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+ inkscape:label="ISI text (left)"
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+ style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:12px;font-family:Helvetica;-inkscape-font-specification:'Helvetica Bold'">ISI: 400 to 800 ms</tspan><tspan
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+ x="125.05293"
+ y="460.79391"
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+ style="font-style:normal;font-variant:normal;font-weight:bold;font-stretch:normal;font-size:14.6667px;line-height:0px;font-family:hel;-inkscape-font-specification:'hel, Bold';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;text-anchor:middle;display:inline;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1;stroke-linecap:square;stroke-linejoin:round;stroke-dasharray:1, 2;stroke-dashoffset:0;stroke-opacity:1"
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+ style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:12px;font-family:Helvetica;-inkscape-font-specification:'Helvetica Bold'">(randomized per tone)</tspan><tspan
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+ style="font-style:normal;font-variant:normal;font-weight:bold;font-stretch:normal;font-size:14.6667px;line-height:0px;font-family:hel;-inkscape-font-specification:'hel, Bold';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;text-anchor:middle;display:inline;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1;stroke-linecap:square;stroke-linejoin:round;stroke-dasharray:1, 2;stroke-dashoffset:0;stroke-opacity:1"
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+ y="473.57187"
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+ sodipodi:type="star"
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diff --git a/figures/Figure5-Results-Age_Musicianship_Correlations_new.png b/figures/Figure5-Results-Age_Musicianship_Correlations_new.png
new file mode 100644
index 0000000000000000000000000000000000000000..12a2b41a55520e994af87fdacd8bb54e25322ae3
Binary files /dev/null and b/figures/Figure5-Results-Age_Musicianship_Correlations_new.png differ
diff --git a/requirements.txt b/requirements.txt
index f26a8cfeb9135820ca57103af2be441d00477401..007d6b5aa015ca9db147ad7638c1e1b2961a91a6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,11 @@
matplotlib==3.5.1
-numpy==1.21.2
-pandas==1.4.1
-pingouin==0.5.3
+numpy==1.22
+pandas>=1.4.1
+pingouin>=0.5.3
requests==2.27.1
scikit_learn==1.1.1
-scipy==1.7.3
+scipy>=1.7.3
seaborn==0.11.2
statsmodels==0.13.2
+ipykernel
+openpyxl
\ No newline at end of file
diff --git a/scripts/analysis/1_threshold_analysis.ipynb b/scripts/analysis/1_threshold_analysis.ipynb
index 220094eb45da81b35b66ad422f4c61b4338f21d8..7c365b53da636b83af39af51e9699756a71aef4c 100644
--- a/scripts/analysis/1_threshold_analysis.ipynb
+++ b/scripts/analysis/1_threshold_analysis.ipynb
@@ -2,27 +2,38 @@
"cells": [
{
"cell_type": "markdown",
- "source": [
- "# Threshold Analysis"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "# Threshold Analysis"
+ ]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 31,
"metadata": {
- "collapsed": true,
"ExecuteTime": {
"end_time": "2023-08-14T17:11:51.646486Z",
"start_time": "2023-08-14T17:11:48.473400Z"
- }
+ },
+ "collapsed": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/Users/nmarin/Projects/ThereWillBeBeeps/scripts/analysis\n"
+ ]
+ }
+ ],
"source": [
"import pingouin as pg\n",
"\n",
+ "import os \n",
+ "print(os.getcwd())\n",
+ "\n",
"from funcs.threshold_analysis import *\n",
"from funcs.plots import Fig3Thresholds\n",
"\n",
@@ -33,16 +44,23 @@
},
{
"cell_type": "markdown",
- "source": [
- "### Set parameters"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Set parameters"
+ ]
},
{
"cell_type": "code",
"execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:51.650855Z",
+ "start_time": "2023-08-14T17:11:51.647421Z"
+ },
+ "collapsed": false
+ },
"outputs": [],
"source": [
"no_init = False\n",
@@ -56,59 +74,59 @@
"AFC_freqs = [125, 250, 500, 750, 1000, 1500, 2000, 3000, 4000, 6000, 8000]\n",
"xfreqs = list(np.geomspace(freq_range[0], freq_range[1], num=100)) # for resampling\n",
"PTA_freqs = [500, 1000, 2000, 4000]"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:51.650855Z",
- "start_time": "2023-08-14T17:11:51.647421Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:51.654253Z",
+ "start_time": "2023-08-14T17:11:51.652226Z"
+ },
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# Paths\n",
"audiograms_path = os.path.join(get_path(\"audiograms\"), 'no_init' if no_init else 'with_init')\n",
"data_path = get_path(\"dataframes\")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:51.654253Z",
- "start_time": "2023-08-14T17:11:51.652226Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 4,
- "outputs": [],
- "source": [
- "stats = StatsFormatter()"
- ],
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-14T17:11:51.657330Z",
"start_time": "2023-08-14T17:11:51.654608Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "stats = StatsFormatter()"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Load data"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Load data"
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:54.236096Z",
+ "start_time": "2023-08-14T17:11:51.660687Z"
+ },
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# Load data from Randomized paradigm\n",
@@ -123,57 +141,210 @@
"\n",
"# Load data from 3-AFC paradigm\n",
"audiograms_3AFC = pd.read_pickle(os.path.join(data_path, f\"3AFC_thresholds_{nReversals}rev.pkl\"))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:54.236096Z",
- "start_time": "2023-08-14T17:11:51.660687Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Resample audiograms to 3-AFC frequencies"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Resample audiograms to 3-AFC frequencies"
+ ]
},
{
"cell_type": "code",
"execution_count": 6,
- "outputs": [],
- "source": [
- "# Interpolate audiogram to the 11 frequencies used in 3-AFC\n",
- "audiograms_3AFC = downsample_to_3AFC(audiograms_3AFC, audiograms_100, AFC_freqs, xfreqs)"
- ],
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-14T17:11:54.735924Z",
"start_time": "2023-08-14T17:11:54.240394Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Interpolate audiogram to the 11 frequencies used in 3-AFC\n",
+ "audiograms_3AFC = downsample_to_3AFC(audiograms_3AFC, audiograms_100, AFC_freqs, xfreqs)"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Preview and save dataframes"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Preview and save dataframes"
+ ]
},
{
"cell_type": "code",
"execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:54.755220Z",
+ "start_time": "2023-08-14T17:11:54.737254Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": " participant paradigm pred time_pred freq_pred \\\n0 cbtxie 3AFC 3AFC None None \n1 cbtxie 3AFC 3AFC None None \n2 cbtxie 3AFC 3AFC None None \n3 cbtxie 3AFC 3AFC None None \n4 cbtxie 3AFC 3AFC None None \n\n tested_frequencies \\\n0 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n1 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n2 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n3 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n4 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n\n tested_levels \\\n0 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n1 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n2 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n3 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n4 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n\n responses len_init thresholds \\\n0 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 6.9 \n1 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 6.2 \n2 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 0.0 \n3 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 -1.2 \n4 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 -1.2 \n\n frequencies mean_threshold nReversals \n0 125 -2.154545 4 \n1 250 -2.154545 4 \n2 500 -2.154545 4 \n3 750 -2.154545 4 \n4 1000 -2.154545 4 ",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>participant</th>\n <th>paradigm</th>\n <th>pred</th>\n <th>time_pred</th>\n <th>freq_pred</th>\n <th>tested_frequencies</th>\n <th>tested_levels</th>\n <th>responses</th>\n <th>len_init</th>\n <th>thresholds</th>\n <th>frequencies</th>\n <th>mean_threshold</th>\n <th>nReversals</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>cbtxie</td>\n <td>3AFC</td>\n <td>3AFC</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n <td>0</td>\n <td>6.9</td>\n <td>125</td>\n <td>-2.154545</td>\n <td>4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>cbtxie</td>\n <td>3AFC</td>\n <td>3AFC</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n <td>0</td>\n <td>6.2</td>\n <td>250</td>\n <td>-2.154545</td>\n <td>4</td>\n </tr>\n <tr>\n <th>2</th>\n <td>cbtxie</td>\n <td>3AFC</td>\n <td>3AFC</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n <td>0</td>\n <td>0.0</td>\n <td>500</td>\n <td>-2.154545</td>\n <td>4</td>\n </tr>\n <tr>\n <th>3</th>\n <td>cbtxie</td>\n <td>3AFC</td>\n <td>3AFC</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n <td>0</td>\n <td>-1.2</td>\n <td>750</td>\n <td>-2.154545</td>\n <td>4</td>\n </tr>\n <tr>\n <th>4</th>\n <td>cbtxie</td>\n <td>3AFC</td>\n <td>3AFC</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n <td>0</td>\n <td>-1.2</td>\n <td>1000</td>\n <td>-2.154545</td>\n <td>4</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ "text/html": [
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+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
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+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>participant</th>\n",
+ " <th>paradigm</th>\n",
+ " <th>pred</th>\n",
+ " <th>time_pred</th>\n",
+ " <th>freq_pred</th>\n",
+ " <th>tested_frequencies</th>\n",
+ " <th>tested_levels</th>\n",
+ " <th>responses</th>\n",
+ " <th>len_init</th>\n",
+ " <th>thresholds</th>\n",
+ " <th>frequencies</th>\n",
+ " <th>mean_threshold</th>\n",
+ " <th>nReversals</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>cbtxie</td>\n",
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+ " <td>3AFC</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n",
+ " <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n",
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+ " <td>0</td>\n",
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+ " <td>125</td>\n",
+ " <td>-2.154545</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>cbtxie</td>\n",
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+ " <td>3AFC</td>\n",
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+ " <td>-2.154545</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>cbtxie</td>\n",
+ " <td>3AFC</td>\n",
+ " <td>3AFC</td>\n",
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+ " <td>None</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>cbtxie</td>\n",
+ " <td>3AFC</td>\n",
+ " <td>3AFC</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n",
+ " <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n",
+ " <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.2</td>\n",
+ " <td>750</td>\n",
+ " <td>-2.154545</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>cbtxie</td>\n",
+ " <td>3AFC</td>\n",
+ " <td>3AFC</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>[1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000....</td>\n",
+ " <td>[30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0...</td>\n",
+ " <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ...</td>\n",
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+ " <td>1000</td>\n",
+ " <td>-2.154545</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " participant paradigm pred time_pred freq_pred \\\n",
+ "0 cbtxie 3AFC 3AFC None None \n",
+ "1 cbtxie 3AFC 3AFC None None \n",
+ "2 cbtxie 3AFC 3AFC None None \n",
+ "3 cbtxie 3AFC 3AFC None None \n",
+ "4 cbtxie 3AFC 3AFC None None \n",
+ "\n",
+ " tested_frequencies \\\n",
+ "0 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n",
+ "1 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n",
+ "2 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n",
+ "3 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n",
+ "4 [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.... \n",
+ "\n",
+ " tested_levels \\\n",
+ "0 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n",
+ "1 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n",
+ "2 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n",
+ "3 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n",
+ "4 [30.0, 30.0, 10.0, 10.0, 0.0, 0.0, -10.0, -5.0... \n",
+ "\n",
+ " responses len_init thresholds \\\n",
+ "0 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 6.9 \n",
+ "1 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 6.2 \n",
+ "2 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 0.0 \n",
+ "3 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 -1.2 \n",
+ "4 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... 0 -1.2 \n",
+ "\n",
+ " frequencies mean_threshold nReversals \n",
+ "0 125 -2.154545 4 \n",
+ "1 250 -2.154545 4 \n",
+ "2 500 -2.154545 4 \n",
+ "3 750 -2.154545 4 \n",
+ "4 1000 -2.154545 4 "
+ ]
},
"execution_count": 7,
"metadata": {},
@@ -182,23 +353,170 @@
],
"source": [
"audiograms_3AFC.head()"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:54.755220Z",
- "start_time": "2023-08-14T17:11:54.737254Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:54.819586Z",
+ "start_time": "2023-08-14T17:11:54.754858Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": " participant paradigm pred time_pred freq_pred \\\n0 nunamh Bayesian Bayesian None None \n1 nunamh Bayesian Bayesian None None \n2 nunamh Bayesian Bayesian None None \n3 nunamh Bayesian Bayesian None None \n4 nunamh Bayesian Bayesian None None \n\n tested_frequencies \\\n0 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n1 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n2 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n3 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n4 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n\n tested_levels \\\n0 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n1 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n2 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n3 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n4 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n\n responses len_init thresholds \\\n0 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.966314 \n1 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.623549 \n2 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.239123 \n3 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 7.789474 \n4 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 7.330968 \n\n frequencies mean_threshold \n0 125.000000 1.324109 \n1 130.362973 1.324109 \n2 135.956037 1.324109 \n3 141.789065 1.324109 \n4 147.872352 1.324109 ",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>participant</th>\n <th>paradigm</th>\n <th>pred</th>\n <th>time_pred</th>\n <th>freq_pred</th>\n <th>tested_frequencies</th>\n <th>tested_levels</th>\n <th>responses</th>\n <th>len_init</th>\n <th>thresholds</th>\n <th>frequencies</th>\n <th>mean_threshold</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>nunamh</td>\n <td>Bayesian</td>\n <td>Bayesian</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n <td>22</td>\n <td>8.966314</td>\n <td>125.000000</td>\n <td>1.324109</td>\n </tr>\n <tr>\n <th>1</th>\n <td>nunamh</td>\n <td>Bayesian</td>\n <td>Bayesian</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n <td>22</td>\n <td>8.623549</td>\n <td>130.362973</td>\n <td>1.324109</td>\n </tr>\n <tr>\n <th>2</th>\n <td>nunamh</td>\n <td>Bayesian</td>\n <td>Bayesian</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n <td>22</td>\n <td>8.239123</td>\n <td>135.956037</td>\n <td>1.324109</td>\n </tr>\n <tr>\n <th>3</th>\n <td>nunamh</td>\n <td>Bayesian</td>\n <td>Bayesian</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n <td>22</td>\n <td>7.789474</td>\n <td>141.789065</td>\n <td>1.324109</td>\n </tr>\n <tr>\n <th>4</th>\n <td>nunamh</td>\n <td>Bayesian</td>\n <td>Bayesian</td>\n <td>None</td>\n <td>None</td>\n <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n <td>22</td>\n <td>7.330968</td>\n <td>147.872352</td>\n <td>1.324109</td>\n </tr>\n </tbody>\n</table>\n</div>"
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+ "<div>\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>participant</th>\n",
+ " <th>paradigm</th>\n",
+ " <th>pred</th>\n",
+ " <th>time_pred</th>\n",
+ " <th>freq_pred</th>\n",
+ " <th>tested_frequencies</th>\n",
+ " <th>tested_levels</th>\n",
+ " <th>responses</th>\n",
+ " <th>len_init</th>\n",
+ " <th>thresholds</th>\n",
+ " <th>frequencies</th>\n",
+ " <th>mean_threshold</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>nunamh</td>\n",
+ " <td>Bayesian</td>\n",
+ " <td>Bayesian</td>\n",
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+ " <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n",
+ " <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
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+ " <td>nunamh</td>\n",
+ " <td>Bayesian</td>\n",
+ " <td>Bayesian</td>\n",
+ " <td>None</td>\n",
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+ " <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n",
+ " <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
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+ " <td>nunamh</td>\n",
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+ " <td>Bayesian</td>\n",
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+ " <td>None</td>\n",
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+ " <th>3</th>\n",
+ " <td>nunamh</td>\n",
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+ " <td>Bayesian</td>\n",
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+ " <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n",
+ " <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+ " <td>22</td>\n",
+ " <td>7.789474</td>\n",
+ " <td>141.789065</td>\n",
+ " <td>1.324109</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>nunamh</td>\n",
+ " <td>Bayesian</td>\n",
+ " <td>Bayesian</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>[1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500....</td>\n",
+ " <td>[60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10...</td>\n",
+ " <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+ " <td>22</td>\n",
+ " <td>7.330968</td>\n",
+ " <td>147.872352</td>\n",
+ " <td>1.324109</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " participant paradigm pred time_pred freq_pred \\\n",
+ "0 nunamh Bayesian Bayesian None None \n",
+ "1 nunamh Bayesian Bayesian None None \n",
+ "2 nunamh Bayesian Bayesian None None \n",
+ "3 nunamh Bayesian Bayesian None None \n",
+ "4 nunamh Bayesian Bayesian None None \n",
+ "\n",
+ " tested_frequencies \\\n",
+ "0 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n",
+ "1 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n",
+ "2 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n",
+ "3 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n",
+ "4 [1000.0, 1000.0, 1000.0, 1000.0, 1500.0, 1500.... \n",
+ "\n",
+ " tested_levels \\\n",
+ "0 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n",
+ "1 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n",
+ "2 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n",
+ "3 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n",
+ "4 [60.0, 40.0, 20.0, 0.0, 10.0, -10.0, 10.0, -10... \n",
+ "\n",
+ " responses len_init thresholds \\\n",
+ "0 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.966314 \n",
+ "1 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.623549 \n",
+ "2 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 8.239123 \n",
+ "3 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 7.789474 \n",
+ "4 [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ... 22 7.330968 \n",
+ "\n",
+ " frequencies mean_threshold \n",
+ "0 125.000000 1.324109 \n",
+ "1 130.362973 1.324109 \n",
+ "2 135.956037 1.324109 \n",
+ "3 141.789065 1.324109 \n",
+ "4 147.872352 1.324109 "
+ ]
},
"execution_count": 8,
"metadata": {},
@@ -207,18 +525,18 @@
],
"source": [
"audiograms_100.head()"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:54.819586Z",
- "start_time": "2023-08-14T17:11:54.754858Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:54.841786Z",
+ "start_time": "2023-08-14T17:11:54.771261Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -236,53 +554,55 @@
"participants = audiograms_3AFC.participant.unique()\n",
"print(len(participants), \"participants:\")\n",
"print(participants)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:54.841786Z",
- "start_time": "2023-08-14T17:11:54.771261Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:55.215351Z",
+ "start_time": "2023-08-14T17:11:54.775475Z"
+ },
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# Save DataFrames\n",
"audiograms_3AFC.to_pickle(os.path.join(data_path, \"thresholds-3AFC_freqs.pkl\"))\n",
"audiograms_100.to_pickle(os.path.join(data_path, \"thresholds-100_freqs.pkl\"))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:55.215351Z",
- "start_time": "2023-08-14T17:11:54.775475Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
"source": [
"### Fig3 - 3AFC vs Randomized\n",
"\n",
"A - 3-AFC vs. Randomized average audiograms\n",
"B - 3-AFC vs. Randomized average threshold\n",
"C - 3-AFC vs. Randomized average PTA"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.498767Z",
+ "start_time": "2023-08-14T17:11:55.138617Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": "<Figure size 1600x600 with 3 Axes>",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 3 Axes>"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -319,29 +639,38 @@
"\n",
"# Save figure\n",
"plt.savefig(os.path.join(get_path('figures'), 'Fig3-thresholds_comparison-3AFC_vs_Bayesian.svg'))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.498767Z",
- "start_time": "2023-08-14T17:11:55.138617Z"
- }
- }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# audiograms_100_3AFC = pd.merge(left=audiograms_100, right=audiograms_3AFC, on=('participant'), suffixes=('_100', '_3AFC'))"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
"source": [
"### Stats\n",
"\n",
"#### Thresholds - Descriptive stats"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.503085Z",
+ "start_time": "2023-08-14T17:11:56.497090Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -366,36 +695,28 @@
" 'thresholds')\n",
"\n",
"print(\"Threshold gain:\")\n",
- "compute_db_gain(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian'])],\n",
+ "gain_df = compute_db_gain(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian'])],\n",
" 'thresholds')"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.503085Z",
- "start_time": "2023-08-14T17:11:56.497090Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"#### Thresholds - T-test"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean threshold comparison (paired T-test):\n",
- "3AFC vs Bayesian: T(27) = -13.24, p = <0.001 *\n",
+ "3AFC vs Bayesian: T(27) = -13.24, p = <0.001, d = -1.79 *\n",
"\n"
]
}
@@ -404,34 +725,26 @@
"# Filter data for mean threshold comparison\n",
"thresholds_data = audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']),\n",
"['participant', 'paradigm', 'mean_threshold']].drop_duplicates()\n",
- "\n",
+ " \n",
"# Threshold comparison (Paired T-test)\n",
"pairwise_results = pg.pairwise_tests(data=thresholds_data, dv='mean_threshold', within='paradigm',\n",
" subject='participant', effsize='cohen', alternative='two-sided')\n",
- "\n",
+ " \n",
"# Display T-test results\n",
"stats.print_paired_ttest(stats, pairwise_results, \"Mean threshold\")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.568315Z",
- "start_time": "2023-08-14T17:11:56.505157Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "#### PTA Descriptive stats"
- ],
- "metadata": {
- "collapsed": false
- }
+ "#### PTA - Descriptive stats"
+ ]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -448,6 +761,248 @@
"PTA gain:\n",
"Mean: 7.185 dB | SD: 2.638 dB | SEM: 0.499 dB\n"
]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>paradigm</th>\n",
+ " <th>3AFC</th>\n",
+ " <th>Bayesian</th>\n",
+ " <th>gain</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>participant</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>cbtxie</th>\n",
+ " <td>-4.200</td>\n",
+ " <td>1.548005</td>\n",
+ " <td>5.748005</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>cmzntn</th>\n",
+ " <td>-10.325</td>\n",
+ " <td>-0.429477</td>\n",
+ " <td>9.895523</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>cwzopc</th>\n",
+ " <td>-7.350</td>\n",
+ " <td>3.969210</td>\n",
+ " <td>11.319210</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>ddmfvc</th>\n",
+ " <td>-8.275</td>\n",
+ " <td>-1.578300</td>\n",
+ " <td>6.696700</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>eqdcwr</th>\n",
+ " <td>-4.850</td>\n",
+ " <td>-2.338756</td>\n",
+ " <td>2.511244</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>erfnpg</th>\n",
+ " <td>-4.850</td>\n",
+ " <td>-0.422276</td>\n",
+ " <td>4.427724</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hnxrev</th>\n",
+ " <td>-10.450</td>\n",
+ " <td>-2.511800</td>\n",
+ " <td>7.938200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hoavur</th>\n",
+ " <td>-6.225</td>\n",
+ " <td>3.662175</td>\n",
+ " <td>9.887175</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>klxufm</th>\n",
+ " <td>-7.950</td>\n",
+ " <td>-2.152117</td>\n",
+ " <td>5.797883</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>luctew</th>\n",
+ " <td>2.475</td>\n",
+ " <td>8.775392</td>\n",
+ " <td>6.300392</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>moiern</th>\n",
+ " <td>-5.475</td>\n",
+ " <td>-2.072791</td>\n",
+ " <td>3.402209</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nefynm</th>\n",
+ " <td>-5.300</td>\n",
+ " <td>-1.907420</td>\n",
+ " <td>3.392580</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nfsmrp</th>\n",
+ " <td>-4.700</td>\n",
+ " <td>5.585122</td>\n",
+ " <td>10.285122</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nlhglm</th>\n",
+ " <td>-3.425</td>\n",
+ " <td>3.220272</td>\n",
+ " <td>6.645272</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nunamh</th>\n",
+ " <td>-6.400</td>\n",
+ " <td>0.480973</td>\n",
+ " <td>6.880973</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>ofgjwt</th>\n",
+ " <td>-10.500</td>\n",
+ " <td>-2.085560</td>\n",
+ " <td>8.414440</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>qmbami</th>\n",
+ " <td>-9.550</td>\n",
+ " <td>-0.821386</td>\n",
+ " <td>8.728614</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>quicsb</th>\n",
+ " <td>-7.025</td>\n",
+ " <td>3.830245</td>\n",
+ " <td>10.855245</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>quvsxs</th>\n",
+ " <td>-0.800</td>\n",
+ " <td>0.946092</td>\n",
+ " <td>1.746092</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>rdzaqd</th>\n",
+ " <td>1.400</td>\n",
+ " <td>6.847192</td>\n",
+ " <td>5.447192</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>regvnx</th>\n",
+ " <td>-1.700</td>\n",
+ " <td>6.190042</td>\n",
+ " <td>7.890042</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>rehvhf</th>\n",
+ " <td>-1.750</td>\n",
+ " <td>1.804668</td>\n",
+ " <td>3.554668</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>tlvjds</th>\n",
+ " <td>-10.050</td>\n",
+ " <td>-0.471109</td>\n",
+ " <td>9.578891</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>tsbvqq</th>\n",
+ " <td>-0.175</td>\n",
+ " <td>9.011714</td>\n",
+ " <td>9.186714</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>udoety</th>\n",
+ " <td>-8.925</td>\n",
+ " <td>-0.141241</td>\n",
+ " <td>8.783759</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vqnuwy</th>\n",
+ " <td>-9.200</td>\n",
+ " <td>-1.026994</td>\n",
+ " <td>8.173006</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>wfoooa</th>\n",
+ " <td>-7.050</td>\n",
+ " <td>1.768279</td>\n",
+ " <td>8.818279</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>wxbjqe</th>\n",
+ " <td>-6.250</td>\n",
+ " <td>2.620391</td>\n",
+ " <td>8.870391</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "paradigm 3AFC Bayesian gain\n",
+ "participant \n",
+ "cbtxie -4.200 1.548005 5.748005\n",
+ "cmzntn -10.325 -0.429477 9.895523\n",
+ "cwzopc -7.350 3.969210 11.319210\n",
+ "ddmfvc -8.275 -1.578300 6.696700\n",
+ "eqdcwr -4.850 -2.338756 2.511244\n",
+ "erfnpg -4.850 -0.422276 4.427724\n",
+ "hnxrev -10.450 -2.511800 7.938200\n",
+ "hoavur -6.225 3.662175 9.887175\n",
+ "klxufm -7.950 -2.152117 5.797883\n",
+ "luctew 2.475 8.775392 6.300392\n",
+ "moiern -5.475 -2.072791 3.402209\n",
+ "nefynm -5.300 -1.907420 3.392580\n",
+ "nfsmrp -4.700 5.585122 10.285122\n",
+ "nlhglm -3.425 3.220272 6.645272\n",
+ "nunamh -6.400 0.480973 6.880973\n",
+ "ofgjwt -10.500 -2.085560 8.414440\n",
+ "qmbami -9.550 -0.821386 8.728614\n",
+ "quicsb -7.025 3.830245 10.855245\n",
+ "quvsxs -0.800 0.946092 1.746092\n",
+ "rdzaqd 1.400 6.847192 5.447192\n",
+ "regvnx -1.700 6.190042 7.890042\n",
+ "rehvhf -1.750 1.804668 3.554668\n",
+ "tlvjds -10.050 -0.471109 9.578891\n",
+ "tsbvqq -0.175 9.011714 9.186714\n",
+ "udoety -8.925 -0.141241 8.783759\n",
+ "vqnuwy -9.200 -1.026994 8.173006\n",
+ "wfoooa -7.050 1.768279 8.818279\n",
+ "wxbjqe -6.250 2.620391 8.870391"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
@@ -455,40 +1010,374 @@
"StatsFormatter().print_descriptive_stats(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']) &\n",
" audiograms_3AFC.frequencies.isin(PTA_freqs)],\n",
" 'thresholds')\n",
- "\n",
+ " \n",
"print(\"PTA gain:\")\n",
"compute_db_gain(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']) &\n",
" audiograms_3AFC.frequencies.isin(PTA_freqs)],\n",
" 'thresholds')"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.568564Z",
- "start_time": "2023-08-14T17:11:56.533376Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "\n",
- "#### PTA T-test"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Thresholds - T-test"
+ ]
},
{
"cell_type": "code",
- "execution_count": 15,
- "outputs": [
- {
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.568315Z",
+ "start_time": "2023-08-14T17:11:56.505157Z"
+ },
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean threshold comparison (paired T-test):\n",
+ "3AFC vs Bayesian: T(27) = -13.24, p = <0.001, d = -1.79 *\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Filter data for mean threshold comparison\n",
+ "thresholds_data = audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']),\n",
+ "['participant', 'paradigm', 'mean_threshold']].drop_duplicates()\n",
+ "\n",
+ "# Threshold comparison (Paired T-test)\n",
+ "pairwise_results = pg.pairwise_tests(data=thresholds_data, dv='mean_threshold', within='paradigm',\n",
+ " subject='participant', effsize='cohen', alternative='two-sided')\n",
+ "\n",
+ "# Display T-test results\n",
+ "stats.print_paired_ttest(stats, pairwise_results, \"Mean threshold\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "#### PTA Descriptive stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.568564Z",
+ "start_time": "2023-08-14T17:11:56.533376Z"
+ },
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PTA:\n",
+ "------------------------ 3AFC ------------------------\n",
+ "Mean: -5.674 dB | SD: 6.095 dB | SEM: 0.576 dB\n",
+ "\n",
+ "------------------------ Bayesian ------------------------\n",
+ "Mean: 1.511 dB | SD: 5.145 dB | SEM: 0.486 dB\n",
+ "\n",
+ "\n",
+ "PTA gain:\n",
+ "Mean: 7.185 dB | SD: 2.638 dB | SEM: 0.499 dB\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>paradigm</th>\n",
+ " <th>3AFC</th>\n",
+ " <th>Bayesian</th>\n",
+ " <th>gain</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>participant</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>cbtxie</th>\n",
+ " <td>-4.200</td>\n",
+ " <td>1.548005</td>\n",
+ " <td>5.748005</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>cmzntn</th>\n",
+ " <td>-10.325</td>\n",
+ " <td>-0.429477</td>\n",
+ " <td>9.895523</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>cwzopc</th>\n",
+ " <td>-7.350</td>\n",
+ " <td>3.969210</td>\n",
+ " <td>11.319210</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>ddmfvc</th>\n",
+ " <td>-8.275</td>\n",
+ " <td>-1.578300</td>\n",
+ " <td>6.696700</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>eqdcwr</th>\n",
+ " <td>-4.850</td>\n",
+ " <td>-2.338756</td>\n",
+ " <td>2.511244</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>erfnpg</th>\n",
+ " <td>-4.850</td>\n",
+ " <td>-0.422276</td>\n",
+ " <td>4.427724</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hnxrev</th>\n",
+ " <td>-10.450</td>\n",
+ " <td>-2.511800</td>\n",
+ " <td>7.938200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hoavur</th>\n",
+ " <td>-6.225</td>\n",
+ " <td>3.662175</td>\n",
+ " <td>9.887175</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>klxufm</th>\n",
+ " <td>-7.950</td>\n",
+ " <td>-2.152117</td>\n",
+ " <td>5.797883</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>luctew</th>\n",
+ " <td>2.475</td>\n",
+ " <td>8.775392</td>\n",
+ " <td>6.300392</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>moiern</th>\n",
+ " <td>-5.475</td>\n",
+ " <td>-2.072791</td>\n",
+ " <td>3.402209</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nefynm</th>\n",
+ " <td>-5.300</td>\n",
+ " <td>-1.907420</td>\n",
+ " <td>3.392580</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nfsmrp</th>\n",
+ " <td>-4.700</td>\n",
+ " <td>5.585122</td>\n",
+ " <td>10.285122</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nlhglm</th>\n",
+ " <td>-3.425</td>\n",
+ " <td>3.220272</td>\n",
+ " <td>6.645272</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nunamh</th>\n",
+ " <td>-6.400</td>\n",
+ " <td>0.480973</td>\n",
+ " <td>6.880973</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>ofgjwt</th>\n",
+ " <td>-10.500</td>\n",
+ " <td>-2.085560</td>\n",
+ " <td>8.414440</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>qmbami</th>\n",
+ " <td>-9.550</td>\n",
+ " <td>-0.821386</td>\n",
+ " <td>8.728614</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>quicsb</th>\n",
+ " <td>-7.025</td>\n",
+ " <td>3.830245</td>\n",
+ " <td>10.855245</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>quvsxs</th>\n",
+ " <td>-0.800</td>\n",
+ " <td>0.946092</td>\n",
+ " <td>1.746092</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>rdzaqd</th>\n",
+ " <td>1.400</td>\n",
+ " <td>6.847192</td>\n",
+ " <td>5.447192</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>regvnx</th>\n",
+ " <td>-1.700</td>\n",
+ " <td>6.190042</td>\n",
+ " <td>7.890042</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>rehvhf</th>\n",
+ " <td>-1.750</td>\n",
+ " <td>1.804668</td>\n",
+ " <td>3.554668</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>tlvjds</th>\n",
+ " <td>-10.050</td>\n",
+ " <td>-0.471109</td>\n",
+ " <td>9.578891</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>tsbvqq</th>\n",
+ " <td>-0.175</td>\n",
+ " <td>9.011714</td>\n",
+ " <td>9.186714</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>udoety</th>\n",
+ " <td>-8.925</td>\n",
+ " <td>-0.141241</td>\n",
+ " <td>8.783759</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vqnuwy</th>\n",
+ " <td>-9.200</td>\n",
+ " <td>-1.026994</td>\n",
+ " <td>8.173006</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>wfoooa</th>\n",
+ " <td>-7.050</td>\n",
+ " <td>1.768279</td>\n",
+ " <td>8.818279</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>wxbjqe</th>\n",
+ " <td>-6.250</td>\n",
+ " <td>2.620391</td>\n",
+ " <td>8.870391</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "paradigm 3AFC Bayesian gain\n",
+ "participant \n",
+ "cbtxie -4.200 1.548005 5.748005\n",
+ "cmzntn -10.325 -0.429477 9.895523\n",
+ "cwzopc -7.350 3.969210 11.319210\n",
+ "ddmfvc -8.275 -1.578300 6.696700\n",
+ "eqdcwr -4.850 -2.338756 2.511244\n",
+ "erfnpg -4.850 -0.422276 4.427724\n",
+ "hnxrev -10.450 -2.511800 7.938200\n",
+ "hoavur -6.225 3.662175 9.887175\n",
+ "klxufm -7.950 -2.152117 5.797883\n",
+ "luctew 2.475 8.775392 6.300392\n",
+ "moiern -5.475 -2.072791 3.402209\n",
+ "nefynm -5.300 -1.907420 3.392580\n",
+ "nfsmrp -4.700 5.585122 10.285122\n",
+ "nlhglm -3.425 3.220272 6.645272\n",
+ "nunamh -6.400 0.480973 6.880973\n",
+ "ofgjwt -10.500 -2.085560 8.414440\n",
+ "qmbami -9.550 -0.821386 8.728614\n",
+ "quicsb -7.025 3.830245 10.855245\n",
+ "quvsxs -0.800 0.946092 1.746092\n",
+ "rdzaqd 1.400 6.847192 5.447192\n",
+ "regvnx -1.700 6.190042 7.890042\n",
+ "rehvhf -1.750 1.804668 3.554668\n",
+ "tlvjds -10.050 -0.471109 9.578891\n",
+ "tsbvqq -0.175 9.011714 9.186714\n",
+ "udoety -8.925 -0.141241 8.783759\n",
+ "vqnuwy -9.200 -1.026994 8.173006\n",
+ "wfoooa -7.050 1.768279 8.818279\n",
+ "wxbjqe -6.250 2.620391 8.870391"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(\"PTA:\")\n",
+ "StatsFormatter().print_descriptive_stats(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']) &\n",
+ " audiograms_3AFC.frequencies.isin(PTA_freqs)],\n",
+ " 'thresholds')\n",
+ "\n",
+ "print(\"PTA gain:\")\n",
+ "compute_db_gain(audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']) &\n",
+ " audiograms_3AFC.frequencies.isin(PTA_freqs)],\n",
+ " 'thresholds')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "\n",
+ "#### PTA T-test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.568951Z",
+ "start_time": "2023-08-14T17:11:56.545908Z"
+ },
+ "collapsed": false
+ },
+ "outputs": [
+ {
"name": "stdout",
"output_type": "stream",
"text": [
"Mean PTA comparison (paired T-test):\n",
- "3AFC vs Bayesian: T(27) = -14.41, p = <0.001 *\n",
+ "3AFC vs Bayesian: T(27) = -14.41, p = <0.001, d = -2.04 *\n",
"\n"
]
}
@@ -505,27 +1394,27 @@
" subject='participant', effsize='cohen', alternative='two-sided')\n",
"\n",
"stats.print_paired_ttest(stats, pairwise_results, \"Mean PTA\")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.568951Z",
- "start_time": "2023-08-14T17:11:56.545908Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### ANOVA on the audiograms (factors: task, frequency)"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### ANOVA on the audiograms (factors: task, frequency)"
+ ]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:56.643979Z",
+ "start_time": "2023-08-14T17:11:56.572006Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -552,199 +1441,199 @@
" subject=\"participant\") # , detailed=True)\n",
"\n",
"stats.print_2way_anova(stats, aov_threshold, \"thresholds\", ['paradigm', 'frequencies'])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:56.643979Z",
