From 5b2e2c9477f7920c45f7131fda4afc6309cefd98 Mon Sep 17 00:00:00 2001 From: github-actions <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 28 Mar 2025 15:59:05 +0000 Subject: [PATCH] Built site for AMR@2.1.1.9230: b972bbb --- 404.html | 5 +- LICENSE-text.html | 5 +- articles/AMR.html | 5 +- articles/AMR_for_Python.html | 5 +- articles/AMR_with_tidymodels.html | 317 ++++++++++++++++-- .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 54352 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 0 -> 160405 bytes articles/EUCAST.html | 5 +- articles/MDR.html | 5 +- articles/PCA.html | 5 +- articles/WHONET.html | 5 +- articles/datasets.html | 5 +- articles/index.html | 7 +- articles/resistance_predict.html | 283 ---------------- .../figure-html/unnamed-chunk-4-1.png | Bin 53370 -> 0 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 76927 -> 0 bytes .../figure-html/unnamed-chunk-5-2.png | Bin 74713 -> 0 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 79145 -> 0 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 70820 -> 0 bytes articles/welcome_to_AMR.html | 5 +- authors.html | 5 +- index.html | 7 +- news/index.html | 20 +- pkgdown.yml | 3 +- reference/AMR-deprecated.html | 5 +- reference/AMR-options.html | 5 +- reference/AMR.html | 5 +- reference/WHOCC.html | 5 +- reference/WHONET.html | 5 +- reference/ab_from_text.html | 5 +- reference/ab_property.html | 5 +- reference/add_custom_antimicrobials.html | 5 +- reference/add_custom_microorganisms.html | 5 +- reference/age.html | 5 +- reference/age_groups.html | 5 +- reference/antibiogram.html | 5 +- reference/antimicrobial_selectors.html | 5 +- reference/antimicrobials.html | 5 +- reference/as.ab.html | 5 +- reference/as.av.html | 5 +- reference/as.disk.html | 5 +- reference/as.mic.html | 5 +- reference/as.mo.html | 5 +- reference/as.sir.html | 23 +- reference/atc_online.html | 5 +- reference/av_from_text.html | 5 +- reference/av_property.html | 5 +- reference/availability.html | 5 +- reference/bug_drug_combinations.html | 5 +- reference/clinical_breakpoints.html | 7 +- reference/count.html | 5 +- reference/custom_eucast_rules.html | 5 +- reference/dosage.html | 5 +- reference/eucast_rules.html | 5 +- reference/example_isolates.html | 5 +- reference/example_isolates_unclean.html | 5 +- reference/export_ncbi_biosample.html | 5 +- reference/first_isolate.html | 5 +- reference/g.test.html | 5 +- reference/get_episode.html | 5 +- reference/ggplot_pca.html | 5 +- reference/ggplot_sir.html | 5 +- reference/guess_ab_col.html | 5 +- reference/index.html | 5 +- reference/intrinsic_resistant.html | 5 +- reference/italicise_taxonomy.html | 5 +- reference/join.html | 5 +- reference/key_antimicrobials.html | 5 +- reference/kurtosis.html | 5 +- reference/like.html | 5 +- reference/mdro.html | 5 +- reference/mean_amr_distance.html | 5 +- reference/microorganisms.codes.html | 5 +- reference/microorganisms.groups.html | 5 +- reference/microorganisms.html | 13 +- reference/mo_matching_score.html | 5 +- reference/mo_property.html | 5 +- reference/mo_source.html | 5 +- reference/pca.html | 5 +- reference/plot.html | 5 +- reference/proportion.html | 5 +- reference/random.html | 5 +- reference/resistance_predict.html | 10 +- reference/skewness.html | 5 +- reference/top_n_microorganisms.html | 5 +- reference/translate.html | 5 +- search.json | 2 +- sitemap.xml | 1 - 88 files changed, 477 insertions(+), 561 deletions(-) create mode 100644 articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png create mode 100644 articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png delete mode 100644 articles/resistance_predict.html delete mode 100644 articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png delete mode 100644 articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png delete mode 100644 articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png delete mode 100644 articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png delete mode 100644 articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png diff --git a/404.html b/404.html index e9c8f51bc..e713ced71 100644 --- a/404.html +++ b/404.html @@ -32,7 +32,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230 -

Notice how in fit(), the antibiotic selector functions -are internally called again. For training, these functions are called -since they are stored in the recipe.

+

Notice how in fit(), the antimicrobial selector +functions are internally called again. For training, these functions are +called since they are stored in the recipe.

Next, we evaluate the model on the testing data.

 # Make predictions on the testing set
@@ -363,7 +389,22 @@ since they are stored in the recipe.

 #>   .metric  .estimator .estimate
 #>   <chr>    <chr>          <dbl>
 #> 1 accuracy binary         0.995
-#> 2 kap      binary         0.989
+#> 2 kap binary 0.989 + + +# To assess some other model properties, you can make our own `metrics()` function +our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value +metrics2 <- predictions %>% + our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function + +metrics2 +#> # A tibble: 4 × 3 +#> .metric .estimator .estimate +#> <chr> <chr> <dbl> +#> 1 accuracy binary 0.995 +#> 2 kap binary 0.989 +#> 3 ppv binary 0.987 +#> 4 npv binary 1

Explanation:

-

It appears we can predict the Gram based on AMR results with a 99.5% -accuracy based on AMR results of aminoglycosides and beta-lactam -antibiotics. The ROC curve looks like this:

+

It appears we can predict the Gram stain with a 99.5% accuracy based +on AMR results of only aminoglycosides and beta-lactam antibiotics. The +ROC curve looks like this:

 predictions %>%
   roc_curve(mo, `.pred_Gram-negative`) %>%
@@ -392,9 +433,241 @@ pipeline with the tidymodels framework and the
 aminoglycosides() and betalactams() with
 tidymodels, we efficiently prepared data, trained a model,
 and evaluated its performance.

-
This workflow is extensible to other antibiotic classes and
+
This workflow is extensible to other antimicrobial classes and
 resistance patterns, empowering users to analyse AMR data systematically
 and reproducibly.

+

+
+ +
+

Example 2: Predicting AMR Over Time +

+

In this second example, we aim to predict antimicrobial resistance +(AMR) trends over time using tidymodels. We will model +resistance to three antibiotics (amoxicillin AMX, +amoxicillin-clavulanic acid AMC, and ciprofloxacin +CIP), based on historical data grouped by year and hospital +ward.

+
+

+Objective +

+

Our goal is to:

+
    +
  1. Prepare the dataset by aggregating resistance data over time.
    2. +
  2. Define a regression model to predict AMR trends.
    3. +
  3. Use tidymodels to preprocess, train, and evaluate the +model.
    4. +
+
+
+

+Data Preparation +

+

We start by transforming the example_isolates dataset +into a structured time-series format.

+
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# Transform dataset
+data_time <- example_isolates %>%
+  top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
+  mutate(year = as.integer(format(date, "%Y")),  # Extract year from date
+         gramstain = mo_gramstain(mo)) %>% # Get taxonomic names
+  group_by(year, gramstain) %>%
+  summarise(across(c(AMX, AMC, CIP), 
+                   function(x) resistance(x, minimum = 0),
+                   .names = "res_{.col}"), 
+            .groups = "drop") %>% 
+  filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
+#> ℹ Using column 'mo' as input for col_mo.
+
+data_time
+#> # A tibble: 32 × 5
+#>     year gramstain     res_AMX res_AMC res_CIP
+#>    <int> <chr>           <dbl>   <dbl>   <dbl>
+#>  1  2002 Gram-negative   1      0.105   0.0606
+#>  2  2002 Gram-positive   0.838  0.182   0.162 
+#>  3  2003 Gram-negative   1      0.0714  0     
+#>  4  2003 Gram-positive   0.714  0.244   0.154 
+#>  5  2004 Gram-negative   0.464  0.0938  0     
+#>  6  2004 Gram-positive   0.849  0.299   0.244 
+#>  7  2005 Gram-negative   0.412  0.132   0.0588
+#>  8  2005 Gram-positive   0.882  0.382   0.154 
+#>  9  2006 Gram-negative   0.379  0       0.1   
+#> 10  2006 Gram-positive   0.778  0.333   0.353 
+#> # ℹ 22 more rows
+

Explanation: - mo_name(mo): Converts +microbial codes into proper species names. - resistance(): +Converts AMR results into numeric values (proportion of resistant +isolates). - group_by(year, ward, species): Aggregates +resistance rates by year and ward.

+
+
+

+Defining the Workflow +

+

We now define the modeling workflow, which consists of a +preprocessing step, a model specification, and the fitting process.

+
+

1. Preprocessing with a Recipe +

+
+# Define the recipe
+resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
+  step_dummy(gramstain, one_hot = TRUE) %>%  # Convert categorical to numerical
+  step_normalize(year) %>%  # Normalise year for better model performance
+  step_nzv(all_predictors())  # Remove near-zero variance predictors
+
+resistance_recipe_time
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:   1
+#> predictor: 2
+#> 
+#> ── Operations
+#> • Dummy variables from: gramstain
+#> • Centering and scaling for: year
+#> • Sparse, unbalanced variable filter on: all_predictors()
+

Explanation: - step_dummy(): Encodes +categorical variables (ward, species) as +numerical indicators. - step_normalize(): Normalises the +year variable. - step_nzv(): Removes near-zero +variance predictors.

+
+
+

2. Specifying the Model +

+

We use a linear regression model to predict resistance trends.

+
+# Define the linear regression model
+lm_model <- linear_reg() %>%
+  set_engine("lm") # Use linear regression
+
+lm_model
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+

Explanation: - linear_reg(): Defines a +linear regression model. - set_engine("lm"): Uses R’s +built-in linear regression engine.

+
+
+

3. Building the Workflow +

+

We combine the preprocessing recipe and model into a workflow.

+
+# Create workflow
+resistance_workflow_time <- workflow() %>%
+  add_recipe(resistance_recipe_time) %>%
+  add_model(lm_model)
+
+resistance_workflow_time
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: linear_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 3 Recipe Steps
+#> 
+#> • step_dummy()
+#> • step_normalize()
+#> • step_nzv()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+
+
+
+

+Training and Evaluating the Model +

+

We split the data into training and testing sets, fit the model, and +evaluate performance.

+
+# Split the data
+set.seed(123)
+data_split_time <- initial_split(data_time, prop = 0.8)
+train_time <- training(data_split_time)
+test_time <- testing(data_split_time)
+
+# Train the model
+fitted_workflow_time <- resistance_workflow_time %>%
+  fit(train_time)
+
+# Make predictions
+predictions_time <- fitted_workflow_time %>%
+  predict(test_time) %>%
+  bind_cols(test_time) 
+
+# Evaluate model
+metrics_time <- predictions_time %>%
+  metrics(truth = res_AMX, estimate = .pred)
+
+metrics_time
+#> # A tibble: 3 × 3
+#>   .metric .estimator .estimate
+#>   <chr>   <chr>          <dbl>
+#> 1 rmse    standard      0.0774
+#> 2 rsq     standard      0.711 
+#> 3 mae     standard      0.0704
+

Explanation: - initial_split(): Splits +data into training and testing sets. - fit(): Trains the +workflow. - predict(): Generates resistance predictions. - +metrics(): Evaluates model performance.

+
+
+

+Visualizing Predictions +

+

We plot resistance trends over time for Amoxicillin.

+
+library(ggplot2)
+
+# Plot actual vs predicted resistance over time
+ggplot(predictions_time, aes(x = year)) +
+  geom_point(aes(y = res_AMX, color = "Actual")) +
+  geom_line(aes(y = .pred, color = "Predicted")) +
+  labs(title = "Predicted vs Actual AMX Resistance Over Time",
+       x = "Year",
+       y = "Resistance Proportion") +
+  theme_minimal()
+

+

Additionally, we can visualise resistance trends in +ggplot2 and directly adding linear models there:

+
+ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
+  geom_line() +
+  labs(title = "AMX Resistance Trends",
+       x = "Year",
+       y = "Resistance Proportion") +
+  # add a linear model directly in ggplot2:
+  geom_smooth(method = "lm",
+              formula = y ~ x,
+              alpha = 0.25) +
+  theme_minimal()
+

+
+
+

+Conclusion +

+

In this example, we demonstrated how to analyze AMR trends over time +using tidymodels. By aggregating resistance rates by year +and hospital ward, we built a predictive model to track changes in +resistance to amoxicillin (AMX), amoxicillin-clavulanic +acid (AMC), and ciprofloxacin (CIP).

+

This method can be extended to other antibiotics and resistance +patterns, providing valuable insights into AMR dynamics in healthcare +settings.

