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-  <meta name="keywords" content="mutagenicity, (Q)SAR, lazar, random forest, support vector machine, deep learning" />
-  <title>A comparison of random forest, support vector machine, deep learning and lazar algorithms for predicting mutagenicity</title>
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format('truetype');
-}
-@font-face {
-font-family: 'Arvo';
-font-style: normal;
-font-weight: 400;
-src: local('Arvo'), 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format('truetype');
-}
-@font-face {
-font-family: 'Arvo';
-font-style: normal;
-font-weight: 700;
-src: local('Arvo Bold'), local('Arvo-Bold'), 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-    <!-- <header class="article-header"> -->
-<h1 property="headline">A comparison of random forest, support vector machine, deep learning and lazar algorithms for predicting mutagenicity</h1>
-<!-- </header> -->
-<p class="author-list">
-  <span property="author" typeof="Person">
-    Christoph Helma</span><sup><a href="mailto:helma@in-silico.ch">✉</a> 1</sup>,
-  <span property="author" typeof="Person">
-    Verena Schöning</span><sup>2</sup>,
-  <span property="author" typeof="Person">
-    Philipp Boss</span><sup>2</sup>,
-  <span property="author" typeof="Person">
-    Jürgen Drewe</span><sup>2</sup></p>
-<div class="author_affiliations">
-  <div class="affiliation"><sup>1</sup>in silico toxicology gmbh, Rastatterstrasse 41, 4057 Basel, Switzerland
-  </div>
-  <div class="affiliation"><sup>2</sup>Zeller AG, Seeblickstrasse 4, 8590 Romanshorn, Switzerland
-  </div>
-</div>
-<div class="author-info">
-            <div class="author-correspondence">
-        Correspondence: Christoph Helma <a href="mailto:helma@in-silico.ch">&lt;helma@in-silico.ch&gt;</a> 
-    </div>
-    </div>
-<p class="abstract" property="description"><p>k-nearest neighbor (<code>lazar</code>), random forest, support vector machine and deep learning algorithms were applied to a new <em>Salmonella</em> mutagenicity dataset with 8281 unique chemical structures. Algorithm performance was evaluated using 5-fold crossvalidation. TODO - results - conclusion</p></p>
-
-
-
-<div property="articleBody" class="article-body">
-<h1 id="introduction">Introduction</h1>
-<p>TODO: algo history</p>
-<p>TODO: dataset history</p>
-<p>TODO: open problems</p>
-<h1 id="materials-and-methods">Materials and Methods</h1>
-<h2 id="mutagenicity-data">Mutagenicity data</h2>
-<p>For all methods, the same training dataset was used. The training dataset was compiled from the following sources:</p>
-<ul>
-<li><p>Kazius/Bursi Dataset (4337 compounds, <span class="citation" data-cites="Kazius2005">Kazius, McGuire, and Bursi (2005)</span>): <a href="http://cheminformatics.org/datasets/bursi/cas_4337.zip" class="uri">http://cheminformatics.org/datasets/bursi/cas_4337.zip</a></p></li>
-<li><p>Hansen Dataset (6513 compounds, <span class="citation" data-cites="Hansen2009">Hansen et al. (2009)</span>): <a href="http://doc.ml.tu-berlin.de/toxbenchmark/Mutagenicity_N6512.csv" class="uri">http://doc.ml.tu-berlin.de/toxbenchmark/Mutagenicity_N6512.csv</a></p></li>
-<li><p>EFSA Dataset (695 compounds): <a href="https://data.europa.eu/euodp/data/storage/f/2017-0719T142131/GENOTOX%20data%20and%20dictionary.xls" class="uri">https://data.europa.eu/euodp/data/storage/f/2017-0719T142131/GENOTOX%20data%20and%20dictionary.xls</a></p></li>
-</ul>
-<p>Mutagenicity classifications from Kazius and Hansen datasets were used without further processing. To achieve consistency between these datasets, EFSA compounds were classified as mutagenic, if at least one positive result was found for TA98 or T100 Salmonella strains.</p>
-<p>Dataset merges were based on unique SMILES (<em>Simplified Molecular Input Line Entry Specification</em>) strings of the compound structures. Duplicated experimental data with the same outcome was merged into a single value, because it is likely that it originated from the same experiment. Contradictory results were kept as multiple measurements in the database. The combined training dataset contains 8281 unique structures.</p>
-<p>Source code for all data download, extraction and merge operations is publicly available from the git repository <a href="https://git.in-silico.ch/pyrrolizidine" class="uri">https://git.in-silico.ch/pyrrolizidine</a> under a GPL3 License.</p>
-<p>TODO: check/fix git repo</p>
-<p>For the Random Forest (RF), Support Vector Machines (SVM), and Deep Learning (DL) models, molecular descriptors were calculated with the PaDEL-Descriptors program (<a href="http://www.yapcwsoft.com" class="uri">http://www.yapcwsoft.com</a> version 2.21, <span class="citation" data-cites="Yap2011">Yap (2011)</span>).</p>
-<p>TODO: sentence ??</p>
-<p>From these descriptors were chosen, which were actually used for the generation of the DL model.</p>
-<h2 id="algorithms">Algorithms</h2>
-<h3 id="lazar"><code>lazar</code></h3>
-<p><code>lazar</code> (<em>lazy structure activity relationships</em>) is a modular framework for read-across model development and validation. It follows the following basic workflow: For a given chemical structure <code>lazar</code>:</p>
-<ul>
-<li><p>searches in a database for similar structures (neighbours) with experimental data,</p></li>
-<li><p>builds a local QSAR model with these neighbours and</p></li>
-<li><p>uses this model to predict the unknown activity of the query compound.</p></li>
-</ul>
-<p>This procedure resembles an automated version of read across predictions in toxicology, in machine learning terms it would be classified as a k-nearest-neighbour algorithm.</p>
-<p>Apart from this basic workflow, <code>lazar</code> is completely modular and allows the researcher to use any algorithm for similarity searches and local QSAR (<em>Quantitative structure–activity relationship</em>) modelling. Algorithms used within this study are described in the following sections.</p>
-<h4 id="neighbour-identification">Neighbour identification</h4>
-<p>Similarity calculations were based on MolPrint2D fingerprints (<span class="citation" data-cites="Bender2004">Bender et al. (2004)</span>) from the OpenBabel cheminformatics library (<span class="citation" data-cites="OBoyle2011a">O’Boyle et al. (2011)</span>). The MolPrint2D fingerprint uses atom environments as molecular representation, which resembles basically the chemical concept of functional groups. For each atom in a molecule, it represents the chemical environment using the atom types of connected atoms.</p>
-<p>MolPrint2D fingerprints are generated dynamically from chemical structures and do not rely on predefined lists of fragments (such as OpenBabel FP3, FP4 or MACCs fingerprints or lists of toxicophores/toxicophobes). This has the advantage that they may capture substructures of toxicological relevance that are not included in other fingerprints.</p>
-<p>From MolPrint2D fingerprints a feature vector with all atom environments of a compound can be constructed that can be used to calculate chemical similarities.</p>
-<p>The chemical similarity between two compounds a and b is expressed as the proportion between atom environments common in both structures A ∩ B and the total number of atom environments A U B (Jaccard/Tanimoto index).</p>
-<p><span class="math display">\[sim = \frac{\left| A\  \cap B \right|}{\left| A\  \cup B \right|}\]</span></p>
-<p>Threshold selection is a trade-off between prediction accuracy (high threshold) and the number of predictable compounds (low threshold). As it is in many practical cases desirable to make predictions even in the absence of closely related neighbours, we follow a tiered approach:</p>
-<ul>
-<li><p>First a similarity threshold of 0.5 is used to collect neighbours, to create a local QSAR model and to make a prediction for the query compound.</p></li>
-<li><p>If any of these steps fails, the procedure is repeated with a similarity threshold of 0.2 and the prediction is flagged with a warning that it might be out of the applicability domain of the training data.</p></li>
-<li><p>Similarity thresholds of 0.5 and 0.2 are the default values chosen &gt; by the software developers and remained unchanged during the &gt; course of these experiments.</p></li>
-</ul>
-<p>Compounds with the same structure as the query structure are automatically eliminated from neighbours to obtain unbiased predictions in the presence of duplicates.</p>
-<h4 id="local-qsar-models-and-predictions">Local QSAR models and predictions</h4>
-<p>Only similar compounds (neighbours) above the threshold are used for local QSAR models. In this investigation, we are using a weighted majority vote from the neighbour’s experimental data for mutagenicity classifications. Probabilities for both classes (mutagenic/non-mutagenic) are calculated according to the following formula and the class with the higher probability is used as prediction outcome.</p>
-<p><span class="math display">\[p_{c} = \ \frac{\sum_{}^{}\text{sim}_{n,c}}{\sum_{}^{}\text{sim}_{n}}\]</span></p>
-<p><span class="math inline">\(p_{c}\)</span> Probability of class c (e.g. mutagenic or non-mutagenic)<br />
-<span class="math inline">\(\sum_{}^{}\text{sim}_{n,c}\)</span> Sum of similarities of neighbours with class c<br />
-<span class="math inline">\(\sum_{}^{}\text{sim}_{n}\)</span> Sum of all neighbours</p>
-<h4 id="applicability-domain">Applicability domain</h4>