- "start_time": "2023-08-14T17:11:56.572006Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Post-hoc paired T-test"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Post-hoc paired T-test"
+ ]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.039282Z",
+ "start_time": "2023-08-14T17:11:56.617761Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Post-hoc tests (paired t-tests):\n",
- "paradigm / 3AFC vs Bayesian: T(27) = -13.24, p = <0.001 (fdr_bh: nan)\n",
- "frequencies / 125 vs 250: T(27) = 0.37, p = 0.713 (fdr_bh: 0.839)\n",
- "frequencies / 125 vs 500: T(27) = 1.46, p = 0.156 (fdr_bh: 0.277)\n",
- "frequencies / 125 vs 750: T(27) = 1.65, p = 0.111 (fdr_bh: 0.226)\n",
- "frequencies / 125 vs 1000: T(27) = 1.48, p = 0.151 (fdr_bh: 0.277)\n",
- "frequencies / 125 vs 1500: T(27) = 0.35, p = 0.733 (fdr_bh: 0.839)\n",
- "frequencies / 125 vs 2000: T(27) = 1.46, p = 0.155 (fdr_bh: 0.277)\n",
- "frequencies / 125 vs 3000: T(27) = 1.76, p = 0.090 (fdr_bh: 0.190)\n",
- "frequencies / 125 vs 4000: T(27) = 5.20, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 125 vs 6000: T(27) = 4.34, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 125 vs 8000: T(27) = 4.24, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 250 vs 500: T(27) = 1.12, p = 0.271 (fdr_bh: 0.414)\n",
- "frequencies / 250 vs 750: T(27) = 1.07, p = 0.293 (fdr_bh: 0.435)\n",
- "frequencies / 250 vs 1000: T(27) = 1.15, p = 0.260 (fdr_bh: 0.409)\n",
- "frequencies / 250 vs 1500: T(27) = 0.10, p = 0.924 (fdr_bh: 0.942)\n",
- "frequencies / 250 vs 2000: T(27) = 1.25, p = 0.221 (fdr_bh: 0.369)\n",
- "frequencies / 250 vs 3000: T(27) = 1.31, p = 0.201 (fdr_bh: 0.345)\n",
- "frequencies / 250 vs 4000: T(27) = 4.61, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 250 vs 6000: T(27) = 4.37, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 250 vs 8000: T(27) = 4.23, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 500 vs 750: T(27) = 0.29, p = 0.776 (fdr_bh: 0.853)\n",
- "frequencies / 500 vs 1000: T(27) = 0.35, p = 0.732 (fdr_bh: 0.839)\n",
- "frequencies / 500 vs 1500: T(27) = -0.77, p = 0.448 (fdr_bh: 0.616)\n",
- "frequencies / 500 vs 2000: T(27) = 0.66, p = 0.515 (fdr_bh: 0.691)\n",
- "frequencies / 500 vs 3000: T(27) = 0.79, p = 0.437 (fdr_bh: 0.616)\n",
- "frequencies / 500 vs 4000: T(27) = 4.31, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 500 vs 6000: T(27) = 3.97, p = <0.001 (fdr_bh: 0.001) *\n",
- "frequencies / 500 vs 8000: T(27) = 3.67, p = 0.001 (fdr_bh: 0.003) *\n",
- "frequencies / 750 vs 1000: T(27) = 0.13, p = 0.901 (fdr_bh: 0.942)\n",
- "frequencies / 750 vs 1500: T(27) = -0.88, p = 0.385 (fdr_bh: 0.558)\n",
- "frequencies / 750 vs 2000: T(27) = 0.45, p = 0.656 (fdr_bh: 0.820)\n",
- "frequencies / 750 vs 3000: T(27) = 0.63, p = 0.535 (fdr_bh: 0.700)\n",
- "frequencies / 750 vs 4000: T(27) = 4.42, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 750 vs 6000: T(27) = 4.10, p = <0.001 (fdr_bh: 0.001) *\n",
- "frequencies / 750 vs 8000: T(27) = 3.69, p = <0.001 (fdr_bh: 0.003) *\n",
- "frequencies / 1000 vs 1500: T(27) = -1.18, p = 0.248 (fdr_bh: 0.402)\n",
- "frequencies / 1000 vs 2000: T(27) = 0.41, p = 0.684 (fdr_bh: 0.836)\n",
- "frequencies / 1000 vs 3000: T(27) = 0.56, p = 0.577 (fdr_bh: 0.738)\n",
- "frequencies / 1000 vs 4000: T(27) = 4.77, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 1000 vs 6000: T(27) = 4.79, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 1000 vs 8000: T(27) = 3.82, p = <0.001 (fdr_bh: 0.002) *\n",
- "frequencies / 1500 vs 2000: T(27) = 2.27, p = 0.031 (fdr_bh: 0.069)\n",
- "frequencies / 1500 vs 3000: T(27) = 1.60, p = 0.121 (fdr_bh: 0.238)\n",
- "frequencies / 1500 vs 4000: T(27) = 5.83, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 1500 vs 6000: T(27) = 5.26, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 1500 vs 8000: T(27) = 4.29, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 2000 vs 3000: T(27) = 0.30, p = 0.765 (fdr_bh: 0.853)\n",
- "frequencies / 2000 vs 4000: T(27) = 5.26, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 2000 vs 6000: T(27) = 4.20, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 2000 vs 8000: T(27) = 3.83, p = <0.001 (fdr_bh: 0.002) *\n",
- "frequencies / 3000 vs 4000: T(27) = 6.94, p = <0.001 (fdr_bh: <0.001) *\n",
- "frequencies / 3000 vs 6000: T(27) = 3.53, p = 0.001 (fdr_bh: 0.004) *\n",
- "frequencies / 3000 vs 8000: T(27) = 3.50, p = 0.002 (fdr_bh: 0.004) *\n",
- "frequencies / 4000 vs 6000: T(27) = -0.04, p = 0.967 (fdr_bh: 0.967)\n",
- "frequencies / 4000 vs 8000: T(27) = 0.11, p = 0.910 (fdr_bh: 0.942)\n",
- "frequencies / 6000 vs 8000: T(27) = 0.18, p = 0.858 (fdr_bh: 0.925)\n",
- "paradigm * frequencies / 125 vs 250: T(27) = -0.26, p = 0.799 (fdr_bh: 0.845)\n",
- "paradigm * frequencies / 125 vs 500: T(27) = 1.36, p = 0.184 (fdr_bh: 0.293)\n",
- "paradigm * frequencies / 125 vs 750: T(27) = 1.09, p = 0.285 (fdr_bh: 0.414)\n",
- "paradigm * frequencies / 125 vs 1000: T(27) = 0.59, p = 0.563 (fdr_bh: 0.681)\n",
- "paradigm * frequencies / 125 vs 1500: T(27) = -0.78, p = 0.441 (fdr_bh: 0.564)\n",
- "paradigm * frequencies / 125 vs 2000: T(27) = 0.80, p = 0.433 (fdr_bh: 0.561)\n",
- "paradigm * frequencies / 125 vs 3000: T(27) = 0.22, p = 0.828 (fdr_bh: 0.866)\n",
- "paradigm * frequencies / 125 vs 4000: T(27) = 4.13, p = <0.001 (fdr_bh: 0.002) *\n",
- "paradigm * frequencies / 125 vs 6000: T(27) = 2.78, p = 0.010 (fdr_bh: 0.025) *\n",
- "paradigm * frequencies / 125 vs 8000: T(27) = 3.33, p = 0.003 (fdr_bh: 0.008) *\n",
- "paradigm * frequencies / 250 vs 500: T(27) = 1.56, p = 0.130 (fdr_bh: 0.220)\n",
- "paradigm * frequencies / 250 vs 750: T(27) = 1.09, p = 0.286 (fdr_bh: 0.414)\n",
- "paradigm * frequencies / 250 vs 1000: T(27) = 0.80, p = 0.428 (fdr_bh: 0.561)\n",
- "paradigm * frequencies / 250 vs 1500: T(27) = -0.51, p = 0.614 (fdr_bh: 0.719)\n",
- "paradigm * frequencies / 250 vs 2000: T(27) = 0.94, p = 0.356 (fdr_bh: 0.495)\n",
- "paradigm * frequencies / 250 vs 3000: T(27) = 0.40, p = 0.693 (fdr_bh: 0.786)\n",
- "paradigm * frequencies / 250 vs 4000: T(27) = 3.64, p = 0.001 (fdr_bh: 0.004) *\n",
- "paradigm * frequencies / 250 vs 6000: T(27) = 2.94, p = 0.007 (fdr_bh: 0.018) *\n",
- "paradigm * frequencies / 250 vs 8000: T(27) = 3.74, p = <0.001 (fdr_bh: 0.003) *\n",
- "paradigm * frequencies / 500 vs 750: T(27) = -0.33, p = 0.747 (fdr_bh: 0.805)\n",
- "paradigm * frequencies / 500 vs 1000: T(27) = -0.48, p = 0.632 (fdr_bh: 0.732)\n",
- "paradigm * frequencies / 500 vs 1500: T(27) = -2.07, p = 0.048 (fdr_bh: 0.097)\n",
- "paradigm * frequencies / 500 vs 2000: T(27) = -0.11, p = 0.912 (fdr_bh: 0.921)\n",
- "paradigm * frequencies / 500 vs 3000: T(27) = -0.77, p = 0.450 (fdr_bh: 0.569)\n",
- "paradigm * frequencies / 500 vs 4000: T(27) = 2.91, p = 0.007 (fdr_bh: 0.019) *\n",
- "paradigm * frequencies / 500 vs 6000: T(27) = 1.94, p = 0.062 (fdr_bh: 0.123)\n",
- "paradigm * frequencies / 500 vs 8000: T(27) = 2.43, p = 0.022 (fdr_bh: 0.051)\n",
- "paradigm * frequencies / 750 vs 1000: T(27) = -0.21, p = 0.835 (fdr_bh: 0.866)\n",
- "paradigm * frequencies / 750 vs 1500: T(27) = -1.73, p = 0.094 (fdr_bh: 0.176)\n",
- "paradigm * frequencies / 750 vs 2000: T(27) = 0.12, p = 0.904 (fdr_bh: 0.921)\n",
- "paradigm * frequencies / 750 vs 3000: T(27) = -0.56, p = 0.583 (fdr_bh: 0.690)\n",
- "paradigm * frequencies / 750 vs 4000: T(27) = 3.44, p = 0.002 (fdr_bh: 0.006) *\n",
- "paradigm * frequencies / 750 vs 6000: T(27) = 2.32, p = 0.028 (fdr_bh: 0.061)\n",
- "paradigm * frequencies / 750 vs 8000: T(27) = 3.06, p = 0.005 (fdr_bh: 0.015) *\n",
- "paradigm * frequencies / 1000 vs 1500: T(27) = -1.51, p = 0.141 (fdr_bh: 0.232)\n",
- "paradigm * frequencies / 1000 vs 2000: T(27) = 0.30, p = 0.763 (fdr_bh: 0.815)\n",
- "paradigm * frequencies / 1000 vs 3000: T(27) = -0.33, p = 0.742 (fdr_bh: 0.805)\n",
- "paradigm * frequencies / 1000 vs 4000: T(27) = 3.83, p = <0.001 (fdr_bh: 0.003) *\n",
- "paradigm * frequencies / 1000 vs 6000: T(27) = 2.92, p = 0.007 (fdr_bh: 0.019) *\n",
- "paradigm * frequencies / 1000 vs 8000: T(27) = 3.03, p = 0.005 (fdr_bh: 0.015) *\n",
- "paradigm * frequencies / 1500 vs 2000: T(27) = 2.31, p = 0.029 (fdr_bh: 0.062)\n",
- "paradigm * frequencies / 1500 vs 3000: T(27) = 1.26, p = 0.218 (fdr_bh: 0.339)\n",
- "paradigm * frequencies / 1500 vs 4000: T(27) = 5.77, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 1500 vs 6000: T(27) = 4.18, p = <0.001 (fdr_bh: 0.001) *\n",
- "paradigm * frequencies / 1500 vs 8000: T(27) = 4.09, p = <0.001 (fdr_bh: 0.002) *\n",
- "paradigm * frequencies / 2000 vs 3000: T(27) = -0.92, p = 0.367 (fdr_bh: 0.499)\n",
- "paradigm * frequencies / 2000 vs 4000: T(27) = 4.19, p = <0.001 (fdr_bh: 0.001) *\n",
- "paradigm * frequencies / 2000 vs 6000: T(27) = 2.34, p = 0.027 (fdr_bh: 0.060)\n",
- "paradigm * frequencies / 2000 vs 8000: T(27) = 3.04, p = 0.005 (fdr_bh: 0.015) *\n",
- "paradigm * frequencies / 3000 vs 4000: T(27) = 5.57, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 3000 vs 6000: T(27) = 2.79, p = 0.009 (fdr_bh: 0.024) *\n",
- "paradigm * frequencies / 3000 vs 8000: T(27) = 3.78, p = <0.001 (fdr_bh: 0.003) *\n",
- "paradigm * frequencies / 4000 vs 6000: T(27) = -0.93, p = 0.360 (fdr_bh: 0.495)\n",
- "paradigm * frequencies / 4000 vs 8000: T(27) = -0.17, p = 0.870 (fdr_bh: 0.894)\n",
- "paradigm * frequencies / 6000 vs 8000: T(27) = 0.72, p = 0.477 (fdr_bh: 0.596)\n",
- "paradigm * frequencies / 125 vs 250: T(27) = 1.68, p = 0.105 (fdr_bh: 0.187)\n",
- "paradigm * frequencies / 125 vs 500: T(27) = 1.05, p = 0.305 (fdr_bh: 0.430)\n",
- "paradigm * frequencies / 125 vs 750: T(27) = 1.65, p = 0.111 (fdr_bh: 0.194)\n",
- "paradigm * frequencies / 125 vs 1000: T(27) = 2.18, p = 0.038 (fdr_bh: 0.079)\n",
- "paradigm * frequencies / 125 vs 1500: T(27) = 1.69, p = 0.102 (fdr_bh: 0.184)\n",
- "paradigm * frequencies / 125 vs 2000: T(27) = 2.00, p = 0.056 (fdr_bh: 0.111)\n",
- "paradigm * frequencies / 125 vs 3000: T(27) = 3.29, p = 0.003 (fdr_bh: 0.009) *\n",
- "paradigm * frequencies / 125 vs 4000: T(27) = 5.45, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 125 vs 6000: T(27) = 5.62, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 125 vs 8000: T(27) = 4.72, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 250 vs 500: T(27) = -0.35, p = 0.727 (fdr_bh: 0.805)\n",
- "paradigm * frequencies / 250 vs 750: T(27) = 0.57, p = 0.575 (fdr_bh: 0.688)\n",
- "paradigm * frequencies / 250 vs 1000: T(27) = 1.10, p = 0.280 (fdr_bh: 0.414)\n",
- "paradigm * frequencies / 250 vs 1500: T(27) = 0.91, p = 0.373 (fdr_bh: 0.501)\n",
- "paradigm * frequencies / 250 vs 2000: T(27) = 1.26, p = 0.219 (fdr_bh: 0.339)\n",
- "paradigm * frequencies / 250 vs 3000: T(27) = 2.44, p = 0.021 (fdr_bh: 0.050)\n",
- "paradigm * frequencies / 250 vs 4000: T(27) = 4.95, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 250 vs 6000: T(27) = 5.07, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 250 vs 8000: T(27) = 3.85, p = <0.001 (fdr_bh: 0.003) *\n",
- "paradigm * frequencies / 500 vs 750: T(27) = 1.07, p = 0.293 (fdr_bh: 0.419)\n",
- "paradigm * frequencies / 500 vs 1000: T(27) = 1.52, p = 0.141 (fdr_bh: 0.232)\n",
- "paradigm * frequencies / 500 vs 1500: T(27) = 1.39, p = 0.177 (fdr_bh: 0.286)\n",
- "paradigm * frequencies / 500 vs 2000: T(27) = 1.71, p = 0.099 (fdr_bh: 0.181)\n",
- "paradigm * frequencies / 500 vs 3000: T(27) = 2.69, p = 0.012 (fdr_bh: 0.029) *\n",
- "paradigm * frequencies / 500 vs 4000: T(27) = 5.44, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 500 vs 6000: T(27) = 6.10, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 500 vs 8000: T(27) = 4.73, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 750 vs 1000: T(27) = 1.15, p = 0.260 (fdr_bh: 0.398)\n",
- "paradigm * frequencies / 750 vs 1500: T(27) = 0.45, p = 0.659 (fdr_bh: 0.755)\n",
- "paradigm * frequencies / 750 vs 2000: T(27) = 0.85, p = 0.402 (fdr_bh: 0.533)\n",
- "paradigm * frequencies / 750 vs 3000: T(27) = 2.18, p = 0.038 (fdr_bh: 0.079)\n",
- "paradigm * frequencies / 750 vs 4000: T(27) = 4.94, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 750 vs 6000: T(27) = 5.91, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 750 vs 8000: T(27) = 4.11, p = <0.001 (fdr_bh: 0.002) *\n",
- "paradigm * frequencies / 1000 vs 1500: T(27) = -0.06, p = 0.953 (fdr_bh: 0.953)\n",
- "paradigm * frequencies / 1000 vs 2000: T(27) = 0.33, p = 0.743 (fdr_bh: 0.805)\n",
- "paradigm * frequencies / 1000 vs 3000: T(27) = 1.77, p = 0.087 (fdr_bh: 0.166)\n",
- "paradigm * frequencies / 1000 vs 4000: T(27) = 4.74, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 1000 vs 6000: T(27) = 5.61, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 1000 vs 8000: T(27) = 3.94, p = <0.001 (fdr_bh: 0.002) *\n",
- "paradigm * frequencies / 1500 vs 2000: T(27) = 0.63, p = 0.534 (fdr_bh: 0.653)\n",
- "paradigm * frequencies / 1500 vs 3000: T(27) = 1.60, p = 0.121 (fdr_bh: 0.207)\n",
- "paradigm * frequencies / 1500 vs 4000: T(27) = 4.61, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 1500 vs 6000: T(27) = 5.73, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 1500 vs 8000: T(27) = 4.23, p = <0.001 (fdr_bh: 0.001) *\n",
- "paradigm * frequencies / 2000 vs 3000: T(27) = 1.80, p = 0.082 (fdr_bh: 0.159)\n",
- "paradigm * frequencies / 2000 vs 4000: T(27) = 4.93, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 2000 vs 6000: T(27) = 5.57, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 2000 vs 8000: T(27) = 4.07, p = <0.001 (fdr_bh: 0.002) *\n",
- "paradigm * frequencies / 3000 vs 4000: T(27) = 5.77, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm * frequencies / 3000 vs 6000: T(27) = 3.74, p = <0.001 (fdr_bh: 0.003) *\n",
- "paradigm * frequencies / 3000 vs 8000: T(27) = 2.76, p = 0.010 (fdr_bh: 0.025) *\n",
- "paradigm * frequencies / 4000 vs 6000: T(27) = 1.14, p = 0.265 (fdr_bh: 0.399)\n",
- "paradigm * frequencies / 4000 vs 8000: T(27) = 0.36, p = 0.723 (fdr_bh: 0.805)\n",
- "paradigm * frequencies / 6000 vs 8000: T(27) = -0.63, p = 0.532 (fdr_bh: 0.653)\n",
+ "paradigm / 3AFC vs Bayesian: T(27) = -13.24, p = <0.001 (fdr_bh: nan), d = -1.79\n",
+ "frequencies / 125 vs 250: T(27) = 0.37, p = 0.713 (fdr_bh: 0.839), d = 0.05\n",
+ "frequencies / 125 vs 500: T(27) = 1.46, p = 0.156 (fdr_bh: 0.277), d = 0.20\n",
+ "frequencies / 125 vs 750: T(27) = 1.65, p = 0.111 (fdr_bh: 0.226), d = 0.23\n",
+ "frequencies / 125 vs 1000: T(27) = 1.48, p = 0.151 (fdr_bh: 0.277), d = 0.28\n",
+ "frequencies / 125 vs 1500: T(27) = 0.35, p = 0.733 (fdr_bh: 0.839), d = 0.08\n",
+ "frequencies / 125 vs 2000: T(27) = 1.46, p = 0.155 (fdr_bh: 0.277), d = 0.36\n",
+ "frequencies / 125 vs 3000: T(27) = 1.76, p = 0.090 (fdr_bh: 0.190), d = 0.40\n",
+ "frequencies / 125 vs 4000: T(27) = 5.20, p = <0.001 (fdr_bh: <0.001), d = 1.23 *\n",
+ "frequencies / 125 vs 6000: T(27) = 4.34, p = <0.001 (fdr_bh: <0.001), d = 1.13 *\n",
+ "frequencies / 125 vs 8000: T(27) = 4.24, p = <0.001 (fdr_bh: <0.001), d = 1.11 *\n",
+ "frequencies / 250 vs 500: T(27) = 1.12, p = 0.271 (fdr_bh: 0.414), d = 0.14\n",
+ "frequencies / 250 vs 750: T(27) = 1.07, p = 0.293 (fdr_bh: 0.435), d = 0.17\n",
+ "frequencies / 250 vs 1000: T(27) = 1.15, p = 0.260 (fdr_bh: 0.409), d = 0.22\n",
+ "frequencies / 250 vs 1500: T(27) = 0.10, p = 0.924 (fdr_bh: 0.942), d = 0.02\n",
+ "frequencies / 250 vs 2000: T(27) = 1.25, p = 0.221 (fdr_bh: 0.369), d = 0.30\n",
+ "frequencies / 250 vs 3000: T(27) = 1.31, p = 0.201 (fdr_bh: 0.345), d = 0.33\n",
+ "frequencies / 250 vs 4000: T(27) = 4.61, p = <0.001 (fdr_bh: <0.001), d = 1.17 *\n",
+ "frequencies / 250 vs 6000: T(27) = 4.37, p = <0.001 (fdr_bh: <0.001), d = 1.08 *\n",
+ "frequencies / 250 vs 8000: T(27) = 4.23, p = <0.001 (fdr_bh: <0.001), d = 1.06 *\n",
+ "frequencies / 500 vs 750: T(27) = 0.29, p = 0.776 (fdr_bh: 0.853), d = 0.04\n",
+ "frequencies / 500 vs 1000: T(27) = 0.35, p = 0.732 (fdr_bh: 0.839), d = 0.06\n",
+ "frequencies / 500 vs 1500: T(27) = -0.77, p = 0.448 (fdr_bh: 0.616), d = -0.14\n",
+ "frequencies / 500 vs 2000: T(27) = 0.66, p = 0.515 (fdr_bh: 0.691), d = 0.14\n",
+ "frequencies / 500 vs 3000: T(27) = 0.79, p = 0.437 (fdr_bh: 0.616), d = 0.18\n",
+ "frequencies / 500 vs 4000: T(27) = 4.31, p = <0.001 (fdr_bh: <0.001), d = 1.05 *\n",
+ "frequencies / 500 vs 6000: T(27) = 3.97, p = <0.001 (fdr_bh: 0.001), d = 0.96 *\n",
+ "frequencies / 500 vs 8000: T(27) = 3.67, p = 0.001 (fdr_bh: 0.003), d = 0.94 *\n",
+ "frequencies / 750 vs 1000: T(27) = 0.13, p = 0.901 (fdr_bh: 0.942), d = 0.02\n",
+ "frequencies / 750 vs 1500: T(27) = -0.88, p = 0.385 (fdr_bh: 0.558), d = -0.18\n",
+ "frequencies / 750 vs 2000: T(27) = 0.45, p = 0.656 (fdr_bh: 0.820), d = 0.09\n",
+ "frequencies / 750 vs 3000: T(27) = 0.63, p = 0.535 (fdr_bh: 0.700), d = 0.14\n",
+ "frequencies / 750 vs 4000: T(27) = 4.42, p = <0.001 (fdr_bh: <0.001), d = 0.97 *\n",
+ "frequencies / 750 vs 6000: T(27) = 4.10, p = <0.001 (fdr_bh: 0.001), d = 0.89 *\n",
+ "frequencies / 750 vs 8000: T(27) = 3.69, p = <0.001 (fdr_bh: 0.003), d = 0.88 *\n",
+ "frequencies / 1000 vs 1500: T(27) = -1.18, p = 0.248 (fdr_bh: 0.402), d = -0.23\n",
+ "frequencies / 1000 vs 2000: T(27) = 0.41, p = 0.684 (fdr_bh: 0.836), d = 0.09\n",
+ "frequencies / 1000 vs 3000: T(27) = 0.56, p = 0.577 (fdr_bh: 0.738), d = 0.14\n",
+ "frequencies / 1000 vs 4000: T(27) = 4.77, p = <0.001 (fdr_bh: <0.001), d = 1.13 *\n",
+ "frequencies / 1000 vs 6000: T(27) = 4.79, p = <0.001 (fdr_bh: <0.001), d = 1.02 *\n",
+ "frequencies / 1000 vs 8000: T(27) = 3.82, p = <0.001 (fdr_bh: 0.002), d = 0.98 *\n",
+ "frequencies / 1500 vs 2000: T(27) = 2.27, p = 0.031 (fdr_bh: 0.069), d = 0.33\n",
+ "frequencies / 1500 vs 3000: T(27) = 1.60, p = 0.121 (fdr_bh: 0.238), d = 0.36\n",
+ "frequencies / 1500 vs 4000: T(27) = 5.83, p = <0.001 (fdr_bh: <0.001), d = 1.32 *\n",
+ "frequencies / 1500 vs 6000: T(27) = 5.26, p = <0.001 (fdr_bh: <0.001), d = 1.19 *\n",
+ "frequencies / 1500 vs 8000: T(27) = 4.29, p = <0.001 (fdr_bh: <0.001), d = 1.15 *\n",
+ "frequencies / 2000 vs 3000: T(27) = 0.30, p = 0.765 (fdr_bh: 0.853), d = 0.06\n",
+ "frequencies / 2000 vs 4000: T(27) = 5.26, p = <0.001 (fdr_bh: <0.001), d = 1.09 *\n",
+ "frequencies / 2000 vs 6000: T(27) = 4.20, p = <0.001 (fdr_bh: <0.001), d = 0.97 *\n",
+ "frequencies / 2000 vs 8000: T(27) = 3.83, p = <0.001 (fdr_bh: 0.002), d = 0.94 *\n",
+ "frequencies / 3000 vs 4000: T(27) = 6.94, p = <0.001 (fdr_bh: <0.001), d = 0.98 *\n",
+ "frequencies / 3000 vs 6000: T(27) = 3.53, p = 0.001 (fdr_bh: 0.004), d = 0.88 *\n",
+ "frequencies / 3000 vs 8000: T(27) = 3.50, p = 0.002 (fdr_bh: 0.004), d = 0.86 *\n",
+ "frequencies / 4000 vs 6000: T(27) = -0.04, p = 0.967 (fdr_bh: 0.967), d = -0.01\n",
+ "frequencies / 4000 vs 8000: T(27) = 0.11, p = 0.910 (fdr_bh: 0.942), d = 0.02\n",
+ "frequencies / 6000 vs 8000: T(27) = 0.18, p = 0.858 (fdr_bh: 0.925), d = 0.03\n",
+ "paradigm * frequencies / 125 vs 250: T(27) = -0.26, p = 0.799 (fdr_bh: 0.845), d = -0.05\n",
+ "paradigm * frequencies / 125 vs 500: T(27) = 1.36, p = 0.184 (fdr_bh: 0.293), d = 0.20\n",
+ "paradigm * frequencies / 125 vs 750: T(27) = 1.09, p = 0.285 (fdr_bh: 0.414), d = 0.15\n",
+ "paradigm * frequencies / 125 vs 1000: T(27) = 0.59, p = 0.563 (fdr_bh: 0.681), d = 0.13\n",
+ "paradigm * frequencies / 125 vs 1500: T(27) = -0.78, p = 0.441 (fdr_bh: 0.564), d = -0.17\n",
+ "paradigm * frequencies / 125 vs 2000: T(27) = 0.80, p = 0.433 (fdr_bh: 0.561), d = 0.20\n",
+ "paradigm * frequencies / 125 vs 3000: T(27) = 0.22, p = 0.828 (fdr_bh: 0.866), d = 0.05\n",
+ "paradigm * frequencies / 125 vs 4000: T(27) = 4.13, p = <0.001 (fdr_bh: 0.002), d = 0.95 *\n",
+ "paradigm * frequencies / 125 vs 6000: T(27) = 2.78, p = 0.010 (fdr_bh: 0.025), d = 0.71 *\n",
+ "paradigm * frequencies / 125 vs 8000: T(27) = 3.33, p = 0.003 (fdr_bh: 0.008), d = 0.83 *\n",
+ "paradigm * frequencies / 250 vs 500: T(27) = 1.56, p = 0.130 (fdr_bh: 0.220), d = 0.25\n",
+ "paradigm * frequencies / 250 vs 750: T(27) = 1.09, p = 0.286 (fdr_bh: 0.414), d = 0.20\n",
+ "paradigm * frequencies / 250 vs 1000: T(27) = 0.80, p = 0.428 (fdr_bh: 0.561), d = 0.18\n",
+ "paradigm * frequencies / 250 vs 1500: T(27) = -0.51, p = 0.614 (fdr_bh: 0.719), d = -0.11\n",
+ "paradigm * frequencies / 250 vs 2000: T(27) = 0.94, p = 0.356 (fdr_bh: 0.495), d = 0.25\n",
+ "paradigm * frequencies / 250 vs 3000: T(27) = 0.40, p = 0.693 (fdr_bh: 0.786), d = 0.11\n",
+ "paradigm * frequencies / 250 vs 4000: T(27) = 3.64, p = 0.001 (fdr_bh: 0.004), d = 0.96 *\n",
+ "paradigm * frequencies / 250 vs 6000: T(27) = 2.94, p = 0.007 (fdr_bh: 0.018), d = 0.73 *\n",
+ "paradigm * frequencies / 250 vs 8000: T(27) = 3.74, p = <0.001 (fdr_bh: 0.003), d = 0.84 *\n",
+ "paradigm * frequencies / 500 vs 750: T(27) = -0.33, p = 0.747 (fdr_bh: 0.805), d = -0.05\n",
+ "paradigm * frequencies / 500 vs 1000: T(27) = -0.48, p = 0.632 (fdr_bh: 0.732), d = -0.10\n",
+ "paradigm * frequencies / 500 vs 1500: T(27) = -2.07, p = 0.048 (fdr_bh: 0.097), d = -0.41\n",
+ "paradigm * frequencies / 500 vs 2000: T(27) = -0.11, p = 0.912 (fdr_bh: 0.921), d = -0.03\n",
+ "paradigm * frequencies / 500 vs 3000: T(27) = -0.77, p = 0.450 (fdr_bh: 0.569), d = -0.17\n",
+ "paradigm * frequencies / 500 vs 4000: T(27) = 2.91, p = 0.007 (fdr_bh: 0.019), d = 0.74 *\n",
+ "paradigm * frequencies / 500 vs 6000: T(27) = 1.94, p = 0.062 (fdr_bh: 0.123), d = 0.51\n",
+ "paradigm * frequencies / 500 vs 8000: T(27) = 2.43, p = 0.022 (fdr_bh: 0.051), d = 0.64\n",
+ "paradigm * frequencies / 750 vs 1000: T(27) = -0.21, p = 0.835 (fdr_bh: 0.866), d = -0.04\n",
+ "paradigm * frequencies / 750 vs 1500: T(27) = -1.73, p = 0.094 (fdr_bh: 0.176), d = -0.35\n",
+ "paradigm * frequencies / 750 vs 2000: T(27) = 0.12, p = 0.904 (fdr_bh: 0.921), d = 0.03\n",
+ "paradigm * frequencies / 750 vs 3000: T(27) = -0.56, p = 0.583 (fdr_bh: 0.690), d = -0.12\n",
+ "paradigm * frequencies / 750 vs 4000: T(27) = 3.44, p = 0.002 (fdr_bh: 0.006), d = 0.78 *\n",
+ "paradigm * frequencies / 750 vs 6000: T(27) = 2.32, p = 0.028 (fdr_bh: 0.061), d = 0.55\n",
+ "paradigm * frequencies / 750 vs 8000: T(27) = 3.06, p = 0.005 (fdr_bh: 0.015), d = 0.67 *\n",
+ "paradigm * frequencies / 1000 vs 1500: T(27) = -1.51, p = 0.141 (fdr_bh: 0.232), d = -0.35\n",
+ "paradigm * frequencies / 1000 vs 2000: T(27) = 0.30, p = 0.763 (fdr_bh: 0.815), d = 0.08\n",
+ "paradigm * frequencies / 1000 vs 3000: T(27) = -0.33, p = 0.742 (fdr_bh: 0.805), d = -0.09\n",
+ "paradigm * frequencies / 1000 vs 4000: T(27) = 3.83, p = <0.001 (fdr_bh: 0.003), d = 0.98 *\n",
+ "paradigm * frequencies / 1000 vs 6000: T(27) = 2.92, p = 0.007 (fdr_bh: 0.019), d = 0.67 *\n",
+ "paradigm * frequencies / 1000 vs 8000: T(27) = 3.03, p = 0.005 (fdr_bh: 0.015), d = 0.82 *\n",
+ "paradigm * frequencies / 1500 vs 2000: T(27) = 2.31, p = 0.029 (fdr_bh: 0.062), d = 0.44\n",
+ "paradigm * frequencies / 1500 vs 3000: T(27) = 1.26, p = 0.218 (fdr_bh: 0.339), d = 0.26\n",
+ "paradigm * frequencies / 1500 vs 4000: T(27) = 5.77, p = <0.001 (fdr_bh: <0.001), d = 1.32 *\n",
+ "paradigm * frequencies / 1500 vs 6000: T(27) = 4.18, p = <0.001 (fdr_bh: 0.001), d = 0.98 *\n",
+ "paradigm * frequencies / 1500 vs 8000: T(27) = 4.09, p = <0.001 (fdr_bh: 0.002), d = 1.12 *\n",
+ "paradigm * frequencies / 2000 vs 3000: T(27) = -0.92, p = 0.367 (fdr_bh: 0.499), d = -0.17\n",
+ "paradigm * frequencies / 2000 vs 4000: T(27) = 4.19, p = <0.001 (fdr_bh: 0.001), d = 0.89 *\n",
+ "paradigm * frequencies / 2000 vs 6000: T(27) = 2.34, p = 0.027 (fdr_bh: 0.060), d = 0.60\n",
+ "paradigm * frequencies / 2000 vs 8000: T(27) = 3.04, p = 0.005 (fdr_bh: 0.015), d = 0.74 *\n",
+ "paradigm * frequencies / 3000 vs 4000: T(27) = 5.57, p = <0.001 (fdr_bh: <0.001), d = 1.04 *\n",
+ "paradigm * frequencies / 3000 vs 6000: T(27) = 2.79, p = 0.009 (fdr_bh: 0.024), d = 0.74 *\n",
+ "paradigm * frequencies / 3000 vs 8000: T(27) = 3.78, p = <0.001 (fdr_bh: 0.003), d = 0.88 *\n",
+ "paradigm * frequencies / 4000 vs 6000: T(27) = -0.93, p = 0.360 (fdr_bh: 0.495), d = -0.19\n",
+ "paradigm * frequencies / 4000 vs 8000: T(27) = -0.17, p = 0.870 (fdr_bh: 0.894), d = -0.04\n",
+ "paradigm * frequencies / 6000 vs 8000: T(27) = 0.72, p = 0.477 (fdr_bh: 0.596), d = 0.14\n",
+ "paradigm * frequencies / 125 vs 250: T(27) = 1.68, p = 0.105 (fdr_bh: 0.187), d = 0.19\n",
+ "paradigm * frequencies / 125 vs 500: T(27) = 1.05, p = 0.305 (fdr_bh: 0.430), d = 0.15\n",
+ "paradigm * frequencies / 125 vs 750: T(27) = 1.65, p = 0.111 (fdr_bh: 0.194), d = 0.30\n",
+ "paradigm * frequencies / 125 vs 1000: T(27) = 2.18, p = 0.038 (fdr_bh: 0.079), d = 0.42\n",
+ "paradigm * frequencies / 125 vs 1500: T(27) = 1.69, p = 0.102 (fdr_bh: 0.184), d = 0.40\n",
+ "paradigm * frequencies / 125 vs 2000: T(27) = 2.00, p = 0.056 (fdr_bh: 0.111), d = 0.48\n",
+ "paradigm * frequencies / 125 vs 3000: T(27) = 3.29, p = 0.003 (fdr_bh: 0.009), d = 0.77 *\n",
+ "paradigm * frequencies / 125 vs 4000: T(27) = 5.45, p = <0.001 (fdr_bh: <0.001), d = 1.36 *\n",
+ "paradigm * frequencies / 125 vs 6000: T(27) = 5.62, p = <0.001 (fdr_bh: <0.001), d = 1.47 *\n",
+ "paradigm * frequencies / 125 vs 8000: T(27) = 4.72, p = <0.001 (fdr_bh: <0.001), d = 1.26 *\n",
+ "paradigm * frequencies / 250 vs 500: T(27) = -0.35, p = 0.727 (fdr_bh: 0.805), d = -0.04\n",
+ "paradigm * frequencies / 250 vs 750: T(27) = 0.57, p = 0.575 (fdr_bh: 0.688), d = 0.10\n",
+ "paradigm * frequencies / 250 vs 1000: T(27) = 1.10, p = 0.280 (fdr_bh: 0.414), d = 0.21\n",
+ "paradigm * frequencies / 250 vs 1500: T(27) = 0.91, p = 0.373 (fdr_bh: 0.501), d = 0.19\n",
+ "paradigm * frequencies / 250 vs 2000: T(27) = 1.26, p = 0.219 (fdr_bh: 0.339), d = 0.27\n",
+ "paradigm * frequencies / 250 vs 3000: T(27) = 2.44, p = 0.021 (fdr_bh: 0.050), d = 0.55\n",
+ "paradigm * frequencies / 250 vs 4000: T(27) = 4.95, p = <0.001 (fdr_bh: <0.001), d = 1.15 *\n",
+ "paradigm * frequencies / 250 vs 6000: T(27) = 5.07, p = <0.001 (fdr_bh: <0.001), d = 1.27 *\n",
+ "paradigm * frequencies / 250 vs 8000: T(27) = 3.85, p = <0.001 (fdr_bh: 0.003), d = 1.08 *\n",
+ "paradigm * frequencies / 500 vs 750: T(27) = 1.07, p = 0.293 (fdr_bh: 0.419), d = 0.15\n",
+ "paradigm * frequencies / 500 vs 1000: T(27) = 1.52, p = 0.141 (fdr_bh: 0.232), d = 0.26\n",
+ "paradigm * frequencies / 500 vs 1500: T(27) = 1.39, p = 0.177 (fdr_bh: 0.286), d = 0.24\n",
+ "paradigm * frequencies / 500 vs 2000: T(27) = 1.71, p = 0.099 (fdr_bh: 0.181), d = 0.32\n",
+ "paradigm * frequencies / 500 vs 3000: T(27) = 2.69, p = 0.012 (fdr_bh: 0.029), d = 0.62 *\n",
+ "paradigm * frequencies / 500 vs 4000: T(27) = 5.44, p = <0.001 (fdr_bh: <0.001), d = 1.23 *\n",
+ "paradigm * frequencies / 500 vs 6000: T(27) = 6.10, p = <0.001 (fdr_bh: <0.001), d = 1.35 *\n",
+ "paradigm * frequencies / 500 vs 8000: T(27) = 4.73, p = <0.001 (fdr_bh: <0.001), d = 1.14 *\n",
+ "paradigm * frequencies / 750 vs 1000: T(27) = 1.15, p = 0.260 (fdr_bh: 0.398), d = 0.10\n",
+ "paradigm * frequencies / 750 vs 1500: T(27) = 0.45, p = 0.659 (fdr_bh: 0.755), d = 0.09\n",
+ "paradigm * frequencies / 750 vs 2000: T(27) = 0.85, p = 0.402 (fdr_bh: 0.533), d = 0.17\n",
+ "paradigm * frequencies / 750 vs 3000: T(27) = 2.18, p = 0.038 (fdr_bh: 0.079), d = 0.47\n",
+ "paradigm * frequencies / 750 vs 4000: T(27) = 4.94, p = <0.001 (fdr_bh: <0.001), d = 1.09 *\n",
+ "paradigm * frequencies / 750 vs 6000: T(27) = 5.91, p = <0.001 (fdr_bh: <0.001), d = 1.22 *\n",
+ "paradigm * frequencies / 750 vs 8000: T(27) = 4.11, p = <0.001 (fdr_bh: 0.002), d = 1.02 *\n",
+ "paradigm * frequencies / 1000 vs 1500: T(27) = -0.06, p = 0.953 (fdr_bh: 0.953), d = -0.01\n",
+ "paradigm * frequencies / 1000 vs 2000: T(27) = 0.33, p = 0.743 (fdr_bh: 0.805), d = 0.07\n",
+ "paradigm * frequencies / 1000 vs 3000: T(27) = 1.77, p = 0.087 (fdr_bh: 0.166), d = 0.41\n",
+ "paradigm * frequencies / 1000 vs 4000: T(27) = 4.74, p = <0.001 (fdr_bh: <0.001), d = 1.10 *\n",
+ "paradigm * frequencies / 1000 vs 6000: T(27) = 5.61, p = <0.001 (fdr_bh: <0.001), d = 1.23 *\n",
+ "paradigm * frequencies / 1000 vs 8000: T(27) = 3.94, p = <0.001 (fdr_bh: 0.002), d = 1.01 *\n",
+ "paradigm * frequencies / 1500 vs 2000: T(27) = 0.63, p = 0.534 (fdr_bh: 0.653), d = 0.08\n",
+ "paradigm * frequencies / 1500 vs 3000: T(27) = 1.60, p = 0.121 (fdr_bh: 0.207), d = 0.41\n",
+ "paradigm * frequencies / 1500 vs 4000: T(27) = 4.61, p = <0.001 (fdr_bh: <0.001), d = 1.07 *\n",
+ "paradigm * frequencies / 1500 vs 6000: T(27) = 5.73, p = <0.001 (fdr_bh: <0.001), d = 1.21 *\n",
+ "paradigm * frequencies / 1500 vs 8000: T(27) = 4.23, p = <0.001 (fdr_bh: 0.001), d = 0.99 *\n",
+ "paradigm * frequencies / 2000 vs 3000: T(27) = 1.80, p = 0.082 (fdr_bh: 0.159), d = 0.34\n",
+ "paradigm * frequencies / 2000 vs 4000: T(27) = 4.93, p = <0.001 (fdr_bh: <0.001), d = 1.03 *\n",
+ "paradigm * frequencies / 2000 vs 6000: T(27) = 5.57, p = <0.001 (fdr_bh: <0.001), d = 1.17 *\n",
+ "paradigm * frequencies / 2000 vs 8000: T(27) = 4.07, p = <0.001 (fdr_bh: 0.002), d = 0.95 *\n",
+ "paradigm * frequencies / 3000 vs 4000: T(27) = 5.77, p = <0.001 (fdr_bh: <0.001), d = 0.71 *\n",
+ "paradigm * frequencies / 3000 vs 6000: T(27) = 3.74, p = <0.001 (fdr_bh: 0.003), d = 0.87 *\n",
+ "paradigm * frequencies / 3000 vs 8000: T(27) = 2.76, p = 0.010 (fdr_bh: 0.025), d = 0.68 *\n",
+ "paradigm * frequencies / 4000 vs 6000: T(27) = 1.14, p = 0.265 (fdr_bh: 0.399), d = 0.20\n",
+ "paradigm * frequencies / 4000 vs 8000: T(27) = 0.36, p = 0.723 (fdr_bh: 0.805), d = 0.08\n",
+ "paradigm * frequencies / 6000 vs 8000: T(27) = -0.63, p = 0.532 (fdr_bh: 0.653), d = -0.09\n",
"\n"
]
}
@@ -756,55 +1645,174 @@
" within=['paradigm', 'frequencies'],\n",
" subject='participant',\n",
" alternative='two-sided',\n",
+ " effsize='cohen',\n",
" padjust=StatsParams.mult_comp)\n",
"\n",
"# Display post-hoc results\n",
"stats.print_paired_ttest_posthocs(pairwise_results)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.039282Z",
- "start_time": "2023-08-14T17:11:56.617761Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "### Some stuff we tried"
+ "### Gain analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/8z/_3_51qzx16ndh4cmqt4fd0h40000gp/T/ipykernel_96358/601146170.py:10: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " pta['pta'] = pta.groupby(['participant', 'paradigm'])['thresholds'].transform('mean')\n"
+ ]
+ }
],
- "metadata": {
- "collapsed": false
- }
+ "source": [
+ "age = load_age_info()\n",
+ "gain_df = gain_df.merge(age, on='participant')\n",
+ "gmsi = load_gmsi_results()\n",
+ "gain_df = gain_df.merge(gmsi, on='participant')\n",
+ "\n",
+ "\n",
+ "pta = audiograms_3AFC.loc[audiograms_3AFC.paradigm.isin(['3AFC', 'Bayesian']) &\n",
+ " audiograms_3AFC.frequencies.isin(PTA_freqs)]\n",
+ "\n",
+ "pta['pta'] = pta.groupby(['participant', 'paradigm'])['thresholds'].transform('mean')"
+ ]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 22,
+ "metadata": {},
"outputs": [],
"source": [
- "audiograms_3AFC['pred'] = audiograms_3AFC['pred'].apply(lambda x: translate_conditions(x) if x in preds else x)"
+ "# First, remove duplicates to get one 'pta' value per participant per paradigm\n",
+ "pta_unique = pta[['participant', 'paradigm', 'pta']].drop_duplicates()\n",
+ "\n",
+ "# Get 'pta_3AFC' and 'pta_Bayesian' columns\n",
+ "pta_pivot = pta_unique.pivot(index='participant', columns='paradigm', values='pta').reset_index()\n",
+ "pta_pivot.columns = ['participant', 'pta_3AFC', 'pta_Bayesian']\n",
+ "gain_df = gain_df.merge(pta_pivot, on='participant', how='left')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Figure - Correlation w/ Age, GMSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gain * gmsi: R2: 0.053, p = 0.241 \n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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bl9s1Gg1atWoFADh9+jRycnJQVFQEs9kMvV7vMiZ7h5MrY+rSpctVH29FgYGBTttE0fYx9Pf3r7SsIvvzN2TIEJexTp482WWsrl43wH3vC3dJSUlB165dsWvXLuTn5wMAtm7diq5duyIsLAwDBw7E1q1bYbVaAQAbNmxATEwMevfu7XSsmr4+lanq8w3YOqdd+Rl3Fc+V7B2YKnZ2+vLLL2EymTBx4sQqO0tW1KxZM0yaNAkrV67EkSNHcP78eSxZsgQJCQnYv38/Ro4cqTxfFel0OoeOafa/AQMGID8/H8uWLcP777+PKVOmAABef/11XHvttQgODsaNN96IpUuXYs+ePXjrrbfw0EMP4fz58wCAli1bVitu+3u0Xbt2LsujoqIQFRWFwsJC5OTkOJRd+drW5XPsKep6OUsT0Lt3b/z000/YsmWLkiCqcuzYMadtS5YswWOPPQatVosbb7wRkydPRps2bdCzZ0+88sor1RqYbqdSqWoUf03U9dj2L/L+/ftX2gMScE4IRITPPvtMub148WJMmjQJQUFBNY7B/mUjy7ISj06nw4gRI6q8X2hoqMPt4ODgGp+7ruzx3nzzzVU+9tatWzvc9uR7wt1Gjx6Nffv2YfPmzRg2bBh27NiBhx56CAAwaNAgfPPNN9i7dy9SUlKwd+9ePPDAAy5/jLjr9enRowcEQcC5c+dw9uxZJCcnV7m/LMvV6rHfpUsXdOvWDT/++CPy8/MRGhqKjz76CKIouuw1b3fy5Els2bIFLVq0wMCBA53KExIS8Mgjj2DUqFFo164djh075vK7KTQ0FN98843Lc+Tk5GDo0KHYvXs38vLykJ2djf/+979Yv349tmzZgg0bNmDDhg3KD9Z///vf6N69OwDbj9jqqMmPuiv3vfK1rcvn2FM4wbrJbbfdhp9++gkrVqzAk08+CT8/v0r3tVqtTjPIHD9+HDNnzkRERAS2bNmCtm3bOpTLsuyJsL0iKSkJAPDAAw9UOgTBlc8//xzffPMNBgwYgKSkJKxevRpPPPEEli1b5nL/48ePY9CgQU7bzWazUgtMTExEZGQk9Ho9rFYrvvjiC6jV1f9YeCNpJSUlIS0tDU8//bTLx9cYjBo1Ci+++KJSOy0tLVUSif3fP/74A6dPnwZgS8iuuOv1iYyMxJAhQ7Bx40b85z//wcKFC6vcf+PGjcjLy6vWsSdOnIjHH38cX375JQYPHoy//voLqampiIuLq/Q+aWlpmDx5MgYNGlTldIdJSUno2LEjdu3ahaysrGrFYxcREYF58+ZhxIgR2LhxI0JCQnDHHXfgjjvuAADs3LkTQ4YMQXFxMXr27ImAgADExcXh+PHjOHXqFDp16uR0zH/++QcbN25E37590bdvXzRr1gzHjx9HWloaunXr5rT/5cuXcenSJfj7+yMiIsKh7MrXti6fY0/hJmI3ueuuu9C+fXukp6dj9uzZVf4ye+WVV5yaKP7++2/IsozBgwc7JVdJkrBr1y6PxO0N119/PQBUOhfr999/j3bt2uHNN99UtmVkZOCRRx6BTqfD+++/j7feeguRkZFYvnw5fvnlF5fHef/9912+Dj/88AMKCwvRqlUrJCYmQq1WY+DAgbBarcp41CtNmDAB7dq1q7empapc7flbsmQJ2rVrh6+//ro+w3KrHj16IDExEb///ju2bt0KAMrY006dOiEsLAz/+9//8Pvvv8PPzw833HCDx2OaN2+eMtbWHpMrhYWFePzxx6t93HvuuQdqtRqrVq1SWqkmTZpU5X2uvfZaqNVqbNmypcpYSktLlbnPmzdvXu2Y7OxNvUaj0eFHvslkwpw5c5Qklp2dDaD8NarY0lTRa6+9hieffFJp+re/bh988IHL/VeuXAnA9p6/WsJsiJ9jTrBuotFo8MUXXyAoKAhLly7Ffffd5/SL0WAwYNasWXj++eed7m9v7ti5c6fy5gOAkpISzJgxA0ePHgVgq335ujFjxiA5ORlr1qzBokWLHD64586dw5NPPom0tDSlOYuI8MADDyA/Px/PP/882rZti8jISLz11lsAgPvvv9/p+gxgey6feeYZh2tPx44dwxNPPAEAmDlzprLd/oX4//7f/8PevXuV7fZm6U8//RT+/v5XbRqsD1OmTEFwcDCWLl2Kzz//3OFHxP79+zFv3jycPn0agwcPrvU57M2tJSUldQ23VgRBwKhRo3D06FF88cUXaNeunXK9XRRFDBgwAFu2bMH69etx8803V9li5EptHt+AAQPw3HPPwWq1IjU1FStWrHCawGHPnj0YNGgQDh48WO3jRkdHIzU1Fdu3b8d7772H0NBQjBo1qsr7REREYObMmZBlGaNGjcLq1audYsnMzMStt96KnJwc9OrVC9dee221Y7I7fvw4ANvn4MUXX4TVaoUkSbjrrruwa9cu5T3Wvn17AMCDDz4IvV6Pr7/+2mECEyLC559/ji+++AJxcXHK/R5//HHodDr85z//weLFix2+Cz755BM8//zzEAQB//rXv6oVb4P7HNdLV6omZM+ePdSyZUsCbBMvXHPNNTRy5Ejq27cvabVaAkCRkZHKUAt7D8vi4mKl919YWBilpqbSzTffTGFhYRQYGEi33norAaCOHTsqvXir6kXsSk16TlbVi7iy3qu4ordrVbZv365MHpGYmEjDhw+nAQMGKAPOX3nlFWVf+9jYrl27OvRelGVZGdt25513OsU5duxYAmxjPIcNG0YDBgxQ5nUdO3as0ivRbs6cOQSABEGgXr16OUx+ERoa6nIyi+o+F1U99/Zes4MGDarWsYhsQ53s76fWrVvTiBEjqE+fPiQIAgGOY3yv1pvX3iuzYm9Xe0wajYZSU1PrbaKJitatW6f0an3ooYccyuzDdQDnSTiIrv761PbxybJMr732mvLcR0RE0PXXX0/Dhw936L07cuRIuuuuu67ai9jum2++Ue7rKhZ7WUVms5kmT56slEVERFC/fv1o+PDh1LNnT+W90LlzZ6dRBfZexDExMZU+1rS0NOX7xP65jIuLU8bk23viR0RE0I8//kjbt28ng8FAH3/8MYmiSACoffv2NGzYMOVzpFarnR776tWrlVEYriaauHJiClfv14pq8jn2NE6wHlBaWkpLly6lG264gWJiYkitVlNISAj16tWLnn76abpw4QKVlpaSv7+/w5fPuXPnaPLkyRQfH09arZYSExNpypQpdOLECTpx4gS1bt2a/Pz8lIkifDnBEtmGFkydOpWSk5NJq9VSXFwc3XTTTfTrr78qwxNOnjxJAQEBJIoi7d692+Ux7B/0zz//3CnO77//ngYPHkwBAQEUEBBAvXr1omXLljklV7u1a9cqP2x0Oh21adOGpk2b5jCEpTbPhbsTLJFt1qV7772X4uLiSKPRUFJSEo0ePdphEgai2iVYIqJ58+YpM/jU5HWt7nmvprS0VBlf/umnnzqU7dq1S/kSrWqoVlXjPOvy+E6ePElPPvkkderUiYKDg0mj0VBcXBwNHTqUPv30U7JYLMrQnuok2NLSUmVmru3btzudz1WCtduyZQtNmjRJ+X7Q6/XUsmVLSk1NpQ8++MDlfOf2BKvT6ZzG7Y4cOVKZeUmj0dCTTz5JBw8epAkTJlDr1q2VxO3qz/64duzYQePGjaPo6GhSqVQUGRlJY8aMUcb1Xmnfvn101113UWxsLKnVaoqMjKRRo0bRH3/84bTv1RIsUfU/x54mEDWQvvmMucmkSZOwatUqbNq0qU7NpIw1Vps3b67WaId27drh7bffdjn/M7s6vgbLGGNNVHJystP4XSJCYWEh/vOf/+D48eMYMWLEVde2Za5xgmWMMeYgICAA06dPx3333Qez2cyLttcSJ1jGGGMuDRs2DIBtOTlWc5xgWaPz0UcfgYj4+itjdWSfDcxgMHg5Et/ECZYxxphLISEhADjB1hYnWMYYYy7ZZ086fvw4JEnycjS+x/uTNTYAsiwjMzMTQUFB1V69gjHGfFVRUREA2wxHRqOx0v10Oh0CAwNRUFCAr7/+Wrkm25QREQoKChAfH+9ygYmKeBwsgPPnzysT0DPGGGNXk56ejsTExCr34RosoCz5lZ6e7pXlxxhjjPkGo9GIpKSkai2TyQkW5QtOBwcHc4JljDF2VdW5nMidnBhjrBIvvvgiUlJSvB0G81GcYBljzIXMzEwsX77c22EwH8YJljHGyhQVFeHAgQNYvnw5+vfvj4yMDG+HxHwYX4NljLEyv/76K26//XZvh8EaCU6wjDFWpl+/flizZo1ye+7cuSgoKPBiRMyXcYJljLEycXFxGDFihHL79ddf5wTLao0TLGOM1ZHJZILJZFJuy7KM3NxcRERE8OxwXlaTmZfcjRMsY4zV0YIFC/DCCy94OwxWherMvORunGAZY6yO5s6di1mzZim3DQYDmjVrxrPDNQA1mXnJ3TjBMsZYHel0Ouh0OqftPDtcw+GNpnpOsIy5gZSZCTk3F2JICMTERL7uxhjjBMtYXchFRSj98SfIFy8q28SICOhvHQ2Ray6MNWk8kxNjdWBa91+H5AoAck4OStf+7KWIGGMNBSdYxmpJzs+HdO6c67KLFyFdkXgZY00LNxEzVkt0lQkIyFgAxMTUUzTMEzZv3uztEJgP4xosY7UkhIcDVQxcF6Mi6zEaxlhDwwmWsVoSAwKg7tDeZZm6VSuIoaH1GxBjrEHhJmLG6kA3ZAgElRqWQ4cAqxUQRajbtYPu+iHeDo0x5mWcYBmrA0Gthu76IdD26wvZaIQYGAjBz8/bYTHGGgBOsIy5gaDTQRUV5e0wGGMNCF+DZYwxxjyAEyxjjDHmAZxgGWOMMQ/gBMsYY4x5ACdYxhhjzAM4wTLGGGMewAmWMcYY8wBOsIwxxpgHcIJljDHGPIATLGOMMeYBnGAZY4wxD+AEyxhjjHkAJ1jGGGPMAzjBMsYYYx7Q4BPsiy++iJSUFJdl27Ztwy233ILg4GDodDp06NABixYtgiRJ9RskY4wxdoUGvR5sZmYmli9fDrXaOcydO3di0KBB8PPzw4wZMxAREYHVq1dj9uzZyMjIwBtvvOGFiBljjDGbBleDLSoqwoEDB7B8+XL0798fGRkZLvf7f//v/0GSJGzatAmvvPIKZs+eja1bt6Jnz55YvHgxDh8+XM+RM8YYY+UaXIL99ddf0aVLF0ydOhWnT592uc+5c+ewY8cODB06FD179lS2+/n54fHHHwcR4bvvvquvkBljjDEnDa6JuF+/flizZo1ye+7cuSgoKHDY59ChQwCAPn36ON3/uuuuAwAcPHjQg1GypoRMJlBJCYTAQAguLlcwxpgrDe7bIi4uDiNGjFBuv/76604J9tKlSwCAmJgYp/vHx8cDALKzsz0YJWsKyGyG6Y8/YD1yFJAkCDodNN26QnPddRAEwdvhMcYauAaXYKtDlmUAgE6ncyrT6/UO+7hiMplgMpmU20aj0c0Rssag9JdfIVW4TEEmE8w7doJkGbr+/T1+fiKC9eBBWA4cBBUVQxUbA03vXlDFxnr83Iyxumtw12Drw4IFCxASEqL8JSUleTsk1sBIly87JNeKLHv3gcxmj8dg+n0DTL9vgHzxIqiwANYTJ1Dy5Vewnj3n8XMzxurOJxOsvXmOiJzK7NuqasKbO3cuDAaD8peenu6ZQJnPkqu6xGCxQM7L8+z5c3JgddWPQJZh3rLFo+dmjLmHTzYRR0ZGAgAMBoNTmX1bVFRUpffX6XQum5cZsxMCAqsu9/f36PmtZ85WWiZfugS5qAhiQIBHY2CM1Y1P1mA7dOgAwDbZxJV27NjhsA9jtaFqlgQhJMR1WfPmEIOCPHp+QVX1R1MQffKjy1iT4pOf0ubNm6N79+749ttvHYbjWK1WvPLKKwCAsWPHeis81ggIggD9qJEQrkikYkwM9Dff5PHzq1q3BipJoqrkZAh+fh6PgTFWNz7ZRAwA77zzDgYOHIiBAwdi6tSpCAsLw/fff4+//voLjzzyCLp06eLtEJmPU0VGwn/yJEhnzkA2GKGKioSqnjrEiQEB0A4YAPMffzhsF/z9oRs0sF5iYIzVjU/WYAHbhBQbNmxA9+7dsWTJEvzf//0fcnNzsWDBArz55pveDo81EoJKBXXLltD26F5vydVO26M7/O6+C5ounaFq2RLafn3hf994iBER9RqHr0lPT8ekSZMQExMDjUaDpKQkPProo8irRse05557DoIguPz79NNP6yF61pg0+Brs5s2bKy0bPHgwBg8eXG+xMFbfVLGxPO61BgwGA/r164f09HRMmTIFnTt3xpYtW7BkyRLs2LED27Ztc7l4iN3BgweRkpKCf/3rX05l11xzjSdDZ41Qg0+wjDFWXQsWLEB6ejoWL16Mxx57DAAwc+ZMzJgxA++++y5WrFiBhx9+uNL7Hzp0CH379q1yH8aqy2ebiBlj7Epff/01QkJCMG3aNGWbIAh4+umnAQDffPNNpfctLS3FiRMn0LFjR4/HyZoGTrCMsUahpKQEp06dQo8ePZzGuSckJCApKanKRUDS0tIgy7KSYPPy8lBcXOzRmFnjxgmWMdYoVLUICGBbCKSqRUDsq3T99ttviI+PR3h4OAIDA9G/f39s377d/QGzRo+vwTLGGoWqFgEBbAuBuJpe1a5ign366acRHh6Obdu2YdmyZRgyZAi2bNmC3r17u7wvLyDCXOEaLGOMAejcuTOef/557N69GzNmzMA999yDJUuW4KeffoLJZMKcOXMqvS8vIMJc4RosY6xRqGoRkKq22911110ut6empuKaa67B5s2bUVJSAj8Xs2jNnTsXs2bNUm4bjUZOsoxrsIyxxqGqRUDs26taBKQqiYmJkGW50skqdDodgoODHf4Y4wTLGGsUAgICkJycjN27d0OSJIcyo9GIw4cPV7oIiNlsRqtWrTB16lSX5WlpadDpdIjgWbRYDXCCZYw1GmPHjkVGRgZWrlzpsH3hwoWwWCwYN24cAKCwsBBZWVlKbVer1SIoKAirVq3Cvn37HO771Vdf4dChQ7j11lt5mUtWIwJd7cJEE2A0GhESEgKDwcBNO4z5sNzcXHTq1AnZ2dmYPHky2rdvj507d+LLL79Et27dsGPHDmi1WsybNw8vvPACJk6ciI8++ggAsGvXLvTv3x9qtRqTJ09G8+bNsXfvXnz22WdISEjAX3/9hYSEhGrFwd8pDYe7X4uaHI9rsIyxRiM8PBx//vkn7rjjDvzwww946qmnsGXLFkydOhXr16+HVqut9L69e/fGP//8g+HDh2P16tWYM2cOtmzZgkceeQS7d++udnJlzI5rsOBfm4wx9+LvlIaDa7CMMcZYI8MJljHGGPMATrCMMcaYB3CCZYwxxjyAEyxjjDHmAZxgGWOMMQ/gBOtGZLVCOn8eckHBVScWZ4wx1rhxgnUzMpkgZ2dDOncOcr4BVLZGJWOMsaaFl6vzFKsVcs5lIC8PYkgIhJBgCCqVt6NijDFWTzjBeposQc7LBfLzIAQH25KtRuPtqBhjjHkYJ9j6QgQyGCAZjBACAyCGhkLglTkYY6zR4gRb7whUWAipsBCCnz/EsFAIfn7eDooxxpibcYL1IiophlRSDEGnhxAaAjEw0NshMcYYcxNOsA0AmUpBF0sh5+ZBDA2BEBQEQRC8HRZjjLE64ATbkFjMkC9dAnJzbZ2hgrnnMWOM+SpOsA2RJEHOzQXy8yEGBUMIDYGg5peKMcZ8CX9rN2SyDNmQDxgMEIKCbM3HWq23o2KMMVYNnGB9AoEKjJAKjBACyob46PXeDooxxlgVOMH6GCoqglRUBEGvhxAaCjEgwNshMcYYc4ETrI+i0lJQVhZIq4UQGgohMJB7HjPGWAPCCdbHkdkMys527Hks8hoOjDHmbZxgGwurFXJOjm1xgaBg2+ICtZzzWM43ACRDDAtzc5CMMdZ0cIJtbCr2PK7hnMdSZiZMGzfZxuICEMPDoR04EOrmKZ6LlzHGGiluS2y0yuY8Pn8eUmYm5KKiKveW8w0o+e57JbkCgJybi9KffoJ08aKng2WMsUaHE2wTQCUlkLOyYD2XDtngehF4y759gMXifGdZhuWff+ohSsYYa1y4ibgpsZghX74M5OZBDA6CEFI+Q1TFmuuV5OzKyxgDAJIkwGrlJRgZq4ATbFMkS5Dz84H8Ctdpq1jJRwgKqtVp+Eu38SOLBeY/t8Fy6BBgNkMIDYX2mmug6djB26Ex5nWcYJu08rVpxZho0IEDLuc81nTpXLOjmkwwb9kKy5GjgNUCMSIC2muvhbptG3cF7kTKugj5UjaEwECokpN5qFI9KV37M6QzZ5TblJ8P02+/AZIVmi5dvBcYYw0AJ1g3IYsF1vTzoOxLECIjIYi+NemDGBwMTceOMO/dB0ElAhoNBJUK2muugbpVq2ofh4hQ8v0PkC9cULbJOTko/eUX6AG3J1kymVC6Zi2k9HRlmxAcDP3oUVBFRrr1XMyRlJXlkFwrMu/YCXWnTvxDhzVpnGDdwLJvP8xb/4RcWgIqKobg7w/ttddAFR/n7dBqRN22DVQpyZDOZwICQd2mLVRxsTU6hnT2rENyrci8Y4fbE6xp4yaH5AoAZDSi9Icf4T95Ei/350FSZmalZVRYCCoogBASUo8RMdaw8M/LOrKeOgXTxo0gs0nZRsXFMP3vf5ALCrwYWe0IOh3ULZtD3aIFYLVASk+HlHnhqsN87ORM18kVsNVkyWSqtLymqLQU1mPHXJcVFEA6e9Zt5/I1VFoKS1oaLGlpoNJSj5xD8POrvFAU+do7a/K4BltHln/2uC6QZFiPn4C2R/f6DcgDqKQYVFIMWa2xTccYFFh5zbCqL121BnDjurZUXAy4GHKklBcUuu1cvsS8Zy/MW7cCVisAwKRWQzegPzTdurn1POpWrWDWbnb4camUtWzJKz6xJo9rsHUk5+dXWkb1VIMlSYZ09hzMO3fBvGdvlTHVidUCOecypLNnIV26BDKbnXbRtGsLVJJ81e3aurXJVggKgqCtvJYkRjW9a7BSejrMmzcryRUAYLXCtGmzU1N6XQkaDXQjhgFXTMkpRkRAO2SwW89Vn9LT0zFp0iTExMRAo9EgKSkJjz76KPLy8rwdGvMxXIOtIzE0FFIliVQIrN3wlpogswWmjRsh5+Qq26yHj0DTravnhkoQgYxGSEYjBD9/27zH/v4QBAGCnx/0qako/fVXQJKUu4ixsdANHODWMASNBpruXWHesdOpTJWQAFV8vFvP5wss+/ZXXrZ/P1RJSW49nzo5GQEP3A9r2jFQUSHEmBiomjf32c5NBoMB/fr1Q3p6OqZMmYLOnTtjy5YtWLJkCXbs2IFt27ZB7cZWGNa48TuljjTdu7uuGahEqFtXv/dtbVkOHHRIrsr2vfugSoiHGBrq0fPbm4+hVkMMDoYQHAx161YISLgflqNpQGkJxLg4qFJSPLKcnua66wAAlj37bE2Vogh1q5bQXX+928/lC2SjsYoyz7SoCHo9NF0bx5CcBQsWID09HYsXL8Zjjz0GAJg5cyZmzJiBd999FytWrMDDDz/s3SCvInfsbd4OoVLh333j7RDqlW/+zGxA1C1bQDt4MKDVKtsEPz10AwZADPZ8DVY6c7rSMuvpMx4/f/nJrJBzcyGdOWubu1gQoO3RHdq+faFu3txja9UKggBt377wf+gB+N03HgEPPgD98OFVd8BpxMQqhiaJkRH1GIlv+vrrrxESEoJp06Yp2wRBwNNPPw0A+OabppUgWN34fIJNT0/HAw88gJSUFGi1WjRv3hyzZ8+GsYpf8u6m7d7N9sV+62job7we+tGjoUqon+ZJcjV/sF1VZR5TNnlFZiak8+chG40u5z52N0GjgSoyEoK/v8fP1ZBpuncDXDXPqlTQdPf9DneeVFJSglOnTqFHjx7QXdEDOiEhAUlJSTh48KCXomO+yKebiLOystCnTx/k5ORg0qRJaNOmDXbt2oVFixZh8+bN2L59O7QVapaeJGi1UCcnQ7r6rm6liomBVMnQGFVMTD1H44hMJtClS0BODoTAQFsTMg/d8ChVVBT0o0bBtHkzqKyzmxAaCt2QwTzxxlVcKpuPO6aSz018fDx27nS+3s9YZXw6wb788svIzMzEF198gTvvvFPZHh8fj0WLFuG///0vRo4c6cUIPU/TuZOtSVZyrCWKEeEQExO9FNUVZLm8U5ROb+sUFRDgsx1hGjp18xSoUiZCzskBYOvV66km+sZELmtpubL2aqfX60FELstMJhNMFcZ4GwwGAKjXljQ7o1darqpH7Y3no+yclb12nuTTCfZY2SQDw4YNc9g+ZMgQLFq0CEePHm30CVaMjIT+xhtgOXAQ0sWLtqbSlObQdO5km/KwgSFTKSi71DYRQVAQxKAgrtV6gCAIXGOtRwsWLMALL7zgtD3Jzb22fZ4XZ/YqKChASD2f36cTbJs2bfDbb7/h6NGj6N27t7L9yJEjAIBWNZhD15eJkZHQDRns7TBqRpZBBgMkgwGCXm8b0xoQwFMbMq+x1/Irq+lUVQOaO3cuZs2apdzOz89HcnIyzp07V+9f6u5kNBqRlJSE9PR0BAcHezucWiEiFBQUIN4Lw/Z8OsHOnj0bX375JcaPH4/XX38dbdq0wbZt2/DSSy+hf//+jb722lhQaaltOr/Ll23XaoOCmmwvYOY9kWU1fnvz7pUMBgOioqJclul0OpdNyyEhIT6bmCoKDg726cfhrR85Pp1gU1JSsGzZMtx+++0YNWqUsj06OhrLli2rdED4lddLvHGdhLlAZJtDuKAAUGtsiTYoEMIVMwUx5gkBAQFITk7G7t27IUkSVBVaU4xGIw4fPoy+fft6MULmaxreRboaWLVqFcaOHYuYmBgsWLAAq1evxsyZM5Gfn49rrrkGf/31l8v7LViwACEhIcofXydpgKwWyHm5kM6dg5SZCbmgoF6G+7CmbezYscjIyMDKlSsdti9cuBAWiwXjxo3zUmTMFwnkja5VblBUVITExETodDrs3bsXsbHly6qtW7cOqampGDRoEDZv3ux0X1c12KSkJBgMhjo1g5DV2qRXcPE4UYQQEAgxOIgnkmcekZubi06dOiE7OxuTJ09G+/btsXPnTnz55Zfo1q0bduzYUa2hfyaTCQsWLMDcuXMr7ZXsCxrL43Ano9GIkJCQauULn02wf//9N3r16oWHH34YS5cudSpv06YNMjMzUVh49RVVavKEVYUTbD3SaCEGBdo6R/HcsMyNTp8+jWeeeQbr169HXl4eYmJiMHLkSMyfP1+5TsuarprkC5/9ZhLLxlBKkuupHSRJgoav3TVeFjPk3FwgNw+Cv195L2Qe78nqqHnz5li9erW3w2CNgM9eg+3UqRMiIiLwzTff4OwVtcbvvvsOp06dwo033uil6Fj9IVBxMeSLF23zIGdfAhUXe2VQOWOMVeSzNViNRoO3334b9957L7p27YpJkyYhKSkJe/fuxerVqxEREYEFCxZ4O0xWn2QJVGCEVGAERBWEgACIgQGAnx/XbBlj9c5nEywA3HPPPYiPj8frr7+OTz75BAaDATExMZg+fTqeffZZREdHeztE5i1Oydbf1oRctm4tY4x5ms92cnIn7uTUhIiiLdFysmWM1UKT6OTEWK3IMqigAFRQwMmWMeZRnGBZ08XJljHmQZxgGQOck62/P4TAQE62jLFa4wTL2JVkGVRYCCosdEy2fn68hi1jrNo4wTJWlYrJFoItyfr723ol80QmjLEqcIJlrNoIVFIMKikGcgCoNbZE6+fXKGq30oULsOzeDSkrG0KAPzSdO0HdqVOTaSKXDQZYDhwE5edDDA+DulMniD68RBvzPk6wjNWW1QIyGEAGA2y1Wz0EvR8EPz2g0/lUwrWeOYPSH38CylYsosICmC5ehJR1EfqbGv+MaNYzZ1C6Zi1gtSrbzH/vgd+to6Di1bZYLfnONwBjDRqBSkpsS+xlZkI6fQbS+fOQLl+GXFgIqvDF3RCZt/6pJNeKrAcP2uZ8bsRIkmBa/7tDcgUAWC0o/W09T7vpw0iSQGazbTrVgoJ6Pz/XYBnzCAKZTIDJVFbDha1J2U8PQV/2V41lz+qDXFgI+dKlSsutp89AGx5ejxHVLzkzs+wauzMyGiFnZUEVF1fPUbGqkCTZfhBJkvJ/278SIFlBZf/iih9HYlBQvcbJCZax+mK1gAostqFAgG0KxwoJFzqdV653CipVncp93dVaF8jSsFsfGguSZUCSHJImJKk8WUqS7bWSZAC+0arACZYxb5ElUFERqKjIdlsQymu39oRbD8lN8PODKikJUnq6c6EoQtW6lcdj8CZVfDyg1gBWi1OZoNVBFRfrhagaD6faZllNk6QrEqiLSxS+jhMsYw0F2a7jUklJ2QYBgk4L6PUQdDpbk7JW65Farm7IYJR8/U2Fc5dtHzQQYkCA28/XkAg6HbTXXQvzlq1OZZrr+vBwLBeIyKmJ1mUCtUrwldqmJ3CCdSPLwUOAKDT6LyRWXypcx1W2CYBGA0GrtSVcne3fuiYBMSIC/hPug+XgQUhZFyEG+EPdsRNUsTF1fRA+QdurF8TgYFj27IWcnw8xLAya7t2hbuS19yspCVJpjnWsbSo1Tlnydqg+gROsm8j5BhS++BIAQAgNgSo+AaqEeIgJtn+FkJAmM56QeRIBFjPIYgYVVdgsCOU1XK22PAnXIPEK/v7QXnON+0P2Eeo2baBu08bbYbgdEVWdNCv+n3tMuxUnWDeR8/IgxsZCzsoC5RtgzTfAeviwUi4EBEBMiIeqLOGK8QkQI8J9aqwka8DIVW0XtsSr0QAaDaBWQ1BrAI3atk2t5vefDyFJsl2nLPsj+//Ltl95G7LMtU0v4/Vg4d71YK1Hj0K6cAFSRibkjAzbv9nZri/g63RQxcc71HTF6OhG32uTNSAqlS3ZqtQQ1GpArbIlXpUKKPtrSO9HKT0d5l27IV/IguDvD3WnjtD06N6gYqwMXZn8lIRYMWlekUQrJlVWZ+qWLet8DF4P1osEPz+oW7SAukULZRtZLJCzsiCdz7BNQpCRAflCFmAyQTp9GtLp0+UHUKshxsWWJd4EqBISIMbFckcL5hn2jimoqiuKAKjE8qQrqgC1CoKoAlRieRJWqQBBAETRtkiCmy+JWE+fts02VVYnILMJ5q1bIWdfhH74cLeeyxWlqdVV8itLlFTxtiyBKiTPptzZp6niBFsPBI0GqqQkhynXSJIgZ2eX13QzMyFlZAImE+T085DTz0MZNCCKEKOjoYqPh5gYb7u+Gx8Hwc/PK4+HNTXkkIgrbK2aPdkKIgSVWOG2AEAABDj9X6hkOyDAtHETyGx2Oo3l0GGo2raFKjLKFhVRWXDl/6crtwMu9yv/P4FkAkgG7P9yYx+rIU6wXiKoVFDFxdlmiOnVE4CtCYlycyGVNS3bky8VFUHOyoKclQX880/5MSIioKp4XTchAWJgoLceEmOO7DU+SKBqztVQWQqj0lLbD9BKWA8fhtCxY41DZMyT6pxgjUYjLl68iJycHISEhCAqKgqRkZHuiK3JEUQRQmQkxMhIaLp2BWBrliKj0dasnJGpJF/Kzwfl5MCakwPr/gPlxwgOtjUrV0i8Qmgo92Bmvk2lstVmK8vAar6EwhqeGidYIsLPP/+MNWvW4LfffsO5c+ec9omKisKQIUMwdOhQ3HHHHfDjpsxaEwQBQkgIxJAQoEMHZbtcVFSecDNtNV35cg7IaITVaASOHCk/hr8/xPgrarqREdyDlPkMQaOBKiER0vnzzoWiAFUzXvGGNTzV7kVstVqxYsUKLFq0CCdOnAAAxMbGolWrVggLC0NQUBCKioqQn5+P06dPK4k3LCwMU6dOxZw5c+rUQ9eT3NmLWDp71o2R1fD8pSZIFy6UX9M9nwH54kXXPRC1Wts13QRbL2ZVfALEmGhbT1LGGiC5qAim9b+DioodtmuvvQbqVnXvHcoav/ruRVytBLt+/XrMnDkTJ06cwLBhw3D33XdjwIABiI+Pr/Q+OTk5+PPPP7F69Wr89NNPCA8Px9KlSzFy5MiaPyIPaywJ1hWyWm09mMtqu3JGBqQLWYDFed5VqFQQY2OV67piQjxUcXENZtUXxshigXTqDOScy4BeD3XLFrbWHcaqoUEmWL1ej+nTp2Pu3LmIioqqcUD5+fl45ZVXsGTJEhQXF1/9DvWsMSdYV0iSIF++bKvhZpZf10VpqfPOggAxOspWw7Vf142Pg+DvX/+BM8ZYHTTIBHv27FkkJyfXObBz586hWbNmdT6OuzW1BOsKEZX1YC6r6ZY1MVe2TqYQHm4bq5uYoFzfFYPrd61FxhiriQaZYO3y8vJw/PhxxMTEOCTcbdu24Y033sDRo0cRGhqKMWPGYMaMGdDr9bV/FPWIE2zlZIcezLbkS3l5LvcVgoMca7oJ8RDCwrgHM2OsQWiQCZaI8Mwzz2DRokWwlF27GzJkCL744gv8888/GDFiBKwVFi0WBAHXXnstNm7c6BNJlhNszVBxsTIxhj35ypcuuR6I7+enTAdpv64rRkVxD2bGWL1rkFMlvvPOO/j3v/8NURTRvXt3FBUVYePGjbj33nthNBqh1WqxbNky9O3bF2fPnsXLL7+MrVu3YuHChXjuuefq/IBYwyL4+0PdqhXUrcqX8iKzGVLmBYdrunJWFlBSAunkSUgnT5YfQKOBKi5OmX9ZlZAAMTaGezAzxhqVatVge/XqhUOHDmHjxo247rrrAAD//e9/kZqaCkEQ8P7772PKlCnK/iUlJejQoQM0Gg2OHTvmuejdhGuwnkFWK+SLF2013cxMyOczIF24ALiY7g4qFcSYmPKEGx9v60yl09V/4IyxRqlBNhGHhoZiwIABWLNmjcP2G264AZs3b8aFCxcQHR3tUHb//ffjk08+gdnVl2kDwwm2/pAsQ7582WFWKikjAygpcd5ZECBGRjrUdFUJ8dyDmTFWKw2yidhoNKJ58+ZO29u0aYPNmzc7JVcACA8PhyTxOoTMkSCKUEVHQxUdDU33bgDKejDn5yurDdmX+SOjEfKlS5AvXYJ1797yY4SGOsxKpUqIhxAczJ2pGGMNSrUvernqrBTIE8szNxAEAUJYGMSwMGg6d1K2ywUFDtNBSuczQLm5oPx8WPPzYT10qPwYgYG2ZuXEBGWpPyEinJMuY8xruFcJa7DEoCCI7dpC3a6tso1KSpQezPbkK2dngwoLIR07BqniNX+9zra0X8UF7aOifGJxbsaY7+MEy3yK4OcHdcuWDtdSyGyGlJVl60RlT74XLgClJkinTkE6dar8AGo1xLg4pYlZ6cHMC9ozxtys2gnWYDA4rZxjMBgAAOnp6biyr5S9jDFPE7RaqJs1AyrMEkaSBPlidvmsVGXNzDCZIaenQ05Pd1zQPiambEH7sibm+HgIeu7BzBirvWr1IhZFsdbXsnyhoxP3Im4aSJYh5+Q6rjaUmQkqKnLeWRAgRkQ4zEolJiRADAio/8AZY27RIHsRN2vWjDuLMJ8niCJUUZFQRUVC063CgvYGg8OsVFJGBshgsA0nunwZ1n37y48REuK8oH1ICH8+GGNOqpVgz5w54+EwGPMOQRAghIZCDA2FpmOFBe0LC8ualitMB3n5MshggNVgAA4fLj9GQIBDD2YxIQFiRDhPB8lYE8ednBhzQQwMhNimDdRt2ijbqLQUUuaF8lmpMjMhX7wIKiqCdPw4pOPHyw+g0ylzMIsJZYk3Jpp7MDPWhFQrwV7ZuakmGuLydIzVhqDXQ92iOdQtyiddIYsFctbF8vmXMzMgZV4ATCZIp09DOn26/ABqtesF7bkHM2ONUrUSbEpKSqPu5MRYbQkaDVRJiVAlJSrbSJIgX7pUNla3bDrIzAyg1AT5/HnI58879mCOiqpwXbesB7Ofn1ceD2PMfaqVYCdMmOCUYPft24e9e/ciODgYgwcPRmxsLC5cuIDNmzejoKAAXbp0Qffu3T0SNGMNmaBSQRUbC1VsLNCzBwBbD2bKzYOUmeFwXZcKCyFfvAj54kXgn3/KjxEe7jArlZgQDzHIdxe0J5MJ1pOnQIWFEENCoGreHIKWa+6scavRgut2+/btQ79+/XD77bfjzTffREhIiFJmNBoxe/ZsfPzxx/jtt98waNAgtwbsCTxMh3kDEYGMBWXJNkNZdajyBe2Dbdd0431rQXs5+xJKN28GLBXWjNbpoLt+CMTwMO8FxpoUMpmhbtEcQh3XKHf7ajpXGjt2LPbt24e0tDSoXazhKUkSOnbsiKSkJKxfv76mh693nGBrRsq4AOnMGUCy2iZoaNGcryO6kVxUBDnzQvl13YwMyJcvV76g/ZVjdSMjG0wPZpIJpT/+BCoudioTQ0OhH57qhahYUyLn5MLyzz+Qsi9BDAqEKiEB2kEDoYqJqdXx3D4O9kqbNm3CnXfe6TK5AoBKpcL111+P1atX1+bwrAEz79wF6/ET5RvSz0M8fhy6m27ktVvdRAwIgNi6FdStKyxobzJVvqD9iZOQTlyxoH38FQvax3hnQXs5O9tlcgUAOT8fcl4exDCuxTLPkAsKUPr7BsBa3noiZWSg5Jtv4X/vvRBDQ6q4d93V6hNXWlrqNDXilUwmEywWS5X7MN8iX7zkmFzt2w1GWA4chLZXTy9E1TQIOh3UzVOA5inKNrJabT2YMzMgna/Qg9ligXT2HKSz58o7U6lUEGNjbDXdePvQoTgIWq1nA7/aetBma9XljNWB9WiaQ3JVmM2w7NkD3ZDBHj1/rRJs69at8d///hcmkwk6F7WWy5cv4+eff0bbtm1d3Jv5Kuu5ypu/pbPnAE6w9UpQq20doRITgGts20iWbWvoVlzmLyMTKCmxTZaRkVmedAUBYlSUw6xUqnj3LmgvREUCogDILn6Qa9QQIrj2yjxHzsmptEzKyvL4+WuVYCdMmICnnnoKY8aMweuvv44OHcpnwNm/fz8eeughXLp0Cc8++6zbAmUNgFzFkKuqyli9EUQRqpgYqGJioOlh68VPRKC8POfpIAsKIGdnQ87OhnXP3vJjhIU5LWgv1rJvgujnB3XbtrAeOepUpunYySvN1qzpqKpDkzt/SFZ6jtp0crJYLEhNTcXGjRshCAKaNWuGuLg4ZGVl4ezZsyAi3Hrrrfjmm28gerizBRHh/fffx7vvvovjx48jODgY/fr1wyuvvII2FWbhqQp3cqoe6Vw6TFu2uixTpyRD269vPUfE6kI2Gssmxyi/rku5uS73FYKCnHswh1dvQXsigvXYcVjT0pRhOup27aBu2cLdD4kxB1J6Okz/K//OEoMClf/rR42q1XvQ472IAcBqteLNN9/E+++/j+MVpojr3Lkzpk2bhoceesjjyRUAHnnkEbz77ru46aabcNNNN+HixYt47733EBAQgH379iE2Nvaqx+AEWz0kyTBv2gTpYrbDdkGnhe6mmyGG+O44TWZDxcW26SDtNd3MDMjZlyrvwRwfV35Nlxe0Zw2Qec9eWA8fAVCeYDW9e0PXv1+tjlcvCbaiwsJCGAwGhIaGIqAel/P673//i6FDh+LBBx/EsmXLlF/T69atQ2pqKl588cVqNVNzgq0+kiRYj6ZBOnMGZLVCFRsLdYf2Pj0JAqsamc2QLlwov65r78HsapY2jQaquFjHHsyxsdwUzLxKLiiAlH4eqrg4qFu1hBgaWutj1XuC9ZaRI0fi119/RXZ2NsLDw5XtRIR///vfaN26NW677barHocTLGM1Q1Yr5OzsCtd0bZNkuOw1bF/QvsI1XVUcL2jP6l99rwfrswlWkiQEBwejd+/e2Lx5M4gI+fn5UKlUNU6SnGAZqzuSZciXc2zDhc6XNS9nZLoeBysIECMjHK7p8oL2zNMa5ILrDdHly5dRXFyMjh07YuXKlXjhhReUVX+6du2K1157DTfddJOXo6wfJMmQzp2zzWer0UCdkgIxIvzqd2TMjQRRhCo6CqroKGi6dQNQ1oM5P9+xB3NmBshghHzpMuRLVyxoHxpSPla3bC5mXtCe+SqfrcEeOHAAXbp0Qdu2bXH8+HHMnDkT/fr1w+nTpzF//nwUFhZiw4YNLudCNplMMJlMym2j0YikpCSfrMGS2QLThg2Qcx3nr9V07gRNl871Ggtj1SUXFpbPv5yRATkzE/Jl12MWhYAAh7G6YjwvaM9qh2uw1WQwGAAAaWlp+OCDDzBlyhSl7Prrr0fPnj3xzDPPYOtW52ElCxYswAsvvFBvsXqS5cABp+Rq234QqsREnkydNUhiYCDEtm2hrjAZDZWUQLpwweG6rpydbVvQ/thxSMeqWNA+IR5iNC9ozxoWn6/BhoaG4vLly1Bd8cG67rrrsGPHDhiNRgQGBjqUNaYabMm334FKTS7L1O3bQduDlwxkvossFsgXssp6L9tmppIvZLme/k6thhgXqyzxp0pIgBgXW+uFKEiSbPM8p6cDRBATEqBu3YoXtvBhXIOtpsjISABAXFycU3IFgMTERPz1118oLCx0SrA6nc7lFI++iFx90dhVVcaYDxA0GqiaJUHVLEnZRpJU1oO5bEF7+3SQJhPk9POQ069Y0D46uqymG69c373akmUkSTBv3AQp+5KyTcq+BOnMGehuvJHXsmXVUusE+9Zbb+Gjjz7CqVOnUFhY6HIfQRBg9dCXfGxsLOLi4nDu3DmYzWZor5i0/Pjx4/D390dMLZck8hWqmBjbl4ursmpMssGYrxFUKqji4qCKi1Pmv7YtaJ+rjNNVFrQvKoKclWUbt/t3+YL2LnswV/ghLp0+7ZBc7eS8fFiPHYOmU0fPP1Dm82qVYBctWoQnnngCWq0WXbp0qdfJJewEQcD48ePx2muvYfHixfjXv/6llK1duxb79u3DuHHjGn3vQ03nzpAuXgSsjoP+xchIiAkJXoqKsfoliCKEyEiIkZHQdO0KoKwHs8Fga1auMEkG5efbhhNdzoF1/4HyY4QE25qV4+Mh5+aCzGZAo3H6DpHS0znBsmqp1TXYNm3a4PLly9ixYwdat27tibiqxWAwoHfv3jh+/DjGjh2La6+9FqdOncLKlSsRFhaG7du3o0WLq8816evjYOWcXFgPHYI1K8vWpNa8OTQdO/C1IsZckIuKHFYbsi1on+N6OkiVCoKfH+DvD8HPD4KfH8S4WPgN44XifZFPTDSh0+kwfvx4fPDBB7UO0l0uXbqE5557DmvWrEF2djYiIiIwdOhQvPDCC2jWrFm1juHrCZYxVjdUaiqbDtLWmcp66nSlCx9Arbb10E+IL1viLwFiTDRPB+kDfKKTU1hYGNQN5M0UFRWFpUuXYunSpd4OhTHmowS944L2JEko/e13yOfPg0pKQMXFoJISoLQUsFohnTkD6cyZKxa0jy2ffzkhHqq4eljQnjVotcqSw4cPx7p161BUVOSV66+MMeZJgkoF/U03wHriJORz50AE2+L2LVqAjAZI5zMclvlDaamtqTkjAxbsKjuIADE6ylbDrTBRhuDn590Hx+pNrZqIc3JyMGjQICQnJ+Odd96p1nXOhoybiBljtUVEZT2Yy2elks5ngCobXREe7ljTjU+AGMyrUdUHn7gGe80116CoqAhHjx4FAISGhiIkJMT54IKAkydP1vTw9Y4TLGPM3WwL2mc49mDOc551DQCE4CDnmm5YWKMfBVHffOIabHZ2NgRBcOhE5CpP++gkUYwxVmdicDDE4GCgfXtlm21Be1sNV+nBfOkyyFgAq/EoUFZpAVC2oH28Q21XjIriOZh9SK0S7JkzZ9wcBmOMNX6Cvz/UrVpB3aqVso1MpvIF7cuu68pZF4GSEkgnT0Kq2Aqo0UAVHwcxPgGqxLLm5dgY7sHcQPGrwmpHEABBBAQAgghBFMq2ufiz7w+hbH8BQoX/Kyq2eDj8l6osR8VyIkCWAVkGSTJAtv+7HOPIWAMg6HRQp6QAKSnKNrJaIV+8eMUyf5mAxQLp7DlIZ8859mAuW9DePlGGKj4OQiOZDtaXVSvBfvzxx+jYsSN69uyp3K6uCRMm1C4y5l6CAIgiIKpsTUyiCIj2baJtm1B+G6Jou/5Tcbs9YdrLfAhVSLxKApYrJN8rt8lXbpMAScYVmR0AIOflwXrsOMhohBAYAHWbNhAjIur/QbJGQ1CrlQULgN4A7AvaX3ZY5k/KyARKSmzL/WVmwrJrd9kBBGU2N1XF67r+/t57UE1QtTo5iaKIJ554AgsXLlRuX+0LloggCAIkSapyv4bApzo52ZOkSgWoRNtMM2L5/5VkKZbfbsgJkSQJMJsBvb7BxlgRSZJtEQVZBlmtsJ44CdNvvwGSBBDZkjEB2r59oG7e3NvhskaOiEB5eY49mDMyQMYCl/sLYWFXLPOXACE4yCc+e+7QIDs5ffjhh+jYsaPDbeYGV0uWKpVjslSpGs0HgaxWmLf+CcuhQ4DZDCEwEJqePRv88npC2esAAJBlWHbvdphMQICtpmE9chSa7t0hEEAWM8hsKVvdiJuqGzLZYAAVFEAICoEY0vCHzgiCACE8HGJ4ODSdOynb5YICh6Zl6XwGKDcXlJcHa14erIcOlR8jMNCh97IqPgFCRHij+a7xpmol2DZt2qBXr17K7YkTJ9bqZNu3b8d1111Xq/v6BEGAoNM7JktRLL9tr1E2smRZG6Zff4X1RHnnDSoshPmPPwBJgrZ3ryru2XDIFy+6HOsoiCJgtYKMRqgq1GKJCLBYQBaL7V+zBbAnX7nht/Q0ZmQywbx1G6SsLGWbKj4O2r59Ieh8bzYmMSgIYrt2QLt2yjYqKVGW9rM3M8vZ2aDCQkhpxyClHSs/gF4HVXyC44L2UVG8oH0NVSvBDhw4EGPGjMGLL76IdhVesOo6cOAAnnvuOfz8888wm801vr+vEFQqqBJ5BZurkS5fdkiuFVl274amezff6BV5tasrV5QLggBotS6nzyNJKk++ZjPIYrUlX4uFO2jVgyuTKwBImRdg3r4dusGDvBSVewl+flC3bOnQTEpmM6SsLMjnM8p7MF/IAkpNkE6dgnTqVPkB1GqIcXHlNd2Esh7MvKhIpar1LfbXX39hypQp6NixI3r37o17770X/fr1Q+fOnaFx8eSazWbs378fW7ZswWeffYY9e/agf//++Oeff1wcnTU18oWsSsuotBRyfj5UkZH1GFHtiDExEPwDQMVFTmWCVgdVUpKLe7lmb3p2tRA4WSy2pdNMJlCpCWQqtXXAYm4hGwxOydVOysiEXFAIMSjQZbmvE7RaqJs1AyrOaSBJSg9mZUH7zEzAZIacng45Pd1xQfuYGKji4yEm2hazty1ozz2YgWom2J49e+Lvv//G8uXL8fbbb2PmzJkQBAEajQYJCQkIDQ1FQEAACgsLkZeXh4yMDEiSBCJC37598d1332H06NGefizMRwj+Vc/F6itztQoqFbSDB8L06zqnWqZ2YH+3/bIXNBrbsSrM+01mM8hkApWW2hKvyQy+vls7VOB6SkOlvLAAaKQJ1hVBpVISJcou15AsQ87JcZiVSs7IABUXQ75wAfKFC8DffyvHsPVgLruuGx9vmyQjsOk8h3Y1niqRiLBhwwb897//xR9//IGDBw+itLRUKQ8ICEDnzp1x8803Y+TIkcrQnobMXb2IWfWQJKH4g5WgIueanyolBX5jbq3/oOpAunABlj17IefmQgwJgaZb1xrVXt2BZNnWtFxWw6VSE2C1XP2ODLKhAKVr11Zarh89skkmh6tRFrSvMCuVlJEJMhhc7i+EhJTPv2wfNhQSUq99UXxiLuIrFRUVwWAwIDQ0FP4+OM6KE2z9kzIvoPTHH201sDJiRAT0Y8fwl5mbkCSBSktty6yVlNiamZlLpk2bIWVecNquTkqEduAAL0Tku+TCQtcL2rsgBATYJsZItNd0EyBGhHtsOkifTLC+jhOsd5DFYpugoaAAYlQkVM2b8zyrHkSSZFvbtKQEVFIKWDjh2pHZAvP27ZDOZyjb1EmJ0PTpA0HLnXjqikpLXfZgdtmXQKdzHKsbH29b0N4NPZg5wXoBJ1jWFJHVqiRbKikuG6fbtMkFhaDCAghBQdyS4mFksUDOynKYlUq+cMH1+1Ctdr2gfQ37OTTIiSYYY42PoFZDCAoCgmwTKpDZDCouAZUU25qVm+BvbzEosEl1aPImQaOBKinJob8CSRLkS5ccF7TPzARKTZDPn4d8/rxjD+aoKMfruvFxDaqTJCdYxhgA25ANQasFQkNAsmyr3RYVgYqKeSIMVi8ElQqq2FioYmMB2DrIkiw7LGiv9GAuKoJ88SLkixeBCkNAhYiI8vmX7T2Yg7wzKxc3EYObiBm7GiottX2hFRXztVvmdUQEMhodrulKGRmg/HyX+wvBwVAlxEPdqRP0I4bXKeFyEzFjzK0EvR6CXg8xIsI28UVRMai4yNZZisffsnomCAKEkBCIISFAh/IF7eWiIsd1dTMyIV++DDIaYTUaYT1yFH6jR9VbnJxgGWM1Img0EEJDbE3JkgQqLlb+eIYp5k1iQADENq2hbtNa2UYmE6TMC5AzMmzzxdfjUNJaJ9i///4bn376KU6dOoWCgkqWRhIEbNiwodbBMcYaNkGlUjpKEZEt0RYV2SYR4WTLGgBBp4O6eQrQPMUtvYhrolYJdt26dRg5cuRV13ptyqvFMNbUCIIAISAACAjgZMsYgFqN6n/++ecBAKtWrUJhYSFkWXb55wuLrTPG3E8QBIgBAVBFR0OVkgIxNhZCYKBtuUbGmoha1WAPHDiA0aNH47777nN3PIyxRsahZivLtpptQQGouATcQYo1ZrVKsDqdDvHx8e6OhTHWyAmiaKvJBgbaZpIqLLT9mUzeDo0xt6tVgu3Tpw+2b9/u7lgYY02IoFZDCA0FQkNtS+8VFEIuLAD40hJrJGp1QeTf//43jhw5gnnz5kHmzguMsToSdDqIkRFQJSfb5pgNDAS4kyTzcbWqwX722WcYOHAgXnrpJaxatQo9evRASEiI036CIOCDDz6oc5CMNSQky5AvXQIEAWJUFPeWdyNBEAB/f6j8/cvH2BYU2OZGZszH1GqqRLGaPQEFQfCJnsQ8VSKrLuvxEzD98QeobOy3EBwC3fVDbOPsmMeQxVLWhFzIUzWyWvOJ1XROnz5dq8AY82VSVhZKf/nFYUwnGQ0oXbMG/vfcAzEywovRNW6CRgMhPAxieBiotBRyQQGosJDH17IGrVYJNjk52d1xMNbgWfbscf2FLkmw7N0L3Y031H9QTZCg10Ol14MiImw9kI0FIFOpt8NizEm1Euy5c+cQHByM0NBQ5XZ1NWvWrFaBMdbQyDm5lZflVl7GPEMQRQjBwUBwMMhkgmw02prueYEw1kBUK8GmpKTgiSeewMKFC5Xb1enYIQgCrK5Wp2fMBwnBwcClS5WXMa8RdDqooqJA4eG2Wm1BAY+tZV5XrQQ7YcIE9OrVy+E295xkTY2ma1dIJ09WUtalnqNhrggqFYSQECAkxHat1mi0XavlWi3zAl5wHdyLmFWfZe9emLb8CVgttg1aLXSDBkLTqZN3A6shslhg2bcP1mPHAasVquRkaHr2gBgY6O3Q3I4kydYD2WjkHshNnE/0Iq6OTz/9FEePHsX8+fM9dQrG6p2mWzeo27eHdO4cIAhQNWsGQav1dlg1QpKE0u9/gJSRoWyTc3JgPXYMfnfdCTEoyIvRuZ+gUkEIDYEYGgIqKSmr1RaB50FmnlbrGuyRI0fwyy+/oKioyKlMlmV89dVXOHPmDIqLi+scpKdxDZY1JZYjR2Fat85lmaZrV+iuH1LPEdU/W622ALLBWN4awRo9n6jB/v777xgxYgQsFguICIIgoGKett8ePXp0bQ7PGPMg6dSpSsusp041iQRrq9WGQgwNtdVqDUbburVcq2VuVKu5iN98801YrVa89NJL+P777xEaGoqpU6di06ZN+OSTT9C8eXMMHjwY3377rbvjZYzVVVUzsQlNb71Wwc8PqtgYqJKbQQwPB9Qeu3LGmphavZN27tyJ1NRUPP300wCAiRMn4vDhwxg0aBAAoH///ujQoQOWLVuGadOmuS9axlidqdu0hvXo0UrLmipBrYYQFgYxLAxyURHIYASVNPxLXKzhqtXPVaPRiFatWim3u3XrhmPHjim3k5OTMXLkSKxcubLuETLG3ErVooXLRCpGREDbq6cXImp4xIAAqOLjoEpKso1x5mGJrBZqVYONiorCpQoD7tu0aYP09HQUFxfD398fABAXF4e1a9e6J0rGmNsIggDdsGFQtzoOS1qabZhOSgo0nTr6XI9oTxO02vIJLIxGyAYDr1fLqq1WCbZjx45Yt24dLly4gLi4OHTt2hWCIODnn3/G7bffDgA4fvw49Hq9W4NljLmHIAhQt20Ddds23g7FJwgqFYSwMAihobaZovLzQWYeU8uqVqsm4scffxx5eXlo164dli9fDj8/P1x//fV49NFH8dZbb2HWrFlYt24devbk5ibGWOMhCALEoCCokpJsC8P7+Xs7JNaA1SrB3nLLLfjkk08QHx+PrKwsAMDLL78Ms9mMWbNm4c0334Rer8dzzz3n1mAZY6yhEPz9bddpExMhBAUD4Ou0zJFbp0o8d+4cvvvuO0iShFGjRqF1a9/okcgTTTDG6oqs1rLrtEZA5uu0DVF9TzTBcxGDEyxjzH1Ilm2zROUbeJaoBqbBz+RkNBpx7tw5dCqb3PzFF1+scv/6biZ+/PHH8eabb+L06dNISUmp13MzxpggihBCQiCGhNjG0+bng0p5QfimqNo1WCLC/PnzsXDhQowcORKrV68GAIiiqEyNWHEJOyJCYmJijRZnr6sdO3bguuuuAxHVKMFyDZYx5klUWgo538DTMXpZfddgq93Jadq0aZg3bx5kWUbXrl0dysaNG4fVq1fj/vvvh06nAwA88cQT9ZpczWYzHnjgAQQ1spVAGGO+T9DrbdMxNkuyrVfLE1c0CdVKsH/88QeWL1+O1q1bIy0tDf/6178cylNSUnDXXXdh+fLl2Lt3L5o1a4ZFixZh586dHgnalVdffRUFBQV46KGH6u2cjDFWE4JGA1VkJFTJybZ5j1Uqb4fEPKhaCXbFihUQBAHfffcdEhMTq9y3TZs2+OqrryDLMpYsWeKWIK/myJEjmD9/PpYuXYqAgIB6OSdjjNWWoFJBDAuzJdroaJ5Bq5GqVoLdtm0brr32WnTo0KFaB+3duzeuu+46bN++vU7BVYcsy3jwwQcxZswYpKamevx8jDHmLg4TV8TH88QVjUy1EmxmZibat29fowN36dIFGRkZtQqqJt577z0cPHgQixcvrvZ9TCYTjEajwx9jjHmT4OfHE1c0MtUapqNSqaDRaFyWWSwWh97DFYlVrTvpBunp6ZgzZw5ee+01xMXFVft+CxYswAsvvODByBhjrHYEnQ6q6ChQeBhPXOHjqpUBExIScPDgQZdlKpXKZSI9cODAVa/X1gURYfr06ejcuTMefPDBGt137ty5MBgMyl96erqHomSMsdoR1GqI4eG2heAjIwG160oOa7iqVYO9+eab8e677+Lvv/+u1gT+//zzD7Zt24bp06fXOcDKfP/991i7di1+/fVXZGZmKtvtzb0XLlyARqNBQkKC0311Op0ynIgxxhoynrjCd1UrwU6fPh3Lli3DnXfeiQ0bNiA5ObnSfdPT03HHHXdArVZjxowZbgv0Svv37weASjs29e3bFyEhIcjPz/dYDIwxVp/EgAAgIIAnrvAR1Uqw7du3x/z58zFnzhx06dIFjz/+OO6++260bdtW2efcuXNYvXo1Xn31VRiNRrz00kto166dxwK/55570KtXL6ftq1evxueff44VK1a4rL0yxpivs01coQdZLJANBpDRCPC08g1OjSb7X7RoEZ5++mlYLLYJrP38/BAWFoaioiIYDAZlv3nz5uHZZ591f7TVMG/ePLzwwgs8VSJjrMkgSSrrEGUAJAkkyaDSUgg6LQR1jaecb7Qa9GT/s2bNwq233oolS5bg119/RVpaGoqLiwEAkZGRGDp0KJ544gl06dKl9tEzxhirEUGlghAWBiE0FOatf8K8c5et+Vithrp5CjQ9unOi9YI6LVdXWlqK3Nxc+Pv7IzQ01I1h1S+uwTLGGgPzrt0wb90KwLY+LcwWkCRBFR8H3ZDBXo2tIWjQNdgr6fV6xMfH1+UQjDHG3ICsVlh271ZuC2o1oFYDkgQ5OxtyTi7EiHAvRtj0eHYmCMYYY/WCDAaXw3cElQqCnx8gihACAr0QWdPFjfKMMdYICH5+tmXwKrnqJ4aEQBUbAzKHQc7PBxUUgof4eBbXYBljrBEQ/P2hbtmikrIAqFo0t/1fq4UqOtq2Ni3PeexRnGAZY6yR0N1wA8ToaIdtgp8f9KNGQLhi7VlBo4EqOooXgfcgbiJuQshqhZyVBYgqiLExEDy8GANjrH4J/v7wu+duSGfPQr50GUJgINStWkKoZLEWoHwReAoLA+XnQzYaAVmux6gbL06wTYTl4EGYt/wJKi0BAAhBQdDdcAPUzVO8GxhjzK0EQYA6JQWo5kQ7yv1UKggRERBCQ0GGskkreBWfOuEqTBNgPXsOpvW/K8kVAKigAKVr1kDOzfNiZIyxhkZQqSCGh9lW8YmIAK5oWmbVxwm2CbDs2eO6QJJgKVs0gTHGKhJEEWJoKFTNmkGMiLSNqWU1ws9YEyDn5taqjDHGBFGEEBoCISQYVFAAOS8fsFq8HZZP4BpsEyBWMY1lVWWMMWYnCALE4GComiVBjI6GoNV6O6QGjxNsE6Dp3s11gShCwwszMMZqQBAEiEFBUCUlQYyNhaDTeTukBosTbBOgbt7cNtF3hV+cgp8f9MOGQYyM8FpcjDHfJgYEQJWYCFVcHAS93tvhNDh8DbaJ0HTrBnWHDpAyMgBRhCox0WngOWOM1Ybg7w+Vvz+opARyXj6opNjbITUInGCbEEGrhbp5c2+HwRhrpAQ/P6j8/EClpbb5jouKvB2SV3GCZYwx5laCXg9VbCzIZLLVaIsKvR2SV3CCZYwx5hGCTtekV/DhTk6MMcY8ymEFn+Cms4IPJ1jGGGP1QtBooIqKsk3DGBLa6Ffw4SZixhhj9UpQqyFERkAICwUZDGULCzS+FXw4wTLGGPMKQaWCEB4OISSkUa7gwwmWMcaYV9kSbRiE0BCQ0Qg5Px+QfD/RcoJljDHWINgWFgi11WjtidZq9XZYtcYJljHGWIMiCAKEkBAIwcGgwkLbCj4Ws7fDqjFOsIwxxhokQRAgBAVBDAqCXFgIyssDmX0n0XKCZYwx1uCJgYFAYCDkoiJQXj7IVOrtkK6KEyxjjDGfIQYEAAEBoOJi2+xQJSXeDqlSnGAZY4z5HF9YwYcTLGOMMZ+lrOBjMkHOy2tQK/hwgmWMMebzbAsLlK3gk58PKiyCtxcW4ATLGGOs0RB0OqhiYkDhFluN1osr+PBk/4wxxhodQaMpX8EnyDsr+HANljHGWKNlS7RRoLDQej8312AZY4w1eoJGU+/n5ATLGGOMeQAnWMYYY8wDOMEyxhhjHsAJljHGGPMATrCMMcaYB3CCZYwxxjyAEyxjjDHmAZxgGWOMMQ/gBMsYY4x5ACdYxhhjzAM4wTLGGGMewAmWMcZYo0JEILMZRLweLGOMMVZnJEmw7NwFy759oJISCEFB0PTsCW33bl6JhxMsY4yxRsG0YSOshw4pt6mgAObNmwGTCdo+19Z7PNxEzBhjzOfJRqNDcq3I8vc/IIulniNqBAn2zJkzmDBhAqKjo6HRaNCmTRu8+OKLKC0t9XZojLEy0vkMlK79GcWfrUbpr79CysrydkiskZEyMystI7MJ8uXL9RiNjU83EZ86dQrXXnstSkpK8MgjjyAsLAxr167F888/j82bN2P9+vVQqVTeDpOxJs1y4ABMv29QbsvZ2bCmHYN+WCrUbdp4MTLWmAh+flWX6/X1FEk5n67BzpkzB5cvX8a6devw6quvYs6cOdiyZQvGjx+PTZs24bvvvvN2iIw1aWSxwPS/LS4KCKY//geSpPoPijVKqqQkCEFBLsvEuDiIYWH1HJEPJ1ir1Yq1a9fixhtvRP/+/ZXtgiBg+vTpAID//e9/3gqPMQZASk8HzGaXZVRYCPlidj1HxBorQRShHz7MqaYqBAVBP/QWr8Tks03EOTk50Gq1uOaaayrdx2q11mNEjLEaE7wdAGtMVHFx8L9/CqxpaZANRogREVC3bgVB7Z1U57MJNiYmBvn5+S7L1q5dCwDo1atXPUbEGLuSKikJglYHMpucyoTAIIgxMV6IijVmglYLTefO3g4DgA83EVfmp59+wmuvvYZWrVph/PjxLvcxmUwwGo0Of4wx9xM0GmgHD3IuEEXorh8MQWx0X0GMKXy2Bnslk8mEl156Ca+88gpiY2Px008/QafTudx3wYIFeOGFF+o5QsaaJk3HDhDDw2DZtx9yfj7E8HBounWDKjrK26Ex5lECeXuyRjfYu3cv7rvvPhw8eBA33ngjPvroIyQkJFS6v8lkgslU3mRlNBqRlJQEg8GA4ODg+giZMcaYDzIajQgJCalWvvD5Guznn3+OiRMnIigoCKtWrcJ9990HQai654ROp6u0dssYY4y5g08n2PXr1+Pee+9Fnz598M033yA+Pt7bITHGGGMAfLiJWJIkdO3aFVarFTt37qxT025NqvyMMcaaribRRLxz504cOnQII0aMwOrVq13u07t3b/Ts2bOeI2OMMcZ8OMHu27cPgG3Mq33c65VeeuklTrCMMca8wmebiN2Jm4gZY4xVR03yBY/yZowxxjyAEyxjjDHmAZxgGWOMMQ/gBMsYY4x5ACdYxhhjzAM4wTLGGGMewAmWMcYY8wBOsIwxxpgHcIJljDHGPIATLGOMMeYBnGAZY4wxD+AEyxhjjHkAJ1jGGGPMAzjBMsYYYx7ACZYxxhjzAE6wjDHGmAdwgmWMMcY8gBMsY4wx5gGcYBljjDEP4ATLGGOMeQAnWMYYY8wDOMEyxhhjHsAJljHGGPMATrCMMcaYB3CCZYwxxjyAEyxjjDHmAZxgGWOMMQ/gBMsYY4x5ACdYxhhjzAM4wTLGGGMewAmWMcYY8wBOsIwxxpgHcIJljDHGPIATLGOMMeYBnGAZY4wxD+AEyxhjjHkAJ1jGGGPMAzjBMsYYYx7ACZYxxhjzAE6wjDHGmAdwgmWMMcY8gBMsY4wx5gGcYBljjDEP4ATLGGOMeQAnWMYYY8wDOMEyxhhjHsAJljHGGPMATrDMLUiWIRsMIJPJ26EwxliD4PMJNj09HZMmTUJMTAw0Gg2SkpLw6KOPIi8vz9uhNRmW/ftR/MFKFK/8EEXvLUPp2p9BxcXeDosxxrxK7e0A6sJgMKBfv35IT0/HlClT0LlzZ2zZsgVLlizBjh07sG3bNqjVPv0QGzzLwYMwbdhYvkGWYT1+HHJ+PvzuvQeCIHgvOMYY8yKfrsEuWLAA6enpWLx4MT744AM89thj+OabbzB9+nTs2rULK1as8HaIjRoRwbxzl8sy+dIlSKdO13NEjDHWcPh0gv36668REhKCadOmKdsEQcDTTz8NAPjmm2+8FVrTYDaDDIZKi+WLF+sxGMYYa1h8NsGWlJTg1KlT6NGjB3Q6nUNZQkICkpKScPDgQS9F10So1YBGU2mxEBhQj8EwxljD4rMJ9tKlSwCAmJgYl+Xx8fHIzs6uz5CaHEGlgqZjR9eFGg3UbdvWb0CMMdaA+GwPIFmWAcCp9mqn1+tBRC7LTCYTTBWGkxiNRvcH2ERo+/eDnJcH6exZZZug1UE3YjiESl4bxhhrCnw2wdbFggUL8MILL3g7jEZB0GjgN3YMpAsXIF24AMHPD+pWrSBU0XTMGGNNgc82EduHf1RWS61sOwDMnTsXBoNB+UtPT/dIjE2JKi4O2h49oGnfnpMrY4zBh2uwkZGRAGxjYV0xGAyIiopyWabT6SptWmaMMcbcwWdrsAEBAUhOTsbu3bshSZJDmdFoxOHDh9GhQwcvRccYY6yp89kECwBjx45FRkYGVq5c6bB94cKFsFgsGDdunJciY4wx1tQJVNXFygYuNzcXnTp1QnZ2NiZPnoz27dtj586d+PLLL9GtWzfs2LEDWq32qscxGo0ICQmBwWBAcHBwPUTOGGPMF9UkX/h0DTY8PBx//vkn7rjjDvzwww946qmnsGXLFkydOhXr16+vVnJljDHGPMGna7DuwjVYxhhj1dFkarCMMcZYQ8UJljHGGPMATrCMMcaYB3CCZYwxxjyAEyxjjDHmAT47VaI72TtS86o6jDHGqmLPE9UZgMMJFkBBQQEAICkpycuRMMYY8wUFBQUICQmpch8eBwvb2rKZmZkICgpSVulpaIxGI5KSkpCens5jdT2Mn+v6w891/eHn2j2ICAUFBYiPj4coVn2VlWuwAERRRGJiorfDqJbg4GD+cNQTfq7rDz/X9Yef67q7Ws3Vjjs5McYYYx7ACZYxxhjzAE6wPkKn0+H555/nheLrAT/X9Yef6/rDz3X9405OjDHGmAdwDZYxxhjzAE6wjDHGmAdwgm2gHn/8cQiCgDNnzjhs37ZtG2655RYEBwdDp9OhQ4cOWLRoESRJ8k6gPoqIsHz5cnTr1g0BAQGIi4vDbbfdhmPHjjnsd+jQIYwbNw7h4eHQaDRo2bIl/u///g+lpaVeitz3pKen44EHHkBKSgq0Wi2aN2+O2bNnO82cxu/t2nvxxReRkpLisqwmz+vatWvRv39/BAQEQK/Xo0ePHvj444+rNWsRc4FYg/PXX3+RIAgEgE6fPq1s37FjB6nVagoKCqK5c+fS66+/Tj169CAANGvWLO8F7IOmT59OAOimm26ihQsX0uzZsykgIICio6PpwoULRER05swZCgoKIo1GQzNnzqRFixbRkCFDCACNHTuWZFn28qNo+C5cuEDx8fGk0+lo6tSp9MYbb9Bdd91FAKhHjx5kMpmIiN/bdZGRkUEJCQmUnJzsVFaT5/Xbb78lABQbG0vz5s2jBQsWUKtWrQgAvf322/X0aBoXTrANjMlkok6dOlFwcLBTgr322mtJEATavXu3sq24uJh69uxJgiDQoUOHvBCx71m3bh0BoAcffNAhSf76668EgF588UUiIrrzzjsJAH3//ffKPlarlUaOHEkA6Ndff63v0H3OjBkzCAB98cUXDttnzZpFAOinn34iIn5v11RhYSHt37+fli1bRs2bNycALhNsdZ9Xi8VC8fHxFBgYSGfPnlX2zcnJocTERPLz86OcnByPP67GhhNsA/Piiy9ScnIyPfHEEw4J9uzZswSAUlNTne7z6aefEgB66aWX6jla3zRixAhSqVROXxiyLNMrr7xCX3/9NZWUlJC/vz+1b9/eqaa6detWAkD3339/fYbtk26++WYCQEaj0WH7mjVrCAAtXLiQ39u18PXXXxMAh78rE2xNntf//e9/BICmTZvmtO/8+fMJAH3yySceeSyNGV+DbUCOHDmC+fPnY+nSpQgICHAoO3ToEACgT58+Tve77rrrAAAHDx70fJA+TpIkbNy4Ef3790d4eDiICHl5eTAajRAEAXPnzsVtt92G06dPo7i4GH369HGan7pXr15QqVT8fFdDmzZtAABHjx512H7kyBEAQKtWrfi9XQv9+vXDmjVrlL9OnTo57VOT59X+L78G7sUJtoGQZRkPPvggxowZg9TUVKfyS5cuAQBiYmKcyuLj4wEA2dnZng2yEbh8+TKKi4vRsWNHrFy5EikpKQgPD0dISAi6deuG9evXA6j6+dbpdIiMjOTnuxpmz56NqKgojB8/HmvWrEFaWho+/PBDvPTSS+jfvz9GjhzJ7+1aiIuLw4gRI5S/iIgIp31q8rzya+AZPNl/A/Hee+/h4MGD+Prrr12Wy7IMAC5nYdHr9Q77sMrZvyQ2bNiA9957DzNnzkS/fv1w+vRpzJ8/H6mpqdiwYYPSa7KyWW/0ej0/39WQkpKCZcuW4fbbb8eoUaOU7dHR0Vi2bBnUajW/tz2kJs8rvwaewQm2AUhPT8ecOXPw2muvIS4uztvhNGoGgwEAkJaWhg8++ABTpkxRyq6//nr07NkTzzzzDObPn++tEBuVVatWYdKkSYiPj8ejjz6K5ORk7NixA0uXLsU111yD33//3dshMuYxnGC9jIgwffp0dO7cGQ8++GCl+9mvA5KL8Wj2bQ11LduGxL7MVGhoKCZOnOhQ1qNHD/Tp0wfbtm2r8vm2b7/aWpBNXVFRER577DHExMTg77//RmxsLADg7rvvxtChQ5Gamoo5c+Zg8uTJAPi97W41+c7g7xfP4G8IL/v++++xdu1aPPvss8jMzMT58+dx/vx5ZRD+hQsXkJGRgcjISADlNbCK7NuioqLqL3AfZX8e4+LioFKpnMoTExNBRFCrbb89XT3fRASDwcDP91UcPXoU+fn5GDNmjJJc7YYOHYrWrVtj9+7d/N72kJo8r/waeAbXYL1s//79AOCyYxMA9O3bFyEhIdizZw8AYOfOnU777NixAwDQoUMHD0XZeMTGxiIuLg7nzp2D2WyGVqt1KD9+/Dj8/f3RrVs36PV6l8/3sWPHYDAY+Pm+CnsNv7KZmCRJgkajUZ5Hfm+7V02e144dOyr73nrrrVXuy2rAC0ODWAVpaWm0Zs0ap7+7776bANCKFSuUCQ26d+9OWq2WDhw4oNzfYrHQwIEDCQDt27fPWw/Dpzz55JMEgP797387bLePzRw3bhwREY0ZM4YA0G+//absI8sy3XvvvQ6TJDDXzGYzRUREUFhYGJ05c8ahzD5r0G233UZE/N6uq0GDBrmcaKK6z6vZbKbo6GiKiIigzMxMZd+CggJq27Yt6XQ6ys7O9vjjaGw4wTZQzz//vNNMTlu3biVRFCksLIzmzJlDr776KvXp04cA0COPPOK9YH1Mfn4+tW7dWpny8NVXX6WpU6eSRqOh6OhoOnnyJBERnThxggICAsjPz49mzpxJr732Gt1yyy0EgIYPH85TJVbDZ599RgAoJCSEZs6cSa+//jqNHz+eRFGkiIgIOn78OBHxe7uuKkuwNXleP//8cwJAiYmJNG/ePHr55Zepffv2BIBee+21enokjQsn2AbKVYIlItq0aRNdf/31FBgYSBqNhtq0aUMLFiwgi8XinUB9VHZ2Nj388MOUkJBAGo2GYmNjadKkSQ7TxBER7d27l0aOHEmhoaGkUqkoJSWFnnrqKSoqKvJS5L5n06ZNNHz4cAoPDyeVSkXx8fE0Y8YMunjxotN+/N6uncoSLFHNntdvv/2W+vTpQ35+fqTT6ahLly60fPly/jFZS7zgOmOMMeYB3IuYMcYY8wBOsIwxxpgHcIJljDHGPIATLGOMMeYBnGAZY4wxD+AEyxhjjHkAJ1jGGGPMAzjBMsYYYx7ACZYxxhjzAE6wjLnZ4cOH8eijj6JNmzYICAiAXq9HixYtMH78eGzevNnlfSZNmgRBECAIAjZs2HDVcwwePFjZ/8yZMw5lJpMJ7777LgYNGoSwsDCo1WpERUXhlltuwZdffulyzU/7sRhj7sMJljE3WrhwIbp27YolS5YgMjIS9957L+6++25ER0fjs88+w5AhQ/DQQw9VuoQbAKxatarKc5w+fRp//PGHy7L8/Hz069cPjzzyCE6ePIkRI0bgoYceQq9evbBx40bcdddduOuuu6o8P2PMTbw8FzJjjcYbb7xBACglJYV27NjhVH7gwAFlFZ958+Y5lE2cOFFZdcbf35+MRmOl55k3bx4BIH9/f6cFIR566CECQFOmTCGr1epwv1OnTlFycjIBoPfff9+hDADx1wFj7sU1WMbc4OTJk5g7dy4CAwOxYcMGXHPNNU77dOrUCRs2bICfnx8WLlyI/Px8p33uuusuFBcX49tvv3V5HiLCxx9/jGbNmqFnz55O5d999x0AW01apVI5lDVv3hxvvfUWAOCrr76q6UNkjNUQJ1jG3OA///kPzGYzHnvsMbRo0aLS/ZKSkvDss89i5MiROHfunFP56NGjERwcXGkz8datW3Hq1ClMmDABouj88S0sLAQAFBUVubz/DTfcgGeeeQYjR46szsNy6cCBA7j33nuRmJgInU6HhIQEjBs3Dlu3bnXaNzc3F08++SRatWoFnU6H+Ph43HjjjS5/QMiyjE8//RQDBgxASEgI/Pz80KlTJ8yfP9/p8divWRcWFuKHH35A//79ERISovxouXz5Mh5//HGkpKRAq9UiMTER06ZNw6VLl2r9uBmrMW9XoRlrDNq2bUsA6PDhw7W6v72JeNOmTfTggw+6XAuYiOj+++8nAHTs2DEaNGiQ03433ngjAaBu3brRxo0bq72WKqrZRHzw4EEKCQkhtVpNo0ePphkzZlBqaiqpVCoSRZH+/PNPZd/MzExq2bIlAaCuXbvStGnT6N5776WgoCACQK+++qqyryzLNGHCBAJAYWFhdN9999HUqVMd7p+Tk+P0fC1cuJAA0LXXXktTp06l4uJiOnv2rNIUPmTIEHrooYfouuuuIwCUnJxMWVlZ1XpOGKsrTrCM1VFxcTEBID8/P5IkqVbHqJhgt27dSgDopZdectinqKiIgoKCqG/fvkRELhPs8ePHqUWLFkrCDA0NpVGjRtHLL79M27dvrzThVjfBPvbYYwSA3nvvPYftn3/+OQGgCRMmKNvuvPNOAkDPPPOMw4LdZ86cocjISNJqtcqi6ytWrCAA1KtXL7p8+bKyr8ViUX5wVDy2/fny8/Ojn376ySGWG264gQRBcNr+0UcfEQC64447rvo4GXMHTrCM1dH58+cJAMXHxzuVWSwWJXld+ff8888r+1VMsLIsU6tWrah169YOienTTz8lALRs2TIicp1giYhKS0vpo48+oltuuYUCAgIczhkdHU3//ve/qaSkxOE+1U2ws2bNIgD0+OOPOyRri8VCa9asob/++ouIiC5dukQqlYri4uLIZDI5HefDDz+kiRMn0t69e4mIqHPnzgSA9u/f77RvaWkpxcXFkUqlUpKv/fl65JFHHPbdt28fAaB77rnHZfw33XQTiaJI2dnZV32sjNWV2t1Nzow1Nf7+/gBs1z2JyGE8qSAImD17tsP+Z86cqbQTk/0+EyZMwHPPPYft27ejb9++AGzDd/R6Pe64444q49HpdJg4cSImTpwIi8WCAwcO4M8//8TPP/+M33//HXPmzMGGDRvw66+/OnWEupr7778fK1aswOLFi/HDDz9g1KhR6NOnDwYPHowRI0Yo++3evRuSJGHo0KHQarVOx5k0aRImTZoEwHbd+MCBA2jZsiU6d+7s8vGkpqZi5cqV2LlzJ1JTU5Wy0aNHO+xrvw6ckZGBJ554wulYubm5kGUZe/bswc0331yjx85YTXGCZayOQkNDERkZicuXL+Ps2bNISUlRylQqFV5//XWH/T///PMqEywAjB8/Hs899xxWrVqFvn374vz58/j9999x5513IjQ01OV9rFYrrFYrNBqNkjg1Gg169OiBHj164NFHH8WePXtw4403Yv369Vi3bh2GDx9eo8faoUMH7N+/H0uWLMEvv/yCt956S+mZPHToULzzzjto1aoVLl++DACIjo6+6jHtHZNiYmIq3Sc2NhaALUFW1KxZM4fb9vI//vij0rHCAHD+/PmrxsVYXXEvYsbqSBAEDB06FACwevXqq+6/adOmq+7TvHlzDBo0CF9++SVKS0vx6aefgogwceLESu/z1ltvwc/PD++8806l+3Tv3h333XcfAFtv4NpITk7Ga6+9hkOHDuHixYv4+uuvMWzYMKxbtw433HADiouLERAQAADIyclxeYzi4mJkZWWhuLhY+cGQnZ1d6TntZWFhYQ7bdTqdw217+RtvvAGyXQJz+TdlypRaPXbGaoITLGNu8MQTT0AQBLz66qs4e/ZspfsdO3YMn3zySbWOOXHiRBgMBvz4449YtWoV4uLicOONN1a6f5cuXQAA3377bZUzNRUUFAAAwsPDqxVHRYsWLcKsWbOU40dHR+O2227D2rVrMWjQIJw7dw779+9H165dAQAbNmyA1Wp1Os7cuXMRFxeH3bt3IzAwEB07dsSJEyfw999/O+1bWFiItWvXQhRFl+OLK+rVqxcAYMuWLS7LP/zwQzz88MNVJnPG3MZrV38Za2ReeOEFAkAtWrSg3bt3O5Xv3LmTUlJSrtrJyc5oNJK/v7/SK/jJJ590ON6VnZwsFgt17dqVAND9999P+fn5TjH8/PPPpNfrKSAgwGG4CqrZyenuu+8mAPTpp586bC8pKaEePXoQADp58qRDfM8++6xDZ62DBw9SYGAgxcbGKh2l3nvvPQJA3bt3p8zMTGXf0tJS5bmp2HHJvu3KDl6yLCvPwZUxHjt2jEJDQ506jzHmKZxgGXMTWZbppZdeIpVKRQCob9++NHXqVJo6daoyDlMQBJo9e3a1EiwR0fjx45Xkd/DgQYcyV72IT506Re3atSMAFBQURKNGjaLp06fT/fffT927dycAFBgYSD///LPDsaqbYPfs2UP+/v4kCAINGTKEHnnkEbr//vupefPmBIDuvPNOZd+0tDSKjo5WnotHHnmExo4dS1qtlgRBoPXr1yv7SpJEd911lzIOduLEifTwww9Tq1atCAB16NCBLl265PR8uRorvHfvXgoLCyMANGjQIJoxYwbdeeed5O/vT1qtljZv3nzVx8mYO3CCZczNDh06RNOmTaPWrVuTn58f6XQ6atGiBd1zzz20bds2kmWZEhISqpVg169fTwCoZ8+eTuepapjOu+++S0OGDKHIyEhSqVQUFRVFPXr0oKeeeorS09OdjlXdBEtEdPjwYbr//vspJSWFtFothYeHU79+/Wjp0qVO42zPnz9P06ZNo2bNmpFaraaQkBAaOnQo/fHHH07HlSSJVq5cSX379qXAwEDS6XTUvn17ev75553mZq4qwRLZxtref//9lJiYSBqNhhITE2ncuHH0zz//VOsxMuYOApGLtasYY4wxVifcyYkxxhjzAE6wjDHGmAdwgmWMMcY8gBMsY4wx5gGcYBljjDEP4ATLGGOMeQAnWMYYY8wDOMEyxhhjHsAJljHGGPMATrCMMcaYB3CCZYwxxjyAEyxjjDHmAZxgGWOMMQ/4/3gfV3ZRHamBAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gain * age: R2: 0.009, p = 0.637 \n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
],
+ "source": [
+ "analysis = GainAnalysis(gain_df, paradigms_palette, os.path.join(get_path('figures')))\n",
+ "analysis.plot_histogram()\n",
+ "for x_var in ['gmsi', 'age']:\n",
+ " r2_value, p_value, sorted_df = analysis.compute_r2(x_var)\n",
+ " analysis.plot_correlation(x_var, 'gain', sorted_df, r2_value, p_value)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "### Some stuff we tried"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-14T17:11:57.044792Z",
"start_time": "2023-08-14T17:11:57.042692Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "audiograms_3AFC['pred'] = audiograms_3AFC['pred'].apply(lambda x: translate_conditions(x) if x in preds else x)"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "Compare thresholds"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "Compare thresholds"
+ ]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.089258Z",
+ "start_time": "2023-08-14T17:11:57.047331Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -814,12 +1822,12 @@
"paradigm: F(3, 81) = 102.98, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "paradigm / 3AFC vs Bayesian: T(27) = -13.24, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / 3AFC vs Cluster: T(27) = -9.71, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / 3AFC vs Continuous: T(27) = -13.73, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / Bayesian vs Cluster: T(27) = 6.18, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / Bayesian vs Continuous: T(27) = 2.65, p = 0.013 (fdr_bh: 0.013) *\n",
- "paradigm / Cluster vs Continuous: T(27) = -2.78, p = 0.010 (fdr_bh: 0.012) *\n",
+ "paradigm / 3AFC vs Bayesian: T(27) = -13.24, p = <0.001 (fdr_bh: <0.001), d = -1.79 *\n",
+ "paradigm / 3AFC vs Cluster: T(27) = -9.71, p = <0.001 (fdr_bh: <0.001), d = -1.19 *\n",
+ "paradigm / 3AFC vs Continuous: T(27) = -13.73, p = <0.001 (fdr_bh: <0.001), d = -1.52 *\n",
+ "paradigm / Bayesian vs Cluster: T(27) = 6.18, p = <0.001 (fdr_bh: <0.001), d = 0.53 *\n",
+ "paradigm / Bayesian vs Continuous: T(27) = 2.65, p = 0.013 (fdr_bh: 0.013), d = 0.25 *\n",
+ "paradigm / Cluster vs Continuous: T(27) = -2.78, p = 0.010 (fdr_bh: 0.012), d = -0.29 *\n",
"\n"
]
}
@@ -842,31 +1850,32 @@
" within=['paradigm'],\n",
" subject='participant',\n",
" alternative='two-sided',\n",
+ " effsize='cohen',\n",
" padjust=StatsParams.mult_comp)\n",
"\n",
"# Display post-hoc results\n",
"stats.print_paired_ttest_posthocs(paradigms_posthocs)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.089258Z",
- "start_time": "2023-08-14T17:11:57.047331Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "Continuous"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "Continuous"
+ ]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.130029Z",
+ "start_time": "2023-08-14T17:11:57.092653Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -876,12 +1885,12 @@
"pred: F(3, 81) = 3.25, p = 0.026 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "pred / F vs FT: T(27) = 2.54, p = 0.017 (fdr_bh: 0.084)\n",
- "pred / F vs R: T(27) = -0.09, p = 0.930 (fdr_bh: 0.930)\n",
- "pred / F vs T: T(27) = 1.48, p = 0.150 (fdr_bh: 0.215)\n",
- "pred / FT vs R: T(27) = -2.32, p = 0.028 (fdr_bh: 0.084)\n",
- "pred / FT vs T: T(27) = -1.38, p = 0.179 (fdr_bh: 0.215)\n",
- "pred / R vs T: T(27) = 1.49, p = 0.148 (fdr_bh: 0.215)\n",
+ "pred / F vs FT: T(27) = 2.54, p = 0.017 (fdr_bh: 0.084), d = 0.20\n",
+ "pred / F vs R: T(27) = -0.09, p = 0.930 (fdr_bh: 0.930), d = -0.01\n",
+ "pred / F vs T: T(27) = 1.48, p = 0.150 (fdr_bh: 0.215), d = 0.12\n",
+ "pred / FT vs R: T(27) = -2.32, p = 0.028 (fdr_bh: 0.084), d = -0.22\n",
+ "pred / FT vs T: T(27) = -1.38, p = 0.179 (fdr_bh: 0.215), d = -0.09\n",
+ "pred / R vs T: T(27) = 1.49, p = 0.148 (fdr_bh: 0.215), d = 0.13\n",
"\n"
]
}
@@ -904,31 +1913,32 @@
" within=['pred'],\n",
" subject='participant',\n",
" alternative='two-sided',\n",
+ " effsize='cohen',\n",
" padjust=StatsParams.mult_comp)\n",
"\n",
"# Display post-hoc results\n",
"stats.print_paired_ttest_posthocs(continuous_posthocs)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.130029Z",
- "start_time": "2023-08-14T17:11:57.092653Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "Cluster"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "Cluster"
+ ]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.175448Z",
+ "start_time": "2023-08-14T17:11:57.132867Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -938,12 +1948,12 @@
"pred: F(3, 81) = 9.40, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "pred / F vs FT: T(27) = 0.63, p = 0.536 (fdr_bh: 0.643)\n",
- "pred / F vs R: T(27) = -4.39, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / F vs T: T(27) = -0.13, p = 0.899 (fdr_bh: 0.899)\n",
- "pred / FT vs R: T(27) = -4.46, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / FT vs T: T(27) = -0.89, p = 0.382 (fdr_bh: 0.572)\n",
- "pred / R vs T: T(27) = 3.77, p = <0.001 (fdr_bh: 0.002) *\n",
+ "pred / F vs FT: T(27) = 0.63, p = 0.536 (fdr_bh: 0.643), d = 0.04\n",
+ "pred / F vs R: T(27) = -4.39, p = <0.001 (fdr_bh: <0.001), d = -0.26 *\n",
+ "pred / F vs T: T(27) = -0.13, p = 0.899 (fdr_bh: 0.899), d = -0.01\n",
+ "pred / FT vs R: T(27) = -4.46, p = <0.001 (fdr_bh: <0.001), d = -0.29 *\n",
+ "pred / FT vs T: T(27) = -0.89, p = 0.382 (fdr_bh: 0.572), d = -0.05\n",
+ "pred / R vs T: T(27) = 3.77, p = <0.001 (fdr_bh: 0.002), d = 0.25 *\n",
"\n"
]
}
@@ -966,27 +1976,30 @@
" within=['pred'],\n",
" subject='participant',\n",
" alternative='two-sided',\n",
+ " effsize='cohen',\n",
" padjust=StatsParams.mult_comp)\n",
"\n",
"# Display post-hoc results\n",
"stats.print_paired_ttest_posthocs(cluster_posthocs)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.175448Z",
- "start_time": "2023-08-14T17:11:57.132867Z"
- }
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.330800Z",
+ "start_time": "2023-08-14T17:11:57.178872Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": "<Figure size 800x533 with 2 Axes>",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x533 with 2 Axes>"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -996,27 +2009,27 @@
"from funcs.plots import Fig4p50\n",
"\n",
"fig = Fig4p50().p50_barplot('mean_threshold', paradigms_data, continuous_data, cluster_data, paradigms_posthocs, continuous_posthocs, cluster_posthocs)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.330800Z",
- "start_time": "2023-08-14T17:11:57.178872Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Cluster vs. Continuous 2-way ANOVAs"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Cluster vs. Continuous 2-way ANOVAs"
+ ]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.363110Z",
+ "start_time": "2023-08-14T17:11:57.330583Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -1040,18 +2053,18 @@
" subject=\"participant\") # , detailed=True)\n",
"\n",
"stats.print_2way_anova(stats, aov_threshold, \"mean thresholds\", ['paradigm', 'pred'])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.363110Z",
- "start_time": "2023-08-14T17:11:57.330583Z"
- }
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-14T17:11:57.407899Z",
+ "start_time": "2023-08-14T17:11:57.360775Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -1075,14 +2088,7 @@
" subject=\"participant\") # , detailed=True)\n",
"\n",
"stats.print_2way_anova(stats, aov_threshold, \"mean thresholds\", ['time_pred', 'freq_pred'])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-14T17:11:57.407899Z",
- "start_time": "2023-08-14T17:11:57.360775Z"
- }
- }
+ ]
}
],
"metadata": {
@@ -1094,14 +2100,14 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.10.15"
}
},
"nbformat": 4,
diff --git a/scripts/analysis/4_p50_analysis.ipynb b/scripts/analysis/4_p50_analysis.ipynb
index d70d12c7ca4a79538d016be3dffd09480703e91e..511cc0fe6d0c9d8b0704b89f2a8b898147e852de 100644
--- a/scripts/analysis/4_p50_analysis.ipynb
+++ b/scripts/analysis/4_p50_analysis.ipynb
@@ -2,42 +2,43 @@
"cells": [
{
"cell_type": "markdown",
- "source": [
- "# p50 analysis"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "# p50 analysis"
+ ]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
- "import os.path as op\n",
- "\n",
"from funcs.p50_analysis import *\n",
"from funcs.plots import P50GlobalFigure, Fig4p50\n",
"\n",
"from scripts.utils import *\n",
"from scripts.stats import *"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Set paths and parameters"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Set paths and parameters"
+ ]
},
{
"cell_type": "code",
"execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# Figure output path\n",
@@ -45,51 +46,53 @@
"fig_dir = get_path(\"figures\")\n",
"\n",
"no_init = False"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Load data"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Load data"
+ ]
},
{
"cell_type": "code",
"execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"random_audiograms = pd.read_pickle(op.join(data_path, f\"global_random_audiograms_{'noInit' if no_init else 'withInit'}.pkl\"))\n",
"sigmoid_data = pd.read_pickle(op.join(data_path, f\"sigmoid_data_{'noInit' if no_init else 'withInit'}.pkl\"))\n",
"\n",
"process = P50DataAnalyzer(sigmoid_data)"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Plot mean sigmoids & barplot of p50 values"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Plot mean sigmoids & barplot of p50 values"
