Bell_et_al_StrokePsychosis_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Association between stroke and psychosis across four nationally representative epidemiological studies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the analysis code for the above named paper with embedded results. This is a Jupyter notebook which can be re-run to reproduce the same results when accompanied by the same data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load R packages\n",
    "library(dplyr)\n",
    "library(foreign)\n",
    "library(jtools)\n",
    "library(table1)\n",
    "library(epiR)\n",
    "library(lme4)\n",
    "library(IRdisplay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(warn=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and recoding of national datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adult Psychiatric Morbidity Survey 2007 (England, United Kingdom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "#\n",
    "spssfile <- \"./Data/apms2007/apms07arch.sav\" \n",
    "apms_complete <- read.spss(spssfile, to.data.frame = TRUE, use.value.labels = FALSE)\n",
    "\n",
    "# Select variables\n",
    "#\n",
    "dataset_apms <- select(apms_complete, \n",
    "                  age = ResAge, sex = ResSex,\n",
    "                  paranoia = PSQ3a, # Felt that people were directly acting to harm you / your interests\n",
    "                  hearvoices = PSQ5a,  # Heard voices saying quite a few words of sentences\n",
    "                  thoughtpass = PSQ2a,  # Ever felt thoughts were directly interfered with by some outside force or person in past year\n",
    "                  stroke = HDoct9, # by medic\n",
    "                  highest_ed = EDQUAL5,\n",
    "                  sample_weight = wt_ints1)\n",
    "\n",
    "# Label dataset\n",
    "#\n",
    "dataset_apms$dataset_name <- 'apms'\n",
    "\n",
    "# Recode stroke variable\n",
    "#\n",
    "dataset_apms$stroke[dataset_apms$stroke==1] <- 1  # Yes\n",
    "dataset_apms$stroke[dataset_apms$stroke==2] <- 0  # No\n",
    "dataset_apms$stroke[is.na(dataset_apms$stroke)] <- 0  # No evidence\n",
    "dataset_apms$stroke <- factor(dataset_apms$stroke, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode psychotic symptom variables\n",
    "#\n",
    "dataset_apms$paranoia[dataset_apms$paranoia==3] <- 0  # unsure = no\n",
    "dataset_apms$paranoia[dataset_apms$paranoia==1] <- 1  # Yes\n",
    "dataset_apms$paranoia[dataset_apms$paranoia==2] <- 0  # No\n",
    "dataset_apms$paranoia[is.na(dataset_apms$paranoia)] <- 0  # No evidence\n",
    "\n",
    "dataset_apms$hearvoices[dataset_apms$hearvoices==3] <- 0    # unsure = no\n",
    "dataset_apms$hearvoices[dataset_apms$hearvoices==1] <- 1    # Yes\n",
    "dataset_apms$hearvoices[dataset_apms$hearvoices==2] <- 0    # No\n",
    "dataset_apms$hearvoices[is.na(dataset_apms$hearvoices)] <- 0  # No evidence\n",
    "\n",
    "dataset_apms$thoughtpass[dataset_apms$thoughtpass==3] <- 0  # unsure = no\n",
    "dataset_apms$thoughtpass[dataset_apms$thoughtpass==1] <- 1  # Yes\n",
    "dataset_apms$thoughtpass[dataset_apms$thoughtpass==2] <- 0  # No\n",
    "dataset_apms$thoughtpass[is.na(dataset_apms$thoughtpass)] <- 0  # No evidence\n",
    "\n",
    "# Calculate probable psychosis variable\n",
    "#\n",
    "dataset_apms$psychosis_tot <- rowSums(dataset_apms[,c(\"paranoia\", \"hearvoices\", \"thoughtpass\")], na.rm=TRUE)\n",
    "dataset_apms$prob_psychosis[dataset_apms$psychosis_tot >= 2] <- 1\n",
    "dataset_apms$prob_psychosis[dataset_apms$psychosis_tot <= 1] <- 0\n",
    "dataset_apms$psychosis_tot <- NULL\n",
    "\n",
    "# Set types for psychosis variables\n",
    "#\n",
    "dataset_apms$paranoia <- factor(dataset_apms$paranoia, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_apms$hearvoices <- factor(dataset_apms$hearvoices, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_apms$thoughtpass <- factor(dataset_apms$thoughtpass, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_apms$prob_psychosis <- factor(dataset_apms$prob_psychosis, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode and set variable type for highest level of education\n",
    "#\n",
    "dataset_apms <- dataset_apms %>%\n",
    "  mutate(highest_ed = recode(highest_ed,\n",
    "                              `1` = 4,  # College / uni\n",
    "                              `2` = 4,  # College / uni\n",
    "                              `3` = 3,  # Late teen high school\n",
    "                              `4` = 2,  # Mid teen high school\n",
    "                              `5` = 2,  # \"Foreign / other\" coded as Mid teen high school\n",
    "                              `6` = 1)) # No or primary education\n",
    "\n",
    "dataset_apms$highest_ed <- factor(dataset_apms$highest_ed,\n",
    "                                      levels = c(1, 2, 3, 4),\n",
    "                                      labels = c(\"No / primary education\", \"Mid-teen high school\", \"Late teen high school\", \"College / university\")) \n",
    "\n",
    "# Set variable type for sex\n",
    "#\n",
    "dataset_apms$sex <- factor(dataset_apms$sex, levels = c(1,2), labels = c(\" male\", \" female\")) \n",
    "\n",
    "# Give variables formatted variable names for table\n",
    "#\n",
    "label(dataset_apms$sex) <- \"Sex\"\n",
    "label(dataset_apms$age) <- \"Age\"\n",
    "label(dataset_apms$highest_ed) <- \"Highest level of education\"\n",
    "label(dataset_apms$stroke) <- \"Stroke\"\n",
    "label(dataset_apms$prob_psychosis) <- \"Prob.Psychosis\"\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(apms_complete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collaborative Psychiatric Epidemiology Surveys 2001-2003 (United States)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "#\n",
    "d1_filename <- \"./Data/ICPSR_20240/DS0001/20240-0001-Data.tsv\"\n",
    "d3_filename <- \"./Data/ICPSR_20240/DS0003/20240-0003-Data.tsv\"\n",
    "\n",
    "d1_df <- read.csv(d1_filename, sep=\"\\t\", header=T)\n",
    "d3_df <- read.csv(d3_filename, sep=\"\\t\", header=T)\n",
    "\n",
    "# Select variables\n",
    "#\n",
    "d1_select_df <- select(d1_df, \n",
    "                       caseid = CASEID, \n",
    "                       sex = V09036,\n",
    "                       age = V07306)\n",
    "\n",
    "d3_select_df <- select(d3_df, \n",
    "                       caseid = CASEID,\n",
    "                       stroke = C10H,  # by medic\n",
    "                       highest_ed = H13,\n",
    "                       paranoia = PS1F, # Unjust plot to harm you/have people follow-nobody believe\n",
    "                       hearvoices = PS1B,  # Ever hear voices others couldn't hear\n",
    "                       thoughtpass = PS1C) # Ever have mind control experience\n",
    "\n",
    "# Merge datasets \n",
    "#\n",
    "dataset_cpes <- merge(d1_select_df, d3_select_df, by=\"caseid\")\n",
    "\n",
    "# Label dataset\n",
    "#\n",
    "dataset_cpes$dataset_name <- 'cpes'\n",
    "\n",
    "# Recode stroke variable\n",
    "#\n",
    "dataset_cpes$stroke[dataset_cpes$stroke == 1 ] <- 1\n",
    "dataset_cpes$stroke[dataset_cpes$stroke == 5 ] <- 0\n",
    "dataset_cpes$stroke[dataset_cpes$stroke == -8 ] <- 0\n",
    "dataset_cpes$stroke[dataset_cpes$stroke == -9 ] <- 0\n",
    "dataset_cpes$stroke <- factor(dataset_cpes$stroke, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode psychotic symptom variables\n",
    "#\n",
    "# Note: NAs replaced by zeros because random forest imputation imputed all NAs as zeros.\n",
    "# See Bell_et_al_StrokePsychosis_Imputation.ipynb for code\n",
    "#\n",
    "dataset_cpes$paranoia[dataset_cpes$paranoia == 1 ] <- 1\n",
    "dataset_cpes$paranoia[dataset_cpes$paranoia == 5 ] <- 0\n",
    "dataset_cpes$paranoia[dataset_cpes$paranoia == -8 ] <- 0\n",
    "dataset_cpes$paranoia[dataset_cpes$paranoia == -9 ] <- 0\n",
    "\n",
    "dataset_cpes$hearvoices[dataset_cpes$hearvoices == 1 ] <- 1\n",
    "dataset_cpes$hearvoices[dataset_cpes$hearvoices == 5 ] <- 0\n",
    "dataset_cpes$hearvoices[dataset_cpes$hearvoices == -8 ] <- 0\n",
    "dataset_cpes$hearvoices[dataset_cpes$hearvoices == -9 ] <- 0\n",
    "\n",
    "dataset_cpes$thoughtpass[dataset_cpes$thoughtpass == 1 ] <- 1\n",
    "dataset_cpes$thoughtpass[dataset_cpes$thoughtpass == 5 ] <- 0\n",
    "dataset_cpes$thoughtpass[dataset_cpes$thoughtpass == -8 ] <- 0\n",
    "dataset_cpes$thoughtpass[dataset_cpes$thoughtpass == -9 ] <- 0\n",
    "\n",
    "# Calculate probable psychosis variable\n",
    "#\n",
    "dataset_cpes$psychosis_tot <- rowSums(dataset_cpes[,c(\"paranoia\", \"hearvoices\", \"thoughtpass\")], na.rm=TRUE)\n",
    "dataset_cpes$prob_psychosis[dataset_cpes$psychosis_tot >= 2] <- 1\n",
    "dataset_cpes$prob_psychosis[dataset_cpes$psychosis_tot <= 1] <- 0\n",
    "dataset_cpes$psychosis_tot <- NULL\n",
    "\n",
    "# Set types for psychosis variables\n",
    "#\n",
    "dataset_cpes$paranoia <- factor(dataset_cpes$paranoia, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_cpes$hearvoices <- factor(dataset_cpes$hearvoices, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_cpes$thoughtpass <- factor(dataset_cpes$thoughtpass, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_cpes$prob_psychosis <- factor(dataset_cpes$prob_psychosis, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode and set variable type for highest level of education\n",
    "#\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed <= 9] <- 1 # Basic\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==10] <- 2  # Mid teen high school\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==11] <- 2  # Mid teen high school\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==12] <- 3  # Late teen high school\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==13] <- 3  # Late teen high school \n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==14] <- 3  # Late teen high school \n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==15] <- 3  # Late teen high school \n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==16] <- 4  # College / uni\n",
    "dataset_cpes$highest_ed[dataset_cpes$highest_ed==17] <- 4  # College / uni\n",
    "\n",
    "dataset_cpes$highest_ed <- factor(dataset_cpes$highest_ed,\n",
    "                                  levels = c(1, 2, 3, 4),\n",
    "                                  labels = c(\"No / primary education\", \"Mid-teen high school\", \"Late teen high school\", \"College / university\")) \n",
    "\n",
    "# Set variable type for sex\n",
    "#\n",
    "dataset_cpes$sex <- factor(dataset_cpes$sex,\n",
    "                           levels = c(1,2),\n",
    "                           labels = c(\" male\", \" female\")) \n",
    "\n",
    "# Give variables formatted variable names for table\n",
    "#\n",
    "label(dataset_cpes$sex) <- \"Sex\"\n",
    "label(dataset_cpes$age) <- \"Age\"\n",
    "label(dataset_cpes$highest_ed) <- \"Highest level of education\"\n",
    "label(dataset_cpes$stroke) <- \"Stroke\"\n",
    "label(dataset_cpes$prob_psychosis) <- \"Prob.Psychosis\"\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(d1_df, d3_df, d1_select_df, d3_select_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### National Mental Health Survey 2015 (Colombia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "#\n",
    "personas_datafile = \"./Data/ensm2015/Personas.txt\"\n",
    "personas_df <- read.csv(personas_datafile, header = TRUE, sep='|', stringsAsFactors = FALSE, na.strings=c(\"NULL\",\"900\",\"950\"))\n",
    "\n",
    "adultos1_datafile = \"./Data/ensm2015/Adultos_I.txt\"\n",
    "adultos1_df <- read.csv(adultos1_datafile, header = TRUE, sep='|', stringsAsFactors = FALSE, na.strings=c(\"NULL\",\"900\",\"950\"))\n",
    "\n",
    "adultos2_datafile = \"./Data/ensm2015/Adultos_II.txt\"\n",
    "adultos2_df <- read.csv(adultos2_datafile, header = TRUE, sep='|', stringsAsFactors = FALSE, na.strings=c(\"NULL\",\"900\",\"950\"))\n",
    "\n",
    "# Merge datasets\n",
    "#\n",
    "merged_df <- merge(personas_df, adultos1_df, by=\"identificador_persona\")\n",
    "merged_df <- merge(merged_df, adultos2_df, by=\"identificador_persona\")\n",
    "\n",
    "# Select variables\n",
    "#\n",
    "dataset_ensm <- select(merged_df, \n",
    "                       identificador_persona,\n",
    "                       age = age.x,\n",
    "                       sex = sex.x,\n",
    "                       paranoia = m7_p21,  # ¿Siente que alguien ha tratado de herirlo en alguna forma?\n",
    "                       hearvoices = m7_p24,   # ¿Oye voces sin saber de dónde vienen o que otras personas no pueden oír?\n",
    "                       thoughtpass = m7_p23,  # ¿Ha notado interferencias o algo raro en su pensamiento?\n",
    "                       highest_ed = NivelEducativo.x,\n",
    "                       stroke = m5_p38)\n",
    "\n",
    "# Label dataset\n",
    "#\n",
    "dataset_ensm$dataset_name <- 'ensm'\n",
    "\n",
    "# Recode stroke variable\n",
    "#\n",
    "dataset_ensm$stroke[dataset_ensm$stroke==2] <- 0\n",
    "dataset_ensm$stroke <- factor(dataset_ensm$stroke, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode psychotic symptom variables\n",
    "#\n",
    "dataset_ensm$paranoia[dataset_ensm$paranoia==2] <- 0\n",
    "dataset_ensm$hearvoices[dataset_ensm$hearvoices==2] <- 0  \n",
    "dataset_ensm$thoughtpass[dataset_ensm$thoughtpass==2] <- 0\n",
    "\n",
    "# Calculate probable psychosis variable\n",
    "#\n",
    "dataset_ensm$psychosis_tot <- rowSums(dataset_ensm[,c(\"paranoia\", \"hearvoices\", \"thoughtpass\")], na.rm=TRUE)\n",
    "dataset_ensm$prob_psychosis[dataset_ensm$psychosis_tot >= 2] <- 1\n",
    "dataset_ensm$prob_psychosis[dataset_ensm$psychosis_tot <= 1] <- 0\n",
    "dataset_ensm$psychosis_tot <- NULL\n",
    "\n",
    "# Set types for psychosis variables\n",
    "#\n",
    "dataset_ensm$paranoia <- factor(dataset_ensm$paranoia, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensm$hearvoices <- factor(dataset_ensm$hearvoices, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensm$thoughtpass <- factor(dataset_ensm$thoughtpass, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensm$prob_psychosis <- factor(dataset_ensm$prob_psychosis, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode and set variable type for highest level of education\n",
    "#\n",
    "dataset_ensm$highest_ed[dataset_ensm$highest_ed == 1] <- 1 # Basic\n",
    "dataset_ensm$highest_ed[dataset_ensm$highest_ed == 2] <- 2 # Mid teen high school\n",
    "dataset_ensm$highest_ed[dataset_ensm$highest_ed == 3] <- 3 # Late teen high school\n",
    "dataset_ensm$highest_ed[dataset_ensm$highest_ed == 4] <- 4 # College / uni\n",
    "\n",
    "dataset_ensm$highest_ed <- factor(dataset_ensm$highest_ed,\n",
    "                                  levels = c(1, 2, 3, 4),\n",
    "                                  labels = c(\"No / primary education\", \"Mid-teen high school\", \"Late teen high school\", \"College / university\")) \n",
    "\n",
    "# Set variable type for sex\n",
    "#\n",
    "dataset_ensm$sex <- factor(dataset_ensm$sex, levels = c(1,2), labels = c(\" male\", \" female\")) \n",
    "\n",
    "# Give variables formatted variable names for table\n",
    "#\n",
    "label(dataset_ensm$sex) <- \"Sex\"\n",
    "label(dataset_ensm$age) <- \"Age\"\n",
    "label(dataset_ensm$highest_ed) <- \"Highest level of education\"\n",
    "label(dataset_ensm$stroke) <- \"Stroke\"\n",
    "label(dataset_ensm$prob_psychosis) <- \"Prob.Psychosis\"\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(personas_df, adultos1_df, adultos2_df, merged_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### National Health Survey 2016-2017 (Chile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "re-encoding from CP1252\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "#\n",
    "spssfile <- \"./Data/ensc_2016-2017/Base de datos Encuesta Nacional de Salud 2016-2017(ENS).Formulario 1_2_EX.MINSAL_EPI. (2).sav\"\n",
    "ensc_f1_complete <- read.spss(spssfile, to.data.frame = TRUE, use.value.labels = FALSE)\n",
    "\n",
    "spssfile <- \"./Data/ensc_2016-2017/diag_main_v8_spss_11102019.sav\"\n",
    "ensc_dx_complete <- read.spss(spssfile, to.data.frame = TRUE, use.value.labels = FALSE)\n",
    "\n",
    "# Select variables\n",
    "#\n",
    "dataset_ensc_f1 <- select(ensc_f1_complete, \n",
    "                          caseid = IdEncuesta,\n",
    "                          age = Edad,\n",
    "                          sex = Sexo,\n",
    "                          stroke = d4) # by medic\n",
    "\n",
    "dataset_ensc_dx <- select(ensc_dx_complete, \n",
    "                          caseid = IdEncuesta,\n",
    "                          paranoia = PS1f, # ¿Alguna vez pensó que había una conspiración para hacerle daño o perseguirlo y que su familia o amigos no creyeran que esto fuera real?\n",
    "                          hearvoices = PS1b,  # La segunda experiencia es oír voces que otras personas no podían oír. ¿Alguna vez le ha pasado algo como esto? \n",
    "                          thoughtpass = PS1c, # La tercera experiencia tiene que ver con control de la mente. \n",
    "                          highest_ed = NEDU2_MINSAL_1)\n",
    "\n",
    "# Merge datasets\n",
    "#\n",
    "dataset_ensc <- merge(dataset_ensc_f1, dataset_ensc_dx, by=\"caseid\")\n",
    "\n",
    "# Label dataset\n",
    "#\n",
    "dataset_ensc$dataset_name <- 'ensc'\n",
    "\n",
    "# Recode stroke variable\n",
    "#\n",
    "dataset_ensc$stroke[dataset_ensc$stroke == 1 ] <- 1\n",
    "dataset_ensc$stroke[dataset_ensc$stroke == 2 ] <- 0\n",
    "dataset_ensc$stroke <- factor(dataset_ensc$stroke, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode psychotic symptom variables\n",
    "#\n",
    "dataset_ensc$paranoia[dataset_ensc$paranoia == 1 ] <- 1\n",
    "dataset_ensc$paranoia[dataset_ensc$paranoia == 5 ] <- 0\n",
    "dataset_ensc$paranoia[dataset_ensc$paranoia == 8 ] <- 0\n",
    "dataset_ensc$paranoia[dataset_ensc$paranoia == 9 ] <- 0\n",
    "\n",
    "dataset_ensc$hearvoices[dataset_ensc$hearvoices == 1 ] <- 1\n",
    "dataset_ensc$hearvoices[dataset_ensc$hearvoices == 5 ] <- 0\n",
    "dataset_ensc$hearvoices[dataset_ensc$hearvoices == 8 ] <- 0\n",
    "dataset_ensc$hearvoices[dataset_ensc$hearvoices == 9 ] <- 0\n",
    "\n",
    "dataset_ensc$thoughtpass[dataset_ensc$thoughtpass == 1 ] <- 1\n",
    "dataset_ensc$thoughtpass[dataset_ensc$thoughtpass == 5 ] <- 0\n",
    "dataset_ensc$thoughtpass[dataset_ensc$thoughtpass == 8 ] <- 0\n",
    "dataset_ensc$thoughtpass[dataset_ensc$thoughtpass == 9 ] <- 0\n",
    "\n",
    "# Calculate probable psychosis variable\n",
    "#\n",
    "dataset_ensc$psychosis_tot <- rowSums(dataset_ensc[,c(\"paranoia\", \"hearvoices\", \"thoughtpass\")], na.rm=TRUE)\n",
    "dataset_ensc$prob_psychosis[dataset_ensc$psychosis_tot >= 2] <- 1\n",
    "dataset_ensc$prob_psychosis[dataset_ensc$psychosis_tot <= 1] <- 0\n",