+
diff --git a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0116fcb2a83f5c78523b139f0b8b4efc201c44cf GIT binary patch literal 54352 zcmdqIcT`i~_a+>`#zzztJ|faoLKBcGy(uDwD!mt_Nk=+?P%NNSN$4eXkQ(W|C_)S! zX;Oo92muMbguFNS{mq(p=Fgc~@0vGjLEv)F+2@@7>}NlF=koEnhVqqvZvO)Ufv!NF zJ<$e%&S!!^RBe|o09V@I&3p#lF1%D#egeD$uS3PgAmEMK?U|7W2*eUb{-TIk+_C|I z?tmaq9_jj~txx-oWJPTqomv)3Q2gB+gR)YR9H>SXU^Hs3nE zjZkmW+A4l>qyI!SR`0`8!%vo;b}#I-=(Y}S=Ll^*Iq#!I|J4RnhGl!$K1$=^+3%^| z-y@e1qb#4_aIBLN=);mx6xd%N7k{?4|9#&?YDF;{6FEt_>(UO38jf;Rekg>^WseY8 z+F_^I<%_JxvY@svE#?>7-XT*5Az#a5%phf|?ndxLEu`Wn{O~JHH zcVL*!j$FM6rULoYb2h0L)?|Y7YZ4r zR&mo-A}c6Za?=meb(PJ4tKMJWP5OCNk)uWp?|Y;&_ug(j5)^Q}W-f zC|it}c@=j!D&Z9n04Ep>cU{RN5|7^rWo((SbV?#una55=F3+=-M60TyUAYe3CngQ{ zIQ%@o+0y0U^jZjBhE@K|x@8jUz~$~W-R9wFoY$R;nY6Y+{fT1dX=ml zuWH&i9HFxE`EM)sRopYK;)0Cfvh72>VHuh>_Hgy;Wmh!G4dsu zCgGYr<xD3w$pypP?+hpQe(zlhc0CE>vlQXgImAfPDK@soutE>-(60Mcs==`ILTFvVlVA?c%8^h<*Nb{Ob_Y0t; z(Dw zkq%$jyE-kMf(2NSr`SlB?E?1~@Y4372t!q*4C1OcfivW*h+QDLZ8BUG&4O`|Q|lhb7&g=$L8}OD4$7w|I`I85WUDYVR^XgEYWcFm&~la5 z>347#jb^LlaoET!AmQa<8?OVd}`s1 z>1I!pqPzJ|(|#2zSSB>t_iSTTtJ$h>I)!`l*IP3dkRn$A2DDY7guXa*aX$L+dZtzQ z*i}UoE@2c$Oq2>@*K@b=fn(DbJT_7ns@r#od?Oq~SQSExuGPGkl*Y0Ua0&fq8JkqZ zFtd$d9(`d3Aw;i}kof2kpMJ0a)9Dl&*Z4zbG=ui;Q>ZDtF*3QJbx*R@!XKuJ-#wZy zcK$9jmgp=k2Pa|=C3WIh96g+8(CW|g1xAwcT{Wu%UAj(pcNc&6lXAfOsv@$n7TT$S z{zWr+$HEN-+}b`Rh`p5Y?UB)0Rds({lt4wcpGkD^^IUsqg_QS56IN#{z%`UJH+r2)O-@X2avcT6s6N~o+SlQ) zbuE~P=DLkNZYkUw7sy{ra<_tO=1*7@RLb}6$$>x$w2{p(^rXv*dMlmN_Ql6$NTpyj z$Ki_&-<(w8`F&AoG0a1{IFx)~(jN4}O^CUylG!mlEq*P`s8PHNw{XG6r<=ufPGUSK zutIjPQ&dNm(b zKL*){d1?BXq^)`zM*6i*pYwzvKkgK$q-hNI4YSqePOwf6?x`jfr8C8%`^gFR9sBg2rV3OauXs})C093~qoYi~hY&om znq|0=GaZIHs*y;f8?e)y_m?e>>UWi*nEnoB6`)Ah75|r4XE(q^=4+EhQM{4x$%H4} z0EDny?H3fg5I(Vu%C9fZk&-Rr?_R0dx}p=e>1{I6D^`&1?46%67GPsnOB0i zxKwU<*PtH`eelaZ=Shd2_inD&UoK4^53Cc*u`(4}BIZXAu{Sg`+qs`g=~Si%2ii}# zUPJ;RVm@wrSUmBeKJgfk5V8Xp+TD92i*7pUcU=2eG;g=Y7mf91B};7i?nlZo5{-)3 z(v%CNw)nL<+6)Sg$|ba2Tsd2Ycd4!B4%~}`3=(O_YWXnj+Gb+XGnfrEx`BP>g1*|^ zhFrgKHzKU?hV#A#p$eRV`7Uov5<&KGr<#6 z5i$?;P_-TH+c5#PR@%f{Q00c8p$gZLmj~^yXHff6Y@7GrZFzc_r1g1AFj23dXwfj! zBGz`LBK+|mU0GU|(B$RW;o1{HY;&zuIu zK$cPqHRuMGZ9JV2S9E?p`OgX0q-KaulTUcvcbUzCB^v+9n7FT2@mj4Ot)}fNKH1M% zlcjNDQrg!mQi3yQY*u5H(n#iv9)gH;ziwDYO$$y^uf(FC7yW+nv~M~gUn;S9KXN9W z$a2*MLWtBoeu@1vzX6ex$)CYMKkP%NI5Om+AwJnHYJ3p*AsaNjh1j&&2Mq;Xy zKWEIMTs)A6bxxCdtvx{ooEzhkbF~hOLBfw{u!Zq{ZVWHihh43A+@;*pgj?nEd$e?h z$MMdu8zx`KF1c`KYO1@d@Hxj+8x^5mPS`|6oyJ3eoy-K0GT_(Iq@^M(OE-!(n(z2Rm%C%&nY?Sy_5O3 z$xUIKXa^gC)J%X^`}2Ww9QFsddW3q7OvVe32+CPKe4XavAgQr@Qf&f_4VIIq+kC4W z&0~^(d-LQEbr(b7URG?yW_qqEY9Ek5(9BcC<{lPTR?)o$kA-q^y_YX!cIl?33!zbJ zi>E_Lc78=n@sT~^-jQuOj+=P3k<^#MfGi9%N>1j;jA7Fm+tLXLl+NF_3X*#;39JOI z=H<6^zPdsnIkP8P?WEbRoyj4C^d8a$lhz4Jygog1bJ>`(fzGFB#D_?4ld7g5UXDb3 zq#MAjSmV+iQ4Hg)(-s+n$4dbGAzq60M^LAND=8mWS;yp(F$gR5syv9#WSV!)xJ%#+ z5bWtI))}Q4LF+&g?<<#0MN2>Msw6r&Fs(fyHa0;+z4%#mu)YTF;Awdc@oMG*kJAZ$ zAbeZobhySyFVxhy)U{e#P*W0*lIy4zhS-^=Dx_meE`6FS|FdisYePy@nSIx_8=fX+!>bPMv9@*Mw;Ho;3 zY%QEos!o!@Ea!7ukj&i1A~R;qUjZa0h<>Kf?<&s0s74qo)Hq^I+1s=Yv`Jmm02ERY zae#0*u|q^R8Ei&X7xo@aMAfoTRj=-Qmd|MLf44C)Af1Q^Z!KmO*pUS9PHLM?NF&Ea z^7~assCGvn8qc1jM0tmXx1;g z-OK($NNXLO-+e`h3lp+zKOi8`89Gn1#)f-9H@ttMYZcx1@4lM5ZN8j=eKxv%X+2K+&=jiqaOz7;hqSWB1rXZ9qb#1KJ%cd-ad}-kJ`ynF2n5Zd%= zBs#e>IT*kgiIX(A+tTka)+EEVrPmFGixTPEV=l?~yAl)Q@*1dkk|on3IxuW2dfVd# zz`8dDWiyw<^r^fyTyoSbo?_cvAVo7F7xUK4DxD6hW0i^8YezMDn;%8Q>LqIoI-ht~ z8@b%Kk|!n3CpgSJHm#YLY!xE{#a)on9+&bS$6*isE=+1P`bVC}ZhH%JlFs)Wv{!L8Jf23TxPM zU*=4CA0&Q3tq8UOftV*Og#I3jh7gV(1kOC^lrF=oKiv7|C?bW~4TA)_zJaE^QpMRe z_O2>18Hh6-uUnI<91O_pTASq!D;Fbv=vmMBYDGtoPs(+wjUKA3W}cJ`O7EANmTxGR z?~R9nK!$DL%w#oj4TplF%Ji88$bKMA4Dm#|=S50%^7$}&DY1;;QK z%mBfnp)CC|_89D^jz1hJe6V#|tt&2o)AXXb?+}CX67Pu?twv7nmac2wAo>-7*FShe zt!n3gfJuh_r;)ni?}~}FAsys1o&lTpu#x;JiMtA#1?jiy9^%Z3Dg!@SG|tDL z+BqZ3dWrSKt?T?kFRdP$x22!?)F|c0T1Z*YTWg3;+Z)WkdF|*IN&J-y)c0Wr#VR%WVAq@FrQkn6m34 z^@S77@F~})*Y)%{B^}bnQm1ZCPG9X_Z>2E*X|DOrI{^Yq4bR5`D5z=IzwGrXSb7eD>O1g0-#7|gWAb7PC3+c zjinsE63C(b<}Qlt-kgU86Wj8Tk>h5aAjIJEKFNY<8`!WcLjwDd)iebt3Lb2GpY$9s zZ7bHf`XOdC-y*pX9chxbfNK0x&n{=0YM#0=b%GzA@RXK)hIn7^gx;QCZ!JECuWP=M zZPm|Dw^rslwQz=Z(5w}5Jq(P$f12HkuF%C~U*GLcQug#G3(%xUb8~d9*6E0IT;YC~ zlU+|ALycBG56~X)o=&n>#UBb(24RywT8SJz4O|8vj!P_hbNao+A6nSiwu3y|k1I)R zU{&FU>05(<`Yd#*mYhCnTCF<2ZJ(juFd5kQ-kBR>F0^!5@Y9H|z-H$QADcR%)wyS^ zbbiOwDB82HfeLh+gQ8crMfl`dk*cMD`ms1;y?4qMg%v5sJ;Kt&HE}aSR~AI; zx-BxfH4=Hcbvh3ANqv!yEmkLKq;B(lw~>w&AIY~!Sqtmkc0j>I5hf{X!gJoxNq$Yj zyNPX#ON(eS(0nPBl^4jNi_#g7UvPEow}_YCd$PV)bI?5D**N(^dwSjJyNSq=?Gr0m z=^WQ<;;~2j&<%PUw&W$!b>Y}?!~XPJKTa3+vXT$gUme3u;Y55}zOL0pO>5Pntpw!& zVT3EKdLw@hnj_F^JYyF*xb1R@O#U8B|BLYlwVOt?=nu~NM9kR?0BNi z{3;{tZJP<=C*8wqf!IbGsY1}U$N4{o4(>RIG|v}i`f-9*Gy)Jr`Kd+U^O3wG7%d!8S2b21vx)h zCMRH!8F-PIxiPO#qHZ&?RZ!zl1FeEweGPp=*FAV^Am^db1>i*Y0Htj^XsV*Xg=rU- zq8Qk)%BK6d6=@+G%4%uuozbBK_o$r@GB_lcn2l zB3r0`zNQ;a^%SR&HNn+0P_N7jLm6wO9|XWpW|I7ZoF`&8#1Zoo#SYsuDO&=xo3`Zr z769d)xE#6UKpZ>iP_N`Rq6sc+F$Ki}BZk~e-1}5C+*(+JsbgD`X)+l0YiBYL; z36wa2xu`vSu%)W)yv=SaO(C0#^Q)m7K$SF;B6O*zG*}V_n_p#!`)%>`?$qr~Oq|qO z6H=|cD*5qGQ-?5Olf7at06gf9oreCxBlj3RpIoO|lqwfpf@m(R`6!Sl6)h(xqAC-! zGWxoqjHz3T5?TfH64;l10u8zUZ(cq2leMm`gia>c09(|2@S!a_n#ExKSGmbbttk$@+*q`NMk0KYRqzoq7{37MHx)%NZvuaujmRHrrJ_6w{ZQwh*yDZ~4 zUrRZoCg0Z<@OT@evK22r5OduTl;!V{Tt=J&y4b)g>QgZJV>D3a^&11^caZ2~%G>1k z;HS?NsK`GIT%vwOeh1yYOL2w#uD}k?1V9M_y?Xb5(X0eC&e`I9q z1#d@~W-JVqHXqOU4frvbj?Moe@9UAK@JlAj4%rp zz~eSa8D^-H%7Q&H72x*Z_nMTq4c)w3=ia04cbzD8#m(R3uBhUR2x`DD3dmR`D3Z@n zBJfy1Z~cDzUS^7P0)0!PauPxxApr^l%j4hg@xNU&j> z+-heF%r&jB#+ICy=#RJYd#*(faWBA6*0wf|ZMDgGJT^+WIPQ}vqNF9`Y2CorZRdh* zA^vWTytss@;A7x|Xwhq80a#HtM_-iMY512{e`)}CCpZ}qj8rr(9i@uMmM9&2&3vh= z!c;e5(Uo55^|Cqou@C>@sQ5;TesO8BboiwAjQ!w#EAdzJK5VvB9bFw9$t<4m3YBT) z!yo=q>&Ed_&rH7V#;c@l5eTaxeaEyvBW;fe45FQ|2uxpRf_fXGQDt0OyDxPiQ<@XU za_RDDG>degR>EREuYQTIBFZYO(Z&DpvRv}L7{}UxSoijy9;7`~YNY+$@p3D4zxMRN zYjmtKYkfAb;eZM&babm4VU9gUhVY;{`uq}td=~lK;bY{z_DjrtU_UJ%6Z#tPy8Vhc z^Ar{vq-vDVZ(jbgLw-k!6?9dtGx);Iglvh=h&h|_F)cr`$0xWbuJ8#i?n~V_uWFf8 zV)R&|0$Uq8s$W{_0K*-l2i#3u;whpwNCKF!(B<&$44&N^2 zP?B#DB;TM?{zB0aI|IYP0|e@u3%2oHlNLNE^9G+mRXY(e;I?3@sB@xUW|=)_&X3_& z``g3g*zUt&yZhnvwFx}fFP?T=pXXRMe~lO4B3r$BE&>Q$1-h$#LxI)V>~8nojs^P`4REKRG#CJHsV`bOFkyjUjwX+GyCZxKXg2__|Lx)KsaOA^ z9Zrbvmo8BYH%F5X0JJlj4Seu?V&L-Yi}e^P@TdvgKfPO=AqO@}X_`APj#u%sAGc_E zJ)Sy4EHy`8A`j`2lg&(yO|>}s$?m<`w6z9a1~eYNw7r|YzX#}?;h~F{$9B18$hT&O z0R?c4dwXaL2eDDkJfY(wg5CsxnsVT77Xwn%G31P!Jl>oW9jZAV&5+~Z(QwIG$}LM8XE2+$UjdT9d93CnT0&qom#U)Rg?^wy8T5y;c7%XSDJXk4HHV zfgSlCE@pGX4ftjEUF9WH%g<(S=A!70tbj<=&R*-X_hV}6xO!tgO$4081mRV{XF)gJ^DUcmwH_2nd+1TCej~#P+2K-fx zEQ*pWx=ysTH6*H@N}kkm6XxFbHO@8|g#(|;2frq}XuGST7D>Wqa~l-&h@<^mxhpIA z2u(JwlB7*rX>y1?H5tZ%Qrt$3>{#>?7P}ELcQJ!XTBef@)iXie{I#ZaHr)x$Lxmpg zp;urQCvyyJCiER1@U*0bK1fUALU=a!gSwrcs-#*+3t+v&KWm$gaf{o?eUnC7i@n_3 z0JZV1X`O|oY;O750w>skhHL$7=o!7XQK~6q_$?*JaCf^q!cSwzfLw1z-?64hE4%aF zajZ<%Zzx~AbuG6KEwdz~ifZ1Q$jau{xV1e>L`=2vbtjgXHSTGOJ5P8%VrZcR4TR1$ zZ%!i?a@v7P478Ma{^2=v4jXZ$5s(ggMGM=hiTozsKl0U6j{te$3O)?7F_II-$a@=a zW-CRAfhF%^dLbHZxM8_~}L;B`tas+k?FgwA^0#{#u{-8ObG)Zt7A(F>nq6 zjfy(H?iiPes?`w&i-0YDNLRN&>xp!C0>88*1_{=bnU55S#(i0nhrqMB1A~CQq@wEP z!p$IEyk&JS(RqpHM-yNJE{KH%Hreb%3RpA%(22(?3JehQ6tIm^%Zv3?^27r06BvAF ze?MdBMROP@qVth_A% zNUj7R2`$W$g;pV1bXHdIbuaF*to(wo(*ecBBruf9pOL1AOG%556&XO-!%uEb4q0X@ zmg?hJl-XcAdw>w>nR)|Jpyi@REEv$m*W+s@_xOx;VT+61iThmkycFqR023`1TJ-3c z?a_2Zn%lJ3*t8@jdXJrTEEy2DR-s-4Er>8Sh2^XF!{_vxuTn0h{tlY`XOArdf^ZdmndFbw`op`G%JCGdGhh5y0kBf5Wq0;!g3%4cJ0+}m}rvQG-< zr=&&0DG9&}v9~eZ=mbCqMkEr$7?if&fLmK%4Q6j5y+)0c)uo~2V-G%W`*kPSrP_G% z_~;0}U``jmy16ND?d$H-*zPK`rWGRv%(M@N7$xuuG_F^$MK$eXGy@6CkW+8`M0Gd3 zshY35mgA7P8W@(%-6s$}2Vd>eJOJR(>l{!XAB9H=1-Vqn`Pfak!N z3{DJyI=NAoJQxgyWkfcSo><8M)iD>XW+4C2I+3h}L0<;=a?g1JzCGRpmx49rw|hiL zYwMN$=16di|J3{jP=YLhRp9|^gl+*+-!H#@DO`zNnOURv{zSDSs-!g#!;xs3`1`JO zR|T_iqc?uD#Hi9%3!Vns8}}jq4zRQM$thFHNg&x8XdClJMZw~A$G^b+GW?M6>|bt^ z&b34b!234EBS(+mbnpOYAo?|vE%#2r5n*gOK-4CIClOQ9nCdWQ_aDe=$FYRkY41q^ zL}X+b^tVl-nYUJLD;;_Awt(u)8v_vcahpVE00kS689n+ATEx04@SJ6kZ;udKW@j!! zpe1ie3W)rl8SB%H`(xqR2X8C*~e008Hj5x>RBvw~b|0UaRv zyQ2HSu@;=+iu{E8YF=WXAm$<$loZZe_A7ot@r>v}p!_eh(7!DKLGk}HlH`A-!SDfS zkp!QA<+frEiPp5hzLz|_ZUNDXYraYr0iYxpRAJ~H&{?jiGPdYSaK`?at!eGDtC^`C zdvpe~N>$2~jfSH&E4WP4UKbJWu6Ptd>Pv7QvvtF%iX0p^?%zMFJ}md1DGZr$Wdy*E z_J~izPcpz3OuObrryAN#{Nu-OofsSLzg6t-pPjwlOifR={0a~j(>`51*xnR9*RHlA ztV;OBf*(wXfLi_RXUPb}iCM+L5im^V?IaSJm8A%!7a|WE0EyqtHKO7riC`SH6M|yD zW`*Ugm}JChLfm)UJ}U8eJk>_V%8n=VE2e7Kh}kal8!SYMc<&0jlLuRd0;LO!k{0LT zW4ORVc7%Bfd$hR@N6vOG*oXB&wT#Uu2MkwnGGJl7J*t_D`jzC~Z+YRu-X5R#*=S>$ zuV2?qYF#WeWDhGD4R@#3n+{jv{7kh2MKOKGh3(q({VK_t1%@X%POMOa8YllJT@@>O zzsO@1?0_PoRHW}$ypDlcO2`NfArv9j6={fHBEk~XbM*H}U8f%kgCe#x1ZwW@3xW8Z^63J+u>Wh&hUCZ7$A^kM{pA+jI{k z#EmB@Eut%-yXyb3c-0rGAkv!V-bJ$REgG}wExMS;7o(|#OuZ*xMa+@xg>I1OTAf|AA4Ou^v3;IN(isfH%u-3!{MM$6# zua{f6J51ZDD^`5^Pd3~1m-V;u$A``CgG$8zEc>Pm^{bl+AtB9@yScm~U)E^hWcNTI za!6}~(&66>GdmF+sD{Af%F4N4Y}VTYe&ZE*ADsjx*EeU@-$N)<-aA^()}%G8(E-3% zSU0V@XLHyCw4JT7c$+Tf)O!LDqf`GHr(^vKwapIg*4qh|qFrB0bb)y_!UDp^o= z>UKe2mXla&HRIb8J{fINad2#pJ##edC0S-atQx5u5)yI^xNs0=~iOvyiK z0fcDzCfx5v(!AV$fW_WY9lf6t92Lp4`zhDgnDCXWPHM*fK~ ze}C5L+zw$K5PUhC>uTFu)Y_GT*hs99xvS)74rd!t*(vDBffLv&R%$Y=y9dxBb$Cqu zQVQ^_J0scflCW&%i;`?^glX!+gcG*~5>Z>GF<2|7)Tfc$NRw!sBCM&_#bs-7*zGj4 zhoLXM?D2!pzx0l7Z%6Pp1|LG7l1xbxP#)PG_##-9&h{(IPH|zt={Ezf$aF^T}X>$^}AlftB*4B>*08)pDgs*282TnTz!(L9aW>c2Exbtx*LN>2L0C;+QUHBQn(O7(I!TXA6JD~X12 z!gk8UpfLbA`)vOB$45S82QGQL(+0qkS_* zlTZ$pcFen~swKnAbIPyGdi>uLtJW}i*#lzKuaYj4SE6C&2f3i1j#xK}U08)Y(5$G{ zU;9k&_o$1I;xxX82PoO9tYPVEGxaU=mX4bjfeYU{Kb7fTk=+~N){f9wD#?}MRlVBs5Eu+Ga_Na~43NUHRyF#3C4>0O07Rkv zae3A&4n`H>xUXLs@=T$|N5-1!$y{Jb>$>5uJln&$z7G0RJC#l|M_csug3@K!6ffM~ z?gL-4{hRj`-P#_D3VsnUKziDYmOyP2_E_R8eBI$OT;pE;$-+Jmt&zV|2p7JmkXIfVqNmw6W24M*_%^pm7<0o~hDfuyr9a3_JBV*pqOP6jvg>yVzS|bqt8W2{i%DA9XEc;ywAY_!Z4?Wv)X~ zMZo5s6XTID5VVqGrj<>~*Uy%zyb6p&ad&|&)Ce7>o`byZ3qr*@JEfXkTm?O5p{o3V zlQ%z0;yqry?SFO-hdeTOHn&O4_Xiw6Sz|f&_#KP(QX~z{TLO( zRPKROc$GumM(jNS&EnlWAZjhfWp)zl*NTpJ?pp&kLAEhMnIRj7%}4oQwr7M8Q`oN1 zxIu|(_%1}FB)%ai{KU(c7?l1s%MsyjRdHa&=zBoxPj~i#o;p1ELc`_)0;Ngl18?>K z3YQnwA<07+2GbVBLtkmjh>jnR(zrBA0S?8^1@!^JUG*0V1J0qdpcJp8watZLJ=Y#U zsj69EUGsWU;Q=T>s&?rB?UAGXDInJ*+5sw?k-167m69wAGEH`ZFWC0;aKZR7W7-L1 zb;p(8qoZSO2G0pqJe7z)IiqGb7J@=rN*>61tNOxw8%uwI|rd8F{0n^fCO1+?QY7KZ+<>vBd zu%>3LCdar5ua|Ck(;dt*5Tv!0ek)P+Ybh7^IW5Uc=CrzW)zZbddMyzm2VY=kWR|&S zbdsA3ND3}4K&b}w)BAY=l`3#ZI3AA|#*4}xz?ts+-R{#BiYq-ZAm75>ZB#Ysp3vKDydz>Gv|8Rp9o7Z8qQ)0p`VW7u+k6d=|co7Ix7bQU;(=aK$r8ntWzG>qHq z|C>f3xOKp>);6WX%{mL!_IQ8i3t=tr3LsG~DsKa-@;{Nj9dl09&C-9T3lEt|UzjG- z=+7}JI|)YOC*#6-ojA0e)(re>mNTSYsPXs#E0O_NYB2S!H&mA2?Ri@>LPlIMZAJ5y zsNKw5Ul`mqhNoJX=TuC!i_Ojr*J|swl6*M4Op8~l1BQV-($9(hAyxV;|Mt?`Jw;=2 z;MGeuPK>&4l>ah6FH%5MNuEC!&hL>Wap&{Oc_oy=^G9Dk+{r$z>yx1M<*B(eA^br> zV**M4?jM7u+2A)nEbWs_CaUcFj)>(x6N>f|RhT}Flzp7vp2z5n9llwf`ikTs&;EphqqHqVyp(UWBrs8zz{eXa{E=< z8%4Mn=etW&=chW>$L&O1_CM;*(L9!5{dE`rkXa)7T8x#R=~LB1sh4Tz9bNJaO%W#E zun!SF#5O`kFnvx%y^Es*!#CC6h=6jJj>W-V(cmG%R%^6FC^F0}@ z1{xnZ&Q^K!-a|oE+rq&q;KIS(KZ-Z`ens^Ornh0f{{f7lxZLi>r(EWVXP|56dwIZd zQPY>NV3@o)FeLd4V9f~lcO7ioSP06)1n0=@cH=Hm%{rsVe4hHX%-z6vZ5-u?%9WDF zP{pgh_y21gtdmCqTqw_MN}>(lWe_Pmjq=o=p)~#Hq_RjkSc1ClO-eLJT-TV+{nk`% z`&3hc%9*)c3T0rKLI2#P+^5{XF<;+f;dv)s;%%F!Wnz^9zngW%NNKJvQkACiRnSJQ zT5`pG)XLvwrgl?SffqS=o?@>~X%y~29-9gAJPor7!T&U8522&pdh#^dFCk&nvCz}J z1EFf1B-bobyZ(2m>?}PqT=W`!gySoRy7!oe3J%W{^3hkqQq7M)iw zDPNzXxwmOOO{(?b2(1jRd}R;&kU-#jnWR*HN^ZPFF9S^UP~tc0l2m#-;<|jUwex*j8>f#@byxYj zg3vE~{|wkwlpUPhc}{D1@F}-NSLTn_tYh0)o!9S~VR~DIG>WFOqOp*7zIU=V+*L|B zUM}AMmg2~BGozQQ>Mx)jDh%KK`ChcQWf+i<>%{W&KpgP=PhHZ5f8N(pSiC=2nOJ5& z|KTFtx0p*_n$Pc(QnU}BEb+6(Tf90u1W|fxAxh|G(O!md-Kjnh3#1r`4Z%lM5_Q)5-{kRZt@+F9gH$}lY_)wO=sV3uxt?)@=W74s zOPk%&VY#KM%8`7&FV2t2HAXTaEjlm9C$@x6X4QliTGcH6!q+TjYUOZSZ%sZTBM!>_ z?oVjC&&;8K-}0wnf1|itE$0F_on0@=WPFvaQBh5N;C#*;;S-&8=iQ(5Tl4B|O-+^0 zh=tC*Q}zn?{_9zQlour8@Q|y$c?}^rOurPWYD1Orj#2MYf79#lf21{k1vX00RJME5 zj_EeX-g=EYv*nMzT^USxcPF~Fz(4z;ON|TrpP+fArTF?= z^EUIHQH>IteA+H9Molc$#nE@aGq&Bu01u2^roA@D_wvQf4(cF#M@RqLzbIZYNqeu? z1#0kJad38aW&mZ}0)CJ7vx~ie0DAvOhQ|l@6fXUSNpCi7$NSmAFGO&tCPbDM78;s4s)~Fy59gY=CGW7XCn_NGc0wZTBi$r{`tO_< z8h|LGrSj4{W)F6C={$S@ljx8jZo$=x#X9VSr8F>~_h2eK? zxq~@U++qPA#`n`Pbp^CJ+DVV@vMO2YPB@#)(m!6L|LmBP=XEu{uh?+FS6W1oqyOY= zmO_%m`7p43w>sA6p?cJIl4+1|vH30K4_s8+!T(8fkF(TtaEeuYC&GE7#1Dg#q%(G% z=rRgU0PjY%U-b{k?=+%k$}cba!u#hgWv_ziBv-{np){&3R^zVyL2bE=pV?ze2}Wo3 zY>MPB4^Z4nc8P`}a!bd5(;@SJ{ZUN0eKr4WA{7bp!KbB8$CoNrKw*(WWwG&<3lUt) z4>fw=cV<(Yv*Lk*D^;3?u)xl3OQi-W~C}TRp4pM*jN7g|q;rV?FO^-usl{{wxHQ(7(&>XQG9P1U++L>~u zkVeQ7+;c}T{YM=+UxGu}vs^gx z2&O=~IreC^>59`ke4lv6*0S+R6i^z$Mh8+s8#;Iqx65=s;NOE7Pf}2DYKWc44?q)r zhQHjoXL&vEXV8tc@FyCaZhWyq_NdslkMDf%v7=>xWW@W@=BDNY`xi$$eJ9`kf-eg5 z@}Fo#NJEt@twdDf&5XOrmyc*hqXNq)W)P29AwMm(_{y>hf8PwhQgB1s`|fwEyFC;W z#ZYJ3fZ*qK@#T&3D&5~uLrs4`FFiv{&JN&05)f2q-D-Iy;l<9*fczqu6BZo{zM|6c zQd&J~OsgbAgJu5G7OYKOVfVwb*~i~}kL%tZmIf8%1qkJhiZ8_(o*|=@D}qO2Mr$PS z%S9sF>XIo0e&foO?qJ>!UY>aG(1v-4-0Pvb8qI}~w)$)RdAg@`Ax7`|ijzfGvjQIf z-5Rir_M!>d|0`_Jq@OZ`FT<5TFUUtADsDJ>dH{m}H$(1SXU z!17%%;tN!)V-B9CeKv_G*hc6)ny80Q_a6v426EkzL|zK*rajwiyhWNWxqR z%KIgOfb60Qw8z;F?*I8kPPntz9v@uK+Mre`9UC-7_BD0n5W>%p^>g$A5aBV!6Nw;Y zr(9R|KfNw}@nHMy_tzwS=cehI`fLMR2%&)LmQ#Ud=j+D=|ClgE`)16`$#~@(?KG6iob1^Yzo-ucv;Mr`td@J){mnxA z_xcNclq*d1}*G)ES}j9;Dbe3@=msTFwWQHuv_cl_^2ZQ)H zjBYvj?H8!PhiSt*>_Rd?^1nx=y|ZdOq=foFSB=Mb&N7jyLSX>ye*r7@zc9gVZ5F&y zk!Brex9pn$zG522d~gmkSBK5@Jm$^X22+ZBXsRz7Gx^vQZ|K38=bn76EPKH33LRI? zBAxNsNriNu2kMxKPjin*xxd_a)UUYSB|QDWK$EEk1^PSS2 z3vh$8Q52tBpDL)rMSay>>Szy`g^-%tc-Mk!nxVe;zBo?gT#!h+cGYen#arbviYwIU zT{IKV&B^V{56BSIZh`6q&xBda*##drNVafnOnNdv;O^HRpW8UMVZFCbdRdUDo_);@ zgr=G-Xnv|7fo$7)*RaeJk7B=RdtHC}Y5B^V#=lO^6FN3q#_PinvP`odi`cHw`5f809DHIJvYRTd?`ck$g6o$2d&juB}&b1?C% zWF`k>t&~ScU#R}YgN{zd66g!&yGWWIDyh-EkC9Y;VEQi6YPlzcyV@xXE$?r^=Jm*H zs2cfy$a?FrsJf_qc!E+=K{})*1nCYz3F#7$loXH#k!Btci;(V;lJ2e%q@-8^$nSJ(Id*!|EbLluidKqpk1YRsc`#?f3mAbgX$du}Mo+CUi0@QobNa+u`4V z&tqQ{H;T0hM|9!MZ?22J6Cyf=X*UA}AVcUEuEaG~EbRWL0K?Wmn&1E>Vo0L7vAJ?9 z|5p9-yz9gpB&2=lB^FVm)j9Sc21VSeQ~BN```zn*yXV`9J$r+tBXE#<@I0r zbQqxJ1inkui+9iTPU#2DRbuU6l^h~#BH`FBatK4_{p2U}Oz7fE^Hf*6RY&}A+$TqE zyH}v)=E$hjyTqjP?G9Y!i`hWL!THoK04heY|AK=H^$)v%Bj6+8bm>Hn<{Mk#>b94T ziDb0WA@u*%nN2^WA4qSjkNDlMA&F$de*$7c6jgEd_*)o%w%)O~3203v?0#U2wKf>nfm6mY)}Ywk&lg{^{SwM07dEQH{POo4;%J&@;bL zm9#Q?|B2pp-v%-eC@4i)RA0lJaed+petN5SetcpDaV%OpWMOb*7&Vm1^AV%(HB;$( zO(3%K@xN#joQQaYiOuwG4Qqbe6FY**=9ft2f(gHX&pWfWsq`JJ4`ecW{x1pwKkQNs z7kegao|3+Jp_Z*ZDH+%JuU%!-zd33T66qA z=DsO~9MW@BJ?l!5Sr6~XU|c~i5DB}~EL^XZKmDdA+`y2wApenhi@Hy6vxhClU&Vsd zTyDDcWS2%H=O*>AXZA7(nEfKbSDOQTmL{JpW;|i9_3oeWU7zy|3@b<{By$KI7U*|q zV?of*^TCgCx`)deJ+%LCV)$C0B>g(&y8yGIrip@_;2h3$7YJ(PGgn+P@sB@fMc(-> z%`0=(M)K!I|M$}^naax?7Qu3!&$-b+8eb%Fr#CB{-nGW z`pePp%`UHh4?EGS9G1{`!0CNx^fX7%j-T956M5!Tl=0#@uw0_M0aRQuHN;nNm_rA7^g?wRKEtEA5uaW-ojcKh9w^7T@X9i`Lbqf zk`DR*>$koEztsTDk_##aC4`$`%|aU)eErOmfRS(l&#*LqX7vf5&SH7L;jYN-KO@MW zRlW@70moV&goxK@GqtZsYaS*7`>&b4*^^&H`-80ai2B2@lqd&@1-eY&1mv`G3SlcS zSGJgH`cW0eAPOSF+;stY3u-5pcRv#-)RUXsxp0h-$|HFa-bb zz0W-f(@yu=&+^oWEyy z#cTlgDFFD_?GwozUk8H&UR(B|kG+uD=4v$yIvg^M4`dqbbC`xtu`4{S+ZIK#Z(zqm z1dxS?rIQMXK?1^WTg{Qn$Yp@qk(I45nI%IP@O7GI2^v#GujsN}aOr!$N?d$uV--(P zyTMBaV_j|&fFleCz9&6zfgzFdh`HkuXO|atoxgS`fPu#^Z6O9gY_2x?!huPSv?;n4 z=mQvB={Cb~^ME~Ku&>$wBXXZvaZo*mQ}RT9KD;+1&GJucAkI4qfVaJ#EFg$~hNnd# z>4lm7&h_InH za_acAvW3iFWnyJc!aIDX6O)n=8bqoLJ5(K#6+xP(*wdq;2@@4||6yR0>72N~iGq4ra0K#BM#CjgdM`JZa0i4 z;#<6s8ooXrOT@^kf|Zm`x)YlH)b;0{T1S?taGl#q~-9s*E)Z) z^T?MPsEH0P{axXjwJucc>B{fAV zU6xS~DWGjd6Zp;dI#puczgqsMei>9JlF8B(E?Mi!Ws5O1kU@XUB^Io9m4DPP@z>`sQkGmO24j7_&;e?Q-jKXEOx+ z5g}p5Q5vLZH$WoVB5mqkf9#joQ_>><;sm&|hW@?uhG+zqslmKE6Xxni(QNEG7^t?zM(JWc6A%FLIn@_?G=bs>QX%vj-AHEX!u zB-I(cuFZqB5&&K+YF>*L`mqJ&zl&HT+sD081KB7{VC$9l3@!!c6WmY=iDbj51UdBP zgA=bFNVnjM8%{MWAZqPK4fkTXS6%)c5)22;Iye>x{f_p5aYgl^7e z4g5IAcP)UoROQB8)W@n92?P9dywA04C0IK7Zw|r2LL=Y`QtCU|Ob8B}^_TugF0mfo z2H|uREfw77!Ht!CO9D+2)0N3f7#WZE8x;p&dtetIy1qgrGV^`ee*wL+3pm!@Y!O3$ zT=Cb@3HSXgihwT3kg3h3WMc4P#lh#?Ab1(%Px@~>zCO>NJU?^>x1aUnU^V4O>#5zb zdF_{pWBjQuiW_Me|NIKMw9xT3&rEC3R_psciZ1peN7+Ij+* z0)O&~P(PtAQk7_iMY3YJO2*E-0j$ZZ5!fHi=c_yU@fWr~H4J%vg|XKV$3*9dGL zFFK_9+l^G!Hw%@(xL6WizGU7(sZ7hl)qzW1zeyW4=RUt1AZG6*W+Q4#h~BDA{(}f# zp+QIFz*+vt*8OjXVIC?1-oiU7rc{w%9jjp7!`0R$9zG;H{ch)u^A_`o#ugi&&q>~{ zD&x%Lshq#j4ad_NV~1?R{i==A79<+uu)h3%u{o<*H+hFFk?bmjNEI`fSabkeGT8$S zq2SwE!{*l7Po-e9EgvP_k<_MMXTo2$E0`r=$qk6b{*^~Kr*tadbM|FU^uHo17qK5a z^s;Y80HN`JX!=A(mR16vmEon4M_4@itBSOkU&BxKzt7Ee+n<13p10{KO{vgMQa;Kb z`nmyAN&wAmAXr0nHeDa!}zu$x?AU#5hfLbT4=UaJtn_W?(2ZN&p!UH{D4+~^Q(|k>+^vvM%@q zC1sb&tIiRhU6s+A{XN&~=fqWfMQ!;i7ko`JN$dT_My9td1nrct;*F}VwIXc zx$Y-S`xr#t`DJv4JuZ3Upd^Szn;)C9K_a^OxrK#MPk@9e#Z$l8wm9JS`__;CdADac8|haubD_d$Tt93c7JlNaR9 zn1&1dzT2(^+Q6S2R^FjQwKIMdItKCb>o(5(x5`UaLC0tplUYQLE>3% zkt#5I@tpiiHo?n?zySzr_NF<)@lX(1X*2ZZBTs6I)r3IM&iiNCdu?C{#$;r0u-wYE z*!{;HdAyRMBBRl^Et3Qx$63!L8UVNAQ*RFVhDVr7VmAImKKt0_H~+klXD7JL!GN0E z@$h8na@xuF69)|bBhMkY0Uwd{(!{?8Rpz*(@K!KRuB=uzdd|hQMCu;NHpY}6Qi}aO44BDimN-i!qIf*H56fSHt97lkNkVs zFIAIo1nW1OiLQc1F{O#tE}jV#W-;%q{l+m8=-$OaPlQ18$);5zSQ7V60tuaGfF zXNjg26``5RPoX{*WJ z1R|V__y2EcDbze~M4s_^j00HKczal2k$9Zk_fpoFBX(q^pjcRc4mGr^6l-CeH) zkh937n)?MM z2zGe+I)dblSoO}uF>G%7Nt){X%z?v~Cnhr}-bgly`hBs;?f32#^dR=R9X^;>QL!N4 zHhxt{1pH_{G`0WjigB@peKL4T|x9e=m z>7>KWcQoQF8oCnP>n)v)nVWtb76Gpd!4M_2nzwer-+tD{GDO@=<r$eEL-f--Q8T}DYxf{_j**JBt8NH*N&ux6` z6&ji#%S(SwO4ykuXg|5Y(~%Sq>$$&jPiPnwZyq*b)0Zfq1_Sz8Sll4vu14+rY5jV!x2;mih$U##OX1Tumeh=Cgk$&&Nl)G#X%V`h82sXNj?0m7^s5Di zBoM&7Dm}LQQ!QX{LEGHopY@O!B7?&t=O2{Sx3o=*|BOD!_sy&)3g55QXmCEVorL;J zFWJ8Fyf>3;*RaRX_M6Cs%YAR}HuA-t%DS%&lAvQxQ&rU+Cl1>8lwKM;#RUaoEWW$v z4b2ZEb76kN=~Aanv0j~N^!T^1LunPAlQg1ZOXd^$Z|jO5Dhqp0t2WhTNr?@Te&RXX zXSApAf5W6$*PRcU$;RV{OhEv?ui&@)5R(=jd%V!}JU2YEeqB9Jd(^LqL$`Qiqp!~_ zBu$Bqd%e#+{~b{(g!XU2x=p4rvAGXR(2m>eeD9n8xZHsIAi+IPC zwJG(`&BEsm)Fh2WEIT)^M2f55R2VA-5xoiF*@9d!MtvE4&fZaLj`NZ=fT%T+e6hTd zPciUmNEwmLTl^gim$T22|HB2?@tO6^>ia!0F%jW`rRd8D^2#P8PNdAf@oL#J)4rJF zF7-f?W}bE|>21saU5~NcoE)pE%DQ@?AxGr|UQ5UMD(Q1DvFR=0)zAYT{4xOTUoBYA z^9J4d0ccz}jo8s2KtKpoG%%PNLK5(FvW1O4 zktVPyjbmahaWH^(2btsXEk7c zY109pPV!>6vQA!0YX}Q>y{f9psD0Jj%e5piF>z>UWz1Wv3t%UK3f9XoSd80t=5+O6 z%eobG9h~*}KH96um!I3G=Gm1ry}9*^pcIU!**jTiggCPUQh^Y#i6J$qCV~?^uM|3m zRd*1O?z?vVIREGt+DQF4W3TSN%I*FaPy>(YZ1PYTwus$15tYu?Jl!}`Q%w>C-EQT8 z{{DUwhgB6??~Sr<kJK&T83 z-_+EUu5yZ~z;pH(f9N7YDwLe(&kqOH66o5XeP9@P0!f*)me-|;$Cw&u(zV;NlfgNl zIJ*-3amRf^ps)q^WC(x*54az$AuI$&i`9(R2!H$Z?4qOExbEs~-M}n_8@u_A%v+Mb z7=DC4uh0X5{{6`!Jv9fD?(XiaxqTBrJVv8ag`i|3N(4MIqUMM#Oc>d6A{wS?nfs_| z6ZqL2EvQMC0>u^c*+G?`hCtU`0TD)mdz*UDbZB(+$j!jO;I*BdL81pKn{JCyCF8?o z)PoqtY?rL;!_6d7G!NyU>gwv;bH-Gx0L@d9XUj2_(P`>OVbnu(SJ&zKFm!C&-~W0BHhj!k`8~Y$_op`VDlMcCxQhre3hZq%h=&D6khi zqF>BK-6Z~Og!(>ArILYmfVGCt>AtY;=0y64R5&`;B_bzSbG9tH0!#sxh zuST>pt!GfjZ3KUgZ~3dkX?$^~BYeAyH47bQThN3>CY#UhhS1&LZm9b&e#);<%AY1s z;JP_6@8leGwd9e6fex-4j)}n(cgK+4s2{wBbCxSE8SFq)ssgb3A}g~$wZFk3E1n@- zFtd^~F~NTt_0t3r_U!n%1{-zXcVb#|t8eOs*Cao}?|#b~pnGP2E(03fPgQLIWSZqS zH0FQ53GWpwCp*0P8?Z@(8hiN*yjD2m$!u@d(;-*YtgGkjX*dgw`HXb)^S3;jzp0h& zP7#H(lrl){v6%JIM87y*uesR{!C5|}iEf8%ULzgjg2nfOY=>~3#@CGwTn53M&~yBc zJNBQaoW_Q62DFDIEAP(XQbr#4!Q&9xu3wirg2xlHJ2R95Y-fvzY*15@VgF`L)iZ#g zJ#Zr2sRz}hz>fF)U&&gKRShEf{99kcj&rQ*$a^-4{JWKQrOF^CWq=Wdqm z3wrf0bx5h-!oFK8B~gwa;R%R75<@_L6*W~s>w@-{p>90I@75J^(&4q?F#_9GfGDAo>g{yd_9M(IQVkC_ zOw@Y(pvriS`VUxra`NB^bBo{9ZPJfqoaTgohj_k%iMxwVA&m|z`B%?5+7^{n@P!~X ztYQnx>cO>8!?7VmZxj24L)ow;=N9cqJ`W65w=Mi|vK)CQ)t5C|;Iog0 z_6(wCNz=m%Y}0Ow*UawsD9Firh&0C?1Ei8NUgiVQK#`~m*YaL<-Ll4;l^VZKL~}`7 zMbI$Rmkw;9bJ3w=BNgXXHX`mqQ7?w!nO0!AdE8%t{wsQ}+e5vs^N%)X4(bZ>CH`Bn z_EYFXBtRhbME`WUDra)?*U&01_1GQD3V+TC9+c}JuO(t=??@n0WoEO2^Q%{^ya9az zm6CGwig0O1++W-!wk3aa2H4Xht2+<({LsHPz(MI$4Xo6Be$&endNx>udqn?3Us@sD z@UhmV$NAD^`&rsuj`ze0xw{{{H9mN#v(t^Uj-h)_E`w7rw{Nzcp$Ah3W&pubGziQ& zYxw&jON2KVbYXqqd1}*V=PBRVdNJ#BbZlNu-LXDgm0(e83?*Mf$PY4#!I=SJc&ZD{ zF&Ye5VMo`*z7Jg_?dFoht;2Kp&)-}yS-?petD^hFF{s(<&r@2y{l6A&;3 zH4HH_;y4rZ6{RtCl>T-_8xXYX2K6&Zns%bwky@Qq5XD8=7dBwkT5iS>CnRq@WaW)9 z+wJb%I_?kd{8+$xr7$nsxi}#C^o^_xKrQH?aR^6g`$%b9mdlZg&K zhpC>qgDbC$AKf~6J`&+-10K`rc+5>YMzl!L>*)KX%X0O19v+oMn&zqzpqrjO1fW=C z<+~f_#l{iSN``n7(9SV6w}*<^R84!N*PC|DF0-IpNL9Kk#XsU-H|H8eWMg}Bn_dS$EnA>Qs>b)uC{rIioqc;RowD#^>MhK#Fb^aBL zvR;DYBh$Sf&u^z-L;cmlU&}99y&!EC8bC9UbZ#T96-NUL;8%=+-otx;4p*=ui%Sf2 z{uPB%`=2PH=ISl!3p2l$m5M4wWKj8R?@gkxl@$DN6vgUO5cDCU1TY7+@Zmdc12$YH zpa(YaDv~MBvB{}l-osG})hjzSa5XNb>vy*3y_Ays{s{gCYYT%Nxr*E|Bxds<1>A_p z^6K)Hy$FGK_qtVuIgO@O;VM0?6i@n%8t6_TDu*7+@vquGwX;$f3zN4dS9QY31_#f~ zt>%O{J1xMK2n!`qnTy~edU?S+k_K$WFmxq8a2MF2w6Qn2fXU1gZjbu~dobyJMN&%kAKbXR!9ufNlTj8XTFOpPm! z{I^>_=!zxJdutA@t7%1(vqn$&03M1RS*5h~Q9Q(QQ`4hV;qFnX#SvCrephp(p? zTsGysoJsC+m)0wp>sT?{67`S`lxfDg2(vi*(iEV9q($Z-uVHBuUG7!To1;DZP9Pva zlQ!*{dsKxDBje*cd%@}WnKaZwuQ9A^Yc=Bh+u}?wUQB4J&==5pA&7YtYT-tJY#w&O zu6aoQ!9V_4WR}Adb`-Ioq>0Omhebs}tz>76Lsn~vKR3Kr&EI9!;yGywp^{}a-x}jL z5{`#u8H?bJQ`Pb}5g?72=Kg74z1mv!8++qx;&TTni~NWzW_r3NleqpWa?HTwSw-Ia zi%ej%d1;M~xtqFo-aeX{3IABsiK2Yg+pIXDWRgXud#ZzS|>88io;FBOZC=G@OoDVW) zvONmdJZ1Fk0QQ|!ZB`N&3a?)6W}vr8s~>USEa~DMvhq?pJF!o%SUZ6uGnq0!&Na@8 z7=0$5mF6AkFm>uK{-zvZI0!m%|Q8q^{!a#UA~dZ!gE9txE>j#?qHQtAj{%#Si#aJlYSP$gEu! zL#a~M)qD%<8o7fC{!&Vlw0b-cG>mZkCaKLS*)vEJmVQQvL{2Eba1#KO<1IDNEUl}% z*s~6J?>#Aj)MBLd`(r}EGD&?77yF`&d4$3jmtyt3xW7*7i$$I!DH&l@`EbuOQZ4$D ztoz&M??Qv&QkG4viUqAI`hT_9z`4~>Mp?3%Y$8x&O60wvf8F7+n1(4=sl8X$55YF6 z2CfL!eMZ+gcwNtNe;dCSV-jH=;9O|%;n5e)uy1`9P|(rJYk5bM z_rrCJ=0n=zN)q-U1Fu0YfVjS`#>*~^tVa0%()WM?65>)K2M7HV79k%5xf;E`G`3kh zq>WVL#lfUc8<6T@sOgWffR@Ab^_J&kqTD}4)d)jjv$|C#HHf16@pFK_&a5tK_$KFh zJ?z{XHSp9D>P#B!yvFcTDTkPi$pk^fjs&OGhfOg5Mj$x?baMeiV(^!s=O25W{ev*` zlfM-%s+ETZ&1)?Pv-+Bl{-68=BJ+8nDdjrx(>#aI8gM<4n zSHo+4&SC0~ddzo1OLQ45w1O*2bF>diUWO^tUH`1tXr-F0Ry(J`*odumRHO@s&X!Uf z`w03@=Om#R*PGv;zn(wc4A80w@kD?rGv^qiAC;};cHwyT_I5YUI{vBi*<0pP z`AM!Xy{kT*a{Uc_z8AMk>DFy?#Uh5e2DVnjmrXHB1D5D!gR5=RibN%w>G*7M?7-ZD z0~*S(ug}dw&n&3bR8_TuVlx{(w2%aTz@40&O!ey~ez-iR&MyCKSwZRa5A|33%V)Sr z4`Red{nC;Q2D-&G9ne!(v7=Ijm&Qy}xgU6zXo|#cirmW?Vo;cNAFrgyQ!2-D>hsnY z6+PrKsy`S8z{@0iQ?k22)0P~guGoXkDY-GTUy6uhY&R3x5G2>Sk#*W(s);P`*g=Gj zuN*Ag!?md$xomU(_pq2I;FPM{1&gZK@r{jY+Eh^z>U~zBoU{o(f6nIJN4LJY4INkK zztNII_Ytvj1Y-8}k6}l|(_Wot+@&K_HC#Rd^cSDp(<;v~+i#h4^Jx~ds9p#;A2Q*L z>C#-@-Y4_@tl6!`2*O6+@57Fr*QTC?XV<9~!~6Y6gCX;K^Ri5Vn*K2H+isD)Sv#`x zn@HkyBb1*h>=D`9V2esOn)OeU5DrCBl18Sc=d4bI-O6|%XWC{l#~{G!XG-YEgo$Ha zL|Ysj9DD7!+e!ck;@Em-X#qDtcCd5n`AT7`VH4X;GX*|IPrS|i2sfbXfvs5o%nypS zJgRMSS(SJtWwOt=c2)nL5YfNhSj;eJF>@9vlnf?nt~k;Qv6h}GVTFErbnD-(IIDhJp+-l6#V2iAvish*45!_xPXzha{i0reU3fWKN!k_T*j2$? z_m=NnPG&_2x=35#29n&gT*TMcG|w$}-bc!z#VF^p%PD%?`Jva`rpLed9HZ&yLzOAw zMR#d;&APDQ#G&#K{=%i)8Ekv+!OMikyC<0MR=}HfNr+Gq%X_D}8TQ;b zy6$9)K9q^vFTc;2pGj(f`&%=j#Q$p-&_B~YfL*QJ(d2uQhLs#2k_IQuMU!T1PF@vU zNg}MFA9{0sX(u^<&EW1pPW~APIA)2-?RM|Yu0G1F6;3}Z)TS7Tx zsgJW)0Eetd84C`n1x6hkQj=2|HvL;#XCm1KiP`AY9oja2qJj%VB^TS2+XzY4d45{m zLsuv-yU+H7KrL+YV>=;_c?UefigIh^Jpsp5&JpJt-4T*^>J7T8WD@ z^H;5ZEXY73`Ngv)0wyvqD;)6Vkuw2hvwVqpjPm$CFR zra2Dqj((`|*lerR9fQ?CiMsE!hCkr<4L@}*^(z5D?%<-t2dn}*%19A^AAQ#g)L*;}yfNoMD)mT|iK`sIVo-NDt*y|3ycV`H5uvcVd&HnN4K`DRw_C7Ur~Z&8Q;yXc*LQ`^iQltQy+hjhuSN;tBJZiHCe&AY;)Ipz(eoT!22xGfha{xjGl4OgmPU#Od6 zc^1P59`(%|RQkZ~?RqgxF~uwgRi!Zn#oKVj^cFt+r2*Jh(b~qcT@S4khZ_G~^A%Y0 zGR<<4>@0a?GrN4(;lD+3={0*(UOX3mC-bsk1BU+pLg8R`i} zkHGy~qJzn&^4;K#L1P_3KfizqAkI+gvXiRvKyD}3{iTgoynkf&?+~f675Idl)r*}c zmuzfS3~*FR`VsB4m9Lcr<*z>=5gcF#AaP`QlpNg4bK>M7)g%A7a_1h1HQJ$ytgT<+ z?S5K%#n1p$AEp(&(QHoVNequRPA=L1~5iB zQ#t9}M(0a%VNGjSw=Q5{B86S(FX2_2yi$*_OS>InGxDcN#^!^S_xKYAPh?em`y3JC zJ8qg22%;o5{;imo>_DLSXr1RkwDp8>L$0t-aCT@-J?7h@W7lfGy`Jq1Ak*q%1UFS) zxvv7Vi^>cyb6ZW~>*G^5!z&CUgKI_p98%|r^SN5)G|x-htLkA3&Li5wyw3}54JApG zO0-8G8Gi7MKZUSH)2KnYu(u#v7=HL08b@c{$P!I7ACkX(!|41G&xMDJ_G9^FA)piL zv4Z{yL{iDrRa$s-Of9HDeYrtMg3dm-DEl!+V&pUCA4;!Yoni4D9oj2`D}+L91k$1X z@Y&viv7oJ052OKNJ3AiTkyu*&c0#ao8$$0F&m4LkVV~~zVvs+stEN6o_5`oOHr6s; z0d8L+k&38pLTvRhF`583$%eZ{8+D^>*=&7S{77FUHo_U16d-CUx)Ymq^msmv4NbD7 z^gK#DBl+P9{Gc#n8~{MFooR^KU0uCaP#YP|nh8q#YN} zo0fYe#?zzlY~n(l$>jW=*#*h32<`pKA?1am6kcVtv60|ChokwA@P}#sv?mT@&LMNO z&(~MEYQ6D7>L|zA@}@fzr!$uJHz{%kP4h|`J|L;@Pgw9a(ul@ty-FA=kM?4 z2VnRld5pA9cqjRMD6;c`z)OEwl#89LY?x;MtXF2r_RITjSB_CJ?o9)a@d5QZYr!_n z*BIKr!4GfK4-}BLwTgr?yhjkY^+^S~ z>|qOnXs<8>C@WJ54VI5^lHSi3iTHnS12Q1olr6gtYkMXD%`_G4$S3o^EgntX6c^k9 zFAo7d@Yue8oQ#1mS>v{XM!pG{cQAsG2Cab+bw56#f4AZ~`B`?&0TAwoYs@BuVcZZD zbWY7Sm%!_*ik4d5$Ezo!kL5m9QvPy;(n1|F58}dBkx2+4dn8a!MJn^74FRGR4>A|) za?{68tvpAwf9WDh>+{tKr-WhKM zM&E`_beRUW(;LW6{i8=&X77&QsfE-^HzE|3(Ej>AT!5GB zSuSD)#^SL=8(jGoKcfZBE`;$_2&IML^CbnN8(zQQ;ZCPvCFW+9lr7B)ky+7)NBSkM z_Df*0YBrXmC4vEhg0LRNSJ>^Hu&c}Ft-f#&FQtB~tG_AHy+|CL*z3D$hyRz_iEC$q z_Amu8z!Yx3I-8b#L3!`u{G6T6@uzpt6;Pe14w_$`zh``!(*{j&bJSlfZo5+LpmUIp zpQtJNrSU1XMEALt*cN(6~t%J34WoC4E?`SHrerR`cl0noYoJ8+wG%!MtCM zA*pCn(Uy%|ktjabdZsdQ{$NJmFrl{(F1EAXH;eD=5^4R=Njs80&hYplQr~iWJG|R_ z^);oV+VR;qwDu)r*8Ih`D>B;|_j`urA|X^QXNM0c>+sN^O^dlzkI|1>@M<&O)jIP4 zI%^u-fbJ>Zr13+YHXV{hXz$N+5e?s-F+y>2P%@BR zjQHW?GUmLL*_-I68~l;qaA}>A%XdB_&1bcYRq|TFK+KLY30kA^N;DCKX8q16CL5Yr z_`*SfYCH^n$nSbrN?cdqk&<)ltkxx*MJcyEl#| z%|xN*9F{!SEAqDQ40@*JDJ&BFEkJOyEo%Hu7sH~~_Kp9))5TqNkluzH#gd69cPE$|<-n_UO`6 z$5K~8vH1#CG9I6wQl9Qrq3-^{MvN$03g!Qy)0^BYMyKmpPJ}XarY_J+Ub(#zNGQo@G;J6BT(Dl0e(1dsZXA}7~&g3rs3kTjn&2I zV2AbOL^NZ+;LGmGU}7&D2d5a(p9ISoHQ*ASU7~Cc67)UrdHS>L?Avy{KDAp>CLZ6YC?f>Sv-M~riXeq(i&0RWuRWD?{;W?w?`9Hn@Jwt|%d1~XIOD-n#4+ZX~i2>^3 zs@{I0f!@Q@15CFzAN|qx&ylM+tCv#8^S*mxtvT^e7eg#RZIlgAetz|StSj7cJkb4& z800m)qxW<*cx?OE4BplO&%Cjz?tEJGXzmsyR%^0y`$DB_@8P_)Vr&6IH-#u&*6Y72g(uZ1WTyi_C*Oehjj`-ibahIP*&LV;c&*fNS}TWtF^(WXm2F zjfjTSL2T;)gO{6RTBrJ&b@~F=X6_DZ_1k-SOhKR7_|qPT(D%Fo0R9}q&eM6&7kp6U zJq>Ndn1=eqX-YXOhVL6=&0;3wTri5D9Kh3!{FxR*buc8M%1|yStxECvqwJ0YZ$z$F zvMXV>SX@bNrHv_OwpeVBROah7Z)K2x2}^yk(FP!}FMY?`qowYsma40XNYSe5YDp!V z_cNo4Q7kI`qT&3>mDSaLG{P?jvlOTB&InBg-6RnMEb=W3Cf+{+jQs&_6Mr#X^dtvP zbi#Vincdxepa`XG)AYPh&lxBPM8w+Vw+r*|194N$t>q{x0jT^>L4Fl)~CL8|;Xu`!qb}7egeexXSSj!e^74(7GvR84uEIo8j`C zVGv5~_H8IPlRP$X)8T$5uHEJO$U39W_8sOuccH`cdQJbn!c(>%muy9!ae``yMX#g* zBFEPULKFXOd@FeUZSoo@AzDW(>+4Y;k^$D^ayfYo=BM#(YkX zv!T&xXAd|y)^VmIwP9cuC^Bce!t`;w=l~ej(`*4XI{~r}c^JH`8~w{xwE+3ZOo<2l0~6o&S?&EvOSvJ4m?anN(y`%OEdEUXUsh_q5q$;6 z*j!urn9azn%@VT(n#BrSP^a+kfeWM(Y$SvvA@Y!xZbC^=QV1)&0aAc4M_j{;Rp`<4 zZK8dtO~K?S4;t*|_KVt=X}54y zT?dEM*({fg%Q%dy1nQ=!)xP&d$H~Df z61NP8jXuoXH*s|rg=R+w5#^O!3Pywz=_P3&o4#&LVODYM<458UL^K&kpPyG*YT4@vxvSaZuW492z<9_?ynr`1mp3>8`G~&grY>~qB z6!^GxTC?P~Vw;lR;phae;gCq85?@7DL9++ zTD!$EzAU1;+0HLACL{2qfxX&wsab>)VU*Lq{~vEQtvy!=LiqXdqp# zRVl0$7#*beVZk;3J8|&WwVuL6OCm?rFqXmoThrAWGe4a4CkkEjO3pn=ce5VzIT~Z1 zCKBIIl|JT0(4~jUk=8VFptoHAf+-cAMKqUZL}&x%zaF_;?7G7D-LpY204gP5(izZ=A6Eg)Kq%= zNi-g{7H6bM5>f7gp$FM0G z!(JK|N8qrU4o^oEf=UL*zh5+@k_YrE`==ZlHeXcq)8}26+e#OZsYzWPobq&d>Z8b+ zS+wejTTnA6k)!r^zu?l7n1}e0M+^2_MaR-pm0!OJ^FH`^3pdr@*^moq>G<~2b$iaN zqDtR7^1C9d&XUY}C+n=r}Y$HREm za^9$LT+TaKN@QM`B<{J_qz|5j=WpX9e!Uw? zxdI(mUdm=eXFQKx4RXL^P(bW5+N+YvB)5WdGso22;fSrMUG~*SlT0Y{gJs}e>*Zyi zd(ez&%1Nt{X_*jrq&egXOB9Eoy51Yp>hEBM;M6O@^f`>iy3Wk68;3PVdtRu;BI$=t zHWL>HX9?(gh#b@fLGDwXM1T})%>S$IEuf-m+kfG~QBaZaQAq*O0USj_T2g%khY$q> zX_Oc`C5IRu5mZWsZjh3e?y$g-l z=T_4`9JrjlRf1O?9+)`;-jMebj+z)aKOo~yUe+JMk`=K=gGMKmDf)W(r%HDZ|7P)MEDgC(5RqmVkY zlyDDxjtsu3YLXGT=~h+EUYVK8u){#p#^}(@_H^(RNKs^wf?}_}o*O&Hq5a(O=xxXL zlPRwc?cM<@13Bf*$6!op zxOkZVEY+KJsVnsEvQxvXNZ#?#{T_vq5HNv=DB%^XH;9pBvln_%xSbMb{R1;duR+oX z@RLDbZ9y|>f3Uw$fBU79-*^*!=o^7cF?WNK`5CILw(-e(PxhyI^j)3{$7?$QZ~V!; z6IV8D@9lA17ha8m9NOtykV$1c=%zBbBhvWy#9t}TG>;Sp-E_!6ov!hn-^XL|GBEWq zdB%O{K3i!5`uqsiRyen?DumvQ{uzaw3Fb{Ouf-tG6tvuI5PZ=eRq%?QCS|g3_nJ;6 zQc4AbU5F?aVT(k=>~!YRncjw_^?A-aiLxlv#ESSJUMinDOwthLx#**Mf0L&eiVa6V zPWI5qV6mI5swzpV^MVcgwC|6d_lOMc_M>UH6V2Z$liqTY23UWuX9sk3ih)3=Mw117 z6nEs=7qxG=RL0G`m8Xc{3z&4}^!3lPbf!~47R%zKWV?(HMNOwNAOijRBj;&v4aL+K z%3FEoKby|=WEZSRXx4qATgwIbbyL}{D&aw3lNsdVwwM6hR9|iFDfsD{{ppOhGd^cZ zi7~Qe)}E%v8`U#?-L`771m1YE-&Zqwu%ut0WcJ}{v`g>3eSsBgxjSd%WML0G^4M3U}M~_XBI|dM{=+@~!5~ zdeL^rQkRpuZFYM7%G|}T-i=DAnS2dDkR>p3jV!AV!un8q0QN2f=kr0%G-f|v**!)$5it!&7*n|(sJ{&GzIcsNEP7NC>lYnZza z6^mZnSj$RRQ#sd=(fRToIT9@TAdUtyqb)Q}gW+>I+!o%@O9cPOG02pXd$SiTxwo%G zC7s}(rQOY*_HOk<@i(JB#9g zR?!d+udE5(&8);6sDmC577?d8PmE`DZe8&;{EHc08~u(j!&~PiDn!_C$OHL4h;8tJ z-yd0ur+D$LC*v0nEDRt~)b%5xunu>x+xTR`wPKo27ruuuoDHT5E42CnIXl(t^vIDC zoPVIF&4mY<=Yh}M`qU)D*)|1}IM6(s?w|_3o4@5<;b`a#;hCCGZ~x&d)0WJxup0K5 zvl@46gYHc|0>L`XViSPLUGF=SDYxLP+E8{sg2r#YTi3!O8*Q`hc$F&p-Q>Bb@}*Wb z*UK#tNSi_6zCzt_ICBinA(<+=5AL^d`t2MP=ma&dbuEmSn3Md@g@bOKiy+Gta9r>H zV-vGcR3rM##qfo(AS=5U^IdG@`0B|)krdaHAyn-rX-_qoMnJ|H&cDHPnU0hFp^>SY zJ7duciPy8Ew;?-Lz7F8SlrI;4YTv*>_JkXKB~1KM06z%FPwS$CrmpQN^s@Z?=gvt`!Y;N0Y^Uo>6bpDKIz9BcNp0QfRzRRXR;?K5z zWWuS$A{$F~$XG%~p1P<(-&9#BPlsV@kM-DJmDjRusCS~>p;aw}?kCZyp=dhaYuq?J z$#XHT{_Fl98E_Md#p8_i$tf?>sVt9L!l{W%6V@qkx3HB=;l|pF8^HwF6z9MG z-d2;hr~w-Jz6WBtsCeDXbSfLaDJ?x4P`n=a@3Cym+PLV6T!;pz2V?j6__8{Mou$zb z!72fsW($Q90hk^#{Ss!bj4PrHlQtB_F`fl3@R+!HTV;rVnu`{dQ}tW=UGNW=O88N4hG zzy(%#%7tp>R_-O=v>oDh@*)#-b=5awZ0l~!V0%=@uN<=P0LuADUevg zw?P^VgkA_5s_ioBH{Z1zg;1snHym#X3(t_XBmaiamU5wKrI(vo!^vut#r5puG35ed z)d@qCsnS)k?oKxo2uQouqRilTU>qk7DM9Lf=GNU=L+M#BcJZ6;8ksR-!@9jvJYZ}mP4gadGn<& zif83Ad}(&G*uH98XWT-j*-3677cp2kZvD1V;dE76DCvlowL_WgKtWg-x6wV=C^Nj; zG<8~E?Beo+nFVfJ8;|>$gJ6xH!T{mM6tBPC@|ttXif(CIy-aou;L=qz4P7|}+2tVo z;ECi#gOoBe2oq_jkCW|Oql1k(Hiui9!YO4!pc!Tx0vFojpl)BD@SiRh1A$%>muL#| zJcPHPm4KyU=Xzqel_-KhHl~HWUJ{7HhK~5h5 zO@?aq`){YsrqMV2dK%dJ3fw(?k(;c=(1<|1N1@3xwds2++`&%CUK##Kxv7I9kZk0G z!!w60sZHO|uDU8unjnkR&W+4gM`%r4U`;oHYtGGTLT17|CC9p5c;bXX-E`A)>;25v z&`i^LXRP?S7-Z!pzN>N8Grz0o?yNQp=bBc6;%Qf|$Rc{i$L3ORnaH%D zX4Yjb2bVf=`ApC$7fMTC=Eh;{I>>m~Q*S@M&FWltmh4?UN%)SJ+8O%x=N2t=$cs0~ zX(`2FF7WDZor$}ON_M>PLyEiwQ-i1cA`sbj*@tyBoU@_&i?j+PTe7cU|Ksb{NzcJ$ z<`KSt5ero>*`Hf`*>{(G#s>fk!V4v6chp=3S5%owB1~J#TX=ymWqGMBmeRu7!3e)l z983md7J9JTWpnhurHp7`f-8>%D-yU=YRp(4fwPVBwFVzx9l~SaOEgQLLO?h)l;WnF z^AxFy0$iR6CiQcuwL16u?1C>E_NLZXPAD*bv4jS=Bq_hj38%pAFR%Tz#xvnD!7%Y$ zaZ%cC)OaNTt2r^(sEImYo;Yo^0V4|$5FRVZEPKB=aG?^&JQUy9H(XO1Sq1MBqo?^} z2a`=F(wtnxN{070Q7B<^|I|;BEGF#1HC9`yN94dpu7N>3e`$w(!U;c>lwK~B;he-sArwC3)EMBqj2$0`SpdPVA-Pm(KtT&ws$A%P7UA4bz@2b7*IPt^# z5b`>MqZO6C=o@Zf?hX4*_jdZtaqcA181SjKLWF;KB#*aQz`LI#3fGkLCjvm3qbo4f z-*+`$e|iu!8K(Cput}=H>~7_s59N8AD3;M(4AoXmOViQP%r*vd*(WVo7Wya|C#=?* zN3whOoNrB0Z7@<#ul=`a4R8ps@Ll!meuFA9~IQ8DlLB+Hn(p}cw5U+wHK z`6nxxIrTtxX%>(%G^V)?TZ8)85-gKJZ_+%{TBYdV%iVWesH4mVvSyCL?b@vl1y~do z_0-hnQAY4Z9M9r@xiYN1+BaCa9@berrN0@0c?0_Ac}4--%}yT4}rUA);XGM&E(|w@3np#^wb9!<`rsOb|lb(e4JKE)sE)0u! z3w%n34<|ud5TN*q-JzPdHW(O`g81zd)<2W~{FFccKR1J~Ox7^g(F0xYh4td{_JDM) zll5iHPoo)JdXgUW$vPybW;Rvt4J)4(d)a#x-QDTQ`TC38iXDQ6wRcX>URTg>zH!{6 zo+0J*Ni%<^c6Y8uv(*Dcb!ibD@wBaYuCAjJe!+l%% z)9VpT;upSTz4)=L=S3iK&~5U2N-PH9l zHLi zJ7w?W@!6xk>VrW0OO2H?!aR|RZ=yRpw`h(P9gEVEW9x+FkT5jGY(5;=_)me(^x{Pw zK2s1*gc8!aKAIgHS;DucH*+mMk_7x#foU&=Lv@V)o%-piKOU^Q%o^Mn2Bsn(0un&# zXImPJPQF8lsD8A}wBLIK*^(9-llS4*kt!!cR^$D@?CC$*oStldQM6MMZRkzvr=g))KByx2Qn6R6V=#MJ z_fLZ_(kSQx=nHJ^?2ZOMJP2fG!S0a95LS-uk`HSBR7&!`Ivh8^>V2mnb#PFtp!e3)`8`%%UPzBogrz}A+g3+rs>}9`wBKK>D`Vfi zd*`Z$SuQ(T@LX0P{Zg_{kH8fhN7mXT08ARV#xtZD^>gE`f^vEli5d z4SNMO=6~n!kg4)-CPA7%ujpjlbQP^`Z~nYlxL;QxA=tEto2z2=7$t4a%>muxV&M0{ zy<`pORE5*0H&u%Xk+F?mR$hn)`p;4$FUt3b1%L}wtgp?=rd##p4a!8ffQw5?Q&Uqx zUy+fKM+<-cT=Dy~=WjqPs9A|SsR7T^tJ*j`AdVk-arzaWz)*Aq&@(hMb?8>wPwK-r z{p@@%WQ!ZnlC-X6Qs_Tt*1r@}29C>jx*y1e?;ZX@qf~qY~&Y78gDuw;*doq(iju${3Cb8PJznW!sPg=b=X|$K4STJBgH74Vt+=Ng6 z#p$<3czc0|Q2i|j#0I|QQPAY6BsCL7QlIgfqdUHXJ zVjBKyW@kcP6jff0ZlS0xDwMc%>5?wuYZ+_hJ|(yZsikR-Ac}5{5sU^O1SZndT8(=V z*+BY?CUNpN(^Qv=lol(PFu0{krIAJ(9p)cPeed*?Z}f{G9uqcsmPapwobI2-z?0kV z1PZqxsyEj+TdqdTqVEV8F{@xTmHNcAwp?bZ640ymo73 z*>E_*xqkbtU`X4HBV!X2KZU=iLAIW$=}fpb`n|A#yfWMaRIdsJGoz?YE!3I<8EMl{#mplD@5QO9STEubk zX~5N^e&b#Li88_Wd)rR@o=g25^=sEjaouM)&DaY0Rb@!iW{t1qra&GIKmHD=>hzLD zP|1FO#kB$R*E5<=;LPeXASRc9ar!NaZdFq=^68z~ho=-5I}VS{D#r*sZ)taU`n2~a zjVjr3$FgxO8d-l|(l2YY;I*kw(2XB^57|KewdcFBlvnN9<%*u|D|Rr>wI|k`vcD_` z06UV^eR-AQ8&l}&)b>|!51FfhbMI5Dy_jR`r(lEB%bS02+Fn!ZmZNy2)ZeMuVO+t< z&j*W9gQp_EwtfB#;=CTT3Auexy%KNyooQ&Y2m z(mPgpZwT=9sN7=+MCGe@s{^krQ6^AJJs~Kb^7A@9sn!JUG4%9zwEHH z&v+fna<}ayufc6!WmtZXPFWe%V#d3Bl;>5jcvHgefgqguzHPV(8f|7!I5((TU(%3V zR-c?W7fWidMYj!+ng(hsxJmYDzr+g~8je1|x1mDL;@`IU_RTyn2JxvJvRApXGW+OE zxA@VkLO1c7>I+%80vcwfeOYa%uguI$f2`G;VVw*aqcn1o=RgFrwr{rpT#`rnPNCe` z+1Z(hVoxynSW|;BsM+(yo0yybX+s%wkB%O^cOjk#$NST;jy+R!*TqGs8F+5}QZVdW zNivF-A>}j2y3R3Mh(={S=|Ht5Bx=xPL_}JI=p`+x)(hTLEP1*-0xmKOaDMsHI;X7u zDIlXOo^1siKx@i@vdVIjTS;4qTS@!nygECJ%od>Jt|V^X zNbdovSUh9jXMVs#m$HwDZkss8%;feM!$OIDTXF(A+@b7ls+2E(n+|DS)sPvbk;l7^_dmJD5`y8rDEatV@EV08}lweS$>wtH^1a|!qVkSlt zWk~(H?5ZnVbD3tgxOB5eIL6E#Ml-@l&20fUQ_IbFBM!qu39e%gC5Nf7Y=i$mm7r-K z)YT0aSI$v`eM;=fZ%*%V3NOBmU#rZy7QSz*I2AbW{U@~8WfW6lHY%iMWue{=TT{xz z0s|gD9d$|t~-vG|+{V#pT3Qy>t-kRiD71HqUX;lpg`IQQenSM|+k3l~o@ z_$x89wp}f>k&$=&9ZI9P^WmGJ;VBnXM5eZH1esZ`WNH^j=6^q=iL+V$U z8Ld%km#kzkGl>_oEuRpOCex8ojzrm)XZK*_{RXAv2D zTOTessc*cMGT$MbJLk=7McMIFt6`|Opq?V;&dE-&Q~|cU>um|;Yr9*7eQ(<2vMXF@ zF48lc%2W<}#I%^Yqq8XO5d!{-qcv`k(eyzi2kevTh#Y8VR4p;-m#;mtS+zx|y&iAI z7OvKzv@suH&8+3Dfg4w{$ZPwseU#|C&4_NRo>C_ttMWZEx5c#YKC0viR5gelOL9`L zU=LR_h?}NHT7=|!JX14_R;!OI6Wd4k(H^dz8(G_NDLdCV{cCUqs#}e{|F_+$KDmZ7 z>9Y=lcM##ZpS`{0P^ zi6%poe7h=sqesd?uUB1dGomNN)zrvdAVV*y=|`61Pvre&3l{AC;9F3AV(jRm^!ncX zvbzEUQ3@&2kkk{XwlT_<^C&iN?#Zyv2gEksn01WrD&#G%!a-_VT!kn4{st7LN zkkNxML~TTRZiM?Jq_LM@53ZKNQ{yg6Xo}oK@;}5=t5ti4`F>W-(&ke&Zi(9>*jnkX z!Y&JwX%|N5aGwjFE`1Gp-7Fs23nXPr@MLqS;-!iGJ*_Gm!xlYO>eaSwjT3t%v%9ag zpO{{NsmJhdfPou*o%*!noh&XB zyrnfALUp9xE@`VY>_KG|eN20)rVlK*t2psMfV!!~2|ZFt<5R^~Wyi)NvSY&|Hq{-y zt9@LwC@Bk-OZ1YG$2|1gCGFDl58(E)y5x%M0R>}YHWD_IbKS$6lG(bUsJ6}U-E73y zshkkyjT(y{{*1j;+1AG0hW0q;j)(afD*A8%c`|Y<)7I5}1=s=|C@;|ku$p1Qv=geS z(9Pg|kE}9MY^Hj99N$=7VcIs~02XXCRB{R`1uj5J*lsmMRPo|X!S$9a!jcB>b)tDB zNQZH83OqBxNOxmO%7(D%jr6IhdB7cjU5s{%$XgXN@<_t;dLZMWz6a(JzFg`C7pQAr z#+KuK4HQBLALkgY`RC~$Q9qnt~gg9yb3dVW{{@tmh55$ z3*UM?UfiiTa0cT=s1-aM-I(7ZjP+&~P-ODnghVhld%h>>NO={1v)FucN`3JR%3T$w zyDDqg{cvNcXO|9OF52iItHf-rcFV{4(Xa<)%!^}M$Senk8XrEAaA|v6g5;khyl@I- z@P7Iu0@W6jE+fX((e}z;49r(3yXvYF+KZDX;Zs@gC|uc+EQw*=&F{KeEvO4s?K*Bc z?&O`ymXO3Z7i8vk@oJ?PnS!maD9d-nzG^(4E!CI*fJ|AZRsPwm*@Sd&DSX{AWa5Mu zfZhYiZo7>P)<*#NY`oy5?_rQ)H6bSnlqHEW^ivO2gCy(3=(us^p9_d#Evf!irHvfn zxWy8e10q-s!E=3==WLp9_TOaA>{P6)i+8l_n3>lfhz=_tWp5L4c`9nnPo;0*lhf1I zxAiIx}_L5mlh*__UzP!q&{j%NFS1>0-JpH-|^q8u`iE986ggExpNzIuyjq&c?;hhIpN` z|M7_okc}EwJSl1KlE(y6>>l}SU~)=ibq-h}XUO~mgWcjJ(-_8gNGFNQwh-TO2k!3nbJS;?OCGhJMP>9bqv|rM%5r_$xuQpgR>oixj}g#> zuyyvUuYJ~VZB=uHYAvsns#XVa^8MvRnubHVF|o~pHXj|nx|ddxn3MljgN*1z8z!F? z@^f*Ze!g;*(o(O%)!J^J7b?taiE=JYm4=C%oPn^$NUU6M=Vw-*&RAIb%91ClYBj}f zvuM-Mon9A?y`My0yo+&q1a){sQS(aV?r!yq*U4oRMnUnQsyA#&{@TnSMqjVxYq|CQ z{FLqhEll|ad4C38?`}l9_D+9%8)V_qOUhrMf^oBwH+kt^)fOOZ(MKlA%@5}iZ&`vT zY{yT=wl91<(C&Y(;9X*x2x~-KBd>kbvfg!Tyt?_P&PIjl_jfk8Y)`|uX^vYo<+&a# zGvYQrrz4yzkXvmfW_I3tcdgFBxG9cb_}sdBF#S|ydl_l5Qks(KV`e;D((+DeRpLiJ zMI&c^$ta}!HKrEi=}F+GJN%EPeGcvei?%<4hsRW2(bhsVUaQ@mxYu7%qW}}Xc)Vr& zZ63{3_2JX}L!(qMWrkxl?*Dm``S0j1!bPmn#dXA?S-~=&-_n(%`)>uD8E$Ar-spCA znM;J@QND3{VqGIb!!kcu?Og|3Obl+B8tr*Rd0`IC!JVP@vyj7U>({ugD@WtuqO~jB zTI}}ijdghdW-dfQmD;+#>dc6Z*Dhiw!coYX4+tlmwc%L_jX|#9ioWUCHpKixQ4WdC zxvzF^+efig8B_T)IHeV9*HC~i(YHCx&eRgNgYTKDCe`n*gF-a1Qu>#=^( zhbKxv2RBrVcb%+kBaV=8QDUcHFl7m_Oss|Z;#xi<|~MT9V}^mJqjHKs-d_82i(wY50@l2kGi6fGa;^)C;UKJQ+wWEFx|1 z-dtkzVeP0F015Q~vUA}7hNS&|Qj|eICzob6l~8id9vE*4}R2nJUqR z`ZX@Yf6wU_o8YOHkkQMjOA&{Mhw+5$=Md;&k>)@1u?FDb+p<30@LE26T6k-AS(2V2 z=?@4l+bt|E#tZ$DP(lVH^lLCZIxQ5K^H4jOdSiqRg;x_ZS}HxlQ6CqASd9^}>U#i+ zg4FIUm2}9I|1$Ti?(^`(wcwu2o7ajNJz*govju04y?N);!H}1G$3WvECN63I)1R6*xREcD@j=GdNlwk z@PJr_O$k`wOy0cD8Z57fl+~$mSuRiPed*$I*#G0lLq(+OXmd+#ZDHZ+B~;S;X#%3Z zU+3ug_CjwzSN5JbWYfVKBQ%Ca8-0sk&Uej$-lI)ye0+RX+bxrbwR3D`HpAtTIFqTS zVEvl2Ij%kZ{S{L9nHr>ajl)!vP=A5Gq|{_nGcyona(@lKx_nuIo0%O0!>s-Y8k4ld z)eMbLD~pyivCFs`ov_R=QACzB%hXEZqg)8^aAo`-qMeLuOi6&YJgHxSleaX>k2AYahX0xra6@_|L#iWrr8R8qW zlB)E+-Bd&%hzVibaZbcdk;|Hj?rVpwpZ!|FbS`M&c05k)HO@6u6l8;%gLvko)I^=+&=dgxV~~k1#dPcXq15{Dc1xp=Oz{z%_W$=KzoBwiYGcsi2^>QRl|m zipI0D1k2;3JhVjE*w_L>*=z@gm&FL_Anq~j<>l3nkjPC-n@UIQU6$*J7h8RbGH|}K z`+^7}L*52fUj2Y7_$*!d1!6&zV>EX9GuS>CSJw_JS6T9}>Ll=1)ipIW!=u#7+(4CZ zGsaZ}d+q#OSy^cr8l+R^h6;f^2Ls1j<`*V`%3umZqja$D?g)~TKL~*xx-G4R1v2uF zS$rBB9CSAnKPZE{FRT3oWx-1Z_3W?AWn`>e&vW`L3S4V=Kh0MYtB z%0I3xv$sRQ;eN)&+++&#cx1_m-JINl>ioh&J8^v8HBi>X0d?rr9H(Pt2tq9~m@9?) zR$g5Llgm_&20B?aHG6`~D-_&mV6x3+HB>QqU?u#I935)*u=q^y6o-f|t)Ytvn(avT zasA7uj8aZlCH7J$$`VK92J_qRW>bZ(XI(=SD&xY!!c;GR3{3JO42C}H5+h6oQ&}27 zoPos`ELE9oh;OzE)ksQ8+9`E?EGvkS#t>>W3-sJqCFT+~0qwaz#a+PZb&rqx5JHTj z`Sf>(%lhcJnE!x;4C4Ta@yCj!W}QXX-Mzh46b{s`Irsz+Bf&tCWRCk9zOU}%a}-N8 z7kL62@A7VD*V#XwD^AI*Ib}&vh>VC!U%!4OZf$S(nw5~onQ4+61VPpV%j&=bSF^XfYiVd`mNa%u6U{wRqV07P*&C# zz~gR+oKZl@<2)tsEeyQR?Dv00-V{?YsehWQIXd?1*ZKg0F-onEi}ZaUp#nj&N18qX z^Zc@>DGvoj{`Y>u6)argu%(6!rD%3_E9qPKSQG=d_Is22NK+e}l2*P>nK%Djr2u{t z*UFzUGGZ+%TWiss*#e-YW?3%eKOjFt9c-c9koq{RRDpF>dIo^%oy?Mw&3RN*k4}fm zZ87}Z-riocatiUx_$B~naSGu^fOh?^FL9~T5!-enmFpv;C%^DO>zW@}*F(MSA; z4i1QyKRN(zY6;h?ukp9a=gBvy+ie={g>|wo*Ft-L3)R&SF4$k7Q??+1S@1F}q=q%t zTa?yNzzC{nH2PMCXHJCUqx|hjY6jkzU*qHB4;`N&XKE3;;ahCpJCk-}gF8aN!OWQv zv3Ps;)s7^{r*L)y5Sun=EyIm(=+y~T-N5$#+`D62tFyQY!pzZ$(taJVfC3O!!le&d z+o}{7M{ImRgtF^W)M?RvtD}Zt-vTd#(YwXPMM)vN2r7EFO0g8+Qw6uXdj9ql zh#VWp9F7#5wuCUTv$HF9i2!rvX1|_O8y&%QWME(h#mAqqKbG?}mrpu{p9x!T)mw)~ zw*!(!i^R9_FSVWh19#Vl!3ZIjI$`P~|D2+(7k3$mk#t>oBALL4GKfZ$^#!_Iu5q0C znao2n!o7U?GQkJdL?J*^`e&@9VWb9x?SS$m@cm#dK=$%c*syHk&Kyw5`h{iC&saADF}*oYS(}rG|3k(N z^~V40{9g#TaRF#Bp`EFik!v=doO21?EUjf8-Uu%NV0<@3#KG13lSSR{RCy8p~(Da zaZ$I#ZT+`4#={GC*+a*9b(Cb*1 zNx3FZPEO_%*&tD7?9&unv(gb>n-{V1NxQ_%nLs3p-MMpzT~E>d32yROijmk25wL&= z%`pr?tEPY&CNK)r?97x)H#LR*0|NL9%aWcbl6uygIgW<(BW#)XT-ys~Ek)yrg|Hg3 z=yKA-nLtv0i%9fw13M^dP~|xD5VsrK;W=9XxZI03?dAt{E3dIZbn?L;{WiMB)R`;b z2#yEKhX#v*2D7av&9>kYw5;?+K$_g9R^GE;Bic44gChbil`Sb1K>Vx~eZ56oj;$)3 zBFF=fs0h~+5^Y9dBx$h(2_|CFL2tq037E3$33DvcL=R?v7iR@Z({1e+U22kqy5tCD zlX29-=)1VMj1gb$_5i3_5#jF5bU;P^`MdO^9 z?TA}@lBS(L$n>Z%ec3Ss6|)N+)>g?c<3}kXlSjuLFu8rZ=|x|MdG0V^U(Ff)*E@+V zf=w7R)4=LWjXwXwg&}2q6~SY|0k<8_hwuOp_i)2$apQmd_`yTlSSigiOsuk7`bt&n zehC)fFZy5Y6L*yZMdJp-iV?u*Y(}&}wXZkhO#JlFe^8y~&?pSHmjzV2b=0Bp(pNK_{z}cDxiI)lx5Jw-?&|3c1`kw1 z!fTia!GO*ZO98X0P`wxdwX{OB%f`xc*_%gVrnMmmTuPW&4H{jS!0 z{QcStf?xNO(dYG=-MgNKO#tE-2!P3&xvM@!6!YQvo0>*((_b?p9)O{#UjtWv)7?5^ z5VKEkQ+pDB#?lr#@t~G0w*L1d&HwOiWt~-}V$-GdZ)buq-IWe?xBgH=?_p84X1HWT zHp)B3q{$%2{mU~im5tDRE64)|{LGMDp z2m0meAGS8sMm(yZ^-p~lk(=a#qeHk48R#9oL4Xi;EAkeH{ ze9(5DLzFO5gyK`&$S)!t8uNPq`wlqqVHlsCKPR>|=hj>i<^hFO2q zR4a^OWbY3uu(n+?38|w=vBLS_3$*Tq(xvbZecxGKUH2gmjg4}%E)OWv;vzjMr>fAG z?2)n7Tkg1B{oC!X{HrD0ue$W)IFq)@YQ~l6IHB9s_japL>@uPSLDK9Izr^Hgy+nZya{9-T`yMdq!4K^}vh8p!T8@hF>d>IX8lg1K)dFFho}}j9ve!~} z*sn`HB58ZdtF0jvTSa?rx5#aD(!-_~2id3VO?gE0QSwdgM=J)cRrVvE0n5gQEhS#2 zo9#h#DdQ!f{p8nEz8gd?KVO-0aXbv0YPZ9iU-w+CY@RJ@jw)*YQMs$I{5x(GWe_%9 z!t7Pjh-nTUcL-sYWR{h|%2=EzyaW`CZ+C_P1Hvw7x;l6!pPCA(*=RgSbw#5m*W+=u z&L^B+nhHmZMe6bhFaj^h+vTOib_(FrxI;Uk*O2)3bqB_H8rDGtl)ev;14F&T-=R4W z7jZ_)mRYPkY}&3VBp|#NfiFZzNTqSQ8jn;u>8{^{NK*xJNo?<%kF0MT#LWE(@&|;Z zi;{_(rb3BwSw%7A{?#!^LJ47OxQfT`iFfSOr3JXE6W<^ThntU$O#9RB zrt(@G{fLI@z&M5A+-wkK*|0*Hh2+jZbTq+bak<}TXS&+KBz71QL^)W}+#ZQg;3AD8 zTvzS$zq)jFs5dA2>=@K3lJ}Ihy6#L$_%AW~ybEr%Xdb8iQG=l2gz3Voz-0n+F@iil z*^6l)C1e`CC)f<4TQ8O>`2E Z;J=itoAY!e>Y$-f4<7wpaL?$U{{*g;*I57n literal 0 HcmV?d00001 diff --git a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png new file mode 100644 index 0000000000000000000000000000000000000000..6f59d476ea87dcb72824ce72070c86971b417c33 GIT binary patch literal 160405 zcmd@5gk5EWEaNe2WX$pV3h zzFZ&$Mp|FY6#{Rhmg*`>z&r3dzS|HCy#42@YUlw1v4x-h5X7zQJ^_KQf}l!wbbZpd zW_{C5y#n#OXylB)ZP%^W);FP7i9^A}u6N?wLck-T6kk^ijwb7|jt*{4WxP*|TkbnJ zjb6jCblxN$8Jofp|1zKudc<`n^hczTL8L!JC$Z(#9szXvz)qaUPH#%ryA&CZofNS; z^sfL}RI3L{4(TrsOY8J+-al6RL*xI~e~HirK0-j&4`rCg3B}X&seARU!`4GX#)ue(D8e1w$}ryJ3qjgl{! z@pq}rmLz|Z3f8e;O%iv2FUjg!L~Cie_A_4YKt1@ifV`bsvR?@uGwOO&+Uirop=x9r z?qKWmJS=l~|7Hn^m`W5k{$1hfA$&dHaz}`MOhDif7tQJ7S)8IoClmXO^eBH@wvOPh z;8;`6^~xJ2OZ0G3^=St+46S{Fr-~(h2OJnoK3)G=)0>w;!c_XPQ@c)DkB$ioA>5+a-cCXt)Mw zEgoe;yR~xqcfsc290oo!;3|ISrug)_ZMgS%S**>^0hXX`q3&9S5$=Ncc@n=@TI^z3 z1gE=cK4Tv2NK{aB#OLN2(_(@jwALyNx#E!?dZy;9>3I%n)70uGN8r-qQaDP`i4h!V znQk@Cdoi-dEKe$+w5IS|U`g%3A&h?DVvFeR#AT?%!$P9u^?;F}WGMX~ZXUN^50Up? z=EB-r;?2Rc@QL8?r)hU;Zj@p7_+k~b5HpBFjKqviu946>A60mB(g*jr2}8Ji+5}#` z?U}EB$#7@ZiLxO*;hAW9J&6Z9VkKD2uDZr8w&9zvuRmCCR{rRu-MphZWj@t>pEi8b z<-TLEP2_lh65`5bOy+BGml088@tKSzp2${|z%S^@Q%ii~C-OhsgFrUYglkqE#9S1VM`|<-uxa)9OyEw*94W0T}8;-I*fs#dHc!O2l=22Mq3D@?UgRDgn z{8MO=_@OYZ{HFzF0-lWpnbFOa%j2reNz=4w9+&yFtS}F7lfkYq!9`viNiQ{>u&Ym9 zUiE(&DwC!W4vE2Tw5om?i!G!_DT`>N!F%LkL+Z&u&qHvN=B@^AmL+JmS;#&&kFn@v zKwD1X?(lnr!@F@;^)wFtAPzFHoPWC8#;=)zr6zNokH69)GpFU-cJP~AUK;M7+ykqE z=Fh~^K9U_Dr6;H^>wRw#_h9#%{-~-@vw@lT&%Ez}SK4o*KYn*hN`^^w;G&Z{S{b^z-#A@UBO?WvEZiMtEd!e|YFdBRw}UKeGI-m6k48 z{$n6&iKOrsj&hWb$!oT1iB;yL%B-YpzDF%s`@s$}W7XdUJ}2?@QTF11-E<+QgFJTg zS0LFR(_tER5N8#NMg=><*)?UGo4ZB%Ne1BuSVSOEBqQ0UWtj*oBQ^BsWr#W=lc!(d z>N-UPcIQUZN4T`!*2PN&SotCQjZfjp}t>~a9%Z;K}Fn>7Xy?m{0LI1cmr`@^BHE^+(2aJ8T zh|YV!GC&u;tWHPf#xXWQ!KF_N8Q=Hg(2B0|j<({vK{lwM`}^3W#3#SyMi5(^fh;Zl z>-VGVF#m7^I3?#Bf+eH2D2K!jd1XXPj5LabcEn%pWtl=&t(>B<{T9THKds*G{y^b__Kc1a2 zuW-hHy^IyQCkyaI2Io z?KZ~+7aIHCB|yc$ac91xdmv9mMq|!_tj*+#_1~20 zNQj|_s9%3RwsaYtmN$pM`itKb`Je(4Y@_Jrx|`xF5v>=qy!k`gPrFe~jJ}6p_>rI* zc>O@zN`GH=ie5NKU%nI(>eoaoE6+SiJou||4zQ!H7N5hYUykF@mKF1tEoPZq3i{->d5rxN53kbN{~A>e zT;?);Gtpv^Zl&;cBB+2nLr^+nnec_MrlZ1L#5csD0`EoTjdd>R`uA;Q8v!v9boXZO zVO}maR|?0AOZ7{YsygDIFDhhac3y%?@*ED7xc1IQY)ckaqw83P1DlN4bY&fMBaJ0= zD|nG);yC{sZBbvi`q-Ch4S1?|;0LpOB?^g^@^=mEl}F{`*8Nbw{C3PUCU9iXa2}=# za~(8shibj?%byD6Pcc5LLG$>org?ss_yET+ha^4Fy@&PxEhxB zQfnNORkInQVl$xFDE->HyXNoN<9FW=kvyU}qKNHbep%ey_d}{k+vXN^#BDX(>LX8c z7ujOC5dY||UR#vQ;pUp*W>+HP=)oP_J^Bu%NMix8!v||q%Z38LAm5DH@~Xr1d}lo6 z(0)zmE@muDS2SOD)je+mpL)QEHWz$I3-`FzEZt+%v^Q^L4PPr)FjwX;zVR+}4ZDT=ghL`zxFTn(M42 zcf?4$e46b0b#iXb(IQ&cpNBE(yY=(ab}&i=VV*@0uIaHbFE{d*;T8B;&DB9zOzs9s zU^Ig=P+qD_v`DY-+KVT`Bc15wV=Gok>GZUXZS2wrf^Qv7CmhM}aqpOnu7;dhy^6rI zsYG%VbmG!cYt}@#_7rdQI0xi+fNB2J?Z%%HJMcg-p9NyDZ&ZMt*~HGpm7t~c3Ia!b zm#W4KJI>8PV=&Zem4yvFlbvf!72S+|xOkMFH($JPLJd9fvd0W?HSz2|lBg&v8ojZ; z6oED1H}fenzi$4#MqRoncKluBfcn(i3A}-x8B=dTc481TQ)lL4-qm%bwifVF;bMqd zWN^eT9^ACT)Mz$o51)HCf&aK@PMn_S^Yv@`Vy0DME!KdoRDn5n9$n|fT9m@-Fc9e3 zw5^vRnbNY+xq&&0JS-Dhx){nYY)jD!LzH$mP%W~4y>W4FTe>!ov_(i+z?`YHAH$En zh>qZr6MV|CwGBkwt=1Fl)<4}Yg(8cv(jPNh&7%XG7RB!vtGry|79*->A5ZJ1TwC5k zY8BQiPaJafr}Q%e!R+y3vXO1B%`tPTYN}Y)d(TXNmYu*oq*s;Prq+1pxa&s>N`1^< z&+iS%NYUqYex_X{0k3~5g?~tonts&klZ(v`d)`SvRkNsiBGwjo|Gf{vmzKh%Uj>V= z7AGg3>^xjyj)U=3XQn+>R=M)_fD<}@eFG4^4dS(SzHUKPe)Qs3QF;jRfcN% z@^tW{rALNAVddVR!}y3tzQZO$h)7;Pnm4yBfPg@~IOz z|CsqW8NqoiN8wb0ahbOh8G0+pT`B2AdaU>Yz1XzG;QNlH22K>0`deVqlg3cKXRPR#RSxmbgQMm}XK z5_o-JNEM7jJ(y|m6qu$*8w&yPp!A{siFmp2?)>`Nm-UACX>|B5T(GW`jR4Qby_~4x zNMjvcvDO+>ggPyzg?;mj+X%+5MZ-~{qMHStJ;kBry0!BWmpx}4sCC@|7nw(!FQw0t znQhfpb~v*Wf1qUVpO_bCw-s!zxoZw*Rrs1bj6Kh8G#BewCVMg^?Mnao@oo>b|g{8o^P=HikeEQBlTLjVWjPH;y`ib&+(gW6fY9uCWY5E z;rGWnNji<_!rvP3qMmO@`uE3)bE zNIfndnQxrS%=UvekX62&^U&IC3a-1?%uTH-&7vXNYb8-2Sc`8QLb>Rg(70gx-e4KxQ!s zekwHIZuGb!10XLY7G$J`tSk66mzEctrWCWoUZr3LGL8F7)2hAo>z{~SYL0RK%S&`# zo>HmGAUSD`-^+9*Zz$JV-cL<;j$DQeX#bu{94H;3FoH|d*HC@91)2Kjb7;$D%NG<_ z#BAHrZocN21FD}Jf3xW7*eJ@^MAlJMPjj*zdAE^_7A?q;8(T?acf(A4Uq)}lwD~lh z_=N=$N>7qO9c-q5>Q3O*^(MWMBlotJ*U&kG4|+OHE$4BltI4Ra?D^Jng>$XFiLpsh|FCLnNyDaGV05X7xE3e)>^0DgUj z*{`3-sE^MV)FvGn)3;PV5j)-C!7e3q_Pv9=_Q$}M#c}7Nydqs}j@z%_l%tl?L~G@K za_DuR9W7l}C51vCQZy~Rx$vlB%23+ofD*au_iKFr98v$+!~YNcf{^&5QF0A6HL#A{ zIQjZ(n?{32e*XUczz^);@DSy*Ro1ck#FUz4ey+)1p~+>wS@MY~8OwZgAjV7wcU%qI znJX$UA&V)ksX==+H8sJkPyZ_%v>R2uH&7FZISd4E`VAhj`)`#!c|S1iYdB5_5>uAn zBn&Z1HVn^`c&TWiKUS5sTZb9$-3owqBlmy*R@&&@Vuj&-$GlF*EujwJu3q^Bx7&7v zHs0`t%rq}HyMW^2;%90K^E2=8Hjs^PB93Ae5*AGCNnU~=k%z>FjDIy?r$Ib;{y9XK z26EPE0fBVxfT+%gRY?fM&p%$J|Nqm6;|J9d7j9XMfZZg#BbxdfxH?#pGoZ zuJJ~aa?Lv1l)@`>#EI%{l1#lP1H>9sbnYWC(^FHcb8a1Mcj%N0)J~ck6x0zNn{I;V z6xh*GjfO6HWMXDa;V8bHmZLUdd5OrJ)D9>%#OY>3gLev74Su=!WYihqRPs)ffRhFtC@Y>(Bg9NykWKagL*0#H2fml3G}_y47TDuzR8m1-&4%R*+#B;*)1)>_B6ydE8d+7 zPG6cka{wg9B@sqUS9V#5!3JV#nL)AQ%g6^!aV z*BAW8NkN93AsG;%q)ks)9j4Wa&B{hA@z(TMi7C*pDa=r%A)bYs$t8GHjP3P=({G(< z-Wrd^BX@4r)#e?VV>QCfk5v#qgKKl|B(&N>Sy)+rtA)MKs%tFwB+{|Ej;F%xBdApM z*5^lEYyiNRF3fZ(`fhvi20vc%d5Tl=1`}Y1j?npH15MCt2_sjHFSEw#X;!$yveHtQ z&yBNla~bMQyuG}2D&G7P3h&Jw&(%Q{!#qfPg%`W?sqEsb9J@6yS@&gCsZolQNv!m} z`8Te2kPqd|lVxVde_b74b#jW@?u?&Z9s2U!c0}vvqa8w-qh%8)Is2psOgwnu`mk@H zh61c*q?bVro)XwSTkuOPJpaHF+LCzi7!d5jJMQ&K_%do}g;HALkorlT%_Dy+C@I#F!A&S|bTT18&AnmbTpFO0NwJRkr5JHVa z$3*K)N?02&YE)svz+T?o&heE`J|ZP^=jP_B;MIUb^{ftL;-9ZKdgHxZaQsPL>N?X% z9M)=^(R^=ry-_~@6rXpJDj&efUw#{9V`Gc6HMz`U;^MMX3Jxr(sMy~&91m?D0}RA3 zfzVD2<6~=!#CO$##cKhKZZbe*dU|^|pQ8VS_s*PL(F5MML~G`IDR-w1QM3N3DaCO) zYec4|rZ6BBfO?55e8KgXVf4!K+aDzI`7Ls=RyyXlE z(Pnvi>6^N6lczVp-{8|SZMqe}vxJQSn1UP!!GcEt0J-f~?S5Y;+w;l-h53=<4uEVp z8Jp_9-g?Z*N&R@Kc`0vXcld7uW8-Q3Ck*Yk=v&AgmznY4Sz}UgRqd7kvy~gEC#M?) z->i$xo1i(^oG;QFrl5@=6`Q_sfi>mo1(yS za5w1L)GyIrqcyWgoo`jrSVY;io+B{$5`?IV+31xBIU&O};yr*QlUw#{CU`i{6`@EAeUE^Ncxo`q1_n`S$`fh6cHzyGJN&td;|ny zQUpp7cG z01{(9H8k+94!CcC0|cPav~rm&oRL%fpK56U9yxaAe;Y#s;nvsw?g0jv6w+{q zt{4uQ&dt6XC}%(0=rQ*x*#(U>7Ou-*o|S0upPP{l2$*o+M1F)!dY+|>hV$&{RCZu- z9_}kRL|JBpkHpNbua2rTZGQqjzfb7r9a+YJ$W8Do3j{==rvSu70sq1gLous6H zJGrF2vll%h719nYe8Tdyb;yP2;Mtj^ArG$u%yw!-yu9%4-~1S0er~4|Z^*@e4g>M{ z^Bf&j)el$6e=JD=EM++QkXC~K8cEiNQ=QNyQ}u-;{*!;-p9PfvvlNmKpyo)t7fc0y_|`kA-r8{N+(WES^)DtST(e;3yEo;)Xl+D!#@1_2 zfQo(-7Nb|61r;DyE}o5w3j(8aGhqzZqQ5J?I2*Gw1FYj6myv5{ZV2ru%D2mAsik3|4y!A{SU_Ve>hMEI7pfGS)zqs{J)F= z!+E(gVOH@lSdF#Of9J$sBDA~8cGpSjBzDU@{mHpeLG%Gl(H;GywDWzGM|IZM0;OIi z5PwbRaV3@DotPEpIYNkY0rRO=?-Ep|{S&t)ASu;nr)>TOt-O4B1`Yl{3<2Jl_ocpp47@G4ee*9~{@EoaY1*}Sx2ExlS^oPo zhCs}K_vl#NrMPsl*KO(?IJ6)im6x(9g^kXJ-TE&xQweFMJQ7<6^kh%OPC})6Q=@=WRF|kZgP!m7vnZ zwSRono$>})-~S0OkcR-xh-DzEcl?tqJ)kJ%2=+mDNt^SUE$ag?^G0OosrTRhTd}2* z16saD@jM$e^^Sq{oJt{p%1EQ8Cos#Tf0Xl`j%}T6QbVP$LffzXHSwFk@J2ulxUTX` z1Kz-RZ1gYbF9FiiCLeID;hL9HNX`HxBp)am^Ll4!DK6dnTPNDF04sD~v$x~?c9r~J zEYW-pOiwWsktIrfdEpGa29-{=We7O6kHpy5bgH47hyoqt%3m-) zn_C6*l;yh-iTV<=0)Zau*`0Us7HPhIW4$>r>cVjda zrpNPU&8ECP{w5TVhgX58)9Hx63c%*KQQ=%&y{qb}EpAKgcaH;tkQs9R?As_*?9CD} z+W2E3{$e>M-v41cp`g?QNeia_h#b2oG{ikzRf#*)p^v@kl#A-?>xCwOnzCzlc23p|8rL|8_2*_%;C1o>}*4Mi4`Ba&yQ!LSS+?90o~V2@$wt3lao`NIyr54 zhR2kB!7&lF?C0QxZ!epGWLo8ET}gw&Y%h?L-UgZ+6i=D?HHqeHgP}cg_#4o@I*&{s zv-Wpu=dyUZnjlF^N;(GgPUPdCh}w}Cqne{LnUhHi5+r-Kbmcu%(;9>&D+n_s@&qGu zs=uRH99noNj>aD5OjlRRBZ>?WfHb zmhP+t8~14dJ&>nPP6?Ak95ssBS%-(-Rh5;x0gc*^<}dZt?Q(#j3ybfie}7cyuf2Av z(k~jzC|hBp%C!c=aRSh}=W;DcjpeA|)hPCiU=0s-NW|_Iy&6wJF8()za0k!_1ppB(O3d7!HUX1~qP%c}DQiNREmwq9ZUdE> zz9srX=IuPEow=sK_qXC(miI|Pq70nhEBSssHs)Lx7faJy*bSgl?nx9f^_xo+H141H zY|+}7t8&{Wf$wD320~FRjYC=6g@!mz zP5LRPBFx1LO%HhQv8LqgfnD~4iQCxGu`iA_Gh9$-f`8fCPrI+rn6Fc|e(9|Tzq7ER zcz(5T!aetqz$&141aD+Nb_b8!p(i3D8a7!Uc|`ZWC=CFzSZo6_mseUFv3`e!RdTKj z=(VK6etaPa1Uy2vsGz_W3WK4eu^KAnd)Ho&&-_c0!X&L)rLR)9j)_oed7~5HtI;sW zvPX__Id*ym2IC~gHO_KRTEnPR1RawldRD@LY7hwZf4I$BxCB{xB+@gRdCmT1LyeZJ zWA-Bfp#kJR(q*RB?N@QloHR_D{L?*$Y20jtsnOr~eL5?70ksbmevK#S?;Cq0S7&b13+5mUZ-7KKzQEmVVH|5_*@mX1o^ z$k66yc{VB6AG%PA(5r>qulnmS{{i@f`~`TH*KodOht}N>?;RX`IKP=F{mUc8ja;)h zlMUx)lj{5n8Wb#TT8dX2c_SH;CqrYdNU{UnT^aP& z%LbC0t)xA|y>1OHfTlj&_Ps;X8~cA0wNgFpd<7QY`r)SXT|ue;lbYBO58A1suB{L9h&7eK2?bmegO|?0Hq?Ww z97aEKD11u#y*GX`xzL(-o@mhm;c@s?PUlS1aBsgzg^=dnzPSLybmUkl+J&5wH$ z1q%Q*^rkveRoDto@!xn0E+FYBTZnb(L(IzH?mff*YNmUcs5jm+V?L-XlY#mhT16W0C03{kgX3Y?KS9+FN|2bIRobx(q-kVY^V*j~#X z_Cftk{jL1?2K#9P^PJm(3h;F~xX~MG1%$%S`%cqiiP~PE2L=qv`aWwNe7_G=beQ&B zXaT{5)Mt~4r|L6>kwAF>|L(tf;gBw>jpz=I#>b9|ls*nt*7O$N{ngesOE+7<5pxYp zz4WATx0`!r@4p?{Tdo=x8Vr}OEKNi{qnq&An703YV zNkpcXFp9N#{Fiee?wRkfDK}^&4SRwtNUHHrz^_jlju8U;Wt9PmDi>;wU3usO3P&fF z<{4}YB@eyoV#6b7T7S@(2IOD885tR|xx&^!;(ssx5@diFmEQns)bPLr2~0MuJiQ@T zZf@hEyMwZI*iOZ8+4pT38cEu&EtNqkBFAlX#6Tn3s4ku)N+$)}1YgUZ8iUa(x<6n9 z(!QZpCGASYtkzIrbIT}s>!5p`i7lP7t;pc+Z!m<#k^V)m18M}Yv}~d0Xu55 z9`lS(PAW}!xY+BI>+BN7N+v&PJL%0;_zeAw-82e3epX`tkpYBo``NV+92ePUsFElw zE!p}M*byX7LumKdk@ZKIkjR z2?#!PIWY0cB+;LP*8E5i3zG_+@+yj=*U^iHSba9In*v69XYktkGCsAXb_tTL_nbk5 zNSXqb?}Wx?7}?+-<-?$n!F7-QOc0x+4H#Kd_n~nfmR0yt99{<6hNvqYmqVw+Ek{AG zQ@^&>iN)3r1EuGw8ijy_o$rMO(+i7Q-V33l+H_tY!gl(z6>+=Q>l)b|oW?V%Zx=~A zP7JQ2^gG-|6P?UuOs97+@Z}nHBmMgEC*Bp2FB6Pl76WVESFDc5SJZzo~RKeNSnm+mduVpET*4A`92 z8+J)XY)syDIC$S+qsKhZ$aI4w#NN8RqI-HyzOwUU)hb4~IN(oOp$jxZl0!u(DYWU{ z@veJ9J5;oFy=bsX>OB_1FB6|RWP?)-jcJGrPGHxXKo5#=-K^VepAk^|=WrtZ; zoc^?{Mu0-G@DAXhsAkyOkm+ZH^UdHLT~S zF{tLqb?DA`;GY84oAl%)FK^E>PRKs$*WqI7DN)L>+-W|A?B(#rm#8DjW|57?41qI0h0dc>(+JP!bCEx{uws@0v~>v2`pXczrsv+<1kh zW+AlXIy|b`bbPbe7;QWoIFEUPdxntPNx~@K+^;ty1L^!2H}NoO3FlGO@%&BbixD_( zj5VKnHed3(((d!lZUxsU8p(Z1S^l?A3|X-=f!<7E1yCbx^7s5K;#9w`WJtK?d4yWn zLZzMXI4q;%OzrLs$r;zyrQq0{5-tUyd1U-qCpe26C~V2qwx#re<0EKsE(bOJ z7Qf5Dp`C?<)jjRE)S*AVA08g=pGL_sAUBCEHE-*vZGfeJ*hbF^)&wUwd;oXRL8l5E zOGmgE1M`HoZ?*5GBU$4=`5#@x_|JM93sr3rK;98E0PPDi@+>kf<6m#GG0SIl^;5tN z@-}_MfI>~Hcg6kg_El1ky16}X2RfnTZ?zPCTkAkf5AfE~8i4bcHkRLR?H^DO(VE`D zzSfw3Q+aE*6I=d+wYcGDU;SDRc33BhIzTr+T+QG1!~To;%*0JXh@gOUQ6sa^BQTW3 z#A{Uux-#~F@knBvHR;Y1Kf>P~3-MYma zsc-7u#f1cb^)!6Ij7=jm)@L-Qy&Dy`+cLYi2e=Z5j?B8uiu}h+3ZpXje^icDI-6?m zwP?^Co6R1!CfXMhfVla~vzZ6AZCN4wNgiT>Wu7qSEUo}E(uC8VfgKG1=i>jhioD7x zR7Wt!)l`Q$?_z!9@^2FPRfeX#tMpt)?Ek6d0Y8-95LxZ@92n2|=#QEfS~mv^CeqbR zyUy}bm5kI&aHv5WrP4Udb3R|OOPpA`ZDdi+-#7MQ0|}Z zS;&nZt$+(5evvu6H45|kQV;C(j34Rt2X>2PsZjilDDRA`9d9#*E%XEDQ%yh_HA!6l z>mYM}<3V$&vGxH2)qqc89%lK_#&FHKVNbn5!pr)yYzV6uEAn(E4}UiAf* z^;*nafdeMcJYiZveS>fIirQfuUv)8pixFGVYcm%uJKPXfZR#>!?L;B=i4??F-WjI& zWJk(*e68a;b=b74)EPKxPyx0WpEF4Sy@?(>;+jqBA~tIg||TWO!UH+T*VD?vv!g>)N}N zH2!2v0oum&RKk!)Cbg~uYbvI=w(l4JwAey!q@*m+4iYO;gb|M1q7Ju_Z@@T;$y9l! zCA3%dIXag;GHE(B!_%0w=TtL025I-5o0+5&$*KL>F&%%R0Hx{y_~@x#Hj!P1yb1$K6;At> z6uE03TIQ;fQ|P1GN))pPM~8>^m1u|?ytk)Dr)Ou`MlnVzb?CVzy-IZVZz^~7-#Wdh z_=8i|Ap0=>I`y}?dYggT(}#~shmu~&iO64j&Q|&rw3>gy#}u=-^FGU zO;UGA3mq()-j&H`d~0+6v^Io{o+;sYKCy-(-hdXuA9*b*$1Xdi_+see4-Ex}29zJF zkw@JInIEEd8nkR`)HVcWjo*4UksH0QV#H31jyhY(5`D#RCD=4z)GM7jj!*3lzU)2r zGr#@!llk=EiGx>pWadL)BSESN~)3@MX} zQ2$nut-cj^izbJ+8^CvHqRi7u6W3gx_%+?@&za5_@$~2Ak>`DAe^}9E zj@o45eoW@-SpGxf!*46zlkB{Q?b0JF=Hio+yZ4U*pbDBT5P(%sOepgpplVeb0@cl`AJpu7?gXYa)(v+Y5$!7EMm z46Ige=*9+NFZ8HQwaA4~U4KvRX%EL!TyX>5r`=-5P_^a=eJF(*n&AoVj{Q$hAke*` zQn+$d-gB{Q%O&hk(66QbU1kgD<^sIVxQ^`L*&%hX589`rW?R9%MPtQ0)ojZp(80g{ z&-a$>z{FP}_hx#pPtwAzQEWE)af7uq%0m&TW+Oys<#RhRF_^h7FI`5R{cygm(`x|^Sk13~<)pHQ`3m+?%Nwx_T>rNjt24g=}qA5*(R~3~D zkU{o08V)sifRcU+`-}>`SlH>@?E9cZILUkZWPhRU1H&J4&OQZLzV3^v@H$8RGAY}f zr6PJ|?WhJ}540Z8^I=rh{&=SnoH99MSyo%`FkkBya09k4&FrG zQH1qk*6i8gXVz0d`C*YP8y`OCXHF@;7m;F&Vl_HNu>;@AK+IWI_2Y&CfF1OlkpS*;JrdBDAgTw8Qq=nx$~m0E>R>H^DwHql zW*&gv#UauNOuwcBeY!W5-+FB{pM$YnW}ZZehL`9c~5m zQ56D_%;@2kd79{U6}A zjA@*i*QnmYNM@g?HBiY{Id#}_H9gV^pZW$<^;2orBd{aH7IHofCXW=Q5r7 zbef3A#^h26_u~u&4d9e#3;-F2hqc8tmeH)p01Jo68+sgR$< zo{iMvw-I4?FL1t~QM@234uw#}--YJnaepU(>~_}tkYX;D5cT*^Y`}IEs5Yg85r9aDU7X~N%zuDy;&vUb@!g_aYCJhPQRe_omY`LD`&O8~&NrfggFrdKU&PXo zt5VlEze^Q-C88hHxMxHUwEJ$xqMvaa#itaW?hI_dlmgCr*(n;%djUk+YI}oSB0|5| zD>!rY81ylDGJvb)u^xUr>dgn9y~_JGw@FK#rYk;v{K!xWw08RMQp+t5tomI?P4Qn0 z)gG6;2|1`rZ@>P634Ni1KTGbdMg!s0{x-)J+2;b6N3__PS%kdm8OLr$@DPS;9b3)B z;vkeQ*v0;5t?ksUMmNScSK_euO&E`KxG&kjMe&W6+KNm(6aFg~cv7{L`C223?-G7`=J96siJqN{FaSxM=4(rE#aaFtNE#??6gVUY!=9@`Iqd%Elk>Yh zl+frEYej-|M#xxhDPjG*c1;!C+ARHNZdSh`@jenI-36)G_$Z&`NhhZEgYNF`9)nUd zIsBaePTznsE3?G{8EER{-gx#x-KINpERAKlCFY3q#S363w448%0m8LS{J=O`PoLs^ zKj@$R)Ty3+wTML!AKWPocJCD_0EI+(jW?>Uc@S7khAY7YRdbPjF)C&vF?_hrwD z(IW!`*zU$?5x;|jL&uoIG~zv{NFerz=s++!iQ&59EA6{)q?-viH!Dg)@~6DCX)?Co zT{zyow)2Gfg4Vg^-j68;$IvW2fJbNGtfG8%xwFu|Sc3x&12C=eEB`BZh(Ka#y|UUj zB+PH<<-hGVTzva#@2$>0A0HpX_~>ZgQI}TGVwawPeH7UyLF165(OXH?H-GN}yoecS z;SV3Wz}e23vvh@~{8oQGVLdE#2`Ezpoutmqx8RT03M|~L$Hc{GKO$iBt_v^~^56u~ zR8(B03tF&7-i}>zXD#S+>Us6)S8(x;4lIE-ya@0d) zwdj(S{k7LZN%7V0y?qEtr9mBx(w6TgsgqLYBhmD_zdn<8>N91@?rbh!zxs`EWxBGn3Vzwse z?3h&x=;WmQ<|?R!dRd&E^Ly=A;CPzU!h8Pb)j*_7_Cl|1;3m!#bn%diF$}+L&KE|X zb>_8}GjQTcn%fLw(KNJ7Aj%l+gbuNFkC$;*Q( zdU|>o|L3%BvMyd7Y!_eQod&O0`QUtA`WG)LMqh!}q*7$L3_p_J#0_9CJPsk9a%H*^ z*y@U;J9i;1z=fFOuiZ47nJYKx5M`JQ8G~E^XE|NGe_^nEwzOYu9aVXJsa z#Jjr=PNMq&6{S@|{a)8nUr~0rao@WZuRd063|n|kE&r%JcMX5L#OQ=61g8{C4?m%; zqg1&ddY`Eug17z5VunY`EBWjlL8e^K%#HK@{mX-vK2Hs^E%sY*E^ROHCmt3H3e#a0 z%hGveo!lD)AWLuG31$iUQZl)YqD&s{%V}gT^2tKQRmw^aLA^u|-fjX0AQk&CSrz5`PQO-6UG2LYXZu}8)$8ygs3LMqVKX!hBjs-oD~-1NY}J_oGZfa1_G zk&u#s)tjuG-zQX}pOM=UNsG}B&L7{u5Zwp*0)amDTWL#4HaK_2W>rEX%FO&7Gza_P zQHf1XIuBh3iwbZiU{$)bjJ%l`BtzsI^=z)C$5Q4vXS|v=CEeU%+hX-Ym_@Ii8hvb- zlK7=dVo46g!bU(PnS((!xGc6(E2O7I%B$e7gy16ggWK{oY27b}(^xjHm4?_PDO8C) zd;?#6CO=5oEc$St{0xoSsiBal1qYXtrYJ88(DwvU`%+;M{&CJ2iE14=Y=_YHa^q~M z6Vtu49uHr<&wVhwfAzWjkdZ%YN6M|#%R`_qy#&MJNKY>>LENc!ikh3}By$K%U@H!w zpt&oQ0iQ=(;?_JLY;+{NQjRI9Ocsv7JUn(+4OJ; zks%z^2!6AF4$JNGfFPo41tzMx9>)?wIz9UoR)6U)OXrt??OC-TX-3FKvIC16+sFG~ zp%IV9K4xm!RC4DKTusqP{rZ>&xCAl*q`1pK{Mm9g@|i8if;KSsxRdeMi#7q<?;@Wq-fm4m7!F-q!$c1TG)DMdvOkU@-{xb*wRMp;Z6gY@b+ff5Mx zJvcl}uzaY1=9(ZeY6nl*_I8Ne4ivHq!0{vXD;LiL(SN7At~R<3czoXiL6Ul^c-P!l zRsfE#*)Rb)L5jyzs8!9l!UL>Hq6T)-la(iF7xWX9oc}geZ0hpRb4fvyCcj@?hLe>) z>wAp99F;Cn|E#$w-9ocyBJzNMDq#}X@-rpU9=N>3R2k~vuri-?u55xmiqOvFP+dOV zx8*g2AX?^WA`*2Ew&6#Yc&_IYTmez`n^MHSuz18Kw27FzwBP9ZbD%DAr7w9hN;~xx zDd_#%dGWs6z-hBO@O1+jOv603M8WEfOvVn;+S-+3NUMi|t)nyN_qwbW!l|;HN}1s| z!!_fc1Z#L-?f6KT$?J8A^Uz?G1kO^v*B2Fb>yV?ysN_Agb*0-v%8Hb>>@FQKh-5kC z_Q{;%0n+qS#0==Q%XAFa2VI!ZqSQMa;CwOl>_Z@k5~-L03E?w!{Zg~QGmz|UjoLj? zz9SOoF~~Rn?G%mEX^&71`ve;6SGMypD0OQqwo@m3togr`5^{O)nw5^)hlt%r9$(5} zgdumk(MD!J5GEZG`kyy6A+*mMW5pJ?)stVPk~+ytNwCk#B1H4-vs4c3G?a)!bUup2 ztLg$FTF?xLS(W}fP2+)MZ?wxVepzT9TCwv9(z_Q*rx@)ZTEo7Er_^C~X%Q8XVA~<; zd;QP-mQGD!Q6kvvxB#gej5QucHkP(NVK4h2$v9!{TVk1`oy5c%0}lj}(jTKAF1fV2 z4Q{@>EE&xQ`uwP7AVTRTPIOyf7s&qlM52e%UsZ!N_I+fNfEZR%e;T#7p^3tA&)4`n zcvf7l4P*0P(kxw!7kwSVJ(e;b@;R=@;N)gVb#p@EPozR>g}2VRnG7ida=CsB$tX}6 z(HJh5@zR)Ft;D}1>q05^on|DSPQ~2$3I|ku;D*w^F{5*m%Knga(KmpCEH34OsSx9#57wvF{o8hC znlbhmQ_x%c#6!F5;H~V={cdiF*CEvhoz6;3?sdOs!+bA+XeP{PsIXq(@qxUr5q2Qu zT+vIj9k$blpG+IP1wRxOrD2vh@IGtR{5?t2R|D)HYy8#|7xyV=&ZDvXw6p|#J>I&Q zkOkzbc;%w%UneF8{pjW2c_EchP|e_(w(DCU!}Te;n+*q9xfuLoN~N1e=_5RXZND{p z+rYbOhwtsvrCE7um-@Zwr@dO~{47 zt@m&7RB>oH)%z8W--xZ_`J;_qU;GiC`+l=!%eZlJpNLAsQ5FO1>IHp>*d+;L6c_gY zL)2GCRke0sZ$Lo0q*GM7yFpqSq`SMj&k>OhDd}$M?vRu&=@g{98@}zm_x*imjOS0r zU_WQCwdR_0u5~t&tLDDNX#2diEE60S!0i+$bE{IZu2mQR{10v|Z#io2EzzjRP%|(% z<{$WgdV-H2c=k@+I>eNu+FUDL? z+n6-x1ji|c8^=oTF@W zY;9_g*R@W65X;v~AY{{1t4J>YKKtOs{ot_Kv)1Ej+wT?g^U7?Q)aRjCm5%s#G#;ys z68y!>`Zb=NQ@sg@ZbFD1S+SdogpD0c(gBY`zE#S|uz)qrUqB!cDxOnRUe4sT>UT|Q zvQ?nye-?*i`NU1roPGV)mk2UfO~kD`k+R|V1vB^MdyET+`Wn`U7wo@37(e(J0Q7(_ zvR=P9G(>%0TC}zL$oau@O++Qkx{>?5)1GBt!g%>R60t3)&X}a;; zok^GS%YY`F4Ad+Y+G$a7(p+AwviRzFm5*rMN!8{fDd@AGmK}UOx6+>Bex(Bsj{D3o zH;KCaw1ET?N!E8dEbONtsvhuCZj}l=P^^ z?X@lEC)DW2&m))Vb`?xW?nVk60h6lL@}Csjn-;8iP)p_y%e=}PFGd|klOCrbse;c2 zvug5fN1Q_+*{s=vEZ%=SmJAI0U6oX3ThTCdtP`;^lB1L&(`LxK(K7h(N>_XTL(~QX zpcJM2)V#xk4nrfKud)sKGgl=Qb{KER0w+h_T{EUI=H@FJG7(r3O+E?!>mTjPMmj(G ziP2+amq;1^FF1ENzvD0(`zGN;a(dSG<%yG^_yin1&vo1n(eo|%^SvYUnds$n#`~z! zu$yvc?f+xMV2IiK+U`T718%(Twd#lYB_2YHR>F15KNs(^U^uM7m93+uO3b4K*>kTyRO32J{*y5vl-LumPU@1*; zM>IXYLzHy9q>d&p!F|(E{@>kl#Nv@q?iWaQ2NOL$Jt}vn8PO*|=FS*2r@d|W>DC-* zMFb5r1?+U*zrTDxq=Umca^|`%+}Ft(?eTk6VCBpH5d8*jXwN8bjN^t{Iw}9)$52bI zBU&Aj&jC9n7#{rYFmfe~IyP-&KlxW*BLgCx^&T2;jkR=a7>vwuI>o0r=#!cvLl%;z zDi+9THQe0^{nZA3M9U^!=Ma%qFbc1_&*;o!%t( z()+qP@4T6rg4;Z!>o(h~t510UzWHh0U`3Yfsnu*XlA>UN1JJ$NX4A-zyh4-a;de@! z`L7M8mz&5t?<6}q4!%6Z?r}dn?V%5){1~=U+g+p>Q*PMq%C#T+LM%Jz>O|*c=*`jm zSbbF9wmN)z5W4ftwwx2sstgTC00u*gU~M(dxB&T zWF!PPd8G3h>v|8lC}KhO6dt+=6|S;1JWswGN@b9Dn( zvmGebvg)hv_I6U8G}QJ*W!3cNl^qjPa>EpPk(GPP_9BHMpAB2h9!ps=>FvyL+3gpH zYkr0OKc1zh0gjIMu}9_Hy4um2oO8+5)2DbOL1id4b)(AB4x+zUEC6V+o1_0*ww!^` zs=#Dib`7xO#|srNeR%N2R4(u4`vFGf^Zn3SC_SgLzIgGw)UdG8mg*84Vk~)SsyLjz zy*<$&BMw|#+`7L*#<}$=Ixk~*If|ayO-%2EI|kx*(itiWG(vrwr8lfq0$n*gXFe2W zQq0fL$YmiEUf_w!#SRSwU9X7W%)qj3?icryT`fjBkUnm}fTL;o`6Uqf^bpO2h9F#A zYYtH42$WqefjBK0oYu5Ez4etphCJVo6+Aa%8XpjV4Rxd~JO@y@PW@qO+h=x`9$4a@ zWc{zHmP7>GCM*|yo`?0<(c9+1@yxbK=TY7QIN#S#O)Q+tf=Ra*Mz5A<-RqCdb5`5% zepYx^RaO=Z>~}Qwve5I6XsU>}i|HTd{5^$XLur$$*2lw^ji+IT*H_6CjT+yss<=ls|@Jn`h2RZMm-DTVc8Qg zCj|0;sped0_n2ybij$U;>G59>UfDQ@h|{|@Z##UlTRa~c9Q;oTQ*h-Yc-L9<#Jqhk6IQGPs<;a_~z?6%)b z+HYlTWo4E4-STx``P}wVxk`jDK#>&#brq(GCk&+i;fV-{Odtc0;SVJL;rBFzN9_nAmS&_AuAJCQZS>M2I zoP|1{-$13f6;6M36Ab1d?)Yzuo5jD%wI&=lVn?G$r?9Km#@X|NlaoUOdUU1NV2!D$ z1~u0WJH~_svm<1H%GxPDdZOjj;wE??OTpOetZSr4htYI zuRubGj}W5cD#!kK7dGs*SLWnUf)I9lhq;zuCE6w&B4@w1N+{%8m$Yh+U^mL;NaNnwjVJT#ekcUQ>y zgW@$7pxFNV65pPdPQP+?zr?C$sm3j33?gsJrK9dvqo&)|R7n6|{Ok8_=-gGfPe3G! zPjhMr@0(ELR2s`qD7f7xu$8fL*Mv}v{(xh$D5h8DSVLT-{p=9* z@^y7cqxZOF4@je*`$H~&+-wV-@`;?kPr|?N>QFfMzYli!Lr3=Cv)cdjtVmUwvKhjc z-XMA75#qZD>y^la?CwBzH~2dlFWmLIF3nTOGUZ6VH{V77`pzcGyA?%627WS~dB<7a ze|j_J!J6W^&p=h(O);XMXbdB`f%(vSaew+Qf=)qEk{&m3YVol%Q3h9>k^T+RPS#KY zK8Nl$98jg87A(eWZmjQQJ$!)30w*FzJ8it^u0)$K{kD8EWm~cfqZRMxGZVm}m2vH% zJrmbE@JCCHjon3=GKR)`?Gs`wC`FB&dOjqnN}#YdV`ZDK9ZICPAn&Xg{)#iqzyNwi z(SSV{P&WDJ9X9SUV~4Q=8i_g?0X*0wU`>w8H8s6AsfUL34W8AFj{a|7fM@SY-;Wi( zA;Of?{*xWGdOeqe4w9`b%-)x-b=rz8TvhTjZ=0?lEifyw<|AL2|V2?9?U<>>z1l@MrAZ_~}CUU7xfM&oM zzVVJ56>aS;wRAmZf~>?evQg#7)6HZ36t3WV<;#d))d;kKI6E?bIo$#9^i5<|I@|8b z*?Sa%xCL8{7zfB$2nGLK{&S@}4xY;5ym*wjV(tQ|+(qWYU26emY->`wi3}Bvah-)p zg@sg_3{)KD@I~!O;SAhx`+=TTNtpJ7*o3<}qr(zRiL&2wz7TwAtXF@RLy7`GKPfzW>}xv{Mla%fF=35_!<+;- zGS1FutLmgqqxpUL_gl`D<-#6^2lNrv$S>w({UF#{nu-zcdxqmSCL(=&y^i6b+^rd# zO)?@K^GlYECiWbyNSy`fLuW!dN&*fV6ct-Zi|ngZX9eu|>_tPHDJyD9ERUN30SQ9= z_L(H%oEi8>^X;{T_s~tA(bS3$T|Yfxy)7sppX9$|0<8;PCe9q*960A;AX%cVNSPy3 z=0?okKpb@XZM(~nVfScheh0%AnRqLK=#Ni9*EkNSVHYd2}LC6goz9gokc1l)A{2 zsF!^=+!}-Rq=w2+dVjEa%yr~>2nPv=3^j+9g$3<8xBZ#a&>b<5Z%}%Nsjo!mK8eZcLD=RJhZ3fp-zoJp2%5S6}7j#yrpc|cxBG5A0fRK?rj zcy|>LuqXDdkjgqKe|Hq0b9hqkQ)`Jk{8{;(UT@D_p!maO)5OoKlI8`EZx@$4@+n&) zunUHx(y(_~{p^*uYk8V--!r9<%(+4>15~~Tge=s~*onK&Ja(@h#2Q{l6NkxugAq?> z+9z#k`(!W@`bCGG^Vx8tTH^xb)?UT*N*lk&9{B?n+_iPxy#Psdo;js%S^nML0I@Xj$Wg@ z)%o%6a>8}1^|X(8nYry^(wtKJqJVh>jwL!-xXyWVFuKB^LnwFp%SQSx&uE~y^ORAv z1uk%Kd7r2lE#V)#->A^*OtPI)9@RGM>ULn3Bjxc2H@@wwfl|%te#Mb<@>KFzQU5If zZsVTY(DMY(O<|6To-y^+NQh*^>g3E4+q)$=z;X9Cb(RjJqSYMBHVpI!Uo!=^w_Br2 z*s!sA?=WtVQ9N(dlof$w$&B|LxpD`~lFLrQLjZNN{Oky7BNsj{#883ZAziM=m(HO) z%zVp)k&*EbRLixjIAd|q(DI@C=Z+^%n5_Uz1at0hW(>pIO(|#649r3?InE`63%RsK zu>U~yfxGAw97O!# z4t=XK3s}#{WM+hu8rN-3`5L@VNAxn9h~cGyrj1sm zu}oYSSRkOlOTVQ5W9)v?=nGaCH${(qVG`b(;o0kh#^crNWKasdr1py~f( zrssO-XDhkKF_0QR_PLml%G`xGg5z^`z?0wRV1fp0qYAAX3e<<)g?k$zGcGK#AQoa@ zx1<3lUU3hrk!uUk$jfs8$Z;vCoQ#qD#G z%(3W9xfJ!S$-5G@)48bIs-IW%{EHs=b614ajd4N0_Z*v;4vEi}G;eNQO|af5+Q{pK%{D?JGbnZS;QxGafxMkK0D`dA^{F3;C_)&fvHcpR9j()kH&i)52Vs^)mf?oe<6)9%hvDOk|iF4uIvHd*z{5ip=ZnOG8G0An5dn zV|xSQPxsdlx3w}@lF|ytL`22993f)Q27-Tmn_6%|}c>r(lyf@rL{s_ULHaHEMHQ@PG z$NzAP&zI*5e6?8QAKEyLstJAn2c3PJKZ>2W)7E_SSXQBMGbJj@fX49^&{fc6E2}*5 z5P|3&MAA=NYv<#yr8OT*{_HV!zIAOHle}@ zK!)hZ4pI&zf&;DGu+SOr4Xr@`QTh+>no&Ttf4RU7E6&;OdYieawPj;hw?6@?XZSCm z#k+FlhPl}=%4B|S3RvH3(}%c~n2k(}TlryFciL7ho$D}MKWw9f+0$2klq&2qS;Pnb zKz|ZPKy+B(<{{VK22=%ULyK&Mc!F4K$U9Lf!{98HW!!Aayo&aLEIPvdV(GUKr{TjV z7Qps=8{+E3;u+1?yZk+CC*ZVRK9$`$mG$b}%BY?HB-j^yXp5I=fxN-byX z<*8Ufj43Rezw4tkg6GfbNk@W$qYB%ECMmLA$M+T!#Z)V`K2#TEpi$Ff1SQzR^dU%` z$ur4|j$P4ki;Hp(U@@f+$4xKm@Gq*~(?c)LNn&I2EhsXba3i&2juL4Vo7CQ>&p%sS zT(tPBL-n#mn1H_hU{OK?%YAix(bEpZ6Eh%A@qzLgX5ogdT<>hQLAU>c9urb}O>i9W zHmjFLVto4ZiW-sNAvZ97-Hsm$u1u9T)J4o~XlFC(3giTb6=c)p4<12HS?KeM=~YqbWF%kz9CX zsOUL}wRj5CDK9iuB|+xlSY#8gA~eNOI9K0hV3STVde_uLVGHM#Ve;n@_Z81eqM2ve z@T?Ns{?oVX<*}yNvrQxQC4Tx=9jtOpi8gdtUVo+RqaQD?n}i7|jT;uzFFXZ4Q1Tt_ z?>qQ(;dbmGDZ)eP#3Y_es!Ns8oaDIec%5$%;B-!zWKVp>J?T8n}=k#_!LRZEU|feN=OvU94GdE zXw}H$MUd7EJ@Y6mw5NJAa$qQtRA|a3)ROJI6CsEnrZwjDhU?KP<+AIW7x1<9WZf@x z-(e+gm$N5{t>xJ3dU(7L+36~2o+79Hkz^ds>s;0}S!J-Wt{uBm9k7^2m$i8e4GRnN z6U(Fx#555DlQ9OYBN~y<*OdZ!i-cDeTTg*{7pmJm8>jJBS1Z-C`*xV)VUE=g zjp430FTH;|HwCp@Vvcm0WMe+ni#zPCabWcOe6WzS{&8EW zzjKIS#zCHnWN2P6i)kXedTuZGw5Sn5>Tu_wEcAz$y4rU9xHnwEV`Ud}Li(r>OgUPK zk@7EH@`YO*2W`Vl-iDTY#W-kR^hWvYEK^UT#XD z(t7%PSWT3^VfmtHvJn8JkRtlvnAB%^&C%&090|A+ln@*I_$cqNM$GgDH|#6qTk-}e zYH1ze@dxXrj9)j|Bc2}$PthX?F7M*T1qB5|By%0p8rpl3#R<}Q9=$X$xuOSQcVLDi z#3?@=9od76?J~|*6dDma$wiHHYmU);ca)}(2DXW!iG?$@zo4-ftbK<*N32F@X?+!T ze6XzCW;hZLIbi3D>cA+Mm2n{$7}$+far8g7`8u)WD%z#{pTEc7EEo1JjA5AfZJAq% z3NoOeg=9k@`P>pRz;4%ZTHnw%GB$h(s%07iS92zCshCek9v%A#j?aS$^nyV9(C{kU zNxRWfw?*hqiG`0J)88!-33iL`y3-4noDF`NYjeYO9`o^CuM$N+t-Ed`!hR~=+}u32 zv9~Xemdw>U<`hyv?h;f}YqPU87U(M9M_fd3Jyx&KYQW8 zVem%;lLm#m8OzYsHBjL#ySOp5HViwh6Lus!nGVL&lC#LWU;`w4t~>YjcKyzzQ@b?z zltD(Wn%{7bAf-tOXHCniApr$svhz24PjDFRvkH;L^1`J3@4=6Sva+&{mzUT0p*uV9 z7VtjK&edoo2w%o=xe53&t10edBai_`73VF67oN?G_fLPN&+=}B1+b55pHYZJv9>jq zmlKy#GKEc3vwlyuU%bij0a{a&nMd3hjD4rHedMPOR+HR< zXS~f6;IKEy*cI7PHx%pbAWF1#yS7{4)(c*~3oX$2>kc-n>2dO9OZFZg=@MX}v>FC} zbzapkYOGS&?}YZQWr)r*W<45h9M5j$$q-KT00`pRBSG`n!R5vPrB(^>uAsKTg%-w{dBeoI=0Nw zEGB|RI^a)WFbT=|C!L@Azl}X*{+9Eg0xOp-@B>V`kMsJn5q67ElKkOGcNKW{_^tj zpM_tie$6bVT-4UlK?9UzNaMCsF-0#1C1Lrf|E|8_sL*XQ8O5F1JxmV+ z4Vf&i-#upZ8_cU#3A?`&Aha5$SLkw1QD|ax>aw}!dHKEerpg;zu}9h+L~SaNAK9) ziYRW?LI*jFX>JQ+Ne4Te@r(!|pdhr;2pWq1>6HBExj>*qcu?K@1E9d*(+yl3Tr--; z49>V{8tdfrXTAsSEw=cns)!qQB=QStznv>gZ)n;0>8$?dN;A8x;VGwjwIpgt3^dEz7&UAT zkng3O_8GY+(Vzkd=Y-1;FFLF?uDPZLzNL?qwIwL{moGln7{@T`_wNd%5>OUswRb0a zj6BOHxn5gr%{=rs6ppp;Pn;p)zhE2^yqGrTnAFw*zQ@m?e1qqQlr?g1Pp!MJx(#m- zek4wBO)}tdS9WqPnp3nf4PMm1rryCt1gc`6S zby#d60vx3V9Zx||{h&OyO0M7Jyh#r*8E=jJrC!)yA-B~#ZAjjO;1~l05&SR@_Vr$~-enjK?w%$miLI{t!RUm3C8Js-ni zDB=4KJpX(tiLQT63Egqi4Z3xK9=(y2r3jaCPcmBE2_9yyXzQPB# zCJS?pNlyt4i;ZE=+Wk@ol|Nz1%TrS>zYgc!htJaR*-)RSB=SAlj^|?TbP>7YHO`O= z+T#swK7BRP6})kRe?k2gN4O)q>~;dS{{ui1{T6!@q@`c?J4LyaS!2-d&sXos$vk&V+O zudpFBpuAm}nj(id`IZx?0b1h|6N3}zH7Lo1jXLTL{?<+mi*=zFEW+3;jZNci1b{Z9 z&4*!*%(I27q&;PRUG(BCl(w=g!Gl*lSqh8fOI)bABWDap&)W;+KHVXy8Ui!kx#9m5L$+(donRSjPXul znlP+#X)1p&J*goFS-X2i!KP4}F^-W|aH>G-^7685!m{@EW9FUe^}JLL1it9{0&N8z z6j&z$qBTTXY-e>AzD82zjZi12Ib24n12@`W#rlt3P~*~ZY=sjchI#|x3e_{czzkrC z==eR)apCWB^~hlXq|P>R*B4N@HF$tLnZ>B9H(KCKnrE4p-0xxnZ?V1*a{@8x)(TkO zISlrJCX=hm=(U5(y01kR98BfR1XfZk79)YI(ALGr+wCRvU0R7Bf1S#xx604|bLRR+ zwLw5tr$}BR?p$5)`myh z^*@S!-ujms9sG7%q>Axss@Gp*)Qxo_TK}3MmFcQ*fu&MCAHfxf=@zd{v1q=kw|q~H zX#=;?Q(S1A&|`n>WacG7-{M`|SWieoVJt>l%jM2$Tg&FonBq5IvWVn_ndZrjQ(a91 z1IYa2^{VhCIJY6+ksh)500)&`-I3(rcrlqF76O)ha9N0<`RWPackB~0m7Zx+e*ARe z0HnY`KMmcYkkb!3N&kD*Aq<1+@E82R!jrbe2aq?YygX;BdqjV*L^NQUoYf!#y3 z>MWnD0F2$d`wu0z!QkZ#`nzcA{*BZzQ#1%&g7(BrtBC}$JIh3c{xEaWh4Xvm%l8e1 zRA2`^83{_!D=-z1)4vDj-DdHbsq^)Lrs;k4QB>cDDoshDT?)4S042}v8i^TEDmFgd znavThvA#jirNVd_x_pqiCjI#XAs%#H$8Lwo9PhD>sG$^{$ zx+jlQRyWHMvHQ(TzNDmN|IAaMADxu%m$DBPaWTfSF<1hX@qCZ7C1-B}e*4x?{CB15s5p4kYx37TN;S+H^LDxaI>0 z&2T^QLWQ#$nl)bzM@U{DUu=^`(VIBS6vy_pKAhWl2SA40bl<$oR#kki`ZA>IO2FjxX_q3{Yf5>EO!L??shl}m zmZwMTJuY3o^wQME1sC)hM}`OJEixkqN2SX;P(_&M4|r;5u{AYKAGRY?Ea3g@sA3~~ z&isUilJH&pm|ATinb(E*F1-t@p(QK3@!%M|SbhS6A|Cd51mB2AM?voe!Gs#mu8-oyK>C&Rd zc$wWexB-~M=8(TGSfAvrxQFAEVjK&ISFvg!-Qg+&g+r~6;2YaGG1P3MBCw)~vj=KO zC4QM2`@ikbse?AVl&+@~bv6@iC_Tnq^*qP-A?DT$&IF*R5dn&vugc5rL7%^nbX$AK zG%6XsNb1>KbP&ar=thjw`nmnjtAznH4Oh2kTLlK+1)-hpKPocvVd9i=e?2$$4fk^q zgzVah%mz<0+f<9hfCefQe=W7rzXXFG-|XQz6w~6n2V9pvGRJpriUW)J@z(zL21r&lPAv0J$BLel{p}qZ>5dmQmf1#++>$)8tL$|PT$+!$2zuHntN&C4z;$0aQzJ_I3F?8)_j+G1 z0%C3lU_%}7|I=&Vsk5kn#2SmhMe&yC5!@sS^*{MM+OvJ+|LIdNK=oGmoO%1V&vW`! zp~rvNDc1m0pgwblFG3Ku$%T6BL`AI1sQWg?$juc$sGvP1QC~R@!ycVo#b}T>ACe zncU5X3DmB2E2|pg!(#{F*w?H2hMHUIHG+MrwNB{}nrL1v%^5c(@`-<{v>IOC7)u3x zo(nG3E~NcPz{{k9WRMs%KN3&z%lRC%_!t};+dzKqc{JIHGkk&&tY3Ykpc zo}Wm_Tpbu}(|5Q9jkY+TsVi7Cd&}pjiF#%>Xk%uDeU}T=-u|dV;A`Ez#9Ew*KJXDJ z22>INSr?H2Xk7c3P34J?d~vZ>HaVJy*14AVg}S>c#Ayv2;Tw|@Si$u>gc-G|6Ug5j z@boF)7_28y4#XvyWRVl{LP4j>p=$84diJzts5kpVdBDj`WS@zTCXBK+|Br;L%I5%z z)-J<`84fsJ!D)t==1PS_DHFTZ=C&jBn!B^jcFqh7p6?qiK)2f{98;N;FMF%Y7jQ-> zZv9l4?84gt9;)a6Jg7Bsf}t>b*5^dR4gDZfj3oYRA~8b9E^{Zus+lW3er3t@c4Z$^ z1_3fsak$OAx9fEhn{nhv*i?*aY~EnE_z@&_e}Q7Fn=oW;d?vU+QQkTTzE1`;q*wZS z3Cuc$xnO`kR|>4&+VmXS!J@Gh&=7giC@oN@FGp%60B{ za2VcgAJ>DF;Q^*H{w?ut-ZEeM4^$=%^&D-mYYBNq1gR+Dg%0QN*68=X)5>P?1^fo$ z#ntIgozwh}pGqKlT(9|96FmL&d1@aA%SY3Di+hqjR7c?A2Q@!0{c&PejG+soH#v(E zdGeahUi*q0>gSg;l%QfJQ+(7eet{$yvk_!uPs0RR#H!dnwo|%YtGFfE?mef>Moe_| zI2t}-H2bo_^Cgj2@Jq$TMeE#wyTOD>iWh5lD_Pq;*-O?O6=jnjS-+EZo|nelvTpPL zTnwTk$cShTX5Q^}NZyPDJXKSMNeJ&{MbCTNm zRP0(V4vBZHZjI4D+xRPA!emwyfPMf3=w$Mk`X7GK0O9-du<53FU8V4s!eJ_5B$xio z@(K?((c#Zgy)+$N?Zv5_D(b77$72|%pZ@X4`J3#+JyiO!dr;4(q09+U`Oer&;@w*M zkn-7n711^)^z+!Y<#rwX@A2r0y(5x1@|Mi6(zaoQeYo)TXw3VNho6tv9W&tgoQjSP zy+c$8)&`4p(eZSAzx4CA|HaQY3I{cr4{%tk@SSp$-i*-IO+Kdo96shCPI#n%GGCHH za5_r>uC(kqp|Hg zWCZFgF{Z?hMOVF-z>9i(@i&kWmmb0OiSDQEx2LikSheu0e6f`Ii~a5i!eZ-{+0 z8%O&ZH8eDWE0MFXtTW|2F`&x>!S;TxR=o5y$8qdRmY)xUki#4M)MVuL_nK^QYlaQ> zVA5Ar$=i}o0kWV(^!RSsivttv?HT6xPb+pDo9GZNDwh{lc)5ulPtT4<6XR!0u8#L6 z9j`MnZhX1m%whMw$8VK$!P7T~a z`ce_L5d}!Hg8#pS)EEaQB!55{I4N#F#R2E8O5rQ<0k|ZjH8DUPwYeO<^N6A6O<(AI z%y?f1-Zx!TfYIJYOQtwTw?E#Fi=`4PJJk&a`sQ(i^jX-)gNSt2Z5idMV=R4dCM1*s zh+3mShYyXX38j<>&0!db59V>|ch={d2+vrW2qUGf-`H`REYa8)lAR32;b;EFLo+bA ze-t63pT-ko>6OJ}gC_x-Mmq`@1Z!R1>~<_zD4c~HXsP^G`W15bQ(14_5u-Zk0VT}W z8*pp@NkYBkY}PSd#~Z@t7TACnKF^HL+>W+(7=U?qbVnGD9@tHa59i)WA-?Q}-zcL# ztJ88JZKFYR_EVUn!F(aCSM1cceE*a9aaGZA;`bB00tOWsM!G*wx%{slk%2U{7hLXt zYCYsvpL^y8WOVT1=PA_W}HxW2! z#gt96hCf_jBr*jo)#Ei{AAv~(Yu_9$xG(3g3^&}Fa7six*ipV|26U+9YtOx6ZO{~0 zJ@n~!Nok*ZgS8tbu3iW8(fdL)w8zoX?Dpvd8~_MmN}X94yv-4Hy7rvqQn#gTlfn^{ zB9p%$Q5JuG(622&T()HJAFSqA31YRV-A|AfS6KWpU3j>*r$KDt$)2`eC2^~J{a9>z z{U918)=6B+kt%qRO)8*=dFQJbGIt-->{b#Ns@S@g8?vKG#QjK`xfN>&nAf|G6bD z_wTqO_kcfcCmsNKx%Olu;6M&tg|Ez&ajT>)Xj58R!9{0AfS#K>eh$1It1B-~@s%e= zmvf^HhTF-_K2gyKkpx~!KR+yq9_cdf5!>X&YbOx3R?=~TzYJ+p$jD#cknMCEYRI!< zLm>MW+=*%NLFBj!cF3o+^GxtE(jd%hrO6Atr|-7S`$w*4>*6WNf-zrJtgV=?KVQG8 zvVd93F%yiPy5ttk(70*b&P4r!Pl6UClsbC>|Aw49Pi~m`Ps4JIdKIV`+$ny3XUw`-TL7J<;(f(m-9$Kv8&V-d?=RTfeT>Y_;|u)=%qKtBZ% z*~Aa~l)JmxeUi;Fx=gbCG98BX)3AOtrv^2dm3>Q@tH+$b?}9FF^7jxw z*XJ|xwrCz4{VZr9lS9j>Y%BRB^*=9w;8;nQPEsx2RyBD><>z3w=Bg!Xb*q$qf)iY(v`ja2z@ zvYykLSprM7`ml{WS^s_@`%&zLUC%fT_kW@IO8F3IrVP)%9TnI}UdC3D8F3D)fDoYi z69UbF?Ppcm_-YbS1sI6qIRXK>W7WiL&Fv&Wph~YN*LA4TtSXk8|8C8rpkC(T@$MP! zgwExq%WAabhDE8Nl;o_AR7=L2+UB?)gYRt!sPa$l8y(>MRJ6FkCc#s1{-^erl*|*)nm}>O*F(i z6&W-!e45ly8piws98uHq>*mm$0Vns2x zM&(N2e3(7XJcGZ4@wKJrQ;kxR>v<+UtotLef=~=B=I1W&>D&GHdrN}`h+vWeGnG8n z2CfJ@m%05|!3wVlqR@WaOAM&F!BxQ#=>hLZBU^yjYXm?_><1?e#)BA%aLS)fLZBk;r3BH{%yhpZLcpniw`N|D`SA)%rlEcA?6rX3ky7T=eR-vzD|xLyvZ#5kdV6h3ew7! zI`2}F87)&xgC!AaIT^9t$e+02HdEh$4xLPS%0hfd78*67^?yUtQl zQVwd!<{y8aJXnZK1Qfcu&gu?$3%;bOEw-{&A95Ou3A}1)0GLw-izCTS%h-0`vvJ3D|04!ZN7jK zrtud=)rOY#)2m6a7Jxd_BB0P``Pc%mv^{kw6T3Z?%)P|1;wM-a15Q+EYZtuv(U)CGV{T4MET%}ymWuQt4I9d^! z^36I+QW{LwqC3=#kw1LdVz2g$8m|H?5~uP#G-Ohy*gs!Y@+E6_*BsUCywhm42GB{! ze?wva8d|O-gVgU-3(8g(-Q-V>hXxP51{avhV^fO4f>wQTmETJue$Z+cGW%&x$=Js@ zN7|XjQ(x08Xf9g+a|;9}CcEmeJC8ub8LQsLX)3Fli#S&hWqGQNtE|Ii>dd1M)PvJ+ zdb#sddsYpNOnDHRf&}0XuYFGF-^L4XvC)|RV<=5Ki*c2yD?A6m?(A?2&K+^++gmRT z_;CX@Vp6hpU3EA^UhhY~sZ!0FKkS&@;UQ;oMAo}Td)u72Rr9FkP0ZJyaEo&97z02=iRPf(iX~f zYN0UNvtH+CgdYQ*h0-bYCD}&G8Z_9#4vVDl+0)VPZKdPd=wG@RgGGNUwk<8Cq^dci zGY*+IlaU+<6Q#o9OH#?)2*e)W!;0i$=3t4Si*#U8()QP%-vn`b=ab^g@uLIe3!g6c zW2eb!-vt)IDa}q!Qi8iRmn!z}HVZTTy}xctDEhsv7=|De(>lFZL8^;<`#3s|!~-?| z=hwz>#4TbG;l@WgJiD86|)5;?1T5VopZu zFeI8)2*-rS#f12(vcv`R{hO+94Y_Wlg?9WIzaqVI^~P{1UHJ@GUBGkRT|oF05$oe? z^m?|=y|TmSefHPVKA)tcc$L?{YuZf)N*PRLQ5h|wF(UN>Hh!kz$_`|JR?L9;%N$3a z2~T;k0Da56+wxYTzIX9O7Yg$Q0xV1hzF@vG3Blckii|=i`i@AJRYRQOA0QC8tlWF3 z1KgfCqm%#r-fUwxut;oGY&7=HY(Mtj#v&$)`+3k*uV2aSJ!;Q#ev`?|Uk&H%#3BMH zw!9JdWO`jAhf=DgY27UNymm)j&#&`h7cr_agbsh=Atf;63wskUmq2nzH;Xm z{YJOUC;XTLm%_&{+q<3lhy=}q!*s2VaRdByH>&?jO$|jvFs{@Nw;%^ZqM7=!Efs1_QBX*5p5{h-5eh|`B{~w6=p5+%jpd_ zx^>@@o{CZGz6Htazkn{6xCVWvuM{r(XW?^sKI61H14$TIllG?uOkU5rm4H1IW4@{? zEoXTu9#`{IRQR{{SAfF8kITB3Sryp7ILg{I8S24x$F!SX`q5S?eq#dXsE~4bV`K`% zq8H;$B3Vba@+d^Ifp$taya2TG{}-G*jY#0`8X(x*AV!OG*y4wHtOE(2zeyS5d4Zy?7=FNjvc z`_>CfMWiIT%_H3fgT)WxNwr-D(aXHdK-T%-TNKl%c{8~*hWi-PD^J%h0>UP%*U$mr=9`Quj05l;jlwz*v&xyr4z&v6NwRKCx;dX4}0>5R10$L4D+aMIJ$KU&*P zxPFB^aqDnqp#!hRvP#jH}1n=4xkO12$#KFQM4x@-}?5ZVt|COKk(6T!8v?)EE2LoE%KT_K5u01x=a( zwfjtL0dLuuQq}u65lT!n#BlZmu7@8)v+pxpt8L7v+`Q=AE@x%DrnzwXo6aXg>5+N)8L_+<)I(b3a)CZ9rHio|X3$b-KXv=YwTK8Kif zU1IQPjlFiv9QUc9D3=XY8ndA1CKCC6ag|>UtPi;a3%~d>p0_l%Up!y*y8K_O=6+5k z1L*DgZ|MZg%Qp>Oqm$v@J;u^}orv5N{3d8p@NuWaO|OB@j36ejU=;=^IIHqzF(QrN z`bxR})iOX!a~OlEHE9*jP;ro>jMObsHaf8D6y;mp(mO1oL+=mENe#lr!;9ph{(aw` z)(%9oOWq{z`WqUqiHqEt;3X+&s0;#7EwF8Zm4@BXklE}vK9rnYbQ+k%gX=sM+4(V@ zqzQ8&ts;{iq774PztTGYI!9k`*2n9-D^#JEeq#sMQpQ%~21|S~>lZdaA-4E(EuToE z=ih*9fyV&>1-X-e5k%SoN9QFCXX27zBIEf-U`L7OVO*~8#JOz08Jq!|m<|i#vpSYT zr`);s7P)o*bis-=sOi=t^KN_Fo7wNgr4x~fVx`m}twEURkjG~Jw6tK1F4Omj-z(V~2us(a|{W$Y=?|(OR zhYY+$+jvdeV<4Z!N-~@GcWHr(O#Wn=d@9!_%mp6umkbBc74t=VwofSRg&XQB&g<#; zJt!Ha0sKw_0Dbj;g(0?O8t}%VW8~3@Xg%~rDt{Df1z6xZ&)<;w9OCU8O=${bu1uS7 z*(-?~cXAxb@86XukChCj&7Nk3>{Oer81NdT9v}j9?d0??(X*&g{vT8C9Z%&S{sG^| zJXQ`7k<8@Sd(V^AK{#gih-@OGtW(*E?3L{(;Yju|Ba|(BE3#Mi_T0X|-|u-IukOG6 z(e1iFpKH9|@9Vno->(zh{5VR%`3Q3SEzYBI^&Rf?IQ*5BTB|4>7v{Ue=$_&$vgF3y z*LGzAjnBedJTmn*PXfl)7Oi|`Q_}X$ud2X51=7Qs_ZT4RW|vmIsPa+c^bRsAxzv9c z;gj-x=)$5-`H)X>iMWEwF1 zIlfA7L8Sr;%TEKRr z(oC)Ct9`8c%w*M@i-g0xjyR9!b!vGx0Q=;qw8xEW1wzUN_Y1rvD`pX3u>?q!d z-x3qlO}bU$Z1$3aey!H!aknh>tHBaXA~ng3892X(Y%X#%kah8_(JLx+i*`bPY;tkl z@j(~7LTJK|+mgmXA1Owy`znGuYnj3+-KQ;Idri()E!MFLzeQK3GKiXJpsgTLw@ok) zRA!M`zFft8Llq5Ajg5`XO-!tg;Hp>EA&Xp5DcT8#zD-``g@gtW5MmC!hFES`vD4(W zzx1a-XNvLvFTUcFz}X7DVa4_7k`#Tjia$ilO~bQp)BXF}77_aHkY5Y!pO0aQ3Bnvw zH=!lPcjmGlW#V}d1N6VjiXZE`Xo^Wax_m1~2MQG}9u8H`l!p-#vkk`Vy;77FsO%WT zrVLdsvFCd1#euZ*m5W<9h~kAOFNw@ha#E`?1k@OZ)(HNEV_Y!9E^UI70dv}5W+#B@ zG2iyq*sEibD?D%p1V3d^;vKJZruHr>kOr{ ziaA2Xs~QkRJz?hl!kC;pil)d8CzCtHq(es+;l!sg3bF&fW>kS!ijvZMo5tqn`Yhu@ z4)l9F6?4uCaT+wT&QreGRh#v}!-8$@o-;Se_9nK@^nPMPPd2QQW;+rMvS`h>&w?6Z zd7ly)bdVIV!A^^LlQgaD3C3cDb?RO|lEEg0JDK&u8S*lHc1G_|$m1r3wxf&Ny}iAc zWC9HX?qGPju5r{G9y%Gs@k@uXKMjRTUe#$Dm9>7{#seU+_{WX^dA5Z9b^lStqOqNU ztcvlbKrwNb)XD{t+hD=(W-Ii1!ATwjgoxdq1$L;GHol(=5~OaV0Q!haD17Z#w7?0f zgluYWa(_?sYu!7dP@%s;?hVhcDvEkXheDSADBv#xAN=E=gd(unneobVKbG-9TjJzn z(j|vcJ!O+@NB&h2#^eUwv7PpaW%Kmq#riExJKUDmjSoW(o`HeG>50NXj@D4+EdbsR zE<$?}3Tq3FK_Cs5lYI`;M$3Sf-yVzKk|y@AjPm1)qasAv(pPa?J-rzc_18OWz-=In zbH^PLsMOChbz;go&%^@cq6()$>}Q?QLUK#?Y6A{z0;N3{vtynTA;{4E|R}B$o*Zp-hw%%Bn zHA%F0MWScquU=IFSp3tRddz=%_>*0-FzqqM=CC`ZubFVZ^fh)}W{u&NZ#Wy%GXASt z_DWsf?@ymMR%ygm`-JAjUOv!pwolgEu*m9tuJC?E;g*5CyRh#YZu3E9eSr`zIEE+$ zhL7r^2Br;vY=hYpKCb+3OCotrnay$2+$|`C^(tj0>ALxOeFd+!09_?UThw?z7p^B+0jc|G=_QwiRqg( zJ|8OEnhAi9u4lPFX%v}BRg6wKUIVay4eY(`HhDRY-@3NfkElOWG1Iz_qgFCJ&GZi!qfkl1=<&XcR$wj(u| z&S2OEe%171RUi;h1Rm@m>5QxcJFvy<2``Q`S&kpaDsy~XecMljR{<9B%g*Lw?`o|c zw=GC4NuS68Ivk}e5N!b0A(Hd>HXO6{@P7YX!lNwWFPoK2pQrTZzMO}+`tGN_YWGZ> zTeue&G$Fc>(i?WXg(LcIc4Stsw_=j?$a^m7k;Mm>d>1DbiM&k=3{=tj7CjnAmWw2vhHYAx(bNOGSqv{JZ4<&Y6zZXd%O!1$XP#s3`1r?gC|FISe3}H)JM;w?g?0BNN`j2rFk; z{ed~M_|nr!L&EO3;>$_CK>QAy0iqq*i^_tPSkLJ{iHFe}k_N9)I|l6V_nT*N(S`r~ z2z<1J`n}}m#{75oR^(Rj{`BI)>cFD-`0>=%52u&i4r4q}U0-2(+|?7o=U2_W4nFqD zAK2e|O>7bC;fI^PRhiYjU|724xjY-I9FP7af(|8VGZ|YdB04eSTEQ0c8ta7?9n9jxHe;m6<{0RRzHvgN) zLWVcRznkBzWOFm#(kMmS%Vqz-IS?9xNG^|$xOfpR{u;^M#!1MH2m135b>~ZCSupx< z*0;9BO2dKqCxVqK#%$u(gg}E`&U|+TaIG!DDK6WM<4R!`u z)?v#E1BgX|_pz?X`PlHj;RQh2`7tHv!$^Obk>zsx-pj@pBnEOETzj2tqJ_ju4cO6_ z8bGs={3S2CxwN&@Y9oxd-_COG@zWVf^eKgxmeN?wM3w|=Tl9n9Kjfl*0O-`^K-dA2 zd6oMhG#M{U)zT!3?v3snwu!_q9NAjDKcD*IjZ0Y>zZzR#OJRDeyg*0coOYkfZ-!7+ zhg8&oe)CPa@tJ@5`0|@y+t~^IzH?dqKBXErJ_rW}Pxd=!>Tw0=kmQ{4+7fnUsx@sh z0``f)w4gI@+NOMTM8#w$)W2twB*>sxwvM|Pl8a7$&qRO+aQ!M&A+9{xyqeuSADe3c zR4Smg_b;ZJXJ{@G6jsZw5jpYPcXyg|SL$JFlsQ&-Yw}zwEPw1F;3;4DY{D*?)u98s zphnLCeTKgm1bxgwx6QUxN34f?7S8eYMp&NnmKH^W#X1gK7_k!XND({dAh2|3c5x^6 zyGLgQ`~^EgT%5t|<8gaa_jdmF?%6efYyRMjc+jEqvB*H&*nT0H+|ii~4SGDexPFeF zR??U-E$_q!R6Ac-UBz{v98G*q`UIBx17MMbvaB|Qx&6$QCB}^l0gE??Fo~OIm%OaX z87OXW_&6}9q)&wp6Ct<#2hJHzXxcs!6Bj>}`}h4O#)M9M7D`ighxn7IoOBMvK!2Yb zT?VjdB;F<5@%=A_{nx)$?&bCZfW~Rc zt;M~M*q}!b%}8=6Fkq=<+xH%YWnNLJ3(C1FU^}gO5H}tzkzXD|xv3K{Fff(GU1KrJ z68l4*4m$145360jf1ves(CZQZkLCJN4&wTh#fnI$#@NWPL=?z%iB_2R)+WwBJB~ zO7KjCF5#VMb8UZCuTve4=Qx(ile>|)2e~^@GQWzBoXp46Kf3zYp4C+?Zk*KK#;H8B zTdL?|8Yt;fhtq1`_g`3D)P|hzd$PbLX`PLOcxhrR#Y-{raboEQG)nu7qkk?_tBy-S zyeuSedv_X<>u<+7QY9_rF$z3Mn(Yb^oPuwa5ex(tmQ%NM5)Bv)A~EsF{mbj2uzlaCZdYI?W#G?aqz;-8h5IFXXpsxKzdxcwm~yIA%$svD%dfu zB{D)pex<3UKor-ieY8PXePSkSsN!J^!@d}GTCm5|Ixknbd8UEn+M_}HiW!A%= z^Wsu@g3EQ_s}0<|nF%EG9?53BSYF678jk)AnCQt954f0B(Y&n_p#A@32Zf>Lztzth zTzzTOhp*Ax!kRmzY9OM^p8fKLy{V63xrf7J*0F(wKYn*4II9jfSjG{b_U3BoY7?~8lp!g$8q=yk^cSPA4i(aXCw@HT)ZOhRz_tDOc&iZ@vajvs;WC#|#jcanu(OS96Sg zbIoqoC-Xom%P0v4VoU2I#(c9wTg|wpV~l1a{)il5oDEpOXt(;20kS`Jov)iBkVWF1 z6VE!To1p+myM}WjfK9Nzd`c8n54*Kop-^#RrSdR-`Q~mqLI;d-TCjpd98W@%pAlyu zRg3fT>u)Pq;|J#~h3Z`IQ4*0FP&4%}?97n)JmY_~c>0AQP(P4y~*DzdHBscpH z1NR`ox+fbfck9ZY9v>x6k`0&v1XGQ4Nn6(n0GB&E&>P~e&li8WXHpV~pDKTg#&!x2 zzwYI04{+zZrrbK^lym{;>1=pKRJ->M_GwHk?3ahd^np*%sXHb+-JI8_(`y2I@bmk#sXfk- z7Q5+$?XK_lBlb$$!vvm)c z^qaLPz!s$rXRme@xMpoU5fM!wHh#32puOZ|K1HGXIp5QPY+Z*>qll1Q)V4W6dmqn% z=!B}*Eb^UL2lp|S>ge(QpmM`?C2QuBN`xJ(UIrR`_<7l zE?F+upY~hYUmyciD$ILu9qVWaUByxS3hPq{jQr{c<6m;((*I^*Pu_> z2Rh(jeK3YyYyl5kBqP7En z8}EM!-*g)01nGF#Tt(MUNS9QGa2I)wGfnwerIGrrG@tu(f!e|o-fH4ABD!kcm9&|Z zVR#NXW5PxN{T^`29kzYei-@oXViJYH$*X{%xA=0sN_yc0u#}9%)Pb9Qd;;dShrAoN z)1GEMw0&*H>q`~~4)?WWA96eI5R7zXktG45zAyY253wuyc;k9$R~xUUGnr2=zEm-% z*+9?~^YYKRUp@MB!44)6@7_8Q4Jc5~Z+l?YPn?RonlH&)5`fFxs477s;1dt+&_UMO z7TS8Y7`U*KY}mS7Y^gRG_UySq;XW>vuRrDQ5(#$?qL?S*_$KllxIv77Aj8NZemZ^Y6|w) zd7@~3ev1SP!G=pa_*tCPLu1dz87J7YChY7dv0q|vIUfb!gNy!0@Aylo`GxT)iPYRnx@(hXFy&PP-GHsC@092lo1O-}G zvgqKtwQ?iDO4~ZTFVrnhg@&nI@s7`Sy(B~I4Qe=2`~xjBUkCvE>94%+X`Mx`o^AP! z&O4;13HJDh^(C4l_s$O3Kgog>b(Jsu#I5OfjvqVi%>1Fd#>@%l@#$dx>kLc5u*mj) z!i|^@SP3xpHo)F>zWGfYOWa0zx!yBs^LF7*6JN$^kcY>-N5%T^bMnxwv!EUxZwiJ{ zxhVAyU)ug9ZOzxZ&w^?+$avFs4dlm7-T1bZ*xkT6f`O&8AX8`O;u>(n3Kv-4Zig0{or5QU^*V40XLygsX)%XG$rWui#K=5L}70T1NOEwk|IJnMzR&68)k0Yz)XE^f*X;!-96lKYq7+G6x*90<$_{ z1|ualm#A?v=H9gqYD=9JE6JDrpNHou2wQA1GBk}E)Pw-bpBLc_=F4I7+f1OkEl2B~ z?a1fYauXq-PF`x~_vvCwI1n_zMB3{oGD)vMOYTE%Z0P@@qLFdw72a}bOXl@6m62}u zX5oWV|8l+I$D1?%%&?s7E|bVU;x}R5l)!%K5-G57SJCp`r$o{i@_a}^i>-9nL%284 z8?zvqg1lD;E`(PCE`ZbQ7fWfMEn7Hr8azwj!IPkZ1g&qws8mV>+#6mO%Jwxi(ro|DJkURTfy1+&nlDnzd;tUpvAR@c2US1 z`DJ+^_EDq?yh|^fsRl0;4b{OqZuaUraVg%{mFdcTEiCJ0;#&;=xRL?g9dOJh ziOxz4fzc(QkWlM~aC`)`drua3$>Y3GZMHRDZgm{bUO5^+V1I`sJVl`UNe8BQBigl} zkJMEhpht1Kbw7=rU2)!fVs`u6?=#ugZ?t%Nu?bi0vl`oZblbNc4*86Y3CPk%cJ!P2 zs3{FGsMcFN8q0_cVW(s!QM-GY&F5NY*eO-Xo3a_il_p8ell1vkEd%PMQ2!>wwFXOF z<}P{rw))4};?d6f$4%SBLeFbZrgrwClF^sYxg!!qCbV?_-YwVJ2C3r2wG`B0@TRjR zV*-WSNKF$hXzmnk?O(o4dh~C-@AI4u|E+nu$%juMirzu5ojavUTbBH^2eBJ*>+v`- z`bjewi%aJN#)jCy*^k@X?d$alA+2~^yq#F{18{xTw`V1Zpm{|NK=OE&s*`!PWv}0S zp~{6i4po?h`m~Y%F_DcmL`4|_w*wT>VXBXeQC;c8LmctrS`GS&zP z6s9*Zl5&G59Yz=K;XbWDR?#U?#G$4ky@jRd-3v9M(^xs-fnZG#)qkU2@;B3%_cGVc zmxIEKKWtm6QHVcsU;-W3fBiU9XayyhN|y^}uY-wkj=!~#!jeL2Rbmt|5rr2mc*{mf z$v5K!tqtr|@g4AWIP$>U(BMYl<1`dEISX${>eVj|0ouShZFTrr@Pb4T`$J_3@052u zQQcvOllUzQoQeX?8+2J$y<@=XJs>QB4(t-BF&P8FYcU_YOOcg@PIrB-4g4XyW@lj6M*BEc!hamxO{L-ecDbmtP$IK$}3V zPnr}yPhe|4v!?`YKWEvI(vEPj7^3@7{=jRA)^izq)s>MV6s?1jFantb3)z%FFVV82 zBvcfa3UY)>KRS`kJ0X|IR*w*lF)U0-xqg82;|4pm!(j}cgNK6RqVgKGP0(u#wzx2WaXyIi`2L}ThZimuYsE=3AC`@XR2S!LHxpV62 z486SEqqSko3ERrjtE9T4DAG|Vhoe>;5?fL*;;N!kzFHsfa-{ele|-b=*UWexz9+RL zEeGN+7^GC^t!US*Z>meB`b7NDMOrKG39RDWKvgPu_@A4Xy%a1xYnuBbEz@s$xD3qu zan)K|upV9MFW4H~A^lIoZl1Z`!0eNG?l86!f#TH9)ERKWAYQOF9i*01*~^6XfIV$~ zH(N5K+5~;t8E$#2feb;Z;xwz!TCa=3cS#TL#{t@YyZW(%Bj#A{ z=I0_}=}p_J0Y^GW2_x?JL9I9V>*W$=gGHQ2;uC)n2wu}dIBY$tOO)zfB$w%BP`_Yh zWG>96a6wW;0}~HcA9UAn3lD}G1Q)08Tllfuli{Wqc>TR{8{D8|`9fj%A)_>_EH=#K z`^nxrA#u6QNdrpeBSi>{+4na`Oef!$ohRjgcv7bRk@_#eRs-Qx=08Z_f))t&$Xvea zGAOef(z%yPG!JD(eLc@+>)%s{T2t%VVJpq0?!L9cv1d&94wB1qM)nlAm261hD>!h| z77G(=Q4QJ+nV3ph8vod?OY<&(!H*vV%PaN2UD8Sb3qdUFB5teWGOLa8%>M>YUhqp9 zhKvh~O8y{DE8l|Wwxb{fOF|`O7e4yo>jP&CM;D(A4UFwuvN|kreB{^4tU8icSHfa106hDJR*Vyow$y>tu^FUyWsm-4@tu2G~nF`0W3g^ zkyG8nN=c-11^rd_BnJH!bF*I^C>-6!uf5q4us|+}2{<|G9*fUo2M~KUve>l2t3bJ1q^+^gT z+I~Dv4z&{WBWWqhSWUG^?%!Gw>GcM5o2^TJuAVxx_A~Cis{&gje)keTm1>{Kp9t_M zppJb0cRca(QzwaFM;IvOj|cgVM&PjU`OM_R!1Jl!I<*V0ZyRrsQr*24sC=kbu<4s& zj+Arbk&^sR3|~+`N^zK7i8)W3S#j<6%FFWQ!Z7-(v+el_e&Nk}Mj8DxBSW!^v0t^T z8OSkSjX4Z#ZUNCD9ptr+r4BnaIz7D+d{sZkNBgp`r@VkNck$WU#^O%X>q=LhUElB+ zM|QMAv8xhT!ttzioi9^gEQVPi#)(5I0AS7%|3=RLS_sT3m;ezEyM~Ui-7@DFw_Dr| zif)O&3qW;NTGKPj*y8GpHNTr}D|QJQs-I_;6hPbp7R{;3N^r+FQX^3-Bkcl)0wETA!fmU}}MRBHBJgagd%EEsYiRm#Tg zxd{jrsY!5VjYg=G(RulPBqsw{2s7ut+I5>vRo~Q>KY6kxEs(v7_a-#(QDagVf~Muc z8sprTRhhZldv80h!SM@+BM==jlwa?#U3c5)Q^y=a9w^*@<-mt7H7JS&b?rRGhQKEN zu>oKToU@|O%KkM30zq^wG2A2H=+U`WlSdcXX+rOIm%>HPf6aZM80XQ%c!h9#25PO}s<^HM$_Duw?M@_Wp4|o#pUW9YjVAs9(#wVH0Z((!`F-4Ciz9Fq#6j=& zG9N2{X)Q>vy)ueWp&vPa0Wv05AY<|ckH^B@1$BV+by8v-Yn0AhGtvLSB<&U+pi+SG zmfIrDWn>=WlQVetfw9yvJrQLe$<>=L9X9-8>%8lk;h2CroNm#xju4#miW)q+2*IvL zQsqk*(oW(=_~frPVj%%kiYEiLJr?#d+j_=G$}+xxb{Ndi+o{+k$$x@pWu#g(d($$#e+&4+Zk>J@ zb6SW|d-FR+Be|-F{i_;uOq#d410&W;;bLgX#GIJVlI%Jv_|Q-xXavZ z2nFM5u?~nP3K8X;LY}MKda#%IqC+I(%MzW}l^+NFCV&mhg}8P(olP`|G=GsHVV z*P0n`icMnB8eSloCQx}ztK76v<%fM=1#);o^K%CTo{w8EeEar_odT;@0=ChJs+yfU zgR*Iie&O`(Oe%!t+bst#WkAk~@KNxzS0V}lANjQYk{xilPQV>a@^rX3vcRTha_okt z*k7)(IaQpYye^}{$pm;w!10FTlKrqElN)h)V8!+8+>m$lS=WtD zVhUUNuyCGP`dBDkui`>`M@#UfZX5XoNR0+Oj;}&QD0%)zi1ek|I-I7r9GYgd`q_Pbud^Zf-)!{ z{0Mww)bXf$_0-(uY|(mS3`h}#WN+Y@(jEA-8((>^3+KOVMENW2XF~vG=N8Zg(dFFN zw_2F#ed(jszZI`q#vt>?1DCsDkm&*YS16>4GU41&xP&4KX&qePHn^(j@hX?bWln>? z`{~?(XfId4Tq8V6{e%OBix&w&S-(@?OWJk!k?R;QWycINO+4=hA4+t#87V=lj4efX zOn57koV`_%wxC)?7F&dTTf?S5HQKF=M8HBmxJhrNNhD(nCn4lv$S!sde~eM=s|>>f zMmqaY*lqAs1ibS2RlN6s3qAq>O|YltTJ>q$dtopePIQ`bfw=2r;$dJgI)d8rY;vry zi@ws8o-{{^!Ij`kQT1PtNs*+Q1VgT;Zhq>OS)t>Z&cv?jzdP5s@`%`7$UZNSjrWCA zQ3im3-K5+2$3keY1s!}_iN!c&n<>khDtW$#%DV{5iy#j1?LN)5o?h&clIUq&Jd6th zJ)1oegU2RaWqbt5d@9|RJz3MCO62lBMW7Z^>7jZLfPTNOI)sz=s}K|J=&*1uPrdN` z_h$;2Xa$qfC@lsobcWL~f}KC5w~Se?bVU~K66GVxxd5vLE-KyD zy@prd&(AaWi0|Uib8$fN6_{v_?<=t{E5zF4w|>pq3m44i!)L*3b#j@*+O~x~JaOj) z&qZWeI%HoH2qu}Y7<)163|x|hiQjutQfSk(AX+zj#?fmG}$)Jv-YE&!bn^b=*46$1X9v&=E3sw7I*v+-AmN?)Z`X z*diKy9ZvqKy$0?3r!fK2gTBgC7kmDLxayj=!59%A!7SluseJP?Wj4>r8(4W)Vx#6s z$+iq#?z@(W-QF|VNS}UzMbwBNWkNJ7#swJAkr#ln3MaUlseR|8=nwW04eaSeLRmF| z(lF~vaS4fHP+8aK=H&F+F@50Rr`MYgf2~q%*(q6|^Ln2vh6?)n90PU3gv2S%ZEHvv z%q-o|eQeZzUc=6^|3YFn>*s(SE}GY{(an~3GZ#OvJ&9peP^&SKmBNll&7JJ_fk4Eb zs_Y>>T91D1y}U8zC4IfMbHc^k)!5SLRe|D5=O1XsgG0IT&*@*Y$KVK)JicxRq3^7EY zmg~1;cg&`%gIUn@4WIqTuk1V%z4EWQoL6?>C7Z}P?GR{}-JWg$X-Wgm32$HN7jeV; zcSj%q53d^^sNxnRznSrega2H_Wv<%k zEX}B)1VnvVax&u+6H?YPe^&ahrs48e)7p>&DInLTJeUccJ&NP+pfCR1u^6ZWy^%Sv zUVFBck?~I(Oxx5TKvOf(b$Lo(91n7qN`U{fdjVS=I^lcu0QVl57~pI3>A~p@fCxQ9 z&oCxrU{C$^;paxN_4J^oi`|oEhF%5~6z7s|`RRiIH%(J;#Q~I+ z22g|l1xqm$QaRwrIPm9-p8K!!S7%RjYSoapVTf^5mu_#mUGzvFsLVZ7(l)Wue9e;eHCGJnX{8zR|CQWm8 zG4oiHS|jg6T1t<9r0wZMdBb;~C#GjjC^mJp3>>Sty*~4K?c!N|hE@a%kjLm8*jGnf zb7p9Cfg9{@oI4#kdiqh}Y@+|xvv1FzTPyC^ss})U2$mxR$pC4nO_5jJMObRxg(?L+ zySP8_rY8@Y*q$yGk*mW+lTt{FxBnC}e9jk6McRAw=@Th*3YEQFv$1Oeup^?R$O&WD0sSi9>?wd2=(iOm4MhyTgz`fNc1h2(*9rQ=>#Tque*}~zMMaf;H^!}(_FmMZ zii~3x7Mu1l0!d3U{Gp)jDpk>!Nnsbku{Ce|-w^;o_${+*o0g%}IkaTpbpMG3fqRn{ zTzb_SXpW%C5%2BJjy_r8nNH@Yj+v>dQl~_9nIa-pO6K_;k zKH!Uoi6=Bky?WO16)SK1*hpb;7A_NT^E~rT))rRmQsNQ#b6x{d# zWi`a0%0Y@YE)8abLBUCLEWVrf43V;4pWB{jL+HRmVEyyi5Ik(aFIQ&v+=C{HXCbq$ z$C)_qU)x=bX#m1VVRFWQ{Vc&&Tl=7P`41EG`;mS_)A_5)e(#K?VL8_+_+o1yn{t2B z;n0czMTi^{&WhncKr>)Aj_LLf;hRoW-*5Z>;--mOt!RfgK5MO3AO&t~H5+!-yv@j* z_T6HXIg<9+^3Fz`x|l9^H2(YEVp=U{oafRpM{L)HyP=xrdpBBn``pbvyQ0;bazEwm z*;Y@X8qU0CI(oTE-(mkfX9$=aV-oF<&8L(ZmFqChvdfQr+H89!xO*7En~&y1uQX?Wn(OS5JFdgYu&i`(MBB z4+~64CL4J0;DL#@b}YCP^e+zEPG7-Lks1o?*+XFO;Vu537QpvDQo!O<(JdD^4Oy5s zaKq@xonWU!wjyn&u8-MWuX*A$1pYf9dep8BjKiU@u0+}M@HZn)0_cuap) zc=g42sek}rl6YtNX=YZJUvsM3ma{g5ApSjX+}F&9Ql;c8X)Gb{qsxWSo_xhQFI$aj z{(-|45KxT?u*a`Azs=jL8X9BtshTxM^xv)J(5>#uu~3+=UA)>>7e_2`JnO+bc!n@N zYbjTM!cxkZQSd$5P?#XZZY&VKW);b`ZP!l_Q{Pzb{hbOBT={Ue&`@YvaimU_nuTg z_NPd#J_<1vHwQS;M?Z2|-&Gp!7;@1>o#ZK@M}x%IXHyUFD2v_KjDLKlxIIx3E)`?@ zckkrwcRNG`Ey1hM&#RR%JX{cK4aVfHb#)sLO?R9tFk7-^Nn!Q;MhC|y#veR1L-}CH z8hfUMZlY{a&>XyMyLq=*hnIdf11W#F!+o~*VZbf?YjO7K%6O0|r|<9EZbHj=U)_m%liA_bdJlcwbMg0G(0D`*beXYU#V7Ht=5y zr~cZLlSBZ$kJ$qDw&pfD&7LiO`#C+jvv1XKzo@TolQ_Tpl^3o&Q%^d-zaTN5lfqWx zmtb`Ry?ebt$D6(NJ5T5O5N@C*#~b1P>fk#{5$TA8YXO;nBAhnlJvGApk`4VeardkE z{jkIX@|6NdzI5`HZe1RGXpR=N{P#Cv=0!^RV=E#9rPIr^?d%bE`r9vSH_kf+ryvzDO0cBQjllytI{k~iGlgpteo7&23 z2Y=oaGu2>jU)&KbdgTs5Kbl+p$=DHT7$b2z5%p8b7gC$}i&v-LA?Jga2GY@pPj+MN z7w$H#wjNh9V+Qx%#`~^k?0`p$_d)#zPe%9S-!hokY=u2xbP7}JxL#(a>7mL3{zn)8 zFNnvTnrA%1;3NXkQLrh^|ET7rQL>@`*5zh<&dY={Z zZmJ!6Vhh`~0MLv7n^^s%YN&7G`RAaAyTgVTD|c}Ree?iXT*32VVZie0pvjom-jJL2 zt&m~A#i(fSo!hquxiBzz_bFZ6==KAvX+r-h)Qwp)zqSA>2`ez9kQ0YPrIn z=Kvnd9m0WTy}%wL$hqD@dBU@5!g6jJz1=>SQ{uT5^9#p^R&?}@&cVFZo@(XC)kg~+ zqD`MQS9<|P3|9k>+@T-O!k*@csEaU z<0$v9M%QYulYp4Cq^rGw0hksCwY{2J_+$PQK4j57j3_hz-t%}ks|M?Rg{jeh8vj}p8V9`(&9nO{=HCDOV54k6o*wl% zHi$-XII%&Y%#`K5M_O`WA{OB-tX+BRYsbopiGB#TMMPBne1eic-)WOVI)-^=W%ejc z9t#A^tOcFCy$_TSdp!8ZbCoM_xO}yFKCM;;nuSjh`MNW^h-Bc-^h+C?gqeEZ^$C|z zCwF%V^RX(;AyIHl!E?_VbqT4`ts`;Y1M*MM^9y8V2;n(X~5Q5Y0OVn-Xz6Z z#6)7iAao#sr6AD`uFu&n3fzGx~$ z!OxMHUD>{clX&d9HpgTcL(F}mM~sK)P6wO93tG{@1IF~G({~Q%fpf;?+%;WA8_hWV zNnbT_Z?3g7ks1SId*wj)dw@D@QB3Zq)OINo$W5^-hr&=wwYUNV^qKP^lLol5`}c<$ zuf8eB{I%-^^Qk@*Ql!w)UjAZLI|V=kzv7OCpj*Vitsh5hX0JYirhJ>Vq9ekp!Nk`NdfVbp4$xp2wY* z!?4auNG-*0r&qo#m4WZ@{K#7Lp#5Z7inYSQ!u(=e$!lIGDnKK2)0P?dLQFTN;)uU< zJA~)bSHJV(en{~pS?V1oP;nE0*TN4JLnMJ1w#q7S9!NGmmzZo}*<`TouTjC)vFf;H zf}Q&}Z9Z%|SKes(y>|Hym`(LKN0t3{3gL$`tF1(Nz|_YP7rr>&6Go0b4y1PMyr#D|H$$24ZvsOyEW$=~EM?1nq-=I}#)V zA!ZwR`}g8dry;OxuPpc4SRLeMI!iBoML}ZqZn7coy_Zb)?VbfqTDy2FwaVK~6^|x& ze>9bI8$PMBUkg&d{Lu?^fBG zXK=ROxWNZi=!zY)n2uYY>ELjY&7QHnph37TVwfvJjFaSzmOpXegavVY4Oxk z7-{}C;rr}@&F^_Z&7|{JnFC8PYln=`?EAzXT2=1FxijLx6-mn#>AaKKq>0Zxgpz-I z$QcD7Q#`c{$l}i*lDKkW%?d_J<2u$C4I5PTnC?R@^O-drytKT}ND)+LEq14Rybu#s z#pdDH^0ZMeo~qT=E{7_g%>aUh8O&a(Cwy<~A;5>~o-?P-(UiBbJu0#)TE(Uw4#xYp z2ZjpMSB;L6v6+4%9prEfMayTB;SYN}#j}MvyZ|mBc-yhV5Wj_FsL6+)U`3TaZgIrCL+N861nT}U_r=-`KaVM2Hh&qw7YQixj zIhPViNJmA7a9vXpV^dmVnYmu4Q;I72G5u+ybf%{qu(qs65|9>k4(TsNsDQ5%wp_`C z1TE~qtZ$n57;Dc;75|VuTXU$+rw>0Ci!Kr>$XXTvx0b&Z0g(AU;BsHMb1qy^Jka z%DX(2xBFIy0SD?kps)b&NQYm~q#-^i1V-?ia6pAlDZP-SukHcd8_Jr*0=;8C#`9tC zp4ZoGiRiCwl@f$hGCbr=<*6sD#LNLZ1_8bEX6r2dCb{)HUpJ8LEQ0#qMs-gYlBY@@o78Vf{T#>E$be{65_h#EQ ztfd4X(S~Lu+usTQO{j6*31+nBEDaN6KV~&E)A5oHAhf3bbpaJ|^s22CJPib{@LRfp zcwl<%LN_Ft{)(=(=kNMwAzA76+F+B-WeHeRfnCVsF=o6Stq{E9t4N(E?|@@`;OeF# z?xhPXm!lu#0v>novDz?6NqB^#YgSJUOf(Ra#J1J}%`SI}RbnEL+ z3$5dz`$;HbAUob`mpb&&{sEt<36u(g_{|Q*gCSlcatCbFIk_-9vi%$(E2u6N_;9lK zCSluw51yhC##1D3lO5FszHo6U%x@((JnF+CGOa2sNgt0Nkoo6E|_ z@GgDOVfd4Y6+H@>fHI*=35BV0C*jnU+{`kK>SxF;G&!of*9!9ZzOPpqr1n0^*SjO{ z6n=~OUg2hHUL8W{_EnvAaGt~$N-Jix3`ExOLgpqBW z1pd6TcZLK0FUda4p@0gHMSjr^>{L_YR@e<-L&X7(s=s2_!P}3w=O+&Ho56`mibC!gyqi;%eZRB_bzp>Pf#Ab`5TpImxlZKx8FkJO_cPN-ov@)F}Q z77?Fs>wqIXrd1?|G6;3*bq1Vlo^O_M`+Z7vKh+vy<}soN?$of4@zX$XR7|j%r>IW1 z{(=~A@`XC@;6(t4GU^W9)l6IBg#V2;yhFe;>Nqs}VzSa#es+XiI`C+`!}G-)*2Ug0 zN1xt(aL5?OQEOxww5sQdJbP!)iWv@=lQ5UiM8lJiGl&QXBI1*YU#&s5qY-k|A-y_> zj5)@%oNj7n{{pbuv!ZL&ERaTZ(PKn8LySd)g?_KwPx%b2V!dk2a`<%0% z{XD;74lGfJ{5WgN(dJHS0v{=tIv=GbB^&NcYmi^Km+NM;tADl~@PN)*RokoxMXv;% za4Di!v041ASp4+&UvEj9S)a?;xDKc0Ydqj?3Zj^0YyAg7?jZVwsmAz`J(RyMQoZ{N znF@~yc8A3NNtd@6ZkO})DBaPuQRA00S#w-uyAmy?b=>||tg~eaelyYE6)FY@cq< zrGTjwtFUY%;EJh>x8x%<(dw~cL;@evsVUg-o&w9d-)^j*3IszjSCPkSp0N<}Cu=w*KnY<>&}%DS&MR{G;;WUHMuf*d9#m~s1+b#Grr6Eu z>vK2m|4F*;zM=rK)iZ2Yf1C{6B&s{_XF9l&Lb4*KSPD_1Xu1#j1lEWBktgxWcUUY; zOSfC<6Ij~BQss*ziqZwJK7n&m1sUgE-1Q$NI?T#|GJR@v|GwtPL&-!8IovFqy-p2w z{Kqs?-ccpqWeBZLxju%ww8-2T`N>wtP#IsZFAW8g~)89vU1Jv1IX@?LKZqa%nf^DC~dd%);iip?)^SLssJjW z(CZQ%Osd%OwE2~1&lM6AB@XYCf^i7i@C`prT%^(oaBf@45L~D*y;(o6sl@i5w2qNm z_}R2E=x?m3oVwSaB}z-D7>>#TNFJk4Y|G z8&j3(1&2fuvHeCQ81np~st!7J704N80FP5p!IM!$2Wo-qoWbtx+6_z+LsPcxz19nS z+#}5P&Fo$NUvCXZ!*6-s_`m~L%~a~&!SC3uoRO@wfRa?D{0YLcy?O8 zi#Dn?AS2{a1MO)J_L)M_3^m|w_ROr+top?YjizUmhogLdstMg0akwFqg=$Hx7&WgJ z)){Zt>fn#+cXo&xi-OWLQG#5=4r8e5ogu2|cte7bQ>?R8g41h0q!C%@=r-=YQaFn| zPKScg91odE)w07%#oUfee9XqrVy9C&IAaS(PKZ~9HG$%$NWNi53z^{FCiP-wGsW^YHjcXIvpL)PE@ za3bhISR8#KnT7dt+!g zl-8~RDCw6EWP#d4X_2qIEi9H)n8yImk0p6t4R8) z<9Q4n;62m?T4@eR-K#SGbaVF6PA(jpTKmoT{)=v`hz>9(TXoBg?3H^l6n@$=Lj`p{(JS?AO?DXCf;-o{T=G z^sDyZ;;9QNx*_~@IMS$rl)+O}ir#EE zw2XIzhjQFX#@Yu<+Gc&~`ALN0A0~G6mwE$9KbKN>l8#{=2E*-*ie09+pS~{#jL-HE z#H1hHBd}a~Q{^)Mpk6M={?