+ ]
},
{
"cell_type": "code",
"execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": "<Figure size 1600x800 with 5 Axes>",
- "image/png": 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"
+ "image/png": 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7d7F48WJ4e3tj48aNePHFF/M9h1arLfC5dTpdqb6Wxx9/HJMmTZI/06pUKvzrX/8CAFy8eDHf9unp6VizZg3CwsLktvJ4D5I73FTuMERAzjBUuUNG5Wa9desWFi1aBJ1OhzVr1uR5j+Ll5YUff/wRvr6+2LhxI06cOFHo9+Xzzz+HzWbDhAkT8gyfKUkSBg4ciFdffbXQff9u9erViIuLQ7NmzTBr1iz5fSEANGrUCF9//XWxjpP7fQsJCYFer5fb/f398fHHH2P8+PEFDjX9dykpKfDw8EC/fv3QuXPnPOs6deqEoKCgAt/DAcC8efPQvHlz+bGPjw+ef/55AH/9DEwmE+bOnQsgZ57O++dOcXZ2xn//+184OzvLw46ZzWa89957cHBwwA8//IAaNWrI2zdu3BhLly4FAHz44YcPfG1EVD5YkCAqZQ0bNkR2djYSEhLw008/wc3NDZ988gnGjh0rb2Oz2bBw4UJIkoRnn302z/6PP/44dDod1q5dW2gnXrt2bQwZMqTAr969e8vb5Y4/arPZyuCVEhHRw3J0dMz3d3zQoEGIjIyEk5MTNmzYgLZt28on2UePHo1Tp05h5MiR8jFsNhtOnjyJjz76CEDOCYtcrVu3RsuWLbF//35cvnxZbl+1ahUsFov8ARAAfvvtNwDA4MGDC8yaW/DI/fCX+wF05syZGDduHHbu3Ins7GwAwIgRI/DVV1/JxY6C9OjRA6dOncKbb74ptwkhcPHiRUyfPj3fa8nVrl27PB80c/n5+QFAoeMoExERVRQjRowosGAQGRmJsLAwJCYm4o8//gCQ0+/NnTsX+/fvx6BBg+Dq6goPDw+MHDkSmzdvhkqlwtKlS3H16tVyfhU5+vfvn6+tqD45MjISDRo0yNNWHu9BQkJCEBER8cCsO3bsgMViQa9evRAUFJRvezc3N7mIsWnTpgLzApDn3hgxYkSB68eNG1fovoUda/jw4QXOI9KtW7c8hZbCREVFAci5wHHo0KHYuHGjPH9G37598dVXX8nbFMXT0xOnTp3KN99XQkICPvnkE8TFxRW4n0ajQc+ePfO1//1ncOLECSQlJaFx48Zo3bp1vu2Dg4ORnp4uv7c9duwYEhMT0a5duwInfu/QoQOCg4Oxc+fOfBeKEpEyNA/ehIhKQq1WQ61Ww9fXF0888QQaNmyIVq1aYdmyZZg5cyZCQkKwc+dOXL58GRqNBn379s13DEmSYDQasWzZMkyaNCnf+ujo6HyTiRUk9+rR69evFyu7wWDA3bt34ejoCH9//2LtQ0RE9vPz88OqVasKXGcwGDBq1CisW7cOI0eOxLZt2yBJErKzs7FixQps3boVJ06cwIULF5CdnQ0XF5cCjzN+/HiMHz8ey5YtwzvvvAMAWLFiBZycnDBs2DB5u9jYWAA5RY/Ro0cXmjn3hMcTTzyBa9euYcaMGZg/fz7mz58PrVaLNm3aYODAgRg3bly+uzj+zmq1Ys2aNdi8eTOOHTuGmJgYpKenF/paAOS7myJX7gf0gooYREREFcnfT8jnkiQJDRs2xPnz53HlyhW0adMG3bp1Q7du3QrcvmXLlhg4cCDWrFmDbdu2Fdl/5yrtfrKgfrmoPrmgokB5vAcp7vuH3OcIDw8vNEejRo3y5C5I7kn5unXrFri+du3aee6QKcqDjiVJEho0aIArV64UeZx27dph6dKlmDJlClauXImVK1dCpVKhefPm6Nu3LyZOnFjgRR+F2bt3L9auXYuDBw/i/PnzSExMhE6ng1qtLvCiyBo1akCjyX8a8u8/g9zXUdjr/bvcn0Pu5NtFuXnzZoGTvRNR+WJBgughXb9+HX/88Qdq1qyJli1b5lvfpEkTdOnSRb6lMyQkRB6OyWKxFHmb57fffouXX375gZ1qYVq0aAEAOH78ODIzM+Hk5FTk9p988glmzpyJMWPGYP78+XY9JxERlQ53d3csWbIEPj4+2LFjB65fvw6TyYSoqCjEx8ejZcuW6NatG/7xj38gPDwcZrMZ3bt3z3ecp59+Gq+99hq+//57vPPOO4iPj8fu3bsxYsQIuLm5ydvlfgh89NFHiyxK536glyQJr732GiZOnIitW7di8+bN2LNnD/bt24d9+/Zhzpw52L17d6EnXZKSkhAVFYXTp08jLCwM3bp1w+jRoxEWFobAwED5w/7f3T9MARERUVWTO9RQce9yzx366ObNmwAADw8PADlDBxckMzMTQM7wQ6WhpP3y/e89cpXHe5Di5ixJwaaobXOHkrp/SCl7leaxhg8fjmHDhmHXrl3YuHEjdu/ejSNHjuDYsWP45JNPsHnzZnTo0KHIY9hsNjz77LNYvnw5/Pz80LNnTwwYMAANGjRA+/bt0bZt2wLv2CnuzyD3/0Bhw479Xe7PITAwEO3atSty29yh0IhIWSxIED2kw4cPY/DgwYiOjsa2bdsK3Cb3Sk+TyYSUlBSsXr0azs7OuH37NpydnfNtbzabERAQgJMnT+LIkSNo06aNXdmaN2+OgIAA3Lx5Ez/++OMDr5jJvRW0Y8eOdj0fERGVLjc3N7i7u+POnTu4fv06/vWvfyE+Ph6ffPIJXnnllTzbFtUHPf300/j6669x6NAh7NmzB0KIPMM1AZCHJRgzZkyeIaEexMnJCf3795eHbLh69SrGjx+PTZs24eWXX8bGjRsL3G/y5Mk4ffo0Jk2ahLlz5+Ypvt8/vBQREVFVc+HChQLbhRDyusDAQJhMJsycOROurq54/fXXC9wnd5jf3M+cuSftC3uOmJgYADlX6CuhoJPS5f0epCi535fz588Xus3Zs2cB5AwdVJjg4GCcP38eMTExBQ47FBcXJw8z9SC5z5P7s/u7+39vikOr1aJr167o2rUrgJyhlv75z39i6dKleO6553Du3Lki91+0aBGWL1+ORx99FL/++mu+ItPDDhmdOwdmUe8H582bB5PJhMmTJ8u/P/Xq1Sv0zmMiqlg4hwTRQ8q95fTw4cMFjpGZkpKCXbt2AQCaNm2KH374AdnZ2Rg0aFCBxQgg5w3C448/DiBnEid7aTQaecinN998s8ihmzZv3oydO3fC3d0dgwYNsvs5iYio9Ny6dQt37twBkPNh/fDhwwD+mqz6fgcOHCj0OPdPbr1ixQo0aNAAnTp1yrNNly5dAORMnFiQtWvXIjw8HJ9++qm8fWBgIFJSUvJsFxwcLE8aWNTQAbmvZdy4cfnuBCzqtRAREVV2ixcvhslkytd++PBhnD9/Hi4uLmjVqhW0Wi3mzZuHN954A5cuXcq3fWpqKv73v/8BgHxVe+fOnaFSqbBlyxbcvXs33z4//fQTABQ6DJQSyvs9SFGioqKgUqmwceNGxMfH51uflpYmfw8LujM1V+7396uvvipwfe6oCcWRe6xvvvlGnifyfjt27CjWxRwjR45EYGAgTp06lafdz88P//3vfwEU7/uW+x7uqaeeyleMuH79erGHjC5M69atodfrcfz4cZw5cybf+vPnz+Oll17CihUroFKp0KpVK7i6uuLAgQMFPndWVhZatGiBqKgoDu1JVEGwIEH0kOrWrYuoqCikp6fjhRdekG+BBXJOJD355JNISEhA586dER4eLr/xeOaZZ4o87lNPPQUAWL58OTIyMuzO98orr6BNmza4desWOnXqhN9//z1PJ2yz2fDVV1/JE4h9+OGHDxzzm4iIyl5aWppcSIiMjERgYKD8oW/z5s15tt2wYQNmzZoFAAWe4GjRogXatGmDJUuW4PDhw3juuefybTNo0CAEBwdj/fr1+OSTT/Jc3Xbt2jX885//xPnz5xEdHQ0gp0ASHx+PV199Nc8EgUIIeZLDhg0bFvr6CnstBw8elO/+KOi1EBERVXbx8fEYO3ZsnmGVbt68Kff748ePh6OjIyRJkud7Gjp0aJ5hcAwGA0aPHo1bt26hc+fOaNWqFQDA19cXjz32GNLT0zFmzJg8n0+///57rFixAnq9vtAJpP8ud5igwoaAKg3l/R6kKLVq1cKoUaNgNBoxYMCAPCf6b9++jYEDByIhIQHdu3eXv+cFefnll6HT6bBw4UJ8+umneT6Db9y4US6cFEfucEgXLlzA8OHD89xZcfHixWJPkB0SEoL4+Hj861//QlpaWp51a9asAZD3+1bYzz73Pdzfzy3cuHEDQ4cOldvsfR/n6uoqv1d96qmncOPGDXldYmKifJdv7jkVBwcHTJw4EdnZ2Rg+fDgSEhLk7c1mM/75z3/ijz/+QKtWreweDpuISpkgood26dIlUatWLQFAeHl5iV69eomOHTsKR0dHAUDUqVNHXL16VRw/flwAEH5+fsJsNhd5TKvVKh9z8eLFQggh3n33XQFAjBw5skT5kpOTRdeuXQUAAUAEBQWJHj16iF69eomaNWsKAEKlUon333/f3m8BERGVwPbt2wUA4ejoKIYMGZLvq1u3bsLb21vuM86fPy+EEOKdd96R/2Z36NBB9O/fX4SHhwsA4tlnnxUAhKura4H9xIIFCwQAodFoxM2bNwvMtX//fuHu7i4AiMDAQNG3b1/RqVMnodVqBYA8/cTly5eFr6+vACA8PT1F9+7dRb9+/URISIic4/jx4/L2AERwcLD8eOHChXK/1LJlSzFgwADRvHlzAUAMHjxYODk5Ca1WK3r27Jnne1ZYHzhy5EgBQGzfvr1EPwsiIqLykvt57rHHHhOSJAkvLy/Rs2dP0bVrV+Hg4CAAiA4dOoj09HR5n6SkJNGkSRMBQOj1etG+fXvRpUsXub8OCwsTcXFxeZ7n+vXrch/t7+8vevbsKZo1ayb3u59//nmxM48aNUoAEA0aNBCDBg0S27ZtE0I8uN/9e7+/aNEiAUC8++67BW5fVu9Brly5IgCIqKioAp8392eyaNEiuc1gMIgOHTrI3/Po6GjRo0cP4eLiIgCI8PBwER8fL29f2HMsWrRISJIkAIj69euLfv36yT+HyMjIfN+johw/flx4enoKAMLX11f06dNHdOrUSWg0GuHj4yO/H7xy5Yq8T3BwsLj/tF9ycrKoV6+eACCcnZ1FdHS06N+/v2jYsKEAILRarfjtt9/k7bOysoRerxcARNeuXcXgwYOFEEKcOnVKODk5yec6+vfvLzp16iT0er0ICwsTrVu3FgBEp06d5J9DUa+1oN+NlJQU0apVKwFAODk5iW7duonOnTsLV1dXAUD06NFDWK1WefvMzEwRHR0tAAgHBwfRuXNn0bt3bxEQECC/10xNTS3W95qIyh4LEkSlJCEhQfzzn/8UYWFhQqfTCUdHR9GsWTMxY8YMYTAYhBBCvPjiiwKAeOmll4p1zClTpsgduRD2FySEEMJms4l169aJJ554QgQFBQm9Xi+cnJxEgwYNxLhx48SJEydKfEwiIrJP7sn1wr5UKpUIDAwUEydOzPOB12aziU8++UQ0btxYODk5CXd3d9GlSxexevVqIYQQw4cPF3q9XnTu3Dnfc549e1YAEAMGDCgy29WrV8X48eNFcHCw0Ol0IiAgQHTv3l389ttvwmaz5dt24sSJomHDhsLFxUU4OzuLsLAw8dprr4lLly7l2bagD6JLliwRrVq1Ei4uLsLFxUW0b99eLFiwQFitVvHmm28KR0dHUadOnTzfMxYkiIiosrr/5PfOnTtFnz59hLu7u3BwcBBNmjQR//nPf0R2dna+/TIyMsR7770nIiIi5M9xERERYtasWYWeZL127ZoYM2aMCAwMFBqNRri5uYkuXbqIX3/9tUSZz58/L1q1aiWfmM49cV/aBQkhyuY9iD0FCSGEyM7OFp988olo2bKlcHZ2Fg4ODqJRo0bi3Xffzfc9L+o59u/fLwYMGCC8vLyEVqsVderUEVOmTBEpKSklKkgIIURcXJwYN26c/DP18/MTTz75pIiJiRFRUVEPLEgIIcSdO3fE66+/LiIiIoS7u7twdHQUoaGh4oUXXijwnMDXX38tAgIChEqlynOsvXv3ip49ewpvb2+h1+tFw4YNxdtvvy0MBoNYt26d8PT0FN7e3nYXJITI+b1///33RUREhHB0dBR6vV40adJEvP/++wX+PzEajeKzzz4TrVu3lt9bRkREiNmzZ7MYQVTBSEJwADUiIiKiqu6tt97C+++/j59//hkDBgxQOg4REVG1M336dMyYMQOLFi3CqFGjlI5DRESkCM4hQURERFTF3bx5E9988w2CgoLQt29fpeMQERERERFRNaVROgARERERlY158+Zh48aNOH78OO7cuYOlS5dCrVYrHYuIiIiIiIiqKd4hQURERFRF2Ww2bNmyBUIIfPDBBxg+fLjSkYiIiIiIiKga4xwSRERERERERERERERU5niHBBERERERERERERERlTnOIVEIm82GGzduwNXVFZIkKR2HiKjCEUIgLS0NNWvWhErF+nZpYf9DRFQ09j+lj30PERERET2MkrxHZ0GiEDdu3EBQUJDSMYiIKry4uDgEBgYqHaPKYP9DRFQ87H9KD/seIiIiIioNxXmPzoJEIVxdXQHkfBPd3NwUTkNEVPGkpqYiKChI/ntJpYP9DxFR0dj/lD72PURERET0MEryHp0FiULk3qrs5ubGN+VEREXg0A6li/0PEVHxsP8pPex7iIiIiKg0FOc9OgddJSIiIiIiIiIiIiKiMseCBBERERERERERERERlTkWJIiIiIiIiIiIiIiIqMyxIEFERERERERERERERGWOBQkiIiIiIiIiIiIiIipzLEgQEREREREREREREVGZY0GCiIiIiIiIiIiIiIjKnEbpAERERERERFQ5mc1mWK1WpWNUGyqVClqtFpIkKR2FiIiIyC4sSBBVEtnZ2bh69SqCg4Ph4OCgdBwiIqom2P8QUUFSU1Nx584dGI1GpaNUO2q1Gk5OTvDz84NOp1M6DhEREVGJsCBBVEkYjUbExMSgRo0aPCFERETlhv0PEf1damoq4uPj4eLiAh8fH16xX06EELBarcjKyoLBYEBsbCwCAwPh5OSkdDQiIiKiYmNBgoiIiIiIiIrtzp07cHFxQWBgIAsRCnBxcYGXlxeuXr2KO3fuoHbt2kpHIiIiIio2TmpNRERERERExWI2m2E0GuHu7s5ihILUajW8vLyQkZEBi8WidBwiIiKiYmNBgoiIiIiIiIoldwJrrVarcBLS6/UAwIIEERERVSosSBAREREREVGJ8O4I5fFnQERERJURCxJERERERERERERERFTmOKk1ERERERERlRrLrVuwphiUjvFAag93aGrUUDoGERERUbXCggQRERERERGVCsutW4h/YhhgMikd5cF0OtRauYJFCSIiIqJyxCGbiIiIiIiIqFRYUwyVoxgBACZTqdzJsXjxYkiS9MCvkJCQYm2X+0VERERUFfEOCSIiIiIiIqKH1KhRI/Tu3bvQ9a6urkhLS8vT9s033yAtLQ1jxoyBu7t7WUckIiIiUhwLEkREREREREQPqU2bNpgzZ06J9lm1ahXS0tLw1ltvISQkpGyCEREREVUgHLKJiIiIiIiIiIiIiIjKHAsSRERERERERERERERU5liQICIiIiIiIiIiIiKiMsc5JIiIiIiIiIge0uHDh/Haa68VuG7AgAGIjIws50REREREFU+FL0jMnDkTCxcuRGxsbLG2T05Oxrvvvot169bh1q1b8PLyQu/evTFr1iwEBgaWbVgiIqoy2P8QERFRSZw5cwZnzpwpcF1gYCALEkRERESo4AWJGzdu4JtvvoFGU7yYZrMZPXr0wNGjRzF48GB06tQJJ0+exMKFC7Fjxw78+eefcHNzK+PURERU2bH/ISIiopIaOXIkFi9erHQMIiIiogqtwhUkMjIycPnyZezfvx8ffPAB4uPjERwcXKx9v/32Wxw9ehSTJk3C3LlzIUkSACA8PBz/+te/8P777+ODDz4oy/hERFRJsf8hIiIiIiIiIipbFW5S699++w0REREYP348rly5UqJ9V65cCQCYOnWqfDIIAF566SW4uLhg1apVpZqViIiqDvY/RERERERERERlq8LdIdGxY0esX79efjx16lSkpaUVa99Tp06hTp068Pf3z9Pu4OCAFi1aYPfu3cjOzoaDg0OpZiYiosqP/Q8RERERERER/V1iYiL8/PzytCUkJMDX11ehRJVbhStIBAQEoF+/fvLjOXPmFPuEUGJiIkJDQwtcV7NmTQA5vyy1a9fOt95oNMJoNMqPU1NTSxKbiIgqOfY/RFSezBYLsrONMBpNyDYakZVthNlkhslshslsgdlshuneY7PZAqvVCovFArPFKi9brFZYrTZYrVbYbDZ52WqzwSZ/iZx19x4LISBsAjYh5MdyuwCEsN37V+Ssy3kAgZw2CEBA/G353joAuPf43hKQuyyvv9eeu/zXYp4HeZvvfwQIm+3hfwBERERERKSICleQeBhCCOj1+gLX5V6VaivkA8zs2bMxY8aMMstGRERVF/sfourLarXCkJqOuykG3DWkIsWQhrspBhhS05GekYmMjMy//s3MQkZGJrKzjbBYrUpHJyIiIiIiKndVqiDxMKZOnYopU6bIj1NTUxEUFKRgIiIiqg7Y/xBVfBmZWYiLv4VbtxNx63Yibt5OxK2EO7h5OxHJdw35ruCvCiRJggRAUkmQIAGSBEkCJOT8C0n62zL+mkPn3r/Sfce6r1lek7/9/r3ytt8/P4/NVvW+31WJ2sMd0OkAk0npKA+m0+XkJSIiIqJyU+UKEoV9IMxtl/J+4pHp9fpCr24lIqpuhNUKkZkJkZEJkZFxb/m+f7OyYE1JUTpmhcL+h6hqSM/IxKUr13Dx8lWcu3AFF69cQ0Ji0kMfV61WwdnJCS7OTnB00MPBQQ+9XgcHvR6ODjl/B/Q6LXQ6LXRaLbT3/tVptdBo1NBoNPf+VUOjzllWq9XQqNVQq1VQqdVQq1RQq9VQqSSoVCqoVSqo5C8JkpTzr1qlyik0SCqo7hUaVCoVIEn3Hhf896qiSE1NxQ/z5ygdgwqhqVEDtVaugDXFoHSUB1J7uENTo8ZDH2fUqFEYNWqUXfvGxsY+9PMTERERVSZVqiDh4+MDg6HgN7657ZxshIiqEyFEThEhNRUiLQ221LScf9Pu/ZuamrMuPR0iPR229JyiA7KylI5eqbD/Iaq8rDYbzsVcxq59h3H42EncLkHxwdXFGTX8feDp4Q5PDzd4urvBw90Nnh5u8HBzhYuLM1xcnODi5AS9XlfhT/RT5bRx40Z88MEHOHHiBEwmE5o2bYrJkydj2LBhimXS1KhRKif6iYiIiKjqqVIFicaNG2P//v1ITU2Fm5ub3G61WnHkyBEEBwfDyclJwYRERA9PLjLcvQvb3RTY7t7N+UpKgu3uXYiUlHuFhjSI9HSA45SXOfY/RJVLZlY29h06hr0HjuHkmQvIys4udFutRoPgoJqoXzcEATV8UcPPBzX8fRHg5wMnJ8dyTE2U33fffYcRI0agbt26eOWVV6DRaLB8+XI89dRTuHHjRp4hAYmIiIiIKoJKW5AwGAzIysqCi4sLXFxcAACDBw/Gzp078Z///AfvvfeevO2iRYsQHx/PN+REVOEJISAMBtgSE2FNvAPb7duwJSbCdicJtuQk2FIMEKmpgMVS+k+uUkFycoLk7AzJxRmSqytULi45j3PbnZzk5SybDVi7uvRzVHDsf4gqr7j4m/huxc84cPRPWAso1kqShODAmghrEIqG9eugXmgwgmoFQKNRK5CWqGhmsxkvv/wyatSogSNHjsDDwwMA8PLLL6NRo0Z477338Morr/DOHCIiIiKqUCptQWLSpElYsmQJ3n33XUyfPh0A8MILL+Dbb7/F+++/j0uXLqFt27Y4e/YsFi1ahFq1amHatGnKhiaiai+n4JAK640bsMbFwXrjBmwJCTkFh7t3IQyG0rmjQSVBcnbJKSq4u0Nyd4PK0xMqNzdIbq457a5ukFxdILm4QuXqAjg4lOikRXZq6sPnrITY/xBVPidOncPyVb/g1LkL+Pt0LzqtFk0a1kdUxzZo2yoCbq4uyoQkKqE7d+4gJSUFgwcPlosRAODi4oK2bdti9erVuHPnDocMJCIiIqIKpdIWJAqi0+nw+++/4+2338b69euxatUqeHp64sknn8R7770HT09PpSMSUTUghIDNYID18hVYY2Nhjbuec6dDUhJsKSmA2Wz/wfX6nAKDhztUnl5Q+XhD5eMDlZcXVB4eUHl6QHJ3z7mbQaUqtddERWP/Q1Qx7dhzCCt/3ojYa/F52rVaDdq1ikDXyPZoEdEQWq1WoYRE9vP19YWHhwdiYmJgs9lyJiYHYLPZcO7cOXh6erL/ISIiIqIKp8IXJHbs2FFg++LFi7F48eJ87b6+vvjqq6/w1VdflW0wIqr2hBCwxcfDfD4G1itXYL0enzO8UnISYDSV/IBaLVQe7lB5e0Pl4wtVDX+o/P2h9vPNKTh4eUFy5Hjl5YX9D1HllWJIxfuffI3T5y7maXdw0KNHdEc8/Xg/uLo4K5SOqHRoNBq8//77mDhxIkaPHo2XX34ZWq0Wn3/+OU6fPo2vvvoKGk3BH/eMRiOMRqP8OLWa3vVIREREROWvwhckiIgqAltSEsynz8By8eK9Ox5uwZaUXLK7HSQJkrs7VN5eOYWGmjWhrh0EdUBNqPx8Ibm4cJxnIqKHdODICXz21VKkpqXLbc5OjujVrROeGtwXjo4OCqYjKl3PP/88du3ahaVLl2Lp0qVy++OPP44RI0YUut/s2bMxY8aM8ohIRERERJQHCxJERPcRVius8TdgOX0KlouXYY2Lg+32bYi0tGIfQ3J2zhlGqUYNqIMCoQ6tA3VICNS+vpDUnBiViKgsmEwmzF+6Chu37obNZgOQMzTToL7dMWxIX+h1HJaJqhaLxYJ+/fphy5Yt6NGjB5544gkAwA8//IBVq1bhxo0b2LZtG/R6fb59p06diilTpsiPU1NTERQUVG7ZiYiIiKj6YkGCiKotYbXClpQEy5kzMJ87D+uVWNhu3YS476raokguzlD5+kFdqybUdepAXb8eNHXrQsVhlYiIytXVuBv4eN5CXIqNk9v8fLzw9r/+gdDgQAWTEZWMzWZDTEzMA7dzcnLC3r17sWXLFowaNQoLFy6U77J8/vnn8fTTT2PFihVYvnw5Ro0alW9/vV5fYKGCiIiIiKissSBBRNWGyMqC9Xo8zCdOwHLuXM7dD8nJgNVW9I4qVc4cDjVrQhNaB+oG9aEND4fKw6NcchMRUeGOnTiDDz9fgLT0DLmtdYsmmDp5LBwcODwTVS6ZmZlo2LDhA7eLiopC06ZNAQAvvPBCniEfJUnCSy+9hBUrVuDIkSMFFiSIiIiIiJTCggQRVVm29HRYr8TC/McfsMRcgPXGDYiUu4AoYieVCpKXF9SBgdCEhkLTMAya8HConDn5KRFRRXPpyjXM+vhLGI0mAIBWo8GwIX0wbHBfhZMR2cfFxQVCFPVG5S+TJk0CAFit1nzrctu0Wg5VRkREREQVCwsSRFRliMxMWG7chOXECZhPnYY1Lg7ibnKRBQjJ2RmqGv5Q16kDbaOG0EREQO3lVX6hiYjILsl3UzD9P/PkYoSXpzum/GM0WjR98NXlRFVBdHQ0Pv/8c8ydOxePPPIIVCoVgJxixMcffwwA6Natm5IRiYiIiIjyYUGCiCotYbXClngH5nPnYD5yFNarsbAlJABmS6H7SO7uOXM+hIVB16olNKGhkDikBxFRpWI0mfDu7P8i+a4BAODu5or/TP8natbwVTgZUfkZMGAAevXqhVWrVqFNmzYYOHAgJEnCunXrcPToUQwaNAh9+vRRJJsl+w5spjRFnrskVDpXaBx8lI5BREREVK2wIEFElUrOXRA3YD7xJywn/oT1+nWIlJRCt5fc3aCuVQua8HBoWjSHNjQUEiedJiKqtGw2Gz78bAEuX70OANBptZjxxossRlC1o1Kp8PPPP+Pzzz/HsmXL8P7770MIgfDwcCxcuBAjRozIM7dEebFk38Gt/a8CNnO5P3eJqbSo0f7jhy5KLF68GKNHj37gdosWLeKcHkRERFTtsSBBRBWeLS0NlqtXYT50OGcy6hs3gMysgjfW66D2r5Ez8XTrVtCGhUHl7l6+gYmIqMws+mENDhw5AQCQAEx8/mnUrxuiaCYipeh0Orz22mt47bXXlI4is5nSKkcxAgBs5py8pXSXRKNGjdC7d+9C1zdu3LhUnoeIiIioMmNBgogqJFtqKixXr8F89CgsZ87AGncdMBoL3Fby9IQ6uDa0zZtB26wZ1DVrQuIkjkREVc6mrbvx84at8uM+PaLQPbqDgomIiP7Spk0bzJkzR+kYRERERBUaCxJEVGGIrCyYr8TmFCFOn4E1Lg7IKuBOCJUKKj8/qOuEQNu2LbSNGkLl46PIsARERFQ+rl2/gQXfrYLVagMANA6vhxdGD1M4FREREREREZUECxJEpChhscB6/TosZ87CdPgIrFeuQKSm5t9QrYIqIACaBg2ga9sWmnp1ofLyKv/ARERU7qw2G977+CtkZmUDAPx8vfH2PydCpVIpnIyIiIiIiIhKolgFidDQULsOLkkSLl26ZNe+RFS1We/cgeXixZx5IS5chO3GDUCIvBtJElQ1akBdNxS69u2hDWvAIkQ1wr6HiHJt2LIT12/cBgA4OTrgrSkvwNXFWeFUREREREREVFLFKkjYbLZ8Q6Gkp6cjKSkJAODm5oYaNWrg5s2bSEtLAwB4e3vDxcWllOMSUWUmzGZYr12D6Y8/YD72B6yxV4B7V7veT/LxgSa0Ts6dEA3DofL3h8SrYKsd9j1EBABWqxU/rtkgP372ycdQL7S2gomIiAp2+PDhQicYHzBgACIjI8s5EREREVHFU6yCRGxsbJ7HCQkJiIyMRK1atfDpp5+ic+fO8rrt27fjtddeQ3JyMnbs2FGKUYmosrKlpsJ84QLMh4/AcuYsbPHx+e+GcHCAOiQEupYtoG3VEurAQE5MXc2x7yEiAFi3YSvupuQM5RdUKwADekUrnIiIqGBnzpzBmTNnClwXGBjIggQRERER7JxDYubMmUhISEBMTAx8fHzyrIuOjsaWLVsQFhaGt956C8uWLSuVoERU+Vhv3YL55KmcSaovXIC4m5J3A0mCqmYANPXrQ9uhPbT16kPl4a5IVqr42PcQVT8WiwWr/7dJfjx25BP57pwiIqooRo4cicWLFysdg4iIiKhCs6sgsW7dOvTt2zffCaFcXl5e6NevH3777beHCkdElY+w2WCNuw7ziRM5d0RcvAgYjXk3ctBDHVoXutatoG0WAXVQECSNXX+OqBph30NU/az8eRMMqekAgDrBgWjVrLHCiYiIiIiIqLJKHvy4Xfvd/ft5LQB3Rz0HtV5f4mN5rVllV4aqxK4zgImJifDw8ChyGycnJ6SkpNhzeCKqhITFAuuVWJj/+AOm48dhvXgJsFjybCN5ekIT1iBngurGjThBNZUI+x6i6sVoMuPnDVvlx6OfGaxgGiIiIiIiIioNdhUkAgICcPDgwULXW61W7Nq1C7Vq1bI7GBFVDsJigeXCRZiOHYPlxJ+wXrkMWG1/bSBJUAUGQtuoEbRtW0PToAFUzs7KBaZKi30PUfWyfNUvSEvPAADUDQni3RFERERERERVgMqenQYMGICjR49ixowZsPztCmij0Yh//OMfOHPmDAYP5pVsRFWVsFphuXABmSt+QuaCb2FcuxbWixf/KkaoVFDXDYXD40PgMnECnJ4bBV2LFixGkN3Y9xBVH5lZWdiwZaf8eNTTgxRMQ0RERERERKXFrjsk3n77baxbtw4zZ87EN998gx49eiAgIAC3bt3Cpk2bcPPmTdSvXx/Tpk0r7bxEpDBhs8EaGwvz0WMwHT2WU4Sw3XdHhFoNdd260LZsAV2b1lAHB0NS2VX7JMqDfQ9R9bHsp/8hIzMLAFC3ThBa8u4IIiIiIiKiKsGugoSvry/27NmDiRMnYsOGDViyZIm8TqVSYejQofjss8/g7u5eakGJSHmWa3EwHz8O89GjsJw/D5jvu0pdq4W6Xj3oWraAtmVLqINrsxBBpYp9D1H1kJmVhS3b98mPRz3FuyOIKhOVzhVQaQGbWekoD6bS5uQlIiIionJjV0ECAGrXro1ffvkFN27cwLFjx2AwGODl5YXWrVvD19e3NDMSkcJsyXdhOnIYpoOHYTl7Bsg2/rVSrYK6fn3o2rSBtkVzqGuzEEFlh30PUdX307qNyMzKBgDUq1Obd0cQVTIaBx/UaP8xbKY0paM8kErnCo2Dz0MfZ9SoURg1atTDByIiIiKqBuwuSOTS6/VwcHDAnTt30Lt3b9y5c6c0chFRBSCys2H+8yRM+/bDfPwYRFr6XyslCeo6daBt3RK6du2gDgmBJEnKhaVqhX0PUdVkNJnw25Zd8uORvDuCqFLSOPgApXCin4iIiIiqHrsvY05MTMRTTz2FGjVqoGfPnhgzZgyAnDG+w8PDsWfPnlILSUTlS1itMJ8/j8yfViLzu+9g2rUrTzFCFRgIfb9+cHp+NByfeAKaOnVYjKBywb6HqGrbtfcI0jMyAQD1QmujZbNGCiciIiIiIiKi0mTXHRJ37txB27ZtcfXqVXTv3h3Jyck4duwYAKBt27b48ccf0b17dxw4cADNmjUr1cBEVLast2/DdPAQTIcOw3r+HGD9a8Jqyds7Z46Idu2gbdwIkl6vYFKqbtj3EFV923btl5eHDOipYBIiIiIiIiIqC3bdIfHOO+/g6tWrWLJkCTZt2oTOnTvL60aPHo2DBw9CrVbj/fffL62cRFTGhMkE48FDyFz2PbJXroT1zJm/ihEODtC2bQunUSPgOPxZ6Fq2YDGCyh37HqKqLSs7G6fPXwIAODk6oGO7lgonIiIiIiIiotJm1x0S69evR4cOHTB8+PAC19evXx/9+/fHvn37HiocEZUP6/XrMO7eA9O+/bDFx/+1QpKgblAfukfaQffII1D7+ysXkqo99j1EVdue/UdhtVoBAG1bRkCtsntkUSIiIiIiIqqg7CpIJCQkoF+/fkVu4+vri9u3b9sViojKh8jKgvHIUZj27IXljz8As1lep/L3g7Z1a+g6tIemQQNIarVyQYnAvoeoqtu2+6C83D26g4JJiIiIiIiIqKzYVZCoWbMmLly4UOQ2J0+ehD+vpiaqsCyXL8O4dx/M+/fDduu+E7h6PbQtW0Dbvj10LZpD5eysXEii+7DvIaq6srONOHP+IoCc4ZqaNmqgcCIiIiIiIiIqC3bdCz9w4EBs374dGzZsKHD9Dz/8gJ07d6JnT/snI4yLi8OoUaPg7+8PrVaLoKAgvPTSS7h79+4D983IyMCsWbPQqFEjODg4wNfXF0899RRiYmLszkNUVQijEdm79yDzhxUwrv8lTzFCFVwbjkMGw+nZZ+DwaEcWI6hCYd9DVHXt3n8UFkvOcE2tWzSBmnflERERERERVUl23SExbdo0rF27FgMGDMBTTz2F69evAwA+/fRT7N69G+vWrYOXlxfefvttu0IZDAZ07NgRcXFxeO6559C0aVPs3r0b8+bNw8GDB7Fv3z5oNAVHt1qtGDx4MDZv3owBAwbg+eefR1xcHBYsWICNGzfiyJEjqFu3rl25iCo7663bMG7bDtOuXbAlJPy1wtERutatoIuKhDYiAlIh/7+IlMS+h6jq2rb7gLzcNYrDNREREREREVVVdp119Pb2xu7duzF69Gh8//33cvuUKVMAAM2aNcOSJUsQFBRkV6jZs2cjLi4Oc+fOxeTJkwEAkyZNwosvvogvvvgCCxYswAsvvFDgvuvWrcPmzZvxwgsv4Msvv5Tbu3btigEDBuCLL77Axx9/bFcuospKWK2wnDwJ49btMB0+DJhM8jp1aB3oOnaAruOjUPv5KpiSqGjse4iqJqPJhDPnLwEAHBz0aN4kXOFEREREREREVFbsvgw6KCgIv//+O06ePIk9e/YgOTkZnp6eaN26Ndq2bftQoVauXAl3d3dMmDBBbpMkCW+++Sa++OILrFq1qtCTQrlDY/Tp0ydPe3R0NADg3LlzD5WNqLKxGQww7t4D046dsF6+/NcKR0fo2rWF7tFHoY1oyrsiqFJg30NU9ezYcwgWiwUA0KJpQ2g0HK6JiIiIiIioqrLrDOS1a9fg5uYGDw8PNG3aFE2bNs23TUpKClJTU1G7du0SHTsrKwuXL19GdHQ09Hp9nnW1atVCUFAQTp06Vej+DRrkTIJ47tw59O/fX24/e/YsAKBevXolykNUmVkuX4Zx6zaY9u6FSE2T21W1akEf3Rm6To9C7cu7IqhyYN9DVDXt2HNIXu7WmcM1ERERERERVWV2FSTq1KmDV199FR9++GGh28yaNQtz586F1Wot0bETExMBAP7+/gWur1mzJg4dOlTgOgAYMGAAOnTogJkzZ8Ld3R2RkZG4evUqJk+eDD8/P7z66qsF7mc0GmE0GuXHqampJcpNVJEIqxWmo8dg3LgJlj//BGy2nBVqNTTNm0PfrSt0rVryrgiqVKpi3wOw/6HqzWQy4WxMznBNer0OrZo3VjgREZWGxOwspJlND95QYa5aHXwdHJWOQURERFSt2HU2UghR5HqbzYbLly9Dq9WW+Ni2eydO/36Fai4HB4cin1+r1WLRokWIjIzE+PHj5XaVSoUffvih0KtmZ8+ejRkzZpQ4L1FFY8vIgHHHDhg3bYbterzcLnl4QNfpUei7doWmtn1j7BMpqSr2PQD7H6retu85BLM5Z7imiEYNoGWhnKjSS8zOwkuHdsGce0FMBaZVqfDftpEsShARERGVI1WxN1SpoFaroVarIUkSPv74Y/nx37+0Wi1+/vlneQiL8nTixAm0bdsWBoMBU6ZMwQ8//ID33nsPPj4+GDZsGObNm1fgflOnToXBYJC/4uLiyjk50cOz3rqNrJWrkP3TqjzFCHWDBnB8cigchz7BYgRVKlW97wHY/1D1dv9wTV0j2yuYhIhKS5rZVCmKEQBgttke6k6OkJAQSJJU7C8iIiIiKsEdEpGRkfKbqJ07dyIwMBB169YtcFtJkuDr64vXX3+9xIFyn6OwK1EfdIXsv/71LxgMBmzbtk2eTBQARo4ciYiICEydOhUjR46Eq6trnv30en2hV8YSVQbms2dh/G0jTAcOAvcmB4VWC90j7aDv1hWaxo0hqYpdgySqEKp63wOw/6HqK9tkwtmYywAAnVaLR9o0UzgREVHJjB07Fnfv3s3T9s033yAtLQ1jxoyBu7u7QsmIiIiIKq5iFyR27NghL6tUKgwbNqzIcbzt5ePjAwAwGAwFrjcYDPAtYhLeffv2ITw8PM8JISBnUtIhQ4Zg/vz5OH/+PFq3bl16oYkUJCwWGPcfgPG3jbCeOye3S+7u0HWJhkPPHlD7+SmYkMh+7HuIqq7dew/DbDYDABo3rGfXcGtEREp666238rWtWrUKaWlpeOuttxASElL+oYiIiIgqOLsG6h05cmSZnVRxdnZGcHAwjhw5AqvVCrVaLa9LTU3FmTNn0KFDh0L3V6lUhU5mmtvOD7xUVYisLGRv3gLjxk2w3b4tt6tqB8GhV0/oo6IgOXJMXKoa2PcQVS17DhyVlzt3bKtgEiIiIiIiIiovdo3fsmjRIgwdOhQnT57E9u3b5fb09HQsW7YMycnJDxVq8ODBiI+Px8KFC/O0f/jhhzCbzRgyZIj8fLdu3cpzRWt0dDQuXLiA9evX59n38uXLWLVqFfz9/dG4ceOHykdUEdhSUpC1ahWyV63+qxghSdA0bwbHZ5+Fvnt3FiOoSmHfQ1R1WCxWnDp7AQCg1WjQqQPvHiIiIiIiIqoO7CpICCHw4osvonnz5vjuu+/k9oyMDIwYMQLBwcFYunSp3aGmTZuGgIAATJgwAWPHjsUnn3yCYcOG4b333kPz5s0xfvx4AMCcOXMQEBCASZMmyfvOnj0bHh4eGDhwIJ599lnMmTMHr7zyClq2bIn09HR88cUX0GjsujGEqMKw3riJzO9/QPYvGyAyMnIa9XroukTDafhw6Fu3gnTfFd5EVQH7HqKq4/ifZ5BtzJlINrxBKPQ6ncKJiIiIiIiIqDzYdXZk4cKF+OKLL1C/fn08/vjjcrunpyc+/vhjfPzxxxg9ejS8vLzQr1+/Eh/fy8sLe/fuxVtvvYV169Zh0aJF8Pf3x/jx4zFr1izoivjQ2rBhQxw9ehSzZs3Cli1bsGLFCri6uiI6OhozZsxARESEPS+ZqMIQl68gc9MmmA8fAe5NtCt5eEDfozscevSAystT4YREZYN9D1HVsffgMXm50yOtFExCRERERERUNB+9HkmDhigdo8qQhLh3RrMEOnXqhIsXL+LcuXNwd3fPt/7GjRto2LAhWrVqhW3btpVK0PKWmpoKd3d3GAwGuLm5KR2HCCkpKTj5w3I0vnoNuHBBblcFBMBhQH/oO0dB0usVTEjVTXn/nawOfQ/A/ocqHoPBgF27diEyMrLA/3v2GDf5HcTfzBlu8If5c+Du5loqx6XqgX8nS19JvqfZ2dm4cuUK6tSpAwcHh3zrL6cZ8M+j+8oqaqn7qFUHhLqWzt82AAgJCcHVq1dx5cqVMp/U+kE/CyIiIsorefDjD96ojHmtWaV0hDJRkveTdg3ZFBMTgz59+hT6obRmzZro27cvjh49WuB6IioZYbXCtv8AQo4cyVOMUNevB8eRw6Hv3o3FCKry2PcQVQ2ZWdm4cSsBAODn48ViBBERERERUTVi15BNqampRQ5dAeQMfWEymewKRUR/EWYzjFu2AqtXwznl3iS6EqBt3gIOTwyBNjxc2YBE5YR9D1HVcOpsDHJv0A1vUFfhNERERERERFSe7CpI1K1bF9u2bYPVaoW6gIlzrVYr9uzZg+Dg4IcOSFSdCaMRWRt+g3Hdz0BaWk6jWg3dox3hOGQw1IGBygYkKkfse4iqhsPHTsrLbVs2VTAJERERERERlTe7hmwaNmwYLly4gLFjxyI5OTnPurS0NIwbNw4nT57EoEGDSiUkUXUksrKQtWYtslevhrhXjLBoNUCXLnB69hkWI6jaYd9DVDWcPndRXo5oHKZgEiIiIiIiIipvdt0h8eqrr2L9+vVYvHgxVqxYgQ4dOsDPzw9JSUk4ePAgUlNT0aBBA7zxxhulnZeoWrClpSFr9RoYN20CjPeGn3F2Rnz9+qgzcABU3t7KBiRSAPseosrPZDLj+o1bAAAvT3d4e3koG4iIiIiIiIjKlV0FCUdHR2zfvh0zZ87EggULsG3bNnmdTqfDyJEj8dFHHxU68SgRFc6WkoLMFT/CtG07YLEAACQPD4g+vRGvViPUyUnhhETKYN9DVPmdOX8RVqsNABBWv47CaYiIiIiIiKi82VWQAAAnJyd88MEHeP/993HhwgXcuXMHzs7OCA8Ph4ODQ2lmJKo2bMnJyPxuGUy79wC2nBM2ko8PHJ8ciuwWzSH27lU4IZGy2PcQVW6H7ps/omVEYwWTEFFZcdXqoFWpYL73XrYi06pUcNXqlI5BREREVK3YXZDIdfLkSRw+fBjXr1/H9OnTcf78eYSEhECv15dGPqJqw5acjIxvF8F8YD8gctpUAQFwfHoYdO3bw5g7qTURse8hqqROn70gLzdvGq5gEiIqK74Ojvhv20ikmU1KR3kgV60Ovg6OpXrM2NjYUj0eERERUVVjd0Hi/PnzGDVqFA4dOiS3TZ8+HZ9++imWL1+O+fPn44knniiVkERVnS05GRnzv4X54EG5TVW7NpyGPwNdq1YKJiOqWNj3EFVeVqsVV6/fAAC4ODshwN9X4UREVFZ8HRxL/UQ/EREREVUNKnt2io2NRYcOHXD06FGMGTMGnTt3ltf17t0bnp6eGDZsGHbv3l1aOYmqLFtSEjLmL8hTjFDXqwensc+zGEF0H/Y9RJXbuQtXYDbnzI1UPzQYkiQpnIiIiIiIiIjKm10FibfffhspKSnYuHEjvv76a7Rs2VJeN2DAABw6dAgeHh74z3/+U2pBiaqinGLEtzAf/Otqb02DBnAaNwa6xhxbm+h+7HuIKrdDR/+Ul5txuCYiIiIiIqJqya6CxJYtW9C1a1d06dKlwPW+vr7o168fjh079lDhiKoy+c6I+4ae0YSHwXHCC9DWq6dgMqKKiX0PUeV26txf80e0asaiOxERERERUXVk1xwSKSkpqFu3bpHbuLq6Ijk52a5QRFWdLSkJGd/Mh/nwEblNEx4OpwnjoQkKUjAZUcXFvoeo8hJCIPZqPABAr9chpHYthRMRERERERGREuwqSISEhODEiRNFbnPw4EEE8cQqUT62FEP+YkTDcDi98AI0QYEKJiOq2Nj3EFVeFy5fRbbRCAAIDQ6ESmXXTbpERERERERUydn1afCpp57CwYMH8fXXXxe4/oMPPsCxY8fw2GOPPVQ4oqrGlpaGjK+//lsxoiGcJrAYQfQg7HuIKq/7549o0rCBgkmIiIiIiIhISXbdIfHPf/4T69atw8SJE7Fs2TJkZWUBACZPnow9e/bg+PHjCA4OxptvvlmqYYkqM1tGBjIX/G0C60aNcoZpqsWhK4gehH0PUeV18kyMvNymZRMFkxAREREREZGS7LpDwsnJCTt37sTo0aNx4MABHDt2DEIIfP755zh27Bj69++PXbt2wcvLq7TzElVKIjsbmYuXwLRnj9yWc2cEixFExcW+h6jyunItZ/4IjUaNsHqhCqchIiIiIiIipdh1hwQAuLm5YcGCBfjoo49w4MABJCcnw9PTE61atYK/v39pZiSq1ITJhMxly2Davh0QOW3q+vXhNHECNLVqKhuOqJJh30NU+cRei0dGRiYAIKhWADQatcKJiIiIiIiISCl2FSR27dqFoKAg1KlTB56enujdu3e+ba5cuYK4uDhERkY+dEiiykqYzchc/iOMm7YAtpxqhDokBM4v/YPFCKISYt9DVDkdvG/+iIYN6iqYhIiIiIiIiJRm15BN0dHR+PLLL4vcZt68eYiOjrYrFFFVIKxWZK1eA+OvvwJWKwBAFRgIpxf/AU0gJ7AmKin2PUSV0+mzF+TlVs0bK5iEiIiIiIiIlFbsOySuXbsmLwshYDAY8rTdz2Kx4MSJE9Dr9Q+fkKgSEkIg+5cNyF73M2CxAABUNWrA+cWJ0IbWUTgdUeXBvoeo8rscGwcAUKkkNG/aUOE0RFQebty8hZSUFKVjPJCHhwdqBtRQOgYRERFRtVLsgkRISAgkSQIASJKEBQsWYMGCBYVuL4RA27ZtHz4hUSVk3L4dWT/+CJhMAADJ1wfOE1+AtkEDhZMRVS7se4gqt6S7KbhrSAUABPj7wkGvUzgREZW1Gzdvoc9jT8B0731wRabT6bDh55UPXZRYvHgxRo8eXeh6T09PtG/fHm+88QY6der0UM9FREREVNkVuyAxYsQISJIEIQSWLl2KRo0aoU2bNgVuK0kSfH19MXHixFILSlRZGI8cQdaSpUB2NgBA8vKE8/hx0DZponAyosqHfQ9R5Xbovvkj6tcNUS4IEZWblJSUSlGMAACTyYSUlJRSu0uiUaNG+ea4MplMOHv2LDZs2IDffvsNa9euxWOPPVYqz0dERERUGRW7ILF48WJ5eenSpejTpw8+/PDDsshEVGmZz51H5lffQKSlAwAkV1c4Pf88dC1bKpyMqHJi30NUuZ04dV5ebtG0kYJJiIjKXps2bTBnzpwC133++eeYNGkSXnnlFQwYMEC+A5SIiIiourFrUut3330XPXr0KO0sRJWa5fp1ZPx3HkRyck6DgwMcR46Avv0jygYjqiLY9xBVPpdj/5rzpU1L3ilIRNXXxIkTodPpcOXKFSQmJiodh4iIiEgxxb5D4n7vvvsuAODMmTOYP38+Dh8+jPj4eFy5cgWLFy+Gg4MDhg4dCpXKrnoHUaVjvXMHGXM/g+3mzZwGrRaOTw6FPrqzkrGIqhT2PUSVi8Vixa2EJACAh5sr3N1cFU5ERKQcjUYDDw8PJCQkQK/XKx2HiIiISDF2FSQA4KOPPsLUqVNhs9kAQL7l9MCBA5g/fz5WrFiBH3/8kW+2qMqzpaUh4/N5sF65ktOgUsGhfz849OvLW7GJShn7HqLK48LlWFitVgBA7aCaCqchIlLW6dOnkZCQgNDQULi7uysdh4iIiEgxdl1GunbtWrz++uto2bIl9u7dixdffFFeN2vWLEyYMAHr16/H119/XWpBiSoiYTQi48uvYDl1Sm7Tde0CxyeHQlKrFUxGVPWw7yGqXE6cOicvh9ULUS4IEZFChBC4e/cufv75Z/Tv3x/AX3d8EhEREVVXdhUkPvroI3h7e2Pjxo1o3759nitRfXx8MG/ePDRr1gyLFi0qtaBEFY2wWpG5eAnMBw7Kbdr2j8B51EhIWq2CyYiqJvY9RJXLuZjL8nKzJg0VTEJEVD6WLFkCSZLkL5VKBS8vLwwcOBCpqalYtWoVRowYoXRMIiIiIkXZVZA4deoUunfvDi8vr0K3ad++PS5dumRXqLi4OIwaNQr+/v7QarUICgrCSy+9hLt37xZr/23btqFbt27w8vKCh4cHOnXqhC1bttiVhaggQghkr10H45bf5TZN0yZwfmE8JEdHBZMRVV1l3fcA7H+ISlNs3A0AOUOrNWwQqnAaIqKy16hRI7z66qt5viZPnoyuXbsiKSkJ//73vzmhNREREVV7ds0hIUkStA+4AjwjI8Ou8fMNBgM6duyIuLg4PPfcc2jatCl2796NefPm4eDBg9i3bx80msJjL1++HM8++yzq16+PKVOmAAC+/fZb9OzZE7///ju6dOlS4kxEf2fasRNZq1cD98axV9epA+eXX4LKlRN2EpWVsux7APY/RKUp22jCnaScQp6PtyccHDivCxFVfW3atMGcOXPytQsh8NJLL+H//u//8PHHH+ODDz5QIB0RERFRxWDXHRItW7bE1q1bkZGRUeD6O3fuYMOGDWjatGmJjz179mzExcVh7ty5+PbbbzF58mSsWrUKEydOxOHDh7FgwYJC971z5w7GjRuHiIgIHD16FNOmTcO0adOwf/9+uLq64r333itxHqK/M/35JzIXLwaMJgCAys8Pzq9MgtrbW9lgRFVcWfY9APsfotJ06mwMhBAAgDq1aymchohIWZIkYdKkSQCAPXv2KJyGiIiISFl2FSReffVV3LhxA4MGDco3NMbJkycxcOBAJCUlYfz48SU+9sqVK+Hu7o4JEybIbZIk4c033wQArFq1qtB9lyxZgvT0dMycORPOzs5ye40aNfDFF1+gT58+Jc5DdD/LtWvI/L8vIdLSAQCSmxucX34JmsBAhZMRVX1l2fcA7H+IStPJ0zHycniDugomISKqGOrWrQsHBwfcvn1b6ShEREREirJryKZ+/fphxowZePfdd9GgQQN5CA0PDw+kpaVBCIHnn38ew4cPL9Fxs7KycPnyZURHR+eZrBQAatWqhaCgIJw6darQ/X///Xc4ODiga9euAHKG7sjKyoK3tzeeeeaZEr5Korysd+8i49PPYMsd99VBD6fnRkPbiBN1EpWHsup7APY/RKUt5lKsvBzRJEy5IEREFYRKpYKPjw9SU1OVjkJERESkKLvukACAt99+Gzt27MBjjz0Gd3d3qFQq6HQ69OzZE2vWrMH8+fNLfMzcCb78/f0LXF+zZk0kJCQUun9sbCxCQ0Nx4sQJtG3bFi4uLvD19UWtWrXw2WefyUMHFMRoNCI1NTXPF1EukZWFjM//C2vs1ZwGtRqOTzwOfWQnZYMRVTNl0fcA7H+IStu16zcB5JyAq1entsJpiIgqBq1Wi8TERPb1REREVK3ZdYdErsjISERGRpZWFtjuTRD896tTczk4OBR5UichIQGOjo7o3r07unbtiuXLlyMjIwMff/wxJk+ejPT0dLz11lsF7jt79mzMmDHj4V8EVTnCYkHG/G9hOfGn3Kbv2QMOAwYomIqo+irtvgdg/0NUmlLTM5BiyDnZVsPPB9oiJoMnoqrHw8MDOp0OJpNJ6SgPpNPp4OHhUW7PFxwcjCtXrmDlypV4/vnny+15iYiIiCqSKvUJ0WAwIDk5GcOHD8eSJUsgSRIAYOjQoWjYsCHef/99vPjii3B3d8+379SpUzFlyhT5cWpqKoKCgsotO1VMQghkrVoN086dcpv2kXZwGv4sJLVawWREVJGw/yH6yx9/npGXQzihNVG1UzOgBjb8vBIpKSlKR3kgDw8P1Ayo8dDHGTVqFEaNGvXA7bZv3/7Qz0VERERU2RWrIBEaGmrXwSVJyjfx6IO2B1DoVahFXZ0KAO7u7khOTsbkyZPlYwGAq6srhg8fjg8++AD79u1D79698+2r1+sLvTKWqi/Tjp3IXvczcO93Tx0WBqcJL0Di7wpRmSuvvid3H4D9D1FpOHXuorzcsIF9/4+JqHKrGVCjVE70ExEREVHVU6yChM1my3OCBQDS09ORlJQEAHBzc0ONGjVw8+ZNpKWlAQC8vb3h4uJSojA+Pj4Acq40LYjBYICvr2+R+ycnJyMwMDDfuty2wo5N9Hfm02eQuXgJcO92c1VAAJwnvwy1q6vCyYiqh/LqewD2P0Sl6eLlq/Jys6bhCiYhIiIiIiKiiqZYk1rHxsbiypUr8tfBgwfh7e2NiIgIbNu2DSkpKTh37hwMBgO2bt2KFi1awMXFBTt27ChRGGdnZwQHB+PIkSOwWq151qWmpuLMmTNo1KhRofu3atUKAAq8MvbChQsA7L/ilqoXy61byJj3fxD3TnJKbm5wfvklaAqZ8JaISl959T0A+x+i0hR/4zYAQKPRIKR2/iIdERERERERVV/FKkj83cyZM5GQkIDff/8dnTt3zrMuOjoaW7ZsKXICz6IMHjwY8fHxWLhwYZ72Dz/8EGazGUOGDAGQc5XsrVu38lxx+uyzzwIA3nvvPVgsFrn9+vXrWLRoEWrUqIHmzZuXOBNVL9a0NGR8+jlst3NOqECvg9OY56ANa6BsMKJqriz7HoD9D1FpuJ2YhPSMTABAgL8v1Cq73moSERERERFRFWXXpNbr1q1D37595SEu/s7Lywv9+vXDb7/9VuJjT5s2DStWrMCECRNw6NAhNGzYEIcOHcKPP/6I5s2bY/z48QCAOXPmYMaMGRg5ciQWL14MAOjduzeefPJJ/Pjjj+jQoQMGDBiA9PR0LFmyBBkZGVi6dCl0Op09L5mqCWEyIfOrr2GNiclpUKngOHgw9I8+qmwwIirTvgdg/0NUGo7fN6F1aAjvjiAiIiIiIqK87CpIJCYmwsPDo8htnJyckJKSUuJje3l5Ye/evXjrrbewbt06LFq0CP7+/hg/fjxmzZpV5AkdSZKwbNkyNG/eHIsXL8asWbPg6OiIRx55BNOmTUPHjh1LnIeqD2GzIXP5CpgPHJDbdJ2j4DB4kIKpiChXWfY9APsfotJw5vxfw5Y1bFBXwSRERERERERUEdlVkAgICMDBgwcLXW+1WrFr1y7UqlXLrlB16tTBDz/8UOQ206dPx/Tp0/O1azQavPHGG3jjjTfsem6qvoxbtsC4YQMgch5rmjaB8/PPQVKrlQ1GRADKvu8B2P8QPazLsXHycvOIhgomIaKyJoRQOkK1x58BERERVUZ2Dew7YMAAHD16FDNmzMgzVjYAGI1G/OMf/8CZM2cwePDgUglJVNZMJ04ga9kPgDnn91kVGAjnl1+C5OiocDIiysW+h6hiE0Lg5q1EAICDXofAAH+FExFRWVDdmxvGarUqnIRyfwYqztdDRERElYhdd0i8/fbbWLduHWbOnIlvvvkGPXr0QEBAAG7duoVNmzbh5s2bqF+/PqZNm1baeYlKneXGDWR++TVERgYAQPLwgMukl6D29lY4GRHdj30PUcUWey0e2UYjAKBmDT9IkqRwIiIqC1qtFmq1GllZWXBxcVE6TrWWlpYGrVYLrVardBQiIiKiYrOrIOHr64s9e/Zg4sSJ2LBhA5YsWSKvU6lUGDp0KD777DO4u7uXWlCismBLS0PG5/+FLSEhp8FBD6cxz0FTl+NeE1U07HuIKrY/Tp6Tl0NDghRMQkRlSZIkODk5wWAwwMvLC2oOb6qIrKwspKamwsPDgwVgIiIiqlTsKkgAQO3atfHLL7/gxo0bOHbsmPyGtHXr1vD19S3NjERlQphMyPj6G1hjLuQ0qFRwHDIE+g4dlA1GRIVi30NUcZ27cN+E1mEs7BNVZX5+foiNjcXVq1fh5eUFvV7Pk+LlQAgBq9WKtLQ0pKamQq/Xw8fHR+lYRERERCVid0EiV82aNVGzZs3SyEJUboTNhqwff4J5/wG5TRcVCYeBjymYioiKi30PUcUTe+2GvNyCE1oTVWk6nQ6BgYG4c+cObt68qXScaker1cLDwwM+Pj68Q4WIiIgqnYcuSBBVRsat25D966+AEAAATaNGcH7+OUh8Q09ERFRiFosFtxPvAACcnBzh78srdomqOicnJ9SuXRsWiwUWi0XpONWGSqWCVqvlHSlERERUabEgQdWO+c9TyPr+e8BkBgCoagbA+eWXIDk5KZyMiIiocjp/8QrM5pwTkoEB/gqnIaLypNFooNHwYyURERERFY9K6QBE5cly4yYyvv4aIjUNACC5ucJ54gSo/Tj2PBERkb3+PB0jL3NCayIiIiIiIioMCxJUbdjS0pD5xRew5Y5zq9PCcfhwaBs1UjYYERFRJRdz6Yq83Di8noJJiIiIiIiIqCJjQYKqBWE0IvPbRbCcOZvTIElw6NsX+ujOSsYiIiKqEq7F/TWpbfOm4QomISIiIiIiooqMBQmq8oTNhqw1a2Has0du0z7SDo5Dn4Ck4n8BIiKih5GdbULinWQAgJurC7w8PZQNRERERERERBWW3bOPHT16FMuWLcPly5eRlpZW4DaSJGHr1q12hyMqDcadu5C9/hfAZgMAqOvVg9PYMZD0eoWTEVFJse8hqnhOno2B9V4fG1izhsJpiIiIiIiIqCKzqyCxceNG9O/fH1artcjtJEmyK1RFYjAYIIRQOgbZyXb+PMR3y4Ds7JwGb2/YRgxHuiQBBoOy4UrIYDAgIyMDhkqWm6qu1NTUcn2+6tT3AOx/qOJ4UP9z9I+T8nJQLX/2U1Tmyrv/ISIiIiKi0mNXQeLdd98FACxZsgRDhgyBk5NTqYaqSPbt21elX19V5mAwIHz7DjinpAAALDodYppF4O6Vy8CVy8qGs0NGRgZOnz4NAHB2dlY4DRGQmZlZrs9XnfoegP0PVRwP6n+O3VeQsJmzsGvXrnLLRtVTefc/RERERERUeuwqSJw8eRKPPfYYhg8fXtp5KpwOHTrAzc1N6RhUQiI1Dbb584G7KTkNajU0Ax9D0969FM31MHKvOO3QoQPc3d0VTkNU/leoVqe+B2D/QxXHg/qfH9fnFCAkScITgwfAxZmFNCpbvEOCiIiIiKjysqsgodfrUbNmzdLOUiG5u7vzhFAlI4xGZHz/A0ynTsttuq5d4DxkMCStVsFkD8/Z2Rnu7u4sSFCFUN5DI1Wnvgdg/0MVS2H9jyE1DSmGnJPDXp7uqFUzQIl4VM1UlaH5iIiIiIiqI5U9Oz3yyCPYv39/aWchemjCakX2z/+DacdOuU3TvBmchj9b6YsRRNUd+x6iiufEyXPInekkiBNaExERERER0QPYVZD44IMPcPbsWUyfPh02m620MxHZzbRzF7LXrwcsFgCAunYQnMaNhYpzLhBVeux7iCqeU+cuyMv16gYrmISIiIiIiIgqA7uGbPr+++8RGRmJf//731iyZAlatmxZ4BAykiTh22+/feiQRMVh+vNPZK74ESIjZ6JDydMTTmPHQFODV2wSVQXse4gqnkuxcfJy04YNFExCRERERERElYFdBYk5c+bIy1evXsXVq1cL3I4nhai8WK5dQ9aS7yDu3MlpcHCA49PDoG3cWNlgRFRq2PcQVTzxN24DANRqFZqwIEFEREREREQPYFdB4sqVK6Wdg8hutuRkZC39Dtbc30u1Cg59+0LfubOiuYiodLHvIapYbifcQVp6BgDA19sLDg46hRMRERERERFRRWdXQSI4mGMEU8Vgy8hA5vIfYT5+XG7TdnwUjoMHQlKrFUxGRKWNfQ9RxXL8z7PycnBQTQWTEBERERERUWVhV0EiV0pKCpYtW4a9e/ciOTkZnp6e6NixI5555hl4eXmVVkaiAgmzGdn/Ww/Trp2AyGnTNG4M5xHPQnJ0VDYcEZUZ9j1EFcOZmEvycoN6dRRMQkRERERERJWF3QWJjRs34umnn4bBYIAQQm5fuXIl3n33XSxZsgT9+/cvlZBEfydsNhh/3wrjb78BZgsAQBVYC46jR0LFE5JEVRb7HqKK48p9E1o3axKmYBIiIiIiIiKqLFT27HTmzBkMGjQIGRkZmDJlCnbt2oWYmBjs2bMHEydORFpaGp544gmcOnWqtPMSAQBMBw8ha+06iHtjV0seHnB69hloQ0MVTkZEZYV9D1HFYbPZcON2IgBAp9OiQd0QZQMRERERERFRpWDXHRKzZ8+GyWTCli1b0KVLF7m9Xr166NChA3r27IkBAwZg9uzZ+P7770stLBEAmM6cQdbKlRB37uQ0ODjAYfAgaNu0UTYYEZUp9j1EFUfstXhkZxsBADX8fKDmvE1ERERERERUDHbdIbF161Z07949zwmh+/Xr1w89e/bE9u3bHyoc0d9Zrl5D9o8rYYu9mtOgVkHfozscenSHJEnKhiOiMsW+h6ji+OPUOXk5pHaggkmIiIiIiIioMrGrIJGUlISwsKLHCm7QoAGSkpLsCkVUEOudO8hetRqWkyflNl2HjnAcPAiSTqdgMiIqD+x7iCqOczGX5eWGDThcIhERERERERWPXQUJb29vnD9/vshtYmJi4O3tbVcoor+zpaUhe+06mA4ckNs0TZvCcdhQqNzcFExGROWFfQ9RxXE1Ll5ebt60oYJJiIiIiIiIqDKxqyDRtWtXbNmypdBhMX799Vds2rQJ0dHRdgeLi4vDqFGj4O/vD61Wi6CgILz00ku4e/duiY/1888/Q5IkLF682O48pByRnY3sDRtg3L4DsFoBAKqQYDg+NQzqgABlwxFRuWHfQ1QxWCwW3E7IuRPJyckRtQPZFxMREREREVHx2DWp9ZtvvolVq1ahd+/emDRpEh577DH4+/sjISEBP/74I7744gvo9XpMnTrVrlAGgwEdO3ZEXFwcnnvuOTRt2hS7d+/GvHnzcPDgQezbtw8aTfGiGwwGTJw40a4cpDxhNiN72zYYN20GsrMBAJKPDxwffxza8KKHbiGiqoV9D1HFcDbmMswWCwCgZg1fhdMQERERERFRZWLXHRINGzbE6tWr4eDggI8++gidOnVCgwYN8Oijj+Lzzz+Ho6MjVqxYgSZNmtgVavbs2YiLi8PcuXPx7bffYvLkyVi1ahUmTpyIw4cPY8GCBcU+1uuvv4709HS7cpCyhM0G4569MP76G0SKAQAguTjDcUB/6Nq1VTgdEZU39j1EFcOfp/8aOq1uSG0FkxAREREREVFlY1dBAgD69OmDS5cuYc6cOXj88cfRpUsXDBkyBB999BEuXryIxx57zO5QK1euhLu7OyZMmCC3SZKEN998EwCwatWqYh1n165dWLhwIf7973/bnYWUYzp8GMYNv8F282ZOg1YLfY8e0HfvBkmtVjYcESmCfQ+R8mIuxcrLjcLqKheEiAAACQkJmDhxIoKCguDg4ICwsDD8+9//Rva9u4uJiIiIiCoSu4ZsyuXt7Y0pU6aUVhYAQFZWFi5fvozo6Gjo9fo862rVqoWgoCCcOnXqgcfJzs7G2LFj8corryAiIqJUM1LZM588CeNvG2G9dCmnQSVBFxkJh379IP3t94KIqhf2PUTKuhp3Q15u3jRcwSRElJCQgLZt2yIhIQHPPfcc6tSpg507d+Kdd97BpUuXOI8REREREVU4xSpIXLt2DW5ubvDw8JAfF1ft2iW7lT8xMREA4O/vX+D6mjVr4tChQw88zsyZM2EymfDuu+8Wa3uj0Qij0Sg/Tk1NLWZiKm2WixeR/dtGWP48Kbdp27SF46DHoPJwVzAZEZWn6tD3AOx/qHJJS0tHUnIKAMDdzRU+3l7KBiKq5iZNmoS4uDjs2LEDnTp1AgC8+uqrGDZsGJYsWYKZM2eWuE8kIiIiIipLxSpIhISE4LXXXsOHH34oP5Yk6YH7SZIEy71JD4vLZrMBQL4rVHM5ODhACFHkMU6cOIGPPvoI69evh5OTU7Ged/bs2ZgxY0aJslLps16PR/bGTTAfOiy3aZo0gcNj/aGuWVPBZERU3qpD3wOw/6HK5fjJc/L/l8CaBRfwiKh83L59Gz/99BMGDhwoFyNyTZs2DU2aNOGwTURERERU4RSrIDFixAi0bt06z+PinBRSgsViwfPPP4/HH38cvXr1KvZ+U6dOzTMESGpqKoKCgsoiIhXCmpCI7E2bYNq9B7h3skMdWgf6/n2hDeeQEETVTXXoewD2P1S5nD57QV6uVydYwSREtGPHDthsNvTv3x9ATl+UnJwMDw8PNGnSBE2aNFE4IRERERFRfsUqSPx97NGyHIs092RTYVeiPugK1c8++wwXL17EL7/8UqLn1ev1hV4ZS2XPlnwXxi1bYNqxAzCbAQCqGjWg79sXulatlA1HRIqoDn0PwP6HKpeLV/4aOq1Jw3oKJiGi2NhYAEBgYCCef/55LF++HFlZWdDr9Rg8eDA+/fRT+Pn5FbgvhwskIiIiIqWo7Nlp6dKlOHr0aJHbHD16FEuXLi3xsX18fAAABoOhwPUGgwG+vr4FrouLi8Pbb7+NV155BRaLBdevX8f169flscGTk5Nx/fp1mEymEueismNLS0P21q0wbtsOkZEJAJA8PKDv1RP6RztCUtn1a0pEVQz7HiJlWSwW3LydACCniNesaUOFExFVbwkJOf8fX3zxRfz++++YM2cOfvrpJwwdOhTLly9H165dCx2yafbs2XB3d5e/eGceEREREZUXu870jho1Cj/++GOR2/zwww8YPXp0iY/t7OyM4OBgHDlyBFarNc+61NRUnDlzBo0aNSpw30uXLiErKwvTp09HUFCQ/DV06FAAORO8BQUF4cyZMyXORWXDlpEB49atMG3bDnH3bk6jkxP03btB37ULJK1W2YBEVGGw7yFSVtyN2zCkpgMAvL084OzkqHAiouott4iekJCAI0eOYOLEiXjiiSewdOlSvPzyyzh16hSWLVtW4L5Tp06FwWCQv+Li4sozOhERERFVY8UasgkAdu3aledxXFxcvrZcFosFe/bsgaOjfR9UBw8ejLlz52LhwoUYO3as3P7hhx/CbDZjyJAhAID09HSkp6fD0dER7u7uaNq0KdavX5/veCdPnsSbb76JSZMmoVu3bqhTp45duah0iexsGHfshHH7Tthu385p1Gqh79wZDj17QOXsrGxAIlIc+x6iiuOPk+fk5dDgQAWTEFVdNpsNMTExD9zOyckJ7u7uAIBnn3023118EydOxOeff44tW7ZgzJgx+fbncIFEREREpJRiFyQ6d+4sj7EtSRJ++ukn/PTTT4VuL4RA165d7Qo1bdo0rFixAhMmTMChQ4fQsGFDHDp0CD/++COaN2+O8ePHAwDmzJmDGTNmYOTIkVi8eDG8vb3Rr1+/fMdzcXEBADRv3rzA9VT+hNmM7B07Ydq5C7bcK7LUaug6dYJDr55QeXkpG5CIKgT2PUQVR8zFWHm5cXh95YIQVWGZmZlo2PDBw6FFRUWhZ8+eAHLmkPi73LbChiIkIiIiIlJKsQsS77zzDiRJghACM2fORIcOHdC9e/cCt5UkCb6+vnjmmWfsCuXl5YW9e/firbfewrp167Bo0SL4+/tj/PjxmDVrFnQ6nV3HpYpBWK0w7d4D8959sF68mNMoSdC2bw99965QB9ZSNiARVRjse4gqBptNIC7+pvy4dYsmCqYhqrpcXFwghCjWtps3bwaQM3Tg3124cAEAEBoaWnrhiIiIiIhKgSSK+473PiqVCq+99ho+/PDDsshUIaSmpsLd3R0GgwFubm5Kx6kyhNUK0959MO7aDcuxY3K7tm0b6Hv0gK5lCwXTVWwGgwG7du1CZGSkfIs+kZLK++9kdeh7APY/VPEYDAb8/L9f8dOvuyCEgLubK777+kOoVXZNRUb00Ph3MofZbEZISAhSUlLw559/om7dugByhn0aNmwYVq5cifXr1xfrLj1+T4mIiKg6SB78uNIR4LVmldIRykRJ3k8W+w6J+3322WcwmUx2haPqS1itMO3fD9PBQ3mKEZpmEdBHRkLborly4YiowmPfQ6ScazcT5Ku2awcGsBhBVAFotVp8/fXXGDhwIFq3bo3Ro0fDz88PGzduxM6dO/HEE0+gb9++SsckIiIiIsrDrk+Ts2fPxp9//lnaWagKEzYbTAcOwnT0GMwHD8rt6rCwnGJEu7byOPFERAVh30OknJu3k+XlsPqcoJ2ooujXrx9+//13tGzZEosWLcL06dORlJSETz75BN9//z3fXxMRERFRhWPXHRLdunXDtm3bYDabodVqSzsTVTHCZoP54CGYjx+Hec9ewGYDAKhDQqCL6gTdox0hqdUKpySiio59D5EyhBC4czdVftyi6YMn3CWi8tO5c2d07txZ6RhERERERMVi1x0Ss2bNgtFoxPjx45GdnV3amagKEULAfOgwTMePw7RrN2C1AgBUNWtCFxUJh6goSDyxSETFwL6HSBlJySnIyMwCALi6OKNB3RBlAxEREREREVGlZdcdEr/88gtGjRqFuXPn4tdff0WPHj1Qu3Zt6PX6PNtJkoS33367VIJS5SOEgPnwYZj++AOmXbsAiwUAoPL3h65zFPRdoiE5OiqckogqC/Y9RMo4fuoc7k0fgVoB/nByYt9NRERERERE9rGrIPHiiy9CkiQIIZCYmIjvv/++wO14Uqj6EkLAfPQoTMdPwLRzF2AyAwAkX9+cOyM6d4bK1VXhlERUmbDvIVLGmXOX5OXwBpw/goiIiIiIiOxnV0Fi+/btpZ2DqpCcOyOOwHTiT5h27gSMRgCA5O0FfWQn6KMiofLxVjglEVU27HuIlBF7LV5ebtWsiYJJiIiIiIiIqLKzqyARFRVV2jmoihA2G8yHDsF8+kxOMeLeOO+Spwf0UVHQR0VCHRCgcEoiqozY9xCVvxRDGhKT7gIAnBwdEN4gVOFEREREREREVJnZVZDIlZKSgmXLlmHv3r1ITk6Gp6cnOnbsiGeeeQZeXl6llZEqCWG1wnzwIMznzsO0YyeQmQkAkNzdoI+Kgq5jR6gDAxVOSUSVHfseovJz5I9TsNlsAIAAf184OToonIiIiIiIiIgqM7sLEhs3bsTTTz8Ng8EAkTvTIYCVK1fi3XffxZIlS9C/f/9SCUkVn7BaYdq3H+aYGJh37ITIyAAASK6u0EVFQde+PTR1QpQNSUSVHvseovL1x8lz8nK90NoKJiEiIiIiIqKqQGXPTmfOnMGgQYOQkZGBKVOmYNeuXYiJicGePXswceJEpKWl4YknnsCpU6dKOy9VQMJigWn3HpjPn4dp+46/ihEuLtBFRULXrh009espnJKIKjv2PUTl7+LlWHk5onED5YIQERERERFRlWDXHRKzZ8+GyWTCli1b0KVLF7m9Xr166NChA3r27IkBAwZg9uzZ+P7770stLFU8wmzOKUZcvATTzh1A1r05I1xdoescBV3rVtA2DFc2JBFVCex7iMpXRmYWbt5KBABoNWqE16ujcCIiIiIiIiKq7OwqSGzduhXdu3fPc0Lofv369UPPnj2xffv2hwpHFZswGmHctRuWy1dyihHZRgCA5OYGXecoaCMioG3aVNmQRFRlsO8hKl/HT5yBxWoFAHi4ucCR80cQERERERHRQ7JryKakpCSEhYUVuU2DBg2QlJRkVyiq+GwZGTBu3QbLpUsw7djxVzHCwx26zp2hbdoU2hbNFc1IRFUL+x6i8nXszzPysp+3h3JBiIiIiIiIqMqw6w4Jb29vnD9/vshtYmJi4O3tbVcoqthsKQYYd+2CNT4epp27AJMJACB5ekIXGQlt08bQtWypcEoiqmrY9xCVr/MXr8jLQTV9FUxCREREREREVYVdd0h07doVW7ZsKXRYjF9//RWbNm1CdHT0Q4WjiseakAjjtm2wxMbCtH3HX8UIby/ooiKhjWjKYgQRlQn2PUTlJz0zE/E3bwMA9DotatZgoY+IiIiIiIgenl0FiTfffBM6nQ69e/fG66+/jn379uHSpUvYv38/Jk+ejEGDBkGv12Pq1KmlnZcUZL0eD9POnbBcvgzzrt2AxQIAkHx8oIuMhC4iAjoO00REZYR9D1H5OXzsJMzmnH6+hr8vdFqtwomIiIiIiIioKrBryKaGDRti9erVePrpp/HRRx9hzpw58johBFxdXbF06VI0adKk1IKSsiyXLsF89BjMMRdgOXZMblcFBEDb/hHoIppCGxGhYEIiqurY9xCVn6N/nJaX69WprWASIiIiIiIiqkrsKkgAQJ8+fXDp0iUsWbIEBw8eRHJyMjw9PdGuXTuMGDECvr4ca7gqEELAcvIkTKfPwHrmDCyn/5rgUh0SAk3rVtA1bQJt06YKpiSi6oJ9D1HZM5vNuHj5qvw4onF9ZKXdVTARERERERERVRV2FySAnAlGp0yZUlpZqIIRFgtMBw/BcvUaLMePw3rpkrxOHRYGTUTTnDsjGjdWMCURVTfse4jKVvzNBCQkJgEAdFotmoTXx+HDhxRORURERERERFXBQxUkkpKSsGzZMhw4cABJSUnw8PBA27Zt8fTTT6NmzZqllZEUIDIzYdyzF9bERJgPHoLt+nV5naZZM2jDw6Bt2RKa+vUUTElE1RH7HqKydezEaRhNZgBAzQA/ODo6KJyIiIiIiIiIqgq7CxKrV6/G888/j7S0NAgh5PZVq1bhnXfewbx58/Dcc8+VSkgqX7bkZBh374Ht7l2Y9u6DSMq5ShKSBG3bNtDUqQPtI49AUztI2aBEVO2w7yEqW1abDYePn5IfNw7nhQdEhcnOzkZMTAxu376NpKQkuLu7w9fXF2FhYXB1dVU6HhERERFRhWRXQeLQoUN48sknodPp8NJLL+Gxxx5DQEAAbt26hfXr12P+/PkYO3YsQkND0blz51KOTGXJev06TAcOwnonCaY9e4DMzJwVajW0HdpDExQE3aMdoa5RQ9mgRFTtsO8hKnu3b99B7LW/7oqM7NBGwTREFU96ejoWL16M9evXY/fu3TAajfm2UalUaNWqFXr37o0xY8agVq1aCiQlIiIiIqqY7CpIvPfeewCANWvWoFevXnJ7eHg4OnfujOHDh+ORRx7Bf/7zH54UqiSEELCcOQvLqVOwxN+A+cABwGLJWenoKBch9JGdoPL2VjYsEVVL7HuIyt6xP08jNS0DAODn44X6dYORnZWlcCoi5RkMBsyePRtff/01DAYDHBwc0Lx5c9SrVw+enp5wdXVFRkYGUlJScOXKFRw/fhyHDh3Ce++9h8cffxwffPABateurfTLICIiIiJSnF0Fib1796J37955Tgjdr3nz5ujXrx+2bdv2UOGofAiTCaZDh2CJuw7rhQuwnDgB3BsJRfL0zClG+HhDFxUFlZubsmGJqNpi30NUtoQQOHDkT/lxWP1Q6HU6FiSo2ps/fz7efPNNqNVqjBw5Ek899RRat24NtVpd6D5CCJw8eRI//PADvv/+ezRu3BizZ8/Giy++WI7JiYiIiIgqHrsKEhkZGQgNDS1ym5o1ayKLH2ArPFtKCkx798JqSIXl2HFYL1+W16kCA6Ft2wZqHx/oIztBcnJSMCkRVXfse4jKVlJyCi7HxsmP27WKUDANUcXx1ltvYdq0aZgwYQJ0Ol2x9pEkCREREYiIiMCsWbPwzTffYObMmSxIEBEREVG1Z1dBIjQ0FBcvXixym5iYGDRq1MiuUFQ+LLGxMB85Clt6Osz7D8CWmCivUzdsCE2TxtDUqgVd+0cgabUKJiUiYt9DVNaOnTgDQ2oaAMDH2xMN6oYoG4iogrh06VKJJ6l++eWX8euvv+LSpUvQaDSYOHEiRowYUUYJiYiIiIgqD5U9O40YMQKbNm3C//73vwLXr169Gps3b8a4ceMeKhyVDWG1wnT0WM7k1bduwbh5y1/FCJUK2nZtoW3aBNrwcOg6PcpiBBFVCOx7iMrWvsPH5eWQ2rXg7+ejYBqiiqOkxQgASExMRGxsbJ42FxeXUkpERERERFR52XWHRKtWrdCiRQsMHToUI0eORP/+/eHn54ebN29i/fr1+O6779CxY0eEhYVh165defaNjIwsleBkH5GZCdO+/bAkJsJ64eK9+SLuTRjh6ABd+/ZQ+/lB26oVNHWLHhqFiKg8se8hKjuG1HRcvHxVftyuVQQ0msLHxyciIiIiIiKyh10FiR49ekCSJAghMH/+fCxYsEBeJ+6d3N67dy+6du2ab1+r1frA48fFxeHtt9/Gb7/9huTkZNSoUQMDBw7EzJkz4enpWeS+6enpmD17NpYvX464uDj4+Pigb9++mDFjBmrVqlXCV1q1WOPiYDp8BLaMDJiPHIEt7rq8TuXrC237R6Byc4OuYweo/f0VTEpElF9Z9z0A+x+qvv44eRZ3U1IBAN6eHmjWOFzhRERERERERFQV2VWQeOeddyBJUmlnAQAYDAZ07NgRcXFxeO6559C0aVPs3r0b8+bNw8GDB7Fv3z5oNAXHNplM6N69Ow4cOICRI0eiadOmOHz4ML799lts2LABhw8frpYnhYTZDPPxP2C5fBm21FSY9+2DuDdGNACow8OgadIEand36CI7QWXHbelERGWtLPsegP0PVW97DhyVl+sE10KNGr4KpiEiIiIiIqKqyq6CxPTp00s5xl9mz56NuLg4zJ07F5MnTwYATJo0CS+++CK++OILLFiwAC+88EKB+y5YsAAHDhzAe++9hzfffFNu79atG8aOHYv3338f//d//1dm2SsiW/JdmA4cgNVggDX2KizHjwMWS85KrQbatm2hrlUL6sBa0LVtC0mnUzYwEVEhyrLvAdj/UPWVmZWNcxcuy48fadMcapVd04wRERERERERFemhP23evXsXmzZtwvLly7Fx40YkJyc/1PFWrlwJd3d3TJgwQW6TJEk+wbNq1apC9/3555/h6uoqn0jKNWrUKDg6OuYbU7wqE0LAfO4cjL//DuudOzDvPwDL4cNyMUJyd4OuWzdogoKgbdYMuo4dWYwgokqjtPsegP0PVV9/njqH5LsGAICnhzuaN22ocCIiIiIiIiKqquy6QwIAUlJS8Morr+D777/PMza3SqXCU089hU8//RReXl4lOmZWVhYuX76M6Oho6PX6POtq1aqFoKAgnDp1qtD9k5OT0aJFCzg5OeVpzx1z3JJ7Z0AVZ8vIgPnQYVhv34Y1IQHmg4eArCx5vTokBJqWLaBycYGuQ84k1kRElUFZ9D0A+x+q3nbtOyIvh4YEooafj4JpiIiIiIiIqCqzqyCRmZmJLl264I8//kBAQAB69+6NGjVqICEhAb/88guWLVuGP/74A/v374ezs3Oxj5uYmAgA8C9kQuWaNWvi0KFDhe5/+PDhAtu3bduG7OxstG7dutB9jUYjjEaj/Dg1NbU4kSsUIQSsly7BfOJP2LKNsJw6Bev5839toNNC26o11EGBUPv6QtehPSRHR+UCExGVQFn1PQD7H6q+srKzcfr8Rflx+7bNy3SuFqLKSK1WKx2BiIiIiKjKsKsgMXfuXPzxxx8YN24cPv/8c+juG+onIyMD//jHP/Ddd9/hk08+wdtvv13s49psNgDId3VqLgcHBwghSpT1/PnzGDNmDDQaDaZNm1bodrNnz8aMGTNKdOyKxJaeDvPhI7Devp0zcfXBgxB3U+T1Kj8/aNu2geTkBG14GDQREZA4PjQRVSJl1fcA7H+o+jp09CTuJN0FAHi4u6JVsyYKJyKqeEr69z8Xi3tERERERPnZdUZ6xYoVqFevHr744os8J4QAwNnZGd988w3q1q2LH3/8sVRC2kMIgRUrVuCRRx7BjRs3sHz5coSFhRW6/dSpU2EwGOSvuLi4ckxrPyEELDExMG7cBMvNm7CcOQvT5i1/FSNUEjQREdBGRULl5QV9ZCS0zZuzGEFElU5l6HuA6tP/UOVnsVixY89B+XHdOrXh6+2pYCKiislms9n1df/QgkRERERElMOuOyQuXbqEUaNGQVXISW2dTodu3bph8eLFJTpu7lVEhV2FVNyrkxITEzF+/HisXbsWoaGh2LBhA9q3b1/kPnq9vtArYysqW2pqzl0RiYmwJd+F+chhiBSDvF5ydYX2kXZQeXpCHVgLujZtIFWy10hElKus+h6A/Q9VT5dj43Dlarz8uGO7lryim4iIiIiIiMqUXQUJnU6HzMzMIrfJysqCVqst0XF9fHImUTQYDAWuNxgM8PX1LfIYp06dQo8ePZCQkICpU6di2rRp+SYZreyExQLL6TOwnD8Pm8kMy+nTsMbEALknzCRA3SAMmsaNoHJwgLZFC2jqhiobmojoIZVV3wOw/6HqRwiBoydOIzEpGQDg7uaCNi2aKpyKiIiIiIiIqjq7ChJNmzbFL7/8gpSUFHh4eORbf+vWLfzvf/9DREREiY7r7OyM4OBgHDlyBFarNc8EcqmpqThz5gw6dOhQ6P63b99G586doVarsXfvXrRr165Ez18ZWK9fh/n4cdgyMmFLSIT5yBGI9HR5veTuDm2b1lB5eUHt4wNtu7ZQuboqmJiIqHSUVd8DsP+h6uf6jds4ceqc/Lhundrw8nRXMBFRxdWlSxe79pMkCVu3bi3lNERERERElZtdBYnx48djxIgR6NWrFxYuXIhGjRrJ644fP46xY8ciJSUFY8aMKfGxBw8ejLlz52LhwoUYO3as3P7hhx/CbDZjyJAhAID09HSkp6fD0dER7u45H6CnT5+OtLQ0HDlyBE2bVq2r/GxpaTAfOw7rzZsQWVkw/3kStqtX/9pApYKmUSOow8Og0mqhadQImobhnCuCiKqMsux7APY/VL2cOHUO5y9cAZBz0vT/2bvr8CjOtY/j391NsnEjIQRIgru7FS9Ohba0PRXqLT2cKi8txWlL3Y1yWqRClVO0uLsWKF5cggSJ6+7O+0eahTRCkGQT8vtc117JPvPM7D2TTe7s3DPP06V9/kOLiZRmy5Yty3OZyWTKc1g/DYEmIiIiIpLTVRUk7rvvPhYuXMi3335L/fr1qVq1KmFhYZw5c4b9+/djGAb33nsvAwYMuOJtDx8+nB9//JGBAweyYcMGateuzYYNG/jpp59o1KgRTz75JADvvvsuY8aMYcCAAUyePJn09HS+++47KlWqxOrVq1m9enWObVeoUIG+fftezS67jJGRgW3PHmx7Modnsv/1F7Zdu8Bmc/YxlSmTeVeEvz+W0FDn9yIiN5LCzD2g/COlx9nzF1i7cSvpGRkAVIqsQOMGtV0clUjxtXTp0lzbv/jiC3755Zc8l4uIiIiISE5XVZAwmUxMmTKF9u3b89FHH7Fjxw72798PQN26dfnPf/6T7erSKxEcHMzq1asZNmwY06dPZ9KkSYSFhfHkk0/y2muv4eHhket6Bw8eJDExkX379jFw4MBc+3Tp0qXEnBAyHA7sBw+SsWMnRmoq9lOnsP2xFSMh4WInD3fc6tXDUrUqZqsV94YNsFSpoquxROSGVJi5B5R/pPTYvnMfe/YddD5v1awhAf4a3lEkLx06dMi1ffbs2fkuFxERERGRnK6qIPHpp5/SoEEDHn30UR599FGSkpKcY3r7+Phcc1CVK1dm6tSp+fYZPXo0o0ePdj6vVatWnrdLlzT2EyfI2LYdR3w8jsREbFu34YiOztbHUrUKbvXqYbJasURE4NGkMSYvLxdFLCJS+Ao794Dyj9z4EpOSWb56A6lpaQBEVAinUX3dHSEiIiIiIiJF46oKEuPGjaNbt260b98eyJwM9HqdDCrNHOfOkbFtO/YzZzBSUrHt3oX9wEG45ESXKaQM7o0bYw4KwuzjjXvjJlgqVnBh1CIiRUO5R+Ta7dzzF7v2HnA+r1erGlER5V0YkYiIiIiIiJQmV1WQ6Nq1K4sXLyYjIwN3d/frHVOp44iNJWPnLhzHj+NIS8e2dy/2ffvAbr/YydMT94YNMEdGZk5aXbsWbjVrYnK7qh+hiEiJo9wjcm3S0zNYtGwtyckpAJQvV5ZGDWrj56vCnoiIiIiIiBQN89Ws9Nprr5GWlsaTTz5Jamrq9Y6p1HDExpK2eg1p8xdgO3yYjD17SPv9d+y7d18sRri5YalTB2vPHrhVqoR75Up49uqJe926KkaISKmi3CNybfbtP8Sfu/Y5n9erXZ36dWq6MCIREREREREpba7qjPbs2bN56KGH+OCDD5gzZw7dunUjMjISq9WarZ/JZGLEiBHXJdAbiSM2joydOzPviMiwYT90CNuePZCScrGT2YSlalXcatfG5OmJuUwZPBo3xhxSxnWBi4i4kHKPyNWzOxzMW7KKhMQkAMqGlqFF0wYE+Pu6ODIREREREREpTa6qIDFo0CBMJhOGYRATE8P333+faz+dFMrOcf48Gbv3ZBYi0tOxHziIbd9eSE3L1s8cFYVb3bqYfX0w+/niVq8elshITCaTiyIXEXE95R6Rq7d77wH+2L7L+bx+nRrUq13dhRGJiIiIiIhIaXRVBYmlS5de7zhuWIZh4Dh5EtvuPdhjYjDS0rHv/wvbX39Beka2vuby4bjVq4c5MBCzjzdudepgqVwZk/mqRtYSEbmhKPeIXJ24+ETmLVpBbFwCAGWCAmnborHmjhApIIvFclXLTSYTNputMEISERERESmxrqog0aFDh+sdxw3HsNuxHz6Cbd9eHHHxOJKSsP+1H/vBg/CPDybmihVxq10bc1AgJk9P3OvUxlK1KqbLfPgRESlNlHtErpxhGKzd+AfbL5k7on6dGtSvU8OFUYmULIZhFOl6IiIiIiI3sgIXJNLT05kyZQpLlizh3LlzhISEcPPNN3Pffffh4eFRmDGWKEZKCraDB7H9tR9HSgrG2bPY9v2FI/oEXPqZxGTCHBmJW+1amP39MXl64lazJm7Vq2myahGRvyn3iFybvX8dYseuvzh3PhaAwAA/OrRrgbe3l2sDEylBHA6Hq0MQEREREblhFOjMd3x8PJ06dWLr1q3ZrvT56aefGD9+PIsXL8bXt3RPimg/fRrb/gM4TpzAkZGB/egx7H/9hREbm72j2YylciUsNWtlzhHh64tbzZpYKldSIUJE5BLKPSLXJiExifVbtrN+8zZnW706NahfR3NHiIiIiIiIiGsU6Az4q6++yh9//EHlypV58cUXqVSpEgcPHuT9999n06ZNvPbaa7z55puFHWuxY6SnYz98GNuBA5nDMsUnYD90CPvhw5CWfaJqPD1xq1oVS9UqmDw9MQcF4VarFpaIipojQkQkF8o9Ilcvc6imrazbuJXklFQAyoYE061jG7w8PV0cnYiIiIiIiJRWBSpITJ8+HT8/P9atW0doaKiz/a677qJ69er873//KzUnhQzDwHH6DPbDh7AfP4EjNRXH8ePYDh7COHs2R39TcBBu1atjrhiB2c2CuVy5zDsiyoW5IHoRkZJDuUfk6v114AjrNm7l6PGTAHi4u9O+dTPqae4IERERERERcaECFSSOHj1K3759s50QAggLC+Pmm29m9uzZhRJcceJISMB++HDmIzEJ48IF7IcOYz96FDIysnc2mzBXqIBb9RqYygRjtlqxVKqEW/VqmP38XLMDIiIljHKPyNVJSk5h+eoNbNq609nWslkDmjSqh1Vzr4iIiIiIiIgLFaggkZGRQURERK7LIiMjsdls1zWo4sR24ABp585jP3s2c0imo0dxHD2KkZiYo6/J3w9L5SpYoqIweVozh2WqVg1LVKTmhxARuUKlOfeIXIvVG7awfM1G7HY7ANUqR1KtchS1a1RxcWQiIiIiIiJS2hX4LLm7u3vuG7jBT7Snr1mH29kY7EePYlyIzdnBYsESEYGlSmVMZcpg9vDAElERtypVMYeUKfJ4RURuJKU194hcrT37DjJ/0SouxMYD4O/nS9NGdWnbqgkeHrn/PomIiIiIiIgUFZ3Rycfc9h3xWLyY3K7BNZctizkyAktEBGYPD8xhYVgqV8JSoYLuhhAREZEid+DwMWbPX8qevw4CYDabaduyCc0a1SM8LPQya4uIiIiIiIgUPp05z0eLMtnvcDAFBWGJjMQSEYHJ2wtzQEDm3BCVojB5ebkoShERESntjhyLZtmqDazduNXZ1qh+LRrVr0WdWtVcF5hIKRAfH8+UKVPYs2cPgYGB3HbbbTRv3tzVYYmIiIiIFEsFLkjMnTuXs2fP5mjfuHEjAI888kiOZSaTia+//voawnM9h58f7hERWKIiMfv5ZRYhIipiqRiBOTDA1eGJiNzQSmvuEbkSJ6JPs3jFWpauXEdqWjoA5cuVpVWzhrRq1si1wYncIObNm8eIESPYuXMnYWFhPProowwbNoyDBw9y0003cfr0aQzDAODNN9/kmWee4YMPPnBx1CIiIiIixU+BCxI7d+5k586deS6fPHlyjraSflKo2YJ5rPn4E3zLl/+7CFERc4CKECIiRaU05h6RK3HydAxzF61g0fK1JCQmAeDlaeWmNs1o36Y5bm4WF0coUvLNmzePPn364HA4ADhy5AijRo3i7NmzHDlyhFOnTvHAAw/Qpk0bDh8+zH//+18+/vhjmjZtyv333+/i6EVEREREipcCFSQmTZpU2HEUS4eSkvDs3g3PChVcHYqISKlTWnOPSEHFnD3PzLmLWbhsLSkpqQB4e3nSuX0rbu7QBj9fHxdHKHJjePfdd3E4HLz55ps8/fTTJCUl8fHHH/PWW29hNpt56aWXeOONN5z9n3rqKerXr8+nn36qgoSIiIiIyD8UqCAxYMCAwo6j2DL7+bk6BBGRUqk05x6Ryzl3PpZfZsxn0bLVpKVnAODv50Onm1rRrmUTwstpEmuR62X79u20adOGIUOGAODr68vrr7/OrFmz2LlzJ48//ni2/lFRUfTu3ZuZM2e6IlwRERERkWLN7OoARERERKTg/jp4hG9+msH8JSudxYigwAC6dmxDo3qaxFrkejt//jwNGjTI0Z41cXVkZGSOZRUqVCA1NbXQYxMRERERKWkKPIeEiIiIiLhOenoG6zZtY/majWzYvN05nn3ZkGA6tmtBq+aNqFmtsoujFLnxOBwOfHxyDoEWFBQEgJtbzo9UJpOp0OMSERERESmJVJAQERERKeZizp5n7qKVrFizkZOnY5ztFcLD6NK+FR3btSCsbIgLIxS5seVWYFDRQURERETkyqkgISIiIlJMGYbB9p17+Xn6PP7ctRe73eFcVqVSBD06t6PjTS3x8fZyYZQiIiIiIiIiBaOChIiIiEgxdO58LL8vXM68xSuJjUtwtnt7edKscT3atmxCq2aNcHOzuDBKERERERERkYJTQUJERESkGLkQG8+i5WtYsWYTBw8fy7asRrVKNG9cj6aN6mm+CJEiNGHCBH799ddsbefPnwegSpUqOfpnLRMRERERkexUkBAREREpBi7ExvPb7IWsWreZ0zHnsi0LCvSndfNGtGvVjDo1q+Du7u6iKEVKp/j4eOLj43Nddvjw4VzbNceEiIiIiEhOKkiIiIiIuNCRYyf436yFrNu0jcSk5GzL3N3cqF+nBn16dKJh3Zp4elpdFKVI6XXo0CFXhyAiIiIicsNQQUJERESkiO0/dJSFS1fzx/ZdRJ+KwTCMbMv9fH2oXaMKHdu1pGmjuvj6eLsoUhGJiopydQgiIiIiIjcMFSRERERECllKaio7du9n5ZpN/PHnLs5fiMu1X3hYKK1bNKJj25ZERoTj7qZ/1UREREREROTGUSw/5R47dowRI0Ywd+5czp8/T7ly5bjtttsYO3YsQUFB+a6bkpLC66+/zg8//MDRo0fx8/OjU6dOvPrqq9SpU6eI9kBEREoi5R+5Hmw2O4ePnmD3vv1s37mP/QePEHPuQo67ILL4+nhTvUoU3Tu3o3nTBnhaPYo4YhHJT26TVheEyWTiwIED1zkaEREREZGSrdgVJOLi4mjbti3Hjh3jkUceoX79+qxcuZJPP/2U9evXs2bNGtzyuFrQMAzuueceZs6cSefOnRk0aBBHjhzhs88+Y/HixWzfvp3IyMgi3iMRESkJlH/kStgdDi5ciCP61BlOnDzN8ejTHD0WzYmTpzl77gJ2hyPPdU0mCA8rS/06NWjbsgn169bAQ5NUixRbDocjxwTViYmJnDuXOfm8v78/5cqV4+TJkyQkJABQpkwZfH19izxWEREREZHirtgVJN544w2OHTvGBx98wHPPPQfAs88+y6BBg/j888/56quveOqpp3Jdd/78+cycOZPbb7+dX375BYvFAkDbtm3p378/Q4YM4ccffyyqXRERkRJE+UcybDYSEpKIT0gkPiGRhMSL3587H8v5C3GcPXeBs+cvEBuXkOcdD/9kNpsICw2hclRF6tWuTvs2zQkK9C/kvRGR6+Xw4cPZnp85c4b27dtToUIFPvzwQzp27OhctnTpUgYPHsz58+dZtmxZkcYpIiIiIlISmIyCfpouIlWrVuXcuXOcPn0aq9XqbD9x4gQVK1akS5cuLFq0KNd1H330USZOnMj69etp0aKFs90wDGrWrMmJEyc4d+4cnp6el40jPj6egIAA4uLi8PfXSQNxvbi4OFasWEH79u0JCAhwdTgiN9zfSeWfomUYBg6HI/Px9/eGw8D+d5vd4cBhd2C327E7HNj//t5ms2Oz253fZ361kWGzkZFhIyMjw/l9ekYGaWnppKWnk5aWTnp65vOUtDRSUlJJTknN9jU9I+Oa98vdzY3Q0GCiKpands2q1KlZlSqVIrB6lNxhmJR/pLhx9d/JQYMGMXXqVPbt20dISEiO5efPn6dmzZp0796d7777rsjjuxquPqYiIiIiReF8vztdHQLB//vV1SEUiiv5f7JY3SGRkpLCwYMH6dSpU7aTQQAVKlQgIiKCHTt25Ln+jh07cHd3p3HjxtnaTSYTrVq14ttvv+Xw4cPUqlWrUOIXEZGSqbjln/Y97uLFEW9jNptzLMvvMgID49Inl6xj5NrHMC5u0Li07yVtznWNzHUNw/h7cebXzOUGDsffWzYMHH9vw8hax5HZZhgO5/cllclkIsDPl+DgQEKCAwkvV5YK4WFUCM/8WiY4MNefm4jcOKZPn07v3r1zLUYABAcH06dPH+bOnVvEkYmIiIiIFH/FqiARExMDQFhYWK7Ly5cvz4YNG/Jdv0yZMrjnMg5z+fLlgcxbrHM7IZSWlkZaWprzeXx8/BXFLiIiJVdxyz++/kFEn4q5on2QK2exmPH28sLbyxMvL0+8vTzx8/PF39cHPz8f/Hx98ffzwc/XhzJBgZQJDiQoMAA3N4urQxcRF4qJiSEwMDDfPt7e3sTGxhZJPCIiIiIiJUmxKkg4/p4A8p9Xp2bx9PTMd7xmh8OR77qXvsY/vfHGG4wZM+ZKwhURkRtEccs/N9/2EAEB/pjyuNLelGvr38sumXg1+xyspnz6mLL1N5lMmLL6my72N/3dKXN51vdgwoTJnLmOyQQmk9nZjgnMZjNmU2Yfs8mcuX5W+98Pk8mExWzGZM78arFYMJvNWMxmzBazs83NzZL51WLB4pb51d3NDXd3N9zd3XF3c8PNPfO5h7s7Vg8PrFYPrB7uf3/NfO7t5YW7u1uOiWpFRC4nPDyc9evX57ncbrezYsUKKlSoUIRRiYiIiIiUDMWqIOFKQ4cO5YUXXnA+j4+PJyIiwoURiYhIaZBX/tE43iIixdMtt9zCZ599xpgxYxg2bBhubhc/UqWlpfHss8+ya9eubH/bRUREREQkU7EqSGRdpZjXVaiXm3/bZDLleQVq1rp5XQlptVrzvLpVRERubMo/IiJSUCNGjGD69OmMHTuWCRMm0K1bN8LDwzl16hTz58/n5MmTVK9eneHDh7s6VBERERGRYqdYFSSyJoaLi4vLdXlcXByhoaH5rv/XX39hGEaOEz9Z28xvfRERKZ2Uf0REpKBCQ0NZtWoVTz/9NL///jtTpkxxLjObzfTv35+PPvqIgIAAF0YpIiIiIlI8FauChI+PD1FRUWzatAm73Y7FcnHSyPj4eHbt2kWbNm3yXL9u3bps2rSJv/76ixo1amRbtn79ejw9PalUqVJhhS8iIiWU8o+IiOTF4XBg/secPpGRkcyePZvo6Gi2bNlCXFwcwcHBNGvWLM8CdG7bEREREREpbYrdf8T9+vXjxIkTTJw4MVv722+/TUZGBnfccQcAiYmJnDp1KtvVrP369QNgzJgx2YbXWLRoEevWraNXr154e3sXwV6IiEhJo/wjIiK5qV+/Pr///nuuy8qXL0+fPn2477776NmzZ57FiJkzZ1K/fv3CDFNEREREpEQoVndIAAwfPpwff/yRgQMHsmHDBmrXrs2GDRv46aefaNSoEU8++SQA7777LmPGjGHAgAFMnjwZgL59+9KjRw+mTp3KuXPn6Nq1K8ePH2fChAn4+vryzjvvuHDPRESkOFP+ERGR3HTr1o2+ffvSrFkzHnnkEfr3709QUNBl1zt79iw//vgjkydP5s8//+T5558vgmhFRERERIq3YleQCA4OZvXq1QwbNozp06czadIkwsLCePLJJ3nttdfw8PDIc12TycSvv/7K2LFj+fnnn1m8eDG+vr7cfPPNvPrqq1SpUqUI90REREoS5R8REcnNBx98wN13380zzzzDwIEDGTRoELVr16Zp06ZUqVKFwMBAfHx8SExM5MKFCxw8eJDNmzezZ88eHA4Ht956K99//z01a9Z09a6IiIiIiLicybh0bAlxio+PJyAggLi4OPz9/V0djghxcXGsWLGC9u3ba5JEKRb0d7Jw6LhKcaP8I8WNK/9Orlmzhs8//5wFCxZw9uxZZ7vJZMo2ZF9kZCR9+/blP//5T465hYoj5R4REREpDc73u9PVIRD8v19dHUKhuJL/J4vdHRIiIiIiIiLFUZs2bWjTpg0Au3btYseOHcTExBAXF0dQUBBly5alfv36JaIIISIiIiLiCipIiIiIiIiIXKE6depQp04dV4chIiIiIlKimF0dgIiIiIiIiIiIiIiI3PhUkBARERERERERERERkUKngoSIiIiIiIiIiIiIiBQ6FSRERERERERERERERKTQqSAhIiIiIiIiIiIiIiKFTgUJERERERGRq+BwONi/fz/Lli0DwGazuTYgEREREZFiTgUJERERERGRK5CSksLQoUMpW7YsNWvWpGvXrgA899xz9OzZk3379rk4QhERERGR4kkFCRERERERkQJKTk6mffv2vP3224SEhFC1alUMwwCgUqVKLF68mLZt23Lw4EEXRyoiIiIiUvyoICEiIiIiIlJAr732Gps3b2bMmDHs3r2bW2+91bls8ODBLF++nLi4OMaNG+fCKEVEREREiic3VwcgIiIiIiJSUvz44480bNiQ4cOH57q8devW9O7dm6VLlxZxZCIiIiIixZ/ukBARERERESmg6OhoWrRokW+fiIgITpw4UUQRiYiIiIiUHCpIiJQQVquVGjVqYLVaXR2KiIiUIso/ItmVKVOG6OjofPscOHCA4ODgIopIRERERKTkUEFCpITw9PSkZs2aeHp6ujoUEREpRZR/RLLr2bMn8+fPZ8uWLbkuX7JkCfPmzaNjx45FG5iIiIiISAmggoSIiIiIiEgBjRw5Ej8/Pzp06MCIESP466+/AJgxYwYvvvgiffr0wdPTM885JkRERERESjNNai0iIiIiIlJAkZGRLF68mLvvvpvXX3/d2d6vXz8MwyAsLIxvvvmGOnXquDBKEREREZHiSQUJERERERGRK9CoUSN27drFnDlzWL16NefPnycoKIhmzZpxyy23aIgzEREREZE8qCAhIiIiIiJyhSwWC7fccgu33HKLq0MRERERESkxNIeEiIiIiIjIFTh79iwvvvgir776qrMtJiaGrl278uWXX2IYhgujExEREREpvlSQEBERERERKaAzZ87QvHlzPvzwQw4dOpRt2ZIlS3j66afp1q0bKSkpLopQRERERKT4UkFCRERERESkgF5//XWOHDnC4MGDef/9953toaGhxMTE8Mgjj7BkyRLefPNNF0YpIiIiIlI8qSAhIiIiIiJSQIsWLaJly5a89dZbBAYGZltWpkwZvvzyS2rXrs1PP/3kmgBFRERERIoxTWqdh6xxX+Pj410ciYhI8ZT191HjZF9fyj8iIvlzdf45fPgwjzzySJ7LzWYzbdu2ZcqUKUUYlYiIiIhIyaCCRB4SEhIAiIiIcHEkIiLFW0JCAgEBAa4O44ah/CMiUjCuyj8eHh6cPXs23z5paWl4enoWUUQiIiIiIiWHChJ5KF++PMeOHcPPzw+TyeSSGOLj44mIiODYsWP4+/u7JIbiQMchk45DJh2HTMXhOBiGQUJCAuXLl3fJ69+olH+KDx2HTDoOmXQcMhWH4+Dq/NOsWTNmz57N0aNHiYyMzLH80KFDzJw5k4YNG7ogOhERERGR4k0FiTyYzWYqVqzo6jAA8Pf3L9UffLPoOGTSccik45DJ1cdBd0Zcf8o/xY+OQyYdh0w6DplcfRxcmX9eeOEFevfuzU033cTQoUPp06cPZcuW5dy5cyxYsIDRo0cTFxfH008/7bIYRURERESKK01qLSIiIiIiUkA9e/bk7bff5sSJE/z73/8mKioKLy8vKlasyCOPPMKRI0d49tlnufvuu4s8toSEBCIjI+nYsWORv7aIiIiISEHoDgkREREREZErMHjwYHr27Ml///tf1q9fz9mzZ/Hx8aFhw4Y88sgjdOjQwSVxDR06lGPHjlGlShWXvL6IiIiIyOWoIFGMWa1WRo0ahdVqdXUoLqXjkEnHIZOOQyYdBylMen9l0nHIpOOQScchk47DRXXr1uXDDz90dRhOa9asYcKECXh5ebk6FBERERGRPJkMwzBcHYSIiIiIiIhcnbS0NBo3bkzfvn356aefqFSpEsuWLSvw+vHx8QQEBBAXF6c5UkREROSGdb7fna4OgeD//erqEArFlfw/qTkkRERERERErsBHH31E48aNCQgIwGKx5Ppwcyu6m9HHjRtHSkoKI0eOLLLXFBERERG5GhqySUREREREpIDef/99Bg8ejIeHBw0aNMDHx8el8ezYsYM33niDGTNmFDiWtLQ00tLSnM/j4+MLKzwRERERkWxUkBARERERESmg8ePHExgYyPr166levbpLY7Hb7Tz22GP069ePnj17Fni9N954gzFjxhRiZCIiIiIiudOQTcXU9OnTadOmDT4+Pvj4+NC5c2fmzZuXo59hGEyZMoUmTZrg6emJj48P7dq1Y86cOS6IuvAYhkGVKlUYPXp0rsvtdjvvvfcederUwWq14u/vT/fu3Vm7dm3RBlpIjh07xkMPPURYWBju7u5ERETwn//8hwsXLrg6tEI3duxYKlWqlOuyNWvW0L17d/z9/bFardSpU4f3338fu91etEEWksOHD/Pggw9StmxZ3N3dqVGjBmPHjiU1NTVbv507d3LHHXcQHByMu7s7VatWZfjw4Tn6iVyOck92yj2lN/dA6c0/yj2Xd+TIEW6//XaXFyMAPv30U/bs2XPFk2sPHTqUuLg45+PYsWOFE6CIiIiIyD/oDoli6JNPPuGZZ56hYcOGjBkzhpiYGCZOnEjPnj2ZOHEiDz/8sLPvhx9+yAsvvEC1atUYPXo0aWlpfPHFF/Tp04fp06dz6623unBPrp9vv/2WQ4cO5bn8hRde4OOPP6ZJkyaMGzeOc+fO8emnn9KhQwfWrFlDs2bNijDa6ysuLo62bdty7NgxHnnkEerXr8/KlSv59NNPWb9+PWvWrCnSMYqLUnR0NBMmTMh1/zZs2ECHDh3w8vJi0KBBlClThqlTp/Liiy9y4sQJ3nvvPRdEfP0cPHiQli1bkpKSwr///W+CgoKYPXs2o0aNYtmyZSxcuBCLxcKRI0do3bo1qampPP3000RFRTFr1ixef/11du/eza+//orJZHL17kgJoNyTk3JP6cw9UHrzj3JPwQQFBRXa+9/hcLBv377L9vP29sYwDIYNG8Z7771HuXLlruh1rFYrVqv1asMUEREREbl6hhQrZ8+eNTw9PY1GjRoZCQkJzvbo6GgjJCTECAoKMlJTU519vby8jKioKOP8+fPOvocOHTK8vb2N8uXLGzabrcj34XqJjo42Vq9ebbzwwguG2Ww2AGPUqFE5+u3cudMwmUxGixYtjJSUFGf7+vXrDcBo2bJlEUZ9/b300ksGYHzwwQfONofDYTz99NMGYHzxxReuC64QJCYmGtu3bze+/PJLo3LlygZgREVF5ejXsmVLw2QyGZs2bXK2JScnG02bNjVMJpOxc+fOIoz6+rvrrrsMwFi5cqWzzeFwGPfff78BGD///LNhGIZx9913G4Dx22+/OfvZbDajb9++BmDMnTu3qEOXEki55yLlnkylLfcYhvKPYSj3FNQjjzxiREZGGomJidd92wkJCQZw2UeHDh2Mvn37GvXq1TOOHDliHDt2zPmoUKGC0apVK+PYsWPGhQsXCvS6cXFxBmDExcVd930SERERKS7O3X6Hyx83qiv5f1JDNhUzCxYsIDU1lcGDB+Pr6+tsDw8Pp1+/fly4cIEdO3YA8Pvvv5OSksLTTz9NUFCQs2+lSpX417/+RXR0dIkeNuLxxx+nbdu2vP/++zgcjjz7TZs2DcMwePHFF/H09HS2t2jRgq5du7J+/foSfRv6L7/8QkBAAAMHDnS2mUwmXnnlFQB+/fVXV4VWKObOnUuDBg148skn87wy+ejRo6xfv54ePXrQtGlTZ7uXlxfPP/88hmHwv//9r6hCvu5sNhuzZ8+ma9eutGvXztluMpl4+umnAVixYgWpqanMmjWL2rVrZ7si3WKx8NJLLwE33vtDCodyz0XKPZlKW+4B5R/lnoJ7++238fPzo3///hw8ePC6btvX1xfDMC77WLZsGdu3b2fHjh1ERUURERHhfJw4cYJ169YRERGR55BzIiIiIiKucuPea19CnTp1ioCAABo1apRnH5vNBuA8OdSqVascfVq3bs1XX33Fjh07sn2oLElGjhzJU089BcDp06d57LHHcu13ueOwaNEidu7cSUREROEFW0hSUlI4ePAgnTp1ynFbfYUKFYiIiHDu/42ibdu2zJo1y/l86NChJCQkZOuzc+dOIO+fOVCij8u5c+fw8PCgRYsWefax2WwcOnSI5ORkWrVqlWNojGbNmmGxWEr0cZCio9xzkXJP6cw9oPyj3FNwPXv2xDAM5s2bR/Xq1QkMDCQgICBHP5PJxIEDBwotjq+//pqUlJQc7Y888ghhYWG88cYbVK5cudBeX0RERETkaqggUcw8//zzPP/88znaU1NTWbRoEZ6entSpUweAmJgYAMLCwnL0L1++PABnzpwpxGgL16UfiA8fPpxnvxv5OOS3b5C5fxs2bCjKkApdeHg4ffr0cT5/9913c5wQupF/5pC5X7Gxsbkumz17NpB50ie/42C1WgkJCSnRx0GKjnLPRco9pTP3gPKPck/BnTlzBpPJRGRkpLPNMIwc/XJru566dOmSa7u3tzdlypTJ9n4WERERESkuVJAoAdLT03niiSc4ePAgI0eOxM/PD8A5lERuE9JlDR+R33ATN4ob+Tjkt2+QuX+F/WG3OLqRf+b5mTlzJu+88w7VqlXj/vvvdw6Lk9/740Y8DlI0lHvydyMfB+WevN3IP/e8KPfklF+xUkRERERE8qc5JIq5/fv306VLF7799lv69+/P8OHDXR2SiBSxtLQ0hg8fzm233UZISAgzZ87M80SQyPWg3CMiyj3XZt26dcyZM8clr3348GGWLVvmktcWEREREbkc3SFRBBwOB/v27btsP29vb+et34Zh8Pnnn/N///d/ZGRkMG7cOIYMGYLFYnH2zxq3N79bxP85tq8rXc1xKIhLj8M/97c4Hocrkd/POL/2G11Je+9fi61bt/LAAw+wY8cOunbtyuTJk6lQoQJQsPeH2ay6c2ml3JNJuefKKffkraS9/6+Wcs/l2e12tm7dSlJSUo5lDoeDcePGsXbt2hzDfomIiIiIlHYqSBSB5ORkateufdl+HTp0YNmyZaSlpXHfffcxbdo0brrpJsaPH+8cu/tSISEhAMTFxeVYltUWGhp6jdFfP1d6HArq0uMQGBiYbVlxPA5XIr+fcVZ7Sd23a1HS3vtX64cffmDAgAH4+fkxZcoUHnjggWwnuvI7DoZhEBcXR40aNYosXilelHsyKfdcOeWevJW09//VUO65vAMHDnDzzTdz5MiRPPsYhkGtWrWKMCoRERERkZLhxr98qRjw9fXFMIzLPrJOhDz++ONMmzaNsWPHsnTp0lxPCAHUrVsXINeJJdevXw+Q57qucKXHoaBK2nG4Ej4+PkRFRbFp0ybsdnu2ZfHx8ezatavE7tu1yNrnG/FnnmXhwoXcd999NGvWjD///JMHH3wwx1W3lStXxtPTM9fjsG/fPuLi4kr8cZCrp9yTSbnnyin35O1Gzz/KPQXz6quvcvjwYbp27cqzzz6LxWKhV69ejBw5kkceeQQPDw86dOjA8uXLXR2qiIiIiEixo4JEMbNu3Tq+/fZbXnnlFUaMGJFtmIx/6t27Nx4eHrz33nskJiY620+ePMlXX31F2bJladu2bVGE7VK33347AK+//jo2m83ZvnPnTv73v//RpEkTKlWq5KLorl2/fv04ceIEEydOzNb+9ttvk5GRwR133OGiyFyncuXKNG7cmGnTprFjxw5nu81mY9y4cUDmcSup7HY7zz//PDVq1GDevHmUL18+137e3t707NmTtWvXsnDhQme7YRi8+uqrAKXy/SFXTrnnyin3lM6/LTdy/lHuKbglS5bQrFkz5s+fz/vvv88999yDl5cXo0aN4r///S+zZ89m5cqVbN261dWhioiIiIgUOxqyqZiZNGkSkHl14vjx43Ptc//99+Pr60toaChjx47l5ZdfpkWLFtx///1kZGTw1VdfcfbsWX7++Wfc3d2LMnyXaNiwIU899RTjx4/npptu4vbbb+fChQt8+eWX2O12Pv30U1eHeE2GDx/Ojz/+yMCBA9mwYQO1a9dmw4YN/PTTTzRq1Ignn3zS1SG6xCeffEL79u1p3749Tz75JEFBQfz222+sW7eOf//73zRo0MDVIV61DRs2sHPnTvr06cPUqVNz7dO8eXOaNm3KO++8w4IFC7j11lt54oknqFixIosWLWL+/Pn07t2bPn36FHH0UhIp91w55Z7SmXvgxs0/yj0Fd+bMGWdREqBNmzZ88sknzuddunShc+fOvPvuu9x8882uCFFEREREpNhSQaKY2bZtGwDDhg3Ls0+fPn3w9fUFYMiQIQQFBfH5558zduxYzGYzDRs25OOPP872QelG98knnxAZGcmUKVMYPnw4VquVFi1aMHLkSFq3bu3q8K5JcHAwq1evZtiwYUyfPp1JkyYRFhbGk08+yWuvvYaHh4erQ3SJtm3bsnjxYl599VU+/fRT0tLSqFy5Mm+88QaDBw92dXjXJOvvwOzZs5k9e3aufV599VWaNm1K1apVWb16NSNGjGDKlCkkJCQQERHBkCFDGDVq1A0xuaoUPuWeq6PcUzrdqPlHuafg/P39SUlJcT6vWbMmBw8exOFwOCf0rlOnDt98842rQhQRERERKbZMhmEYrg5CRERERESkJGjTpg1nz55l9+7dWCwWzp49S9myZVm1ahVt2rQB4IEHHmDGjBnEx8e7ONqCiY+PJyAggLi4OPz9/V0djoiIiEihON/vTleHQPD/fnV1CIXiSv6f1BwSIiIiIiIiBXTfffexf/9+brrpJhYsWEBISAgNGzbkueeeY9u2bcycOZPp06dTo0YNV4cqIiIiIlLsqCAhIiIiIiJSQE8//TSPP/4469evZ9WqVQAMHTqUzZs306RJE26//XaSkpIYNGiQiyMVERERESl+NIeEiIiIiIhIAZlMJr788ktGjx6N3W4HoH///litViZPnozNZuPee+/lX//6l4sjFREREREpflSQEBERERERuULh4eHZnt96663ceuutLopGRERERKRk0JBNIiIiIiIiBVSlShXeeOONfPuMGzeOqlWrFlFEIiIiIiIlhwoSIiIiIiIiBXT48GEuXLiQb5+jR49y5MiRIopIRERERKTk0JBNIiIiIiIi+ejcuXO25z/99BObNm3Kta/NZmPjxo05hnQSEREREREVJERERERERPK1bNky5/cmk4ljx45x7NixPPuHhoYybty4IohMRERERKRkUUFCREREREQkHw6Hw/m92Wxm8ODBvP322y6MSERERESkZCoVc0hMnjwZk8mU4+Hv70+tWrV45JFHWL16da7rmkwmOnbsWLQBlzAnT57kjjvuoEyZMvj6+rJ7925Xh1Roli1bhslkYvTo0Ve9jY4dO2Iyma5fUJc4fPgwJpOJhx566Kq3kfX78tVXX12/wK6jhx56CJPJxP79+wtl+1f6O58Vz+HDhwvU32az0axZM5544onL9s3t55nX3zOTyYSHhwcRERH079+fvXv3FiierPf05fZ59OjROd77BV137dq1uLm5sXPnzgLFdCNR/ilcyj9XRvnn2ij/KP9kiYqKIigo6LpuU0RERESktChVd0jUqVOHnj17AmAYBklJSezdu5dvv/2WSZMm8dBDDzF+/HisVqtznRdffJGqVate1euNHj2aMWPGsHTp0hv6pNLzzz/P//73P3r16kXlypXx8vJydUhSAMuWLaNTp06MGjXqmk5wyZX54IMP2L17N7Nmzbqm7Vz69wwyr9yMi4tjxYoV/PLLLyxdupQtW7YQERFxrSFfs9atW9OnTx+effZZFi5cWGgnRIsz5Z/CofxTMin/uIbyz/XLP4cOHcq1/eTJk6xcuZKyZcvSoUOHUpnvREREREQup1QVJJo3b867776bo/3AgQPcc889TJ48GYvFku3KvNz6S3abN28mNDSU2bNn64OXSD7Onj3LqFGj+Pe//33NE13m9ffMbrdz6623MmfOHD788EPee++9a3qd62X06NE0btyYuXPn0qtXL1eHU+SUfwqH8o9IwSj/XP/888svv/D5558zevRoOnTowObNm7n55puJi4sDoG3btixYsABPT8/r9poiIiIiIjeCUjFk0+VUrVqVuXPnEhwczNdff822bdtcHVKJkpGRgbe3t04GiVzGhAkTSElJuaYhTS7HYrHw5JNPAhSrIZIaNWpEgwYNdJL9H5R/ro3yj0jBKP9c3/wzZ84c7r77blasWEF6ejoAzzzzDLGxsTz44IP07t2b1atX89lnn1231xQRERERuVGoIPG3kJAQHn74YQB++OEHZ/s/x6c1DINvvvmGtm3bEhgYiI+PDzVr1uTll18mJiYGuDjG7pgxYwDo1KkTJpOJyZMnO7ezd+9eHnjgAapWrYqXlxeBgYG0atWKzz//PFtcWduaPXs2v/zyC82aNcPLy4uAgABuueWWXMfpPX/+PP/3f/9HtWrVsFqtlC9fnq5duzJt2rQcfQ3DYMqUKbRs2RJvb2+CgoLo2rUry5Ytu+wxyxqL+siRIxw5cgSTyUSlSpWAi+MaJyYmMn36dNq1a0dAQACxsbEAnDt3jiFDhlC9enWsVitBQUF0796dOXPm5HidSpUq0axZM06dOsXDDz9M2bJl8fT0pFWrVixbtgzDMPjyyy+pV68enp6elC9fnvvvv5+jR49edh+yzJo1i44dOxIQEICfnx+9evVi586ddO3atUAnupKSknjttdeoV68eXl5e+Pv7c9NNN/Hdd99lmwTxUqdOneKJJ56gQoUKeHh4ULFiRZ588klOnDiRo29sbCwvvfQS9evXx9/fHx8fH2rVqsVLL71EYmJigfcTLo4L3alTJwDGjBmT67jfhmHw/fff07x583zfc1k/H5vNxjvvvEPdunVp1aqVc/mOHTu4++67CQ0NxdPTk1q1avHWW2+RkZGRbTtHjhxh4MCBREVFYbVaKVeuHD169GD27Nm57kdqaipjxoyhevXqeHh4UK5cOQYOHEh8fHyOvkePHnVu28PDg9DQUG6//XbWrFlToGOWnp7Ou+++S/369fH09CQwMJDbb7+drVu3Fmh9yDyeX3zxBQ0bNqRu3brZlm3bto0777yTkJAQPDw8qF27Nm+//TZpaWkF3v6lkpKSAAgLC7uq9QvLvffey9KlS9m+fburQylWlH+Uf5R/HsrWT/nnIuWf6+N6559PP/0UT09P1qxZw80338yRI0dYu3Yt9913H5MmTWL69OnUqVOHn3766bq8noiIiIjIjaRUDdl0OR07duS9995j/fr1efZ54403GDZsGOXLl+eOO+7Aw8OD5cuX89ZbbzFnzhy2bNlC3bp1efHFF1mzZg1r166lf//+REREOD8E7t27l+bNm5OWlsbNN99M7969OXv2LLNnz+bf//43bm5uOSYcnDx5MtOmTaNXr160atWKrVu3MmvWLDZv3szevXvx9fUFMseuvemmmzhw4AANGzbk0UcfJT4+npkzZ7J48WLeeusthgwZAmR+QH3ssceYOHEiVapU4Z577iE2NpYFCxbQuXNnvvvuO/71r3/leSzuvvtumjVrxoQJEwB44oknckzw98UXXzBkyBBatmzJvffei9Vq5dChQ3Ts2JGjR4/SoEEDHnvsMef+L1iwgBEjRjB27Nhs24mNjaVt27a4u7tz5513cuTIEX7//Xf69OnDfffdx8SJE+nZsyedOnVi5cqVfP/99+zYsYPNmzdjsVjy/bm/9dZbvPzyy84THr6+vixYsIB27dpRrly5fNeFzBNwnTt3Ztu2bVStWpUBAwaQnJzM7NmzeeCBB1i4cKHzxN6lsj7A9unTh6CgINavX8+ECRP47bffWLFiBbVq1QIyT3x06NCB7du307p1a+6//35sNhvz5s3j7bff5tixY0ydOvWycWbx9/fnxRdf5NixY/z888+0bt2aNm3a0KxZs2z9pkyZwurVq+nVqxePPvponu85yHwv3X///fz222906dKFm266CYAFCxZw6623YjKZ6N69O8HBwaxYsYKXX36ZlStXMmPGDCwWC6dOneKmm27i2LFjdO/enVtvvZUTJ04wZ84c5s+fz48//sjdd9+dLb6nnnqKrVu3csstt9ClSxcWL17M+PHjiY6OZsaMGc5+mzZtolu3bly4cIE2bdpwyy23cOzYMWbPns2MGTOYMGECjz32WJ7HKyMjg169erF48WIiIiJ44IEHMJlMLFy4kLZt21KjRo0CHfddu3Zx/Phxbr311mzt8+bN49Zbb8Vms9GtWzeqVavGoUOHeOWVV5g5c2aBtn2ptLQ0vvzySwDnSe7iIut9MWvWLBo0aODiaIoX5R/lH+Wfi5R/Min/XD/XO/9s3bqV3r1707JlSwCWL1+OyWRy/t0ym820a9dOBQkRERERkdwYpcCkSZMMwBgwYEC+/bZv324ARt26dZ1tgNGhQwfn88DAQMPT09M4f/68s81msxm9evUyAGPJkiXO9lGjRhmAsXTp0myv8+STTxqAMWPGjGztq1atMgCjV69eOWI3m83G3Llzne0Oh8N4+OGHDcD43//+52y/++67DcAYNmyY4XA4nO2HDx82QkJCDA8PD+P06dOGYRjGt99+awDGfffdZ6SmpmbrGxUVZfj4+Dj75icqKsqIiorK1jZgwAADMLy8vIyZM2dmi7tdu3YGYIwdOzZHjJUqVTIAY/Hixdm2Dxi33XZbtjgHDRpkAIaHh4exceNGZ3taWprRsGFDAzC2bt2ab+x//vmnYTabjUqVKhn79+93tickJBgdO3Y0AOPSX5OlS5cagDFq1Chn2/33328AxuOPP25kZGQ428+dO2c0a9bMAIyJEyc62zt06GAARmRkpLF3795sx+bdd981AKNVq1bOY/PDDz8YgPH8889niz02NtYoU6aM4e3t7Ww7dOhQgd7ree2LYVz5ey4qKsqwWCxGREREtv25cOGCERwcbJQrV87466+/nO3p6enG448/bgDG559/bhiGYXz44YcGYLz88svZYlm7dq0BGO3bt3e2Zb23IiIisv3MUlJSjNq1axuAcfbsWcMwMt8LlStXNgBjypQp2ba9detWIygoyHB3dzf27NnjbP/n7/w777xjAEa3bt2M5ORkZ3taWprx4IMPOt8jhw4dyvNYG4ZhfPrppzneC0lJSUZoaKhhsViM+fPnZ+u/YcMGw8/PL8fPM+vnU6dOHePFF190Pp577jnjX//6lxEeHm4EBAQYX3/9db7xZMl6H1SsWDHb9v75aN26dY73S9a6lx6v/CQmJhpmsznbz/NGp/yj/JMX5R/lH+Wfkpt/rFar8eyzzzqfP/XUU4bFYjFiY2OdbU8//XS235HiLi4uzgCMuLg4V4ciIiIiUmjO3X6Hyx83qiv5f1JDNl3Cy8sLIN/b1M1mM3a7PduwARaLhU8//ZRZs2YV6Gq11q1bM2zYMHr06JGtvV69esDF290vdc8992TrbzKZuOOOOwA4ePAgkDlh4a+//kp4eDgjR47MdkVkVFQU77zzDvfeey8nT54E4P3338dqtfL5559jtVqz9R03bhxJSUl8//33l92f/DzyyCP07dvX+fyPP/5g1apVNG3alOHDh+eI8ZNPPgEyb4W/lMlk4osvvsgWZ/fu3QG46667sl1d6eHhQdeuXQG4cOFCvvF9/fXXOBwORo0aRdWqVZ3tvr6+vP/++5fdv5iYGKZOnUp4eDiffPIJbm4XbzoKDg5m4sSJAM79utRbb72V7f1iMpl44YUXaNiwIevWrXMOx1C+fHmGDRvmHJc5S0BAABERESQnJ182zqtRkPdcFrvdzvvvv59tf7755hvOnz/P2LFjqVatmrPd3d2djz76CD8/P8aPHw9k/l5lbTclJcXZt1WrVsybN4/Ro0fniG/EiBHZfmaenp707t0byBwWBGD27NkcOnSIO+64gwcffDDb+g0bNmTs2LFkZGQ4r+jMzX//+18gc4LhrL8RkPk+++ijj/D3989z3Uvt2LEDgCpVqjjbZsyYQUxMDLfddhvdunXL1r958+Y8//zzeW5v165dvPfee87Hhx9+yNSpUzl58iSGYbBly5Zc/5bk5fjx49m298/H2rVrC7ytvPj4+FC+fHnNk5AL5Z+LfZV/lH+UfzIp/xTf/FOuXDn27dsHZN5JNGPGDJo0aUJAQICzz5YtW6hYseJ1eT0RERERkRuJhmy6REJCAkC2DxP/9MILLzB8+HDatGlDx44dufnmm2nZsiVt27alcuXKBXqdAQMGAJkfordv387evXvZuXNntrHD/6lFixY52nx8fLLFvWnTJux2Oz169MDDwyNH/4ceesg5TnNSUhJ//PEHISEhOYanAJzjkW/cuLFA+5SXfw4PsG7dOgBuu+22XMfG7t69O1arldWrV2drr1ixYo7hK7KGbLj0A/Y/l11OVjw333xzjmWNGzemTJkynDt3Ls/1N27ciMPhoFevXtlOVmWpX78+VapU4Y8//iApKcn5MwOynSjLYjKZuOWWW9i2bRvr1q2jcePGtG/fnvbt2wOZJ0x2797N7t27mTt37hWNIX2lCvKey2I2m50nY7KsWrUKgBUrVuQ61ryXlxfbt28nNTWV/v3789Zbb/Hzzz+zYsUKbr31Vlq3bk2HDh2cJ/6uJr5L32+5ufXWW/nPf/6T4/2WJSEhgX379hEZGUn9+vVzLA8MDKRjx44FGtoi63cqMDDQ2bZp0yaAHMcuy2233Zbr7ydk/h25dF4AgOTkZHbu3MmYMWP47LPP2L9/P/PmzbtsbAAdOnTId+z+0aNHO+cluBZBQUEcP36c1NRUPD09r3l7Nwrln4uUf5R/lH+Ufy5VHPNPz549mTBhAsOHD+fAgQOcPn2ap556Csgc5u2LL75gw4YNPPfcc9cct4iIiIjIjUYFiUvs2bMHgOrVq+fZZ9iwYTRp0oRvvvmGBQsWsHTpUiDzBMSzzz7LmDFjLjtm9OnTpxk2bBg//fQTiYmJlC1blurVq3PXXXfx+uuv57pOfiepspw9exaAsmXLXrZv1pWbZ8+e5b333suz3/Hjxy+7rfxERkZme37+/Hkg78kO3d3dKVOmDGfOnMnWnnUFY27yW3Y5WR/SQ0JCcl1+uRNCl9sfyLyK7uDBg8TGxjpPWPj4+GQ7OXSp8PBwAOfr2u12xo0b5xyf2s/Pj+rVq9O0aVOio6Od79vrrSDvuSxly5bNdvUmXDw23333Xb7rRkdHO0+affbZZ8yePZsJEyY4rxpt3bo1H3/8cY4xxgsS3+V+PlknGbP6/VNcXBxw8WeS3zYuJ2tbl/7cL7f9gm47i7e3N82bN2fatGnUrVuX+fPns23bNho2bHhF2ylMfn5+QOYxL1++vIujKT6Uf3JS/lH+KQjln8tT/sl0PfPP0KFDmTNnDuPGjQOgQoUK/Pvf/wYyJ2v/6KOPKFu2bL53moiIiIiIlFYasukSs2bNAjI/gOanZ8+e/PDDD8TExPDnn3/ywQcfEBwczOuvv87LL7+c77oOh4M+ffowZcoUhg4dSnR0NKdPn2bVqlW89tpr1xR/1gfNvE5gJCcnc+rUKZKTk51XydWvXx/DMPJ8rFix4ppi+udVm1mTjv7zhE+WjIwMzp8/n2Ny0sKS9eE068TQpQzDIDo6Ot/1L7c/ly679MrEpKSkHFd5Zjl16lS2/v/3f//HyJEj6dixIzt37iQ+Pp7NmzczYcKEfE9EFaXcrs7NOjabN2/O9z2WdYVxaGgoo0ePZtOmTZw7d47Zs2fzr3/9i7Vr19K1a1dOnDhxxXFd7ueT1Z7X+y3rZ5D1M8lN1hA0BY0lMTHR2RYcHJzv9gu67X+yWq3OE2iFdcLwamUN43ElJxxLA+Uf5Z9LKf8UnPJPwWNR/rl++ScyMpKNGzfy4Ycf8vHHH7N582bKlCkDQKNGjRg6dCibN2/WkE0iIiIiIrlQQeJvf/75Jz///DPu7u7cc889ufY5deoUzz33HFOnTgUyr4ysV68ezz33HMuXLwcunlTKy/bt29m0aRM9e/bklVdeyXZl2tV84L1U1lVoixcvxmaz5Vg+dOhQwsPD2bRpE76+vtSqVYu9e/dy+vTpHH337NnDU089xYwZM64ppn9q2bIlAL/88gt2uz3H8pkzZ5KamkqbNm2u6+vmpUmTJgDMnz8/x7LFixdn+/Cem2bNmmEymZg9e3aufTdv3sz+/ftp2LBhjitSc3uvGIbB7NmzgcwxnAEmTpxIYGAg33zzDXXq1MnW/1rfM4Up64TEypUrc10+dOhQ5wnUyZMn89xzzzmvFA0KCqJ37958//33DBgwgLi4uDy3k5+s99tPP/2U6/Iff/wRIM/3m6+vL/Xr1+fIkSO5Dk8SFxeX7zATl8q6cvzSceVbtWoFwPTp03Nd51p+/44cOQJkPxFZHMTGxuLl5ZXnFdqlkfJPdso/yj/XSvknO+WfTNc7/4SFhfHMM88waNCgbHeHDRgwgNdff50KFSpcl9cREREREbnRqCABLFu2jF69emG32xk1alSeQ054e3vzxRdfMGTIkBxXlGVd4XjpulljVF96cibr++jo6GwnRJKSkpy3ejscjqvajypVqtChQwcOHTrE2LFjMQzDuWznzp1MnDiRcuXKOT/8PvHEE6Snp/PUU09lm3zQbrfz0ksv8eWXXxIaGnpVseSladOmtGrVim3btjFy5Mhsx2Dv3r288MILAM5jUdgefvhhAF599dVsw4PExMTw4osvXnb9sLAw7r77bk6dOsWgQYOyTUgbHR3N448/DuS+Py+//DK7d+/O1jZp0iS2bNlCvXr1nGNU22w2kpOTs115bBgGr7/+Ovv37weu7j2T2/vzenrggQfw9PTk9ddfz3Ey5ddff+XNN990ngDat28fH330EV988UW2fna73dmnIEPB/FPfvn2JjIxk1qxZfPnll9l+J9avX8/YsWOxWCw88cQTeW5j4MCBQObvy6VXMttsNgYPHpznlcb/lDUG+KUTsvbu3ZuKFSsyY8YMpkyZkq3/tm3b+Oijjwq07X+aPXs269atIzg4mHbt2l3VNgpDWloa0dHR1K1b19WhFBvKP8o/yj/Xn/JPdso/155/VqxYwaFDh7I9L+hDRERERESyK1VzSGzcuJHBgwcDmR+ok5OT2bhxI5s3bwYyhyYYOnRonuv7+/szePBgxo0bR61atejVqxehoaEcP36cuXPnYjabeemll5z9s06mvPLKK/z+++/ce++9NG7cmNq1a7N582aaN29O27ZtSUpKYt68efj6+hIeHs62bdsYMWIEr7766hXv44QJE7jpppt49dVXWbx4MY0bN+bkyZPMnj2bjIwMfvvtN9zcMn/sgwYNYv78+UyfPp3atWvTrVs3LBYLS5cu5a+//mLgwIGXHT7kSplMJr755hs6derEuHHjmDNnDu3atePcuXPMmDGDlJQUXnrppVwn+SwM7dq149lnn+Wjjz6iadOm9O3bF5PJxJw5c4DMD/F//vlnvtv4+OOP2b59O1OmTGH16tV07dqVlJQUZs6cyYULF7jnnnt49NFHs61jMpkICwujefPm9O7dm7Jly/Lnn3+yfPly/P39mTx5svOEzb333stXX31FkyZN6NOnD2azmVWrVnHw4EGaNm3K5s2befjhh/Mdiz03We/PyZMnk5CQQOvWrfO8OvtqlC9fnvHjx/Pwww/TsmVL+vTpQ/ny5dm3bx8LFiygUqVKzgkzn376ab766iuGDx/OrFmzaNKkCQ6Hg1WrVrFz507atGlDhw4drjgGq9XKjz/+SI8ePXjqqaf49ttvadKkCcePH2fmzJnY7XY+++yzHFf+XuqJJ55g/vz5zJgxg3r16tG7d2+sVitLlizh4MGDNGjQgO3bt182lqz39ObNm53vB6vVytSpU+nRowcPPfSQ8yrkEydOMGfOHOrXr+/8+/RPl/49y2Kz2di3bx/z5s3DYrHwxRdfFMmdCLt27cr3vdOhQwcGDhzIn3/+SUZGBp06dSr0mIob5R/ln39S/lH+Uf65dkWVfzp27MjgwYN5++23nc+zfk8uJ7c7skRERERESrNSVZDYtWsXu3btcj738/OjQoUKPPXUUzz11FMFmnjvtddeo2HDhnzxxRcsWrSI2NhYypYty+23386gQYOynUC57777mDZtGmvXrmXjxo00aNCA5s2bM3fuXEaMGMGiRYuYMGECERER3Hfffbzyyiv897//ZfTo0c7JSq9UjRo12LJlC6+//jpz5sxhw4YN+Pj40LlzZ4YOHUr79u2dfd3d3Zk1axafffYZU6ZMYerUqXh4eFCjRg0mTpzIgw8+WOAPW1eievXqbNmyhTfffJMZM2YwYcIEvL29ad26Nc888wy33HLLdX/N/HzwwQc0aNCAzz//nKlTp2K1WunatStvv/02PXv2vOwH6tDQUNatW8d7773Hzz//zKRJk3B3d6d+/fo8/vjjDBgwIMfEp2azmQULFjBu3Dh+/fVXjh8/TpkyZbj//vsZNWoU1apVc/b95JNPCA0N5eeff2by5MkEBgbSpUsXfv75Z06ePMldd93F3Llzeeutt65ov2vXrs2gQYP49ttv+fjjj4mLi7uuJ4Qgc9iCqlWr8tZbb7Fs2TISExOJjIzkmWee4eWXX3ZOmlmxYkU2bdrEBx98wOzZs5k0aRJWq5Vq1arx1ltvMWjQoMtO1puX1q1bs3XrVsaNG8e8efNYv349/v7+9OrVi8GDB2f7nciNxWLh119/5YsvvuDrr7/mhx9+wN3dnWbNmjF+/HimTJlSoBNCVapUoW7dujmG/rjpppvYvHkzo0ePZsmSJaxcuZIKFSrw7LPP8uCDDzqvbP2nf/49y+Lv70/37t0ZPnw4bdu2vWxc10NMTEyew5IAeHp6MnDgQNasWQNAjx49iiSu4kT5R/knN8o/yj/5Uf65vKLKP6NGjco2vNbIkSML5W+UiIiIiEhpYDIuvYdcpJRJS0sjJSUFb29vPDw8si07ffo0ERER1KlTJ9fxm0Wu1H//+1+eeOIJtm3bRoMGDVwdTpFr1aoVcXFx7Ny5M8dJUpHSRvlHipLyj/LP5cTHxxMQEEBcXBz+/v6uDkdERESkUJzvd6erQyD4f7+6OoRCcSX/T+o/cinVpk2bRlBQEM8//3y2cbDT0tJ47rnnyMjIoG/fvi6MUG4k999/P2FhYUyaNMnVoRS53bt3s379ep555hmdDBJB+UeKlvKP8o+IiIiISHGhOySkVEtMTKR9+/b88ccf1K5dm5tuugmz2czixYv566+/qF69Ohs3biQgIMDVocoNYuLEiQwaNIj9+/dTvnx5V4dTZPr378/evXvZtGkT7u7urg5HxOWUf6SoKf9cff7p3LnzVa1nMplYvHjxVa1b1HSHhIiIiJQGukOi8FzJ/5MqSEipl5CQwHvvvce0adM4dOgQNpuNqKgo+vbty9ChQylTpoyrQ5QbiMPhoE2bNtSvX5///ve/rg6nSKxfv562bduybt06mjVr5upwRIoN5R8pSso/V59/8rqzwmQykfVRysPDg/T09GztJpOpxExqrYKEiIiIlAYqSBQeFSREREREREQKQUJCAr169eLw4cO8+uqr3HbbbQQGBnLhwgWmT5/OmDFjKFu2LL///jshISGuDrdAVJAQERGR0kAFicKjOSREREREREQKwZtvvsmWLVtYsWIFDz30EIGBgQAEBQXx8MMPs3z5cvbs2cPIkSNdG6iIiIiISDGkgoSIiIiIiEgB/fjjj/Tu3ZvKlSvnujwqKorevXsza9asIo5MRERERKT4U0FCRERERESkgI4fP065cuXy7RMSEsKZM2eKKCIRERERkZJDBQkREREREZECKlOmDDt27Mi3z8aNGylbtmwRRSQiIiIiUnKoICEiIiIiIlJA3bp1Y/ny5UyZMiXX5a+++iobN26kV69eRRyZiIiIiEjxZzIMw3B1ECIiIiIiIiXB4cOHady4MfHx8TRt2pTevXsTHh7OqVOnmDFjBlu3biU0NJQtW7ZQvnx5V4dbIPHx8QQEBBAXF4e/v7+rwxEREREpFOf73enqEAj+36+uDqFQXMn/k25FFJOIiIiIiEiJV6lSJZYsWcKDDz7Ipk2b2LRpEyaTiazrvFq0aMHkyZNLTDFCRERERKQoqSAhIiIiIiJyBRo3bsyff/7JunXr2LRpE3FxcQQHB9OqVSsaN27s6vBERERERIotFSRERERERESuQqtWrWjVqpWrwxARERERKTE0qbWIiIiIiIiIiIiIiBQ6FSRERERERERERERERKTQqSAhIiIiIiIiIiIiIiKFTgUJEREREREREREREREpdCpIiIiIiIiIiIiIiIhIoVNBQkRERERERERERERECp2bqwMQEREREREprh555JGrWs9kMvH1119f52hEREREREo2FSRERERERETyMHny5FzbTSYThmHk2a6ChIiIiIhIThqySUREREREJA+HDh3K9tizZw9t27bF19eXkSNHsmXLFqKjo9m8eTMjRozA39+fZs2a8eeff7o6dBERERGRYkd3SIiIiIiIiOQhKioq2/N33nmH9evXs3btWpo2bepsL1euHI0bN6Zfv360bNmSb7/9ljfeeKOowxURERERKdZ0h4SIiIiIiEgBTZo0iR49emQrRlyqYcOG9O7dm59//rmIIxMRERERKf5UkBARERERESmgAwcOULly5Xz7VKhQgRMnThRRRCIiIiIiJYcKEiIiIiIiIgXk7+/P/v378+2za9cuAgMDiyYgEREREZESRAUJERERERGRAurQoQMLFixg8eLFuS7/5ptvWLJkCZ07dy7iyEREREREij8VJESuUEpKCp999hmdO3cmPDwcd3d3goOD6dChAx988AEJCQk51pk8eTImk4nRo0cXWZxbt25l9OjRLFu2rMheU0REri5PFJXJkyczevRoYmNjs7WPHj0ak8nE5MmTXRKXSEkycuRILBYLPXv25O6772bKlCksWLCAb775hn79+vHwww/j4+NTpP/3iYiIiPxTTEwMJpMp2yMmJsbVYYng5uoAREqSdevWcffdd3P06FHc3d1p2rQpTZs2JTo6mrVr17JixQreffddpk2bRqtWrVwa69atWxkzZgwAHTt2dGksIiKlRXHPE5MnT2b58uU89NBDGk5G5Co1aNCA6dOn8+CDD/LLL7/w66+/OpcZhkHFihWZOnUqNWrUcGGUIiIiIiLFkwoSIgW0efNmunTpQnJyMs899xwjR44kKCjIufz48eO89NJLTJ06le7du7NlyxaqVq3qwohFRKQoleQ8MWjQIO655x7Cw8NdHYpIidCjRw+OHDnCjBkz2LRpE3FxcQQHB9OqVSt69+6Nh4eHq0MUERERESmWVJAQKQCbzcYDDzxAcnIyo0aNyvUW/IoVK/Ltt9+SmJjIzJkzGTlyJN9//33RBysiIkWupOeJkJAQQkJCXB2GSIni5eXFPffcwz333OPqUERERERESgzNISFSAL/99hu7d++mevXqDBs2LM9+ZrOZoUOHUqZMGdasWYPdbs+z70MPPYTJZMpzjgeTyUSlSpWyte3bt48nnniCypUrY7VaKVeuHB07duS7777D4XAAF+erePjhhwEYM2YMJpMpx7BN58+f5+WXX6Z69epYrVbCw8O577772Lt3b7Z+y5Ytw2Qy8fLLL5OamsrYsWOpXbu2y4ekEhEpTq5XnlizZg39+vUjLCwMDw8PKlSowAMPPMCuXbtybKtSpUqUK1cOu93O22+/Ta1atbBarQQHB3PnnXeyf/9+Z9+OHTtiMplYvnw5AJUrV842Z0Ruc0hkrZOSksJXX31Fo0aN8PLywt/fn+7du7N58+Zs8VxuHopKlSphMplytB89epSBAwdSqVIlrFYrZcqUoUePHsyZMydH38LKnSL5WbFixVU/REREREQkO90hIVIA06ZNAzJPhLi7u+fbt1WrVpw9e/a6x7Br1y7at2/PuXPnqFWrFt26deP06dMsX76c5cuXs2XLFt5//32ioqK44447OHz4MJs3b6Z27drUqVOHunXrOrd14MABunTpwpEjR6hatSrdunXjwIEDTJ06lVmzZrFo0SJatGiR7fXT09Pp1q0bK1eupE6dOjRv3vy676OISEl1PfLEhx9+yPPPPw9AvXr1aNGiBbt37+a7777jp59+4scff6Rfv37Z1jEMg4ceeojvvvuOZs2a0aVLF/744w+mTZvGmjVr2LlzJ0FBQXTo0IGQkBCWL1/O2bNn6dGjBz4+PkRFRV1230aOHMm7775L/fr16dKlCzt27GDBggWsXr2a7du3U6VKlYIephxWrFhBnz59SEhIoEKFCtx8882cPn2a+fPnM3/+fF588UXeeeedXAsZBVHQ3CmSn6zi3NXI7+IUEREREZHSSAUJkQJYv349AG3atHFZDG+99Rbnzp3j5ZdfZty4cc4PxuvWraNTp058+umnjB49mk6dOtGpUycmT57Mww8/TP/+/bMNHeJwOOjfvz9HjhzhjTfeYMiQIZjNZgzDYMKECTz11FPcfffd7NmzB6vV6lzv66+/xtfXl3Xr1tGyZcui3n0RkWLtWvPEqlWreOGFF/D09OSnn37illtuATILDuPHj+fpp5/mgQceoFGjRtkKAGfOnOH3339n8eLFdO7cGYCkpCR69OjBqlWr+OWXX3jiiScYM2YMkHlidfny5XzxxRc57iTIy6effsqPP/7I3XffDUBGRgb3338/P//8M1999RXjxo27qn2+cOECd911FwkJCYwcOZIRI0bg5pb5r+nq1au55ZZbeO+992jevLnzta9UQXOnv7//VW1fSoeRI0dedUFCRERERESyU0FCpABOnz4N4NLJPmNiYgCoWbNmtg/FrVq1Yty4cezdu5ekpKTLnlSZPXs2W7ZsoW/fvrz88svOdpPJxJNPPsnmzZv573//yy+//ML999/vXB4fH89vv/2mYoSISC6uNU+89dZbGIbB2LFjncUIyPzbPHDgQLZt28aXX37JJ598wgcffJBt3bFjxzqLEQA+Pj4MGjSIVatWZRu26WoNGjQoW0HA3d2dF154gZ9//vmatj9x4kTOnDlDr169nMM9ZWnbti2fffYZ9957L2+++eZVFySuV+6U0i23OWFEREREROTqaA4JkQLIut3elWNNZ80B8eSTT/J///d/bNiwgYyMDACef/55xo8fX6ATYXPnzgXIMexHln/9618ALFy4MFt7pUqV6NSp09WGLyJyQ7uWPGGz2ViyZAkAjz76aK59HnvsMQDmz5+fY1nfvn1ztJUtWxa4eEL+WhTW9rPyzGOPPZbr1ed33nkn/v7+bN26lTNnzlzVa1yv3CmSlwsXLjB//nx++OEH5s2bx/nz510dkoiIiIhIsaaChEgBhISEAHD8+PEC9T9y5AiHDx8mLS3tusXwwgsvMHjwYADeffddWrZsib+/P127duWLL74gNTW1QNs5fPgwAA8//DAmkynHI6vocOTIkWzrNWjQQMMViIjk4VryxLlz50hOTqZs2bIEBwfn2r9OnTrAxb/hl6pQoUKOtqy/14ZhFCie/BTW9rPyTK1atXJd7ubmRs2aNYHc97sgrlfuFPmn2NhYHn74YcLCwujVqxf3338/vXv3JiwsjAcffFCFCRERERGRPGjIJpECaNy4MdHR0axZs4abb7453742m40qVargcDg4fvx4ridyLie3CRDd3Nx45513GD58OPPnz2fhwoWsXLmSxYsXs3jxYj766CNWrFjhvGo1L1knj9q1a0dYWFie/f4Zt4azEBHJ27XkCYvFUuDXya0AcCXrX43rtf1/5rYrKWYUpG9h5k6RSyUnJ9O5c2e2bt1KeHg4PXv2pFy5cpw5c4bZs2fz3XffsXXrVtauXYuPj4+rwxURERERKVZUkBApgF69ejFnzhy++eYbXnnlFdzd3fPsu2rVKhwOB1FRUZQvX/6y287tJMvRo0fz7B8QEED//v3p378/ADt37mTAgAFs3ryZkSNHMn78+HxfLyIiAsgcImPAgAGXjS9LYZ/wEhEpya4lT9jtdjw9PTlz5gznz5/P9S6J3bt3AxAVFVVo+3C95JbX0tPTOXnyZLa2yMhI9u7dy969e6ldu3aOdex2O/v27QNy7ndR506RS33wwQds3bqVJ554go8//hgPDw/nsqSkJP7973/z7bff8v777zNixAgXRioiIiIiUvxoyCaRAnjggQcICQnh4MGDjB07Ns9+NpuNkSNHAheHRMpLaGgoQK7jYs+cOTNHW40aNahTp06O8cnr1q3rfM1Dhw5ddl+yJj6dNm1arst/++03atWqxYcffnjZbYmISKZryRNubm7OuQ4mTZqU63pff/01wGXvvnCl/PLa3Llzc9zB0KVLF+Divv3TtGnTiIuLo169epQrV+6yr1GYuVPkUj/++CPVqlXj888/z1aMgMxJ5SdMmEDVqlX56aefXBShiIiIiEjxpYKESAH4+fkxYcIEAF577TWeeeaZHGMDHzp0iO7du7Ny5UqqVavGiy++mO82q1WrBsCnn35KUlKSs33ZsmWMGTMmR//w8HB2797N2LFjsdlsznabzeY8CXPpFaZmc+avd0pKSrbt3H777URFRTFr1izef//9bCdpjh49yv/93/+xd+9eTWAtInIFrjVPvPzyywCMGDGCqVOnOtsdDgfvvPMO48ePx9PTk2efffaa4swrN1wPWXntq6++4uzZs8727du389xzz+Xo/9hjjxESEsLs2bMZMmSIc7JpyJy8e+DAgQAMHTo0x2sUVu4UKYgDBw7QtWtX5+/TP3l4eNC1a1cOHjxYxJGJiIiIiBR/KkiIFNDtt9/O1KlT8fX15ZNPPqFcuXK0atWKW265hdatW1O9enWWLFlC3bp1mT9/Pr6+vvlu76677qJs2bKsWrWKatWq0bt3b1q2bEnnzp3p2bNnjvGs3377bby9vRkzZgyRkZH07NmTXr16ERERwddff014eLhz4k64OLzFZ599xi233MKoUaMA8PT05McffyQgIIAXX3yRqKgo+vTpQ/v27alWrRoHDhxg3LhxNGzY8DofQRGRG9u15IkOHTrwzjvvkJKSwn333UeDBg3o27cvNWrUYMiQIVgsFqZMmeI8IX+1snLDbbfdRr9+/Vi6dOk1be9SnTp1onbt2uzfv58aNWrQo0cP2rdvT7NmzYiIiKBp06bZ+pcpU4aff/4ZX19f3nnnHSpXrkzfvn1p3rw5PXr04Pz58zz77LPce++9znUKO3eKFISHhwfJycn59klJScl36DYRERERkdJKc0iIXIF7772Xjh078vnnn/P777+zb98+Nm3aRGBgIB07duTOO+9kwIABeHl5XXZbwcHBLF++nCFDhrBy5UoWLlxIpUqVePXVV3nppZeoWLFitv4tW7Zk48aNvPXWW6xdu5bly5fj4eFBVFQUTz75JAMHDsw2SXX79u154okn+Omnn5g1axbx8fHOZa1atWL79u2MGzeOefPmsXDhQsqUKUPHjh154YUX6N69+/U7aCIipci15InBgwfTokUL3nvvPdasWcPu3bsJCQnhnnvuYejQoTRo0OCa4xs6dCh//vknO3bsYN++fdxyyy3XvM0sHh4eLFq0iP/7v/9jwYIFLF68mPLly/Pss88yZswY55CBl+rUqZMzHy1YsMBZqOnatSv//ve/ufXWW7MNf1jYuVOkIOrXr8/s2bOJjY0lMDAwx/JTp04xc+bM6/I7KyIiInKkZZurWu/cJXcgZznWvRfJV3HRRNT6NVcVg0huTEZuswKKiIiIiIhIDt999x0PPvggLVq0YOLEidSpU8e57I8//uDxxx/njz/+YOLEiQwYMMCFkRZcfHw8AQEBxMXF4e/v7+pwRERE5BLXUpBo+sembG2bGzejTCkuSJzvd6erQyD4f7+6OoRCcSX/T+oOCRERERERkQK67777WLhwId9++y3169enatWqhIWFcebMGfbv349hGNx7770lphghIiIiIlKUVJAQEREREREpIJPJxJQpU2jfvj0fffQRO3bsYP/+/QDUrVuX//znPzz++OMujlJEREREpHhSQUJEREREROQKPfroozz66KMkJSU555Pw8fFxdVgiIiJSBGJiYihbtmy2tjNnzhAaGuqiiERKDhUkRERERERErpKPj48KESIiIiIiBaSChIiIiIiIyBXYvHkz3333HQcPHiQhISHXPiaTicWLFxdxZCIiIiIixZsKEnlwOBxER0fj5+eHyWRydTgiIsWOYRgkJCRQvnx5zGazq8O5YSj/iIjkz9X5Z968efTt2xe73Z5vP/0NFxERERHJSQWJPERHRxMREeHqMEREir1jx45RsWJFV4dxw1D+EREpGFfln1GjRgEwZcoU7rjjDry9vYs8BhERERGRkkoFiTz4+fkBmR90/P39XRyNiEjxEx8fT0REhPPvpVwfyj8iIvlzdf75888/ufXWW3nggQdc8voiIiIiIiWZChJ5yLrF2t/fXyeERETyoSEpri/lHxGRgnFV/vHw8KB8+fIueW0RERERkZJOg36LiIiIiIgUUOvWrVm7dq2rwxARERERKZFUkBARERERESmgV199lR07djB69GgcDoerwxERERERKVE0ZJOIiIiIiEgeHnzwwRxtdevW5dVXX2XKlCk0adKEgICAHH1MJhNff/11UYQoIiIiIlJiqCAhIiIiIiKSh++++y7PZUeOHOHIkSO5LlNBQkREREQkJxUkRERERERE8nDo0CFXhyAiIiIicsNQQUJERERERCQPUVFRrg5BRERE5IqVcXfncIvWrg5DJAdNai0iIiIiInIF7HY7y5cv5+TJkwA4HA5eeukl6tSpQ6dOnZg3b56LIxQRERERKZ5UkBARERERESmgs2fP0rhxYzp37swff/wBwKhRo3jnnXfYs2cPy5cvp2/fvqxfv97FkYqIiIiIFD8qSIiUEKmpqezdu5fU1FRXhyIiIqWI8o9Idu+99x47duygZ8+e1K1bl/T0dD766COioqI4dOgQ69evx8PDg88++8zVoYqIiIiIFDsqSIiUEGlpaezbt4+0tDRXhyIiIqWI8o9Idr/99ht16tRh1qxZREVFsXr1ahITE3n++eeJioqiefPm9OzZk2XLlrk6VBERERGRYkcFCRERERERkQI6fvw4HTp0wGQyAbB+/XpMJhNdu3Z19qlYsSJnzpxxVYgiIiIiIsWWChIiIiIiIiIF5O7uTnp6uvP5woULCQ4Opk6dOs62mJgYAgMDXRCdiIiIiEjxpoKEiIiIiIhIAdWtW5d58+YRHx/P+vXrWbZsGd26dXMuP336NL///jt169Z1YZQiIiIiIsWTChIiIiIiIiIF9MQTT3DixAnCw8Pp0KEDAA8++CAAP/zwA82bNyc+Pp6BAwe6MkwRERERkWLJzdUBiIiIiIiIlBQPPvggx48f55133sFisfDCCy/QvXt3ADZs2MCZM2d45ZVXuPPOO10cqYiIiFzO8cX/uqr1zsWm5miLXvEkaYGeV7ytil2mXlUMIiWVChIiIiIiIiJX4JVXXuGVV17J0f7yyy8zduxY/Pz8XBCViIiIiEjxpyGbRERERERErlB6ejozZsxg+PDhPPzwwwAcOHCAhIQEF0cmIiJScsXExGAymbI9YmJiXB2WiFxHKkiIiIiIiIhcgRUrVlCjRg369evHuHHj+OabbwD49ttviYyM5L333nNxhCIiIiIixZMKEiIiIiIiIgW0bds2unfvTmxsLK+99hq33Xabc9kDDzxAkyZNGDJkCDNnznRdkCIiIiIixZQKEiIiIiIiIgU0atQoHA4HK1euZOjQoVStWtW5rE2bNixbtozw8HA++OADF0YpIiIiIlI8qSAhIiIiIiJSQKtXr6Z79+7Ur18/1+Xe3t706NGDnTt3FnFkIiIiIiLFnwoSIiIiIiIiBZSYmEh4eHi+fTw8PEhMTCyiiERERERESg4VJERERERERAqoZs2arFu3Ls/lNpuNZcuWUaVKlSKMSkRERESkZFBBQkREREREpIAeeugh/vzzT4YNG4bD4ci2LD09nWeffZa9e/fSv39/F0UoIiIiIlJ8FfuCxNixY6lUqVKB+58/f57//Oc/RERE4O7uTlhYGA899BDHjx8vvCBFROSGo/wjIiK5GTRoEN26deONN96gSpUqTJ8+HYDbbruNypUr88UXX9CoUSMGDx7s2kBFRERERIqhYl2QiI6OZsKECQXun5GRQbdu3fj0009p0aIF77zzDn369GHKlCm0a9eO+Pj4QoxWRERuFMo/IiKSFzc3N2bPns3YsWNJTk7mwIEDGIbBzJkzSUxM5JlnnmHZsmV4e3u7OlQRERERkWLHzdUB/FNSUhIHDx5k7dq1vPnmm5w4cYKoqKgCrfv111+zefNmnn32WT744ANMJhMAtWrVYsiQIYwbN44333yzMMMXEZESSvlHREQKYtasWVSpUoXhw4fzyiuvsHfvXs6fP09QUBA1atTAza3YfcQSERERESk2it0dEnPnzqVBgwY8+eSTHDp06IrW/eWXXwAYOnSo82QQwH/+8x98fX359ddfr2usIiJy41D+ERGRgnjsscf48MMPATCbzdSuXZu2bdtSp04dlxQjpk+fTps2bfDx8cHHx4fOnTszb968Io9DRERERKQgil1Bom3btsyaNcv5qFevXoHX3bFjB5UrVyYsLCxbu6enJ40bN+bAgQOkpqZe75BFROQGoPwjIiIF0bZtW5YvX45hGK4OhU8++YTbb7+d5ORkxowZw6BBg/jzzz/p2bMnkyZNcnV4IiIiIiI5FLv7icPDw+nTp4/z+bvvvktCQkKB1o2JiaFKlSq5LitfvjwAZ86cITIyMsfytLQ00tLSnM813reIXI5hGKSnp5OWlk56RjoZGTbS09PJyMjI/D4j83ubzY7NZnM+MjJs2O127HY7NrsNuy3r+8yvdpsdu8OOw+HAbrfjsGdgOOyYDDsmWzomewYmw4bJYcPksGM2HJnfGw7MDgOzw575PQ7MDgcmjMznhoHJYfzdbmDGyFzXMDD//TAZYDKMv9fJajMw8Xe7YWA2wIQBDtefiLmelH9EpDRyOBykpaU7/xal/Z3H0tMzMvOZ7ZLvMzIuyWeZuS3j7+d22985zW7HbrdhOOwYjgwMh+3iw3CAYQPDAYYdcGR+j92ZqwwcQGYOAwMDA3NmEgITmEwGNrvNpcdszJgxtGrVilGjRjF27FiXxXHu3DmGDBlCo0aNWLlyJb6+vgA899xzNGjQgBdffJF//etfWK1Wl8UoIiIiIvJPxa4gcS0Mw8jzH25PT08g80NXbt544w3GjBlTaLGJiGulpaURn5BIfHw8CQmJxMXHk5iYSFJSMknJySQlJZOckkxiQhJJycmkpKSQmppKSmoqqamppKamkZqaimHPwGzKwM1sx83swNvLQqCPBwFWN/zcLfi4mfG1mPAxmfEyGVgBT8PA3WHg4TDwdhi42R242RxYHAZmmwOz/e+vNgOTzYHJbmCyGWAzMNkzv2I3IPc/X1IMKP+ISFExDIPk5GRiY+OIjYsjPiGRxMREEhOTMr8mJZGQmEhSYhLJySkkp6aQmpKCPSMFw5GKYUvFRDoWbJhNNtzMDjw9zHh6mPD0MGP1MGF1N+HhbsbqbsZqNWP1sGC1WnB3N+HhZsbHzYSHuwl3iwl3DwM3D3DLym8OB5as3GUzIOPvHGYzMGxkfn9pbrMbkKP97+eOv9dzXLLcDjgMnnXhz+DYsWO89NJLvPbaa8ycOZO+ffsSGRmZax548MEHCy2OBQsWkJqayuDBg53FCMgssPfr148JEyawY8cOmjZtWmgxiIiIiIhcqRuqIHEthg4dygsvvOB8Hh8fT0REhAsjEpHLSUhM5OjRYxw7foLo6FOciYnh/IULXIiNJS4unri4zOJDYlISNtvFqyndLRDoZyHQ14KftxuBQVYC/a2U9XSjoruZQF/w9TbwzrBjTbfjkWbHPdWGJcUGqQ5Ic2R+TXFArB2MdBceBSnplH9ExDAMYmPjOBMTQ8zZc8TEnCXm7Flizp7j7NlznD13jtjYOJIS4zA5kvD1hEBfCwE+Zvx9LPh5Z37197ZQwduMb7AZnwoWvDwteP1dYDCbMl+HDA9Idfs7nxnOnGakXfI83YAEW2YxIc2BkW5AuiPzedbj0uf/uGHuRq+f9+nTB5PJhGEYbN++ne3btwNkm0PIMAxMJlOhFiROnTpFQEAAjRo1yrPPpf//iIiIiIgUBzdcQSKvsVyz2i/9oHApq9Wq25lFipGk5GTi4+M5cvQYBw4e4vDhoxw/Ec3JU6e5cCGW+IR40tKyFwLMJgj2txAe7EaFUCvNavpRrkwoZS2hBGbY8U3JwCspHfeEDIi3YSTYIdEOZxIhpXgMk2O4mf5+mMHNjGEx4XAzg8WMw82MYcl6mDAsFhwWM8bf7VgsOMxmcLP83ccCFguG2QJ/LzfMZkxZ31vcMtvNbmCxYLKYMZkz18FiwWTOXGaymDFZ3MBsxmyxgNmCyWIhI8MGt6519SErNpR/RCQ/6RkZHDlyjCNHj3HiRDQnoqM5cuw4x4+fIObMaXw9bYQFuVGujDthQW6EBrpRL8CNoPIWAnwt+HqZsboHAUHA339b0ozMPJZkhyQHRpIdotMh2QFJdozkzOK5keLAnuqAZHvmHQZyTYrL3AzPP/88zz//fI721NRUFi1ahKenJ3Xq1Ml1XQ0XKCIiReGOZXOveJ202LgcbQ+vXow1MOCqYpjWsedVrSciheeGKkiEhIQQF5fzDxfgbA8NDS3KkETkMhISEjl3/jxnzsSwc/ce9uzdx8FDhzl9JobY2LgcV/Z5W01UqWClVR1fIsp7Ex7uQ6gVQhJS8YlLxXTBBudtGBcyYFUMxNtzXLl5rTMfGG4m7FY3HJ7u2L3csXlZcXhZMTytGJ6eOLw8MXl5Y3h7YfLyxuTlg9nLG4uXD2YvH9x8fHH38sXN2w8PL2/cPT1x9/LEw2rF3WzJ88R1cZN58uIRV4dRLCj/iEgWh8PB0WPH2ffXfnbt3svefX9x8NBhzsacpHpFDyqX86BCqAc1gt1o28KNMjdb8Pcuj9mc/ep6UhxwwQbnbBgHUyHejiPejpFgy8xtCfbMOxmKkGEy4XB3w+HugeHhjuHhgWG1gocHeFoxeXhg8vTE5GnF7OmF2csLs7sHFqsHZqsVi4cHFqsVN4/M701WD0xubpjc3cHdPfN7Dw9MbpaLz93cwc0Nk5sls5+bGwnJyRASUqT7fqkBAwa47LUvJz09nSeeeIKDBw8ycuRI/Pz8cu2n4QJFRERExFVuqIJE3bp1Wbt2LfHx8fj7+zvb7XY7mzZtIioqCm9vbxdGKFK6ORwOzp07T/SpU+w/cIgNGzdx8NBhzpw5y7nz57ONse/vY6Z2ZU9qVwmmckVPKoR5UdbdQcDZZExn0iEmA2P7BVh8JvPEzN+u9NSM3cOCzcdKhp83Nn9f7EFBmIJDsASXwS0oBGtgGaxBQXgHBeETFISHvz8mD4/rdETkRqH8I1J6JSQksmHjJtZt3Mz27TuIPn6IqLJQPcKDSuWs3NHYnbJd3Aj0qZSz6JDogDPpGGeSccRkYFywQWxmYf16Fhvs7m44rJ4Y3l7g7Y3J1xezrw8WXz8s/n54+PnjERiAW2AgZl9fTN5emL28MXl5YfbyxOTtjdnTEzw8ikXR3JSR4eoQiqX9+/fz8MMPs2rVKvr378/w4cPz7KvhAkVERETEVUpsQSIuLo6UlBR8fX2dk7j169eP5cuX89Zbb/H66687+06aNIkTJ05k+6dbRIpGckoKBw4c4sChQ2zfvoMDhw5z9NhxzpyJcQ5l4+9jpkoFdxrWDqBOZW8qhXkQ6mbHeiYV40Qa7IjFmHcaztkg51DVebJZ3UgP8iM9uAyO0DDcylfANzKKwIoV8Qkvh1toaOYJFpEroPwjUrrFxScwb/5Clq1cyYmjB3A3YmlQxYuakVY69/EgLKgcHu7mbOsY6Q44lobjeDqcTsc4kwExGZl3QVwFu5sbDh8f8PfDHBiIW2Ag1jJl8AgJwa1sKG6hZbGUC8MSGIDZ3x+TW4n9l7/Y+uijj5g8eTIHDx4kMTEx1z4mk+mK53BwOBzs27fvsv28vb2JjIwEMotbn3/+Of/3f/9HRkYG48aNY8iQIVgsljzX13CBIiIi16ZMoCfHFt3r6jBESqQS++nk2WefZcqUKYwaNYrRo0cD8NRTT/H1118zbtw4Dhw4QIsWLdi9ezeTJk2iQoUK+V4lJCLXT1paGgcOHeavv/az5Y9t7Ni1h0OHj5CamgqAn7eZGpFWWjUOokFlL8oFWAh2s2E+mopxIA5jYSpEp+MoQOXBZnUjNTgYW3h5PCpFEVCtOkG1a2ONjMTs61PIeyqlkfKPSOmSkWFj+46dLFi4iF07NkPaKZrX9OSORp6EdfYk0LdC9smMbQbGsbTMgvrx9MyvZzIKXE13mM3Yvb0hwB/30FA8y5fHWrEibhXK416hAm4RFbEEBhbOzkqBvP/++wwePBgPDw8aNGiAj8/1+38jOTmZ2rVrX7Zfhw4dWLZsGWlpadx3331MmzaNm266ifHjx+c5b4SIiIiISHFQYgsSufHw8GDRokWMGDGCWbNm8euvvxIUFMTdd9/N66+/TlBQkKtDFLmhHT8Rzbbtf/LX/oPs2buPnbv3cO7ceSxmqBjqTqV6gbSo70/Nsu6EBYBHdCrGjguZY2NHp+PI50JRh9lEaoA/tooVsVavRmDdugQ0aYK1fPmi20GRPCj/iNxY0tPT2bBxMzNn/kZM9G7KB6bRqLoXXXt7UDY4DMulQy85DIyTaRgHUjMfh1IxZVy++mDz8sQICMAjPBzvSpXwqFoVj+rVcYuKxBLgj8lsvuw2xDXGjx9PYGAg69evp3r16td1276+vs47SAvi8ccfZ9q0aYwdO5ZXXnkl37siRERERESKg2JfkFi2bFmu7ZMnT2by5Mk52kNDQxk/fjzjx48v3MBExOnEiWg2bNrCH9u2s2PnbvYfOIDNZqdcsBvtmwTQuWkg1cu6EexjYD6cgrEqDmN3Mo6kvCsQKX4+ZISH41G9Gv6NmxDcsgXWsmWLcK+ktFP+ESldMjJsHDx0iN/nzuLArnWE+8fTpaYXFdt6E+CTfWJgI9GOsTsZ468UjANpmFIuzmX0zxkWDJMJW4A/bmXD8K5eFc86dfCoXQv3ChUyh1NS4aHEOXLkCPfff/91L0ZcqXXr1vHtt9/yyiuvMGLECJfGIiIiIiJSUMW+ICEixVf0yVNs2LiZ3Xv2snb9RvYfOIi/j5l6la00qx9E80grFUNNuB1KwVhwAWNvMo603K/6S/X1Ib1cGB716hLYvj0VGzbE8vf4/CIiIoUl5uw5li9fwtYNCzGlHad+ZXc6dLNSNjA4Wz8j2Y6xMxn71mRMh1Mw/Z3O/lmAsHtaMZUrh7VSJXzq1sHasCHuERUxBwUViwmh5dqVKVMGt2IwL8ekSZMA8PHxybMYfv/99zvnOxIRERERKQ5c/5+0iJQ4589fYOXqtRw4eIgtW7exbfsOAn2ga4sAOtcNoFq4GwFudoyNcRjfJ+KIt+fYht1iJqlsCKaatfBt24YKjRrhU6ECJg01ICIiReDEiWiWLPmdvVsXERWcSNe6ViqVC8DNcslwTCl2HDuTyfgjFbfDSZgM+Of9DA43N4xyYVgrReHbrDmezZriXrECZi+vot0hKTI9evRg3rx5JCUlXdf5I67Utm3bABg2bFieffr06aOChIiIiIgUKypIiMgV2bN3H0uXr2LHzl2s37gJL7d0OjXxoWe9QGpWtuB+LBXHnPM49qbkmMDT5uZGYrlQqFYdv44dqN6kCZ5hYa7ZERERKXUMw+DQ4SOsW72Qw3uWUzEgjrvaeVHG3z97vzPppC1PwrI9HrPdwP0f27H7+GCpXAmfJo3xadEC96goLKEhugOilBg3bhwdO3akf//+fPLJJ1SpUsUlcaxbt84lrysiIiIici1UkBCRArHZbKxYtYbVa9ezaPFSHOmxtK3lTdcawdSq5o71UArGF7E4ztuyrWcACWEhZNSsiXfbNlRq0Aj/SlEaM1tERIrU/v0H2LRuAacOryfYep5eTTwJCbg4N4RhGNj2ppG2LBGvo4k5ixDe3pgrReHbtCm+7dvhUa0aZm/vot0JcYnIyMgcbampqfz1119Ur16dwMBAAgICcvQxmUwcOHCgKEIUERERESkxVJAQkcuKjY1j3oJFbNm6nSVLl9C8shvdqpeldk0PvE+lYkw6i3E2eyEizepBfFQERqOGlGvfgQoNGmC2Wl20ByIiUlolJiayYtlcjuycj5c5hva1rJQNujiEjZHhIHldKqyJxzMulUsHWnK4uUHVKvjWr4/PTe2w1qiOJTg454vIDc1sNue4+8XHxyfbcE2GkXOOrNzaRERERERKOxUkRCRff+0/wJJlK9i0ZSsHd2/i8WZ+tGnsS2BSBo6fTmOcysjWPy44kOTqVbA0akKFli0Ir11Hd0OIiEiRMwyDHTt2sGXNr9hi/6R5FQvlgrOfQE7ekIZ54Xk8k9Ozrevw9satTm2Cu3fDs2kT3MqX13BMpdjhw4ddHYKIiIiIyA1DBQkRydOmzX+weu16Vq1eRdn0Y7zcJphqldwxzz6L40T2kzdxgf7E162NZ6uWVGnekrKVK7soahERKe0uXIhl9YpZxBxaSYhnDA2aeGIxXywoJO63Y591Ad+YxGzr2UPK4NWwAf5du+DZuDGWoKCiDl1KgKNHj+Lv709gYGCefWJjY4mPj891uCcRERERkdJMBQkRydWuPXtZsnwlm1cuootvEs2bBxNyOgUmngPHxX7x/r5cqF0dS6MmVO3ShfCoSi6LWURESjeHw8EfWzZwaMc8ks/voVGUg9DAi4MwJZ6BlJkJBB08x6X3OzjCy+HTsiV+HTtgrV8Ps69vzo2L/K1y5cq8+OKLvP3223n2ee211/jggw+w2+1FGJmIiIiISPGngoSI5HDk6DF+mz6LmHXzGFDOQvXafngsOg9nLg7PlOjrzZmaVTDVqktou3bUbNgIN4vFhVGLiEhplp6RwfJFv3Hh2Eqs9hO0r2fFzZL5r649w+DC7DT8tpzBy36xqu7w88OrbWsC+vTJLER4eroqfClBLjc3hMPh4ODBg7i7/3NqdBERERERUUFCRLKJiTnL/6bPwr5uHnfW9iQizQY/nIG/P3sbZhNHKkWQ0agB3k2bUadNWwK9ffLfqIiISCFKTklh6dxviDu+mhrhKZQLvlhYSDphYP8+hqDYZGebw90d98aNKNOrJ97t2mL283NF2FKCXDqxtclk4r333uO9997Ld526desWRWgiIje8mJgYypYtm63tzJkzhIaGuigiKUzWwAD6Lf3d1WGISCFSQUJEnOLjE5j1v+l4rphJp/qeBP2RADGX3BUR6M3BurXxatqSKl27ULlsOU3yKSIiLnXhwgVWL5xEypmNtKoJHm4Xr0o/vcRG4NKTeP49bI4BmGrWIKhTR3y6dMZd4/tLAbVv3975P8/y5cupWLEiVatWzbWvyWQiNDSUl156qShDFBEREREpEVSQEBEAUlNTmffTL3gvnU6zymYCll2A9MzbIgyLiYNVIkisWROfmzrQvE07/D08XByxiIiUdidOHOOPFd9gi/uTFjVNzomrkxPNxP+QQOihGGdfh58ffjd3wbdrV6wNG2By07/BUnDLli1zfm82m7nnnnvynUNCRERERERyV6BPYlWqVLmqjZtMJg4cOHBV64pI0bHZbCyZ8i3eq+dSOTgD35UJziGaUst4sa1GLXyq1yDo5m60rFoDD80VIUVAuUdE8rN/3072bPoJc9Jemla7OJzO2b1m3KedIjTx4hBNpiqVCel/F96dO2MJ8HdVyHKDGDBgAM2aNXN1GCIiIiIiJVKBChIOhyPHsCyJiYmcO3cOAH9/f8qVK8fJkydJSEgAoEyZMvj6+l7ncEWkMGya/A0e6xZQ0YjHa/PFEzjxUYHsrF4XnyYtKN/+JhqGhGHWEE1SRJR7RCQvO7au5cjOmXhlHKRmlYtF8rOL0/FfegqLI3PiasNiwdqmNQH978KrWVNMZrOrQpYbyKRJk1wdgoiIiIhIiVWggsThw4ezPT9z5gzt27enQoUKfPjhh3Ts2NG5bOnSpQwePJjz589nu7VZRIqnk4uXkLZuAVFxZ/E4keZsP1s/jB1R9QnocjP16jeksp+uKJWipdwjIrk5sG87R3dNx5/DVI7ILEYYhsG5aSkEbTnj7OcIDMC/Rw/8+92Ge1SUq8IVERERERGRS1zVZWJjx47lzJkzLFq0KNsJIYBOnTqxcOFCEhMTGTZs2PWIUUQKSer2Pzn26/dUPB59sRhhhug2kfxRtTF+t99BuybNVIyQYkG5R0TOnD7Ono0/UMZymMrlLhYjzk5NzlaMMNeoTpmHHyLwkYdVjBARERERESlGrmo2v+nTp9O7d29CQkJyXR4cHEyfPn2YO3fuNQUnIoUnbc9ejv/6EyF/7cOSYM9s9DRx5Kaq7PKrQcitt9K5Sg0CPayuDVTkb8o9IqVbYmIcW5dPopz1IGFBmcUIuw3OfZNEmf1nnf08WrYgoN/teLVrq4mrRUREREREipmr+pQWExNDYGBgvn28vb2JjY29ms2LSCFLP3CA+AULYN2qi8WIIDf231SdnZ7VCO3Tl45RVVWMkGJFuUek9MrISGfjkon4OnYTViazGJGRbuLCxHjKHM2cV8YAPNu1I+jRh7DWqePCaEVERERERCQvV1WQCA8PZ/369Xkut9vtrFixggoVKlx1YCJSODKOHiVpyTLiF8zBPS49s9HPwsGuNfjDvQblevahY8UoQjy9XBuoyD8o94iUXptWfI8pYSuR5TKf29MhdkIsZaIvAGCYTHh17EDQE4/jUaWyCyMVERERERGR/FxVQeKWW27hs88+Y8yYMQwbNgy3S26HT0tL49lnn2XXrl288MIL1y1QEbl2GdHRJC5cRMKiebjFxGc2Wk1cuDWStaZaVOjWi/blIwj39nFtoCK5UO4RKZ22rZ9J8un1VA9LB0wY6Q4Svowl+GRmHjPMZry7dCHo0Ydxr1zJpbHKjS2r8L1mzRqio6NJSUlxLvP09CQ8PJzWrVvTsWPHbDlKREREREQuuqr/lP+fvfuOrqM69///ntPUe5cty7333rsxENMDgUCAhBAHkqyQ5N6bC4EECMS5Cak/bu43CaFDIJDEYIp7L3IvkuVeJTfJ6uX0M78/jnyMcME+VrU/r7W8mqqbJQAA1UlJREFU1pk9e4+eEdJ60OzZz37qqaeYO3cuzz77LH/5y1+47rrryMrK4uTJkyxYsIATJ07Qo0cPnnzyyaaOV0TCFKito37xElx56zAPHws2WsH7lSw+tA8jffx0xmVm0yk2rnUDFbkA5R6Ra8+h3esoO7SULilVWAwD02dS99dy4k7UAmBarcTMnEHi/fdrMkKa1cKFC3n44YcpLi7GNM0L9jMMgw4dOvCXv/yF66+/vgUjFBERERFpH8KakEhLS2P16tU8+uijfPLJJ7z22muhcxaLhbvuuos//OEPJCQkNFmgIhI+0zSpX7kS1+YteHbsDDYawJ1pLLb3xzdwOBOyO9I9PrE1wxS5KOUekWtL6cn9HMr/kOzYUzhsBqZpUvdeFVHHGiYjbDZibriexHu/qskIaVarVq3ixhtvJBAIcP311zNjxgy6detGfHw8hmEA4PV6OX78OPPnz+e9995j1qxZLF26lIkTJ7Zy9CIiIiIibUvYa4k7derERx99xPHjx9myZQtVVVUkJyczfPhw0tLSmjJGEblC7vwCnKvX4Mo7W3/f+FIyO5K7sCl9IN/u3ot+icmtGKHIpVHuEbk2BPxe9mz6Fyn2o8RGBR/4OpfXE5VfCQTLNMXeeAMJ996DvXPn1gtUrglz5szBYrHw4YcfcuONN1607/3338/s2bOZMWMGv/jFLzQhISIiIiLyOZYrvUBERASRkZF4vV5uuOGG0FtCItI2+MvKqFuylPoVK0NtxqQESvpk8m7yaKZ06s6QZD3IlfZFuUfk6rZ3+wIi3IWkxAVL43gKXTgWlYbOR0+aqMkIaTHr1q3j1ltv/cLJiDMmTZrEzTffTF5eXjNHJiIiIiLS/oQ9IVFaWso999xDZmYmM2fO5Jvf/CYQrPHdu3dvVq9e3WRBikh4TJ+PusVLcS5dBj4fAMbgGNyTUvibYxIdHHF8qUcvrJYrnpsUaRHKPSJXv9rKY1Qc+oSsRA8A/lNezHdLOTPtaB80kMRvPazJCGkx9fX1ZGRkXNaYtLS0Rptei4iIiIhIUFhPIU+fPs3IkSN59913mTp1KkOHDg1t7jZy5EhKSkqYMWMG27dvb9JgReTyOPPW41y1Cn9JSbAh2QY3p/AP70gq602+PGAQiY6I1g1S5BIp94hc/UwzwJ5N75AZWxE8rvfjfvk0Nq8fACMnh6RvPoSja5fWDFOuMbm5uSxcuPCSJxh8Ph/Lly+nU6dOzRyZiIiIiEj7E9aExE9/+lOOHDnCa6+9xoIFC5g8eXLo3Ne//nXWr1+P1WrlF7/4RVPFKSKXyXv0aHDfiM1bgg0GWO5MZYOvC1sCKYzPyGZYVofWDVLkMij3iFz9jh9Yg7V2O3abgek3cb5aTkSNGwAzKZHk++8jcsTwVo5SrjV33XUX+/btY9asWWzevPmifQ8dOsRtt93G3r17ufPOO1soQhERERGR9iOsTa3nzZvH2LFj+drXvnbe8z169OCmm25i7dq1VxSciIQnUF9P3dLl1K9YAYEAAMbEBJwd4viXMZTkY1V8ZcZI1d2XdkW5R+Tq5nWWcWLXXDLjgiUGfR9WEFFcB4AZEUHCl+8gZuZ1yl3S4p544glWrFjBsmXLGDlyJAkJCXTt2pW4uLjQz6PP5+PEiRMcPHgQ0zQZM2YMP/nJT1o5chERERGRtiesCYmSkhJmzZp10T5paWmcOnUqrKBE5Mo4V63GtXYtgfJgyQsy7RjTEvnQNpTafae4d8hwUuPiWzdIkcuk3CNy9TLNAPu3zSXRfgKAwKYaLBurg+csFmKvn0n87bdhRKjMoLS86OhoVqxYwf/93//xxhtvsHHjRrZs2XJOP8MwGDZsGPfddx/f+c53sFqtrRCtiIiIiEjbFtaERHZ2Nvv27bton/z8/Mve/E1Erpy3uBjnho24d+QHG6xguTONEks8a2tS6eQpY8aQoa0bpEgYlHtErl5VJ7bhLl1HXAyY5V4C88pDm1hHjBtD/N1fwZqc3KoxyrXNYrHwne98h+985zv4/X5KS0txu92h8xEREaSmpmKzhfXnlYiIiIjINSOsPSRuvfVWli1bxieffHLe82+//TYrVqxg5syZYQdWVFTEgw8+SEZGBna7nZycHL73ve9RUVHxhWPr6up47rnn6Nu3L5GRkaSlpXHPPfewd+/esOMRaQ9M08S5ei3OFSuhYbNfY3oSRpaDf9tGUFNwgPsnTMThcLRypCKXT7lH5Ork91RzpOBDUqLrMQMm/n+WYXiDOczSsweJd92lTaylTbFarWRmZpKbmxv6l5mZqckIEREREZFLENb/NT/55JP8+9//5uabb+aee+6huLgYgN///vesWrWKuXPnkpyczFNPPRVWUFVVVYwbN46ioiK+8Y1vMGDAAFatWsWLL77I+vXrWbt27QX/h9/v93P77bezcOFCbr75Zh566CGKiop46aWXmD9/Pps2baJbt25hxSXS1nkPHKTu008J1NQEGzpFYEyIZw+ZbD9pJaeyjmEDB7RukCJhUu4RufqYpsmx3QuI8u3HiIBAXg3GIRcAgZhoku+4XZtYS6vbsWMHaWlpZGVlNWr3+XyhMk67d+/G7/czcOBAZs+ezYMPPtg6wYqIiIiItHFhTUikpKSwatUqvv71r/PWW2+F2n/4wx8CMGjQIF577TVycnLCCmrOnDkUFRXxu9/9jsceewyA73//+3z3u9/lT3/6Ey+99BLf/va3zzt27ty5LFy4kG9/+9v83//9X6h92rRp3HzzzfzpT3/iN7/5TVhxibRlpt9P7byP8OzeE2xwGFjuTMU0DD5gCOV5m/iPL39F9Yyl3VLuEbn6uGuOUlG0kpTIAGaZl8D8ilCpprjrZxJz/fXaxFpa3ZAhQ/iP//gP/ud//ifU5vF4+NKXvsTSpUsxTZOUlBRM02T9+vVs2LCBJUuW8MYbb7Ri1CIiIiIibVNYJZsAcnJyWLx4Mdu3b+d///d/+fnPf86LL75IXl4eW7duZeDAgWEH9d5775GQkMAjjzwSajMMgyeeeAKA999//4Jjz5TGuPHGGxu1T5kyBYDdu3eHHZdIW+Yu3EX9ipWhY+O6JIwUOxst3dh3pJ74U2WMHT2yFSMUuXLKPSJXD9M0Ob57PomOMsyASeCfp8+Waurdi8T77sMSqU2spfWZponZUArzjBdeeIElS5YwaNAgNmzYQGlpKadPn2bLli2MHDmSt99+mzfffLOVIhYRERERabvCWiFx9OhR4uPjSUxMZMCAAQwYcG4JmMrKSqqrq+nUqdNlXdvpdHLw4EGmTJlCRETjP0I7dOhATk4OBQUFFxzfs2dPIPjw56abbgq179q1C4Du3btfVjwi7YHp8VD74Uf4T5wINiTbMEbF4cHKx/6+VKxZwlfGjcVuV21jab+Ue0SuLp6ao/jK1+OwQ2BNDRwObhDsi4km8757sWVnfcEVRFrPW2+9RWJiIp988gmZmZmh9sGDB/Pxxx/Tp08f/vKXv3Dfffe1YpQiIiIiIm1PWCskunTpwi9+8YuL9nnuuefo0uXyNyAsLS0FICMj47zns7OzKSkpueD4m2++mbFjx/Lss8/yl7/8hd27d7NgwQLuv/9+0tPT+dGPfnTecW63m+rq6kb/RNoL1/YduNauCR0bMxIxrAbLjL4U7y3Bd+Aod91+a+sFKNIErsbcA8o/cm0yzQAle/5JtN2FedpLYMHZjePjZlxH9MQJrRidyBc7cOAAN910U6PJiDOSk5O57rrr2LFjRytEJiIiIiLStoX1uvTnlyx/XiAQ4ODBg9jt9su+diAQADjnDdUzIiMjL/r17XY7r7zyChMnTmT27NmhdovFwttvv33Bt2bnzJnDM888c9nxirS2QH09tR/Ow3+6LNiQZccYEEMNkSx2daFi9UcM7t2LDh2yWzdQkSt0NeYeUP6Ra5On5jDu8u1EWBpKNfmCv1/+Xj1JuvsujAv8Loq0FQkJCcTGxl7wvM1mw+VytWBEIiIiIiLtwyWvkLBYLFitVqxWK4Zh8Jvf/CZ0/Pl/drudDz74IFTCoiVt376dkSNHUlVVxQ9/+EPefvttnn/+eVJTU7n77rt58cUXzzvu8ccfp6qqKvSvqKiohSMXCY9z4yZc6zeEji3XJWFYDBYZ/SktPEz9/kN8+fZbWjFCkfBd7bkHlH/k2mOaAcoPfESkzYu5thqOBEs1eaKjSP/KXdi7dG7dAEXOo6SkhKqqqtDxjTfeyOrVq8/b1+l0snTpUpXrExERERE5j0teITFx4kQMwwBgxYoVdOzYkW7dup23r2EYpKWl8eMf//iyAzrzNS70JuoXvSH7X//1X1RVVbF06dLQZqIADzzwAAMHDuTxxx/ngQceIC4urtG4iIiIC74ZK9JW+auqqJv3MYEzfyDnRkDPKFzYWVOdRsWaD+iQns7ECeNaN1CRMF3tuQeUf+Ta46k+hPP0VuwVXsyFlQCYQOz0aURPntSqsYlcyOuvv84bb7xBx44dGThwIBkZGRQUFPCLX/yCJ554ItTv1KlTzJ49m+LiYq1+ExH5nCOjxoY1rszrPaetaOaN1IexMhogd/3asMaJiEjTuOQJieXLl4c+WywW7r77bn71q181eUCpqakAjd5A+qyqqirS0tIuOH7t2rX07t270QMhCG5Kescdd/DXv/6VPXv2MHz48KYLWqSVONfl4dqyJXRsuT4JwzBYb3SltOAQziPHmPyVLxMdFdWKUYqET7lH5OpimgGqjszHYXHj/7QCGko1Obt1JeuOO7DExLRyhCLn2rRpEwUFBeTn51NQUMDWrVs5fvw4AL///e9DExLr169n7NixmKbJ0KFDL7p/kIiIiIjItSqsPSQeeOCBZnuoEhMTQ25uLps2bcLv92O1WkPnqqurKSwsZOzYC8+qWywW/H7/ec+daQ+nvrhIW+OvrKTuk08x6+qCDT2iMXIjAVjh7ETFurlEOuwq1yRXDeUekfbPU32I+pItWA+5oLA+2Oawk33HHUT07tXK0Ymc39ChQxk6dGijtoqKCgoKCjh48GCozWazMWTIEG6++WZ+9KMfEaUXQkREREREznHJe0h81iuvvMJdd91Ffn4+y5YtC7XX1tby5ptvUl5efkVB3X777Rw7doyXX365UfuvfvUrvF4vd9xxR+jrnTx5stEbrVOmTGHfvn3Mmzev0diDBw/y/vvvk5GRQb9+/a4oPpG2wLVlK+5t20PHlusTAdhFFgd3l+A+dpL+/frSvVvXVopQpGkp94i0b6YZoKZoIVaznsCnZ39fzSGDiZ0+rRUjE7l8SUlJTJgwgQceeCDUNmzYMDZt2sRPf/pTYrTaR0RERETkvMKakDBNk+9+97sMHjyYN954I9ReV1fH/fffT25uLq+//nrYQT355JNkZWXxyCOP8PDDD/Pb3/6Wu+++m+eff57Bgwcze/ZsAF544QWysrL4/ve/Hxo7Z84cEhMTufXWW7nvvvt44YUX+MEPfsDQoUOpra3lT3/6EzZbWAtDRNoM0+ul5v1/YbqDG4Ga/eIxshwArDK7U7ZkDQA3f+kGLJawfs1F2hzlHpH2zVN1gLqTmzG318ExDwC1MdHkfP3rWBPiWzk6kcvn8/n44IMPePzxx/nWt77Ff/3Xf/H+++/jcrlaOzQRERERkTYrrKcjL7/8Mn/605/o0aMHX/7yl0PtSUlJ/OY3v+E3v/kNX//610lOTmbWrFmXff3k5GTWrFnDT37yE+bOncsrr7xCRkYGs2fP5rnnnsPhcFxwbJ8+fdi8eTPPPfccixYt4p133iEuLo4pU6bwzDPPMHDgwHBuWaRNcW7ejHt7w+oIi4H1huBGuaeJZcMBH+7jp8jKzGTaFG0OKlcP5R6R9ssM+Kk9thSLr5bAwoqzJ8aPI3KQfj+k/VmzZg333XcfR48exTTNULthGKSnp/Pqq68yc+bMVoxQRERERKRtCmtC4tVXXyUjI4MNGzaQkJAQanc4HPzgBz/gK1/5Cn369OG3v/1tWA+FALp06cLbb7990T5PP/00Tz/99DntXbt2PafkhsjVpPYf74PPFzwYkIKRHKxNv9roSfmKDQAMHzaYpKTEVopQpOkp94i0X+7qg9Qd3whrqqEquK9KWXIiA77+dQyt5JN2Zs+ePcycOZP6+npuvvlmbrnlFrKysjh58iTz5s3jgw8+4JZbbmHdunUMGTKktcMVEREREWlTwpqQ2Lt3L7NmzWr0QOizsrOz+dKXvsTHH398RcGJyLl8RcW4tm4LHhgGTI8GwIOVteXJ1O45AMCXbriulSIUaR7KPSLtk2ma1B1bjlFXQ2B5cO+VgAGBSZOwd+ncusGJhOHZZ5+lvr6el19+mQcffLDRuQcffJCFCxdy44038vzzz/P++++3TpAiIiIiIm1UWK+kVVdXX7R0BQRLX3g8nrCCEpEL83z8CWZ9PQBG5ySsKcFf401GF46u3gF+P6kpKYweNbI1wxRpcso9Iu2Tt/4E9cXrMZdUgCdY2uZ4RjqD7r+/lSMTCc+SJUuYMmXKOZMRZ1x33XXMnDmTFStWtGxgIiIiIiLtQFgTEt26dWPp0qX4/f7znvf7/axevZrc3NwrCk5EGrO43fjWrg0d+8dHhz7n+btRuWEbAEOHDMJht7d0eCLNSrlHpH2qL1oCZZWYG2sB8Fmt+CZOxJ6d1cqRiYSnoqKCfv36XbRPt27dqK6ubqGIRERERETaj7AmJO6++2727dvHww8/THl5eaNzNTU1fOtb3yI/P5/bbrutSYIUkaD4XbsJnDwFgJEQg62XFYD9pHNkXzWBeicAN86c0WoxijQX5R6R9ifgraXu6CoCn1ZAw76/hzpmMfJr97VuYCJXICcnh+PHj1+0z9GjR+nWrVsLRSQiInJpSktLMQyj0b/S0tLWDktErjFh7SHxox/9iHnz5vHqq6/yzjvvMHbsWNLT0ykrK2P9+vVUV1fTs2dP/vu//7up4xW5Zpl+PynbtoeO/aMSsVsMANZZenPk408BiIyMZMrkCa0So0hzUu4RaX/qilcROFgKe4MT5s7ICOpHjSYqPb2VIxMJ35e//GX+8Ic/kJeXx+jRo885v2rVKj755BN+/vOft0J0IiIiIiJtW1gTElFRUSxbtoxnn32Wl156iaVLl4bOORwOHnjgAX79619fcONREbl8/j17iTpxMnhgs2IdGVzgVEk0xz3Z1B8LnuvXtzd2lWuSq5Byj0j7Ygb81OxdQODTsyua9nbqyKSvfrUVoxK5cvfffz9Llizhxhtv5D//8z+56aabSE9P58SJE8ybN4/f/OY3jBo1ittuu42jR482GtupU6dWilpEREREpG0Ia0ICIDo6ml/+8pf84he/YN++fZw+fZqYmBh69+5NZGRkU8YoIoD7w3lYGmrnG33TsUQHyzWtNXpQu2FHqN+k8eNaJT6RlqDcI9J+uMp24991GE56AaiKj6Vy4CASsjJbNzCRK9S/f38Mw8A0TZ588kmefPLJRudN02Tt2rX06dOnUbthGPh8vpYMVURERESkzQl7QuKM/Px8Nm7cSHFxMU8//TR79uyhc+fORERENEV8IgJ4S0vxbd4SOvaMjyaS4OTEsej+7Fn2/wHBP3Rnfen6VolRpCUp94i0fdXb3iewojJ0vDunAzPuuqv1AhJpIvfffz+GYbR2GCIiIiIi7VLYExJ79uzhwQcfZMOGDaG2p59+mt///vf8/e9/569//St33nlnkwQpcq2rm/cxZlUVAEZGMpEdg5MRh0gjyRtDZXkFAJ1yOpKZobrccvVS7hFpH7w1JXh25sNxDwC1CTEc7tyFu3JVrkbav1dffbW1QxARERERabcs4Qw6fPgwY8eOZfPmzXzzm99k8uTJoXM33HADSUlJ3H333axataqp4hS5ZgVcbuoXLw4d+0efLXVRYO3CqZV5oeORI4a1aGwiLUm5R6T9qN7wPoE1ZaHjnbm5zLz51tYLSERERERERNqEsCYknnrqKSorK5k/fz5//vOfGTp0aOjczTffzIYNG0hMTOR//ud/mixQkWuVa+sWvAcPAeC32/EONQEIAO7EIWxbvynU94aZM1ojRJEWodwj0j4EXHU4t6+Go24AXAmRrMvKYsDA/q0cmYiIiIiIiLS2sCYkFi1axLRp05g6dep5z6elpTFr1iy2bNly3vMiculq/zUXzOAkhKtrJtF2JwAHyKCXI4Wi4mIAEuLjGTl86IUuI9LuKfeItA81Gxfg33gydFzYpSu9uvfGYgnrfztFRERERETkKhLWX4aVlZV069bton3i4uIoLy8PKygRCfJXVuLatDl4YBi4JqWGzu22dePAynUEAsHJioED+ulhj1zVlHtE2j7T46F2/Udw0AWAN97Bso4duOfWm1o5MhEREREREWkLwnp62blzZ7Zv337RPuvXrycnJyesoEQkqOZf/8asrwfA0rEj0bnB1RF+DGypI1i79uz+EZMnjm+VGEVainKPSNtXt3k1vm1HQ8cHunWiymuQEB/filGJiIiIiIhIWxHWhMQ999zD+vXr+fOf/3ze87/85S/ZsmULt9xyyxUFJ3Ktq1+yNPTZMrE/sUbwjdO9ZDE6LZc9+/YDYLPZuH7m9FaJUaSlKPeItG2mx0PN6n/DvuBEeiDexrKe/bhl2LBWjkxERERERETaCls4g/7zP/+TuXPn8uijj/Lmm2/idAbf2n7sscdYvXo1W7duJTc3lyeeeKJJgxW5lniPHg1tZm1ER1M1AOIDwXMHHD3wbN6B0xmcoOjWtQtJiYmtFKlIy1DuEWnbXPnb8G7fGzo+1qsDBZU1PD1zRitGJSIiIiIiIm1JWCskoqOjWbFiBV//+tfJy8tjy5YtmKbJH//4R7Zs2cJNN93EypUrSU5Obup4Ra4ZNXM/gEBwBsLRtw828zAAPizEZoxk2fKVob5jRo1ojRBFWpRyj0jbZfr91Cybh7mvJngca2VpjyF0dAWIjIxs5ehERERERESkrQhrhQRAfHw8L730Er/+9a/Jy8ujvLycpKQkhg0bRkZGRlPGKHJNcq5aE/psm9qfaHMFALvIZlrHnry8bUfo/A3X6+1TuTYo94i0Td4DB3Fv3xI6Lu+fzlbDyneuv64VoxIREREREZG2JqwJiZUrV5KTk0OXLl1ISkrihhtuOKfPoUOHKCoqYuLEiVccpMi1xr17D76iIgAs8fGcyC4jKViSmyMRPehTWsbJU6cASE9Lo3/fPq0VqkiLUe4Rabtqli0ksK8ieBBtYXW3QVTu2MvkR7/fuoGJiIiIiIhImxLWhMSUKVP40Y9+xK9+9asL9nnxxRf5/e9/j9/vDzs4kWtV7QcfgmkCEDFsKFH1+wDwYCU+eRjzFy4O9R08qD+GYbRKnCItSblHpG3yFhfjXLsUgmmL+iHJbEruwPB0HzZb2ItxRUREROQC+g4eFdY4n897Ttu4KTOx2eyXfa3CbevDikFE5JL/Sjx69Gjos2maVFVVNWr7LJ/Px/bt24mIiLjyCEWuMWYggHPdurPHk3sSyWEACunA+PQcfrbxb6HzE8ePa+kQRVqMco9I2+fM24B//8ngQYTBtm69OFK4j+/edmfrBiYiIiIiIiJtziVPSHTu3Dn0FrZhGLz00ku89NJLF+xvmiYjR4688ghFrjGubdvxnwg+2LEkJ3MyoZiUhnJN+81OTLA72L1nLwBWq5Wpk1WaRq5eyj0ibZu/rJy6JZ+CL7g8IjAknuXp/bAu/JgRzw9t5ehERETkapBit3N45JjWDkNERJrIJU9I3H///RiGgWmavP766/Tt25cRI0act69hGKSlpfHoo482WaAi14q6efNCnyNHDie2fj8ALmz4fNkcPHyEqqpqAHI6diAxMaFV4hRpCco9Im2ba9tWPA2T5BhwtHdHDpdWM3HEMJUTFBERERERkXNc8oTEq6++Gvr8+uuvc+ONN160jreIXD7T58O1YWPo2D+pGxFm8EHPTiOHLl4rK1etDZ0fNKB/i8co0pKUe0TarkBdHXWLlkBdQy3iPtGsSh9A2Ttr+crPn23d4ERERERERKRNCmunwZ/97GeMG6e69SJNzbluPf7TZQBYM9I5EXWEtIZyTaej+pBeB1u2bQ/1nzhhbGuEKdIqlHtE2hbXzkLc+dtCx+4RyWyypJLm9tGnd8/WC0xERERERETarLAnJAAKCwv561//ysaNGzl27BiHDh3i1VdfJTIykrvuuguLxdKkwYpc7Wo//jj0OXL0KGLrg6sj3NjokDYU7+kD7Nt3AACLxcKkCeNbJU6R1qDcI9J2mF4vzmXLCZyuDDZkO9ie1Y3Kbbv5kvY2EhERERERkQsI+6nNr3/9awYOHMgf/vAH1q5dy9GjRwHIy8vj3nvv5fbbb8ftdjdZoCJXu4DLhXvr1rMNUwcQRfB3aJ+RzfCULEpKy6isqgKgU05HoqOjWiNUkVaj3CPSNnj27MW1ZUvo2Bgbz4q4flSs3sDdd97eipGJiIiIiIhIWxbWhMS///1vfvzjHzN06FDWrFnDd7/73dC55557jkceeYR58+bx5z//uckCFbnaOVeuIlAZnGywdejAUev+0Lny6J7YLRZ27todahs4oF+LxyjSmpR7RNoGMxCgfu1afEeOBBtiLZT1TOVwqY8OsXHk5HRs3QBFRETagNLSUgzDaPSvtLS0tcMSERFpdWFNSPz6178mJSWF+fPnM2bMGCIiIkLnUlNTefHFFxk0aBCvvPJKkwUqcrWrm78g9Dlq7BgctXsACABZ2WMAOHjoSKjPuDGjWzQ+kdam3CPSNngPH8G1YROYwWNjVDwbI7pTuWkHY8eMat3gREREREREpE0La0KioKCAGTNmkJycfME+Y8aM4cCBA2EFVVRUxIMPPkhGRgZ2u52cnBy+973vUVFRcUnjly5dyvTp00lOTiYxMZEJEyawaNGisGIRaQkBlwvX9h3BA8PAPnM0iYHgaokjpDGmQw8Ajp841dDFYPJEbe4r15bmzj2g/CNyKVybN+PZE5w0xwbGqDjW27tTuW4Lt8y6sXWDExERERERkTYtrE2tDcPAbrdftE9dXR2GYVz2tauqqhg3bhxFRUV84xvfYMCAAaxatYoXX3yR9evXs3btWmy2C4f997//nfvuu48ePXrwwx/+EIC//e1vzJw5k8WLFzN16tTLjkmkudUvXYZZWwuALTeXQ+YezjxyPRnZjUirjX1Hi6mtqwOgY4ds4uLiWilakdbRnLkHlH9ELoXvVAnOtevA4wHAGBTL0Yg0ThytIN7hoH+/Pq0coYiIiIiIiLRlYa2QGDp0KEuWLKGu4eHo550+fZpPPvmEAQMGXPa158yZQ1FREb/73e/429/+xmOPPcb777/Po48+ysaNG3nppZcuOPb06dN861vfYuDAgWzevJknn3ySJ598knXr1hEXF8fzzz9/2fGItIS6BQtDn6MmjMdbkR86jk0Plr9YuWZtqG1Av74tF5xIG9GcuQeUf0QuhbugAM/OnaFjY2w8eY7uVORtZcSwoWFPCIqIiEjr0F4XIiLS0sKakPjRj37E8ePHue22284pjZGfn8+tt95KWVkZs2fPvuxrv/feeyQkJPDII4+E2gzD4IknngDg/fffv+DY1157jdraWp599lliYmJC7ZmZmfzpT3/ixhtVRkDanoDHgzu/IHhgsRB14xRSfcHSTKXEMTpnEABbtm4PjRkzemSLxynS2poz94Dyj8gXMZ1O6leuIlBVHWzoGkkgM4KtgY5Ub8nn+hlaBSQiIiIiIiIXF1bJplmzZvHMM8/ws5/9jJ49e4ZKaCQmJlJTU4Npmjz00EN87Wtfu6zrOp1ODh48yJQpUxptVgrQoUMHcnJyKCgouOD4xYsXExkZybRp04Bg6Q6n00lKSgr33nvvZd6lSMtwrliJ2fDGt71zLke9u4hp2Cn0iC2XIZGRAOzdF3wAaxgGUydPap1gRVpRc+UeUP4RuRT+ffvw5J9dwWcZF08h2ZTuPY4lYDJp0oRWjE5ERERERETag7BWSAA89dRTLF++nFtuuYWEhAQsFgsOh4OZM2fyr3/9i7/+9a+Xfc0zywIzMjLOez47O5uSkpILjj98+DBdu3Zl+/btjBw5ktjYWNLS0ujQoQN/+MMfME3zgmPdbjfV1dWN/om0hLrFS0KfI8eMobJkc+jYkjIEgCNHiygrLwcgIz2NpKSElg1SpI1ojtwDyj8iXygQwJO3Ad+x48HjZBv0imKjpQsV6zbTp3dPoqOiWjdGERERERERafPCWiFxxsSJE5k4cWJTxUIgEAA45+3UMyIjIy/6UKekpISoqChmzJjBtGnT+Pvf/05dXR2/+c1veOyxx6itreUnP/nJecfOmTOHZ5555spvQuQymH4/7m1nSzHFzLqelEM/B6AOB0NyxwCwdPnKUJ/evXq2bJAibUxT5x5Q/hH5IhGny/Dt2BE6NsbG4zFsFHjSqN25l8mzH2rF6ERERETkUthsdvoMUgloEWldYa+QaIuqqqo4duwYt99+O3PnzuXuu+/moYceYv369XTo0IFf/OIXVFVVnXfs448/TlVVVehfUVFRC0cv1yLnho0EKisBsHXswElrMZF4ATho6UhOXCIAGzaeXTUxfNiQlg5TRL6A8o9c7WKOHCFw8FDwwGFgDI0l39KJ0zsPQSDAjddf17oBioiIiIiISLtwSSskunbtGtbFDcM4Z+PRL+oPXPAt1Iu9nQqQkJBAeXk5jz32WOhaAHFxcXzta1/jl7/8JWvXruWGG244Z2xERMQF34wVaS71CxaGPkeOGsneY2vJaTh2xvcNndu1e2/o88TxY1sqPJFW1VK558wYUP4ROZ9AeTnx+w+AzweAMTgWI9LCZqMLFasXkJ6WRufcTq0cpYiIiIiIiLQHlzQhEQgEGj1gAaitraWsrAyA+Ph4MjMzOXHiBDU1NQCkpKQQGxt7WcGkpqYCXPAt0qqqKtLS0i46vry8nI4dO55z7kzbha4t0tLMQADXlq2h4+gbrieh5I8A+LDQIze4OWjRsWOUNNS3T4iPIy01peWDFWkFLZV7QPlH5GJ8u3YTfeJk6NgYHUctERTWxuI8dJTrbrqxFaMTERERERGR9uSSSjYdPnyYQ4cOhf6tX7+elJQUBg4cyNKlS6msrGT37t1UVVWxZMkShgwZQmxsLMuXL7+sYGJiYsjNzWXTpk34/f5G56qrqyksLKRv374XGA3Dhg0DOO+bsfv27QPCf+NWpKm5CwrwnzoFgDU1leoMk3jqADhspNM/JfgQc9myVaExHbKzWj5QkVbSUrkHlH9ELiTgcuHLW4+tvj7Y0DkCI9PBNiOX8u3B1Xs3zJzRihGKiIiIiIhIexLWHhLPPvssJSUlLF68mMmTJzc6N2XKFBYtWnTRDTwv5vbbb+fYsWO8/PLLjdp/9atf4fV6ueOOO4DgW7InT55s9MbpfffdB8Dzzz+Pr6GsAEBxcTGvvPIKmZmZDB48+LJjEmkOdZ8uCH2OGDKYg0eWhY4rorqH3gxfv2lTqL1bl9yWC1CkjWnO3APKPyLn49mzF9+u3aFjY1QcAFuMzpSv2oDD4WD0yOGtFZ6IiIiIiIi0M5dUsunz5s6dy5e+9KVQiYvPS05OZtasWXz66aeXfe0nn3ySd955h0ceeYQNGzbQp08fNmzYwLvvvsvgwYOZPXs2AC+88ALPPPMMDzzwAK+++ioAN9xwA1/5yld49913GTt2LDfffDO1tbW89tpr1NXV8frrr+NwOMK5ZZEmZZomro1nJxpirrsOR+27oeOMDuNDn3ftOrt/RN8+PVsmQJE2qDlzDyj/iHyeGQjg2rCRwJmN1mMsGP1iqCKKfeVWvKdKGT5siH62RURERERE5JKFtUKitLSUxMTEi/aJjo6msrLysq+dnJzMmjVruOuuu5g7dy7/9V//xapVq5g9ezaLFi266B+9hmHw5ptvMmfOHKqrq3nuuef485//zODBg1mxYgW33HLLZccj0hy8Bw7gKy4GwBIXh6d/RzICwbr4x0lkRMd+AJwqKeFUSQkAKSnJJCcltU7AIm1Ac+YeUP4R+Tzf0aO4tmyBhk3djRFxGDaDHUYO5VsKAZg+dXIrRigiIiIiIiLtTVgrJLKysli/fv0Fz/v9flauXEmHDh3CCqpLly68/fbbF+3z9NNP8/TTT5/TbrPZ+O///m/++7//O6yvLdIS6uYvCD3gcQwcwL6jSzmzO8SpiM7YLVYAli5fhdnQr2f3bq0Rqkib0dy5B5R/RD7LtSMfz+49AJgGWEYGyzVtoxMVq4O/JzOmTWm1+ERERERERKT9CWtC4uabb+Z///d/eeaZZ/jJT36CzXb2Mm63m+9///sUFhbywx/+sMkCFbmaONedfagaPWUyvsoVZ49Tz9bi3vCZsk5DhwxqkdhE2irlHpGW46+owJW3HrNhM2ujdzRGoo0aItldYuCrqqFjxw5kZWa0cqQiIiLN747ll18S1F1ZdU7b19csISIxIawY/jn5hrDGiYiItDVhTUg89dRTzJ07l2effZa//OUvXHfddWRlZXHy5EkWLFjAiRMn6NGjB08++WRTxyvS7nmLi/EePgyAERmJfdwwMrcG94+oJpIRXceE+u7ctSf0edzYURQdOdKisYq0Jco9Ii3HvbMQ965doWPL6ODqiO1Gp1C5pgnjRrdKbCIiIiIiItJ+hTUhkZaWxurVq3n00Uf55JNPeO2110LnLBYLd911F3/4wx9ISAhv5l/kala3YBH4fAA4evdi76k1JOMH4Ig1h76OKACqqqo5fvwEAImJCXTJzdWEhFzTlHtEWobp8eDauBF/Qw4iyQHdIgHYTg6Va98C4IbrZrRWiCIiIiIiItJOhTUhAdCpUyc++ugjjh8/zpYtW6iqqiI5OZnhw4eTlpbWlDGKXFWca9aEPkeNH0fN6R0kNxwHEvuFzq1cs5ZAIABAzx7dWzJEkTZLuUek+Xn27sO9oyB0bIyOwbAY1BLB7hIDf20dUVFRDB40oBWjFBERERERkfYo7AmJM7Kzs8nOzm6KWESuet5Tp/DuPxA8sNmInjGVlJ1LAfBgpX/ns5uDrsvbGPo8dLD2jxD5LOUekeZhmiau7dvx7NsXbLBZMIbHArDDyOH0pp0ADB86uNE+LiIiIiIiIiKXQn9JirSg+iVLMd1uAOxdulDs2kUsLgAOWbKYlnj2De+dhYWhzxPGj0FERKS5+Y4dw7V5C3i9ABh94jCirABsoxOV64LlmmZMn9pqMYqIiMi5ipd8NaxxZZWuc9qOr5yNOzHysq/VcdrbYcUgIiLXFktrByByLXGuWh36HDV2NMdPnF0FURfdLfTZ7XZz5GgRANHRUQwa0L/lghQRkWuWu2Anns9sZs34GADqcLC/3I6/rh6AieM0US4iIiIiIiKXTxMSIi3Ed7oMz96GEhiGQfR1M4hxnt2kOrvDhNDnvA2b8HiCb6d269oVi0W/qiIi0rwCNTW4Nm0mUF4BgJEehyXHAUC+kcOJdTsA6NihA+np2rNFRERERERELp+ecoq0EOfKVZi1tQDYOnakOraGVLMKgKOkMCS7d6jvmnXrQ58HDeiHiIhIc3MX7sLzmXKBxrC40Odt5FKRtxmA8WNHtXhsIiIiIiIicnXQhIRIC6lfsSL0OXLEcA4Wny3fVBqRi/UzqyC278gPfR43dnTLBCgiItcs0+fDtW0b3kOHgw0RDgIjgqsj6nFwqCKCQJ0TgMmTJlzgKiIiIiIiIiIXpwkJkRbgLy/Hs3tP6Dh65gyMmgOh45jUoWf7+v0cbHggZLfbGT1yeIvFKSIi1ybvgQO4d+RDIACAtXc61oa9LPONHE5u2gmAzWZl+LAhrRWmiIiIiIiItHO2cAdu3ryZN998k4MHD1JTU3PePoZhsGTJkrCDE7laONflEaisBMCamYE/J5qMslIAyohhWOexob478ndS17BpaG6nHCIiIlo8XpG2SrlHpHm48nfi2b37bMOoaMADwA4jh1PL3wKgb5/eREdFtUKEIiIiIiIicjUIa0Ji/vz53HTTTfj9/ov2MwwjrKDakqqqKkzTbO0wpJ2rXbg49NnSry+7Dy0nk+DP1VFrBzq73VS53QAsWrIs1LdXz+5UVQX3maiqqqKuri50LNLaqqurW/TrXUu5B5R/pOUESkqp37ARs7YOACM7g0AnHxbAiZ2T7iQCThcAo0YMVx6SVtfS+UdERERERJpOWBMSP/vZzwB47bXXuOOOO4iOjm7SoNqStWvXXtX3J83PWl9Pz4IC7A3He1KTsVbugYZnpiWeNFauXBnqv2r1mtDn2Oio0Lm6ujp27gyWzIiJiWmR2EUupr6+vkW/3rWUe0D5R1pO/K7dZO3Yzpn1ePWdHcRagqWb8o0cTm3YFeobYbc2ylkiraGl84+IiIiIiDSdsCYk8vPzueWWW/ja177W1PG0OWPHjiU+Pr61w5B2zLNiBfW1tQAYyUn0v2MmlftfAIJvng7vex2d49JD/ee88P8BYLFY+MaD9xMfHwcQeiN17NixJCQktOQtiJxXS7+hei3lHlD+kZZhOp04CwrxVFQGG2JjiJmQCARLouUbORQvex+A6Ogo7v7KnVit1laJVeQMrZAQEREREWm/wpqQiIiIIDs7u6ljaZMSEhL0QEiuSGnehtDnyP4DqHTtIaqhLvdBSxYzO/YInT948DCVDRMP2VmZ5OR0bHStmJgYEhISNCEhbUJLl0a6lnIPKP9Iy3AdPIjzwIHQsaNXNzzx9VgBF3aqrTnUVwQf/g4dPIjk5ORWilTkrKulNJ+IiIiIyLXIEs6g0aNHs27duqaOReSq46+qwr2zMHQcPXUypaX5oePaqC6N+i9fuSr0uU/vns0foEg7otwj0rTMQAB3fgHevXuDDVYrliHxWA0fAAVGB+wnKkP9x48d3QpRioiIiIiIyNUkrAmJX/7yl+zatYunn36aQCDQ1DGJXDXc23bgP3UKAEtCPNZB3Yj3HAfAj0F29qhG/Tdt2Rb6PGL4sBaLU6Q9UO4RaVrew0dw5xdguoOr9uxdOuPv5A6d32F05sCny0LHI4cPbfEYRURERERE5OoSVsmmt956i4kTJ/Lzn/+c1157jaFDh563hIxhGPztb3+74iBF2qu6pUvBNAFw9OrNaedeEs3gfhJHSGNkdv9G/Xfv2Rf6PHni+JYLVKQdUO4RaVqegp14dp3dsNrWqzNO25FQuSZPdBd2578CQGJCPFlZma0UqYiISPsTkZjA7cs+ae0wRERE2pywJiReeOGF0OcjR45w5MiR8/bTQyG5lvmrqvHs3Bk6jpo4nkMl2zmzK0RpRA6RtrO/giUlpZwqKQEgNSWFjh2unVr5IpdCuUek6fhKSnEV5OMvPQ2AJSUFBkRh5Uy5po6kOy34/cHVSD17dGu1WEVEREREROTqEdaExKFDh5o6DpGrjruwEN+xYHkmIyYGx4he2A8tD52PThnYqP+KVWswG1ZT9OzZvcXiFGkvlHtEmo67oADPrt2hY0ef3ngSy6GhGlq+0Ql/3tbQ+d49lJdERERERETkyoU1IZGbm9vUcYhcdeqXLIWGOveO7t0o5zhpgXIATpLA8NyRjfqvW78x9HnYkEEtF6hIO6HcI9I0ArV1uHftxnvgYLDB4cDerytOcx02guWajPgerFv9IgAWi4WuXfX7JyIiIiIiIlcurAmJMyorK3nzzTdZs2YN5eXlJCUlMW7cOO69916Sk5ObKkaRdsdfUYG7oCB0HDlmFAdObiGj4fiwLYfh0Y1r3xd+5k3ViePHtkSYIu2Sco/IlXHv2oV3927w+wFw9OiO2cWHLRAs15RvdKR/bDpvFx8DoEe3rkRFRrZavCIiItJ8UhIjKVp8T2uHISIi15CwJyTmz5/PV7/6VaqqqkJlZgDee+89fvazn/Haa69x0003NUmQIu2Nu3AXvqJiAIzISByj+mKc2hI6byT0adS/rLyc4ydOABAXF0vfPr1bLliRdkS5R+TKmD4f7p07cX9mM2tHv77UOYqxuILHuyyd6L7rbIm0sWNGfv4yIiIiIiIiImGxhDOosLCQ2267jbq6On74wx+ycuVK9u7dy+rVq3n00UepqanhzjvvpOAzb4iLXCtM06R+2QrwBd80tXfuTFXEaVIDZUCwXNPAjsMajVm1Zh0+X/BN1e5du2IYRssGLdIOKPeIXDnPvn149+3HrKkFwNahA/a+uQRcwUlxJ3bsCb1Yt2ZdaMyoEcNbJVYRERERERG5+oS1QmLOnDl4PB4WLVrE1KlTQ+3du3dn7NixzJw5k5tvvpk5c+bw1ltvNVmwIu2B/+RJPLvPvnkaOWIYR05vJ7Xh+KC1I3ek5DQak/eZ/SMGDezfEmGKtDvKPSJXxjRNPAWFuHfuDLU5+vfFn1GFreJMuaYcxmX14EfrnwcgKjKSfn17s3bt2laJWURERERERK4uYa2QWLJkCTNmzGj0QOizZs2axcyZM1m2bNkVBSfSHrn37sN39GjwwG7HProPZv3x0HlvbDesn1sBsbPw7P4R48aObpE4Rdob5R6RK+MrPobnwH78J04CYEmIx9G/P5XufaE++6ydiC+vpqq6GoBhQwdjs13RlmMiIiIiIiIiIWFNSJSVldGrV6+L9unZsydlZWVhBSXSXpl+P84VKzDdHgDsnTpRF1dDaqAcCJZr6pM5oNGYktLTFBUH95uIjIxkxLAhLRu0SDuh3CNyZdz5+bh3FoaOHf36YuuaDvXBzaud2IlM7NNo1d6kieNbPE4RuTxFRUU8+OCDZGRkYLfbycnJ4Xvf+x4VFRWtHZqIiIiIyDnCmpBISUlhz549F+2zd+9eUlJSwgpKpL3yFRXh2X32dyNi+BBKKndyZj3EfktH+mb0aDRm9Zp1eDxeALp364LD4WipcEXaFeUekfD5Kyrw7N+P98DBYIPDQUSfPnhij2PnbLmmyTm9WbFqTWjc2NHa0FqkLauqqmLcuHG89tprzJo1i1//+teMHDmSF198kZkzZ+Jr2NNMRERERKStCGtCYtq0aSxatOiCZTE+/vhjFixYwJQpU8IOrCnf9Pnggw8wDINXX3017HhELoV7ZyHeI8FyTUZEBI5RvQl8plyTK6YLkfbGEw5r89aHPg8ZPKhlAhVph5R7RMLnLtgZnDD3+wFw9OqJvVcvTpfvCPU5aM2lR2QsO3YEN4ZPTU2hc26nVolXRC7NnDlzKCoq4ne/+x1/+9vfeOyxx3j//fd59NFH2bhxIy+99FJrhygiIiIi0khYExJPPPEEDoeDG264gR//+MesXbuWAwcOsG7dOh577DFuu+02IiIiePzxx8MKqinf9KmqquLRRx8NKw6Ry2G63dSvXgMNP5/2Ll1wJtaR/JlyTT0z+jQeY5oU7Dy7AfakCWNbLmCRdka5RyQ8AZcLz549eAob8o1hENG3D9auadicZ8s1xaX0YeOmzXgbftYnjBuD8bk9j0SkbXnvvfdISEjgkUceCbUZhsETTzwBwPvvv99aoYmIiIiInFdYExJ9+vThn//8J5GRkfz6179mwoQJ9OzZk/Hjx/PHP/6RqKgo3nnnHfr37x9WUE35ps+Pf/xjamtrw4pD5HJ4Dx/Gu/fsxqCOIQM4XbsnVK5pr6UjAzJ7NxpTfOw4x46fACA2NpYhgwa2VLgi7Y5yj0h4PLt24dm3H9PpBMCW2wlHz57UubY3Ktc0NacfS5evCo2bMmliq8QrIpfG6XRy8OBBhg4dSkRERKNzHTp0ICcnh4KCglaKTkRERETk/GzhDrzxxhs5cOAAr732GuvXr6e8vJykpCRGjRrF/fffT1paWthBXexNnz/96U+8//77fPvb3/7C66xcuZKXX36ZF154ge9///thxyNyKVzbd+A7FnzT1IiNwTa4K776RaHztVG5xEVENRqzYuVq/A3lM7p17UxUVOPzItKYco/I5TH9ftw7C/EU7Ay1RfTrh6Nvb4pP/IXYhrajtlxui09k+crVAFitVsaMGt4KEYvIpSotLQUgIyPjvOezs7PZsGHDec+53W7cbnfouLq6uukDvEQul6tRLCJyflVVVa0dwhdqDzFC+4mzrWsv38f2EKdivLa0xPcyIiKCyMjIZv864Qp7QgKCG4z+8Ic/bKpYgLNv+kyZMuWK3vRxuVw8/PDD/OAHP2DgQL11Ls0rUFuHc10emCYAjq5dcSXWklR2tlxT9/Te54zL27Ap9FmrI0QujXKPyKXz7NuP9/Bh/KdPA2BJScbevTtmMjgOBVfo1WMnMXUABw4eorSh3+CB/YmJiWm1uEXkiwUCAYBz8tYZkZGRmA3/b/p5c+bM4Zlnnmm22C7HkSNH2Lt3b7N+jf/35kfNev1L8e37ZrV2CG3Sj596rrVD4H9+/uQX9nmw+cP4QitXrvyCHve0SBwX9UUxPhleadUm94Xfy7bvUn5um9sX/0zqd+eStYffnavg9waArz/Q2hG0yPeyZ8+e9OrVq9m/TrguaULi6NGjxMfHk5iYGDq+VJ06Xd5miFfyps9nPfvss3g8Hn72s59dUv+29JaQtD+eAwfw7j8QOnYM6kep5xBnHuXsMnK4MbNnozF+v59du/eEjidPGt8SoYq0G9dC7gHlH2k+ZiCAe/t2PAWFobaIfv2I6NeX8hOrcDSUayowcpjWqR8L3vt3qN+0qZNbOlwRaUGPP/54o8n96upqcnJyWiWW3NxcMjMzm/VrtIUJiYkTVQavrdJ/GxERudpc6IWVtuKSJiQ6d+7Mf/zHf/CrX/0qdHwpmxwahnFZm4DClb3pc8b27dv59a9/zbx584iOjr6kr9uW3hKS9se1eTP+khIALElJWAdk4anbG5qQqI7MISU6vtGYg4eOcPJUcExSYiID+/dryZBF2rxrIfeA8o80H+/BQ/iOH8d7+DAARmQkjl49sfXozOnNb5DU0O+4I5ec2DiWLF0RGjt5oibJRdq6MznxQvnpYnkrIiKizfyhGhkZ2aZLCjSVhISE1g5BLkD/bURERFrWJU1I3H///QwfPrzR8aU8FGoNPp+Phx56iC9/+ctcf/31lzyuLb0lJO2Lv7wc16bNoWN7967UxdeQWH62XFOXtB7njFu+clXoD8Xu3bteE3+IiVyOayH3gPKPNA/TNHFv24ancNfZcoJ9euPo3Ru38yixnpMA1OEgNW0wNTW1FOwMrqTIysygc+7lrTISkZaXmpoKXLgOcVVV1RXtrSQiIiIi0hwuaULi1VdfvehxU7qSN30A/vCHP7B//34++ujyluW2pbeEpH1x79uP98DB0LFjcE9KPcdCqyMKjRymZZ47IbFh49lJjBHDhjR3mCLtzrWQe0D5R5qH78gRfKWlePY0lAa0WHD06U1Ev74cPvwmEfgB2G7kMrVTX9asysPfsFJIqyNE2oeYmBhyc3PZtGkTfr8fq9UaOlddXU1hYSFjx45txQhFRERERM5lCWfQ66+/zubNmy/aZ/Pmzbz++uuXfe0redOnqKiIp556ih/84Af4fD6Ki4spLi4O1QYvLy+nuLgYj8dz2XGJnI8ZCODKW0+gshIAa0YGRvdEvPUnQ30qIzqSFZvcaJzX62PP3n2h4ymTJrRIvCLtmXKPyKVzbd2GZ+8+THfw587etQuOHj0wYu3UVp3NP6VR3UmPimHx0uWhtqmTVUtbpL24/fbbOXbsGC+//HKj9l/96ld4vV7uuOOOVopMpHn5fF52bd/Q6J/P523tsEREROQShDUh8eCDD/Luu+9etM/bb7/N17/+9cu+9uff9PmsM2/69O3b97xjDxw4gNPp5OmnnyYnJyf076677gLgRz/6ETk5ORQWFp53vMjl8hUV4c4vCB3bu3WmJr6OhEAFECzX1DG5C5bPlZnZtWcPp8uCJZ3S01Lp2ePcFRQi0phyj8il8RYX4yspwb0jP9QW0b8/Ef36UnZyC8n+0wCUEkevzMEEAgFWr80DwOFwMFyr9kTajSeffJKsrCweeeQRHn74YX77299y99138/zzzzN48GBmz57d2iGKiIiIiDRySSWbAFauXNnouKio6Jy2M3w+H6tXryYqKiqsoG6//XZ+97vf8fLLL/Pwww+H2j//pk9tbS21tbVERUWRkJDAgAEDmDdv3jnXy8/P54knnuD73/8+06dPp0uXLmHFJfJ5rp278B5sKNdkGNgGd6HCVUxsw/ltRi7Ts3qeM27Z8lWhz7169sBuv+RfRZFrinKPyOVzb92Gd/8BzNpaAGwdO2Lv0hlbp04cW/8WqQ39dlpyuSW7J4W79lBdXQ0ESwiqhJhI+5GcnMyaNWv4yU9+wty5c3nllVfIyMhg9uzZPPfcczgcjtYOUURERESkkUt+Cjp58uRQjW3DMPjHP/7BP/7xjwv2N02TadOmhRXUk08+yTvvvMMjjzzChg0b6NOnDxs2bODdd99t9KbPCy+8wDPPPMMDDzzAq6++SkpKCrNmzTrnerGxwcfDgwcPPu95kXAEampwb96EWVcHBB/4+HMisbmOB89jUBqRS4f4c8u8bNqyNfR51Ijh55wXkSDlHpHL4zt5Eu/x47i37wi1RQweRES/vrjqS7HVHwm1e+N6EhcRwdIVZyf5pk+d3JLhikgT6NKlC2+//XZrhyEiIiIickkueULipz/9KYZhYJomzz77LGPHjmXGjBnn7WsYBmlpadx7771hBaU3faQ9cO/Zg2ff/tCxrVtHauOqiax2AVBINj2SsrBbGldG83g87G/YBNtisTB5svaPELkQ5R6Ry+Paug3fkSMEGvZDsWZmYs/piKNnT/Yf/phEswaAg6QxPGcoAEuXnZ2QmDhhXMsHLSIiIiIiIteMS56QePrpp0OfzzwU+tnPftYcMQGX9qbP008/3SiuC5k8eTKmaTZRZCLBzazdO3fhO3w42GCzYRuUg8d5mDOPLHdYunJ7Rvdzxm7dnk9VVbA0RlZmBp075bRM0CLtkHKPyKXzlZbiLSrCte2zqyMGYu/eHRx2Tpds4UzGOWrvxJi0XMrKy9m3/wAAuZ1yyMrMaIXIRURERERE5FoRVuH6P/zhD3g8nqaORaTd8B4+gmfXLkx38PfA3qkjrmyDaG8pANVEQmQaHRMzzxm7bPnZN1H79umNxRLW3vIi1xzlHpGLc2/bjq/4GIGyMgCsqSnYOnYkon8/yioOkuI5BoAXC/Epg7BbrKxekxeaOJs6eWKrxS4iIiIiIiLXhrCehM6ZM4cdO3Z8cUeRq5Rn9248u3aHjq09O+KMqMBC8KHOBqMb/ZKSibQ2nvMzTZNNW7aFjseMHtki8YpcDZR7RC7MX1GB98gR3Nu2h9oiBg3C0akT1sREDh5dRjRuAPYaHRiZGyzXtHDx0lD/qVM0ISEiIiIiIiLNK6wJienTp7N06VK8Xm9TxyPS5vmrqnEX7MR/8iQAloQEjH5pmK5joT4HbZ3oltrjnLGnTpVw+MhRAGw2G5MmjG2ZoEWuAso9Ihfm3rYd34kT+E+dAsCSmIitcy4Rgwfj8brwVu4N9a2M7EJmbDI+n4/1GzYBEBMTzaAB/VsldhEREREREbl2hDUh8dxzz+F2u5k9ezYul6upYxJp0zy7duHZtSt0bOvZBVd6gKhAHQD7SSc9Mpqc85RrWpO3nvr6egA6dsgmM0O1ukUulXKPyPn5y8rwHDjwudURA7F36IAtI50jp3aQ7T8BQA0RdOsYXJ23bXs+9U4nAKNHjsBmC6uSp4iIiIiIiMglC+svz48++ogHH3yQ3/3ud3z88cdcd911dOrUiYiIiEb9DMPgqaeeapJARdoC0+fDvbMQz779wQabDVv3dGptpUT5g00bjG4MT8kixmY/Z/zqNXmhzwP798MwjJYIW+SqoNwjcn7O9Rvwl5biKw6u1DNiY7F360rE4EEAHC1aTS8CAByw5jA1qx8Ai5etCF1jxrTJLRu0iIiIiIiIXJPCmpD47ne/i2EYmKZJaWkpb7311nn76aGQXG28hw7jKdwJDSVj7F1y8edEYncfAMCJnVp7Bp1Te54ztry8gr1nJjKA8eNGt0zQIlcJ5R6Rc3mLi/EdO4Zr29n9VSIGDcSWkYG9QwdO1ZQS4zwcOmfE9yLaHpzEW75iVbDNMBg/dkyLxi0iIgJQuG19WONKS0tJT09v1LZm2QLS0tKaIiwRERFpRmFNSCxbtqyp4xBpF9y7duH+7GbWXdNxpjqx+YLLI7YYnekcE01OXNI5Y/fs28ex48cBiIiIYNxYTUiIXA7lHpHGTNPEtWET/opKfIcPA2BER+Ho0Z3IhtURe46soat5GoAS4hnQKbh30fETJzlaVAxAzx7dSE4+N2+JiIiIiIiINLWwJiQmTZrU1HGItHn+igrc+fkEysoBsKSmYMmJxWeWhH6Rtlm6ckt6F+wW6znjlyxbiccTXFnRo1tXkhITWyhykauDco9IY979B/CXnca94zOrI/r3x5aWhi03l3JnHbXlZ8+dcHRicEpnABYtPjvBN23K5BaKWERERERERK51V7R7YWVlJW+++SZr1qyhvLycpKQkxo0bx7333ktycnJTxSjSJnh27cZT+JnNrLt3xN0pkkh/FQDHSCLWEUNucudzxtbU1LJl69nNRseOGdns8YpcrZR7RIJ7Grk2bcZfWYl3f7BsoBHhwNGnNxGDBmEYBvtP5pPtDa6CCACp6UOxNOxd9OnCRaFrTZk0vsXjFxERERERkWtT2BMS8+fP56tf/SpVVVWYphlqf++99/jZz37Ga6+9xk033dQkQYq0NtPrxbVjB95Dh4MNDgeWTnHUxVQS6Q42rTO60yM+kYyo6HPG7ztwgMNHjgBgs9mYPnVKC0UucnVR7hEJchfuIlBbg2vjJmj4XXD07481ORl7t67UeD0cO7WNEdQCUGyk0S9nFABl5eXkFxQCkJqSTJ/evVrnJkREREREROSaYwlnUGFhIbfddht1dXX88Ic/ZOXKlezdu5fVq1fz6KOPUlNTw5133klBQUFTxyvSKjy7d+PZWQj+4F4Rtq4d8XdOxuo5CYAXC4etHeiZ0Sv09ulnLV22EpcrOHOR2ymHrl07t1jsIlcL5R6RINPtxr1tG76TJ/EdOQoE946I6N+PiEEDMSwW9pUeIPozm1nXR3chITq4T8R7/5wbmtC7fuYMLJaw/ndQRERERERE5LKFtUJizpw5eDweFi1axNSpU0Pt3bt3Z+zYscycOZObb76ZOXPm8NZbbzVZsCKtwfT7cW3fgWf3Zzaz7pxMTbqbqIY9IXYYnciNiiA3Pv2c8U6ni42bt4SOBw3oT3RUVPMHLnKVUe4RCXJt207A5cK1YWOoLXLoUKwJCTh69MDp83HgxHZGBILlmupx0CVnYqjvvI/nhz7fefstLRe4iIiIiIiIXPPCeiVuyZIlzJgxo9EDoc+aNWsWM2fOZNmyZec9L9KeePbuw7NvH4HqGgCsWemYXZKx+YpCfVYZveiZlEFSRMQ54w8cOsSBg4cBsFgsTJo4rkXiFrnaKPeIQKC2FvfOnfgOH8FfUgqAJTERe88eOAYMwLDZ2FNxAqNmPzYCAJyw55CT0Q+AQ4ePcOhwsIRgx44d6NG9W+vciIiIiIiIiFyTwpqQKCsro1evi9cb7tmzJ2VlZWEFJdJWmIEA7h078BSeXR1h6ZKKM8fAHqgHYD/pYI+nW1qP815jxcrV1NcH++Z07EBf1eoWCYtyjwi4Nm0O7mu0cVOoLXLkcCxRUUT06Y3b72fviQL6+A8BEMAgMW0wDltwwvzv774fGjfr+utaNngRERERERG55oU1IZGSksKePXsu2mfv3r2kpKSEFZRIW+E9eAjf8eP4ioKrIYzoKIwuKWA9Huqz2NKfnjER5MYmnDPe4/WyNm9D6LhP715kZ2c1f+AiVyHlHrnW+cvK8Ozfj2f3bgLV1QBYMzOx5eQQ0a8fht3Ovqpy6iv3kIATgBPWDLIyhwFgmibzFy0JXe+uO29r+ZsQERERERGRa1pYExLTpk1j0aJFFyyL8fHHH7NgwQKmTJlyRcGJtCbTNHFv3447vwAaNv+0dsnA0zUShz9YvukYSZy0pNE7tQtRtnO3ZDly5CgHDgTfUjUMg2lTJmKcZ9NrEfliyj1yLTNNk/rVa4IbWm/ZFmqPHDkiuDpiQH+8gQC7T+2mu/dg6Lw1tgtpiZ0A2LJ9B6dPB1cQ9enVk8yMjBa9BxEREREREZGwJiSeeOIJHA4HN9xwAz/+8Y9Zu3YtBw4cYN26dTz22GPcdtttRERE8Pjjjzd1vCItxnf0KN7iYjy7G97Itlqxdk3FF3s61Gex0Y/BUT46JXU47zVWrVlHdU1w8qJDdhaDBg5o9rhFrlbKPXIt8xTuwl9SgntHPqbLBYC9axds6WlEDhmC4XBwsKaK0rK9dCaYpyqMeNKzx2MYwf/d+/s774Wud9sts1r+JkRERJpIWloapmk2+peWltbaYYmIiMglOPeV7kvQp08f/vnPf/LVr36VX//617zwwguhc6ZpEhcXx+uvv07//v2bLFCRluba1rA6wu8HwNatA96ecUQ01OU+TSx7LB25Kz6eDtGx54z3+/2sXL02dNyrZw+yszJbJniRq5Byj1yrArW1uDZuIlBXH8xLABYLEcOHYYmPx9GnN37TpLD0MFmeA6FxnugcOqT3BcDn87FyVTAnWa1WbrnpSy1+HyIiIiIiIiJhTUgA3HjjjRw4cIDXXnuN9evXU15eTlJSEqNGjeL+++/X2wnSrnmPH8d75AieXQ2bWVutWHqk4k6txhGs3sRSoy8DIlykJQ3Fbjl3sVHxsePs23f2wdC0KZOwnKefiFw65R65FjnXrMX0enBt2RKaJHf06Y01Pp7IEcMxrFaO1FRx+PR+bjOPAuDCTlLmWKwNm1kvW76S2ro6AIYNHUxc3LkT6SIiIiIiIiLNLewJCQhuMPrDH/6wqWIRaTPc27bjyS8Anw8Aa9csAh1jsTc86Kkhks1GV74c5yA3Pvm811i7bj0VlZUAZGZmMHTIoBaJXeRqp9wj1xLPgQN4jx7FX1GBd+++YKPdTsSQwVjT03F07Yo/EGDH6eMkOA9gIwBAbUQH+mYODV3n3ffnhj7fefstLXkLIiIiIiIiIiFXNCFRVlbGm2++SV5eHmVlZSQmJjJy5Ei++tWvkp2d3VQxirQoX0kpngMHcBfuCjZYLFh7pOPq6MTe0Ge50ZseDg/RCX3IiIw65xoer5cly1aEjnv16E7HDvqdEGkKyj1yrQi43DjXrcc0TVzr1oMZXKIXMXgglshIokaNBGB3dQX7yw5xQyA4YREAYtKG4YhMAKCuvp6Nm7cAEBkZyYxp2vhdREREREREWkfYExL//Oc/eeihh6ipqcFs+AMZ4P333+enP/0pL774It/4xjeaJEiRluTetq3x6oguGZipkdisJ4BgGYw1Ri9uirXRIzEdwzDOucbevfvZt/9suaYpkyeoXJNIE1DukWuJa/16TGc93n378R0/DoARE0NEv37Yc3OxZWZS7/Oys6IUS81BEnACUG1Lp2v2mNB1Ppj3CV6vF4AJ40bjcDha/mZEREREREREgLCekG7YsIGvfOUreDwevve977FkyRIKCwtZunQpP/jBD7DZbDz88MMsX768icMVaV7+snLce/d9ZnWEgbVnJq6uPgyCDz9XGz3JsvuJTehCbmz8ea+zau06Sk+XAZCWmsrI4cNaJH6Rq5lyj1xLvMeP49m7l0B9Pc689aH2qPFjMewOIkeOAGB7RRknKo4yMrA31MeR2IfouLOrheZ++FHo81fuvL0FohcRERERERE5v7BWSDz//PMA/Otf/+L6668Ptffu3ZvJkyfzta99jdGjR/M///M/TJ48uUkCFWkJzrw8PAUF0PAmqbVzOv6UaGyOkwD4sLDC6M2kaDtdEzOxnmd1xImTp9i8ZVvouGePbuR07NAi8YtczZR75Fph+nw4V60GwLkuDzweAOzdumLPycHRuxfWxERKXU6OVJVRXn2MzpwGoM4SS0bO5NC1Sk+fZmfhbgASExMY3TCRISIiIiIiItIawlohsWbNGm644YZGD4Q+a/DgwcyaNYv169ef97xIW+Q9dBjvoUO4dxYGGwwDa88s3N29WPADsN7oRqTVRmJCJ7rFJZz3OjsLd3Hw4KHQ8eRJE7Barc0ev8jVTrlHrhWuLVsJVFfjPXwY36HDABiRkUSOHoVhtxM5dAimabK1vJTjlUUMDuwPjTViu5CQ2C10/MZb74bKm02bMknlA0VERERERKRVhfVXaV1dHV27dr1on+zsbJxOZ1hBibQ00+fDuX59cDKi4U1Ua24a/swoHI5SILg6YonRj4HRkJuYjeM8kwxOp4tNm7dy8lQJAElJiYwdPbLlbkTkKqbcI9cC36kS3DvyMd1unGvXhdojR4/CEhVFxMCBWKKjOVRbTUltBQdrKxliHgHAi53kjlMxLMEFsKZp8sn8RaFr3Hv3nS17MyIiIiIiIiKfE9aERNeuXdm/f/9F++zdu5e+ffuGFZRIS3MX7MRfVoa7YGewwTCw9srG07U+tHfEcqMPbksUGQnZ9ExIPO91du/Zy7bt+aHjfn16q1yTSBNR7pGrXcDlon7pUjADODdsxKwPTq7ZcnKwd+uKJTqaiAH98fj97KgoY2/FMaYF8rERAMAXnUNy2oDQ9VavzeP4iRMAdMjOonevni1/UyIiIiIiIiKfEdaExP3338+CBQv48MMPz3v+n//8JwsXLuRb3/rWFQUn0hICdXW4t23DnV8QWh1h6ZSKN9eBw1YFQBVRLDQGMDDST1ZiDjE2+znXMU2TzVu3s3tvcGNRm83KDdfPwGYLa6sWEfkc5R65mpmmSf2y5QRqa/EdP453T8Mm1XY7UePGYBhGqGTTzqpyTtdVUFFfwTAzWCLQZ9iIzZ6ExRYduubLr70Z+nz9zOktej8iIiIiIiIi5xPWk9Jhw4YxZMgQ7rrrLh544AFuuukm0tPTOXHiBPPmzeONN95g3Lhx9OrVi5UrVzYaO3HixCYJXKSpuDZtwldWhntHw8oGg+DqiE7VoRm7j4wh2CwWchPS6R2feN7rFBUfY+OmLXg8wQ2xe3TvzsD+/Zr/BkSuEco9cjVzb92Gr7i4YUPrNaH2yBHDscTGYs/NxdGtG9UeD/uqKtlRfpJZgW2hPOWO6kLHjCGhcfkFO9m0eSsANpuNB+/7akvejoiIiIiIiMh5hTUhcd1112EYBqZp8te//pWXXnopdO7Mxolr1qxh2rRp54z1+/1feP2ioiKeeuopPv30U8rLy8nMzOTWW2/l2WefJSkp6aJja2trmTNnDn//+98pKioiNTWVL33pSzzzzDN06KDSOdKYr6QU9959uNblQcPPprV7Fr5eFmwWFwBHSWGj0ZUpUU5SE3NIiog877V25BeQv3Nn6HjI4EHkdspp/psQuUY0d+4B5R9pHd5jx3Bt2QKAa/MWAjU1AFgzMnD06Y3hcBA1bixAcCPr2jIiPKX04xgAPiOCmPQR2CJTQ9f8v7+8HPq5nzRhHCkpyS15SyIiIiIiIiLnFdaExE9/+lMMw2jqWACoqqpi3LhxFBUV8Y1vfIMBAwawatUqXnzxRdavX8/atWsvWALH4/EwY8YM8vLyeOCBBxgwYAAbN27kb3/7G5988gkbN27UQyFpxJmXh+/IEXxFxcGGSAeWftkEMitDff5pGU6q1UuH+Ax6XWB1RG1tLavX5lFZGSzxlJ2VyfChg1WuSaQJNWfuAeUfaR2Bujrqly4H08R36hSeM3sZWa1ETRiPYRhEjR6FJSaG4rpajtXXsq38NA8Ftoau4YrpRreMgaHj/QcPsjZvPQAWi4VvffPBFrwjERERERERkQsL62np008/3cRhnDVnzhyKior43e9+x2OPPQbA97//fb773e/ypz/9iZdeeolvf/vb5x370ksvkZeXx/PPP88TTzwRap8+fToPP/wwv/jFL/jf//3fZotd2hfPvv34jh3DuW59qM02KBdvdw9WI/hW6SajM4eNdG6JriEytiNZ0THnvdbOXbvZvqMgdDygfz96dO/avDcgco1pztwDyj/S8sxAgPqlyzBdTgL1TuqXLIOG1T4RQwZjTUzA1qEDjl69cPp8bCorYX/lKXIDx+lCKQBeSzTJGUOxxZxdkfd/f/5bqHzgoIH9GdBPG72LiIiIiIhI2xDWptafVVFRwYIFC/j73//O/PnzKS8vv6LrvffeeyQkJPDII4+E2gzDCD3gef/99y849oMPPiAuLi70IOmMBx98kKioqHNqisu1y/T5cG3ciHvbdszaWgCM9AToGo8lKVgqw42VD42hdLU5iUvoSu/E85e7CAQCrFm7niNHiwCIjY1h8MD+dOmc2zI3I3INaurcA8o/0vJcGzfhO3kyODGxbBlmfT0A1sxMIgYOwLDbiZ4wHtM0yTt9kgq3m8KqamZ9ZnWEP64XyelDQ6uHjh0/zvIVq4Hgz++3Hnqg5W9MRERERERE5ALCridTWVnJD37wA956661GtbktFgv33HMPv//970lOvrx6xU6nk4MHDzJlyhQiIiIanevQoQM5OTkUFBRcYDSUl5czZMgQoqOjG7WfqTnu8/kuKx65erk2bcZ7/PhnNrI2sA3OxdulDmtDRZglRn+qjWiuj/XhiEolJybuvNc6eOgwGzZtCR3379eXwYMGYLFc8XyfiHxOc+QeUP6Rluc9cgR3fjAHuTZuxn/iJABGdBTRUydjWCxEjhiBJS6OnZXllDidFJQfZ4h5mCyC5QE9tnhSM0dgi84MXff//eVlnK7gHki9e/VkQsPeEyIiIiIiIiJtQVgTEvX19UydOpVt27aRlZXFDTfcQGZmJiUlJXz00Ue8+eabbNu2jXXr1hETc/4SN+dTWhosP5CRkXHe89nZ2WzYsOGC4zdu3Hje9qVLl+JyuRg+fPgFx7rdbtxud+i4urr6UkKWdsh77Biu/Hxca/MgEADA2jMLfwcDa2zwIU45MSw1+jLQUY8R14fByalYL1C7fvOW7ezesyd4HauVwQMH0Kd3r5a5GZFrSHPlHlD+kZblKy2lftkKME28hw7jyT87OR49dSqW6GhsGZk4+vah1OVkZ2UZp121FNc7ecDcHrqOJbEvcWmDQsdl5eUsXLwsdPyNB+7V5LiIiIiIiIi0KWFNSPzud79j27ZtfOtb3+KPf/wjDocjdK6uro7vfOc7vPHGG/z2t7/lqaeeuuTrBhoeDn/+7dQzIiMjMRtqK1+qPXv28M1vfhObzcaTTz55wX5z5szhmWeeuaxrS/sTcLlxrliJ79AhfMeOBRujHRj9Mgl0reLMlMMHxlAMw0LfhCTSYhLpEB173usdO3acZStWhmp19+jejWFDBjX6nRCRptFcuQeUf6Tl+CsrqZu/ENPrwV9ZSf3KVaFzkaNHYcvMCG5oPWkCnkCAvNKTePwBNp8+wXhzD8nUAeC2p5CVOQprRFJo/F/+9io1DWUIu3btzPXXTW/ZmxMRERERERH5AmG9NvfOO+/QvXt3/vSnP53z4DUmJoa//OUvdOvWjXfffbdJggyHaZq88847jB49muPHj/P3v/+dXr0u/Nb6448/TlVVVehfUVFRC0YrLcW5ahX+ykqceWffdLYNzMXXsw6LNVj+pYAObDNyGRHlwozOYUhy2nmvZZoma9atZ0fBzlDboIH9GTigf/PehMg1qj3kHlD+kQsL1NZS9+l8TJcT0+ulfvFS8AYntO3duuLo2weAyGHDsCYksKmshDqvl22ni3H7vMwwg2XDTCAqZSCRyf1C166pqeGjTxaEju//6t1YrdaWuzkRERERERGRSxDWhMSBAweYPn36BcsAOBwOpk+fzsGDBy/rumc2ZLzQW6iX+nZqaWkpd9xxB/fccw/JycmsXLmSL3/5yxcdExERQXx8fKN/cnVx796D9/BhXJu3YNYF3zC1ZCTiG+jAGh8sl1JFFG9bxhJv8ZObmEP3hCTiL7DaYf+BQ2zeuo3KymAt76zMTEaPHE5c3PlXU4jIlWmu3APKP9L8Ai4XtZ/OJ1Bbi2maOFeuJlBZCYAlKYmo8eMwDANbVhYRA/qzv7qS4rpaDtdWcsjpYbpZQCzBXOWNyCI5cyQW+9l888rrb1NREbxehw7Z3HrLrJa+RREREREREZEvFNaEhMPhoL6+/qJ9nE4ndrv9sq6bmpoKQFVV1XnPV1VVkZZ2/rfVzygoKGDQoEF8+OGHPP744+Tn5zNmzJjLikOuPv6qalx5eXiPHMVzZkWDxcAyqgOW3LpQv7csY6kzIhkVA7aoVPonnn9zXL/fT96GjezIP7s6YuCAfgweOKBZ70PkWtZcuQeUf6R5mR4PdZ/OD01AeAp24j10KHjSbid6+lQMux1LbCzR06ZS5fOyrfw0VR4PW8tPk2uWMtUsDF4Lg7i0QTgSe4euX3r6NP+a+2Ho+O4v344jjN8DERERERERkeYW1h4SAwYM4KOPPqKyspLExMRzzp88eZIPP/yQgQMHXtZ1Y2JiyM3NZdOmTfj9/kalBqqrqyksLGTs2LEXHH/q1CkmT56M1WplzZo1jBo16rK+vlydzECA+mXL8JdX4Fy5MtRuHZiDf5ALS8PGEUuMvuwxsulsdZGS2J0BSSnYLecvd7GzcDeHDh/hyNFgaZWYmBjGjh5FRkZ6s9+PyLWquXIPKP9I8zF9PuoWLcZ/+jQAngMHca0/WzYwetIErAkJYLUSPX0a/ogI1p0owu33s760GIvp42uBNVgJrtLxxXYjPn0kFmtwv5NAIMBLL79OSWnw+ulpqXz17ouvyhERERERERFpLWGtkJg9ezbl5eVcf/31FBYWNjq3detWZs2aRWVlJd/85jcv+9q33347x44d4+WXX27U/qtf/Qqv18sdd9wBQG1tLSdPnmz0NuvTTz9NTU0Nixcv1sMgCXFt2YLv5Enqly7DdHsAsGQnE5gUhSUquG9EEcl8bAwmyvAzJimOxOhEusSev2yKx+Nhw6bNrF6bF2ob0L8vw4YOav6bEbmGNWfuAeUfaXrBCfHl+I4fB8B75AjO5StC5yMGDcTeuTMA0ePHYaSksLrkONUeD9vKT1HpC3CLuYU0agDw2OJJSh+OI6F76BrbtuezZNnZa95+601ERUW1wN2JiIiIiIiIXL6wVkjce++9LFq0iDfeeIMBAwbQrVs3MjIyKCkpYf/+/ZimyT333MMDDzxw2dd+8skneeedd3jkkUfYsGEDffr0YcOGDbz77rsMHjyY2bNnA/DCCy/wzDPP8MADD/Dqq6/i8Xh488036dy5M2vWrGHNmjXnXLtDhw7cdNNN4dyytFO+kydxb9uOa+Mm/KWlwcboCCxTsjEzagFwY+V1y3j8hpVp0TUEYnowNDk1VFP+87Zs28Gu3XspLj4GQGxsLOPHjqZL59wWuSeRa1Vz5h5Q/pGmZfr91C9fgffwYQC8xcXUL1kGDfuR2Hv1JGL4MAAi+vbF1qMHa0tOUOJ0crSuhoN1dfQ1jzHe3AtAAAuRqcOJThmAYQn+71tVdTWvv/UOx0+cBCAxMYGv339vC9+piIiIiIiIyKULa0LCMAxee+01Jk6cyB/+8AcKCgrYv38/AP369eN73/seDz/8cFgBJScns2bNGn7yk58wd+5cXnnlFTIyMpg9ezbPPfccjgtsMHzw4EFqa2vZu3cvjzzyyHn7TJs2TQ+EriH+6mrqFi/Fe/hwo30jbOO7EehVx5nphn8ZIygxEhhoryM+qScdYxNJiTz/26V1dXVs2rKNNevWh9rGjh7J8KFDLjiBISJNozlzDyj/SNMx3W7qFi3Gd+IEAL4TJ6lftAQCAQDs3boRNW5scBPrzEwiRo1k4+lTHKuvo8brYVPZKWJMF/cE1oauGYjvQ3x8J+xxZye/5300nxWrzk6A3fXl24iLi2uhuxQRERERERG5fGFNSLz44osMHDiQhx56iIceeoi6urpQTe+YmJgrDqpLly68/fbbF+3z9NNP8/TTT4eOe/fujdnw1qFIwOWmbsFC/CWnqF+xKtRuHdQJ/3APFlvwZ2UrncgzupNk8TIgKR3scQxMSrngdTds2sKWrduorq4GIDsrk359e9O7d8/mvSERafbcA8o/cuUCtbXUzV+Av6ICAF9JCXULF4I/WCLQ1jmXqEkTMCwWLDExRE+fxraqCg7X1uA3A+SVnsAXMLk/kEc8LgC8jhSSknsRlTYcwwhW2yzctZt33vsnbrcbgB7du/Hg177aCncsIiIiIiIicunC2kPiF7/4RaMa2zExMXTo0KHJHgiJXAnT56N+4SL8ZWXULVkGnuC+EUaHZAIz7FhifQCUE8O7ltFYDJgSb8EblcWwlHSibOefp6uoqGTT5q1s3rIteD3DYPy4MfTr2weH