    "dataset_ensc$psychosis_tot <- NULL\n",
    "\n",
    "# Set types for psychosis variables\n",
    "#\n",
    "dataset_ensc$paranoia <- factor(dataset_ensc$paranoia, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensc$hearvoices <- factor(dataset_ensc$hearvoices, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensc$thoughtpass <- factor(dataset_ensc$thoughtpass, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "dataset_ensc$prob_psychosis <- factor(dataset_ensc$prob_psychosis, levels = c(1,0), labels = c(\" yes\", \" no\")) \n",
    "\n",
    "# Recode and set variable type for highest level of education\n",
    "#\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 1] <- 1 # Basic\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 2] <- 1 # Basic\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 3] <- 2 # Mid teen high school\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 4] <- 2 # Mid teen high school\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 5] <- 3 # Late teen high school\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 6] <- 3 # Late teen high school\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 7] <- 4 # College / uni\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 8] <- 4 # College / uni\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 9] <- 4 # College / uni\n",
    "dataset_ensc$highest_ed[dataset_ensc$highest_ed == 10] <- 4 # College / uni\n",
    "\n",
    "dataset_ensc$highest_ed <- factor(dataset_ensc$highest_ed,\n",
    "                                  levels = c(1, 2, 3, 4),\n",
    "                                  labels = c(\"No / primary education\", \"Mid-teen high school\", \"Late teen high school\", \"College / university\")) \n",
    "\n",
    "# Set variable type for sex\n",
    "#\n",
    "dataset_ensc$sex <- factor(dataset_ensc$sex, levels = c(1,2), labels = c(\" male\", \" female\")) \n",
    "\n",
    "# Give variables formatted variable names for table\n",
    "#\n",
    "label(dataset_ensc$sex) <- \"Sex\"\n",
    "label(dataset_ensc$age) <- \"Age\"\n",
    "label(dataset_ensc$highest_ed) <- \"Highest level of education\"\n",
    "label(dataset_ensc$stroke) <- \"Stroke\"\n",
    "label(dataset_ensc$prob_psychosis) <- \"Prob.Psychosis\"\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(ensc_f1_complete, ensc_dx_complete, dataset_ensc_f1, dataset_ensc_dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create combined countries dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select relevant variables from each national dataset\n",
    "#\n",
    "subset_apms <- select(dataset_apms,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_cpes <- select(dataset_cpes,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_ensm <- select(dataset_ensm,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_ensc <- select(dataset_ensc,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "\n",
    "# ...and combine into one combined countries dataset\n",
    "#\n",
    "dataset_combined <- rbind(subset_apms, subset_cpes)\n",
    "dataset_combined <- rbind(dataset_combined, subset_ensm)\n",
    "dataset_combined <- rbind(dataset_combined, subset_ensc)\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(subset_apms, subset_ensm, subset_cpes, subset_ensc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to calculate prevalences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate stroke prevalence in total population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_stroke_prevalence <- function(prev_df) {\n",
    "  # How many people have stroke in the total population?\n",
    "  tbobj <- as.numeric(table(prev_df$stroke))\n",
    "  nstrokecases <- tbobj[1]; npopulation <- sum(tbobj)\n",
    "  prev_matrix <- as.matrix(cbind(nstrokecases, npopulation))\n",
    "  \n",
    "  # Calculate prevalence\n",
    "  prev_est <- epi.conf(prev_matrix, ctype = \"prevalence\", method = \"exact\", N = nrow(prev_df), design = 1, \n",
    "                       conf.level = 0.95) * 100\n",
    "  est_plus_95CI <- sprintf(\"%.2f%% (%.2f - %.2f)\", prev_est$est, prev_est$lower, prev_est$upper)\n",
    "  est_plus_95CI\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate probable psychosis prevalence in total population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_probpsychosis_prevalence <- function(prev_df) {\n",
    "  # How many people have probable psychosis in the total population?\n",
    "  tbobj <- as.numeric(table(prev_df$prob_psychosis))\n",
    "  nprobpsychosiscases <- tbobj[1]; npopulation <- sum(tbobj)\n",
    "  prev_matrix <- as.matrix(cbind(nprobpsychosiscases, npopulation))\n",
    "  \n",
    "  # Calculate prevalence\n",
    "  prev_est <- epi.conf(prev_matrix, ctype = \"prevalence\", method = \"exact\", N = nrow(prev_df), design = 1, \n",
    "                       conf.level = 0.95) * 100\n",
    "  est_plus_95CI <- sprintf(\"%.2f%% (%.2f - %.2f)\", prev_est$est, prev_est$lower, prev_est$upper)\n",
    "  est_plus_95CI\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate prevalence of probable psychosis in stroke population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_probpsychosis_in_stroke_prevalence <- function(prev_df) {\n",
    "  # How many people have stroke?\n",
    "  stroke_table <- as.numeric(table(prev_df$stroke))\n",
    "  stroke_pop <- stroke_table[1]\n",
    "  \n",
    "  # How many people who have stroke have psychosis?\n",
    "  stroke_psychosis_table <- as.numeric(table(prev_df$stroke, prev_df$prob_psychosis))\n",
    "  psychosis_in_stroke_cases <- stroke_psychosis_table[1]\n",
    "  prev_matrix <- as.matrix(cbind(psychosis_in_stroke_cases, stroke_pop))\n",
    "  \n",
    "  # Calculate prevalence\n",
    "  prev_est <- epi.conf(prev_matrix, ctype = \"prevalence\", method = \"exact\", N = nrow(prev_df), design = 1, \n",
    "                       conf.level = 0.95) * 100\n",
    "  prev_est\n",
    "  est_plus_95CI <- sprintf(\"%.2f%% (%.2f - %.2f)\", prev_est$est, prev_est$lower, prev_est$upper)\n",
    "  est_plus_95CI\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate prevalence of stroke in probable psychosis population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_stroke_in_probpsychosis_prevalence <- function(prev_df) {\n",
    "  # How many people have probable psychosis?\n",
    "  psychosis_table <- as.numeric(table(prev_df$prob_psychosis))\n",
    "  psychosis_pop <- psychosis_table[1]\n",
    "  \n",
    "  # How many people who have probable psychosis have stroke?\n",
    "  psychosis_stroke_table <- as.numeric(table(prev_df$prob_psychosis, prev_df$stroke))\n",
    "  stroke_in_psychosis_cases <- psychosis_stroke_table[1]\n",
    "  prev_matrix <- as.matrix(cbind(stroke_in_psychosis_cases, psychosis_pop))\n",
    "  \n",
    "  # Calculate prevalence\n",
    "  prev_est <- epi.conf(prev_matrix, ctype = \"prevalence\", method = \"exact\", N = nrow(prev_df), design = 1, \n",
    "                       conf.level = 0.95) * 100\n",
    "  prev_est\n",
    "  est_plus_95CI <- sprintf(\"%.2f%% (%.2f - %.2f)\", prev_est$est, prev_est$lower, prev_est$upper)\n",
    "  est_plus_95CI\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to run regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression models testing unadjusted and adjusted associations between stroke and probable psychosis in national datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_prob_psychosis_models <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glm(prob_psychosis ~ stroke, data = funcdf, family = binomial(link = \"logit\"), maxit=500)\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  un_prob_psychosis_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[4], summ_of_model$coeftable[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glm(prob_psychosis ~ stroke + sex + age + highest_ed, data = funcdf, family = binomial(link = \"logit\"), maxit=500)\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  adj_ed_prob_psychosis_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[9], summ_of_model$coeftable[16])\n",
    "\n",
    "  prob_psychosis_results <- c(un_prob_psychosis_OR_CIs,\n",
    "                              adj_ed_prob_psychosis_OR_CIs)\n",
    "  prob_psychosis_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-level regression models testing unadjusted and adjusted associations between stroke and probable psychosis in combined countries dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_prob_psychosis_combined_model <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glmer(prob_psychosis ~ stroke + (1|dataset_name),\n",
    "                      data = dataset_combined,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  un_prob_psychosis_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[4], test_model_table_OR[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glmer(prob_psychosis ~ stroke + sex + age + highest_ed + (1|dataset_name),\n",
    "                      data = dataset_combined,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  adj_ed_prob_psychosis_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[9], test_model_table_OR[16])\n",
    "\n",
    "  prob_psychosis_combined_results <- c(un_prob_psychosis_OR_CIs,\n",
    "                                       adj_ed_prob_psychosis_OR_CIs)\n",
    "  prob_psychosis_combined_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression models testing unadjusted and adjusted associations between stroke and paranoia in national datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_paranoia_models <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glm(paranoia ~ stroke, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  un_paranoia_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[4], summ_of_model$coeftable[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glm(paranoia ~ stroke + sex + age + highest_ed, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  adj_ed_paranoia_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[9], summ_of_model$coeftable[16])\n",
    "\n",
    "  paranoia_results <- c(un_paranoia_OR_CIs,\n",
    "                           adj_ed_paranoia_OR_CIs)\n",
    "  paranoia_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-level regression models testing unadjusted and adjusted associations between stroke and paranoia in combined countries dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_paranoia_combined_model <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glmer(paranoia ~ stroke + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  un_paranoia_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[4], test_model_table_OR[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glmer(paranoia ~ stroke + sex + age + highest_ed + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  adj_ed_paranoia_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[9], test_model_table_OR[16])\n",
    "\n",
    "  paranoia_combined_results <- c(un_paranoia_OR_CIs,\n",
    "                                    adj_ed_paranoia_OR_CIs)\n",
    "  paranoia_combined_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression models testing unadjusted and adjusted association between stroke and hallucinated voices in national datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_hearvoices_models <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glm(hearvoices ~ stroke, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  un_hearvoices_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[4], summ_of_model$coeftable[6])\n",
    "\n",
    "  # Adjusted by sex, age and education\n",
    "  test_model <- glm(hearvoices ~ stroke + sex + age + highest_ed, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  adj_ed_hearvoices_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[9], summ_of_model$coeftable[16])\n",
    "\n",
    "  hearvoices_results <- c(un_hearvoices_OR_CIs,\n",
    "                          adj_ed_hearvoices_OR_CIs)\n",
    "  hearvoices_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-level regression models testing unadjusted and adjusted associations between stroke and paranoia in combined countries dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_hearvoices_combined_model <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glmer(hearvoices ~ stroke + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  un_hearvoices_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[4], test_model_table_OR[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glmer(hearvoices ~ stroke + sex + age + highest_ed + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  adj_ed_hearvoices_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[9], test_model_table_OR[16])\n",
    "\n",
    "  hearvoices_combined_results <- c(un_hearvoices_OR_CIs,\n",
    "                                    adj_ed_hearvoices_OR_CIs)\n",
    "  hearvoices_combined_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression models testing unadjusted and adjusted associations between stroke and thought passivity delusions in national datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_thoughtpass_models <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glm(thoughtpass ~ stroke, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  un_thoughtpass_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[4], summ_of_model$coeftable[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glm(thoughtpass ~ stroke + sex + age + highest_ed, data = funcdf, family = binomial(link = \"logit\"))\n",
    "  summ_of_model <- summ(test_model, exp = TRUE, digits = 4)\n",
    "  adj_ed_thoughtpass_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", summ_of_model$coeftable[2], summ_of_model$coeftable[9], summ_of_model$coeftable[16])\n",
    "\n",
    "  thoughtpass_results <- c(un_thoughtpass_OR_CIs,\n",
    "                           adj_ed_thoughtpass_OR_CIs)\n",
    "  thoughtpass_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-level regression models testing unadjusted and adjusted associations between stroke and thought passivity delusions in combined countries dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_thoughtpass_combined_model <- function(funcdf) {\n",
    "  # Unadjusted\n",
    "  test_model <- glmer(thoughtpass ~ stroke + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  un_thoughtpass_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[4], test_model_table_OR[6])\n",
    "\n",
    "  # Adjusted by sex, age, education\n",
    "  test_model <- glmer(thoughtpass ~ stroke + sex + age + highest_ed + (1|dataset_name),\n",
    "                      data = funcdf,\n",
    "                      family = binomial(link = \"logit\"),\n",
    "                      control = glmerControl(tolPwrss=1e-3))\n",
    "  test_model_CIs <- confint(test_model, parm=\"beta_\", method=\"Wald\")\n",
    "  test_model_table <- cbind(est=fixef(test_model),test_model_CIs)\n",
    "  test_model_table_OR <- exp(test_model_table)\n",
    "  adj_ed_thoughtpass_OR_CIs <- sprintf(\"%.2f (%.2f - %.2f)\", test_model_table_OR[2], test_model_table_OR[9], test_model_table_OR[16])\n",
    "\n",
    "  thoughtpass_combined_results <- c(un_thoughtpass_OR_CIs,\n",
    "                                   adj_ed_thoughtpass_OR_CIs)\n",
    "  thoughtpass_combined_results\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results reported in paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"Rtable1\">\n",
       "<thead>\n",
       "<tr>\n",
       "<th class='rowlabel firstrow lastrow'></th>\n",
       "<th class='firstrow lastrow'><span class='stratlabel'>UK<br><span class='stratn'>(N=7403)</span></span></th>\n",
       "<th class='firstrow lastrow'><span class='stratlabel'>US<br><span class='stratn'>(N=6082)</span></span></th>\n",
       "<th class='firstrow lastrow'><span class='stratlabel'>Chile<br><span class='stratn'>(N=3403)</span></span></th>\n",
       "<th class='firstrow lastrow'><span class='stratlabel'>Colombia<br><span class='stratn'>(N=10870)</span></span></th>\n",
       "<th class='firstrow lastrow'><span class='stratlabel'>Total<br><span class='stratn'>(N=27758)</span></span></th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Age</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'>Mean (SD)</td>\n",
       "<td>51.1 (18.6)</td>\n",
       "<td>41.5 (15.8)</td>\n",
       "<td>49.4 (17.9)</td>\n",
       "<td>43.4 (16.8)</td>\n",
       "<td>45.8 (17.7)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Median [Min, Max]</td>\n",
       "<td class='lastrow'>50.0 [16.0, 95.0]</td>\n",
       "<td class='lastrow'>39.0 [18.0, 99.0]</td>\n",
       "<td class='lastrow'>50.0 [17.0, 98.0]</td>\n",
       "<td class='lastrow'>42.0 [18.0, 96.0]</td>\n",
       "<td class='lastrow'>44.0 [16.0, 99.0]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Sex</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> male</td>\n",
       "<td>3197 (43.2%)</td>\n",
       "<td>2747 (45.2%)</td>\n",
       "<td>1226 (36.0%)</td>\n",
       "<td>4384 (40.3%)</td>\n",
       "<td>11554 (41.6%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'> female</td>\n",
       "<td class='lastrow'>4206 (56.8%)</td>\n",
       "<td class='lastrow'>3335 (54.8%)</td>\n",
       "<td class='lastrow'>2177 (64.0%)</td>\n",
       "<td class='lastrow'>6486 (59.7%)</td>\n",
       "<td class='lastrow'>16204 (58.4%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Highest level of education</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'>No / primary education</td>\n",
       "<td>2278 (30.8%)</td>\n",
       "<td>540 (8.9%)</td>\n",
       "<td>766 (22.5%)</td>\n",
       "<td>4007 (36.9%)</td>\n",
       "<td>7591 (27.3%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'>Mid-teen high school</td>\n",
       "<td>2103 (28.4%)</td>\n",
       "<td>835 (13.7%)</td>\n",
       "<td>895 (26.3%)</td>\n",
       "<td>5152 (47.4%)</td>\n",
       "<td>8985 (32.4%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'>Late teen high school</td>\n",
       "<td>938 (12.7%)</td>\n",
       "<td>3604 (59.3%)</td>\n",
       "<td>1040 (30.6%)</td>\n",
       "<td>895 (8.2%)</td>\n",
       "<td>6477 (23.3%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'>College / university</td>\n",
       "<td>1916 (25.9%)</td>\n",
       "<td>1103 (18.1%)</td>\n",
       "<td>668 (19.6%)</td>\n",
       "<td>713 (6.6%)</td>\n",
       "<td>4400 (15.9%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Missing</td>\n",
       "<td class='lastrow'>168 (2.3%)</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>34 (1.0%)</td>\n",
       "<td class='lastrow'>103 (0.9%)</td>\n",
       "<td class='lastrow'>305 (1.1%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Stroke</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> yes</td>\n",
       "<td>180 (2.4%)</td>\n",
       "<td>171 (2.8%)</td>\n",
       "<td>95 (2.8%)</td>\n",
       "<td>79 (0.7%)</td>\n",
       "<td>525 (1.9%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> no</td>\n",