=X=GF0K$e_7FwdFwXxLPxlIA0&n&9reKAl6!@^+=Zf( z*3__OlpZE?DtcFRnpp#8qX;>0^koKu*}~s`c29f+NjE$>_~Cz*Oy&BN9BSne>Ls$p zDH^Db(=pX{Qb%sh4<;v`tp4~{!t!Qb5Wpy--^7PqtvEk_uGrvWy0^JGhDJ4G0ziWx z{M;#A8v_*^I0OeUpIS;O)q<5*7UiM@i7W$}GaCk)uT@j`8oq@V^bA?KuGaaR0pW%` z43wz%=Z!s&`Az6+kW*OG3M!YK+;Q4u7t}bGsyl92{*0D*9&~vSlc4*z?8rS)k8Rs7 zEXGP#uq*V#x{^n>E0>hZMe?0?J>#uRv`QQUF*p5^C{Lg*-o59Ko`MNz?2VI)({Z0e ztKn7*2%CGAMj2I9Jp{9Dxju1=-Rdb8g$*wzGk>5_dZ%xAn>ZDodEn`*;$#`)0jR*$ zt9&1;K+@OwTtLii=sbq74wgddL?^HgFAPSsF!ue7d1`1>xl-Fl>iW!hN2>c#w%jp0 zl7=s%NN#ko$w!i&MbhT!Ye5?qk)6t12fkB>$mGlX$kSl2?vGuLQ%sAGzmqN{BAB(* z+gQ6h)As!I=>+ADU{6sq!!>AWsbWn~1vIKDkY0=;1mrRE_{$P6bnxw=rQ^-!5T1of zA}Phf?fJBR7kTn{7FU&d)L02hc(ZwTv#m{D_H6mJO^iAV_o~AV#?T>h>@WUWLZ? zZrPK0167mltM`OAlYB_bF5~T1vA#y9`3;F~hY@zf-yioC>B^57%x6&i(tPXG_{HwX z+eLvI8`~1Ar~T7n8)+U_?+dft4A0Uw)hHD>AMUz^^YraQtt{7DHOL*1B+CQNAdESK zH?Pv$gTzN~wxxSz7MO6?&fn#gRK0jKsf|cg8V>%Jn?^?wf|kPMqR3(B(yit}smeKj z2pftt1sfRIxC|Qp@CQUy)2N#2r#!gJs$18qUb8kzNck==u1;fVd~dMOsjUp;T9o(i zs>k=tsP`?p4hZjwe|jPl2d>bG1%{)GoAne=kF%fNj<`) z1EJxa{L$Rpt+t|fr3zu|bdU)x9QVRYL%lz^x1W_3m9eq08wdVoHT;@~Ar=qQB0lBu@`&_^ zE}c51_nGh}=YzkSx@|yZk*yFnTP2t?at!2ZdN>MxTjs%hWwtTJT7J$ior`PVeU}H@ zpDyq1K_$)^k(o462DX364{M2^D^2s?_DsSfZm0>TIK0XHv6GzaVk78{%zpc?1gPBS z9+tKE1b#D-AWq%vQM9@)I@>Px!vx)7kH&uD(^NTy{s_#&Li_HEUX*Ufx018sB zYIERJjYX0%mL!hhr=if-Lt3SGzC#c2hUl_z9yR#o51uZ~t{a{#Bn%T<jZZUok%D0XLoo zj{9VbR1m$|*tkA|g2F$2yw+m@WWn(=Gct2UQmV)JgvjCyPSD5lm<$dEvkCcLoq3d- z2|8tC91_))3sBXp5YeHgqOIm0si|;iMv+4X6mA{kpaflSt`HfL@?gY|RK^CDn|C9I z7^)M>D3$?K$Crcv5&BX)gZp$?Xz^_~#W3%E|nEbncb)y!Wi;MQ;5Wux)Brc2(LfH>eL)XPfs{jq=|dQbtrsW#$=$@HsJj_Go(R@C zYB%|B(aDGhHnwgR7Jupb6COjQ!A^6t_@I!9nW z--pC%&vfE?Hn*Mlm|I*1@*muUnN&%)^p)Nvr9K3~??RF}%arX*OTwz^dJC&JeMndA z71h-VRXu)km~{%2!TCoyp(Pk2HQ}uAk%7L=i42!!5jz%}$y&>OND5uct?mU^A88zx zqKOd#A8a`EaeuvSjK@$DR<7FAF*7g?_EH^E!$fSJZF?BcCbLlRpi^aUu)3k8r_TqI zAz!?)e9%MNZ(9Rm10a`D>{psz9}haltAY%k28DBkf9|=ST}n(O1M-q1Y@X8CoQw3Z zhL=!1tk5=m2+0dhO6AcQ|41|vl70EtET##qM=x*nb_l}taW5@eh)Odmhn>=3Pql>j zdWCZ*TbbwcZr9y=zV|vGraief1)4uzKl-^_2831;Sc@kyKuxBf#bG1&p8dWdM@nFs z#eR0T)J~|N(f~bk!d`XX(!;2w%AvWZ9A=(a;mjf{sSZTI4)LX1Ju7oJiDv<$S6mhS+3e^zTm36u&^S)uzEX_j9ofVkO{x`ph+vny4x zc23Dtd0F|BKW}?`7gygW(6mTTwUTS!&92>knO{_%+59%TRChL%le~3DoilKbG|;dy zBwMDr&S2ipPB7#-dPR%BaNgUSdBR$dw)x+K*F!wDbmk<-m4K7antN0P@{xyF*IygD zD3(C;Z{+59bS0u*QN%x~(XlCb0H!>JLqQ*EaJDus+WnPOA(QkKD?Y0=t?H1E`Bc5j zGr`n|y~|7?ZpR(suh3C@gYBhu3jm7vf(JUpk`BHQp71fJ$HiL999QRrOJYQsp)=W9;eG2GD38`ksIcN=n1G&BF=*b!5G6+Xg*ZAf150Z5_W|}M3;+m3*#*tnmIR& ze#byB;0Bcw1rk4|dMihcMd#E}*|}*WnI&SKJbo31%zS-^YADpd=c-Yx1F|^nImeFS zL8l$RKq>~B&XlvsSt+HVlR#)V?WdTX)>g(n=@y%}dn`-FFp{Or{+*U)16rs0C-@xU z8+u0oZsz;#Z~W%n$@(L)x=6f)ns7+vEDdAt%N5BI(4!5!%CLsNo8=BqfI~DOWHCZgS#v;#R4S0`FgxcuH9HY^ zN6G!^ajozCgP4n(>1pHCimlm)q;j=501rCcONR$tD9xOQdheKpYtmv)^^O3I^3PUd zn47pzM|Z8>H=o*iT;@OqA?CV8zpBI?N;t5Gw=Xl|IUA*IJJE7G2W5+dgu@rx4y1EwAuPpYF2#Ps;dKt$t~G<@S4*vA0R)XprMDE&a=M1uGy z{BUowyJov!=1-0P=l}|Q_uL@!D=@LD6P7*A5Wj|lprHEb@;%Mg^zD{l z8hap7q|2J8qv`o-5|FL6Rdnnh)O+$8q(jjy%ronHHf_JxR05MagbMtegT+P<@*t$| zo%oV}l|}z!nbq{haP#Q8WRDYR@BsedCEFNo&&8OS6DcYJm_|7MTqH`Fyn5a61pymB zkOIx;*17~4C+J#EG(Q=fH^1{n3!#BV9Y>~`_`2-uNKd)|K4HtMmcCXDOYQS@`*$4k z1u$P0AC($J&D3k7QLVApI+U*%tUnQS*2Vme6yjAfu0lyV_ku`MBx@SQErw_%;H!%5 z5($9O3c`m!0{eSz^>ANCg{)P71wrG_I`!l?Pr?_!iM+%)!)%JvDWv5uZNzP9*DwKJ zoT64p;CbgC#<%zM7**1K$x;pa$LeT#1asfEC?I(Q8~N=cfDlTW%kC>aIBD)%fKl~K zrIxP4j%+Au(jVyPr2bZ5FZ7r6_Q58?Z$*oxw{ShybChANlSxkJ&ht!zYH#LoZ#{#@bqheJ0of(D{W_x8EeYh~^vjV{6U5UZR0=@(niagyqUb@Ila=tBXEeD}jk}rPJW9^)EXDx` z_?zK7qs$TUn{WiU3i-Md`I8JJOb~z_96iv6n~P7gEw)?Xnsgul+~%oN@tDLsoZlj_ zt^X1rEDHPiE!a)_Zlhg|{)i ze@9EPeTA;sH?%NXOuH-yQNXLi_c+k<^KALVo%KT3XVD`PIzrmsY~Rc`RufysesBQH zba7mP7WR9zvW_!2bYG`=)&{DbvEkbd{FfL1R*sK%bVY|;b*b5+pQgCTmvS>L?S+T$ znD(22od;{T^UhHNv>VI2(!lzD=8mQ^vhCf+`#xF_GMo`LSOjV;4@E775wkSWUI&MY zxNn8Z;Xgol2N|T#CL^1=V)0>^C75fm6YDb}`t>V4_>;td?xQCB4sA$CZahbA2!P~) zxBSE>vsEmZwVlOif!lU4#*3AKbLJENOg!x6$m*4zEdFvlSs)gS8fG{VVqJO-*_s2D38*Hua!LdOc_HU^lP%Q~Ab^W*tLEjTn8;#(W13m0bg6er)!&>hk&D z`XU(TE0Sq5L6#7a6dq5wHCL=l-$vk< z!Be6`rYS3%+%y&jo~#ItG;?vY|;nQt0ZcU7v1h;k#tL1zxgNNS(@w^Rd zQp|jJgOk8H?iO!}F4i2SRSg^Z7g_Z-;RymJ2Dm6eYq0>dfpAY>-CYNmP3eU>NWb}$ zo>cU-VQGE;Iu*n5hKJ^>AaRWW>D(w5mm*)r0BzTkzr`3CXarnH0;G|K;^d{vDj5e* zKqCIU)@2^DZH&E(qpRL*ew3cbfU2Iu6_6XZ$dUR;3@Er z`N@rhuyN))WC);Z%QU}IX z`F?}XUFL?Jm!E7uf$*O^x@skHeXy5f^h4O#B*T&aZk|B$-?lm7jq5pHGwiq^B8J7v_%T*h`EwHDvwXvsH37cnFmUSa77kU0kllfTx8uzRLV)^K4=eY5?qXf7)gZtX&z%LH7$Aehg^!*)gm>?S z0ye~Gke&tBd$;Zg$09?Ecae4gl2EBWg@|to6@Be7yb&Mu`~&sA3+lOQ zfA+oC@Oto_v^jWg)Ufs3iw$3AJ?-I!{nIHmJ=GVh>iI6-S`COnTDLZJ5xyoNEyNuP z5JCp+kIX!l4H`r-jQ_-pb!93f1rQPRq}w8LsuoojV;0 z+ct}tPiO88EY82mF;#vxuEwtPnF9FT*1&{7wsAgbzm40(>q&ba^TaWQiC5Dk&tA^q z>t`keK7iDC;l}ePen^eci0bg`+ie~oS9v4u{GC#jJsE^z^jXHoJdjN)559Wxgt;d; zDnQ96f^IG{RNtuaTspYu00n%2BG**p$1z{4XV`~5H<5r5H}xvIU)7^ezyAL#?`(d0 z?BIH!Djb0gw-<}n$us6L5W)0i9twZFqv~y5tk(y@$i8x{uZ4D{O)1il89+w=;JXzO zThl0h|E2D|-)wdg0hZ#X&-G?QVIR#xNjkg&x?+l3S{g{yR$dotJ9QLz_ml;`Zys$3 z+Tq@Q)jvk(bxy*>2(cApHXWC2!*#=U7U}^9Tw-n(EijFJ<4>*q2m1k2I73 zEK_Qs`M9};^SS{Ve3aU&YsS4|B|m50y<46<=e_ZJf)+lphP?+##_?l=;Eu}3RSF+- zfD}b|p1%y(ceY0pVS8ggY1akejYj13)M$t^i~HSD7(JxSYG?(U4Vz4Az`(@*Yvv;$lbkJg2Z##ITMr92m_)ku|Rb=SR;TOA(8}9 zq#*aGb_8pG=cP%NKfx2#s@{_}G^(V{8NkB-& zzy1@nqe@zA?P}5_yu6t%fQ!&HQp!CS}-7oe6KdkWG*zSuxono*w`-6H0c$L3H z6jI`)HV|iU8b;NjQ1?8B>ΝbPg20D6!;WM)2c>@Sn?@%t)1i(-J9p+qu#MUy+=h z>YkwslhVQlM_Hwkqd6H~S0lfsu^DrZz{|d8L8&de28-+M*Y14%7-xVd^sL}(SM6DF zZ!KjW$~6zA3a~<6S%xQnpaNNt1Z=oqZ_5gy#bkO8m7=Pt+D(b5&8Maa*g#H!_sTUR zZdiJsvpW`oATewtQId>JdB7+$s=*6SIS9`FS62N@#{` z=vO*vjK!!5)^Kmnp=740{7GHyalEoikP1UG=p?GY-|e+xly!_l1l-uF41Y(Me`uHm zf;=O#<<3FFlCy6Aqxs7Edp&>nQuy);3JRQ`{tTYRn*m%=db=FqW{-*4VQY3lK|wS} zK~F&1rY*dTUs4|Iop8>i;YsI70X`$nJOHpjEr z)}Q)MuUl))JPTGZzfaEm=lhCQRJoI%XV0N*t_=vSZ1u=QlA<^t5CRF!Ao^RMKfajk z*V0Uc&ZfF^fAkM>pjDpxZp}a!1=Dx4HumP&- zTsbD{y=R4_L0@?crSDy4K&>=rBvEYHQ1&H%1+32+Q`Puax%%oFi{6ZZNe;Fcm{vEJ zG1hXD;zbhh7km3DVr7^-!=IS*F-k<{_B}xIDh;1&;FjR$jKK1OvdXS_=b;3a%Ikr} z-598q=(bGe;nmvgebuWI-^UcWby^5Nqe^qnVGp*>^KP$NlwRIepdt*^f8ZB6)RVyr zq3m)RG=wTOCHH=)!_MKM!?7N&6W=g82)>Y*YznKJKdBA$^!T620aaNGi|>^S=mfn# z1Z-Yy><J{e16m*igs~?! zuGLLFyWGLMzY)-XWWaBpKUxy75-V{-3rJ$#W`|yE3=jKhB^|V8w*|#bk1Ajx<2r5^ z-7*8V$`0usfgF#@N|Vb)R3n~eJD$xg4G{Z2JUH7$~q7q{u-dy6`d zVN89Rc~b(Z2Nln%1Ov7)YHV`?%-hxepUdWaJ~t)c-18pD^RE50#R20G_T(HDmo73z z+r}8Z*3bC3v&Ej4oR+7{&kbS2LViIBlLBxEc1Iyfm4K}tCEQ0_^NQ8jvt7UxfgZ(4 zY~+IoMaLW)i4NAjRy?l|boW-APu6B><@+E%yVB&gK7YAzPbTnSC7TM3a}W-?CO$R{ z1FW)y%eQpWfBMAxoJQ$P$<f$Fpr`Bm?|)uXT}_sLaedSm{-4qX*$f-B`3ySqV$}| zd!dNr1Qi)&m9SNvK|iy9<E_yb20M^(ES0Zh#AfG4O#buln+YXYFI9&e508 z+6DaE*}A#I)01o7Xn-AizC_<^nZQiKi$fT0c($jgOQHJA_eLrvskC+3%&A&F>Ns9d z)Ubi6PgodyXPRdmV<4x>1nAT?vO(Ak#%m!+@a}-e%KqW#Jo}M1+bd=0T|1xaKn;it zWh9AN(r~LZ7~o#?h=1SkNQ&ypWDQq7P4;p?pDE;#?96D>^>coC_aV^+eAb^@7fE_! zC`)G@x}HC&b8`yh#ILIAbZnODBIZG?{>X#L_(cAf${=mk>%K`~g!=5d7`(jSXZ^S| zNC2~W1;u8=g|G>tYwzZ^MXI1)mg;KJD8XQ$e|_%Skk!p`0C5SzVBhlX7#1LDtAh6* z=J0={3N*_Nb-9ls@@>vy4nlV1)J`LA))cUjw3v$%c%WCR>27?`vv#&up%1_N*z3J% z`+wXVV&WLz^~AR{%a@}&<*k3r%2VW*(FyTVYDR#ZKctlp0}cRx_k%VXR|#qpqXJ<& zu^M>)Y~#s^Ex28Pq+8k)Hpo0<@1nL+aao!?s%dRAy+K#iR6gI{<|DD(-iV6>vmhyO zinW41cNt@}F7G>-?5XqLR3sW-nJ+!9`dB;W12Rvk-Y%tU$JPccRu81GNNnSP(-`16 z{-Q4sKj5YuRhpoek?BRDi0^Khp9s}^uV)C#0eaF3lpVUQFoVX<#P@3P0WCIFBk7@6 zp98miTYsUb1>JR=E&#v=_8)Nv-8cwSqp6Qdbq8Q7Jy)r-L8?H^#O~J7m+w!yw|@#& zj%9%k~>g>&?&`EAvL3E|Io`s^?}sz%>eDW zJZDL28a&d?b~cqF3j>_5?=!(@4dyTRvSfJ(&UY96=b}>QX7S=OPM#Y|*vhhPQqape z%t&O_Zhv6j+5BUk?dR{+RJ&KN73g21?;244Dgyp&Tv(n9A)K&_GDPg+HLiUoISNqcMPH|ovec=RxLQ9DJ zbBO#1nZL1x;O{?2Gu4mkMuo@KYMRA8$Kv7{_Ww^mFl%E;bZjmiuH%G$-NBD2zd4v(s0ZBWB zVkO!NQKCo5&Yi+yA zCHmr~@_ur&1WM%EC3}ixty5h8or`i5$Y(DgCriTr-+eY=qg;UamasIhV$(3~Ys%I^ zTFpG&@TX&dFt8lJTxO%82`dOsD1p8j^olVCjhnqP+9-gb^gne*G`YCzDoBA>kR){g zz-L+s*J>G=rEkzXTWuP@5BTR5Ze8fRWSPEMIq1|c5?dyp6(2Vy%&6adY+uHFjN1(9 zG!&_>X(_%R5F{MH!`37*enTxSrOl$@zbk{qO6`9Qpnz7~(oluoGKPOrl=I2$^=j0u zLeL%)w}$6bKo|$RdBFIB$ihmfD%J9ip9aKi;9+?C z+>(=yt~GA$-oF18@sr{(}mJ~JUacsJsO<9PoM@1+R!+RA}i?Zfg)RxU!V zr8h&z3g?q?J~z_CGEH+8rwYcrcb%(up8Qg4Htxw!6#1~-{HSn3d+785oHo^S2nUF& z(;PR^P&)(vs9e-V=Ar7iW;0v(=VYF8W7q6QML~DeDFJpfMomc7N4#4-d6p94oyP#} zXu149D*Kn0@gdvRq?O(*)Lp|@1H-aaVPiTqG|D?cgzR~rT`gYPu+a1oZ_udxr6W1kjdJqSK(NmoT_{+G^Qc?)@;12FtIlK zJvkb})4KY=<+JM3(1ED6N8B~VAZ6!@45U7cA;Rx;Bsar6RpA=vuS$Ql`KA=5^gB)S+!-iawk5 z<=6EM^^Q|Lf|~wPHx}`NVxwk@sN_5tq8P#kW5WfI(BF>{3;=!G-ds483&EFy(oFnb zcv#8V;!Hp^OR`C#l+U0osK6w`2ftCfo|#loEsQjT3W@ z&|=Q6^c)MxNH8(*J98_jj6z0cURCf{fYd`=sfXVK?qy^UXuliu_~jCZ6YtX^ev;Y? zw)j#;q*3|Y1E1xJN#4WQk=Aijxu)8ec(m#{bCRb zF$jmgfp!52+?-#C3sB9VuwST1*+FFv=G0RNv<{Y6N@HC-FZcY@WYCk0+<-Pe(nYL` zkr({?RN+Dt(IJpH;H(SP&H&udDl&I40}5}8_UMtr*z16<>5C79-NYKPLbnm#M$Er3 z_&~z(m-TED2fw#=nEdPy(dwi9;p+}+LAbeZC}oJ3=F<0D{ao!0Jm$Wofm8L_pS8gn zBKU6$s;)K+FkFuop7T-?!NYU10=?*5R3AQ?^#$HZsKlvMwYZXI#(itz3{MsdX@68& zR4JsiZ~HZqU2)30K!9wdCXo>PG)VQW#U=By7)$OjTDrb1L+ivmM)|@~YpX4k*zFs) zs24AJNPnsNk-+VI-Qv#4{8e_K%W~)6ZwIcuPmfa`t)%$mkJn)ViLBE^%VEs59sk1x z=;xc`=yoi2sCt2?^Zm0Fs0QBN>4NV5qfTc#e4`eTx_iT9)} zGa5(`ug){ryx;?TMLt_p4+@do;c93zZv!qsH?ri%>(|_*B(}28gIc2+Vn~zCRPh-GzNZR^go&s0 zS=3vheQS$#=bhDIAuyXrs2(=Jh70;|5we2TSFGU!8%o}-7^ZT^y!$I#ZvwWSf!}Ez zITgE3VGIsn=bcvhIrXpLl%RN~YJSTzwR>#p>4`O!WxXCRy!>ybgIg zEaj$)V1PqFQ!+e^S>zZY(2?LXc`LPbSvC|}ppv>r9K#?Dq>)+QSlQDff*HZ?xFGJPjHQuSK0Btm-;x zSM>ULk&gV-L_Fw~m@0k!0b!xZn~D7<}O7YQHrHHjpN zfWiRCD*(8%^{yq(?UZVOp%)P+`c=XPbRU9Mhr}Gq43UdQm86;PB9d~pP^HS@|N7fB znrNzA3GErzfzR#o+&cDyH>FKiCr-F-m=2@CjNv9fW(17<}1@%vTfn3CI z+m}%G2xyZ0*gsN!74S_=JKAGtMGqmcdl{Rye9uX%p8(Eo_i251F;FZlYm}6^6XR*} zRfr-m)Ba6@Cn9A?^Xz@+iYnvcfMS?v zTSKo$w#9}#?7wc*Dc{w=`TO4qvB5RPWGn*i)yyIdqte~hgxx-zG|JNIY?=jqzMb9u zZL^33o}kMPJ9|%hCS*2$+w?+QcJ61rnwG zg{f~l4afMj-}Rkt&K@>&7sOLZ_V(A~5affK9XxEkGPGn?7U$Yq+P)NUeFwNdjdFZs z)9ibEk31w*+o@gKA+Rmvql3xtfqy>6U-n?yZt8eKeEQ@7WJlfRMRq-Zf5=tCWpCdT z3Gq7t6#M*6c(F{XW)t-E!Zntx1s*v7uk|aw2_mKNHyO~#1NA$41cm-57LJecb0xq3&XjNBaz#JGX zMwjIBhMkEDlPZa=nI`*=AOV=CL6o!1g%&IE!+rUO`)yjwU|D|CLiE)qzB-9myWfbI zBTOiJC`YWo8m_JMgvZtw>$5LfEQNVRESa?SnQ*NgnGl~N(nw? zME(zV-AClV4Mhvy+4L##5U?$2*s!!%J>9cUewt|k7Q3@(0Xex#VAHf~0oRMA*N zgd{tfGkf0EXn5GrP5IrjOGM(;93e-5#&?CbOf5HmSo)@no$5)dPsm~Ld zh&+AE)LNG4mM?k8JG45cEa zVr}wQlo$A}j*}OLEV)IF^UnO@RnbLk(mN51{H;eEuTkxcFivmJ;?R`uQ|I@H{`Z|* zYFr2Fg7iU+Tef`fHL#pI_Y)+!aKEO|8-a4a?~{xjG7$}JVmgjfW?xHGUkwl~U@U>Y zh}PemHz`{kYnx+OQyPLQ-A{hkP#{N0SEKAc^7qy2r^B5;P41k&Mw#{ft*=;%R^<%i z&1Pi$pSrH@eSY&R8th4tL@7Ry4n93Zj@L6>T@c6}7u814VKRGRXb&#+`z#)8?+sY7 z0p{*^sQv!%>hPb{A9Q|q{)OiQMjFj5@D|%Zx?T`mch60AXg67gBqmj zZsP~4a=;cxt;v5a&hvl}>6R(MQEFGsbg84Z5>n8-$OlQEYApt8$-d6YSQ!W940(T1 zpu$b9pFQbdn$)|^N*>$t+L_X#6bnY6=LK z+dCjim!eRvAAfpqT{YbUykG*-^fG7HXOfe5$B!G!^`uX=cvsXrJb7jq00aHS z^Zyh!oE^&Cuqy3K7fDbaq0nhgK< zy+F~l=tktbw*l^{>ca9O$y8^BBU$DcS!#8#w)MG?oUp&(jF6-)tq7O$6CPJW*GetM zCVQ8&J3wLA^h2zDwQ&{_DGU~poUL$TVo$Wk*bG63pNqS}#ktXvbi#mT%6yB-s8EY9 zln(t;QI%C*?NbqS)(QF%Pm#N`Ol)j~-diVtXwPZQdGcp}S^4RB-W^ffI#|~~_eM~G zXnBUvvL`WaNUoRSM;l+!42CSD(e=(akJIcQ>&S2a)=JLqqVDXfWbftXcEXjdFR4&RBhAs$38>Q$cek;6I z?ZT8Tf|l=hVNKoV1RttiV{=|OvbXLGIc{wK_w~Mv#ilb4JUPx#v2UdmUec0Er_{J8 zmC7dfJ(G{vwgHxK{|(A3O%|Jn%KyC2&JhVn5J694A?pgXxxZbMK(`8BhS_JWa}mt>d?&U_dV;J|`5O zpa^*k4tO8Xv6bU)oKUQF78bWXoPK|)5T9l{k86iT4yn;In-rdDZclnpv|qyhxjHgh4h)8!`ewX>L4Pq{cU` zCN^7Y2toP`cqspF<~Ky{I%?jhBvdhlK(b{@DDjL5vBf%6%m~r|*p`2tD{U=&LXelhHLI3N^-?XO?xh;F=&-v-+qxPa< zvMEw~B0X~>+3+gfjiXmzQw4khU`Q?Y;af!76J)jyAH2B;XmQ2oInOTWB*mt_<9oygH6+MVkbyy{8NKZtLu%=&*@?aglf# z9Z}g(<`$zKVBzq)D+C=$1hfT8r+k-B8am3V$`4s-Zqpcy^={w8;aU9xo%bZFaf7}j3pkJ87Q|z-bmPv>^ zII{~MtkuApO{n(bx)i2(@-gVXjr1cR>z;{?Ie6cA#?J}dbeJkO^XyCz%G&h0u3ooX&cFx7imd(jX^W#l2 zK#e=iRc>Z9vA>r*zd6ZNSSrG6@R?mL6i7My?6mOctQ;-Mu` zAV7*fO!!HUR){1EUXBmH#0 zHlN_5z1aoK#*3!C;Jx+zvX1>SdGt}bx9!}hzvC?z-nc&2yGo(gvEqnd2iJNEJL9{wS> z2FZ50EvackftAqQbv>7npLM~b?^a8#MZRIOL^Sh^$)vGw_o=>a;~;Ai*YkV{N5+3y zuBPyX^8f&Pf*j5X)GBY(pS4x`ynmU>rk4Rgk;IztS?P{7{f|sT-k~LhTMZ3;dN0Ga zb*s)A@J?wsu@?*lL)Rjmr^??$Tfv9%41^V(T_yzUE2nA-h}cN9DTkOdcrx$o593rR zPkC)BK7pQ$AYttrs-_FtS~(kZxI)bq&g_sN;2L+#-r zQ5ZRU&eaClyel`Vs3s^b7S?mD&n9hSa0nER4MU4ec3G>CGb0H6nRCnIdY8s1Z!zUV zvj<_WWNTmAKaFYw4-(Y3=T@7CE4*_kqo`Kd%&2l$9*VGxQF0=&k@qtC%kA}_CtD5v zT}Svm=Q3t$?;woG7eIY7CU{|{_)56RmpvErD%T)8x>$9%TfYE^1CUCv8572(#zChe zZdnPMUrIWdU0qbxm92V<7L=m2VYt7#$Rop)sKdCx)%3U5I zi{-Rm8RVU3~n>rAC4AM#E~@DA&4i3LxP7NMgsb?F&tl}*AlB9}Kl6ZnDqzM_t=%VrM@CP!FrE}Slh z_aW-I$GSsyM&Mcvc*NY%3r6g4<~vY@yCDRgF}YF%8PL`x;cnj5JXu2?blDziB^Uocx;zzC#uMh6#E!67)b0m3(p9 ztvQ>S;k7JXab}GCm}xjmqI)W9Uvf=z0YO?lgpk9lA*@riT=70qNPOmMm@NbzdiYID z2lx#H{;SbLfMSK>6~!CJR)0r*%439aUG^d9xMD@$Uu`RlXjz)u?}Hb6EHkbDKt;BAqg;`{ z>=WF(8jGY@)lZEwc4L8C8|!Y@xx`zuS@8luBr-LqOztC!5X{ZnIXG#y3PaF+%p@_ zJO@i|6lhz$S{no?IS?tWuSs?MyBV#$UeTsMx-w4;lE12D`@r)(q*sb0t}plyLfJ=- zBn>Q#*-zF%_aw$Cle9awdw(D(Xz>8dxzTgKeF?PlD?U;poN@!2@Y1VmM#pemi0^=G zrp1l&ku7qdO<)eUNp@)sbA5Aabm#Z>-%M;jbJZ`vO(qazYv60@emnM0IJ!|gw>eV1 z%53S)my{{R1b5)G>1k6nZZ9@7m-0Jm=WW4}py!$hl#>Rp!I+R=;RtfV58xenk0oWTMk*^>sCBF4{l%oNr zh+s0h@v4&H55c=2%?4|zjfTTSKo$M=z4M8eosb&YCPlV2dDKfCK3++ZsO^}3o}IwH z)%Y8QY(0hQ58gflKl~u;9&|xA^a#85R80BW%$5mQJk#OPx$*TlB=wev-F7{A0X%R` zqk4u2yP8!!$8#HDl(?ZX090XbS)=r6>)5Z(5!;htXVt6uY)iB11{2beV)JJO#75dX zZ%fX+>gaZ2lp`|(9Lyf_=>DKpGH9%D(oNlu$VQtjMkt4oZAD%HxMSXS58vPL7 zd7HQL!jsIgb!nG`JJ(#%o1x~s$yWKQkXFwSoVq!cCXPyG4Dun!PiK2n$|3XSX_SfO)xdx3sTB|CnIq?M%wVKoL1_%03$~l=it1Mgq-RkzZu02C7{q0{-P!$ z5jnmX0xwS$S@b9xN^>j;CnWs(F&J^(8%vg)5F53w{S%kl)j4wR=V#&$PIw|&o5~Bc z5kJWn@5l)xT&_?;H?D)_m^{(|31Gbv@?dpdw|A{C8Kb5r9<3%6gb!U3+rgy4GslG2 z!^{%c`q?hSB(Z~yx)y?*`F!F8Tl1*$VnJ){~DBp{HzMC;mQ$O8t5sMZcOiE%cyinJB} zI(KuW0JCQ^%{9EnbjHNqea35kX?mz~_tY$~z)!{&*(70EcmRTMFVA}t^w@$07JPdR zoiJs3mEV|_U;R;=y<-B?osMF&F2<66eKxNEUseE&@qVj-(w96D8L2{*ULV-KTuP*& z6x1L8(td(~tTIpII=#+}GB#8V82`kyY|Q@X8{-(mR)4$)VwxsR4*HmjN=rCcsX^Q+ zet(`edPr!L4LrbkTIYfrT4kZR#t#_@TqB2S0)Lw4c9`az`%n z77yR!dsNqs;-CZhP7Qd995t%cWS0Mto>Dzp-WlffHVOH$k z$?Ugn@AL1u$QiZ@=!@$4;j=QnXY_9xVi z;MBj~`0qgvW7^2SfdudIyh;N}v!x!VklAVLX=}aokb`5?cAb7xDJ|X@13_5t9c9()m?Myfn>VQ^}jE|y<2RW8818*a9DN-qU;UH!bN*_{h3Yt-_<&HGzJ{d zl;P)RT|T|^+piUyV$u7D0R300|5WS9t4Szg4z^mZWApMW<{m#fF9Le`&Ueah)<4;p z9m>`#tFv)7Ota|1l&n?#dued&Mp+L?FB-yD7r|Cz^nL6?dTrFMRY?epo1X3Kqq}CXga`@OD;r z<|3+}FL1L;Z=?CC&HuG;h&km+sSYBBwgRL1Q&*fgXe963Ewtkr3ZD?GQSTsT)R&yW z8YPZ}a)aqEEtMa7c#KGn!3gX5*-pr->TIIHVW6{^>p=+EXrQ0e9XO64{YdZm6x`A| zp8>cG*4`aMz{fl}w4y6O1~11QrNq&)D09U0+F&-477!Tw@f_Sgym5Z%)7BV7#)~%I z?jOZN`9AcJ7I<1CUVIkoVpWrsTczs|FtVWe? z?9+~1X09)O+Y3SdbalTyeIUq_WZ!}X7YdthpDw#UpE7x#*}khROyImAVHXAI8ca@H zVhU*NH4NYH70+n>yLlXX6}oi}g@G~1@9B~9D7!fUaCGFcVnmnV7o*S7Ym-(c#cVOW z@!%D4zElGXah%~1Qu9bjY)FA|CikuH=?O^Ugw@)MByBLzk@~1&kj`=dAmc*3cJak- zv6=#?-tH83#*&t`)x)wk@T9|*?I}lzYg>Aoc5N(S=mRGmvv>DfX?rb!|52)xjohb_ zER`w1fY9tMj!)e@h!|&v=~u?{ZDvvR_y~XV*~xvebCOg&;`NUSL;k7igEw9u;!nCi z`ASw(5odp%(7wr=YEKD{Hb^9mPdCaRwz+?s4>Ud6znZ&UT|YMPT<_FyefQm7H)(?m zoIeiBGmjkmZCxdwJj*DfF^Xy_9T=VkB2h#|5=wmnzW*g)&Yw;HP*X;xn1@kcznR`% zyJ$r}^5qf)DoVacc5jU%-DT?3%c1s$kCWV}j#ZDuX!Fxwlu)>f%8e+v3q+uS`e98eIMHyI-@xlXRbuD-k?j9)0p8t&s;DsY zbtHf~IB>3NB*yQ@0V8?g^5+8*#ZP5(hfI@lDA`lr34O^-IX1-~k5TXM#VudT1Ple>E$Q^f<8&J`&ux~RE)b67rufoWY%i3H6l5NBh0 z{9_Qf?x<4kbSu|BN;DO)rfQd(h$T&@Q)4W*e9dqqXw<~@yi;Al2ve{Qp0qMxU3lqd zbWeJn6#gvz=kL7KvQBZ(o{NBg1i}P^$L4W_&ILFtcl8xsbP@!yC~w=1$(8&tMWD^A zFWd??;&mvBA9l=1)aL}@sr&;1c7gL;*O=BLk8;%+Jmfco4g4S$rTq)OLHk$tuS-b{ zCMT;VG4F#u*+;oB^z(pe{-fThl+nr)-ZHr`+mAO81Zsc2b!gr+zBJf&7jAL3&gXrZ zGglI1c+6~msM~v9-bggq>anllx~b_*R*)(3flnV#kr~Dh!L7&R4T%w)^B%4qn{Ii1 zkHv747Ii>h^X!bAn`doa@1LAfMqOb|&OPP5i3T{e%K8$nk_C!=*L_g(Y&gzjuE=0> zVbL5DUNR!AtTX>Up>JY%$Zx{Lh=qZ*95Qa-JkyUUFxMU!WlrE1m6)M@pH%=_{eJ#+ zaW*@QrYuh{)j%q}&Zz0*Xn1_u(<6fq@~8f5+S`z-=$GyJg&Can?xDfnE|6cg9v7X6 zw~X3mRcgdR=n2USUh44EN?P^(Wz;O@)h;nTv3lc*>t_ zLkl2K`9W{~{mjNX1Cv>YS#UCGbkej&AyD1`Hjp%Dt0NVU{emBVHci>$dAF^C;N6sE zHy0z*3zh`UyZvA+_yt#=`EPbZ}afHeJ=h?eN3n<$gy9E^ZHYHgT$Oer40|+Pa|Xe)a-()NF+*{eBv9N*&tP`6jfhLWan( zb$Kk5q=I~F1;b#CnG!g~L=$qVAGoL>9@70wq8LiR+#bV4{X3I}tixu(S*c+QAP9;| zB>x8{3li~1)fMzXnIV4qr^K6bQwudxS8UKqI4S9yKA4-Yb&}y2JshX?S(YxY8``Q6 zT$|cghCj1EPaFCIpO3`7$dMFsmuWA`a;rMSt+wTICmzo9wp+%MafI;(?pA+;6COn1 z*5+_LibhH`pBbG`Xy+x6n_8ewEkh<1hZP6Db(V=;(i6U1#1Lci#TJC!PCXhyEY$!# zEHgOZdyZ?C&STH0-;qP}y)fZ%QDXb8XZKG*D!z$#i_srs{&qYm-0mt|SQg9s!kRzb zXFh+(j_}V%5KdLN5B?c=U1MFY=Nx5wuw$4xqIadm_OxEC_t42%e&99R>=dNhDsZ~q z(-Y>jmIAYo1!H5r7h#=|&-_(YXEq1cHT||MXzr7Y6e{>>yJT2ih+wZWFgAi3(Cvi1 zOC@DLd1PC9F@$`r+BgaYW^(1JLn`F-+j*b#uRVTn(zEbT>YHNr=$dWZPoTj&r>zGo zKo-+?UoSwSS`K13r6JZe;+0})NBUDCZ0>*PpsauZjnbqyd+?UmUl;dBN>|6Fv_t-( z#MF?vHOtOgtMC@Ib6j0h`wB{_UCu!ERe{&H-6NL@#+zeQ;2`T|Hqc5ag$bnv`YNlQ z{}ov^MsOYoQj8VR;bjBx-=$r7xv~wv6OcLB;;=JqOC{{DDyEF$O6`Q{=qK&GY0&Mz z_V6{~7!5JuJ#|+aI$qdMK^l%VYxo_p@^&qFj-mN;$S3u=!o%9~gIk`db6vBlhXsor zeCMb|P9_4CN@GbeGd(c==8dvss32qI&)>)&rLd$~kZUAExUI0HLAT3Vggr}TE1R&= z%**hKF@0zdcZM&>Yq|}BVgX#CM9qa_XFCVR-iLnu9Qfa;TpAXylE85aB^;Dj2W;T# z_B%BE)i?z$3smQo{P{@`kT4S!?@lNsb%cpkA9W;PhYu5gKHr~i0`A{j!&c;^H_7g6MoL{K6C;nj^N=mwe}4;pMk*0H1=`jV$= zBmLk*&gTE%J<@?u+aL{z;S@I5^JYm>H#I>H2D4TAZDgbMR2z7@&(-r$iEcou8L^wEAQ+T>azHLZi$>-6o3Sh zgC&;Po-=t5+sEx|583hoWgtMZYy5H#rlv^?1Vge@F@fh7@3;T49;84+c15@^@_Jfe zL|W-r?rxh%PX{0Tj?(gKr$X4OKI^5|PZQ_RFIVK~1GTy0aLJ$713&+|*Kqlfuih(e zPU{&n(TO;9jz2Y=*{~5pm=nfbHDH?`J~Ps3cll5=IBc0Yu1t}?6vOa6&AD8QquirC z5tlj?DN72OSq;=2(lLa}gE4k`8y26HK<2WM@T zvM-HpY2=PwLN|aBlV)`>=pm34(|3)Tw!I1+_iateLtwV)8bi@QBZI&f!)2!VJqvcZ7wcgxq|UtJUwn+WGrx_)9iPqGXh#l8ve^H zkmyP|+qet|2B)|Rz@FCdOSB74SaEc%&C-%7dDAYnS4pb$9_MkW;z}3^o&4 zqT6MbV9G9pAcXfk?VMwi+h&z#rNC;m%e#%|zG3MrhX;->`CU`U=}D^Pvq*Q_KU8N_ z17(~*8b7E2`%|$D2{BlOEImR--Ga5@qXkt5I8soMOc6&4Rd0 znEXbzA!|ar;a*TV-UxywKfo_NfJH5nq`?58L#76!G7ND&Ea2L|e;vnNEp#Xt-uynF zY9)~}`sAWL#Ynl$+jifTt#MXEVoJW+2}&#)Ozyi_!=}gJI}Scy)>l_ZX8wIV@TII^ zmc1Ove_7@=yWxiPUEaxQ+DVc!taaS4b;^@;O_1jT%-R7HUNwskqvvo2+rYHr_E-A- z0ZXNtvk5W1l!N^nF%O$}Fxi*`vay*`cuaiMTejs)^G1QNC3pmfL_GPs$^<5Bjq>2Hm{ z{3wHpL>HhnuHw*asM&G8Srgh}0BVE>P~L)G&`-wLqn#+fG2?%MHsBV#28*U;?H3BJ z_N{(rgC6xP$ZvnfUx^G)vmKZ0VDyd*v?HCOLZ4r*Tc`d~5{oERFF)V14W-$5J#tlW z?hZUzeDJK)2|~&?J?DhKIb^$6ZkWD#A8K~`szsQ^2P^yO=G>=-^BVkqOliq%ZYoDU z2`>3?LMw;embDD-18W`21)2{R8A~A8H>@P9&f3-w} ztcD2Us31gYwqEpe&cr9BN3}ALapVhj1LB0LQ8X96AHlJMI`P`iDwDDNpP3OiK`Mj+ zgeY>8gfHQFz)pY=YzW9Lpf5d_V={58NnN+Zb(36 zRQH5k^4K9KQmJ*OUR zWPAv=DqPuZZ`UH5*RE8l-8r14kjP&wdhdyk-ddPrEAClxDHQK@A3v&p${70rVFGx! z%ta}bz6Zt0o}=(5Nob&dST_LxYPb2wAiJK(VY^EvoVPI|(TITl*uvi?d~&1D6&pGD zD?KP-`x-RlH`XH^2f%ozaBxUFU;Z@8$BqflmF@IOKk6h@YIf#mLF8t+k3^h|X3fW( zjkgKMNzzqt@<8whwFiH}E}N-)?9r=AY9$>XgzQ~khi^wM_}N^yHOP-2zF^7rKS@A0 zf1V-OgmK~Ai~#`%V4lMj#D)qlu04!Pcc7uj(zt!wejFdU(8 z9dGK6H$&>8YykBehtYQ?-qZcv2V_WrGdbwUt$*y;Of9$9ZRp}8QH|o=$Y&4ERZ@eakJlqhj;RKd+D?6<#YbLz9g*8|-zXZ<{Hz(v#{SV^SwQ0&m(2CA2zu&4I7 z5c&e{s8HUUH&t0H30?&-raZNQT2DvMqYohoZqF;d8*gNt#C~h3DagIdt;tGTu~a@N z)|@rxUEEY(ocSx$P9dZ4UY;8V+e~OE`X3axT2hyyOYKkfh~h=D7w<{P3vRk2brEAGpj zQcUurbPA&vq`s|plG8?38^L@@pIln&pI^=q+Ne@TvcY1a!qC>QQ-66FOmq-do-ImO zx<**LY7@2+#F*^(06x!TS1F^?6%*A}cyJdnstZr_EyC%6BAO!|*A4BWsJ$adNaUFP z0nlWvFl1+$fWDgvk1MuVP1?39eAfP>r@&qS+!D;>D+D0Mtne4_(NhtoZBJQ`GwS7?%=gk z^^s)bHyJVJ{Pgh=###3rcwl^MKE_BD3LBLjD-LwI89E1G4gAWY>7&OKU+M2S40SRU z_3xVSm2yjL#UBlF3MCVL4r^9OOGu5q-Y{bzT-sP6gLFX08OQpAVXz5VYd22lC$8G^e z$6pvrhK}i;ox*Z=kblR8or418Bw5uL;ZN z{m04Jcp8Zs3k@F5J)?O871m4sMBD9|wmW&OkYlA5**ol<`8*`H*?DqLDe98zZiEpw z8C{cW4f%vdh#OZlyAJaa;_Z>6d|W>}oA{@#gMr$*r(O%cZpE;R|3HfRQvzcR5;5aF z!mMs5|JGb}21rbWEjtR{on$hRt!*!s_Klh1NDnoo5OLSSc&7ZLg-uLXE@@vM_yv{n zpYjm$F)Lt?e#tRHrw@6@v?$thP$=1_6W@1t8JLSd_xa$qL=uS&WJ+& zUzvZPe|vglqEpzES=dP@ICa0x4a-A7uHsJs2cT+YBu!X;V$b+pOgq5)wx%zTfTqSV z=PdljLOr)cuu>MVT*|O5B({mjES7ey+2nC-xjdT^)`b@pKWbZcIc6-e5tP^db zCs{8S#e0_LT1O#-lU_3yG9Z#<*BHQ8B{5gT5&?Tb6M8y+5sfItlcWrZ)l7e^S&3zFVnrFPVkUEv?{BZ<8H8Sm*Xo%3Dv#mG)8aZWlvb4dYKxBBQ890=vm&FZP&7-@99 zF!5%c%q}BO#-HCCH#_mjtxfVTUiH@-9OcSo$UFSAt?nfI2!S>Z^_+(N)qVV}7#xotL=XZ|i7+BqJI^%mKcIY0X3zM{a^3kxB- zz}BfbbZ?Z(hDZZ;j=X>kEfXbI_BgiF>aPsW14T@1%y_!E5R-^XxSQ^+gJ?5mgk2n{ zP9A3W4DwD&0fKUr&(9wSkLpZLBKv>r5coo>*VS6S{oifKm!;iB4c0AC1`&byYBcsA z0otUnMEDi8Il)eYVfq^ElFXqrvGYUDuiBQ8i-1l(MAiQ4+i&s~Gcx-tMPMH?go#1u z@%RY1un`9{%@sX$D_kGQX5CVm(uZp0$H!_IG*vK8MHx`O_=>QOE3nt-XrJmUGzH3Y zrrgS_G1daFIyKJDdG!&?tT1#G$Bem0kc|5TZsNUK(uc)-REtlK5{`968}XDSGOqe4 zX*KrMkJl(RpLF+?IO}Sn9KBjwMk*xX*HcizL)&Ex$ug<_MSC8+nXFIf(imRV_raWp-y?YYolA4E73(;#~n`CUOyq zu>c+_i`5hJFV$#>goY3*@_EBw24-(x?5eLc?LU*)5Wd2~0u461XhO5LxY_gL zL$SbQ8^9$m9^CHw_({XBT;!WAYg4-z08wP$ofm(}m!&|Ce@aPJSzsA;oe`U1YY8Rm zE0^v{CgcXu%F9lT4WdXb1@e_!M~*l`7ig@_m*S5Kld>dHdzIys8~VB*kf0p@ya{S( zm=X$U&nBk6-7s}gS`fv@W#o4iP&9+6vk3=-PbEi>6P$2mk3KI*>5E6Ye&g$6`o-E@ zwGJdM{3zYKC7D9(N2d)InecV%;vzY+QFTo?QrR5*2=kK!@c1^d0MOC`Qr@F(crXKR zJN8MH$*3R$OjLPUdcNRs2j-NN{|R-gF%P~42Z&4d+J|7$no;hZsvPcK<_jom!L2dq z;z>>HQ+Gh#tYd?vQE9t)jBrib-W+_>pBG_SfNdC%EDVkR)ETnLSpJu}8|2kOnu z5qj1%mk_Ub%KDM@`2|Sxx;tjd|2C9HfAf5z#mss+yg1 z@@n2b?~Hhv>%Z7d0V};w%?U<#VuV*fUvjL5UnF<4Oe8(!+w z+<&S)@Qz0W1C=mYS5V#zXdsV2e=UW(FoZlDd0ssiGcOHAXk^>K<9?HejOb}hw&?3J zZHF+1ihUb45&;+7X(jOPuC2boWMy_s$DMiYgx@!I>eKwIu07?5&;K7|h00daoif5bY^x7cr{Od)E{MihDqzFHL%i{5NGf7ZDa78hs4fTKhH$SCz8 z2SX$7%^(iFXuZR)#4XY2jGk|;S2cqY3l>Ks4EIYT6oVBt%?osCQ@R`JUK{uImWD{4 zznb2}0;*7iF#qG+%96Xs30^!I2%Xd@mTn--8diL~SK+sF{B&iwb{%OcIjuWz6oN%} z?B5_G)E-=4u27@)6-&_CO4ey~I1&uKj=nbkcvEDrr-W?R~`Hm5!|W+<0I1 z{y0czg+-%HvZc|0WZsaC!lXYyKsjObV~Mv8-lBSb?Nn|pJ{1~^jbi9KksVy@(SFCi zKAd4v`?JS0fw_@k0=I^`{53Q6CUfDiX2ESYOj;CqY`@zqEYP22s@>+4%RB{Fs6Xdy z%w+U1B%OqKPo~H?t)=l3vSR9cVXXxq^l9q|)=>8BY+r}2)R!#0WNM(TJu2*N?XQSN zO*Yyn$Bdu0@;4h-N657TkANm#9*o8Pqs{jk`h*A|{hpbAMp_pI{w6PdG^d2FmDSY% zL5rb2baDJ&6y>Rq2eGN+!m)VZ4JNUSl+jG&3t}L9=0F=7a7M*dpHywQR65f}%(Qf{xGkc$$YzDu$;Rc@ti}!G@NI zI@BnS@bn?FU^YosF2!rFnszPMLGKfoTMC|=`5wfRc%!Ud>Z|^7U^$)hIcX(f zQV1TWq(s(!Dqojv4i9zBmoDL$FqDb6$?KeW%FiQ2vvJnyS`CqT<;>|x3U7qQM=+1khAG6 zC-k~>C!7ED8#-L9uct1x$7mr0ePAWS_zuiO2d$!imaWBq%&|bArl$!lcU#Rz%{B>7=$96Zeh;xkG1>^S(J_X?zb>+h%V@w`wvO!?R8^xlO;Uht<|Ic`Q`@HoeS#wM9n`Oh`a zv>O%Y{4$D6n+-7>`m_SXUaTzPQ&YSTWm|ajqxMAPB%RD3^4*;CCVsO2``!p=-)6JQ z-gZTJ4o4G)OahCLqpS-Fc9C>e@AbrSQ+||_C*R=1gx-2g!;OPOhGB-0@+Sss_tzZ$ zz}t0g8jq$=hhxJ5mi&QG^b4_hMM;VFXrrj8 zxnJ>SB6!RO6AFpJXtw~g?7BW{pcd|C!#5`LibRmLLb z%{Z_K4!j?nq9%WfM6}eWJJYSuqvkOq$LW;r(2x?+t% z4o{=fmuc>rYLmBe;-aVB^_M$5Y3!pgzw4WxVEdQpy2eC!(p=Os7*f$13R>DxJbgIv zlS1ahKQ~m@aTdOFEJy&5yCpl;QM?uy97NjIT2HHK|SdUwcPXP>?%AE6kaTPWO(1qa~~h(&HWwn7RPL$R0RrCra}H}568#AALy={ z&j;6m`f)v7H$dFH$>c%}bqYLS)m}Dj9?KYqNkpa9Z5kfMxu(LLZXw6y;;tu1?`8sl2_4O!+?sBMyxdz zYuFla*VTpZH;V=ro{+R)`RA(`4qdn1cAoJD7ofRzM(U3?0%~w-8|j%~Vn%8~FNE$R zmCe9)p1$6&cT>hbriyIYHC)HU*TAtW1xqzchZ(&srGsxsfYD$Zl4vcF3j!%n-XZeQh_pJcTjCN&la}p^F zj`x1`{UEvurt8){zXtEWZ)Gm2%u?_jwg4q)$qT&%1OPQa2@ra*9>+qcNEa7@tL9cQ z5Z3wdMzu3RJ{6@!?JL6FgogHJKNWO)=Q$cBs!C{2IiD`U|N!0lfg0yo_ves!W z{f)#gw5vKl4zGB(ck$8MDC8x(ee3@=$&4|^YWAzKa)Y6-P+>12DMwQ_YY77viZoz!hM+qR(+v+t}L{ zdU8#|wmT)XCJp_J?1Z|LA>dPJ{PL;zm$VaGO}F{c_WY+d)#c9%K8M%Bu>kPheOc6XJ5w~^Ij(2t z{C(sLzzP^vQ(%VrD91+Awv4y`Q5(IDR@gJ-TnbYKI*Q)6`WEU0F>K#&;#UX_zJwRKWXnIjJsRF6JcRGgOw0`F5d-!`^C9}hO)>H9YzE-lZ zWF_Kgsc;%(!jOJZKIo&Yt3`5z`Fq&c@wnIv@Q!P;@1&l3R4|=?9k&R!+BVHYUJ=@O z6}w-IC!z92uV_YdH!Ry?pAy0O!QO~Id=pp64(>pU+JJ5J2sQuZh#G9dv}#Rnr|EUn zp}(*h*LF&Jrfbn8z8c9`kzY=)H|N2nAJ>s9$~UYh>4aF7|Ko zbicl{(o2B{gf&vNj=2K@6dB1w4612d`PqG_QzQ`cVjwphKu<`=y!wEH{dI=P()k@a z#YJ(tv%`3-%$C{rkw@kFllI~ogTCVjt@V%T9$;yZWgqfg*5Oo*jeCQ2D7+q*ix_Q# z&4;hh#`y*EqrO<6;@tH7b&^O$!l<{w(wfV1b+F=eIA?wsHmELlF73zZXDg+NUQ(g+6*KPcumXpxWznRNI&L=Ta{<2&`oJaqD<3v&69Mv+nYQGSKC}7+c{;SbypkO@A*}&)o5B6;tde8i zy(QY<<)e>^G z-;lHI_pSeMdPyEbKUP*t^M$lz242|EpA}FdxpM^afWR;pg~6mH5lON4M3lvqOGfNS z&*rBu>U!N5;K|ce1jRq=w@pZY2do=a?Mvj1~?<5p&);O)x1sp)ZP{8M(RfrRK> zJt=EK7ZPg%C_7T83V*V(Ke+l}<1k0ueR$0j#qM?3QsU?{I4jU%(ZBYPuL3)9%lPo5 zVY@CNUF5l^YWwylb!H#}6fc`JQsq+T}hH8Ho*o&2B{VQYVHPqrAZ9KeblNs6fg%QWG=WMF7Gp7Zvd0V??I@ z)nxVY-wQ;a3YxwHTn6^fXwYj@i~Dxq^jDo1rbRYiSRVBst-ylS0ywZdD6?bWuMtZ* z4xp$}@n}kSb5j&vOLAG3i0}=x();5wAyMf53I#pmf2rX7gd-*!wj#zT>dh@NaLP3- zLCRa72zlX8uutL}Dk%x{UHPm^fVZW5={eXDyswG~YaFy7gXHIO*qRWm1z<^+041TZ zt^lI6BDhsi%vX(AvyC&w0-SdPdGD7ou>NYW1&#R?-leee2h|AUS5Ye89cRc$Mcu5L zq#>QCFFdk8}Fw^v2vq)turX~NT6I=Vg1V=r$HX%VE+<#J9C zrLpfiwOAWzMIXmBLQTX_sq+NWZhws45CH24$AdP1d41p5HhC((nvh$xyH&Nq;+sbD z8?LpwmGm!NeQs3wvJ*{K z%z`~`>QW3D4!I~imVhj-uD^(Q1kCbggovR4@$QqF9JSuMuO)3+k?%aPFEl=Uru(f9 zS+eM)`2NCw5*EU~B_@N6Zj_4yUND&w6C-sw~NLfS5(@cwN^eHPlO;T^*O^qp)8C^99`}|0R~qS?AOxr zWdaF}wnnByYtQDbmx!`O+8WbsuSDx>Dz(Ixhq){9wBIl%Y8_c~ueGP3G8rfQJwQ6@ zxlRCm6Tnc|-A68@+in9+t_`&Ba4&p+{3 z_{;Z}_RZPwC}n&j;pOWB)_-e7$lt9NvpZd`uLcCIr_-7)rLeMky?9;qj zxtP(9O>h%Xrdr6Mcv*x4ilEN)gT?-oL^f#`|3`1#3`yVX>G_5!n7?r@_JZOEE02N6qE=R5zFIJ?@d z-W)v4Rq+?a*dbAT(Dtrihc9r--D|^Hu?EU)X?V)QRagfe7LH3f1oR>oQ zIGTlF;}6N>`^rkr7}D9KzQ_UmUj!Jyv3j}yalIOZcdPM;SUL0Oa!{bcOtnG-9Dv`o z=apVy$i+_h6^mwQey~Ns8%=uGmbco~zOk0!p^_flBDeS84w(v;W|($d)f6rnFRkl= zu+U4S0{nO62sTX`O3G;y!y5f`-`E(%Mtgtz>|cP34@wCS{e8bb>Q+@a@{%|ICBcPl zx-&K4*pvMK)_4^3g<>Tb1fGE|eLoN}`zQMq!`foXYWpa_V?}!ehOg zXDL2n(NxH497(M48hyg1TCxY9ezxiP1SrKU@3VMh$R%9u9QCyQC|Vj~EVcQr{f77} z*R1M`vSYj3KJ$tBI0?a5$i93jPg$i*2M{Uz79V%7Z8VYdw7<=9R&DNMf94~u0nR%h z5WM1jxf6FK9c8@|v^^(+9`F?mqYbVc<=F4*oy!kAM2xOaqcvRD>m9aTnol^=)(Ts1 zmvgCP(9>r90jgYk@Xn)3OW(L;huuftx%Kh2i8}Pc8Yf+a!UY_7oY4Tf0B22Fgk3PF zqDD%uWy-2keMq={Swa`*jEZQmbkPY&p1pvTgP-xgEt|xHi13%T#!4kg$D*zEJD0>_yPCpl z^I6nkqrD6n_i2Y6!S2@ik;aXC9kdsrxu?nI25H_~1@&ag^)H*z?oEyHxJO%wtYZlA zwqC-?1kw<<-yThks*;M2+HDv*W;ja_hXrU%N9Sr$GhE^Gw6`*5N+#`|a@=C5$uD$%yiEPfe#LcRi>b-fRS1_AARuqGf2@^EFe-E-79 zi~_6?f0bQ=g*RMe)|MHA$x&>fdeglvKo1%JFxX0*Hz64=`7U~@I#p<4tZF3_?{X#; zre5$746hnpannJVr8(183aeYYi-Uf&8BN#rN6T(tvb|xmEQzASo<*vK3&w>OayE>i zY~tTX)wX**fOp^B@u@8@957J2za8Pg@$YTomj>Uax4=qWJ){Hl##=1A!WZUBl%GX~ z3^YoIT6Gve${Iu7h2>%6Y0U&OtVjUwvf3(EymJgA7RYTI7D->utI(d_YrUID338_i zEkUb)Eh+F-$i-aLWfL|WPs%j(t)$@PHh$o4q$4IpSkIV1&cJWKhC7$e>NNn^Z1NQa zn5D)bh4BxX{yUXQdwpN_570+3+Sz$8DFwAz78aK5q%Q#A3v!>*dH79=KwPS&CbrZ! zcL@dNC{z#$&Y>eRU+lfgj97JZbrCWn=P21%k^Oq^}ky`ii!-aE582dyf{UWl_FXD*7kukmB@(olZzaj{k+s5%dB6q_;SkVII|emn95 zBX3ePX1?l)x%=m0xn&p!9yLP7#AP{eQ`=>V#b1~WmXec42~s8VHqzA=Pxm5D*b{hq zguqI=vC5z+1{pL#(N0K0+T5$;AM*FD`h)In5u%9*B&YM|*_fE9kD-hkSJZ4rw+f1k z?h!h4B2X*ntNY1}82J}(f(Hd>aB>nIa`zUj7*0A$#bG;U2W3~A%3tMZ^lN-VmFAYP z2?nG?ao)nGOb`FHXgc==#^&Zcg}44By0vls{uxMcey7yu&n#1oy{QiOdgst?#Zs4X zJ*!!x<6yvNBHyDZr|7lt2oXsY7%4NndtvrwFL2>u>b1@^K9#J#w$sqAAKj7fjBHP< z!2oOcS%N2NP-#(#_w-yZk^gS%W^Sem*A+|qTKo?ES1eoLofh#KzU9L090wvv5KJL7 zRs##c8t+XQmUNu-SGt`5^`O1xU!R=C6Xh=o_Mb9h9uw z&sqI=GHe%IigG4TXHOuT;0{+i)SIJv_^+EH;?HeV=e?urim1v;{|^OHo#a&hpazV4|_aaa4&)3NP3hB95~{)LE~>zqo%w5IcY zD7tA1Qzd%526bo=GHA3u3i>SqDAMwA+*U>o&!cvpIqW!r9twgcoXFXni&x_0DqHR& zF)g=|sS`mEDWUFewW>VfZwLGB za%Xvh#?^+nk%YhdLccuRhDDDbCy2E!B&W-H;ki?S8wE&7?M?rdxpPmVy%IqE!bGc% z&>RN5ZJLQnI&UTCE_W;n^xAyoVo~09B4FOF{pJV}$~`jA_QK{Le)n&)Fr=4W;(}&B z4IKko{Q>_tny*EbX^4aiRPT88%^8n#6SA zlndC6k8`};iy}{OGv#sdr+qbE=voye#m4g8L!tCuwYjc3!*DctsHVWK23-3mG_VYBKNi(0SY@AG1iP7^Dt$wd(Ff^J}N3VOUoFd zY;$*eKsrA%M*@W_M=bl{r@?JncG4bikhu}5q?wiBq zwO`k-EE%%AG_J}@lL1c4ti-1GML-l*q|j|A0VfMAzWQ;B@8J}1fp;zfL55D-C%WM` zZ$h!=2;9v7Eg*=;?*B0M)j?IpU6}7Bvc z4bog1q?>*HzTMf`+1Z)>;+JblQ&tp5jS*}sylEC8t+p7 z?$p>4i;aE(n8EPkXaA0BNv4LO1!}9y=CjT|2e_8^S~8Cgz7}0hFR*pCZSC{#ehoU> zNOE2TDX+?VkZ+FG=Kp$ymUgxu0b|IqBrJUlI4%Z)(Yq;;iKH2&FOjai0RG|mBU*qx zEss9o4S}3KZ_zs(KmuFJz!k)tfOro#(AnM#4Ahn2|AK%0E+z=A0E^BAhi|#6>wy>$ z0__S1F}x9Br(=~il6{xzl=0LrIAYqeM%u)zrSmIaiOI`wP8MroMYDl%#WH^n6ZM$W zO8RLMapk5%QQN);7PJ~rsy)d&)#LFxPHF2J9}NgLli++HK#c2t8OT$J?=>y|} zgg0G^QVBs^c=%TLUbA|-*GOHmDs zQn*sTw8MtbhC^LY0m+r;edalAB{#MG5p1M4t`5UB^u6q(Xssn3YmbYXl~wu>{|-%Z ziYPb$&}%zIG&O=J>x~ZIFjg*P1(e8UlNu)mE|9LmF&rO3ez3)7^Mp6ktooA~vSK2g zy0H3{Pj6CKs)|rK=nUd%-WB}McCK@4*`*@;{#DcEqLhHbyU+!i~E zynZmwntEE*uZ-qGOz9S!;y8{8Pb@-STzV1s-8etrL~(d#LHLk@i?$1b^%N+>vT+$4 zy$g4E8a7F|pu6592obVKbC3YPQaI(XQ2&K}JCUhgmXYeuNV|UmB-#Z=4l3Gz^LPYvI_<`;x#UR95_)wT`A(r)KDM7oT1CW5N=n_VU)MXTb zRBNhc2miHW<4N3bRBF>8hLqW3N17G~I`eY`euG2JyY>3Qcfl>*#!HNN6hqtdm z-@tb>2p(h*a{5UBeTZI4{F5`akF|AYeTwvUr}+iB4wexb@LnRb(L98wFrz?`e8$mQ z%cK4a9x65!c26ruXWya{aYDVEEek-D8A~77HQUUnOTgBdqbWFwmc&4&z^|n}rPojR z^Iz-M{rZ(+=#x-TSV9yC0alDp9Z$W0uIFaMCgdq^GUOvQ`Jg_pfR6CBf{k zveD;e&Ug10PxciHV0)RhJ#FqS7rlO_&$`Wn)TN3#Ps*K(wY#Oz|Hp`FHHZijYPya@ ziGjEIMtkzfX_S~Jybdd-H=aKp8*n~7dCgP=ov=>lUiBGmyuo~3?>e$06}9k;*Swxx+C8A~q!J>0!3797rMWDXmW|4MF#Ac^ zB|_SON%u7U9`-u&=HWR3V!2w)z#gs_Zc+0vX}xiH;~uYhKi{aZqsId`c!5Rz<9XQP zHW-YdlrQA+`};I@%Hw1zj5S+pvN%o_KjP-c?fE}8XCH>VN|)t^s+?;LA_rhxf0)5P zwMb>bJLlB$e~4;9uxp zB&kvR_b$0cbQUr6QBm&W%eSY*ys-!8-ZW+`e{{CpBi+5ZRd*Ty>Vl8s9imXM%cAt3 zL_+94oh2^dNOl{o10wOVH~-l>ph9~V1-5dPVVc~PuGre4dazsnWP4EWoH^l5)w9nT z`q@f8VwfwLsM4vr%1%ty0l0AsHeA)CmhGuh|N6D`oSq|9)5hLjNH1DhP}}2T2#poO z2cm?MX3l%wvhWJv#HyIKH?e#uVBGy$G$%OxO)<(`7mt1mi+)RkXZy?qFaBg?iBYVIG$#>Nb8KV-p}LV{Y?T`$={q3?!!({g4erY2h|aps zOx_sE%=-P}RPG{-RShfY^+05R&e%qCJgT#PR?Pa$g2O-bb?_ERZ^YRIh?kqXcqA`{^pXWV+mVo8B*R`Jdt}^ zcClV;Ak^9T`|r@t5{mw~M(J#nV>&GBm5U^dgQY2R(+SKQ)~(B3k!^Y;9a=c;`#vT3 zWd`f)!kXZ$26HGb_d4UQn)djUVlSs-JyzaVc6zBI$)eSa@ED33j|>tPmMCNOH$#wE zKdrhTsr^tI0xTR1#z-!#FSzDBRB%bqveoX#DN3kU_U%thrMP=?V_3xEVKZW%KTzr; zDgK~6j8kXnQ0sA9g61AI7DGcLX~u;oCVZNMYBI3JW4fMVeih+@5=wXK!W){?`nKob zquTHKZJxisgED`;ThRGmTmUR&AfHvh-+}#hSoXXNM;_(f?L}#S>T+CvYEYsDXnO{8UTFdL-t@n4X{z)P7V&YFYk75i6S$=ozMe!lIpZL0tG#?H zp~#|B4(%AJbl&k)pnMPIh{NMOh@{oI>jQ7ZFvjxv2=c32fNJ1g86cnsTV-$5#~YFJ z@JASm17)6mVz-$Oy4)yz@ z55=xB_ZqH)7GXXe1=Xy^eXC7FLBdRpf61Q!wxSB)O9Xf-^f%s}!Q&KKIEb4&K%NHJ zL{tGPu`M*H4Jx{qZBQRN2?)2@wpMYuvnOLe+t7@Ur<(6X${85Ce{L-d$mPn>Sa=!1 zrC<)p<3p<d543~$Rve_f4o>!cAM2^%|LJxFU4{v4wWn3t6J00wd`*=AWc- zhBY58U!q0ji)1K0)~ecJ0Nd|XyTnqj^9P6=q#w&6&sUnZxWE4kpp)Nnj!l(L^ZsRp z?PE-t3$CRbstGYQocMeDR?J(IQ#Hl%H`hJ=;Gp=*md3-1_RqWDQqmhOGTVHMf;tE0YQ-Ph7r@6hgLf(rhY!K3Dv;uZ#3F<7JkP3CjO zE(@~{>zRIhaT0*NjJiJxwIy%RG<~AqH(>Gk3I)JV>k0V<@kPp$InaevTwxrPr%^(h zk-NdsIj_%sz#?Eq8yYEd$WluEZvm6nHLZj+gkLrgr-ni46HybNEjACxbZk@38L_t` z>a8CCqWTyV`DRAHt~o0Og9*6&FIuP&d@58tPkOo}Mhm@p)x(d=s*;wDsT>i8#Fy*!mj?A&Ti?L*wrPZ++)sGVgWYn?q zzoO&uM&ys^_peAY^{U0x=B6EgFrUX%ushTy{Ja@RJ*1BcjgAspj1bcm_OUMNyo$XT z;dlRJtFP|j$GOycIZ`(i<*#)p1zlparwJ_?R*C8AM{<;}TPDUG>`_ zo+%3TMuPN>v6aj}AxP-uN2$Zp#v@%4>{I)~CXI}p%A^;70B6BoLOAYDC$(^+!w{#> z6Bw)gGz^Q1SbHf^o~}_O^-cSdd5>okf^0E)6ti6{M>Oy6weqqliW}6? z(EBJ?hN$vSn&B3uT^KqNTniP>O72MEPt%0@Wbt#OBX48gX4fshQFycgLg9X9Cua2u zQxvi#yro|%_ZD}5YxL14yh^}Cx*`Q`{6Nc@MZ>lritZz%s~p>vAx8P{W1K!TF+!s^ zVf+7kE&bV%4RG7Nm@L5T5{Go4uybxuXJtA0*<#m7DV*L=k@e+&NP+bfGDvceKtf;w z8pD=8)Ci8?EYGh9k^11D%xXC)9lUyKX4n$pv`KSFEdCs1LH|^H757dL3GIOHMIX-L zH!B3HWaLwI4H#pY6rT3G`~ewx+zQK3!sjCj1h_K7(5T36mIhxFrk>f_Uwo6O{<{Zu z-;;>PFme~%+_SPTn`}JSO_4!wWI?}jBsG#Y{R6>hd8dQ@ zou5*y_z_doiLFS>wUuz^UcJ@1Zb|L-`q!xZ#uu{t_aCqW9{Cq$bcIB1zBOryG>fN- zW?n4K{BA>+v87Sbo4$QFMJpvIz~2Z~4Qi-~zzQxm51zwyrrfWn5>XmZtU_B>3GQEt zWiW9nROEHPFLgO;zZ>V@Ht9Cg{^*gV*Zr})bsib8_&7(4YTM7eg$dl<8r&WMccy?G zL<`$m?<-*ReC0Fnz!D1H9`2jplisAnSw9_n8hQ^crZkGzeTrogb3nKZKAEg|smwg* zjRWcbFyMbDGpjBY-OMO?v}65mir3>eEiT?K&$ReIJ5ydc8zNG}X%kc-5+cVnb!mN7 z8ZYj{$h?zJwq47f6RU2L-Tx%Ji56_fqy-Dvchb3xxZ29311vXV#0wN6IFUJ+$Csc`qjX?tD9p#;i#$KIEy37>L z(iIN)SzO*W-dnR-bLkc!zpR5Swn3a+_52^uD`>ojZSFS7jigWkrlvRlBB!zbG^Ja( zu~37eSumA+yE18nJl@#!ml!%wE(6nns{2Q$-+GP+LnN>?geX)Ww8JLgMm_CP0%bzx z+|w99WcPu732>lUmQq@a54E9pJYz2YLX1$z|9PpZvI`Fee%TLL1LB7B5p;<~uB0Z| zIm^(MVuUC=Fa-FPc(*;tqhiw!UTl%RtwPW6>h45dB`mL#zX$0VsB8ASc=iSf%sl_r z$b!4^^v=s|xnst1KZjgKe@b)fPhxueQ>!+!_( z)lC4vX|H#wY@dL(aKGyi)iHkT#fHSFp&%EA#TS}{MZ*NMjR;dJ5U`Rpwe=|X(PU2) zviY)LTx>JJ8BVw`3VT~mUT@L$3=0XcTMoGSD0Q(a+t-!?a?h3udw(2iyhZ9pwkWcr zeDl?Z;ve+UI^JiGN=X<+l{=ZyX(5z%(k(WJZ~PnQe?FC`#fy_GYsk` zf^!dia|h2G$_|I6m>G8`IUqp&@QwV|NM$@_$>r=Pqxz;+?R2BS-ji=s%NO9VCpOF$ zz_Qv;JF^H0dtN0RC}58aC@7?%vn5n;Lqof&8y1>P-T{$lbRN_u`Nh z@u@?OusMq+q$|vZ8YcjA8s0gE%B->QfD2quEBOZDbR&R3at}iWCa7saJkX2{x-kj# zB5wE_(nb)0ln7F+8}S*NKUsn<6#>}(0iz8ZfHcGsA^s-%uWE=}^W7~noVYuN{(n=Ig%te3ELq^@D1HjRP>BrtGC33jyTASBkMo{r)4 zBjsk|CP!xhR6xACP>Flz7idCf8RtD5p|KbI?Kc-6?&q47)Y0tD+~tO95?`*47aTN! z9sUDh7l-yW96M+r?j7Joah8}t6wrdzW5KWUlkoY~H#;#??W8>-$rHIsHp-jh*=-y4dQ8lY zwmtJRJGh>d_7rH9JVIpX&Xs}PTU!>K`_p3?0_jb>Me{Owy^o2TrMRPrjp)C9%lkmaOAuuhW-qp8oF)o=Rl)g9_7JO3t5I zb*4*t>a(ER@@{FId85Hzm$ezZ|?6(W7ThHdu^=+e3U`M{5sjSpQq{AZS2S%W8I6Czii#v&Enqh4kAS0$ z%U6l9wKfYo@omTRpv0*&QOvJ~H8|4SmC0*t^2-6QgZw5qySPbe&{DXI2J@usn5Zme z@$+SHZR4TVI*}(Nlt-svZ&wh~I#{moh&YX?H4`Y_Wdb&m>@Lny6g0k?A_q1)OlEP& zSafy%b{i_{s|?=x{JpPhamBg1dWjOrUf(ozGza;LMvB@GDKD(gQH2ICfl}7?pa=MY zWxf1B+lv%#ipIHoG;RajalksK^kMqHKo{z_x4f|TM@^7BVVFTS`U*G~^&lGRZRGDZ zCti7eAE_S{g7wx7{`i42`ZnzJ;@ShYb}A6+L!;!353PIO1u^3Y4Ji}VK_IZ~6T-2g zy85uJDffUzsv&NZUm^RWSW(^L<*{`X)t3AmN#f-{kIT~xL*rbBNSAu$IW^)TBystxpA1f{bsD9c+TFU>$4};Jl=gMH)7G~lyWy;eL z78R^z)-Ah&o-c!?jm6X2gT;8%t=iQ7-UN00&*%)YO$LZkg((ngYOF!KU_9-<2h6TI zTk8^++eb<-;DJq}(D!1>$HN;_#6+nv%+kf=S6SXG%j1Vy-*|+X0?T5W%eeJ48hYe; zggog|#7yiH!=ifLdk~LVO?@IBOYQz+e)waM>o!18yUQ@NB%XdEr@2h)X6ffe0prux zX;X>l?vOTuHo_CN5T*X;aHZh8=A!_6@#3pZ^aVr{p94;Z$ZA` z;Stl8c+T-X{qULT9*Cc12VM+;@htQS&>^wNRl#;=STm z$gctOn7pae3nhn)^FRH6M>R({&gr^J9V=3*oISF9W>V`~-5%vxf=e~)QlD3q#gqh( zAyL1XIBhML-FW}rU0<74JIot7bigm~yBkBb(pQ#n*r>{u76^m#WVE1vLOL_Zm9itG z{Yrh|7~4x-f4gSLmhmtjM9ZTVg0!N`*(Gr!=TX5zHB=vjA(e1yNE(EK3INQVbA zq1l#IS$T@W%EmPMhtka4SCyfI=KA`xyFvU9n_^$;#CV`LT0Lzf{}*WPZBvYt!`x>qLq6m%KbGeFoI<%Ft~oWk z-|dBRM7Dpx;#fFGQ&+=4R?CoSNS)VG7TFJGrd=n@LW8QgoUHWG(c^qjUKc+5ER3DLjPb=z1RIQeZ%^NV|{APBkP&e-sfcrTTG26$x<7pqbJ9|=;8B>NTB4(aYGx% z>Yl(C)v3cr@P+fCQku9%ZZ`7GfJQXglhD`wXZKE@EiS>aI3bIG#-yi)s+5=J}~Xz6+T68 zfDod?u*7fZ+pm(Z{GMIpXukE zahYvj<#y+~j_2~FfpEK56;FMKftWAYy;Lh+XYJQo_wriAfiH?EhE5u& zK7Ox*$&_lt^vJ0)oX^}-*?()_x&f+>5BT6Pmg8tocYz;XW&E-l&ElWW%Zou zOF(7XqpA#z{%Kl_9W!D9K$3jp%!)&f7Ro%;o?jM3dl*0o0bPRr2EE+BdNi81)qRQk zQ9XlmS~UDy2^Ww%SYFhK#~p+|dQkTvcVt)bE`UH z!p?da%nS@IBC_Xiyz9Nb<3fXWBm`fICa<*~y615a`*sjm1PNh!f6Cx=|9@3d_TfDK zmdDNblqAtG9)*c24`+iKRc*h7Bkss2@46}tt_LLtA6x*}ftWx~18qv@=kGD*b&Qy$ zB0yHP6q6_|?3;tRrxbJ?E5sdarwotBD(sZ>UwO+!%zo6c8jovW;!T8WjKPDUS|&XLy>9D`fGU5JsCx+EKXQr2%_`4A9c?SQd-zsqganR}|7w z(Sf=MQ^d>Cuy5-zg$NcBaAV5HGdlrhoMH)QWa+-Vn14faDi~*xKmyx1E$a4^>Hy}) z0uenO8F@`sTewWVf9;PcC@v=l-=r_~Tod8V=<++YKyTnqL@H#JRq7J*j&tG09Lc*t zz2=x^oiB7zBz_8YW{#rY%dG|VRC82J{ITigS*WS>cK8yS8D(fZ^=}sOu;T`in%oi& z%6DTo8;>*<$(Mvs4yE4*{99$uHFA`%#VT-&8;Ft4>|j$byCTOvY*4o+4sayANeN}9 zpFDqxh0XpIU`sFx)teIK59`-Vnv-Gq#5D|l+h9DahXv|fXSR($ynn2N4hQ6zR+FM| zz0L1nSwwQa6_6~AS~mX4j!uK!STtuBIuH&YY5E*SiD!z%EFF!1bqlsQX4bU6b#Q(N z{Z#it`$>>YtUCbFUMY#f%O&mp!t*E|a7j+l=k+K0F<|6() zf|EXtRX?+HO(oq-i^{%8e}hYu#Y>e@uYuUpw$7QTLV-ryzCPW@4Qv zy?S6$Ow<8Fhx8wMYsQ7OM@!`=mEQjSA<}oZeY|4Wy<+&H@GQ*Lad@Fe4MDv$qo4Fu zFqM>9o#~I$6~`XmUFy=pm@S|FbX%mx+nK_2&)Sh%g1a%x5ZxPuy$deka68!qnO7n@swyYqr}MXl4zy$ zVibQfB^Nk5seX}3@R)O%o4D;LS-867d7fOpbMm*kD_mjZK(B_xG(^Z|baj?3tuv)A zN`dJq$PB0r$**3l2Ci1!;O*{rdtdtemZS^M`V33xg1ewAkI2WoS_5gBtm3ltko((p zH%FAQ7=_r~9c$k%v4?Y^2$VB7-U5xQyLk0d&53XYokusVqUF(7AJ%=(ZBZoCUdkmh zgyhM`WNq){QDt5{3$BN(rd6~D?C*&_9E)bP{PqT*z9VtDdk+C7De*HzQUZ~V@T)_$ zdLJLgd5jeL-s}PJMq%8=Z-&y})q;y(V7-5;hsc5$ zAcm(YF~zDW6jbfqAcR*!%Y3RC3DGR!8TfEg-4qN`%$s%5n_moPFL zQs9cb3IAr?T}?A~VBYdN_DLVkd5)@Cq5k0rNEZ6BjB-o{ z*WH`?CG8irzN8!6@^Pl>V;9OX^p8019qzhm4jSiq&(8X~srUzlwTsy)+_ylNaOeFP ztA#JP_#bg8&P{MHE#BFgr&U6<5(g-OZxr1K_~qlhTU?0a-5ywu3vMl6x%HRGAhi-6 zW!Zf#IPrYjNFdTCjRm`8R^#QZNt7|K2u}Ep4WkFe-nk*7xoC-foqrIQlm@y)0g@Jf zWRhsVG4cBk57xkQvKAS`=4$bN2C*155x(6=J*EPFtk@syoVEJbr9nA4^4grGwvy9> zrgAx_D>&N(x94Z-lN4vU`PPKA=3V67xyu=skjoMlNh|jn2#V1MYTbhw>Y4_jqKV0y z4g$F%l<1FJaPz}a6>l;aR;ow#i3o=bbQ}aIr3Df5M(|bGvAT9h**I$u60lowf*
)mWy&X z#e+ajhQ^c^leY1t0_WX$DB?vW->@;bQ;ZSuu@}sBSLF41X}7N3ZX?KmN;jJ4I@|8fct4>6 zD#{AX=)E+)WXZ&pU8pOlpixvZ*(^jWV-J!nMtx91r%t$*{I@zJT!H(PTq`)WPd)wW zT0GtZbRXjjeFTn$OSBZ!ARwKwE4J$)ppQ%(SXqVyk|bYtl6SLf;XIP6rK45>zIe%V z+qe%zpn`OG$ywYFp4IKGjTmWp8w|4#Zop>k7I8rz^ z`6%I3WO6?mq;S^RfEDZs{vx}uojB50F>Qtz*Uw?s9dYk9oa%vO3fgCc@R2<3G}eq8M-wH64TPunT)*NF**NV| zw@I!_7Ldy5n@(n2YVCtAa{u)&uo88%7P~d5d{3AX&(O#EN5pf3_t|R0OmEHUGBnIt zxu03S^r@22W$;gY-Hj8?4<}heV+7pYdHOPV-t;+YEC9& zm;up5eeb8~t0!@MpL-PA$eczCao;W|pcpm1ws3y$3M+RL?aeFCZjw1~XH)O%-8Cm5 z#7b;pJYZLF=Pusq{~*gaXQN~qaOOE-!XD2zHy!fKcHRZ(RtY(dCba46lwdi}Ae{QW z6_$-arI(LFf(B&!EaGsmQ#@7W#!*hOn!INkpDZouu77~N+`5fc`}UKXM=bC_g?n+x~!{)J_5JbiROIQm0$e@)0P3tNBj<#U9S5K zKK)0*{fL{J=NZy!Qz1hNYf>$}L4rSHNaj#7n6^8CzoM90tC!q+mNH-xZF_6>^4MqOr459hd2|q>hpgkDpq&*G(W@- z2X6KE5Uqog-4?6}zieqJx?&=pRSase@WZFW5H7O?%DX=U-VM1WPXSvB($-g>A0Wgo z3XIS`Ar}4%=GDxj^ILJ^VztR2(j&{3<8Gqt)Yg(gcLpiuHJH-mU(iFl@6 z%Y!k7J{JMPQ7e<*?+8?k{j?O6?)pSVjKv4njkIu5`xH$mIGBVcuX4J_ z4TdJKzx)-)m_l9A^biS$TwPz~%yFuxJBiGMv{i}pIB?`AqU4OWXfBDIQXkb3CKJzM zu&vgXNTa`y6fnlhK(b+hLL-G#Jbe``q$IJJJ+~;FBueaGW~SG%5QVh<5xM(A^NV06vjzT=7|D11Q~?!n?M;;M zcKOV{=c667$BWVH*64d41+M6V<{C`;8VwuY@_mlbWiKtYrz+s*DP`Xf`Ujh9J{Lsq z#8E>{a*j@kW&9Y{1QZX06qC`~?MD1EG|IqaEvAOZLMca}h>mG9#IS{L5^>58l1fA_ z?*g~l%!#`=Kl7ZX&j4oC%a8H^9W9HE&+Rk!y-vraie+9;wSPU6?Ne;pt(yEeFeC$n zvdC*l=I;Ta?#>MvMI|$07;Hs?5+4?=VI}nP_M0%Qzun4MD641GIAiZ!Gh7+v``7X9 zEwc6bpBdBTqP~4PK$Os-fGqW5HmK~eJ2K9Pu79xfMm0a>+7K8s8d=JC_VIDU@&_|3 zIiJNM{VRI~ZYA_t8z%GOzmJU*Qd0GRn?oXS z((yBHPg(#Ghw%Fjd}Jzof)II1b>l8@)APEu8UoOb{GI!b<|jX`z)(35AK@BRPM0Tm zaf6(LB2ioGKfPWfP^A$6Fm@y`5-#_|C&U+}O;qLA-uU*w3{u{3hnT62iH^aRe`QSm zFJhS%vz*c96~c36l@As^Apf$haTDIElQLMmZy!@%b_ptv%uCq*2_VJ3*zMF&DM12) zN0&GPvh}nDFp{{oEKFR~6K8m&>S;5oOMkLHXZ1FEPX+Dm|L=D`hLUG7#(oua77g84 zlcj?yDn{$uCop^cTG6PT(MTfx?9+xk9?o8mCMGBNY{??E;*|$m__>aWPMDtq zLnA1qSS%fJ>)-6m(L0N=0s$g=rsn13W@Z*PrMo?ZJ!h1EWmbaw6cnVtn9ZQt=aro4 zEGjhWO;H-~VqDTqNjl8TX9#~DQY%cfSC`^erv#QCb)OzD2J?PBB8Otj++I^(N`<0H*H#R;9@*dx(7nhct`7JWi_>Uux+){$`|x!&5exsNi~yk;FQB|K6#`AAu6 zX0^<7H#~{!34pVD*RFIk=9hj(4PYM4#BN8|TorYQS)7)XRoPH^>{|YUsz1?oIyF;T zxO1#{fjzV1#FE<9Xgz6cLwJH*JmIMGd7}1p|EFHxf?mHxGgh_ydI0!PCI&t|yYu;X za55C*reenaJ&2jwMLAiJVFgCZrnBn z2X`Um>wfmIprAGzzSU+=?)oB9Xgzo;$){g0m+61%O)Ln=rr~PDp)yN)W`oxuu;lhH zll#+*(Onf86q|x?%VEj)<3OX=H4`?X7yd*4HSZctXa#qQT6!{6c>26t!7uYO3A16+ zKHH!I_e+QV@9$jQb4JB~F&gv97esUKY_vUeTL{WOP@$>k#L9?w<#oB{vm+D-Nb}w- zIDdcSeIljA{MKnacxyM|ogy1EwWDFGJmjRluO&^HX=vc~^Vq;`;+SS7ucC!Rd{Q>r zaiETN1E@18Jwds>uiK#fqVS=`M4XnT(gihny6Jsh{8bu%>U4;SoxxHx{<1rsPiD4crA91c!7m4({aZ6OI=FRT zDzf2@p>*Syc-prUtk(@#`(;x>wYRW@^xa>-t#IbDz=jq6>MMFhm9(MnV=cZN|KO?U zeZ}Cama}Jq>ya6v%rd~##;WgIjjt1o54XVbJAJhO{+$(9{H{^H$JhnvN@em>Q#rEJW{ zcd>-Fx68gxWuT%_<!|?ajWl|su6ctaZ+0; z6idx3>AuL>QB!{@azaLO`UbS34tehuE=V4nE@yZRl;iFXKbq$`7z*7xf3{G$HFGXy zQFM3tPc`-3%Mh~9E+1u5RF4l>j+_jq2-cTKvh$R)mKU?A$z$M^E((2LeNUAsWW8Xf zE)X`w3?WB3HU&n?$FJT~4XI1!7vN6r;%!BO`<(3cdAl<10&=U%H2QXMj*G9ciT}iL zdpTn+y?cSMNd*6J@oB@iXJJllPUZ_mXDd>D!fTj?@mH@o@+a&_lei{y2KcMkw{Wm$ z4mm|Q^0RG=@-$` z*BvM(sThm@`09WU@O?>ivnF%B@1hA$*;jJbOx!>tA$b_0uO|loWV|CK@x#YMzr$~b zT@F^I-(=RvSO&u|eb8vc6D;S_OHWFyVRm;xP>MzqWhV8x&tDe|!6)l|zBKLCa zf*3ja9{zNzgPpgiH^a!dN|RVi4czs{+b&TG0&2_bb=Lh)-JF=cW-fJKnIf zU)OwOX)1m~XS=x|H8ZhuUZ&sLw< zHy*N(=MvZwU3Bu)a6P5Ouep1z=7qVOl{}|&GZF&J4k%*0>9DvT{rKtQloTB3OF9EN zlnoUQ44tZWH>0g>9U&QF`_f$%&}>Y@Od1 zGg1lU!CSEv%&Bc7LkMrhE7;6&V;aR}@fj0|KzaYzO5<(Rasxw0|xdGF#YSk63)E zE70vAaPa-gH~v&HazJn4hqsP4fe5lc-~WmAaSEgg*Op$$mM4K&hty^Ji#d-OP$DMr zTautDHkfF?s|~Pv-4Ze47!^?aTay8-`aryI_zeT?`K=;sXtuqMq&M5DlIcs+5l>7t zq+2rQ;fua}0QZ_S!BfGs*>bN&?zY4+P#+28#_98v-^SMw9Jft& z$+{9NXH-4pc|S@aY@KD5>`X@Di`-4xNGCrkWA5PnMCuFX&dnB1^46l}*EfYXU&USh zCMcWlY+{ECtp^vg7ztY0$(89Xvg>Ry8;aJEh6*=6?f&&1$dfZ_6Jt^QjJ~bC+56{b zj7v+DfQqVt`Y3X!x{MPt6_{I6L5tsk)s{Gq)h$a2T~2&4VK-R(69O&}Ta-`bl1lGg z=UE17Hz9uLPz^X(L}C?12yI#3+bE`dJ|(u$J6-CiH4?!9%;WqptoFw@e=jPhQHIq; z5|0ITLoc(BS4fz#*E)$ymyyjdLvJBejtd|$(mE(hd-qsarFX?duJG+zFSb;~ehshW zti$`NAZ5iC7+ZSMo?2@)E7OvojPeW82TLE$X&VS{`l_#!|5fDCmCmv)c56Rv{X<^s zjiI=p=M`E*^K_snak+S#>P)cB+AV&H-!0nYYYxhQ#ltrsD6#s^v^Y}^Z_CMvV`G{E z3cFAh{pfKB5N%~*YAU=`^vtczP{RVW630+rM_hL~*-BD!>l;xuw*w{P{{qHl6GqzCVNO@$(0O52CKlRYU850U!00PUOfJY7- zi{eIte?U1#BbvlWVlPc+c>hh$b0)pwQG|h|uIf=lTeo7hu;^X(Ywq=OI{1ty!GxjD z$6b?3lcZjabsuA8^FUO1Or7=BXI3#T{Z?J~ z_5z^h7SVLlzYX4{2R>5PLYJ1a#L$NJb<8_r2}S@Wa#{05?#WU1;VifIerCm;$l7cZ zqAqDx}*fN=mH*fs1?moZZGW*AbjZ z84byHEdk61C;nJ+UeJp58Ngv4!PZ>jzFT+U2z3*?nc_>o( ztwM63}%loIRr{+vG!{ ziI@aj<8W>GV9a9zIyIMi{%fE}RZ5&P?jC+b@di#m@&Q=U5c(#Jb)Rh*HX+E?sNd}M z?GYcSPO{PuW*{?eV!@gZ)SKZq)OtGV)!xp^f0X!oxa}^l{0L--yVO)vDgTt3X&he_ zr5>##Bsp>{eAQ4rS8?`hZzhlp_jb?8@OMb|%U(f#k!j6Og)dY}-1WeH&Zqo0T^g*N z@xVvO6FYfssE-5~6Y6wYv32*~jlDwUjgZ5FxlvGmuIs(4(nkANpGZ3ivwCP**WmmI zI8GlC%wEcynW=A;&z9P=4N=b}DAKGgk_Fm*w z0#t5%C#%0#a!`nc-%dCxY1h%~`~5f)nn9K}$QfM}_)6R~deNGVXu+Zvf-%J@OU0Mi zul2E&|J`|@z@j>J=Dg!zDBQF+ok6r7hRez&Qj(C%yGsS%7%I9|xr_cBjLJ3eL1J zkI#U1a%k}1_eNWaFEgBs6d&i>PfqEXb1(svMIrH=>vi5D!E*mbwD<1r zHgdljL((*-CX6h=B|yp5C=cpedAs>-)Mja_-2g!~OB}O;#^&_r`npCqTC3FWxkay_ zIKS|8h_iPEWp{z7Sjg42ZG}h9Xp^7x)J&b=!;81DU#~Md?cPlCixNgs?+b(*3>H>l z;<>jqZgI=~B2$bU`Y7}DPbQ5^{~$R1)~8op^Mizeo!={Nk|$Sp0+>^{KSmJnQb}T= zj{4P7Ag8Lhu#HH0%WO}JZ2Tj_? z>kmcAGybq2%FJF5G$tX}UbL@Ob!g*SnyWjnUo**cc&*)@7oIF<=JfAp9bAKG%;OWm z+sv(6r=;(*T10IZg?j6vr?EEnkuTtBG=uI*Tnq}HnNKoU%PU(lZ`0sc0}OS~BDwBL z3ZH|pMNfq1ETYTb-lF$&VWrVfV{$YH;7k2des{9Qx3BF%4S{X^KL~rPpt!#1OZXze z-Ccr9aQC3W-64b!AXsqM1Pc;ef@?!?cXxs`?jGFTZSL^`3 zwbou|@6%dj^byzA(%e!nIphj22E3QE=rjW^mJz499m#KVkcBE=u{mQ0D{O$nyvV&m zywUvM9G?*@JF0H}Dy!o{G z^(_g|ZW!nR|DH%UY3nb1E!aq20u>g>Xad1pdhY4RJr676u>|vb&K}naxX@ON+)9`p_ph-%g8K?VjrO2=2h78P4Jwe9>f3W-^i3=YaAC%d$3@wx zooAi?#ae`7N#gC)v5eSY3f=IILf%5jNFD7IZd0^W%Aw{TpKcRv$`3i81pbhAq$K7O zc4t~@Z!XrH8_~{sju-laKTvk*>7}lyt8iBciY@9gmnvyN3Iy3+(vLd&ePbH>foqlS zJaE}+3iQO$-ZLn;I6(I$2MCfdI(DTOBbpAM%5T3HJ@OM&BSJ^NxOL!!8F|}+5y%M2 z#JUjiejGf59@F#)gOVW2`TPMtT+XOyYDEpz@TfC+g_6~mj*Siv@VV=HuxOd!i$7HC zD>q@ZzuFusu^wBMaJYD+U&2F2A{;zzHvi=rNRXIHrm(3mgB(I`sbiiT1ldP5 zx$D-9AP=NMQZ#&rGd`xe(2m~eySdZ@yufzOsTaZcu5s?V!Zem3oP%5xng9Vqf<_@e zQT4+?C_55+!Hg6p0M4z#LJJZp87=NIR$%)e1RyouT)&@tj&5buZG#q6dPvsWeTW2` z{wRL%Phn;#W17`S`v1XN?Id$F0i~QtqBXMWZ9p#T@N>PL91=Ar;GF3T>cu44wfHs3p>~hfLrq*&YjlHXHZPAD_mK^{ zX>^(InD@VRyy_+^;CTH2r$}&!E_$1K)e?3}t%P9v3u??Yt#JrxRyRlCj_kh=lgp-< zxOykY<2yVxA<(g8IxmY1-Im@6ifKwXAewxFdyG_oHQ0`9NZnp-Y7;gU0vBY}pJ{j? zl-I;?-q8)}0`|81QRxF_j*PA=Wh9t&RhTQ7I|kr|>G2i6Gnqp@T?Z>Dl_)-;h)$sR zV#5S0sfM6t`Yp_Rz=3H+^Ot@IFm40?xQNguX;% zVh$EV(Dgukt{Y6il;>|gBAz|;wkK(bwb!Sg{xon4-B>^#y~GK+RO2nT^kA#EkcC-C z11xI^YUcTbV*YPfRjOc>9+|t>5y}4qk^71VX!YN$h=`mupccK)(70|%9qJQD3~01l zQy})vB|vtWb0voGpi`ksm__z|(U0=XEF5tuujIjk{ylKok3=YOY3ud0{VdMV`}fa* z|MgvuszCsE)oY~BjWX~%HOTKR$?H*~S>Bq-5$%;0Ib`yyT+t~pFqV(6>`zakNDJ3B zDu-v6rx?aLura8DaP)-?u;SsfeK=sAIugo@S#p)9_f^Z-@IAf0Pqj1n>)$|lNo&3I zy_I&R_I_iZ>YtEkUN|`4As@dEU6aBH#Vv4eZpv?(rV0 zm@)5>n~DhmLjeZl@dC#)hJAmvo04eR8^NRj*X(zj`gubV0BNH#mqP_h>TxEQWR&1S zz<5EeCAO4)(!;OyuN~MvFo~#IOK|2ZJRt5%)8&zjP0MNpWogi?b`QeL0(_by3lQ>V?biA#)CJi3^FG z5riPFIx#}nr9a!Of-`q1FNiZ_6m~^&a-|{7-+KJ@8#X$QRv!wSYRub~)Q8+bh4kUu za{XF^ftA_>pOURTBOX@O$ZYF|zh%zpq05}DAmVNGYslEJ#!@Z<4M+p0jmmJwxlI8C z8{WGpa$yp|-B;XBbRZRp8WbT;81YcVy$*^B33ytL^C+LcrX4n$Pz4zwW}{0`Qg1Po zAFiH9m^?|c4&Ws55Pph`Q6KUI7g;)&taTHuK2}n5z@ajIwxYI*#3{1$+DCo1V{IZs zc9>Di$X`Ym7PT)a)*q$xMB16qkrcHExU&g=st?^F(!f0i#=-!qFaZeT=uK7HzqUWW z{gQ^9(S)2><(jW>TYJ=`TSS(``~J6F$I6h z(`Lxd7#{6C7eKXsB6z<%$De@OgZgXqjh@cbX;j+IDXc_|aqG45vqh;0 zS<+CFR8=D~ONMAZvMUzez%^E|U|D+z;;i^c+`2{;9gv7^cMRAz2Hj!oKM01pJf{!j zU9+CdBQ6g2{cqD1L=pw44(Cb<_-HcJGtjC7fXG(jmyW0Ozt}jrpXy2ZGv&1MiuabP zSn%)leuJB)14e>m?Zpdy4D<)g8M{;OJ}oe9es^yFg;o6y59JwpbG*dvyZ#+f;gTrz zXufI_Q*`X{&@8ouL15P;o_61cf6b@HZA|p`Cka>t*};y>UB6zBp{XZVIee(CF@xW5 z$yTwF5XgQkP7Sa~xKP(_EBVXQfdIHhxuOEG0b(*=Gx-JKgbMfH!WhF5bh?G)wh2p3 zd<3Fl1G<`)W0mXp4It8;jyTP`u7jB^A!N?Zd?s9$?_iSa;Ei^R=}RJ^BS`La_t{MH zD{o}vF|1^(g^k>^!5WvE@szn#@l8{h^IXDcM+a^ENwF*$FpmFA-0AOntVv#aC{J|8 zn$A$|HBoG)^)GSK|M5@3#(a(>PM9Qu@C*^fTZriY5ejp*QZ8~(A7S(V?WB~H1KtXd z<$2Ee=)Y%fk5TuFMgA+a<@x(qBts5AdnkC=WdtufSW@4@2QVPGuDsd`)LTW1j|={> zMvlg^V>5Gv{r_xK0uK|wC4z2>MZ-pu9rc{dxbygzL{s@%*PV%=6f9`dhG(0n{_5cS zEx*yZNdxAqcmGogl(Yj=$g?(p>$+)?rj$7*czT(G>B}gK6|J=JX0UK=y-$8h1#s@2{2b^HbJZ5v zQ+G75Eu(%32+D{aH7Jyey71@vUIe7=@LBWGjxj?^lLF9rtKzBG{XiVmC_5=l981wLf*qm8rN9f~s1?5wc z7kWTVU?4;zQ=Hg_YS(hA#5^Fp+5naCDm}B66>JM*s%>TS?mfsy-eak#M`-`Zqu0`Y zHU?03p9V0+?$#-lkNo#?LNS(oE=QA73j|@;`=fofXv&0jXz+I^cTEOdnoo4Z%;?b@c@>@3Y)JSpPXt_NxQj36N!N1Mn=keARev)R3f z)bg#h?)USb4bQZ~6Mpmk6$+ER)yQ1d#;svcUmnOiUn(adeO`4^lvF+A-1#UfwCsDm z=g?4mh*?Z3^fKp6<`M4bjTtC3cTOfbzKt1ML0d1ep=4EG%3fYOGRID==w6o?@tJEt zWM&E4MPmSIR@Kp2q|yloW&ZT)Bn4Qne<3r18|nqXfr$bs||#d&ABQ3gu|Uwt5*wZn8sL3`LyF9t-BwF`mT3*3|qLs%h ziHo1&37Nx6g8$i`%*Bs*9#E`>)1-%zisKn?j zY~p`%IQ@PqMi^zccz9_~p)-RX86zkv;)o=Zbz(SG_&D2&6}5`zmhqx9yzHvtR(iN> zbi&7t%BZ(4MYq7)qpU}8%%|x4>r&)gsb&nynZ>{?4NCpRl_kDJ1 zHWS#r#g=UE6y%R&cy$j5BL>jkZqiaJ%S?jK;cC};r354117xLid3qOz=VM4%eZJ-_5LA<0p^8&ls{5>>a*$6 z^jqg7I6*xr5_f{RsA*;YtEOD_McFk`2gS^Oeu2Bzv1;kXPMse^D&^Gn-;$tuDjo1t z#_677$T|GhV$E826c1MAnG>C|7!yCjo@qpVIB<4#J#2aU4)q&tSSWCTVnV8`CoiH( zYe6~MZ%;f;-wk5!=1PEKyu_8~{Tx*jgRE01l67d1I)K0`Z?J(wLOfOLFkM|8L7of} z6g6K|Wr+&7)j2DVgNuF)vJO6iH)@#cw}nUEw(Z~blInhFphymG6lQu^FG4D-Sx;|8 zFoR;8Erd_NxT#}GAO>g@xvV!ImJ3H?U1$RxuvAh=p6SjnWr!3W>%Z>%#Y{asJVbYW z^r0VoZ&%!fZI}2g#HyR0{QNqAJL&72#X(}&@| zTVr}66l+AkWFsPoBP%%;KfI=Frlq^O#nm;d*L0xnH_xlQLiz3A0-PeZi%|sy$wnRi z{aKWfKi|r0WZZc-*WRI|V0LoB0B%x&4fS^SudnKO5YK~~q6_W@f-?lNms!Ap0FHC@ zkImCiBFghb?;%CN$4^9w;$v8IkaL$}>Nu4;go_28QP#TRq^7vm+ZVT0TIeDATp7QS zyJ4HQ>|1dJT^(7atcr%M%iHbGu5J7MNm2#5t%XH(&kw^b(@u`uZ+;lAukbb2Z|+<7 zYJw_+LXvr~@fORR}|@FnQ`TSHB9FL=qQJoQg}*jDeB_&a?+e74o6&-t4`3=P7vI~X$}4{$rd zS08Nux9wcfUVezqUS!~{F|(+tRh)KM$12(L=(?Hv-0V2PesHj&Gq*3?Q;lKtEeW$6 zv=j)P?oCfh4DL~XdIKbM$6n5I+poq?M$IfPdfkkVtCbpTjuM#JCeq5Ur`wZ|kPMAk zqCxMMl$DkL)r5cqD>9fZy2i2AN0zq~vC>|Q;RM8jJ?WRBFe2BTaF|A%E1dkx4pPgm zD|o=xrRTOs*nwI9O1GeY8kW;<=NC($U3kSY{n8%Hh=KkbS@R5Nn3(=qSQjb5kyUzJ zDQLZLFWcXDs>l5uDS=MI3E0EQ5(2@u?*;CEns?n^wg3PtS%~N8HE&13!@rssZV`Lz3xAso?tEQ#@qXJRS z%XgU8X=ki7RAQH%YOvN5IBlIMN?l3AspooKc|h`MhPTsIkH$@s*xsM@t z<3J=#pT0er<)2kFHH2}HnVBHYmjU3zUdK_WRDP=`Mey?eN$Xr^p;xs!k?goH1amG3 zR-3e?sfqZz#C5-LiomWVKMzl9-Mk~ntF%xv?qlNLBCq3&aNnZ=>CSdOmyN1P%2Vkh zF%LfN`YYm&&|l{j;I%kKqSSqSK8L7)VRf4Jxsf7HT`sZ*LtF)a_4H6&p4nDY{0iATnwkdvZ8OGX*P+T-!_D|59 zs9$dz`NNtCv)&KGiFX~Ctb((iskZdaEV<8IPe1B(NFh3`weejl%8Y}nPEWi z{;;;Sp1wSuzFzd&M2p_&VRv>Ixv#jIN)GbFJc$OpyZlKGNFaG8VO9HxDJz-k>nztF z$FDvc#pVCbW!%+Ix_D^GAuyh78pa+4F!)qqWb z72aso-}nY>MIq+K$Ux@Fa2ojUJ`ZT2SF={cnK$7AMsv!|z0F4}$xaf%U< zCBn+6(fI?bt=(tnqb?u&H;1w!3)oq!hJu24!_!|9ayoRJjCD|C0@UF3mSBLG(dvJc z#Ax@jvfTz2JV(Rk-P1fJ8gMBd6k20}C_rY!nc`SL+ePcH^xFj|##G+JgBr*~K`Ajx za&pheS4pHFUF%+RuDZKUO}bg$W7yV z)jlq3tl0oYHhU9@iaSCWG2e#!JE9^j#7=IH?8f{$~<%O3U9x~M6qigGTvyf#I-b?O2-TDN;-FkG2QiNjc*lmb- zQ_s)y?U?7m7itseAZFt_Mg48^nXmV(*YYwhn;c8t6nGAb>HEV({x5xPQ1F!wausyz zH9#XjnVvceYEP`LV2VA>w8#q0L*8>I5O^u&><<~h?K>V2mW|5~H@61!>8}f4R>YEh$1_>~ynI$})wh-A z8Uq6ZsIb%4V1}=+FQV$rb#h4wG2d{f!asonL-w{SOakh{nO`S9#y;nLYndM;+}+*% zSu#{2LMeu)6MI72!_MKaB>Z2+kN*-B>)4-3Ln~JzbYT7800$=x&tp-Me(~yF%T21u zMoxbP3#{5NK<4PX`=ys*#MX@7*A2cA2Ot~vIp0n;$<)@|gq)0Uk1b<|NejVbt1*ha z+$`C@eyK7mu5S)0XozS&HwPNIt0rn*!P4k z7G0Lr+>y^{e|`7kc9+#1?Qy4#vG#xm2>axrICA8GZi- zM`&366^!%N61G(kBr^qg&Yn`8+6zfGZ?3OJotEqx=t?+ejlc|9!4R|zF1gU==4LrC z^dQAd$_=K^E}Sw5=`CUJcyjV=hOc2AK5o^hI2~PxVv(QR5`T!SqM0I( zS1BC$v3X6&t9%LbTNiZ5s1T^^BZ=740lh=pcLAm&0;ozL$4TJK^gH}geX z55i|V=D!eN{*A1NBep*siT3?uDe$uRx4FI20S4^eRAncxC30sZ>xPE;^6y65j(=)}$i2X$!aU2dZQhRe939yI=18aRl_VvcW~iU@OMO$;j_hXkTxA z|5WPzVG3oY@gu%|`X!8f9D?93HPjP4VL{)SqQB5B>pc!u0iNtTtm{r7-t~8#dbM zgOFKSkn^NIYm^H?ger`OwcbReWp33kZ`dyAf9|v&Bm1X(yXjHI2aD?3#*YS!PHk(K zA-ai*El}2-X11ZoYerk*nbnlQ%3ZRN=!$M)^~8E!^D9=T51?KxnFtmRMAg73Ue#nh z+d6=JlpYWz!?5q zNU&!mc=?xZRA8CI$)Rvh;qUM%T3i5Fmj}jy#aPvfZ;bf3)B~WYpis)xmK^jSHurkA zZ8Tqp9DXZl82Yd^u&(aaD4l+DIRul@I6c zp9ZN@`uyh~J;AE<(kPYI)pb2cS_tCDfwRs-`q=#4yooyDzzt|mb487` zl}~vU9$gVNG1A8rH28`qeJSUvh1%5>J?5iDzSM&#&nW#w{#N+u1)VX|QAA*n`GN#c zeP=E4U*u-~iySHF?S%UZTZs#{G9ob8iiEo(2oV1i`HUT4lJW2l6u*C@U#=klW0xnX zoAdsN>ln~`LKqS5(L+i^KyvU9)F7foM-P8g=6hrpge`JpOSH83aj2G(<1He1)3f{O&vIHijA-dyFWR2@PPog%sBina z^Bei*pzn8CKWF?;oaHdu3VRphtL7Jc#nw)hd^)eoaZm;w2{%sFcB6A}#w)ECE$CGZnVV5YNnyngqNZFozZ&v-)q$&Xyx zP#&5E@^WD{gI*ltbmveFp)Josyl2>zb;MeC?ap3vy!^O`O8NIhApgv)64t% zn|mKa%7~fg*P|6r7o#~}hP%_Y+vB=ywcuc?uwYaqhY2>tYO}>^R=K!$e_PU(M8eF3 zRkFlH_y6V#i|&6>Y1zF>$)t@>arUwwh{~{|kIR@hu+?|_B45uc6JZ|mmd*hi84u~l zJWAc1;R%;N@aFyUd#x(q%n!QJ<@)aQnF>dCrs3rASy~Q{Vokk_>?K(8WjxB-^97}2 zg*RvJb^wz$0T!u9%?zB-Fer5QNp^}yxy5G`DO(g!Wi7{qxhtzVd1Vdg5IL-WOMpO9 ze(;NI_emA4=Pnr#^L~B4rF1vGjc&gmwVxGxn25enV0y&}c(d-k_I%<#=9%}p9*Fsp zs&SwSBXW7S8NF8X)UHuxX)45fC9+Xv+RiarLI@U6^hmb z9~)Ea31kx3uK60?%)!*UDTg^%;YsMC9s1Our51lCdLOI5W);6t0V81ox!A}3Xf1Vx zy}I`&A&5y308suY3qoBY6er6&$%F4-Z(N!;G2ReehMcdDEiO{DLDU40fl#xsQAD%9 z{t>|&Qy);y169YA%3U_Qi?xwA#8q7>`nnU{qhTZriG*5)?z@!zu6Kwq{YK-$Ic9#B zoP*=25#q%OatX8759~GNX};Ki-6K&<5`o1&iI&QA^7+ znG=P?>L{28VcpPWEx(ngus~qmMilK@<4j++Jo+%J=p$@taCkFR(*~LRr?LmZ^178p z&0!$-*7aSXICn1hDjRQw^I8R2dy$%ui=h}lbnpSi-i}&QZU{N6 zx-30zE8VJ4NYdXp^dPmsYEhV%CZHX)6OmxfMEtOwEuB;hP67wD?cD1-OV-2VR=g^7 z8Pw`2X@f+hknXEbA5Hwga!h5Z_UvKQrq2$ns-+&0mlJe7-ZIN~w&V5X(Viiw6=nT4 zQKRD(WajD5mEs;x=L1Os@~2?toZTz!V4t*bk1c5?i;`?tO$je8^% zko@?o@MT{aGI_f5XIm^D`a}s@Re^bh_Ve+ryR7lxdaQT4m8`FcE^~*##|~$U&+)_e zZFnfKcR7&h-z;~P@9sD>i>W*2kUuSIU@UQwecPn=Dv?>{axWlI^Q!Hi|1jYs*S|a; z8(+1RQgU4*fwUp{(~4;uEuhhq$O^EN{E3h5YeS&OWMc?%aA!slmzZ8Utr4KEv22}p zr)RxIml;gGyR9W56%B%t+7`WD?o_&|02$oNRVRHB?snAWUtcDY#@YQXE~bZpkLAQ6 z=SIxj9s&ew?n90@2nls%R~~fze3apI{NN*gbyko$nbaxXT4*HH>i_NLt45fsWAL{h z5%KMP^{}1!$7p+i!4!|3nUyTiCXyoBV%$ea*zrO^uxQW=h@h|Qu-0xiVs>tp2;SqW ziku%k->!8eVqLUE#9Mj;VAx7TMMeEz5-WVRe@sEy9hc4it0CwBuS!hU!@vyyE+ABCwC>_j{e=PZ^HsKBF927XlrWDE;Lc z8=Ktqw=+7^xyJ1$*<5NSazT;xCV9$y-H{43yL7{7Tpdmm8sw`u9{nYBkWoFZ}(s!}^llAZldkEYXZp(<@f zNcXcPVfF9C73)vQFPDAaSlAlxpGzv>G8msm#h1Ge#FHDhvV=z+l_p`HvTtGB>XfjM z;RF2J`Bwvc{*M>nZns+GX5P#nVSSA4{5MX3f5)BN)NEPx{s=4`5KvV4 z?>l2^{YSfZx^ABxbq%B!(#n>*gh&+qoK>ma#ev?uv#!Pjf9(R79yGkgpPzrQU;CG< zOWpc^9B2;AZmrXKtSLQw;R7OjH%)XRbi1&r8ru?HpY7V$rmW=Zv4X?Ig0UYcQ>u@hXrS&O%K6b@1)1hCW2Fz;`xI$h zXFOr7UgmC7cY&=r_jY3Ia=@c%{~#5e1UB)ZdE z&mzeP&`P@;`S||Cr)H+L9f4Vs!i(g=DF06(OwrL?xI=5(UkPSYL60FmG_Yh zP~(dHKC6>wm0G}S07mn8cy_qZ$`Wt^?o>T2PaA25XHTspMNqzG;m>>I}aI z2mIwYu%BJKl_-m&{G>ty`aYR`t>;_8YH_8GhnLj7*6rz*)BirH5zpQ=txDeAF zdYbJNudUD$dRo0t+58sq+Ab7GWyz@jFNhvivLPrkB?(K{<+{4SDz*NG2TX|L5v}AA zCLR8>$HVU4=sc|?%2QBmtZsa2&s?qW_pWM8VJGIr#;C34&smqHbuc`D(&8!&T-_u& zFiwDw#TOuVuT60VF&2|*Z-evdcXS2Xp!y17D~T8WVnnJhj?eYwgRc?MBP!xc-PLSM zAGod;cd{>%n6@$KXJ!vCr%nZ9j_&riMficOE@>~kuq#FaleB?g4tMQmT`6`5c%Kmt_%C;b!)XeX+bVp%%{jws@q~|?8q64(Z6cH zN7{(+&9x+HV_8%iBKm{3mK^oNBp*dF5bMo*$JuiVQrFKFc0poeEL9CgJ{bW%&!c)r zbNJM?Bas`|B$9+l*ZwDE_q$jALon1O2rc##YGtBxmQZ_qe z(iaiO?ySPf>5zeI{;!CO#b^=EYE@~uWKoT@Axi&O?$W0MHr)<1B$eo|Buv``Qn49GmZsOdPn z)RLQ8>$PT)W1{=-b`}-#&{RQ<#S-_|Ksc$CXx7zx-d94E~-6Bm4%&Q`>O~ z#p&jv%g2weB!H0h%otVJFiQ4V;fUUqf%hCXYl~3TJG2aA$i1;~toT-NS0znfWzO^t zLINhJbBuALHnL*}-(G96G@AO+qcHgn7xsgwLbhOeelS*xp0xZk)|Wgt8C=Lnd<`e7%IA(Saoa3wxsFI!kOCi)e_Ez;+Z5QlW%|X(+GL4|> z6|)oOPp{7)%^Y?4bMrHc?2i^kM~jg_ZK2lhCWA7exb$dzv`a7Gch!iUF!MM}Er=Bn zCL)0cx>je25TnFFFuc6oTH*rkC`pQ1D0<5O}@h zWnI@>fAFLxhlSSk19RyIW{vsxBWtYAS_oOaxxjM2)Rj&|BOSj!I+C!CnQi{pCK|JE zM}vR$E=Cmm5p-)N(qLtWoCucHQ8@ep8q4Dp!Fv^WmHsC#)SvoenQLTs><$MV z8N{4~l3I^SaM;{+YiS@YZ5oaIU3t&aZN4+~g)UCrm+hifo;K~ecQf^cMgr1E?JpsO zkJr}(l4B`18o&yEFyYaw5uuvdNP;>ckRNDZGIKuLYZIYFG%LpG`mhW5QE>HVszMst zPWoXh1h4`8utdggYm}M0=O!PY4&cUnl6j7qHxfo> z=jwlwR6EO0%~lKC9v<_nnfIr9#3Ql7e~{^USb_jdf5cqS_Wh?0nrq#|QN`3u5F-y> z#uii0Ex4|G$(=Pj6!1jkq)(R&SnS2N`&CUwNvc17W;>+wLt(82ax~()tcxsd>O|6! zo;QAPNfqF=9Oz<~5~zQ57U&q=c&c|%>x=qK@gpWBpeh!HJ9WyYxI>m8ouHUp z(`mS`GpD-ICI^DNJw(4Q_lyCo%?TiY_i`~8iX*wq%kTVD-juQ#9wTQGP1pQUrT$cO z-}CVzNiZ8vXL>V3^WQJwlhMs6@5`wawv>&H^W&MJ4fow$Oibvs8ZyLi?1jiyBgK9cB&JqMYJ+Inrd_& z-^p8b=#A{)eP>SDxnyXoKb>PVPHx$*s1L?6@OwsQuVRzjV_G`d3Mv3P@qI)nWRjn} zp`;e|xV)@lg&lwC%1P7}j7`{7{(E5VCh|9X{g;zgeOU?Cgv}RveqcSuA?nLwkLyn@ z@-A5%do`6kZ#s63UCmgBeq+VQSj!wUA7y!pq3`PZOTzsCe44Cn*#F<8@T)9<&WiSy z47T!303ubyW$3V20Oq^$EGvz7Ak37L9!+_OhR~T8^lg7N&{Ss%W7^^X7^#RF+IZ^% zdF_P~!%cnpDT1@;sq#D0U$th#o(FX1AZ=x00a!pmdfoM@+*H&+oWHlG-%UJV$`&n_ z{;!MwBBZS##2Ya{WO~){0?PrXGrnQf%ZAfp2{WPnF=OrUfRs8v9=K7uckXzECeQj98 zEr??DpjI*p*y=;I344Y|1XbS_Wv=f0?zvy~b%9omylId2Hs{?g51%!wffd6%RMRJg zgp{iNUml;QNY8s$))s4NFtbN-ZCU@X1Bk!0XNgw^~ev_9whe&!wC2LLF%()*NZ^X+!V zvEta?o)zkYgTm8)A3CLeOj=ehZ}9~90Fik;#2$Y9PGfm-La6@5lDH<_0hLWG_uyq*w^G*U+~ST)6= z<0Q0thTQd@v=Vcpf1adzHvg#!)D`Zyu2NFMW!+lkrnyq}Q z4m;L*k2t;oX1BRpl9wDTLOU z@}q>>F@>aPpf`C%00ngBp>L2?S(oX*Gv?d&$NQKR>u}dpZ4LfO{*BX#X2!|}IpRR0 z`73BYsB--4 ze2ij*d}A6>UDo)W1)K${FWGrhXDm1`N1SHxjZ(`GhI7Fq6KN*Zcn>+bz1E6e?(;zh z9&PG+8WJu-zrEX0%@X~>fh*%F^;d*}XPdjZ$%3eU_&(TRH+`-kyWT$D;AzAEu0!|22h4qC=Pp2*)!d&}b9{-;oKGAO@T=WdWbAh!<0qP3xI~(bMCQL4 zgld3&=05~&Fcd3DAcyy(rprvE( zpqa#U(p53O{E<1yIDAd_oq4@>M9<$&$|z*9qYu2&2}(TweDFBPnabTI9eurtJ{i^p z-D2axT1O_JZ+_^imW!gFvd^e8QeYZIq8^b=cc&5N53;bK4}uD*RM7Uq1P~?Lm*va3 z_Eion<_2Gwi5ww*COtt}`<|^s^jqZmZ2)0JkiPg|mm@El%z5Dr0dFZu-6w8cj>y%( zFzM4S>3^%Q?o&HYI&1!B_7HGu-!F3}J7WNx)UqHy!KT_uu)9Tf^0TGe{UGzA3}gei zYH~UTc_2`m^#Gw)txZC{7P92dZ;chLqDUAu(Fxn$=ITSFgAg7biKX}J}liAQZ@7=ys#dL)6 zL=#vylvB$Ct<%L3U&5`)KLlvJosyWdbaoa9lSBC+R%xuO8c92CQ3 z{f5QyZW42Xrwp5xV>sX&<9LFJ{HRcsQHjlDt*o>87h;7I06-p56@;d?i?~6@+P&p%j8PHQ{<#QdyL za@b5l3px1EdVCe@v1A2^!v-kTrMBkqyoT|etl1$~^=aXRB$RmNRg-B}v-F$mM#?=t zvY0zas_`6i*A#H8SC^fqTbWy~^cInfU-04D^xaGC{ZC?foY4s$e{lJXf+uAjU0cL{xE1)(ksR<_jCXx1xIq-vo@eV%&NQ;&2UBpigTjO=lK!T>!72}px=R9#1$wHjDxpP6hs98(=j~JW!zR44feGa_)8vf7cW9&iW z{pG<}LbuwA(z?8@=tMQekr)7=S-nUKkINz|`-Lcv_1_%sn>ZBfHqCnP+>PLJIAIpZ zGE`xo0q~16E4bWLvhe6tJj!rM7saMt4abm0LK%_n;yq6~JoadMcl~JS`+9Z_`ImD` z;Kaa6&31P=MIggJJNITs%yWA!_P_o60nEk)#ytXqlT?zS;M{bU#p@ia!}mfLAm@V* zsj_$!pOa_O?yBxcL4E?utGB&|Jr40@Y=^1la#keGGryr5B1(ZS`~#Czp{y}Of*fWY2}907Iw&U9^yW_KDnIw^=3{~*c_&;y zeKEo?0XjpP^YT&Y_Q=oS<&7rnBR0Y{z(EHoqy2#T>d&TK#p(1fRKF`H_XRdx2q{vjB#V95DL3sN`^nwygR9CDDJm57cm5E1uc@=& zw4b`TKBoOxAKkFt9)G-WrXhc?RwfSBWVTQgg9qZWA7pn6{J(D9Lcp|a+%y9}E+z2# zZ5wr^+-i{o7l+Hxq~*uq`=G^;E%|ml2|{%5(HsAdCk{fF(=SC~W4K{_G~yjrX`S`) z=lLh1%dm#si%Aa{Ugd~b)`tOOdI|hx9X6PO?694)*7rGPRnr|duEwJ=H_y|b>Kt_6 zqMY(V$V49f-oT5QiD%Zg7Dg3Jig%2cMtJrVwN0Op!3$x|+8?46|IL8h=R|RkxXJJw zA&E&R4wcr*YBK#+{|O(xt{GBn`u6R#L-3w^@ZK0ab-i{tmIM}wZ2f1w%{M(GP(3%d z)!NJO7<5vFQ}F##QJDynfl~%3B*imSUi%T9o5b4;sIL<2Ky4fvG=cA{22PHya=`)` z`5q)H5||)9{@+e|ADx{U01-EVq&**|nfBZK-Q(euSvQDI61jt9hGvxa6w^T4{=QP- zNYF;uvETJL`Tc#*t#TF#ZNxwc*X4D%xiRjs8hDh>27)h`(2J&Yjmlz5B^#a2 z72NtC=n1_El9>6w|LKF~a+xAVBQRfciUz0| zQby5pN3Za?Kh#KfIR@QHsMb9c#%J=+fZ4iBXNZVxq)nK7_QWx$5l*0C%yw{49GZ5c zIbe1?hX1a_t9{dGsP-7Y(C!p$UomZe2trf*_H)=@BY z9fA^a`<3n@b28gFJKY2+T?8qQfJ(Rks{A8vvsT;6^Egw>!ex*8&iRmL6thMn58O@- zBxBS5^%=Ar)DgXd*H+%E3dVp;?jud#{QRG=6EIhhMSZYSJ*!d1%_E(umu@mlq_ay8ZsSCDs`4VOwQPV^>& ziVp_#jl1hdpnt0VY_LY@x+$Lila4-3eL#Z0q_iGDc@1JsR%3z+Tb*qDK;lgh@#!xdj_CRFzJ&%zb0&FDyu!G zMpc@i9w{6h#A~NBlf_ugU-ixdsz!lN3W9UnkGfaFeEB>-kuqu%mPhgx=;%}liqvgw z>qs?q@wCKwMen(lHZ8?WkM3`7$SWA)yj2%Y+DXjz6Ddv;4)y)P@p1n>1qoTM35!9N znfI(>XWyN;59YwP$(DtY&3jCu!+E6tY2%tmQ{lgtIYp`!a(MW&+I7M&?_0m)g~-e) zDT8V=Uhlj%vyV^zP5UCaCVA`e6k%Kzs=-xUM#|8-)lkj?^-TwdpyREsQNa6i+Ake4 z2kl=xN0BE!h5i$19f&zd&w#{bA8_A{)g{tYR8k#o*!Gu=!h+RMB{y+!gKfEX6{0h9zHPO|?}a#1bLS*czkWA{H`KXIYc7Fk$%?4L zIh%jSSUSJDA&J_|gNs0psGK>VoXd;Yc*KQ#^tKKnUAYgb1?Uq-3v zkQja4maBye-dTRY`TP;;7^fk|-SPeL4_GEGWB7l2TLMV`7hi7~7iHAF3*SSdq_mVE zAf3`F64Ejt-7P5$-69~3(jbjPcQ+`d#L%5mLx*&q+vhp|^S)os;WwX$*?X_O*0rv6 zt-W$?gZwe%`PaU^zN;M(>Dc_I!NNO5N$A*xR3fbr)Y%Pn)T0y&u*eC0hX(kq<-=f=#DVq8E#X z@G+Cm#uVqC|6-Fp^JG_obh>=l2fIB#x)50?5W$-GNlX9kZLViuOM)+bX$7b+5zM$MwFWD{UHfD@qAF$lOTy=ng7c!>ij09?QpdgZ1OvE7^;6HBTkw1@ml zn6qO=U4{&(7hr~WYr2pF!~{`I$3$i2+lrbE`(UXdpa~P0lYSV-I3mZi0C$?N*eq3wMhMj**pOdEcdvhjAs^1Bp#~u6P*(a^(ck%<4*NVap~` z6!KuWWg3kpe?kdmlj&fS31GsiDuV_CK_lGNBwbIr$gSLat7}g3GqgFDctkW=q5)*+ zh{uLihwWoRfmN#Uq{$!HDv-eEJu>tWT9f&vi0Xm{A~ek(^&xwf!C&e~4=UsrKL)rA zxfRUh;*;DC5l!$Vme7Xr)3cb@{5cQ<}LE8PQz%HP>c>S{*!*m`h3@a zWHdWy%CtK5_s6;ye$L0chmcEifCo@RZ6tZTFLZ!<Z_HrKuEvE+OF;NSj*c)w} zJeE&BFZXSC*~l8+h6n*46k|T|Ud#QD7C{z#i}ni)EUevVxmWzjkl0cGvRpMB`3d*D#s%1M}i> zk6g&eT6x2S|8tV?=OyBqP{oi0D{4B-@SsGzE)~A~B4dq;u}$U_21VHjf*QryK>FY> zmOmt(D=sDtM=$H7R8i+}Th3or+=*uS6mg6@D5)RForLPxf(Fet4jOd50KA2tiRM&%35GUd1l=mWg53RujgWt}d zXS~|KfMoL(ila$kj%a`6xEuq1vhvISqoNI~y7n`;9RISdg=)5iYR+FIZ8~|iah&W- zmi_>?%6~2lWti^MBuCz%RSx?r%WPN0z4v=anLf`;Ut6!R^0S-$KiI&_B**Y~xt=qu z;Lv#Zdt)YR-M~s~vZ0mQf|99X&szFn46MCHw9vFN0khzxvH44V{!;rT*BXs-J|m{m z?Gr3c0_lfd!=_nDLkBEzrxeiHYOhxmKfny5De_{lFd@$fuTVt-jl^GL{ZZsGvg~zN z2!r)HH-jZM4+|WH-|;cs0stt>8k66&qHXhVmt&E}qAK7I`R5dnAj{n5H|u=`zoh`E zfqSAQukEA1YU#`4{|Z$tVPPW_9?sNcP<;9cfv8|lAHX!Vd<}Toxf_0NeHeAirCubW z!g7HNjunn!)Nrb8E{{eexn#F<>eeqo%F2|Wiiv#t^35m@9zgbZg+GQiOW;98(sjL- z;zhU2X{2(_+TKm#T4D7{tg=ve0Vq1Tw%+4?qd577ouB7P7uXn|2n~JprjMzkm5U-+ zG)`>q#B%~EbuMLgHvb($4$Xf8eh53-TeN!K5{5Bn6!ARQ7l_(x-M1JPIt~YHE*-~D zf=5KPDgSxXaEecW6d>`oL|}c{sHe7ft}7(YEDns`RfK(QQ;bbwx|WYAy1sAQi;4yw z6i+$^LeYTYe6Q@K>)K<~unY&RnM3R*KntCi9Jek%7fVXdL$HFc3 z4P)~Siv*$Dw63aZ{vVkWtKqF~)^)Xajmb8O#<#A-jP6M=4@U5TNu}aHHHX@7zYa$s zd)x#Sd4+rjPKG5PmebVx>VgP?v;?eY*R1V5paj)B=i1SDIW>-@ z+D}$dz_O=;QdlS&Qh1zVJ|e`Ckra6sEtHR8;pzD|$^ioW4HX7fXp@$+qJDnc+0`AF z0R90Z6pGp3iFxo-Be24UVimoJzq#QW(?d7+w#+Ag=dEANEx74gWFkvDzj z4#S9f_p7O;kHam&3uA>(%iUaaDm<{IKw+74FYlV;;IN1hX{Q>bGjVK*_)>qa%Qmae zD~l~xLZdj;;7(2|jAO$}6i1-o=Ss|yE%r1-%)~y|w$gs|L$z?<5JC?zn23WEb;4R) zx`MZCTDMn^5qOVPgOo(=iOu9))^2e^5;`2F+#gjmvak4lnt8HARO`J`nSi?xDJGyv zPO0N>tSrN#6yX56)rj#Jk0;)1HDE12?O*Wzzt{33v6Rg*>O$&rJf!R>yQiS<32gs( z7)WisVZ(If?{9TTW(&Pq6pEFQmHKsVd#piJ)g1tq-Y6$G^_;WYW3Z?l3>nj=$ekGY zVwWTYuh`Hk4QTu-MGO7i`p>n_{w%~m-y^L$T-B71z+qiQAPKUhPmh&9kbgGO`0esX zBbj7fv6EDbJ+z;olL3B;6DYu9Spa3#%6loa?fpWvd1>t6B6!_MyKHV6(IvqZac%I! zCl&fzZsHZBizIiMBv+Csct(pw8hB+xJ{w^gO@_lKYCb#bHI87D=DhOpE`&aXs2htD zJKf}>L`D2?H&6iG;8V~I#?rq0{?84@*&EQK!rzGE-`FO>O277XkTQDd2*A@LnTOD# z+>=$tBjwde00GQ8%AME}@=ZzBMfcaBkq&`IddC5D)HdEf4U9E4<^JRF?~$z8_|ik2 zdjCf3Gn+Jb!jn*b?T7AP3I9oozUWRp2y;idE&7``HLU*bXv^Wgz-AsvS)o;xuX9>g zZU7T?fm{m$yoqUud9z?y{lSpFR*r3oTCeA+dSK-bvwI?qjHLkMZD)~NMQ`gmUk|ZI z2R!gO|MR+ z=6IXu+m6If9FPZm&)$k%$)cNS;(*P_u6<7a+=4gDTrYz0NNf_T_kqrY;`6J?P=D?x z3C?SGgO3xW4X&3kj>e#{g7kRUbYOw_yXvNBifL!UI10(!>7}NxcdR2>1+K zKExH$NZ7emeRF>#7W?pbRhzc6;oDnK9zph%Z!bLviWV$&2{RQ5KUlWfwUNtw}m zb5n!{=+>6*z%!Bkzvc|tTVIe&9n`^n$%waf@zv?zlAshpqYp|g$*IxIy(Zfa!_Tin zqx+tpAkrh8^*tdU+*+0mFYAlJZ&s#D_CC~|yMR;zeTLYe>t$X0cPyVa<{duaL(AZl z=)!0gl2}-~DB|kiAUpI2l!lP*D(y!#iPgUsPBWa_j(JTbJ~(0-TK1k7j8k`9eY|R_ zXiPV*-RQ9)){P**0Gxls3VOdaT&k4ki+mnqK!f}8@62?y08KQ4aK=;nMU>WP$+2HB9H zFdoPksz4l#t!!vq^hmi>OFajrRAIHF{dz9Gk+o{yYU~~}sRRH@@h-oYO$ee&Bifta z=rbxse7qKM68cxfpo{W3*7%fog>>M&&_H);Ot|?qkgBpQwrpOMU?I=aQSbsxj4V$f z#RQT!Jn3IFNXD=4L9o+>J)Plr?~z?bYOV~Vs4EUJ!=IexgruK5#(SX|T=;%GGecls zm%U#5B``;!*)snro9lO%M6A3Yo&`RG&hwp@iHfO&YK+hQ{bG%K4pznJ74z)|skkWR z>X*5!9XLCVYczLYX9U-&=Yz?dB+?xua!NTqBDMw($mSq(l|ojuuO{Gz$*{Nx@5|Ok z?&5tKRkt=J@*g#s2eL&AIqR00DW@Qd{{L>fgWdnDpl*or!UCbm`Wj$k4DyWGQM#@> zW81$ex;_$8RXBHXzbJblZ(j8E-eo(RZsUyj*WS0Rrd{jpa@v28r&rU~8MF3Dfq8fh zQrl*(*=~mrrNxZoMsJ5%+&l<6LI-GvN(uJ>B-I><`4185T=>dw-l)M{#DQA*d+Wjk$qBl*Yv1W5<={JLfa*3&TbIH78@VSApMC7$ zU7-X%snXM#E(dpgM5$!%B(NI|bgrR@s_`VD5I^LJS#81vkBS;?X)JKi}Z0^Sh{b(0CUHy?NcPAH2`he3iz zuIe{F#*Urp86fSjmRgtoS#Xl|K@@OB{~@`H$8?XbE>uAKLFRXO_4|N_MzR3!sqk$A zhfn%cdO{tSeT>1bZx!d0@i`2N9>HJrM7Qzp^ez=IlakTb|E@UA_Z&gWs%Npu(#Ern z20B6V6Shj;IRJ;RRywTp;aEI^Bw>m)1?JcbiYn=+tIyEnDHk7_%6kW^7;^7`m5^3i z0sp0_69c;^>9JZVU>W@5kAuQxBc8#mU4a+>;PLY0qijPkiP-tXs%ii~u}yQQK!ewu z)v*NA$P|{@#e|(tObd6-e}U8{KsVTL^fTHYBvCu_EB}sAg25%Pn?B$@VEU%;A0|Ye zY4)j!-uAzLZOZqKweD6z_D%cGs1C_}hD>j^uR=@3PwS@WgC~F}Hn=L0G;hLK6v5B- zbt10Usa5^QIxc9PSe4xC+&x|OoRV8gSRH~0uL=$?+V~Hkjsj!G-jNg)R&!Yp*?^v~ zK&(EcWolJ#Gduf!dVsW0-Ld;>lf$R9rGDgYAhETBux}zTCM-*==8}thI4a|89!aI$ zunbbr`j}}H;1q`}v?EKvXBcL@D2X{z(|C^xT=QzHwDj913^I{TmurGq=oE({3r`{y zcx%vfhvzH*{Q0(@dT5PEzZZMQ`u?%kycEAA_YPc6A3XaNY_QCto%&DT_p*#d=oyj& zSs{x0V2lzNyb9qnN~u{ai1GfNoO}^HRqmk30x?O0Npz7%vcW|Xp}&oK8aYWJ2}l-M{WL}c5O z2EbB8TH)vqzyI%kkVuIF{(#AYrPvheQ3dJrGRl!{{C{{3`ragWDZbg1c3-dyUSA1b zrb&kfq{rnEnp2+R8l%g-SXUqPyboxMapKU0&vQt&`4d^?R`1DeT`H zu7)P%S~T~%x7UEvoe2Fo(Nw+ix0nOfoHVm+wx=LEMQ9Rb!*i0bzL4lRB*TELy4 zr>L(##^aXkEXarUds8(o{bg)wDUi6-&Yxm`3jw%UMD-0~Kw1HjT|MzIYuy`a-%+*j z)neXbiC^$+0Il0q=;7^W@n-MPgn2;fm27+rtanQNISWl&yWNQ=KA-271g;VhQMT?GpB#6cuJr^x`T@b-7k~aFX@Y+kk6M*5?)aC z+Lg^lh|zEk%z%$|XSS0&1ma9*Dp0q2!(d*# z)>WJUIZS-!QS)6W==??g35rPpT0;X+F}n!PD*1SVIU=82c8GQqz@Nhe=5@IyEj7$T zs6a~GKdF>s4;rX2G03Ns49u)qxJom8~9&X6#y8tUs$o|1$+;LXx|fXu>-h1b76ydnZT~662wVav4yYDf#i!IW^lVt zwjL9;=|~C@>s$R_9Q8*HwX&WbPIV~QM_;NmNcwvtlM*xP%>FUh^PfUs&Gb8%NAbPH z9~^eEZJW~TMnX0`*>(5vg2G9YzyB+N=3AkSWEt1r8&@9vTsKR?_nDc#bOCvg1Tk|< z^kB3~9JuRaJcq?F8OStu(CzhW68kQNChz}=p1g_0HJFvtjp6 zYsr^LT2=PiXOcTTUFF;p590h_%`qHYkq3u9txi8- zpyD@a?zUYueJ8W@`TRIh07dqV<5;~lD5IbJK%Yc4uf@`8f$b%V`o!lE-$m=f(w$Ao zu3p8e*wexa)MBvE3r0FLx?nbVJ{H}0RFwcgEgj}rvO`gt5JQpRe`mse51)=_K}*G3 ze!H#~4rTb?F8NW1U~e8sNf{t}II6h6qVBIA*2Z3Z{RlR*SZud|87&fKU}V z@JxNWp+uvsUtMA_vyBDG;!ze=HGnD>B_z)sfii&nG6=}EVkVVodzZy8c8Y+W8C%*q zXWa@#dM>Zf&wJKHT16jUKs!Pd@)LNLy{@9L&0oW|8Aa*SSYgmi4M0tg-lKY^8(%+S z*cYSXte>1F>6#GD2Dh#}FaZkr1?FF59)=L8Z;o6csnd8=HS<1^k>to7oJ%?d1b`k=|r1w;j7Ms`{9u`YRSeuCeAeJ zqgPKYXRA#uV4-{xyS>#Uij)&v7Ul}_rVWgiY5`?I&yN`x!kqi71um}ci?pWytYtbd#z~lS$$ssTWlCyT0v{Ze@ZjGz<>Eok|}LgZBU1h?s~)#}A)i0pg>o zUXr@Y8c#;cQ%B`=x&09CSRwI`AmBF_=HpA^`$TSp3ZP~$zA9@GKfQUPK7;&QIIQl% zH{oSXS(ZX);UoPE?7v+c`SkQ{nl>FHU%ROlTYkn+>gFZI3kgC%9TYTWx}i@}Lb7SV zUebhp$MOI4D_oC&?nM@$H8$`-Nonm~0}nM|?!QY68fgmA%?YGFRt27ymAx6)>eVHc zN@m|p+j=l~!SW84boZv=@WBq)`fG=ug9~z@A1NQh4!6sb9%TK19EQTozfY{QEA}Qq zc!mW}c&j+{hQ%724qN)oXDI9JPBUjHs>@IJ7ccBRo^&b!D{n1bHH-DfzAh)PfN%-K zpcZ6-ayZ5eNt%XQ4WtlB5$p^oNqMXCf&zrC;7({bKsxuB4*z(6HL@GqL zmEAG=s-ElreFm+;`tl4bFvYI1OW3+Y@-6zSCoF+7kOp9HS+CA>S8KA3mhLV3-kb+5 zKTWZT>yPPz0=}!!@Mk`VC;NrYUg+@)SODhEo_cuyo|9C+^m z_y;5+X?3gaOd4xc+V;K76iOia z@ConKoCN)jGh(~8rc8G0SNd-fImG&-0qsN5^nufmL_cu_F#~YC-&Bg}a#Lnf`j;Hu zsr}zGAJnXa1ounc{Q-#V{PK%Ww?m$hi_;RzSy#`p4774AH+ZYm4tR)R0WEpg3`tKH z3yaV0UE{Iof^|xwnWY|B0tuu@JKO#-Ibxh*L2E;R#LP@4`jN+fSBqV=@s+Y2=J(!; za20wMY?|yMh6c!uZb=hNj3H`g>6Zl~UmY#CH)N5oI_EZtbQrExSu_y!zN{~`>08)!25hz#W>{PdRp(zmKE*n6 zg&B{gu7Y3CA!)tHiH|c>UQ9)%=PZHlscVOCQ6@g}t-CQ-?<3=*=VfZz;sLIs!%?c1 zuspv0Z`QdS#t8?HcLfC+%nRjt5$z&SZ|z*o!`8!Ok$4N9Azx`MI;{pm^exsvTGS(J zo#v>s_do|=vayQmgmJV6IO&VFibp)a7Pm1_~8KMiTIEoj_T&8y-yEQ zExUBOo$6S00?9nRkQA%U_roU=74P zG+23m@F)-1QY?hl_9XVdfTFyYFaf8*NiXyA1N_}a7iYZC!Y8awwVOA2C$Fqr#lDFe zsvE&ckn`-_%Cg_sD~7q_ha{o3`Ol%=WI+0FtBNhm6m2UP)?(gm#ffi0MGoH<37Q`j0ZXt^&bq& z7re)H>-KI-{CfS9v5{d?NAKaxY+4RU=>D_kf+) zFN!&1mvv6>Q+o(loMABH!3If;h>!5JtNv996hf#?ch`|jpA(ry{ z1j-b3@`{nNq&gE)3c^V!wKKd4(Df!bN-b*csPBDKEgyW_zTER5Eu<;nm+*}iip%sn z>-NLPsX5ebzYg6Vk5|kZmn`Ivx1`-fLDj@HSQoGC+d<(RnRJ-IX1Y0iA`0dxX9rRr zeS?OGrh3|8b@ioIMs^!H`IWr`oE7<@cO6_m0Y~#spMA8i;k1l^q}whS#l*f_0xgyD zGl|L%=kP%3(I$J@BJO=<=~Ao1)m0;uN5D#gx3q}vM_A9Qb-7MT02`37dij}eFn~(k z$CcM1KNmJjY26!8pOQlB?awujE_q^1d$4!Y_3Of8VOj=@uAUcZKq8eGhIpc00WF@^ zn~!rQq5K#wblj>T!Xp@{;3j+KN0hq27;r=(!fWjk@|CaY=ikD@AXsX9Wp%}i@XwJ` zdjn7T_C%-0(^lS6?4DcLHx}^y_;x;7{2?&BPD114b`*U-R@EAohvaKIN%HC<72Z?G z6`yPJ2f`9XM3Fb~fYe}mgO$bT2|n*iw55yB`7~=DWg_@gVE1+106Pb*QpVT?u%#>< zNg$fUn0DT&jUwftvsDHqM+beBl95}*qv*zFp`4ukG(kY`@Fba0>!bpA|y63IIR4cko=>_O)%omF>;;T)v(kmCY5R3g;wo7D9G$ zrnm|J|5)&e0Mj*(#_vOFl|&$zt14tilFa~u?|NFHauq9LH!PJ%o+bk z+x<3A!u1?|EA+Sh!DXi2{ySjgo6P4#SioAsTqP&mU7efhWUlG=1#$ek{7CD|)D=>( z2YlKC>g%?Q8EnpAg84*P3}g@`<(=Y22JB96SJlozX_K+xXV78nX}6Ny?FJ_|W^*EI ze~ISbj2Dn}v?Mtl*VIPXvzSoPRhveWwLejAH!orJB*^i>+14s;q{`jSePK)qpku;w zdOKQt7>l^R-ws!HuFZ8jgT|^1YQFsK0`cbk=Ht4tcIW(eVRmo~e7tG61*KE6{G{S} zFo79rT{Vya`9K|L3eW}iRlP*LmPP@<+3$-$v({dggDDMNH=|;L1U|b87xPm#Sawem zflkc=#E&yR5yiDD9C05oOA-_FzR$-OS>EjLeBbNj>K?$jZ$P=7)^qjg#_ptJOL%E| z!I~sGoz;Ee$|7g?_+ z?ym8-a-13-%n7W31Ty8e7cNaxPJI@&=S>&<{lR322Hp1ev?3}zNWn;*`!^fLPG?<; zeC~wumRBChen#f;r_qzy)$L;F=m2%WaVZtaVHcF_Byzd-9=j8N_39@t#{RrLB8Yi zxbT2AavJ zeVdl!jjNNgrc?5}`COID`+h4bi(1_HS8T|^RY`((?$*^YUoC2V|db|#%r-k?f--&$8C=W%9Hb-%cOci2=TS(YM-vWsV^YdG^@pJQ%u zrtL_rt!-5y{#N(4vd=eL*tv@!LpYIb@k-Nr*nB%5j80#ZdOc&B3^=v95*5R8v3kFs zuvvdUE9z{b1GZv*-X$6w?-rpoVWBowDLeXtkplBb7JNeN{JOYKTwFoOfcN(YM4I16 zH&mPU*>ZgJDO?Nzn!TvC=|fo(ofLPvq8hjTjPlF-?I8XNr%YRVeNP1t2t`a~M$|6h z0@gK6sBH=^KailAzFGEuJo3rY^!N%baQVIcGTMqapbcDt%@v`@$l)5DowR%NH-B`) z>IuEQn}B`O?KnZYIbC@JGr5)&#$blHlr60t$Z;7Pk{4DQhZ7MtxKD^COx{Y zy*9_q1yRxXOUd%Bk<|#=+hgtKYuZxpr2(TkHt=d9NA#X&4R@Wieo5{74_I)V^NdM5 zms3>u%LFj%)zC`mlVo!-xLSb8!E-QDY~YtaMp$0|>~GIxpyAZ3;j$a7JTy>KQvLN- z*WIUvb7?-K+evQ@om^$~M^CDg@yPv_tAL=lM7*d|Dh1#jciFW4%##lQQOg|w`a3Wz z+P0x@(Hd6WH2_N=Hv!$1HJu&LdrmRq1dKpQv?Ch!p>R^^+OM-DHIub9cVK2<#_^YJ zQ<4kgNBGN{+L9NKsRPcNJ$L#@ehs9p@B5ja{WzKZsXW&-wN~8I#0A-kUfaa=aQ$=5}`x@Q{7YsqRHQi)Be87vZCZBqo?B=l3T+h#j{Q{F5-S~t`%yu3ixgsA0b z?7CRQb^Gk!UOm*Xg)hxgwV?p-h9AZ&P7l1Za}#_U!RZ=BD@To)W4g5nX=jr;Cvv18nIvsLE^%9eS>U38ZU#L6a6No$>#R z5-WL@d(+X3IKie9_yS3Po~AW;m)4{$EYGW77v5qUX&14+}Lcp`7-UeBhd5Oa*NGt63i7JsRDlGWEi9O%C_5 z27$?FrlNo@r_^-qV1Ut(MgC=~UtNeGij*4F9VitJc**(7Q6IDT@l6GI3_>4#7hR@n za*Jwaf6BKn&E9w_`F{{nivnRz=m1Jt zLO-eizF6Wpre|CZf7}Sd$K=k!Pv7l%yo+a-Cj^M- zJ%xBOEy_q6=q#}h2lTprNyOWFm(J`aQO#B=wkGP4=-zq}+Vopca&GkE3e54tFbk)L zm2cl@Yd%ouIeR${jR#y!mbb?aU+Jdqg5Ph}#RJ3b{OgHFzteRx^e!2!z)&oLZy!Hf zt3QwqrYz#ayWrKN8|w`Et0q1+d7+PvycP+104*74~NpV-6Zg{}RTjh7sks%J~x5VN**JC8zwGQNF-85$b8 z`}!{6;^NL895{Mh9+`fQjD2VOc$90&g__Sz;K2XNVZ$ZGHhU_2tuMBO=!NQ5Obp)Q z^mKu@xA!W8LVg^Bm$F@1Ssf^?y=gxr{OXQqTURTqL#DT^+}wm8+;`9l30@napL!z} zKNz|lH1|C=jCDFUK9x>@o`#;RJ;-|yw+0xK0?JJW zM*FC34gn_T(d@+~iq{&bNBIvk{=rMZ9;M&x5@GQ3l#4Y8<2-3LpwN{pBfndkf?8ESTizlF)Hp1hZ4zrPA;qM*vQ!FyF+aHKQ7Wyo`SxNz!?Sz!OHOO~h zk(*$$d}5Fh2qO`1C_GDx>@#GNIJ0QKbQphy@%(CaP~QsifDNn*u$F>5qQvxED42lj z?eVEAOkWA&%^zMjbqT(I`uKM*dHipSIH8zmq!&{Jy#{MyoL-sL->X&%3JRVLm)YDQ z(WrT99$H&Y`PVYwOD2nozU#lfqA5LwbR2!eU%sXMdg2IqyQO@Taro40HGd(4Ruml= zRK^!S{UXWFPeSljO$OYLSDeWwsk04J7=W)8f*Brk7K5vRE=;KY)`~Hw6e`n*2OzaBX+~uME>6&RUS8?pUV7+ z9@gE?ZgMsT%at0*M>MA z!Z4U_Q-;g;0*C`oy-G*<`ue(9k%fS8={Uy2x4otY#+tiqIpeuQigGD+0hXdJFZ|_x3K-K z_1W7p0nfYD)BTUI3Wo#pkSmz~fqCpXj&C_hfHoM_%xYft!Ums=vJ-BE7iNVuNAV1- z32(Zj@)>+)#LRGZOi2k(Uh?sHsUm}Jo>U%0zY1%*mF;sfcwisYo`Qwo(`SBIQCI4L*Z$#UXz0hnYn6y5 zEZ}RdHEX|qW5P5MA6coNGr< zdQ%Om7Yr{X$(Ec?J3d{>Lx}S_@7V)m9Vf2RLU!i%8{FAeIsnhUh|kvH$M=t!Xx^T^;uxgnE{$7jYpR&>5FAA%lZcKADU~xkTPorjYP27h$t(gK_5qWnf z!Yoo3p`&(b^DzI-xL&81Zsb0;OD9g?bBmoFtHrGU%_d|(hZUA=0?#+uHv+bbrhWQO zuELXOI!57p>5+9)RdoUwUoP8eDb3BSRKZC`1{Kq57ws2q7*G>pHfAJV&SKD2&xp6^C+a*}r_IZ;a#A zadrZqCC6nAOHbq3bLj;jrd|+f#MIS5QRwXIuTiJ!RVN5wb}NP&FjD-L3FobEABQBO zcL`tmBI5IXKSc7|!RRAe`y_biMOPUBmIXNHMatf%jKAod$C;0Fu_G#ynd)|@Sr3myoI3{j0~ z?+!**RZCv+8+{_Mz#{V)gnVJt%68PENHefgi8SeEH?ablamRz-Ddph&Cz|$N%~)q& z2MlqG!QEyB`nMt9FaRo>1@TwltIZ0L6JcbJWe+3r29+oRE z8Y4SCj7xn7d*jzWYaLh}In2f`Jp5M4oF4+eEgFykj4C_-9Iy4AO-yKVD#Qg%iKxk3 zJ;VAt#{Vzn%M*gnFpE&=NXR@3IQ)4^qDIn^^#L(tQx-Y6Fg!^89=U*%ri&aJ`#Gk( z0-OCgJq>G(<0;tDJH!lhmW1{A*U-iHfVqbO254_pxL%podCU>v%qRqe>M}CwaSS|3 z5!|B#ZtNWw=lWR&{(+|>Og*G#@7mAt`IVAoz8q8T%HWGzbj>?8QD(=Y0>Zaqfo>$( zdGk(pgc$ymQp*vG0ew?#FD|~((&`RZ>F@tT1Z3p9KL$@nUgKL@TU$R?h$9voW@Cmw z0{%|vHGS6x534!2UzBj>k^nfB89ifO#fB5}ozMr$f^|%=xJAD+i=Yj|>#Zx{e~u4) z9aEc1XdX!^-O)0tt9DveIQ{TRu5=E8IJld~ zufc;56hY%ba5|y32wwY2my!^2cu29Cu}C!liL`~2P8}N!kZVi8WCkDQa#l%L&b4op z)?%O!z$u@(!+*P^W_{R-oF(m5dDmp%-YC`Q+GKDZ8XEe@nTm`zRe6+L1FA?;51UQ{ zf5(7e5}R0igC077wufi5=cQ-&*knvk6kd1m0da=P>G&ol7kN9$OXco{p9BK^bumikJp5Z4$KaAcOR>z3lZ{1 zP!1<^*hO=c=gW}+Zrr+&(D z7GLD^&ZJ$!Ydl~_QBwDFaouVox}DmP&kjE@DoN_&2plD9QT;RN>t9^2q-z1bnkkW& zy)v1|5XGWj!eq8(d9$;#HV^lArsh*yNm3gwR7?K5#DV^R4|#T(>s9Ma=IYihzlkk- ze;5AvQ@NqAIu`#^B4qA->^CH8!R z8K&|Z&dSQT0R?4sYl90)GJZ2bhtC(CFXuMTgR%@SE~E2RiL%lzZl{4xi9u+gxkjssgCKzp= z3OP}2V%yE)As!@Hhe!WfBkNi-DOJgoJShRnNYAQJPzw@7PW;27U->roff3WVPn~d% zaxek>4;hYV;;i9sn#J2o+oaUr=h_kbqFXR1(W9^pPZ5Hi=iIi zj~tzv!?WV-SAGxfro`s$UicC>8eoypbngHuO~{YT;KsOCKgZrFfRPW%fNoQ@&);Xx4Vz)-7DfIP zis>pZFF!Io^wyU7`}%hXJD1GXiaV-Msu?LA@H1yKzwSWkz1(a))j`lt501s0`9{#~ z_gnTgEE`2M-m4SDlzO@=cRy$|O?xCqy?!q&;j;b)R|+@!n0in->vsJ`q%)O=XXf-* z289=dmgCVZ!33)=(H4J)4V#3>hzKsH3=7hq7fdw!zcAedNj!p45Ef>^%JBmo)z_1@ z!+&ld)`cZ)W-oDpqtZHIJJso0j}UX$KQ&n$7@b6AV*hcZFTf>j5E241GB0=2A7{XF zc$fw^Tug~&|BU9zo3{>nMtOAHERy-y$Ui?K7o1l~wWO2&%G$nPZ-54Sy>{SvaG;j1 zz&|38*tFZ;5$V`*W(PO|A5=6&ANGaHf)QRUE(C zHd_$Z(CD=9r+9bef+oT;JH-p%B($P$$> zClBB$om?kdpD%;MDw~v<#ua2h3ZJvgjk7Hnv~R6q=0gi?r49sfX%kJSvC~9T0yGjJ z1u;3f`E}it_|%+Ahi(2q_rRJU=_h5SKBt%aCDU0MXKfuSW_K^v{Qu-c3;oBao`XiE z8<&V~Rpz#+M;U5L&dJU$6K$L(OQ{~wXHc`J#vAnddt@p;zxJ4O>35**W1&Gzo~UC} zY^r}-)d26-boEu{LvsIy(By~JIKQJ$w56zps6v?R(ffs(tQp{?FfDrBoMI~lUhg0I zE^wmP>eK*%4(tOT;(b|k*cJ|7ERlM}RP!)7QR#g@I5)t52^{?d5Dhjd`@b$X+2?lN z(6`Qiur4>c*^~5JzQ|Y(gJIDHH@*ZNFUOBTEjvTY%`d61!5=S720CAFfp55=->hG81_z0m7HR{QmMUGd(pI z&3`JGFko2${?+8=t?7#of2#ZI4uZ{ZmSXX#-Xz@MbkgC2;vk+>P}A|v+0m*VU`q9R zB=v$26xx_RA8snq5`Pl~0QAG6>e{v%_vPR*je&P|^oii2>$vtf;+bB_^d#9dZMN{=*NrAF$Z5w&1nMw5#yTwy-Z0AxSzRq3YfJ7tGQU@<|dMWq4>yh zY%h*Foxu3mEfUT|m4uEJGNUOUL#0q@X3+;`0H3zEx54;z?$*Sgla?;V`; zoF_9;E;u=#?}1mbQ&SPDbq!x>4~=znK*g#qV&QRG9QCe3}Vd~Iz6Xlj2zT_YEJ2Zu*o^Qp%{NNRsUtVzS`-Y6&bRIctgm`!29p|d&UsanVkU6gOfAZbK&-x#%6bcQIkdee^yO=Ic}5ie z410d`p6hi_31ZZIwx36M(5E%fUJ5YZS9V(H*jj zp(?ZkGX-aU%$EKNva$JcmsOVG`gIcCiO#b0qznh)ImVF(rK8d}$*9g3y^V>J;?+-K@m(A&s?p4C)gK{C!{jGp zKqMK{E_3I1=-4Kbxv5HV%6jCKOLYJ6sd&Wuo3Mi3)e%T0S%Gfe3D@IU|7K|c%8lJp zup082GutCzfvnB+{MCc=ZJR$w2!OCPP*PGdCj0Y&tYmTgJ1R4d zL<(n){d6km4a%K&S|skPXshg9Qd8BNZ{6{mw==7f4EETr6AanWOZOnt`w}>wBxS)* zU}#!!`NK&=*5``uEg69aVrdW0@2F$iu`VEkG-8zU89XI2@%dLe%b_)EYS*@PzIPJF zNET6&9!MqLGH2HMt?cd4Rlc>9vH?$jet#wLflRZRs~5u|k-V6B)Xcb%#**t-CD)(? zg71w}*ccQLLljQRF0^lv&Zwx+1oup0ecS(z=0gYa%i_^bud#l8wfiGTO6!dJ8OOby z)u}yD29d-mXAOuBkTs}@g6zqbllXJM1~t(L!?|zd12Ys7i7Q@0ONMc;an#Th#J=@2 zSGxJMF4auUWOpgtQKAq8S9@q5x{qm-JJh=}%W#0PHqYdGx1ld95JL*Un~EOYtx8WU zNuL`mLNWCZ8+WaH<(JVW@c^4OF#50=(l7)h=n7^gAEC%Q2LAT*5$}EY&Y7gk1k?0! ztR0%1x)JBb4YHs_HP}eC|LXaRotymyM8$5&?Vr7>2saWCCLJ+HCBT%;f_~qSkLVzd zC;huP=01^p`>kRvxFdZZ2q>z#00nM@5J8?+1*CtadsRxG={zP(e3W5^L^O|+WF&ui zicKXgaJ*+m=X1${Rsit;cxXJ4))&HFLR1#Kr)>}IFBA##uD`RiXnS54^OTNN)?jCn zR9ctRk)#0!gf4OliY+P)mW!Jrgg*p*)~~UyVsX-U0?>{S~BS z7=QodD+%$c`{oXAI^1UAxQ@zC)>2fts6i{RP5dqU(qn*nucA-6FyL@FPc{L{%w*JU zCw%QyD1KL^x3_n#Yy@h{e#Y^ay^9DM{i5@1U}M8YXj&o13GXUY5smO%*x1-GOYkf& zd(`DJC2(vf4LP#Y;dUFCCM0}YnQXF$UGSA$7nf2~QjYMYCBaMKI7)tDY5T8b2D{#P zx(Baczuv8^ti(DkfZ_Vy$q#}byA8~YfdAIY>Y8i*ehsS1 z%G)3&^WK(DcFSt-+wYiifOz^CervY2{d!4iQag+e;~Vm-9dGM6gI=nV=Q8q~eoF?1 zvfuBWq}1Hu9ZxU2F+4YC<1X6jh(AR8`PCm306P&fw>M_MB_$n5UiY#Z77fsC^ZPUL z=5VC`G$iZbE&hAJ5A!|btLmiW;b~{TJ$guHEtlb9-PHagRYbsGZWv$i6rBdm7rJOS zM!-Bck3*>cvglRK-md&5hZv~8H7y&*vrg$pCi(0NL{&RI@r5GaR}w>D{4FBK*y^}k zJYNC%zkA0@^SZhHnQub;Zp-uM&*gjZ!2a{EGySj&2$s4qInaYIdP~*KfZMHqnqNPm zzppjyS)M+*+FDQD;0sYn92BO8njCl5M>%Kw8rIwUOwEu4Vxia|Q zfYR7oj|Kdv=GYZU1^liwm|W_X@4;U$uVo=tCxI-FT(9rdx=fWu5Ip}%Q11FL3kV1d zwY9Y^gZX57^{fA*qAj)e_VVE`W%l)Qs}#?fSa^v#w+~H0o?cG|;VYxSTf4S&dhjlM zzJ%<;aq@%n&>)AZ&-C22?V?fvqX^~v$2D7{L;FOW@>eQI^wvyh;+!7}o2@4!wxOlie&6uvAA5kg>2ucR>{13Z~`g%Xc z29IIUa%@gc&dgx=qr<~PwT!EliJ=blWthqpQzxm4=z2V;N^H)Quk5S2ySd>ra8}}E zKmkrn7#ZLBle+SAr5gPBbVG;g;r{DR0YO2*?osylZOMNn-GOLo>0D`8&zHp_L&)W! zx+8e^ZfQe@y{+w{%dp$Ki}Er%9dFIG&;mMX-@UL$p*nsR z-kwbxU$yF zOnlzsB{h|SSzvV3dk|wE{&Pd;-EoYD-7t|luG7zeJ56kd7+zfWUNAZ89$L-m1xlWT);H?^vrAu@8a4}-4%$jyD_>FFR$Xu%J@Wvb{$Dap~W-lgO--PDpaSjteE98D!S4Yuae0Nk7Z#mXX=f@X$_t&)x0$t44yc^ntV4y{cO<{E20ZT-+q^mR5^7?^6%@~(!qq_fc(Y2 z=65IXG`i^mUKbj4D-H2m+uPfu_jpaiZe}K{oGp@6u{h5`+LWpPw8g&ml#6^)O@5{1 zG(oDOn}EUd!3p7G#kcX5AR&D03kYbi3LDIG^uvfen4qu2%OpT~Eqd-zNjwbG0oLIM zm4*~VNhFSYAV7K&Mxodimp{EK>>y;d6z>+jTunU4w2Z;+9n7PN*=-MS484cJ{0wRc!}t5xXXI*QfT{1 zr@Y|kkI&vh$5H$EjH?P~S-B^_McojDvZ18ComOdM#Csx$ST;sQ7)NE6hfyr*n-3^o zrXu>{=(?Pn9Xh6#k&M1m@PXS${P|hv>7QfNd)HeF9(P-ulC~rqgu>T>(B*LxuFE`r z*nt|2Kj`2Bj53_MA5+LCLJnPvUA6*2%=>tM!h}ej=St?(E&`Q^GG6t$I7IgM6U&WL&r8CFH`(NH6!M#7qU-g$*)! zJu`rrdwDFd6K>F&J7dM{^Y_a#XDCU?hsa=;;UT~_T9e{Lhc0qJ{xyQ6L~M0bP&3422z$`;I2ONz`{(l6eSyhCrTdM6jGU4<#~>W%(z)y zs@SB9xSfk}u(8+irLPBZ?sq0k)paiug=I$Ef$07?>lLI>-D#B#N|}%YhamwiHjhcv z+5E_N#e0GpwDTOFnmjGgWu5QYM|+;NJb&mKrnoZMb;dM7?{SIqL5nQ=b|nlT0oIQ+ zWiQ?T*rx~S5UFntZ*W6an)n;#^k!VNX>YZ`qX!JaC1_&abEa$K-YcEv{J4A9ua@>- z>A?e6G)%wuYbcMG1y<*nwD3`na+jpyhqVhY>_nIlL9vcUbND)k1|%`aw*Q@e6*>ty zB{G91gR4y-&FSXtDG}lRtfz<*D1?OXNwCeCDp4i!xFC z(}-CGthoX@SxG#R>iuS}vE%!A1murZp*Xd(;6h#dW7*3h&SC5xTp zYYh3~bs304BW_GT_H$o5*xBopar|?|n3F61ma$^kyvK>X$oRfFnDU**^YRd&s{k%` z8mI&~QeMA|A~lW>SD6H)-P^SehhQ$*8+AH8b;^}vnWUNt|5u8;{*2KBMRF|*-Tf(QS= zs_ZZhZgu2=VYDAX{X}Ao!95;ibfwC=UA3{3vE1}=yItUcE`br{5P8JnQoX^OQ$bc$ z9rA%=$q*djsfCiQI_uS&Sd_TjS%f#omjBga69PYeJ640l@$3qLE?t%c+ z4{(O9px+%hi2sd3|L$UvL>IgBrR>X9Baz4ya*(X=MI(Av2%$Ib-N5t z=#KKrwnQx{dMVffJaPiBeV~8XMda@$F-ZIP5ld!Cp|PhDrh0yt{~5d+CpUu_ptGec z9r|wW3!EFV2Y;W8S$m$(tZy}RL3mk8Zdy&NrvvQb5br#3-W!md^lFHjizvbEcIIBN zLe={~Yw99R4oR!R)Bd$ihm*Uuzt(ML+7frf6GjNdY$qURb^rM-gw`1aGF>ae(bwr>y3i1`;c>!G!~Dh??$ znaH9GNSg=B3ih@y&z&>9KtlTWR$F!X5Z-mn&!cqGBZ@6N!g6N)S?$4^s1<(A)%Z_O zhuXjaw=%Ro)7hTOdwcI_e_>yd1U{kCvFF zi(hRqK>oi>N$&dp60qEVV`nLY`WYrbERZJIrRh)NAJPmIQWfug?}tiWV|3By#A(!R z%2gBAUPbRAZeBWFPkn9`bCBV)Ono@A)K@wIc7#w0q!C?6wCbP`A3U9K>aJXKrlxV0 zd)LZkw`!EZu<)y^XCX(eK;!O0al-iL?Qw4_l6#I-eC!*|Kl~Nn#MzE-3qO20Ni3Hu zmbX7CT93OsEj*PxWmmgDam8ub4ogF~J`*e3`PmABV%X3BfL`5U_){St#)IT9oowK= zsNOn=@KK3Hh)SaL4PL&vK?FrK(M=(iVoUcIOpd*@Ra83X`iFZF4Sc`lS2)EyI&ADeCn$iAG&$-4T6J@_-eYDudq4M{Sv7f zfVfegH~S6tv2B0QOG`^?dT3$K6nEZ5WtqzP5OQu=UAtC4*85%h0yn>PE>BEo7^%RQ z`~)u2@Cn!7f35a|XLg1F8`$EFLRd_R;9xL`B z?B%6tP1io0@wzb*>ok&AzZ$;OT6r2$zmIJ*SM7O>99af0lb*XrmGmS(ncj^%EyiNH zRx5K-3po=uzUg>dXvG)2`4+&aGT`Hc4eDbS^fI4SsF_&!c zMdbEZo>&*_he}rQhZmsf_7kg&Zu_$yB5RgU@HEIT+x_%_D3g;Z+m`uWKqqK1B*w@XbX=mt~WFq6RKjJ`XLm_5D#fg`FG z3e}Xn?3PC>3;pInHCI_Gwc?4@DaE!UYqO~hWqm9S@eOyXemRKs2b$wBKuBA#*>N$v0nkJ1XiLT4L*01;5SZrPG z?iy8Znv0R<&ItR{7v%5QiJ*WP0AE8BKYwnRi6t8|gvgG+i2*ChtFbA29hbCf9F3S7 z4b#JQv^77Q*{wAq$S0}BWz<+1`__8(l$%LYL+a0fU5K!T4kXITm>!FlMmqh*V89m#`JX%WdDgSWBx=a5N^&iXa6^FbO z*<FSmSEhXu(SYCk|zV`L> zI6HoMafYFHZbP3in)qIlG|e|;9vH>yH4h!WRM%${U);S^vyqzkc%5=>m0^tz0ul6` z7Ho^YVWzpg)skmrd>eICd;G@yh_B?cjmC#ByuawJNY4LAv4R&{Br7&HR{6n$rr1^% zKOZ}(RKMo!!<0_~pqorUqjpTu2uoA6^}>Uj_GW(xm?I`#pHCcDEVcw7)30C+==h=) zEvB>uq|jkUmB;ZIOXS4+tU{5T**xd;jn9=BU;0DiCl%u5nZ8qeJP?$Y1}w|k-hNy| zM`ybfv#gUsSULw5e*7b$GHIn2fs_kPPF@l%r0Ws|=PXNC`yGepwxL78o*WsAG>F_( zOZnE@NbSxVjh1{m5%%=~T(gRMrB)~(i9?j+O3XymT|MldjW8QK@* zx_}kl&?;kAvtQ&Kui8Xh;AU)BmL00Ka;;6szzNCy42J$cK0&OIsmn}f;y{JzmlvH(OiTb* zO)&V(Cj4odk>}R7lMLp4yFMCMu1nlG0cwt`*@q;MsTMqO)N;<+N{@Yp=qf%R$4BqJ zR-PDNshpYdh#%s0qMHKC({dG9pg&)fV&+V>SAC zA|~<|d^|GVDOR~_L(h-84Dzx*(l3BmbBheDKk8PU`Lt$RpOP~cL6C~h>1pED)?u>e zSHAF)jA}*?ygrY&C5&(R6@&e(GJA9vD0*7Gyf(YxI}w;nMyKSR-d)fjFoL-GdlV<& zT-k>5JSN*(@vT^K!6K%8kDHgYwV6zO+vYZ^hQF8LSgqo- z{nPkLDsK1vB|+@?qrtJqB`(tuyz3D3-9SmH{YzF>7HxX>tXS82xmQXYB`|}>#VOK1 zUZEbZeBoOn_I1v(GTp-HAgV06R>+|b$^ceq9{RL>Ck^LIjf%O$xTnz4%p&bEV#g7P zBm0F+?0AUvL1N#*GwcIShTohs=ieR1CyNirU60b4qYiieu|UZ!D{@ELjqIXViq+G# z-N@~g<1EWHYLqMJu9|)ik*8VzR*IG8%FAQhyh33_^V&RhR_~_^kl?8(ra)lfEe2bp zPeC6fawF=uxx!9I>)wcduxoQ?XtF{2Pc)88m>vog=_-gf( zH@@plF61aCW8gV2K-(hUW#aoJHlyTxHBbD{v9TvJM3CtR1Bco&2Y_CQ;e2Xm?)_z$ z*e+?UKYv!IC6aPlaP=w4ivIh{jPQ6{z z^jtD(-wE=EosH3$xuL@mqoSRu^ja1OI)oiiN84n-ADq8EWl$omg-| zm_D>k%mECwFBbSlt%?l z_6dmT!*7^sYC-STTVSSuUj#JzOfh&bzyzBU*@-6P+9h38XS6bebjsG}vb|C1=X#k8d%Ip~6e}&RV zJ5y*jJ2x111HSiOeof9LtIPC9{wL|)i}9B$+ewMnk)qOTi{r8{QV8I8up7>TIHGc3 zIgqMYRS(d2|EZV8I+H3tt6UQu-SF{{?gJ2|W%O+9?8xUA7Agh?1`4A~yF~{syRmCU zNQ*Y1)8+=luUTtm0IXHavo^5f?A}cQIB3rJUwgceVjPm>rIjr&#{VS!-=X# zR+jr&DX$*#md3kAN!(KYsmM_iilCk;D;a-vD4hB-06AicN=BDAUzYd?~~=O zmWQ^F2*Qzba4!+KA0KyJeE_ls84DXw`7r)u`lV%gd3m#LWrerpZ#Z{Yr7;ix57H|3 zILUWHj2zSaCHobWawuu0CS8z(!NL8$BA4EgE1|>Zvez(jFJ1Yq&Fzj44?}qI6x=ei zi~tT~9#+9Q%>4Yk@lY9h0A@oeglVwqYem_&qotnAB*koi{T_ybBi07?YjGQuUWlb~*s{wUU72ZD_1wW-;Im z25x{)_v`N`lnF7ovB%U2=VjL}XGkGKt0t49o;<_*$q&>~Ud@ri^4A@Aj9bT?xeMN} zagXR~L%2vi z(c^2IAzRM=kBjR^A7~HtR^HDDJRpCCn%~Xi>A<=?ezJWALr9i9LE-nvOidx2^0MvI zr}`|BttNL{T!NC%W#)8w5Zj4ZTJA?wQ$2ax+5G$7(G4sj{UCP!W<95w1SAs(;3*BE zb;s!Fk=h{r8&hPMw7 zpOD_SnG1bLdj5yty`#@(X5HObb|kxU^szn0f98BwMe4zE3^e3mS=nxsyEJ>DMqAm+ zXep9!EJ))jYp21JH%93Z$hZ8ikWrn2cp2f&r*ROo2ohcUnFLSoM*Pd3PXZbO>i!W! zQ#`UFg2_j+$6L>>17KHt56AM1uW=uJjPU^969XS)>vc&+9scgDr?uoh5JHX05Dk}b z$()56LKN7eNnw)MKWr7o$5gjc{Yl`-@A)vDm2+WYh_;HY(Y9TmzHWRzfMD%=e_4^v z&XqdotlnqDkh&H?#h9e8#nXv)QaiyJI*2v}fsbX zI+_D6HwLC@aBys_c6|Ggsf7;Mm$I^`bg>3DEKD?gyFonCZT!sGJ`bPfTZDy=-{BYC zkM1@6r=VNZ@MPueEx0>$%GX39_q2cgFh^}nZ8XNJ1>wko%)4-#+{ozj)oO_#WABgv zibE9~e+vr=mfzY6RpZxd^6{e2%75S4JAcL+nGn^^Tt?pdbJ27^{IHvD33Qr2ln2<@ zmax%RJv?Q6L@lRpAQJP&iPGwdP$#({ow=m=;PDXsumiAN@P0Ra{x);a?zTNJv%V`V z(bW!2in{4Y!Do`eYW14P@B0CQ-&>~uL5cCrx7>R)1%O_?5=2x^o0$l|QQAC;BX!kC zT8f8Bd=m=>gFX*Fe7ThrMZb7#%LPUWwdnHWIs}D|dm$iL5@{x{6%6vuK~c zJ1^}13P%e0EB+WBzWQ@uz+nQQ7NEPvTM~sli^_C%+lzx0 zC2TZ5k6JPk)?YruoX$2L)En50Cw2+WRoQT=P~Fj(l}YoiRc*M-=;Ki;Ps>M2VR8t1 z%l1}r$eh2jq@cjQHY2=$7$k8|4mK|61v_Fp>oX}g&KT`Ko7kwB2$oHm->TX0AP_@Z zSqg)Irf~s@rvbT%d)P_Jc1ObH$7qV)&%4csS+!qE%Ztr?qUeP`L+_H(0SAOwd0Rw~ zOjxm%N;cQpxqpwHzum;}8!&)?Q0#e~7SA6f$6HCk+~Qpvx3iODq@aNCNcwKCoRqF|7dOqiw7` zvxKEoBJS$3d)!{TY;gDYRLtR-*=rYNg#!}KfN=8})H=5M2L_INM7M9FmySNIn=a~g zVMHfJ{*!{LVaK~mR`_pFzs)FJIZkDMsQd8}#A^>5FghgjcC2{z0-GKZt6`UAwRVFW zoG>_QKAmHw(L0C5b1l}HF4siYMMID>FPws2Wnf5M2SgrUS^I?WWl+)krEglcmB0!% z<7VaJn=OVALDsLQAT3_lw2b?TFCW`xNJqs)Bj|nkM+k=%)zh)`zu0d}UtECE@GzAK zoJ0o`cpx~Kw3-!>W8H$1Nl{a#h&|0?H&-dYU!{gs>{m|5DPp7djFaz%KLj<~sZbCB zL#a%ve(&=y>mzND&UnLwkQu+>LmlDNtInmxza@lW=LU}e#rVVgFt4ue2(-osw8Te% z*>NdXNss_gOY$X<;Kp?DiGw;Sqb~UPb;?;7H>O_0SC7&yUX#ar=;VIleVwuWaBZut zu30#h$+DpM_g!`WEeR!Xpo{;dp25MvT97V>rx5Zh`pdya??!w8b%pdLX*v(fM8v}+ zaf~JRqmkp`=em7rO-dxt2N-3wVshEs4?CjwGorR=C~BKT_SZM!uJW7})TMXC5cc@@ zOrKQTF3&_g`tTeA`U)btZM>zWB`!-Pv3z{J#)#&GsBt)0K_>qiR9l8wwq;*q#KjXQ@;rm)&>f+N+mn_Ul_N5v2q(L~cFQ`A-b1^a+ zyS?l4ET7M^ny=+m@;Uh}%g)k)B*Ox~38Z9VR!16e<7+hVoR-cz_@KPG--A$M@k>2C zYG1?43A0pG&~%X;kkqKNFc5#&Qc_Z~gfqC3p+zzDlF<2XYPFVlpYe6$xd}D->uQ!^ zZ9%wm|6uzEkLcqk53M8?{4{%b&IkQ=-n(b%)Na+(p{;0``vjY@P2*P4@3WaG9_b2F zBu?KuckbZ8GU-@##23Zh-}u@~kWXEhSPo`;b_r--G8Ql*U^|5CE2ZtHbiU6PHNFeR zEPttM?p%LB{`h*2G+wvJttDrIQk)D*D+c+UKWj(mWt>MJay@Kgj5TOw(O~qT z!tBd-y0g6_(S0i>c>Zqb-g@h$#42_vfjTG z2=ZSAgcXWtR8UpYInuUt>b*zkkXF##*fsgS;euSvEn!z>V1Kd((2emUav=cs+>EaAGFC73caN-lq{6|3;5q&n6-F*j4MB9?z>ZDhi&u^(ms56CzSk4ngP5h- z>DOyJ-e(C81)6yH=?*pip22Tgz&Wsvtx{{2Ix!eIwzppQSAkJ=za~7f2Y^)jQ%^o+ z&soVuzm=ZuxSdq`?C<4uKJ*LnxrcX-J^q%DsGjHfwevm2eLfJ&j>IOV2<7Md1YQAz zL&1;Lt0jyco)*p<@Z+3*u}da|$h9(_F+bAO^#1jUqw)-&bhT*hr_7;DO^f;;d1d1u z-)K4&00y-INgO69my~ zsMWBAf@=H6+;QGUL3?$}Xf#^fm1~(S%_UyzyW(C1+WzQCmg4cF9$LghHkD|nc7a;& z`Mo;8e)^bhvrr1#tkMa9Sbmc0K&Bl4%Xn_j^-Z_OIfOTxSt%tU+#ha8q<%>m(n0YS(dMMm)vk#P=viHHhu|M_ni0 zBA4dtyIYc2ZDW4A8RKRYNLm@6e+E5>Db~!{^UOdkej;uC9ez0R1uer48TI`+%HM>xy1Z z3#TX>dxKeCq4X;%E8FzX@WUb|wxz{;f;>{TnUFYJp^@L7%nmLPBLIZr5VrP}j_S!% zsEw`%#$vsq+sB|*AsuD36Sy7p)`80dwm5ni|DdouaO{l~pjdyM-QBmny4Kxba4|Kz zm3V5yb->6yA>%1>er|5aB?FU$p`<-Z5x`n?-~b+Rh>Lr(OS!O!EbaB~e zmF;#xMgP7p2l-Oefd=d@ft|-LC+>1*l2ZgOGvLKw@4ZG-G46#n0p#vak~cuZQovat zs78B;CQSg>F^4q|8!ET#OgMEg)V3B z;j8=E{&lXNg2pFma&7UcV@bw?J&T$-3c(lEl3-aJ@syw8ekZ`&TgLpj#bhAXDuFX2 z)JX6BpmziEl%s;pW$}nF+lSG~7-xK~S1H&yaagNMU1HT0!I-b^iwKeZZBH;%kxD&2 z_|U}MygXJ-D^4ij>_2Re!q`{w$@LHKi;Iin-bTE4#v2XRCwE#Nr*W!!AA6JiBXl#P`u7 z1FICx<*8eaAo`^R&fKF-K(Xy8Fapc6l4rem4fw_fT>5~dISiYYlc`L&!!_6j`uX$pJC-nVyVgB`St^6PFFM!C0Hgi0U9UpEj^!qyP)qjQ<< z4`q&CxZf1mr?kjcWiXn&z9!~IVM}V2DvrE_(pgP1+93{$bEi3i9-_jcFsi=^q@kQw z!=KJ{;Jy>qfe~s5CFTQfWH~EN+Y&?wTn}7fO{JGB8E? z{#ZtQX1lJg&QwIWc0M=ncmFVpqSw*;*W6WpE*FVR-y};t@=e`M+LKz(F7CCWV2rF; z!{)N=#F|MT=P28ca@bvXU5{yPaC6+FZxsQd<(grXg6yMg)xJ`jUeIve7(oolG~Z05 z|It4j(dZ!3B|a=#x1z>vYZ)~1?8c>#gZzv~VSFsAnVN1J$VhJH$O&v0;kYWki}OL8 z9-4)}#n3*?Q*@D+)1iMjh|LIrtXruc5Uuc2UiLmZ^IbKWIo$8qZ^E={%kQ3Qv@#Xv z(a-46!u@8|WS2D^FDZB5#7iOPN{YP?hO$|ldn~Xte(RlTBrDa{m_gk4s4}k9I4VPzYwNxQbNzg{ z@OXT&>q37Cpm%?!j3Rqnw}!0hosV`2EA%S_(CSdYH)DmBTJapGzERl<>cL_yB3Gb* zT-)+*OBHD)a=BM79%bL`-&82+uFp{P&=@9xUbt<3;QRj zJ=vuPd3fAuSF^udoxNY@S5W4r=y6Vi3mj}E&Rx6F<75(6oSvSpi2YbrttPc=-Xa32 z)0a)|e4%{yNl8;53kK%(B?|9^?^DF`&HTD3aosCGdissF{q6R^x|clev36z;U} zs%Aq2UB+7bX*ZQ(+-(H;?o3RKjQ?E z&WVm>@d`z3LG9LXNfIVfDHQ3o5hc$&MWky z{%cP(ZyJ=@HrIdEc72|tcPpoUP(r202sgJ%=&2Akky@?{t1k5$5nzTXwK~Npx$x)@ zJrk2-7sMq`U_N!90H;&u2DIT2Y?}H2q8}*;D9Fs-x$)s)eXD%mM?Iu$lBo)aqd%N+ z`|ra^{Lg&@wT!pA+n2{Ln^YZ;bggsq^$fG}_EI&I zeqDXP=yn!g3-j?U5Y*w^fs?pMeA!D3q)|=yIUdC3wuKNj=QW8JTkVI+=Dxo0=7EPi zHxHN)xqB&0FZY2F$0UKSU9`3JAU~=ltgIz|317+Q;7bC@R05d|vS`fEEPpx_>KBqX ze+sFDm_E;=678a6>2*-KvD6tCxp7omG3xv(Np$K*iuZWN@)DDsB?Qbs$YH!@@x>W3 zp_BX_G;^-(!$eZ#0{n;qmUBG?@Vvzcti*QAKc`LNj>$e&vPUN-VbmBstXZYJ`9S2ozHZlxpb+oi3l$ zj1e~1%M`8Q9VS)>8|}Ej&QuImbU7T(*v^2G5(J4PfV_Me6wrM@rqK`AJb|1X019=G z76dvzsFiel?y#(mKMAXo{QE+{N+g|B;B|su?4j8@X@pztKQ+e(!o6Px|KJisnSm3f zeJv|3Wwf=m^}z$ljbJ#iS_uAll6alxpUZPEYmNK(xqH1vDXi4(n-WXWV+N4mt}N|o zuD1o5#7Po1+?B+B7BtP-L@N$CgNLsq99XAy&IPInS65Y8AMf^8JlKfRp-d!hDtc+H!&-q9lhn5L>A1DEd!w4jVX^pr{rRqyy$~27s+KZNv!(j7AGS>L# z!GpHWW$7e$H(xqYV4&ZTx84?ac6N$`xHSNfI~s0^f}my@U;@{Wila{3c_9|JXTIU` z5Vat`k;ZOpHEtzF$Bxya4|-t(&jw!1@PD}-3SPF!JD1vHk3~RH)t7%ph@Q!({dEg0 zFF|Q%HFLB8Z#r@XQocUAf*z&2K@X{4fs5bIUjLI>)TeCMl7)3wfVjiovkh7i!Swfo zhPi^GqSZd5YPWoF2-Hd2$T`Tr8+ImGGM&C>H3+max*S-BhNw+om8%gC{!2XsT)r@amc?W8c^fG28=^YGKu;3n zcpbE@f$yP))9zE@!fs&w55$sHrGX~!WeW7#H++RB3w-=9Ikn%ZSX2XjEhP8;)|8cV z!0XCLc}E2P7UJYnQBmsqpXhWo(?+30)2=v$MK9HYF?j zLvV<36tM)e0zT@ECoPYM?}C_z`?|ZGEbm^GY}aom_(BZ<4U|B<0iW*;K$1FzbQr~S zNs3E!iI0f7zrsW~ND)K#n<=mV^DhcOnPH1=8z=_a8evp*V%umC2c`(fsVCoCe8|jX z>jjxwF=lxV^x!oC6F-q&;BV8yz%PxFJW^o4cQmGQ)DvGh2Myhwu$-+xVx3`k7ib1O0rbQe3~T?-5= zonr}}4(k#;SKK%unO#`G0w0+`eozSzpTQ@OJlma)OJ$w2Mc*kq1vSLKI|E`E_*^ak z&0e6o=AfugNb>HbLxHv^h?5d{1pKW(0&d#?K3r~IGUSS{Og^c+5kZO!CV5lER6vBd zsDYHdr*-nrkVAJ*4>oFJO>%p*L>s6Ps^>+)sZO$>tN>pNV$=XxUVdSrA8TB@dnO#xC3NR;+_<}EMZx~G6x%grzxd=88i?yx(#I&2GYhFQL~wdES6@F!dW zaVa4A9K^f&B>CUXDrxi zPjou)poSCMY81f#ulbZtp4)uq5)`bBZ=X)t^!4+D$MFkBJowXM2sc&171HRGpv%IL-pHLU)}5M#o?H8#QS(EraGwaWbiN`QiZNq7nLo-WV~RkN~x zvPO*b|ALZC+ta5{ho_Q8yup^zl$j*&L<~1GeEDmzf&xBi8T6>6jARS}zqA8(<2}nN z6M~7=B-MYu=PE5PCjo+$LqIYy#2d`~&(Fa4-@~Z<&xaE(DE#yJe}Depo)fr5h>jd> z2Ws))MI%&hS7M}59a6|-kbYuVTkly6-pJutq~i3$SC;Z+m3$hZeM+Yl%8aaTqIe$K zEnT-nCV1x73k)}JiwkI(BnO3v|J9F0E8O8EhsGy~W{VMyWO}r*Qd~B%85F3FtH|vF z{jz!5rkd9KNI$N*yQmHl^wJ>^-6#w6O4z0Q2aJK7>}M9XyX<7<`w?WMORL4ioF=Rh z4l}(#Pt5_Ca|t;hLMVv7`>+QEB!ht@nXQZAdC;vU>(IqOk`}CO|HC#KV`*cjp4?ZT?|W25IEwVbSe(eR(?4t{*W_ zOd)S;1xP~Ch%%;M3CD&YiOP5xz--K|B z;Q}a3g3PX{yj)04GaQ^rlaZ7$+!oDoTzJFC?_aU{-2)cmWq|(CBg4ZI=%o?1!(mbP zqKq4O7y8b!!z9cm7}kt}-_GAtTIIa0!e~2H%{;!DE=00UJgW#7icjbir(Zh#RO8i& zz11Uf$e+6Df;D83UtYd-3mK_c40MRqCaWJm`}>cQGkC-abroKl^Ihb=aO?l0-Z_`- z3~Q>>4&EEx@)LATXlahQdnje+zFh6?h#_e@nP$$J9k5Aq$tcnrXu2r2iyEXQ<>o&Y!=!r_UZOI<_tWx>T9$ zhzddc)u%cQ)U>;g2bi-8%FErJXSgGu#%%}?Mk{m*jSmA5?x&8mpvzghQzO;^n}*Od z!5TGsn%?Tfe9po9hKdENXP!B2&(~-?(feVxh2@YYRH-ORz zEK*`(JDc{gxtdafq{a6b(#YM&)9O*7l6B#7x?N|~r6Pn|0!8tEVY`TW(tpms805w^ zb}w7xGtqH?2HzKaRgZya@NDDn`tg+X5vo4J z4<3kIj}pF88Wb?{y1f5H$(J)O(L=Y|MZlt*jOqE42&Z@90yTcbAc_Jal=hKE>a&kd zj|5>f*F)lH2xJ7P8uyO#<3M}HbE-KW+jjWa{?3zT#rvU}2_ z2hJZ0m;k_(V9oQBUEdF!;*_2e{dZ1=Rn5Z^zfE0O0gXQ*{2;7-Z+yaH$PAcX)s}~V z@iTCS*kaE+a8!nv>--scu~t!?+sKyqpw{`}U*J#=`zIxbvV~{Vja$5Z7{WiLfFfG* zES+#vEB!dw*8vKhpt-B2ku=V)?;Rz#G)@rRFW7WYl^N|4htNaUyX?FMdKQi=M+5uf z+D-*u%~m{I4q8X_U2*e6JMlqpZ)B|6-EH*Z_P=zuk`kxl7CyWl6^NDc>l7TQ(FbLz(z3F$m0@%tNG|q?k$lKVW*b&q zl(Bc8woWo*i0l;x)KRnO_EQ4`gF=LnQ}boPBHhLFs1En@*+(9m^L>f(8`2m2v9vQg zLRX&vXSbp6q@!&SX*zLs{0Ww)18{Li)M^cuVIzjA`_^l=r4Rl#F;GfB1JBSQ)N-)F z83!w8Qa^!h`DW0{J1_lHo?H^s$G^x%7>ytQB9WOshw-WY9eVpISiL&dARqE)82dDt zKS}!~0Ctc2FEn+B{r`a+>{gQzT}3>M+-lYL{%TT{Z(QpYj#*YlwukeLxXcp&5JRq8 zbtbs37KI{iRJs8eTLNs0$L1a13=&C7HeeU)QqMOt1#FFFrisj-wZ+#*e35=UDuMwO zv19BU16;;a)gz2y8SA*@wHbRlf3AKKG~( z^ITuT=!`(P6fxPC}_K2K&}?WrB*>b3jLTJ~%{6E5$2tZw%?+q33# z7Q|)kNUxeTmGR5I(skhUazJe6`h8iJCu&nq&1ik~X375#T3PqZS8Xqz``hZqr<2bz zk1>9^Sh@~kxLEbNrv~4zY5q0u`E}l;<|Jc=eRcNL+^eaJ4R5cCy?Xbm_grbE_Y!CSFI7CTujZ0@cxU`y}b^XID zpUz)3zqAMR (for R) - 2.1.1.9229 + 2.1.1.9230
  • Conduct AMR Analysis
  • Generate Antibiogram (Trad./Syndromic/WISCA)
    • -
  • Predict Antimicrobial Resistance
    • -
  • Download Data Sets for Own Use
  • Use AMR for Predictive Modelling (tidymodels)
    • +
  • Download Data Sets for Own Use
  • Set User- Or Team-specific Package Settings
  • Conduct Principal Component Analysis for AMR
  • Determine Multi-Drug Resistance (MDR)
    • @@ -66,8 +65,6 @@
    How to conduct principal component analysis (PCA) for AMR
    -
    How to predict antimicrobial resistance
    -
    Welcome to the `AMR` package
    How to work with WHONET data
    diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html deleted file mode 100644 index 0a083f3b4..000000000 --- a/articles/resistance_predict.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - -How to predict antimicrobial resistance • AMR (for R) - - - - - - - - - - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    -