3d4i9yZyLVPukbbOX1ZO7YfzQpMR/rIy6uYvBG8w79g6diR6ymQMi6VhE+vpFLrq2Vddid80WVdyggqvlxHmQQZRBEDAsBOfNpKo5H5YI5OB4Gq9V15/m4OHDgMQFRnJ/fd+hcSEhBa/ZxEREREREZHLEdaExPTp01myZAler7ep4xG5IqZpBmt2nzyBK289gdOngydiIjBuTcGSGHwo5MTOny1TcRoRjI5yYovvTvf4BHJjL1zqYt36DazN2xD6ue/bpxfZWZkMGti/2e9LRJR7pG3zHj9O7byPCNTVAeCvqKDu0wWhSXFrVhbR06diWK1gGESPH8fBCBs7K8sJmCbrS09w3OUk2azly+aG0HVtyYOIjM/Bkdg71PbJgkUs/cxG1pMmjmfalMktcp8iIiIiIiIiVyKsCYnnnnsOt9vN7NmzcblcTR2TSNhceevxHjqEa8tWPLt2BxstBpabsjE6BB9i+rDwF8tUThqJdLS66ZTcjaTIaAYnpV7wuocOH2Fd3kb27N0HQITDwcjhwxk9cgQx0dHNfl8iotwjbZdn/wHqPl2A6Q1OPniLj1E772PMhp9Ta3o6MddNx7DZwDCIGjeWY1mZbKs4jWmabDx9imJnPYYZ4L7AGiIJTp77ozoQm9idqLQRoX2K9h84yJtv/wNnw7W7dM7la1/9ComJWh0hIiIiIiIibV9Ye0h89NFHPPjgg/zud7/j448/5rrrrqNTp05EREQ06mcYBk899VSTBCryRdz5+bgLCnDtyMezdVuo3TqpAwwM1ncPAK9aJnDQSCeCAKNSUrE64hibloXVcv75ubq6OpYsXcHqtetCbSOGD6Vzbg4D+vdtzlsSkc9Q7pG2xvT7cW3chLugABr2EXHv2o1r7brQsTU1hZiZMzAaSvtFjR3D/qwMdpadIhAw2VxeypH6WjBNvmxupBslAPgtkSSkDyUqdQgWe7Asmcvl4uXX3mTf/gMAREREcOcdtzB40ICWvnURERERERGRsIQ1IfHd734XwzAwTZPS0lLeeuut8/bTQyFpKZ6DB3Gu34B7127cGzaG2i2js2HG2R/z94xR5BudsGAyPcHAGpXByNQMYi+wB4Rpmixaspxt+QWcKikFICkpkQH9+zF50oTQG6si0vyUe6Qt8VdVUb90Gf6G0oBmIIBrw0Y8BTtDfWydOhE9ZVJoMsIxehRb05I5UlGGaZpsqzjNwdpqAG4wtzPe3Bu8FgbRaSOIiO+GPTYHgEAgwIcfz2fh4mWh608cP5Zbb5qlXCQiIiIiIiLtRlgTEsuWLfviTiItxLP/APUrVuLZtx/XmrWhdsuQLJjlCB1/agxkraUnYDIjxklMQn96JSTSMSb2gtfevHUbBw8dJm/92UmO8WNHM2TwQNJSU5rlfkTk/JR7pK3w7NmDc10eZsN+JqbHQ/2yFfiKikJ9HAP6EzlieHADa8AycgRrUxMpq63BNE3yK8vYV1MFwKTALq4380NjrcmDiYzvTGTqkFDbilVreOcf/6S+vh6ATjkd+fr9X1WpJhEREREREWlXwpqQmDRpUlPHIRIWd8FOnHl5eA4dxrliZajdMiAN83YHZ6owrTG6M98YCMDEKCdJyX1Ijopm4EX2jTh56hTrN2xi2YpVoQdAXTrn0rd3b0aOGNZ8NyUi56XcI63NdLupX70G78GDobZATS11CxcRqKgINjTsEeHo3SvUxzd8GHnJ8dS5XJimyc6qcnZXVwIwPHCQ281NZ79IQj9iE7oRlT4SwxJcWbFp81Y+mb+I3XuCKygcDjtf+fLtDBqoUk0iIiIiIiLSvlzyhITH4+G1115j6dKllJWVkZqayowZM7j33ntxOBxffAGRJubcuAn3tm14i4txLl0Wqtdt6ZsMd0djaShhsYMc3jNGgWEwLMJJdmpv7PZIxqRlhvp8nsfjYdHiZWzZuoMDBw8BwQdAY8eMYuKEsTguUOJJRJqWco+0Fb5Tp6hfupxAbU2ozXu0COfKVaHNq3E4iJk+FVt2dqhP7eBBbEiOx+vz4QsE2FhWQlF9LQD9zGLuNc+u7COuB3HJvXAk9sIWGVyFt3vPXuZ9Mp+Fi5eGuo0fO4bbblWpJhEREREREWl/LmlCorq6milTprBt2zbMhoe+AO+++y7/7//9P5YsWUJs7IXL3og0JTMQwLl6DZ49e3Dv3Y1r9ToIBH8ujV4J8NW40EOa9UY33jFGYxoW+thddE/ridUWyYSMbKJtF55UWL5yNQWFu8n7zH4U06ZMZujggXTpnNus9yciQco90hYEXC5cGzfh2bMnNPFtejw489bj3bsv1M8SH0/0zBlYE4IllEzD4OSAfuSnJWIGAtR6vawpPUGV1wNAV/MUDwVWYKHhZzsml9iU/tijs4hI6gNAUfEx/v3BR3z0yXy8DeWhOud24pvfuJ/EBJVqEhERERERkfbHcimdfv7zn7N161Y6d+7Miy++yEcffcQf//hHcnNz2bRpE88991xzxykCgOnzUb9kKe7du6nfsBbXyrVnJyO6x2Lcl4hhCU5GLDQG8LYxhoBhobPNw8CM7tjsUUzKyCYlIvKCX2PX7r1s2rKNhYuXhh6CDh86hF49uzNh3Jjmv0kRAZR7pHWZgQDunYXU/OM9PLt3hyYjfMeOU/OvfzeajLB17EjMzTeFJiPcdjtbhw9iR3oKpgmnnPUsPlkUmozobJ7mO4GlWAkELxCVTWzaUOzRGUSlj8YwLJwuK+O9f85l7ryPcTqDKzAyM9K5/957GDSgfwt+J0RERERERESaziWtkJg7dy5xcXHk5eWRlpYWar/zzjvp0aMH//rXv/jlL3/ZbEGKAAScTuqXLMVz9Cj1yxbhP3oydM4YFI9xRxKG1SCAwbvGaPIs3QHItHoZkdEZmz2aiRnZpERGXfBrlJdXsHT5SuYvXIzb7QYgt1MOI4YPZdSIYXobW6QFKfdIa/EdP4Fz3Tr85eWhNtPrDa6UKNx1tqPdRtTo0dh79sAwDAImlMZEsnPIQLxRUZimyZ7qSvIry86sg2CYWcRXA6ux4Qs2RKQRmzESe1Qa0RljMSxWampqef9fH/CvD+ZRUxMs75ScnMQtN83ihuunq1STiIiIiIiItFuXNCFx9OhRbrrppkYPhAAyMjKYMWMGH330UbMEJ3KG9/hxnMuW4z11nLpFizDLz9bwNmakYEyOxTAMvFh5yTKZ3Uawfneuzc2I9M7YHbFMyMgm9SKTEZWVVcyd9zGLlizj9OkyABLi45k+dTK5uTkM6N+vWe9RRBpT7pGWFqitxbl+Q6NNqwF8J07iXLWaQHV1qM2alUX0xPFY4uIAcPr8HExPoXhQf7Ba8QUCbCor4WjDfhGYJncYO5gQ2MGZ6QTTkURc5hhskalEZ47DsNiodzr59wfz+Mf7/6a8PLhRdlxcLDfPuoEv3TCDhPj4Zv8+iIiIiIiIiDSXS5qQ8Hq95OTknPdcp06d8Pl8TRqUyBlmIIBryxbc27bjOX4I59LV4AyWvMBuwfKVVIy+0QDUE8H/WqZRbAQ3Au3rcDMgrQvWhsmItItMRlRVVzN33sfkbdjE3n37AbDZbFw/czodO2Zz/XXTsVguqcKZiDQR5R5pKf7qatzbd+DZuw8C/rPtlZW4Nm7Cd+To2c5WK5EjR+Do26dhVYRJqcfNgZ7dqOkRXJl3vL6OLeWl1PuDP6N208d3jFV08RefvU5kBnEZI7FFphKTOR7DYqesvJwP533Cu+//m5OnSgCIiork5i/dyM1fuoHu3bo2/zdDREREREREpBld0oQEgN1+/g2AbbZLvoTIZQnU1lG/bBnu4sO4t2/Eu+MA+BvqbcfbsDyQjpHlAOAEyfzZMokKI1hSaVS0j9yUXlhsdiakZ5F+kcmImppaPvjwYwoLd7N2XV6oferkiXTJ7cSsG2biuMDPv4g0L+UeaU7+iorghPeBg2AGQu2B+nrcW7bi2bM3tHcEgDU9nahJE7AmJGCaUOF2cxKTU8OH4M5Ip97nY2tFKcfq60JjUs0avs9S4v1nV1dY4nsSndwPmyOB6KzxGFYHh48cZf7CxcxfuISjRcGJC4fDzqwbr+fWm2+kd6+eLfAdEREREREREWleeqIjbZL3yBHql6/AXbwX15qNmGVnSzSRE4HlvjSMOBumCcut/fmQQQQMC1ZMJiTYSE/sTqTVytj0rIuujKitrWXuvI/ZvHU7S5atINCwQfaQQQPp17c3s268npiYmOa+XRERaUG+06dxb9uO9/DhRhMOpseLOz8fd34BfGYFjhEdReTQodh79gDDQpXHQ4nbSXmnHKoG9MNvt7OvupKdlWX4PnO9MUYRdwTWYjeDK/tMw0pk6jAcsR2x2GOJzpqAxRrJtu35LF66goVLllJcfAwAq9XKDTOv4/ZbZtGvb5+W+caIiEiI2+VkyYdvNGqbdvPXiLjI3xYiIiIi8sU0ISFtSqC+HteGjTh3bsO9YxPe/ENnV0UAxpg4jOuTMOwWas0o/mqdxGEjWF8+0ggwKTmBhNgM0iKjGJOWSdRF3qKuq6/ng48+Zc3aPFauXhtq79qlM2PHjOTGmTNITk5qvpsVEZEWY/p8eA8fwbN7N74TJxqdC7jcePfswV2wE9PpPHvCbidi4AAi+vfDsNup8XopcdZQEx1FxfixeNJSKXO72HyiiEqvJzQsDg8PWLbS3bsPo2E7a9MaTUzmGKyOBGyRKUSlj8I0HCxbsYqly1exYNESamuD+00YhsGMaVO447abGDRwQPN/c0RERERERERayCVPSHz66aecPn36nPaNGzcC8I1vfOOcc4Zh8Le//e0KwpNrhRkI4C7YiXPjOjzH9uPO2954VUSSDcsdqRhdIwHYSmfeto7GYwTLuaRbfYxIzSYmMp6eCYkMSkrFYhjn+1IAOJ0uPvjwYxYsWsKmzVtD7X1792LSxPHMmDaFDh2ym+dmReSSKffIlfJXVuLZvRvPvv2YLte553YW4tm3v9GKCAwDR58+RAwZhBEZRY3XQ1lNNXWBADW9e1LduydlPi+FJcc54az/zBVNbrAcYqp/Cw7v2YkNIyKV2IzRGFYHEQk9iEgegNvt4dMFn7JoyTJWrl5LIBCcfI+KiuS66dO45aYbGT50SHN+a0RERERERERa3CVPSOzcuZOdO3de8Pyrr756TpseCsml8B47Rv2qZbiP7MJTsAf//hMQOFvywhgThzEzCcNhwe238aZ9HDuMTgBYMBkSbdA1pTt2m4MRqel0iom76NcrKSll/sLFfPTpQgp2Fobahw4ZxKgRwxk7ZiQ9GzYmFZHWpdwj4TA9nuBqiD178J082ficaeI7dhxPwU58xcXnjLV36UzE8OGYcbGUedxUVFfiDQRwpyRTPnQwxyPs7Dp9khKXs9G4rkYl97GBFO+ps18LA3t8DyKT+2KxRhKVNgx7TAeOFhWzZOlyPvp0Abt27w31z8xI57oZ0xg/djSjRw5v4u+KiIiIiIiISOu7pAmJV155pbnjkGuQv7qa+rUrceZvwLt7L749x8DrP9sh2Ybl9uCqiEDAIM/fg3/bh+JuWBWRYvUzMjmduOgk4uwOxqVnkuCIuOjXLNi5ixWrVrNg0VL2HzgYah83ZhSDBw1k5PChDBsyuDluV0Quk3KPXA7T48F75CjegwfxHjsGfn+j84GaWjwHDuDdf4BAZWXjwXYbjp49cfTtiycmmpNuF1XVlZgmeJISqerdk0PJieyqKqesyt1oaAIevmLZQR/vHiycLTGII5mYtCFYHQlYHYlEZ4zG6bWwbPFSNm3ZxoKFSyj9zOqfAf37MmHcGMaPHcPgQSrTJCIiIiIiIlenS5qQeOCBB5o7DrmG+MrLcW5ahXPnFrx79uPbVQwu79kOVjDGxGNMT8RwWNjnyeTNyLFUGsHNpQ1MBsXY6JbcFbvVSq+ERPomJGOzWC74NT1eLytWrmZt3gaWLltB6emy4LUMgymTJzKwf1+mT51M1y6dm/PWReQyKPfIFzHdbrxHjuA9dBhv8TEIfG4SwuXGe+gQ3v0H8J86dc54IzaWiL59MHp0p9owOO5146qpBsCTmEBprx4UxsdwuK6GmtLG+06k4OJm6176+vbh8J4t22RaHEQmD8Ae2wnDMHDEdSEieRCFu/excvUa1m/czNZtO/A1lIiy2axMmjCeUSOHM3P6VNLSUpv62yQiIiIiIiLSZmhTa2kxvlMnqM1bjHvXDnyHjuHfdxKz7jP1vA0whsZiTEvESLRR6orjDcZyJCo91CXVGmBoahaJkXFkRkUzNCWNOLvjol+3vLyCT+YvZPGyFWzavDVUp9tqtTJzxjQGDezPl66/ThtYi4i0cWYggL/0NL7iYrzHjuEvKQUz0KhPwOnEV3wM76HDwZJMgcA517FmZmDr05f67AzKfD7q3U4a9p7GnRDPvi655MdHcdLlxPzciohso45bLYV08x7E5j+7kbUJ2GO7EJncD8PqwGKLITJ5ANWeaD764GNWrV7LuvUbqaurC42Jj4/n+uumMX7sGCZOGIvDbm+6b5aIiIiIiIhIG6QJCWlWpmniPlyIc+MKXPk78R84gf9IKfgav8VK32gsMxIxMhyU1cfwkXcQW6K7QsPG1ImWAAMSEsiMSyfWbmdwchodY2K/8Ovv2r2Xf30wj4WLljYqjZGUmMi0qZMYPmwIM6dPJSLi4qWeRESkdfirqvGdOIGvuBjf8eOY7sYTBKZp4j9dhq+oCF9RMf7S0vNex5KYiKVrF9ydOlEZ6aDW54WGTa4DhkFpaiK7M9PJj4nAY5rwuT0ielkq+BI7yfEexULjHGY4kolOHYQ1IgnD4iAisTceayZ523eGNq0uKTkbl2EY9O/Xh3FjR3P9jGnat0hERERERESuGZqQkCZnmibeimJc+etx7dyBd18R/gMnMU9Vndu5aySWmUnQIYIjtcnM8wxhf1x26HSsJUD/+AQ6xqfjsFrpGZ9In4Ski5ZnAjh56hQrVq1l/oLFbNy8JbQqwjAMBg8ayIhhQxg5fChjRo/EaJj0EBGR1mUGAvhPl+E/dRLfqRJ8J09hOusb9zFNAjU1+E+eapioOIbpdJ73ekZ0NHTujDM3h5rYGNxnVkt4vfjNABV2G/sy0ihIS6LGYT/zBULjE3Ez1XqYQYEjJHhLMDAbXd8SlUlEYk+sESlYLFbscV1xGtls3LqLdes/YMPGLY32KwLI7ZTD2NGj6NO7JzOmTyEhPv4Kv2siIiIiIiIi7YcmJKRJmGYAn/M07r1bcRZsxbfjAL6i0wSOl4HL17iz3cAYFIMxJh5/WgTba7L4ODCUkqSzJZOiDJO+8fF0TkgjxmanR3wi3eISiLBaLxpHTU0tq9as5ZP5i9iydTuVVWcnQZISE5k6ZSI9e3Rn/NjRdOmc26TfAxERuXSmaRKorsF/ujQ4CXH6NP6SEkxf45xhBgIEKirwnTyF/+RJfKdOYdaffwICwEhKxJ+dTX1GOtVJiaFtps2AH5ffT03AT1FiPIUZKZSmJIdW4p1hN31MsBYz0jxEuv8kVv/n4sGCPTYHR0JPrI644JiYDtT4s1i5YTdLlv2D/IKdHD9xstG45KQkxo4ZxcAB/Rg5fCg9unfThLiIiIiIiIhcczQhIWELeGvx1p7EfbgQ94G9+LYdwPf/t3ff4VFUbRvA7+27STaFNEgICYRO6KF3BKlSLCAKBBQpgoDii/AKBlBsWD4LKogUQQSxUF+aCKJIlxqKIL2TkF42W57vj3XXLNk0ICTA/buuvSBnzsyeOTuzz9lzZs6cvmofhMgw517BTw1FUyPQwAvxZiP2WitgqzIKGQEGZ5YgNVDJ6IsQrzLw0+lQzccPEZ5GqAq4IyLbbMaOnbuxdNmP2PPnPqSmpjmX2e+KqI0WzZqgaeNGqFsnCqoCBjaIiOjOEYsFtuRkWBOT7AMP8fGwJiRAsrNd89ls9nzx8fZBigT7C2ZLHlsGoFbBVrYsMssGIy0oEGaDPabYIMi0mJFhtSBVBOd8jbgcGIzrAf6waFybP55iRhPVRdSV8yhvuwS1OTv3+yg10BorQuMdCaXaAIVSA5WhPK6kaPHHpiP4edPXOHL0GNIzXO/o0Ov19jvymjRC40bRqFa1MpQFxDQiIiIiIiKi+xUHJKjQbOY0WLJuwJJ2Gaa/jyH76N+wHDoL27V42K6kAJnW3CupFUA1A5QNvJAc4otD2SH4XVEDV/wD/s0CQSUPHcK9A1FGb0CQ3gOVjT4I9fAs8OrR9PR07N13AEuX/Yhde/YiPd21IygkpByaNWmM9m1boUnjaHgYDHlsiYiIbpdYLLClpMCalARbYhKsiYmwJibClpzi8vBpEYFkZMCWlARrYpL93xuJ9sEHq5tYkvM91GpYAwJgCiiDdP8yyPbzg6iUMFmtyLRakZWZjkyrFakqBa4E+ONakD8SypSBTZVjEEAE1RTxaKQ4j8pyBT62RCjdPPwaChXUHiHQeJWHyhAMhUIJldYHSSYfHDgSj19+/RHH/zqJM2fPwmZznc7J19cHUTVrILpBfbRo3gTVq1XlYDgRERERERE98DggQW6JzQJrdhKsWQmwZMTDfOkMsv8+C8vRs7CevwLbpRTghsn9yioAVQxAlCcSQ/1wSoKwW18ZJ3zDIErHAIOgrEaJUE9fVDD6IdjggXBPL4R5GmFQF3xYnjt3Hj+uWI2tv2/D36fOwGx2vSOjQlh5NKxfD00aN0Szpk0QGOB/exVCREQA/hl0SEuDLSXVfjdDcjJsKSmwJSfDlp7u8gwGyc62D1CkpNjzp6Q4ByFgdnMnnRs2Dw+Yy/gi078Msvz9keVthEkEWTYrTFYrsjLTkGW1IlujRqKvD274+eKGnw9SjV7O6ZjUYkVVXEVtXEEE4hEs8dDY3NwFAQBQQGUIhsYrDGqPclAo1VAotci0emH/sSSs+flnHDv+Fy5dvpxrEEKhUCAivAKiatVEo+j6qFGtKqpVrQJ1IeIaEREVnzVLZxV5nevXryMoaKFL2rdz3kdgYOCdKhYRERHRA4m/kAk2SyZs2cn2AQhTMqwZCci+eBHmY3/DevI8rJduQK5mAEn5dB5pFUAlPSxVvXApJBhHteVxwBCBq74BzkEIJQQhOhXKefggxNMbwQYPhHl6oYKnEUaNtsByZmZmYu36n7Fy9f9wKO4oMt08xLRiRDiaNW2MNq1aIKpmDfj5+d5qtRARPZDEbIYtPR22tDRIejqsqamQ1LR//k2FLTMTEIGIANnZsKWlw5aeBltaOiQtzb7uP4MPYspj4DoPVk9PZPv5wuTjjXQfb6QZjcjUqJBts8FktcJks8KalgIAyNTrkOJr/GcQ4t8BCLVYEYRkNMAJVJPrCEUCfGwpuR5I7UKlh9oQBLUhGGpDMBQqLWzQ4UK8FTv2n8H2PSdw8tRpXI9PcLu6waBHjerV0LRJIzRu2ABVq1aGr49PkfadiIiIiIiI6EHAAYkHhIgNYsmA1ZwKW3YqbOZUWLNTYM64DvOpv2E5eR62C/GwXU2FXM8EErKB/GbNUAAor4Ul3BMJ5fxwKiAUf+lCcdy3PLJyTIvkoQSCdFoEe/igotEHYZ5GlDV4IFjvUag7IU6ePIW1GzZix649OHHib6Slp+fKo9VqEVkpAu3btsZD7dqgcmQlXo1KRHQT5zRJmZn2f9Mz/v37nwEIW3o6bFlZ9oGGzCxIZiZsGRn2f535MyDpGbBlZBT6LoebWT0MMBm9kOXlhQwvT6R7eiDFwwNZKiWybVZYHHdZWO2xyD744IsUoxdSvI1I8fYCNAoEIA3BkoSG+AthSEKgLRleko4CHxWtUEGlD4TaEASVIQhKjRE2UeDy9Qz8ufss/thzGoeOn8ONG4l5bsJo9ELFiAjUqF4VLZs3Rc0a1VGubPAt1QcRERERERHRg4K9tvcRmzULYsmEzZwOmyUdVnMKrJnXYc6Mhy3hKiznLsN6KQG2qymQhEzIjSwgwQxk5XPVqINaAQnRISvIA4mBvjgeGo4473Bc9AmCSa9zZjMogQidDv4GI8K9fBDh6YWAf54L4avV5vtMCKvVilOnz2DX7r3Ytn0nDh6Oy7MzSKVSISK8AhpFN8BD7dqgWtXKCPDntExE9GCR7GzYsrIgWSZIViYkK8s5kCCZmbBmZEBSUmBNsk+rJJmZsJn+yW8yQbKynP8618vKAtw9T6GIzHo9TJ4GZHl4INOgR7rBgDSDHikGHcxunqUgsCFTq0W6pzfSPD2Q5amH0ksBjQfgq8pEWaShHs4hCKnwkzR42Ew3byBPSo0RSp0fVLoyUOn8YBY9Ll1JxMnjN3D01F4cOXEZf52+hixT/gMsgQH+iIyshGZNGqFRw/oICysP/zJlbqV6iIiIiIiIiB5IpXJA4vz585g8eTLWrl2LGzduoGzZsujVqxemTZsGPz+/fNfNzMzE9OnT8e233+LcuXMwGo1o164dXn/9ddSsWfMu7cGdJTYLxJoFmzULtuxUWE2JzpclKxG2pHhYE27AlpgC2400SEIGJCkTkpgNJFuBJAtgLsSgg4MSsJXRwhRgwI0gP5wpG4LD5SrhojEA6Z6eEKX9waBaBVBGo4avzoBgDyMqenkjwtMIf70B/jo9dHk8vFNEkJSUjLPnzuNw3FHsP3gQx46fwPnzF2C2WPIsllqtQvnQUDRr2hjdu3RC9WpVYTDoi1SXRET5KS3xJ1JvgPnYMWRYrPbnLqSkwpaaYp8WKf2fKZLSM+yDCdnZkGwzxJwNZGfb/zZlO5flfKbDnWJVqZCt08Fk0CFTr0OmTocMnf3/WXodsvR614dI/8OsVsPsqQW8lFB6KqHyUECjE2i1Vug1FlRABsrgOnyRAU/kGHAo7C4olPbBB403bAoPZJg1iE+24tL5dFy4nIyzl87g7MUbOHc5FWmZ+Q+6KBQKBPj7o0KF8qhZoxoaNWiAWrVqIDgoEEpl7n0jIiIiIiIiooKVugGJ5ORktGjRAufPn8czzzyD2rVr47fffsOnn36KnTt34o8//shzOh4RwZNPPomVK1eiffv2GDVqFM6ePYuZM2di06ZNOHjwICpUqHCX9yhH+WxW+/MazCn2qZPMqbCZ02Azp0MsabBmp8GWngxrcgokLRW21HRIWiYkNROSZoKkZQPpFkiGDUiz/vvKb2ql/MoDwOajgclXj+QAH1wJ8MffQeVxvFw4Ury9YdZoANifUe2tViFAq0VVvSfKGTztgw9eRvhodTBqtNDk6JxxDDicuX4d167H4+rVazh3/gL+PnUa585fwKVLl5GZlVVg+ZRKJcLKhyKqVk00bRyNFs2aIDg4KN+7LIiIblVpij/ra9dF2gtjkXandq4QbAoFzBoNTFoNsrVaZGs1yNZqYNJpYdJqYdLpkKXTwqTTQDRKqNWARmODWmWDQq+AQq+EQquAhxbw1gnUGjO0Ght0agt0SgsMCjM8FSZobzVo5SAKLazQwmzVICNbgeQ0Gy7fMOPc5XRcSbiEq9ePIzHVjOQ0K5LSrYW64cPLywtlg4NQObIi6teriyaNGiKsfHkOfBMRERERERHdQaVuQOKtt97C+fPn8eGHH2Ls2LEAgDFjxmDUqFH47LPPMGfOHAwfPtztuuvXr8fKlSvRu3dvLFu2DKp/rtBv0aIF+vTpg/Hjx2PJkiWFLkvLBsEwJx1ERpYWYs2GWLMgVhNslizYsjMhpgzYMtMhmRmQrEz7tBmmLEhWNmwmEyQrG5JlBkxmwGQBsq2ASQCTDWKyASab829k2oAsG3D7s2S4sKkVsBh1yDLqkeLjhetlyuC8fxDOBIfiYrlgmHX26Za0CsCoUcNHo0OEzgP+OgPKGgwI0enhK0ogywRzZibS0tKREn8dCUkn8HdyMpKSkpGYlISk5GQkJCTi2rVriE+4AUs+dzrkJcC/DCIiwlG1SmU0qFcXrVs1h5en552tECKiPJSm+KO+jYFXAWDRqGHRqGDRqGHVqGHVquwvvRo2nQpWgwqit79sHirAoILCwz6goFTD/lIJDGqBUSVQq2zQKLOgUWZAq7BCqbjzd104yy+A2apEllmBrGxBWqYNiakWXEnIwsVrWbh8w4KUdCsyTDZkZtmQlmVDWqat0LNMeXh4ICgoEOEVwlC1ciXUrF4NdWpHoWzZYA54ExERERERERUzhUgxzOVwGyIjI5GQkICrV69Cp/v32QQXL15E+fLl8dBDD+Hnn392u+6zzz6LuXPnYufOnWjcuLEzXURQrVo1XLx4EQkJCdDrC77aMSUlBYkDHgEsYr8DwSKurxKuNQFg8dDA5KlDhpcBaV6eSDIakeDti8tlAnA9wB9Jvj7IMuigFoHaKlBZrFCZLFCZTFBmZEHSM4DUdFjiE5GdnILMzExkZmYhMysLmZmZSE/PgPkWH1iaF4VCgcDAAISUK4uw8qGoVrUK6taJQrUqleHl5XVH3+t+k5ycjK1bt6J169bw8fEp6eIQISUlBT4+PkhOToa3t3dJF+e2lab4c/2pnhCNEtAqITqlfdRYp/znZb8bAXolFAb7/5UGBZQGBRQGe16FsnR1rFttAlO2ICvbhvR/BhDSMmxIybAiJd2KxFQrEtMsuHbDgmvJVmSZbDCZBaZsG8xFvKHCw8MAP19fBPj7IzS0HCqUL4+KFSMQGVkR4WHl4cmB7iJj/KHS5n6LP6UB6zS369evIygoyCXt2rVrCAwMLKESEREREZVeRWlPlqo7JDIzM3Hq1Cm0a9fOpTMIAEJDQxEWFobDhw/nuf7hw4eh0WhQv359l3SFQoGmTZti4cKFOHPmDKpXr164Al25s53x7tiUClh1aph1apj0OmQZdMg06JFm8ECqhwdSPTyRYvBAqk6HVI0aqSo1UpRKpApgMZlgTc+ENSMTlvQMWFPTYb1yAda0v2BJTYc1PR1iLvqdCrfDx8cbgQEBCAoMQNngYAQFBSAoMBChoSGoEFYeIeXKQaMpVYcdEVGpiz+6F8sVfSeKkSn7nwECs31QIeufwYWMLPsAQ4ZjkCHLhtQMK1IzrEhKteJGqv3uhkzTrY/iKxQKeHl5wtfHB74+PvAr44cyfvYBB/8yZRAY6I+gwEAEBQUiMCCAUywRERERERERlWKlqmf4+vXrAIDg4GC3y0NCQrBr16581/f394fmn2cf3LwuYL+qxV2HkMlkgsn07wM0U1JSYFUrYVMpYVOrYFUrYVX/MwWGWgWLyv7K1qhhUqlgUqlhUqqQpVIhS6lGhkIJk1KJTIUSJoUCWQoFsgBkCJAhgkybIF1syDZbYDNbIGYzrKZs2DKzIUlpsGXfgM2UDZspG3IL0x/dCqVSCYNBD4PBAINeDw8PA7y8vODl6Wn/18sTxn/+9fXxgZ+fr72DyNcHvr6+8PHxhtZN3RMRlXalLf64Y7EKrDaBxQqYLQKLVVz+dfw/2yzItthfZse//6SZzIJss+2ff8X5b5b5n0EG07+DDTkHH4pyL6VarYZOp4XB4AWDXo/yvnro9XoY9DrodDp4eHjA09MDHgYDPD094eFhsKd5eMBo9IK30Qij0QtGoxHeRiM8PT34EGkiIiIiIiKi+0SpGpCw/TMB9M1Xpzro9XrkN8OUzWbLd92c73Gzt956C1OnTnVJMwQFomWrh6BSKqEQgVIAiA0KARRWBRQ2BRQWBRQKBRQKgUJhhVJpgwIWKJRKKBSAUqG0L1fa86mUShiVSvgolFAqlVCo9VBqFFAq7X+rVEoolSoolQoolSqoVEqoVCqolCqo1CqolPa/lSoVNGo11GoV1Go1VCr7/zVqNTQaTY6XGlqtFmq1/V+dTgud1t4ppNNpodVqoNfr4WEwQKPRcP5sInoglbb4E+CrR88nnoZWo4colLCJ4p/YoAQUgOqfOKFQ2OOG41+lQgmlSgmVUgmlyh4zlEolVB72ddVqFfQqFVQqNVQqFdRqe7pG7fhbbX9p1M40rVbrNrZoNBrotFpodTr7v1oNtFotBw+IiIiIiIiIKE+lakCiJE2cOBEvvfSS8++UlBSEhYXh+7mzOI8qEREVm7zizwfvfcD4Q0RERERERET3lVI1IOG4Oj+vq1ALev62QqHI8wpUx7p53QFgv2PA/dWtRER0f2P8ISIiopwCAwMLjP9EREREVHSlal6FgIAAAEBycrLb5cnJyQgMDMx3/eTkZLcNR8c281ufiIgeTIw/RERERERERETFr1QNSHh6eiI8PBx79uyB1Wp1WZaSkoIjR46gZs2aea5fq1YtJCcn48SJE7mW7dy5E3q9HhEREXe62EREdI9j/CEiIiIiIiIiKn6lakACAB599FFcvHgRc+fOdUl/9913YTab8dhjjwEA0tLScOXKFZerWR999FEAwNSpU12uUv3555+xY8cOdO3aFR4eHndhL4iI6F7D+ENEREREREREVLxK1TMkAGDSpElYsmQJRowYgV27dqFGjRrYtWsXli5dinr16mHYsGEAgPfeew9Tp05FTEwM5s+fDwB45JFH0LlzZyxevBgJCQno0KEDLly4gNmzZ8PLywszZswowT0jIqLSjPGHiIiIiIiIiKh4lbo7JMqUKYNt27ahT58+WL58OcaPH4/ffvsNw4YNw8aNG6HVavNcV6FQ4Pvvv8f48eNx/PhxTJw4EQsWLEDHjh2xbds2VKpU6S7uCRER3UsYf4iIiIiIiIiIipdC3D2Bk5CSkgIfHx8kJyfD29u7pItDhOTkZGzduhWtW7eGj49PSReHiN+TxYT1SqUN4w+VNvyevPNYp0RERER0O4rSnix1d0gQEREREREREREREdH9hwMSRERERERERERERERU7DggQURERERERERERERExY4DEkREREREREREREREVOw4IEFERERERERERERERMWOAxJERERERERERERERFTsOCBBRERERERERERERETFjgMSRERERERERERERERU7DggQURERERERERERERExY4DEkREREREREREREREVOw4IEFERERERERERERERMWOAxJERERERERERERERFTsOCBBRERERERERERERETFjgMSRPcInU6HqlWrQqfTlXRRiIjoAcL4Q0RERERERHeKuqQLQESFo9frUa1atZIuBhERPWAYf4iIiIiIiOhO4R0SRERERERERERERERU7DggQURERERERERERERExY4DEkREREREREREREREVOw4IEFERERERERERERERMWOAxJERERERERERERERFTsOCBBRERERERERERERETFjgMSRERERERERERERERU7DggQURERERERERERERExU5d0gUorUQEAJCSklLCJSEiKp0c34+O70u6Mxh/iIjyx/hz5zH2EBEREdHtKEobnQMSeUhNTQUAhIWFlXBJiIhKt9TUVPj4+JR0Me4bjD9ERIXD+HPnMPYQERER0Z1QmDa6QnhpkVs2mw2XLl2C0WiEQqEokTKkpKQgLCwM58+fh7e3d4mUoTRgPdixHuxYD3aloR5EBKmpqQgJCYFSyRkA7xTGn9KD9WDHerBjPdiVhnpg/LnzSkPsKa1KwzFPeePnQ3RreO4Q3RqeO3krShudd0jkQalUonz58iVdDACAt7c3D3KwHhxYD3asB7uSrgdemXrnMf6UPqwHO9aDHevBrqTrgfHnzipNsae0KuljnvLHz4fo1vDcIbo1PHfcK2wbnZcUERERERERERERERFRseOABBERERERERERERERFTsOSJRiOp0OsbGx0Ol0JV2UEsV6sGM92LEe7FgPVJx4fNmxHuxYD3asBzvWAz1oeMyXbvx8iG4Nzx2iW8Nz587gQ62JiIiIiIiIiIiIiKjY8Q4JIiIiIiIiIiIiIiIqdhyQICIiIiIiIiIiIiKiYscBCSIiIiIiIiIiIiIiKnYckCilli9fjubNm8PT0xOenp5o37491q1blyufiGDBggVo0KAB9Ho9PD090bJlS6xZs6YESl18RASVKlXClClT3C63Wq14//33UbNmTeh0Onh7e6NTp07Yvn373S1oMTl//jwGDRqE4OBgaDQahIWF4YUXXkBiYmJJF63YTZs2DREREW6X/fHHH+jUqRO8vb2h0+lQs2ZNfPDBB7BarXe3kMXkzJkzGDhwIIKCgqDRaFC1alVMmzYNWVlZLvni4uLw2GOPoUyZMtBoNIiMjMSkSZNy5SMqCGOPK8aeBzf2AA9u/GHsoZI0ZcoUKBQKty9vb280a9YMK1asKOliFknbtm3z/C4pbaxWKxYuXIhOnTo5vwOCgoLQrVs3fP/99yjJx0/Onz8fCoUCW7ZsKbEyEN0NRTkPHd+ZZ86cKbkCE5VicXFx6N27N3x9feHl5YV69erho48+yrO9mpSUBIPBAIVCgdmzZ+e53bzaKjlfN5+X165dw3//+1/UqVPH+RuiatWqGDFiBI4fP34nd/veIFTqfPzxxwJA6tatKzNmzJDx48dLQECAAJC5c+e65P3ggw8EgFSuXFneeustmTJligQHBwsAWb58eQntwZ23YMECASCxsbFul48ePVoASIMGDeS9996TiRMnitFoFI1GI7t37767hb3DkpKSJCwsTADIM888Ix9++KE8+uijAkAaNWokZrO5pItYbC5evCihoaESHh6ea9nOnTtFrVaL0WiUiRMnynvvvScNGjQQAPLSSy/d/cLeYX///bcEBASIp6enjB8/Xt566y1p0aKFAJB27dqJxWIREZEzZ844j/UxY8bIBx98IO3atRMA8uijj4rNZivhPaF7BWNPbow9D2bsEXlw4w9jD5W02NhYASADBw6Uzz//3Pn6v//7P3n++efFy8tLAMiaNWtKuqiFtmLFClm0aFFJF6NACQkJ0rJlSwEg9erVk0mTJsmHH34oL774ooSEhAgA6d27t5hMpmIvS2xsrPz0008uaceOHZPPP/9cLl68WOzvT1RSinoeOr4zT58+XWxlSkxMlNjYWNm8eXOxvQdRcTh79qwYjUZnm/3dd9+Vtm3bCgDp0aOH2/bqzJkzBYDodDpp3LhxntsGIGXKlHFpq9z8SklJcebfuHGjeHt7i0ajkccff1zefvttefvtt6Vv376iVqtFo9HIN998Uyz1UFpxQKKUiY+PF71eL/Xq1ZPU1FRn+qVLlyQgIED8/PwkKyvLmddgMEh4eLjcuHHDmff06dPi4eEhISEhzh+O96JLly7Jtm3b5KWXXhKlUplnp1BcXJwoFApp3LixZGZmOtN37twpAKRJkyZ3sdR33iuvvCIA5MMPP3Sm2Ww2ef755wWAfP755yVXuGKQlpYmBw8elFmzZknFihUFgNsOoSZNmohCoZA9e/Y40zIyMqRhw4aiUCgkLi7uLpb6znviiScEgPz222/ONJvNJv379xcA8t1334mISN++fQWAy482i8UijzzyiACQtWvX3u2i0z2IsedfjD12D1rsEWH8EWHsoZLn6FybN2+e2+X/+9//BIC0bdv27hbsPmexWKRNmzYCQN59991cnTRZWVny2GOPCQCZOHFisZcHgMTExBT7+xCVJrdyHt6NAYnTp0/ne4EOUWk1cOBAUSgULheK2Ww26dq1qwCQP//8M9c6DRo0kIiICHnhhRcEgBw6dMjttvP6neDO4cOHRa/XS9myZWX//v25lsfFxUlwcLBoNJo83+9+xAGJUmbx4sUCwO1VNEOHDhUAzh/AX3/9tQCQd955J1feIUOG5PpBea/p1q2bAHB5uQuC06ZNEwCydOnSXMs6dOggAOTcuXN3ocTFo1KlSuLj4+PsDHS4cOGCAJCHHnqohEpWPJYtW5brc7/5i/7s2bMCQLp06ZJr/UWLFgkAef311+9Sie88s9ksBoNBOnTokGvZH3/8IQBk1KhRkpmZKR4eHlKjRo1cDdbff/9dAMizzz57t4pN9zDGnn8x9tg9aLFHhPGHsYdKg4IGJDIzMwWAVK9e/e4W7D73zTffCAAZNGhQnnnS09PFx8dHPD09c8WGO40DEvQgupXzkAMSRHkLDAyUli1b5kr/6KOPBIAsXrzYJX3fvn3OAb9t27YJABk7dqzbbRdlQKJjx44CIN+7jBzn/5AhQwq1zfsBnyFRyly5cgU+Pj6oV69ennksFgsA4PDhwwCApk2b5srTrFkzlzz3otdeew2rVq3CqlWrMGfOnDzzFaYe4uLiiqeQxSwzMxOnTp1CgwYNoNPpXJaFhoYiLCzsnv6M3WnRooXzc1+1ahWioqJy5XF8nvfrsZ+QkACtVovGjRvnmcdiseD06dPIyMhA06ZNoVAoXJZHR0dDpVLd0/VAdw9jz78Yex7M2AMw/jD20L3g6NGjAID69es70y5evIjRo0cjMjISer0e3t7eaNGihcuzJlq0aAGNRoPr16/n2uYbb7wBhUKBrVu3OtN+++03dO7cGd7e3vDy8kKzZs2wfPlyl/UyMjLw6quvomrVqtDpdPD390fbtm1z5XP3DIljx45h8ODBCA8Ph06ng5+fHzp27IjffvvNJZ9CocDLL7+MdevWoWnTpjAYDPDx8UHv3r1x9uzZolRdvr788ksAwIQJE/LM4+HhgfXr12PJkiUwm80AgJSUFEyaNMlZB35+fujSpUuu5zw4nv/w559/YvLkyahQoQI0Gg3Cw8Mxbdo05/N32rZt6/xeWbBgARQKBQYNGuSyDce2z5w5A4VCgU8//RSLFi1CnTp1oNPpEBAQgJiYGNy4cSPX+7t7/oS7z6eo+1WY7Rb2eKEH162ehzlt2bIFCoUC8+fPz7Vs0KBBLnHbarVixowZqF27NgwGA3x9fdG4cWN89dVXzjwRERGoWLEiAGDq1KlQKBQuz1ZLS0vD5MmTERkZCa1Wi9DQUDz//PNISEjIVably5dj48aNaNu2LXx8fJCUlFTYqiG6Ja+//jpiY2Nzpe/fvx8AUKlSJZd0x7Hfr18/NG3aFBUqVMDChQthMpluuQxnzpzBxo0b0bRpU7Rt2zbPfI899hhWrVrljHkPAnVJF4Bcvfjii3jxxRdzpWdlZeHnn3+GXq9HzZo1AcDZoA4ODs6VPyQkBID9oSn3qpw/iPN7SNP9XA/57Rtg379du3bdzSIVu3LlyqF79+7Ov9977z2kpqa65LmfP3PAvl95NdBWr14NwN7pk189OH6Q3cv1QHcPY8+/GHsezNgDMP4w9lBpcuPGDVy4cMH5d1paGuLi4jB58mQEBwdj2rRpAOwDaW3atEFCQgJiYmJQqVIlXLt2DXPnzsWjjz6KzZs3o3Xr1hg6dCj++OMPLFmyBC+88ILLey1ZsgRVqlRB69atAQCLFy/GgAEDEBUVhZdeegkKhQI//fQTevfujZkzZ+L555+HiKB3797YuHEjnn76aWcH3KJFi/Doo4/ixx9/RK9evdzu28mTJ9GqVStotVoMGDAAoaGhOHfuHGbPno2HH34Yhw8fRmRkpDP/1q1b8cknn6Bfv354/PHHcfjwYXz99dc4ffo09u3bl2tgsKgsFgu2b9+O0NBQVKtWLd+8TZo0cf4/OTkZLVu2xOHDh9GpUyc888wzuHbtGhYsWID27dvjq6++wuDBg13WHzlyJC5fvox+/frB398fS5YsQWxsLLy9vTF27Fi89NJLePLJJzFixAg0bdoUMTExBZbpm2++wcGDBzFw4EDExMRg27Zt+Prrr5GcnHxLnf23sl8FuZ3jhR4Mt3oe3o7nn38es2fPRo8ePTB48GBkZGTgu+++w5AhQ5Ceno7Ro0dj+vTpOHPmDCZNmoRu3bqhe/fuiI6OBgCkp6ejbdu2OHjwIAYMGIAaNWrg2LFj+Oqrr7Bp0ybs2LEDfn5+zvdbtWoVFi5ciF69emHy5MkwGAx3ZD+I8jJs2DDn/+Pj43H58mUsXboU8+bNw5NPPunyuy8rKwvffPMNoqKiULt2bQBA3759MWPGDKxcuRJPPPFEru1bLBaXtkpOgYGB0Ol0+P333wEADz30UL5l1el0Lr9DHgQckLgHZGdnY+jQoTh16hRee+01GI1GAIDNZgOAXFcvAoBer3fJcz+7n+shv30D7PsnInezSKXC/fyZ52flypWYMWMGKleujP79+2P79u0A8j8+7sd6oLuDsSd/93M9MPbk7X7+3PPC2EMlYdy4cRg3blyudJ1Oh1mzZjmvaly2bBn+/vtvrFixAj169HDma9++PR566CH8/vvvaN26Nfr06YOxY8di0aJFLgMShw8fRlxcHN566y0A9gHF5557Dp07d8aKFSugVtt/Lk+aNAmPP/44Xn75ZTz22GMAgA0bNqBbt25YuHChc3ujR49G+fLl8cUXX+TZwTxnzhzEx8fjwIEDqFOnjjO9Ro0aePbZZ7Fz506XAYndu3dj/fr1ePjhh51pWq0WX375Jc6dO4fw8PBC16s7CQkJMJlMCAsLK9J6sbGxOHz4MKZNm4ZJkyY5B0bGjRuH6OhojBgxAl26dEHZsmWd6yQmJuLgwYPw9vYGAIwYMQIRERFYsWIFxo4d6/wMR4wYgWrVqmH48OEFlmPv3r34888/nXe1vfTSS+jUqRNWr14Ni8Xi/AyLc78Kcu3atVs+XujBcKvn4e34+uuvUatWLZe7ycaNG4fKlSvjs88+w+jRo/H00087BySio6NdzsmpU6di3759+PXXX9GyZUtn+pAhQ9C6dWu88cYbeP/9953pc+fOxYYNG9CxY8e7s4NEOQwZMsR5rFerVs15d6TDTz/9hMTERPznP/9xpjkGJL766iu3AxIXL17M85zdvHkz2rZti4sXLwLAXT237xWcsqmUO3nyJB566CEsXLgQffr0waRJk0q6SER0l5lMJkyaNAm9evVCQEAAVq5cmWdHENGdwNhDRIw9VJLGjBnjMoXa8uXL8dlnnyE6OhqDBg3CkCFDAAA9e/bE7t270alTJ5f1T58+DcA+uA4ABoMBAwYMwK5du3Dy5ElnviVLlkClUiEmJgYAsHTpUmRkZGDgwIG4cuUKLly4gAsXLuDy5csYMGAAMjMzsWrVKvj6+sLHxwdbt27FnDlzkJycDMB+RaTJZMK6devy3LcRI0Zg9+7dziswAftApmMKJkeZHRo1auQyGAH8e4V0XldmFoVjALEoA4k2mw2LFi1CaGgoJkyY4NKpExoaitjYWJhMJixbtsxlvdGjRzsHIwDAaDSiZs2at7UfvXr1cpliT6FQoEmTJrBarbh69WqRtnWr+1WQ2zle6MFwK+fh7apYsSKOHz+OGTNmOO9+NBgMuHjxIo4dO5bvujabDfPmzUN0dDQiIiKc35UXLlxA+fLl0bhxYyxdutRlnU6dOnEwgkrMyy+/jCVLlmD8+PG4cOEC6tatix07djiXO6ZrevLJJ51pDRo0QOXKlbFhwwacO3cu1zYDAwNd2io5X44YXxLn9r2Cd0jcBTabDX/99VeB+Tw8PFChQgUA9ts6P/vsM/znP/+B2WzGm2++ifHjx0OlUjnzOxpI7q5SdKTd7i28d9Kt1ENh5KyHm/e3NNZDUeT3GeeXfr+7147927F//34MGDAAhw8fRocOHTB//nyEhoYCKNzxoVRy3PlBxdhjx9hTdIw9ebvXjv9bxdhDJa1evXpupy547rnn0KFDB8ybNw9DhgxB8+bN4ePjg++//x7bt29HXFwcjhw54uzwzWno0KH45JNPsGjRIucc6EuXLkXXrl1Rrlw5AMCRI0cAuHZI3OzkyZPQ6XRYvXo1Bg8ejOeeew7Dhw9HVFQU2rRpgyeeeAItWrTI87sgPDwcRqMRc+fOxc6dO3HkyBEcOXIEGRkZbvNXqVIlV5ojLrubQ76o/P39oVKp3Ha23OzAgQM4ffo0GjRogISEBHTr1g0ajSZXvlatWgH495kfDnnty+3sx52sn/j4+Fvar4LczvFCD4ZbOQ/dPc+qKJYtW4ZBgwZh/PjxGD9+PGrUqIGWLVuid+/e6NSpU76x/Pr164iPj0d8fHy+V35nZmY6/+941hZRSXDcxdO3b190794drVu3xoQJE7BlyxacOnUKmzZtgo+Pj8szVADAy8sLIoJ58+bleh6Fh4dHgdMsOe6mK8y5vWrVKqhUKnTt2rUou3bP4oDEXZCRkYEaNWoUmK9NmzbYsmULTCYTnn76afzwww9o1aoVvvjiC+fc3TkFBAQAgNsGd86rLkqLotZDYeWsB19fX5dlpbEeiiK/z9iRfq/u2+241479W/Xtt98iJiYGRqMRCxYswIABA1x+rORXDyKC5ORkVK1a9a6Vl0oXxh47xp6iY+zJ2712/N8Kxh4qzdRqNfr06YNff/0VO3bsgKenJzp06AARQZ8+fdCvXz9UqVIFgYGBLncgAEBUVBSaNWuGb775BlOmTMHevXtx8uRJvPfee7ne59NPP81zKiRH51vLli1x7Ngx7Nu3D7/88gu2bduGr776Ch9//DGGDBmC2bNnu+1k3rhxI3r16gV/f388/vjjePbZZ1GlShVkZGTkutMDgNuO8TtJq9WiXr162Lt3L44ePZpvzHzjjTfw/fffO+NlQZ3oNw9cFse+3O42cw5aFHZguTAD8zcPhtzq8UIPhls5Dw8ePFik97j5mKxVqxZ27dqFuLg4bNq0CX/88Qe+//57fPnll+jSpQuWL18OrVab7zabN2+OiRMnFur983o2GVFxOHr0KNavX4/27du7TI8I2AeXq1Wrhj179gAA5s2bB8Detp0+fbrb7c2bNw+TJ08u8kU3jRo1AgD88ssv+eZLS0tDjx490KBBgwdmQIKXL90FjhG1gl6Oht1zzz2HH374AdOmTcPmzZvddggB9gACwO2DJXfu3AkAea5bEopaD4V1r9VDUXh6eiI8PBx79uyB1Wp1WZaSkoIjR47cs/t2Oxz7fD9+5g6Oh95FR0fj0KFDGDhwYK4fKRUrVoRer3dbD3/99ReSk5Pv+XqgW8fYY8fYU3SMPXm73+MPYw/dCxwPX1epVBg7dixSU1Oxb98+fPbZZxg6dCjatWvncmdfTsOGDcPJkyexc+dOLFmyBMHBwejWrZtzeeXKlQEAQUFB6N69u8uradOmSEpKgqenJ+Lj47FlyxbEx8cjOjoa48ePx4oVK3DhwgU0bdoUc+bMweHDh92WYejQofD29sbhw4fxwQcfYPDgwWjZsmWu79u7acCAAQCAd955J888V65cwYYNG1ChQgW0atUKfn5+2L9/PywWS668jod4Vq9evXgKXESO50jcXMc2m81lCq/AwMAi7Vdht3s7xws9OIp6HjraojnldUwCwIkTJ5z/T0tLw5YtW3Du3DlERUVhzJgxWLp0KS5evIjevXtj7dq1WL9+fZ7lCAwMhLe3N5KSkpwPu8758vDwQHZ2tsuDq4v6PBei23H69Gm8+OKL+Pbbb90uVygU0Ol0sFqtmD9/Pjw8PJCcnOz2d+JDDz2Es2fPYtOmTUUuR61atVCvXj3s2rULmzdvzjOf4/lCOdsk9zsOSJQyO3bswMKFC/Hf//4XkydPzrMxDdgPVK1Wi/fffx9paWnO9MuXL2POnDkICgpCixYt7kaxS1Tv3r0BANOnT3dpOMbFxeHHH39EgwYNEBERUUKlu32PPvooLl68iLlz57qkv/vuuzCbzc4H6z1IKlasiPr16+OHH35wabxbLBa8+eabAOz1dq+yWq148cUXUbVqVaxbtw4hISFu83l4eKBLly7Yvn07Nm7c6EwXEbz++usA8EAeH1R0jD1Fx9jzYH633M/xh7GH7gUmkwmLFi0CAHTs2BGXLl2CTqdzTrnkMHPmTLfr9+nTB76+vli4cCG+++47DBw40KWT7NFHH4VSqcQbb7zhHPhweOeddzBgwACICP7++2+0a9cO06ZNc8nj6+vr7KzOa77oS5cuwc/Pz+VZClarFV988UXhKqEYDB06FNWrV8eCBQvw9ttv5yp7YmIiYmJikJKSgtdeew1KpRJPPfUULly4gHfffdfljoHLly9j6tSp0Gq1ePzxx2+5THdyvm3H8eEYOHaYM2cOrl275vy7qPtV2O3ezvFCD45bOQ9vltcxuWHDBuzevdv5d1paGtq1a4eRI0e6HOcGgwF169YFkPuYzPm3UqnE448/jiNHjmDBggUu+RyDGj/++GNRdp/ojmrevDk8PDwwd+5c5zNSHBYuXIhjx46hS5cu2LBhAy5cuIC+ffu6xOWcBg8eDAC5pnMqDIVCgQ8++AAA0K9fP+zbty9Xnu3bt2PChAnw9fXFCy+8UOT3uFdxiLKUcdwq5OnpmWejtH///vDy8kJgYCCmTZuGCRMmoHHjxujfvz/MZjPmzJmD+Ph4fPfdd8V+i29pULduXQwfPhxffPEFWrVqhd69eyMxMRGzZs2C1WrFp59+WtJFvC2TJk3CkiVLMGLECOzatQs1atTArl27sHTpUtSrVw/Dhg0r6SKWiE8++QStW7dG69atMWzYMPj5+eGnn37Cjh07MHLkyFy35d1LHLfOdu/eHYsXL3abp1GjRmjYsCFmzJiBDRs2oGfPnhg6dCjKly+Pn3/+GevXr3derUJUEMaeomPseTBjD3D/xh/GHipNNm/ejKysLJe069evY+nSpTh69CheeOEF1KxZE+3bt8cXX3yBhx56CL169UJSUhI2bNiAa9euQa1WY8OGDWjWrJlzKiSDwYD+/ftj9uzZyM7OxjPPPOPyHhUrVsT06dMxceJE1KtXD4MGDYK3tze2bduGH3/8ESNHjkSVKlVgs9nQrl07fPbZZzh37hxatWoFrVaL3bt3Y/HixWjWrFmuKaMc2rdvj3Xr1qFnz55o3749rl69ihUrVjgHt3/44QdUrVoVzZs3L4aadc9gMGDNmjXo1KkTJk6ciMWLF6NHjx4ICgrChQsXsHDhQly5cgVjxoxx1tnrr7+On3/+Ga+++iq2bt2Kdu3a4erVq1iwYAFu3LiBL774wvnsmaIyGo3YunUrPvnkE0RFRaFdu3a3tX8tWrRAaGgopkyZgsuXL6NChQrYu3cvVq1ahaioKKSmpjrzFmW/CrvdRo0a3fLxQg+OWzkPb1apUiVER0djzpw5yM7ORu3atXHkyBEsWrQI0dHRzilqypYti5iYGCxYsABt2rRB586d4eXlhbi4OMybNw+VKlVChw4dAMDZSbtq1SoEBwejSZMmiI6OxvTp0/Hzzz9j8ODBWL9+PZo0aYL4+HjMnz8farUakydPvjsVR+SGr68v3nnnHbzwwguoU6cOBg4ciICAAGzfvh0//fQTQkJC8Oabb+Kll14CAAwZMiTPbfXu3Rve3t746aefkJCQAH9//yKVpV27dvjqq68wbNgwNG7cGD169EDjxo2dcWDJkiUwGo346aef7vmpX4tEqFRp0qSJAMj3df78eWd+m80ms2bNkrp164pOpxODwSBNmzaVH3/8sQT34s47ffq0AJDY2Fi3y81ms7z55ptSrVo10Wg04uXlJe3bt5ctW7bc3YIWk1OnTkm/fv0kICBAVCqVhISEyLBhw+T69eslXbRi16ZNGwkPD3e7bPPmzdK+fXvx8vISjUYjVatWlbfeekvMZvPdLeQd9vnnnxf4PfD666878+/fv18eeeQR8fX1FZVKJRERETJ+/HhJT08vwb2gewljj3uMPQ9u7BF58OIPYw+VBrGxsXkefxqNRmrVqiUzZ84Um80mIiKZmZkyevRoCQ0NFY1GI2FhYfL888/L1atXpX///qLVamX8+PEu73Ho0CEBIC1atMizHN9//720aNFCvLy8xNPTU+rVqyeff/65WCwWZ56UlBR59dVXpWrVqmIwGMTT01OioqIkNjZWUlJSnPlu/i65fv26xMTESFBQkGi1WomMjJQJEyZIUlKStG3bVnQ6nXz66aciIgJAYmJicpVv3rx5AkA2b958C7Wct/T0dJkxY4Y0bdpUfH19Ra1WS3BwsPTs2VPWrVuXK/+NGzdk/PjxEhkZKRqNRnx8fKRDhw6yYcOGQpfX3XftRx99JGXKlHHZ/5u3kV+MdhxHp0+fdqbt2bNH2rRpIwaDQTw8PKR169byxx9/yGOPPZbr/Qu7X0XZbmGPF6KinIfujvWTJ09Kt27dxGg0ik6nk+joaFm1apWMGzdOcnYBZmdnyzvvvCO1a9cWLy8vMRgMUrVqVRk3bpxcvnzZ5X3Gjx8vRqMx1zl35coVef755yUsLEw0Go2EhoZKv3795NixY848mzdvFgDy5Zdf3tmKIiqElStXSps2bcRoNIparZaIiAgZOXKkXL58Wa5duyYajUZq1KjhbFfkZejQoQJAPvroIxGxx+e8fifk5dixYzJs2DCJjIwUvV4ver1eqlWrJmPGjJGzZ8/e6i7esxQihXgiExEREREREdE9bu/evYiOjsbcuXOd0zAQERER0d3DZ0gQERERERHRA+HTTz+Fn58f+vTpU9JFISIiInog8RkSREREREREdF+bNm0aUlJSMH/+fEydOhWenp4lXSQiIiKiBxKnbCIiIiIiIqL7mp+fH6xWK5544gnMmjULajWvzSMiIiIqCRyQICIiIiIiIiIiIiKiYsdnSBARERERERERERERUbHjgAQRERERERERERERERU7DkgQEREREREREREREVGx44AEEREREREREREREREVOw5IEBERERERERERERFRsXsgBiTmz58PhUKR6+Xt7Y3q1avjmWeewbZt29yuq1Ao0LZt27tb4HvM5cuX8dhjj8Hf3x9eXl44evRoSRep2GzZsgUKhQJTpky55W20bdsWCoXizhUqhzNnzkChUGDQoEG3vA3H+TJnzpw7V7A7aNCgQVAoFDh58mSxbL+o57yjPGfOnClUfovFgujoaAwdOrTAvO4+z7y+zxQKBbRaLcLCwtCnTx8cP368UOVxHNMF7fOUKVNyHfuFXXf79u1Qq9WIi4srVJnuJ4w/xYvxp2gYf24P4w/jDxER3XkFxZfKlSvj5ZdfRnJyckkXNV83t7PuRNuNiOh+pS7pAtxNNWvWRJcuXQAAIoL09HQcP34cCxcuxLx58zBo0CB88cUX0Ol0znXGjRuHyMjIW3q/KVOmYOrUqdi8efN93an04osv4scff0TXrl1RsWJFGAyGki4SFcKWLVvQrl07xMbGspF0F3344Yc4evQoVq1adVvbyfl9BgA2mw3JycnYunUrli1bhs2bN+PPP/9EWFjY7Rb5tjVr1gzdu3fHmDFjsHHjxmLrEC3NGH+KB+PPvYnxp2Qw/jyY8YeI6F5xc3wxm804f/48Nm7ciPfffx+bNm3C9u3bodfrS7CUhRcWFoZx48ahefPmJV0UIqJS54EakGjUqBHee++9XOl///03nnzyScyfPx8qlcrlyjx3+cnV3r17ERgYiNWrV/OHHlE+4uPjERsbi5EjR6JcuXK3ta28vs+sVit69uyJNWvW4P/+7//w/vvv39b73ClTpkxB/fr1sXbtWnTt2rWki3PXMf4UD8YfosJh/Hlw4w8R0b0ir/hy9epVNGvWDPv378eCBQswbNiwEihd0UVGRrI9T0SUhwdiyqaCREZGYu3atShTpgy++uorHDhwoKSLdE8xm83w8PBgZxBRAWbPno3MzMzbmtKkICqVytlIL01TVNSrVw916tRho/wmjD+3h/GHqHAYfxh/iIjuVcHBwRg3bhwA4Jdffinh0hAR0Z3AAYl/BAQEYPDgwQCAb7/91pl+8/y0IoKvv/4aLVq0gK+vLzw9PVGtWjVMmDAB169fB/DvHIhTp04FALRr1w4KhQLz5893buf48eMYMGAAIiMjYTAY4Ovri6ZNm+Kzzz5zKZdjW6tXr8ayZcsQHR0Ng8EAHx8f9OjRw+08vTdu3MB//vMfVK5cGTqdDiEhIejQoQN++OGHXHlFBAsWLECTJk3g4eEBPz8/dOjQAVu2bCmwzhxzJJ49exZnz56FQqFAREQEgH/nNU5LS8Py5cvRsmVL+Pj4ICkpCQCQkJCA8ePHo0qVKtDpdPDz80OnTp2wZs2aXO8TERGB6OhoXLlyBYMHD0ZQUBD0ej2aNm2KLVu2QEQwa9YsREVFQa/XIyQkBP3798e5c+cK3AeHVatWoW3btvDx8YHRaETXrl0RFxeHDh06FKqjKz09HW+88QaioqJgMBjg7e2NVq1aYdGiRbDZbG7XuXLlCoYOHYrQ0FBotVqUL18ew4YNw8WLF3PlTUpKwiuvvILatWvD29sbnp6eqF69Ol555RWkpaUVej+Bf+eFbteuHQBg6tSpbuf9FhF88803aNSoUb7HnOPzsVgsmDFjBmrVqoWmTZs6lx8+fBh9+/ZFYGAg9Ho9qlevjnfeeQdms9llO2fPnsWIESMQHh4OnU6HsmXLonPnzli9erXb/cjKysLUqVNRpUoVaLValC1bFiNGjEBKSkquvOfOnXNuW6vVIjAwEL1798Yff/xRqDrLzs7Ge++9h9q1a0Ov18PX1xe9e/fG/v37C7U+YK/Pzz//HHXr1kWtWrVclh04cACPP/44AgICoNVqUaNGDbz77rswmUyF3n5O6enpAOyN99KkX79+2Lx5Mw4ePFjSRSlVGH8Yfxh/BrnkY/z5F+PPncH4Q0R0b3O08xISEgDYpwucOXMmmjVrhsDAQOh0OoSFhSEmJibXs54UCgUef/xxZGRk4L///S8qV66MPn36OJevX78enTt3Rvny5aHT6RAYGIhu3bph48aNucqRmpqKSZMmOduRgYGBGDBgAE6dOpUrb17PkLhx4wbGjh3rbHdERkbi3XffdZu/bdu2CAgIQEpKCsaOHYvQ0FDodDrUqVMH33//PQDg+++/d7aZgoOD0bNnTxw+fPhWqpmI6O6RB8C8efMEgMTExOSbb9WqVQJA2rZt60wDIG3atHH+PX36dAEgISEh8swzz8jw4cOlRo0aAkCioqIkOztbdu3aJePGjZNmzZoJAOnTp4+MGzdOdu3aJSIix44dE6PRKFqtVrp16yYvvPCC9OvXT4xGowCQWbNm5Sr7Y489JgCka9euMnLkSGnRooWzHKmpqc78ly5dksjISAEgdevWlREjRsjTTz/t3PY777zjzGuz2eSZZ54RAFKpUiUZPHiw9O7dWzw9PUWhUMg333yTb3199tlnMm7cODEajWI0GmXcuHHyxhtviIhITEyMAJB3331XAEiTJk1k2LBhkpGRIadOnZIKFSoIAKlTp448//zz0qdPH/Hw8BAAMnnyZJf3CQ8Pl8jISKlUqZJUq1ZNRowYIV27dhUA4unpKUOHDhW1Wi2PPPKIjBo1SurWrevcf4vFku8+iIi8/fbbAkB8fHykf//+Mnz4cKlUqZL4+vpK9erVJedpsnnzZgEgsbGxzrSEhATne0ZGRsqwYcNkwIAB4ufnJwBk4MCBYrPZnPnbtGnjPF6MRqP069dPnn/+eWnYsKEAkMDAQDl69Kgzf2ZmptSpU0cASLNmzWTEiBHy3HPPSVhYmACQfv36OfOePn26wGM9ISFBxo0bJ3369HFuc9y4cfLtt9+KyL/HnOMYK+iYCw8PlwYNGkjfvn1Fq9VKly5d5M033xQRkfXr14terxeDwSC9evWSZ555RipXriwApFu3bs7P5/Lly8796dSpk7zwwgvy6KOPik6nEwCyZMkS5/s5jq0WLVqIp6en9OvXT4YNG+bcbo8ePVz2d/fu3c7Ponnz5jJq1Cjp2bOnqNVqUSgU8uWXX7rkv/mcz87OloceekgASFhYmAwZMkSee+45iYiIEA8PD6lXr54AkNOnT+dZ5yIihw8fFgAycuRIl/S1a9eKVqsVpVIpnTt3llGjRkm3bt1EpVI56zzn51nQ91lWVpa0bdtWAMjmzZvzLZPIv8d0zn12JzY2NtexX9h1HX7//XcB4PyeeBAw/jD+5Ifxh/EnJ8Yf9xh/iIjub4VpL3/yyScCQPr27SsiIkOHDnW2f5599lkZNWqUsz0THh4u2dnZznUdMdoRv3v16uVs837zzTeiUCjEz89P+vXrJ6NHj5auXbuKUqkUjUYjhw8fdm4nMTHR2S6qVq2aDB8+XAYNGiTBwcESFBTkbA84uGu7Xbp0SSpWrCgApGnTpjJy5Ejp2bOnqFQqZ/s9Z/42bdqIt7e3NGnSRMqXLy/Dhg2Txx57TJRKpSgUChk7dqwAkI4dO8rIkSOlZcuWAkBCQ0MlKSnpNj8ZIqLiwwGJHA4ePCgApFatWs60m3/s+Pr6il6vlxs3bjjTLBaLs4Pil19+caY7fkDd/KNs2LBhAkBWrFjhku74sdS1a9dcZVcqlbJ27Vpnus1mk8GDBwsA+fHHH53pffv2FQDy6quvunRCnDlzRgICAkSr1crVq1dFRGThwoUCQJ5++mnJyspyyRseHi6enp7OvPkJDw+X8PBwlzTHj3aDwSArV650KbcjSE6bNi1XGSMiIgSAbNq0yWX7AKRXr14u5Rw1apQAEK1WK7t373amm0wmZwfN/v378y37oUOHRKlUSkREhJw8edKZnpqa6vxRXVCjon///gJAnnvuOTGbzc70hIQEiY6OFgAyd+5cZ7qjQ6hChQpy/Phxl7p57733nI0TR918++23AkBefPFFl7InJSWJv7+/eHh4ONMK0yGU376IFP2YCw8PF5VKJWFhYS77k5iYKGXKlJGyZcvKiRMnnOnZ2dny3HPPCQD57LPPRETk//7v/wSATJgwwaUs27dvFwDSunVrZ5rj2AoLC3P5zDIzM52ds/Hx8SJiPxYcDb4FCxa4bHv//v3i5+cnGo1Gjh075ky/+ZyfMWOGAJCHH35YMjIynOkmk0kGDhzoPEYK6hD69NNPcx0L6enpEhgYKCqVStavX++Sf9euXc6OXHcdQjVr1pRx48Y5X2PHjpWnnnpKypUrJz4+PvLVV1/lWx4Hx3FQvnx5l+3d/HLXQC5qh1BaWpoolUqXz/N+x/jD+JMXxh/GH8Yfxh8iIiq4vXzlyhVnTP3yyy/l4sWLolAopHHjxi4DDzabTTp27CgAnBfjiNjjq0qlkrp168rly5ddtl2tWjXx8vKS8+fPu6S/8cYbAtgvcHEYOXKkAJBBgwa5tLtSU1OlQ4cOhWq7OS7KeO2111zaoitWrBClUul2QAKwX8SRmJjoTHe0225u11utVunWrZsAkOXLl7utTyKi0oADEjmcOHFCAEjlypWdaTf/2ClTpoxoNBrZvn27y7qnTp2SVatWyYULF5xpeXUIzZ8/X1599VUxmUwu6UlJSbnez1H2p556Kld5V69eLQDkvffeExGR69evi0qlknLlyuXatmNbMTExzk6S+vXri06nk+Tk5Fx5v/nmGwEgH3zwQa5lN8uvQ+jmq/H27t0rAKRhw4YuAdjBcZVw7969XbavUChyNR4ceZ9++ulc2xk3bpzbur+Z44qCefPm5Vr2559/FtiouHbtmiiVSilXrpxLZ5WDo5Oxfv36zjRHo8JxRWhONpvN2Zn1559/iojIr7/+Kq+++qpLp4WD4+pIhzvZIVSYY07k3w67ZcuWueT96KOPBIDMnj0713YyMjLEaDRKnTp1RETk448/FsB+NXfOThcRkXXr1rl0tDqOLXfbffnllwWA7NmzR0REfvjhBwHsV3i747jSJmdn283nYNWqVQWAHDx4MNf6iYmJ4u3tXagOoeHDhwsA2bJlizNt8eLF+Zbvtddey7NDKL+Xt7e3jBw5UtLS0vItk8i/x0FhX7fTISQiUr58efHx8Sl0/nsd48+/22L8ccX4w/jD+MP4Q0REeQ94jxkzRh599FHx8vJytuEyMzPl3Llz8uqrr7pcvODgaF/lbIc54kjOi0gcYmNjnRcp5LR8+XKX2GMymcTT01N0Op1cu3YtV/6//vrLOaDgcHN7Jz4+XlQqlVSsWNHtnbQ9evTIFe8cbbecAywi9gtbAPtdmzdztDHctTGJiEoLNcgpNTUVAODj45NnnpdeegmTJk1C8+bN0bZtW3Ts2BFNmjRBixYtULFixUK9T0xMDADAarXi4MGDOH78OOLi4lzmDr9Z48aNc6V5enq6lHvPnj2wWq3o3LkztFptrvyDBg1yztOcnp6Offv2ISAgANOmTcuV1zEf+e7duwu1T3np2bOny987duwAAPTq1cvt3NidOnWCTqfDtm3bXNLLly+PsmXLuqR5eXkBACpVqpRrO45lBXGUp2PHjrmW1a9fH/7+/s55Kt3ZvXs3bDYbunbtCp1Ol2t57dq1UalSJezbtw/p6enOzwwAHnnkkVz5FQoFevTogQMHDmDHjh2oX78+WrdujdatWwMATp06haNHj+Lo0aNYu3ZtkeaQLqrCHHMOSqUS3bp1c0n7/fffAQBbt251O9e8wWDAwYMHkZWVhT59+uCdd97Bd999h61bt6Jnz55o1qwZ2rRpg06dOt1y+XIeb+707NkTL7zwQq7jzSE1NRV//fUXKlSogNq1a+da7uvri7Zt22LlypVu18/JcU75+vo60/bs2QMAuerOoVevXm7PT8D+PZLzuQAAkJGRgbi4OEydOhUzZ87EyZMnsW7dugLLBgBt2rTJd+7+KVOmOJ9LcDv8/Pxw4cIFZGVlQa/X3/b27heMP/9i/GH8Yfxh/MmJ8YeI6MFw5MgRHDlyxCVNrVYjPDwcw4cPx+TJk6HX6xEWFoY33ngDgD3GHTp0CMePH8cff/yBJUuWuN12SEgIGjZsmCvd8byGjIwMHDhwAH/99Rf279+PBQsWuOQ7fvw40tPT0bp1awQGBubaTpUqVRAVFZXvs4oc7eWHHnoIKpUq1/KOHTvmGddvLvudaIsSEZUkDkjkcOzYMQD2YJKXV199FQ0aNMDXX3+NDRs2YPPmzQDsX/pjxozB1KlT3QaXnK5evYpXX30VS5cuRVpaGoKCglClShU88cQTmD59utt18uukcoiPjwcABAUFFZg3MTHRuc7777+fZ74LFy4UuK38VKhQweXvGzduAMj7YYcajQb+/v64du2aS7pSmffz1/NbVhDHj/SAgAC3ywvqECpofwCgbNmyOHXqFJKSkpwdFp6eni6dQzmVK1cOwL8P7LJarXjzzTfxxRdf4NKlSzAajahSpQoaNmyIS5cuOY/bO60wx5xDUFAQDAaDS5qjbhYtWpTvupcuXXJ2ms2cOROrV6/G7NmzMWvWLABAs2bN8PHHHyM6OrrI5Svo83F0Mjry3Sw5ORnAv59JftsoiGNbOT/3grZf2G07eHh4oFGjRvjhhx9Qq1YtrF+/HgcOHEDdunWLtJ3iZDQaAdjrPCQkpIRLU3ow/uTG+MP4UxiMPwVj/LFj/CEiKt3cDXjn5bvvvsObb76JAwcOQKvVolKlSqhVqxYefvhh/O9//8uVv0KFCm4vSNm7dy9effVVbNq0CTabDWFhYahatSqeeOIJzJ4925mvsHE5vwGJwrT98pJXm/N22qJERCWJ3145rFq1CoD9B2h+unTpgm+//dY5Gv/hhx+iTJkymD59OiZMmJDvujabDd27d8eCBQswceJEXLp0CVevXsXvv//uHOW/VY4fmnl1YGRkZODKlSvIyMhwXiVXu3ZtiH3qLrevrVu33laZbr5q08/PDwBydfg4mM1m3Lhxw5mvuDl+nDoaBzmJCC5dupTv+gXtT85lOa9MTE9Pz3WVp8OVK1dc8v/nP//Ba6+9hrZt2yIuLg4pKSnYu3cvZs+enW9H1N3k7upcR93s3bs332PMcVVHYGAgpkyZgj179iAhIQGrV6/GU089he3bt6NDhw64ePFikctV0OfjSM/reHN8Bo7PxJ3Lly8XqSxpaWnOtDJlyuS7/cJu+2Y6nc7ZgVZcHYa3Kj09HUDROhwfBIw/jD85Mf4UHuNP4cvC+MP4Q0R0P1ixYgWefPJJqFQqbN26Fenp6Th69Ci+//57NGrUyO067toLZ8+eRceOHfHnn39i4cKFSE5OxpkzZ7Bhwwb069fPJW9BcRMoOHbm1/YDcEvtDSKiexUHJP5x6NAhfPfdd9BoNHjyySfd5rly5QrGjh2LxYsXA7CPRkdFRWHs2LH49ddfAfzbqZSXgwcPYs+ePejSpQv++9//uoyw324AclyFtmnTJlgsllzLJ06ciHLlymHPnj3w8vJC9erVcfz4cVy9ejVX3mPHjmH48OFYsWLFbZXpZk2aNAEALFu2DFarNdfylStXIisrC82bN7+j75uXBg0aAADWr1+fa9mmTZtcfry7Ex0dDYVCgdWrV7vNu3fvXpw8eRJ169bNdUWqu2NFRLB69WoAcDam5s6dC19fX3z99deoWbOmS/7S3GhxdEj89ttvbpdPnDjR2YE6f/58jB071nmlqJ+fH7p164ZvvvkGMTExSE5OznM7+XEcb0uXLnW73HFLb17Hm5eXF2rXro2zZ8+6nZ4kOTk532kmcnJcOe64OhwAmjZtCgBYvny523Vu5/w7e/YsANeOyNIgKSkJBoMhzyu0H0SMP64Yfxh/bhfjjyvGHzvGHyKi+8O8efMgIvjyyy/RqlUrqNX/TvxRlPbJTz/9hMTEREyYMAFPPvmky1RHN2+natWq8PX1xc6dO90OPJw8eRKHDx/O9/3q168PANi4cSNsNluu5XnFZCKi+xEHJABs2bIFXbt2hdVqRWxsbJ5TTnh4eODzzz/H+PHjc42MO0a5c67ruCUwZ+eM4/+XLl1y6RBJT0/HyJEjAcBtcCqMSpUqoU2bNjh9+jSmTZsGEXEui4uLw9y5c1G2bFnnj9+hQ4ciOzsbw4cPd141BtinaHjllVcwa9Yst/Mj3o6GDRuiadOmOHDgAF577TWXOjh+/DheeuklAHDWRXEbPHgwAOD11193mR7k+vXrGDduXIHrBwcHo2/fvrhy5QpGjRoFk8nkXHbp0iU899xzANzvz4QJE3D06FGXtHnz5uHPP/9EVFSUc45qi8WCjIwMlyuPRQTTp0/HyZMnAdzaMePu+LyTBgwYAL1ej+nTp+fqTPn+++/x9ttvOzuA/vrrL3z00Uf4/PPPXfJZrVZnnsJMBXOzRx55BBUqVMCqVaswa9Ysl3Ni586dmDZtGlQqFYYOHZrnNkaMGAHAfr7kvJrFYrHg5ZdfzvNK45s55gA/deqUM61bt24oX748VqxYkWue0gMHDuCjjz4q1LZvtnr1auzYsQNlypRBy5Ytb2kbxcFkMuHSpUuoVatWSRel1GD8Yfxh/LnzGH9cMf4w/hAR3U8c7Ydz5865pG/atAkLFy4EULj2SV7bOXv2LF577TWX7ajVagwdOhRZWVl47rnnXNqvGRkZGD16tEusd6dChQro0KEDzp07h7feestl2ezZs/N8rhQR0f3ogXqGxO7du/Hyyy8DsP+gzsjIwO7du7F3714A9qkJJk6cmOf63t7eePnll/Hmm2+ievXq6Nq1KwIDA3HhwgWsXbsWSqUSr7zyijO/ozPlv//9L/73v/+hX79+qF+/PmrUqIG9e/eiUaNGaNGiBdLT07Fu3Tp4eXmhXLlyOHDgACZPnozXX3+9yPs4e/ZstGrVCq+//jo2bdqE+vXr4/Lly1i9ejXMZjN++ukn5xUEo0aNwvr167F8+XLUqFEDDz/8MFQqFTZv3owTJ05gxIgRBU4fUlQKhQJff/012rVrhzfffBNr1qxBy5YtkZCQgBUrViAzMxOvvPKK24d8FoeWLVtizJgx+Oijj9CwYUM88sgjUCgUWLNmDQD7j/hDhw7lu42PP/4YBw8exIIFC7Bt2zZ06NABmZmZWLlyJRITE/Hkk0/i2WefdVlHoVAgODgYjRo1Qrdu3RAUFIRDhw7h119/hbe3N+bPn+/ssOnXrx/mzJmDBg0aoHv37lAqlfj9999x6tQpNGzYEHv37sXgwYPznYvdHcfxOX/+fKSmpqJZs2Z5Xp19K0JCQvDFF19g8ODBaNKkCbp3746QkBD89ddf2LBhAyIiIpwPzHz++ecxZ84cTJo0CatWrUKDBg1gs9nw+++/Iy4uDs2bN0ebNm2KXAadToclS5agc+fOGD58OBYuXIgGDRrgwoULWLlyJaxWK2bOnJnryt+chg4divXr12PFihWIiopCt27doNPp8Msvv+DUqVOoU6dOvnOFOjiO6b179zqPB51Oh8WLF6Nz584YNGiQ8yrkixcvYs2aNahdu7bz++lmOb/PHCwWC/766y+sW7cOKpUKn3/++V25EvTIkSP5Hjtt2rTBiBEjcOjQIZjNZrRr167Yy1TaMP4w/tyM8Yfxh/Hn9jH+EBE9ePr164c1a9agf//+6NWrF/z9/XHkyBFs2rQJrVq1wtatW/HWW29Br9fne+drjx49EBsbi48//hgnTpxAlSpVcPnyZaxatQqNGzfGqVOnsGzZMlSuXBkDBw7Ea6+9hl9//RVr1qxBVFQUHn74YdhsNqxbtw4pKSmIjIzE33//nW/ZZ86ciZYtW2LSpEnYvHkzatSogRMnTmD9+vXo3Lkz1q1bd6eri4iodJIHwLx58wRArpfRaJTq1avL8OHDZf/+/W7XBSBt2rRx/m2z2WTp0qXStm1bCQwMFI1GI6GhofLUU0/JH3/84bJuUlKStG/fXgwGgwCQefPmiYjImTNnZMCAAVKuXDnRarUSGRkpL7/8sty4cUPeeecdMRgM0qJFC5eyO9bNafPmzQJAYmNjXdIvXLggI0aMkAoVKoharRYfHx/p3Lmz/Prrr7m2kZ2dLR9++KHUq1dPDAaD+Pj4SKNGjWTu3LlisVgKVb/h4eESHh7ukhYTEyMA5PTp027XuXr1qrz44otSqVIl0Wg04uPjI+3bt5fly5eLzWYrcPv57b+ISGxsrACQzZs3F1h+m80mX331lTRs2FAMBoP4+vrK448/LqdOnZJq1aqJp6dnge+ZkpIisbGxUqNGDdHpdOLl5SXNmjWTuXPnitVqdcnbpk0bUalUcuPGDXn55ZclIiJC1Gq1BAcHS//+/eXEiRMu+TMzM2XixIkSGRkpOp1OgoOD5amnnpKjR4/KL7/8Iv7+/hIYGCiXL1+W06dPCwCJiYkp1H6PGjVKfHx8XNYp6jGX1+fj8Ntvv0n37t2lTJkyotVqpXLlyjJ69Gi5dOmSS76zZ8/K2LFjpXLlyqLX68XHx0caNmwo77zzjqSnpzvz5Xds5fW5nzp1SoYMGSLly5cXtVotZcqUkUceecTtOXHzOS8iYjab5eOPP5a6deuKXq8Xo9Eo7dq1k19++aXAYz2nWrVqSVRUVK70o0ePSt++fZ3fKREREfKf//xHDh06lOvzzOv7zPHy9vaWzp07y++//15geUT+/Uxv3uebOeo252fvWLegl6P8H330kQCQTZs2Faps9wPGH8af/DD+MP7kxPjjHuMPEdH9zRFfCtOGcPjqq6+kfv364unpKT4+PtKhQwdZu3atXLlyRapXry4eHh6ycuVKEXEfXx22bt0qHTp0ED8/PzEYDNKgQQP55JNPxGw2S8+ePUWn08mECROc+TMyMmTq1KlSrVo10Wq14uvrK927d5d9+/ZJmzZtJGcXW15ttzNnzkhMTIwEBweLVquVGjVqyMyZM2Xx4sUCQGbMmOHMe/M2HfJrd+XXniIiKi0UIgXcV0Z0HzOZTMjMzISHhwe0Wq3LsqtXryIsLAw1a9Z0O38zUVF9+eWXGDp0KA4cOIA6deqUdHHuuqZNmyI5ORlxcXFQKjljID3YGH/obmL8YfwhIqKSl5ycDIVCAW9v71zLxowZg48//hg//fQTevXqdfcLR0R0F7FFTg+0H374AX5+fnjxxRdd5pk0mUwYO3YszGYzHnnkkRIsId1P+vfvj+DgYMybN6+ki3LXHT16FDt37sTo0aPZGUQExh+6uxh/GH+IiKjkNWrUCKGhoc5ncTls374dc+bMgaenJ6cXJKIHAu+QoAdaWloaWrdujX379qFGjRpo1aoVlEolNm3a5JxHcvfu3fDx8SnpotJ9Yu7cuRg1ahROnjyJkJCQki7OXdOnTx8cP34ce/bsgUajKeniEJU4xh+62xh/GH+IiKhkLVy4EIMGDYJOp8PDDz+MChUq4MKFC1i1ahUsFgs+++wzjBgxoqSLSURU7DggQQ+81NRUvP/++/jhhx9w+vRpWCwWhIeH45FHHsHEiRPh7+9f0kWk+4jNZkPz5s1Ru3ZtfPnllyVdnLti586daNGiBXbs2IHo6OiSLg5RqcH4Q3cT4w/jDxERlbxffvkFn3zyCXbs2IHr16/D29sb0dHRGDt2LLp27VrSxSMiuis4IEFERERERERERERERMWOE6kSEREREREREREREVGx44AEEREREREREREREREVOw5IEBERERERERERERFRseOABBERERERERERERERFTsOSBARERERERERERERUbHjgAQRERERERERERERERU7DkgQEREREREREREREVGx44AEEREREREREREREREVu/8HZ3cuNxULQmoAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 1600x800 with 5 Axes>"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -98,36 +101,36 @@
"source": [
"figure = P50GlobalFigure(sigmoid_data)\n",
"figure.plot_sigmoids_and_barplot()"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### STATS - Effect of the experimental paradigm"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### STATS - Effect of the experimental paradigm"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
"source": [
"#### Filter data\n",
"\n",
"Filter the p50 data from the 3-AFC task, Randomized task, and the random conditions of the Continuous and Cluster tasks.\n",
"\n",
"Print some descriptive stats and run one-way ANOVA."