-<p>The applicability domain (AD) of <code>lazar</code> models is determined by the structural diversity of the training data. If no similar compounds are found in the training data no predictions will be generated. Warnings are issued if the similarity threshold had to be lowered from 0.5 to 0.2 in order to enable predictions. Predictions without warnings can be considered as close to the applicability domain and predictions with warnings as more distant from the applicability domain. Quantitative applicability domain information can be obtained from the similarities of individual neighbours.</p>
-<h4 id="availability">Availability</h4>
-<ul>
-<li><p><code>lazar</code> experiments for this manuscript: <a href="https://git.in-silico.ch/pyrrolizidine" class="uri">https://git.in-silico.ch/pyrrolizidine</a> (source code, GPL3)</p></li>
-<li><p><code>lazar</code> framework: <a href="https://git.in-silico.ch/lazar" class="uri">https://git.in-silico.ch/lazar</a> (source code, GPL3)</p></li>
-<li><p><code>lazar</code> GUI: <a href="https://git.in-silico.ch/lazar-gui" class="uri">https://git.in-silico.ch/lazar-gui</a> (source code, GPL3)</p></li>
-<li><p>Public web interface: <a href="https://lazar.in-silico.ch" class="uri">https://lazar.in-silico.ch</a></p></li>
-</ul>
-<h3 id="random-forest-support-vector-machines-and-deep-learning-in-r-project">Random Forest, Support Vector Machines, and Deep Learning in R-project</h3>
-<p>In comparison to <code>lazar</code>, three other models (Random Forest (RF), Support Vector Machines (SVM), and Deep Learning (DL)) were evaluated.</p>
-<p>For the generation of these models, molecular 1D and 2D descriptors of the training dataset were calculated using PaDEL-Descriptors (<a href="http://www.yapcwsoft.com" class="uri">http://www.yapcwsoft.com</a> version 2.21, <span class="citation" data-cites="Yap2011">Yap (2011)</span>).</p>
-<p>As the training dataset contained over 8280 instances, it was decided to delete instances with missing values during data pre-processing. Furthermore, substances with equivocal outcome were removed. The final training dataset contained 8080 instances with known mutagenic potential. The RF, SVM, and DL models were generated using the R software (R-project for Statistical Computing, <a href="https://www.r-project.org/" class="uri">https://www.r-project.org/</a><em>;</em> version 3.3.1), specific R packages used are identified for each step in the description below. During feature selection, descriptor with near zero variance were removed using ‘<em>NearZeroVar</em>’-function (package ‘caret’). If the percentage of the most common value was more than 90% or when the frequency ratio of the most common value to the second most common value was greater than 95:5 (e.g. 95 instances of the most common value and only 5 or less instances of the second most common value), a descriptor was classified as having a near zero variance. After that, highly correlated descriptors were removed using the ‘<em>findCorrelation</em>’-function (package ‘caret’) with a cut-off of 0.9. This resulted in a training dataset with 516 descriptors. These descriptors were scaled to be in the range between 0 and 1 using the ‘<em>preProcess</em>’-function (package ‘caret’). The scaling routine was saved in order to apply the same scaling on the testing dataset. As these three steps did not consider the outcome, it was decided that they do not need to be included in the cross-validation of the model. To further reduce the number of features, a LASSO (<em>least absolute shrinkage and selection operator</em>) regression was performed using the ‘<em>glmnet</em>’-function (package ‘<em>glmnet</em>’). The reduced dataset was used for the generation of the pre-trained models.</p>
-<p>For the RF model, the ‘<em>randomForest</em>’-function (package ‘<em>randomForest</em>’) was used. A forest with 1000 trees with maximal terminal nodes of 200 was grown for the prediction.</p>
-<p>The ‘<em>svm</em>’-function (package ‘e1071’) with a <em>radial basis function kernel</em> was used for the SVM model.</p>
-<p>The DL model was generated using the ‘<em>h2o.deeplearning</em>’-function (package ‘<em>h2o</em>’). The DL contained four hidden layer with 70, 50, 50, and 10 neurons, respectively. Other hyperparameter were set as follows: l1=1.0E-7, l2=1.0E-11, epsilon = 1.0E-10, rho = 0.8, and quantile_alpha = 0.5. For all other hyperparameter, the default values were used. Weights and biases were in a first step determined with an unsupervised DL model. These values were then used for the actual, supervised DL model.</p>
-<p>To validate these models, an internal cross-validation approach was chosen. The training dataset was randomly split in training data, which contained 95% of the data, and validation data, which contain 5% of the data. A feature selection with LASSO on the training data was performed, reducing the number of descriptors to approximately 100. This step was repeated five times. Based on each of the five different training data, the predictive models were trained and the performance tested with the validation data. This step was repeated 10 times. Furthermore, a y-randomisation using the RF model was performed. During y-randomisation, the outcome (y-variable) is randomly permuted. The theory is that after randomisation of the outcome, the model should not be able to correlate the outcome to the properties (descriptor values) of the substances. The performance of the model should therefore indicate a by change prediction with an accuracy of about 50%. If this is true, it can be concluded that correlation between actual outcome and properties of the substances is real and not by chance (<span class="citation" data-cites="Rücker2007">Rücker, Rücker, and Meringer (2007)</span>).</p>
-<p><img 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" style="width:6.26875in;height:5.48611in" /></p>
-<p>Figure 1: Flowchart of the generation and validation of the models generated in R-project</p>
-<h4 id="applicability-domain-1">Applicability domain</h4>
-<p>The AD of the training dataset and the PA dataset was evaluated using the Jaccard distance. A Jaccard distance of ‘0’ indicates that the substances are similar, whereas a value of ‘1’ shows that the substances are different. The Jaccard distance was below 0.2 for all PAs relative to the training dataset. Therefore, PA dataset is within the AD of the training dataset and the models can be used to predict the genotoxic potential of the PA dataset.</p>
-<h4 id="y-randomisation">y-randomisation</h4>
-<p>After y-randomisation of the outcome, the accuracy and CCR are around 50%, indicating a chance in the distribution of the results. This shows, that the outcome is actually related to the predictors and not by chance.</p>
-<h3 id="deep-learning-in-tensorflow">Deep Learning in TensorFlow</h3>
-<p>Alternatively, a DL model was established with Python-based TensorFlow program (<a href="https://www.tensorflow.org/" class="uri">https://www.tensorflow.org/</a>) using the high-level API Keras (<a href="https://www.tensorflow.org/guide/keras" class="uri">https://www.tensorflow.org/guide/keras</a>) to build the models.</p>
-<p>Data pre-processing was done by rank transformation using the ‘<em>QuantileTransformer</em>’ procedure. A sequential model has been used. Four layers have been used: input layer, two hidden layers (with 12, 8 and 8 nodes, respectively) and one output layer. For the output layer, a sigmoidal activation function and for all other layers the ReLU (‘<em>Rectified Linear Unit</em>’) activation function was used. Additionally, a L<sup>2</sup>-penalty of 0.001 was used for the input layer. For training of the model, the ADAM algorithm was used to minimise the cross-entropy loss using the default parameters of Keras. Training was performed for 100 epochs with a batch size of 64. The model was implemented with Python 3.6 and Keras. For training of the model, a 6-fold cross-validation was used. Accuracy was estimated by ROC-AUC and confusion matrix.</p>
-<h2 id="validation">Validation</h2>
-<h1 id="results">Results</h1>
-<h2 id="lazar-1"><code>lazar</code></h2>
-<h2 id="random-forest">Random Forest</h2>
-<p>The validation showed that the RF model has an accuracy of 64%, a sensitivity of 66% and a specificity of 63%. The confusion matrix of the model, calculated for 8080 instances, is provided in Table 1.</p>
-<p>Table 1: Confusion matrix of the RF model</p>
-<table>
-<thead>
-<tr class="header">
-<th></th>
-<th style="text-align: left;">Predicted genotoxicity</th>
-<th></th>
-<th></th>
-<th></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>Measured genotoxicity</td>
-<td style="text-align: left;"></td>
-<td><strong><em>PP</em></strong></td>
-<td><strong><em>PN</em></strong></td>
-<td><strong><em>Total</em></strong></td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>TP</em></strong></td>
-<td>2274</td>
-<td>1163</td>
-<td>3437</td>
-</tr>
-<tr class="odd">
-<td></td>
-<td style="text-align: left;"><strong><em>TN</em></strong></td>
-<td>1736</td>
-<td>2907</td>
-<td>4643</td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>Total</em></strong></td>
-<td>4010</td>
-<td>4070</td>
-<td>8080</td>
-</tr>
-</tbody>
-</table>
-<p>PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: True negative</p>
-<h2 id="support-vector-machines">Support Vector Machines</h2>
-<p>The validation showed that the SVM model has an accuracy of 62%, a sensitivity of 65% and a specificity of 60%. The confusion matrix of SVM model, calculated for 8080 instances, is provided in Table 2.</p>
-<p>Table 2: Confusion matrix of the SVM model</p>
-<table>
-<thead>
-<tr class="header">
-<th></th>
-<th style="text-align: left;">Predicted genotoxicity</th>
-<th></th>
-<th></th>
-<th></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>Measured genotoxicity</td>
-<td style="text-align: left;"></td>