    How to predict antimicrobial resistance

    - - - Source: vignettes/resistance_predict.Rmd -
    resistance_predict.Rmd
    -
    - - - -
    -

    Needed R packages -

    -

    As with many uses in R, we need some additional packages for AMR data -analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2. The -tidyverse tremendously improves the way we conduct data science - it -allows for a very natural way of writing syntaxes and creating beautiful -plots in R.

    -

    Our AMR package depends on these packages and even -extends their use and functions.

    -
    -library(dplyr)
-library(ggplot2)
-library(AMR)
-
-# (if not yet installed, install with:)
-# install.packages(c("tidyverse", "AMR"))
    -
    -
    -

    Prediction analysis -

    -

    Our package contains a function resistance_predict(), -which takes the same input as functions for other -AMR data analysis. Based on a date column, it calculates cases per -year and uses a regression model to predict antimicrobial -resistance.

    -

    It is basically as easy as:

    -
    -# 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"
-  )
    -

    The function will look for a date column itself if -col_date is not set.

    -

    When running any of these commands, a summary of the regression model -will be printed unless using -resistance_predict(..., info = FALSE).

    -

    This text is only a printed summary - the actual result (output) of -the function is a data.frame containing for each year: the -number of observations, the actual observed resistance, the estimated -resistance and the standard error below and above the estimation:

    -
    -predict_TZP
-#> # A tibble: 34 × 7
-#>     year  value se_min se_max observations observed estimated
-#>  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl>
-#>  1  2002 0.2        NA     NA           15   0.2       0.0562
-#>  2  2003 0.0625     NA     NA           32   0.0625    0.0616
-#>  3  2004 0.0854     NA     NA           82   0.0854    0.0676
-#>  4  2005 0.05       NA     NA           60   0.05      0.0741
-#>  5  2006 0.0508     NA     NA           59   0.0508    0.0812
-#>  6  2007 0.121      NA     NA           66   0.121     0.0889
-#>  7  2008 0.0417     NA     NA           72   0.0417    0.0972
-#>  8  2009 0.0164     NA     NA           61   0.0164    0.106 
-#>  9  2010 0.0566     NA     NA           53   0.0566    0.116 
-#> 10  2011 0.183      NA     NA           93   0.183     0.127 
-#> # ℹ 24 more rows
    -

    The function plot is available in base R, and can be -extended by other packages to depend the output based on the type of -input. We extended its function to cope with resistance predictions:

    -
    -plot(predict_TZP)
    -

    -

    This is the fastest way to plot the result. It automatically adds the -right axes, error bars, titles, number of available observations and -type of model.

    -

    We also support the ggplot2 package with our custom -function ggplot_sir_predict() to create more appealing -plots:

    -
    -ggplot_sir_predict(predict_TZP)
    -

    -
    -
-# choose for error bars instead of a ribbon
-ggplot_sir_predict(predict_TZP, ribbon = FALSE)
    -

    -
    -

    Choosing the right model -

    -

    Resistance is not easily predicted; if we look at vancomycin -resistance in Gram-positive bacteria, the spread (i.e. standard error) -is enormous:

    -
    -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()
    -

    -

    Vancomycin resistance could be 100% in ten years, but might remain -very low.

    -

    You can define the model with the model parameter. The -model chosen above is a generalised linear regression model using a -binomial distribution, assuming that a period of zero resistance was -followed by a period of increasing resistance leading slowly to more and -more resistance.

    -

    Valid values are:

    - ----- - - - - - - - - - - - - - - - - - - - - - - -
    Input valuesFunction used by RType of model
    -"binomial" or "binom" or -"logit" -glm(..., family = binomial)Generalised linear model with binomial distribution
    -"loglin" or "poisson" -glm(..., family = poisson)Generalised linear model with poisson distribution
    -"lin" or "linear" -lm()Linear model
    -

    For the vancomycin resistance in Gram-positive bacteria, a linear -model might be more appropriate:

    -
    -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()
    -

    -

    The model itself is also available from the object, as an -attribute:

    -
    -model <- attributes(predict_TZP)$model
-
-summary(model)$family
-#> 
-#> Family: binomial 
-#> Link function: logit
-
-summary(model)$coefficients
-#>                  Estimate  Std. Error   z value     Pr(>|z|)
-#> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05
-#> year           0.09883005  0.02295317  4.305725 1.664395e-05
    -
    -
    -
    -
    - - - - -
    - - - - - - - diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png deleted file mode 100644 index dce431473f1b6824d3ddde72ce39abef5bba9254..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 53370 zcmeFZcT`hd_bwWcA|miA3er^qh&1WF*&tK}k={f=q<2Ect|-V(q(e}e(t8h}C>Xj_ zX#pjG)X+mqayP;6JHK}BU-yi0#~pVMC2^Oz)|%y+&zyVV%UcFoOeZ)`Kp+q%nD%ue z2!u8R0--s7oDMwmkMn*O_(6A1SL-_X2_C!G>Lb7p1}|-MUkHTf`Qcxx*qJp)2;@8j zc3tyMQ0meIeB9sBT7uL=JgEnK(AJ2&bos-1X2sMqXXm~Ox5xl}Qp#<5dgYx<4|sh4 zAYI~(;NcHn{yuYB@yGm&3o+;B-dzI}G@d=WcK(El%Bl)scY2h7$M!fCSLRj@VfPy+ z@x!j@?UJFo?ZD!YiD8TV`XYa82FmRI{~!NnHPAe}MS?0n*PH8>zLX(d{C!o)&bdp8 zs}lS3l>@@25~13 z8qy>EQU7=4?dPHm?i!cEbosA%o z&sKl^F_c|sHx$?aLS-=3T(NGu?p-)C{_hm{$K)|d!s<&mU4D;OE!KTk!(8cKVec;( z9#o2+YJw^@;kQx%6=#FeTcV4NG{kL#b@|%e@O=QuI7`H$aJR+U`faPjR4`$dd#PI4 zsT(I3_N*4(!NS9|-B_02eWU&cU&QC$u~XMu7lLi$<^1Tz zj}fY!?i>C41E0BO8eEeT4yT&++z-j-SIR2r`)Uz zJJQKbc~^cq{QTs|$|HyzG7Qn+_I;n6h_mErlVY(BZ}aZXwo$^NuK_AKyIzS% z8X2x{c&8q=TVnusIxg^L0bwA1+Tx6Ql4^C&=w=C=j+XV@0iP|5V`+K?M(TOtKS5GU zdt>40Hh{0Z&111fnp1vr&zj3W4)jvCuqLz39lAm&e7iYhj>YmHJ8HxB)yB8BrsW1E z#1VC3K-XT+IR_$XZ-wL_s;=O!&+$03yExY`R`Qcv#Bi8Qc1X6S1WNS!Xzy3;UnR%s zM3Ssw;)e6lE+OsbpR}m>wf88CVjPSwZ|vu8idD#BER0^{XG$>;#jpIY|k;~*rD8XPieP5{dGA7rk zv47IBPNdg;r9+=H;9{xt41Vt;luu{tL0^yl`fm=k{Wjw|s-XM8SgzaLoZxQri>*;? zm5A^$|5U)ogxqW62AV}oc)xW4=_voHix`OYK;gWF{c%L2h4gpG;I%JMl>UfeBXX9^ z<~}IjUl&$4!Pc_yM7*Z}l5HihRVFhWYEllh^$%zLy_q-1UIhG*w`WNUvp8D{|{q@EMGOb$! z$YTdJGXz7SA>=;kB%i?UIP;a30oacP83Xf_HDAbYa08JSUkVyX1@5$ zktF7Cb3?_IykGrC%y`Rucy5+VpcklpaQ!tN=3+h;pLtm=5b$lkEKzVNL{{FgNN(9) z7KKjsiE=4E<>(hCK8TMyyNGuC8Vn&fxrmfn4i^Ms5>aRY3~C5zEN`9Cr{j%G&@(5E z`rXqX=|3oWpp=eEDs32q2J&9=jYs4RLdkJH>ry%s+hfVNP6oA&Mu$+Wjt)VxKQuRu zzkTJNZTgU?(B=Jsg8F1&fb$L^a6*2uFz}gv%jsTg_p$oTc)r|m&#creeMDq>4puvP zB4X^Ld=AkDR_nXeCH>XM<0jc`u@2O!<=BUXw(*+&2G(Bv;Tt9kLnG<9Y5w1TQ0X$i zl?&%T#JHB(tSoR}aG9%7b+;Zgp{|?vA1#^`=P)BTH^SYyT>+2n&#^N9gqR#)4q`*x z2b?=Bn8 z59vVuNF#8SkGB|auX!7sN$DFRDSKKP)v@k;<7f}M)6Oz~yYS{S7tq;S8>(psw+(8o zcd1wleD4AdTB6eU={A|I^#Vaft)CyaY)nBlFk{li)YTJkA*`W84V}U1O8M~NE%!m^ zawGYI6o0-6AuF6``W5&Chuzv&1ogqbxk~p*WYb)UTH&zD zMS-~b?~N$j*flKqhu?n63#!w-ZgjZ=ogw?@7E#GK;j1fF`SYSfSPv1#q5aBN{>x@>PM@JVI#Rh z-UQ@lox0vxP+Lu@KlZ;Vth}42yXunJm^9Q-{q3S_8Hjwx02TPFEo{o(UCj-(a6My* zVwZhxd;Z}NYnS?PuB8*d->`-c70mkoTS=}1OpCLXv83w@`y9M}cI{@@GA){!_xc$U z_}Z|-)2B{u46>;eyNd|32geqe&%%X?5!Hn+eLv-u2CvB5i#7|&V%)h!gsW#&ik;G; zrS~p&iWyI|4bfj7nv7o{eYCM$QzT2?AMtc*K;&LC_E09iOm-AR>zBO=8m)0(@)i~o zX{}AH5*Q@jj9GY<{cyife^Q@A)B%s38&`8YAD=fvF8J-cZOEbUex=+7@VQr_y2wvA zspyzuo3daDuOCftmRv5h5D^x;xZrV?%V}WJUv}on)^{Z9s+e%#or60%Qcn7&W7{RC zN=feRjn&(afBivzhTjZpyNnp(a$jnS?xSrhG~T;Yu=lzgF)q2dkFghKUt0K`r&PO= z2ZrX@*uA9ssRwo9$vGmAE9X0et1sgRqZg|;rnQW>eEPZ+ucovucMZ^L zjctwP(hn(z+J#$Md-;{J$PLoA#WEr5wcF5wi{6ksy}PmcO&wv#FZ9F+%%&umjX zbUy75FGPoARfHB=G|tBtYng_22XbC+S`=3xg>=mo=8V(I!U$)rQMycfgRi#8UXSn) zw|oBYem-pFLfX6UIkgW?+z!vqd0j^;TCptcE<#$q*(i>GgaDo`^sdLba<>%v=O_0%J==7dHYs{`}=9er3MLYu{jGf zE_%K(y-w)rk<-2IgPxDWqHN^u?ZVSC1^ZFCF=evZjt(Xxv0P={AGit0MLjvC5B#r{ zg*Ig`obFAyAC$miTVU=ii@El!U}GS8CZ&;o$@?tIcyK+y$XYOi@AihdtN(J=#2+v- z$bNiy{+oB59qqZKNZ!C;<96-(>79o+IQV!=u|vr{d~z542G6d~<)j9I3gBaKZ}S~{ zwQh6x)SHm^ zF7BLegXK9Z_`P(7-zyKtY^=&dCt&0>*CES7bI-ou%r2t5OA$6v(8b54%Xu=#2_0sn z`nH3K1N-}9{+^RX!qC^@%_?nx?{2}y!bs!I!W*BWbM$e80(@0j72SCjf%prHe(-|_ zc{b^-GIPh>heA@ss3z_bx3i-BZDjtfxgm4ddVI1E%AypTEek#{?pmjo()wYL3A0X& z&(qZ~uvmaFYVgHAr~c=5Q}ef4UO;D>vc3@rT0M17lzLBlP_@TW+Jfb`o7N_i?{JeMvrJsHr0KIAjcNib#e#549melk+2gZA zvt;~-Z~aIv*IE71grCWQgPr*glgfu~Tx~*^%|R52Mc@DMrTW*6CyZ5~Jvqn2P14`_ zw6$clF6V<8qh2gK7HzMF+WV{_4%>e$R#{8#9>ad}__X7hK zRl(+B*=d~M|3hYhG#MbYo1eV(n)chjSFDvaa!jW=I1a8^Bs_ng zD|UmwY~i_y(VjeyDdJEoj+UY+<_jNS)^R>d`5Q^e@W-&+8%p7?EhShUE*@nF*ZlA? z`4DEW0F#>pld)&;v|N%m;B49%;sMB;c3L9q8bMSR7*O606>57Nu=30eCe0$;O zDf=PSh2>t0#c8xM2Jg575j5G>sXeh%fW-ApONZwkWQtm-m{e`5;Rln5c>yM5AOD+M z(P)Q9WeeNY=KEgDKDIVAe1?iqq;ce;ZM=`jPldYCDu3(AP>0v+t>#W_?ki0jcL@_D zhu2$8Cep<#U%q4rE~dDMwO5(3fsWU|GkUILfa$GFkf=3sx(&Luo?O*)O3nv8Tv@eH zO@Fe1l<${vkTM5f=s+}N+{1bb?QSIdvu781)~Q)$dYH-MCyo5spDX|qCJ_X2E;G3i z^oYsHRBdTtp;3POz(eOo82}ttRog10CZETYE*e9-|Ix%z_a1}=c*(E-JNwrUNphh zH<3f^Liei>Nbhi~%EEc~`WJ=na?y3}>M)+#Qwu3MRo^Z?%oKmvaq)mhuy9(iFoFFS zchp*?Nt0^Wl)tq+pMJ`m+Rks}0r_>#DV%HwoIUwd%+O&+As?&mwz zO(t8!vFjrpzXPTBik{{SRMwmu7_1`NcQts^O()J)eTRXmrQ33%5NSdrZ{^pYtuEs0 z($pW4@3%m@OBz~(rab500SCD6NaGixhNFEB60>X!>Ogw6MH>yvTcZRPo`rX;T0V?3*}9?96&Yq-XAQ6nbvDUiUb@J` z#=h0w8B9AqoJ0CeC)P)u{Km}`K{lqoo{&mvGI)CBfY)sls z$)rk6`b9coh}lAYmCIFj5XRW48+$)H8*75=Tv7higvM=sBB|G07!-oVCJKC%EX+Ac zT3thGUQI{giiF>}3{v6Kxz4A18?-MNXh(?541K92UED7&FzkC&?<$`atjrsa`z#%= z+7kxnUwk-*MdC!xZp0Y+j3xWW>g*Rvj*dsP{g{od<@wY|Bx`@No!V&vPBp1eY|{a=j(b$b-5$9T-7@e#wF zAyelZd;0TTXVDUwo+0DuxEp6T*t~7Amp~ATFfYgQnKU#3xE{gR_7)%zVV1U9MHeg> zh_J2(klrTPCCcAlv+QSfuoP|VH=ZE;>d77NlIeUd-C*10s2uJGkLe9SxA_gx-CdOT zFv@=Epg%nK$xv@S<`o z(bkN|`n87N_EAeRC$`U1bvb$IFBl?i5*9?h;lHo4W-p`R{?)|=BkD79Ir&C6y?ACT zps`Eulw*`l8f^Uq0*pY?t^9aHtet>>15bA(tZ+_Eb;ddm7!qw406u-GY^ zw|#T?(e06VpVRx%Fp{dHSCW0*Xo7s^p({_pdbBx8rrXNGQ*d#@k2?n5cui!1`RV?4 zumhWk`V!*wev&ZJt=N74vGHlL$zu(H8Lcg;GBLJE9^k+i2)U0Y)tcz=Yz!rD+1Rd; z%6Qc4RJ0z{V;z**?)OCVP93Lku4Efcvs9l~hQ}3M2t$3Y2OFj8vU`v0U4HxT8+hG$W{sB3gs%THo5wx)fD{1IOPo6R+ z#c#h5Zqqeqvh{wuoc}ks>>$&?z&DURnv>dcC6_1t6N{>rZHGgOMY9bIH{;LKXbUi*fy6v$Dti zzA)Hx)cpsJRB?O}iWv5k;4yRRV=OJ_b*&qvXW;>Bg~oGQ`YoJ@&ixwsYIcm*XZ{F>{MPAAK@&Ur3>E0Ke=CZ~bS zuTnMpTbn;xAuwxPCqO$-i`Ev3*Kxs&J`*R*bmio`?>7uo2Xo9$_UTO~Oh51?>pSJk zJ8iWho2CmI6Z?EzlSbI07ZI@Tq|joxejp=FxLVg=wvKRV`s>EC0fMbud$h5tM*9`#oNzv~ zGx@^R*h+8R6{)3;jy$kW;t!C4ABFdZLY1?_>l-RxY!A$mQaWusJMRVRxTZ=wUz$EZ zDtok|&xYOxSL3SH&NA6!P}1Eau$yqILv(B{v%~jE)uO7P@BQs1_ML`-K=~_rtZm6} zywa8MbtKlL1CNB;TO&8~xDMizn ze9l`SX^5BVjXn5OFJs6_c4%J&xBR`N8`y*dYp^J11pB*9Z*F5tVU6-PM9V=lrBz}R z2j2**d+;S<7RK?}Cr|s|6ZHJt_|Yx;oBuU{KD*9wDA^S_xZY|gF4n)6x#;^u4qV~J zlc-_Oc`PWbnfQ}chmC`D$p5EuZO_%+=}~67qSi8n+$8W+?SugN5t#GRI__3$FfdJZA2pR z!B*|=38n(Oe(hE2B3t__8CBbkbt6>?g!T|^9X?l~>`mI;LgW~9GQDrXH96{e@?j6n z75(FhVb;J)t?Yy5M$h)igH_~MVzC~qH%tz*Z@lpRO>hUwy)-cZs;G!r6DG=|kn8e$ z;X3!G!_Y3tL%tm#b80(mW4O0UD}0}+u-2CGE*R0C`?OK8sGcb0HOXpBD&`&CA9t0q z4y%e=pliyW#ZL&c*U{D2Du`{pnS9XplA{sTm^G~cz9x*fK|KEW<>p%E$OhT|&af0j ze}^y0t>pUX#;5TE{A$|kVHPD@0L2AUPMQm>|&( zcw{(+KDMJtswYD(Zp>*1{-b4<;|v7SEQN@qi~P2pHGWWY(DHzG;^}ak;wcD3BNLiI z1u^5KyFhdJelz3j>E6T7|4$xnb|M?hk?*&4eI}uSFrO4C=w#JUcEQ?!7;jL65J*WD z$d}R3eA#{xzzt;_O3{ModofsdS2|`E58b^Ah|puzi>p;AS6=)1==khDgXThOzcn8a zfdf5YF{C4{&&BWP!NS-qXuRRM(($m6jcb!q!`{J`p2si4<|7Dc~Ev3j!_;$v9(J%(=30dsx6$e61*k5m13*&)-EC&%i;s>R2j<@pMv+JE(*b z&Y8_NLnV5Ghk0E40}%~M7)|R#X9j1vu6Pk6RF-ZP?x17J^SDZAL>*%@@$_f~WtOmY zU*X5BMS|cKf5ODYDEs>w^BY z>HxMn>wD0Brlcl*3?m{n{eB=GT?jMKT^B+RlhbiH55K5Ns= zi`a0?{O;-AiJg?gw^-P2Xb4jSp%Dq)csLpITmWtg&~Mu`31f z00mnghNZv5eS#Ul)<18zc#d0i%=rSLelgyMA~n*C5#!1$IOoq{=LJR%zMkq(ZKxhs z9~AZNKo0e~hFz1$tkh&fspB;c@6KrD5QY~R&+&Yh8UtUg%SF*|@v2|BSGy4$%L4YY zlaWI`EC%b#9McpcLk!K7^fC2vd#_#VqH<9H)pncDi>tBm%!Cz#kuC zk6*jy5FgUw_v`Qy#P1q)U0B4=kWd)FXmqnnMG5l@-GhoP<&g#i=r6 zEi?k>`k>(JS`HYqc9|!zh_Ffno>08%=PhVPeYG+sqFb3V*T|~*M-&4_-hDu&8X*GP zhLza@g~DOiZ4>Tm0fUOIw1?Ba!piVxr4U+c2((c$f^0{)8pa%^hQxjOBf@!jN92iE z!}T}F_%4LOq~1CXNwX~#;@(bxlNyd4&N-6PISW^6kQR8pLyxa_!VK&(Lg%204fm4x zpt}Kh@5F0}1|f{{G@dd`QL2}n9t~0$={+0UIGjAdop7IwY-$lhaF2(K_l1yuLYvZY zmSKx(0vNt(UdhApA&mHvrIB~79Y%CqsbAxv61T>*UR+u`=0y$(ku~hW%=E%D+jcWWs<6K*}*^u>qdW2hR+2MJJ zg#gt=qwijNlOo2(IKCWRO)BxB*pe*<^dxND){4~1J6u03CaL6gCL;7Ah8SGzskdHr z^8>{?4=@ymSkhikgwAc*OiE)CfKXM;7$Q{A(d$dT@#zk5+reAhTjY}sVb}^-?oK1D zh6AR;V!%k3@%{Y!J-W?O_7)puDlh{bA7fyUBM0PY+ZamD9B!=YjrKKVOC#>S_6 z>p3s896oM#>vZpiw*w%tEqM6PNTyFOHD?aLL8hDzb1ry1cuFI4_z5ZOrQbX#%sQ!7 zbLgv&H>?w+&iz{8K8nr6mo$EayTG^*#sNr8#qA_Js-tiP;C=!hdtjnai~dkRvr%v; z;F(q7mhQLb1PJ9qOA;Uf?>to=y2$eV=hlX40##b7onx4Pq)T7L>ydmUaaIqCQL2ShAmjL_LQ zmn*bOgw_j+Is^iy0&w@qke*~oif0}i;ovN~SQOTyF6Ur=DC8&*6vKKZ zQ|}iGcZ^^yBg|?k7Z|lb{K9_^4&)%{tj|N+^vqg+z|`V!;pky`u!Yb6;|QSck2M_o zNfQ8t>j4|V#rPzgF03&fw+vH5NGbdp1d}M$Lles9cqni+pujH>>%RbI8K=6bKi%{QZX|977GO}b0{Uhe zZVHNbSNN-&KaVmkL4CRCoZkBb{ogrbs*DT8gjY=ei}S(QDM=nuah3WA-tKB^P$IGS zM3{yIiVWP(CLAC(P9Cwq7@Ey>MRm+JcH>*iGxp7+bQ@-?`5Cg->8<>!e_pTPCJ_>*o5G=$EdJ zwXZfxICud+nn15C!=LT2I{dEn92><4G}=I06LuTt2uYS8!37T<1PE-|VLf~*#pfv} z>YV=hvTcpP38UxaWp82Cn2?lFdzBc#bd&qJEI|IR+qn9Drd-hw`EQZ=Hj)A(|0c%& zg8~^IP)4)305Wj!_&+XWY?cS2`|BfAC^w0bjh?_;Eq8>3CC$(bj<$S2CHt2Yi%SiK zq}qMf){u@#5ZZm1>{5BKiM-ou`k(G*2M!FbIjTwtP=y&^o1rLy5!_QnS$J*-I2=N7=Xj3y|0E>~Hcm$?M)a z6b^Ae3=$JzqOxrg&INY&a=daqVJG(!sUBQIGIql^`NThmw?)oWcmncJ^JRUMj?dS( zGt9bW`PH3{Fq^ly3$T^6KK#bNtA_>w_Ux|_+aD=$Y8UgbOk>C02728P>vsvAGIzTq zt^2AB*7B-VDQ_*0;-p#XZKTt@o9=pwxgj({45LaHuXqL1VO+QL$51Ik9+QvY9xy2p zi=)J!9f!6?c}0x&`pW{LgvrD{leUay*tTJrY2f=x0}7%=I-aMRfIR-EyG1 zOByUa4VMDrl`R>60}KCYNl}m8CvA` z2Y4>HYXh_i)n3H(BMLhIxs;f5=f?YjHF(B3Ou>y2~gCKA#X#-S1n1T?H5Rfl+LWgrUi7E=#D4gQR#-rvT zniw3xpx1bw3z+;4pJf;XMWyoy=7DBmM6-XQXbs|)#lG8ur2=C13PWOsF zWyw-19moGd&yYSjC0Po>O@~-VJGB3;-7cdVK}W3#xqG!@=ot^r%uafNTA$XM*&F}9 zVw-S}1v~w`Rg~~-{tG3H z>@sVNhxXkX&tovK7z3Xm{8}y$n&p`^J;&Ve6f!mBC0r;WvR}z)}is z4{DkVeNP!*?hIHEEQ9++H<&5eD#N{Q>QLtY>WEZaW#`_g2JWMv`vN1$Qrf@#!LT(? z`^&VY<04nEI>7!RLqH(7ZOfPD4y*ZVF!9%)PaKXADR2>VkTxN>1JKfDMOs16JHo); zYXvkSWyKQQ3kM+j^b*9XmsJ-?l=PNCB?7|&fI?An5zNOCU+PgCn7xl%R@V9|)8%KM zJ4u(6i0HkoYb=%9MF1mX7qb^*l- zdAu;Vad!QY+%eu&Ay|>ld}i=n^)y^FbkZf`i16z#gJs7$!7)iCu3i8WP2a9q_W4}B z-))@(=N@GWG<vwFkWvtP8TUA{ZgBKc+wdbfuG+f5G?qfrSULW;RDs(_%CA6hV ze$3GkY;xm2u&uT&)uwx`WqbSjYDGT6hwF%>1!K1{Ev(0x=W8(;U+M54i;Ru^bUtMV z^T6d0QYl9V#W<8|wR=8nlfQ`biWxMp;^8*J_L zYb3=Q`dE)eQRq?$_kXT7nCdUh)TgTAH7J7rSIyl|t94XsNEnv+e(?uOp9Ltr5Ztrn zuXaFZny9;s(KQBowS*KeU`k;tiMSWg{Vcx`Z#W1TR;jp0*y${o>!IApS|Bn#mJ1PD zDo=4%H|_uKoYcXk%ZU4ADA?kna5y_7ux+7q1%m)o)G=V=AmF={B|=3669KJN!>|D3 zNL&kycU=E(vk1WsHRq8^8>;TKfvH)Sj*o82o3Aj^gH$#4zfskw+u^cXuole= zGebEEf#*W{|09kN5lijC>pOkW9{qJ!nD{(8&;-cbiw6m1nvsQ)=X}?G+fvdKwUQQm zFZ75z#YfS|=>BpsH{^^E&rW$G3)X>op4idpstYr2`s*W&W#Oghx0V7Fes!2PJes8l z0u7?+KM4oJz|vAkn1hb%A82g|eyswjNl(ALU>Q(CNtKaL`9aSL0o}0a+H6Gl)Yem3 z$F1Us;`Z!Ay+YU`LH@~%4(agLrjDIz~=xFw#WYU!J?7De;DREJEp48QB}tTSS#c~NKpx%IPg4wZBy z79#hA6;ZIAwhn(u2cz)&l9SL3bI!Khlbkh5 z_v?f)%V2}jsI+rx^Uumv&(x#xvqOWrj%p9Yk2zpm7NgDrCwsuem(gm{7)ICjk)%GB z&03~5N}1VPL+NJi`hY;Ecpl>pWeMi-`*jKdq({XWzo#4HGO z!}>b;!CDnezVeGBXp{XP-1om$C6E81gnD_bnWp2nQ^t!Z@7{aP+a8&LzS2Uvf@@icYs&5JUX3$cK?JI?RP(>vS zztJRZTd@=R#>PBM&7^#35T-V+?bn%hq>?MqB8Y~tuTnyes#Ia&KwuT;DaLF6`x5H^ z(F)*@j8b%GtSpET(-JKY1kG%vdapJGIM4;GnZDA(j-t)Z94(BIkhhv`60FtIk>Yg9 zVWi-jnE{LI`_6g+#hRx+BUu#`0u?#4xdd%`wi9ST9b7M& z4__XDN5=wRuyL9#`1>)~4FbXD+TYCkKb17S^fJ>m{_P+0^sP!1G6*$&8k9=Anc9_~ z3T+E03n&U=;$Y0Mqjp^!uI%uaiyi{5mOG)z1h8i=V>SDpDS_fuqKP@Dd!GyK&O>|j zd=6+9!z3NgbQ1)33D54mhey2Y+k?9vb5y;UeRh+;d5O}l`k8Up2zZIoDI0C}vj?=a zng+NgZU{%<|6*0M;A*saPZQcP#=D_hA_kS_z>NS4Z7ii5nvz4i8IQcah7wYI2vP(n z8HsxYB`5dIv=R{GiT6VQ%WOZ4w;sR>sK_ib%!@ldSL7CZQvBMh`xuh_68cR zZ)2yEi-mT}p%EyYYm%r-g8*h3p(2NI5f0PhOTrO_8J3KJP>(O?DAqKCAOZr{ddv;g znv^lBMi!dj9vf3Bc=XJKFl