       "<td>7223 (97.6%)</td>\n",
       "<td>5724 (94.1%)</td>\n",
       "<td>3287 (96.6%)</td>\n",
       "<td>10791 (99.3%)</td>\n",
       "<td>27025 (97.4%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Missing</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>187 (3.1%)</td>\n",
       "<td class='lastrow'>21 (0.6%)</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>208 (0.7%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Paranoia</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> yes</td>\n",
       "<td>569 (7.7%)</td>\n",
       "<td>68 (1.1%)</td>\n",
       "<td>59 (1.7%)</td>\n",
       "<td>2340 (21.5%)</td>\n",
       "<td>3036 (10.9%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> no</td>\n",
       "<td>6834 (92.3%)</td>\n",
       "<td>4926 (81.0%)</td>\n",
       "<td>3304 (97.1%)</td>\n",
       "<td>8530 (78.5%)</td>\n",
       "<td>23594 (85.0%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Missing</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1088 (17.9%)</td>\n",
       "<td class='lastrow'>40 (1.2%)</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1128 (4.1%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Hallucinated voices</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> yes</td>\n",
       "<td>68 (0.9%)</td>\n",
       "<td>293 (4.8%)</td>\n",
       "<td>152 (4.5%)</td>\n",
       "<td>362 (3.3%)</td>\n",
       "<td>875 (3.2%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> no</td>\n",
       "<td>7335 (99.1%)</td>\n",
       "<td>4702 (77.3%)</td>\n",
       "<td>3211 (94.4%)</td>\n",
       "<td>10508 (96.7%)</td>\n",
       "<td>25756 (92.8%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Missing</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1087 (17.9%)</td>\n",
       "<td class='lastrow'>40 (1.2%)</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1127 (4.1%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Passivity delusion</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> yes</td>\n",
       "<td>77 (1.0%)</td>\n",
       "<td>33 (0.5%)</td>\n",
       "<td>18 (0.5%)</td>\n",
       "<td>534 (4.9%)</td>\n",
       "<td>662 (2.4%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> no</td>\n",
       "<td>7326 (99.0%)</td>\n",
       "<td>4962 (81.6%)</td>\n",
       "<td>3345 (98.3%)</td>\n",
       "<td>10336 (95.1%)</td>\n",
       "<td>25969 (93.6%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'>Missing</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1087 (17.9%)</td>\n",
       "<td class='lastrow'>40 (1.2%)</td>\n",
       "<td class='lastrow'>0 (0%)</td>\n",
       "<td class='lastrow'>1127 (4.1%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel firstrow'><span class='varlabel'>Probable Psychosis</span></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "<td class='firstrow'></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel'> yes</td>\n",
       "<td>66 (0.9%)</td>\n",
       "<td>36 (0.6%)</td>\n",
       "<td>27 (0.8%)</td>\n",
       "<td>505 (4.6%)</td>\n",
       "<td>634 (2.3%)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td class='rowlabel lastrow'> no</td>\n",
       "<td class='lastrow'>7337 (99.1%)</td>\n",
       "<td class='lastrow'>6046 (99.4%)</td>\n",
       "<td class='lastrow'>3376 (99.2%)</td>\n",
       "<td class='lastrow'>10365 (95.4%)</td>\n",
       "<td class='lastrow'>27124 (97.7%)</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_combined$dataset_name <- factor(dataset_combined$dataset_name,\n",
    "                          levels = c(\"apms\", \"cpes\", \"ensc\", \"ensm\"),\n",
    "                          labels = c(\"UK\", \"US\", \"Chile\", \"Colombia\")) \n",
    "\n",
    "# Relabel\n",
    "label(dataset_combined$sex) <- \"Sex\"\n",
    "label(dataset_combined$age) <- \"Age\"\n",
    "label(dataset_combined$highest_ed) <- \"Highest level of education\"\n",
    "label(dataset_combined$paranoia) <- \"Paranoia\"\n",
    "label(dataset_combined$hearvoices) <- \"Hallucinated voices\"\n",
    "label(dataset_combined$thoughtpass) <- \"Passivity delusion\"\n",
    "label(dataset_combined$prob_psychosis) <- \"Probable Psychosis\"\n",
    "label(dataset_combined$stroke) <- \"Stroke\"\n",
    "\n",
    "# Display table with descriptive statistics including missing values for unaltered data\n",
    "display_html(table1(~ age + sex + highest_ed + stroke + paranoia + hearvoices + thoughtpass + prob_psychosis | dataset_name,\n",
    "        data=dataset_combined, overall=\"Total\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insert results of missing data imputation for CPES dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As the missing data imputation did not impute any new symptoms for cases in the CPES dataset\n",
    "# and replaced all NAs as symptom not present (see CPESVariableImputationCheck.R),\n",
    "# we replace all NAs with \" no\" in this dataset\n",
    "\n",
    "dataset_cpes$paranoia[is.na(dataset_cpes$paranoia)] <- \" no\"\n",
    "dataset_cpes$hearvoices[is.na(dataset_cpes$hearvoices)] <- \" no\"\n",
    "dataset_cpes$thoughtpass[is.na(dataset_cpes$thoughtpass)] <- \" no\"\n",
    "\n",
    "#... and recombine the datasets\n",
    "subset_apms <- select(dataset_apms,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_cpes <- select(dataset_cpes,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_ensm <- select(dataset_ensm,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "subset_ensc <- select(dataset_ensc,\n",
    "                      dataset_name,\n",
    "                      sex, age,\n",
    "                      stroke, prob_psychosis, \n",
    "                      paranoia, hearvoices, thoughtpass,\n",
    "                      highest_ed)\n",
    "\n",
    "# ...and combine into one combined countries dataset\n",
    "#\n",
    "dataset_combined <- rbind(subset_apms, subset_cpes)\n",
    "dataset_combined <- rbind(dataset_combined, subset_ensm)\n",
    "dataset_combined <- rbind(dataset_combined, subset_ensc)\n",
    "\n",
    "# Remove intermediary memory objects\n",
    "#\n",
    "rm(subset_apms, subset_ensm, subset_cpes, subset_ensc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prevalence results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate prevalence results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stroke prevalence\n",
    "#\n",
    "strok_prev_vector <- c()\n",
    "strok_prev_vector <- c(strok_prev_vector, calc_stroke_prevalence(dataset_apms))\n",
    "strok_prev_vector <- c(strok_prev_vector, calc_stroke_prevalence(dataset_cpes))\n",
    "strok_prev_vector <- c(strok_prev_vector, calc_stroke_prevalence(dataset_ensm))\n",
    "strok_prev_vector <- c(strok_prev_vector, calc_stroke_prevalence(dataset_ensc))\n",
    "strok_prev_vector <- c(strok_prev_vector, calc_stroke_prevalence(dataset_combined))\n",
    "\n",
    "# Probable psychosis prevalence\n",
    "#\n",
    "probpsych_prev_vector <- c()\n",
    "probpsych_prev_vector <- c(probpsych_prev_vector, calc_probpsychosis_prevalence(dataset_apms))\n",
    "probpsych_prev_vector <- c(probpsych_prev_vector, calc_probpsychosis_prevalence(dataset_cpes))\n",
    "probpsych_prev_vector <- c(probpsych_prev_vector, calc_probpsychosis_prevalence(dataset_ensm))\n",
    "probpsych_prev_vector <- c(probpsych_prev_vector, calc_probpsychosis_prevalence(dataset_ensc))\n",
    "probpsych_prev_vector <- c(probpsych_prev_vector, calc_probpsychosis_prevalence(dataset_combined))\n",
    "\n",
    "# Probable psychosis in stroke prevalence\n",
    "#\n",
    "probpsych_in_stroke_prev_vector <- c()\n",
    "probpsych_in_stroke_prev_vector <- c(probpsych_in_stroke_prev_vector, calc_probpsychosis_in_stroke_prevalence(dataset_apms))\n",
    "probpsych_in_stroke_prev_vector <- c(probpsych_in_stroke_prev_vector, calc_probpsychosis_in_stroke_prevalence(dataset_cpes))\n",
    "probpsych_in_stroke_prev_vector <- c(probpsych_in_stroke_prev_vector, calc_probpsychosis_in_stroke_prevalence(dataset_ensm))\n",
    "probpsych_in_stroke_prev_vector <- c(probpsych_in_stroke_prev_vector, calc_probpsychosis_in_stroke_prevalence(dataset_ensc))\n",
    "probpsych_in_stroke_prev_vector <- c(probpsych_in_stroke_prev_vector, calc_probpsychosis_in_stroke_prevalence(dataset_combined))\n",
    "\n",
    "# Stroke in probable psychosis prevalence\n",
    "#\n",
    "stroke_in_probpsychosis_prev_vector <- c()\n",
    "stroke_in_probpsychosis_prev_vector <- c(stroke_in_probpsychosis_prev_vector, calc_stroke_in_probpsychosis_prevalence(dataset_apms))\n",
    "stroke_in_probpsychosis_prev_vector <- c(stroke_in_probpsychosis_prev_vector, calc_stroke_in_probpsychosis_prevalence(dataset_cpes))\n",
    "stroke_in_probpsychosis_prev_vector <- c(stroke_in_probpsychosis_prev_vector, calc_stroke_in_probpsychosis_prevalence(dataset_ensm))\n",
    "stroke_in_probpsychosis_prev_vector <- c(stroke_in_probpsychosis_prev_vector, calc_stroke_in_probpsychosis_prevalence(dataset_ensc))\n",
    "stroke_in_probpsychosis_prev_vector <- c(stroke_in_probpsychosis_prev_vector, calc_stroke_in_probpsychosis_prevalence(dataset_combined))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display results in table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Country</th><th scope=col>Stroke.Prevalence</th><th scope=col>Prob.Psychosis.Prevalence</th><th scope=col>Prob.Psychosis.in.Stroke.Prevalence</th><th scope=col>Stroke.in.Prob.Psychosis.Prevalence</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>UK      </td><td>2.43% (2.09 - 2.81)</td><td>0.89% (0.69 - 1.13)</td><td>1.11% (0.13 - 3.96)  </td><td>3.03% (0.37 - 10.52) </td></tr>\n",
       "\t<tr><td>US      </td><td>2.90% (2.49 - 3.36)</td><td>0.59% (0.41 - 0.82)</td><td>3.51% (1.30 - 7.48)  </td><td>16.67% (6.37 - 32.81)</td></tr>\n",
       "\t<tr><td>Colombia</td><td>0.73% (0.58 - 0.90)</td><td>4.65% (4.26 - 5.06)</td><td>13.92% (7.16 - 23.55)</td><td>2.18% (1.09 - 3.86)  </td></tr>\n",
       "\t<tr><td>Chile   </td><td>2.81% (2.28 - 3.42)</td><td>0.79% (0.52 - 1.15)</td><td>1.05% (0.03 - 5.73)  </td><td>3.70% (0.09 - 18.97) </td></tr>\n",
       "\t<tr><td>Combined</td><td>1.91% (1.75 - 2.07)</td><td>2.28% (2.11 - 2.47)</td><td>3.81% (2.34 - 5.82)  </td><td>3.15% (1.94 - 4.83)  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Country & Stroke.Prevalence & Prob.Psychosis.Prevalence & Prob.Psychosis.in.Stroke.Prevalence & Stroke.in.Prob.Psychosis.Prevalence\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t UK       & 2.43\\% (2.09 - 2.81) & 0.89\\% (0.69 - 1.13) & 1.11\\% (0.13 - 3.96)   & 3.03\\% (0.37 - 10.52) \\\\\n",
       "\t US       & 2.90\\% (2.49 - 3.36) & 0.59\\% (0.41 - 0.82) & 3.51\\% (1.30 - 7.48)   & 16.67\\% (6.37 - 32.81)\\\\\n",
       "\t Colombia & 0.73\\% (0.58 - 0.90) & 4.65\\% (4.26 - 5.06) & 13.92\\% (7.16 - 23.55) & 2.18\\% (1.09 - 3.86)  \\\\\n",
       "\t Chile    & 2.81\\% (2.28 - 3.42) & 0.79\\% (0.52 - 1.15) & 1.05\\% (0.03 - 5.73)   & 3.70\\% (0.09 - 18.97) \\\\\n",
       "\t Combined & 1.91\\% (1.75 - 2.07) & 2.28\\% (2.11 - 2.47) & 3.81\\% (2.34 - 5.82)   & 3.15\\% (1.94 - 4.83)  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 5\n",
       "\n",
       "| Country &lt;chr&gt; | Stroke.Prevalence &lt;chr&gt; | Prob.Psychosis.Prevalence &lt;chr&gt; | Prob.Psychosis.in.Stroke.Prevalence &lt;chr&gt; | Stroke.in.Prob.Psychosis.Prevalence &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| UK       | 2.43% (2.09 - 2.81) | 0.89% (0.69 - 1.13) | 1.11% (0.13 - 3.96)   | 3.03% (0.37 - 10.52)  |\n",
       "| US       | 2.90% (2.49 - 3.36) | 0.59% (0.41 - 0.82) | 3.51% (1.30 - 7.48)   | 16.67% (6.37 - 32.81) |\n",
       "| Colombia | 0.73% (0.58 - 0.90) | 4.65% (4.26 - 5.06) | 13.92% (7.16 - 23.55) | 2.18% (1.09 - 3.86)   |\n",
       "| Chile    | 2.81% (2.28 - 3.42) | 0.79% (0.52 - 1.15) | 1.05% (0.03 - 5.73)   | 3.70% (0.09 - 18.97)  |\n",
       "| Combined | 1.91% (1.75 - 2.07) | 2.28% (2.11 - 2.47) | 3.81% (2.34 - 5.82)   | 3.15% (1.94 - 4.83)   |\n",
       "\n"
      ],
      "text/plain": [
       "  Country  Stroke.Prevalence   Prob.Psychosis.Prevalence\n",
       "1 UK       2.43% (2.09 - 2.81) 0.89% (0.69 - 1.13)      \n",
       "2 US       2.90% (2.49 - 3.36) 0.59% (0.41 - 0.82)      \n",
       "3 Colombia 0.73% (0.58 - 0.90) 4.65% (4.26 - 5.06)      \n",
       "4 Chile    2.81% (2.28 - 3.42) 0.79% (0.52 - 1.15)      \n",
       "5 Combined 1.91% (1.75 - 2.07) 2.28% (2.11 - 2.47)      \n",
       "  Prob.Psychosis.in.Stroke.Prevalence Stroke.in.Prob.Psychosis.Prevalence\n",
       "1 1.11% (0.13 - 3.96)                 3.03% (0.37 - 10.52)               \n",
       "2 3.51% (1.30 - 7.48)                 16.67% (6.37 - 32.81)              \n",
       "3 13.92% (7.16 - 23.55)               2.18% (1.09 - 3.86)                \n",
       "4 1.05% (0.03 - 5.73)                 3.70% (0.09 - 18.97)               \n",
       "5 3.81% (2.34 - 5.82)                 3.15% (1.94 - 4.83)                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display in table\n",
    "#\n",
    "name_vec <- c(\"UK\", \"US\", \"Colombia\", \"Chile\", \"Combined\")\n",
    "prevalence_table_df <- data.frame(Country = name_vec, \n",
    "                                  \"Stroke Prevalence\" = strok_prev_vector,\n",
    "                                  \"Prob Psychosis Prevalence\" = probpsych_prev_vector,\n",
    "                                  \"Prob Psychosis in Stroke Prevalence\" = probpsych_in_stroke_prev_vector,\n",
    "                                  \"Stroke in Prob Psychosis Prevalence\" = stroke_in_probpsychosis_prev_vector)\n",
    "prevalence_table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of directed acyclic graph of major causal relationships between stroke and psychosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download graph from dagitty.net and display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Bl4qMrIyHDtkOVwOKtWrRoZGQkt0IjF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bCwsNDT09Pa2rr5W5lMZnMsc1NTE8dxGE4qFPbLab2G\nCgAFBEGIRqMFYYc2tdkV8Hq9u/51AgAVAWG3heKtWJ1ORxAE5sJOlc2q8nt8JBJRxJCVfdi9\novg6Ojrq6uouXLiAodYpEkmSwuFwS0uLx+PZllqczWZFUdws7Gw2m9/vpyhKjZMegLxfrqpx\nhgBQS6LRKEEQ8tiWDMMwsVgM59ZqAEAg7LZRfMUOYT8Yq1bFDiFkNptbW1snJycrDxPe2WC3\njd7eXk0HF6+vr+dyOa/X6/f79Xr95gWscsvatsUhPp+vxvZ68cj75UZHR9U+CAAoAMMw11xz\nzeZ3BIZhJEnSukUAHHpA2G2h+B47hP3yiZqF2O1Ka2ur0WiscEI2nU6nUqn9hR1BEJoOLg4G\ng3V1dRRFyanFGxsbhQyFdDpNUVTxL0jVIQjC7/dvbGyAIQscAvR6/eZynfwVi8Wyuc0OADAE\nhN0WSqrY4SzsMpmMIAgqCjt5QjYSicj7T8uD4ziapi0Wy/4P025wcSaTiUQi8rYJhJDJZAoE\nApcuXZInQjZPTmgFOc4QDFngsLKzzQ4AcAOE3RaK77FDCOn1emxLRDzPEwShSo9dAavV2tra\nOjExUbZJGolE9i/XFdBocHEoFDKZTJurAm63u7m5eWRkJJ1Oa1HYoarFGQIADsjCTlsfIIGj\nBgi7LRyaip08ObHXzEHNkA3ZsndmR6NRp9NZ5IMLwcVaWfUjSVIoFGpoaNj29Y6ODovFMjw8\nnEqltCjsqrpfDgDUhWVZQRDi8bjaBwGAPQFhdxVJkiRJKr6lCefhCRUnJzYjv8evra2trq6W\n+txEIpHNZous2MnIwcWzs7Oa6PHiOC6bzdbX12/7OkEQ/f39mUwmFotpUdihau6XAwB1oSjK\nbDaDGwvgDAi7q8hD7MVX7CiKAmF3IFartaWlZXx8vFRDluM4s9m8bSb0QDQUXBwMBl0u167p\npjRN9/f3C4Kg3RgXMGSBwwrLsjA/AeAMCLuryA1zhyPuBB9hhxBqa2szGo1TU1MlPevAoJO9\n0ERwcTabXVtbK4xN7MRisUiSFAwGE4lELQ+mFFXaLwcAqgNtdgDmgLC7ilyxOwRxJ6IoZjIZ\ndScnNiO/x6+srBRvyIqiGI1GyxN2CKGOjg6Px4NzcHE4HDYYDPv8B8ohdi6Xa2hoCM+X2YHI\n++UUiTMEAHxgWTaXy8nLuAEAQ0DYXaVUKxZbYZdKpSRJwqdihzYZsps3K+yDHO9etrBDCPn9\nfpyDi4PBYENDwz7TLel0WqfTBQIBkiS1G/nb0tJiMpnAkAUOEwaDwWg0ghsLYAsIu6uUIezw\njDvheV6v1+O2mrqtrY2m6SInZDmOs9lslaxYkIOLKYrCMLiY47h0Or1zHnYzctaJTqcbGBjg\nOK6QWqwtCvvlKokzBADcgDY7AGdA2F2l1B47bCt2WDXYFSBJsq+vr0hDtuwGu83odLprrrkG\nw+DiUCjkcrn2nwsphNiZzebe3t6ZmRmNvpHI++UqiTMEANwAYQfgDAi7q4iiSBBEScMToihi\nuBAaT2GHrhiyExMT+xuygiDEYrHKhR3CMrg4l8utrq7uX65DW9dOeDyexsbG4eFhjaaHKLJf\nDgDwgWGYbDaLbQsvcMQBYXeVktKJEUKyUYhh0Q5bYYcQamtroyhq/wlZjuMIgmAYRpE74hZc\nvLy8rNfrXS7X/g/btnaiq6vLZDINDw9j+EHiQBTZLwcA+GA2m2mahqIdgCcg7K5S0j4xhLGw\nS6VS+IzEboMkyd7e3uXl5bW1tb0ew3Ecy7Il/b/YH6yCi4PBoM/nO3ApyDZhJ7cMplKpmZmZ\nKh+wKhSKtWDIAocDcGMBbAFhd5XDUbHL5XK5XA7bih1CyG63Nzc3T0xM7PWjU6TBbhuF4OJ9\nBGUN2NjYSCaTO7dNbEMQhFwut23tBE3TfX19i4uLGq17yXGGZe+XAwCsYBgG9k8AeALC7iqi\nKBYfYocQ0ul0BEHgNnHJ8zxBENhW7GTa29v1ev2uhmw2m00mk4oLO3QluHhkZETF4OJQKOR0\nOg/8vyP30u3cJ+ZwONrb28fGxpLJZLWOWDVkQ3Z1dbWM/XIAgBssy6ZSKY22vQKHGxB2Vym1\nYoewHIzled5gMJSkUGuPbMiGw+H19fVt34pEIhRFWa3WatxX3eBiQRBWVlb22TZRIJ1OkyRJ\nUdTOb7W2tjqdzqGhIdw+URSD1WptbW0tY78cAOCGxWKhKArcWABDQNhdpdQeO4TlVjGcJyc2\nY7fbm5qaxsfHt/0AZR/2wBa0slExuDgcDpMkeeDYBEIonU4bDIa9fgi9vb2SJI2NjSl9wFrQ\n2tpK03Sp++UAADcIgrDb7eDGAhgCwu4qpVqxCNeKnSaEHUKoo6NDp9NNT09v/mI1Guw2o2Jw\ncSgUamhoKObDw7bJiW3o9fqBgYH19fXFxUVFD1gLSoozBACcgfkJAE9A2F3l0FixmDfYFZAN\n2VAoFIlE5K/wPJ/JZKoq7JBKwcWJRCIejx8YXyezv7BDCFmtVr/fPzU1pcWCQZFxhgCAOSzL\nJpNJeBkDuAHC7iqHQNhJkpROp7VSsUMIMQwjG7Jy8SwSiRiNxhoI00Jwcc1Sc4PBIMuyRf6v\nOVDYIYS8Xm9DQ8PQ0JAW+9WKiTMEAMyx2Ww6nU6LH66Aww0Iu6uU0WOHm7BLp9OiKGpI2CGE\n2tvbCYKQDdlq+7CbkYOLV1ZWahBcLIri8vJyMWMTMsUIO4RQd3e3wWAYGRnBamFaMciG7P5x\nhgCAOXKbHbixAG6AsLtKeT12WA0n8jxPkuT+S0hxQ6fTBQKBYDC4vr4ejUadTmfNbl2z4OLl\n5WWEkNvtLubBkiRls9lihB1JkgMDA4lE4tKlS5UeseZYrdbm5uad0zMAoCGgzQ7AEBB2VzkE\nVqw8OVG9kdIqwTBMY2Pj2NhYPp9nWbaWt65NcHEoFKqvry/yY0Mmk5EkqRhhhxAyGo39/f3z\n8/NanEVob2+nKAoiiwHtwrJsIpHA6l0AAEDYXeUQxJ1oaCR2Gx0dHZIkURRF03SNb+3z+Vpb\nW6sXXMzz/MbGRpFjEwihdDpNEETxZVeHw9Ha2jo2Nqa5leTF7JcDAJyx2+0EQaiYeQ4AOwFh\nd5VDEHeiXWGn0+mMRmM+ny9MyNaS9vZ2ObiY53nFL760tGS324uPXE6n0zRNl1R2bWtrYxhG\ni6nFB+6XAwCcIUnSZrOBGwtgBQi7qxwaK1btU5SDKIrJZNLpdBYmZGuMHFx84cIFZYdMSx2b\nQEVPTmyGIIhAIJDP52s25Ksg++yXAwD8YVkWBmMBrABhdxWtT8UKgpDNZrUSYrcN+SNvb28v\nQRAzMzO1P0CVgotXV1dFUfR4PMU/pQxhhxCiKKq/v391dTUYDJb6XHXZZ78cAOAPwzCxWEwU\nRbUPAgCXAWF3lfKsWEmSMPmVlm1EjVbsOI6z2+00Tfv9/qWlJVWsjWoEF4dCIa/XW9Lrqjxh\nhxCy2+1dXV2Tk5PxeLyMp6vIXvvlAAB/GIaRJAna7AB8AGF3lfKsWIQQJu9GPM/TNC0fSXMU\nEuwcDkdDQ8Po6KgqhqyywcWpVIrjuJJ8WFSBsEMINTY2ejyeoaEhzaXhd3R0kCS5bb8cAOCP\nXq+3Wq3QZgfgAwi7qxwCYafRcl0ul0skEoVo4q6uLoIg1MpmUzBwYQmaAAAgAElEQVS4OBgM\nWq1Wm81W0rMymUzZwg4h5Pf79Xp9LbelKQJJkoFAYPN+OQDQCtBmB2AFCLurlBd3gkDYVQzH\ncSRJ2u12+Z86nc7v9y8uLqr1IViR4GJJksLhcKnlumw2K4piJcJOTi2Ox+Nzc3NlX0QV5DhD\ntaZnAKBsGIbZ2NjQ1kcp4BADwu4qkiSVIewIggBhVyGyD7s54MPhcNTX14+Pj6vVv1h5cPHa\n2lo+n/d6vSU9S86iq3B3iMlkCgQCs7Ozmqt+dXR0FPbLAYBWYFlWEATN9bYChxUQdpcRRVGS\npFKHJxBOg7GpVErTwm7bF7u6ugRBUHFZViG4uDyTJRgMejyeUlseM5kMRVFlvA634Xa7m5ub\nR0ZG0ul0hZeqJYX9cpqTpMBRhqIoi8UCbXYAJoCwu4xcGSq1YoewWRebyWQEQdCisEun06lU\naqew0+v1gUBgYWFBxeYVObj44sWLpQYXZzKZMsYmUGWTE9vo6Oiw2WzDw8OYTG0XCRiygBaR\n3Vi1TwEACIGwKyC/i5Qn7HCo2PE8TxCEUpqglnAcR9O0xWLZ+S3ZkB0bG1NRmpQXXBwMBk0m\nE8Mwpd5OQWEnpxZnMhnNZf+CIQtoDpZlo9EotNkBOADC7jKydNCuFcvzvMlkKmkPFSZEIpGd\n5boCqhuyheDiCxcuFFlDKm9sQkZBYYcQoml6YGAgFAqFw2GlrlkDdDpdb29vMBjkOE7tswBA\nUbAsm8/nq7GTEABKBYTdZcq2YnU6HSbCTos+LEIoGo06nc69vqvX6/1+v7qGrBxcnM/ni8wQ\niUQi2Wy2vr6+jHspK+wQQna7vaOjY3x8PJFIKHjZasOyrM/nGxsbA0MW0AQGg8FoNEKbHYAD\nIOwucwisWC0Ku0Qikc1m96nYIYRcLpfX61XXkC0puDgYDLrdboqiyriR4sIOIdTc3FxXVzc0\nNITDC7V4Ojs71dovBwBlILuxap8CAEDYXaGS4Qkc3i81Kuw4jjObzQeme3R3dwuCUHlicCUU\nGVyczWbX19fL82FzuZwgCNVolPT7/SRJjo6OKn7l6iHHGaq1Xw4ASgWEHYAJIOwuI6+dKKNH\nDYepWFEUM5mMRoXd/uU6GdmQnZ+fV3chYyG4eGlpaa/HhEIhg8HAsmwZ189kMgihagg7nU43\nMDAQjUbn5+cVv3j1UHe/HACUBMuy2WxWjqIEABUBYXeZMvaJyej1etX3cqZSKUmSNCfsRFGM\nRqPFCDuEkMvl8ng8o6Oj6oZ3uFwuv98/OTm5V3BxKBTy+XzlTbGk02mdTlelbb9ms9nv98/M\nzGgrIq6zsxMhpOL0DAAUiclkMhgMULQDVAeE3WXK2Ccmg0PFjud5vV5fXlOXisRiMVEUixR2\nCKHu7u58Pq/6pqyGhoa9gos5jkun0+WNTaDqNNhtxuPxNDY2jo6OyqVBTaDX63t7e1XcLwcA\nxcMwDLxQAdUBYXeZSip2qvfYabfBzmazFV+goijK7/fPzc2pvrpnr+DiYDDocrnKXghWbWGH\nEOrq6jKbzUNDQxpKLVZ9vxwAFAnEFAM4AMLuMqIolrfHCYe4E+0Ku+LLdTJut7uurk7dCVkZ\nv9/PMMzm4OJcLre2tlbe2IRMDYQdQRD9/f3pdFpb06aqxxkCQDGwLJtKpTRUEQcOJSDsLlNJ\nxU6SJHXdWC0KO0EQYrFYqcIOIdTT05PNZlU3ZAmC6Ovr2xxcHA6HKYraJ5PvQGog7BBCNE33\n9/cvLi4uLy9X+15KgcN+OQA4EKvVSlEUuLGAuoCwu0wlPXboSgyeWqRSKc0JO47jCIIoY+kW\nPobstuDiUCjU0NBQyfKP2gg7hBDLsu3t7ePj48lksga3UwSHw6F6nCEAHAi4sYDqgLC7TNlW\nrCzsVHRjs9lsLpfTorBjWbY8MV0wZFXfzFgILpb77RoaGsq+lCAIuVyuZtt+W1tbnU6ntlKL\n5ThDMGQBnIH5CUB1QNhdphIrFqkq7HieJwjCZDKpdYDyKKPBbjOYGLLoSnBxJBIxmUyVyLLq\nhdjtRSAQQAgVs0sDE3DYLwcA+8OybDKZLLTeAkDtAWF3mbKFHUmSJEmqKOxSqZTBYCjv8GqR\nzWaTyWQlwo6iqO7u7tnZWRxWoJrNZoIgUqnUPsHFB5JOp0mSpGlawYPtj5xavL6+vri4WLOb\nVggO++UAYB9sNptOp4PPHoCKaEkNVJWye+yQ2oOxWpyciEQiFEVZrdZKLuLxeNxu9+joqOqG\n7PLysl6v7+np2Se4+EDS6XTZOSllY7FY/H7/1NSUhswjHPbLAcBeyK3DIOwAFQFhd5mye+yQ\n2lF2WhR2sg9byZyBjN/vz2azqq/JCgaDDQ0NPp9vr+DiYqjZ5MQ2vF6vz+cbHh7WinmEyX45\nANgLaLMD1AWE3WXKtmIRCLvSqbDBrgBFUV1dXbOzsypOd8ZisUQiIY9N7BVcXAxqCTuEUHd3\nt8lkkmd7VTlAqWCyXw4AdoVl2UQioaGxJOCQAcLuMpVYsSpuFZMkSXNZJzzPZzIZRYQdQsjr\n9bpcrpGREbVESSgUcjgcheGVncHFRaKisJNTi5PJpIYGTjHZLwcAO7Hb7QRBgBsLqAUIu8to\n1IpNpVKSJGlL2EUiEaPRqOAYb09PTyaTWVhYUOqCxSMIwsrKyuaUk53BxUWiorBDCBkMhv7+\n/vn5+dXVVbXOUBL4xBkCwDZIkrTZbCDsALUAYXcZjVqxPM/rdLraN91XglI+bAGapru6ui5d\nulR7Q3ZlZYUgiLq6us1flIOLBUEo3tyUJCmbzaoo7BBCDoejra1tdHS0DB9ZFfDZLwcA22BZ\nFtrsALUAYXeZCq1YFYWdthLsJEmKRqOV7N3alfr6eqfTWfvI4mAwWF9fv/OVQ1HUNddcE4/H\ni0yJS6fTkiSpK+wQQq2trSzLDg0NqbtJpXjwiTMEgM2wLBuPx7XyewQcMkDYXaYSK1bFuBPN\nNdjF4/F8Ps+yrOJX9vv9PM/X0pBNJpOxWGyvbRNycPHKykoxjWvpdJogCNUrr7KPLAiCVlKL\nwZAF8IRhGEmS4GUJqAIIu8to14rVlrDjOM5qtVYjhpem6e7u7loassFgkGEYi8Wy1wPsdnt/\nf//c3NyBwcWZTIam6crzXypHr9cfO3ZsdXU1GAyqfZaiwGe/HAAU0Ol0VqsV3FhAFUDYXQaE\nXW1QvMFuM7U0ZEVRDIfDPp9v/4e5XC6/339gcLG6kxPbsFqt3d3dk5OTWgmKA0MWwBBoswPU\nAoTdZbQYd5LP57PZrIaEnSAIGxsb1RN2CKGenh6e52uwI2tlZQUhtG1sYlcaGhpaW1uHh4f3\nmZLDStghhHw+n9frHRoayuVyap/lYOT9cnNzcyrGGQLANhiGicViMNkD1B4QdgghJEmSJEma\nq9jJ04saGp6QlU01GuwKGAyGrq6umZmZVCpVvbsghEKhkNfrLbIvs7293ev17hNcjJuwQwj1\n9PTQNK2V1GKPx6NunCEAbINlWUEQcFhmDRw1QNghhJD8oaqS4QlJkmpftEulUjRN6/X6Gt+3\nbDiOs9vtZf+ci6ShoYFl2arukOV5PhqN7jU2sSv7BxdjKOxIkhwYGEgkElrZyorJfjkAkKEo\nymKxgBsL1B4QdghdEXZlV+woikII1b5oBw12e9Hb25tMJg8cWSibUChks9lsNlvxT5EHTmma\n3hlcLElSJpPBTdghhIxGYyAQmJubW19fV/ssB4PDfjkA2AzLshBTDNQeEHYIISS/0ZYt7OQS\nFAi7/cnlcolEojbCzmAwdHZ2VsmQLXJsYic6ne7YsWM7g4uz2awoihgKO4SQy+VqaWkZGRlJ\np9Nqn+VgVN8vBwCbYRgmGo3CqxGoMSDsEKrYipXNUBB2+8NxHEmSdru9Nrfz+XwMw1TDkF1b\nWxMEwePxlPHcXYOLZc2keojdXrS3t9vt9osXL2qiDVzF/XIAsA2WZfP5PJSQgRoDwg6hiq1Y\nkiRJkqxxj50kSdpKJ5Z92FpGtfn9/kQiobghGwqFPB5P2a2NJpPpmmuu2RxcnMlkKIqqduth\n2RAEEQgEcrnc5OSk2mc5GBX3ywHANgwGg8lkgjY7oMaAsEOoYisWqTEYm8lkBEHQnLCr5R2N\nRqPihmw6neY4rgwfdjM2m21zcHE6ncZ8tJmm6YGBgXA4HAqF1D7Lwai1Xw4AdsIwDLTZATUG\nhB1CCImiSBBEJcKu9lvFeJ4nSRLPxqydpNPpVCpVY2GHEGpsbGQYRsEFWcFg0GQyVW4obw4u\nTqfT2PqwBex2e2dn58TEhCa2JNV+vxwA7ArEFAO1B4QdQpWtnZCpfcWO53mj0YjDEqpiiEQi\nNE3vs32revj9/lgspoghK0lSOBxubGys/FJoU3BxIpHQhEBvamryeDyaSC2u/X45ANgVlmWz\n2exeAZYAUA1A2CGkTWGnuQY7p9Opyq2NRmNHR8f09HTlhuz6+noul/N6vYocDCHU3t7e0NAQ\ni8UqfPnVjJ6eHp1ONzo6qvZBDgYMWQAHTCaTwWAANxaoJdp4O6k2lewTk1GlYqchYReNRmvv\nwxZobGy02WyVG7LBYLCurk6OLVSK7u5uhFAoFNo1uBg3dDrdwMDAxsaGJhaz1my/HADsgxx6\novYpgCMECDuEtFmx05CwSyQS2WxWRWEnz3XGYrFgMFj2RTKZTCQSqXBsYif5fF6SpF2Di/HE\nbDYHAoFLly5FIhG1z3IANdsvBwD7AG12QI0BYYcQQqIoVhg2odfra/muLIpiJpPRirCLRCJm\ns1nd+YCCIVt20G4oFDIajYovus1kMgihgYGBncHF2OJ2u5uamkZHR+XD40wN9ssBwP6wLJtO\npzUR8Q0cDkDYIaTBih3P85IkaUXYqdhgt5nGxkar1To2NlbGcyVJCoVCipfrEELpdFqv18vh\ndvF4XBPtawihzs5Os9k8NDSEf2pxtffLAcD+WCwWiqKgzQ6oGSDsEFKix67GcSc8z1MUpWyz\nV5UQRXFjY0NFH7ZAwZAtI4yN47hMJlNfX6/4qQpZJ7K2W1tbKwQX4wxBEP39/ZlMZnp6Wu2z\nHEBV98sBQDFAmx1QS0DYIaSQFVtjYaeVcl0sFhNFUXEHszyMRmN7e/vU1FSpHmIwGHS73TRN\nK36kdDpdyDrZFlyMOTRN9/f3Ly0thcNhtc9yANXbLwcAxQDCDqglIOwQ0qYVi/muggIcx9ls\ntrIXcClOU1OT1WotyfHM5XLr6+vV8GHRVmGHtgYXV+N2ysIwTEdHx8TEBP5xcVXaLwcAxcCy\nLM/zmph8Bw4BIOwQUijupJbDExoKsav9JrH9IQjC7/dvbGwUX2cKhUIURVXpv2KbsEObgos1\n0ZTT0tLidDqHhoZqPBVeKtXYLwcARWKz2XQ6nSZ+o4FDAAg7hBSq2EmSVDNtpxUrVhCEWCyG\nlbBDCJnN5vb29snJySINWXlsokpLPnYKO3QluPjixYuaCKwPBAIIIfzHPuT9cuVNzwBAJRAE\nAUtjgZoBwg4hhXrsEEK1KVpks9l8Pq8JYcdxnPwXTe2DbKe5udlisRQTWRyNRlOpVDXGJhBC\ngiDkcrld94l1d3czDHPhwgX87Rs5tZjjOPx3s/r9/ng8DoYsUHugzQ6oGSDsEFKoYodqJex4\nnicIQhM9dhzHsSyL4b4sgiB6e3s5jjvQkA0Gg06ns0q7XOVoq10vThBEX1+fVoKLLRaL3++f\nnp7G/K1Lwf1yAFASLMsmEgnMOxaAwwF277iqoEjcCaqhsDMajRiqpZ3g1mC3GbPZ3NbWtr8h\nm8/n19bWqjQ2gRBKp9MkSe41bKvT6Y4dO6aV4GKv1+vz+YaHhzFPLW5qarLb7ZXvlwOAkrDb\n7QRBgBsL1AANiIMaULkVS5IkSZI1E3aa8GEzmUwymcRW2CGEWlpazGbzPu/x4XBYp9O5XK4q\nHaAQYrcXFEVpKLi4u7vbZDLhL0N7e3sr3C8HAKVCkqTdbgdhB9QAEHYIKWHFohomnmgl64Tj\nOIqirFar2gfZEzmymOO45eXlXR8QCoUaGhqqNDaBEMpkMgeavBoKLpZTi3men5mZUfss+1H5\nfjkAKANoswNqAwg7hJSwYlENhZ1Wsk5kH7Z6qkgRCobszhmFWCyWSCQaGhqqd/ddR2J3oqHg\nYoPB0N/fv7CwsLq6qvZZ9qOS/XIAUB4sy8bjcfxbZgGtA8IOISWsWFSrKDtJkrQl7NQ+xcG0\ntLSYTKadhqw8NlHV4miRwg5tCi7GXDAhhBwOR1tb2+joKM5ZLZXslwOA8mAYRpKkWCym9kGA\nQw4IO4Q0ZcWmUilJkvAXdjzPZzIZTQg7+T0+EomsrKwUvigIwsrKSlXLdagUYYeuBBePjIzg\n36bT1tbmcDiGhoZwLk6UvV8OAMpDp9NZrVb8f38BrQPCDiGFhJ1Op6uBsON5XqfT7d9xjwOR\nSMRoNGqiFxAhZDabW1tbJyYmCobs8vIySZJut7t6N5UkKZvNlhSkoqHg4kAgIIoi5sOnZeyX\nA4BKYFkW2uyAagPCDiFNVey0MhKrFR+2QGtrq9FonJyclP8ZDAYbGhqqmimTTqclSSo1IU8O\nLj5//jzmwcV6vX5gYGB1dRXnvsAy9ssBQCWwLBuLxURRVPsgwGEGhB0SRVGSJA0JO/zLYJIk\nRaNRp9Op9kFKQI4sXltbW11dTSQS8Xi8Bj4sQRClFl/lyVODwYB/cLHVau3p6ZmcnMTZeyp1\nvxwAVALDMKIoJhIJtQ8CHGZA2CH5w5NWhic0MTkRj8fz+TzLsmofpDSsVmtLS8v4+PjCwgLL\nstX+OWcyGYPBUMbUMEmSWgkubmhoqK+vHx4ezuVyap9lT4rfLwcAFUJRlNlsBjcWqCog7C4L\nO0UqdjV499KEFRuJRKxW614LFXCmra3NaDQuLy9Xu1yHSpyc2IaGgot7enpomsZZgxa/Xw4A\nKgfa7IBqA8IOyWU2RYRdtSt2+Xw+m83iL+w4jtOWD1uAIAi32y1JUg3i9w5cO7E/WgkuJkly\nYGAgkUjMzs6qfZY9AUMWqBkMw2xsbGD7OQc4BICwU9KKrXaPnTwLibmwEwQhFotpa3JiM5FI\nxG63T05OVrv+WknFTqYQXLy4uKjUqaqB0WgMBAJzc3Nra2tqn2VPmpub998vBwCK4HA48vl8\nMplU+yDAoQWEnWJWrE6nEwShqp/DeJ43GAyVa9CqInfKMwyj9kHKgef5jY2N7u5umqYLE7JV\nonJhhxByuVyBQGBqagrz4GKXy9Xa2jo6OppKpdQ+y+4cuF8OABSBpmmTyQRuLFA9QNgpacVK\nklRVN1YTDXYcx9ntdszV514Eg0G73W632/v6+lZWVqqnliRJKmZRbDF4vd62tjb8g4vb2trs\ndvvQ0BC2WQ/77JcDAAVhWRbz31ZA04CwQ6IoEgRReU+VXq9HV2RildCEsItEIhr1YUVRDIfD\nPp8PXZmQnZiYqJIhm81mRVFURNghhNra2vAPLiYIoq+vL5/PT0xMqH2WPdlrvxwAKAjDMFCx\nA6oHCDtlFsWiK8Kuqm12+IfY5XK5ZDKpUWG3uroqiqLH45H/2dbWRlHU1NRUNe6VTqcRQgpu\nENFEcDFFUf39/cvLy9huaN11vxwAKAvLstlsFuePYYCmAWGnzNoJVH1hJ0kS/iF2HMeRJGm3\n29U+SDmEQiGv11tQ+SRJBgKB5eXlarT8p9NpmqYVNKy1Elxst9u7uromJibi8bjaZ9mdnfvl\nAEBZTCaTwWAANxaoEiDskCAIigg7giCqui42k8mIooi/sHM4HDXIClGcVCoVjUa3xdfZbLbm\n5ubx8XHF/7cqMjmxjUJw8cWLF7HtY0MINTY2ejyeoaEhbFOL5f1yYMgC1QPcWKB6gLArv2KX\nTqe3vX1uE3aSJD377LNK/fbyPE+SpOJqQFk0tyK2QCgUslgsO2uN7e3t1TBk5bUTyl4TXQku\nTiaTY2Njil9cQfx+v16vHx0dxTPNS44sBkMWqB4QUwxUDxB25ffYjYyMbHu/3xZlt7y8nEgk\nLBZLpUdECF1psMO5GJZOp1OplBaFnSRJhbGJbZAk2dvbGw6H19fXFbxjNSp2MpoILpZTizc2\nNubn59U+y+4UpmfAkAWqAcuy6XRa7rUFAGUBYVd+xa6joyMYDEYikcJXNgs7SZLm5uaampoo\nilLknPiPxEYiEZqmlRKytWRtbS2Xy3m93l2/a7fbm5qalDVkqyfskEaCi00mUyAQuHTp0ubf\nIKyQ98tVO84QOJpYLBaKoqDNDqgGIOzK77FjWdbn842Pjxd61TdvFVteXs5kMk1NTUqdE39h\np91NYqFQyOPxyOMvu9LR0aHX63castlsdnp6uow7VlXYIY0EF7vd7ubm5pGRETzrFrIhu7q6\nivPPENAu0GYHVAkQdhXFnXR2dhIEMTMzI/+zULErlOtomlbqnJoQdlr0YTOZTCQS2dWHLUCS\npN/vD4fD28pL6XR6fn6+1EmFXC4nCEK12yU1EVzc0dFhsViGh4fxnPawWq2tra3j4+NgyAKK\nA212QJUAYVdR3IlOp/P7/UtLS/LvZ0HYyeW65uZmpQ4pCEI2m8U5xC6RSORyOS0Ku2AwaDKZ\nDtyBxjBMU1PT2NjYZkO2vIwbuUBVgzkY/IOL5ZSWTCZTpbzAymltbaVpGtvjAdqFYRie5+Ez\nA6A4IOwqjTtxOBz19fVjY2OCIMjCTvHuOoRQKpWSJAnnil0kEjGbzdWY9Kwq+4xN7KS9vZ0k\nyc3ea9nCTq/X7+P8Kgj+wcU0Tff394dCoXA4rPZZdoEkyWrvlwOOJjabTafT4VxQBzQKCDsF\nNk90dXWJojg7OyvHnSherkMI8TxPUZSCSlFxNNpgF4lEstlsfX19MQ/W6XSBQCAUChUM2bKF\nXc0UsCaCixmG6ejoGB8fTyQSap9lF6q9Xw44mhAEAW12QDUAYafA5gm9Xu/3+xcWFnK5XD6f\nV7xch7BvsBNFcWNjQ6M+rNvtLv5/FsMwjY2NhYkZkiRJkixV2GUymVrmEWoiuLi5udntdg8N\nDVV1KV/ZVHW/HHBkgTY7oBqAsFNmpZjL5fJ6vSsrK5lMRvFyHcJe2MViMVEUWZZV+yClkc1m\n19fXi/RhC3R0dBAEUTBkt4UXFkO1R2J3oong4t7eXpIkR0dH1T7ILsiGbJX2ywFHFoZhkskk\nnh9mAO0Cwk6xlWKyIZvP5xUv1yHshR3HcTabrTZNYwoSCoUMBsP+epTn+ccee2x6erpgw+l0\nut7e3kKEoSaEHdJCcLFOpxsYGOA4Ds/UYqvVWqX9csCRxW63EwQBbXaAsoCwU6DHToaiqPb2\ndoSQ4uU6hFAqlcJ5JFajQSehUMjn8+2/zMNsNvf09KytrT311FMFebc5wlArwg5pIbjYbDb3\n9vbOzMzg6U/J++UgshhQCpIk7XY7CDtAWUDYKWPFyjQ1Nd16662Kl+uy2Ww+n8e2YicIQiwW\n05yw4zgunU4XMzbh9Xqvv/763t5eWd5NTk5ms9lChGGpwk4QhFwup9bOX/yDiz0eT2Nj4/Dw\ncCaTUfss25H3y4EhCygItNkBigPCTjErtnrwPE8QBLYVO47jSJI8MAcON4LBoMvlKnI6lSAI\nj8cjy7tIJPL000/PzMx0dnYuLS1JklSSsKtZiN1e4B9c3NXVZTKZ8Ewtttvtzc3NExMTYMgC\nisAwTCwWw3ZiHdAiWAua2qCUFVs9eJ43Go3Yqk+O4xiGwfZ4u5LL5dbW1kodmyjIO7/fH4lE\nRkdHLRbLxsZGSSkY6XSaJEkFV5KUAebBxQRBDAwMpFKpwk4XrGhvb991vxwAlIH8kTgWi6l9\nEODwoKU34yqhoBVbJfCfnNCcDxsOhymKKi94jyCIgjkrSZIoivF4vPinq9Vgt43u7m6WZbEN\nLqZpuq+vb3FxcWVlRe2zbEc2ZHfulwOAMtDpdDabDdvyOaBFsBY0tQGEXSVkMplkMqk5YRcK\nhRoaGvYfm9gfuXp33XXXdXR01NXVFf9ElmXb2trKvq9SEATR19eHc3Cxw+Fob28fGxtLJpNq\nn2U7drt95345ACgPiCkGlAVrQVMD5IoL/sIO5wY7iqKsVqvaBymBjY0NnucbGhoqvxRBEK2t\nrZ2dncU/xWKxeL3eym9dOfgHF7e2tjqdzqGhIQyl5879cgBQHizLylGgah8EOCRgLWhqgPy7\nhLOwE0UxnU5jW7GTfdhKSl+1JxgMOhwOHPxQ1aEo6vjx4zgHF8t+N4bH27lfDgDKg2GYUjs6\nAGAf8BU0tUEWdjgPT6TTaUmSMBd2ap+iBPL5/OrqaqljE4cYo9GIc3CxXq8fGBhYX1/HMHtv\n2345ACgPiqLMZjO02QFKAcIO94odz/M6na5mO+NLguf5TCajLWG3vLys0+ncbrfaB8EIzIOL\nrVar3++fmprC8J1v2345ACgPSLMDFARfQVMbCqvc1T7InuA8ORGJRIxGI7b9f7sSDAbr6+u1\n5R3XAMyDi71eb0NDw/DwcEnJMjVANmQL++UAoDxYlt3Y2JAkSe2DAIcBfAVNbcDfisVZ2HEc\nV15iiFrE4/FEIgE+7K5gHlzc3d1N0/Tw8DBub35gyAKVw7JsPp/HcAAc0CIg7DRgxeIp7CRJ\nikaj2vJh5bEJbZUYa0lbW5vP58MzuJgkyYGBgUQigWEvoGzI4hmnDGgCmqZNJhO4sYAi4Cto\naoMgCAjxP/qDeh1BkMZbPjezbeA888M3MqTpVf+pQCp49ud3eXXGl38jUlq9YZOwU/AwChCP\nx/P5vIaEnSAIKysriqScHGK6urqwDS42Go39/f3z8/O4+cU6nc7v9y8tLXEcp/ZZAK0CbXaA\nUhx1YSeKIpl69js/WxcRkrKnv/ffl7CKEsrn87lcDs+KXSQSsVqtFEWpfZBiWVlZIQiipDDh\nI0ghuPjcuXMYpu86HI7W1taxsbFUKqX2WbbgcDh8Pt/Y2Clsw8YAACAASURBVBgYskB5gLAD\nlAKEncCffeTBCNH8W789oM+d+d4PpnH6syw7Ynhah5prsAsGg16vF2fbHRPk4GJJkoaGhjAM\nTW1ra7Pb7RimFnd2doIhC5QNwzC5XA7DLghAcxz1NzlR4E4/fDpKtr3mr+597YA+9wxeyo7n\neYPBgOFshyAIsVhMQz5sMpmMxWK7jU2kpn/66fe88oaeesZsNDsaOgZf/KZ7vv70SlVfBflz\nf3MNRV/3idHd75K473dNhOm138tU8wz7gnNwsVxTzOfz4+Pjap9lCwVDFuouQBmYTCaDwQAv\nHqByjrqwy6/9/KEzSV3n777mxmte9ao+XfbZ+x+Y3Oc9PTP34Kfe/MJAvd1kdjQP3vaef3ws\nvMmtyi099Nl3/vY1TQ6z0epuv/aVf/yVp1e3FzwkfuL+u28/2cyaLc6WY79515ee3tx0t+0K\nb7j736YzOC5IkAcnGYZR+yDFEgwGGYaxWCxbvipFHv7Q80++/IP/8shq3XW/8/q3vvmOF/YQ\nI/ff+7bnn3rlF87h5fXVmkJwMYYlKIqi+vv7V1dXg8Gg2mfZgsPhaGhoGB0dxa2aCGgCOfRE\n7VMAmueICztp9Zc/OsOT3Xe89lpK3/eqVwb0ufMPPDC+x9/k/NS//d6NL7/72yPU4Cve+Ibf\n8fNP/Mv7XnzLe368KiGEkDj/7Tfe+NI/+9pTibZbf++Nv3uDe/l/v/CuW5//3p+ubhJu4qV/\nf/2tb/3OxsCdf/gnb3mBY+nRf33fy9/+rZD8iJ1XePzf77nr7f+w5Qp4wHGc3W7HsJS4K6Io\nLi8v7xybyDz58Xd++pn8iT/9+eT4Ez+672tf/vLX7nvwuemhb7+pbflnf/H2T19QqcXMcvvX\nphen//nlaodS22y2Y8eOLSwsYBhcbLfbu7q6JicncVvEJC8OxnB0F8AfhmFg/gZQAOkoI4b+\n7oVmQtd5z7M5SZKk3DN/1adH1ImPXMxfeUT6f95gJ4yv/OaGJAlLX38FS1I9b3lgLit/M3Xh\n726xEfS1947kJXH9e6+rI8n63/n8ubgoXzx25qM32wldx/sfTUmSJGUefLuHRIT5ursfW5cf\nISWf+vM+PWG587/ikrTrFb7xzuPWq1fYdBi1OX369KVLl9Q+RbGEw+FHH300n89v/XLuzN0B\nPcH83v2xbY8X17/1ajuhv+bD53LbvpNPp3Ji5QfKPffXx/TUtfeO5A9+7JbnZbJC5XcvkXA4\n/PDDD6+srNT8zgczMjLy5JNPZrNZtQ+yhUgk8vDDD3Mcp/ZBAI2RSCQeeuihVCql9kEAbXOk\nK3Zi8H9++OsU2fni1xzTI4SQ/ppXvbJLn7v4wAPDO2t24uL3//0XG9bb/upTr265PAhqHHjH\n3e960W+wyWAGbTz4rR+t6a/7k8/+4XGrvNSAsF37wU+8rQPN3n/fE4XgCNJ9xz133+y8vPbA\nfOKWa60on+QzEtp5BQlZ2+58+xvbtl4BA3K5XDKZ1FCDnbxtYkd9UYxtxESUT/HZbRVRgr31\n/f/wqXvfdbOTRHJOjb7+ru88+uk7Ai6rmaYtnsBv/cl3JtPC8mOfefPNXW6L0eTqvOWtXz6z\nsfk6xfjyV5DWfvWBkzad5fj7HlwWEUrf/5pCj51893f8ZPKHf/6bbazJQJmcrdf81ju+/OvN\nDn5m9icf/f0b2p0Ws6Pthtf//aNLv3xPE+W96+eKvGpwDi72+/16vX5kZETCKbXY4XDU19eP\nj49jOHoC4IzFYqEoCtrsgAo5ysJOXPqf755N63pe8pJ+vfwV/eDtr+jQ54cfeGBohwmXO3/6\nXJYaeP5NrqvLqAjmtr/73//75cd+05wfPzeUIluf//z2zeLBcOqFz7NLa0MXl678fdf3X39i\nU5sXodPpLl9u5xUymYyo63rRDbYtV8AAjuNIkrTb7WofpCh4no9Go7vF11F9152wIP4nd7/+\nww+cX90sgsiGF9z1wQ+++8XNhd+P2I//5Hf/6mnHy95795+/+QbL7K8+/6aXv+oVL/qde895\nb7vrfW+51b38xNff95q7H7qSG1+UL38ZKfrE377yjs+ONr31Wz/97Eu9u/1Kps5+7Hdf96VL\nba/+ow/9xdueb1946Gt/9LK3fjMoXr7XfX/wgts//INZ9sY7/t8rBrIP//VLb/6TX24oKXSw\nDS6WU4tjsdjc3JzaZ9lCV1eXIAhgyAKlwjAMhp+gAG1xhIWdOP+D7z6ZkfKj/3iHnrgMfcPf\nT+RRfvSB713cruxSHJeWSGeda9cfmRiLxSXS4XJu/a7e6WZIMRaNXZFlhNlq2X1L6c4r8DxP\nkpTXy265AgZwHOdwOLSybjUUClmtVpvNtuM7RP3rPvX3r2zShX7x0TtP+DwdN7zqrnu++J1H\nRlZ3TqNKqVXyJd84/di3PvvxT371Fz//6A10buKXT3g/8tjTD3zxE5/4px8//MWXseLSI78a\nzSOEkBT5wYf++P5F122ffezCY9//+lf/48enR/7vIzfR41/5o48/lt565cSzn77j9o8943rd\n13/2T7c37t6zKMXOXTC+78GzD3/zc/fe+4///fQP399DRn75348mEEJS9If3/On9wbY/uO/s\nmR//x1f/48dnzn73DfTEdELhCha2wcUmkykQCMzOzmK1rVWv1wcCgYWFBXiTBkoC0uyAyjm6\nwk6c/cH9T2dIz3WvfuOb/vgq73/zLQ26/Pj3v3d+m7IzWCx6Qoxv7K6wCJvNSkjRSHTr26kQ\n5WISabVZCz/nPcXQzivwPG8yGTa2X0F9ZGGn9imKQhTFcDi853JYQ987vz90/idfvuftrzjl\n4p790dc+/r7fv3WgwdP14nd8+hfzm/Wdvv/tH7rzsu7Std9wg48kvb/3F+8OyCPLpPemW3r0\nEs+nJIR2cdV39+WRlB7+p99/+YceIW/7ws/+7ffb9Hv+Z5CuV//l3bdccfAt1//WTSwhJBMp\nCUnRn37zv5cNt37gY7c3yc/XNb7iIx94kUlp1Y1zcLHb7W5ubh4ZGUmn0wc/ulY4HA6v1zs2\nNgaGLFA8LMvyPI/bxydAW2AkF2qLMP39+09ndS2v/MBH/vLuz17lc1/51P/r0OUnvv+953Jb\nnkD3Dwb0+YtP/Dq2SbulHryr0WB5/mdnyJ7j/UZh9vHHZjf/Dc8+9+hTMeQcONZ08M9Zv+MK\nPM+bqbnir1Ab0ul0KpXSirBbX1/P5/Ner3fPR5D23tve/dGv/s/T06ur07/+4dc+/ke3D5oX\nH/rqB1924xu+NV/4v6mrq68rlNMIg8FA6OqbG6ktX7nyjyJ9eTH8/Xe+7H0/DQlSLBRO7ltg\n0/eeGrRe/SdhMFwZmM2PnD3H6zp/4/pNFi5Rd/1vdFZhXhnn4OKOjg6bzTY8PIzVwbq7u8GQ\nBUrCarXq9Xoo9AKVgItcqDXC5APfPZvT+V/yEr9ha089de0dt3fo8lPfv//ZLcpO133H637D\nyP33xz76yJW29ez4v37mgbDY8cIXtugct73uZc78rz/zZ/8ydLnPSoo8ee+ff3VKanvt628p\nIreC2HEFPhka+8bnir9CbYhEIgaDYXsgHK7I2yb0+t2LYaIobJIBeqb9+le87S+/8P2zU899\n9c4maekHf/O5Jze9BrbXwPb0oov05YXFZ89Rr/nC59/QlHvu03/2lal9gs8Ig8m4h4MfjXAi\naXcwm+9FMg62Or/Z2AYXEwQRCAQymczU1JTaZ7mKXq/3+/0LCwuxGB4LngHsIQjCbreDGwtU\nwhEVdsL4A/efy+n67nxxF7F9xxR16s5Xd+nz0z/43tltyu7dX/z/nm8Z+uxtx657xZv/8L1v\nfdmp573/f5Mdb/nEHw3qEeG685OfeXXDyo/ee/OJF/3eO/7wba++cfDFH3ki2/uuL/3184va\nCLbjCu9/42vf+MVnSrhCTdCQD5tOpzmO221sAiGEUPK/XmOlTC/+0uKOEo8l8Ad3v8mvF5ZG\nxrgyWtWK9OXJutu+8OA33vveT37s5a7kox/98/uCZdSaCLPFTIjxrT2YUjwWq9qQKLbBxTRN\nDwwMhEKhcDis9lmu4nK5vF7v6OgoVqVEAGegzQ6okKMp7ISR733vYo665jWv6SDEHctDqRN3\n3N6pFy794Hunt/Y5GAc/8OMnvnPP7a3Rp+7/2lfvf1Y8+ZZ/ePDRL7+8jkAIIV37m779xE//\n7q3XGcYf/ObX7ns05Hnx+//lkUe/8FJ3se1O267w3Bp767s/X9IVaoCGhF0oFDKZTHuuxzD0\nBDp0uWd/9svwznfc3PrahkjafT57GT/6na46Qrv48rrWm1/QSSOy8fV/95c3m9d+eM+HHyxd\nRlK9x/toYfrM2bWrT5XWzp6eqmYXHLbBxXa7vaOjY3x8PJFIqH2Wq8iG7OzsrNoHAbQBwzDJ\nZBK3TlZAS6gdpKcyZ8+enZ2dVfsUuxCPxx966KEqJa+KohiNRmdmZp555plHH320+LvIp0qn\n09U4lbKIovjEE08sLCzs/ZD82KdvthCk65Z7fr6w+b8ou/Tgn560ELqO9z6UkC4nSxt+85+C\nhWTi3OkP9eqpEx8dKuQLC7Ofu4XWtb7//7KSJEni2n+91kXqfK/88sXE5dOsP/HhmxlS1/G+\nR3hpZ0Bx6sw9xw0E1ffBJ5KSJEmp795pRMbX3J/e9e6SlPnVuxtJw0u+EhYlSQx/83YnSfnf\n8eOgfLF86CfvCtAEIj1vfzBTwQ/wQLANLh4eHn7qqadyue3p0iqytrb28MMPb2xgkC0OYI8g\nCI888sja2praBwG0yt6DeEcDURTx3IvF8zxFURRFHfzQokmn05FIJBKJcBwnCILNZnM4HJ2d\nncXfJRKJmM3mq637GLO+vp7L5fYbm0A6//u+9Y3h337T1z/20u6v9l1/XV+zw5CPLY2d+fXF\nUNp2/Yf+7SMvLK+TkHDd+cnP3P/k277/3ptP3P/bLwxYV5/95c9/vYgC79nDVTde+8FPv/2+\nl375S3/2hTf934cGSrqX93Wf/fyPn3vbV++47tzvvPSUY/3sz3+51veC3slfxWi6rNMXi9fr\nTaVSIyMjg4ODWG0N9vv9zzzzzOjo6LFjx9Q+y2VcLpfH4xkdHb3uuut2WAQAsAU5JTQajbpc\nLrXPAmiSo/4nRhR3WrFYwPO82Wyu/DqCIHAcNz09ffbs2aeeeurSpUsEQXR2dt54442nTp3q\n6Ogo6S2Z4zin01n5qWpAKBSqq6s7QLPqW+7817Pnf/j377mtMz/91M/uv++7P358Wux++R9/\n8RfnH/n4CxxlW+Al+/L2F/3NJ+70pk9/6gNfny2xF0vX9oZvPf2zT7ymK/nUd7/548m6N3zz\nwXtvMhCk1Wap9isbz+BinU43MDAQjUbn5+fVPstVuru78/k8bkHKAJ6wLAuDsUDZEBJOq3hq\nz5NPPtnR0VFfX6/2QbYzMjJCkmRvb28lF3n66adTqRRBEBaLxev1ut3uSsSiKIqPP/54X1+f\n2+2u5FQ1IJvNPvnkk4ODgyzLqn2WKiNtzDw3vm5qPR7wFgp0mV+8o/W2/zj+z3MP3uWtdnum\nJEnDw8PxePzUqVN0lWuEJbGysjIyMnLNNdfg81FkbW1taGjo1KlTu8VlA8BVIpHIhQsXbrnl\nFjwNJQBzcCxW1RKcrdjKK3aFYN5EIjE7Ozs9Pb24uJhMJvd/1l7EYjFRFDUhlYLBoNFo1MRR\nK0VcvO+um2940Qd+Uhi8kNZ+9p8/XdOfePELajF0g21wscfjaWxsHB0dzWR2bhJRB7fbXVdX\nB5HFwIHIRgqk5ADlofvbv/1btc+gJrOzs16vVxHTU1mmp6d9Pl+FB2MYxul0rq6uulyu5ubm\nfD6/srIyNzcXDAaTyaQgCDRNF69rQ6GQKIpNTU2VHKk2jI2N+Xw+rBq/qgXp7rQP3/ef3/n+\nz8e5eHjymV99+94//psHlprv+ud/emOP4vsndoUgCLfbHQqFIpGIx+PBZ9ec0+lcX19fXl6u\nr6/H5FQOh2N+fl4URa2MlgOqQJLk+vo6QRDwOgHKAIYncOyxy2Qy+XxeEbnJMMzg4OD58+fl\nrFSCIOQRCo7jpqamcrmcxWJxOBxOp5Nl2f1FXiQSwcfV2odIJJJOpzG016sD2fi6b/yfue+v\nP3P/f33yf8IpY0Pv8970+a/81btuKb9DsHTk4OJnnnlmbGysr6+vdjfeF4Ig+vv7z5w5MzMz\n09XVpfZxEEKIoii/3z80NOR2u8GQBfYB2uyAsjnSwk4URUmSMBR2PM8TBGE0GhW5ms1mO378\n+Pnz5xFCfr/faDT6fD6fzydJUiKR4DguEokEg0FJkhiGkUWezWbbVuEQBCEej3d2dipypKoS\nDAbdbjdW/V5VxtT5qg9/+1UfVvcQcnDxc889NzMz09HRoe5hCtA03d/ff+7cOZvNtu+IdO1w\nu91ut3tsbOzaa6/FpI4IYAjDMIuLi3iWHgDMOerCDiGEYY8dz/NGo1HB32ebzTY4OHju3DlB\nEPr6+uS3E4IgbDabzWZraWmRk+04jltdXb106ZJer3c4HLLIM5lMCCGO4+QhfKWOVCVyudz6\n+jo+ORdHCjm4+MKFCzRN42PZsyzb3t4+Pj5utVoxWYXn9/tPnz49NzfX1tam9lkATGFZVpKk\neDx+JFpKAEU50j12+Xx+YWGhublZ2bi4yllZWUEIKVtgoGna6XTOzs4mEom6urptpQKCIEwm\nk9PpbGxsbGpqMpvN2Wx2eXl5dnZ2eXmZ53mO42ia3nM9FzYsLS0lk8nu7m6ohaiCyWQymUwT\nExMWiwUTFYUQYlk2Ho8vLS3V19fjUP/Q6XRGo3Fqaqquru4olZaBEiBJcmVlRa/XH4khMEBR\n1P8bpyJyxQ6HP/TbUCrEbhtWq3VwcJDjuJGRkX1ibiiK8nq9vb29z3ve82644YaWlpZcLheJ\nRKLR6NmzZ6enpzmOw3asLxQK+Xw+UHUq4vV629raRkZGsOoQCgQCCKHx8XG1D3IZj8fjcrn2\n/00EjjjQZgeUB3aappYIgoCOkrBDRWu7AiaTyefzdXV1SZLk9/tdLlc0Gj1//vwTTzxx8eLF\npaWlVCpVjXOWRzQaTaVSR2ZsAl8wDC6WU4vX19fx2W/r9/szmQxWKcoAVsjCDqQ/UCrQY6dk\nj10ymTSbzRWWi0RRTKfT1UtgsVqtJ06cmJycLD7Dj+M4iqLkzIj29vZcLheNRiORyPz8/MTE\nhOzhyj15er2ar6hgMOh0OpUaOgEqoaurK5PJnD9/Hp/gYovF4vf7R0dHrVYrDvYWRVHd3d1j\nY2Nutxsf2xrAB5Zl8/l8Mpm0Wq1qnwXQEtgVq2qJslasKIpnzpxJJBIVXieVSkmSVNVoPYvF\nMjg4WLyi5TjO4XAUBCtFUXV1dX6/f7NXOzY29vjjjxe82tp/yszn82tra4VMZkBd8Awu9nq9\nDQ0Nw8PD2WxW7bMghJDX6wVDFtgLmqZNJlM0GlX7IIDGONLCThAEgiCU6sfKZrOSJFU+h8Hz\nvE6nw6TIISMLu12/JXu1/f39N9100+DgoNPp5DhO9mqHh4dDoVA6na7NIcPhsE6ng7XZ+ECS\n5LFjxyRJGhoawqcps7u722g04qOlenp6MpnMwsKC2gcBcIRlWRB2QKkcaWGnbESQvLmockGW\nSqWw2oTB83wmkzkwAJ0kSZZlOzo6rr322ptuuqm7u5skyZmZmaeeeur06dNTU1ORSKSq7+6h\nUKihoQHGJrBCDi5OJpNjY2Nqn+UyJEkODAwkEolLly6pfRaEEKJpuqur69KlS2Xv+gMOMSDs\ngDI46j12CjbYZbNZvV5fuVKs3uREeUQiEaPRKKfZFYk8VyvHtaRSKY7j1tbWlpaW0JUtZw6H\nQ9nY/VgslkgkBgYGFLwmoAhGo3FwcPDZZ5/FJ7jYYDD09/efP3/eZrPV1dWpfRxUX1+/uro6\nNjZ28uRJ+GQCbIZhmFwuh9ubAoA5R13YKVixy2azivinPM9jtbmL47hKziMHm/l8PkEQYrFY\nJBJZWVmZnp6maZplWZfL5Xa7Kx+5CAaDDoejJPWpOLlcbmpqyu/3V2nOenx8PJFINDY2siyr\nrQERi8UyMDCAVXCxw+Foa2sbHR21WCw4vGX6/f5f//rXCwsLLS0tap8FwAiTyWQwGKLRKA6v\nUkArHGlhJwiCssLOYDBUfh2e5zF580MISZIUjUb9fn/ll9LpdPLkLEIolUpFIpFIJDIxMTE2\nNma3251O566rzIpBEISVlZXe3t7KD1kJ8/PzsVisehUXWRknk0lBEIxGI8uyDoeDYRh15WyR\nOByO3t7e0dFRg8GAQ5EMIdTa2hqLxYaGhk6dOqX6+hmapru7u8fHx10uF0zIApuR3VgYCwOK\n50gLO8V77Cqv2OVyuVwuh8+Hs1gsls/nD2ywKxWTydTY2NjY2ChJ0sbGRiQSWVtbu3TpEkVR\nzisU/8NcXl4mSdLtdit7yJLI5XJLS0t+v796wq6pqWl5eVkURZ/PZ7PZNjY2Ll26lE6n5don\nwzAMw+C8V97r9abT6ZGRkcHBQRy2JMlzu2fOnBkfH+/r61P7OGDIArvDMMzc3JzapwC0xFEX\ndsr22FUuyORAV3xqMBzHWa3W6q1cIwiCZVl56iKXy3Ecx3Hc9PS07JG5XC6n08kwzP76OxgM\nqr4qan5+nqZpj8dTvVvYbDaapuvr65eWlgiC6O3tJQiC5/loNBqNRufn5zOZjMFgOHnyJLZG\nbWtrazabvXjx4smTJ3H49KLX648dO/bMM88Eg0EcKiI9PT2nT59eXFxsbm5W+ywALrAsOzEx\nkU6nsf29BnDjqAs7Za3YylNPU6mUwWBQ3RgqUGGDXUlQFOXxeDweT09PTyKR4DguEoksLCzI\n87Zut3vX8OFEIhGPx+WFUWohl+t6enqqWmghCMLpdKbT6ePHj1+4cCGfzwcCAbPZbDabZVGS\nSqXi8ThWQTk7kYOLz507d+rUKUVaFyrEarV2d3dPTk5arVa73a7uYQwGQ1dX1+TkpNvtxufT\nHaAuFouFpuloNAo7dYAiOdJxJxj22GE1/SQ3dSnuwx4IQRA2m62lpWVwcPCmm27q7e2lKOrS\npUtycsq2ZbXBYJBlWXXbkhYWFmialqeAq4rT6YxEIna7fXBwMBKJbAtjM5lMHo8HwxV5m5EN\nUKPReP78eUyCi30+n9frHRoayuVyap8FNTQ0MAwzOjqKScwegAMMw0DoCVA8WL8HVBsFrVhJ\nkhSZisVK2Mn7p9Vth5LLeIFA4Kabbrruuuu8Xm8sFjt//vzjjz8uL6sNh8MNDQ0qnjCXyy0u\nLra1tdWgL8rlcgmCsLGxYbPZTpw4EY1GL168iE/2b5FgGFzc09ND0/Tw8DAOcqq3tzeZTMrx\nQACAEGIYRv5rDADFcNSFnVLljVwuJ0nSIRN2HMfZ7XZ8fGGr1dra2nrixImbb745EAhQFDUz\nMyMIwtzc3LYyXi2pWbkOIaTX6+12+/r6OkLIYrGcOHEikUjgI4+KB7fg4kJq8ezsrNpnQQaD\nobOzc2ZmJpVKqX0WAAtYluV5HpM9eAD+HGlhp6AVK//KVWjFSpKE1dqJSCRSex+2GPR6fV1d\nXW9vr8Vi8Xq9jY2N8Xj8woULjz/++Llz5xYXF2u2x6yW5ToZl8slCzuEkNlsPnnyJM/z58+f\nFwShNgdQCjm4eH19fWZmRu2zIISQ0WgMBAJzc3OFH6+K+Hw+MGSBAlarVa/XgxsLFMmRFnYK\nWrHZbJYkyQqDdtPptCiKmAi7XC6XTCbxFHYyPM9vbGy0tLQ0NTUNDg7efPPNfX19JpNpfn7+\nqaeeevrppycnJ6tdxqtluU7G5XIlk8mCcpXlUSaTwadlrXjk4OKFhYXFxUW1z4IQQi6Xq6Wl\nZWRkpGYfDPbB7/cnEgkwZAGEEEEQdrsd3FigSI66sFOwYqeID0uSJA6jggghjuNIklR9TnAf\ngsGg3W63Wq3yP3U6ndvt9vv9N95443XXXdfQ0BCPx8+fP//EE08MDQ2FQiF5ma+C1L5chxCy\nWq0GgyESiRS+YjQaT548mc/nz507h0P7f0nIwcVTU1Orq6tqnwUhhNrb2+12Ow6di0ajEQxZ\noAAsjQWKB4SdMj8BRdKJeZ43mUyYZJNyHOdwODA5zE5EUdxnbELuxjt58uTNN9/s9/t1Ot3M\nzMyTTz555syZmZmZjY0NRRyu2pfrZJxO5za7kKbpwcFBSZKee+45zTXieL3e9vb2kZERHAoS\nBEEEAoFcLjc5Oan2WVBjYyPDMOPj42ofBFAflmWTyaTmPrkBqnCkhZ2yPXaVCztosCuetbU1\nURQPDATW6/WFodrrr7/+/2fvzR/SRqP2bxMIhDVhERQURWUTF6SdTtuZ57+fmU47LYKCgPuC\ngIKEHcKW94c8j19ftZYlG+R8frRt7lttm8vrnHMdu91eLpf39/f//vvvRCKRzWbHlkGi2HUs\n8/PzbJD1U1Qq1d7enkKh2N/f59yb5JuVlRWHw3F4ePjy8xIelUq1tbWVz+dzuZzYd5nz+XzV\nahUKsoDBYEBRtFqtin0RYAqQe0Axhz12nITYSaT02W632+22YNHEY5DNZm0220hNjTqdTqfT\nuVwudsXFw8PD2dnZ8fGxXq+3WCxWq3WkfVxi2XVzc3PsQo6XH1cqlaFQ6PDwMBKJhEKh6Uq4\nlVRwsdFoXF9fZ/9uiLulDcfxtbW1s7Mzs9k8Xd9QgFtQFDUYDOVy2WKxiH0XQOrI2rGTYI+d\nRBy7UqmkVqslcpmXtNvtSbZiP2bj/fnnn+/evWPnTL9///73338nk8n7+/tfTiGIaNex/Oxc\nhUKxvb2t1Wqj0eh09WZJLbh4aWnJZrNJIbXY6XQaDAYoyALQZgcMiayFHYel2Ml77Pr9Pk3T\nEtFSbIOd2Lf4KdlsVqvVTu5usisu3G73+/fvP336107XgwAAIABJREFUtLq62u12k8nk33//\nHYvFMpnMz7SRiHbdL2G1nV6vj0QijUZD7OuMgNSCi71er0KhSCaT4l6DbfurVqvZbFbcmwDi\nQhBErVabumAjQHhkLey4LcVOKOzY7iKJVFukLOwYhsnn85yvbMdx3Ol07uzs/M///M/W1pZG\no7m5ufn333+/fv16dnZWLpcf5y1Et+t+CYqiwWCQIIhoNFqv18W+zghIKrhYoVBsbW1VKpWr\nqytxb/JYkJVCDgsgFuwSIGizA36J3HvsOHHs+v1+v9+fsDGo2WyqVCoMwya/z4TU6/VutytZ\nYffw8NDtdvnbh42iqMViYRtZGo3Gw8PDw8PDzc2NUqk0m81Wq7VarUrWrnuE1XapVGp/f393\nd1civZvDwCbzRSKR8/PztbU1cS+j1WoDgUA8HjcYDOK2nDqdzkKhkEqlQqGQiNcAREShULBt\ndpL9zxmQCPJ17BiG4UrYsZOVkzt2EqnDlkolrVYregP7zxhjbGJs2GGLvb29P/74w+v1IgiS\nTqdvbm4Yhrm8vKzVagLcYWwQBPH7/fPz89FolKIosa8zApIKLrZarUtLS8lkUtxZY/a7Wa1W\npTCrC4gFSZJSSAUCJI58hR3bxMOJsGP/x59c2EmnDivZeViapkul0s/i6/jjcd7C4XCo1Wqb\nzcbOW3z58iWdTrPZKwJfaRhYNcAmiTzNNJY+kgouXl9f12q1onf+aTQat9t9eno6dXE2AFcQ\nBFGpVKT5vw0gHeQu7Djpset0OhiGTdhxJZEQu8FgUKlUJGv153I5jUZDkqQop3e73Ww2u7a2\ntr6+/v79+48fP66srNA0nUgk/vrrr4ODg0mC8fhjY2NjeXn58PCwWCyKfZcRkE5wMYIgwWCQ\npumzszNxb7K0tKTX60Wf5wDEgiRJhmEkXisAREfuwo6rUiwnIXZSEHbVanUwGIilnN6GYZhc\nLsf52MTw3NzcYBj22F2n0WgcDsfOzs4ff/zh9/sxDGP3W0QikaurK0lNpLrdbrfbnUgkpGCA\nDY90gotVKlUwGLy9vc3n8yJeA0EQn89XqVTEvQYgFkqlUqfTQegJ8DbyHZ7gVthNWIelabrf\n70tB2FEUZTAYhOlgG5VSqdTpdMSaWuj1ere3tx6P56U1y+63sNlsDMNUq9VisZjP58/PzzUa\njdVqtVqtBEGIPkLrcrnm5uYSiYTf7+dv9IRzpBNcTBDE2tra8fGxwWDQ6XRiXUOr1brd7pOT\nE5PJJNlGWIA/oM0O+CVSfH8LA5sGxFWP3eQNdgiCSKHHrlQqSbbBLpfLWa3WyYOgx+OZXfcq\nCIIQBEEQxPr6ervdLhaLxWIxk8koFAqz2czutxBRNLtcLoVCkUqlGIYRvk9xPNjg4mg0GovF\nwuGwuD9yuFyuarUaj8ffvXsn4k2Wl5eLxWI6nd7Z2RHrDoBYEASRz+cZhhH9Z0VAssi9FMtV\njx0nkxOi/0Pt9Xq1Wk2aDXadTqdYLIolR3q9XiaTWVlZGf57hOP40tJSKBT6448/fD4fiqIn\nJyd//fVXJBK5vr4Way2E0+kMBALsbK8oFxgDSQUXBwKBubk5cbvc2JkYiqKgICtDSJLs9XrT\nlU8JCIx8HTtJ9dhJZCS2XC6jKCrNzLNcLqdWq8USnaxdN14Fk52ofSzU3t/f397enp2d6XQ6\n1sNjc0cFw263IwhydHTU7/dXV1eFPHpsMAwLhUI/fvxIJpPBYFDEm7CpxT9+/Li5uVleXhbr\nGlqtdnV1FQqyMkSlUmm12nK5LO4WY0DKyFfYcbhPbHLHTiIjsRRFEQTB1ZeFW9ixCVFMTdau\n29jYmPD0x0Ktx+Op1+tsofb6+lqtVlutVovFYjKZhPni22w2FEUTiUS/319fXxfgxMlRq9W7\nu7tSCC7W6XQ+ny+ZTBoMBhHHjFwuFxRk5QkbeiLizxWAxJHiK1wYuEonZhim2+3ORjpxqVSS\nZh2Woqh2uy1Wy/8kdt3P0Ov1q6ur79+///z588rKSqvVisfjf/31VyKRuL+/7/V6HJ71Klar\ndWtrK5PJiB7hMTzSCS622+0OhyORSIgYKcfukKUo6u7uTqw7AKJAkiQMxgJvIF/HjqtFsZ1O\nh2GYSYTdYDBot9uiCzuappvNpjQnJ7LZrMViEaXkxJVd9zPUarXT6XQ6nf1+n6KoQqGQTqf7\n/b7RaLTZbPPz8/x91haLZXd39+DgoN/vvzrtK0HY4OJkMqlWq+fn50W8CWu7Hh0dhUIhsb50\nTwuyYg0VAcJDkmS325WIHQBIEHDsJoUNpJ3k7dtqtRiGEf2fKEVRGIaJmOPwM7rdbrFYFCu+\njt0SK0DGikKhsFqtgUDgzz//3NvbMxgM19fX//zzz7dv3y4uLnhKxSNJcnd39+7uLplMMgzD\nxxGcI5HgYja1uNFonJ+fi3gNl8ul0WjS6bSIdwAEBsdxHMfBtAN+hnyFHVc9dp1OR6FQTGL+\nNZtNpVIp+g/cFEWZTCYJ2jb5fB7DMFGsRNauW11dFbLvkG3F83g8nz9//vDhw/z8/MPDw7dv\n3/7999+Tk5NKpcKtAiMIIhQKlUqlo6OjadF2bHDxwcGBuCnQarU6GAze3NyIGPvMFmRLpdL9\n/b1YdwCEhyAIEHbAz5CvsOOwFDsbDXassBP7Fq+Qy+UWFxdFUZyC2XU/Q6fTud1udn3Z0tJS\nrVbb39//+++/k8kkhwtqDQbD3t5euVyWQp7IkGxsbJhMplgsJu7iVJPJtLq6mkwmRdyNodVq\nV1ZWjo+PJbjODuAJaLMD3kDWwk466cSiZ500Gg2apiUo7CqVSrPZFCW+ThS77mdoNJqlpaVw\nOPzp0ye3293pdBKJxN9//310dFQoFNi07UnQ6XR7e3u1Wm1atB0bXIzjeCwWE2DW5A1WV1dN\nJlM8Hp/8uzA2KysrOI5DQVY+kCRJ03S73Rb7IoAUEf+NJRYc9thN2N4uhawTiqJwHBddX74k\nm82aTCYcx4U/WnS77lXYYYvd3V0295hhmGQy+ffff8fj8bu7u0kkjlarDYfDzWYzFouJqFGG\n5zG4+PDwUFwxGggEBoOBiLqKjSwulUrTtQsYGButVqtSqcC0A15FvsKOwx67GSjFUhQlwXnY\nXq9XKBREGZuQlF33KuyC2mAw+Oeff25ubmIY9rjZIpPJjFeVw3E8FArRNC26DTYkbHBxq9US\nd/hDqVRubW0VCoXb21ux7qDX610uVzqdhoKsTIA2O+BnSPSlJQAS6bHrdrvdbldcYccwTLlc\nlmAd9u7uDkVRi8Ui/NHStOteBUVRq9Xq8/n++OMPdpz26urqn3/+YRXeqC1oOI6Hw+FerxeN\nRrvdLk935hA2uLhUKok7narX671eLzvdItYdVldXcRw/OTkR6wKAkLAxxWLfApAishZ2Uuix\nY3uuxa2BVqvVXq8nQWHHjk0I75lJ3657lafjtHt7ewRBZDKZL1++fP/+/eLiYvjufpVKFQqF\nGIbZ39+fCvuHDS7OZDLiBhcvLi4uLCwkEgmxBDFbkC0UClCQlQMkSTabTXGHhwBpMk3vLW7h\nsBQ7SY9ds9nEcZwT73BsKIrS6/UYhol4h5fUarVarSbK2MQU2XWvwiq89fX1jx8/hsNhk8l0\nd3f39evX79+/X11dDaPwVCrV3t6eQqHY39+fijcHG1x8enoqrqbxer0qlSqRSIhVF9br9Ssr\nK+l0eircVmAS9Hq9UqkE0w54iXyFHSel2F6vNxgMJnTsoMHuVbLZLEmSwn9xptSu+xlGo5FV\neL/99pvFYmEV3rdv3y4vL99WeEqlMhQKqdXqSCTSarUEu/DYSCG4GEXRra2ter1+eXkp1h1W\nVlZUKhUUZGce9uc3EHbAS2bh1TUenJRi2ULVVAu7fr9frValVoft9/v39/eijE1Mu133M/R6\nvdvt/vDhw8ePHx0OR6lUYhXeG1VahUKxvb2t1Wqj0ehUaDspBBfjOB4IBK6urorFoigXQFF0\nc3Pz/v4eCrIzD8xPAK8Cwm4iaJpGEGSSIqboIXbsD3wEQYh4h5ewMfrCLwOdMbvuVR4j8ViF\nR1HUGwqP1XZ6vT4SiYi75mFIpBBcbLFYXC5XMpkUSw2zE7LHx8dQkJ1tSJJsNBrwXQaeMbNv\nr1/CSY9dp9PBMGzspQgMw7TbbXEdO4qijEajuE1+L8lmswsLC8Krq1m1615lSIWHomgwGCQI\nIhqN1ut1ES88DBIJLna73UajUcS059XVVQzDTk9PRTkdEAaDwYCiKFRjgWfIV9hx0mM34eRE\nu90eDAbiCrtSqSS1Omyj0ahWq8KPTbB23crKygzbda/yTOE9PDw8U3istjObzfv7+9VqVez7\n/gIpBBez+rLX6x0fH4tyARRFA4HA3d2dWBVhQABQFDUajSDsgGfI6wX2FK567CZssENRdMLF\nFZPQ7XYbjYbUJiey2azRaNTr9QKfy9p1CwsLAp8rHViF9/79+w8fPszPzxcKha9fv/73339X\nV1c0Tfv9/vn5+Wg0SlGU2Df9BVIILsYwLBgM3t3d5XI5US5gMBiWl5fT6fRUZE0D4wFtdsBL\n5CvsOCnFTh5ip9VqRVlvz0JRFIqiBoNBrAu8ZDAY3N3dCT82IVu77lV0Oh07afHhwwer1ZrP\n5798+bK/v6/X62022+HhYalUEvuOv0AKwcVGo3FjY+P4+LhWq4lyAbfbDQXZ2YYkyVqtNhU7\nAAHBkO87TAqlWNFHYimKMplMIirLlxQKhcFgYLPZBD43k8nI3K57FVbh/f777x8+fDCZTJlM\nJp/PK5XKg4ODu7s7sW/3C6QQXOx0Om02WzweF6XDHUVRv9+fz+cfHh6EPx0QAKPRiCCI9Bsk\nACGRtbCTQikWGuyekc1m7Xa7wMMcvV7v5uYG7Lo3YBXex48f3717Nz8/j6Lo0dHRt2/f8vm8\nlN0CKQQX+3w+pVIpVlHYaDRCQXaGUSgUer0eqrHAU2T6GmMYhmEYKZRiRcw6abVa7XZbUg12\nrVarXC4LX4cFu254DAaDx+P5n//5H6fT2Wg0jo+P//rrr4ODg/v7e7EmFd7mMbhYrJcfm1pc\nqVSur69FuYDb7VYqlVCQnVVIkgRhBzxFpsKO9RgmFHaDwaDX640t7Pr9fqfTEdGxoyhKrVaL\nvvfiKdlsVq/XC9zzB3bdGCAI4vV6vV7vYDBYXFxUKBTJZPLvv/9OpVIURYk1r/Az2ODiw8ND\nseJaNBpNIBC4uLgQpTfxsSAr/c5IYAxIkqxWq9L8sQoQBZm+ydh/AxPW+9i1E2P32LFBEuIK\nO0nVYRmGyefzYNdNEU6n0+fzZbNZgiD+/PNPr9fb6XRisdiXL1/Ozs4kFWjMBhcfHByIFVxs\ntVqXl5ePjo7a7bbwpxuNxqWlpVQqBQXZ2YMgCIZhxBrQASSIrIXdhA4NK+zGXjvRbDZVKpVS\nqZzkDmPDMIzUhF2xWOz1egKHA4NdNyGLi4ubm5unp6c3Nzd2u31nZ+fPP/9cXV2tVCrfvn1j\nw/BEkTLPkEJw8dramk6nSyQSopgrbrcbRdGzszPhjwZ4RalU6nQ6qMYCj8j0ZcaVsFMqlWPb\nfuJOTtTr9W63Kylhl81mbTabwEoX7LrJsdlsW1tbV1dXrGhQKpUOhyMcDn/69GlhYeHu7u7f\nf/+NRCLZbFbcMQvRg4sRBAkGgzRNi6KuFApFIBDI5XJQkJ09oM0OeIpMhR0nPXachNhNcoFJ\noChKp9OJmI38DJqmKYoSuA4L2XVcYbVa2WCRp5IFx3GXy8UO0hoMhvPz87/++iuRSBSLRRFD\ng8UNLlapVMFgMJvN5vN54U8nCMLpdKbTaSkPMgNjQBBEpVKRWm8rIBYyfZ9x1WM3YYidiCOx\nUqvDZrNZjUZDEISQh2YyGYVCAXYdJ1gslt3d3dvb2+Pj42cvGHaQ9vPnz8FgcG5uLpFI/PPP\nPycnJ6KsQhI9uJggiLW1tXQ6Lcokx9raGoIgUJCdMUiS7Pf70l/lDAiDfIUdgiATBvNOGGLX\narXEcuwGg0GlUpGOsBNlbALsOs4hSXJ3d/fu7i6dTr80D1AUtVqtwWDw8+fPq6urtVotEol8\n+/bt5uaGbVcVDNGDi5eXl61WazweF77bjy3IZrNZKMjOEiqVSqvVQjUWYJHpK42TfWKTCDua\npvv9vljCrlKpDAYDkiRFOf0lpVKp0+kI7JxlMhkURcGu4xaCIEKhULFYPDo6+llhCMMwp9MZ\nDoc/fvw4Pz9/c3Pzzz//HB4eClmiNZlMgUBAxOBiv9+PomgymRT+aIIgHA4HFGRnDLYaK/Yt\nAEkgU2HHydqJSXrsms0mgiBilWIpijIajWIN5L4km83Oz8+PPV88BmDX8YfBYAiFQhRFxePx\nt2cUNBqN2+3+9OnT7u6uUqk8OjpiS7TCVJRsNhsbXExRlADHPUOhUGxtbVEUdXNzI/zp6+vr\nCIKIuEUX4ByYnwAekelbTfRFsWyDnVhLWiXVYNfpdB4eHhYXF4U8lLXrBD5UPuj1+nA4XKvV\nfqnt5ubmEARh/bPPnz+73e5arfbff/99//49k8nwvV+VDS6Ox+OiNCdptVq/3392dib8+1ih\nUPh8vtvbW5ACMwNJkt1uV1LhkYBYyFTYiV6KFXEkttfr1Wo16Qi7XC6H47iQ9wG7TgC0Wm04\nHG40GrFYbMiS32NOyocPH0wm09XV1T///JNIJHh11MQNLrbZbE6nM5FICH+6yWRyOBzJZBIK\nsrMBjuM4joNSB+ZkK+wmL8V2u12GYaZR2JXLZRRFjUajKKe/JJfLgV03k+A4vre3R9P0qJnA\nOp1ufX3906dPm5ub/X6fXWVxeXnJh/oRPbh4Y2NDo9GIkloMBdkZA9rsABb5CrsJS7HsO2Ya\nhR1FUQRBSMSsKpVK7XZbyAkGsOuEBMfxcDjc6/Wi0eiodVUURefn53d2dj5//ux0Ou/u7r58\n+XJ4ePjw8MDtjAWKojs7O3Nzc6IEF7Opxc1mU3iBBQXZGQPa7AAWmb7bJnfsOp0OiqLj9fsP\nBgOapsWanCiVSpKqw1qtViFzksGuExiVShUKhRiGGUPbPT7B5XL9/vvv7969U6lU8Xic3UXL\n4aYypVK5u7srVnCxWq0OBoOZTOb+/l7go00m0+LiYiqVgoLsDECSJE3TrVZL7IsAIgPCbkwm\nabBrtVoMw4ji2NE03Ww2zWaz8Ee/pNvtFotFITVWv98Hu054VCrV3t4eiqKRSGSScqrBYPD5\nfH/88cfq6urDw8O///4bjUbv7+85kWLiBhebTCa3251KpZrNpsBHr6+vMwxzcXEh8LkA52i1\nWpVKBdVYQKavt8mHJyacnFAqlZOEG48NRVEYhul0OuGPfkkul8MwTEiVeXNzA3adKLCWGIZh\nkUhkQqeNnbH48OHDu3fvNBpNMplkDbzJO/B0Ot329nYmkxElgmRlZcVsNh8eHgpsnimVSr/f\nn8lkoIo3AxAEAd9HQKbCjpMeuyltsDOZTGLFrDwjl8s5HA7BLgN2nbiw2k6j0ezv73NSLWIN\nvE+fPi0tLRUKhS9fvsTj8QkXKpAkGQgEzs7OhK+Kzs3N+f1+hmFSqZTA55pMpoWFhXQ6LXyL\nIcAt0GYHzMlZ2E3u2E0SYieusBPl6GeUy+VWqyXk2AR014mOQqHY2dnR6/WRSISrwK3HDjx2\nAOLg4ODr16+ZTGbsEVebzba2tpZMJoUPLlYqlVtbWw8PD8LvOtvY2Oj3+1CQnXZIkmy1WqJk\n9wDSQabCTvRSrCjCrtFo0DQtkQa7bDZrNptxHBfmuH6/f3NzA3ad6KAoGgwGCYKIRqMcxgIj\nCGI2m7e2tj5//myz2dgMvJOTk/Fa1lwul1jBxXq93ufznZ6eCtwpxRZkb25uoENrqtHpdEql\nEr6JMkemL7nJS7ETDk+IIuwoitJoNIJpqTfo9XrFYtHhcAh2Ith10oHVdmazeX9/v1qtcvtw\nlUrFrinz+/2NRuPr16+RSGSMAQsRg4vtdvvi4mIikeB78cYzzGaz3W5PpVJQkJ1eEASBNjtA\nvsJuQudm7B67brfb7XbFEnYSqcPm83mFQmGxWIQ5Duw6qYEgiN/vn5+fj8VifLgLKIrabLZQ\nKPT+/XudTpdMJr9+/Xp9fT28VGKDizUajSjBxR6PR6VSJRIJgbNXPB5Pv9+/vLwU8lCAW0DY\nATJ9z00o7Pr9fr/fH0/YNZtNBEGED7FjGKZcLktE2LHbJgQbmwC7ToKw2m5hYSEWi/HXzfY4\nYOFwOG5vb//5559kMjlkex+Kotvb23NiBBejKLq1tVWv1wVuelMqlT6f7/r6mnMnFRAMkiQb\njYbAdi8gKWQq7Cbsset0OnNzc+MNTzSbTbVaLbx1VK1W+/2+FIRdtVqt1+uCySyw66SMx+Nx\nOBwHBwfFYpG/U9gBi48fPwYCgVar9e3bt2g0Osz8rIjBxTiOb25uXl9fFwoFIc+1WCx2uz2Z\nTEJBdkoxGo0KhQLa7OSMTF91E/bYscJuvLUTYk1OUBSl0+nGuzO3ZLNZk8kkmGeZyWQQBAG7\nTrJsbGy43e5EIsG3gkEQxGazhcPh3377Ta1WHxwcfPv2LZ/Pv61gRAwuNpvNKysrqVRK4F0C\nUJCdahAEMRqNIOzkjHyF3ST+DU3TGIaN9wQRhZ0U5mH7/f79/b1gYxOsXbe6ugp2nZRxuVys\ntsvn8wIcp9frA4HA58+f5+fnT05Ovnz5cnFx8UbpSsTg4tXVVaPRGI/HhUwtViqVXq8XCrLT\nC7TZyRyZvu0mL8VOV9ZJv9+vVqtSqMPe3d2hKGq1WoU5Duy6acHlcnk8nlQqlcvlhDmRnZ/9\n/PnzyspKPp//8uVLOp3+mTcmVnAxO8PR6/XS6bSQ51qtVpvNBgXZKYUkyVqtBvt/ZYtMhd3k\npdjxGuwYhmm328ILO9aWJwhC4HNfksvlFhYWhPHPwK6bLpxOp9frTafTQsbzKhSKpaWljx8/\nbm5u1uv1r1+/HhwcvOpUiRVcjGFYMBgsFAqCSV4Wj8fT6/Wurq6EPBTgBKPRiCAIVGNli0xf\neBOWYsd27Nrt9mAwEF7YURTFdtQKfO4z6vV6tVoVzD8Du27qcDgcgUDg9PRU4KIngiBWq/Xd\nu3e7u7sMw/z48ePV6QqxgouNRuPGxsbx8XGtVhPsUAzDfD7f1dWVkIcCnKBQKAwGAwg72SJH\nYccWFybssRs76wRF0bHLuGNTKpWkUIfNZrMEQeh0OgHOgmHYKcVut29tbZ2fn5+dnQl/uslk\n2t3dfZyu+P79+7NxXbGCi51Op81mi8fjQsZYWK3W+fl5iCyeRqDNTs7I8Z3Hdh5MWIodW9hp\ntVrB8ttYut1uo9EQfXJiMBjc3d0JNjbB2nVCLrcAuMJqtW5tbWUyGVG03dz/TVd8/PiRIIhE\nIsEOz7JxJyIGF/t8PqVSeXR0JGTwitfr7XQ619fXgp0IcAJJktVqFRS5PJGjsJvcsRu7x06U\nyQmKolhnXuBzn8F2nc/PzwtwFth1047FYtnd3b29vT0+PhbrDjiOezyeT58+WSyW4+Pjr1+/\nZrNZhmHY4GIEQQQOLmZTi6vVqpB9bxiGeTyey8tLKMhOFwRBMAwDc83yRI6vvQmFHcMw3W53\nEsduvHPHplQqkSQpsE34kmw2a7fbhenzA7tuBiBJcmdn5+7uLp1OCxwO/BSVSrW+vv7p0ye7\n3X52dvbvv/+yi0x2dnaEDy7WaDSBQODy8nKYdGWusNlsVqs1lUqJ+F0ARkWpVOp0Omizkycg\n7Eam2+0yDDNFwk4KK2KbzWalUhFGafX7/UwmA3bdDECSZCgUKhQKwi9+eAaGYW63+9OnT06n\n8/Ly8suXL3d3d1tbW8IHF1ut1uXl5aOjo3a7LdihPp+v0+nAhOx0QZIktNnJEzm++dgeu7Hf\n+mzT9BjCrtfrdTodgYVdq9Vqt9uiN9hls1mDwaDX6wU4iw3LALtuNjAYDKFQqFQqxeNx0RuG\nlEolu5psaWnp+vr68PBwcXHx5uZG4BnetbU1g8GQSCQE+4KwBdmrq6sh1+wCUoAkyUqlAj6r\nDJGjsJvQset0OiiKKpXKUf9gs9mcm5sTbJUWC0VRarValF0XjwwGg3w+D3YdMB56vT4cDtdq\nNSlou7m5OaVSubKy8unTp6WlpXw+r1QqT09PhdmZwYIgSCAQoGn69PRUsENtNpvFYhF4dAOY\nBJIk+/2+wNE8gBSQ48uPDbEbu+ds7MmJVqulUqnGUISTIIU6bLFYHAwGNptNgLNub2/nwK6b\nObRabTgcbjQasVhMInn6CoWClXculwtF0WQyeXp6KpjoUalUW1tbuVxOSEHp8/lomoYJ2WkB\nwzCtVgvVWBkiR2E34T6xSULsBHbOGIaRgrDLZrM2m00ARQvDsDMMjuN7e3s0TR8cHAicM/IG\nCoXC5XJ9/vzZYDDc3Nx8/fpVsJ1jRqNxbW0tnU4LZsk8TshCQXZagDY7eSLH959YayeEF3b1\ner3b7Yor7NrtdrlcFsZCA7tutsFxPBwOd7vdaDQqZE7vL8Ew7N27dxaLpdPpJJPJHz9+PIs1\n5onl5eX5+fl4PC6Y0rXb7RaLRfRZFmBICIKANjsZIlNhJ8qiWOGFHUVROp1uvNtyRTab1Wq1\nRqOR74PArpMDKpUqFAoxDCM1bYcgyNbWltFoxHFco9HE4/FoNCpAipjP52MLwXwf9IjX6223\n2wLPiwDjYTKZut0u294NyAc5vgInLMWO7di1Wi3hhZ24dh3DMIKNTYBdJxNUKtXe3h6KopFI\nROC9Xm/DBgijKErT9Pv375VKZSQS4TuXRKFQbG1tlctlwVrfVCrVxsbGxcUFFGSlj1qtxnEc\nqrFyQ47CbsJS7Hg9du12u9/vCynsBoNBpVIRV9g9PDx0u1273c73QWDXyQqlUrm7u4thWCQS\nETLO7ZcolUo2uPjq6ioYDIbD4Xa7/fXr15Pt1NePAAAgAElEQVSTE/6qpVqt1ufznZ+fC5Za\nvLCwYDabIbJ4KmBDT8S+BSAocnwLTliKHW/tRLPZRFEUx/Gxzx2VSqUyGAxIkhTsxJdks9n5\n+XkMw/g+COw6ucFqO41Gs7+/32q1xL7O/0OtVu/u7pZKpbOzM6PRGA6Hg8Hgw8PDv//+e319\nzVNci81mczqdyWRSMAvT6/U2m00oyEofgiAoihL7FoCgyFTYje3r9Hq9fr8/Rtdas9nEcVzI\nvV4URRmNRoHTVZ5C03SpVBJAbIFdJ08UCsXOzo5er49EIpIqC+p0uu3t7dvbW1b3WK3WDx8+\nrK6uXl1d/ffffzyNzW5sbGi1WsGi/tRqNRRkpwKSJDudjqR++AH4Ro4vwkmEXafTmRtr7YQM\nG+xyuZxGoxHAMgS7TragKBoMBgmCiEajkgpiJUkyEAicnZ3d3d3Nzc2hKLq0tPTx40er1ZpM\nJqPRaK1W4/ZEBEGCwWC73RZsxdni4qLJZIKCrMTRarUqlQqqsbJCjsJukuEJmqYRBBmjtijw\nSGyv16vVaiIKO4Zhcrnc4uIi3weBXSdzWG1nNpv39/cFmEIdHpvNtra2lkqlHgthGIatr6//\n9ttvCoXix48fqVSK28FelUq1ubmZyWQEy9Lz+XzNZpNd4gdIFoIgYH5CVsjxXThJj12n08Ew\nbIyKqsDCrlwuoygqQMjIz6AoqtPpLCws8H0Q2HUAgiB+v39+fj4Wi0nKmXC5XE6nMx6PP3UT\ntVrt9vZ2KBSqVqv//vvv7e0th46XyWRyu92pVEqYCuljQRYqfVIGYorlhkyF3SSl2DHqsIPB\ngKZpIYUdRVEEQYhoYmWzWavVOl4uzPCwdh2704nXgwCJw2q7hYWFWCwmqVbx9fV1s9l8cHDw\nbHqXJMnffvvN7Xafn59///6dQz26srJiNpsFSy1eXFwkCAIii6UMSZKtVktS2UAAr8jxdThJ\nKXa8dOJms8kwjJDCrlQqiViH7XQ6xWJRgDos2HXAUzwej8PhODg4EGbxwzAgCBIIBLRa7ctN\naAiCLC0t/f7773q9fn9/P5lMsi28kxMIBObm5tLpNCdP+yV+v7/RaLD/GAEJotPplEqlpMxs\ngFfkKOwmKcWOF2LXarWUSqUAqR8sNE03m02z2SzMcS/J5/NqtZpvZflo100SXgPMGBsbG263\nO5FIFAoFse/yv7DBxQiCHB4evhxZValUgUAgHA43m82vX79mMpnJrS82tfjh4UGY7je1Wr2+\nvn5+fg4FWWmCIAi02ckKmQo7gUuxAjfYURSFYZhOpxPsxGdks9nFxUW+s13ArgNexeVysdou\nn8+LfZf/5TG4+GclSzbxzuPxXF5eclKZ1el0Pp/v9PRUmNe5w+GAgqyUgTY7WSFHYTdhKXYq\nhJ3JZBIyM+/Z6e12m+86LNh1wBu4XC6Px5NKpXK5nNh3+V+eBhe/+hsQBFlYWPjw4QNbmU2l\nUhM2ydnt9sXFxUQiwVWF9218Pl+9Xs9mswKcBYwKQRCNRkNS65UB/pCjsJvQsRuvx054YSfY\ncc/I5XIWi2WMr9JIgF0HvI3T6fR6vel0WjphHM+Ci1+FrcyGQqFKpfL169cJg0s8Hg+O40dH\nRwIYaTiOr6+vn52dQUFWghiNRoVCAW12MkGmwm48m2cwGIy9T0wwYddoNGiaFqvBrtvtFgoF\nsOsAKeBwOAKBwOnpqXQ2Xz0LLn7jt3348GF5eTmZTL6cqB0etr2vXq9fXFyM94SRcDqdBEEI\nNrQBDA+CIEajEaqxMkGmwm48x268tROdTqfX6wkm7CiK0mg0Qi6lfUo+n1cqlRaLhddTwK4D\nhsRut29ubp6dnf2sACo8L4OLXwVBEJfL9f79+263+99//409VKFWq4PB4PX1tTDTJD6fr1qt\nwoSsBCEIAhw7mSBHYTd2j914wq7ZbCIIotFoxjhxDESvwzocDl7b+waDAdh1wPDYbLbt7e1M\nJiMdbfdqcPGr6HS6d+/esTOn+/v748UOm0ymlZWVZDLZbDbHuu8I4Di+trYGBVkJQpJkrVbr\n9/tiXwTgHTkKu7FLsZ1OR6lUjvpnW62WWq0WJkGXYZhyuSyWsKtUKo1Gg+9tE2DXAaNisVh2\nd3dvb2+Pj4/Fvsv/8rPg4ldxOBy///67SqX677//zs7OXmam/JLV1VWSJOPxuADvdafTaTAY\noCArNQiCQBAETDs5IDthxzAMwzDjyazxQuyEbLCrVqv9fl8sYZfL5cxmM6/eJNh1wHiQJLmz\ns3N3d5dOp6UQyfFGcPGrqNXqra2tQCCQz+e/f//+S6vv5XGbm5v9fl8AvcV+atVqFSZkJQWK\nogaDAYSdHJCdsGN/2B27FCtxYUdRlF6vFywJ+Sm9Xu/+/p5vI41drAl2HTAGJEmGQqFCoSCR\nuLW3g4tfxW63f/jwQafTff/+/fLycqTPQqlUbm9vFwoFAfTWY0F27LEPgA8gzU4myE7YsZWI\nsUux0hd2Ytl1d3d3KIryOjYBdh0wIQaDIRQKlUolYRJAfskvg4tfgmFYMBjc3NzMZDKRSGSk\ntjm9Xu/xeE5OTqrV6rhXHhan06nX61OpFN8HAcNDEES1Wh2jlA9MF7ITdhM6dqPGszEM02q1\nhBF2/X6/Wq2KWIddXFzktZUQ7DpgcvR6fTgcrlQqw/tkvPLL4OJXsdlsHz58YLvurq+vhxep\nDofDbrcnEgm+s2oRBPH7/dVqVToZ0QBBEAzDCCDrAXEBYTcCYzh2rVaLYRhhhB3rsRMEIcBZ\nz6jX67Vajdf4OrDrAK7QarWhUKher8diMSkMCQ4TXPwSlUq1vb0dCASurq729/eHn0L1er0Y\nhiUSCb49S41G43a7T09PaZrm9SBgSJRKpU6ngza7mQeE3QiMMTzRbDYVCgXfaxhYKIoiCEIU\n3ZPNZkmS5FW/3t7eDgYDsOsATtBqteFwmKbpIWcX+OYxuHjU/bY2m+23335DUZTNuhvmjzym\nFl9eXo5z11FYWlrS6XRQkJUO0GYnB2Qn7Ngf0McTdmOsnWg2mzOfYDcYDO7u7niVXKxdt7Ky\nAnYdwBU4jofD4W63G41GpbBDkw0uTqfTbwcXvwTH8VAoxA4rHBwcDLMZFsdx1up7eHgY975D\nwRZky+XyqIIV4AmSJCuVihQaTAH+kJ2wGwwGCIKMIey63e5gMBjVexNscqLb7TYaDVGEHbsc\nyWq18ncE2HUAH6hUqlAoxDCMRLTd8MHFL1laWvrtt9/YNRXFYvGXv99isbhcrqOjI74HV7Va\nrdvtPjk5gYKsFCBJst/v12o1sS8C8IgchZ2QaycEm5ygKEqhUBgMBgHOekYul1tYWODPSwO7\nDuAPlUq1t7eHomgkEpGC8tjY2LBYLOPth2Xry4uLi/F4/Pj4+Jftg26322g0CjBEsry8rNPp\nILJYCmAYptVqoc1utpGdsBt7nxhN0wiCKJXKkf6UYI5dqVQiSZLXXV6v0mw2K5UKr2MTYNcB\nvKJUKnd3dzEMi0ajUtB2fr9/+ODiZyAIsra2FgqFHh4efvz48fYKMjZJuNvtnpycTHDfoW7l\n9/spioKCrBSANruZR3bCbhLHTqVSjaScer1ep9MRzLETpQ57e3trNBr1ej1Pz4dhWEAAWG2n\nVqsjkYjoS07HCC5+BkmSv/32G5tj/PZEhUql2trayufzfIeSaLXa1dVVKMhKAWizm3nkKOzG\nTiceo8Fubm5OgOGJVqvVbrfNZjPfBz1DgLEJ1q5zOp38HQEAc3NzCoVie3tbq9VGIpG3jS4B\nYIOL2+322EsylEplMBj0+Xzn5+fxePwN889oNK6vrx8fH/Pdd+VyubRarXTW9coWkiS73e5I\n0dbAdCE7YTd2KXaMELtms6lWq0et3o4BRVFqtVqw/RaPFAqFwWBgs9l4ej7YdYCQsNqOIIho\nNDrG+AK3qNXqnZ2dUqk0SZ10YWHh3bt3zWbzv//+e6OtamlpyWazxeNxXidI2MpvqVRix60A\nsVCr1TiOQzV2hpGdsBu7FDteiN1s12Gz2azdbudPdYFdBwgMiqLBYNBsNu/v74se0M8GF+dy\nuZGCi18+5N27dwRB7O/vX1xc/Mz/83q9CoUimUyOfdAwPBZkh8lkAfgD2uxmGzkKO8EWxQoT\nYscwjCjCrtVqlctl/uqwYNcBosB2+s/Pz8diMdGHB8cOLn6KQqHY3Nz0+/03Nzc/s+UUCsXW\n1lalUrm6uprgvr/G5XJpNBqYkBUXgiBA2M0wchR2Qu4TE8Cxq9fr3W5XeGGXzWb1ej1/AStg\n1wFiwWq7hYWFWCw2alww59hstvX19TGCi5+xsLDw/v37drv9s7KsVqsNBAIXFxelUmmSg97m\nsSB7f3/P3ynA25Ak2el0RJ8TAngChN2wjDo8wTCMMMKOoiidTifM1rJHGIbJ5/Ng1wEzjMfj\ncTgch4eHvAqdYVheXh47uPgpWq323bt3FoslGo2+Oi1rtVqXlpaSySSvs6tarXZlZeX4+BgK\nsmKh1WpVKhWYdrOK7ITdeMMTg8Gg1+uN5NjRNN3v94URdsLbdcVisdfr2e12np6fzWbBrgNE\nZ2NjY3l5+fDwsFAoiH6TsYOLn4KiqM/n8/v95+fnR0dHL0OM19fXtVptPB7nNbV4ZWUFx3G+\n8/OAN2BDT8S+BcALshN24/XYsT+/jiTsms0miqI4jo961kgMBoNKpSJKHdZms/E08DsYDK6v\nr8GuA6SA2+12u92JREL0cN1JgoufYbfbw+FwtVr9/v37s9gLBEGCwSBN02dnZxOe8gZssbtY\nLIqumGULtNnNMHIUdmM4dmPsE2MnJ/heBVGpVAaDAUmSvJ7yDJqmKYrirw4Ldh0gKVwul8fj\nSaVSfKf4vs3kwcVP0ev17969w3H8x48fz9SVSqXa3Ny8vb3lVcvq9XqXy5VOp6EgKwokSbZa\nLciLnklkJ+zGK8V2Oh0Mw0b6g61WS4CRWIqijEajAFF5T8lmsxqNhiAIPh4Odh0gQZxOp9fr\nTafTby9y4JvJg4ufgmHY7u7uyspKIpE4Ozt7+kCSJNfW1o6Pj3nNal5dXYWCrFjodDoMw8C0\nm0lkJ+zGLsVKM8RO+AY7vscmwK4DpInD4QgEAqenp5Okyk0OJ8HFT3G5XNvb29lsNhaLPTXP\nXC6X2Wx+e2vFhLAF2UKhAAVZ4UEQxGg0QpvdTCJHYTeeYydBYdfr9Wq1msDCrlQqdTqdhYUF\nPh4Odh0gZex2++bm5tnZGa/9Z7+Ek+Dip1gslvfv33e73R8/fjy16AKBwNzcHK+pxXq9fmVl\nJZ1O87r0AngViCmeVWQn7MYuxY4k7AaDAU3TfAs7iqJQFDUajbye8oxsNmu1WjEM4+nhYNcB\nUsZms21vb2cyGXG1HSfBxU/RaDThcNhoNP748aNYLLIfZFOLKYri1aRcWVlRqVRQkBUekiQb\njQZI6tlDdsJuvFLsqCF2zWaTYRgBhB1JkuPF8o1Hp9N5eHjgqQ4Ldh0wFVgslp2dndvbW3H3\n2XMVXPyIQqEIBoOrq6vxePzi4oL9oE6n8/l8Z2dn/Fk7KIpubm7e399DQVZgDAaDQqGAauzs\nIUdhN4YSGrXHrtlsYhjGk631iPANdrlcDsdxnoZwwa4DpgWTybSzs5PP59Pp9ORDDGPDVXDx\nU1wuVzAYvLm5SSQS7Oyt3W53OByJRIK/