{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting from an HMM in split data\n", "\n", "In certain cases, you may not want to use the built-in cross-validation (CV) functionality (covered in [*Predicting from an HMM*](./Prediction_tutorial.ipynb)). This may be because you are using a different scheme to define your folds, you want to train and test on separate populations (like patients and controls), or you are using training/testing as separate steps in another routine. This notebook shows how to use the toolbox's functionality to do this. \n", "\n", "If you are new to the (GL)HMM or are unsure how to fit the (GL)HMM, start with the tutorials [*Standard Gaussian Hidden Markov Model*](./GaussianHMM_example.ipynb) or [*Gaussian-Linear Hidden Markov Model*](./GLHMM_example.ipynb). \n", "\n", "Simulated data and pre-trained HMMs for this tutorial are available from the [OSF project page](https://osf.io/8qcyj/?view_only=119c38072a724e0091db5ba377935666).\n", "\n", "**Note:** Due to rendering issues when viewing this notebook through github, internal links, like the table of contents, may not work correctly. To ensure that the notebook renders correctly, you can view it through [this link](https://nbviewer.org/github/vidaurre/glhmm/blob/main/docs/notebooks/Prediction_split.ipynb).\n", "\n", "Authors: Christine Ahrends " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline\n", "1. [Background](#background)\n", "2. [Preparation](#preparation)\n", " * [Load and prepare data](#load-data)\n", " * [Train HMM](#train-hmm)\n", "3. [Predicting individual traits from an HMM in split data](#prediction)\n", " * [Separate training and testing with deconfounding](#pred-wdeconf)\n", " * [Separate training and testing without deconfounding](#pred-wodeconf)\n", " * [Classifying from an HMM in split data](#classification)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "The different methods for predicting from an HMM are explained in detail in [*Predicting from an HMM*](./Prediction_tutorial.ipynb). We here only cover some considerations for separation between training and test set in this context.\n", "\n", "When working with split data, it is important to consider on which levels of data processing this divide needs to be preserved. In our case, these levels are: preprocessing (not covered here), fitting the group-level HMM, feature normalisation/deconfounding, and model training. While training and test set are always kept separate for out-of-sample prediction for the last step, the other steps may differ. The gold-standard recommendation is to keep training and test data separate on every level to avoid leakage of information. However, in cases where the purpose is mainly to explain relations in the data (rather than generalising predictions to unseen data), the divide may be more important for the target variable. By default, the toolbox functions keep training and test set separate for feature normalisation/deconfounding, and model training. Whether the group-level HMM is fit only to the training dataset or to the concatenated data of training and test set is up to the user. \n", "\n", "We will here show an example of training the HMM only on the subjects we want to later use as training set to follow the gold-standard recommendation. Note though that when using the Fisher kernel, heterogeneity between the training and test set may bias the estimation, since individual subjects' features are here defined *in reference to the group-level model*, which test subjects would then not be part of (see [Ahrends, Woolrich, & Vidaurre, 2024](https://elifesciences.org/reviewed-preprints/95125) for an in-depth exploration of the effects)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation \n", "If you dont have the **GLHMM-package** installed, then run the following command in your terminal:\n", "\n", "```pip install glhmm```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Import libraries**\\\n", "Let's start by importing the required libraries and modules." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from glhmm import glhmm, preproc, prediction, io, graphics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load and prepare data \n", "Example data for this tutorial are available for download from the OSF. This notebook will fetch the relevant data from the OSF project page using the osfclient package. If you prefer, you can also directly download the files from the [OSF project page](https://osf.io/8qcyj/?view_only=119c38072a724e0091db5ba377935666) and skip the next two cells. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# checks if osfclient is installed and otherwise installs it using pip install\n", "# skip this if you have manually downloaded the data\n", "import sys\n", "import pip\n", "\n", "def install(package):\n", " pip.main(['install', package])\n", "\n", "try:\n", " import osfclient\n", "except ImportError:\n", " print('osfclient is not installed, installing it now')\n", " install('osfclient')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|████████████████████████████████████| 434M/434M [00:26<00:00, 16.6Mbytes/s]\n", "100%|██████████████████████████████████| 1.35k/1.35k [00:00<00:00, 6.15Mbytes/s]\n", "100%|██████████████████████████████████| 1.82k/1.82k [00:00<00:00, 2.95Mbytes/s]\n", "100%|██████████████████████████████████| 1.82k/1.82k [00:00<00:00, 7.89Mbytes/s]\n", "100%|██████████████████████████████████| 30.0k/30.0k [00:00<00:00, 23.1Mbytes/s]\n", "100%|████████████████████████████████████| 408k/408k [00:00<00:00, 7.95Mbytes/s]\n" ] } ], "source": [ "! osf -p 8qcyj fetch Prediction/tc_forpred.csv ./data_prediction/tc_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/T_forpred.csv ./data_prediction/T_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/Y_forpred.csv ./data_prediction/Y_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/confounds_forpred.csv ./data_prediction/confounds_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/family_forpred.csv ./data_prediction/family_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/hmm_pred.pkl ./data_prediction/hmm_pred.pkl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will create a folder called **data_prediction**. Load the following files from the folder:\n", "* data: simulated resting-state-like fMRI timeseries: 4800 timepoints from 100 subjects in a parcellation with 50 ROIs\n", "* behav: simulated behavioural items from 100 subjects\n", "* T_t: indices for beginning and start of each subject's scanning session\n", "* twins: matrix indicating family structure (subjects x subjects), zeros for unrelated subjects and positive values for related subjects (diagonal will be ignored)\n", "* confounds: confounding variable for 100 subjects" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(480000, 50)\n", "(100,)\n", "(100, 2)\n", "(100, 100)\n", "(100,)\n" ] } ], "source": [ "# load data from csv files and convert to numpy arrays\n", "data = pd.read_csv('./data_prediction/tc_forpred.csv', header=None).to_numpy()\n", "T_t = pd.read_csv('./data_prediction/T_forpred.csv', header=None).to_numpy()\n", "behav = np.squeeze(pd.read_csv('./data_prediction/Y_forpred.csv', header=None).to_numpy())\n", "twins = pd.read_csv('./data_prediction/family_forpred.csv', header=None).to_numpy()\n", "confounds = np.squeeze(pd.read_csv('./data_prediction/confounds_forpred.csv', header=None).to_numpy())\n", "\n", "# check that dimensions of input files are correct:\n", "print(data.shape) # data should be (n_subjects*n_timepoints, n_parcels)\n", "print(behav.shape) # behav should be (n_subjects, n_variables)\n", "print(T_t.shape) # T_t should be (n_subjects, 2)\n", "print(twins.shape) # twins should be (n_subjects, n_subjects)\n", "print(confounds.shape) # confounds should be (n_subjects, n_confounds) or (n_subjects,)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standardise timeseries for all following computations. This is an important step, especially when looking at differences between individuals, to make sure that predictions are not driven by measurement noise." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_preproc,_ = preproc.preprocess_data(data, T_t)\n", "del data # we will only use the standardised version of the time series going forward" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split dataset into training and test set\n", "To illustrate a split dataset, we will here simply use the first 50 subjects as training set and the next 50 subjects as a test set. In reality, these may be obtained from splitting functions, e.g. sklearn's [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split), or come from separate populations or datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_indices = np.arange(50)\n", "test_indices = np.arange(50, 100)\n", "\n", "# separate time series for training and test set\n", "T_train = T_t[train_indices,:]\n", "T_test = T_t[test_indices,:]\n", "# to get time series indices, use aux functions:\n", "ts_train = glhmm.auxiliary.slice_matrix(data_preproc, T_train)\n", "ts_test = glhmm.auxiliary.slice_matrix(data_preproc, T_test)\n", "\n", "# separate target variables for training and test set\n", "target_train = behav[train_indices]\n", "target_test = behav[test_indices]\n", "\n", "# separate confounds for training and test set\n", "confounds_train = confounds[train_indices]\n", "confounds_test = confounds[test_indices]\n", "\n", "# separate group structure for training and test set \n", "twins_train = twins[0:50, 0:50]\n", "twins_test = twins[50:100, 50:100]\n", "\n", "# for this purpose we will not need the full dataset anymore, so delete it to save memory:\n", "del data_preproc, behav, confounds, twins, T_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train HMM \n", "\n", "Train the HMM using only subjects from the training set:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "np.random.seed(123)\n", "hmm = glhmm.glhmm(model_beta='no', K=6, covtype='full')\n", "hmm.train(X=None, Y=ts_train, indices=T_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can load the pre-trained HMM (which was fitted to all subjects)." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# hmm = io.load_hmm('./example_data/hmm_hcp_preproc.pkl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the parameters. We will use these to construct kernels, so it's a good idea to make sure they look reasonable before proceeding. In this case, we have simulated data where one of the states' mean is correlated with the target variable, with the addition of noise. The state covariances are kept the same for simplicity in this example. Note that it is not necessary for the true features of interest to be the state means but they can be any of the HMM parameters." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "K = hmm.hyperparameters[\"K\"] # the number of states\n", "q = ts_train.shape[1] # the number of parcels/channels\n", "init_stateP = hmm.Pi # the initial state probabilities\n", "TP = np.zeros(shape=(K, K))\n", "TP = hmm.P # the transition probability matrix\n", "state_means = np.zeros(shape=(q, K))\n", "state_means = hmm.get_means() # the state means in the shape (no. features, no. states)\n", "state_FC = np.zeros(shape=(q, q, K))\n", "for k in range(K):\n", " state_FC[:,:,k] = hmm.get_covariance_matrix(k=k)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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abr31Vj74wQ9ireU///lPvk2ZMoXFixc3dZzddtuNSZMm5a833nhjPvzhD3PPPfcMWJLs//388pe/BOD0009vKD/jjDMAuOuuuxrKJ0yYkM8GAnR1dXHMMcfw1FNPMX/+fKD17+iYY46hs7Mzf33EEUewwQYb5G17K3j66af561//yic+8Qlef/31/Hvo6enhfe97Hw899FAe1Dxy5Egee+wxXn311besfWEYctxxxw0or7//li5dyn/+8x/22msvent7+fOf/9xQt6OjoyE2IQgCdt1114bf58iRI/nXv/7FE088sRrOAp588kkWLlzIF77whQY/9UMPPZRtttlmwP0GK9enNHO/Zpx44okNsRH33XcfURRx2mmnNQgbnHjiiXR1dQ1oq+d5fO5zn8tfB0HA5z73ORYuXMjcuXMBF3+RBfYaY3jjjTdIkoSdd9550N/GYYcdxsSJE/PXu+66K5MnT35LfxtvvPEGv/nNb/jYxz6W32P/+c9/eP3115kyZQp//etfc3evle1PC9YODjzwQB555BE+9KEP8cc//pGLLrqIKVOmMHHixAFuZENR33fFcczrr7/OlltuyciRI5v637v33ntZtGgRRx99dMN/p1KKyZMnL9MNr54TTjihweVu8uTJWGs54YQT8jKlFDvvvHNDv3PzzTczYsQIDjzwwIbPnzRpEh0dHQ2fv7b10wUrRmEQ1FEqlQa4+owaNWqAP93KYIzh+9//PltttRVhGLL++uszZswY/u///o/Fixe3fLzzzjuPRYsW8Y53vIN3vetdfOUrX+H//u//mt7/zjvv5L3vfS+lUonRo0czZswYZs2a1VRb/v3vf7No0SJ+/OMfM2bMmIYtG/w1E6C11VZbDSh7xzveQW9vL//+978byjfbbLOG1//85z+RUrLllls2lI8fP56RI0fyz3/+s6F8yy23HOCv/I53vAMg9/1s9Tvq334hBFtuueVbKjOXDWCmTp064Lv4yU9+QrVazdt+0UUX8cwzz7DRRhux6667cs4556xSo3cwJk6cOKhSz7PPPstHPvIRRowYQVdXF2PGjMn/TPpf6w033HDAd9f/9/nVr36Vjo4Odt11V7baaiumTZs2IIZiZcjup6233nrAe9tss82A+21l+5Rm7teMwX4bg7U1CAI233zzAW2dMGHCgEDkwT7r+uuvZ4cddsh97MeMGcNdd93V1G8jO+Zb+dv429/+hrWWb33rWwN+G2effTZQ66dWtj8tWHvYZZdduO2223jzzTd5/PHHmT59OkuXLuWII47gueeeW+7+fX19nHXWWWy00UYN/xOLFi1q6v8z67P333//Afflr3/966aDmzfeeOOG1yNGjABgo402GlBe3+/89a9/ZfHixYwdO3bA53d3dzd8/trWTxesGEUMQR3NKo+sDBdccAHf+ta3OP744/n2t7/N6NGjkVJy2mmnLVeWcjD23ntv/v73v3PHHXfw61//mp/85Cd8//vf58orr+Qzn/nMMvf97W9/y4c+9CH23ntvrrjiCjbYYAN83+faa69tKrgna++nPvUppk6dOmidHXbYoeVzWhZD+eQvLyixFVb1d/RWkLXr4osvHuDHnZH5WH/sYx9jr7324vbbb+fXv/41F198MTNnzuS2227LfTxXNYN9b4sWLWKfffahq6uL8847jy222IJSqcQf/vAHvvrVrw641kP9Pm1doNw73/lO5s2bx5133sndd9/NrbfeyhVXXMFZZ53Fueeeu2pPqgneij4lY2XiVZrlv/7rvzj22GM57LDD+MpXvsLYsWNRSjFjxgz+/ve/r/bPXxGy++jLX/4yU6ZMGbRONqGwMv1pwdpJEATssssu7LLLLrzjHe/guOOO4+abb86NxaE45ZRTuPbaaznttNPYbbfdGDFiBEIIjjrqqKb+J7I6P/vZzxg/fvyA95envJUxVB8zWHl9X2mMYezYsfz85z8fdP9sImNd6KcLHIVBsApoZTB6yy23sN9++3HNNdc0lC9atCgPSG6V0aNHc9xxx3HcccfR3d3N3nvvzTnnnJP/gQ3VvltvvZVSqcQ999zTII967bXXDqg72DHGjBlDZ2cnWmsOOOCAFWo7MOjy/F/+8hfa2tqGDM7O2GSTTTDG8Ne//rVB637BggUsWrSITTbZpKF+NltYfz5/+ctfAPIArla/o/7tt9byt7/9bZUYQ83eW1mgW1dXV1PfxQYbbMAXvvAFvvCFL7Bw4ULe8573cP755682g2Aw5syZw+uvv85tt93G3nvvnZe/8MILK3Xc9vZ2Pv7xj/Pxj3+cKIo4/PDDOf/885k+ffqQcpTNXufsfpo3bx77779/w3vz5s0bcL+tLM3cr820dfPNN8/LoyjihRdeGHCfvPrqqwPkSgf7bWy++ebcdtttDW0aavA01G97VeixN/udZefu+35Tv43l9acFw5edd94ZgNdeey0vG+o+u+WWW5g6dSrf/e5387JKpTJAwWuo/bM+e+zYsSv1/7mibLHFFtx3333ssccey5xMeDv10wWrl8JlaBWQ/YE2k6lYKTVA+uvmm28eIFnYLK+//nrD646ODrbccssGyc2h2qeUQgjR4Kf/4osvDpqVsb29fdD9P/rRj3LrrbcOkBIEBrj7DMUjjzzS4HP58ssvc8cdd3DQQQctd4b1/e9/P8CAjKXf+973AHKlpIxXX321QXloyZIl3HDDDey00075LE2r31GmpJJxyy238Nprr62SwfVg130wJk2axBZbbMEll1xCd3f3gPez70JrPWCJd+zYsUyYMGFImdbVRfbd1l/rKIq44oorVviY/X8PQRCw7bbbYq0ljuMh92v2Ou+8886MHTuWK6+8suF6/epXv+L5558fcL+tLM3cr0NxwAEHEAQBP/jBDxqu8TXXXMPixYsHtDVJEq666qr8dRRFXHXVVYwZMyaP8RnsO3vsscd45JFHBm3DL37xi4bfzeOPP85jjz22yn4bzbhmjB07ln333ZerrrqqYaCXUd9PNdOfFqz9PPDAAwP6eKjFpNW72Q3VNwz2P/HDH/5wQNzbUP+/U6ZMoauriwsuuGDQvqnZ/88V5WMf+xhaa7797W8PeC9Jkry9b6d+umD1UqwQrAKyP8tvfOMbHHXUUfi+zwc/+MFBEwN94AMf4LzzzuO4445j9913509/+hM///nPG2bwWmHbbbdl3333ZdKkSYwePZonn3wyl5Ts375TTz2VKVOmoJTiqKOO4tBDD+V73/seBx98MJ/4xCdYuHAhl19+OVtuueUAv9lJkyZx33338b3vfY8JEyaw2WabMXnyZC688EIeeOABJk+ezIknnsi2227LG2+8wR/+8Afuu+8+3njjjeWew/bbb8+UKVMaZEeBppYOd9xxR6ZOncqPf/zjfGnz8ccf5/rrr+ewww5jv/32a6j/jne8gxNOOIEnnniCcePG8dOf/pQFCxY0rIq0+h2NHj2aPffck+OOO44FCxZw6aWXsuWWW3LiiScut/3LY9KkScyaNYvvfOc7bLnllowdO3bAzDS4xE0/+clPOOSQQ9huu+047rjjmDhxIq+88goPPPAAXV1d/O///i9Lly5lww035IgjjmDHHXeko6OD++67jyeeeKJhpuutYPfdd2fUqFFMnTqVU089FSEEP/vZzwb9o26Wgw46iPHjx7PHHnswbtw4nn/+eX70ox9x6KGHNgR+96fZ6+z7PjNnzuS4445jn3324eijj85lRzfddFO+9KUvrXDbB6OZ+3UoxowZw/Tp0zn33HM5+OCD+dCHPsS8efO44oor2GWXXQYkj5swYQIzZ87kxRdf5B3veAc33XQTTz/9ND/+8Y9z+cEPfOAD3HbbbXzkIx/h0EMP5YUXXuDKK69k2223HdQQ3XLLLdlzzz056aSTqFarXHrppay33nqceeaZK31tJk2axE033cTpp5/OLrvsQkdHBx/84AcHrXv55Zez55578q53vYsTTzyRzTffnAULFvDII4/wr3/9K88x0kx/WrD2c8opp9Db28tHPvIRttlmG6Io4ve//z033XQTm266aYMAwlD/fR/4wAf42c9+xogRI9h222155JFHuO+++1hvvfUaPmunnXZCKcXMmTNZvHgxYRiy//77M3bsWGbNmsWnP/1p3vOe93DUUUcxZswYXnrpJe666y722GMPfvSjH622a7DPPvvwuc99jhkzZvD0009z0EEH4fs+f/3rX7n55pu57LLLOOKII95W/XTBauatlDR6uzCU7Gh7e/uAuoNJ4tFPLtJaa7/97W/biRMnWillg6ThYLKjZ5xxht1ggw1suVy2e+yxh33kkUfsPvvs0yAJ1qzs6He+8x2766672pEjR9pyuWy32WYbe/755zfIpyVJYk855RQ7ZswYK4RoOJ9rrrnGbrXVVjYMQ7vNNtvYa6+9dtBz/vOf/2z33ntvWy6XLdBwTgsWLLDTpk2zG220kfV9344fP96+733vsz/+8Y+X2fbsWk6bNs3+13/9V96Od7/73QMk2rI2/fvf/x5wjDiO7bnnnms322wz6/u+3Wijjez06dMb5NCsdd/FoYceau+55x67ww475Od88803N9Rr9jvKpBD/+7//206fPt2OHTvWlstle+ihh9p//vOfDcdcUdnR+fPn20MPPdR2dnZaIP/8wWQYrbX2qaeesocffrhdb731bBiGdpNNNrEf+9jH7P3332+tdRJwX/nKV+yOO+5oOzs7bXt7u91xxx3tFVdcMeC6rghDyY4OJTH3u9/9zr73ve+15XLZTpgwIZf/639uQx2j/3W96qqr7N57752f/xZbbGG/8pWv2MWLF+d1VsV1vummm+y73/1uG4ahHT16tP3kJz/ZIK+Zta3ZPmUwmr1fB+vP6vnRj35kt9lmG+v7vh03bpw96aST7JtvvtlQJ7u+Tz75pN1tt91sqVSym2yyif3Rj37UUM8YYy+44AK7ySab5L/VO++8c8D3kPVfF198sf3ud79rN9poIxuGod1rr73sH//4x+Vej2ZkR7u7u+0nPvEJO3LkSAvknz9U3/n3v//dHnPMMXb8+PHW9307ceJE+4EPfMDecssteZ1m+tOCtZ9f/epX9vjjj7fbbLON7ejosEEQ2C233NKecsopdsGCBQ11h/rve/PNN+1xxx1n119/fdvR0WGnTJli//znPw+4d6219uqrr7abb765VUoNuI8feOABO2XKFDtixAhbKpXsFltsYY899tgGKe7BGOp3P9R/5VD90Y9//GM7adIkWy6XbWdnp33Xu95lzzzzTPvqq6/mddZEP13w1iOsXQkzr6BgJRFCMG3atNU6E1JQsDay6aabsv3223PnnXeu6aYUFBQUFAxzihiCgoKCgoKCgoKCgnWYwiAoKCgoKCgoKCgoWIcpDIKCgoKCgoKCgoKCdZjCIChYo1hr39bxA5dffjmbbroppVKJyZMn8/jjjw9Z9+qrr2avvfZi1KhRjBo1igMOOGBA/QULFnDssccyYcIE2traOPjggwfVai8oePHFF4v4gbcprfQLcRw3JHTacccdufvuuxvqzJo1ix122IGuri66urrYbbfd+NWvfrW6T6OgoGAV8NBDD/HBD36QCRMmIIQYVLq9nttuu40DDzyQMWPG5L/3e+65p6HOjBkz2GWXXejs7GTs2LEcdthhzJs3bzWeRWEQFBQMSSZpePbZZ/OHP/yBHXfckSlTpgyZUn7OnDkcffTRPPDAAzzyyCNstNFGHHTQQbkOu7WWww47jH/84x/ccccdPPXUU2yyySYccMAB9PT0vJWnVlBQsIK02i9885vf5KqrruKHP/whzz33HJ///Of5yEc+wlNPPZXX2XDDDbnwwguZO3cuTz75JPvvvz8f/vCHefbZZ9+q0yooKFhBenp62HHHHbn88subqv/QQw9x4IEH8stf/pK5c+ey33778cEPfrChT3jwwQeZNm0ajz76KPfeey9xHHPQQQet3rHCmhU5Kih4+7LrrrvaadOm5a+11nbChAl2xowZTe2fJInt7Oy0119/vbXW2nnz5lnAPvPMMw3HHDNmjL366qtXbeMLCgpWC632CxtssMEA+dbDDz/cfvKTn1zm54waNcr+5Cc/WfkGFxQUvGUA9vbbb295v2233daee+65Q76/cOFCC9gHH3xwJVq3bIrEZAWrHGMMr776Kp2dnUOmbV8RrLUsXbqUCRMmIOXQi1uVSoUoioY8Rv82hWFIGIYNZVEUMXfuXKZPn56XSSk54IADhszK2p/e3l7iOGb06NEAebbT+rTsUkrCMOThhx/mM5/5TFPHXRdZXfdUwdpPM/3CsvqEIAgafpPLYkX6hWq1OuD45XKZhx9+eND6Wmtuvvlmenp62G233Zpq17pA0QcUNMtbOVZYFRhjWLp0aT5WGIwsK/uy6qw0q83UKFhnefnlly2w2raXX355yM/u6+uz48ePH3Lfjo6OAWX9k8xZa+0rr7xiAfv73/++ofwrX/mK3XXXXZu6DieddJLdfPPNbV9fn7XW2iiK7MYbb2yPPPJI+8Ybb9hqtWovvPBCC9iDDjqo+Qu8DrK676liW/u3ofqFvr4+Owo15H7jx4+3CxYssIsXL863/kkNM1akXzj66KPttttua//yl79YrbX99a9/bcvlsg2CoKHe//3f/9n29narlLIjRoywd9111wr8Ut6+PPjgg/YDH/iA3WCDDSy0Pota9AHF1ur2VowV+gOt39szZ860o0aNGpAUL0NrbQ899FC7xx57tHTcVilWCApWOVnq8Vvvf572jlWXhryneykffd87l5naPIoi5s+fz8svv0xXV1fDe0uWLGGjjTYa8N7qsPgvvPBCbrzxRubMmZPPDvq+z2233cYJJ5zA6NGjUUpxwAEHcMghh6xUGvh1gew7vz7YnDaxakOfLt73slV6vHo6RnYtv9IK0L1oyWo5LkCpo7xajlvp7lstx9VJL3PvP3LIfiGKIt5Ec0P7FgPunV5rOGb+3xk3blxD+dlnn80555yzStp32WWXceKJJ7LNNtsghGCLLbbguOOO46c//WlDva233pqnn36axYsXc8sttzB16lQefPBBtt1221XSjjVN5md9/PHHc/jhh7e8f/b9/vTOF2hrb+53JYRFCFDS4CuDLzWBiglVQokKAVVKSTd+1INfXYqqdCP6uqGnG9vXg+mrYPr60JUIE0WYKEbHuu74AulJZOAjPA9VDlFhiCyXEW1tiHIbttSOKbWhg3aSsIPYLxN5ZSIREtuQyPpoK0mMwliJqWu/EhYpDEJYPGHwRIJPjCdifBvh6ypeUkUlfai4ikyqyLiCiKqIpApJAkmCTWLQGkzd/4xS4HmIIMD6JWyphAnbScJ24qCDStBJhTZ6bRu9SUBPFNATefRVJL1V6KtaqlVLNTJobUlik/+PKSXxPIHnScJQUi4J2kvQXja0h5qOoEq7V6VddtOeLCGoLCGoLEb1LEb0dWN7ujG9vZi+PnfNq5Fru6m/OkOztBqxw/dvbGqscH1pc9r6hdP2Ypja/Y+3ZKwwe/Zszj33XO644w7Gjh07aJ1p06bxzDPPDLmquKooDIKCVU62zNbe0Ul7x6ofEDWzXJypdbT6Xsb666+PUooFCxY0lC9YsIDx48cvc99LLrmECy+8kPvuu48ddtih4b1Jkyblf/pRFDFmzBgmT57MzjvvvNxzWpfJvvM2IWkTapUe2/PbV+nxGo4ddKye4/p6+ZVW+Nhtq+m4q1fDYnn9QmfZp1023jvKaOih6T/+FekXxowZwy9+8QsqlQqvv/46EyZM4Gtf+xqbb755Q70gCNhyyy0B10888cQTXHbZZVx11VXLPK+1hUMOOYRDDjlkhffPv99gJATN/a9YQEqLVBYhLdIzeF6CryICVaUs+yjpgFLkE1QkKpBID4QEKy0ai7GGxBi0NW5MisCmA1MhJUIplFLIwMcLfGQYIEsBMvQRpQBbDrClEB2GJGFA7JeI/DKRKhHbgMgGJNYjsQpjBdbW7mMpTG4UKKHxRVJnDAg8DV4CKrGoyCJjg5QapEXEFtLzQFjwJFbXGTO5QeBB4GFDH1Py0aFPXAqpBgF9XpnAlAl0CT8OCSKPoKQIqoJS5IyCOLZEkUUbi03H60oJlALfl5RCQTmE9pKlrWToCGLafZ92T9EhNOU4pqSqeLKMslUEMVbHGKsx1mCkwCiJSVro81I3oWbGCh1ln/Z+/ynSaqg0N1ZYGW688UY+85nPcPPNN3PAAQcMWufkk0/mzjvv5KGHHmLDDTdcbW2BwiAoGKb8etykAQPHXtt8hxIEAZMmTeL+++/nsMMOA5yf3/3338/JJ5885H4XXXQR559/Pvfcc88yB/kjRowA4K9//StPPvkk3/72t5tuW0FBQeuoskT18ydW6Yxps3/8K9ovgIsdmjhxInEcc+utt/Kxj31smfWNMXnc0bpItVptOP8lS9yqWHevQLcQQyClwFfge5ZSIBoG3UJYpNJ4KkJ5AVL5oHw3e95/JdJYjNZYY7D5TLsB6QwEqzUm0Qit3Wy81tg4RvgxJAnCS5A6RqkYT0cYoTBS4RO7tmAxQjYYBEJYJM4Y8ITGEzGejfFMhDLuOEpXUUmE1DFCx6BjRFyFOMJGESQxVmtnDNTPsBuTvxZCglIIz0cqH5VU8KSPLwNCqdBWoZXC+Nm1kwghEEIQKYHvW5KE3O9eCPA9gacgDKAUQOBbQs8QKE2gEnwRo2yC0jHCaIRJwJrGVYzahUDIFuJGWqirSkP3C6uT//7v/+b444/nxhtv5NBDDx3wvrWWU045hdtvv505c+aw2WabrfY2FQZBwbBElhVqgNUPVJo/xumnn87UqVPZeeed2XXXXbn00kvp6enhuOOOA+CYY45h4sSJzJgxA4CZM2dy1llnMXv2bDbddFPmz58PQEdHBx0dbqb45ptvZsyYMWy88cb86U9/4otf/CKHHXYYBx100MqfdEFBwZCokkSpfn/8uvU//lb7hccee4xXXnmFnXbaiVdeeYVzzjkHYwxnnnlmfszp06dzyCGHsPHGG7N06VJmz57NnDlzBmiTr0vMmDGDc889d0B5ot3WLDL9ioUQxInEk5ZECrRVJMYjUT6xCvG8EsqPEEGEjCNEOUalU94ebkZd+h6m3vVGCoSUSN9D+tnqQIgIAggCRBBiPR+rFAiBzY0Mi7AGZROsENjUZUVi3GsrnLsT6eoABiUSpFufyM/NCokRHlImGOUjjUZ4Fosb5AulQAegU0PF1hkEQroVAt8HPwA/wCrfbUJihUBY93lKaDyZECiJ8UV66hJPCWIfEi3QhrTdpG5aOIPAt4S+peRrQi8hUAmeSFBCI1KDxAqJlZ4zxvwAwgRpDUiJqPrIzKBpEs9rfmi7rImCZunu7uZvf/tb/vqFF17g6aefZvTo0Wy88cZMnz6dV155hRtuuAFwbkJTp07lsssuY/LkyflYoVwu55OF06ZNY/bs2dxxxx10dnbmdUaMGEG5vHrcOguDoGBYsiqs/o9//OP8+9//5qyzzmL+/PnstNNO3H333bm/8UsvvdSgYDBr1iyiKOKII45oOE69P/Jrr73G6aefzoIFC9hggw045phj+Na3vrUCZ1hQUNAKQgmEapw5FLSuVtNqv1CpVPjmN7/JP/7xDzo6Onj/+9/Pz372M0aOHJnXWbhwIccccwyvvfYaI0aMYIcdduCee+7hwAMPXLGTHQZMnz6d008/PX+dxYD1Vaxzg2kSpQRSQuRBNRZUIo8eX1EKfEpeSNlvI1QjKIVjKJUqhCMrBLoPP+5DJVW8pIJIIoKkijAajEak/yU2m4kWsjbgl27mP3tus9dSYdNJKqVjhDX4ooIVKjUK3LFyoyE7xbpTFXUDei19tPRhOS6P9QZEPbbfvZ99bn07LILAVPBFRIcnwANbSt+v87vvf6zsc0UaESGtyY0LaZ0hILVBWI2RijjoQPtl6FjfrRbg6mNtwzk3i+3uAZrLCaCClZ8oePLJJ9lvv/3y19l9O3XqVK677jpee+01Xnrppfz9H//4xyRJwrRp05g2bVpentUHN54A2HfffRs+69prr+XYY49tqX3NUhgEBcOSVWH1g/PfG8oVYM6cOQ2vX3zxxeUe79RTT+XUU09tuR0FBQUrh/IH+eNvYWBZTyv9wj777MNzzz23zONdc801K9SO4cxQEo/VyCK85r83ISxKCeLYGQeeAt8X9FUVvqcIfY/QC/Hr3Vmkxg80XpjgCY0indFOB7miTgQiG3BnA97+j9JoSB/rB8UqjiA9Tj4A7ocblEtnTEiVuhl5aOmhpU8ifTQe2ipi6zvXHqtIrESb9DF1kbK2ceAuSAOuhUVJjSdc0LUnEwJRH7hcwcuCl3UVmUTIJEIkEULHbuXB6EZ3HylyIwnpYZXCKi9fgdBegJE+xgtIVEDkh87AEZ7brErPyxkllsbYiuXRLZY2XXdV9Av77rvvMoVBskF+Rv8+YjDWhNBIYRAUDOChhx7i4osvZu7cubz22mvcfvvtub/s2sKqsPoLVh3D4Z4qWLtRgQv+bChbfbHZBauJsetJyu3NB6jL1IVF5q4sFindo5LgSYuUbthprCBO1X5io1DCRwqDFBaJRYhsAD+IQZD6+wtpURgExu2LQaKR1iCtzjdhLcKmRkLdTHh2vGzwbtKVBSOVGyynA+fEerkRkBiP2Cji1AiItUIbQaIl2oI2qUHQLz4hO3/fc8aArwyBVBglCaVzW5JSo0ziLp61bgZfx8g4Ah1DEiOS2Bk3mVGTGQNKYT0fYX23giI9txoiVWoMhMTZlgVYp+eSGA9tBYmVuUHTLD2V5lcVin6hRmEQFAxgZeXh3g6sytnAgpVnONxTBWs30hMor7FPkKJ1d4SCFWN5ftbN8t4N/0XHMuQk+5N64mMRaJsOz61CW4k2sjabbpwxUIndc+cXLzDGDaidj/zAmFdZ5zOvpMVTTtnIUxZPWrxU8tSTBk+YfDZeSZ0aGyY3IESd0WGtyNueWIVBusFyrPLBf6QVcSKpJoI4EVRjQZRAHEOU2FR11KJ1owqQkCCFwPMEvi8JA0U59FM1IE1boOnwI9o8nzYlEcqgdIQChE6Q1T5EtRcqfdgowkRVF0SdrXgI4WITPB8RliAM05iFEKN8Eq/sZE1VO72mjd6oRG/s0xv59EaSvqqgEkE1gjixJIlNDZrm/sMrvX7T90fRL9QoDIKCAaysPNzbgcLqf3sxHO6pgrUb6Suk109oQBSTBG8Vy/OzbpbR1dfo9JvPw2ERGKkw0neuNsIjViGJ9ajKkMg4nR9tPayFxAiqsSSKBdUYooTawNq4QWkm1iMluaqOUqCkwPPAU4LAc0G1vmcJPNswE+/JmptOvbToYAaBSd1+tE0NAe1WL6qJJIolUZK2M3YD6GpkiCJLHBuSJHt0OQKssRhrkUIglXR5AkqKMJDEZYk2AlAIAZ708KRHIH2MUHmMgbDGuQil+Q1sVMVGETZO8hUCqxRCa0RgUvUir271QKCVTyIDqjakLwnpiQO6qx5LexU9FejutVQqhkpFE0Wu/UY3P0ivVprPeVL0CzUKg6BgWFJY/QUFBfUoXw7oE9QqTnJXMDTL87NultgrEXutqaxkQbuZG46yiXOZEZpQKBLl0WFl6n/v8gFoo9BW5MnCjKntb2ymtNPoOuQUdly4rUgH+fXPszqSgQZA/+BfISwWkSoNCZR17fWlIjGa0JPoQJIYSaJFvqrhNrfqoVNl0Wx1w7W9cVVDSnJZVt8zThrUc0aKtoqKCdFiJNVSGT8chd9RTeVOI6RJkCaVDbW12AqbBljb1BAz0kOrwBliMiAWAVUTEls3k+9LQ1ug8ZWloywZ1SlItCLRtXNoJQSwr6f5/3ohB0qatiRxOowoDIKClWYoveg1SWH1r928He+pgrUbqQbpE4oM4WsdFdWO7zWfTHBA0C8Wz0TOjx87wJdfps9zhRubBRKng8z+90w2+CVVGkoVhgwqnVlXGCFdQHD9lrox1bsG9VfrMQjqi7IYgFwJKHtcTnl+rEGoDzbO22IFOo2niLSHoYy1LoeD6RegbC0gBrZVirSGAWEsyrhkaVkgs8gTrllCFVP2qk0bS8uju7vFoOJiogAoDIKCVcBQetFrkuJHvnbzdrynCtZulC9R/bIlK4o+YW3Dt1V807yPeP9g3VoAr20I6M2UgZZnCNSrAVkh83oIsChXLx8Yi9pseb1RgExXHlSmW5QPxusZTM7TNUUMWmeo+uAG2v2pNxL6GwMu5kI0GAKZMeCClJ2oaGYf9Q9YBpEO8N1r56rkDAIrTJpMWAIGJchXQ3IjQOT6QnXHbA5J8ysExURBjcIgKFhphtKLXpMUP/K1m7fjPVWwdiO9ok8YDox+6m662krNVXbRsyBVnoDL+iE2KGH8EiYoEwftxH6ZyGsjkiUqtkRkAiLtUdU+sZHEWhIZiUmDi5f7sQ0z5fXl/d2Cln+sTB3IWOe25B5rgc71bkHauCTJzsXGonW2v21IUgzOVUhKkcc+BD74HgSecx8KfUvgGUJPOzlWmeSypL5I8ESMQjcoJ9WTxR0YIQdIo8bWI0lXHyrad9dXS+Kk3u2pdk7uHJZ/rTL6etqarltMFNQoDIKClWYoveg1SfEjX7t5O95TBWs30pPI/nFFtugT1jbm/+ZReoLmVgiEdN+53xbilUP8rg78rg7UiJF4I0ahR6yHUR7VsJM+0c6SpJPF1TKLen0W90iW9FiWLNX0dCdUKhWiSkISa4wFawwizXXjbA6JlC5YVynpch54Eqkkvi+Ryqn6KCnS92rByFLUMvxmBkTmM28MqUqQe0ySbDN5wG2SaOKqTh9dG7U26DhBa4M1Fp1m+rXGIqRAKYXnK4JSQKktoNwe0t7h097u09Wp6GwXdLXhVJJ8TZtXoU32Uk6WUoqW4kU9qGovMq4g4qoLNK7PQ6B8rOc3GF5R0EHFa6ePdnooO4MgVnRXFN19gp4+6OnV9PZqKn0J1aomjmrn0yxRpXmXoWKioEZhEBQMYFXJw61Jih/524vhcE8VrN0UwYPDg/V33IquUvOTBcJzqwMiCBBhCVEuY8vt6HInutyF9kpYBFIYAhnT7itEmyX0fNpLklGdHpXIoxqViBOLMYPPtkNNcSjLy5UN9JWsLVbkr7Mkx3VGQP8Vg0zm1NpM9tQ9Jglo42RFtXZtihOLSQ0GrY0zIrRtUBeqz32mlDNegkAShoowlJRCSVtZpPKjhvZQU/J0npTNIjBCEXslsBYrJMoLEIELLK7XNbVS5UnItFci8UJ0modAWIsnXBK4tsBdI09JSoGkvazo61DEsU8Up+dj3Lk1S7VPLb9S9t0VEwU5hUFQMIBVJQ+3Jil+5G8vhsM9VbB2Iz2J8vtPEhTKY2sbLx54Gh0dzechAHJfdJEmGJPoAUnDQtNH2XYzRmqkrxGeRpQHCTKGmv9KOoLPFXWEwgrhlHXSmIEskViW/8CQKRllORFkHqTbTDZe0U/ZqPG92vPBYgaWd5x6f/36a5YdL7E+PbILZBfCb923v9Y2TZvspU32gg+2PESsxAqu6ncvXcrMZttS9As5hUFQMIBVJQ+3JnEGQfEjf7swHO6pgrWbQVcN+0/1Frzt+dOr61Nu72q6fn3CsMBPZTWVpqQSSl5ESVYJRQVfVwjiXvzYucKoag+i2ouoViCOsFEESYzNnPQhS0SAUJ7z/fG8xlgFP8D6JYwXoP0SRgXodEtU4DIPKw+Nl+ccyAbBgn7B0Fkm5DQMOct8LLJHLNLoVDHJ5uWDBUNn2DSjcO7vLxVGerlCkhZenTHjoZFo66ftFMs0ZjKjIsux4BSFNAqDEgkKjbIJysQomyBNgtIx0iQIo5E2SeVM6zIgN8mS7t6m6xb9Qo3CICgYljirv1ghKCgocAy6amiKPmFtY8cJC+noaD7xVLYqINKVASW0ezQJyiZ4cZQORiOkiZE6cYNkv4SQChG2IZLYDUqNRtQL4ue+QQqrnIuM2zxnBHil3ADINPgT4ZNYn9i6QNskUamCj8yVe+rJVHryFY5slSMdZEthENKiMEilc8lOSWoYYAcoLUE6iBfplRHCGQENA3+FMS5LcpbR2ViX98DlaUiViIzIA5/rr7lzhXLtVdKi0kzNnjR4UuMJjScSfBnjCfddKC81EIwzEAZdnWmCJGi6atEv1FEYBAXDksLqLygoqKfoE4YHWzx7a/MqQ+AG7JCrDSEkVqk0mleB9LCe53zepYdV7rnxgjyngM0y9da5CGVkbkJWKJcJWfok0iehNvBPjEesFVHsEWs3qHaPTlFHG5EqA4kGNZ3MBSiT63SJxKw7Felm35WsDbqlqM3Ii/T5UHr+9TkQsgRs2gqMES7ZmZH9kp25NsZ6oKpRfxWgLEDaKRi5jM0uc7N1CkZpErRAuTgCXzoVI08k+CpGeRpFgjKJM24yw6BJKknzQ9uiX6hRGAQFw5LiR15QUFCPUAqhVL+yok9Y2zBLFmPiSms7KeUUgTzfPfd88DysH7pRkM0MBS8PhK1l2PUbkovVGwP1s+xGKDQuNiA2NXlNbSSxUW7LjYHaIDszBrTJAohrza4ZBLVgZSmFm3mXomYYyPqEX85FKs+KLGrP+5O5+2SGSJ5vwAh0mm/ApCsAUlisEM4oqQuONmpwSdDs/WzzlHPbUnWT71lGaAlE4K6xlEhr8IRGCj0gH0EztBBTjJAyV4uqL1sXKQyCgmFJYRAUFBTUM2hukhakDAveHogNNkS0l1vYIXXp8TJXnhDrBSReCe2FLgeBKhOLgKoN8xwEkfGItCKOnUa+SWfL+w/al8fy6kphkcrNooMbJPffP1Mb0gaipKY25GboRUPeAWOy/ANODcntW1MYstY6NSQJvufkT31fUA5dLoK20FIODW2BpuzFtHkRZVUhFBVC3UsQ9+LFfXhJxUmOJhFCx86VqkFlSNRcqLwA7Ydor0TslYm8ckPOh0oS0BOHVBNFNZFU4ywnAXk+gtbyEDSf2V5IiVCFQQCFQVCwGjnrrD/i+c2nmF8eSdzTdN3iRz48+eEHfoznd6zSY06/+VOr9Hj1fP/I/1ptx15dmEQvv9JayKCTBIVBsNYRj9qAuKO5/5UsS7CRzhhIVECiQiJVJiKkYktUdEBvJaASe/RUFX1VSW8VeiuWSsVSrRqq1STV+zeprr87frZYoJRECJHnHsgevXTA7XkC3xP4PvksefaopHVJwkTme18b+Zo8+ZjApIPjKIE4hmpkqFYtUWyoVjRRNaFaSahWE+JqnOr3J+hYk8QJ1hiMsXn+BM/3CMoB5fYSHV1lRowqMXJkwPqjJKEvKHsx64VLGKn/Tcei+fiL5sN/FpAsXEhl4essWfAmfW900/3vHiqLqkRLYmzs2q7KkqDTpzQipG29NtrW76RtzEhKY9ejbexYWG8s8chx9HaMY5E/hqrw6Y08Xl/qsWgpvLkoYcmSiN7uKr09VeJqgo6T5u+R6krmIVhH+4XCICgYlhQ/8oKCgnqkJwYGD+oiD8FwRliLFU6xx1qbB9lKq5EyVeoRFk8YlHT+7Ym2+FoQBiId+EuEhDhyg/xM2x9c3gFINf2laEg+Vm8IeE6AyD3mWxZsm8YFMFDC01rnupPoNKGZFEiZ5TGQLk5AZa5EtVwbSgmUkiSeIvE1KlZY4/IRZCsEnq/wQ5+w5OOHKjdkwK1GJFYSW8+tngTtqHInqquCl8SUcPkd/LaQsKtM3BsR90WYxP3HSk/ilwOCjhJhV5lwZCfBqBF4o0chOrrQ5Q6SoJ3IK5PgodNVkew8gkAQhook8TDGopREaw9rmlsmECpq+h4ZVJFQtz4pcvnll3PxxRczf/58dtxxR374wx+y6667Dlp333335cEHHxxQ/v73v5+77rorf/3888/z1a9+lQcffJAkSdh222259dZbV1vunsIgKBiWrKofeUFBwfBg8BiCFpyNC4bkoYce4uKLL2bu3Lm89tpr3H777Rx22GH5+9Zazj77bK6++moWLVrEHnvswaxZs9hqq61a/iyZVJFx89+bSF2GjIqRJnYqNirCUxG+iih5AVUV0uH7VAOfasmnqhVR4gJ/o0QQJ55LCKZr7jvQmFCs0WfepgN3N+j3pM19/T1pBgT/1isJkT4fLOBXZ8HIWhJpQZwI4kRRjRVR4hPHEMWWODbEaUZjo2uJyuoH1FkGZc8ThKFLUFYOncGRaEF3JcCYTvr8kMXlkZTbNyIc20eY9OLHbgvjKjKpInSC0P0SkynV4KIV+2V6/TKxV6aq2qjaEhUTUokDqmkQcDk0CCEph4qRIxRxHObn0YrLUKW3eZcy4SlEv7GC0K31CzfddBOnn346V155JZMnT+bSSy9lypQpzJs3j7Fjxw6of9tttxFFNaPl9ddfZ8cdd+TII4/My/7+97+z5557csIJJ3DuuefS1dXFs88+S6nUQkB9ixQGQcGwpMhKWlBQUE8RPLj66OnpYccdd+T444/n8MMPH/D+RRddxA9+8AOuv/56NttsM771rW8xZcoUnnvuuZYHOMJolxW36R2cb7vErRgAePWrBdKtFngyQQmNLzWhUsReYyBwFmhrrRjEIEgDeMlUgAZX/mkwAoRFCZMHzPZXAqpXAcoNAqtIlJMAjbXKlYqcYSBS40WQaEWcQKJrcQXGDAxYVsoFCStVc2eSwqkIVRMB+CRG0qd9QlnGkwm+0nhegt8WpxKutTwImRJQfznT+oRsifVIEkXSL9Aa3KpJKTB4Kgu4zq63aC2GoNT871oqhew3MdD/9fL43ve+x4knnshxxx0HwJVXXsldd93FT3/6U772ta8NqD969OiG1zfeeCNtbW0NBsE3vvEN3v/+93PRRRflZVtssUVL7WqVwiAoGJYUs4EFBQX1CDXITGBS9AmrgkMOOYRDDjlk0PestVx66aV885vf5MMf/jAAN9xwA+PGjeMXv/gFRx11VEufNX/sDvR0tpapeNB20ThBpISmJCqUZE3BqJnMwcs77sD3mx+s2tQlydQZB5mhYJCpS1HNaMjyGNQ/z85hqAG1qDNqBuQ8yMozw0W4Y8f4JNYNH+tXNepO0j2Ywa+FJzSe0pQUWL9/EPXKT9x1d7cQVLyS/UIURcydO5fp06fnZVJKDjjgAB555JGmjnHNNddw1FFH0d7uYmOMMdx1112ceeaZTJkyhaeeeorNNtuM6dOnN6y8rWoKg6BgWDJ4DEHx519QsK4iBpkJLCYJVj8vvPAC8+fP54ADDsjLRowYweTJk3nkkUeGNAiq1SrVajV/vWSJG+Q9+MImTWcqzqQ6M79937OEabbikp+4bMXKZSsu0VtT0Yl6UFEfKupFRBVEVIEkqWUqro8qFsKtNKV5Daznp/KmPtarucwY5aFViFUuV4GRHlp6GKHyzQ6iDZqvZKBzPX5lEoTVSJOW6STP7Ct1kiZQS7X7TS25V/+LY4V0KkBSNbQx8UKXTE2GbrMBkfVdPoVsZt+oxiRldcnJshUTT6axGdLgS40nEwKZEMgYX0QEpoJvqnhJFS/uQ+kqst4NKYlBx6kea/MxgEt6W0hct4x+IbvnMsIwJAzDhrL//Oc/aK0ZN25cQ/m4ceP485//vNzPf/zxx3nmmWe45ppr8rKFCxfS3d3NhRdeyHe+8x1mzpzJ3XffzeGHH84DDzzAPvvs0/T5tUJhEBQMTwax+ilmAwsK1lkG9RXu30cUrHLmz58PMOiAKXtvMGbMmMG55547oHxpjyVuwX+k3iUm9J07jQ5ErtvvSYVGYoVwg2+jkTp2kprVPkSlF1utQBxho8gZBJmEtXTqQlYphPKcUeB5CN8HPwA/RBqNNQl4IcJaNEHD4FzIms6+taIhz4GwJnfFkVa7IGgTO0PAJEgT5+0VOkHq2BkAOnY+/UanmcNMahw0Zlm20nP5GDwnDSq8EOE7A0JYi/VSlSYhUUi0aPy95JKoJluNSNuNQGExNn3MzkdkKxFp4jGb5MaAF/c6YyB2BpiIq4MbYc3Q23yeimX1CxtttFFD+dlnn80555zTfDua4JprruFd73pXQwCySe+vD3/4w3zpS18CYKedduL3v/89V155ZWEQFBS0QuEyVFBQUI9QcpA+YcViCFa1osiqDLodLkyfPp3TTz89f71kyRI22mgjJoyBthbUrLNBaL2ij5f69idGUkl8tJH0yRK+HIHXVvOPV9YNWt1g3M3I00KCrCFa1PBKGo1k6JgIlyk5DTCWHon08wRpRkgMKncZyp5rKxvci7LnA1tSi1uoTwDmBu3Za4PEEIoqJVVBKFeWrVwALWURdiflNi08+oIubDiytf2Xw9Kl3cBZTdVdVr/w8ssv09VVW43qvzoAsP7666OUYsGCBQ3lCxYsYPz48cv87J6eHm688UbOO++8Acf0PI9tt922ofyd73wnDz/88PJPagUpDIKCBmbMmMFtt93Gn//8Z8rlMrvvvjszZ85k6623XtNNa4lBXYaK2cA1wnC5pwrWblbVCsHqUBRZlUG3bzeyQdGCBQvYYIMN8vIFCxaw0047DbnfYO4ZAONGVGjvCFpuR71ven2G3r7Epw8/L8tmuU0/3/uhfNv7BxErafCEwZMGT2rnLy8SfBHjiSQ3MpSJUSZxM/225tZTH5iLSDMgKx8tfbTwcheeqgmoGp9Ie1S1RyVWVGJJJZJUY6jGLl9BrjaUJizLUMplPQ58l5gsDKAcOKWfcmAo+4lLSuZVKVEhtDWFIS/uRUV9iCRCxpFz6+nvSiVF6jblVh+MF6D9kktO5peJVcnlg7Bhw7lEOnVJSpySUpaLoZWg4t6WEpMNLUDS1dXVYBAMRhAETJo0ifvvvz/37zfGcP/993PyyScvc9+bb76ZarXKpz7VmAsnCAJ22WUX5s2b11D+l7/8hU022aSZ01ohComFggYefPBBpk2bxqOPPsq9995LHMccdNBB9PQ0nxTs7UBm9Tdurd/ul19+OZtuuimlUonJkyfz+OOPD1n36quvZq+99mLUqFGMGjWKAw44YJn1P//5zyOE4NJLL225XWsTw+WeKli7EbJ/f6AQsnWDoF5RZNttt+XKK6+kra2Nn/70p4PWHz16NOPHj8+3e++9t0FRpH/Q7Q477MANN9zAq6++yi9+8YuVOeW3BZttthnjx4/n/vvvz8uWLFnCY489xm677dby8aqxR2UFtmqiiLTKB5zaON/3jEwSNFCaQGlKXuIGxUFCexjTUYoGbF2liI4gpj2IaQ8i2v2INi+i5MWEKna+8jLGEzoPzM1wCdMURrqEaVr6aBU0bEn66OIN3MqASefuM7KZfSUtnnK5FAIPQh/CAMJAUC5JSqGkXJaUSuljKGkrS0olQTl0xkApsISeJfQ0gUoIVIIvEnwR4ZkIpSPn559mJ5ZxBEkVEVcRSTxgI47S9yJkEqESt7+fVPB1Fd9UCUSVUEaUVESoYspeQugllIOE9lBTDrTLnFwyzlhpZguaX7EYOE4Y6F2wPE4//XSuvvpqrr/+ep5//nlOOukkenp6ctWhY445piHoOOOaa67hsMMOY7311hvw3le+8hVuuukmrr76av72t7/xox/9iP/93//lC1/4Qktta4VihaCggbvvvrvh9XXXXcfYsWOZO3cue++99xpqVeusCpehVmcC58yZw9FHH83uu+9OqVRi5syZHHTQQTz77LNMnDixoe7tt9/Oo48+yoQJE1o/ubWM4XJPFazdrIo+YXUoiqxo0O3bie7ubv72t7/lr1944QWefvppRo8ezcYbb8xpp53Gd77zHbbaaqt8BWTChAkrpJjy/Esepbbmhy4yldfMkoP56WA58Cwl3xB4mpKnCZUbxPsyJhRVfOsGwZ6uonSMNGnQrh2YMdfJbMo8UDeb2c8G/SAwMnXtScsTGbgVCU/kykP93Xqy1zVlIZU+CqQwhDImkAllFef5CrLcBbmrUD+1oQxRJ4fqErMZfJG4wF8R4dvIBfzGbgCvkgoqH9xXEenKgEjSoN96KVirwQqEsVhrXIBzXEXEFaTy8ZRPoDzalI9RHkb6GOW7IGvpY5SsC7SuSZg2y1K/+UzFg6oMtdgvfPzjH+ff//43Z511FvPnz2ennXbi7rvvzuNmXnrpJWQ/ieN58+bx8MMP8+tf/3rQY37kIx/hyiuvZMaMGZx66qlsvfXW3Hrrrey5554tta0VCoOgYJksXrwYGKibW89QahBrklXxI29VW/jnP/95w+uf/OQn3Hrrrdx///0cc8wxefkrr7zCKaecwj333MOhhx7aUpuGA2vrPVWwdrMsg6AZNRFYPYoiKxp0+3biySefZL/99stfZ77/U6dO5brrruPMM8+kp6eHz372syxatIg999yTu+++e4XcoRYurBCWm3cZUsplGPYDSRhIyiVJHEJ7SeApQQAo6QyCsuyjbHsI4x6Cajd+1IOMepFRFaJKOvjtF+CaJj6zSjl1IamwfoBVPiZzlVEhxnMz/kjQqf+/xnM5BvAapEWXRb3fvxJ6QHZj2S/GQTC4r40zK5xBkOUSUDZBaefK5FYDIhfEnM7wizR4OVf/0bpRAaghlkACBqFduRUakSQui7BUteRlMlVnEtI9FyJ9TGMnROsr+153C6vPSrmtf1mLnHzyyUO6CM2ZM2dA2dZbb51nvB6K448/nuOPP77ltqwohUFQMCTGGE477TT22GMPtt9++yHrDaUGsUZZyR/5qpgJ7O3tJY7jhoGvMYZPf/rTfOUrX2G77bZruj3DhbX6nipYq1nTaiIwuKLIcGDfffdd5uBGCMF55503IHhyRdh4ozLlthYy0cpaBmEnPepWBwLfECiDr9wgNjYKQQkrJXEQ4vmd+LqSD46lzaQ8GwOA87wCmYxnthIgFVYo5xYkaqsD+cx3XaCvRCOQIAauEphslcDWQnzrA4W1kXXxD6JhhcC1TzSsDhhoyDeghEVJl5BNCU2gYjwvdisEuoKnq3iqivKqtRUCIfJWWnCD/vzI2YUXoNxAPzeUPB+rPCd1qny0F2CkP0CKNXePSldehgqKHopu2eIKQSFAAhQGQcEymDZtGs8888xyo9qHUoNYk6zsbODKzgQCfPWrX2XChAkNrgAzZ87E8zxOPfXUps9lOLE231MFazlObmZgGc2picDqURRZ0aDbdZX3bLqY9o7WlX4agorrBtnZnHZV+1S1zxLa8rr1890WMWjUZTZDX5/ES0ItIzEWlSr1yFSdR2Wz8+i0jkFQc0US2JpKUKoglAgvzdorXQyErWX6zYJwE+OyFSdakGhINGjjVFKzgGJjnRuVqDOSAg8C32UJLqUuVCXP+fWXVIUwqLiAYr/P5WiIPaRUyEiCiPsZBeSz/06GNXT5GPwSxi+ReCUSv0zspUHFhFTTwOJYe1RjRZTUsjC7jMWtZSpuKai4MAhyCoOgYFBOPvlk7rzzTh566CE23HDDZdYdanl9TbKsH/lbMRt44YUXcuONNzJnzpx8WXzu3Llcdtll/OEPf0C04A85XFjb76mCtRwlB1k1dCO8ZtREYPUoitQH3WYGQBZ0e9JJJzV3busQHaqPDq+1oUv/TL/GSueqk82oG4WGvMzUbfVJtwYLVc1n24ULTJbCooRBSjf77gmDkRopJB614GJV58pT17K6Mrf6YJCp8eD0/POVAZtt0hkARlCNBXEiiBJnDCTpo9bWefZYF8QuhMgm8Ak8p+JjrMBaWcslIHz3ScoipEWqND+Bl1oWaUWRCIRUWNNfZci5UTmFIZecrb8xUKXkjAHtU9EBkVZUYmcMVOOaYdOqylBfXwv/r8voF9Y1CoOgoAFrLaeccgq33347c+bMYbPNNlvTTVoxlvEjX93awpdccgkXXngh9913HzvssENe/tvf/paFCxey8cYb52Vaa8444wwuvfRSXnzxxWbPbq1i2NxTBWs1QnkueVS/slY5/fTTmTp1KjvvvDO77rorl1566QBFkYkTJzJjxoyG/YZSFBFCrNKg2+GOL6oEonXZ0XqPk1bcT1aUwXz3By9btiJOlhdA2SRfgVBC4UuFthKtJInvDJzEyIbswcYIdGbM2PpBtc0Xy6SwTjoVJ6GqpHsv1hKdDta7RRtSjHSfHVpkyeSGTX3egv5khlj6ibkxZhFY7aIYTOre5EuNJwwlL24IiHbHaY2e7lYyFa+afmE4sG6edcGQTJs2jdmzZ3PHHXfQ2dmZB7WNGDGCcrl5v801zbJ+5KtTW/iiiy7i/PPP55577mHnnXdueO/Tn/50g/sQwJQpU/j0pz+dDyaGI8PlnipYu8lkR/uXtcrqUBRZlUG3w51M/aZZ6pNnCWyq+W8RaaKxLAtwFh8gTFqezni7xzQ/QB48m0+jp49yQFBsthnpu8c0nqA+riCPJeinpGPrfJNEuk5ghcClHhN5XYvEKjFgBaT/c6jFImTU8i3UlIkyo8JY4dySEpG77SRaYAypCw/5lmcrbkyCnHvoZW5JbnPSqIGy+J7BlxpfuccsZ4MS6SNJHvCcJUJrlqVxd9N1V1W/MBwoDIKCBmbNmgW4ILF6rr32Wo499ti3vkEryKr4kbc6Ezhz5kzOOussZs+ezaabbpoPfDs6Oujo6GC99dYbMDvo+z7jx48f1km6hss9VbCWs4rURGDVK4qsyqDb4Y5nnBxoK9SMAmcICOsGmFlCsNwYsGbwgX9+IOneqzMEoJZEbIAxILzcGNDKxwrlpDXz4GKZuwX1D/7NqEmPNkqT9lclsoO8rse5HNXnL3CnJ4Rt+FwhLMKmhogAiXDSrdLV62fr5saA6H+pRC2YW4p0JUJkxoLtd4zULUtkjlMidZQSqcuUGLDPsjCtKBOtwn5hbacwCAoaWJ4M1lrDKviRtzoTOGvWLKIo4ogjjmg4zupSLFlbGDb3VMHaTfHHPywo9S2irOKVO8ggMVxWuNl7pN+81GU2oy9kqpkvGgb9Ti3HyYpqqzAoEqvqYhhkGqsgW3aPGSpz8oB6TbpHZceTANISSI21gtDTtWzNdccydvAVB2i8vFLUZFJFagzIvJ5bxzBAYjy0tQi8fPBf72LVSthdt27hd130CzmFQVAwPFkD2sIrEgMwXOMGCgredhR//MMCP+rFb22BoJbUql7bPpMJrUsolkmG2gYN/Nqgf7DkWFn9+ll/k+oKaeuRpIpABueOo61ywcBGpfKgoG1NOhSymfuhz6d/LMJgA/Dl7TPY/tlgXWUD8lwpiYZBev8B+1BtrTcS6pWdBlN50mlQc1ae0Wr8AEBP3IIghZD5Sk9D2TpIYRAUDE+KP/+CgoJ6pFM+GVBWsFbR2zkG1dGxwvsL2/wQs75u//wD9dRWFGrGRH18QJal2Pn713IQ1Hz9B89UDAPzEAwWI+DchxrdhuoH2/WBvQPaXrcyYa0gsam6ka0/JrXXeebjfvv3iyGAmvpSZlBkKkxC0KDEJIXBk7VEadn79UnYWsEPepuvXKgM5RQGQcHwpPjzLygoqKeYJBgWvCo3oUMuXyI2o342O1PGkcLN4SuRpB78GmWSPFuvTGMKpNF5wDG2Jgs6wKjIJDjT2ASyxzRoOQ9KrnvtApbTLL9DxiwIkF5DRl+jfCflqTyXAVl6aOWyIGvhkcgAjUJbD41bkXCD+MbZd5MP9CWJlWAUsRXEWhJpSZwIqrEkTqAaQxRDNbLEsSWKDUliSWLjJE21GRhULF2G6CCQ+L4kDBVhICiFUA6gFFjKgabsJ4SewZOaUEaEooovIjwT45kIaRIX+G2GNsj6szR66zMVDwcKg6BgeFL8yAsKCuqRHvSXE5TFX+DaRk9cgrh59aXMT12INCuv0HWPCl/EKCHyem4y2jpjwCROdcgmaeBx3WC/nrpB/wB1Iq3BJGA06NQg0O4992iwpp/QvjWpK4tASImoy/QrPQ/rO11/5QUYL0CrEOWFaBWgVJIbBqmp41yZstUDUVstcLEMFoxzq5JpfgIglS2FKIFq5IyBSsVQrWqiyBDHmjjSJLFGa4M1NjcKpACpJEHo4QeKMFQkiYfWEpAo6TJGZ8aJEhZfJAQiIqBKkPS5DMk6QuoovfbNKw1VKy0YBKmhNaBsHaToDQtWGzecP4LOlVja7c/Sbo9t7mmycvHnPyyxxmJNa8vHy+OSw65bpcer59vPfm61HPesra9YLcddnZQ62lbLcZO4SReQZWQqLlh7+PuCMuXu5uWKZap0E3gW37OEniX0NWUvpuTFCGXxSPBNlTDqxo+68ao9yGoPotKHqPZiqxWIIqzWWK1d6t+6DxBCuEel3EDe90AqJ3XteaDcYB4/wGSDe6mw0sulShkiRmF5CKPxdQ9+K7PipIo+6YqDUQGxVyLxQ2IZEhFSsaU8YVhf7FFJFH1VSTVWVCJnJESxdSsFiU1XG9KszULgeW4LfLcqUEpXBdrDhLYgoc2LKHtV2mQvJd1DGPfgxz3u2kd9yGovxBEijkBrrE6Wc0Y1/N5K8xdCDjJ5WBgEBQXDiMLqLygoqKfoE4YFz/+lh7Dc3PcmhEApQRAoSiVFuSzpaJN0tilGtElsSeCLBKk0ftJH2Pcm/tI3EItfxyx6g3jRYqLFS4kWdxN1V4h7q+goySclhBRITyE9hVfy8dtCvHKI117G72hDtpVRHZ2Itnbn+hOG6LAdHbaR+G0kKiRWoXPzEZ6TH01jAGoJv2ouTZ6J3KOuIpMIL6kgkggZV5BRBaIKIo6wURXiCJsZMXHsnPytm8kXUiCERHgKEQRuC0tQKmNLHeiSa2McdlL126mW2qjYNiompJIEVLRHNVG5a1GiZZ7R2V33Ws6B0NMEnskNsJJMjYCkm1JlKX61G9W3BNm3FNHTje3twfT1kvT2oStVTBxj4sStojQ5GdRTbSHqvOgXcgqDoGB4UrgMFRQU1GGVwvbrA/q/Lnj709EZEJaby1TsXFcEgS+d/3pJUA6hFBgCzxDIBE8myNRHPXdJkQKUQvg+0vdQYYCXOB926akBBoEKPFTgOWMg3WSphCyXIQwhDLGlMiYoo4My2isRe2Vi5WbkE+s5n/8si29mEFjrgm/RqFTK1BMRVkg8Mneb2qqCxHk8iexRSEQSu9UAa8Gkbjd1KxoiCMB3m/X8PF6hQXGpPlGasP2ChbMYXNtgEGS5C4QYqHzUoMwkFVL5oHy3mhIECJ0gtEal11go5VZmmkS2sNJS9As1CoOgYFhiZZo1sl9ZQUHBOopUA90G19GZwLWZA99Tpb2jhUzFqYqNFBZPaDyR4IvYPZoqvq7iJVWUjrBSocsdSM9DdIzEGxvhx1VIEmwS5z7/Dc7ywqnUiNQlyHo+BCWMH6D9Um4AJF5IokIiVSYRPrENiK2H1irNDpxudW2X/dqvhHGxD57G8xL3nKRh9UCaBGXiAZmX+8c+ZC5DWTZlrYI8ODmWIbEIiGxAbHyi2CPSHpFWxEYRJ5JIN2YwNrYxgbOSkGhJ4lliLYg9SaQVFeXTp0JC1U5YGkFQrhJ0VPCTPvy4D5VUkEnkNh3jJYmLwWghn03c0wdc21zlol/IKQyCgmFJ1tH1LysoKFg3KfqE4cFm/IVOWohNszg1H1IVIGyj8k+qHiSswUgFfgnjBYiSbVD/aVAWqhv5WiHzmACTDq6NF6Az9R8ZkEifBJ/E+s4IMG5gneUicEnKajKfGZncppS2wThQ/YwEiUFJjVJuNSFXU8Kk52YHle6sz5SsrSJJE6hl7YvzdkpiLUl0zRAwBhIt0P2MgfpLZK3AWIE2Fm0EiZIkniQ2ish4VGRAIMv4MsYPYvzQqQspE+fGTZZNuhWDoK+7+XiKol+oURgEBcOTwi+woKCgnsKNcFjwAu+gndZkR7OtfgAthcHLZtiFkxtV1s2uOxeiuKY0ZI2bbR9sYFqX4CyfcRcKKyTCWqTVeIZ0IK/xRboSIBUa97w+f8Cg55AnALMN2vwuxiBzMjINScMgdc1BNZT1J/tMKQy+dSsnVkhK0h2xvn1ZjEMmWZp/xiBZk2vqTrWcAjK99p7QueGicC5b7rprpDX59c7bJySIFnJItBKcvYr6hcsvv5yLL76Y+fPns+OOO/LDH/6QXXfdddC6t912GxdccAF/+9vfiOOYrbbaijPOOINPf/rTDXWuvPJK5s6dyxtvvMFTTz3FTjvt1HK7WqEwCAqGJYVfYEFBQT3FTODqY8aMGdx22238+c9/plwus/vuuzNz5ky23nrrvE6lUuGMM87gxhtvpFqtMmXKFK644grGjRvX0mfdeHdCUGpecUZIgVISz1f4viIsKcJAEoaSUuhiCsohtIWGkq8p+QkllRB6Mb6ICUSEJ5yx4AasetCBaT5Qb8hJoPF0TTIz269Wd4j8Aw0HrmVKdoZGlvxMNSRAyzIl23xtQOYDeBg8KVn9SkJ2fiqbodcxKpX9lDpGJhFCx86/P4lBOxcqkblRDZZDAZzCUupWlbnnuP9nL1dZcs8zY6qmvJQZVhZaUmBKVPNBxe56igFlrXDTTTdx+umnc+WVVzJ58mQuvfRSpkyZwrx58xg7duyA+qNHj+Yb3/gG22yzDUEQcOedd3LccccxduxYpkyZAkBPTw977rknH/vYxzjxxBNbas+KUhgEBQ3MmjWLWbNm8eKLLwKw3XbbcdZZZ3HIIYes2Ya1iOtovAFlBWuG4XJfFay9FAbB6uPBBx9k2rRp7LLLLiRJwte//nUOOuggnnvuOdrb2wH40pe+xF133cXNN9/MiBEjOPnkkzn88MP53e9+19Jnvfriv/GDvqbqCimQUqJ8hed7BCWfsORTKgeUyh5tbR7lsqK9TVItS9pLqWJO4Fx4QiWx0mUX9oR0icysasxgTD93JAwyG/inhkCW3Kz/SsMA3/7+hkYmRZo+1g+Ys5UI56bkZXmLMSiMIAtLdodNh/393ZGsTVdPUpepzBjwkwoqqaDiKjKuIGKnXiSqFadeFFedDGscY5JUAah/0G+qYoRSLnjZ88H3EJ6P8HxsfSCzF2CVc7VCWZcJWaYrG5kr12piVYwVvve973HiiSdy3HHHAXDllVdy11138dOf/pSvfe1rA+rvu+++Da+/+MUvcv311/Pwww/nBkG2WpD9Z74VFCOkggY23HBDLrzwQrbaaiustVx//fV8+MMf5qmnnmK77bZb081rGisG+fMXxZ//mmK43FcFay9Fn7D6uPvuuxteX3fddYwdO5a5c+ey9957s3jxYq655hpmz57N/vvvD8C1117LO9/5Th599FHe+973Nv1Z7//wVpTbW3EZcgGuSjopTD/NRxAoQ6A0gYoIVUwgEwIZE4gqvqnimcglx4oihE5yF6L6AXzDTHI2aE8TiuXqPNlzqTB46ex+Wi9T8RFDz+IPhdu/kWwFo9njWCGhLm4hViUiVaY3GJG2p+aQVO/WlKkOZY5KgyGz+Af0AAlVaU3eVplmhK7PDI21aTB0PHjsxnJoJTHZsvqFJUuWNJSHYUgYhg1lURQxd+5cpk+fXjt3KTnggAN45JFHlv/51vKb3/yGefPmMXPmzKbbvTooDIKCBj74wQ82vD7//POZNWsWjz766No1cJNykBiCQmVoTTFs7quCtZZCeeytY/HixYBzjQCYO3cucRxzwAEH5HW22WYbNt54Yx555JFBDYJqtUq1WlMTygZnh2zwh5YSXmauOlkwcaa+I3WMSLRzgTHaucMYPdAdJs0wPGhG4TSTcOYOYz0f0tlv6wWYNEBZeyUMHolXcgHGMiARfkMgb72vfu3wdcPxdECdyZA6//vaoDp3aRpscF0XPA3khopRPlp6aT6EEpEIqZqQviSkogN6Y5+eqqK3IumtQk+vpbfPUKloKn0JUayJq5ok0Wht3fUBhJR4nnPTCkseYagot3m0lRVtJUFHG7SXDB0lTbsf0eZFtKk+SvQS6l6CuBdpYlTsFIeESb+HJvF7e5uuu6yxwkYbbdRQfPbZZ3POOec0lP3nP/9Baz3A9W3cuHH8+c9/HvJjFy9ezMSJE6lWqyiluOKKKzjwwAObb/dqoDAICoZEa83NN99MT08Pu+2225D1huq41ySFe8Dbl2buq7fjPVWwliOk2/qXFaxSjDGcdtpp7LHHHmy//fYAzJ8/nyAIGDlyZEPdcePGMX/+/EGPM2PGDM4999wB5cq4JF2tMEBVKJt1zhSC0joW3KhIKYTW2HRFgCwOABqTY8lsRUA6DX3pYXzn/mK9AO0FGBWQeKVUcahmDCTWJ7HKGQNIl4Ogn8oQNgseBmldUK62FimUMw6yGXeZRgxYjZRpnAOpq1J+vv1n14ULbBZeQ5u0VXkbBDZfVQmNwJZFOtgXhKEiig1x5IwBrW39ZUEqie+7LQgEQSAph4JyCULfHVOKWhC0RpIIHylDhGdTdygf6cfOeGtBZUjr5v/rlzVWePnll+nqqq1G9V8dWBk6Ozt5+umn6e7u5v777+f0009n8803H+BO9FZSGAQFA/jTn/7EbrvtRqVSoaOjg9tvv51tt912yPpDddxrkizAqn9ZwZqjlfvq7XhPFazdFH3CW8O0adN45plnePjhh1fqONOnT+f000/PXy9ZsoSNNtqIl4Mt6Qg6mz5Of7nNesWe7HV9WebqUlPzGRgvUE/NT7/RjabmXtP4XOu0nqXhvfx4gyj2mLS8pvaTvrY0GBLWkmYMzuo0HrMhfYLIFIDSQb/U+MrgS00gE0pBD2PCmguVr12uBi+pNAYap7P3DQpM/eRYXeCwXzOQspwH/SRZe00bS0wXCRIjJQkSqwTaE0Nem8HoiZufQFpWv9DV1dVgEAzG+uuvj1KKBQsWNJQvWLCA8ePHD7mflJItt9wSgJ122onnn3+eGTNmFAZBwduLrbfemqeffprFixdzyy23MHXqVB588MEhB29DddxrkmKF4O1HK/fV2/GeKli7KfqE1c/JJ5/MnXfeyUMPPcSGG26Yl48fP54oili0aFHDKsGyBk2D+WsDLOjuZGkLsqP9GaiXXxtkmibr1ZMbFunbWb6ALEOvEM6QULImw+kMj0ajpP5Y9Z+ZJSvTRjotfyNJjMsFECfuMUog0RDH6WNiSRKLMRatQWuDtbZhccPzXAbnUkk6N56yT1dZ01VyMRXtsoeO6A3aev6Dv+Q/iEX/Rr/xOvEbi6i+sZi+15dQWdRDZUmVqCci6UvQsbuCypd4ZY+gPaDUFVIa2U5pVCfh6C5K641CjhwFI9cj6VyPqH00PaXR9IguqtqnOy7RU/XpqUp6q4IohigGbZpfIaj0Nj+Tv7L9QhAETJo0ifvvv5/DDjsMcKtk999/PyeffHLTxzHGNKyKrwkKg6BgAEEQ5JbrpEmTeOKJJ7jsssu46qqrBq0/VMe9Jin+/N9+tHJfvR3vqYK1m6JPWH1YaznllFO4/fbbmTNnDptttlnD+5MmTcL3fe6//34++tGPAjBv3jxeeumlZbqjDsaGnW/Q0RE33zbqZ9ada45JB9qJkSRWok36XIt8sK1NLQFXffKtwdQ13eDfBS5LCUraPIhZSosnLRaLJ01DYrHsMXcL6rcC4fIApG0VkkRKhAaQGCMQotamREOUWOLYEkWWODYkiXs02qCNxWYJ2qRASYEfKKpVRRxLolgSJ4pIh8RGEgeK2A+Iu0q0hR0EbV14XaMorbeYYOli2pYsJenuIentQ1diTBxjEufnLz2F9H1UycdrK+N1tKPa2xCdXdA1EtM+krhtBJXSKHqCkfSYdrqjNrrjgJ6qR08as1CpQhS7c7LWYppMQ1DpbbIiq6ZfOP3005k6dSo777wzu+66K5deeik9PT256tAxxxzDxIkTmTFjBuBWwHfeeWe22GILqtUqv/zlL/nZz37GrFmz8mO+8cYbvPTSS7z66quA+72AM66XtfKwMhQGQcFyeTtYrq1iB1kGLBRF3l6sjfdVwdpL0SesPqZNm8bs2bO544476OzszOMCRowYQblcZsSIEZxwwgmcfvrpjB49mq6uLk455RR22223lhSGALb4y/+jq63c/A4qC/YNMUGICdpIgjJR0EE16KAi2+jVbfQmJbrjgLjqUYkkPRXorVgqFUulookiQ5K4rf/AejB/+TAQlENB6EMpsJR8g/QtgbBO0UgleCLBF4mTM03jAep95Y1QaBTaekTWJzI+VelRFR7WeiSpr3ycQKVSC/jt7U2IqgnVSkxcTYijBK1NPmAHN2hXSuIHTo611ObT1uZTbvNobwvobA9pL42gozyOjjChrSumPLpKWfYRUiHQfU6eVLvswpmsKlBTVZI+sQqoqIBIlYhkiaoNqeqQ3iSgL/Lp7Vb0pasBvRWoVC3VqqYaufiEJHbX3LQQQxBVmg8qXhX9wsc//nH+/e9/c9ZZZzF//nx22mkn7r777jzQ+KWXXkLWCRj09PTwhS98gX/961+Uy2W22WYb/uu//ouPf/zjeZ3/9//+X25QABx11FHA4IHNq4rCIChoYPr06RxyyCFsvPHGLF26lNmzZzNnzhzuueeeNd20llhViiKtZB+8+uqrueGGG3jmmWcANyt2wQUXNNS31nL22Wdz9dVXs2jRIvbYYw9mzZrFVltt1XLb1iaGy31VsPZSqAytPrKZzf7+z9deey3HHnssAN///veRUvLRj360ITFZqySvvEJSbn71UHgKEQSIIEC1tSPL7chyJ6ocIUsaQtDSI5IeSngIYd2MewJxbPMBdrWa5Io6DQZBmvTM8yR+oNDGS2eyJUoJpATfNLoj1Wfs9USMZ+NcJah+UG2EQqZtcmUCLSSxTF2QhAVq8QHa2NRo0blBEFVikjhBxxqd6DT3gEBIged7JLF2W2Lc4Ds2xJFHHHtUypJKpOgJFe0lj7IfUPLaCFVMqGK8IMEXMUpopFvPcO3MEqNZSYwLVI6MTxx7VI2imnhUYkklcoZAJXIGTTWyVKuGalU7YyA1wHTiXJ6aJao0H3S+qvqFk08+eUgXoTlz5jS8/s53vsN3vvOdZR7v2GOPzX87bxWFQVDQwMKFCznmmGN47bXXGDFiBDvssAP33HPPGpfDapVVEUDYavbBOXPmcPTRR7P77rtTKpWYOXMmBx10EM8++ywTJ04E4KKLLuIHP/gB119/PZttthnf+ta3mDJlCs899xylUmnFT/htznC5rwrWXoqg4tVHM4O1UqnE5ZdfzuWXX75Sn+VNnIjX6gqB9EAppwCUyYEqD2k1vq7Sppbi+TFlVWVkGFBt96mM9oi0Ik4kkQ5JdIlEiwFuQ5nLUOYmJKUL0lVC4ynnJqSkwRPGPRcaTxqEsGgrgQCNS8Il5CAxBCgS49SItHX3qxKWQDljxlOWwHdJ1aqdHlHiUY1C4tjFEcSJxaQqQNaCMRYpnREhlVvhUErgedkGgSfwfQi8miKQEGCsIDYSg09sFEpYlNQDXJ5MbqSIPA5Cp4/WCqSwhJ5BCSgFOFctLdBGYoxEGw+dumoN5qa1PPp6mv9dF/1CjcIgKGjgmmuuWdNNWCWsih95q9kHf/7znze8/slPfsKtt97K/fffzzHHHIO1lksvvZRvfvObfPjDHwbghhtuYNy4cfziF7/IlwSHI8PlvipYeyn++IcH1fGbU+1ob6puQ/KuLPlXXcIwrCWIe/CTPtrqkoRZIcETWD9VBBKNSkD9j28R+XMjVPraDeYtIs8x4IRCa6+1VSTWG5B/ICMbWPdXJPKEQSiLrwxtvkCXBlccMjYzYNK2DXGdsrMYKE1af/ks2gi0UcDyfzd5kHVdYHUWaO1Jg68s0nerJfVB1llMRXaM/nEVzdDd3bzKkPvO5YCydZF186wLhj0unfvADZxiTf02mB97ln2wPpFOK9kHAXp7e4njOE/O88ILLzB//vyGY44YMYLJkyc3fcyCgoIVY1l9QqtcfvnlbLrpppRKJSZPnszjjz/e1H433ngjQohcjSRDCDHodvHFF69Q+4YzWoVNb0b6tU14GOE5//B0tJolKpM6ziU1/biPIOrBj3sJom6CqJuwupRSdcmArVxZRLm6mHK0hHJ1MWHcTSnudsm1dIXAVPBsjCeS1E2o5sOfBTnXBzfHRjVsTllIDpq4LBs8K6mdVKjSlLyEsp9Q9hLa/ISOIKY9jOkoRXSUIrr6bZ1hTGcY0xa4rRwkhL4m9DWBp/E9g6cMsm7lwlqBTrfEuC3WcpAtC9B2wdqZcZIhIQ+sdqsnSZotOiFUMSUVUZJVQlWlrCqtbbL52LRsoqD/ti5SrBAUDEuWNRu4OrMP1vPVr36VCRMm5AZAFmg32DGHSs5TUFCwalhVKwStuhJmvPjii3z5y19mr732GvDea6+91vD6V7/6FSeccEKuyFNQxyCqMMsiT0IGuf9J5uveP1GZsBasqQX21mnrNxynDisVZEG0ynPGh/JJVICRHki3wgAKg3P7SYyXqhsp92gFxtRWIUzqVgMMKlXaX6Eoe569dsaHdUHK1JJ/1edRqF9xcO1Kw5qte65zBSaVDvyd4aItGCNSl56aC1X9gL9ecrXRjaqmtqTSpGRSuNwHSrjNEzrPxJwlWxss/8Oy0PQ0XdcIiem3ItD/9bpCYRAUDEsMg/z5s/qzD2ZceOGF3HjjjcyZM2dYxwYUFKwtZPKT/ctapVVXQnDZuT/5yU9y7rnn8tvf/pZFixY1vN9fRvCOO+5gv/32Y/PNN2+5fcOezGm/SbKEYfmgH9NgCDh1HJsn1xJZduK6zMZYgzDusbEtEuvS8mKVj1A+0gvQ1q0EJIAUCvDTmXWZuglJIu2Mglir1BWnllAsIzMAlLS5q42UIBAIYVyZ0KlakQvuVSQok+RBytIk7rzqMzRDnXuUQksPLf00a7FLEhZJn8R4RMIiROb65IKYja33+6/5+tejpJNF9ZQzCqwFPBAGZPr9ZasEzsDR+Tl4NkYajbKu7dLWGXBNECfdTdWDwpWwnsIgKBiWmFShoX8ZrN7sgwCXXHIJF154Iffddx877LBDXp7tt2DBAjbYYIOGY+60007LPaeCgoIVZ1mTBEuWNPocD5UHI3MlnD59el7WjCvheeedx9ixYznhhBP47W9/u8x2LliwgLvuuovrr79+uee0LuJVu/H91v3K67F1BoWVqnFGeBBjYyif8iwWIZPZ1NKtEGjpkcjADbCtR2zd4Dq2HolRJMa5ByUmy4kwMLswgBEgrRt0mzSOQVqLEYLESjxjUMJDCh8lQ1Q2qy4MUqYRC8q9hoErBFALXHYxDTINAK6tElgr0sRqJk+wZjyB9t2qxmAZkeuTtal8pcAZNUo4F6SsTcZKkjTuQVuVGgjuXESeo6G177vbD5quu6x+YV2jMAgKVhvHfGMxnt+8/NfySOK3bhlwRbMPXnTRRZx//vncc8897Lzzzg3vbbbZZowfP577778/NwCWLFnCY489xkknndR02wpWLXE1Wm3HPme7wZP5rSxn3Pyp1XJcgEsOu261HLfS3bw2eCskcV9T9ZY1SdCMGyGsmCvhww8/zDXXXMPTTz/dVDuvv/56Ojs7Ofzww5uqv64R/Oclgt7mVIbygbxSbhbf87Eq3bwA7QVor4RWAYkXksiAWIZuEI8bxGcz+plSTuZm0/A56UDYGLC6MRA4D+wdEOg7+GC68bguENjY/p83cB9jG9/rX3cwMpcetwrhXHtUuiKhZM21R0lNWcZIL30tdOrClAUE2wFuPbn7U31Qdd11zJ7HRlHVflom6gykxmvVCr0tBBUvq19Y1ygMgoJhibUKY9WAslZoNfvgzJkzOeuss5g9ezabbrppHhfQ0dFBR0cHQghOO+00vvOd77DVVlvlsqMTJkwYEGRYUFCwaknnTgeUwepzI1y6dCmf/vSnufrqq1l//fWb2uenP/0pn/zkJwtXw6GoVJqWQxGQGwMoVRsdC4ElcI9SoZUzBLLEWZEJiLRHZJz0aH0WYyejuepPS/Yb89Z/Rm5A1GVLzvz3Xf6BrKz2fubhlO1Tf6yG7MoKPCXwlHPv8T1L4DkFIzwQSuPjXHsCmeDJBI8kd0+S1uT5EzKjIFNdsgjnkiNrCdaSVFkpsWCNwKYGQqzT4Oo8Q3RjnELW9mbo621BdnQZ/cK6RmEQFAxLVoW/cKvZB2fNmkUURRxxxBENx6mfbTzzzDPp6enhs5/9LIsWLWLPPffk7rvvLv78CwpWMzYN6uxfBs25EULrroR///vfefHFF/ngBz+YlxmXtQrP85g3bx5bbLFF/t5vf/tb5s2bx0033dT8ia1j/GO7w+no7GxpH5n/I9QH3dYCVpWJ8UxEoCt0poNbaTTSalJnFoQyoBjoh5+5DUmFln6qXlXnMiScT35snfuQG/S6+IHMZUibxpnxjPrg3Hp3m0yZJ/O/z/IbZMnO6oNyXcxEev6D+OBnA3f3XGDrrlZ94HFWbhDE1ieyzi0n37fu/7U+iDn77CzIORCakqw0XH9pdb65WAedxz1A87EDGUvD5r0JltUvrGsUBkHBsGRVWf2tZB988cUXl3s8IQTnnXce5513XsttKSgoWHFWRZ/QqivhNttsw5/+9KeGsm9+85ssXbqUyy67bICr0jXXXMOkSZPYcccdW2rXusT197YTljqaqiukS7zlB4pSWdFWVnS2CzrKMKJNO/nNoJcutYT26E3aev6D1/06cvHrmEVvkixaTLxoCdUlPUTdfcS9ETpKsKlRJ6REehKvFOCVfPz2Mn57Cb+rA9XZgezoRHR0Yds6Sdq6iEtdRGEHVb+dimijakOqJiDWjQYCuMG0lG7g72fSojIilFVC00eYOFlUL+pBVbqRfUsRfT3Y7m5MXy+mt4+ktw9TjdDVCB0nYGyt7Z5C+R4y8FGlEK+tjGwrI9vbEe2d2LYOTLmTpNRBHHZSCTqpqjb6bJnY+lS1T6Q9qv2CoiF1PZLWtVsZl9lYxpRlQsn2EsY9hNUl+H1LUL1LED1LsD1LMd3dJEu7SXr6SHr6iPuqJJUIkxhMogf/kgdhaRQ3XbdYIahRGAQFw5LMP7F/WUFBwbrJquoTWnElLJVKbL/99g37jxw5EmBA+ZIlS7j55pv57ne/23Kb1iV0YkiS5maMhRBYJZBKkiQuW69Osw1nR8iFN3WM0DEijrDVCqZSQff2EfdUqC7pJequEPVUifsaDQIvVHiVGL8txKZ+PsJTCKUQvo8IAggCZFJF6SpKh0ilUdLN6Cth0MIgrHDZiuulO3Gz7TWp0drKhjQJ0sTIJELGESJK212t5MZA0ttH0ldFVzJDxmKNRUjhjKXAQ5WC/FyEp7BBgEhiyNSWqCnx6FQ2NdIekfaoJIoocbKkzl2pFlTsKYtWAptKn0ph8fDxpcsFYesH3daA1tg4xkQxuq9C0lcl7nVb1najm/MZqsbNxy4WY4UahUFQMCwprP6CgoJ6VlWf0KorYbPceOONWGs5+uijW953XeJD+/u0d/hN13fSlhpPxnhSE8iYQMT4IiIwFYKkF7/Shx/1IJMIlA+dI/H8ENXVRTghojOKMNUIqzVWaxc9DCAlQimk7yGCwG2lEiIsYcvt2LCNOGxHh23EfjuxX6aq2ogIqZqQ2Ph5ErLEyoZcBHksACqNH5AkVhEJn4osEZQ68MNR+O1VPF3FTyqopIKKq4gkIkyqlOIIdAxaI3RtgA84yVTPB8/D+iHGL2H8kMhvIw7aiL1yviJQ1SF9sU818eiLPaqJpBJJ4gQS7Tata3EPUgiUAt+DwPPwvYBSYCj57ZS9EZS9KqX2iHJnHyXdQ5CtdlR7CKI+StVeRFSBOMLGMSTJQMnXZbCktwK/fKipuqtKjng4UBgEBcOSLANk/7KCgoJ1k1XZJ7TiStif6667btDyz372s3z2s59dofasS+wQPUZX1N7SPoMmHstyDaT5B0hzEFiloNwOYRt0jcp92TMt/IZoXylAyDoFIw/thQ0KRokKSFRILENiERDZYIAEab0xUK8oJIWzPWKhckWeWHhp7EDJ5SXAoDyN57vYAZXFEKTBvv0DfoW1aayAC/zVwnMyqdRiHSLjBv9R1aOaKKqJpBpLKpEgTqAaQZRYkoR85cUFMtu03c4gkFLgeYLAF4SBIvQVoe9RCkJKvib0uigpl5nYb48JO6r4IsJLYzqUjmqxHM1GFAN93S0oEhaThzmFQVCwTC688EKmT5/OF7/4RS699NI13ZymSbvEAWUFa5619Z4qWLsp+oThwetdmxG1EFScBxPbWvCqsgnSJCgd5643Komcy5BOEDoGYkRinOtM/Qx7/4GpVG7krl1iMqETbOp+JHWM9EooFaO8BE9GeCokUT7aesTSyZrWS3EOaL+weTbiTOozT+blcgunz1PFH1MzAqRNk7ClAcVZkK5NcyeAQMq0vqxlPhbSBQE7iVGLkgpPWjwliRNB6AuiRLiVASPQurai4drs5ExVrlwEgWcJfEvoGQLPUPKSPLYglBG+iPBNFV9X8ZJqagzECKORRre0QuBVm5c41ki0LfoFKAyCgmXwxBNPcNVVVzUk11pbMIN0rv21nAveetbme6pg7cbaWtBjfVnB2sXtT21IqW35ilAZjbPUUAqgLbS0hZq2IKY9qNLmVWgTPZTjbhfsWu1GVZaCqbhA3TSmwCaJc2Gp+cYgpFMZymMGPB/heSjfB+VjgwC8EOMHef4Dozy0Cl1SNOlhpEozBw9Ut8kVdqxtyDicqwbl5brf61pmZZHrdqbHSnX2rVRYITDKxwqFUT6JCnKVpMTz85WDTC40NgqdujhpKxpWNuoNAilcMjMlXHCxJzW+SAhkzV3LN1X8qA+VVPGSCjJ2m0hdnURS5y7UgtZr0Ftpum4RQ1CjMAgKBqW7u5tPfvKTXH311XznO99Z081pmeJH/vZjbb+nCtZuikmC4cFf/vwfgrDaVF0pBNJTeJ6k1OZTKvm0d3i0tym6OjxGtCtGtHkkgYf1BcKz6WpBBSkkwljnHB9F2GoV01epxRGAiyEQIo8lEEq5gGLPA9+HIEBEPng+0g/AS40Ez8NTPkb5zt0oHZhbIQdmSq5bkaif5aff88wQyPZpkOocbHZdyPzzpIyccaJ891x5eNJHq6CWdVl6LqhYulwC2rr1CWuFS9hWLztKmp1YODlUJTQ+ce4O5KcGgJf0oeIKMq46QyCqIOKqix2IImzi4h+sGWRlZhmYvlYMgqJfyCgMgoJBmTZtGoceeigHHHDAWjl4M2kn1b+sYM2xtt9TBWs3xSTB6mPWrFnMmjUrl17ebrvtOOusszjkkEMAqFQqnHHGGdx4441Uq1WmTJnCFVdcMSDjczNss+2YllYIlBIoCb4vCHwIfSgFlnKoKfuaNj8iVDFKGKwQLklZ2IkVEuWXUG0diNgp+ah+6juAm20XLpbAKtWQFZl0kG2lcqsDUmGUhxGeK5MqT9xFngtg8HtyoBa/Xeb7ookBdL3rkFuhELmikNukC2rOsgyjcncaIVxchRXCGUX920vNBUli02wOXp5UzgiJVj7KK6OCKFdMEjpxcR06dudktDPMWnAZEj3NZS+Ht75feOONNzjllFP43//9X6SUfPSjH+Wyyy6jo2NoKd0f//jHzJ49mz/84Q8sXbqUN998M1cr60+1WmXy5Mn88Y9/5KmnnmKnnXZqum2FQVAwgBtvvJE//OEPPPHEE03Vr1arVKu1GZslS5pPG766KP78314Mh3uqYO2mmCRYfWy44YZceOGFbLXVVlhruf766/nwhz/MU089xXbbbceXvvQl7rrrLm6++WZGjBjBySefzOGHH87vfve7lj9r8jYV2juCpuu72Wqbqg2ZVMLTugRe2Qw2BiUSDIqqaiORATLoTOU9deqOYxuCczNs3UA+G2BbofLAXSOcO5ARqvY6i2ywdUnBkMtVtzF179e7u9XvZ4eo0/T1ynRPLQ02R//zzq7rUO83HAt3ftq6gN1E+giZJSzTDYnT6h+h9aRkAEu7u5uu+1b3C5/85Cd57bXXuPfee4njmOOOO47PfvazzJ49e8h9ent7Ofjggzn44IOZPn36Mo9/5plnMmHCBP74xz+23LbCICho4OWXX+aLX/wi9957b9PZc2fMmMG55567mlvWGoVB8PZhuNxTBWs3RZ+w+qjPxAxw/vnnM2vWLB599FE23HBDrrnmGmbPns3+++8PwLXXXss73/lOHn30Ud773ve29FmBTAhk8zrzUAvMzdxYGrIVp0HH9WQDdwAkCCsbgnMb6uYz+rVZ9kzBJ3vMjYDsvUGkLtMjDGkUDGYM1GcJ7l824PkQg9z6QXu606CD/8EyETfu1nh8gcVaQZaJwBllonHgL9xxa99PLeZhZQyCRDZvML6V/cLzzz/P3XffzRNPPMHOO+8MwA9/+EPe//73c8kllzBhwoRB9zvttNOA5SuY/epXv+LXv/41t956K7/61a9abl9hEBQ0MHfuXBYuXMh73vOevExrzUMPPcSPfvQjqtUqSjUGPk2fPp3TTz89f71kyZIBGTjfavQgVn//1wVvDcPlnipYuyn6hLcGrTU333wzPT097LbbbsydO5c4jjnggAPyOttssw0bb7wxjzzySMsGwT/+00W5r3mXIad44xJlecriSUvgGXylKSlnXPgyJqSKIsE3jbr+mRuL1M5dKAvcbfgA6gyDzH0odSWyUqWvRf6YvyekS9BV53IzqMtQ7tpDzdgYxN2nvyGS1Qew/XJj1FY23Dx9bhplrkFW5lK92qpcBcmSxgzYrG56vH6/pfpVGZUmVpPC4EmTSqMaPDRKJE461SYoE6dKUC5IWho96KrM8jDV5mVHl9Uv9F+ZDsOQMAxbaks9jzzyCCNHjsyNAYADDjgAKSWPPfYYH/nIR1b42AsWLODEE0/kF7/4BW1tbSt0jMIgKGjgfe97H3/6058ayo477ji22WYbvvrVrw4YuMHK/0hWB8Y49YP+ZQVvPcPlnipYuylWCFYvf/rTn9htt92oVCp0dHRw++23s+222/L0008TBMEAn+dx48Yxf/78IY83lNvgP1/VlNp00+1SyqkMeZ4gDCSlAMqBpRwq4kDR5rsZ6kBECCyejgiiHvxqNzLqRVb7ENXeWpIsU5eBK0MKN7xOB/pOeUjW8hQIkb92xkDde1AzCPobA7kaUO04tu54NlUnIg1KRkgXj9DP2BhgZKSfl7szpUpHWqQBxMIjwSO2vhM2tQJt0wRqRhJribGCRDs3IGcc0E9lCKR0sqWeypSGskdniAlhcdETBmViPBMjTYynUyPMpvEEtrUYAr+vednRZfUL/Sehzj77bM4555ymj92f+fPnM3bs2IYyz/MYPXr0Mn8Ly8Nay7HHHsvnP/95dt555zyWp1UKg6Cggc7OTrbffvuGsvb2dtZbb70B5W9ntB1EW7j4818jDJd7qmDtplghWL1svfXWPP300yxevJhbbrmFqVOn8uCDD67w8YZyG1x/tKLcPnASYSiUBCnBV+B7Ft9zWvglXxOqhFAleCJx8QXGuIG0VGgvqMl2KtUggTmk29AgA3hSA2DAoB5q7zP0ykD+Xv0KQyZTKpWLPUiNgGyAXx/L4PYfJBtvP5emLIDYrQbUlISSujwJNpUWFcKFQEtpwQiMsPT3InLuWRYlbcMMf+p4la8+JPjuYMoZKEp6GOkjPN2QU6EVlaEkbv6/vj5/Qn0ZOHfXrq7aatRQk1Rf+9rXmDlz5jI/5/nnn2+6Ta3ywx/+kKVLly43vmB5FAZBwbDESYkVAYQFBQWOYtVw9RIEAVtuuSUAkyZN4oknnuCyyy7j4x//OFEUsWjRooZVggULFjB+/PghjzeU2+DkTRfS0dGciky9r7ushfLWBRqn8QS2FtiqpUcl6EQEHS4hVl3cQKbcU28Q1A/kbX/3oX4Bx8Cg7jz9j5OX1e+fxiD0V/1xLjw1957stcscnAYq25pLz1Dz7APiEvrVr3cJUsIghcCra3JjhuX0OmVxACKN4xAurkCbNPOyUGnStVIeRyCFQSibf0fgvrtW6NZLm667rMnDrq6uBoNgKM444wyOPfbYZdbZfPPNGT9+PAsXLmwoT5KEN954Y5m/heXxm9/8hkceeWSAwbLzzjvzyU9+kuuvv76p4xQGQcFyWV4gy9sRYwS6+PN/27I23lMFazfFCsFbizGGarXKpEmT8H2f+++/n49+9KMAzJs3j5deeonddtttyP2HchscE/+LznhoicahGGqQ3n+A7mbZPfe+qvnZ58dZRhbbwQJ6+w+w61WFhto3w9TtaxEYU/Pz7+/Tn5elicJ0+ugMg1rSMGvFoEHB2SlKYWseTHUD+v6D/Px1+jjYms1gV8q1F7A1udL8m1mOalGz9CTNu5StisnDMWPGMGbMmOXW22233Vi0aBFz585l0qRJgBvMG2OYPHlyS59Zzw9+8IMGKe9XX32VKVOmcNNNN7V03MIgKBiWFH/+BQUFDQySqZiiT1glTJ8+nUMOOYSNN96YpUuXMnv2bObMmcM999zDiBEjOOGEEzj99NMZPXo0XV1dnHLKKey2224tBxSvKI2z79ksv0bUS3f2m6GvNwIaypchDboyg9ihPitbHcjyFRih0KjU3cZLXXxcELA2kkSmgcDSGQfaSqS1uXGQnX/9oNfadAWNWkbvLCag8XnNuHDPa/sPuBZpHIEQtcBu577l3IiUAE+ZNKNxLauxU3+qrShk6lCtIGXz8QZv5eThO9/5Tg4++GBOPPFErrzySuI45uSTT+aoo47KFYZeeeUV3ve+93HDDTew6667Ai72YP78+fztb38DXLxOZ2cnG2+8MaNHj2bjjTdu+Jwsp8EWW2zBhhtu2HT7CoOgYFhSuAcUFBTUU0wSrD4WLlzIMcccw2uvvcaIESPYYYcduOeeezjwwAMB+P73v58nYapPTLYi2EzBZwUYyvWnQeKyv1tQfabgIRxu+tcdNKPwsrIJ9w+YzR31ZZ7ALMskbFSAUT6JCtDSJ5EBiQqIrU8sfZLU/z8xikRIjLXpCkLqApQ+ZkaCNiLNNgyJFsSJINGCKIE4gSiGJLFEsSWOLUliSBJLHBuMNmhj0doMyNUmhUAqiedJfN9tQSDwfUkYSMIAQl8R+pbAd3EdmfqTL3WqUGSde1J63Qdb2RiMVmRp3+p+4ec//zknn3wy73vf+/LfxA9+8IP8/TiOmTdvHr29tcDoK6+8siGeZu+99wacfO/yXJVaoTAICoYl2ritf1lBQcG6STFJsPq45pprlvl+qVTi8ssv5/LLL1/pz3JJv5oPKobagN0Kl0/ACpEP2KXVtcG5TWMM6gbv9Qo3Q2b/rR8N1xsQgxgKDfX7Ha8hNgEJGETdoaWQIGKskEihySVHbS1GQQiLMBYpLVLIxoDgfu5L2gqMFCRGInRqGEgQpuZCZC0k2qZGgSGONFFkSBJNkhkF2mCNzVcMpACpJEpJPF+RBAqtJdYqhHCZoz1PNNR3qwYGTxh86YyCTJ40yx3RLAFx03Xf6n5h9OjRy0xCtummm2L73RfnnHNOS+pGgx2jGQqDoGBYohnE6l9OFsiCgoLhSzFJMDxYqkZhvc6m6+fBqhik1Sib4JkIaRI8rRE6wUsqiCRCJlVEEiGiilMVqpcajZ3CkDV1ijf1EqO+B1IhfB+Ujw0C8EKMH2C9AJNuWoUY6aFVgJaecwGSfmMytPr2UzNGBsvmK6wh0H2Etjd1gzJ5VmW3/8BkauCMI62cok8ifWIVEtmQqg2p6IBKEtCXePRUFX1VSW8V+iqK3j5FtWqoVBKiyBkIItZobfJYACnd6kAQKPxAUSp7lEuSclnSXha0l6CjrGkPEtr9iDavQln2UTK9BEkvftSHF/ci4ypSx4g0B0SzLOlpXna06BdqFAZBwbDEGLf1LysoKFg3KSYJhgeLdQc6ad4gyPzSldD4IsGXERZBkA6ala4ioz5UpQdR6cb29mB7e9B9fZjePnSliq5GmChGxwk20dh0altIgfR9VOAhwwCvHKLaysj2dkRbO6K9A0kHiV9C+yWioIPIa6Oi2qnakMgEVLVPHDt9/8TIhkUDJS1SWDzpZs4z/f5AxvgyIjAVfFvFj/tQSbU2iI4rzqiJq04qNYmxWjeuZGTGix9gwzZMWEaXOonDDipBJ33lTnpMOz1Bme5SwNKKR7cv8ZSk1xOpgmptkC6kaLguni9zY6CtrGhvk3S0QVebobOU0BVW6fB66RBLaYsWUep7E693MbJnCaJ3Kba3B9PXh+mrYJLsujf3J16tRE3fH0W/UKMwCApWG2fceyptLS7tLoteq/l4k3X1IIFC/V8XrH14vofnr9puK2o1Yq0FBku6tiq48P1Xr5bjApz75GdXy3HP2WnWajmuNM1d42KSYHiwNCpjonJTdbOA1GxA7auEQPqEwkP7HkZIwrqYBGUNwlgwGqk1aI21FmvcyoA0FgOuDriEZFLUPdbFNlgDWoNxWY5VEuGpCCN9AlFxeQkkqeymQstaNuBa221u0GQGgS8SfBHh2RhlE6RJ0uy+ScPKQJbvQAgBSiEAa0QenyCUlyY069dma5HWZQ72RYKvEkKjiH3pFIysO1clFb4nCEOVxhWY/PeklDMIAl8ShoJSKGkvQ1v4/9l773C7qjrh/7PW2u2029ITSKOFGqoRhUEFB+sMCAiMowHnxfEdscDIq4yvBhwlYEEc9QWxYBsHy6BjHYwI+pshIMKAAoKA9CSk33LKbmv9/ljnnFuT3JtGcrM+z3Oec88+66y99r57f/f6rm8zRL4m8nJbIVq0jiEfdMdq12uQdty+j6RZS2GcbjByAje2kwuDOIXAMSnRY5gB99Wb3OFwuEWCycJDTwVExWBcbVvBrZ4C34dCAFFgKAQ5xSCj6CUUwwbFQo1CqZ8o7iNo9KEa/YjaAF6tH1WrohsNdCPGpCk6zdor1UJKhBS2cJnvI3zPvrcWArIMkTRs1pwsQSZ1Aq+X3A/R0rduQ8pvVwoemgIVGOUeJLMcYYYU7NLNz3mK0NYCYIQE5WGkQvghpqXkGD3MQmDTqiqM8u2r6dJk95vj6xijBFLlBDKl6PvEkUdS9khyRZpJMt2sVqzVsHuplbrUU7YQXKBSApUTerYQXCgTItkg1HWCrI40uZ3we9ZagZCIMERtrTr0VghqjXG3dXJhEKcQOCYlLoDQ4XAMxS0STA7WvNAgLIxTIRCivVodBpIwlBQjQangUYoUcaRIA0XuK7Qn2xWBfSHakyMhJFIphFLoJEHmOaaV576pECAkwlN2RVupdvVhjLZKAQ0wBpXnGM+69Gjl47eyBzUrDdsUo8MzKA3GEAzPTjQq2LmVIUkIjGyOXjYrFY8RQ0DTMmKkAqnQymsfvzA23sLTKUjaLleBVGTKI9WKLJCjah8MRQ6xbvgyR0ltXZ2aFg7fJHg6QTSDutuVl5tKCVKB50OQI0a6O23z/z5+y6yTC4M4hcAxKXGBQg6HYyhukWByMHNGRFSMxt1eqUELQehbC0EUaEpBRtFLKXgxRVmzAa1pDZXWkWmMSBNElmLyDLQZ7q4iR6Q9bSbcF0ohPB+CwL6HIcYPMa3AYj+ywcXN9KG5HLQOaDHaQgDDrQTS6Hbg8EgrgdDWckDzJVpBuGOkO7XKh0GIVvalwaxLLdq1D1DkxtY7yLRVBlqvTEu0aaUuHV70bDD+wbpB+aalRA26Q0mZI43GyGwwvWrLzanlNkRuz6+eQKrZCaSlNWPUJxlVr2QfwSkEjkmJUwgcDsdQnEyYHBw4J6dYHl/GmVaF3VZgrq+0DcpVGYFMCEVs3VbSGkFSxUvrqLiKbFQRcQ3SBLIMk6VNZ/MRaUJb1oGWy1AQgB8gwggTRjZYNyig/YjML5D6BVKvQCpDUhGQmIBMN4uL6cHKw+3+aQVEN99ljidyFBmesNmSlMnw8gTVfMk8QeY27abIm8qAHr3KLpouQ60TZaQalp5VC4UWksx4pMYj0T5p7hFrRZIp0rzlMmQVgqGr6q1iZJ4yeMoQKI2vJIGRaCXQUg7GUBiNUM3sSE1FxugMZI7QNv7B5CAmUGwMOf4JvZMLg2xfdQ+HYw+nGQ826jVRvvCFLzB//nyiKGLJkiX89re/3WLbhx56iLPOOov58+cjhOC6664b1eaKK65ACDHstWjRookPzOFwTIhWddWRL8feRTlMqYzzVQ5SSkFKKUgo+QkFLyFSCYFMCFqBuTodnv9fKoznY/wQwhBRKCCKJWSphKqUkeUyqlIe/LujA1muIEplm1moWMIUSpioRB6WyIMCaVDaojKQGrvqnoxYfW+9Eu0N+c63E3P8ZiGysF2YLPMiMi8i92xGI+2H1iLReoWF0a+mstKyXOQqJFOBtVoIRY6HblZFNoZ2WbbWhL/1asVTD91mLQSjJ5mtegi5kWgkmWwWWFPB4NiDon2FRRtTEBYxURnT/LzNV1Ac9/XUCioe+doXcRYCx6RkZ2QO+M53vsOll17KDTfcwJIlS7juuus4/fTTefTRR5k+ffqo9rVajYULF3LOOedwySWXbLHfww8/nF/+8pftz57nbkOHY1fjVgInBwuCp6gE5XG3b7nhDH23rjCShJCGKpIrifatT7xG2slqM+OPaVayNYx2LWnRcpNpu8Jg/e5bNRCUMIP1EFp1AjA2jSgpQrV+P/yCbMUTtCbQBjumVPvEJmxnJWoXH0OQS4FGYKQgV61xD1YoHkor+FdgULI5Pm1QZrhlomVhCWSj+Vljox3y9jGPee5b5w1Jjj2XuvmeGp9GFrbPc47ANK0HOmi58jRdlybowlON+oC/H1dbJxcGcTMRxzCuuOKKYSWyAQ455BAeeeSRF2lE20duDPkI824+weXAa6+9losuuogLL7wQsOXDf/rTn/LVr36VD37wg6Pan3DCCZxwwgkAY37fwvM8Zs6cOaGx7O1MluvKsfcylpVwe6yGYC2Hn/zkJ1mzZg2LFy/mc5/7HC95yUvGbPulL32Jb3zjGzz44IMAHHfccVx11VXD2l9xxRXcfPPNPPvsswRBwHHHHcfHP/5xlixZsn0DnMR0r36QjtI4V4BbgarNLDq5H5L6JRK/SMMr0zARtTxiIAkZSHz6a4qBOvRXDQMDGbVaRr2W0qglxI2UNMnQWY5uVTkWAukpPE8RFALC0CMq+kSRR7HoUSpKSkVbiKsUaYpBRsHPKHgJoUwISGyAbR63U4gOddsBQS49awWQAYkJSfCJc79dOKyeKGqxpNqAegNqdU2jkVOv5yRxStzImlWF82Y1YYMUolkrQOEHHmHkNcfst8dcLtjiYeUgpeQnhF5ii4flVYK0hp/U8LKGrXnQznI0WLDNSIX2QrQfknsRSVCyNRhkkbouEGc+tdRnIPEZqCuqDajWDdWqHX+jnhHHOWmSkeeaPB//8ztp9I+77c6UC3s7TiFwjGIyrGDrMW7yCRQ6JEkS7r33Xi6//PL2Niklp512GitXrtyhsT322GPMnj2bKIo48cQTWb58OXPnzt2hPvcGJsN15dh7yc0YK4Hb4TI0UcvhHXfcwfnnn8/LXvYyoijimmuu4S//8i956KGHmDNnDgAHH3wwn//851m4cCH1ep3PfOYz/OVf/iWPP/4406ZN257DnbTkhS7y4vhdQlpZfLT0yb3QutVIn1ZdXSkMgcqJPImOBEJIPCUIA59SSRHHAXEc2Vz7qSbXZlgBLtWuyivxg1Y2I0EhFEQBFEIbxFzwcwpeSqjSdrYdJXKUzppBwyPiE4zGCNW0KtgAYiUyFJJAZhhlV80FBikVSkoCTxAGkrggiUuKJPFJU02WGfLc1gloBUcrJVBK4nkC37cZmMJAEIU2PWvoWwsBgDY2liA2ISisO5EKUHmClyeDgc1DlBkjFFr55M2qzKkMSWREYgJyY2MXbI0Ie36MkUghCDxFXJAkZY80NWSZVQYmsp4X18ffdmfJhcmAeyI7RjEZVrC3Zgbs6+sbtj0MQ8IwHLZt/fr15HnOjBkzhm2fMWPGDq1qL1myhK997WsccsghrF69miuvvJKTTz6ZBx98kEpl/NU390Ymw3Xl2HvR2qBHWA1Hfh4PE7Uc/uu//uuwz1/+8pf593//d2677Tbe9ra3AfA3f/M3o/bxla98hd///veceuqpEx7jZObPXcdRLk9MVgqaE/im247AoIzGEzkVNUCnp5GRbk++B7P6DP49rJ9tzE7NtoodjuGSYoTAiNFTMoNANusC+MQUWq5PnsD4LVcoMcy9qP1qutqMTAlqt43tjjTSZSrTklQrGnERbUrN4OdWZiHr5NTKMDQU0XRHUq13ad2OWu5HUmiKXkIlaOCJvJ3a1Dpz2c+DR6K3ec6H0t8/wKfH2XZnyYXJgFMIHKOY6Ap2HMfEcdz+PHLC/WKgc4Meoea3Pu+///7Dti9btowrrrhit4zrta99bfvvo446iiVLljBv3jy++93v8nd/93e7ZQwvFhO5rvbEa8qxd7OjVkPYOZbDWq1Gmqb09PRscR833ngjnZ2dLF68eGID3AdY1ddJUXeMu72Upj0ZVVITyrxZsTglEg0CYoLMZhry0yoqqSOTOjKuIRp1SBNMGkOSYPIck+eDAWmymSJT2kxDeD54tjgZyscEgX33A7QX2tSjyiNXIUZ5zbSjyq6mD6mYbBBDlI+moqKzbaYcFUNSjgqj7d+t+gQj8/i30nwq3wYU+xGZZzMhxapITERdF2gQUM8UtcS6JzUSQT2BOIE4MaSpIc8NudbtXQgJSgqCoGl9CJrWksBQCDXFIKfoJwQqoyAbRNQIc5v21Uua/4O0gUwakKeQN7MkjZNSbfwmgp0hFyYLTiFwDGN7VrCXL18+yj/8xWZrFoJnn32Wjo7BB8pI6wDA1KlTUUrxwgsvDNv+wgsv7NRV7q6uLg4++GAef/zxndbnnshEr6s98Zpy7N3sqNUQdo7l8AMf+ACzZ8/mtNNOG7b9Jz/5Ceeddx61Wo1Zs2axYsUKpk6dOq4+9yV8Lyfwxj9js6vUGikNXjPQF+yKdip8hDAYT7TdYDy/iAwTvIL1jZdZgshttVyRD5lgD91BM2e+UbbAmJYKI73hRb+kQguvnW9fC9VWBloWBdGsNNxSBqAZS2C0bdP8Lm9lCzWDSkOz9YjPw9sNxbQKoTWVES0UufTJhUdmbHYhKWyaVulrApVRCZVNNaqt5UBr0XS5EdAMXm6dEluaoVmcrKmQ2ZdpFiqz/8PE+BhRIvUClFdBRRlKp003Kd0swjaxGXr/wMC427qg4kGcQuAYxvasYF9++eVceuml7c99fX2jVuF3N3luRgUhtT53dHQMUwjGohXYd9ttt3HGGWcAoLXmtttu4+KLL95p4xwYGOCJJ57grW99607rc09kotfVnnhNOfZutiYTdpfV8Oqrr+bmm2/mjjvuIIqGF9d65Stfyf3338/69ev50pe+xJvf/GbuvvvuMeMS9mVKQUIpSCb0G8kQdyFh/dFzFFpLMjybGUholK+Rft7O/z/SZUVsYcI9kqET+qFth27fUj8jtw3WBFDNv6VNBYq0BcOQQzINKZt5qLUNq/hoYy0PQ3uWZvg5UcIgddOlZ4gLT6hilMiRaBv43IxnkKZZCI0hlZOHjVtYZaOp/OTCswHS+OTN2gaZ8WjkAf26QK4VaVPRyHLRroBsSymIkSUgtkqtOn6L8tbkwr6GUwgcW2U8K9hbWk17Mcn1GFmGJugXeOmll7J06VKOP/54XvKSl3DddddRrVbbvsNve9vbmDNnDsuXLwesqf/hhx9u//38889z//33Uy6XOfDAAwF4//vfzxvf+EbmzZvHqlWrWLZsGUopzj///B095L2KbV1Xe+I15di72dqDfzxWQ9gxy+GnPvUprr76an75y19y1FFHjfq+VCpx4IEHcuCBB/LSl76Ugw46iK985SvD3JMc4MscX07AQjB0ct50c5dDUmUaBBisy44RgGorAu22IyfyYoyJ/Ig4hda2kQrF0HgEgRj2W2BElqEhFYNbr+ZkP8cjM6qtCGRGkuvme2sy3YwFaKUdHWnYaBVtU9KgpMYTdhUfCT4GmilJJRpPJ7YQmrbZkJROEXmGNFm7EnJLKTCtLEPCa7tG5SogMz6olkuUaisr6ZBiZ3EmSTNb8CzLIcttPbiJBBXXq+NPUzpWPZJ9tT6JUwgcW2VvXcHWeowYggkqBOeeey7r1q3jIx/5CGvWrOHoo4/mP//zP9vuAs888wxySAn7VatWccwxx7Q/f+pTn+JTn/oUp5xyCnfccQcAzz33HOeffz4bNmxg2rRpnHTSSdx11137XCaRvfW6cuy9bK02yXishrD9lsNPfOITfPzjH+fWW2/l+OOPH+d49bA4mr2Fq6++mssvv5z3vve97eKMjUaDf/zHf+Tmm28mjmNOP/10/t//+3+jXK/Gwyy5iorcOTFFw1bjt/F4GKkU2J8MTjyHTuBb9Q5a21uBvxrVDAAWg98bMayfwb5H141tBe7qIYqEaq7m+9DOPDQ0wHesvkeN3TRrFzQPURtBQweY3NY6MEPqMOghv28aHra4j+aXiNzAEB1uaMx1SzGLvJzIy6mEw7dvD9WB2rjb7m4LwcaNG3n3u9/Nj3/8Y6SUnHXWWXz2s5+lXN56bY2VK1fyoQ99iLvvvhulFEcffTS33norhUJhh/odilMIHMOYLCvYeoybfKSCMB4uvvjiLT7oW5P8FvPnz2+ndNsSN99884THMBmYLNeVY+9FG9POHz9020SZqOXwmmuu4SMf+Qjf/va3mT9/PmvWrAGgXC5TLpepVqt8/OMf56/+6q+YNWsW69ev5wtf+ALPP/8855xzzg4e9e7lnnvu4Ytf/OIoC8gll1zCT3/6U773ve/R2dnJxRdfzJve9Cb++7//e8L76Fr/OB318acdHUpr0t6akQ773JrQt2arIz63245kiC/+yNiAXHptv3xbnEu13Xzy5uq+de0ZLMI1dDI/1NowrLiZ0NaFp+nW1MrKY3vSbd/7LWVJAoZZHTLhkxmPxAS26Fnu0cg94kzRSG2dg0YCjdgGEseJIY41WaZJEo3O8+YqfstKIpACPF/ieRI/UO10rEMDjKPAUAhyQi8jUBmBzAhkgi9SPJHhmbTtnrQtN62h9HsTiSHYcW+CifCWt7yF1atXs2LFCtI05cILL+Qd73gH3/72t7f4m5UrV/Ka17yGyy+/nM997nN4nscDDzwwbEFye/odiVMIHMOYLCvYzi9wz2KyXFeOvZedYTWEiVsOr7/+epIk4eyzzx7WTytOQSnFI488wte//nXWr1/PlClTOOGEE/j//r//j8MPP3w7jvTFYWBggLe85S186Utf4mMf+1h7e29vL1/5ylf49re/zate9SoAbrrpJg499FDuuusuXvrSl05oP0mxi6RUmtBv2pPJIUG4opl5p/Xeysgjmu4vDPnNyN8PdmyVAVuIy7rHGOW3MwmpZg7+TNnCYtZKoDFm0F0mG1ppeIi1YKQiYGyNXzyRN3fdVDFEjiJD6QxlsqZLTzo6E9HQY2iOW0sf3aoToEICFZKIECUipAgQwgdaLjuyGYAryHKDUjb1qJQ2K8/IxTAhBaJZAE1JgVLC1nfwB7MNFfx8WKG2SDYITIyfN/Dy2NY4aLoliQmk/tED1fG33UlyYTz88Y9/5D//8z+555572pbCz33uc7zuda/jU5/6FLNnzx7zd5dccgnvec97hqU1PuSQQ3a435E4hcAxjMmygu0Ugj2LyXJdOfZedpbVECZmOXzqqae22lcURdxyyy3bNY49iXe96128/vWv57TTThumENx7772kaTosq9KiRYuYO3cuK1eu3KJCsKXUw/eyhBLjTzuqpPWTb8Ue2JXohFDEhLpOmA7gJzX8uB+ZNBC1AUS9iqlV0fUauhGjGw3yOEGnGSYbMjGVAqkUMvBRUYiMIlSxgCgUEIUSplghL1TIojJpUCYOyjRUiQyPXCvi3Cdu+s/bzD0jMvUAntIoaQiVHbtSzWDf5viDtEaQVG26zriKbFQRjTqmUYM4xiQxJsswaWoLqmlt06RKgfB9RBDY8UZFTKGEjkrkhQ7SsEwjqFAvVKjqEvUspJoG1BKfWiKpx5I4VcSJT5YZsty0ukYIgZLg+4IwsIXOCqGmFGYU/YyiH1NSdYoMEKX9RLXNePV+VL0fUevH1KuYeh3daGDiBJ2NOO/bwDTGH3S+Nbkw3uxj42XlypV0dXUNcxs87bTTkFJy9913c+aZZ476zdq1a7n77rt5y1vewste9jKeeOIJFi1axMc//nFOOumk7e53LJxC4JiUbK0OgcPh2PdwiwS7jptvvpn77ruPe+65Z9R3a9asIQgCurq6hm2fMWNG231qLLaUevjR53yioj+ucUlhV7ADH0IfosBQCjWlMKXsNyh7AcYXKJ1BIhFpgqhX0Zs3km3uJentJ+kdIO6rkww0SKoxearRzcmp9BReqPALAX4xIOwoEnSWCDrKeF2dyM46qpLY1XkhyJWPknZCmRvRDqZtZIoklc0AWtFaxEdJg+8JQk83j0cTSGEtBSa19RPiAYL6ZlStDzGwGTMwQNbXR16tkg7UyGoxWb1BnmToTGO0RkiJ9CQq8FBRgF8q4JeLqEoZVa6gOjrxS50E5W6iQidR2EktqBCqEqEXEvoBUaBoJJIkFSTNIOCWMiMleAoCD0LfugUVg4ySH1NUDUpygGLSS1TfhF/bjOzfBP2b0b29ZL19JH0DpP01koEGWSNtjn38CsFAmo277e7MPrZmzZpRWcM8z6Onp2eL98Kf//xnAK644go+9alPcfTRR/ONb3yDU089lQcffJCDDjpou/odC6cQOCYludbkIyIIR352OBz7Dk4h2DU8++yzvPe972XFihWjUqnuCFtKPVytavIJ+JMrJfB9QRK0qutKwG9X0pUqRwXWJUXkKUrb/PetyZEQAuEpVODhFwPyJLMr7Vi3GBV4ze9CvEKICgNkENhZsdEInSGyBJXUCaQPCIRv8L2UUKXEnk+mPVJtc/wPdRkSAjyp8WWOJ3MCmVrLAA3CvIaXx0idAtgaCEGEKOQooxG+hwwC/LK1bOgkxRiDaT4HW5YN2RyvLBaQxRKiWLKWgrCAVh5aqPZ4lMgJZIb2rEucEgZPSYJc2KD9ZvyDFAZPge9pfGWIvIxIZfY4hD2/QLswGkGEiIrIPMcT1nLhFSL8SoO8kaKb1g0z3md4ko77+tgZ2cc++MEPcs0112x1P3/84x/HPaah6OYx//3f/307TumYY47htttu46tf/Wo7Vmln4BQCx6TEPfwdDsdQnNVw13Dvvfeydu1ajj322Pa2PM/5zW9+w+c//3luvfVWkiRh8+bNw6wE20rVuuXicDWCaPxTF89TeJ4kjBTFgqJYkNSKiloSERc90sAj8QPKpYiiX6AQFPCiErJQwi9X8Ab6Cao18lq96TaU0k6KLwWiWaVY+p6dYAcBMgwQXnOMTauDl+fItIHv9xH5EVoF5O2Xh/bUsOxEQDsguF2kK8tReYpsxQnkKTK3q+FG+VAogR9AsWJjCrLMluE11jIwMu+oUB4oBZ6H8UNbUdmP0H5E7ofkXkSuAgwCRUYoYoSy7leRUuSBdXVqxUAMdm1zJCmZ44mWQpMRiJRAxKg8BQRa+uSBzZIjPR9RKKEqDVTSwCQJZKl1dTJNV6dxJgFQ9fFn59qaXBhv9rF//Md/5IILLthqm4ULFzJz5kzWrl07bHuWZWzcuHGL98KsWbMAOOyww4ZtP/TQQ3nmmWcAtqvfsXAKgWNSorUmH1FuUDsLgcOxz+KshruGU089lT/84Q/Dtl144YUsWrSID3zgA+y///74vs9tt93GWWedBcCjjz7KM888w4knnjjh/WWpRqrx/d+kFGTkSAFZKsh8SZqZdp77NJPEniLUPqkIyFRI5heRQYIME0RzMq2gOelvYPK87b4iWr74StmXpwb98pXXzlRkg5YzRK6QIgFjkF6O1Ckqi20wcrMA2UiE0YOZgoxB6HxI7v9W3n8JyrfBwspH+HlbEaBVWXmkVUVIm0FJehivFQztY5SH9gJyFZKpoJkxyR6HEAaFxpCDBGGsG5M2clh2JLDbbXEzgyczG/wsMsSQ8mhGKrTyEV5uz4k12YDnI4IU8hSR55jWsYwT6TfG3XZnyIVp06aNK0HGiSeeyObNm7n33ns57rjjAPjVr36F1polS5aM+Zv58+cze/ZsHn300WHb//SnP7ULfm5Pv2PhFALHLuOW9/+MIKzstP6SuB8+cci2GwI60+hMj9rm2LvJ8xwxgaJE42FbqWJ3hDzfuWNtsSvH/LETvrRL+r38Jxfskn5rJmc8yTmd1XDXUKlUOOKII4ZtK5VKTJkypb397/7u77j00kvp6emho6ODd7/73Zx44okTzjAEcOQRnRRK4w8qlsJ67/gKfM/ge4bQy4n8nFBlhCrFk3aVPZUhIuxASw8VVvA6Gsi0gcgSZJ6isnT05LqZZci+S4y0k1otVfOzGvXSwmtmJlJD6hTIwZSnQ9ByaLyEGKx3IIbUMhjSBzDq7xZj9W8YUjMBm/momdwUgy1uZhAYPZgFqVX5uDkilMiH9T1YpI1mZWhbPTkhQCPJlE+iCs2sSKmtgKxza/kwOTK353lo0TNgVDXkLZFOIMvQ7pQLhx56KK95zWu46KKLuOGGG0jTlIsvvpjzzjuvnQno+eef59RTT+Ub3/gGL3nJSxBCcNlll7Fs2TIWL17M0Ucfzde//nUeeeQRvv/974+73/HgFALHpEQbRpU634WphR0Oxx6Osxq+eHzmM59pF0saWphsezhyv82Uy+P/v42sTtwuCyZsBd7BVW9hrQSeT8Mrt6sLt3L5w/BCZmMVKttaG2FG9zFWNbThlYqb6UGb1oNWTYNWPYPUeORGNeMPBuMQbMYim7VIa0Gum8XHhuxOtsottGIpBEhpmmlOB7MzKaHb222tA7v63z6XonVux/6fCExTsbB1F7LmtLNVeEyIZkVkObSi9PDzIrdVNW4EA6p/3G13t1z413/9Vy6++GJOPfXU9j3xL//yL+3v0zTl0UcfpVYbLK72vve9j0ajwSWXXMLGjRtZvHgxK1as4IADDhh3v+PBKQSOSYnONXrkTZ67h7/Dsa/irIa7j5GpV6Mo4gtf+AJf+MIXdrjvXHtkemJTl6EFvVoT23aef/RgPn9jV6hb77KZw1/qfJjbzhg7ABi1Kt9a9R8sarblFf6xGFr1uKUYDAYdazzRHJc0NvjYSHSzvkHefG9VF2659AxNazrWeQKG1UhuT9xprvQLYy0EhjFqKY8+5yMVh5YrUfv/0Tz/bQUNM6yg2pYUr61hGH9hMhusbEZt21X09PRstVjYlgqcfvCDHxxWh2Ci/Y4HpxA4JiXWDKhHbXM4HPsmzmo4OXjgmTKFYnlcbYW0K9++Z1NgBr6hGGgKQUbRSyl6DUIZU8gHKCR9BI0+vNpmRLUPqv2Y6gC6WiVvxOR1W4vAZPmwLEPCUzZjTxjYDENRhBxaiyAqoqMSOiyRBiXSoEjqFUhURGoCUmyWodwoW5xsRFCxEqZZfCzHx1bwDXUdX8d4WYyXNVBZA5nGyLSBTBqQNBBpgkliaAYWm9y63wwpFoDw/aa/foAJIgiL5FGJPCySBSUSv0TDKxETUdcFGnlAPfOJM0WcSeJUkuWCLKdphWiOW1gXLU8ZAt8QeprIz4m8lKKXEIiUAlWidIAg7sdv9KEa/chaP6ZWhUYdXa+j6w10ZjMkYcafZUhPoA6BcyUcxCkEjkmJWw10OBxDcVbDycEzz9YJC+OrQwDYDEOhIookpaKkUlR0lgSyYAiVQqIJshphbRNe71rYtI58/XoaazdQX7eZ2vp+ahtr1DfWqW+Iyfpy8rq9blRB4nUogg6fqCuk0BVR6C4RdZWIejoIujvxerpRnd2IDutnn3shBkGG355kx7lP2nTzyfVg6k4pDL7SBCojVBlI8ESGpxPCZICg3ouq9yH7NmL6esl7N9Po7Sfe1E/cqp9QjUmqCWktJU80RptmulSJX/QJKxFRZ4FCT5mwq0IwpZtgSg9B1xSijimE5alUC1MwUpJoD20EjVQxUJdUG1CrG5LUkKYG3dQIlBQEgSAMJIVIUIokeUGgpD1vnkiJkgEKtfX4/euRm9aTb9pAY/1GGus309jUT33jAPXNdeL+hLSakcc5OhvfRL06karGTi60cQqBY1KSj+EX6DKKOBz7Ls5qODlYMK9AoVQYd/tWkSxbmExTDHKKfkbBiymoBp6xOetzL0AWysg8RwlJIYoIujspzhwgq9bJ6jFZIxlWh0B6alSBL69YQBULqEoZUSpDqYIuVsijMplfJFd+O5uQErb6MICSklwrhl6hShg8oVEyRzV99DWSVEWIwGCExPNDVFhCdfSgplTx4hpRvY5pVVlOEkyaorNmtp4molWHoFWDoFBAlCqYYpm82ElWqNCIumj4FWqUGcgK1LKAOFNkuY1HEAI8T7TPs24qM0oJPM+e91asgjGQa0mc+3iigApsYLiRCt+PkIUSYUcnwbQ+Sv0DZNWaPe9xgm4WJhuvK09/ksJXx5f338mFQZxC4JiU6HyMQKF9VOt3OBzOajhZOGnBasqV8fmIt1JcDg0iltic/tI0c/o3g3izoETuRcjyFOS01BYq0zmhzol0K4WnGREU3PT/FwIjPYxU0MwklCgPLW0qz1zav3PpkQtb7EuiCWWMb1Iiqaz3vBnDsX/o8QhDYgISAqQqgwIRGagMHmvreIf+ZizaGYNQ5KZ5ZoytK9AOVM4kaWwtF2kumu5Bg1WJPQUiBBOMHruUoGRLKTBkuaCeeORakOQeVVUgVN0ElVmEHTH+jAQ/b+DlMV6eIPOEIE+J8myL535LeNUafPXH42rr5MIgTiFwjOL555/nAx/4AD//+c+p1WoceOCB3HTTTRx//PEv9tDGjTMD7llMhmvKsXfjrIaTgw69gUo+/sJTLYamrByZ6ccISS49kB7CBOCPbrel4NaRAcT2bzUYUNwOCFbD0oEKDIocKWxef4OAEfpAOxB4jDSgWXMSb4xoxx60golbk/3W37avscetjZ3cG0RzFV80+7T7b2UoynXrO9uXzURk37cUZ63kYBuA3ECmJXEOGsiNIJOSVPrWFUqV8LwURYZqpSBtBxiPf9V+wB9/ULGTC4M4hcAxjE2bNvHyl7+cV77ylfz85z9n2rRpPPbYY3R3d7/YQ5sQeZYjVT5qm2P3M1muKcfejbMaTg4eGFhEkfHXIWil0PSkRkndrpwbqMwG6IoYXyT4eYyfx3h5HZknqLSBzBJEliDyFJGloHMbiT6yDoFs1iGQCqMGrQRGerboWKv+gPKtsiAVWqhm0a/hykOLkZmHRn0nmpN4OTKzUUuJGF2HYGR/BrFFS0FuJLmWVvEwslmRWLTjHLS2NgmtB9OZDg0qbqUx9Zrn3pMaXzXPvczalYt9keCZFD9voHRqrQNZYisxN600TCAmAEBXa9tu1Grr5EIbpxA4hnHNNdew//77c9NNN7W3LViw4EUc0fbhLAR7DpPlmnLs3TiZMDn41V11gmgiQcXKBhZHikLkUShIKkVBuWCoFHIqYULFr1NWAyiTIfIMv96HqtpsQ6ZWbWca0o3GMF980crWI6XNNuR5tlKxb99lELSz+OAHGD8E5aP9ANOsCjysaJmQ7Yl8i2GuQC1FZGSxrpGFu0akRx3L1WbQ1WlIwTTpk6uAXPnk0ifzAnLpkeGTGb9d96DlVrSl1KbWPYtm7IPBkzm+aCkBCb5J2lmS/LSOyhpWAWsWgRNpAllTCcusy9B4MwwBeLXxVyp2cmEQpxA4hvGjH/2I008/nXPOOYdf//rXzJkzh3/4h3/goosuerGHNiFynSNHVInNJ7jK4Ng5TJZryrF346yGk4N6NSXPx59W0vMUnq+akzwfIQSeEvieIPQlqSdJlSKXilwMrugjPWhWG6a5Cm/fc6sIDEFIgRgrsT8MVjFuYuSIdkJusQ4B2JV+wbYnqGMpA0MViFHt0bbvIc/FIdUSEEYjjSY3HkpkeDLFF02FQHropuVgaOVi3bRGtIrASWzNAU/kKLJ2hiRPJyid4mcNawnIbczGqHEKCUqBEYitnKNRx6bGP7V1cmEQpxA4hvHnP/+Z66+/nksvvZR/+qd/4p577uE973kPQRCwdOnSMX8TxzFxPOjT2dfXt7uGu0V0ZtByZKDQvpk54MVmslxTjr0btxI4OUiTDCGycbUVUtjCU60JshRIYeeYUkjsFDhoT2pTzycthhS9AkFYwS91ouIqMq6j4gakCSZLB12GWvEASg1OXltuQ54Pysd4nnUNUj5G+WjlbcF1yFYm3jrDn2HDYhy2YhHYWnGvUVYHY1BZA29IIG/LCtG2RgwN8N3CfkcpOWIL7lAtF6fm+bHfb+s8bJ1kYGIuQ04uWJxC4BiG1prjjz+eq666CoBjjjmGBx98kBtuuGGLk7fly5dz5ZVX7s5hbpM8H8NCkO+bWv+LzWS5phx7N85qODnIx/D53nLjwaqzQghSJYiVQCqBlALPE3hKIqXXnjQbT6ADRaQCQi/EC0t4SR2RJcgsBp0jhqS/NLL5y9akfqgL0Ah3nJGxA1oOBhoPDTjeHbSqALcqMkudoXSKyDNknlo//lYMRRpDmlgXnpZSlGa22FnedKEasbovhLCKklJ2xd73EJ4thGb8pguV52O8AO2FGCnQXtDOzDQ8xmJiCkJV7rmVivdknELgGMasWbM47LDDhm079NBD+fd///ct/ubyyy/n0ksvbX/u6+tj//3332VjHA86z9EjHv4jPzt2D5PlmnLs3Rg9+kE/ziyGjj2IciUiiMZXh0BIgVISz5f4vqJQUESRolSUlApQLhg6ChmlIKXsNyjJKkXdT1Tvx08G8BoDqEYV4hoiaWCSBPLcvgBk05VFKTv59XzwPIzngxfaWAEvQHsBeJBLO8nV0ieT1k8/Fx6aQfeboTEEwiYjRYjWN7qdeWfwpRHtFKo2CFe03+2qftstaGsX/MgJvdFWmQkKEBTGdjvaxg1kRr5vQeERRjcVrvG7gm2VCQQV51qPsVCwbwoGpxA4hvHyl7+cRx99dNi2P/3pT8ybN2+LvwnDkDAMd/XQJoTJ9CiXIbOP5hZ+sZks15Rj78a5BkwO9pvbQVQcX5YhIWyRrMAXhAFEARRCQznKKDeVgLIaoKT7KDQ2E9Q24Q1sRPT3ovs2k/f2Ue8fIO2vjVmYTEiB9BQq8PCKIV4hxCsW8MolVKWMbBYmk6UO8qhCrkK09Im9IjERDROR5D5p7pE1M/m0UoAKDFIalDD4MseTOaFMCERCKBp4OiFIa/hpFRXXUHEVUbcv06ih63VMo4GOE7I4weQ5ZohvvPCsEqPCABkGtjBZoYgoljCFErpQJo8qpGGZOCjT8ErEpkBDhzTygEbukeaKJJNkWrQrLMPwDEO+0oQqJ1QpoUqJZExEjSirEqRV/HgA1ehH1gfaYzf1ug3gbsRjFlXbFklj/IqFkwuDOIXAMYxLLrmEl73sZVx11VW8+c1v5re//S033ngjN95444s9tAmR5znCuQztEUyWa8qxd5NnOVK64MG9nUpZUiiOz7VGNPPg+97wSsWlIKXkxZTVAEXdTyHuHVQG+jahezeTbe4l6e0n6R0g7quT1mKSaozONHlqJ4zKl0hP4hcC/GJM2FEEbWzWIaVQQYDIUswQ1zQtJLnwSI1Pon3i3CPJPZJctdN5gs3Uo6R95UoQGoFqBui2Kh1LkyPzzGbniWuIWj96oB9drZFXq+0Ky3mckDVSjNYYbYYrMoUQr1TASzOUMUilIIjsWJVH6kU0vBJVXaaaFRhIQ6qJR60hqSeCRgJpCmlm2kYEJcHzJIEPgQ/F0FAKA4pBSsn3yTyF8YW1bGQNFNhMQkmMqdfJ+wfIBqpktTp5I0U3lYLxUk/Scbd1cmEQpxA4hnHCCSfwgx/8gMsvv5yPfvSjLFiwgOuuu463vOUtL/bQJoTO81ECxLkMvThMlmvKsXfj3AgnB6cd+BSVSmXCv2tXLTamnUWnFYhbDztpBBVk1/5NN5jB732j8RkMzB0ZONuOARACIxSpVMTCxgrkwmu7BOXGIzPK/p03c/4bgRKGQGX4zUnp0LoB7X02qw3HuU+qFQMUUKILERpkqJGd1rVICVuJeWhl5nYfIzIV5UAK7YJnrZd1XZLktNKJSkxu332Z0xHUqQQCUxqsY7Cl9XTZHPtQtychIDU+vXkX/V4HorwfsqJp7XVY2+ZrosT9/XDjf4yrrZMLgziFwDGKN7zhDbzhDW94sYexQ+S5RozU+vdRM+CewGS4phx7NzrPR1kJ99UH/97MtF98mY5onO6EzZV64XvIKIKoAKUKplghL3SQFDqphV0MyE768zL9aURfw6e/puirQn9VU6/n1GoZjXpGmmRkad4uxGXrkYl2alPPl4Shwg8UhUgSBJJCOOiuFAWa0DOEfo6vckKZo2ROqPJ2is52cDOtasM273+mPRq5R5x5NFJJI5HUYrtCX6sbGg071no9o1FPadRT0jhtjjkjTw16iIVAKTtmP/QJIp+oEFAqB5TKPqWSolySVApQKeaUw4ySn1D0GlRknUjXCLKarSGQxzb4eGjaUCHaGZVyLyL1CsSqSEMUqesCtSxiIA2oxh4DdUl/HQaqmv6BnHo1pVaz40/qSXv8E3HjSZPxt3VyYRCnEDgmJTrL0SIftc3hcOybOKvh5CDesIlGGIyrbdt1JwxQxQKqlFoLgZBI5SGDAspkSGEnkMZAlguSDOLETrKrAynVgYRGPSFppGTp4ARSCut601II/NAjjXyC0EPnXjP2WFoXHQmeEnjKoM3gyrpqjXXISrodzHBrQasqcK4FWS5IM0GaQaPRHGc1o1pNqFcT6gMxjXpMUk9IGrFVCJJ02PUulcIPA7wwICpGJOWILMvJtUGbYEi9BomvFIFS5Ea1g59B2CANY5rpSAdTkRrksABm06y1YKshK1JtYw8aiaAWDyoD/b0x1YGYWn+D+kCDpBGT1O34jdbt9LHbIkur42oHTi4MZffluHI4diMtM+DI10T5whe+wPz584miiCVLlvDb3/52i20feughzjrrLObPn48Qguuuu26H+3Q4HDuHnSUTYOL38Pe+9z0WLVpEFEUceeSR/OxnPxv2vTGGj3zkI8yaNYtCocBpp53GY489tl1jezG44oorbJrJIa9Fixa1v280GrzrXe9iypQplMtlzjrrLF544YXt2ldjYz+NDX3jfPUSb+ylsX4z8fqNJOs2kK9fD+vXoDa+QLRxFZXNzzKl9hwzxSpmFzcyr7uPBdMbHLSf5pAFikUHFzn0sC4OPWIqhx41k0MXz+bwY/bj8GP247Bj9uPQxbM45IgZHHL4NA46pJuFCyvMn1dk/zkBs2coZvQIeiqGzpKmFGoiX+NJjWpO/LWRdoKsfRIdjHhZF6FMe+TauhkZY+fhVsGAKBJEkaRU8ujoCOnsKtA9rULP9E6mzOpm6uypTJ0zjWn7z2DavFlM3X8m0+bNYtr+M+iZPZWeGd10Tq1Q6SpSKAX4vkIKyHNDvQF9NcGmAY91AxGrBzp4vjaVZ5L9eFos5JnwEJ6pHMmzPUfz3NRjeW5a8zX1WJ7pPpqny0fydLiIp80Cnonn8Gx1Kqv6O1jbH7Gh36O3KqjWDEli0LlBKokfeBRKIaXOIuXuCl3Tu+ma3kPXjCl0z5w6rlfXjCnjvp52plwYDx//+Md52cteRrFYpKura1y/GY98aM07hr6uvvrqCY3NWQgck5Jbb34ZHR3DM1H09fXR2Tn+Pr7zne9w6aWXcsMNN7BkyRKuu+46Tj/9dB599FGmT58+qn2tVmPhwoWcc845XHLJJTulT4fDsXPIM40QI4MHJ+5GONF7+M477+T8889n+fLlvOENb+Db3/42Z5xxBvfddx9HHHEEAJ/4xCf4l3/5F77+9a+zYMECPvzhD3P66afz8MMPE0XR9h3wbubwww/nl7/8Zfuz5w1OLy655BJ++tOf8r3vfY/Ozk4uvvhi3vSmN/Hf//3fE95P3FcnCMZXmAxAehIVeDYwNUlttp08R+U5Mkvx8lbu/QQ/jIn8CoVCmXoQUS8E1FKfRiqJU0mWS7IRKfeFaLoONV9KmiEBwYPZdpQ0SNH085dNtyAjaNoaMMaQMxgvYEzLZcj69WdatPcrm/vxlCDXEAYCkFZJ8CR+kBMVPNIkJ8s0eWZrNwxdYRfC1mKQSrZTs3qexPftOnGWGZLUAIIsh0Yi8T1J4Ck8BZ6yxyWbxyUEw92dDOTajr9l0WhZX5LUvuLEkKaaNNVobZACPF8CvlUOQt9a+83oWgFbI4nHf1/vLLkwXpIk4ZxzzuHEE0/kK1/5yrh+M1758NGPfpSLLrqo/XmisTbOQuBwbIFrr72Wiy66iAsvvJDDDjuMG264gWKxyFe/+tUx259wwgl88pOf5LzzzttiysyJ9ulwOHYOO2slcKL38Gc/+1le85rXcNlll3HooYfyz//8zxx77LF8/vOfB+zq33XXXcf//b//l7/+67/mqKOO4hvf+AarVq3ihz/84Y4c8m7F8zxmzpzZfk2dOhWA3t5evvKVr3Dttdfyqle9iuOOO46bbrqJO++8k7vuuutFHrXFBhDbYGIAOSSQtRUIK5veO3JIjawt1csaVBDMuCdZLfcg03QnGvadGV9hLtEckJTja789aDPcjWmoctSKexjarulNNOrv8dCK0YDB953N7rYQXHnllVxyySUceeSR42o/EflQqVSG3YOlUmlCY3MWAsdOp7UKkcbjrxY4Hlr9jcePsK+vb4vbRn43Vs77JEm49957ufzyy9vbpJScdtpprFy5csJj31V97iu0/ucT8Q0dL1la3+l9Dva9a1aaduWYpRr/6utEqJld85CttfyWtyEX0kbfKF/hPLPX03hkAmzfPbxy5cphRfYATj/99PbD/Mknn2TNmjWcdtpp7e87OztZsmQJK1eu5Lzzztvqce0pPPbYY8yePZsoijjxxBNZvnw5c+fO5d577yVN02HHt2jRIubOncvKlSt56UtfOmZ/cRwTx3H7c29vLwDTPv3VUdbfiWKAkVe5BIrN1yB587W7GGvSO9ZoJ9qnYjBaYUvo5mvX3P8TQ7Ij69V9fZrvfmp8c4UsGRhlfZioXNiVTEQ+XH311fzzP/8zc+fO5W/+5m+45JJLhlnqtoVTCBw7nf7+fgBu+exxu6z/zi34/gRBwMyZM7dY1bZcLo/6btmyZVxxxRXDtq1fv548z5kxY8aw7TNmzOCRRx7ZrnHvij73FVrX1J0/euOLPBLH9vJfu7j/LcmFlkz43W1vHvN345UJsH338Jo1a8Zsv2bNmvb3rW1barOns2TJEr72ta9xyCGHsHr1aq688kpOPvlkHnzwQdasWUMQBKP8pbd1fMuXL+fKK68ctd1VLHeMl/HMFe5ZcfaY309ELuxKxisf3vOe93DsscfS09PDnXfeyeWXX87q1au59tprx70vpxA4djqzZ8/m2WefpVKptE2YOwNjDP39/cyePXuLbaIo4sknnyRJxq5UaIwZNSZXEXfPZyLXVF9fH/vvvz/PPvvsDq8k7q6+3Zi3v99tyQUnE3Y9r33ta9t/H3XUUSxZsoR58+bx3e9+l0KhsF19Xn755cMsK1prNm7cyJQpU15UGbCr+3d973j/u3uu8MEPfpBrrrlmq2P64x//OCzQfmcz9F456qijCIKAv//7v2f58uXjlmdOIXDsdKSU7Lfffruk7y1p+0OJomiHA/GmTp2KUmpUJowXXniBmTNn7jF97itszzXV0dGxSx5Mu7JvN+bt63dbcmFnyATYvnt45syZW23fen/hhReYNWvWsDZHH330Do/5xaCrq4uDDz6Yxx9/nFe/+tUkScLmzZuHWQm2JffGcs8Yb1aWFrvyftrV/bu+d6z/3TVXAPjHf/xHLrjggq22Wbhw4Xb1vb3yYcmSJWRZxlNPPcUhhxwyrn25oGKHYwyCIOC4447jtttua2/TWnPbbbdx4okn7jF9OhyO3cf23MMnnnjisPYAK1asaLdfsGABM2fOHNamr6+Pu+++e6+VCwMDAzzxxBPMmjWL4447Dt/3hx3fo48+yjPPPLPXHp/DMZRp06axaNGirb6CYHy1M0ayvfLh/vvvR0o5oeyFzkLgcGyBSy+9lKVLl3L88cfzkpe8hOuuu45qtcqFF14IwNve9jbmzJnD8uXLARtw+PDDD7f/fv7557n//vspl8sceOCB4+rT4XDs2UxULrz3ve/llFNO4dOf/jSvf/3rufnmm/nd737HjTfeCNjMMO973/v42Mc+xkEHHdROKzh79mzOOOOMF+swJ8T73/9+3vjGNzJv3jxWrVrFsmXLUEpx/vnn09nZyd/93d9x6aWX0tPTQ0dHB+9+97s58cQTtxhQ7HBMVp555hk2btzIM888Q57n3H///QAceOCBlMtlwAbdL1++nDPPPHNc8mHlypXcfffdvPKVr6RSqbBy5UouueQS/vZv/5bu7u7xD844HI4t8rnPfc7MnTvXBEFgXvKSl5i77rqr/d0pp5xili5d2v785JNPGmxKiGGvU045Zdx9OnacRqNhli1bZhqNxl7Ttxvzru93ZzIRuWCMMd/97nfNwQcfbIIgMIcffrj56U9/Oux7rbX58Ic/bGbMmGHCMDSnnnqqefTRR3fHoewUzj33XDNr1iwTBIGZM2eOOffcc83jjz/e/r5er5t/+Id/MN3d3aZYLJozzzzTrF69epeNZ1dfQ3vj/bo39707+t9dLF26dMx5wu23395uA5ibbrqp/Xlb8uHee+81S5YsMZ2dnSaKInPooYeaq666asLnSjR37nA4HA6Hw+FwOPZBXAyBw+FwOBwOh8OxD+MUAofD4XA4HA6HYx/GKQQOh8PhcDgcDsc+jFMIHA6Hw+FwOByOfRinEDgcjknBb37zG974xjcye/ZshBD88Ic/3Cn9Ll++nBNOOIFKpcL06dM544wzePTRR3dK39dffz1HHXVUu9jOiSeeyM9//vOd0vdQrr766nb6uh3liiuuQAgx7LWzKnA+//zz/O3f/i1TpkyhUChw5JFH8rvf/W6n9O2Y/OwqGQC7Vg7A3ikLYNfKA3AyYXfiFAKHwzEpqFarLF68mC984Qs7td9f//rXvOtd7+Kuu+5ixYoVpGnKX/7lX1KtVne47/3224+rr76ae++9l9/97ne86lWv4q//+q956KGHdsLILffccw9f/OIXOeqoo3Zan4cffjirV69uv/7rv/5rh/vctGkTL3/5y/F9n5///Oc8/PDDfPrTn55YHm3HPs2ukgGwa+UA7L2yAHaNPAAnE3Y7O5ZR1eFwOPY8APODH/xgl/S9du1aA5hf//rXu6T/7u5u8+Uvf3mn9NXf328OOuggs2LFCnPKKaeY9773vTvc57Jly8zixYt3uJ+RfOADHzAnnXTSTu/XsW+yK2WAMbteDhiz58sCY3adPDDGyYTdjbMQOBwOxwTo7e0FoKenZ6f2m+c5N998M9Vqdasl6SfCu971Ll7/+tdz2mmn7ZT+Wjz22GPMnj2bhQsX8pa3vIVnnnlmh/v80Y9+xPHHH88555zD9OnTOeaYY/jSl760E0brcOx8dpUcgL1LFsCukQfgZMLuxnuxB+BwOBx7C1pr3ve+9/Hyl7+cI444Yqf0+Yc//IETTzyRRqNBuVzmBz/4AYcddtgO93vzzTdz3333cc899+yEUQ6yZMkSvva1r3HIIYewevVqrrzySk4++WQefPBBKpXKdvf75z//meuvv55LL72Uf/qnf+Kee+7hPe95D0EQsHTp0p14BA7HjrEr5ADsfbIAdp08ACcTdjsvtonC4XA4djbsIneBd77znWbevHnm2Wef3Wl9xnFsHnvsMfO73/3OfPCDHzRTp041Dz300A71+cwzz5jp06ebBx54oL1tZ7oJDGXTpk2mo6Njh10bfN83J5544rBt7373u81LX/rSHerXsW+yq2SAMbtGDhiz98sCY3aePDDGyYTdjXMZcjgcjnFw8cUX85Of/ITbb7+d/fbbb6f1GwQBBx54IMcddxzLly9n8eLFfPazn92hPu+9917Wrl3Lsccei+d5eJ7Hr3/9a/7lX/4Fz/PI83wnjR66uro4+OCDefzxx3eon