    *JlpeCIHv+#7+PQ9Zx8Q*n#@?ii%oMna#nDbD4f} zg$u&Zv%fDM{ld<@-{_(hAr3n8;tpeP zTJucv{VDF>lW7g7b__F4*T~FP3cvv9;h$@l2HCi!qnyj)v{^zTd#EQl9}$kO08mHG zJmrk%A9hqy@j`!Ze+2_i{e70!zta2hiw*~`xtu}Pil%or-&tMOV5cr@Nqa&O4ah|H zM=nNvk_7wcUV^LlU%TqGh=Zez+@fh<9CO#E4C8V?h;@w(MHO@Ll7?<(yVpvj0reb} zExq7L2i~K37+nrOE=oJQ#fpIFw0H~kUTIEVj7pI<)NdE_KAWL?JU~kRiWk=#=xV^D zFTV4T(!Wm?nX_=KYjo<_xhh~!+2}eL2#P7ZieikKpm{on)?h3-Vb-S`s|OTjkR=Oo@~H+ssq^&5vDXFZ@+W z`|WwQ49^dQ-@XHb&CAVxcGqG#^|2EABH2z5GS(5x|)SF+v2O0B8#u92sH#zbAe`m`1xldTb@a5~2 z{TD7Xy%XJ*eL;MGFZCUdH=t5F#Sys8I?T&aoxP^^FIAipO{1#z6V6k>Lwdmw}6X2z$h`o5b8&?tAsQeTclH@7qgXV{> z$L}v|)Mp0T!JEaJlj4R0ju{>^j{Nk?6-8%2Z9uQ|OiJ^wYIJT?G4`(f;b|ED=M2g+ zvYJZX_knjKjRNBi$E{pvku3U8E4<0m`>b1}V0dMXrZ!%6Da778$1#ZUN zlzseqQOVuE3*t?9(i7d^e)F#-pJ~e-&WdTA>VPv*Mq_+swYFmN(j{gT3Ii4XE4aPN z??f*sz*o!DPU5r2YHk#n=5*NCi8E>u(Tx$d>ye&Mrcudz_YQqMhKOK_HU`PVRalxQCWZsz2~W$<7& zqS}rJJX2?|I)3K*?<#7E3zx3;T-d2Appk<_&^9u3GCJ5&xpjvx{2f!KqLqq#% zoIVKl;JkD+ePMlaMtzRY_?4L}(m+d1W!!o}8^JM&4qYjh?(K z*IV2v2|Y$B!qh+icT6kHz|b#s`|piAq+iKeH|Y=>N?X=SazpA!?+&^?@M zmRmVwqKn#ZPgJArJQ)7gypR}bwNo|`96?X61#XfU5>HssEiFlsIi^H@5iO;@J3xa^ zm>Kkb$$FYmydgK$$?ILH5q9oh!OO2BNc$jCZpxKLHQJRS_{?O_d*xshmD({q{+}3S zDs5^+lp%G!RHuE`0!p{k2k4(_jJ3F7g(tXWEO zv!nOo4dYwA^w%(o5=GQ9HJS3pB2gpF@(jW1WE!)cp9-nBkCfkc>5MZ)!hSppH)F#i zyXu{hU;g1iO$)+MO(szy*ww@`}KY|G3Qf zOA1b8X{H?aTOGH!nG+j!-}y04{UYAmifxGRby6I66_YVD#4{H6KimC75f7SalhlLl&ppoNc~C&}6#@64$Go_hXI5|JuH^)=i?!W9}Re>&7!If~faQJ(C- zZ5ByEWtaPP=|Ryw%gqta!lsP96QUZqJ=B|5e6;XW6IUmn&V1}|scG64X+Bo+ue|`Q z#&qM_gkcZpU%>7AF3XVE*E6kCx5$R#I$tFk4Y!C~aiUTRW6N;3^|m#Y{g@|18wz^q zCjR_Rmo~(hAz(3_HHc|dXR11G*D}gDQXZmCi=|CHmdZGud{eD<1-ZH#PxX7{Br1Wx z>Ki%bx@A3O$}xMCu#kCS$HqNhzg}brR&_m@UsR;ln3aY503N1L1WZXu>4Ap_37?ab zb3;r_EMaJHkmuYA_dDzPGa2gKYE8-47(4zs^BzUlcueK^!ON-hXzcHBsspN>3!0zz z*wX7QgJm8-1pnD=@K_Wo%J+f(&IRak^TjU zePBRt+@L`h+tPD0bTXrCnGR@cR9^4@z9sZGP`*JuXFHP!2I(sB-*!TxW+hVt?C3u? zOr52LnNT2vuKdyO%A_F{UiisG5}C4}`n0+$-~8zpaU+WUnbO}hA8kYuevH+`nwICZEoMb zeecT?2Geh8vCnLkp8pfD{0E(Gr{x);nm+MkY?)!Iuz4&J)~xUdVNW=9=KYa%vrf6L z{r(o+(b1vXmu7qWcBxEvD3Yshgoio|imNa)xS4@3`Dx|JjO~vUU5J%RNk;2W)=y%-@*VLq9WEa;9p|A8Y zRuWSPUKsr$a)dH2uNVIO36etWg3go1CGSxXE5bKJS6#uiw?oRkmj3treNXqV6~gB2>K^M^;{1lAL>}uF+Lg+i#Ayc zHqSvX%AbC5=gGODBd%h^9K1K5E0L_^zd%q@R)#_8$W-KK&z^0E?QavCM00ZNfrnIk z7pG1<;;9?COC5T0=gU70rd0xw`BBwdtQ(@e+P9XL3$>Mx?Fg(gKDx%M{9C`&Rvm)n z-bj|dXJdFY(UyX$s_lJNLA{_h`{z7W_e*@i01G-;Ym=xUllI*|OPMJSdkl&OX%=ut)n-^5_*`Af|`v^FQLv;#kHziGZZ z^~gge}H!u=AbqD0zIDW5O~FAib^ zS}BTd>htOv&!dZ*Grx$jkV$H}wp3SJ4+cK(nuIAron+1y#Ys3B^CDkS-CI8{7q-lI zz6g~->U?u6{CJEo>hK_IR)iHL~kZU;2g)F?;>u8hz1 zBuY+4-Iv;}9n3dvBeuM`_N2Zr;&>`^Dr@6`d-Ly&b8!!;>U6oh_*X@y&OBmcRc+#k zrxH5pgH}HNDnAfmE)r{XX4$`-zojx@tF-9IqG+`nt1lNY#IJgg*{9RNkMyI$=4m_} z{VLQ4glXT0%hxuU?G%V^#UkBqv2MTP@o%a;1;C;3OYw zfJUR;{L;9{RW=6~eTUSew7rCFBgJ4>AXf&8p=WE-5>jgpWGb!Y18 z=hLIB77p6_`ud-77=$OE-de5h#ki@}78deV&^r1f9&PVSMf$1z2FjaJU)qF@zsk;T z4$AoFfxr%f)v=_r_FBpHk)}_lpVFw7_517H4-KSS{99z4aq;vd8{N3^ZRgJ1*|TQ_ zE{lj1JFcdk%tD^r6T0Z~=lmrdPoW2HO{xvNy5F3S2RjAUCTG|ND4l58MHe1{UY2A* zL&HJWe0Q3h`{>#sFjhMWY>A}MKA$#7w_Z0Y@@k~`~E z@rMuhEb4rtqoY};+hQXeQ+Ktkf4hBtkCmm8IJR=K*bqWuP+>mcHq-J__@(yfix1Tm zhD3u~UTW+L4Q;|nxXZ+f}V$~|K_8c3Kw zgs_4R9V#$44GahXw6lhogWF zzyUYc(fxN7`-aXCWB`4tA=?8ia*dxpeVSft<5S-!?c*mCx*BAvMjr78>O&R$u23yO zwpl!noDSCR2&R^wOr6R>t5c&R<<1g+Bm#QURksXPw+5mY7Z!$?ZwGT`ifYWo)2v!d z~NnFv{)2%HSNupKt^PYEO~3^rB$D~NS9 zO6~W06#1CVU>h+r(lQc7<=N)>&ygt=9Wj8wMtes`gf$5GS8cEt(mWB(*-wwJ`j zh(s{IPFC2rJbOIS>9{;vsZJYyqCr(%oxIS{paPmR;_59^;Mr^1KTDQ$4Nz`!4ys0C zZT$DfV-2B_E9;|{QO~~w9cg|ha$#X1K>}n&Icw8x?AvD%dV;La`NDTb>I0TX0Tim~ z!9<;ZEXxg#<>lpy>S~s6mav{*7oLn9f?9Fd*G_6R-rB~~mjo`U@X;e7lds2{xIey@ z)H;%$J^xf9^q2Rcye{HdRA!D|l93vivzVgU#4ck3Jo>ykhM502M9D)WsMG0RGura; zy-Z>}^K@E^Az&}&ro^k)s3rbc{kS!JBw?#&@_6Wzj9bKKtChpYCS&=JxYAr)Zp`C;Wf$_1^JRzv2J*IgXi-9T{aLBeM5s z$lHkQ$d+Vf?{$=x8JU@dgpBO%q)-SMQPz<{=CL=w>(J-({r&NMeEriO<#k@?zOVav zJ+J5Wyza)Ef)67xKOmUe$y^yT`9tQ=q9oLHEL*-(LEM)oJjG;0MDD}qCP}qytGIYX!o}Mgv&oSEG;Qze>kk<5q`EX`?=Ce3 zn~(Ua)^Kz&umGbMQ3>zf-)~zb$0ob%v7V*27Odgg`4-+76v=bFH3dH9{pYPmcDBbc zMr=(4x*FM_|2MQYv#^@Fx?5oxkN)Zq{Z~=0F9nlz*&0hJjjVD^ zHc_3b6E-o-x@KqhS2(#-NKjBAKJ)tdNHq}Y5^3n3BNtI!zk00f%AW9aCMS(CZ`vEI&# zii-7$7?+zrhli;?#fQg*|Kd{`yw(aqFsM5I`pHV#DP6&uyA-%PQt2o6n38%pr>Eg!5 z2exMn_uyw35OuCB?nRs^ET+ov<$AeCzDr>d&d+!gO}uz6(N`mU&Oh>vrk)Q=Cy|SN z^smH=djrI0jxA_wLWoK)baDP~8@92r z(P8i4aQJdzv$&w3t`hiY?F&trzt%Z9Ie6N@nxb*u&fb0&AlyjT-28B~!eMYMX+bXA zgvJobjHSWqVY#rR*vk(-uD+I!I3(^#GQ&*a*+4fT0p;4`5+s$F?XCH>wD9D_br)2=eoEe!}y_fdc zLD&CNYS*ilY5ow=kp9~8e~-t!Rt{%i30(Vjd#XDl*iWcsIygnxy7irPw7l0JRi3o9 zkXM~r%yYj4lu+da?1WROM3le(Ge9sO1!5m_DV2XGLlp?va;miZcS8dMO%{y+kz@@P z-`_Fg#!%ZD{ph-(b9RxyBe9Ka=d$4`mUohSN#|yk+!=W$vwvmzaMD`2K zom(T)s(ymOy~o;FHg#@J&Y#himKM9gN~kHum~9ne{cMjpGnXo-rbu#t_W4zHb@ge0 zh3rQ9Pvu=YncceANqSU!-dHo1klc&lJ!WHKrST#z4dL* zY;PO>S6lSajpP?1>;3dZg*z-f>? z6~J|u3HR*t9Dm4dG|$I5K5Pz#DJF>@En`86J5hT`qD$8$Pf;!UM(zj_bQW)u>^-os zHV(#jLiSun2+KEV>&|TX3H;#YnICUDY=;jK;(76<{AV+plneGTV8xmV;83voO{D`P zpM6{GS4@?BrVJ>FIuyU?LF324!~=)@lX z<(dvbY&^z3lPgu1W^9-^^@Jl?v3f|Eb^E^-NEV(ecKxXHcD~Bz4uJjxj-sq9f5q+S}{cF3_aH9ytzL3dTa5l>(b)qt++VR<5=x|>kY-; z$mS=505pbDxjzPAZ!GD%-1gvWNTi_78WXE>-k3Ds(xbiHM^&ecC`O4 z=VUVPzf_%lP8$lyp5ES-1c-}41_p-SA3s!qK+A@F_4M>?I={sg@V$RvK%OG?rm1Pe zThQWYeSLk4HP64m3vj@o#)td+*AyYaDSq){i%sV*BJl7?)5#2az8c0k+03>n!)Z-* zHtq<-+b?${3LSRBRtUb7afzg9$28;q ztTsESemq;mz)}(8dgH`uf=X+F{|G_Y@$t2? z`_I2OO!J$~lccn9i*>sSNoPj#c?N=(MA%jikag!V+a^EfB*ovHe+k-AYN5RE{!7K+ zgD(%C-Qc2Oas5Q?Z1Uc!#k({uF?$41a%UPd=3+u7M^GO+c01rVDwmo?NlHFo0nxL% z_KlWvQn58I_c&9D=$V=ZPCXzZ@6z-fgFnWqKm*Jwi+PbfLT5-da~FFDi+n(^s)wag zk}Y8vy4KUT?Sjp|bl#q*XK1nl=!{~^|9O;=k*^I$%zwUSc+7qM`d1sKbgL}(h(CbB z$wD+j%tGCmpw%<&>&x!WM$*MGL^0YY+f1T#HNfsT(s)hr@$fq}I< z0g0Je5dyh{iBLXm*#FsIL=aJZoP~;qGnA2GU8eu)oj+*!RC;A)HdzSP z0o9u>McpgA|EgP34==C#9UhUDm6cyze{=9|wv0ZwdK}(a!=?)h4W6c~sjaQ88@r2j zOL?bPW8yyk>u_+{^+3Ygoc~$jpi;Z)z3_AW(g!BX*j5C2&%bQ&r7KNV@kLjCR21dL z()i(6HT-s6=>V$sv=7on>=M-lrr-6^cX^M)NK9&pL_zcLFhL7J+dOUiH(1hv9CsBP zo3x@!c?&9s=<_#Tu7yauPqB={@!*I#-Cy6v=06{GB%(;mt4ec0YGJ}DKR7hX`AFb# zpAV6)A}2dVvip$~k4e2f9VkK6`8StxtxgGN5EUUda$m%`^?^W`0J&UF&V8!$$HQmA zD_?lnw>r!ZR!SYGxp}KT8KKjWj)d*jCb~5fyBJw3Gk4?8xg(4d8AO^;1U&T__8LRC zOl#YId`v;&U~fO=1R|QD`*c^J4JaMO!-N+D8bdWg+{Vy-`6tVw(!zbIzy)P?!k&uvJ3YFlXxR{tpB@}_9KRU1KEQS>z z8d#(hSQIG*2wr}iqpT%QtZQ!Lh6bq2gXoPBk`ZQI!Laej2~NsQ0v-Yi;liUd88#k* zTvU;y7pK{~ZGQC~1go}<%^y#w#$`%XsHW^Np0GOmU!dkGx>aF6z*Cmi5x6;8#pFZv zAtRE!IkAZ#fOB~1B5zg8y6a0xl~#6Gr;&_x!WLWfI5J-N&vA+o%TM#@Rb7(bnaLXb znl4{w3%CQ>JuFIoV~E^*qcm~90m^Ehr@C=mXB!FDRzCZ9D!R%yaUNWXZA&@sOY1b{ z7ZjL#_G_yIN+xSi<`?M#voaA$HhHm7&y^<$&_Au*DzEsakBMnhK*>2WLY_$fD6;d| z#l^*m;9E2_LQ%5m)E$^4>%I64kF2*US7NgpKk1|IheyczkA!c8Fb__e`QHi9(KycR zT#;%}_9kk*6w9*>w7L04D`RZjGA1^T9zL!LK8{Kv;DSiejBD{F5y5A?j7OoCt!?+~ z)3fPp*{ae~w`LI37~7JgxhS}b2-fyvmbch87b7CfhbG6q#VlVvKGu0!Ha0e*qRAN= z=gk>`F^HXp$7hjHv3;K$yBM=9IWzXxgfu|bZOQw8*OQN)k#TeHx3AiX6DRHho#CRm zE^(qKccXe!(!yFQU8RS1M*?e?d7j4G=Kp*Zh%MIS&ZU`|p)>#UDTl}rHU);Y>Or!4 zx*3039b_wQq;fyjwHkJus3xu=MGhSMf3 zr=E{G_CfTX?H?Qr&V>3b{>vSanAs7h;mZBT7FRRrs^~HNA$vhqnh_D>C-i^;QEdt1 z?o8#`Ra4j?W%50f#*RA(cAt!!g7$SMTO)T5zNxV9AL=Am*>1M+4{ym>FLJc|UeG~puRJSP8^!Dw{)9uyi427gEXkti0 zw`V&JlL%}!9|WsQBArm5ia54@zb|C8DtDP^UhaEyqwY<9{w#ibirW^P z#U&-pU1#%h5AIKQr8#$b4Tl4+%VV}N&gkKfiG~EWcUJ?s?MfckAxv4A6vZ8lj^bQw zPXWP_awXu)St~QjdP2o^wiIsl1QsJ`92UV*u`(Bi$1gqQjPlV3{WhVux0ieFd!hfG z3+{2*CcUe<9#?shsf;ydk|xKU;y6^0W{t0GDTw zBx*K8X*HAL7Dl*u4e@pz&D!C+9?#5*iDh<^GtO@DE;O^P7Tw@HKDgJzQ_#0VT=ze* zp!y$AK0)W8-ua>r4kL@`G?GRBBtUu7t2d>mrx&N0CbMpU%@zPMBz+MKJZ|Z?(NSvC zXY5&VyJAzvJNL3YG`jKHE?E_7w=5;97W92~=#a)6Q2TDo|9~qt3ZmZvZ=>@zi;bIm zTt?KU`G9!4_}m41f6`aS)KB%xot5SX57wf3`7T_@tghao6$ZY_qGhx?N_0JjQ)(eGAH@}e3VW6euMCLGU zd1a;7{o%?5CnF7Y>tX$gtL;Xv44I0Q0phLet)(;;!Y*_-k@G}s#NSaAACnS~-pTsO z=x3Y%@d6m&&)K6#k22&}K646Pyl}cYyZ8I|);Bj&6zcAe*4}=6R$jGT72rMkch9d? zO{@r(?ZHDbU5DplUi1h?RK^_7lpB^N)AIt&F`SGV6dHRP`TOsF#P`uup^{KFcZHHg zeyd+stHBz+H8A#|*f<9uDNRBmMJNNrMd3nugWt$1gza^+VG2144dk20#evLHWKyg% zr`rjS&U-r%N!y-q{tJv0}ZDuhXH*qv?AIcT)+j^o8g*j3|=s(fAPD zBB^IT=Cj$tRuj#SCaGlSOPac0iqDlaT0v7dSs`$z6V9ros?~JhZz06YndUi7Z<8+7upNG~~y;Py&(H=AVt^l-@y=wX;s>Bh4$b9?ug6rBh zmEiZawL4$0#-wuPIlC^P8=1CTq}a4vaG3<&h$fzUH%f~$sz)&_nQ;eAK`j%jqW>C2 z1aA=xlpOcZmplyV{_RUMlxnAQLcqLn&mGhg6)!KZgSyXh&-eBMroN_ZX2EIRxBt@~ zf5S*qJGTEx^@$$E+&VWH8;@t*XmOXZw=u!rcFpe|yT7cv zkL0!1Ha-DLjt^$qyA?K-4CW z(Q00fCZ+7u#a_ayWs+7iyd}!_d$!AW`P=paW)ee$2}d)YqFYxalsl2i-a6cNbhdxF za^T&eG??C`L6#Q)aaw{xLRxiQmt22+8f*m)#48Sq^w?tXJ`=QB%&-)gach&{Y6f#q)0{Li5DvQ+wRMJ23vFKi&5(Mr_kwiqA~@Quom{ z(U#OJF&H%@F2;-J^}R_whyagMuD8kjl&+R++dF(0>23NmJ>C8JPLN1)g!^h*|DP+h zia83WRk1m!PxW*@p;+3r-nZ0)k`sf7M-BC6nk5B>A>a?gP!KBb+Fwh?6@w7 z1)mR~qX~Fu|LQQ~-BAf4##d2Qwe;mq5J+DBc!Z_8WBFAAn_+azklEmX&c#QLNPwh^ zC`bcGTjn0(j+W(cEu1(i3A2UsDk?m=nOHb(0fiH4bj%Lkr7n4ioi9G1JC#bbLHJ=J zd%uQ#DBaFVr|4M$LjK$v&(>H?tPIuw8%(Cq=qZbn#X2cnSDc8)p8-2Dd|h{Ab-LU6 zjABrLv3LZqqeJe+)a)Kb*S1oz%=)D7c2R$Y85THa0t5Lj??#q|uwr}(bI%`tj|5N0 z(c1MLkMk1Z;+}G=ouW(8vgcHLu^_`4Oc@ZAg*el!1s#xm_?zqgMCe#$cx=uQaqN5RQ$P~0Pq5K2E z*PB<_j-JB2jwedxKq+S%{5K^|dUXL92Q}d3W*bo<)n1H%5>+H2P7{kLiOK9WP`p#oYF*|9)k&xastkpopt- zZ-Vqig?o^ro@l_x$Y_2`Pj9XC7yMLKN*Eg(OGt`~o9f=YiEDvwgQSfp6@K+i(M5zs zs4kXk^KRJbiLH3Tx(6y*jD4m72V6MO2Um$@$-J&>5sm%Vv%4)$XdXU(eae|g^W24* zLpmQu7fKgF7l)0$qdXYti7&I@CpZZd{J%s*PZDT=g~ej*SXo(fp(#322db4Wv|K6g zh-QGE{vBCNbU(L_csU(s92<8P+T_CV7Ci3dTlK{mTjq%F(8;A!bk-_aR8P8#i5m$V zbN#Eld2nJMiks%qN1W@R5EyUuyLXPOd)upUeNYs15v+k5 z2ql5!{GyFLc(4DQhIVY+M@UmVq+XY_P82(n+$H#<Xjr)PuIkMJa&Q2-)mP`qESwGkMc_=1t zb53rGR{s*k=*BE7jZ0n6A4eS$nGl+cX#KCP?!4+T8GQ~ay-<#<_iTS2JCT4A|9#t@ zhGf?gVt0cdTv#VwTTVMA*Fcsx=S3Qe+Zr@?zMp2w!1vdt@YXsEAJ!8SgPF%|4`9q^ z4l-64pZs&h`Nc^3uKeEqt6B8!-Md@m_r66)(Z-^`DycAh-#29V9(OH3Hj^qmBBj?P zOX{(l+qW!C4Q8EiKW%qz8O;2co5#&QonXimjC72QJiTcdU?u`a;f6I7e#0$)?dB6> z86^_}sZ?y=CTbwR65$})rJ+*{+c(%}s??QxXx66v<+G1FY})HHq>V9w$StbcUHPjx z-8yqb28xYor~e4KZUsKUj;rqP?}|~KR>6@Chje|FMg*{JX$$;_bm|i^&CJZSaASG) zpS)blD<@O#KP8Z4D==x^p6u^WNps==lJT zYxuZu8)bRm9;%})@pL!i1A#{?6hmz79+4o^+yIeImAU5EEHY5#&fW^$R0F?ABR8}^ zO-V|gU_%Gwk4FK-yyS^S=vH4|#B5rdxoi9^vHZ9Q=$box_XUJ>6p-XGR$I;3>=+){ z_sE##VALe%U?BaiXd(rcXH5J=_l~k-CU{6MgQ{&%3E@W{5_FNY`+s5~47zgjZP(VdQR*qj2d{VMFSjZR1q4tIY2qsO6(1u*RvO0~bT`aZD%*GtRc*D!b_c|xX=DM%%_7}(!1*Q9A1qOvXcfd@Rk*ksH zwhs2uyF4`A`#p;wwR$fNy`S6%?%eP`ksAy(dLu+Lg3&0(N6}Uf8hd;R1Jm2Ail+%( z#Br5DR_WX``VvuCL{$1+L=#~UGF5&2iR|5v-ssJi`l-02L3|G$bSLPn&;yqq>;lHi z4L!7Pk(cQ)qfiIi4M7j}4>SzAW+%eo-GT}869kOh?m%mtYISABfQyCTqth1H;X0xalkSb&R`~8NI9i*eg7&r~ zB_t(54i6ZuN86y ztmVXJUU%cZx~uA}diC?eh0>+8m}Py;wCa9O3nf;au%V)^p8ua4TH3KSf+?8E5gKC9 zRAs=+$mZ<^n#_G0D9KOtcS&$faN4g|RC_5+qn4=JcF`G>Zezg&FFr0GB2eskvMcp+ zB8>tcD;lqr?AADphKrgUxlZFA;)PE|*hT5TooV6@-&IJv?v%dGt==bbQMlAYb$x~G zV9%5C)L=x$^MvaSa1@0QjZ9IxykVNCM(U^~q(NJpmf*9XHQqk6p}L)0M~azPm`19U z@Pd2rdCk-$6R<87fctiLPRc#@%I*_cq$;Pn$wjHIs0e3u%dv%+Wq#{+?!oeFt;uY^ z829&APATF~FmnzpV6evdRVWO{Pl2MyWy1mD;Eg+b_fD*`MMosIy-@3_;TJJWnlS#> zgdRzoI5LlAv&3th-<7R)Qi3{J6bvG<*&4vh75GH%|7U4teJ8*XpM+C5CksMeiw*Lg zoZhTFGA`QD**Q39gxMP7Ve3IE~eu{M~riFGoJBH7RA|G!)B^5tNe z%OAE)usAk{bPASEuV46(y58{PWIfXb_R}OM%d;w3eM0*sxbvvM#8w>$5pfvbH3B^q zIA4wQ-kPyoId}#2Vvi^012txxMB3TxPCY=b+c!0=z=KLAQxfVfl%S?-8YPy6QojCZ zn4@>u2HTu8xF=ik)WSVdv9eP~o|S>M@P zrke^B6KUjxM-LQY=&(AO+$}YQgu&9O#Cv>GQ0W24-YEXu{IVNG+0k5U-SwE_j2B+) zOv&p4gU9?(o%hliXu@%+{(b5|vUZ46ByDp>-amVxdShA)I~p@L2QiVjK%}64GgN^_ zis=@q03si4^&o-ucE}CPRZKokdf@dDe_fxRB+O_}?bp&^8eL$ZLfqbY=EO5}sf;&a z{*$Tq==%HS=H_X<11;jiCrL?3@B7e`!P9K2yRwoLHELMJ5b&yYMn^Cr%x8EiUts$N z97MEk#O^SH5rpcp_PXfA`b9!nuO>>lKF&<+TkA&ko~fXCBHKloOo!_yqq=DV_{B zs|1f-+r0Z~xOq+(50qsR&SNIVdV1H?)ycuDskjrP!uG{4C0y}vyIbhKlhb}zz3=K6 z_&|2|=vJYDJ(~gT(LtD>*neMqI`W<(!Yt*=UO=sbSm5@_56QaGIVo`&H&RdUr(jyKtChI+03XLR8PWvJ-*(mcovjoO#a2m04HZ{Bp729~U~ z$o~fVmEbe|)eIc5YJQ|=Z zZQ5d5txSLU$*JqXN|zo^vr#^;IIud(9t;(r#^(Uxy^vP3y}cd5u6WQ#*zl5Dm1dbf zGXoJt*+%x@?#2l@Wx1AZr-u53nLpxqtkiiK79MV%@nsKGZ5vQ1RD188G9-^0=9ny1 z$igp+YP%&K4v(5ZJgsiYOQe|9gr$$yC}%_Fw!io}(Z>O;yW zok$Bv`5t6ezrvzBE)=1oKBGxV`S{*uJ(LNn&V1U5_!U-4NE7S9peL3|8p=gT^SN=; zV10K|FUamntwUYaef`bdb-B&F;nC@lolwn}`OX=;^0?DDGwiuc{^seo*NPEEB!Y-W zqCHudLiUI~*20AbL62sHTNbb{jAg?Nti%$BzCF=X zWm8n)ZnH#)RcNN_QwjO=d*k8yyQeE~Q#3{e%}!(dg5`u9kqI(@jC>8hfO-;mjSZV5 zZ2digRn|+d(Y?xP`1|OOAHJR}HE~JqAt|e=s{<^psZ@Ixmc(N9LSBQy`N*`8!!I8h z^_c}V%3*N=7}}4{$!!c1W}K$;h{zgLkZ^pi-i$s`IbM%lv%41R?W-1@N5| zhD)cbIk-GBHH>Z7jJqfmXUgNki_>kkpV=qn|KyA~puMLh_!56qY4Qp3-B}X5EKT^@ zT-z^$$)uQ>nYl<2i$*B`or?Di(ATF|PZUhs?YT;2dh3A?UttQVw*-5tjfrT95RR)v zxMPSL(;XgtUN!z)J_sgHi$rhA5wrX^L%wcEz}!+{<9p z*hx9s32|C)+y|0DDt3jvKvPrGVz9|^#;e7s#>BHpTfCcQCa-6R6qA^8(Z)>{9%Hcs@oM$#qAeLOni zhp=u>wqJr7iMiq7;b1w(p^~LY7U`92@I_7i{AsL{N0%buWN%(|f3z8p%%lW7B~5Md zmt(WlY{{d3%cEGx0!~#^OGTH^>@58DQcuS(c>4-1c0Dv9An@;Y(Aj~xx7hB`M;Gar zpojF<>E69tVo>gW++~*ZTbrQ~JcPzmjUfq?;y6p4k8AgJt_jMR7mkLF0L9IfP1ttmyAnx&IOagW3fCc7}; z0fa9vD_UV^gje@XXQ%gAf1Yl!qmvWs8I|)8=BZ%WV!3wjoU7a`#vhrT(Qmo6YU}GiudS_>)Hh^2VDQ|+P23o|s}q}DN;`03hOha=)I@Uh z60(bDO>7Y{yKyO(e2V}_aYZ&N3tjni_`Z%ua7$MvHR<-D6jSzvIQA)WLL zm%!wA#G!z~OD8QZU5nYPMhxywpC`o)H>~>>mMjA@TGADG+P4BkNe_JujjYd%E8g+@L{E+ z$DcWk%(d;hjUJG1OqjJE>t~xjFgP;zwX3X;`7iCO!4QExY^0em^ocBCHZ8>}psT;u zb1v##LBZ}c!0YutA`^deGtyTvD5`qdR*hJ$JW_c3!_OdS&$rfNrUVvNHFNpJO-=@pWP_^ruqT+g7_%B%Mdecv3If zfgam5>c7;W1)n)=eK=ltyUI%lx++a~yVC<*S)?HeivfI(69@3S2ccYK>wdbeRTAu+ z#Wu59ADg6f9)PwN)C(g?!LooSkL3d!ii(Qtd$Uz{fok73170yW$#($NwF8MuueJ(n z#bM^BblKw(wew8$!X`9gn5b&11Qgc>sUIF%=jaj0=_; zdlI{BmcHtQxhSvTEnFh~S^)pG%X#!M$(UzGtrP^5gz4QZfxc{8$O(CEW&f`ZW_`6_ ze>{Bu{=M*=*<~;fviM*_7?JwW6>Pjvq(ROg;6tz_nR!M^arJLU2z%>#DwG1ou)@e9 zuKMS)%tKT@Cw-8oN31&=Fl6-}SIKK84Nl z@t|GPYU{SBVMaldnREr0L2J^c55X2u>(hP6k?wI+O>P#!O^&9ni~K>n_q0cJhn-rn34grSr(Cfv&CSV;0VjrJVGB#% zMZfPxc{8R7Sbls$rC8ik1MfQu4NcEmFq_lD_=_miKzx&C!>N5hxW4>s15YC21!>DY z)m{XRC(kejEs+8Y0=!GcY@MFV+fleqM5I<~h z1dW*jN*!qb(f*}_IfLFrTrG_lWqivsGMYMTZ9?KATOuEZhUBB;h}_|7=9?b%j!CBo zpsptQk8vAlUYG3IhdQOPX==F>1|sTk4#3 z1XR^}KrE{Th3LCcr#KvAwr6~R+_UBH9jd` zA~HF-NA9Eh*Ge_E)Cizz@=+H|3ORV=X-ZZnm=boPuYgEHR(EA-O|m&`ItD|Ey=UPX zU3@5OnR^>#q2S1~6(n+;z5Vza|sXrFfz>*<5fZEX1y+@fQBu(#c0212^|YFZWW zttfbADlI+@_AAnrl38}&zN<(upJ#YW5-03M|4IwhXoPWx!XfbeTnWyEk#R2 zap_ln|Ni|wq~OHLswx-)(DL{9@56q0cvx0YFgJ_`xnF)`wi$sNNgp1P2%VAlTl;L% z+WRR1`#)ZQs;a8obl<6DQ3fHi`twD2%v+(?K`Yd^ZNFA%mH#=p_mm(Vd>8tUSHqJ0 zB_rAa%o)uh>Gu1xEZ#)<^5@UQdi?^TA}43(rZ;(cf4xCe^c#fiQh9BE zFWEtJ3^p$H%L^X%vB4qFx$jO!(4CrtQ!6Doi;tdQ-fVUU~Ae#AaG}Ct8OniKz2##g31~4m{Yfs;T z*?Z#r>75L^4mT?KOd$wRQw?2bc2w4kc&6qoZMAtvm^we>V34Z z{J8lN(a7^ZFQH8t()=<&9ashIt-gF$**?{~l;#}`DB0S-4W|6o=}gQH^8&Bm4XEeZ zGvMj7ZcO7)JDn8x3GVSwDT6Dh&J@WdL98Ga2ITasz!VMKG>EL8KWe(~a8nuk(Ko^w zDcCMD#epT$SIZ zanp1N(OK1l=ww7+N&uQwUak!$M7b+58wnlN^-pB9=jRPSkEp&l-%tr);|Kvi`_`RT zI1xquCCIzS#ljiunkm{b^aJzjR~BTmBrEcs-CpnU;+Jx*1ahW#03G!FHgEbM{ZN6< z_5yi_TPH31zM1*6&@T3{T)($omE1V4IFD#V2FT~5Rquhd28KxVH$wbx0?p9}@y8CU z`1(c;VFjv5Lk9AQs{0F2*JYIcpeh=5Gs=JIZ}Ru*Zr}9Pr350 z2p}8DO`cEJ>thmMM|0+#TtEa7r!r26_hGc&V!;mq)q!rzs>=0v$lbh@kLWTFjj?k2 zMbqetYh`}E_oie_sZC&~6$A+=kx^e=_av|y7N(BRxZ+b*L&8IL6j}oqsxP?*u?AKR z-s34>&bv1{Iw~8Mx-{hmEB}sO=f89|-xKk5&Ud3O$Put09V2G^;IQ^k*0WW|%=5Ek z5RP5n%Bjo_u?b8lc3yC8B%0YwnR5bzJd;K7m1#)E)~X(MfbFjtm9_AVLi9o?eN^$e z5&QA7o_>nX^W}e~PJ=8!hyjmlkY48=@CJjm^AYgjhh_1PVMUB@Prc=Ew2}oM^e{Ax zJMe+G3zAV&5ljn7kJsR7lN+8dMR5)y^p!Wh3i;o!9#$H%D3fGW${zx|i@uB@jE`kk zXOrz4WqkZqmVE+zE)g-%j5#CjyK)C))b4E67nEV!yt?>@nHw4gDW0!#9#8k1c*Hoy zru!!39tZBi#QpTfC|CI@xp6k=dN2o{F)TD*e*yj0AS)OtWz|;P1l?!pE!d3h5BE0@1Du>Ts_ucwH7_^aZ?^ss zIpYEMtN?203|Y$G>vk+IlsJn|gYjH>uaB=s5qqwm;q&WR72X2JIxyhhc-aGJ`@_dn z><6zH%>$NML0~NeY&;B;`2-?((fsVJUjW!)C80KR`_FmOpxCTos%AP})*9?N?pa-z zFryTykw+0;tKVU%d)}YCc<5R+bU8(Les6HZ@4kPSYa_ zLMHJ^QYZM%(L_-%+5=oX8H%2>{i$KyvS4eJtSm3@B;JVTHJHVZmN2`}mI$uaL}DDQ z{1jlBZ`F|DC5ddL9So)o8tUu+k#~k~w+n-7>C{*8e-qSpR7YeZZFk_t0hMSr`PV($ z&@)TCmJCpz*K|D2Y=^OPbd+mK+P?aPQl2XxPyhClK=O^d}y z!%bl?S{Dl3DjvPEt&EQD#o4J6LUM+@4~)noxo*=Xixv-zxB)XjWt^&s&w(5rYUb!9 z8W=KGUc}og*rKF*mBh)a#k!?Aee5p#dDEgD-h<~($Ef7UdAIJoi7WiRr{Asw1KEoe>^~o0i&#F#n|E0by>r9i~ z%!Rt8-W{@?WE6-a<$z5HVI=r&@D+TF0=#Pd^)TXcxsfyTvK^;8m zVh10@xzI;q22ZlS5u99@oXhf0r9y7}wRV)m2|O260Gd84bX>3TB|~@I5T+pL0(hn% zk=YyHzMTuTkvxM<5pq@y>;5~)cHS;IONbnutYkRx9yoch)$x@BIpxmp8Q zM=^&4TGyzdp|ehP6pz_kVyK#T-<{6UZI-TgnG(OdD4`dtlh^+`Dk{n&FfdU6+$tzl zx>p~c-1hIUA%0sY0<8%h$K$WEe2-|6gjmJrs~6OU>7>O*Iv^;GRSuf@Urf1V*{txW zGkCd$F(XynAz}X!1V3NsQ9SXoi9V~`I@yC@QGuIwvZ7?{)%fCfu0bIR`{Lo@!6F6M z9=OlV%{fOF^xy~jwO}M^;&3G`Q8U*F9P>PrpYfpOa}4gkmT9p!vG64+Z3nNr1QY25 zM(p$8(tc%Kh8DIZ)N@sl?219gp6luxuN;s;K*SPKqGT7V&#?Q=yb3S7(>{z(EvIEX zX7DN$OPXeGUVeMwnYKQ-xB&wv{NR)gUa4y7An@>IKB+-MdDe03eLC*0^|=!94<`7S z9%aVvv9vm&h;6!}y8>7W@QT(R1mA&0Pv+%;hDeyn%?DZUCl5fqrfGxUIzPy6EOtcz z7n6qXZoR*QuhpkTg0@%wsKdr-Jrj8d-|rV(gjU~Akci4yvhHLglhyZ5;J`=dP9iY; z7n|e3hbeA92rs2+ih|m>55gS>_7sN+S(@FFD;DmU2wnwV+zo&|Vl5o7yRne>rz7zY z#8LJ=3%zc*Vllh-J;GEWKV+Oh-q7y>%2<{sbsSzwT(G3v`qTZe_hK=nPa!caI5K-yGbbWQJnNTsml{DjqOC>R{K z!T1s75kLuT1mtQVyh+ptT$_Uz&FXz_eCkdqZ$R^-19wmB4o=pSZCNKc958JWl;l+3 zgH`Lyw>(0qFf>kI?LnfrK=QGc@EnA0}qf}^qqVw1RIbQ;ugDzPWA~@_qmy#F% zE26G`fyw+3RgBx&G@JSHkm)ec#$)L&lJ_j{a^lWl12K;kS__dI#E%gvyQc?}d`-@% z%0S`BK7bhKJ{G?xty$JfQ0eVKCbdy1T%QG({QrY`XyCYjn zJce4Uoaiu3ZgcM4-OORGR&WpU)BW}TmOYbM%Z`MLfuk{r`i6#s58jpn*4kN=AXu9gLMe!;c;?kc)n+EV z$b=d$dU50Yu2B0qH>*t>VXUy1v1c%GokM+Sn`ISn_0iiK$E=n)8Q*$pd_}z!k%AW$B{FeW;G@VX96s0Y)>f)^u3%mYqJjRj3 z6;4}Smmd=UX*gxm5=I{kHLI#he=@vQf0Gefuuu^0c@aVnqnR+~=p{r3$GZeG@&G~i zek6sG$`k-YdkZ;P_mZ95E21BvEUlV^Y#in1ZlAV%Tq**Dpa803KVPVq)Wa~~c!V5h z^%>M|%|*B;E&u)dqIg}!KHy&i$dgrG7jSl00MLY0W z1+I1`iwZ@`bnI`AA5P&1Kwsij*>&;zU}*1_TkqE;>C8}Ux;T51%=fVmgU_DmV+WuR z>*Pgf%`sxE0G1G($aUX~2^2{8LT|o>KxuC?4>{zlZ~Z)|A6jXtE!Fh7#;`Bgj9fn& z(CG|tjpD;3Fd0n)c4FtU7MtXNrFDb9EU2Qf@!;jTtB>mo-@kV?F*Ric=DTF??3_^t z4zx31&IqFawe67o@3=y5+ernHFP5NKVe<6{eIe9x6B$}&W1K^j$i z09*5HSUCDBI+)rkESR`DqU0P@Av#57XV*f4INFxIsm(I7Is6W&F<6dbVd1c|e=Km= z4=wMAZQ&2+Xg>TXpPm{+rh3*1AH@=~TLgQ|)sK3Xe920Pwl35)va6#}KL&Pj%+0eI zhKYiukgrJ65m6lPQb)gcB-w^gom#B@$vok%qWw84|8#maAZmcY>n0621Jy^^qMCCD zSt}N#QJq#X&6k-jzsDTnOzWp)XrnMl5s+50GHuzsP(~hd_-bna{E~LqA<_Vb)2Mo; zt&=$AD;>qj7zNCYYv%K#|84#87fw*2&SrseDMo+E6z`949`ZPh&{5-GFg3}m-85dF z#l*}a(f-ssi#epSEls{v+x#KcA>K5`9s$owzJ1AaB>~*pZ#QT*y=Bf_zPWS$QX)=$ zNh&+UKRoGB=n8*n(5tMhNoeqRdwLc|{_oOEdRtB{Ycwwucb7RxZi-pb#yKN%?|?Wj zlt>&z7V%Y%5*2ht*9^A%?Vi>Z_l_(UFFvQ%5i*Pqyd%$%tm) z$B%7u2uq=R6}~DeD)?)%%)?}KpNZ4_Y!ajX`E1Q=Eg)Fuh0XlUX4c1*JRsKaSu#Yw(v@ zj=}{1hS$>aK6#xA%Il$@5SIlPqzq^M8weH-xU4j&>5H6Mg3U;-XmnHea_z#S7W@b1 zd1AhW4)pvGKpKa%S>1?xPQo65Lv<_c&g*V>8UZ1j)h{q;5HHf;_BwazX$hm6xr+7F zXNmiyE~=`Bgm{XVLNv2wn&?}9G6|JRG*6|`DVI(*qBRuBcEuSkna)1Aa}h_}e7B=% zGNfkvdZy-!ooQ%+}3_h)&>$bNhZWf@kE)o#FFqxPAXq5H}Mej3B*C!50i%2XUY=;btb@ z`JVl50~vJXR$k1as|RgZ-!4zWyK=U3do@dHGXJs9k~W>Zh3RNAEDPBzEwHVEP5%H@ z$|jKdD{XPSy1k$yX|cO~K3A-Hq5b{5-&axDS0`ba{!B46&tMI#b=;GYA`sK zRyJk0&yDC^geY7pp%c@JV&^EA|NFI?ZF-kr{HxnnMdYF3og+(bm`P0$`aa@7btAsJ zj(zpCPr(onmhxX_$o;0@hs->QlTS~V0z$>p8Y-z^nv>}QlA3p1nuvY;KjHhIugfJK zJG=>@fSaY@8|+71l!`SZ5~DGw>pvw?J4`%(w!XyBt{xD(N|_#TfY*whr# zsh^&5n)l8OrikGXC1SB2@awaK0!oQy^f-HLB*qA136;nh7ZRMm#ks&H>i@5~?~H1K zYZ?qh#DXAV18G74BMJ!8tNI9ps`M%#(mSCO2vKZ+pdkuKRXWm?UK6oII!KkS1cP7z z37r6Y6QB3n@8|B>vwJpw#N^hw(`WA78{a=OKRkJ9Z(kPsY#i(f*j1%clyV|)?0``t z^P%GBiis0Q!sr2(-$o5-H)!$iT&C`c>~O7629EEMQ%sio0aw94YG9?i&JH7wV4jrCW0JNPw^e*E`ASJeCzZLE zwxMd=OH?aMGLZX*kYQ=vEs>bEk4s`QHGZJ;~`_jF1Mxp*J0sDS4@!<~= zT*6T(5#J*~6P(Dwln@+KrmYDggi|th_yZfFwQ3O;r#R(!bX^~`6XvcPKz9=67$Dy&hwc-GnC%aQ>s zE6QA7#1Y}r`!*DO1(t-qC#6e5)*%ASJc+TQN3_YuDEvIi35Rtz9|DfVL$F~giIK{* z^*QvmqUwx|3OF4v5DrM?e)g!V7iJbL4YcsjRNbcqlH7GHv8m7Y9s|cg`UA_$o z$w_1T;KV5PV`fehJi+G>WBWas*t?|x!+?MU7I^zGnmPESfIh$oBlO#N+c%~$;{~Pl z#Hx2qig5Fpdgicv=z)luiV_ufsHiCJCN@loeO3rRkfb`8gQr}O`A)=pqDn~sh;G@tGq;$gO$r2I7&ZOtmz3Uz5b47ymRlq2ZS!VA9FzeF2 z9rw46lyjf9B8;8Fgl9hidL+08pbPFQgL$g>YfA}r=1il)9Smyw+D`XL6uz@%rNO%u z_gGP44=xyq6flDXH8vF%Pv{_1Y}A{BUcC%ZotWbkJTmq?;3#5F>%mdELVdC?ExDW= zb@}KMvC52J^OIlLr~GXS==`Df{u0lhw48g|xtDW@Hy;DJvId3i5V0$+fD*4*X7Xm&n9uM+5y}|s7 zV@2TLbuu|B^H>;iS5D?9rS=z{eVVLY^xy5*HlpX$KylZ*Hkzu4n-4La;ioIo%Psql zq$EsUIj6V1Lzg&RQ$l18p~^pW%rSu+PWzJza#(iNKIa0zfn6tx<~_x_@qlhSobEoM zxEFdpnCb$+4hPpcrsZ>qn|ZvS?U+#@e6Qv!e3D+|`cT!;*$cOK(+ch~W`73MgFv#P z+7Z;hG5a~8G_^7n% zK{+=t7r>W5a;t{_TgMQ*FyQc?s%qHdhyPOr2!uHPt%DC*e`;Zb59l4?C;$4~>BGGJ z>v^XC8FT-yTbn-P1v)Lw6z-p39%EM7-*`ZUY3yJc>t)yr5csCI)#(w|;Gv5~qx(v{ z8;8G7-t-~L5Lyq;FbYT(;sIr zW%|GzvUixR%MT^Hq)vGIR^;#aZS>WeJ}85Hpr#ogMx8+qGA^FxL|fpV<}BB6_?lsm z9MD^5Y>zX>${7QJjhy9Rz%mVE|7E#G8T37Dj+TAd+}6Z0F0a7ghG6G~v?Fl;NMjhB zu@mevo$A{ri=xSlDPSi)f~lY5L$7ybp81#i=BQZqA|WpS$pxTKwGLUjia{!4l>gE( zIC~iYc9Y#8i1`VHb!9@uzd3P;wiE!>BDP7Eg*IfDFd_HSoo6P=n)b(}*a~zM=*ArE zIo=nq2Jh0jay|=o&wXahlTdbTMkXoH#=3i=C?2Iho1bDXy)q?kf|^w9J zv`oUtfzsTs+}CH_!lWElw|MM1eI*&LX%la=)k zwLBYw;k%)44{o^sbd_|FK%6&UKc%?W{I@ywvW6;i4yFOi{<@f*DdQ{(|BjCXn$3lP zBW7Iq>vn#R?F<{ZGOn!^s*GI?DTy@rdgZ+3@rx&5tLoEH>X1YvFZ!~tSkF;w5Yp$( zJlm+Q=zN)ssQW|rXfi4JW%teK^ z$ydjN=0kXfA#4&6cM#`wPeJ(Z?0wrbw!d+en=ay4LFHObpsD7JH&s2V>m(p|yt^-rElqcJ?*e_|9RshgOt9za!otaW^XKkr8>ufHUs9X8MrbCS zwijNyTn=aW^sy$#00B|F1Arb;s6MxkzZ+zwfas9@FsM#1xI zIoM5SjDVx40}l0dA=32Yn86eWeKA6OhfpUh_Nj2F9PVHOMZpZD{QMPYc=j*~tpwb_2+G})YZHDy2fOZH5-E6qOwELZ|0koyF%(nTSE5A>R={95% zPCOuvP>qA^UR_XcSlB;z3~?STq7ZP2_^68tFiPtr&=RbXym)4XBNnH?QM8rvfa}^J z{73__%3lmU>Jx#v(q!T@lfeGSeKh{sBeguq2CmOJ^VK0`XDg>?+r3g$5{PM9*mt34 ze07+pYD~N*{TQjcSR4e@0dhXD6nHh^>xB22%_1o&?mQzKkJoPrJ&)*aVgyziQ0~l3qJ~EX zk_RI&g?E9w^H zXs@Bsj+jiS`DZ>?NI0{F`A6EOhWWo~wcIDOa+m#FpHVp;!|wYJK=k&1W|BSVa#MOg zG#DCAzC@-H>i&G>UaF581x}vWznTO#2t=HpC)_quhgSZBechV;47D) z!pIi`&{Gg3hC+}bAPJgN>{B_%W^RPm9#E)_3YCX^5bAn8#nXcCL|ue73;fq1g=?%Y za2+4Q2Sol7PU?6?P+i$|*<Dsqo2a_S_U(#m-KDc{={7d@Jch-!kl8Y7 zVU!3jPL>rY&=K@WdjRP+9!4h!3wQjv&nyE{Rtg8yPmD^t=>HPtumG=V5r|P7P#+i# z1++o%a1ub56JMAECg-BIzmz?#I;e2&arvc&M=i18_?UCoK#>_8Jp489OzFww1A5R0 zVrjW169_++S>j*J#Nxz&vG^$4yeOw7kQrRZCo4p%I#8Q-Ejq%iIgyhPwkLugUJ-_c zerEDsdEg&(-1p3@goQ&>el3%&XoxwJFgEsNL$wgO`#&Nad#mL{PhBBvsbI19f)~q7 zI-x&@`EzkhPj*%Ei99W>r_-I+oo7Xh*%y4~FpJN2y74C!WM9SQN@cd}d8c9jl9f>; zu-Y)Y7N?(W-D7WY4x1EQ5%&U-i&PAn8PAlVcfpqSGU@^FwJP1^{WKwBq`x=cX{)=z z^7Rbzv@&)ncr~3s@q3{2WrmP>C|SON5K0F&@O^=$wOyC(x$gHSAt6?f`0ToKh{RpC znV&q%dyC~q6a*iai}-?f0&7Azhs7Re(L3cd)-fwFuSLBui8qQ?Yq_s;N)erpKNiMw zYHs&{G%oQmA^&i){DE4z9~WjoDFAsSZ^PZ5Q?!)fuMA=~aR$e8-W-4a_5y0^{sHL# zCUuG1yPceO&WVw4gm~a;AlX74MiTXy!x(uc^e?B5Adr2_84*W6T7a*>6aC7D!s3}* zK6ol$E7|d~gG>`jh9=j-HuFO*#sje&|5Zz(4jQ%2l*E(?^K4O2=w*oZoU6d7E7CZb z^*kZrDf$^T=^y-`Ne@hyinPkX%74;c@zP=eR^G2%WUx&*{3I~YL0o?3w=yA#dK#_n ztM(nuezRU6gwFw5=gGf6{%rBf1)rFUzqNK^ndmFSn*5}1%*AcWXv&sQ5fL}aj$#Ud zzVg!I<=?l~uG0N=y!nObO1c`W0=vZt;|Dn<+9A!1Jrzk+f%JqN-m(2-`m+?;etUry z=MZrWYm{=-fXJ7(oS+7WiWKK*j!I}dCQu|~H)otR?Z(vD?>tGk=fK%n0jj zR{3ssaFQD1ZYtvXTUNTMZ=10n+J}KgJtKroWfHFb@Q@Tld{cp<5^3^WzXdCe{)OvZ zcZ~S2@DtywG;FVKzMeYdTKH~&Q_iWwWtCb7665F#EmSCtGQ%4k8T+6cD<{c}7F(&N zY;Lj^j2(ep)X;tIb9Bl0-Kh+DsEY_gtTFO*%<%pvze@%v`^rh(sVU>wKf&(V9jDxo z^XoNubYxE501^rPJ(YW(d;-(~zV!Fj4XiMP4t$Q;-K_`4#It^OPe2 znDe5Da5-@#8?z|WHH;|%8NGKUNeAkC$X8m5yMXxxbe#z;Nc#bjAm~fBQ#wloMR8bC zJxgSZq^Ff)zTjX$myh}SE&4Xn$4esv+9gc;4Rk)B-U)!qV;Z?@oTV`Iz6Hnw7w#HS zYHj%ZlW|#R{-KjxI@t3zCzb-I$k78sP29ldN1fWsWE8A4YTa~JiJZsO9F;qU;%F*s z8LboANL|^DDBNG#oi@p5s(~od$Yhe}aHQdZIN@UGlrXsBy{J9sA<7i>8`Zt(phmEB zhcvhW6*6T?@FR#}^22Rh{Kwuq(z8Q8jhvdzS+73R#0Q`10cr$&Z|4&)Dwy14hi2S1 zKTA4xtN9>le%Lg6VLrszO^68Wj-1;O3AEU5<%CR;r;Pj6CMg#*-u&oi-g{qq^-CY& zd&3hqre+P-Z@m?+@AiYIIOiboC_lsHh{0RAM>Yfz_93iPyKy zds+!|G362#cAERWtd&qL$YKP$Xv46~l5#MaTxQg9CN|au5X^hY91C03@(`~5{PI94 z-8}+|S%3g^=$WXBD6WI|pM%<31U!=|xFeS1zamur?$PFal#6iNUw`Pka*$Wx3@$bK z91T<7J794@&j8#3f1ZBX*M*b1xW8WL-*zTu?os!m`VQ=58g`>>Dn@JXVZ?^390r}D zUk9M4M5D1WRr26%D0zY3%xk&~X9&o72Z?s)HBT%>+KY!v#CWo*wdtDY3_(tmlc>M&@5pcnzZ~?|JY&Kvs8u4cRd)#20SzLV9@%R zDJZyOG7}NE9q{wdy5g;xrsNx;O3cYTe`IU4P^$m7bpkPFTB#$r=CBVi{K*62>8wyz zrc8)IM;yL-g|lKyVM+G2B~yf=ubQvjz9Gi40n_~qo=03nOnFY3wTN2EY@ZHiT5^S7 zoq-=w;p(lD(+uGI&+rm4Cra8DPJwIZ`5NoqcDhc@k&vbFW8{Aa${DU@t`EonKG!r> zHrD-|TY^4<|9SBYun~|>69%`p{mQW4z>sk7|M?*4Cm=&x-!SuF-fJ9rrL`hw&+0&B zR3iXG7iohSXW5|;*(FU7J1$rIynCb;x}jQrH2nHsI~_Ph{gle@mcLV+oL1bm3t#8I z*ls#>kQUz6RELlVwZD2yFghvmv^LxR6VZbEv~D^G*6u&L|IcFu_Vd{(&#q`bfwejQ zqsi-ZR@dUL%5jG)nllclPfwj-_pa?b=)4Ju-uD4^;PF}cy7dl$hOof#yaq${B2XbN zQq5yvt~vir(9TwWSrDjyGv0vB&plqg{o542NG3M?eK9_7qXefk7#zr_otXj+ldZ-! zo^72$EXX^gONwri=ls&PVWHvJnooXE`ZLI38fTHRk!M@i2X`Zkv9XD1sia!mwC`1H zUlEZqM<&BIyAj^SUfXdn{BYPudJjl(Qy+4}ghxHqzv6wUr&wsOwlasS)Mry^7REEs z5%!1T_n!vOwF?xeqV=(vy8gaha_l0ZZaRO0rdX5W7%x9(nI7t)gx{uYI_XxgemzKG zJQ7~a$1=N?ZXF)khxHm)d1XtVEUGFSGTscWB2&9|REi=Sn=9{F)4zw57)5zcKCdE^ zFMJ!k=H9)BYOQ8`ZHJ=C`I_MrFo@2C(XeU z3U(vRvGTIV_~_j=z@u4w1kNd4M6+sZrpey!OPGM}G6!NJjU;L8ZLqP4YgwLvt?T(O z;_gx_u(Z@?a+zr)yHR#tdT)E~Lp^W)(8;ZQkbN!zZG`UOF@D@MwHoUrY5Tz(ajW`(26C3KarFMvr8ex z!H79}-~Rl9$;0Z@T1^M08?SpXnj641f~QMi0wpII|9z4J(Ko+^BZbVJjNg zZ?iwd(!7SQtb8*YV)FebOrveq)S~|cVj-1Ohwz?LIC*-t9HyR0Dl=}TFwOt`0ZTAk zE`p)^{Vqtk8)Ad=<0P62fIXMyqq{7On8+jz@DZ7(X}5J87R3^2`oy<{0vT+hXsbFb z)QI@DWBLUwlcID0Vm2>e)V#crRSLyi;?KlpqQ3?TO1(d0?7z|gDB|;QDmJrOsjGhL zJ&rfRb_he*XP!@*&8GjPpe&+Zwy1Q}tbOPK9e5De`^=2DqkpVkK^SkYYlt7uV!ROke>w<8tzNNp9HBM|w(+@BIHBB| zbZ-9l4Xm}0x~1LLjlpzYr}S~Ge@Rj%pu}yF(2$`kgyW}RigojX_IIZfZB9cuE$lcD z{$}9&ec@OYwN}-7_I;uXBCXl=?96!^^{{n1*#7_dWNB5`vqLOAtPuvf%jK{H?d2*2 zztxVX4;HQG?}ZJ9g`%;N7t0eIu`1%N*Ae{2JKNcaUBTA*L{!newPvx_`JcmNR;)^7 zvy_|msN%?-$c(2X45GO{X0-*zQP{Mb1P^zRk()*C6_p5lS@kpESk z?>|2CPGB-CbQ=J3%c|ae_+~AkXr$Q?Yo1WK*?*$i9B-eFZx4TE?L~6!+r+^P4g8(3 z?M2DmI{uymYgtc1523t1(r3xmC%e2Bpv0=#khgu=do$E{_vCK(KU~ui@L!=IBGp=J zV0Gv=*~p`ly@)QoW$d%HiMQ?Bg9&Tb1eRso)U;M#LC(z8!ZN>CH4(|OLsUOO< zG5H+o=hf7mV-s!Gdu!)z-~?{{3c4c^OxAZ9ZR+8e!9021@s8}^C9PAP-T$c9yVW;$ z$@68a1yBJ%y15vCoCV1+RR$wINteFHtC3u!!{g_!G06?fI}Nu^DmgVZDWXaX+%ikO z#WwRt>Jx0Pgg-t!nDcnv zKRCmx!HI~W%YP5hx&3CuEYI>Ut{xo6K*l7pEzBivj87h+mg289iU88^I%U$(>Pon0 zsil7<$7cV-)0Q21q;si-Z{}$#Y1g!>0oW|Nh2@L^)D}jlqoT!qfq#*(<3)DySr*qU zII%xtuM@W_0iJb)Mw=|lQk5IJpg8gnHFquUg0rzduCsq2Fp|IPm9Vuy{9ZAc zGu3Szxk7`j7iH`@VI}n}%I2w$f{Mc`+tIlD ztaIPPY4!O~lYEYG-udM0a3gP?^+p)_=Faa@Sa;ex{UI_Z#ezuFJK5f=6mgEQUs1HR z6OOzvo=(bv&o?VmJE2vk)@ZTT-4~;}?3ps|BmUvo#TfP1TlRyzUc#AqmUrUEO^ILJ ziyhr_@ETS3INs) zmp=ah{;l5Cq1C|1lUmD)|ES0BHIjRaS6=Ou8z|PNlM39qS6{*mwd?%GE^qaAzB2%v zBQd+;>qF+1FYPk4eSe;O+v)p|&_9S?irX%jR9#oQSd#=ZH1xN`YQYp0XT0&&1Jg~$ zYR{W4Jw%6X0ehfxt}`*)i_~{HHcrYG;DL`VG9ZPs2Mpf38rb{`Fp*TiP0$lmZGn2r zLAX|C;s>g8fZ|i>umGN&S!c_9z)@NOMSpJj$6wkK?%uzcB_Xw zXNQ7bdi(HiCyu3TO14z`^zzyUs$DdinwKWK83_1SQkVl?3)q^^2c-KbX-3RC&wlu0 z_fzHNbG3koRY1q-NhJG{l0LEGd;tnx?I)d7}<&!8GX&&PMR&Ex;#UX_(s^Z6W#O0Bx?UOOLOe*WUWm6qA+si?#eF&lIG7JG-I4|Y<&?$Q+_ z(cfq@>@{TNpSL4aOEuXhG-Z=$EWzegEnk)}lwsJAs`%!uXM9k)Va4pVIK3bkX|@qS z)RC`@n*A@mi*{v)&R!eR74Q#ux!#|F-iZ#ZkAL=QRaHIl8y)3tM9Crpu!mn)q z4HaD%L(5?0Rx$T^wJ;V+=X5eGxPK&k7E5_so97=euvw{%DQi!2@r&r_H9gLlG5y~e z^TCXw-PxAEt_{&=Jc+mPSiJ7RBTOB|(e#|#d%+J(gI^9JO1sOABd@;zsPfe+TtKJp z=@Y7*$9#lbV*U~L`XOQX63Aw%bxNJ$W|g$3)J|6S%Ql z2M`Gpg8+qpB25Gi^<4AXBVLBf{}XZxJE^;T9~V1pmpdl1jye3X(KCFMt`J1PGox>% zk_K^xdOo`T`dB;N<%@7TGEh9ouEv|+%$Vw1PrCsms{BRkPXJm)DK$djp{t=lJi71F z+_i0|ab$pUt0nw;^`v%+#P=6CRK0L(Al-iZ2JaKNa8*2 z2*+2%KUYltmplDmXsE$K^T$F5{C}m5@@JA>jI42ZUY2Ie6GoYZWg^}gUnFQ2@~&zh4AqZ`t+_Lc2$8@|519w?t$RSWN7>u&OY zEwL?N2Aq)w=N8ryW{sFIwq6W3`)jI0nu5$8+@G<={uZd{3LYG+6Ur4-og=}z4VM>S znflxpBOqBh;)u z1q-&SIzM7~D56-Jq&_fHQ?+g>i{##DNF;T^#GW)Z%eRVDQN%6|i7I*}DM)0#N>7Ik ziPSCPg4PBGn;K*O*m8|1c_$?^Kw;4mgynFt!0imqk*yKC#cS03c+VR7*5@fDu8oP( zFE?>;zYeRY3534~mP@SCSW;upk3CcEPJm*hjtXOh%TsY|vF~bfj|b!NYZbxq-+n$< zY!swH5ew-?$rC{J#`0oFXh>T>?qPtg<^Ht?l z=75N>XNv_BL$&aK7$dv(ayVI(@7QDSamU;6^TBCRBlYMcQZ$?b^xvJ4EwTjXbThA- zRrpxiKsL+l8JW<^zXu{-{+tBeT<-ladD(e9=}z+Fwq>@5|&wVYj2F#YtqIGIU~&^s;Ky3Bht5HewBIUJ+AQYZY`jT?d4J1 zRgSmRl^!NYH8)u;6ueOOXvd5g;km!VtP%V#NcmWr1J>|~TmCIU^tWWWIhNUZ=P)7! z^3-333j)cqRp!dzi0jH%@#g>O7N5RE?MrMFYc=og9rWJb?dSDkg7`)o1KU1o!KjAn#IB14{qoAhPpFhK+#a{>}5 zmzcPr>Ma)f8S6zs_`9#tiu5Ga#9Gl7{%8iU0!U48d4Vy+^3uWiG)rspLPyXvK^4&~ z(|S)Uve~riZ6e9eNYlj7Khg`YjJT!hoE}j%B$;^@c#X6f3n`90gQa!PZDzoRa*y8= zcuATa2_xnme}lfzs*jj>A(;sc6Kh>G?>5x-zmBC!U-)Mc;jf9+#i9z{uQOIai0PDO zR)Pc7SLk7ac7~H;xSlnj;+Cuy*OP8jPU^+lz&>=HqqixWAuCq}l5DZB@erw3nA*lTW|x$E9VE-oXS4M>ejW z=uQx;Gq37;e2G5z|MO22ccVlrAHv|b($g2|X{1TfP)qC?EU*_6oyT^QVd_}YOWfqp z5mW2@>RQUXO9F0dP8-T2efACbTUDVB_q;?~7fmS{HB)O|pQ=hnLhyYfD03{}NIt%d zGu6T&vAP zy`WC44#%PdTYn)+4VI&Dcy&X)W!(X)cw*Y%Qg!=BwhN1ki8KT@Q_sIU-jVL5noWN{ zOP&PVX6McPrp2XRXSMhOMvM>Lp$yF9N9cV}IZ9tOH7Xj%<0F9|6Ng0`cb~H5_YDtCF?tzAIp1pF8>PVZu>SG<6|a*aQL=Xqu|uLZ zc~eP1EAQSB2BJyHNdDki&Po%(HWGlUf=7_t| zIK>wAG}0Oj{oMcBr)>9myQid1x*0l%b4e!FTNW$!{+Dd zJ@F048j6H^VP#yxTv{WF){$OSJ5LZ3FG+qf-jgr55^b={;iqY8x1N$_;iG!>m|Mo= zg+^ilr7>kCavc_G(q_{spaT>XA3nfu7Wu!C8`*P@7uFbiA2t|EYCEU)TA*`1N4f zA}n+^xw^(U=4E2FVlqit^doS(E*gGYtE(bUYig+bv{fr3CN4QNxMkll)PcwFYcFRV z^fQ#+P~8jBVG6R*+e>aGh8x+mXEem}XUkEYN46v0K-YSo3;D{KF=0|P=;sgck94;y~H+)aJ>zHi&PPI@vPYq8NSjN_ohXzMq zzMDvW6DZ;BI_h-W`|8dxUcMD-9KSsQ9Jc}p$)9n1;f+S zFxA1WFMZSQ>gciP2)x>dmR~t?#Lkq)bkfTarPbO59e?Xps*LFGb$)oAig)Q`_2Hi2 z96ifRbG5i%;z35<59Hm!x`oIK(!_$2MsABc5(}zu;jCbzn6cQ(I{e9@+SJF)5G@E2 zc$GWI9!13Nj`9T&xbP`0TRT<$#9zjGanuQ1%aBleS$AET>2M)G`Qk^(2CvauSfi(W ztGjc5)^MRgS`n8%&!jMeXfI1GaK4@voU#8 zXKq%RG^Ob@meJbmHQKHA`%uWrbKoi`p!UlPj(P=n8y&hi&D&P~<+3+!Ms(x$s=|iV z9_$`xYt7|KN_}#`Z`My=-yi3k9y3v0c(e2S%^PB{{x@>Iah{8ITR#t!Y?hD&kiSff zMx)LB;f8a&%9Vvu_-MSi6nmaVty_mjpnP5jIUGfCxp#F(-SC_D z*ToR2mqE%EPX_H)AxYLQA(VcW5!?T^6#2;{>5E^yPwt={cc3|LZ=sMg9S2+i-($;@ zs`b&@UfdFmuGsRVZ06M6tHGioR%u%!-AUhqjE`uBW>&X{-5c_Gv*UT=k*ZT$P&QJY z1JxYXc%ed{D($u6bLMkru!=8$Ysu0?j#K>2Qw!-1e>OMa*+U74P4ypBA-lh^+Xda* zzcr(Qf@1^}oaAmhlLTr<;Al<>uI!!UyW(XZJQR*(7n}BZ`2yzRyir9a zEcYYQg0I?5UXS021LCOwq9Re7HrA?Y@3ME+e~Cs*mhq$Irt>e<<%FSZ zn^6W7r|R*4%uN$qT$a;5s~fA{O8;(h36U}?50`IM4(|xEM%0Ww(_OZKp0brv9o2Hnd5}N<%Ov0wQS;Dk;+5rgfS$gQ@r_4DG^-HO$50PBE z!B|7lI&+=xG_`^5b+OG&Sa;Fx4)TakLvf;XS~cm)#Y+4mNzuB>uw5;9b_%O7!B7uA zyyv^RD4>SwL!E6EFCv@g$MNu!fV7Sj|Ik~Wy`Z<8Hn-Ve^`F-gD3C67k?GAskM=UBFyEjTu zYiECCj9spe+c$i{T3rVR0iJsdtvQ z`Bt2!Ux*v$k%wECu066G;T<+eL8HVf#tFwD!#6bPtZMPy+#fCFC$|IVsM>l-b?J57J2M(l7~^2etG677t9vquCFQ{$8hN*D z#Nf`z?G8qJE$S?HA~I5>Z*SPQb#6}JjLK0IiS0oarFy0bG85h|^Qd3t;0MQ4t(UqHsyFa5^(aM`5$gOW(32>A77Vt$%W>{dMv z`6~%aoA-C1hnaap_h%o$zn<4{9YY$$Qx1dRSlI6v9Cemd3T3E9*+}CUamfXxqdexxFl=SSb%TFOuiD@JA5`x3FaQ7m diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png deleted file mode 100644 index 13317f8baf2c0ee9ab25f3d839fc35b4d927b648..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 76927 zcmdSB1yq&&w=KL;QEV|NMNmK#1PnruvJga4IxSj2y2HXmln?<4=|+&wS5c5|kVcU% zrJHX)ivM%&IA@IeefQk^-FqFcFI!+g`+0t`)|_+A^?NKUeR1n1x=kb!X{&_zd3h3v zJcLAA`+{OUzEicSQ5#>@Uzfai9$)ci?p%%wzHGcLu4+Ld(LW^ql6my}(jk!;NfPJJ zDA>LkZnv}Dt(af*E2FM?*CX-sG_pHxpI5j~c}?~VB{@0IsqNq0p13~R-2KRce79I) z|D!H?yGe5&ovZuyTvTP=!uXWp?vtZWLTJMdM&G|6&d7XoNZO`dgm8(O94lb zDlgWC_CAHcSm8d)sU(>|a~ij66u)l{|9g7jLC)XTu0@YY+^hfihiY92+3M?bM$*>R zm+#2Q^jBZHa!`n_U44D)42f>_<<-q=s#jl<)OWZNqeA@odiVe9r3-8)YcB5DyEpFZ zSCs_q0^zpLWCzbmNIY&fxqjn@4vkpEz`+=&3-YQ=4a-XlJUXSjr-qxal$4b8{*u0Q zX_HOO<@$SHc9$|gi%UqnC(#9>RHC9cUQ88Dd2A^`} zN1y20Z6LVcqs=6v&BP|GnN@}BHLrQ2s@{OxUX$u)v?nKVrH46R(kIgPX0&SR_=_8A zzf@=x=e8Nu2y~imo*!FTHZ^Wdmm()8KhyBmU!|QlX2Z)PuV*;E&NL2MP8qB$FX^@i z3s@OuKHZfZ-yc8<-mu?R^udD%mo8nZb((3{6CN|qid|aVG{q$(q$#h;*feORTr^m9 z?P1|t)%Ut3Bi2S!gAFb3uHCaTT~7+>pmY@@B{l86ouav1)nh*J&z-eu?{(dm$9)Tk zN6+pLeJcH!aW5lzlUPbhN~(F!C86cn?!mUK_w$njVe#?tx2fqVa!m8*#KJCmr0cY& zN_8p6DJ7g3a#mAQ%Y3@_&A9bgN5X}tR1%(A_5&=RKYd!4tJU789PP3!@<=srwDjvQ ze#d4V-x!CPQP1#ig%8WRf7MSVkVyA8lIhp0iix=i*o?*}<)n0objT<87e!Vk zL_EWTo##uod|yjCxSdR20dp*^EBnt(-;}+gib{0gX`8|9p~TJNodqr~KCfQ6#iUxz z)Jk&sE!b7?G=0s_tF5hFZ&Si)7!}JaFDo08pq>$HGuEN2=35qFl$58WmBjaqWn1vP ziMD*d@zH%cLW)NQC636Jo~q6M*NJh|=0e{2d1cABY<9!*-biJ$3oDJT-Ww_*D$1zw*E zQU-DJ6ZU!b7{9gY^yLaPN>L=sCSnzIA`~0pqsJn9LX8WuVW2% zln=hJj_%pVj~`trD>>BDQ;pSArQVb--aBX0W_FQAa*e2GkmI<7h28AWZwC?zzujIZ z$hlJJskyu~<}&n|TAQ1p%AZHlSIgeOeJ*}+-}5I=YIn&99*__)|3Sg$K<-L$fkbC@ z!fAfeb=NZ=c4fC@ZqdT;w@ZTs2KQ0<(Sebp8 zwYbo^BJ4;`782~TtQx1B^op4X5PCs&y00a7=jJ)g4E6??@-lQfFO1m#{IG#T)y7!l zp=nkRm4#iY_mNlLWF*-F3ehm>@7>)EQ8Ek@zZw(N3svYszDY<+Z_6zIStld1^@f4# z&tCCm4va-0>z}NXQ9gV2uJb}`=V1QAXx;qOkP%OCYSU0dZ$-e=*Bc%&dR0M&jq!fp zzkm1k_m4Gc&mO2eJyt*5{C=u}uTz2lVSAW|uwjROM5JWS+4i8~?&BH1=7*93sd z6niI@U8uDk&C+zfCf|5?NG2#=*lGSS3(Exw35o2VA2;({|3s0Xl6>~_=g(*83y8ql z+Lq^#k+qB!sJ&fyHblU(KLpDu&A3&`ro%1@14lisQM52UQn#5aHzYNcd#RVN;JAl+ zRglnB^||dwgoK3p?QiPPr_}NHy}7}ur>CcEo0rn9t)^xuZDFxI*T;V3h(vO7a!bCG zLqzMlZk2rkmg3l-4H3RtvEkv&5s{H>92`=J9I?`GPRYetHAFhi#Th##hq^zIZ^WcT zL`4~WdPFHO(`MoG;zbCiPP0kVCNRil*>0q>KqPvqUQd0ClIAqW>C>%k!BiY-udAqc z?^c+1{?XUhn$6Xt*u;A;VS`Ve9;dJ5Q);e8p_#U@_mu*Y8#itotdH^9?=&grG}q&M zz_;m%waEpS8$Eq8c2-NzDAJN*o@UqYiEGU;4N{F6UT-eEb!4%yKrn4-bMLrI-29JH zmG=C-Y7Qx}i7I*8ic^{c0|RvY=HV^r#&N`cM8MlFH2GqAB6vk1JIJ(PNy&>MEHpv4m@a=Xj5CQg~_ zq>Q2qA{nnW0z>!w^lA+tQpOqg(|)ddrEt55!rZd+{6G|$-N-2_t)!;eA?+2l zk@?WjP<)DISakH^F1w^A^#Or<#@fPf8>!h_RE*2~gMv&x6&E*UCR{6YqeZf9NQ@J1 zxo%u<)%kqCkXqaf6BifPj}*0bVv>70-;WinbcjnyC0L6*+(LVOH#PN!JX59giR?)} zYsckTl_^@vRalWduiv>{@TaREAJ&ay{@Xy8`gFl%7tI{&glU?u&8+Ip`Fjl=VvRJD zjS5N&s266&_*zm8u8hpHUVeQw$Tw4i^9G_~L*!WV`>2<}!3>jTi6t1(a;xG`pA_t# z@8053yi2xm>t0?JrOI*a!+q}(JcQ>DM>|@-VOnTSHxBSiH|@+nlfXc#zI|*Bot3^Z%V+cL@L=@cyTvc5Or`o)E5B)pNdCT0n0P0N@5 ze5NK6s!qegD-=o^8X7)H&CRB&*ltD+u^#p*ChY=&2u`J?rLny2sRnhEGX*P}`#mo5 zwcS4FentFNd$!`e^;=^VRbwYGY_H3dwVkZ)y>^a0WYm<{P#<%pqL?f9=UvW%MRWCZ z#n!5C6P2f{G&%safCf2w){ws5BXJM@c+46^F)Odo-ro2=C8a*c#`L7qd)*3dmt{wK zgfNWoxP2bm;ltIDQUUc5lGG=)0r}!`+orSng05V<_LeyYV`D#2!RO=S!{S@UlGMbW z)D#yQdXQewR(7Z#@Y$7Chm60w{8!S#PagXd-m)p8Ja5X{?-4Z z#A|*uXKb=)uW7V)fwNCy*>i!LtcAxWzc6{M6SV1e=Ti#M`~K;%d6otdkJGG2)B;bL zoyBI3-<9uWk@NgBfJNNgL|=dhqD3qgyf_fA?ovm#Rh<1~{{e}4L_>|7d-C_PvAhvTARgM40yT^UGeEZmr-JS(@}(zippNkAmoOyvfsVUo#Lm zCsY`O?CTwcj~`F2oe9GrzZyAbb=Dx@gYEantUs)C8e$dMk>U<^+3MW9xvs6;05O+< zQgNEW?M_IoR?MRXelLz^*gm)meTB=o3AX*Y{+e_t`u1;yHckuE&ZJTfsCVL zF0zS&LDul7SAt2W@YVz&RYm1t0U4jtN+E0(D`X8L?BrhlOJ-)t!t?!1X$EygmlwM3 zZ8x0kuTeuJ@$~eZY%xkYa{9D7mM8v07(w!C*NN;*HSp4zN&OYPv)HDi{sl6bHdgPw zxmf_4#!R#BTg^&`g9{N56fa+XHaFY#;aPQ7QR#bpN6`=eFf|od?bF&|1sBHx|$RbTJTVtx8gJ@r^BedCS|1h(xQSwo*pNLOavpko4`BZR}+c z%YNCDBOzz*#a;%sc2CsI@iz$IG17I!`0)Fizu7pFOaqsv%4U>(kDQ8+=W?U8 z-@7?7p3|4T>%_1mXI6O2Ojm=l_SRF6q2YF3tUY;a>NI$YL5EaZ@H`bbwRBE|( zU@`2_g$ozN`YHn}tQ2`ZUv`=*QA#r{eDrL3_a)8bxFByAuKmul-`FzDetdbksgE^5 zE$sxhpOS$+#(WDM_m|5KolZ04X-Pc4#=NLGHB_RknuW+O%E`T~NjFxvSQ^3SG$_cq zU8~|Zw`M#8_Sgs!;%TPWhw$C{xN_xpvyrb{+ebw zMeCX#YV1a*5Pzhr8_R-IGyC-bt4xrFfj~}+o$*3|EcwLT4XXgYXJ0mow$|i3S&T+T zMk)oQ9|&|&hytYS5@sUe(g|ay$r7dXv6fBB4kY8jfM<7>C{${K~-z{JiT*Ep2iy)P?QWbxZ}-Fz1y zury945~JhmyK5WFbK1#VdIGT^=YM#~q~2;yag_`$bwHS|=vkRhT9F4aHEOd4Ko|4a zZ!FvaX#ap4`6DfBv6MY&oz+Gi&H9xTXh5D&@17TPr&H|CZ?-zR zJeA}^9-k38U6AxM+E9ny_1r)hJ_1v@*thRBQ* zjz%%3VRff>*~aU`5VLiI)%SoA0(}K|XxsJpT&ioh7)*WHWmoJ~VV8q373vj((}6xd zI+a-t*t8Fw5}Rca^=RGRB66!E26!`Lcx9FrW|B@gIZdh)(_t7ta-`mVyia6BwWg+~ zA@WzS+K<#a=_^TEd9hMsEBr6Kyuz@DLrUf8R~(AM!^7+CoUHwXow8qXX`2W;W5?EK zTMY>$H9fETcCt#aPD9vn_I6+y1u0r&U5Fej!7ZNbVK>a>uLKvW=)nS8pXag2KSIo@JAOdZjZOySs z@?(9?f99TF7$+OsrI?r)GM$)mx2=22d&Vc^8eeHzec6WhUIDP3s=p#*fXzQY{JzR? zv3Bs})qh;+>b_bXT{KnMJ?xnI!JAJ-sgXLz3Cpi{JRzflP9bPBXC`BpnG~N)%IffE z)<W2_-gIiL)IIY#}P;nk+O zhXosV9eRDHCo0JP64S-!@w|V=+|`M8;@-k-;4@}bD$8Nl$C4AbBs+wU8Netp{ISt=&DgN{i?ZRVgMwdri`UYjc zDn1+xP2rVS{_yNna<>YN8|9Ap@q#-tR?ZUvo$GuO4=FWKpT_!JS?m+(?Y>%Y3c;EX zczVrmPOZ4Q?)2TbMTY<{uPbjWu=bT(DNT^@&&vm7+-n$d z01@&my{iF`Dc8P9nLMXT#1VK_=lQ{WWI19jG^ixb_h-c4>Xh#`mYI^1xtnFq#5UMu zrnD^bqdw}>r}GxU@f7;%BR{_tl8KzxvT3*S=Qi+U8A>0r_`32ib9UCYtZ6&x-FBuB zIhK`Oe8K(w`UIIKC~v6`dqp3X+TLUHMZsgwTZMsjfREUY9#ufpee(2ak9k(P$ckjz zV!>7ADRvtGN+0YksB#9?A!o>XDk~})-tb%0XP9d9n09CoQGd#eVrIHDK+rCw=o#&m zA+PzXs#eV=b6llT{@fewG_9N7$|^RB4T43cw=|6iQ1x^sjWgYe{VuXIf)jbxnq9l` z$B!Q&6G3K0w7n@SU!qIzcki5^Z{f>nuHn5$c`MKA)IKufyu~k#^MezHgQlg3#H^kz zx%3zyR_Qm@NQBH10Igni1F5&#mjT#cguARPblT5;d&uGwXj&0?I-}@@qW01&#gW#i z_s(&Xid#H8pkAfpc#9pP%eOa0e%-#V}hT==f`Y z8Xq6GL}(uZ!G4}OGUqtcZeu^)tXEMCO{ykVF%DXc$N)mjxUC?-{E1SNV{C=TZtdNu z=rum(;Ov~QGbG@H&|I375h>~B6hD90K*uRreJ-W(&{uyiuN$0A+{M7F5Uq1MjOssC$oSe7HO#<5q-FRsx*xy+etx9ODdd?OtNlo=MXf#JT_ zN^Xmi=7SBSXa*vL5_HCHuI^WN@l?UeLcyNeQ~RE~+D+#J9J=nRy1KGxN_8a=1Ut>7 zT@4YSesOL>Q9&V`TFdr>zrRn=w%XIU=~dx3icGRf>~{4is|G!K!!N@ERNEdhb$Pa5?fWTY+5bXkK}XRk zIiT>HEMiZ2F#`N%w&13rt(lkGTy83yt9d<>AUvC2-W0wlw6;`xf5(mm!8j2jIq_L8 zk4Z`KslC6x*ZPgiaC?rjvWD4%@RXOYu`D*tZwiMQ(j!=%awGyuFlP{DN(aG#l;l_`zOTnKxSrUee99mZgA_A+LvC-eDA-4 z(C2wqupT-j<{>ownltOi&WS9fb-w5k^3$eqE6D+;%&xnK{V=z%SSvPDpONph;9oY; zSCv$k{ZaKH9Ub@%R+b580Yb-_9@Pw!K%Lf%lzeaHrmUA`6Xche4N5#&&h#8{IhT30 zuQ}1w-L7+Zdb-tggHPqkNkZlKc`jh2?-WEIEMlELcjZA@Pi<|4B#T6ohJ{o@lCVBS zA479<^I%6_js~YMqD(06t;v4V<3I7~<=3FQG3+m`gADmrNU4FHLWVam(sZY-Z74v5n4VSYq?^cZHbxR| zfhwP7u%)p2)8kzg#n5C#BL?Wyg7u2~JQ|Fb6@COPsCtS$-$$zuPh>YDdU``H_c1UxK+7xD zeD})`SwNVzzRiJpA-W!k=AuO^vPVp*D1NK4-(+gKCCx~W(*>I68J`a#(L$yjxrTL# z>KT=_T~`lt=Ffh2Jux%XBm*^v!a&oy{;jx9Q*!2bDSO4~GHNO+Lx`MYeB2XyG;aIt z`w!5II1AnvihkoWbzxJ2J9!)cyUkAITqF$ zx+L#nkptloKi*1nOjIw>>Xnwu($uAl1>^%Z9v(%4WndtuCaOf@Qd1S=Rb7vAamf&B zMuthdcR&Dp%+aeI&@^65*OiXUX}_9ToTxGavnBKOQPl0Pe%m{a>K(U9+bMAB&MiB$ z+f8(Ibqx_t2T}eJU(jcHSV(#M$S_J+R!9X#P^RX;J!Dujoe>^>9u=xEDlV<~0F&$z z!Ng^P&R#f}^ZdfCTk*P8LB_()LkSs{sDc?3Kd0{%XgA4#xEc|cm>qlebjJ09_*gD^ zedD7l$B!Q;v(R(~UKGHdn8GrA9rh_#D+wfWgF8e^Kn7pG^ot(*>A3Y@u=Kn;ccUX? z{+&giG-NUfC_{Wv;YdjH%L`*xh-ijwnP$2zluTmLkZpVtqux&0LCREV@0H?tH;SM0)Wu`{Pme%i zqD56@PwmAE7icj)Vo)_|A|!oX1)-xAU7*(7J(FCgJx}e!2u6UMz8T>l=r+r=#Q68Mk4zla@ zb1l|N%BY=ctj}LmRn$Ifa3hUl=!S=oQk;3ZVMFPcl9Cv6C+m#QE${3*&CSirQxWrC z`P3B;rqkXM`sq>?dD-v!sldvR=S_BffqJ|5>}gQh7T0PQz2vSLEaG-;MDM(ehMV|b zFTk-e^>&|1E>t92=S?8Vb?%lg0!4Icwe}-gLYUSBK$PF6rX?=I? zT(uqcf!epP8(KSJ=Ua2_EFO$}HG4j6@#71nMZ2$t`QwP8^`!dkVxom7wu>yj184|O zDVw1_sv>(p3gW9}*FR(!bMm|nLeyotxrmt@KuD-%SpNAiq|u_qAj$xn_JBv_dwT~= znOd%`*@M13t)xGd@&}Yhq@%82E5GGL&6zGk#A)6VF+A=}xd-pne@G+kIeb?+b7fUk zA07;(FaG%Lc0X!X_2Cj;h8j)krg;&?`yGz<8*eC!o!@zBZ(ITp$eE{99GeUi%if-Q zI7rZ9Xfs+-#*My3xu(`x05B*8Yx`YkR?qRR-<~58pfbb0kE-QvIlU$iyYdBO>&Lg9%r5XAl z^9mV*#X}J!%7Fq_gW;HEXz5Z!EhjW=NU0~GqXSZ#_S>i{oW&jEzTUt$?=_)#HjBsygd~x8MC1M8w#ntj1v!E@8$HyOq#vO}b zHwYOF1YEarEv*ER`Xdmx=I8o?(`}W;P~?nwxK10@(?g@U4KnjMONUw2HAPR z(;_bMi_cCe&YH}(#jBnq)SI-@*4I@c!@~qods!P~sIGi4BB(wJTCx%(aUW(5m3N}u z#U9%&9v^&qjiz_W4HX6F(#xG|Nbc^1(PP0}Fv!q7CQ>0X%L`_#c=oPC8m^k#=mo+% z7kl|U!vUs^Q1L+_*mY3DejuU+^;A5*Pe6eBHP>yi(;%M#7M}`!746MN{h(0eAW8Hv z6ejp(9x3pD^eqW@TNC~pEI zd6xFJC5(7;k_NSbUMm*9Jlo4BZIW@ioaM^xQu6QFF^bh}+2c2T+Y04J93zXt*I!uXCOo3K{6o;PHe0F2Tc7_OK+q zmRxt=O_q6e5pkkNTahrPWF~WBQFHu%j);G>gJ5nIO zfLJTZtBRCSUpOC&sxN+_hLtIlg_V_ESXj%`a{Pw`64fzZ3JNI=jW+~@&&?IgFFbG^ zn}Q9l3{M64Q&1)N{Ts-M^78WD!NEyT({582TSK-w(5JI$+v;QfuXb(yC!_g4%MkWo zzVxGQHe~TPY1}}?ra*=bz;p-!ml!@mPvvr%law7oJ-PuTW(p~UAP9pX(usM~YMPKi zK^9*1P1y>nrQCc>6rDbPJPUi3B#Kxb7;{uM>?ifDM%z@u165=f)~(*}x9aFM)u*k8 zt5Gy$7aW_izgHUU=NALbZe4iQmoG#_REk%5+ZW^@4e9Y!56_l!UWcS=OT1WlEP5}q zfN_qtWzh-Qr}kC_CqWq9)T)}Q|B|TP9lueuE=^|jV|H-58*bW$OtDmYbPNztkw<%Z%Eb?GT0*bu^gH7bl zUAyj8<^Y}B3GSa7ZCAJHbjm>3!)~fi(UC;vkV5`P04`LUOSJ=mOG4+2o(qRss;srO^*}=17v3wi54ixI(9jh1EGY@L6q$PeL+2F@@&SmfS>#ZfS047?V-XS zpPq9lX86!E1bM}L6Pop5u{DfxTs11E_H7$HdGJ-zsQg39lEon|rxOSLT!JqPiG{Oi z+r*STQkX3Hz(nEzN0+Hhy7Rph3Wc~Qm_BTYw6_u^J7BnxB^W<@Qf?@b*x;feD&)K2 zt5i41y5N~=+9{l%nWJ8pb+oGP>thh!zj3a)`nXQ|G)LP7 z(%>@$xC==6g_`R^Di@PplOI&Ydi_ZJ3%lT>=Kb{^q*Uj?HB7+qrQFM50lx3;E_ns0bTX%NN!ru$XV)1b{h|UaKhax3 z4EfYpXRx-~>KfoawJwC^!bM;D^_?1(KEG@Qd(9QVaAa;2J&Q44!0scsR-B^{)P78l zV*a-J3?jm_97JiI+hyORt>B3*Ovrv`&jmj#`bXJ=W&Y-QUL=O7I*H(CkHqlJk4>z> zY*SyU*KR$M^q}XFO8(n%e0<^wDOB>pQ|^t3gl2Kt#aVp+v=O4Fa+sL=39fcX5$QzC ze)Z}QQVPn&`nYw-|Nj?|Mbf5Mk!uY2>;6<4289 zpezQ%b8~ZD8JO;fd&1xZPZVw~^~aB!&?6*0rGo@jza8ol4S)3X=@m?tx34cNe4y~S z9+0?vS>gKiQ05p=g%&a=(Q{M)p5ETOd;0oE#oWz z7mEbOE#s)Em6qeL3u*c_XW^CbRG5zIsti1BH4x#u{&GeQ;b@cB)zz&Am-zk1bzYRD zro;De++F@OCN#nz-MICT#5|NBQ`L^x;g&Q>SoN|F!5gFX>brRsvtllpthsp#^KrI~ ze=Z<;?;I{*k=|z8?d};4Nn6o2FFIPBT{+R4nXuKp>PdLkI?^~fP-g{GW;Ebz??gVM z&77zO;iVYuU3D>sd^U1X zdU!)_fZLY(XKvjm}W4iev*n3%}gc;MhGydPks z?f$#~O{M^Qs-G7kSgOa|S4TLcfiw_G>4rM*%0_UYQcfTt2PWc$1 zY&Jh)>Jo>vlUvJ;M{2;Uc=F9u{bMdGIY7*b2q)(t7{^<;uMX0VZSIEaxX%_Q_37)= zkhvGRQda99{m)*|3-;7@7re4JYczAaQ(rJfSGGoEHtC)juak)1j%SA9aXnjWpnC0+e-G{J$pX$uRr+8 zCHUDO|CY*pX*;bXUoYy_L3!1!cZhK602F-VTz~bFxVS8kBT5|~HFq=KgoyFv@#B}w zJv}`&RR^ylCmlI(;LKmdit^RxqM}P|r)OZ=+G_FGwfa+05dlP@0bgYnIj98F3$i_n z1SCIHQ$T)p_#?>?cveJ2d%p*3+vntVgQ!3%bFDSivIYi6?oe_c&Y%C{d+z*+6|M*S zJ^H*)9XQkdW8uI9VcM7FD;iDRzhr~4bbaj{1osobcdkh@sYwzcw^8FFv;R=4vuptpG0Q((= zlLf|3u0Gz^2c$_Zz>Gsu{^8~2<*t1h#;uXq{4vT&T67}LxuKzK4BSC~VD6WpDgP?i zs17qRor45WZBZo@m6@68D(H4izcyS#z-~MoI#&#s&+nXg(W4QiB_D5Zg=?oF>(s&! z$&IwVdHQdM$#CKBeFe5KPT^r=PF9{e$CW=D+Tk>#37f}dU^W6urs8+@$ zYC=(<2bS2IUv}hyW?zz#X(B410G1=o?=QN*M9ubN;eQPMQi#Ug8hi$gOxAM}8gd*` z4p_nynAlbI0wel@U9`46Jcp-{zjp1~gGH2;e2cr~;B&Qt+(WQC{1kUx53kG*O9A4h z>G{o>89R|hStKx=Ji$-h-4y|X%ExDE+}_fj)QyIgNvI$V=p_2{=jcU}S}nxv-zizy z(9rM?8iv9m;VBG3+_ed15h$z761cep?lEL7E{PUse*YN31&TU$<+56;{#)b<*S_>y z&7m;!xig657iC4Sg5;0`jHHAV%4$zSR#Enggz(@QPJh~3;fuPw{6Psr0JjJ`)WnZS zw7`J(&jh4AFJ>@S*S`1f-&Zs-$7E)zoYbqVhySd?eD$V9&*84+0Y#W4;QUpLp{A=_*BgtR3 z=s%#u5lpyj*tA#RLyiTSf0%0W&$fL2uU7~pj7VBjQ{eoG*P!;i+qL`XT<8xCEiGcN zW@Yl6+rC@k(xp%tu}|YuC16Am`B7$9D7;RL{+$sD5+)2OU9C`g_oC zq=JP|dw=7uNj$}1{QF8z<|V8-f0$%cU&4L8c6>gv3J5RGd}%o_u4#ye;q<<3Qo+KS zo2fbLzr18pL}dk7(-08f5L5>$KeUHPg835sEj)!ZdzXB3s!nrM3C9xugMU?eAyOH^u^uGw+mrM>?zmjOkxrSHK7xB z%yiq%pmm@Vg>^!-6apZ}60IVT(IZMGZnN*_QHdKu`wA>z3y2G^)ke5hC;qEDViI%^ zZg7gByNtVK|z8}`&fTK7!KZQn2qAvl;EZ1_vNYHL11!d zImCh^&=`+F?WZ>aNBH>ox{AdaJweS5A3y%mg8VlypLp^ahMx9CBal4k;AP;dn&doIEe|Y5 z7Ny%dx$gG37>i3wqh~4muSP|;b4~Rb6^JZwEfT3J4QsNC2i$0{X|9}Ss(wxACcCFk zo)8Uc6197jHRQU}QCJ|zC9I-in3iL~GX>9DwE_CWQ*szrn-oRQoh8jv)6&us{cz(G z6BTbKFi}J%Agsz?Km`_Of05N`&u&3e1>!P%Z1tFGo##rGm6eTNGQu&nwenVD9YmUm zPu_>}ZzK{RirM+DeSy+ZoRH=bu{o})tTbu6a7YM@(RCtRJ?{D zRm-D;5)OMuLY6@-kY~(aGZ1PC2h9J3AcJIEj}Zsg)?S*Qiks}czl`jGUZGeZxQf@u z!)jkPdQO6)7@{+v1x-H$zz35u!i^|KZeP!L;QTo+Z*O>aPa#7bZM#5r_BTD94~LMB zcE9^s=SB5*?%WCAUHB?HQ3b{O4Hp3)U$l;F`wb{Re7OI5-AmZ9344UD)gb^EbH< zMac&bcUl{9>e=%bFS7IU zz7nRTDSH$;7`Y(MsoNnCH5HAQ(U?j^3pC5dJZBI|gb+Q5_Jjr> zWvwGob*cuidxo({z9L+;geMXHUdt+>8FUXului=*5j+4ygJOeS@G89ghjU$s%Qx#B z9_K|{z%|ycIn?{m(TlD?O(N~X>(5@Xs+j+;p%RdPQuV6#6F9+7FQ~?oMbbMcC=pRy z6OlNMnXoxE04}iq_+QN>)*X0K^4ANnaveP=p5cUT83GJJ$63(GNU72&z+V$pgTMAh(H7$0NC#<_oquO#+qw`% zsy2?JN4=TRhWNIYTgYMBn>i5ftFs-D#w0x5w$ZB{&<0RRELgUes3oi)T4g0AFBPaP zr<<^92*;FQ1}F_-ok40fKeg}7$Y1^8{w^|o2_#vRw|$xMAW5~{!A{eD%mc6pZ?P3d zWJj#`3`g}>!M}+*7rj5L#6p30CAHwcC&x2E-a~1j6wU$kZYe>`+>mMe1xLp>z8T`@Dy&p{mu1x@4aQH;jBSyBt^PK+}YezbgzpUW8ktx+vobNk2B>3%o zui3MW7TaVfuJm0yCbuJE7c0f4blFNzUa6Kt98#^}B})tUc#KBwcpj4S5@u*h8`_jL zCNus#$AZtU$D%-lFc0&X{}BJZSD16!6A8G4;4=tsJzP1VfEQ^k)Y)){ft8z@E#Tqi z~f;VM~K@Hn81g_8xi`sOXeM0z&h*TUrdZ+KC&UxYu$$Ss&#?DEvxZ zWHUng7IcN#5rl#}z$Wo<`j?ch&Ea^#< zFfiIDQ#lYW;l4<46b@EOP>VpT{2-+1EyhW~l(5vbeeYIQR+bc?RLYpD-H8yrhh0e@ zCBV1;IKV$#3V^$nuZ_{GBpeTf$So-&vXp9)JrF6jnVPpA-S0%hw!yEgc%?WtcJ@{U z>T1eCE_WeB?08ACkFWgw>E>Ui#Ku06OxclflxlPEo*gr{c-8woO_Q2h_3u*bkU@Rp z=KE$#FdVhpj3%lHjj~T`1_uor8X7)l9%K~6u^k|=Lnnec&|_M-Uy{Tz0BgLb^yD2A z8ezW4_AzJTpov=I>;cq|+|;z{$HpuL_DKqP_2HuRr#k8JfBJv44CluTxDS zGl4ID5*y)#W#o`VDTE+M<^fJP$l|D`(^Lb8urjoVxRaT8p6l`k=KlCl#MpI6a5QlARu za1_?|ojnjCL}poAVk6-=#UQFa6sA!y65#S92g^XftYu_cx7dU@y1^1BJ7mNJ;ZO-f z6rrIi^>)jgc4PZntJB?ocwMzXP3T#HWQuE&XGV(|T#ic69lI(T z7SeL_!~mN1eVL)#MV4e@JoC`;=qX(uDok6@nM7n^)O}$PlszqCyLKg&|uZJ2FyiWwuykvdy9jB)I%_&CNxGpPMKAAW|L5o2-?5 z+g$pm4PHr8GY}n4^qaEP-`h_a&N&LVTelj!kIG0(H^S}~MF?1fQ-t^GcebD_V&CxR znOysi)5dEs4ty&Ajy_M&g4E0^6i6)LROD@q?FNyp{{H!C06ibvU_x-Kr!?P6XuO)y z3SmN!XgRE?e1-O(cSJ!PZZA!uCms}>>ZnS{Q_gJh%~VHEm9XE!+|n);R>y=#MU@$* z#bVMMBS_caz}=*oC$FU?yAkJOdo6wyKo z2BIM;DR~7Q%giw;DW1enU}ndLdDS{Hx>G0wO>BFnRo3*cUvw@Y)HAeK69=>)h!VUE z^gD_02ccQVz{(N#2=h%8+#6`;HTc=}foS?bxvP5k`~9uxPQlVHy~R6JbCJO~nPCcu z1GZN-xQZkZeCsg>#DO153(CP;7__RJcZf1k;;-C){F*^e#y$J4+-q_}7U(p?KHo0^G4@-Cx?|8qN5xg3NhxvcHD~F|two%fush z=C`AWc<=x5m%i+FjV76MO|s&ZD@@gKqhq@0&MmEW$4sk2ko8b-j6J z+xYX%8*lGVKQ@0#n7`pWX8K6)jn~V=gC3gsV}bj~xQP(zY<51%Gbo5Nu7NG1m3~Ox z^&N2l&h=z*Pc}BT-4yGtYf$b8`m?kxq{TGKzI=I6q3dP&{e4pBm3#E@jmNCIqvFV;xI0HZJa|6~9~)oM`Wg$KM}WT@U|-+3r>O3jj&K zK90=*w?wC>D^WdVY3#AI|JN$Jz0W`o4^fCd5wRqJXfmOC1+63=-gb5 z4_9S9y}i%tb*CTvyD(i%qqhfPo8P1ni!Xbu{;#Y4G+kHq^1|Q68B!Lft?@25_e~f7 z-G41!OI6`uuKI4jF8=X<`yRh=undr@Nk%{y|C4|FHto79`lo;Qi0DTq+vi*VwUIW) z;i>Q*gpw})_5XSg^S--%m3RK_J<$mXis;!A+%FR^dFS7L%N6MqkN0K7x;T3`x9HzH zW;AK2ufMNm95Qj>@1yRpp$O_;`*$0dMERK`qb(FV4b@EW zX`4i((MY_t&)DJ4pQRxBCN41%tvs@j*5f5V9AR`_&Jbm+j$DF$ymokLZf=f9ZM(Ou zKQ}Xia|56vHxl|5;V@E)ORJ&5p#X8(OP2^44h0J0pc(>}!0tr&RBP+%0t9h3kdcCt zQY1_$fgj^m6W8}E#>NTzwrzO2x#-9meF?;4;)n$GHZ6JiP=1TvFE>vZz+obluB@bF zSQC0-)B1b1~a=0bpJevBw%h$L5@0$-=&`&+yL*#AVYDZq>hAOz-thzwz7u!y~2Mmqze zW$@?gawaB;urjsjYh#T1!#vo)h@hZ({A6FXl?iM>hQ|yMvKYYT{VKEPqmN=eEzYQgPI%9SY4xjKtZ>@1U0hHyi1yp}uur&; zqP&$8zRNVx4WFPNz>HO^kEUb)6FN_zmQyc3U1gX8exi(wmZkq@_)!kIzq!uuBM_c`t0CU@F0M0D?nJP1r@E0Ry;JjN#O6 zQR3_l{Q{BYUmhm{D*X8^bXAhxW@oFxd1Y$mfZOuH=4iaJw&sPm<@b!^@BOaq{;phD zK#N(>$h({QnV){q#oERt_0TI;@wrrXu>1N%F4p>i#F`(=Joeb-ot;J z)~z|=cbTKtC{SuCc^}tQ(ay1PCEGkLqQTcQyo@s^zS#-En0blyrinC6Vaz3c6GwJX z+}~DCSd+jBi59}Cf7WjQay_{pD8cvh^%~e#SR@Fo7yFE$jy$a{*&JwBh=nCPEprL> z^lGANPCP(86dDy@()sgXXz`My7I%viJ1^j*kKeDWsKj@}&<^5qT%JJ?t&P~EoZf+- z!%(PI@vR=96wUZfL;z-DmvHRAJT-DMUdiaGN8L}~7gvwb%x9F`tc3O(f^XIxjEVB6!P%q#)#wy|08yyS(!1yMd$42}t3ZvA%VDnz0kn4>8>5N$o3@sq)tt*agVGS6!eU^PuTHa6C9@&+?V)Rij)O93T8HT}NuLx;SMLr-1T{`zQ`#s;LK zsHk|~`jI-$$N-n}BKF+=4W85v`TqmJ1Sty$5leow71@w_#|~AEGkg-0zSaGA8TYO3K!w? zjJf1LTiR7WHac1sMGlg`Sc4KbSjON_$DoM&=aolO1%H!904pyh5V@KYtH z{j+S7Y}P77>JV>$LAcgN77sA+Jn&q0_TAY3Sma|Ul&0MJejxB3#3Y=tr}Ne$|H57 z=4uGlNr0f$WoKB=la{t(>a-nyJqSYG`%P!jLB$fN?^~lm(dJ3?q7W1u=>_h=sFaiw#Mxtn zQ5gkkHO()w0?BOsh#K55SrB@0#VF1|AQ474qERKtVU(ZHgox8Lc)()P3q=2XF5_jO zD+p8Z7u|SI(r#Vg50Qkxa0PJMidIzI-aZ>p746v90k_k^+htdl$H&dd*W95|UU#1r zE&;?j#DZi_atd|$sF7U5cPR_bFD(;4FUGbwdD082ztQTRu)Lm}-O|!RqkKPod|?TK zBpVkOwOQy*-T=ZI(tL}T&?BtM>kn8@XLi(>d$9(GseiU=vcmEf5W4a?M3KA?G z<<&(yveSnofUt;=Nc0vF#Wu_&&ZRr=>5a~N=;egvv~#{O1D%I);mnO^S9ADv4w@~o z5TIankW76gI(gx!EAN(bIBcaJM_CZnO>E&*gEvSG1Pnt{q+xxu9MaCqfB7Nz(zqP} zri0?vC@FeA&0)~&7)?F0EDz4uAzC)71Sc-P|7KBmXkshaIT4x&!Eyce{qb-l2N*2< znFP@dsE$;lo^FYA96suu*Z6ue1muEMCw0qZrKP1+9^jm`JOuao&XpzYEDcVHTF5wQz_IW4Z#OPx zi|oGzsHl^srl_Pu_3z0L0{Nc*h73Iv4kr6WxR7`Qq&!h+dfogW6YqnKs02JH%}x0W z5Myk5I&aY)D=8ix9waTL-^li{y6$^baQK%MxGxwxewI3aOn2jax#*=N!$ktaizoC~yUzmaoj zWWMm>TbnA0BO@QcOq@>gMfd#xoH;c_=pW9<<_EcK#6fIF zz+|%vptZkbTNR1KRq#^`KiXKazx5S!*6ITbqfV}mE(9rme{I-E>~Wgd*$*k#Xo!Qd zVAyEaFR~yGl_WgmM3QwVTYb8n*u=^xeIu6q=7nU^vF&7&c{3ex@EjF18xyr}I6nxb zJ3CuI3VvzgumEEmwc<;hP(rw9bSu2PQ^Z4{Eo@!Y>a1ocJkirB~;xWeAI*M=n`>4fE5}+9*hR?TSQ_4A506f z{CWF}YQ$f{DKt1Z2ni%rJ!efnEN2f)w$q*nFPVN{C7e@JfCk2nX~9*`^NV-T35b&* zz)wI}8LzkO5%ooZ*hD>{@lY$g4RlMV`S>z~WLD>ud0#;%S57-8x~Z^R9~mi=jA_LB z(@Sm|S_fl6$_CR>?Hhh`lu9yeh3k%rB&;F$(ey(OMlIND9+;>frBaF!`+pew5^$>5 zzUxhrBvU0+5-J)bwvxE_=2_jP3ttyaG< zmf-!QDl_{4lol++4k|Ev&_0YBKSzAQk%8vyIP_~v1|?silwSM`|e$q-PahpLmKV&AywgZ-|FRWq+E-p=`UM z&J^`0-lqCJk%ys2c+a^hr@n9wR4YBp6X;{fJCe~g@&=D)D5^q|^J?+gq0=dr1^4c0 zlda@lgssH&Y)8Sc5RcN9gO4@|P5p)Z1oa!5noz%K;@k0;0v4J&(N_e8>&y5QRHD$u zzh*n&YCFstAERfJJTUjC-G?N2ufut;zW-R&W~E`ac!>Ul_szX)&c|$J&~z9(J8xaY z#<;jex@6Dt5#6x;g_9TLE@ z(aSDlq#n>Zy36@4Iai>Ai|J7 z2b*@CUi4jD!f}B3u#tE|T(1eU5S9$3W*=befYUqZk4Y&CJA{T76zl3zwGwZl!aOpY zA0a}#k3brFB{?8CHXvkp&NdJbqG9476|}6z6A}7H#P0(pgO`Z3dHIx>lG*WQHd<=^ z**Z@W9(2OMPBwQdfONYl0{o^}{`1`X8Seh^%N29oC=$69=5f_zQ6>?7;87%>0;r~2 zfhR~k%+%rSAw>mH^#A&;BWdaBsL=NV8G*Gq?wnf#fdG%x_RsHML;^^p zF@}8O2=DGkEBt1ml`vOKVm(fsOG%yAnJy~th|ycNbSa8Y>8QgDEOC{ziYNELGY~6X zgrc=tM4@<*LqRmEil?m?+O^kJRr^rMp!$YCYjXqBri_5RcqzoXx{1oBO}9{%@j)lU^l?<;xhla!fN{ zzkQ3*h;D!-w=p{@qD2tU%*+s5dHk)@Phv_I%UJ!FG9busY@l)DP!6`kAYS=?GUyCd zS!1rv9wMVgFywHU0Y!%yJT<&~>P`h*9bx;#pA0=1yLmz4d)NG!#SBz#2K*0D`oI3I zY_l-_fCVSV7kP`nT7Y47%7@cmG?zLd)(;@xf zAg8kg4qgjEhoG{4b!`oidP9#W0_sik`drgNee-P`;++72YNZ92U$OQ1A!7a+Ecv4Y z>6;Ox27f>U4eU~!3*635K-BD=TG{uV=s+A}tq2&**+2#I;17x-#4nR0G3n0NGI zF5#a)%1=;~&MImFIOw7QPKO5r@y$@_joTW>@L?}@!6>e+A2w)p;7CZs5a0?3cngb8w4`3o0ATPWB#npnf;(kY;YLoo43I{eUUacU< z+<3}StJb}fbU@f5x20^ZK>94T7*@6=@WeZtOUJbY2Z&+?Ee@GtUw2gnSDJwC(-v)6 zm0xvTAp5*6ML)wnx({wv&+k%uA7o0|9FrgHg;Dg$=8``t#2pU0?YMWcgPRD{ReR2q zzR6tmfrP>I`Wn1*<8R--4J?r9J5ZfLT(5&&PAD2Ch&RtAZ$J$O{gIB;NZ=Jj`~>2$*}_TFEKX(-3GK}4Bij;E%F`uKt|zW zqHPdmv#kVk{3s4XtPeP+d|+_J?)m5Qo5rMF92lP54=?>1$Y)(4SWZWo?hcd+D8PEa zB+#`Is+Av_!ap!aONW6LYAJ`*sG9^$Rh;hMQ`mE5ClwA2)1THW)}p@OXJAk_AcL+0 z6tf+$TB1J&jG9dB=No-fk$4Yzj_xD#{)!W&96#ZN^9lrC;SBK}Cnd=AB>jH4)u$OR zk`a*tvlww3m2>GFDEB0I(W@R>dM|L#x};U#URy}q#T@K$H1-Kov-lq{HP#Q~G2hPZ zBK8r$bJoU8>(k`pk)G>Jq;rbN9Q=S3zhbcg{Li{sl_ge zwcc+458eC3B?nt#UG@0PpN^SX^D9`d5(^nJsX)klv|-5Q+s|f19QHaugyH141nLv{ zl^OBD@7Dd#h;wrjPmo9&guRUz#e28uKXs@gII5s#!BS_ClK-PU6+y*}_T!QH)!q@e zV~^W+JQm&nMECH_Wb^$i*_cTiTwXGvzVzPZ5D0aB&Yl5lY9s+qkO5jB#6Rt&bLa^=-$R`liPeJyLCN!ku z8^_cH=7)ZpOq@WFjdLFm)2ajeok1qeYS|74y-dVb8Gtr5OoZSJyUXDlKS;D%Xd~}J z$M69h^src}If_8?&NRc|=fY=xy46a1_wE}27c0w|8&4^w$5>cBf#ITg}Zz` zUWgT5NLVs#3h!Z4Itaki2~(h_wCXSvecOJIj!wOFvzf3~8z!og97d~N4Q{E;iU<#{ z|0LOrsuZ}~AhgMj^_QdQ+bvLtmkolbyLd4eX>R%)A9UYjQ~%SMoZ;U*`XT0yv{RO7F^EY%G%?I$>n894&~n>_Su$WT0coI^Am`(@0K^)rRUfDtZW-D1w2IU1%R0 z=W)ozlj@6j*yY)`O2WaZq%~(W_ZI2eO^=O44J_X;_b(Pf%Wh@o)U+4DDS&Br6+0h_q)cmsI8{FIp8a2w^j=->fItf0dBAB@Cf9;Caoz&=XGf@A2jjy^xgfr;co2TR;N|ADij|TPt8UU;NQ~#uEkUK8!Ef zQ6D8Hs?>R_eEE`m=Zx z842oexbZ3na%c(2u!s4c03v0lf7X||+Y6fFl1M)Rru)9MeFUKqc#Rp=n0t@C$o1>} z1cZ(Jr$*UE>+^2)AfjOh5F8x5ZmOZ7p${qZCWe==Yar&4eV`T}iiLxPVgxG9K|l(i zCCS8>_q~ibJ--Hp5`lwMIpmYkU>^orhSA=$G!KY(zZMRPfAIKLbi_Y(RYI|^22hDu z>}|}{sH6DakKPFUdLE8@k;g?tR~MrZ%$oo;pI>>C|Ip&)7QgTKNhMt@wdm_WO-S^7 zsI%|FVJaO3CDDr_?V)l@LFzypFdB2nI)dKz(75?wN40G#*vK z#{utoliEpA-T7*XohBMWoiZB#`uD__95ZA~1Xh$aWfC$?wYFvmBY>Qn5P|dxzoCba zMuxo7!HnKlXZ>yG4%{D1k8%j23IhdnCOAQ(4r~>MC?D z7I*^zf?;={2~-jSm>OvEp_X}SeE2Z@noNIf5p)+#Fx(oci=ehLBH;LFMb3*IJR^iR zV(2|4!V0wl)8gt%0K^h(^Xc{avSrTak_^GUH6hL#HrCO8dIX*Kbh^2hvb-S|F^7mpWNkHggXDY-9FW&m|fc%%3rH+Su{ zP0q_NMnsY}(s4IMp|%5f+{2?USz#l)-nD;>yhQllu5ip(##_dm8kvWW^_sLLzL%R1 z`AO4i1hLu^UiK2W7*e080Y+JsS-f~-#oBIDQu`0AzFJY=hsm2*?4u3x@-ZhRyG{S1 z#M5g1!k69X=pV4B7^hO^!YhB35Tr^3Rfta5x9JO72RzI{G)=@{6@w-~nbC2Oc?)ug z6X+nI$X8EA>f^u+=p;`6>91TBub_6bpzwTA zp<4iQ2+{@#3d+PZ@0mXXphNv17b339!o3{rkMggb5L=Ch8)y6>+S-4y|YEjXjh-W9nDMlR`aZ9$% z2FSi4#3=L*z%=-+Hb2{rsRf=r;&?@^&mMqX2tH6>b*<6eq3RaIyGRV zRfi4GI5dWd8co@)jm1X}A4iRXKyxSz&A{z-Jy>p|W9=t&!iuLpe2aa^{%__cq%E@H zaQSpqe8Hb%Rdyd|uOmK}pr0};CsCEqhJC{Ugb`6OF8!mO2M?%b_-sgQ%?)3))8|mW z(-9+&+xZ_HG2t#~cXehr;;|tXhXT%*n*O}Sd*P_V*wi$ssmQ9-vU{j>q)5#04$&4s z)Ji7UR8Fu#q4_e1(5rex*$$Dgo@oUt+Z`TD7b=oO5OXG$_#Bi;sEm7~PYg?j zNtxgHb9xF)L~Tq`GP{40{vC#g$Iu%QE6b=cD2|ZX7kR$exiy^Y%g&CYIj^ASz`Wxm zF7B-MbjZPzFkzb>dtA~8Om3srD)*y^aKuIG;8Uh!@Na^%fJSaRarh$kEv*i)5GQjy z{H7DhdA)H67>L7J);2n;f!xE6v>DTJS*W#%T_TAeu+wYEx?h};5j=lWP1=2yHQdT& z>MU>Gyg6`*(>dLXOC{pXG6J?sn_a5u8Nr`-8&3;ef?Vz?{PS=nAuc><=ItE7H||)U z6bMNR(I%$8-~WrCzvx90jrJK@=N9OU`Z!h#T{^O$W{>>nbx8O)CzEFD`;yhf8HhSFYM^Z*=w{%B_|1R zL$U|@e^O(R86^xGgZNU(4O+4dJTENKMN#VRTJ8DpCG!9=3Bk-E8Gj^173@%Ip$Kko zbc*5F(r3>SAIgINiT?ZFTw4epb@}pbhae?NNO&u(UQQ3SIKS>ea&!eMeYz#_5>t)C z^3!M`S;XN>rYt|!Vj1#vHJXmBf-n3ru=te4bZ#SDHER|WpN8|@{=;6sQphkf z5yjU|z+A#`?!=Sln-{10{O60QZKcO-=qPxu>NC=!-{P*4p$_qt9)sij(0#d3O5yO^ z^&Kfz4WLqI2{b|`VRNil=fM^1|izw+ko+XnPy508{F!-~`l z9in0veS%ym8p{MLy#!RT%0zI0rZ%f$mfV3wW-m0JSSxuOqRJp;AByQgBBKVCGm5!o z=*2FHr;x_qef(=CdWCdUe5;xw=A3~-YvK4BFOT5-+;Qe+WF%jg9p^%>#qldhC10Dg zz%!b@>A2MZS-$ybC z%gh+fz|%t~!0;}wLwiohQpu=2AD<>5G@}jIU0~$vRQnewT?6?9@Eq`r3e^FTNB{$f zZA}Qv35Yn`cJRQYhXDEgC@n7Q`_eZ4m7pPw6Cio{@_M^@F(fa7Uq4EGL-Ao?wjhr| zur@F@z6oe2Lq~1JUliEY87cy7U(_~Nz^*ePssnPsixtrVhf3NZB3(*OzVfZ___nCV z$0s`2y$`7|&!MzR%Ri4#0qhc)l_l0Jynf?GKUDG#V{R>=jUYLcwrdc@tv}C}Z%2Khi;B@<)a`CYMgtme*IM=EwH|y98gu%$8ugl9zA8BVtX~|rViJAp z4HT!ulj!x0HC(Re>Urp^)HVO!y$!%R2Y@1ix&H*fP%$O)J3ugCwE7t=ftUE^-r2&p z5Fk1}hWhwL;Zj!>+&399`|J!|6G8{Z1ZRAq#tX~PQNF;7wg}JI=)i&2JJW`ShM4(yCA!~NjcbH53HU9c-8g;pdU;b* zlSV%OL*L3{tWySy1o;QN@1xR}J3di%94!f3S-=r#r3sn!!dz6j^*>sdk7WEY`6BbR z@L!yRxY(Z=g_E1k2moIO5L7_iGV!g(o+2s@;e(&24%+%j+DEAHP`T(XH*+c#+atT%i0E8UY#cS$NL@a{S3`7~z=-y1tv9v%iL}u; zz2e=i;!j~ZLgwskK02psT1_&Iq3Z2}GtewB4v&bwT`kxr#iE$S*u(<>Y}?^03tct9 zi$hEqCISZ$G#x#ceZJqSvc^y`F~T!5Z}dgEgatS&j^^fqYWL@kf=7>To^)C7N@5T( zTzK=D)sy+{t5-y+b_drzpOFFUm3Kd%2Sj#Zh403TAVRxep&{oJi(^eieLm(pCxNVK z9r%MO9F7l!%2?g+Qp1e<)_JPWsR*_8TWcwYvz*QR?^wS7I(P2A#(UJgVbDiK2j|~J zk-AfCp6>Tm?Bhqko^E$I)55|1fSdHRkkf#G`{| z`!}}{PbC3)cj~>+lhxi0Gn{>hVf*P4DZ}|S+dsM`q2k9g`-y|Gj4%~ggftQ5FmnIY zpfMSQ3~MOzIJ5hf#G1M}%H_pk5S*wsk%zER*)4ee#)jKdq2Z@aUGzpbM#b@o&{MsL zmlc#0`Z%#27$RQv0|FZo+XdoP*3q{-6TLaX$3jdUp8`bqd6gb^%nhd5TCnlB70z%$ z5ikjjP97EjPxiKr8#kgdjRJ*TQ`*Q7x&$jDY}QE?13u&=ESw8L1*NRfUeY>v7LYb+ zf2rE^Pwi^}u*!X}T1xMo1RD_X-H_xsw2k9RBC4$GeHiA+;$j~NDa*?U>W)n&sWPtu% zmVns1%r&FHLSX6$_^q95j%Kdu2eyxaeE>_43jr+W#quD7zQ^rRz%Wp|_5A-T!_5y^ zS)~k}_6Q29N*IJTh1KKB6bcVvv&EdAfB%lY!NG$Y0jnZw;+^Ux$~)|*wO(*jn@did zM{xb1DHN({LNG;IXL;Tv(2X*kw0c?-FDaE;eoT^_--R);Ix-Tn)v}x%xEm@?7k3EW z8<>&$!Z%Y2MAq)6FfRtjOe8g zr#T=Gyh4NByLTffu0Mm2K8VaKpRyIjGG=!zu5VyWTrXC~;a0geqAyHGycYR!#fK9) zCREJG5vdT`#M(!sekOtz)PO4EM00~dBOkP(!sbHs_wi!loB}zy4l-4da~e_6pli_Z z?2j6gj?@iH#(FsnztP%Fivlj=l8GDdHpln8?n_9K)0S&<6}yPc7dvzmn}f_mE-1v( zUaN)n(-6nZ5!?p_-Cnoh@hLJ5j6kCqK~H`);&{Ml1Bf6Q|3dM!5@1Flf*fj$Q+1w@ zD`A0UqP04$WTo^;UCxJ{iE;AlnUZv*#&6_20}`=84{!f@JjAto_crLgU|p|28_ONv z-^Cjd*K31aR^(fDi zab)rcV3WP`>iFCsH;z6~w*!dE5hAwOe?-CT+=0Q8X#}`t9CDfjK$Gwk#7`2cz(Y~-QD{hm30MZI?R3wB^Bs0 zQM>_tDz%Vk`Ygx@{Rr`v#A3Ot|IIO~>JHiTmAQMm@%DH;zQ>pM=1@UnD+<7I& z%M(JEAf8h18v}&kRX5!MV?XPPZ9^%pV0<+0(pg2HCKL{26nNKFfwkxK1n~sXvv@%z zr%F)DbN>XwzPVwmFT+A{lrs=dsysgNWOy6q7oNQIB);oVWBdM@4v+gS;`*d3>KvT) zxiaappw1}N3utH(!MfzWvbX?SE}XALAhRF0$UtT7jVa3WB_kAfOhXp}q*x2j8IIKo zW@Ih^60&%B1cN2-gSe(1+~_?lWf@ zg1#WBxm`HtgFx8vdAYuk62N8Oj12xD1I=& z4PCB@v?~N+5$kvY^uJF!x``(Il&f0?Y(s;KEWV%1Aj^$4W|x~4nBQK4!%Id z!8NEI2O*H*IAV|vJgpgP_7n&xa&Q6JJg0R9Icva|=6OxKqYMtg(RrAU)(HyQ&dkU_ z;CFm=eKmghudTJj0-B|ZC}^5<)c4D~m~_yj+dNzj;-GbckBzp`Go=Hz9Xcho^bqko@>1_T z6;?A)YuM3su7C`o0on6ljQslpzd>>Uehp=Pee16l!0e>9B`{BTZVI94CtkYHNnUHT zy8-WQNGR?Cc_UQ`I66D(Ulxuy8o(G%>o%N53AR8Yd*SDs2l^NX`;?M25ks;q;Yq?MYvN7Pw*%*zxS97bW8wtiM%Yg{^H68iiD9?L)_|~1a0nxX4tx-7K_oyScKC(etmH|M>lfH0kRk|A zhNMG*j+-292Va4>I^Og~{^i5R&c744laHTK^f)81YMc<9R{GZ3@-Z3IM0n-^boebU7zCKU4c- z1Xn|MvEqD|P zaW^oroNUbga(Y_iY=ZB=d-ziK#K8>a8In>Ch8N!AOok4rLum)zB2({8Z=6=J#xM{i zzPPx!c^ESerHCOuetyilF|x~)##dKPPP%76V%+vEEj9LnMp7vPmFSz}B0{tO@CSue zikwqe4gK6fhrL@a2wFhK>4v#2e^&#Q6oVs}>hR1mU7y&cqeEH6#!S9{sQvMlRUC1~ za5oKqBrm{$UnQ&eZLIu?nQt@GB?=2-$Hq=mW>_PqEUEkquGzP5Yl7p1t|Kr(3JQOFm?>$MP94(SC#yEal6X;<(~W8Aw~ly7hSEq{>EJ(eD}H~s$#DXq**?A zd9=$W$m?NVUP4X|S1s+jyhpA{b)|=F47+LOHp^-RUnh{~UD@8#8=($ieqU{u*Nt7X z%DIEazuI8QzA9@e#e-}rV-(Y+>f``cg9$mD5PWX@Nykg@9?6Hx$%DqqwM2ohDZ-r( znah3HH5Tab@q|}i`gX2&=PgBV1x&;-DJzLCeGZs(yBu_76F^BuU`)pkeQ}?(c%y=9 zb~DFBi#$gb*zZYiWzPE`a_u{cR{^$A7vYAZ)1v$E>p*2A60RlpvqtsS&NJfBl`V+h zsr&Jf!wMwEOUO|fW(C~nXl;gfHPqIgNAf!PY^vl(%(v(~FQJt0o?4dkZFbj~JhPMx zSr*AA8&KyPx7mG>uZoQ41-#d1f5DQNz>4V7%0FAC$-{%_Y#znrA&rZqu)B?R88EJ9jn97aRn`peE+0(} zbb28KB{qSiFVXW9RDB)2n9~g=a~i;OOL4G^A3CvTpIs7At+cH_bzgV%{MS7~q1PVm zyZ6!I@hRC=SUp5__LFZMv-M@4UAhp>m}^!k0H_tf%}OCtQ{t-qfy}~1I{F!C6#r4U zFwg$uf^J&X&~OR(kh+w;SGbrMz#-kmP)y;{L}%qO%B1(7Uhm}L!_x!%ZR9PHb^>vh z2^%(JSwzWiD?9@{`vM*QJPbL!jZ<8W?rN8VgF`l@&GGR(ikWz`Pmf{HMmy^5HF3%i zG`19;+DLo})8xy6i1s^buKwX)^#EZb2W%AIX509N&Difnqng@?i`F|Za?{d5HSeSM2X;)X2NgK_$ zYG2XvOaTcnF`jq+a6!1S7tPxbDP#7)iXNF$154jKZdXa*G}^EXEjvb#piXMCc`l&; zNXP12sm1-~PxU!=)L{=Rj`$9+-L@WpiKo8`Urub0>_)*+4y>6oQ0+dBK79}2hD(G< z5a8{F*B0SEr)c<2+p|Mggu7>(+hQh0nw?!(+YtjtnX~_hVD7BtTVoI6 z60#$v;LLoGb?4s`=8rT|sHo<-7(V^eKvnhPLx&ub%76yMqFD#8|*IWYiYJLe0lYI#LGDDqaXm)bt*ZOw!%L9<=H-6VNZc%J9-zBkvE1bxr| zF=Nt6J^QwBC=2$vaNhjL?I#4@p;uDq*`H;$5YY;7BitiZF>m$b*LM`)Oe1b9L0x@o z-03bz+B~ih4QC7pjuo0`8e3v@0dzi`&G!k)gB=vnLLXuJ6^BM65n-vKPG$s@;JSm# zSb2;a3uk51Ej6L@FiA~G6B07GJR~1a9I($pD@XCUd6Nf?CIhATpBveo-ZSIg3=|L^ z0>vXCCnyw<2ZA9*ofj5;61UM*(|-Z4eU~O4L8zV3PpX!TW0$=5o|&Gg+~aitSce7H zP0`ovjEol$`bg1Mp=WO?IJurw&AcJy3gaZuoO$oOfT(@U;A&2@wJimMcpqYxrAwEx zO-|64f?8`lH~>HzCZ9PxKaWz8+!J5(ceHSKHiK8GIJ^AU#_$}=F2 zDHM!+HIgXF;JP5~^pjPxF?y_t3_l}Bqr}+#=aqnBy^q-)WL*vfHYE{mWe_BwVSV8o zd=zShqBh@XTfCYUqyoq&0ue{!^YWG09?D`M|^gvSa{KAW-XOTqf6-TL~Qd)_N275z|3vsERWmeyK@AtE2X9TWFAsXz=~B>QXBiA?Zwn0ccO>SJk_W;$tE>RcL;NAkNXC$%D9H)zFN}V; zV)yv`RvRTauBr)zdVQN}t@iT65g(mdPreSE()fW=xg15tyYG?oL_v$zTo(=s3_q3| z_NF@WSG^Km|3y{JGH3{WfT!^z#S4O+P=J=Aux7hjagj#7j1!$1#@Ew}sl*(A&)5ou zsH$zq4GY(sDzRgBIb>H+_Yq)zY(s_e%Sn9zF4EG5i@7C3TU(g?lPaBpWjb9 ztFZ9)W|@F;-qz)78v0%9)>2hH`$B`){0h#Sze#l5nM;L#Q9jF>kY#3>IwI)yH&riSIP({EBpC?GKa z(^xBaxG;%F1an%uJ~64gE5(UP z&r`;vzu8=fLRhe|Sx=qTb)~Z;r3Nyb?Vx*U?miDlDdR_QJY*z$1Nuv$# z!jp&AvM^>CZ$txQk!PAv-ydJ!-%{_zvXq1L2@mBVU6{Xd&D^U9JsM}jAAoWf0@tlj ztGk{)EyN#!l2$pjzLZ8);bcnD5#hh4m#T`#MKeM3_E+wxj*6?{h0YgOp*G z^SFV>GwPm$imd+;uC?~M>fAQxyE*BFQ)61~gx@54Z7;RpM6_bQ$bh$DaQL_G5+(*b zK&*9&&tB0N#lNrLK>i&%y^~z}wx}E8>w7~s_{0L50McUb2B6eE3})X$vs*R1cC zJSm5;;TLdVU8HUz(j}D4(|;V%s!HJ4Wt1aGYp)OC@#v*40l+!^$|~7y5qtd3yIuon zsS=3jDLQ{2gtDuvYkWaLq-SjIUf|~n6P@!W&J{C^+uGY-Y9B85U4AG4kT{$1uIrwt zu2HSx8katiwa8epk1|b$rS>ekLQ+G-`;|Zhp5@NJAo%Q<+R*CWPg7X@=tRi9;vAQt4{mFl7(G{t* zMmsT|SWGa|1Y+rXFyI09u75?3G7@B5AgXrAe%P~{cYc}EuZ% zlEe^^ljUw`&yT7iVJzt7^YG2A2+-p2W0+bqS3QcBwY9g`!sLTSz2_=MWje29@j|{J z>h^73rTSawaNw_upMv=K=MRo@smyJX*X&(RyvPE5eSN4h`Tc5t7f(F7lcSPpth0!f zKSU$tO@jAP%I>+v6!7vWa{u?KY3&FVVTs!x->2n04RE>9Xy;qsf2qp4=fZ^pq7DqM zLZQz9Z;YfWPA|ksqqv73*Vz4lWcP2FyVQVp*Zyv2UbQdxYu}Dhp)E63xz1;r$phaH zC)pUYy%KLy*7_*8{}d&nTI%-gsXbkDzfQZZrmuGJ;0iPG)hQ@ zb+J9fBaXiaYSJImPctI>8KAPiv|e^|5?>8r-HPy*;5zy*^!Xv-|ZM^mU9) zy?p6g=Lf5gkXcCEsg#IAM<;aY*OYwUO^*F>dk6?f{msU^T6d?`cR~4Fj>pRtsK)%k z8|?-d@3%@;YO2C47aqoAv3wQD&>fMTa<2>nrEauMkzf{;Q!E=TAAq*O~IcyRU^! zZtCUHt$$)g)9)H>z|V^wXKZBtt$C)KR~=ju$YjH>|E_y$Wch`LDc;VlkuFh!EC2vh z71lv4OO=*RNKfBbbwi&_QIo=-@jQh)kO}PsR{BcRNy?eV8x6v>Q^FM1uMgJ}2(Q?B zL)#b6|5I;onfTpm^D!RV)yhuEs0vR&J>!0vA3pl+`K$jK>K&;g9F~_7e@tJuthx{aN-L+I5Ryk;I-wEy8 ztExH=BP=`nyaLr2!S?u?W`61RrrwFvT_+QxhvTkZwpH&vjiMwgBV%aPe+zeD=arpb zdH!E^PM;I1eFes3aw-p=4IPp&OTia7-N^*6$a|(4Hrv&u+&?I z$F}Ci3#LS1A0roR8CRXlU?Dp4%Xy8`8zmxjwIkGKuIsMbbxtzAsAyH9s7tx%XSO`k z#YhnNNvgf;y5qWK%Y}>dyB2-(^YJsm;OfeVTO-%}8o(;Ls?us@!m~9Pf-5SqgZ6vdlGhH0s0rOoODlz>T(Pe> zVJ{1EzXZhs;RE=Wm#p^Rl(+#)>rnP`g*XlXUHvXJCIIFHWP14M5&KzPAPF$tyH5X|&f*S`hXb!j?~ zN&3gD6=mOdR6ur3d$oBH;vYHBmbzc9A;LHvX76k6=*3*d{08a~pDrn@;327a#b2M6 z;GnBT6esYoonUz`Xs28-&HOrkbpYcu$q)dZ>G4#s z(Bt%uze3-@0FWII`WJ*e{%WHQ`?7gf4DHk-CB4^|NSowgARdd`6w6@^*G`nM@Bfy4 zVp+MlOHeeh_^RN++rssS;EC`rik3aanwgo2a<-=56`5Dn%4#jFwr_}smoE&jWb!X# zz)Hz$65aUfkN7!ATa$+)rEIj@=fzm{|Kh-ocx}#3zwpqbkWJMfP5_}z(c#NQz877p zBD2yD_xtkbdA-y(ZmTW`66gknET6ZdP^R%B`nrdstm_W7qE`?mAr#e(Idy!;@U7(j zxqA5e*&Gu#yj)~KPIdi$m-1vcxhoLEbwi_4WPLOBLMOIj5^RG?Sp(~I{%h>QE8g5X zkA{1Qcr!oVL%pMct@pgC^UooVec*r2?3l~3d~oMOC61qhd<*`($B%nJt0+nE4xNk> zz3lYl1AcBvTCsxK*-7)*n`nkSfU4}sdpR?bC`hfCDCuMhV-PhUz_Md(C9Gc+_dZaw$zK88fD(xN-Q?Xs~+ecQ-cSz#f8^X{1N|E|C} zKX?^<3iY?vOtH}jRxlr)oEM;XZHIQqB-eU(_Eh!I=u;?nu#1wDmd*u?10viDvG}2F z5SO@nZES2zKSVmJO43-BDvNR!r6QUijU+6#fVy=KsRksY!n~>r!*MH};@~UXg|(Lt zKCNyaGyRfl@1k`DW<*+7m;iR=UP!F%3#fRYxZP?Vng9e#V1V#?;0Hm^US`ApqfT<3 zY)WDxBj$Klk}(HTV}VOR9D@?)7DUTUGxL5-P2&WZ&1(9W06Id-aHeUxy{(O$`91{Q zu$`g{>cAtFVQUFfa|HzuIU;EUxbl1iO0pogm;Bm|w8Sue__5Jm0blj=w8gK1g3`jie7HX zcNXp*k@5N3Ic|?q+Zm|XnCucS%6wQ?c!5agaMJB*(5ChKc!v0HujiYBAmQ&mRxAUa}MgFNSKu#+at z2c}t$zlV;UfdVB;okLn{Hy-w(Y)^Il7v$(#qka31^w*_GKAlU0+JPEL1gwS-12i8Z z6Egs1@FVgWTXB0Z3eYoDR~s7CrSi7U>qF&?)Pf%n2i7&yMDbud17F?O=I;XRKRzX0 z9axHsf-E*4_AgZ@r6t7&EL2uCK~4yr!Qfhb?`s;)gjYJ#_udc^E4U$Nx8t%eViNUt z-!4V|hwJFOjsG^b^PM~E$lieB0_m9r3cx`43)yH+v0m^2*L7__m!T;{i$+F97SJ1` zKf2UtXJxJ0z=2DJaz+aprLo5gK6qm^wQdZ7__Mf83s6`epv_&VIt1z%=OeiG4Gb_+ zpc@%~QjI)Gp%5dp?DiPF^*+TLM<&lH^ea#h>Bwp{&N2%GHjz-mOTWf}{X(HW6+NYZ zW;%cT61<#Px3-pZ;RPQ|dn6SWPG%g5Yqc+jbHOk9+s6P(0du>6tYZncWzzT>U88Y6 z*serIU6Y6LYTjJ@st7Q}b_x>q3JgWbgW`FhDRKexX}mac=p~Lj^QW$p4nk*d@ob*n z|DR($!)tz6^vtO3UoF7ltMJRzoRS}HFhpaN4^Gp6`nJ#4eNol+{k8SaeHWdzWwBa@ zic*;~Ut9V!^~S*AUbj(q&un$OKo_lhhu6(AnO=I${jO}R9#|CZ z{>?7Wl=%yVoELMhU+1ujr)SYai)4+Y^B){{ojtn)?iVeCQhZkau@x2}8sST%{Lntq zah+Rn*F;7SHTAph7=5g^@WG6$foBNAGqeVUC6FpS!cjmaZ_oz*hU|nek3_({=Bn+9 z6AQj)Un@{dyJ$$|$9A~(F&;hJ^JJ~??@=AUnMu2W_!Enhxj+<};Q%{|4~oT3@XXJk zx^7P0SFsnW_utLl)6G8P-%w_u8xVH4PjtnQmUe`a$Rg+yToeU8Lv0-*`Xcrf}Pwj0j zf*NqTM39BVP7EhqfmBS0JJ16BYYRqx?60i&=Q?GoXLs`5jX zj*xk1tE$KnY$i~Jq~uc?KTuox?(Nu+H^fAQ#WCKxL7LaX|dS9%)V54hfaERQ~>3D}!D>=)Pqy!HWV0HA2dP)=*8gu6v^Gzy*2P{G5Myo}lh8K+$;^Zb$|c zPl#3z$;VEegr3KC3aYuVC(&zKi#>Ouhliw5W+?JST5BDkre~f8Hm%b9RoALxp>?+Z zP`Heo+pzTQiPi`6Q8>Uwz(p zd`6-{8hW!|#mUik8tqan70TFOpOTHaQgQ6HtzBMW^NUca$w9#UTbY$QgPX+ z{ThvWyY{{?e@Hhv34ViXtAk#CNBHZ0@J75xmo@7qAA(S&6_;%3(GQYW6y*hi`uOxnz&bV3KRgOGu_UuQ7=rrj>T{NtNsqmF@g$0wQ9 zo@t5$$l&>qmN8IZ%*ldcfI>QkC=%!$*EIk9Z`|xfD7qFv)$z|E z@C1BP6xYX`2lAb|uC0!nP4%>lKtPjEJ^-3DOWvb_(`EYaTZdNkIAbt}f+^o4Pc&a| zz&i&~g~w{08~JTdB3r2Ejuvv}xZRTxyzQ>Q^|8F)Ic89vjljH~p-Q_k3syhi#;IHJIFiap&>j_66}CrziFTY-Rz-`3$nVjd?@pd?8~@ls_b~xY(Kc)jIRc15`}mLM$9M_Lhg|u_8wODm;&(=WFa3b;Q3RZkd1Zu5MYa)KKqU+ zFP>%ti6KH%Q0ka3tL^oo^R->jj+PUHAvnQI6@BIKFC%gu3I(%b0uOKgXWDl3)#nsC z4ov+Bfd{JH{>w&TaqYtb&QGqQgn!xOo;b3blgVTg4xro0P?8GhS>`Hvh@k$x;%fhn5&FSm~a7DEpGjqpBc$(0fjY4{eq)AaMuXf|gXmR4y#^Nu>d1 z{7m8NS1c&KfgY@;+{dhK#-SqqnXwZyeqf^bPzsV}7c6Y7^6J#Iw1x0td_j!qq@R7W z#l!^`rW>SZYc2~Ruz}3<1J;#*$<485_1Q3d-2tJ2fJg|%e${GBMTf4!0}t($B@|+! zqM{ZPy4S5pnUC`##Ykz6;vS(a01qC$0;Gp1b~K9;D=q+pvP7^alrSlu5FK&B+gSz%!V&S1F8@#sM$Ck z^ipWI*Fq-7Xz3`+=Uoy2>e_e2?{t-l)J0Rl2Wk~64^-D&A)gxy8&(5<0!|(8VV<5K z5Z-7fjr4~SVmViG#x+^4x~3*SH@Pg=9k$zV_Y?p>8ALG~sP@UrWUiLn1v&^e({a8n zV!3S0^9arZf4n0h7*tjD^76WUFL4A+Tq#Iiz&v(&Dk<6r*B=nl7DnpB0We+Y(HHo9 zeZ(y%On-@Ls>{Ipj=I;@)iFkHX-VOd9e;na%cY>Eu~9mCdf$d+Na%837^p`fWHi-v zx_|SI+VyFGix4P?&94E}cE5etMR7j82@Kham;nq5=EIvAbAzFjfT&^wD%?{wrx$Lx z!HO|kC79j#-Al@O4jc+-WtWo^mvZ7(jj`j)o3hL|I+Q@C>7pn%_i<~`4T2X3L5DrS z7ME)T7*4$8w+rTOcFg-49v62NOY#VySiX#4m&ileyWqN3{4-_C5%|B0ZrXHgLYK;n z0=)w2))15&Waqua75%F4r#D;fTI<5=f_q2qyVcXc;vd#8uaSiS8a-mV!^0`0^J z|2LmicI{e#3I!1f1|i6$t*0+Cr_|E+nwSJY9VQZ9x#scdHHq)n(sAPKbDuX(K&284 z*L6-bF8R=aJK=QpcyQ7DRjqLkFmU{JuH6&WL0kez_k3`az3tt3AJoX`9-cO#-e-+1e+H~^I0KFG4Ta$D{3S16Q{4ip&)PU>pHpxTZA)cNrXoH^|U z3P7wZiXbP8#80CD(aQ(~$Wy;pvFQ^ed$|2f6JIw^(0sJ7Tjw2lEsGb61cfw(C31V! z3CTzc$w-Rpf|_WC-38i6ypE5PGBX{_%qk7{?v?1yc~I>$*B5CczMWU1h(|1{r5W|R zxRe7qgdQVSd3><1?->T=eK90#vgsz9kp@))nI^rF6@XdL%dnBwUuxH5unIwhsurxC zdzLPH1vz~{Ns4gm7haF%X7?X~3WMY93|=cxQ%(#%oaIj!8i)hOW}4|Fvl1mDTTI|x z(qWM?j<5c7^geMVHybl{z$|yZc0GJv-C7*Dfjc-&{uNp&2mqyTqWgp9gaKE30jC>LGzxtvXpY0 zz7jRoE@dqpK0S-AYrlhbQ26VO4^2($PD-nNSi}vD1@e>MJ2k2h-@&V2zI1)grs$H@%F1s%g>4w@Uk(3EdgGD{$chjBOVdWB^j_HYEY+% z?_CPxDRMY+hUVzVOd+bB8k&q))a+%6Mu7pvWt}W{;O`rq6Cfm&(LuD?<)Z2*~g+h|ma*71a%!y*gT z=n3F)dRtw+M^&AA(>BjEt;i3Hp>U-Tf09wZTMe>&;}wm_#p z^o!2zVvRMx)BzolFfldF7FVyy$)pPeNzF3--n~zo{mMl$jf1dHbz|lDAP5lMyx9s| zrR`dfM&Lo2V6w+ZF~hiy8vn46)-=>BX9ond~Uo?R-QS{8;dtju4vB=BQUU5l3 zQO#(0dQRV;R9?=*lbD|`6S7ZVoNUild$L1KHgPg3V|CD0{x^hL=Eshg{}ZCFl>oni zi~?AWxtbN2$X^oxIC1vIiJ;=coAa% zuP*e}L<>N)!r>;!+uVVd>OKb_2c~9H=qiTvda}u@2BcYjUt&;;%6W=}>$=1tNY$o+ z!)UgbnHN1yPuKZkH}^RbbL6XgQ9wnHc0{-|NKfR-ho+k!nmUx1J%tF+49986c>R`v zPKJ|(i8JVBeDk3G-n~AsyToRZI<~TxS9Xd1qi}hj(=sao{!$8OOj507I2taa7Se7< zd~}JJ1G?uCh0xSAVtv`bcDb8t!?lR+4;s1oC_q!%p*}`m3OpyKwzV9EG1N$90GhxI zqEW4_tu1X5?OCa6BlLRh{{@ehRh5iXJAQm49@E8M0mu+WU`2=V)UkpsSihJRlQ})a zOOCjGxo+=P+R74g7g?ai4#84qsZt3vWdwbwE}Vf3EP0r$)33mFu%u_4^o_O5KhlU6 z1;MnzF7KfOmEZrN52yY%ZSyL0;-4SH4!*<2#Fm_&u5Wy=(GO8;6VFAkBBR@n%aPy* zRPMvzh7W&`9RPJM2Ydx+>fPQ`9}G%179Fat=IOaK3rhIc_pg*n$OR7nh@c#nRDE%+ zQyvohAx}40ab6JpaX1qh4H`N@G*SH+&kbCHRf$Xj8iXP^o$wn6Z6qJ42H1;AzwGOZ z-AT)@RxGd&RY_)2<`iUMUVQWU=+#~?6lTDt zK!UKJ4MshSgToi4H{LFPJ;}{_eepBD~Cg`O~BmZgaVvYcY(lbMdE`9N>@qMN|fDo4%Lb?a%DEY4s&3VAylJS?XzTaEe%=m3t0TyU|vcsD9Dec&W$cY(mk!cKSI)* z<;_AuLdsa<&VQA@O8ieSXFT8b-n}iLWWp<72-H~a^&XZ6$cyQwx|GUy$0SRSNiy^m z|AP)vr|WWnh6X*Y6%~1!MZe!HD#|<3^3bgJKE%lO=z?kO&e_ zYnK>3R`;-9?Cr9p(Rjyrl{ss`xZ_B$AD0dIxyG;=M}!=_*EZAf_L#f{KhZxqp(TG0 z%gFu>bYhhDo-U=sX*%kl(#v|+@Kq6OU6*;U^a!uP{qY3W+7MfC<$PGAx*$~)_}=L^d4n8rt@{H+s*x6UJ95!x?j;h>VB*0 z3&eT_n(2+&2`E^s`*hWQ?s>pegYK`xxoq+FPRz7;evQJ^1G>b}+8gKTba%NBb9KKy zwBM7HlUyN|uVjk*U1gC0@xvRUQM@`JU~=mKWfCGowcWd`e8!ubn=JtsP4uju3BcUa zm9c($=qry=6Jmhq9q=G?wJpHxOK4c~&9c^9t+mU$rQqq#pPpzBAQWnpBe|{lkCt)O zat3ekVfQa1tz*i}_DJ1{cV~((!rXVIy!%q3Ob3Ne=>!VS(OiKwQvbw^(@eP(26M(zqO9{nk*fWA?8 z@$Z{13n7l;U|R%Z24zl*923CAjED$1vg8|)!~ud6=>^D4z*t~yP=$$Z*-`=DnS|O` z52CrPA{(aOL3&}GOBPL7<=sDaUhrtC8w0XC^`RH1s7E{+|JI>+JC)A%^M_;UnwpxT zfvMQ{osEKO62JyL;ZckT;6I_$vHJ8&^8gA~l&`2+5jNK^4+yBU zFxu4w08NLuFLZc95er3OSmh_5)uMBYA!%f#+&m?WIe-;R$}Jv4$BQ&giUtss)fT6y z#%V?y0N;zb8e_disHsu!K!nJnuHVMPbmZAI=nQ-uE%q{fi@fE{&fMlsz!vH`J#ZGJ zY|=M0lYHQHQBT`&1m++9KW}5(>WM6U`9|!pRev+(H!myb^d~o!q@0w&z<{En3vE@OlH{u2kvf|YDXl@oJJT`y%EZ{A` zYgbHTvmLojD1y6+uuA`J&!hf zH?L2Dr$c`?TeF7U06@BoPsb9MebZWYaJF0(O3bxbG$)RYM2~~=2>+L`>oQMHoij)? zLV-mpEz`^zyFBu!)KsbWDDEQiPw=E^zq!?kW5C_)>|Ip;myjf01cw-LSSV0572O{2 z0mzIA&$eR++T~!k41948_iJmE7El=5WtXZm1qQg(-({r12XFr>d^pwbWsfr%AG#_( z#tVgX>!3n<a_a!e)s%<&Wr7R@Ge~?!RaGrh}Mi$Nu z9_)Bd*O5SfZ&Ktg{qfw7-N0au#mfpMcA*ufFD%RNn=V36*F{34oWSb3KVasd1J^pa z@A|?Ef;XUm~rv|>&*N*xvw79eh;S4EW>1hL$)mOV}l7RIM85*V2g3)SpILskLrpYLSw z07;6>)*(nZLqbA+4c)7aXE21)L+=Zg5JgqTw+i&gPXDHZ=P7)HmNl4IYN6C2wR15H zY1ms1?+n$n!0X{u56w)*@@C9_cI!W`q;IWTF4MM9|IQe4SpXw|(cnwy%s zG59q+TKW@Kf->hOB#5nG7d&z2lhS8vqG&e+t75XUviE^iB1I51p3GLkx4Hh?z`HHQ z6GAARbG)V}^^1=R2_^o-X<`&IIZ^%xCc)Ma$zDeMLBWl(dsbU=4D}yT6@6ap!>p<2_Yx|S3iTqlThZ}{i$(Ze=MN#vK_@BGwK<#ATc^1i~I&YPHv*}A}PQz z6L$%N|3CT9z|fuW>oDpDGdiDwK{y^VIXw%5gDSJmo(63~xTPmw3)4HC&{vB_k@KrU zbJWy?m$Cap-2&32{MdIk*OGFGj-YY%eCCOQlQ>JhjoQuu-nz=f0bM<*v|E0Jo(tAU zYN(D3Qsd^TjdYuSqxiwCCnUf7^2WW!#>_E#3zRwI0RipX=L^MMk`yIV znVKhaB{YdrDW!U^d-pu=Z+OqU);VXLKi+j()_Odty}$eW{d_+6eP8!=U)SfeZj~EA zUtgjvIvS1*^%$E5@u?76oc*u)S9o{GG9n=@oC|&|HRk`XI@^2u#)Gx~LP2_>g3_fx zhkHbqB(5SRk5EJJR6DY9-UZw!krGL*1cW%~>~s$do-*K3fTB`Tgoq&m4lJ5@uW1aU z{~5z)9HJYlC_WE=^>)^O>1$hHurl4Rhs!9l7mG*Q!)UJDnhUm)Rz!NO3*>rrTwE6U zb;rnbPD0-Day*{>nR9bzOD5x_!Wb+nwIc>m&S_jOSI^0*?fmZzCpsW4FkZrz$zX#U zhDiW4PL{xU9bJ1c++`@msK7&dwUj;cn8m_*H~zpcqS@099BJ^V7}|nKrFXVH;%5{< zgYiR~S3>22m42JR)5{fMpaO-q#W|#F(J&C3k{*DUabNtPJ{*&CLXp{mtLc%rza^NZA}aTY+T~3ft&(PHXX}`SjQ(%tc)zx zow6E>@h_M`NY4tO3Oee-iz=sxs)gUs2u-YR^^-{esK+p}v#V?S4JLm*DF51+gR9yK zx@1&-{!Dbk&i_7)!2zt5{ZO0=ZuZ1c+&4JH_tTtP{gn#Lx9Fcdtx01Alwx z?*l(F0iGAs=KofGfR*$_(i|uw@$!q<3m>72ix~mgtA?-=Z3G2}%DBSX0no$^(htKl zNCK8)O!w6)lQ5zyJ8tix;rOZkcMxi>o4mk=KXf5v z)+*-8eM3f}`pIvgU29QFKM0V>X=oMlrOVI$+Hu7&M?Ta5Ly*8nX~ZOT&iACn|8fC9 z_h*O9*K^4$k3g}p*niJkj5a<0Z81V%^16Q`^ZJ6uFGmz7FL<^mbAgR+%}&P05aq++ zhcsux_+tmg<$QJPO$LPWY**Dc_JmKrVK^^t7^gOln5RHFGa#j|KU-{}#VrOXXGp04 z7(CSzSs4IZsvRKZGuX&xo!{RTJ~eTME(#Z7RN(yoeFBM7iV8?xSdbGevK+-hgtF`v zUoD2?P=p-Dg-qA#@3d5r#Q4NX=s3^2Bx<}2^A{&d=3O{lH~(~?Tta!x8AS3Ne7X&&kpx>KroJ*5(D!(vIWSmI0XLF{4%05&3t zvVU|O!XbR`&$sItP$NX>ZeaKAb7$W@=SQTBa-8NYVbiWaRID*AO!pw9Q->tVZ*FRxNC+Bp7% z?no@D$XM2cY;VX}Yi7s``@55*CuY8VUAo(Hw&&H}{r)n%1bnsNjl&yLKL5O=lxiKHi%g(q__TOY%l6zs|Dtn;0!BpC#ZY5G?2|%>6tWZ{>f{HCrl%5#U?5oX`z#6$R1<NoK$2o6I+neHkzHL~7;{a}HurD3iKZ<> z$f+TC?aV`?QS03Up^48ULR=GZ2({p+wqJ?R5N9w@m1OL1c--(%9qED)ijjLjirvt> zQ?L;f3ld8@ApqfyQRj3JZlKmjMaL6Kdz_)h#-Z`a2CkR_Ky42`*_xQpR8^eI3QO2C zqoMx*2pe!0IK5zY^b9m0iS}g>@oB$7sb_Q9Eb4ia>!rsfjp}8})Qy(>ot>2>+1&7|I%I_F8y@l)LP4%`(#$x&&%92RraLm)VIsT zjm%buu6|~fA?|#F|Jdp?{d%jT0<9F%*aqC1&-__}R+iSuMv2D)r;Zj7o6~W7yxdmK zi(Pg0pH?}<>)+m}baifBshhX7QmOC!rhtYQ%C#R#J%=}=p#xbLlHM|bI@J2bIr`W> zT%}jAE{3RLBH>)SX9DRU13NT`NTJX51Y+cODWs6-ciLwVv*dwNK?gk{Ypow~b}B>2;?mf@Q;= zKnuwT1LTWZkU}q_V?{R!W7t(tE3bmq^0TKV`P(!XE!qIdtQx@A5TL1!(i=;rLnuK~ z52zk5ZK}kicFdJ4(~2WjJ$(GWfi#{lXV?nffH5pENskK>YPZdP7u*kyVeS2D$`<_I>GW0so zPdD}Q^2%xg>_wOI@|j}b5m1&hGBSpSlPjNG{g2z8fN z_r`Ge(6LN__7@>Wd@+)z;TO3PPDKjP>V%VURPZDvU^Q5DX3&}pYMRGn8kGqt<6BVA z#Y1v)b4%GcmJvBdjwfN8s5IJV)YNJozI?k& z=>*+FvC|_L$i|P$jabA8rxmX;rT)s`!{OdxKy#+;%OURu^|Wz2B*C0VDgiQb+*pHV4P2IK)`4i*qYE($$UcQR8~? zH>0)u4B1xWk!wyu=MlW1)rQxDKt%Kf%^#P)qR9eS5FLRSTOjd6h&8V*>tZsQ&4>xu zjHk%J49w2|<#=pfD$NAQTxa$KD|FkE0J^)vf+K6ENzNrR6qfR^uQw`6x{Kx<1phK# z*2v zoDgoCX|9fKEPwzD?JfxT2AEN~Q8}&)unC_Lb zva~JvN8{U1#uOx2=1q3>dL($4SO>o|VkzI#f5YRnvIM0>@NsB^c z%)PwsKCn&4OMtu?kKGKj_R%Z#@DflPGi%p90uf#~^2W#}oUvUKlVSMi>cuY%Mty>u zw%*dEOND>@HVnwF*s5%~^tgf7??r|9-iHI{0^!%nblVs_1^;{I^!VbrLy)yJbr8S@8i2>;^2xODOje+Q(oYGHZdwk3#>0~0{- zJYMJ4lh_^p`mnnG?TT8rmSOjxqXwov#p07EKe;)^bV*-dYIQx1+QpK1JkbTStXqUiKY?O0ooZkRV`x?@v3Cq*#P3lA~WW)E9$=HU;}1izdtEy%~8$R6C|#mJTg} zgVo~28xdo0_9$%8gj8lCHlN&_IWdJ&S&T=pu-ctrfzU73DyLmg^5gJ}LsLUv<1B0H zdePQ%`}Q1usQ-!3`BnR~1@<4X7nHowg!fPPYJVrN)YMY#da?h;lHy|FLtM^&iOG{2 z?HZ(|q+Yc?U9uy?0&D+kr?&-PP#|>v2HSmbl}N*;eb(3Bw$9&f&z?PMflg=N-B}nT zFc!wDy4y!@kL%u%J1e|EEU(0G3o4pJE9XRm{JhQ|4(vT-hJ`GPQ!=H4OrfA#+luB? z(~qPj;%C}&(;p7y7S?rv>}&=T>{ge#?QfgDREU^#tcvpuUw?S{d*{1KGU}Zx79I)9 z_X6^kzEeLX)9$|Y{5hP9wjx~`X_CAp)<@osWNVC#bF~QdNEYNCNQM-pX(fsD;igB2 zjgx>zo`&0J{h%gtaYEP95d&YMo1^3rfxzQM1R_275>)<)ya=St=>~z`1}~g=-~)PG zQApc10{Gm5jn+16`eRc>vD9yy*=ef~QV^N#JmTw-i?&YST@<-eUR&_WY@?&2Q{e8O`*Nn|t38>2wmPEW_Uvg#jR=DgFvffqwptk}B{qKY#EvXS zU5+LD6G_88?gkNi!D!D{J2Lu~345qZJ4?et`0*HXU;K)&^~R0CC`u*DK7h1wir-_@ zR0#OoOyp*#8Ek&`3rZ5Ez=HmORwp`@vc)IySy+Kt^fB zVHC6~5OF>WN$Bw~c@4nL!zYj8(WqSy60yc1q#sSEa9IIK-ZR~~zn+M!au^Y_T-=Kt zUetr7-v)kZITlqKW{}1#T7U25ix=exAwBTofM3nWVegnVLD0gN;vECenm69iXu)d0 zd1jJTh!XPM-7u&bATxlkmzz>v&hhmTi(2RV*v^GE5F4wy4>B>rH4$3X1*q7X;lUubgQ&ES?UUhqkh43 z(~PnYQu`ic1XqT4A|)~B_F1>$*^*8DG0t3R7_2$Hy}jG?w`~dT0lBa;aHcShe#}c`PJ&q=?sSc zB4qDfCp1RuzH0Y+)z))TV@!RMhX&>mzk1!*n;|gH0wWy{=exim0h>fOd|cuOG+I2M zJeqv(-o1D3e!nv#_F}5a^Ck+l)lfW2>3k@~txo>(<;#Hyk>yMUh4mP%ODR9ogVWK* zy;!xUreYSX(Xm`$wu`7XYj{P*`}QIC18_1AvTZu5ari_gSm>24vCq$0A!?#>`?^wk z_BDJDe>}b&w33y2%imCdoKZ{57BSm4ZISLG#ESYHYd)OY+Ba4lOe+`sjE}n)0*X2> zenHGvpgU><$|fzi;N^-;*@KQvrDXQ{7Z4IfB6U2h*&2B(MuAN+OrmFdBuz1HWjO4u zeM$tI@C47mg*x(4^jON7nJlC<7a{pg&e$J5d=M-m3r$E*uPalN{eWW0;M*F0q$4OG z$Ex&V4?2Q&G-JNwy>n}JUKKCt-j8%rPG;n+8-(W`Ik;v4gC<8*w4%=g;KOP_;pG8H zsu|_w1V@EaFVEDOq88ykVK&T%NH$VhzTyp=Hl;VJ2(RD!2)XL*y|Vb=!n`a>dpa5F z%p^Y*VExGCfiAr)EltlJdW33xg10L!sBH~Wwl zMqcE^jvYHFck(=rYZ)V;*^7w@djFypk1?9Hj#)L^In8>HuzqCKc_*xF3QFI#xb-zy z9gZsLuv?@f;QZzjV^MyS~UGuWWj~74wC=$BSa*;Wp{HMNOB39_2LJ<{xVROWM5|#&1cO&Fb zkgQB0pTuJU+=Y0_fE(z9ZU$o^5o2~2cVfs9$3_AgTh)5E%iu5j%JIXQ7!aHtp{jX) z(uo9=uHd{S;cx>Ikx5=4p5NaX)9b2B8xL=Xh`9DWUgpKVrGnE10hu6>1h+0azupnduJh+3Q8*`~Z{AvydL5Yw_vur8K?Eqq zEPW9wHFh=T;_76V)jHuOxMK1!54yT9pFdCB zZ;b#(RmI!(-#&)stDM!gUY!X-#PX_8yr$8_;e?W$a#@hp41S0iAF~XSuBv2FW@MWP==)>E}X6k@XJKc zry3x2!dv~zfgDOU_Nz~syk#&;g?Gqr;1HyNzT~#m0>@B!%B!he>fImcfCCVJIH=&_ zfJBPU=jY$4c3oUXMiG@S6ugXz#^*kL`jp*to4@}d6qT`!^hAE1lR5E5? zf>v-6B2+||H{byhrbf>(5vCT95B557;tPdeUzTT| z!dNA3On_W9(C0!1*aN>_CyYE;-QLB@x&Dd-V&Sj?$TIb7rt36vS|cO{fJhAz(l0$e zo~Q|($gmiTKBdJL2DW1nLK5psN2s{vy(rN2yjlb?%fv~qL%gVF0Y5^H=qYuo69aJKI z;PTaM453gsLQ?~W^F`){o0^s=d#eFGmcBKdk1znMirS9{>ueftluAkBG{jVB8@drI zn!M1>{Oy4HfV}@m&+j8rst1p-Zxh!-AjWRLm&Oeki;8X4gjlfl*Th^F0e5ItK+E3_ zrL#UtQRH~wThIq1Lu{50;I~)p4S!&Yf>o@KL?s8tNIxI4U29T!zKPIHfp5%e3@k2I z-Uo+1PKk5$mX`?o`1lav+j2Dn^)7hF=Ab61!q6pmNKF3*nl`Oyz$V|h90p%G#9$8$ zo(GyWV_QAinPE|N&uu3eRJB&boz3-XPe!_?uym<0@{7-O^QXixY=n_#X2dG5u7}Z+ zQV|%~q|D2r`&x3fZNIt)rjgch(3iCSW&r?!`Da;@JfgHm$(mJYAC*H68CBfE#bsRT z)&N{{HysLJceaG?Ea24EIl!2-t56`qifAZE6r|+j#WuN#r~)0U63ds@=Ho&GU8h8H z8@}h4tZHxk-s%09>%29~(e%e(oPsP4vYoR^AHkM$UXI z-5Q9W8aGR@toqi$pSCP}!Pj2*^Im6En7%%J`n0&K5k`vLSrx!7UTXR^oRCMA8(wF(29G36HDHZo)@!fn{6&z_yIeNv>?x~*0y zZ-oF6G<~o!mx^mw!LbYv-tdj~`ghCJ2g0d@8bSG*}2I4t8qxU7lzr^58qKOdRt;Uy}ie0&>zUlH8|O!V?)+; z3NSmTLJBi2Fut0P7f872vo6o4XEQ3Cxhxd)wHVn(KwV5*4XJenZ0O|M4_aGTc$c)c zMQD|tG26HHT7`JaQ3Dq1^tA}RJM}+Dl^?5Bisnmie2zH|$1Hwecz@0o#|bn!Y%P~| zDIF_3xoMaFgpwD5W5O?R*YM@?W4Doi9;zW&Vak5QG~3tUv!|DL%}Gh|-;b9a9`^S+ zkY%y(nz~yw2B^JD9$+UlgU;gvi?O*k7HE;J3P9xw$O&Eqe1*C4HWa2PN*p&t>@Xqp z?FH%!wgH~p-)DQ^NP4}v@FYh67>Qe^M7{#k_RjtWU6S7?l;Fq1;UPiAr|b9mY8?8t z>x7-6qT;5Ct2;bBSBbxXOAk8fDVXalwwq>y6ARJD6I@aNX&BLCp;cXlmQ*A_?wrS4 zcdqdIo=8oJ{ya25A!`4DZe)E9V2KdisaSWMY*wkjnxcaFM=xBwxWxZDIsracMdlne z*!<4#XkBRNEFg^2r%(SnllJvnXTo2$-Yoqjy9D_%bp*t-Y%fl&jZ?18_k0^aI&l+@ zK`e8;Br@r!;8CNWHtg?v?+UX^)7(grarUs7MYQ|nt%;0jqGxy{ia#Om#RAO{a*MR~ zy}QG@Y<=MLp3<8(EsJJtxTX^75%2rt?cj}4FP!J+p=V_R&Q(ddeFs^4m>-ct9uMKH4xLGmIR%yP3PX; zb-B(Y=g#Vv8MvDyluws5yg?Tpak0bV57y^cFJ^Iy8k1^jgT2syf-heLeWsX0IGc9P z5n&iFzyn1tU-6?w%kTzbC4;4}WAgDnkdd$}R}vw{?|_f6+f0i;a3U)IhmIc|vnn={ zJN2x!eEkn`ZWDpk7fX+v>$*{snsaSVKH64>lq?;`?OqrK3WjACBUK*EV{fDl>su04g*X$jAdI1&wo4^yWjkZ~^kb9fZn zz#Q_V2pz`sIne?uU5+s84%64Qu_eFqn(eXn7r=EX%?Xr66BBh(z1^t zW0=sGX(WUI4aj44z}-mrRl14m2zlGt_|;> zLu@wSXh6BI{Xsc|pIXT(98ERruWy{va@HX9^h)&c$x1>H&IvNZ{op`bxOQ4{s2N2z zjJWqQE15WR(U#cRv3h-1omC%DBNep4S@vcs<~343VL;)YjW*V z6#^1xiGw%1M;Yk>xrc$==K&|t%wLK^8RKU~>&Pve6|UxT%`C;jxV;ksTMwb9G5x82 z;4Wv~K!<{P0fAHh(_bun@6`8=M~h%@Xk@gHFOroks9mMn=2?-H(EE1kI;y~-3h4fw zm*UlwJbWw0h2J1&-vdP$FY08@*ZcyNrI}4XCuWi$dFM-wA#&jZU!^6QRp1657{i3$s8ldHD zIVJCoZLnR;eb?x#6@kWg6cTu+dyR-nl%(P}|&@P<%+?XZlu&g9)kL@)5xjc~p;o!eU;eFCvN z2E~8oA6cVOK(?xoyPdX{wQD~UweG?W{m3i z@5lK@96p>dU_v-9&wnA<43O0w2y&x)H+SAVa;T@;;`JVP1EpEF|5Kf{?zs!jVjKpW zfyl-+VxA=$Dy=4d7qpVSzue89-#T4&D6I;0t5jq*^_*X8}<3L0>%!XNc^4IwK#CKpByf$ki00?-uQyaWj74 zaZO7xz&~q0<<-m#lg|M6gd9Gh2Iqd4poOEqVx$u>7c#OkWe+ecfx*dp!Ik(YM(9X; z(&%(}c=-9(D7_if*~Nb%ClP`{!JQ#1InpwcU%J1(fK43_LrC3l8wsP)-Zj$SG5Ku% zu>az@!fSSFbr^@i#p@W1#=q-N{O7&Vlu9SjhBfZcr;A$gdhKA=*-4aj-7SaW{5qNylY{L=_A2;qC2}@to0OI|0mHlWXj_LwBqiKNMxxY)z&ir6IQ~aAPEEj}A56WGFOVhaIntM` z+(-+V{{H{Xw`b3*!kK2e0$h$ya9JRyHQ481*O=; zq9^l*`kfZpC4!W=c8Aa=?qos`fNe8g3P##JEzT$y_x?$TQ7VjSz{6s`}T!uY2 z>RK;C`GRH3vPU);=G*8%4~RkrJ6Z+sZwpwz|AqwFCA=4W>wK5pWR+T@VK{OjqUyX0 z!IqIJ(}b-z3Z8$DdC_USe}ZZAHTE~MUBk-yZ?8s1aM(K4Gxsf-#$A!M8jSU3GE!@LH;4s|H&TG$MBz}p$hU$4^&1R zL3wY|{L$mXRrN<5)j#+X&9x(NcY?cS56Fkj@J2$@Ws!0bZZ!Vi1Wi*g1(@hEQ&)wI zw9(lDepdQNi}=6q+iUPRoce_L^rHbXkuki__l%-DKu_IESg6*h@FAc=bna*)b2QuU zfO$$@^5_$8K@JUOvo84+QU&j+Nw;s@IDzJ2&^shm9L`gxAkF4J*-w(4HCJ^R5PE2x zT>{LpVcEx6tl8&|SrA0nLOd=HSg2Ff#18;G0SOOQV>PgRn<6Epz`6#<3#c%Vf0rXf z6?PXU#Kt;6uq}7s$Ng7}HjWD1NM72Q%Z~%`qy0n97UZM8~?D8eLC*Xs&rZW-HPC1imqA9a`56#?dIopdGcTagzuAwNR~PHRTTv4d6U0 z$74vty&90&_Ydep{%3@vpg07o@?}*%Bv)9QQ3w1D!AV73TKrvp3;XwKtrC=8NHAeM`IJfI~qm?YX7vi=3LW<|}MOglrn{nq&Hi@>|c z!>8uZ?F)DR(6FC)85fx_8_s#1fltq}KT~4Ae%(3{f*h|Ffn^$v58?=AYXl|FOESQJ zOGu|?-Puz60d%&B4h-(k$RWVa)#J;(1SnKcP>{(=waSt|X!y`?kjWx9kgG*fs*X$+ zol0HaU>glzVWK7K1XNp6)p5#7S<21q{0FmOmC*w+33$)pO()0d(+xH1mD8@>+#>%C zknUB(_ya{MK%+PwunkoTA(1Bz8KnP_QIMA>q(%ja+VD=g2goT@TtD2?D4xh*=-Q+A z)1!fgC;onPW?2^5sEt4?uG5J0hsf?9l?yAuT-bkfL)g0&DhrpN?I?%S)I7k+NWs=~ zAS{^D(^lISx_0LB1k<&il+U3-$?fO^G&y)(me=liwQb3*xASJddn>cvwHVX9*oyo2Y?YPD|Bzn(n}&$0dze$K$GU*i(72ej z#mwodyNW35T%mHCPrKKB#Mi*efMr7$Yd7y55aL!_HC)L&D{7lwJ8X~k17@4Zppq6l z;4!$-uUcgL0CXHT6x}{x>Q`~i6^F;1{@;No6WTkyp2)yvuwBmDh?KHwRq3zp(Jo? z@<8j&ZNK|I?EHCI7J;|_dnn{};R$;zz!MN~dMo(wauv1^v(cY`=)M)7STQ&p`~IN! zIlk|SjBFKN1Ly)?#^KS?Kr|}{W#XH@e*N=v;n>+hNP;R`oR`#2#Y5sNjbqrXCwPO2 zkQLVQ^T=J4YAlVeHJQ1jFomXN3%YCv1zaczv(G?wc`lWgQeYU&O?6+@yE76rfKDFawzQjU# z`Oi;Wuw)NwfdBxmN}#Ea3(7c?^xCG-=N3JpCi7;^dQ_K5e3-An{{fZsUjrOm0er0+ z>Aa*hVQoupea&Rf-XJn&NqNdK2C_Y&O zA`?*!#7VB^f6>&%tNU?I+=@ZshW>68!)7cxigU3!(Nocf=V!cs)&X2Um2{|9$V->b z){A!3F$Z6-iu7%jLarOmBcvKIzd~i36Y6?SEFO7ngR<@&B&fBxi-R)OCePZvoDj|K zOa%Kc2Q8MKf>9zS+>j=yr*z}5ReC(z*~Asy?4fjU^#6Kgvys=#9p~(ziRQ2hl~sns{sQJ$48*JM<5iW>ShER zeQ-)oAG`~+@Hnw4DGP~xsxBrv4PAr2K5y(5N7c9y?H6Avqo?4dzCb1L zkaic=sVNe2*&{eFTCl>9qs2tbqaUkUDp;mHN(mh{$?VS#I4{YKar6Q)MgXRKDlvGQ zZuj@Iga!%&&+^q0Nw{tFGyp2Za)y9l^LQD-fEl#M@3df8}Su zZ#$M$V|@F&lM;3@M2`T1CgbHE#2B;XzcG1Qj;h{Tga=T``hX%S8A)1$L?lP^FBLs8 zASvj{qF7pw?AjFRqh6RO<5(j=!8EhO!V#tOd7%Wyj?8z0Y;f8L}LSB;25X zPxPE3yA4qQtsG8lcXzjv%S=&8sO|EvV8zJ((4~TzVq@ur@=u8wnW*F;r)p9XaxV?0?Um)tfQDdD7&`qi#xVIklW& z5(%AkayGsRINN>WI%;E4LQEMr!InPI+)sZC)E2t~J#f49ItfPWdlCXGyWRs}Ylfz` z5gG_0y1YXt&=sZSu@t1DgdL-MEI+94jB`wK`GHvEl$%3Z7?BNq$RBl;Bv2bPkk)KgIUhA+CRTRMQ ztFm+XMV%>$gg{9g8k5xN1Y%r`R%SOSfLt(Pt2!m`yz5@bE#~DC4$rY~bbzKMvjsPz zq=_^V(7yuQ`=eXY3`xK|mlj$^JSzlZe6sOF?u~5ty~{;uAy@HV(w}tle%9F`YZlby;f{fR^dX5MR^SeeEz46Z(L}@yK!@wlb=Qheta1~LCuLXk z8e*(=A{wir(!e0x?B_`x#1X0nz?;1C6Ii9&mcWu_rr?f$p3Tyzi41+^gv#k1)|X%5 zx@YU*)FW~LXuaS{S)h)n*?rk29(`K;YSq?>sB=+_?!`n;|M$#N#C0YM3#bx3HxE#F zlEKyLxiq%H1?7&_U1!UnZ@cX6ognwoeOTluf(9M)6$GeBw{`vLh9!ayw4GB=?c(jD z8}tBB!_CKJ2k=_-MpGJ98eV}ri>F&lggRgh?!k6GEpa|j)NG3z>$GOgNfP~TA@zXe zMd&e~*}w}n64?!$JB9)tzogQxKzQoJfv;rd^x)Ap(5@;Wxq2*o zp(bu}8*JsuDL0eGjD89Md+@I)gMZL*jy-Dq>i+xkHto5vgRp@UZ9jbMriN(-3N2W$ zVAj>M@7gL8LeQs}!$Ns5bQ8=meEQrcf)28Q*yV6PI^o#sj-s<4ZbmpvFIvk#X56k5>1b%|hE9b_U3+z;Z`pJF|ZC~7Rn&&5z+03cz4stzg4+8~)#wz!tP z`xPF8Io!|wW&^uJU<07E(OWYDBz&~>HYQ~1q}&tELW;Z+1_;!^2c+VO+X9Xf;z5!X zHzuYS75=E314NHG)OHMrF}vwoQ`535W`dNA(tLAB(Uxy+h!0599f1)OU6KG>tYAg` z1#Pv|k`kV_L>p*9QcYil?N9%|jWSZN+s1?z?^0q#?-rW9)$sN3r{C(*!DD*2FNi)> z?!8CY(sS!yzJ|o^ce)&uAt)%vRWgn$q{iXGP_7|2aLzR;MV;({(A@%YWI@lcK=E_6 z&iGd9H?;tNJ>*MOsBO7_pW$Iev;w3mvybqN$Msfi$t{C$Yd;j+<57M*Z}&1^UIXL^ zN6luG{jCdT87-51d}yNZsPx@LV<#&0?M%CeRdYmNsLwYCATI}wpl$lHs-pgFQ_SNU!5)5VB9WEI+Z~(wnXDX;OVKEK8Yon0oO4KGl)$o z1LLors_Pn@r!@LV5>>-dEL>|i#uXSNBJtfEr^n6ul-TfR7ad1m4DRkk=?0Dc;sC(;nTsA|~L4@jSRTmbA=hEW`SI?|V zu~g2(D8BGr7hYDxP{-0?L|PFPi0Kam_TNl4^TX+}A-v4M`0D~=wN-GCuIaIsJ1DQse zjf2)W=&(M8PQ=c&S;&l+@S1&Mt_Q4SbXrFT(2JsC?8dyAek`y_(kaPBO;NG{oo8^Yh<8T#1?HE=>*(@+ znr@M(8{jW0IGr#!VH*4~-}gQ&Z)3oL_AwZ_10~ea6?G$KA!Pnc5B~)ZiVOHmV5u1I zRpdnI4(Su?8Vn>S&7VMR%|Lx zR6x$yrZs~xZavm+Cq@5MO>`rjeLx(%CFl6JZ8MTe=wc8JXo~mXgt$6FTxt} z?x^J~FDIvNAB-~se0v1!nl z9ab`VtCwL9s@hm;w4wNH#t}d;%kIp#yOvC=!bT%w4m2kIX@uJB`2;{_E(;Cu&Q*KB zvP7nbzbC!6K$Zg0LoTWgq;3Ws5mOieIh2@Tl->+ypMvxSFsw z`<^MP9O>JiYMrwgHYqfWDbM0n=P(D~3o`_y?i1YB@z$Rtx`tU_z_=W|!DC!r0pqd- z&$EnpJ@Jf>iNnjedn$k0ws7@CEkp6|uBcLZd$|NT-SBi}o(> zh5wEgXtt8Kc@}=wY-f{SeY3 zCh?4rl@48Sq1GyF;F~c|T7^_?F@S%>_zl1+P#j%iqW5nB3yfOy#wYC$?Zb3S6~*L+ zP9SNUPz%{=+ewX!2N%+4Eu&T6+1WX94kN2@`SkK*#Sg@U2`PuBR#Y&f>KcY#JOgg6 z1ztYnfB4|c8K#GZ-To>ozu2WW?)(>aq1`U=b9_g~jk|sNM6%J|tg_FS&P>Q!s~#v9 zc%Z(SZ?(R)gPVe5oPo<05rJ6G?c%$ZY3~+WA{wNl$FDz$^Wyd3ppLy^_|@}<-%2(- zAMo5*a`nUEh^9ZEEN@;t()GGG7hJT>hK7b^JClbFmNs_w`DMR##wbJD5JIUVWmBL8 zP_Bbbvzi_rl4Hz=rOsUGlL#GaPAu?5YA#R&z~B^uW*pAH&WNeJhtFULPaNOu^J{T- zZ_Uw(Pusl)T!*_A-e7VC10l%1e>tHzlLy4r3}`0QWAPtNzrWreQBGjm=NisZRSe7l zVKtY8^io;pWMCAwvgHaI8di8~;ar)(JBf*M%cX-Q?aa0HFahN2?sw==j?j}^>a%54 zKBX7_vg8Z0^zgW|Ps{*{pm7t*98l&V-~F~r#Sv>c-}=m}ugRB``6MU*4F6y<^yzF+ zkZ{f-x?sN}9c-ibp+o@y$`mvaZC#q*y>TEz@T=6QBn0@`eKg4uun{$&?Ic+HdpPJ5kI z&(#YTVo--VqMpf5-)9bwgZ`xr?Ozvp_(7*1srf(8e%@?8n@wuhH$XukjEUi+!+rx0 z7`THPomGJo?eM1|WaE@Ypt!finb6_VJ1@uongjVKd;H*j`sZcJ&Yx#bJdVT-)Q-KV zh6rNbmvsQGPU;asgn9R#J;^{hn%bR!OC%>Imf^j&y4;7k9?&2JNUPLae{chsEcC7q zO)DMu&+E2^jP{sz$Dv$z)5@MOCJltqxzz(_GhIr~U_XykcgFRt2KnjQgrr`G}>j%S<&N^aP z0x#}Q%bjs<`!w2b;4r2jKOd_(52_WYS2^@clqZ9@R)Z7L6FB{~e-3M;5sGRtjw{Ei z&}=&h24<)D`Y>5f>~nm>>=b`M3X+)o{z1Uc;Mz`IJ33$Cc{vN{~t7&u}yFiNO~h*Th1dx8u_|%Z1leznSv=yq<$k`@v`80jeIQ;7*C`r z`eH9I;0ZC%-~W$%X*^wqg(B|R$?X<1#?&|NSI1NlNCr-xa@xV5DmH1lk+PG%txBcozvTtm(&#F6G_;khmu4U4a%0Ig-IMCBaim~wAm~l6Hy8 zG8cd&ft3G_`fSFYIV(jN?D13y5^s;=@%y{w*%&@{d^NjS+box@cX+ zFVIoBr7p<`)fWDL`6y<;Y{fIXCyMU)CR{8h78tChv@k=(1=kby(a*Jg!{*JLXXOm- z%9<=Ea|mHSvcj9O1?hQsreYZp=_C4g6Rzpa573~JQ9NVebQ9nMD00c;@-=Z6Ed zI*7o6%=1l=far3yCrgC5$5F&h+6W3A+Ln2RI|-{ozArEsnG1+(1Lg!~h7|3y!=BKu5)!jUGwS`*a$DtD zO|7cvBbmInpLUXp09FMG*THv%MMZ7MOwTg`*3WWYYN@)IVNsmCJZDzE1cP0ja!qRI zjvYr>P0IEq`p(vjhSj{QPqJF}Bf;}C=m|-9U)Ha!hZ^W!u_`cG`FQFD(?)*!)eBOe z@euK9Iptpuw4m=A%CrH>EhZ(Q?Y)|}qJ^I!G|as*zyPPy+@%hajZDmWvUC(5RG{8q zRe-4dzr5U{3ip`p(=^bswS|T@+NB>sChKTe z>n1P?PVfxVa2=hmw#Uu)10RCa8V)VPUOSyS}JC1M%)W6Y1&b_H-m!6*(eL8v1=A z@Yu9t>5iPl9SKz7Hm$+DFcb$&HfeSSP#j0o3gfWl-S`B@Y4fpu`6b$7rV||s%z53B zG+zx_jFAk@d@IYi8Z@xEEb5tU4L1v#zzE%i&tUOs0z;M~S@FRk^DR;LSrtG<=BO=Z zcg)7Kb2yOfm-wSR<`hyOzfa;~7*PYjN!j>+&eU!_bF99{5M5wa{356&4~SRXtdgKd zj?0wij|v)#NUMEe$F!5kXqO|$*hq~qCc+C=do3**l`+t-K(U5WN^3fQRiQg@B>& zQFmDoKFciC;-X-E=!;^a8gz@Tbw9CiKV{b^%lwdmQzH;|S?B+D%oYcMdQJtq)44_9 ze_}XVSbY!9;=mL+?kENp-O25v^DIsu^p{0npEyUiGc&1;jewAZ&M+}cz>ctLTo<~n z(e$HhAx_CM%cb+TE?>-0*%cZtb%Db{Mmn1&y$H1=<}6bC1j)amJmDXsa6sn=xuOvi^l?#{NZkR!?oo#~Om6bE&rG~;rh)WodnZ@eI6DO!i zG6egy3f)L13vdW^;0!|u$Wxc$C8XM;KxtN_*f3=F>PixZj%-H5r#lhqAlw-^;AWx~x`CsDl6A0UlmBhrwH1i) zo(X%*LZr*+n1nG=%^viDxGA^&V2*Wug%q`+F}U%!A1*!BV9}Cl0?qXzOW6V!AR@px zAmTR#HQ;TnV8ueNp_EJEz^9)<%mr;roiO6mvU!u^&0H)WyZl;lx5LQ=jIxn0t|Ov?9W1d7=GXhDJF= z!X)qf#6dv#4&J%zy}7Y*)Rik&x(aI%#0J3@F(xO|A9XqI3205uWodYQzk(+mL;BS3 zmZDVax=5Ulf)ht^j^kd=SpugH>OgX05#=oHOTxkQ4_5r6iFWnFFF^GF+LO**;g?L0 zv_0J@s4(%S8@vzTFgjozhs<@?6NBI{r@E4gR3IbD&FEvW*?)uRZ#CSC$rh*J=h2rZ{DU|cLhzo;r7On_#jCL4B~wV$nHmrF9w60 z*66jQyBp#{slSBL>l8AM=`UI})Ns(SdXhAUPC;}D(@J*Y?IPW_3(zT07O2@>rbn_3 zKYP)3@1Gk1g&+tJyRa&7=t-&kS;&HD5Tu39GXc13<2fX`8+zUUc+l-BHb`x&v;mEC zs0jbFYObPgetWrNB?jtg(#~ZV9g?t1m*%m~1P3FJ^T_Id`RV{;~3cC52tn)};D_RPn_Lbdn=+TG-V+;Gs}E z0{~z?N9vOo@OiOSo9MTX{QKLW?EU-=Mw#A^gb}2W!y_CKz6@GB;lt3iaQwOou!E#e z0euvhCrI&%;Cg-) z-0Zq`a1BB6W|5o$RLyz)rl894p5DW%&ZxbLNr1~ zijNvL&7!!F4HWTFQt{)_#bZNM_730}!VgFwioV>F33O$FygaYtjapL`Y!k4u&D)-| zcsQa#&xyt01=TaiATMKT=c6lzJ~7N@_WpnUj9H#IMh|-@E z0wi1k5_!!-2)HB=nENBQO?M_18ABqC7daV>Z8rgkBx9C`plT8xpjs97FBj2(27M-l z#8F5=0fUI1z_0&gg@AYhQc^dy1!(LOIkkzUWfFo7V-7eHVC{{$=whB013bs28kZH*~grk%t%6hDvmf{|mCTZqq~vIJW7 zx?y^mCKoVbaU7~pDwu~Rh7yoPl{gpm9j@}=;(G!(#lu|a>*9 zxhi0RTZHGupL~AKUAQV?`!vv@LO>Op6;d?$ZU%FKd`jNPjaS)0d}*Zk>2ud#vY2sZ z0s1sa2r~UH=^K7okh(<52!&t(cB!{?zyT?PC2k^431FXEDK2eN?pr>0dgDlVd`;Vf zr!%;{t(1#X`io!jnWR}tjn<=j_GZ2%Wqpd8q&@*TzsG7b|4o*dsD-G0p^OS3f6Bz7 zDRA@Y?@9MhrBfXAfk0$!P9N_kCK}+*aYlw)Ah<9aj0`L%E{K5?GthDhzLlN2={3VK z9kd>(w^n1On=ki`{&a!2-22gdl=MOxNZ(y;a;<7O!Go^0@G3O{NSE{>zJ7;$v zP{A}*34HHk6B49oerVQ`CS-cA7~#yJ2I%b zIoPn%mBE;hNxA?Ib2K#G!9rE2Wr!wXj}>Ah<^kKq=}eMVdZm$!gmQ$!qY@hG5|T_{ z?nJ9o=jXt+AFG}$IR`anJr3+krrin2-Ns8Ca7z9HwN;VDr|8v7N`sX|?P@Fv0Symt zVYn}^=Mu98liQUv6R7eV!2RNcO3S8*M7aU_<47s``jy}*G6=GIk^rS?_W3oeIT|-!dU?HS}0ECygK6d=an5qb6h`6+kAM{ zcGk|3kfX8#JSHXPbjVXr^f`C%Vk7F$_v`_r;7$fgH1nU3VTU1JNpqz!1JltyzELAd zFz0`gxfAUmzTSxR(yy;BOL547q{ipGVed>PNKO#xpzW2Q-%(I{E+X@H~On+vJwN0D0 zMCt=@`Tz>;8UQhUM7fR0~oIk%%jDChYVf)jAGMnb@Lz^UDb~gv-!B z8l!m%N6vGQO|i2{TDf57gU-cv`I_}Z+LR+6*3ys;w#w!@V*lKJKe#0f%|1>n5^sda zl#;=k9$FNeP47i0M74sY$UI?vIUg&O6N}Pn;lj>;dmtNb_D0(PY|;9efxfz1K|2C! z7!$&?ClzmvdEDZ$F8R*~EO6g!?z}l|m3L=RAz083VPiBQYX-x5a{%4Hb$p*E5liEq z$AS-GajxfJD!!o>302yJcdj^m`LYiyHZKF&FmkTVgQh>|Uyp9D6Ee5)G%aYym@Ab9 zG&3FfUoN|;3qRKkKC&5nXQ5uDT(|Qo%}rr+)&E9+7jzUkvFBe!{57e6%*)l0PX;dQ z+~PUcb;+Yiu}96+Y~>)%%VYrp9`9p_RTS$!z9~8DCVh%=cQmqFe{UN&zEXw`fR-Yr zo$M#^5+Eu0E@)R({}%YX!DA?F*-<3oC0p&)D-_BUp>#5Z>SDnUvSsuOZyy zmL&h7^y0PGVCUoXOzFI0B(wgfX0xwNjl@6EUHgIr&3X3ZUa?A)e6o669+wq@xk{I; z1E!xwyCd8`uKBI|Wy21i$v2OBE1oy^T%CXLt1F^Tnb)I9F`w>Y8`hOsP5calFktr=v*s_IvbqD-j(+n)T9|V{A6X1 z)Hyj(tP+}3&>~^CPqy854V)bqRjbz*o~RSlP;O<{Bp5|`tzkrOP(?;p!S)kcRK7E^ z#CH+VkW?zl8)Q{juhfT07`P(1ix&kJ675GqYE$*Sdu1uv0nB|CiTdicb;yxC02SVΜVe3k|T+{#Ne1NZT4@cCAw`kyB_~-bjgaPOJU_PF<$IYGVMUiqU2C1N)y^-9dk;Z^8MREax z1%<1IdhxC4>bv(=7QR~VQveQ};qYB;Of}H^uv;q&cQS$-la))I0h#Hvr2xgSweKghKf9Lbt zNn$UCgu)G}2dEP*q^I-XV+)|>F`vcE9bW89WEFI?SS!7EVdFdvr>esH6|s z^hNKRlEWlX982D5Jajq5}7 z+5gF;lc~sA5g#!@Y)E!-qKFx3KH4Pm%u*e)k^p%~6!{Tv{yeo>evU}%z_y3*iwN2% zZEtf@L5f)4#H$IAQUMYNbU(sjbrxiV2IM2*C9!ReSSLE=WW2VrBKZJJ<#lc)T{0*^ z>b??y&2V4g`TymaxJoVInG|6x3qxyu3u!rbnM6AuedYzI4C7dK#^i z*aKd2F0)iqYP*4d<5)3FC6#)s7MI}c`N=7|`ihyhh3L>CL4w5sd zC@4sfC|N{ulAPJqmj}&eom6-cC&p(N7s$8@y#e=640Y2!EAtLM(1#_#x=IhEsr-&R{lt6Gsr+uey@G~S&f zx+KyL68ZEA#aqt@TI_CZR?06L$*68%+PY~2?dBD18@A}2W>%Cted5IW^1GQ~oI8HJ zKlVd|=AI9a2V`QxP3AKU=fkU5DB2_r2kN-dtA9T>Iw_#{@UH5>G*VeY-;v z`1?E3+pQ}?X_mfE-$7cx^zECKGzLrGx*nvHT)y3#e6A8xu}ViLEmFzSdVS6ZSpw{du5flEQe`DZU* zh&yl#3pX^kk;&wC-oOX;jL+`OwBA`Ti2X)LED&*5gK$6dOBprF|BaITL< zMSax8Vd{I$=VvMot+VN`yO5NWls%N)|6Y@gBq_eZ^^B|CSl7xGPq_1c{Pcd|lcBx+5B~S0m)3C(+S?*@3 z^x(u6Uw?n)pgojE6)r!)jb*{2D*C}T{)=-3RQ0BG(@Q!!I&wBqLdqEz`c7P5)8(*s zGAB($^Az!9s#B4&!7M{RP9Na$y5L%|Fx8g$@+If)-Mf2R9fk$Fb6SR2Dzt5!bgy5R z=4SY*mNyV{*@@lu7+avAvA@i@b7}SyMyB81yvycWPOAJ)C%MbDzJGn@w}ffSoE8y_ z@2+)ms;>&>JNf$R;)8f|f4$e1dc6Me9phaM4Gn5pmao|z1`gW{HLE(!j?3HH+Gcvt z|J8zri~suSY*VsH%P{!94^D@U+tr}#3mNFNhF<6$LFc?s&61SOoqW&Z2BAInLc3^WFcv6muc_2k}8joRmNb8}bW&TIzL>S@D= z+X@OwN=n`sF)wy7<19oQ!96gSP#F z#tA-n?bjX@Mus&7C{77&^-5k;JD`zS`TT{1OTJo$S$x#KemrbxEzWdP!bOL3FUdyof454IU_8+GQ1JNN0KaKd`UQ%B-RMu5ybz~~%BWtY5tq2B zR)@qovAe4n{1uP;N|_qQI@slDiI0BTMbl5Ow{IO!q7Preyk(kEtqf)SS0O|7S{4%L zX5vX>LR|~n!Hl-P4sQvwPn^p<&&bQmOEPr(A5?El=WX2MTsD9AR7RV#1M|6Ml9U&> zeyp~#$9mhSxfQn4-O?U$-<>l**G8>d%a?m4KLnx5oAr~THl}LF?)C6t6)wt47P0(E z$KbHil}-w=aRdSAy7rMDkBVE8fMmxHi(}m4osJF3x;otJ8MUw*x~#8Wz53cEf_0&gOOt6v z@9kCWZ>~3ly>K_rpV70jFY(>)?@L3fd_yPsO6x~QM{VRqjCu;eQS>dBM>N+|)3N<*t*AK>Lv>u7%aQyY| zkUy8ey?ghv=SNG+bPkKy^lkpt;lJgiFhdCLsjoRJh0Czi|DXt7`)geq(l>9CK;K|o zO8JpU@9m;nt@CkfXAqOMt~AT78x14(@7#H!U}4bQW;k!kXkm6@r{~_m3Hvm=F+K6w zo>zShDUpFk%@Z;+k0Ccjt7n*HcX){kI82&2Elkwcdg)Ak+U5O}K`nZ)E+IRiqGRM~ z|1E^~ml+i;yVt^PRGY6-OtqKYwu{;tjQ;%GR~_jcC}bLgoT+K?ZY9g%tA8oSUEUc` zx}|23_0Sd9tn!wen?a>6xRvp@%Ne3hxzo!?OUog(*%WWTY0#2wW5Zs0t1aKj9ue*L z;zdH+eAm$~=YHHIyW_7DNM0*6z2o9e*^0~!q?GkOfBsx1L?w+{T2k^w$E;j*SPbuV zzmSk}Q-*n><#yWv4}5@OUrluOPs%x8?B>)|f!C3d{eCK1UmtARds0n#-(abB)WpY! zLdpTj{=UBZcI`T~>&&A{;qK?J{`zZ0ux+DZ_GF{+M6*?eA$4)##KFj&O;_LarI-o} z3wKSX8hm~KT>J9n%bim`AtC1d>57V%n%1SWD^3JwwU`~$%Ds#|l2I-;c78V(R}_}S z{-@8`@sCW}HCegJsa8B*N0nc=aK{V_zf33&oQ=AunUw7`SMgADqdE*q}mJD<4(g5>SiS@DX?yd?t(KXBBS{WH55hcvU6#&2oHD)FUFDf-?w>Qwb>v&Gu_vH24$g^5H_9jBgA?myw@ z=eIIAF?%>PF7C_aOpCCu**5)ZV|#b+-iEKVp~8Hpxj;MH!A4y+VapFaHPM$mONUSg zd;$Vg`jefI!982^99HEGB3x^EOZ!ANKg>?`Iqo$3)Q&fAybAHqOl@ z__*H7seQuUvtV%|QIMC9o4e%Gr%#6-dU!1Fa>#4t4%lWgHyUm1l=RZ`o?6|bN9%SS zKb~J_vz}Gx%f15#;%=y}|GM>*e4DAL{kVa2Tk+>7d;2w%W1IBeOoaZpIoWcvZJVK? zp`cm!`&rN+C0<5YcmqQrD<*{@K~6eVl{0{jlMCnY0vpXiRYTlG7dXV z|6-}08fd8gR8hfTFHzt1E$LcU^0lj1WAe_daVzdvy|3SGJ?jA;eW@pVFVbJGcjs_s zRy;16;In`bDLao1(H82@Kr#^Pq4Bi8k)_d?YWQTPl`0ey<2XN3EHd_mKTWsPUoF7t z`&$}K>#tJw)PsprhvW*=)5*+4K$Z zQBnIsJ!^#6yJM7M$D7&~(;J8DfA!0o*XJ_}8u`w=3Fy0(r-iL*4BU~%%crPZ$CPs+ zP=MUq+k0Z-hdol0O{-n^$`*%auXN@$q8agr>C#MTuXUFAVYgq3&&%A(rxk}Pv_i}` zNyL_Y?4)q3^klFLXLOnRN3TibzlpxsL>UT@%*odg5o!v`965;+ceXGwsSN7dvS-AZ zwM7~^7~Qu|Hfs?Hb{NVkDKAer+>&ZoJw8=H)nxZR3vlBUe@fbxL=U1dSWi}N!?mIsRXIbpAK`Et~SAff1U0la$o ziwkoq4mpD<6~|+N^2iD@G7npCb*$Ib*DoEkE$yEu>7R(n%)GpcfxTvSqJJV%VuAeN z!2_Gt*4c!LUh9%W>$bUt=@hy>8^fAiDW7uD)zec}_a{)*M7?(XRGSN5K^A8TSz{jZ+I4{I#_;0uQB|@zKIWT}>@8E76->+^GEG`SWq3bJApTk_oGkLO}Yq zN7xS<$UjE4F@C6T8{QNg;pbn@-j(VwWk%pALEeg?Yg%uLgC53-Q7_wh-RH7R9R|iP z$vu-boIkhhxUoq+$6{ynpkGjsF@5_c4vs{CVI@`G9PiJ{8Rj8HbuZ##cx*KzWjgEG z@mSdd$)%~LO+sTVA3H633mGI7gM>`Kq%w&8ay{eC9**n@1;?-gBbg@ zPmg!%&2HFrM)BFuKuU#B^+0Q$XsT!Yj;HDd@fvLe3gk7ej<|55TfM+JFX=`{k(znO z*e$W5H#&u1Y{VvNF4aa%6vWF@Zd8a*F|&I|Hjblk3s%#!2t+knW6gUS(_){cgJKmC zsB`X&a>@bGKDnqnhP!wJN~f{$JYy5{MXnUQaGGp51AzT{e9IIS3Cfu5&W8l2cH11! zni@rZO?!KLzs`w+)dkKoS`*vu6qd|hWwtTD_RhC<&~{a`n-gGq{q*)+)Hj;BUL^PU zBf*-chd0}rsw3v#n$|0)WSiFMUS8Rjy>`u-mWizwso1=+=k--Y876LZRP>E` z*P6B-;?$?37j2xnbWvP4q7B71t(T>G;;bp^jwd!*P-%?W{bz0>d(><5*Y7&gcuBMG zq2@&RkDDWvrx=uSymp=SQp`SXi0Ha8@GXhE+96NtT4m@7y+()OyrWU3@t)MfcJ1%) zO{n>|oU+)=#KejmFBkyWH22u;;gNL?2|hdob$ZsFH`Tf}+L7e?i<9I@T4bVEn7O69 znD~&%YVkIAMw8~CWdS&etL}?qH(u5yZ{D(H<)d~L4GoR`o>U1|B`5ZWR~B!lS~}S* ztU9zAd%a6UE!{-bRD|j=o0^)+QgE*D&?CSAT1RT_!tyu32CmkkH;3ZJ(!VLWpH!+B z`e7~N2{0N_qB}cGy^>y*;FRRNU_UlCJw2^fTx6Z4apuXM3wL7HzDwof>G#n2#_HPo zM(32ID#LaW#eU0G^lSzZ!?m$0eue`^9${~cn^=yJOr@IQjOCKjUA^o31+%StuSSH3 z*;)Ga4j6TZv77gCKV5qLdr78e=O#U~f`UZlb!3(#(4DOo z=M<&Pb4T2S7qqDp+D293>BRRg|>84GzxqQGVM_LHfXJ!A^m4+rm-K``+L{U2} zZ3CHL>iIKWtie|0!fCwbHBs_uWBKeVZ{DsL@~Srhi~^2%O{I3I1(@-8_!|C=cy zs%sp3H5zwY7pM<}&^3;4t%2)Vs?{5|p8CqzRL2-+7WA&CJ~71ZOT1TMd*ygtbmhIa$udz_Wq##d30ASWWZKaAm1{%RChL$L7 zqUsk303x-Prj+6i(i~?j#};yi^J781>K(FdpSxEa>sV(s`>VZhf_E1ww4KrQOx{3K z#zh|GxTuNtWBv7sM|gd&SQSt9{pdK&aYYQ46Lnyhve7@sv# zP*8X&Xk2H|oOy%bH|4nU$B!RDGlagU-5l29zj)}{9a{b~nu?0PRaI5XN)-G6AD>ol zDSoxiPt)cTeOb^^73|H4pHD6tb(aUvB5N2gMAqtK!O|jn zKHE8j-v#i}wV!#g0Z|54>UtHH!Unh0SL_M=6(vs*kvewmlu77%jZpuOVdG7`}Sbo~Oeh z_I=T=^hcFrhwZ*mcZRUsy?G(S9yy|76Xe)<@SuDUc8Suu!pi5uR^!DIi%E5R<5V`EpS|K> zFm0}`B>rxFA>FOn4C<5E{`c4z97<_Evuh%XA8em{f9R3x6+h~7N2Af8w)s6#)@e!E z`QJVtTDO3We7J4ZocARxD4yy1P&c%t6TzZ46NK`VP!86oncAkjGNs-1ja_3)K~r;c zvy5rNPA|K#NsE-x`AuZgR(j=(;f56bbKDG;nz!2TP4H+44&*QXdItTX!DIS`!_Ze~ z0;StQL zRxJ6+9@S)%obtj&i4Px(i`Day+MNLCjb^knXHK6z8;d$Xh>irjDGw37X(l<$>XN_L zY%a6KY|EA{(NR&-yaDI%pIJte<4jvjCzZk0roeJ)xJ^4ND@!faz=w7{%VxPDNJ?eT z_|GEoC>a^W5Xo!QnQm&9J5bCnx#-chO`}pyZ{o5|Gk!>v?BBoNM$5m&CMqGuXx9WZ zSoMx>+d+?qZ7LsMNY;)FKTzNxu)ER$V?dKiyNePF)>e%L`ie^LZ>)7XWg65nU=Z|< zF1=NR&C4}iD@ot@)b=qW(N@LloxekQdz?wzUfJiB*o|D|Lbvr#s(VvS8W!<3ABRJ= zX|wMMbLaBYHiuH0QRh@ES9~zGF{2{7@deAv7k@Q6zZc{xQ`HaFl?^#|gY$rHlvmTB zGDyjnvC{xhsn)#;rIUV|-R&XHv!9>S>qTuRNp?V%wh^-UE-C)&?eeaoC2fcdTSH^k{5WafPqpkbMeSl8>eA?`ahw{R_)RoiBduvQD`d9}yGK$y^7|3!6 zTXH?xy&96}Q0|zl6K!P>-p&$ z3&Ye!^Vb6O?z^r$&r2*=6#4$1|62gH@T$?qEn8kA4I6)ddpi)KB_PiP09sH8b;R2x zHI?b)sf6KL6>Y&t^MVCiq7Kw+-VFY&kwCiP=8mb7*a_B>dc7sg@)#Qp!&^ek=u3-p zi#z4{OmpsiyTyv;WYz)Zd`KSH3UM)fwpPd3K(wCBI3rb@=p3`pqse$MH}%NqfNZC! zFzb!_So6Cpx32B{Q!SH<%e9QW^+3aSeAcRX7Z3SZPwk*rZrY zv|PeAeMWmNh|lYD0Wy??{Mo+8!^aKQ)pJ15A|NBA29�?EJ^>j}7j3`1=itZfv@A z$Yys{!ll734%hTC7jHR}wtP)O(P@)dobK2u-Xru}_J!ECL;gbIp)@;^iCXK)B3Lr8 za?mzsqJ$?=QN{|m{%Q55y|Qs5G@C!twg>xNi8@itm3!r_TbBB zuCW}nL{L1drY=quEVpO57jw8Ktmu7$(~bW4jKFGKN+Z6b*x%p3Zq_17?y{gvV|Lpk``jSEfVwyrd%pgg zm!0i%mpHSrf;0I^A-fHKe)2idX?VV#0|G3eje2oN@M1q;vFZ&q>i;g9yIzamQ&Zs_ z6sa|XV#yy#$W5g#>ll<|Wo6$oY=OpF4H2U7^2lAnT^%Geg2%SgsA^ zHikJ6^rH^@Wp3a0F6vTjO?P|z;)T{V2b5bqxf6v&wqH^@=C9cVQ3Yq~3l?={r~2#p zqQpEqXJ4V&rJ`x$hrPu^Zp^%K?X8?#m_byBztW(nLgS^VzXJ;TYUHz1>X|J=Z|INy zFgC|ar<8Df5AgBXknTQNch7}^$iU){cGHk*q<%iXQ5ogAjlXDqOJk>{;ZSoHO^7HP z{Xy;g=G;%JIkxd?X-2PAQ}lZ0h6@;`_r60z1r(5w*=g4?hZ60!#Jov0zkVlVX0+KN z!cMsx4K}9D0-`7?EANRGle8WENk$Xw1YYGOEv*;fqpo&SLoNNO!O960)b!l$7Y?#H zH;3sn^csnE2|DqYZ{ssJadE?wmfFbDR*bQ}io@+6s{Ovd62=rClqgw|WMx zp)t)kAOX}=X0Z9;U~){pw2kq#cMl&tSk2r&`pWxw6}WyRbepz;-Ca<((Ec+X@A;ZN zRxVnWDsDdx_IUTee)sLaQzFQQBASJ%AY)e2+}_2nph?KyY`)=M!c3NM0CE!G$HPXwMiXx&#sjtp_Omz!ZJoN%x2uZ8SM=n~}4+tySafs=pN-<#p7 zk#hYq;hK>`E2!lULH3M z7qMxey6IHp|FX|3LWX4-Pkw>6k?WLJVn#`4U0wdXs*-kJRnf_V{a6$h1!46xBi)}} zUCKFPIZaph#kHn4H*4^lDkxBNJI9la$VHleVV|v5 zeTS!JH%H-{Nwy7NrgYA#UHsz(*gA02*hVC_X$n+{V=Uzvzdmi6SfK%1Jx6H(AA|I; zwMdyw{Uq|aLt2ww_u+<;$?f|G_t#`!?Y6zw=#1j5AHi|>>go7iarTRn4G$kaG%DNl z2>rmo(gE=8;DAd>j*uwnM27LBJcDyrn$&F)2^OQ~y}ka^@v~lRhXV+$L_OPtBv}}` zO5ng4bc@fu)-v1$PA>26tn{lw?41I?l7r$>S-KsPZo{b}Z4Hg*`SYXa7TOtH=!}HR z0tKrP)cwt(6@3#xv&JJoKKenF*YflCkHI@v@ZL-Sk>k^nOj@^e;`@lBKG-NyIbQ8q zUS5VtL)DIou;{3mk(|4x=Cyf`XxS?o3vN=O4AqAA(~>Ul5@}xfHhPz^uqwgL7DodL zq$!K@-4gwv3G&clB(tk8N@$|rSB={qM~~}wV->B9myj#>?b&mtJNV{{?3=?0sBW#{*BXzbuu|z#D z|HNI|USukJH1|?1J7tLVP4hb3l51y0beHYXu}i)3ZW+;C2l7%F>ngKp&<*HCb+5rT zh{4m=8mq_KPB!TG<9-3~oL-}?s}7i51?;W}?Rw9JK%(DA$Px)zU4d#-!G1RTsv45` zpdu2v5qg_bptwUi1l6!FU(O>U`m4_I5nW6HlW&KDvCGoMnah?|bLT0yp4tAydZ6)C zkbg)4WH=E(niNs>YmmK&4i4L@Bb4VQO?m;c!6HcV&Q(7?dZgjm@=*Fq%F1fN3|<>2 z<|)5A>n&7lORs!QxU0c#F=>=-#JJs|$BKAHJdeBYlw$tkn=yi_dHGImHqw0HYhU&F zgrq*h8MM5we?ziO zH*4W0+fUT0Y;#^s;$;49;n{J=as2DqBkNA->gukz?n-x9zno1tShf=kxn@yx3$32V>WGttq}+&nyMZyx4EYrir;+j-m2RTAmINrwNf zIjH^6xfm&`@`K7jGJR zN!mG5t4YSY(n>t~W%sg^oA4Lzh3`g~HWX8*`zP#mna?~ux8kp&g2iP%A8rorxG^tv z-BKjDMpNSJR~v?1n_S&S$Huarp7?Oz!{ZXVI{?_^JgL(+M1qZ4bF;WU58CobNJtP@ ze)9Zz+|#Gbot>Tca`s6|!-e4Y{P_Vg3M4l#FTK?R*Gd87y7O2v?tS~{ZpynphK#rm zWLCkhISlO&T;fi*#WCN&z?dIDu4ZW@STIG(OP@OzYo+D(ed!(jpLcov=NIcg>P_?L zv0!>D0cAxV%K+S_kNFh-x&w8rmW-K;+Z3Kt7eX0oLU!q<`nIc&bU#=F% zUN5Ue5493Zf~RcCKJP?lMM+83U#mAQtJ2rgbr`;O)A=8U&9CL3ilM&h&O0OUsY{|9w$R-OgA+y_we*o zLJx5K-71b1;ezW<@#}q&!=F(F+T?8im=^!~xPOal0#D%~$VW%WRh405u2J{Uk}PlY!yD{{22& zXlzH62#7mO`f{1JWY;F=0w)D<0m9IJsrdZ)u*4ItfYR3hXhy%jzcT`__?K62DL3pX#crpVlI==c{9sc*s>iZ3j!6UY}#io2XMKoPFiO-E8E}cRgseaEpt#oIeWP zTy;+H*0so_w(WoCEFY4`yXkSR@r{>2@K@0Lw`c6hgvDGCCB{Y+=T z#25aGeV`;nYY`Spo3XN^=O~kdO_4-*8|EiJw?4W$1cNnnBb7P3GiPYUV>jHGAKpmf zB;1#1pcHDZ3zdz^xqlsIOR+Dk8}^HcsKIL2&Pyw&53ObW%?++1?TznPR)kKJGBV$| zaicb3WVmHu!oF;3;&Z>nF#)Gp>w_AZa%hS9mUb#sybu=zO%OD!I*rIgOYu^MS<84% z+kz~``q%dp2QR;o#L_E3wWMWWj{_tcGhX+1sJotVC#9CST=2i{)v2+?yZD;yS*e91 z*0xTgGM#zf-559$+6&!PN-_YLwjX!A43*(|_~`esr-!czT>U^tYz0Up_S5a|Y36Om z!9pV%CyGCQ?9Cl3kDVNYodLEMr8(isr^ndnQ4{+PaC#*U$#kCLmeG!+9dVHQAav`Y4n2?Rwru*S!&h1uxS48ER3 z=cpc{m38UC3`J3{6YyG1G#V!=P{I*W>i!vf_;P>Ufe0N2C`#t0 zrdgm_LKwG}xix_jrf}`&;o<4gUGfi0p4vf*AJz7CVwe`a_3MhCA^Kr^$g>N>F6OGO zeFKeY=RhEz?K%JSN-J-VZO(2b=U?85M_1+fEG0exp%tM@t?`R)tY(k74yvU_Rds`R z5mr{N-qBLNj05IM@J$IK`ZS#;YWpg~?h3at{Y+ps;N|9~a3TJ}>;COlI7tgB@KKev zTNrc{dD>%_l(82R;qv3bUPucOa6`nxUE|*sPjD1CPwUW_Wj)l^YuyKFv>vySfn)KgS@W?8TvcI_FMs;vZ=tc& ztSLR7@DiiD$}N$2Q7Z(5&gje2!}bG7#W4d17hoGOKpM^l*%yGm4b;{LuZ1!)G-2|r z&m0J36SaNhp0)GpH2X>EJgM*z7djm>c{Rh%(;r5}|N1pO($UVlhH)d+opI~dt)q-5 zPqVR~yf2Mz&U;ipe0%?g1D(!^6EqAv=T)89Pcob82(#SlV_(rDQIP#~?jDm?)XKG6 z4IuvgPU71|BTyw{z*zd~6SXB7n@V!?k6Z{juKC-mh;pR#ECTHexPSu)guQ^3Rl&A4 zgKE582KMXCjV=$`TO)YX&9YlBQMFz&u_i`={q!nf*SSH8?{^f!jZnD&RVTG zbN$9KHmL3>6l~P~m0kt+Pt&{K&Gxwe)8ZHuZeyJt8>jpDinWc8tt&kAUX$*xfcpO3 zk5~O2t8Xp;`W1HP&N92aRtu)(bTUZ)8#isT`SD@($lNy02i3ibK(SoWK+6#MerFZb z9b}cBpP!%nj;pq`yIe3}5q|sMu$DsM1Zn~~v|w6EM}`oD0<@d39Kj4u{ChC~gNO@3 zF=%R;Wr#L^G~A2?sN9{qA~X!HrDc^Uh3Xk%>9|F7oL957s+(I}!z%Z~hr(_)pCzEbZj~1kL!biI0C7 z*pRZ$TwD2+*)0sJxA8<@EsR?fh~_S^Y-&wDUF4 zRs=Y#)4bX88b)5C?wd3y034xPT0=NEK+9bnq5ArArI|L>J!itC3=Fo#mF3JUV_uI`^dpN5brrJYq+UqO`q6ha6gzAC?MaP{{^+8`5d zO`$SWprzy?1j@o=(uzm{%mvfJ$W2~WyPdwfU?jcSxfv<%L3hyvAq(pRv(Mu`2 zWNn=aGws%z$-gg5lDx*aeQ22gt$v78I+r0Zs0gA_a`+k<47(0qzyrMjlBpGF0`Fef zcs)S|?291i;4x2c_Yr;uqI(2wF%Ii@1{P~VRw1U9ph2oqQUNCk9f@$_2q>O?<2*ld z_QJCxuNy0q%*o>7e2{se;zHE^WzJ=|f zL!3XR6Si~hhljbmzD6>0`o+Z$BTPO;1y8grEsszOcD|fmbU6pVICRCQ=QhpINyg?* zjY=_ThHK{78ovK)H33+x(H@bZ5VH`d#w}uU{_t!6oLC z;6qW75r#`xjyv9Z?HH>ZUj@WZSo_J`49C5<@t+}_{~sP~@`I@cUX;solY`ZO|0f%@ zmcrl-D@{)bnsSDd0}a0gQJ8~?jyYt<8jKV~H<{&>u@y8N)o}M77K#GyR>>KFrULHP zcg00e_{NXF2vRQp>P$4cRFrTyL!y=e_aY`cUUbs z2f2v_T08n@a=j@P5>$;UJ_h>nk->RLrnHZS2Zo`lQn(04)RT;%u(d{@&ajYhMdvb2 z5Q9(%^i}IJzGhkJlNFSe{o%S+8nmqy(1H$8O!pH=)2;CEy1R!zFv`#O^Gq=Le4tBu`F4$!hUe5;T5#LsbEVT9)1aa4#W zTnp$GGZ;C6{8eF>Mi6GDQ?{_Zko{atrDbLPx$Ji>r5*!oI>}W!XqXd943t))fA3ns zM(=Kx*-lHYdMV|>-FXoEy2OtMAExbW7vIl)@L(-$X2j#fLc;EH)P9_EH8cyzgHnTr z!}p_fOA*3CLWH0y8NsSjQc-as;FTk++bh3Ht+K zkZe#F-n;iKVEkVwwKc@9Flk7xv&1iRh~b2y#3g^ zGgbav*!6?WS(o5CtHUdcrcgGbU2R7Uwh_Ix<5_8Y2!m0!bu>{8Ks=-Is}BQmFsG|@ z7Oq}EIXTO2cN+3h;Nfd${KVsT_hJiH-dVYp@KmE8P3R{q$1JX-_1=AVif#iLt~aFm zlX+4f{z#(H;o+zMJz*tK2>~rBD2iiQ`++@P0-?bolJI*|xYkd)s)aP=x@J-st{O8@f! zlE6>PtYaH?T>SuZC-ia10pLgIK^oGAV!#YVY4dMg=HWy1^@&pZ0VQTd=*)(dK6`|O z{Eb(ghH(l%tI5lBLhSTrZ1xWyPGb&iNm7rxND92cw6w&e|GN3;|2u-}w0jQ8^MMX? zW4==kn#z&Hj3V|UQ7?G&r>}z1%fY@8bJzv83x8hC81zC<7khK4AV1rb3D);K3*?tc7;yr)70EvDWhhD|uCYpG&JHqdN8TTxQzG66XGe7 zZTyjAe+KeBSEESbZOc#uAS2oIR!JQ`d>D)l5_|Lz41`3Pp@ajos+8oQw+grbsY(52 zi%kG}OPvXxl=qDusjy-6GfJoU6=o^(1vLtk&%%+!opbu z?+8p>;Ns%?bm-Bc09v{i)R|qlM+}nb99Yko4VVcG1L<1UdYq3sI{jj7*=Y)-q=fCi zr~@rGZzfZ5df|4zlS zk||Zy)o5&8MGx#8$`7g((MgKUdyM(hR187NbRnE&@Zs$y$HmXp7=d(Y+VMtT{+ScFDDsD_8D6dNkvV4b7rn(&f!;Y;o9_ zY8E>}nLUZpK&A}X=FHt(&baT+-MhL+p81A^3=b=~s_oCQeJr!r^P7o~gvyEh#y?p^Zb0~DFz#7<#h27`fhw0GYZ7Ah$!{v2|ytdTl>y2kDRO=>ur zNz&Yd%Llg|cgV)D7qOxk9MdPdJ1qzKO421@N6A_&e3*%+r-9pAgJsN!{HU1 zpM3sFAG)1>6@uc}Ru8E&uY9bSXo|-g8_Y7S=y!ep>#x5o23=i$Hl)AROZjY;F_**= zYFo0*AYuDr>~R_a&D4pucQbL>d=f2EvZn`why8G{t^uNb{9Fgq_!@ zf_+KF>{b~A`+}lJnp|@Sx^BZBBiML!Y1IWbW7X569?O`YysY$i4kPK(6u|zHm9?~Y zPk^`vaKTlX`#$si`;$(1DHIuf`H%}_@&ovz|GDs< z6_pfV^X9?BgwuJu*saQzpePU}VhT#jVNj4<1xn5R=%S1a<=vG^h*LyAcFLp^&1G?z ziIbZK3D>P?PeM7e>GhNbqE8;+(0u&}G56Y-bnR?beg?D}KQ3h1B>IX!+K*%L_aD4k zxX-N;@a>m;{v2%?MA&^1wtne-(=hxI10*UUyf_SjXzk5)?NuUQyr=?An&{vwim1<` zaN2*ry=t5oY(*Nb?yZhQn}=&*=w?ARNM8Bqx>%e%Lgv$4@hCKO2V%*hmGk@4^LwVs zu7Z|l&G0Ofy>%<|z*Y~S&p*L9lx_n`(!63T&0Zu<+Z;_hJG({Rl~I28@6#dv$=qcc zXjMpFJ;b~jdJy8@Y}uR(USFS>KdJE)UN~Aq5$Grr2@H~^8~`m`juC`u393hw?L2N0 z=X7v`AQ3_+(V9m-5;Uj`l`)s?`HW#vw0Qg*OzNN`s_07eA@B`gvxD%G0W}>}FdXSh zsi|Prgl?2knl5xh6n^#me}4Z&7s|s4PdDSwosStMcgby#+*OHR|N8sm4U#H~A*4X| zf5=bBj(^VanO;Hj&k;d;>Haw)==h0$jtKg-df7iNpo@(Ev}hdf{BuOm_f0GQpSkq= z@8Bzoyvr7mcew7p`#yZcmE|ytClXU~gUOH1wYNW9IrPa0@C%6x6=YsxgPf>dit3qH z@ACeZ`}gg; zZ@h}(#EBC&?8cKZn?d^9ghU5b@JwZ$@mqkR`H05X;28KAF{^p+zc5NIUGX`zqiAN4I75G zJpsu0^5d-JVdixYVcP(r{++sIUom3zzB}o|^im!n{ZGWty9;znDulo`438DgC#DRJz}W#$!MOc!_k+DJcDN?cs9F5^OvgLh{sdXz zBcwXR)?7QK1Q(z@phpQZCE{X80#1*R$>_(V#Kk_*`hU6X`}gmyf%H|foN?I95J?2{ z*swXQS85b~u=P)p`k#5I|DsKiQt-zZV_^nDktler=#l-fJ}e!?U|pWv29`CblVHk( z)8R~$7u?%0h@XAkkMlQ(gyRHr2rWO^8z1D|1QUZw64`)!_9fxEV62A8UtY*7%1_7YTb78cIsi()YLWYDD357jR3d zHEY+#T0DoR2*vmbhtSeOd$QA{a(R4w3G;VB$po--4Qy4{4>8w((yi&Yva%A?E!m81 zfZH9O9lahCc}Eh#}Vm%cc+! z8yS?X+EWbTiiUsqm%tffKm@HYmCjj|C>|((kiF0oKj+s3ggzT8GQE+;dcAM(IB{ZC zoU-w~3CtqrLy{sqq{K}BP%+3#!Zv=rp7bWOOK6J292gC(H8qQwDrJiA7|)Lo-TJNxUfVE|(Hr8V)bQQ8e;HNtmyPd$jlKSq{H< zSE4#nmOjDN?G(vrE0pw0*y<=brDck|PlAKv30b@f4Mn-$<3QWH$rwXVED>klv}srL z_@4VOK+c={xrlj1VoenkHV%`PE=b3m`u&&(#&M-75U+WH?KFfugrU|D(dUE%c}`NS zK^-lShc$W}eHX&bM3E`%fpkmE_Y4hcIZY@%E7NOno*T5Wrw7;+AuJ^Q5V##x z5BQ4=vKkoZO6cpQWHn!qxLs5}cJr^V2EGkn60fY-`s(YxyYJp+c>v(wXie5F*IB{x z_n&`03sdw9Ao&@ttAGBYJF&rJ(n0*T*RYl#anc0=u((ZqwE(corDx4QX*_%Un2}5= z(|c0bZc*?Hm`w>5=C9}Y^1@NkOO4X{2%ku;Q7z^Wc9Y#1IO;H(A&4K5nY&p30etQy z#0KRoXM~=}aPC-Ns`*=O&Rx3}G?&Tg16LxR7I~LPZ7SDXk&F+TN)u+3KELNuFqIv5 z3+Ukz;7XF)I?J+-wm{p;p0?b_$HzHwFN(TK2VR#i*91PcgsC)navKVZ(opeO@bSp% z-o?f%hYZjPC1hMeO<8K{5|ID;^>A!Z!hJ%V{FPkF`-F=)yoG=o7+YC_8t|`IF)e8$ zlCMsYG(8~^pQQ*G)kJY;>~}aD9>-v4d!gh83R^{l zSN#%(%Md1;YTx~r2pt8cN5Zx)LncR$PGc8lFV6PII?>nGG6?KJDctID&uS}8F$$nQ zFIq`!7N}TH6QNQAffBuxnVij`%IEhynG0+&%LvF3TD9zUt&tyEX)R_`L`{2IrZ=@h zEU0WxKxt?9L&kmB6>Ze6DU%t)l!ke&i`V2@em14-bWM1VZZ_cPVbyqL8$hsa0>YtO5xz!@|b~OV-z{jiY&G-%*8Yncp zio8p=zDqLnzL%GOi z!S&DMxGv(*H0<_BV!Q)v#bu^cyF5Dtem+97BV#YWy&$&8uYL}QkigZS^V1|#$9`}|tlt+2}p%Nu$( z>b*kHl)&0w5J~`{T0-~8hBH;rP1M}FiZ%!RU@El|0e>RIV)b{18S9r&M0Ri0v>3?wlz8vsq zc-5S0!=SB%N{zsFaeKwHIQwSG8K-_JOf7tNe2w%@KoSTjL{B}0z25{f1Tnb@%j!VG zD-_6rx@zW{$hl?wOC-eAsuH0BlOF*GaeTxj)WTf34S}5hHf`YeelLG$uxzCB;*36_ z`Vb99sI@kj6eKHvj-hu@i-w&h$_>h^?^kJh`oCYvtxEyCn%0=aP|i+(D;u3Le=b5< zWf3rt^P5|}e*Fa5$$%wyD^Z+@3lqr!2o_LD@zo7zI)vT|Cw3GPvIgzlcj_2{{QY&I z4nudqT#seyv1`6;mxRq(i#lB_!ICT>uyf*t{eZPdnoc1t5|TVI_=kzw%ZAfzEG%lh zIlF~|E)buCZ5oEnL!2b^vj|fdzrQT#qpV1SmRYIjYU8}R9+*UH(K6&BXHWm|m;eI) zJ=lXJ?i&!W7p7(`7NoVD0G!qm4vK0#JKp>IY{FhJP~u3GP(rrEfa&AX&e8IJB2pkz zB;z|XddkG_>CwMy+q|I3!!c#Rv2FcF*9)+CW&@E-;9y8~AgGDH{H2#qS|6!( z@R;N0D~G1%nV0@CsV&-S?b4!?NHNt>%X*M%Tmt_5^)Jeq4Uz%Az6QXf*Z^ZU_y74D zzGy2319O~J`xU2*z9uXOpfMx*|MvG|Y9vi)`LM1mz$dzfp1ux1A`WL^zNs=b6$mC`k$t}k$?#1JN?Wz2VEV%}2|N7UfN zDR~{8FpQ8Jd9sT)wwUb#3M0TSP_PY-%KU6HX?&J(=}}#oYI#P)VSspH-?x9N9yCCi za0B*YCF(FtL3kzLx|gztnF7bqZ6&{s7a~j?0N|TMmK<7dg=4gOhp}B-N|D0rFfGCL zWt?)XLxX{UP+m$WT%4?S`A615-r+)kNeboexK|5|GwAty{^COyL>(~ zIi2%9@Av(>@8@=_nob}I?z8_ios|A6_X&nWH9XqtFKnvrsZ<)f9aY+bZ{9c>GB?;J zG(@lZ(um{JU8A3CpGdYF+@ZpHUuLJ?KngW|=y*t&|2jr??%pi}Tt{WYz5pL|x1O3O zo}GsdQo#&dBthN6!c^c0UwqzyAxO}J0|Fijc2eWK5@f^eXr1_}bVubgq5-XI4w8}Z zYxkopO-;|ex-hFebc)p{|Wz215;vCe0n z)f+G8Ut5~18|`^V}wvm6y++yCd^zbWWp<)=m8AnR2{V{RbXPPd5A@5#jR3eOA1E z{X*KW+h+gXSm~YTgepdpX1Tzj=4TX&=R>Zg|NEc+C&Vwss& z?^*xvFK%`>bHGTEupyJe5G)Y6bVr-=k^_0@@m4RDJk?%C)cY5V-}yQJ_iz54saA+9 zwwIC6YK4TV<-A_7#ti0#vAji!^Y>>KWXD-=&1h@%61vyf>fe&e#vz$tinLe)=h<`rx$TVvNXRW7Jp<6K`XH6p> zQ2mUW598NJa0YIxobUtm4%E*0X>HZ|%5?|JmFzuf*hv}=Vn%Iyy(6|KK_2*;vdNEP z3ho<`3IKb2xzC@P&ARE|{=cs)lMG2w+2IGc{A~>D>Kli)>nuYag%{`0c@j4zOczQ~ zE8-bRLH*9Y2w-vyqQUXT1pOM$PVx@p^vf@jPxLXba{7Nvlp{BDt884m_Og##)z%ru z29G1K@g=u-At@?$rQb4yG1ScZtI42vk?8>C3$iS#Kdmo)Abns$=Ws&)=bCx@QS{6& zfO~CB0Z%L9TbbrLX@3V9^C(Zal3nI@Qyxyxb|3gk?jei>D%+4`(K6A$Zl;^;C)F5& z`Q|@+waE|Y=R)^fMidr|33HV{;#OPI12w3iuyVIeBrl&o-p2^Q@_$#fRIL!kG6ZE^ zNRO#?zPjCRZ8**I(UO-gB`Fv1p@=I&rNkp1GKv#hvWuyT!qVh`YL&e1QF|iZ)n<4T zm;_iaaCw{$NG!S05*rV-RAFU(H;7x~@k#nKd2eDR>pODfJn6wM-T0?5hM+e|yZ^Q3 zIZEKQ98V&&lDL3@x4sM&id@uB#7vxp9pThP@#mab`q5r#LlFof1+zT z&o0Q*h_rakF(wmi$u+@CEG0HnJ_yftP^}a1FnxPS%)jOv58Cu>2@gp)27_$}CZ`|t zIoUK{@s4L6zS8dX#pB}S5#tMNja(!edg8yN>VmuoBT0;a3Bqoy((z zGoXrhMYy{{5-bqEg)e#38f((kck3i=o|G!-i^barTq$X4XcB@tgBcd`S?wx*6o}!u zkbAsupFS7f<;on6`Tx%IxY~BlHe1ROGrGlra@r~v<7bh<)@s+#N)tzksO${cPDZo& zD(J_X7!h}RsrM`&@siyd|Ay+Jji-q|$BVHVDW*h=2>tp!Fy0qUGJbTOg@38bs1{xE zZ6Ez!5z4=8#BeVJV~j}B?m{HtdC(g>1o6m-Z}1}`4-rKP+49wMR=9QGvFPKo-=!z|2s7(H$g(Jy$Fb5PMr{{KAa?SnietroVBn}!Sf_C}&-F=d9@>X_R z!$I?}PLQlwf)_@tNxcYQ11h6EcB~bu7E$61L?Ez=EFWfqjR%HxXet+uxIZHrIt#H+ zk%<_{j2~a#DB%H}R}o*;fo5R!$T`Ha2w*A1E+5m8I)_EW1?J;L5zq-P99q$=rrmn*5D63rz6 zT;R9g%f8nhdft1VK7Bg+z_Z7Xzb)#OdEK#|vYMQPUPcltB3}bdN6F~mgdkAo8yC*S zozM;a>jxl<#2=KAN{VzWlx{ik!b!(R=xU;?iUt|IWI77Kuij5na{@pN&>6$7yf63{ z?fsJ0!o9UI!OT31;0064Dul8y*kG>@mkjNPIQy3-2mYU+YeWclgQ)irfuS){Ce7+r z^Q7C(ew=5Ms9b!)n-9RFcSPi~cOI{L{8xf-q>on#$}8=@__ng=%#7-fz+XVJzJLw4 z?|%?zHq?56k@$Fb7M(tQS}Rv!p9P|XcBpU3JiuX2nnKRS$G;Aa zzoB1SZQ6?Bdh1FEn)ceGA}HT^O#A0v-sxjDid#}M5P45Ro_629Q#fN*x;zdX#x2P| zL+($aVDJ?l!@R2JqNedrD#X{}DMi}L>Gl`RK^(p%TP97K6xq$4+fD9jjGyG>nuTQ7 z77t?+heLM38wi!q29Bz|Q>(LPxp``e42(-MP`4-0YbnY?^bQGte$V+dKA#!}~jSY(sFU!|^4WedW9z-zcG_7WTqPYE-w`zdbbNwnxR1wlI za846&zLk+z81w!tOgk7kv`3E~(#rPU9rM0<5YR%9oDGD?z2u8r8g5ZU+9*t+*jH2u zp!NWt;-A?$x65#OoH*c5KQ5Yfq`wI#agKn%+24nByqm^JnqA@D{KvHUO)dl78JO3J zw*tHWW&HR`tNO;;-06aCk3H&cnEqB@hc5DfS3t788;!J0=yJW~&vyOx+mG-ge19cZ zV;2>aJ6OLRwOB`^)5Rma7>!-zg72>0X&(UzYyITF9#pPeO0ewS{!eKQ9xgIZJ+nW8Ta+0rvxI5WE zle-|~`XEZYDVZAKX?4q{{;XX4yr*(q^Pi2^<@G$cuBzsLOUo;Y^zPjI^{f5Z=&>Wh zO)vGZ@2F#Y5QBS4?$+R-2lR8>n+!DO^X&lKD3{G8me&(qlNrdJ^dqHIfkfS7S zkQDWyCvsdG`f86^0Xi^}=K5bx?TlFb@xPMw*!kLUfr3h#ug#pP{>RmQ^`6gHKXWxrO%?k1BrZV_ z9+NYuj@NSKUH;#nFKj<1Pc-~SMp;SGj?B0N;57DQJp$XsEiS)xB+c%#1q4e1?i;+S zPI)sM`Rt!`7PPQ$QApEsLaas{LjhCrjioGFDa%M8 zQyu0XAGShlaHBltu*?2)UzY9j>es*jVbij)L0Mxod8#ewX|U?1-wFPN;+qT&e|`xC z3vk(54p(HB60d0X*3*yPO47u<9z1^f)O6XhWggjeDh5!fm_DJRS9=cn(a^H@p+}XY zSB1Tmp5_0EHO}Lf^E`)HQJ9gwHBe(*S>Rm1)3ix;%lC8t-v~QYQBkq}(04tM(y6CL zJA5oY^W>gIj5w7%I7@EI2jUEnmrwB2)V(&+e?h&5;U=t)Y>%e3mM z&8PHTEP&l;*e^>fDWj}`fekOx%zDA%8rIvx&2fF*uU{sbCQN^S`SReBtIw8gh~9Ig z=-jCPy$(r#mr~`jAhjGIZqA3wnra*G#W6G>B6)UxB>^wbD<7@VnSt1~3_*8vv}0iaLlG4{xN*{! zI}1L&%^$@)#1zG+B%c2!O|ka=lq-vOYX;Ig4i8$6#_(O_h6|sb9{=Ezx;pHwrrGU+ zS#MhyF75U&{-*IW<2+869C{^`eDM&2>DCc|m@+Jg9*8;alTX9FcPA(+-IzrE=@(Z3 znu6C%+gU^nk4gpyd0syRcMpO$4$}PfxFZv1O`hrlJhp&1*?o_MNd4!SQP*?4kp16)}Cvt3yPW z1T$9l&%Qy0p>CDa#8UXnFTa?Q3MVrF&Dh~{>vj9~KZHDo3og4}L?^xacEKbVCU&5F zmYI_FlO8I8-Ye|&6&mEXD@UX-6=ihQJw9D>a_M4g^G{^v{}VCT|rb}`Fm0k9{~(QX9J^Rl0|@2*K^Qv2sX zits2hKcA(nZVmC{|H_UjGZ0AYE${1v=9a7kH>$#rqm3AYX6n*viJI>)@L^)(#aKg=2i@c&imyo~OG;!GGbvk5ls23jVXp}syd;+aS7+l!jRG(0LjZCM zOHHH~f+@a+e6JZv@zx%9YrElHU(suQ35k^9Dmyv7lPHuZIF}t%Nmuvx_T@BB`YR&?k2$x&w|4-R^M4);_MK(gS1q$@ZK2%RatFdR#cG=doXD25D=!_h7a z#tNJfxPB#36sq^qP2W;l5DgFEP=HNcX@g2Gc5AaX(o$|cv32o_=iZzlF)hzdT#z8! zn|OUhc924NlXMa}MYsR`Fvgogue~c{??i6KRHbot^7aory@>*Jil2~M=C)+gkV`gh z_6pyRV#S2xnFHs}GpfB=u>fof_6W(bjX}lN9j`-eLdm-gOumEg;i4Q{e4D`!<^-RB zDF$#Aif1u?vq7VJ~PxNpa4P1`nl|I?Ud^G=asiB>FgXd zc(2k91t(-@1f|MIJ2{Wo_67Luk2lwhWMN|_sdF#qzmf@2 z#vvNxwIx$)9uvPtgO5)~Q;%I>Q^OZe-8#R217?W{e z=7*NYZok}K+v+NwpQpFyMhMT|`|Hr-g>14)-EDU7=zmZ?ojr)Eh$@>t@M!S;#Z)G`DS3Rfv6_1YwuM6ml>eg>+j76TT#Q<3z z@TND~51vaizO`;zzv;I6S5s%Z7W7%H%gCT)Aref*{Qm7^=@omqss^^O{^eg$o=y+R zT}A~QGHyrxN2p19_WY$bIP#~~tzV`W32!Mnff~xKp>N!fe|K*sp`G-0n|cP=cqQT>S5qaNl4F6)5-_JDpGi&=1mJALh{q<@v>S%gz^R z|3Z>qyTJK^qlCTt`c`B#7a*U?Z)oYXeOi0X8Y-ZkY0-j8T)AV<Qx+~Hd_PA*s^DlrMH%aJj4244ahk1Bc60#%_P_jVu z0~*gT+M%vQHo}Z$3|6d~mzu4cr@9&3H|%<0D=eN@4y3vYROE&=E6xwM2+naM|0KZVEZ;IV#XP%+VaKOSAy1$()epL; z=T_axE5Z1FT4sLa-5j-zjN9VeHDWZ%+dMU>Y&SJ8FXLwM*>ROTOa(iBb0;PEgZKi% zEJ6fAi+-ZW{%u41ZT&e%xpG3U3cWHL=Px%JSqDX+Ae zHeteVPNPPSym8MylE$68*Cq?3&RAx02Py?l?*xHmQB4fDwOaaMP_(_C5HLCqO?zk`p zWq$hfcy)yCUJL%A^z9KAdIszA^DXGJFTY*s8c1ICK%tjqaik3VS1m$B&c@o8>^tTs z46?P#JEcL^6s1kkI%T?`_n<*>`9(VQeb)HIA4#nH8P9cO-OnHxSwwe-ZYsy!apNc+ zv}J3K?$CgM?N_o-t)cpe6CU5&sl_fC8d=wNcg(n?e(Ig~#r8b=;vsUc52dXvZlydQ zwcou>>(+mT*R^~4eGNy|r^H!foMv|5m@CK`^`+zA<=;1Nd$vrP!;J(vf)N1(%SNib z`}Y@48%vFqU7B2nNHB&;1r~@GE36-^aXV6BHEXdF=2%W~7%XuU3A~EZ!2In$3pvY0 zb$IUOfn7~CbRH(vG878eIk3Z&KY=6>3j@V&3Oxb2AXP#%;&dUd7h8Yr5*kg{+U~sM zRCvnSC)_`6t<)~369^=7SZmlfas3DApYs>m)|_e2JJMtcm``NHb*%+wzNFkT7b6GV zYo{Bd{TBZC7}kNKW@E%_B(X}|Cr|1rnH&-fSVmHkC@KSV051RH`O0(zGO5nrX9$iv zDQO_UB(p*@mK6hot)S zOEl;-vi+8*2G=gYg&V`w9uaGSZ1&?o;^~q_o-(=a1jqRH_1_l>OUlJZ6H;_Tj{fS> zI5!6L_N9JWcMY6143Az>-B6~4Dva-3TkF1_5V8xb)IsBxTeWK)+mmygU;lfy+wPSr zu2&3FV|`KmYjpkNYBdJCmU{rA0;DZVEsq4719!~dxwmD*lQWl}dhoKYMyAHU=Z)4E zTywJRBqvJxhgOEVJs?UPx^`;YHY0YbqWZg|!iBYn4_8Md+U>vhs~BsfX(e=AjE^)X z#hX^td^qNJ}EU zlUYU?cxWiKQpygY3h%WdZZXKYm~_?X2c(KS4?5>gP{IumAo}*_lg-qs-cu=PZ$t<{ z=(2DUB_9|AYYcAOEyR=LIY2W-@~A_ue`p?RPPD7E1%zsgs3gh?7bAb}&Ug5%9493D z`bwS^2uXneXiw^2Z0eCBVZ1c)rvRcaCLvigddY|fYvXLCq4e9In);p=niRg?d{0JN z&Ca<&J^~G`fKQdFlaXE2K+QS~HuU_dOP3IIs)P*Lb8gC9sVSy$&eOxsmVMs@jUs>C z<9^$@GmD^AV>UZ_Tqh`5Jy&<_Ob)7C+5@`BYz(He$qKpVkTVzB@loZ>KTOLm&1B%` z0Piio2?|Xz9yu1`8OjA=hyOXODP)U`q7hGOMJvuhWT*(kkwCkw74iFl1Bsmclf`mk zfA_Kbt`9{DM^@V9z3zm_GN5>I-P+Mba|KsW#1g$AEGH5HcE;%zue9~n^qP-wHO78cXyr5-5`5vWB~9KazcQ0!E1bj)tw*Ys1;(gNq#asE9J}Ec_|w%X5P~0u8%w# zRMW#6&iRM0qhrTFWTTOm1EUkxf&2#|$>dHvftkN2$nd^_H>XTGoA_arpGiU4jD}<$ ze@i=cBJPe;T*sF_i^rCKI5~3NU)td{Cv$qT448Z+a#`SJ5})a&^*53om7UhyK8?xE zbN&Ovx&@AkRp=a^fRvihONA#v=$nszJ%<)x^WK_wc6U?=8}1|oQ{p%mo&xBgHih$` z3J3`5Jlxx`kzd|z{Rfh)wrLPwNZG}tP^(+lUtXbi%gf-A+Zs5!=8ByR#;HJkdN!4@ zms3)bK52VCC0_4rI48YGF}hc;*eGWvBDr(SR!cbmoO@ka1UQysU8QO$^2jqVx3BqX z??x_%OU)SSA9Q%+bXZoElGQIi6nZX+0*3BDm1$l zx}z15Gh76bBJJ=z7m?~XO=K-zi-louVaCkJD|-vKE(D3^IUaGxfA61@<0by(?aJ#G z2Ot5cm56Ggd>%b$Yl}cvtsJR)QDRsjEW#xB=z)9p}zC@4+Rrf zn8|HImui(eLVT2bq+m;5^xq8hlD17a-VreeGv5S|1Sc)yA7qe844+scLFkIIUQLFE zHTTk8vyo-hOfIUd)UI34eVSFHE=unV)N#epqnCSq=a}UYr8*s&YUpjbEcJ+%n8Zvg zamQ*(9)<9bV8t#DKP)yxS}w1=G0|a07U`RXZx$cmpuX8nHkXQ4kP;^(J`POdot0w? z{%#{PCR|+G)~(M-oGIs(@Q|23v^p<`RtJpipJ-pxr27SG+kdxcPdVaTaPLEV?}Lvf zUWV1>Ep*$-#uz*iH!VCK5kZZLndnafdoK=Ie7`R^rVL#|y;78Hc_-dOmrjjb#}h1( zt722dcUBV^4P0g)ySJ^Rq&{Bd2C*&%R*Av!e_tbeGMv*EMeoroilEG*6D{kHj?<4H zUIcPLO5tY~jlJZ81g59|N?!}HWXZR4@X-6tmQoMSug>#5JID`(13&teHbxKq~g|NC(6OM@%(FJ4wK`^rR06+7QtLg05@7 zM6Xmmj_&6#(R4I!(IT`+4xBo+cXw;_cOR%yoV}l4-9$$SDE)E&9|B5`IP}?BCHLn{ znzYO5lY#QdqqMFsHyETMC(Ls=l1RUh>k>m!8O|+Mq*}< zd@v~TW9ukZFO;EgOWtfTpoVtRS^seg4cdQbr%j=HlT~j?BaA4Z99i&G;>x{zZ|q_3 zV{BNQE_?zL+0Q%f8LxUSa0^7F*dUwD>!B?Cq3Qmd8%P%WvLeFfOX>yo4ax=7A0|7#Sm*q3m{U606kzKk{jDqO5%7)WyKEyQ||A^jHq>bd}1- zuC5{ntJ>ApxRcMCMHch?_C$r4>v1|isX8>g~A0b zF)iJE7EdE>!0=!1y4OEd`K5HtqHv2!$LCvSwi}MB%)sqQRTqsId!NjrU(kD{iNY$n zjPB;I)T z4uzcP=2G})VsUjzb(M`rLEU=+r`s=a|FtD!PtHK^{?Dy+mNJWK`n}KuISSpJJOazS zi9{QRufi=4vEljHHxbCgx@0jniQ-g}&hMu68M~owRmEHTeZBhkf0}>goWK7HutS)f zvjSOB=jkm_*V&P2tTAHcJpXSOs#>>e_bK)Iy#8vHowmA$Hb0-=_g=9jW1rfA3xUHb zUnNS+6MF)T)*HMS&~6`&tbb-2iaTBQ*|2{}RAW(S&4eYEJqKYifnf-WO}F+3QDm3t zA@maxe2TDe4hMuO?OU>omj2zj@!Kx3*l_I{Cu_CCk7xaZWI$ZRX7|OqAYL&>j_!ea zrFuleN+%_VF8+)nj*u)Y#QZwPC|7IMbGuQ=rCDckK#MDTOKweV;7 zsK+^NpBu_VEYb&NID6oGuhvQi2v3Q2cI;wv<;EggTia8i7MU3tD-h)ftelcDL=b0C zMvQ%vNHvFH6ZRJZPI1g)i-ba<9-rm#oz#l{wK+De6J!5Lps!8$rXdR%vlQ;E;Ov3>62C$ zsZ3oB(uHs0uy*&2ZktwMHPHO-Vse2utg*Sh#ROJQZGZ2PI_I53vl_{?9~lwVcHQPk z%e?j{<@hD=1An>2?JZXby&4iWe&|H1c(_aXCZvg%UVdO&_B9u7|X+9N0c{pm*mu&rY|i&h-1IC_*bPZQ|g{)!Tcti;Fu+ zRKKf-P|aXuO0bR=3@kwBDi@o8e_}$Eg=fr`14}+s{ZpDUM48VoXjG?5lQ@6x#R2L0 znX@NrRxteQxtze>9wtKO+gdhZY=N|Cza7cX0a2U90 z<;1aHVBqe|d>{sK5lJCK`pfF-S%#$98EHog^V8P(g*H|7@$N^&T@FT}q<^zN>EOan zICUNef;2df?RGx;O6CGgDSbce32tW55Idax7^J`53wf%#4qcxiyg)T zjAZ|+*A?!ZG9JoaMug4>7R|#_C>c0h?s&HIlzeDwl-I4dx3=rid-HDAG}=0Ds0kz& z;8YojXP@EI?EPnJDZF)GwNZlWkf^vNH3^Vxt{u?|>qD^}eJ2qte z?5#bA({{}bPB^vai0RVTq0thvNSufe^tAV+_4fp@2aWwKicrehMOxX{H)c!0lH?6} zlYED?^(QIGvWG%YuUjKei^&A%2hWrh9oMDnv&b?O=jH(G+w;JeOi{HV8t_A|A7x@- zpnCU)<2&m+-x&9SQd7iCHiz5#Q+@UBpO5*t7f<&Y$X}Eqqba*(SYHq#OoX77m0NbQ zV>XLu)gfHy99X;i3@Vh25nH-Z7353a(K?G{St!ErVoY~0xpN)xLob?%xN=e6l(sM>a^u7Xwq_aX*m-wA-;>8{tIiawR(%-gm>vzC~&cWm)zsYtTFj39YnzmdyL2R7kKJa}{uA^bwgCC=dSxID%(MyaiPQD&B1G(X^m=?{@ zy%CB#FY12E##b}$G-?>I17w^^v~?9RzZ=%DG~Ep2S3>c$nKS1?DrFT6*X0TKC5)Dn zU}})l(8wN(Yd%!Vc^oTDT`XJkref#loCmDR20cFTbKTqG8zw_1C%%La_w`+I{G3@? zB$W))zPL=gd#{l-ub|W;T1^gJu;8HZH4N^O0`g>VQ-cZhyLb4pq}5+#LB}eeW?!iJ z@fovVz9el#=3K#i==in5AMg@%6a3;qkCv+=@INdD7LD@XcJB_pb}im&#EsF^g&||E zE$f%w=rhRXt%zF=IqmM&3UZakw6z+n=8(YgN|IP#b8ecE^~HC9lLH^Qw+=BQu&G?l&zS@ zWr0HS=A(m%@Zpz}jd}x-&*ZmF6l!gL+SjreC`I5Pp3FL3YP8vghb{wiMd7oR8zwxr z{Xk1`(GgiWlmb~gYP6znVn9bFYpj=gweHL{Mh<#dGdDGA0pj|1SGZJIXn3lU_p0tc zLwCu2k=`VD)sv@B1<}_sA`>+cPf}2>i~(UkjNfvk${UWF>4K(Y0BRA&$kURL7r$< zN{5d7Ab2kctvh#l`!uhk2;IJ@)h{)#5d3)tAxAHZ9d6h+Px#1Tf2VN`UFY{`-|xR8{0J=>88bK@ ze7oBO_4quRP8J_}B^K}9a8nHPpPm-H8#G{mhsCEE`)9RQmY3v9_|iFE@5iE&n)fw8 z+*iO~X8G%Q?qQ+y_cRPhfgmpg!g>8Jl)#?*uB`AH-sp4h`-SQou@AG-FjNFnixVw3 zcu35*^~ajm(#Ya`nu8K6YTCM{2wgeh+&5DistMAGJQN2S{DLPZ0Uj`{MGbJL>7FU@3I-Oe?8*m)e zevFCVX8u#ue#RU^H7x10H!{ZIkAAGuLz zEkE-dduj^nk1|iKymE`H4e7#am1DO0L~r{ec36WppEEZNj%~dkQ7RhihbJnh+1kdu zddQsvM80ry9Y-cg$##6EuReV)NPNE6hHZxEM397&C83r%!273tvH$oum{XHc>M16x z$IoIy2a+4P`Nc=7yf%IoPnrZ|?`Ks1cIC>I#?G5AC*|hl5(Mvlf6lylpHnySrhdyO(9?W^A z;`x-bpZXd^-%m>AwnRcptMO0xER4&zb3X0+n$GywgyLd$svK>j=s?R7LS1_y&xv`c z=zfE8Sd+5pFl=*P@}#t8C%wI=)_K>08`d=6`!Vq>Vo}Gg?EF((g}MPxF}WopoNAr1 zYLK7Ar})(ONC{isqLHm)fHzYcwB_qBR;u1v*>c-g^j+S4v~|D9@9^Og&RKN=URl++$R66nO4{#ji5fZ`YP*bv z2gl^yh_O$i>LoW)@vgXCip}T!*ye4wHt{^v0;&x5`E_Vztjq6UUtmbM4KRsErQV?g zLzh2mKYGA8=!qQ8n?KnW|O~?XW{?oJFCTfOXn-s*(q-2S`5jaUMpyFNwkCgT@vr#|u$DXNjWa?dgN=T@hhOF`+0Q9X@T? zDcOg`RLI4fYTLx~gr|Fm_L}J+o4NHhjSHBC;OPtzDC`Kp8KPWR$51(VtXdRmQovX1 zsN|&7)uRe5H-4oLTeiFAMDqUg<;&S}C{aWkH*1Dw_M6jjsf$IY-1usQ!{>lfir64= zq{6QsujfLvYRPrF^+b`796DQbsz19Yf+Qhuuuus~T3*oJ#@R&9OmR-+>!~a5mQb0Q z5wbBld1@m3_j&&%OW9p@8M$O_nzTz^B7*nH~QT`@Eyb^EcVj!Cfh`6kH(2B;JD%@`l=Q#F!#=nf0~qheCquL0+Vhg^tvpy1b{5?DI1I_o<%Eyc(#h0LtTlGEApstzmM}39T z_^xkNiQq+@!dvW%T{K(AFuhb$ZGe$rb*my$&Zkv;(iN^YIQI~8Hn|<>i3-eW;sb8b z7$Q#buq$+BKk7N|=;JM0-A4Y_{NX&mD?$!pV4#WVZu_G@uSvHi%RR5Lc_HKDN69zm z6t_#e6NSI3ZL3!ApTy|CBS%z(qyVZciQ|V;gRWF_Y+PFu+n9xRu~x22Xovw>k0olG zGe_ViON73W+%|-MS)?BZqJg+w?8|XQ{}otW5m&vkFiw@%HQ;x|*?3kvMfn0N9BbLk zu636#Dd|`6Yz!UJnl%|ebkwZwp%rACEu(kU$dmnQB)R|r?AI<>a0rP?Ho0qLId3$$ zj_plijb$}E9?9KW!!J?lBq4xY344ypy?gdZ0=&+ucRFH{0A@D(BaATvxNatHZQb`5 zRDF2&k&<-*fXJ9s)HUhww-yRz^6@(j7jC%1pd!D4vK?L=wK2;R_a`pqK+za;n!Tb* z>0miw49HiF@`$w4z_s_KWG z;4t*pTPian^`+a5Dt5wBTj$4|YfTx}P{o8u$F8B%T#?>M-^j3i+qRL_$KNK}Xf4w` zfGnDJ>0)>_%MHYA_Q69poBMMPjRfrksB|IhF7RJpaB4B)>Z}Hs@gyuwvt)7Guvj*M z%?k|WybvdwIAYPm2(^}YOvFae zxCJH?6z*k1scB$%6DLj5Vmnb86L#1Foj_)#50O;IaqXRQ%I^ z*>2P2v{ACGPV6vn3h^tZ$M*jOh~^m8Gl{muND^YS$EMOi3b!uHhQuuvsMpcu$Jd3j z!9FEljJ;U0aI((JXPpgTc+la(uR7>0VR&_H)7+!YoR3|5yOK(y_DB7ITL%xi*?i|a zEV|?MQ$rnOX)%LUeeTYkkrCs^j~}($L_4lz<%wtQlpF33`%?qCmvSDhx~wSe3i_zZ z>tniRjz$WGz&Z%b!Aw-caLbh%uUgcLDPJ=9RJ6|(5|sT$xz(+-{N>d@Tl4(hb6T0l z4?ps_{pNrf!1>8Z4EFwS-^V|pQ|rye6_LI}*9`^#Of;&VIn?|@&q&$e!?`xo)J8Vv zVMS1_vldT0fo9a_9{hH~b{2TDm#9^UO|&&Y$EDdpXD>04`llg&u}IB2x+q!2SFZ5t!`kdpFx%uwbuV39`XTvDq{0b5Y zYSZylI8ZFICX1ZImqn9Yw97uxwG4_vaVky)sTwV^#>eQu67v-a6J{jXz zjaZ=6I|`ug-A;eZu^75=%4$lI&mia2EqA(a{i7&Ce9ycf@#d3Gwa#p-^>}&w#-WbS z_iq|~DzCcJuG74!0jS)>4x6$%ktrUQT~AEgkYqBH$-P70Hq8?ywzviaAORHtxg^m~ z%i*-@F*%RpH<2n;h&b*ET?m;xUZ$=`b3Ys~xP)yr3n9UxU)pT{tIaIE(;_5GxGwu> z@^a}wch>b&XT*-bBPpDb&2_H`J2O6@=_s*VUUzO*XS(wN*zKU7$;FdP4peQpFn zGaQKi@2*n^Pg`$}JzL8C!uagG>8tYCm=`l<8Nvw{<*66)HEAbox%8YqHaST*dF>8v ze~y4@(p&A{zuz%V^U|>alJXMjqLtd2r_tUjGU6AyHWI=8h~`(GI_Aqz!m%p*0a$fH zoP=1!+{#}0ZsZ;`wYZ^tC9^ceB2GmgJveHl)3I$t^cyBk*JE|Ess8|6EaOYG(iz2( zLLEK{l|*-y(&#ho&hv%wkB6zPib$#p2i|)u0BFUX?FrfcY603!BdJGtKapj=ON19j z)hom(6}qyeRp}6I^;el%tUCERPcQoCtyMmC?hR|_b~2hx!SLg1)t!2d+|t{Rn$Wo2 zI4}2^fO9m8eN@{vL(!cjys0KpW5XJ!fwwbfwRH$)fPb&Gs_md-KyVUlr^@-6i@!%R z()|5cQuY1I$kskJx|fj{xK1q=m8nqsG(tvNZgWzI48#d!($6TBh&7{(UK3a;WSUuH zJTX2cBeTO)eRB}k*O0FpF=@}~+FEUxTUEuRK8@$wH~iX+A%tO0g78GuAoI19h1v@2^&nEI!DCA2Y~bl#Ps-M@d5 zEW*Zb@>OqI>D(E;DNo>fmbr54?7(!4K&?U7Oxl{ z`q$Te(AZ?bSEYh!owe+u}3b9M6Dkh_g?bHFM@4a}l1hIpJxPvDXFR z!59`K+=b>8>Rw1F0$MDoSi>tbRnWt={vPumKHEPrU7>h(Agk9%J^yp(Y|(iE?~653 za^WGT;It&65Ag7g-fbI601?MA&B|d`a^MK>hf@ulU-pfK?`z}8QO)wE*R<|Rgd*~)G2WMSdWmnJd=7lH?@%V7Y^d+M3~ zj7^HVprnq-b`HK1g6~B7%cZRB+-3VV9OE=dIq{qS-qXrDsq2<>HBq@O+gY!B!_sr= z4}iyH)}pmE1J5>rhkCnP^^SE~$rnTKlUS@&ehWRnYhnDG&|ZKWhmk)#ypnWn#aT%% z`d#wtVvyCe&0FCE4?rR~T#e6C02GRFrpA4ZBsbj~uV2FF_xJxsT zlifOrx|iI(FIIKH-tbD4n>mNxDAZfuu>aOc3&QPs;qeP}?(q`R#BFE& z?hmPtH9%s~9MIh8ii|;GP`3ZFyu&X2>WU2_=cX_nzN=8EkLBI`7mBE3FWh>_qvcs? zo15joxiTKkKl{~%1EJ&VD*R07U2WLKu#A1={Bd6<&TG;_CPqDQAzdds<(6(3bj&!9 z?I!Z?73#)#vuGZo9>|0VxC$+zP<%gF^X9s{YaE);8O2*I&Qy1(M_7I8ZK~1S!jhhuso2de9-2K=P-uB=X zh2qDKSy!VNFQ^-`pC-hGRU4{)%To--3>!asYO3Ds_SxK{b%(Z7DBQjp)hWY(>(Iwn zEG=ZLED#?Tegm&Nz$=^AxS?dI<(&phC0Bv93gF_Fq3e*`%SwR*XMPzhSq&__d8(15 zJ>6T!s7H^4L6s_v9ALru2MrVnL%`4w8cIBaCjqX;V$H^n`)BN;z!7RGL|~Q_qjU#vOn%c%`uZwalTEE8WmR3&W=X8>@p4J5NS%|vlGmXDTYMhz5l|MvEo1VYm z!u_T2kZ;8V>27%v?Z0fMKd>W9)pdXKduI?%ioE1(33yJYHfPk_Z*nF63;ip5eM@y@ zn6ANYE2_M+C1Jogqn|xpHIM-wN`g}oOGAa@7ls&#@0N{<;x6YVd;EUw!;JOfab*-! zG82X(ca6TkGksyWg%_vW=fMg;>;Dli9MiAgqc1faZvZ5#40oahWowch2T6^rkul{p zUqR*Q#f3=F6Z!B(xO}pKlmDbt5I8CkrDX$n{GOsZbL3N(AwAIaO8X`zHjQ$eUtN(J zynQb>o#8%-M`Cun_3k>)Zb`{?&L>+bjU}(>SA73&-+!Rx4L|W2agztUoJ(86z%ELE z(&KkCq<=2?Q8Bg&Lg_F7LK?}Tr=_$@D2OzU6P8aJSTUOc(Kcg&(I zQ#=uEmk$1;ckK6>eliJ8oLFzLhF=xb5rXW$BKP%vu;E$9-~@}Do>|Wx;m4CmX}cu2 z-;Y(PN6=PN`eJ-fo4qgHT#%Xb6c@JyjbvnGB)o~kzz??o9VUpF?-^}_<=SsnUU$D! z-|5JkYlsa!V#9hz#x82)UD7+XkM?)xN{>BzK(Ctqs$&>KLdWK=b($^bx>26MRE{#~ zamodWu&8(M^Y2P;g9b{r`l*LlW**x1%_Ej}2|!A>?i-=Vd; z;jpITcP(6-cji&SN!wN$8jp`YbSvn;vqRJTX+Ca!-0ol9nDx@)P+Z%lF-kU5I?!6m zvHRdpukU&`F@OD4r))FK*oHL=`X6z>|4*q^_YTj~8sDBZ_fzTZVm&wa^P12oFJphw z*`F1CPePICl(;WnG1YqUl~z~fSI=ch+O0UjW~G~=6U%#m)uW4bK;4gYD3-rZIJ7YA zZ9JB8s~c;#eQ!5oZ|tIViGTD*+sx0ah(i<;ya##Bx?LI@58?~NqjW@*gNP;Feebra zx0}q^l1=mHALmYOf7kYLVvC^k(0LN+%IPt>B=kmkp19tMX4-^p@d;&mQ1OMs_`dlb zzMmNfj(0aMt#R|+Kie*9RG(6`K65ML8_4&o!tk-!8}3b#=D%!m@{4KIn3HeS*Ll9@ zzCIu(h|CC;9i}GF%QCQXcesz_aN`KF`Azi*8@5fup%(Q3*o-cd0r^-XQ-vb_&$#S? z)7f{%*zDqAzU$SiSJ;($jQx?m<4$80{^zcRtkY+2QW5g7Du^f8}$G?Ng!j)rv~Z@!(mIO z)+wXQix--lD`K`HhmJ;sW|$71?;o#v_exv-jnGZmV=p1};Y@q-?C*xHB?