CUp2Qvb4+BjcIyFh2+ygGjt7gLAbilFLsQI02NE0\n3Ww2hRd2i4uLfGS4gF0HTBckSe7t7RUKBU4GVMeGq+Dip8zPz7Mpd/v7++w4hcfj0Wg0R0dH\n/H2mq6urGIadnp7y9HzgVaDNbiaRnbAbo8eOYZhutztGiN2IVxsNiqIwDNPpdLye8uzEdrvN\n09gE2HXA1GEwGEKhUKlU4lXx/BIOg4sf0ev179+/R1H0+/fvtVqNTS1uNBrn5+dcHfEMFEUD\ngcDd3d1jhx8gAARB1Ov1lwtIgKlGdsKOYZhRPaFut8swzEg9dgJsiS2VSiaTie8A5Kdks1mL\nxcLHUlqw64ApRa/Xh8PhSqXCSWjweHAbXPwIm3JnMpn29/cLhYJarWZLtPx1whkMhuXl5XQ6\nzV/ACvAMgiAQBIE2uxlDXsJuMBgwDDOqYzfe2gm+hV25XDabzbwe8ZRut1ssFnkamwC7Dphe\ntFptKBSq1+sHBwdiOR/cBhc/wrpobILx1dWVyWRaXV1NJpPPtpBxiNvthoKskKAoajAYoBo7\nY8hO2M3NzY0q7GiaRlF0+O0OnU6n1+vxKuwajQZN00I22OXzeQzD+JCSrF23vLwMdh0wpWi1\n2nA43G63RdR2nAcXP7KyshIMBq+urlKp1MrKislkisfjPH2aKIr6/f58Pv/w8MDH84GXkCQJ\njt2MIS9hx/5nNKqAGGNyAkEQXnvsKIrSaDQ4jvN3xDP4G5tg7bqlpSXOnwwAgoHjeDgc7na7\n+/v7Yo12ch5c/Mj8/HwoFHp4eDg4OPB4PIPBIJ1Oc3vEI0ajEQqyQkIQRLVaFauRAOADeQm7\n8Ry7MULscBznNV5O4KCTcrncbDYXFxc5fzLYdcDMoFKpQqHQYDCIRqNiaTvOg4sfMRqNrCt5\neHjo9XoLhcLt7S23RzzidruVSiUUZIWBIAiGYarVqtgXATgDhN2vkVrWCcMw5XJZSGGXy+XM\nZjMfBiHYdcAsoVKpwuEwiqKRSIS/1Le3eQwuLpVK3D6Z3U6hUCjS6fTq6urJyQlPJTwURX0+\nXz6f5/xTAF6iVCr1ej202c0S8hJ2bCl2jB47SQm7arXa7/cFE3a9Xq9QKIBdBwDDoFQqd3d3\nMQyLRqNiaTs2uDiRSHAYXMyCYVgoFDIajdfX12azOZFI8ORNEgSxtLSUSqWgICsA0GY3Y8hL\n2Anm2PHdYKfX6/lea/FIPp9XKBRWq5XzJ4NdB8wkrLZTq9WRSKTVaolyh42NDavVym1wMQu7\nAWxhYYGiKARBEokETxl+brcbRVFxd/LKBIIgKpWKiFmMALfITtihKDrqBMBIPXYMw7TbbV4d\nO4Eb7HgamwC7DphhFArF9va2VquNRCKNRkOUO/h8Ps6Di1kQBPF4POvr6zRNVyqVy8tLbp/P\nolAoAoFALpeDgizfkCTZ7/drtZrYFwG4QY7CbtQ/NZJj12q1GIbhT9j1+/1qtSqYsKtWq/V6\nnY86bC6XA7sOmGFYbUcQRDQaFUXb8RRc/MjS0tLm5ibDMFdXVzytiyAIwul0ptNpWI3AKxiG\nabVaaLObGeQl7MbYJ9br9fr9/vDCrtlsKhQKPtYzsLD/9giC4On5z8jlciaTifPKMth1gBxA\nUTQYDJpMpkgkIsrU4WNwMU8FU5vNFgqF2IIsT6nFa2trCIJAQZZvoM1ulpCXsBvDsWPXTgwv\n1PienKAoiiAIYfRQv9+/v7/nY9tELpfr9/tg1wEzD4IggUBgfn4+FouJ8uJkg4vL5TLnwcUs\nJEnu7e0xDPPjxw8+Bh3Ygmw2m4WCLK+wwg7a7GYD2Qm7MdKJEQQZflJBAGEnWB32/v4eQRDO\nxybArgNkBYIgfr9/YWEhFotRFCX8BfgLLmZhI+76/f63b9/4qJkSBOFwOKAgyyskSXa7Xf6W\nxQFCIi9hN0YpttPpYBg2/OgAr8Ku2+02Gg3BhF02m11YWOA8aRnsOkCGeDweh8NxeHgoivPE\nX3Axi9Fo3Nraomn6v//+4yMAZX19HUGQ8/Nzzp8MsKjVahzHoc1uNpCXsBujFDtGiB1/WScU\nRSkUCoPBwNPzn9JoNKrVKudjE2DXAbJlY2NjeXn58PCwUCgIf7rNZvN4PKlUiidlabVa19bW\nWq3Wjx8/OA/wUygUPp/v9vYWlAd/kCQJX97ZQHbCjtdFsb1er9vt8ufYlUolkiT5WNj6kmw2\nSxCETqfj9rGsXed0Orl9LABMBW632+12JxKJu7s74U93Op1LS0t8BBezrKys2O12mqZ//PjB\neYCfyWRyOBzJZBIKsjwBwm5mkJ2w43VRLNugwJ+wE6zBbjAY5PN5zscmHu06pVLJ7ZMBYFpw\nuVwejyeZTOZyOeFP5y+4mMXv92s0msFgEIlEOJePUJDlFYIgOp2OWJHaAIeAsPsFIzl2zWZT\nrVbzVGRstVrtdttsNvPx8Gfc39/Pzc3Nz89z+1iw6wBgbm7O6XR6vd50Op3JZIQ/nb/g4rm5\nORRFt7e3B4OBSqWKRqPcajsoyPKKVqtVq9XwtZ0B5CXsxhieGKnHjtfJCYqi1Go1ryO3j+Ry\nObvdzq1CZRgG7DoAYHE4HIFA4PT0lKdJ1TfgO7hYo9Fsbm42Gg29Xr+/v89tgJ/JZFpcXEyl\nUlCQ5QOCIEDYzQDyEnZ899jxLeyEqcM2m81yucz52EQ2m+31emDXAQCL3W7f3Nw8OzvjaSXX\nG/AdXGy1WpeWlur1utVqjcVi3Gq79fV1hmEuLi44fCbAwi6NFfsWwKTITtiN5NgxDNPr9Ubq\nseNpJJZhGMGEXTabNRgM3M7egl0HAC+x2Wzb29tXV1fCb1bgO7h4fX1dp9M1m02bzRaNRjn0\ngZRKpd/vz2QyIEE4hyTJVqvF+VAzIDDyEnajlmI7nQ7DMEM6dgzDtFotnhy7er3e7XYFEHaD\nweDu7o7zsQnWroPsOgB4hsVi2d7ezmQyx8fHAh/9GFx8fX3N+cMRBAkGgzRNoyi6uLh4cHDA\nYTizyWRaWFhIpVJ8lJLljE6nwzAMqrHTjryE3ailWPYHlyGFHU3Tg8GAJ2FHUZROp+NvBe0j\nxWKx3+/bbDYOnwl2HQC8gdls3t3dzefz6XRa4J1ObHDx+fk5H8HFKpUqGAyyFQA2nJlDbbex\nsdHv96Egyy0IgkA1dgaQnbAb1bFTKBRDasFms4miKI7j497uLYSsw9psNm4VGNh1APA2JEmG\nQqFCoZBMJgXWdrwGFxMEsba2dnx8vLi4uLS0xOHiDaVS6fP5bm5uQIVwC8xPzADyEnZjlGJH\nbbDjIz14MBhUKhUBhF273S6Xy9zWYcGuA4BhMBqNu7u7pVLp6OhIYG3Ha3Dx8vKyxWI5PDx0\nuVzs4o1iscjJky0Wi91uh4Ist5Ak2Wg0Op2O2BcBxkdewm7UUqxERmIrlcpgMCBJko+HPyWb\nzWq1WqPRyO0zwa4DgGEwGAzhcLhSqfAURPIGvAYX+/1+BEGSyaTb7Xa5XIlE4uHhgZMnezye\nfr8v/FjxDGMwGBQKBfigU43shN2ojp0UhB1FUUajkW/Hi2EYzrdNgF0HACOh1WpDoVC9Xhde\n2/l8Pp1Od3Bw0O12uX2yQqHY3t6mKOrm5obVdvF4nJOaLFuQvb6+5jZRRc4gCGI0GkHYTTUg\n7N5CIunEwjTYPTw8dLtdu93O4TPBrgOAUdFqteFwuNVqxWIxIWN4URQNBoMIgsTjcc41pVar\n9fv9Z2dn5XLZ7XazNVlOZiksFovNZksmk1CQ5QpYGjvtyEvY8ddj1+/3aZrmI8Su1+vVajUB\nhF02m52fn8cwjKsHgl0HAOOB4/je3h5N09FolHP/7A14DS622WxOpzORSNA0vba2xs5ScCIg\nvF4vFGQ5hCTJer3Ox8Y5QBhkJOwYhmEYhqceO3ZxMh+OHUVRKIpy2/f2EpqmS6USt3XYXC4H\ndh0AjIdarQ6Hw/1+PxaLCanteA0u3tjY0Gg0rGpcX19fWFg4PDycvIqqVCq9Xi8UZLnCaDQi\nCAJfzOlFRsKONep56rFrNpsYhnFodz1CURRJkqOuuB2VXC6H4ziH8xkMw1xdXYFdBwBjo1Kp\nwuEwgiCRSETIZQD8BRezqcXNZpPdtOH1em02WywWq9VqEz7ZarVCQZYrUBQ1GAxQjZ1eQNj9\nlG63OxgMhhd209tgxzBMLpcDuw4ApIZSqdzd3cUwLBqNCqnt+AsuVqvVwWAwk8nc39/Pzc15\nvV6LxRKLxRqNxoRP9ng8vV7v6uqKi2vKHWizm2pkJOzYNuThS7FskM+QPXY8CTuappvNJt/C\njqIomqYXFha4eiDYdQDAFay2U6vVkUiEbfkQBv6Ci00mk9vtTqVSzWYTQZBAIGA2m6PR6ITa\nDsMwn893dXU1uf8HEARRq9WEnN0BOERGwm5Ux67T6SAIMqQ04UnYURSFYZhOp+P8yU/JZrNW\nq3X4+d9fAnYdAHAIGxei1Wr39/eF1Hb8BRevrKyYzebDw8N+v89qO6PRGIvFJvzsrFbr/Pw8\nRBZPDkEQDMOARJ5SQNj9FLbBbshNEq1Wiw9hVyqVTCYTH9ssHul0OsVikcM6LNh1AMA5rLYz\nGAyRSGTyquXw8Bdc7Pf7GYZJpVJz/9d7p9fro9HohAd5vd5Op8N5d6DcUCqVer0eqrFTioyE\nHesqDy/shg+x63Q6vV6PJ8fObDZz/tin5PN5lUrFYbUX7DoA4AMURbe2tgiCiEQiQk4s+v1+\nPoKLlUrl1tbWw8NDJpOZ+7/PTqPRxGKxSfZZYRjm8XguLy/BbZoQkiQhpnhKkZGwGwwGCIKM\n5NgN32CHIAiO4xPc7hXYhX18N9ixYxNcmYJg1wEAf7DOltVqjcVigr102UMRBDk4OOC26Uqv\n1/t8vtPTU/ZzQVF0e3sbw7BYLDZJiJrNZrNaralUSuCVuzMGQRCVSgW+htOIvIQdf1knOI5z\nnkhCUZRGo+FcLz6lXC63Wi0OxybArgMAXmE70hYWFmKxGCebG4aBDS7udDpHR0fcvuntdvvC\nwkIikWDtQLbizDDMhCLS5/N1Oh2YkJ0EkiT7/T4Yn9MICLufMpKw46kOy7ddl81mzWYzV9oR\n7DoAEAaPx+NwOA4PDzkfWf0Z/AUXe71elUr1uOsCw7Dd3V2apifZbIZh2MbGxtXVlZD9iDMG\nO7cHbXbTiIyE3aj7xIbvseND2DEMUy6XeRV2vV6vUChwODYBdh0ACMbGxga7cbVYLApzIk/B\nxWx3Xb1ev7i4YD+iVqtDoVC9Xp+knK8sAN8AACAASURBVGq32y0WC+cWo6yANrspRUbCbgzH\nTsQQu2q12u/3eRV2+XxeqVRaLBZOnsbadUtLS2DXAYAwuN1ut9udSCQKhYIwJ5IkGQwGOQ8u\nxnF8c3Pz+vr68RPRaDShUOjh4WESg9Dn89E0DROyY0MQRLlcBmU8dchL2A2fTjwYDHq93jCO\n3WAwaLfbnAs7iqL0ej0fO8oeyeVyi4uLXI1NsHbd8vIyJ08DAGAYXC7XxsZGIpHgfEXEz7Ba\nrXwEF5vN5pWVlVQq9Rhlp9Ppdnd38/n8o5M3Ko8TslCQHQ+SJHu9Hnz1pg4ZCbuRSrHsvP0w\nwq7dbjMMw4ew49Wuq1Qq9Xp9cXGRk6eBXQcAYuF0Or1ebyqVYnNDhDlxeXmZ8+Di1dVVo9EY\nj8cfxyaMRuPW1tb19fXNzc14z2QLsslkEmynMVCr1TiOQ5vd1CEjYTdSKZZdyziMsGs2mwqF\ngsO1DXNzc/1+v1qt8irscrmc2WzWaDScPC2fz4NdBwBi4XA4AoHA6enp2AJoVNbX1zkPLkYQ\nZHNzs9frpdPpxw+azWa/3392dpbL5cZ7rNfrbbfbgn1lZgxos5tG5CXsRloUi2HYMEKQjwY7\n9ickgiC4fewj/X7//v4e7DoAmBnsdvvm5ubZ2dnl5aUwJ7LBxbFYjMPgYgzDgsFgoVB4KuPs\ndvvGxkY6nX54eBjjmSqVamNj4+LiAkqKY0CSJDh2U4e8hN2o+8SG+Z18CDuKogiCGF6Gjko+\nn0dR1Gq1cvW0brcLdh0AiIvNZtve3r66ujo7OxPgODa4GEVRboOLjUbjxsbG8fHx0wS1paWl\n1dXVRCIx3sqNhYUFs9kMkcVjQJJkp9MRckMxMDkyEnaj9tiJK+z4rsMuLi5ykqgMdh0ASAeL\nxbK9vZ3JZI6PjwU4jqfgYqfTabPZ4vH4Uy9wdXV1YWHh4OCg2WyO8Uyv19tsNgVrQ5wZNBqN\nWq0G0266kJGwG7XHTixh1+l06vU6f8KuXq/XajWu6rBg1wGApDCbzewwaTqdFsCgYoOLK5UK\nt8HFPp9PqVQ+04sej4ckyfGWyarV6o2NjfPzcyjIjgobeiL2LYARkJewG6nHbpgQu2632+12\nuRV2FEUplUqDwcDhM5+SzWZJkuTkzmDXAYAEIUkyFAoVCgVhpkF1Ot3W1ha3wcVsanG1Wn26\nFoydrlCr1eMVfxcXF00mkzB6d5aANrupQ17CjvNSLFsU4Gq2lIWtw3IVL/eMwWBwd3fH1bYJ\n1q6DVRMAIDWMRuPu7m6pVBJm9QIfwcUajSYQCFxeXj4NzENRdHt7u9/vHx4ejrFwzOfzNRoN\nKMiOBEEQ7Xabw/FngG9kJOxG6rEbshTbarXUajW3Uw68Ntjd39/Pzc1xMjbxaNfxmqIMAMB4\nGAyGvb29crk8ngYaFT6Ci61W6/Ly8tHR0VNVwS6TbTQaqVRq1AeyBdmLiwuYBhgeNiofQk+m\nCBkJu+FLsQzDdLvdIR07buuwrVar3W7zJ+yy2ezCwgInShTsOgCQODqdbm9vr16vC6Pt2ODi\neDzOYXDx2tqawWBIJBJP74/j+M7OTrFYHGMpxeLiIkEQEFk8EgRBgLCbIuQl7IZ07LrdLsMw\nw/TYcS7sSqWSWq3mfMyWpdlsVioVTsYmwK4DgKlAq9WGw+FWqxWLxTgMJfkZ6+vr8/PzsViM\nq8odgiCBQICm6dPT06cfNxgM29vb19fXY9RV/X5/o9G4vb3l5IZyAOYnpgsZCbvhS7HD7xPj\nXNjxWofNZrNGo1Gv10/+KLDrAGBawHE8FArRNB2NRjkME/4Zfr9fr9dzGFysUqnY4YxnDXwm\nk8nv95+enhYKhZEeqFar19fXz8/PoSA7JCRJNhqNMYaRAVGQkbAbvhTb6XRQFP3lpCfDMK1W\ni0NhxzBMuVw2m81cPfApg8Egn89zMjYBdh0ATBc4jofD4X6/z+2iiFfhI7jYaDSura2l0+ln\nRV673b66unp0dDRqodDhcBAEMUaXnjwxGAwKhQKqsdOCvITdkI7dkJMT7XZ7MBhwKOzq9Xq3\n2yVJkqsHPqVQKAwGA5vNNvmjwK4DgKlDpVLt7e0hCLK/v8+39cJHcPHy8rLVao3H471e7+nH\n2eDiw8PDUe03n89Xq9WgIDsMCIJAm90UAcLuFYYMsWs2myiKDvM7h4SiKJ1Ox+EDn5LL5ex2\n++RjE2DXAcCUws6TKhSK/f19mqZ5PYuP4GK/34+iaDKZfPZxr9drNBoPDg5GMiNxHF9fXz87\nO4OC7DBAm90UAcLuFYYMsWu1WhqNhsPAOf4a7FqtFkVRnIxNgF0HANOLUqkMhUJqtToSifAt\naDgPLlYoFFtbW+Vy+dkDH4u/8Xh8pOFfp9NJEEQ6nebkerMNSZL1ev2ZXQpIE7kIu8FgwDDM\n8D12wk9ODAaDcrnMk7DLZrN6vd5oNE74HIZhrq+vwa4DgOlFoVBsb29rtdr9/X2+tR3nwcVa\nrdbn852fnz9Ly1MoFDs7O61W66Wf9zY+n69arUJB9pcYjUYEQaAaOxXISNjNzc1x22PHrbCr\nVCoMw/DRYMcwDFdjE/l8nqZpsOsAYKphtZ3BYIhEInzvTuU8uNhmszmdzmQy+ayazBZ/Hx4e\nLi8vh38ajuNra2tQkP0lKIoaDAYQdlMBCLtXGL7HjkNhR1GU0WjkY+lqsVjs9Xp2u33C57B2\n3fLyMth1ADDtsMtYCYLY39+vVqu8nsV5cPHGxoZWq32WWjw3N6fX64PB4OXl5UgGodPpNBgM\nUJD9JbA0dlqQi7Bjp+45LMX2+32aprkVdvzVYW022+SS8e7uDuw6AJgZ2NY0i8USi8X41nbc\nBhezN2+1Wufn589+yWKxeL3eVCpFUdTwTwsEAtVqNZvNTn63GYYkyVqtJkDMNTAhchF2wzt2\n/X6/3+//Utg1m825uTmNRsPJ9Xq9Xq1W40PY0TRNUdTkdVgYhgWA2YPVNHa7PRqNDq+ExoPb\n4GKVSrW5uZnJZNj9109xOBxOpzORSLD/Sw8DjuNut/vs7AxW3b8BQRAMw9RqNbEvAvwCEHbP\nYfs2finsWq0WhmFcqRyKolAUnXy44SXZbFaj0RAEMeFzWLtueXmZk1sBACAdvF6vw+E4PDzk\nqg3uVTgPLjaZTG63O5VKvWwT3NjYIAhipACUpaUlvV4PkcVvoFAo9Ho9VGOlj1yEHfv/yDDC\nrtPpIAgyjGPHbR2WJMkhWwCHh6uxCbDrAGC22djYWF5ePjw8LBaL/J3CeXDxysqK2Wx+mVqM\nIMjm5iaGYYeHh0MGoCAI4vf7q9VqLpeb/GKzCrTZTQVyEXaDwQBBkGEy5zqdDoZhv/ydnAs7\nPuqwpVKp0+ksLCxM+Byw6wBg5nG73W63O5FIjLp6dSQeg4uPj485eWAgEJibm3s5+sBO/rbb\n7eEDUDQajdvtPj095Tu9eXohSbJarY4UFggIj4yEnWRD7GiabjabfAi7bDZrtVontNnArgMA\nmeByuVhtx1Xs3KvodLqdnZ27uztOgovZ1OKHh4dMJvPsl1QqFRuAcnFxMeTTlpaWdDodTMj+\nDIIgBoMBV9PNAE/ISNhJNsSOoiiVSqXX6zl52iOdTufh4WHyOizYdQAgH1wuFxs7x2tmr9Fo\n3Nzc5Cq4WKfT+Xy+09PTl1VCNgDl6urq5YzFq7AFWYqieJW20wuGYVqtFqqxEkcuwq7f73O4\nT4ym6X6/z5WwK5VKfNh1uVxOrVZPmHgMdh0AyA2n0xkIBE5OTm5ubvg7hdvgYrvdvri4mEgk\nOp3Os1+yWCzr6+vJZHLISBetVut2u09OTqAg+yokSUJMscSRi7AbaVHsL9OJm80mgiA4jnNx\nNb4a7HK5nMPhmHCVLdh1ACBD7Hb75ubm2dnZSFscRoXb4GKPx4Pj+KtjGcvLywsLC4eHh0Nq\nteXlZSjI/gyCIMrlMiezLwBPyEjYcdhj12w2cRznZIi10Wh0Oh3OhR1FUe12e8KxCbDrAEC2\n2Gy27e3tq6urs7Mz/k7hMLgYRdFgMFiv11/tqPN6vVqtdsiklceC7N3d3YS3mj1Ikuz1enxv\nogMmQS7CbvhS7DA9dq1Wi8M6rEaj4cr8eySbzVoslmEWo70B2HUAIGcsFsvW1lYmk+FV27HB\nxdFodPLgYhzHg8Hg9fX1y8FeBEG2trb6/f6QQ7JarXZ1dfX4+BgKss9Qq9UajQba7KSMXITd\nkKVYhmF6vZ6QIXYURZnNZk4e9Ui32y0WixOOTYBdBwCAxWLZ3d29vb09Pj7mqfrGSi6FQsFJ\ncLHJZFpZWUkmky/XTmAYtr29TVHUkPVll8ul1Wq5imWZJQiCgDY7KSMjYTdMKbbT6TAMM0yP\nHSfCjmGYSqXCeR02n89jGDahXgS7DgCAubk5kiR3d3fv7u6SySRP2k6hUHAYXLy6ukqSZDwe\nfykTdTrd5ubm5eXlMDVWdt9aqVSCguwzIKZY4shI2A25dmLuV/vEBoNBu93mRNhVq9V+vz/h\n4OpLcrnc4uLiJGMTYNcBAPAIQRChUKhUKnG1MeIlarV6d3eXk+Bidu1Ev99/dfrBYrGsra2l\nUqlhhmTZguzJycnLYVs5Q5Jkp9MZfhUvIDByEXZD9tjRNK1QKN729lqtFsMwnAg7iqL0ej23\n4qlSqTSbzcXFxUkeAnYdAABPMRgMe3t75XJ5+CVdo6LVatng4qurqwkfpVQqt7e3C4VCNpt9\n+asul8tutw85JOtyuXAchwnZp2g0GrVaDdVYySIXYTd8KXaYBjulUjlMiPEv4SPoJJvNmkym\nSaYxwK4DAOAlOp1ub2+vXq/H43GetB0bXHxxcTF5PrBer/d4PCcnJ686cz6fT6vVHh4e/rKr\n77EgO2TEsUxgQ0/EvgXwOjISdkOWYgVrsOv3+9VqlVth1+v1CoXChGMTrF23tLTE1a0AAJgN\ntFptOBxuNpuxWGzyQYdX4TC42OFw2O32RCLxct6Wndjo9XrDDMnqdLqVlZXj42MoyD4CbXZS\nBoTd/48hHTuNRjP5ldh/Fdw22N3d3SkUCqvVOvYTHu06TixJAABmDBzHQ6EQTdOxWKzX6/Fx\nBIfBxV6vF8OwRCLxsjUQw7Ctra1SqTTMkOzKygqO4ycnJxPeZ2YgSbLdbk+ePgjwgVyE3fA9\ndoKF2FEURRAEJynHj2Sz2YWFhUnGJsCuAwDgbXAcD4fDvV6Pk/C5V+EquBhF0a2trXq9/qp6\n0+v17JBssVh8+zlsZHGxWHyZkCdPdDodhmHQZidN5CLsuO2x40rYcVuHrVar9Xp9kjosa9c5\nnU6w6wAAeAOVSrW3t8cwzP7+Pk8FSq6Ci3EcDwQCV1dXDw8PL3/VarWurq4eHR39cpWCXq93\nuVzpdBoKsizQZidZZCTsOOmx63a73W53cmHX6XT+P/buPLyZ/qwP/cxoNBqtM1osybItW7Zl\nWZb9eElCyUnykpM3BcISwhaasLYQoC1rC1xNoXDKeoXTHmhZ2sPh0HJxIKcJ9AC5Agm0p4dA\nSAhvvDy2LMv7KsmWrdG+z8z545coQstotHgb3Z+/eLRZfsl1Pd/nvn/3/ctms4MNdtFo1Gw2\n99Mmvrm5gWFYAIAcarV6dXVVpVJtbGzcx/UMA1xcbLVa3W737u5uy/rf1NSU1Wp9+fJlxwQ5\nNTVF0/Th4WE/X0Yx4JjdkzUswU5mK7ZjxQ5t7un/jB3HcSRJGo3GPj+nhuf5m5ubfraciKJ4\nenoK5ToAgEwkSa6srGg0mvX19UKhMPDPV6lUy8vLA1lc7PF4TCZTu10t8/PzKpWq409BDdmb\nmxtoyGIYxjBMPp+H+uUTNCzBTk4rtlKpCILQMdjRNC2nqysN9WH7OQzX4ObmBsfxkZGRfj4B\nynUAgK6oVKqlpSWdTre5uXkf2Y6iqIEsLkZbSyqVSssBCHT1RSaTOT4+lv4cg8EwOTkZDofv\n6XDhM2I0GlUqFRyze4KGKNh1rNjJuXZiUCOxAz9gh8Ymeh7FgHIdAKA3KNsZDIb19fWOJ9V6\nMKjFxRRFLS4uxmKxaDTa/CxN04FA4OLiouMFYpOTkxRFwYQsjuNwzO5pgmD3BeVyGcdx6a28\nAxmJLRQKxWJxgMEul8ul0+l++rBQrgMA9IwgiEAgwDDMxsZG/ztKmg1qcbHJZJqZmdnf389k\nMs3Pms3mmZmZvb29ls/WEASxsLAADVkMwxiGgYrdEzQUwU4URZnBjqIo6fboQEZiE4mERqMZ\nyGgtEolEGIbR6/W9vR3KdQCAPqFsZ7VaNzY25FzD2q1BLS4eHx+32+3BYLDlEr6JiQmHw7Gz\nsyN9dAxNyO7v7w95Q5Zl2Ww2e0/rDEHPhiLYodOyHQ/GdVxiJ4riQCp2g+3DCoJwfX3dz5YT\nKNcBAPqHZgtGRkY2Nzc5jhv45w9qcfHc3BxBELu7u+2epSiq47VpU1NTarV6yCdkTSYTjuNQ\ntHtqhijYyazYSbygWCwKgtBnsBNFMZlMWiyWfj6kXjweF0Wx57EJKNcBAAYFZTuXy7W9vd3/\nnWDNBrK4WKVSLS4uplKplof20E7jYrEoHdoIgpifn7++vu643FjBCIIwmUwQ7J4aCHZfIGfX\nCUEQHS+TlZbNZiuVygArdpFIxOFw9DyoC+U6AMBgzc7OTkxMbG9v30foGcjiYp1O5/f7T05O\nWqZPjUYTCASi0WgkEpH4EJPJNDExEQ6Hh7kXCfMTT9BQBDu03LJj9Om4nRiNxPa5o4TjOL1e\nP6jyWD6fTyaTPY9NQLkOAHAfPB6Px+MJBoMDnzAY1OJim802Pj4eCoVabldmGMbn8+3v70un\nFo/HM+QNWZZl0+l0nxukwWANRbCTWbHreMbuCR6wi0ajBoOh50XHUK4DANwTt9uNsl2fo6zN\naouLg8FgP4uLZ2ZmdDpdu+N0TqdzdHQ0GAxK3KuBGrKxWKzlfWXDgGEYDMPuY1wG9AyC3RfI\nacX2GewEQUgmk4MKdoIgxGKxnscm4GZYAMC9crvdaJS15eq4flAUtbKykk6n+1lcjON4IBAo\nlUpHR0ctX+D1erVabTAYlBikMJlM4+PjQ9uQValURqMRjtk9KUMR7FCVWDrYCYJQrVbvO9il\nUilRFFmW7edDam5vb6vVqsPh6O3tNzc3xWIRynUAgPszNjbm9/vD4fDFxcVgP1mr1fa/uJii\nqIWFhaurq5ZlRZmDFNPT0yRJDm1DFo7ZPTVDEezkXzshccaO5/lSqdRnsOM4zmQykSTZz4fU\nRKNRh8PR26dBuQ4A8DAcDsfCwsLR0dHp6elgP3kgi4tZlp2ent7f3295bQZFUWiQQqLoSBCE\nz+eLxWL3MQj89KFjdtLbYcBDGpZgJ2eJHSZ5n1g+n8cwrP9gN6g+bLFY5Diu57EJKNcBAB6M\n3W5fXFw8Oztr1/Tsmc1mm5ub63NxsdvttlgsOzs7LdupDMOg+yokTpIxDDM+Pr63tzeEDVmG\nYQRBkL6uAzykYQl2cip2JElKvCyfz1MU1U+xrVqtZjKZQQW7SCSi1WrRwdVuQbkOAPDAbDbb\n4uLi5eXlwLOdy+VCi4v7yRZ+vx/DsFAo1PLZ8fFxdCOFxI4Vj8dDEMTAf7unT61W63Q6OGb3\ndAxFsON5/ilMTnAch9Y59vMhiCiKsVhsbGyst7dDuQ4A8PCsVuvy8vLV1dX+/n4/06zNaouL\nC4VCb5+AthZzHNfuLCC6kUJiDlelUvn9/mg0OoQNWZZl4Zjd0zEUwU5mxU7OErt+vgbHcSzL\ndvwmctzd3VUqld7GJqBcBwB4LCzLLi8vX19fh0KhwWa7+fl5o9G4tbUlfc2rBL1e7/P5jo6O\nWmYUNEiRzWaPj4/bfQLDMGNjY+FweNj2urEsi0YDH/uLAAwbnmDX/0WxA6nYDaoPG41GR0ZG\n1Gp1D++Nx+OFQgHKdQCAR8EwzMrKSiKR2N3dHWAUqC0u3t7e7jlXORwOl8vVbncdTdOBQODi\n4uLm5qbdJ0xPT+M4PmwNWZZlq9Vqy+kT8PCGItgNpBXb53biUqmUz+cHEuzK5fLd3V1v6+vg\nqgkAwKMzGo2rq6vJZLLdcuDeDGRxMdpd1y50ms1mj8ezt7fXLsSoVKr5+flIJDJUDVmKorRa\nLXRjn4ihCHYyW7ESWadUKvE830+wSyQSFEUZDIaeP6EmEonQNN3bMjwo1wEAngK9Xr+6uprJ\nZAab7fpfXIy2FudyuXYt18nJSavVur293W4AlmVZl8s1bA1ZOGb3dAxLsOvzoth8Po/jeD9n\n7AbYh+35tolauU76NCEAADwAnU63traWz+e3trYGmIH6X1ys0WhQy7XdRbfz8/MEQUgcE5yZ\nmcFxXOI0nvIwDAODsU/EsAQ76YqdKIrSFTs0OYHjeM/fYVDBLpFIFItFp9PZw3uhXAcAeFJo\nml5ZWSmVSltbWwPcANf/4mKz2Tw1NRUKhdAG0wZohDaZTLbLjiqVyufzXV1dDU8Ri2XZcrnc\n8j8XeGBDEew6nrGrVCqiKEoHu376sLlcrlwuDyTYRSIRm83Wwwk5KNcBAJ4gmqbX1taq1erm\n5qbElrhu9b+4eGpqymw27+zstKwm6nQ6v99/enp6d3fX8u1ms9nlcoVCoSFpyGq1Wo1GMzxB\n9ikbimDXsRWLxuPvL9glEgmtVkvTdM+fgFQqlZ7HJqBcBwB4mtDBOFEUNzY2el5W0szlcrnd\n7n4WF/v9fkEQwuFwy2dtNpvb7d7d3W23PA81ZE9OTnr76c8OdGOfiGEJdtIVu3K5TBCExPaQ\nPpfYcRxnsVh6fntNNBpVq9U9VP6gXAcAeMooilpdXVWpVBsbGy1XjfRmenq6n8XFJEkuLi7G\n4/Grq6uWL/B4PCaTqV1VDzVkLy8vh6SOxbIsx3GP/S0ABDsMwzqNxAqCUCqVeq7YiaKYSqUG\n0oeNRqMul6uHo35QrgMAPHEkSa6srGg0mvX19Z4vkGjW5+Jig8EwNzd3cHDQshaF4/jCwkK1\nWm1X1TObzaOjo3t7e8PQkGVZtlQqFYvFx/4iw24ogl3HM3bS24kLhYIoij0Hu3Q6zfN8b9tJ\n6iWTyUKh0NvYBJTrAABPn0qlWlpa0ul0m5ubg8p2/S8uHh0ddTqdwWCw5RFAtVodCATi8Xg0\nGm359pmZGVEUh6Ehq9fr1Wo1dGMf3VAEOzln7KQP2JEk2fNGX47jDAZDb7dE1ItEIhaLpYeD\nejc3N1CuAwA8CyjbGQyG9fX1Qd1k0P/iYumLYk0m08zMzP7+fsvDfCRJzs/PX15eDkPiYRhm\nSPrOT9mwBLuOrVjpJXb9TE4MZNFJtVq9vb3tbWwC3QwL5ToAwLNAEEQgEGAYZnNzM5vNDuQz\n+1xcXLso9vT0tOULxsfH7Xb7zs5Oy6qe2Wx2Op17e3sDXMX8NMGa4qdgKIJdx1Zsx4pdz8GO\n5/l0Ot1/sIvFYiqVymq1dvvGm5ubfD4P5ToAwDOCsp3FYtnY2Ein0wP5zD4XF9M07ff7z87O\nbm9vW75gbm5OpVK121o8OzvL87ziG7Isy+bz+QGONoMeDEWw69iKlT5j189ILPq3S/8H7KLR\n6OjoaA9jE1CuAwA8RziOz8/Pj4yMbG5uDmrWsra4uN15OGlWq9XtdodCoZbn/1ATOZVKtQyO\nJEn6fL6LiwtlN2QNBoNKpVL27/j0DUuw66diVygUeq7YcRzHMEzHm2qlpdPpbDY7Ojra7Ruh\nXAcAeL5QthsdHd3e3u55z3ADtLg4HA739oG1/SYtm6parRZtLW754Var1eFwKLshi+M4HLN7\ndBDsMJ7neZ5vV9OqVCqVSqWfYNd/HzYSiZjN5h6qhlCuAwA8d16vd2JiYnt7u10PtFv9LC6u\n7Tc5ODho+QKbzTY+Pr67u9ty64fX6+V5vt1BPWWAY3aPTvnBThAEURQlgp30tRP5fB7H8d5a\nseVyOZvN9hnseJ6/ubnpYWwCynUAAGXweDwejycYDMbj8YF8YD+Li9F+k1gsJrHfRK/XB4PB\n5socasien58P6uDgE8QwTC6XG+DNv6BbQxHsMAyTOGOHtpxLBDuNRiN9RK8djuNIkjQajT28\nt+b6+pogCJvN1u0boVwHAFAMt9uNsl0sFhvIB/azuFh6vwmO44FAoFQqHR0dNT9rtVrtdnso\nFFJqQ9ZkMuE4DsfsHtGwBDvpip1arW43l9DPSCzqw/Yw8VAvEok4nc5uT+lBuQ4AoDBut9vr\n9e7t7fU2+tCgz8XF0vtNKIoKBAJXV1ctY+jc3JyCG7IEQZhMJujGPiIIdve466T/A3bZbDaT\nyfQwNnF2duZyuaBcBwBQkrGxMZ/PFw6HLy8v+/+0PhcXS+83YRhmenp6f3+/ec0ySZJzc3Pn\n5+c9HPJ7FliWhYrdI1J+sEP/FJPopXbcTtzbAbtCoVAsFvsMdpFIhGVZvV7f1bvi8Xg+n3e7\n3f38aAAAeIJGR0cXFhYODw8HUvHqZ3Fxbb/J+fl5yxe43W6LxbKzs9NcEbTZbApuyDIMg+7S\nfOwvMqSUH+w6VuwkltiJolgsFnur2CUSCY1G08+VFYIgXF9f91CuOz09hXIdAECp7Hb74uLi\n2dlZy0Ns3epncTHab3JyctJuecr8/Lwoint7e81Peb3eSqXS27bkJ45hGAzDFDwg8sRBsJNq\nxRaLRUEQegtnHMdZLJYe3lhzc3ODYdjIyEhX74JyHQBA8Ww22+Li4uXl5UCyXT+Li6X3m5Ak\nubS0dHd3d3V11fCUWq32+XxnZ2fKa8iqVCqj0QjH7B6L8oMdz/M4jktMMEgEu3w+TxBED6Uv\nURSTyWT/fVin09ntQC6U6wAAw8BqtS4vL19dXe3v7/dwQq5BP4uLJfabYBim1+u9Xu/h4WFz\ngLPZbCMjI4pcWcwwDByzeyzKD3Zyrp1oF4PQ5EQPY63ZbLZSqfQT7PL5fCqV6rYPC+U6AMDw\nYFl2eXn5+vo6HA73n+1qi4u7CQgDbAAAIABJREFU7SFK7zfBMGx0dLTdCO3c3Fy5XG53Su/5\nQvMTygusz8JQBDuJopcoipVKRaJi1/MBO71eLzFs21EkEjGZTAaDoat3QbkOADBUGIZZWVm5\nvb3d3d3tP9uhxcUvX77sdnGx9H4TDMN8Ph9JkqFQqOFxtVrt9XpPT08V1pBlGEYURYX9Us/F\nUAQ76QN2oigOPNj1ecBOEIRYLAblOgAA6MhoNK6srHAc1+4K1670vLiYYRi0tbh5vwmGYQRB\nBAKBliO0drvdZrPt7e31H0yfDrVardPpoBv7KJQf7Hie73ifmHQrttufKAhCKpXqpw97e3sr\nCILdbu/qXVCuAwAMJ4PBsLa2lslk+s92aHExSZI9LC6emJiwWq3b29stL9TS6XQ+n+/4+Lh5\nqsDn85XLZYVNyMKlsY9F+cGuY8VOpVK17NXyPF8ul3tYYpdKpURRRPPevYlEIna7nSRJ+W+B\nch0AYJjpdLq1tbVcLre1tdXnBjW0uLhSqfSwuHh+fh7H8eaWK2K3210uVzAYbCgHqtXq2dnZ\ns7OzltW+Zwods1NSGfK5GIpgJ72dWKIPi2FYDxU7juNMJlNXsaxeoVBIJpMul6urd0G5DgAw\n5GiaXl1dLZVKW1tbfV5Cr1arl5eXM5lMt4uL0dZijuMuLi5avmB2dlaj0TSfCHQ4HFardSAn\nBZ8IlmWr1aqSoupzofxgJ92KldhOnM/nKYrqIZ/1eZNYNBrV6XQmk0n+W6BcBwAAGIbRNL22\ntlatVjc3N1ve4iqfVqtdWlrqYXGxTqebn58/Ojpq2YgkCGJxcTGbzTZ/rM/nK5VKipmQpShK\nq9VCN/bhKT/YdWzFDvaAXbVazWQyPQc7URRjsRiU6wAAoDfoljBRFPvPdiaTKRAI9LC42G63\nj42NBYPBUqnU/CxN036///T0tGFnXm1CVjFVLjhm9yiGItj13IrtrQ9LEERX9bZ6t7e3lUrF\n6XTKfwuU6wAAoB5FUaurqwRBrK+vt4xW8lmt1t4WF8/Ozmq12nan9KxW6/j4eCgUavh6qCGr\nmAlZCHaPQvnBruNU7MCDHcuy0iuRJUSj0W7HJs7OzqBcBwAA9UiSXF5eVqvV6+vrLS/7kq+3\nxcVoa3E+nz8+Pm75gpmZmZbJb25urlAotDui97wwDFOpVNCBdfBglB/spFuxEmfsCoVCb8Gu\n5z5sqVRKJBJdra+Lx+O5XA7KdQAA0ABlO61Wu7Gx0e3C4Qa9LS7WaDSBQODi4gJd/N2gXfKj\nKGp2dvbk5EQBDVmtVqvRaKBo98CGItj10IotlUo8z3cb7EqlUj6f7znYRSIRrVbLsqz8t0C5\nDgAA2lGpVC9evDAYDOvr633mpN4WF5vNZo/Hs7e317JqpdFoFhYWLi4ubm9v6x93Op0Wi0UZ\nDVnoxj68oQh27Sp21WpVEISWqSifz+M4TtN0Vz8rkUhQFNXtPWBID2MTUK4DAABp6MoHhmE2\nNzez2WzPn9Pz4uLJyUmLxdLuXRaLZXJycm9vr6FfPDc3l8/nLy8ve/7CTwTDMBDsHthQBzv0\nD6+WFbt8Pq/VanEc7+pn9dOHTSQS5XLZ4XDIfwuU6wAAoCOU7SwWy8bGRlfn5Br0vLh4fn5e\nFMW9vb2Wz05NTRkMhmAwWH9nhkajmZ2dPT4+fu4NWZZlS6VSn8ccQVeUH+wkhidKpRKO42q1\nuvmpnicneg520WjUZrO1O/DX7Pb2Fsp1AAAgB47j8/PzIyMjW1tb/Vxg2tviYpIkFxcX7+7u\nrq6uWn63hYWFYrF4cnJS//jo6KjZbA6Hw8+6IavX6ymKgqLdQ1J+sJM4Y1cul9VqdcuyXA/B\nLpfLlcvl3oJduVy+vb3tqg97eno6OjoK5ToAAJADZTun07m1tcVxXM+fU1tcfHp6Kv9dBoPB\n5/MdHBy0jJUURbU8bOfz+XK53HNvyDIM00+YBt0aimAn0Yod4HbiRCKh1Wq7PZaHRKNRjUYj\nf2wCynUAANADr9c7MTHx8uXLhgjVFbS4+PT0tKvFxQ6Hw+l0BoPBlmuTzWaz2+0OhUL1XUvU\nkD05OelzqvdxwTG7B6b8YCfRim03EisIQqlU6jbYcRxnsVh6+YoYFo1GXS6X/CN9qFzXW4gE\nAIBh5vF4PB5PMBiMx+M9f0hvi4vn5uYoimp3RM/j8RiNxobDdqOjowzDhEKh59uQZVk2n893\nNU0M+qH8YCfRim23xK5QKIiiqNVq5f8UURRTqVRvfViO44rFovzbJqBcBwAA/XC73SjbxWKx\nnj+kh8XFtYtiG47TIe0O283Pz+dyuZbn854Fg8FAkiR0Yx/MUAS7blux+XyeJEn5cwwYhqXT\naZ7nu1pBVxOJRKxWq/zTclCuAwCAPrndbq/Xu7e31+09sPWmp6ftdntXi4tpml5YWDg/P29Z\nL2x52E6j0czMzBwfHz/