1mzZo1aDT300EN3mvuBw7Ez2FVyAPZ+WQA7Tx6Akwm7G+cy5HA4HFvBGMO73/1ufvCDH3DHHXewYMGCXbo/rTVxHO9QH6eeeip/+MMfhm278MILWbRoER/4wAdQSu1Q/0MZGBjgiSee4K1vfesO9fPyl798VBrHP/3pT8ybN2+H+nU4dga7Ww7A3icLYOfJA3AyYXfjFAKHwzEpGBgYGLYq9eSTT3L//ffT09PD3Llzt7vfd73rXXz729/mP/7jP6hUKqxZswaAzs5OCoXCDo358ssv57WvfS1z586lv7+fb3/729xxxx3ceuutO9RvpVIZ5dtcKpWYMmXKDvs8v//97+eNb3wj8+bNY9WqVSxbtgylFOeff/4O9XvJJZfwspe9jKuuuoo3v/nN/Pa3v+XGG2/kxhtv3KF+HfsOu0oGwK6VA7B3ygLYdfIAnEzY7bzYPksOh8OxM7j99tsNMOq1dOnSHep3rD4Bc9NNN+3wmN/+9rebefPmmSAIzLRp08ypp55qfvGLX+xwv2Oxs/yGzz33XDNr1iwTBIGZM2eOOffcc83jjz++4wM0xvz4xz82RxxxhAnD0CxatMjceOONO6Vfx77BrpIBxuxaOWDM3ikLjNm18sAYJxN2J8IYY3anAuJwOBwOh8PhcDj2HFxQscPhcDgcDofDsQ/jFAKHw+FwOBwOh2MfxikEjjZPPfUUQgi+9rWvvdhDcTgcewBOJjgcjpE4uTA52SMVgj/84Q+cffbZzJs3jyiKmDNnDq9+9av53Oc+N6zdVVddxQ9/+MPt3s/DDz/MFVdcwVNPPbVjA3Y4HLsUJxMcDsdInFxwOHYee1xQ8Z133skrX/lK5s6dy9KlS5k5cybPPvssd911F0888cSwlGLlcpmzzz57u7XU73//+5xzzjncfvvtvOIVr9g5B7AXY4whjmN839/puYkdju3FyYQXDycTHHsqTi68eDi5MDnZ4+oQfPzjH6ezs5N77rmHrq6uYd+tXbv2xRnUJCfLMrTWBEFAFEUv9nAcjmE4mbD7cTLBsafj5MLux8mFSc6LmPJ0TA455BDzile8Ypvt2Equ4aeeesr87//9v83BBx9soigyPT095uyzzzZPPvlk+/c33XTTmH3cfvvt7TY/+9nPzEknnWSKxaIpl8vmda97nXnwwQfHdRybNm0y73vf+9p5hefMmWPe+ta3mnXr1rXbvPDCC+btb3+7mT59ugnD0Bx11FHma1/7Wvv7JElMd3e3ueCCC0b139vba8IwNP/4j/9ojDEmjmPz4Q9/2Bx77LGmo6PDFItFc9JJJ5lf/epXw3735JNPGsB88pOfNJ/5zGfMwoULjZTS/M///E/7u6F5lR944AGzdOlSs2DBAhOGoZkxY4a58MILzfr164f1u2zZMgOYxx57zCxdutR0dnaajo4Oc8EFF5hqtTpq/N/85jfNCSecYAqFgunq6jInn3yyufXWW4e12ZHz75g8OJngZMLOOP+OyYWTC04u7Izz7xhkj7MQzJs3j5UrV/Lggw9utYreN7/5Tf7X//pfvOQlL+Ed73gHAAcccAAA99xzD3feeSfnnXce++23H0899RTXX389r3jFK3j44YcpFov8xV/8Be95z3v4l3/5F/7pn/6JQw89FKD9/s1vfpOlS5dy+umnc80111Cr1bj++us56aST+J//+R/mz5+/xbENDAxw8skn88c//pG3v/3tHHvssaxfv54f/ehHPPfcc0ydOpV6vc4rXvEKHn/8cS6++GIWLFjA9773PS644AI2b97Me9/7Xnzf58wzz+SWW27hi1/8IkEQtPfxwx/+kDiOOe+88wDo6+vjy1/+Mueffz4XXXQR/f39fOUrX+H000/nt7/9LUcfffSwMd500000Gg3e8Y53EIYhPT09aK1HHcuKFSv485//zIUXXsjMmTN56KGHuPHGG3nooYe46667EEIMa//mN7+ZBQsWsHz5cu677z6+/OUvM336dK655pp2myuvvJIrrriCl73sZXz0ox8lCALuvvtufvWrX/GXf/mXO3z+HZMLJxOcTNjR8++YfDi54OTCjp5/xwhebI1kJL/4xS+MUsoopcyJJ55o/s//+T/m1ltvNUmSjGpbKpXGrEBYq9VGbVu5cqUBzDe+8Y32tu9973ujNH1jjOnv7zddXV3moosuGrZ9zZo1prOzc9T2kXzkIx8xgLnllltGfae1NsYYc9111xnAfOtb32p/lySJOfHEE025XDZ9fX3GGGNuvfVWA5gf//jHw/p53eteZxYuXNj+nGWZieN4WJtNmzaZGTNmmLe//e3tbS3NvqOjw6xdu3ZY+7G0/rHO5b/9278ZwPzmN79pb2tp/UP3ZYwxZ555ppkyZUr782OPPWaklObMM880eZ6PeW529Pw7JhdOJjiZ4GSCYyROLji54OTCzmWPyzL06le/mpUrV/JXf/VXPPDAA3ziE5/g9NNPZ86cOfzoRz8aVx+FQqH9d5qmbNiwgQMPPJCuri7uu+++bf5+xYoVbN68mfPPP5/169e3X0oplixZwu23377V3//7v/87ixcv5swzzxz1XUtL/tnPfsbMmTM5//zz29/5vs973vMeBgYG+PWvfw3Aq171KqZOncp3vvOddrtNmzaxYsUKzj333PY2pVR7VUBrzcaNG8myjOOPP37MYz7rrLOYNm3aNs/F0HPZaDRYv349L33pSwHG7Ped73znsM8nn3wyGzZsoK+vD7CrFVprPvKRjyDl8MuvdW529Pw7JhdOJjiZ4GSCYyROLji54OTCzmWPcxkCOOGEE7jllltIkoQHHniAH/zgB3zmM5/h7LPP5v777+ewww7b6u/r9TrLly/npptu4vnnn8cMSaTU29u7zf0/9thjgL3BxqKjo2Orv3/iiSc466yzttrm6aef5qCDDhp1obfMkE8//TQAnudx1lln8e1vf5s4jgnDkFtuuYU0TYfd5ABf//rX+fSnP80jjzxCmqbt7QsWLBi1/7G2jcXGjRu58sorufnmm0cFao11LufOnTvsc3d3N2AFU0dHB0888QRSyq3+D3f0/DsmH04mOJkATiY4huPkgpML4OTCzmKPVAhaBEHACSecwAknnMDBBx/MhRdeyPe+9z2WLVu21d+9+93v5qabbuJ973sfJ554Ip2dnQghOO+888b0fRtJq803v/lNZs6cOep7z9u9p+28887ji1/8Ij//+c8544wz+O53v8uiRYtYvHhxu823vvUtLrjgAs444wwuu+wypk+fjlKK5cuX88QTT4zqc6g2vzXe/OY3c+edd3LZZZdx9NFHUy6X0Vrzmte8ZsxzuaUUZGYC2W33tPPv2HNwMsHiZMJwnEzYt3FyweLkwnCcXJgYe83ZOv744wFYvXp1e9vIIJUW3//+91m6dCmf/vSn29sajQabN28e1m5Lv28FHE2fPp3TTjttwmM94IADePDBB7faZt68efz+979Haz1M83/kkUfa37f4i7/4C2bNmsV3vvMdTjrpJH71q1/xoQ99aFh/3//+91m4cCG33HLLsOPalkDcGps2beK2227jyiuv5CMf+Uh7e0sr3x4OOOAAtNY8/PDDo4KXhraB7T//jn0DJxOcTHA4RuLkgpMLju1jj4shuP3228fUEH/2s58BcMghh7S3lUqlUTcuWM1zZB+f+9znyPN82LZSqQQwqo/TTz+djo4OrrrqqmHmtBbr1q3b6jGcddZZbfPlSFrjet3rXseaNWuG+ftlWcbnPvc5yuUyp5xySnu7lJKzzz6bH//4x3zzm98ky7JRJsCWtj30uO+++25Wrly51bFujbH6BLjuuuu2u88zzjgDKSUf/ehHR60atPazo+ffMblwMsHJBCcTHCNxcsHJBScXdi57nIXg3e9+N7VajTPPPJNFixaRJAl33nkn3/nOd5g/fz4XXnhhu+1xxx3HL3/5S6699lpmz57NggULWLJkCW94wxv45je/SWdnJ4cddhgrV67kl7/8JVOmTBm2r6OPPhqlFNdccw29vb2EYcirXvUqpk+fzvXXX89b3/pWjj32WM477zymTZvGM888w09/+lNe/vKX8/nPf36Lx3DZZZe1Kxu+/e1v57jjjmPjxo386Ec/4oYbbmDx4sW84x3v4Itf/CIXXHAB9957L/Pnz+f73/8+//3f/811111HpVIZ1ue5557L5z73OZYtW8aRRx7Z9h9s8YY3vIFbbrmFM888k9e//vU8+eST3HDDDRx22GEMDAxs1/+io6ODv/iLv+ATn/gEaZoyZ84cfvGLX/Dkk09uV38ABx54IB/60If453/+Z04++WTe9KY3EYYh99xzD7Nnz2b58uV0dHTs0Pl3TC6cTHAywckEx0icXHBywcmFnczuS2g0Pn7+85+bt7/97WbRokWmXC6bIAjMgQceaN797nebF154YVjbRx55xPzFX/yFKRQKw4qNbNq0yVx44YVm6tSpplwum9NPP9088sgjZt68eaNSj33pS18yCxcuNEqpUWnFbr/9dnP66aebzs5OE0WROeCAA8wFF1xgfve7323zODZs2GAuvvhiM2fOHBMEgdlvv/3M0qVLhxXpeOGFF9rjDILAHHnkkcPSeA1Fa232339/A5iPfexjY35/1VVXmXnz5pkwDM0xxxxjfvKTn5ilS5eaefPmtdsNLTYykrFSiT333HPmzDPPNF1dXaazs9Occ845ZtWqVQYwy5Yta7drpRIbWkzFmMGiLkMLvRhjzFe/+lVzzDHHmDAMTXd3tznllFPMihUrhrXZkfPvmDw4mXDTmP05meBkwr6Mkws3jdmfkwtOLmwvwpgJRHA4HA6Hw+FwOByOScUeF0PgcDgcDofD4XA4dh9OIXA4HA6Hw+FwOPZhnELgcDgcDofD4XDswziFwOFwOBwOh8Ph2IdxCoHD4XA4HA6Hw7EP4xQCh8PhcDgcDodjH8YpBA6Hw+FwOBwOxz6MUwh2gG9/+9s7VJq7VqtxxRVXcMcdd+y0MW2JY445hssvvxyAjRs3IqUcc7/XX38955xzDnPnzkUIwQUXXLDLx+ZwTBYmm0x49tlnufLKK3nJS15Cd3c3U6dO5RWveAW//OUvd/n4HI7JwmSTC/V6nb/7u7/jiCOOoLOzk3K5zOLFi/nsZz9Lmqa7fIyOXYNTCHaAnXGTX3nllbv8Jq/Vajz44IO89KUvBeCuu+5CSskJJ5wwqu0111zDr371Kw4//HA8z9ul43I4JhuTTSb8x3/8B9dccw0HHnggH/vYx/jwhz9Mf38/r371q7npppt26RgdjsnCZJML9Xqdhx56iNe97nUsX76cT33qUyxevJhLLrmEpUuX7tIxOnYdbsa3D3DfffeRZVn7Jl+5ciWHH344pVJpVNtf//rXbetAuVze3UN1OBy7gfHKhFe+8pU888wzTJ06tb3tne98J0cffTQf+chHuPDCC3fruB0Ox65jvHKhp6eHu+66a9i2d77znXR2dvL5z3+ea6+9lpkzZ+62cTt2Ds5CsAX6+/t53/vex/z58wnDkOnTp/PqV7+a++67D4BXvOIV/PSnP+Xpp59GCIEQgvnz5wOQJAkf+chHOO644+js7KRUKnHyySdz++23t/t/6qmnmDZtGgBXXnllu48rrrii3eaRRx7h7LPPpqenhyiKOP744/nRj340rvHXajXWr1/P+vXr+c1vfsN+++2HUor169fzX//1Xxx55JHt74cyb948hBA7cOYcjsnJvigTDj/88GHKAEAYhrzuda/jueeeo7+/f3tOpcMxadgX5cKWaB3X5s2bx7Vvx56FsxBsgXe+8518//vf5+KLL+awww5jw4YN/Nd//Rd//OMfOfbYY/nQhz5Eb28vzz33HJ/5zGcA2ivqfX19fPnLX+b888/noosuor+/n6985Sucfvrp/Pa3v+Xoo49m2rRpXH/99fzv//2/OfPMM3nTm94EwFFHHQXAQw89xMtf/nLmzJnDBz/4QUqlEt/97nc544wz+Pd//3fOPPPMrY7/E5/4BFdeeeWwbS2h0uJf//VfATDG7PgJczgmOU4mDLJmzRqKxSLFYnGcZ8/hmJzsy3IhSRL6+vqo1+v87ne/41Of+hTz5s3jwAMP3M6z6XhRMY4x6ezsNO9617u22ub1r3+9mTdv3qjtWZaZOI6Hbdu0aZOZMWOGefvb397etm7dOgOYZcuWjerj1FNPNUceeaRpNBrtbVpr87KXvcwcdNBB2xz/E088YVasWGF+8YtfmEKhYC677DKzYsUK85nPfMYA5rvf/a5ZsWKFWbFixRb7KJVKZunSpdvcl8OxL+BkguWxxx4zURSZt771rdvcp8Mx2dmX5cK//du/GaD9Ov74483vf//7be7TsWfiLARboKuri7vvvptVq1Yxe/bsCf1WKYVSCgCtNZs3b0ZrzfHHH982I26NjRs38qtf/YqPfvSj9Pf3DzPLn3766Sxbtoznn3+eOXPmbLGPhQsXsnDhQn7/+99Tr9f5X//rf3HwwQdz//33M3/+fM4555wJHZPDsa/jZIJ1LzjnnHMoFApcffXV4zhyh2Nysy/LhVe+8pWsWLGCzZs3c9ttt/HAAw9QrVYncAYcexJOIdgCn/jEJ1i6dCn7778/xx13HK973et429vexsKFC8f1+69//et8+tOf5pFHHhmWhmvBggXb/O3jjz+OMYYPf/jDfPjDHx6zzdq1a7d4k8dx3BYMP/vZz5gxYwY9PT2sX7+eX/7ylyxZsqTtD9jT04OULpTE4dgW+7pMyPOc8847j4cffpif//znE578OByTkX1ZLsyYMYMZM2YAcPbZZ3PVVVfx6le/mscee8wFFe+FOIVgC7z5zW/m5JNP5gc/+AG/+MUv+OQnP8k111zDLbfcwmtf+9qt/vZb3/oWF1xwAWeccQaXXXYZ06dPRynF8uXLeeKJJ7a5b601AO9///s5/fTTx2yzNR+9f/u3fxuV/WOkT+B3vvMdAJ588sl2IJDD4dgy+7pMuOiii/jJT37Cv/7rv/KqV71qm2N2OPYF9nW5MJSzzz6bD33oQ/zHf/wHf//3f7+t4Tv2MJxCsBVmzZrFP/zDP/AP//APrF27lmOPPZaPf/zj7Zt8S9l4vv/977Nw4UJuueWWYW2WLVs2rN2Wft9aWfB9n9NOO23C4z799NNZsWIFAG9605t4xzvewWte8xoeffRRLr74Ym6++WamTJkC4LR4h2MC7Ksy4bLLLuOmm27iuuuu4/zzz5/w/h2Oycy+KhdGUq/XAejt7Z3wWBwvPs5XZAzyPB91QU+fPp3Zs2cTx3F7W6lUGvPCb/kEmiER+XfffTcrV64c1q6VoWNkiq7p06fzile8gi9+8YusXr16VP/r1q3b6vhnzZrFaaedxgEHHEB/fz9/+7d/y2mnnYZSipkzZ3Luuedy2mmncdpppxFF0Vb7cjgc+7ZM+OQnP8mnPvUp/umf/on3vve9W92Pw7Evsa/KhfXr14+ZiezLX/4yAMcff/xW9+vYM3EWgjHo7+9nv/324+yzz2bx4sWUy2V++ctfcs899/DpT3+63e64447jO9/5DpdeeiknnHAC5XKZN77xjbzhDW/glltu4cwzz+T1r389Tz75JDfccAOHHXYYAwMD7d8XCgUOO+wwvvOd73DwwQfT09PDEUccwRFHHMEXvvAFTjrpJI488kguuugiFi5cyAsvvMDKlSt57rnneOCBB7Z5HP/93/9NpVLhyCOPBODOO+/kxBNP3OpvfvzjH7f7TtOU3//+93zsYx8D4K/+6q/aqc4cjn2JfVUm/OAHP+D//J//w0EHHcShhx7Kt771rWHfv/rVr277EDsc+xr7qlz41re+xQ033MAZZ5zBwoUL6e/v59Zbb2XFihW88Y1vdC6FeysvYoajPZY4js1ll11mFi9ebCqViimVSmbx4sXm//2//zes3cDAgPmbv/kb09XVZYB2WjGttbnqqqvMvHnzTBiG5phjjjE/+clPzNKlS0elHrvzzjvNcccdZ4IgGJVW7IknnjBve9vbzMyZM43v+2bOnDnmDW94g/n+978/ruN45zvfaU499dT25wMPPNB88pOf3Opvli5dOiyN2NDXTTfdNK79OhyTjX1VJixbtmyL8gAwt99++7j263BMRvZVuXDPPfeYc845x8ydO9eEYWhKpZI59thjzbXXXmvSNB3XPh17HsIYV5XK4XA4HA6Hw+HYV3ExBA6Hw+FwOBwOxz6MUwgcDofD4XA4HI59GKcQOBwOh8PhcDgc+zBOIXA4tsBvfvMb3vjGNzJ79myEEPzwhz/cavvVq1fzN3/zNxx88MFIKXnf+943qs1DDz3EWWedxfz58xFCcN111+2SsTscjp2PkwkOh2Mkk0Uu7HSFYKInxuHYU6lWqyxevJgvfOEL42ofxzHTpk3j//7f/8vixYvHbFOr1Vi4cCFXX331PlUUzskFx2TAyYSdh5MJjsnCZJELO70OQevEvP3tb+dNb3rTzu7e4dhtvPa1r91m6fmhzJ8/n89+9rMAfPWrXx2zzQknnMAJJ5wAwAc/+MEdH+RegpMLjsmAkwk7DycTHJOFySIXdrpCMNETMxKtNatWraJSqWyxXLdj38QYQ39/P7Nnz0bKLRu3Go0GSZKM+V0QBK4684uAkwuOXcV45IKTCXseTiY4dhVurrB97HGViletWsX+++//Yg/DsQfz7LPPst9++435XaPRYHahzCbyMb+fOXMmDzzwwLAbPQxDwjDcJWN17BycXHBsiy3JBScTJidOJji2hZsrTIwXXSGI45g4jtufW3XSHln+LroOOZS+/Y6g6nfxwPp53P9wTJZqcm0wxjB3bomjF9QJVcLqvg6eWycJfJg/I2FKoUqmJUnukRnJ6k0Fnno+J800OjdobSgUPKZNUUQBrNtkeO5ZWyp84cIK+03T1GLJn5/N2Ly5wezZJQ6bl1EKEop+QlHUaJiIP63vYdU66O4QzJtWp+glDCQhvY2ANBNs6hdUa5qOsmTBjJiyH9OXRGyq+mSZoL8Otbo95tYix+xpcPDUjUSiwZq4h9WbCiCgGGgCT9NdqLO/fJogrfNscCAPr+4mzQZ/Xyka9u/qpahietMSaweKJKmg1hDUGlAuwsEzBujy++nPi6wbKJNpwczKAFP8jQzoCr95pIdHH9nElGklFh0QUog0qzdInnuujhCCmTNDpnQIPM/QWcjwlKYae/RWFVkO2oAxEAUwvTOhFCQoYZBCA7CpUWTjgA9A6Gt8NVgfT2vBM2sFT/65nyzNEUIgpCBp9PPNjx9BpVLZ4vWUJAmbyPlG6QCKYvjKQM1o3rbmCWbMmDFs+7Jly7jiiismeOU6diVbkguPfvJ9dB54MP2zF1Hzu3hg03z+8KecNMmp11PSOGe/uR0csVAT+RnrB0I29ApCH2b3pHRGDXIjyLUiN4KN1ZAXNgpybVBSgIDAs/eQp2DVenji8T6MNhx2eBcLZybUYo+H/2zYsKHGzJklDplnKIcpXWGNDrmZ1IQ8XZ3Ohj6fKNR0FxN8mZMZSZortBas6/Ppq0KpALO67f1RSwP6Gx5ZLqjHgnrcvI805Llh5lQ4aOomQpGwKS2zsVZAa0GSC7SGnnLKgtIqorzKC8zm6c2dZPngymlnMWNeaS1F0896ZvD05k6SVJDnglxDGBhmddYpejGN3GcgDtFGMKU4QI+3maopc9efp/L4EwNMmVLgoHmSYpCzaqPHc6sShBTMmenT06GH/N8gzQSNxI4j8MDzDEoaIk+jpEEIgyftbzZU7f/L92C/Kfb/VUt91g+ExIlgzXrN6lUDZGmObu4mifv5t2uO2qJccDJhcrAlmfCnay+lY+FB9M86hGrQxe9eWMC9v6+htcZo227W7DKL5hsiP6e/4dE7IJESukqaKMiRQ67B/obPpgFFmkF/1VCr5YShZEqXJPRh1TrNU3/uRUo49phuFs3qpz8JeeBxjxdeqDFtWoEF+0lKQc6UYo1uv5eGiXh84xTWbJBUijCjKyHwctJckaSKTAs2VyXVupUJ+09pUPIT6rlPLfbJtKAaK2oN0BrSDHJtmDPNcHDPOkLRYFPWyaZ6iSyX1FJJlgl6KikLy6uIdI3VejaPr+0kzcFXoJShEuXs17GJgqixKevi+b4KjUTSOwD9A5pKWbJovzo94QC1LGJdtYjWglkd/UxT6xmgwh2PTuOPD26ka0qRBfMKFEJYu1GzenUNIQTTphXo6pD4LbkqDf11yaZ+g9F27tRR1AhhMMbKCSkNgbL/j41Vn94B8D2Y3ZPRFTWoZT4bqwFxIli3ybBqVZU802htrwk3V9g+XnSFYPny5Vx55ZWjtncdciiV7m5K6SYyL2PD9IN5Zm2ZOM6p1TKyLCcqlqlUFAWV0qs78PsEYSQolhJKBfvA10aSGUl/ViIqakSckyQanWmk71Mo+RRDqOeGSqedoJYrRUqVHOMplJ9gjECoMl4BgjAl8hsUvQCpQypxBx2JoFyGQjEg8DIiP0D7ijiV1DNBPcnxQ0WpHFMOGqSNIr2JDwJ8DSEaKQWBL1ASurs0nZUGgfDo9zsIGgWUhI5iTiHI6AgkBdVDkNWJRCeFUgdeBp4CJaEQalQUILyEyA/o8XzSXGB6FY3c4EeCSgW6ghSyCr26AzK7bZrYREF5dPV0UuqAjq4SPT2ScpQTa5/efqstl8oBYRGiwFAupgRejpf4CF+Ra4GSBintZL+74BOqAACDwBhByQvRykMbK+C0EXjKEAUaCZTriqgkyVIrFKQUSGmFxXjMw6XAoyTUsG3C5FC1qwYdHR3t7ZNZ499b2ZJc6DzwYCrd3RT1AJmAdVMjnl3nE8ca5aXEQU65o0xHR0rBz6iZAn0NgR9AVMooFQKMEW3ZkMiQgUSS5ZBmdqFAeJKwAKFvKMeC7qn22u3uLtDTWcdrBAQFgxACL6hQKEExTClFHmVlSExIZDrxEo8gNBRKCYHKSXOJryVpLokyj3puCAuCYjml4KfoxCfGQ+YCLUEPeUZJAR0dmko5JZApcdxBXUTkWlBs3g6dhYRisYvAFIiyLgpJB7m2MkEIQzHMUSWQskKYVeihRJJJequS/hqEHhRLAR1BDT8LSVVEbgSViqRH1ghFRGdnB4WSR6kSUa5AKczpzH02VzMAoqJHENkJv6cMQliFIErtwUSBJvQ0QoAnmwoBtp0xkKqAXEg8BUEhR4Ypvqco4RFkgoHEEG5SKC8ny3KMNujcyohtyQUnE/ZutiQTOhYeZOcKeR8psGp6kce7I9IkJ45z8lwTFsqUy1AMUnIVkBirEBTLmmKQ4UlNoHKEMIggomE8VAq1JMeIFOX7FEqKQgjl2FCs2OdRsdxBuQx5HKF8e21Jr0xUUBQiTbHkU/INngmppB0MpJJiBIVSSqByPK2QmSTLBV4mIdF4oaRcCSj7DWQWEosQkwsCYeWCMRA21886OnI6KzUC4ZGknVQpowyo3D5nu0ox5VIHofYpZF0UShW8HAqBwfcMpTAnKkEoi5SyIj2yRCOVJFrSW01RgUdnRTElTPCyEv2mgywXdFYM0/R6osCno6OTsJhRqpTp7g4pRYZYCzb12vlUVCwQFq0yVS7b+18Girx5v07tNvSUrKKXajt/aGEMlIVt63vgF3JEWCLIJRWpiDJBPYdCr0+W5ejckGcao1uLrG6uMBFedIXg8ssv59JLL21/7uvrY//996dvvyMopZtQTz+CF0ZMP/JwOiozqHmCOM5JE5BCoIRBCWvyyTVkmZ1caux3nszwjcD3DNoY+9BJc+JGRhBIwEdKCDxBoWQvYN8HJeyKexzn1KoxjUZOkvokniJTHrmRGCOQ0hAGdiKfaoXI7f6FsA/xLIc40SSZfSAKDLkWVOsQJ4bmdYsUEIV2BS3yczyRI4W2k5fmSpjvaYpeiic1mfQxniDP7IUshFUIvOZKe3/sU888PKmtENSSDUJRq+dEoYcUmkA3UKJCkgnSTOBJTdToQ0eKYghh5FEsSiqRtYyUIo8glBg9aI2QAnylCWWO9gRZJKxlwNeEXoYShlCleEIPrpAaicAQ+ZokF/Q2JI0EiqGgGICnNL6n8Dx7bKZ5/BPxE1WRRI3wHVTNk93R0THsJnfseWxJLvTPXkRRD+CvfhLfD5h6yOGUS9PwPEGW2fsp8AWBl+PLHCkgSe3kvbmgiBAGBQiRo6RBKSs7Gg1NvZ5TKik6y4rQhzCAzs4AKaFSsNey73lkGTTqCWmqm9YGSW4kGkmOJNOCNIPME+RakEtBbiSZluRGkOWQ502ZlQsyJclySZza77LcPgyFgMC3k3rfG7SiGUT7wRl4Gl9pApWRGbvQkGrVttLJ5uQcoD8t0JABmZYUgxRfKTb2B2zuzdFNq0lLnmba7sMTOWE6QB54+B54nsTzhL1PZY7v+YTB4L2WZgI8KwM8ZVBiUB4Wg4zIy4etBgI0p/QEylCKmqt8uUDHPsZYuSYFeB4oZa0aRhuSJCNJs3FdU04m7N1sUSbMOoRS3od6/gm8MGLqwUdTLFZoSDtXaCmMUhqktMqnNjbFohCm/V3kpSihqTYXr7SBekNTHUhRSiKEIvCMlS+BQkj73Ad7P8ZxTnWgQaMRkeUembaTckNrIcu0F+20sfIg11YuaS2IE7u/QmQ79aRGa0EjkVaWDJEJoWffI1/jiRRlMowRxJndl+9Zi4evtF0UlT55a/W9OVcIPYMUhjj37TiNoBykeFKhTUB1IKVYUHhSE9LAkyUrq3JQIieI+ymowM6XlCQIJKXIUI4yotDH82R7Yt56hgfKEHiawJeEgR1PRyGlJ6qijaSWBaRaWe+OTLbH7DePt5FIjLHzNCVBBYbAk3ZfxqDzHGNM23o0HpxcGORFVwi25JNV9bvIvAwvjEApApHQWQZPSeLYXhDN+WL7hhuKMaCFQBrQtC4qgc6FvXgChVKyeTOCkLRvRCXtA8oYCAJFVAhQyt4IcSaJPUWoAxLttW/Q9j7N4A2uDVbZ8CVKCjItybQVFELaB5vOjF0hb2r+ALkWJMbHYH+jtb2JjbHjyrQklgWUzEi1QorBMdhxCNJckhuDUSDF4M0hmw1zo0hlSNZcuRMCK6BUQC59PAXFon3QK2lQwq4ohIE9Z75nx6SNHU/WvKECpdFGtPdpWlYaINeKzEi0Fk0FRgPSHpu2feUGRPP7ILDf5c2bM8/GnyVXFbZ8k4+XgYEBHn/88fbnJ598kvvvv5+enh7mzp3L5ZdfzvPPP883vvGNdpv777+//dt169Zx//33EwQBhx12GGDNlA8//HD77+eff57777+fcrnMgQceOKHxTWa2JBdqfheZAN8PMJ5PIFK6K1ALJFmmEMIq9C0rQN50GWhNPgXGTqab16VpKu+qeT9KJRDC3rtZbrcXCxKl7AS4JWsCXxIVAqQSaG3vtzT3iFVIogOy3MoBYwatYm25oIW1njUXEjLdul/tPaUkpAzKgxZaCytzsO2zXLTlAoA2klgHpMIj1cNXvKApV3L7sG0xch+ZlqR6+ORBt+SC8PAUFAqKMBB4zclV4BmKBSsX2hMkA7kRND0E21YBK2cEtP4fTcWgNQ4pDVEw3H2wpdhoY+WXH9hj07m2FsZ8fI8xJxP2brY4Vwi6SAEvjKxMkCldnYpGJNHaUK8JfF8Ceftaa70Aa83GPrtBYrD3lVVA7fXmec3nZtPiVij6yKZCkDUn9p4niAoBnifbin6sFbEJSbSddMsRcwWtBVnTZU8IO0eRAtJcEud++15tKTF5bucq1urX3IcJ0UgS7bUXCXyaz0zT+l6Nkgm6ecxJ7mGMsMfRVGDssUuUasoEYRcRhLCLK7lR5F5IJgM7VyiHRJHCb7oB+h5EBQ+jTfvc2fHQnB9A6NOWCZlR9nwYOeT/I5qLAdb1uEWWi+Y47Gchaf5/aSsgOh8t/7aEkwuDCDMRVWocDD0xxxxzDNdeey2vfOUr2ydmW/T19dHZ2cnXVmxiv+kh0wu9BCIhMjXCrErd7+Cpxhw2VkMCZSgEGQLD2r6QNRvtQ3bBjJTuqE6qFbXU3lS1WNFbtVdQS0tPc6g37IO/qwJTOzKUNPTXPaqNwYmCEBCnMFCzk9MwEO2LuUXoQ7mQ40lDLVb01UR7u++Zpl+w3VchhI6ifZit2SBZuz5FKkGpqAh8QRhAKbL9D9ShWrM31bQuQzHSZLldNdDaKhxKWstHrlsTIEgy2jdT4LWEh31JaU2Gnmr56tmVk5YJXxtBnCqSXBD5mo4wxpOaeuZTSzxyYy0KVpAJ4tTua2pnzuxKPxLDmmqFdb32Qe17dowwuMIxrRLTE1VJteL5vg429Mm2cAA7zjS1QjBNjV3BrfXxkbd209vbu0WtvXX9/GzRkZTUcKFQzXNe98gftvr7odxxxx288pWvHLV96dKlfO1rX+OCCy7gqaee4o477mh/N5YVY968eTz11FMAPPXUUyxYsGBUm1NOOWVYP5ONnSUXvnHbRmZPjZha6LfKQLqGUt9qkmI3T/iHsa5Wbj9McgNrNirWrM0oFiVHLsyZWeqjmoW80F9srjbZa62tFEhDkgkGanb7jB7Dft1VlDD0JyHVuOXmZieqjUTQV7WLCmFgH1zatHx87X08vdO6B/Q1AjYNWCV3SiWnHCXUEp/VGz2qdSuDZnSlADyzzmf12gwpBKWiXZG3SktzUq3t5MD3oKtsKISaJJX01ezCRRRYd76W0g52jPVEtOWGP7SvQbd/a6YvGLpL1hqpjWhPTFormoGnKfoJntBthSc3gs31iL66QmsrM3MNc6ZkzOvYgBCGp3qn8vQLHkrZcxP4prmgYVASppVrTA02k+Gxtt5FX8MnTiUDdSvbtB6cGNUbmjg2NGp9XHXRlC3e104m7JnsLJnw5f/cxOzpRaYWqwQypUP0Uoo3Efslns7msrkeNe9veyOs6/VZt8kQRXau0FOoEec+vfWQJLfPtji1k1bfs9a1NBNsGhCkKfR02Lg4IaC/4VFr2HvD90xzFXtQJvj+4PO3dRkEnp0rKAEDDUlv1c41OkuGUqhppJINfYJ6bOgsC6Z1WIvaqo0e6zda653vy6b1UBCFNBfOBhWWjqJVrJNMUG1YmRB4rYU80164tIrL4HO5Zcmzc6SmUtG6533rYqSkaW4bfl17ShN6OUoYUi1ppNbiWGtI6oloy1pjYEa3Zk5HH0LAqv4KazbaxZzQH5xzSWHdjqdVYqZE/WTaY/VAhc1Vj7wZR9FaLAD7Xo8NaWplwrK39bi5wgTZ6RaC3/3ud8NOTMvE1zox4+X+h2OeWVumozKDzjK8ZuZ9VP7rB3R2dlI6/BQ2TplFX1bmhYEKcfMGti4z9vcGuxq2qeqTZtZlaGqnnbB3RAkFL6E3LvDwMyGb+zJmTlEc2LEaJXIeM3NYtd6nXISDpg/QHfTx9MA07npe0Nc3mKIqDBWzZoZ0lu3nLLcaf7Uh2NRn8D3B9Fk508sDbKwXufc5yfp1dQ44oMyCaQ2k0Gzqt3ERLRJPsHGTafo+GsJIEfh2QhD4kiRT9A7As8/VieOM/fcvs2C2fZhWG9ZNIclgoKpJEmNXNpuTiSldgqmdOfVY8uwLht7ejJkzAg7ZLyHyMp7fVOC5tRCFgsP2bzC/0EtuFLH20UbSFdaYXkjIjWJ9vcLmekBcF6xZl1Or5ZQO9unu3owk59m8g2dX2+PyPIFSNg7A9wS+DzM6DT1iPakfsjksMuAHxCn09tvJf2dFMHOKnaUkqSDJBPVwAi5DvkSpEVq/nJju+4pXvGKrpsexrudt6dfz58+fkDlzsrCz5MIf/pTz7Dqfcmka3RU4I/ofNn71K5RnT+XQ1/w106YcwsZ8Co+t76LaEPQNaBqNDM/zkcLgyxRtCmzok/QNGColQUczgLi7lFD0UnrjkA2bPTb1ZsybKTlQ/AkvT3jEP4Kn1gaUIjho2ma6/V6ers7kyWcVvb0xRhvy3OAHimnTIjrKcvBhhQ2s3dxn8H3BAdMb7BeuZq03jT88UWHVqiqLDqnQFdaRQrNKdTHQn9qAeuEThpKBgYx1a2voXFOuhJQrAWEo8T27wrWuV/DIo/3UqwnzFnRxyHyJlIYstcp7LYYNm3LqDWuyjyJJ4AumdlmlZaDh8ehTmo0bY+bPKzKrMyNSCU9v6uTJVYZCJDl6fpVZ0Tpyo2iYCG0kFdVPJV5P7gX8ubiQgUaJOBes36SJE83sHsGM5BmE0TyWT+exxwfwPMn0GRGlgl2F9D1B4MGCrgazev9I5hepBYfT1/DRGqp16/rV3WFlGNCcaEjq1fGtBjqZsGexs2TCvb+v8Xh3RLFYoatTce78p/B++FWKPd10HXcyvV1z6ZM9PFedQj1VxCk0Yo2QEl9piqpBknus7VVU69Z1txjaGKL9ugbo9nvZmHSxbnOZ9RtT5s5QHFF4FKUz/kcfwcNPGLo6FccvHGBasIFn6zO4e11If39GvZ6RxBlB6LH//gV6OkR7ZT8X9pm9cXNOEEjmTc/Yr7yBNbVu7nsInn9mE4ceMZ3506xivkZ49PXZBQO7ei+o1TI2b6yR54aeKUW6usOmTLBWyPWbrUyoDsQsOKCbg+bJ5lwBGomdQG/clBE37BjCUBGGgoPnGvbv6KM3KfDbRwKef67K3HllFh+gibyMx9YUeezPDTo6fE46ImZe8CyxLNCbdZBpRVfYoFwaIDMez9WmsbYvpNoQrNuQ02jkTOvy2J+nEVrz5+RoHvh9L54vmT69SLEg8X1BMbILr11hjf1rj5AEZXqDQ1nb67UDvrU29HRKpnXlSKCRShu7WZ2AN4GTC212ukKwrRMzXrJUW/99T+ApSd3voLOzExEV8dMqHd5GUuWT6Q4aiTXRtcxxWW6zCyW5Ik7tCranwGv60kqhm4ZB09RERdMtJkeZrK0pKwm+yghETKBylPLb42v5qWWZXT0Au2pohLFZdrRpugwZfJm3LzA94txISdsEbvuwVoiW72MQyPaEOs+tVpxlduKhc9tXK3jP9t/KSjLoamOPp2kdaQb7ZpkhSfKmO5I9F1kuaDRyhLD+0AAaa7rMjcCTOYFI0Eh8VbKmVmHHnGWaOBU0TIRCU48ljYYVXkEoUbnA8+yD32hsdgUZWZ9n7OpKktlVwDTVGKPaK4dCDJpwx4sKFGqE1q/Gzi7m2A3sLLlgAwU1nieoBZKkp5vy7Kn4HWVUvY/OgVUk5RAlu5qrXqL98LRm+JAkV6SpvQfAPjg91ZIHrfvU7k8IUHmKzFOQgytcntR4JsWXGs/zmi6BpunHqoljTVVZ39q46epm/W81SlmTtzIZXtOnpnXPamPN8lJYd0Ww+8uypqxJMrI0p1AatKFnOTb2YIgrvZStVXd7jK2VtCQxxA0bW5HnglwCiKaMMM195c1MbtZ9IMkEtZrN9pUZKzlzPBp5QK4VRVlDabtzG1Ng72UAnRuSXJL4RYTRNBJB3MjQoSJJDL5vCBBtH2GNJPOLpH7BulfmVplpHT/N2LBWzFTgwTg9hpxM2MPYWTJBa02a5DSkoBFJYr9EsacbGYXQ6KdcfYG0EgJT2pa9FpmWZKY1V7CKgufJ9v0wFGNoP3OVTlG5fb7luWnKhJwwrxHKHM+z7oeDLkLNlevEXute87uWpU9r68/vkaGkdaUdSWuu0HKLyXNDlmnSOCPPNVmm2/vK8pYVn7Z7smp6RsihMkHbY8oyK5e0MWjdche0mX/aciczQ9yaIUnsoqMxwrpiGkGcW3fFogdBXkdJ3wZPKzt+K1+0tcJ4VibUY0G9GuMHHnGiCYLBRUwlDbmRpH6BVIXoVLT/F1kzCYQ2tGWClGbIMY4PJxcGedFjCLZErg21WkYc58Sxz1ONOZQOPwU/rRJuXk1h1Z8QC5fwQGMGG3rtxdbyp63GktwEDNQV6zYZkkQTTFdM68jxpUYJ044rkMKa4T1l8HWMMhm+zPE832YZUTUqjfVUgm6iKCQMFVmm7YQ7N1Rr1oUnCq0/sqcGg4OguQIhaxS8EM/zkKLlwyhsKtHQ0NPdDFJsaNJU06hn9PfWyXNDuRLQUW5NKCAeMNTqGmPsRDnwrVuPFIaalO0gZHsTa7QSGNOKjRAEnrZxELFmoC+mPmXQJ7OewMaNDYpFn1qiSKKAahaytj8izQTzenKKoh+DoOCViXyfhifJMqvAbOwzPD/QgxDw/Dp4YVUfUknKHRFhqJqZFyRCWlPram8aYH0KS6EmzRSNhqbRyKmUlZ3QNBWzXFrBNl6kJ1De8B+0Up469l7q9RTlpc0JouKJuYdx6Gv+GlXvg2eewP/9fcx+ySk84s9GG+uCVy57BL6gt+ZhTAe9NUV/VdNoaEyHpBTmgxlxtCLLB68bJQwqAdOX2QABAABJREFUi5E6xXiCRsMQBQJfpBTSfop+TEelQK4D6rWM6kBKnhs2rK+zAejqiYgCn0IobYrhWjPAEQiyOp6XWQVBQpYbBtLAKhsKurp88twQJ3astVpKrb9BluaUOwooZWVYrWHIMkGtrvE8G/MUhbLpt2/abkFJCgP9CQP9MToP8X3Z7sOTGiEU2hiyVJOm1uwvc4+BOmxYXyeOA6qxTyOMqGYFnu8rk6SCaHrC9KyBRFIsNegoFGg9WrLMsHlAsqpzDgBrNkL/5hp+6BGGHlnmUy4rykVrxR1II9YWF5AYnw29BfpqknoDanVtF0K0lalKmmYshsBk47uvnUyYnBhNc56Qo7Xh6WwuXcedDI1+5PrVBE8+ypTDM/7kzaUWy6aLoF2YGmgofFli/UDIug0Z1YEMT4VM6bBzgkxLGjqikXskqSHP7T3lp3WkTq3MSDXGQEE1KFU3UC5OpVwsk6bNRS1p45L6+jKqVUGppJA9Ng1nktoJsucpfJUTUaegUqJCkSCwrnW6GfBbCAzdnXYc/f0ZSZJTryYM9NXQmabSVbRuQDlU64Y4EcSJJop8PE9RKioi37of1aRNDmIn9jlJnDVlgWouJjA4yU801f4GcVxpjyVNoTYQ43mSVEty4VHXBdZXC9RjSce0BqX6ehCCjmIX1UJArj20sfOxzQOC1T0zMUbw3Jqc9as2EkQBxVIAhPi+oBTZoOlG5rM22J/U+NQTz84HcqjXbcbIrk7VXOi1MsHGOI5fIXByYZA9ViGwq+82mxDAxmrIximz6PA2Ulj1J/JVzxHOOZQksw+LMBBEkX24xamdBNdiqNZy0kST5QolNUrmzQvd9iukFQ5KGKTJ7au58i0lBCLGT6sEQYrngefbaPa8uVIQxzm6OeEuRoMTd7uKb1fffZHajCfSCgewAT2ymWqzEEoSCXEz73iWaeJG2l4JCJt+yXFiiBNjBdCQ1X9f5m2rRisgJ8/tGIUUzexKYliqvzy3Wr9u92MtG/VqghCCOPWbWYEk1Ya0/sBG4OU2PZivsmZfzf9Vqmk0DH11u+Lf359R7W/g+Yog9JornoM3XZIK+uPAWieEzaAkpSLPBy0XrVWa1oqpFBO4yX2F9IZr/RP5vWPPJI1z4iBHN31e19XKTJtyCJ0Dq/B/fx8bH3qC6QcdgjfD/q9tUL9AKUEjAVBUGzbzV2tFzVPWigfNIPgh+xMCpMmQOrdB/rl96Cih8fKYwEsJA5uQIE2ssqtzQ6OWkCQZni9pJD5CiKZVQpM1V8+FyZHotnUvz20wYcuftxgJkgzipKXg56RxSp5r8iFO/2lqZU2WWRdBAmUDpKUZds23LI+NWkIQee16LmBX+FrB/UabpqVSoKW0CxzVGKUEceZbN8Lcp79ms4PFuY/QOYIcX6SEKsdTChDkuaYRQ19SAKBa1aRJhjGGuDkJiSKbdEFJQ5Ir+rMiifaoJZIkhSSzMq8lc1XzuDyl0Ui8ca4GOpkwObHPY43ONfWajWPp7ZpLufoCwZOPUn/meQrzN0Fn08I3JEtenErqqWdXqWsZ9VpKkgbIZmYsm4DABuK3n6kCpG5aDRV238bgk+KldUIRE/jg+4IsG8yA03IDFkKQdcrms7PlB28Dmz2dND0KBJ6vmgkOrEySEooFu3jQ39+UJamVCbp5/C2GygTftylWfd/6+Q+NZ4DmXGFIqs7h59Z6a2RpTpbZjEWa5hynuS3TPlooUuMxUFfUYjtX8JMqAFGpQaBy/FbWQGOox4a+Zp2Tvr6UWt8AeVagUU+JCh557uF7Gt+z2QkHsmIz+1Bz/mSshSJNckzTy0EJjZRNWTIBbwInFwbZYxWCuXNLFEu2qITvY4PysjKJCuCAlxLN6SUOO3jZ3OdI9g94tr+HZ9fZwylFurnq59GIPeLEkGTw3IZCOze+7xnqsSTLrIVgoCFZVZ6DwLBqU4H1G62v/SPR/vQUplKtB+w3DaZ3+yhptdQsh00DgmrNpiIDe6H6PnR2ePieYHPN52kxi2rsU4gk06fbmgJr+yKUNMSpLaZms+rYIN2u7pApU2y+/2JBtoMeOyt2pUFJSbDQQ0praly9OWoLlkJoBUdSspkRokhSLNj4CikM/bEN0uvqkijVie9LntsQomSI0TB3fgeFgl01BZt5qJHYVcgX+gtE3QsBWDtQZtOADe6ZPdNnxnSfUmQFjjYwpcfngEXTUUrQ2eERRYLusmF2Vx1f5sRakeaKNJds6rerkHkOnZ2Kzk5FpdRywWoFS4u2KXM8KF+O0vrVREwMjj2S/eZ2UO4oE/iimU0oY30+laQcMnvJK5h+8KFkXdM5svI4B3UWebJ/Jo+vChDSBqxFgUZrSbGgSD0bc7O+z97TrQD9RiJRylAsKOoJrJ66CIHmhXUFqtUMIQR/7p3G5kKFOFbM7smZ3iXIcp8kC2gk8NyajM2bYkolv+3q5yno7PQJA8GmRsRzpQPYVC/ie4Lu7ggpYGO/h6fshLxUAC8VNBoSnRu6uiPKlZmAdR1oPdhLBUkYgOxUzJ9TaC8MrO+z8lA1kwjoomDq9AKlsk+p5NPVqfA923ZzPaCRSIpFmDqjhJTw/MagHXg8/4BuigVJKcya2c9s6uRqXbOqr0S55wgAVle72dDvk+WCni7o6ogoFyDOFMZAT7fi4CNnoaSgXLYWnOndhvk9fQQqI8k94tynnvn01wSb++wkp1Syk6PQt24eAuuKlGaCNB+fXHAyYXIya3aZqFjBGNMMts3pkz2klZAph2cU5m8iqUzlkPLTzCsXeLJ/Bn96rpVu3DTr3xg6Ovy2e2FfTeApRZIWiPyQvrrCGE0YKgbqirWzDrAxgKs98jyjWtU8XZ1BozOimhSY0pHTURSAAHwaieS5FzSbNyftjIWymS2oUlaEgaQ/9lnlzWFzbP3op04vEfiC/obXVLBpPmMF1YKy9RVmlOmZZoMYS2WvqVxDFNmYHCkV4WyFknbBb22v314MLRWsK1G1IyAIJOWyT3enwvchzTRr6x1UY49KxTBz/278QLJ6o4en7Er/nLmdlMsenkxITEgj8xmow0DN8MJAha4ph2OM4OnqDFZtDMg1TJsi6eny6CjRzKwm6OlRLDxyAWHkM3tOiXJJMaPHxjAEMiHRAXHuk+aSvppkY58taNbZ6QM+pUJzERSbwTDXNpvReHFyYZA9ViE4ekGdSkWhmppaNQ14YaBCpgW/b0wnyeBlc59jzs8+S2P9Rqaf/y6eE8ciBHQVbV7bchAipTVhrdlgeOzxGkZbN5xi0bqk+E1Nft1mwaaBTvIc1ryQsHZNv33wP+Hj+ZJDDgo5ceE6KrKPYtJLVN9EHHXyYMcRPLnWTt5tCjyb4aNSsBfm8+skjzzpE0WSad0wvVuweQAee9qaGadPhZ7/n70/j7Utu+/7wM8a9nyme+705levql5VsVicRJF04siWugkNcRIzgB2rk0COoMRA0AGSJjppCIjjRAngRgLIaqMNKR0oCYzutg0lcox0uo0ocmJZNilGiiiJpIpVZE1vfnc6857X6j9+e5/7HlW0X8kEXSq9BVwW37nnnnGv3/oN32EomNuyUw7an2puHBSEpuHePOPeiehvX5q2TJKCSbji8uY1bJ3zW8kf5f/7BUvbep59JuZwx3X4XUWTGkYp7I1qFNK9P55LInF536MPLcczxW9/bUNdtdy8OeRjNz2RrYit4CPrVrNcy4iyKAx3jwWbvd605EXN/m7Ax29s2AmXrJqUk01C4xTXL7Rcv2AIjWMQbghNw35wzMGDr6DrgpOLH+HN+hnKOuLN2y3vvDXj8OKQj7wYMEqa7WHvvd5iIZv3gOvT5l2q/j+ExL0P2nrlWcdoJEZ43ivWZcA3j8cYPebV4BL2wPPK8Jvs/tJ/RXU2Z/wv/gT3ku/tFH8cWdRgtKGsDWVlyEvPm7e7ScGW/C5KYjtj2atfKi7ROrj7oOXsrGA+UxwdWYIg5OaNkM9cvUvmFwIBaks20YTfHN/km7dTQK7bthCy4qWhjLRvH1u+eXdHlDVCKaqLEt6+K5PEwz3D3rilqDRFKd3Jgz3D5f2WwHjun1keHMt9xwOYZA3TpOCafZugyflq+wq/8mWL955rlwMOxo44VESBpWoscQhpJO97vtbcPRb1o8lIMx1rZkvP117d0DrP889mfPIlR2hq0qAWQ7fWcDZvmc8ryjLkzpE4es4XLZtNxXQa8pFnGyZRzqqOOFmFOAeX91qev+gBT+8+cDE949ritzFVzr2Dj/GNxSUWueXhScvDBznjScTVSwFJJM2cuuNkiD67xM0nWU9jwgdzvfSMZzDwHW5c+Hq317vALq/ZazCGFwdvc/hr/y3takn2A/8K97KXO+UcMSQbxIrDPdvFBc/DE7flN4gjthw+cWyYLeHL5ors44fSqZ+dlXzhKyFRuMuVQ82HDs9ITU7mF6TljHW0wxftC7xaWaxVVPV58/Agkev3wcxy93QCwGgAo0GI83A0E07DZOCZDhuKWrPOpcDe3zVcP6gJjONkZThbCidqnHnS0LGT5lwN7hC2Bb9Tfoi/92WFaz03roUcTlqKWgOWvDSPKRrN1pYHp8JTmk5gdydjvnR89dU1beu4dm3AKzft9vNbtSmLMuL4tGWxqHEu5GR5bRs3T45X7O0nfPIlx06cs65DZrnIM18+UOxPdwktTLKWKKi4kM64tvoKpi45nt7kdnORqjWczDx373Yx4aIhiTxRINNb59iqRPW8zidZT+PC+XrfFgSRqUhMjVHyZW/qgLIRuc2TucCEyqsRxfEpi7cfslvMtgpDWnk0Tgw1rOsqYkW+roS80/kPBIEmCAxaCWltkwtJZbOpqcsG5z1FXuGd5+qVlFDVJPWKOD8jXJ2gvCNKGmxnbNSbb2glEl1NK/g7UQYIsMYQh04SksqJq54zWyKM0QJtiEMYBAWhbgiDBO/ljdnOfCjROeH6FFOsCQcNRSFjvbaNt/J9oe21fj2RFcKf88GWYB1YMQY7M5Z8XVHkFTAUsyIt0mHn2ukyKi2KtjN/8hR5S121NGNLbCoyvaLS50THyIoxmVWOLCixqiFu1pjNHOoK6zpCloeiaNksC6rdjNB64qABLHVjeJcp5hMtpdUWnvXobU/XH+wVBw1JIDyf1isWPmBddNdpt0+eH6VUZ3PWd48ZFnPiUPai7uR5BaLWGQ+VUHUQvLYVyE8Unk/VqkYUburGk+fihNk4R1nIniouhwSqIqpWhNUKW6xQ3pGFDVEYdX4GHryIF0SBE6neSnE2b4hCzXQiLp5lJXjkRwUArPHYTiksjmAUy/6chbZTIFLdfcQ0LSnnBOWKIGkpSyVwABcI7E6LFGlgZf+HXWx0XlOUHmsh6ZQ9FivYbGqaugUysi4uaOVwnSla28WFzUZipXew2YiqSjMSMmFiS/I26BTYILYt4ygHevM0TaRLTLlB1wUKT9lIol9VHXzQy/cVdUZnwGOwriddT2PCB3PFQUsa1t3+FnXBvDY4p7acgeuDhHa1pDo+Ja6WRIEk+ppHzL66SX3TqI4n6Lf/tYEmiTVaK6rGM19LzCmK3hTVs5xXrLRiOkmwuiFSOXG5JNqc4ZTpkAlqO8FrH5EC9R42JWxyQRuMMrm9qKAozyE+gRGzsr6RGYcwjkoC01DUhvla0AvWeALriExDUi0I6jWBbSlygQg5H24NCwMrsKQogNB2poStTP+sUYyH8rvlWnKoqmqBAWnUEhjZiU0H52mdfGabjeMsULQtLBYVm3VFsxMTmpbUFlTOdp4sEpMGicdqTxqKRHOkSkxdbmOC82LcWNWeqm5x3hMFUvT0YgjO//728tO4cL7etwXBvcWIuTvXfy0qkRZtOw3tKFTcXu5w+H/4P7JbzDgbXuOVZE7VWmZ5zMPF7jbpzaKWC7sB5kMTmkdUekTKU9Rv3DbxFUMuczgUJ8IOjGaM4q3lPmmwwyC7RjrKKV3IySLZavmWlQQGo1VnMCRGXmlqiaJe81w28cVDwdpnMVujrvEABqkiDh15E1GqgKozDmtbmK0tTTtgHUXk+wmBqnk4GzDZEcUhpSEvpZuQdN7mgXUUtcV5xWylODqRJCQJtRBxNIx3YpIsxGgoakulzNaAqag1WaKxJqBp5fPxHnRqcYnAkk7yjMpZ5kW8hSkcThyJkW7iWZHSOM2ZHTK4ehWlPEebAUcLgVcEgWf/4pjJRD6T1mm0Es3jXi8ZYKOfzJEUujFg8C1jwHdRbni6/mCt41XExidbhY7ZSqRFe4K91vDW6pDJv/gTDIs5m2yfj4/usHEprx/v8PZ9QxrDztAxGXiGiWY0EGnLshJpyzCQA9AaKRhmi2araJGkwgcQ1Q458N7ZXCC1U5KsJB1uKFxMuTRkiWjx54XsmyxRjyiCSadOa4kBWdyilQFkpB8FvY+IZ2/imQw1g6TdFutZ3DKdmK2/yDK3KDJsdpMwqZnNYuJEknT5vemUeXyncuI7GIFwKxbLhiTWTEeGNHKkiSZNA8qy68Y3llqJ07JHsS4NWQpaRZ26h3w/xgS4gSVLhX+0aWJWRchyIzH2YAypKWi8ZVVF5LWlcQcsd/8YSnlunY259VC6tE3jiJOANDEMEscwbraKaErBKBbc9TrMn+jaeRoTPphrWVhaE24T7bwUzltvLugdvLk8JPuBf4W4WnKWXORZs6RoLcfLmIfzmNBCGjsGiScKDGFgqDv57qJoCQLNIDOEgRCBZwvXTQ5gNAofa1zVNdxfj1kEqcSF6RVKFwoceCxk3qrxuBbgvEngnaKqHMZIgyCJHCCvA9h6DSnlmQ49WawZpU6M/rbOyL2AgBCEFSk2fRYbNsxnIeMxHXdIYgKIsEkaSROzd0AvaxFAiCKNNYosdowGmsk0EZGWQCC8lddUjTQC81IzGamtwWtdC/RxNArJsoCdiaVqPYs6ZVGEzNeidHS40zJNCpFmbiyrytD4PVbjP4JSnuP1ea7gXEuaSkyIQ78tYHynPpZFLWkIoX+aK/x+1vu2ILh9pAkWamuYYy0dDl5+4lhx68hyW30P1sAryZwb7Wvk4YjbZy/wtW827E0DXrjSkgYNwcQxyaSKPZ4bZkupjKNQcHxNozrZP0+WGYZDizHnSQbAN+4KZi2JEpJoZ2sAJqo+IlnWtkIGlE59ZzKWibPneUEggQfYmnsZ7UlT17n3wqqSxKCozjsK87ViXRiUMtwygjnOK9gZy+vTCjalYAdHWUtoJNAUtaFqFGdzx/HDnCQNmE5ikkh1aiYhTSMdwnUpQaLtqvfWKbJEOod5oVjnDlCE4bmM2fEiYG4tq1yxWElhsjdSxKaiaEPONiHztWZTwHwR0jovUq+dtlcYKg4Ok05ZqKVxGqsdoRZtYa1EGSpqiye+frR9Ogb8IK6TuWJRKKpaCLGbTdv5DOitmtA37obcS76XOPR8fHSHq/e+SDk65Kv19/Hqq3OuXB1wda9lFJUUsWWSWapWM1sZ1oXqJmx0WH7Fcl51yaklTS3aKNLEbBsJbzyI0TpmmHgGcSPun40micTAR1S4hLvQu/tCL3sqxcAorjEKlDJdQSDdTqs9u4MKox1WuS35OQlapkPpflaNYJ7LOiSvpxgFpytDmvTSyLDYiNzhOPNEnZuoJNaKsvIs5yWuDTFalEiSSBMnthMlgLw2gNnCdJoWBikkkelIv917SbU4i0ZQNYalilgVelu0ASQ6p/AxRWOZbywPZpbVJqZuvEARN7LPlVIkiSVNNMO4YhiKoEEvD5uagkiXrJvFE107T2PCB3PNV5rK66207nrjKUo5R1VHDn7tdsC97GWiAJ41S66YW2yCIbdPrvLW7Zq9acDeSHKFyFpCa6k6yGrTeKJIM8pkX54uFPO5SH0mqWUwsB2xWQizVeO5dxZiTUhg0+1Uq2kV06FIj1ZLyRlkWukxTjrcZSlS42HgScOW1inyUm8LjqYVb5FJJoasYiSqabrcArqiqBK53qoOWJU7aAWLjWY0ZFs0LDbS+BhnLZEVA8LWCSenKGG9rlE6ILSKJGwZJorxOKSqHVEo033vNJtSb4uWyRBAs1gLfBBgNDQksTQ5RII5Yr6x21wh2nfsRXNKF7Kpx6wKy/EiYJknwr+qvUxxvUx1siwgjhShbQltK27PThqhkZGmia3LJ75+nsaF8/W+LQjCAKL4XFu7Lwjg3JkOzkdpVWvJwxGVjhmlLdcvB2SxJzAyUmqdEOHqRjaXeeSxetvwKJKqV3TCzx37rH0cErTtPHT/7TsFUahx3neE4/POdp/QV41s+tD2bnzyuqpa5POUEtWMfvWvrR9pCtTn/L07L48TdvYIvc7vo6v1aqvlbYyYqUXxedKvu/fd6/727oP9CM5oTxT0r0eRl/K+pBgQxYPWgWqlcIrj88cp2pDKiS1677L66MTFduNToHOFlM+tVpqmI0/L7yTwbZqAJ13aavS3Son5P5xV/wdpRQEEYScb2F2r1gbb4r2/vmRqp9i4lHJ0SBGPGWUtN54dMR0rrK5FUcgpmlY/or197t7rOmUuUb3wRJEhDGWv2k5LXGQBASeJeVHL3qo6J28QdS2tpfhuOoK8Ur3bqBiWGRVQtWrrFO48lI3CdKNrozWtdtvJXeP0ebPEnDuL9oZHgo9W2xjx6PJdTFDK0zh5jiQNiL8lLsSxwVqRR+w/nz6+WAODpIc6KJYdv0epc3ngxil0q7fE5/72jUupnTQ8dOcz0q8w0JD2ZGi1VUdpnaJqxSW9j4fOayodsnpCBuHTmPDBXFp3P8j1GMcK1Z0dWxhvr+jjoGgtm2BI6SOGacuViwGDhG2uAOcOuEb352PnkO3kzItjQ9sKjKjfZ7o794w+b2RKjDl/rT2sJezO296kzHV7LgiEy1PVio021I3avrfWKcrmHCaoEb5Tq9W2sw9dkWHOpTcfLRRslz/0MaGXQG+dOI1LPJTnTFNLFOrHHiOOFdaabY7TP0b/mHEoxXrdaFbm/PX0kvA9/0crT5aen/+Fi6id2b6H/rtzTnKFpHusuvNCCAOJSXVrutig0d3nW2v9NFf4fa73bUHwzGFFmlW/BxfWtJp1qSlrGWNN0hqtPLM85vbZC4zSlu9Jv8p48TXKwUXejl9hWScsC8udIyHnie+A6saLMlZPE8X0QKAqy1yRF7LB4+iRyYSWC7m38m59DxXypLHicCrqRZtCs9hIIKgbMeNqW8+DY5kgjEeG3bFCK89io1iue3hPb0feqQUpwRX2MIadoWMnrWicpqx7zN558dEbewHUjcI5MWbrMdajgSJ7Nu0whjJ9MBr2J33S4bediH6TD2LHwWBFqBvuhGM2haVp/TYZMrpPnmCUOq7viaX7ogj4xtFIEqrQsTN0DFPFzkDhvMZ0+EVJTnolIelieC9Qi7LyW61k7z3F5r1tchN8a9X/h1Nb+IO0Lk1r4qx55BASCcq61cw3lqKSoiGLHVp7Xj/e4av19zHKWj45epXP7v8u+c4Vvmk/xLxMWOSW06WQ+fuCu3WwWEsHP00ULz4bbqE5zSOJ76P/Bdnri1YSdTEQlPhx/UK/3zzHc+HFhAFcOLBUNbxxu6VuPDtjUdcItGOx0azz3rNEGg1JpBimdOpkMtYPLRxOagZhRdlaZpuAqpZpwN6ER/aX7FWJC2prZuY8jDIYPy9+JN7D2cqiFNy4IvtHK3m+flnjGcQtB5nEhXvrMW86UVPqY4JMNMVBeZi2fOR6icIzLyK+9nBf4pz1pLHDGt0lUYo0gqRzJO9NpJzznCwDjn1AXonGukChZLJZbJ7s2nkaEz6Ya5I50sF5Mh9ZR2AcjdOsCiNKftYTBtJuO17G3D65yjBt+cTkG+w2X2czusjb9iarOqJsNMuNXHtxJJLCzkFewMrJbc9eNdtcQM6n8/3husJDpneP/w4kxhzsyPm3KTVnS2lSRiFcPBCDtLfvOeoaJmNR4TLas9hoTua6a9bJew07lENf8PRn+yRryaKavLacrSxVI/nEUNR/RYmnu/Q3pUYrgWQXlbz+NIabz8h5WzVwshRuwqW9vsiAVS75itHyngZJy2G2JtAt98MhVSP+StayjUEieGLYHTk+enWFwjMrE75+NO0ey29hjUknIz9KBS7oUeSV6SY3XpSfNqZzXO6LG/FSKTYhT7qexoXz9b4tCHaTNVlicPSEQcGuSpcoxHuRxpzGazSOh4tdvvbNhuuXA8aLr3H0136B/T/yMbI/doNlnVBUmuNT0fYfjwOyVKYPvV7vaKCYZE23yQPKqjcs66XJBI+vlKhbVN1h2nQHv9GibhTbmqZNyc9UV3xItV3VsFjUlGWLUjGjTkZvvfHMFzIG6XksSWJgJKP33lE1DBSDqGEnWlM5y1LFXZdQbbGDoMmr7nV1RUJZy/gPZMSfxR2MqBLJxTiUjWyUMPQfnWyAkIx2gjmRy1nGCcZ0HX9zPrHpXaLjwLGfyPh+nu9x/0TUWq7sQxYKlMJ3Pmh9Iud9h09uhTB+uhS99uWqUyvoHBibxlEVqye+ft51DOj+cG7yD9IaxwVZIsFeIUTaQNeUbYT3I0CI+1kkRcPb9w2vvjrnxrMjPrv/uxz91f8Xh3/8U0Sffp6yHrAuNPOlXPDDTJFEcj1vckdZOQaZ5WBcERjHugzYVHqL5e09P/oium66g69rEjStcIimWUlkGx4sUx6eiS/IMJXnmq/h6KhgOcvhxoSLu9J9q2oZufemf23ryQaWaiwqJd6dTwjjoGESrlg1KWdelDvCwBOH7hFMdT85kcM0rxSrjRQ0+xPPTlZTt5qTpSUvpfu/O6yx2rEsAla5dMyCTqYxDRv27TFRu6GIQ+7YgAo6cQR5bZsu7kyHnsPoBKU8p/ll3rkvn8vlfU8S9jNR+d+drNqSjhtnaL1iWUacLgM2hWe1csxnJU3jWMwL8lVBXS6f6Np5GhM+mCsOW9JQzlCjPcOw2PJUAp2R1yLlG5oW7+HhPOat2zVXLgbsNl8n///9LUYf/yjJR6+yKGPqRrw3AJJMsPxFpVluPEXhyRLN/qjCKCceBpVcU70JYNU14XoocT/l71dgYZg0hKalaSOOZ7I3xwMYJI7ZSnN6WrGY5Rgz4uoBGCXxZb5oHyM7R5EhTQXnH4Vq28BMw5pBUOB9TOuk8RCHwhMAHlPhEbiPYlPCYunQGkb78h7rVnO8sKzzrnGQiv/Q2TpklUuHP42kSZAELZNgQahKNlFIHFrqR70WWuFOeAcXdzyH4UO0d5wVz3D3SEjSe2MIYjFmjDop90lasxuv8ChWgUANN5XheG4oO0n09Vrc1etKzMrK/Ml4RfA0Ljy63rcFQeNEo9Yoj1MyGqq6n978whrLIIywWjrze1OBCZWDi+z/kY+hDy+yk98jjAry8Q3eCkXdp64dm1xGc1Got12+nnhX1ucqAEXFdmwXdDgj10F5evOs7ejPaWon7oRxJLyE3kwMYDoNUUqRdrCafnknhUOSiC53migmA78dJ643QoKuGkPRhtvRWp+QPDr2e3R8J/rKAD1/AU6XUmgME0+Q9VbnisZLZyWLBHZwtrKsC8hiI1jFjqTTk7LPIUaPfmeKvI1QiGLAZChwA607aFRjKGrphsShI7ZtV/Vrii7RikPp8NaNJs81rRbNdYAyOHdV/ketdx0Duj+cY8AP0mo7p0ylhAC3biKcT6haw3wjpmNNqzHadOZecOXqgOlYke9c4fCPfwoOL7Pv75FMct4ODpktQ+rGb3Gwzov/RxJLjJhtgnM4TsetyavfK4Pbw41kLK8IfKdU1PaGPBIXRGWoMxxr4eAgYTqNO5Kw25qu9cTlKJLrNks1085Bte/mGQ11a9i04qYqakL9eF1t4UH9hMB0imZJKBynntd0tpZuYBp5sth30wSB7mntGSSO1sG60KxyRWQDmjQgUIbGa6quMWKj8ylLH5vqVrF2mZifAYNME3YnT9MKDGJdqE5NyBKaEO8VZedTUjW6SzoU3mmKwqJrx2SaMhrHlMUTyo4+jQkfyKWVx2qZCGrEKK9qLVVrOF5F5KUiCjzDRHWTKdibCkxoM7rI6OMfxU0PmLYPibMCqw85WyVi1FlB1cjZFIVqiyw4WQYdYqA39ZSGVtWwRQb00Lb+rFfd/c/hNmY7hWid5B1NK48xnYYMhwHjofgftB2R3lqB2qapwA3j7dTwvOvvveQijbN4JAaFQVeod3luf1+jhRch7sSKvjCvGjheinFoFosKUP+6m9YQWMd0KNPQTSn7N7QWn2m078zENpIrRKFArryCsIM8OmDlhmgl05zDXUFMxKHfwqPWhbyXLLakNsJBpzYpjVCRIQZQlKVCOTDaEkWe0P5jQob+kMaF921BULUW5zVWN2gvF8LZOqCsFUdnQj4rSovWCZF1xIHjhSsig/V2/ArZH7vBTn6PwTd/nWy1wn3mT/G7g+fxS8gLx3LZkGWWiweGLPasC8W9E7buwkrL/1+tJRk/3zCCo4vCTkmow8UrBXlltri6ycBTN4q7R57j44rJJODFZxTjpKJsDJtSRl+9416aWg52RTUgCVvGcYEG6mbAg2PB5c03Bq2T7WcksCXVFQM9brof4bnuc4FxKof7Ww8Md+4WTCYh11+sOEjmLOuMe8uUulGMk4a9eEHeRtw9HXPrTkUYhKipJ2hykQTsnJLj2Dw2IRACtCgKaSXawNf22y4h0BSNYbkx3D/xNI1nf2rYG0uAOVmI18EgVRzuOELrUMqS5yJLmCaGOFLkm/dSEDyt+j+Iq+26xga59h+uEk4WWqZKa+nqp4mhbsR0a2fouLrXYnXNN+2HiD79PPv+Hruv/SrT1YL0k/88t9KXWeeKvHDMK0+aaA6mQv6frzVv3pFJ1iATgn3dwOnMscmlIpCkQDEeG0aZFMpp1Dttw6qwHVlYbePC7QeOk5OSnZ2Ql5+liwt2G0PEn0AO/57MOIhbdtMco1vOipSTpRx669JSNRm9G3kUOJr2nIwoiUbHv7DnpOKgEx24fRLy1h3HaGj48LWC3XjFok65O0toWsXOoGGabdjUAQ9OY+7er3A+5JlxTKDldfdNiyzRpJGXRKHTBs9LzYP1cNt4uTh126lK2WhWueb+scM5T90YmjbGe8Uyl6Ini2F32BAYR2hDykpInIPMEEeQr59sXz+NCR/MZbVo4ce2xnnFw9WAh3OB3hydNOSbhtEo4HDPEgWiJrQ36nIFe5Pko1eZtg/ZeeNL7KyXxB/9QW7HH2aVi5rQZuNIU82lfU0SOU4WmlffkO7BxcOA/YkUBHcftsxmNWGoiSKDMYjJZ3IuVNCbH+aV2UKcRqn4ED089cxmNcOh5bkrimHSUjfCJWpavRVTCa1iMmQ7CR1HIs15vEl5MLMdNMdQGEvjNHEo8qHOsW0O9BAea2CUtkRWxDzqRlO1irvHmjfelrzle54vOUhmLKoBdxeSK0wHNZdHS/Im5LfeSrl1u4AbMS9OFcbVFLXl3v2StvVcOIxIYrUthkBex731BKsdqa350OGaxmmWlUwA6kbx8LjthBcsSiV4D6tcvtc08tsJptYRm0Lg4EmsCQLI1++lIHgaF/r1vi0IGq9pvCbwCofqLlbBzYpltaOsxG3Ye0RuKmhQygtnoE4Io4JstaJ++JCkXhCFMpaqajET8V42aRR4NqWiKORQC0NF0HXZZDwnah3OdxW+MdIp6AqC/qATcnJHdDR+S2JqGrm40rBlEJQoQopabxWTQLoHoYUkdCRBQ2Sa7eOAYOgFk6i3o8meROW6CUHfkYOeyCP3M8qBBecMm01Nklqs9qL2YaLt31ntiFRJo63IMBYNdRNuiYwegRv0e6WfELScS6KVjcYoTxy0RKah8SI/6Dops/VGNvloqLdk56ruJMq8IjCu++mKLaT4SiLgyZXEUMagjPmW2/5wbvIP0mq9cFCUakU/v9IsVrJHi0KgZbVVlJV03yYDzygq8V4xLxPKekAyyZmuFlT37pOWM1G/qIGSzmfj3KdjofT2caMwgKTj69SOsmi3CmRKKdr23D24P/ihnyTIlPE8LniKvKEdhQyihkm4YamSziiobzZIZy0OBf6ThA2JLdHKEZoIa+y2w+8eIcFtdc7dOZ65bcHb8ylGYESjvNHS5VuvG6JIExhHrAtyHXVTvb4D2xIYI13TvKGsQlpvcFqkSNtWZFl7adP+PTgkLmwqcUs1iq3/QdlIXKgaKCsn8KhKMN/SYBBd9iiQuBDbljDwBIE0QdIY0S9/QkWQpzHhg7uU8nLOIQntOhfVv/WqId/UWCu8O6XkmulzhVUdsShj4qxgZ72kPToirpaEViZobQtF0RCGgUzcA3mO9arGedidBtszvCwd+abGtQZjRNs/4XzP9fsfevy+2oqFOC+5Rp43ZJlI/46jgrUOKZtzd2G7jQmuy3lqElN2nfZ4+3m03ZTA9zmL9uecnG3O0D1mV1Bp5bucQc7m1bIijg2haUjVmtzEAlFuFVY7Yl3S6o5fsakpqxjHOTSxLFtc63A+wujHeZjOKfLaYLUmCypSvaFSIWsdbT+funaUpTR+i+p8clE34EJFoFtC0xLasCs0xJ8hCsA9eT3wNC48st63BcG9s4Rlk2HNuRpP0GH5wwND66RSvH8ixNSLewHBRDpey0IuoM3oBu4zf4qkXrIMp3ziuvgUPFgmzFbBdoNWjSIwsLsjF10a0eFvlZB3WzEnOjppOjy74IN1l/wb0xl5FJLkP7rZ9qeKvZ0UrWG21qyKIXHgmCQVPlGyWUyMNfI3q0JgNevy/Iq+etFuD/iThXT96vqc/JPF/jFyTz/aqxDica94ohTs76ckieb+wrCpL9M61WmSw6IIyetDqkY2x95+jNFwNz/gzI5Z5wG7E0kgtiYu3WdT1/I5OmfQGspQE1l5/tlKvqvl2pNvms7kTL4joz0Xph61K0XPujSsCkPrYDrpSI2/j+ndu7oPtn84N/kHaZ2uIyodYfS5cd4wU4CCcXdo1CIWkJcwTDRFLCY4i9yyLjRv2UPST/7zJNWCZbzHy9EpVWs5WmfM1nZbTJeNxmjYmUiYnI5EzrduFNZY8omlrj3rjcN5j1bn6mPeQ9PFhz5hcB1W13vY3TEMB0OiEM7WmnU1kSI9aIkDR+vEO0R3sEGB1AU4P8QoIUwOYpHcK2opjFwHO+i5Bf0hbM35tEKIxLprSATb6d7OTkAUKk7WMVW7T9lIAp+EsKkMD/2AqhG44XQaEVg4LoZsbEzRGCYj+exDe55w9KtpBb7UKEVoPUGnblRUeuvf4lrxUnmULL4zgJ2BTDvrVm89SQbpeWKzKUX57EnW05jwwVzLIkCFMWsjzau66bD0VmNNRNOGW3ndplFEgSHq5HaK2nSqOodEH/0h4mrJPDnkilmxN7QM05izcdydq566FZXAvX1JvscDMUZTynPpQsBw2J3VnfLVo4pDzkEvorOVHm57eW/YGWvSJCUKYVV46jbFdNy8yCqcN9u9XdUK7w1lI67hCk/RmA4iDGV93nArKrWdBvQ/vY+L8H1UN8W35JX8XRIpLl1OSWLNLI9x/iJFYzs+pRc4VjmibKzEhN0Ya+GkGJMHMWWjOTiIt/E5tH7rb+Cc6jiZRgjEJsZ5gVvP85Ci0uQd/8h0IwXnJA8YJh6VSr6S11Z8TFqZnPiOy/io+MOTrKdx4Xy954LgV37lV/jP/rP/jN/4jd/g3r17/M2/+Tf53Oc+t/29956/8Bf+Av/Ff/FfMJvN+KN/9I/ysz/7s9y8efM9Pc9bd1riVA7awCoOdjV74xarPfujFqMdt08SXv/GhnxdYe1k6zNw50gIxG+FhlcHzxOF8Inrcz60/AIAD/Y/xIPRLmVjOV1HlLWQ8Iapw2rPMKpJg7LD4lkccH+RcjYXwmvdeGztu0O2k96sJUmHcwhNEMC1/Ya9dMOiivn67ZDlquX6JcOl4QKrG4wekEaRSCSWiuVGdVKk8lgHO57rewW1M7z9MOB05ihLx2JR4VrH4YWUqxcksdb60URd1ETWhWK+ElWC0CoO9w3OwTv3PXWtmU4MV/YaAtNytAg5mkmhE0dwsCub8ZsPEyAhjTwHY9lpZWcP3jq1NW8pSk1dC5QoCjVRIHjIk5ljvW4py5b1St7YJnfkpZHAM9kwDtfMygHfeJCw2ggBenckUKm8lMd5L+up++B3b323YgLAg1PFqtLbyVwUyMhda5kSWuM5XgS8edtR1Y7RIGCSWZpWc7oUAvFsGXI7FU3yly+c8tzyNwDY3XmBo3SXorWcbeRgskbUekyn/T0KS1qvGMYBdatZ5ob7p5q6Fh5QL0PcPHLw9xyjupHk11o43BHFsE1luXNiWW80h7uKa9M1ZqtkZrqOnCTym1IxX0s3bJK1jOKKxik2VURRSTGwWAr0ZpAZxoPzYqTHO9fdvq2ajivl5PVMx9JoeDjTPPBRJzbgMFoaHScLvZUt3N+VPX7vLEIpKc52hn0C1E80zwsDwUer7URU6z5RERfWovSdWaTr1FMkYRinNYmtKVvLsghYd5LHvdypKA6J+suTrKcx4bu7vltx4WxlKLykMn03vofs7Y7EIXyxUTw8kUlfGBhCa3Eelhvh85ytEu7GLxNazxWz4rK9AxZGu4ecDcRYc13K9CqwcGFPuIeTgcBtQtNidz3NjkD1ThdS7D46rau7SeH5FE0S16KS+02H4mNS1IaThea4MkxHnsNxKU1RH9K0prv2JR4oBTMtvMUw8KSRwPFWuQgJNC0UhUw9RwNFGsmZqlUnRa49TSvnfF5pZitp+MUhDA9kzx8vxCskjTyjtN1CnsQoVSYWe7uBxIR5jNExTQsX9noIoyewnqruDGYrUSkUXoPCuYA8MtSN7vxU5DUrpbYyqU2rCJRnlNYkQSPqSetAVNO8fN+9HHwPh3rS9TQunK/33Htdr9d87GMf46/8lb/yrr//T//T/5S//Jf/Mj/3cz/Hr/3ar5FlGT/0Qz9EUTy5qRRA3ci4qColAQawvSSVFoMerYWQ65yY7/Q+A23r8c5TVo7FsmW2cJStdNxV25A2C6bhkmFYdIRXec7ACK5WK7nBKFExiUxD1Gn6OiedrKoSeJHpkvAgONff7jsE3tGpGzTS1Ws9eeG+bfXa8wCqWg7Jsuo0dzuzDaXYKgxUZUNZNN1r8Nsueg9T6mXPpDsnSkpRKMlTEkkwzgtH3chrtFrG+EUhn1tPPAzM+WN5D9aIeZrRdLyKDs7UnEOJ+o1Z1nQQL99BCh5/v30XUStHoBq0co91V3RHgDyHZTz59SNEIfMtP+991PBX/spf4ZlnniGOYz7zmc/wpS996dve9/u///ulQPyWnz/xJ/7E9j4PHjzgX/vX/jUuXbpEmqb88A//MK+//vp7fl3vp/XdigkgLptNSwf7O/fn6DtXgW7Pk1In11zVahp3rgdeN551DosN53HBtWTNnL3olEm06Ubscv/QyoGmO8yvUp7YirJJGAi0QClRD2oagedYc06Qf0xAYDv6l7hgjfyNqBI9fr++I95PHB+VMgQIdNvFq3MRhLoWE7RegvB8j57Hly3fqFNA6Q3LsljuJ47rfVzolMBK+cyt6VVFzpN9OJcLfJS43L/mRxE9IsmqtuZmrTvf7+pbNrhRTjDCym+hT/1z9QXOo7DLf9T6TsUEeBoXnmR993KF7sys5Ed3SWgUOKLAEQaum8DLOVQ3In/ZJ5PQy2iK+WfRdsWFb8nMmt14xTgqtjFBOHJCgLW6v3YhCRoGUbNV8mlbQRLkpSTD4hnkt/4A8LjXkDWeyErTs2llytn7hRh1DjVqHzvbO2hdKfsjMO6xorw3TM1z10kr+8dgS86LSmHT6m2jQBAA0gAZJK3wgYouKTcCLwKZzlVNFxNimQ7270dtvwO/VSbTHS+zeYRwrZR8FyIUo7Zyzb3UeP/99I0V2+V+potLdft7ydvvdT3NFc7Xe54Q/MiP/Ag/8iM/8q6/897zMz/zM/z7//6/z5/8k38SgL/6V/8qh4eH/Hf/3X/Hj/7ojz7x80jS7ajrthv9BYziautaq5ANPxiGaCNugr3GtzEwHgfUtSMvHFUND5cxD/Y/RNosGJ++ye7ZFyguPs9J+H0czUTFYhhVWCUM+WUVE5qG3XBBpHJWcQSENF2h4lpHNgi5fjHgwqSgqC0ny2CbBLe9XjkeoySZL0vPelWzLgI2TUigZUR21GkL9xuhKB2LRYPSAoWKTI1SYhAWhoqigPWyoCpqrl4fbuVSj5chy63alnTkykqSBGsVV/ZqbgzuM2+GzFcTjo5KtAoYhSWRqbmnYubziigyXLugOByfv69+4ysl7ykJW+Lg/BKqa8dgYBmkcp/ZwrNYttsgvE2EOotwY/qOod9+n6bnPDxiDAd05mjqveECrUF9yxhQtebb3Pvd19/4G3+Dz3/+8/zcz/0cn/nMZ/iZn/kZfuiHfoivf/3rHBwc/J77/+Iv/iJVVW3/fXJywsc+9jH+9J/+04Dsj8997nMEQcDf+lt/i9FoxE//9E/z2c9+lq997WtkWfaeXt/7ZX23YgJIR6nu+ALGKMYDzU5Wde61ch+tRY2jbUU+cLaS790aGV/XjQgLUMLDdcrezgtkzZydN3+d0RuvYV58hdP9P83dE8M4g1FSbw3DFlVEaFqm0YpYFxi1y4OzhKL0rNdCuM8yw/VDx+FgxaaOeLiMv0XHH0Ljtoe8FDiOstbkTYDVjlVhOF10kJ8tDFASH6XATh2jcEXtAo50LKZopWc+K6nqlvHIMskkCJ0sRTaw9zhRXVwoSkcQKG4cFDwXvc1KjfmNOxc5mzsGqWYnqQh0y3wz4PRMOAYXpoq9YUHVGFaldAj7rqzRnkHUENuaVRVxNA9YLB3DTHe+KuK7ss7p/EX8tpmgjZIf1RuPSbIV6oZaG1xn6GaNJBliAKmIQ0X4hAXBdyImwNO48KTruxUXlmvPpmrJC4e1ihefMVyZiER143QHIxKIT9M4VmuH9wIHjCORFs0rIRC3LQzTmNHuIZlZc/nkt7C3XqO5+gLz6Ae5f2qYDDz7w7JrJgpkJzQte8mSWBUo9vlmHrJYNBR5Q1E0jCcxVz9iuDaes2lCjlbpFspT10CAiImEOY3TzBaK46OCNEkxymOVIy81x2dSFWvdn+9+K27w/HXJkVqvKCqJOZvc8dYbc4q8IvrYAc9frFF41kXMciO5Ul5qtJZJ23LtCAPF5as5z9tvsDBT/v7iCg+Pa9I4YCfeEOuSs80+dx+0Ej8uK/YGJY3TVI1BWBZ9Q0/kXq12zIlYbxSzWcXeXsThVOLb3WPF/QclxijhLIQi8NCjCZgGJJGIpKRBTWqLTnI96nyeoBMiFOK2gvY9ZLZPc4Xz9R3VVnrzzTe5f/8+n/3sZ7e3jcdjPvOZz/CFL3zhXf+mLEsWi8VjP0DX9XdCbK1a6VjbisTUWN1KV9l60tSQdE6is6VnnXfwgVQRhpqmdhR5w+lS86DcZW72sGf3WfzqPyB+83cIjNvCc2JTEVvZUJvKUrWWUJVk5RmprboK15OvK2YnazabmkHsOIxnTJONXLTf0hEU8q9s4rJyFHktXfjWUrYB68KwWHkWK99hHD1l6dlsGtarWiYEuiHUjWD/OqnAYlORr6STkoU1adBDcfz2Z7nuJhKNJB7TeM3F2e9yUd8lDKAspdWY2JLMrFEK1ivxSohDxyTcMAgrsVbXnc5yl3iFprN5D6Tz4FrxaIhDmSyUlefkOOfsrKCq+iAmCZ10VM87BAAKtyVLP1oMwLmEqjFPRh6EDhf4Lj/vZf30T/80/8a/8W/w4z/+47z88sv83M/9HGma8l/+l//lu95/Op1y4cKF7c8v/dIvkabpdpO//vrrfPGLX+Rnf/Zn+dSnPsWLL77Iz/7sz5LnOX/tr/219/Ta/qCs309MgG8fF1ASG/K8ZZNLpyi1cv1bLUm20WI+qLSY+q0LMa5RSgz/AitTq83GMVsZjspdVnZC+8Zr/ObP/RL5F/8+kWkoCrnekqAhtXU3qjc0TpOZNZP6IcMw38r5lZVjuawpS88kKbjELfbjGXHQq/p0h5elI/A5FF3XshIjorqVBGNTKtYbx6Y4n2A6T6fyJdOKVK1JdC4HYCukxvWqYrUoZYweNlt99l7+uOqgjXUtU07vYD+es//Or3Nh9Tph4CkKSTDSoGQQiOvXalmxyVuSsGU3WjKJc+LAbSczWolWemxrhkFOYhuaBvK8pWn7zqQkHcfHNSendQcj9LROkvtHncuBrvnTovDbqaNSMimOrBOideSIoydUGfoOxAR4Ghe+E+s7mStsNuJZMzstmM8qrPHsBHN2gjnDICezJZH1Hbleru/5omWduy4myMR7s3HM5xVnS8VZmZK7BHvrNW7/zV9C/86vEZqWTS5n1CgqmIQbAu227rtDvWCnvE8WVOSFY7EoOTlacf/WCSdHa9Kw5YB7TMPldrLwKJog0DKRCEzLetMyO92wySVOaOUoa8Vi0bBYNOS5oygdq1XDydGGk6MNTQuZLUlt1U00JSYd3Tnl/hv3WK9FiSkNulhWONYbz3LjWa6lGMjzlqr27IRLdm59mYP1mygF81lB08LQrBiqBd7D6WnBctmQho69ZMlOvCEJG0IjAgiDLi9JbUVmSwIrKnCrZYVznjQURcXlquXWGyfceWfGaiXxU5AhAjMGCE0vVFITqRKr3XbKKdNQv51M9jyJJ11Pc4Xz9R0lFd+/fx+Aw8PDx24/PDzc/u5b11/8i3+R/+g/+o9+z+1JYtFBQBhqrNXULczLBEW3kRCsnlKeINBYqwhsp4bTddu1giyz3TgOysay0gnFxecZfaqkPbjCXrzkpatKTEYaYbiXjRUybmtYtCOa0LLOI8JQMxwGWKsJIkOWBbQe8jambANc5wNgug6l0lDWhmWdUTaG0dDg2pQoVKxKuzUbMUZwjrZLYpzXNI9Yb+dt1PkbiFtpPbDs7A+pipgwFDKQMPflsUAeT3XEIWtFqadoQxajyyz1DmEA02lCFELRhluVktE4Iow0Za2ZVSlFbbdmZWKCpjvohMZ1Hc4s07QuJIrUVlElCGA8iVBaEYXy/QicQTa4MWoLH9jUAUYN5L9aoE39KLEnCbVO0bZPjhlS5l2q/ubJN3lVVfzGb/wGP/mTP7m9TWvNZz/72X9oIvvo+vmf/3l+9Ed/dFvNl6UwpeL4XA1Ca00URfzqr/4q//q//q8/8ev7g7J+PzEBvn1ciALAaLLO2K9qFPNS9m3TSuldVBqtxfwqDNQWw9+P23ufgZ6gV7SWVTvAvPgKH/4zc5IXbrIXzXnlhuzBvBY977IRwl3VaGbNmCYIWJVCvE+TbvKlFUmiaZ1hE40o2oi2g//AeaFbNIZlFVM1hixVKB0QBCIqIO9F9m1vALiV+I26PeE0Kzek8bZTRYI41ox3YpraSZxozzsTYaC2EwKjBSNrjCEKYdPEbA6eZZnso3MYDoUomDchjZYnHgxDokhT1DCrBhSNpeiIi70Wu1IiF71RMWUrjz0YyGNV9bmiynAox461/f6W4gR6+WJxZ17HIUZJ86SPqwIBExK5c+cx5EnWP25MgKdx4Tu1vpO5QhRpTBBgTHfONIrTaoKjP8s1q0IDDhtogkATRRpr5XoqKsHNp6kmDAMCK4V53kQ0117i0v9+jr56g/14zkefnaAQdSIQDxDdTQ/n7YQqilhtIgKrSFOJHzYwDMcxzityM/g9MaHPFfLaMquFqDseWdylEUkkxnximApR1PmrJLozPVUoJVLkoYXKWdpOjCCwkKWG3QsT4iwmjg1FE0gTwolikagbqg7uLJOCKNSsm4T1hRdYxnsEM9jdS4gCWLcZtQ7wXklMCDWbSnNaDmidJq8sbZcr1FpyhdYqaifk7Sw1NLsJcaSpWgeI79LB5QnWasbjkDiWWFEWhtZJw2RTaRyW0MS0VlO0ElfiqPdgkM/yUUjkk66nucL5+ieuMvSTP/mTfP7zn9/+e7FYcPXqVfZ3DUkWAEFXzcLX3om23WOlJekPAggCMfSKwo5sVkoXLQo1Fw8MoZUO1ek6YpaHnITfR/DcH2U3XvHc4jd4cXafo+uf5gtHL7IuFFHQm/9oZusp3k9pWtH+HQ0C6jqgaWOCQPDIDzcDykZIPG3Hhk+77/FkaZitB1jjuXbouXFRs8rh1kPTYfrYGpXFoTzvKFXsjM6lxu7M5SLRGnbHnlGmGY/GNI08T69HXjWiXd5XyabzTugToofLmLl9CYfYgX/oOYPRjofrdPv5X78a4j2cLeFkkTwiFSYHb171duW+wyYrLkxhfyL26OsO/jkaKKbjcPtatPLklWa+0rQtBFbIXM7B7dOY0EqADa0ntJ0hWyGbvCdqvgfzQdS7VPm9tNi229ytKIqIosc9Do6Pj2nb9l0PrFdfffUf+fxf+tKX+MpXvsLP//zPb2976aWXuHbtGj/5kz/Jf/6f/+dkWcZf+kt/idu3b3Pv3r0nf3N/CNa3iwuDxBMlMB4IPHC1gZOZfUzVxhgxw0kT2cthZ5S1WAuZvfcZ6HkBZ5uQRRFwuv+niT73L7IXzbl2/wvcOLnHyUt/nL9z/yMs83NOQFlbzlYTnJ+gu6lDEsHOUIt3ioFVFXDLH1I2hrwUgmF/SHsPJwvDkTNEAVzebQit42QZcOuBSJ8msWLYeRokEZ1KBySR6iYVmrfnuzhkX2ax/G46FmWPOIRlcU60zBI690+/LS56HfS7yyEP1GdwpYz5n7kor/HuTBIN7+HaZUnKj+fw4DRFbfe1vKcokEP5tA1RSmLIZOAZZ5LgL9ZSiA1TONg5P7wBNqVmuZZOaVk5NrnIQ9dtwDw+P6Ks8VvxhT42Og/55skLgn+cmABP48I/yfXtYsLuRJNkBqXkuzxbwdFs0MkD95DVbtoXawaZYZR1104By43Ei0v7uuPBeDaVoWwS/n74WcJP/QD78Zxn7/89bh7f4+Tl7+eX7n6EdS6iHztZReM0rz6cUtaCq89S8SDwTq6hOFbUTcv9Yo9NHZCXmrp5PFd4MLM8nI8IDDx32RFc0yxzz5sPgs4fCXanZmssGgYO5xRNG+C8Io0cs0IezGrPZNASWk30iT3q2pOligfzvnkir8kaSEIe4SDK+f7W6ZDb5tOwEnGBj72ggJbby1EndQzPXBGVsodncPc4JrSKJD6XI+/XozHn6iFcPbTiuryWJ72457l24TzxBc9sZWhdSF05qtrz8EwTWMMySYnDGKUgi1uyWAq6dXFuvtp7rzzpepornK/vaEFw4cIFQMgQFy9e3N7+4MEDPv7xj7/r33y7wBuHQl7TXVfodAGzRYNWqlMYkf8GgeBOe5xt6zp8ajdezmLBnFaN6hJ2xdFMU9Xw4hXFS7P7NK9+lezgOYpKsdqAT+S5W6dY5YLbDQMxyNEamkCep5fuWpeiBtIfcFqBNucXZtNAEouZRxI01E3Cau07PX6ztRsXAo7rsLLnXflezjQOPbYb0dtO51hcU/v7nssNPmqC0pOI8lJzupTf7QxbhrahagzrUm8r7GEin9VsKZ4BQaBF1lH3lbfakhj78X4Syeh1uTGsBGFAGp2rlPQOkkppqlptXRwlYMt3ZYySjkYsr7dXV3mUW9E7yT7JeldcYPfvq1evPnb7X/gLf4H/8D/8D5/8wZ9g/fzP/zwf+chH+PSnP729LQgCfvEXf5Gf+ImfYDqdYozhs5/9LD/yIz+yPbA+aOv3ExPg28eFfp9EgQT/5VpxNn/8wkgTw85YPSKzJx080bl3JLGY9cSBo2zOJTvvnhiKIuSVGwE3Tu6x/vJvk139EGUNq7VDK01ghMi2XEuykaWK3dH5HutX3SoaF3Tk2S6B7TgzrRcVsU3uGQ8VadiQBaXIom7EYyUMAtEcf4QwrTX08th1o7ba3L1qiVJsCYvOnT+vUudyxD3BzygRCGidYra2LDYSF3ZHjiRqWZeGZS6FTBLKvmxaxWLtWa2Ee5ClvRP5eQLQd1tD60ki1xEkLVXTiywoBnHnet5NdLzXFJWQ6qqOFN22inVogN7USd6f850OuZc4L5/FE1yI/JOPCfA0LvTrO5krRMHjRfNqozg+rXGtKFe1rSeKDHFs0FrUbaJAzpiVEyENa5TAzwJH3SrKWs6q+6eGTW555cYON4/vsfzNL5M982HWOZzNW8YD03XvFWcrxXwheyOJxGVcYL4QGPEGWNfhdrLWNzF6gv46F2jfaKC4uFMzCErqdsBiKQ3O8ciQRbJP01gw9XAuQOA6HyClRBAl1B5i2ENvDcl6OU/neSw+9vLufQ51utQsVtJkuHYgimab2nK6CjoIoMSEulHMl575vCZNDUqbrYT6eY6kto7Iw0SEEJaFSEAD7Aw807SU5kZrOnlSTRT2kqMCfS61TAbLWhOHMEparBHI1lY0oX1c7OBJ1j/puPB+ignf0YLgxo0bXLhwgV/+5V/eburFYsGv/dqv8W/+m//me3qsozNP3vrtOGsyhAu75lzNQnlWheZoprZmQk2junGaYjSQbHVdSEcpMB3DH0lWQRjrR9c/LcVANOLTV+5S+YAHmzEny4DAeq4fNISmZVUFHM+NEBJLT1EITnmQaeLOtbi/popKWPkiSwbpWCr5o0VI60JaB7tdl8w5UfAQLwOF0UY6aiu3VQaKO5e/qhH8fdXAOpfOx2So2B3J1b/KNZuyK1K6Tr1MCHr5L9l8TQsPzgxlZcgS2Bk4tBY89Xwl980SmAzNuXGagt1hzTTZdCPTmLIxnUSaoShlWjPquC7rHI5nqnNX1NuuQX94x11g2058lBRPD07FDCaOpQADKBWo9hwO9SRLGf0uZiMSYG7dusVoNNre/m6HzN7eHsYYHjx48NjtDx482B5m326t12v++l//6/zUT/3U7/ndJz/5Sb785S8zn8+pqor9/X0+85nP8L3f+71P/N7+IK3vZEwAuHsMg1KmgUbD4dRz/cL5IahURxBcycQsLyU57uPCILNoJd2phdLnEywN4wzGmVzwJy/9cbKrH6IOB/zg9VepdMzb6wu8fRxLB+9SQxy0lI34ZvTa2etuitVP1aIumQ4DyEtx39UKdoaeq/stjVM8XES0LqZu4HBPbxOFx+OCeiwuBIHawn+iUBJt0fRXnSEb7Axks61LQ9MpgmxKvZ1MBtZs9944k1j04ExT1Zo4EkUykR1VHM8kDu8MFQc7ZlvQA1zcKbmQnuG94qgYc7YJBbaxFA+X0MLFXbnvbAX3jm0Hs1JboYI4BN1NWXTXruyfo6xhthQy+SAV2dFexrVpwT1hN/AfNybA07jwnVrfybhw98gxKP0WHjgdwbVD+Z57KNum0JwuFFUjPJrThVzPcQRZorupnQZ013SQ4n0y8OwMRfHq5OXvJ3vmw2zSPX7ghXuULuS0yDhZRSgluYK5IF46JwuRKM9L4QQao1gXhjg02zMvMHJtr0v593QEWSwu4/fOIupWTEOnE3mfvc9I3YhjuzWSK8yWorQYR3oLoREuo6esRXa8bjzDTJzSoZP/rKUht9ooWudlmtBNC9JIYkLTwt1Tw1t1QhLBMJUG63ytmS9Bac90rLi0H2479CANx/1UguGiiiQGtYrjhe0aBiKzCvK5v3k3FlJxx/FqWhhmmmEmr6n3V+iVyfJSGpDOi/dCFstj9VKr783E9Gmu0K/3XBCsViu+8Y1vbP/95ptv8uUvf5npdMq1a9f4d/6df4f/5D/5T7h58yY3btzgz//5P8+lS5ce0x9+knX71orhOCDJApJYc/3A8/zoHka1BK5E+5a7g8ucrcZsctkQbQtRpJgeaCZZwzI33DuRDsDujmaYOgLjGUYVsanIm4gvHL1IUSk+feUuz3z5b0DTMPjUn+JocYM0bLk5usMkv8/t0fPcO9njdCb25ItZjg0Me/spg4Ho6U9HUmmvcsXpWUMYKq4dwpXhgpMi49dfNRwdFVy9kvLCVSFEPpgFHJ3JSLOX3ZvNau68fUZTt+xfGnNwkEgQ6cZ6m43j9DSnqR0ffnnMXpaLTrFL2ZQy/VhvzsnEIDjF6xcVu4OSeR7ylddq7t6a8czzUy6+7ImDhqNZwu07BUlieOWm5eJ4TdValqXAly5kC66VX8crzcPkOsflmLLR3L7fcHZScOVqxs2rDg3cO1K89rvHGKMZjGLCyJKmlvHYEFolASdtt7AFkJHpW2+vWS0Krt+YsHu981dQWpSb/nGVA7p/j0ajxzb5u60wDPnkJz/JL//yL2+vXeccv/zLv8y/9W/9W//Qv/2FX/gFyrLkX/1X/9Vve5/xeAwIeejXf/3X+Y//4//4H/WW3rfruxUTAL75jQU7eyHjcUiaaF68uOZ59RqmrTFNifYN9/de4NeKK6xzmSou5xVxYnnx2ZCDccVsE/DmHVEq2plY9ibSXRwl5xrXf+f+Ryhr+MHrrxL/V/9Xgrrh5Z/4d/lG8zGy2PPS5DbT5TscjW7w2+UV8hLuPWx5cH+Ncx5jBOO7u5fw0g1NGrWscsvDo4Y41nzocsmN6G2O3CG//PaYu3fWXLuW8eI1UeS4dxby8FQKfdXxgc7Oat5544S6atjZGzLdSwkCzXgs8We18dy/l1PVLS+/NOTGfoFRnsal5KUkDw+PRPUkCA1JLHjqS/uK/VHFfBPw1ddK7t6aceP5XS6/okiChvk65tadgiwLuP6S5/JgRtGGnOQpziluZHe59M3/BbQhvfF9OH+J2Sbg/pFjsah59pmYly4s8SjevJPxpV95gzAOufTMLqNRyGRiubJPZ9hYMA7XNN7ycDNkvrEsc8Xb74jfzHM3x1zdl9i5LqUQ89+BCcGTxAR4Ghfey/puxYW33piTDhVhaEjSgO//XsUrydcxriaoc7SreXjhOb6srzBfK2YLx3zeEMeGZ68a9kcVJ8uAV98QIY+9/ZgLe5oogP1hySgqWNXRFib0Ay/c45m/9//ANw23f+DP8dtvXWE60nxk7w57m3d4sPss/2t+kaKE0zMh/WrdYe4jw2BgubiniALPulCczhqiUHPzcsMzg/s8KHb5n14LuXt7wY3nJnzkOZnmPZorzLoO+MlJyduvPaAsKg6v7nF4aYi1ijQxRKEIExwdFdRVy/PPD5kc1CgFpz6grBV56bl7tyDf1CRpwGAo+dYL1zxXR5K3/NbXHG+//pBnXzrkMx+zpGHL3eOQ11+bMxzHvPApy7OjhyyblFvzEUWluT4647nZ/4pXirs7r3BH7zHLQ27fF+L2jWcSru/leK/46jcCfu3v/C5xlnDluUNG44jJJODKgaAi0rAhCytqZzhexiw2isXKc+v2hrJoeP75ERevi9LiprIybWyfvIv+NFc4X++5IPj1X/91fuAHfmD77x7T92f/7J/lv/6v/2v+vX/v32O9XvPn/tyfYzab8c/8M/8Mf/tv/+3HyBG/nyW29y3GNxjfoH3bKXSwLQacF0vscwtuGSH1OtdWi96+7fStQbpfqw1UPoCmwVclUbshjRxJ0GJ9jXKSuMpjiaKO6/wPvPeP4eUkwZXug1JCVjZKbMHbFppanrfXTLcm2LoFSkdMHtd5eWyjVQeLOtfs9t5Tly1N03aavz0k53GFnnOcrUferuq6qDJKbeoW73olpP5v/GN4bJDfOSVyo3iP4nwe573CdWNZ78+7tN7TPX7nQdC6jiAEPddRa7/9ruQzYXv/f9ypmNLvYkeu3xuB8POf/zx/9s/+Wb73e7+XT3/60/zMz/wM6/WaH//xHwfgx37sx7h8+TJ/8S/+xcf+7ud//uf53Oc+x+7u7u95zF/4hV9gf3+fa9eu8Tu/8zv82//2v83nPvc5fvAHf/A9vsP3z/puxgTfXZxaC8zMKI9tK3Rbo12Ndu2WNFc3AstrGrk+lTrX6RYomlzHomsv2GHbeZAsc4EJVTomqBvqVUFWrxkkkIYO62u0b7pC/HxvOiceKF77c+nhTvnoWxV0rBMZ5baFqmqFHGwkNtkOFtmPh0XT/3xTqE6VR4i56hGvAU9Ty2P2e/rR55X4QgenkD3overiZbdXu89lKwmsOm+Tb4l1vd65omNqq8c3ba/qohWYzucEoKkbtNHiE/EI9AckfgRKMnz5HCQGen8ea/vH2focPCE84DsRE+BpXHjS9d2KC1qL+p7SPckWjGswfUxo663HTX/NCJRId9e42zbbes+PvgGntccq2cg9TKh0Ib5pcHlBoCqSSJJ7o1rJFTjfs9DFLK26PSS3qS0R/3xzGuXQndqe685PkGIgMG67jx/19mg7WFQfFx9V8dvmE62jbd32eY1y2zxBd/r3/fLuEb8P1WK6eNi2IiFuur/f3v9bDupeSlwp2dgKvVUm3ManxknegdzPe09bNzR1Q1O38n56jCV97ufxSuJBaAWO5V2fV0j+1EMKnVdbwvaTrKe5wvl6zwXB93//9/9DMUxKKX7qp37qXUcg72U9++yQwTAl6Mbiy1zzur8sh7pu0dpz9yzh/oOKzaYmTQOyzGCMjOVbJ54AWnnCUHU+AxIYGq+ZVwllY4kC4Qw82IwZfOpPEbUbbFvyx5IvUgUZR/4id+1FjuYJm06GcDyJGE8irFWMR4JnC7rRt/dC8LuwbzuYjGdRp5SNYX/XEMcDpmO1TYLHaUtg9ZYL0LSKLInY3RWCyuV9uDApaJ3meBmy2GhWgWKziairVvwX1glGCbQgCWUcFwTymDJW750JYVaE1I3m0sWY8fiQnbGmblq8l8/i8FCC8b0TxcOzAUHHnQit5/5mTBF9BI9isYq21fjuVD770UBR1BJZB5nm+nO7W23hIFA4J94SVQVRaIgCQxg4pklBaivxL7gxYLlKyDLNKu8xgyKV+F78ar4T7oN/5s/8GY6OjvgP/oP/gPv37/Pxj3+cv/23//aWPPTOO+9s4Q39+vrXv86v/uqv8j/+j//juz7mvXv3+PznP7/Fzv7Yj/0Yf/7P//n39Lreb+u7FRMAXv7whJ2dhGHiscaxrCJeDV4BDd4qPIoHRwl3H7TkeSuj9MQSRQJfWZcBTasYZIooDJiO2Pp4OK9YViFFI2NorTRvry/w8k/8u2T1GoAfnvwDqiDjvrvCO+Fl5mcRs5UQ5EdDQ5add5OUEt8Da1q8l7H2xUOJC5s64I69xrKMmU40xgyZjnsjMsEzH0y1FNxdkjLMIvb3ZAS9v6PYH9d471iXgqsFxXocUXfqJiebBKU8RScEEIdw8TCgbYNOss+LNGsDs01AWWuuXU3Y34+YjDTON5StIY08V69IXHjnCO6c7BEFMM4kUbmVX2Lx7L+AQ3O8GjDfSPdxugOjoUANlpX8/f5U87E/8izWanYmlqiDApwsQClD2aQUmUUp0S8/GDRENqJ5NmNTiELb/TPbYa5lylNsnqwi+E45kj6NC0+2vltx4Xs+sUM6GG35c8vC8Zvule45AANn9yy3H7KV1E1SSxILdy6vBUZ48TBgdxowHogDse18BhZVSt0aDnY844HhtMi4/QN/jkBVWFfzz934KpWJOa53uWUPmZ1GzFfCMRoODMPBCGuU8IUi4QLZzlBwkEiuoBQsy4B31AU2leXKRcvOeJ/JSOA8zhtCK5wj78/leUeDlEuXbgBwOO1jgsCpyxriyOBcQl07ggA2lZCve9jgeADDmzEQbw3etIKy9jzYjKgaw/PPGi4cPsN4qLCmpvWa/YmHlyd4D2/dh1tHl7YcwMB67q4mLAbfj0exXgZsKkPdKKYTS5YKb3JeCPzm4qHln/6hj6A0DDPJFbyH+6eCDpgMI8aZxWrPMK6ZpI5RFhIEAza5QMVuH0sqKwaKnmLzHiYET3OF7VL+fcZaWiwWjMdj/p+/fEo2HGIUOOBsaZivOgJdZz5xfNry9psz6rJh73DI3n68lR/tiW5tVzlfPfBc3xHG+LKK2VQiLVrW+hGHXWHq/7Hki0T/4H/AXr3O//bMv8xX7gwoSlgsW1onRODRVgHEE9peJ1suokdl+Hq+Q+NUR7iT58iiputMSHe/cYplKYeyOBZKNXyYLtjnPpWJeTu/xMNFzHytuHNP/AKm05C9Hb0l3j16zQlmXxJq5+nUls5JiP3r7H0TylqI10UFt+5UzM4KxpOY61fDjmT9OFlnS1g0j3ch5bGkGHmU0LjawMPjmrb1THcCdieKLHZcmyzYCeYsmhFvzcZsCk1eyeZuexfqxlNsFvzUj0+Zz+ffdozXXz93fvr/xCh5HO+3yEsuf/4v/UP//ul6f67+e/3FX33IdJwRmRqP4v4y42gu5NeiEGfQ9brl7KzAtZ4kDUhTSxRpDnYNw1TIsb0Kxe7ocaxrXplOWlQ6enXnBjpIpBgI/qf/hvDZZ/nCcz/Bb34j7hx0JcZMRppxdt4d7OWOk1AgLk2rO8nMvutFF4NE7CAOHVkkHcbGaZyTWZxzqiMBdnFBO3bjFTv6lFqF3M0PON1ErAvNw1PxGxhkmlH2uOKHNb5zVvasC8Nyc04u7LkUWSz+LjJxlBhWt6pT/dK8/mbFg3tLDi4MeeWFgEHsmG/ERM174RH1ZMUk7JMfT9iRrqtWPSJB6jsxAs29I4E4TieG6UhcYC8M1wzshk0b83A1oKg1p0vFyVnb+Ug0nWP7gp/7v1z5tvv6aUz4YK7+e/2lL91iMBgC0HjNm0cD3rrTir9H7bbE4n4iOBqFDAaWMBDlwDTy22tdK0Tv3krhULWGuulkjK1M+k9WEbePhHD7z934KtO//9+grt7gV/Z+lN/6ppVJQBcTxkPDeCCE2mlWkgY1VWtZVUHnnq46V17VuSd3cqFx27mEq454D3Wjtxj9nqMUBo5hJB4s43DNSM1pVMD9Yo95ET3GZ0hiEQ2BnoQsgiC7g5LYNMzLmOOFkIZ7ZcLAesap8Ci9Pzd66wVPVoXht3634vZbpxxeGvORl1NGqeN0qXl4Ip/3eCTGr4/yBfsljQpHaPxWMa11itlKcetORVU59vZCpmNNFnuuT9dMwiXrNuHhWmLC8Vxz70FD0zj5qR1lvuD//n++9DRXeI/rn7js6Ldbm1LjrdmOmpSCQdoTZuSgER8ChfO+09VWW5lMY9hCfEA2XA9pCU0DoWz2spYkPbCeNGxJgpYqyMiuXqcd7TIJlzx/wXK0jFgsoa4c67W4oIq2t+ogCPLcvYFO04qm7zgVc6CyMSw3hrIWAuAolmKgagxVY8TPoBQ1gKJSrJAkP7IpSTymciGrImRdKPKCrWmHUoos9l3CobZFSb/qVlxJQYhG4vIpBMemle7/cCBOrFUTCnyq8QShZjAMybJz2dZejhV4LIj1SkxRKIokIMm8GL+orRTqOndUlYwDz/WCFc6LXGPtDJtCs+ycTEXeVNE+Ylj2pEuZdxkD/j5MiJ6u99falBZbhARWQpfzcg2L1KbqCMSK+UzROLn+dKfXDeeQgV7pqm4UrVfbjrSOPFWjKWtL3Uqxm8ViolMFGdmzz+J2DphGK168FjBbW+4eiVP3at1BFjtSn6iH+G2Xv2p77xSJC0nQUDSWVW4pa7nW6c6lqhHlnUdXVSs2nVa40Sk2amicZVNbNqUIChSlo+oKAulGyoRAyPzdWF1L4tHHhdBKUgRsydFp7NhJa4zyzIqQdaEpKtFBn0xTBgOL1lI0xKHrJFf74uJcHrR1kMUKm7To7vFXvWuylbhQVB3cqoMryshf1JgcXVyoNHn3eEGgO6iW7GfvnuwYexoTPphrWUW0ZYxHbdXyJuNz4rvznjx3nJ6UXbf9HBrrO2jJo2dnHwuUEgPO0LSiUuYkV1DKMx1pUS80MerqDZrRPpM457krQ+ZrzYNjKXDzUnKTKFDEQbB1N+5fmzQn5HmjwJNG8rqKSu4TWE9kPSgoOhQBSB4kQiOGqpaChQGYqKVprUibVoa8VGxyUSmKI00YSCOw35tlrdlUAbUxHSfnPKZmcQeVKg1rDJGV4kMpz6IIWeZioBgnht2DIcNxuFUrSiPP/vR8MhkFQpZe5ZJ3BLYTElBezvxHGo1iwMg2p+s5VACtVzTeULeWTSnP3zR0vhKKulYYo8E/eWr7NC6cr/dtQfDGrQYTVJRlSxgaPvR8yM2DFYFpGJgNoSp5LbnCG98MKPIKYxRhoLZmFdbIQbNaCy62rA2NsxhTsxsuCFXJoh0xW09Z5XD9oOHm6A7W1xz5i7z5zL/MJFxy453/mWdufZP5Z/4FvnnrFU6ONpRlQ5VXJIOIZ5/f4WBXE9pOFtR4jleah8ctUaQ4vNlyJX3ISbXDa7cD7j8oeeZaxIWROBC+M0t4565svCAQ5aL5vObBvSVN3fLcC7s8d/WyKA8cCyFns6k5O1rRto4bz2Q8v3uGUp63ZxPunaptFa6UvP/5okEpxWRouTDasCgjXn0T7t1e8pGP7PDxC8ckrJltnuP1b6yxVnPtWsrz1yzWeOJQuhV76Zr94BjlPSUxlQ+5v5nwla977t2acfn6DjdvhCgF949q3v7mCVopkizCBmY7PlZKkWUBVW0IjKJsAwobc1YkvPaWOE7uHSRcPLDde+mKreA94AKfbvIP5PraG54wkalRGGheuqG4uT/DakegaoxyvDHf5+jIUhYNxgjBznaKVq1T5BWczhxV7bDGMowDYtsyjVZkZs2iGXG22mG5FjWhlya3sb7mvrvCq8/9BNNoxQu3fombb7/O+lM/zF+bf4aTk5rb85LVIidOQp59fsL+VBMFisYqNDBbaR4ct4Sh5tLNmuvJXU6aXd64N+HBw4pLF0MmmbzOo7nmzr0abVRH/lXM5y0PH6xp6pZrz4y5evEQ72G+6l1HW44frGlbx/7ulKuTJUp5bs2GW81v3Z2sy7VnNpPH370RcGVnw6oK+fLrhocPcz70YsbLu6cSF4pnef1NibGXLwa8dCNGa0/YaaBeGG2YhCs0ntpbGmc5ygd88XcUDx+suXptyEvPKNCeOw8db3xjjjaawVDMHvulOpx1nygVTYAGzjYJb9/zLFcN45Flf0eghJtSU1aWYvPuikDfup7GhA/m+q1vWExgKMsWaxUvPev53mdXWN2SmIKAmrfXh3zhKyHLuVTBwp+Rv+9hdXcftpSl4+JhgN3zJEHDXrJkqBcs2jG/+3CXs5Xi+kHDR/buYFTLcb3L63s/yiTOeXHz63z07G0ePP99/K31Te4/KDk9bSg2NXEacOOZjN1xslVKVAoWG8XRqSOKFB99puRKesRJtcMXX8u4/6DkyuWYm5drtPI8nBvuPZRcQXccxdWq4ejBCtc6nnluhxtXLmxjwqbwbPKGk+Mc13pGgxGHQ9EFv9NknC6kSfrgRNR6RJa5IQw0h896nhmfMq8yvvhqzK131rz04pDvu3lKonMeLq/ym18tCQLNc8+EfM+L4lnSeimkntldsR+eoPBUPqLyAUf5iNfftjx8sOHS5Yyb1+Tzf+3tlm9+/RgbWKa7KXFiu3xOk8SaNFbbBkveBEDG8Trh9XdEtGAyCbfFR1kbgRdvwie+fp7GhfP1vi0IZrMC7xWbdUmchLx8c8pOl8gPi2OCes0k3sMG+jGioe1gMaEVjVwZFYrUpgMcikjlZOUZbWTwfio+A6Zlkt9HuZa79iJfuTPg+QuWZ259k3f+P3+Pa9efB16hyCsWpytWZ0vSUcbBhSHjkVx8SSTVd1XD2VlJmoqD8KCZsTYZm9wxnxUUhxFaOYzyrAt4cH+NUorROCIINKcnObdfv0td1QShJYp28N5xdFSyWpSUZcNmmeO8wH1GdoHCo/WYshK+QD9SLErHelWjjcZoQxaU5E3AfNbw4NYJz92cMHAz0uIMeI7T4xVRHJC+kHI4LrdkZ6VgaDdMlnfQvqGIJ9QmZhGkzGctD955yGCc4HyIUbBaVpzeO0VpTZLFmMBiA0OchhijO6KnEUiG09LprAxnJytOHizIBgHWCN5ajMmkW/DEy+jfq1Nq9Lvf9+n6A7NOTjYopSjyijgJeeGZMTvBHOtrknqJbUvm6YAgkCTRGNXFhPMJQdPCJm8pi5Z8YqlbTWAcsS4YVic0QYDzO1S1Jw5apst30L7hnfAyv/mNmBevBdx8+3Ve++t/h5cOL2LtZyjLlrOjJUe3H5IOMw4uDJhOwq1RjvIyNZvNatLUEuiWYX7MJs4oSs/srGAyCbaExLyAk+McbRTjSUwUak5Pc25/4z5119rXWtQnylImb/mmZrXIcR2ubxQI78GaAXXdTSt6M7C8Zb2qCEIjRVGQizP7wnH/1hnPXM/I/IKsOAWe5fQkJ04CshsBF0YbGq8paovzisyW7NV3Ud5R2ZTKJqxszHzueHDrhPEkxvkA5WGxaHh4+xRtNcV0SBRZgsiQpuIA309UenhCPx04O6tYLSuyNCOJOsiV1oRWYZ+UQPg0Jnwg14MHkuSuVwVxEvKh53bYD0+I2g3Z+gRb55TjiCjcY7WdFPrtdECpjkczq8k3NcOhwIk9ilgVDIpTqigSCc+Fw1zw7G3eQbmWW/aQ3/qm5bkrQz569jaL/+Xvsjfew5qbNI1jtSg4O1oQJxHjcUQYWAKrGCRyDRclrFYNzhkS27BT3qcIYxbLmId35xITlNy3bmC1qnHuvCA4frjinVdv09RyxsfxjnwWm5aqdOR5w3KW07YO50fC1cOjVUZde6ras1w2VFW7/TzjxBIaGKk5pQk4Pta88/o9Ll5KSXRO1sypm2vcvzNjMEoYvWy5Nj4lbyJO8pTGKYbBmoOz18A78myPPByztgnLpefo3ozJNMZ0xN3ZWcmt332bMIlp6kMGo4RsELKzExIEuoMhCqyrbjVaiYfB8fGG5Twny4JtTLBGUVs6EZUnXE/jwna9bwuCS5cylBlQFC3GCOb37dW+HKThDmHQsC5CXrwZce1auu189WYYvba47oi9yw3cX6QE1rOKIxKzz2YT0rSi572qAm6Pnkcpz8NZQlHCw0XE7I/8Sa7deJH19Dr/u8M1n/7QmHtnB9w7lisujvRW1ac3AgoDmE6FdHy6hjfMs8yLmChU7O6nOO955zTrDjW4/ozgH88VgjK8uyqdvsMBgo5Q7O5G7OyEKKUIgl2MEX+Gd9aHeBRVrRll50EOwHmDc5Gw8j2s6piq0YwnIReu7dG2nq9tnicJGhYbzeVrO0SROKg2TlPUmtnKdPq/e7QDGdeXbdRJkkY8/7xld/8lslRGjgCTnYjnP3INbbQkQKEWw6iRFCsCq5Kfo0XIMgxYbrTIuA5D9vairRZ02+nIP6m8IIAyFmXs77nt6fqDvS5cyLDhkLp2aCPwujdXFwlMyyAoCUxDUVlu3ggpLodiUtN5WPTww35pLUZAy9xQNhqjdtmEKasyFnffVFE2hqPRDZTyzM8iWgeztWX16X+WFy9cprjwLP90dsxHribcPrvM2/cuAZClnamOF6iP1lKYyCGnmOUx7wxe4GyTYjRMdoR0e38Wbr0Fnn12+Nh7391L8C9eoqlbpnspYSixJwwFXsA05Nq1DKVhOvI8KCaATEXGAx5T3rDWolWC6UyEFnVKXlvSVLF7OKKsHK/lN4jMVVa54dLlAVGkZfTvNesy4OHcdtCrMWp4TTqtbUhVi1Tx1SuG0egqw6FlvpbnHgwUz334EsYokrRLjjLD7lgO/aruRARaBVjyQGAPw6HFBpos1VtCZm9gaJ+wmfc0Jnww1/5+grYDyjLBBgK1u5UfEumWQbpHqCpWVcqVQ810klDXAos1Wm33p3Oyj1wrUKO81LQuQLHPWTBmtYmoGvH/WJeGe7vPofHMTiOcE1+TB89/H7uTA1bjS3x6OuOVaxH3F1Pun+xulQ43ubgih7YzTrQwGlmCQHGax7w9uMnZJiXLDBeujLFWcbwIRJXQwtXLMa0T4qxzHhuMCMJnaOqWgwsDAiv7LE0MYaAZjy3Xr6VoDTtDeLCWmNI6GA8VdaOwNqCuH3EEt4pN5blfHzIvY7KB4uL1fZyDr88ukwSHrAvF5Ws7xIlsvnWdsKgi7p1aygpCs0sw+RBKeVbtgE0VsiwjrlwOGA4vkaWas5V87oNhyPOfeB5rDbv7KXFs2BkbLuw6QuO2cEFQnUCCZV0IFySKDKOhIQrcNofXSuGCJ79+nsaF8/W+fdcvX2+wCVS1kFyOZvDFOwpjAuI4wlq4tAf/1LNHhKrmreU+37gb4LvqObCOwJiOYwAnpw3zRX8ghmgdEoaayVDwcsdzw72TPdpWRm1l2bJYwhu3Pwx8mB/Y3/Cx//X/RnnnHvHHP8HypY+xjnb42uIZbh3ZzkBHMLFxCFcuGFoH79z3/O4bCUGgmIwU42HA6dzzm7+zQWnFSzcTPnmzpHGa+SYQ1Z6J5cZVYfBXjSjs2M7cJ4k8O0nBlfgeYVvwavE8X/hqQOs8Vy8q9sZS6fdExDRSDFPTyZ15jlYxdaO4eKDYGU+YL1r++79T0NQt12/EvPJi1El7iYvrycLw9W8UrJYltw5SLh4eopS4BzetqBR86uaaQVBwZznma29BVXkuHlg+/FyKNcLNCEzNTjjj0N1Bu5pv8tKWrP3w2FEULYOB5cZVSxyarlg6n/w0rXpPVf93SmLw6Xp/rReve5JMjHmcU9w/hTdvaaw1jIYJUQiXpi2fuXqXQFW8s7nAGw/irdxf/9++iM9zx/1TjdGaB2cJRiedWADsjjyrwvDb5RUpBFYK7x13j+Cvzz+FtZ/in8pOePGLP0t1/yHf+9GPs/jwR9mEE76+us7tY/Hv2JTnxoCXDiR5f+fI8vqdCVopggAuHoasc8frb9YYo7j5TMDLV3LqVnO0jNiUiunY8szljjzZFdNKCW/HGs/OoOHa4JhIFdzOL/D1ewnOyfs4nDzu3rXJDOOB2Tp83psnVLViNETUOzaO/+FXpAK/ds3zoWcV1ojzcNlYzlaW175ZsF7VHF1IOb6wh1IC06xqMTZ85UZFYmvuLyzfuCXEzgv7hpeeCbdSx0p5dtMlV8wtjG/4en2T334zoW48M9XhiDVMJwatDcNEiNUC/5IMoA2eTBfjaUz4YK4bVzRxYmhaMb1arOHXjqQhN0gHhIGIB3zo8AyrG+6vx9w7E4NQ50R6vG6EH2OMoApOF6La9c08JC+kcM1SIRKfLBS/kYva13wFzrU8OHb8rfVNrLnJpyYzPvr1/zft8UPUcx9ifvMlFnbKl+5c5bW36g7CKHDCKICDqfBvJCaMO0Ui2J3ErHN4606L1nD9kuHZw5zWaWabQJzK9zXckElh1akEASSxJNAHk5YXdu4Rqw1vrK/w229Ik+TCruLCTkPjFJtM07TnHXHnYb5WnCyGW2O0yWiH5arlf/r7BW3ruXwl5MMvRNvO/dEm4Whm+NrXN2xWJcdnQx5cutoZGwonYJDCS1crBkHFnXnGq2+2NI1jfzfghWd20VthFM/BYM318BbWVbxe3+Qr7ySUlacoHXXjiULNwb4ltJZB4kWMQYHWWoqi92BV/DQunK/3bUGQhRVhVFNZ6d61TrNYSPs5igw20BzsBAz1gqRekQY7gJSFvQdBv7QSzfG6FvZ/r0s+HAaMBgFaC8nwdNZuScjOe+rKcXK0ocgrPvnihPLOPY6/8iYXhgMGSUYwuUgaXgbsVhqwdd0EqiPWFaXj7KwkGwTs7QhT/mwBq2XZkWUSBkFB46Qb1rTilBgGvfuwYdWR9eLQkYUNwyBnsrqLrdaE6Q1mZ6J+dPHAbnG9DZpHt0QvZ1hU3RSjg1bNF3B8f06VV1y8MmSQiCpS21mh1w1s1hWrRU4Ui1qL1qrTd/fEkWUQFEzVMWdhRtuGtE4IyJO0xmhHaisC3TLWMwazu+Ba0ukzaDXAe5GCWyxKokgkDgdxQ+uE7AnnSil9gfBEy5h3GQP+4dzkH6Q1iGrSSKTv6lbjnGU+L4V87kKSWHMwUWR+QVStSO0UrWP4lvNBdfrbzgvhrlFQlKLRn8SaJJIkuyfZtu6chFvXjpMTUfl65WpCdf8hp7/7FgfDAcM4IRofkgSXgaDbd/J3Pam/dYpi7jmbiyHRdEcmcptCUeRNB++TfVVpyyIQ59/ACoFXKelgbkrVKY0IJjkLKibtEVG14qHdE/6UE9fiwLgtHAmEj9MrnDSteoSwC0pp8rzl7GhFU7dcvJiQRe12WicqTLDpIErZIGCxFgGIohRll8Aa0qBiHKyZBTFVLfEisIZxUom3Sf+d2g3Z+hjtauLkxhbmCbLvoxCyRImcspVGgXtEevGJ19OY8IFcWdiSxI6muzbnK8Vy2aCNoq5FxnKYaFKTE6mcRZBijcB8e+db7+VScFuSsRDdF4uGxaIkTQOSRMwG61rgf95Lpx5EBe/+g5Kmcbx8LaI9fsjmjXcYJAnDMEGPa8LgCk3taAK9FdXo4c1Nq5gVnsWyJY41o0wTh56iUhRF2xXGmsyWNM5SBAbv1bZ5B7AqpEkC5ypEadhKTCiXROYCy7XBO/BT23myKJrgHGIM8rp68y9jpAgKLCxXcHq8oi4bDi+kDBJJwptW0AllBZtVyWqesxxGnA0NWinywlFWjiCwpEHFKFxxFCSUZUtVOcIgYJK1Wy8YhWcQFGTFKbqtCKOmi1OesnSUZYvRAVHQE5bPvYyM8jil0LyH9TQubNf7tiBIg4o4KGiMpbSGKEy2v2sa15l2WdJqTpyfMciukUSJQAQ6iU/nIY4Vxghhpm48rpXuf76usFZT1wFNIBbjs1mNax3jScRoaFivFWXZsDhdcX92QPzxT3BhOBDy29d+i+TyCemLn6Bu5LU1rWyqLJZDujdM8d5jrWIycEzSmlUeESdSvCShJzJ19/eKvFJMhy0XBisC3TKLUk4D6ajFgUMrT6QrouVD9GaBzRyz04K2ddR1hlYez/lJGVlHGsoBerywnC7E2GMylALjNJGt470nDDSDUPwAFoVMK8pKDFJc46grIV0ppdhsGoqiZjAYoJUjcCWBbmVvtYo4lATFqJZQN+cO03WBamtCXRNYSUC8h7pscd53EoUttTL4RtF232frFG37HjKAp5v8A7km0YYstl1BYInCAd55GufJNw11pamagLDJCasVSVYyTPxW0q9uZI+Ox4a2NWKaZaQwWK89ZSWHq+u6z70DcduK3PBkpFmtPbfnJWdHS26dXuFTH/04B8MBvq6p/7cvEV29SvrR76GsJC4o3Rv2yJ5rWjHOKfKGKAzZH3uGSYNSAaenBq0VYeAJdbXFObcOJpHj8mhOoGqWdca8jLYcH4AsKBnM72I3c+yFlzk6rmhbz/5OvDUH6o0Jta4JjKN1intnAQ9PnMgwjhTJwFOUhqZuqYoKaxSDsARglsfklaEoxdzMd0ZoZSUvYrFsKPKGJImJTMNALUnCCUZbvBY1o0Egj9W/7kTn2HqDahtMKhBR3XqqSgqDKJRGQRxK97B/z708bN08KYfgaUz4IK7ddEOaBXivKJ3haJ6Q5812zzWNBjSZXxCXS1I7JbBpp5In026lIEk0CWw5eM5D0WHwvQO/HwmxtfScnsn0bDgwjIdGbjttWC0KHsynqOc+xCBJ8FWF+uqvM7ryDKPDj3XqRp1JaSf1G9jzBmZdO7LMMBm0DKOapo15aCUWBdYT6gaP6mTMFTuDlouDBVY1nIRDtIq3BQ7AMCwZzu9glydElz/MaqnFzMxZAtPinCIN5Yw1nVFo4zQnc8vxcUmSWnaGlmHqWKz11gwsjjU7SYEDTlYR60KxKfzWWKwoGlYraV4sFjVFUROFGVY5EpVvG5cg09hJUnQmZfJBxLrE1Dm6rbFJK6pKHcSrbf1WyS2NRK2xbsWzpah158D8HkqCp3Fhu96/BYHakNqQ1msiFxIFcrh63zsGyyaK8zPC1QnpKCeJdmjavnKXjlcUKqxRNK3H1nLIuNaRr0uCyNC0soGKwrOY5TjnpSDIRFq0yitWZ0vuHTmWL3+MQZLhvvZbPPi1rzJ9aUnycknddQnqWg7/OFRE1qGVSAR65wmsYpQ0TOM1Z1lInIhecRx6IlXitOiOVzUExrMfHBM3a4L4EBhvib1KeUJVYs4e4udnmGsts5Mlbd1SNfsY7bfuxXhFaB2DoKLxmvut5fi4JssMBzuacVKTJpG4AzctQSAHv/OauQ/JKyWdvW6TN7VInQIs5wXrVcF0mqDwBHVOYFqsUTjbTTNsLuRpHAqHbSpUXaCamkBVRIEXDfbWUVcNrvUExonmMYqavlg57+Q88Xq6yT+Qa6RnDIzHoSlNRBwOtu7d61WN0lA3IbYtscWKdLhhEDcUtWHR6i3MZpSprrvV/TSKuhYCvmCL5dpb50L6d86TZSPGmUiLrhY5R7cf8s69Syxe+SjDOKH+377EO3/nyxy8ckr2iZyyGnfFgNrymeLAUavOiHBTMxqFTLOS3XhFUe8IjtlDaM7jAshrTMKGi+4WYbVinl0gMlMar9k0IXVrSPWG4Pg2zM+wF1uOH65EYe1GvC0GAi2u6yGezJZUzvJOE/DgQc5gGHJp37I3rDhbRTRNS1mIulBmJS6c+ESkj0tP2zicl4lrWYoT7GpZsVoU7OyEhLomqRfEtsZaaYDEgSMzubhJI4d43K4xVQ6u7UwnZYIjj9viBoYkcqRhJ//YyvSwac+LvCdaT2PCB3LtBHOyQBphpY8IbUJVykUhjt4aCEnLGdHmjGR6BWt8p2iltlP9LJGJW59Qi7dJw3K2FilLBsI7Kj0nRxu88wwHI8YDeZ5iU3N2tODeyZT5Cy8xDBPUV3+dh1/4baYfmjO89i/hnMW18tznfMdzb4C6liR6FFdMoxXrJMBaMSezxhPqmtabrnkIB8axz33CegPRNcrG0jpF3UoTLbUFwfEt3PFD4msVy7mc502bYpXDm97hVxFoR2gaaifwq5OjNeNJQnTdsJNWHMex5AptSxQqxuGa1huOvUCbikJyBNc6yqJmvRYC9Ox0w2ZZMBpFGO2I2g2haem91OPQMwqFGN56mXzEbDCVFASBajDmfILhnTg8x6EjCVuJA61MXTal+Cjl78HE9GlcOF/v24Kg8DHaRVLFO3HyiyLTFQTdmK5VlPEY5Z3YifewnW5M77oN51Vv0S1Y/GwQorQiTQOCgK5LCDYwQtTp9LHDQJEMItJRBsA62iGYXCS5fML0pSXRhQMC1XC4K0S45Zrta+srXWuUKHnYc+UMAGv1Fk7QYHFed527bmRJQK0jPAqrHa0Xh0LnFIWPaacX0ElG2QakwwTXOHEj7jSOQbabfB7yt6GF4VAcGrV2OC/vMx3GAlMwUDuzfe2mU21Kh6LYEoQWpWTiEUSGuA0JQ0XjLWU4oGlkpGq92Jt7Oo+BbuMXwYBkuIdyLYWLt88RRYYki4gijXmEKOAe+Rx7F8onXkpJdfattz1df6BX7SMqH9GiqZzgYYPQiIV9200Aa9hEE5R3FC7ewgBaJwRja8+Nd3pfAqMhywTbmyT6MaKqc36rZAZdYp+EpEOJC5twQjQ+JLp6lYNXTkkvHbBWG65dEMjRYiNdyEcJzb0/glKI0Q99fBKlHQfUPqTx4tprDZJEa0vbwR2ManHdNNB5SYbayQEmCKmcJck0rotHdavx2hFo0KqbgvguIQ9FBCBNjHQr8YQWBqMEaw1BIPdtvdrGrMAqskGIsZogNFuIj7WaJBN1kMZZKpvSlqbzhlGo7rmly2lxaHIzIBkeyPfVhhKPtWiLqy4OGyUxpXVmC2fsvwv9pHHhaUz4QK7Cx1gvZ1TlRJUnjCze++15VdaKdbSDU4bSPS5J2e/LPiZAD+cRha+mnjAcx8SxIjByHetu/GCNIgq8+AykAXES4R0s7BQ9rhldeYbph+aEly+R2orrVwfnz+vO4YQgOUgQiFCA94rWSxCyVmF89z5cSN35bxgt+6g2EdqLoWFkm87nwGw/j3r3MjZKyduIJLO0rRQdZWu3k8MectN6KbajEMaThMEw3MbCwMJomhHmIWEgyXvtzPY9WKsYjBOCyBIn4dbwNYqlGRBFhsYZyjCl9ZoglHyh93GS59cyPSVlM7qA8p68jbrYKBLMEMh30Zk0Nq3ZehrBueT6E6+ncWG73rcFwWvHU4blCK39dsNevBDRNJ71RjpH8zV8ZfwKUdJwskhk/Lft+HXGVo8Uf2Iiprh+MWAQixlY3UhHfZCJwo33nvFIk0QSTJ59foeDC0PSRPO1xTOk4WXSFz9B8nJJoBrG7TE/PH6To/Q6X7x1hdO5BJOqUxzKUsX+fkSWaPLaoFRK1SgGA/nom1Yxqwa0Tg7jvgN5L9/HaDHzCXSL9opNHVI1mrcXe9wd/AkYwNFpyEc+nuC9J0tEAcV0nch+vL6uJQBe3Cm5suukqGgseW3IYvjwR/dpGk+WKB4sU0CCZBY7kfa7OaRqBhSFZ5O3aAWTSUgYKEYDxdFmwDp4hkUZMR4AKALjaJyl8ZpVFVG1hntqgglEfHg9t1suw8XDgOmOWMYb3XbJgqaqdZd8eKLAo5r3MCJ4WvV/INfb6wNiP97ihesG9vdjytJxcpxTbCpu32/4zfFNsrChXBqqRm8hQwJtUaRRr0AGjRLvkuuHjklS0DrDqgqoW9UlshqvH8G0B/Ds8xMOLgwYZJqvr66TBJdJP/o9ZJ/IWasN+yev8Sdmf5fFtY/yP599kgen6tyID5lcDodSBBe1YWVinFOMhlIQVDUcFRNar7DaM86kwD7mEBs1GC8yqdqHzF3CujS800w5zn4QPfScziNeelHjnRzkx8uQLG5Jg5pI1xRtyKoWyNHV3YIb+65LQiRu7QwdH/vEHk3rmQ49izruOEWSsIwHMHlZYsXJTPDTAPv7EYMsIY3g4WbA3CacrCKisC/sW1qvabzlrMzIa8sdN8ZxRSYvs65rGAt8KTCaJHJknRtrXluWG7M1bRqlHuufMC48jQkfyPWN012G9WgLOVEKrl5NqGvhAJRly+0Hji/aF4gCt+XBPLqUlrNIa7YO5XEIVz9iSMNMuvdNS+thXRgGQ0l4x0PFNCuIA/EZGI+lMPnSnauEwRVGhx9jeO1fIrUVl9q3+bG9X+N4epN/cO95jmc8lshmiaLdCUgTxaIIaNyAqlVMJ9LkKyrPneV4+x5Hnev63fIQqxyhatiLFrTeMNcZm9ryYDXgHX4ElyrKh4oPv3RehNw+iYhDz3RQE9uGqjUsS4FeXd5tubwboZQnMDWN00yHLd/7PWOaVp77/nq4hS4pDdOJ5mBXXH3PFp7jY+FJXrs+YDSQxsNpnrCpL7IuLRcPZK8nYUPlpCm6rqXguVsNma8vCQy7E2qJQxgPDFoZMU5MBKZcNgmrXG8nPVHAe1IZehoXztf7tiC4ewSjSlxuVScjOh6Is6hgcDXrjefNhzHW9BtLNnNZiXGR2XalOtnB7mK5MCk4jGcULuLBesi6NMShYtCpbsShEH2M9hzsasYj6VrdOrKApW4k2BzuKn54/Cbpb/9dLn7oUyTR5S25uO9gRQGMBprAiiug7y3D43Ps/qYOty7KcehwwOlGNuMorhkEBQqpnMtGs8o1dx/KmH66A1cOpNgpa1FMiAKRPg1692InHbbpYM00mFH4mLvrKXllCazn8r50AJtW5NOEB+EJA4c1LUkkHbmThaaq5fCdjDTjTPDIizxgWdiu2+i3pOrWK2onQWZdiCTpaiNTlEGmyGIZl44zIOuM3boJgQRtuRZM4AmMp7HvoSDQRn6+9ban6w/0OlkE2MpunYadl/21NnACVFXD2UnBN29nRGFElogyV9OqThnLE3QHqtGeRvWTPDgcrLjELTbRiFv+kMYFW45B255PFKyB/almOpFC+/ZxAASUVUJZjbl2wfMnZn+XB//tf8+Ff3bN4NrHudeNwp2ThNtaiGMxTKtbTdmKMEEc9Q7gMCtCjPKdvrZshkUto/4syMn0Gt/FhapWzNea2ULTtJ7JSHNxKvtlsdEsN6CVRg08RknRXdSyH/aHS3bVEbWOOKp2WdchWdRy9UASrF5ZqGnVFraXxZ5pVgp+t0l4550GpRVZIs/rvDQnnA8oql75jS3HqfESF5YbwyqHo5OWpmnJMmnOhBZGqSOL2g5G2HQTT8VGag/CQBof7knjwtOY8IFc9080q1qmelrJtTYdSaK6Xotq0GxW8WolakGTsWY69I/J8PZ/Z41Hjm7h8VwbzzngHrkZcL/YY12HxKEhikyn4udJgxqtPbvjhDCwbHLPa2/VNLVwHZ2z/3/2/jzYtuyu7wQ/a609n/GeO7355ctJicQgnIBIg7EB2QIcDtOmykPQhU3Q/gvoKOR/IKICG0c45Ap3UXJEM3Q7wEV3FAUtlzFl1LjbqEAGWwJZQkKAMlNSDi8z33THM+9prdV//Pbe575MDe8JkZ2k34p48d69795z9jlnr9/6Dd+Bq5f7fN/O71L++q9w7pu/hd7wGneaCV3b8IwC6GdC4M1L3RUurRN7bRUnC3EIz2JHFEoRf7RKMcqz11uwFcyxOiA3EYU1HM1DPv6HK2bTnEcfH/PWh2Q6cPs04PDUM+wrtnoyNbQuYFnIfjg/WjOJZ9QuYFr2yGtDFlkGOxUexbIMmK1Md04bLdzJvVFJaCwv6pTDQ8H9b48V57cqaqtZ5IZTK/5Mw0xyhUBvmofrKqCoNTcONZ/61JR8XfHwo1tcu2SIAk8SOsLAEQc1aVBilEOrhKKS/C9rTGnvK59/EBe69YYtCLaGin5/AxM5O8FJYvlmFG6gAO1UACBLVDf+a3+vtmCbmyavAuZBRmHDRsFIsINpLD8cGjpb8+jMO3RWMQSgqOAgu8r5r/h6Fv1zXEhWDNOIotYUleCE22KmVcqprSQjabPJl7miqER7vFXQ8F41z6OYEVLURjoUzdRBq5YsremlkCVtEm2o6rYwUl0xUFSS5M/LBKXG1M502v6B8egAwHfdihY/WdamcSp2aONJIkOWms6OPAp995q8V11AVQrWZUDteo01+xkYk5bX1RYN8jlt3tRVKfCw+dowW6nmfTaEAaxW93G76s8RFf4L3eRvppXEjij21MEGviIKIZrxJBGt+p60h5ynOSgaoloMcSSJw9l40v57VcVMkx1yG1PUhqqW0fn2jvCXBj1R9XBeIAKt6VgLVQRpOuSlZnblqzn3XUvKS48xSXMevZDigLpR9okCGA4MSQzWedalTMyyWPZvVcPRzBAaSGNHYDxGaSqj0cpTOMNSp80hKve1bsbqII2IJJQiIg82xNvSBRgXk1sxZHMe5lWKjrax1rCqI0orjxc3ibZSNNAC1SUAXbGuPGkMW9uJuJInyGTSbn5WKcgSiYFlbTgpBlTNBLDlRoWhxLMk1k1DBuJAOEXOKxZljHWK2dp0+GCtFGUVsF7eY1x4EBPelGuQbe6vdiklBWivJ9LjYlDYQPQc5GVz1gfypz2Pzv4+wKqOmEUTchuzqkLyShxx2wm/1lDaAOs0RgtXMI7EHb0OG/hfA9s7nDzGuW/+FtY7V9kL18SX4y5XEBW9zbW05ypIc9I1ucI6p/El0kTB2ev1zMoU2MGjWFQxZS2P2+uHXbEeGkmSQtNA8bTAmMsmHrTPuapCAtXHekVhDXXz+kwTCwLtiUIxfC0qaba03AtRP4JerzVVE0ijIDKk2REFArVSylPWhjmRQIUquQatoD8Q8ZUkkecW6KTAhJxTzAqBHc/WgYgceJn+RlZUoO55PYgL3XrDFgRXd9ekWdRg2qFsblbpsCmyZGP84xtJwLxwxJFmfyJqPrXTrEvBly1zTVXJQXY0D1mVA5xTQmpt5L8mQ9lgrZSWqIL4rsNYVJtkuWo4Ax9+6RJpfJELyYon/B8S2ynXt76aT9w8R1HRHOjymmorB2RgPNtDecwXb3puvLIiSQMunE8Y9lSXZADcOTHkhRYt7pESN2QNo768Dzsjy15vJd34OmOZy+isrGWKUlaKVSGbPC8T7pi40/sNjGzsJKjvSpBKa3jlSPwStoaKcWZJAosjwjXYxFHPkUU1ZW1YF5qiktfZiyVTOJoHnM4FO9xL5X1ogzQ0B34TEEIjk4G8NhzNA4oKFkvPyVTUl4yWgF6sN26KX3Q9qPrflGsrK0l7pRSubLprRa1JopC8DEXBw8o0YJ2LUk0Uaa6eg0mvoLSGRR50qmDtAXxnnjDL97BeZD2lO+d54ppuDiNL0BTsdbAxNVoV8u+WQDxbwW+ePEn/ytuZpDlXzfM8lqx4JXqEpw+2qa2in3rGfd8poi1ziTXjnsUB128bbtwsiCLN/l7YdQnb/Vc1muPGSGcuDn1naBaFin7qGMRyKkrsNI16WMTaCFxwsZaCIC8zbpq0I+0BpJGonGglRmTiayKwq+WazgApUI5xr+bhS7Kx+6mYCXkvMSEvRH981BOJwtnacPtUCqxW6tBoGPQknvczmQwE2tNPShJTMytibhyHrHNYrh3LpcV5OG6ut1jf7bHwedeDmPCmXPvjkrRXbdSnmmZYYBR6oqlHMv0uq0ZwpPaUc0lK97ZE4UtIuqYR5JAz2ns4WGSc6PSumKAVnN9pG2qORRk2k0NPP5XHDQLdcZZaLP1/uvkoveE19sI1j/pPEfkZL42/kj+4tU9Vy9nYT4VwXFuFrWS6mUYW7xWHp4abt0vCSLM1CsgSdabpqZguY0CSjRb+ZB3s74V4F7Iz9iSBnKFp7ChrmcTllca5kFWpyUt5jw5cyGkQdJNUpaRBEGop+JOgJg7EuHGZB5S179TTBGXh2N02zbV46gZuuMwbr4RUpoxKeRa5ZtVMJjo+h4GLFxKc94z6jQdKo0AYGcuyirh1ItPH1dqzXIlKodHSmMxXD3KFL2W9YQuCLCiJghplpbp07bhdiT01NMTh5nO3TtSHnPei1x1UDVte9MY3DHXpGoJUz2eVa6RzuOnQu2aTa0VXwbfKAEoL9OV42sAW0ojYTgmPX6E3frjpfqu7CDv1GdJLqyxQ157ZNKeuI8qduOvQy2sSXe/FoiYINP2eIY3l8cLmPYiMI2g4Bm2HpO1YyutTd6kmKCXVu8k8QdMV0Vq8B6QL6JvfCykK1yUSRksy1E5MAi1JfK11I/VK99pAEpblyhKGmjgSMpY+M7FpFZOMaicFvvu9trgrcov3XohHRlPk977JvTH4V1X9r/76wfqzt0ItB4LVqhu3tzK7aaxRSpJWmwNeDuSicOIUbCAOGvURZYDNfpRpgqK2ulGykT0ThZDFtpncyTU435hqeRmJt/d0Czuoa7h9rLjpDY9eSHksWZEsDkh3LnQxJAzEYKuy4qzaPl8YtBhnz3JZYm3YSSO38apVRVutHUGgiAJNHHKmGyd7KmiI/Ub5jnhbO4X3hqreaKHLc6tOyq+dUmrlMMrj8Xi1ibkiW7ipHkLjSGPX/bsjKLpNXAjbeGeloGinBrppFASB/B0F0qQIjDx3S4DOy9Yw0lOWLRRDIJfF+t5khh7EhDfnioImJjSEVO8VVji/Xce9lRG1VnKGFlLcJplgmkn13dwCmSRsimHnpbnVNrO0avcUXfIcGIjDxuOj2e/eweEp3PGa+HJM5GfEJzfpjR7C6D1qpbpz8Gz+AXTKgdZBUVg5y5sE/CwKoiihqjxKb3w7QKb5AHHoumm80XST0raIapWPfDNZLeuN0aoUSPJEEis84AmaxMq7Tb7RKhe1zb/2OTbxZvNY0IjDNDV9myOohtcF8jrUXbmDxJ9VAcuV+J7UtcAUnZJJZqv8eC/rQVzYrDdsQbAoY5Iw6irORS4W9rUVbXDXmOgkUSvdpTqo0CrX1FYIb20h0Cat7c3ejrJb3GFewmItm7+XSLeutnC4EMONKJQOeIuVS6INXMl76VBe3/pqeuOHCV3Bt43/M1WQ8HTxGJ+9LTyHXuKJAtncYo0ujoSPvWVCHCn2Jop+aruAIJW2JktCMQhpXisaAhrCcGGo7QCHBK/29Zxl3Gfx2U3ejiY9aVRTWc3xMpbRYmzJwhrrBVaRNFOY2TqgqAyztWG6aD8h0xGnk0iMyEA+J5DEYn9Hiok2yVB2U5y074FSoMqNvGgv8aSxIgoNYbiBhgUG1qu71SG+4NKGBgt19/cerD/Tq24MyayXgn5VBk1CrZivG1dvI4eJ0YpeqvBeJmx56bk9z7qxttbgXiVb2RbaWkIB60KxWEunrJd4sshRWsXpQpMXksiK4dimIPBdrBEC8cvRo2Q7F8jcnG/PfocqTHnWfwXPHfQwWrrqbSKwLMRcqJcqrlzpS1zYEidO34zdfWMwuCpkf4VmAy9o92FRaW4vRQUpL3XTpZT3yTddvl7SaK2XciBrJGmIA1E5OlxKJz8JLUlYN40IgfcArMqQyhmWhUwbQK7Nh5KUyeRCPo9NXPDsjO9OulRT50v3VmBBRmtWpSEwMVWtGKQSc/PS0MtasQH5DNer+5kQPIgJb7ZVWUPQwGCdUyxyzXwtZ1NZSa4QGMkXJLncnI2rQlPWcfdYLTqgLdDPavq3RXXL1QNFPxXxjdoqpkvx7WljgpiOye+4MzGhqDUvjb+SbCS5wl+afJwySHl2fY3nbovXQRb7rmm4yMWBOUvg0sWUwMCwJ5BdkLjjPJSRoBjaxkDbIGzv8LzUHDTqfpJ/0CkagTQN0qgRRamhKmXiGAaeLLLUVnOyivBekUaWJLQdD8MYOdsXuRjJzlea5bp5/sZUDWCQQi+R32ljhtYIjxD5rNr3qoUlCuxTYrgYNIZUdRtfFEVlKJocIjDCwbhnGCE8iAtn1n0VBO95z3v41//6X/P000+Tpil//s//ef77//6/5y1veUv3M3me8w/+wT/gl37plyiKgne961389E//NPv7+/d1YdM8woVy4DmnWKwVJzOPc5517qhrz2gYMEiF/KObjnNtFbOVYn2iSGIY9303/vNtd6u50YJAdV2qVQ7HJzVKw7ndoIEJae4cWk5OCiaTmEvnDKbBzsWBEHrKBsZUVJpP3DyH0Z5v3foo+n3/d/r9jPPf/aP8/uIcWarYHorT8KIImC4loZkMPRd3pdueRTWhlosTOISinwT007vHaRoFGpT3zM5svCzZJASu4RGEgUxMQMiLbcIUB44sqDipEw5ONWUFu2NFOBTMbktwUgpOF5K4L1YwX8j1WWfIEulMDnuWOHAsctO9rlHPszOspdvaBGqlNhyFVjO48xiwwqvYHVqiwJLFAVnDFUljRxh4Vov7mBBog3/Vpn711w/Wl2e9nnGhsobQaWonMJaTheZ05qmtY7WSTtFoFHJhoIhD18Hiaqc4nBruvCougBziZyVB286iVp75WnHnQCqG8/sBwUhMb24fWk5PK7a2Ii7saeJQuDZJuCHFOyf3/jMHE4ye8G29/0T+P/000ajP3v/xx/nwccagp9gd1gyikmkRc/s0xDoYpAIrjAPLOM2JTd1NQ7xXFDYkrwXbu8gNedlKJUssXBWKk7kQH1tXcpAD1TaQwSyR1+VcQFE1EMlQ4tAsD7l9oqlqODdRZGFNoJuxfSj7tk3cl7lisWq7eCLJqBX0U1FJW5WaecMH2ho4dtMC6yXhb1WLWj14SbYAFP4M92MycITGUdS6ayRksSMKHKvFPU4IHsSE1229njGhrAy6wcu3ifnxqRXoYNM5HvQNe6mIe8ShnCe1VZzMNcu1xIRhJt1956RBaJ00GKz1Xa4QGFgWcHzaTBp3A5JIpouHJ47FomY4DNibCMY/DDaJfXv2FZXmD27tY7TnL27/Aen7/yf6vR473/5uPnIS0ss0k4GjH9fM85CjmTQIewlsD0U9MG4kN9vlvKKsNXkjXHJWvaidvq8KxXoqjxWFDeKhKSbaqWIUts1D8RpRWjyVelHFdB1zNNPUNUyGwi9s+ZdKydRltlJopViuJVcwRhGFmsAI/2qQWYyCRa45bl7X7tgz7tVdY1XU4wSK2fKpWk7AwuiuyTjIJB6Vle44IUnkCQPHKnqQK3wp636U3fngBz/ID/7gD/LhD3+Yf//v/z1VVfFX/spfYblcdj/zIz/yI/zbf/tved/73scHP/hBbty4wd/4G3/jvi+sqiXJLiuRDATpCIWhaNGKZnYzlus6zw3R0Mkmrmt5nJZQF4Yi99f+Cc5U70pBFMnN2xKStYY4VmRZ0JiDtN0DMcKoz8B7XHOYFZWiClLCfobJUgw1g55IbnlPY6AhCXcYbMZ2ch2iylM5c9fjB2YzSmy7F+3rar0WOkiU35Cfz5KEXbNxw6ZzUVtxdXROuu9R21ms5ZA+S4Z2TdKuG23wJNEEwaZjUlvVkbPb96O2gl2ums1dNupQZ4nZ7Xus9UYb3rGxj78vjfFXr1ZK7NV/7nP91E/9FA899BBJkvCOd7yD3/u937un3/ulX/ollFJ893d/913fV0p9zj//7J/9s/u+tjfKej3jgnObvVd397QijjRZpskyQxy1RWfjcO3lbzn4fHPIyH3ZKv60+yI8IwDQHnZJohtim1yDxApNlgWE4ea55CDTVLVcn+06XY2jbpgSjfqYJCZQNaOBarr0shdtuxeDTUyQw130u2sXdPHBnhm5b+CBm+faQBjOxpcGfuk2saG2d+916xWlPXMtTVyomiKshQK074U4nErSnsRSaLSxqLZtw2Szl71nE9+aWNZ+NtBOe5s/QbNt9ea9ADpH1bOv/Z7WlykmwIO48MXW6xkT6jN7r4XRRJEmjhVJYkgT3U212r3q2j9n4Lllc061PAKjNznD2VxBKxEniCPdwGGa6VysyDJDGKrmzFR3/WnvU+8baGylKE2C6fXQSYxRlmFfkyXtJMF0/EaZ/G/u+TYHqayWM9uKT0gLxbUNBMi9KtYpvbnl20KgbR5umnWq4SNJDKgbmXLrN5MWoItzSkmcOPseBQayVDd+ARseQhsT2mJFN9/LK5Ffbj/Ds5DEViXSGIkhbUw7+xm2ucSXtB7kCpvn9P6ew+lr1sHBAXt7e3zwgx/kW77lW5hOp+zu7vKLv/iL/Ff/1X8FwNNPP81XfMVX8KEPfYhv/MZv/KKPOZvNGI1G/F/+1SlJOuwS0a0BjDKL1r7DqZ6uQl450BSlazaoPEZVnykKGlfA3Ylmf1yLCk5jxlFUhqO5oagEVjPsWcHMNqN+ozxJaNHKc7yMuH5LkTd4ZFHUUfQy1eF320r5/DjnfHaCocYoMQyZ2hGfvDHh6NSzN1FcnoiL79Eq4XRhukrY2o1HhlaioDBMLbVTHM9lGtB6MTjv2d8JODcRwt58ZVgVZ0adriU6ynWNep5eLJCHk7lmlcNkCJcna4y2HC5TDqeyEdLmcK+tjEdd0/Xf6lXoJuB4BOLwyoFmuXL0e5pxX57raAqnsxqtpIgLAtVJOCol5MFBKt2TNBIM6KoMOJwFlPUmOYMN9nC9nPHDf33MdDplOBx+wfvnxr//nxn2srv/b7niwl/+3i/4+2fXL//yL/N93/d9/OzP/izveMc7eO9738v73vc+nnnmGfb29j7v773wwgt88zd/Mw8//DCTyYR/82/+Tfd/t27duutnf/3Xf50f+IEf4DOf+QwPP/zwF72mPwvrTzMu/MyvnZJkm7iwO7Sd7X17HpzkCS8fBhTl5vfbQ8towdrOlw7nPJOxYXfcEtYkrpS14WBmKCvYGnjODXO0cqyqkHVlCLSnF1eE2nK6Trh+EJDnvjlcBcPaJhFRQxQMA89ef81efEygatJ6TlitOUnP85EbV7h9JHHhwlaOUZ6DhXTjzkbnswpnWSyEQ+dgXaqOZLxcyeva3zFcmFQoBdNV0EAc7k6gW93uUU+IlVWtuTM1rHLYGXmuTpaEpuZw1ePOtHEajnynetIexuOsYpyI+3BuRaVoUQS8dMewWDp2J5qL2wLruXkScvvQNZ+HakyeNp/NIPMM07rjXhnlWJUhxwuJ02fjQtUQNtfLGf/n7/78ceHLGRPgQVz4UtafZkz4v/5vpwTxkKKUfd52nAXiJlOqeRFy+1QEK1pDMEns5Z4qKpgtxPR0PNRsDzdnU6gt6yrg9mnQnZm7Q5G8bCW1o0BkeNOg5vhMTICm+242uP6W02C0Z2dQspPMMcoSqZJA1ZzWYz7xyhaHJ469iebydo7WnqNFzPG8cTkvfOe11HIae+lGonRVSJwTGXaZJExGmt1xI1+8NM0k7mzzQfIPY2Bn5NjKSvI64OXDgNnCs7uluLKTE5mag0XGnVPd7dkWSrlqRE3GfcskK1B44XI6zbIIeOGWYrl0DAeGSbPdbh56Dg4KwlDT70uTJQpF4U0g2p40bmJGE6NXZcCdBtkQhRueRCs9/8Viwtn750GusFl/Ig7BdDoFYDKZAPDRj36Uqqp45zvf2f3ME088wZUrVz7vJi+KgqLYaETNZjNADrZ1aSlKRxRq9rY0e/0FobZkekWoKl5U53n6+ZDZrCLLgkZibIMtLUrP4WFJXTt2tjJ2shWRkSRd41jUGafLAXUN2chxaTDDKMusypjlEVlUcym7Q78+5TnzMJ96LuXkpMB7cS4NI8PubsywL5j7lvD72dsJv784x6CneOcjz3Ph+d+md/Er+XA+4aWXV4wGPSbxjFBVTPNzzFdyrdNZTZFbtG7Iv6Hm0vmQoUB5WeUwnVny3DKbFljr2NkasZ1JQrQuehSl6khHLQ+hHXnujDw7vRWzIuazLwfcupWTPJ6xl5yQsmRePMTBsSTv6a4iiy15pVmXCmfl4L/SuyPJkc0oXIh1PY6OLXduLbl4ecDuWLompzPL888eoQPDcJQQxUHTVRXX5mFPSyEQOLaSFanJOVIjnl8FzBeWfs80JmdtcPPkq/sgCimNf5X74Ku//mLrJ3/yJ/n7f//v8/3f//0A/OzP/izvf//7+fmf/3l+9Ed/9HP+jrWW7/3e7+UnfuIn+O3f/m1OT0/v+v9z587d9fWv/uqv8q3f+q1vikO/XX+acWG2hHUD+4sjxbW9kkvxTYyvieo1ylte7j3CZ29scTKtcVZUhsJQc24vII3loDw6KsjXNYP+gK2sbOKCkFgXZYxzhtXac3nXci1+kcCVvBJc4VY9IA1rrqY3GKwPeWnwGJ9+RZ4rX9esVxXaiOlYkhiGA8O4LyP+5w56fPg4YzRQ/B/O/y7Zh36N9Cu/nsX6Cp/5zIzsq0ZMzi0wynK82mU6d5SlZ7msqUo5yCW5UGxvx+xsyV6rGh7EOnccHuQS7yZ9ttOlND7qAcvc4BpssHeitJLnHmOk2bKVrFiUMdO55s5BwbifcD66RVrNmQdfwcFJgDFwaRcGSUVlBfKjkd+9pK4DcBTtM696LMuAw6OKo8M1/d6QUVygleMz65DnPnNCEBjGk5QkNcSRRvc0KoQsckzSFYFyBLpG4zjWQ146TFksHWmiGWTyPixWIj6Qr9xr7p/Ptb4cMQEexIUvZf2p5gproHSsc0cYKB7ah0v9IwJqEtYEruRmeJEbx2NWayGlF4UjDDXn9wz91FFbzelpxXpdk6UZ/aQmDiyjaE3PLDmthtyZDilKTy9xPNS/hcZxXZ3jdGHIYs+l7ICt4hYv9R/l06+Mmc0tVeWoKnkuuxV2PgP9VKb+z92O+chJyLCv+Y63PM+F5/4DvUtfxX9cbfHC83OG/RHbyYJA10zXu8wWcv0nJyV5Q6Y3RqGN5ty5hCholMMK2Rtl6VksKpyDyShhOxN8cV72mC0b1EO9aWY416iU7VsuZMecVgM+Oe3x4vMzkreN2U2m9PSCaX6V41OZzkrxsGZVRVR1iLKK7SznavSSfPZssagz1pXh8LDmzq05V6+Nubwnlcjpac2nP/kyYRSwf3mb/iCi1wvYmUg+l8aOrbREq8aPRHk8PeZLxXxeMxoGxM0EaJ1LsfQgV/jS1pdcEDjn+G//2/+Wb/qmb+Irv/IrAalooihiPB7f9bP7+/uvqXba9Z73vIef+ImfeM33h31NGJtO5aa2cLjqiaJFEBNox7IIG+JrSBzrDVTAitMnwNaWlI5aw7RIOtydHJSCg+ulMno6XPcw2lPURvT7a8NRucXC9JnmCXGsGAxCgkBjTGPV3dN3jdChHZfJxp/aEb2LX8k82ubKbk0v6THuW6ZVQ/ir5LHiSDEcBNSpJMxRqBoDkhZGI6P6OFaEoRQ/ILj7VSXdO++l4yEwCHVmQiCPVVs4zROKWjMeasIwZZB65nVGoYXAPRnLC6ltYyzkNrJp68pw2jhCllaMRGqnGA0N0CNLdYdFzlLDuUsj0RZP5DXJ69RNwUM35tQqZW0iFmUo198zYo3uNu8nkcJVd5MRv9Dy6nPgApV83R4k7YrjmDiO7/peWZZ89KMf5cd+7Me672mteec738mHPvShz/u8//gf/2P29vb4gR/4AX77t3/7C17j7du3ef/7388v/MIv3NNr+rOw/rTjQi+FOFVdd3tVhtwJdptuYI1WjtN1KjjT5l4DUQMqK5gu5d7e2opwo4g4EmJyoSXOKGRCEIfiQlo7xW13rukGJt34/LDaZpX0OFlljf6+Jo4iRqNITLhCgdWJz4CM90VeU5FGcJKeJ/mqd7AcXeRKUhH+uRFbfceskk5VWSuSWBGGnigKcS7sppJtNzCNN+ph0BzOu0IaTCNYVAlKifO5OTMp9F5+tpUt9N5xmqeUtWE0UARBQhJ5pm7MOuxRl5rxQDXXBSfL6C5p5GUt/g0Aqzoht+KpMBgEQCpxsIi7Kc25i8MGiiXwCoGCNj4FVjEvE7QSXpXCs6oC4hB8Ku9pOymJIyQW3yPy9U8aE+BBXPhS1usRE8JEkybCR8krz63VFkY7UlNhtOMkl30VNX4XUSQ/W1TCkStKGI0C+v2AOIK8MtRWYHIzk1DUAaGBYV/gP7fyHbk3y4CwuSePyi3WYcrRuiciJ5lGKdNBXtJYdbmCc2f0+jNBNxzXW/QufTWzaJsr+5Z+JjFhXklHsKp18xgGiKgGYbN3JLb0e/L4zgl8J6gVQarI0hjVqAW1JqhtruA8uLA9VzdQ3bzS3Mm3WNcBWyMN14YMezCvepRGHmN7q3m/S80hKVWtO2n2WRFzEOyjlGdZxazrkNoqxuMQbYYkiW5EXMTT4erj59BGMxxGDSxZdTDGotKcKjFpjIJQJMor3fhGyUShFYlpcylXP8gVvpT1JRcEP/iDP8gf/uEf8ju/8zt/ogv4sR/7Md797nd3X89mMy5fvsy1/YJeX7oBtdO8fBjy/MsKMASBuPWliWZ3CwJjupu8rOH2oWM2q5hMIh6/CllkOV0qnn0lorYiXVeUMra6si/W3QeziI9eN1gLu9vS1ZuvDM++HLJaO0nY+4rJSDPuO4apkGDWlemMRVojsF7i2R46vIdP3pjw4XzC5R3LU8lHyBYv8vLWU/zWc1dYLD1Zqsga9aJ0W8aUSWjpRwVGeWZFzDwPsE54CHqoGaSec+OCWFuO1wmvHIuNehLJ+B820IAk8mRRjUdxMA15+jBgkCnecn7NOF4wLXt89nBEWStGPcvjF9YUdcBnb4TcOayIY81oYAhDuHNqOJwOG3nARpJVw8MXHeaSZrZWHE3lec/vKJ64IoVKeQY/CQ0ZuxZnVevgem6oKnkvdkaOcMuzWBvmK/mNNJbXtTL3gW7T+nNoC0vicPny5bu+/Q//4T/kH/2jf3TX9w4PD7HWvobgtr+/z9NPP/05n/J3fud3+Lmf+zk+/vGP39Ml/sIv/AKDweBLws2+Udefdlw4v1WS9atur908DvjkZwfABo4m5kAwGWt6CfQSS14qnnvZcnCQs7eX8taHoR/XnK4UrxwFjYuxNBN6meLidk0W1dyZxfzmi0Oslccb9TyLdcBzN8fkhe9wxuORYXcksIHWBVj4Qp68FNJbL3HsNkT7j9y4wmJ9hUtRzVPmQ6T+RW5sfxO/+fw1lmtJcrZHUgz3YiHURoElC0oxJrMBeS2mhWUtxjyjvuLS1YokqJmuY64fSnEQBhIHWuEFgDRyDOIS7xU3pwlPvxTRS+Hh/ZxxvGJapvzhrR3KGrb6jmt7OaU1PHcz5PDEkiaa0UCaIa8cpxyGyWs+w4fOezgv0IRnX5ZYsDOGb/oaGtlAiQMtNtp5iQkHp3cfuFEohM/JwIv7bC6f82QgkI5Vem+k4j9pTIAHceFLWX/aMeHydk5/II2/ymqevx3xsT8C0CRphtGKLNUM+6LOE4dCvi8rxYs3HcfHJZNJxCOXZCq+yJWQZy2czhTLlWI0DHjkouP8VsXNk5gPPBviPFw6H7C/ZclLzYef7TGbJ2SZZjyEYU8z7luGieyzWR6Sl5tcoXU6ngzkTPzkK2M+tPparuxbvjn9CNnqRW5ufwO/89I1lrmQinfGvjFAE3GRLKoYxrk0LMqUWR415ocaUIx6cGlrTWwqTvKUG8dSzAspt3VB9x0cJzSuy7c++emI4cDwlVdzth9acFL2ePZ2n7KG/S3LV12as65Dnn4l4eZtRxwJeTswMF8FfMZtARtYVhjAY5ctcQB3poqXblmcg4cuGr7hiQDrNctCdxyoVuHpZK5YrHQD8Qo6vlf7XnSiBk3RM8hgdT8UgAe5Qre+pILgh37oh/i1X/s1/sN/+A9cunSp+/65c+coy5LT09O7Kv/bt2+/ZvzRrs/XiemHBf0oR+HF1tqGHB6scd6jlUJpxe5uwt6WJolcR9xxXjXa4xalFKO0pB8WLPIB84VlnTuWi4p8XeFsxrXzmjSssS7i4CCnrhxJ0meQiRTfrdsF09Oc7d2M0SCkl3jGWcUkWTYku+wusi8I9r7XSHoenWpeenlFL+mRLV/EfuI/M9x7nNPZJU5OKvb2InqJKCUNkro79IfBEoWjchNm67CTTlMK+oljJ54R64JVvc9yLZ38OOQu90KALBIFE+sVt1zI6WmN0SGDaM2uu0kZXGG6zFisPMNMMYrWrHREWQdMTwt6/YhBXzZLO4YE6KUykUlj2OlXpEFNaTPKpkOQxp7d3hKPYl2HVFbj2RCXamuEaFnB6dSyWtVsb0dc3PZkUUVZaaxtSYSiDGGDe4MGwBdWDnjppZfuwgV+rvvvftd8Pue/+W/+G/7Fv/gX7Ozs3NPv/PzP/zzf+73fS5K8Npn6s7hej7jQi0rSsKI2Qqhbrg03biy7aRjA1lbChXMhcSjFwDCpMCqkqj3z0zWTScIoLRlHK9blmOXKU5SesnJUpUPpkChw9MIC6xJuvLKkLC3GDOinYrh1+07J6UnOeCvh/H4kB2xas50sxCnUJBQ2YF0GLHM6Q8JBVFI4w+2jgM98ZkbwtSNSXiT/yIcZ7T/KbPEQJ9OacD9k3BcJ0FFSEJuKxJT09RzlPXM9BKTraXTQuSe38Lt1NWG+kkbJ1gCCpiDQWrgWWVQxDFc4NC+7lJPTGqUC+mHOtjqgMBc4ngl0s5co+lFBXktcOD0pqHohaRJ1kCVRF5HkXVxQPaNU8Nd5mXA6q/EOLuwYttNlIyOYkle6I3la56lqxWzhmg6q+AyMBobtoSMJLLUNu3shCh29uBIpmHtYr3dMgAdx4XWJCWFJP8wJtKOwIesi5JXrJwBEUUAQGrZ3ewz7EWEgEJQssqy0oapgdrpmMAgZpJZRnFPZjMPSsC48hwc5p8cr3IUh4RVNPyyobMyNl2fUlWVrtEuwLSqGt24X3LkxZe/CiJ0tmbIN4opJvMB6Q+36nfiArTY+R/24pqgNhyfwwvNzeumIbPUi9cd+l9H2VWbLa0xnlnjPkERi/JeFokiYBTljfYL2FhXtsq4DqHVHRM5ixySekeicVR2xXIed8lgcOowSHwejG9MvXVM5w7oIufHyHHexzyDK2XevUAZXmS5SlivL/haMwzmBzijKmMPbS/rDmDhOUEqxWIoKG8BwGJKlmtFAMUwq+mHObD1iPq/x3tNLFBeyY2pvODZDVlVAaTV5KQTjsoL5vG7uAU0YagY9UW2MAyfKaU6BA5NIDmTDB7nCl7LuqyDw3vPDP/zD/Mqv/Aq/9Vu/xbVr1+76/yeffJIwDPnABz7A93zP9wDwzDPPcP36dZ566qn7urBZmVDlmWwgJ4nnI4/0m+uQnzEaThdgtCGJxB0vCmA0NCiVkCUC+1FEJKHj6gWBIC3zkDz3hKFisYaqTrEOLl+SA3YyUmSxIzCKh67E5PsxznuOp56TGSzWMSc96UiUjdKPKHX4RloTFoV09fcmitGgx6hneXnyFMO9x5lHE/7CW5fkNuD2FA5OpbtZVpH4A8QReRKhlWNeRN1zrMumi+kMpd0lMHKAtuQcrTxFpRqfASHhlLXh2CaiHlJDmsoI82DVp0qusqojdseO7aGiF0swsM6QxorBKGbQN01XxZPFIm0Gcui3qiEvHycddKF1ZT5dKFa5XFg74hd1JLoORRq1ZG4DiKtxUStKG3Xur97DYg15aVgv773sd8rglHnN9wCGw+EXJQrt7OxgjOH27dt3ff/zHVif/exneeGFF/hrf+2vbZ6vwVoFQcAzzzzDI4880v3fb//2b/PMM8/wy7/8y/f8mt6o6/WMC6sqwpUhtdVYD+MBPPGWQeNOLNwCraR4LUrQymCUTKm2RgaujZmMDUUdMFcpWnv2t1VjjiOyvGEobuazdUBVw5UrPZyXuJBEDqMVF85HjMeSnC7XjlWuUCokr7Y2ChhnpnSt98G0iLu4kH3ViHHfcWPnmxjtP8o82eGbnphR2JA7c8PRTDewqExkQqOaYZyilWNZxazKoFHrESjRuoDrp0MCM6CqhTPkvHTgq9ZkqDEiLOqAO3bYOCXLQasUHK17uOQ8yypid+yZDDXDtMJ5jQOyRDEaxQwHhp2Rb/xaNs2QTtHIwe3TEO9DyhomYzlqljk8dzTquqSt3nhL9gxD2N/WjdqQbyRMfaOUErAqVEcWP5waThcpxT36EPxJYwI8iAv3ul7PmLC2IbqOG5lfzaiv+IqvFCKnMcLHixrln7wEEBWtqlaMRwpjhowGRsw0dYRRIgdeW0WWZqzOp6SxYr72VLaP93DtkTEA46HIAIeB59LFhPE4JAhEdlP8URKW6WZS3vqFpPGmcTfPRWp4b6IZ9iUm3Nz5BkbbV5mm+3zttQWlDThZiXSy0YY4FJREP0nIU8kVZmXKIjed0ZdzMFtprgfbItlbGXbGvlMgKypNYHzjOu5ZVwFzF3X51tVrQ3qZ5rTIUMlVllXMuW2P29L0opLCRdTO0EsVO/s9Bn3D7pbAlrLE0MtkbyVxK+MMN04SIGGZw2QiOdR0qXhW7+M9LHPTuMxvcoUggL2dsHNlF/+FdvKqO/ly5z2zpWJdKNbLe+cAPMgVNuu+CoIf/MEf5Bd/8Rf51V/9VQaDQYf1G41GpGnKaDTiB37gB3j3u9/NZDJhOBzywz/8wzz11FP3pBpwdp0sQ6ZlKK6WGs5PHNd2hRbfqgDdmSV8+kVHUTrO74cNUQe2R4phz8hhWhjySjNOSy4MBA+2qiMKG5xRwvBsbykev2wbZ2LZrMPEcW4oRMPrxz1+/5MrFvOCJA1J0kDcg/sBaaJJEzplgrzUTJdiC35le80knjGtevzWc1c4nV3iL7x1ydtf+ldgLZ946G/yqc+mgkEMtegdp4ZRXwxINnKCsFwL3OmgdHxqXmGt59FHMp64KKTio0XcPW8UyghwkQecLBqjFSd4Ra3ghTsRSkVsDx2XRnMiXVPYsPkjBdZkS+BF24OaqOnOd7KijQ786dLw7HMlJ8dr9s/3ePiSmDhdv2m5eWMleOdeRBRpisKyXFQoDdeu9XnonIz8J8mSfrDmtBzw2YM+y3xj+e6852TqKQp3z46k8CfXFo6iiCeffJIPfOADnRyYc44PfOAD/NAP/dBrfv6JJ57gk5/85F3f++/+u/+O+XzOP//n//w1o8ef+7mf48knn+RrvuZr7vma3qjr9YwL8zygYKObvz+uGMdrnNcsqkiM9uYBL94QQh+EKCX+IfsTz/ltg1KOdSlxIQ0tVyYihdhOsha54aXb0h3f39G85YolNK7zASCGcU/4BnemEc88V5Gva46PDVGkCQLNcKBJYil4xz1LGDiWheH2aUhg4MJWzuTcglmV8ZvPX2O2eIhvemLGV7/wv6Jczcev/W0++WzaaIZLXOhlEZNhhNGest64e/qGXzRfeV6+6bDOc/ViyMP7EheOlzHzlXglqEiw+fM86DTFnd/EhZcOQ26akGHmuLi1JtI1zgvXxzkxYtrdDhj14dx4TWoqSheQ10Enq1w7xXxluH7DMp9X7O3FPHRemiXXbyv++NkCYxSDgQgNlKXrOoCPXou5vC1KJllQEumSWdnnxeMey1yKgXUhfjQHR448rynW+T3dO18OvfEHceHe1usZE1ZFSKFi8sacandoeWhXNofzghqY5wEHp2ImWNVizKU17G15Lu8Jj6aoFUUdkoSO/VGBUv4uoYHnboXM5p7JGL7qEQiM7DXrFXHgeeyiqHodzUOef1nEP+4EsneDQHUQxiiQc89ozyIPOJoJXv7yds52smBepfzOS9eYLa/xtdcWfM3st1DO8rHRX+YPPxPdNQ0d9AyT0bCREm8bhuIJYB2s1p4bd8Sb4PI5w0M7EuuOVgnTpSEKIAnFO2BRBJ2yWT+FJx7yWOe4M414+ShiMrBcm0wJtaVyhlWdUDgjMOULoUCaBiVRYClrQ1nL3lJK9v7pMuDp5ypOj3P2z2VcvSDNyVtHnqc/U4k6WywCKC0XVGm4uB+wPxZ/olGck5iSadnj+YNUVBctVLUUOrN5TVU6inX5mvvk860HucJm3VdB8DM/8zMA/KW/9Jfu+v6//Jf/kr/39/4eAP/j//g/orXme77ne+4yG7nfVdcKlKjLGKMambBmDKRAe9Vo84Ozm2pb7MRF+g9oMKrgU0Wg5dAJtcF5jVGSdNa1/L7RvjMGg2a8rhxGCVZNadU9bjvSblfb8QLu0tvXyhEqCU6LpefkpCK3AViLqkTdJE1Nx/Cva09Vi6xnKynWmgppJZW8d1CVtvNacF6h2eght9fj/KYD18qJRU1Xv6zav5V0/7zeuMC+SofYN0G1lWvtdM+bYsVahz+riUgr++ow6MYMTl7f5yLvG+Ux1N3n67z8XKjBWoUxEhhe5Sr/BdeX4/B/97vfzd/9u3+Xr/u6r+MbvuEbeO9738tyueyUBL7v+76Pixcv8p73vIckSTrCXLvaUfirvz+bzXjf+97H//A//A/3dT1v1PW6xgWr0E1HuiXya+VQDQm19e1olbo2vhyKULszvhoKUCSh7G+AQLumo97sw1JMjQLtuv9rie5tJ1ypjcqH1huvA+d9ZxDk4AwspnERVR7TWPQu13AylYJcObEIjUxNkpguNtW1qCW11+39GRd2WvlBgd3UlTvjsHrGK8VLN7OF7rW8icBA2yBrk4o0Us2+l2Kg9uIObbRv4kKL+9evijuN90NTzHsvUKWzogt17Xi1BY7WrYa6vDca0Dg0vuM9gMSwMFCdPHOrzX0v68tlQPQgLnzx9brGBKfwVqR3Wx3+VjykbR4GxnT3FzRnM3LmGwUWMSL1HuJAdcVAoJzEF2T6WFVNrGjw9s4bvENykiZPMLqJP80Gtc5j/NlYtDHis27jB6C1J9C1EHEbRcHSBihnUbYiUCKv3sYC60RWvc07zvr7KA3tthEVIfFf8c0h2nk2nb2u1rvAy/sSB46ymUDmpZCandeAxXndoAmamBBuYoJv/BC0vjsnaHM17zdTijaHqyuH0ooo2sQTpcXluH0s8TLYxIP2fWt5Y857jFbUWnXv/b2sB7nCZt03ZOiLrSRJ+Kmf+il+6qd+6ku+KID5GsJ2s9SeW0eak3m/Ud6RTnxRafZ2BELTS9oRtDgVL1ebm0asvWOMFsjRdB2xzGVEGBgYDkw34g5MyCiz9JOSsjZcP01Z5nKgPfFYilIpaSSj8jax8F6ILS/elI06GsjIMTBNJZ6fI680WarY24u4PYVPPPQ3iUxNTy/5O28/YKmG/PHhPndOpYsw6UtXMTKCw6295jBJmK001mn2dvudfODHPh0KX6IxOpIRu7iMgtiFA51r4lmb9TsnipuH/eazE+KyYPY9+1uedaF57qahKEVJpdc8Viv5FxjPww8lBI8k1FY+N4C9HcOF/ZF8Bm5jVhIFSacadTwXS/NlMSIOBlivROc8FPhXGkrCVNZSqKwW9z4G9J9jDOjV/W3yv/W3/hYHBwf8+I//OLdu3eLtb387/+7f/buOPHT9+nX0l+CG8ku/9Et47/k7f+fv3PfvvhHX6xkX1oXCNYpZFXD9IOSGGXeFs2qK/P0d+azjUO5l52V8XlZ0iYFWYF3Q8VoWuWFVSLGRJoqoceC5eRI1ssLiRFzWmoOpZp0LYe6xh0KMDohCT2Q8Dim4y0YO9PptQ21FzWyQSlF9sIg5Xu1S1kpUUvYFJvTxa39bigFd8DeffJGl6/Hs0Q6HU5Hb3B8VhNrhkIO3dpp5HpCXWqYRgxDfQCM+8Zyoiwwy1Ukir0qNbkQQxsLFbpKYjXt4VcPhVPHS7RjnYDwUMnX7vo16MqH47K20M06KGqWVZS5NnCiAhy4GRIHAM04WckAPe/C2JzI5xE2bFGhRVVMC+7o5FZysUr3u+tLYkSV0zRmggY0FrBb3dox9OWICPIgL97Jez5iwLAyRkn3rPNw4DrilGuGRyHdKY+N+m7DK15Ir6K6Q6Apsb7Be4CzrQpRzrJP/Hw03uYKIa8g5nzu4M5WcIgzg6gVx8W7PXO89eSln7zJXHJ6K91CW0J3ZR4uY6XqXqpYcIN4znKwUHxv9ZQLlCE3Nd3zNMWsb88JRn+OZop/B9qDuXIPrpvGQl6qBDyvO7wgcMC/hD18SWHQSSWzUypNXmqKBAg/OyPEv8qBLuONQzus7JwKf6aXiD9CaoMUNSfv52xHWNoZuDd2nbTIoBY9dE2SC+BtJDNsawvZYnrj9DEQIwTVKUJ7DWQAEBCbuTM4GqWeQtg2gNiYonAtYLe89tX2QK2zWn8iY7E9jtWYR//j/eUKcyonlHKzXlqKwhJFhshWQxooolJtCa99hdstKcftYNP29E81egIsXEq7sy78PpprZQjS4s0beKi9glcvBeHEPzg1zZnnEx5+F27eWXH1owJOPFfTDnNhUxKqgJuC07LOqIm6eRPz+HyyYTXMefXyLtz4sG+BkLmo5xtCpCZ0u4PCoIk0Nf+ftn2b7f/9/EFy4xB89/rd59mBMPxFd30jXpKYg1WsqH3Er3+J0FaO1mCg5r3jmuuHjH7uNMZq3ffUu1y7Ie9HyDgLjiULfVfyRseS1mKws13DnsOL5Zw4o85K9SxP2z/XoZZqr50Ux5dZpwkc+sWZ2smI8yZhsC0ng5KRkMSvYO9fjG75SsZMtuTnv85mXJdl4+KLn0miO85p5GVNUhl5csRUvUXhemG7x3CuiMhQE0t3sZ7AzsiSBZRgXjMKFkMq9waFZLOZ885979J7MRj77ux9g0O/f9X/zxYJH3vHt92VC9GC9MVb7uf6z950Sp8Ouq3U6q1nMK6LIMB6HZIkk2FsDS6A32P2iVtw80kxnFmPEOMwYRZpIDHFedSTaIFAMepu4sFw7lBLc/6RvWeSap5+rOTpc89C1Ad/w+Jp+mBPpklgVVD7iIB9zmkcczQyffq5guSy5fLnPwxcl3B5ONdO5I4k12yMh+B3NNDduVSSJ4W8++SIXPvS/wN55/vjqd/PpwzFbvYr9bEakKzRieFj6iDvrEafriDgQkq0GPvVKysc+dozWisffMubyviT8bXKTxWJS2HbetPJUVnO6DFgXcPOO5Zk/vE2+Lrjy6D5Xr2QkMWwPHb3YcjQPeeb5isW8pD+IGA0FMrRY1KxWNbu7MX/uMRFfuLkY8tkbAdZ5Lu3CzkAgPmVtcF7Riyq2ojlKeV6c7fDZGwF17Tsd9e1JwGMXK3phSRJUpCZH49FYgXPM5zz5tV/1RY3JHsSEN9dqP9f3/oqYFbbGWtO5ZTarCCPD1iggawy7JoOa0LjOobuoFTcOJSYEgWqkLkWhLG5krw9PHLNZTRwbticiR1xUdA3H0UCK/HWpuHHbslhUXLyQ8JVX1/SCgkjXRLqicBGvzEecLAJmS7h5u6QoLBcuJDx0Th6rzUvSWLEz9iSR43BqePlWTRxpvuNrjnn85gcoBzv8cfIOXjrt0U8sk3RNoC3WyZTfOsO8CMkrTRI6xqmYHT5za8DHP7kABLJ7riFDtw2RMJDnBDplNNgk6beOPJ9+5oQyr7j00BaXLghJu5d44tBzPNc892LJalUxHEaMG95Qngvkd7IV8LYrOeN4zY35kE+/LOf/lXOSbzmgshIT0qBmGInM4IvTMc/fFHhjVTmsFVW3a+dECS4yNbGp0cj7qJRnsZjzF5+89iBXuM/1JzIm+9NeQigDp0GbzQiorqFstOzLhizXjp/0mVGRMYosC1CaBmPXjstoRu7yu0HjVO29JAbOyRiyRSK1I+naaWonb5nTMj6zTpQ8jIYkDahrwcsHDfTIOumYxZE8VxRuIFDOeZZqyP6FS7jBmHFwysM7uhvTly7oRpy1D5rNAjhFycbFNEkjjNGEgeo6M+1o8uyqGzhQ3UGCRKs9jAO8F6O11lVYTIyku5emAXUdE0abqtkYRRgbgkA375uM+rSQ/bskox0PymbXrGspKKp6Uy1bC751P25w2rXTFC5CsXkRZ6EJX2x9IaLQg/Vnd50di4Ps4XZ/WuvF5bpS5KUQ5gLjCbSMklvDPmNUg+ttpwQbCI4x4pdxNi4AzX0s8AOQeNTGpMpqSh3I/2tN7QOsVxjlRXEn0lgbEkeKOLDduL4sPWEoHcY48Bs4U+1Zuh7sncdlQ7aCYx7b2UiGli5EKyMuoD7s+DyV1RR1gEImInESCnRAt27fqoMSnW0DyZRTSNpa+UbbXHVxIQg2fg61VZRWDvIo1CRJQBTq7jPRRhFFhsAIzLB2mzglfiiOUNsGeiCQhcpqVjZpPCA2cUFgmb67XoE6aSoVovBEGlTzudzTvfMgJrwpVwvfbaEy7fLOU1YCN9VakVeyT5SSJmILKWyNO6MmJhj92j2i1GYSVtVNruBeeya112KbXMGjsN5QOSPno5YGWBjpBt4oe0IgO1CWjjAwjeqPw2iBJNW1Z21jysEONkiYRKeE23UD3TXUjRuwFASSP3ivsF5R1AFaC2SoPcPbSSq89rWC7PN2chKH0lgMAjBGowPTwKdpRFREsMR78V+JY4M2Auvr3j99Bq7FBhKkfDuhtGivqJB8qnKaZZMrFJXGu8177pqY0L3nXlM7iYdaOQyvfT1f8P55EBe69YYtCFpr+yRuP9zNB5TnjqKAKFIUpcZoxagPWeZQyhEGQpBLU8PetpBsvZdxeEu4ae3DZXQmo7w2QKxLxbwIWReaMPQMR2KqM12FrEPTjKUksISBYA3DwHPhfEK5E3NuW5FFojde1QHTWc1wEJBui7RoWUUchWJB/seH+6wf/9uMg1MufPa3OPfyi7i3fh0f6387J2uR9dMMsF42hjy3dCna7vrDj47RWsZ4LWyqJRdJItTAhHLNupDANup5xn2HVoblckRRWPb2EnYnjbJJoVgVkVTwlyKsi6gqcUP1XqTEhsOQXmYoase0SChrSSS0gkB7NB6LFBbrQjNfGV4qRQe5qhoFCC08iLL2VJWoMFRKM7Uxi1JefxpIFyC3xee7XV6zvlyupA/WG2v55hCLmnF0L9MoJTCZonQUJeS5Ji80gZFO23ZfCGbeC7E9jjXDHncp5LRqFr1Mda7jUbCJC9DAF12jEpJoRuMEreBgHjMLo+4aBcPsO6jL/l5IVYXsTzzjNMc6wyukLJc1URTSix2jpBA1oSa5fvZoh+rqd7MVHHPhU7/B7gvPYb7qz/FH597FtEg7Pk/tNKerkGUu3IKTBpvvHVy+0hOiXqRYFRsTM++hDloulGK2NizWEhf6qWeYOYqxZv/CkLJ0bG1FZInEZIkLApvY2zF4L94traJSOBQJ1F4qimRzJUaIQXPSxIEj0jW1E4WkdWmYrw036gTnJa61vIwgaOQEgdJqTB2w9gGeFK08o3hNagoK7o1A+CAmvDlXVUPsIW6SeTEFlM91Pq+Zz2GZGta5+OlMBp5xT2A2znvq2ol3wEBgKmWlWJcNTEgr4tiQJOL/kyUO6wynZ4oQrTf5hNZQVp7TVUgeisFp24hUSmRAQbM1kinYuC97oraKdeE5OSkB6bxnYU0cSkOzrj0vHPXxk3cwiU65+sL/jnvhM/i3fR0fH30bp+uke66Wn+C9nL2zpTQSqxr29gSOF4UyLWyvCzZJtG+gf8en4gw+3PH0k5q8DBltJRSFZdA3Ypra/GzVPNbutkB7ilKknLvXnRrCAEprWNdR05RslM9CkVqvnGFZReSVYboKWOZx01CVIkAphda+4yjYpuFQ1IbaCnIiiypibanc/UCGHsSFdr1hCwKQTRY1V1iEirLpXFeVkP1sQ4IxRtHPFIFxOHR3g0ehYpg50sixyA3z1YbIA5KQiuyWVOJAlzy3ybcxEmCglRHTrEvRxtVaMer5RorQM+zJAdZLXMfEtxaK3IoDsfFEgRVcYaCoa8+dU01Vj3l4R3Pu5Rd56df/E1cHQ9xXfDvL3HT4+3ZpJdjkVS5BIgwV46F08MLgDOHXbYw95HWJvOBs0cAUhuKVsE51p/bRyzS9xDcBQar+KBBTIK1hvhLegXPynrQGULWV7kv7fhlNRwCSDglUtSQlx6eOuvYkifgYaBTeO5zzmy6m91grj6cVqFQeq3JfHtnRB+vP7rL2bpJqECjiWIh2eS73lmtI7EGgGA80RjuM1kKsa8Z+cSj7Ni837ppGbyYDUYP9bUltZwmBIE6/cSQXIbj7DYFfCm5HYES1rJdAHUIvtsSmpm46/SIMEBIaR2wqec4mLhxONbUd89gO7L7wHJ/51Q/xlkEft/8dLIqgaRTIJDMvpZvXNjt8w9cZDTaHWlsIWCfFgmumdhrBFs+XMsUcZhAFjjTS9PshRek611Can61rKZ6yWBKgopLveycxVWtFFNC5Onf7uJniCkHbyYFu5f2bzn3X4IgChW8SLd1OZ63CGplOVLUQGJMgJNQW6+9tXz+ICW/OZd0m8eyI6UbJxLC01LUT4Qsv+7aXaOkmt79vJdlMIoHDeW86KUuZGgppNQpF9z4wd98zWrUu4NI8cI0ZoZy5inUpcWWYeaLQiYRwIsl7FLZiCAKJydc1ZS9Eowi17SZz1nmOZwqte4TbNe6Fz/Dyv/89rgyH+OG3sy5buc67k/yy3uxZpaThAbK3OnffM937dtVWGq9tkRMHljgMSBJJyMKGRGydeAmtcokf/Uy4QTMUq7W853EskxfhDrbCJRveRmBEYMGr1qNITAuPTgUy2Ko0yetS3aS0JUW3ngXSiHSY0FO7L4/s6H9p6w1bEFzYha2xIwkt1imKKuD4RJj1+bqmrh3jrZjdie48CNoVhTId6GWirR8HtjPAKSshwxaFwFyGmeq0uE9PK5zz9NK4kQ9VTKcVx0drvM/Y3RLJwMnAErYqGw20SBQ5XjuqUs2GCgJFGoqUXi+OyFJDVUuy0E+cKP287eu5Ohzh9i/zaPQ8Fy/0OSq3uLPMOo4EiB3R9lC+OF0YDk+bJGmgNuO95n6OQ3E+dl4xWwWizqGgF9eM4jXrMmgUgTy9FPaGBbXTrIqI5cpjY0UaKzSeooTZrMZaf0ZeUcxS+lGF94p1IRtR1BsskVYM4orQeLJEE4e6K8jaBKWFacURjLKaNKiZ5RGrXB4rMDJ6XVUh97qc0rhXVfmv/vrB+rO3zu/CYOAIA+HKFKVmsagpS5GgrErLeCthf0+kc/upJVAOqx1prOj1A3qZZpDWpGGN91F3+LdqXC6ExMm+zUs4OpIO9KAncaGsFNOp5fh4zWSSMhkFhIFnHDuyaKOJ71EYpTeQl07RY7OXjRFjoMSUZFFNL4uoa88gg61eJWonX/0kbxkOUHsXeKT+FJeHGTMz4bQaNrDCGKOl4RCHUjxPl4bjmTxJGktC38GgmslFP6rwKKImWIQBDNOKUZxT1v1OFaSXKs6NS2qnuXkcMC88KQrdEPoWa8V05gR2GDbTgFQRaE8S1ORmIxEWGkeiC4yyDOIIowRyFAWaqpEabj0NAqMgFh3zQVKTBhWLMmKVS5GxDgO0iljVm+nMF1oPYsKbc13c9YxGtuu0L1YBq5XEgnxdUVeWeL/P7rYhjWGYuQZe7ImCZgIQS66QhRVVvWkqFqVnubSiINZMq/ISDg9ynIdhP2uEBgyzWcXhnQVBOIRdgSxu9S17xnXKXu1jnFU8alcLeQoCcSDOgpx+kjDoiShBP4N+YiWGvO3ruDIa43fP85h+his7PQ7dHreWg25S4JwijT17I/EkOp4HHJwInHDQU42DsAiktLzE1qkYDEVhyTLNIKmZxAtWaQOXdiJUcG68lte9ilguLd5rskTjtBQIx8cF3nuSJCCOxWsoMI44qDE6orZiMquVJ1E5lQnpRXEDzzKAEL5bNbRWRch5RZaI03wSVFiXbPgOOqB2mry8t5gAD+LC2fWGLQge3zlmNMgJlKX0IafLXYpCiMXz6Zoir5hMEq7t5fTDnHUdsyglYUxjoFHGGMQFsalZFmFTDHhmMyG+1XXI1jAkMOKO+cqLJzjv2d7eJw4cCzS3b855+dM38O4yD18ZE4WOc/0Fu+EhNSE317scr+K7igHX4F3br5WSCrkXiQNxnkSM+inWCUlxK1tjveJjvW/DPfFtPBo9z85H/jfsbMb+N3wrpv/1lC7AOhlB9sKSrXBKoGr+WF3h089bnPMkScwwk02eRlbwwMaSmJraaY6DAGuFTL2dLNnVt1mnAgcqS8dW3/FQ9gq5z3j56DynpxWDQcC4r8HIJr9za4GzjrBxgDQmox9VbCcLlOqzWMtIMjSWSFWEpiZI60bW1FANBF+9rCKWhaGqGylEJ9rHe9mcTK+o3Q43jkWJobKaVahZLe+nIHhQ9b8Z16PbJwz6knSXLuD2yZiDOyuqsmY1z6mKiv7gHBe2xaE41KLS5VEMMihHAZMhTNKcNCiwXjNdRp0yUFF6Eq+wicI4mai9/MIxALs754gDxwrNndtLXv7MLdzj57l2eUgSOS4Op5x3L2F1wCH7zKqMymhRLXEi0+vZ8BBEalCRBeJAPIxTJsOI2ir2RwU7yRyAP9r/K7i9d/FI/Smif///wp9MufrUU/QfeYo86hGoHeIgphdV7ManBKriWXORF16RhD7YFo3wJPKMsoq4US4LjcAaj8IIraQg2M0W7Kg7FNlVrA0pS8tk4Hh4cJO1Szmc7TGf12gVCPchdBSl4ebNJc56+oOIJAnQKiA0jl5Ysq4CaDhPsanp+RlWB/hY0QtDrDMUPSO+JuuIk7nuZBu9kynsTrog1TmVm7AuwwauEVLWhtWXwZjswfqzux6fHDAarAhUReFjbhyf4/R4RVXULGYrqqJia6fH1b2KUVyIp4bXWC1JcZaJgtcozklNQWnF/KsCVmvLyfEaSKmt3Henc8/1z9zBWseFC9foxRV5pTk6WHL96ZcJgqu85dqYKHCc78/Y5RaViblR7HO0Epm+VxcErdyxVjLxHMbiQJynEZPRkNqKH9AkXWO94eOjb8MPv53H9LP0f/OXiadzdr7lnZjdv0BuI4He1AGjJOd8fEDoCj6lH+az14XzN+iFnWraOM2J9Mbjp3QBWoXkeY21IdvJknP2Oqs0wfuYunbsjCyPxC+ySvo8ry5xdLimHscM+xFaix/AjRePcLWlN8pI0gjoY656ekHBiU4pCgCZkPTcDKsCXKRJTEQdBQwTSe5PliHHc4FEttOMcd8xSZYkumBVRSxWrWiCNB3XD3KFL2m9YQuCROVEKkArqa6Vki62sw5rPd4JhjYytZhqNTjidszXShC+us6TKnyzEzuIjYW6sh0pV+AC8r2qrKhr2yX4obYk9ZJKxxhtmxFWQ2x0m24gNNcSCs/BKEkHtHLd9YUNprZ0ASeNHOrFC33sbEZx55B0eUI2yglcRF5HgMFoR0yOsZWYo3TvSWsC4lF4TOOhIF4KqhulGi3a7cZVaOUasl+jg+wqjJLgYK1AeVz3Hgne0tVWSEVadRMHo6y8PnUGptS8J0ZZ0RRXoituvaJyhlxrvJFOYBDICDJQNYGqG6zgxqCs7d7e63J8jk3Of5mb/M20YlUSaUkA27jgrKOuLHVlm3sWQiOeIq3HADRwleY+M9o2+8J1kpvt+FyfuYetharcHJaBljF6XVmqosTWkt4rBaGqiMoF1kQEcd1omMsDWddOCFQXG1oCs1Ye5Zt9qiWWhNoR6YrShUyLlEURcGnYw59Mmb90QP8th6QXT1GxIzYjjI6IdE2s1rKHtcfZjcfKWX12o638UQIaCozHGE1gIFA1oS0krumwed8g9CW22U+t/nf7uN6Ljrh8Dg4b+Qbz23pE3P0ZKuT1GmUJlEG1koFKd5KCNLAnrzkTFyqMapVR6MQfanePpOIHMeFNueImVzC+xiHQEfG+cbha7kulFKER6U5sQN0U51rL+WM0HSlV/r77OZTamJw55ynysvPekZjgm/u/wtoNWiFQNVG1QnuZVJpOR3/zBO6M7r42uvEpcWhvu1wBmn2gLbUznK4T1qXh8m6feDpneeOAZH5Cur9GKU/pAionPgqRXRPavIEmqQ422UJ7A+W6pon1WjxAGo8PYyDQNUFVYgIrYgoNRzC0BUGYNLmZ5CBtruCsp65qbBOX61ByK93kJq9+D5QXLxmtxPPFYwmNbqYFMhmQ96CBkjcxwdDkKg3xug7k+qpNyP6i60Fc2Kw3bEFwq5gwD4e0WtuLNcSJOIH2BzIOylLNzWmPKEwpK5HJEi1tIa0KU77fGWbsbclhcn4nxPtNBWmddAp2L4wwWnFxF/azGXGQ8cjj24RRwO5+n7KGxdpwGmeEyb4EFGCYVMwIuXNiyAtPHGr6icjwDTK4dD4ki2FWxFRuwryIusIlMpbUFF3HwDk4KrfY/4ZvJV2egPc8/PJvYrMxz289yY3lFoU1LMwlAA5nIbGQ8YV8ZAXPPF0KWz+NPcNUNJMHmeWxK6J0sq5jbgcXWZYR45EhTTWLteIz8UOUVvCI43GEMYrpQsjJzjm2d3t4J4pEQajp94XUM696LMqQspbXcLyMsW4iZi1NELReVEKsU6zKgFWh0UrIzbtjTxpaKheypIdzIhXXGjm1ydq9LhkDvrrq/y9zDPhmWidVn6IY4lEdFrU/iEl7Ef1hirWOODHcOgk4jQJ6iSUNrZDPKplEFZXiJM+ITEztNOOeVJrBRBL42mnWpWDVw1CxtTNAacXulmI7WWB0xpWHRgBMdjJqK3rl86rHtCdW9cY7euGawok2eZ57VoWmyEKsV2QxbG/H9FJFYQPmesiyiilr1alyiaym6QjEM7PNQ089Rf8th3hbk370N+jt7LF663dxx/VZVDG130cjrqBh6Doyb1mL8klVx2gd04stg1ggSf245qHzhiSQ/XcS7FGuA8bDgDiS9+1mdY7SBlgHvcwQBgKzrK0YPm1NpPPZ7wckiWKQNUpKLmgEGOQ6ZkXM7eCckMBt2Kmi5JWRyWFuOtO5fioQqEEi0KbahyjlyWLBLreJ0lnjsi+0HsSEN+c6qUeU1QjfKPNZB5PtjLp2DMYZzjqyLOBoYcirDdG3VReLI8kbDlcZoUnIayPYfgOPXg156NKIKBBCsHOKJNbsX94BYH+iGEVL6MNDj2yhjWbvXJMr5IajaADxFVHFUjV7vQWzMmW6jClKKCMh31uvBJp3LqHfU8zLFBXtMivTTvRAVMJ0o3Yo/JsDu8vut7yTZH6CT1Iu3vkYdTrghd5XsyxHLKqYl/wVlPKcrCJ6vY260KrQDecgJTRxV5g4L1O5tz2R0Us8uY04TC6R5xG72wGDnqGoPS9xlTyPqGsYjROyLMA33MUkMeye38J738TnkNEwoKgt8yqlspo4luebFyE3ost428QLr8nrgFVpGrJ1Cx0WblYaOdKwpvZBB8UyGrw5I/5wXypDD+JCu96wBcHNk5QoT7tO0HLliUKxAR/2dUNmhZtH4P1GSst7YfnXtWe58tw+lDvj8vmAqzs5kbFC4NM1axvzyrTHMtckiWJvLyUMFefGObvcIk1GPHL5InG8RRBIkbHwcBxGwIigkdDrhzlFbcgLwTNnScgyE0zvMLUMUzng53nAbB1SNgTEwMi0IdXi5qUZ4DzcWWaY/teTjXIefvk3Wf7vv0F6bpf+ux5nXe5QVIrZSlR/itKTJKYj89ZWDurjqfAkhgNDOQyJQs9Ov2BruKJyhmmRcrRKKa1iayDBZpnDH10XyI8xYkhUlDCbW+pauqvb2/Gmq9LouOeVAWIWuaGsZDOezDXzVSwqApFvFB02Ls5FJdeZRIq9YcE4XndSq4UNsV6Txu4uxYRXOx9+oeUan9NXf+/B+rO9jlcpa5U0SloyoWobBMZs8Lm3Dy1KKSZjw2Sgm3Gy3Jt5CUfzkMAE9BPLMCkJtWUYLcjUkqXr88J0h7zURKEk/UGg2B1VbOljgrjm8vl9tB4RRXpDjC1iYjPBKEuic3p6yVKnlBWs1o68NOS1xKo09uxsadIY8joEMlZlQFG1E4rGGbwpBjQwrfocPfIU6cVT0o/+Bi/+2u8wfnif/mN/ntpeIq8MB2XU7eU4vptUXFZwPJOpx9YwwI8UYeDoRwV7WYFHUdiQtR1Q1IZRX9SUigqun/S7SV0v0xgj72PbyBiPm6ZDT3hMSSSCAkUdULsNMXu2MtRO/GXaqytqOfStawsXiY2DVPxIQi1SrRUiqZrGoiWv1b0XA/AgJrxZ18m6x5I+Rb1xyR5vSZesk+ZONCdzxXQZ3MXzM1o4KtbB7VNJh5JIkv/QOIZJSS8oKF3AaS6KWUkM+xcGGC0xYaimmNhy7dI5kmSLMBCuonMKrRKKOiAOanbiGVvBHNgBYqrKyzlYSSe8l0AUaMIAZnnEug5YNAUyNNMwL9Kioo6muLPsE+7+BdL9NRfvfAw+8bvE2xMGT17jJiMWRcDNk5jKSte8NRYF2b9FpSiqsDP76/ZzVnFxLNypvI5Y1dsUdcD20OP64vf02cNRU1jBaBSKXHnDxUoSzd45MRYcDMQLIg7FZHSGND7SWH5+tjTUdrhRJ9Sedak5XWz4hq3s6zCtGUY5qmncVBhqq2W6ckYl6b5kRx/EhW69YQsCznSEtaJjmrcE/9ZBr+2AWbuRAYtCRRSqRj1go4XfqtS0h0j79UYloNUl15QmoXRRk8A6QHgALdRH9H8V2isUGucbT4NAd1rI3kPtN1Vrq4p0ljhXe03lo0a7vHltThLjwEXYbEx6bheztYX2jkFSoXXQSf9JEvSF7/4O8tPZjZu7JMbOKhK1o1LdqHwERuRd22RLro+zKrAyPjSOOHRE4caXoZVii0JHZFxnDd9W9GEjE1c7TWHD7nPZqAhsioF2XHuvy2NeM/bz/4WOAd9My7mNFGWLez+b+MJGClA1MLl2r0XB5r6E5r5qICdKaSoXUuqYyocdzn8jYyhxoFKR6Is3++ds3HBeDmyHQvsIjxzextCpZLTqF85t4kjRFAmVPZPAO03po85noG5iQh71ULGjt7PH+OF9sv1tVkAWVZTWUFRBdyCqs3tZ0b2m9rVbD4EXp/fKmc7/Q/xV1F1dNteQ/9t92x7QSkFtzsbOjecBNDBE7YnDtqhvrqn5P5nInPE30RvIp8RPjfJeDuiOm6W664d7hww9iAlvzlVbjXlVkh/HgpVvYabtWdTCA9v9EZjNvXYG6SOJrRMd/7rxBYLNpDoI1EbSW4XUNvicSejG/0NjvRHuzFkdftXCi32nYtZC4ah1l3ALekBhncE63cEOa6fIbSQJcjog3p6g+kMUjiSscQ0SwrnXXlu72jO5heoZ3fgxNV4htt1vbvP32fepfT9EiUm+LoIm/r4qr1aN07gYpqpGEc13xX1rlHiWY3FWEME1cGNxLLfdY2qFKJOpz03Y/kLrQVzYrDdsQZBFjmFmCYNGuioUNQrb2GAXpWfYV1yYWALjOF0GTJcyRt4aOPpxTVkbpivTafK/eCdEqbBR4hHsrm462GUteELr4HAeYfQFFnnEzUPHwUHB9nbM+W1RM0pC123kVRU15jqKyUjR7xnSSDZZVWtmK8Uqb6245XnXJSzXwrA/TBJgi8oaiqpJFppDOq8jnt96kv67Hkd7R+BK3ho/wzzb4sVwn2URcDzXrNYbfH0b5NJEPBSSeHNwz9YBy2KAblyL06imqEOmC5mqDPuK7ZHvOvi1hTSBcSNfeDqH41PbcAmkQEsTzVa2ZhQuGUQJWZQ2yYRs9ihwbCUrEl2wtjGnRXbX/1uvOFmG3JlGDDLL+cGCWFcsVdRBwNrEpLw37qC8hw+q/jflKq0iU3JfeQ/jviEMmi597qkqL/fkQA6a2op5YWBgf1yRhDWVNSyLoJPLXZWx+AnoBK22Gs1uOSDjCEYjg1aKZaG4sd5jVQVMF1AUjijSpJEYDnpPp3gzdSnOK9aV6Tp/gREYQet1UjXqGWUdYHTQSYO200SjRp3PQF4qlIqFQGxGrN76XfQf+/OsgFqHPJq+yJQtrJuwyMUYrC3ug0DiT1skWSdfW6coak1exVgXd5rgWnmKWlNWsufapEmSlo2ZYxo7tILpUpPnEjsXS8fMyXRxf+RITYVJPWEQdkmYwAhFbSRQjlUgwga1VZ2ZHNBdWxo5TCqcg7wWqKFzm+Jjvby3ff0gJrw516rSGKsIA0+IZ5iJJHZtFcu1xIQkERhMqxK2LhWhgXHPkkWVFOC16fx+FutWajwhDOKue22UFLdZKlC5Ra65Fe2wquQcXa4sWWpIE8knvIfKKrw3THWP3EQsqpgogF6qugLFOs2qEGf0IJCvAyNTzbqW4iEvFXMTNtDbVsZTBDpKF/BC76sZPHmtky24mt5gGo3Jqy0p8kvx+wFpJHZGYX4zrWuNxqargEUeNNr+FqM9eaWZLhV1LVOVJKIzODVGioFRr+VBGRYLhfPy/i89mL4iCUVoIDKG7IxJWhsTImOFk6Fi1kWE0TJNFTlYxSI3nC4zerFjt7dsxBEcUQhnVclfzQH5QutBXNisN2xBEDUJaxZUogBiZcNWNRQLMb3QWglD3tTUts8yF/OLraxkK16S2witU8paczzXHJ+6RjJTobWMrLZHniDYkFas9cxWGqUSlrliOi1ZzApGo0huzKgWYowS2cOy1hS17vD6aby5Ga2D5RqmM0scK/RQdx3LovCAPFdg4g5O0xIapetluLHcYl3uMEgq3ho/w+jws4SjC0zjAUYlLPPkNe9dt0mtarrwEgTWhUj7RQHEQ0eoJXCs1o6idAz7Ab3Y4aBTRolDkQINjKeowkbT2dOqhgD0goIhpwTBoHndRsaaTpOYmlEwI7ULwmBIYUMqZ6SqB/LacDg1HE89zhsuDj2RrhrX0o1WMmw6vfeynNddh+Ps9x6sP9ur7XSFRv6RxgqjBede1wIhSmM646H5OmC2koKgH5WMowUrm1DWPcGqllo09L0UDtZKstxLWvleiRNKSaJwvIpZFZp17ihL13W42iRW3MQVy8JQNmY9cShJhMD5VNMs2BDf8kIk9QKjuu57XmpOiXBOscyleDDaEAcxRkfccX1qe4ksqng0fZHR/GXUwHMrGGO0aVR65PG7aV1bIPiNG2uL0V0V8rpHPUhCR1UL7EFIyaqD60lzQRGHjqxRMlsVUgRZ6ylK8YJIG633yNSNcIFr5FY1DgiUIwtKAlXjUcSBQI6S0JEEdVcIrQuZCPSjgMjIFKX1XKhqaYTk9+ZL9iAmvElX3SjVBc15JjKaUtQXpezpKJAmYxjIeLtuCoheXNEPc2oXkBtRtREfog20VSvZl+O+JWo62HHU/j9M85h1aVjlnrJwjbmgOpMHyD9WVUBhDWUtKlrtxLJFDhSlSKIHtQIkZrWFr2quJQx0NyEDmRrktRCIl+WIm4xIwpqr6Q1Gq9v4TBMHo0b6d+NH0C79OYoCaD2XxH8hMJ5EWYlfhTQPN74Am2ZDGkniHmrLPJSCyTfCBt57rDMEWjxXQm2JmmvyXsCRWjliUxMoSxUYwiAEWn+IisoapquI2QpAs9NTnRBDaMCqDRrifiYED+LCZr1hC4KtdM0w0gTadZt0upCba7V2VJWYDY2jBales4xjVEMMqZ1uWPYbiJB10tGra0eeNwlAzzDsCda/rGG1Ei3tRSgbcp3DalVRFDXGKLbSnEG4JtYlkSrIfcKLsx0Wa91BbLSSnaUb2VExTLKEYcAg9fQbp8OD0jUEHC2HbRMUyhqyBHphidGOwhoJSjpgnm0Rji7gtOGSuk6dRtTuYT7zosY6z9ZIFEP8mU5ga8AkHgGK5Rp8U0O0I7eidORri9EBvbjCecUq1x0fwXsxQlquNxuuHcd6T4d1rnzAtJAJQWSkcu8SAWWIVMFefIzDMK36TIuEdWVYrESmrJeF5HVIoOxdxiKtOdv9FAQN+vo133uw/myvSb9ilJZERqRsp6uQg6l0rVZrSUb1yDBJc2JToehRVBFGewobsKgzcht0I/fO9duKJHFROJJEd0VAbWGx8s0BrPBekuflyrJeVTCJ2OrX9MKSXliQ6RWFj7leT5gudWei1h5QbUewbFy/w1Ax6iviULEuYL6SyWEWi4OpwIhazpGnFwn/aVHF5JWhtIYpW6iBx6qAK707VFnIi7Nt7hxJ0d5LJfkB8WiRWOWJjMcBi7VMV9K4VRCRpCkvxBhoMjIMEpEondqgmWzobqK5WENtfZektCtovEhyF3GySnFAGlqSoG7UghQOTWwq9voLnNcsq4hZLtOCxVoSEKUURdbG9rv38P0QCB/EhDfnmgwqxr2C0IjZ3SJPODxVDZKgiQnasJWtiU2NIqNsPG3WVYD3SQeXa4vk2tLFlKJ09DJDFGhIJDFfNrlCEhvCwLAuFKt1zXpdMxoF7I0tWWQZRAVZkFO6kNuLPkfz8DXwpHY5hxhxpYpRT3gMs5VmtW66+lo67NYrytpQ1tKEHCU5gXIsqphFEeB8yDQa4zONxfDw6IB6aHh+us3RSeMj0DNksX9NcaC175oQ07k4FY96NApFEvfK0jMaBIx7tUgFLw3rhjNRW0FhnC7ETM174XPWNdS1adTTahZ1wtEqFfGQuCYLy04RzqNITMnFsesIxos8oqgVi7XwSeNQsa7lMzwLtTwLgb7X9SAubNYbtiC4rF8kNRNqHVLolLwc89LLa5HX9CI7Gj0ccHH1LNHymPVuyktGMt2iMswb4iHIYVxVMJuVlEXNcp6Tr0q2dgeMRyMCI6PF4+M1VWFZrWKyLKAoLCcHC1bzNUGwzaXkJuPFDeL5HczJHezkHDf6f5Ubd8TNc9RXhEYRIEWBdYrlyjKbFqSp4dy4YCeeUdpdPjWvqErL3m6fyDhKhLy3ymEygK1wSkzOwlxitopYFYYXw32m8YBL6jpbH/k16uMTHvmrP8i/m+5S5DV7OyPi0GHUBp+rG6nB2mmOZhFHxzXVwHBxGyJd471iPi3J85rAxGwnc2oXcKQj8gKq2rNey1TAOnGBBZopgW94BwJnyn3ES4cRVQ0Xt3U30nMYKh3TL48ZHL8IzvLS+W/kpdMBs5Xi5q2cO7cWwIgruzEeRV5veA5Vo1xU35eU2IMx4JtxXevdIMvG1D6kcBGfXmU8/Yzo9QeBwRjF1QspV4IXSYspQe8x1tWE2ipOVyEnPmqkeeXxyhpmc0dVeaanBctFyWgrYTJKCANPUSpu3VxjnWc5ihkOJFE+vL1kMVtz6XKPK/1DxvaA/vQG4eHL2PEeh72/wulMuAdxO1oPBVqkrCQUhwc5O7sJl65WbCUrrp8OefmmwzrPeBDSiyuKOuAEcTGNQ89ufEqs1tR+n4MyoqgCrJtwKxhzpXeHq5/5/8LpEeHX/R1+d7VPVVom44QkEh5PEgpMxzW46NppbteG09Oaqmc4N/GkYc2xDzk5qSgKy0MXM/bSmRT8qxHzVWPYtBKxAaM3HCPfHMRaiyR0rApuVyM+/bIQBB++qBgO843qmA8YBXN28uso73g6/Fr++E5MUXqKwlJVjmIYkMUhSWgoqzONggZv7e/x8H8QE96c6+H+Dfq9Ic5rCh/zfJ7y9DNzlIYkCQlDTXzBcDl8hbScEWQPsyi2qK3iZBFgnXhqJJFA4PJSkeeevHC88NyUg1eO2T43Jv7aHXbQTBdwcJDjrMO5FOsMq7Xn6HDN/HTN5cspj2/dZmwPGExfITx8iWr7Itf5Tj7+hyt6/ZD9vVAgNzSYfacoSsdiUZGlMZe21kziGdeDbW7cESf28zuacZpT1AGzpSEvYXfoOR8fENk1L/kr3DwRMnVebREHIx4eHfDwp/4N7vAOybd8Px+eS0w4v5cxafxcNn5JkviX1vDirYDnPjNlPEk5vx0RBzXWJRwc5OSriocujbk0OKF0IbP1mMVKnOJns4q6dgSBJgxlb7Vu0f1+QBKUDMycw3zAp54XV/a3PBQy3lp1nKrKG8bBjN3lCyhveTb5Wn7/1oh14ZlO68YfIWKYxWRxwLp8rXhC8QBe/CWtN2xBEFVronqNDxRG19LJK2pcYzMOzQ1crTH5klBVDbGv1abeOIRqmsPDOmztKPOKYlVQ5gl1LUQVaz115ahrS1VaCqMoikbX3IsnQWRzgnKJXs3w0xN02oO+TB6M2RB9nFco35KERA9Za4i1JdaFWH5b33kHdHh6t9END1SNsXJXV1WjtFQEGJXIZOD4hMVLN0nsEtjtNJFVUwyERjTWxSPA4/HN42/IvXcZozh5jaGq8ap9Hb6bcFSVa3TTW0LU5pc9MgGobcAqh6rylI2FukNh0WhvUN6h13Ooa4yylLWiqKAsHXUlzpJFHVHZVg7trE+E7/ST72VZNPZVYz/7X+gmfzOtxC6JfAoKKiW4+/VSMCNJGkEkcJmwXhMWC6K0wigZJ5eV4Iq1FsiLUq2evfhrlJWlKGrqym1G8h7KylJXjioLKUpNWTYa51YSiFjlxOWCYDWF6QkmjNADT23brvyGoNuN6JvnBEiCmtTkBGaAdb57fpkNtAT7xkdAVQSuQrMh+S3yxkgpC+H0iPLGDVK/bJ6HLgZo5Ym1JdA11htKG+AaXf+63hAw2+dsfV+UgliXHSu5toILXq/kvYqTANOoPSgNho34A7SNEZnOVvXmyHFed1CBKJ+Bs+jIdU7yZeVEz9z6BmahOglG72lip3ge3Mt6EBPenCtxK2IXUusQh3AGl4sCYxRBYLrJfWRzwmpJENXdPixrSSCjsPUj8N3UyVrI1yXL0zlJL6GqfCPr7alKyQ2qylFVhqqSZpm1Tjr5akVczAnmR7jDOwRxhssUs2neGO59buMs52QPxaYi0TmhaaF2frOPm2sU+I8ndAWhlSK7dft2XpMbTT00uMM7rK+/QmoXwH5HBA6NbPgWgqQbjkTrtZCvSvI0xPu42yXiNyDeLbEqOpxRXXuqyrGYF1RlTdqLCMNG6cn5Lj8BUEiDcrGoJe7WSXMdm5hglCVanaBsRZA61oU0JqvKNXmap6g0Wkuu0H5m/kxsuNf1IC5slvL+HqMp8DM/8zP8zM/8DC+88AIAb3vb2/jxH/9xvvM7vxOAPM/5B//gH/BLv/RLFEXBu971Ln76p3+a/f39e76g2WzGaDTigx99niQbUTfQn5dPUm4cbA5YrWEyhCsT6ULfXva4fWIweqNV25JWvYejmeHgWIy22hVFmvFA5LCKSjB8ncFFU3jEkagLjXqwPyoIG2OfQDkKG/LSccrxTOS8dkaWyDiWhWG20ncZaqUJ7I1qktByugq5cypdP+vkINZKiDlai3fB9sCite/sxo1R9DPpMu4MKx4Z3CCxS6Z6wnExpLKaWR6xzAU21IsFL5lXupH0E/jPuhB+we7IkUaCCyxqufl7sSULRa3k5aOY46knDBW9dAN1aDXCs9gThfJetsF1mWtmK9mMcSTXqrW4mQZGfAZGsQSuO4s+N45NV5i0KiWtCsk6h+VaTKbaOzRfzfjH3z9hOp0yHA6/6P3T7w/u+r/FYs5ffPLaF/z9B+v+1+sZF37rPz9P0htTNco41w9TXrltz+xVkdG9OMkJteV4nXAyl+w0DOiw/u3BsWphdE3xLveuYjyAKPDkpUh4ui4B3dzvSsNW33NunJMYKXKlwxbwyqzPyVwkBPup3P/CV9gU/yKWIMTGMHBCmGuk9lrFEdgcbv0MtgfCjWhFFITDIPtmd2h5dHyblCULBszKPrXXzIuIdakJA88gFvzuqgqZ50KsXuYifBCFMBl4ktCJqlEtL3SY1gzigtIGvHSUcDwTvkM7+Wj3rlLynrXwpLZBMl8rpgt5b0cDxTCT/29fXy+2TNIcrRwvnQ546ba8t5OhopcIn2GZqw5qVZRnixhPvprxT/5P2593Xz+ICa//ej1jwm/83nWy/qgzvHzxIOWlmxZjGs+MEIYZ7I1KAu2Y5WHn09OuVoVIKxEYaQn/s4VjtXLEsWYy1o10pvB+vIcwvFulyFrYGsC5cUFkLIkpiU1FbiM+fWfEzUNPL1Psjrycz6VmVWw4Ad4LWXd7UBEZy6oMO2EU2CS9LQdp3N+8ruNlzPFc3dXs29+yfMWWNAimbHFYDDvfj6IWI8A4EBx+WWvWpTzXyUIxmzviWHF+G/qJFUWjUpqJw0wkgUtreOko4fDUN9LnqosHpjNfFb6EUnSNmOlSc3gi5/tkrNnqN02INtYlm5jw8rTP9VuiWDTIFEm04RC1UOKqahuYdDHhJ/7eg1zhftd9TQguXbrEP/2n/5THHnsM7z2/8Au/wF//63+d3//93+dtb3sbP/IjP8L73/9+3ve+9zEajfihH/oh/sbf+Bv8x//4H+/7wv745hZpTz4IkRf0XLsg5L0kFP3/m6cJ/+8PBeQ5jLc8WyM51GorUnRy6Ev12Es88b5sgHGvphdV5HXA0TxkXSq2h46dnrj8HS5TjueGXuJ5dPuEYTDj+nKfD/1RyOlJwelxzunRnGyQ8lVvT7i0B1ni2OutxEnQDliu5bov7lq2s5xVFfLKccRyHTAZwhMXc5xXfOzTIX/4iTskacTDj44ZDzWHp55PPy8diDgWnwHwrMSugM+8qPj/THeAHb77L2m+4eX/BYzhk5f/Or97MCaOhGhpPZwuDHeaQmh3ormw45pESDNbaS5v5/y5/rOE1Zrr4eN8+nAsGMmGLzDswSN7C7KgpLAhqypEa88kXtDTS+4UO3zg4ymvvDRj91yfyxdCdAAHx46TE+nchqGQqcvSkefCzxiNHaOhZtBTvPXikv3kiFv5Dr/3bMrpadVBw0CkXE0zsbnXdVY27ez3Hqwv/3o948KLpyPSctglk0nkeeyKRik52LT2HM4C/sPHA4pCkaSeLBWJu52xwAKqM7r3RsOoL3+L4khNZXWXLG/1a67t5mjgaJVyvBDs7eXxnGG45CAf88zNlMXSc3BYcnhnQdpTPPEWzfmJIwktg7ggUI4DepzMhWx3ZbdiO12yrGNePEiZrww7I8/D+znewyeei3n6UyfEScjlKz1GA83xzPPCK+AshKEkKV0yruHOkeL3VrvALt/59QVf+8r/CtrwzOXv4BM3dogCT6ADaiOE3VtH0nmcDOHynsU6eV+WueHcVsVXbb9C6Apu2XO8PB1QNt4hIE2La7trkqDsijOjPINwTarXHBRb/Mc/Trl9e81kknB+Tzq1ixXcPry7fWetoq6lm5imnizV9DN4y/6M3eiI28Uu//m5Pscn0lFsJyvtKtb3hg94EBNev/V6xoTPHgxJV5uELgzgsau6U80KjOPONOS3P67I157RCIYDUfMZpALjk+adovZC5s1iyTUePV+RBBV5HXJ7GrMuYNTzXN2tUApWpWFdaKLQsz9YkQUlt5cD/uC5iPnSspgr5lNP2gt42xPwFVeFW5cEFqU8By5hPZVk9+KOZTtbs6oibhxHLNchO2PP1e0lHsUfvpTx7LMLwsiwt5fQyxQHJ57PXhd0Qq/n6aVyP5cNbv/oBH53vgfAX/lGxzecvB+04ZOTb+fj14dNE0Cc3edrw50TiQm7Y7i6J0Zsi1xzOAu4MCl5+9ZzRHbNy/4KL56MGh8DadKO+nB5OycNKnIbkFcBWnl20iV9s+BOMeF3/ijl5s0lk0nKub1AYsLSc+fw7FRWpi1lKVOUXs+TpWLc9ti5NbvJKQf5mD94MWU6szJVrTfVnVZQ5A9yhS9l3ddc5K/9tb/Gd33Xd/HYY4/x+OOP80/+yT+h3+/z4Q9/mOl0ys/93M/xkz/5k3zbt30bTz75JP/yX/5L/tN/+k98+MMfvu8Layvg2kLVkFc33Si/8QJooD7uDJykGxs18lytU6bRvusCiD35plsIdMo3Rm1svVXzs9B0852MBW1lcbUQi9pq3LbW5mdeh3S/m46Y26ikqGaEL/bgGmNEeUA3aiDObSAyxvCqEbynyGvyVUXldPefWZCzt+UZ9WSs2BZDZ7vsG8hCA5doCL9Oi1Noq3vcPu+rV/taW2UA24wz3Zmx4OZnJKl3roFP2WbcV7m7frZ9j89axmutGh15hTrz73tdrXLAq//c7/qpn/opHnroIZIk4R3veAe/93u/93l/9o/+6I/4nu/5Hh566CGUUrz3ve99zc/8o3/0jzavq/nzxBNP3Pd1vZHW6xkX2vuz3UstES4wvttT0JLepSvtG1jJ2bhwlozadrdfvdo90zpti4a2PN9ZQ6xOArOBtbnmee96rEYM0HWx5u7nlImA6vgNSjd7oBEI6J7L+ka143Nfb1VaityKoIJu4oJZsTeqGPfqTmP8882Fz04vWmKd9wrnNj4gRqu7XMM1m4NEXqeR0b/nbijjXRDFM3/8GQilf+3Pnl1nY0EbN809Wph/uWICPIgLX2y9rrlCkyO0ZFL9qn3ayWs295h1n3v/nCWjtvtdITCaNgdol8B3XPfY3dSQzfTLuzY/aaHHnzvOfC5S/FkIzBdbbax7tdeAeBc0MaGwVPZMTAhydseOrb5wDmXfqw5+1+ZLIsii7ooJ7R/rzuZOr92zbYxzTUwQD5FNbDz7O+2Etn1+dwYm7H0TD7/A8d96rUgsf5ArfKnrS+YQWGt53/vex3K55KmnnuKjH/0oVVXxzne+s/uZJ554gitXrvChD32Ib/zGb7yvxz9rGgKtk658yCstnTHn4eGHEqxNuhtGa5pRt77rRm5Hgs6KzwBEnaqPdbBYa6zLOm3fKBC84IunY7QeUVaay+cV5/cCqqpHWe+ilcBpiqox96kzTKMXnDVKPvOVYV30ulFgHErAOlrEeK8YDRRv++pdwkAeKwzADBRJEuMbPOGrz7utkWZvZwTAdO345OW/ThbkXFw8zSOLX6fYucIfxE9xc5piNAwHurMUn75Ks/twHvFJ+zhaedaVwWhPFCh8LBAm6+CV0x5GZ10iBXBsYozeoqg05/cUk/GkKyCsg2Ff08vS7rOUzRx1UohtEmY03J5nLMqYZWHo9xRxFDXBuYUkyeeeR/d+uzqvOjOZs9+7n/XLv/zLvPvd7+Znf/Znecc73sF73/te3vWud/HMM8+wt7f3mp9frVY8/PDD/Nf/9X/Nj/zIj3zex33b297Gb/zGb3RfB8Eblspz3+tPOy4A3WEPTVevkqnTWSWfKxdDrAs7tRBx8RbpXZEVlMOyjS/Ow9E84KgJie39uSwMdRMX2nF5XmpeOh0QmD7WKbaHnnEfdrcSimuy8cMAZitNHihKqzFKIEPt6Hy6CijqAc6JpOHWQOLC8TLGexmNP/6WseCJI3lhaawItu/GHotpo7y+XqqYjBOUgkVZ88zl7yAzK84ffpIrN36V+txVPjn+Vm7MJB4NepumyenC3JWAHC9CnraXCBpDwbbgco0+uvNwc5pgdNKZCQEcuKSBNCgmY8Ww378rLmQJZMnmuVrIU9hsgbrxmbEObi97LKuIRRkSRzDZCrom0NmVrz43HvvV68sRE+BBXLjf9acdE0IjkpftXi5rgfmB6nIF7+HalQjno67obWGwVa06OGwLCdRKNVC1RFSIvOQK4nSuOPZhl3A7r1gVilfqHlr1sA7ObSv8RAjLtc262HT7NJDrjV2TK4gbOtAQhXsdlLCNFUerBO8FKnPtWl+geQ0ct5cpetlr7/+W6zfoGc7vSV5SVJZPTr6dLMi5OvsEj996hmr/IT7R/0vcnPZQCoa9jZzq7dONIpL3cDCLKOtraO2pGs+Q1rdAKSnKbpwkKJV0jRml4GQ5QqsRZa3Y3oLRYIDSm8fNUkWWyofX5hdigBii1SYeANyZJ8zyPfJKE0ewNTJd01N+XwqO/HN1Mz/PepArbNZ9P/onP/lJnnrqKfI8p9/v8yu/8iu89a1v5eMf/zhRFDEej+/6+f39fW7duvV5H68oCoqi6L6ezWbdv8UAR+6QolIdZqwlwSQR7G85lPKNlvYGn7tusLqt2U8Y0LllztewWgvmTeA1sCoERqMUndFQbRU3j5WYoPVUxxHQjdte7TSny6DTCV/mku0GZvNcqwKKUvC2o54nCsSuvE3MewlsDxt5Lqu6YDDMpEvRWpTDWec+32Hx5ivD7352zN6W55HFr/PC/+0XufgtX8PgO9/OSzZDK8jiDdZuVWwcXo2G6VJx8yjAOxj2Ff1Uuq3eqy7g3TlpdJ6DDV6yKEUhIEkED5lGlkVuOlxzmmyKn/a6w0BgHUrButQdbON0oTma6YabACppOhtniMXAXW6UX2x9ObSFf/Inf5K///f/Pt///d8PwM/+7M/y/ve/n5//+Z/nR3/0R1/z81//9V/P13/91wN8zv9vVxAEnDt37r6u5Y2+Xs+4oNRG97/dH85vDo008uyNJC7M14bZSu4j6ySOtIe/tRtXY+9EVrcoBV7UFud1oVgXZw10ZHI5XWqqSuBG++OK0DjpmymRwTucR8xXkmzUVuAyZwuQZa5Y5sJ5SiJPEHmqWjFfyT2axsJbsk72bG3ld3uJXEfr0XF2L4eBSAwb5VmXmk/c2GFvVHHlxq/yqZ9+Hw99+1eTfc9T5OUArTxZLN23Za5YtnGhcRpdrOF4ZvDesDWAYc82xo0y1awtglduOBdpLK9vthQJ0yRW7IyEZ1RW0sxxXvZ3HLafnRy8SeQYJOLvcroKOV1I4XUyN5yqDf8jDNpYsDmsvYfwHruBXy698Qdx4d7W6xUTjPHN2SIyukUlCjxtlx6E47c/Fl7eq2NC+3eboFq3mUTOVyIPHBhFkqjOLEz8CTYuutbB8UxRVZ7RQHFuqyY0jtC0fMOAl49iDk8lvpS1FNZtYeI8LHOYLSVXGGRyxheV7vgOcQi9bd9IJcsejKNNcbEqdAfpa7dEFnsm/YrQWI6XMR+/PmR33OfxW8/wB//sf+bR73w7g7/5Dl6opNDIYpkgLnOYLkRBLGp4ErMV3DqS/TIZKSYD2zRaWpiS/I61EhPixqB1lXvyXGA/+xOJUcvccDqHystnk0btZKDN7SQmGO2ZrkOmyyYmLBTOSTwNjBRN8ntnphwO1D26l8vvPsgVuue73194y1vewsc//nGm0yn/6l/9K/7u3/27fPCDH/ySL+A973kPP/ETP/Ga7w97jjSW3doWAGVN1+kWVrx0A1oo0dkG8tlKs/277RScXa1pT0tY0696nLZbcHadHZe1coJnx41nR1sbmMLdz9k+h24MzpzfqIa0RkdKSZLQbvI2+HhNJy0aGN91EIvdq1z6i28nvHqVgZlzbTtmVsYcTEOcPcO+v8sKXDavd/I64lDUiJZ5owGuJei0lXrexOO2e++b5CqvdNdp8Q5MLAQioCt05G/5QIpKU9abKcGrm/9t0ta+d87fn5SY/RxVf/v12eQSII5j4ji+63tlWfLRj36UH/uxH+u+p7Xmne98Jx/60Ifu/UI+x/r0pz/NhQsXSJKEp556ive85z1cuXLlT/SY//9er1dcGGU1WSz4UOukGFg1BL+ykvvSZYokUo0zr9zXrbIGbPasDzZJvvWbEbc6c9CX9cb4KokgijxtQioj7qZYZ6MC4rWjl1h0Mw7bhI/29wB/BvLU/JHrksfUjXeK7bhQdMWDjPHFSdixgRkCXcPCetUl3tX5a1z7y28nvnKZiT/grfuaWZlxZ55Q1neTAAMjHdc2Lsrr9iSBeINIcr95X9umwXy1aTp0EErX6LmfhSFqTxS2eGHdwTfWlUEjcaF1YZUmjsM6RVmpLgYIgXADN8rX99Yp+JPGBHgQF+5nvW65QmrpxWKS57zCOsO68GdyBY/Sml6iu0ZC3DTV/Zk8oS3WW7iw1qohxgp0r0UslBWsC/ELGfUlIS8q3cQgObNrpzBaoHbeKBSeJPIM+6o777rY06Adyg7GK89vlOQCUUAXE87mEar5/yQUUnBrqNauV0MhZe81TY1z13jsr/45kkeu0dcLrm5nrKqQ2TqUPKvNFXSLbPAEzSTWO0ngk9A1z6lZrJqYEKmuabJc+06IIQg2zs1t0t7GhChA/Jm87H/XxL1VKUlBXuqu+dGauZ19XWUDL29J3a7J5e51PcgVNuu+C4Ioinj00UcBePLJJ/nIRz7CP//n/5y/9bf+FmVZcnp6elflf/v27S9Y4fzYj/0Y7373u7uvZ7MZly9f5tJo9v9j789jNc3u+z7wc5Znfde71b21L71zFymLoiPJjihZsg07sRggMgQrEZQECELBiexxIgwSyYBnnACCZSSWYTtQlHjGHsGZicdOPBGhoUZRIokyRVlik2ySTbK7a7tVt+727s92zpk/fs/z3qrmomqK7LSZOkCjut569/c5v/NbvgsmTZiVEbXTVI1ivvA4d6YuUeaCXo2tIksCw54kCtKVU23XzK9hQI1TSOEoP7ZWal1lLgpYLOUKjSJN1v7mXQcRhIvQ0B3ysjPTqLXNblU5Hr7gu65l04izX7eZIyuPkdG6Xj+uG41ZA1nsUAQmC8PxRN5XlsqYTCm1lhbtp7TOpIFPxt/J4AffzcDM2D16iYvzEw4uv4/Z6lmq5VkyYpVs8DgKpDHkifxbL/HkLdn67qHm4EHFeBxx7Xwgiz33TiwPjhzOBfJckyYaH2C6FHxb3QZK036nw6zBecW8sCIxWoqqSydvKN8FbAzE4bDx0jVtWvfR5SqsA7pzUCxfB5L8GutrVf2XL19+5Paf/umf5md+5mceue3w8BDn3JepXuzu7vLZz372sd/H69f73/9+/tv/9r/lueeeY39/n7/6V/8q3/3d382nPvUpBoPBH/wEb9H1ZsWFq70DTA9mdUblDAe15ehEzHLms4qydGyfy0giK513Exi0qjZSsIq0YGSFryKTR7n+ZXQvHbEkkvssS83Bg4YQAud3I0a90B7k6st4AkYFIu2INORRjeoHKm/FaKstXupGsPiNY10Qq1ZKMNUBFct0btkqEq2nGW1cGOU1VnvqJuG4Pas6vGwcQRo1JNoRGYvVYij4qdEfI/9z7497Mt4AAQAASURBVGczPGDnc/8Lm/f3qb79e/mN+LuoGitxsu0CDjJJ2LulgV5S04tKlk3Ml+aWO3cLNjdjnrkscer2YcStOxXeB4bDiDzTrZ47a8WWsuqS/MAwrfBesdKW2onLdKeuVNUia5rEimHmRHfdWSariKpWzJeKg8OapglUlcSicrV6rGv0DxsT4ElceCPrzYoJFwcnpD0oXUTlLI2LOD5pWmKqXCPDYYxWlsgq8iQw6jnZZ6U4XydRYJiLSpjsT03TkuxLi+QYsSTg86Xi7t0CpRSDZ1K2+iXLKuL+kWE2azAmYtTTNJEij8+aDJv9mo2eGKcWdSeLfsbzqxuZuvtItYWAI7KKNJL8oKj1uvDoGmlpHBhnYkwGGWUdrRto3epeJ408mwNpJv5+/scY/BvfQU8vOHfyefZWn+Bw7518snm6lSVvjU01bAwcw7SibCyLUqYV47xmGK9YNAmTueHmzSWbmylPX9Hkqef2geHObdmXW9spw77GWokHPihWlRRPxogK225f/I+OV6m4PpeKe0ciB6/02aRie+QYJLU0JxpD4xXlTDOZ+bV0ssSExzctepIrnK0/NCDJe09Zlrzvfe8jiiI++tGP8qEPfQiAz33uc9y8eZMPfOADX/XxX60Tk5sSZStWjcW1hMCqEkJQXYlRlrVqXTmCdMfkPZl1pz2x0jGrMNKJR+7/cGegS/g7st7DJNy1ikdbxa+PyqCETKcDUVudd0Q6eQ9qXWV3o8uHnzM2Eng6062ueDibMgSMkk5aWcoDoyhg2m5b9+/WKCIrWsX7k4xbLuf6VsLF+QnhtZfpb1/Fmme+jGQjxkEyYpWKuw1AxtN42VzFqqHpWxIrZkVG29abIZCmZxuoq8YbF1pZV+kGREa6pKp1JW0cFEVYdwCFLN1ao0eOopYWjeCEJWD4VpbVB9Elf9z1+qlMdxvArVu3HpES+0rX3zdrdbJ7AO9617t4//vfz9WrV/lH/+gf8eM//uNv2vv4Zq9vWlwIM7QeUOiYxks3aVV4ysIxn5UUy4osj6gau4aZJO3+kD0phMMOuuaDXhcE6075uhso4/miEB8U5yIh5T7swfFIx64jNnsSXWOUw/iElbGEYAganAa87IeHY0IXS6z2os1fn8mPdnHE6EBiHEY7tE7WhbXzYFvss1UeqxuioGiMxgfF3WmPohrwwq5h8/4+R5/4DDvXnsZuPyRsoFk3UGLj2+9C/j01DYmuqZSlrqXw6vcjYhvIbANEzGYlwUOe23U8dY/EtbAmIyfa0ShN5UIb28Wlua7DevQfW3kvqZXgYo3Ft5yw5VLUhsqioakdVXEGI/la660aE+D/GHHhmxUTMrUkaTluHZqgLMQMqyobXOOJY82qNPhw5gLuvGqn/3JeJdYRG/eQ820r9NFOCDqYivNBXMoBSElNQ20MPpjWhMvSON1C7NS6u5/aBq1Eltj7qHXdbn0P1EPTQ85IvSCTiBCgbPQj17BqUQKxbqQRYRKMFhKw5suLAq0CkZEcZ3/S49W6z9WtnPOr30Hf/AL90XmsuYFqJ5ui8qdIrSMzdesuLtOKxDQkpqbylrIMzE5X9HoRcaTIIgcYFnMZrY43Eqw9Q2g0nVeCl88WmUBmSmrtsSZZE5k7V+QkkUaotXLfPKqpvZCUaSXTq6pzRJacqX6SK3xd6w0VBD/1Uz/Fn/yTf5IrV64wm834h//wH/Jrv/ZrfOQjH2E0GvHjP/7j/ORP/iSbm5sMh0N+4id+gg984ANfF3FwUvdIo1gOSAPWmDVRxhtRnklTzTDvunmywV1QrRSWkHDSdqzdtKRksTNvTXcs7WEYHkmYReFIUTuRxCpKjw9m3UlPrCe2fr05OpnTqlZrgk1kA9pLIOlY8mkcyOOGqjHMC7tO/rsxptGq5TsEIuMwKsjkYyCJcpp0Y/TuIJcg0RECTQsdmJQJB5ffR3/7Kl5bvnP4ItVGykuLG7x002KMkKaTyLGsDJOFJB9bQ1o5NCErjTcSslRT1AGPjBKVVhjajd2c+RRERngbK6Va+JZIw2ogsQEQwxatz8aFXadjttQsi2TNjUhjMFqmPw8HtdUbIBW78BXMRtq/D4fDP1BbeHt7G2MM9+/ff+T2P6iL9UbXeDzm2Wef5Qtf+MI37Dnf7PVmxoVDdkmaAY2X31JriCPpSHuXEKeWwUBcQCMrE4JunN44uUazWG6LjPh0dMICZSVJaWe6Z1roUBQbUSjxsCgMdQOzRWC5clhrWVYGHym0rolblZHCxeK47Syr2lA3Wg5vG9p9r6ja/ZPFMpkrG8ussGuYUIfxb6x0EbNE9pTRApHYGNo1P6ojWosDsWFZR5wuo/Vhp1VgUmVU3/697Fx7GpeP+A5+i2qrz0vuBT5zMyNqY6V8L4ZJ+/itgRT1TdDkmWJjKyPPNKsqEEKE8zAYJHgvTRrvwUTCd4hsYFUqnOvgF/L9dCpvRskkppcpmli1nVJpFpwuLHUjnTCrAyZ2jPqWYkeKIVEwg2L5eHjhP2xMgCdx4XHXmxkTTpoxvUYKgiYIDzCO9dpIM/hAvx8x6qu1W3hHJC1rgRwqJWamWgWKRtxv60axKqThwEPIgNhqspbIWzcwKVMRH2gezSp1m9RHWg76yhmcl6nYspJmhm7hSV0h3DXUYuOJjWNVW+alFMPAQx4f8hq2JfMHzlTA1spHnDUztRae5Wxl1jmKUrCsIw733kl/dJ6gDe+1v0/Vy/lk9iyfnAhUp/FiFtbJtDsPehSITYPzhl6u2d7t0+tZikoDktv0+vH6/C5a/yPxJGiLFn2WH7lgcEGLqpMWSNLmWGLtw9/n6cJSNT2JpUa8lvJEMxqaNYQw+McXGoAnucLD6w0VBAcHB/zoj/4o+/v7jEYj3vWud/GRj3yE7//+7wfg537u59Ba86EPfegRs5GvZx3MczZtRB7X4jJoI6xVbZWp8UFIKtvDmsQ2FLWlqM3aaEeaRopRLvJgVaOYzBGXwTqsYTwdcdnoM1xw44T4W1YwmTYs5jXeJwzyVrIrDvSjChcUi1rgAGUtpGbnhTgX2YANAa00zkklnMcNg7ji2KWczCVBGWTiYQBdRQ557EiNdBOGWUQ1jATqYyR4pLHH6pY8WOrWZ0DUhPIEDqcR/7x4Fmue4TuHL5L+479H4j3P/PBf4ZP11bYq9+RRxaLMeXASKEtPbA2jTJKHYR5QyqDVGQFyVbSSqFpGeVUNUaQY5mIEtywNHRwrtp5YN3jlyWKN1oYsls8bgqKoBRJRNfDgRJKrjZHlxnlHFjXEJsK2HU6jJUgsk8fDCsvveBYgH77tcVccx7zvfe/jox/9KP/6v/6vy+O956Mf/Sgf/vCHH/t5/qA1n8/54he/yF/4C3/hG/acb/Z6M+PCa6cjNumRx9KhiwykqcY5RRQJJn08MuSJFO1GtyJ5Svb0fCmFd2Q8iWnQKqKsRYmoKD1VJXhjIS57ImvIUr2GKs6WEhdOT2sW8wqtMkZ9mT5GxtOzJT5o5nVCURtqp1sFM0mQ87Rp1c80RRHIEsUgqRhGSw7ckKOpGBaOB4KN7pYHenFDZBqM8gySmjBSuIe4R7ER7kDlpLC4dyTX+6AnxcXhLOU34u/Cbge+g99i9Yt/m+A9T//Ez/Dx1fMkrRpSYhpmRcSdA3EMbs5HRFviLtrP4OJehNYCFZwr+WzjcUQIgrt2TkjFo9yRxQ0TE1G1nTyrZYICuk2EhAw66rXT0FqIkc6LmEFVi3nk5e2KLGraZoN0emMrUKvl4vH29R82JsCTuPC4682MCXemAzZ1j35c41oxjCQxrYSlnNmjoWFn6IitazmAMjEsKpjO5HqsGykIVpXmdC6NgtnCs1q5Fv8uwhdJDP1BRGghbofTiKKCsvpymIpWbeIcFLMyksLBqfU1LgImZ1PxTi0nMnJ+znzM0VSKh0EO/YdiAshUA84SWCVDh7OioJ06GhUoar32GRj2BDo1XUV8snkaa27wXvv7JP/T3ycBnvqhv8Lv1HsYI2gHFxTLyrB/KLmCVhGJTWi8ZtwHLqRoJWIEi0LTNIHBKF7DKpcrj1KaNPbksaNxAt+CVuksyJ5WSmJEngbSWBohIhjTCZwEmkYxGmgub0vRNMgUSp0ZzSkFy8XjXz9PcoWz9YYKgl/4hV/4mv+epik///M/z8///M//od4UQFUraicHJ5xh5rpOfmgNRM50fwXD3xFY5T6C0cfSQnrkH6wVR9MkPpMqVKolvngZEZ6N7hS69QhYV/FtJ6Jz4jTqUT3ds9dXZ8+rWev2uxY+0438O8k+ee8txthrTNdtj870ubvnlaq9hSq1xLwOnuQdVC2uv9pISbzH1w2xWwmWr3UQdN480oF3HqpGRnG6I16GR/WQrZXfo3NVfr02cEdEDAEab+XzPsT4X49BnXQI1vd/3YRPcNWgQtflPeuGPM4SeIH6stveyPrJn/xJ/q1/69/i27/92/mO7/gO/ubf/JssFou1ksCP/uiPcvHiRf76X//rgJCLPvOZz6z//86dO/ze7/0e/X5/jaX9y3/5L/Nn/syf4erVq9y9e5ef/umfxhjDn//zf/4Nvbe30nqz40LVaCJz5nIdRwqn5ZoMIawlLDsiu3T/ZE93Y2vnFY3WaxI8SHErDrxyqgqkQDhFWgeUPhvDa6OIYvPQ88lzVt6u9zCc6Yl38Bl5Xy1Ep3UYDkHhW5nkplUPgjNpVb+GAHTPKwVLZD22hRI0TuCQjdd4FR6Jd2eiDIqqEbOgaqtP8B5XNaTN8pG44INeTy+hEw0w7fsWlZRuuZYYLbeddSi7fS1wibO/uyDfkWtjmHsIxinwRXFB7n4/78Mjh7NWoRVDaD+/DjjzePCAb0RMgCdx4XHWmxkTikow+Va3Z5eCJJGEv4OvCfdO9oPzklt06mTdfSqnUEo/QoKX69qQxLo9M+X+WarXogCdXGkcadLMypSs3fON19RePH7CQ9dxlyecKRvJBC2O5D13j+sMVl/vVdCdx43XVN6u40MHNequc+8VlTNrJMNZTOhyJmicRilN1ctJgOA9sSoZ9AxRpPA4Kmcll0I+f+2gbOR5tWbNuew8YoxRJPHZ/eHR2Pvw7Y1XrFyE89JA6VSFtQ6ocBYTunJLPBfUuhECZ0pNWj2qQvc460mucLbeskLHy0IRJoajVnbOaNgatwn42h5bKlwfIk7nipOJx5hWPjOXjfrqfYP3pu0sKZIcLm3XbKYLShdxf5axKsWS/Op5OdQ6El8SwXhgMbqrPuVwOpxa7jlLbOH8Rslmf8GsSimqVCBJtUgPaiVSo9ujQOPgwSTino/WaknGnElxNk4zKaQ7Nl1ajq0QAge5Y7tf4oJmurKsSs2qVBxN47Vk2c6m7KyHlXm69dLiBs/88F8hdisiV/JDlz7OItng05NrvHwvQ2u4sANgWJXw+Tvxmui7PWwoas1sqWk89HPo5+YRXgWI3vpkIQlFGstt908tNx/014ouHSY7ic5MnbpE//y2wm1arGnVilzcFjq0n6tN0B5/j39N5YDHXf/mv/lv8uDBA/6z/+w/4969e7znPe/hl3/5l9fkoZs3b6K7qAbcvXuXb/u2b1v//Wd/9mf52Z/9Wf7YH/tj/Nqv/RoAt2/f5s//+T/P0dEROzs7fNd3fRcf+9jH2NnZeUPv7f+oyzmR7D2eyYVmDWyP4UyKUg6JyUKIYtK5kwngsAc7Y0mWbx/F68PYWsgsXD9XsJNOWDYpd2cDTheyBy/syGHeSX3GFrauR1hj14d64xT7JxE3G4ErXd4q2BnMmNUZRZW3RHmF9/Kco15gYwAhePYnKbd9tpZK7CaBHdRpuhIZxdgaTqIIawL9pKEflzhvKOqEVamZr+B+0/GnYLOddHeqYQ83VV5yL/D0T/wMabPENiU/vPNRlr0dXlw9zxcPMiIDV/YESrko4JV7FmNEIvncqKHxirrRrbKQyI5qJbDIyIr6yIOpoW4MkZUJBcCDU8PN+/laeKArQs58SUSNBZAOoRdlmHl5Br3qCqaugXKG+f4Drp1vQEyAJ3HhrbYmc6i8xodYztw+PHulhaa1P2/deE4Xdn0+F6UkpHkKwx2ZBtw91O21rNbKOhcvr9iIZyyajFePBxzPZArfPX9Zi6xuL4XdG4HYwLIKTBaK6VJxNLE0zpLEcHHLcX60YllHPPAymawaETYwGrZHnv6uo6g1tw8jVmVElkA/O2saiuKO7EnZBwatZGI36gXGeY0PisnSUlSClnjtnsX7QL+n2BnLd1LWIqTSEY4BPpk9y1M/9FeIVYn1NT/8wovMzAYvHl7kU/dS8hSuXxDo72QBn78dtfE3cHGroXKaZSHvb9w/84XoCMp1AwenmrrWZClsj+Qz3T/RvHwrR2v57qPozFOqiykbfUkGNvodLDuwKM2a5LxuwiqJb69P8L/WepIrnK23cEEAhRMoiVaK8+cM2yOH1YHYCmRmUUYcTAxlDQ+OGg4PViSJoXcjp5d6jmeaO3cLlsuanZ2c3R1DPwtc79/j/OlLTIcXObXPczyDjX5gq19iVOC0iJktDUkU2Bsu6UUl8zrlwTwVU5Jp4PCwZjCwXNrybEanKDXmwCTSlW9EGi+KYGvg2e4tmZQpL920nJ42ZJng7uIWrhQbh/NCrJvO/Zopb4zimSuKjeGS2hsW5YDaiV760bEQ63bPxVzY9sIdWMhoLTzUjXzppuWT9VWGfc0PXfo40f/433H+hef51MV/ny++WnJ+L+HS5YrEOD57N+NLryzI84iN5yNGaYHVMcsipnFyqPdTv+5cAixLw/6RYrnyjIea7ZFs3DsHcOvm4iGbdU3WcjC6QNHhu9NIsNFFY9dBu0sQlJIpQXhdh+UPWr4dCb/+tje6PvzhD3/VsV+3cbt17dq1Nezrq61f+qVfesPv4ck6W502+OlEssKLe4Zzo7odNYsXwOkq5u6hpigD01nDbFKS5RGjpxM2ejUni4hX73gWi4aNjYjNkWaQBZ5KXmPntd9hee4G99X7mS7FC2BnWK018icLTZ4ELm0sGUQrpnXO/iRjVSoOjjz3768YbyRc3/FsqQfoeIt9kxGCWuuXWwMXNms20iWTMuOlmzEnpw1JounlGmU6PlAgBEn0Zwu5rkQeVHPtvOFcXkr30Sfio1IETk8bmiawtxtz+Zx8R6dz82U+A5+5mfHx1fMM+5of3vko81/8u2y8+3nMd/yfee12zYW9iBe2V8S64bP3hty6XZBlhp2RZZSWNEFT1EKe9LSyqAr6aUVuK06LjJv3Io5Pas7txIx68v5v3Q/cfG2OtZrtHeEiyLS2NVdLRYNcA7YlN5eNZrY0a0nTtSus6iaRjwkZ+gbFBHgSF95Kazb3TBY1i3mNtZpve3vC5eEUrfwaQXCwGnL/OGax8iyXnsWiJs8tz1yL2BlWHM5ivvRaxXxWsXc+5/KeZpg7nrZfYOO132O5+zS3zXcynUtMuDycYpTj/nLIg2lML3VcGx0zVBPu1+c4mg6ZzgOHhyVHDxaMxhkXtxI2kylW9Tm1orxXV6LMl8SK6+cqLuTHHBQbvPhyzN3bM65eH/L8NWkazgvLshQhkuNTT1F4ytJRFA1KKd7+fM7FcSWQxULO0cnM86UvTCiWFc+9fYer5wRGef80YjJ/VJr1kxPN79R7DHqGH37hRfq//PfZeO5t/G7/x/j856c89fSAt11