*LkjLwms zOUmZ?3or7$MOjSa7j?axzVkd3FY!#reo&wHyQ+o~_enX7PB^g7ic0?ZVjl3}_*c43 z+WG5}EA#lDI$i23bmsT)=qQ1m)Tf)(-DpOs@UrYdVhrW}+?*uY6mY`#YmI!bkQ>WR zeZ5YuH} zBS$7y2mV+0(?NMP_hqcdo(|AE8zqi-@~Nx>T0?&vO0X-M))ZDnwYrO(WubuR+&Onm zc!~fP1HdLkwZ!l6z8?EhP!qB%xT?c{&14B4(^k8&ou1LpyOc5uk9kWEhI+HfI{mC+x%*%oUC9(@w}fCg@qef+H(nJ2C;>|kAv*qi|W`<+!Hr0uDo z*I-oDn0{+$+1GLIq3u4=A`Ykvn5vknFb|IA#(m~I^ zb(H}a8UDOyO`9obv+sxsZ4)+l}|qzkcbZ<~gRYK+<` zz72HR7v%SJ#zUqO9`$80!_j(;)D-TK)2A869Uu1Kuxm}P=T{0uk8jCd`38iDB0Tg^ zcs)t8>Su4nQ+~WKHxL|s;H}dxJBEJ9kX@>aASuK*HY`!rp@BH z^tgWY-Xg(KJyUDQzNE|czb;WFvh&dEJs zont?yb8{r_GeAq?<(3&ymRVnM*Cy35XMo7rpI2(PZX=7QA6AVBnSDVe#fHcQ65d(ph{E6Z?v>~$2~L?^-Vr$95B0+63i|i>KEBSg zGYYsJ>;W^x2q&?;jMV01ToLuE;$#zol54$m7=AyuaQm@i<%k9TazxG=TXgu__wN!0 z3e_QaoG4@34X`+e>`d#yeAa)`9tcdM`>hWjDY^>ydbn+}C<(Zc<<{-oN^z|}v!*B$ zg?)yB7j;9*^& zJtp5mks5>dBv6ct*hOTEY{u-;U7MR5jnHs;QV%(Fbcn2jwt$41DhudDGbkLm6PmzD3t4eYTrJG4*xY2R8;XOMAhO3qOxnjVGkO1U_6-(bi~Ys9JPxjMZw6Zpmd!{ z6qnZ$jc7o48i6T+GC;n&4lg1S^e;RGGdc|St6mD8po(rnuuc>e_YB>*u&3gcL?i~7 z<^Ub2wU`G%j8u?4CMb7(MFr4dm*gIa0}1?E^xm8X{_CJ)Ji)=0S;=}%utCEoxRTi%cvYjOBd+xVp$}FlEb1&XOeBP1 z4(E9hg27;dL1&*}*ikKX#~lmyE$b(-rL6o2FoT-egU zk7e6mpzzgvyTgXdC78`2E!&0adoa;NJ$k|0_!H(qde@?mfaquzCjbH_xm@UNu?sF; zT+$xzCUe-%*o(@*@}T>r9o)brPctE$rmjLaYYcXxxixC|7EW*rbe|Hs(oZdgL9?W4 zY|aTBh&PkYee1ZG=hy@Ui|={K^A`0K?!JI4=g|GYOX`$88SMTkR{~~XW`b8W!D>0{ z2ay&u2=i4SPB6VmS^xWn@5m&KdVv01j(edjcQsyWwss{H`g6iS!0yeDOAL zlXX*qF+m5<&7L!4UnH%xb^AvQ&u2BwA~QlHA%t&MGK2Y)GiGlcT815uOw#?g(vad@ zQYw}8-MSA%Bgixmb$vnqcF`9%H9L0tpXQ3^^L2*%WL{j@VZgV}caU`o60T!wIjzE_ zUMFj}R!=1*g5MTmQhtL(X9seCkN;#+q?f;>Fwtq81P!Rf8!41B@a=``CnS8YNKr3y z__X_q`cNOklf&Exsh35n$D7CI3<{0<)LpHzuEdxPaxG|rB!aX%ql{rE0323Sex7U) z^s<`S20(wwrJ3ZWaIM?3aqmN0Bm1WBxTcZanq%jTYa^t*>gvyBZ6ckq-*LE6?lCVzEgMmrVj!$OyAJqd<# z1cc&T{&efE@t^?($rHaeX|}2&Q3F}y_wPyHS2rhKgOsEg@<|$Vn(0)>SCyiE$-Z1Vf-CLz|wxe{Kp`D6&VRZygOoMAAWg!})jD zQAYjkQW87M^;?@tR+e14?z_Cv?N8@UKgzuS2gh}QVV$xDOg6L^c19V$f8P6BJ_Jz_ z$i_uA0!nW)k5~p_SOeYeVkDP-W0f2 zR{ns~NtmwC%`<%UQ*sJ^?%7iZZX-0e-M-jUQyOfynlhzfW`A!@6kqurWnpvd^CLaU z+z~`*jqCSLavKy|>?;BpRziE9hrjd5ANSXyD-Q=Z4RssV{UD2Gvcf}flALnCgT>VD z_Aq5)sIE_3H|fq2BTx+|INpmpfvNZDn$UYb1@+1tqW_sWFR)@wyN6r!7Q?ivRi`M5 z-p|hqyLfT`J`1Ql)ybk2)h=R*$MFsm>fEeP*>4O8u z^Y%c;o<_{do{yt9It90>9M7^*Nk{GK{B_*sz-`RG{rE94z%VCM520@ z@#2oT;fc4q=AfvcZw#JeY3Rp^CHa>qLjReekdZ&|+K(@)dEeyM%0#!yqY{nHs&7MT z${Z$ZM4BN8gzOP}3#xz_%DYwAgIo)D7$v1$qVszWd(T&IVY+{ewM3 zb%AQe{MJf`UpwrQ1?EwN5xgU?z+ZhMKcy)syI1xYCpjiH#Xs$!|4FzDzC{oA8qO|F z$RiYK1u&M^<5h+K?dhvBpmSC8L~tu>x0MSOhGDu;H;%phn#CN1g}}0%6GNwKIBi^= zn}6JXc5T0h2O>_%zY+T`+(k>y$?U|L@FE@VzTsYV3Lc}BFPWA4a1OD3ODQE1nb5KG%S?c`0jSn%!y9)bm^| zv`qscF%{thc+)iB-EDWqr}~$UajUwW>RP^v0ru(GSM#)H)$hgzf$AcgYJXZ0Z8nEe zJVJ)*Bh%Tn@H4jA1Rng9@`pIwB~6f#SaMY)B4+rJXp(9qSCErRd+h2f^Wy3BXz0we zl1Y{rpj!9UsW41{jjthS1aeR@#WQ27h#FXTF1N_>AVBn^`#%+vE3>%$kFW3Z9t|*% zHM{_j=0KQE{#V5k=W(y8tyiMCE2`K+K=!S<>=O}N8ojIO3j7!^B&|UjVs0#X)7s&E zFwi1E4!4!X}R)%ruMT4cGL;l`4a4Go4Y_KZgu z+>A_Z)nqk3s`{oR5z3k|Vka>75p9zx00H=!thacbBwHh;gM|y=uibd3&oJRA>3Q(0 zvLRfYbb}Xem%!~wl@(lC#nTcR#}xn_;l)XmGf6`h4Y2j{J|`Y63zNXsc!tbgMuIS5 zetQ4?!x={~z~R@?&jww*_|*OsBjWZhjXt|Y!p?ia4i^>{*3XwsL`?7@v-Qsc!7EvR zVVBDSrgK;=b-SwqU#h;*7|O0rhdeiQ8?xhCgbFRra=U*u$O~1$fro@7D!P=5gS9!$ z;cJ{Na76V{s`F>C#5c1|-7YX*ErQ*G-M~1)?wRA z(7nY7?Ply(7j2Hn$`UuleJ=yC0KZNswr4JvRTQs20G&kksEMQMeUmHGN$)z>qxYok z*_O;CjJT^#m8Ci)T3IK#O8OLmeLC7uR^9_@-hB2d?pWdbSd(|tfl7KyQ^icP!2{hr zzIxx-_LBM_>W!%OJ$0Inr@JUN0V`E(8;6#F&k%(*6mdN*DFPKN6>>`1EUf2&XGgrD z5J70h*cpI`e1Dz|`-|dWAezrK2xSh03M4S2|ATrwx0mZV10eoWZZ8UgfWo6%PRBE-hQ}BAQbL zXi?bKN7ZnS4$|=exe%|}5F>+hw+TBNqv0Cu{Nr+BF`1$C&}cBq>_aOt6jC$o9i0J6 zHcwH-jM{!9bmVRN6~GQnKbJ3D9?6=SZy(L@lY;ovIw_OIIiR=R9r=d84ypac-!z7n zM{Y-bfRb3D*s`cT$PJ*wprJ#r-jkT%>D>0e@7p(Q>8JHfb~E-c>iuY=dKmp0G4H%! z_y%pJUIcTR_nz>RB`{7Wy%CTke@`(!#l^Sh#@p7MuT))8``1Pikg~d}`b)~zUk@jg z78lqbd8&D^S?f9thyUr``j0U zOD7{5>SUZ4NCp}8N`HFY8*#QegtV{CD-#iE5$^7}PR>|v|4(;s9#?bz|NWn`Z-W_Y z$d)Z+tB|c3GdK!~WUK62$X1pj8QU0&Ii##LgcLO-R8(fDu|!fG$`VQCNRdQZuKV+3 zzMtRcce}3Z_Wl08*Y(e@+vjsL$myK-`}Kakp3legv4kgTNW_W=2nZN6zB~V3wo#hT zNEkU7zF_S}+%ItmAILSKb(snK-I_AZHWKcINJD2`Tp}9BmAiP)@OZQ#1C8rOyy|r? z(5v;n>ZcW1AD0n`n3vbcs^gfPh`~>2lg4a_ucH_GWWz6tMx)5!G~nRg8euQJKaV&bIu_Shz_A@oN!`n~)1b>AQ}Z7EFBN8STr>>&h^ zoX=K+nc^W3#qqz`y+uM_;D5JDBN0Otdp0aygHsyFKYuAmMnul1t&r zI%nUJFi4A-YC>L}<~eIf_9W~87&^BSQ*%VV{s#00PM{xh`pS4XXG_nBih7Z&W3Ntr zx|sU@!n%@0mz-HSQhOD!VoI$;f4x^h8F+}8*q{S*v^$gGTCitH8A^*uOXOclZVqJTqfyF3 zry(C^gx$qwi$f)a?zQOok3E-BVd~FVHq6vCqm~or+Nz(l53v#a0(X-@&H0QfwkWNI z+UZkAowv9Z56)E^yWW|ZHi2qC9f=i>AtRUe7P3tzG|1l#q*mS z_Q6nwH?Ddb)rH0xgSPJZ6Jxm~X3@$*9y(`R>ZTBE^1aDLu!cWYQq)T2_- zmr^e>^!ncZ=JN=8N9nc%_29F~M=&VhV4lq3?hc@9h|O?VTh1;aMS!eCOw6I+4W;4o zG}+rvd<4=>U+(TO>{YqITCzgIHXP-|@)**VH?r*O`zau(G<3~Uw#?xHFSY81sN}r& z0)iriaDp5j^HODTMRnsDVqIYmma`Okces7~wq&-;+XLK;{(e+eCQ9LaFvwKFOEs3C z8W~+Sr!k)3@v#KFxs>KnZRW~5$CYJ`UsB_`wVF_ZrY!iQL1*pFBkbq({o~uS+}YzK z3~2b#Jeea?oN0!~UAQn+5Ln895l3%4d-?LEBhdpkrh&I3A1#>)6aqw zejt}M^_@7x3TR&iN`rQ9tw|$(R7E3gaBlW{ypEi(Q+8cyI2ie9#FCzu0NyXT$RJ;A z>6l&U-^=O8GX?^58riIw`V2IG>VFv>saH~32|J7@N=U(CB3UESa0FYm{}NaA3Pdms zMzKaCA}Q1vQVGnK(FfyVD>~&fQ@#`DQSjoAYkg4yX@n1Nt7VP^w@U%K_kn?2?~*gC zKy+?I5BzCy+RNtRl|V9 zJD5eO@b9mj50;Q`q-+`+Pc$4&Prd7h6m3mx;++0s$HNY3KTb$bQ+L;F4){sOR9Afw z;xN)kdkz=34m)8(F#rw`qCR`Jlb@#2hPymBJb->D z-Zzyd(hd6IB@HEpj3xRRyuS+FxBy1zGQTA8S5&L%ijNZam}HnF@<}vMAI38n_I|ZX zdl@QHg*gEKAuKa-o{glo!4_RNz^|wjgab8xxUJpNb{2I*84;xw6fK~N5ZJ~qNsRdQ zK-?AfDaH}ze5hu{;9;avtOSqFaH>F3{DJwk=TN_*BdhKXHACR{I1~e4xtspq%0)<@ zU-Jg0F;oA*BzKlf^a&7nV3>pjT>%)f5M&pc4&Hq`iVw}eDu!KU9vH`S_M(7k3Y`fJ|T!&DD)_ z-uIbG^#kuhfFaT7$_1yzqHl8M(;%Psi|EQVPebhZ!CfBeAl>5;wLWb=Kg+BmzG1vf zeqeRQDUsyT8@W)l<6Fu`?azHZYp7YZY88X4K8(LuSVEU%s+RJMBp}f{bDA(+@j4Mh z*C{b4+6+7dS!CdGCxxbq8uGMp6^nBwK5=M5r!HKj_ZC*`{jQ{DFjW##xg6^-t^B}; zknLECbVslmCNZDQptZrF;pwn4QH!+qffQl(clw_W>iZA;XA>Kt-F*|2`gp`*NUfJwrEcFCBY2L6;=HZThL5K`C3?7p85C?z^Rl| z*i)#iii*aAf;8Ejt17pX4Gtq7d>+94WgT}@(kFyY&BDD}#^$OE*orkw!h*Zo>D0vj z2)&`!hZKL!yE7&?scqwJb+71+O9jl9I+-{&;| zj>#&F0o^=L`e0uN95^L6wI@Fzc{1x6UG2l5yq+7^bmC7AcGpXB-$U?ef#v3*1_r@@ zo+GUeASK<^>LDASGF7s*hCc;i5#tOoN9GjlU0^w=oQo7waPrXX9N1TeEEY_!o($?2 za=??i$F8H^<@wwazx_gZaSVSPeMp!p$*r5@&xB7TqGnZaJ^i&osqfJ4{gfIp;Z^89 zld}}cROxm;uf;>1v9ypu#s7tKBOkb8FZeb;wVM_+0XsHka4_D&JuRJ`Gy-?Ysh$;- zGs~t;NXIHu9c#)a1oi*j9KFk7OBx)6THCRQ&f_UwpAB~2uzo(Vb*E-+d=^i0&3Rnv zu&5-$|0ATPiZfNGD!wcO>fj075Kwn{;C;t^c+gr|?TI+9aP1YldQWh1@p3xIjwf|q zpQ3B<>wf&t4aPw(O|9Pt!llhOowgfa}TN zo5ZilxQO^Q92kPBer!Dt<+2{b1ZhqzO5Zc%+NM{8$A_(1ia;f@Jof!qwi!+=Og}>Z z_`YX$5go8*LkvzD4B$pitbMr$BQQSJJ1VKE{_yE#CCX?S09w{>G;9!K%?>`^m^r z`k`nWe`ug5V1BISM4$*W<-E2P6&uWRITSdgBcq#V=g~vYR{KQ(2TY1e68rHTp20{}PCFrI6b4WkDW#UAK{~0Tg(5z9FIU9?N;;lZm2wUC=* zPyU4{o5J0H$A|iNU|aFelNxaMlw*LPKL)+e?Y>1Oy>MQpIpmBTnJL6~e4L+oTMm zqV9$ud@kQuT~vJ&du`a`=})WH!w!*ZNs#cbyDCIEll;|Adg~UGBcovg4Fd^Rkhy)Q zjsg$abj!9$KB!b|)T8N->Yd}qnB^T_btRzjrfqeNbz3%ZtZk;3Qh%y(Q1ibX&27ij z(rfZV*QT4c@OOR@dMVOpFdEz#Z70JX#3-4Kb~FodH1BG4-+N5ZaUCCX`4PM7x-D$t zWFDE&mBHpz4h?vj_7Rx}Gj_^-Qv`)esh#qp@U%eTN<&8gk6u=4S*Iyh#td=HyES<< zOTJa%Jy3q{cnl_R&dfFU>|i08PLSS9VZQl3kVViA(kD`Z9cDyihK0x6`U~8LujJ;^ zzaRXA?fX(s)azi{B47pzlEY`EyfTfYbmlseMkHJw2{`u#L@IlobaWu z2%;?(OWKYy$rndoF@WzHtgGul6hMK) zCF+BqrJRHG;;&4pWEv0AfAds z(||S1Dtwr!Uq5HRR zydz>D70;0Pmzw8>w!dfgj_McPtAdWII)!OS$u=GzWeV2I0l0q zI(wg7z&`&<$B}0s zwn|p&4^IG&+z4Y&uJK1w1^?qN#kq;!M?k|IFqjkYfb#J!Jac|%lc065Lq+v3jxdtF zOe$qCr?U`F-QPYw(7k0dZ>D=wbycimc-fb=x*5}=S%AWi8S6J5uH0IzEu6bHTidfSR2b;!H7VPdT&q#$AagBxCG z{y-jS^U6OO%&kSYE|xwjkzkHbl3H}`U)z)_VofGLpT3B!)j<>T-lK`y_uFripP%C$ zvFSPiq1|z=O}+F8-+A$xIdvi)VuCh-cBqBSx)l{on&j7&wniw^r|C*Z84-yr2gEGl%{-e7;HyF)%$d#GV1tSn>4L91foJ;FJ-ZsqW$AkDn>&ep6mK+rl`g zUC5GFt7w=69=%z z={yU@c%3U3AG`;EBN<5|dVsYj1Z)v|fWp_ahXCier1a`v$`_+nY1ddsZWd_vo~b2o zPKa5`;)Ch#M=owG|KKFj3pjYT~BgMu%b3OPUeHHXuk}E+TSu`jZqP8mnxV^O#0__B7o%s z*kN>}x_jr?Q+5S0s{FlyxPmIJ5gow$cF0}D+Y}4TkdK}eW$$)3@LRBOq0^)D3$i)kds! z7O#{=@7Hw7EL?qeZtcS;ftD9~drz&Jc$Q}l0y5~|<0%=Q#DEI7W7u+IEAE0O;}Kjx z!=yKvQ5GsUocl5ghL*9~z#1Is$HE)EI=}bondf#VmzdhoVtjeN$@vTcE)0Y-(5VUe z6Kc$DNQSWIJQz5eEP2?Um9ybac5Ow|U)Z0S)a9>!5-?!?>@`Z+-n`?F={zxKqyggS zZ$M&lOzJzGa$O*_+Su#yis9Le@+XrR_Cd<9N)3`3iwqo4|Rbg7* zJCdB!s0Rvf02~d+e8`@6Btd~L9ojJpp-!@iEt3-xiq+=5c2^9JJlgXT3jT4;J<6u^ z{J!z7d83<-n40TxiyltRG(K$oTdWK4SyD%RR=Q*HPv~T@z{Epml_N9~0jo>`MDj=d zCJ1Kv$3ijLR2R5U!)$$Llv`(|m>L1Iyq@Mmyc2YYb{BT+A4WmV8AL(7Tng&B)0vPm zUF+E6=TSmJ<$8aRHT#pl|hB_sVkK zlfc9x#$!GrF7h%VgUCo-vE1*_cl+~zT@kiP*c>(w=6>^;4VH378}hFruDt&hVx1T+ zyk<)Z%jsP^)E|uzMPEXp8EB{YCJ02++FHlFc7PNqse{!dMV=Bsu=|!1b0?+;p zw0}|4v>GUxZcjz`r?DA^Si9*d9mO-zA2C_u0);+VD?@yR73Yc0WrH3)n4>=Qvcsby z6vFh=UvbBxzm7t(D;7*Lb4~-HlpX`ASe&Q}kOwqvk~;*C06=)qlV9i6eJ@6A;yY@*B#*vD!e`5s|5Wlnm6k7 zv+j?(`OiKE2yD$=HjwoC^-ukKDU9#PCN-mKNwKG$?UZHlifTqcnBiC3%Eadi)p$!Nd+-HkDp zbrlmHFod2FVFqM{{NO9#6;yCcr^%;>V$MWE@kq8ozT=#b=c zk*Dmnon!{rENbtQK6Q<=N3*>>plmH%{RDO6aYo>e>HpTI()9(4ot2o>Fnlfr#u2Yo z@iAi9#w~~$FN|D%X2fyRVH_agwnvy!;iEJBuof~rXvFnxDwk^H={ngP zS0L-PVh{kTqt~mg0hS%#k2(Iu|9ip}q99OR-6N2Sb}s1MR0_P(@DVFsY^k?5Xe5XI z2Pg=JqeeYbPtIP9b3`~*O~$&@hebjHW&6WQ!+mPXN3A-4mI>7Y#cd6~q?oW5SjGF* z8D?gs;b20gbH1&%WnvGu${{rY07GQRKrQL9YnC0bJUQm}Z-Idk;`_mgVz#z8Nb`;d zi?WRedB>yQQaCah_-9W`hP^IXEMYt~_T#rclX&`4s#|9se|No6h~c@R$+u6-hYE7~ zo!f4Obm+C6C)~4j{dZJDDo;neWGg0GA~ABSIK1laeR;kR4L;S(5J8g+z-~RtlNq-R zB^=q!WTG$Zz|!HxlR)D$|CqZu%bn*y2xg`9fu5aPhEf9^lP*_6bC{0W6<$W0Dq|jW zO+Eoydx?W};rOfn#zsaE0Si2GoHjJM8z?xUS~id7DF>wg!x zdGx)_0uecnCbb7N5ut2ILS)3kO&ifIY`i+SC0*00?GJ)XA#_lh9T08+v)oV^6$ABr zq~a4+IZk1M(HjTKW)^fbKrRonNZ~4AI&tl6j8Fjh4EE!5JF@=YM(4@cZfumV30bcE zL!sNroGxdEG)H+y)?`GV&R;%6l*Qb(ow-8};ySv^Lv6aiZmsbhF_e@P$Xp^wO`*2d+AQz~^M4P3*bTtOVhA@zymWtIXKd$K9@G7D9 z3%)}Y7}{aJ=^^2IIy0n6elfd>HSEsVj&Wn!x>a|0HD`v!$uElPZMsQ>yrXd_iACzk zic2&M2a*?Gj04-Cjn#scEMndjb5Z$wV8pgQi(if4=XaYp`3~AUT5Zyc0)3sCYoUvgkFs9g4>;ELu3>^N1!B9xS^zgKFN8+J4|&huVf7 zix`9J@yzEuX#dF%dcdVczWXJ^;Rvw}$JZIfeVWImOD>F&IUUcq0Q8Rf8wW0XdS&LJ zd#MKwRI-NodDCQwiUx1YxV%{s)&hA2Hj-`5W%jG9?-JXI5z43NMr*AxSB7~wWK#3x z%sK4V48mW{BrNw_ig8Wt*9$qVTrsk~5m1dcd$49st5Lc;YEssLkPm&RE_l*hJ03VW zQ>S>C*!e+Yx#}#A`_GN#SblBe9}tlL+(4TZ1ST@?NRt1CpkgugoblL8m~>pVN`8C4 z$>7I9RPqgz?m=&r&@|?k57=uo$M?xoqgdM-V&x~$Z-YyNyEOjhn~V-?9zTC4;z;o% zCqZIb-a%QgKwTwkTWw?$Zbe4SWxF!i$)uHyHUl8Ky609kNpxn&`+u?tO{3zEkchb76wU?ESjt6oe?6OdGKk^o@Ns5G$HQVWg-JS)RcSb z%}U+PNNr_vgfM}KG*P`mUQ*%Mf}-wIHyRXVVPP09yX$SFtPtBl``h2qNJ#Q5jfjcw zPC6>#G!Xsf(RXM3-*@hOQ1;cnhIUBt=`2JKDS&p5EDym@VsdT(u7=L!)_A-=ed$=s!M z(Yfg)yckNu9_1?`-f$<+j2Wk$3o0t=Fb*=`Of-k|8@+AA)A+0{!C1^PxF|mQBWB9N zW6N7;{N6=7Gx2^E{Q2$CI*Q^p+(apLBr65T6Zz@_(?f{U6Fu%}f4uR0`SJQB`HFdz zL((3h%xKtA@jSCd836z=cC@fn72achj@taW*yth1mxfrumQoT zshH2YzoDEu0o#sD1mhpHMY`f(KW>$G_qog?#Og>hyo%-bfLz!st+geWLO9&D<7k_u z7I9Tyg{E0fki*xCOfgPA?t}!Y&NDprv37Y-jxZ4M>@34Qg<_BPO?czCnD})l5VT1C zg-$=}wg1KzaVY9-)L#8 zwRL4!!cgq(d`O_xEKuLx}){*LTesApDs={ zwcukB_}c~~$Sw6PCQM-NCssHwW8B*-tRC_7idsR6@^aGe&eB@gL%{{nriR@tdE!X9 zGHYg{3ddHC0DWLxE*tXLJf{sk$%af_x0l$28Ncg1VFyrJ3ypC?!$AWF&Q3BoO+g~Q zbOcnvV9ECJXTb|XEs2LB1d8+3ZlZDzV>aDdG+^x5v9^v~{MG*@rsC|PG2>CnB0_N} zl%B%$`40*G58b=6lm&J3`SMRgnpmyL-8h!QhCle+ze)DUc}iVcI{a=AyyD7};>Q)kXJR5FoI_w6-GL(1RfswMr~7=sK++9}8Q z6xvBT^i!OhB7UcrVMt?5Z6jr*g-Y-(A5%Q*suDw#(z@lfDY-oLCZC?0+%H8E}y9F{) zcw?FduW{-ya^tL04=i$b*Zf$`X!0KpqCEr-Rj^!@E?s^QV=f+7@y(ZLQd}K4pWU|l zG`cOidrtJwN_zkZX(Xo@C8z-k8KE%4yzKqGIh>R5x)+icZb$@2+x?K0?>|1EUg zZ5U_)^P*y-DHr!_)PmRlyu?mkLi8H2>AESyY$L+mu5cZZKM;TfhdQG4GyH1-AVpt; zE+{{o$W})oz3|xzG72<~%${*S%`1OciG-j_D_Bb9`hb zVNXxi!<4)u-wt**_!J*w7Fp0^Ha6=%P;VAm?ljiHE@ZQ${ zi<$Y9`|$SUuhF!^Qy`>uzs$HrF-v>Dy0Btq2VR2SF(0<7_|k{A|Blv8T--RsJlJ5- zq@IvG6rnW?w*U6)n>YUD+6=D|^^7f8V?c>Ek2dDvSv__RxKdZ~DJ;T8UvXEV$z}?SDmTHC|9!2gKs* z*IFs=YQDT}wF)%tZ~p)M#{nBUDB>1J%_Ny{Lx)-+-$j1Dlhulg?{oi?W^r8iy`tpO z3Xy4&MTpFfQ>RXJ9M|*kU-gHKIsO|Q9)rC%UzTS&N5xe2t5;j~Ufn!o)aJe404~`3 z;}=00pi~*f7o!gDXm;=Q z71*S9?b)Y~SmJe2-kGE8Jw}9}>Munn{Ow=o7%NiNuZN-^UiJ*|t{hasVc!4eyC_!! zX;mlkR4zSUxs%AV5+uUJ{*6T9HM=MtT=1jzw{&*LIiZxYK-V9x>)o#{Xwn09=dY_r zF`=neM5_Jm|BOWBUH$Cok$PioPHbpjjU;p1nFe*f?QyWR?kvZJexdM+#becjrXMheBrPBm^7yrup>O%I~i>a<5p0Kw0C zfyD}P%Phnu!hQq?{OKC=J-ZX-<47W*`*;8NW`?*FiF8l$!vX$wFU9x+ey-?Pp$9v1 z#*osJ+=dIeydOGM!FsYOmB^FOvhx>xi7h>(}!|ugd%0 zU}of<(3R_Rd7h~GY5DQ-x+)U%^auwBhF(H*X4}TS-4g1DIJQVAPtwP)@W@-@99hQW zcpE}AVg83#{}RawlcC(KEIzdcz|@9~TY>>7?VVUae&fy63uY_o*WGk*W38b@waok1 ze>wazfs71Ft;9DT$vy{)6xTq5S837&HkN4@>~Qj!KP{Q9*rc`a=qJQwkPzx>TE0Hq{q_6%e8wr<_;Y*4`V7o6%2RxeNr<&2DzY&{3n}ACDwgg}69nD#h~S zOd>ip7Yo@qjwcvlYdsxcb+I6xH$`RmWL>*Dt>;?{UKCotTesfNu-v5AT2axDud8`$_Sn-v`ZDab*K*{@*I zt=ROJC{8PU8F~Rzxba+-Qn&-mxerTR6scTUoqqlNEw6Wdzi4)uG?x$GQ9gW$eE965 zbt$fw{IfRVvRn8p{+Y?P89BQ38yg1cD0=?Yzp=>mz=LhU!At+{x_j+#qdsq5IQ)LM zG=Df?X1Ka@=eWneR?+TF$w);&^0Sg`F;2<7?H}uV4=X4^kh)pyca+?% zVQXBYw+t-lXZ7XX>Dr3yYRo9vB3$?@QAdEOcS8C2?jbZdrAh?yQSp!^l&#~9zr0o} zYH>{hPhq6OQq@ImDc35h06hX2NHE+98XC=hx5T;|&U!Ag#`FfD9#^EtMPA zQJ?xem3J7|D&+H}i^;4rmuo|s>TP>Ivvv8lcCN))mlYM8@$Jg~HMipZRU9qZaHWhL z?~*9WQ$C8sHDVYI6Qzq16VD4WqXmrN%7kHWJkn>FRgSKA1Y7Ibv)sf_J+sPvNue%10Y(@1g@{yixp!?Y*%q z(v?r^dCzGUvaT5T%LBis-n;rDZxVDWX#hL1cXqQ@`8=rg>u#I7c0?eNyVRf_Zs)F*Q>EXl5d{Vu*TR){`)`u(zSo5MZ66|**VIYr`FH4OwO-N zm@k>v>(h{UTi^#bx3-xUPRj-V(~wRl%q#ILVtDYkbJVnli8R`X`UqarZLKq+EJB*cbV#{%usV&a%$jxJ&Fh%xdHtZP2l_o2*x7(O31a6)4u zX<6#|2!wGp#Z-p(*Rn&0LjN}M?tZQDwrF;JiRmG6CB&0M#L~FjhcI}(-GMc!FX)&` zl;j%NKJ|YL;RkuQlO0Rp-KiCZ=rs=w8=3%-xgWEZn5t9Tu@;2o^SE5fK4Eb(3^5_6 zmln|(+ftS-_Her_5|`uTEU}BiOcN38uztT$ zc-to+b3@zvIwwiW9-P$@m0yZVvQ{s>j11QEY09U{{_*SUinO0_T4gHrnk36eDXt1e z=|%B$_3^t=l^^ypPw%WG(J@ZK+_1%kyaRY76E!}!=mQMIcvOJi5xzLXZWLCnkVQdR z0JHfYeDo-JCk#kZZ!9#$mtx(`RR{2$WG22vWWJjoW6B4wZx7pnU(>n> zor1M_YiSnrILN@amww55R&^3p*CYxnEBZ~y!$s#tQIQBZDE<>*+-97D@wWz`P!Q8A z0phe8J6Kol!%n{Ww!Ml)dqQSc>#$x^q#4!h1W|u8cTNXlQwOrHAFJ*$iKEcmXTaF# zFPHZ-RKQ%u43d=+|237lMQ#9pGP~*63KZ?V-M=xA;c|H6uP=57^Qc$jKj}mFfZ5UO zQZ+9Q#J9&_>-O#7PFEh6-@2>5yRjiyz4{`XvTfFR#(=GbJi|mNf_Qd)VI>HpR=MyC zW^Mb^o%^cemelzmDqsf3Hr3LK2W8Z6*YZq6o&0vAHItsKJX(m3h!)f zt)sYn$Jq7p!%`0pIt*r0M}Zcsj&g_Lb7CaVb1C*fA_qna6y9y{;X(kkO0T25q53s$ z_`EtnN19R&uFxm<*uMP&1N$k(x?CnUtTl+ZW2vQc_g6n;zEN6>?KffJPVLsa{J;*v z`=}uel%kUYwhK$2^eE~$R(x0W{V5bUe{Ebu6*T*4%`W9H@zys>IXizsH7(O&@|NCY zx>78^eLPg;icq%QEV?kpj<#=#oL!@iuw|_p-0a^N*PQ}JKa-i%gnbAaEw4KCpI7C5 zB46|jABE5VSgHvd&?F!){>L-KJFLaa&k%Uc)zvk6BzTxmXp|z8W#}v>dOL3%W55UI z@*3{aWW;YAp*i;+a0Hq6tvN0(TZ+7%(a}^C{h?2icrJhmU%GGPM@jxJ7CE+_+RqyI zr)~}7zl0NYEO|c?`Yyhr&NMA@>=8))JI9`w?FlS{u+*IPuJO zV{@bT<+3$#>v^(&=I$rwEJ%#@c>SP%W9e?FN@-w= zA5B-ac=h0E&6RdO_f@c`=E$qn;n!Xw(4skbw$yW>DoG`N39`9DY>!&XTN4SF$Xa2u z;PIAuYN?A}iQjdqGXP`bO^%U|$RJyv5jcf;MAR3P-v9jkdFMp3FKR{Mv`X69lHuC( zm^WcSb1M@+9{g$a@wGbm_M!nT<=nC9xW0H3TtmV&?F%k0zI%8wgh_}2LO3OHh#gp% z`}DcC2oO-=ye_$DvZJkeE)h63w2vMzw>Fdqw)v+N8&?jLDq$f^uP3$I*)8RHz%d{P z(Y|QzNl1uW7GNTuL~A3%;sT# z1keV%r$OcFBe(GMKB|4lTl)`qXOm@a=oydsmI;#~7|3C4&@#8eYO2ueBlW8Xl%h1e zS6C|f^puj?WBBG-|Hd+u;HFKXc@m68rs9$wyfyt4rw>dN0bp8^an590LP}*un6^$5 zz2Wg&qj?tmRMhd*X6cv|8LpXYxg4`?nJ30@j@VpswG(nO79GVN>wnU8QGZx9^T?NZ zuRL#LMQ1KCmCRzjp(}&KBG}->Kt@WRt$EXbqu-mQ5>JY0u}ufp6vPQFjRp@^?^%FH z@&nBPoJ8$0Ya4GX}UPo5P-e=e2mjf;OD}V<1%~RSHo$DQ@S^ojyWXSkm?8xQP=@!FC^K z!n$?hxV!&sfvVyXixyL{5~~RsPRXP z1jH7yV#?|n4I`pE5v(Q~A=$J>TfFjtU1OhpYT>_Hu za>xL-1upa3jPdEHD&u-xwu`URsMR=C;_i+SV4%~&p1oc_G0fB9@SU|0Yaft$KxXb# zHqhlG*1nhESEx!dQbJna0X3MR|D9aYLw_?^`tXn6XBIyBWZ+Z7{j%E0Zj-oYfk>e3 zcdwW>l-jdWzpQE7f!FseI5Yj+vGMasBs|n|+CYyl1sDPOCB6$jpMG6J&%5=$4!xuB zTxxXpr7qvDnlQAejET*Yk3tJEw3CoV@WIO+_U_p;XWT$B5qAAT-!wb#;NHDC8AdRZ zY@$5@F4e0ScUuRK={JA>ybM>`#&EBV4mwN}mN98;!>t(zayH^<5w>y9Ltpd_JLdd| zfk#Z@8P_^)a8Bo5%6jtgUf`%_nlC2@woI|9JUp!|ZeNE7>LNdjBVmIfu@5q6%UNA= zx!QUI88eUfEU-L0w) z;JuqtYkmX`EhC(sL#Q{wJDVoDiJgIgHhqbSd47fj{%VchzG2?ApVdGCeHKm8?xZ0( zcG#LHRUW;-5WLi@ag!!z-D1z7 zs;PzU1q6v2DMk1=>BU#OWJUPeyW3a8=+0LqK`~C(zciROR0UlqKQ`g#E$PmFQ{P3i zx?}A9CydDmUfLhA$?2p*2m&fEi;T*nC09QHM(odeyS~#CA#u6ik-3tJK;VLmaC|K6 zv!Gqv<2u%=xe&u$nX5w%Otrr4^`n87PzTW_V zcio8YB%!9>*s|(T)KQmI(>7~E@Ai#-lI69mXgf@FWqh~K!&gijf|*8UR3Ya^?#Fd6 zEZwS(4%&qB+$j3)AMw3YB4S<}Mtlh;+KS}**zp?cVK$|!WmiYa@U`7;p0L9JXJ*uQp}+sCx_$S0bmTF$ zW%|{Ao0cD?L+=SL$ZHWb19WAdj#Hj zv3v*>NoZ+KKcodKx)q3-%ew%vNlRVk@}~S)VJ=K)rAng3B9exv%Zv`~S#xm~erf8} zPcyo|#kbeM2^&hAMj1^HTR2oO0N>%#;1?(<0s3Rzi!3^EUXM|U*`y04F6>kvxF$C=vc#>FM?hP*-x`bw_^ z{gKHqosOQg$|H;#oyP-6X${WPd%gFGh^b|9wSSIuew?NHUIL0HS93C|g24^aLOS;N ziY0ON5Wgr>IU#l1D~kn`p%cBph(M^JKppa^w%rrIbS3;_utF@;htg|Eh=_5poQZ~lymrhwy=J{`&g zPQjBIB3$vE0eC7jE|t-=FB>^#ax==im{C4US}c>ald&~w*NH+p1mswc4RxR~jm zukfLT5SeVa$S>jE?b~9uZ9)sDS6Xzqp7JZS_Gz3zFQR_a z@x4A9+JL_Lw@-!wdSUwCORq}foA;T7@`4cSz0*BX-<5v)~6&y{t%4A< zNrnK_d3GofEQp{fK!G6S^HTgl#NvM_2~xQl5umde8T^r&_k5>j7-Iu*y(cyO9PJ|} zIPGK39#JT2H4$F`QLrgncf3$WH0!y{4YoFvC-D+Pz${F13xJA3=+A{IMJp-2Y~`5m z-I?xKs6>73u*E_P?EnnW8Ve{ES6$^vpi{bFAmG1%$6dT=KQMy zTHFQ@c1?v~am9Dm-izrzk7n2$LcK|(kcEA_jna$5>Jn5I>xv0T=>AS-K*M)iQct#*p|h4WIfaw$ZFFpNCWP~xl@;bc%= zi8x}=q$kOu-+>?IjEyp?=HKX{gd#<~@I5voHhXN5FD+B*Qgrz$&aZ3G><3-KgUnp6b-#X)gHA8+Z2*l;Ugl^<_ z00>QkLdJtIW!x{zTHTxTPcN-xJ&AJ39a2W&!*g9zo(FAXqw+(b27z$v%RyERzN(Xi z#uV5su-iY4*=$-Z;H~5AcKNV3o4O5AWVhZnSD5-jE@XfB7byL6$A68UE&rPUwH$K) fHyidF)j9|Nbe=Y8UF-4kqm(~Q{4xB8IY0jo3ZS8y diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png deleted file mode 100644 index bf2bb411052a6851f1ed50ff7424bdb495bfde81..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 79145 zcmd?RcRZGT{62hD8p>*tnP|$&CMz-$N=RlXSs58cMpF|}QAUL98Is+NsO&95lD(2W zuitUHbN`<2^Ljn6=lSpV$K%y~7q06(&+Bu(KkxT(9Pi_OK0K|aNWGbHGl@i^R#uYN zAd%LGlSu2HZrXsKRBvuF!e1LqR21d$H@@cNay{`ErJd4w2NH?pKJnig-=6VHB+?#| zvb?P3^^oCq7dOquCGz-8#YI}mJrB-&OwE21@!@9dY5O(zbQ5Van;xB@h&ufCw$U2d zw?B(a{s{}vm4EY|N9+0R6I=HF)HqS|=)zWt&7T?H`8geZ^`&BI$**dJ>J{g+i~B_c zTqN0ikFj1etCE~?X#PG|k=@rqRm?}Ra&07%%-wHEG=Kk1q6<@!S@-vwh}#r`fB(7X z@c)?~lby&*GhBxbDdS5w%OgO~x{Fi^Cex{yc_mC^C7FKsc8rD7&wk(Hd{6&o}cdxwzX6h{lu8k%;j6g z;aj#{Wa#eaEq%-bt(gfPi`{Hi9eFOT1@5_x?24h}yz%SDG6NpIo!|;E@jmgGp0_D3 z=5>7M@^n^fhQ+h5<>kfVUdLT}UYx6N;1v)!tEHvo?UN7@as11dFJ|sz1q(KjLu>c4 zY=gU5ql=!tKJz45Tu4ZZCotikaw;db-(#L+iQ&1v>M+|X z5!cCn-SThN!rb~FPP6(dCY(!Ynx7hCV&=kqR*_xHDC;I=bb9ZPS?R2{B)R)E7L`Fl zuD|>g8?tPTLT`*_C++>RL8&X0VHJGC*YHc~N|`$Ev4o-N#+F<~C@uO)8` z-e>bj6@RXM&mpnrmE?{0V(z$sgFpVc^T#zG_uR|)^N6!ZYypm9S@K|O@6h@!y9CAG zi#hbZtT!!va^~vQt5|wn0nf*Mlar3^u0u&eQdxs}?ti`HgK3_G^kg9;1KCd7Hl+2s@%2Bx=7mDSQn{=G0z%xO*ozw^$3mjgB5%oTzS= zULL1aeaZLF;_zobj`{vb-yr_uA9XWBIV>zJ2AfmDq?Tt*y_v=87Ok6;4d(HVU;+8? zPj=#=vfLr*@80dz7`qf`5S@`BI5GA01s9WknGt#ZoI}pp7`4Ir725$(T=PBKp|bgG^r?qB)-qOCC;QJN8RqNyHxzn$ zhC0?MX9irUns^@{f3jL?A#rYgy3MxvCBJD!vqNu%g=2Y-K$WN*!X{%*TsJA)wqVZ8 z!L_&I)z%a?Tvsb%T3JIwL(sft&xF&D$fu+zWG@P*Pvf8HjdU2OFNA)KIu*4&?^e?8 zYr`#XkhAVdj%9UvFimb+zLM3x>r2f|?QU<84!)SN{Fx5da*<}21#Gh~1CA&C)uiSw zhI-5oaBef1NG|CjlaG;YsAuI+IAWuqYbvCMb1x;Bp5CJq}R3n!{1QmPmV<_EbX7k?Ks3YtG{eSakZ zyE4?FN{ms`HA6c|cVA%n&ZcQ5amU!~Q{+Itu<96Esacts?+(7M)5CEM4VsrOUml__ zoW0kSY|z$U@B~qxfJC9oq41>UP)vx4O%^uqP{)n2Mg(B7l+RulsuO4NqRN5A#YM)j z`>o4J&1NDZmqVQb>gwxXJb$h^v`4YFWtXB4b1jy_5qF78Qqq7$!nxtWR*uOs>E$z@ zK7FE&Phjsm7w*N%>nB>qxZUc~rMKG2`f*5CLmBmIXDuz`)kM1}h`bOaaM_23S(Hv& zveJdeE8);kKvzTb)2ls3gd9e_wMC4)Y5wUg6rHMikgPA^Lvf6XB*QmtGC}6%by?39XsBX z?$D=3EN4!C#N&o&RVJt5mNO#X?e1VLwff#~W)5+zjW}WaX;=i|=+H6KaU-SUk81mJ zm;PhjI-zdlXMq@027TK(^RO(BTX&e%ULQLz9IQ%nUk-^QY;Jj6ol8g_;e2|_kbQUQ ziAzkKE`6cBg6hm`NjG1blGg0v*1f27lyf&q3XRokuH+9zA(wxAyfqad?b!d6|H4O- z!|T>%@oS^j*5BT}?U;8S>$no)Iv6+A)JMKCQ7IzVBw$=bkzUc7X%+b{4+%82FX0NC zrk2+3l>F(S+H8kD*FJT1brG!g`7vSUW7ku~ssp(7o_znV-(4Y4otQrQm?!7+!NZ5+ zvuq0QY@}sew~>bGM!O`ddXm$Lc?Qb|r6C-5Hc%h!%bu)Lv2ycRobE(jOj?+E%v-Q2 zNQAMisBdDoIZOG-*ov#bQG+GKF` zvO@))WRHpTD{b#zjkS$97x!Gfx6|Q=`$uFIa|SQt>&I9lP0M_G7i{yVw3Cp;MXHa8 zT>bSqHNb+wE6t*QzeB-4cQ)RaL^hLAk=bG{_^`aE?m(EENde8U<(}u&(qw&uT<1pA zn8Bolnm2OdV|f#ks76h?(c$4T?(>ti*n`EiIg~*+T;3cpD$vhrcMhgu5!bZ+_UF%4 zZwCJ3DuF!Ixyw^S%?X#QU$>^2Du(tDX@Qzi@L;006r1~`UykFzHot#d_PSG@2(BNX z5?AEO{^c)TUWfn|Uz)79YA;lfc|31^C1Jjjir4P7AXI^AMA|tr-U>)eFM#d z`AxkPo-l0H9%?>k=yC!(Ub`i!EYhAwSYEK1o6#n3T)`nHDk`ceX8qbEqe4SqD~%}` zBRMZB%K2lh&3c|xe&4J8q&y>Bm=g_hwJ(R*d(p5S)481eE&cM)+dxBqAR=GSMbo7L zb`4ZvOR+(TB5V zb%-kwWBEW0AaAmU(p>P19IfPz$@S66QnR?g*jwv12KBUM*`^>A`%%ZSsr;+^&bqi{ zOU?ay7VcL8;G!|a5w9*e_553kk%EGkq5G5;%dzXHZL;*F!qo9+=gyttZnLQ#k{k?{c5)UpE)<2 zRm0f$4S;s!M74D15ec&jhMhZh+|BeUoU63e)YXlba-TO}nx8s+=n(%dZ8~*6-OT&-^`DdzPJLCOQ^>kOeNO=C zd{`tV0g}HTNyl!ZW)ePy`mokus#2E{`#%gNC2^_+*UuI}e0KR>_P{+Deic43RZ z4)hM$c`(0{_AC%{Z#KAgXy3kl7qiU`Y~Ow!?Nc(eecR90O?&M3%@DO1p4{Q1S6VZ^ zE#b|;&yv-Te0z%Mz0kL82K`Z@LbGRvL$=H`xCUX<6FsOcIMh+w)@tz6h1qk zPBUL&mOda-IOjcwy8F%elYTIhZC>qg0FQ{sxs<|X*JpzghGu4FHMDH^Y1w?tmk^6* z8jn#YZRIK&FboIuYI%Ppc)UJrS+GB?G}tC5uF2J1D$M@drNCF00QxkF2OOJpJBFqh zMcN&oo%fjRuVpWH8tZ63lI}FrFMYB3qS!9AM4K#$e*2*?ijP$J4tbmUd{xiY^-f)O!!2pzS)byy z5^g6~XL3vUw{`L{cC5dz)eI%z_HtR?bgLD0kVsu$QYSv!ov*#Uy-f$xfeFKpA~8*s zGR1G9A@XVU;t>IX`q;$8M1D`x+#h^Y#UFqrDqN&zf85znxrIc!un#nP+x4^DS~K1B zv)2~s|M@7dWw69<(^()HTlMqjPghfWPylx|ZB5OFgWG3&L>v03w^q^TEAgbHeM$2= z$jvPj==b76b+Mb z#S!TXA8$Ur6yby{pV5>gwylQMw@gVzMIi9(Iq5{+yx+IaCxectb~tw#{_kaH?mmD1 z{M59xyPIcqnEUkk(4KW!pwcXs+#*hcn(5yiswK}|4!xyZNaW#;9tD#HCWYedHQELO zSnB!7f%^Uu^Ngyt`>d{?kF=#273!8x_16|35%FAJbnQ;!S~G$`tXGq$TwI*%3rpem zJiDz%q~m&|*jN~{&)+r9`5_Z}j8(m+b3EtTSTa#$xwI2wGo|H~T@j#_1LTE5@>y)T z{Bku_RchA?C(t9Iulmp&dM6R&Y)u@6sVEpVikQ^{o@yMa8DDjy*_j{x*?l) z-ysPJiGC*+7njdhZ*jEJvWN@z^G|s_YT|FuoC@XeC-j1m@7eXk*D`oOoa3$$Yrj0- zT)3f8&*+*EU_ZndoSo(zQCxv^n=1zVI24hN6FOYhvW<158s9m2^5ozVwl5B8>_MjO z5&KZM3YVu+Dx000Qs@{ao*z0}G@AZi*gLypK9J?%67#@BjF``zjjY#YK+l{4D2F2= z04x~!F38{pq@+XR$($~}Q7t+1vc7O>#MWwrb`D|y-@?PHuV3Rc*~kHQOYa7Q*bkXo zKLPz0Ht#SG$-)+P?R|B*qWRf=)%eV+kI7Ha%$eH$d7o<_ zNU!R#5w<4Kud=pjkO{2>?M8{Gl{rpB6$8e_UiS<;J4&d z!&#!TNBEO0Mtd6HzI&%()s$F^-x92kAdn{^HUTx)bV9+Xa48ztC>Bz{G~%2q?mWz} zL**dSfJh8vp;71&DjBF>D4h8lXju~#+1xdQMQod}ZoC5gT48VG?lifKeNrn>5Hv46Aj&iH3LLhhJ@-r)yg*r^ zK$Qu$rki@tzCu?;r8rnq(S# z=y)iHmvL-r>S;EQ**9iYA#vDyg|ggS$XWWfugtBtAou#$-MZu1*z^3X8fAjM`(F1v z`Dexq1YN6W?ppqEA*;;^5gL>-B*Dr-*1ZeAqVV0cqg zer-X=NxyFd5Dgj<-PAWgox9|j6@Tktz0&o`TE)tN)9Y%o+ikdYQa`$}h_;x72V7lx z_cN%AezxS)6=iac{GYrRJ2Ev&k$RYv!KF)= zRO9P;e{Z~#v%v@=vbhCuZ*CdQFK*km?RjWu5+Kk(`?XG?PNTf*Y1%1CcV>@Bdh=&s`X8$TInYDUo;UY7++(?)L1 zF!}UogYCwIz-xKf)H>Z|zO)kAwWn3xsoKY8c{6wK-tC>t)mnDi^q#n45I|yFRW-1i zPp45?*4{+b3s#BI4}OhryHZxx8m$_f+9x?)vb}QPW0T7p`9Xc_2feyk<_P~FG0AJA z-@~VX8)bca)n6Tb+vFPIp8sNRdKicMaz=ycvAVtzD4`8+1JPjJLg(-mvV|nBK5IPprLEr z7w%%-f>{#-=Wmz%f(rPx%T6hN`S>3MgeLN!eg${6Eu=TspAQdIG=taH zK?jIgT9`>lOgv?nat^FDR)`Q?nbLYhO2!xRA;pycjyyAJaeecY-j7*1X z*W6kXRoOh)EtMMfqx)0c6qoK=J>I_FUSX_BNS43l;;bk1$>x5ZzS%;fmbp(Cs5pF>v7Z^&&Toi`B0s&9WH5iR04symfLsPLx9hv{ti4 zx*w-d8Cx>S3(!dVak)XK1132}QedTdr=J;=`cc(2Nm&;*R%Cpp6m0Ey0?Ws=n}Y{maSxMp2w%#A1NHqj-~~< zc8q>YSM2m8yGI6&%|Pm0?{uwJ=hnjsLdnl@`k=XV(cl8PHRC2aacOrc1cR(AUhFt-UkNfzAj0 zmYNWc!MGUB^XD!0)11}NSgDnIicwjfiImJGW5u-6)|0*-s*<+ro{=H6U(IbdmfrpA zYWDM&FU_miEccXn4K^kO+CXNWm;%;>9)7{Zy(U~%HVvC6!;#Q*Axz0GU6h)vkrUc> zt}o29VD28fw6u}*+!ysCR=QDS6j>vhC15onK)cj}9p^MP`)xfJg6DGheb+0xxu3Gwm5jHDAc7-IY5^wXQre)MT=xT>+zTrwSl(eXe+OW^!*|TR(o$fQQQlK=k z_{CiAMNdtQ_<*u~%5oG^FYJ*ngv5AAq?=}!{->IGqX23Z2S0yp2oaP?XUw3>d9~Y! zlxJ8qSIsVsdD;%Q=Qe{k36^VVYcoPc6@*G@LACp6$Hlk$4FZ{Kuq1x&@idTd8Bpcjyw1etT%@FO(ELI4h>z+euB5`CU$T#n7u>p zhDVe94Gi9rii348N7$0Cj6+k|IFbTUj|qLUx94NpMuZ7eWQrJM_vH_(rhnHlU&UL9 z+wCn@#<*>Wprj@vwUPJJY}&;m^YXrfQ~m~JN0-l=XnMW?`~}H96h!c2(-nSvb%h_N zOA*x1CFJB$8a65YzP#ETLNc9p;H+jVHs4(~i^N;`CsNTjIV-Pui2BhdhzJtC#tfyw zwgtPdO0$Yx(PxnTq4DA*rx8Uq-?*E^|f+4Vr)=#y30%VMBK<_;20wU#(}=B`;M46B4X` zGUFv-o9_(IV`*km3Md@qmZ7)rJi6?~8P1WG_vq1Pj!@~%V&D}kUVwf3-YU4{w%c&- z-_Q8);X?%^soTAA@$sAI*J^Zjrg%(ob))H(JX5ZdQTQ?0FhA#?wxsvGzxYjkP?V$Kotwn8^M$p~1mSS3Bt+AS;r9FY3L}%&4d_;Y7|$x05m_3 zY@7sji9gX5_jg6sLZzm?@vTs@h%NbpHDe};-GxzjNG*Z%qn8DlADb@4G3lH#<#9V4dZn$ z9TMNX_L#T87CDkAb(>I)z<$pj2(dfyT?js@B ze?H7XkBmvu^M_2hujI7Ovu?tkV8a={l^^RsIk;|uIJ8T>2Y{U=8kmIox{F6YR?jB; z1ktZ!rnwI`Nl z0qkc)^N%x1y&2H?5tt~u;fky-^3FGk3te}68QU~t zRbDE4pmOM#2t3*JBpJ7qRFVqRVqO=e>>mYiWC2sfF2xlRB!s<7Pv?GecpCbBf7CNIqVbVlEtNpIeZLNiMg>2WT;J;($$)#&3CTGpA=uw) zWXDqC+z$u? z)&rx-f0yBlO;!T<>AWX-jBK$mJ;E7=)va>r)s)QL z7++BcqMyPzBXR^V(6T<-oAXQ0)wqa=qMr}w4k0Q6%ev2;KmV%n&7R{&1q9IY=8T)4 zZ(v;Z%i4f8FJ5pCy`k?mS&dzLjsvXCtDQ5d%QwiS;agT+YBHKniFjD~l3%|PIK;pI|MFu_9TpkR-Cl1ZBK9In zs}L|tioEC`7OFNi(ss`G*dHYwryVh=O9lSjzBiX)m*~z;Z zf@%{*07|4^{XoObHR~qJjg~dgpG(SI{9xbRfQFjt>tkYxPM{4dUtA?xYv&VE3hH2d zu*@5qm@5kMEc0Or5_hsZJ%{&Mpk<|-Y5C%{>)@e%kO!;{i}?giOQ~MGcrozm^6N9O zJrFerz5t?~20va(TF9mHfHFif+q9 zKtO;c+)U`~4}VVYP7+ zNO`dCt;*-4KyiVbNZI@*6YpWg@&7e`{dPxnb+w4}GtR*Bw-8em2=51~Hgl8s-&MHT z_|uZEsPs>k{3vWY%ruIhkD{OUj?)6t#Ma;}q*qtijU0b`TodZtYT+OMtQIh;7T%YN ztB|rHAOJcpjAV&8Hgamw7WH4vwp{wo_e8rfzSS%}Gk)p{cT3Fp!wDge*%^)nL60<} zZp9ySk7kRVM5WTyySMjQE&ebRT~0F^Qdq02p{)EcioEZUoU*Fw3r>iZ$Q}aUzCU-V z7VBe1!$gkgt`Ln@Xlnx~auCq{3D&Dz2jVaVEyxNcE1{ghO+>HG3B0Xwehw}lJ+1OV z+|&(Iv_1jVU3Lfe?fW#yN4FmPC|#UFUI(?ak$ul*ASiLi{tHeF5BR1n+~9N)Dh%5r z#mUKeP)zI_@p{C2Ch>Y;2f{~_Pz)38i1TRhpzK;*^3dAIWY5-HNx^7h&gC4MXtfrG z_v{?uQ5nq{;>p(MnHuXff}W~jW)|%x6^1ZMDsW#IyuF3zDJP4Rn+_Zgjo@*F&e#r6 z!FcrIroxPs`%D*!I)SVTdEiiB`M#89D<>sVQ4Knl9!EB>Lj;gbj(iy|7kmEkabf#% zVRCx#(G*QdQdjJmNI}bnSjGwP;u~MTe5nl)b3j~$mHD!zfR0;XTYasrZZ3JaJ@eX_ zAt9(Y^&9|(g|#4l$^n=N9eQ19-;Jm~-=|mF{?$aiKt=E}PF3%IP~64msX0M-EQ|4T z$`w!09e2jdi!-ub#=lR9`OJ<5|6E#56&g3Xvk&FS+q3X0wGXwI@ymy1h{dm8Pdc;~ z(xTm~Q^8X@pN$VL9+%kAaRq z*PlKR0+OGkzh)1Kh#<=l6n)r(f!!0(-x2z1-I}57@Aze3puBZQE1X#;j69bzcOB8W z;LOxz%kAtI)4QylpVSq&YvrIp!OcTL+bfnYI-EGMp8NcoJ*TGUAD2jjUJa!b8v1A8 z0E}UTq=Xj13r<2W+!`#h!?!FF_N_s*$+VGbDrc5_BiX{tr=_L(Y^P~gj>}lpnQ4R; z0f2NA{9~3tcmxiFM=S|Vg2TML>bN<;eBo=9WXR3bBH#Dml)h5)N^l_LeETU1g$uUQ zb6Q$2cyjvpqKR?Bv7 z>7z8=1q+L~KNAy9@In(_yxMD>?lHhbR=8#)jHhVc16-Yq*iG8|(X8Z5nLPR&a1vBk zHs6YTykWZXm_;2Y^sUs=MM6A}CIs101R_DIfX3LRZNKJuz#`E!JKjC@;|{HtHTMz( zKXY^qau!B&g{)infU3=ZmOLb^C;QbxbS}3$9XfL4$%0?%x{d5dczG|=6^Rz@xjTNq zj;;vZfBxSmLw?g9*U`|x5A92&=^g@&LsU89v%&8t+uhXg@Q*}n-?~F59;&v1{Rs!v zaUBV=%*|Ww0{128!e6Y#LAkiP4Mo*vH?lj5hc3o$RRk(*e5*oF6eR2%?fcuEsE#v) zzklrTVYFifFqZZS~z)^*@*;7ZzaZo9&W&PS-z$}mDr5X^i}*%a4=-+Pd-tHzvJSB4{? zcCGkw&029O{Jr`g@x2GW7er2hOInvjA@cw73bz%afLr-fK~2r4k)1?ZyNV<2{{irf ziYgi8Bask4D<{4AL1ySO$=qf2+n48bb-%Xok!seiun`=GzmktiS9+bymJ@3ki4Y-` zWWYS6rKzdIvVpXR;xEJ4V^%xAo1`oJ-?uw=?Lz-h53dQ|#TmX*d1X!k)qJh2%oL|0 z$*}x)#^2^Ou_iFWWLb& z*ty*Hj>A6PO$R$tid8Scn2{>JNvFJTV%&FyAlCexb(2ETeBMakNu$x&KR6nD`T}9xfu+szA;+V$2O71 zef|cXIX{ZN`4Sb$9$lT4pf%sB&lC9Q(W8~4$^Lh=r_Y{o6!ZO$AKvUikil-U@#72mCvikY7(WJ#@ zC3dR$hSkrP4xAR)JhZZ}1Xiyx$Q!I38Gv|y(CX#XSYxTtEhIj5&8gL#V%FuHt9AM` zuq~PxtGiY3F{C}nR4YGxe!+>v{SY1mG^{dRrz{CWC8DS=#KDJXrvj;dHA`<4^Mp2q z#!NDB|8elZvnhu8j+H{q{fg4_egtuFK|1R2Shzx{gJ}A%+dx&r>p7>AiEY*N2kyzU zXebD|{Q0#%1!@w$D#2%u!;rl4am_fx$>k%?R;5pN6OP}S1psY?f3P4t;;OHXo^Za= zuh314RfM9&+i4UOju7NvSbIi-w9B(HapaRv$A~6lXZJ4Kn6W9CiQ^7oh zp{W=ljba;>`m(Xz%3Ki?!d;jpT^oo@wkv##57`ykeuiloO(-;3!u_#g28H4Z=kOa; ztxV(M4FrN<@8jm?ZbZ8YtL<2?Dl$+h(%AsAF$JAFRxit@HUCByL`oymg0VufA$W2< zV0Z$?Z}xF=_7h(H6DRJiDAzp`fBsCiTBTT^;g;p_65Hl3;GBAqTg^zydrg<9`_1P@_ z5Eqw9`sL~X5NzDd=h%9JonWW?l)ruZ)_VhEc7shx!MlCKj2VJNY+s|T6b*Z~qU-jR zYoQZ@2TB|Bi>aF^&6Pp@{5T0JM!57=m@5y=n#y&AjVrtw*55}v;S9?9epThOOUi$o zcJh_yuTD6r!)GJ_%Q0vXJ|k@15{_e-e|>&vYWl=V29{AdgB^yR8nJtH?owo2q`7T} zz9?+t4t-VrQ7HxU@uhjt7W|`l407&_j^;v33PEh$~4rKLL;Px&KF zymsl`O;9N_=6~1J(aX4oD9W(>`9p%{R7OKCF|z`ljD5<$Yo50|X0l9HDXXLjLy8xG_UGeviaXrEvl%^us<8I9Vv45#~v^-qT;~Il@%gA2W0WhHfOb9Qt%` z(4#d}KQ?|By_ZRBN@5;Tq|+^((*j$_6$6q&m^5NEaJp-SrX%6IOA3>Qd3z8I*vLVj zoz4qn4=dbQi^=!$<)&F}(Db6PSypto-(`6SL+pHG!3R4$XU?Dl&`Um1LYv!OQw;5G{J&Z8R1PR!?4E2*g^XD)yA+yj}@ z?MnJLK{%w~LbA#kO3p?3BSx*bbu*N}!hl_ZFxf|p6m8-8Afw^5)(yb1_1&e%K;Yp_ zHXmf+C5lToG6x1s(#vX^&u$*Nq9yW%=6+;T)8GSD;b89m$2)&29$!n2{z={ZV-{OR zw=rMd>K`d@b?4mBg=o=w|FL@x4h}tFqA^ySqpyC_;$Nlds5ZafNLCr;-1}+!ycd$KArHMja@)Wd+#x*dd5xxM6;p zp>C|w9(fABtVwX79|AvtDZK$aO$aTs4N1H!aa8+rVy=H-H)+p9ii|^m15gd>U=j$2 zO~w-59_X$0JJ0FAA*`Whul;}56v4qbl{5|Rj7qy%aj zldVp-K(yZQD{Q~>|EjsgZby=0V-NN#9xLI2B8KzCWn^d)=!rlfJbA=g#0r=gK?H|;UI}?$;k!tmM9+eZqmQ)CuNq)`atW7jo4RW zF5+YNc+C_T%qGfyGErKaQjAhCtLSBZ8% ziB7~MExuHpVe;2*{KP>D_n2I>WCQ@QW!I5SbLU-Ma|nT%aLs})L#<}@T(l?h3c>OX z;U^2PP6wR?d2V7VMmo=Qo4YCrZ#p!*r13fMY9UOJ5h57a4Pqj20}XR5Y9p7JSa#Li z{~PaBV*r!-|3rI}LLK}`q<_5t1O<+4W+JZYrqbsxUyS$gkYuQZ?~rMd42_MAzf12W zBrBR09Ye!pY@?Mu1gRIpNnl^v_iu0dQc)qekS`6J(YlrrO6*Li8VTUJ!||Kbs>u{6 zGjBiXF+*b2LQly3!+)Kdc#l8z(mqrsM!C)-kiPL-OQ(a_bv0-qC(>~AVv8O^1J)la zRhn-XlG--%_4DVa^#jk*EEH#!R|3z$P*#Lms5tcfnnAmWl|cYmPG1UYp3 zPOqZcNHm7tKhSPLV6QSwb3veK(%|vGgi`RNy*1_x)z=8i`HrJ=N0gzy)x$LyPPi&xMOw|R(hEk=EKLZg zU=kOy0d)G|sNRw=3|q+*M0y3Wh?dnGO7R9HUy}>KnV4tRK3~koshpslWb(U-w7O7F z2{lDqTNf*@V*`$1W+JAKJJFhIEcblxBT3=NH+ppTkVC%O_>w#KLg?ibnvJ*>;Jw7? zxwxPSIe;m+vQ)}j0L{_I6GF>&1*I|O?OT|<-oS67#uLaDF%{tvk}&?5oRs#3kBUI@ z-h(~|0gweQS3Pk%y^AtWN=WlKMYqfoOX@<)1Cakw%QdD_@MMKDLjJ}V&OIz%XTkW~m(2EEc1nLsMcm-7U(@~KmrCMJ8A3QD(PN1|6spw%uz zQ6He<&wNiFc*bxYKK!%R{xKR3FK3Eaeed7s62+b&sxc8^a$@R zvTyUBg*<+TLJ&~t=knT@NDBMIq3IgM{(L|V`%piSA-+Ri>4$1odC%VSyAKcs5me#x z=fBV@*q3;2w`ADRj5EL}diO2`;2u5-%}JSb+ipeZ8+iVL{7jrNLn}&Rf_ZGiHthXZ zGewJbY|;>9pjpgq<=Y_CquW0*M-1xSy*sN^D2zHxUV={!UpG^0W&}u!>InMC+$2?X zb>HA$4rf;*9F6V26qoxX#-164j3A|QFe?wQMy#g3{;9BKl3fU7L?O!pkEQt&qW&#^ z1-F~pK26mmx>gtlBA%&<$H`8F;2{=+z)(bd{0$@X9>gS}9I!(lF@VAd;1oKS^Zsfb z`SMstcVz6?l5gSVYw2J1|57mFyiKuYYxaYKtQptko*#a6dUK0z!Z8X9-2~Qwxi`#h z_b0E%Xo()yl9#o*b@;Z)&d1vFhkh8GsSV$BV$Zd!>cU^lu6cMyi7?}Lxt&WDm!&v}#!r2;>QLJ3UJPTdtdGe{i=e-P+9tvvzkUBHSmo|q3^L0d zn%$21?dpK~f#I%A#Y|ZyE01j1<8=lfMiB#+f@pRS8=a7z;}a4tF?b=$92dvStY&|A z-8yWMq400=yvcB4>@rDI7%g|gFGHAvQjDZgBI1bVMrsiwH{k$*mEsI`*Gf1eU77q{ zz6Y|J$iuE{S1$HQa(=E^;#r*XaghgOfr zzH+>6tH;|!9PIVqq<)h@7Fi)Y=5KiHQCNAa#p+wv5N}PCS^ZQpcu9*Y8A3P-d~p#If|JRg3n{Wdb~ zTt=Wey(~2d6&eNxp;hQoqUr1e+%aOegw}>-ORDjFrL9N(i2I7tAfGijXo)K(5`Jt! zfgv#=g4RZZ**91xr|@8f!G^Qyjj6`PM;_3;Ilgwj6NJ+ycSLyPTY6061o3H z2`0x*BP4=Hh{0t91!QRIBEyx!62XF-QzWaZ>Md;B;a1`-qpGS(JjsJ7LTrmaHXfyl z+{hsxTeEJylbGF=yfJ+cr~v27BBp`S7w?DJ3|?S^eafx~OW^(%GixU&qIt9jPiw&A zJ%%vjMl=d0OSY?O15tqTP5*xn_dAsLe!3x>rsgdizKIOWKR|0KMR8F00 zfGJ|Xa%+y0Rl+4SiUJx!9Bl|i2RS+Ebz7S0eMo+eXm;DrtszeOBi7Q=QmKQK8e$Q2 zMYqG*>`8TBJ+hFFOHaiDXtoP{y?ay}|TfrBUvQOZ9mCr6yvFT_(t;5c8GB)Yh$RRMngu5Dzbz!^`k z;LH@Wa%i=PQT8Vuk#W7Md)yp)hd6HNx@t-28xZw#dV{QTvIB|p-qbm5V4#WsNUaH# znw(7arJ+e!w}~DmzeYThBndV>^X0|4qyWUtfESZZ4aV;-re{`tTUcnGeO57IpRMm7 zRa&axlHVZ{;R{f7aKuw7SMK~qunkx)TcWiTFB2VmB!)4& zIe0P}@C+oLeCH@T(d9Aq_5sJbQqD&VUhJszc(Ma3_tT7HuL7Sw-BT~f{ba*GKgr*{ zeSK}GUOnl*z_1~#_)H0zwdbXRZ$M{~y}dni+rgh?_tp-6N#u7}D_q4n9nY4B+^HY9 z$isXIqqO2j44;@S=jogiW%fy3Ix;~nHRf@1TwH72g%ur{4v0%iYQ#vP=Gn85KO3~m z7e@qsc6Up-wFj{AVEX5`Z82YldF@_;jH1kFySwKT4~Bs$2Xp2pfSL*GI3Z6EPq6Vv zFDeC(wXnokJFq?>yb9U>EaBL%Ny{dci1#>sa6=m6pHg`E`0-hN{g6iEJrAsfnJAME zHikajTvYE@z`HfuT3GK?->YBRRezHV3r~C!ar*f z)`M{2)I#EhFxnQ{{_oCxh*yKI+$6=+6E@PGzCN~zmXMGTI{CY=;Z&)ttih=LVP)4J zw>Cca^V_Nu0D%p&H2U7&-o(_GcB0N34B)H}Sm28Aom@ZWoV!oxK!U`LxD$9V456T7 zl!Rhi2O&lSF-D(ywYQk>IsB#=qa#|PMBh}m{X^+%d;EKVtpa?qgK$h`|-rE5j#B;uQSq=SLQsM8X9&?EikEU z+rAyCTrB!bR8NoAa>v{QHz?(hLLq{LP(0bYtQ>0*;#dF0XLPDR07X)C;z*Q1rokY7&H(WA!%a*o? zr~t}-omra^wY9CzlUfmB#kgZ-zfFipU#EWR+DF0r#3eZ?tE z84yp2p>tZI4rE=C#GESYawTnAih2QAgibilTH926qjIol~`V zxC0@l(i^#@ZJ=h{alIq#*RqG(aycD z!h`3UG5?StvuzQk7Jp6x`nE6G5*JI7KNugjMt*HEpCe51@kWIn*dPEiZ-at@cCfLf z;PPbF?2p(&+3%@`-VIR0e!`#Jq-T5OS}0`X@b<&Kklx$fXWDas4l~Ru8PT_jZu~=7 z-ovEHvHZXK`mBH%gQPt@!hKJv5Lp8z#0KHom2tV66+nFe06T(WMF6Gfs2Y*;cK^-y(>3qLvrc?g-c_Gjr8oYhZQxwKYjUTZ4FD z3pyK)&@-)&m)l#f5z8d=D?TYn3kZx5A8L4thbyie*xHD;XVJ7z#6SK|pVECWHobBs z3B2{wR(2gsNdq!}_|EqTyOV+^7rukOs?IZnH3?cYV9YJ1p}l|RQa+-h<9Au2j1<^D z8kj1?NRGAB!A<_Nv$HZOl{q;%AKfp~&4HdkO_~BI^t`XL5yY?vk-)M^6@nng1`VW-s#OEMwjgOA@f)9O9;wn`*jvK1O0=KH-Dwo)V z!Y9WiPLgR^r79GMj~vN>o* zp#fqibeHh6f7K2;*;q(G@_2CM$~DPvLJX4pyjBk#@!>-Y(MlBEze-U^n1NnB8UNp2 zvJ6Ey*wUVDtLLsD*xduozi-*(Kv0^!JO8q!A_jsjDXsE4J_RtKJ^rh2J+<;yM*yFd z>miLq&hM7Ffh~{$QyI zwyp^+Q{6;hu9dDf`=zY5b-vx*-92`{W7xQ%6ahUw4+&Wb(SWgPG$LFBPEe9(o*;U` z3gN~;sbnGmnN1dOtB=JF3zv2A2xe^u)4MUy!eQ)Sb&^Eg2(E03bK1IFx9Z;ZZQEXM zQKTg6w+uL4E=5A05uW%glR&fZog=$f(k{Zhw{Wo${Tm(RepErs?Gcut`y zFkp_uDcql&ymr)koZ`V)QzSyYv7v$4f%c8kGf-WqCd!ttYih{{U87(PfuAutx)h!- zPH0ys_x?e)@4BX33FH6=KJn}XfCV98VO!pGp zv8OK)XmN0G5WW<}KVX-eOC`D)zjZ%h6vI|9`gF+e%nKD#lY7h}cQ6h?HKq*sXD1}mzxsXyag_I%leZ9Y& z`~2=*_ul{guXWFQ*7Ka_oNRl4_h)#&U(*|V+C%0zddsmze-_WcXK)Y zFWlf`#O}toyu7$aaYqjx8RsrDXzj87`@LJKT?C&A;NTSRR&IxXl^U~dW1k(tp8{p| z07Nt?C(!Fkp%dSG6Q~+UVsl_Rk3X87^7u8j*I2%QHX@hO5sglhRG~PC&|V`D3smwW z`PGqA$F|Jg=(~5-V$6YpdhlQ!gNr-rD3~){%a_qbdW@$t=p|f-%tPGkf&HMoYz*dB z=CwR}G#W%V-;A*%t2meW^tkBGb~Y(NDkq_ouz#c)l7ELUK2j!oO;s1*!m~l;g%gK4l)};5zj0 znYX|hXWoH1V`tvxmt!hiK=qIOa$8t0JoCSOKu5eGxgDAdiq(2ndkEjIezsfYXV>0+ z_-&RD+NXaH+~M$i{A^SrsT=ZIyD6oT!cehl9Z)v!%xTV0TI*-P&J3^+d#nBrWg?t5(^M~AN5W(U_#*SmCi)wNILLjR2g*^e&(V5 zZ8q)?uyU6ps1*k-=^&9Fntb5ZBvjtXAB%v>U}?qF)YO<(ZJ=MJyc4H1iv=Mt7u+xWy&Cgs6~P&nssWsaE%*W9KfDpV(dH{QPXix5sU<|pHZG3 zW8T8+{`U<1gU)aH6Hf9nwbApks;awV`i`7q;*kL_R%?(kKF|TNU&$GRl$jUpG@vFa zA;FyomLj?QF$Bpwv~NT^p({YeXrPb^We!-@=9Fq{IHmU0F!i$X&PY5?4+w|?4j{@C zu;^}8Rn-UE4`u%kn1-bvtdW6TpyQLB6;aD*+Z#QiR~=UH3dHyT(A>wRkifvMVR~fwuyMBE!RE6!92jMUeL^@D)OCMsK zG{6jo0l${YqzG7@;cl7kP@`YT>wliA+2xgiD>tuNI5`(BGPq8c=F%SPM<>@ zXPT$!>839Z^2qj4KCy$%2C2n0XWfU3n&`)H*z2D66#aHhH5T*(BvE@sRCVAvK$8aA zNH5^%`@>JVt5S-epqq=I#<6J@>iNHaPr!3=3F?#|8rwE;_VdubXO}d6h{Ew$(=I>U zGiO^r%lzb8WYUfwwi=D`!HZc!vvdF9!%DEd$&g8rBnqLms6i_6qmFrVj;L8E24x*< z*iQCd2o2zFx(}VT+Bs2L17|%?pXMauQZ(+8hvI1%88rh}gS4Mk*%?wbFrb_YE{c1e z`v|`fkOH*t$p*Kcp+SJ&w*`*fOJ!lFir&2|@2)g@NIHE8B%qD*1tS}a(_mU^1Q;H` z27}%Mw$Xp1HWlv3!VRb|GDBr;Z0Ta4r72&$_KXVjn6T#xY6;XZAS`5M*Za;yH~+rTxBscivd)oTKY*g4PRR-Qj=P*-c44h9SdWLx zqeik3orHEB>+LdZ5A`CN$$j(*+c{D?zR|g6HVb zqf-po0R@dVbId~ztG~UG9^?@dUy~p@p zEdab_6f);{?k4g%p;}C_uDuT}*v~FOe;ANvjVQ@T?G;K&*|m=P?D%L{lP--*hv;O3Q+v%lEJ!jOLO8h1uu^6U?1KLUCPDhMwy@SZm108K zg!H(SS-JeL(1zO|-S1t^{>jvS#ZPc4qpVo?5{QZ;gk@|I$(sfcJ$}F~yIEuwUpY7$ zQ{3Ny(brcUak3Z!=KMxxiWR&lfb=HnEJf693r?F|yLShX&IwSNaCb6N7VeCj6 z&D82Pa>d|b4C&6lWaQd?RlFIQTg^Igw6i_FpoH_Dg67BwX<)J!VN=dH-tj|GLU|p~ z>zim;Tq%`CzHX%$QpJei5I4tC4pnqbPe5G6J_t{Uxd6wx8ZmxRS(yQuo`P0M*F97V2jC55}>0-%}ryd8^UD&OKJ5{~3 z|4jIFd#ms2g9UuhMkdrc;vIGshy|FDaT0P$;FY(4rRc-w-7+|hseVt#jJB-~+Cb_< z@`JBlgiJ?YO8)8l0Y4BZey)i=59>L# z-`%VDHl>%UBFltJmR~fb_%zd+dPw7v_Fe`YGuUrDs;p5a)L#pA4k|JTmUCna3JRKj z?`_W<@32oz-xhLwKr_dY!xT#^52*h5qA5oX9Wn=HL_-3I(U~eJqr7(RNn85e%HQc1 zY=lop_t>%DvZ+Nr0Rfe`S?AMpoSnp#WFAVdMd&M#7>E9Z+c-uMgBSjzDEtBB!n?gp zxR#33E3r-|q^IkHr2#?^IhmAl)Z>D-jxl$#cnjy_cU#-JYa$<{?{kK84fUu5!BRp9 zD5oHdk6eY7XiE!C+HA;kF9 z{M{bUlhC<(yyuDr<~^s`Md2GH;aCe|OfHL0MUVxpM$mI{_^<#)RAbEVr=;lO4SVLp zDqn73lX(Z6_%`OSX{*i9LwdtGnvy{wiQ&Vjsqt5&Bto@}tLec(+k-quw!q)GGx@()rjceELf`W%>)*+A- zQ$xM(BdI8;NCVw;JbNIVd|Qx>X_KfGe$>z9e9WQ8b00Ql+^NMHUM5Z0kRN;`Z{8*B z0D+F4H!es>NZg0(lpYxHpk@97-qZGJDtBP9lYfY$jHEAf%E&bEM2_|n1kOTNO6E!I z!&BH3Q3el3MO}~3ZmEO-5V$C`W;^tlkL3kK?>nyN7((^Z3g;zH{~;cT3uo_hxXduY zLp{}-T&o6$dE76lG^>eQ(ORw=)nq|cdt>((ahQc4RXUK_y`E#Og)9+0$=hw52MeZqK&Hyox(kga;R+ktZa)LgHUU9 z|KFW596xNGZI0vF(`hbvK4NbnR_cBdF{D22{2h@^%tN>&4hVrdN}ki!hfeukxIp2Q z3)!Mcv5oakzEaB}o6cOAd1=GCZS`#P=a}gE{NYQ3d!}%$BY4EQAnV$TF`gxdeHsw=?@`1?F$r(f!q2hbyCW|9F}qn;nE#H zk9W?>=y$(S{y#0xM)-MA`%xyxz~d6G99%ptx}50^A;$nI+-N{`7C)>2i2uL;8B|5o z9(stj=<=GCt{xXKHP`HTIQoN}kag>Xr|i^>eNvw2r@DNk2cC-zNp-&KU}exFTvMHX zqiv+UxK85k)XS)1xdtf41n zknGUnKftCInoVrN37TBHPn`(^1bI3O#(I0VoN;h^ykLqMvT~%Dh@J98tHO+(S5 z6xXHHz;1wxtz;fN6?lFzx3DO|jV|xF)*y!{Pgl3QRpnA z`}uhSA$&m{)_X0V?lCHCj;2`e^Wzoe96z+r9V)5T`6GYNlhbT5BC)9^MZ35R3i%aP zH<$wbi#IysPi;FVM_Q6#?9L2%lmu9CQ#YxwUhFH-+_2B?)yySQQpG?J5&Apd91~Xh zp<9QOc*cDV<|T?JVV!20`ahw_b>d(cKuG*v%n8l7Fcpz4xj(~aK^B^oX0ax4W0{QFiy zThZP>TFao;%1ariA4?@1O}cb|hMF4B0ddyCt*JAL(Swl`0x;QCWC>GjL)l#|`LAbZ zTmfy0P;N*U*nrJo2PlS8j;hlk7`AjF&`LtW%A*Y7`)O%=NtR4AJKkNB-9kguk}S%X z%$z-YzMUbi7OERak%v-*Ons=Gm(h+Egl>YIR*=F}>~!znO$W=iXX3Ab9OF!I{`Gu9 z#xr;ND*4)Ktk;DMj9YN&>ecdw#C(zChJ0cie2desdi21Y1i>Iql5rtxfNd;Auv#Es zm3Rn1nyJbZHyJ?fO=dUvHPGvnz$M{`1e^Pjk&!ntN?dUakiP<7T@so)a9PUua!I|2 zaUSd6U0)TOTeuY(FDO}-WnxGT)|3v!A85Mo&;XB-q3n@ZFE1~r)ZXg4VO=8M(V2DO zyFtaKiOP77m6cUS^*qU`z zXrpl+E(RTh88E@9cQHZYAw=;|h?(KIpH1MoQ0J>TU zi`+w!5~49EMotmYx4R@A#%fTUp_!Znb72e2MZvJz)gIZ8=&Z~;PT z2)!X0zbMHcmD-I&x&u(--~nx+l&)M zK;6*oIwN1lAl@=GhDI5ZVp2%S?bM^=42*J{yZpVf8F^#mp^rNh`a>(I7z+xip}Q~( zfOqQz(VDTUlugsYz5}1)h=Y!wVlzU6VDbddOeaCr+&`BV}up;*&>Pgn#_+Vr-RYI#Sm>9l7KXzkkvF{!fm zC`)FEJ-zL%*310-)(J{~pHKgz%DQcY8x|+?WpS+f$XrMC28-B$lQ-5|zQP~N8fb6T zl-qYWH0JZEKN{>3oZrXPO(B%$912IuZe_bX+!I&D$=VZt6GNav#7}O#srw$l5R{7A zAS;9aBUQ{7U>?^?{t=s9tXrsg$@qY2RGWdPz|j&6pC!oQIB31XF=U4V?nzamFS3N? zo7^U8H^r^f*Qg1))H2HH!1Xg~r3yg4L$@KA5m)XTt*K&WoFu#Oo4 z@@t8tAwnMTD-g&dMVJ{WK37%zhd^i@uz8b)AZgwA5y)5~3o@*ho;e?tQMyp@30$(C z-IRNe1;5Y!-J06Ef9MH&Yoq6fga$rg(HP8t!In`+)Bi+XfMs&uU}6)Uc(5+R$P_?P zK+0myxiVeIm*!gH0`XQ1;Dq&39OvP{J{SE%{5OT9Hd!rSqc`6Mz#|PAz*->voUZ9m zh*RJHy<&2l&j1B2;-go-I5)XEsii3nGGVi#u=OI+Mu-bTChu9|2*54F6mc7>K{cj> zu_-?@?{a*@qEny;W~rXu1%qgcJl9zdgeXu*6U2p6a=%LF?4_r?D0L^5?PNNs@mJ%&@GRaU7JB;t}#s^4Z}`rhS+U^B8efWbXujFwS&{ z-6OUrTVGcdu?RWYQ*(=fz0ny&=TdifH)3YuP{_o9{6=p={23Li@V!l8YXdN(A#q48 zS%NNS%w!r+W&gVIk4W+)(cu2=`6XF-{Qi~>^=Yc}{mr}TtC;_}P+@9-`vncm-P&w1 zpI5H_6tOq;`P_29*NCG5q!9~ z=-naHw?F4GXqX|KE;#_Zf|X!K=HtIilb*pZwNL>vZ+%<2S3kl#JRWK_3Vh}57&UhK-mV-HrBbE8hlJW9G!ngfExXLuKm3YkG#HVK#%cOe`>&|~RNJx~U&{Dht8Uy| z{Cns^)ANBf{YmQb`7fR|53GM@Iz8#X+^Ezb{IoWsLqY66N2o;%@X`G?~(y{y*sp3?ZX@A?H#00zlXpeL)%A(LXW{ z3t#(BJ%!?)FN4}H0R9apceWGCNK>TOF)_uybm>)>2y1kYUbkDxXX0{@X) z`1SXhECY7Qo1_ey0|d?!x!Ye*0nx04a+9t!$UCq=wl>IK+ygb0=cz4LR?F?bN|`+Q zhy!dHLeJ9z=Usjuv9qi1)R-3YcI&-XsrsD@ceFgxPCL|bCs-+LNCDBg+vNxU3uhrq zN^N``CUJDl$Rpx*9V-4gk}RKFZ}S^Gcx7`X*%&;`jzymq#nhkWkRT@TP}d(llCj`x zZ}P^dn#Xna|Dhog5q*#)-i%VDU1uYSzA2a&<-WqM&+R*%-LvmK&Ay)+l}25fXecyN zS{!If1;sdqY=F)LiBommCS8Xacj6SH7vHZB8y7l=9f364@o*SMYyrn!!j`9jS3>6@ z5Wv=>`HZO+N9uPTb65KCGs4WS(iAVxN-S`HwHSEYP9TYOXipZjb%RX51+0G2!G|NR zvkh%MQUBb*?cYp^6_MK>;pGTNMTN6N`@vj+evNSXJ&2b;or+l^8j<=>ap@ngu{)1l zNS>VT=F-Nyc5!HEApuVR>NQti!6vmD1K(^j5$qcuTH%haD=>Pu=K4F98<@s+I!clY z0~x3-f24<21`T@p`25y*B`%!&Ud<)aZmiW|7Pw(u&5WaBRMq)ntVg+3yZs1772*r)a|0Pe!6 z1evIC=N|p|{;ME}%Vz3WAbqQ9>4Uqb6*W#NX5u-bt9t-Bktf$t)DejP>c(x>ikSam zjO3-9-I(&fRNoT;g|;O_NXUQFzY#%abvAF_{Ke}c?q+qJC^hCN2w*sl@taTs@He>_ z?cXnr96vi19&YGdkr{-=KVE;&f-za;0;_jkHyfCI|GN-C7>G48HWs*iq5CA(XP32^$45Bd;V02vQSO>hwu$Y$z7;7@L(p&#qAwXn&L zzhL@Gl|-Cv*LErbom!k;b2{n{Ej)|>h3K>#_3;`cmf(^Wb~J>c2eTdSP|P-nT?%7UdCcUJfH0Wgf}+ z8GF&s!?%VHV_|0Yh;XK2_9hMGRLE$sn>6O*h0y@)f)R=Y{kix*`Jz%|a2ZcBH*5kxG9F-M>BUPMJPpe@gsO_47J ze+)+~CGoHv#-f0ftI)IGVfN3MoRcpYuaAp~pmdhQST>jPUi5fZ-ZwsBKF`!KiIhn` zH2lcQC;PJUx-tFd+3+!W0&CNkCZCHK{i(aoUr^50c;`{3gXi?M9+>l^yDn}5i}hha)4BzN&=Jf$Av}M+ zySnMq4`dHNrEK(%z(4YOhRJ-62KdC#8JxQ5Vky> zSWA&G0t5!ODbRAeiCdf|TwvoQs!k+6TK`0OzelsXCN*J##x@Fbjs6iV#BlrBhhBBz zJU=ZmP5xF&N<|CbX1Go?A<_s8UD^g#^FO|mxpL3X6*L}u$|<=59};mHkcU1uMlr%9 zO1^t{=e8PlKB}CLi82x1GtpF!T3J%$1-N`*o`&_J%#bBut#>mxB&2lUsV`hlFv3=e z>~^>bX&uTpT%1P9*L&wFp4v+hibU0+^wb2OG;6C*J7`&G?ogdWkFIFJvuH<2YQ$}t4AuqD(oVSRl+Zt=+*hhOh>3>HBbBp`EPK1F3M&rJ~4E6<8UugreUg$s` z^IGsFoSaTHeI)~1FW*)!)~y+V(mj}rJ~62R zlN-xnveI7^nU8^^*t)BKb)aIhL|o-99){%R^TO#Cl{Hhe_%ale2|MC{Kp3a&E6 zVyT2&l877{lPtoy()IEO^BYa>gZPJZ2&c;4H`b}gPsZUyyeG`!3B8p3h4~Yai%M6I zW~q5JK;@LNk=#|@ms!e9V<&Q#H@F7irr_0$pTZdBXl{rK0QdfB;i;f%Ms*L$1&~)V z;$&{VS;%W}tU<{1pERKN=S78BJn@n1ru74MN{ESxF%?1>9qFksIFOJlKpu$Z(0{ZA z7Y1UoVAA-0&3!572WVqx^n(o$&{&%#atN-biR}ygr?5`1z!D(g8UpsJAFcvhN-*b} zqM~V=b(qie0M;NTrJb8kmp}@)!4s4bf%;{e;c>K8@)$w`aUsAY*7F%%vxF&74Tl5D z-fOz=H-u|moTs3VFj`%dJr52cV>(7tX~+TK*Z7+*fH{F=VZ_TbRO~49e)rX6JMgAs(oL$a{1rM z_jTJWlCLIT&@L+po_Us2Vu4aYM_rKm;r3o$C+k%b3$ok4AC`EZzdp?V`qSg>mT3o% zHM%PG4)vkvPoSY16^{=UCSjtx>)+h@{uvrC5ioOo)ix@$6+3-)HX(1L0zu$2eN;#b zkqM6@1^|FZhQ$)!yJJV;rA#Ym6fB-(vW9$( z24CpkL=^F*RUGh z4~0J-5}M0uFX;^#bJC*#7;o7JsYoRRuiX230W+3jOjBHT9mHi7zoGk&ZvakRSK&wR z&*NoNd%G^y&gkzn!S4k2Ukq|eoQ=0l>4DUnQEc3&Z1-$Z2ZQZoSMGO-C;ibbDRDMQ zD8W;hWEMeRIu>~P%~(O(ve1E9v9{)T_L)h>$SLxXTLD8U|Vm3e#3(=&>7 zH8pXR{zCIra9dEqIg}*3XxLbdb+1RY%EIYZxN&I2uKb1#B*k@ws!GqvsVB1aAza}I z@~^DS>Ph~Zy@?*E8vN3GcJKb?fSO+Bxp*{ddAQ)jOlaz9d4kd)K>zTjn;?^M4Bax8 zxpucU0I?cdGz0A(R~L{m2_It52eUF~+;gs^9l43Yy{;WDb(k;F+Us1`Uy)k&`EvqF z3Cdh0xD&~TxL|n~i-|?mxsk0HAZ}jwCuU8>u35J(Uqb>+mgu5KtXe%n|BJ$1N%0V|1wgf|f!YpA=S#;yoP@ctX>GIw0Fxq* zLmj9~U^L3{7F3zZ1oP692{6dU8AW^!VC4o5-%@%iU(TAXe$#}yTt>87a)G*&>%cDF zO3J!a6TeMEh-u)%pj$S)hu|=op)s3{OW z0RC|o{9`qyM9_ez>%M4nD#|GPD7~KTb~)kpbyawjqK9iH6@Rtei~YA{n7#JhyLT<^ z?ZM(;>Uh(NaCR+Xqq{y_^9^pxz%rX?WxS8>F!6Jn7tkacIPG=efw1~WTh=%Z$94S= zY9yMLnkYnSl$H_WZ3Xa}4!cjEK6zz^2kk%cb^H)yz~l^tjxGtFZvr75r_U5ge8j1VCwx7!e=ja9hw7U^CjpX? zjF3)~bg0$_+Fa7G9~?mHPwgKh76K6!el5vCTkCwsp%$F?KJ9I`kKGr<3gguD*?4)d zfA|zE_i}C~{5{_LULv6<K`fjzllh&sc>mtV;QH{bK z;;EdIQkh$T%NmJC{+lK>yGUag?fTi8(-ZAYKm$LlY_%1tTI6K7-bN_2ZI4{(>h60x zUEeHGTmfj&sJ|qHOt!5laryIH)MLT?Q%TpVPDl=0B!zjC3cva-=&{p{ouod@t4 z5TNGha@bYB`gA1hXQ4ygjAv%6Erx1kXzmCJmi57PZoYMi zpd=zO1*a?#y4V8YNXeWdVYf)>S>kixH$ZvDBH7Sjj%E?s+y?f*=G4^GRt&X&7x~^D zd{3CpD^@`M77qDWX|>h{*=i>Bf^Fy4sT+pb#!BSMNR&T)dGNWchKAG4b+0~6|NXIj zqyHw0dKb%$O{22NgRkTGq{_@oU>^S%jkbF z)4}s&)-XG#hqKFlxY1p|5KJAfrt_iL=x7zC;m&|$u(qz>#pY|_NIU&Jvi5Rb9Qtth z_A7ABC&34o#_50@kLVexH>XOheR@0p+nk=O3Bkd$XjhW3p?sbxz+Dud6&L~tl!o&7 zuqcQa>91*2oW0J@#0V8N;!av42T&(kSXbPj8}C7r$sYhOhd5R8&;m_YAfDiS}32#8>|8QK~PH4;qLN$ff;A&I#RjQvF?sZhb_cO3hoK3 z$=Ssv+<)ASmu{)5QCbhS+ANv7V_<*vu;9WcQy{yK*uyx^KmQ8z9V=ydAzv%7Tq~L*B8Ut>Td6UutyYP`d zisY}w^sdWg*`FeUf=s)m;_u%VvaQ~$1k7L2W$yrC*pyXCbJ{A->mUd$`mg(nW}A_` zdy1#&qRDw_JNVZw%M4;#6BKnKbKQnFK>&~%j9K*Y4O!=!L7wNf%ca}}$$6mw+E!ne zBAF>v^#n$fz^IPuIO0pi*^toWpRYkKiY|ay(tA*>o%2TB@59Eo*7w+~pKic}#;7e< zP*A`gI8Mcs+Co&G82o9qytnO1269m>pW4jz&2lwRlG{{w0yN|YVBFE@ZSoFH!}p1F zrSe(d(LI+z=+JMK4Vn+!w%-1eTTu!>QC(VDS=kC_+HM=0hnRaLT)DwnhMJSI($Xko zWGc4X!3a&`-=KW56Dz=Jy^np&fpZ(}zo0F^X=-{woO|4=`aG`RbpF6KhV|A4qz1TA zoJ}%i;se-$B!_qJk97|e&&1(hkwVFowRT>wR%<9=?!2TG$4Z;e8!DV4t;*<&h={0bqLNg7i*w?4Lm?$OY!8HD#&l*RdXZzbC%#=oNuZ5bPTIIL zZ!hYe+r42w84*bOOZ5Rvixj7edmUCO3s}k0znq?ore!eAX~BGwqFVx^A)g`XT!6t4 zow`&np$xxmLRi?N6(|m2sYHxQ6NVwru?+{VPN7${<>sCGwTV1}H1rQ-_r{oBRBbGl zq-p4nNc+$DyD&eKPp3w7?CqaGw6lm32rtatGYAu>L0wuJirBk-Q#S+FW(?CVeLfjGtk}WZrZfT zFypJvn&9@yDeLwXy?(7FC7>-OP+}UOFsrvFd#F0w9D8R-VhPEalaWV#0VD|3)hbb1 zd-U`+hyY9wUtDcqt0tN7h!mw!t+c5^B8Rr*B~O+gROgaQ8Y_I z0PhIUx}*~m;Vl?rD;Ec`TdXP8vG`#$K>&VJodu>=bbRz5Ro43;Xzj-DKd-1|dW=@F zw!^J5UBNQ#)%Ooa8AwAdY?stat|-)ZZ;wWt<)im`X%Kkkq0ktrMwJ6^@QrV(YC1y2 z1|zdm#tWj@3-d=IsO|A>d{b7c1C6ShCP7sq3)nxzMa(-Akd2vF3`bSV@g2HHS8ig< z+kWvdOz^aIU3(k?B~z>@e#x*k^)-x z-WC*#y@soWXtE2G(syWcZ#!t5M$9i~3aBNB|l*9N?qenSaxa%v2c$BB5uF@pm<`f^?TRF>ck+0-w8MN_M- zW=76luD?1s1lTx>G=E zXl*Qq%s+x&l$9~Z>#M_+=*yQlyP&O%-Fd2hM%Q#&>mC#Dr>5$mF~DUcV*y|+Ds&^| z4B^Y2sK#ZyQc6nd%fbqtkNZ8WaTQ^r-ki#v&A^85D)xT}D4ERwtP(DJ;SMSTdEY(G5YauGZb16y8WVzz;8@y^nr9p%6$W2(A!F>6C+Q*NlLSBmf z`E}E zvb9$W-Fc|Z&vQO^v3nRY{~di*RtxP}E#xRS{b#*X#-UJ&{MF}Bytl4e%LTixqVOk$0~ zfdN-Z(gssQ&X!Dwn{Z}R#&M1t8eS{>tW!6%e?PkNl#KeUM9$Uus~x)(n%mpj8n~8k z?OLw3yYWp2UtbLJ4WDeU9<&?n?Vy``wtZCA8Ci+klci^uC=-CC%h6AwQ;L75w}KBG zGdLl#t^;#Mt~wleaTV3+`5Dip;a)|rj>|`Lo2Lc}ek?KZ@V}?(C-1SqJ%{=nWiHMl zJNaox>w5l! zby`J&w{ZTmWjDh^k%1Wu2D8t}IHv@7BqSt2zY>0DL&vk-6jPqW#CRqgkXUcA^RBV1Hq65^k%g?f6r z`We)70G9&t@{}5?+c{XqvmaI#ivBZK){F5FXz$&!W!wy2PL>QOWX}~`S%Mp?w<%}i zN*z1Sj0FI%plQ!rZHGH9h>flZ&$m=o`$F6JCJ1}0h60%lqJ+EHf zaQMV6{R8pKgxnhDN2i3|xKaM;Qww2rfsk0QLdg>DfB7-LcH7i*o5}YI=VSST%C?_B z%TT2)XOqSVEq=X<3Zd~HS7uK;kE@}9`FT}9YpBcc^CkQx0v(jSi$g5G z>|1V>Rip0)g%|&H<_eNHhbyJKr$?Uo<^XaaKlmq}(h-y&h?*-w7UXiaJc(VQPHBc4 zE2z3{p6w9!>E1$tNah1LfKe$cDXAZQ112U5;@9PQ;(Ith&$&Q93y>YRNS|=f_3P%# zM=(Gzaa6z){S23~z75JKPI$mky4lO;MHASZnmrUxEg`4nM;Skk%8m`q#&AE?GBtr{ z13u_tD&AXqMIP+3S{=IBr1GQs?y<9KfeTsWN3 zKUVy<)wk#pk#$8PN|gxn2(@R$W2mU0BF4Ac|4`Sr@mqznI%J>I^<;ceD|nv{V-8PT z1FW1S?F^lMDG(bD8IIpa9~hKr03AzZB}C}QVB&EU&F;Fh;@+J*4KMw<&7OCX;Sy?g zM5iLT7Bgy{^69aAT*1w%^Upuc%&U0SbyR}l3HO&>j0>*_d6VS3VFTkUL4ayEn4J9D zSv~Yh*n}4>pLT+cC>dl#Jzlqv3|4`^ow7TwJI~m@Y(e5ipA#S8Y9gOs|BKmKk*IFq zFp_BRPvzybbf%E_~%< zn+wk|1{$zKFN{u_;e(;0L|F?0up0*LFo&TZKMs?S8YG@qx!ZTxXv;|aKxNxn-R}kG z@%tDpz-2WR(7Tmn)GR8}M0Sfh4FjPgt*M{H9nf}lCqHzxdfPFb*28af&sylcN7qBr zXWRXoCl|}o`-No*m+gt4MYCWb1`rM21bv&^&cmQj(?Pq$VPiYmT=Y_Meyf>>i z(@h}(P-|*d_wwY_RI;~Yux};Q6od^CLk)PPDK!G!`DRMP%y!G1+Cg=?^5dqaCIG~l zqi+ME>2Vd6GFeh1y!9aX%R#b0HrukG(&~SN(Ba^&q%_xvp`SS_B4pNoKu4xEFH`Ne`tcO<_G_^3DlS0KT_Wn7CUg3#oYp)Fd39-gRns9Nl* zGXbgxMq}r-CRUks{x9%!&woF=9O$U@jErzJDas|0-FYuf2DId`M^S0wKZs9R^}U}) z^ClyhB-`eAnAS)5zCpF&qLa*((M(S;4<(jcoDE;z*6hY{o*s%ohIh!z-cNs6^|l;e zrd_~n0fDZJWF`k16jN5`MVl$t>26bQX=-U91Lo+Hr}_oL(^A=A?nhTBD?tKX@q2dG zuX773G5P@q7b12tzWG&ArCXbEycG*Reb8;>QY+>i8;DH@oj<|w2trB&qamwX)!vDi z1GYBOXkDIVWqo$m3`CcC(7Ess!^CFamhSXa~lUsbaAn>X?>zA z{|g+gMf$@9+Z0!R??=Mx5N1_C*iU%v6?aWRT#!2(teOn z9O;61$KT}?6oAe@6Z0vtjyW-)^LX)5BiI}+_@7Q zC9@XVs!Mu7U$QmogWGWpf0eq9#n!1V>JQKPEb7^C71&z;O-bbFY-*jNCY`w9qryDi z3|L5Lg(i>Ofm`u#dqq)%v-}}*rRs05ewGB_bU>xtp$X!y=r2NeN z9Fv^9ClKiCBHfy{=4LH%{zNy=Ww2>IIsC4K>`J7wgRrL*14H95N!GRJNdyKhtv{wh zS{};#(2CGO!bD5m$WYw`*I@+t(QKJ*-Jf1VxN&htlE6324v=0c>d3{(5i6E+_@3Ky z`^QM`sj0tqS}vxj_2*9=l;-gf;TV39Uf@yo#VMn2DD*a(oYLvWt1PDbtUL7Pie1M% z1Q|DKYFHVPTD`tyo-Vzczz`mHy7CK`?s$kP$BetwT!fKT#!xsNNnvhF4SrVS;qy}oUYbKCIR1l9S##UaP2zvyccnDVsx^wu0KYDND&sW`1 zSynbBUGwMU>p=CLMq>U8s=w!29W1~A8)@4C5``#{{JazH`+7HA|2yP{4|0qcry%PKR{>MI*y6@QVG{<>9X-(xd%0w^ z2LAWX;Cz8&^JoUo+>E=T;ex`s=LX7D`%YjLnp>K6wMSP}v_5ZhfNWLKu_+~SUz&3m)` zh(sRxE~zx9EBG=-4Yyw2Ht(`S{Tr*J^$M{n3opC5_a6>WsL+EVuv{hU{(Xy_%32I8 zjGhsdd)^d!W^%ev5QYBo4F{rL9T?wnZEw&Y04XM!Yw`X(d=T^Nrd^Zg>$337^cfNhV&lmQ#S}4r|qsi!i%D$3rhE|L`pt1^@zuPcG z94e@U4Vng_IY57i(BI6uKghZ#-#5_$5MANeqZlUS93ELoqApP%2qmVi=bWi_7NnwI zs}q`GeH(FJ;K^NtW0LIS)-E*+QGgv7Iu;#(Plt=bu4~VjO$JQD>}g6ls;YYcT*-@z zqaC?9zd0l&e-^IRAX*pZF=9};4T*_F-~?iL5)z!` zxg{2WC(zSdci#%C<%6L=*d4RDJyc#76hwBb^2@$`ewas?pwqdGTmlT%2K20`ub zT>l2gIi8hk*+4d0)%ZVatHk(t_KL|@S636qj_(crCOu@S_R6B2qw+ro%S^k}q8z*Mo6Jl`_ zuy&$zj+m(nZxhhLe+t*~CeW?wab^0n^X8cx+YqN)yI3AUHvb%Z%D*;VfHg(xUu2P6 zwV@3gg+8n0*ZNyCzoeUoz5K zZS?Z1>>G@kkz62w;q}Sx%1{GRNisY#;DgwHeuX7LBfPPL)}v0OC+ab5mg(su4cY-G z<8IJNJ9hZuY9x0&b~H+X*l!%y&H|f5GT~|zK7Od{{|nS4j?`X@QGi4cgGMWG6BAiN zUq-j;B$B$pQIlcBkST%scx}Krqm4=dhE6lBg7G-%1@umoIq^Cr&cH6FqjJXi1x?z~ z%^VDz&yVB(ew|7iuq4~0&uYuRt(<70rx!vdm7oNytTCV|M=L%)eiXIBzQIxf>{BIW zG9FPk$xt8GVxS7zI-~5y18!YW^Y1~B8|VNEv*6pDkWdDE5Z4js{{Y^DK7(=z-Ui); zrLx9P&g!^qxpj#wmFU+U3mm~AJL0I=5l90C;BJ$fewjXlu_3S1&I2j~OyHTFYPTUY!(J$`cBpnGVWW>s;(ts z_DFyw`}7_0X40?%5O6K&UT5{`;Bx3fuR7&Fm-#z1yvfBOb`pxX<*|3}OkKZTA`oCV zx;x+vw(^sXmh?8R!MPM;E`N>N$!F+h=fIGkt=cB(5a0J-1?oFb92Dfvci;j`Mbp@O z2g3bVMrLIjD9n3*_MOjVoTV$LoG}gE+&}yb!_L78O+e|LX#X`SDJji#<8{=u)Lf!^ z*W5NIxyr8LnYo#l`JZ(I{{GXzj4u_Kv=}*LkV0qy$3?(%*~gEI*}Ae{;lW5%RcQS2 z1Hy9Wm#$H=PJmbz8(o5dq~BOZBj~j5B(PV|Yn1jjUY*fi_Ts%|OS8*x4*tj673Pui zh&?k!8R;mW$L>~`t=b|(@ACBDlNuU>&+=}7KxCGXpX~LcjW0~^iF{Jzlm~$npxA#` z_u>9{H(wyT4kHuP*^<@) zKU-Rkfk45G_6I1UdU~8hJPmUb1eSb2nzl15;Ujs{{8cY0wr&q_4Dw*zTQ5DW-ic>Q5<}fxfT#IzYehGCCK?8~{*HQ#0d`mq2rgjtpp)NI)%Je#$RK z3;t0can<3smzYCI*w~VzAKyP^f#@3;%$&mQ2nfgm`AAOf%$+BUgDgL~g5I8tpbl1E zsq7a%gGt5igWu*mqUdZvSv_>~#v03DXg^{Qj8o{pkA9zrDWE8#_D2sQ5NA-B;5eOd z@g)C?y1^go2y)?GrnrOTkmPtLU5RNeV$~3=mbY31x=1OZi}E7l0by&ww8R`pa%Spc zks0Zj0eUAy-<_9>v%h|J_FXbbl5!e@JRYKZVV5*2_O3^V28zu7D8oMIaNux5RWo|- zh+*}jqysvao2TR91|Y8N!-oZI+*K81jZJftruWBG-DC_zk4Qq@AtYP>sp6;aEJx1L z+L}g6jDlTgN{~_JWdgy)w7I({`-8(UC^Sep+FYr=5%(#jXm|G_migGS4EkvgJFsha z(0egYMh9@MM0Q<=!xVLv=@Q0>Q1Kf_8@J(+*MW;FenZfe_n2uW1c@fY#L+t)NXQcV z@~00yC;b>7eymqyJRc7iw_$$cpVtlYiE|J8Qh9Z0+2~O;bJ}^-lt9Q9OG~G;c;;Li zmb?~YzC0o>!WT3}p*8*}%eV$#v;k>XJifjzEJV6B?mHPz+r`W(P>e(*Ak~=pD5LKE z_oX(&FTa_77|YMW!2x$OwDxM+z+-c26&rKK2xg4-E_N}MKk|T?s!c)NQA6SL}DjeDCZ(BhF zf@d9zuvrPInJm8m+*2CYSH5Z%yq1IY81s-~+B^zgGGFNp8aym4(~ zs;Hc30-m9af(ke`O4J7!=(d#ctXPN^aL<4>VtX)8&4ZoK?8Cbm8P&~FYR7OkM|}>S zn86Z%0>Qyk!z;67lVrO+G#7y@AmbI4wxlJ-d4OO8@m@vLhwgv^nvn*J0PRf-c^5k$KRkc+PDGbZ#rpt^F32-a)}U@08bX-e|+D zdYYd@x&G}nS<~VTL!O@WKv6d(Q?eecO(azS2KcR$Qpb_xAB$riwYVc@al2J3BCm^B zLZ~zx0m>D0^kU=*g_cvk9$Ew>XF3f2?M!P!F)PBpu%THE{!w%Dd*EQ_q=Jj+M|vL3 z2j6G=eEh_}vuTlQ8$kBxFT9@rc;!Ol^H8Z#kWS$0xXnnduK;(X(V@d)OA1aYQRETJ zv&k^|)px2QUJHne-FxgIU^LqU9bV1U>#APM$3t$4B=KgHffrp8aJp%au$N*%0QH#P zTZQ)qrLmjqx4CtKomU<4tBoJ~YRC)HL3&z%0EY)Wt}9VBko5$Kdn>2iW92A9(heeC zGhdniHcMg3SKBI~zK!?>(;Ct2#9x0hp|y@KI(7-A;PjqAzu(>ON;batsEB6NfQ&&s z^^OX(+1F9ni8ufHnrj!Z z0%IL0JOQVk15HMh*YZ<5_4g5it`EicQtlkuWPI!0cRz2#nY?>lWEj-fxCT-(C8dar-xDs! z9j$f4ZFOCj15QQP^`r9(9M(NJk3tc|@8MMew=dCI z@&AX?aEq7sYDGc=fj-;}r*Tt9?7=uskI1JP}% zfw7S~>tnFmEUc^={xpvKJ*8N5Bk#kB@WNjs8`gAYuIYZ+vgLK2%oK9gX?_~?ffGXe z$Fs#+Qp&ud{5~&-hCuj{bBktfJme9!jIetgl$IqH_5X;-;3P5EDdnH-noq&)m~Q6iA4iGG&>XADYkB+6Je7({jue2D`5~;g%fGw(LbvBUwYt+i6V87}V?-I{ zlu(l0Mra)bEi^zd*x~lyXYdwK>=;IApP_RA;2u3k5VS*)1E41=fo4u{{oLjhJ9uyz z5OR6z%W7v=ELetipBU=N09XYj%=_9qfN23& zqLo&toqg>jy)k&WP9SpUi#8`3d(vO&KBVUge{v;h=jfA@RRr#bR2_V{DgE*gfCp-x zs+v+E*?)#gk&}N8lX?{yCzr)BsDTYXyFNmva4kcoKqG5Guo^`S)HK#hNdKhYK*kG3 zN*{$E_qw|DpYFD&vnHL3Gd=x1Rkw-g;8Xqhqj6Vn2z+*(oo zevw2Fqx1EQR*IoQ>*!Gj`Mk53Q5$d2=i=-vFzKAICRYYUR|{SIZ`{3kIF)VNKD<;k z(O@V^QY|DZN|Gr}rjVhe5{hJ~P^JvE8tz6@l*$lAWTt^4Q8G1|he$$YCPj+y+mEID zdEa;2zTdZf-}l#V`)v2KJr&lv)^(ldaUREh?E8Kky)E_+dJhRj6;)OFPaH2fg35^4 zzLZ}(bK?>4P=bEVSd4ZT;<((~==Balezi}=Y)YRiON{-?1z6-M2t2QGq@huZ7}$J+ zrhnB1M@r4P|4UpD@jt}{QW4aE06NC|-^2wohiM)FYkPHRsT!DlELl{_c8ZFe|!>_;klsG;TokjT#5x(?*1 z2@;HPqZkGI8I9`@3L6RA1Q{crafo0 zGY4Jb*)`W!3D4++dDjpOH)}fcKG*ugSoBrc z9p_!Xw9U==^$(IcXE^B;s`R*K(F{>?9PT+g1ZESuJs<&W@1={;cn$SUY)P$@jy}u8pSj;LN^*ULzSC#fH0vP}mQLzvp*%uCZ1{umfmv zBfT$ak05X*DH1AcqVa6{n2sshSg>+%c?pM!#KdGHG!e*P4JB3?Ty;PsRo?4TwS%s( zuz!@6mz66D&##*?dyflgIb0iXCSQ`(5sKFJK=yw|@BWQtXpOGfXQ`nvIm+^X1HA`< zsFrupVlv1W;2%6nPsb?4jYt2t;QPE_B&4V@z~ko&8AlR}S&exOR8)LoHr?$i2Bb+}zs$Rc_x#viJaa~%I_P44 z3)cfyL`t?1u>fckP(S|h@!?oY;KO~7#FCHRuEEdRI?ZLSyUFwECO%44WI|RnuYw$$_D0#bcLJ#ULha2 zg6Qa1$g#_2ZA46f1L(5pX*~bW&+tNj3qBnkrMc?hKwpEpk-9)NKr|2y zREwy}_|$-Oql>NrRSG0%m=y5}iFahA8c{Vsh-}+8jgdzN&FtdGf;WmmdWl>ofT=o) zQ?$yjEYTp7Q*cTi7kYYn{^iL-y{NlzxV8{L|5tr&hUSd_dR~RKo^%O*myq~g+#KdN z;XK+``Vg)3?DQIq#0sIQVmjNWGB*7O;}%~x{_%w^v1ebu2LG0R4Z3EsGoAO7k~7G( zGI(!HVFd2^myXI9|DCX7*tFw|jxU#3HM27;j)PHNRbC#5<_a4Q1P8UrnOlu_{3a5t z2>9NKMr*fl5M^+iETxQV) z$Q0U!Q0#V;wP$-Q!B7L6P_s>SmjAJ)+kJbcq2MqAv0YJpBYp-VA}vTk<~~S_4Z!|G z`bCU&s2dT=9zGMyNKaperWHclh48D$-=)UILd9?2g4-=86;yQBuQq=OWW##%dNzts zx@un-97*gM>HyS$4KC2Dj3{ZvgtaK4c{gCmoSvRODLUjAm}=Ml-Y4&C%n7qyEb1(sWlpogBMT zq^5ti$`S2;OS_O5YS_*;FE!L{wA}alzTx-U^*{_!Sx4=@kP2#nSZ{^9R#hQ?RK=i4 zt(>k%z1-~V4(0d$wp+GvHu@iak@LHBRqS|kau4ub@DVSS2$rKVWZG_4afk&I6khLE zTPCokM!}#bs($vOM+WQ|7$64+UvNAg+CERk*|COHarKV;{QNc$rfKPMGU7_moq;5? zM=&?i<~bp{qLKhY2*j$YI^f2yGmFkCv?ytE!RIe;&}qqRyK~v_=!Pu}K?ekvql)3K zg$%AO?2xMV0!1hccv=6PmfcT;D?WH2)q-=t)=8`&U$0;{EmU6v)p3L{!V^wbq!??aake#fE^J-y!^#$=~17fkq9# z1(92q-)XrfM}^Ohsa~@gVKAK!9MsU6^7qF!%(=Ca83`l%%8qP|F5c*5kjY zhBev~@TE3d%70%8h`+6G_52$);|Gpl5Vi;0Um}_T&9+2B?~3o~|NY)0HPJ{AB=o(I zB?%+Uf?HOCyL#d8%fcB#g+yZL{WlmOrSnAizyiR%Q}-E%;bwzaP!8ijH2u5i+SDTl zxWt!@2Tk>tNG$OjSLs&t;Hb1Z3!><52a6YX*uh`)2#N7rHboX2fL|0s(*X9plM z_KRtPq>DGIyUiH6h@KLWLKb>`BUV2t>qrF(UN*aDKn5+zjcKS?Ni~koLJeN(@X*Zn zYZdLnjymW76Z;v!#|~slw75ypdM`EAv2+BLjUIKqfNhoB=)ps0%sQ}(Jj~Jc`_kv$ z#Y zs*ssGr1T!Yn$B-i3doS&N=!Fots*d5K^!IS8zF8a*X409u!kHRutOC(2LOaaN>keB?DO zM_4WBZ_|)bpl2YY>yaTS4J8;s+eIH90TBEpkS03Tw;t3kx@rgS&OMLmy#2g47Pmy=lxsG z(PQ}Zh^!u}v2jod(+Ed;r31Utx-s9Z9C{(x?k&%gIixd%5rCE1U&!62;02fVVCVZ& zJ^&i%m4AJ|f4FfBLr785Maav;jg)u*%yuM2?QVO(hKLT@()tXXR{({>kWkwOkt7*l z#Pu_qajSf8+J>-qajTqFNDQ%(GCq22#TFU4R1#QOR~L_^VNMOXs``3+BW(t2b0?1# z*R2Mpw)&dimABkc7N?y_M-9bMNH|y^O~)RrH$z&l1xD;hI#}0*M}nder>JK9JETPZCtsM3&I5U75%1oz{+36FB=&E6 zD7tTgy+kTs1$%)qxV&Ifb^D!W)`E#o;P2q5gkD%Fwd$DS6Byj$N=jMg3C5y(%O-jV zB1RKGZYi5rd91FxZ0nq;EniP~Qc)q1FU37`uY;koyQ=iW)#c|PxX z+BX?MCeP(`*{<49D3kjW&4G*U<~tnWWM}X9Z0Mbjyzc6s5B3QXK1J?Cgf&c?G=a>b zsCy3h0YzB7EqltKyv$ zLyuR{t&ZYNPP*$Utuaf9TggAo7UVL#zEk#`pebw+2zcj?+`q3k$~WVYy9V4Q zMt)aLJdTq5?$+yePzc2OdZGzk319-wfZNN9n|~kzI-|rOQ+jmXs)3j!;qT?p3H(BIn1Ak~NlY5i9Ew*d<Fn?3967PR@P+N-x@EChS$oi2#?URkleUpyKeE9< zA9v-b!qKSbn4WrJP6XM3lJUBfaiZY&?|H~Gv?O*0h;*H~jpI5H1Je2*A5(^E7lKXF zLWqU`*$*69o@Ty{TxtU2GPr-?bg>x_{$rE#3@yCgkB1RWUD(+I5wdAKRda9xT$#XV z$!q3R>C@LTm*w(l1jL!jUVi`^dVlcRx2Rx4FY*D|% z7@3M?w{5I{8*!4!PDmSu^J=Eukh+(ysWDxmg%)IkZN?xDGXk@i?O<5HNzI|v4sHl^ zv1zEBEbF7T^cYQlTedaH#uUsx);;(vT`HM}-o@C%sZxD3AOtjljf)v-SJBatKP+_l z&*CM0i8UwrBi2I9LM#TDN(+~DTtfuD0!lAdaX<2ld~2Y&*K)q6ZPSGv4EJPfXo%$Ppth3bWl4Gf`&Jbf1r0S>J1Re9)TCX?5c5!M-`vX z_i_G55PmIH4stMx;d_k4z)E_~qN~opGsprfupcI2J7A^L5IIcaCXv4F$TTDId>7qs4BD{_E9X$N28M^br;oZ2 ziRvDGmg+wTc5O;tRqyuG$sD#56%?>2nZq#;jDeE5R>b;!Ja=!3bcx1syiQihxpZ zja$zLC%wsxoB1-_Kx05wocA`BE8*T^OE_)CjLf~()zwvd+80|I$bH(n*}Qjw>wumN zzAsXp0QmlzG!CQSU9@f5{$@wTaG*=Z8X6jjI|Rg^wF#I2P(Vg1A6t9~%_KMyY%P)v zFKoF|*n(cL#)QDGm#QtJCn)*?tw ziupD6wwfsLK-_x|SrPIUL-g4pO7HeNJ4sSf63Yz(tZ?UC3$-!NqfXLk&kE+{-{OwsGUfyTi9x3WMd`xJ3_)*Gl3S5a^IN zJovQ&ue_`dqei&1q8O5A*maT&2RJBgXg9G}I`;h;)Jb4S5WQ7x8tlx$U%`oxPQ{zx zgrv2sQ{&fbxLtIXJ}4Uq5>y1`&O#e!#|{Df0LS>-8tNlMLq}e`LXK)f#xD#p%K2gGw~f8JLiWn}V~OxFzxr zq__UHZ9@e?gh#b7n`{p5apHPb91FL4^Vd3wIugT$dk5v@-jXv2^JO0zG6Y z@5Ng7kFfHZ^bZ^m!}TkSUw;9mkEplMdO``T0BSi}v9zo(?+Wr2)F?ZR=a|PbM2*Tk zCSF*mp@YlHRlp&RpD_}hm8vxDTp8?zXhJ8l&{Es%Czx`Ah=*NZinJ+|cLJkJt&_*X zYWx$mL_c}53-_8`k_xIr-9=hG#CeXDDcFfLj1Ps<06EOTjzT_q62oUL>`~Eq05@&P zr&KoCDWC`SFeC=|&>p!|LeS^V#9z|w58sz~XW(YEV-A?Lx)Q_c0sz0`$|6~o48CdX znpX58Y9v(*`$N5j`{7iVet1vfFJXTieB@88yC3m)SrKTpU>acUO+C+EI{}k|50Ef` zCYexuKco5c;4t9bvf2LiIVZ<1T&jj5ovVfp3F5Z}4q+Bub?I{rj7_OtiqRgobX#UE?0VYg5tXz-=Lp zzZ<{LbG7L+{0dEAb&C8FgjuNbWw_4e8yFT~9nYBDV1idyPr#`?d4{k)`w1A*Y{j88*Vh8aj$oRnF$>Hi$(ysIAYZ)UbHzGL z?_igS0{=88=|fA-=PimTxgNlG8Mg;YKgfulnpI$skX{$lSzLI!|6PV|BemKs&Z0j( z0G3YdfxdMWp!Z?|kpI0cdvVK1SSukbz_541_CerGS$U!{rGq+xoGh zHd-sl84kwZNZ0wjNST1Vy}!J}y{ z2-z@3rh^w@5s!*yWq|!Pjh_)L{)xeqYJb^OOCwzapH0ekw3xG@5Ks^_bHACFi=snT2NtU%C`evREof*YvpqXxJF znB#Y88{m9;m^h|gq4s68ylnjuhtjJ@P_1qp`_cq%t_T(jw0!Kau0X6t3|qjae>jeB z&3h}SOOX(QQVPr#!f>3#ZYdj_r@4w9TZ>F^(89~f5Qz2oYoHy6>j;7&3vSy__6HGd zLBW6%5BmW+BChrKpKoxLx%rfB&AK1hRSp^obS@ z&{O0Xt{M{=RyR;*K;=ijE6Dz=0kd())9<2zniXYr74PK@PzdJ3zicxwtc!*bLQC+4 z*M^b%xai$GQmEiV`ix5^Y?sfPau4(-_Np&dKb7Uax01+g$g5F1%K7=Qw2E3rHo;Hp;A z>}P%>09gnocsk=P08eDKMEaM#IpK#-O;@8i1@;vnVPmCWS$HU@VDVD5lP~T| zQ^o~hXf+wC5v$n<&Pl|6oGjuln%r0hxdo=u*(2q}0bGHuU;jTVy*7{^MwnhJ%MvYX zGNa8#0R}NHdu}Po?MkohT5{z=&wm3g@AU%R$=%Eg3O!a9t{+w53|t9hF^v}rQK7}5 zUoiC|gK-nw9L!lR?-69EZr~W=qcQaZ82dT)u)8n$)-ya5^28hY*+@Qm003#EB7~R0 zg)QxC(flCOO>9ID3?Th7;PHrTIH6e?s!BPr=F@@2Q2&xK6UkCvcuqme*N1IQ+S=^w zy}<|GFPQ8Uvg*JkNO&}qjl5Cg;4swE=42F2LgWSA!s`}8^6btQ37G5?SkMdSWzdp* z$cbkGSk#>nUV)~N+ej?_0<#l58GL@DTXiWnFW z6voX6=)q3V3uR2nHRF$5??he?Gzy6L>7bDK-y8g}b!B!m>}j{Ic(R#9l>n7M4ncFI zA7&jz7|n=igN^Q<;{>io5EGlTFdZouQLc~y^<<@E%4$wl7+R{&t+ai?%)h7C^M`6EO$p?nA%F*g;B&J?N-fDT z8>xYo(8K2=G(u)y9v2a4hrkr)WrLRORt|a3F7wN?QA@oQnLjX$!5%PGh@y+#svdOs zS<9tg8=@zGoIS=u#>P#Uc?xPh&}z0IfY6{+XXk<@`VV| zB>WAW=NtpyIcM=v^Rm^BNDF&2Mo{>aIh^=0e*vb1za#lpSJL|qiZ_2!*>-V&%jbRr$iwZ7Vz*?=57*Sw{dP!_4)cp(oy z4_FCZezMVRX_#eZU_9sUW9Ql4raXC(;sYIYe99>Lf%tqYCt6rD1+qs`}n#FTY_#O zN=EKHvVT96ST*(`&7Pg#(ES8{f7s3Ev6FPzt!9-u@2s{TDp`IO56G)9_Z5Q|Z?4~Gt| zM5qOl?F7%T3%X2`dR}nDwSvSkH$l8R#d;#+({?-$8nmgzMTMJ4)PL~C;myNBcm(+> z^`Bl|+0InJAV~=@EDh-rY;R9=I56x%{)8z$E&r{;-22kx#klaquO?QZ40+s>1vZHPD;GTweQ^K&=c7;B z-iE7F0{vw6w14*RblTqx5?Zirh;(y^`iCQK$g5{fag9{>Zoaa0;dOcEgE;+GMY^sW}pgWo315s&;Y|D zbYUCa1NtE{oxo_a)VXtch0r%Qq+9Li5zXuGMbAukcw+Fg)g^gNj&+TVI{SCcncQF- z<52IbzqGP<8Ly6MS&7ESGZjGxc8!SatdOhv@IhEf^+g-9^qH!^)X)%~e>4KF$rM(I z@rh9+t7CFjdwh5~_jA8hh`9e@A@l+7OJ6RO^X)&J1-vel zmQDT69ceOph1!QK0nqSwHW@*B|49}6wkyoN?oIzVjl6?8!XBq(CUh429SqAPjz0Xf z07r;KyHx~Qy9^~<4%QcqE=J|;Wvj4TIB~-f85-6(j4t@xM#x?XMa_7bwRquTORjVU*;H508G)ri&u4=b;8Oj6cPkp_!q#q4|S3 z0Xh0vPX9We1-&hT+vO5XMnc65+$PJzZz{uPE+C~xleT-p_i_8Xb0>$js*XNn* zkR07BK++nYmF4*+JCcK;DNwd$^6<|hE*U3%R*z+>>ObBYGt*BGgT3((%5~C&gvuYL zSE1<>G)EZzj=x!#`F;W@0yqCI^vfGpU%wmTMQgGw+L|@UE6r1Y^WQ06z1j2_!#(q`EJEf z%Up^%;&95w$?n6!MrZor#zyBmhN{TEeUV@LxwRE5^w@UGW5f-heXXupJ9h1| zb>n7OZf3I(P$=MR!ahZpf3kPDQ7$M9;P$O=b^>z%tv4-A5b%CE0Vauyo0oo9l2ys3 zA&Q>aGME1l2u*AVU#hpUY9U#iC&Q?vDK21-V2J6B`EoD7yb@Og5 zy@9M1uDc2``O`TVse6FgjgwLsQ`w(M)Hf~iQ!a8889_ow@|seW-UU!k5?UeREC3H& z-~AJ^dm6lh5{5s5gTc2OumJ&bqYvig7O~4@%}pnAA~-p>&Azyq#^5ecbk-#C0~3|E ziOD_K(%Hf%5uHi-SYbAdfQ~j8iCeMZ5b=oujr&;33~^=r66(hL5%~;tHuR_# zVzJ@*bG&&wT%dwbfesx82LYXA)NeJYx=4DGkeK);=4#$+SW#n_69Yr@G4MDvIDu!V z@v<-SMy`pzBXyu@(%AWK&jnK^^<(@?JJBpkTIa`t=7<^ftP(R(-?HtKQH4(+;1MlI zk_W+m6|2_JON0eE5cE3ygLf_?p{KG4^!1~waJp(ij)9_`9?0f5*!LJ-b7%Urbku!N zt@SAIy#V(ISe}Ml4B%=S2mw?3z^j0OHF4kd;T3}70ku7IHmy$&8ytiLNmW(IpLMPFz; zeHu7^zq1|#Rc_CSasbj=FdCRLvx(RVOJ>aJevgE(U@Ciag#KqIdFyZ*^&b>GXS$;1 z-dz5Ecjc*ilNbynIorWB!08KkyyYy_p1BH_H!ntgh5tI*+5}xr@#lrg*#@Fq)=*AP zO%0%dq)Z5is32#97A&Xn6f zt=Ph5BB}BA|NL1n?HwXWjBVWl$whb#;O0P#NWjf`W&>y`5aT3ZiU$r@G|zf&$f8QC z12qKbN3%!}Sbk_xFj3|+ZpI>^fQ$y8{tz4f7v;bSKtKnek$3OjJv@})1TdWlAF%?* zV7Y)-pyhxwuM?hyBt+Jg96qKXyc<*&NX`cyiv+cenhIgMZ!^qzu_EqPYS}Sb*zj)d zQgGvXdU{OCp3tJu)WVS#gL3Ow?G|1P8XQ0_X%DL$Nu#qXmM$$N#~p0^c*LdB5ulOm zl^z{!C0qfD{YT`|*oC|Ha1>dv_kZ)%XLR17g$*&Lcd%~bG%$Cnw)m%V(r7YomTYM}7ayY^b+ zB_de5@K-FkP#^~)4Yy=vA+VPXZ@#NSdZn&Fpu8%5s3h~F)a z3j0sunON_;E!PbG93r&7zJ(h<6N(^xu)hJ($@1`u4Xq2p*DCK*Q8;aBRbg}Bw`z~Q zYjUKv)K}-9o!#9khs&P3?zPoH&;J5O_XLVw)g%7$JC@n%`w zZOX~*QN5P7gDpZc&f_W;!FB)$2Nvj_AJLgW;L!5F7^L0%oY8zxu;6UC4@86q^=%fY ziC3Dh67cUVU8=TZ$s7X=5HZH5iw-hB_as`;8-jjt`HMlES-?5fP+xymsAL(|Dr($g zi6z?qtAXlTf*WN>wA$skxa1^}XggO%%v&blBF2GzV_$7XvF zYH$n!z`Mnfvwe<$Ks$;poVPc5+Vxud+{r93{`3yCgn4;6(4s)5hI|_TAwotA52|Wc|r!n8ds0-PCOeV_4_?fWiIU4`E-9 zzjDif2i{f^6N(YbB#UJKprgl#LvP;<5mR!H7aB$6<6T!*w}1W20{}n3H7s)9JfF;# zs~j*m=bTyycY#_2@Mek75A@Z2;wB%vgA%6= zBhxN;oBpc0A2!hLGsZFPF7`J1{Ls5sSpg$_MbXw0z!{IZ@G^t zjtjVVZd@1sZqeWmcV(I;6N?(o*9a$(%q?656i$y1|r2D;sH^HgbD#5 z1AHdFST|8mke&F<7ZQSff9R*pQIy4@lQIHt`Yh0#${hp-p*sQO(}{fLYJGRQtQbzM zV03nIB7NUA(}JybU`;JV0fsExA!j5Vq4+~e#8IqTcwfcg=IuM!={Gm0aeYS`a;T{~ zt6YKTfI@Hzu}T>jCYB8}1WWm+-8F4mh?C+RM%*L4T4P(zRPE*P5u{^D<~Fcf3~bJ+ zIKS|O!l!OTf6@(smgNE;IBaaUFh22W$IAkBXol0s3iOF$Y-2X3*x|vr0}Mm^3Rw;S zbJo;LIJ3fD$mtS(Jp}KWJ6JAg7PX->Q33hx+xI7OtwF&bLA{rZ;UfiI8K0)+#KK* zC25Z4W7IW7SDY_@s1*4P@)7_j4>5*|Ek5L&Yii;4vI0ZwXoOq^QB>Ph0WwzOO778= zB@8FgiF;oPHY!d2x`h$16)S2Q;WSK=L-M^N4Y{QCqOas{gQF8Cam)$0_%&O;9co~u2`x^nyk;O0)`iz-RK zK9D^e8)-4i_7D!@Nk*_hUKu6ii81bzV&^?UZb7H2Q}=w8=Psz>mPuXyjeRWo8@e>I z>qKba^_w8aI!nH{qi8-D(`e?SIdpTzhuV!Ph|RBq7x4f_YLz&>weg$de)8Lyh1{O& z3ttnF3{`mHBL@i7NEHW3ATxI3vU7O9{$tb%@!NF&HZWK}k3&S+ z2=ej6&Z5e?R;TOs=*aTd#L-7Gr()KJhW|Mv>4b^`6|i~2Au35rV7u9pc+TcyLp563XSW{=i6MsX zu3&+hW2%{r@J7Kro5Dp+pFsN1P?7&Q7-RLrBb&HO6sO@B1E7Tr4H}KsHcL?G%i76QTLBLb@wjCWO z6$#H6e)om)$0(r;FvNgsAt@D8m~Gf#6R|oFtfmnyP#(8ggj~4^KiGSPFdY(?(b|vF z_d%{G8jfyApwLhXsAxxrpT+$gbaP9%L8_u8^0n(i%Yrw|+UiBqw|j*lG3)N6FDRA? zzV#TD+cpbc9e&H>@8smshD27M?yhH?r?HZSkN+uIa@`eIfr&RDE=`-}=l1auB z7kq%G`kn&ujqts~u)uo-dhi7KIWZ$FEX=?kHJdKIF5k$yA7uXeG9?SJNNOZ7_aYnA z!SMf_vr}zCDkmbgrF`(SM8sW$`=f}D1Po}fIRUzkAy?+VJ*2M%f}&1w@FdM=KoH)q zk~g4!0|mEI&OdsX=(f6}2KPO8GZF$EO$jiktgTa=rDomIFgf=I&UrtNIenmy@r7A` zW*Y$eX2HgrFU;yQfVU0<-z; z2Pkj07|Y}|Hvs{r{2w@N{q};zowc@UK9}eNEIAo#Bz$nA&c9b+hNwmBi-chsrbRHs z*y92jDO1wm56wTrDdJ<}gH7i4>_<3LY-79>R`21A2KifynIanv#wLn0 zO>Mw&i$y`>G0{M+YW(xI(FRB%dm^f!8{k;tk>TdtK>QZ7dcT07=J6{lXILp4pKz7# z>K<9lgJYZ$we-2?jq-xjc3v7bK z$VlUu%JQkJK7jBai)g`iPj0C@i8m(-eo&a*X9!tnIz7=R*~lg4&eesXY|RxZ+f{=8 z9RDqjX#S5^7|fR_X!{3Gjtx5Xn(OOF(*2PYrTG8O#nh#gky-Wh~L--iZQL3vDh1;f^|2n+_CaoJhlhhZ_&c^xRfeGtX(5zlw zk*NuB@nsmGOl}xM8d3>5oeSQF$#erz;evINo*Y^rWIc~oDQGBA5wgWemt0&?>VnM^ zN}>Z4(y+|gLK8MC?wl-!eueIN+o{PSg5TF6v5dorXz@kY3+MiuwEO>d+k)Dwjcnin z{>{ZMw#8Y&9@&5n>b}w%>tzaX6rckVW6d@|=#{RLS=x*MGJoguGCcq*LqIoSL>E1t zfZ4$W(5yqHsOF#3=6T091U6L<*YWV?!x90V*QlsGFa~F#R{*#`%h18W zA*W@M&LL6;>lz^u_!qdZ?gY6AZY!I5dQq)CxFzvI_E-$sr6E8As1}1T0BtZ$#1`&6 zIHUtkd!(dSPI#qLcuTBPMeDd>`@10(XWRdM_gMljHKkwFJa5@Gn zl+#R3q|NWIIy&N66R&K5X0{<$A35?G(A&wOZ7^doP6UnZuI|+H>7X0XJRn*_!z1-D zM(VIP9}Xe%>sghPK?bZ_ZIH`9gr_{7F!VY4c=*=Z13e3h09PhrzJ})5;Mizm#YcZQ zFAdPJVNCq9)PDDs%<;)&4esDz=iO^OOnQ!!qa|azfH%)~w)S2I9tsft%L2C(;^Qez zFI_3yV!--{l!}#;)qDG$#Yw+|Cn0fl_PE0cKe1FTcIC9v_Jd5$Hp<+=$n19#*TZ*i zEmbS@pWsY$D?6Y8!nC$Bmzuq?+wXurA@|w ziT1g)LB|>Ay7cVeyPM;Gzf&Q z5_x0-{^uDM$ddy6mv$dg2IbEuK!d?5lUh6oI4nJ;?$V^K~izPnkChgz&+W%{pBTdO>h3l z3O>%ZUw3<4p7Ry`&kp&qL!JPbJ4bi9*uFPA;XWH0mwoG{@-(81&d&&dyd!DVQAjNwTRT$<=z|$4o`z^4~9=ltA z@d$&_#e3N3y0(2eBi9ja=_}~>gM6`4D-&7Qwk(yuN+Nnj=-lvprmRjq(ptyY->1(T8Co zx|3tpDD8gbl`>7>m3g-+HC{~kCCqNe_m^*_$$ddL4!Ps@q7a!^=2JrLFl6)5G=^GS zcAku4*-!a1NxNikv`JskWG+srQEEF8d?0^X8R*TBVWuz7R1uzm(Y~4H{m>U$twKUj z93+tt9sLq)ESdfMk%gdvsJQ-qX))qH7fB*9OOVF)?qvD>w8G4vk!Rn~&L8Zo5dx(A zKPUj|bv6L0>9A{dqLYb?0f9j)&#S4XGXpTu{ z*)E;NlshgF8GOmNjkqDkUH7#`jReoh#&E71P8Ym5Cw^PFZNp&esh?4u!VHT$)UpKY zwlIJp4kD2ye|Wy>?%j%QiS(Ja210$%=rFvlH7ts4eF$0_3UtYKD_IYaNJlP7ZBrSz z3OK0$t;309+vfqR=`Yh*$m8J&Fr?1(*$;%~Tj^6wp3)K}bM2{m{5dn%|Ir7ygwz}G zrNqOPzcxdrE<9uMWJ|tMvLW%a`v6HyG~`3Io(T&GA~o=}$8A9se-F@y!t;xU=KZTz zNl9JaXdwjV%9qCTQCr$iO{?*q{=s{!v;HV^_e;OgvC4F@cBbNz6Vehq0lSAzY(t|( zIoV}v&GJO1Wz%fF%+?=;(^&rGQ@tbz-F^U_*sVb8hJd! zl&o}Ykng#LW2I%c*+tjI2PdmrN|)@(iul2|PKovM(mPe3cZalNjHcI^>VmWb`++&h zTmisFX7@vlWG+ni|uzWPHr_1%?i7Oz*n!4UlM{`XqJdE>A@wNUY zXT^$+U$e&gZfG0AMGUk8=pypFU0>gq_WP&e!s{=Wba%Hrmdr*01}5)K9i=&b9~;Xe z87k^!l0P>M^k6#473cdV3wk@g6RR2(k2BOdF#gaC#84kQh$m4)qI(^Z0Vdst}N@ELP1)c!ChJp9{ENFbozsd?rBHe;< zdfMR0#aS?w8NloMx|@PK3U#_&r{xLoaXT=g{r81J$ocS8*Dr}kK{AJ<{FIu7&Q34U zN1JDE)U9|^)asH6;R?=Dpwdf3{3SqH<~jpX1%33pxN?|O)z}Lamz4Nd@c=tsx9_Sr zKt$X$G^easCRR%)aKVUd>6w?-2r_ls7`s+&?{(-4L zc^3*{`anK9Xs9x4!2enwKYEaSfF7H6s9psCM@Dmqhu2cP{`qJDbc;b0Qx6}2EBh}; z@T6b(fSQ;Y9ws~2|G$$Jwgk5wO0#*c1eL+IK(u~UzkjpsfEaf4!n;=r!ZTFP!))`tPF;se3dLkZei?l9fO~!(~0;1oQ zH$;VpkW6@3>Ife!(lft?*fX_2l~C&b3FZ~~a|STIzpjp$_j3@(IuVe-2{A@sJ>>bY z$lLDrI*;F`8#X*|>roRT+8$Xj7ChRWic%7jZRKOC7hHsjuRjMVq~CDgQ|w=7q+w#~ z1Ok*%GhGJ|6!ZA!z;7?&UhS9o*yV~$pcA#0zy0h~B!N@_000#}Z#90IGp3bJO$C_= z>w$W1&B1qJ8wO!-Uxm5m{k{%fmcYmyz)ECKn}F_V!=U8Kl{K)R z(opu~*IWazDq^%ff;bxmLk%-OQwz2&l!Gbf-Q$VYN|EY6Rp`WFvD5)XeRoSm z3ONX-VI`R7oY2Mv@;ZNcB_Og9o%e{$DTt|QN z=Ci2rTV;3kfqU8kI%yet>y;QQ<|`<>5y_H6c;NioVJz267cV-EygvbU0b+x6&+I?{ zOmc1miXLbG9;8n%|C#Pv92apc2+>qKFV0(f`#iq8m}7y*-@jg*x>@Sw2Cb%x+UpgBmuty5uZx+*d zNhHSpG74aUM8N*Kae5M3?Ch`qBVYQ`lmsP4=N1hO_^Kvk?TareD$-8bG~zX%H}q}H zw{MR5FJH2kgh_2q&U{}#zhDqeQrCW)+v?)$9O@k#b%`N0l09lH+GYV z?AswEbQYip*~SG{J~+7TvggFyP!w~In;$)XT-*GS@vUH90{R$_9!oHuLi}+o-#%6^ zT)Kyc;XvkPmtgwXX+CF=i?vqCtq0v(*`OFZR?aC>?$Fo&%cs9Mv4^3*FfcH%pA1t2 zD{pU_%)TJd*f!y$z0O>7~Ds{3D8@XVUq`54G6Gs za$vZx_WI@czS|Y8xR)Ktlaxc@hFKLpeq{{CS&`sR|Ni;^!F6sqTeUzf&$Yc=jNBH8 z1Ow0YYY`DCNe&-vSwFgIA{Vzlf>Zq}oEYVI zw}OuT7=CXAx9rXK*rDRlUkdi6{fEK2^y!lxSoqY?tTp7mX8xF}u(J{V)w?>SNi?aWjW@zo?FjjY+u-~#_ z9A0ENuvAeo^VKu>WueWH@CpTc&R#%V_tJZx;d&D+d~3+jaP-e_Gyj`#hER0r_&gve zdZTiUnfJvVQoXa)QtvN2(owgtNY|yu7?B%q`uNkhVEbySJ#f-x@dK_BA zVqC+F+UUD?=h415!1$R1W3Yg-pnm~~o^(G2)Iveo{b#QP2U~}M22hudhT1{;_DV1$ z9~BNg*ekkylHJoIAD_O5e%FH0;6rnp_GGEaJXma&aG4gv_gNb!C6ZjD{Lz3p=I|}+Ud_EJ;>1uK(-uv^ z9k1l=DvJh^%5bKSR7)h(aZcDPS|G(3RL)Phnf6L$!abFQ=7K$0j1Lc%nMFlKRblw8 z7FconvL8*Kv|6gUYEPm(&Xn1y%aR}cXnEwaVs9o5;?z3I2}^N2gFq5L3@+ld+SrBmZQNqRYY%pWQ$KyZcdfkQ=xOW(Ri^(ij6G1 zhAcDHeT8m*Trq1}$7(Zs-UNYvzSn9`5aNh~K4D-|Db=X2MzOs#hul78wLHB7(<&^G z4=rz_51%+ZbISIQ%~gpeJm;|X zY_Rq^>XI&`2|SK@*1QI7WF!*-`vjBcg@JHZh8E-At$M!FRSq&?Sha)(RpH4;Xki#8gj0Y93=C`xGPH1n=@3#(D0|W2i&b&V) zPq7IjlRe80WtF6Ke*!G`~FI=!+xVN=Vxo11~ zuT+^aX}2RgveZPU^}An-zIyfQvij!cW;w@CYAE>i5IBtBN{#%T^h&>y@LVgIW+Mv{ zo;&(8Ciw1fGOBPDD=^NDd)6{QI63myuZpVhIU*xnV*KJXvxU&*8t*=z6g=63PpjpP z@1B!QKYD&ct_=4kl(d!bPpC}TxssTLEkbKiH-$6X(X12c#E7gw=Wed%6o;cpc!+Y( zXQfnuBkF4(>6Sc^>+GG7f`s*!YxsL+t9d_IcEqmYh>PH!GJO-3cmJF}-w8=cW$%eW zC6~6+4FHy_@Gd$IbEl{q7uf6Wf24SOReB&xUCr_1ENVtyy~Ce?{343c@k-9IBsRe)Vf$9~rq$h}(VGrq^eL)L1IKh1rymrq+j%3`Op^0#t z>~aiMbc4tP&%q;Fh7qdXf2w!V_TM6tZ?7As>b<-Ck?MroG3k9B{w6$+JSXWdOuRJT zye?^Kf$`byR|IU(8=%%v+iY-wqTj3bqGK+ zjq`m|x{c4L3EY;RRk$us#?<~T4(hufDON@1CuEo;>D}{5Vn*(p*UGaGZHbP$N5ls= zNHF)(@DW^uOb-s8WJ7{cFV~oRpSl|WuQEdQ3@+H8XKGnHx+fSls~pLEG8WhGq`kRz>=R>+VDD^J6`dFlU*o{Q$@UiBTL|)zh`*Vy zGy@xskiQr8ufcnk6c<;A{$cQMT<+ve;5l&U5eB-MG)$G#p+g1iFp2iM44seFO}j9y z19n^m%j#!B+mGeYE&k=1miD(BYHN+YbBiKX~Yg zr)AAnA_SwW^P%%N4n!?j5E?o=KdfnCSs2#14~Q8jJ=I{~D1IEbHOnW-=3e5Z8Wj5^ ziTPHSf$ltgRRJiGn$I3zOG^u82p&b%M9Nl0@1ciiG*{i(GTo)Ud`nzh z9L={@QRSF7h4a}<`@Mw4jX$^=qVr5XN?(@&$||GQ2oi@lEMvvv-=pDI`80SP0CHmbXq5wli4l-8 zuGH8`m7PBv4Df%GBHP+*$h;-6AFLc(C~eLBS4v5ZM~JCYrvlQv+3wALw@vIKZ;C#~ zX;uOaTd76!Vj%hK?yIFnHON_k(bTWBtGs8B-49+i*SZCB@Vz{R_P)0Iu z>c>7=hDBN~OGANR2*lK)9Eug4QSt`BVqq>pBxm&NlFwk{&i5$;b-+ z2oI{eb5@#}-t|N7X9vWDAZ|GJZOtzzh{sM6T6-i6nRR@39&{%I`E$)}nqOYbL?^xq z%Y=RgtX-ki);=`8xZ~@iu7<3d0mql)xy4YwEjjrX7Z+dh_1%Hm%)b8?U5vX7d#y~F zK(pc@7iMZfp=FHRT1*QZp3|u3CA8`u9P}q5hxQlDCP0h0TVZzx~fBMOYqLd z?{NgjW45~8a7~t*!w6bXI}v6YddASrq}ofX{O0O|51m?&SFDLIm&G~dvU~|~C`)t4 zZ^{%MTpcpYt4yASs;1~X(wqaS-+%u6DA}o{iZ`#v2!bCWUct#p^HcykfEpYzoPqR; zM_3`X?q+G=L-ZS$6(7`cu>(<&aq1$wG@mtT@#4iKmeE3Ca=pLS(!c%La8D2u)E%1U zdCd>H(cU%IZv?dry}X{1ck%&PTpY=O!ktDo5vSVDE}kigjZU^C_30Jm<^9DH5p5tP zfcF7~KmhpF$j`}+@5IQER%ZH}0syQ#x}Us3ZjKLIPR)OS8f+@_Z485m_z229W73N8 z0_}7W>`B)g|NdFKe#QdDXz)z6>(QGT-AeOv2=3{ql*}(I%t|V0a%=-qR0d$w4dYV` zkQc_eL;wuNps*E)FP*@fpFp&fg9#JOyuyL>i;r-ds}Eyeh(7-NlowO9#L_e!yCrL> ze(m#vh9fm7sF_;Oi~qRc6&#(KDx({rx;K!Vo`9HFXKq8bn|yvrvU$!k^;Wg-Fp7-a zH*Wqj%ZiGDoi7G)vUZ?AygPzY!@$rmkSV!pmEn~uSFTmBO5?$d0RFvNn>TFV>H5rd zbY~>e#7E1eq+ahxzk_U~ZRN?|myvwOQfnKHCrna(Vt+DU5vmh@0f8NHUitG%iRwW< zh{JT>Sdp{$xj+TNU{w0OwLUgn*N)$E%ucWGzF*(HCOHeE@?NEbwe-YVtLALOk(5AI z@;#T%>NUwUGwHeS_|BbW5Jz5q0=MHJc^l*BXhkJkxBjx6vF)b15u_;8oRJ2kbH# zb3!EnHJ-#Qk$C16K0dy^DH^^)cTs(l%~&AX2Eg=i{y3mY)O+Mnx(F{TG=mLvsZGHH zCj%ZiWfm{qh!6^x_&Sl9(HPNofv%+;Q;C9^p!dd~92-Tj zymClB^u>!8m559#zdr}zj#ZqU%GdFuAMY}YIgYX|qM!;Vf)rpHpN=iSde{zCk4lgK z#fxRI+I2<)YWM^Dw(JBzfnQ`;+`=JC=_(BnA$vkZj5eSPm;C&U_wT=huGSrniCi|H za!+M;r`N|{_4oB<4|GE6Mb!;8xzTK?jNP`7AW6#tA4-N?zJJ!5{tiqz#;JGKg6H^%vB$wf0)zThh@?2p^n)+- zD@jkQ1*_dibXALKLzS)IIgi1fBoP%4h+cjrE!VpA&Hga^jx%^FvC3R9 zhb+nTNqI+d<#e#%o`Wk179{!PqrfIzm~obZZC+65IkNcPeriilC^g^ z3>c%@N!i`6Du)ORyevn2R=2CCZ_0+laJ>0#nzGA#+Ggvpge=9w@}W3;6Oq~(MR@CovOr%2T#v_;%ITJYvm;?x8cE{&TF*ZLr@(8Xyc&`w;VBS&mWb_*%W~b}P{DC769y}O;E(E@CT24+*=!8^Zv{~naEEm8jOR;>& zY@tg015aKlya1V6$gP|`-w?xC3r*t$r-eAS&{UPu8iL{0-j<_lcJzgy?y$vFF!)*c z!ul{CI2{d+Ot$>-{d+&CCVBvkF=iqRIC3IP-%|$WpSw*h^4ipW#B^~%%gq)})kDBm zyIg+8;aJ0gUzN0HEeLdyVq#lTo(;t#rr9~0!LFzLu~Q4xnNq-0!Qvr=!G7T5a6On9 zaCjZjMi0;sabXP*Rt$e|SN>R^S>)oo#yt7*%?N>Qt#%pp10c}b;c}BR3D5-t#0D9Y zSxH$aaMP`Uoh$kNycl1?tP+%&JX!_#EGi-u$$OPH1@>epR3k%I_#T zf1bcVAj-Zb`N(Uggv5Mg26D`KQ0ecxc7MkCyhn`<+mNE;7&o|UiX3QBF`R>v@AslN zrfy{wJ$v3fTiQVVqt8Ia5E+&OXqj;!dmwH_+Sg7O*&h#@+fA{JcKMH7wZiQ!Z1L>v z9zkLcf}t#P-rSXD{U#cR$CDCd6f@Xvok;K!e%8ub7PNtRS`MR!FOCY3yGP@k-WEt* z0RUP5tGO%xYVu5@E~leC>Nr+u1tjV*T0s^SaYMwVj((t`LfDtKIRE&K#(ka)y%6V>-1PkC3k|7dh zfxEMkh9OJ{MaX=~vfe68%h5y&l2gTqd_7WD8#R(9pTWFU4U-j#Btbc%yFo&lkrgf$@yA#2`VefVn4 zAlEI~ z_Ep(I$YG^-asek=HEO|N2U}0JHa4z0e;{RT$jyd9w0kA*c;Nm=O3l8!U3|WCwzZ1`j^K|wR?@z*Vx2_t4uVKa zwmx8Pamcmy{~c5e=`UmuNU{f?L9SQd7pam-fWT*QS}3kgugKC0EN0Gaf!7hV2R9%i z-Fo`;=|Xcp%IV^KHv%VWYir#fefR(s0h`%+a{6AhzX5Gmnv*s}hh7fEbu(E0{V@?& z!!NxSKZ<}2r&cr){<@Ph4B5;W#yiC5?wpLH09$HcvU)MRuQPGC;hLaJ;em1uVs^P* z38D!?US;39L$GSaIfA|VVdQPi;9Ot6t*+OySl0#|gv#jyaQEDMaouTy1T}ZoK1Ny?$$a?!Q81)eC2cGtqZ~XWMucMKX)j4Z_L4<XY z{X1W*pLZ@msgI%0Az4QUKI~-(%+7P5l8blGvF~$gaeb$iIsH2DIAP7F7Qc+ur6~+= zuYvH4XEL^bd?+6OnXKbUNpqR(#^cX;9G{sBM_M{QEMW+sk~CdTNik*co}vQ7>Xuv< zp;@yN!VJjOc-XTZaQI>dUx<}d-a4Xp8PZSa&6>pvTb9b~^nK81qzYy0`6{e^`(LS4 zM@?16E7YbsjMs>pyWPss*f*!{F^aY~x1O!4)0ZE7tChKS()=<;E2!Nk8il5SA=YvaBJ@z;^~gd&s$)mp z0-^SpX^;C?Pdsiyw|UPQq#z!3761l?dfueU>zj|WN%|hFi}OTK2YQ*v2F4XD?E{=B z6p_IX5*VkNJgBfSBDCcdQln>J8vL_@zJ1|qA^%<2^0&=^a~Cs&9~xK%9Eh>y$<3XE z>kNhpdLJWws-5^$ordS*iXlQaP0m zzcR#310ze^aTKo9t3qCW$B+)XtVm0vTCdk;qP)19_=R|&@T`qLU@jV?V3ZrJVNV)t zmKuzwukC5vjueV|n1d3{vm2J$IYf7&uP2C$Hig5(2plK?STS-qtK}a8L6kfSH&sbv zHg*~vm_Xu+V?Zl|eLiQ9e#W-FgN)oRY-iP)LQxfWqV~%Le$qTe63>IkNQWPmwARveJwx=ppvrikwmL&jW$|>XDAI(RAnvFxDAak{B#r zx^CTbt7T<3NtO-!FeSX^1SZTAL=6r7b0PX{EgFWC!E1aLkqV@16U6G?)>v?PtPe-{ zWP1)V5BbS+2BFDvq&W(W6F=~e7%#O4$YsSnK0omJoyxw@1%Lt+aLwro8^vz=3*mj! z7V$q~IP_ZK@8b?4u>s%Aadq{Jh?>;k5chU=>bkJM5(q^VHyAS@a$-bh+KN~ZrrJ#% zndYq_%EcQZ?o89$Jr(sS`2kqGHwQUoX!e_nh$R%_@qP6;fVAu$KLJ*^2ee@mW`h-| z!A-ge3PCg>Ie_|E-mM1}6$mxAfpHBH{HKXO-~a5;YLdl1JZIF1!DB@s*9CMFX`AdA zh9>+xh$_eVFTR@TT?xXCHeB_ZKbtcv8%P{bT`lMMz2~Gw8nw zp7oB^a)ggJ%>bh00T`|xsLg01FY$B#`r;c@KLF{D!wusG@GOruqX}6nDwY#8OIaEj q^m94~3L^OYg%c+HzoQ;6$0qOSUE_GdkL^!yhuOCKJL#71fBFx*cp4=D diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png deleted file mode 100644 index 346282b23b699b37d6878162efd488d1965366c8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 70820 zcmd?R1yogSyDp51jhLhYA|NV?sFX;U=n_GtB}GKKLmCqU5nKpJ2?|J;bgQT|NG=2^ z>6Vs0*Yw42@BjbDf5zB*?0wD|<1oJWGmy3Bn)7+?`>OkSaz*C+rVY#+sHmtmkuIE- zrJ`CLLPfR8bKM&JP34AqP5iRvhSd49_!VC>XR=-J%X+g5*DR^1SRN7ot?=j?)uE!= zO+`9;O5Qebu-VR5zHXs#w6g5xX41M-H0Nkmnmk@-@_573jr(d(JDKf&x+g$BTt0E- zJ*jg#$!(XV=&nY_ou&)Fe)rzv4EZf7E7PLx?AWq(%Y}6hIKBMJezdZgo9GsC`f*Ml zE$&>fe=S-WCB_$;W>Ir1>Hm<#U{ksln=H=AutWeY| zqP*2y9P>TE%d2Q$5be5|jdko=O5;#f==n2?n%vynr9(Cp+o1O0Rf`KFF6RSXy371- z<*}b}mG`e`H`ALM{61!%tCDFEULUWn+L5)AimH}@I%Mmok?BX&D`n=#ZAE7n=SETu z>JEraHE0vJqe5b7UUKWrRbfSqMJ>4wsg5%iV)MTW`oF!oZ$HudCf8wFGQ0m|t#MPD zG>7D~#MqnwldNJRhldXz&K626ULAO^7_YY9qmUzk!V_E>F%X|!@!o!=d$E$HQ}{VI zdJ_4y%c9+v!oo+6tZ_Ekwxd5imnbIbsw2768txRPVez)Sx#1Xv#r}IEzM9UNym`NG zkgD50-tQWpF;K&nx-)x+iogG$kdP9Kh*jj(k^wI_C+?w`?bPk3dmx9wQ99a~(6#A|Mh8*Y4ooh_gx}SA>#N^$it!UQb>_FG_T&QwK z@A2TsmnzmHZunSj7GulsL7PIHiMM*e$|11@=_XdU?X2QEJ%E&18 zR0K^<=P#*;O1dAFXv%lV7spERQa-ag3m<3y{{2}K)l!pH=h^hXg`DcvZFL-X$4sIRPiO4=s>20%I}W?dxMlu$dv2q3e{C^2YlVCH z=O^3CyFT7utK>g?Grb8*sZNbm%s$1Hmc<~Cc2O(#UB!tJQ7$en5?Ro=F$_;9tX@aq zYoAsBi}S26I9Wt(6F*8{SdU7(^^PQqbg;M>=eoi$=?_c7yJ#2MH@mE}Gc6`_U5(zf zMLj$`yxRiTvWxZ+SJ&4sU;32tXT4`cerQiA9HBW=6LDEc(_^Pl#d};;@n(tn5N{r( zi{vbup=i0wVyV?B+{w*8-rjvPJ;6ent#&;@%z{R*X6zeN^izj39jCtQ4t&=Q7SL>} z+c@i^YI4Lk*!Ji8($ck5REaxR=+%;BWy#oLT6wh2JGR}a4I_DZz7ZCm8%*9;zvl7l zwvcCaD_YuGo~fpr)D1Uhb5zVFxGeB=Zk@loYOTsOfsiWO@p7T^uDOx?{Kg6Hm}ckM zz6g{JAI?;x@3Oi<);_#BzfOn0dw21`h{AlGlIFRb;q9B)#MS!x`p6~ep@Q3YD)!XM zT~>HR%fjisdFOkVCFfEK+Q4=0mER(hYc5MPt5sB1dI+~p4K)eajoq9aZdOG-mb0*U zUm-GZka#J>`q-!{+iK^0&0xo|Ctshl#Kfy-A2@bQ4u#a9B_}h>s_*TI;fynu-DOp2 z#!Ux?{KneuZ}#8rqcCl&ZA|GIpDDARpQ&^aY|WqTk13zNzr>r-cJIKzuP+Rmy;w&I z9;{+7U%YsOZD~E&a1~p>>b*{Br%g6TqYJjTAcg7ow(jYX%hwxk;RzIK|rhV_0~Vy5_V*+PA)p;EKU(t=8smHy|R7v4RdK^4Ls zG?p{J+6(B!H>WR^mXwrfnN)~PtKt3~U}t|*O?p1qz%VyGLg_5_vM%QG-6tTRP#z$p zl<&NNHGjCTw<=WNK+N7D9%boD2A)P`<9-ztmFRFOUxOk~c9HS2W8S!~Tjl;lUHF!w zUo(N;bxyvb*vRcgbZHukxMRbQ*Yt-}UX3u&(LEGnMRiS$)}EUhs>^e77@rJYoQQZl zUJ-PHqBe{j)$rz2Y);EX?h#>CyWG_AU59yiYO2Gf1gv_mbai&_JAV8cQ4|-ZTN9S% zdnB|R-6rAKDjc3_^6k4Nt8#?wE-$wYoe%OZ02Wlw)hg5bE-NK0+v}JlNmg;GlX|N~2RlvhL+$l|F}5pWR=(nYky>QpQ_H z6CYdh)TvXcI%@};vnjpka-W-qC+&F-mG)ak?lq{9{PV#x=f_HU9G!nIw&ptA4lkTe zt`y%`Y*?80Yi{A|u}V=n9Xpwb8s#jjsH?KF+s5)*M>eb83LD}HN$YW=Gmd>{t&VTK zc==L(C~G)pTqZO!GP2~ojHGJplPr^_H)kFhSc#(iDh^~-1PE0gMDB%#RM|S>8r#{xo}e6&RUcgRaOd>df>}8 zzp={XKHZKT4~*tsi#e24T3A|^PY20Fv|~p_TmEwTP%hMJ#OSJJo2wCOH{RVnXPUoY z6F#F`_DbvBPJX>BX9xIc3K}xZL;VtTD_?Kz0VIsv+`GbE#5}us_IHOzIpyP-O%IYy znnhAgnpFlU2?=R~Bl$}j3~FX~#ecWmzgW}R)fFWdA=8M*)af06-zzuIjQ+4jEJ|8G z3R!}n@ztv@W+w+CX2yHYt1ULB8rH?DpD=FDN-eR%V|<7Qwe{-ZU{vBO5kWToTwdeK zPPE_SGBSk2+|_Kp>nOa_OuF$9gmuW%X_=jnepf`*(Y9;-bYJf$iI}zB$@~4&?QuWz4^{j5-=j}GJT?dL`co40G>!?H zwixkhT9ylJ-JGm_K6?|r=*TDHEwKLs5-L%e#*1n4TQwuz9Q*n6=fqF9!~O{)*(2hm z!JkCXLdv_^oB(gXZP{{1GDvi+h=X18dNlKWQ|!C`mfUPVaTIBQiW-yw1x3ZZ`K<|W z-|lgmA1!1*<~QA67gK#+KH`$!jpqZtK0fb+T5QiaOb>e(cD!gE@ogPZB9We;DMSbu z*7s8K7p;q82Lu!-lbLF~fLaB7Ry{$t{YMhiv+ErtejgSRO0Ukc>iu>KKsn21q~%b0 z(;>Va_HS2Dk6~T3LjRANl4_vf-Gvet^C>g~OCd4Mq&XTudS=0S#!;L_!dZ<#9vUtS*6I#JsamD) zJ9fSI(qaUtu_|&9YPOQ|w%dE@)ozc%P-0sHiN1c$Ds~{h)%5lDyHsiFb3f5%W4g-& zbn}LBX^W#CE|D1-4Wju%MhziShjZo06W`8kt}}{|4~_3}^EQqZK@F-#zckJ>sf+gZ zIjm`t#)KD-Q%&z@$p8J&?-syfKcK`}FZpZD^r1<{XdFV*lc))0({i%1pIALoQc_eX z$2mC8NX&h&ETg=b7d~p)eaX_U_uHF`7cXuIsE8kkyE$Z@HfGQB-Q~Fi-XD#=o=!>a zjr&d^e+pB2Qyj6#2<}prf~pZL`Hb-mK;7l2s3`lPREpK_Us|{|_@5KL%gnFm>9^S7 zAwhfM@WX=kZ-6p7d9<&K;*V-1DCN&JkCah>^w$Mc^e<@2Rps0=`jJtkMmz8F_(9N+ zqHNI&3Uq^KB@mM{9#ud>giH|g)gT^Appk%rYJjgR8yR?GiIr2s!a{doY} zIA;!?{iV+>Bj4@O=P*fcYRIumHgC43JRGDPs}L^O$HNojx%hdZHR5vc0k$XUhMKmb ztQ728i(h} zbwfQjgE}^g{yG^xHw}VB7k}iCY8wC8_;|s)>A3|-+~x9=f=8P-2UHB%Jfdkg>nwV4 zVEPN-(&_k0@3^Lt1ye==wtE~Z6q*9Q(~E@%l@miG?cz+*SKx83BRTwP`?x{hW~h;l z+znDtS1 zR3|$l?EKM%+j%Put-OK&>raZN3yW+wcxz0Yl)1Ti(JLsDNr;MjQV~3`fs-2nDY{DTXCid2nojj$}HFJY;YE8&xnpYr5`HdEH z6Mfq6MhNu8Za%!vZ+Y>09q_a}Ly9`P)!H7tp@jEs*q z_ZKf-Tn){!NohjM5krsf9yp@?RbJ)RuW!46MB-|4gHQv1WLlmn;oZMK%G!=wWU^MS zyld53T4hCEEb!)aCqrehGs1OBeH2c-00lBE>k@aGJ*(#Yiy_y{t6y}sH?(`~)e-Gl zU=?Ypj~V z(9pP;ggg~8nn`*pW$C`QljJ!6JobHTTd{t8W;Zjz&OpS9i;D?$;>L})GYs=O z;XXXTfTh#I)0}~#;N36<<;E5j&H#tN;*d6zjILV|EkVmV!rnr;vNnhy2dWlx1b|F!S90E3!_q{B1?>(*afdwEg8J9xg6 zduw>i^SU0lAiOJjAltZgm;|qIkPIOtbr1AcoR4X|AsrZNoGka316rF^F5Zy<80of1 zH)%dF)ND9CsrbHIShd+%rrvH#am`Mdo+T57`>P@pZP5&Fp&w>vyn9q^Y|@zcd``PqMQ(aj@aDK>_p1bM znZQWU4oXznoi;X_Xn)78pmTYI+O^uHZ{uS(H#r%Z=X)=|j_oS-4H&6}+{vR9PopYx zy?&Ys=;!Cp8*~f|P%DZ5b&S4p`KZ}_-$7BSz9v*DK?6{)=IuGR+H})aPkV+PJ9<%j z6#@Arx8~*Rgcl}i%DY0(d(=nM#54{iX&0tg^~sJ;MqUk#R=@pg z3rj-YtU-{2tENRUR~Fz6r}x^<7-q$=z0SYe*J8zMnWMn}S@Ef;DG#2#5jJc4khmhx zYxLp1BS)^TT0`@VkUS>sQ*V!GKqXcq$h8`;Z`jyjPLzVm$xj;(^Jh%1b_D#ghXPbi z5woB8_LPn%MYocbz{29em!0z^!i0t)Qkj0AHT?OxLql0v@7gtYKS15j791?e)zKdU z0a%)4VhyG5#eG~6-~YAYxvTQ`w6^p8n|BF^5Rklk0A0tgsmn@)heh&Tl7?x4H|0v@ z{eIz~3Qvu1A3KDlHZXOF+Xl967YG~hRlnzOm<=+VqKqW!we*bZgGZ${x?Vq=J3-DAg}ww{TANwcmXsD}zY^MA+s)ln?jm*&(aO(LdF(pA z%_i`~qk^qRc{`ybeWF#p(yW81&C*SN=b zMOUK3(LUnnOd7pV_-m7pjIMEdY4rFP6a3xHS9jI3ZO*-{gs4tqxaEg-V%}qKV?(HZ zMZ(=5z3#1E+9wRadXY`rG?vnI(k{u2_3Y31?BS!n!OpY#FUbU!ettUl5aK~U#3MdUiJ49>YhKGz zpF>#J-v|vlj{|p#BqLM*jOkbzWjnVFO_SxAzhV3lUx~%v9bU5OcYgo2b%*%CplqL1E zdP~@|uII6TvRc_LFR&=uTxL~#&e%oEnV{P(Pxe(l*)Aa|QZZFb&(lOde_;Vtv~$$f zYW}zRA(fOOP{94Kw+nO+#O*lIbIKYTJU@^DsVg^~5~s>d@)vVR^ScGFrOrpYXEKI; z9qma<7i&!XkPWov#x!HqI11aZzKCE~n%h@ZKi(a`mVB!&>vo3x;!X8mzZ)kA%W2bk zXsO>5wF=5!?_>4g?tD>j$6+|LQ0+CJ);*o%c*VcC>@F1tqp#T%PM zK3kAJs;#()@Qfa>^zaa$jm&fetd|tkY9Q`_O_#Tl|588>?r^+%Og>(=y9a!4b6=|c z+pmtf-Zy$gAAd&MG2*=pqyYK(RvX{`{UlIq?NlA*+@+oVZKQcZD^XeMRifDJ+%pOs5{+{A8GD{D6?R*mU^DoiF^W}AI!zRF z2mj}665?-92G`ByztOLc^|P?B;Pk#N898LeZ>^t>3W#=oRMUg$*gMHnjRac#^O?8RW>XFkt7efsnn z@dQT3D&6Z?8Qak z5LaF|4GESKPwkmPj3U!o+Fg8DCt8fI?bCDaXd%pOj=%1jqv8eVdy&b{$Yk<{E_Y^u zl1s_J^<9CL+McLZux^V%3phQXK>KySS19A1Jcvr8V_ne_E|9#HcFBqCEjz$I=lHR z90zy!cTCVryfQWKUvr|ZlQeF+pkR=^G}OodhEH=`165E!d}-b?`I1MWwoyyYcc|d! z8!XFE7Z0UpP0?NNnvo37EjQg`q*pQJLu?`z>UO%D~bDc#PRnpPb*&y zd;Rda;tIf28zWT>jn{s>0s?hpdqrIUAgxL{($_qvx#%)O%49v?;E4*wG`50Bww~Wu zkL44v1)>CVD|cOKA0-sG#+GO(7Pvc;D!y_} zHgj+p&iz=!lZ=b=Jo;&w@yUQFY0mIwVe66LJ|L472Cf z^zlRMb;lA4m3uS2R0D_VPZl6ThTRTW<8Bo85-a?)!XJP zOg;QjVi_seFGbbJH~5`_5K9)OnmBj|q9R^zuv4c{rD!SN&Z~XrUY8QDUHClis+ek; zkv`;B1r-ga4STQU8dt45xzklE#k4i=G#=cLim`Zhe7dXF2q7yOGFC|xURI=P9J1ND ze*JoOJCp~Ot1Z_RhnHOG@3f?w246D)?Ch6UG;SP%YGZlK*q2w`Aa}S;hMGX+zwC@x zep|blmBwL^W7EmCJ^8(F-3(I1u!$yDoH6@w6uQNoKcJ;>x~DQ2TPNICBbrc#7iK5j zGJa?kxaxzG(6wB?bg34Mz_b}~W~+A%$Um5nLqFQ`=Ycd!ClZNdKR1-llfx#e>HsIT zNSD`Fip^=_1z}f@eb#RJB;>K@2@iq?N(>i^2 zB;2#hl_jg?CuN%#)rB35?wFl zQf?M+8)`}?bnE)K^p{7>ADS4lTvu?d3w`ntughXs!L zg@oKGe-nuK@P&%{tO&A1U;tLzwiR(2xhk;!ssTDQBaBn*ozPI~pu_WPf2C1(nyh2* zFjm|Pl{i4iv>qJB3imw*Rm(yo2rIR^(s}9_6byo%)R;Pt2OKCA68laFbPMCbOY$Zr z@vJWM=97cp-Egrk#S9O!H>m5#Se&u!4$@ch8kGx`laWB0>J5cJE>0 zosjnDCjlWTIy&Z5S6A2KK@x9-#Z^0$-fGfx{8rtcsv<`~W7Hc6HhVTP^Iw4Q0b3hF zRZS{IBPQat{Y9u4_1Mc%xTnf_&L_TnN(=fO`lhB&9wInLN$F&o+QGoJFETowpD61> zK~#r-kb0o7f1%Zrx^n%tc#hLy28|}Z2d`}-3zDC3@YH}YTYg^#uDm=A+8$ziBa*u{ zb5U=m4HXrAm^x7$^e#Xn`#4A>WKOOUG^{`9&U8$Y%#SwW&Uj=`iGaZ?-ptNzPNQEK zpzO+?I(2v3_U)nS;cE!mUWX>TKg#iaWgFW?{5EO z4?mZwxTyvTbvgS50^LHPRKFU3wE!Q$S0DNe0f?crv&e~c!^7|RxMde!O|SM=MWFtg z;I*5W&;H7!6}dfZMEmB*tujWt+?IWm$aW%%AZXOEJ4*D`;>;AB%x%{OX$cxg%Wl|-s5JOVopRcr#wg36x(ei8jmuH%` zG7N3lJ&R`uijfkdMOdd1$@A0n`K^Zw_};29Nby%&h34!Ke#TBp*wGX8nsK|DWm1zn;qWez}%E;rlvMjP(&T_ z=F_K7uky7>SYKXyr8!B6+dz7~8oxD`?B};1f(mH;`RoIU7XT#?hT`^q<^dBkI|tM!!D(iekV7qok}j+pM4 zmcHw-)OdXz?ea7DUl=$0?|SP0>5$xGH=EV5ke4=W*g)vOy?~mOEb9T{#z8XcO)6w{ zi+qMsa7ZIZIoN5+@AL8c1S6`a2I}O4i&w2|xZWdn8Yl ziHXVk9ovx`4@9hbL!eYhrbsV;{4JY~(A^>QXMVe}&J81T3yY7(Orf=6rEDez2L#0Y z_@M^qcuDTvrK^zHbnG@2e1p6~kUTSV4mvAYv~lzn|?0{ESr#HllvJ+1eL~yVFegWl&HYxIsymg+Abk zi0!Ws9Xlc8Motnc-;q|S3m2~7k|0g7Jq#0|Idkk*8S!T@=Su%U`sKHL{Pfj^lN28< zMwRzBpK$g8PQ?APL(Iq|XB4R{2#YtawV-~Ix=!_62TT6eUhwEli)y}eE@5Rtjw)_9 zJDX#?{9;=UQHLmq+g;b!kK*i0sgc?4F7ONKmjrbWW>cjAm6XJ)er z@ksh%dcY{9s}fu;jwe&T)N(#ZrR#n21KT=NEk05IPu8muGPI7}3Di{>-g3AhK^3Co zmq=Y7d(niWHS?WsWq}->1G3H}I006$0m$)FbC6<$zi}( z`it{3jqi2b;2zZiO+W#2(^Of0%C#r(lo#ESI4uwMnBk6O4q7dm_6}xsdY5qT-UTg< zXqW7*Q1-0aKCFPjBQ0z9%MQDX`RL8Z*h3*O{|qO3tKgaYN9ng95TiLppKOVq+a@8`sr2V+G>!^$T_>ALG8jccydvq{&)p!{S z`M**R--mFW*?OvgKEw3s5zG9gA$1JDaictB16Y$)@f!}3eSB_-YpE+MH|)Eo;(qsE zolH$euX5hbqioU`)g#$SIH|AD1p?sPA~)rAjGr5ulw&1NqTx;NK}^@{*H) zs6JhBWgr?8D2)b)jC3b%z1wnwo7RA}x2}oNS$}!Ae@onQM{(wr9zowZ^{d{VqZ2>8 ze}C0jS1A{XhF`inbdL6{UtHKbHHN^I#AKk{vtts}3PSZ?F{2H^}F=IT!zwoYUsQ$3?=*IEX))ArmsUN+T2*eD@BEfS> zb7|IhuS_6g+8|u0@>fUBfc9ufBuKT~AvP1%zWwj^M+H+pi^zrmfbMH{7|BJ_!eeY~ zjNg7jPu;#RoJf6m>nL$aNJw<+exiN-ATcuXF=?IlIFgL0SI9`QXSNzV+?s zh_4>-xl-@lGdklaV$pfwrQxa=>b>k`$fejQTz`Ro?dIm@hkwGiUTbhYUMeKy4qoc) z^=&|0r}wfyWB=&2rMsu+e6~nhOeyCBbNc#FjmU#nw$3~29GNCGqw$_f%Fi!usm=@b z=#4S>6i_gH<8!APJ$I*h+pV%Do(PeyR5$-Zr&ZJ0+1dM$LF3!}%=jd7a|d{Mq@Yg8 z2a7vJAKbZIH{P$dF&{?m%K`7uwdo=Z1sp zQj~zZ+cbpJj_;xc`aM3X!I;Eurk1&;koUP^9g&Hsil=^CIx=18_;)noL-BcARuSj< zjCp4lfPVN?)F?F<$Uq%H&dfyZ1v3VBLXhVNrxKsr-s4ncg_!XXlDeh zd5a4m$!cl6aTM3YH+hhR*135T)ms~iJTOn^^c<2_NqS0!HUt#M5D2WeFOGM(e_vhq zC$04!p=3q0OrN&3mX6|VLd{3^{8|@P*%P{akLK5*@3VaL7`1HO;XqT+_UfzW_K}Cj z(~{VK)gGs`HGDCTWA1>R{?FHc^9;%V8Efs|O}-GIzJGfG-vmrJ!>$tV#w_cYdFOl) zo54u9evwkXyl5c`-G}$>`-bu`2_>ozg1JF!UT%E;qP@CxodUECt#Qb^xd36nb>2_i z-0BdWZAPH0q^Ky8e*w|k+Bav`!sbbf_i)On+otMJthq zn8$DOL-=@5`w91Dpbx+z)LL3zt^j#e+&egsM>svS7y*XJ=&$iTuN`DywiBqG zNSMKJ+bYzz6LZo$%Y)XT7MWexG8IB7(>!q?4@v1>Pipi$0Fjdhj#9N-162#$NwDu zCwk#GCPFCWgjTO_K#30msfL^Y!7L7QAb{J5{hY&IjQ3nZGTR&53`z%NmD@-Mk$_Cl z;QU3bqw!Pq37QE=1-N!LrRaNc?&sz%bUX~83Zv-A!1n8muEb0bE;$l%yW$AuZ~lBG z?pmQoI&pjWC1#Cdn#@V) zOmt&DK0X)8GAD*o673-T9w0&LIa4&}=jTV}OrY!>TSUJuujDKqvKh$ic`XOgyxe~I zx=%Wtr&5t6sJ=c#Gw*lbp#*dG5ejY5++B?_d=TH#t_=a{h{li?DK#P?NYs$SM)Kw= z94iMV4dCJRSA{;BY2iu8d5e&4Ge#t~8Y2t%=c{%ts_{uDv#(6L!1jX;Z=fVUm=N~~ zLzWXCPX-*JY6YIW*F^qqOJS@#GAHJYkO^E9jm%mkY~X&(3Dn}_#-X~QhZ-VR^X*Xj zk3A#;1h+Vhh!L?QI@kWkwXkZ4P=@^M3^JdgU;rxK%~ynjmgeHP5c;W~+cxO|SchRT zl*6~`bjqB+3v*IZ_?cX{erf!0;BI>;5_;uJZjV`HBD@C7XrR(y!|tkKwD_58=IH3S z!*CVP>TX^%jOT3*?cfcltQ;$+KVkKt;y<-8zV7|Oyelut9vHvHVdmaO-f!4q{K$_< z2MS-shxOrHfICfef4eHg`8O8tWEAH(awMsG?EcbSM3=`?5mK7yt4Y27apfi!5qW4j z5b^8&Nv!aqLW8hIW5;Cu`m_=5+XtxZ2_Wo9EJ{IegD9OFy^8_r{s1C8(G!-zm6S-6 z)97{Kxph^1$9FyVJgoSb#oT;8;HiMxeQmFTc0Kv&ZSJcJ`2~*$$qWbFpqc)Z^e8^iw0lUaj1jxO#V1F%@DeV%28REo0X(86xS<98TzepAIYBby;`UJfr|vs(b8- z&>GkZ%>Qu~6FRhdd9`G3y|Z`~BXJmb3!QFt(Ig_*V634~qzJis_RC>st>>+~7~?s1 zfBUkC45PP?Izc={m09`@tD{ZU5efyu-~zz*e*{@Hz)0|0JV=A8w+}(DV!tQr4x}}i z#PC^NIEkH-f{JyK0*rg>xeiMV2qH0MNFo#!yG!4G5wD}8qk|@In-a6q4YI6XLFW#H z>*pUoaxLw$h(cjGaPZ(uc(*pz*I#_tq5;P%u$F0)Chim2%L{yljUX(zG2J;rpfm&& zuyDWb4L*b@^Is(m5?&d1ECyjz+V>mrUy_NC!^S1v)SHE|Dv{r?{u0U)WRvsHcj0}E zlziJA)}LH!o~hQ~oSkkzJ*?iIjBP44+xq$Lin~EBFfu(ygYFZnjU0w8B&p^LPdy4I z0$i2^q)KdvkQpi;!_gm-#Apx+!7l|Oa?~6j{q=2eWhMHAM~2C!AT7pcp>*29?~>fK zcb-^T_(GdGTR1+6BO`aulo`u&G3WtblH6H-Y%CN9NMhV~U?m6@B0^v>&NE%?!MJ_3 zgHgACQa@brFK)9n&J?~GF}@~%4`^BR{G@5pb{FGy6aC>7#E*!tJF~7-R@8*Ofp`%C zpbZd{Ez-q3_&2IE=JWEU-pp7RISh5@eWNvk`xpXKM>P*ZVbINEn-{iE{LoN9KbX{D4gRKaSi5Pn#1TjpL@aw*bD+6Rnz&M8C=+7@IP^geNCWexrT}V+h9E=-^ z*WYJyebZMHF%X+vQH@OcRzYb<_eA3_50=nG{3#l82fta{$w~a9`li3_%Ep}WQcXiI zXb^z&`E2Gi6w0IAMh6cZkOj*l0PM|Mw=i?t|9iCK4+($$+T^)|OJ~v68OOYRly!># zaQj-Ywz4MHG4k6_t|Bl1Dgy zzY7>J8@zy|s+!uaLVi~;I9KWrP$Uksmfh5UvjD)7;=vx;%xCb!Om}Ruo5A|s&=yuh zx?2-6^KqXI)^>nc)6N3x4Bi#J4Q#jnx?fx9{wp5nf6*BKa+Ue-r2B3edP*VUFdT`| zY{dkPIQZl-k&zb&6%q3mVFa@xGTZ-6vTIChCYOObAD)blgUZ z1Vo2IhlSYf07J8+yu1!kPJW$|9T@p{?Zkznw^xsL6i#9^Wk04i30V|04jQE>xV)lr zCDI5lp?bdHIIoge_xFynxwieTLKh&^Z)gg=2!K?d7|97G{1QYnl1;LQ1WA}l%?%^8 zq9Rpg<;eBh_ZE#z8-BR60=bxUBA}X!VMlaMcLH|%9n)1TN;hs2?CS3B$0)55@IIC@ z5f#Ut>4oba@41Nl6Cra0-_Z#Q|E=MZ>(N8vkisJK!$`*XHfeNp6nF}Z^c&0VA8)`_ zFOvz*t5k##?Fh#4qi~n9a7X~OTTUd6VY^EQi6}sZ#;_m-3Gp*yAW|gqOD`|&JYP@G z5HP7;46$z@%`FW!J2>u`%v~Ct9^9Lqsd?eq?)u%k_c8E1U3+{V!zuZF_jcUy2q&+) zP-FkG^!NserbiR|HYnfac)RCfH2s#$9dwBy`z{DPeY%#1S@nS;|C)8ABC+p-!y_J6 zg$GHyJ-;9H%X#hb$fJ;NJjk!IbHF5TrtG$O2ZoDb^hW_%6QkYXl)u{Lo0BV5tc#nU zKHWf)1vp|hK6d>B6-oBbbU)_Ac9U=m^b$2MU3x|kcv3ZHckidMK>C(dug=UvS3%m9 zIu7FY)nzw}$+aqWV3{u7G264^5G>(IAPq9q{=1S*j$_5#WMV~A#~sGe5Z2bIoPAg| z##XH-$7X>9XjFm&0}n>UFIqXlt?d>hMMp|`)VGe{JdKh*D?$4~nII7od1|DUErl=% zeTWhJZ)!cGwka4I-^41~K%|l@_wU(r8Y|kMU19lioE&(v?lzU~Q+)V=%`U(1V>B)v z>)^1Ss*zEwN$#W~!fh96&zn``!AGv%OzQr6_WXG%vVnmC46X9B*3da?F{&+wh_(MQ z-RCHX1ggN8A%TTto1YC5I}EY6O&6{CDMS>+d0}>Mx(9-;kntm|2h;ZK`T1z6PMw!Q z=aSK9KALl9&+gm1ckhokANEX|c0*b#MpEqvvohDO+yR^H`*~hkn_abji5YR+t=#8i zz&7cUE-5Kloi4cT_uNbIRF96rPsocus{$WPf;wnmF}A1i^~wCB^y0@CSH|`5Q1B)M4{Tn{ znWOSwMiEfJRJj#0r5DK%vI#d5P;4L245aiLVA9L4t{~$b=>yNJa|%!|HD>e0n>WMu zaBvi`tT_mrazXFAeA^nBxa|Qw{E~*x)idBuY(CXZb#5^KI4Ww)` zrUn8H1G)TZOx)9F&-h1Jm6Vk5c6-aVemszrlmys;4Yc9*zQhYwRw;It2{}1|g|x<> z+RgR}3M%5!M#sf%x-Fb|0U@QZEbbgU^MYNQVt-m>c3Nc0D=N|tU%CF`@#DuK@9%K? zi{4MeMMv~(hz8%)YgqL%jKKS3!C>PEKX7MziJmQLVmWKL^L4}Cv! zNG;S#i`01(6zb#`s`%!8z}d@#l84qArW72*EGP z4H|DlLoxE)E4MRhX*j~)pV{O`)7dtCTaUtmo;t~uBXWj^wcC9-Ls+;?Z@ApezRVpR zx~suv{!&g@80R>{Sq%-1(7EBpR4FnM4&dj3Vl$-&5e~ZFyBRLJ-`WemsqNZarf|P* zvHUeZR5oJuct?8nYn*P7-!qM{pciL&M8sOIXap26k;O^E5f%E0nmRh+=p>v`CiIiV znAs$V2}WG6V40Uq-sPMg9aWH*zYrbJ^;b95-L~x;nUH+O7@;)~5E)v2No2J-=OadX?frJr9>R6^(AkamG>u?*RU6vMay`4L&7_0mSH=`My_tXGp z?Lo=oAl;z!1PtWQk4ceaG52!0qhuMy?Y4?sv2d73`w& zyo@tf?Az|HO~E@tEz&7T0;wsf`%eJ z+zK}mbA5(LMj#Gx6^;VVuZagjF@QAbo)?%X4lxRJF$Vt{gT_}vjgbdWwH>{I9~vN# z0Fi>oo4JM3AARoM+UfP*0~E`X+4AsM+q$0GY^S5s$xBSi$x*{z(|W#65z`Gmg&kK> zIx^1OJqLDBmNkt6ev8DJVhl+H@E>N#5h;Z?LmNf!c1_yjdG6@eyYAiFtM}#e=U(I^ z+i4N5%5Lrs7(n?en=T3&LP8!xl?vD?#A_m$+hvo@RhPReIxS5QbzlQ%1tOH#Nm0=; zF$FCD@QMPy&hZ&sCgZT#>3FU>hR79v!O*((+x{Mgb;on}|8;*|Ffu(?z(V~gCOY~e zPU$-F%oDlKr;i_da$++8Vz?gP^C#&B42b(n^k58TS_the?TIN9fZc zkVT!Ez72LR1TgyTFpwZ=d6S3plzFoO?&2MPr16Cdp zJt8Aj5rs4?-+Ag7kTQ(9MdEl8;-f*+`*X$;lAgLE1oX_ozDe>j2$>2?h3{<)o0S|@?2b2Lno8z)g|swTnqh_*$fcue4%G$PC( zX!0ZSJew1Q`xAO>D3Pu6-9ljY#1!NX7M3{hRDB{z1gLXETYG1qE(SLt9s^3$?^k14 zew!3=bR{hP$wtEjfy@O=^I#_0pQhjn@FKI(;X{XrygJGgzt$&eh}%Vw`$606J71hf z>RTtf%eF9UQg;ZOMP+Xm!#otv^ z!C8C9#1)M}icEw?5FuOBq>1=3fCAxA5_2sW?cgflgC=*LIR6fX8L4a$$KPh(oCy%Z zxgV$AsP$N667%Fk4M`l4aR^BvNBs`TMm?MWLa)ib{85!Xs3%D9JqUx4jPYWMP?i&t z&E#4=mOfw;%Y1~>))=1v4CWNLU=R@=9u*R@TTdM*l}H|A;HZ@ESxwI0E7ud_47vOh zA6xd^xraC_;NGnRYcW?Ila+;WWLun8vvWIi}St^CZlx6un0 z8}5Ea!JRK4BN(QV#qPtO9{#3HgF^S)>=Bh4y7eE9ruE?l?W6l6?OwZAIISe+qBx`A z)kr&!t}W5cXdd3{A-5DCj$=NSj2T_KVRm6m#sCAA1ns@EVih5A;Ejur)(83Rrz8aP z=1$0xWHDF-{G5u>SSTlipwA5Se2jYei3XZqI2rr_i5xBxYK7^0>FZCOLTIhDkuJ+r zTN)LLGU>jBuh3&Jg-lywVR#c^S6TJ3<8axKjIF}%lfQB$gz4C=UieRF3Q(zwyv4rcaD+A|4#U#G1#r*+Us(zGXIW6Su!s{#qx$? z4rRTBkA{fZB4aA$bFfNUsCmc4&rh>N2)}h<__p+G;-H?tGzyOs!fjYq!0Nl=rB;t7I~a!nVV<02#5y6;ItX-3!;4BGrYMH0|~rho{O zGqe^QBAq-T(-P)H6mU*`#EiKwF(Vp!fsCwl^-XdoiXu4-Nln4Ic{nOWlKr^lcQ_5i z2nJ$xtp7%oe%xaF7u8s`zE=_lV;LhsLFUI)`t~$wI2OA7IKV}q@fOY?h20dN@%CCz zYqG8f#xLTatE~~U=UDLkMZ2!7{}3;)Nqy4tN()K#ykqhi6h~fFH3srX45R>VBSS`4 zj5~6WRFZE=-ygyaLRP_xv#*3FL9{PZjMal!=UX`>BjLz(&z>aD{XT2iHkyeMcrVs} zUFCLMWh~5d@}HhJ1<3$MA2s3tIK?;>oeD)>Burq|hBP*+5b6@5xT1YY|34IEb?$>U z1TbE5VvZ;ITGC%p$B=!6Qy}J=`iNta!n%FSE?{U264XHwkxL*cW^Y-_*-=Or@rFz4#b(pGx zABlqauq+EEcdoku)&DaF!tMCt4JX`Fg5eQ^@FAz}GaGy1BH)qnoH(IM9K?wdWQA#Y zLxjZ3^G_}}96c`Ad}pz>DN>-quo2p6Il|t)eOtyL=;P}f180vA2L3S_(6u)b!T`bo z*QWEJH)|va5p>}{L_=`Wm#_RZt=|WSo8P7&s=$pP+z$df5)x{5Gr7NIg@UT;PAvb` z5K3`&9vm$By1Ke{+O_mB$X;kM66vvn2Wf5#vs1|Z8(+39e-f$()a(D|`%YeX5Vj_z zvZsb{$_YXZu1TCaM$0CCkVJ4h1g9rD*p`%uLw#^suky&kF#@nbkC zkbowruKuaPa5I4t1Tbtzn9C<5tCRnu`10VC@?> zc5L5XbKOZ=+MP%jVPUC3Xr#Q84lr6eExP;ydYffVtQeY}OSK!n1s*>Db%Ho8={Bhv zG04bAY|hHW$sQ0QiSxtom;tF{a&msCwdA|x6Rfq|@7VcV`$;nA5DrBp4tpdbsC^i5 zgK3@KePDU_QN<0XNSV`qf{k&F;qCBn^po|xz-!u-gz^dg;V zsyLRpA9E4J>>TE9Q=qmJHxCs0_)y2+%lb5sqI;4^V_~8-X+HceAU;mJN)?~&Utzo&wHh5hnh7;7f z^WkA*7T_SMnur7eq@Bh<_BSM?Cy_re#9$-2|Dv%dRxfgCD@nS`uaOu^} z!-$YzsyNdih96-^+=gWri z$YV1HiD=nI`h)8XB=oB}VWiyaJhuEkDXR`$Jn4dAJ_;_pgHVLud2Y^ZXb0zPKr?MzHKs#)&# zQ4L{RNf6XiPWtC3ms;H(Kh~RYUEjDD;)az-pWIG!Vdo5?eu$alGoKh?p!QPm(t8VO z^s`)HDRD2WJVSeYwSBu!FkNg5CuWvZlkP-!9}Bvy!` zLP{FZK&i|jnkS7Cnv|hQWz{~;v^>xAecyNghP{9L^ZD()`|yX$=YKJkH}d z&JorbO2|0wvB$52WCB$%lk#$$t50V_qlV5979)^vN*tbLXM;+^WP$DoREPc_&3SaC zh$GhiLaHJY^dBanI6(X_-8j4z7~~X`Q{ZFMZ{If*A1rY2(wtNR02)#W#MWbL1hzB8 z#dEJPU12c@jDi4)#{hV78sHST9UVP}0A9eV5M!Z|L%RB8)~1&uUi3#jy~{)Xt<{Z7 zD|)^5tSpJlggH~y?X&qCJ+rcm-fpt7DXTQjkLr5XP@>S>Jcf@`46=&_^@b$!u3&bb-pqPQ8TRzF5q^~35`gN1xE_<*ES^A=_yqKrO2r(gGe zeBpJT4kF<|e5GrgkosyRa!{5~u~)oAqSu~1do-SKZsC=+`sckjZP-vq04zFgjrdIF zNpB$*7iID){0s-9+ou&NOMu*u6NA~M*ci?|upMck!`+MAKMhP-;u4|CZV5fqrh_C^ zJEXS1D;3RAxbKFN0Tx`A%#of^jyGt|3o6dORsI5@y`lNeaHA4kupkDVIUMqXNe3)*wrtyk@Uu1IhXzc@7oO;6}EUA;R*7ix+RF zrO8&+J(`Q66m6T|x|_O*3qEr19w?e*s>(%@b@G{zhmSIe(40ua_hN=kF(efU0M>|5 z2I9|&8n~H}V=y}H7vNHgbJGzPc;?WFd=^|fw4)vgFJ&reI7x<3o17Gf6eT)g=FbER ztr!-OQ6|4W2;yv{?#cQ9VXKlwoP}YQh)rbuhD=zApG=-5ND-l!{CxpPt4gAVl4Pzc zkC$pGjl#ZCEINx@+%YFoTSoFH@&N9#(_B$hika zmax`&7%e#T>jz?=OU&u$afk5lGSuMF=%s{PA>Zt+{^}V;M%|MpO`>L-rIY8Jyz znf{M-Y6Ok+p@<@SFXf0h_D4>fdFxf+f+ZlkzUqQYDsfdEfmG<*lLrfZ@r@M=9@dHz z1qvTff-b*mKsy?IS1^qW3w5jU%;=?2q%)e3R*)?jMiNA$!lB0td1wiqKz=DWu6NTk&H8=0~Ktchh@MI*Skrfpc>$~5A0uM;Cv?gO1B12;=zwza@suLNS>B(z(I@aNwKD#vZmI{`LZ z!03q%?GEUO$mwD$GXEh|F#NHniAU!J_|?QzTBmDbJpi`Phjwu*;BDNnmFC2{qlWdt zD!b!=C&jWne?J+nMBP*T`D7f!XhQF;2!%CzC~vKBw1Rt5j6oKH%t8nnW_$?)oL3vy;!Yf7cSq=Ll#ui!K^ zAAFmb__PhFRraT_?GThnJ@LSy0Stx3NVu^oCsuq#34eh)fFUga1jv3K6)sP5XCK?` z%bje39qGJmCJX+-uN*uUHtq(KMaJ{p<0`Q_(99kgr-kMi!&NRcyz}32KYGr87z&r(wiKR%vVPrU zSO9J9{cHgBHCY%afZ1A!~c0x&$>oSf!}etFFAOjM*kZ%7KeKGsnVhfYr=;2^ol_rGbXi zy?|QJKC28k;Rq>uK7^~IPZyYZ|AK5v>QqN*iJ&>YRZEeb1P1v$$bbcnUDjfuVsD*i z5G?_yN(q3D8RwhL<4InKV?xt<&`umyltr@62B=?eT)K40_V}<~b#jsi)-?zzNdtP%E=TommjOBC z^tssrID-L23sO_+QA0_c?J{dwzp0|(^XAU&0BqNSL$-ucBdmV%y^u^@Gjk$xPY*ZL zLV1lQCl2!qA;7zV9wZH_4-sRK3Ez&7FU3M7?-)9W>84UC?og}5VynDL=#{+T@qZ5! zWxHSn`dG2CUydsJbZyy|CU0bUNI9!wGJC&bN3=&lYS+533PBG`{6oGV;rBld?Lx|? z;UtlN2%n(y(Qw{I$a|wQ2l@}8o~J1fBp;YNSKs~Q*>Q}cxK5(SfDL`A61(0Jo+7u= z754i1R3r(eBrriJuM)nWsl=bY+&H1k&{^h4OenJcz5Dj9ytG4%3G*5~gi1uK1<~R? zR{%NTmCV7iUW|hWZh3@gBcxpY)(cAn+aqD#M_xyj16uhUba>F|oYz4m@#NFGa4oCI z{)UpaTFur8VcYtm731sLfw+G)?JZWA28cb&p!N8jM33*T$zkX|Dkwy`E?$~5QB6gK zpt|PLxR9;K4@eWwp9a(9n}@*fsEG(s{g{0Ldr>;>3V8#b@ffF+UwZv|1Jg7uivOF7R4aB<>bi~A`VFEmi6&ik?=rHIFD)8bE8#V zU42uC%_r3tR?g3zv!xe+JzQni0IkB z)lo%#EQzLO?w>$sC?!g^v44*P=}M(;<}F6iUDM zN}u)Ct3K@wP*tP1?%2`n7)6b6&x{6Kr$Zz-CjXgavN}WlY==VTE!UlTHk<8+j!dQd zXTAG_PyuPM(7`Kj>;mq90Wb%@3U4W^ST=sp+=pZ$BgsaeqzHqWgC7xgf3ZwPFE-;> zw-*UBpU8O85;SQjoAj1^Y_T95;jry&Y3~v+|FIgCoqyF&n7%>Z z^4+Ba^KJ-(V+nK|dE!m}5X;6BF$?}4E6kRVf9l~;!rE3=alPGNDME!xotd&k?-uA& zq$bOEL;o3-2-vC$Kqy)G%5rOkqNo=6???yc`Z?zbfg*s3G?K(0MBRlHuAs^HpnmS^hM7c0!9*S%FlWFfKq&=4UtMg+ zT@|o;G}}mVPj8FP0r`YJt<$Db0(qh(;vOm)bg@7W@}`ZR9D2>N)^-yjR&23_;r z#z%Tvw=RP4&kN(jh(Ox{)F_{Bd^r0nz&0{4#KwBsiS^Et`S4KJpg;}2?{jU*|R%KGb9!-CLeK}YUIa*;{aIS8{MtuecW0*;RAK{;=Xk) zC(oSG18)^G5vXuOFNRDfnNDBNMh_fuO0oj>40haNF|i2*f`k7Co<>AkVZ=XoJPLuW zMn`L6?%K$p$rTQQA}RFzt~h4ldJzEj<1EBM zRfOoN@`IeB=s8;1HD!~M$W+#iN1!x>83e4eD#riwc_bUwmqmc~ELx;Wy zJMg2el;>@*%tCUolXzAC;5^F`<%Y507v6|E%aywq1of3c;FVj6&+siU@9UMA zB?mB|q>l4LPoDh<;_FA3V_8tOb@!rH10y3VJ1KLFL);|0IU_E{Yj}jbq0J$rDjMuO zuz}D;xPod#%+D0-Fb_mx*l&YuR+^-wdc}}wH3qBaJ^xo_2!$wuOJT? z%z~yb!Gr?k^Y{kzzXA6!b+uPqi;R4+amzuZo#e)Op)Pznl=C7SfbHTv_mLgIiUz?4 zT_dJHs@kiG9_;BwJ|idGm7OWI4(g@*>qb9#`BEK_3iEb|g~{g4lc^sHgopa|BLyom z@ow#(_hQRK*bmf&oLf+#QRRkW*7o>tLQpksr(doXelfZA7@E#7IT>QS0mTLNL@e5x zn@=mWLJfN!>TB#6!gKhiOu32o9Ry}UY@t^M&jh-a-KHVXx1gs8Sp$A{9J)_})B3_D z=1k59La=IbK2q}{1c6QEo&cgh{~)8ln5Szc5f3XDxHK!iK*uvDymCE;?g1SF)*Nk; z866qvyEau5h|}{IFOUzxx_mvik9JmVUU#BDb$JcNdy6BHXlXb)O5p+^Zv1SosX5`* zhLF*6PPqpYGcZnoxnFURUMiN5ZVQY(SW*)x$|jV5v27~;=UrUD2_f+;Qx~TmDjn;; z;KyZm6VX1U zA!@+MyJHrINE1F~N2s?k&=T0`oC=V{VL%FN@Gb&J==?blLOzMgz&AAlvst3O2Kv{w zz_5~LI)R zYUVGb3YDlhhq|p>YcdQ#ki*Eh1QMxWj-DvGf<$lAtNWH5aa2xH!FQjZ-KH3tDX>8Igt&O1>`b_bY#$i9-jx9 zS+Yot1M8(7v*r)o{n^iON+kfzLPO^c!veP-tVhkTs=9nMmF6+2#O^>lNn?<<1wH5G zR_en$UE(}EzI$?mxR65+{K@1j`hb+LO8c6{uFSfeLjyLJp5$ab`TBS#n739No;h(s z#-guRGNR?{j2Sa5#YJQtU$V+Mt1K@22ahB0s0W6+y7q-#2#)@31}4hN$~nV1$9vLZ z>{jY5K6r@R6qr(b-){GfK0Q6X;2eTlRSP)xO#jrUF^y=EIrNYrDpJUh1AU0rnjrmr z&LXqukeq>^*@|yIVF2lNp?REp(t#`Gh0`YqZW*t@ibOt0>@YYS>5;~W_-EwshFXkl z67W=63>qg0O7^mVc97dH=aBzFi75w7RsQnPW{jMwuW6%}S8pIV5;q8dWerZ=1N^q% zK8%nd0%T{N5&2Pve6k41>0Bn6p#AMt-u&Q6V&EvHDv2I6L7)U)6*yD3b`XRIY6c4| z^Gc+TGfw6QCpk210&nj9QLE3nS7X4}Sot91{{8JFRzl_hPSspe$^x752S0l*qFAE> zW+>t1i_m?5?bJ0GVC@G4yJ6$T19#cb?*pZiAeW-Q5fApx83d%SslibrK(T*2noqRh z5kH|4#u!J+SBXT`+z|z5<(!tz&?L>xV7W^Ipa@JiZ77nF^nsEPp(gCALKd(`MvQ|JJvN-3+1tFN6=FCN zk>Ir$7BmYD$8i!2he4uTeT$7|Rua_4WUk>qKWb>Dz2CU2M`LtlTCdZskQUtgz_OMS zm>J$QuzsM5z6H7hILM13;0HEz0ot%!F5aJTh!bU!+m|eA5+V>H`Cn^=JV*G@m*qGJ zRgsOX@n15u=)Fr-xzmmzz>&x4Z*`VDYT7G084%orTVd;8*b?Lz4-%y&K(w_jI8Ug7 zL(?_aW*qo)lalqhE3lyN-u)3VkSq-}3Q)^M##I>3`(uK}PH#rjUt+8_v(5jOLYT7D zdFb^<-#lAF^M42qP6GH(+Bc$8)ks_+K@M>;fp5j3lR`GQFpyRD=0GM9%Nu7}TJgb3@6 zzu%cN7a1kR#V=brgZ-LGXNlU9rcX%2=N8dA0}KJQP=aObt}~jD;*i}99AB#6SG`5| ziS+8L9Ioc?uOvuck{nJpLA<@^|XXCOYyQWkJ%+`ZDzk@0%d$VPTk>ah5n8-$JBRGeK18KP<+F*_ye_AC~rWWrVI8}$(-bJ z7}mtVoUR4a?DVXyt+lyjk-bouuKn15IB3aw6&0yNDUX3=)AmB-e!Dc_&VLI>>*#Pq zv*Ye72Q)0fYFV`;J_TN&iy-0BHS)UB=sT?9BK&6BMWYgrBN2DFcmUhNwVmH81V8~U z3y1fNhwg$_+jgG*(CHS!&g8w)gX5CWOX$*wmVmWMS}NV@ak)@SJOQZzyhaIRcfb|~ zNe|DSU6KrgW2Mjhq-(8MYM`ED9nZ%{gFLLE{j#WAYpm#}Lt{B+FjB{pPlC2bLoW_-2DKe5@8zXJ4T{6Z z0v?jA=KjP!oUbr+JrQm{V~|mWz`Y+*RDWR%^dWK2Zs7 zfX+Bv#}ty!^*XgZG?pNV#V^kew+we$a zPkG8O&wdqhB^bTy;2gkUpY7QZ7Ho-PCGKr39NasdWG=2opEbhwT|WT5_O?BPfZoVv zQxo{*y_GFPgr)?MykdW`K;bwi@T;BW`Ez=u$H*aKjdu#=NAC;b>uK7wVf=uLHv3rz zf;qi5O-c=XiN=6w{z}L+p=o9oqR%Gu#Q^mbRu7YfxhJm!L!tCA@Bh^!1S?&%+xErt zG9CnINsaQnXtt|~f9Py)4MFaNCliv>+4990z99H4E2eg~qzDfj$t<-8=Y*(Ols+BV z6@UA-F4~qmrhbHOyM)>eI|m5mTi50>hrDv5Om32p z>4o_|>PCDi3r{Yck`w;!WDEZyh0}Yb$Ei#1jjojap>25F`lj-MVaaDykDjjIaQ?V} z;okMBPB-;+ZfiTg8Lj-dnV(OY|A$Lp$f3Ocb2YognX_3A_dkB~d@JK%FPwF}UcTp( zLoRzr=bG7m^=o5Re@hmFNjbS9=y)0|aA>Wp4k1x^XS6BdQe-rm#-Vq{okeD+drDBE z&3OmpZIrYTZih5?G2k-&P$cMp2CpQa0ek^m#~d?)#~xEQx)=mY7(6m!!jjw}aDmqC z*jVy2E&xR|Hw~1F!7rJx1H9pwe0m*L_e>V%unb?~9SUr6u!As9{VhDJ!6ZCl&NQm& zl{X2a%k7x>L6RiE?1(nyO{?HW34A6RBz)j0TkxPzg2t2Q z@#PgaD<6+pBdNI=dyf_roMg&dWpT2?TRjT;k4V^pE?Tu}8VA{Th z{+S1~w{44pl^PQc+(oRK#h#FRbleML$5yryAr0#wRQlF2Hbm6|pne(o-Z=6&Tzcp3 z&6_vtmPs_$BF_=yE%So;7IIV{*W)%*>kR+hf@# zA7*U7vwb7>{G#}g&SQ=#k3IfW7W9YP8izHx4|^kkFq}@$DRSK?++hRV?#bu7_M4ll zJ1t6NBD~-SBF?zq%*c>`KIQR5;Q%#qCs?;`oo-nIuEN2Aq_E#zbPqnYE(xj;Jn+?# zBH=l875o9~#lG+^4Z+q=ceV;TJQnJho6t~BQs5+ar^;(a9A~sxR5wA52~#X2qf2LD z_!a2&X&+e{ZiXDAoqVs={7PZ|+4Thghd0}Cc(RXNcOBv)^>9p9ZfNhJl-(S}_R+Do zU28`psk~lrixHigt`KCkQ)J{E6|WbwkG4I<-)DdOOaFfU&+liL_02vyw6?g^1=YE( zy7a9U6BBz*p7?`bUsyl5(%Q?U6EOT#K#x=LByJ8HIY-Xr+s+_`kTu;U4}izJ%2k_H zeRna$U^dbuN+%>HCQ5wbyo~s4kFnwK{;_ZhE4Yd+@?%M>4g7vQ7I9XK?J%aR;7naL zZ8kL%fT%}{|Dk{eDr5{}6Kt&^ICJ-l(>ij(K@7^`!IP@An($OxW+sAbJf3f_?#Q7x ze9v9nATF~B@`E@oHvr`x?`YZweT9p(Z04%IN_dF#SI%je&-+eTi5IC;as-6kMdM<$qm9qP(|)Q}cP^hJwPhqn#!O5)oxA54W=jr>wR;d{^Gv6R9fqQfHARA*S?!?j|I39;0r%=j^<9 z>e?M-CxP6Jr|MqV-cjetH{9I)%|%Y{MtVlZ)sev(juXSjC{)$&vUD_A3e<8~QjSYT zO$1l4Ws5L0;3KB( z84+uw`ndPo;E)ib0S&Qf^PdmX)N$-gQChWXm5IQ$(&vxrzi+pyD=jVE5;Q!7hkF>B5=BxHC;M!# zw|a>m_&VGT$t@Ks43Z4V+I=`$eSGMVa&-(($r|R-(WpZVM(W63T)N{ra{d{{33Y%$%8fWne)b52lJ@rYDgNu@!seYjfqqeKOKVfhfyTMc&U6{x zOD*B*bk5rDSeG&?9~N72fB%-_pFV>v&oEAJ;nAd&gO3ZYUc5LJY^5ba{u@}n6J|Io zOyo2G)aPV#n-@tXgS$y}2Lh4NMNv5bce13PYXZgKLf%2+6*P&EzPz8#WFEo=QDL ztf<)UAU*U7-dpa-d*|lnrbO<(Qyw&``Pz~*&?Z~TDk|3g^9AIAbXumZGTQIxx!di- zGe74b4wj}qsd9KDFkOQpnw*`R){lHKwh3gd%74BB(ae@i8@~bB=u;7ZBAaK8YK|Ot z2Cy#8%{Ll7#^n{Os=-0lGR(whO6MN*x}d}Hq1(SeDi-a-MIGcS!>KwKFBN=9ey!I~ZL z#;u5mXTJA1`>{c$bcC8$H+BT*vmmUini-{DyS5gosGgi~8QS+rcJ&yMmZC`mC~;Yi z-`=mKIx@!@4w>5^Qa}zFMc$6?3K&vlfFz&-gBXH3wXVoq1DTAn(`w%$&PSYM$MCkc zu(;EI&!M_q^!xfB5Z^_hZ_pFllJ(>D6698Cb-pu*D7kprc*yjqo0bzK5d`22)CVW{ zPDQlB8fPB!bzWZTXVIFewGibNrbSvbqv=dH4x?KSJl!!AjJ@yStb!H#bz7NO{6VMf zUi^TM$gfBG0}@_6r>Z|)2?TP9sdkz`%PyEsGczoc(j+M;%Ms{_^A6#O$DL?IL$gav z+CAjf8=6O7U6YfOLz4Z%hu}=W%Gg8yzHQwOOU^rvJB%F;1IpMYTBK%1Ubxy)M-W1* zKe_KE(m1LY$TWBtO&cHT#0En+IVEKSOFaF{UF)*xAbT;IK&aCS4hsyN$2__I2D2R^ zl{hdY{8R|U0XWlyjnT?}HFQIj#=cV+T?ZcFK25LokXpHxUL-nqA~AqAUWDDK<{VUP ziA)n!R6a;IpiyeS(B}OvYw6kQNG=gPH ze6dnDaPzJ}EC6J==RzKng_a%$Km_EHDo%T$py?1FkMv!rdVpC!}a=a&Onfug}#^JqG9Wvr@FiSvY|Qzw@|An4NJ!9d0x zP*9bD5kH3sVNPO=OZ^0dk|d37vobnC_iI$_uUB+4%mi1_qt()6O_WEsnrGI)l>sq3 zQ*=d{ywl$c*V)RHh(6k9?e*UE4!$R}Y^T!g9PhXHBimFA%02FnN*A;;?Vc0ixZvrs z`@CVtT8BA;&>~$Ovg(SgVo%e<)}9oI*vWqd1bX9yBkf03x~Csj3eR?uReXFJ8$Ayl z97yCahA$t7&lEouGN_H)ac?~m3LFHaUwd(8Rt%aC`Yht!f&))EE*ej0O2Ha^j#mEA zS~0Yx$o=C-#~?FQ-@qWR*D=arAR9G}b5PNe0`o0!DcuZS8IIDd!W6%Rr;OgiZJU-( zy%(v)`w7JYc=qYD677JDgQI#0^2#~U^V~6AoD{-D&PAAR0b_^8zH9{qTK^qGxgs!5 z`?$Qg7DRdY3dpH`)X=_zcMQ6?X&veQ{Ww||2?vCImpsugI&Hi@Z?nF>e&dm+qVUT= zA~g4$df`{q4jY_aELq{i)TE?{`$FZS*stjI?zgtyL`7Ibimj9oG=&C~Bq3eYn$4GJ zKeFOU8zxXbsY}G?#Q7Q8Y`-%Q4@Bzisq59}WojKv4{>=we{V3X1Ih_jakj%jQd$@k zc@1=X*&JM*ao68d7cYY4jh@5j zH+PQofJsZP#Gp!H`2#E43_IOW%*CaK0CTvY^Get)P=`cb7|tIrI=!Su`aF(UpIVvM zy^DYRNL*pAw9`2(_7-F2_9kI;TdrxVW9jmUh!;L6cc%bh4h0Pr9Iu5;8j%Xh-O8(W zY??e$>ez6&J2y%tt3>hVW8So-8gf>Eq-hwP_@LqyM?HgyRm6$_m6Xx>wdx({@^hH* zi`$-HNs?Hw!W~=UQa3caoN|LGGOZ~xgr)og$nJ*bM&&)=WoOhV4y`v z$g(vPzxt!>-w*R7qOF(gRr_FdN9Fz1vyC4g#>dBR>l>=!yxX65tqOx?_45Mdwj!U;Dk756 zy{VjnKD4}lWD=tqXgBj(8@1XXrY67hw9mnoL>^}x%fo`}t?ABeP+8=0v9ayLMvrxZ zQ>K6zM+y*NL8&!*_^_pL|3bMG4>XO856t9ipys)SB(EB>O=f$BaRPxPz!F3=F0c_9 zHCUP7{cYSoIdJm31;7h53P~~zykfhJ@z* z*+q)%K(e|AhDj)YBndDPpiB|` zPbA?RQhg>n?;FgGfaQ(`J@`(g6U6^eHEMyjyAhqr!Esukl);Cw#BVbvIx8%H6S{u1 zoEo744E}Q#!K50#n(n*qUvZ>BoJ6{YlfJ;|^oF_A?H4azYzg8HfqAb!yQZwP)Oq@^ zDQxuf$U&~Tr6n%@cK(29=3!K5V1k5v*uOJKdtn4}4rC3HX#(X8RC`dK=w?q_;b*re ze*iqZB~wpDU%NJkYD3WTP`$HcUwH4gvC*_ZQo{)rSwHrY*HSxd%G} z!+xod)`;x!fKWC<1{66F19iM!iHCxQ-qzdmWfxa3y6DM&`__rSO3gE~X=?W1mkceiw4%bo z>kz`I-q2wdVk%Q^wkhXI7Jb~#WfdGrs}BZ+&>Td)BTX3}wr#OBu=Mcf$NM+L4umxJ7v1sqUtZ(Yq z1|WMUvevCdYyrm*jVr<%_a4&kTga`MEO_fjkdTh_iXbMXwm^paBzXRxkJJs+#~A7! z=?wc%uZ3&fH%&uVR+^0(_ni^vI%lgQzSlx5X6XhGfYKz>Y1Q$yuKAB~ewY9AhZI3I zwv^=??S9h=anmZEbK}k~Mr}?9ujA6GeLs}s9iJDJmhRJRXlSVX=L;;@cxk6*ccg}Y zuFXay`UQV03O}srTWsiT zk3(-w6Lb0QO+4e*j~UOXB5>$NVaG(YH%JSNeI@l)KyP?D!!wV^XDyD#n~48!L8TIL zo)nfxDOIGny_-RKtJT0M08e%8%`2{EzXCE&5LE0)XJ*yG;fIw9rPmK!ydPh6JTG^d zRew%qbTkuOG+jJWY+gdQ!3CkC{=tJCtk`9B&>Pdq@ zbV9;G_9)~<`@z{^MF6fyrVMCqIry7uNZ0)R{egC_L{Acu*B}PcmBkiv!#YfmJwTj8 zAhpoIFm@$60RXW3bpCNJ!E}YrGXO@gR8nW*u!|M+`8Vg1-b!5)lZ39Wu0h;fPMG*b zc;Ee2R+>($6PdtIJM;T@nXeM9=x=y(uDd@9dNbrgjc{GhgumQYc&8-&>c|X52#-Ny zV8I5?CX6_-n=SqZc1oD`_-=aNV4gxLm?Z=`n=WvEN_~>rAq6mjZjnVQ$?K31kR}qv z_29r+kae~#6tMsCjdMx}$V#lUfr}%IXm5pwzjvR>*&VjS5~aNr@T(JN%7mjn0F|~G zak>oQrd5tp42Op$ihB1Kh4n{Z4;!Gs{d)HR;8VoiGURb)+1G~NhKsEdHfj!WO>Jm0 zP35;8EaHuT`DTgO;~~!>Lxzhq70t*;O`HZTPh1$1l75KVwvyqc0^nVuaN{wZWl)xg zmO})9h5Xc$oDa`d4&!bld6}jr2>3bT=^JU*3y!PUPlF~9P=S&o4}_;kJ3~4o27X9* z#Bc!Ke2I?eac97g*pJI^Km>@mGFuD273&BKvAh zE~VCCe$XGEg5GlqBl3|M5145le_CfC_^K-eZwDHBlvi)g}sxH@SL{sWsT@3$tpyUU(G$1^eLcOT*KFMti4y ze=!4c{zGzYph=M$olX=mi}C>^mhB#>0Hj1-*zmgTgyBI~&%>by{b&(SzE>v^E=Yxz z_NErbY9w1E&r81rq1xyMr&fN2b0tRQ=*kJFeg^P^sHICo2#6Ilg8OiU)y5)ntBHN_ zVm+C1p?Vi9&|xduYx1z6*#L_&*PRc`1Yf$zFs)zbO8-md%JiCi218NPMB?HT2^ zn$7di3h`nR8{48u0J#+-KXbUNU#8s-|6;&<(TRy?J+BQd z97EgD;aZ!)zNYoAtHyVAv^&tpPOQa7=nXcGJ@1iu#_NfSVt#egO!Kh*4VeIsfjbaR zb0F2D&l&0GMqqzuizg`%Q2^Hf3CbOW!o_ zg@>0Mt;gX;RA`DT0|G-D1iiM9>(Ly;16FmH%fkU%Me0T?`PKz81c$?oTX?; zGvMe5jdS8R;Z+3xJfIIS&$;3Pna1d_#v(#v=uD&GP*<;tk)4=7Vj)>%WAj6D4pLJK zIwGa$W?hi7H8mrRJaw~#1GeJWgRs8yc6>izWqlJTKb5(q(rX8T{-w67U1?!%{+@Sn z5H666fE^s}0aMX6ns{IZYFOs1!xt+n`2_<9Yr8iFXgFsHg zjgyv=ilCxz-n^&1zD~P$*Q-w2Z#16YNJ?tes<7`P)^|t%SOEbl(!yAvHBzN=z%YNE zv&1ytYhJ&yz0NZ{AR%SufIJ||L|43VIim-(F$NRhWlQJsZE^afuP5VWU?LUOaAk|f zkg$lhm7wJ>Vpc%pmC(&G?`;@9%u%AVV~QWHt3{K{zpcyOfzrWfLUj6Ca);I(SXLwe zE8S+pZ*xkN`|HtX1{uJ&Gb&aM9g9~ac=9uL21k}ibb!2iA?eMs@ifMk6#+2G>0Qdr zo2oR)r@VoAJ9=*W|uVK?8GLeVG2F&@&gVucnsMZ>_+m@?x1B0 zES1~{Q9zz6YmwhQA<_bdYFj`l&qowZH(G##T1&q3JmhdR=YVMFFlH%0Bd*V}|IbxC z{ZbV+TzmuQ0`e2@HcoxKGhZn24yqs|rqrm10h|sX)ZM#x6XgYR^El71mX?;lG6(>9 z-{}T!@{Qi!-iWsN{H*_{L5|)(HL%PP&KOdz_oM%oLjL=F?tk|mHn}}csTt+)<;cs; zD`q0C42%S#p}T#;m(Sl_Q1*gHAr1GCkKez=7Ej3w&(;yr}8c(=L?7eXnb4F!zT z^fN%CK!RdLp!cpi_Z*QK0DRrf$e>Opm7K&0@9emrnQ4q*I7EDciTVBm2ewEF4Hkx| ztQm`daAjsHJTG}94t@jv;8>yG{@%x?N#YFExsx~rEwC|gfKcbq|3h_(woDMcUQ*sx6UMlA`-B6VZ06k*zx7FWz&oXhP47lqChIWxzI^PlF~9 zMldQ7z1EE4)~*Q?CQ#z^;ro5!93g1zha7NFVvF1inDu_J;Ehr$0iOf0rp5u;RKuJG z=^q{S=#Zz!dVP2{hP2#}kbGTvh107TH)dE8S%=YElCB6rA6p%yL9l|js9;JJi2O)RXbofj4U$nb8FRu z%_Nk;+@`(L){EqFfW%y0?ndLrs)Y6?whU1usMv9Qw>QWsosGG7qRg*Z1D=Jyh~BA- z^CH;f-;_K}t3x^a@rnr&kVLdenX`sDEEL$FfAMzvie?t*$~#u)es<2GQzMCsa*#laI6mS;}Uaz{M9xt{oOX!)AoR1XHZc6 z`LY?2?NND7AHKWy9FUTflstCf(U{>Q$Dg$bjus!jvfV9%SzDt#uy2|# zya|KTUN%|P6=jw9^@F2r%j_P#1a2oodh6aKkg3~6q&ZmD1cGpSH^KLtA5lq8PuTlC5>JcU(ZAxwFai2AC z)Xy_Kee$X2u}wubD**yPjf4@BZFj3Njs!+3%Zak3tM(H*!0YgUvcUXC@`a&R7K%X_ zyM2raojbJ8Wlo}8i?pEaNm?XFz-&#|jte(`pHM&OkO={WTa}51#;J%iiD{`?D&RHT zBr7fQ1j^05$e=bm!rG7>Jr+GGt4?I8IG@^@zdH>O7ZQ10&GZ1c0#wJO^OlI|Z$!Na}H;FFtdk!S1W! z1LJtO2}EH?uUZ8>X*Hs)I3W6evXDC`v=EgfH8l$8@MF;UA!$G~m#H6jy3vHgLcR|m zN7v{~J-^j}!{hXUDCW^TVx_~^5RlFXNl(?e;A68K@i2tfvB)@-g~~h8xaG`r2!ra$Qj5XFh}Y)35C2(@bPN8cdL-!bW!F-VA%yZ#j|x|u z#M6dacu|l=9;Q(BURa-iv7NA^MX8m##n{+7BhC<7Tq+Ha3DMLlkgfC5639jl$3+@2 zoWReAGK^{b7h(s?BDnMyDIZxKXuaMj>l`|K9`!t$eUv=AuhW~cB0&CAm)!TJ(XN_2 z98t7t1_vSZ(o_Ywlk1=00=o2Dau%cHcXe9A) zk86g9@le;bw|EkVh$(ZJt5+)0qD$t|&Rwnt;r|zhkViTKQ2e1MDL_rK6$AC4T)ooi zzrRR|DmW6)VJ%e0!^0~fD%5QQ3KI+#rx4|YR<$|Uei@2uInPIH{JkiZDT4m!(>|~N zz9p#c*3M<-6UxictsJ7me*E}x;XmlGdRg6A{ATV8PxLEy_V|NM&s@vJsh$79MCIX-Xt>n=#_0Zk(GUAt1n;|jN@+g; zDpxtVQ$vq+r^H|xLRoqmPd36R-CbSO+JNebZiAqPqzDw7!ACQGBytx~r>Zb09`L`! zNx=`GJ-zGBcDnT#nI!5Y_a<{%#bm)#;JP4|q9naZ%{~VM8LSPEq3L)_I##`aZ}iKC zmJ|Dts2?|-hFSy-r>%z$9io@vw=yk~Dj~pS|CJz6dGHiKqD$)!(qA@h<9^3>wY!JV zEW`qE&@^I*iM<^gK4wWZtuTSH(Q$G9@1Ls5u1b5ro2~&=s;n41Yc7;Y$R@Gm3^xm6 zu(uwUCOA0Ia)3vJjV-s#{+zAM(x=PBgHvoxxXpWwhVBm6|K}>w``Spj5oyR+&I>FU z8rgOmO%6Kb=LN_W#{8dmM$)V2Pd!B}8aJH$c!IH*;MoYTsLc@R2ejm&8Hk8d3{|d6 zZUcucvT2@oe+&$2l@$Bl<)ys|$ga<*@Fd!0lDSS{FNzcho{GuOH(U77#=}apEoS@= z@L)eK37s?aFt3<6KK>4A$Tlhp0=$0hCTW1A&I!aqyVQ_dnCM)vA7`6A90$r41~75~bJ ztvW6_luHk`A_CAW0@GQPNj6SFpRmfweUi}x?$;)L=N})L%vskQ%wW@k#A;J;D&7g| zX)QQ(LY16S!aH=sHqG&FyYW9sU`>wWXvJ;$hdGLf*6b3NGKcN~#D{;jEzU7KFqW?L zAs9GBMlFnBmOg|D@g|_sAQdDf|9Ke}68esV2q3J?@t4ylk3Gg<{?DfH=p4n<;DGE; zwS6_+h&FaaaFtb@`K^!r!^NfR!&Dp%7<+hlwb4IG3TXj&Nuz&KS+ZieKKF`poH*g1hrClUg;uHc9To zN&zj7*57a>|6lGYd^_#`GA%7la%4W9dDIk^$#~QsqZJKbG_-%$sJe;D;C0H6Dr9U}(47qoO;I zd)4Fnb3s-FEK54wvS58j(|l-(EOVBP&R5K>T~+P4S@+-!%C#A^57AI=bT&b$m`|>Y z^>>if0~{-Dsp!3tYZIE9nwsA4E-^pS0%aTmV1z^Ji~{rX>r*uto{ILo&I^j99>j~Y zem=CC)3ARa>9;)VQdRW9f-N`9azBb!cNn%w zJ)h=qkzu@X^KN;|XW?rPFAP61n|toort~klUwJdr)!rFjs{YjJF6TaaP5V1niS)3` z@4DSTvllHcOovtZSgxIrmhY1{L!idg$~)w*v1vo!+S2(ClYjmC#U)kdIv;LNzrXC~ zZ&a#O=i|Y^$6;JZhHI={Jv`(wgkc2NB|z-28a797*`Dq!1_b9Y^zGI46^w(GDh_aS zMCVfa&pw z4LA9bznjNof3jbV@r`F$EN&eErvS5k`?$HF^D)cbrt;@&&ze&2enkY6OGww-4z@;Y zLyP(6TN}?Chkl`*cVL^=>SfFLBr-bqG4)H$7fBniE?mBULtK5o@y<|qL7o%fjB<8em z&w~E`e(i+%G+ipM^(`@^4lk&QCb4GJZ2~7aUAz~s_ zHqn`ng!2D9CY6tT8ac!e@=YNBgI)NlP0a^nf(9%;@@u0oD zSbSlu+>$kU9ULye#s{O;^1W|r^279Eb19+dr*qbP|M7#H>%{fZ7dD96N$ruae=rk& zHr&*0)_%FkMf^)9{(9nXDE*bKe301HssHrp8N0Km_<0_;PR_S72}{qa$vyEk`p}2I zW(EDsAa|4M$+g?MpLQ2}Bvp@U&u~(7*ZTb(kHo$Yefwul&OeHJHZ_a8p|y2_qM{;~ z(z}pem=hN^t<{-MsIcWt(AYTWpq1cZf#l*e@HGaA^(x+ zH=dOdx+@4f<)7d^$UDs&dyE^)l`A;!7_bfoz2?G&qxUBZAyU$7e!wa}raUaXCtX%8 zHGOCu7nhV=`^P#~(fK0KpjqMIW%>7YYQ1hvzOKM?J;hYEQQy7Hfn-tJBt=WFZ80TK zDXg=y5>wGpF*jd`y~8IgoO>>RYAFW7?_>Dw;?a=cuII5;?n&p)sZ*0{J(E z_7L-ueT$w;h|Ul9!{}=IdbuAD4x}I;Un6DBA= z^7K@IlaKAE*HfzNkIW7SIe6NNJ)<$`xVpLL4ZCdR)bIzH2O;J$`n(PXaLOQ1rMng4 zc&9t;<|Yfj!sTGDyxg-QB-Km;NQaDLBOm%I6XI`dS#0q-@DM_B?=~AR)4BKVg1(W9i~WR;OTW^9ybH^;REf!S2?u zaplEBfZ?*O?Vd=0dUG%t;QC5%CbK~}G1nMa}I z34?oJ_`rSyZq2tJKa_D_&z(IBJ?uzF7yjHTUDO+fk6?mBUH5KR1k>YyrygZzkHR$^ zncY}XDSJ6M^W5XlAT>NepGpUvq=T-=i;O%nPNK4Et2a>En@U_z$R|LzsE zv_C%_){lDuqxGXObd_1Sxa+_PMji8oxpD`}FD4&aGHLXJI(~?VIfz2r0J-ZVO4Y zy8NbO)d#)Wmj({apM%%Pu9B3TOlzeKSG0cZ+F_9!FLQf&c}WiJIs@_5apWthONLzw zSg9iq$%c!IdYCUrEX$Yl#Db0I3+~}+RsK~NR{CMw2;_8)MNz!;I0P=Z!Y*T}^^F4a zvmqg;;O!nGp?7gMp%%uI3^+biO0&(Jw|*8!X2JE%`h8S!V@rl7TNb8Er!jM6j*Lus zp<>WFES~3ke(Z>ekDrLgx^CC5>zf&NP?^2mpkPUl1PZxnC@Y6US#qP`J%qT)_p6VM z6U@)@@?w4+slE7uo~W|4&;%%*^1+%JrLs2^i3$=0gOU_@eHZd1ZMb%*_Zj^1;mmLvNQNUuyUb}W}+VWjI7_@!$y7N@z2F!KQTa@Pqu2%h# zJrk!n9c$o{dyJVldbAR56&b_!h`seKE-TxO6iddVH>u{FXT`Z#n4q1TEz3q?f&S@Z5(5pzm!-P&C_ z>;8+&lPBqn;!~Z3mck=k#7QLR&N+S9wP1tKhQ-s7#y5diulmEyPzdwk$D!ckv05AARMDGUfxq#&Zxk?QrUHJp$MA2ozlPG?%1k{(=)PJCO<)W_ixS&OK@< zu7-K#+{1_8oR|{e^(PQ3m z%|ulp9;M zVu7t?rF8AgQ5_Yn#tX&d%TM+=#!pp_9(_GVg{Z->RAmys zjz)#hQG{1;wS1MYDY>}FKzMz{?MNba*GxsvnV2Hc%G!flcS+9m?#EXG9>@(nf7Xq< z0w1~RyGXbqA|pS%xh#pYZ7%=h$)W>CstQrye1~c=yYW_xukT2j{<;uuGoU&7UsNz( z^XhUYLo8hJTBWo6?pIaF7{64e@*>|_)+3~<^9;)g!65)`EB)@{cQJy^{vIn&43lzq zqR+<)c=i@Yq>SH{ISAd@sB7Wx=yQFA7OL>At>Pw`OJ7AC6$4<9uC=n{*l@5(OX=n zu09&wsET3dF6(XH?Bm-08KvLbMmM;{J%Ktu(Xvb&_1Qsf$B(h42_^wq@GDqfwg@)| zZ1B^d4v!(6cFn>S_R<$O9i5kqPmf3!9v;qx^z>U-7kWYR%fKUWY$?@j{x&|%<@;O& zr0V^;%fuukBsK<1jUkG3n$Aj`;7@U7Ha<6ApbHAf!el3!jQ0V?F+?<@Zx)y*VpV}# zyOxi~$Innlzz(fioTajOev3mj*6}cuP!IJkP1TZ8Ljp@_ct%mq zMHUb82b@Y)tGf&&PZozBxU91Ins)he{v2STJF4}X*PMZbGaqeT=iw%AOk1wb?V@Bc zhClz|xE`-d-t*_CuXgKLNS5s(4{!g%U7L+W3w(;BVPPhtRL!;ne84^Rn$A8FebcBP zjF35t7QI@uzG|sSW)(%BhzK5tA)&(<#mE}FsgPa_MBCk^X2yIxzwnEy?(TBrUpDb% zV!Qc)11hertIux`{l#Wa;^+V1ctfd@&S#u*unj(pJVa{c%Ig|I;wMg@eg@UrGRTIb zgvQ*ysT-XL2e`p4($J4%PR9M`GDMQoG~VjRjt`X(#f(OS+|2d?&H*|=zkg7bgT2J2 z|D(J&kEe2d--Z`alm^mhNTyOzgfb*bgi>Ul3z4x53mKcXk`#)P$dt^Hd2S*pAw!nT zQ)Vh9qUX5O{(gV&```Qi^*o=)XYc)Ki?!}`-`90s=XspRah$6mVpu~==5)^Z!LNG_ zPNXsb&2-s26U@eg3xuQ9OvQdcCs0_I+RyIubY|J15%EzSFluEQ5&viX#G|E3qhpf30G7zf|Te&=K?d)E{$_5h-ti?4T z3JbzPD+|@3`ne126vQNR@t>dSVG#2Kz5}uo<%#aa1`7m{=-U4o6Y1(;H$c1}g2!eIxBCpkt ziQ-v>N@qY8kB`2OJR(Kem8SWooEvAGT?CI>f~Jb}<7Z*l1vNX)bqx$yO))tn-PGvn z>e}l&U)*S)z|PKo+0W09Iw1CFEBn&Kt(JSSPB~?$Z(}8KX-q3tP>(D254V@doSz!9 zqz-Z_tlEVsXI0~8>ngq3eNLd**al70D9N z{C;+8^FgGiXJWT@V}I%7w1HvO%lw&$ni?H&mygK1X~JnaXGpROcL07%Cn45aarKQ8 z$-7Z)p%kb?w(8(Pwjf+itCW+o6OO*&f_QcO3o@T>R7fZ$Ux7Trb4KiO8hF)S@XQhn z-GRyo`d6;CJ55H|O{sqUJxAfBK>4ta0|a4%Q9j`$SKF$ru>E;Q=<|%4K7O>GJXC>A zdlErS^3DkWQ21x9uasjFKsmtTYx87q-w@WYzf*?1nwp#AQd3z_47lP^WcD}phuJ55 z8u(}sB(Iu#o4wYJW%w|l4Y7M~lS7sQM)6TLK7Ra`_IR1UzyDE*Govw>3V=#EC;!3C zn>Tyc(O$fG(KSBqILUY$6?4_jC(ADS!O~-+?7H0xtdb4^dJ2X{%fsd#dH&mtDkH?H^&UP2q>RY-rB7NKMny&4g`5yD_E{|8lG&s7`q?PI_O_LC zC6SX%@G&F4ts_#=Syjq%M!p{JNLE!sEVz&fnkt*tIivS>>R$Ubb7MP4|WF^ zx&#X|OIRJQxN`aOU5VHXVIkX9wvcS<=aj(`&A+!Q26uV-=TCiEA>u3D8|{JBjR?TG z?|j1u>o;z0?hw$Yg?M@C;e;#req}auJE}-NLBU6tVt3nvHhHVM?{!U$WTDCNilnQsy6HhIKEIQP1U}459=vsdIRKdAIfKi9 zd9_sqZ6@q{yw)<45j^F@ksV z7ZA(H*hy?~`F;s|wCDQDnEUD;^tDx6&Lz!`)V=TmE3Mu#eiM)oi`};BdZTqs+(4ka z`uZ#z!`~s(KkUO1mXVRs7@Bc-&hke5$?7#8R?}}erJP>Ip1%f0^fauQGC|PNkvMmu ziMb^=?rf`|t>L`mR(;zc4`L=dISeP;Z76DXo_VLB!JZbep2a#{Wrr)C!Au0I^pIoe zmTLANkyxv&uFW;-yi>@3L0Et>{@49QPX=?nghJ=#okvPbOV>6oZ+80UdLZnj_d53n z9TnCVWH#d;a)j(I42BfP_F5Tf~i7u#YA-5OA$UVx@^ zG)w_2|Jh~c)jPNg?A7ZtM`!F?H`wgQBf5O$%4R94md=4hG#CiXH8!@&``S@&=J&bP z!Z+T1v~8N_gUH68fo#hYz>6x6b@l2?5JR9z@X^ff(Y%B3rts=M^0Xid zJQ`}1+km7laMONn+%badX>GgXU^=u7zgUObKIz576$@3;+pwK7nx>5S)qMDAq$Mxf z2YWY?eZcKoPa;5e*REP2hvCE^t7_(`hAsX`mLQH;n^%Mz454`7lP9~X#teWQpcj*s zJc=`#|EPS5*0z+@G3ZW^-4?smU{2r|@NIGN!{2)d8OF?q*dbqoz52Q7;>uTUB`iQG`YcaB6YU?S_ z_8N;c*yq2*F5Z2ldk?(UkZEIfXo%N0NfmRrr%ZhNCO*9Ev>HUMccDN0TWU2{y(DSn z`BM&RQuKyO&uEu~<6K50o>#85U(FnL>-_$~;bGgU*_~6L6EP2NGx*K1lC;a$ZcE(0 z&|jC`6xoa((P5u+x`Lsb<>a`Lw64u5!e|D5jHMCZat@f&yV4u_t0HXA!SdQa=!}m|EXh&PSPKF%_ zpD4=d%l`iNIZx7pk=>z!sATKe8>m;Ah;37^bL$sjR8T&s1l?Z&9sTw` z8Z#l+pA5flglzkGN+Z=H9fiRT!(0|T&BJPH2a$lG=IiR|xrFnV1-@`Uq}-Hn)ICF_ zI0rp$9DMk61Ge(>D}Lz-EAoyfbe@ z1cM9qd2b#IZZikimrwWp#DL9=Jsu7YytSx%0RN9VC^*i_J`xn#xcf8<$S4lfPh_*m zc}wF%z1^6Wv{S^p`e<8J`pye*?D0jU;Q{oXs__k~uyP!N{J-B?@B20JVj8N!BTbGg z0Rv%>#VR(odWGP7YuVT?qLL$rD3IyP?eBfHwAjh$(4UGiGstRAf@6xFz)KWXn2Qt$ zu%+#A6Hq#-qjM*{k)^=kIS65Rk#$scd40qS&Cn_foW!BOf*5#tXErU|VYFi=&^_pZ zyV@SxIX^#2s%csK8FlNqU%w`x@Gt|RLl0Ia?X4B$J5cELAd!4};>IuX5s47=7fjv6 z41TPjkUlu*wl2pldQ%Co4#E3Uv;`J250^GvMh~(`w*Y1)`=n4ISE-BT_qR>yUqE(YQuMynW(M9J#!nY@)by778l zL^F-bbwlYA#0l*qA2EaR3XuMfE?$Vu7jQtolPJdYQrJ@aqcRR1Mj-h$|D((;K;>OF zl)NUw@?fzZ=M8Bq>M(JtcK z`(R{%;3!`Pyt{tx2=e2T;Pp|HSl4oLvfNR%Y;GNfR5=s*Jb}soz-9y{C-cb@xidq; z!~Frsb0N&x-MQMNo;{6?16zJpHYIC=Lx&hqcpNt{Sc;yj;-N!8+@1}VS#=d;6-r)xa!jJgR9~b@i}!RYk7-JFYVxmDSDMr5gWNdnRYFXL-neU_5 zL5qBd5vy#<*61j6!CC;gaM$qgGTe^vy}2Q0Xl$IC@7KwH?-?I|&&KI+7^faTKLgsv z>Y~bvTa>M>A3K!$3HQ+(UlCqpe%CKI6l#Z547zr#7sfil1=|$0IszFbldMr6cS0a? z0bO0W`Dt@bv>>B>W|G=>uEc06#?oJ3)a=@eThJW(Btd5k01{B}rf9D(Dn+Zs(8=DR z_k=?9rzhH+9s@JUglit)#^3UY*(sJNh4MkndIJ4G;@wHZXl&h16!_0j4jImkLT);V z9FiOSna6E%WkHAKiwc1dlYR$ssiT==0snFVm{?d8k&{PuOS+qjm=s@yu6VO)f+c* zY@MESGh0)pL1Ntz<-QxV7GgE}tL6%M)R-yqI3r_E(L3xOVkC=0q7)#5T}#%hRL!ku z-4k5(!8XzGkdTWcVp#kFiNBn*UPPqa#y&~?LSTEGnrfGKL?qlCJ}HD;V?3nOx|=Qy zy53799-zD>74ULR9T!;W$m=`h^ z-4rSug7BiD=XMUe6etDW5rZm$W2L{%4brAXULY~~f_t}_9PmB-mC zvrFu&Q4w`HO4t|Z`OzBpj5VgLBhd$0huZp>bLb+YNAbG4`XCzW>`NJ+zj#3^g`zPI zu3$gGGoo~(rFZ?P`*GK|{JHD>=Jd1`@C>_%x|fYaco>O(u+hf`h49=54H+-AR#1yK z3!{I+0V&}>#7?!5;UMB7c@LrtYcmIw1=Eg0==%~OWl_+Zb9oHkP~_~w*efH{?Wt2m zAWwkjEB$J#FnWCOA>u%MIGr|*#0Q|Lv^IT794?Ng9=Z2I(O6;iNXSx>$RuPcRoqZ7aEJQQ3TWRZb^od}!Vn6tP0o;*${n3pdB^l$&+`$*c8C#x{f z($UTTdKmRfE>b)-IUB=hfP$o118cf8^=uq`yD*Y2kV~d1DWrdfkWd~!W+EK~)GeS7 zpxGC{qWa7(x=|n8G}HgrQkD9Vw`d)zxYD`rhLm#jG+G@BP&30#kaVlau}Bu8YjyUP zDSKPnfBgOVcJ9n*G{?t&jVZ4nlSJ}PcE2_89F=$)`krze4Kf@$oj~ec%(VJj^3TP! zkaM=-IS}6rEXNT>p5etw z1|qX7t7fy97#lFWS8xuvb?;u1+XK>we<0JGBq;tN0BAh2>j{iC?EdxhYd(6>08LoY zz(kD(#&UckqpejBU^Qfu{DOkdJ336mU%DnNyMVG9@It6G3t`E{s$XEX=0H9=m~OHm zxl#s$SVle-tlkgD-4urGT)`C81RIIxFfdHjE@?vMMPkt1J&sX^Z{tS}Pu52hiDhbR>-np%k%sveyy95VvA?-HEHh}ojLbl`BV3w67 z@3&HKjX8QaIOSPHZr-?Izh`NyHrVW+I$4Gvamu={B4@$_KCuog#&e@Ii4+?5-m~TD zuFs6G@i5)0vN-a&;Njft55qB66SSS|C7q&Wbh1JOjT? zOzg5{lW{(zr1Ygbb@eKS3$rKp#Excw>7`sfF62}YIiAMRRq$l?j0auYix-#N3JaM> zXNPda>j)el^v&NS8HA7620)S$D0m=y>H*kiAhL}ypwhm8roh4qLjUtcb@ejn&n^O= z_rcHxdJ0;Hf#5%N!QS+Vw2G0Q-c@3gxH#Mwy-SDPqD?UOvA5w^zuvmiddCSZEeqFH z(%;%x)PNfCQ$Me&Ref&5Edg*sTtf3yyI`xNlZV55tYNP4o?tNlY;cvugBj=1^y#R= zEu#;X^~xbPpG~>n{fl9&va&LlcaG&OR3qB-%;LXxkp3Cn0!9Og{Rm%l3Z_=?!_IwkgZW!SZ} z_$a3GJLX%m{t=EgOxpi7om1uyEmA}I6g^Ferr&-;fZd?!ke%VpdfXa#ysUz?Xbg3f z&|LujbRj=oQXA=I0(_C%XZmESLC#V%wXU|->}Qn!|?Ar%o{FLG{3%@N@Nd!%68n2yZ(~8qaW&hXQ+rMu~ zQ=npK1L08 z50MciytnJ6CinJU6SQ4^Mit(xp^Bt>mo%A?54rh!h0PD3`8ZE?IFeVlH6f5sz;3lC zSeaV^=q`l>DMd3go-xd91tx=tA`A2H>4E*fuXW6gxupq;*a^wXa>Ao$SAH6-MZzNr z+VMClJ~rsQDSLsQN7v}6ePehD0I`C1p4LLgjvaffm8?EHW88vFG82;M>#YPSMwuQ` zIsg^UEAXU*9kzs50}y?p{?p@5bst(0c*;)(gH}3;&_a*jQ2HRWzj^Eph*ZeWunn#r zh^ZBLh-HmD^S>m-h`Gia)3dX~Ult?E7n}78T8M4H9jN>G@uLviod?J>{CJpF9m+g0 z;%~1aFEjpaLIhkib_%j~o3o3jVFXJ7*(I>${VeDmJot>TNv5ln4Vc04kNRIL#fk7u zC=_&z_1BaY4dQSnwad98)S82DA`id9^^ws7I~>+=a|a;~$nfy;Ru=wf_e@Ln%|*^a zbqjUL-mv?$p2TDua_M(8vp@W#hNN9|_Hhp%^0f6@{UaExrlNwkJypX=Pz1Dku#sF< z){a_>+CMi`lMzyYwz=~%&R!%YmqllaY17{0aK#kp|)XX zpgBFD$$_ADY>AQ;jWAS@U&0XhMW=L}^34hq5TMXauL%72V@NT@N*&C5O`H@`;eI!s z1&qEJR#Jl12iVuFIf(**JkIvIdZc`$beq75$%+(Q3C;Mhou)x(E0{y_u2X5Eq0yE5 z^ofo5Mszb-l!G_{M$v(eg%Vs8vptSHkX{46tjx;9tpQ{;ktP#L3iRv|iRh6O9smhV zJIA?AjkCzPDv-n$YWVR%O zJoYE;epksLj&7sK4gAClhHOuqEc@^iQGXNjok+}A{`Uob>Me@+v#iN~IWIC>lcej< zmlM-}>AxTVZzg@R|9ttI0m1&y*Z+;bxH4%G&!7DK+>)1 zKG1}a`(>@ZH{#o@v}P$II=q)5%dqayJ$CH9&bTO{>gR8w@}X&r^CZyUUpXcEXM5qw zsAWZ)3XIZcTdJ#5w0{dU3oz09{pK>S+GpLN7PaS|;MKP;HMS4{%dV|(4drYlmzx=4 zA$bl7dcD0VW-8>p3|^01TsHYVYy@uI0hPMN_6-2|HQssgd3nH$2beVKQpwP{UT5u#D>vy9uUYQg5!@B*#fc2~~Ye zYZD1hacZ1Dj|p4Fuu4*D)`$LV=Eu0B3m32MDO=y1M;WRGH+h`2HK#ZX^Qz6ayDDjN z542-IJSS$KcT$T0c_ZOjiDHCGUMnQIj8?BJ^CI&O-$NBKG_TPXBrFY_v#jjw4EK&K zPSCuDGqVRMJBaN|OQ*Y+5oBzXrVAn-J^(})gs^uOH6g4c9)h_BmGM%LLMG5-CO+76$Rl)^=3JyW_FR8<7-W{0n5TLUq_)!~D`IQP^?Y3$MAP|x zuquunNcJ(eDU!`)%h{ssYj1763h9-)sF0A#j;&iiKOrkkXeK48l(r!#E%NfMNHUdx zPQ2MSEj`^K=Lq{DFQh0GKjK_vY-nm~+NK?cm0Bf;V3->xXDFs zso)`Bi6&i{WleZ#$mAIx7JQhUp3XMVL6J{E&t|utxEzue3ON6^d3WP!BEe6cE9PnS zNUkPnTdLo$wy>Vk|*`eNamMLB3@7h)!%B!UKu+K6_b6=Woulw!d z;=AS$D>xC;$PfWnpF^4Z0{Lzi*3A;G7SnfwDZ&L`Gw(24JE=mmwP(?1;3+iWGNd2*`6-!_oU z7$qL^dmw-$UIDJ804*bl?B1_?T$et=^Pfq6I?i`BG8m63a+IE|wuZVn?KGXd8HBB2 zpj)`T2Z~;@-^@@9`|#Hrq&$5Y35zS&GZpyNT`|k0;9Ebz)K9{Q2j%ZGMq9Vxj@rE) zgspT+`V)j4NrW8dyk!#~RpT4$=5mmfPJ;{XI*eH(Cqd)*2m}+I#~^_HW!k)fmAbLN z2Vc+IoH1pTuFSmSx!!3b5<%DnRKRmg6ofum*z+#7{RActJq7sL>4~3c)zXQN%a6DU z=q8V{Npa|Y&Ul1Y&ftn~G-)%%vDcsGZQhp-t(aws4HDEY%d{wZgllB5|0ZJQd zT8!!Pe=5+n?%Os+iZw5_XHujPDCsCTz_|*IV}nkq_~z)Ex+Z!vXeb9Kcjy%wJL3|@ zYVHL&Bea6HOz}f3btAj<%F}PU=H88*V`s@5^EW&B?*f?xl?JU>J07K`aa$xPoF&8Y z@$nSWU$o8BOCbB{Y{I-v5PAxWtb>AIx9QPLN9%Ok>^CViu*MJVX|ikPl+6v1ajxiz zq=q~VY9|{oci!f4M#KxQAaEN)bS$}orDpc})@D5z-s*2Dy~x8OB*aX40&t01 zJv=;|2?jW&99o0XwHaEtON-N+Opg|Tnmg9}tX5G?G5t-%yp64?YaaQax9~xWj8C}R z3AptKR!TvuoO61~3eJxw@r%@&Ces^J+9y8skrk+-RCI7JxXk46#;m(%v8y`}bHVN= z9U>syPy#YgZr{0c5>g*)FqpxC7&UoljEAjKwPtphAR#d0g;l#}{Gg|9fcn{rAwuN`8gD3Z1yArI?QY0LT%_2>-47qQI`k@iT0{nu`lI?(U07 zqdg32elrZHE9`&_DBC!uFK7@#?3XZ(TR4>Z0_JkS-N43vSl8T$l&=^qb-*b~&3sM* zx!-kdXOeHvGLgfpEYlKaiKZ2fG2!joUjX&76VC;UhxEG7=bah-8|lMJY5GBF+?q?| z8N^Lv#I!n%i;SWX(iXNH@*?n6L7%;lpT8F%@+puElro$)&`hc;xiS&Z2e!>gpuvF| z0IWpghQ1S)R$NRB9m$4)6^XhIX^{?6C(CQav2r~0VNvw}Z-(F$oIhCI+=kbrbIZWK zK8e_a#tlCSTI3Z-h{lCX>jEnqL2OWvEM$J< M^2v_SLA13QD1@Z>UiNEX-4u)=NDGl7 z4^#jmjq!=~3sTf0C;!x)uT^fW7o|rVm7UT_7A!Q0&s|l|TlOvKVSan{;{(jMr9|jK1XF=s1J9gkklzMFdyGseF z1oVcWyEry=TqZZoXo>P4rG0@P(ImJ7ET>Qax;#tEYhOwbI7WgpP|DFuxClHt&xUx> z{2FsBI|%vNvjJE{JVz6UdDEt7YBj1l_N7;Y zLI>I|M;){XS4+i(r?&6x9~EctGGgz5GkFOmA5DJDN+AYS#$SFWB}UnVd(A^*5PbW# zh4@bv@#erN@KjI(H%`uh=7RG!peneQ@&phVwaSVjX&+5qITC%^h36$w{^G@=h4p~x ze1u(dcem9F3J)qe(uYr(0v90Dx{jA3k7RDJ$4u=pk!GN z%{Ngs`Erm2;bgMo8CrLWioFNgy}lkLCwLn7EJ|*OaBGJ$fB*PBze#R(WH%~DqCCbM zo{v5jS7!!o%f~#&O}JJ5U=>_9ueTN_LM=q3b1*RNLWj&DPraIrljN1xNvFRUl1aRs z@ZSj1dw?|T7x-JV%XSvmiu_E~OqxJWw}|owQnI3;!-KPKIfbZ9A?{iGXtSmog|fO= zMNvVkl0US{C6sTh>;#R0a@N21x*t@VS^=-8PoJWf+o4mPVN`GtP#{2n z#4SMb4I6cv=2PdkAwPA!Nh}R%xp3+aAZi2f3HS)<#K?#x#~{tH{fiVDd?4vICVN-2 z)?#4N>WZ729OpLqk%w#akADN*s)b!>33BA@Q%s`G9J9Kk3dO4BcjxVQ-cWCr;H)Ur zsPBhHc9gAF82zV+IirR9P5WD8zkZj^dCx8dGIjmiUXuPsB6-7K7hdx#VN~Ml^*ay^ zhkY=#hDlLFeAW~Z+QnXCO8!Mc&J-Ony$Ejo&zmEh^q@)2 zNcdaac=e}49tKT87zHGmZ-V7VJlRun7naZ%06OxJdG};fM-AHEliE7Uy_*>lLt?t| zBOf(5c%&aXgGPr@Gsqf>&AL9Ia> z7C#C26PSrRTqA268pyz}SUyv3a>on)MF&4Mpfo)P4lP!_;T)%9*_%jdZsX5H+kS7VG*!_PRk*ZKTzX@Vre?$I{6=A$SYd4?e;w`R zX-9-A=9<*+ZTxn?NKoST88x8_Gc98!W7XApThFaKQ)yWtz`(q@>dntz^Al}puN|^D zid1@kc)gAEt$_*Pz?{_msY=g|_9cF%xBg?#KFtU&&7ZITGr#R8_A+Y+X5{LTL0W^y z0~(PpYR4R4Y_XGU1hu+6Gt6=@ufyj=hDnK55!GHhRZjz1ZWH=-w?jg@oz!7(YA1PL zxe@_;F7uVu8=RMxPg}BKsvDiw0!kfT$GGHtqm}L2?DVoR-HjW}8wy=*bFAwQgjxP{ z8Fi8~{%bucV1t}m+Vgv=PP&nPeGEH?c_6}VBC$&jX>)Lsk(7}!gz82YOzo(zDEAxD z9$Uk~0rJyc*z@e@_;j(!+Xy95@zqkhtQ~69qPE1UBSmdvlUAMAUFTfO>bK%>L=-py zmr~+)jd!cIvOPif=k1RxA?>h1v&E+N;e`d4y)O3qxD^ZIPGqQdG3I2(7kUEPNt%an zuz`ITwxe3p#wMAJ4pym$rfLd=sQ!DpxOW@OKXGH5-{NOnyf_(@Hn|SIPDguMwg&F8 zI3!`w^JnGhH!iH2hk?jBu0xwH0V)h51H)~Xy|20fRfmyQKXiI^AihT zFn=|!EM3D6;}r~H`3wS(CSsI;3d)A>+kyZ<9tsf$WrcCc63>EVx0Q$?tEdv%Z6bKG zhoI!cEaN1U%V&@Y$r*-&A49(6H*|g8_X|e11;y?8=gjfMAfp>b&v2uZQ~Xd*LUWQqct8$2e%P3r9b;Q zRO$&wXHxdqzWD;<{Id#>StOrG3`*8YibYp!2>sle%shv-dLnQ_1AZHb%t*@{LDyb3Ci*NLu!8BrNUuFDELK6r%(6#fRED*pjr)!PV(>QQd4$%nFo(MU7MalrxDGD_X&HGLdg>V&&{K4kc$ z{teG095?vZT^?%A$bhJJ2w6ECM(S{(u0Ow6z5`uqRBo+^(POk>lHtIzFi}?4od=hJ z*@vKVZ{4y*8C$;|Og32`$%?yd9%sxnO4Sav`Qly3j3gumQl=Ak@zaQ{WDnq7bbwG_ zn1ueu2xA-Tv6?OQ!vaMEf`V#xlV*HKd>Pix8SZz!ULh5K9O&h*5fkTN3-q)5sey4z zzo4vg^C&^u;IS>Bgs7?LQ+Bs;q1ZO*s9tZ5Bib4y#-oT~Un94ysjZMrUh6h8HOVI~ z-tabhe+Ssl?wrc-HgYb_*rpWSJN*e+!R>Ja47qr^tq@LaBHN#!)UxHj2SVcjNp7Ti zO1*wMl}dqiQ1hl8@_>PY{>QoC2#Dc3Kl=p*1f-gMvgl`U;{>5O(#kMt`&8!J(dV;H z*q}Fc+Q&#?4O0H98qc*>jM;e|35hMf3RqBh2k3eVi^s0!;Z3gS3_8-Xs}74H@&7VC z&lvM>BxQjs&n^B&aC~YrF*)X(M|<6G2?2pu!?P=~3My@5w{|01MZp zz#qs(b=D5d@6|wja*?WF9mke6GI2`)C{j~~$!|P22lfd&mZ{Ctd~u-hC)KupfHlYV zY}Ng;vPL0fq23%fUB&j-o!Qc_N5CSAUxC!l;RE=EZ7O7$kJEq6gyqK6Seqe|?M8?d zN2Y#}#kg&oD!NpvhYufLIK%33sOEquPmUM#Tnc^W3YCY+oVyTl03#XZlRAKNjHBH6 zyST2s*djGAh$dm=0Ow8-%>LWc)}Db!5)u!vu2*XOZfk<7GJ%-giD&OG(a%<%fz7Y_FXZ3t577nP)>y+sfK{f1vi zPR;~5Z;dUKh%=zr1F%aVQz}uy8)Tk3bYU2yM7IkF90JpzW}AhtQ=Vz1hg-9;cRH47 z$h?|lVfSN)QfNb?zT#+lJ~ zbiPU08mwjE&cKR} zaE#VwS*oejAcnWtFCj zY>h~OG!d_G-{O%>dgl1ocj62Q=f?n%Pkm1hA3N3zejmRIcI10!VQh>Lb|e+>rSfuq zA1M`wte~*i(ZwbEYbp60b$ccPw++pYw-s%v^1l86C%UZn+278PiK=KK5PD}3UOoBp z6MKkLH)P&+_vG*3xFuOdFDD_a`kdNt-%(mJKR@e84w?Emh7P0*;^4!o)FXRdR@@9t z*S?Jdu+!|eTc)}3JO_D_#GmNfXU74ODN$G8@H+v*C7uZ4+#ZtVy#IuRp#+@StkzMR z=JW^f@7ie|xTCrr61~QlgG=&$0ETvzE5ePp#YXlxzud~wau9s9h<%>9&zq$J81j|-ozCSCRRDV1_lviIx}j)CM!|t`Q?+^^fN6s>VF3ZgiBLLwyMxTLdG`M zeO`Cl1X->&1dpU9u=AgYNB$<`sZiw9Q?cAi?J^KW4;NGOBiE(|r~@EM)k5LyI?<_M zwJf|6)+c+nKac)~<{RensUV2H34iE-BwTK~a}j2Wl=poie%W{*;)jceI3QVy)Xlv( zynfoNP3XKzjGk0hJ5>c zDd2BFCo!U-E%b`tm+S_Mv)08wlp zewB9ld<$~0?u@kX$FU>(QOuAs1e1pGI_&G$KgMTUjwzV1+|We~Ja(%3_3Pu{Wnq3~ z5|;n8#XHK0kHfNm?!?5Ak9c-4^-BY%oL%qP_3k|=KQJ|38;rfL`j$eSwz&8PNc*y( zekcK_Oin-aKh;9;{rf2fIW|JLS2(R1btYroagEzB%?iu2$5|oCBCrl4gyJ#Vk%;nJ zPRyODyZC42JPbs4RTBz!B-<{iwnA1c`3-}tG@Kt~t{i`~h7GXk!{muzV|s;oh5aYB zJb|d&_~hh49HwfMlam5mEBvhdM3rERp@oP9VSvKlM1Vp$KJ)MUQ7E13`2RkB3dQHY fKk~oRw|_5*(7ZDjKHk6Za8wSdE2bYXyZFBVXC0~O diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index d5e840d9b..43296ef61 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -31,7 +31,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230