ThiyO4yaTCbqxD0bhwU4URelg127XSQ99WIPB0HLAVlqlUulqbALK\ndQAAMBBjY2OondrPdILP5+t2cXFtd13LoNbysJ3L5WIYpt3ClKcP1hQ/JIUHO3RS4WGCXW8H\n7GKxmFqtlv/es7MzKNcBAMBAuFwuv99/eHjY1Yhrvd4WF09NTZlMpp2dnZZv8Xg8aLNd/bk6\nn8+XyWSeaUOWZdlcLtdyagQM3FAEu5Zn7ARBqFQqAwl2PM+n0+neDthFo9HR0VGZYxO3t7fZ\nbBbKdQAAMCgOh2NxcfHs7Ozo6Ki3T+hhcTE6TletVsPhcLtnC4VC/WE7mqZnZmaOjo6eY0PW\naDTiON7Pdmgg31AEu5YVO1Q2b3myrVAodBXsUIWZYZhuv14qlcrn86OjozJfD+U6AAAYOJvN\ntri4eHl52XO262FxsVqtDgQC8Xi85SE/jUbj9/vPz8/rD9uNjY0xDNMyCz5xBEHAMbsHM+zB\nrvlUXLlcrlQqXQU7juMYhmnX8JUQiUTMZrPMoHZ7e5vJZKBcBwAAA2e1WpeXl6+urvb393tb\nLNLD4mKTyTQ7O7u/v5/JZFp+Jbfb3XCNrM/nS6fTz7EhC8fsHozCgx06vtCyFVsul0mSbH4q\nn8/jON7VrpNEItFDH7ZarcbjcfljE+hmWCjXAQDAfWBZ9sWLF9fX1z1nux4WF4+Njdnt9p2d\nnZbnzzwej06nq+/w0jQ9PT39HBuyDMNkMhn5xxBBzxQe7CQqdhJL7DQajfzyW7lczuVyPQS7\n6+trlUpls9nkvBjKdQAAcN9Yll1ZWYnH4z0vBK4tLr67u5P5Fp/PR5Jky5+I43ggEGg4bDc2\nNmY0Gp9dQxadVoJjdg9geIPdoEZiOY4jSdJoNHb73SKRiNPplDk2AeU6AAB4AEajcWVlJZFI\n7Ozs1N8AIR9aXBwMBmWGGLS1OJVKnZ2dNT9bO2xXu+ICx3G/359OpyORSA9f77GoVCqj0Qjd\n2Aeg8GAnfZ9Yu+3E3QY7s9ks/zYwJJPJZLNZmWMTUK4DAIAHYzAY1tbWMplMz9mu28XFWq3W\n7/efnp62vKDMarWOj4+HQqFSqYQeoWna4/EcHR3VH797+liWhfsnHoDCg90DbCdGwa7bLxaJ\nRFiWlfmDoFwHAAAPSafTra2t5XK5ra2t3o6Fdbu42GazTUxM7O7utsxqMzMzWq12d3e31q4d\nHx83GAzPa2UxwzCpVKq3rAzkU36w6+qiWFEUu9p1UigUisVit8GO5/mbmxuZYxNQrgMAgIdH\n0/Tq6mqpVNra2qpWq92+HS0uVqvV8hcXT09PG43GYDDYHH3QZrtcLldr1+I4Pj8/n06n+7kS\n7YGxLCuKYssRYDBAyg92XVXsCoWCKIryg10ikdBoNN1eU3Fzc4Nh2MjIiJwXQ7kOAAAeBU3T\na2tr1Wp1c3Ozh1sTVCrVixcv5C8uRofnSqXS4eFhyy/j8/lOT085jkOPaLVaj8dzeHhYa9E+\ncSRJ6vV6OGZ33xQe7Lo9Y1coFFQqVcuzdy31dpMYGpuQM3gL5ToAAHhEFEWtrKyIothbtqst\nLpY5xEpRVCAQiEajsVis+dmRkRGXyxUKhWrfZHx8XK/XP6MJWThm9wAUHuzatWIrlYoois0V\nu3w+L3+DnSiKyWSy2z5sLpdLp9MyxyagXAcAAI+LoqjV1VWCINbX13uojaHFxTc3NzIXFzMM\nMz09HQ6Hs9ls87Ozs7NqtToUCqE/ooYsx3Etg+AThI7Z9bZKBsik/GAnce1Ey2Anv6+azWYr\nlUq3wS4SiTAMYzAYOr7y7u4OynUAAPDoSJJcXl5Wq9Xr6+s9DKJ2u7h4YmLCZrPt7Ow0n+1D\nu1GSyeTFxQV6RKfTeTyeg4ODZ9GQZVm2Wq22zKxgUBQe7Nq1YkulEkEQzfeJdRXsEomEXq9v\nOVrbjiAI19fXMst1p6encDMsAAA8BSjbabXajY2NHm59qC0urr/7VcL8/DxBELXKXD2tVjs3\nN3d0dFTbkzcxMfFcGrIURWm1WujG3iuFBzuJil3/u056OGAXj8cFQbDb7R1ficp1k5OTXX0+\nAACAe4KGIQwGw/r6ei6X6/btaHHx7u6unMXFKpUKVebOz8+bn3U6nQ6HY3d3F5X0ag3Z6+vr\nbr/Vw4NLY++b8oNdu4tim4NdtVotl8syg50gCKlUqoc+rNPpbLeBpR6U6wAA4KkhCCIQCDAM\ns7m52UM/savFxTqdzufzHR8ft9xaPDc3h+N4rUqn0+mmpqb29/effkMWgt19U3iwa9eKbRns\n8vk8hmEyhyfQ8U90+Z1M+Xw+mUzK6cNCuQ4AAJ4mlO0sFsvGxkYPN592tbjYbrePjY3V3zlR\ng0p6t7e3tXN7brdbp9Pt7+93+5UeGMuylUoF/YUL7oPCg127VmzL7cSFQoGiKJIk5Xwyx3Em\nk0nmi5FoNGowGOTcKgvlOgAAeLJQ63NkZGRra6vb42LdLi6enZ3V6XQttxbr9fqZmZn9/X1U\nO0Rr8BKJxBNvyNI0rdFooGh3f5Qf7Nq1YpuX1XV7wK6rPqwoirFYTM5tE1CuAwCAJw5lO6fT\nubW1VdsYLFNXi4txHA8EAoVC4fj4uPnZ8fFxq9W6u7uLMqJOp5ucnDw4OJB5j9ljgW7svVJ+\nsOuqFSsz2FWr1Uwm01Wwu729rVarDoej4yuhXAcAAM+C1+t1uVwvX76UOeta09XiYoqiFhYW\nLi8v0a1FDebn53mePzg4QH+cnJykafqJT8gyDAPB7v4MabBr2YqVH+w4jiMIwmQyyf8mkUjE\nbrd3bN1CuQ4AAJ6R2dlZj8cTDAbj8XhXb+xqcbHZbPZ4PHt7e83TuCRJLiwsxGIx1IFFpcRE\nItEyBT4RLMuWSqUeNgICORQe7FoOT/A8z/N8yzN28oMdy7Jy7gRDisUix3Fy+rBQrgMAgOfF\n7XajbNft9Q9dLS6enJy0WCwttxajyyr29/fRsK3BYJicnNzf33+yDVm0AhaKdvdE4cGu5Rk7\n9L/1hjN2xWKR53n5wa6rPmw0GtVqtR1HaKFcBwAAz5Hb7fZ6vXt7ezLvlqjpanGx3+/HMKxl\nm3ViYsJkMtVmLFBDttaffYKgG3t/lB/smutqKNg1XDuRz+cJgpBTKisWi/l8Xn6wE0UxGo1C\nuQ4AABRsbGwMRbTLy8uu3uhyuSYnJ+UsLkYrTu7u7pp/BBqJLZVKJycn2Ocbsre3t902iB8M\nujT2sb+FMik82LVsxZZKJbVa3fB4Pp+naRrH8Y6fyXEcRVFyLntF7u7uKpWK0+ns+DIo1wEA\nwPPlcrn8fv/h4WHtIleZPB4PWlzccbubXq/3+XyHh4fNqYiiKL/ff3FxcXd3h2GYwWBwu93h\ncPhpNmRZls3n809/nfJzpPBg164V2+cBu277sCMjI8330jaAch0AADx3DodjYWHh6Ojo6Oio\nqzeixcUvX77smMMcDsfo6OjOzk7zKy0Wy/j4+N7eHnpqamqKpunDw8OuvsnDMBgMJElC0e4+\nKD/YtWzF9rPErqtgVy6X7+7uOt42AeU6AABQBrvdvrS0dHl52VW2qy0ufvnyZcfFxV6vl6bp\n3d3d5jV4MzMzNE2HQiFRFFFD9ubm5gk2ZHEch27sPVFysBNFURTFlsGu510nuVyuXC7LD3bR\naJSm6Y6vPz09dTqdUK4DAAAFsFqty8vLV1dXXV3whRYXV6vVjouL0bVm2WwWnairh+P4wsJC\nOp1G7WA0IRsOhyuVSg+/yL2C+Yl7ouRgh/7RI6cVKwhCqVSSE+wSiYRWq5WfwOSMTUC5DgAA\nFIZl2RcvXlxfX4fD4Y7XS9TIX1xM03QgEDg/P2+uxmm1Wp/Pd3x8jKYxJicnKYp6ghOyLMvm\ncrknmDifOyUHOzT13XJ4oiHY5fN5URTlBDuO4ywWi8wvkEgkisVix7GJs7Mzp9Op1WplfiwA\nAICnj2XZlZWVeDyOGqMy3yV/cbHZbJ6cnAyFQs0jF3a73eFw7O7uVqtVgiAWFhaeYEPWaDQS\nBAHd2IEbxmDXfMYun8+r1eqO8w2iKKZSqa76sDabrbntWy+RSKTTaSjXAQCA8hiNxpWVlUQi\n0fI8XDvyFxdPTU2xLLuzs9N8LG9ubg7HcVT5QxOy+/v7T6o8RhCE0WiEYDdwQxfsRFGsVCrN\nFTs55bp0Os3zPMuycn56pVK5vb3t2IdFp+ugXAcAAIpkMBjW1tZSqdT29jb6W0kOmYuL0Yk6\nnuebW7do6d3t7S26D2NqakqtVj+1CVmWZeGY3cApOdi1PGNXqVREUWwOdnKiVSKRMBgMHQt7\nSDQaVavV0uU9KNcBAIDi6XS6lZWVbDa7tbXVceK1pra4WLqmRZLk0tJSPB6PRCINT+n1+pmZ\nmf39fbSBf35+/vr6Ws4VFw+GZdlMJiP/vwmQQ8nBrmXFDq1DbAh2MpfYdXXADo1NSG88hnId\nAAAMA51Ot7a2ViqVXr582XzZaztocfH29rb04mKDweD1eg8ODprvrhgfHzebzTs7O4IgmEym\niYmJcDgs/wvcN5PJhON4xys3QFcUHuxwHG+IVuVymSAIkiTrH5TTiuV5Pp1Oyzxgl0wmC4WC\n9NgElOsAAGB40DS9trZWqVQ2Nzfln3Xz+XwMw3RcXOxyuRwORzAYbP5kv9/P8zxaqufxeJ5U\nQ1alUhkMBujGDpaSg13L+8SaJyfK5XK1Wu0Y7JLJJFqoKOdHRyIRi8UivRUFynUAADBUKIpa\nWVkRRVF+tkOn6OQsLp6bm1Or1c078EiS9Pv9V1dXt7e3qCEbi8XQtWNPARyzGzglB7uW1060\n3HWC43jHgMVxHMMwzR/YrFqtdhybgHIdAAAMIYqiVldXCYJYX1+XeVOqzMXFBEEsLi5ms9mz\ns7OGp1iWnZyc3NvbK5VKJpNpfHz86TRkGYZJp9Pyx0pARwoPdnK2E+fzeZqmOya2RCIhsw8b\ni8VUKpXVapV4DZTrAABgOJEkuby8rFarNzc3ZWY7mYuLaZr2+/2np6fNBbmpqSmdToe2rkxP\nT5Mk+UQasizLiqKYyWQe+4soh5KDncxWbKFQ6BiwyuVyLpeTGeyi0ejo6KjE2ASU6wAAYJih\nbKfRaNbX1wuFgpy3yFxcbLVa3W737u5usVisfxzHcXQL2fn5OUEQPp8vFoslEol+fouBIElS\nr9dDN3aAlBzsWrZiW1bsOh6w4ziOJEmj0djxh6bT6Ww2Ozo6KvEaKNcBAMCQU6lUS0tLOp1u\nfX09l8vJeUttcfHV1ZXEyzwej8lkal6bp9FofD7fyclJKpViGGZ8fHxvb+8pNGRZloU1xQOk\n8GDX3IptecZOTrAzm83Su0uQSCRiNpslQhuU6wAAAGCfz3YMw2xubmazWTlvsVqtPp/v4OBA\nYh0djuN+v79SqTTfD4uuGguFQtVq1ePxEASBpmUfF8MwqVRK/s0cQJrCg11zxa7h2glRFOUs\nsUPBruNP5Hn+5uZGemwCboYFAACAEAQRCAQsFsvGxobMdW6jo6MdFxdTFLW4uBiLxZovJatd\nNaZSqfx+fzQaffSGLMuy1WpVZrQFHSk52DWfsatWqzzP15+xKxQKoihKB7t8Pl8sFuUEu+vr\na4IgbDZbuxckEolUKuV2u2V8fQAAAMqH4/j8/PzIyMjW1pbMjqScxcUmkwldO9Ewl6BSqRYW\nFtBVYwzDjI2NhcPhx737gaIonU4Hx+wGRcnBrrlihxY81lfs8vm8SqVqGKdowHGcRqORczVF\nJBJxOp0SA7aoXCfnowAAAAwJlO2cTufW1hbHcXLeImdx8fj4uN1uDwaDDQfpjEajx+NBV41N\nT0/jOP7oDVnUjX3c76AYCg92DWfsyuUyjuP1l73KuSVW5k1i2Ww2k8lIjE1AuQ4AAEA7Xq/X\n5XJtb2/L6Y3KXFw8NzdHEMTu7m7D4263m2XZ3d1dlCkjkcjjNmRhTfEAKTnYNbdiS6WSWq2u\nn4HoODkhimIymZTTh41EIgzD6PX6di84OztzOBxQrgMAANDS7Ozs1NTU9vZ2PB7v+GK0uJjn\neYnFxSqVanFxMZVKNW8tnp+fL5VKJycnLMu6XK7HbciyLFupVGROBwNpSg52LVuxDSOxHScn\nMplMpVLpGOwEQbi+vpYYm0DlOhiGBQAAIMHtdns8nmAwGIvFOr5YrVa/ePFCenGxTqfz+/0n\nJycNNTmKovx+/8XFRSKRmJmZwXH8+Ph4AL9AT2iapmkainYDofBg19yKbThO17Fix3GcXq9v\niIPNbm5uMAwbGRlp9wIo1wEAAJDD7XZ7vd69vb3mmdZmchYX22y28fHxUCjUcNGFxWIZGxsL\nhUI8z/t8vqurq0eMVnDMblAUHuykK3bVarVcLncMdnIO2EUiEYfD0bw2D4FyHQAAAPnGxsbm\n5ubC4fDl5WXHF8tZXDwzM6PT6XZ2dhq2Fs/MzFAUtbe3ZzabR0dHUcgbwC/QPThmNyjDFewa\nthOjQXGJ4QlBEFKpVMc+bD6fT6VSEn1YKNcBAADoisvl8vv9h4eHFxcXHV/ccXExulKsWCw2\nDMASBLG4uJhMJi8vL2dnZ3EcPzk5Gcwv0CWWZUulUsNNaKAHSg52zcMTDRW7fD6v0WhIkmz3\nCWgXNsMw0j8oEomYTCaDwdDyWY7joFwHAACgWw6HY2Fh4ejoSM46ErS4OBgMtmtoUhQVCASu\nrq4aTu9ptdrZ2dmjo6NCoeDz+S4vLx+lcqbT6SiKgqJd/5Qc7DqesZNzwM5kMkkkP/RTYrGY\nxJaT09NTKNcBAADogd1uX1paury8lJPtPB6Pw+GQWFzMsuz09PT+/n7D/KnL5bJarbu7uwzD\nOJ3Ovb29R2nIMgwDwa5/Cg929RU7QRAa7hPrGOwSiUTHPuzt7a0gCHa7veWzUK4DAADQD6vV\n+uLFi6urq/39/Y4v7ri42O12WyyWnZ2dhq3F8/PzPM8fHh7Ozs6KovgoDVkIdgOh5GDX0Ipt\nee2ExAE7dHVdx8mJSCRit9vbVfWgXAcAAKBPZrP5xYsXsVgsHA63W1mHoMXFFEVJLC72+/0Y\nhoVCofoHSZL0+/2RSCSZTM7Pz19eXj78jCrLsoVCoWF0F3RLycGuoRXbEOxEUZReYsdxHEEQ\nJpNJ4kcUi8VkMtlubALKdQAAAAaCZdnV1dV4PB4KhaSznUqlWlpaklhcjLYWcxzXMJbBsuzk\n5OTe3p5Op0MN2YYR2vtmMBhIkoSlJ31SeLBrqNiRJFmLeqVSSRAE6WDHsmz9NRXNIpGITqdr\nF/6gXAcAAGBQjEbjyspKIpHY3d2VznYdFxfr9Xqfz3d0dNTQ+pyamtLpdLu7uzMzMzzPP3BD\nFsdx2GbXP8UGO/TvjIZg19CHJQiCpul2n9DxgJ0oirFYDMp1AAAAHobBYFhbW0ulUtvb29Ll\nNK1W++LFi5ubm3bhzOFwuFyuYDBY3/rEcdzv92ez2Wg06vP5Li4uHjhmwTG7/ik22KGzBfWt\n2OYldlqttl1BrlgsFgoF6QN2d3d3lUrF4XC0fBbKdQAAAAZOp9OtrKxks1mJU3SI0WgMBAJn\nZ2ftFhd7vV6tVttQ/9NqtV6v9/j4WK1WOxyOB27IsiybzWYrlcqD/UTlUWywk1Oxk+7DUhSl\n1+slfkQkEhkZGVGr1S3fDuU6AAAA90Gn062trRWLxY7ZTnpxMdpanMvlGm6JdTqddrt9d3d3\nenqa53mJy8oGzmg0qlQq6Mb2Y7iCnfwldhzHSfdhS6VSIpFo14eFch0AAID7Q9P02tpapVLZ\n2NiQrm9JLy7WaDSBQODi4iIej9c/Pjc3J4ri6empz+c7Pz9Pp9MD/gXaQDOLEOz6MVzBTv6u\nk47BLhqNarValmVbvhfKdQAAAO4VRVErKyuCIGxubkpnO+nFxWazeWpqKhQK1T9LkmQgEIhG\nozzP2+32UCj0YA1ZOGbXJ8UGO+kzdoIglMvldhW1XC5XLpclgp0oitFotN1tE1CuAwAA8AAo\nilpbWyMIYn19XXr9m/Ti4qmpKbPZvLOzU9/YNZlMU1NT4XDY7XbzPH92djb4X6AVlmUzmcyj\nXH2hDIoNdujfFvWzEfXXTuTzeVEU22WvRCKh1WqlB2bL5bLT6Wx+Csp1AAAAHgxJksvLy2q1\nenNzUyLbdVxc7Pf7BUFoWI8yOTlpMBgODg68Xu/Z2Vkmkxn8L9DEZDLhOA7d2J4pOdgRBFEL\ndpVKRRCE2hm7fD6vVqtbzj1gGMZxnPQ8bDQatdls9Y3dGijXAQAAeEgo22k0mvX19UKh0O5l\n0ouLSZJcXFyMx+P1I7QoDuZyuXw+b7fbH2ZCVqVSGQwGCHY9U2ywk75PTGJyQhTFVCol0Yct\nl8u3t7ct+7BQrgMAAPDwUGjT6XTr6+u5XK7dy2qLixvuE0MMBoPX6z04OKgPVRqNxufznZyc\nOJ3Ocrn8MA1ZlmXhmF3PFBvsGq6dKJVKOI7XbnSVCHbpdJrn+ZZTEUgsFtNoNC2T3+npqd1u\nh3IdAACAB4ayHcMwm5sKbJsBAAAgAElEQVSb2Wy23cvQ4uLb29uWi4tdLpfT6QwGg/XTGCMj\nIw6HY39//8EasizLptPpB77QTDGUHOwaLoqlKKrWmZUYiU0kEkajsV2XFsOwSCTicrmaNxuj\nct3U1FS/Xx0AAADoHkEQgUDAbDZvbGxILCiRXlw8NzdHUVRDu3Zubg7DsLu7u5GRkQdoyDIM\nI4riwxzpUx7FBrvmVmz9ErtCodCuria96ITjuGKx2HJsAsp1AAAAHhe6E2xkZGRra0vimJrE\n4mKCIBYXF7PZbP1eYpVKFQgErq+vLRZLuVw+Pz+/p++PkCSp1+uhG9sbxQa7hlZs/RK7crlc\nrVZbJjCe59PptESwi0ajVqu1PiMiyWQSynUAAAAeHY7j8/PzTqdza2uL47h2L5NYXEzTtN/v\nPzs7u7u7qz1oNBqnpqYODw+npqZOT0/vu5wGx+x6puRg126JXT6fx3G8ZSs2mUziOM4wTMvP\nrFQq8Xi85W0TJycnUK4DAADwRHi9XpfLtb29nUgk2r3G4/GMjo62XFxstVrdbvfu7m79mC3a\nfnJzc2Oz2fb29ppHaweIYZhUKnWvP0KplBzs2lXs8vk8TdP1z9ZwHMcwTMunMAyLxWIkSTZv\nQkmlUlCuAwAA8KTMzs5OTExsb283XBdWz+v1tltc7PF4TCbTzs5O7UQd2n6SzWZ1Ot19N2RZ\nluV5XmIKBLSj2GDXfMZOzq6TRCIh3YdtOTZxfHwM5ToAAABPjcfj8Xg8wWDw+vq65QskFhej\np6rV6sHBQe1BtP3k/Px8bGzs9PRUYrVKnyiK0ul00I3tgWKDXXPFrn47ccsQVi6Xc7lcu2CX\nSqVyuVzz2ASU6wAAADxZbrfb6/WGQqFoNNryBRKLi9VqdSAQiMVi9e+12+12uz0Wi1kslt3d\n3fvrlrIsC2uKe6DkYFc7YycIQrVara/YtTxgx3EcSZJGo7HlB0YiEYvF0vxGKNcBAAB4ysbG\nxubm5sLh8OXlZcsXSCwuNplMMzMz+/v79dMSPp8PwzCSJEul0v01ZBmGSSaTcMyuW4oNdvWt\nWHR9Hgp2giAUi8WWOQwtOmnutGIYVq1WW45NQLkOAADA0+dyufx+/+Hh4cXFRcsXSCwuHh8f\nt9vtOzs7ta3FKpXK7/dfX187HI77a8iyLFupVJoHO4A0xQa7+lZs/X1ixWJRFEWJYNfy066v\nrwmCsFqtDY/DMCwAAIBnweFwLCwsHB0d1S+oqyexuHhubk6lUoVCoVr9jGGYycnJWCxmNpvv\naUKWpmmapuGYXbeUHOxqrdhyuaxWq1HOy+fzKpWqeRFdPp8vFovNE69INBodHR1tmJZNpVLJ\nZBJuhgUAAPAs2O32paWls7Ozo6Ojli9ot7gYncNLpVL1jdepqSm9Xl+tVvP5fLtCYJ/Q0pP7\n+GQFU3Kwq2/FdhyJ5TiOpumWZ+8ymUwmkxkdHW14HJXr9Hr9QL84AAAAcF+sVuvS0tLl5eX+\n/n7LF7RbXKzVav1+/8nJSW0xHhqbzeVyVqv15OTkPhqysKa4B4oNdvVn7OTsOpHow0YiEZZl\nG94F5ToAAADPkcViWV5ejsVi4XC4ZQu13eJim802Pj6+u7tbLBbRIzRNe73eeDxuMpnuoyHL\nsmypVKpfkgw6UmywazhjJx3sRFFMJpMtgx3P8zc3N81jE1CuAwAA8EyxLLuyshKPx+uPzdVD\ni4u3trYaFhfPzMzo9fpgMFjbWux0Om02W6lUyufz7aZue6bT6SiKgm5sV5Qc7OrP2Ekvsctk\nMpVKpWWwu7m5wTDMZrPVPwjlOgAAAM+ayWRaXl5OJBItd9HhOB4IBDQaTcPiYvR4qVSqP6Xn\n8/lEUTQajcfHxwNvyKKlJ4P9TGVTcrBrPmNXrVYrlUpzsOM4Tq/X16p69aLRqNPprL92FoNy\nHQAAgOfPaDSura2lUqnt7e1aBa6GIIiWi4spigoEAldXV7FYDD1CkqTf708mkwaDoV17t2dw\nzK5big12Lc/YoeMCzRMSHMe1nIfN5/OpVKphbALKdQAAAJRBp9OtrKxks9mW2a7d4mKGYaan\np/f392v1OZZlJyYm8vl8LpcbbEOWZdlCoYD20QI5FBvsaq1YURQrlUot2Gk0mobymyAIqVSq\nZR/26urKZDIZDIb6B09OTkZGRqBcBwAAQAF0Ot3a2lqhUNja2mq4LhZrv7jY7XZbrdbt7e1q\ntYoemZ6e1ul0Go3m5ORkgOMOer2eJEk4ZiefMoOdKIqiKKKKXaVSEUURnbFrecAulUqJosgw\nTMPjgiBcX183jE2gch1cNQEAAEAxaJpeXV0tlUqbm5u16yVq2i0unp+fx3G8VszDcdzv9xeL\nRY1G024mowc4jsMxu64oM9ihejIKdvXXTrQMdhzHmUwmkiQbHo/H44Ig2O32+gehXAcAAEB5\nNBrN2toaz/NbW1vN2a62uDgej9ceRFuLOY6rbSfW6XQzMzOlUimbzTZfX9EzOGbXFWUGO1RM\nRi3XUqlEEATKbS2DXSKRaNmHjUQiDoejvm8L5ToAAABKRVHU2toajuPr6+vNZ9rQ4uLd3d36\nrqhOp5ufnz86OqoFr7GxMZZl1Wr10dHRoBqyDMPkcrnmuAlaUmawa6jYoXKdKIqFQqEh2FWr\n1Ww22zw5USgUkslkQx8WynUAAAAUjCTJ5eVltVq9ubnZnO1aLi622+0ulysYDNZe7/f7BUHQ\naDR7e3sD+VYmk0mlUsExO5mGItihA3alUkkQhIZgx3EcQRAmk6nhEyKRiMFgMBqNtUegXAcA\nAEDxULbTaDTr6+vNJbeWi4u9Xq9Wq61tRVGr1eiwXTqdHkhDFsdxk8kE3ViZhiLY1Q7YEQRR\n21SMcBzHsiyO4/UPiqIYi8WgXAcAAGAIocNzOp1uY2OjIdu1XFyMHszn88fHx+gRi8XicrkI\nghhUQ5ZhGKjYyaTMYNdwxq4W7LRabUOGa3nA7vb2tlqtOhyO2iNQrgMAADA8ULYzGo3r6+sN\nl0m0XFys0WgCgcDFxUVtumJ2dhbtFwuHw/1/H5ZlM5lM8zYW0EyZwU4QBBzHUYarr9g19GGL\nxWKhUGg+YBeJROx2e/2cLJTrAAAADBWCIBYXFxmGWV9fT6fT9U+1XFxsNps9Hk8oFEIn8AiC\n8Pv91Wo1lUpFIpE+vwzDMDiOQ9FODsUGu/prJ9otseM4jqKohrhWKpU4jqvvw0K5DgAAwBBC\nPVabzba1tdUQqlouLp6cnLRYLNvb26i0ZjQa0V+dh4eHfTZkCYIwGo0Q7ORQZrBrd59Yc7Br\n7sNGIhGtVlu/r/j09BTKdQAAAIYQWjvsdDq3trY4jqt/qra4uP4Osfn5eVEUa/OwbrcbjSf2\n35CFNcUyKTPY1Sp21WqV53mKonieL5VKHYNd89hEKpXiOA5uhgUAADC0vF6vy+Xa3t5OJBL1\nj1utVr/ff3h4WDtaR5Lk4uLi3d0dmofFcXxhYQHDsFQqFY1G+/kOLMum0+nmC21BA8UGOzQ5\nUbt2AhWBtVpt7TXZbLZcLjcEu0QiUS6XnU5n7RFUrmu4LhYAAAAYKrOzsxMTE9vb27e3t/WP\nOxyOqamp+sXFBoMBXVOBHtFoNHNzc4IgHBwcFIvFnr8AwzCiKDac9gPNlBnsaq3YcrmM4zhF\nUfl8Xq1Wq9Xq2ms4jtPpdDRN178xEonYbLbay6BcBwAAACAej8fj8ezs7FxfX9c/PjU11bC4\n2OFwOJ3OYDCIrotwOp12u72+RdsDkiT1ej0cs+tImcGu1ootl8tqtRrHcTkH7Mrl8t3dXX0f\nFsp1AAAAQI3b7fZ6vaFQKBaL1T/evLh4bm6OoqjaShSfz6dWq5PJZD8NWbg0Vg7FBrvmJXb1\nwU4UxVQq1RDsotEoTdMsy6I/QrkOAAAAaDA2NjY3N7e3t1c/M9G8uBhtS8lms6enpxiGkSSJ\nDtsdHBw0X1YmE8uyqVSqtjwPtKTYYNd87UR9sEulUjzP1zIcEo1GR0dHaxuMoVwHAAAANHO5\nXGhm4uLiovZgbXHx9vY2GnGgaXphYeHs7AyNVrAsOz4+Lopi/fa7rrAsy/N8NpsdyG+hVMoM\ndvVn7NASu0KhUB/sOI4zGo0NR+6KxWJtbALKdQAAAEA7DodjYWHh6OgIFeQQtLg4l8vVztJZ\nLJbJycm9vT00wjg9PU3TNMdxDZ1cmdRqtU6ng26sNGUGu4aKXalUqlarDcGuoQ+LxiZqN8lC\nuQ4AAACQYLfbl5aWzs7Ojo6Oag82Ly6empoymUw7Ozuo5rK4uIjjeDgc7q0hC8fsOlJssKs/\nY5fP53Ecrw3A8jyfTqfrg12lUrm9vR0dHUV/TKfTUK4DAAAApFmt1qWlpcvLy/39/dqDDYuL\n0Sq7arWKdhTr9frp6WlBEHZ3d3v4iXDMriNlBrv6VixaYkfTdO0uimQyieN4/d0SsVhMrVbX\nLo09OTmx2WxQrgMAAACkWSyW5eXlWCwWDodreathcbFarQ4EAvF4HI3Eut1uhmFSqVQPDVmW\nZSuVSm2vCmimzGCHWrGiKFarVY1G0zA5wXEcwzC1nIdhWDQadblcaGwClevgZlgAAABADpZl\nV1ZW4vF4KBSqZbuGxcUmk2l2dnZ/fz+TyWAYFggECILooSGr0WhomoZurATFBjuVSlUul0VR\nRK3Y+mCXSCTq+7DJZDKfz9fGJqBcBwAAAHTFZDItLy8nEond3d1atmtYXDw2Nma323d2diqV\nikajmZ+f760hC8fspCk22BEEgf4d0BDsyuVyLperdV0xDItGoxaLBZ3Ag3IdAAAA0AOj0bi6\nuppMJmvrTrCmxcU+n48kSVTYs9vtNputh5XFDMNAsJOgzGCHztiVy2WVSoXjeLFYrAU7juNI\nkqwV5KrVajwer41NQLkOAAAA6I1er19dXc1ms7Vs17C4GE3FplKps7MzDMP8fj9JkgcHB7X7\nKuRgWbZcLqP9KaCZMoNdrRWLJidEUawPdmazubaFOBaLqVQqm82GQbkOAAAA6I9Op1tbWysU\nCltbW7UrKOoXF2u1Wr/ff3p6mkgkSJJcXFxET3X1IyiKgqJdO4oNdqhip9FoCoWCSqVC909g\nTRvs6m+bgHIdAAAA0CeapldXV0ul0ubmZqVSwTBMrVYvLy/XFhfbbLaJiYnd3d1isWg2m51O\nZzqd7qohi4Zq7+sXeOYUHuwaDtjl8/lisVg7YJdOp7PZLOrDZjIZKNcBAAAA/dNoNGtrazzP\nb21toWxH03T94uLp6Wmj0RgMBgVB8Pl8arU6HA7Lb8jC/IQEZQY71MivbSeu78PSNK3VatEf\nI5GI2WxGfzw+PoZyHQAAADAQFEWtrq7iOL6xsYESW/3iYhzH/X5/qVQ6PDwkCGJ5eRnDMPkN\nWZZlC4VCb3dXKJ4yg139GbuGYFfrw/I8f3Nz43K5MCjXAQAAAIOGOrAqlWpjYwOFsPrFxRRF\nBQKBaDQai8WMRuP4+Hg6nY5EInI+Wa/XkyQJ3diWFBjs0CRO7YxdLdiJophMJmvB7vr6miAI\nNDYBp+sAAACAgSNJcmVlRaPRrK+voznW+sXFDMNMT0+Hw+FsNjszM6PVavf39+XU4dD1UdCN\nbUnhwY4giEqlgoJdJpOpVCq1YBeNRp1OJ0EQmUwmkUhAuQ4AAAAYOJVKtbS0pNPpNjY2ULar\nX1w8MTFhs9l2dnZ4nl9ZWcEw7OXLl3I+Fo7ZtaPAYIfmq0VRFAQBhTySJDEM4zhOr9erVCoM\nw7LZbDqdRmMTUK4DAAAA7g/KdkajcX19PZfLYRjm9XpZlkWLi+fn5wmCCIVCNE1PTU1ls9m9\nvT10LO/m5iadTmMYdnp6ur+/X/+ZDMPkcjk0mQHqKSfYlUqlfD6fTCZRmAuHwxiG8Tyv0Wg+\n+9nPJhIJjuN4nr+8vMQwLBqNMgyj1+szmczd3d3k5OQjf3sAAABAudBqYoZhNjY20uk0juML\nCwtocTGGYYuLi8lk8vz8fGpqSq/XR6PRq6srDMNubm5isRiGYYlE4ubmpv4DTSaTSqWCY3bN\nnn2wK5fLn/3sZzmO+/SnP41uIEYVO5TiS6US2mQYjUZTqZRara5UKoIgxGIxNDZxcnIyMjJi\nNBof+dcAAAAAFA3dQmG1Wre2ttLpdP3iYpqmfT7f8fFxIpFAE7KoCqPValH3VqVS1a4pq32a\nyWSCbmyzZx/s1Gp1tVq9u7ujabpSqZTLZfT/Zr1eTxAEmpwQBCEejwuCUCgU0un08fExhmEj\nIyNQrgMAAAAeDNpy4nQ6Nzc3OY6rX1xst9vHxsZCoRCGYXa7vVqtnp6e1oKdWq1uCHYYhrEs\nCxW7Zs8+2OE47vF4rq6unE5nNBq1Wq3xeBzDMIqiBEHI5XIkSaLLJzAMo2k6lUolEgmHw5FI\nJHZ2dqBcBwAAADwkr9frcrm2t7cTiUT94uLZ2VmtVhsMBsfGxjAMOzk5IQiiWCyKokhRlCiK\nDZ/DMEwmk0FtOlDz7IMdhmF2u91gMOTzeYIg1Gp1KpUiCAIluUKhcHd3p9PpqtWqTqcrFAoa\njSaXy6H/6RSLxcnJSZ7nw+EwlHMBAACAhzE7OzsxMbG9vX17e2s0GpeWls7Pz6+urhYXFwuF\nQjwex3GcIIiTkxNRFIvFIroXtKFoxzAMjuNQtGughGCHYdjMzMz19bXdbr+9vUUzsJVKBQV8\nFOawzw9SiKKo1WqPjo60Wq3NZiNJEh3R02g0t7e3f/u3fwubrAEAAID75vF4PB5PMBiMx+Nm\ns3l+fv7w8DCVSi0sLFxdXdE0bbVai8UiQRCFQoGmaQzDGv6CJgjCaDRCXaaBQoIdy7IsyxaL\nRZ7ntVqtKIrlclmtVmMYNjIywnEchmGiKI6OjlYqlUKh4HK58vm82Wx+7bXXaJpeXl4+OTnZ\n2dmxWq3onwUAAAAAuFdutxtlu1gslkgkMAwLBoMYhnk8nmKxWKlUGIYRBCGdTqMCTbFYbPgE\nOGbXTCHBDsOwmZmZu7s7i8WC+vH5fB4Nxt7d3aGE53a7U6mUKIpTU1PFYlGv1x8eHrpcLpfL\ntbGxkc1m19bWpqencRx/7F8FAAAAULjb29tEIjEyMoJhWCgUKpVKer1eFMXNzc1MJmMwGFKp\nVCAQwHH84uICBbvmlhrDMOl0unmuYpiRj/0FBsZoNNpstlKpVKlU0LUTtadQ9Q7NwOI4brFY\nTk9PVSqVz+dLJpM7OzsTExMej4cglBNzAQAAgKeM47hIJKJSqex2e6lU4jgOx3Gr1ZpKpdAQ\nJIZhe3t7ZrM5kUicn59jbYKdKIrpdJpl2Yf+BZ4qvHnM5PnK5/Of+cxnaJoulUqCIKhUKp7n\ncRwXRdFkMmUyGfTLqlQqURSnp6fPzs4oivL7/TAYCwAAADywarV6e3t7c3NTOzGlVqvL5TL6\naxrV4TQaDVpAi2HY+Pi41+tt+JDXXnvNZrPBvaA1yqnYYRim0+mcTuft7S36XwDP82ilIUmS\nmUyGIAiSJEulEs/zLMseHR1BoQ4AAAB4LCRJOp1Op9OJEt7l5SX6y1qj0aD1dQRBoCodqtG0\nnG5kGAaO2dVTWqbxeDzVarX2R5T6eZ4fGRnheR71Z3Ec53n+DW94w8zMDKQ6AAAA4HGhhPf6\n178+EAiIoojjeMN5d9RwQ4W9Bmh+Qkntxz4pqmKHYZhGozGbzWhjoSiKS0tLL1++tFgs4+Pj\n8Xjc5/OFw+GxsbHZ2VkYkgAAAACeFLvdrtFoqtUqwzCRSKRUKjkcjtPTU6/Xu7m5aTKZmt9i\nsVg8Hg/8nV6jqDN2AAAAAADDDBqRAAAAAAAKAcEOAAAAAEAhINgBAAAAACgEBDsAAAAAAIWA\nYAcAAAAAoBAQ7AAAAAAAFAKCHQAAAACAQkCwAwAAAABQCAh2AAAAABiY6vpPBNR4PYIgtYzL\n/yXf/FO/v5e7959f/vj7HCr6q/5z4p7uX8h+8Ou0uPbdv9/i3tonQWlXigEAAADg0ancb3n3\nK5Mq9IdqJhL+7N/85e/99F/96Sf+zSc+/sOL1ON+OUWDYAcAAACAAVN/0ff/1u98I/2FB4qn\nf/B9X/6e3/rEz/3sH33HB7/R/HyvdtW/6zePLou0VfPYX6SNJ9KKLRz9yb/9J+/84jkno6N1\n5tHplbd/20/8p0/f8AP9IdXNn3yhpt7wC6HBfiwAAADwnJVKD9BWpKe+7mf/2Vs1YvKvP7FV\nvf8fd39wrcU15rLQnV/5OJ5AsBMT/+NfvLL2VT/6v/9/8ZE3fMV7/9F3fP1b5/DdD//8d77y\nunf++83C515V/vMfWpqe/+4/yj7qdwUAAAAUJZvNfupTn9rb2yuXy/f7k3Dz6CiNCSkuJWAY\nhgm3n/6NH3zn6z02vUZjsM/+T9/0U394WMAwDBOO/rc3aVSW9/x+qu7NwsWvv6olTO/87Rt0\ndK509rEPfMdb/U6TVmeeWHnHP/nVv4z93bgo5vc//P53rU2wOr3FvfTq+37t0/WH7ipX/+8v\nfc+XvRg362iDzfP6d/7Qb3w6LtT9tHbfDcOw4oe/se6MndQrH4n42Ip/9c+8JK5b++d/Hqt8\n4dH84Qe/3avG6df9zBZ6tPiH32zE6a/5v9K9/6TKxr9aItWv//ndap9fGQAAAFAOjuM+85nP\n/MVf/MXx8THP831+WuWzP75AYvQ3fKjQ8ETptR9fJDGV90c/VRbF3F//+KoOVzFzr773e77v\nH3/blwfMKpyc/K6PJgRRrIY/8EVqYuTb/ihTey9//mtvo3HTu34nLoiiWDn4P7/GpcLVI0tf\n9t73ve+9r3qNBE7Nvu8jN4IoiqWPfZedUC98yVtGDZ63fusP/ct/+U/f9cKswgnr1/5OREAf\ndva73+RW44TR86av/Y7v+tavfP24Fsfp+X/80Rv0vNR3E8XCh76Bxuhv/HCx4ysfx6MHu8rf\nvt9P4sw3fbgxsQl3v/u1Jpx88VObFVHsEOxKuZyMsAbBDgAAAGhFEIRoNPpXf/VXn/zkJ6PR\naD8f1SLYVbOx0J/98nv8OhwnfT/yyYIo5j/ybRaCDPzYX+c+95LcJ//5PImbvunDGVEUq8Gf\ne52acH7nn3z+Wf78V99GE8zX/96dIIr81X/6apZQz/3DPzgro6cLL3/xLUacQn/Blz72XXYC\nw3VveP9f3n0uX+U+9WMLJK7/hv87I4qicPf77xkhCOdX/LvNDHpeSP/tz7zZhKumf/AvCmKn\n71Yf7Dq88nE8eitWSKfSAlYt5MsNc8k4+z//4L/5wM9/75st+PX/8WU0/a7fzYjFP/oWE06/\n/T9GSx/9h1b15A/8RfbwQz/41mnG8q7fiaO3dyiu/h3i7X//kTWjSr/8Ax+7/txrxEzowz/5\nnjf7HEad0T79uq/6wV//y8izPgoAAAAAdIbjuNPp/OIv/uLR0dFwOLy5uZnN9nX2qfj779bW\n9p2QBqf/S3/og3tV19t/9j//qzfSGFZOpgoiThsMn5/h1L3xJ//s5fanP/AqjWGYyvfV7wyo\n4h//yKeKGIZhmHD1x//1r8vMl737y804Jlz+19/+s5ThHT/+ga91q9Gb6cXvfv/3vu3vsbnI\n5w8LErav/4n3v9nyuREN3epbXm/Aqrl8ScSw1Md+9yO35Bt++Jf+6bIBPY8bX/+jv/Cd09jp\nhz/4yXKn71ZP/isf0KNPxaoX3rCq/w8f/ej73/tT+l/8/q9eHqnNQBOjX/K+H/0SDMOw4lt/\n5Dd/c/7Xv/9XXvN/96/8wJs9b2TxAwzDxMTHf/grf+1j5re/+3u+dFmPY5hw/nvf+pbv+NAl\nPfXGt3/TOwzxzf/x3/799/75xzf/4BO/9hUjDQM4YvKT/8s7v/6XQuP/6IN/8ktf7iAwDMOy\nn/7pL33HT/9NcfT1X/p1bx0p7H/iz371+/78I5/4rf/+u9/sUT3gfxMAAADgEahUKo/H43Q6\nj4+PX3vtNYfDMTMzQ1G9LCf5O+tOMJw0OKYDb3rXt3zNEotjGIYZX/maVy0f+ei/fmX1b/7B\nu7/yba+8+U1/LzDhD9Te7f/qr/b9zM//6Uc+U371FQrlOvPXvvvLzDiGlbc+s1lWL77yJusX\n/mLHmXf84n97B/q/yxiGYWTgi1b1X/g2uEql+tyrq+HNnQIx+corf+cvds3r3vpG0y9/aGf7\nSnh1Uvq71enwWzyORw92uPM9H/hf/3DrB/74z37mG/78F5ip133Jq29/+9vf/urb3rgw8vlR\nYtr797/Fnf/YD//KxsRb3vvt32zEsPIBhgmR//Jr2+//ePBff7EJxzAMExP/z7/4oQ9fWt/x\nSx/7L9+/bMAxTMy89nNf8epP/sb3/9w/eNsvv1Ifn7Pr//br3/Wzn7W+57f/9D+8awz9/7ay\n/ovf8/OfEd7005/44x9/A4tjGFY8+r3v+vvf/sEf/rGvefuH3uN4vqPZAADZBEEQhHZlftAd\nQRB4HtYQDIYoig/5H9PtdjMM8/+3d+/hMd15HMd/55yRhFxIQkKYjESoBI80FK1UxT0lLq3G\npftYSpe4Lur62NUKdaeoWKVufeIWspq4RKV5EkzEqlhC69JKltrEJUQqt5HM2T8iRIWMfTCc\nvl9/zpPfmd/vTM45n/me+f3O5cuXU1JS6tat6+XlJctPd4vvkeVOHiZ7Dt5y0H7+rKWb9qz5\nbM8/VEmycw/oNXLW/CnvGmyEELpmPUMazl2wNzZ1YbtWWTHRySaXvqFdqgshRMGtW4Wq7FLL\n9Ukdkqo52Fd82Tbn5v6myp6uLg8317nUrC6bc3NyzZX2zfJRWIXVg50Qtn7Do0+/sz8yMjru\nQMLB1Ni1R2PWzpEUJ+/2/cImzxjdxfMxS8Woao2eUyfdS3XifnF13u+LqxvaL4vaYlzQruO9\nz1AtPLOqf4+piXJwxL51/euX7YG7RzZuOmNuMm3ZlNJUJ4Swa9B/3qR1O0ft35Fwe8CAGpWM\no7i4+MSJE5zFLPrTVfkAAAeBSURBVMQZ/xl6wWd8AH8oly5d0ul0BoPh2W5WcvALDd8aGm7K\nPn882Zh0IHrjxqiZfdLyD6bObW0rhK55zx5eC5bujT01xyNlZ7KpZr9+nRyFEELY2tvrJPNv\nt3PNosbjs91jizGSo6ODpObczHn4B2AlObdyVdnD0UGuvG+Wj8IaXoJgJ4SQnRoHh4UHh4WL\n4tvpqQcT4g/siY7am7BmUtLelA3J2z70rPCj0/m29H9QaK28uKoXQghzVvTw7id/yCwRdplZ\neQ8+VvPl48czzXaehae2R/74oH3JfxV7qeDCTxnFwr+SfaUoil6v1+R3fUmSFIWb0c+GJEk6\n3ctx3GkCO/NZ4TB/hjRwmKuqeu3atYyMDFmW69evX6dOnWe7/ZJzO+dFpjl3CAtr7+7a6M2Q\nRm+GDP5L21CfAd/GJ6TPbt1YEaJKQK8ehiUr9sR853boiMltYGiQQ2lbmyb+vrqoNOPR3NGe\nZYUYURD3sU+vzQ3mpSWOr/fkt9Y1at7ErmTf4UMZ5lY+99OF6UTSkVzh0rRZPbmyvj3VKKzA\n+v95ZnOJEEpZiVdX3atVyNBWIUOnz/3p68Fdh+/459+/SA5dElilgpaSo5Pjg0heeXFVCCFE\nya+p//bqv3yZsmBC5OKJX314YIyPIoQQ5pzsW2b1zqFFgw898kaK0528yh85V/rLUwtHDQDA\nyyknJ+fChQsFBQV6vd5gMDztTVhLqAUnt82dnX7YuXPbcQ1Lr/BFmRmZRaKKW+2yy7hNy17d\n6y1ftWH8sqy7tQf3Cyqr5CgN3x/QOnzirtnhiR0XBblIQgjTua+X7Mwyew9t7ymLSsorknPw\ngO4usTuXTFzddXNYU3shhHoz+fPJa35W648e+LatRX2zfBQvnrWDXd7WD2oN3N1mxS/xo+o9\nvBPsff88bdCSXXN++fHsLTXQraLGklSu1GpJcVUIIdcKXh63YYTPdaeEuCGx4ZO3vL/jTx6y\nEJK9o4Mk1x0Zf3FlEM+wAwD8ARUVFV28ePHq1avu7u7Nmzf//6ZNWELXbNCIoC/HxH/Sxn9P\n10BfNyX79Pe7Ey+YfcdNfO/+ZEfb1r3erROx6ud0RR8W2q7q/cZKw7AVn33bccrS4GZJnTu3\nNsgZSbvizuR5D5s7xl93b/LEE0iufectiUoeGj068PWoru19Ha6nHth/9FfhO3Ll39pVFUJY\n0jfLR/HCWXu5E9tGvt7K3dR9B7Iejdh3s2/cNstOHh5Oluye0uJqxuFDGeW3VK64WvqCYgh8\np4GNkOsOXDA9sNqNmBkz426pQgjFs1nTGuL6qZNXyjdXbx6KmDr50+1nWfMEAKBdJSUl6enp\nKSkp+fn5AQEBvr6+zy/VCSGE4hO2/fu147s3KDgRsz4iYtP+9BpBY1cnJi3uUP3BJd/urd7B\n7rJQ6vUKbfvQNAw7/092G7fN6G3IORK1dk1UqjlgyKK4pIgeFqYpxWvQZuPeBR+9YXsu7pu1\nW5Iy3TqNW52YtLxbTcnivlk+ihfOekvolSo+uzjQXpJd356x/3JhuddNV+ImBNhLivfohDuq\nWrZAcc9NOaqqqmrR7sEukm339dnl1nY239ga6iorHj0j0u7ceyXbODOwuqx4j03MVx9doLjg\n2IzmtlIVv0nGPFVV1bz4kQZFrt1j5en7CyJe3RfWuIrs/F7kNSuuIQ0AwPOUmZlpNBqNRmNW\nVpa1+/KQu8em+eoUr3FJhZX/Le6x9q1Yobw2NnLDma6D1s/u1nCNX6s3/PTOtsW5V84eO5qW\nWejYauq6We3thRBCqlqtqmRKWTt9TlbbbiP6VrClSourv2fXctLiYVu6RaycuHzQwalNq3X4\ndOXHCX1Xj27TdHtI8Ou1TOmHY/aeyK7ZY9W8UOuVVAEAeI7y8vLOnz+v1+s9PT1fsgk0pn9t\njTqv1h8X2sZKE0xfTdZOlqXunI9dOKrPW6/VdXWw0dnYu3o2eyd0wpfxGeUeNHfn2NLefjWr\nVrFx7bPxamEFFTtVVVW16D/fLRzWqamHk51NVWdDi5C/fnX0etlj7yp4pJj56vb+tWW5RtfV\n6SWqqqol11JWjwtpYXCuZufo7hPQ5aP5+zL4ngAA0DKz+WW7LWXKzy/KTfuii7Os85v+g8na\n3XmlSKpa+XxPAACAF8SUMNK786orZkmnH7D56Dcf1LH2hIBXidVvxQIAAJSj8+k+YsTdTOcW\nfYcPCSLVPR0qdgAAABpBDgYAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATB\nDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAA\nQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMI\ndgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAA\nABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCP+B6nbf18T\nZPk7AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(dagitty)\n",
    "\n",
    "g <- downloadGraph(\"dagitty.net/mWB5lCB\")\n",
    "\n",
    "plot(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test whether there total effect can be estimated by covariate adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjustmentSets(g, \"Stroke\", \"Psychosis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No output, so this reports there are no minimal sets of variates that will acheive this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression results reporting associations between stroke and probable psychosis, and stroke and psychotic symptoms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unadjusted and adjusted associations between stroke and probable psychosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate probable psychosis results from regression models\n",
    "#\n",
    "uk_results <- run_prob_psychosis_models(dataset_apms)\n",
    "us_results <- run_prob_psychosis_models(dataset_cpes) \n",
    "colombia_results <- run_prob_psychosis_models(dataset_ensm) \n",
    "chile_results <- run_prob_psychosis_models(dataset_ensc)\n",
    "combined_results <- run_prob_psychosis_combined_model(dataset_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Unadjusted</th><th scope=col>Adjusted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>UK</th><td>1.26 (0.31 - 5.17) </td><td>1.11 (0.15 - 8.26) </td></tr>\n",
       "\t<tr><th scope=row>US</th><td>6.90 (2.83 - 16.81)</td><td>6.22 (2.52 - 15.35)</td></tr>\n",
       "\t<tr><th scope=row>Colombia</th><td>3.37 (1.77 - 6.42) </td><td>4.04 (2.10 - 7.78) </td></tr>\n",
       "\t<tr><th scope=row>Chile</th><td>1.33 (0.18 - 9.94) </td><td>1.33 (0.18 - 10.09)</td></tr>\n",
       "\t<tr><th scope=row>Combined</th><td>3.06 (1.93 - 4.85) </td><td>3.32 (2.05 - 5.38) </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Unadjusted & Adjusted\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tUK & 1.26 (0.31 - 5.17)  & 1.11 (0.15 - 8.26) \\\\\n",
       "\tUS & 6.90 (2.83 - 16.81) & 6.22 (2.52 - 15.35)\\\\\n",
       "\tColombia & 3.37 (1.77 - 6.42)  & 4.04 (2.10 - 7.78) \\\\\n",
       "\tChile & 1.33 (0.18 - 9.94)  & 1.33 (0.18 - 10.09)\\\\\n",
       "\tCombined & 3.06 (1.93 - 4.85)  & 3.32 (2.05 - 5.38) \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | Unadjusted &lt;chr&gt; | Adjusted &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| UK | 1.26 (0.31 - 5.17)  | 1.11 (0.15 - 8.26)  |\n",
       "| US | 6.90 (2.83 - 16.81) | 6.22 (2.52 - 15.35) |\n",
       "| Colombia | 3.37 (1.77 - 6.42)  | 4.04 (2.10 - 7.78)  |\n",
       "| Chile | 1.33 (0.18 - 9.94)  | 1.33 (0.18 - 10.09) |\n",
       "| Combined | 3.06 (1.93 - 4.85)  | 3.32 (2.05 - 5.38)  |\n",
       "\n"
      ],
      "text/plain": [
       "         Unadjusted          Adjusted           \n",
       "UK       1.26 (0.31 - 5.17)  1.11 (0.15 - 8.26) \n",
       "US       6.90 (2.83 - 16.81) 6.22 (2.52 - 15.35)\n",
       "Colombia 3.37 (1.77 - 6.42)  4.04 (2.10 - 7.78) \n",
       "Chile    1.33 (0.18 - 9.94)  1.33 (0.18 - 10.09)\n",
       "Combined 3.06 (1.93 - 4.85)  3.32 (2.05 - 5.38) "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collate results into table and display\n",
    "#\n",
    "probpsychosis_regression_table_df <- data.frame(\"Unadjusted\" = character(), \"Adjusted\" = character())\n",
    "probpsychosis_regression_table_df[nrow(probpsychosis_regression_table_df)+1,] <- uk_results\n",
    "probpsychosis_regression_table_df[nrow(probpsychosis_regression_table_df)+1,] <- us_results\n",
    "probpsychosis_regression_table_df[nrow(probpsychosis_regression_table_df)+1,] <- colombia_results\n",
    "probpsychosis_regression_table_df[nrow(probpsychosis_regression_table_df)+1,] <- chile_results\n",
    "probpsychosis_regression_table_df[nrow(probpsychosis_regression_table_df)+1,] <- combined_results\n",
    "\n",
    "rownames(probpsychosis_regression_table_df) <- name_vec\n",
    "probpsychosis_regression_table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unadjusted and adjusted associations between stroke and paranoia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate paranoia results from regression models\n",
    "#\n",
    "uk_results <- run_paranoia_models(dataset_apms)\n",
    "us_results <- run_paranoia_models(dataset_cpes)\n",
    "colombia_results <- run_paranoia_models(dataset_ensm)\n",
    "chile_results <- run_paranoia_models(dataset_ensc)\n",
    "combined_results <- run_paranoia_combined_model(dataset_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Unadjusted</th><th scope=col>Adjusted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>UK</th><td>0.63 (0.32 - 1.23)</td><td>0.99 (0.46 - 2.16)</td></tr>\n",
       "\t<tr><th scope=row>US</th><td>2.71 (1.07 - 6.82)</td><td>2.50 (0.99 - 6.34)</td></tr>\n",
       "\t<tr><th scope=row>Colombia</th><td>2.01 (1.27 - 3.20)</td><td>2.34 (1.46 - 3.74)</td></tr>\n",
       "\t<tr><th scope=row>Chile</th><td>1.92 (0.59 - 6.26)</td><td>1.71 (0.52 - 5.66)</td></tr>\n",
       "\t<tr><th scope=row>Combined</th><td>1.38 (1.00 - 1.90)</td><td>1.66 (1.19 - 2.32)</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Unadjusted & Adjusted\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tUK & 0.63 (0.32 - 1.23) & 0.99 (0.46 - 2.16)\\\\\n",
       "\tUS & 2.71 (1.07 - 6.82) & 2.50 (0.99 - 6.34)\\\\\n",
       "\tColombia & 2.01 (1.27 - 3.20) & 2.34 (1.46 - 3.74)\\\\\n",
       "\tChile & 1.92 (0.59 - 6.26) & 1.71 (0.52 - 5.66)\\\\\n",
       "\tCombined & 1.38 (1.00 - 1.90) & 1.66 (1.19 - 2.32)\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | Unadjusted &lt;chr&gt; | Adjusted &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| UK | 0.63 (0.32 - 1.23) | 0.99 (0.46 - 2.16) |\n",
       "| US | 2.71 (1.07 - 6.82) | 2.50 (0.99 - 6.34) |\n",
       "| Colombia | 2.01 (1.27 - 3.20) | 2.34 (1.46 - 3.74) |\n",
       "| Chile | 1.92 (0.59 - 6.26) | 1.71 (0.52 - 5.66) |\n",
       "| Combined | 1.38 (1.00 - 1.90) | 1.66 (1.19 - 2.32) |\n",
       "\n"
      ],
      "text/plain": [
       "         Unadjusted         Adjusted          \n",
       "UK       0.63 (0.32 - 1.23) 0.99 (0.46 - 2.16)\n",
       "US       2.71 (1.07 - 6.82) 2.50 (0.99 - 6.34)\n",
       "Colombia 2.01 (1.27 - 3.20) 2.34 (1.46 - 3.74)\n",
       "Chile    1.92 (0.59 - 6.26) 1.71 (0.52 - 5.66)\n",
       "Combined 1.38 (1.00 - 1.90) 1.66 (1.19 - 2.32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collate results into table and display\n",
    "#\n",
    "paranoia_regression_table_df <- data.frame(\"Unadjusted\" = character(), \"Adjusted\" = character())\n",
    "paranoia_regression_table_df[nrow(paranoia_regression_table_df)+1,] <- uk_results\n",
    "paranoia_regression_table_df[nrow(paranoia_regression_table_df)+1,] <- us_results\n",
    "paranoia_regression_table_df[nrow(paranoia_regression_table_df)+1,] <- colombia_results\n",
    "paranoia_regression_table_df[nrow(paranoia_regression_table_df)+1,] <- chile_results\n",
    "paranoia_regression_table_df[nrow(paranoia_regression_table_df)+1,] <- combined_results\n",
    "\n",
    "rownames(paranoia_regression_table_df) <- name_vec\n",
    "paranoia_regression_table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unadjusted and adjusted associations between stroke and hallucinated voices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate hallucinated voices from regression models\n",
    "#\n",
    "uk_results <- run_hearvoices_models(dataset_apms)\n",
    "us_results <- run_hearvoices_models(dataset_cpes)\n",
    "colombia_results <- run_hearvoices_models(dataset_ensm)\n",
    "chile_results <- run_hearvoices_models(dataset_ensc)\n",
    "combined_results <- run_hearvoices_combined_model(dataset_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Unadjusted</th><th scope=col>Adjusted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>UK</th><td>0.60 (0.08 - 4.32)</td><td>0.85 (0.11 - 6.29)</td></tr>\n",
       "\t<tr><th scope=row>US</th><td>2.33 (1.41 - 3.86)</td><td>2.03 (1.22 - 3.38)</td></tr>\n",
       "\t<tr><th scope=row>Colombia</th><td>4.30 (2.20 - 8.41)</td><td>4.06 (2.05 - 8.03)</td></tr>\n",
       "\t<tr><th scope=row>Chile</th><td>0.71 (0.22 - 2.26)</td><td>0.71 (0.22 - 2.29)</td></tr>\n",
       "\t<tr><th scope=row>Combined</th><td>2.01 (1.39 - 2.90)</td><td>1.89 (1.30 - 2.74)</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Unadjusted & Adjusted\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tUK & 0.60 (0.08 - 4.32) & 0.85 (0.11 - 6.29)\\\\\n",
       "\tUS & 2.33 (1.41 - 3.86) & 2.03 (1.22 - 3.38)\\\\\n",
       "\tColombia & 4.30 (2.20 - 8.41) & 4.06 (2.05 - 8.03)\\\\\n",
       "\tChile & 0.71 (0.22 - 2.26) & 0.71 (0.22 - 2.29)\\\\\n",
       "\tCombined & 2.01 (1.39 - 2.90) & 1.89 (1.30 - 2.74)\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | Unadjusted &lt;chr&gt; | Adjusted &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| UK | 0.60 (0.08 - 4.32) | 0.85 (0.11 - 6.29) |\n",
       "| US | 2.33 (1.41 - 3.86) | 2.03 (1.22 - 3.38) |\n",
       "| Colombia | 4.30 (2.20 - 8.41) | 4.06 (2.05 - 8.03) |\n",
       "| Chile | 0.71 (0.22 - 2.26) | 0.71 (0.22 - 2.29) |\n",
       "| Combined | 2.01 (1.39 - 2.90) | 1.89 (1.30 - 2.74) |\n",
       "\n"
      ],
      "text/plain": [
       "         Unadjusted         Adjusted          \n",
       "UK       0.60 (0.08 - 4.32) 0.85 (0.11 - 6.29)\n",
       "US       2.33 (1.41 - 3.86) 2.03 (1.22 - 3.38)\n",
       "Colombia 4.30 (2.20 - 8.41) 4.06 (2.05 - 8.03)\n",
       "Chile    0.71 (0.22 - 2.26) 0.71 (0.22 - 2.29)\n",
       "Combined 2.01 (1.39 - 2.90) 1.89 (1.30 - 2.74)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collate results into table and display\n",
    "#\n",
    "hearvoices_regression_table_df <- data.frame(\"Unadjusted\" = character(), \"Adjusted\" = character())\n",
    "hearvoices_regression_table_df[nrow(hearvoices_regression_table_df)+1,] <- uk_results\n",
    "hearvoices_regression_table_df[nrow(hearvoices_regression_table_df)+1,] <- us_results\n",
    "hearvoices_regression_table_df[nrow(hearvoices_regression_table_df)+1,] <- colombia_results\n",
    "hearvoices_regression_table_df[nrow(hearvoices_regression_table_df)+1,] <- chile_results\n",
    "hearvoices_regression_table_df[nrow(hearvoices_regression_table_df)+1,] <- combined_results\n",
    "\n",
    "rownames(hearvoices_regression_table_df) <- name_vec\n",
    "hearvoices_regression_table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Unadjusted and adjusted associations between stroke and thought passivity delusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate thought passivity delusions results from regression models\n",
    "#\n",
    "uk_results <- run_thoughtpass_models(dataset_apms)\n",
    "us_results <- run_thoughtpass_models(dataset_cpes)\n",
    "colombia_results <- run_thoughtpass_models(dataset_ensm)\n",
    "chile_results <- run_thoughtpass_models(dataset_ensc)\n",
    "combined_results <- run_thoughtpass_combined_model(dataset_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Unadjusted</th><th scope=col>Adjusted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>UK</th><td>1.07 (0.26 - 4.40) </td><td>0.67 (0.09 - 4.94) </td></tr>\n",
       "\t<tr><th scope=row>US</th><td>2.17 (0.52 - 9.16) </td><td>2.12 (0.50 - 9.00) </td></tr>\n",
       "\t<tr><th scope=row>Colombia</th><td>3.52 (1.89 - 6.55) </td><td>4.27 (2.27 - 8.03) </td></tr>\n",
       "\t<tr><th scope=row>Chile</th><td>2.09 (0.28 - 15.87)</td><td>2.17 (0.28 - 16.95)</td></tr>\n",
       "\t<tr><th scope=row>Combined</th><td>2.51 (1.52 - 4.14) </td><td>2.68 (1.59 - 4.52) </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Unadjusted & Adjusted\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tUK & 1.07 (0.26 - 4.40)  & 0.67 (0.09 - 4.94) \\\\\n",
       "\tUS & 2.17 (0.52 - 9.16)  & 2.12 (0.50 - 9.00) \\\\\n",
       "\tColombia & 3.52 (1.89 - 6.55)  & 4.27 (2.27 - 8.03) \\\\\n",
       "\tChile & 2.09 (0.28 - 15.87) & 2.17 (0.28 - 16.95)\\\\\n",
       "\tCombined & 2.51 (1.52 - 4.14)  & 2.68 (1.59 - 4.52) \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | Unadjusted &lt;chr&gt; | Adjusted &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| UK | 1.07 (0.26 - 4.40)  | 0.67 (0.09 - 4.94)  |\n",
       "| US | 2.17 (0.52 - 9.16)  | 2.12 (0.50 - 9.00)  |\n",
       "| Colombia | 3.52 (1.89 - 6.55)  | 4.27 (2.27 - 8.03)  |\n",
       "| Chile | 2.09 (0.28 - 15.87) | 2.17 (0.28 - 16.95) |\n",
       "| Combined | 2.51 (1.52 - 4.14)  | 2.68 (1.59 - 4.52)  |\n",
       "\n"
      ],
      "text/plain": [
       "         Unadjusted          Adjusted           \n",
       "UK       1.07 (0.26 - 4.40)  0.67 (0.09 - 4.94) \n",
       "US       2.17 (0.52 - 9.16)  2.12 (0.50 - 9.00) \n",
       "Colombia 3.52 (1.89 - 6.55)  4.27 (2.27 - 8.03) \n",
       "Chile    2.09 (0.28 - 15.87) 2.17 (0.28 - 16.95)\n",
       "Combined 2.51 (1.52 - 4.14)  2.68 (1.59 - 4.52) "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collate results into table and display\n",
    "#\n",
    "thoughtpass_regression_table_df <- data.frame(\"Unadjusted\" = character(), \"Adjusted\" = character())\n",
    "thoughtpass_regression_table_df[nrow(thoughtpass_regression_table_df)+1,] <- uk_results\n",
    "thoughtpass_regression_table_df[nrow(thoughtpass_regression_table_df)+1,] <- us_results\n",
    "thoughtpass_regression_table_df[nrow(thoughtpass_regression_table_df)+1,] <- colombia_results\n",
    "thoughtpass_regression_table_df[nrow(thoughtpass_regression_table_df)+1,] <- chile_results\n",
    "thoughtpass_regression_table_df[nrow(thoughtpass_regression_table_df)+1,] <- combined_results\n",
    "\n",
    "rownames(thoughtpass_regression_table_df) <- name_vec\n",
    "thoughtpass_regression_table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis platform details and software versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               _                           \n",
       "platform       x86_64-pc-linux-gnu         \n",
       "arch           x86_64                      \n",
       "os             linux-gnu                   \n",
       "system         x86_64, linux-gnu           \n",
       "status                                     \n",
       "major          4                           \n",
       "minor          0.3                         \n",
       "year           2020                        \n",
       "month          10                          \n",
       "day            10                          \n",
       "svn rev        79318                       \n",
       "language       R                           \n",
       "version.string R version 4.0.3 (2020-10-10)\n",
       "nickname       Bunny-Wunnies Freak Out     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.0.7’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"dplyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘0.8.79’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"foreign\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.2.1’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"table1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘2.1.2’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"jtools\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘2.0.31’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"epiR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.1.26’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"lme4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘0.7.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"IRdisplay\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}