-<td><strong><em>PP</em></strong></td>
-<td><strong><em>PN</em></strong></td>
-<td><strong><em>Total</em></strong></td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>TP</em></strong></td>
-<td>2057</td>
-<td>1107</td>
-<td>3164</td>
-</tr>
-<tr class="odd">
-<td></td>
-<td style="text-align: left;"><strong><em>TN</em></strong></td>
-<td>1953</td>
-<td>2963</td>
-<td>4916</td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>Total</em></strong></td>
-<td>4010</td>
-<td>4070</td>
-<td>8080</td>
-</tr>
-</tbody>
-</table>
-<p>PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: True negative</p>
-<h2 id="deep-learning-r-project">Deep Learning (R-project)</h2>
-<p>The validation showed that the DL model generated in R has an accuracy of 59%, a sensitivity of 89% and a specificity of 30%. The confusion matrix of the model, normalised to 8080 instances, is provided in Table 3.</p>
-<p>Table 3: Confusion matrix of the DL model (R-project)</p>
-<table>
-<thead>
-<tr class="header">
-<th></th>
-<th style="text-align: left;">Predicted genotoxicity</th>
-<th></th>
-<th></th>
-<th></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>Measured genotoxicity</td>
-<td style="text-align: left;"></td>
-<td><strong><em>PP</em></strong></td>
-<td><strong><em>PN</em></strong></td>
-<td><strong><em>Total</em></strong></td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>TP</em></strong></td>
-<td>3575</td>
-<td>435</td>
-<td>4010</td>
-</tr>
-<tr class="odd">
-<td></td>
-<td style="text-align: left;"><strong><em>TN</em></strong></td>
-<td>2853</td>
-<td>1217</td>
-<td>4070</td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>Total</em></strong></td>
-<td>6428</td>
-<td>1652</td>
-<td>8080</td>
-</tr>
-</tbody>
-</table>
-<p>PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: True negative</p>
-<h2 id="dl-model-tensorflow">DL model (TensorFlow)</h2>
-<p>The validation showed that the DL model generated in TensorFlow has an accuracy of 68%, a sensitivity of 70% and a specificity of 46%. The confusion matrix of the model, normalised to 8080 instances, is provided in Table 4.</p>
-<p>Table 4: Confusion matrix of the DL model (TensorFlow)</p>
-<table>
-<thead>
-<tr class="header">
-<th></th>
-<th style="text-align: left;">Predicted genotoxicity</th>
-<th></th>
-<th></th>
-<th></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>Measured genotoxicity</td>
-<td style="text-align: left;"></td>
-<td><strong><em>PP</em></strong></td>
-<td><strong><em>PN</em></strong></td>
-<td><strong><em>Total</em></strong></td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>TP</em></strong></td>
-<td>2851</td>
-<td>1227</td>
-<td>4078</td>
-</tr>
-<tr class="odd">
-<td></td>
-<td style="text-align: left;"><strong><em>TN</em></strong></td>
-<td>1825</td>
-<td>2177</td>
-<td>4002</td>
-</tr>
-<tr class="even">
-<td></td>
-<td style="text-align: left;"><strong><em>Total</em></strong></td>
-<td>4676</td>
-<td>3404</td>
-<td>8080</td>
-</tr>
-</tbody>
-</table>
-<p>PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: True negative</p>
-<p>The ROC curves from the 6-fold validation are shown in Figure 7.</p>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXmcHVWZv5+36u73dvftPd3p7DvZIWERCQECIsjmiPxQBhRcR2RQcURFRUFRB3EZGB1FZEAkgsiA2wgikWGLBAkhe2frvdPr7bvfW8v5/VE3zU2TpUPS6Sz1fNKf1Kk6dc576lbVt872HlFK4eLi4uLiAqCNtgEuLi4uLkcOrii4uLi4uAziioKLi4uLyyCuKLi4uLi4DOKKgouLi4vLIK4ouLi4uLgM4orCMYCIfFBEnhptO0YbERkvIkkR0Q9jnhNFRImI53DlOZKIyDoRWfo2zjtm70ERWSoiraNtx+HCFYVDjIjsEJFM4eXUKSL3i0hkJPNUSj2klDpvJPM4Eilc62W7wkqpZqVURClljaZdo0VBnKYeTBpKqdlKqRX7yectQni83oPHIq4ojAwXKaUiwAJgIfDFUbbnbTGaX7/Hypf3geBeb5cjAVcURhClVCfwZxxxAEBE/CJyp4g0i8hOEfmJiASLjl8iIqtFJC4iW0Xk/ML+MhH5uYh0iEibiNy+q5lERD4kIs8Xtn8iIncW2yEiT4jIZwvb9SLymIh0i8h2EbmhKN6tIvIbEfmliMSBDw0tU8GOBwrnN4nILSKiFdnxgoj8h4gMiMhGETlnyLn7KsMLIvJ9EekDbhWRKSLyVxHpFZEeEXlIRKKF+A8C44HfFWpl/zb0C1ZEVojIbYV0EyLylIhUFdlzdaEMvSLylaE1jyHlDorI9wrxB0Tk+eLfDfhg4TftEZEvF513soi8JCKxQrnvFhFf0XElIp8SkUagsbDvhyLSUrgHXhWRM4ri6yLypcK9kSgcHycizxWivF64HlcU4r+ncD/FRORFEZlXlNYOEfmCiKwBUiLiKb4GBdtXFezYKSJ3FU7dlVeskNdpxfdg4dzZIvK0iPQVzv3SXq7rXp+Hgm0vF/2enxSneStQCD8qTm18QESeE5HZReneLyL/KSJ/Ktj4goiMEZEfiEh/4d5cOORafFFE1heO/2JXPnuwea/P0DGBUsr9O4R/wA5gWWG7AXgD+GHR8R8ATwIVQAnwO+COwrGTgQHgXBzBHgvMLBz7H+C/gDBQA/wd+Hjh2IeA5wvbS4AWQArhciAD1BfSfBX4KuADJgPbgHcV4t4KGMClhbjBPZTvAeCJgu0Tgc3AdUV2mMBnAC9wRaE8FcMsgwl8GvAAQWBq4Vr4gWqcl9EP9nStC+GJgAI8hfAKYCswvZDeCuDbhWMnAEngnYVrcWeh7Mv28rveUzh/LKAD7yjYtSvPnxXymA/kgFmF804CTi2UaSKwAbixKF0FPI1zPwQL+64CKgvnfA7oBAKFY5/HuadmAFLIr7IoralFaZ8IdAGnFGy+pnDN/EXXbzUwrijvwWsKvAT8c2E7Apy6p+u8h3uwBOgo2B4ohE/Zy3Xd1/OgFX7zW4FpQD+wsOjcawvn+AvprC46dj/QU7j+AeCvwHbg6sK1uB14dsi9tLZwLSqAF4DbC8eWAq1FNu31GToW/kbdgGPtr3BzJYFE4cF5BogWjgmQAqYUxT8N2F7Y/i/g+3tIsxbnRRMs2nflrpt6yAMpQDOwpBD+KPDXwvYpQPOQtL8I/KKwfSvw3D7KphfsOKFo38eBFUV2tFMQpMK+vwP/PMwyNO8t70KcS4HXhlzr/YnCLUXH/wX438L2V4GHi46FgDx7EIXCiyADzN/DsV15Ngwp8//bSxluBB4vCivg7P2Uu39X3sAm4JK9xBsqCj8GbhsSZxNwZtH1u3YP9+8uUXgO+DpQtZcy700Uriz+nfZRrn0+D0V59eGI6Rf3kVa0YFNZIXw/8LOi458GNhSF5wKxIeX+RFH4AmBrYXspb4rCPp+hY+HPbUccGS5VSv1FRM4EfgVUATGcr90Q8KqI7IorOC9bcL5S/riH9CbgfHl3FJ2n4dQIdkMppURkOc6D+RzwAeCXRenUi0is6BQd+L+i8FvSLKIK5+uoqWhfE87X8y7aVOFJKTpeP8wy7Ja3iNQAPwLOwPki1HBekAdCZ9F2GueLl4JNg/kppdIi0ruXNKpwvja3Hmg+IjIduAtYhPPbe3C+NIsZWu7PAR8p2KiA0oIN4Nwj+7KjmAnANSLy6aJ9vkK6e8x7CNcB3wA2ish24OtKqd8PI9/h2ri/5wGl1A4ReRbnJX3PYCSn2fGbwOWFdOzCoSqc2inAzqK8MnsIDx0AUnwtdt23QxnOM3RU4/YpjCBKqb/hfLHsauPvwbkZZyulooW/MuV0SoNzU07ZQ1ItOF/ZVUXnlSqlZu8hLsDDwPtEZALOl81jRelsL0ojqpQqUUpdUGz2PorUg9PEMqFo33igrSg8Voqe8MLx9mGWYWjedxT2zVNKleI0q8g+4h8IHTjNe4DTZ4DTZLMneoAse/5t9sePgY3AtEIZvsTuZYCichT6D74AvB8oV0pFcV5yu87Z2z2yJ1qAbw75vUNKqYf3lPdQlFKNSqkrcZr6vgP8RkTC+zrnAG3c3/OAiFyAU3t4Bvj3onM/AFwCLAPKcGoU8NZreyCMK9redd8OZTjP0FGNKwojzw+Ac0VkgVLKxml7/n7hKxgRGSsi7yrE/TnwYRE5R0S0wrGZSqkO4CngeyJSWjg2pVATeQtKqdeAbuBe4M9KqV1fNX8H4oUOvGCh03KOiCweTkGUM9TzEeCbIlJSEJ3P8mZNBJwXyA0i4hWRy4FZwB8PtAwFSnCa4mIiMhanPb2YnThtum+H3wAXicg7xOn4/Tp7eaEUfrf7gLsKnYx6oXPVP4x8SoA4kBSRmcAnhxHfxPn9PCLyVZyawi7uBW4TkWniME9EdonZ0OvxM+ATInJKIW5YRC4UkZJh2I2IXCUi1YXy77qHrIJtNnu/9r8HxojIjYWO5BIROWVopP09D+IMCvg5Tq3pGpzfa9fLtwTnI6MXp7bxreGUaT98SkQaRKQCR7x/vYc4B/UMHQ24ojDCKKW6cTpnv1LY9QVgC/CyOCN8/oLTaYhS6u/Ah4Hv43wd/o03v8qvxqn6r8dpQvkNULePrB/G+Yr6VZEtFnARzmio7ThfavfifGkNl0/jtANvA54vpH9f0fGVOJ2CPTjV+/cppXY1yxxoGb6O01k6APwB+O2Q43cAt4gzsuamAygDSql1hbIsx6k1JHA6ZXN7OeUmnA7eV3DauL/D8J6fm3C+ahM4L8A9vWiK+TPwJ5wO/CacGkpxs8ZdOML8FI7Y/ByngxucPqH/LlyP9yulVuH0Kd2Nc723sIcRZfvgfGCdiCSBH+L0k2SVUmmc3/aFQl6nFp+klErgDBC4CKdZrRE4ay957PV5AH4KPKGU+mPhHroOuLcggg8Urk8bzv308gGUa2/8Cue6biv83T40wiF6ho5odo1QcXE5aETkQ8BHlFLvHG1bDhRxJhjGcJp5to+2PS6HFxHZgXPv/mW0bRlt3JqCy3GLiFwkIqFCO/mdODWBHaNrlYvL6OKKgsvxzCU4nYntOE1e/0+5VWeX4xy3+cjFxcXFZRC3puDi4uLiMshRN3mtqqpKTZw4cbTNcHFxcTmqePXVV3uUUtX7i3fUicLEiRNZtWrVaJvh4uLiclQhIk37j+U2H7m4uLi4FOGKgouLi4vLIK4ouLi4uLgM4oqCi4uLi8sgrii4uLi4uAwyYqIgIveJSJeIrN3LcRGRH4nIFhFZIyInjpQtLi4uLi7DYyRrCvfjeFncG+/GcS0wDfgYjt95FxcXF5dRZMTmKSilnhORifuIcgnwQMHXzMsiEhWRuoLffRcXF5fjkt6eHK3NeTo6oKld0dOt0bN+J20dNksuHcf1nw6MaP6jOXltLLv7iW8t7HuLKIjIx3BqE4wfP/6wGOfi4uJyMFi2wh7iWy6ds0hmsmTiMSxL0bg9x6aNwo6tOi2bDVrbIvT0CaCcf7YimEoRSGewdJ2SsXGclWFHjtEUhT2tcrVH73xKqZ/iLLjBokWLXA9+Li4uRyT5TIaunV20D2TpjucQEfp7hUxaI53S6elWdKzqo62jlNbOclLZMkQEXZlY6CAmQa9BbYVFdVmOykqDqd1rmbT1JSrPm8/kL18z4mUYTVFoZfc1URvY85qoLi4uLqNGNpXEMoy37FdK0Z3ME0sbpPMm/V1dxLsH2LbJT+v2KB3bA+xoLSNneFDOhz+iFIpK8PvRdEW0NkNVfTe1tW3UTBeWnTaJkyb5iA6k0WfPRtM0yM2G7SfDzJmHpbyjKQpPAteLyHKcxeUH3P4EFxeXw4ltWdiWVdg2iXd3kc9mUbZNNpUYjOcNldCfcYRBKUjlDBJZg/adQk9rCS07QjRuGEtz8wyUYQMa4tGwdSFckSEUyuP1J4iUxKmr76VhXBuzGpKU1WmUVkxgUtU0SqumEHh5JXzsDtA0eOQRiETA7z9sggAjKAoi8jCwFKgSkVbga4AXQCn1E+CPwAU467OmcdYmdnFxcRlRcukUyb5eMvE4uUwKTdMRXcc2TXSvFzNQQkb3oaJRNJ+Pjv40AwNZLFPI9fpYv8bHtkY/2zcFGOjT0ZQNhgFKIeSYOj7G5Nl9VE3ZxsSJ7Uwrh6gngjdYSkV0PL7IBPTgXLxldeiewiu4rw++/g146iknPHcuJBKOKBxmRnL00ZX7Oa6AT41U/i4uLi4Afe1txLs6gUITDqD7fISj5VRNmEggHEEpxebOOO3xNN19KTx2nIgXRARMiK0v4enlFuu2liIolFJYRpZSX5ZJ9c2MaWihfn6KhpkxyurGgUdoKJtAQ9k7KC+txuPfy8tdKfjTn+DOOyEeh0AAPvUpuOIKp7YwChx1rrNdXFxchkMmmaB90wZAUTNxCqGyMgBE07FtG9M0sSyLjp3dNPfE2dyZoKHMR0nWz+r/K6Vlm8nOTqF1Z4hErw1Kxx82WXxCN1Mmpagd34JnbgwCfmZUz2JS9WQCvgABf8ARk+Fwxx3w29862yefDLfcAvX1I3NBhokrCi4uLkc1mWQCZduD4d6WJvLZDAC+QJD6GbNANLLZLL0DCbZ09GNZFvGcTd60MRUEzAxWo8Gjz9fw0j9KsXMGCi+i6yjRqa9JcvH7LRbOT2CUx2izmwkGg0yrPpUJ5ROI7K0msD+WLoWnn4bPfAYuugiGKyYjiCsKLi4uRxW5dIpYZwfJ/t7BfYFIqbOhFKJp1E+fhS8YwrJt+vpjxGO9DLRuYCBj4vPolBopMu1ldLRUsGZTJW9sqieT82EbNrrYnHxihoXv8FFTZ1JZlqckZJAx0uxItxCp01k4ZiHTKqaha/qBGd/cDK+8Av/0T074He+A3/1uVPoO9oYrCi4uLkcN8Z5uupu24fH5qZ4wiUh5JSKCFLW/m6ZJOp2mt2kL7W3NeFMdbN4WYeP2KK39U2ltLaWjw4/Cg7JMyOcBGF+d4MRpPSw9XygbEyRa78G2FcoGW4Tu/E4mV9Uyt3YuIW/owAy3LHjoIfjJT5xO6enTnc5kOKIEAVxRcHFxOUroaWlioKuTSHklNRMnDwpBPp/H6GsmH+vE7Gsmb5hk8xYvv1HJX19tYO3mE4mnwmiahkd3mmc0HSaU9zG5upe5c01OPCtEVa2wszVMxTg/pp1HiYHHp+PxeOmzE/jDPmZUzThwQdi8GW67DTZscMIXXghHsGcGVxRcXFyOWJRt07VjG5ZpkkkMUD1hEqVVNVhGnvTAAPHO7aj+JrR8Ajs8hlxgEk+snMCvHg0T6/Pi1QTdthlbleWEKQkml7YzZWyC8bVJ8jkNGTceCYWw8jYdTTaxTIx8GmK+GP6A35k8BqDBxNKJRAPR4Rufz8PPfw733+/UFMaMgS99yWkyOoJxRcHFxeWIw7Yt+tvbGOjaiVI21RMmER1Thz8cIda+jfTmv4Gy8Xp9dOXH8/KWU1i3uZSXXwrQ1asQSzGuPMVp01tZNLOPaXO9Th+ux4M2ZjIiglcpglEPKSNBd7abAWsAT72HmopxRD1Rpkan4tW9b78Qd98Nv/qVs/3+98P110PoAGsZo4ArCi4uLkccnVs2k0nEqahvoKSyGtF1kskkXZtew9u7mbQ2jt++Mpun/hKhpcmLaSk0QMw8U6p6ufS0ds5Z3EloSgNa/TTQdSzLwjRNbNtGKWe0kuYB22sTCoeYEJlATaiGiO8QtfFfcw288QbccAMsXHho0jwMuKLg4uJyRBHv7iKTiFM3bSaaz09fZxO59rVouTi5VID/+sMifv/8ONI5Z0GYsN9kekMfcxu6mT25n6knleGrrcFXXk/OsiCfd+YNeMAX8uHz+eg3++nJ92AqE000JpVNYlzJuP3atk9WroTHHnPmHug6VFbCffcdEcNMDwRXFFxcXI4oBrp34ouUEkumSA80E+lahaWV8eBz7+SRx2rIZjXMvMnccd2cM7+TxTO7Ka/2EJk/DYnUMZAdIJkZwCZEWUUZWbI0J5sREYIqiJ21MW2T+kg9deG6A+84Hko8Dj/4ATz5pBN+8km47DJn+ygTBHBFwcXFZRSwbQtlKxK9PWSTCUQE27aJ9/WSiPUTqq3DZ1tk27fy6BMV/M9zi+nv96NJngWzBrjonU0smmsSmDIevaQB0XUMw6Av1snm1GbG14xHD+h0m93YyqY+Us/ksskH10ewJ559Fr79bejtBZ8PPvpRZxLaUYwrCi4uLocVI5+j+Y3ViGjYtk0wWk7eskiuW4u3cz1+06RvrY/n/zGR3/9jEX3pSjyBAFNmKK64tJ+5swyCZROI1PoxDINMzlm3wBv00u3tZmrDVBbVLRrZQvT2wne/C88844TnzYOvfhUmThzZfA8Drii4uLgcFizTpG3jOoxcFs3jpWzcRGJdXWRWr4JEjFC8na6yCTzw6pn8Y2Ud6ZQH0TUmzLS44vIki+YlUZaNv0onnu8jFVOEw2FKKktIqzSt6Vb8AT9zqueMfGH+9jdHEIJB+PSn4X3vGzUHdocaVxRcXFxGnHwmzc7tW8lns5SGSxhYu57uN9aDZbJxQ5ZnuxaybvsFNO2sRRMNny5MnWaw7B09nHZqFl9AAwUpfz9v9DZSWVZJTbQGUzfpt/op9ZUyvXw6taHaA3c9MexC5J0mIoBLL4XWVkcMRtmB3aHGFQUXF5dDjmWa9LY2O/0FmkY6kcBWCn8qRnLVCvozUX6zeR4vrKqjJ1aG+IJ4NY2IXzFnZobzlsaYPcvA4xcqx4WxLIuuRBetuSbmT5zP7NrZh68wtg2/+c2bE9Hq6pxawQ03HD4bDiOuKLi4uBwydnkr7d28iZ6N6/GHSzEsE7ujBa+Z5pWNpTyx/kJe2T4eNB2/R6OuTnHySSlmTk4yZ45BIAglVT78oTBKKTKZDJayiPliTK2ayuyawygITU3wjW/A66874T//GT70ocOX/yjgioKLi8vbwjJNbNMkubODXHcPuY4OjFSKVCpJMplAlVYRp4GtW0tpWlPOysZx7OwtQbx+QmHhrCU5Fs+OMX1KlnC5l0DEQyDiDA+1bZtMJoNt25SVldFqt6IbOpOjkw9P4UwTHnwQfvYzp9moogJuvhnOPvvw5D+KuKLg4uJyQPR3tmNks/Q3bSfT1kGmpxstECLjj9BiVtHeNZl1m8rYsq2S3k4/um2g1BgsT5CaKsWyMwdYdlaKoM/E69eJ1ofRvYJhGKTTaZRS6LpONBolEonQkm4hnowzt2oupb7SkS/g1q3OSKJNm5zwxRfDjTdC6WHI+wjAFQUXF5dhE+toY+e2rXjQ6FyzhYQay183L2RNYw1d7TqZtAcPJiiFrnuI+ExmTEkzc67O3Pk5ZkzP4w8I3oAfhRfLNsmbWcQSgsEgZWVlBAIB8pKnN9tLW7yNvmwfM8pnUBmsPDyFtG3YssXpO/jyl+HUUw9PvkcIrii4uLjsFTOfJx0fIN3WSqKtja7Nm0iaYTa2juelrUtZt7UG0xR8moWGoipqMXF8lrknejhxsc3MmQY+35ujgUxTI5/PkzPyaJpGaWkpkUiEQODNJSy3xrbSkmgh4o0Q9UdpqG6gIlAxsgXdtg0mTXJmIE+bBnfd5fgrOgoc2B1qXFFwcTnOMfI5jIyzfGUq1j/omiHZ10uip5e+jZuJxy3W76xlY8dS1myeRCqp49NMdN1m7hyDd587wOzFYWrrtMLp5uA6yOm0OZiXz+ejoqKCUCiEz+fDUAbJfJJ0Nk1vthfDMujOdDM1OpWxkbHDX+v47ZJOO95MH3kEvvMdOOccZ//pp49svkcwrii4uByHWKZJKtZHd9N2ADxeP3h0jFwOnz9AdvMmBtp7+O0L9azeejYtfTWYhqBpHjQdxtVmWbIkx7L3+Jg4yUapCKZpkslkB/PQdZ1AIEA0GsXv9+Pz+dB1nZSRYlNsEykjRd7K49f9hL1hTNukOlhNbbiWqmDVyF+El16Cb34TOjsdB3bt7SOf51GAKwouLscZRjZL87o1gCJSWY0/WkEsFiPf0wMtLWxpSvDs836e2vQu+uIR/H4vognTpuU58TTFGWfkmTnTQtM82LZJJpNz3FUEg5SWluIP+MmRG1ygJp6L0zjQiFIKTTSSRhK/7md6+XRKfCX4df/hvQDxOHzve/CHPzjhmTOdjuXp0w+vHUcorii4uBxHKKVo27wBO5ckqvrIvb6SfM5i3VovL66dwJr2qWzpqgHxovQgk6bkueoag5NPMSgtVUAe0zTJ5QyUUogI0WiU0tJSLLHozfayKbaJrJmlxFcCgGmblPnLqAvXoYmGIIS94ZFvGtoTmzc7i9309Tmzkz/+cbjqKqem4AK4ouDickxj2xZW3nCGkQ50ke7bSTaZImLESLV2sCZ7Bv/91+m8vjGK5vGi6RqeoGLOjBz/dHmMU0+3Ma08lmWRTjtper1eotEovoAP8Qhd2S6a+5sZyA2gi05FsII5lXMO3WI1h5Lx453O4wkT4CtfOaLXSh4tXFFwcTlGUUrRsu4N8gM9GL3bCIkBeUHaM/x1ywx+v+EytuysJJ2zCEVs3v2uHCefnGfO7Dz+AKBbZLIGkUiE0tJSvF4vHo+HnJ0jZ+V4rec1bGWjiUZduI4JpRMo9ZXi0Y6g14pS8L//C0uWQDgMgQD89KdQVXXMOLA71BxBv56Li8uhIJdOEevsoK+5iWTTDqpSLejioyU7iT+/MYe//KOBuBnCtBWhUJ5lp/dx9WUGE2cHADAMG8MwCHgCVNVUkSZNkiQ74zvxaB66090EPAGCniBzq+YS8ARGucR7ob3d6UheudJxXHfzzc7+mprRtesIxxUFF5djAGXbpGL9GLksfR3tZHa8hrZ9B9UBL2+k5vPI60t48dUQygIUjBub4swz45y+KE1tvY+qMQFyuTc7jGtqarB0i9Xdq7GURU2oBo/moSpQRU2whqpg1ej0CQwH24ZHH3WGmmYyzkzkefNG26qjBlcUXFyOEmzboq+tFaUU+XSaXCaFXlhJzMhlMHqbQBRWbxve9hh/blnG79afRFOLD9NSeDXFme9Ic/ElOWbNtgiW+AAf2WyWTCZDNBol7UnTZXTR2NsIQMgTYnbVbMLe8CiW/ADYvh1uuw3WrHHC554Ln/+847vIZVi4ouDicoSibBvLdCZ+ZZJxjGyOZG8P5fUN+AJBynx1KIRcy2skky3k7DS9+jSeeX4KT64Yx4BRhoZQWmpwwbtTXHZhjjHjvYBgmpAu9ByHw2EqKytJ2Sna+9qpCdUwoXQCJb4SvNohXr5yJGlvhw98AAzD6TO4+WZYunS0rTrqcEXBxeUIw8hm6WlpIh2PAaB7fVhGnpLKakpr69CCIQZiMcy29fgGtmMreL1rEn/8XSUrV9WRTnvA42HmFJNLL0mx9GyDcKmOwkM2m8W2bUKhENFolEAggN/v5/Xu1+nP9lMeKGd8yfhDv5bx4aC+HpYtc4aa3ngjlJSMtkVHJSMqCiJyPvBDQAfuVUp9e8jx8cB/A9FCnJuVUn8cSZtcXI40TMMgkxigt7kJy3JqBh6vn9pJUwlHyxFNw7Iskj2txLpb0Ha04csPYPWbvLhhHA88O42Nm8rweHR0r5cFC7Jc/dEcC0+0EQGl9EExKC8vJxqNEjNidGQ66O52FrZXKBZULyAaiI7y1TgAcjnHtfVZZ8HswhoLX/+6O6roIBkxURARHbgHOBdoBV4RkSeVUuuLot0CPKKU+rGInAD8EZg4Uja5uBxppOMDdDRuRNN0AiUlVDaMR9M96B4PpmmSiA+Q2fYSRqofzcrR2l3HH/46iTWrw2ztqMDSPFiiU1qqcerJOZYt6WbGLJuKhiC2bZPNOp3H0WiUaDRKc6qZtv42UkaKykAlU6NTqQhWoIt+ZA0l3R+vveb0HTQ3w4svwi9/6YiBKwgHzUjeBScDW5RS2wBEZDlwCVAsCgrY5aS8DHCdj7gcNyil6GjcSCBSytgZswb3ZbNZunt6SCaTiJUnnOrnte1zeeiXflatrwZANA0J+Jg0Ic/iExO86+wM1bUawVIPmvfN/oJoNEooEqI330tzqpmOVAdTyqbgL/EfeXMKhkMq5YwqevRRJzx5Mnzxi64YHEJG8o4YC7QUhVuBU4bEuRV4SkQ+DYSBZXtKSEQ+BnwMYLw7A9HlGCAV66dz62YAaidNwbZtUqkUvb29GPk8WjbO5pcS/N+zXl5acyrtqSgKnUCJl/MvMDlzaZ4TTsgQDKpCikHy+Tx5I4tHPFRVVRGJRNB0jZfbXyZv5wcnmNWGa9HkKHyJvvACfOtbsHOn45bi2mvhwx92+hBcDhkjKQp7GsSshoSvBO5XSn1PRE4DHhSROUope7eTlPop8FOARYsWDU3DxeWoQClFvLsL27ZI9fdRUllNxdhxpDMZurq6MXImZtLkr79o4fH/m0znQAMmPpTHQ1m14vylaS6/IkHt2N399OTzeWeyWSBATU0NwWAQS1ms7VlLLOd0Vp9Wf9rhdzx3KEkm4ZZbIJGAE05wXFRMmzbaVh2TjKQotAKU80qBAAAgAElEQVTjisINvLV56DrgfACl1EsiEgCqgK4RtMvFZVTIJOL0tOygpLKaYEkZ4WgFOzZ1EI8n6OrQ+eMTiudeqiCdrsL2B6mZAGedmeXss3JMnmSi6eD165imiWE4DumUUoRCIaprqslKlgwZtvdtpzvdDcC86nkjv0DNSKGU86dpEIk48w16e51hp64DuxFjJEXhFWCaiEwC2oD/B3xgSJxm4BzgfhGZBQSA7hG0ycXlsKOUonXDWvKZNJHySsLlY+nY2s+2Tc3saBFWvFzP3/4qGDkLj0+YNyfG+z6s88535gebypVyVixLpbJ4vV6qqqrw+/14vV50Xacp3kRTvIkyfxmmbTKzYiZVwaqjr89gF93d8O1vO6ufXXWVs++CC0bXpuOEEbtjlFKmiFwP/BlnuOl9Sql1IvINYJVS6kngc8DPROQzOE1LH1JKuc1DLkc1lmli5N5cbKajcRPJ/jS1k2aRTtl0rmmlK2bwxyfDPP1clFwmj4bN4hNjXPPeTuYu9mOXBVBKkcs5rqoBSkpKBtcwThkp2jPtSE7IW3k6Uh2MLx3P5LLJo1XsQ4NS8OST8P3vO01Gb7wBl18O/qO46esoQ462d/CiRYvUqlWrRtsMF5e3YJkmPS1NJHp7sAyFZerYNqRiKcJV4xA/DMTz/O+zlfzx92Hy8Rw5TWfZ6Tv5+IWrGTO7HoVghWrI2jqWZRGJRCgpKcHj99CUaKIj1YFX82LYBn7dT124DoCgJ0htuHaUr8BB0tYGt98Or7zihN/5TvjSl1wHdocIEXlVKbVof/GO0rqli8uRgWkY9LY0YRoG6dgA2bSB5q0hUl6J7hUs8khZAguLp58p57e/LSUxYEMmw9TZMc79YIbzZqcozYfJlU50nNIZNv6IB0/QQ3uunUR/AgBNNKaXT3ec0SFH56zjPWHbsHw53HOPMyEtGnX6D847b3C9aJfDhysKLi4HiFKKbCpJJh6nv6MV24JIZT2WChCMBgmVe1DeHOl0GhGN1evKue++Enp6NPKmYnLtTt5zxhomnaaxqDSGlfCS9pRiZLPkfDl6VS+2aeNJeagMVDKjfMaRuWDNoeQvf3EE4V3vgptugvLy0bbouMUVBReXYWDm82QScTLJPDu3NmPks/gCYfyRKEoroWdnDos8wYBB2tSwTI3nVvtY/kApOxoDgGLsmBhXLnyKhfWv0jc+SAI/K8xatNIawiENv68VBGrCNUwsnUjIGxrtYo8chgHpNJSVOaOLvvpVZ3bykiWjbdlxjysKLi57wDQMbNMg2d9HvKcbI5Mjl1WIBNF9AaomjcdQQjKRwCZOqNyL1xtE0zw885zGg8v97GgM4Pdo1JcbXH3KX3nX1NfIlQiv1U6mYcKJLKqbRmmoFI/nzcdQE+3onFh2IKxfD9/4htNX8MMfOk1EEyc6fy6jjisKLi5F7Jpg1rK+EdsSjLyFL1hOoKSGUNSLJ2iTU2mSZgpN0yitDKFpGkrBSy/5uP/+EBu2CLomVJUJFy7r4YrK/8ZXA40TZtPtNxlfNZ5TJpyCrh1nY+2zWfiv/4KHHnL6EbJZ6O931zo4wnBFwcWlQLwnzc7tbXQ37SBcUUvDrMl4fTroNql0kmRqAFsT/D4fHk8A01LkTMVr//Cw/OEwmzZ5SectysoNrnl/lvec3UVJy3P0t1m8NLGBivISzhw3j7qSutEu6uHn1VedkUUtLU5z0T//M3z8486ayS5HFK4ouBy32JZNPmOxc3s3Pa3N5FIJAhEvdTPqKamrJZePExvIYNs2mqYRCocGl6D888o8zz4T5I1VYRIDzhd/uCTPBy7ezHXnvMGAN0NbWy9GV5pMeQ2T6mdxysRTjtwlLEcKpeDf/x0eecQJT53q9B+ccMLo2uWyV1xRcDmuUEph5g2a17WRiiXIZ1PYZpryMVFqTzmJvG3T39/PwMAAHo8Hv9+PVuSBs7lZ5/77Qzy9wkNYM9HEZlr1AO8+o4Uzz1pPSu/ndX8JWp+ioitCSXQiY095B9ExY48/QQCnvyASAY8HrrsOPvQh8B4jQ2mPUVxRcDluyCTitG5YT3dzAtF06qbUEi6vwxcuAY+Hnt7eQV9CxS/wlliWNes1nnuqhJUvhLAs8PlsLntnI2edazN5psEmo4+tVoSKvhDjG+OU+MqpPOMkyqZM2U1UjgtiMWhthTlznPBHPgLnn++4uXY54hmWKIiIDxivlNoywva4uBxy4j3d9LW3Yhl5chkvddMX0jAzSjqTor+/n1hfHyKCz+fD4/GQyluYliIeF1Y85+MPf6ykq9WPrgkBD7z73C6uPW8lgbI48frFrIo3AzBDJuDp3ELlwlOpmDPn+BMDpeDpp+G733Uc1j36KJSWOq6tXUE4ativKIjIhcBdgA+YJCILgK8ppS4baeNcXA6W3pZm+jra8fjLCUTq8QQEfym0tDZjWRZ+v59wODwYP5lS/GS5sOn1ErZsCGDboGtCZVRx3pIulp66Ck/pVjZ4/dihOsLpdkq8JYxnDNrra6maPJnozJnI8SYIXV2OA7vnnnPCixc7o4tKS/d9nssRx3BqCt/AWRznWQCl1GoRmTqiVrm4HCTbX19PJpEi1hmjrHYcuq8cE5u0FSebVoRCQQJFI1/yefjDH4L88uEAXT0Q9uuE/bBgQZ5zTm5m1tS/EZMY/b4w4ZIpTCmbSpmnDCNvYMXjeBs3UNHQQNlpp41iqUcB24b/+R9nvkEqBeEwfOYzcMklrouKo5ThiIKhlIoN6SQ7urzouRwX2LZN+6btxHtixLr6mDBnNpUNkwmWe+nt6yVvmoSj/t0mi9k2PPW0j3t/EaKvV8NWMHVanqven+bkk/PEVCM9LStoDlVTW7WUen85ET1CLpfDyBuUlpQQyOfxTJ9OYNfi8ccTt90Gv/uds71kCdx8s+vA7ihnOKKwQUTeD2iFtRH+FXh5ZM1ycdk/ylYYeYtMMk/75q30t3cCUDluLNMWz6ekppTu7m7iXVn8fj/+Ie6X167T+dE9YTZsdIaUTpyU431XxnnnO/LErXY2da9GMr2MC44lOm4ZPt2PaZqk02lKw2HCbW3Q5PQneGbOQI7HUTXvfrezTOZNN8G557q1g2OA/brOFpEw8FXgvMKuPwNfV0plRti2PeK6znbJJPJkEgYDPc4tGO9pwswnCI2rAU1QSmHbNrZtIyJvGQpqGJBKCdmcs1/TFJGIje7LAwqxTZRSeERD0wOIpg+uciYiaACFNQ7E5zu++g8Mw2lrK+qHQSlXDI4gAoEADQ0NeId8pBxK19nvUkp9AfhCUeLvBX57oMa6uLxdjJxFJpEn2Z8jlzEJhL2UjwnhC5jYhokqqac0GqWsrGzw5f1WMRB6ejQSSaHMC1FRlJcrysoymHYaTdmENB9KNJQnAKJh285Hk6Zp6LqOmCYqlwOPBz10DDusG4ptO0th9vQ4cw4mTIDjqfxHCUopent7aW1tZdKkSW8rjeGIwi28VQC+vId9Li6HHCNvkerP0b8zjcen4Q96qKgvQ9MMOho3Yxo5AiVl9OfzjC0pAXjLUFAFdPdArF9DKQFRlJSZlJZl0UhhWuDTfHi9JdiaDgi2bYNS6LaNpusIgGGgDAPxepHjyT1DJgPt7Y5ra3DcWrsroR2RiAiVlZV0d7/9VY33Kgoi8i7gfGCsiNxVdKgUsN92ji4uw8C2bLqaEmSSBh6fRlVDhJKKN1/EzWvXo/u8hMfUEYsnUMpA38Ni7vm80NGpkc0KoAiX5CmtyKLrCp+VRxc/ms8RE1splK0AhcfjQQNUJus0DxWaWcXvR/P5DsMVOAKwbWeoaV+fE/b5oK5u96YjlyOOg505v6+aQhewFsgC64r2J4CbDypXF5f90NeRIpM0GDO5lGBk95ewUopsOkWgpo7+gTjBYJBsNrtbHMuC3l6dgQHBUja6rqiozhCJgE8CaFYOEQ92YfGaXf0PXq/X8XpqGKhsFjwetOOpVlDMLkEQcTyZVlc7zuxcjmn2+gsrpV5TSv0cmKGU+nnR3yNKqZ7DaKPLcYBpWHS3JGjb3E/HlhjpgTxVDZG3CEJvazMbV75Id3c3pq0Ih8O7NReZlk1Xj2Lbdp3+mMJUFsGIwbhxGaIhG78SdCOF2Ca2N8SupiJd0/ACks9jJxKobBbx+dCCwWGXQdd1FixYwJw5c7jooouIxWKDx9atW8fZZ5/N9OnTmTZtGrfddhvFgzz+9Kc/sWjRImbNmsXMmTO56aab3pJ+Lpdj2bJlLFiwgF//+td7tWPp0qXsaTDG/fffz/XXX/+W/UopbrjhBqZOncq8efP4xz/+4RyoqnJqBRMnQm0tmVyOM888E8uyBs/9/ve/TyAQYGBgYJ/5FNuUTCb5+Mc/zpQpU5g9ezZLlixh5cqVey3PcNhrGYaQz+f52Mc+xvTp05k5cyaPPfYYAE1NTZxzzjnMmzePpUuX0traCkB3dzfnn3/+Qdl2tDEc2R8rIstFZI2IbN71N+KWuRw3xHsytGzoJ9mfIxz1U14XpmZiKeHo7u3W/Z0dtG3dQl73MnbW3N0mnykFsZjG9h06/X06tg2+oMWYeoMJ9RpBsfAAgkLpPmxfBIWGbdt4NA0tm0UZBogggQB6aSlaIHBAVfFgMMjq1atZu3YtFRUV3HPPPQBkMhkuvvhibr75ZjZv3szrr7/Oiy++yH/+538CsHbtWq6//np++ctfsmHDBtauXcvkPbiFeO211zAMg9WrV3PFFVe8jSu9Z/70pz/R2NhI4z/+wU+/8Q0++clPOgd2dSgXhPG+++7jve99727NdA8//DCLFy/m8ccfH3Z+H/nIR6ioqKCxsZF169Zx//3309NzcN+Zg2VobOSnP/3pm2UYwje/+U1qamrYvHkz69ev58wzzwTgpptu4uqrr2bNmjV89atf5Ytf/CIA1dXV1NXV8cILLxyUfUcTwxGF+4FfAAK8G3gEWD6CNrkcZ/S2pyip8DP+hAqiNSECYS+BsBdNe/OFbFkWOzauJy8alXX1+IoEYeMmjd4+YWcX2Jbg90P1GJP6OkU04kEzUoiyULofpfuxNd/gqCKfz4emFOg6eiSCFggckj6D0047jba2NgB+9atfcfrpp3Peec6o7lAoxN133823v/1tAL773e/y5S9/mZkzZwLg8Xj4l3/5l93S6+rq4qqrrmL16tUsWLCArVu38swzz7Bw4ULmzp3LtddeS25XR3