    • Conduct AMR Analysis
    • Generate Antibiogram (Trad./Syndromic/WISCA)
      • -
    • Predict Antimicrobial Resistance
      • -
    • Download Data Sets for Own Use
    • Use AMR for Predictive Modelling (tidymodels)
      • +
    • Download Data Sets for Own Use
    • Set User- Or Team-specific Package Settings
    • Conduct Principal Component Analysis for AMR
    • Determine Multi-Drug Resistance (MDR)
      • diff --git a/index.html b/index.html index 462452554..85e92faaa 100644 --- a/index.html +++ b/index.html @@ -34,7 +34,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
      • Conduct AMR Analysis
      • Generate Antibiogram (Trad./Syndromic/WISCA)
        • -
      • Predict Antimicrobial Resistance
        • -
      • Download Data Sets for Own Use
      • Use AMR for Predictive Modelling (tidymodels)
        • +
      • Download Data Sets for Own Use
      • Set User- Or Team-specific Package Settings
      • Conduct Principal Component Analysis for AMR
      • Determine Multi-Drug Resistance (MDR)
        • @@ -50,19 +49,20 @@
        -

        AMR 2.1.1.9229

        +

        AMR 2.1.1.9230

        (this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using the instructions here.)

        -
        A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
        +
        A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)

        This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the University of Prince Edward Island’s Atlantic Veterinary College, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.

        -

        Breaking

        +

        Breaking

        • Dataset antibiotics has been renamed to antimicrobials as the data set contains more than just antibiotics. Using antibiotics will still work, but now returns a warning.
        • Removed all functions and references that used the deprecated rsi class, which were all replaced with their sir equivalents over two years ago.
          • +
        • Function resistance_predict() is now deprecated and will be removed in a future version. Use the tidymodels framework instead, for which we wrote a basic introduction.
        -

        New

        +

        New

        • One Health implementation
          • Function as.sir() now has extensive support for veterinary breakpoints from CLSI. Use breakpoint_type = "animal" and set the host argument to a variable that contains animal species names.
            • @@ -116,7 +116,7 @@
        -

        Changed

        +

        Changed

        • SIR interpretation
          • It is now possible to use column names for argument ab, mo, and uti: as.sir(..., ab = "column1", mo = "column2", uti = "column3"). This greatly improves the flexibility for users.
          • Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
            • @@ -192,7 +192,7 @@
          • Added console colours support of sir class for Positron
        -

        Other

        +

        Other

        • Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way
        • Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations
        • Added Prof. Kat Holt, Dr. Jane Hawkey, and Dr. Natacha Couto as contributors for their many suggestions, ideas and bugfixes
          • @@ -201,7 +201,7 @@
        • Stopped support for SAS (.xpt) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.
        -

        Older Versions

        +

        Older Versions

        This changelog only contains changes from AMR v3.0 (March 2025) and later.