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -151,39 +154,49 @@
"paradigm: F(3, 81) = 191.91, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "paradigm / 3AFC vs Bayesian: T(27) = -19.55, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / 3AFC vs Cluster: T(27) = -12.79, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / 3AFC vs Continuous: T(27) = -17.65, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / Bayesian vs Cluster: T(27) = 6.65, p = <0.001 (fdr_bh: <0.001) *\n",
- "paradigm / Bayesian vs Continuous: T(27) = 3.68, p = 0.001 (fdr_bh: 0.001) *\n",
- "paradigm / Cluster vs Continuous: T(27) = -3.02, p = 0.006 (fdr_bh: 0.006) *\n",
+ "paradigm / 3AFC vs Bayesian: T(27) = -19.55, p = <0.001 (fdr_bh: <0.001), d = -5.24 *\n",
+ "paradigm / 3AFC vs Cluster: T(27) = -12.79, p = <0.001 (fdr_bh: <0.001), d = -3.72 *\n",
+ "paradigm / 3AFC vs Continuous: T(27) = -17.65, p = <0.001 (fdr_bh: <0.001), d = -4.72 *\n",
+ "paradigm / Bayesian vs Cluster: T(27) = 6.65, p = <0.001 (fdr_bh: <0.001), d = 1.85 *\n",
+ "paradigm / Bayesian vs Continuous: T(27) = 3.68, p = 0.001 (fdr_bh: 0.001), d = 1.08 *\n",
+ "paradigm / Cluster vs Continuous: T(27) = -3.02, p = 0.006 (fdr_bh: 0.006), d = -1.03 *\n",
"\n"
]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/nmarin/miniforge3/envs/beepbeep/lib/python3.10/site-packages/scipy/stats/_continuous_distns.py:7321: RuntimeWarning: invalid value encountered in _nct_sf\n",
+ " return np.clip(_boost._nct_sf(x, df, nc), 0, 1)\n",
+ "/Users/nmarin/miniforge3/envs/beepbeep/lib/python3.10/site-packages/scipy/stats/_continuous_distns.py:7313: RuntimeWarning: invalid value encountered in _nct_cdf\n",
+ " return np.clip(_boost._nct_cdf(x, df, nc), 0, 1)\n"
+ ]
}
],
"source": [
"paradigms_data, paradigms_aov, paradigms_posthoc = process.p50_stats_pipeline(var='distance_p50', factor='paradigm', filter_on='pred', conditions=['Bayesian', '3AFC', 'none'])"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
"source": [
"### STATS - Effect of predictability\n",
"\n",
"#### Why not both?\n",
"\n",
"Remove participants with problematic p50 values in any pred condition of either task, and count the remaining participants."
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -217,12 +230,12 @@
"pred: F(3, 81) = 18.94, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "pred / F vs FT: T(27) = 2.70, p = 0.012 (fdr_bh: 0.014) *\n",
- "pred / F vs R: T(27) = -5.44, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / F vs T: T(27) = -0.44, p = 0.662 (fdr_bh: 0.662)\n",
- "pred / FT vs R: T(27) = -6.18, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / FT vs T: T(27) = -3.88, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / R vs T: T(27) = 4.57, p = <0.001 (fdr_bh: <0.001) *\n",
+ "pred / F vs FT: T(27) = 2.70, p = 0.012 (fdr_bh: 0.014), d = 0.53 *\n",
+ "pred / F vs R: T(27) = -5.44, p = <0.001 (fdr_bh: <0.001), d = -1.26 *\n",
+ "pred / F vs T: T(27) = -0.44, p = 0.662 (fdr_bh: 0.662), d = -0.10\n",
+ "pred / FT vs R: T(27) = -6.18, p = <0.001 (fdr_bh: <0.001), d = -1.68 *\n",
+ "pred / FT vs T: T(27) = -3.88, p = <0.001 (fdr_bh: <0.001), d = -0.61 *\n",
+ "pred / R vs T: T(27) = 4.57, p = <0.001 (fdr_bh: <0.001), d = 1.10 *\n",
"\n",
"Two-way ANOVA (paradigm, pred) on the p50s: \n",
"\n",
@@ -236,23 +249,23 @@
],
"source": [
"_ = process.p50_stats_pipeline(var='distance_p50', factor='pred', filter_on='paradigm', conditions=['Continuous', 'Cluster'])"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Continuous stats"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Continuous stats"
+ ]
},
{
"cell_type": "code",
"execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -276,12 +289,12 @@
"pred: F(3, 81) = 6.92, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "pred / F vs FT: T(27) = 2.12, p = 0.043 (fdr_bh: 0.052)\n",
- "pred / F vs R: T(27) = -3.22, p = 0.003 (fdr_bh: 0.010) *\n",
- "pred / F vs T: T(27) = 0.20, p = 0.841 (fdr_bh: 0.841)\n",
- "pred / FT vs R: T(27) = -3.60, p = 0.001 (fdr_bh: 0.008) *\n",
- "pred / FT vs T: T(27) = -2.39, p = 0.024 (fdr_bh: 0.036) *\n",
- "pred / R vs T: T(27) = 2.63, p = 0.014 (fdr_bh: 0.028) *\n",
+ "pred / F vs FT: T(27) = 2.12, p = 0.043 (fdr_bh: 0.052), d = 0.44\n",
+ "pred / F vs R: T(27) = -3.22, p = 0.003 (fdr_bh: 0.010), d = -0.58 *\n",
+ "pred / F vs T: T(27) = 0.20, p = 0.841 (fdr_bh: 0.841), d = 0.05\n",
+ "pred / FT vs R: T(27) = -3.60, p = 0.001 (fdr_bh: 0.008), d = -0.87 *\n",
+ "pred / FT vs T: T(27) = -2.39, p = 0.024 (fdr_bh: 0.036), d = -0.41 *\n",
+ "pred / R vs T: T(27) = 2.63, p = 0.014 (fdr_bh: 0.028), d = 0.63 *\n",
"\n"
]
}
@@ -290,23 +303,23 @@
"paradigm = 'Continuous'\n",
"\n",
"continuous_data, continuous_aov, continuous_posthocs = process.p50_stats_pipeline(var='distance_p50', factor='pred', filter_on='paradigm', conditions=[paradigm])"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "#### Cluster stats"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "#### Cluster stats"
+ ]
},
{
"cell_type": "code",
"execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -330,12 +343,12 @@
"pred: F(3, 81) = 14.62, p = <0.001 *\n",
"\n",
"Post-hoc tests (paired t-tests):\n",
- "pred / F vs FT: T(27) = 1.33, p = 0.195 (fdr_bh: 0.234)\n",
- "pred / F vs R: T(27) = -5.28, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / F vs T: T(27) = -0.83, p = 0.416 (fdr_bh: 0.416)\n",
- "pred / FT vs R: T(27) = -6.29, p = <0.001 (fdr_bh: <0.001) *\n",
- "pred / FT vs T: T(27) = -3.16, p = 0.004 (fdr_bh: 0.006) *\n",
- "pred / R vs T: T(27) = 3.85, p = <0.001 (fdr_bh: 0.001) *\n",
+ "pred / F vs FT: T(27) = 1.33, p = 0.195 (fdr_bh: 0.234), d = 0.25\n",
+ "pred / F vs R: T(27) = -5.28, p = <0.001 (fdr_bh: <0.001), d = -0.89 *\n",
+ "pred / F vs T: T(27) = -0.83, p = 0.416 (fdr_bh: 0.416), d = -0.18\n",
+ "pred / FT vs R: T(27) = -6.29, p = <0.001 (fdr_bh: <0.001), d = -1.35 *\n",
+ "pred / FT vs T: T(27) = -3.16, p = 0.004 (fdr_bh: 0.006), d = -0.51 *\n",
+ "pred / R vs T: T(27) = 3.85, p = <0.001 (fdr_bh: 0.001), d = 0.83 *\n",
"\n"
]
}
@@ -344,28 +357,30 @@
"paradigm = 'Cluster'\n",
"\n",
"cluster_data, cluster_aov, cluster_posthocs = process.p50_stats_pipeline(var='distance_p50', factor='pred', filter_on='paradigm', conditions=[paradigm])"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Fig4A - p50 barplot"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Fig4A - p50 barplot"
+ ]
},
{
"cell_type": "code",
"execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": "<Figure size 800x533 with 2 Axes>",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x533 with 2 Axes>"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -374,14 +389,14 @@
"source": [
"fig = Fig4p50().p50_barplot('distance_p50', paradigms_data, continuous_data, cluster_data, paradigms_posthoc, continuous_posthocs, cluster_posthocs)\n",
"fig.savefig(op.join(fig_dir, \"Fig4A-p50_barplot.svg\"))"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -474,10 +489,7 @@
"print(simple_effects_freq0)\n",
"print_anova_results(f'{paradigm} Simple Effects - Frequency (Time = 1)', simple_effects_freq1)\n",
"print(simple_effects_freq1)\n"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
}
],
"metadata": {
@@ -489,14 +501,14 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.10.15"
}
},
"nbformat": 4,
diff --git a/scripts/analysis/7_catch_trials.ipynb b/scripts/analysis/7_catch_trials.ipynb
index 7187ab9fc38886366a141db8aeae6218059a049f..11e6c46b31c884ef3c1d8a669055b68a5758cc9b 100644
--- a/scripts/analysis/7_catch_trials.ipynb
+++ b/scripts/analysis/7_catch_trials.ipynb
@@ -2,61 +2,70 @@
"cells": [
{
"cell_type": "markdown",
- "source": [
- "# Fetch catch trials data"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "# Fetch catch trials data"
+ ]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": true,
"ExecuteTime": {
"end_time": "2023-08-15T09:28:42.024228Z",
"start_time": "2023-08-15T09:28:39.018636Z"
- }
+ },
+ "collapsed": true
},
"outputs": [],
"source": [
- "import pingouin as pg\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
+ "import sys\n",
+ "sys.path.append('../..')\n",
"\n",
"from scripts.utils import *\n",
"from scripts.stats import *\n",
- "from funcs.plots import Fig4p50"
+ "from funcs.plots import Fig4p50\n",
+ "\n",
+ "import pingouin as pg\n",
+ "import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
- "outputs": [],
- "source": [
- "data_path = get_path('dataframes')"
- ],
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-15T09:28:42.025245Z",
"start_time": "2023-08-15T09:28:42.024405Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "data_path = get_path('dataframes')"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Collect catch trial data from all experiments"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Collect catch trial data from all experiments"
+ ]
},
{
"cell_type": "code",
"execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:42.939337Z",
+ "start_time": "2023-08-15T09:28:42.024504Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -564,32 +573,128 @@
" paradigm_data = paradigm_data.drop(paradigm_data.index[-1])\n",
"\n",
" all_responses = pd.concat([all_responses, paradigm_data])\n"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:42.939337Z",
- "start_time": "2023-08-15T09:28:42.024504Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Do some clean-up"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Do some clean-up"
+ ]
},
{
"cell_type": "code",
"execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:42.939712Z",
+ "start_time": "2023-08-15T09:28:42.939222Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": " participant paradigm pred isCatchTrial responses \\\n9 tlvjds Cluster both False 1 \n10 tlvjds Cluster time False 1 \n11 tlvjds Cluster frequency False 1 \n12 tlvjds Cluster none True 1 \n13 tlvjds Cluster frequency False 1 \n\n feedback.rt trials.thisN \n9 [0.32247939996887] 0.0 \n10 [0.4047361999982968] 1.0 \n11 [0.32659319997765124] 2.0 \n12 [0.1770447000162676] 3.0 \n13 [0.4016340000089258] 4.0 ",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>participant</th>\n <th>paradigm</th>\n <th>pred</th>\n <th>isCatchTrial</th>\n <th>responses</th>\n <th>feedback.rt</th>\n <th>trials.thisN</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>9</th>\n <td>tlvjds</td>\n <td>Cluster</td>\n <td>both</td>\n <td>False</td>\n <td>1</td>\n <td>[0.32247939996887]</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>tlvjds</td>\n <td>Cluster</td>\n <td>time</td>\n <td>False</td>\n <td>1</td>\n <td>[0.4047361999982968]</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>tlvjds</td>\n <td>Cluster</td>\n <td>frequency</td>\n <td>False</td>\n <td>1</td>\n <td>[0.32659319997765124]</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>12</th>\n <td>tlvjds</td>\n <td>Cluster</td>\n <td>none</td>\n <td>True</td>\n <td>1</td>\n <td>[0.1770447000162676]</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>13</th>\n <td>tlvjds</td>\n <td>Cluster</td>\n <td>frequency</td>\n <td>False</td>\n <td>1</td>\n <td>[0.4016340000089258]</td>\n <td>4.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>participant</th>\n",
+ " <th>paradigm</th>\n",
+ " <th>pred</th>\n",
+ " <th>isCatchTrial</th>\n",
+ " <th>responses</th>\n",
+ " <th>feedback.rt</th>\n",
+ " <th>trials.thisN</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>tlvjds</td>\n",
+ " <td>Cluster</td>\n",
+ " <td>both</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>[0.32247939996887]</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>tlvjds</td>\n",
+ " <td>Cluster</td>\n",
+ " <td>time</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>[0.4047361999982968]</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>tlvjds</td>\n",
+ " <td>Cluster</td>\n",
+ " <td>frequency</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>[0.32659319997765124]</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>tlvjds</td>\n",
+ " <td>Cluster</td>\n",
+ " <td>none</td>\n",
+ " <td>True</td>\n",
+ " <td>1</td>\n",
+ " <td>[0.1770447000162676]</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>tlvjds</td>\n",
+ " <td>Cluster</td>\n",
+ " <td>frequency</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>[0.4016340000089258]</td>\n",
+ " <td>4.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " participant paradigm pred isCatchTrial responses \\\n",
+ "9 tlvjds Cluster both False 1 \n",
+ "10 tlvjds Cluster time False 1 \n",
+ "11 tlvjds Cluster frequency False 1 \n",
+ "12 tlvjds Cluster none True 1 \n",
+ "13 tlvjds Cluster frequency False 1 \n",
+ "\n",
+ " feedback.rt trials.thisN \n",
+ "9 [0.32247939996887] 0.0 \n",
+ "10 [0.4047361999982968] 1.0 \n",
+ "11 [0.32659319997765124] 2.0 \n",
+ "12 [0.1770447000162676] 3.0 \n",
+ "13 [0.4016340000089258] 4.0 "
+ ]
},
"execution_count": 4,
"metadata": {},
@@ -614,27 +719,27 @@
"\n",
"# Display the first rows\n",
"all_responses.head()"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:42.939712Z",
- "start_time": "2023-08-15T09:28:42.939222Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Compute false alarm rates"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Compute false alarm rates"
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:42.939745Z",
+ "start_time": "2023-08-15T09:28:42.939425Z"
+ },
+ "collapsed": false
+ },
"outputs": [],
"source": [
"def calculate_false_alarm_rate(group):\n",
@@ -646,32 +751,204 @@
"\n",
"# Add column with false alarm rates\n",
"catch_trials = catch_trials.groupby(['participant', 'paradigm', 'pred']).apply(calculate_false_alarm_rate)\n"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:42.939745Z",
- "start_time": "2023-08-15T09:28:42.939425Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Descriptive stats"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Descriptive stats"
+ ]
},
{
"cell_type": "code",
"execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:42.939803Z",
+ "start_time": "2023-08-15T09:28:42.939451Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": " count mean std min 25% 50% \\\nparadigm pred \nBayesian bayesian 28.0 0.017363 0.047050 0.0 0.000000 0.000000 \nCluster both 28.0 0.138486 0.147954 0.0 0.000000 0.108187 \n frequency 28.0 0.161256 0.186996 0.0 0.000000 0.105556 \n none 28.0 0.159449 0.146078 0.0 0.000000 0.133929 \n time 28.0 0.170022 0.154999 0.0 0.046875 0.154762 \nContinuous both 28.0 0.162968 0.141666 0.0 0.000000 0.142857 \n frequency 28.0 0.092069 0.142826 0.0 0.000000 0.000000 \n none 28.0 0.094487 0.124734 0.0 0.000000 0.000000 \n time 28.0 0.209779 0.179157 0.0 0.000000 0.154762 \n\n 75% max \nparadigm pred \nBayesian bayesian 0.000000 0.222222 \nCluster both 0.179972 0.500000 \n frequency 0.285714 0.600000 \n none 0.275974 0.500000 \n time 0.250000 0.666667 \nContinuous both 0.238095 0.500000 \n frequency 0.166667 0.500000 \n none 0.148810 0.428571 \n time 0.343750 0.555556 ",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th>count</th>\n <th>mean</th>\n <th>std</th>\n <th>min</th>\n <th>25%</th>\n <th>50%</th>\n <th>75%</th>\n <th>max</th>\n </tr>\n <tr>\n <th>paradigm</th>\n <th>pred</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Bayesian</th>\n <th>bayesian</th>\n <td>28.0</td>\n <td>0.017363</td>\n <td>0.047050</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.222222</td>\n </tr>\n <tr>\n <th rowspan=\"4\" valign=\"top\">Cluster</th>\n <th>both</th>\n <td>28.0</td>\n <td>0.138486</td>\n <td>0.147954</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.108187</td>\n <td>0.179972</td>\n <td>0.500000</td>\n </tr>\n <tr>\n <th>frequency</th>\n <td>28.0</td>\n <td>0.161256</td>\n <td>0.186996</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.105556</td>\n <td>0.285714</td>\n <td>0.600000</td>\n </tr>\n <tr>\n <th>none</th>\n <td>28.0</td>\n <td>0.159449</td>\n <td>0.146078</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.133929</td>\n <td>0.275974</td>\n <td>0.500000</td>\n </tr>\n <tr>\n <th>time</th>\n <td>28.0</td>\n <td>0.170022</td>\n <td>0.154999</td>\n <td>0.0</td>\n <td>0.046875</td>\n <td>0.154762</td>\n <td>0.250000</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th rowspan=\"4\" valign=\"top\">Continuous</th>\n <th>both</th>\n <td>28.0</td>\n <td>0.162968</td>\n <td>0.141666</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.142857</td>\n <td>0.238095</td>\n <td>0.500000</td>\n </tr>\n <tr>\n <th>frequency</th>\n <td>28.0</td>\n <td>0.092069</td>\n <td>0.142826</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.166667</td>\n <td>0.500000</td>\n </tr>\n <tr>\n <th>none</th>\n <td>28.0</td>\n <td>0.094487</td>\n <td>0.124734</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.148810</td>\n <td>0.428571</td>\n </tr>\n <tr>\n <th>time</th>\n <td>28.0</td>\n <td>0.209779</td>\n <td>0.179157</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.154762</td>\n <td>0.343750</td>\n <td>0.555556</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
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+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>count</th>\n",
+ " <th>mean</th>\n",
+ " <th>std</th>\n",
+ " <th>min</th>\n",
+ " <th>25%</th>\n",
+ " <th>50%</th>\n",
+ " <th>75%</th>\n",
+ " <th>max</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>paradigm</th>\n",
+ " <th>pred</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Bayesian</th>\n",
+ " <th>bayesian</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.017363</td>\n",
+ " <td>0.047050</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.222222</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"4\" valign=\"top\">Cluster</th>\n",
+ " <th>both</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.138486</td>\n",
+ " <td>0.147954</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.108187</td>\n",
+ " <td>0.179972</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>frequency</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.161256</td>\n",
+ " <td>0.186996</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.105556</td>\n",
+ " <td>0.285714</td>\n",
+ " <td>0.600000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>none</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.159449</td>\n",
+ " <td>0.146078</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.133929</td>\n",
+ " <td>0.275974</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>time</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.170022</td>\n",
+ " <td>0.154999</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.046875</td>\n",
+ " <td>0.154762</td>\n",
+ " <td>0.250000</td>\n",
+ " <td>0.666667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"4\" valign=\"top\">Continuous</th>\n",
+ " <th>both</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.162968</td>\n",
+ " <td>0.141666</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.142857</td>\n",
+ " <td>0.238095</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>frequency</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.092069</td>\n",
+ " <td>0.142826</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.166667</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>none</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.094487</td>\n",
+ " <td>0.124734</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.148810</td>\n",
+ " <td>0.428571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>time</th>\n",
+ " <td>28.0</td>\n",
+ " <td>0.209779</td>\n",
+ " <td>0.179157</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.154762</td>\n",
+ " <td>0.343750</td>\n",
+ " <td>0.555556</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " count mean std min 25% 50% \\\n",
+ "paradigm pred \n",
+ "Bayesian bayesian 28.0 0.017363 0.047050 0.0 0.000000 0.000000 \n",
+ "Cluster both 28.0 0.138486 0.147954 0.0 0.000000 0.108187 \n",
+ " frequency 28.0 0.161256 0.186996 0.0 0.000000 0.105556 \n",
+ " none 28.0 0.159449 0.146078 0.0 0.000000 0.133929 \n",
+ " time 28.0 0.170022 0.154999 0.0 0.046875 0.154762 \n",
+ "Continuous both 28.0 0.162968 0.141666 0.0 0.000000 0.142857 \n",
+ " frequency 28.0 0.092069 0.142826 0.0 0.000000 0.000000 \n",
+ " none 28.0 0.094487 0.124734 0.0 0.000000 0.000000 \n",
+ " time 28.0 0.209779 0.179157 0.0 0.000000 0.154762 \n",
+ "\n",
+ " 75% max \n",
+ "paradigm pred \n",
+ "Bayesian bayesian 0.000000 0.222222 \n",
+ "Cluster both 0.179972 0.500000 \n",
+ " frequency 0.285714 0.600000 \n",
+ " none 0.275974 0.500000 \n",
+ " time 0.250000 0.666667 \n",
+ "Continuous both 0.238095 0.500000 \n",
+ " frequency 0.166667 0.500000 \n",
+ " none 0.148810 0.428571 \n",
+ " time 0.343750 0.555556 "
+ ]
},
"execution_count": 6,
"metadata": {},
@@ -679,60 +956,67 @@
}
],
"source": [
- "FAR = catch_trials[['participant', 'paradigm', 'pred', 'false_alarm_rate']].drop_duplicates()\n",
+ "FAR = (\n",
+ " catch_trials\n",
+ " .reset_index(drop=True) # Drop the index completely, avoiding conflicts\n",
+ " [['participant', 'paradigm', 'pred', 'false_alarm_rate']] # Select required columns\n",
+ " .drop_duplicates()\n",
+ ")\n",
"\n",
"FAR.groupby(['paradigm', 'pred']).false_alarm_rate.describe()"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:42.939803Z",
- "start_time": "2023-08-15T09:28:42.939451Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Save catch trial data and false alarm rates"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Save catch trial data and false alarm rates"
+ ]
},
{
"cell_type": "code",
"execution_count": 7,
- "outputs": [],
- "source": [
- "catch_trials.to_pickle(os.path.join(data_path, 'catch_trials.pkl'))\n",
- "FAR.to_pickle(os.path.join(data_path, 'false_alarm_rates.pkl'))"
- ],
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-15T09:28:42.939829Z",
"start_time": "2023-08-15T09:28:42.939506Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "catch_trials.to_pickle(os.path.join(data_path, 'catch_trials.pkl'))\n",
+ "FAR.to_pickle(os.path.join(data_path, 'false_alarm_rates.pkl'))"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Fig4C - False Alarm Rate Barplot"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### Fig4C - False Alarm Rate Barplot"
+ ]
},
{
"cell_type": "code",
"execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:43.250292Z",
+ "start_time": "2023-08-15T09:28:42.939527Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"data": {
- "text/plain": "<Figure size 700x533 with 1 Axes>",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 700x533 with 1 Axes>"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -741,49 +1025,47 @@
"source": [
"fig = Fig4p50().catch_trials_barplot(FAR)\n",
"plt.savefig(os.path.join(get_path('figures'), 'Fig4C-catch_trial_barplot.svg'))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:43.250292Z",
- "start_time": "2023-08-15T09:28:42.939527Z"
- }
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### ANOVA on the False Alarm Rates"
- ],
"metadata": {
"collapsed": false
- }
+ },
+ "source": [
+ "### ANOVA on the False Alarm Rates"
+ ]
},
{
"cell_type": "code",
"execution_count": 9,
- "outputs": [],
- "source": [
- "stats_params = StatsParams()"
- ],
"metadata": {
- "collapsed": false,
"ExecuteTime": {
"end_time": "2023-08-15T09:28:43.278100Z",
"start_time": "2023-08-15T09:28:42.973723Z"
- }
- }
+ },
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "stats_params = StatsParams()"
+ ]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:32:05.047478Z",
+ "start_time": "2023-08-15T09:32:04.992329Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "28\n",
- "0\n",
"One-way ANOVA on the false_alarm_rate (factor: condition):\n",
"condition: F(8, 216) = 5.78, p = <0.001 *\n",
"\n"
@@ -799,60 +1081,60 @@
" within='condition', subject='participant')\n",
"\n",
"StatsFormatter().print_1way_anova(StatsFormatter, aov_results, var='false_alarm_rate', factor='condition')"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:32:05.047478Z",
- "start_time": "2023-08-15T09:32:04.992329Z"
- }
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:43.278891Z",
+ "start_time": "2023-08-15T09:28:43.228735Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Post-hoc tests (paired t-tests):\n",
- "condition / Bayesian vs Cluster/F: T(27) = -3.84, p = <0.001 (fdr_bh: 0.004) *\n",
- "condition / Bayesian vs Cluster/FT: T(27) = -4.21, p = <0.001 (fdr_bh: 0.002) *\n",
- "condition / Bayesian vs Cluster/R: T(27) = -5.00, p = <0.001 (fdr_bh: <0.001) *\n",
- "condition / Bayesian vs Cluster/T: T(27) = -5.02, p = <0.001 (fdr_bh: <0.001) *\n",
- "condition / Bayesian vs Continuous/F: T(27) = -2.58, p = 0.016 (fdr_bh: 0.047) *\n",
- "condition / Bayesian vs Continuous/FT: T(27) = -5.13, p = <0.001 (fdr_bh: <0.001) *\n",
- "condition / Bayesian vs Continuous/R: T(27) = -3.15, p = 0.004 (fdr_bh: 0.018) *\n",
- "condition / Bayesian vs Continuous/T: T(27) = -5.56, p = <0.001 (fdr_bh: <0.001) *\n",
- "condition / Cluster/F vs Cluster/FT: T(27) = 0.56, p = 0.578 (fdr_bh: 0.717)\n",
- "condition / Cluster/F vs Cluster/R: T(27) = 0.05, p = 0.957 (fdr_bh: 0.967)\n",
- "condition / Cluster/F vs Cluster/T: T(27) = -0.24, p = 0.812 (fdr_bh: 0.941)\n",
- "condition / Cluster/F vs Continuous/F: T(27) = 1.64, p = 0.113 (fdr_bh: 0.214)\n",
- "condition / Cluster/F vs Continuous/FT: T(27) = -0.04, p = 0.967 (fdr_bh: 0.967)\n",
- "condition / Cluster/F vs Continuous/R: T(27) = 1.76, p = 0.091 (fdr_bh: 0.192)\n",
- "condition / Cluster/F vs Continuous/T: T(27) = -1.02, p = 0.318 (fdr_bh: 0.445)\n",
- "condition / Cluster/FT vs Cluster/R: T(27) = -0.71, p = 0.484 (fdr_bh: 0.622)\n",
- "condition / Cluster/FT vs Cluster/T: T(27) = -1.10, p = 0.283 (fdr_bh: 0.424)\n",
- "condition / Cluster/FT vs Continuous/F: T(27) = 1.40, p = 0.173 (fdr_bh: 0.296)\n",
- "condition / Cluster/FT vs Continuous/FT: T(27) = -0.84, p = 0.406 (fdr_bh: 0.542)\n",
- "condition / Cluster/FT vs Continuous/R: T(27) = 1.36, p = 0.185 (fdr_bh: 0.302)\n",
- "condition / Cluster/FT vs Continuous/T: T(27) = -2.30, p = 0.029 (fdr_bh: 0.070)\n",
- "condition / Cluster/R vs Cluster/T: T(27) = -0.32, p = 0.753 (fdr_bh: 0.904)\n",
- "condition / Cluster/R vs Continuous/F: T(27) = 1.68, p = 0.104 (fdr_bh: 0.207)\n",
- "condition / Cluster/R vs Continuous/FT: T(27) = -0.10, p = 0.925 (fdr_bh: 0.967)\n",
- "condition / Cluster/R vs Continuous/R: T(27) = 2.00, p = 0.056 (fdr_bh: 0.125)\n",
- "condition / Cluster/R vs Continuous/T: T(27) = -1.28, p = 0.211 (fdr_bh: 0.331)\n",
- "condition / Cluster/T vs Continuous/F: T(27) = 2.71, p = 0.011 (fdr_bh: 0.038) *\n",
- "condition / Cluster/T vs Continuous/FT: T(27) = 0.21, p = 0.836 (fdr_bh: 0.941)\n",
- "condition / Cluster/T vs Continuous/R: T(27) = 2.42, p = 0.023 (fdr_bh: 0.058)\n",
- "condition / Cluster/T vs Continuous/T: T(27) = -1.01, p = 0.321 (fdr_bh: 0.445)\n",
- "condition / Continuous/F vs Continuous/FT: T(27) = -2.49, p = 0.019 (fdr_bh: 0.054)\n",
- "condition / Continuous/F vs Continuous/R: T(27) = -0.15, p = 0.885 (fdr_bh: 0.965)\n",
- "condition / Continuous/F vs Continuous/T: T(27) = -3.07, p = 0.005 (fdr_bh: 0.019) *\n",
- "condition / Continuous/FT vs Continuous/R: T(27) = 2.97, p = 0.006 (fdr_bh: 0.022) *\n",
- "condition / Continuous/FT vs Continuous/T: T(27) = -1.54, p = 0.134 (fdr_bh: 0.242)\n",
- "condition / Continuous/R vs Continuous/T: T(27) = -3.22, p = 0.003 (fdr_bh: 0.017) *\n",
+ "condition / Bayesian vs Cluster/F: T(27) = -3.84, p = <0.001 (fdr_bh: 0.004), d = -1.06 *\n",
+ "condition / Bayesian vs Cluster/FT: T(27) = -4.21, p = <0.001 (fdr_bh: 0.002), d = -1.10 *\n",
+ "condition / Bayesian vs Cluster/R: T(27) = -5.00, p = <0.001 (fdr_bh: <0.001), d = -1.31 *\n",
+ "condition / Bayesian vs Cluster/T: T(27) = -5.02, p = <0.001 (fdr_bh: <0.001), d = -1.33 *\n",
+ "condition / Bayesian vs Continuous/F: T(27) = -2.58, p = 0.016 (fdr_bh: 0.047), d = -0.70 *\n",
+ "condition / Bayesian vs Continuous/FT: T(27) = -5.13, p = <0.001 (fdr_bh: <0.001), d = -1.38 *\n",
+ "condition / Bayesian vs Continuous/R: T(27) = -3.15, p = 0.004 (fdr_bh: 0.018), d = -0.82 *\n",
+ "condition / Bayesian vs Continuous/T: T(27) = -5.56, p = <0.001 (fdr_bh: <0.001), d = -1.47 *\n",
+ "condition / Cluster/F vs Cluster/FT: T(27) = 0.56, p = 0.578 (fdr_bh: 0.717), d = 0.14\n",
+ "condition / Cluster/F vs Cluster/R: T(27) = 0.05, p = 0.957 (fdr_bh: 0.967), d = 0.01\n",
+ "condition / Cluster/F vs Cluster/T: T(27) = -0.24, p = 0.812 (fdr_bh: 0.941), d = -0.05\n",
+ "condition / Cluster/F vs Continuous/F: T(27) = 1.64, p = 0.113 (fdr_bh: 0.214), d = 0.42\n",
+ "condition / Cluster/F vs Continuous/FT: T(27) = -0.04, p = 0.967 (fdr_bh: 0.967), d = -0.01\n",
+ "condition / Cluster/F vs Continuous/R: T(27) = 1.76, p = 0.091 (fdr_bh: 0.192), d = 0.42\n",
+ "condition / Cluster/F vs Continuous/T: T(27) = -1.02, p = 0.318 (fdr_bh: 0.445), d = -0.26\n",
+ "condition / Cluster/FT vs Cluster/R: T(27) = -0.71, p = 0.484 (fdr_bh: 0.622), d = -0.14\n",
+ "condition / Cluster/FT vs Cluster/T: T(27) = -1.10, p = 0.283 (fdr_bh: 0.424), d = -0.21\n",
+ "condition / Cluster/FT vs Continuous/F: T(27) = 1.40, p = 0.173 (fdr_bh: 0.296), d = 0.32\n",
+ "condition / Cluster/FT vs Continuous/FT: T(27) = -0.84, p = 0.406 (fdr_bh: 0.542), d = -0.17\n",
+ "condition / Cluster/FT vs Continuous/R: T(27) = 1.36, p = 0.185 (fdr_bh: 0.302), d = 0.32\n",
+ "condition / Cluster/FT vs Continuous/T: T(27) = -2.30, p = 0.029 (fdr_bh: 0.070), d = -0.43\n",
+ "condition / Cluster/R vs Cluster/T: T(27) = -0.32, p = 0.753 (fdr_bh: 0.904), d = -0.07\n",
+ "condition / Cluster/R vs Continuous/F: T(27) = 1.68, p = 0.104 (fdr_bh: 0.207), d = 0.47\n",
+ "condition / Cluster/R vs Continuous/FT: T(27) = -0.10, p = 0.925 (fdr_bh: 0.967), d = -0.02\n",
+ "condition / Cluster/R vs Continuous/R: T(27) = 2.00, p = 0.056 (fdr_bh: 0.125), d = 0.48\n",
+ "condition / Cluster/R vs Continuous/T: T(27) = -1.28, p = 0.211 (fdr_bh: 0.331), d = -0.31\n",
+ "condition / Cluster/T vs Continuous/F: T(27) = 2.71, p = 0.011 (fdr_bh: 0.038), d = 0.52 *\n",
+ "condition / Cluster/T vs Continuous/FT: T(27) = 0.21, p = 0.836 (fdr_bh: 0.941), d = 0.05\n",
+ "condition / Cluster/T vs Continuous/R: T(27) = 2.42, p = 0.023 (fdr_bh: 0.058), d = 0.54\n",
+ "condition / Cluster/T vs Continuous/T: T(27) = -1.01, p = 0.321 (fdr_bh: 0.445), d = -0.24\n",
+ "condition / Continuous/F vs Continuous/FT: T(27) = -2.49, p = 0.019 (fdr_bh: 0.054), d = -0.50\n",
+ "condition / Continuous/F vs Continuous/R: T(27) = -0.15, p = 0.885 (fdr_bh: 0.965), d = -0.02\n",
+ "condition / Continuous/F vs Continuous/T: T(27) = -3.07, p = 0.005 (fdr_bh: 0.019), d = -0.73 *\n",
+ "condition / Continuous/FT vs Continuous/R: T(27) = 2.97, p = 0.006 (fdr_bh: 0.022), d = 0.51 *\n",
+ "condition / Continuous/FT vs Continuous/T: T(27) = -1.54, p = 0.134 (fdr_bh: 0.242), d = -0.29\n",
+ "condition / Continuous/R vs Continuous/T: T(27) = -3.22, p = 0.003 (fdr_bh: 0.017), d = -0.75 *\n",
"\n"
]
}
@@ -864,22 +1146,78 @@
" within='condition',\n",
" subject='participant',\n",
" alternative='two-sided',\n",
+ " effsize='cohen',\n",
" padjust=StatsParams.mult_comp)\n",
"\n",
+ "\n",
"# Display post-hoc results\n",
"StatsFormatter().print_paired_ttest_posthocs(pairwise_results)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:43.278891Z",
- "start_time": "2023-08-15T09:28:43.228735Z"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/8z/_3_51qzx16ndh4cmqt4fd0h40000gp/T/ipykernel_33320/332840592.py:16: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " table[\"p-value (corr)\"] = table[\"p-value (corr)\"].apply(lambda x: \"<0.001 *\" if x < 0.001 else f\"{x:.3f}\")\n",
+ "/var/folders/8z/_3_51qzx16ndh4cmqt4fd0h40000gp/T/ipykernel_33320/332840592.py:17: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " table[\"p-value (corr)\"] = table[\"p-value (corr)\"].apply(lambda x: f\"{x} *\" if x != \"<0.001 *\" and float(x) < 0.05 else x)\n",
+ "/var/folders/8z/_3_51qzx16ndh4cmqt4fd0h40000gp/T/ipykernel_33320/332840592.py:20: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " table[['t-value', \"Cohen's d\"]] = table[['t-value', \"Cohen's d\"]].round(2)\n"
+ ]
}
- }
+ ],
+ "source": [
+ "import re\n",
+ "\n",
+ "# Replace substrings in columns A and B\n",
+ "pairwise_results[['A', 'B']] = pairwise_results[['A', 'B']].replace(\n",
+ " {'Cluster/': 'Fast ', 'Continuous/': 'Slow ', 'Bayesian': 'Randomized'}, regex=True\n",
+ ")\n",
+ "\n",
+ "# Create the 'comparison' column\n",
+ "pairwise_results['comparison'] = pairwise_results['A'] + ' vs ' + pairwise_results['B']\n",
+ "\n",
+ "# Select and rename required columns\n",
+ "table = pairwise_results[['comparison', 'T', 'p-corr', 'cohen']]\n",
+ "table.columns = ['Comparison', 't-value', 'p-value (corr)', \"Cohen's d\"]\n",
+ "\n",
+ "# Format p-values\n",
+ "table[\"p-value (corr)\"] = table[\"p-value (corr)\"].apply(lambda x: \"<0.001 *\" if x < 0.001 else f\"{x:.3f}\")\n",
+ "table[\"p-value (corr)\"] = table[\"p-value (corr)\"].apply(lambda x: f\"{x} *\" if x != \"<0.001 *\" and float(x) < 0.05 else x)\n",
+ "\n",
+ "# Round t-values and Cohen's d\n",
+ "table[['t-value', \"Cohen's d\"]] = table[['t-value', \"Cohen's d\"]].round(2)\n",
+ "table.to_csv('../../data/dataframes/false_alarm_rates_posthocs.csv')"
+ ]
},
{
"cell_type": "code",
"execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:43.278939Z",
+ "start_time": "2023-08-15T09:28:43.228771Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -900,18 +1238,18 @@
" within=['paradigm', 'pred'], subject='participant')\n",
"\n",
"StatsFormatter().print_2way_anova(StatsFormatter, aov_results, var='false_alarm_rate', factors=['paradigm', 'pred'])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:43.278939Z",
- "start_time": "2023-08-15T09:28:43.228771Z"
- }
- }
+ ]
},
{
"cell_type": "code",
"execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:28:43.278991Z",
+ "start_time": "2023-08-15T09:28:43.228800Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -952,18 +1290,18 @@
"\n",
"# Display post-hoc results\n",
"StatsFormatter().print_paired_ttest_posthocs(pairwise_results)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:28:43.278991Z",
- "start_time": "2023-08-15T09:28:43.228800Z"
- }
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-08-15T09:52:33.148951Z",
+ "start_time": "2023-08-15T09:52:33.128489Z"
+ },
+ "collapsed": false
+ },
"outputs": [
{
"name": "stdout",
@@ -981,14 +1319,7 @@
" within='pred', subject='participant')\n",
"\n",
"StatsFormatter().print_1way_anova(StatsFormatter, aov_results, var='false_alarm_rate', factor='pred')"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2023-08-15T09:52:33.148951Z",
- "start_time": "2023-08-15T09:52:33.128489Z"
- }
- }
+ ]
}
],
"metadata": {
@@ -1000,14 +1331,14 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.10.15"
}
},
"nbformat": 4,
diff --git a/scripts/analysis/funcs/linear_models.py b/scripts/analysis/funcs/linear_models.py
index 8f07168a0eb8eac597fca76c11418d928b9692d4..1f9609ec09f88aaf5f0a946f72fb1fa659083305 100644
--- a/scripts/analysis/funcs/linear_models.py
+++ b/scripts/analysis/funcs/linear_models.py
@@ -43,7 +43,7 @@ class RSquared:
results = model.fit()
return results.rsquared, results.pvalues[1]
- def compute_r_squared(self, paradigm, data, xvar, yvar):
+ def compute_r_squared(self, paradigm, data, xvar, yvar, exp):
"""
This function calculates the R-squared values for a set of prediction conditions.
It fits a model for each prediction condition within a specific paradigm, and returns the R-squared value for each model.
@@ -59,14 +59,20 @@ class RSquared:
R-squared value and the second being the p-value of the fitted model.
"""
- if len(data.pred.unique()) == 4:
- preds = ['none', 'time', 'frequency', 'both']
- else:
- preds = ['time', 'frequency', 'both']
-
- R_values = {pred: self.fit_model(data.loc[(data.paradigm == paradigm) & (data.pred == pred)][xvar].to_numpy(),
- data.loc[(data.paradigm == paradigm) & (data.pred == pred)][yvar].to_numpy())
- for pred in preds}
+ if exp == 1:
+ R_values = self.fit_model(data[xvar].to_numpy(), data[yvar].to_numpy())
+
+
+ if exp == 2:
+ if len(data.pred.unique()) == 4:
+ preds = ['none', 'time', 'frequency', 'both']
+ else:
+ preds = ['time', 'frequency', 'both']
+
+ R_values = {pred: self.fit_model(data.loc[(data.paradigm == paradigm) & (data.pred == pred)][xvar].to_numpy(),
+ data.loc[(data.paradigm == paradigm) & (data.pred == pred)][yvar].to_numpy())
+ for pred in preds}
+
return R_values
def compute_and_report_r_squared(self, data, xvar, yvar):
diff --git a/scripts/analysis/funcs/p50_analysis.py b/scripts/analysis/funcs/p50_analysis.py
index eef15ba32147a69e317672a963ef17783b0c2a5a..d3a52c205de0d96f3c6a668fc47a08c8041e30af 100644
--- a/scripts/analysis/funcs/p50_analysis.py
+++ b/scripts/analysis/funcs/p50_analysis.py
@@ -1,3 +1,10 @@
+import sys
+import os
+
+# Dynamically set the project root directory
+project_root = os.path.abspath(os.path.join(os.getcwd(), "..", ".."))
+sys.path.insert(0, project_root)
+
import pingouin as pg
from scripts.stats import StatsParams, StatsFormatter
@@ -51,6 +58,7 @@ class P50DataAnalyzer:
within=factor,
subject='participant',
alternative='two-sided',
+ effsize='cohen',
padjust=StatsParams.mult_comp)
'''
if factor == 'pred':
diff --git a/scripts/analysis/funcs/plots.py b/scripts/analysis/funcs/plots.py
index 9c4d0f19fcdfdf4819d1ce3f386d60da317bae07..e6d4d1becee54a3897b280ead2bc03096915909f 100644
--- a/scripts/analysis/funcs/plots.py
+++ b/scripts/analysis/funcs/plots.py
@@ -22,7 +22,9 @@ class Fig3Thresholds:
@staticmethod
def plot_mean_audiogram(freqs, thresholds, error, color, label):
- # print(thresholds, error)
+
+ thresholds = thresholds.to_numpy()
+
plt.fill_between(freqs,
thresholds - error,
thresholds + error,
@@ -89,10 +91,10 @@ class Fig3Thresholds:
participant_jitter = np.random.uniform(-0.1, 0.1)
x = np.array([2, 1]) + participant_jitter
- plt.scatter(x, participant_data.groupby('paradigm').thresholds.mean(),
+ plt.scatter(x, participant_data.groupby('paradigm').thresholds.mean().to_numpy(),
s=8, edgecolors='black', facecolors='none', alpha=.2)
- plt.plot(x, participant_data.groupby('paradigm').thresholds.mean(),
+ plt.plot(x, participant_data.groupby('paradigm').thresholds.mean().to_numpy(),
'black', alpha=.2)
# ax = plt.gca()
@@ -513,7 +515,7 @@ class P50GlobalFigure:
color=color, alpha=.5, edgecolor=None,
zorder=zorders[0])
- plt.plot(data.random_distance, data['mean'], color,
+ plt.plot(data.random_distance.to_numpy(), data['mean'].to_numpy(), color,
label=translate_conditions(pred) if paradigm in ['Cluster', 'Continuous'] else None,
lw=2, zorder=zorders[1])
diff --git a/scripts/analysis/funcs/threshold_analysis.py b/scripts/analysis/funcs/threshold_analysis.py
index 38e34a62bf27991627409ec57c1ae08cae36fd5d..3af03f0323966ba4028c08fd48109f25733e6fb7 100644
--- a/scripts/analysis/funcs/threshold_analysis.py
+++ b/scripts/analysis/funcs/threshold_analysis.py
@@ -1,3 +1,17 @@
+import sys
+import os
+
+# Dynamically set the project root directory
+project_root = os.path.abspath(os.path.join(os.getcwd(), "..", ".."))
+sys.path.insert(0, project_root)
+
+import numpy as np
+from scipy.stats import t
+
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
+
from scripts.stats import StatsParams
from scripts.utils import *
@@ -83,19 +97,138 @@ def downsample_to_3AFC(data_3AFC, data_continuous, frequencies_3AFC, frequencies
def compute_db_gain(df, variable):
+ """_summary_
+
+ Args:
+ df (pandas.Dataframe): _description_
+ variable (str): 'threshold' or 'pta'
+
+ """
precision = StatsParams.pval_precision
+ prefix = '' if variable == 'thresholds' else 'pta_'
# Group by participant and paradigm, then calculate the mean threshold
avg_thresholds = df.groupby(['participant', 'paradigm'])[variable].mean().reset_index()
# Pivot table to have paradigms as columns
- pivoted_df = avg_thresholds.pivot(index='participant', columns='paradigm', values='thresholds')
+ pivoted_df = avg_thresholds.pivot(index='participant', columns='paradigm',
+ values=variable)
# Compute the difference
- pivoted_df['diff'] = pivoted_df['Bayesian'] - pivoted_df['3AFC']
+ pivoted_df[prefix + 'gain'] = pivoted_df[prefix + 'Bayesian'] - pivoted_df[prefix + '3AFC']
- print("Mean:", round(pivoted_df['diff'].mean(), precision),
- "dB | SD:", round(pivoted_df['diff'].std(), precision),
- "dB | SEM:", round(pivoted_df['diff'].sem(), precision),
+ print("Mean:", round(pivoted_df[prefix + 'gain'].mean(), precision),
+ "dB | SD:", round(pivoted_df[prefix + 'gain'].std(), precision),
+ "dB | SEM:", round(pivoted_df[prefix + 'gain'].sem(), precision),
"dB")
+
+ return pivoted_df
+
+
+class GainAnalysis:
+ def __init__(self, gain_df, paradigms_palette, save_path):
+ """
+ Initialize the GainAnalysis class.
+
+ Parameters:
+ - gain_df: DataFrame containing the data with columns ['gain', 'age', 'gmsi'].
+ - paradigms_palette: Function to get color for a paradigm.
+ - save_path: Path to save generated figures.
+ """
+ self.gain_df = gain_df
+ self.paradigms_palette = paradigms_palette
+ self.save_path = save_path
+
+ def plot_histogram(self):
+ """Plot a histogram of gain."""
+ plt.figure(figsize=(4, 4))
+ self.gain_df.hist(column='gain', color='k', bins=10, ax=plt.gca())
+ plt.title('Histogram of gain in experiment 1')
+ plt.xlabel('dB')
+ plt.tight_layout()
+ plt.show()
+
+ def compute_r2(self, x_var, y_var='gain', exp=1):
+ """
+ Compute R² and p-value for a variable.
+
+ Parameters:
+ - x_var: Independent variable (e.g., 'age', 'gmsi').
+ - y_var: Dependent variable (default is 'gain').
+ - exp: Experiment number (default is 1).
+
+ Returns:
+ - r2_value: R-squared value.
+ - p_value: P-value of the regression.
+ - sorted_df: DataFrame sorted by x_var.
+ """
+
+ from funcs.linear_models import RSquared
+
+ # Sort values by x_var for proper plotting
+ sorted_df = self.gain_df.sort_values(by=x_var).reset_index(drop=True)
+
+ # Compute R² and p-value
+ r2_value, p_value = RSquared().compute_r_squared(None, sorted_df, x_var, y_var, exp=exp)
+ print(f"Gain * {x_var}: R2: {np.round(r2_value, 3):<5}, p {'< 0.001' if p_value < 0.001 else '= ' + str(np.round(p_value, 3)):<9} {'*' if p_value < 0.05 else ''}")
+
+ return r2_value, p_value, sorted_df
+
+ def plot_correlation(self, x_var, y_var, sorted_df, r2_value, p_value):
+ """
+ Plot scatter, best-fit line, confidence interval, and R² inset.
+
+ Parameters:
+ - x_var: Independent variable (e.g., 'age', 'gmsi').
+ - y_var: Dependent variable.
+ - sorted_df: DataFrame sorted by x_var.
+ - r2_value: R-squared value.
+ - p_value: P-value of the regression.
+ """
+ x = sorted_df[x_var].to_numpy()
+ y = sorted_df[y_var].to_numpy()
+ a, b = np.polyfit(x, y, 1) # Slope and intercept
+ y_pred = a * x + b
+
+ # Compute residuals and confidence interval
+ residuals = y - y_pred
+ std_err = np.std(residuals, ddof=2)
+ confidence = 0.95
+ t_value = t.ppf((1 + confidence) / 2, len(x) - 2)
+ ci = t_value * std_err * np.sqrt(1 / len(x) + (x - np.mean(x)) ** 2 / np.sum((x - np.mean(x)) ** 2))
+
+ # Plot data, best-fit line, and confidence interval
+ plt.figure(figsize=(5, 5))
+ plt.scatter(x, y, s=30, facecolors=self.paradigms_palette('3AFC'), edgecolors='None', alpha=.65)
+ plt.plot(x, y_pred, lw=1.5, color=self.paradigms_palette('3AFC'), zorder=5)
+ plt.fill_between(x, y_pred - ci, y_pred + ci, facecolor=self.paradigms_palette('3AFC'), alpha=0.2)
+
+ # Add inset for R²
+ insetax = plt.gca().inset_axes([0.7, 0.725, .25, .25])
+ insetax.bar(1, r2_value, color=self.paradigms_palette('3AFC'))
+ if p_value <= 0.05:
+ insetax.scatter(1, r2_value + 0.15, marker="$*$", c='k', s=100)
+ insetax.set_yticks([0, .5, 1], [0, 0.5, 1])
+ insetax.set_xlim((0, 2))
+ insetax.set_xticks([0, 1], [None, None])
+ insetax.set_title("R$^2$")
+ insetax.patch.set_alpha(.2)
+
+ # Add labels and title
+ plt.title(f'Gain in experiment 1 wrt {"GMSI score" if x_var == "gmsi" else x_var}')
+ plt.xlabel("GMSI score" if x_var == "gmsi" else 'Age')
+ plt.ylabel('Gain (in dB)')
+ plt.tight_layout()
+
+ # Save the figure
+ filename = f'Fig5-gain_correlations-3AFC_vs_Bayesian-{x_var}.svg'
+ plt.savefig(os.path.join(self.save_path, filename))
+ plt.show()
+
+# Usage Example
+# analysis = GainAnalysis(gain_df, paradigms_palette, '/path/to/save/figures')
+# analysis.plot_histogram()
+# for x_var in ['gmsi', 'age']:
+# r2_value, p_value, sorted_df = analysis.compute_r2(x_var)
+# analysis.plot_correlation(x_var, 'gain', sorted_df, r2_value, p_value)
diff --git a/scripts/stats.py b/scripts/stats.py
index 6c8c6abb1f0b10ba838200ba586716b5746d4e21..a15932ef88ce533d032b155cf842ac9b538133ea 100644
--- a/scripts/stats.py
+++ b/scripts/stats.py
@@ -65,11 +65,17 @@ class StatsFormatter:
@staticmethod
def print_paired_ttest(self, pairwise_results: pd.DataFrame, var: str):
+
print(f"{var} comparison (paired T-test):")
for _, row in pairwise_results.iterrows():
+
+ # Extract Cohen's d effect size
+ effect_size = f", d = {row['cohen']:.2f}" if 'cohen' in row else ""
+
significance = ' *' if row['p-unc'] < StatsParams.alpha else ''
+
print(
- f"{row['A']} vs {row['B']}: T({int(row['dof'])}) = {row['T']:.2f}, p = {self.fix0(row['p-unc'])}{significance}")
+ f"{row['A']} vs {row['B']}: T({int(row['dof'])}) = {row['T']:.2f}, p = {self.fix0(row['p-unc'])}{effect_size}{significance}")
print("")
@@ -77,8 +83,14 @@ class StatsFormatter:
print("Post-hoc tests (paired t-tests):")
for _, row in pairwise_results.iterrows():
+ # Determine significance
significance = ' *' if row['p-corr'] < StatsParams.alpha else ""
+
+ # Extract Cohen's d effect size
+ effect_size = f", d = {row['cohen']:.2f}" if 'cohen' in row else ""
+
+ # Print results with effect size included
print(
- f"{row['Contrast']} / {row['A']} vs {row['B']}: T({int(row['dof'])}) = {row['T']:.2f}, p = {self.fix0(row['p-unc'])} ({StatsParams.mult_comp}: {self.fix0(row['p-corr'])}){significance}")
-
+ f"{row['Contrast']} / {row['A']} vs {row['B']}: T({int(row['dof'])}) = {row['T']:.2f}, p = {self.fix0(row['p-unc'])} ({StatsParams.mult_comp}: {self.fix0(row['p-corr'])}){effect_size}{significance}"
+ )
print("")