ARv/jFL5g+fTpnnnnmXl9sTzz+OFdfeCHS2sqp06cT6+ujo6PjLfEeeughLrnkksHw1q1bSSaT3H777Tz88MPDui5bt25l5cqV3H777YM1vMmTJ3PhhRcO6/y98cQTT3D11VcjIpx66qnEYrE9luG+++4bfOFrmkZVVRUA69ev55xzzgHgrLPO4oknnhg859JLL+Whhx46KPuOJoYz+iiklPqziNyplNoK3CIi/zfShrkc+yhb0bnNaXaoaijZa7x8Pk/zlkaSiTjj5ywYfJmYJixfHuK+BzXu/kEvHq+irNwmELR5eXvf4BePmBlniClZbGU7S15qGqIJWBYqn3dGFHmS+7R32Qm1wyqXZVk888wzXHfddYDTdHTSSSftFmfKlCkkk0ni8Thr167lc5/73D7TrKmp4d577+XOO+/k97//PdlslqVLl/LMM88wffp0rr76an784x9z4403Dp7T0dHB1772NV599VXKyso466yzWLhw4ZuJKgXxOG2NjYxbssTpL6ipoWH8eNra2qire3MxoHw+z7Zt25hYtGTmww8/zJVXXskZZ5zBpk2b6OrqomY/s5nXrVvHggUL9jgoYChXXHEFmzZtesv+z372s1x99dW77Wtra2PcuHGD4YaGhreUYVdz3le+8hVWrFjBlClTuPvuu6mtrWX+/Pk89thj/Ou//iuPP/44iUSC3t5eKisrWbRoEbfccst+7T1WGI4o5MSpQ28VkU8AbYA7j93loIl1pcmmTcZOi+7xuGVZ9Pf3s7OtlVhrE9HqWryFoZCdnRp33FHC+g0eUkYSf8BmTL2FpoHfo7NsZjVim2DlEBXA9kawlcKj62iFiWfKssC2HUHw+w96Elomk2HBggXs2LGDk046iXPPPdfJpzBvYk+83ZEimzZtYtKkSUyfPh2Aa665hnvuuWc3UVi5ciVLly6luroacF6ymzcXWn4NAzo6IJl0+jaCQceTaaGWNNSunp4eotHdf6fly5fz+OOPo2ka733ve3n00Uf51Kc+dcjKuq9+k6HsaRLu0PxM06S1tZXTTz+du+66i7vuuoubbrqJBx98kDvvvJPrr7+e+++/nyVLljB27NhBdyg1NTW0t7cfkO1HM8MRhc8AEeAG4JtAGXDtSBrlcuxjWzbJ/hzRmiC+4O63oVKKRCLBzo4OurdvwaMJkWgF0fqxADz3nI/v/6CE/rgiEs1xxTWrKCutJ+wveqkr26khiI7lCaKUwuv1IrkcyjSRQADRdcTjOWQzknf1KQwMDPCe97yHe+65hxtuuIHZs2fz3C7voQW2bdtGJBKhpKSE2bNn8+qrrzJ//vxh57U/TwS72OuLOJl0/nSdhkmTaMnnBwWhtbWV+vr6t5SteITXmjVraGxsHBS+fD7P5MmT+dSnPkVlZSX9/f27nd/X10dVVRXRaJTXX399cDW7fXEgNYWGhgZaWloGw3sqQ2VlJaFQiMsucxw8X3755fz85z8HoL6+nt/+9reFS5Pkscceo6ysDIBsNkvwAAYcHO3s92lQSq1USiWUUs1KqX9WSl0MNB0G21yOQSzLJpPI07SuD9OwiVTsPtwzm83S0tJC644dDLQ149U0aqfNpHrSFCzby3/8R4RbbyuhJ2YzY36KT96+gnedoaEVXn5iZtHySTQjhUKwND+Ijs/ne1MQgkE0nw9thFxUlJWV8aMf/Yg777wTwzD44Ac/yPPPP89f/vIXwKlR3HDDDfzbv/0bAJ///Of51re+NfgVb9s2d911117TB5g5cyY7duxgyxZniZMHH3xwsNN0F6eccgorVqygt7cXwzB49NFH3zwYjTpDTCdP5uLLL+eBBx9EKcXLL79MWVnZbs0uAOXl5ViWNSgMDz/8MLfeeis7duxgx44dtLe309bWRlNTE4sXL+aFF16gs9PxRbVq1SpyuRzjxo1jypQpLFq0iK997WuDwtbY2LhbG/4ufv3rX7N69eq3/A0VBICLL76YBx54YJ9lEBEuuugiVqxYAcAzzzzDCYVlQXt6epwJi8Add9zBtde++d27efNm5uxaMOg4YJ9PhIgsFpFLRaSqEJ4tIg/gOsRzeRtYhk1va5LulgSBkIfxJ1Tg9Tlty0op+vr6aG5uZmfjRlKdbXj9AWqnzcTrD9DZqfG5z5Xx6P94scXiuo8l+MmdOWqqFDNKZwzmIbaBrfsw9RC2N4zX58Pn86HSaZRpooVCaIfBcd3ChQuZP38+y5cvJxgM8sQTT3D77bczY8YM5s6dy+LFiweHbc6bN48f/OAHXHnllcyaNYs5c+bssZO0mEAgwC9+8Qsuv/xy5s6di6ZpfOITn9gtTl1dHbfeeiunnXYay848kxMnTXImcIAz96C6GrxeLrjgAiZPnszUqVP56Ec/OjgqaijnnXcezz//POA0He364t7FZZddxvLly6mtreWHP/whF1xwAQsWLODGG2/k4YcfHqwZ3HvvvXR2djJ16lTmzp3LRz/60bd81R8o+yrDggULBre/853vcOuttzJv3jwefPBBvve97wGwYsUKZsyYwfTp09m5cydf/vKXB8959tlnD7oj/Ghirw7xROQO4J+A14FJwOM4HlK/A/xYKZU+XEYW4zrEO3ppWteLshW1k3afkGZZFt3d3QwMDJBsb8E2DGqnzcRT6D/4+999fPs7EfoGoLTC4JtfTzJ3lqIt1UZLuoVTQhPpz2jMmDoZrDyWN4zX63NmJOfzzlBTQItEji/ndeAsdNPe7vwPUFsLlZVvK6nXXnuNu+66iwcffPAQGnjks2TJEp544gnKy8tH25Rhs2HDBmbNmrXbvkPhEO8SYL5SKiMiFUB7IfzWRj4Xl71g5i1aN/WjezWUpRg7oxyv/82RJ/l8nrbWFlIDA2R6ukCpQUFobdW5994wz73gwbAUcxZmueULScZWa5iJVjpbnmaCL0ogk4XgTGyl0LxBfKKBYWDn84PzDsTrPejp/0cVtu04r+vtdUYZeb2Oi4pI5G0nuXDhQs466ywsyxrW6KFjge7ubj772c8eVYJwsOxLFLK73GMrpfpEZKMrCC4HSsvGfjxejTGTyxABj/fNl0kikaC9tZXebY34/H5KqmsIV1SSy/u49ychfve7IDlD4fFZXHlFiuuuMujN97Gl6RWsbA+aL8KY8eeSUF7seAJdvIhho1Ru0GndcScG4NQK2tredGC3y0XFIXiRF7e1Hw9UV1dz6aWXjrYZh5V9icJkEdnlCVWAiUVhlFLv3V/iInI+8ENAB+5VSn17D3HeD9yK48zydaXUB4ZvvsuRSmogRyrmvJQaZpQ7cwIK2LZN0+ZNdHd2oLIZvD4ftdNnoXs8vPiil/+4O0Jvr0besjjxnQk+8MEkJ0zwsCG+iWS8mbrcAKV1p+MNjSFjCF7LQFcKAcTrOf7WOBiKiLPmgc8H9fWui2uXA2JfovBPQ8J3H0jCIqID9wDnAq3AKyLypFJqfVGcacAXgdOVUv0i4s5/OAZI9mfpbkkSKfdTM75kN0EwTZN1K18i0ddLtKYWf1U1wbIyUmkv99wT4alnvJi2YvykHB+4rpdlJ3toS3ewZmAnAPMNm5Lqk8hFJjoroOXzhLu6SFZWovn9yHHSrPEWMhkIBBxB8Pth/Hhn7sHxLI4ub4u9ioJS6pmDTPtkYItSahuAiCzH6adYXxTno8A9Sqn+Qp5dB5mny2HGthWW6QzlyyYNBrozGDmLcJmPyrGR3VxVDHR3seHVv2NZFmNnzMIfcWYxr17t5TvfjdC2E3SPzbUfTnHF5UlEUzQmNpLODTA5PJYqWyPoyTDgr8PasJFywyAYCuKfMgUtkzk+BcGyYOdO/j975x0eVZk27vud9N5IQiCJhIRQEkLooBgQlEVApKnxhwVYZFdRcF13hXVV9HNFXRQW14bi56dIwgpEVFBXKRZUejHUUBJSSEJ6JtNn3t8fJxkSUggkIe3c1zUXnDPvnPNMIPPM2+6HkhLo3h0q19aremuVa6Uxm9eule5AZrXjLGD4ZW2iAYQQu1CGmJZKKb++/EJCiPnAfIDw8PAWCVbl6jBozRRmazEZlSWOjs4arCYbnv5K7+DyDWkmk4mMc2dx9vSma88oAPR6wdq17mza5IbZKonsZWDBn9Ox+J7hcAm4aFwwm8sZqKvAwyIQZgNaJ38ctDr8HR3xGBCHU3CleuL48ev6/tsEZWWQm6v4PoS4tNxURaUJtGTfsq7ZvcvXvzoCvYAxwL3A+0KIWs4DKeVqKeUQKeWQqi37Kq2HxWSlKLcCRxcHukf70iM2gLA+/vSI60KX0NoJQa/Xk5mZia64CHdPT0wmSElxY/ZsfzZtckMIuOWOIuYtOY7F9wyBroEMDhjMwICBjMITDxc/KoKGUCgjcDF64VdUhFtY6KWE0Ea4bursAQNY/9ZbkJWlJAR3d0VR4e8PXL06+8SJE4wcORIXFxeWL19e7/uTUjJ27FjKysrs51JSUhBCcOLECfu5nTt3Mnny5BqvnT17Nhs2bADAbDazePFievXqRWxsLMOGDeOrr76q976NZdmyZURFRdG7d2+++eabet/D008/TXR0NH379mXVqlUA/POf/yQ+Pt7+7+fg4EBRUREmk4mEhAQslWqUzkCjk4IQ4mrr72UBYdWOQ1GWtV7eZrOU0iylPAecREkSKm0Uq8VGfkYZ0iYrayQ71pgzuJyysjIyMzMpv5CNgxDkFgXz0EP+rF7tQVmZoHcfM/c9kcnI23Px9TTT1a0rkV6RuBjL0Fw8ja0olxICsWq1BFRU4OPjg+fIkbj0anv/Ta6LOtto5NB//sM9Y8Yo8wVduyp66yaUx/T392fVqlV1JqLqbN26lQEDBuDt7W0/l5SUxKhRo0hObrw4+ZlnnuHChQukpqaSmprKF198QXl5+TXHD4rlNDk5maNHj/L111/zyCOP1Kj7UMWHH35IZmYmJ06c4Pjx4yQmJgLKrvKqHdPLli1j9OjR+Pv74+zszLhx467Kw9TeuWJSEEIME0L8BqRVHg8QQrzRiGvvBXoJISKEEM5AIvD5ZW0+A26pvG4XlOGks1cRv8p1pjhXh8VkI7iHN86u9Y8+2mw2Ll68SHZWFtqcTMy6CvL1fXjqb104lwVegQbuejiLcQ//jLnrL7h6ncamPY63Ng+ni6mw978YftlFRY4ZrzITQWVluPr64Nq7N5p2sJqmxdTZR44QP3UqZwoL2ZaezsBx4+gfF9ckdXZQUBBDhw7F6Qo7vS9XZ2u1Wnbt2sWaNWsanRR0Oh3vvfceb7zxBi6ViSw4OJi77767Ua+vj82bN5OYmIiLiwsRERFERUWxZ8+eWu3efvttnn32Wfvu6rqsrlX21ypUdXZtVgGTUT7AkVIeFkLccqUXSSktQohHgW9Q5gs+kFIeFUK8AOyTUn5e+dx4IcQxwAr8RUpZeI3vRaUFkVKiLzdTXmTAP8QDR+f6J3WNRiO5ubmYzWZsOi3SZiNbP5DnX/BFb5D0GVTAw38+RQUl2KSNGI9IeiJwMZgwSR90x9MxVbjideuddOnRw26rvCpONn04oha9b29Us2ZVZyvVgwgKCLikzv7sMwxmM2Oio5umzr5Kdu3axbvvvms//uyzz5gwYQLR0dH4+/tz4MABBg0a1OA1Tp8+TXh4eI3eRn386U9/YseOHbXOJyYmsnhxzYrA2dnZjBgxwn5cpc6+nDNnzrB+/XpSUlIIDHrdg0oAACAASURBVAxk1apV9KrW69TpdHz99df8+9+XFlvGxsayd+/eK8bbUWjMb5tGSplx2QagRs1oSSm3AlsvO/dstb9L4InKh0obRltspCBLi7u3Mz6BtY2RVqsVo9GITqejuLgYJycnHGxWdEWF7DvVmxXveKM3Geg/KpMpc47j6R5MqHMk/oYyXA1laPQFmF1DqMgzg6MPIXdMxvMKbv4GaeQHeHPS7Opso1HRW+t0ypLTKhwdOXn0aNPU2ddAUVERXl6X6l4kJSXZ75eYmEhSUhKDBg1qNnX2ihUrGt22MepsUL6wuLq6sm/fPjZt2sTcuXP58cdL5WG++OILbrrpJvwr52dAmStydnamvLy8xvvvqDQmKWQKIYYBsnLvwWOAWo6zkyFtEu8AVwK619QkGAwGiouL0WqVAjUajQY3Nzf0JcUU5BWyZkMkP/zUDYu0cMukTJYsdMbL+UY00opT4Uk0uotYfHpQkVWOsfQC7v4BBCbcjGtTEkIr0WzqbCkVPcXFi8rfHR1r6SmarM6+BhwdHe3K68LCQrZv305qaipCCKxWK0IIXn311QbV2VFRUZw/f75RH7BX01NojDq7qt2MGcoWrGnTpjFnzpwazycnJ9cYOqqiKpl0Bhoz0fwwyjf5cCAPGFF5TqWTYLXaKLpQQfXPoSrF9fnz5zEYDLi7u+Ph4YHGZqMs7wJnT1zgmeW92PF9KI6OVqbMTmXu/CJ8XLxwMJbisGcz1sMH0F50pOREHlathe633UbY7RPaZUKoTpPU2QYDtjNneP3VV5WE4OsLkZFw2XBLk9XZ10Dv3r05e1aZ8tuwYQMPPPAAGRkZpKenk5mZSUREBD/99BO9evUiJyeH45XLhDMyMjh8+DDx8fG4u7vz+9//noULF2IymQBlmGvt2rW17rdixYo61dmXJwRQ1NnJyckYjUbOnTtHWloaw4YNq9Vu6tSpbN++HYDvv//e3tMCKC0t5fvvv68xbwJQWFhIYGDgFedcOgqN6SlYpJSJLR6JSpvEalV011KCT5AybFReXk5ubi5OTk54Vn6DlTYbRp2OgnOn+e2cP6veH0p5uSuBgWYSFx6kf6yFcM8INGVZyP070ZeZMUcNxjGwK128vfEJCsKxCbK2tkZ1dfb999/P5s2beeyxx1iwYAFWq5X777+/tjo7MRFdaSkCmHTLLcqu5Hp+JtXV2RaLhaFDhzaozg4JCWHQoEF1rsjJzc1lyJAhlJWVodFoWLlyJceOHas17j9p0iR27txJVFQUSUlJtT6cZ8yYwbp167j55ptZu3Ytc+bMwWAw4OTkxPvvv28vWvPiiy/y97//nX79+uHq6oqHhwcvvPDCtf6oAYiJieHuu++mX79+ODo68uabb9qlfRMnTuT999+nW7duLF68mFmzZrFixQo8PT15//337ddISUlh/PjxeFy28W/Hjh1MnDixSfG1J+pVZ9sbCHEGZanoemCTlLJpa8eaiKrOvr4YdWby08sJDPfCxcORkpISLl68iLu7u30Fh7TZKMvL5WJ+ASnfhfHlVz0RCGL6GbjjkV1Ed/ch3CkETVkZTud+RJtvxDthMj6RkUo1tGYY4qhLFdwuyclRlpo2k8CuOblw4QIPPPAA3377bWuHcl2ZPn06y5Yto3fv3ldu3EZoKXU2AFLKSCHEjShLSp8XQhwCkqWUjV+YrNJu0ZYYERpw9XSioKCAoqIie0IoqjBx4lgaNqOB/Qd8+XH3EC7meuLsILjrLj0xk3fg4+pBhGsYlr17sRmLMZrL8Rs1Ff+O8AHeVKxWZd7A2/uStC4kRNmd3AYJCQnhoYceoqysrFGrhzoCJpOJqVOntquE0FQatdZPSvkz8LMQYimwEvgEUJNCB6esQE95oQG/rh6UlJSQmp6Lg7MrF/OLMZUUYjJZObnPnd17hnMh1xkHjYbuwTYee6ycuKHF/FYs6evTF9u5dNAV4NDdEd+IW/HoGdPab6310WqVlUVms7K6KCJCSQZtNCFU0dT9BO0NZ2fnOst/dmSumBSEEJ4oIrtEoC+wGbixheNSaQMU5+nItpg5mpVPYVERzs7OdHOUeJXlkJnhTcpXvUk/54bG0YFuITbuukvL+PEGXGwl7Du1AT+NM25lWvSH03AI9sM3aiweYXGt/bZaF6tV8RWVlirHrq6K3rqNJwOVzkNjegqpwBfAq1LKH6/UWKXjkF9ioMxdEuZiJCbKH6tex5nfsvj4014cOtYNjYOGgEAb991XmQx0mZSVlVJUkgaufvQIm0jxhXwcglzpPvkOXNxq72/oVFwusAsMVEpjqglBpQ3RmKTQU0ppa/FIVNoEOpOFX88WohGCwkItfq42vN3dcHRwYt1HBjZuHYTZ5oq7B9x1l45p03Q4uVgwZP1IqvYsJvcueLp4EOTQHWNWLh75+QQMGKAmBKtVGS6yWpX5g5CQJvmKVFRainqTghDiNSnln4GNQohaS5QaU3lNpf1x9mIFUm8lRGpwcjET4ueO0ejC00tcObC/J86ubowcaeKRR7T4BZjYV7gPh+wMnEzlOHUdSm9NNzSZeZgu5uEXFoZ3bCyubVBed12oWtknhLKSqGtXJSn4+am9A5U2S0Ob16q0gP9GqaB2+UOlg3Esp4ysnHK8K6wYTeUEhjuTdVbHo39w4MB+DX5+kmeeLeO558oIDLRhMmtx0F3kRqcgBvWYSoz/AOT5C1gtZrqNHk3Q6NGdKiHUUGdPmkTJb78pO5OpVGdPm0b0iBH0io5umjo7Pr5Ba+fVqrM/+eQT4uLiiIuL48Ybb+Tw4cN1Xrcjq7Or4o6PjycmJsa+EVBVZ1dDSlmlGOwrpdxW/YEy4azSgcgvN5BfbiDQInB1NuIZ5MDBn/L461Ndyclzp2eUhrfeNXDjjcouVCxGyvP24qovRnp1x+Lsg7akBCetltAhQ/AKC2v4hh0QNzc3Dh08SOoPP+Dv5MSba9ZAcTH6iormU2ebzRw6dIh77rmn2eKOiIjg+++/58iRIzzzzDPMnz+/znYdWZ1dUlLCI488wueff87Ro0ftu79VdXbdzK3j3O+bOxCV1kNKSXpaCSJHjzTqcPK0kZHuzitvxKC3eDNkmOC1laV4+JWTXZFNTkUW+ee+JEubgV/X4WiduqHLOI/3mTP4d+mCaxsrfnNdyciA3FxGDhhAdnExRESwLjm5+dTZhw4RHx/PmTNn2LZtGwMHDqR///5NUmffeOON+Pn5ATBixAiysrLqbNeR1dnr1q1j+vTp9sqO1ZXaqjq7EiHEPSjLUCOEEJuqPeUFlNT9KpX2hMVsRV9uJjejjPJiA14+FjTGPNJ+c+H5ZQHoDQ6Mvc3IX/9azgVTFnl5p3DVl9KlrAJbmY6QgFi8z+kRFYcI9PfHrXdvXOr4htsa7Mzc2ezXHBM2pu4nqgR2NhvodFiFYNuRI/z+D38AR8emqbMrCQoKuqTO/vJLDAYDY8aMaXZ19po1a7j99roNsx1ZnX3q1CnMZjNjxoyhvLycRYsW2fcnqOrsS+wBClEqplWfQygHDrZkUCotj7RJzh8twmSzUWCyYAvQoCs8T3m+Ey//KxqjxZmRCQbu/MMBjpbrMZYXEX3+PD4VHlgcAzB79UZ4BuIfEICXfwBOwW1LYlfvB3hLodWiNxqJv+su0rOzm0ed3QAnT55sdnX2jh07WLNmDT/99FOdz3dkdbbFYmH//v1s27YNvV7PyJEjGTFiBNHR0ao6u4rK8pjngO+uXzgq14vC3ApSc0pxDfNAOFrxFzrKjd4sfysard6V2IFafvfH77FoXAircMXr+Bk0Jom+30AcQsMJ8PXF29v72grgdARsNuXh6KisJAoJUeYUjh5tujq7ETS3OvvIkSPMmzePr776ioCAgDrbdGR1dmhoKF26dMHDwwMPDw8SEhI4fPiwPel2JnU2Uso6H8D3lX8WA0XVHsVAUX2va+nH4MGDpcq1Y7XaZHFehUxLvSi/+DFdnjl9Rh7Zv0/+9N9f5dhRF2RsP51MTNTK7Xu+kdv3JMuszz6R5994Wp7/5GWZk5kp9Xq9tNlsrf02anHs2LHrdzOtVsq0NCnT06Ws9rPw8PCw//3AgQMyLCxMmkwmqdPpZEREhPz222+llFLqdDo5adIkuWrVKimllIcPH5aRkZHy5MmTUkoprVarfO2112rddseOHXLSpElSSin1er0MCwuTaWlpUkopH3zwQbly5UoppZSjR4+We/fulTk5OTI8PFwWFBRIk8kkR40aJRcsWFDruhkZGTIyMlLu2rWrwbc9fPhw+/3eeecdOX/+/BrPJyQkyB9++EEaDAbZo0cP+79Jenq6DA8PlyUlJVJKKf/yl7/I2bNnS6PRKKWUMicnR3788ccN3vtKpKamyri4OGkwGOTZs2dlRESEtFgstdo99dRTcs2aNVJK5ec5ZMgQKaXy/2fs2LHSbDbLiooKGRMTI3/77TcppZQFBQWyT58+TYrvelPX7wNKxcsrfsY29DWvquRml5ZLSSrXk5J8HcW5Omw2yfHzpymvuEhOiUTj5Msr/+pDfpE3XcNKmDHnS7Ql2fQ0BWPML8c1bhD+Qyfg6tb2ayO3KFYr5OdD1bdgIZRzdfSWrkmdfe+96HQ6hBBMmjSpwVCaU539wgsvUFhYaJ/cdnR0rHNJa0dWZ/ft25cJEyYQFxeHRqNh3rx5xMbGAqo6u3YDIXoAOVJKkxBiFBAHrJVSljX4whZCVWdfPTarjcKcCrTFRvyC3fn5QgE5B35mQN8bsNq68e+3u/Dbb04EB5qY88d3iQkLxtcrAv2JPHxDuhE0YnizVvBqCVpcnV1dYCcEdOmiKCo0jVnA1zFQ1dntx5Taoups4DNgqBAiEvgI2AKsAyY3+CqVNkNZoQFtsRHfbh4cyC2goLCQyAAfvtvRh42bPDCbwcPbyOw/fIqHnwGvkJswlOvw1BQQEBvT5hNCiyKlkgxKKhfcubkpiorOMr5cDVWd3TloTFKwSSnNQojpwEop5SohhLr6qB1gtFhJSysm83wZLn4ulJ4pQastpxvuPP/qQIrLPUDA2JuLGDL2EyL9Bf7BYzAbzPj6+OAe2AXHTrDaokGEACcn5c+gIPD379SKClWd3fFpVDlOIcRdwP3A1MpznaNYaTunqMxI5vkygsM9cHU20sVoxebSlYUPO1BQ7Er3Gwq4956dBIadwubkQbcbJqM3O+Ct1eJ24QKiswrbzGblUVX4JiAAfHzA2bl141JRuQ40JinMBR5BUWefFUJEAEktG5ZKU7FabJw7WYyTBnydK0CA1uLD4sU+XCjQEnpDOg8t+JIeQd3w8J+At3t3DHoDft5euGRl4dyjB86hoa39Nq4vUip1DvLylN5Az57KJLJGoyYElU5DY8pxpgohFgJRQog+wGkp5T9aPjSVa0WvM/PN9gwkNgKDJAgnCgo8eO45b7JzNHQNzGD5n44Q0ncauPpgsVjQ6/T4+fnhdvo0EoFzjx6ITjSJismkzB1UVCjHnX3YTKXT0pjKazcDHwPZgAC6CiHul1LWLVJRaTVMBgv56WWUlBsRToIRA3zQass5fNibl5e5UlKqJahLJvP/XwqBfWdj0jhhrqjAycmJrl274lJSgklvwH3Y0M6TEKSEoiKlVrLNdklx7e3dqecOVDovjfnNXwFMlFLeJKW8EZgE/Ktlw1K5WkrydJw4fJFT+eXkOINPqAulpaVs3hzA0ue9KS2uoE/UMZ778xFuGpuIVTgihKB79+50tVpxOHYc0+kzuERF4uDp2dpv5/qRk6MMF9lsyrxBZKTy5zUkhBrq7DvuoKTkkiLs6NGjjB07lujoaHr16tWm1NmbN28mLi6O+Ph4hgwZUq/mQq/XM3r06Bp7HVasWIGrqyulVeVF67lP9Zi0Wi1/+MMfiIyMJCYmhoSEBHbv3l3v+2kMUkoWLlxIVFQUcXFxHDhwoM52JpOJ+fPnEx0dTZ8+fdi4cSOg7J6Oj48nPj6e6OhofH19Abh48SITJkxoUmztjcbMKThLKY9VHUgpjwsh1AHWNoS+3MTFHC3Z0kLXHt4EuAoqivPZuLEL65PdMOoKGJ3wPYkPOtC713hMJhNSSrqFhGBJS8Ocl49zjxtwDArqXAkBwNcXdDqld9DEISM3NzcOHToEXHIRPf300+j1eqZMmcLbb7/N+PHj0el0zJgxg7feeosFCxbY1dlbtmyhT58+WCwWVq9eXev61dXZzcm4ceOYMmUKQgiOHDnC3XffXaM+QhUffPAB06dPt28KA8V/NHToUFJSUpg9e3aj7jdv3jwiIiJIS0tDo9Fw9uxZjh8/3qT38NVXX5GWlkZaWhq7d+/m4YcfrjPR/OMf/yAoKIhTp05hs9koKioCanqW3njjDQ4eVBZYBgYGEhISwq5du7jpppuaFGN7oTE9hQNCiHeFEKMqH2+jCvHaFCX5OgpMFhw8HenqbMJQks/27/xJTvZAGkqYOW0zsx8LIjryRiwWCxa9ni46HaYDB7Hk5ePaR7GbdoqEoNdDQcGlYw8PpXfQzHMII0eOtFs6161b16bV2Z6enva9KBUVFfXuS7lcnX3mzBm0Wi0vvvgiSUmNW3ty5swZdu/ezYsvvmjXV/fs2fOKO7ivxObNm3nggQcQQjBixAhKSkq4cOFCrXYffPABS5YsAUCj0dClS21hQ1JSEvfee6/9WFVn1+aPwELgryhzCj8Ab7RkUCqNp7xIz96TBQh/RwKsRnQVjuzd68/bb7phMxRzz5Rt9J3kQkiXSKw2G4aCAgLy8xFubjiGheEYGIhDB5xULd9+mUhN2qC0DLTlyjxCUPBV10j2GnvLlRsBVquVbdu28fvfK2VH2oM6OyUlhSVLlpCfn8+WLVtqPW8ymTh79iw9evSwn6v68Lz55ps5efIk+fn5NeoQ1MXRo0eJj4+v0duoj3vuuYeTJ0/WOv/EE0/U2juQnZ1NWLXCTlXq7JCQEPu5quG8Z555hp07dxIZGcm///1vgqvV/8jIyODcuXOMHTvWfm7IkCH8/e9/v2K8HYUGk4IQoj8QCaRIKV+9PiGpNAabTZKRW07a8UJsbtDX34ybqxvff+/BihWeYC7j7sn76X+fIMy3P1jBZDERUFqKe0gIrv36IZw67naTGh/gFRXKyiKTSZkr8PeHwMBmV1To9Xri4+NJT09vd+rsadOmMW3aNH744QeeeeYZvvuuphy5oKDAPs5eRXJyMikpKWg0GqZPn86nn37KggULmu29Xk21s7p0PZffz2KxkJWVxU033cTrr7/O66+/zpNPPsnHH39sb5OcnMzMmTNrJK2goCBycnKuKvb2TL2/FUKIv6EoLmYB3woh6qrAptJKXCzWc/RYIU4ugrAACy7OLqxd683y5V7Y9FqmjD1O3zuz6ebTE3/hD0BY1644S3COjOzQCcGO1aokg4wMJSG4uECPHhAc3CLOoqo5hYyMDEwmE2++qZQhiYmJqTXxW5c6+2q4krOsiqv9IE5ISODMmTMUVB9iQ3lvBoPBfnzkyBHS0tK47bbb6NGjB8nJyfYhpIbU2TExMRw+fBibzXbFWO655x775G/1x0cffVSrbWPU2QEBAbi7uzNt2jQA7rrrrloT0snJyTWGjgAMBgNubm5XjLej0NBvxiwgTkp5FzAUePhqLy6EmCCEOCmEOC2EWNxAu5lCCCmEuKKsSQVyzpZw/PBFnIWNEH8TXq4evPyyP8nJbmAzM2/qPm6ceBbh6o6f9MPNzY0QDw9Me/YiHDRoOst/8IsXFaOpEErPICJCcRe1MD4+PqxatYrly5djNpuZNWsWP/30k/3bt16vZ+HChfz1r38F4C9/+QsvvfSS/Vu8zWbj9ddfb/Aeffr0IT09ndOnTwPw8ccf24vNVzF8+HB27txJYWEhZrPZXnf4ck6fPm1PMgcOHMBkMtWqqeDn54fVarUnhqSkJJYuXUp6ejrp6enk5OSQnZ1NRkYGQ4cOZdeuXeTm5gKwb98+jEYjYWFhREZGMmTIEJ577jn7PdPS0ti8eXOtuNavX8+hQ4dqPerSTkyZMoWPPvoIKSW//vorPj4+NYaOQEmQd9xxBzt37gRg27Zt9OvXz/78yZMnKS4uZuTIkTVed+rUKbsxtTPQ0PCRUUpZASClvCiEuKqvVkIIB5SKbbcBWcBeIcTn1VcyVbbzQpmzaNqatE5E6pliLN4CH2cTUnjy3HP+pB4BjSafWTO3EBiVji3XjbDyYDxKTuPl6YnJasMxsAuusbEdW3An5aXlpF26KLqKoKCrnj9oKu1Jnb1x40Y++ugjnJyccHNzY/369XX+Hxk/fjw//fQTt956K8nJyXz11Vc1np82bRrJyck89dRT/Otf/2LixInYbDY8PT1JSkqyTyy///77/PnPfyYqKgp3d3cCAgL45z//eVU/38uZOHEiW7dutV/zf//3f+3PxcfH21dsvfLKK9x///08/vjjBAYG1miXlJREYmJirfe+Y8eOJk+EtyfqVWcLIUqA7VWHKPUVqo6RUk5v8MJCjASWSil/V3m8pPJ1yy5rtxKlutuTwJNSyga92J1VnW012ziXXsK582WUluuJitYgjM48v9SPEyckbq55zJn3BcOG98Crwg1yigm8ZRx+/v4IIRAODogOXCXt+PHj9O3WTbGZhoV1KqX19eLgwYO8/vrrNcbgOwMJCQls3rwZPz+/1g6l0bSUOnvGZcf/vsq4ugOZ1Y6zgOHVGwghBgJhUsovhRC1d+tcajcfmA8QHh5+lWF0DE6dKORkRim+XZyICABzmQN/+6sz6Vk6fP2KefTJXQzrcyNu2SUYS4rpMiCegDrKEXZI8vOVXclVX3DKypT9ByrNysCBA7nllluwWq2NWj3UEbh48SJPPPFEu0oITaWhGs3bmnjtusYo7N2SyuGoFcDsK11ISrkaWA1KT6GJcbU7TEYLJzNKCQjR4FZxjtI8yUuv9SHrgiPB4YW89qqNbl1/h3X3HowurgTdeit+Xbu2dtgtj80Gn30GK1fC0qWKoiI4WNmRrNIizJ3budabBAYGMnXq1Cs37EC05HhCFhBW7TgUqL6uywuIBXZWjuF1BT4XQky50hBSZ8JisbHt+/NIR4mXLKJMCyvfG0h+kQs9QrNZ9NxxwkKHYykuxmg0EjR6NH5XWCveIcjMhBdfhKpVO66uitW0M6yqUlFpQVpy4HUv0EsIEVGpxUgEPq96UkpZKqXsIqXsIaXsAfwKqAnhMjJzytEZzPTuqUF7sYzX3+pHRqY7ocFa/v7kr3iGdMFqtaJPT8e3W7fOkRAADh5UEoK/P7z8Mvj5qQlBRaUZaHRPQQjhIqWsvY++HqSUFiHEo8A3gAPwgZTyqBDiBWCflPLzhq/Quckp0ZNfbiTrfCkuriYMRTpeeyOKM5neBAdbeeXxHyjv6oTJakWv0+Hn4IBfTExrh92ylJdf0lHccYcyqXznncpwURPdOSoqKgpX7CkIIYYJIX4D0iqPBwghGqW5kFJulVJGSykjq2owSCmfrSshSCnHqL0EBZPFxtmLFdhsNoKdDWhy03jpH/6cOudPly6SVxdtR+eZQb7U4IEHXXJycHN2waGjjqWbTPDuuzB5Mpw/r5wTAh54QJ0/UFFpZhozfLQKmAwUAkgpD6MsT1VpIU7klqEREOxiRZeXx5rkeE5lhNAlyJFlf9mN1ukw+VZvwsrcicwsxkmCx8gRaDpiMfnffoP77oP33lN0Fb/+2toR1Ut7VWdXsXfvXhwcHNiwYUOdz3dkdXZGRgbjxo0jLi6OMWPGkJWVBXROdXZjkoJGSplx2bnaO2BUmg0pIYAKjm/7jrffCeTEmUB8PfT885lTlNi2oz9XTmiBM5GegbiHh+F586iOt0tZr4fXX4e5c+HsWQgPVxJDGy4cX6W5SE1Nxd/f3665qFJnL168mFOnTnH48GF+/vln3nrrLQC7Onvt2rUcP36c1NRUevbsWev61dXZ99xzT7PGbrVaeeqpp/jd735Xb5srqbMby7x58/D39yctLY2jR4/y4Ycf1tJqXC3V1dmrV6/m4YfrFjBUV2cfO3bMvgv8ySef5IEHHuDIkSM8++yzdpNqdXV2Z6ExSSFTCDEMkEIIByHE40DdVi2VZqG4TMupHdv5ems3jp4Kxttdzz///DO+ARlYdY6EBg6k16Tp+A4bhmvfvh3PY5SaComJsG6dMkz04IOQlAT1GD7bIu1JnQ1KDYEZM2Y0aDntyOrsY8eOMW7cOABuueWWGtoNVZ1dm4dRhpDCgTyU3cdX7UFSaRit0UJ6QQXnM4q5cDKTC/t8+WbfABw93Vm8tIywoQO4cOhH3EvdCL4xBs+OXPvAy0vxFkVHwzPPwGU7MxvDuSNN++ZZFxFxtd37ddHe1NnZ2dmkpKSwfft29u7dW+f9O7o6e8CAAWzcuJFFixaRkpJCeXk5hYWFBAQEqOrsy5FS5qMsJ1VpIXQmC79llaLBBhfysGWX8+mOYWhcXZk3r4JBg/Xk/7aPCxnHCL1pLF064iqjQ4dgwAClZ3DDDfDOO9CvH1yjmqOxH+DNSXtVZz/++OO88sorDX5Qd3R19vLly3n00Uf58MMPSUhIoHv37jhW/t/rbOrsK/7GCSHeo9pO5CqklPNbJKJOSLHOjMVixr04g/NHC/lgfSw4enL7RBN3TNGy++JunHJPETr8JuL639za4TYvRUWwfDn897/w/PNQNYwQF9e6cV0DVXMKpaWlTJ48mTfffJOFCxcSExPDDz/8UKNtXersAQMGNPpezanO3rdvH4mJyve+goICtm7diqOjY42dvA2ps0HpSfTs2ZMFCxY0qM729fW1q7M1V/BTXU1P4VrV2WvWrAGgW7dubNq0CVAmwjdu3IhP5co2VZ1dm++AbZWPXUAQ0Oj9Cir1YzFbKczWknrgPFnbv+Xgl2d4L3kQ/Nb1cQAAIABJREFUJpsnN4524JFHtBwuPoQtJ5+RXYcQ33MYmquT1bZdpIStW2HmTCUhuLoqRtMOQHtTZ587d86uwJ45cyZvvfVWLbVDR1dnFxQU2Gs8LFu2rIbOQ1VnX4aUskYfTgjxMfBti0XUSZA2Sd65MlL3/0xp1mGshi58sOVOLBpPRo4wsHhxGUXmfMoqSkkgDJ/Inmi8vVs77OYhNxdeegl+/lk5Hj4cnn4aOpDArz2psxtLR1Zn79y5kyVLliCEICEhwb5yDFR19pVfIEQk8I2UMqplQmqY9q7OtpismAxWcjLLOH78DMb0ryh1GM3qpOEYjI4MG2bi738vw8kJdmXvIlLvSpTFA8+bbkJznWsCtAipqfDII6DTKRPKTzyhbEprYo2HulTBKs2Lqs5uP6bUllJnV12omEtzChqgCKi3ippK/Vwo1XPyeCFGnYWSigqCTQc4kjWCDd/fhM0GCQlGnvhTKY42M7oSPX5pWdwQ2BfX6MiOkRBAWVEUHKyUxXzqKaUQjkq7QFVndw4aTApCmaUaAGRXnrLJq+1aqNg5fKoQd72NkC4WujucI2lzfzb9EIuzGyQm6rjvvgpsu3+lUFeG1tmEq4crngkJOLu4t3bo147VCuvXK70Bb29wdoY1a5S/q7Q7VHV2x6fBpCCllEKIFCnl4IbaqVyZ37JKMeQZiO3ji64smxX/7sIPh6JxcXfmT0+Uc+s4Pba8PLLKsskb2J1uwaGEeXXHpT0nhFOn4IUX4MQJ5e9Llyrn1YSgotJmacwi8D1CiEFSyrplIipXJLNIR16ZgYggd/Q2HS+9EsieA954+Lvz7LPlDIopx/LbKXRFBZSEejAy6iZCvEKufOG2iskE778P//d/Sk+ha1doQJ+goqLSdqg3KQghHKWUFmAU8JAQ4gxQgVJRTUopB12nGNstNpvkeG4ZF0oMdPN1Q1NRxkvPSvbs9cTbB15cVkbfiHJMBw5iEgK3UcNxF1ntOyEcOaL0DtLTlcnju++GRx8F93bc41FR6UQ01FPYAwwCOteAWjNitNjIKzPQJ9ATpwoL//uljX37PfDwtPDqSguRYQYMe/ZiBYpH9sCgySbANaC1w752MjNh3jylTOYNN8Czzyq7lFVUVNoNDe2EEgBSyjN1Pa5TfO2ag5nFCAQBrk6cPKnl08/9sFktzH8wh8huFeh378EibbiNG45BY2Bw8GDiAtvfTl47YWEwbZpiNk1K6nQJob2qs3fu3ImPjw/x8fHEx8fzwgsv1HldKSVjx46lrKzMfi4lJQUhBCdOnKhxvcmTJ9d47ezZs+1KbrPZzOLFi+nVqxexsbEMGzas1p6Ha2HZsmVERUXRu3dvvvnmm3rfw9NPP010dDR9+/Zl1apVABQXFzNt2jTi4uIYNmwYqampgLJTOyEhAYvF0uT42gsN9RQChRBP1PeklLLhLZednOwSPTqjlZGRAehL9ax6yw19mY7hsVmMjchA/5sGvYOFvIGBOBoyCHIPwsvZq7XDvjrKymDlSqUKWpVobfHiJu85aK9UaS7gkovo6aeftquz3377bcaPH49Op2PGjBm89dZbLFiwwK7O3rJlC3369MFisbB69epa16+uzm5ubr75Zr788ssG22zdupUBAwbgXW2hQFJSEqNGjSI5OZmlVQsJrsAzzzzDhQsXSE1NxcXFhby8PL7//vumhM+xY8dITk7m6NGj5OTkcOutt3Lq1KlaS2c//PBDMjMzOXHiBBqNhvz8fABeeukl4uPjSUlJ4cSJEyxYsIBt27bh7OzMuHHjWL9+PbNmzWpSjO2FhnoKDoAn4FXPQ6UOCrVGTuWVczynjAAHB/QFOt5+o5jjxxzxdizn8TvOYjEYyI3wIW9oVwJ9uzI6dDT9Avq1duhXx/btcNdd8Pnn8OqrirYCOm1CuJz2ps5uDJers7VaLbt27WLNmjUkJyc36ho6nY733nuPN954A5fKvTfBwcHc3cQ6GZs3byYxMREXFxciIiKIiopiz549tdq9/fbbPPvss/bd1VVW1+rq7CqFSF5eHqCqs6tzQUpZdz9SpU7KDWYOni/B39OZbk5OOGvN/HykgA2fd0WYS0kcvwtzpD8nPawE+El6+vWkp0/tYiptmsJCeOUVJSkAxMcreus2lgzO7G9aJa+6iBw8vFHt2ps6G+CXX35hwIABdOvWjeXLlxNTh4l3165dvPvuu/bjzz77jAkTJhAdHY2/vz8HDhxg0KCG15+cPn2a8PDwGr2N+vjTn/7Ejh07ap1PTExk8eKa+2ezs7MZMWKE/bhKnX05Z86cYf369aSkpBAYGMiqVavo1asXAwYMYNOmTYwaNYo9e/aQkZFBVlYWwcHBxMbG1qsU74g0lBTa1m95G8dmk/x6phDNRSN+FgcsZjOFWj2r3gnCVlHBjT0P0X3sRbJ9fendrR9xwXHXrE1uFaSELVuUamhlZcpqoscegxkz4Aq2y9agsR/gzUl7VWcPGjSIjIwMPD092bp1K1OnTiUtLa1Wu6KiIry8Lg0SJCUl2e+XmJhIUlISgwYNarb3umLFika3bYw6G5R5GVdXV/bt28emTZuYO3cuP/74I4sXL2bRokXEx8fTv39/Bg4caFdnOzg44OzsTHl5eY3331FpKCmMu25RtHPS8srJKtYjrZKefu54ddVQUKRl1XPO5J7REuqbwW13HcYY3p1JfW7DvT0uzywvhxUrlIRw442wZAmEtOOlsy1Ae1VnV//WPnHiRB555BEKCgrsVcmqcHR0tCuvCwsL2b59O6mpqQghsFqtCCF49dVXG1RnR0VFcf78+UZ9wF5NT6Ex6uyqdjNmzAAUgd+cOXPsP4MqOZ6UkoiICCIiIuyvq0omnQIpZbt6DB48WLYl0gu08tujufLU6UJ58kCuPPDDSXny5Cn57N8LZd8b8uWwmAy59oPVcsveL+X5/POtHe7VYbVKabFcOt62TcotW6S02Vovpno4duxYa4cgPTw87H8/cOCADAsLkyaTSep0OhkRESG//fZbKaWUOp1OTpo0Sa5atUpKKeXhw4dlZGSkPHnypJRSSqvVKl977bVa19+xY4ecNGmSlFJKvV4vw8LCZFpampRSygcffFCuXLlSSinl6NGj5d69e2VOTo4MDw+XBQUF0mQyyVGjRskFCxbUuu6FCxekrfLfdPfu3TIsLMx+XJ3hw4fb7/fOO+/I+fPn13g+ISFB/vDDD9JgMMgePXrY/03S09NleHi4LCkpkVJK+Ze//EXOnj1bGo1GKaWUOTk58uOPP77yD7gBUlNTZVxcnDQYDPLs2bMyIiJCWqr/363kqaeekmvWrJFSKj/PIUOGSCmlLC4utsezevVqef/999tfU1BQIPv06dOk+K43df0+APtkIz5j216/vx1hs0nOFlTQ1d0JTbkZC1osLk68/HJXNiRpEDYzf1twEKcoG9FdexMWGHbli7YV0tPhoYfgww8vnRs7FiZObHPzB22R6upsNzc3Nm/ezIsvvkjv3r3p378/Q4cOrVOd3bdvX2JjY+usL1yd6urs/v37o9FoGlRn33rrrfWO92/YsIHY2FgGDBjAwoULSU5OrrOHMWnSJHstgqSkJHuxmipmzJjBunXrcHFxYe3atcyZM4f4+HhmzpzJ+++/by9a8+KLLxIYGEi/fv2IjY1l6tSp9iGuayUmJoa7776bfv36MWHCBN588037yqOJEyfaK6ctXryYjRs30r9/f5YsWcL7778PKFbRmJgY+vTpw1dffcW//vUv+7V37NjBxIkTmxRfe+Kq1dmtTVtQZ+tNVnJK9WQUVij7tMyC3LOpHM/w5INPelFaqsHDUcef7jlIt7HZXHT1ZPqgu3B0uLbSktcViwU++gjee08petOtG2zYoIjs2jCqOrvluXDhAg888ADfftu5yqlMnz6dZcuW0bt379YOpdG0qDpbpTb7Moqw2CTh/h6EeLtw7KdUftrrRtLnsUirZGCvAp64cz8OAcc4ZfVmVK+J7SMhnDyplMSsmoy8805YtKjNJwSV60NISAgPPfQQZWVljVo91BEwmUxMnTq1XSWEptIOPqnaDkaLlRMXyjGabYyIDMBFwPEDZ9nyRTmffhOPA1YSbz3L5OH/pdyjgnwXM/16TKC7b/fWDr1hLBZ4911FYGezKb2Dv/8dhg1r7chU2hhN3U/Q3nB2dq6z/GdHRk0KV0FxhRmt0cLAcF9cNJKjew7y1RcO/GdrLA7Syl23Hif2rtOkW8x4uPchtvsgeoX1au2wr4yDg1IRTUq49154+GFVYKei0klRk0IjMVlsHM0pJczfHR9XB7IyM9myUc+6rYPRCCu3jfqZ2DmluDv50K/EhEfkKHwCu7fdvQg6HVRUQGCgMnH8zDNQUABx7di9pKKi0mTUpNBItEYLUkJ0sBcnDx3k6y8r+Oiz/rh6OTN5+D5GzMiij1MPvMqz8AvtiUtQaGuHXD+//AL/+Ad07w7vvKMkhW7dlIeKikqnRk0KjUBrtHA4qwRfdyd0Oh3btxlYkxSPcBLcMjmd0WFf4kUI3hXZ+EcPxym4T2uHXDelpcqO5C1blGM/P+Wcr2/rxqWiotJmaNF9CkKICUKIk0KI00KIxXU8/4QQ4pgQ4ogQYpsQ4oaWjOdaOZxZgouDht7BHny9+QRvremNyQLDRp9iWv8kgvQ6wn3C8I+7HaeQfm1P+yAlbNumCOy2bFFWEy1cqOxBUBNCs5Kbm0tiYiKRkZH069ePiRMnsnr16loqaRWVtkqL9RSEEA7Am8BtQBawVwjxuZTyWLVmB4EhUkqdEOJh4FXgnpaK6VqREgb38OP4qVReXuFJhcnG0LjT/GHUAW6ocMAy+HaCh4yzu1LaFFIqK4mq/PKDBinH4eGtG1cHRErJtGnTePDBB+3W0EOHDvHFF1+0cmQqKo2nJb/SDgNOSynPSilNQDJwZ/UGUsodUkpd5eGvQJsdiC8qL+KVlwopyPenh6+RJTPzCPV1QhMRRveRY9tmQgBlvqBnT2U10ZIlyhxCZ0gIQ4bU/9i06VK7TZsabnsV7NixAycnpxo7i+Pj47n55pvRarXMnDmTPn36MGvWLLu76IUXXmDo0KHExsYyf/58+/kxY8bw1FNPMWzYMKKjo/nxxx8BxcD65JNP0r9/f+Li4njjjTcA2L9/P6NHj2bw4MH87ne/u+KOaBWV+mjJpNAdyKx2nFV5rj5+D9RZfkkIMV8IsU8Ise/ixYvNGGLjWbc5ld0/xeDu4saSWUfp0sOCs7eFgJiRODk5tUpM9ZKTA9Vd8g8+qOxKbqNG045CampqLUV2FQcPHmTlypUcO3aMs2fP2msbPProo+zdu5fU1FT0en2NQjcWi4U9e/awcuVKnn/+eQBWr17NuXPnOHjwIEeOHGHWrFmYzWYee+wxNmzYwP79+5k7dy5PP/10y79hlQ5JS369rWstZp1ODSHEfcAQYHRdz0spVwOrQdFcNFeAjcFsMpKRdpyP/xWGo4ML/29iNr1iBRpjLn79EnAJbkP7EGw2WL8e3nwTXFzg00/B3x8cHaGymEinobEqlOnTlUcLM2zYMEJDlY5wlV571KhR7Nixg1dffRWdTkdRURExMTHccccdlaEpcQ0ePJj09HQAvvvuO/74xz/ae6b+/v6kpqaSmppqV3VbrVZCVIOtyjXSkkkhC6hugAsFci5vJIS4FXgaGC2lrF06qpW5cDqNL9Y7Ul7qT98wA/cM2QPCEV8/f1wD21CBnLNn4cUX4cgR5TghQe0VXGdiYmLsdYgvp6rKGCh+fovFgsFg4JFHHmHfvn2EhYWxdOlSDAZDrddUtYe6azNIKYmJieGXX35p7rek0glpyU+NvUAvIUSEEMIZSAQ+r95ACDEQeBeYIqXMb8FYrhqTXsfFjHMcPVHK7l29cRAaHp3yG04+RrxCw3CPHAkahytfqKWxWGDNGpg1S0kIgYHKstOXXlJXFl1nxo4di9Fo5L333rOf27t3b731h6sSQJcuXdBqtfUmlOqMHz+ed955x54kioqK6N27NxcvXrQnBbPZzNGjR5v6dlQ6KS2WFKSUFuBR4BvgOPAfKeVRIcQLQogplc3+iVIH+lMhxCEhxOf1XO66k3smDZ2unDUbArBYYeTQi/QeqMHZ3x3f7tHg1bW1Q1R4+ml4+23FaDptmjJklJDQ2lF1SoQQpKSk8O233xIZGUlMTAxLly6ts9gLgK+vLw899BD9+/dn6tSpDB069Ir3mDdvHuHh4cTFxTFgwADWrVuHs7MzGzZs4KmnnmLAgAHEx8fz888/N/fbU+kkqOrsOsg+eZyKsmIOFLuy+OEInJwd+b8le+nmm0H3ngE4Rd8Kzm3EDXTokGI2/dvfoBEfKh0VVZ2tonKJpqiz1UHnyygryMegLcPS3ZMP3/HDSePEbSMu0K1kO37+LjhF3Ni6CeHAAVi9+tJxfLyysqgTJwQVFZXmo40urm89ii/k4B0YzDcHrJw6EoCbq417e32Ke3QY3jcmtt7kbUUFrFoFGzcqx0OGKBvRQLGcqqioqDQDalKoRllBPhaTEVffLvzrtSKsVkniqIOEeurxHjET0VoJYdcuRWCXn68sL507F/r3b51YVFRUOjRqUqhGRXExXgGBvPtxETnnPOjhVcTU6F143XYPrr4B1z+gkhJ47TX4qnJPX0wMPPssREZe/1hUVFQ6BWpSuAy91YuPVoOL1cKfbt+F15CbCegT2zrBvPeekhBcXOCRR5QCOOreAxUVlRZETQqVWEwmdGUlrE7ypLTEkZjQLGJH+xE8cOD1LZQjpeIrAvjDH6CoCBYsgNA2q4VSUVHpQKhfOyuRNhtn0135dJMzCHj4jgx8+vbFzcvrOgUgISVFmS8wmZRz3t6wbJmaEFRUVK4bak+hkjK9ljffd8RitTBx6FmiI4wEXK8P46wsRVFRtf/i229h0qTrc28VFRWVaqg9hUo+//InDv3mg6ur5MFx2fiOGIGzs3PL3tRmg08+gXvuURKCn5+ip5g4sWXvq9JiCCG4//777ccWi4XAwMAWL7Lj4OBAfHw8sbGx3HHHHZSUlNify8rK4s4776RXr15ERkayaNEiTFW9UeouDHTq1Kla99Dr9YwePRqr1Wo/l5KSghCCEydO2M+lp6cTG1tzHm7p0qUsX778qu53tXz99df07t2bqKgoXn755TrblJSU2BXmffv2reGLWrFiBTExMcTGxnLvvffW8FC1ZEwNtZk7dy5BQUE1fp4mk4mEhAS76qS5UZMCYC46x2cp/mgcXLh9aDkBXZ3wb+l6xWfPwpw5sGIFGI1w++2KomL8+EtzCirtDg8PD7sGG+Dbb7+le/eGjPHNg5ubG4cOHSI1NRV/f3/efPNNQJHlTZ8+nalTp5KWlsapU6fQarV2tXZVYaAxY8Zw5swZjh07xksvvUReXl6te3zwwQdMnz4dh2r7YpKSkhg1apS9qNCVuJr7XQ1Wq5UFCxbw1VdfcezYMZKSkjh27FitdosWLWLChAmcOHGCw4cP23f9Zmdns2rVKvbt20dqaipWq7XB97Rz505mz57d5Jiu1Gb27Nl8/fXXNV7j7OzMuHHjWL9+/ZV+LNdEpx8+klLy319TOXR4CG7OjkxLOIf/4ME4VbNatggnTsDRo4rS+m9/g1GjWvZ+nYirrI3TaBprV7n99tvZsmULM2fOJCkpiXvvvddeJGft2rWsWrUKk8nE8OHDeeutt3BwcGDq1KlkZmZiMBhYtGgR8+fPJz09ndtvv51Ro0bx888/0717dzZv3oybm1uD9x85ciRHKm2527dvx9XVlTlz5gBKj2LFihVERETw/PPP8+uvv9ZZGKguPvnkE9atW2c/1mq17Nq1ix07djBlyhSWLl16xZ9NfYWImsqePXuIioqiZ0/FXJyYmMjmzZvp16+fvU1ZWRk//PADH374IaB8uFYfDbBYLOj1epyclFrs9TmrmjOmK7VJSEiwa9OrM3XqVJYsWcKsWbOaFGNddOqegtZo4ZeDR/goyR0HmwtDe+fQ3bsEn5ayixYXX/r77bfDX/8K//mPmhA6GImJiSQnJ2MwGDhy5AjDhw8HFB/N+vXr2bVrF4cOHcLBwYFPPvkEUL6F79+/n3379rFq1SoKCwsBSEtLY8GCBRw9ehRfX182Vu1orwer1cq2bduYMkVxTh49erRW4R9vb2/Cw8M5ffp0g4WBqmMymTh79iw9evSwn/vss8+YMGEC0dHR+Pv7c+DAgStep7H3A7j55puJj4+v9fjuu+9qtc3OziYs7JKpPzQ0lOzs7Bptzp49S2BgIHPmzGHgwIHMmzePiooKALp3786TTz5JeHg4ISEh+Pj4MH78+Fr3GT58OPHx8cybN4/PP//cHtM3VeVurzKmxrSpi9jYWPbu3XvFdtdCp+4pGIwmHC6kceTgKFxcXJg44jf84gbg5O3dzDcyKGUwN2yAjz+GiAhliOjuu5v3PipA47/RtxRxcXGkp6eTlJTExGrzQ9u2bWP//v12G6peryeosvjRqlWrSElJASAzM5O0tDS6du1KRESE/Zt09WI7l6PX6+3FewYPHmwvuFNX/YWGztdHQUEBvpd9WUpKSuLxxx8HlESYlJTEoEGD6r3u1S7trupdNYa6xJ6X389isXDgwAHeeOMNhg8fzqJFi3j55Zf5n//5H4qLi9m8eTPnzp3D19eXu+66i7Vr13LffffVuMbu3bsBZfjoww8/tPc6rjWmxrSpCwcHB5ydnSkvL8ermVdIduqkIPRFfPJ5EAa9M0P6FnCD9wW8A8c170327VNWFmVlKRvPDh5UkoJKh2bKlCk8+eST7Ny50/6tX0rJgw8+yLJly2q03blzJ9999x2//PIL7u7ujBkzxj7JeXlxnqq5isupmlMoLS1l8uTJvPnmmyxcuJCYmJhavYuysjIyMzOJjIwkPz+/UXUc3Nzcaky8FhYWsn37dlJTUxFCYLVaEULw6quvEhAQQHH1XjFK3YeIiAhCQ0MbdT9Qegrl5eW1zi9fvpxbb721xrnQ0FAyMy9V/83Kyqo1/BMaGkpoaKi95zZz5kz7xO53331HREQEgYGBgFL17ueff66VFK6GxsZ0pTb1YTQacXV1veb46qNzDx+ZK9h1MAwXBw23RPzIDT164hTQTDoLrVZZSfTHPyoJISoKPvzwupR+VGl95s6dy7PPPkv/ao6qcePGsWHDBvLzlXpSRUVFZGRkUFpaip+fH+7u7pw4cYJff/31mu/r4+PDqlWrWL58OWazmXHjxqHT6fjoo48AZXjpz3/+M7Nnz8bd3b3RhYH8/PywWq32xLBhwwYeeOABMjIySE9PJzMzk4iICH766Sc8PT0JCQlh27Zt9vf59ddfM2rUqKsqRPTjjz9y6NChWo/LEwLA0KFDSUtL49y5c5hMJpKTk+1DaFV07dqVsLAwTp48CSg9t6qx+/DwcH799Vd0Oh1SSrZt29agin3MmDEN9hIaG1Nj2tRFYWEhgYGBLVMfXkrZrh6DBw+WzcV/t/wqb+iRJ4f2zpA733lHWiyW5rnwwYNS3n67lIMHSzl8uJTvvSelydQ811apk2PHjrV2CFJKKT08PGqd27Fjh5w0aZKUUsrk5OT/3969B0dV5Qkc//4CITHykIcoEgOkgGhCTAhZglK8XeS1IjNRQhmRBVcR2S1lQ4HjluIsVskgNeMDcFzGUgaQ144uysOhHFDKAZWHQsAXCEZAecMgJAHCb/+4l7YJedwm3Um6+/ep6qrue0/f+zv9+vU9595zNCMjQ9PT0zUrK0s3btyoJSUlOmjQIE1PT9fc3Fzt06ePrlu3Tvfu3atpaWm+7cycOVOfeeYZT/sdNmyYzp8/X1VVi4qKdNiwYdqxY0dNTk7WiRMnaklJia/sgQMH9N5779Xk5GRNTU3VIUOG6DfffHPFPsaOHatr165VVdU+ffro6tWrL1v/4osv6vjx41VVdefOndq3b1/NyMjQjIwMXbBgQcD7C9TKlSu1U6dOmpycrNOnT/ctHzx4sB44cEBVVbdt26bdunXT9PR0HT58uB4/ftxX7umnn9aUlBRNS0vT/Pz8y16jS7p37+6rk/9tzZo1Vx1TZWVUVfPy8vTGG2/Uhg0batu2bXXevHmqqrps2TKdNGlSpa9FRd8HYLN6+I2t8x/5QG/BTAoTJn6sSTf/pOOHfqg/7tsbtO1qUZHq7berjhmjumdP8LZrKlVfkkIk27p1q+bn59d1GEZVR4wYoV999VWl62uSFKKyT0FV2b79Q/6+thUNzpdxR9dDtGp7R002CJ98Ajk5TgfyzTc78yanpNgAdiZidO3alX79+lFWVnbZtQqmdp07d4577rmHlJSUkGw/Kn+xDp06yAdLd3P65+a0alXG7aMyadjwKvPjoUPwxBMwcSK8++4vy2+91RKCiThjx461hFDHGjVqxOjRo0O2/ag8Ujhz4Wd2bO+MSENyso5wQ2L7wDdy8SK88w784Q9w9iw0bgyh6PQxxphaFJVJ4cCxw2wubItKDEPvi+HaxgHOuVxU5Jxmeulinb59YcoUcE9nM8aYcBWVSWHD+mKKixvRPuUCXbNbEhNIM8/27c5ppufOQYsWzlXJAwbYeEXGmIgQdUnhwsULfLy+GUoDevU+T9OmAf67v/VWSEpyOpEnTYJmzUITqDHG1IGoSgonSk6w7dAXfL+jHTEi9O5/8bIrRit07hwsWOBcdHbddU6/weuvQ0KATU7GGBMGour0mG9PfMuZH9tw/nRTmjUtJi3t2qqbjnbsgPx8mDMHZs36ZbklBGNMhIqqI4XYBrHs2dyCstJS0lJ/omnTSsYYKS6GuXPhrbecaxCSkmx4CmNMVIiqpKDnzvPBoiLQG+g5tHnFTUeffuqcWXTwoHOdwYMPwsMPQ6hnYTNB9cMPP1BaWhq07cUiwPY/AAAMtklEQVTFxV02xHEwjB07lvfee4/WrVtTWFjo+XknT55k0aJFTJgwocL106ZNo3HjxhQUFHjaXqDlTWSLquajLz/8gW++v54GCQn0H9T6yiFqi4rgscechNC5M8yf71yUZgkh7JSWlpKQkBC0W6AJxsvMXBXNquXFyZMnmTNnTsDPM8aLqEkKJ8+cofCLGGjQkM5pQmLitVcWSkqCUaNgwgQnIdxyS+0HaqJG7969adGiRZVlzpw5w9ChQ8nIyKBLly4sWbKEqVOnsmfPHjIzM5k8eTIAzz33HCkpKdx5552+UUCrUlX5BQsW0L17dzIzM3nkkUcoKytjypQplyWiadOmMcu/n81EjKhpPjp94igHvm5Og5gGZGaUEBd3HRw/DjNnwq9//cscjpMm1W2gJqzl5ORQWlrKzz//zPHjx30T5MyYMYO77ror4O2tWbOGm266iZUrVwJw6tQpcnJyKCws5PPPPwdgy5YtLF68mG3btnHhwgWysrKqnN2sqvL+s8PFxsYyYcIEFi5cSF5eHo8//rivyWrp0qVXdZRj6r+oSQoAe/a35aIIPborsno1vPAC/OMf8P33sHChXYBmaiyQmbm8SE9Pp6CggClTpjBs2DB69ep1xQQ2GzZsYMSIESS4Z8VVNx5/VeUrmx1u9OjRHD58mIMHD3LkyBGaN29OUlJSjepm6qeQJgURGQS8CDQA5qnq8+XWxwHzgW7AMWCkqu4LRSwnTsTw46EmNGl0jv6rZsCWjc6KHj3gN7+xhGDqpc6dO7NlyxZWrVrFk08+ycCBAyscDC3QqS4rK6+VzA4Hzkxly5cv56effiIvLy+g/ZnwEbI+BRFpAMwGBgOpwCgRSS1XbBxwQlU7Ar8HZoQqnq2fQqMzp7nj1Dqu2bIRmjaFadPg5ZfB4/R3xnjlZWYuLw4ePEhCQgL5+fkUFBSwdetWmjRpctk0lb179+btt9+muLiY06dP867/aL0VqKp8ZbPDgTMP8+LFi1m+fDm5ubk1rpupn0J5pNAd2K2q3wGIyGJgOLDLr8xwYJp7fznwioiIOyFEUH320QXizhaTc/0OpH9/ZwC7YE29aeqduLg4zp49G9TteXGpT6G8ivoURo0axfr16zl69CiJiYk8++yzjBs37rIyO3bsYPLkycTExBAbG8vcuXNp2bIlPXv2pEuXLgwePJiZM2cycuRIMjMzadeuHb169fI9f8iQIcybN++yeX+zsrIqLZ+amsr06dMZOHAgFy9eJDY2ltmzZ9OuXTvS0tI4ffo0bdu2pU2bNlXuw4QvCcHvr7NhkVxgkKo+5D5+AMhR1Yl+ZQrdMvvdx3vcMkfLbeth4GGApKSkbpf+uQRi6pTz/PV/T/DnqTtJe6jf1VbL1FNffvlllXPqGhNNKvo+iMgWVc2u7rmhPCW1okbL8hnISxlU9TVVzVbV7Ouvcnjq52fE8vfC1qSOs4RgjDGVCWVS2A/4XwKaCBysrIyINASaAcdDFVB8vPUnG2NMVUKZFD4DOolIBxFpBOQBK8qVWQE86N7PBf4Wiv4EEx3so2NMzb8HIUsKqnoBmAi8D3wJLFXVnSLyWxG5dGL0n4CWIrIbmARMDVU8JrLFx8dz7NgxSwwmqqkqx44dIz4+/qq3EbKO5lDJzs7WzZs313UYpp45f/48+/fvp6SkpK5DMaZOxcfHk5iYSGy5OeO9djRH1RXNJnLFxsbSoUOHug7DmLAXNQPiGWOMqZ4lBWOMMT6WFIwxxviEXUeziBwBAr+k2dEKOFptqchidY4OVufoUJM6t1PVaq/+DbukUBMistlL73sksTpHB6tzdKiNOlvzkTHGGB9LCsYYY3yiLSm8VtcB1AGrc3SwOkeHkNc5qvoUjDHGVC3ajhSMMcZUwZKCMcYYn4hMCiIySES+FpHdInLFyKsiEiciS9z1n4hI+9qPMrg81HmSiOwSke0i8oGItKuLOIOpujr7lcsVERWRsD990UudReQ+973eKSKLajvGYPPw2U4SkXUiss39fA+piziDRUReF5HD7syUFa0XEXnJfT22i0hWUANQ1Yi6AQ2APUAy0Aj4AkgtV2YC8Kp7Pw9YUtdx10Kd+wEJ7v1Ho6HObrkmwEfAJiC7ruOuhfe5E7ANaO4+bl3XcddCnV8DHnXvpwL76jruGta5N5AFFFayfgiwGmfmyh7AJ8HcfyQeKXQHdqvqd6p6DlgMDC9XZjjwpnt/OTBAJKznZKu2zqq6TlUvzWS/CWcmvHDm5X0G+G/gd0AkjKntpc7/BsxW1RMAqnq4lmMMNi91VqCpe78ZV87wGFZU9SOqnoFyODBfHZuA60SkTbD2H4lJoS3wg9/j/e6yCsuoMxnQKaBlrUQXGl7q7G8czj+NcFZtnUWkK3Czqr5Xm4GFkJf3uTPQWUQ+FpFNIjKo1qILDS91ngbki8h+YBXw77UTWp0J9PsekEicT6Gif/zlz7v1UiaceK6PiOQD2UCfkEYUelXWWURigN8DY2oroFrg5X1uiNOE1BfnaHCDiHRR1ZMhji1UvNR5FPCGqs4SkduBP7t1vhj68OpESH+/IvFIYT9ws9/jRK48nPSVEZGGOIecVR2u1Xde6oyI3Ak8BdytqqW1FFuoVFfnJkAXYL2I7MNpe10R5p3NXj/b/6eq51V1L/A1TpIIV17qPA5YCqCqG4F4nIHjIpWn7/vVisSk8BnQSUQ6iEgjnI7kFeXKrAAedO/nAn9TtwcnTFVbZ7cp5Y84CSHc25mhmjqr6ilVbaWq7VW1PU4/yt2qGs5zuXr5bL+Dc1IBItIKpznpu1qNMri81LkIGAAgIrfiJIUjtRpl7VoBjHbPQuoBnFLVH4O18YhrPlLVCyIyEXgf58yF11V1p4j8FtisqiuAP+EcYu7GOULIq7uIa85jnWcCjYFlbp96kareXWdB15DHOkcUj3V+HxgoIruAMmCyqh6ru6hrxmOd/xP4HxF5AqcZZUw4/8kTkbdwmv9auf0kzwCxAKr6Kk6/yRBgN3AW+Neg7j+MXztjjDFBFonNR8YYY66SJQVjjDE+lhSMMcb4WFIwxhjjY0nBGGOMjyUFU++ISJmIfO53a19F2faVjSYZ4D7XuyNxfuEOEZFyFdsYLyKj3ftjROQmv3XzRCQ1yHF+JiKZHp7zuIgk1HTfJjpYUjD1UbGqZvrd9tXSfu9X1QycwRJnBvpkVX1VVee7D8cAN/mte0hVdwUlyl/inIO3OB8HLCkYTywpmLDgHhFsEJGt7u2OCsqkicin7tHFdhHp5C7P91v+RxFpUM3uPgI6us8d4I7Tv8Md5z7OXf68/DI/xQvusmkiUiAiuTjjSy1093mN+w8/W0QeFZHf+cU8RkRevso4N+I3EJqIzBWRzeLMo/Csu+w/cJLTOhFZ5y4bKCIb3ddxmYg0rmY/JopYUjD10TV+TUdvu8sOA/+sqlnASOClCp43HnhRVTNxfpT3u8MejAR6usvLgPur2f+/ADtEJB54Axipquk4IwA8KiItgBFAmqreBkz3f7KqLgc24/yjz1TVYr/Vy4Ff+T0eCSy5yjgH4QxrcclTqpoN3Ab0EZHbVPUlnHFx+qlqP3foi/8C7nRfy83ApGr2Y6JIxA1zYSJCsfvD6C8WeMVtQy/DGdOnvI3AUyKSCPxFVb8VkQFAN+Azd3iPa3ASTEUWikgxsA9n+OUUYK+qfuOufxN4DHgFZ36GeSKyEvA8NLeqHhGR79wxa7519/Gxu91A4rwWZ9gH/1m37hORh3G+121wJpzZXu65PdzlH7v7aYTzuhkDWFIw4eMJ4BCQgXOEe8WkOaq6SEQ+AYYC74vIQzjDDL+pqk962Mf9/gPmiUiFc2y44/F0xxmELQ+YCPQPoC5LgPuAr4C3VVXF+YX2HCfODGTPA7OBX4lIB6AA+CdVPSEib+AMDFeeAGtVdVQA8ZooYs1HJlw0A350x8h/AOdf8mVEJBn4zm0yWYHTjPIBkCsird0yLcT7/NRfAe1FpKP7+AHgQ7cNvpmqrsLpxK3oDKDTOMN3V+QvwD048wAscZcFFKeqnsdpBurhNj01Bc4Ap0TkBmBwJbFsAnpeqpOIJIhIRUddJkpZUjDhYg7woIhswmk6OlNBmZFAoYh8DtyCM2XhLpwfz7+KyHZgLU7TSrVUtQRnBMplIrIDuAi8ivMD+567vQ9xjmLKewN49VJHc7ntngB2Ae1U9VN3WcBxun0Vs4ACVf0CZ27mncDrOE1Sl7wGrBaRdap6BOfMqLfc/WzCea2MAWyUVGOMMX7sSMEYY4yPJQVjjDE+lhSMMcb4WFIwxhjjY0nBGGOMjyUFY4wxPpYUjDHG+Pw/Xild2BkOR64AAAAASUVORK5CYII=" style="width:3.825in;height:2.7327in" /></p>
-<p>Figure 7: Six-fold cross-validation of TensorFlow DL model show an average area under the ROC-curve (ROC-AUC; measure of accuracy) of 68%.</p>
-<p>In summary, the validation results of the four methods are presented in the following table.</p>
-<p>Table 5 Results of the cross-validation of the four models and after y-randomisation</p>
-<table style="width:97%;">
-<colgroup>
-<col style="width: 32%"></col>
-<col style="width: 15%"></col>
-<col style="width: 10%"></col>
-<col style="width: 19%"></col>
-<col style="width: 19%"></col>
-</colgroup>
-<thead>
-<tr class="header">
-<th></th>
-<th style="text-align: left;">Accuracy</th>
-<th style="text-align: left;">CCR</th>
-<th style="text-align: left;">Sensitivity</th>
-<th style="text-align: left;">Specificity</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>RF model</td>
-<td style="text-align: left;">64.1%</td>
-<td style="text-align: left;">64.4%</td>
-<td style="text-align: left;">66.2%</td>
-<td style="text-align: left;">62.6%</td>
-</tr>
-<tr class="even">
-<td>SVM model</td>
-<td style="text-align: left;">62.1%</td>
-<td style="text-align: left;">62.6%</td>
-<td style="text-align: left;">65.0%</td>
-<td style="text-align: left;">60.3%</td>
-</tr>
-<tr class="odd">
-<td>DL model<br />
-(R-project)</td>
-<td style="text-align: left;">59.3%</td>
-<td style="text-align: left;">59.5%</td>
-<td style="text-align: left;">89.2%</td>
-<td style="text-align: left;">29.9%</td>
-</tr>
-<tr class="even">
-<td>DL model (TensorFlow)</td>
-<td style="text-align: left;">68%</td>
-<td style="text-align: left;">62.2%</td>
-<td style="text-align: left;">69.9%</td>
-<td style="text-align: left;">45.6%</td>
-</tr>
-<tr class="odd">
-<td>y-randomisation</td>
-<td style="text-align: left;">50.5%</td>
-<td style="text-align: left;">50.4%</td>
-<td style="text-align: left;">50.3%</td>
-<td style="text-align: left;">50.6%</td>
-</tr>
-</tbody>
-</table>
-<p>CCR (correct classification rate)</p>
-<h1 id="discussion">Discussion</h1>
-<p>General model performance</p>
-<p>Based on the results of the cross-validation for all models, <code>lazar</code>, RF, SVM, DL (R-project) and DL (TensorFlow) it can be state that the prediction results are not optimal due to different reasons. The accuracy as measured during cross-validation of the four models (RF, SVM, DL (R-project and TensorFlow)) was partly low with CCR values between 59.3 and 68%, with the R-generated DL model and the TensorFlow-generated DL model showing the worst and the best performance, respectively. The validation of the R-generated DL model revealed a high sensitivity (89.2%) but an unacceptably low specificity of 29.9% indicating a high number of false positive estimates. The TensorFlow-generated DL model, however, showed an acceptable but not optimal accuracy of 68%, a sensitivity of 69.9% and a specificity of 45.6%. The low specificity indicates that both DL models tends to predict too many instances as positive (genotoxic), and therefore have a high false positive rate. This allows at least with the TensorFlow generated DL model to make group statements, but the confidence for estimations of single PAs appears to be insufficiently low.</p>
-<p>Several factors have likely contributed to the low to moderate performance of the used methods as shown during the cross-validation:</p>
-<ol type="1">
-<li>The outcome in the training dataset was based on the results of AMES tests for genotoxicity <a href="#_ENREF_63">ICH 2011</a>(), an <em>in vitro</em> test in different strains of the bacteria <em>Salmonella typhimurium</em>. In this test, mutagenicity is evaluated with and without prior metabolic activation of the test substance. Metabolic activation could result in the formation of genotoxic metabolites from non-genotoxic parent compounds. However, no distinction was made in the training dataset between substances that needed metabolic activation before being mutagenic and those that were mutagenic without metabolic activation. <code>lazar</code> is able to handle this ‘inaccuracy’ in the training dataset well due to the way the algorithm works: <code>lazar</code> predicts the genotoxic potential based on the neighbours of substances with comparable structural features, considering mutagenic and not mutagenic neighbours. Based on the structural similarity, a probability for mutagenicity and no mutagenicity is calculated independently from each other (meaning that the sum of probabilities does not necessarily adds up to 100%). The class with the higher outcome is then the overall outcome for the substance.</li>
-</ol>
-<blockquote>
-<p>In contrast, the other models need to be trained first to recognise the structural features that are responsible for genotoxicity. Therefore, the mixture of substances being mutagenic with and without metabolic activation in the training dataset may have adversely affected the ability to separate the dataset in two distinct classes and thus explains the relatively low performance of these models.</p>
-</blockquote>
-<ol start="2" type="1">
-<li>Machine learning algorithms try to find an optimized solution in a high-dimensional (one dimension per each predictor) space. Sometimes these methods do not find the global optimum of estimates but only local (not optimal) solutions. Strategies to find the global solutions are systematic variation (grid search) of the hyperparameters of the methods, which may be very time consuming in particular in large datasets.</li>
-</ol>
-<h1 id="conclusions">Conclusions</h1>
-<p>In this study, an attempt was made to predict the genotoxic potential of PAs using five different machine learning techniques (<code>lazar</code>, RF, SVM, DL (R-project and TensorFlow). The results of all models fitted only partly to the findings in literature, with best results obtained with the TensorFlow DL model. Therefore, modelling allows statements on the relative risks of genotoxicity of the different PA groups. Individual predictions for selective PAs appear, however, not reliable on the current basis of the used training dataset.</p>
-<p>This study emphasises the importance of critical assessment of predictions by QSAR models. This includes not only extensive literature research to assess the plausibility of the predictions, but also a good knowledge of the metabolism of the test substances and understanding for possible mechanisms of toxicity.</p>
-<p>In further studies, additional machine learning techniques or a modified (extended) training dataset should be used for an additional attempt to predict the genotoxic potential of PAs.</p>
-<h1 id="references" class="unnumbered">References</h1>
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