        diff --git a/reference/count.html b/reference/count.html index 3371388fc..8932044b5 100644 --- a/reference/count.html +++ b/reference/count.html @@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible( AMR (for R) - 2.1.1.9229 + 2.1.1.9230
        diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 8faed2c26..8bc368e26 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
        • Conduct AMR Analysis
        • Generate Antibiogram (Trad./Syndromic/WISCA)
          • -
        • Predict Antimicrobial Resistance
          • -
        • Download Data Sets for Own Use
        • Use AMR for Predictive Modelling (tidymodels)
          • +
        • Download Data Sets for Own Use
        • Set User- Or Team-specific Package Settings
        • Conduct Principal Component Analysis for AMR
        • Determine Multi-Drug Resistance (MDR)
          • diff --git a/reference/mo_property.html b/reference/mo_property.html index 346c2cf71..406216f88 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
          • Conduct AMR Analysis
          • Generate Antibiogram (Trad./Syndromic/WISCA)
            • -
          • Predict Antimicrobial Resistance
            • -
          • Download Data Sets for Own Use
          • Use AMR for Predictive Modelling (tidymodels)
            • +
          • Download Data Sets for Own Use
          • Set User- Or Team-specific Package Settings
          • Conduct Principal Component Analysis for AMR
          • Determine Multi-Drug Resistance (MDR)
            • diff --git a/reference/mo_source.html b/reference/mo_source.html index 64129987e..2fea3d04e 100644 --- a/reference/mo_source.html +++ b/reference/mo_source.html @@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p AMR (for R) - 2.1.1.9229 + 2.1.1.9230
            • Conduct AMR Analysis
            • Generate Antibiogram (Trad./Syndromic/WISCA)
              • -
            • Predict Antimicrobial Resistance
              • -
            • Download Data Sets for Own Use
            • Use AMR for Predictive Modelling (tidymodels)
              • +
            • Download Data Sets for Own Use
            • Set User- Or Team-specific Package Settings
            • Conduct Principal Component Analysis for AMR
            • Determine Multi-Drug Resistance (MDR)
              • diff --git a/reference/pca.html b/reference/pca.html index 54e8f71ee..ea20ccc25 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
              • Conduct AMR Analysis
              • Generate Antibiogram (Trad./Syndromic/WISCA)
                • -
              • Predict Antimicrobial Resistance
                • -
              • Download Data Sets for Own Use
              • Use AMR for Predictive Modelling (tidymodels)
                • +
              • Download Data Sets for Own Use
              • Set User- Or Team-specific Package Settings
              • Conduct Principal Component Analysis for AMR
              • Determine Multi-Drug Resistance (MDR)
                • diff --git a/reference/plot.html b/reference/plot.html index 6f0e39935..303327f2e 100644 --- a/reference/plot.html +++ b/reference/plot.html @@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                • Conduct AMR Analysis
                • Generate Antibiogram (Trad./Syndromic/WISCA)
                  • -
                • Predict Antimicrobial Resistance
                  • -
                • Download Data Sets for Own Use
                • Use AMR for Predictive Modelling (tidymodels)
                  • +
                • Download Data Sets for Own Use
                • Set User- Or Team-specific Package Settings
                • Conduct Principal Component Analysis for AMR
                • Determine Multi-Drug Resistance (MDR)
                  • diff --git a/reference/proportion.html b/reference/proportion.html index 2a9fc4734..d1c06f6fd 100644 --- a/reference/proportion.html +++ b/reference/proportion.html @@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                  • Conduct AMR Analysis
                  • Generate Antibiogram (Trad./Syndromic/WISCA)
                    • -
                  • Predict Antimicrobial Resistance
                    • -
                  • Download Data Sets for Own Use
                  • Use AMR for Predictive Modelling (tidymodels)
                    • +
                  • Download Data Sets for Own Use
                  • Set User- Or Team-specific Package Settings
                  • Conduct Principal Component Analysis for AMR
                  • Determine Multi-Drug Resistance (MDR)
                    • diff --git a/reference/random.html b/reference/random.html index f5a58f683..590a481ed 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                    • Conduct AMR Analysis
                    • Generate Antibiogram (Trad./Syndromic/WISCA)
                      • -
                    • Predict Antimicrobial Resistance
                      • -
                    • Download Data Sets for Own Use
                    • Use AMR for Predictive Modelling (tidymodels)
                      • +
                    • Download Data Sets for Own Use
                    • Set User- Or Team-specific Package Settings
                    • Conduct Principal Component Analysis for AMR
                    • Determine Multi-Drug Resistance (MDR)
                      • diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 83530ce16..4ac141cb6 100644 --- a/reference/resistance_predict.html +++ b/reference/resistance_predict.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                      • Conduct AMR Analysis
                      • Generate Antibiogram (Trad./Syndromic/WISCA)
                        • -
                      • Predict Antimicrobial Resistance
                        • -
                      • Download Data Sets for Own Use
                      • Use AMR for Predictive Modelling (tidymodels)
                        • +
                      • Download Data Sets for Own Use
                      • Set User- Or Team-specific Package Settings
                      • Conduct Principal Component Analysis for AMR
                      • Determine Multi-Drug Resistance (MDR)
                        • @@ -183,6 +182,11 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of year_min = 2010, model = "binomial" ) +#> Warning: The resistance_predict() function is deprecated and will be removed in a +#> future version, see ?AMR-deprecated. Use the tidymodels framework +#> instead, for which we have written a basic and short introduction on our +#> website: https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html +#> This warning will be shown once per session. plot(x) # \donttest{ diff --git a/reference/skewness.html b/reference/skewness.html index f7b7773e3..a810b8501 100644 --- a/reference/skewness.html +++ b/reference/skewness.html @@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                        • Conduct AMR Analysis
                        • Generate Antibiogram (Trad./Syndromic/WISCA)
                          • -
                        • Predict Antimicrobial Resistance
                          • -
                        • Download Data Sets for Own Use
                        • Use AMR for Predictive Modelling (tidymodels)
                          • +
                        • Download Data Sets for Own Use
                        • Set User- Or Team-specific Package Settings
                        • Conduct Principal Component Analysis for AMR
                        • Determine Multi-Drug Resistance (MDR)
                          • diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index e87d2872e..4983d73cc 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                          • Conduct AMR Analysis
                          • Generate Antibiogram (Trad./Syndromic/WISCA)
                            • -
                          • Predict Antimicrobial Resistance
                            • -
                          • Download Data Sets for Own Use
                          • Use AMR for Predictive Modelling (tidymodels)
                            • +
                          • Download Data Sets for Own Use
                          • Set User- Or Team-specific Package Settings
                          • Conduct Principal Component Analysis for AMR
                          • Determine Multi-Drug Resistance (MDR)
                            • diff --git a/reference/translate.html b/reference/translate.html index 4e9faf794..eeb4d05b4 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 2.1.1.9229 + 2.1.1.9230
                            • Conduct AMR Analysis
                            • Generate Antibiogram (Trad./Syndromic/WISCA)
                              • -
                            • Predict Antimicrobial Resistance
                              • -
                            • Download Data Sets for Own Use
                            • Use AMR for Predictive Modelling (tidymodels)
                              • +
                            • Download Data Sets for Own Use
                            • Set User- Or Team-specific Package Settings
                            • Conduct Principal Component Analysis for AMR
                            • Determine Multi-Drug Resistance (MDR)
                              • diff --git a/search.json b/search.json index aba7feb97..18274ea24 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 #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. 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 Retrieved values from the microorganisms.codes data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> 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. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), 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), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> 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 #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% 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: 2,724 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 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 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R 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 #> # ℹ 2,714 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:2724 Length:2724 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-07 #> Mode :character Mode :character Median :2015-06-03 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-11 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.6% (n=1133) %S :52.6% (n=1432) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) #> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=176) %I : 3.0% (n=82) #> %R :41.0% (n=1117) %R :36.0% (n=981) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first 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 1854 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 #> #> 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 #> #> 1 Escherichia coli 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"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: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS 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 #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R 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 #> # ℹ 2,714 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: 981 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 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: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"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:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'. my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 16.000 R #> 2 0.005 S #> 3 1.000 R #> 4 >=256.000 R #> 5 2.000 R #> 6 0.025 S #> 7 16.000 R #> 8 0.025 S #> 9 0.500 I #> 10 0.005 S #> # ℹ 90 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) #> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ── #> ✔ broom 1.0.7 ✔ recipes 1.2.1 #> ✔ dials 1.4.0 ✔ rsample 1.2.1 #> ✔ dplyr 1.1.4 ✔ tibble 3.2.1 #> ✔ ggplot2 3.5.1 ✔ tidyr 1.3.1 #> ✔ infer 1.0.7 ✔ tune 1.3.0 #> ✔ modeldata 1.4.0 ✔ workflows 1.2.0 #> ✔ parsnip 1.3.1 ✔ workflowsets 1.1.0 #> ✔ purrr 1.0.4 ✔ yardstick 1.3.2 #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── #> ✖ purrr::discard() masks scales::discard() #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ✖ recipes::step() masks stats::step() # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antibiotic selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalized Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organizes entire modeling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antibiotic selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram based AMR results 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.00e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.00e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , IPM , MEM # Evaluate model performance metrics <- predictions %>% metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics metrics #> # A tibble: 2 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 predictions %>% roc_curve(mo, `.pred_Gram-negative`) %>% autoplot()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR with tidymodels","text":"post, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antibiotic classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antimicrobials: 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 S #> 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) #> [1] FALSE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) #> [1] FALSE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #> 1. If CIP is R and age is higher than 60 then: Elderly Type A #> 2. If ERY is R and age is higher than 60 then: Elderly Type B #> 3. Otherwise: Negative #> #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #> Negative Elderly Type A Elderly Type B #> 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,745 (87.25%, NA: 255 = 12.75%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_sir(5000), isoniazid = random_sir(5000), gatifloxacin = random_sir(5000), ethambutol = random_sir(5000), pyrazinamide = random_sir(5000), moxifloxacin = random_sir(5000), kanamycin = random_sir(5000) ) my_TB_data <- data.frame( RIF = random_sir(5000), INH = random_sir(5000), GAT = random_sir(5000), ETH = random_sir(5000), PZA = random_sir(5000), MFX = random_sir(5000), KAN = random_sir(5000) ) head(my_TB_data) #> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1 I R S S S S #> 2 S S I R R S #> 3 R I I I R I #> 4 I S S S S S #> 5 I I I S I S #> 6 R S R S I I #> kanamycin #> 1 R #> 2 I #> 3 S #> 4 I #> 5 I #> 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 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF 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 #> #> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA #> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA #> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA #> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA #> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA #> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 #> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups: can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) + ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 5: S < SDD < < R < NI 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 78 679 rows 26 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, mycobank, mycobank_parent, mycobank_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 26 March 2025 16:19:17 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (1.8 MB) Download tab-separated text file (17.7 MB) Download Microsoft Excel workbook (8.7 MB) Download Apache Feather file (8.3 MB) Download Apache Parquet file (3.8 MB) Download IBM SPSS Statistics data file (28.4 MB) Download Stata DTA file (89.5 MB) NOTE: exported files SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values. Example content Included (sub)species per taxonomic kingdom: First 6 rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antimicrobials-antibiotic-and-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antimicrobials: Antibiotic and Antifungal Drugs","title":"Data sets for download / own use","text":"data set 496 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 antimicrobials, load AMR package. last updated 19 March 2025 15:05:24 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (75 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (10 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values. Example content","code":""},{"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 34 376 rows 14 columns, containing following column names:guideline, type, host, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti, is_SDD. data set R available clinical_breakpoints, load AMR package. last updated 13 March 2025 14:38:39 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (76 kB) Download tab-separated text file (3.1 MB) Download Microsoft Excel workbook (2 MB) Download Apache Feather file (1.5 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (5.6 MB) Download Stata DTA file (9.6 MB) Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 534 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 26 March 2025 16:19:17 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (50 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (65 kB) Download Stata DTA file (83 kB) Example content","code":""},{"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 271 905 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 28 March 2025 10:17:49 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (0.1 MB) Download tab-separated text file (10.1 MB) Download Microsoft Excel workbook (2.9 MB) Download Apache Feather file (2.3 MB) Download Apache Parquet file (0.3 MB) Download IBM SPSS Statistics data file (14.8 MB) Download Stata DTA file (22.6 MB) Example content Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB) Example content","code":""},{"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 15 June 2024 13:33:49 UTC. Find info contents, (scientific) source, structure data set . Example content","code":""},{"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 contents, (scientific) source, structure data set . Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 971 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (82 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (56 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB) Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 20 October 2023 12:51:48 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain trade names LOINC codes comma separated values. Example content","code":""},{"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: 34 × 7 #> year value se_min se_max observations observed estimated #> * #> 1 2002 0.2 NA NA 15 0.2 0.0562 #> 2 2003 0.0625 NA NA 32 0.0625 0.0616 #> 3 2004 0.0854 NA NA 82 0.0854 0.0676 #> 4 2005 0.05 NA NA 60 0.05 0.0741 #> 5 2006 0.0508 NA NA 59 0.0508 0.0812 #> 6 2007 0.121 NA NA 66 0.121 0.0889 #> 7 2008 0.0417 NA NA 72 0.0417 0.0972 #> 8 2009 0.0164 NA NA 61 0.0164 0.106 #> 9 2010 0.0566 NA NA 53 0.0566 0.116 #> 10 2011 0.183 NA NA 93 0.183 0.127 #> # ℹ 24 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. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Aislinn Cook. Contributor. Andrew P. Norgan. Contributor. Anita Williams. Contributor. Annick Lenglet. Contributor. Anthony Underwood. Contributor. Anton Mymrikov. Contributor. Bart C. Meijer. Contributor. Christian F. Luz. Contributor. Dmytro Mykhailenko. Contributor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Jane Hawkey. Contributor. Jason Stull. Contributor. Javier Sanchez. Contributor. Jonas Salm. Contributor. Judith M. Fonville. Contributor. Kathryn Holt. Contributor. Larisse Bolton. Contributor. Matthew Saab. Contributor. Natacha Couto. Contributor. Peter Dutey-Magni. Contributor. Rogier P. Schade. Contributor. Sofia Ny. Contributor. Alex W. Friedrich. Thesis advisor. Bhanu N. M. Sinha. Thesis advisor. Casper J. Albers. Thesis advisor. Corinna Glasner. Thesis advisor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides --one solution antimicrobial resistance (AMR) data analysis One Health approach Used 175 countries, available 20 languages Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy extensive info antimicrobial drugs Applies recent CLSI EUCAST clinical veterinary breakpoints MICs, disk zones ECOFFs Corrects duplicate isolates, calculates predicts AMR per antimicrobial class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, NCBI 100% free costs dependencies, highly suitable places limited resources Now available Python ! Click read . 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 antimicrobial antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-available-in-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, available in 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 antimicrobial columns. can done using -called antimicrobial 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 antimicrobials AMR package make sure get meant:","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\")) %>% # antimicrobial selectors: select(bacteria, aminoglycosides(), carbapenems())"},{"path":"https://msberends.github.io/AMR/index.html","id":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antimicrobials yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates, antimicrobials = c(aminoglycosides(), carbapenems()), formatting_type = 14) antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), mo_transform = \"gramstain\", formatting_type = 14) antibiogram(example_isolates, antimicrobials = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work mo_transform = \"gramstain\", ab_transform = \"name\", formatting_type = 14, language = \"uk\") # Ukrainian"},{"path":"https://msberends.github.io/AMR/index.html","id":"interpreting-and-plotting-mic-and-sir-values","dir":"","previous_headings":"Practical examples","what":"Interpreting and plotting MIC and SIR values","title":"Antimicrobial Resistance Data Analysis","text":"AMR package allows interpretation MIC disk diffusion values based CLSI EUCAST. Moreover, ggplot2 package extended new scale functions, allow plotting log2-distributed MIC values SIR values.","code":"library(ggplot2) library(AMR) # generate some random values some_mic_values <- random_mic(size = 100) some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) interpretation <- as.sir(some_mic_values, guideline = \"EUCAST 2024\", mo = \"E. coli\", # or any code or name resembling a known species ab = \"Cipro\") # or any code or name resembling an antibiotic # create the plot ggplot(data.frame(mic = some_mic_values, group = some_groups, sir = interpretation), aes(x = group, y = mic, colour = sir)) + theme_minimal() + geom_boxplot(fill = NA, colour = \"grey\") + geom_jitter(width = 0.25) + # NEW scale function: plot MIC values to x, y, colour or fill scale_y_mic() + # NEW scale function: write out S/I/R in any of the 20 supported languages # and set colourblind-friendly colours scale_colour_sir()"},{"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 antimicrobial selectors select series antibiotic columns:","code":"example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance() for gentamicin and tobramycin # and get their 95% confidence intervals using sir_confidence_interval(): summarise(across(c(GEN, TOB), list(total_R = resistance, conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) library(AMR) library(dplyr) out <- example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance(), over all aminoglycosides and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antimicrobial (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 rOpenSci R-universe platform:","code":"install.packages(\"AMR\", repos = \"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":"objects -called 'Deprecated'. removed future version package. Using give warning name alternative object 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":"ab_class(...) ab_selector(...)"},{"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 tbl_df (inherits tbl, data.frame) 496 rows 14 columns.","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_antibiogram_formatting_type numeric (1-22) use antibiogram(), indicate formatting type use. AMR_breakpoint_type character use .sir(), indicate breakpoint type use. must either \"ECOFF\", \"animal\", \"human\". AMR_capped_mic_handling character use .sir(), indicate capped MIC values (<, <=, >, >=) interpreted. Must one \"standard\", \"strict\", \"relaxed\", \"inverse\" - default \"standard\". 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_custom_ab file location RDS file, use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo file location RDS file, use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules character set default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline character set 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 2024\". Supported guideline currently EUCAST (2011-2024) CLSI (2011-2024). 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_substitute_missing_r_breakpoint logical use .sir(), indicate missing R breakpoints must substituted \"R\" - default FALSE. AMR_include_screening logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_locale character set language 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, English otherwise). 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 antimicrobials, 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, user read file automatically start-. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put : Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131 doi:10.33612/diss.192486375 ). installing package, R knows ~79 000 microorganisms (updated June 2024) ~620 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 BibTeX entry LaTeX users :","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"The AMR Package — AMR","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.s.berends@umcg.nl (ORCID) Authors: Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Aislinn Cook (ORCID) [contributor] Andrew P. Norgan (ORCID) [contributor] Anita Williams (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Anthony Underwood (ORCID) [contributor] Anton Mymrikov [contributor] Bart C. Meijer [contributor] Christian F. Luz (ORCID) [contributor] Dmytro Mykhailenko [contributor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Jane Hawkey (ORCID) [contributor] Jason Stull (ORCID) [contributor] Javier Sanchez (ORCID) [contributor] Jonas Salm [contributor] Judith M. Fonville [contributor] Kathryn Holt (ORCID) [contributor] Larisse Bolton (ORCID) [contributor] Matthew Saab [contributor] Natacha Couto (ORCID) [contributor] Peter Dutey-Magni (ORCID) [contributor] Rogier P. Schade [contributor] Sofia Ny (ORCID) [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Casper J. Albers (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor]","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://atcddd.fhi.) 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://atcddd.fhi./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] \"bactopen\" \"cloxacap\" \"cloxacillinhydrate\" #> [4] \"cloxypen\" \"floxacillin\" \"floxacillinanhydrous\" #> [7] \"floxapen\" \"floxapensalt\" \"fluclomix\" #> [10] \"flucloxacilina\" \"flucloxacilline\" \"flucloxacillinum\" #> [13] \"flucloxin\" \"fluorochloroxacillin\" \"galfloxin\" #> [16] \"latocillin\" \"orbeninhydrate\" \"rimaflox\" #> [19] \"staphobristol\" \"zoxin\""},{"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. antimicrobial 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 antimicrobials. can lookup abbreviations antimicrobials data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antimicrobial class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #> `Identification number` `Specimen number` Organism Country Laboratory #> #> 1 fe41d7bafa 1748 SPN Belgium National … #> 2 91f175ec37 1767 eco The Netherlands National … #> 3 cc4015056e 1343 eco The Netherlands National … #> 4 e864b692f5 1894 MAP Denmark National … #> 5 3d051fe345 1739 PVU Belgium National … #> 6 c80762a08d 1846 103 The Netherlands National … #> 7 8022d3727c 1628 103 Denmark National … #> 8 f3dc5f553d 1493 eco The Netherlands National … #> 9 15add38f6c 1847 eco France National … #> 10 fd41248def 1458 eco Germany National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` , `First name` , Sex , #> # Age , `Age category` , `Date of admission` , #> # `Specimen date` , `Specimen type` , #> # `Specimen type (Numeric)` , Reason , `Isolate number` , #> # `Organism type` , Serotype , `Beta-lactamase` , ESBL , #> # Carbapenemase , `MRSA screening test` , …"},{"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 antimicrobials 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\" \"Fluoroquinolones\" if (require(\"dplyr\")) { tibble(clinical_text = c( \"given 400mg cipro and 500 mg amox\", \"started on doxy iv today\" )) %>% mutate( abx_codes = ab_from_text(clinical_text), abx_doses = ab_from_text(clinical_text, type = \"doses\"), abx_admin = ab_from_text(clinical_text, type = \"admin\"), abx_coll = ab_from_text(clinical_text, collapse = \"|\"), abx_coll_names = ab_from_text(clinical_text, collapse = \"|\", translate_ab = \"name\" ), abx_coll_doses = ab_from_text(clinical_text, type = \"doses\", collapse = \"|\" ), abx_coll_admin = ab_from_text(clinical_text, type = \"admin\", collapse = \"|\" ) ) } #> Loading required package: dplyr #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #> clinical_text abx_codes abx_doses abx_admin abx_coll abx_coll_names #> #> 1 given 400mg cipro and 5… CIP|AMX Ciprofloxacin… #> 2 started on doxy iv today DOX Doxycycline #> # ℹ 2 more variables: abx_coll_doses , 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 antimicrobials 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 antimicrobials data set: vector_or(colnames(antimicrobials), 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://atcddd.fhi./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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of an Antibiotic — ab_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" ab_tradenames(\"AMX\") #> [1] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" 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://atcddd.fhi.no/atc_ddd_index//?code=J01CA04&showdescription=no\" # smart lowercase transformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\" ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\" # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\" ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #> #> $cid #> [1] 33613 #> #> $name #> [1] \"Amoxicillin\" #> #> $group #> [1] \"Beta-lactams/penicillins\" #> #> $atc #> [1] \"J01CA04\" #> #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #> #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #> #> $tradenames #> [1] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" #> #> $loinc #> [1] \"101498-4\" \"15-8\" \"16-6\" \"16365-9\" \"17-4\" \"18-2\" #> [7] \"18861-5\" \"18862-3\" \"19-0\" \"20-8\" \"21-6\" \"22-4\" #> [13] \"25274-2\" \"25310-4\" \"3344-9\" \"55614-2\" \"55615-9\" \"55616-7\" #> [19] \"6976-5\" \"6977-3\" \"80133-2\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"101477-8\" \"101478-6\" \"18864-9\" \"18865-6\" \"20374-5\" \"21066-6\" #> [7] \"23618-2\" \"27-3\" \"28-1\" \"29-9\" \"30-7\" \"31-5\" #> [13] \"32-3\" \"33-1\" \"3355-5\" \"33562-0\" \"33919-2\" \"34-9\" #> [19] \"43883-8\" \"43884-6\" \"6979-9\" \"6980-7\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\" # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\" # use set_ab_names() for renaming columns colnames(example_isolates) #> [1] \"date\" \"patient\" \"age\" \"gender\" \"ward\" \"mo\" \"PEN\" #> [8] \"OXA\" \"FLC\" \"AMX\" \"AMC\" \"AMP\" \"TZP\" \"CZO\" #> [15] \"FEP\" \"CXM\" \"FOX\" \"CTX\" \"CAZ\" \"CRO\" \"GEN\" #> [22] \"TOB\" \"AMK\" \"KAN\" \"TMP\" \"SXT\" \"NIT\" \"FOS\" #> [29] \"LNZ\" \"CIP\" \"MFX\" \"VAN\" \"TEC\" \"TCY\" \"TGC\" #> [36] \"DOX\" \"ERY\" \"CLI\" \"AZM\" \"IPM\" \"MEM\" \"MTR\" #> [43] \"CHL\" \"COL\" \"MUP\" \"RIF\" colnames(set_ab_names(example_isolates)) #> [1] \"date\" \"patient\" #> [3] \"age\" \"gender\" #> [5] \"ward\" \"mo\" #> [7] \"benzylpenicillin\" \"oxacillin\" #> [9] \"flucloxacillin\" \"amoxicillin\" #> [11] \"amoxicillin_clavulanic_acid\" \"ampicillin\" #> [13] \"piperacillin_tazobactam\" \"cefazolin\" #> [15] \"cefepime\" \"cefuroxime\" #> [17] \"cefoxitin\" \"cefotaxime\" #> [19] \"ceftazidime\" \"ceftriaxone\" #> [21] \"gentamicin\" \"tobramycin\" #> [23] \"amikacin\" \"kanamycin\" #> [25] \"trimethoprim\" \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" #> [31] \"moxifloxacin\" \"vancomycin\" #> [33] \"teicoplanin\" \"tetracycline\" #> [35] \"tigecycline\" \"doxycycline\" #> [37] \"erythromycin\" \"clindamycin\" #> [39] \"azithromycin\" \"imipenem\" #> [41] \"meropenem\" \"metronidazole\" #> [43] \"chloramphenicol\" \"colistin\" #> [45] \"mupirocin\" \"rifampicin\" colnames(set_ab_names(example_isolates, NIT:VAN)) #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% set_ab_names() # this does the same: example_isolates %>% rename_with(set_ab_names) # set_ab_names() works with any AB property: example_isolates %>% set_ab_names(property = \"atc\") example_isolates %>% set_ab_names(where(is.sir)) %>% colnames() example_isolates %>% set_ab_names(NIT:VAN) %>% colnames() } #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x) clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antimicrobials 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 antimicrobials 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 a wildly guessed result: as.ab(\"testab\") #> Class 'ab' #> [1] CTE # 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 # 'antimicrobials' data set, such as 'group': group = \"Test Group\" ) ) #> ℹ Added one record to the internal antimicrobials 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://atcddd.fhi.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 antimicrobials data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\" # even antimicrobial selectors work # see ?amr_selector x <- data.frame( random_column = \"some value\", coflu = as.sir(\"S\"), ampicillin = as.sir(\"R\") ) x #> random_column coflu ampicillin #> 1 some value S R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #> 'ampicillin' #> coflu ampicillin #> 1 S R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x) clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways circumvent automate process adding microorganisms: Method 1: Using package option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location package option AMR_custom_mo: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file: Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file: Use clear_custom_microorganisms() clear previously added microorganisms.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes: AMR::add_custom_microorganisms( data.frame(genus = \"Enterobacter\", species = \"asburiae/cloacae\") )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter 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_ENTRB_ASB/ # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\" # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\" mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRB_ASB/\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Enterobacter\" #> #> $species #> [1] \"asburiae/cloacae\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] NA #> #> $url #> [1] \"\" #> #> $ref #> [1] \"Self-added, 2025\" #> #> $snomed #> [1] NA #> #> $lpsn #> [1] NA #> #> $mycobank #> [1] NA #> #> $gbif #> [1] NA #> #> $group_members #> character(0) #> # the function tries to be forgiving: add_custom_microorganisms( data.frame( GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\", SPECIES = \"SPECIES\" ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #> set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\" # taxonomy still works, even though a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\" # for groups and complexes, set them as species or subspecies: add_custom_microorganisms( data.frame( genus = \"Citrobacter\", species = c(\"freundii\", \"braakii complex\"), subspecies = c(\"complex\", \"\") ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #> internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\" mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\" mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000) # add ages df$age <- age(df$birth_date) # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE) # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\") df #> birth_date age age_exact age_at_y2k #> 1 1999-06-30 25 25.74247 0 #> 2 1968-01-29 57 57.15890 31 #> 3 1965-12-05 59 59.30959 34 #> 4 1980-03-01 45 45.07397 19 #> 5 1949-11-01 75 75.40274 50 #> 6 1947-02-14 78 78.11507 52 #> 7 1940-02-19 85 85.10137 59 #> 8 1988-01-10 37 37.21096 11 #> 9 1997-08-27 27 27.58356 2 #> 10 1978-01-26 47 47.16712 21"},{"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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Create detailed antibiograms options traditional, combination, syndromic, Bayesian WISCA methods. Adhering previously described approaches (see Source) especially Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) Bielicki et al., functions provide flexible output formats including plots tables, ideal integration R Markdown Quarto reports.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"antibiogram(x, antimicrobials = where(is.sir), mo_transform = \"shortname\", ab_transform = \"name\", syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE, digits = ifelse(wisca, 1, 0), formatting_type = getOption(\"AMR_antibiogram_formatting_type\", ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, sep = \" + \", wisca = FALSE, simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\", info = interactive(), ...) wisca(x, antimicrobials = where(is.sir), ab_transform = \"name\", syndromic_group = NULL, digits = 1, formatting_type = getOption(\"AMR_antibiogram_formatting_type\", 14), col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE, sep = \" + \", simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\", info = interactive(), ...) retrieve_wisca_parameters(wisca_model, ...) # S3 method for class 'antibiogram' plot(x, ...) # S3 method for class 'antibiogram' autoplot(object, ...) # S3 method for class '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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Bielicki JA et al. (2016). Selecting appropriate empirical antibiotic regimens paediatric bloodstream infections: application Bayesian decision model local pooled antimicrobial resistance surveillance data Journal Antimicrobial Chemotherapy 71(3); doi:10.1093/jac/dkv397 Bielicki JA et al. (2020). Evaluation coverage 3 antibiotic regimens neonatal sepsis hospital setting across Asian countries JAMA Netw Open. 3(2):e1921124; doi:10.1001.jamanetworkopen.2019.21124 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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"x data.frame containing least column microorganisms columns antimicrobial results (class 'sir', see .sir()) antimicrobials vector specifying antimicrobials include antibiogram (see Examples). evaluated using guess_ab_col(). can : antimicrobial name code column name x contains SIR values antimicrobial selector, aminoglycosides() carbapenems() combination , using c(), e.g.: c(aminoglycosides(), \"AMP\", \"AMC\") c(aminoglycosides(), carbapenems()) Combination therapy, indicated using \"+\", without antimicrobial selectors, e.g.: \"cipro + genta\" \"TZP+TOB\" c(\"TZP\", \"TZP+GEN\", \"TZP+TOB\") carbapenems() + \"GEN\" carbapenems() + c(\"\", \"GEN\") carbapenems() + c(\"\", aminoglycosides()) mo_transform character transform microorganism input - must \"name\", \"shortname\" (default), \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input NA consider microorganisms 'unknown'. ab_transform character transform antimicrobial input - must one column names antimicrobials data set (defaults \"name\"): \"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 n_tested available numbers per pathogen added table (default TRUE). add lowest highest number available isolates per antimicrobial (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). option unavailable wisca = TRUE; case, use retrieve_wisca_parameters() get parameters used WISCA. only_all_tested (combination antibiograms): logical indicate isolates must tested antimicrobials, see Details digits number digits use rounding antimicrobial coverage, defaults 1 WISCA 0 otherwise formatting_type numeric value (1–22 WISCA, 1-12 non-WISCA) indicating 'cells' antibiogram table formatted. See Details > Formatting Type list options. 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, SDD, , instead S (default TRUE) sep separating character antimicrobial columns combination antibiograms wisca logical indicate whether Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must generated (default FALSE). use Bayesian decision model estimate regimen coverage probabilities using Monte Carlo simulations. Set simulations, conf_interval, interval_side adjust. simulations (WISCA) numerical value set number Monte Carlo simulations conf_interval numerical value set confidence interval (default 0.95) interval_side side confidence interval, either \"two-tailed\" (default), \"left\" \"right\" info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) wisca_model outcome wisca() antibiogram(..., wisca = TRUE) 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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"functions return table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine one four available algorithms: isolate-based, patient-based, episode-based, phenotype-based. estimating antimicrobial coverage, especially creating WISCA, outcome might become reliable including top n species encountered data. can filter top n using top_n_microorganisms(). example, use top_n_microorganisms(your_data, n = 10) pre-processing step include top 10 species data. numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). ideal e.g. advanced plotting.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"formatting-type","dir":"Reference","previous_headings":"","what":"Formatting Type","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"formatting 'cells' table can set argument formatting_type. examples, 5 indicates antimicrobial coverage (4-6 confidence level), 15 number susceptible isolates, 300 number tested (.e., available) isolates: 5 15 300 15/300 5 (300) 5% (300) 5 (N=300) 5% (N=300) 5 (15/300) 5% (15/300) 5 (N=15/300) 5% (N=15/300) 5 (4-6) 5% (4-6%) - default WISCA 5 (4-6,300) 5% (4-6%,300) 5 (4-6,N=300) 5% (4-6%,N=300) - default non-WISCA 5 (4-6,15/300) 5% (4-6%,15/300) 5 (4-6,N=15/300) 5% (4-6%,N=15/300) default can set globally package option AMR_antibiogram_formatting_type, e.g. options(AMR_antibiogram_formatting_type = 5). note WISCA, total numbers tested susceptible isolates less useful report, since included Bayesian model apparent susceptibility confidence level. Set digits (defaults 0) alter rounding susceptibility percentages.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"various antibiogram types, summarised Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(). clinical coverage estimations, use WISCA whenever possible, since provides precise coverage estimates accounting pathogen incidence antimicrobial susceptibility, shown Bielicki et al. (2020, doi:10.1001.jamanetworkopen.2019.21124 ). See section Explaining WISCA page. note WISCA pathogen-agnostic, meaning outcome stratied pathogen, rather syndrome. 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) WISCA can applied antibiogram, see section Explaining WISCA page information. Code example: WISCA uses sophisticated Bayesian decision model combine local pooled antimicrobial resistance data. approach evaluates local patterns can also draw multi-centre datasets improve regimen accuracy, even low-incidence infections like paediatric bloodstream infections (BSIs).","code":"antibiogram(your_data, antimicrobials = \"TZP\") antibiogram(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data, antimicrobials = penicillins(), syndromic_group = \"ward\") antibiogram(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), wisca = TRUE) # this is equal to: wisca(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"grouped-tibbles","dir":"Reference","previous_headings":"","what":"Grouped tibbles","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"type antibiogram, grouped tibbles can also used calculate susceptibilities various groups. Code example:","code":"library(dplyr) your_data %>% group_by(has_sepsis, is_neonate, sex) %>% wisca(antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"stepped-approach-for-clinical-insight","dir":"Reference","previous_headings":"","what":"Stepped Approach for Clinical Insight","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"clinical practice, antimicrobial coverage decisions evolve microbiological data becomes available. theoretical stepped approach ensures empirical coverage can continuously assessed improve patient outcomes: Initial Empirical Therapy (Admission / Pre-Culture Data) admission, pathogen information available. Action: broad-spectrum coverage based local resistance patterns syndromic antibiograms. Using pathogen-agnostic yet incidence-weighted WISCA preferred. Code example: Refinement Gram Stain Results blood culture becomes positive, Gram stain provides initial crucial first stratification (Gram-positive vs. Gram-negative). Action: narrow coverage based Gram stain-specific resistance patterns. Code example: Definitive Therapy Based Species Identification cultivation pathogen, full pathogen identification allows precise targeting therapy. Action: adjust treatment pathogen-specific antibiograms, minimizing resistance risks. Code example: structuring antibiograms around stepped approach, clinicians can make data-driven adjustments stage, ensuring optimal empirical targeted therapy reducing unnecessary broad-spectrum antimicrobial use.","code":"antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = NA) # all pathogens set to `NA` # preferred: use WISCA wisca(your_data, antimicrobials = selected_regimens) antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = \"gramstain\") # all pathogens set to Gram-pos/Gram-neg antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = \"shortname\") # all pathogens set to 'G. species', e.g., E. coli"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"inclusion-in-combination-antibiograms","dir":"Reference","previous_headings":"","what":"Inclusion in Combination Antibiograms","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antimicrobials, Drug Drug B, antibiogram() works calculate %SI:","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"plotting","dir":"Reference","previous_headings":"","what":"Plotting","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"types antibiograms listed can plotted (using ggplot2::autoplot() base R's plot() barplot()). mentioned , numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). outcome antibiogram() 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":"explaining-wisca","dir":"Reference","previous_headings":"","what":"Explaining WISCA","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"WISCA, outlined Bielicki et al. (doi:10.1093/jac/dkv397 ), stands Weighted-Incidence Syndromic Combination Antibiogram, estimates probability adequate empirical antimicrobial regimen coverage specific infection syndromes. method leverages Bayesian decision model random effects pathogen incidence susceptibility, enabling robust estimates presence sparse data. Bayesian model assumes conjugate priors parameter estimation. example, coverage probability \\(\\theta\\) given antimicrobial regimen modelled using Beta distribution prior: $$\\theta \\sim \\text{Beta}(\\alpha_0, \\beta_0)$$ \\(\\alpha_0\\) \\(\\beta_0\\) represent prior successes failures, respectively, informed expert knowledge weakly informative priors (e.g., \\(\\alpha_0 = 1, \\beta_0 = 1\\)). likelihood function constructed based observed data, number covered cases regimen follows binomial distribution: $$y \\sim \\text{Binomial}(n, \\theta)$$ Posterior parameter estimates obtained combining prior likelihood using Bayes' theorem. posterior distribution \\(\\theta\\) also Beta distribution: $$\\theta | y \\sim \\text{Beta}(\\alpha_0 + y, \\beta_0 + n - y)$$ Pathogen incidence, representing proportion infections caused different pathogens, modelled using Dirichlet distribution, natural conjugate prior multinomial outcomes. Dirichlet distribution parameterised vector concentration parameters \\(\\alpha\\), \\(\\alpha_i\\) corresponds specific pathogen. prior typically chosen uniform (\\(\\alpha_i = 1\\)), reflecting assumption equal prior probability across pathogens. posterior distribution pathogen incidence given : $$\\text{Dirichlet}(\\alpha_1 + n_1, \\alpha_2 + n_2, \\dots, \\alpha_K + n_K)$$ \\(n_i\\) number infections caused pathogen \\(\\) observed data. practical implementation, pathogen incidences sampled posterior using normalised Gamma-distributed random variables: $$x_i \\sim \\text{Gamma}(\\alpha_i + n_i, 1)$$ $$p_i = \\frac{x_i}{\\sum_{j=1}^K x_j}$$ \\(x_i\\) represents unnormalised pathogen counts, \\(p_i\\) normalised proportion pathogen \\(\\). hierarchical modelling, pathogen-level effects (e.g., differences resistance patterns) regimen-level effects modelled using Gaussian priors log-odds. hierarchical structure ensures partial pooling estimates across groups, improving stability strata small sample sizes. model implemented using Hamiltonian Monte Carlo (HMC) sampling. Stratified results can provided based covariates age, sex, clinical complexity (e.g., prior antimicrobial treatments renal/urological comorbidities) using dplyr's group_by() pre-processing step running wisca(). Posterior odds ratios (ORs) derived quantify effect covariates coverage probabilities: $$\\text{}_{\\text{covariate}} = \\frac{\\exp(\\beta_{\\text{covariate}})}{\\exp(\\beta_0)}$$ combining empirical data prior knowledge, WISCA overcomes limitations traditional combination antibiograms, offering disease-specific, patient-stratified estimates robust uncertainty quantification. tool invaluable antimicrobial stewardship programs empirical treatment guideline refinement. Note: WISCA never gives output pathogen/species level, incidences susceptibilities already weighted species.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Implementation: Dr. Larisse Bolton Dr. Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # \\donttest{ # Traditional antibiogram ---------------------------------------------- antibiogram(example_isolates, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 10 × 7 #> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin #> #> 1 CoNS 0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3… #> 2 E. coli 100% (98-1… 98% (96-9… 100% (9… NA 100% (99… 97% (96-9… #> 3 E. faecalis 0% (0-9%,N… 0% (0-9%,… 100% (9… 0% (0-9%… NA 0% (0-9%,… #> 4 K. pneumoniae NA 90% (79-9… 100% (9… NA 100% (93… 90% (79-9… #> 5 P. aeruginosa NA 100% (88-… NA 0% (0-12… NA 100% (88-… #> 6 P. mirabilis NA 94% (80-9… 94% (79… NA NA 94% (80-9… #> 7 S. aureus NA 99% (97-1… NA NA NA 98% (92-1… #> 8 S. epidermidis 0% (0-8%,N… 79% (71-8… NA 0% (0-8%… NA 51% (40-6… #> 9 S. hominis NA 92% (84-9… NA NA NA 85% (74-9… #> 10 S. pneumoniae 0% (0-3%,N… 0% (0-3%,… NA 0% (0-3%… NA 0% (0-3%,… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram antibiogram(example_isolates, antimicrobials = aminoglycosides(), ab_transform = \"atc\", mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 #> #> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-… #> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram antibiogram(example_isolates, antimicrobials = carbapenems(), ab_transform = \"name\", mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 5 × 3 #> Pathogen Imipenem Meropenem #> #> 1 Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48) 52% (37-67%,N=4… #> 2 Enterococcus faecalis 100% (91-100%,N=38) NA #> 3 Escherichia coli 100% (99-100%,N=422) 100% (99-100%,N… #> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N… #> 5 Proteus mirabilis 94% (79-99%,N=32) NA #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Combined antibiogram ------------------------------------------------- # combined antimicrobials yield higher empiric coverage antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), mo_transform = \"gramstain\" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> #> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693) #> 2 Gram-pos… 86% (82-89%,N=345) 98% (96-98%,N=1044) 95% (93-97%,N=550) #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # you can use any antimicrobial selector with `+` too: antibiogram(example_isolates, antimicrobials = ureidopenicillins() + c(\"\", \"GEN\", \"tobra\"), mo_transform = \"gramstain\" ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> #> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693) #> 2 Gram-pos… 86% (82-89%,N=345) 98% (96-98%,N=1044) 95% (93-97%,N=550) #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # names of antimicrobials do not need to resemble columns exactly: antibiogram(example_isolates, antimicrobials = c(\"Cipro\", \"cipro + genta\"), mo_transform = \"gramstain\", ab_transform = \"name\", sep = \" & \" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 3 #> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin` #> #> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694) #> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847) #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Syndromic antibiogram ------------------------------------------------ # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 14 × 8 #> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem #> #> 1 Clinical CoNS NA 89% (84-9… 57% (39… NA 57% (39-… #> 2 ICU CoNS NA 79% (68-8… NA NA NA #> 3 Outpatient CoNS NA 84% (66-9… NA NA NA #> 4 Clinical E. coli 100% (9… 98% (96-9… 100% (9… NA 100% (99… #> 5 ICU E. coli 100% (9… 99% (95-1… 100% (9… NA 100% (97… #> 6 Clinical K. pneumo… NA 92% (81-9… 100% (9… NA 100% (92… #> 7 Clinical P. mirabi… NA 100% (88-… NA NA NA #> 8 Clinical S. aureus NA 99% (95-1… NA NA NA #> 9 ICU S. aureus NA 100% (95-… NA NA NA #> 10 Clinical S. epider… NA 82% (72-9… NA NA NA #> 11 ICU S. epider… NA 72% (60-8… NA NA NA #> 12 Clinical S. hominis NA 96% (85-9… NA NA NA #> 13 Clinical S. pneumo… 0% (0-5… 0% (0-5%,… NA 0% (0-5%… NA #> 14 ICU S. pneumo… 0% (0-1… 0% (0-12%… NA 0% (0-12… NA #> # ℹ 1 more variable: Tobramycin #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # 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, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina #> #> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… #> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # WISCA antibiogram ---------------------------------------------------- # WISCA are not stratified by species, but rather on syndromes antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), syndromic_group = \"ward\", wisca = TRUE ) #> # An Antibiogram (WISCA / 95% CI): 3 × 4 #> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹ #> #> 1 Clinical 76.5% (60.8-88%) 95.3% (86.4-99.2%) #> 2 ICU 60.3% (42.1-80.7%) 90.6% (76.4-98.1%) #> 3 Outpatient 66.8% (38.4-91.3%) 89.7% (69.9-99.5%) #> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin` #> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Print the output for R Markdown / Quarto ----------------------------- ureido <- antibiogram(example_isolates, antimicrobials = ureidopenicillins(), syndromic_group = \"ward\", wisca = TRUE ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam) # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) { cat(knitr::knit_print(ureido)) } #> #> #> |Syndromic Group |Piperacillin/tazobactam | #> |:---------------|:-----------------------| #> |Clinical |76% (60-88.6%) | #> |ICU |59.8% (40.7-79.7%) | #> |Outpatient |66.2% (37.9-91.3%) | # Generate plots with ggplot2 or base R -------------------------------- ab1 <- antibiogram(example_isolates, antimicrobials = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"), mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates, antimicrobials = 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/antimicrobial_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antimicrobial Selectors — antimicrobial_selectors","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions allow filtering rows selecting columns based antimicrobial test results specific antimicrobial class group, without need define columns antimicrobial abbreviations. can used base R, tidyverse, tidymodels, data.table. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"CZO\" \"J01DB04\" picked using:","code":"library(dplyr) my_data_with_all_these_columns %>% select(cephalosporins())"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) aminopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...) antifungals(only_sir_columns = FALSE, return_all = TRUE, ...) antimycobacterials(only_sir_columns = FALSE, return_all = TRUE, ...) betalactams(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) betalactams_with_inhibitor(only_sir_columns = FALSE, return_all = TRUE, ...) carbapenems(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins_1st(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_2nd(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_3rd(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins_4th(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_5th(only_sir_columns = FALSE, return_all = TRUE, ...) fluoroquinolones(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) glycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...) isoxazolylpenicillins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) lincosamides(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) lipoglycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...) macrolides(only_sir_columns = FALSE, return_all = TRUE, ...) monobactams(only_sir_columns = FALSE, return_all = TRUE, ...) nitrofurans(only_sir_columns = FALSE, return_all = TRUE, ...) oxazolidinones(only_sir_columns = FALSE, return_all = TRUE, ...) penicillins(only_sir_columns = FALSE, return_all = TRUE, ...) phenicols(only_sir_columns = FALSE, return_all = TRUE, ...) polymyxins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) quinolones(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) rifamycins(only_sir_columns = FALSE, return_all = TRUE, ...) streptogramins(only_sir_columns = FALSE, return_all = TRUE, ...) sulfonamides(only_sir_columns = FALSE, return_all = TRUE, ...) tetracyclines(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...) ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...) amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...) administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...) not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL, version_expected_phenotypes = 1.2, ...)"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"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) return_all logical indicate whether matched columns must returned (default TRUE). FALSE, first unique antimicrobial returned, e.g. columns \"genta\" \"gentamicin\" exist data, first hit gentamicin returned. ... ignored, place allow future extensions amr_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antimicrobials data set searched (case-insensitive) value. filter expression evaluated antimicrobials data set, name %like% \"trim\" col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). version_expected_phenotypes version number use EUCAST Expected Phenotypes. Can \"1.2\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"used inside selecting filtering, returns character vector column names, additional class \"amr_selector\". used individually, returns 'ab' vector possible antimicrobials function able select filter.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antimicrobial Selectors — antimicrobial_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. selectors can also used tidymodels packages recipe parsnip. See info tutorial using antimicrobial selectors predictive modelling. columns data functions called searched known antimicrobial names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antimicrobials data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. amr_class() function can used filter/select manually defined antimicrobial class. searches results antimicrobials data set within columns group, atc_group1 atc_group2. administrable_per_os() administrable_iv() functions also rely antimicrobials data set - antimicrobials matched DDD (defined daily dose) resp. oral IV treatment available antimicrobials data set. amr_selector() function can used internally filter antimicrobials 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. not_intrinsic_resistant() function can used select antimicrobials 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 Expected Resistant Phenotypes' v1.2 (2023) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"full-list-of-supported-antimicrobial-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antimicrobial) classes","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"aminoglycosides() can select: amikacin (AMK), amikacin/fosfomycin (AKF), 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: amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), 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), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), 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), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), 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), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), tigemonam (TMN) betalactams_with_inhibitor() can select: amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), 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 test (FOX-S), 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 screening test (CTX-S), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), 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), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), 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), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), norfloxacin screening test (-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), 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), bleomycin (BLM), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN), vancomycin-macromethod (VAM) isoxazolylpenicillins() can select: cloxacillin (CLO), dicloxacillin (DIC), flucloxacillin (FLC), meticillin (MET), oxacillin (OXA), oxacillin screening test (OXA-S) lincosamides() can select: clindamycin (CLI), lincomycin (LIN), pirlimycin (PRL) 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), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL), tylvalosin (TYL1) monobactams() can select: aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), carumonam (CAR), tigemonam (TMN) nitrofurans() can select: furazidin (FUR), furazolidone (FRZ), nifurtoinol (NFR), nitrofurantoin (NIT), nitrofurazone (NIZ) 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), azidocillin (AZD), azlocillin (AZL), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), carbenicillin (CRB), carindacillin (CRN), 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), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), 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), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC) phenicols() can select: chloramphenicol (CHL), florfenicol (FLR), thiamphenicol (THI) polymyxins() can select: colistin (COL), polymyxin B (PLB), polymyxin B/polysorbate 80 (POP) quinolones() can select: besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), 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), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nalidixic acid screening test (NAL-S), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), norfloxacin screening test (-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), 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) rifamycins() can select: rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP) streptogramins() can select: pristinamycin (PRI) quinupristin/dalfopristin (QDA) sulfonamides() can select: brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfamoxole (SLF8), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13) 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), tetracycline screening test (TCY-S), 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/antimicrobial_selectors.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antimicrobial Selectors — antimicrobial_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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # you can use the selectors separately to retrieve all possible antimicrobials: carbapenems() #> ℹ in carbapenems(): Imipenem/EDTA (IPE) and meropenem/nacubactam #> (MNC) are not included since only_treatable = TRUE. #> ℹ This 'ab' vector was retrieved using carbapenems(), which should #> normally be used inside a dplyr verb or data.frame call, e.g.: #> • your_data %>% select(carbapenems()) #> • your_data %>% select(column_a, column_b, carbapenems()) #> • your_data %>% filter(any(carbapenems() == \"R\")) #> • your_data[, carbapenems()] #> • your_data[, c(\"column_a\", \"column_b\", carbapenems())] #> Class 'ab' #> [1] BIA DOR ETP IMR IPM MEM MEV PAN RIA RIT RZM TBP # Though they are primarily intended to use for selections and filters. # Examples sections below are split into 'dplyr', 'base R', and 'data.table': # \\donttest{ if (FALSE) { # \\dontrun{ # dplyr ------------------------------------------------------------------- library(dplyr, warn.conflicts = FALSE) example_isolates %>% select(carbapenems()) # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates %>% select(mo, aminoglycosides()) # you can combine selectors like you are used with tidyverse # e.g., for betalactams, but not the ones with an enzyme inhibitor: example_isolates %>% select(betalactams(), -betalactams_with_inhibitor()) # select only antimicrobials with DDDs for oral treatment example_isolates %>% select(administrable_per_os()) # get AMR for all aminoglycosides e.g., per ward: example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), resistance)) # You can combine selectors with '&' to be more specific: example_isolates %>% select(penicillins() & administrable_per_os()) # get AMR for only drugs that matter - no intrinsic resistance: example_isolates %>% filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>% group_by(ward) %>% summarise_at(not_intrinsic_resistant(), resistance) # get susceptibility for antimicrobials whose name contains \"trim\": example_isolates %>% filter(first_isolate()) %>% group_by(ward) %>% summarise(across(amr_selector(name %like% \"trim\"), susceptibility)) # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>% select(carbapenems()) # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB': example_isolates %>% select(mo, aminoglycosides()) # any() and all() work in dplyr's filter() too: example_isolates %>% filter( any(aminoglycosides() == \"R\"), all(cephalosporins_2nd() == \"R\") ) # also works with c(): example_isolates %>% filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) # not setting any/all will automatically apply all(): example_isolates %>% filter(aminoglycosides() == \"R\") # this will select columns 'mo' and all antimycobacterial drugs ('RIF'): example_isolates %>% select(mo, amr_class(\"mycobact\")) # get bug/drug combinations for only glycopeptides in Gram-positives: example_isolates %>% filter(mo_is_gram_positive()) %>% select(mo, glycopeptides()) %>% bug_drug_combinations() %>% format() data.frame( some_column = \"some_value\", J01CA01 = \"S\" ) %>% # ATC code of ampicillin select(penicillins()) # only the 'J01CA01' column will be selected # 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) } # } # 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 #> #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> 7 NA NA #> 8 NA NA #> 9 NA NA #> 10 NA NA #> # ℹ 1,990 more rows # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #> mo GEN TOB AMK KAN #> #> 1 B_ESCHR_COLI NA NA NA NA #> 2 B_ESCHR_COLI NA NA NA NA #> 3 B_STPHY_EPDR NA NA NA NA #> 4 B_STPHY_EPDR NA NA NA NA #> 5 B_STPHY_EPDR NA NA NA NA #> 6 B_STPHY_EPDR NA NA NA NA #> 7 B_STPHY_AURS NA S NA NA #> 8 B_STPHY_AURS NA S NA NA #> 9 B_STPHY_EPDR NA NA NA NA #> 10 B_STPHY_EPDR NA NA NA NA #> # ℹ 1,990 more rows # select only antimicrobials 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 #> #> 1 NA NA NA I NA I NA R NA NA R NA NA #> 2 NA NA NA I NA I NA R NA NA R NA NA #> 3 NA R NA NA NA R NA S NA NA NA NA NA #> 4 NA R NA NA NA R NA S NA NA NA NA NA #> 5 NA R NA NA NA R NA R NA NA NA NA NA #> 6 NA R NA NA NA R NA R NA NA NA NA NA #> 7 NA S R S R S NA R NA NA NA NA NA #> 8 NA S R S R S NA R NA NA NA NA NA #> 9 NA R NA NA NA R NA S NA NA NA S NA #> 10 NA S NA NA NA S NA S NA NA NA S NA #> # ℹ 1,990 more rows #> # ℹ 10 more variables: VAN , TCY , DOX , ERY , CLI , #> # AZM , MTR , CHL , COL , RIF # 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 #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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 #> #> 1 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 7 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 8 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 9 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 10 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> # ℹ 952 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 #> #> 1 2002-04-14 F30196 73 M Outpat… B_STRPT_GRPB S NA S S #> 2 2003-04-08 114570 74 M ICU B_STRPT_PYGN S NA S S #> 3 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 4 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 5 2003-08-14 F71508 0 F Clinic… B_STRPT_GRPB S NA S S #> 6 2003-10-16 650870 63 F ICU B_ESCHR_COLI R NA NA R #> 7 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 9 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> 10 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> # ℹ 746 more rows #> # ℹ 36 more variables: AMC , 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 , … # filter with multiple antimicrobial 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 #> #> 1 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 2 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 3 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 4 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 5 2008-12-06 501361 43 F Clinical B_STNTR_MLTP R NA NA R #> 6 2011-05-09 207325 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2012-03-12 582258 80 M ICU B_STPHY_CONS R R R R #> 8 2012-05-19 C25552 89 F Outpati… B_STPHY_CONS R R R R #> 9 2012-07-17 F05015 83 M ICU B_STPHY_CONS R R R R #> 10 2012-07-20 404299 66 F Clinical B_STPHY_CONS R R R R #> # ℹ 16 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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 #> #> 1 NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA #> 3 R NA NA R R R R #> 4 NA NA NA NA NA NA R #> 5 NA NA NA NA NA NA R #> 6 NA NA NA NA NA NA R #> 7 NA NA NA NA NA NA R #> 8 NA NA NA NA NA NA R #> 9 R NA NA NA S NA S #> 10 R NA NA NA S NA S #> # ℹ 45 more rows # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #> (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #> (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #> 'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #> (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #> (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #> (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #> (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #> (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #> (rifampicin) #> # A tibble: 2,000 × 5 #> OXA FLC AMX AMC AMP #> #> 1 NA NA NA I NA #> 2 NA NA NA I NA #> 3 NA R NA NA NA #> 4 NA R NA NA NA #> 5 NA R NA NA NA #> 6 NA R NA NA NA #> 7 NA S R S R #> 8 NA S R S R #> 9 NA R NA NA NA #> 10 NA S NA NA NA #> # ℹ 1,990 more rows # amr_selector() applies a filter in the `antimicrobials` data set and is thus # very flexible. For instance, to select antimicrobials with an oral DDD # of at least 1 gram: example_isolates[, amr_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For amr_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 #> #> 1 NA NA NA I NA NA NA R R R R NA NA #> 2 NA NA NA I NA NA NA R R R R NA NA #> 3 NA R NA NA NA NA NA NA S R NA NA NA #> 4 NA R NA NA NA NA NA NA S R NA NA NA #> 5 NA R NA NA NA NA NA NA S R NA NA NA #> 6 NA R NA NA NA NA NA NA S R R NA NA #> 7 NA S R S R NA NA NA S S NA NA NA #> 8 NA S R S R NA NA NA S S NA NA NA #> 9 NA R NA NA NA NA NA NA S R NA NA NA #> 10 NA S NA NA NA NA NA NA S S NA NA NA #> # ℹ 1,990 more rows # 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) #> Warning: It should never be needed to print an antimicrobial selector class. Are you #> using data.table? Then add the argument with = FALSE, see our examples at #> ?amr_selector. #> Class 'amr_selector' #> [1] IPM MEM 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: #> 2: #> 3: #> 4: #> 5: #> --- #> 1996: #> 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 #> 2: B_ESCHR_COLI #> 3: B_STPHY_EPDR #> 4: B_STPHY_EPDR #> 5: B_STPHY_EPDR #> --- #> 1996: B_STRPT_PNMN R R R R #> 1997: B_ESCHR_COLI S S S #> 1998: B_STPHY_CONS S #> 1999: B_ESCHR_COLI S S #> 2000: B_KLBSL_PNMN S S 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: #> 2: #> 3: #> 4: #> 5: #> --- #> 1996: R R R R #> 1997: S S S S S #> 1998: S S S #> 1999: S S S S #> 2000: S S S S # 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 #> #> 1: 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R #> 2: 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R #> 3: 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R #> 4: 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R #> 5: 2002-02-05 067927 45 F ICU B_SERRT_MRCS R #> --- #> 905: 2005-04-12 D71461 70 M ICU B_ESCHR_COLI R #> 906: 2009-11-12 650870 69 F Outpatient B_ESCHR_COLI R #> 907: 2012-06-14 8CBCF2 41 F Clinical B_STPHY_CONS R S S #> 908: 2012-10-11 175532 78 M Clinical B_ESCHR_COLI R #> 909: 2013-11-23 A97263 77 M Clinical B_KLBSL_PNMN R #> AMX AMC AMP TZP CZO FEP CXM FOX CTX CAZ CRO GEN #> #> 1: I S S S #> 2: I S S S #> 3: I S S S S #> 4: I S S S S #> 5: R R R R R R #> --- #> 905: S S S S S S S S S S S #> 906: S S S S S S S S S S S S #> 907: S S S S S S R S S #> 908: R S R S S R R S S S S #> 909: R S R S S S S S S S S #> TOB AMK KAN TMP SXT NIT FOS LNZ CIP MFX VAN TEC #> #> 1: S S S R R R #> 2: S S S R R R #> 3: S S R R S R R #> 4: S S R R S R R #> 5: S S R R S R R #> --- #> 905: S S S S R S R R #> 906: S S S S S R S R R #> 907: S S S S #> 908: S R R R R R R R R #> 909: S S S S R S R R #> TCY TGC DOX ERY CLI AZM IPM MEM MTR CHL COL MUP #> #> 1: R R R S #> 2: R R R S #> 3: R R R R R R S R #> 4: R R R R R R S R #> 5: R R R R R R S R #> --- #> 905: R R R S S #> 906: R R R S S #> 907: S S S S S R #> 908: R R R S S S #> 909: R R R S S S #> RIF #> #> 1: R #> 2: R #> 3: R #> 4: R #> 5: R #> --- #> 905: R #> 906: R #> 907: #> 908: R #> 909: 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 I #> 2: R I #> 3: R I #> 4: R I #> 5: R R R R #> --- #> 905: R S S S S #> 906: R S S S S #> 907: R S S S #> 908: R R S R S #> 909: R R S R S # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"Two data sets containing antimicrobials antivirals. Use .ab() one ab_* functions retrieve values antimicrobials data set. Three identifiers included data set: antimicrobial 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/antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials antivirals"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antimicrobials-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antimicrobials data set: a tibble with 496 observations and 14 variables:","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"ab antimicrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antimicrobial susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 179 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 153 drugs iv_units Units iv_ddd loinc codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.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 616 Antimicrobial Drugs — antimicrobials","text":"av Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://atcddd.fhi./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","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/antimicrobials.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials #> # A tibble: 496 × 14 #> ab cid name group atc atc_group1 atc_group2 abbreviations synonyms #> #> 1 AMA 4649 4-ami… Anti… Drugs for… Aminosali… #> 2 ACM 6450012 Acety… Macr… NA NA #> 3 ASP 49787020 Acety… Macr… NA NA #> 4 ALS 8954 Aldes… Othe… Drugs for… Drugs for… #> 5 AMK 37768 Amika… Amin… Aminoglyc… Other ami… #> 6 AKF NA Amika… Amin… NA NA #> 7 AMO 54260 Amoro… Anti… Antifunga… Other ant… #> 8 AMX 33613 Amoxi… Beta… Beta-lact… Penicilli… #> 9 AMC 23665637 Amoxi… Beta… Beta-lact… Combinati… #> 10 AXS 465441 Amoxi… Beta… NA NA #> # ℹ 486 more rows #> # ℹ 5 more variables: oral_ddd , oral_units , iv_ddd , #> # iv_units , loinc antivirals #> # A tibble: 120 × 11 #> av name atc cid atc_group synonyms oral_ddd oral_units iv_ddd #> #> 1 ABA Abacavir J05A… 4.41e5 Nucleosi… 0.6 g NA #> 2 ACI Aciclovir J05A… 1.35e8 Nucleosi… 4 g 4 #> 3 ADD Adefovir… J05A… 6.09e4 Nucleosi… 10 mg NA #> 4 AME Amenamev… J05A… 1.14e7 Other an… 0.4 g NA #> 5 AMP Amprenav… J05A… 6.50e4 Protease… 1.2 g NA #> 6 ASU Asunapre… J05A… 1.61e7 Antivira… 0.2 g NA #> 7 ATA Atazanav… J05A… 1.48e5 Protease… 0.3 g NA #> 8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… NA NA NA #> 9 ATA+RIT Atazanav… J05A… 2.51e7 Antivira… 0.3 g NA #> 10 BAM Baloxavi… J05A… 1.24e8 Other an… 40 mg NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units , loinc "},{"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 antimicrobial drug code one antimicrobials. data set antimicrobials 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, language = get_AMR_locale(), info = interactive(), ...) is.ab(x) ab_reset_session()"},{"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. language language coerce input values 20 supported languages - default system language supported (see get_AMR_locale()) 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 antimicrobials 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://atcddd.fhi./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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Input to an Antibiotic ID — as.ab","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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\") #> 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S #> # ℹ 1,990 more rows #> # ℹ 37 more variables: J01CA04 , J01CR02 , J01CA01 , #> # J01CR05 , J01DB04 , J01DE01 , J01DC02 , J01DC01 , #> # J01DD01 , J01DD02 , J01DD04 , J01GB03 , J01GB01 , #> # J01GB06 , J01GB04 , J01EA01 , J01EE01 , J01XE01 , #> # J01XX01 , J01XX08 , J01MA02 , J01MA14 , J01XA01 , #> # J01XA02 , J01AA07 , J01AA12 , J01AA02 , … # }"},{"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://atcddd.fhi./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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Input to an Antiviral Drug ID — as.av","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 0 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 0 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 0-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame( microorganism = \"Escherichia coli\", AMP = 20, CIP = 14, GEN = 18, TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of 5 variables: #> $ microorganism: chr \"Escherichia coli\" #> $ AMP : 'disk' int 20 #> $ CIP : 'disk' int 14 #> $ GEN : 'disk' int 18 #> $ TOB : 'disk' int 16 # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) { df %>% mutate(across(AMP:TOB, as.disk)) } #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli 20 14 18 16 # } # interpret disk values, see ?as.sir as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #> OK #> Class 'sir' #> [1] R # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2024... #> OK #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli S S S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE, keep_operators = \"all\") is.mic(x) NA_mic_ rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE) mic_p50(x, na.rm = FALSE, ...) mic_p90(x, na.rm = FALSE, ...) # S3 method for class 'mic' droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values, e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). .mic logical indicate whether mic class kept - default TRUE rescale_mic() FALSE droplevels(). setting FALSE rescale_mic(), output factor levels acknowledge mic_range. ... 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 numeric vector. Bear mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2011-2024) CLSI (2011-2024). 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.: -called group generic functions implemented MIC class (!, !=, <, >=, exp(), log2()). functions stats package also implemented (quantile(), median(), fivenum()). Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. rescale_mic(), existing MIC ranges can limited defined range MIC values. can useful better compare MIC distributions. ggplot2, use one scale_*_mic() functions plot MIC values. allows custom MIC ranges plot intermediate log2 levels missing MIC values. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_. Use mic_p50() mic_p90() get 50th 90th percentile MIC values. return 'normal' numeric values.","code":"x <- random_mic(10) x #> Class 'mic' #> [1] 16 1 8 8 64 >=128 0.0625 32 32 16 is.factor(x) #> [1] TRUE x[1] * 2 #> [1] 32 median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16 8 8 64 >=128 32 32 16 df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #> x hospital #> 1 16 A #> 5 64 A #> 6 >=128 A #> 8 32 A #> 9 32 A #> 10 16 A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32 1 1 1 8 <=0.128 8 16 16 is.mic(mic_data) #> [1] TRUE # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002 # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1] 0.128 1.000 8.000 16.000 32.000 quantile(mic_data) #> 0% 25% 50% 75% 100% #> 0.128 1.000 8.000 16.000 32.000 all(mic_data < 512) #> [1] TRUE # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4 <=4 <=4 8 <=4 8 >=16 >=16 # interpret MIC values as.sir( x = as.mic(2), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.mic(c(0.01, 2, 4, 8)), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R # plot MIC values, see ?plot plot(mic_data) plot(mic_data, mo = \"E. coli\", ab = \"cipro\") if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Chromista, 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()), only_fungi = getOption(\"AMR_only_fungi\", FALSE), 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. only_fungi logical indicate fungi must found, making sure e.g. misspellings always return records kingdom Fungi. can set globally microorganism functions package option AMR_only_fungi, .e. options(AMR_only_fungi = TRUE). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate info must printed, e.g. progress bar 25 items coerced, list old taxonomic names. 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' return MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel scientifically validated (doi:10.18637/jss.v104.i03 ) matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. implicates e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","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 \\----> kingdom: A (Archaea), AN (Animalia), B (Bacteria), C (Chromista), 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 inspect results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument used clean input. 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":"for-mycologists","dir":"Reference","previous_headings":"","what":"For Mycologists","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"matching score algorithm gives precedence bacteria fungi. analysing fungi, sure use only_fungi = TRUE, better yet, add code run every session: make sure bacteria 'non-fungi' returned .mo(), mo_* functions.","code":"options(AMR_only_fungi = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coagulase-negative-and-coagulase-positive-staphylococci","dir":"Reference","previous_headings":"","what":"Coagulase-negative and Coagulase-positive Staphylococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Becker = TRUE, following staphylococci converted corresponding coagulase group: Coagulase-negative: S. americanisciuri, S. argensis, S. arlettae, S. auricularis, S. borealis, S. brunensis, 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. marylandisciuri, 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. shinii, 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 Coagulase-positive: 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 based : 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 newly named staphylococcal species, S. brunensis (2024) S. shinii (2023), looked scientific reference make sure species considered correct coagulase group.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"lancefield-groups-in-streptococci","dir":"Reference","previous_headings":"","what":"Lancefield Groups in Streptococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Lancefield = TRUE, following streptococci converted corresponding Lancefield group: Streptococcus Group : S. pyogenes Streptococcus Group B: S. agalactiae Streptococcus Group C: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis, S. equi, S. equi equi, S. equi ruminatorum, S. equi zooepidemicus Streptococcus Group F: S. anginosus, S. anginosus anginosus, S. anginosus whileyi, S. constellatus, S. constellatus constellatus, S. constellatus pharyngis, S. constellatus viborgensis, S. intermedius Streptococcus Group G: S. canis, S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis Streptococcus Group H: S. sanguinis Streptococcus Group K: S. salivarius, S. salivarius salivarius, S. salivarius thermophilus Streptococcus Group L: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis based : Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571","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 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 June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ 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 : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )( ?[a-z-]+[-](resistant|susceptible) ?|e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy)) as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant #> Phenotypes' v1.2 (2023). 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":"Clean existing SIR values, interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI. .sir() transforms input new class sir, ordered factor containing levels S, SDD, , R, NI. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2012-2024 CLSI 2022-2024. breakpoints used interpretation 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) # Default S3 method as.sir(x, S = \"^(S|U)+$\", I = \"^(I)+$\", R = \"^(R)+$\", NI = \"^(N|NI|V)+$\", SDD = \"^(SDD|D|H)+$\", info = TRUE, ...) # S3 method for class 'mic' as.sir(x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, capped_mic_handling = getOption(\"AMR_capped_mic_handling\", \"standard\"), add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, conserve_capped_values = NULL, ...) # S3 method for class '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, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, ...) # S3 method for class 'data.frame' as.sir(x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, capped_mic_handling = getOption(\"AMR_capped_mic_handling\", \"standard\"), add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, conserve_capped_values = NULL) sir_interpretation_history(clean = FALSE)"},{"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: CLSI M39: Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. CLSI M100: Performance Standard Antimicrobial Susceptibility Testing, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. CLSI VET01: Performance Standards Antimicrobial Disk Dilution Susceptibility Tests Bacteria Isolated Animals, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/. EUCAST Breakpoint tables interpretation MICs zone diameters, 2011-2024, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints. WHONET source machine-reading clinical breakpoints (read ), 1989-2024, Collaborating Centre Surveillance Antimicrobial Resistance. https://whonet.org/.","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 S, , R, NI, SDD case-independent regular expression translate input result. regular expression run non-letters whitespaces removed input. info logical print information process mo vector (column name) characters can coerced valid microorganism codes .mo(), can left empty determine automatically ab vector (column name) characters can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2024 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2024) CLSI (2011-2024), see Details. uti (Urinary Tract Infection) vector (column name) 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. capped_mic_handling character string controls MIC values cap (.e., starting <, <=, >, >=) interpreted. Supports following options: \"none\" <= >= treated -. < > treated -. \"conservative\" <= >= return \"NI\" (non-interpretable) MIC within breakpoint guideline range. < always returns \"S\", > always returns \"R\". \"standard\" (default) <= >= return \"NI\" (non-interpretable) MIC within breakpoint guideline range. < > treated -. \"inverse\" <= >= treated -. < always returns \"S\", > always returns \"R\". default \"standard\" setting ensures cautious handling uncertain values preserving interpretability. option can also set package option AMR_capped_mic_handling. 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. substitute_missing_r_breakpoint logical indicate missing clinical breakpoints R (resistant) must substituted R - default FALSE. (especially CLSI) breakpoints breakpoint S, meaning outcome can \"S\" NA. Setting TRUE convert NAs \"R\" R breakpoint missing. Can also set package option AMR_substitute_missing_r_breakpoint. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". host vector (column name) characters indicate host. useful veterinary breakpoints, requires breakpoint_type = \"animal\". values can text resembling animal species, even 20 supported languages package. foreign languages, sure set language set_AMR_locale() (though automatically guessed based system language). verbose logical indicate notes printed interpretation MIC values disk diffusion values. conserve_capped_values deprecated, use capped_mic_handling instead col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Note: clinical breakpoints package validated , imported , WHONET. public use AMR package endorsed CLSI EUCAST. See clinical_breakpoints information. NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function can work four ways: cleaning raw / untransformed data. data cleaned contain valid values, namely: S susceptible, intermediate 'susceptible, increased exposure', R resistant, NI non-interpretable, SDD susceptible dose-dependent. can set using regular expression. Furthermore, .sir() try best clean 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 invalid. 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 capped_mic_handling = \"conservative\", MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (capped_mic_handling = \"standard\") 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.mic, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.mic, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.mic, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\") your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir)) your_data %>% mutate_if(is.disk, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.disk, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.disk, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\")"},{"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 clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024, veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024. Thus, guideline argument must set e.g., \"EUCAST 2024\" \"CLSI 2024\". 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), : veterinary guidelines, might best options:","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL) options(AMR_guideline = \"CLSI\") options(AMR_breakpoint_type = \"animal\")"},{"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. determine isolates multi-drug resistant, sure run mdro() (applies MDR/PDR/XDR guideline 2012 default) data set contains S//R values. Read interpreting multidrug-resistant organisms .","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 34 376 rows 14 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. base R function .double() can used retrieve quantitative values sir object: \"S\" = 1, \"\"/\"SDD\" = 2, \"R\" = 3. values rendered NA . Note: use .integer(), since (R works internally) return factor level indices, aforementioned quantitative values. function is_sir_eligible() returns TRUE column contains 5% invalid antimicrobial interpretations (S //R /NI /SDD), 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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 :0 %S :25.6% (n=417) #> Mode :character Unique:90 %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I : 0.7% (n=11) #> #2 :B_STPHY_CONS %R :73.7% (n=1201) #> #3 :B_STPHY_AURS %NI : 0.0% (n=0) #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %S :68.8% (n=251) %S :70.5% (n=665) %S :40.2% (n=543) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.2% (n=3) #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %S :71.4% (n=1342) %S :40.2% (n=543) %S :86.1% (n=862) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 4.8% (n=91) %I : 0.2% (n=3) %I : 1.3% (n=13) #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %S :54.9% (n=245) %S :85.6% (n=620) %S :72.5% (n=1297) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.4% (n=2) %I : 0.1% (n=1) %I : 1.2% (n=22) #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %S :71.6% (n=586) %S :84.4% (n=796) %S :33.5% (n=607) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 1.0% (n=8) %I : 0.1% (n=1) %I : 0.0% (n=0) #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %S :84.4% (n=796) %S :74.0% (n=1372) %S :65.1% (n=879) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.1% (n=1) %I : 1.5% (n=27) %I : 0.5% (n=7) #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %S :36.3% (n=251) %S : 0.0% (n=0) %S :61.2% (n=918) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.7% (n=10) #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %S :79.1% (n=1392) %S :76.0% (n=565) %S :57.8% (n=203) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.3% (n=6) %I : 6.9% (n=51) %I : 0.0% (n=0) #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %S :30.7% (n=314) %S :78.9% (n=1112) %S :64.5% (n=136) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 4.9% (n=69) %I : 1.9% (n=4) #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %S :61.7% (n=1149) %S :24.3% (n=237) %S :68.3% (n=820) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 1.9% (n=23) #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %S :87.3% (n=697) %S :71.7% (n=814) %S :42.3% (n=801) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.6% (n=7) %I : 0.5% (n=9) #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %S :38.6% (n=586) %S :42.3% (n=801) %S :92.7% (n=824) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.3% (n=4) %I : 0.5% (n=9) %I : 1.1% (n=10) #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %S :94.1% (n=780) %S :85.3% (n=29) %S :78.6% (n=121) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.0% (n=0) #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %S :18.8% (n=309) %S :93.0% (n=251) %S :30.2% (n=303) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 1.1% (n=3) %I : 0.2% (n=2) #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) # For INTERPRETING disk diffusion and MIC values ----------------------- # example data sets, with combined MIC values and disk zones df_wide <- data.frame( microorganism = \"Escherichia coli\", amoxicillin = as.mic(8), cipro = as.mic(0.256), tobra = as.disk(16), genta = as.disk(18), ERY = \"R\" ) df_long <- data.frame( bacteria = rep(\"Escherichia coli\", 4), antibiotic = c(\"amoxicillin\", \"cipro\", \"tobra\", \"genta\"), mics = as.mic(c(0.01, 1, 4, 8)), disks = as.disk(c(6, 10, 14, 18)) ) # \\donttest{ ## Using dplyr ------------------------------------------------- if (require(\"dplyr\")) { # approaches that all work without additional arguments: df_wide %>% mutate_if(is.mic, as.sir) df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df_wide %>% mutate(across(where(is.mic), as.sir)) df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir) df_wide %>% mutate(across(amoxicillin:tobra, as.sir)) # approaches that all work with additional arguments: df_long %>% # given a certain data type, e.g. MIC values mutate_if(is.mic, as.sir, mo = \"bacteria\", ab = \"antibiotic\", guideline = \"CLSI\" ) df_long %>% mutate(across( where(is.mic), function(x) { as.sir(x, mo = \"bacteria\", ab = \"antibiotic\", guideline = \"CLSI\" ) } )) df_wide %>% # given certain columns, e.g. from 'cipro' to 'genta' mutate_at(vars(cipro:genta), as.sir, mo = \"bacteria\", guideline = \"CLSI\" ) df_wide %>% mutate(across( cipro:genta, function(x) { as.sir(x, mo = \"bacteria\", guideline = \"CLSI\" ) } )) # for veterinary breakpoints, add 'host': df_long$animal_species <- c(\"cats\", \"dogs\", \"horses\", \"cattle\") df_long %>% # given a certain data type, e.g. MIC values mutate_if(is.mic, as.sir, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) df_long %>% mutate(across( where(is.mic), function(x) { as.sir(x, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) } )) df_wide %>% mutate_at(vars(cipro:genta), as.sir, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) df_wide %>% mutate(across( cipro:genta, function(x) { as.sir(x, mo = \"bacteria\", host = \"animal_species\", guideline = \"CLSI\" ) } )) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", nitrofuratoin = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", nitrofuratoin = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df_wide %>% mutate_at(vars(cipro:genta), as.sir, mo = \"E. coli\", uti = TRUE) } #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> #> Interpreting MIC values: 'antibiotic' (ASP, acetylspiramycin), CLSI 2024... #> WARNING #> • No CLSI 2024 MIC breakpoints available for acetylspiramycin (ASP). #> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin), #> CLSI 2024... #> WARNING #> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP). #> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin), #> CLSI 2024... #> WARNING #> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP). #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `cipro = (function (x, ...) ...`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `across(...)`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #> OK #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), EUCAST #> 2024... #> OK #> microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli 8 S S R ## Using base R ------------------------------------------------ as.sir(df_wide) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: column 'amoxicillin' (AMX), EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'cipro' (CIP, ciprofloxacin), EUCAST #> 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'tobra' (TOB, tobramycin), EUCAST #> 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'genta' (GEN, gentamicin), EUCAST #> 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK #> microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 65 × 16 #> datetime index ab_given mo_given host_given ab mo #> #> 1 2025-03-28 10:45:00 1 ampicillin Strep p… human AMP B_STRPT_PNMN #> 2 2025-03-28 10:45:00 1 AMP Escheri… human AMP B_[ORD]_ENTRBC… #> 3 2025-03-28 10:45:00 1 AMP Escheri… human AMP B_[ORD]_ENTRBC… #> 4 NA NA NA NA NA NA NA #> 5 2025-03-28 10:45:00 1 GEN Escheri… human GEN B_[ORD]_ENTRBC… #> 6 2025-03-28 10:45:01 1 TOB Escheri… human TOB B_[ORD]_ENTRBC… #> 7 2025-03-28 10:45:01 1 AMX B_STRPT… human AMX B_STRPT_PNMN #> 8 2025-03-28 10:45:01 1 AMX B_STRPT… human AMX B_STRPT_PNMN #> 9 2025-03-28 10:45:01 2 AMX B_STRPT… human AMX B_STRPT_PNMN #> 10 2025-03-28 10:45:01 3 AMX B_STRPT… human AMX B_STRPT_PNMN #> # ℹ 55 more rows #> # ℹ 9 more variables: host , method , input , outcome , #> # notes , guideline , ref_table , uti , #> # breakpoint_S_R # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #> OK #> Class 'sir' #> [1] R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in index '20' truncated (38%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" as.sir(\"<= 0.002; S\") # will return \"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 # as common in R, you can use as.integer() to return factor indices: as.integer(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1] 1 2 3 4 5 NA # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R: as.double(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1] 1 2 2 3 NA NA # 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # }"},{"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. antimicrobial), 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://atcddd.fhi.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://atcddd.fhi.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://atcddd.fhi./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) antimicrobials, 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://atcddd.fhi./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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of an Antiviral Drug — av_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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://atcddd.fhi.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # lowercase transformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, include_n_rows = FALSE, ...) # S3 method for class '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 antimicrobials 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() include_n_rows logical indicate total number rows must included output ... arguments passed FUN translate_ab character length 1 containing column names antimicrobials 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, SDD, 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\", \"SDD\", \"\", \"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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 8 #> mo ab S SDD I R NI total #> #> 1 (unknown species) AMC 15 0 0 0 0 15 #> 2 (unknown species) AMK 0 0 0 0 0 0 #> 3 (unknown species) AMP 15 0 0 1 0 16 #> 4 (unknown species) AMX 15 0 0 1 0 16 #> 5 (unknown species) AZM 3 0 0 3 0 6 #> 6 (unknown species) CAZ 0 0 0 0 0 0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS `E. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #> #> 1 \"Aminogl… Amik… \"100… \" 0.0% … \"100.0% (39/… \"\" \"\" #> 2 \"\" Gent… \" 13… \" 2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \" 0.0% (0/30)\" #> 3 \"\" Kana… \"100… \"\" \"100.0% (39/… \"\" \"100.0% (30/30… #> 4 \"\" Tobr… \" 78… \" 2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \" 0.0% (0/30)\" #> 5 \"Antimyc… Rifa… \"\" \"100.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 6 \"Beta-la… Amox… \" 93… \" 50.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 7 \"\" Amox… \" 42… \" 13.1% … \"\" \" 10.3% (6/58)\" \"100.0% (30/30… #> 8 \"\" Ampi… \" 93… \" 50.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 9 \"\" Benz… \" 77… \"100.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\" Fluc… \" 42… \"\" \"\" \"\" \"\" #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` , `S. aureus` , #> # `S. epidermidis` , `S. hominis` , `S. pneumoniae` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 8 #> mo ab S SDD I R NI total #> #> 1 Gram-negative AMC 463 0 89 174 0 726 #> 2 Gram-negative AMK 251 0 0 5 0 256 #> 3 Gram-negative AMP 226 0 0 405 0 631 #> 4 Gram-negative AMX 226 0 0 405 0 631 #> 5 Gram-negative AZM 1 0 2 696 0 699 #> 6 Gram-negative CAZ 607 0 0 27 0 634 #> 7 Gram-negative CHL 1 0 0 30 0 31 #> 8 Gram-negative CIP 610 0 11 63 0 684 #> 9 Gram-negative CLI 18 0 1 709 0 728 #> 10 Gram-negative COL 309 0 0 78 0 387 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 8 #> mo ab S SDD I R NI total #> #> 1 E. coli AMC 332 0 74 61 0 467 #> 2 E. coli AMK 171 0 0 0 0 171 #> 3 E. coli AMP 196 0 0 196 0 392 #> 4 E. coli AMX 196 0 0 196 0 392 #> 5 E. coli AZM 0 0 0 467 0 467 #> 6 E. coli CAZ 449 0 0 11 0 460 #> 7 E. coli CHL 0 0 0 0 0 0 #> 8 E. coli CIP 398 0 1 57 0 456 #> 9 E. coli CLI 0 0 0 467 0 467 #> 10 E. coli COL 240 0 0 0 0 240 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. dataset contain breakpoints humans, 7 different animal groups, ECOFFs. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2012-2024 CLSI 2022-2024. 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 34 376 observations 14 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" host Host infectious agent. mostly useful veterinary breakpoints either \"ECOFF\", \"aquatic\", \"cats\", \"cattle\", \"dogs\", \"horse\", \"human\", \"poultry\", \"swine\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antimicrobial code used package, EARS-Net WHONET, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\", can NA uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI) is_SDD logical value (TRUE/FALSE) indicate whether intermediate range \"S\" \"R\" interpreted \"SDD\", instead \"\". currently applies 24 breakpoints.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different Types of Breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported From WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate great development WHONET software. import reproduction script can found : .","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response From CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET software, developers AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download-note","dir":"Reference","previous_headings":"","what":"Download Note","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"AMR package (WHONET software well) contains rather complex internal methods apply guidelines. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered implementing breakpoints use.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","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: 34,376 × 14 #> guideline type host method site mo rank_index ab ref_tbl #> #> 1 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 MEM A. xylo… #> 2 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 MEM A. xylo… #> 3 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 SXT A. xylo… #> 4 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 SXT A. xylo… #> 5 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 TZP A. xylo… #> 6 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 TZP A. xylo… #> 7 EUCAST 2024 human human DISK NA B_ACNTB 3 AMK Acineto… #> 8 EUCAST 2024 human human DISK Uncomp… B_ACNTB 3 AMK Acineto… #> 9 EUCAST 2024 human human MIC NA B_ACNTB 3 AMK Acineto… #> 10 EUCAST 2024 human human MIC Uncomp… B_ACNTB 3 AMK Acineto… #> # ℹ 34,366 more rows #> # ℹ 5 more variables: disk_dose , breakpoint_S , breakpoint_R , #> # uti , is_SDD "},{"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_S(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_R(..., 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 antimicrobials, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials 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, SDD, must merged one, output consists S+SDD+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 antimicrobials 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 antimicrobials/variables test . See example two antimicrobials, 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 considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- 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$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" and \"SDD\" being susceptible. This note will be shown once for #> this session. #> [1] 543 count_SI(example_isolates$AMX) #> Note that count_SI() will also count dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 546 count_I(example_isolates$AMX) #> Note that count_I() will also count dose-dependent susceptibility, 'SDD'. #> This note will be shown once for this session. #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" and \"SDD\" 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 #> #> 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 instructions, 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, overwrite = TRUE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_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, overwrite = TRUE) #> 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-multiple-antimicrobials-and-antimicrobial-group-names","dir":"Reference","previous_headings":"","what":"Usage of multiple antimicrobials and antimicrobial group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"can define antimicrobial groups instead single antimicrobials rule consequence, part tilde (~). examples , antimicrobial group aminopenicillins includes ampicillin amoxicillin. Rules can also applied multiple antimicrobials antimicrobial groups simultaneously. Use c() function combine multiple antimicrobials. instance, following example sets aminopenicillins ureidopenicillins \"R\" column TZP (piperacillin/tazobactam) \"R\": 35 antimicrobial groups allowed rules (case-insensitive) can used combination: aminoglycosides(amikacin, amikacin/fosfomycin, 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(amorolfine, amphotericin B, amphotericin B-high, 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, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, tigemonam) betalactams_with_inhibitor(amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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 test, 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 screening test, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, pazufloxacin, pefloxacin, pefloxacin screening test, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) glycopeptides(avoparcin, bleomycin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) glycopeptides_except_lipo(avoparcin, bleomycin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) isoxazolylpenicillins(cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, oxacillin screening test) lincosamides(clindamycin, lincomycin, pirlimycin) lipoglycopeptides(dalbavancin, oritavancin, telavancin) macrolides(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) monobactams(aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, tigemonam) nitrofurans(furazidin, furazolidone, nifurtoinol, nitrofurantoin, nitrofurazone) oxazolidinones(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) penicillins(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, ticarcillin/clavulanic acid) phenicols(chloramphenicol, florfenicol, thiamphenicol) polymyxins(colistin, polymyxin B, polymyxin B/polysorbate 80) quinolones(besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nalidixic acid screening test, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pefloxacin screening test, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) rifamycins(rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine) streptogramins(pristinamycin quinupristin/dalfopristin) sulfonamides(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea) tetracyclines(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, tigecycline) tetracyclines_except_tgc(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test) 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":"x <- custom_eucast_rules(TZP == \"R\" ~ c(aminopenicillins, ureidopenicillins) == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"R\" then set to \"R\": #> amoxicillin (AMX), ampicillin (AMP), azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)"},{"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, overwrite = TRUE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_group rule_name rule_source #> #> 1 33 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 2 33 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 3 34 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 4 34 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 5 531 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 6 531 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 7 1485 AMP Klebsiella oxy… R I \"rep… Custom EU… Custom E… Object 'x'… #> 8 1485 AMX Klebsiella oxy… R I \"rep… Custom EU… Custom E… Object 'x'… # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is R and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is I and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is R then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 503 observations 9 variables: ab Antimicrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply, either 13, 12, 11","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 503 × 9 #> ab name type dose dose_times administration notes original_txt #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 mg/kg… #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x 6 iv #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x 3-4 iv #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1 g x … #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g x 3 o… #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g x 3 o… #> 7 AMC Amoxicillin/cl… high… 2 g … 3 iv \"\" (2 g amoxic… #> 8 AMC Amoxicillin/cl… stan… 1 g … 3 iv \"\" (1 g amoxic… #> 9 AMC Amoxicillin/cl… high… 0.87… 3 oral \"\" (0.875 g am… #> 10 AMC Amoxicillin/cl… stan… 0.5 … 3 oral \"\" (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version "},{"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 notes expected resistant phenotypes 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\", \"expected_phenotypes\")), verbose = FALSE, version_breakpoints = 14, version_expected_phenotypes = 1.2, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, overwrite = FALSE, ...) 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 antimicrobials 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\", \"expected_phenotypes\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expected_phenotypes\"). 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 \"14.0\", \"13.1\", \"12.0\", \"11.0\", \"10.0\". version_expected_phenotypes version number use EUCAST Expected Phenotypes. Can \"1.2\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance (applies rules contains \"expert\" \"\") character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; versions '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 antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() overwrite logical indicating whether overwrite non-NA values (default: FALSE). FALSE, NA values modified. ensure compliance EUCAST guidelines, remain FALSE, EUCAST notes often state organism \"tested susceptibility individual agents reported resistant.\" ... column name antimicrobial, see section Antimicrobials ab (vector ) text can coerced valid antimicrobial 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 antimicrobials. , 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 June 24th, 2024, 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":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Apply EUCAST Rules — eucast_rules","text":"define antimicrobials 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 antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), bleomycin (BLM, L01DC01), brodimoprim (BDP, J01EA02), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), chlortetracycline (CTE, J01AA03), ciprofloxacin (CIP, J01MA02), ciprofloxacin/metronidazole (CIM, J01RA10), ciprofloxacin/ornidazole (CIO, J01RA12), ciprofloxacin/tinidazole (CIT, J01RA11), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), clomocycline (CLM1, J01AA11), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), demeclocycline (DEM, J01AA01), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), eravacycline (ERV, J01AA13), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levofloxacin/ornidazole (LEO, J01RA05), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), lymecycline (LYM, J01AA04), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metacycline (MTC, J01AA05), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), nemonoxacin (NEM, J01MB08), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), nitroxoline (NTR, J01XX07), norfloxacin (, J01MA06), norfloxacin/metronidazole (NME, J01RA14), norfloxacin/tinidazole (NTI, J01RA13), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), ofloxacin/ornidazole (OOR, J01RA09), oleandomycin (OLE, J01FA05), omadacycline (OMC, J01AA15), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), oxytetracycline (OXY, J01AA06), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), penimepicycline (PNM1, J01AA10), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), rolitetracycline (RLT, J01AA09), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sarecycline (SRC, J01AA14), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/tetroxoprim (SLT, J01EE06), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Apply EUCAST Rules — eucast_rules","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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, overwrite = 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(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R R 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, overwrite = TRUE, 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 Expected phenotypes #> 4 1 COL Staphylococcus aureus - R Expected phenotypes #> 5 2 CAZ Enterococcus faecalis - R Expected phenotypes #> 6 2 COL Enterococcus faecalis - R Expected phenotypes #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Table 4: Expected resistant phenotype in gram-positive bacteria #> 4 Table 4: Expected resistant phenotype in gram-positive bacteria #> 5 Table 4: Expected resistant phenotype in gram-positive bacteria #> 6 Table 4: Expected resistant phenotype in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v14.0, 2024 #> 2 'EUCAST Clinical Breakpoint Tables' v14.0, 2024 #> 3 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 4 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 5 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 6 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 # } # 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 #> #> 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 #> #> 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 antimicrobials class sir (see .sir()); column names occur antimicrobials data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , …"},{"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 antimicrobials transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Unclean Data — example_isolates_unclean","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"Export Data Set NCBI BioSample Antibiogram","code":""},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"","code":"export_ncbi_biosample(x, filename = paste0(\"biosample_\", format(Sys.time(), \"%Y-%m-%d-%H%M%S\"), \".xlsx\"), type = \"pathogen MIC\", columns = where(is.mic), save_as_xlsx = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"x data set filename character string specifying file name type character string specifying type data set, either \"pathogen MIC\" \"beta-lactamase MIC\", see https://www.ncbi.nlm.nih.gov/biosample/docs/","code":""},{"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 functions 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":"methodology implemented functions strictly based recommendations outlined CLSI Guideline M39 research overview Hindler et al. (2007, doi:10.1086/511864 ). 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 previously-mentioned sources, 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. method makes sure duplicate isolates selected patient. method preferred e.g. identify first MRSA finding patient determine incidence. Conversely, 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 ICU cases, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates[first_isolate(info = TRUE), ] #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #> microbial ID was available) #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … # \\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: 441 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R #> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA #> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA #> # ℹ 431 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # filter on first isolates using dplyr: example_isolates %>% filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #> microbial ID was available) #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # short-hand version: example_isolates %>% filter_first_isolate(info = FALSE) } #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # flag the first isolates per group: example_isolates %>% group_by(ward) %>% mutate(first = first_isolate(info = TRUE)) %>% select(ward, date, patient, mo, first) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 865 'phenotype-based' first isolates (70.1% of total where a #> microbial ID was available) #> #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 452 'phenotype-based' first isolates (70.0% of total where a #> microbial ID was available) #> #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 99 'phenotype-based' first isolates (82.5% of total where a #> microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> #> 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 TRUE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # ℹ 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also factors. y numeric vector; ignored x matrix. x factor, y factor length. p vector probabilities length x. error given entry p negative. rescale.p logical scalar; TRUE p rescaled (necessary) sum 1. rescale.p FALSE, p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following components: statistic value chi-squared test statistic. parameter degrees freedom approximate chi-squared distribution test statistic, NA p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test performed, whether Monte Carlo simulation continuity correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals, (observed - expected) / sqrt(expected). stdres standardized residuals, (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007, section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers. Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed . Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R : df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio. x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 4.1471, p-value = 0.1257 #> # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible. # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills. x <- c(1752, 1895) g.test(x) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 5.6085, p-value = 0.01787 #> # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...) is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023). *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function hand, returns TRUE every new get_episode() index. specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, episode functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c( \"2021-01-01\", \"2021-01-02\", \"2021-01-05\", \"2021-01-08\", \"2021-02-21\", \"2021-02-22\", \"2021-02-23\", \"2021-02-24\", \"2021-03-01\", \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #> dates absolute relative #> 1 2021-01-01 1 1 #> 2 2021-01-02 1 1 #> 3 2021-01-05 1 1 #> 4 2021-01-08 2 1 #> 5 2021-02-21 3 2 #> 6 2021-02-22 3 2 #> 7 2021-02-23 3 2 #> 8 2021-02-24 3 2 #> 9 2021-03-01 4 2 #> 10 2021-03-01 4 2 # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices #> [1] 33 15 22 12 31 27 11 45 38 19 3 29 4 42 41 41 13 36 23 33 38 46 31 45 21 #> [26] 24 9 18 10 6 11 41 1 12 38 26 11 36 1 41 32 14 7 37 22 26 16 44 14 7 #> [51] 44 4 45 4 2 39 20 43 9 42 29 11 6 36 25 30 23 8 14 4 28 11 14 20 40 #> [76] 23 6 11 16 29 27 9 8 15 45 31 35 44 35 46 5 39 44 24 17 12 31 32 36 34 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [13] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE #> [25] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE #> [37] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE #> [49] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [85] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE #> [97] FALSE FALSE FALSE TRUE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 1 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-07-16 241328 78 M Outpatie… B_STPHY_CONS R NA S R #> # ℹ 36 more variables: 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 # 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 [95] #> patient date condition new_episode #> #> 1 067927 2002-02-14 B TRUE #> 2 067927 2002-02-14 B FALSE #> 3 0E2483 2007-06-21 B TRUE #> 4 151041 2006-02-10 C TRUE #> 5 155435 2014-06-18 A TRUE #> 6 167474 2008-02-15 A TRUE #> 7 174209 2011-10-03 B TRUE #> 8 175532 2012-10-11 A TRUE #> 9 187841 2012-09-30 C TRUE #> 10 197007 2012-07-17 B TRUE #> # ℹ 90 more rows if (require(\"dplyr\")) { df %>% group_by(ward, patient) %>% transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) %>% arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups: ward, patient [93] #> ward date patient new_index new_logical #> #> 1 ICU 2002-02-14 067927 1 TRUE #> 2 ICU 2002-02-14 067927 1 FALSE #> 3 ICU 2007-06-21 0E2483 1 TRUE #> 4 Clinical 2006-02-10 151041 1 TRUE #> 5 Clinical 2014-06-18 155435 1 TRUE #> 6 Clinical 2008-02-15 167474 1 TRUE #> 7 Outpatient 2011-10-03 174209 1 TRUE #> 8 Clinical 2012-10-11 175532 1 TRUE #> 9 Clinical 2012-09-30 187841 1 TRUE #> 10 Clinical 2012-07-17 197007 1 TRUE #> # ℹ 90 more rows if (require(\"dplyr\")) { df %>% group_by(ward) %>% summarise( n_patients = n_distinct(patient), n_episodes_365 = sum(is_new_episode(date, episode_days = 365)), n_episodes_60 = sum(is_new_episode(date, episode_days = 60)), n_episodes_30 = sum(is_new_episode(date, episode_days = 30)) ) } #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> #> 1 Clinical 55 13 35 44 #> 2 ICU 32 9 19 25 #> 3 Outpatient 6 6 6 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 [95] #> patient mo ward flag_episode #> #> 1 88B975 B_STRPT Clinical TRUE #> 2 904485 B_ESCHR_COLI Outpatient TRUE #> 3 167474 B_STPHY_CONS Clinical TRUE #> 4 777154 B_CTRBC Clinical TRUE #> 5 E58140 B_ESCHR_COLI Clinical TRUE #> 6 E58549 B_ESCHR_COLI Clinical TRUE #> 7 D65308 B_STPHY_EPDR ICU TRUE #> 8 871020 B_STPHY_EPDR Clinical TRUE #> 9 B13995 B_STPHY_AURS Clinical TRUE #> 10 CB6026 B_STPHY_CONS Clinical TRUE #> # ℹ 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE, labels = NULL, labels_textsize = 3, labels_text_placement = 1.5, groups = NULL, ellipse = TRUE, ellipse_prob = 0.68, ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2, points_alpha = 0.25, arrows = TRUE, arrows_colour = \"darkblue\", arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE, arrows_alpha = 0.75, base_textsize = 10, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default biplot strict sense. scale variables scaled lambda ^ scale observations scaled lambda ^ (1-scale) lambda singular values computed princomp. Normally 0 <= scale <= 1, warning issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component biplot\", lambda = 1 observations scaled sqrt(n) variables scaled sqrt(n). inner products variables approximate covariances distances observations approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. # \\donttest{ if (require(\"dplyr\")) { # calculate the resistance per group first resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here; filter(n() >= 30) %>% # filter on only 30 results per group summarise_if(is.sir, resistance) # then get resistance of all drugs # now conduct PCA for certain antimicrobial drugs pca_result <- resistance_data %>% pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT) summary(pca_result) # old base R plotting method: biplot(pca_result, main = \"Base R biplot\") # new ggplot2 plotting method using this package: if (require(\"ggplot2\")) { ggplot_pca(pca_result) + labs(title = \"ggplot2 biplot\") } if (require(\"ggplot2\")) { # still extendible with any ggplot2 function ggplot_pca(pca_result) + scale_colour_viridis_d() + labs(title = \"ggplot2 biplot\") } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #> only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(data, position = NULL, x = \"antibiotic\", fill = \"interpretation\", facet = NULL, breaks = seq(0, 1, 0.1), limits = NULL, translate_ab = \"name\", combine_SI = TRUE, minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R = \"#ED553B\"), datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = \"grey15\", title = NULL, subtitle = NULL, caption = NULL, x.title = \"Antimicrobial\", y.title = \"Proportion\", ...) geom_sir(position = NULL, x = c(\"antibiotic\", \"interpretation\"), fill = \"interpretation\", translate_ab = \"name\", minimum = 30, language = get_AMR_locale(), combine_SI = TRUE, ...)"},{"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 antimicrobials data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+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.","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 antimicrobials shown plots using ab_name(). can set translate_ab argument. See count_df(). geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antimicrobials x axis. Additional functions include: 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(aesthetics = \"fill\") + 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( aesthetics = \"fill\", 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 or values outside the scale range #> (`geom_col()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_text()`). 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 antimicrobials 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 antimicrobials 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 Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing 'EUCAST Expected Resistant Phenotypes' bug-drug combinations microorganisms antimicrobials data sets.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set Denoting 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 Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 271 905 observations 2 variables: mo Microorganism ID occurs microorganisms$mo. Names can retrieved using mo_name(). ab Antimicrobial ID occurs antimicrobials$ab. Names can retrieved using ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set currently based 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). data set internally used : not_intrinsic_resistant() (antimicrobial selector) mo_is_intrinsic_resistant()","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 271,905 × 2 #> mo ab #> #> 1 B_GRAMP ATM #> 2 B_GRAMP COL #> 3 B_GRAMP NAL #> 4 B_GRAMP PLB #> 5 B_GRAMP TEM #> 6 B_ANAER-POS ATM #> 7 B_ANAER-POS COL #> 8 B_ANAER-POS NAL #> 9 B_ANAER-POS PLB #> 10 B_ANAER-POS TEM #> # ℹ 271,895 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\", \"html\")) italicize_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))"},{"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\", \"html\" \"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 html. using 'ansi', ANSI colours added using \\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 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed 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\" #> [4] \"status\" \"kingdom\" \"phylum\" #> [7] \"class\" \"order\" \"family\" #> [10] \"genus\" \"species\" \"subspecies\" #> [13] \"rank\" \"ref\" \"oxygen_tolerance\" #> [16] \"source\" \"lpsn\" \"lpsn_parent\" #> [19] \"lpsn_renamed_to\" \"mycobank\" \"mycobank_parent\" #> [22] \"mycobank_renamed_to\" \"gbif\" \"gbif_parent\" #> [25] \"gbif_renamed_to\" \"prevalence\" \"snomed\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% left_join_microorganisms() %>% colnames() } #> Joining, by = \"mo\" #> [1] \"date\" \"patient\" \"age\" #> [4] \"gender\" \"ward\" \"mo\" #> [7] \"PEN\" \"OXA\" \"FLC\" #> [10] \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" #> [16] \"CXM\" \"FOX\" \"CTX\" #> [19] \"CAZ\" \"CRO\" \"GEN\" #> [22] \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"NIT\" #> [28] \"FOS\" \"LNZ\" \"CIP\" #> [31] \"MFX\" \"VAN\" \"TEC\" #> [34] \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" #> [40] \"IPM\" \"MEM\" \"MTR\" #> [43] \"CHL\" \"COL\" \"MUP\" #> [46] \"RIF\" \"fullname\" \"status\" #> [49] \"kingdom\" \"phylum\" \"class\" #> [52] \"order\" \"family\" \"genus\" #> [55] \"species\" \"subspecies\" \"rank\" #> [58] \"ref\" \"oxygen_tolerance\" \"source\" #> [61] \"lpsn\" \"lpsn_parent\" \"lpsn_renamed_to\" #> [64] \"mycobank\" \"mycobank_parent\" \"mycobank_renamed_to\" #> [67] \"gbif\" \"gbif_parent\" \"gbif_renamed_to\" #> [70] \"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 antimicrobials 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 antimicrobials 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] 1383 # }"},{"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) # Default S3 method kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for class 'matrix' kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for class '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] 3.032783 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.05102003"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE) x %like% pattern x %unlike% pattern x %like_case% pattern x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE) a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE a[1] %like% b #> [1] TRUE FALSE FALSE a %like% b[1] #> [1] TRUE FALSE FALSE # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC , 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 , … if (require(\"dplyr\")) { example_isolates %>% filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC , 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 , … # }"},{"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, esbl = NA, carbapenemase = NA, mecA = NA, mecC = NA, vanA = NA, vanB = NA, 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, verbose = FALSE, ...) mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, verbose = 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 antimicrobials 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(). esbl logical values, column name containing logical values, indicating presence ESBL gene (production proteins) carbapenemase logical values, column name containing logical values, indicating presence carbapenemase gene (production proteins) mecA logical values, column name containing logical values, indicating presence mecA gene (production proteins) mecC logical values, column name containing logical values, indicating presence mecC gene (production proteins) vanA logical values, column name containing logical values, indicating presence vanA gene (production proteins) vanB logical values, column name containing logical values, indicating presence vanB gene (production proteins) 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 antimicrobial 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 Antimicrobials . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (doi:10.1111/j.1469-0691.2011.03570.x ) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Samenwerkingverband Richtlijnen Infectiepreventie (SRI) (2024) \"Bijzonder Resistente Micro-Organismen (BRMO)\" (link) Also: guideline = \"BRMO 2017\" former Dutch national guideline - Werkgroep Infectiepreventie (WIP), RIVM, last revision 2017: \"Bijzonder Resistente Micro-Organismen (BRMO)\" Please suggest implement 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":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antimicrobials 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 antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), bleomycin (BLM, L01DC01), brodimoprim (BDP, J01EA02), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), chlortetracycline (CTE, J01AA03), ciprofloxacin (CIP, J01MA02), ciprofloxacin/metronidazole (CIM, J01RA10), ciprofloxacin/ornidazole (CIO, J01RA12), ciprofloxacin/tinidazole (CIT, J01RA11), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), clomocycline (CLM1, J01AA11), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), demeclocycline (DEM, J01AA01), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), eravacycline (ERV, J01AA13), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levofloxacin/ornidazole (LEO, J01RA05), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), lymecycline (LYM, J01AA04), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metacycline (MTC, J01AA05), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), nemonoxacin (NEM, J01MB08), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), nitroxoline (NTR, J01XX07), norfloxacin (, J01MA06), norfloxacin/metronidazole (NME, J01RA14), norfloxacin/tinidazole (NTI, J01RA13), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), ofloxacin/ornidazole (OOR, J01RA09), oleandomycin (OLE, J01FA05), omadacycline (OMC, J01AA15), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), oxytetracycline (OXY, J01AA06), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), penimepicycline (PNM1, J01AA10), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), rolitetracycline (RLT, J01AA09), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sarecycline (SRC, J01AA14), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/tetroxoprim (SLT, J01EE06), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") str(out) #> Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #> Negative Positive, unconfirmed Positive #> 1994 0 6 out <- mdro(example_isolates, guideline = custom_mdro_guideline( AMX == \"R\" ~ \"Custom MDRO 1\", VAN == \"R\" ~ \"Custom MDRO 2\" ) ) table(out) #> out #> Negative Custom MDRO 1 Custom MDRO 2 #> 870 804 326 # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% mdro() %>% table() # no need to define `x` when used inside dplyr verbs: example_isolates %>% mutate(MDRO = mdro()) %>% pull(MDRO) %>% table() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) #> 1617 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 class 'sir' mean_amr_distance(x, ..., combine_SI = TRUE) # S3 method for class '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 antimicrobial selectors combine_SI logical indicate whether values S, SDD, must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #> [1] S S I I I R R S I S mean_amr_distance(sir) #> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 1.8973666 #> [7] 1.8973666 -0.4743416 -0.4743416 -0.4743416 mic <- random_mic(10) mic #> Class 'mic' #> [1] >=128 0.125 0.01 64 64 8 0.002 1 >=128 #> [10] <=0.001 mean_amr_distance(mic) #> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587 #> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587 #> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641 disk <- random_disk(10) disk #> Class 'disk' #> [1] 39 35 46 27 13 50 30 49 41 20 mean_amr_distance(disk) #> [1] 0.3216338 0.0000000 0.8844928 -0.6432675 -1.7689857 1.2061266 #> [7] -0.4020422 1.1257182 0.4824506 -1.2061266 y <- data.frame( id = LETTERS[1:10], amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"), cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"), gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"), tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #> id amox cipr gent tobr #> 1 A R 23 >=4 1 #> 2 B I 26 <=1 0.5 #> 3 C S 20 >=4 >=16 #> 4 D R 27 >=4 0.5 #> 5 E S 31 >=4 2 #> 6 F I 27 >=4 >=16 #> 7 G I 31 <=1 8 #> 8 H R 30 >=4 4 #> 9 I S 27 >=4 4 #> 10 J I 20 >=4 0.5 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #> and \"tobr\" #> [1] 0.12237407 -0.92897174 -0.07982527 0.24282355 0.22020411 0.34940813 #> [7] -0.12306945 0.80126097 0.09975463 -0.70395902 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 S 20 >=4 >=16 -0.07982527 0.00000000 #> 2 G I 31 <=1 8 -0.12306945 0.04324419 #> 3 I S 27 >=4 4 0.09975463 0.17957990 #> 4 A R 23 >=4 1 0.12237407 0.20219934 #> 5 E S 31 >=4 2 0.22020411 0.30002938 #> 6 D R 27 >=4 0.5 0.24282355 0.32264882 #> 7 F I 27 >=4 >=16 0.34940813 0.42923340 #> 8 J I 20 >=4 0.5 -0.70395902 0.62413375 #> 9 B I 26 <=1 0.5 -0.92897174 0.84914647 #> 10 H R 30 >=4 4 0.80126097 0.88108624 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 #> #> 1 B_ENTRC_AVIM S S NA 0 #> 2 B_ENTRC_AVIM S S NA 0 #> 3 B_ENTRC_CSSL NA S NA NA #> 4 B_ENTRC_FACM S S NA -2.66 #> 5 B_ENTRC_FACM S R R -0.423 #> 6 B_ENTRC_FACM S R R -0.423 #> 7 B_ENTRC_FACM NA R R 0.224 #> 8 B_ENTRC_FACM NA R R 0.224 #> 9 B_ENTRC_FACM NA R R 0.224 #> 10 B_ENTRC_FACM NA R R 0.224 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 971 observations 2 variables: code Commonly used code microorganism. unique identifier. mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,971 × 2 #> code mo #> #> 1 1011 B_GRAMP #> 2 1012 B_GRAMP #> 3 1013 B_GRAMN #> 4 1014 B_GRAMN #> 5 1015 F_YEAST #> 6 103 B_ESCHR_COLI #> 7 104 B_SLMNL_ENTR_ENTR #> 8 1100 B_STRPT #> 9 1101 B_STRPT_VIRI #> 10 1102 B_STRPT_HAEM #> # ℹ 4,961 more rows # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #> code mo #> #> 1 ECO B_ESCHR_COLI # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Escherichia\" #> #> $species #> [1] \"coli\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #> #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #> #> $ref #> [1] \"Castellani et al., 1919\" #> #> $snomed #> [1] \"1095001000112106\" \"715307006\" \"737528008\" \"416989002\" #> [5] \"116397003\" \"414097009\" \"414098004\" \"414099007\" #> [9] \"414100004\" \"116395006\" \"735270003\" \"116396007\" #> [13] \"83285000\" \"116394005\" \"112283007\" \"710886005\" #> [17] \"710887001\" \"710888006\" \"710889003\" \"414132004\" #> [21] \"721892009\" \"416812001\" \"416740004\" \"417216001\" #> [25] \"457541006\" \"710253004\" \"416530004\" \"417189006\" #> [29] \"409800005\" \"713925008\" \"444771000124108\" \"838549008\" #> #> $lpsn #> [1] \"776057\" #> #> $mycobank #> [1] NA #> #> $gbif #> [1] \"11286021\" #> #> $group_members #> character(0) #> # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 534 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 534 × 4 #> mo_group mo mo_group_name mo_name #> #> 1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #> 2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #> 3 B_ACNTB_BMNN-C B_ACNTB_LCTC Acinetobacter baumannii complex Acinetobacter di… #> 4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #> 5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #> 6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #> 7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides frag… #> 8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovat… #> 9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulg… #> # ℹ 524 more rows # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #> mo_group mo mo_group_name mo_name #> #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulgatus #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: June 24th, 2024) six kingdoms. data set backbone AMR package. MO codes can looked using .mo() microorganism properties can looked using mo_* functions. data set carefully crafted, yet made 100% reproducible public authoritative taxonomic sources (using script(https://github.com/msberends/AMR/blob/main/data-raw/reproduction scripts/reproduction_of_microorganisms.R)), namely: List Prokaryotic names Standing Nomenclature (LPSN) bacteria, MycoBank fungi, Global Biodiversity Information Facility (GBIF) others taxons.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 78 679 Taxonomic Records of 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"tibble 78 679 observations 26 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. status Status taxon, either \"accepted\", \"validly published\", \"synonym\", \"unknown\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism. Note fungi, phylum equal taxonomic division. Also, fungi, subkingdom subdivision left since occur bacterial taxonomy. 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. oxygen_tolerance Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 68.3% ~39 000 bacteria data set contain oxygen tolerance. source Either \"GBIF\", \"LPSN\", \"Manually added\", \"MycoBank\", \"manually added\" (see Source) 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. unique identifier, though available ~33 000 records. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon mycobank Identifier ('MycoBank #') MycoBank. unique identifier, though available ~18 000 records. mycobank_parent MycoBank identifier parent taxon mycobank_renamed_to MycoBank identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF). unique identifier, though available ~49 000 records. gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon prevalence Prevalence microorganism based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version July 16th, 2024 (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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Taxonomic entries imported order importance: List Prokaryotic names Standing Nomenclature (LPSN): 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 June 24th, 2024. MycoBank: Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. Global Biodiversity Information Facility (GBIF): GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Furthermore, sources used additional details: BacDive: Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Systematized Nomenclature Medicine - Clinical Terms (SNOMED-CT): Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed https://www.cdc.gov/phin/php/phinvads/ July 16th, 2024. 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Please note entries based LPSN, MycoBank, 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Included taxonomic data LPSN, MycoBank, GBIF : ~39 000 (sub)species kingdoms Archaea Bacteria ~28 000 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). ~8 100 (sub)species kingdom Protozoa ~1 600 (sub)species 39 relevant genera kingdom Animalia (Strongyloides Taenia) ~22 000 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 37 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 1 entry Blastocystis (B. hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg bacteria) syntax used transform original data cleansed R format, can found (https://github.com/msberends/AMR/blob/main/data-raw/reproduction scripts/reproduction_of_microorganisms.R).","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 78,679 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_GRAMN (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 2 B_GRAMP (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 3 B_ANAER-NEG (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 4 B_ANAER-POS (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 5 B_ANAER (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 6 F_FUNGUS (unknown … unkno… Fungi (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 7 UNKNOWN (unknown … unkno… (unkno… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 8 P_PROTOZOAN (unknown … unkno… Protoz… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 9 F_YEAST (unknown … unkno… Fungi (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 10 F_AABRN Aabaarnia unkno… Fungi Ascom… Leca… Ostr… \"\" Aaba… \"\" #> # ℹ 78,669 more rows #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed "},{"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 developed 2018 subsequently 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, optimalisations algorithm made.","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 : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","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. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), 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 0.3809524"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_fullname(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_shortname(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_subspecies(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_species(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_genus(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_family(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_order(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_class(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_phylum(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_kingdom(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_domain(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_type(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_status(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_pathogenicity(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_gramstain(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_gram_negative(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_gram_positive(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_yeast(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_oxygen_tolerance(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_anaerobic(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_snomed(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_ref(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_authors(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_year(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_lpsn(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_mycobank(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_group_members(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_info(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_url(x, open = FALSE, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_property(x, property = \"fullname\", language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_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() logical case mo_is_anaerobic(), mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), mo_is_yeast() named character case mo_synonyms() 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 renaming) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (-per-session warning name outdated) 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 yeasts reproduce asexually process called budding, new cell develops small protrusion parent cell. Taxonomically, members phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) Pichiomycetes. True yeasts quite specifically refers yeasts underlying order Saccharomycetales (Saccharomyces cerevisiae). Thus, microorganisms member taxonomic class Saccharomycetes Pichiomycetes, 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 Expected Resistant Phenotypes' v1.2 (2023). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antimicrobials). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. MycoBank URL used fungi wherever available , LPSN URL bacteria wherever available, GBIF link otherwise. SNOMED codes (mo_snomed()) last updated July 16th, 2024. 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 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 June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ 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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of a Microorganism — mo_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 mo_group_members(c( \"Streptococcus group A\", \"Streptococcus group C\", \"Streptococcus group G\", \"Streptococcus group L\" )) #> $`Streptococcus Group A` #> [1] \"Streptococcus pyogenes\" #> #> $`Streptococcus Group C` #> [1] \"Streptococcus dysgalactiae\" #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\" #> [4] \"Streptococcus equi\" #> [5] \"Streptococcus equi equi\" #> [6] \"Streptococcus equi ruminatorum\" #> [7] \"Streptococcus equi zooepidemicus\" #> #> $`Streptococcus Group G` #> [1] \"Streptococcus canis\" #> [2] \"Streptococcus dysgalactiae\" #> [3] \"Streptococcus dysgalactiae dysgalactiae\" #> [4] \"Streptococcus dysgalactiae equisimilis\" #> #> $`Streptococcus Group L` #> [1] \"Streptococcus dysgalactiae\" #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\" #> # 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_synonyms(\"Klebsiella aerogenes\") #> Hormaeche et al., 1960 Bascomb et al., 1971 #> \"Enterobacter aerogenes\" \"Klebsiella mobilis\" mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_mycobank(\"Candida albicans\") #> [1] \"256187\" mo_mycobank(\"Candida krusei\") #> [1] \"337013\" mo_mycobank(\"Candida krusei\", keep_synonyms = TRUE) #> Warning: Function as.mo() returned one old taxonomic name. Use as.mo(..., #> keep_synonyms = FALSE) to clean the input to currently accepted taxonomic #> names, or set the R option AMR_keep_synonyms to FALSE. This warning #> will be shown once per session. #> [1] \"268707\" # 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 #> #> 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: 19 × 2 #> `mo_genus()` n #> #> 1 Escherichia 467 #> 2 Klebsiella 77 #> 3 Proteus 39 #> 4 Pseudomonas 30 #> 5 Serratia 25 #> 6 Enterobacter 23 #> 7 Citrobacter 11 #> 8 Haemophilus 9 #> 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 Leuconostoc 1 #> 19 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\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Klebsiella\" #> #> $species #> [1] \"pneumoniae\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #> #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #> #> $ref #> [1] \"Trevisan, 1887\" #> #> $snomed #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" #> #> $lpsn #> [1] \"777151\" #> #> $mycobank #> [1] NA #> #> $gbif #> [1] \"3221874\" #> #> $group_members #> character(0) #> # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(path, destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\")) get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$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 \"/Users//Documents/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(\"/Users/me/Documents/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 Helpers for AMR Data Analysis — plot","title":"Plotting Helpers for AMR Data Analysis — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_y_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_x_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) scale_colour_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) scale_fill_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) # S3 method for class 'mic' plot(x, mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), main = deparse(substitute(x)), ylab = translate_AMR(\"Frequency\", language = language), xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class 'mic' autoplot(object, mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\", language = language), xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 = getOption(\"AMR_guideline\", \"EUCAST\"), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 = getOption(\"AMR_guideline\", \"EUCAST\"), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 class '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(), ...) facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL) scale_y_percent(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = c(0, NA)) scale_sir_colours(..., aesthetics, colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\")) 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/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting Helpers for AMR Data Analysis — plot","text":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values (using rescale_mic()), e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). Note: rescales values filter - use ggplot2 limits argument separately exclude values plot. ... arguments passed methods 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 prevent translation. eucast_I logical indicate whether '' must interpreted \"Susceptible, increased exposure\". TRUE default AMR interpretation guideline set EUCAST (default). FALSE, interpreted \"Intermediate\". 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 expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable nrow (using facet) number rows breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\" position position adjustment bars, either \"fill\", \"stack\" \"dodge\" translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() 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, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE datalabels.size size datalabels datalabels.colour colour datalabels","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting Helpers for AMR Data Analysis — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-mic-functions","dir":"Reference","previous_headings":"","what":"The scale_*_mic() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_mic(), scale_y_mic(), scale_colour_mic(), scale_fill_mic() functions allow plot mic class (MIC values) continuous, logarithmic scale. also allow rescale MIC range 'inside' 'outside' range required, retain signs MIC values desired. Missing intermediate log2 levels plotted .","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-sir-functions","dir":"Reference","previous_headings":"","what":"The scale_*_sir() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_sir(), scale_colour_sir(), scale_fill_sir() functions allow plot sir class right order (S < SDD < < R < NI). default, translate S//R values interpretative text (\"Susceptible\", \"Resistant\", etc.) 20 supported languages (use language = NULL keep S//R). Also, except scale_x_sir(), set colour-blind friendly colours colour fill aesthetics.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"additional-ggplot-functions","dir":"Reference","previous_headings":"","what":"Additional ggplot2 Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"package contains functions extend ggplot2 package, help visualising AMR data results. functions internally used ggplot_sir() . 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() allows set colours aesthetic, even shape linetype. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage number isolates, using ggplot2::geom_text(). interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, default guideline EUCAST 2024, unless package option AMR_guideline set. See .sir() information.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting Helpers for AMR Data Analysis — 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)) # \\donttest{ # Plotting using ggplot2's autoplot() for MIC, disk, and SIR ----------- if (require(\"ggplot2\")) { autoplot(some_mic_values) } if (require(\"ggplot2\")) { # when providing the microorganism and antibiotic, colours will show interpretations: autoplot(some_mic_values, mo = \"Escherichia coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { # support for 20 languages, various guidelines, and many options autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", guideline = \"CLSI 2024\", language = \"no\", title = \"Disk diffusion from the North\" ) } # Plotting using scale_x_mic() ----------------------------------------- if (require(\"ggplot2\")) { mic_plot <- ggplot( data.frame( mics = as.mic(c(0.25, \"<=4\", 4, 8, 32, \">=32\")), counts = c(1, 1, 2, 2, 3, 3) ), aes(mics, counts) ) + geom_col() mic_plot + labs(title = \"without scale_x_mic()\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic() + labs(title = \"with scale_x_mic()\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(keep_operators = \"all\") + labs(title = \"with scale_x_mic() keeping all operators\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(mic_range = c(1, 16)) + labs(title = \"with scale_x_mic() using a manual 'within' range\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(mic_range = c(0.032, 256)) + labs(title = \"with scale_x_mic() using a manual 'outside' range\") } # Plotting using scale_y_mic() ----------------------------------------- some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) if (require(\"ggplot2\")) { ggplot( data.frame( mic = some_mic_values, group = some_groups ), aes(group, mic) ) + geom_boxplot() + geom_violin(linetype = 2, colour = \"grey\", fill = NA) + scale_y_mic() } if (require(\"ggplot2\")) { ggplot( data.frame( mic = some_mic_values, group = some_groups ), aes(group, mic) ) + geom_boxplot() + geom_violin(linetype = 2, colour = \"grey\", fill = NA) + scale_y_mic(mic_range = c(NA, 0.25)) } # Plotting using scale_x_sir() ----------------------------------------- if (require(\"ggplot2\")) { ggplot( data.frame( x = c(\"I\", \"R\", \"S\"), y = c(45, 323, 573) ), aes(x, y) ) + geom_col() + scale_x_sir() } # Plotting using scale_y_mic() and scale_colour_sir() ------------------ if (require(\"ggplot2\")) { plain <- ggplot( data.frame( mic = some_mic_values, group = some_groups, sir = as.sir(some_mic_values, mo = \"E. coli\", ab = \"cipro\" ) ), aes(x = group, y = mic, colour = sir) ) + theme_minimal() + geom_boxplot(fill = NA, colour = \"grey\") + geom_jitter(width = 0.25) plain } #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. if (require(\"ggplot2\")) { # and now with our MIC and SIR scale functions: plain + scale_y_mic() + scale_colour_sir() } if (require(\"ggplot2\")) { plain + scale_y_mic(mic_range = c(0.005, 32), name = \"Our MICs!\") + scale_colour_sir( language = \"pt\", name = \"Support in 20 languages\" ) } # } # Plotting using base R's plot() --------------------------------------- plot(some_mic_values) # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\") plot(some_disk_values) plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"nl\") plot(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 antimicrobials, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"SDD\", \"\", \"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 antimicrobials 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, SDD, must merged one, output consists S+SDD+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":"automated comprehensive analysis, consider using antibiogram() wisca(), streamline many aspects susceptibility reporting , importantly, also support WISCA. functions described offer hands-, manual approach greater customisation. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Since AMR v3.0, proportion_SI() proportion_I() include dose-dependent susceptibility ('SDD'). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. functions meant count isolates, calculate proportion resistance/susceptibility. Use count_*() functions count isolates. function susceptibility() essentially equal count_susceptible()/count_all(). Low counts can influence outcome - proportion_*() functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antimicrobials/variables test . See example two antimicrobials, 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 considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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) #> Note that proportion_SI() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> Note that proportion_I() will also include dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> Note that proportion_IR() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [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 #> #> 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 #> #> 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 antimicrobial 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 #> #> 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 #> #> 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 2024 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] 0.025 2 0.25 8 4 >=256 64 0.125 1 #> [10] 16 2 0.025 8 32 <=0.001 4 2 128 #> [19] 0.125 4 0.01 >=256 0.25 0.25 1 random_disk(25) #> Class 'disk' #> [1] 48 8 6 21 6 34 30 30 42 26 10 41 26 22 46 19 16 38 34 12 50 42 45 42 44 random_sir(25) #> Class 'sir' #> [1] I R R S R I I R R R R R R I R S S S I I S S R R R # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #> [1] 1 16 0.025 0.001 4 4 0.125 0.002 2 0.25 0.25 64 #> [13] 0.5 0.005 256 8 0.125 16 0.005 0.001 64 0.5 8 64 #> [25] 0.001 random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #> [1] 4 8 2 >=16 <=0.25 <=0.25 >=16 1 1 0.5 #> [11] 8 <=0.25 2 0.5 2 <=0.25 1 4 1 <=0.25 #> [21] >=16 4 2 1 <=0.25 random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #> [1] 2 8 0.0625 0.0625 0.0625 4 <=0.025 0.125 8 #> [10] 8 0.25 0.25 0.125 <=0.025 0.25 <=0.025 0.0625 2 #> [19] <=0.025 8 4 4 <=0.025 8 8 random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #> [1] 8 24 46 50 48 11 23 30 47 33 21 43 17 16 48 16 39 38 16 49 33 47 14 18 42 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #> [1] 12 13 15 14 12 16 15 16 13 13 12 13 12 11 16 17 11 17 11 14 12 14 11 12 12 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #> [1] 20 18 24 24 17 18 26 24 26 24 20 20 26 24 19 15 17 17 24 20 17 22 22 19 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 class '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 class 'resistance_predict' autoplot(object, main = paste(\"Resistance Prediction of\", x_name), ribbon = TRUE, ...)"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates, col_ab = \"AMX\", year_min = 2010, model = \"binomial\" ) plot(x) # \\donttest{ if (require(\"ggplot2\")) { ggplot_sir_predict(x) } # using dplyr: if (require(\"dplyr\")) { x <- example_isolates %>% filter_first_isolate() %>% filter(mo_genus(mo) == \"Staphylococcus\") %>% resistance_predict(\"PEN\", model = \"binomial\") print(plot(x)) # get the model from the object mymodel <- attributes(x)$model summary(mymodel) } #> NULL #> #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 35.76101 72.29172 0.495 0.621 #> year -0.01720 0.03603 -0.477 0.633 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 5.3681 on 11 degrees of freedom #> Residual deviance: 5.1408 on 10 degrees of freedom #> AIC: 50.271 #> #> 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) # Default S3 method skewness(x, na.rm = FALSE) # S3 method for class 'matrix' skewness(x, na.rm = FALSE) # S3 method for class '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] -0.01927257"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Top n Microorganisms — top_n_microorganisms","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function filters data set include top n microorganisms based specified property, taxonomic family genus. example, can filter data set top 3 species, species top 5 genera, top 3 species top 5 genera.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"top_n_microorganisms(x, n, property = \"fullname\", n_for_each = NULL, col_mo = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"x data frame containing microbial data n integer specifying maximum number unique values property include output property character string indicating microorganism property use filtering. Must one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". NULL, raw values col_mo used without transformation. n_for_each optional integer specifying maximum number rows retain value selected property. NULL, rows within top n groups included. col_mo character string indicating column x contains microorganism names codes. Defaults first column class mo. Values coerced using .mo(). ... Additional arguments passed mo_property() property NULL.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function useful preprocessing data creating antibiograms analyses require focused subsets microbial data. example, can filter data set include isolates top 10 species.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"# filter to the top 3 species: top_n_microorganisms(example_isolates, n = 3 ) #> # A tibble: 1,015 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 4 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 5 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 7 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> 8 2002-02-14 067927 45 F ICU B_STPHY_CONS R NA R NA #> 9 2002-02-14 067927 45 F ICU B_STPHY_CONS S NA S NA #> 10 2002-02-21 A56499 64 M Clinical B_STPHY_CONS S NA S NA #> # ℹ 1,005 more rows #> # ℹ 36 more variables: 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 , … # filter to any species in the top 5 genera: top_n_microorganisms(example_isolates, n = 5, property = \"genus\" ) #> # A tibble: 1,742 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,732 more rows #> # ℹ 36 more variables: 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 , … # filter to the top 3 species in each of the top 5 genera: top_n_microorganisms(example_isolates, n = 5, property = \"genus\", n_for_each = 3 ) #> # A tibble: 1,497 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinical B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 8 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 10 2003-09-28 1B0933 80 M Clinical B_ENTRC NA NA NA NA #> # ℹ 1,487 more rows #> # ℹ 36 more variables: 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 , …"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale() set_AMR_locale(language) reset_AMR_locale() translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like : save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\") # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" # setting another language set_AMR_locale(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\" # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\" # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> [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-2119229","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1.9229","title":"AMR 2.1.1.9229","text":"(beta version eventually become v3.0. ’re happy reach new major milestone soon, new One Health support! Install beta using instructions .)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)","title":"AMR 2.1.1.9229","text":"package now supports tools AMR data analysis clinical settings, also veterinary environmental microbiology. made possible collaboration University Prince Edward Island’s Atlantic Veterinary College, Canada. celebrate great improvement package, also updated package logo reflect change.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 2.1.1.9229","text":"Dataset antibiotics renamed antimicrobials data set contains just antibiotics. Using antibiotics still work, now returns warning. Removed functions references used deprecated rsi class, replaced sir equivalents two years ago.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9229","text":"Function .sir() now extensive support veterinary breakpoints CLSI. Use breakpoint_type = \"animal\" set host argument variable contains animal species names. clinical_breakpoints data set contains breakpoints, can downloaded download page. (new) antimicrobials data set contains veterinary antibiotics, pradofloxacin enrofloxacin. WHOCC codes veterinary use added well. ab_atc() now supports ATC codes veterinary antibiotics (start “Q”) ab_url() now supports retrieving WHOCC url ATCvet pages antibiogram() function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), powerful Bayesian method estimating regimen coverage probabilities using pathogen incidence antimicrobial susceptibility data. WISCA offers improved precision syndrome-specific treatment, even datasets sparse data. dedicated wisca() function also available easy usage. MycoBank now integrated primary taxonomic source fungi. microorganisms data set enriched new columns (mycobank, mycobank_parent, mycobank_renamed_to) provide detailed information fungal species. remarkable addition 20,000 new fungal records New function mo_mycobank() retrieve MycoBank record number, analogous existing functions mo_lpsn() mo_gbif(). .mo() function mo_*() functions now include only_fungi argument, allowing users restrict results solely fungal species. ensures fungi prioritised bacteria microorganism identification. can also set globally new AMR_only_fungi option. Also updated kingdoms, welcoming total 2,149 new records 2023 927 2024. EUCAST 2024 CLSI 2024 now supported, adding 4,000 new clinical breakpoints clinical_breakpoints data set usage .sir(). EUCAST 2024 now new default guideline MIC disk diffusion interpretations. Added Expected Resistant Phenotypes EUCAST (v1.2). default rules eucast_rules() now: c(\"breakpoints\", \"expected_phenotypes\"). Updated intrinsic_resistant data set, now based EUCAST Expected Resistant Phenotypes v1.2 .sir() now brings additional factor levels: “NI” non-interpretable “SDD” susceptible dose-dependent. Currently, clinical_breakpoints data set contains 24 breakpoints can return value “SDD” instead “”. EUCAST interpretive rules (using eucast_rules()) now available EUCAST 12 (2022), 13 (2023), 14 (2024). New function group scale_*_mic(), namely: scale_x_mic(), scale_y_mic(), scale_colour_mic() scale_fill_mic(). allow easy plotting MIC values. allow manual range definition plotting missing intermediate log2 levels. New function group scale_*_sir(), namely: scale_x_sir(), scale_colour_sir() scale_fill_sir(). allow plot sir class, translates system language default. also set colourblind-safe colours plots. New function rescale_mic(), allows users rescale MIC values manually set range. powerhouse behind scale_*_mic() functions, can used independently , instance, compare equality MIC distributions rescaling range first. using R heavy lifting, ‘AMR’ Python Package developed run AMR R package natively Python. Python package always version number R package, built automatically every code change. antimicrobial selectors (aminoglycosides() betalactams()) now supported tidymodels packages recipe parsnip. See info tutorial using AMR functions predictive modelling. New function top_n_microorganisms() filter data set top n taxonomic property, e.g., filter top 3 species, filter species top 5 genera, filter top 3 species top 5 genera New function mo_group_members() retrieve member microorganisms microorganism group. example, mo_group_members(\"Strep group C\") returns vector microorganisms belong group. New functions mic_p50() mic_p90() retrieve 50th 90th percentile MIC values.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.1.9229","text":"now possible use column names argument ab, mo, uti: .sir(..., ab = \"column1\", mo = \"column2\", uti = \"column3\"). greatly improves flexibility users. Users can now set criteria (using regular expressions) considered S, , R, SDD, NI. get quantitative values, .double() sir object return 1 S, 2 SDD/, 3 R (NI become NA). functions using sir classes (e.g., summary()) updated reflect change contain NI SDD. Combined MIC values (e.g., CLSI) now supported argument conserve_capped_values .sir() replaced capped_mic_handling, allows greater flexibility handling capped MIC values (<, <=, >, >=). four available options (\"standard\", \"strict\", \"relaxed\", \"inverse\") provide full control whether values interpreted conservatively ignored. Using conserve_capped_values now deprecated returns warning. Added argument info silence console messages Argument antibiotics renamed antimicrobials. Using antibiotics still work, now returns warning. Added argument formatting_type set 22 options formatting ‘cells’. defaults 18 non-WISCA 14 WISCA, changing output antibiograms cells info. reason, add_total_n now FALSE default since denominators added cells non-WISCA. WISCA, denominator useful anyway. ab_transform argument now defaults \"name\", displaying antibiotic column names instead codes ‘Antibiotic selectors’ now called ‘antimicrobial selectors’ since scope broader just antibiotics. documentation updated, ab_class() ab_selector() replaced amr_class() amr_selector(). old functions now deprecated removed future version. Added selectors isoxazolylpenicillins(), monobactams(), nitrofurans(), phenicols(), rifamycins(), sulfonamides() using antimicrobial selectors exclude non-treatable drugs (gentamicin-high using aminoglycosides()), function now always returns warning can included using only_treatable = FALSE Added new argument return_all selectors, defaults TRUE include match. FALSE, old behaviour, first hit unique antimicrobial returned. selectors can now run separate command retrieve vector possible antimicrobials selector can select selectors lincosamides() macrolides() overlap anymore - antibiotic now classified either Fixed selector fluoroquinolones(), now really selects second-generation quinolones (first-generation quinolones contain fluorine group) Added agents used screening, ID ending -S: benzylpenicillin screening test (PEN-S), beta-lactamase screening test (BLA-S), cefotaxime screening test (CTX-S), clindamycin inducible screening test (CLI-S), nalidixic acid screening test (NAL-S), norfloxacin screening test (-S), oxacillin screening test (OXA-S), pefloxacin screening test (PEF-S), tetracycline screening test (TCY-S). ID cefoxitin screening renamed FOX1 FOX-S, old code remains work. reason, antimicrobial selectors cephalosporins(), cephalosporins_3rd(), lincosamides(), isoxazolylpenicillins(), quinolones(), fluoroquinolones(), tetracyclines() now contain argument only_treatable = TRUE (similar antimicrobial selectors contain non-treatable drugs) Added amorolfine (AMO, D01AE16), now also part antifungals() selector Added efflux (EFF), allow mapping AMRFinderPlus Added tigemonam (TNM), monobactam Added bleomycin (BLM), glycopeptide Updated ATC codes, trade names, DDDs Added valid levels: 4096, 6 powers 0.0625, 5 powers 192 (192, 384, 576, 768, 960) Fixed bug .mic() failed translation scientifically formatted numbers Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\". argument also available new rescale_mic() scale_*_mic() functions. Comparisons MIC values now strict. example, >32 higher (never equal ) 32. Thus, .mic(\">32\") == .mic(32) now returns FALSE, .mic(\">32\") > .mic(32) now returns TRUE. Sorting MIC values (using sort()) fixed manner; <0.001 now gets sorted 0.001, >0.001 gets sorted 0.001. Intermediate log2 levels used MIC plotting now common values instead following strict dilution range eucast_rules() now argument overwrite (default: FALSE) indicate whether non-NA values overwritten Disks 0 5 mm now allowed, newly allowed range disk diffusion (.disk()) now 0 50 mm Updated italicise_taxonomy() support HTML output custom_eucast_rules() now supports multiple antibiotics antibiotic groups affected single rule mo_info() now contains extra element rank group_members (contents new mo_group_members() function) Updated ATC codes WHOCC Updated antibiotic DDDs WHOCC Fix using manual value mo_transform antibiogram() Fixed bug antibiogram() returns empty data set Fix mapping ‘high level’ antibiotics .ab() (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high) Improved overall algorithm .ab() better performance accuracy, including new function as_reset_session() remove earlier coercions. weight given genus species combinations cases subspecies miswritten, result correct genus species Genera World Health Organization’s () Priority Pathogen List now highest prevalence Fixed bug sir_confidence_interval() isolates available Updated prevalence calculation include genera World Health Organization’s () Priority Pathogen List Improved algorithm first_isolate() using phenotype-based method, prioritise records highest availability SIR values scale_y_percent() can now cope ranges outside 0-100% range Implemented new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024) Added arguments esbl, carbapenemase, mecA, mecC, vanA, vanB denote column names logical values indicating presence genes (production proteins) Added console colours support sir class Positron","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.1.1.9229","text":"Added Dr. Larisse Bolton Aislinn Cook contributors fantastic implementation WISCA mathematically solid way Added Matthew Saab, Dr. Jordan Stull, Prof. Javier Sanchez contributors tremendous input veterinary breakpoints interpretations Added Prof. Kat Holt, Dr. Jane Hawkey, Dr. Natacha Couto contributors many suggestions, ideas bugfixes Greatly improved vctrs integration, Tidyverse package working background many Tidyverse functions. users, means functions dplyr’s bind_rows(), rowwise() c_across() now supported e.g. columns class mic. Despite , AMR package still zero-dependent package, including dplyr vctrs. Greatly updated expanded documentation Stopped support SAS (.xpt) files, since file structure extremely inefficient requires disk space GitHub allows single commit.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"older-versions-2-1-1-9229","dir":"Changelog","previous_headings":"","what":"Older Versions","title":"AMR 2.1.1.9229","text":"changelog contains changes AMR v3.0 (March 2025) later. prior v2 versions, please see v2 archive. prior v1 versions, please see v1 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 #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. 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 Retrieved values from the microorganisms.codes data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> 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. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), 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), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> 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 #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% 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: 2,724 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 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 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R 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 #> # ℹ 2,714 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:2724 Length:2724 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-07 #> Mode :character Mode :character Median :2015-06-03 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-11 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.6% (n=1133) %S :52.6% (n=1432) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) #> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=176) %I : 3.0% (n=82) #> %R :41.0% (n=1117) %R :36.0% (n=981) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first 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 1854 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 #> #> 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 #> #> 1 Escherichia coli 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"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: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS 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 #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R 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 #> # ℹ 2,714 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: 981 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 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: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"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:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'. my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 16.000 R #> 2 0.005 S #> 3 1.000 R #> 4 >=256.000 R #> 5 2.000 R #> 6 0.025 S #> 7 16.000 R #> 8 0.025 S #> 9 0.500 I #> 10 0.005 S #> # ℹ 90 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"Antimicrobial resistance (AMR) global health crisis, understanding resistance patterns crucial managing effective treatments. AMR R package provides robust tools analysing AMR data, including convenient antimicrobial selector functions like aminoglycosides() betalactams(). post, explore use tidymodels framework predict resistance patterns example_isolates dataset. leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) #> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ── #> ✔ broom 1.0.7 ✔ recipes 1.2.1 #> ✔ dials 1.4.0 ✔ rsample 1.2.1 #> ✔ dplyr 1.1.4 ✔ tibble 3.2.1 #> ✔ ggplot2 3.5.1 ✔ tidyr 1.3.1 #> ✔ infer 1.0.7 ✔ tune 1.3.0 #> ✔ modeldata 1.4.0 ✔ workflows 1.2.0 #> ✔ parsnip 1.3.1 ✔ workflowsets 1.1.0 #> ✔ purrr 1.0.4 ✔ yardstick 1.3.2 #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── #> ✖ purrr::discard() masks scales::discard() #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ✖ recipes::step() masks stats::step() # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modeling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.00e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.00e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , IPM , MEM # Evaluate model performance metrics <- predictions %>% metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics metrics #> # A tibble: 2 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 # To assess some other model properties, you can make our own `metrics()` function our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value metrics2 <- predictions %>% our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function metrics2 #> # A tibble: 4 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 #> 3 ppv binary 0.987 #> 4 npv binary 1 predictions %>% roc_curve(mo, `.pred_Gram-negative`) %>% autoplot()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Conclusion","title":"AMR with tidymodels","text":"post, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antimicrobial classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"example-2-predicting-amr-over-time","dir":"Articles","previous_headings":"","what":"Example 2: Predicting AMR Over Time","title":"AMR with tidymodels","text":"second example, aim predict antimicrobial resistance (AMR) trends time using tidymodels. model resistance three antibiotics (amoxicillin AMX, amoxicillin-clavulanic acid AMC, ciprofloxacin CIP), based historical data grouped year hospital ward.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"objective-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Objective","title":"AMR with tidymodels","text":"goal : Prepare dataset aggregating resistance data time. Define regression model predict AMR trends. Use tidymodels preprocess, train, evaluate model.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"data-preparation-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Data Preparation","title":"AMR with tidymodels","text":"start transforming example_isolates dataset structured time-series format. Explanation: - mo_name(mo): Converts microbial codes proper species names. - resistance(): Converts AMR results numeric values (proportion resistant isolates). - group_by(year, ward, species): Aggregates resistance rates year ward.","code":"# Load required libraries library(AMR) library(tidymodels) # Transform dataset data_time <- example_isolates %>% top_n_microorganisms(n = 10) %>% # Filter on the top #10 species mutate(year = as.integer(format(date, \"%Y\")), # Extract year from date gramstain = mo_gramstain(mo)) %>% # Get taxonomic names group_by(year, gramstain) %>% summarise(across(c(AMX, AMC, CIP), function(x) resistance(x, minimum = 0), .names = \"res_{.col}\"), .groups = \"drop\") %>% filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values #> ℹ Using column 'mo' as input for col_mo. data_time #> # A tibble: 32 × 5 #> year gramstain res_AMX res_AMC res_CIP #> #> 1 2002 Gram-negative 1 0.105 0.0606 #> 2 2002 Gram-positive 0.838 0.182 0.162 #> 3 2003 Gram-negative 1 0.0714 0 #> 4 2003 Gram-positive 0.714 0.244 0.154 #> 5 2004 Gram-negative 0.464 0.0938 0 #> 6 2004 Gram-positive 0.849 0.299 0.244 #> 7 2005 Gram-negative 0.412 0.132 0.0588 #> 8 2005 Gram-positive 0.882 0.382 0.154 #> 9 2006 Gram-negative 0.379 0 0.1 #> 10 2006 Gram-positive 0.778 0.333 0.353 #> # ℹ 22 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"defining-the-workflow-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define modeling workflow, consists preprocessing step, model specification, fitting process.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"Explanation: - step_dummy(): Encodes categorical variables (ward, species) numerical indicators. - step_normalize(): Normalises year variable. - step_nzv(): Removes near-zero variance predictors.","code":"# Define the recipe resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>% step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical step_normalize(year) %>% # Normalise year for better model performance step_nzv(all_predictors()) # Remove near-zero variance predictors resistance_recipe_time #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 2 #> #> ── Operations #> • Dummy variables from: gramstain #> • Centering and scaling for: year #> • Sparse, unbalanced variable filter on: all_predictors()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"specifying-the-model-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"use linear regression model predict resistance trends. Explanation: - linear_reg(): Defines linear regression model. - set_engine(\"lm\"): Uses R’s built-linear regression engine.","code":"# Define the linear regression model lm_model <- linear_reg() %>% set_engine(\"lm\") # Use linear regression lm_model #> Linear Regression Model Specification (regression) #> #> Computational engine: lm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"building-the-workflow-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"combine preprocessing recipe model workflow.","code":"# Create workflow resistance_workflow_time <- workflow() %>% add_recipe(resistance_recipe_time) %>% add_model(lm_model) resistance_workflow_time #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: linear_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 3 Recipe Steps #> #> • step_dummy() #> • step_normalize() #> • step_nzv() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Linear Regression Model Specification (regression) #> #> Computational engine: lm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"split data training testing sets, fit model, evaluate performance. Explanation: - initial_split(): Splits data training testing sets. - fit(): Trains workflow. - predict(): Generates resistance predictions. - metrics(): Evaluates model performance.","code":"# Split the data set.seed(123) data_split_time <- initial_split(data_time, prop = 0.8) train_time <- training(data_split_time) test_time <- testing(data_split_time) # Train the model fitted_workflow_time <- resistance_workflow_time %>% fit(train_time) # Make predictions predictions_time <- fitted_workflow_time %>% predict(test_time) %>% bind_cols(test_time) # Evaluate model metrics_time <- predictions_time %>% metrics(truth = res_AMX, estimate = .pred) metrics_time #> # A tibble: 3 × 3 #> .metric .estimator .estimate #> #> 1 rmse standard 0.0774 #> 2 rsq standard 0.711 #> 3 mae standard 0.0704"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"visualizing-predictions","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Visualizing Predictions","title":"AMR with tidymodels","text":"plot resistance trends time Amoxicillin. Additionally, can visualise resistance trends ggplot2 directly adding linear models :","code":"library(ggplot2) # Plot actual vs predicted resistance over time ggplot(predictions_time, aes(x = year)) + geom_point(aes(y = res_AMX, color = \"Actual\")) + geom_line(aes(y = .pred, color = \"Predicted\")) + labs(title = \"Predicted vs Actual AMX Resistance Over Time\", x = \"Year\", y = \"Resistance Proportion\") + theme_minimal() ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) + geom_line() + labs(title = \"AMX Resistance Trends\", x = \"Year\", y = \"Resistance Proportion\") + # add a linear model directly in ggplot2: geom_smooth(method = \"lm\", formula = y ~ x, alpha = 0.25) + theme_minimal()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"conclusion-1","dir":"Articles","previous_headings":"Example 2: Predicting AMR Over Time","what":"Conclusion","title":"AMR with tidymodels","text":"example, demonstrated analyze AMR trends time using tidymodels. aggregating resistance rates year hospital ward, built predictive model track changes resistance amoxicillin (AMX), amoxicillin-clavulanic acid (AMC), ciprofloxacin (CIP). method can extended antibiotics resistance patterns, providing valuable insights AMR dynamics healthcare settings.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antimicrobials: 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 S #> 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) #> [1] FALSE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) #> [1] FALSE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #> 1. If CIP is R and age is higher than 60 then: Elderly Type A #> 2. If ERY is R and age is higher than 60 then: Elderly Type B #> 3. Otherwise: Negative #> #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #> Negative Elderly Type A Elderly Type B #> 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,745 (87.25%, NA: 255 = 12.75%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_sir(5000), isoniazid = random_sir(5000), gatifloxacin = random_sir(5000), ethambutol = random_sir(5000), pyrazinamide = random_sir(5000), moxifloxacin = random_sir(5000), kanamycin = random_sir(5000) ) my_TB_data <- data.frame( RIF = random_sir(5000), INH = random_sir(5000), GAT = random_sir(5000), ETH = random_sir(5000), PZA = random_sir(5000), MFX = random_sir(5000), KAN = random_sir(5000) ) head(my_TB_data) #> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1 I R S S S S #> 2 S S I R R S #> 3 R I I I R I #> 4 I S S S S S #> 5 I I I S I S #> 6 R S R S I I #> kanamycin #> 1 R #> 2 I #> 3 S #> 4 I #> 5 I #> 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 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF 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 #> #> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA #> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA #> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA #> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA #> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA #> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 #> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups: can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) + ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 5: S < SDD < < R < NI 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 78 679 rows 26 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, mycobank, mycobank_parent, mycobank_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 26 March 2025 16:19:17 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (1.8 MB) Download tab-separated text file (17.7 MB) Download Microsoft Excel workbook (8.7 MB) Download Apache Feather file (8.3 MB) Download Apache Parquet file (3.8 MB) Download IBM SPSS Statistics data file (28.4 MB) Download Stata DTA file (89.5 MB) NOTE: exported files SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values. Example content Included (sub)species per taxonomic kingdom: First 6 rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antimicrobials-antibiotic-and-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antimicrobials: Antibiotic and Antifungal Drugs","title":"Data sets for download / own use","text":"data set 496 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 antimicrobials, load AMR package. last updated 19 March 2025 15:05:24 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (75 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (10 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values. Example content","code":""},{"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 34 376 rows 14 columns, containing following column names:guideline, type, host, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti, is_SDD. data set R available clinical_breakpoints, load AMR package. last updated 13 March 2025 14:38:39 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (76 kB) Download tab-separated text file (3.1 MB) Download Microsoft Excel workbook (2 MB) Download Apache Feather file (1.5 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (5.6 MB) Download Stata DTA file (9.6 MB) Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 534 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 26 March 2025 16:19:17 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (50 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (65 kB) Download Stata DTA file (83 kB) Example content","code":""},{"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 271 905 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 28 March 2025 10:17:49 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (0.1 MB) Download tab-separated text file (10.1 MB) Download Microsoft Excel workbook (2.9 MB) Download Apache Feather file (2.3 MB) Download Apache Parquet file (0.3 MB) Download IBM SPSS Statistics data file (14.8 MB) Download Stata DTA file (22.6 MB) Example content Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB) Example content","code":""},{"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 15 June 2024 13:33:49 UTC. Find info contents, (scientific) source, structure data set . Example content","code":""},{"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 contents, (scientific) source, structure data set . Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 971 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (82 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (56 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB) Example content","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 20 October 2023 12:51:48 UTC. Find info contents, (scientific) source, structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain trade names LOINC codes comma separated values. Example content","code":""},{"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. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Aislinn Cook. Contributor. Andrew P. Norgan. Contributor. Anita Williams. Contributor. Annick Lenglet. Contributor. Anthony Underwood. Contributor. Anton Mymrikov. Contributor. Bart C. Meijer. Contributor. Christian F. Luz. Contributor. Dmytro Mykhailenko. Contributor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Jane Hawkey. Contributor. Jason Stull. Contributor. Javier Sanchez. Contributor. Jonas Salm. Contributor. Judith M. Fonville. Contributor. Kathryn Holt. Contributor. Larisse Bolton. Contributor. Matthew Saab. Contributor. Natacha Couto. Contributor. Peter Dutey-Magni. Contributor. Rogier P. Schade. Contributor. Sofia Ny. Contributor. Alex W. Friedrich. Thesis advisor. Bhanu N. M. Sinha. Thesis advisor. Casper J. Albers. Thesis advisor. Corinna Glasner. Thesis advisor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides --one solution antimicrobial resistance (AMR) data analysis One Health approach Used 175 countries, available 20 languages Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy extensive info antimicrobial drugs Applies recent CLSI EUCAST clinical veterinary breakpoints MICs, disk zones ECOFFs Corrects duplicate isolates, calculates predicts AMR per antimicrobial class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, NCBI 100% free costs dependencies, highly suitable places limited resources Now available Python ! Click read . 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 antimicrobial antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-available-in-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, available in 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 antimicrobial columns. can done using -called antimicrobial 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 antimicrobials AMR package make sure get meant:","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\")) %>% # antimicrobial selectors: select(bacteria, aminoglycosides(), carbapenems())"},{"path":"https://msberends.github.io/AMR/index.html","id":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antimicrobials yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates, antimicrobials = c(aminoglycosides(), carbapenems()), formatting_type = 14) antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), mo_transform = \"gramstain\", formatting_type = 14) antibiogram(example_isolates, antimicrobials = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work mo_transform = \"gramstain\", ab_transform = \"name\", formatting_type = 14, language = \"uk\") # Ukrainian"},{"path":"https://msberends.github.io/AMR/index.html","id":"interpreting-and-plotting-mic-and-sir-values","dir":"","previous_headings":"Practical examples","what":"Interpreting and plotting MIC and SIR values","title":"Antimicrobial Resistance Data Analysis","text":"AMR package allows interpretation MIC disk diffusion values based CLSI EUCAST. Moreover, ggplot2 package extended new scale functions, allow plotting log2-distributed MIC values SIR values.","code":"library(ggplot2) library(AMR) # generate some random values some_mic_values <- random_mic(size = 100) some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) interpretation <- as.sir(some_mic_values, guideline = \"EUCAST 2024\", mo = \"E. coli\", # or any code or name resembling a known species ab = \"Cipro\") # or any code or name resembling an antibiotic # create the plot ggplot(data.frame(mic = some_mic_values, group = some_groups, sir = interpretation), aes(x = group, y = mic, colour = sir)) + theme_minimal() + geom_boxplot(fill = NA, colour = \"grey\") + geom_jitter(width = 0.25) + # NEW scale function: plot MIC values to x, y, colour or fill scale_y_mic() + # NEW scale function: write out S/I/R in any of the 20 supported languages # and set colourblind-friendly colours scale_colour_sir()"},{"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 antimicrobial selectors select series antibiotic columns:","code":"example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance() for gentamicin and tobramycin # and get their 95% confidence intervals using sir_confidence_interval(): summarise(across(c(GEN, TOB), list(total_R = resistance, conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) library(AMR) library(dplyr) out <- example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance(), over all aminoglycosides and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Apply AMR function predictive modelling (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antimicrobial (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 rOpenSci R-universe platform:","code":"install.packages(\"AMR\", repos = \"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":"objects -called 'Deprecated'. removed future version package. Using give warning name alternative object 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":"ab_class(...) ab_selector(...)"},{"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 tbl_df (inherits tbl, data.frame) 496 rows 14 columns.","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_antibiogram_formatting_type numeric (1-22) use antibiogram(), indicate formatting type use. AMR_breakpoint_type character use .sir(), indicate breakpoint type use. must either \"ECOFF\", \"animal\", \"human\". AMR_capped_mic_handling character use .sir(), indicate capped MIC values (<, <=, >, >=) interpreted. Must one \"standard\", \"strict\", \"relaxed\", \"inverse\" - default \"standard\". 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_custom_ab file location RDS file, use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo file location RDS file, use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules character set default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline character set 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 2024\". Supported guideline currently EUCAST (2011-2024) CLSI (2011-2024). 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_substitute_missing_r_breakpoint logical use .sir(), indicate missing R breakpoints must substituted \"R\" - default FALSE. AMR_include_screening logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_locale character set language 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, English otherwise). 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 antimicrobials, 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, user read file automatically start-. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put : Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131 doi:10.33612/diss.192486375 ). installing package, R knows ~79 000 microorganisms (updated June 2024) ~620 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 BibTeX entry LaTeX users :","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"The AMR Package — AMR","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.s.berends@umcg.nl (ORCID) Authors: Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Aislinn Cook (ORCID) [contributor] Andrew P. Norgan (ORCID) [contributor] Anita Williams (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Anthony Underwood (ORCID) [contributor] Anton Mymrikov [contributor] Bart C. Meijer [contributor] Christian F. Luz (ORCID) [contributor] Dmytro Mykhailenko [contributor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Jane Hawkey (ORCID) [contributor] Jason Stull (ORCID) [contributor] Javier Sanchez (ORCID) [contributor] Jonas Salm [contributor] Judith M. Fonville [contributor] Kathryn Holt (ORCID) [contributor] Larisse Bolton (ORCID) [contributor] Matthew Saab [contributor] Natacha Couto (ORCID) [contributor] Peter Dutey-Magni (ORCID) [contributor] Rogier P. Schade [contributor] Sofia Ny (ORCID) [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Casper J. Albers (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor]","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://atcddd.fhi.) 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://atcddd.fhi./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] \"bactopen\" \"cloxacap\" \"cloxacillinhydrate\" #> [4] \"cloxypen\" \"floxacillin\" \"floxacillinanhydrous\" #> [7] \"floxapen\" \"floxapensalt\" \"fluclomix\" #> [10] \"flucloxacilina\" \"flucloxacilline\" \"flucloxacillinum\" #> [13] \"flucloxin\" \"fluorochloroxacillin\" \"galfloxin\" #> [16] \"latocillin\" \"orbeninhydrate\" \"rimaflox\" #> [19] \"staphobristol\" \"zoxin\""},{"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. antimicrobial 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 antimicrobials. can lookup abbreviations antimicrobials data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antimicrobial class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #> `Identification number` `Specimen number` Organism Country Laboratory #> #> 1 fe41d7bafa 1748 SPN Belgium National … #> 2 91f175ec37 1767 eco The Netherlands National … #> 3 cc4015056e 1343 eco The Netherlands National … #> 4 e864b692f5 1894 MAP Denmark National … #> 5 3d051fe345 1739 PVU Belgium National … #> 6 c80762a08d 1846 103 The Netherlands National … #> 7 8022d3727c 1628 103 Denmark National … #> 8 f3dc5f553d 1493 eco The Netherlands National … #> 9 15add38f6c 1847 eco France National … #> 10 fd41248def 1458 eco Germany National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` , `First name` , Sex , #> # Age , `Age category` , `Date of admission` , #> # `Specimen date` , `Specimen type` , #> # `Specimen type (Numeric)` , Reason , `Isolate number` , #> # `Organism type` , Serotype , `Beta-lactamase` , ESBL , #> # Carbapenemase , `MRSA screening test` , …"},{"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 antimicrobials 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\" \"Fluoroquinolones\" if (require(\"dplyr\")) { tibble(clinical_text = c( \"given 400mg cipro and 500 mg amox\", \"started on doxy iv today\" )) %>% mutate( abx_codes = ab_from_text(clinical_text), abx_doses = ab_from_text(clinical_text, type = \"doses\"), abx_admin = ab_from_text(clinical_text, type = \"admin\"), abx_coll = ab_from_text(clinical_text, collapse = \"|\"), abx_coll_names = ab_from_text(clinical_text, collapse = \"|\", translate_ab = \"name\" ), abx_coll_doses = ab_from_text(clinical_text, type = \"doses\", collapse = \"|\" ), abx_coll_admin = ab_from_text(clinical_text, type = \"admin\", collapse = \"|\" ) ) } #> Loading required package: dplyr #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #> clinical_text abx_codes abx_doses abx_admin abx_coll abx_coll_names #> #> 1 given 400mg cipro and 5… CIP|AMX Ciprofloxacin… #> 2 started on doxy iv today DOX Doxycycline #> # ℹ 2 more variables: abx_coll_doses , 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 antimicrobials 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 antimicrobials data set: vector_or(colnames(antimicrobials), 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://atcddd.fhi./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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of an Antibiotic — ab_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" ab_tradenames(\"AMX\") #> [1] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" 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://atcddd.fhi.no/atc_ddd_index//?code=J01CA04&showdescription=no\" # smart lowercase transformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\" ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\" # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\" ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #> #> $cid #> [1] 33613 #> #> $name #> [1] \"Amoxicillin\" #> #> $group #> [1] \"Beta-lactams/penicillins\" #> #> $atc #> [1] \"J01CA04\" #> #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #> #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #> #> $tradenames #> [1] \"acuotricina\" \"alfamox\" \"alfida\" \"amitron\" #> [5] \"amoclen\" \"amodex\" \"amoksicillin\" \"amolin\" #> [9] \"amopen\" \"amopenixin\" \"amophar\" \"amoran\" #> [13] \"amoxi\" \"amoxicaps\" \"amoxicilina\" \"amoxicilline\" #> [17] \"amoxicillinum\" \"amoxidal\" \"amoxiden\" \"amoxil\" #> [21] \"amoxillat\" \"amoxina\" \"amoxine\" \"amoxipen\" #> [25] \"amoxivet\" \"amoxycillin\" \"amoxycillinsalt\" \"amoxyke\" #> [29] \"anemolin\" \"aspenil\" \"atoksilin\" \"bristamox\" #> [33] \"cemoxin\" \"ciblor\" \"clamoxyl\" \"damoxy\" #> [37] \"danoxillin\" \"delacillin\" \"demoksil\" \"dispermox\" #> [41] \"efpenix\" \"eupen\" \"flemoxin\" \"flemoxine\" #> [45] \"galenamox\" \"gramidil\" \"hiconcil\" \"himinomax\" #> [49] \"histocillin\" \"ibiamox\" \"imacillin\" \"izoltil\" #> [53] \"kentrocyllin\" \"lamoxy\" \"largopen\" \"larotid\" #> [57] \"matasedrin\" \"metifarma\" \"moksilin\" \"moxacin\" #> [61] \"moxal\" \"moxaline\" \"moxatag\" \"neotetranase\" #> [65] \"novabritine\" \"ospamox\" \"pacetocin\" \"pamocil\" #> [69] \"paradroxil\" \"pasetocin\" \"penamox\" \"piramox\" #> [73] \"promoxil\" \"quimiopen\" \"remoxil\" \"riotapen\" #> [77] \"robamox\" \"sawacillin\" \"siganopen\" \"simplamox\" #> [81] \"sintopen\" \"sumox\" \"topramoxin\" \"trifamox\" #> [85] \"trimox\" \"unicillin\" \"utimox\" \"velamox\" #> [89] \"vetramox\" \"wymox\" \"zamocillin\" \"zamocilline\" #> [93] \"zimox\" #> #> $loinc #> [1] \"101498-4\" \"15-8\" \"16-6\" \"16365-9\" \"17-4\" \"18-2\" #> [7] \"18861-5\" \"18862-3\" \"19-0\" \"20-8\" \"21-6\" \"22-4\" #> [13] \"25274-2\" \"25310-4\" \"3344-9\" \"55614-2\" \"55615-9\" \"55616-7\" #> [19] \"6976-5\" \"6977-3\" \"80133-2\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"101477-8\" \"101478-6\" \"18864-9\" \"18865-6\" \"20374-5\" \"21066-6\" #> [7] \"23618-2\" \"27-3\" \"28-1\" \"29-9\" \"30-7\" \"31-5\" #> [13] \"32-3\" \"33-1\" \"3355-5\" \"33562-0\" \"33919-2\" \"34-9\" #> [19] \"43883-8\" \"43884-6\" \"6979-9\" \"6980-7\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\" # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\" # use set_ab_names() for renaming columns colnames(example_isolates) #> [1] \"date\" \"patient\" \"age\" \"gender\" \"ward\" \"mo\" \"PEN\" #> [8] \"OXA\" \"FLC\" \"AMX\" \"AMC\" \"AMP\" \"TZP\" \"CZO\" #> [15] \"FEP\" \"CXM\" \"FOX\" \"CTX\" \"CAZ\" \"CRO\" \"GEN\" #> [22] \"TOB\" \"AMK\" \"KAN\" \"TMP\" \"SXT\" \"NIT\" \"FOS\" #> [29] \"LNZ\" \"CIP\" \"MFX\" \"VAN\" \"TEC\" \"TCY\" \"TGC\" #> [36] \"DOX\" \"ERY\" \"CLI\" \"AZM\" \"IPM\" \"MEM\" \"MTR\" #> [43] \"CHL\" \"COL\" \"MUP\" \"RIF\" colnames(set_ab_names(example_isolates)) #> [1] \"date\" \"patient\" #> [3] \"age\" \"gender\" #> [5] \"ward\" \"mo\" #> [7] \"benzylpenicillin\" \"oxacillin\" #> [9] \"flucloxacillin\" \"amoxicillin\" #> [11] \"amoxicillin_clavulanic_acid\" \"ampicillin\" #> [13] \"piperacillin_tazobactam\" \"cefazolin\" #> [15] \"cefepime\" \"cefuroxime\" #> [17] \"cefoxitin\" \"cefotaxime\" #> [19] \"ceftazidime\" \"ceftriaxone\" #> [21] \"gentamicin\" \"tobramycin\" #> [23] \"amikacin\" \"kanamycin\" #> [25] \"trimethoprim\" \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" #> [31] \"moxifloxacin\" \"vancomycin\" #> [33] \"teicoplanin\" \"tetracycline\" #> [35] \"tigecycline\" \"doxycycline\" #> [37] \"erythromycin\" \"clindamycin\" #> [39] \"azithromycin\" \"imipenem\" #> [41] \"meropenem\" \"metronidazole\" #> [43] \"chloramphenicol\" \"colistin\" #> [45] \"mupirocin\" \"rifampicin\" colnames(set_ab_names(example_isolates, NIT:VAN)) #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% set_ab_names() # this does the same: example_isolates %>% rename_with(set_ab_names) # set_ab_names() works with any AB property: example_isolates %>% set_ab_names(property = \"atc\") example_isolates %>% set_ab_names(where(is.sir)) %>% colnames() example_isolates %>% set_ab_names(NIT:VAN) %>% colnames() } #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x) clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antimicrobials 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 antimicrobials 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 a wildly guessed result: as.ab(\"testab\") #> Class 'ab' #> [1] CTE # 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 # 'antimicrobials' data set, such as 'group': group = \"Test Group\" ) ) #> ℹ Added one record to the internal antimicrobials 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://atcddd.fhi.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 antimicrobials data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\" # even antimicrobial selectors work # see ?amr_selector x <- data.frame( random_column = \"some value\", coflu = as.sir(\"S\"), ampicillin = as.sir(\"R\") ) x #> random_column coflu ampicillin #> 1 some value S R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #> 'ampicillin' #> coflu ampicillin #> 1 S R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x) clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways circumvent automate process adding microorganisms: Method 1: Using package option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location package option AMR_custom_mo: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file: Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file: Use clear_custom_microorganisms() clear previously added microorganisms.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes: AMR::add_custom_microorganisms( data.frame(genus = \"Enterobacter\", species = \"asburiae/cloacae\") )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter 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_ENTRB_ASB/ # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\" # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\" mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRB_ASB/\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Enterobacter\" #> #> $species #> [1] \"asburiae/cloacae\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] NA #> #> $url #> [1] \"\" #> #> $ref #> [1] \"Self-added, 2025\" #> #> $snomed #> [1] NA #> #> $lpsn #> [1] NA #> #> $mycobank #> [1] NA #> #> $gbif #> [1] NA #> #> $group_members #> character(0) #> # the function tries to be forgiving: add_custom_microorganisms( data.frame( GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\", SPECIES = \"SPECIES\" ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #> set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\" # taxonomy still works, even though a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\" # for groups and complexes, set them as species or subspecies: add_custom_microorganisms( data.frame( genus = \"Citrobacter\", species = c(\"freundii\", \"braakii complex\"), subspecies = c(\"complex\", \"\") ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #> internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\" mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\" mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000) # add ages df$age <- age(df$birth_date) # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE) # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\") df #> birth_date age age_exact age_at_y2k #> 1 1999-06-30 25 25.74247 0 #> 2 1968-01-29 57 57.15890 31 #> 3 1965-12-05 59 59.30959 34 #> 4 1980-03-01 45 45.07397 19 #> 5 1949-11-01 75 75.40274 50 #> 6 1947-02-14 78 78.11507 52 #> 7 1940-02-19 85 85.10137 59 #> 8 1988-01-10 37 37.21096 11 #> 9 1997-08-27 27 27.58356 2 #> 10 1978-01-26 47 47.16712 21"},{"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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Create detailed antibiograms options traditional, combination, syndromic, Bayesian WISCA methods. Adhering previously described approaches (see Source) especially Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) Bielicki et al., functions provide flexible output formats including plots tables, ideal integration R Markdown Quarto reports.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"antibiogram(x, antimicrobials = where(is.sir), mo_transform = \"shortname\", ab_transform = \"name\", syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE, digits = ifelse(wisca, 1, 0), formatting_type = getOption(\"AMR_antibiogram_formatting_type\", ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, sep = \" + \", wisca = FALSE, simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\", info = interactive(), ...) wisca(x, antimicrobials = where(is.sir), ab_transform = \"name\", syndromic_group = NULL, digits = 1, formatting_type = getOption(\"AMR_antibiogram_formatting_type\", 14), col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE, sep = \" + \", simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\", info = interactive(), ...) retrieve_wisca_parameters(wisca_model, ...) # S3 method for class 'antibiogram' plot(x, ...) # S3 method for class 'antibiogram' autoplot(object, ...) # S3 method for class '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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Bielicki JA et al. (2016). Selecting appropriate empirical antibiotic regimens paediatric bloodstream infections: application Bayesian decision model local pooled antimicrobial resistance surveillance data Journal Antimicrobial Chemotherapy 71(3); doi:10.1093/jac/dkv397 Bielicki JA et al. (2020). Evaluation coverage 3 antibiotic regimens neonatal sepsis hospital setting across Asian countries JAMA Netw Open. 3(2):e1921124; doi:10.1001.jamanetworkopen.2019.21124 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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"x data.frame containing least column microorganisms columns antimicrobial results (class 'sir', see .sir()) antimicrobials vector specifying antimicrobials include antibiogram (see Examples). evaluated using guess_ab_col(). can : antimicrobial name code column name x contains SIR values antimicrobial selector, aminoglycosides() carbapenems() combination , using c(), e.g.: c(aminoglycosides(), \"AMP\", \"AMC\") c(aminoglycosides(), carbapenems()) Combination therapy, indicated using \"+\", without antimicrobial selectors, e.g.: \"cipro + genta\" \"TZP+TOB\" c(\"TZP\", \"TZP+GEN\", \"TZP+TOB\") carbapenems() + \"GEN\" carbapenems() + c(\"\", \"GEN\") carbapenems() + c(\"\", aminoglycosides()) mo_transform character transform microorganism input - must \"name\", \"shortname\" (default), \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input NA consider microorganisms 'unknown'. ab_transform character transform antimicrobial input - must one column names antimicrobials data set (defaults \"name\"): \"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 n_tested available numbers per pathogen added table (default TRUE). add lowest highest number available isolates per antimicrobial (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). option unavailable wisca = TRUE; case, use retrieve_wisca_parameters() get parameters used WISCA. only_all_tested (combination antibiograms): logical indicate isolates must tested antimicrobials, see Details digits number digits use rounding antimicrobial coverage, defaults 1 WISCA 0 otherwise formatting_type numeric value (1–22 WISCA, 1-12 non-WISCA) indicating 'cells' antibiogram table formatted. See Details > Formatting Type list options. 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, SDD, , instead S (default TRUE) sep separating character antimicrobial columns combination antibiograms wisca logical indicate whether Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must generated (default FALSE). use Bayesian decision model estimate regimen coverage probabilities using Monte Carlo simulations. Set simulations, conf_interval, interval_side adjust. simulations (WISCA) numerical value set number Monte Carlo simulations conf_interval numerical value set confidence interval (default 0.95) interval_side side confidence interval, either \"two-tailed\" (default), \"left\" \"right\" info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) wisca_model outcome wisca() antibiogram(..., wisca = TRUE) 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 Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"functions return table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine one four available algorithms: isolate-based, patient-based, episode-based, phenotype-based. estimating antimicrobial coverage, especially creating WISCA, outcome might become reliable including top n species encountered data. can filter top n using top_n_microorganisms(). example, use top_n_microorganisms(your_data, n = 10) pre-processing step include top 10 species data. numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). ideal e.g. advanced plotting.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"formatting-type","dir":"Reference","previous_headings":"","what":"Formatting Type","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"formatting 'cells' table can set argument formatting_type. examples, 5 indicates antimicrobial coverage (4-6 confidence level), 15 number susceptible isolates, 300 number tested (.e., available) isolates: 5 15 300 15/300 5 (300) 5% (300) 5 (N=300) 5% (N=300) 5 (15/300) 5% (15/300) 5 (N=15/300) 5% (N=15/300) 5 (4-6) 5% (4-6%) - default WISCA 5 (4-6,300) 5% (4-6%,300) 5 (4-6,N=300) 5% (4-6%,N=300) - default non-WISCA 5 (4-6,15/300) 5% (4-6%,15/300) 5 (4-6,N=15/300) 5% (4-6%,N=15/300) default can set globally package option AMR_antibiogram_formatting_type, e.g. options(AMR_antibiogram_formatting_type = 5). note WISCA, total numbers tested susceptible isolates less useful report, since included Bayesian model apparent susceptibility confidence level. Set digits (defaults 0) alter rounding susceptibility percentages.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"various antibiogram types, summarised Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(). clinical coverage estimations, use WISCA whenever possible, since provides precise coverage estimates accounting pathogen incidence antimicrobial susceptibility, shown Bielicki et al. (2020, doi:10.1001.jamanetworkopen.2019.21124 ). See section Explaining WISCA page. note WISCA pathogen-agnostic, meaning outcome stratied pathogen, rather syndrome. 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) WISCA can applied antibiogram, see section Explaining WISCA page information. Code example: WISCA uses sophisticated Bayesian decision model combine local pooled antimicrobial resistance data. approach evaluates local patterns can also draw multi-centre datasets improve regimen accuracy, even low-incidence infections like paediatric bloodstream infections (BSIs).","code":"antibiogram(your_data, antimicrobials = \"TZP\") antibiogram(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data, antimicrobials = penicillins(), syndromic_group = \"ward\") antibiogram(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), wisca = TRUE) # this is equal to: wisca(your_data, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"grouped-tibbles","dir":"Reference","previous_headings":"","what":"Grouped tibbles","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"type antibiogram, grouped tibbles can also used calculate susceptibilities various groups. Code example:","code":"library(dplyr) your_data %>% group_by(has_sepsis, is_neonate, sex) %>% wisca(antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"stepped-approach-for-clinical-insight","dir":"Reference","previous_headings":"","what":"Stepped Approach for Clinical Insight","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"clinical practice, antimicrobial coverage decisions evolve microbiological data becomes available. theoretical stepped approach ensures empirical coverage can continuously assessed improve patient outcomes: Initial Empirical Therapy (Admission / Pre-Culture Data) admission, pathogen information available. Action: broad-spectrum coverage based local resistance patterns syndromic antibiograms. Using pathogen-agnostic yet incidence-weighted WISCA preferred. Code example: Refinement Gram Stain Results blood culture becomes positive, Gram stain provides initial crucial first stratification (Gram-positive vs. Gram-negative). Action: narrow coverage based Gram stain-specific resistance patterns. Code example: Definitive Therapy Based Species Identification cultivation pathogen, full pathogen identification allows precise targeting therapy. Action: adjust treatment pathogen-specific antibiograms, minimizing resistance risks. Code example: structuring antibiograms around stepped approach, clinicians can make data-driven adjustments stage, ensuring optimal empirical targeted therapy reducing unnecessary broad-spectrum antimicrobial use.","code":"antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = NA) # all pathogens set to `NA` # preferred: use WISCA wisca(your_data, antimicrobials = selected_regimens) antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = \"gramstain\") # all pathogens set to Gram-pos/Gram-neg antibiogram(your_data, antimicrobials = selected_regimens, mo_transform = \"shortname\") # all pathogens set to 'G. species', e.g., E. coli"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"inclusion-in-combination-antibiograms","dir":"Reference","previous_headings":"","what":"Inclusion in Combination Antibiograms","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antimicrobials, Drug Drug B, antibiogram() works calculate %SI:","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"plotting","dir":"Reference","previous_headings":"","what":"Plotting","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"types antibiograms listed can plotted (using ggplot2::autoplot() base R's plot() barplot()). mentioned , numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). outcome antibiogram() 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":"explaining-wisca","dir":"Reference","previous_headings":"","what":"Explaining WISCA","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"WISCA, outlined Bielicki et al. (doi:10.1093/jac/dkv397 ), stands Weighted-Incidence Syndromic Combination Antibiogram, estimates probability adequate empirical antimicrobial regimen coverage specific infection syndromes. method leverages Bayesian decision model random effects pathogen incidence susceptibility, enabling robust estimates presence sparse data. Bayesian model assumes conjugate priors parameter estimation. example, coverage probability \\(\\theta\\) given antimicrobial regimen modelled using Beta distribution prior: $$\\theta \\sim \\text{Beta}(\\alpha_0, \\beta_0)$$ \\(\\alpha_0\\) \\(\\beta_0\\) represent prior successes failures, respectively, informed expert knowledge weakly informative priors (e.g., \\(\\alpha_0 = 1, \\beta_0 = 1\\)). likelihood function constructed based observed data, number covered cases regimen follows binomial distribution: $$y \\sim \\text{Binomial}(n, \\theta)$$ Posterior parameter estimates obtained combining prior likelihood using Bayes' theorem. posterior distribution \\(\\theta\\) also Beta distribution: $$\\theta | y \\sim \\text{Beta}(\\alpha_0 + y, \\beta_0 + n - y)$$ Pathogen incidence, representing proportion infections caused different pathogens, modelled using Dirichlet distribution, natural conjugate prior multinomial outcomes. Dirichlet distribution parameterised vector concentration parameters \\(\\alpha\\), \\(\\alpha_i\\) corresponds specific pathogen. prior typically chosen uniform (\\(\\alpha_i = 1\\)), reflecting assumption equal prior probability across pathogens. posterior distribution pathogen incidence given : $$\\text{Dirichlet}(\\alpha_1 + n_1, \\alpha_2 + n_2, \\dots, \\alpha_K + n_K)$$ \\(n_i\\) number infections caused pathogen \\(\\) observed data. practical implementation, pathogen incidences sampled posterior using normalised Gamma-distributed random variables: $$x_i \\sim \\text{Gamma}(\\alpha_i + n_i, 1)$$ $$p_i = \\frac{x_i}{\\sum_{j=1}^K x_j}$$ \\(x_i\\) represents unnormalised pathogen counts, \\(p_i\\) normalised proportion pathogen \\(\\). hierarchical modelling, pathogen-level effects (e.g., differences resistance patterns) regimen-level effects modelled using Gaussian priors log-odds. hierarchical structure ensures partial pooling estimates across groups, improving stability strata small sample sizes. model implemented using Hamiltonian Monte Carlo (HMC) sampling. Stratified results can provided based covariates age, sex, clinical complexity (e.g., prior antimicrobial treatments renal/urological comorbidities) using dplyr's group_by() pre-processing step running wisca(). Posterior odds ratios (ORs) derived quantify effect covariates coverage probabilities: $$\\text{}_{\\text{covariate}} = \\frac{\\exp(\\beta_{\\text{covariate}})}{\\exp(\\beta_0)}$$ combining empirical data prior knowledge, WISCA overcomes limitations traditional combination antibiograms, offering disease-specific, patient-stratified estimates robust uncertainty quantification. tool invaluable antimicrobial stewardship programs empirical treatment guideline refinement. Note: WISCA never gives output pathogen/species level, incidences susceptibilities already weighted species.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Implementation: Dr. Larisse Bolton Dr. Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # \\donttest{ # Traditional antibiogram ---------------------------------------------- antibiogram(example_isolates, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 10 × 7 #> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin #> #> 1 CoNS 0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3… #> 2 E. coli 100% (98-1… 98% (96-9… 100% (9… NA 100% (99… 97% (96-9… #> 3 E. faecalis 0% (0-9%,N… 0% (0-9%,… 100% (9… 0% (0-9%… NA 0% (0-9%,… #> 4 K. pneumoniae NA 90% (79-9… 100% (9… NA 100% (93… 90% (79-9… #> 5 P. aeruginosa NA 100% (88-… NA 0% (0-12… NA 100% (88-… #> 6 P. mirabilis NA 94% (80-9… 94% (79… NA NA 94% (80-9… #> 7 S. aureus NA 99% (97-1… NA NA NA 98% (92-1… #> 8 S. epidermidis 0% (0-8%,N… 79% (71-8… NA 0% (0-8%… NA 51% (40-6… #> 9 S. hominis NA 92% (84-9… NA NA NA 85% (74-9… #> 10 S. pneumoniae 0% (0-3%,N… 0% (0-3%,… NA 0% (0-3%… NA 0% (0-3%,… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram antibiogram(example_isolates, antimicrobials = aminoglycosides(), ab_transform = \"atc\", mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 #> #> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-… #> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram antibiogram(example_isolates, antimicrobials = carbapenems(), ab_transform = \"name\", mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 5 × 3 #> Pathogen Imipenem Meropenem #> #> 1 Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48) 52% (37-67%,N=4… #> 2 Enterococcus faecalis 100% (91-100%,N=38) NA #> 3 Escherichia coli 100% (99-100%,N=422) 100% (99-100%,N… #> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N… #> 5 Proteus mirabilis 94% (79-99%,N=32) NA #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Combined antibiogram ------------------------------------------------- # combined antimicrobials yield higher empiric coverage antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), mo_transform = \"gramstain\" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> #> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693) #> 2 Gram-pos… 86% (82-89%,N=345) 98% (96-98%,N=1044) 95% (93-97%,N=550) #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # you can use any antimicrobial selector with `+` too: antibiogram(example_isolates, antimicrobials = ureidopenicillins() + c(\"\", \"GEN\", \"tobra\"), mo_transform = \"gramstain\" ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> #> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693) #> 2 Gram-pos… 86% (82-89%,N=345) 98% (96-98%,N=1044) 95% (93-97%,N=550) #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # names of antimicrobials do not need to resemble columns exactly: antibiogram(example_isolates, antimicrobials = c(\"Cipro\", \"cipro + genta\"), mo_transform = \"gramstain\", ab_transform = \"name\", sep = \" & \" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 3 #> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin` #> #> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694) #> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847) #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Syndromic antibiogram ------------------------------------------------ # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 14 × 8 #> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem #> #> 1 Clinical CoNS NA 89% (84-9… 57% (39… NA 57% (39-… #> 2 ICU CoNS NA 79% (68-8… NA NA NA #> 3 Outpatient CoNS NA 84% (66-9… NA NA NA #> 4 Clinical E. coli 100% (9… 98% (96-9… 100% (9… NA 100% (99… #> 5 ICU E. coli 100% (9… 99% (95-1… 100% (9… NA 100% (97… #> 6 Clinical K. pneumo… NA 92% (81-9… 100% (9… NA 100% (92… #> 7 Clinical P. mirabi… NA 100% (88-… NA NA NA #> 8 Clinical S. aureus NA 99% (95-1… NA NA NA #> 9 ICU S. aureus NA 100% (95-… NA NA NA #> 10 Clinical S. epider… NA 82% (72-9… NA NA NA #> 11 ICU S. epider… NA 72% (60-8… NA NA NA #> 12 Clinical S. hominis NA 96% (85-9… NA NA NA #> 13 Clinical S. pneumo… 0% (0-5… 0% (0-5%,… NA 0% (0-5%… NA #> 14 ICU S. pneumo… 0% (0-1… 0% (0-12%… NA 0% (0-12… NA #> # ℹ 1 more variable: Tobramycin #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # 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, antimicrobials = 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) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina #> #> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… #> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # WISCA antibiogram ---------------------------------------------------- # WISCA are not stratified by species, but rather on syndromes antibiogram(example_isolates, antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), syndromic_group = \"ward\", wisca = TRUE ) #> # An Antibiogram (WISCA / 95% CI): 3 × 4 #> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹ #> #> 1 Clinical 76.5% (60.8-88%) 95.3% (86.4-99.2%) #> 2 ICU 60.3% (42.1-80.7%) 90.6% (76.4-98.1%) #> 3 Outpatient 66.8% (38.4-91.3%) 89.7% (69.9-99.5%) #> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin` #> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram # Print the output for R Markdown / Quarto ----------------------------- ureido <- antibiogram(example_isolates, antimicrobials = ureidopenicillins(), syndromic_group = \"ward\", wisca = TRUE ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam) # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) { cat(knitr::knit_print(ureido)) } #> #> #> |Syndromic Group |Piperacillin/tazobactam | #> |:---------------|:-----------------------| #> |Clinical |76% (60-88.6%) | #> |ICU |59.8% (40.7-79.7%) | #> |Outpatient |66.2% (37.9-91.3%) | # Generate plots with ggplot2 or base R -------------------------------- ab1 <- antibiogram(example_isolates, antimicrobials = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"), mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates, antimicrobials = 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/antimicrobial_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antimicrobial Selectors — antimicrobial_selectors","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions allow filtering rows selecting columns based antimicrobial test results specific antimicrobial class group, without need define columns antimicrobial abbreviations. can used base R, tidyverse, tidymodels, data.table. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"CZO\" \"J01DB04\" picked using:","code":"library(dplyr) my_data_with_all_these_columns %>% select(cephalosporins())"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) aminopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...) antifungals(only_sir_columns = FALSE, return_all = TRUE, ...) antimycobacterials(only_sir_columns = FALSE, return_all = TRUE, ...) betalactams(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) betalactams_with_inhibitor(only_sir_columns = FALSE, return_all = TRUE, ...) carbapenems(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins_1st(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_2nd(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_3rd(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) cephalosporins_4th(only_sir_columns = FALSE, return_all = TRUE, ...) cephalosporins_5th(only_sir_columns = FALSE, return_all = TRUE, ...) fluoroquinolones(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) glycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...) isoxazolylpenicillins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) lincosamides(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) lipoglycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...) macrolides(only_sir_columns = FALSE, return_all = TRUE, ...) monobactams(only_sir_columns = FALSE, return_all = TRUE, ...) nitrofurans(only_sir_columns = FALSE, return_all = TRUE, ...) oxazolidinones(only_sir_columns = FALSE, return_all = TRUE, ...) penicillins(only_sir_columns = FALSE, return_all = TRUE, ...) phenicols(only_sir_columns = FALSE, return_all = TRUE, ...) polymyxins(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) quinolones(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) rifamycins(only_sir_columns = FALSE, return_all = TRUE, ...) streptogramins(only_sir_columns = FALSE, return_all = TRUE, ...) sulfonamides(only_sir_columns = FALSE, return_all = TRUE, ...) tetracyclines(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...) ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...) amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...) administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...) not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL, version_expected_phenotypes = 1.2, ...)"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"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) return_all logical indicate whether matched columns must returned (default TRUE). FALSE, first unique antimicrobial returned, e.g. columns \"genta\" \"gentamicin\" exist data, first hit gentamicin returned. ... ignored, place allow future extensions amr_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antimicrobials data set searched (case-insensitive) value. filter expression evaluated antimicrobials data set, name %like% \"trim\" col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). version_expected_phenotypes version number use EUCAST Expected Phenotypes. Can \"1.2\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"used inside selecting filtering, returns character vector column names, additional class \"amr_selector\". used individually, returns 'ab' vector possible antimicrobials function able select filter.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antimicrobial Selectors — antimicrobial_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. selectors can also used tidymodels packages recipe parsnip. See info tutorial using antimicrobial selectors predictive modelling. columns data functions called searched known antimicrobial names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antimicrobials data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. amr_class() function can used filter/select manually defined antimicrobial class. searches results antimicrobials data set within columns group, atc_group1 atc_group2. administrable_per_os() administrable_iv() functions also rely antimicrobials data set - antimicrobials matched DDD (defined daily dose) resp. oral IV treatment available antimicrobials data set. amr_selector() function can used internally filter antimicrobials 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. not_intrinsic_resistant() function can used select antimicrobials 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 Expected Resistant Phenotypes' v1.2 (2023) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"full-list-of-supported-antimicrobial-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antimicrobial) classes","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"aminoglycosides() can select: amikacin (AMK), amikacin/fosfomycin (AKF), 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: amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), 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), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), 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), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), 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), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), tigemonam (TMN) betalactams_with_inhibitor() can select: amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), 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 test (FOX-S), 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 screening test (CTX-S), 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/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), 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), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), 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), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), norfloxacin screening test (-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), 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), bleomycin (BLM), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN), vancomycin-macromethod (VAM) isoxazolylpenicillins() can select: cloxacillin (CLO), dicloxacillin (DIC), flucloxacillin (FLC), meticillin (MET), oxacillin (OXA), oxacillin screening test (OXA-S) lincosamides() can select: clindamycin (CLI), lincomycin (LIN), pirlimycin (PRL) 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), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL), tylvalosin (TYL1) monobactams() can select: aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), carumonam (CAR), tigemonam (TMN) nitrofurans() can select: furazidin (FUR), furazolidone (FRZ), nifurtoinol (NFR), nitrofurantoin (NIT), nitrofurazone (NIZ) 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), azidocillin (AZD), azlocillin (AZL), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), carbenicillin (CRB), carindacillin (CRN), 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), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), 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), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC) phenicols() can select: chloramphenicol (CHL), florfenicol (FLR), thiamphenicol (THI) polymyxins() can select: colistin (COL), polymyxin B (PLB), polymyxin B/polysorbate 80 (POP) quinolones() can select: besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), 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), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nalidixic acid screening test (NAL-S), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), norfloxacin screening test (-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), 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) rifamycins() can select: rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP) streptogramins() can select: pristinamycin (PRI) quinupristin/dalfopristin (QDA) sulfonamides() can select: brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfamoxole (SLF8), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13) 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), tetracycline screening test (TCY-S), 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/antimicrobial_selectors.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antimicrobial Selectors — antimicrobial_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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # you can use the selectors separately to retrieve all possible antimicrobials: carbapenems() #> ℹ in carbapenems(): Imipenem/EDTA (IPE) and meropenem/nacubactam #> (MNC) are not included since only_treatable = TRUE. #> ℹ This 'ab' vector was retrieved using carbapenems(), which should #> normally be used inside a dplyr verb or data.frame call, e.g.: #> • your_data %>% select(carbapenems()) #> • your_data %>% select(column_a, column_b, carbapenems()) #> • your_data %>% filter(any(carbapenems() == \"R\")) #> • your_data[, carbapenems()] #> • your_data[, c(\"column_a\", \"column_b\", carbapenems())] #> Class 'ab' #> [1] BIA DOR ETP IMR IPM MEM MEV PAN RIA RIT RZM TBP # Though they are primarily intended to use for selections and filters. # Examples sections below are split into 'dplyr', 'base R', and 'data.table': # \\donttest{ if (FALSE) { # \\dontrun{ # dplyr ------------------------------------------------------------------- library(dplyr, warn.conflicts = FALSE) example_isolates %>% select(carbapenems()) # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates %>% select(mo, aminoglycosides()) # you can combine selectors like you are used with tidyverse # e.g., for betalactams, but not the ones with an enzyme inhibitor: example_isolates %>% select(betalactams(), -betalactams_with_inhibitor()) # select only antimicrobials with DDDs for oral treatment example_isolates %>% select(administrable_per_os()) # get AMR for all aminoglycosides e.g., per ward: example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), resistance)) # You can combine selectors with '&' to be more specific: example_isolates %>% select(penicillins() & administrable_per_os()) # get AMR for only drugs that matter - no intrinsic resistance: example_isolates %>% filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>% group_by(ward) %>% summarise_at(not_intrinsic_resistant(), resistance) # get susceptibility for antimicrobials whose name contains \"trim\": example_isolates %>% filter(first_isolate()) %>% group_by(ward) %>% summarise(across(amr_selector(name %like% \"trim\"), susceptibility)) # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>% select(carbapenems()) # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB': example_isolates %>% select(mo, aminoglycosides()) # any() and all() work in dplyr's filter() too: example_isolates %>% filter( any(aminoglycosides() == \"R\"), all(cephalosporins_2nd() == \"R\") ) # also works with c(): example_isolates %>% filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) # not setting any/all will automatically apply all(): example_isolates %>% filter(aminoglycosides() == \"R\") # this will select columns 'mo' and all antimycobacterial drugs ('RIF'): example_isolates %>% select(mo, amr_class(\"mycobact\")) # get bug/drug combinations for only glycopeptides in Gram-positives: example_isolates %>% filter(mo_is_gram_positive()) %>% select(mo, glycopeptides()) %>% bug_drug_combinations() %>% format() data.frame( some_column = \"some_value\", J01CA01 = \"S\" ) %>% # ATC code of ampicillin select(penicillins()) # only the 'J01CA01' column will be selected # 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) } # } # 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 #> #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> 7 NA NA #> 8 NA NA #> 9 NA NA #> 10 NA NA #> # ℹ 1,990 more rows # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #> mo GEN TOB AMK KAN #> #> 1 B_ESCHR_COLI NA NA NA NA #> 2 B_ESCHR_COLI NA NA NA NA #> 3 B_STPHY_EPDR NA NA NA NA #> 4 B_STPHY_EPDR NA NA NA NA #> 5 B_STPHY_EPDR NA NA NA NA #> 6 B_STPHY_EPDR NA NA NA NA #> 7 B_STPHY_AURS NA S NA NA #> 8 B_STPHY_AURS NA S NA NA #> 9 B_STPHY_EPDR NA NA NA NA #> 10 B_STPHY_EPDR NA NA NA NA #> # ℹ 1,990 more rows # select only antimicrobials 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 #> #> 1 NA NA NA I NA I NA R NA NA R NA NA #> 2 NA NA NA I NA I NA R NA NA R NA NA #> 3 NA R NA NA NA R NA S NA NA NA NA NA #> 4 NA R NA NA NA R NA S NA NA NA NA NA #> 5 NA R NA NA NA R NA R NA NA NA NA NA #> 6 NA R NA NA NA R NA R NA NA NA NA NA #> 7 NA S R S R S NA R NA NA NA NA NA #> 8 NA S R S R S NA R NA NA NA NA NA #> 9 NA R NA NA NA R NA S NA NA NA S NA #> 10 NA S NA NA NA S NA S NA NA NA S NA #> # ℹ 1,990 more rows #> # ℹ 10 more variables: VAN , TCY , DOX , ERY , CLI , #> # AZM , MTR , CHL , COL , RIF # 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 #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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 #> #> 1 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 7 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 8 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 9 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 10 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> # ℹ 952 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 #> #> 1 2002-04-14 F30196 73 M Outpat… B_STRPT_GRPB S NA S S #> 2 2003-04-08 114570 74 M ICU B_STRPT_PYGN S NA S S #> 3 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 4 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 5 2003-08-14 F71508 0 F Clinic… B_STRPT_GRPB S NA S S #> 6 2003-10-16 650870 63 F ICU B_ESCHR_COLI R NA NA R #> 7 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 9 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> 10 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> # ℹ 746 more rows #> # ℹ 36 more variables: AMC , 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 , … # filter with multiple antimicrobial 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 #> #> 1 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 2 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 3 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 4 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 5 2008-12-06 501361 43 F Clinical B_STNTR_MLTP R NA NA R #> 6 2011-05-09 207325 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2012-03-12 582258 80 M ICU B_STPHY_CONS R R R R #> 8 2012-05-19 C25552 89 F Outpati… B_STPHY_CONS R R R R #> 9 2012-07-17 F05015 83 M ICU B_STPHY_CONS R R R R #> 10 2012-07-20 404299 66 F Clinical B_STPHY_CONS R R R R #> # ℹ 16 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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 #> #> 1 NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA #> 3 R NA NA R R R R #> 4 NA NA NA NA NA NA R #> 5 NA NA NA NA NA NA R #> 6 NA NA NA NA NA NA R #> 7 NA NA NA NA NA NA R #> 8 NA NA NA NA NA NA R #> 9 R NA NA NA S NA S #> 10 R NA NA NA S NA S #> # ℹ 45 more rows # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #> (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #> (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #> 'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #> (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #> (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #> (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #> (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #> (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #> (rifampicin) #> # A tibble: 2,000 × 5 #> OXA FLC AMX AMC AMP #> #> 1 NA NA NA I NA #> 2 NA NA NA I NA #> 3 NA R NA NA NA #> 4 NA R NA NA NA #> 5 NA R NA NA NA #> 6 NA R NA NA NA #> 7 NA S R S R #> 8 NA S R S R #> 9 NA R NA NA NA #> 10 NA S NA NA NA #> # ℹ 1,990 more rows # amr_selector() applies a filter in the `antimicrobials` data set and is thus # very flexible. For instance, to select antimicrobials with an oral DDD # of at least 1 gram: example_isolates[, amr_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For amr_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 #> #> 1 NA NA NA I NA NA NA R R R R NA NA #> 2 NA NA NA I NA NA NA R R R R NA NA #> 3 NA R NA NA NA NA NA NA S R NA NA NA #> 4 NA R NA NA NA NA NA NA S R NA NA NA #> 5 NA R NA NA NA NA NA NA S R NA NA NA #> 6 NA R NA NA NA NA NA NA S R R NA NA #> 7 NA S R S R NA NA NA S S NA NA NA #> 8 NA S R S R NA NA NA S S NA NA NA #> 9 NA R NA NA NA NA NA NA S R NA NA NA #> 10 NA S NA NA NA NA NA NA S S NA NA NA #> # ℹ 1,990 more rows # 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) #> Warning: It should never be needed to print an antimicrobial selector class. Are you #> using data.table? Then add the argument with = FALSE, see our examples at #> ?amr_selector. #> Class 'amr_selector' #> [1] IPM MEM 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: #> 2: #> 3: #> 4: #> 5: #> --- #> 1996: #> 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 #> 2: B_ESCHR_COLI #> 3: B_STPHY_EPDR #> 4: B_STPHY_EPDR #> 5: B_STPHY_EPDR #> --- #> 1996: B_STRPT_PNMN R R R R #> 1997: B_ESCHR_COLI S S S #> 1998: B_STPHY_CONS S #> 1999: B_ESCHR_COLI S S #> 2000: B_KLBSL_PNMN S S 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: #> 2: #> 3: #> 4: #> 5: #> --- #> 1996: R R R R #> 1997: S S S S S #> 1998: S S S #> 1999: S S S S #> 2000: S S S S # 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 #> #> 1: 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R #> 2: 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R #> 3: 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R #> 4: 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R #> 5: 2002-02-05 067927 45 F ICU B_SERRT_MRCS R #> --- #> 905: 2005-04-12 D71461 70 M ICU B_ESCHR_COLI R #> 906: 2009-11-12 650870 69 F Outpatient B_ESCHR_COLI R #> 907: 2012-06-14 8CBCF2 41 F Clinical B_STPHY_CONS R S S #> 908: 2012-10-11 175532 78 M Clinical B_ESCHR_COLI R #> 909: 2013-11-23 A97263 77 M Clinical B_KLBSL_PNMN R #> AMX AMC AMP TZP CZO FEP CXM FOX CTX CAZ CRO GEN #> #> 1: I S S S #> 2: I S S S #> 3: I S S S S #> 4: I S S S S #> 5: R R R R R R #> --- #> 905: S S S S S S S S S S S #> 906: S S S S S S S S S S S S #> 907: S S S S S S R S S #> 908: R S R S S R R S S S S #> 909: R S R S S S S S S S S #> TOB AMK KAN TMP SXT NIT FOS LNZ CIP MFX VAN TEC #> #> 1: S S S R R R #> 2: S S S R R R #> 3: S S R R S R R #> 4: S S R R S R R #> 5: S S R R S R R #> --- #> 905: S S S S R S R R #> 906: S S S S S R S R R #> 907: S S S S #> 908: S R R R R R R R R #> 909: S S S S R S R R #> TCY TGC DOX ERY CLI AZM IPM MEM MTR CHL COL MUP #> #> 1: R R R S #> 2: R R R S #> 3: R R R R R R S R #> 4: R R R R R R S R #> 5: R R R R R R S R #> --- #> 905: R R R S S #> 906: R R R S S #> 907: S S S S S R #> 908: R R R S S S #> 909: R R R S S S #> RIF #> #> 1: R #> 2: R #> 3: R #> 4: R #> 5: R #> --- #> 905: R #> 906: R #> 907: #> 908: R #> 909: 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 I #> 2: R I #> 3: R I #> 4: R I #> 5: R R R R #> --- #> 905: R S S S S #> 906: R S S S S #> 907: R S S S #> 908: R R S R S #> 909: R R S R S # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"Two data sets containing antimicrobials antivirals. Use .ab() one ab_* functions retrieve values antimicrobials data set. Three identifiers included data set: antimicrobial 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/antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials antivirals"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antimicrobials-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antimicrobials data set: a tibble with 496 observations and 14 variables:","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"ab antimicrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antimicrobial susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 179 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 153 drugs iv_units Units iv_ddd loinc codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.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 616 Antimicrobial Drugs — antimicrobials","text":"av Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://atcddd.fhi./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","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/antimicrobials.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 616 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials #> # A tibble: 496 × 14 #> ab cid name group atc atc_group1 atc_group2 abbreviations synonyms #> #> 1 AMA 4649 4-ami… Anti… Drugs for… Aminosali… #> 2 ACM 6450012 Acety… Macr… NA NA #> 3 ASP 49787020 Acety… Macr… NA NA #> 4 ALS 8954 Aldes… Othe… Drugs for… Drugs for… #> 5 AMK 37768 Amika… Amin… Aminoglyc… Other ami… #> 6 AKF NA Amika… Amin… NA NA #> 7 AMO 54260 Amoro… Anti… Antifunga… Other ant… #> 8 AMX 33613 Amoxi… Beta… Beta-lact… Penicilli… #> 9 AMC 23665637 Amoxi… Beta… Beta-lact… Combinati… #> 10 AXS 465441 Amoxi… Beta… NA NA #> # ℹ 486 more rows #> # ℹ 5 more variables: oral_ddd , oral_units , iv_ddd , #> # iv_units , loinc antivirals #> # A tibble: 120 × 11 #> av name atc cid atc_group synonyms oral_ddd oral_units iv_ddd #> #> 1 ABA Abacavir J05A… 4.41e5 Nucleosi… 0.6 g NA #> 2 ACI Aciclovir J05A… 1.35e8 Nucleosi… 4 g 4 #> 3 ADD Adefovir… J05A… 6.09e4 Nucleosi… 10 mg NA #> 4 AME Amenamev… J05A… 1.14e7 Other an… 0.4 g NA #> 5 AMP Amprenav… J05A… 6.50e4 Protease… 1.2 g NA #> 6 ASU Asunapre… J05A… 1.61e7 Antivira… 0.2 g NA #> 7 ATA Atazanav… J05A… 1.48e5 Protease… 0.3 g NA #> 8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… NA NA NA #> 9 ATA+RIT Atazanav… J05A… 2.51e7 Antivira… 0.3 g NA #> 10 BAM Baloxavi… J05A… 1.24e8 Other an… 40 mg NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units , loinc "},{"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 antimicrobial drug code one antimicrobials. data set antimicrobials 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, language = get_AMR_locale(), info = interactive(), ...) is.ab(x) ab_reset_session()"},{"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. language language coerce input values 20 supported languages - default system language supported (see get_AMR_locale()) 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 antimicrobials 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://atcddd.fhi./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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Input to an Antibiotic ID — as.ab","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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\") #> 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S #> # ℹ 1,990 more rows #> # ℹ 37 more variables: J01CA04 , J01CR02 , J01CA01 , #> # J01CR05 , J01DB04 , J01DE01 , J01DC02 , J01DC01 , #> # J01DD01 , J01DD02 , J01DD04 , J01GB03 , J01GB01 , #> # J01GB06 , J01GB04 , J01EA01 , J01EE01 , J01XE01 , #> # J01XX01 , J01XX08 , J01MA02 , J01MA14 , J01XA01 , #> # J01XA02 , J01AA07 , J01AA12 , J01AA02 , … # }"},{"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://atcddd.fhi./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://atcddd.fhi.) 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://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Input to an Antiviral Drug ID — as.av","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 0 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 0 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 0-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame( microorganism = \"Escherichia coli\", AMP = 20, CIP = 14, GEN = 18, TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of 5 variables: #> $ microorganism: chr \"Escherichia coli\" #> $ AMP : 'disk' int 20 #> $ CIP : 'disk' int 14 #> $ GEN : 'disk' int 18 #> $ TOB : 'disk' int 16 # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) { df %>% mutate(across(AMP:TOB, as.disk)) } #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli 20 14 18 16 # } # interpret disk values, see ?as.sir as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #> OK #> Class 'sir' #> [1] R # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2024... #> OK #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli S S S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE, keep_operators = \"all\") is.mic(x) NA_mic_ rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE) mic_p50(x, na.rm = FALSE, ...) mic_p90(x, na.rm = FALSE, ...) # S3 method for class 'mic' droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values, e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). .mic logical indicate whether mic class kept - default TRUE rescale_mic() FALSE droplevels(). setting FALSE rescale_mic(), output factor levels acknowledge mic_range. ... 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 numeric vector. Bear mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2011-2024) CLSI (2011-2024). 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.: -called group generic functions implemented MIC class (!, !=, <, >=, exp(), log2()). functions stats package also implemented (quantile(), median(), fivenum()). Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. rescale_mic(), existing MIC ranges can limited defined range MIC values. can useful better compare MIC distributions. ggplot2, use one scale_*_mic() functions plot MIC values. allows custom MIC ranges plot intermediate log2 levels missing MIC values. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_. Use mic_p50() mic_p90() get 50th 90th percentile MIC values. return 'normal' numeric values.","code":"x <- random_mic(10) x #> Class 'mic' #> [1] 16 1 8 8 64 >=128 0.0625 32 32 16 is.factor(x) #> [1] TRUE x[1] * 2 #> [1] 32 median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16 8 8 64 >=128 32 32 16 df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #> x hospital #> 1 16 A #> 5 64 A #> 6 >=128 A #> 8 32 A #> 9 32 A #> 10 16 A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32 1 1 1 8 <=0.128 8 16 16 is.mic(mic_data) #> [1] TRUE # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002 # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1] 0.128 1.000 8.000 16.000 32.000 quantile(mic_data) #> 0% 25% 50% 75% 100% #> 0.128 1.000 8.000 16.000 32.000 all(mic_data < 512) #> [1] TRUE # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4 <=4 <=4 8 <=4 8 >=16 >=16 # interpret MIC values as.sir( x = as.mic(2), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.mic(c(0.01, 2, 4, 8)), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R # plot MIC values, see ?plot plot(mic_data) plot(mic_data, mo = \"E. coli\", ab = \"cipro\") if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Chromista, 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()), only_fungi = getOption(\"AMR_only_fungi\", FALSE), 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. only_fungi logical indicate fungi must found, making sure e.g. misspellings always return records kingdom Fungi. can set globally microorganism functions package option AMR_only_fungi, .e. options(AMR_only_fungi = TRUE). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate info must printed, e.g. progress bar 25 items coerced, list old taxonomic names. 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' return MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel scientifically validated (doi:10.18637/jss.v104.i03 ) matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. implicates e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","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 \\----> kingdom: A (Archaea), AN (Animalia), B (Bacteria), C (Chromista), 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 inspect results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument used clean input. 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":"for-mycologists","dir":"Reference","previous_headings":"","what":"For Mycologists","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"matching score algorithm gives precedence bacteria fungi. analysing fungi, sure use only_fungi = TRUE, better yet, add code run every session: make sure bacteria 'non-fungi' returned .mo(), mo_* functions.","code":"options(AMR_only_fungi = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coagulase-negative-and-coagulase-positive-staphylococci","dir":"Reference","previous_headings":"","what":"Coagulase-negative and Coagulase-positive Staphylococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Becker = TRUE, following staphylococci converted corresponding coagulase group: Coagulase-negative: S. americanisciuri, S. argensis, S. arlettae, S. auricularis, S. borealis, S. brunensis, 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. marylandisciuri, 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. shinii, 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 Coagulase-positive: 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 based : 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 newly named staphylococcal species, S. brunensis (2024) S. shinii (2023), looked scientific reference make sure species considered correct coagulase group.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"lancefield-groups-in-streptococci","dir":"Reference","previous_headings":"","what":"Lancefield Groups in Streptococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Lancefield = TRUE, following streptococci converted corresponding Lancefield group: Streptococcus Group : S. pyogenes Streptococcus Group B: S. agalactiae Streptococcus Group C: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis, S. equi, S. equi equi, S. equi ruminatorum, S. equi zooepidemicus Streptococcus Group F: S. anginosus, S. anginosus anginosus, S. anginosus whileyi, S. constellatus, S. constellatus constellatus, S. constellatus pharyngis, S. constellatus viborgensis, S. intermedius Streptococcus Group G: S. canis, S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis Streptococcus Group H: S. sanguinis Streptococcus Group K: S. salivarius, S. salivarius salivarius, S. salivarius thermophilus Streptococcus Group L: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis based : Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571","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 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 June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ 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 : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )( ?[a-z-]+[-](resistant|susceptible) ?|e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy)) as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant #> Phenotypes' v1.2 (2023). 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":"Clean existing SIR values, interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI. .sir() transforms input new class sir, ordered factor containing levels S, SDD, , R, NI. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2012-2024 CLSI 2022-2024. breakpoints used interpretation 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) # Default S3 method as.sir(x, S = \"^(S|U)+$\", I = \"^(I)+$\", R = \"^(R)+$\", NI = \"^(N|NI|V)+$\", SDD = \"^(SDD|D|H)+$\", info = TRUE, ...) # S3 method for class 'mic' as.sir(x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, capped_mic_handling = getOption(\"AMR_capped_mic_handling\", \"standard\"), add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, conserve_capped_values = NULL, ...) # S3 method for class '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, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, ...) # S3 method for class 'data.frame' as.sir(x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, capped_mic_handling = getOption(\"AMR_capped_mic_handling\", \"standard\"), add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, substitute_missing_r_breakpoint = getOption(\"AMR_substitute_missing_r_breakpoint\", FALSE), include_screening = getOption(\"AMR_include_screening\", FALSE), include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL, verbose = FALSE, info = TRUE, conserve_capped_values = NULL) sir_interpretation_history(clean = FALSE)"},{"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: CLSI M39: Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. CLSI M100: Performance Standard Antimicrobial Susceptibility Testing, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. CLSI VET01: Performance Standards Antimicrobial Disk Dilution Susceptibility Tests Bacteria Isolated Animals, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/. EUCAST Breakpoint tables interpretation MICs zone diameters, 2011-2024, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints. WHONET source machine-reading clinical breakpoints (read ), 1989-2024, Collaborating Centre Surveillance Antimicrobial Resistance. https://whonet.org/.","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 S, , R, NI, SDD case-independent regular expression translate input result. regular expression run non-letters whitespaces removed input. info logical print information process mo vector (column name) characters can coerced valid microorganism codes .mo(), can left empty determine automatically ab vector (column name) characters can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2024 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2024) CLSI (2011-2024), see Details. uti (Urinary Tract Infection) vector (column name) 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. capped_mic_handling character string controls MIC values cap (.e., starting <, <=, >, >=) interpreted. Supports following options: \"none\" <= >= treated -. < > treated -. \"conservative\" <= >= return \"NI\" (non-interpretable) MIC within breakpoint guideline range. < always returns \"S\", > always returns \"R\". \"standard\" (default) <= >= return \"NI\" (non-interpretable) MIC within breakpoint guideline range. < > treated -. \"inverse\" <= >= treated -. < always returns \"S\", > always returns \"R\". default \"standard\" setting ensures cautious handling uncertain values preserving interpretability. option can also set package option AMR_capped_mic_handling. 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. substitute_missing_r_breakpoint logical indicate missing clinical breakpoints R (resistant) must substituted R - default FALSE. (especially CLSI) breakpoints breakpoint S, meaning outcome can \"S\" NA. Setting TRUE convert NAs \"R\" R breakpoint missing. Can also set package option AMR_substitute_missing_r_breakpoint. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". host vector (column name) characters indicate host. useful veterinary breakpoints, requires breakpoint_type = \"animal\". values can text resembling animal species, even 20 supported languages package. foreign languages, sure set language set_AMR_locale() (though automatically guessed based system language). verbose logical indicate notes printed interpretation MIC values disk diffusion values. conserve_capped_values deprecated, use capped_mic_handling instead col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Note: clinical breakpoints package validated , imported , WHONET. public use AMR package endorsed CLSI EUCAST. See clinical_breakpoints information. NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function can work four ways: cleaning raw / untransformed data. data cleaned contain valid values, namely: S susceptible, intermediate 'susceptible, increased exposure', R resistant, NI non-interpretable, SDD susceptible dose-dependent. can set using regular expression. Furthermore, .sir() try best clean 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 invalid. 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 capped_mic_handling = \"conservative\", MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (capped_mic_handling = \"standard\") 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.mic, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.mic, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.mic, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\") your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir)) your_data %>% mutate_if(is.disk, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.disk, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.disk, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\")"},{"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 clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024, veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024. Thus, guideline argument must set e.g., \"EUCAST 2024\" \"CLSI 2024\". 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), : veterinary guidelines, might best options:","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL) options(AMR_guideline = \"CLSI\") options(AMR_breakpoint_type = \"animal\")"},{"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. determine isolates multi-drug resistant, sure run mdro() (applies MDR/PDR/XDR guideline 2012 default) data set contains S//R values. Read interpreting multidrug-resistant organisms .","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 34 376 rows 14 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. base R function .double() can used retrieve quantitative values sir object: \"S\" = 1, \"\"/\"SDD\" = 2, \"R\" = 3. values rendered NA . Note: use .integer(), since (R works internally) return factor level indices, aforementioned quantitative values. function is_sir_eligible() returns TRUE column contains 5% invalid antimicrobial interpretations (S //R /NI /SDD), 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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … 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 :0 %S :25.6% (n=417) #> Mode :character Unique:90 %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I : 0.7% (n=11) #> #2 :B_STPHY_CONS %R :73.7% (n=1201) #> #3 :B_STPHY_AURS %NI : 0.0% (n=0) #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %S :68.8% (n=251) %S :70.5% (n=665) %S :40.2% (n=543) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.2% (n=3) #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %S :71.4% (n=1342) %S :40.2% (n=543) %S :86.1% (n=862) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 4.8% (n=91) %I : 0.2% (n=3) %I : 1.3% (n=13) #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %S :54.9% (n=245) %S :85.6% (n=620) %S :72.5% (n=1297) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.4% (n=2) %I : 0.1% (n=1) %I : 1.2% (n=22) #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %S :71.6% (n=586) %S :84.4% (n=796) %S :33.5% (n=607) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 1.0% (n=8) %I : 0.1% (n=1) %I : 0.0% (n=0) #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %S :84.4% (n=796) %S :74.0% (n=1372) %S :65.1% (n=879) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.1% (n=1) %I : 1.5% (n=27) %I : 0.5% (n=7) #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %S :36.3% (n=251) %S : 0.0% (n=0) %S :61.2% (n=918) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.7% (n=10) #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %S :79.1% (n=1392) %S :76.0% (n=565) %S :57.8% (n=203) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.3% (n=6) %I : 6.9% (n=51) %I : 0.0% (n=0) #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %S :30.7% (n=314) %S :78.9% (n=1112) %S :64.5% (n=136) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 4.9% (n=69) %I : 1.9% (n=4) #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %S :61.7% (n=1149) %S :24.3% (n=237) %S :68.3% (n=820) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 1.9% (n=23) #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %S :87.3% (n=697) %S :71.7% (n=814) %S :42.3% (n=801) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.6% (n=7) %I : 0.5% (n=9) #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %S :38.6% (n=586) %S :42.3% (n=801) %S :92.7% (n=824) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.3% (n=4) %I : 0.5% (n=9) %I : 1.1% (n=10) #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %S :94.1% (n=780) %S :85.3% (n=29) %S :78.6% (n=121) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 0.0% (n=0) %I : 0.0% (n=0) #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %S :18.8% (n=309) %S :93.0% (n=251) %S :30.2% (n=303) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 0.0% (n=0) %I : 1.1% (n=3) %I : 0.2% (n=2) #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) # For INTERPRETING disk diffusion and MIC values ----------------------- # example data sets, with combined MIC values and disk zones df_wide <- data.frame( microorganism = \"Escherichia coli\", amoxicillin = as.mic(8), cipro = as.mic(0.256), tobra = as.disk(16), genta = as.disk(18), ERY = \"R\" ) df_long <- data.frame( bacteria = rep(\"Escherichia coli\", 4), antibiotic = c(\"amoxicillin\", \"cipro\", \"tobra\", \"genta\"), mics = as.mic(c(0.01, 1, 4, 8)), disks = as.disk(c(6, 10, 14, 18)) ) # \\donttest{ ## Using dplyr ------------------------------------------------- if (require(\"dplyr\")) { # approaches that all work without additional arguments: df_wide %>% mutate_if(is.mic, as.sir) df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df_wide %>% mutate(across(where(is.mic), as.sir)) df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir) df_wide %>% mutate(across(amoxicillin:tobra, as.sir)) # approaches that all work with additional arguments: df_long %>% # given a certain data type, e.g. MIC values mutate_if(is.mic, as.sir, mo = \"bacteria\", ab = \"antibiotic\", guideline = \"CLSI\" ) df_long %>% mutate(across( where(is.mic), function(x) { as.sir(x, mo = \"bacteria\", ab = \"antibiotic\", guideline = \"CLSI\" ) } )) df_wide %>% # given certain columns, e.g. from 'cipro' to 'genta' mutate_at(vars(cipro:genta), as.sir, mo = \"bacteria\", guideline = \"CLSI\" ) df_wide %>% mutate(across( cipro:genta, function(x) { as.sir(x, mo = \"bacteria\", guideline = \"CLSI\" ) } )) # for veterinary breakpoints, add 'host': df_long$animal_species <- c(\"cats\", \"dogs\", \"horses\", \"cattle\") df_long %>% # given a certain data type, e.g. MIC values mutate_if(is.mic, as.sir, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) df_long %>% mutate(across( where(is.mic), function(x) { as.sir(x, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) } )) df_wide %>% mutate_at(vars(cipro:genta), as.sir, mo = \"bacteria\", ab = \"antibiotic\", host = \"animal_species\", guideline = \"CLSI\" ) df_wide %>% mutate(across( cipro:genta, function(x) { as.sir(x, mo = \"bacteria\", host = \"animal_species\", guideline = \"CLSI\" ) } )) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", nitrofuratoin = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", nitrofuratoin = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df_wide %>% mutate_at(vars(cipro:genta), as.sir, mo = \"E. coli\", uti = TRUE) } #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> #> Interpreting MIC values: 'antibiotic' (ASP, acetylspiramycin), CLSI 2024... #> WARNING #> • No CLSI 2024 MIC breakpoints available for acetylspiramycin (ASP). #> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin), #> CLSI 2024... #> WARNING #> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP). #> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin), #> CLSI 2024... #> WARNING #> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP). #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `cipro = (function (x, ...) ...`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #> #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #> OK #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `across(...)`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #> OK #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #> OK #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #> OK #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), EUCAST #> 2024... #> OK #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), EUCAST #> 2024... #> OK #> microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli 8 S S R ## Using base R ------------------------------------------------ as.sir(df_wide) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: column 'amoxicillin' (AMX), EUCAST 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'cipro' (CIP, ciprofloxacin), EUCAST #> 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'tobra' (TOB, tobramycin), EUCAST #> 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'genta' (GEN, gentamicin), EUCAST #> 2024... #> NOTE #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK #> microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 65 × 16 #> datetime index ab_given mo_given host_given ab mo #> #> 1 2025-03-28 15:55:56 1 ampicillin Strep p… human AMP B_STRPT_PNMN #> 2 2025-03-28 15:55:57 1 AMP Escheri… human AMP B_[ORD]_ENTRBC… #> 3 2025-03-28 15:55:57 1 AMP Escheri… human AMP B_[ORD]_ENTRBC… #> 4 NA NA NA NA NA NA NA #> 5 2025-03-28 15:55:57 1 GEN Escheri… human GEN B_[ORD]_ENTRBC… #> 6 2025-03-28 15:55:57 1 TOB Escheri… human TOB B_[ORD]_ENTRBC… #> 7 2025-03-28 15:55:58 1 AMX B_STRPT… human AMX B_STRPT_PNMN #> 8 2025-03-28 15:55:58 1 AMX B_STRPT… human AMX B_STRPT_PNMN #> 9 2025-03-28 15:55:58 2 AMX B_STRPT… human AMX B_STRPT_PNMN #> 10 2025-03-28 15:55:58 3 AMX B_STRPT… human AMX B_STRPT_PNMN #> # ℹ 55 more rows #> # ℹ 9 more variables: host , method , input , outcome , #> # notes , guideline , ref_table , uti , #> # breakpoint_S_R # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #> OK #> Class 'sir' #> [1] R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in index '20' truncated (38%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" as.sir(\"<= 0.002; S\") # will return \"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 # as common in R, you can use as.integer() to return factor indices: as.integer(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1] 1 2 3 4 5 NA # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R: as.double(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1] 1 2 2 3 NA NA # 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # }"},{"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. antimicrobial), 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://atcddd.fhi.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://atcddd.fhi.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://atcddd.fhi./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) antimicrobials, 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://atcddd.fhi./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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of an Antiviral Drug — av_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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://atcddd.fhi.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # lowercase transformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, include_n_rows = FALSE, ...) # S3 method for class '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 antimicrobials 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() include_n_rows logical indicate total number rows must included output ... arguments passed FUN translate_ab character length 1 containing column names antimicrobials 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, SDD, 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\", \"SDD\", \"\", \"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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 8 #> mo ab S SDD I R NI total #> #> 1 (unknown species) AMC 15 0 0 0 0 15 #> 2 (unknown species) AMK 0 0 0 0 0 0 #> 3 (unknown species) AMP 15 0 0 1 0 16 #> 4 (unknown species) AMX 15 0 0 1 0 16 #> 5 (unknown species) AZM 3 0 0 3 0 6 #> 6 (unknown species) CAZ 0 0 0 0 0 0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS `E. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #> #> 1 \"Aminogl… Amik… \"100… \" 0.0% … \"100.0% (39/… \"\" \"\" #> 2 \"\" Gent… \" 13… \" 2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \" 0.0% (0/30)\" #> 3 \"\" Kana… \"100… \"\" \"100.0% (39/… \"\" \"100.0% (30/30… #> 4 \"\" Tobr… \" 78… \" 2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \" 0.0% (0/30)\" #> 5 \"Antimyc… Rifa… \"\" \"100.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 6 \"Beta-la… Amox… \" 93… \" 50.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 7 \"\" Amox… \" 42… \" 13.1% … \"\" \" 10.3% (6/58)\" \"100.0% (30/30… #> 8 \"\" Ampi… \" 93… \" 50.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 9 \"\" Benz… \" 77… \"100.0% … \"\" \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\" Fluc… \" 42… \"\" \"\" \"\" \"\" #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` , `S. aureus` , #> # `S. epidermidis` , `S. hominis` , `S. pneumoniae` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 8 #> mo ab S SDD I R NI total #> #> 1 Gram-negative AMC 463 0 89 174 0 726 #> 2 Gram-negative AMK 251 0 0 5 0 256 #> 3 Gram-negative AMP 226 0 0 405 0 631 #> 4 Gram-negative AMX 226 0 0 405 0 631 #> 5 Gram-negative AZM 1 0 2 696 0 699 #> 6 Gram-negative CAZ 607 0 0 27 0 634 #> 7 Gram-negative CHL 1 0 0 30 0 31 #> 8 Gram-negative CIP 610 0 11 63 0 684 #> 9 Gram-negative CLI 18 0 1 709 0 728 #> 10 Gram-negative COL 309 0 0 78 0 387 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 8 #> mo ab S SDD I R NI total #> #> 1 E. coli AMC 332 0 74 61 0 467 #> 2 E. coli AMK 171 0 0 0 0 171 #> 3 E. coli AMP 196 0 0 196 0 392 #> 4 E. coli AMX 196 0 0 196 0 392 #> 5 E. coli AZM 0 0 0 467 0 467 #> 6 E. coli CAZ 449 0 0 11 0 460 #> 7 E. coli CHL 0 0 0 0 0 0 #> 8 E. coli CIP 398 0 1 57 0 456 #> 9 E. coli CLI 0 0 0 467 0 467 #> 10 E. coli COL 240 0 0 0 0 240 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. dataset contain breakpoints humans, 7 different animal groups, ECOFFs. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2012-2024 CLSI 2022-2024. 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 34 376 observations 14 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" host Host infectious agent. mostly useful veterinary breakpoints either \"ECOFF\", \"aquatic\", \"cats\", \"cattle\", \"dogs\", \"horse\", \"human\", \"poultry\", \"swine\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antimicrobial code used package, EARS-Net WHONET, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\", can NA uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI) is_SDD logical value (TRUE/FALSE) indicate whether intermediate range \"S\" \"R\" interpreted \"SDD\", instead \"\". currently applies 24 breakpoints.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different Types of Breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported From WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate great development WHONET software. import reproduction script can found : https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response From CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET software, developers AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download-note","dir":"Reference","previous_headings":"","what":"Download Note","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"AMR package (WHONET software well) contains rather complex internal methods apply guidelines. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered implementing breakpoints use.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","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: 34,376 × 14 #> guideline type host method site mo rank_index ab ref_tbl #> #> 1 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 MEM A. xylo… #> 2 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 MEM A. xylo… #> 3 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 SXT A. xylo… #> 4 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 SXT A. xylo… #> 5 EUCAST 2024 human human DISK NA B_ACHRMB_XYLS 2 TZP A. xylo… #> 6 EUCAST 2024 human human MIC NA B_ACHRMB_XYLS 2 TZP A. xylo… #> 7 EUCAST 2024 human human DISK NA B_ACNTB 3 AMK Acineto… #> 8 EUCAST 2024 human human DISK Uncomp… B_ACNTB 3 AMK Acineto… #> 9 EUCAST 2024 human human MIC NA B_ACNTB 3 AMK Acineto… #> 10 EUCAST 2024 human human MIC Uncomp… B_ACNTB 3 AMK Acineto… #> # ℹ 34,366 more rows #> # ℹ 5 more variables: disk_dose , breakpoint_S , breakpoint_R , #> # uti , is_SDD "},{"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_S(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_R(..., 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 antimicrobials, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials 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, SDD, must merged one, output consists S+SDD+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 antimicrobials 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 antimicrobials/variables test . See example two antimicrobials, 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 considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- 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$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" and \"SDD\" being susceptible. This note will be shown once for #> this session. #> [1] 543 count_SI(example_isolates$AMX) #> Note that count_SI() will also count dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 546 count_I(example_isolates$AMX) #> Note that count_I() will also count dose-dependent susceptibility, 'SDD'. #> This note will be shown once for this session. #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" and \"SDD\" 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 #> #> 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 instructions, 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, overwrite = TRUE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_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, overwrite = TRUE) #> 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-multiple-antimicrobials-and-antimicrobial-group-names","dir":"Reference","previous_headings":"","what":"Usage of multiple antimicrobials and antimicrobial group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"can define antimicrobial groups instead single antimicrobials rule consequence, part tilde (~). examples , antimicrobial group aminopenicillins includes ampicillin amoxicillin. Rules can also applied multiple antimicrobials antimicrobial groups simultaneously. Use c() function combine multiple antimicrobials. instance, following example sets aminopenicillins ureidopenicillins \"R\" column TZP (piperacillin/tazobactam) \"R\": 35 antimicrobial groups allowed rules (case-insensitive) can used combination: aminoglycosides(amikacin, amikacin/fosfomycin, 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(amorolfine, amphotericin B, amphotericin B-high, 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, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, tigemonam) betalactams_with_inhibitor(amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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 test, 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 screening test, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, 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/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, 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, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, pazufloxacin, pefloxacin, pefloxacin screening test, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) glycopeptides(avoparcin, bleomycin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) glycopeptides_except_lipo(avoparcin, bleomycin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) isoxazolylpenicillins(cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, oxacillin screening test) lincosamides(clindamycin, lincomycin, pirlimycin) lipoglycopeptides(dalbavancin, oritavancin, telavancin) macrolides(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) monobactams(aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, tigemonam) nitrofurans(furazidin, furazolidone, nifurtoinol, nitrofurantoin, nitrofurazone) oxazolidinones(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) penicillins(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, ticarcillin/clavulanic acid) phenicols(chloramphenicol, florfenicol, thiamphenicol) polymyxins(colistin, polymyxin B, polymyxin B/polysorbate 80) quinolones(besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nalidixic acid screening test, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pefloxacin screening test, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) rifamycins(rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine) streptogramins(pristinamycin quinupristin/dalfopristin) sulfonamides(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea) tetracyclines(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, tigecycline) tetracyclines_except_tgc(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test) 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":"x <- custom_eucast_rules(TZP == \"R\" ~ c(aminopenicillins, ureidopenicillins) == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"R\" then set to \"R\": #> amoxicillin (AMX), ampicillin (AMP), azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)"},{"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, overwrite = TRUE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_group rule_name rule_source #> #> 1 33 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 2 33 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 3 34 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 4 34 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 5 531 AMP Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 6 531 AMX Klebsiella pne… R I \"rep… Custom EU… Custom E… Object 'x'… #> 7 1485 AMP Klebsiella oxy… R I \"rep… Custom EU… Custom E… Object 'x'… #> 8 1485 AMX Klebsiella oxy… R I \"rep… Custom EU… Custom E… Object 'x'… # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is R and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is I and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is R then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 503 observations 9 variables: ab Antimicrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply, either 13, 12, 11","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 503 × 9 #> ab name type dose dose_times administration notes original_txt #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 mg/kg… #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x 6 iv #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x 3-4 iv #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1 g x … #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g x 3 o… #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g x 3 o… #> 7 AMC Amoxicillin/cl… high… 2 g … 3 iv \"\" (2 g amoxic… #> 8 AMC Amoxicillin/cl… stan… 1 g … 3 iv \"\" (1 g amoxic… #> 9 AMC Amoxicillin/cl… high… 0.87… 3 oral \"\" (0.875 g am… #> 10 AMC Amoxicillin/cl… stan… 0.5 … 3 oral \"\" (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version "},{"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 notes expected resistant phenotypes 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\", \"expected_phenotypes\")), verbose = FALSE, version_breakpoints = 14, version_expected_phenotypes = 1.2, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, overwrite = FALSE, ...) 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 antimicrobials 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\", \"expected_phenotypes\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expected_phenotypes\"). 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 \"14.0\", \"13.1\", \"12.0\", \"11.0\", \"10.0\". version_expected_phenotypes version number use EUCAST Expected Phenotypes. Can \"1.2\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance (applies rules contains \"expert\" \"\") character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; versions '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 antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() overwrite logical indicating whether overwrite non-NA values (default: FALSE). FALSE, NA values modified. ensure compliance EUCAST guidelines, remain FALSE, EUCAST notes often state organism \"tested susceptibility individual agents reported resistant.\" ... column name antimicrobial, see section Antimicrobials ab (vector ) text can coerced valid antimicrobial 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 antimicrobials. , 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 June 24th, 2024, 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":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Apply EUCAST Rules — eucast_rules","text":"define antimicrobials 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 antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), bleomycin (BLM, L01DC01), brodimoprim (BDP, J01EA02), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), chlortetracycline (CTE, J01AA03), ciprofloxacin (CIP, J01MA02), ciprofloxacin/metronidazole (CIM, J01RA10), ciprofloxacin/ornidazole (CIO, J01RA12), ciprofloxacin/tinidazole (CIT, J01RA11), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), clomocycline (CLM1, J01AA11), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), demeclocycline (DEM, J01AA01), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), eravacycline (ERV, J01AA13), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levofloxacin/ornidazole (LEO, J01RA05), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), lymecycline (LYM, J01AA04), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metacycline (MTC, J01AA05), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), nemonoxacin (NEM, J01MB08), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), nitroxoline (NTR, J01XX07), norfloxacin (, J01MA06), norfloxacin/metronidazole (NME, J01RA14), norfloxacin/tinidazole (NTI, J01RA13), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), ofloxacin/ornidazole (OOR, J01RA09), oleandomycin (OLE, J01FA05), omadacycline (OMC, J01AA15), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), oxytetracycline (OXY, J01AA06), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), penimepicycline (PNM1, J01AA10), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), rolitetracycline (RLT, J01AA09), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sarecycline (SRC, J01AA14), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/tetroxoprim (SLT, J01EE06), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Apply EUCAST Rules — eucast_rules","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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, overwrite = 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(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R R 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, overwrite = TRUE, 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 Expected phenotypes #> 4 1 COL Staphylococcus aureus - R Expected phenotypes #> 5 2 CAZ Enterococcus faecalis - R Expected phenotypes #> 6 2 COL Enterococcus faecalis - R Expected phenotypes #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Table 4: Expected resistant phenotype in gram-positive bacteria #> 4 Table 4: Expected resistant phenotype in gram-positive bacteria #> 5 Table 4: Expected resistant phenotype in gram-positive bacteria #> 6 Table 4: Expected resistant phenotype in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v14.0, 2024 #> 2 'EUCAST Clinical Breakpoint Tables' v14.0, 2024 #> 3 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 4 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 5 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 #> 6 'EUCAST Expected Resistant Phenotypes' v1.2, 2023 # } # 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 #> #> 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 #> #> 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 antimicrobials class sir (see .sir()); column names occur antimicrobials data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , …"},{"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 antimicrobials transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with Unclean Data — example_isolates_unclean","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"Export Data Set NCBI BioSample Antibiogram","code":""},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"","code":"export_ncbi_biosample(x, filename = paste0(\"biosample_\", format(Sys.time(), \"%Y-%m-%d-%H%M%S\"), \".xlsx\"), type = \"pathogen MIC\", columns = where(is.mic), save_as_xlsx = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"x data set filename character string specifying file name type character string specifying type data set, either \"pathogen MIC\" \"beta-lactamase MIC\", see https://www.ncbi.nlm.nih.gov/biosample/docs/","code":""},{"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 functions 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":"methodology implemented functions strictly based recommendations outlined CLSI Guideline M39 research overview Hindler et al. (2007, doi:10.1086/511864 ). 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 previously-mentioned sources, 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. method makes sure duplicate isolates selected patient. method preferred e.g. identify first MRSA finding patient determine incidence. Conversely, 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 ICU cases, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates[first_isolate(info = TRUE), ] #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #> microbial ID was available) #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … # \\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: 441 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R #> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA #> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA #> # ℹ 431 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # filter on first isolates using dplyr: example_isolates %>% filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #> microbial ID was available) #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # short-hand version: example_isolates %>% filter_first_isolate(info = FALSE) } #> # A tibble: 1,387 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 6 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 7 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 8 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 9 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 10 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> # ℹ 1,377 more rows #> # ℹ 36 more variables: 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 , … if (require(\"dplyr\")) { # flag the first isolates per group: example_isolates %>% group_by(ward) %>% mutate(first = first_isolate(info = TRUE)) %>% select(ward, date, patient, mo, first) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 865 'phenotype-based' first isolates (70.1% of total where a #> microbial ID was available) #> #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 452 'phenotype-based' first isolates (70.0% of total where a #> microbial ID was available) #> #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 99 'phenotype-based' first isolates (82.5% of total where a #> microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> #> 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 TRUE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # ℹ 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also factors. y numeric vector; ignored x matrix. x factor, y factor length. p vector probabilities length x. error given entry p negative. rescale.p logical scalar; TRUE p rescaled (necessary) sum 1. rescale.p FALSE, p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following components: statistic value chi-squared test statistic. parameter degrees freedom approximate chi-squared distribution test statistic, NA p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test performed, whether Monte Carlo simulation continuity correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals, (observed - expected) / sqrt(expected). stdres standardized residuals, (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007, section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers. Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed . Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R : df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio. x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 4.1471, p-value = 0.1257 #> # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible. # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills. x <- c(1752, 1895) g.test(x) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 5.6085, p-value = 0.01787 #> # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...) is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023). *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function hand, returns TRUE every new get_episode() index. specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, episode functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c( \"2021-01-01\", \"2021-01-02\", \"2021-01-05\", \"2021-01-08\", \"2021-02-21\", \"2021-02-22\", \"2021-02-23\", \"2021-02-24\", \"2021-03-01\", \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #> dates absolute relative #> 1 2021-01-01 1 1 #> 2 2021-01-02 1 1 #> 3 2021-01-05 1 1 #> 4 2021-01-08 2 1 #> 5 2021-02-21 3 2 #> 6 2021-02-22 3 2 #> 7 2021-02-23 3 2 #> 8 2021-02-24 3 2 #> 9 2021-03-01 4 2 #> 10 2021-03-01 4 2 # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices #> [1] 33 15 22 12 31 27 11 45 38 19 3 29 4 42 41 41 13 36 23 33 38 46 31 45 21 #> [26] 24 9 18 10 6 11 41 1 12 38 26 11 36 1 41 32 14 7 37 22 26 16 44 14 7 #> [51] 44 4 45 4 2 39 20 43 9 42 29 11 6 36 25 30 23 8 14 4 28 11 14 20 40 #> [76] 23 6 11 16 29 27 9 8 15 45 31 35 44 35 46 5 39 44 24 17 12 31 32 36 34 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [13] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE #> [25] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE #> [37] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE #> [49] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [85] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE #> [97] FALSE FALSE FALSE TRUE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 1 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-07-16 241328 78 M Outpatie… B_STPHY_CONS R NA S R #> # ℹ 36 more variables: 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 # 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 [95] #> patient date condition new_episode #> #> 1 067927 2002-02-14 B TRUE #> 2 067927 2002-02-14 B FALSE #> 3 0E2483 2007-06-21 B TRUE #> 4 151041 2006-02-10 C TRUE #> 5 155435 2014-06-18 A TRUE #> 6 167474 2008-02-15 A TRUE #> 7 174209 2011-10-03 B TRUE #> 8 175532 2012-10-11 A TRUE #> 9 187841 2012-09-30 C TRUE #> 10 197007 2012-07-17 B TRUE #> # ℹ 90 more rows if (require(\"dplyr\")) { df %>% group_by(ward, patient) %>% transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) %>% arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups: ward, patient [93] #> ward date patient new_index new_logical #> #> 1 ICU 2002-02-14 067927 1 TRUE #> 2 ICU 2002-02-14 067927 1 FALSE #> 3 ICU 2007-06-21 0E2483 1 TRUE #> 4 Clinical 2006-02-10 151041 1 TRUE #> 5 Clinical 2014-06-18 155435 1 TRUE #> 6 Clinical 2008-02-15 167474 1 TRUE #> 7 Outpatient 2011-10-03 174209 1 TRUE #> 8 Clinical 2012-10-11 175532 1 TRUE #> 9 Clinical 2012-09-30 187841 1 TRUE #> 10 Clinical 2012-07-17 197007 1 TRUE #> # ℹ 90 more rows if (require(\"dplyr\")) { df %>% group_by(ward) %>% summarise( n_patients = n_distinct(patient), n_episodes_365 = sum(is_new_episode(date, episode_days = 365)), n_episodes_60 = sum(is_new_episode(date, episode_days = 60)), n_episodes_30 = sum(is_new_episode(date, episode_days = 30)) ) } #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> #> 1 Clinical 55 13 35 44 #> 2 ICU 32 9 19 25 #> 3 Outpatient 6 6 6 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 [95] #> patient mo ward flag_episode #> #> 1 88B975 B_STRPT Clinical TRUE #> 2 904485 B_ESCHR_COLI Outpatient TRUE #> 3 167474 B_STPHY_CONS Clinical TRUE #> 4 777154 B_CTRBC Clinical TRUE #> 5 E58140 B_ESCHR_COLI Clinical TRUE #> 6 E58549 B_ESCHR_COLI Clinical TRUE #> 7 D65308 B_STPHY_EPDR ICU TRUE #> 8 871020 B_STPHY_EPDR Clinical TRUE #> 9 B13995 B_STPHY_AURS Clinical TRUE #> 10 CB6026 B_STPHY_CONS Clinical TRUE #> # ℹ 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE, labels = NULL, labels_textsize = 3, labels_text_placement = 1.5, groups = NULL, ellipse = TRUE, ellipse_prob = 0.68, ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2, points_alpha = 0.25, arrows = TRUE, arrows_colour = \"darkblue\", arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE, arrows_alpha = 0.75, base_textsize = 10, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default biplot strict sense. scale variables scaled lambda ^ scale observations scaled lambda ^ (1-scale) lambda singular values computed princomp. Normally 0 <= scale <= 1, warning issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component biplot\", lambda = 1 observations scaled sqrt(n) variables scaled sqrt(n). inner products variables approximate covariances distances observations approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. # \\donttest{ if (require(\"dplyr\")) { # calculate the resistance per group first resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here; filter(n() >= 30) %>% # filter on only 30 results per group summarise_if(is.sir, resistance) # then get resistance of all drugs # now conduct PCA for certain antimicrobial drugs pca_result <- resistance_data %>% pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT) summary(pca_result) # old base R plotting method: biplot(pca_result, main = \"Base R biplot\") # new ggplot2 plotting method using this package: if (require(\"ggplot2\")) { ggplot_pca(pca_result) + labs(title = \"ggplot2 biplot\") } if (require(\"ggplot2\")) { # still extendible with any ggplot2 function ggplot_pca(pca_result) + scale_colour_viridis_d() + labs(title = \"ggplot2 biplot\") } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #> only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(data, position = NULL, x = \"antibiotic\", fill = \"interpretation\", facet = NULL, breaks = seq(0, 1, 0.1), limits = NULL, translate_ab = \"name\", combine_SI = TRUE, minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R = \"#ED553B\"), datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = \"grey15\", title = NULL, subtitle = NULL, caption = NULL, x.title = \"Antimicrobial\", y.title = \"Proportion\", ...) geom_sir(position = NULL, x = c(\"antibiotic\", \"interpretation\"), fill = \"interpretation\", translate_ab = \"name\", minimum = 30, language = get_AMR_locale(), combine_SI = TRUE, ...)"},{"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 antimicrobials data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+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.","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 antimicrobials shown plots using ab_name(). can set translate_ab argument. See count_df(). geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antimicrobials x axis. Additional functions include: 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(aesthetics = \"fill\") + 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( aesthetics = \"fill\", 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 or values outside the scale range #> (`geom_col()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_text()`). 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 antimicrobials 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 antimicrobials 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 Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing 'EUCAST Expected Resistant Phenotypes' bug-drug combinations microorganisms antimicrobials data sets.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set Denoting 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 Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 271 905 observations 2 variables: mo Microorganism ID occurs microorganisms$mo. Names can retrieved using mo_name(). ab Antimicrobial ID occurs antimicrobials$ab. Names can retrieved using ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set currently based 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). data set internally used : not_intrinsic_resistant() (antimicrobial selector) mo_is_intrinsic_resistant()","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 271,905 × 2 #> mo ab #> #> 1 B_GRAMP ATM #> 2 B_GRAMP COL #> 3 B_GRAMP NAL #> 4 B_GRAMP PLB #> 5 B_GRAMP TEM #> 6 B_ANAER-POS ATM #> 7 B_ANAER-POS COL #> 8 B_ANAER-POS NAL #> 9 B_ANAER-POS PLB #> 10 B_ANAER-POS TEM #> # ℹ 271,895 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\", \"html\")) italicize_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))"},{"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\", \"html\" \"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 html. using 'ansi', ANSI colours added using \\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 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed 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\" #> [4] \"status\" \"kingdom\" \"phylum\" #> [7] \"class\" \"order\" \"family\" #> [10] \"genus\" \"species\" \"subspecies\" #> [13] \"rank\" \"ref\" \"oxygen_tolerance\" #> [16] \"source\" \"lpsn\" \"lpsn_parent\" #> [19] \"lpsn_renamed_to\" \"mycobank\" \"mycobank_parent\" #> [22] \"mycobank_renamed_to\" \"gbif\" \"gbif_parent\" #> [25] \"gbif_renamed_to\" \"prevalence\" \"snomed\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% left_join_microorganisms() %>% colnames() } #> Joining, by = \"mo\" #> [1] \"date\" \"patient\" \"age\" #> [4] \"gender\" \"ward\" \"mo\" #> [7] \"PEN\" \"OXA\" \"FLC\" #> [10] \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" #> [16] \"CXM\" \"FOX\" \"CTX\" #> [19] \"CAZ\" \"CRO\" \"GEN\" #> [22] \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"NIT\" #> [28] \"FOS\" \"LNZ\" \"CIP\" #> [31] \"MFX\" \"VAN\" \"TEC\" #> [34] \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" #> [40] \"IPM\" \"MEM\" \"MTR\" #> [43] \"CHL\" \"COL\" \"MUP\" #> [46] \"RIF\" \"fullname\" \"status\" #> [49] \"kingdom\" \"phylum\" \"class\" #> [52] \"order\" \"family\" \"genus\" #> [55] \"species\" \"subspecies\" \"rank\" #> [58] \"ref\" \"oxygen_tolerance\" \"source\" #> [61] \"lpsn\" \"lpsn_parent\" \"lpsn_renamed_to\" #> [64] \"mycobank\" \"mycobank_parent\" \"mycobank_renamed_to\" #> [67] \"gbif\" \"gbif_parent\" \"gbif_renamed_to\" #> [70] \"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 antimicrobials 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 antimicrobials 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] 1383 # }"},{"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) # Default S3 method kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for class 'matrix' kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for class '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] 3.032783 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.05102003"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE) x %like% pattern x %unlike% pattern x %like_case% pattern x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE) a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE a[1] %like% b #> [1] TRUE FALSE FALSE a %like% b[1] #> [1] TRUE FALSE FALSE # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC , 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 , … if (require(\"dplyr\")) { example_isolates %>% filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC , 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 , … # }"},{"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, esbl = NA, carbapenemase = NA, mecA = NA, mecC = NA, vanA = NA, vanB = NA, 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, verbose = FALSE, ...) mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, verbose = 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 antimicrobials 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(). esbl logical values, column name containing logical values, indicating presence ESBL gene (production proteins) carbapenemase logical values, column name containing logical values, indicating presence carbapenemase gene (production proteins) mecA logical values, column name containing logical values, indicating presence mecA gene (production proteins) mecC logical values, column name containing logical values, indicating presence mecC gene (production proteins) vanA logical values, column name containing logical values, indicating presence vanA gene (production proteins) vanB logical values, column name containing logical values, indicating presence vanB gene (production proteins) 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 antimicrobial 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 Antimicrobials . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (doi:10.1111/j.1469-0691.2011.03570.x ) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Samenwerkingverband Richtlijnen Infectiepreventie (SRI) (2024) \"Bijzonder Resistente Micro-Organismen (BRMO)\" (link) Also: guideline = \"BRMO 2017\" former Dutch national guideline - Werkgroep Infectiepreventie (WIP), RIVM, last revision 2017: \"Bijzonder Resistente Micro-Organismen (BRMO)\" Please suggest implement 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":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antimicrobials 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 antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), bleomycin (BLM, L01DC01), brodimoprim (BDP, J01EA02), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), chlortetracycline (CTE, J01AA03), ciprofloxacin (CIP, J01MA02), ciprofloxacin/metronidazole (CIM, J01RA10), ciprofloxacin/ornidazole (CIO, J01RA12), ciprofloxacin/tinidazole (CIT, J01RA11), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), clomocycline (CLM1, J01AA11), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), demeclocycline (DEM, J01AA01), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), eravacycline (ERV, J01AA13), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levofloxacin/ornidazole (LEO, J01RA05), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), lymecycline (LYM, J01AA04), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metacycline (MTC, J01AA05), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), nemonoxacin (NEM, J01MB08), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), nitroxoline (NTR, J01XX07), norfloxacin (, J01MA06), norfloxacin/metronidazole (NME, J01RA14), norfloxacin/tinidazole (NTI, J01RA13), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), ofloxacin/ornidazole (OOR, J01RA09), oleandomycin (OLE, J01FA05), omadacycline (OMC, J01AA15), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), oxytetracycline (OXY, J01AA06), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), penimepicycline (PNM1, J01AA10), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), rolitetracycline (RLT, J01AA09), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sarecycline (SRC, J01AA14), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/tetroxoprim (SLT, J01EE06), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") str(out) #> Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #> Negative Positive, unconfirmed Positive #> 1994 0 6 out <- mdro(example_isolates, guideline = custom_mdro_guideline( AMX == \"R\" ~ \"Custom MDRO 1\", VAN == \"R\" ~ \"Custom MDRO 2\" ) ) table(out) #> out #> Negative Custom MDRO 1 Custom MDRO 2 #> 870 804 326 # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% mdro() %>% table() # no need to define `x` when used inside dplyr verbs: example_isolates %>% mutate(MDRO = mdro()) %>% pull(MDRO) %>% table() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) #> 1617 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 class 'sir' mean_amr_distance(x, ..., combine_SI = TRUE) # S3 method for class '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 antimicrobial selectors combine_SI logical indicate whether values S, SDD, must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #> [1] S S I I I R R S I S mean_amr_distance(sir) #> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 1.8973666 #> [7] 1.8973666 -0.4743416 -0.4743416 -0.4743416 mic <- random_mic(10) mic #> Class 'mic' #> [1] >=128 0.125 0.01 64 64 8 0.002 1 >=128 #> [10] <=0.001 mean_amr_distance(mic) #> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587 #> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587 #> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641 disk <- random_disk(10) disk #> Class 'disk' #> [1] 39 35 46 27 13 50 30 49 41 20 mean_amr_distance(disk) #> [1] 0.3216338 0.0000000 0.8844928 -0.6432675 -1.7689857 1.2061266 #> [7] -0.4020422 1.1257182 0.4824506 -1.2061266 y <- data.frame( id = LETTERS[1:10], amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"), cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"), gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"), tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #> id amox cipr gent tobr #> 1 A R 23 >=4 1 #> 2 B I 26 <=1 0.5 #> 3 C S 20 >=4 >=16 #> 4 D R 27 >=4 0.5 #> 5 E S 31 >=4 2 #> 6 F I 27 >=4 >=16 #> 7 G I 31 <=1 8 #> 8 H R 30 >=4 4 #> 9 I S 27 >=4 4 #> 10 J I 20 >=4 0.5 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #> and \"tobr\" #> [1] 0.12237407 -0.92897174 -0.07982527 0.24282355 0.22020411 0.34940813 #> [7] -0.12306945 0.80126097 0.09975463 -0.70395902 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 S 20 >=4 >=16 -0.07982527 0.00000000 #> 2 G I 31 <=1 8 -0.12306945 0.04324419 #> 3 I S 27 >=4 4 0.09975463 0.17957990 #> 4 A R 23 >=4 1 0.12237407 0.20219934 #> 5 E S 31 >=4 2 0.22020411 0.30002938 #> 6 D R 27 >=4 0.5 0.24282355 0.32264882 #> 7 F I 27 >=4 >=16 0.34940813 0.42923340 #> 8 J I 20 >=4 0.5 -0.70395902 0.62413375 #> 9 B I 26 <=1 0.5 -0.92897174 0.84914647 #> 10 H R 30 >=4 4 0.80126097 0.88108624 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 #> #> 1 B_ENTRC_AVIM S S NA 0 #> 2 B_ENTRC_AVIM S S NA 0 #> 3 B_ENTRC_CSSL NA S NA NA #> 4 B_ENTRC_FACM S S NA -2.66 #> 5 B_ENTRC_FACM S R R -0.423 #> 6 B_ENTRC_FACM S R R -0.423 #> 7 B_ENTRC_FACM NA R R 0.224 #> 8 B_ENTRC_FACM NA R R 0.224 #> 9 B_ENTRC_FACM NA R R 0.224 #> 10 B_ENTRC_FACM NA R R 0.224 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 971 observations 2 variables: code Commonly used code microorganism. unique identifier. mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,971 × 2 #> code mo #> #> 1 1011 B_GRAMP #> 2 1012 B_GRAMP #> 3 1013 B_GRAMN #> 4 1014 B_GRAMN #> 5 1015 F_YEAST #> 6 103 B_ESCHR_COLI #> 7 104 B_SLMNL_ENTR_ENTR #> 8 1100 B_STRPT #> 9 1101 B_STRPT_VIRI #> 10 1102 B_STRPT_HAEM #> # ℹ 4,961 more rows # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #> code mo #> #> 1 ECO B_ESCHR_COLI # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Escherichia\" #> #> $species #> [1] \"coli\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #> #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #> #> $ref #> [1] \"Castellani et al., 1919\" #> #> $snomed #> [1] \"1095001000112106\" \"715307006\" \"737528008\" \"416989002\" #> [5] \"116397003\" \"414097009\" \"414098004\" \"414099007\" #> [9] \"414100004\" \"116395006\" \"735270003\" \"116396007\" #> [13] \"83285000\" \"116394005\" \"112283007\" \"710886005\" #> [17] \"710887001\" \"710888006\" \"710889003\" \"414132004\" #> [21] \"721892009\" \"416812001\" \"416740004\" \"417216001\" #> [25] \"457541006\" \"710253004\" \"416530004\" \"417189006\" #> [29] \"409800005\" \"713925008\" \"444771000124108\" \"838549008\" #> #> $lpsn #> [1] \"776057\" #> #> $mycobank #> [1] NA #> #> $gbif #> [1] \"11286021\" #> #> $group_members #> character(0) #> # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 534 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 534 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 534 × 4 #> mo_group mo mo_group_name mo_name #> #> 1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #> 2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #> 3 B_ACNTB_BMNN-C B_ACNTB_LCTC Acinetobacter baumannii complex Acinetobacter di… #> 4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #> 5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #> 6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #> 7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides frag… #> 8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovat… #> 9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulg… #> # ℹ 524 more rows # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #> mo_group mo mo_group_name mo_name #> #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulgatus #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: June 24th, 2024) six kingdoms. data set backbone AMR package. MO codes can looked using .mo() microorganism properties can looked using mo_* functions. data set carefully crafted, yet made 100% reproducible public authoritative taxonomic sources (using script), namely: List Prokaryotic names Standing Nomenclature (LPSN) bacteria, MycoBank fungi, Global Biodiversity Information Facility (GBIF) others taxons.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 78 679 Taxonomic Records of 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"tibble 78 679 observations 26 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. status Status taxon, either \"accepted\", \"validly published\", \"synonym\", \"unknown\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism. Note fungi, phylum equal taxonomic division. Also, fungi, subkingdom subdivision left since occur bacterial taxonomy. 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. oxygen_tolerance Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 68.3% ~39 000 bacteria data set contain oxygen tolerance. source Either \"GBIF\", \"LPSN\", \"Manually added\", \"MycoBank\", \"manually added\" (see Source) 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. unique identifier, though available ~33 000 records. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon mycobank Identifier ('MycoBank #') MycoBank. unique identifier, though available ~18 000 records. mycobank_parent MycoBank identifier parent taxon mycobank_renamed_to MycoBank identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF). unique identifier, though available ~49 000 records. gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon prevalence Prevalence microorganism based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version July 16th, 2024 (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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Taxonomic entries imported order importance: List Prokaryotic names Standing Nomenclature (LPSN): 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 June 24th, 2024. MycoBank: Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. Global Biodiversity Information Facility (GBIF): GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Furthermore, sources used additional details: BacDive: Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Systematized Nomenclature Medicine - Clinical Terms (SNOMED-CT): Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed https://www.cdc.gov/phin/php/phinvads/ July 16th, 2024. 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Please note entries based LPSN, MycoBank, 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"Included taxonomic data LPSN, MycoBank, GBIF : ~39 000 (sub)species kingdoms Archaea Bacteria ~28 000 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). ~8 100 (sub)species kingdom Protozoa ~1 600 (sub)species 39 relevant genera kingdom Animalia (Strongyloides Taenia) ~22 000 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 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 37 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 1 entry Blastocystis (B. hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg bacteria) syntax used transform original data cleansed R format, can found .","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 78,679 × 26 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_GRAMN (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 2 B_GRAMP (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 3 B_ANAER-NEG (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 4 B_ANAER-POS (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 5 B_ANAER (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 6 F_FUNGUS (unknown … unkno… Fungi (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 7 UNKNOWN (unknown … unkno… (unkno… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 8 P_PROTOZOAN (unknown … unkno… Protoz… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 9 F_YEAST (unknown … unkno… Fungi (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 10 F_AABRN Aabaarnia unkno… Fungi Ascom… Leca… Ostr… \"\" Aaba… \"\" #> # ℹ 78,669 more rows #> # ℹ 16 more variables: subspecies , rank , ref , #> # oxygen_tolerance , source , lpsn , lpsn_parent , #> # lpsn_renamed_to , mycobank , mycobank_parent , #> # mycobank_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed "},{"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 developed 2018 subsequently 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, optimalisations algorithm made.","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 : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files GitHub repository.","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. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), 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 0.3809524"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_fullname(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_shortname(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_subspecies(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_species(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_genus(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_family(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_order(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_class(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_phylum(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_kingdom(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_domain(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_type(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_status(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_pathogenicity(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_gramstain(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_gram_negative(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_gram_positive(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_yeast(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_oxygen_tolerance(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_is_anaerobic(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_snomed(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_ref(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_authors(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_year(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_lpsn(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_mycobank(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_group_members(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_info(x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_url(x, open = FALSE, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...) mo_property(x, property = \"fullname\", language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_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() logical case mo_is_anaerobic(), mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), mo_is_yeast() named character case mo_synonyms() 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 renaming) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (-per-session warning name outdated) 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 yeasts reproduce asexually process called budding, new cell develops small protrusion parent cell. Taxonomically, members phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) Pichiomycetes. True yeasts quite specifically refers yeasts underlying order Saccharomycetales (Saccharomyces cerevisiae). Thus, microorganisms member taxonomic class Saccharomycetes Pichiomycetes, 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 Expected Resistant Phenotypes' v1.2 (2023). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antimicrobials). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. MycoBank URL used fungi wherever available , LPSN URL bacteria wherever available, GBIF link otherwise. SNOMED codes (mo_snomed()) last updated July 16th, 2024. 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 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 June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ 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":"download-our-reference-data","dir":"Reference","previous_headings":"","what":"Download Our Reference Data","title":"Get Properties of a Microorganism — mo_property","text":"reference data sets AMR package - including information microorganisms, antimicrobials, clinical breakpoints - freely available download multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. maximum compatibility, also provide machine-readable, tab-separated plain text files suitable use software, including laboratory information systems. Visit website direct download links, explore actual files 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 mo_group_members(c( \"Streptococcus group A\", \"Streptococcus group C\", \"Streptococcus group G\", \"Streptococcus group L\" )) #> $`Streptococcus Group A` #> [1] \"Streptococcus pyogenes\" #> #> $`Streptococcus Group C` #> [1] \"Streptococcus dysgalactiae\" #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\" #> [4] \"Streptococcus equi\" #> [5] \"Streptococcus equi equi\" #> [6] \"Streptococcus equi ruminatorum\" #> [7] \"Streptococcus equi zooepidemicus\" #> #> $`Streptococcus Group G` #> [1] \"Streptococcus canis\" #> [2] \"Streptococcus dysgalactiae\" #> [3] \"Streptococcus dysgalactiae dysgalactiae\" #> [4] \"Streptococcus dysgalactiae equisimilis\" #> #> $`Streptococcus Group L` #> [1] \"Streptococcus dysgalactiae\" #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\" #> # 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_synonyms(\"Klebsiella aerogenes\") #> Hormaeche et al., 1960 Bascomb et al., 1971 #> \"Enterobacter aerogenes\" \"Klebsiella mobilis\" mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_mycobank(\"Candida albicans\") #> [1] \"256187\" mo_mycobank(\"Candida krusei\") #> [1] \"337013\" mo_mycobank(\"Candida krusei\", keep_synonyms = TRUE) #> Warning: Function as.mo() returned one old taxonomic name. Use as.mo(..., #> keep_synonyms = FALSE) to clean the input to currently accepted taxonomic #> names, or set the R option AMR_keep_synonyms to FALSE. This warning #> will be shown once per session. #> [1] \"268707\" # 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 #> #> 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: 19 × 2 #> `mo_genus()` n #> #> 1 Escherichia 467 #> 2 Klebsiella 77 #> 3 Proteus 39 #> 4 Pseudomonas 30 #> 5 Serratia 25 #> 6 Enterobacter 23 #> 7 Citrobacter 11 #> 8 Haemophilus 9 #> 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 Leuconostoc 1 #> 19 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\" #> #> $rank #> [1] \"species\" #> #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Klebsiella\" #> #> $species #> [1] \"pneumoniae\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #> #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #> #> $ref #> [1] \"Trevisan, 1887\" #> #> $snomed #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" #> #> $lpsn #> [1] \"777151\" #> #> $mycobank #> [1] NA #> #> $gbif #> [1] \"3221874\" #> #> $group_members #> character(0) #> # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(path, destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\")) get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$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 \"/Users//Documents/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(\"/Users/me/Documents/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 Helpers for AMR Data Analysis — plot","title":"Plotting Helpers for AMR Data Analysis — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_y_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, ...) scale_x_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) scale_colour_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) scale_fill_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\", \"EUCAST\") == \"EUCAST\", ...) # S3 method for class 'mic' plot(x, mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), main = deparse(substitute(x)), ylab = translate_AMR(\"Frequency\", language = language), xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class 'mic' autoplot(object, mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\", language = language), xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 = getOption(\"AMR_guideline\", \"EUCAST\"), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 = getOption(\"AMR_guideline\", \"EUCAST\"), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"), language = get_AMR_locale(), expand = TRUE, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...) # S3 method for class '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 class '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(), ...) facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL) scale_y_percent(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = c(0, NA)) scale_sir_colours(..., aesthetics, colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\")) 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/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting Helpers for AMR Data Analysis — plot","text":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values (using rescale_mic()), e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). Note: rescales values filter - use ggplot2 limits argument separately exclude values plot. ... arguments passed methods 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 prevent translation. eucast_I logical indicate whether '' must interpreted \"Susceptible, increased exposure\". TRUE default AMR interpretation guideline set EUCAST (default). FALSE, interpreted \"Intermediate\". 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 expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable nrow (using facet) number rows breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\" position position adjustment bars, either \"fill\", \"stack\" \"dodge\" translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() 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, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE datalabels.size size datalabels datalabels.colour colour datalabels","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting Helpers for AMR Data Analysis — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-mic-functions","dir":"Reference","previous_headings":"","what":"The scale_*_mic() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_mic(), scale_y_mic(), scale_colour_mic(), scale_fill_mic() functions allow plot mic class (MIC values) continuous, logarithmic scale. also allow rescale MIC range 'inside' 'outside' range required, retain signs MIC values desired. Missing intermediate log2 levels plotted .","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-sir-functions","dir":"Reference","previous_headings":"","what":"The scale_*_sir() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_sir(), scale_colour_sir(), scale_fill_sir() functions allow plot sir class right order (S < SDD < < R < NI). default, translate S//R values interpretative text (\"Susceptible\", \"Resistant\", etc.) 20 supported languages (use language = NULL keep S//R). Also, except scale_x_sir(), set colour-blind friendly colours colour fill aesthetics.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"additional-ggplot-functions","dir":"Reference","previous_headings":"","what":"Additional ggplot2 Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"package contains functions extend ggplot2 package, help visualising AMR data results. functions internally used ggplot_sir() . 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() allows set colours aesthetic, even shape linetype. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage number isolates, using ggplot2::geom_text(). interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, default guideline EUCAST 2024, unless package option AMR_guideline set. See .sir() information.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting Helpers for AMR Data Analysis — 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)) # \\donttest{ # Plotting using ggplot2's autoplot() for MIC, disk, and SIR ----------- if (require(\"ggplot2\")) { autoplot(some_mic_values) } if (require(\"ggplot2\")) { # when providing the microorganism and antibiotic, colours will show interpretations: autoplot(some_mic_values, mo = \"Escherichia coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { # support for 20 languages, various guidelines, and many options autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", guideline = \"CLSI 2024\", language = \"no\", title = \"Disk diffusion from the North\" ) } # Plotting using scale_x_mic() ----------------------------------------- if (require(\"ggplot2\")) { mic_plot <- ggplot( data.frame( mics = as.mic(c(0.25, \"<=4\", 4, 8, 32, \">=32\")), counts = c(1, 1, 2, 2, 3, 3) ), aes(mics, counts) ) + geom_col() mic_plot + labs(title = \"without scale_x_mic()\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic() + labs(title = \"with scale_x_mic()\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(keep_operators = \"all\") + labs(title = \"with scale_x_mic() keeping all operators\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(mic_range = c(1, 16)) + labs(title = \"with scale_x_mic() using a manual 'within' range\") } if (require(\"ggplot2\")) { mic_plot + scale_x_mic(mic_range = c(0.032, 256)) + labs(title = \"with scale_x_mic() using a manual 'outside' range\") } # Plotting using scale_y_mic() ----------------------------------------- some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) if (require(\"ggplot2\")) { ggplot( data.frame( mic = some_mic_values, group = some_groups ), aes(group, mic) ) + geom_boxplot() + geom_violin(linetype = 2, colour = \"grey\", fill = NA) + scale_y_mic() } if (require(\"ggplot2\")) { ggplot( data.frame( mic = some_mic_values, group = some_groups ), aes(group, mic) ) + geom_boxplot() + geom_violin(linetype = 2, colour = \"grey\", fill = NA) + scale_y_mic(mic_range = c(NA, 0.25)) } # Plotting using scale_x_sir() ----------------------------------------- if (require(\"ggplot2\")) { ggplot( data.frame( x = c(\"I\", \"R\", \"S\"), y = c(45, 323, 573) ), aes(x, y) ) + geom_col() + scale_x_sir() } # Plotting using scale_y_mic() and scale_colour_sir() ------------------ if (require(\"ggplot2\")) { plain <- ggplot( data.frame( mic = some_mic_values, group = some_groups, sir = as.sir(some_mic_values, mo = \"E. coli\", ab = \"cipro\" ) ), aes(x = group, y = mic, colour = sir) ) + theme_minimal() + geom_boxplot(fill = NA, colour = \"grey\") + geom_jitter(width = 0.25) plain } #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #> NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. if (require(\"ggplot2\")) { # and now with our MIC and SIR scale functions: plain + scale_y_mic() + scale_colour_sir() } if (require(\"ggplot2\")) { plain + scale_y_mic(mic_range = c(0.005, 32), name = \"Our MICs!\") + scale_colour_sir( language = \"pt\", name = \"Support in 20 languages\" ) } # } # Plotting using base R's plot() --------------------------------------- plot(some_mic_values) # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\") plot(some_disk_values) plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"nl\") plot(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 antimicrobials, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"SDD\", \"\", \"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 antimicrobials 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, SDD, must merged one, output consists S+SDD+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":"automated comprehensive analysis, consider using antibiogram() wisca(), streamline many aspects susceptibility reporting , importantly, also support WISCA. functions described offer hands-, manual approach greater customisation. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Since AMR v3.0, proportion_SI() proportion_I() include dose-dependent susceptibility ('SDD'). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. functions meant count isolates, calculate proportion resistance/susceptibility. Use count_*() functions count isolates. function susceptibility() essentially equal count_susceptible()/count_all(). Low counts can influence outcome - proportion_*() functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antimicrobials/variables test . See example two antimicrobials, 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 considered considered considered considered susceptible tested susceptible tested -------- -------- ----------- ---------- ----------- ---------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- 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 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , 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 , … # 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) #> Note that proportion_SI() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> Note that proportion_I() will also include dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> Note that proportion_IR() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [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 #> #> 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 #> #> 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 antimicrobial 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 #> #> 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 #> #> 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 2024 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] 0.025 2 0.25 8 4 >=256 64 0.125 1 #> [10] 16 2 0.025 8 32 <=0.001 4 2 128 #> [19] 0.125 4 0.01 >=256 0.25 0.25 1 random_disk(25) #> Class 'disk' #> [1] 48 8 6 21 6 34 30 30 42 26 10 41 26 22 46 19 16 38 34 12 50 42 45 42 44 random_sir(25) #> Class 'sir' #> [1] I R R S R I I R R R R R R I R S S S I I S S R R R # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #> [1] 1 16 0.025 0.001 4 4 0.125 0.002 2 0.25 0.25 64 #> [13] 0.5 0.005 256 8 0.125 16 0.005 0.001 64 0.5 8 64 #> [25] 0.001 random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #> [1] 4 8 2 >=16 <=0.25 <=0.25 >=16 1 1 0.5 #> [11] 8 <=0.25 2 0.5 2 <=0.25 1 4 1 <=0.25 #> [21] >=16 4 2 1 <=0.25 random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #> [1] 2 8 0.0625 0.0625 0.0625 4 <=0.025 0.125 8 #> [10] 8 0.25 0.25 0.125 <=0.025 0.25 <=0.025 0.0625 2 #> [19] <=0.025 8 4 4 <=0.025 8 8 random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #> [1] 8 24 46 50 48 11 23 30 47 33 21 43 17 16 48 16 39 38 16 49 33 47 14 18 42 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #> [1] 12 13 15 14 12 16 15 16 13 13 12 13 12 11 16 17 11 17 11 14 12 14 11 12 12 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #> [1] 20 18 24 24 17 18 26 24 26 24 20 20 26 24 19 15 17 17 24 20 17 22 22 19 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 class '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 class '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\" ) #> Warning: The resistance_predict() function is deprecated and will be removed in a #> future version, see ?AMR-deprecated. Use the tidymodels framework #> instead, for which we have written a basic and short introduction on our #> website: https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html #> This warning will be shown once per session. plot(x) # \\donttest{ if (require(\"ggplot2\")) { ggplot_sir_predict(x) } # using dplyr: if (require(\"dplyr\")) { x <- example_isolates %>% filter_first_isolate() %>% filter(mo_genus(mo) == \"Staphylococcus\") %>% resistance_predict(\"PEN\", model = \"binomial\") print(plot(x)) # get the model from the object mymodel <- attributes(x)$model summary(mymodel) } #> NULL #> #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 35.76101 72.29172 0.495 0.621 #> year -0.01720 0.03603 -0.477 0.633 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 5.3681 on 11 degrees of freedom #> Residual deviance: 5.1408 on 10 degrees of freedom #> AIC: 50.271 #> #> 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) # Default S3 method skewness(x, na.rm = FALSE) # S3 method for class 'matrix' skewness(x, na.rm = FALSE) # S3 method for class '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] -0.01927257"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Top n Microorganisms — top_n_microorganisms","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function filters data set include top n microorganisms based specified property, taxonomic family genus. example, can filter data set top 3 species, species top 5 genera, top 3 species top 5 genera.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"top_n_microorganisms(x, n, property = \"fullname\", n_for_each = NULL, col_mo = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"x data frame containing microbial data n integer specifying maximum number unique values property include output property character string indicating microorganism property use filtering. Must one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". NULL, raw values col_mo used without transformation. n_for_each optional integer specifying maximum number rows retain value selected property. NULL, rows within top n groups included. col_mo character string indicating column x contains microorganism names codes. Defaults first column class mo. Values coerced using .mo(). ... Additional arguments passed mo_property() property NULL.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function useful preprocessing data creating antibiograms analyses require focused subsets microbial data. example, can filter data set include isolates top 10 species.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"# filter to the top 3 species: top_n_microorganisms(example_isolates, n = 3 ) #> # A tibble: 1,015 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 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 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 4 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 5 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 7 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> 8 2002-02-14 067927 45 F ICU B_STPHY_CONS R NA R NA #> 9 2002-02-14 067927 45 F ICU B_STPHY_CONS S NA S NA #> 10 2002-02-21 A56499 64 M Clinical B_STPHY_CONS S NA S NA #> # ℹ 1,005 more rows #> # ℹ 36 more variables: 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 , … # filter to any species in the top 5 genera: top_n_microorganisms(example_isolates, n = 5, property = \"genus\" ) #> # A tibble: 1,742 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,732 more rows #> # ℹ 36 more variables: 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 , … # filter to the top 3 species in each of the top 5 genera: top_n_microorganisms(example_isolates, n = 5, property = \"genus\", n_for_each = 3 ) #> # A tibble: 1,497 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinical B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 8 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 10 2003-09-28 1B0933 80 M Clinical B_ENTRC NA NA NA NA #> # ℹ 1,487 more rows #> # ℹ 36 more variables: 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 , …"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale() set_AMR_locale(language) reset_AMR_locale() translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like : save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\") # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" # setting another language set_AMR_locale(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\" # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\" # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> [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-2119230","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1.9230","title":"AMR 2.1.1.9230","text":"(beta version eventually become v3.0. ’re happy reach new major milestone soon, new One Health support! Install beta using instructions .)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)","title":"AMR 2.1.1.9230","text":"package now supports tools AMR data analysis clinical settings, also veterinary environmental microbiology. made possible collaboration University Prince Edward Island’s Atlantic Veterinary College, Canada. celebrate great improvement package, also updated package logo reflect change.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 2.1.1.9230","text":"Dataset antibiotics renamed antimicrobials data set contains just antibiotics. Using antibiotics still work, now returns warning. Removed functions references used deprecated rsi class, replaced sir equivalents two years ago. Function resistance_predict() now deprecated removed future version. Use tidymodels framework instead, wrote basic introduction.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9230","text":"Function .sir() now extensive support veterinary breakpoints CLSI. Use breakpoint_type = \"animal\" set host argument variable contains animal species names. clinical_breakpoints data set contains breakpoints, can downloaded download page. (new) antimicrobials data set contains veterinary antibiotics, pradofloxacin enrofloxacin. WHOCC codes veterinary use added well. ab_atc() now supports ATC codes veterinary antibiotics (start “Q”) ab_url() now supports retrieving WHOCC url ATCvet pages antibiogram() function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), powerful Bayesian method estimating regimen coverage probabilities using pathogen incidence antimicrobial susceptibility data. WISCA offers improved precision syndrome-specific treatment, even datasets sparse data. dedicated wisca() function also available easy usage. MycoBank now integrated primary taxonomic source fungi. microorganisms data set enriched new columns (mycobank, mycobank_parent, mycobank_renamed_to) provide detailed information fungal species. remarkable addition 20,000 new fungal records New function mo_mycobank() retrieve MycoBank record number, analogous existing functions mo_lpsn() mo_gbif(). .mo() function mo_*() functions now include only_fungi argument, allowing users restrict results solely fungal species. ensures fungi prioritised bacteria microorganism identification. can also set globally new AMR_only_fungi option. Also updated kingdoms, welcoming total 2,149 new records 2023 927 2024. EUCAST 2024 CLSI 2024 now supported, adding 4,000 new clinical breakpoints clinical_breakpoints data set usage .sir(). EUCAST 2024 now new default guideline MIC disk diffusion interpretations. Added Expected Resistant Phenotypes EUCAST (v1.2). default rules eucast_rules() now: c(\"breakpoints\", \"expected_phenotypes\"). Updated intrinsic_resistant data set, now based EUCAST Expected Resistant Phenotypes v1.2 .sir() now brings additional factor levels: “NI” non-interpretable “SDD” susceptible dose-dependent. Currently, clinical_breakpoints data set contains 24 breakpoints can return value “SDD” instead “”. EUCAST interpretive rules (using eucast_rules()) now available EUCAST 12 (2022), 13 (2023), 14 (2024). New function group scale_*_mic(), namely: scale_x_mic(), scale_y_mic(), scale_colour_mic() scale_fill_mic(). allow easy plotting MIC values. allow manual range definition plotting missing intermediate log2 levels. New function group scale_*_sir(), namely: scale_x_sir(), scale_colour_sir() scale_fill_sir(). allow plot sir class, translates system language default. also set colourblind-safe colours plots. New function rescale_mic(), allows users rescale MIC values manually set range. powerhouse behind scale_*_mic() functions, can used independently , instance, compare equality MIC distributions rescaling range first. using R heavy lifting, ‘AMR’ Python Package developed run AMR R package natively Python. Python package always version number R package, built automatically every code change. antimicrobial selectors (aminoglycosides() betalactams()) now supported tidymodels packages recipe parsnip. See info tutorial using AMR functions predictive modelling. New function top_n_microorganisms() filter data set top n taxonomic property, e.g., filter top 3 species, filter species top 5 genera, filter top 3 species top 5 genera New function mo_group_members() retrieve member microorganisms microorganism group. example, mo_group_members(\"Strep group C\") returns vector microorganisms belong group. New functions mic_p50() mic_p90() retrieve 50th 90th percentile MIC values.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.1.9230","text":"now possible use column names argument ab, mo, uti: .sir(..., ab = \"column1\", mo = \"column2\", uti = \"column3\"). greatly improves flexibility users. Users can now set criteria (using regular expressions) considered S, , R, SDD, NI. get quantitative values, .double() sir object return 1 S, 2 SDD/, 3 R (NI become NA). functions using sir classes (e.g., summary()) updated reflect change contain NI SDD. Combined MIC values (e.g., CLSI) now supported argument conserve_capped_values .sir() replaced capped_mic_handling, allows greater flexibility handling capped MIC values (<, <=, >, >=). four available options (\"standard\", \"strict\", \"relaxed\", \"inverse\") provide full control whether values interpreted conservatively ignored. Using conserve_capped_values now deprecated returns warning. Added argument info silence console messages Argument antibiotics renamed antimicrobials. Using antibiotics still work, now returns warning. Added argument formatting_type set 22 options formatting ‘cells’. defaults 18 non-WISCA 14 WISCA, changing output antibiograms cells info. reason, add_total_n now FALSE default since denominators added cells non-WISCA. WISCA, denominator useful anyway. ab_transform argument now defaults \"name\", displaying antibiotic column names instead codes ‘Antibiotic selectors’ now called ‘antimicrobial selectors’ since scope broader just antibiotics. documentation updated, ab_class() ab_selector() replaced amr_class() amr_selector(). old functions now deprecated removed future version. Added selectors isoxazolylpenicillins(), monobactams(), nitrofurans(), phenicols(), rifamycins(), sulfonamides() using antimicrobial selectors exclude non-treatable drugs (gentamicin-high using aminoglycosides()), function now always returns warning can included using only_treatable = FALSE Added new argument return_all selectors, defaults TRUE include match. FALSE, old behaviour, first hit unique antimicrobial returned. selectors can now run separate command retrieve vector possible antimicrobials selector can select selectors lincosamides() macrolides() overlap anymore - antibiotic now classified either Fixed selector fluoroquinolones(), now really selects second-generation quinolones (first-generation quinolones contain fluorine group) Added agents used screening, ID ending -S: benzylpenicillin screening test (PEN-S), beta-lactamase screening test (BLA-S), cefotaxime screening test (CTX-S), clindamycin inducible screening test (CLI-S), nalidixic acid screening test (NAL-S), norfloxacin screening test (-S), oxacillin screening test (OXA-S), pefloxacin screening test (PEF-S), tetracycline screening test (TCY-S). ID cefoxitin screening renamed FOX1 FOX-S, old code remains work. reason, antimicrobial selectors cephalosporins(), cephalosporins_3rd(), lincosamides(), isoxazolylpenicillins(), quinolones(), fluoroquinolones(), tetracyclines() now contain argument only_treatable = TRUE (similar antimicrobial selectors contain non-treatable drugs) Added amorolfine (AMO, D01AE16), now also part antifungals() selector Added efflux (EFF), allow mapping AMRFinderPlus Added tigemonam (TNM), monobactam Added bleomycin (BLM), glycopeptide Updated ATC codes, trade names, DDDs Added valid levels: 4096, 6 powers 0.0625, 5 powers 192 (192, 384, 576, 768, 960) Fixed bug .mic() failed translation scientifically formatted numbers Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\". argument also available new rescale_mic() scale_*_mic() functions. Comparisons MIC values now strict. example, >32 higher (never equal ) 32. Thus, .mic(\">32\") == .mic(32) now returns FALSE, .mic(\">32\") > .mic(32) now returns TRUE. Sorting MIC values (using sort()) fixed manner; <0.001 now gets sorted 0.001, >0.001 gets sorted 0.001. Intermediate log2 levels used MIC plotting now common values instead following strict dilution range eucast_rules() now argument overwrite (default: FALSE) indicate whether non-NA values overwritten Disks 0 5 mm now allowed, newly allowed range disk diffusion (.disk()) now 0 50 mm Updated italicise_taxonomy() support HTML output custom_eucast_rules() now supports multiple antibiotics antibiotic groups affected single rule mo_info() now contains extra element rank group_members (contents new mo_group_members() function) Updated ATC codes WHOCC Updated antibiotic DDDs WHOCC Fix using manual value mo_transform antibiogram() Fixed bug antibiogram() returns empty data set Fix mapping ‘high level’ antibiotics .ab() (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high) Improved overall algorithm .ab() better performance accuracy, including new function as_reset_session() remove earlier coercions. weight given genus species combinations cases subspecies miswritten, result correct genus species Genera World Health Organization’s () Priority Pathogen List now highest prevalence Fixed bug sir_confidence_interval() isolates available Updated prevalence calculation include genera World Health Organization’s () Priority Pathogen List Improved algorithm first_isolate() using phenotype-based method, prioritise records highest availability SIR values scale_y_percent() can now cope ranges outside 0-100% range Implemented new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024) Added arguments esbl, carbapenemase, mecA, mecC, vanA, vanB denote column names logical values indicating presence genes (production proteins) Added console colours support sir class Positron","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.1.1.9230","text":"Added Dr. Larisse Bolton Aislinn Cook contributors fantastic implementation WISCA mathematically solid way Added Matthew Saab, Dr. Jordan Stull, Prof. Javier Sanchez contributors tremendous input veterinary breakpoints interpretations Added Prof. Kat Holt, Dr. Jane Hawkey, Dr. Natacha Couto contributors many suggestions, ideas bugfixes Greatly improved vctrs integration, Tidyverse package working background many Tidyverse functions. users, means functions dplyr’s bind_rows(), rowwise() c_across() now supported e.g. columns class mic. Despite , AMR package still zero-dependent package, including dplyr vctrs. Greatly updated expanded documentation Stopped support SAS (.xpt) files, since file structure extremely inefficient requires disk space GitHub allows single commit.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"older-versions-2-1-1-9230","dir":"Changelog","previous_headings":"","what":"Older Versions","title":"AMR 2.1.1.9230","text":"changelog contains changes AMR v3.0 (March 2025) later. prior v2 versions, please see v2 archive. prior v1 versions, please see v1 archive.","code":""}] diff --git a/sitemap.xml b/sitemap.xml index e2056ab0e..733473fc3 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -10,7 +10,6 @@ https://msberends.github.io/AMR/articles/WHONET.html https://msberends.github.io/AMR/articles/datasets.html https://msberends.github.io/AMR/articles/index.html -https://msberends.github.io/AMR/articles/resistance_predict.html https://msberends.github.io/AMR/articles/welcome_to_AMR.html https://msberends.github.io/AMR/authors.html https://msberends.github.io/AMR/index.html