YktqKTy42+eKXZvR6MXtbEVuZNFghpW4Ug7RhlBQoFdoYZThZxbx6B46PC65cybm8LT3/L9zUfOp375DkMRevbDAcRq0EsiA50hiGaUUn66xUYFFF3D8VqFbnMdXBpUTK/fGvnye5wtl6yxYE/RyiVJEmrXOoFiyZ1nJAGB0oa02nGZ7EQvRJUtP6CsjBOx7HZLklbUd8Za2YNAPy4SXmeoxHOlWNg8kqlgu4OXNCnpYJq0YwsHUrDxpbRa9n2rGhoggpzUNmGWcqJTKGnJYJZaMZ5Aqjo/Wh1kEaisbStO83TfSZAk+rfNBtKCElQkihHhgap8/wh92YTSactPA8gfa08qqLZIPzLzyP277IMK146lpGPxO4Ue01aQw7OylRJFX+6SqldppOE1wkRLu/n43l0gSU0q1xkIxL00Qx3kyJI82gJx2ByHSHuGqVh1r4k1OEIDjsJBLlpLpRa9+B2CqCCevO4OOshxWbHr7tyfqXeyVxILGiJNateWFbhR/Tyl3qtYNvlmq8i0lTuX/XTR4OTKteIddr1SjmakR/6wrzdAtfSlzwHibLqOUg6HUxO69iKmdY1bYVE5Bxf38Qk2cCr6l1gnPmrNvFmYpR3WjmVULVaHoZKGUfSXadZz0+t62BYqeaZI2Q/1t2RKsOJE7Gdc/gnOCS17CB8KjPQCczmjj5PMveDhvvfh598Qq9qOLqpQHDXJSZai8d/s3NGGsFljkpBB/QSaY2jW6/80BkLUaJaWMSK3o9i7Vn5kVJrBhvpFir6fX0Wrf8Ye+CuumghHrdfIhsaH0h1EN+JArWJO0/eD2JCd+aa9DXmNiSZ2btij2psrW6XQiwKCWPsEaRJBqlozWkRab5gfE4Ik2lWReCGGNOzSbpznWm2TlYyB5rHBwVvZabaNZE30nVozQRp2UmBbKBLLeMxhn9geQWHYy2m9YlsQh1WIM0xOoBq8YyHBj8xT69XOO8p3JnAhtKSVzTGvJci/qZgV56lmBqHYisIks1482MIotI005prfM1UaBFWrRDRhgj0MmZ2WDjubdRb11gbBueenrAzrglYruIJAqcO5dLTKgVJ0VOZyom+9RwEjKJEZw5GmeZYjiMsUZRthCkLNPsXt4gjgyjkcgWw0OwqDZHMi0fonNBFrTBmUcVdI0UETF43PUkLpytt2xB8OzunMEAtPK4YHj1qM+t+0IGLkuRHR2PNRd3RKUiizWb4xSQQ6+oFIMscPW5CqsD96aGm/cC03lgMh8TR2PiCIa5Z2PguH9i+NTna5zzXDifsrsp6hiffQUmpw2jccz5c4rYCuHv3IZGay+a/YtNnBeSm8nDQzAgxb0TyxdvW8ZDzXPnVwziFQ+WfV49iKlqcQpelqIFPuoFtoaBXtKwlS7QyrNqEiZtgEmsJxnKlXpxSzZMUYtD4ZqMZM8MjrQWRn/Udto+PbnGpy7++wzTimeil/m20V2ON57iU5PrnC4Nu+OaZ3cLVk3ES7dSXvqCZzw2XN0TJ+J7J5Zbd2XnbW9FjPtyUF/carDas6oNs6VE5PNbget7cqAntsRoCZ6dYkLX7ZfRpXQzemlge1AQaced0x53H0ilP+hp0kSxKt/AJucrjAF5/Mc/WW/NdX60Iu/Fawz67aOYz73aKX6FVvoSxkNNlio2hwajz+Rsj2aWPAm8/UpBZDxHi5SDU81sCZ+4c5442kOv5EDdGnrun2g+/fkS5wJXLmec3/IUleb3XjZMp548VwwHkmCPh4oLO5bICrn3QbXFshFok5j+edLI44LiYGKYzDSjgeLGbkE/Kjha9bh1GLWO4grnBe/fzwLDHIZZzU4+x6qG2keULqLxmjRyjHqS9O9tymFZNyI40B3+D/sMdN4splUqe3Elk4FeVHHJ3OK5rSNO+pf4wuwiyypha1BzaaNh1VhevhPx2S8FNkaW6xckLhxMNDfvNCgN589FbPSlQNvd9Oxuih/BZCGJ/95W4OmLcuB77/FAVWsWhRRVMkXpHOMFdhBZz0avxijPvUnK/gMJdiKioCirx9vXT2LCt+Z6/tKK0cBgtRDfXzvq8c8/G0uuUHma2jMYBDbHMBoITyhuO2ZVA4dTSy8NvPfpktg0nK5SDqeWo6nhN6aXUOoS0ano5V8557h7bPj9z8g1+PQNw5WdmkVp+NhnUw4PNb2+YnMsxcPGwJJcNa3hXy1FgzMtNO4hyLBX3D60vDjpMR5q3nG1YBAXnJY5B5P4kelYEsFwW873QSq5gtUNhYspGomNeeywJjDqwfmtmBASnId5oVtRg9a3JBafgc54UKTZHS8eXuR3+z/G2Da8MHiV77xyh+PBVT47ucyyNGz2a669p2RZR3zmVsonPxvY3IzamOC5c2R55TXJFc6di9kYSNFzdS+gLwg8+d6xxOVr5z3veUriZOMdzotASSe8sixZ+6XkifikGBXYGVYoAgfThNsHMuXovJpW1eNfP0/iwtl6yxYE42jGOK6JfUGtE26bPpNJQ1U55tOSumpQesTlXUsaeawRxQ7nFUUlY7Wo5zmXTcj0ilVzkbrWrArPgwclZdmwuZnxwlOGgW0oK8PdW6c0tWM02kVvycW4f3vG/VtH7F3ZZmM0bnHynlEmWL1VbVhVtp1UBKyGLG7IbU3pDcvCcu9egTUZ42TOjt+nTq+iVCyqGpUoFSQxjPueXtwwSlbs6PsYX3PfXuRomQHyvJH2GO2ItShuHK9ypst4XdF204k4CsRGpEXzqMJ5w8v3Mr74aslT1zK+bXQX/bv/K5vvBadusCoVF0YVF+xdpnbM71UJd26eEsKYpy5a0kiUAY4OxWwkTYVoZQ3045rMlkDOaUtU3k4btrKFBLFW5WWhEpaV5WF1thBocd4S6DJTk5oKa3KWS4/zQmLsPCced3nPl40N38gY8cl6a67clgzjJUY5ah9xK8QcH5c0jaOpPcEHtnZyNkcxSQR54kkjgeSdzC2rUgrPrXROqgtqt839kFJWcDLxFIVjMLBcOw9Z4qhqzd1bp/jGs7OTtDKacHCw4t6tEzZ2BkRXByilyfqB7YEcUiHAopYpArQeAraVHXaGZWE4eFBibco4WbKlHuDT8+wbKQg6f5LIiopXbD2jpGBbHRC5khN7jpUb4IN0N9NIYJRZ1LSwqZRFYR6JC5GBOBKfgU5lyQfNFw8yXrtdc/XSgOe2jsg+/3F4JtD4yyxLzWZespseMXN9XixH7N+aEPyQK3sWhaiNHR+vREkt62GNmDiNew2xcZwQczKTIuD8puN8f0oIimUTU3vDUlmKyq45UE0jMs26xQBEFjJbt93BhKKQuJAkktg9blx4EhO+NddmMmcrqUgoqFXMq6HHndsL6qphMStoasfe5Q22NnokEfRSTxY7Gqc4mlkWK+hngXPZKblaEMIe908tywIODmsmpwVb2xnvflYxTCterTNee/kA5xx7u9eI9xwLDLduLrj58j7nr+4wHm4QWRjkno1cstPGa4rG0Hi9zhfy2NFrzUCnc81rr0zh+pCt63N2/R1UepXbRzFFez4K7ybQTxsS69hM5uy5m9i64jC9xLLZarv0gVSJ0VpixU71pEh5MLFn3CkjDcRhWpGZei16UjnLp+6la5jQd165Q/Iv/he23v1dVM1VpkvF7rBZ5wqfKGJufvGQptnk2vkUowPzReDWl44AMGYba2J6GewMa/Ko5n7I2D+UScmNvYbrvbs4DLNmQOkitEqYrSKRIW45H9Z2gi+aXuLIbY3RjkOdsFw5vOvcpZ/kCl/vessWBDOXQzNAM8Q5Ma+6sCeVcrmTtBr4msNTxZG2a4hON5LvyK2zusdKJzROsTk2OGeACKU7G2zpXPcyuPb0JsHDxkijlKeXwjvfucHTz45pmsBk6phM4SjV5KmYbeSJJN9SDAgmvnaa4zrFBcXWCPLncnppYFL1qOwVlk3M1tBT1YqDE8WDo5ok1mhlWGWaRWVZpgla+Tap6IxJzrR1uw57FjsubxVCdp7FTBYtZCcW1Y5lZViU+Vod4cL5hH4WONp4mq33aVb5Js/EB1TDiIDi0J9jXqf4AL1BSr9vyeOGXlSxM464cWOA0nBpB7b6BZUz7E9S6kaKFqNF6O1oFnG6GIkEXCTfzaLUHE1aadZUEjOtYWsoaiFKBY5WOSHkrfxig3OB2cJRVvoNORW7r6Ac8EaIRk/WW3MVLsI0CRpogqafBa5dzXE+0DTCvdEaTmeB6RzyTJO2CiFGdwQ9Ge8vdUrR2LZTB/1c1DZMq429KA1pAjee2SaEwHio0Tjy1PPCcz2uXe1R1YHFwrFYOorScDxNhDs09PQSOZWy2LVKQgJL9EHIzeN+ShoHTsucwlxk2USMep4sVhxOFKcTRxQpyrEmizVV06fMr2K0o1zaVtdcJHwFkmM4DhIj0jhwfqPCB8XxPGK+OlP6UUpUgyYr8RmIDFw8HzHMPcf9y2w+C1Xc55n4Ls0wovQxp/WQeZ2gFOSDhF7PMkhFRnl7ZLlypY9WQkTe6heUjeX+qRAjtYZeJrF5ujQU9XgNFxRZQcXJTH6/NFHkaUuK7otsqfMCU2q84mSu1uaIxycwn2uK5eOd/k9iwrfmWjYppukT6R6N18RR4MrVfosmGNA0nigWN9vZQjHoafqZFNJGw7Ane2Na9VmZlNLJFDG2ijyNaM5HJBGAY1lb8hRuPL9LCIHRQK2n9y+8MODCxR4hBKYzx2wOk5nmME8Fdjdw5LFrmwO+hctqJiu5tnc2FPk7xvRzOKl6VPYqizphc+CoG83xTHE8CVgLyzIiiy2LNGKep1jjWRURRS0+Jp3ymIgeCHKilzoubZU4r3gwjZnMZZ+VjcD8isayKC2Nkz3YwYSOBtfYfo9nlW3xtHlAPZYYcxy2mdUZWitGW31Go5hB1jCMS85vW555+zm0Uly7aNgZ1dSN5mASU9bSwOzlEhNOFhEvh0t05mvOK2ZLzeGJGKRmmSZPBTK50W/oJTXOa45XKY1XnM5bh2cXmM8dVaUeOybAk7jw8HrLFgQP5n0mfkjVuojmseeZi9UaPwZw+yjhk59ZslpUjDZSxuOYJFHsjBX9TA7h/VneVsOBS9sN1gSGcUlmSwoXc7DIWZSajb7n/NtkhF43jsYrBv2a9+wd0venvLR6mn/60YLDexMAgg/kg5S3v2uHizugLeJEqAPHi4QHp9I9f2Zvxbn0hFmT88XDEZNFzs7Yc2k0wwfN/mGfVz73gCixLBYjBgPbGndFaKUYjwwbA8G0Teai51tWntlEug4feF/Ke/ufxyvDi+5Z9o8sSazIE8HhThaaByfynV3YgUtbFSEIfMhxnWfiA66/+iuo5Zz9t/0J/vnBUywKkW/c2s7YHGvG6YqBXZJs1OwOYqzy7MRHDIpD9tNrfOzFDb708iFXrm/y9mckAfjS7cDNV6fYyDAcJcSJpiprppOSEAKXLvdJdzVx5Lk0mjK2Ex6UW/yLV/scnzrKsqJYNfgAi0Ulms/F44sLfy33wSfrX941LxNqk66JpBu9mvOjpsW7y2GyfxLzqZeWLJc1eR6RZpY0NVy/ZNgayMG0P0lF6k8H+plMGDeyijwqWTUxd08zZivNMA9cfHsrixkaQoCNvOZtW8f0wpTPr67zz3694eTBnKZ2NI2jP8x497dtc/mcHPyDpEYrz+Ei4/6JxIW3XZhzPr7HxI/51L1tjqdSJFzcWBGC4tb9hM996j5RYtm9MKTfj1qt8gilI8ZDy6gv/ICqlv+K0nNyUuNc4L3vSHjn1l0Cis+6SxxPzbog0CowWYrPAEgSf3VbXveLswt8zl/imfguVz7zzwiLOUfv/VN8bPICi0IDgc3NjK0NzUa6ZGCX2HHDIM2x2nMpf8C4us9+fo3f+uSIV75wxI1ntnn3cxqrA1/aN7x2c4Uxiv4gIo40RdEwORW34UuXe5zbkHh/YTBlaKccVpt88taAycwznVYsFhVN7Tl+UFNVDVU5e6xr50lM+NZcDxY5szBoJYYDvcTx7qfOjEN90OwfWz792QWrRcV4M2M0iklTxYXtwChvaJzi7jRfQ3+HuSMyXq5xM2fpc25NRxzPI/pZ4P3vtq1xYU3jNYOk5rueOibTKz4/uciv/G8Fx4dzgg8EHxhu9vj2944YbNcYBVG7F0+W4jNgzZnK2bzJ+dy9AZN5xt5W4PrmBB80BydDXv7cCcZoRhspaSrpWwgJ2ih2tixbw9CqrIkX02LpePCgwDnPB97X5z0bNwkoquY69440Squ1Us/RLGL/UDbE9Quet11Y4oLic5NLvNhc4an+Ic/c+v+iljPuPf+9fOLoKZaFxpjAhYt9zp8zbGUzBmZOslOzO8qw2rOXHjKsDtnPr/LPPz3k1ZcPuXJji7c9E2NN4NZ9+Ph+jTaaPFfEsZz5J8crgg9cvjpge2TIU4kJY3PCQbXD5+8OOZ04VkVNsWoIPjCbyaS4KuaPff08iQtn6y1bEDReQSMEs85pL7XNWjVAtGYT6spRrCqyXrw2GwNRvGgCayKwjMzbEZqpyfRKrK+hdc3zpJGM1kKwNF5wfhkL8uKEzNY0taNaVYQQcI1DGzHg8EEOyg6XG9pDGsBoR8aCUsdCXFwGtoZKTLtaM5GqkOcsS0cci5FZVXnR983OMH9VLZjIYiXKAuI/kBLVK7w2okrykK66WIBDWXaddUNiHLXXnC4Nq1JRDmPUck6YnmJ9hfdnDsLaqLXaicYRqZrcglGOtJkTVzNMIu9lMVlSVeP171fVnsW8wEYWG2m8txRFQ1nIF9M08j1pBZFqiH2BVp6yChSFo659638glX9VNlTFlxu/fLX1jdIcf7LeWqtzGO08Aqz2pKYS/XFn8Vq3ogKBpnaUpRx6nX+G1Z5GCZG9aiCJFEkU2kPakeiKpuUcNE7iRhY1GBUonehkGyVxoVcck9pLgJLYUFSURYW1hsaF9RRPK8G8gkCBxA24IatnrKIeVQOLpWdzqNdxwXsoVlI8V5U0AbwLVJV0vpJYk6Vt/KhlP1WVxBDvPJAQeUmyrfGEIJ9JiM2y98qyi6USj2pvWFYJy1LTDCPCYo47OcG6cv39Czm5NXTUHqMaIi1mglZ5YkqieoWJHGXpWM5WNI1Ha4XWQbgNiwpjNdZqgoeydDRNZ87YSQ4GrG6IQoVWnqoWx9imke/BO09VNZTLirp8PMDwk5jwrbm8V2sncqUUJpFcATqPD4isxTlPVTmqylPVHtsqBFjtaZyhbn2MOslfqz2pLklZ0WjbioDIxDuPHUZ5XNCt/n8g0yt6zYTU7uJcoC4bvPM454hXgm4IKHHp1mfxoWljQmwaenpOpeM2Jjj8hibSDpB9XxU12hrK0rWfHdlfCgY9g++z9jOpaokJxVK4kT4oYrdqfYZaPwJ/5knQ5QrinqxIbUXpIpalYbpUXBlFqOWMMDlpcwWZ8BktkN7YglUOoxyRrulFGqs8qV8Sl3Nsr6Es3DpX6L7nuobFvEJp8TRw3lAWDXXLFO7gO0YHeW5XopWnrmG5lGlh8OL23NSeumqoq8efEDyJC2frLVsQ7A2EVGy1bLrjVc6dk4zGKVZVq7fv4ZlnBsBADqiWUGt0oHKKxHpGmRBep0XMg6ng9vdVilIbj7ze6dzw4FRgL0kkZJ+qiTldPgU8xXSpuXo94/ylAXGkiSIxEullZ74FIIz6XuLYaT0TDhcZs/IaPihGPccwV/SSRgiBQZOminOXNh+R5+xlsNEXec/5Sq31god9xbBvMdpiTSIQiLTmZvQsruUzDFt79l7iia1jayj65c7DqoTP3s1IY9gd11wYyUG6/7Y/gfUVkSv5/vFvM0+3+F9uP8P9+zXTnmFapvhYsagTpoWoJE2znF7vIpMi49qVlPH4WquIIF3LvR3LcLCLVoo0kd+lrBKm86wdtYqfQdUo7i1HnNgeZWM5vw2bo5jDU5Fsc06UBKLYYG3J464nygHfmmsrnzMYaKxy+KA5KXNeOxlJsb0SLopS8PSNHtAD5HqMrCTCsyJC68BGv0GrQFEbFoWQbyfLPtAHZL9lsej3TxapQGWSQJZ4TouY0+IGcIP5ynDlSuD8+QxrBL8aRbA5CC0MTqBNASHg7m22cWHZY2ZfoKlkOtlLFcOsFlOwoBgPNVee3sVazcaGdDN7mWJz4GUv1WdqO5LXKDZHhmsXc/E5yGvuub01LGFjIDCiXlKTmoatgaY5L3yFRQGfvTcksrA1qNnMS0ofc/TeP4V1JdZVfG/2Gyw2t/h18zY+ddiwWBkmVYaLNNMy5WQZCxQpv0Qv2WGyzLh8Mabfv8JoaFgWAuXa21Jsj8ePmB2WtWW5SggB8lQxWwmJMIsGVHHEsonZGEASW45PNU3tcbEhii1umFEVj0kqfhITviXX+eGM0UCSRRcM+/MBL98TmGxdd8ZhgStXZH/H0ZnMbePgZBETWc9mX3gqlTOsKsMKw8lyhxB22n0ke22y0Nw9FEzyzjgw7jVMi5iD2WXq5gqLQnHxUszuXk6aipKWCJgEFpXFts1N3Upubw7l+n0wz5kUVwkBdjccuxvQiytqbwQ3n8GlaxsorRj0xTRskKu1HHvZiCcCiPKftYrRwHLt0hijhdNzO1whIMZsmyNFFot3QWIa9CigVUTtYLKATy42SaLAZr9md9igVODe898ruUKz4nt7v8l8e5tfv/sct+9UxFHCg/GAIo6ZlClHswijA6eDjGG2x2SVceliQq9/lTwzHM8URit2NmF3e7z2dPEeVqVlNE5wLpAkmtlKCoUHyZAqiZjXKeMBJHHM6dRzehrkvqk0II2Jvtrl8mXrSVw4W2/ZgmArOmZHnZAWU5yJmeh3cfsAisJxfFywWlRcuTbk3c8E8rimqG07+lJrs5xeEthOpySqZFXv8uA0UBSByaRiMa8ZjhKuXo4ZZIHJXHH7TkEIgd3dlJ0NxXwJL39hwfHhnItXNnjHc4K/78cV/Vg0wO/PciYLva4wlQrkUUM09JSNYf/Y8uBYsTHSPHdxxSheUXtD6SJqJ1Kfu3s9okixs6nppYFzw5Jr+R2sr/lCco1P3xRb8K1RoJd4eknNVjojUg2H5YiXD8frzkY/C20HoyYyntQ6RpnIgH3+TsyXXlmws5Py7G7BBXuXo7DDPz94Cu/h+8e/jf4n/x27166y/cJfZj4tmfYiTlexFGXziPvH4nZ4P+uRRD2SKHD1XM3T5z2LynI6l67L3qZjkEjG0h38RWOZFyKX2q2qVtw5imhcRD+DvZGovxidM5kaqBzWaiELqcff5E+IQt+aa9OesqmXJPUcZ2Ie+Bd45W5guXQcHa5YLUqu3tjg21/wa7LeqhZJ27JWzFeafubZ7C2x2nGw6POg1BQlHJ80zGcV/UHMlYuiPHJ4qrh1p8B7uHwp5fyWYlFoXn6l4vhoxYWLfV64oegljn5c0rMlPmimdUrZCOnWe0VQisQ2ZIOG2mvuHCc8OLEM+4qndgv6cbkuBpqgGfUCV6/ka/Mka2BvXHFjsE8UKvbrPW6e9PG+5U7pwCBtOJdNSXTFUTXk9mSA98K/GvZESaQXlSS6FqfjLU9RG165Z7l1u2BzM+bSRsNuesRpPeRjkxcA+N7sNwj/7/8bW9cus/Oep1kta+YLy2QV47zmaB5zeCpx94FN0ToltnBh23NtL7AsxahJKbi8XXEum+KCFBRlY9aYZ+8V0yXMFlBEYE3CqoVFjHsNg1zkiRfLiKb2mJ4UYOXq8SaHT2LCt+baMYfs+EPicoazCXfDt/PylwqqyrOclzS14+KVEe94xpInTjrCbY4wX2nmK8XmQHNxOCPVJYflkNOFaP7fvS/5Rn8Qc+1SJFzAGbz8eYEO87Yxg0wxWxn+xadL7t055eKVDd7+bEI/c2xkBaN4gQuGe4sB06UhjgKJFcJ8GokaUN1o7h4bjk9ha0PzzkszxtGM0scsm5TaG3pp4NKFuG1OCAx6b7ziqeQ1Ildyi6t88XAEnHEox72GS4MTElVyWI157WSE8/LYzYEjjTzDeEViamLTkNiEsjF8/nbEF78049y5nGvvKblg73IctvnE0VN4r/je3m+i/sf/O+euXWX43P+J4wdzjFEcbCYsM8PBqeHuPdmX98cp/VxiwuVznut7iqOZ4vY9h9bwthtwbXSCC4ajoseqNqI8NpBG5rKA+cJTVgprEuZZGxP6jnEflDIslgZVe4xRYkQbnuQKX896yxYEcz8gMxafGpyO8AtFmoi1eJ5HKKXIMk1iKyLtqJR55PGdxv/KJTTaUjUGrRTGBJJE7hsnutWwlcMqy8xa67eooGoC1mqSNCKOxTGzk+rzQT8ECZDXq5xpPQFkjNiNnToYU9lYljqWkVhLCDQaermY83RkxtppipBjVLNWKek+k0fGe423BCW25GWng27l8Hee1tvArx2dA0LKyfOIKNKsmoipHTOrspYzAPN0i91rV2HrHKltuHBpQL8vzqCr2tA4GempIC7CddOx/s86dN1neNg71AW9LgpiG9bW6t5DUB0cCuoI6tZV2prAcGBpGrPWKI/M41+uT6r+b821CH0SleJii1NyPWSpRilFWcYYo8T0z9RE2lErjdDcz5bzsKwjImOoW+KdUpAkGucjkjYuNE6hNPR6Eb69gBetm7gxijSLiGPZcx23qUvqOxiDemh/aCUwhK550GmmV85QNLHsba9wQUjQqcj9r6//xmtWPsMpQ+WEayTP3b22og4WPFRO/BE6OIPW8vhlE1MpSxM0jZcYZozEPmsVq8Yyc33mddJyBmCxucXWtcvoLi5czOllmsZ5VpVdy5r6djroHY/wFeT11TpOumBo2hjZfRexCXgTSCJDHctnbpwQpnXrdwAyvR0NxXm1e87CPl5ceBITvjXXnAFpHJGZmEbHUMBwGFFVcn43jafXM8SRqGtVQRO8XKCd+aXzsGpinNaUjV37ZkSRIs/Fs0DcdhVKBwajlBAEejcvDMtSEUWa/jAjzQzWtHkCcr3X3tC00JTGQdkYrFbrPeA6WHMk1/SqibA6p/GG0huc11LEW7X2SHJeUTWGZdrHRilFEdO4DsYjn8t7ReUj0BITyvos3smERLFoEipvcV4UkHwQidBeT7xHlnWbK9QZy0L8EObb25y7dhV2dkmt4/zlEYO+5D9lLd9tmp59x51sapdfGS3xVnyJAqWPcN60eYHErCRi7W3gvfguNO7Mjyq2nUcJDPoiGBOCSJUX9smE4OtZKvxBdmlv8ppOp4xGI/4v/+CE8eaorYRhlDvyuMYFzbIylLVIT42zAqMCkzJlsrSPEEQ6eFFX7Z0pjQTS2FPWmpOZoqxEBaNz09w/Uhw8qIhizcZIVAWSSHSIBft3pvIjEl6dXq5qq29RMFJneTKNg/lSiozOGl0MhcS3wHu1diHsglT3uM65t1ND0e0BD6LQsRAlUNJEXrdqxM24aQK9XDHMz+7ffS+nMyirsB7dKwU7G4rtYUNqG3bzU/rMOPZbvHh3k8n8TBnIB3FVLiuBafUz2ZyqLZaA9SHuvGJZiDV8LxUCl5jKmVYdBU7misVS3BqHPfmMvVQmDEqxDlKL+ZR/47t3mEwmDIfDr3n9/I3/14Ss9+h9VospP/mh0dd8/JP11lzd7/qz/89TRqPh2qBr3GsYJuKcuygjykbiwigRTsppkXG6jB4hiZW1TP86CIBtn2uYC6ynqDWHE82qDGwMFDsjIRPffGC5u1+RJIbdHUMvFW+BLBazHDnY5QCrG90eNLKnfavm0yUKnaZ41cherJpAnqpWBanlI+lA487iQmdABqwlOsVz5Ox5u1VUiqKF1ndKaFWtOJ1DXQfy7Oy1tD57L8dTKMpH9/HulmJnWJHahp3klF6YchR2+PS9LaZLGOYSnz0CvZwvpUAYtp4sDyf0IPFSPAdk/8vEpsQqz7KOWNWGutFMl4plwdqQzWjpCm5klcSW1r18MZ/yZ//o3lfd109iwrfm6n7X/+v/44ThcLT22tgZOkZZuRYaaLzGak9sBFc+LeJ1rtCt7szsJm5dTOhngTz2LCvNwYmiKAObI8XehnS/X71nuX23JM0Ml89bhrkkG93+eVhNq6jO5DAf3qudMV8aB5JI8pKjqaKsAr1MMeqdNdFCWwgsCoFDKX32+KaRuNAZnkmuwPrsrxvWMEOBRcvfJ/NAWQZ6uWbcpzVYFeXEqhbFs1XhW0lP4QxsDFhPHfd6EwZ6ykmzyWfubTBdwKgvsTkExWRpmK/kebOYlqMgCk8iivCw2SBreOZGX+DeZWOoGk3VKE5miuUqrN3Nu7i90RO55y5XWM6n/Bvf8yRXeKPrLTsh+NxnT+gNIUkteR7x3rdbrmUypqvSmCacZbhdcn7WMZO1KODWnYqyaNjaTtnd1mRxYHdUMI6XTKuco2nGYukYDwznR6Jic3DS5/RExoRPX7Hsjkoar9dj7aJSrKq2u5964kikS48nYpqWppp+LlX2qBfIE8d0aTg4FDWNwShhc0PUgHY3Apu9Uir9siMTBU5PhQg0Hscid9hWx5IIiIKAGDFJUm+MdC2VEs3egwei0jPeSFDKkESwPWwYpQWnKzEdu3PzlN4gZWs7QxvF/fs182nJhUsD/sJ3zNj79EfIn34/v9tscnzSYHcsO6kEvLI2LTYTVlo6J2ks3AUQh+a6Fnfn07kUH0YrtgcSmH1QVE6hvMK5jmQcCEG3koOei/mh6M2HmDpY5uHx1ERAnJu/rOp/S5W+T9bXs77wxTlZz2KtJssMf+TtivPpAwKKIklxwRAQZRHfOvnWrVJZd8jNV4q79ypWq4bNzYSdLUNsYXtQsJXMOK363D/Omc895zYMF/unANw52ub+/ozxZs7z11P2hkuacNZRXFWGRaHWzsAge3axEjnUKFLEUSef5xmkNSeLmMMTx+lJyWiUsLMlcWHUEy5S5TTL0tA0MCsDs5kob/VycVTtEmWlpGiYLaFxgW5qYFoXVKUkibhzt2A+q9jYyri4FxFHinOjhlFaMikSPvulwP6tCfkgYXMzwxjFpw4bVsuaCxdzfvhdU8699KtkN97H77stjk8cvdQwTKX6WBZpOx1l/b2nsSePHSHA6TJitlTrrlzwkMaK1DQkpsaoQGQsq9pyNLVMpg5rFXmmsRbObzguZvcxoaFRES4Y5s3jxYUnMeFbc730qWOSvMEYTd5P+Fffn3A1voUi4JTFK0MVEuZOOu4gyTF0zt+BRaG4dbtgtazZ3ErZ3oqwBrb7JdvZjOOyz93DlMmk5sJOzI3hAQC3Hlzg9qvHbJ0b8N7nMq6MjlnUGQ+W2dpwr2vYKd125RtpAAR/dmbHkZx5G/mKo2XG/n3P4f0F27s9uBCtxQ+SKFA1UNeKZSGSx0eHK5zzjMYpo1EkaAMj0/xVDfNlWAuuxJF6pOE4X8LNm0tmpyu2d/twISVL4OKW+AidFDmf/Gzg5hcPGW31uXCxj7WK23cqjh/MOX95xI9915QLn/4Igxvv4zO8n9nCsz1W7PbnLXJiyHyl2+mBvPYwh81eSUDxpfsJt+7WGK3aSY4iiRSZlZiQWo3zhmVtOTiJOJk0xJEmy3QbvzwX8mMMDTVtTOBJrvD1rLdsQbC102M47pHnmiTWpFGDCwaPZtEk1E7jgqZ2cqEVtT6TEUyFUNtLDXEUUzfxeoQtY2jLXKWsGisqN5GMpSsnX0cUwWic0uvJ6M8HGV0fTY1U2ZUw+IXErLFGcIAbQ0XTQnwe7vB3OuFJoun1YwZ9w6AtGFal5l6dnrkHWnCJauVHg2BkK6mEs3ZSUVTiRCoTAM1QuJNrQo4xMB5HNH0rFueK9XdkdUztNOOxIYQxvZ5ha0MkCac9w7QX0e9rjv0W+dPvZ5Fs8Nz5BZe2Y+rGUTkZ6cUWBj15var9TiKryGIha6rK4r3Btd0KrUTVZf8kwZrQ4ig9RisGuUGrdrzYdjymK8tdu41pO4E+nAXWx1neB7wPX3bbk/Uv99rayugN0rZDpIhthQsGh2XRZK17r1pr9C9Ks54E9FNHHjckNsKHmLKK15MGH4Rns7IJlTOodnwfAhTuzG343N6Aft+idVhPJU7mlqqRQnxVylh/1JeJWONkX3TQmE5Fx7fQQB8E8lT3IoYDw6gvCUrVKI5m0XpPWwsZCq1sS5KWbp3A62RS0ThNWQXqOtDvaQY56y5cN7Lf3Izp9yPyTK87h41X6wbLxsgS/JBez7K1IYpNi5VhvrD0Ms1R2CG78T4W6SbP7c24sh0RQr2WfLVGdN27w39VKSKryKMKTfsdl0a6kO10sqoVdyY9MZeMpOuYWMcgt3Rwry6JmRWWW3a3VXMTBZlF+XjwgCcx4VtzjbdyeoM+caxJU4M1jlJnhKBY+Yw6WIomYlqK3v+qlGtd0AKOLHLE1sL1lLJK19MB08LsShfhvBZfglxgMbMmB6Sg2L0wYjCSGLFqEqZVwoNTQ1nBsggUhcdaxeZY00vPmnodibZTGaqcYlnH1I0iiTX9YcKgL5PIrlvfwXsBklgRgqZpybd5brFW9lscidO394qi8NS1Z9RKFWsFtZNzW2JCSq8X0evZdSOjcvK5fVBsbkY0zSajUcz5c9I8iaMEY4TcfNJsMmhjwvWdOec3IhSBeZ3gWzW4fsbadbhpoJdCbBxKBYZ5zMbItt5QEtfKGm6fZBidMsgc/ZYTOeyB9yJtblrfqWWlubfaQLUxIQRYrB5/Xz+JC2frLVsQPP9UwuamZpAKxCQxDaWPKF3EwSxdY3lnC/kxe5mw8JMIzvXnbEQT6Q9uCn5+v9jhC/dzylr0dqfWrjHxg57sglkZoZGL9erlmNhC2nW25obPfaFguahEb7x25IOE+JlBCwHyjHO3PrROZnLYLwqZJngPo4Fh0DcMe7A1kHFap8udZZYrl2KBLSSKcV8eP5nDdOaIY8V4oBnlDSFYVitxVd3ZjHnqnGju3jntcXAio7Rr58UZtajDums5W2qWhZCSru4FnroopmPjdI5WgWmZctrKo714d5PfbTZ57vyC9x7/z5jDfR68/YP8+v23UdYw6nm2Bo55YXl1H+bzhn4esZEssbpBqT61E9yjVgqXKI4m8JmX5jgXeOGFAS9cEtWgxFqqgaaqNctSxqr3jhQvv9YaJ7VOxcUyfuzrxztw7stve7L+5V7PXNX0ByKlaXUgjyqKkFK4mDvTPrOlZrGCk4nD+UAvh34uB9K53pwde0iTR1wbyTThsBiwf5IILra0NL4nhbMRbk8IcLTKMTow6nne8awcRrHxFLXlYGL5/BcLlssa7wKu8fT6MeO35Wz2HlXFWlYR05UkuN4rlpUkF6OBJktjtkdCEgT44r2M1+7UxJHm3LYhT0BnnaKQNAWKrlGQSAe+rDWLpWO1bNjaSLm+I8+1P0k5non62DOXBd63qgLTpcS9utEUtbitX78QuLJn6aeOzXSK1Z5JlTFZxTTO8+l7W/y+2+K5vRnvufOP4cE+03d/kN9afBtlrcljzyh3zArLK3cV87ljmBu24ilW1TRhh6LOHlEZOpoofv/TBU3jeefb+7ztwozEBKINTz3SlI1hthJZyLuHmhc/L0VREus2LiSPde08iQnfmuv61YyNjYReGoisJ7GOSTOkdJbDRcZ8ZVgU8OBIYsJ4qBi3qlu7vQXjaEroaZ7bVHgUR8WI/Una8gsN0ypjVVmyFJSWBsOtyRCtpOh/59tyrAEXHEernHsnls98brkmNDe1oz/KOLc15NyoekjmUzEvzHofLgtN3USUtWLQhyQRcZPtgUzfXrkfc+tORRRpdrYs/RzyVDPsi9lhaDk8cSTncy9xNC5iOq2Zz0q2Nkdc3ioAuHuSMpkLTPfpK9JpLyrNfNVyrAoNyHdw/ULg2vmUQdawlc2wyvFgPOBgU5TBPnNvg8/wfq7vzHnP5FexJ/e49+y/ym/sP0PdCMxvb1wyKyIOjmE6bRj1Y/pRgVUNbmRIoqTlaAgk6sFp4F/8/oymdrzr3Ru843JFamsubsK5kaZymlUpjdaTmeK1/RjvIY5kOlIsH0+KGJ7EhYfXW7YgyFJPP3X04mqt4d0R9upGMGfLQkbozgWsicjSlqCmGxK/woSGqJFD8dSOgLztBLLG42kFtAScEMArOTBDotbuwx3paD4rmU9X+Mbj2hlT1fSlCjaB1DpxEa2FCBt8SwJuL66obWQlUSC2ogtcVorpyZKmSXA+FjwvAQytf4K48BlzBntQSiAIde2xBnIrF7/ROSFIRZ7Fnixq8EQsCkk8mhZ3HFlxT00jRy+qGNglGoePhdC4qg33jw3HJw2XtmPM4T7lF79A/tR7hb+gFLEJpFEjjoiNuIeGEIlOsGrWvxkIZlCpQN3AdFJQrSrqpwcYFdDK441akzKLSjKesvIcH4sOe5IYotg8tpoIPCEKfauuPHb0Ekekpfi2yktc8IaqFtz8YuWZTCpxLVYJWSLXf6wbErckUoZIV3itWdoUpRJoScSVkkNGqzO8ficOEBmPTdUaD+9b7tBiXjOfrlr4XsC0ngdaiwiBVXLh1d5gtHTS/UPE+8ieYYgz00qPepjPKtLUEoJZ43qTyLckPfMIF6HbP00TRIFHQ7qOCynBy32z2JHZhhAi5upMJrhpi/csdigCg1jiglENLtI4r1lVloNTOD5xXNmO4ME+y89/kezpd58R/60ntQ2lEZhTUTQ4b4lUhQ31+ruQ9yV/Vk3g9HhJVTUUZR9FwGhPQk2kNYrAUhsaJeZrR4crvA8tqVtTrh7v8H8SE741V5ZIYt5vm4dGBRovRN5VqVmW0jicTmuaxrf+HS1JVztiVaKDx3gBsq+iFKNT2Ru0kzzkej0ryMXpOLIC4+n4c41vc5N5yXwiUB7vPFHSog+MW3exgxF1LRHmkGtRtZODDuYXWYitPMY5WC7rVhTFEhnwWs7X9bTBnUmvS4yMaBrR5lcKMiufUakU13IT89STRQ6wLArxQGmc5FlGSy5hdKAflwzMHKMcRRyzzITLeXiqmC085zci7Mk9mluvkV6f0bRTCKMgtTUrbXGudRr34s8SKRF/SCNH40TkQCmZcp4ezqmrhuVqLN4NQGIaIq0x2qw9pqpaMZk0OOdJEoO1j+9eDk/iwsPrD1UQ/Of/+X/OT/3UT/EX/+Jf5G/+zb8JQFEU/KW/9Jf4pV/6Jcqy5Ad+4Af423/7b7O7u/uGnnv/SFP6iF5qsQa2+iXjZEmsG9KthsZrDuYZZWlYrRyNC6wKIZndiUfM0uyR51ssI/IkPLTZZLzUKfRsDWrO9yYoFdhfjFmWYle+nS8Y2gU+7HBrt0eSWupKqv4othRF4GiqSWODJ8bqwHQlxDqt4dyG6PwWjeH+iaEoIU9aiVICWaoZb+ZEsaGuYbYUiNBi5XEOQvBrlaLTGRRVJJjkFk7kPJROKo0QFNZKULh3In4FZS1QBpBOaZ4IOeneiaVqLLsbEclGTaRqFnXC0SxaW5fH5yx14zh4x/fTe+q9OBPzx7c/idOWmd5g6TJqZ1DK4lxgsYLjso/VnptHObfvy66KI8FK1rXn3J5IJWapKCF1WvC10ywKzf3jzpRNcf581n4uUQ4wr1OL+Vqrc4h8/W1P1jd3fTNjAsDdY8ugiUiiiNgGsnHNwMzomQXpuYrKWfZnfcoyZrlsWq+LwLJU7C9GrNKkhQFYnBfvjo7g1o3Lu2JAazi/UXK9dxcdHLfKC9w8ykmiwPnRgp4tCWHEwV5Ory9SmE3jiWLDg5NA3Yjnx0a/ITKeRSkcA6Nhe1CzkS5ZNAl3jjPqpi1IvJV9bGA4jLFW45yM0OcrRVkZ2fttE6NpddGXZcx8JQ2EJJVuf+2NGJFp6QR6D7cPIyBaH4K6VfPxQNNoDiaaqoatYYTZcMS6YVqmHM2lAzfMoZcaQqiZvOf7yZ/5NpSr+aD7n/BRwnH/KY79FpUzWBuvuQ1z10crz63THp99xQlUsG/aJkng6o0NALbH0iQIAeZ1wqqyLEohdJZVwBrFs88KyU86reGxJwRPYsL/fuubGRcOjj2lVyRxRBzBpa2KcTIntzA6t8IFzf15H+9jlks5k6YLIbWn0YBly0ksakvjNNXa5EyK9sj4dQMxBNgYOK4OT9DKc2e+wd1jyS2ubc0ZRAsSu8WDowGzQUJZOsqiJkkjTqaBV1TWIg+ksO8mmtbAubFjKy+YVzFfWEZMZ448NVSt0l4USUzQRvbCFOERTGcN3gXS1JCmmtICGGaR4XSO8K16sShytbBorSQm1A3cPjCAaeFLkleM+4Fh1lDWhtuHltkicHHHku3URLpmUqYcnMp5POrD9lgM1/af/V6y64Lf/4Hdf4HTlqnZYu5yYitFR/CBooJplWNUxssHfV6900gjMxM4JASeen4HkOeuvaEGlpWlbGQ6cDSRxmESKy5fFPSAa3mWiX2SK3w96+suCD7+8Y/zd//u3+Vd73rXI7f/R//Rf8Q/+2f/jP/+v//vGY1GfPjDH+aHfuiH+I3f+I039Py3b6+YzFLiRDgE2Q3LuawiVhW5m2EpScc3uPNgTF0HnJMkelUqloXFGEvTCJk1hMDWWHNu5LBGNmLHSFdKOnab2ZLL5echBFbJO7nrI6wJ7ESHjGd3aHqGvXO7xLGiLD1l6VBKsVw5qtqTZwYfBF83mcNs7kgSzUav5krvgNN6yIPTIUXpqRq9VvbpZbC5JQfaqvCUlYzUDu7NaRrP1k6Prfbfj0/F0VOk/WTjNk4kFDtJL2sEz//gSJxCVSvNZ62mnxv6qWdeaG7dbTg6XFHcGLA7iMktTIuIgxOBUW2PAnnqqZzif733PFoF/vj2J8n/p/8GgNF3fT/3dt5JGUVobWkaz2zhOZjKe/3Caw1fevmwtVnPiRNLnlvO76VEkcCyytrgER5FRz6+f7+gWDVcvNTj8q58R/OVZlmAfkTM9GuvJ1X/m7++2TEB4Pbdiv6iIYk1eaa5OFIMykOMb9hpCpR39Dffzq3hHj6Igk5dB2YOXvERsY3WxH3nAuOhZmMQ1so+wFryLgTYy0+48MVfAx+Y3PizHE9zNgaKcTxnu76LGlzhcG+b6cJQViIq0DSBg4OS27caNrZSblyyZIkXzfOlqIGN0xWX1E2m6SaH0UWmS9nLRSMhObYwGMj/i0IJTKae/f0FTe3Z2MwYj2XfF4ICoGljgjFGcMJtQWB0IEuEcHzrTsVsVjIYJIzHQirOkpZjVGlu3mk4Pl5x+XKfYZaRRQ0nS/EZiCIh9Q7TitprPjaXycAH/T/jS3/t59BW89SH/wLVUx+ksDGRTdBaOqaTqocHvnjL8+Lv3CJJY3YvbTAcxgwHhqevaNI4MMrK1hzKMisijmea6SKwv19QlQ1PPdXnuUsVVnsWZURRa+bzx7t2nsSE/33WNzsu7O8vOZlEWKtJM8u5kaav58RuRW95SFQtGG+9jcPNq5xEiroOTKauPT8taWyZL2H/XklZ1pw7l7K3LSRerQOR9tQt4cZ52MlXPHX6cQieSf+Pc3Bk2NnU7MRHnDv5PNH4Be5duMzJwDCfexaLGu/h8LDk8FBkjHe2DJEVwYHZ3JFnms285Gp8i8PoHJ/3m0ynNYO+FARaS0waj600CKrAciVT9LuvHdHUDTvnNzi310MpxXyuWmRDIIo0UZSgNGtoIAi3b7EK3Lm9YjGvhN84igWKZ2AYF5z4jFdvNtz60hGLt59jd5TRizRHs4i79xrSVHPhhme3P2deJ/zm/tM0Dv7E3u8T/dLfIlKK4Q/+a7y29T5im0jz0AeWq8DxKsV7xac+u+KTv/l5kjzl4o1dRhsp43HMs9cj0tiTWGkA105zMrcsCpjOPfv7K8qi4emnh1w917TGk5ZVqVk9fqrwJC48tN7A13a25vM5P/IjP8J//V//12xsbKxvn0wm/MIv/AJ/42/8Db73e7+X973vffziL/4iv/mbv8nHPvaxN/Qaqr1qO2ttF5SQioMmcKYk1HWJOvVU0Q8PNI1Uu3XtqarwyA+sCGt9/m7krggEJW23h59fhYAODUa1mv5anb23ENZdxYdlxFp+7Po1tPLo9vEPr9cLvorebvun8/jGEfyZZKhzksT4hyjwIisW1jCGrovR3bdTIVq/Hx2+7L1a5THKtUVFWGMcgdbQSaQanbbtbxIw5YK8mZLZEq3PCJNiIa9oGo+rHc759j17Qjj7vgXCpNbkqrVMmz/7Lbv7rr+rN1C0d5//9f99s9bx8TE/8iM/wnA4ZDwe8+M//uPM/4BM5e/9vb/HH//jf5zhcIhSitPT00f+/dd+7ddQSn3F/z7+8Y9/0z7L17PejJgAoPSje98FhTMxTtt2qHymOhZeH+TbEXbTyPXhX7c3jBaI4MPSoCEo0AaMCBrI3hBYnwpn+/phmcGH36t+3abv7qtedzE/fJ13r9E9VwhdHJS9792ZtKFu4YxV2xR5+HWMOosLa48AL+om/qF9ppWM9bUKLalfkgmrPbb9fL6NTd1X6ryirDVFpfFRirYapTVqtaDfnJLbooU2emnMOEls6iasIZfeCdnx4dgs6mNWdNvb2LD+7G1s0g/BEd9II+9JTHjz15ubK8h5J+Z+Fqej9cYMtMpW7lHTqaYR3HrThIfOqbN9atqztbvm1vtUKVBnuch6T7cxwTy0j85kzxXG6EfPX9UqDekvjwl/0JJYEfCNw9Xu7NzUco43zVfIFdrP0kGkwyN779G/PywjLo9X65hg9NnzdhAo30oslzU4bVFKEYLkCj3m5LYSeFNL4q1qkRJtGo9zDu/O8oWH46gLiqrRNC3Ucv26D3X213H1dTnW46wnceFsfV0Tgv/gP/gP+NN/+k/zfd/3ffy1v/bX1rd/4hOfoK5rvu/7vm992/PPP8+VK1f4rd/6Lb7zO7/zsV9jby+h1xcCbGShbjyHqwGR6ZHZPpFpOJj3WSwdxcqhc0scC0PdmvZPq0jT1oTMQtkoGm/IYsEgg8a0vgLzOuUguwrAdC5dbuehJKVIx6xcui4ylsuG2aQgSsxaFjSNhchjdQAMTWOwVg7OpcupvCW20Ms0kaE1BRLZr5OTCmMUw6GYhlmriGKL0oooNlijwEDTCOEhBLlglZbu3mYyJwTFsUkoK4XzgTzXpKleFwfGqHUwi2xgeysiTQ2XdmAnPiJt5kyznPtZr7UOlw5+bOVzxSYw0xuMvuv7MeUCvZiw9elfJbvxbQx639Me/GGtsxzFmt4wwxgtnyFqDU0WDq0VdaNpnKiYxFFopVs1o3FCUshleTrvyFbiMF0sH79sdz7gXpctvP7v38j1Iz/yI+zv7/Mrv/Ir1HXNj/3Yj/Hv/Xv/Hv/wH/7Dr/qY5XLJD/7gD/KDP/iD/NRP/dSX/fsf/aN/lP39/Udu+0//0/+Uj370o3z7t3/7N/wz/GHWmxETAC7uRaS5XB9Gw+kq5Uv5DSLtyHsFkarZX2wwmTqWywZjInq5bg/eFgecyD5USuKC0VLY95OG1NZUznLsYopK86AYkV//bgAO5/211nkdLJXNWblECL6ljO7nswprNTs7Cb1Mk6Wisx+ZLonXxDZQuJijeJdlkwICq1OKVjVNJELn8wZtFNHQYoyoofUHMU3t6fctg54kOeLiKc8fPK1aR2AQrQgoHviU6UKghMNhtFYj6Qr5NA7004rIWs6fi8izHlf34FL+gJiSIr/EA5viW3GFZZEKdyn2RNZz1L/BUx/+C6jVgrBcMPqN/4HB2/8Ivx3/aaaTkuNexP1e3PKqGsbnRmtOVFU55nPFHWWJI00/TxjmAoFsnIhE5Jmi14uIY0PTBO5PJD4vCpmGrpaPlwU8iQlv/noz4sLOTkaaC7zUWvGuuL3cITaOYT4m7RW8Nt/l7n3HdFqtp1JKiXpXCKLYs7eb4EPCoCfTgcgGYiOwOWflNucV0yrh7sY7UAQWs4hRe/5XIWHV22bu+iwKmfhPpzWnx0uSNOLK1T5bY4EMim+PQPwiK/LBtTdM2GBRJySx7NU0OfPwaBwUxVkCnCSKNLX0RrkQlwfJeqpY11IQNE2gqqSxGEcx29kCj+JkMWJZSENkaztlvHEGuxNOgsB0FIFz52KM2ebaRcNeekjql5wOMu6PU5SCyVKkRbUSArFRSK7wg/8aplyglnPOv/g/M3zqvfxO9q9QVY7FwjFZyIQzTS3nr5/HRoa8J+aSReG4cyAN2CTWJPGZ58rGIKC1ZrFIKEsRGDiZy+RjWSqqGlbF41+jT+LC2XrDBcEv/dIv8bu/+7tfseq4d+8ecRwzHo8fuX13d5d79+59xecry5KyPFPjmE6nAGwNFYkoe61xrqerGKMDaRRhtedkblkVNXXl8JlZm2ZIUXBGAurwtnWjcBrSSLpnQXmUksBQNobDcgQITq0zFKlCTG1SKmfFJKwJFEXNYl6Qupg4Es3wOArkcdOOuxV5KrJ+AUXpI2ovEqZJLJCczj69KD3zaUmUGCkIrMJajY0MSitspM8IxbaVKnPQtcvT2NPTCwIKozdomvb2RLqlTROo6o7Y2/7oJjDui0rHZq9gUBwSVzN6vYskUW8trdo4kRbdGjjSqGHpMu7tvJO8mbL16V9l9ru/Tz/rkWbfA7RdCcc60OW9GKWVuDxbTfCB5bKFMWnQSrTF4wgS68licZ/u3udiKQGrqmTKU1WPXxB4/2h3pLvtm7FeeuklfvmXf5mPf/zj6833X/1X/xV/6k/9KX72Z3+WCxcufMXH/Yf/4X8ISHX/lVYcx+zt7a3/Xtc1/+Sf/BN+4id+Yt0Veyusb3RMgK8eFzaHnjgNa4376cowL3pENjDMMhLjOJpFLJcVVdng+8JB6jTAO3OvPAmtpLA8v1KB1NYMohUrlXCiYpyHk2WMD/L7TZZRG08CjbdUNqOqrbhs155i1TCfFmS9mH4v4/ymGBnGRozLQlCozLcSx4ZZ3VtjeqMW8tq5mZaVNB7i2LTQJ+EHpanFxYE0FdUg52HaYn+7ZRAOQqZXeIRQvSwkAenkRr2XRoE0XDy5FfGGjb7FGsX2YMW4uk9Ur+glO2id4ltzRR/EQHCUO1LbcOI3qZ/6IP3mlNFv/A/c+ZXf5nKWYc/9aYpVzXRacTzI1pPO4SjFB2loNI1nuWxomoA2iqI0FJU0CvJEmhdxpEhTveZMHculIBDLMjw2gfBJTHhz15uVK4yHmiTX6+72qoKDaYI1sMhiYuPYP4k5OpyzXFT0ehFZ2uUVLdTWQpaeJeuRDa2oiBfHc2/WucSiNNzR25KAVqIAlkSeKkSs4hGrKqGsBN9eFDXLmWSnw77i/EYtYgQtJ0Fky1UbUzTzJqd0ErPyTK8FB0Dea1l6lIY8k4ZjkhjSLKaJHFkvIs/kzot2eiBNTE/wgchA38zxGLQaURTiTTDsyzlclLBcyZcoMBrJjTYGCmtidkY1w+qQuJwzzPbo56kYrq4E1tvPYG9cktqauct5bet99JBiYPrxTzBIM5L4X6GpBc68WIliW5IYts4NpAEaGYzRlKU0dADy3JJlhiRR7G2KfLT3hl7PoNvcaLaSPyVvCRTV4+/rJ3HhbL2hguDWrVv8xb/4F/mVX/kV0jR9Iw/9quuv//W/zl/9q3/1y9+YDaRxWGt2N05Rrjqret3i42BnK6IZScfL6LODHx4duYupl2pHZZYy1mtoAUBRG4paTuWiUi0ZUXFvOWZie8yrmFEf0sQyHPSZbWdEVjHsq5agLBJljZaufNIqCokOeg8XJAnu8LqnC9O6Ggd2z/cxRtHLjXQGBgZjcryHXk+6jECrjiDL+y5QeO6V2y1BWpOmai0/BiLZ2dmhAyxL+YwS8KQrcTe9jk0aJkUmuMlW9aCDCcwLS1EbqtiwiiNyOyC78W308wHl7jV2Vc073jbAGklsfICNkSGOc7RWZIlad2Ieti7vIA+rUlHVUulvDCEMOoITremaJo7B6scnCvmvMPbrNn13kHQrSRKS5PGIiV9p/dZv/Rbj8fiRSvz7vu/70Frz27/92/y5P/fnvu7nfnj903/6Tzk6OuLHfuzHviHP941Y34yYAF89LoAk5LTXU90oSqeoGgVYrBGoydZWTD2U6YB02c5gM2eYUbWG5nitmFeJkNdcq0pkpfAQp2MRILBGYtGDVZ+5TZmVEb0M4siQZSmbm4kYaSXt/nGiFQ6sFcBAJE4X1ZlbahwJcX62NGs/kp2dBGsUvUy18UR8CEC4R2ksHcuNkSZL2ymI7iCLgYNyUzp9jSJthQy61zcRpInEzLrRnBR5Gx8DWSxY47u965jIMVlmJJE8d+eLEILIK6+MoXKGwsYsbc7gHe/nct6juf42trTjhbdviRlbGw83xpZer/fI79lNb7RS66Kh8zGonZChhwPdGjmd+UZoJZ3D2DxeXHgSE9689WbmCpGV83YNgwki9601ON/FBNjeyWg2UjbGds0Rch5c08FpOgiedJkbpzghYWlFaANEIbBxipNWArtuJFeQmDBkZjNmZUI/hyiyZGmP0TgVL6VY1LxcOHMyhzPlwFlhWdUSvyILo4HEi5O57HmlYGvTrj+zNNUsSvfxLjAaWtJWmdtohfOCVOj3WyKxhoNyEx8EqpNn8h3ZtuhIYtDtBqwbOFnFa2+RXgZlo7mbX8P2GiYrIUertqjqcqzJKmJlLLF1xDZhYTOGT7+PQdZjtfc027XjhbdviGFim31ujC39fq/9XGfcB9c+b5fPaqUQQTFD1Uju1cs0USTxUu7T5nnu8ZPgJ3HhbL2hguATn/gEBwcHvPe9713f5pzj13/91/lbf+tv8ZGPfISqqjg9PX2k8r9///4j1cvD66d+6qf4yZ/8yfXfp9Mply9fZpQ19HMxo2i85vZRzL0HTiSxWlfbC3sR77m+JDUVR6seh9Mz9YxuszkPDiHvzBd+jbOTZFsqzizxHE0Nt+8JW39rM2JjIJXvpz4XmJw23Hgq5/3PLuhHheCGCTTB8mDZZ7qSgLEqBRaTRIFhz9E4xZ0HmqNjx2BgePqSZ7tfc/s45fNfqnDOc+NaynNXpJovG0/jxNOgH9fryUVRG6wObOQrerYUCS7lxeXvZIv/3+9l+BA4f06xM5Iu/XQpQa2XsbZTny41+0eKtHUi7Mc196YpH3txTFE0XLuScvWcdDBOF2JSUjXw6n5nnmLR2qJ1j0Hve0jT7+EcNe/MP893bt3jYPQML51cpKo1e+OaQVxhtCM3AuVosNQ+ogmGSZlxsogpKsX+oWcyadjbjXnX1RX9qOCkzDmcxa3ZnASF5eLxr9WvhAPs/n758uVHbv/pn/5pfuZnfubxn/x16969e5w7d+6R26y1bG5ufs0u+Btdv/ALv8AP/MAPcOnSpW/Yc/5h1zcjJsBXjwsgB2gaeVxQzFeGw5OHJ0eKzTG843pDbBy101Koe9Wq8cghs+QsOe+KggeTiKaJSGIY9wNZ4jmeGe498Hgf2NwwbPRFsehjLyomE8/lS4Z3XK/Io4rENMS6pvGWg2Wf04VtsfYSh3qpGCE1TnH3geHwqGYwsFw7HxhljvunETfvOnwIXLtoee6S7MUOP2t1IGpFEUJgjV/eHUk8sjoQG+mq3T7t85ufER3xzbFieyTFQ1GdvZdRLp31B1PDrfutc/qmZ9xrOJhEfOzFIWXpuHwx5sK2fMdlfSb7/MpdRdOAbQnESvX57fhPYnf+JFva8fzGPn9keMJdLvPZe2NqB9d3a7Yy2cguGFwQZbhOUvponnA0bZWJWofV8VBz9VxDFjVUzrCqzNoNWqnAcv54E4InMeHNW29mrjDIA/2+wFpdgPsnhgdHbs0vBNjZ0rzveU9sHJULlI2mbloMu5MmmGklPMtamoLOwWIp0uC93HB5FwaZ43BquX3P43xgc2wZ90UB7OXXLLNZ4NLFiOcvS0wQvL2n8YbjVca8ELnMRSH7cJDBIJeYcPO+4vCwYTSyPHvFMUxr7p6kfO4V8Th55nrMU7sybaicoXEaa3wLeQyUjaNqRE41jXw73fBr+eFbpwP+t09nIrKyAbubXSNTphVRq34UAhycal69A1mmuLoX2BnWHExi/vGnB5SF49LFhMvnROBkWWiRWi3h4Bick+aM5AsJv5P9UZL4j7JVed62eYf3D465q67wqf0NmjYmbOcSE/xD8aD2Eh+OFzHHM01dw8GRwCM3x5anL9St9Lldx4ROHGIZvwHZ0Sdx4ey9vJE7f/CDH+TFF1985LYf+7Ef4/nnn+c//o//Yy5fvkwURXz0ox/lQx/6EACf+9znuHnzJh/4wAe+4nN+tYrLGnEbTrRru+4xy6Wo7JSlo6k9u+ciNuIZPT2n8paJtSin1ocFnHUM6jpQFG5NNPbO43zMzliwZ0UJJ0cFzgV6PYMaivze/q1T7t88YHPrOfpRwaY6JPIlUb2ijPssomvMCtt2EFl39xLrAc1i6Tm4twB6GKVbDXA4ORbdcvtUyna+wAfNpEwpak0/rtlKRe93VveAhMh4RtGCIady+PsKrwyvqE3u3JpKwjLeJIsdZaPXY6LIiI4wtPKEK49SGqs9mS2p6owvvXzIYrJkPL7G0+dbqVArgausBMtclg7nAk3TjhTbiuvtLwz4QLhH8sXfZ+v5jMSeJwQYJQWb8SmWmryaYlyFMzGlzXHG4sM2i0pk3laFZ3JasLUZ0Y8KxvaUJmhmUSQqCVaSHd18Yzb5rVu3GA6Hj1yDX2n9J//Jf8J/8V/8F1/zdV566aXHfk9/mHX79m0+8pGP8I/+0T96U17vcdc3IybAV48LXcfImoBqC/+y8ngXWmKgZzxMGScrMluybFJmKkE7jdF6/RwPE2S7Sdh05lmtHP2+ZdQDq9vielrjXGDYwl6ch4P7C+7fOqI/uExma0bRgr6akdVTKpszsRk+RIL7LVspvOhMxWG+ED19yOC8IdLi4zGb1YQQiK1hM13QeM1MpRSNJbUNvahCK0/lLWUjjsmZqYlNg1GORAmk4jZ97t+XGDPs94mj0OqCq9bpOJDFDT4o6sZwfFLT61l2N8VBtKgiXvnCEcvZin7/Ctf2JOnwQbrxq0pMx4qiQSnhIzjnmU5KilXNC2/f4o8MT9jY/xTVpYzIjgDFKCnZsYcANCqSQgBLHSwuGJaVJBJwFrOHA80gqejZkmUTE0KMD0JwNDqg6sfzJ3kSE9689abmClpc7ztvnxBMe9Z7qkqagJtjy0a6IrcF0zoHuucxjxBptRJ337ISuM3pacV8VtFsZVzeta0EKUwmFU3j6eUGpUQh7OD+kgf7pwwGF+hHFcN4TqZWJG5JGecs6/OcOnE1lwlEIE8URoFTsFh4Du7NUGpAYhX9qABSTo+LVpAjZpys8ChWTUztNIlt1k3CWZ0xJcHoQC+qSExNrBsGZobCc1cP2N9fEDyMBgPS2LeeKPL+YxvIY4fzirrWHB8XInN6wZBHNVUT82qbK/T6V7m+JzlG0UkgN6KQWJbCWXAt6bdqZdpfePsG3zk8Znz305RXcmI7Zh0TzAMAnLJ4NC4Y6hDJn27IfCWyx0XhmU4rBj1DLxaI51ylNC4hBEVkpRAifgOeRU/iwnq9oYJgMBjwjne845Hber0eW1tb69t//Md/nJ/8yZ9kc3OT4XDIT/zET/CBD3zgDZMHF6XFVhG+1eDfHjl6z4qL3/FUSKZ5AvMmp9IxkyJlvpIR2DD3pJGn8eK+51o1m6KU8VK/b9eknKqRMX0UwaXLPUIQrB/ICO3i1Q36o4xez3JnNuIk7hFpR2QcTaOZlnIhdnAgaKUyC4Pzin5PcfHygDzTTFeKyuU0DnbPy4iscbA/6wtfoelUdxRK9TEqMK8i5oUhiTyDOMXagVixh5jGWRaFYWevL8o/hnUHom4DzqpSa5iQNYK3jCysagMISePK9U2qakwv1yyqbjwZSGMZ4fXziBDE/2C2kA5C04jqhzVwMHqGreczinjAdXMPh6XwCUfVBlp5elGPOK6oQszKpfh2nLfbW7BMYk62UpomI4kV8zoFxsyqlGXZmaQInnNVP74olndfARfY/n04HD6yyb/a+kt/6S/xb//b//bXvM+NGzfY29vj4ODgkdubpuH4+PhrdsHfyPrFX/xFtra2+LN/9s9+Q57vG7XezJgAMqKvG4VpC96LWw0XNhWV05zONUUprsTzOmHlIuZFzLwQOM0gd2wOAnU7zXNerc18tIJBT6A31ojMZ+NECODGtVQ0shMpRHqp4vKVAaNxymhouTe1nEYpWTwmtTWuNBzNhWyslPh/gOy/ZSXX9M6mot8bYoyQY4sqpWrg3LlkPfbenw8FOtOIWVphNKtWNtB7tZ4amEwmhoWPuV+PcF4xWyk2NwWqYYyY94jZkBQnq1IxMdHac+HcTiw44gpOiDEabjyzTdN4RkPDshS1FaOlixhZxTA3OG/XyiJNA8e9iOm0wlq4y2WqSxlOWd69u49DU/mY+7V0yJQKrfyzpXACn8qihmfOyyTg7nHEZC5wqVUtTZdlbVmUpvVq0GgdWFaPd4w9iQlv3noz48JspdGxIY40WsHuhmdnbFspa8WqDAx7sKhjKm+ZFjGTpXjgbA095zeC+HA4hW8hMoLrh+3thM3NmDTR1I3AemIL169lBC8SwiB76MLFHuPNlF6uuTPp8SDKiI1MJVzQLEp5zdgqyGjPeZgXEouGA7h6fUyaag4miulqxKKA3b18Dcm5O5NrtGljl9ExJzpDqdBOQ8VQNTbSZJg3KYfFgMZrTuaazU0hXysNi8KIgEjVcoq8yLCC+ARduZJjjWJVwv2Q0Ti4cmOLqhqTZ4ajmWo5WaH1J4FRX7hXRQXLlagJLRbCGTBacVddobySU6uYd+we4NE03nKvFu+JjkBde0PZWAKKyHiu76xYNRalIuJYkaaKWRnTeImJ3YQg9gqt9WPHBHgSFx5e33Cn4p/7uZ9Da82HPvShR8xG3uiaLAwqMjSpIjaeC4MZGxunFCHlznyT6SrCGs/RUi7ww6llOhey3dXtip1sysolnBQ5ZaPx3lDmkhj3c1o8nxzG8yUMe/DMZY9RgaKV08tieOZ6jA8xRQWfeRWcix9SMlLCK4glYe0lbl0MTBbCJRj3YWesKWvF0UQO5iyBG5fkq5+t4Au3JZBFkRCiV6VmvkpbHoN0E+LIkMfyWSdlxq3DmGUhh/3lC9FD35tqD3zZjCulANUe5LA98oSgmC0Np95gNLz9GXm886IiYrTAqHqJJ4sbNpIlRjmOyz4H06SVFqXtKsBLJxdJ7Hmum3tc2v9tVPC8euG7eflwQ9Rb0pzIeMrGMC/kN7i6MeOCvUsZZyy2LwERaSy4xXkVMVsZZsvWit0qCUxvQFzYeY97WF+uve2NrJ2dHXZ2dv7A+33gAx/g9PSUT3ziE7zvfe8D4Fd/9Vfx3vP+97//Db3mV1ohBH7xF3+RH/3RHyWKoj/4AW+x9Y2KCSCj/LSWBD+xnqvDI3arm1RRzt3RRaZVRtkYjuYxjVPMlqJZ3cs077xaspscsfA97i8GLCvTkpOluOgS/rpRTOZiaHZ+C57fm2G0Z1alLMoImzmev6bwIWK2VHzhlqeqA0ZbrG2Jcq14QJ7CqOeITGBeGGZLuf3iVs0oKZlWCZ+7FXE6bdgcC3zIaDiZK754V2KEtd3EU1TG4ExiL4kgshFaeU6WGS/flqlkLw+cP2fOCPpFa2JYdfhcgSIJeTcw6gk2erJQnMw0vQze/ZxGazE/mixkCrA5cOSxI48qtuIpkaqYuz6TqkfpDPd7MceDjCiCz94bE9kR797d59rL/x+JC8/+KV68e0kKsFy+l6rRzNu9/ezuhKvhS1RpRmyusR+lGB2Yl5ZlZSkqxbLsIA4S11arx+MQPIkJb631jYoLJ7OAU4okluLxud1TLvMapc3Z39xjWibUTnO6ivFervHpPNDLFe+6PGc3PmDuB+wvxqxqQ2TNWvVrdxPyOFA5z2Qh5pmbg8DV7RWKwKRIOF0Idv+ZK2C04WSu+OwrkgTL0kSx5vw5zTAXZ+NeKgnnfKU5nsom3RzC5XOK+Upx655jNmvY3Iy5ekH28aKAl2/rlifZcRcCwrNWJInw9eJIkceG2GiOlhkvvaKYzxv6fc/eObvmX57OziSLvRfhgsie+RBd3m4oG829Y8P+oaKXw9ueEWGX45ni9j3xWrp2HjZ7JbFxa0j1tMo5XqVUtWayiFisIqyFT+1vENsx79g94OnXPgIh8Mr1P8Gn9s8DZ2IPZS3fg1Lw3Pk51+xrVFmK4jKRlYbF6cIyVcK5qNqBQFfIPW5MgCdx4eH1hy4IXs96TtOUn//5n+fnf/7n/1DP25GAO018TUDjMJzp+fsglXK3OkJx9++CtRcrc63PyGhCZg10iUC3NF+uYyt63vL/4mcQME7hLdggr/Lwa3arIwt2B3envf/w7Q/f19PqgevX3e7PMM7S0ZTJQ93ISF0rhW7JPc6faQsLz059RV3ejtjrAxhYE/m62x/WANYqYHVDpERB6fXfjQ9Q1a28KRYVPHiHVTVpC1USVQVN7RRVrdrbvnJyH4Jakxb/MMu3Wsevv+2bsV544QV+8Ad/kH/33/13+Tt/5+9Q1zUf/vCH+eEf/uG1asCdO3f44Ac/yN//+3+f7/iO7wAET3jv3j2+8IUvAPDiiy8yGAy4cuUKm5ub6+f/1V/9VV555RX+nX/n3/mmvP9v9PpmxYSHl1bd3hI/APXQ3H+9b3y33890vruudKcx3qgzLoFodMv/P6zJHVCP7gkAHejk8F0LV+ou6U4VLLJ8xdhw9hn8Wnu8e62H7+t8WPsYdLc//D66eNIponloyfgCVxTSYUsEfijudEpsX+177f60WoyZOoWyh2GYGrCqxoYarTyeMzhXF09qB6BwaPl9moZI1eTJmbqTb6e33YEucaH9TR/Sf5d/6zDGj/o0PO56EhP+913frLjwqHZ++5+XmCB4dCXeBC1B3QfJFTrvAN16B1jtsVpjTVgn3B1P5fW5gnT3w/o1H75OZYLuH1HF62KCxIPwyGMfhjh3S87As/uIPLoU9SqwdlaX+PDoHnl4X3ivWvlReS9dTAgB6tC9t9Yz4Wv027rvQr4b+X66uOBbPpNSAatkjxuV4VvfgIdjQtPGBI9uv6i6zRXOvAfWIjKV/E7OmzbGd3Gbh+K0euQzPxwTH3c9iQtn6xs+IfhGrY4gkkYi2XdvMeCWG7IqNXcewGzWsLUZcXXPkVjP7tizPRS4zbSImKy2BVsYOdLIUcbieNw4OJ0G7ldBrMD7ogiyWMH+Azls+j3pkBUl3HtQM59VjDcSzp8TdYI0DqSxxyjRFzc6sKosRzMZnycRbccNDk4Up1NHnhnObyuyJHA6V9zcF4L0uW3DjYthbYpk2v8iI92F40XCyUw6eT4omqCJjePillisn84VB0ey2Yd9USQyiXTutYa4xVaGAPdPLXcORF3k/FZgO204nkd86Xagqj17O5a9TYdGCq3KKVRl1/Clm0c5X3itoWk8UawxWrExMuyNa0ZJQeETXr3w3VhVsz1/je+f/wZNb4PXBt/GYTnE+WitSnCwyCizq5SN5dah4eBBw+aGZXfsyeMaayzWdIoK4u+wMo+PC3wzpcQA/sE/+Ad8+MMf5oMf/OC66/Vf/pf/5frf67rmc5/7HMvlcn3b3/k7f+cR1Yzv+R6Rb/3/s/fm4ZpU1b3/Z+9d0zufsU/PEw20KLSIBFFRVBTHGxHnxDBcY4xTHC6/awYFvFdxiAZjDJprohGDogaMRo2ioD4KElRGAWXoppueT5/pnWrc+/fHrvc9fXqAbmiapru+z3Oe7lNvvbWr6tT+1lprr/VdX/ziF+csP/7zP/8zz3zmM1m9evVjdv5PFHh54VvZSxEC1k2PcE82jzAWbJmwebhDg4pFIxmBkzGvMfvd6dBnoruon8evhM2b7RnNMx1BN7SBg1rZRpumWrB2o03vGx2SDNY0YSzZuE0zM5NSrQrmj9qoYtm3fCWERskMKQxxqpjpqlxT3zBYs3Nx86TLvV0Xz4WRAVg4omiHsH6rfdPVK7B4FKTU+I7OOUHj53NgJvKZ6aidXsqSkpuxcpEgSR3aoaCVP2rlwEp4KmnrcXpGiSNtE6ftU4oNW60k8vxhw4KhjJmO4v7NtpHY/GHBkpEYY2yq4VTHtYXaZhRHaDZMVbhvgyZJDUbb8xsccFgxZldBYu2x7piX4oqEoeYDvLzzC9LqIOsGns72sAG4NIVNX1g/WWWqdDxpJploKVodu6I7WEnwnIyW8jB5HYPtYmpw9aEnOwoFJxwsDNYFI4OGeskKcWxq1rg/firdyEaxZ2YShoYUi+YJAs8wNpjhj9p35lRUYjJcjqs0ZSeh4sa4KkBrlzgVbBoXNFtWkWfBiGGwatgxI/ntvS7GGBaMOYwOaDqh5PcPZExNRlRrHqPDLp7rUvKtvaCkrdlxpCZOFa3QpgEKAaMDdnVu87hhaiqlWnVYvkhRCexqxpYd1lgerMPS+T27KMNRJg/Y2WtpRi4zbdVXLTJGUPFTjl3uEqeBzYZo2+e9XBJWHc3p9Ukx/cBCqgVbJyX3rpeUSpLlCzQr56dMtl02bIUkgdEhOG6ldVpaXcn9W33qZY+sYVOV7tte5ba7uqSpJggcfF/N4YRUO6xd8SIckTDc3sAr0l+SlgdYP3ACO8IGk9JjsqlIQsOGiRLd+mrSTLB1ymGmbahXBIuHY8sJocdES+XqjpbjTHpoSpTDoc0Lh6xDYIw1aH3HvmA2TXhs2JwRhglbN83QboasPHYey+YrfCelpBICFRNmHvdur7Nlh2GgJlk6muGrlMBV+K41BWaaGTvGu9QbPkMNj2pJMz4l+P1d46RJxrKjhlm80KPd1Txw3w4mNk+w8ilLePJRFQbKljQqjm36k2rHRh90hammot3JGBtRjNRTm9c8Y1j7+x3MX9xg9VKX0UqbTlhn86YOWaZZONZgcaNpo5N5BFOJDE8keXfFIZodH9VzqLXEVZrRiq3K74R1JidjjDFUylYe0Hc19VKKq2wDNk+mpNph/fYqG9a3GRgKWDFfMlxqM9VusH7dDO1WSL02Rs1P7D0KXZLEplolmb1vm7Zr7r9nnCzJqNRLlCsejlOm5sUMeVPsiAe5Z3yQwNO8sPULHvz8lxk8eiHzXj9CU64mzmVDtYEdM4odMyWiBDZtjpmeigiCCr6TUXGiOd0QfZWhZIab7Xu3EZ3pfuHzztseKwwNDT1kY5Hly5f3Iz49XHTRRfukWPBQxz3S4Di2gDBwMjIjuGerxz33tojClOZUhyROOfrJ81m1ABp+l7IKKckuHV3mzm2jrN9iqFYkC4aso+wKkzfKE7S7MD5ulX/mDQqqQcrmcYf//tn9pEnKmmespHK0S6sL9987zbYHJzjqyQtZvdyjUYqpuhEV1QUgyzuqT0Y1tk6VaHdhZEAwWopItOKe9XD/vZPMX1TnWWtguNTm/h0N7vx9RJpqnry6zEgt7M9fR2oCGVExMwgMW535pLoG5IWQQOCk1Ot2jmyYqrF13D7v5UDh583/6kGML7P8e7Y4ef3WMusfaDEwGLBqkWRBdYYwGeCB9V267ZiRgQHmlWbIjGJ93KDZEXQjRZjYFMbfr9PcetN6dKoZmNeg3ghsvnipzagzztZkHrdvWkzZN7y88wvu/ug/MfaURSz4n8O0nCeTZBIpFImGjdvg7raNerpOhhC2MVHZTai4kRVvyHtQVPwM38mshbIPKDjh8ES9rBmqRLYI3yjWbh3g1tum6bYjxjdN0JlpseLJyxkdGqFaMgyVQkb8aULt87vtQ2zaDmPDgieNtSnLDgZBN1FkWrFla8SG+3cwb9EAS+cHDJUj1m4KuPHau8iSlGeeeTyLhgVNDff9bpwNdz3AUWuO4pjlIwxUMgZKIXWvY2sEtUOqHZp4dCJFlECjAgOVlDCRbN+ecM/tD7Lk6DH+YLXLwvIEv5dj3H1vQppohgfKzK+HVrnPiVEiIxAhFT2DMJpN3hLSvEGYFAaDoOzGDAxaw/KByQbbxm1AolxSlDxDOdAsqDYpqShX/VJ0M5d7NpS54zcbGVsyyFOP8lhR2cS9ZhE3bU5pt2LGRgZY3pgk0i63zzTYsClhsOHguz6Bm3H/gym3Xf97sixjwYoFDM+rUalUGCm3GVXb2ZKMccfmBQSe4RXpL3ng0/+PwaMWsPCNbyYtH0eUKYxRxIlh03bD5nEHY2yBcqYNlZJDww+puh0kDVphQKaFbXKqDNrZD4eg4IU+DlmHIPCsYavyhj47QyrbuMtxbH2BIzTaSMLMFg1ZTV2r5WsMpCZPV0ltTr4xpr9k2GtWJIXVu7fFuSJfehT5UppEqd5SokaJzC6TG2mPrW0em5S2m6iN2NnVBpvSo/qqP/1W5xJUHqvUJu8+nH/uSYGbRwJlXsjXi/YHKkUK2yxFM6vb3b83ore0KJBCIgEtrESjkuD2iq9yeS4psffSdfrpCUCelmQbuYmdlkNVntOklERIW0ehZIaDTR3oLeWllUEGj15IacEYHWMouyGZEZR82/gozURfHlbl9Rj2WHbpNsp6XVvt8RwjiLN9f1wfSjmgwBMXPVk5IQyYXkdyifYVrm9fGnZ53v6tU+MQmoAkX53yPYnnzC6tp7pXbCvmpB7Mjgde4FnOyfufCGE5SDqyzxW9aSiwL+LUOKRGkejZnH+wjkKvH4rjqH4BcZZ3LrfnPtucTxtbdKeNRqIJlIMws8vrElDCcqDaKRWhh16KBPRSqQSpkHmqkpX67N1Dx5ntlwD5vHSslHJmFKmR/Tkr5dw0JD/wyDKNUqIv9bgz+v1kqoOMPWURlSXziYSySiiOg+/atIzYtQ5A/3u7pFz1UieNgTiTyLyYcl9QcMLhid4zr41E54W6jitxPQcv8MjSEl7g4jk2Dc42C/VsJ968dk8K2xgsFnZ7r0+AUgLXd/pzQ2O3BZUSaZIi5KxameM6eKUA13Nsao6cOx979oLexZ7pRebd/JwdR5EZSWpUnoYnEPmLVQMy7yJshCBRLplwEMJgstkUJyVsGpTlhLkpNTvPT8uBDonUZPm5ZVrZDsFlDy/vmJihMAhk/t43OSdk2n6upMjTe2yvBSnALwfoLOs3Wd1T6qQxkJYHGDxqAeVF80ikwpEpvsoIfOj1cNHGYLQgU3mwuJ++aFMle/e0l36dmT0MthcUvDCLQ9YhmNeIGSy5+CpBG6uM4zgCz5dU6wGe79CoO1S9DhU3YjIsM9nxMMbq+NvldvuySDLJdFuxY8p2vM0yQxC4BIHtPiilIQjsce1nyjY78QSlik+pElCpuLaYzonxZIpCo5G0Yp9m5BIlNs2oV9jUMzRc13bmDEqKOBN0UxdtoFLxyLK8GVHs53rAisxAzU9wSlZGUAnboM1zNYNBh4Yzk08CRYa0ih+uRGvTN1YybZuJCWHwHUPJs0aI78K8eQG1isR3IiTGriY0fBxX5hPQGi2dUDLVypuV5A2DHAWNwTJZZqxj4UkCX1BWIeV4hopboRqU0UbwQO1E5r1+hE5OPovi+2gFw3hqlChzbTOjRBEmkpmWQxxrKmVB2YkpyZCmKTHZst2hbS62odPe94YgOtO7efmPpddf4OAgyGXlekZrJYB5YwFxbPB9hyhK80Y8CYlWtGKfMLUKNp5jWDQ6S/RRKokTW6Rqa5YMritxHNF/sZVLgoXLhzHaMDjg5HLIgmrNJxyqUSo71ukmnzu5asZkVKEZucSJ7BcXg+1smma28G9gqEQQKDqRQMkSaSao1WY7F8epfdkmmTXca74HPiiREWUuEstdvpNSdmIbCMg1vHdGT6EEJF3pEGd5bVXeHdVzbNOmSkWitaaT2uLLas21HcYNTMcldN78MNO2fsrkucy1qmJs8WB/flmDwVgnQrgIYajlPQ/WDTydBf9zmEgoQq/GgJxE+RlxVRGnikogiWoyF0awggq9Jo9ZHnzpRvbvk6SKllR02t4+PTsFJxyeMLmB3Ek9tJH5e65MFGvKFY+wm7BwUZWBSkbZSwhTh06uxqWkYaRh03qacUBb+kx3PWY6tk4vCBRjC+s0GnkjskwR+LD4qDHSJKNWsWl1AEPDZdJkjKGRMp5jZvP+jTXw24lHN3EI85o7KazD3lMRq1YdxpYMU697tCPJhKrTDhW+b/A8a2QnmSJB0U48jIGK56M92V/t69X9eCrrpxdaJ0CycxZMrx4nSiQT3QBH+f3aqiSTlHzBoqWDNBouqc5opjWiVFIu9xq+wY6wQpbXcFYqCs+x98wYQakEi1aOkWX2b2A7ENsxew5M2bcntH7gBBa+8c0kUhH6dao0ST2HhUMu3VjlQQjL0dMtQaerkdIGTzUSnQcIMg1RInN1xf1RJCx4oYdD1iGoeDG+8nCEJsUahEqBygS+byNrvifwVIojUlItmW7bXPvBmqbipbYRT6rQeUOedttq6RtjI/lKiTxVJ48E+g4601btJ185cFyFcm2EwFUJrsxQIkPkcYE4U7RD68m7eadeJa2WcaZtNz7Pd3Ad+9D2olmeJ61DYCBKFBqrLpSkAlcZtJFIbGGdlRGzKQOlrIUWikT6SKP6EVMpZ4sjoddsRAAaKWdlRyvlvM5A6jySAJ4v0dqZ0+E5TiGKbVFj5tuCIaVE/x45ro0oei62iCiL8by430huPKrTlKspuyGL4vsI2uMYIan4VVyZEQovj1I6+J6D51m5R1fagmSNbXSSZuCkNgLRDffX69e7bSvwxEavxgZsJMxzTd6t0pCmLkpZtRGwL8Fu4vQlBsuBpuTZ5n9WecxK/fb6BPRWF/oFa9g5U697tuGgN7uq6HkSP5/XO0cBrbNux212VP/F32uynaS2jkkpQVCyq5xxKghzQ9vzZlcUdF4Q2Y1tDYISDhXXxRE2Wg950b/QOCLN9butVN/OL/+eMEOmTe5cWKEFbUy/E2m5JPE9y2qJziX8XNlfNYlS1X8pm12K/n3P3qMk0VZzvNerJHdOBMZymhZsDxu0nCfjq4QBOYmfdkiUR9kt2zoqJfFcmcsvy/7fvHcdvXOwDpy9t2G0b89OwQmHLzKdR81zJ7VcknieAHyCkkO9pvDdGE9ltGJFK8wVvJTBDTQSCFO7LYxlX0bX8wSViksQ2FXHTNvMg3rDJ8tthV7RflByqNZLlErKrg70BAPyeWylNGW/63GvILe3Wu66gmrNw/etfGgnsQExxxH9AEgv8yDMOxoLAYHycKTNWJCyV4+ocUSGQZAY1RdG2HV1wMqOqr6ksK0rwgYy6y7lkiTTmiizzVc9zxb5am3riXS+2unlCom9bueuI2gM2gCrXf23NYc9fuzde21gR9ggLR+HI1OqNHGzkECFVPwER9n6ilQLklTSjawwiRS9+yr719a7HmNE3hxtH5+dghf6OGQdAiV6S/6yvywl5ewD6XmSobph1B0nSNtMOrW+DGetLPIOn4pmx+bqNduGKLKFvPZYVku/G0uEsB57ueyQ5RXnM21BN7RNTRxXUakoBr0pGnIKV0c4aUTo1tgsBghtI8BZVYJstp25FNa7d5zZlKE0o38ujoKKn5Bkkpm2LSws+Xa5UOapPpY0JN3Mx3XqeCKiGk8gjKbsjRHHPQ/Xw3VMX288zawhXcoDaI4ylHyBqyBKHVoioB1Jwm5MGKZ0I79PipXAKgnEKWyfsm3Ek0RTLtvPe01HjIEUh0x5xMYjShVJJkgzl1jZToOtYBgjJCqLWTp1G0YItg4cy/p0jDRvAtPtZnRDl2ZcAs9GQkrebMdp24xq358fnWr0LoVFu/5e4ImHnkoQWOK3qX7gIahWFUEgGR0wLChPEsiQVI+xdcpBiFy3HohSQasrc6cXwsj0lYK0MaSZoRNJjLGpOwMDszTZ7NhuxwB+4FCtKEbKM1TdDoEICbI2oaqwUTdodWcdCCFAZPRThByVpy/l6Qo9Q7enTKKkpOJZXmh27SqC51ouFNKQaUmUClKt6DhenmqY0nCaSKGZCEpkmej3Cul1II0Sm57ku7YgV5PPLcfeyziRdIRDNxJ0uilJnBEltoGgEIZqSRN49qW8fcp2eQVDo6HIMkWzKeh0UmT+8s+wedNx3xBySTKJ5zgoPyNRHj4hq80dIGFTaSVrp4Zs34XYdiuWUjBT9vEdx65clExf0QhA7mMBYMEJhydk7jCmedpdLw3IcQSuKzDaYWzIML88hS8iUjPC+Ixdqfdd+17JtH2mtbErU2Fo7CpVqIkjq2w41VJ5BBoGB92+ETrZFMSJdfIrVY9GXTFWa1FxIkqyi09ISJlNcY1N47LPWb3V/CQVef47VCoOQTCbEpdms86360DJSUm0ZLrj0IkEUirqnoMhI0yd3J4BKXwSRxGomEF3GolmR1Cymvu501TybXDErpAKyr6hnMuh9s4RoBMppPBpdiTtdkwUpnQjhziR/Ui/79rGrNsmTJ8ThoY8sgzC0EqwZtrkHcmVdY7y3iiT0iPKlO3t5DkEKsQVCaud34MDm8RSNszUrd0UQ7eraXqKiW5A2XOIUkXJ7ylK2XNOnH036AtemMUh6xBIoa13m9loWE93GmZzTOcPdJm39Q5UZ5rqkiVMz3goJRisCowPYSLZssPQ7mR0Oyntli0+c1zrrUZRxnTLpg0kKflLzb6Ux3ck/ZdzUPZo1ARjeiPVqU3IJEQkIaXaCMpdSqtjHQx3J/nPXp6rUoJyWeXnrPuSoe1WgpDgOQGDfptu6rMh8Zic1gSeJKk7SOyL30bLYCoqE2Uu8/wJahMPILtNGouPJgwlaaIR0mqzGyPpRIIwtC/TWslG2Wyerj3HdqToxoqJpqDVjInChFbbtlb3HEOjnDJS02ye9LnzrjYz0yHz5ldZMN/2R2i1MzqdDG0g0S6RU6abBbRCRZzM5guWfAdPjVLxqyyZvp3smv8gnm4x/+w3cL+7gDCWdLuaTjtmcsphWzOgEzhkWlArZ1b1oFdv4O77JM/07oVC+6stXODQgyPty3lW5tJGo1wHqmWBkoLlw02WztyGijrMDD+HVidv0CVtfn47FGwZ13M6HANIZb8fx4ZmO+954Fm1H4AdM7B5u+5Hj4KSw3ADFqkHqbTGcZIOKu4S1uahWcL2HRmuK6hVJE7OtL2XluPYRmg9qWit7Qu12bTGtOcKBr0mnSxgUxow3bTR86hi39JhouhGlgcz7eE7LvOqLUY66/HCGcZrQ6SpjdC5DtSClChRTLUk3QgqJUmjovMCxNnVj3YoCGOHyaahOW0LnDtdnzgTeMrqjQcqZeN0hVt/GzI10WHZykFWLbXnshGHNDV5eoC0TRQzp++ANYVCClvkHFcVZbfMsdyJ85+XE03MsOz157CW04kTQbNtmJ5OSBIHJR1816FayhitRXNWUHz2rai44ITDE57KV+tTmQcJDOXArvJXAqtSt6Q+w9LWHagkotV4Bs2ubSyWBQLfFbRD2DaekSR52puwaW/tVkIUZXS7ys4zT1KrSBblnLBlwvDgpgQhrcM+OOixYFiz1H2QSjiBSrqouEunPp/p9kLuumuaas1n0cKAwLdBu05kX5a+ByNDKndQ7EpFktpotZDWqa97HdqpTzv02TFl3/UDJQdXSTqxnd9CQDfycB2XRQOakdYDeJ1Jtg8ME8dezgkuVT+lEzts2SFodzKGBhSBJ/pSw46ydsdkS9DsuoxPaiYnuiRxSmPAp11T+C4MVlNKTsqDkyVuvr/J1HiLFceOsnqlJbeN26DTSUlTu/qYGNcGJLtWWnSyaQuIAx8WDrlU/IRj3XuoXXclyfQMy1/6BtabU4lTe54zMzFZ5uI6DoHnUC1paoFVnYtTaZu27UdRccELszhkHQKYXRLqLUu5rl2u7unl+jJDJiEkMULYHgEwq3GvDbkG7+5FIz1kGSS5mqXn2CLaOCZPLTIIIfKCYpA6AZ0hsgSRJohc7s4ee3aJqr98ZXaKvkl7Pf3t+TX0tJB3Pt9M2+X2LC8Onu1BYJcdNQp0Bmna/24P/eLFfEm9t4RGrmDkqJ7WvyA1dlm0V9FujCHNZD8tw1NW2izLDHE3Rmv7N5B5ipKQthAzNYpMOf0OxGCJThtLKlHm4ubKJvF0i3CySTXu4vi6n6ak81bncWq7zvbvjTAIzZz7uy842MoBBQ4e7OOaF75KK50nBLmkpsFXCSruWsddmH6Oby/fVRv7vKWplZvLMm0FBNSsFneW2bkvfQjcnpGg+qsIQgpbiKsMyqRInSCy1PKD0bn+d9Yvwu8V9vUeQSVBuPTlhO2KG/19e8WBAhvR6xXz9oqCs1yTHOinDmgj+31AduWFnk56pm3/ktQT/QK83ipG7/NMW/7Y+X5rLdDKpif5Kl/KTzVx3kAg8OzxPVcilRVjyPL86bm66JDk8zlO85RHqYkmZuhsn6IatnADKx3YGzvLDEnau4fWYFHCSqYKLVBi3+Z1wQmHL2ztm5hVJ3RnZctdR+PJGJVEfU7IMtjZ5jP5uzeK7Jx1nNmiefu5IckDhLWK7D/vUkji2BbTlwJpC4MdjaNjZBYjswSZJQhjZcjDbkJQcvPUHctHczghn4smTyXq2Qpql5TgTNu+SGlmV0Z6GQk7HwvylD1jbRa50zyxPY9yTsgMcWznmDa2z4HM7RbI3+Xa3h+jZ79jU6UEjrScoGRAmmQkfU6w48mdTrzPCXmPJCEhCQ1xYjBG0o2tbCouJNMzhDumqMRtXKVxcpKyYxvbp0BAybe1IAKb1ZCZPRcw7w0FL8zikHUIJsMyFWe20GW0FjHWMCSZohk6xIkg0oodC47H0QnbO1Wk6CnVWMMg8DSjQ4p6TRKGDp1uvhyfK4MoJfq5/YFvtboBfE8R+Dant1JxSVNNmsF9rKY8tBxPJrgiJtQB7WmHakXMMUx6L1WwGtr1isy9V0GaWa96xYoqYCfbuulBklSSJBAEdjWknVi1g07sECWibzgIYZhOqmxY8AyUyNg6WaMxoDHa6ql3Y0mUzBohmbYrJXInY13spNxUKcHiJVXS1NCo5YoCWti6BmNlvJ70pBrJqhqlwEZcjLG50FLaZcXpqIQ2I4DtQKyNZFu7xI6ZvItgpgiFx9aBY5l/9huoxl2i6girg3tplRt0osVEUYlG3SHwNJ7ShInK0zbsOVvC3vdZXhQKHZ7Y0fZJlIenbPHcaLXDioHQFvgnAXGmiDOHzfPWIDBsmGzQ7mR4rqTsw2AlxnUcklQRxWpOXq0Uom+4R7HtPjxYkwyUQpQwRGmZNFW5Q2G/FyeC3yVHE5RWoMpW5jfMPFpTkkoFAl9SLduC5ji1qwC2S6+h7GniTDDdzmuHXFi1ws9f7oYHZkaIU5vP7Dh2zKmu7RTaDhVxmtc5KWv4tBOPu90TkZ7mwekqpdJsQfFUxwofxAn984+SWee/EvTmvd2/5AsWL6nYVMpAMNMB31UEjosShpKbcfyTq4RRleGGoVGK0EZQLfuEke2PsKPl04kdSm7KMWPTaCNZP1ll4zaIXUElkDhKsjE4iuWvP4dq2CIpNzjJv41Oo4HrrGCtcK24g2/vYZoJZkKbA9lTZ+mERVHxkYyJtktVzNbJjdYiBvwOmZGEqUtqJLH2GB86GoFhvF0lSWxue72srZR44AAOYbSLSTRko9xZZvodfX0Pyl6KkoaBmk804iGEoBzYORzGknuSo/H8FKeU4YqUbuYjJaxcNUgQSBpVgefYWr04se/qSslQ8jVRIvtpSI4Di8ascmKUGB6YHiBKJFFsDfY0hcm2i6NMf8XQUVDyrf0Tpg6/D07EKWk2TNaoVEyfE6a7LmEsEdLg+/a73UjmtpMh8MhTiqyyb6kkWbKshtbg+5JOmHNlqggcSa2UccKaQTrdAUYGBL6TkplcNjhPNZ5oeyRZHVdpjl3QItOKDRMlNm2fDVikmWAzS1j+0jdQidvEQZ2ner+j3aij5GKECAgCQaM6m0bcyv9uNpvCdmHfVxS8MItD1iGYaLlo5RC4GifX3R8S48QyYLMzSjPySDLF2mQ5ANtnPJTKcBzRb9gROBkjDfJ8VEk3Uv2ugD1jOYrJFUigUc6saodrl85sW2+bG5tmcMfGKlJUrQKROxvBqgSzKTlCGMLYpiUIAbWSoeRludKRLRYqebB8vv3+RFNw/8aelCf9gsh2pAilpBNJwpi+hKoEpqOADVM14lTQ6kKjPusAdCOZdwi1xzPGpj5Iae+Lo8yciF3ZNywem829BGsshIltfOY7mictjlDCEGeKbuzkCkrSyppKmGx7tGOHsUqbhc4mAKLSMnbMlHKHRCEwrE/HuN9dgONrVgf3Mnz/jQzUh9k4NML2SZ96VRC4Ga7StCNFqzvbj0JJ+t1M9wVGa8wuy367/l7giYcd04JMSCqBfWmNeFMsmL6L1C2zrbyCZmrT6u6dWUiUSjZsU3Q7IZQdSr6g4XfxlEeaBX3HuRep682LMIZOF6JII6Wk4bVxRUpYcUhSv1/YZ4w1oG9bW+oXz/U6gYJtcBh4VifdkYaZrq0RchTUSylDpQ7NOGD7lM9MSzM2LFkybJ2PzdMB922y9CyEXb1MM5hs9qKBs0W1jjIETspM6HHnNp8w0jjKNlPq7TvVkvnL1uQOvb1OR4l+o8UktaojaZqnLwzY4ze70GxD4kG1pHCVQ+BkHLewmXeDt8pPceZQL1sjA2DHjEAIh6MXpCwz9wOGqdLx3N22RkJUswXE66YHWcfpuIHhJP82Gr/5IQODQ0we9Rqm24O4yuY7C2Hzradac1cdup19e/kXnHB4YroFmbBpg4EHw0GTJZ27SdwS27wltHJOeDBdgDaS7TMecaIpKUEtSBkOWpQdHyFKhLG00fB81a3kazxlbPrxhKQbagLPip4oYWhUbHBBiF49gqEbCe5Yb999Ss7WCwQerFis8uJ5ndsgkiyz+5R8zWApZkZ4bOhIms2UeSMuYwPWLhmfcdi8wxb6a2OsQ5BZG0LJ2UJlJW2Pjoqf0Ao9bt7SoBvZFchyaVYedLot+3WGfu5TdyLLCYNV27MkTB3i1CGMbGBgpGEt8GYXWm2N1pI4tbVNVS/hKUtiBMZKseef+R6USvYeTTQlra7HitEuy50HEEbTra9m87jTFzlItWDDTJ315lRcpXmq9ztG7v4pQ/VBJsdeRqtbtymiJdurKEklzc7cAsMo3vfgYcELszhkHQKwkaw4s3m/iVYkrk9qrMfe05+OEp/MMKewt5eaYwt47Y+StqC2993ey0XrWQ9dCOtIeK5NIci0wFWCJFe1CCNyhSL6UXvP6bXz7un/2vPoGdiOMnmn4FwqL7ErESXPWu1KytnCWWmQ9CT1BEbZcw08O1ZmrGpSN1HMdHaKNlZEnwh6x3Kd2ZWQXiTR/pi+IdMztj3X5hLHqS2sMiJPMdKzRGOXG1Wu+ZtHJp1c5SO2DkLH94g826woSh2iZHaFAqwjEcZWCapVbjBQHyYt1ai4MYtGfas6oAVhovrRzJ5qRCoh2rdUYQCyTCN3Uw44Mif54QTXmVtcnuKQumUSt0RsXGLt0E1dZrqONXDj3VMFLT/kNT7S9I/XUwsyRlr1kHxVLTVO//PA6xUe91TDBEmelijzNCMlIQhsBLCnfe4ojZKSnvMhBDhC91N5dG7ceyrt9zTo1TYoJejJaksJ9Oafmr0XaV5rFMWGKDK4VUG13CtinuUj37PnpdQsVwB9pbXeMr7rQNnTSGmsQeCSc6ZVUPKdDD/vy2ILIB2SviY5facDrBJbHJQQRpNm+QUwGw1MM8s7rmPoNBoMDA6hyzVKKmRsIM0VRux+USLoRnPTObr7rDJUcMLhCNeZrd8Dq6sfe1US5ZMYt2+cRqkizWyArb/ybHMQ+jVJkNcD5qo7vqMJ3AyNg+soovx9aPsAWEe/khfiOipP8xN2HqaZ6av/OY6tR/KcXuqumZOqB71UQTvnekEFpcBzsjy9x+mn+Pb6Gu2cetTjg15acJIpolTQjQzdrqZUklRKs6lHPQU0z50VFdAa0p3Ox64g2ne960A5sEZ4phVRbAMgPUUkV2kCJ0ECCbNyyUqC79s0wiTJlY1Sh7gU5KlUtl7D5MIKPSW2OLVCEO1GnaH6IFlQI1AxowPZTopjOSfEzAlqduN9f34KXpjFIesQ+K5Ga5gObVV+qutM+WWglzoDk02HtQ9mhGGG65pcZiw33vOK+x0z1rCcN2hYMtTpN+sA6CQuD04EZNlsVE8K28mw1/8gylwSLRlvemwb14Rh/jBmGt9XLBhzaVTy4kZll99FbB9oaazhPxh0mBEBnVAxNZ1RryiGgjZKGNpRg8mmIMt6+fw6j0AKHCUYqGrm1SNSLZlsu4xPK1od2LwlJI41xx5d5rhFbYQwbG2WmWr1pFct6TQ7ku2T9noXjAgC1yoLtEOHMIbhumZxYwZXpGzpNNi4w+2/qLMMamVl1T2U6BcyGoPtCOhaZ2DzuKYbaiaHA9ojiwHYMK7YtDlGKcFMy0qLWjUhO9E60WI2Do1YZ0A9yOrKFsbrK7lx03LGp2zKRqej55Bf1N33JQKdarQqlAMONyweTvBKWR4ogG3dATrek0m1w47pEp1Y0uwItu3IbI5tqnMjPa+b0YooUzS7VsVmsAqNcmKbe0kb7e4ELknm0fZsJHpbp4YjNZ7KmF9r5465S6qtozuVc0dvPN+XDNRtOoLvGKqBjSZ2YoXRAiNzudC8AzHYl5mjDGUnzuWGK4SR5cBeDZKjbN2Oo6BaMtRKWb5kL5nquLZIL8qIE81QXXHs2AwAW9sVJpu2t0q9lOE6mqm2w7bJPF3SEzhKA5KSZ6WGB6oZC2szODKl5NZwlG/TdTqCHTMOtbKDO6jxSWglPs3QzY17G/CIEqsQlCSGTRMunlqOEIaJlsJ1bDCkG1nnK4ytChyA66xg8qjXUFIhw/FGFme3MjF4FDdtX8WOGcFMUzM9nVjJ5kxb2eZuZ5+enYITDk8sHEpxS1l/ZWpzq8a09yR0Ivor2jMdyY6pXtqPVfhLUkM3VrTcgE7i0uraNLxayVAvJzhSU3YT2zxPBTRLZchVAsebAY4y1IKEoZJtutlNrYJWGEvCSBNFuu+4lgJJo6r6GQ+esnMgSQUtZVfaPaXxVIqrXHzPwfcllQAafpg7ET5JYjnBNj2zvNYLDjQqmnopRRtBK1RMdzxaXZieTkkSzbxhxdHzbSf1bc2AyZYNDow0Mlxlck6wJzxYFShh5UvLvuWEwWrKwtoMSmRs9+s5J1iVpW2TLvUKLBoCX9li5cmW01dymj9kjfRtOzRhqBHCRbAEIWDrlGM7ECvbZ6Ab2WLjdqcXNF3M5NjLCFTMCFtZ6t7FjupSfrN1KTtmBK22Zno6RfeaykpB1N336GHBC7M4ZB0Cq8VvX9pGW9nNlmtlNSu+LRRqh7B+3RSdZsjoggbzxkp9Wb8kTxNqtu1Laf6Q6C/992ICjqziOX4/kt5D2YmpOzO2+7ETkGqHVuQQhhkzMxFJZAtnShWfoUEXKvZ7jtQ7FerkS/0qo6RCusojSQydTgooqk4XRYrv1JDCwQhDnNocRa3tNTuOYHTAMOB3iTKXbdMeE9OGmWbKti0t0iRl9TFlxoIdCAyt2GfHjMylTDMCN6MT+nS6mVVUGHJwZYYxqi/h5UjDgDONp0MmnYqVC02sFGOaGls0VZvtCJqkPd1hje9oktRhejpleiokTUuAXcHZtj1leipCKUEcazxP0u1mdNoxWhuiqMT2SZ9Foz6rK1twf/0TRk/MCOPlbB+Pbd1GrvjQI9VoP9z+TGvkzpWRHLnKAYcTGkGI9BN05KK1YCZ0mcmN0ZmOdf6nZjTbtnaJ44yg5FIqOf3OupkR+UsbOl3DYBVKjn35e3K2GWAlcCHXt57uODjKMK+aUnU6aKx8aaIV3bwL585FiUKCqyQV36a/9bqLO8oWKRljuwtLdL/QzxgbTfRkjMwj6HFshQ1s13EBPn2HwncNdT8iNZIw8elGtp9CkljlpEqgGfV2ALYeaUoofFczUAoJnIQkrREnMuebXKqwv1piqHgpdWcG18TEnks3sA2VJmYk0zMZoEgaEldKwkQx0bSBAt+1xd1JJkhTQxhmTLcEm90AKQytTt4BXtqVWbCOw/R0gjGwVrhMtwcZG0hZnN2KuvV6hk8wwCra+X47trdJU41OM1vHETX36dkpOOHwxEAQIvwKxlgemGo7bJu2pk0vAj/VhE2busRJRrnsUqlY2dA4FYSpQ5TX6iQpiLKh5Ka4MqPshPgiwjiCwAv6jflmOgLPEQyUNQNek9QooIIUDkI4JDkXZFmvENdFCmULjvPgglVIc/siAjLPUHCkxnVtryTPNQTKiqYomRdDG7vKL4TpB0eVhJKnqXshiVZMtcvMdKDdMYRhSppoAs9jNJgCYCach9bKNhb0E8puQpxWSFORF+2KORkT2kgqfsKAmsTNImLfpVVy6EaSiWmYnE7R2mVezglJJmmH9t4P1gzVwHJGFGtmZmI8T+A6HkrCTNsWCRsDna4mTgTdrs7VhAxCBLS6dUYHMpa6d1H63Y0MH2PQemnfGZgY71gOVRKlJHEY7vPzU/DCLA5ZhwDyiJlvK+2thradwEmqkNJ2sBtbUCMerjAw4FKr2ihYT7LLGCtFqHOVnamoulM7b0M3tcar69hjb51y8mU/ILAv/MmwRCdWNDuSatU2RdPGqpP4vqRRnVXYCPPOosaQN/+CTuywQzRoxS7lkmB42ENKmIpr/T4D1XLuxCQ2Xcj3oFrK+wa4WV41b7t9aqOolF2gQRJnSAFbwhEEhnZkJ7hdUbERfUcZBhu9Jix25URrQSUw/Q6g26NhpNBEqUO1BInbS6vIDZM817qddy8GK+9Vyuss5o95DA/Z4r8gz0UcGnQIgorVZi7bqGY3dJmcsr0eGnWHetXWNYzXVzJ6YsZMbRFLnJh62WWiKdi+w2oXK2kNiLBTdCo+0tFJXFynVwwMaTqb89sNIc7TbBoDPtpAuaQol6y8p9aGZuQTpzby5rsC19FEmUOiDYm0tS5RNmtMRAk0uzYS5zs+nkotL3TsakQY50vinhUj0FWF5wpKvsZVdrVvJrLPbZIKAr/HCy4Tsk4ncfBcaNQUUhhm4mr/5T885MzJlQ98aFRMPzLpygxhDCVP5waCIKo7pJkhSQVbI6uN2IrtRE8z0b82gIGa5UZHGaK8CM91dM6hgvF4yK6YpHZSS0G/YzzYYkKBoRUqZtqzOcqea1MxB+qSek32FV/Aiiz4nuznXCtpHaEkccgyg+/ZPimpFkwMHsXwCYZWfSHz0hDP9dhStQGcdKcIXtTdtxqCghMOT3RSF6+vTGf60t52JT8PBGQm5wRDuaQIfKv7b4xducu01eHXeXpuN3GIpU01cmRAmDn9OoBuBDOtvCi54lF1SySZw3i7RDu0hrDvSZR0kdI6wEEgKAca30nRWtBOvPw8BX6ejteJHQwVotQG9WoVa7dMx5X82mCgoeakxpQDGKjaOoeSm+ZpyzZoChLfFWSZ11cv3B4OALnQSJ4imGqbki2loVHrqToa2jlv2ONlZFqyLR5FCk0rsVLOUlo+8Fx7vDiTKKloh5KZVk9lSKK1Ik4FQwMOtYoiyGVhpYB6RVApObl6od3W9hVZ5pJpQxCIfg3ljupSho8xtCrzmO+ElAOPLRMeUgl0ZusqpBKEnf1YISh4oY9D2iEIPE3Zg8zARFMx3bQPdRhaLfBGQ3H8sS5eLlEnRNaPFE40rXE6Nqj7Rar3bi31J1O/46hjcwC3TgjWPdDGaMPUiiqL5zl0Qsnv1yVM7mgxMlpmxRKHsm9fyK7SKKlRMsOR2hr+TYcktU29RusZGhifcVjbcWyRXkOzaNgQpYL7tlfzazSMNLK+zFimBY1yyrxyE0ekJNrmRQMsqLVYVDeEqcvSUZ8o9Zhuw3//3q6MVCuir5TUDq1z4rualQus9xsmkqm2g+9qRmohJZWwo1vm5nVVotiwYATmN8I8F9jFGImQVic5jBXbJw2bt4QYbcm1VJIM1OCEZV2qbkgrCZjsehgjGBvQ+E6GkpqyE+PKjGZcYlszIE4FgZfnZmrBjZuWE8bLWeLErHFvo+ROsH7lCdxamk+S2mio6xg67X0vFMrSDCmz3bYVeGJjvOVTwekXx7e6gnbXRuc7XU2SaCoVxZKFbr/wrBbEZFqwo+ky0XQp+zBcS3GVto2/QjdX3pkNPoDlhqmm4IH1NvqUrqxg5lXpxJIHNhsmJ2NqNYehAUWlZA2Kkq9RwjYb9FRKK/bZNOESxlArwVDNvmgmWooN4yV8F+plw3DdFvU+MGFf/iVfc/SihNkGRbYAcqTUwhFp3p3Tdv1UJUPVc4jKirLvEqfWKPnV/ZZjfM8a8kkqmO66OMrBkYYlI3bFrRXZrsquYxisJJSchOnQ57YNNeIEBmswUEnziKbtAisEtmGaVGyfEmzeHGKMybu6Wkdg2byUmh/TTZy+CshgxUYjd8ZM2UdJy50l3157kgpu2r4KWMW8NORo9x6CbJoNK1ZTLY2SZjvzwr6pDBWccHhiou1Rk6pfA5OkNgUtTW3DyzjWNBouSxbY/heBZ/Ac++6Z6SrGpxUlH4Zr1snuJg6TbTfPU/fJdN5ANMiolzKaHcWGBzsYbXDdKo6s0okU96zXjI93qNc95o06+K51egPPZjQMliJKTsxMFLBl0qUTWUGSWtnKFG+bkjTbgpIvGBmwnBDGkrXbS2hjU5lWzLdpsz2J1aqfMhS0cURKahxSLVEyY7TSZqRi05jqZZ8osauitz1ga/x8b7b+KE6tDKinNEtGbK+kdqTYOuXiu4bRekzZSZjoBvx+U50kgYEaDFQzPMc68aWSRKlc1CQV7JiGzZu7GG1ot30qFUW9Kli1MKHiJTQjj6m25YTFwzEN30b0e7w2GQYo5RDF0KhaHs+04Ddbl6L1UuY7Iaud3+M70zy4YjW3l+aRpL0aM0Onve9dTAtemMUh6xBoLZBgq/H1rF5vktg8/jjOqNcU9VJK4Ka2gZeWJHnX4SSxk9FzrEPQCm3ufU8NxBbYzRYFZxm0ZkKyzNBslejUFc0uTE2E7Ng6Q7niEXiKapDiqayfA2h2KhKMEpuGU/KFJZxet+RWhqko3EFD2UuIM492aCMXnguBk7cYF5LMGEpOSll2cERKmwpR5iKEwZcJnkxmW5Jnkul2wNSUfcH6noewjnu/w2HgQcm1JJJknm2FrgSuzAhUjDFlJqZsHcZQw8PN1Q9cx7W9F3KVEoAoNoTdFGMMfmgjJrqqqLohA84UMEArdtEayl5CxYlwpKYkQxyRgAedwCHOJJ6yf5cwUYxPwfZxuzJQcidwt6+nUV9MJRglSSWBq/GcDJnuRw1BlqF3WQbc9fcCTzxEscBL8wI4k+txJ2ZOvxEhrEyl79qGNTUvIs4cxo1LJzT2+VeaIJ+j7chGtHvdgntSnta4MHTbsY0khmXCRNKNBM1WSqsZ47gSKRWeY537spfl9QgaiT1mN4ROaMUBXKX7Rf2ttiYLJEM1q4iWpA7t0PJJOYCKG2MQqNQhU4KSk/TnUmpcEhxbYCwyvPz9F7gKKW0n5olJO1+GBh3cXFc9TmwthfKyPi90E9W/7t65p1ow3bS1Qb7nUCvb8+oVGPZEGVJheSGOUrQxeJ6yRdDa8k7Fiaw0aGzziT0no+LaKuAsbzgZOArfdXI1JdPXVN8xI2h3DJ7rEWTT+DNbaVTmM1AZJMkEpVyRzNP7lkpYcMLhiSgWBKlA5Sv1vT4jWhviWJPEGeBS8q3Ur+dkeE5GkilMRxHFuUJQnsrTTawgQZL3KEpTuzpXCaw9oo0iClN0pul0rQJRJxLMzCQ0p7v4vsJzHAIPyr6m5NmgoSuzfs+MMLbpPL5rV+i0sHOz2UwBx6oWOtYhaHfJHQIrdwo2qq+NIHASAhmhsEGCBEsErsz66Yhl30HmioU23Q8GG5az7LEEpLZ/gqdscLIdKcLYrm5YfX+bqTA1bRuS+p7HgI034Cjyjus92U/LyVGYYrQhihykElRKksBNqbldUi2ZEbm0uZNRdTv9AIc2ktC1TcesMItdSexxQqutKQcevjNNMLWRenUB9dJw3jzRCjg4WWErPBLsl0Nw0UUXcfHFF8/Zduyxx3L33XcDEIYh73vf+/ja175GFEWceeaZ/OM//iNjY2P7fWLrtwmqXYWbu7FCQKMmMEZRq6o8HYi8iZXTz71LRd7xs5WRpPYzL08jqpaBvCFGT3q0F5UPAlhx1ABaQ62qbNW6hpF5Jao1j+FhL6+uFyRC9R2BVEsyLWh2Fa22IYw0nqso+7NpOtWKwnOxRUs5eWQZs90HfVs0PJ35ZJlkJvRI9Qi9bpxZ3pitLTwEhlRLwlT10ySyXM2g14jMpkz16hgknrIORZrZSZukgo1TFZQsM9GURFFMkmjGpwxKlnGUoRJkDFU1M13bybAbWkdh0eLKnL+T1jAZlUmNZDoqMdOxk1xKF0doogyapoQGkkz1m0OFiaIdWTWhbqhJU81EU/DAijUM1BfTVVWWNabyVue26sNNu/v8/OgsIysm+UHBweSFLeOaZqRxXZtX73swWBfYfH/ZL2LzXd2XHwYbUevGMNPMyDKF53h4rsHstCLgKNNfmk5S0eeMo45u5L1KBBNNO78adYdKuUKtIqmVbKRaSpPPV0U3cTDGRiDbXU0UGcJY9VNzHGULDXuF+Wnm0smL6ZSyy/SBk5BpSddYJ7oVeyR6qJ8WYJuWSaJMkWSyr+LVU05LU92vb+ilXM607e+NqpPzoJXt6/VJ2TIdIIXPZEswM2NreSamJMbY/ikD1YwFgxnN0GHTuOxLnB51lLUOksSu4iplU4o60qOTOISxTU9qKa+v8JRoywdaC6qlbFaJLbXKIb0C4i1Vnw0rVlOvLCATDqvqm/v8a5C00n2rISg44eDhYHLCtglNJ7E590pZp3V4wDqbQwOKLLP9dnw36yuJ6bzRZxhbZx0EUvp4jtdvqtlPkcuP2Y1sj5/Ag1Wr6nlATzA+bfsBDAx4VCou9ZqiWrIy5DI3ZNNMEaUlpAiY6Tp0uoYk0USJtQm0sQHKRt3BdQXt0KYTdSIbpOgrFqrUzvlU5SuHAe3YyyVMrdpfT8XIkXYFtBvLfsH/bPNWe+/iFKKm5aSyL6mV8nmVByuNgW0zPuPSZ6ol6Ia2oH9qRiOETdFuVDRjA5purNgxYxXDPBeOPqbeb7aoe+qMqUNLBHQTp994zfYVafT/LgZBlCqqJd1XhkxSSZTMFhBvmfDYsOJJNKoLCCmxuD5rK2gj8Qpb4RFhv1cInvzkJ/OjH/1o9gDO7CHe85738N3vfpdvfOMbNBoN3vGOd/CqV72KX/ziF/t9YmvvbxJUJI6j8DzJkkUB84f1bAdPYR+eJK8pqPgZnsxQUhLFhpmZhG5X0u0qlLLEMFw3cyS94tROvDSz3v9wruff6kK7ayfhgnkOjrJOhZK2O6ZJBQkSjc2vTzNbZDQ5nRCFGa4rKfs2p85Vdskr09DsQJZPAq3BlVByMxpui0h7tGLPNgIJJZsmVL/HQcm3UcUwFn0t/p4R0wlt4aF1BPJivswWTMaJASSeY89F69mVjE3bDZ2OJk1Twm6KNjA+HjE9o6jXHJ62KmFReZxNzgj3POAyMRGxYEGJJWP2GFMtG73LMhhvejRdl04kaeaCH56jcJVVXZhsOXQiKHlQK2c2XzKStLrWcAlDW0C8fUfGbaUxKsEISxvTLE7uQ5qMjj9AJEt4zr69+CH3+tODN8knJiZ45zvfyXe+8x2klJx99tl8+tOfplqtPux3jTG89KUv5b/+67+4+uqreeUrX9n/7Mc//jEf+MAHuP3226lUKpxzzjl8+MMfnjPvDgUcLF7YvKmFX7JRaNdTrFheYqRhC4FtGp91mJPc8Ba5sKAx0O4amtMxceSQJPbFWy0LqqXZPiKONMRZ74Vsa3mWjNqo2ZZJh2077PweHbSrEI6yxfUqdwbSzHbKbHYUnQi6IbTbNm2hVJJ0y7bxj6ugVrYvyt6qQBRDNzK4Ti6TrEIS4WIoWeMgdOjGNpWhl55kjKATyb7EJ9hjRjspLPU6qScJjO9I6HRSwlEfz1E4yq4AypwXNm+3KiBRlNFux+jMkCaadselUXdYMRqzqLSVDc4Yt//eYcd4l6OPrnPs4hgpDFunfSZm8nqlRAEe7UjRiXqOiZVCzLRVE0kzq5g0WouQ0jATerRDSTeiX0CslKBaGmWgMsiq+mYWbb8ZkSWkpTqpW6bUbe/Ts1NwwsHFQeOEzR1KMx6OYzsFL13s9TmhFxToyZD35ci1JM7sc9ZuZ0SRfe/2JIPLfk8UhH4gsPcMVwLDgmVWzefBcYfNW1N8XzI6JPsrkxU/mxXiyGwhcqtrC5fDyCropKkmyuuQejU1XsM6xa2ONfBtcMJygpQGX6WkWpFmvlUzikW+by4bLGelRN2dsmZ0njVhhUJ6jRXtCsh0UxPHhkZdIYTqywb3Gqc9uA063cyKBHRt1H9qytDu2ODs0pGEheUJtnQHWbvJY3o6ZdFCjxVjqVWDbDk0u1bRqBvbc+/GVtHJGJhqKTphsFN/F2vz1ALb/K2X0tiN6RcQSyW4ozRKvTTE4voUC9O1CGPoenVrK7iFrfBIeGG/GcRxHObPn7/b9unpaf75n/+ZK664guc///kAfPGLX+RJT3oSv/zlL3nGM56xX+OkSdZXmemlB8Cs1r8QBiPoRwW1EX39bqPzdtQStJGoflrPrDPQOy7MGte9wjcQs8WsYla/v4esp2FsxBw9bLvN5MW4e893twWRpq9z3jNaeisXkHc7znoa4qLf/Xjn59bkxVJ7Or5dLTD9eyNMr87C7tNzILLM5HnT9v/EGWneaKWnuGLyz3vf752jNr20DXu/eiSz67mk2WwjpZ46Qm/i73zsTNv26UkqMUYgTYbQGY6O0ULhmP0pFMrQ8uBN8j/6oz9i8+bNXHPNNSRJwnnnncdb3vIWrrjiiof97qWXXtqXVt0Zt956Ky996Uv567/+a7785S+zceNG3vrWt5JlGX/7t3/7WFzGI8bB5AXlZP250/uT9jSzZR45B9DM9hcRIlcrSzUy0fk8na0Z0EagjckjiKJftNw7LuQpN/HsPioPLvRW8oyxKxE6f5H3oHeZPztv322bNv1rkjkvyNyw6S3HZ7o35/Lz1HN7Cuy86rEzbBG2dRRssaXod2neGT1eSBNbbJd5yv5f22tXJkUJ00/NAPryqTuP1eOe3orAztzQu45eaqPlG9P/rFcImqa63yk+ySxTiixBpDEq6iCzFDfcR9nRghMOKg4WJ2SpJk2z/hwzxvbV6K2AKaHJkPn7SeSiInYf+xwahKbvOJv8PaZ2uf29ZxZmexmB5RTHEX2u6NUy2O+IneZr/l42Jv+ZPfauf2ohAd1TFNqpm3qPE3YKbGbazmujBNKAEaAVZLtwza5zvccTvXRLrZkzT2evwYqo6FwxqTc3RaLJMissoLA1Rlrnuv5i9h7tynm72k0mtyF612JMz06gz+W9/bQ2/fNJUtunShuJMAZpMlwdYYTENfuuSFjwwiz22yG45557WLhwIUEQcOqpp3LJJZewdOlSfv3rX5MkCWeccUZ/39WrV7N06VJuuOGG/Z7kvYs2vch3YogTQa/xz6yawOwSl8wb/WT5i9/1FOWSwvesSk03knnXWztpe4ZqmkIkQArrUkSJ3ZblTUasMSv6HXOznV74rmNQriFKlNUEVrYLZ8m3zNHqSjpRLguWK4y0ujA5bVDKEKcylyyDkpMiSgZHOSSZJbBes7GdDRSrobxTqoMjEdKmUOxsZPeeadt8KZcSMwbPEdQqEs8VNNuzkUAhhD1WT3nAeGRG4LpWXckYQytX9OiENj9TKRtVcR3biMXLFUhcR+PndRauA04ewUwzgeiRquw1QxJ5szNhi75cq77U8QdwdIybdAnCaejsWyQQ8gjILt7JY6Ukdtddd/Ff//Vf3HTTTTz96U8H4DOf+QwvfelL+du//VsWLly41+/ecsstfPKTn+RXv/oVCxYsmPPZlVdeyQknnMAHP/hBAFatWsXHP/5xXvva13LhhRdSq9Uemwt6BDhYvKA19uWvrQ59N9R0QqvKobXV049zLXCAemAoqxBtJGnmMTMdMjBUplpRlAM7p7oxueM/268ALPckqc2n7a0wdDopmVZ0IomSMp9Xve7BdoUA7HzwXJBCMJHPWdcRlHNemGzZyJ7vwVDNSvuNTyu279AIaWWWJRmehIbfJXBcuq6DELZGp1ffk+VGSm+FQJue45MbEDtFQLSGOM5sbq8xeI7BdXS/KysIqwrkCyYmYWJ7QhynuJ6Dqthu55kRpMLFGMtzQclFG0M7sook7RC6obZdzIV1FBwlcZ1ezwMbPY0zSZJapbh+aqTZxZHKdD9y57tWcc0gSUt1VNTBmdiCaU7hdPZNYrDghIOLg8cJPYPV8kI3MrmKjm10aVVuRL93ScXP8FWWO6uKJM5Q0qEUWDWyXt1cmtkoec+oTzMbgIiS2ZqYMLK2RpIIokTh5F6ElBIlZusLwL7rrOhHzjPC9DuFgz1uN7TpdoEHKrApfjPNFCVnuUUKTdmzUslSOra7uDM7z3cO2PUCctbw3+memVk1JtsDSc86HcI6Fb0GiKVAIqWg3cloNm2g1g8cWytk8hVZLCd4ua2QpNDM6wx7qZAyd5hcR+Np22fJGDu3e+mbUSL7gcU4lXOuWwjbpFEqieNY5SFP2ayNrlfH1RFe0qHUnUC0C1vhkfDCvum15TjllFP40pe+xH/9139x2WWXsXbtWk477TSazSZbtmzB8zwGBgbmfGdsbIwtW7bs9ZhRFDEzMzPnB7AG7k4vs0z3JEdF/1/bvXI2aq5yuT5jTF+WLvAF5YC8E6/Nrbd1A+T5q7ORqiixP72ltTS1jkgU58VF2Wzn4ygR/Zx8N889VkqglDVOXMfmFWfaLt9n2hYdVksZjoIossZMkkl0/mfwVErJTSl5Wa7n3eu8TC6datFLOUpTO4mUmu0o3Pu8p1cMc7sU9womAx8qZftSNxriyBoJvXuujSAxjpUldGx6RpZBJ7TpVGFoiGPT77fg5E5Hv1uiNH0Fpp2jJr2mZ8bMLnH2H8bcsfDyIutIlohVCZXFeJ1JvO7UPj+rOs32+PNY4IYbbmBgYKA/wQHOOOMMpJTceOONe/1ep9PhjW98I5/97Gf3GEmLooggCOZsK5VKhGHIr3/96wN3AY8SB5MXAIw2xHFqjdvI0I3tfIwzkRuas/wgAV9GeDK1qiOtEJ1pAt+mqjjKpupEfV7Ia3Hy90OaWQnjbiTtMx+lRGFmOwIn9iWe5B2+w9h20Y0SO8cqftaX9rVza1bkQGsII02mbfPCehDbNJswJexmZHmAQ5FSUhFVN6TixZQ9TeD1Op32VtbsfE9Sy11xXly9M3qGto38Z/3mRk4+N3tOfdmHWtnKCMZxStSJyTJtX8QiN46M7VjuOALPsxM4TCSdWNp7GZk879nksqI93gHfMfhORuBkfYlByFcrtF1N6GHnqKbr2JQwgNQtYxwP05yie/86uuvW79NzWnDCwcPB5oQs1TarINX2nZ3YORmlgjiV9t98mzG26NaR1giOY7ti6OaqQHLnYGHGTpH9fJUts3U/YSytoEGic1shn4N5V+0ks3nvYWw5QeQ1CZ5DX45UqZ7Ur+WEbmRyZT2bFug6kMSaKNZ9OVAlDL60ogDl3Fbwdqp1kPk7tbeiESf2J8t6K5r5Kkiv5jDLV+z13BVVmWdU+J6VE3YdQZpokjgly3T/+zrnhJ4t4ji2fqMbSTqh7Q2TZqbv/FtHZi7veMraS0rutPKiLZ/vzAlCWhtLKpHbG7ZHQt9WSEP8mW34ze37/KwWvDCL/VoheMlLXtL//wknnMApp5zCsmXL+PrXv06pVNqfQ/VxySWX7FZ8BBCHTaS0KwJZKgk7KV3fvpRE3ulT56ogAB2Z4mchndQl7LjEYYvI9el2/Nle3OTRKBeUsik73a6dxEpBT6I7DCHs6nxfG/U3KUid5xPmDoWSIFJN6mg6HYeomxGFGd2OR6eVd+NsK8KOQSeCjjJkjqbbVrbrroBOS9Jym7ZGIItItKKTuHTaLlnWm+A2P9lOrNlVjSwz1jCJMoQQhJ6DMpZ8wo4mTjRdz6Hj2xd/li/FpRn9/N2wo4nDNnGY4jgRSri4yqHdymiZpjX+Ox5RN0UahRISTP69WONIRacNMs3oJpJuvoLQVSluFhJnDp22Tze0vQgy177g09TWQ0SJ7UAcdWPCjk+nLZBpipt28ZymTRPqtPG6HZptmxpgzFxjZ09Iopndlv2y1EYNdn6RAPi+j+/ve4+DXbFlyxbmzZs3Z5vjOAwNDT3kC+4973kPz3zmM/nDP/zDPX5+5plncumll/LVr36V1772tWzZsoUPfehDAGzevPkRn++BxkHlhaiJzjRxkqIzh7Cj6LYVmQMmtd2Gk0wQ5SsEba9LO52hlRnCDiRRkyiUdNsaxxi6kY3KSQGesMdIUkG3Y6NnOgGT2pdl2NFEYQejHcKOj2PmPtPWCcm1uY1GO5pu2yHqJkTdlG4nodNK7cuy7RB2NApJp2Qt+aiTEHVDhBB0Wk6fFyJiMqPopB6dTkKaGwZgX5rdtpUU7K0OaGPsuXYTlJKEHRdXCMLQdvWNw4iwI+i0BVkuvZrmjlAUW16Iuhlx1CSJYuJQEHVTQseh3YpopU3akUvY8fvztpX3J+l2BGEnw1OKTitDJLZrabebq5/oDJKEJJN02h5hBFIbfGy36E7o0e3Y3O6o2yGOmkRdu6+nY1ppk1K3jRt2cDoh3TCm2bWqRQ/HCwUnHDwcbFvB9FNJFGFH023bFQLXsSnGmaGfTeCZFCeJclvBI+p28RyXbttFu3NTc7Pc0M60fVdlGkhBZHnj1I4h6nbBOIQdD6mtfUFmw3zdSNINd7Y77KpC2MlIEkO37dDxd7EVUkFHQeba64i6MUIKOm2Hlt/MC/KtxGgn9ui2XctVZjb9rpfK2Dtvk8u1R2FuKyiF0NamiLq2k3HYcem06WcTWGfBijFYWyEjDlt2RUW5COMSOi6dlqZFk3bXEHZiwk6G7yh67UG6IYSxQWSCjpeBN8sJxljO1Y7OhR8kWSZIHGsrWbEDSRTb84i6CXEYEnYSOm2Fk6V4aRfPbeKaGNFu47c7NDu2qLiwFfaTF8yjxNOf/nTz/ve/3/z4xz82gJmcnJzz+dKlS82nPvWpvX4/DEMzPT3d/7nzzjsNUPwUP3v92bBhw16fp263a+bPn7/X71ar1d22XXjhhXs81v/+3//7Yc/lrrvuMh/+8IfNMcccs9v3R0dHzT/+4z/u8dj/8R//YVatWmWazWZ/G2CuvvrqOft98pOfNPV63SilTLlcNpdccokBzNe+9rW93oNDAQUvFD8H+2dvvFBwwqGBghOKn4P9U9gK+8cLj0qWoNVqcd999/GmN72Jk046Cdd1+fGPf8zZZ58NwO9+9zvWr1/Pqaeeutdj7OpxVatVNmzYgDGGpUuXsmHDBur1+qM5zT1iZmaGJUuWPGbHL8Z6bMa68847HzLPLggC1q5dSxzvuajIGLNbUc7ePP73ve99nHvuuQ95XitXrmT+/Pls27ZtzvY0TZmYmNjj8h7Atddey3333bfbsvnZZ5/Naaedxk9+8hMA3vve9/Ke97yHzZs3Mzg4yLp16/jLv/xLVq5c+ZDn9XjiseKFO++8k+OOO+4xfd4O1jN9uM/TgznW+vXrEULslRcKTnj8UdgKxViFrfAE4IV9dh2MMe973/vMT37yE7N27Vrzi1/8wpxxxhlmZGTEbNu2zRhjzFvf+lazdOlSc+2115pf/epX5tRTTzWnnnrq/gzRx/T0tAHM9PT0I/r+4338Yqwn7lj7i16k6le/+lV/2w9+8AMjhDAbN27c43c2b95sbr/99jk/gPn0pz9t7r///r2O9YEPfMAsWbLEpGl6wK/jkeJg8cLBeAYO1nN2uM6dw3Ws/UXBCYWtUIx1+I+1vzjUeWG/HILXve51ZsGCBcbzPLNo0SLzute9ztx77739z7vdrnnb295mBgcHTblcNmeddZbZvHnz/gzRRzHJi7Eer7EeCV784hebE0880dx4443m5z//uTn66KPNG97whv7nDz74oDn22GPNjTfeuNdjsIdlwI9//OPmtttuM3fccYf50Ic+ZFzX3W2fxxsHixcKh6AY6/Ea65Gg4ITCVijGOrzHeiQ4lHnhUdcQPFYoJnkx1uM11iPBjh07zBve8AZTrVZNvV4355133pycv7Vr1xrAXHfddXs9xp4m+fOe9zzTaDRMEATmlFNOMd/73vceoys49FE4BMVYj9dYjwQFJxwcFLZCMdbjNdYjwaHMC4dWa8Od4Ps+F1544aOq6H48j1+M9cQd65FgaGjoIRuLLF++/GEVD/b0+bXXXvuoz+1wwcF4Bg7Wc3a4zp3DdaxHgoITDg4KW6EY6/Ea65HgUOYFYR5u5AIFChQoUKBAgQIFChy22K/GZAUKFChQoECBAgUKFDi8UDgEBQoUKFCgQIECBQocwSgcggIFChQoUKBAgQIFjmAUDkGBAgUKFChQoECBAkcwDjmH4Gc/+xmveMUrWLhwIUIIvvWtbz2mxzPG8MEPfpAFCxZQKpU444wzuOeee/Z7nEsuuYSTTz6ZWq3GvHnzeOUrX8nvfve7OfuEYcjb3/52hoeHqVarnH322WzdunW/x7rssss44YQTqNfr1Ot1Tj31VL7//e8f8HH2hI9+9KMIIXj3u999wMe76KKLEELM+Vm9evUBH6fAEwtPVE6AI4MXHktOgIIXCuwZT1ReOBI4AQpb4YmIQ84haLfbrFmzhs9+9rMH5Xgf//jH+fu//3s+97nPceONN1KpVDjzzDMJw3C/xvnpT3/K29/+dn75y19yzTXXkCQJL3rRi2i32/193vOe9/Cd73yHb3zjG/z0pz9l06ZNvOpVr9rva1q8eDEf/ehH+fWvf82vfvUrnv/85/OHf/iH/Pa3vz2g4+yKm266ic9//vOccMIJc7YfyPGe/OQns3nz5v7Pz3/+88dknAJPHDxROQEOf144GJwABS8U2B1PVF443DkBClvhCYv97lxwEMEemi8cyONprc38+fPNJz7xif62qakp4/u++epXv/qoxtq2bZsBzE9/+tP+cV3XNd/4xjf6+9x1110GMDfccMOjGssYYwYHB80XvvCFx2ycZrNpjj76aHPNNdeY5z73ueYv/uIvjDEH9rouvPBCs2bNmj1+9ljfvwJPDDyROcGYw4sXDgYnGFPwQoGHxxOZFw4nTjCmsBWeyDjkVggOJtauXcuWLVs444wz+tsajQannHIKN9xww6M69vT0NGCbUAD8+te/JkmSOWOtXr2apUuXPqqxsizja1/7Gu12m1NPPfUxG+ftb387L3vZy+YcFw78dd1zzz0sXLiQlStX8kd/9EesX7/+MRmnQIE94bHkBDi8eOFgcQIUvFDg8UVhK+w7ClvhiYtDtlPxwcCWLVsAGBsbm7N9bGys/9kjgdaad7/73TzrWc/iKU95Sn8sz/MYGBg4IGPdfvvtnHrqqYRhSLVa5eqrr+a4447jlltuOaDjAHzta1/jN7/5DTfddNNunx3I6zrllFP40pe+xLHHHsvmzZu5+OKLOe2007jjjjsO+P0rUGBPeKw4AQ4vXjhYnAAFLxR4/FHYCvuGwlZ4YuOIdggeK7z97W/njjvumJPTdqBx7LHHcssttzA9Pc03v/lNzjnnHH76058e8HE2bNjAX/zFX3DNNdcQBMEBP/7OeMlLXtL//wknnMApp5zCsmXL+PrXv06pVHpMxy5Q4LHG4cILB5MToOCFAocvDhdOgMJWOBxwRKcMzZ8/H2C36vOtW7f2P9tfvOMd7+A///M/ue6661i8ePGcseI4Zmpq6oCM5Xkeq1at4qSTTuKSSy5hzZo1fPrTnz7g4/z6179m27ZtPO1pT8NxHBzH4ac//Sl///d/j+M4jI2NHdDxdsbAwADHHHMM99577wG/rgIF9oTHghPg8OKFx5MToOCFAgcfha3w8ChshSc+jmiHYMWKFcyfP58f//jH/W0zMzPceOONnHrqqft1LGMM73jHO7j66qu59tprWbFixZzPTzrpJFzXnTPW7373O9avX7/fY+0JWmuiKDrg47zgBS/g9ttv55Zbbun/PP3pT+eP/uiP+v9/rK6r1Wpx3333sWDBgsf8/hUoAAeWE+Dw5IXHkxOg4IUCBx+FrfDwKGyFwwCPc1Hzbmg2m+bmm282N998swHMpz71KXPzzTebBx544DE53kc/+lEzMDBg/uM//sPcdttt5g//8A/NihUrTLfb3a9x/vzP/9w0Gg3zk5/8xGzevLn/0+l0+vu89a1vNUuXLjXXXnut+dWvfmVOPfVUc+qpp+73Nb3//e83P/3pT83atWvNbbfdZt7//vcbIYT54Q9/eEDH2Rt2Vg44kOO9733vMz/5yU/M2rVrzS9+8QtzxhlnmJGREbNt27YDOk6BJxaeqJxgzJHDC48VJxhT8EKBPeOJygtHCicYU9gKTzQccg7BddddZ4Ddfs4555zH5Hhaa/OBD3zAjI2NGd/3zQte8ALzu9/9br/H2dMYgPniF7/Y36fb7Zq3ve1tZnBw0JTLZXPWWWeZzZs37/dY559/vlm2bJnxPM+Mjo6aF7zgBf0JfiDH2Rt2neQHarzXve51ZsGCBcbzPLNo0SLzute9ztx7770HfJwCTyw8UTnBmCOHFx4rTjCm4IUCe8YTlReOFE4wprAVnmgQxhhzIFccChQoUKBAgQIFChQo8MTBEV1DUKBAgQIFChQoUKDAkY7CIShQoECBAgUKFChQ4AhG4RAU6GPdunUIIfjSl770eJ9KgQIFDgEUnFCgQIFdUfDC4YlD0iG4/fbbefWrX82yZcsIgoBFixbxwhe+kM985jNz9vvIRz7Ct771rUc8zp133slFF13EunXrHt0JFyhQ4DFFwQkFChTYFQUvFChw4HDIFRVff/31PO95z2Pp0qWcc845zJ8/nw0bNvDLX/6S++67j3vvvbe/b7Va5dWvfvUj9lK/+c1v8prXvIbrrruO008//cBcwBMYxhiiKMJ1XZRSj/fpFCgAFJzweKLghAKHKgpeePxQ8MLhCefxPoFd8eEPf5hGo8FNN93EwMDAnM+2bdv2+JzUYY40TdFa43neY95yvECB/UXBCQcfBScUONRR8MLBR8ELhzkeR8nTPeLYY481p59++sPux0PoBa9bt878+Z//uTnmmGNMEARmaGjIvPrVrzZr167tf/+LX/ziHo9x3XXX9ff53ve+Z5797GebcrlsqtWqeelLX2ruuOOOfbqOyclJ8+53v7uvAbxo0SLzpje9yWzfvr2/z9atW835559v5s2bZ3zfNyeccIL50pe+1P88jmMzODhozj333N2OPz09bXzfN+973/uMMcZEUWQ+8IEPmKc97WmmXq+bcrlsnv3sZ5trr712zvfWrl1rAPOJT3zC/N3f/Z1ZuXKlkVKam2++uf/ZznrIt956qznnnHPMihUrjO/7ZmxszJx33nlmfHx8znEvvPBCA5h77rnHnHPOOabRaJh6vW7OPfdc0263dzv/yy+/3Jx88smmVCqZgYEBc9ppp5kf/OAHc/Z5NPe/wOGDghMKTjgQ97/A4YWCFwpeOBD3v8AsDrkVgmXLlnHDDTdwxx138JSnPGWv+11++eW8+c1v5g/+4A94y1veAsBRRx0FwE033cT111/P61//ehYvXsy6deu47LLLOP3007nzzjspl8s85znP4V3vehd///d/z1/91V/xpCc9CaD/7+WXX84555zDmWeeycc+9jE6nQ6XXXYZz372s7n55ptZvnz5Xs+t1Wpx2mmncdddd3H++efztKc9jfHxcb797W/z4IMPMjIyQrfb5fTTT+fee+/lHe94BytWrOAb3/gG5557LlNTU/zFX/wFruty1llncdVVV/H5z38ez/P6Y3zrW98iiiJe//rXA7aN+he+8AXe8IY38Kd/+qc0m03++Z//mTPPPJP//u//5qlPfeqcc/ziF79IGIa85S1vwfd9hoaG0Frvdi3XXHMN999/P+eddx7z58/nt7/9Lf/0T//Eb3/7W375y18ihJiz/2tf+1pWrFjBJZdcwm9+8xu+8IUvMG/ePD72sY/197n44ou56KKLeOYzn8mHPvQhPM/jxhtv5Nprr+VFL3rRo77/BQ4vFJxQcMKjvf8FDj8UvFDwwqO9/wV2wePtkeyKH/7wh0YpZZRS5tRTTzX/3//3/5kf/OAHJo7j3fatVCp77Eq4cwvwHm644QYDmC9/+cv9bd/4xjd28/SNsS3MBwYGzJ/+6Z/O2b5lyxbTaDR2274rPvjBDxrAXHXVVbt9prU2xhhz6aWXGsB85Stf6X8Wx7E59dRTTbVaNTMzM8YYY37wgx8YwHznO9+Zc5yXvvSlZuXKlf3f0zQ1URTN2WdyctKMjY2Z888/v7+t59nX6/V+m+9dP9vZ69/TvfzqV79qAPOzn/2sv63n9e88ljHGnHXWWWZ4eLj/+z333GOklOass84yWZbt8d482vtf4PBCwQkFJxScUGBXFLxQ8ELBCwcWh5zK0Atf+EJuuOEG/sf/+B/ceuutfPzjH+fMM89k0aJFfPvb396nY5RKpf7/kyRhx44drFq1ioGBAX7zm9887PevueYapqameMMb3sD4+Hj/RynFKaecwnXXXfeQ3//3f/931qxZw1lnnbXbZz0v+Xvf+x7z58/nDW94Q/8z13V517veRavV4qc//SkAz3/+8xkZGeHKK6/s7zc5Ock111zD6173uv42pVQ/KqC1ZmJigjRNefrTn77Haz777LMZHR192Hux870Mw5Dx8XGe8YxnAOzxuG9961vn/H7aaaexY8cOZmZmABut0FrzwQ9+ECnnPn69e/No73+BwwsFJxScUHBCgV1R8ELBCwUvHFgccilDACeffDJXXXUVcRxz6623cvXVV/N3f/d3vPrVr+aWW27huOOOe8jvd7tdLrnkEr74xS+yceNGzE5CStPT0w87/j333APYCbYn1Ov1h/z+fffdx9lnn/2Q+zzwwAMcffTRuz3ovWXIBx54AADHcTj77LO54ooriKII3/e56qqrSJJkziQH+Nd//Vc++clPcvfdd5MkSX/7ihUrdht/T9v2hImJCS6++GK+9rWv7Vaotad7uXTp0jm/Dw4OApaY6vU69913H1LKh/wbPtr7X+DwQ8EJBSdAwQkF5qLghYIXoOCFA4VD0iHowfM8Tj75ZE4++WSOOeYYzjvvPL7xjW9w4YUXPuT33vnOd/LFL36Rd7/73Zx66qk0Gg2EELz+9a/fY+7brujtc/nllzN//vzdPnecg3vbXv/61/P5z3+e73//+7zyla/k61//OqtXr2bNmjX9fb7yla9w7rnn8spXvpILLriAefPmoZTikksu4b777tvtmDt78w+F1772tVx//fVccMEFPPWpT6VaraK15sUvfvEe7+XeJMjMfqjbHmr3v8Chg4ITLApOmIuCE45sFLxgUfDCXBS8sH94wtytpz/96QBs3ry5v23XIpUevvnNb3LOOefwyU9+sr8tDEOmpqbm7Le37/cKjubNm8cZZ5yx3+d61FFHcccddzzkPsuWLeO2225Daz3H87/77rv7n/fwnOc8hwULFnDllVfy7Gc/m2uvvZa//uu/nnO8b37zm6xcuZKrrrpqznU9HCE+FCYnJ/nxj3/MxRdfzAc/+MH+9p5X/khw1FFHobXmzjvv3K14aed94JHf/wJHBgpOKDihQIFdUfBCwQsFHhkOuRqC6667bo8e4ve+9z0Ajj322P62SqWy28QF63nueozPfOYzZFk2Z1ulUgHY7Rhnnnkm9Xqdj3zkI3OW03rYvn37Q17D2Wef3V++3BW983rpS1/Kli1b5uT7pWnKZz7zGarVKs997nP726WUvPrVr+Y73/kOl19+OWma7rYE2PO2d77uG2+8kRtuuOEhz/WhsKdjAlx66aWP+JivfOUrkVLyoQ99aLeoQW+cR3v/CxxeKDih4ISCEwrsioIXCl4oeOHA4pBbIXjnO99Jp9PhrLPOYvXq1cRxzPXXX8+VV17J8uXLOe+88/r7nnTSSfzoRz/iU5/6FAsXLmTFihWccsopvPzlL+fyyy+n0Whw3HHHccMNN/CjH/2I4eHhOWM99alPRSnFxz72Maanp/F9n+c///nMmzePyy67jDe96U087WlP4/Wvfz2jo6OsX7+e7373uzzrWc/iH/7hH/Z6DRdccEG/s+H555/PSSedxMTEBN/+9rf53Oc+x5o1a3jLW97C5z//ec4991x+/etfs3z5cr75zW/yi1/8gksvvZRarTbnmK973ev4zGc+w4UXXsjxxx/fzx/s4eUvfzlXXXUVZ511Fi972ctYu3Ytn/vc5zjuuONotVqP6G9Rr9d5znOew8c//nGSJGHRokX88Ic/ZO3atY/oeACrVq3ir//6r/k//+f/cNppp/GqV70K3/e56aabWLhwIZdccgn1ev1R3f8ChxcKTig4oeCEArui4IWCFwpeOMA4eIJG+4bvf//75vzzzzerV6821WrVeJ5nVq1aZd75znearVu3ztn37rvvNs95znNMqVSa02xkcnLSnHfeeWZkZMRUq1Vz5plnmrvvvtssW7ZsN+mx//f//p9ZuXKlUUrtJit23XXXmTPPPNM0Gg0TBIE56qijzLnnnmt+9atfPex17Nixw7zjHe8wixYtMp7nmcWLF5tzzjlnTpOOrVu39s/T8zxz/PHHz5Hx2hlaa7NkyRIDmP/7f//vHj//yEc+YpYtW2Z83zcnnnii+c///E9zzjnnmGXLlvX327nZyK7Yk5TYgw8+aM466ywzMDBgGo2Gec1rXmM2bdpkAHPhhRf29+tJie3cTMWY2aYuOzd6McaYf/mXfzEnnnii8X3fDA4Omuc+97nmmmuumbPPo7n/BQ4fFJzwxT0er+CEghOOZBS88MU9Hq/ghYIXHimEMftRwVGgQIECBQoUKFCgQIHDCodcDUGBAgUKFChQoECBAgUOHgqHoECBAgUKFChQoECBIxiFQ1CgQIECBQoUKFCgwBGMwiEoUKBAgQIFChQoUOAIRuEQFChQoECBAgUKFChwBKNwCAoUKFCgQIECBQoUOIJROAQFChQoUKBAgQIFChzBKByCR4ErrrjiUbXm7nQ6XHTRRfzkJz85YOe0N5x44on85V/+JQATExNIKR923J///OcIIRBCMD4+/pifY4ECT3QcjpzQ44Bdfz760Y8+5udYoMDhgMORFwC2bt3Kn/3Zn7Fo0SKCIGD58uX8z//5Px/zcyzw2MB5vE/giYwrrriCO+64g3e/+92P6PudToeLL74YgNNPP/3Andgexrnjjju46KKLAPjlL3+JlJKTTz55r9/RWvPOd76TSqVCu91+zM6tQIHDCYcrJ7zwhS/kT/7kT+ZsO/HEEx+z8ytQ4HDC4cgLGzZs4FnPehYAb33rW1m0aBGbNm3iv//7vx+z8yvw2KJwCI4A/OY3vyFNU57xjGcAcMMNN/DkJz+ZSqWy1+/80z/9Exs2bODNb34zn/70pw/WqRYoUOAgYH854ZhjjuGP//iPD+YpFihQ4CBjf3jhz/7sz3Ach5tuuonh4eGDfaoFHgMUKUN7QbPZ5N3vfjfLly/H933mzZvHC1/4Qn7zm98A1kv/7ne/ywMPPNBfQl++fDkAcRzzwQ9+kJNOOolGo0GlUuG0007juuuu6x9/3bp1jI6OAnDxxRf3j9HzzAHuvvtuXv3qVzM0NEQQBDz96U/n29/+9j6df6fTYXx8nPHxcX72s5+xePFilFKMj4/z85//nOOPP77/+a6YmJjgb/7mb/jQhz7EwMDAI7uBBQocZjiSOQGg2+0ShuEjuHMFChy+OBJ54e677+b73/8+F1xwAcPDw4RhSJIkj/JOFnjcYQrsEW984xuN53nmve99r/nCF75gPvaxj5lXvOIV5itf+Yoxxpgf/vCH5qlPfaoZGRkxl19+ubn88svN1VdfbYwxZvv27WbBggXmve99r7nsssvMxz/+cXPsscca13XNzTffbIwxptVqmcsuu8wA5qyzzuof49ZbbzXGGHPHHXeYRqNhjjvuOPOxj33M/MM//IN5znOeY4QQ5qqrrnrY87/wwgsNsE8/u+Jtb3ubefKTn2zSNO0fZ/v27QfmxhYo8ATFkcoJgKlUKkYIYQDzpCc9yfzbv/3bgbuxBQo8gXEk8sJnPvMZA5h///d/N89//vMNYJRS5sUvfrFZu3btAb2/BQ4eCodgL2g0Gubtb3/7Q+7zspe9zCxbtmy37WmamiiK5mybnJw0Y2Nj5vzzz+9v2759uwHMhRdeuNsxXvCCF5jjjz/ehGHY36a1Ns985jPN0Ucf/bDnf99995lrrrnG/PCHPzSlUslccMEF5pprrjF/93d/ZwDz9a9/3VxzzTXmmmuumfO9W2+91SilzA9+8ANjjCkcggIFchypnPDMZz7TXHrppeY//uM/zGWXXWae8pSnGMD84z/+48OOWaDA4Y4jkRfe9a53GcAMDw+bF7/4xebKK680n/jEJ0y1WjVHHXWUabfbDztugUMPRQ3BXjAwMMCNN97Ipk2bWLhw4X59VymFUgqwxblTU1NorXn605/eX0Z8KExMTHDttdfyoQ99iGazSbPZ7H925plncuGFF7Jx40YWLVq012OsXLmSlStXctttt9Htdnnzm9/MMcccwy233MLy5ct5zWtes8fvvetd7+IlL3kJL3rRi/brmgsUONxxpHLCL37xizm/n3/++Zx00kn81V/9Feeeey6lUmlfbkGBAocljkReaLVaAMyfP5/vfve7SGmzzxcvXswb3vAGrrjiCt785jfv170o8PijqCHYCz7+8Y9zxx13sGTJEv7gD/6Aiy66iPvvv3+fv/+v//qvnHDCCQRBwPDwMKOjo3z3u99lenr6Yb977733YozhAx/4AKOjo3N+LrzwQgC2bdu21+9HUdTP+fve977H2NgYQ0NDjI+P86Mf/YhTTjml/7nWuv+9K6+8kuuvv55PfvKT+3ydBQocKTgSOWFP8DyPd7zjHUxNTfHrX/96n6+/QIHDEUciL/SCAK997Wv7zgDAa17zGhzH4frrr9/n6y9w6KBYIdgLXvva13Laaadx9dVX88Mf/pBPfOITfOxjH+Oqq67iJS95yUN+9ytf+Qrnnnsur3zlK7nggguYN28eSikuueQS7rvvvocduzfx/tf/+l+ceeaZe9xn1apVe/3+V7/6Vc4777w523pFST1ceeWVAKxdu7Zf4HTBBRfwmte8Bs/zWLduHQBTU1OAlRiL43i/IyAFChwuOBI5YW9YsmQJYCOUBQocyTgSeaFnB4yNjc3ZVynF8PAwk5OTD3vuBQ49FA7BQ2DBggW87W1v421vexvbtm3jaU97Gh/+8If7k1wIscfvffOb32TlypVcddVVc/bpeew97O37K1euBMB1Xc4444z9Pu8zzzyTa665BoBXvepVvOUtb+HFL34xv/vd73jHO97B1772tb5M2Pz58/vf27BhA1dccQVXXHHFbsd82tOexpo1a7jlllv2+3wKFDhccKRxwt7Qi4DuajwUKHAk4kjjhZNOOgmAjRs3zjleHMeMj48XvPAEReEQ7AFZltFqtWg0Gv1t8+bNY+HChURR1N9WqVT2uKzXywk0xvQn8o033sgNN9zA0qVL+/uVy2VgNgq/81inn346n//853nnO9/JggUL5ny+ffv2h5xwCxYsYMGCBaxdu5Zms8kf//Ef89SnPpV7772X+fPn87rXvW6P37v66qt32/a1r32NK6+8ki9/+cssXrx4r2MWKHA440jlhD0dt9lscumllzIyMtI3DAoUOBJxpPLC6aefzrx58/i3f/s3/uqv/oogCAD40pe+RJZlvPCFL9zrmAUOXRQOwR7QbDZZvHgxr371q1mzZg3VapUf/ehH3HTTTXPy60866SSuvPJK3vve93LyySdTrVZ5xStewctf/nKuuuoqzjrrLF72spexdu1aPve5z3Hcccf1i3HA5uEdd9xxXHnllRxzzDEMDQ3xlKc8hac85Sl89rOf5dnPfjbHH388f/qnf8rKlSvZunUrN9xwAw8++CC33nrrw17HL37xC2q1GscffzwA119/Paeeeupe93/lK1+527beisBLXvISRkZG9vEOFihweOFI5YTPfvazfOtb3+IVr3gFS5cuZfPmzfzLv/wL69ev5/LLL8fzvEdxVwsUeGLjSOUF3/f5xCc+wTnnnMNznvMc3vSmN7F+/Xo+/elPc9ppp/GqV73qUdzVAo8bHk+Jo0MVURSZCy64wKxZs8bUajVTqVTMmjVrdpPZa7Va5o1vfKMZGBgwQF9WTGttPvKRj5hly5YZ3/fNiSeeaP7zP//TnHPOObtJj11//fXmpJNOMp7n7SYrdt9995k/+ZM/MfPnzzeu65pFixaZl7/85eab3/zmPl3HW9/6VvOCF7yg//uqVavMJz7xif26F4XsaIECRy4n/PCHPzQvfOEL++MNDAyYF73oRebHP/7xPo1XoMDhjCOVF3r46le/atasWWN83zdjY2PmHe94h5mZmdmnMQscehDGGPM4+iMFChQoUKBAgQIFChR4HFHIjhYoUKBAgQIFChQocASjcAgKFChQoECBAgUKFDiCUTgEBQoUKFCgQIECBQocwSgcggIF9oKf/exnvOIVr2DhwoUIIfjWt771kPtv3ryZN77xjRxzzDFIKXn3u9+92z6//e1vOfvss1m+fDlCCC699NLH5NwLFChw4FFwQoECBXbF4cILB9wh2N8bU6DAoYp2u82aNWv47Gc/u0/7R1HE6Ogof/M3f8OaNWv2uE+n02HlypV89KMf3acGUIcLCl4ocDig4IQDh4ITChwuOFx44YD3IejdmPPPP7/Qoi3whMZLXvKSh209vzOWL1/Opz/9aQD+5V/+ZY/7nHzyyZx88skAvP/973/0J/kEQcELBQ4HFJxw4FBwQoHDBYcLLxxwh2B/b0yBAgUOfxS8UKBAgZ1RcEKBAocWDrlOxVprNm3aRK1W67fyLlAAbHv3ZrPJwoULkXLv2W5hGBLH8V6Psetz5fs+vu8f0HMtcGBR8EKBvWFfeKHghMMPBScU2BsKW+GR4XF3CKIoIoqi/u8bN27kuOOOexzPqMChjg0bNrB48eI9fhaGIQtLVSbJ9vh5tVqd0xIe4MILL+Siiy460KdZ4FGg4IUC+4u98ULBCYcHCk4osL8obIX9w+PuEFxyySVcfPHFu22/59P/m/roEKYyQFppcHPlufz8Vocoymi3YuIoY9mKBicdE1N2EzZOV9k0Dp4rOHpByHCpRaoVYeaSacWOjs/WCUmSGqLIkKYGzxPUqxLXgckZw9YtXaSSPOXYgKNGp5mOStz8e8WWzW3mL6iwejlU/JQBv0NdzZAYnweaI4zPOFRKmtFqiCdTuplLlDjEmWTblGK6qalVJEvnJVS9iE7iMdN1STPohIJuDBhAgBSwYEhz1OB2fBGxIxlkW6uMMQIpDRKolyIW+NvwdZfNegFrdzTIMoEQ9hj1IGNRfYqKbDOZ1tnaqhGnkpm2oNU11MqC1QuaDDrTtHSFbe0aWgsW1qcZ0xtpOw1+unYpd941w/BomaccJSn7Keu2evz+3hZCCJYvqzA2ZBDCoHInOkwEra79pVoyVH1tP1cGgUEIkMI2xp4JXWbaEiVhqJpS8lLiTDHTdUhTweZxzcYH2yRxSpYZssyQRE2+/smnUqvV9vo8xXHMJBmXDxxNWcyNDHSM5k1T97Bhwwbq9Xp/++Hs8T9RsXdeuIBao4EIAnS1wX8PvoIfXK+JwoROKyKNUxYtH+Kpx/mU3Ixt0w7bdmRUyorVSyLmlabJjCIxCq0l29oVNo4rktTQammiKKNScVg8Jin7mvXbBHfdMYGQguc8a5A18zYxndb4xd1V1j/QZHSswqqlirKvGShF1L0OsXZYP9lg+5RguGE4aniKsugwndbY0SkTZZJtk5LJqYx6XbFqQUzNi2klHpNtlzgRbBnXbN8e4nmS4WGfUiBYOJxxdGMLrojYkQ6ztVVFAmU/wVMpJSehISdxTMqEGWFbu0aUSrqxJIwF9Ypm+cAUVdliMq2zpVkjTCRbJ2D79pjRUY9nHTPFfP0gbafORDJIaiTzvAkGO5uYKs/nil8u4sYf3c6KJ6/gRafXGa50uWdLldt/2wTgqFU1Fg1rHGlwnQwlDe3IYaajMAaqZU3Zy3BlRtWLcGVGKwnY3gqIEkkUQyeCkgfLRkMGgw5R5jLZLRGmkjvv1dzyy7XEnbD/TKRJm59d9eK98kLBCYcH9sYJ9/7z/6HaGMCUymRBldsqp/HLuwLiWNNqxiRpxtIlVU5YGVJSCQ9O11i/BXxfcNSChJFSi8xIwtQj1YLJjs/WSZG/d0Brg+9JGlVwHcP4lGD9hg4AT1ldZuW8Fq3I5+bfS7ZtbTNvrMLRyyRlL2O43GLQmSYxPhtaw+xougS+Zqhin/0wdWlHDqkWzLQlnQgqASwciim7Me3YY7rrkqaCTgRRDMZAltn36IJROHZkHJ+IbfEQW2ZKAPiOnYP1IGKBvxVPR2zWC7hvfACjwXEMjjJU/Iz5lWlKost0VmO8XbW2QkfS7ECtDKvmtRhwm7R0iYluxdoKtSlGs820nQY/W7eUu37XZGSkzJNWCMp+ysYdHvc/ECGlYMF8j6G6QUmDI60dECaSMLK2QqWkKbkaKQxSGpTQAAhhr3U69JhpS1wFw3VrQ0Wpw1TXI0okm7dr1j/QJE00OrPfjaMmV/5tYSvsLx53h+Av//Ivee9739v/fWZmhiVLluA87TnUoh3o+3+H8DyOetaz+f3YPMJI43gxYTelWq8SVBJ8J8GLyghlcH1JuepTK0FqHNzUIzWSSAa08xdOGCe0uzHC8ajWXCqBASWI0jJKCgYHJQP1FBOWMVIRhhGZqSA9FyfI8EoBgesijUuQNfAShV8yBBUfX2aIzEGlCpVK/EghuxmOr6hUE2pehI58ZhIXAygfAmUffs+1/zYaKYO1Jq6QhHEdL6sAUAtSPCej7EjK7gAOFcrxAI20SpoJfNfkL2KNcT0imeG6inmeIs4UoXaJZlIq0qFahSG3i9RVpkyDNBM0agkjHU2pJKnV6/glQa1eZmhQU/ViWmmFbRN2QgRlH+VZgqwGGa7SRImiEtmJNVBJqbh2Kc4gMMZO/swIjAHjughHIQQoT6OVQSpBVQkyDc0IShUH5WbEYYJIMnRmPfl9WR6ulRwqUs3ZpnQGU1Cv1+dM8gKHHvbGC5XVJ9CIZkjW3o9sz7DsGMHIvGE6nRRo021HBOUG1ZpL2ctopi5eO8MrSapVj0Y5RSPQRqKNJFQ1xtsOxAbRTUizGKRPUHaplDOqXUV9SCCVoF6vUq9Pk8Z1hKqQJhGGMo4f4JU0fjmm5HuozKEc16hmgnLFUKoYAuWTJBViJ8BNFFNdiXITXN/BL2cEfkQU+sjYQQBekFGu+vi+YnDQo1qGwYGEWrWKKzy6cYNAV5HSMFAKqbgRSrgEQiKEwU/qBFRxtEDFEj8VlHyNCBxSNYCXOQw7HnGqmA4lRnQQqkStmtJIfaRfoxkOIbSkXo4YjjO8EgwODVKuDVMfGmLesMdIkDGZ1KgPugDU6yXKVY2rNGUvxZEaP3FRvn3VDFYSGn4XKRSukEihIa4wnZYxSqA8CMrgOuCWSuBVUEZSdRy8TFJrQLk2iFIhWZrZZX1p+eDheKHghCc29sYJavXJ1JJJuP9uhOez8pRn8MCC+XS6BukkdDoJlVqFes0lUDFBUsfxDK4vqNUSBsqazCiizCXVkswp0U4lSQLtTkbYyXB9h1JFUvYNkRHUmz5SCgYHXYYaGhWVcAOF1hmOV6NSldSClFpJUnM0sfGpiDod7VLyNeVKjCszVOaA55BmgthIYm3wS4JKNabqxqRhgIkdDOAYILcVlASlYHAgY6DWwRUOnbBGOSsjBdRKKb6TUnYUgTOMEhmleJB6XEVrCDyDq+w8FZ5PIjN8rZjnKxIticZ9djQztFRUa4IBL0GmNVqmTmagXk0YaWdUSoahwTrVmkNjsMTwkH3vd3SFRjPFaKhUHUrlWQdESUOUKMLE2gr1UkLNs7aCBoyx/JxoSaYFNVfheBIpwfczhKNtMNeVpJmgFQvKNUUSZXkAUaOynlNR2Ar7g8fdIdhbTlanPEzqZCjPQzgurogZagg6kSKOXYQQeK79Y9uXu8AYgzFzj5MZQaol2tiJJCVIJVBSIAVoA9oIHAfKJYVSdrJlRmIQeK4kKHlIJeykTSVh5hAqn0QrUp0buCZ/kLH/TzP7MNuH2K5CpJkg0oo0k8j8XAAybZDCHkcIe86R8TEI4syShRDWsJb59YY6wBEuibYPspIg8ui7MRBnitRItBZk+f0RAnqOcGokMT6pUUjI74UkcUuk0kNJCEoOnieRIkMIg+sYymWnP17vurUWaGlXMEq+nYhSGLSxg+n8b2Enuv1x8n17f680s9efaXtMIcH17LUZbXfS2dxJ+1BQJYnaJXdQabOXvfeMVqvFvffe2/997dq13HLLLQwNDbF06VL+8i//ko0bN/LlL3+5v88tt9zS/+727du55ZZb8Dyvv7QdxzF33nln//8bN27klltuoVqtsmrVqv06v8MZe+OFqDqK9iSyWkF6Hp5IGB5yCQJFmmqkkgSluc+JMWD0zr8LMqPQRmKMffalAKUErqeQSqANZFqgFFRrHkLY5zs1LtpIHEcQlD0cR5JmECeCOLNGRaIV2U7j6Xy8zIicF+zL3PclSgqSTBBlNkooBPmPsD/SnovlFMsLGkWsLS9IQ34dggxF15QQwhBrpz8/d74PUWrnb5w5di4CUghcV6GUINGK2CmTGA+TO++pcUi8Con08T1BbbBGpeKhpN6NF6S0nNrj3d74gWdviBKaTCuM0EipMLlz1jtXV4F0wVH2i4lW9v5pgc7vjxd4CCnIkgytNdJJ9+mZKjjhiY29cUK7NESmEhzPB6XwiBisGTxXEMX2ufQ9u68NTtn8cKNzRxL7DGS5IQq5DSBBSoHjSJSi/35SEioVBynt/3vvNDfnBKVsACzVkiRzCFVAol3SrDeX83PAPte9Z19KcF1rM2Ta2gpaiz4/AWhjkMwauXY/Dy3t3NUa8tjbrK1gAqTRJFrZ4+S2Qs+miLRC5RyisdcDs/ZJohWJsYFVIQwCgUaSOT6p9HCUoVRxCXyBI7UNAriaUiDRevY4vfO1dpjBczUSUNKQ5WMaZgOHmZ5rK/S4sccHvSCjkgLPneV8kWaFrfAIIYzZ1YR+dNj5xpx44ol86lOf4nnPe17/xjwcZmZmaDQa/OuPJlgy5jM/mMQVMbVonFJngrA0yP3yWMY7ZSB/mLRgy6TD5m0ZlbJkzcqIheUJZtIKG6ZqdPOode9v3DM60wzCyC6/zRsSLBrq4ghNN3WJEkVmIEntC7wdCiZnDFobAl/2CSZN7fHqFZg/mOCpjOmux0RTIgQM1TKqfko7dtg4rmh3DEMNwfzBFCkMG3e4bNluI9+OkzsQniTwLdnEqR3DcWC0YagGKZ1YMdmUxCmUfagENnWnRy5pZpcXe5PROgv2fOVODrMQUAk0g+UYRxrCVBGnypKCtuQVuJrBIETJjDD16CQOmRaEiSRJRX+sTMPS0ZSjGtsQaNa1xli31UUICDxwdpqfShoWDXQYC3YQG4+NrSEmWu6cYxmdO2saOl1NGGZE3Rk+9tZRpqen9+q1956f760+noqaSwrtLOOld9/+kN/fGT/5yU943vOet9v2c845hy996Uuce+65rFu3jp/85Cc73dPdIxLLli1j3bp1AKxbt44VK1bsts9zn/vcOcc53HCgeOHLP55g4UjASKmJJxIGk62U29sJS4OsVccw3qnYOaDtS2PrpGTrtoRKRbHmqJSFlUk6WcD2doUolYSxJMxrypw8GJBm0O7af4cbsGAgxJGGKFMkmTVek9ywn+lItu3Q/RRE35P9F5cUUC3D/IEYz7G8sGPGPpNDNU0tSGjHDg9uU7S7mqGGZHQgQwIbxhVbtyUIIazjoKyxUA7sCzXNIMtsJH24bnmhHSm2TdnIZqUEjYolvDgVpNks5+38rusFJnrv0965l31NoxTjSE03cQhTm/LT48NqkDFaadsUydSnk9roaitUdCNJkkGc2HNcPj/l6MYmBIb7mgu5b5OLUjY1wnMNaWbPTwpYNtJhUbCVDMWWcITJrk+cSKbbgjiFJIEo1mgNSWJTCaPuDB/9s5G9zuuCEw5NHChO+MqPJ1gy5jHm78Ajot7dht8aJy4Psq70ZLZ3qv101UwLHtzhsWFTSqWiOHFVzMLy/8/en8falt33feBnDXs6853ePNeriVORIqWyJFumFFm0WohNBx1IAQzZTmwDDgIkIQIDAuykLbthwEHLghNDMhy5EwfdsduIbMPdHUWGLLdkW6ZCijSHIllVrGLVm+98pj2vtfqP3z7nvEcWpVc0QZdKbwEHVe/ec8/d59y9fus3fIdj5m2Pu7MheaUfKaStCWgNTauY5XL/7U7g0laBVp5lE7OszLqZB1DWArdxTs7t1dnXOjnb+insjloS6zjNI04XEjPOjB3jrGJZR9w6jFgsA5ORYm8k+cG9E8P+4aO5QhQpskT+X/ZGIIoUZ7YCo6ylbAzHc03TQpZAL+lgvutmpFzvww2Mr1+r/KGXSMwy2uO8pvW6ew1544l1DOMSqzyViyi7pkNRW6ouV1iWggC4vNtwffgArTyvz8/xxkGMVnKN1oSu+FJoFbi0lXMuPaQNEfvlFrMypmoVy1KaMa3b5GFl5anrQJnP+L/+6Z0nucLbXN/2CcGnPvWpRz6Y1Yhv9cE89ut8ruKrO33Go/NsjxX/wfBl5v/z36F3fpf3/tCPcbR9k1O/xa3ZFstKU1TQtnKjKgVGtThvOJxqTmeeyUizNZSxVS92JN0B/ZU3YD5vuXre8px9GeNqXo6e582DAf008NTulEk047X5eV593TM9LQk+EELARoad3YzBwFJGiqbVKALLUnN06okixc2zFZeTu9yz5/jNuzFvvnbE+z90lmfPl0Ta8cCMKQqH9wFjpCO4vyyZTUu88wwnGaNRTJoa0lijteHBiebznztlMSt46tk93ntToDdNK4dmUcL9g4Zi2ZCkliwzxLHi/K5mb9ywKA1feMWz/2DJzZsjLjzTktmK2yc7fOErFf2+5Xve03Klv0+LpfERAUU/nXIpK2iD5W65y/4spalh/9CR5y2Xdy1nF6+iQ8tXwzle/WqO0ort7YQ0VWjVdfgixbN7JecPP0+TDDmJPsy9JqZqYL4Ufsf2RLMzkii1KDRFpSmW0WPfPybSGPN1Vb9+e7XvRz/6UX67evmt7uffqb6+du3a7/icd+P6dsWFX/31KZPdmOFwws52xJ889wUO/rv/jv7ZLT7wsR9hefYmR+lFXj69wLKW5HiNBQ7S2crbmLvHlmUR6KVyQEc2sDuoGMYlJ2WPz51EHB/XXD1neUF/Ft3WfDn+Lj53f8Agg+fPnrBlj3l1cZkvv+I5Pc7xzuOcJ0kjLl4eMRkbmhZmRYQ1luO55uBYOmfPnCu4Et/mfnyOT32xx63XjnnufXs8dU6gBNPegNNE4xwsl8KjKcuWxazEucB4S+JCFGl8MORVxP5x4KXP75PPS248d5bnbibENkjXMAg2/8F+Q1E4okgTJ5ok1jx1SXFxvGRaJfzmS4p7t+dcuzHmu58FqzxvHMR84aU540nKj3yk5IZ9jdpkTP0EFww78QnXzAmNTvgy1zmaxeRlYH+/pqocl84knDt+CeUdX7EX+cIXTjFWs7WTkaWaNNGMBoo0hnPpIRe+9i9xvRGnWz/AouixLOHoxFM3nnN7lqcueIEdtJrWafLl4/lrPokJ76z17YoJn/lSy5uHI7bGl9kaBn5MvcTx3/279M9t8+wP/yhnd29yYvZ4Y7ZLXhnyUqbyzgm3TeNovOFgqpktAqOBYmsg0NtxVtO3FadVj/3jiJNpy7Xzmuf0FzG+5aXoA7xyJ2LYg/deOGXHHnGruMC/upcwncr9X5UtNtLs7GYM+2Y9FQTDstQcTz1JrHnmXMnV+Bb3zTk+eX/A7TdOee69O9w8W2GV52A2oCzduvhQWlEWLdOTAu88g1FKfyC5Qi81WGM4ONV88aU5y0XFjZtbPHtNY03oJvKKvFIcnATKspvsayk2VrnCsjJ89TYcH9dcvpjygastiWn46uGYl99wDPuGjzxdcCm9R0tE5RM8mqGdk7kFrY65Z8+xv8yoGsmL8txzeRfOzV9Gecdr5jyvfLXAGMX2dkyWbvZoHCkGZ0rOHX6BNukzTz/IoUuoGs0il2Jg2IezW1IoFZWmbjX54m1MCJ7EhfX6thcEv9MH87irrluqylFWmrwylGe26J3fJRoNUNWSYXFAlaW0fpuqUdINN5JwNk4gNaWz1I2QgvxD0IBVYSYQm248pkAFjwoej3Tfkw6qY3BY7TFGobXCeZkUeC/Ja1V5kthQO4VSet29s0bGg0KoFYz86sYTmJOM7ayV0VjwAe8Czoc1Fi74jfSV9wJNaLsJudKquyYh7Cglz3EemtpRNw5t5OB3bjMeDUHeQ9s4WifjujYYWic/18SbUb8PWrCVQWOjFhtqlPYYJb9Td9cWQqB2iro/QHtHMdeUZYs2irqJsNZgtHQvlJIxZJv0aaOMNmw2Y9vK5xrCBjZgjXRC27dxt5rYYL6u6jdvLSbwZH0H1rcvLnjKosEYRZoaymyL/tkt4nEfVeUkyyPSZAuQvQACBVIKaqcp2oSysVR112GOFeah/fP1SynQrkGHFo90vdsYtPIY3xIZ4enYyNBCB01U+BBoXehghjJtazuC4ooQqINDEQS+aA1ar+AECmtkEtm6QNsKwTF0e/br40LbQtXI3lFao41Gd5DIr18+SOPEGCnQtaaLH07ihwvUtaNpgkAevKFpoC5bmtoRUBjfEqyi8hG1s2TGYlwt3+sO0uAlJnjnaVpF0x+hvKPITRfbNFXpMFoRRStcdMAFi0/7uDij9Rbv5e/YthIblYLY+jXx0CiNM493Xz2JCe+s9e2LCY6q8uSFwIjrnS3657aJRgN0VdDPDyhHg/WEbA0T1HIOVSHpoLmyP6E7U7v9s4IZrZYGjG/RbQ2R3J8+gFWOyFXrvbRawQdCdw+vOvhNq1HK4zxds6LLEYKTs7VrDoLEBDreQBRrvJN8JnSxZJUruG5/KNXlAK3Cfd39rR+Kc76DQDVNoKq9QG/iFR9nA6Vq20Bd+266KFDq1ima2lPHAjUKdJwsn+C8JokqrG+6+OawOgiM2EsxVjtNk/ZRBIrcUFUlxmjqWpq2xihsx5dog8HFGc6muGA6mDIdVFP+Xkaxhi+uIOCPu57Ehc36d84h+GarzFsWUY1zgbqOeO3qs7z3h34MVS0xJ/v07rzOzvv+AIviMgcncuP2MkNkFad5RGCLk4VlmXuK0jMcGFHp0aGDFOgOI6ywVqFUICiN14aysczmfo0rs74mNo5eL6GqIkm2a7ljlouaotA4HzPoWbJYsSygbjxKacElKpkc9HuG0XaPKFIUjaXRGmtgMrE0TWC+cLS1o208ddl0hYxAEYyBvBQ4UFl5ktQCKVmm6cUtSsGylKS+qgN53rCYiURbkpoOWrXC33WJf91SVYFlE+ODZlnSJfGaso2oQkLeptyf96kaxfUdx5mQE5QmNTWJTags60LkdKG5NbkBGm7tK44ezIQ0mViCh15P08sUkYV5nfJgcJM6xJQLu+YklKWnaTyTsSE2ojwQUrBGo98Grk9bhbGPVv1a/TZz0Sfrd8Vq6oZ8XlKXDW3ref0Dz/CBj/0IqsoJixnmcJ/x+zM8N6kaOfT7fYM1SmB2zZBZrpjOHGXpSJOYyMpBInHB0q6mjEZhVrwcpamdZbF0xJFBEYh8RWpaxuME79N1EW6MHNpluZlMGK2YL6V5oHWHm1caowL9vmE0Toki4SGEAFniOb8rCkHObVRF2sbRNhJ7kkSuc5l7FstAUXrSLMJaTZpKgbFKEECS9LbxtK0jSQxRpIgihe32mVaBug5UecVy2XCaJ6SxYTp3zE9zotjS+ghvDWUQiMWy1CRnGs66GhVCxyvYcJUAjmaaN7afA+Dum4piWaGUkiKq9cSx/J40Dixcj+O9Z6lMj+ksoWpEWaWqHHXt0SpiENUY7XBeeBmR2ygO/XbrSUx4d66mcaIm1Fiq2vK1S+/l2R/+UXRVwPEDkrtfY+e94MIVlqUk91GkUUoxzS1ajZnmEctcFAi9l+JU64Dzoj5Ut5IwagXWeExbol0DkSSlISgi3ZBUSzLbEEfCPXAuECXys8tFQ1m0tG3MsGdwXnewuoBzwg+UgsCRZZrBKCWyisZrPJBEga2xpW4Cy9zR1IG2dRTLCt86xlt90lTQAFUN06DIy4CNNGkWkaaaxDqsCTStkWZFA9NpzWJeMxjGJEmMNeqRBklVefJFRVGmVM6glaWoYblsUFpRtJYmRMzdgLvzIXWjiXdazlSvE5Sml+2SRilV2zVDXeB0Ybg7vgHA7Tc0JwcLbGSIIoPzll5myFJFEkHeJByObtCEmPkyoWmVoAkWjqbxjIcRkfVrBUNrAqp9/Iz+SVzYrHdsQdDULXXVrsl1h3mPo+2bDIsDendep759m/jmTCAyhSdJ1JqQU9YKsCxLRd04vAubrkD3+iuc4IrABxCUIigjRJ3aU3cseB0cVnmsVcSxwbmA1h7vQ1cYyAFbNxatBA+7+p2+q56VEpJTmkZopQSLjGDkskS6dUvluuDg111AEGKTUoq2lcDTtgFtNFFiiCPVdeq7CjyoTqLT0VQNbRNtSLmr6+kqbOc8beupWo1W0glsG4d3vuMQSBI0ywVnXU8sJtQEpbGRlwJLbZL0soKTUrgds7mjyivpENaONjE4p9echrK1TNshzhtqp9fBx/lA03iCF8yn0YHIBMDj7ONvUh0ZtH206n/4Wp+s353LNVLINrUUrof5iOXZmyTLI8zhPvX9faKnppBI90urjqinFGUtBfWygLKUBDOETrGjuzfasJmOGbOKCxoVHK2XDlbTTei0d1jtSBMhMmstXT2ZEEhnDaAyQnxbYd69DyI+0E0n40gRJ6vYodBKSzGcgtZapmpdt9A7v+6qrppaRSkchqZ2xLHBRrp7z5tDfV0UhLAmWJtOXMGojdSfczKFqGtP3QgDsapamqqhqVt8EPJUEyQuzJZQ7UQo12LoEinFenIIkJeBw2IAdId41aCtTAq0VjiXrCUJG2eZx1tULqZsTNdBDbStSAoqBVa3RNrhlccFRWOax7p3nsSEd+fyLlA3Dtedcwf5gLO7N+nnByR3v0Z56w7xtRNCsuLQhK4JKBP3ZWXJK0XbynkIZs058EEJH6m7TSQfCSjvUF6STtmPCkuLcTUmcrK31g9N8LI/6xCIY0PVdNChVs68VcxRQXKLOBKi/4pgrFAi9JHINZSlounee9u0uEYUt4RboGhdwHvJFaT41kRWJo+re95304mqcpR5TZzYdTHlZbsTAAEAAElEQVT/sET4Kia0begI1HqDJqgdrbMCLXaWeW4oa2i2DNoJOStSwjuQ6aHC+0BZw7QW9cTF0lMsS6I4oq5a4ljjYhFcMBoqZ5i7IW0w1K0IpLQOmsZTd3wiq7vmoQHQ2LcB+XkSFzbrHVsQPPXMNr3BGGNWakItp36LMsvYeZ8huTml6G3z/ZPblJdT7iwmvLEvneY0FnmrEAyjoaGKJbE/WYi6T9TBV8parTfRsjQ8GF9EmcDxIqKpa5a55/Z0RNGPKRrL1fMKfzZaH4pVA/tHnvm8JcsMzgkJWGsYDCzWwrSIuGPOMitjrFWMRkK0neYWo2WDZYn8TJoaIfJmlp09SayzVBRNIqvIUogtTIaGK+fl+yHA3eN4/bn1U9BKU5zrMxgljMcx21uGWNTNyCvpDEwmEVpPiCLNvSPBGwJcuTbqpg4y7nNBEqllHjhcZtyZPAXAg+WQ47nFeTiza9jdNgwyqBohJW+NDTffd5Eo0mxtRWSJYnsUuLiVE+u2U1gwlM4yWxpO5hIYhwPDcGDI0q548YK3bFpN4x4PKwwCp1Jfh5n4+n8/Wb/71uUbO6S9MVor4kSS96P0Ikm6w+T9GfbmnKq3xQf7r1JtZdzOz/DmYYpfJf6dCk6/b0kSmcBNl6IysYwMsRUOUAgC8ZsXhrsjUXS4dydmOl0SQuD1vW0Wg5S8jDm/E9ibGJQyKCXk+HkORSXJ96rDb61iPI6II8VpnvCmusSijtEaRqMIa2BZGapWExvBMMdeEVmJAZOtlMlWitIQRxrbFSzDvsSNJBYsszUSmw6nUhBlKSSRKK0MBhHGaMZjy85EEVuBWB7kA+alZTgMnL28RRxr7h36tcrKjefPMZnE9GyOV4bWW2ZLOJ067k57jPaeJwTFg6OM2UIO0/Pn0u76IK8lvoxHhmfefwGgU29RXDxnuL4zJTMVbbDM2x55G3M00xwcufXno7Wil4h6WRug9UaKNPd43KInMeHduS5fGdDrD6RhF4FSNSdmj3I0YPt9mvj6CU1vzIf7X6Ha7vHV+QW+fEv2XWQDsfWksaKX6Y6LBydzg9GGqNPrL2sh9qeppqhhf+dpFIH9g5T5QgqDVyfnOO2NWJYx53YCO2PhDhkNVaO4dxiYTht6PdkLTScKMBoaYqs4KVNs7yrzIiWOYHs7xhg4WUZrsYNeJzZSlIq21Uy2UgbD8wCkqSgfaQX9TLrr2yPN5XObXOH+SdTB7gJpLCTrnZ2UXi/qYoImtgCBaRFRVJrBwHL2wghrFXePIyIj++3qtSG9VJFFpUCLvWFRwCIP3F8MGE2eB+BOvsPBLMZ5xd42bE8SRr1A0QjpeDzSPP3eC9hIszW2pKliZxS4OFmQmFZUE72lchHzwnA6l2bPcChKamksiA9FoG4Ful09yRW+pfWOLQg+8mxDf1DjwobJfmu2ResVi+ISdQvfN7nLpf/vX6fcP+LST/xZDtPfRwgw7rWM4oo0ighBzCtmS7j7wHXjQoEJgZCD41hxPIMv+B18gHsHnrIQDsNv5RZrY566bPnwhbv0w4y4zYnrJWU65nPbz/Pa/XitilNW4ifQz6QTf/9I8ca9TCQNI9jbMdQNPDgKKA3bI5gMHHWjaRoJOttjzaXdhkg7DheWw1OBDG0PPYPUsZXmXFZvEDUFn3Ef4pf/ZYvzgWef7nNxx1Fnin4aUbcR475nuy8j9eOlqB8BnN01nNszHE8DX3xpTts4nnluzAtPBxJbM4xLVNchmS8D01mL85aj2S4+wGwRKArHZGx49lLNOCmYVhkHsxjv4cKu58Z56ThEpsEoz5lsypXZ59BNxcHe87xRXGBZRdw/ElO48STh8nlDlgSs8bRORqVVp2gkHcvHWybS3zAGNOrxg8ST9c5c738uYTiM0V0HqPWBl08lwfTcgBheGLzGzi/9D9RHJ5z593+ceusHaFq9lgSOrAaEcFy3gXsHHR7dyDQuBOEGRVZx/xiW5Vmch6++XnJwd8rJgWG5GJL1E65fjvjQpSP6ZknPzcmqKVU84CvN09w5zsgrOD71NE1gNDRsjyRO3D3UvHq7Jw2MVIrqEOBoJl37MxPPyLZreEMcafa2NdfOVMTG8WCWcf9YdYe/wAnOjEqeSV8jaZZ8zn2AX/4NwfBfv9Zj3PdopdjdNtSNXMe5SYUicDBPuHsY4wNsT2B7MuTwpOXLLx3jW8dz79vj930wohe3DKMlDkvRRhweNezfX1I3ffZP9gCYzhzLZcv2dsyzV1rGacm0TDmYSvPg/I7nmYtS7Oe17Otr2zOezT+FrZbcPfNBXp5fYJpbbt1tuHdnzs5en5vXE/ppYJg52iBk4qq11M6Q1/Fb3CnfuJ7EhHfnev/1mtGoXE/5amd4Y7YrSjXhCiGG7xq8wvB//e+xR6d85Cf/U+70vw/vBZqXRbLPhn1LEmvyInDrnkyjkkT2nlJSaI+GmpMFfKa9jPfwtbuBk6OC02PFwYHF2oTrVxM+fOWEgVkwao/J8kOq8ZjPjj7AV+/LvSqKXZIrDLpc4fah5av1BGulsTc8JzDe2/uS5O9MFNsDR9loloWmbQNn9mIu7gpG/3Bm2T8W/P320DPKxET1knqTqCn4nH+Bf/YpjXOem9dTzm+3MjW4KOqCkisUAOzPUu4faZSGvW3Nud2Yk3ngpS/nOB94+mafF55qiIyjbyspCFrLwZFjelrRNClHuxckpp16ZrOGnZ2I91+r2UpypnXGwTyhdYqzW54rex3H0NQY7TmXnnDp+N+gm5KDs+/j9fISizJm/0Sxf1AzGFjO72nxU7B+DUcqKokpRfn4+/pJXNisd2xB0I/EgVg8BDTzNmJZaapGWPFF4SkvJ5T7R0zf2OdMcUoShW68JoFBEYijbryuZXwmY0W9hgsJzlZGbLOljPqr2os0nwssFw1aQX3eEquKtJoTVzOiYoYKjl7UkERRh92XLrdS0nlzXvD886XrpEQ1SSRfX+ah0+cVnV1vpIMYRaK2MYorItOyqCOUNmuojdWeRDek+SlRvcRmnuWiJoSAc33R9wV8HIgsZLEns02n2RtT1dKV6CXSHZkuoFhW1HULjOnFDYkWiNTaN8AH2sZTlp7TbqMUhbi6em9IbUvPlOQmXkurTWxgnAqHYeWPkOoSU+XoOkcHUYSqnaJp5LWcCyRRWGuWrxK4b2Vp8xZjwN+DSh7vtpXFnix2a/JqXhuWtehdV43ce89NUuqjE+a3D9hbnpBttShEMrQNkkTHVg7Z1glUwIeAbgUOsIoJuoP5nMwE/lPkzZrAN5uWgms/F5HomswtyMpTkuUROqvJkobIpphGrYnESrHmKlQ1TKcNaWro9wxJtCIGg1MrcrKM963pfAsiGMYliW6YxTFaWZReke4DmW3plSfE5Yyo56m7PbUWVNCy95WSAiIxgn1yXrEsZBow7AvHx2hFlVcC79EwTBoy22KUx2Nw3nQwpZblouG0S5rK0nWwC0htS99WLLUoiIUAiQ2Mk7ITEkiotPCRTF2ga0lGilpgB0Xh1lyR2EryFpkNbFA8DNRaN/13Wk9iwrtz9WxDZhq08gSEnyeTcJELbx1U2xnR0SmzNw+5UEzJYiH8azZnTGw3HjgSE1gT/a1R2FSIrk0LpwsR91guG4HZ+kBVNp0zb0KkG9KQk5YnxLN9tHf0soYkinEdIZYAEbJ3vYeyUpzOWnqZYdRTJJGnrDVN0/GYgpCdbZcrWCu5wrBzKl+U/bVEqNEQGSdGbEvJFaK+J89FlMD5VGKLllhgjUiMJ9qtz92qlt8zzEQeeLZU5F0MhD69SBTRxDvJrGNp23rywnE6k3xqvhAVwtHIkpiWzJQsdULdCJ9x3POM00riHRL3ElWgqyW6LlHBbzr/tacqRTI2iTa+R/AkV/h2rHdsQXB/PiBuBx3mT1FUUFQb5ZAkUdyZj7n8E3+WM8Ups/FlnuOI0sXcng544yAmiWDcdwwzj1IGay1tK13Btu02TSTd96oOzBbC1PcukGVddzvWa17AG/l5MrtLf1jQG+fUIWE5l1H/KvlvW+EESHEiwaWpPXGk6SXQSz3WbIwwYhs6Q6LAuB8YZIp+Kl0w72z3HNnkZa1wPsL5Ebr/HPGgZnocM9mWrqYPMMtN182QgiAEyJsIFzSnC83xaUsSa7JEkxmxZB+MUqqyFZx1Y2m0YdFIJ2NWWBnV9yzGbHDRMmWxRBEUjSXSPWZlzGwpv3NnBIlpcMEwrxPqVlO7s8x2fgClAg/mQ+4cRaL20rZkvYheT3cdG7c2bgHIIgk6S1M/9v1jIo2Jvq7q5/dm1f9uWkczQ+42YatqRH8bWI/L7xR7nP33f5y95Qnl+DxX0jssGfDF/bPcegC9TLE9FCJxGmvSxKz3b9MEtBaInlLyteVSkty2cUJ8iy2DUbo24zquh1Q2ptcbkma71Dolz0VXOwQh5RWFYzQ0ayx/6wJl0QoBOIZBJiIG6/fSPc9qz9YgkESaQSbmQqHDNa+aWHklU7TEZhxMrpL0SmanMdZWaENXLEkXMokknlgTutdCku9SYhQoIoN4KvQStJFiq2hspyLUozHiS5Blhsl2jyS1a8OkNDXySIQgvWhSZkXE8akE7os7MIhyah9zkmcsCsP9aIzd/QiWlq9Oz3H7QIqBIm869RXhhlkdSIwjMw1KBTLT4GLFwi8e6955EhPenevurE/PDdcGfstSkZcPGWcG8b/47p/8T7lQTFmMLvBBe4/cZbx6NOH1+ym9FLaGnnEv0Es0vTTqFHigaUPHt5F9mVcyCWtbyRcGwwTVQet0t3HvLLY4iYaMersMh9ckV1jExBYh5OaSK+ixZtQTbH3TBoq87WKPIbGBXhIYjzTBSzHvu1xhaxDopxITVGcCak2g35PCPK80zifUrSH0nycatBwcpaRpI94dNRwvok5hTeKB90IQdkEzyxUn04Ys1YwHAqWMI9WJmQgMclnHGB3WBoTzwpBlAUiwVndka4E36mEk05cmItJ9ToqE45n8bXaGin5U0XrNvE6pnaFoL3J0ZgdN4O50xJ0jQ1VDngt3LIo0vdTTj6WpsWoKZFEn9vIkV/iW1ju2ILhzpIkSqKqwHuGLE6kSNaFI8eaB5Sj9fSRR4DmOuJK/RN7b4aX8vXz+C3POX+ixczMwTBoi40ljqWJPF5plIQlEL+0wt7Ww7Z0LJIkcasZAlglWt3Xw6v0UrVIG2YBB6ghBDLpWmN2q8lS1ZzzSxJEQc0GtO2ZZ4hkkLbExWKM7aU3pUhodGPfaNUFYTD90B3FY6YgrXKFYljGLahejAycLweivtMZnuXT4Rp1VeAiKvI5oveJ0Hjg6LOj3Y87sJFgTiC30B+K4CtJx1UoSiNYJmz+yQKY7ErV0UONYCxnSQtkaIGGemzWeMgSIdUvt5TXnueF+Y1kWKU0rusdl5whljCLLDFmqyaKaXlQLTtjr9cEfm5bULR/7/tH2SdX/blyHJ564cOv7fXUoG6Po97u4cJhSb/0A2VbLlfQOu4dfpj88y6eqc3z1lVPOXRxwYUcxTBp6sSFLJC5Ml4ol0h1fGwM2cgg1ndyniQxRYukPIoYDSfCP8oyFTYhNn9i0+KDJO4dUHzrSXtngfIJW4DrCcVm24vAZB4ZpSwiWhdGPGIdZHRj3GoaZkIQbb2gwHflYDuayIyYabegnu8Sm5XRpiGLTNQoCVSMJTWTDWkGlcULQKysoC9eRjfXa8KjXj6k6nkLZ6M5NNKUyEWVr6Pely+86WUWvA/3MkMTCi6pajVIx06Xi+LjsxBsi+izQuk/tFIsCqibiZHEWgDsH8OBBSVM7ykLig+4SFqs9cddh1Igai8aT2Olj3TtPYsK7c93ZhyilE92QM8r50AkKCP/mK7ci7va/jywOfNDe48r936AcnePz1ffzhc8fc/HKiIs7gXFS0YstgzSidqJMNs83XXetxfxq/0FO2zgGo4TBUNyJk0SteT1vHkQYHdFPU7JkAsg5H0eyF5dLR1U5+r1kbWLYtqIOmCQGozWR9WQJOC/3bGwDrkM2jHpunSuAOPvGJjDIpAFQ1uLfM8tjTnPJFQ6nmiSRmFDVgdO5IBLG/UBsZCJfNOKYPp0HTo9L2mEMZw2R8URWfl7eS1jHuGWpZbrpIEs76dcmrCcb1irSVJqHeW0JZJzMDccnYrzoLygyXVKSUDSW06UlryKm80Q+k2ITC1ZEbWsl+e9HNS5sXIsTI1Drx20SwJO48PB6xxYESQRRLHjaEDpprk4OMOpc+lZSlc4rSheT93YoTZ/toePpp4eM+hCZeq3osdL4XkGFVh08cclUZD2Ld4EkkQpUKmeZIKye65GNXbWSrK9cQEEIR3Gs1km+93L9aSrd9bLWKGXXyibQaQG3irZrHa70dFdufStnUJDiIbZy7b5zJdZKnI1BXBFXwWW1XPf5tE7eR68XkWarTS2fY5YZokgTRd3nGTbdFWuEFBiCIi8VrruYlZ+DD6J3rDtlhuHAdN9zlC6m6YLZSgJx9bNJojseB+vvx5GQg6rWdrbv8vd2VhO5iGX9eGoiANpq9NdLiYXfm1X/u2n1Mk2SGXynltM60dqWqZioCYkGt0ZhWDKgPzxLEY8Y9z03bo4ZDxVp1GB1oHadK3enSLRStgC5/43pZHs7RZ60FxFHhiTedAObVtR4Vqpizqu1k7FW0O+LFGgciT9BCHSkaIvRwo3Ja0PTdr4nHZQpb4S81zqFC+oR5Qznu+ea1b6XRsBKttQH1of3KoY+fMZ5r6hbcWNXWp6bJLrrggZiqxgMY9LUkiQbZbKqNWtH9FFfkcSGsgws883ofhV/WqdolDRMtre7xEd7KlIqLxOUOJLn1638UGRhMIxoW4u1mqqKyXp2LRVdtBGKvvhAqIBWnoV/TMjQk5jwrlxxLMl4ayF44eqJEuBKFWgjjd06Re4yytE5ymTE1tDx9LNbbI8VkanwgAua1quuoSf7Z6XtD7J3s15ECJZezz7iJC4xaHNt0lTr5Db9ppPd6+kO9iN70XlJnHs9iRVlrVgY2WsreKTz4BsRRvEmdF4JG4Wi2m1yEWvkobv3vcp70lR3XXuZMK6mHkp1qkShyxW0iJvEnSdR28WbrCf7MrZqreoEdHlZ1zxEscgVs3bjqmwfgmhWrcSZ8UggjxpP4VNqZ3Gr/OyhzzCONEZH8lwleUwSqy7vs3LdnWty4w1We5bNk1zhW1nv2ILg+pmS/iBew0bE3EsUMU7ziLJWpLF01RWB29MBL+XvZXvo+APpJ+k3n6bp3+SLyfdzUmYsSsss3xiMZJ0E2TwX+NCwr3jm6sociPXh3nSW2A/fpK2T6ns1bq8bIfY9fdkTG8+sUBxO5ftpApfOW4oKvnqrpa48Z/Zirp5zGAWnS8NsKb+j7ZL/JFb0M9aKB3UjB+fetlib184wL0XNJEsCSSeysZIUNbqTJ3VShBQC5WdrCHuTtCuiYLrURAaevSad+NaFdfAyWvgXo6zlbH9OpBpuL7Z5+XZE03byZmrjP6A17I0cz5yZowkcFAO+ejhCK1EzGGVOlBwSwfz2U8cgafFBUTWmC2ahUy6y5JWMVV1nzNS2gap4fKdibTUm+vqq//emtvC7aT19sWY4rDamOV663LXTnMxFHtd23a7Gab64L5OBcd/zPeMv8rH8C1ST89zqv4dF06N2MUczKSqslX28wvgGD1miuHoxQutOqSfytE4xy0XrOwQ6lSJQyqybFCs+QxLB+24qrDaUTWBZyuE/7CuSyxnOwe0HIn/a7ynGAzmkj+eKe0dyv68KlCSGQRbWcomrBsHeuGEQV1St5bSIaVqLVnD5goT3VeGuwkMNlFrUw3wHI7py0XYa6xJHJkOYPJd2v1/giFWjWBSWxsHWwPPBSyfEuubN+Q5fedPQuoAx8pp1KzGyNLA3bnnvhQVGeaZ1xiunnSpK5Liw7Shqw3Qpse/CbuC5y4IFX1YpTavW13yysNwuLbNFIpOPbjuX+eO5kj6JCe/Odf1sQ3/YrLvlWokDceMN09xSN0o4NolHA68eTfh89f1sDR3fl36Kj0W/RT26zivJi5xUPeal5Wi24RmmSdd1r6TQGPQU555PMTo80hBc5QgrCA5IArwo1PrMdV5UcZ65IjK7yyqs85KtEWyNUsoKbt2TCcLOTsSFXdmDs1yQDZKfSHMh7tQHZaov15jEcGm3ZZxWFG3ENI+oGkU/hX6qH2kM2K6wAIEZzXOZOvZ78OzNrHsPAtU0Gm5esd3nLBwMoMPyi7LjXrYg0g235tu8esfiXCDt/AQA5oWCwrAzcjx9ZolSgeOix5cebK9zhUHmiDv4VQiKQRYYpPIht6vJsPMcziKcjyhryAuBaAuXEsr88SFDT+LCZr1jC4LtdMkw2xCEI9ViVEvpUwJbgKWfOEaxZLtvHMR8/gtznn56SL/5NJ/+6f+J9/5H30f2xz7C/bZPWYv+OAhMKLKdKlApZkHjoeXMqCIynsaJQ2frFIvSUDWPVv2NE2xhCHIjti4w6Gn2egWZrWjdhFtL2Xg7E8W47/FBc/Ag5/RwQRSd4cZ5RWQ9jTPMl6IjvlyKG2jWj6iGIlsauiBijKIXN0ziBXmbsqhEsivuZNNCkARotUlBOg9NK4ZmAGe3A7sDKSgenEYUJSRD2B2WRNpxWqScLOSWkA5DYBDXnAu3SaoFRZaQxBNArfWKnZOCJQS4uO24yJto7zjR7+HBUSCJFed2AknksEYRGyk+trOSrXiGQ7NsMyoXMStjjmaWvAgsFo7ptKJtPItZQZnXNNX8se+ftxwD+t+bm/zdtM70Zowyt9HNDxqPOBDXzXDd3QoB2qC49QC++sopN26O+Vj+Be787f+Z89//AUY/cpFCi8rFfClQg/FQdzKcsr9lBC7Jb2Q8o7RmEJWUbcyb9DntOohlV3A/DPWRbmCgnyrODxekpuZBPuJkISpcaQKDnmKRw/5+xWJecf7CgN2JQatAXsLhYfMIjynLBOPf0Y/WJOVJUrAdn3KqR9yfpixLEQ3YHspnlFcy0ofNlKBq4HQu/9idKCYDgUBWjXTe+qljK6ulA1/HzAorhdBS5FTHfTjHbXrLI8r+B/laPMFXm9/hnDRLtIYrOzXXmy+jvONL9gXuHguJ+sK2YxhXQMrJQncEw4bL/UMAil5K4w2LOuHuSUpewb0HLXduTdeOzb71jx0XnsSEd+faypaMM43V0hy0yqFxVCFBqzHLyhLbjZrQ6/dTvvD5Y55+douPRb/Fb/1f/g7v+fHvJfsPXmDfDSgqzWIpRfCwL8TdquMSNE1gMlJc3C6w3d5YlHbNXwhhA3GD1YRAzvG6lWS1nyrO9AtSW3NPjTg8lTN3exQYpJ6jueHV10ox67I7XD6jOzMxIejKmevxLhAnmmFfJph10/EStJiV7iQz5qbHrLCEIA3ULJbEepUriHCBfI5NC7OF7Icz25qtoaN1iqOZpihgMoS9UU1kHIsyZlaYTvRAcpBxUnJW3SWuc4pewq14TNOqrpEi8TSvRDjh4rbjsn4DReBUPcfdA+EoXNwL9GLXIfgFPnlmVLKTyB6vfIzzmtMqY/9Us8yF4zWb1XgnLvZt46jLJ7nCt7LesQVBE0TreqVQU7hkrVt/srCd46AhjSIU0iU/f6HHqA9N/ybv/Y++j+TGdc64uyRbFbeTPeZF0pGKH6rWUxl7awWnRfyIQ1/rpJNWvcX0afWcJFaYLgkpO7KjRw5751aaxkIW3DvbYzxJ2ZpYWu/xrRD2tGatUQ4RvUwzHkhSviJTx7YbmbuEykUd/KmDE6zGZa1aQwliK5V/GgPdlKV1iuNlAkA/DfRTOskuS41siEEmdupFpVkUil4c08YxkTK4IMSeugn0exIovRf+he8KkrnZQluPrxWDvnQxQb5Xt7rTeAdrEiLTwwfFok4oW0vjNMMMkkgBhqqyuCgQxQbvPNXbkBJ7yzGg/705Bnw3LRfkPlSq48e4lLyNxV08l6LfB5EW1UoIxOcuDhgPFdXkPOe//wPYc+cZLe4RZSX14AZ3k75IkDYbs8IkhrTb47NcRAAEs+sp20iIi4XEppWKz2o0vlqq26NVp5MvBbwcjFUDy0Kw/9vbMZOJcBJWz9MKdAdT6mViQJilMBn4ta65+KiIRnfhM2pvsSaQRBIDWtcZpDnWkKgVFjqJYDKU7zsPpwuDNTDIHIkVycXKGUAO/VHWUjaG45lmsXAUlaXVcWfkqNeCCnGkSToV0FXHtHaGRW8HHRw0K36TPKfx4rtQlHKdy8qySHtCDm2FFNk4TWQDPRS9niZJI6w1KC1KUHX5eIf3k5jw7lzOW9qgUeJKReESGm+onWWaR+SVIok26IBeChevjNgeK+rRdd7z499Lcu0KO/Ud4lFFYs8xXaRr8ZFiBcGLBR8fAhwtks4bQHW5hOQJqzxyZXa6nh5oiK2CDjpTtFbgx16RxLIH80pgfFUDOzspg2HM1sQCHtc1H5SSGLMyLctSzWQgOcSylPgXdd4iS5dRtrIZV02EtnudqlEC9dWKNPaYTnlsMtLracbpQvKiYRYY9To1pCDXqLV08n03bZzlFqsTXN8SlHgM1U3H60jUGrWwmqDUrWFmdtDIBzQaSK6g1Ao2rNaTkzSOSU1GCJ0rsjeUjaafymeqlKFpLM554sQKR6J4O07FT+LCar1jC4KqjShchEYIMw/mGYdTsfpe5p66EdWOEGLiKDDuO3Zuio7tF5PvJ/tjH+GMu8vWl36N8XzK8MU/ypvx+1m0G4Z/Eiu2x6IItCzhtTtCAh4NZLzWOpguOhlSv1HYGQ6MQHq6g3WlUXySx2glet67Y0/TKu4dwtFxw3Boec8NwyAN1M5R1qKl3TrZ5Fmq2J1AP3X04ppJkmNU4LDoczCLRTmgtrS+T9txAoCuYJGD3XdeCCsTMmsCaeTZHnh8UNw5injjrmc8NLznSsFuMuO0HnDntEfTKraHLeeGS4o24kvHKXfv1ygV8/QoJTY5pbPSofCBrZFla9DBJ5RMIhal4VZ0Zu3weHZLNqX3Qr6eLjX3D6TDUTURzWSIh7W7YZbA7mhFAE8IISaEwKAvSizF8m04FT+p+t+Vq2ojmmBFtjYYDpZ97h5bEQWYOcrS0e9bQLr928PAhR3hDNzqv4fRj1xktLhH77XPkC7mPPXiH+Frgw8wLxSzuUjrDgaGy2ehnziO5pY7D4TEXJ2J2B1rikpzdOqZTtsOxy/uxIOBqOvAht/kA5yWMbGx1E7TTwVmcHjiOTqq2NqKeeaqYtJrKWpYlJq64xIksXCS9rYEGjjKWnayJUYJJO+gjfBBMStj6fh5TRJ5rJHDPq+6gqGV2LCWI7SeNIKdoXh93Do03HvQsDW2XLxZs5dOmTd97s/7tE6x3a/YSnMWTcorVcr9u3O2JlvUF1Nam1K5iGUucoPDvmaQsU4qQoB5GfGmuSiykEFxcasQh2FnyJuIeW44PnXUjSdNIqyWuHC6MBSVxIXtgSPqe6omYjpJcc4zGESkqabMH48A+CQmvDtX3kbEbYw3LS4oDhY9DqZytspZ70lTxXhgia2oCV3cCUSm4pXkRbL/4AV26juMXvp1BrMpg+/9o9zqfYhFrigqmQo8nCvMcsXLbwhkbTzSjPqSK5zMRCzD+UBTy301GFiGfeEYxClE3eT9tJBcoXUCiWlaxYPjwOlpw2hkefaaZph5mtZRtZvzXszXBNKXxoFB2rKT5RjtOcz7PDgV47FFZWn9oFMlkgYhiBqZ91I8FB28KO4cjIeZY3vgab3mzpHl9v2W0dDwgWsVu9mcZZNxkGe0TjFMW3aykryNuXuYcvtuRXMt4fowI9YlVWvIC0E+DPpSvFgC1sj1LErNmwshOwcUF3daQhBH6LrVzHLNvX1xR26dpXX9TjRFYKH9FM6MG2LjuJ+keG/xvuOYxVAs3w6H4ElcWK13bEHQBo3zBt8p7hSV5nTm8T5QlDIuq2K9xrwPM88wafDASZlxv+2TbFWM51PKW3fIXjglMquDujMeioSIl8ah6/iJ0kYSW9KuahcMu4z6VmpBzsno/mHlAa0CdaMekvxcjQyhKEQ3V0zFCmZ1Sl7qdQK/Kih6iacXO3pRTc+WaAKpTdfa5c7LZnF+A0+Qr7E2RnuYsKwR2cLUtrRe43zEcumIY02iHT2dk5t0PdYEUQZqtQTTPG8o65g2WIIyHflJZFm1ptMEF4vxVsmEYlFZjAJrPKl1YkDkBH7VtCJv2NSOvJQq33c8jKLqJBGNI7EtaRwRR/L9fiqTi4jH36RKa5TW3/C1J+t395K4oPFKd2Y4mmUhh3ZZOuraE8Weplm5kovKmNWBRdOj0AlRVpIu5jSHRyTVnDgK2K7b3jQe73WHz5f7rapEyaisjBgItqIc1jS+4xzpbpJp1l34Fe8odKT7EPRaR1y4NyI76kYx/cQxjEsgZVFKR29VaFizilGexDoS06Dxnf53Rzr2irK16+mG0YEG6Vyu1JhWsUGp0MUbT2wcWgWCNxTLhl5msMqTqpJcpV1s6RIR1WKVNEaqshF4Qojw2tI2au13oNYShh3OGemiLusIrQJZ1IocsTc0XVxoOwhEUzvKKhJFIy8xIS8kibBG3nNko7VyTJpqBj2FeUwfgicx4d25Wi8kYN3FhLzSzBZBvAEqKQiUMtQdOX7cEzUhD5xUPfbdgHhUMZhNKW/dJf3QCbEVnhwVj+QKcSSd7uVSIGu9DsYnimeBppEkuChEDnNF7F/lCiso0SpXWH3daWiaQJ639HqSK0ySgqWOqV28nm6sBFFWfj1Z1EquoDyJTUkigRK3TlGiH/o58WjyfiUyIkpBppsiQtdAtI7We0KwFHlLlmpiI7lCbaJOXEBhlOxHqz11G1gua4oqkZigdCfUsCE8awWhe7+r6WVeW7QOJB2cq/WaotnkCqt4nheGsifvpSgFsphEitg4MtuSxp4oUgQv0580Bv34A4InceGh9Y4tCI4WKSUyllvdsJORJIir0XoIMFuuVABEGiugWJSWslbcTvYYvvhHST8447R/gevRgmrLcrhImOd6k8i3gsudjOTj2BrCpO9wAWIrLn5lBdO5YGNXpLlVF1AScLWGEVWdo24IMBnBoN8jtjDNNXk9QCsYpB6PEBF1p7OdV5rWRRSRoWzlAK2dIY19pwoiG6V1iqJmDTeKH/or+sDGF6FV1M5SNBtlkOHQEkeKgzyj8ucoG9tJEULjFAfFgKoVxaGdnRRr4EG5wzwasKgjJiNReDFaZAUFptQlPm6lpCRdh8h4Wi8qKnWrWIkEmU5yte3UYQbZCr4kduZ1lyikCWty9KLQFMXjb1KlNco82eTvtnW4TKlNH6t7Half00vBxYo0iVnpjle1kN7SWNOLDbWD2klHrh7c4KkX/whJNeekf5GLdsnuwDLuJ0yXcWdQ5NcSvJOJbLBBT7rrSinO7hrGQ0PdBspSOAjGbAqB1RIyrqZ14SF4AYyHhvjagF6mWJSa1g1wQfaANKvU2iHVe8hLTetiGjfG6EBRW5IodAW17mANG4Ljak8+nHSsTJa8h9wZFl6wz1EEFy5kpKniKE+p3HkaJ3sltoFFHdH6CWVr6GWaK9e36PcUB9WE3GbkdcT2lqgb9ZKNqEHd0hkVKlpn0RqaRJNGRjTHC9tBn+gUxySuV12yNMgEb601LEoLSNGzvdX5s0QbNZXHWU9iwrtzHS8iWp2su+AhrOBwqoPwGOHSFQGlEZ+BuNPbLyxlrYnNOfrf98dIyxNOBxe5kc2oXMTRMmW6FEKt0bKHIws72xE+bNyDW6+IrDQol0Xg9FSK5Cja5AKuUzmCTYx4uFjfnmhGgx5RtMkVrBFMvY8UzltWeUbrFEWlCUH2plGe2hmyxHd7T87dVbPPdT4GDyMaklggjN6zbnQsu8ZfCDCZxCSx5ihPafzZtepfZH0nT9qnbA2xVezt9Ygs3C+2mEc9FrVAIEPY5Cer4mD1WYgUcvf5GGidFiO2LldYPX/TzBCexeoaaidxxHtFP9tAwuoOFv6460lc2Ky3XRD82q/9Gv/tf/vf8ulPf5p79+7xD//hP+TjH//4+vshBP6b/+a/4W//7b/N6ekp3//938/P/dzP8fTTT7+t3/PgWNGvZdSmlFR9W8PQOW7Kf08WmrsPZKxkrSWNxbRnhSWeFwlvxu8nMnDNLLkefY1gFcP4AkdZn9Zplp3VdWSENGM07AwbxkmFC4peHNE4zSw31K2mquSuaxpoFTi76civNnnTCIHIGsX5Hc92v2ZZRdw5MixyOLutuLhdYrUHEkCMkZYlzL0UN9aYTm/YM0ylei5rQ17JBl8sPc7BoC8jy6+7n3G+kz9seYRQOBnJE+8dGe6rjDQW8zajIK81x/PNJj6zK7jiW8cZSmUStEawCkoCe+owykH4EstONszH4CPZrKtrbpqOwGQ2nIbIioJAGjkZM3YOk84LR2Gl2FJWsMwf//55yzGge/wJw5P1+Os7FRMA9k80s9KscftpIhMko8O6sJ0uNfcOBMKSJuIz4L3iaCYE4rtJn68NPkAcBS6YnEvJPUKimCTbnA4yGm+YF5ZlKQfg7riT3Es8kQlERhQ1QBLVg6lwhLRmLTX6sKyx7L9uL9ayVydDuLjnaVoxATpqDf1UGhFaeyKrGGRyoC9L2UNKKY4Quc5BFugnHhcEWrPiTrQudFNOKahhkzCvmisrjtC8kMnioAdbZ+VnH5wY3qwFEjkZeCLrmeeGe4XEo34Go8sRRsOdk01c2BmzcVXWG55EVQeMVhSl6qSXLb1E9vgsFw+Epg1EVszgQH4utjDqO7LIkdeGo5lgq9MYzmxtCJvOQ/uYp9iTmPCdXd+puHA8hdKptXlYL4Fxv5Pl7DiBJ3PDrXsSE3ppxCAVb57juRCIp4uU270XiG3gRjrjavgqaNjfusRRb0jjDLPCigFgBHtbUqhuDxqGSUUIimEqctmnS4tzlqYRouxqrbg8qyJd5HYlX7BWYMbjrGFZW+4dGfJCyL2Xt+sObgerorh1Mr1blobDIHtzMvBMsprWa+o2oqglsT6dydRiPDJrk9PIbmKU82qtIlbWEhOMga2x5Ar3jw13g2GYwdagxZjAorQclrZTRYSze1I03ToSryaAyWAzsfRho4K4uv6yVl1M0ngrsS6v5JqrWiY8cp+sFNsCW/2GflxTtZZZGa9jwDALax5H3Qjq4HHXk7iwWW+7DFoul7zwwgv8zb/5N9/y+3/tr/01/sbf+Bv8/M//PJ/85Cfp9/t87GMfoyzLt/V7nA/UbehIrPI1a8Lm0Tnsyc0i5B8ZP28qxbaFRS5ThNoZAgodHANbsJMtGaW1jOv86vXld6wSe6U6Z8yoJY7CupMvMAAxP7Gm80zoyEIrxn7wKx3/QKSdGAG1UNeexrEmS8NGnWClztG2kgBLlayw2nfFw0qeFOpa1JFWXgErreK1ZGoHF1g9v22lozbsSQLlQyej5iEygdg6VjKqTduZtiWCeVzLMIYuiHTdEtMVZqvrfvh9yNhPdQZn8v2H95gPYT35kdfy68CxIlCZTif5Yf+Cx11CFDJf93j7Vf/f/Jt/k2vXrpGmKS+++CK/+Zu/+U2f+9GPfrTTvX708WM/9mPr5zx48IA/+Sf/JBcuXKDX6/GH//Af5pVXXnnb1/VOWt+pmACsncarWh4gXfX1w/iHPD7Cuiu3ugdDEHjRvIDpUtx0g1IY39IzBdvpklFcdTK8PPL6qz1mdJAxetQSR379u3y315yTPZnG8t/Vflx17X3Y8Huskd9TVJ2zuRKzoUc6iN3PNo0UFCu508j6tTfBqhioKvlcVpOKhz0VNq+l1kpKzssUYJi1ZIkICpSVp3Ws42wIcn1VJ+maJfJ5uIe4TBKXpVmzim3Bb+LCaq06g1Wjus8qPPL9h6crRokZmeliTPCPyjqu4s/jxoVvV0yAJ3HhcdZ3LFdwovS3EgYA2RtxV7zHXUxYye86v9LsV+v9WLdBcoVcrUUAtHcM9JzdZMY4KSR59Rt1r9hujMGUCmsISxKFdSNz9fsejglxlys8rEoGXVNDi7+GNA/C+sxciSg8LF/qu3O5qqWIDkGMDFe5QuiadE0T1jzIVYK++v7D6kjr+OUlVxj3wzpXWF2LNaGDCkuu0LoV3JlusipIAdjkCau8an2+u0cnqTLFUGsJV+fo4EZf9wEhEGiBKoW1YMLDv+tJrvBvt972hOBHf/RH+dEf/dG3/F4IgZ/92Z/lL/yFv8Af/aN/FIC/+3f/LmfPnuUf/aN/xE/8xE889u/RSlFVgsXTRtHPLL3YrXG5ANaYbiSnqdvA6UKvb4heKtX3Mpcb63CRMIwvMLAFl07+DfHdV6kvPsOvxT/EvSPLqA/DntxoziumVUJsHFtpTqorYIs7KqVtgyT1jaffN9y8AOeGc/Im4f4spazlAnyQ0eLqerxXFKVnPm8ZDgxVa7HasygMJ/OHkm0lmy8vBJ+8NdQM4orGG05URNPIgX18XNHUjsFgwCCTDXo0M0wXUrhEkRycVS3PjyLFc7sV1/t3mPsRn761x9GxY9AzbKUlkWk5WY45OnZEkeKpS4rdYUnVWuaFFVhVl6goBYPUkVjHorKczA2LpSPLNOOBvOHTBRSlVPmr6VtdyyYOXxcJE+vo2bqDM9h1kFnhLZUS0xn9Nop2ZQ3q66p+5R4TW9Ctv//3/z6f+MQn+Pmf/3lefPFFfvZnf5aPfexjfOUrX+HMmTPf8Pxf/MVfpK43rYmjoyNeeOEF/sP/8D8EZH98/OMfJ4oi/vE//seMRiN+5md+hh/+4R/mpZdeot/vv63re6es71RMAOGg1M5TVQ5jFDuTiN1B9UhcWEbiMq5bRVUHpkvVqQDBeCjCBLO5FNPjfsIk2aZnCs7PvkSy/wbluRscR7+f/VPNqAfDzHUwGNnXVnu20yWZLnBhl7LUzJeukwgO9HuGi3uBs4OceZ3w4DQmrzbvwRjoxZ5RXBFCwjJXnExbYhux0yn/iH+IHJB5SUeu2ziF7040W2mO84ai7kuSXcPhQUlVOS5f6XN+R2QFF4UmrzfdQKMFi5sXEhf2RjVP9W4xC2MenOwxm7UM+jGTtCY2LcfzIccnLUmsubCrODMsKVvLrLCdUkmnZ642HIXQJRFF4YmGhnHXLZzncDwNawgASEJXd8Ud/ZXfQ6AXt/SjSj5zY2jNphhYJUggMJDHWd+OmABP4sLjru9UXLBWFP2K2qG1YnesGGfyebvOpySOxHBUaymET+Zy0ygl0qJtK0UvFRwtU/a3LjHQc87d/hS89mV46nlORx/n3nHMMINxzwlXJijmXa6wm83JdAHscUtntK2nqj3TVmLC1bOes4PFI7lCCOBX97UOmI4b1DSdQ2+taby4hC9KzfFs9Z67CUMnsgKwN1b0oorWGyChaiRenpxIrjAe2XWukFeCVtBaSPtad1Cbbqpx/UzBjeRN5mrMZ+6cYzYPqJFiK82JdctpvsXJVCBRuxcCu/2Kyhnmpe2MGrvrNOLC3osaZlXC/ollOnOMR4ZRT2LF6UIxWwQgrNWZ1vGAR/0dYuNITU3jzRpGHXcNG8xGoS08Pqf4Sa7w0Pq2AqVef/117t+/zw//8A+vvzYej3nxxRf5jd/4jbf8maqqmM1mjzxWq20Ds1nNfFoTgiSOsdlYdhsdOqk/2dDLYqO5H3VQnraVrtk8VxwVfRZtRnz3Ve784v+O/eInia0nL6QzlkaeNJJO+bIyNN6Q6ZKRP6YfyYnuHJRly2xaiX9BVnFe3WEnndKL/RrTGtnOKbB7Lx7ZnEXeUNWBphUn3qKCxdKRF34tWdY68SSYz0U3OTMVmWk67CBUVWAxK5mf5ngvRKDUOtERnjuWuaMsA2XVHcqlp21hJ11w9u5nuFC9Rmwhz+X1e7ZkaMTqe3paURSOLHZsJwvGSSmdSLNyfpRNnkUto7ggixx1I1rAbbuZkixzz/6DkqOjqrNpF3K2d5vuIXSvpz2JFstxeHhiEzoZRTEsSeJv7Bh8s6WNecvH21k/8zM/w5/5M3+GP/Wn/hTvec97+Pmf/3l6vR5/5+/8nbd8/vb2NufOnVs//uk//af0er31Jn/llVf41//6X/NzP/dzfPd3fzfPPvssP/dzP0dRFPwv/8v/8rau7XfL+lZiAnzzuFDVnrJ0LOY1ed5iNAzjkkFUktmWyPhOcldgPk0TWBai67/qZEUWytKzWLRMl4rTKqP0Ccn+G8z++f+P7LV/Q2z9egKR2pbMtuvOmlaBgVkwcsdkVgi2Rd6ymNdMT0uKQkz39uIjttJ8Y3boN4dbbB2prTE6UFaexbymqMKaNi/jc4lnVR2oG4ljy2VLnjtxQLYF/aggjiTueB+YnhacHMyp69DFM3nFVfe0ajZQnrojQI7igt3jV9j19zG6iwseMlvRt2LeMp9KXEhsYBIvGMXlOiasp4U6YNQm5sk1uzXcqpd4kWE+aZjNW5qmK7K8ED9Xog0iyuCJTUuimy7mbwiVdvV7uo7g6u/yO61vR0yAJ3Hh27G+rbmCknsozx1lKY20vq3o2brruMv5FUeSKzRtYJ6Hda6QxlKkN40UsNOl5qgasvQDeO3LfPH//r/TfOo3SK1bSw1nUUtixVhzUYoUZk/njKt9hnGxLoqLvGV6WlKW4mNyTt1lO5mRRH4tQPDw45G9UzqR7vSiIFbUisXSscw9TdNNNpouJnQym6muSIxkw00juUK+qFjMikdyBSHoSoxbc33cRknxbHrKmVuf5sLiZdJYJgxGS8wZmAVKwWzWUBSefuLYSadMkpxe7EiiDXzTaOhFDaM4J4taqjown0vMXDX9Fnng/v2SgwPJFVbE7K8X+lFK0BaxarDKr5ELsMkVYiv5Qhw9yRW+lfVtJRXfv38fgLNnzz7y9bNnz66/9/Xrr/7Vv8pf+kt/6Ru+vtLtHQzjzvIapoUocKzGXNKNXx0KG6zsCtO+8hkIXrpYrRMDo+rSs5z/oRPUpeuc7U350NNjccxtNWXQHQFXCb637dMYy6IWgnOSKHywKKVIU0PjHHk6pGoTWr+BK62S56pVLOqYplVMRoY46pElUu0rJRh8a0UPOIlX1t8a140tlYK8FYMeELxelmkm2z2axpHEUlisrMnjToEjiTu7cAHtk8RKDM12rjFPRO5raysisp1ucxBsYn8QkSSaotacVgOKVnCTTbshTQNUrUGphMZpskTRdkZqKzJPHCkmW4moEUW6wxUHinKjiFTWAola1hGKjLwRGcUVFGzVaVhhD91Dpmu/01LmLar+9vE3eV3XfPrTn+anfuqn1l/TWvPDP/zDv20i+/D6hV/4BX7iJ35iXc1XlRSVaZo+8ppJkvAv/sW/4E//6T/92Nf3u2V9KzEBvnlc6PcMJrKyZ4x00E/KHiAHZwh0Xhehg7cJcW0FDWxWRPaBwfvOiMwbijahOHeT8YtL3JlL7GYLnr8ylIOz2YRJgQEo5m5IY2KWVdwp3lhcHIgaQ5oaWg+571E5cfaMrMQg4yVOla1hWmdUjelEElKyRFHW4nfQdMphRgsfABRtFLA26pKHQOUS2rAi/0KaSFxIs5g01bjgUWxURFZkQqMlLjov/gZFm3A6ucpCT0gi2N1NyFLxNwCJZZOtlDhWLCvNQTGidoam81FZ+ZysmgVGyWcvCiwSF1bxwxjo9w1aK3qZxlooS01dCwTT+dB1TjVlEmE6YQWtJTaGIFwnYC1oUDePFxf+bWMCPIkL36717cwVIgM60vT7VhL7Vq1jQt0aXBDOjNyfqpMJZk3Yr5qNz0AUqU5gw5C7lHDzvTz7x6bEN25wJj3hQ09t44KmdgbfKprO3Mt5xcINCIliUaYYA2mqUSoiig39vsEFRWEG1G28hsWuOt9KiahH3gq3YTLSWJuQxCLnvWoGWivKY2myyhUUbUei8QGWbfYWuUJGXSfrXGG1bMfbie3DxHxNHMGizViceYpFuoMqA6OhwZpA4VJaZTcCJbGmbGBWDyidpWo35qh+BcdyRnIYp0kTxWgUEUfCV1BKEUewtZUIVzTVRFZhrcd5Q+iUiqoGlNIsmxije1QuWsczHxR1pzZZt52S0pNc4Vta/85Vhn7qp36KT3ziE+t/z2YzLl++jNEwGGq2x6IsVJTwlTfkOasNHUJYH/xRtFKlgXkuyh9pqtgaqg7fH1hWmrJJ+fX4B4k/+Ac525vy9N1/xjMPbnH6gT/EL99/gdlyI11VNYbT5XhdXCSxJLejgcYH0TSeVxFvcpaqNZS1WmPjkkiu5XShOZqJjv7Niy2JdRzOY249kGo8TYUYHFsY9gJJJCYkO+NNwLgzGwB0EAdxP90apWv+wDSXDaoVjAaiEDTMNjjfVWJ967TPPfNd6IV07J65Imoqd2ei8Vu3irNnIoKH+8dw7yhbH+IroqQ1ggGumwitRQlpexTYHgn5ceV+OhkqLp7Rj2All6XmdKZEcaUNHJ14rFVUTUQaR+uOSdSZNy3LTWBZSY497lJvUeWr7t8PT6EAkiQhSZJHvnZ4eIhz7i0PrC9/+cu/4+//zd/8Tb7whS/wC7/wC+uvPffcc1y5coWf+qmf4m/9rb9Fv9/nr//1v87t27e5d+/e47+53wPrm8WFszuKrKcJQeLCIofPnWyKZ9jICislB96KXNt2uPkkhstnV/h+z7ywLErDcfR9xDd+H7vZgqfmn+b50wfcu/a9/PM3nmJZwO44MOo5itryymJrLY836EGWbA7lOIJFCXfZomlFwrSfbiBHIQi87/5xRhLB5TOO5IJnVmiOZoq2S55XMKdRwlp6eJV8Gx3Yzwf4TnKzl/ouUchonUxCBCOt1vCcyNKZ+QgeeNSTn717mnJfP4/RgVHf8YGbAaVa5lWCDylp7Hn2uji23z0IvPKG6P9vjVSXlMh1adVdRyeJeGZL0470mkAcgsTFi2dNZ44mXb3pUjxZ6lri9kErDYXWJQwyUXNJIk9sJcaczFdYagksj+tD8G8bE+BJXPh3ub5ZTMgSyPowGUpDYJbD/nH0yM+abnJvU7XuXAtfRviAK58BQRYEZoVlWQ05Hf4R0j/0Y5xJT7j2xj/j2v4dZi/8e/zqyYdZFIosEUngstG8erQNbK/3XxorjDYYLcl01VruFXtirtU+ypMD8eOZ54Y4Clw7KxPz42XCnQPdEe+hn8n5vjWQCWDtFON+J2HuA2+cDNfveWsYGPYU2+N0LRSyKM26aTnoqc6MMKzhuav15umIN/lu1FKK/OvnpfF6fy6vH4Li8jmJwUczw+F0sG7Mrv5rdKAJYoZ6omJCUOxNAtsj8R1awZ8GPTizpdfTPqVCpwKpaJqAc4HjDp7kfEpf3FYxOtBPA2WtOVlsmrEgksWPu57kCpv1bYUMnTt3DhAyxMPrwYMH6+99/UqShNFo9MgD6DTEN466zgXmc4HRLBYti4WjqmSmpJRab3ilhBhTVV7UNqJAL/HdhEBY7K/ds/zWy5Z7ywnqwS1OP/kZBtPbopKTy1jbd/r+J3PYPxbCkchpCuaun8rhVjWaaRF16jibqnS1MVY8htbBMKnZSRdihLZ0LBYtzslriiSYJ7HiRTBI5QEiuZl3Lr2RDWSJZ9z3TAaeJOrIyl1n3namZHEkASOJvOj02sCiUNw50OyfaoyCUSomYHkpRiAr2cAkhrIKHJ+0zBeugzhtiMqrrkpRSRERR2K5bruxa9NI8Br3WoZZyyB19GJPFsumtqZzcK288CqWgdmSNc5aq/CQkoL8t20348HHWStc4Nc/AC5fvsx4PF4//upf/auP/8KPuX7hF36B97///XzP93zP+mtRFPGLv/iLvPzyy2xvb9Pr9fjVX/1VfvRHfxT9LpU5+1ZiAnzzuJDGnn7qGfYcg0zIr8fHNcfHNaenDfO5jM9D2PBdzErxx2+kbvuJY5i2WBM6tQ7NmweWL74R8WA5JD59gPvKFxiUx5R1Z4bYHeIuCCH58FRgitaI2lGaiApPEnWeHKWhaqWDuCLdxh05uaxhOvdUDQySlq20IIk8ZSn8odat+DPdXo69wG5SeYDAGota9m1sBFY37gdJFuLQKY19vfzoBobXTyU2LEvF3UM4mmli45lkJaltxUG4m1gMM0c/DSxzz4P7S46P60cc39uHRAO0kinB6m8lcUEgS0ZDP5X3ksWiLpbGYd25DSGs40JeSRPhYX8XEUOQz6iqwvrxOOvfdUyAJ3Fhtb6duYLRsq96idz3TQMn05aTact01jKbO4rSdx4ZGwleoyWvWEHXVtr+Ssm5vig0X70f83+8knIn34H9Oxz/68/QP70juUS5OZNap5guFQdTxbxQazfwVazqJ2LiOa8iito85Dy8edStnIGtU/SjWvD6kWeZOxYL18mYdtPAyJNETnyLurjgvGKeq3UjTT6TTa4QWTFAWxkfruDNK9+kxAay2JNY4R29eV/x4ETuv5WXS15rFqUUAr1U8o+qEVO2Rb45r1exQLr7El/rVozdRpmTxmItHIu4yxVG61zBkSUbiFcIrJsFy3JlTqbXMUH4SqwFaJ7kCt/6+rZOCK5fv865c+f4lV/5FT74wQ8CUmF98pOf5M/9uT/3tl7rZOpwKpAlAn05s624et6uiWsAy9JwPOvUNWpRwdBKMewrxkN57rKUTrMYaXVd9L5U2s57Tj/whxhcfZ5icIYf2H2T8mrKveWE+6eiR37lrCOxokk+XWrqdnXzyTVED8F9Rj05bItKM8vld20NAtmOaAPfm2bcDRllrdjZ3piGSGINzhsiYyhqmC1CJ6e6kQjrpYrYir73sugmBgOYDFaFg1kz/w+nGjENY93Fiy3EQ9msd48NbWtIE+kQpDGcLBTHpx5jxK353I5dd+cBdoaO3b5of06rVHgWreo6fBJgL5xZwTbgpa9Z0X1O1VptJY46ONFIEZnVmG+TVOwfS/ey36khaSUwj9Y9vrwggDJ6XeU//DWAW7durQ8T4C07gbu7uxhj3nYiC6Ku8ff+3t/jp3/6p7/hex/+8If57Gc/y3Q6pa5r9vb2ePHFF/nIRz7y2O/td9P6dsYEgK/dVQxGCmtEknhnDFfPWUl2u7gwLwz3j1fqGkESUQNZosi6CezRvIPDPFQ0jHpA1zW/d+17GZx7ljIe8oM371KHiKNy2LmRw4VtKSYWpeFoptdSeXXt0VrR72mB7RnIYpli1o34h6ziwvltcN5zMIu57xNaB4M+rKYAq+sTV1GR3Zwvu+SlkxV9WOJ09X3vA5ORZm8sp/JK6SsEOJ4L/GgFsRR8v6iPeR+4exzRtBFpDMOe4IZPF4bZUq5nZ9twdm+0btgYLVKEO5nEhVmdktcCJ5ouheicpRup0IOp4u6+cL8GfZmyCrRTYEzSWZXnrpRUilpxOBUoQBKL5CNsip0ifrwD8t82JsCTuPDtWt/OuPDgOJC3GyGN3QlcO6/FmNN4lAoUNZwsRHUnr0RoQ2vFoCdnkUwWZKK20uvXGoYZDDPxNJi98O8xvvocTW/MD2afp7rY41ZxgTePMqwJXNxpSKxjWUUczAxNBadzETzRSmKBMZtGZ2whr8QxWGvYGYnEeOM092c96rZP62B3u1Pua2VK3hgAkSZfFHB4LNLrcRw60z4RMUnjTa7gPQz7sDVYkYr12p/gaKY7tbGNWqJS4rHgvMiO3moNvVSkTU0UmOeG/RNpxA4z2BvLxG41+dgbN5ztSXf9uOwzK2PqVmReV6IhF/YkV5gXcP/ICiqkLxAmgUcJrCmJFMlDA58VhOh4pnE+kMQSv+ChBsWTXOFbWm+7IFgsFrz66qvrf7/++ut89rOfZXt7mytXrvBf/Bf/BX/lr/wVnn76aa5fv85f/It/kQsXLjyiP/w468H9nLxIyPoRvczwzHsqnrMvo4InKE1Qigfji3zB7zBbKmYLz3Rak/Usz1w1nBlVnBYxr90RB+LJyDIZyo047DnSyFO1ml++/wJN+wI/sPsmF//Z3yY0DaMf+U+4c/w0vSTwzNY+E3fAg/5FPlfssCzg+LTl5GiDX1FasbeXsjdW9JOWooo5PnHEseLGuZar/Qcc1Fv8+hd63Lu35NKlPk9fFlLcgxPD0Yl/RGLr8LDizuuHNFXDzvkJu2cGxLFmPLKkqWKx9Ny7V9DUjve8Z8QzZ4uuSOrROtEkv/egpixaBsOYrYkVo7FRYNJrOM0jvviVkntvnnDt6V1efL8hi1vuHSW88fqMwSjhxsWEa5MphYs4LVJar7g2PuTK7HMEpXkweob9aJvTIuVrdxWnpzXXrqQ8fU4Sg3/5xZRP/8YbRHHEuctbDIYxo6Flb0u6BGdGFdvpkjYYHiwGnC4txzN4880lZdFw7caYs1vSAShr6RLydpQDtEJ9nfbY6t8Pd5e+2YrjmA9/+MP8yq/8yvre9d7zK7/yK/xn/9l/9tv+7D/4B/+Aqqr443/8j3/T54zHY0DIQ5/61Kf4y3/5L/9Ob+kdu75TMQHgi5/fZzBsSHsxg2HMH3oRXtCfRTu5OYLS3B3dZFme5WQGy6Ujz1uS1HD1YsTWwDPLNXceyIRxMrHsjsUPY5g5UttStpZ//sZTlPVT/ODNu1z99b+Nr2smP/ST/POjZ+mnnptbR2yFQ+6kl3hwPGa+cJyeNpwe5xij2d7tkWVi5GUmooQxL+Do2JGmmmtnaq70H3BYbfNrX+xx/96SCxd73LjYGfRVep3Iz5dCHjw6qrj9+jFN3bJ3YcLe2R66m45qrVgsWvbvL2hbz9PPbnNhW6R861gSnWUB9w8ayqIlSQz9viWJFTcuei4M55yUPT735cCbXz3k8o0dPvTehDT27B97vvzSMaNxyke/N+OpySHLNuP+ok/VaC4NT7g+/QzeWF7pfRcnyzHzXHHrbsNy0XDzqR6Xzotowet3+3z2k7cwkWXn7IheP2ZrK+bCGU0vCZwZluykU1pvuT2fcDS3zJfwtTeWlHnD08+MuX5WJpuNEwWZPHm8wPBvGxPgSVx4O+s7FRduvbmgP4rW9/Rz7695Tn8R41tMW6K842DnJr/VXuF0oZjOHPsPcrJexLnnUy5uFxwtEl5+Qyb3W1sRZ7cV1gTGPZEdr53hV08+TOs+wg9mnyf+f/wstm556j/+83xh+V7GA8X14QN2yjvcGT3F3eNdTqaeo6OKwwdzlNakWUQcG3Z2M567rukljmVpOT51xLHm2fMVV9PbPGjP8CuvDLh9a8H1GyPec00cge8cx9w/kObfdC7v/f79nNdfukNdVOxd2uPcpQlRbGi2IrJUs1h6HtzPqWvHs8+OubZXd7lCgveaZQl37jcUy4YktfT7hijSXNgT9bGTZcSXXpZc4fqze7z4PkUvbrl/bHjt9SX9fsSL7zdcGR+Ttyn3533qVnGxf8z1w08SlObe3ge4o/c4zRMeHHpOTmpu3sh4tosJv/b5jP/j118nTmMuXNthNIrp9w07k87zYVSzmy1pg+Yw77MoDbOl4pVX5iwXlcS66yKRvKwEuu2jb3KzvMV6kits1tsuCD71qU/xgz/4g+t/rzB9f+JP/An+x//xf+TP//k/z3K55M/+2T/L6ekpv//3/35+6Zd+6RFyxOMsbRTaaCEEGrDKY1yNCh6vDUEZlAlr4opzgjXzruMUGN+pT6i1os0K17bS6i2DZrYUOEB5NSU0Da6sSJsF475Ad6xq0N6hzEoRY6OQ41dJfDfeF5UNv8YyK9V5CKgWo6Tj31QO7+X6BEZguuertSZ3CIHgfQd7UNhIY61ey+t5D23jaRu3UetBlDeUYv28FUnPh0B4aEQp8ImAcyvIVUDxkPb5Q7Kgmo2Kh1EeFQLgO3nBTjGo+/yVYq0UBOAah1ZKiEFrXWG1/qysalGIrnG0sopnpUHM+rVXeslfr9v82y1lzFtU/W+PQPiJT3yCP/En/gQf+chH+J7v+R5+9md/luVyyZ/6U38KgJ/8yZ/k4sWL3zBG/IVf+AU+/vGPs7Oz8w2v+Q/+wT9gb2+PK1eu8PnPf57//D//z/n4xz/Oj/zIj7yta3snre9UTACZACothOLV/tJtjQ4tQWlUcB3Jr5PqbD1NIxKlm7igO+LZBpazhtN08qLLooMJhQhf1/iyImlz+qmoakSruMBGW9s7j+/2lHOeEOR+W0EU1h4lQVTSLA1aebwP1LXEhYc9VuS5nZ+CC901e5xz3euqTrxgc5i1raepmkcgU6vfu4oLK6KeTCDlm7bzAQkh0DZyLavPAxS+dV18WsURiTfWBDQe5R1KKTSbveucxB9REtvEhabDN660xuVBZzbniWig+1sJz0Ct45UPEOhcaINaGzA+zvp2xAR4Ehced30n4wJIUSz7zGN8K3HBNXJvsvEJadvu7AxW7nHl1x4DqzNxBSkSJa2AbxWLQmBC1cUetm5xVU3c5B2HyGOU687RjXqWc93Z5x3B24dizsbDYH39ymN8g1YB52SfhCDno+xP2euh8zxZKZe51hG8R1uNjTRxrNcxQfhU8n5X+YpmIw8MXS7Q7S/nDLrb81qJlLBzgbZ1BB86vfyw/rkQREFRuEqqM4PrUBwhPDzsBFb5k/y8VRsZIdc6nHPdNTyqMGR0kM9WhU5FTmM6c9NVnrRaq3zhLSwMvul6kits1tsuCD760Y/yVoYRq6WU4qd/+qffcgTydtb7nh8wHguhzWgo2oiXo+fxaMrG4rzieBFx70BwaN4FksSQJDKGWtlsjwaKJLZsDcWBWCnR4i4bwfH1Uois5t5ywuhH/hPSZkFanvCH239MnWzxNfd+3mzOcjqNmC2FgDQeG7a3hOjrvDiDjgYKq+WgyxLPmV0hztXOcNRskTcRezuGKBpxZkcT2xajPOO+AfQaMx8CbE/6XLki3b8zW4G9UY0PgWUlzHxrNFWd0dSCizwuUowOtF5t8LgXY9o2RukNcal1innnLnj9Wo+z5zK2RgqlxAl5MoCnnh6jFTw4URzNtshicQyNjeduvs1B+gcAKOaiKFDWmq2xot/LGPUCtZNbam9b894PX8EYxWQincjgRYd8qRTWSNA3KtCLGvqTmkGaEvyAvBT84OF0pWfeyU3mj29E8JbKAW9zk//4j/84BwcH/Nf/9X/N/fv3+eAHP8gv/dIvrclDb7755jfg+b7yla/wL/7Fv+CXf/mX3/I17927xyc+8QkePHjA+fPn+cmf/En+4l/8i2/rut5p6zsVEwA++gNnGI6GaxOsyrV8Of4uPFA7cQq9dzvmq6+XFHlD28ghY7SMncWMMFCdiSgrsznMu0KgclKg744Do77mqBwy+aGfJGlz4mrOx9T/RqNH3A7P8krzNIfLhKL0+BDI+hFJOkIbxWhoRVGjD+cmFbFxJFGCUhIXqtZw0OyyaBK2JwZrh0xGGqXcGndbVJIkZ4nq4E4pk8l5AM7tGc5OWrQKa2WP43lMXQ8o8kbUmHTzSNKRxnD1YoQP4meSl3J4F5XmuOxTtYZnnrJcOH+FnTGcHS6JtOP6hSFKncG5wJdeD3zh1V22J4Yb5xtGacui7fHl8feKiELZI4nkszx7JqaqIwYZVF1cGPQ1l58+S5JYLl/KGA8EMrl/7Dt+Uo+wpbDKs5stOddvORoNUXrAYilyhJ99xRCCWRd8VfH4pOJ/25gAT+LC467vVFx4z/NDxuMeaSz387KJeSn6AETIA9g/SPna3cBy2eBcYDBK6PXEX2dRi+PteKTpZZqdiWJ7IHvHB0VeRzROCMSRhVvFBZ76j/88cZNj25KPD/8pdTLitrvJrfBBTo5j6kYESC5cyLh0MQM2Yii9FIwWzH8aB3a2DNZA3kTctxeYVSk72wZjJp2oiqL1mn7qOburH2mSTcZDLlx8HoCzu5rzWw0+KMpGeH9xZKiqjKJwGC0cBg00reRHaQznz8bUbWfGpjq55hZOlgLzuXa1x5kzKdsTjVYNzmsmg8DTNwf4ALf34c7hLmkM47409x7kE/LtP0hAcTLvCZ+q0exuw2jYo5/CohH4zZkdw3d9/1MYoxiPDEkshdt0viqeku7aAoOoZpwU9OIMH4Ys8wFpIrmCD7AshIP0JFf41pYKv92O/XewZrMZ4/GYX/rkbSajPpF2uKC5Px/y4NRQt2IqVNWepvaURSuHZmZJU0Oaai6dEVxr1WrmucF52B44dnqiqT2tEpaVIQS1lqlaKQmN+54/3P5jDv/u/8zOC8/wuY/+Bf7VV/pdtS8dt90tze5YOl5Nq9e22r24lclDayk6abyNG6mQDJtWMew5xlmNUYGisZSt6WQ25bWyxLOVVUTasRVNGbdHNCbhTnuRozxjWQl+r2kCvUwxyDqXw5VBx0OrbrSM0LovrzDHg0y4ESvlExDZs9Ypikrz2u2Ww/2C3TMZ731KM0gdB9OIO/vyM6OBJks3hC6tO8JnLEXRvIxYFLpTN9moiewfyaG+t2PYHYtO+pn+gqFZsHR97i2GFLXhZKE4OvGdj4Sjrj1VMeNv/JfnmE6n33SMt7p/7vzMf8koexTvNysqLn7ir/+2P/9kvTPX6u/6m7/1JYbDPlp52hDx+myPWweWuhE/j7oW75L9O6c0dYuNDCYyTHb6/MCLfW5sn5K3MUd5Rt1oIuuJzDcPgWVHLuynno+p/43F/+v/yei5G3z5Bz7BZ25tkZcwnUn3PEmEN2CMop9J8jAZOK6MTkl0zVE14sE8w3lFEonLcOtlv8nXwto34GhuOJkHjJbXEmnAsN5LZwc5u/ExAUXuelQ+4mDZ5/Nf1SwWLVcuxTx1Tlj6p7nsxcjCpC9eDSfLiAfHgp0e9mHUk4N8mDSi/29a+qZAETiqRxzlGUczw6//+j6vf/41nvnwM/yf/08DzmQz9osRtw7jtQpaL3Hr99U6xSBzjFKB9dw6Srl34On3NNfPtYzTitsnPT7zxYoib7h6bcC182Jm9NTwLlv5XU5753l5donTpeX1O56XXzqkqVuaqsE5T1PN+Sd/60PfdF8/iQnvzrX6u/6T37jHZNQnMeILcHs65O6hTAHrRiZQ84Xj5KigbRyDYcJgGJMkmjM7mlEvrB20QfbsKleYVwmL0j7SUc9LQRYMevDx4T8l/3v/E+P3PM2nv+e/4pMv99bXZwzsjhXbw9VETOHCo34D7UNu36tY1HpFXsreSePAIG072VGFC2r9WiBTtDRyGO3ZSeZMOKbVEfv1HqdVxryw3HognInhwLA1/EbfjlWe2rRq7Xi8WgKxFtdnrTdu7c4rvBcZ4i+83HDnzVPOXRzzwffEjLKWaW45mkreYdaGgtBLNo7mD7/WSvhg5TZ8NDPcutfS1J4zezF7k0Av9VweTRmbKUvf595yQtEYjudmnVcUhaOuHeVyyn//X114kiu8zfXvXHb0m61lHaOqDKM8AdlI/TSQOIXRmrrRLHNxLKWDq6xuPB9kk8mjI5l42Uwri3GVSgJeNYamU/rpJYFe7KiTLXZeeAZ7/iKTeM6zlzOmueHugaeuxdDkWJt1512mDqG7Tk3j1JoF308cvaihwFI1lrKWicQoVThEJzkv9VqOUB6a1qXiyNkP2LilCTHLJmZZGSEZlnItvcyspyg+bLT7V6P9uhViUQjSDUg6w46iEuWiLPGM0gajAidFzCwXgmRkFaNx0nUaBVKUJZ7dLYkeK7m11omqgUw3NGkHQcgrCZpKQ9IpC5W1wBScD4+M9pzXNDqi9paiNuSVFGlRpDrdd/mdwWked3274AFP1jtrzdo+rpEA7YN0ywYZtLF0w5pObet43+CcJ4otUWKJI1EBK9uYso0oKt3tFU3UmR1a7dddwaK2uCAF9Aom1OgRo+duYM6eZ2AWXNnrczSPmM1Zm2o5p4hjRZpo0u52Xen5185Qdo2CXswam7wohDBsNKhYYDkrGFLwYZ0w1K1C1SLvmUUJmekTUCyalMqJdGrdTUSAzlk8dJ9VR0z0Cq1UB58QuFNk1LoQyWtLjmWU1gzNAq1ED3y2NBQl9IcpOxfPMN7KZO8Gg1GeQSa/J438elqZV6pTFtIMkq7p0IopHKz01ROKzt3dGLWBVaFoQkwd9amCCBjklXyzN0xpG0fbJrRNS109nqTIk5jw7lzLMsYkCaWJ1ufosLe654WADnB6rPA+oLQU7as/vTQDN8R73/kQrXKFYbZCFUiSbk1gPFBkiadORozf8zT67Hkm8YJnL6fMCsODI1EvmhcKpWynUChJtVZAlwy3TlHWcs5lcaAXN1StZe4lJjzcuG6c7ryXZIkakmFZGfEzUIEoaWm8ZdEk61yhrIQvNR4assR3AgQbOXIdJFa0Tsi6sMoV5P+LSlMgcXC7V6GVZ1omzDoRkyQxbO0OGA5t5yIuucFkKBevu2LKeUVRS+xJY0U/FeixuLJvVBy17sjTtZgVCuxZCpDWa5wxND4iry3LUtO0Ky8naNtVrvD4qe2TuLBZ79iC4KU3IrQ1FKUijjTPXdc8tTtdV+kAd2ZDPp1blouGKNZkmcYa0QZflEIumS4EAxdbTT+JSIxjK83JdMms7XO6HLPIRU3oma19rGr4mns/xUc/wiSec/X+v+L6vTeYvu8H+V8XH2I2qzk6KimWNVFsuXylz2QkOtpiygPHc8ODQ08SKy5stVxK73FodvjiacS9+yWXLmaMewatAg9ODXcetGilyDIx5Tg5ddy/O8c5z42bW9y4NMR7ODhVzOaOsnScnpS41rM1nnBxvEQRuDcfcDTXHX56xbiXwGSNYnIucG6UM68SPvuK4cH9nOefG/DcjXukFBwsnuLzLy2xVnP9WsYzVxXW+PUhf2m84L27CxSegCageFBO+LXXI+7fWXDl2oj0ukAu7ux7Xn35FGM0/UFMFMsGCyFgjF4nbi4o8jamDZqjPOOrtwOLRcNkErE9lmBe1pqqDpT54zOFnmzyd+f6za8OMdGAqhL8+3PX4PmzJ2jlUcgh+/rpNov5kNm0ZDBKxWwv1sxyxZv0WZaKo1M5bM7sGAapOGBup0sGZsHcDXllscV0qbiw3XJz64hINdwOz9L8wPsYmAUXDv8NV4/vcv+ZH+T2/k2Ojyua2tHULWkWkd4YMOqJ6sZx0UMpOJxF3DsUONy5ScvF3iEnzZg3DyKOThw7W4ZhJt0z76GuJXGuGznsVl1O7wPXrg+5cm5HlINmirwM5IXj6LAQflGbkpq262wm62SH3GCNKADNl13cSRTnB3NmTcpnXk25d7/k+Wd6nLt6SOpzjvOEz32pQCnFUzeHfNcHR2RxwIeWk6pHP6p5z94UgNylVC6iqFPevNNyfFxw5cqAQSpd1sMTz+03TrGRYbHo0+sZmrYljjVxHJOmCqNFMvWoHlHYlP3FkFdvwXxe0+8bnntuLLCGJtC6QJk/Hub8SUx4d64vvaGwsUzZIqt49hq898IpVjki3WBp+erWOQ4OLFXZYK0mSdS64bRSrTmZCc8vspphaomNYzeb09M5Czfg1aNt5rni4k7D9eEDjHLcdjdZfM9/xSRecOP+r3PzzutMX/hh/v7ph9g/qLh7p6UsGuLUcu3qgO2xTOp6iSTD80Kxf+RJEsXl7YbL2V2O2h1euz/hwX7D+bMRWwPhCB7N9FvkCi13bk1xLnD9qS2uX7rQxQRY5o48rzk+KvDOc2Z3i4ujBUoFbp0OOZquCnEARVEFlktRGdy5pLg4Wa5zhXv3lrz3+SHv2T4kDTm/tXyaz36xkFzhasJ7npIGZmIdWgXODxdMtmbrSa5Ds19M+PXPWR7cW3L5ypDnrsnf79Z9z2uvSq4wGCUkiaGuPXUlSULbSvfeeVi2CTDiIB/w5a/BbFaxtRWxM9GAop+JqtOTXOFbW+/YguDgoMJ5xew0J81inr66xSSaYXBYX6ODo+hdw1qRARRZL6n6V+O/qhHsedsG6tbQOI3VnlRXDP0JrTH4MKaqA4n1TNwB2jvebM7y2a/GPHs54/q9N7j7//5VLmyfIYo+RNt6picFh3eOSHsJ40lCP4vXGP2gxYF3Nmvo9SyRdvSrE+bRiGXuOTlcMh7HtE6jtVionxyVaKNxTsaYx8cFX/vSLeqiQqmnSOItfIDT05o8b6mrltlp3pFzJvRt0ZGZ+hRVJ8Naifyg74qCOJKCZWBLqtZycuK4+7VDrlzp0wsLetUpIdxk/84pWT/hvc9mnBmWuKBwXqMIjKIlZ4qvobyjiTKciVnYjPk0sH/nmPFWStMmaB2YTlsO756graYY9YjTiDixxIk4lz5MGq6dQKbyUnN6WjGbVvT7lixekTHFbVa9DfdBGd+Yb/zak/W7et26lYOCfF6S9mKevjJmyx5jfEvkK7R3LAYpWT+hqhy9nmU4EGfcqoZTr8gLuT+bRrpmAFYHMl3Qb6c0RjDFRSkwna1wiPaOV5qneeV+xpW9PleP7zL/V/+and3LGH2TtnUUy4rlvKQdprRtf90VW1adyU0Oi0VLmhoi7en7GYXJqGqYz1sG/U1HTaZ9oRMNENjD9LTk/q0jXONIUkuaiKHg4bGo+TS1oyxqgpefN9qti6QVCXEFPSiqQF15tFFEVtMzBYWLOD5puPXaIWfPXhKhAt9Q1ooHd6f0+gnvfTblxt6Sxhvy2tI6yzgp2Pb7BKXB7OL8ytSn5vjBnK2tlNZLo6AoHLPjBdoavA8kaacOM4iwVq8JiSFA3sQ03ogC2XHFfFrS7w/ZGcvfpWk7iGPymPv6SUx4V66DgxKlNWVek/Zinr02YMceEbmKpFpiXM1pNsLapCMeS65gu4nUSiykLD1N46kaTes1kfZkumBUHRASBWxTt5BYx055B0XgVvggv/VqwrOXU27eeZ1b//ifcXnvPFp/iLJoOdqfc3owJeunbG2lDPqSqPoYNDJBWyxavDfEpmVYHFKmPap6zGxWM5nYteBHXrLOFdo2Ikk0hwc5b375NnVRotVTRNE2IPGkKlvKsmW+zhW2GESiAqjUkLrtSMbduVqWArmxVhFbwyjKabxhOnXcf+OIa1cH9PycXnmC80/z4O6U/iCl/2zCpfGcxhuq1hJQ9G3BbnHrkVxhGWXMp54Ht44YT1JA3Ian04Z7X9vHRpbxzpC0F3fXqLCRWYsZCMTbYFQscs+HBdPTgl5vQhqv4EYK2z7JFb7V9Y4tCM6dS0APGE9StBFYymvz81jtiY1DK0/RWG5esTStXRtirDR0V6NnUQ6QzTTLDWWkCWGLWdRn2cSdA7Eirw0P+hfBwOk0wvvANDecvv/f48LOOcrtC/yBwV0+fCXjzek2X7u/Q/Cinb3S864aMfHRSmy9o0hzkme8ObzJSd4jSRRbu320hv1p59YZw9VrYletu/HHzm7Gtecv0zaOvbODtRLIeByt7dmjm0O0UkyGsF+ILFXTavpZx/jvCezAexnTm86Vddb0KBrLcKg4c2mb1gW+tLxBah2zXHH5xg5pasXwzGmKxnI0t+LyurOL62uM8tQ+ogmWWZ1x5UrCYHiFXk9zMJWufpoabjx/Hm00vZ7FWkW/r9kdq45TwZpjUFiNNZaiFlvzJDH0e3o9Xl3hD+3bKNqVsShjv+FrT9bv7nXmTIayfcpJijGKWa55dXEZqz2ZbTDKkdcx1y9H1OeiRyaKD2NjVcelqdvAohSCvAu7nNgxyyqWMbSRSeOdVJLjg0VCXsLRPOLeMz/Ezt4V5qMLfP9oxgefSjiYDzg8lV+YJrLvlIJm7S4Mg4EUxIfLBKWuMa9iVBcvAE6Xdj06v3hWBACqeqUI1MPas3jnmWwl6/fR7xniSBNFin5vSGQFMnFc9NbwyazzLFiN9Ic9RTO2KCVQvoNqwryK6fUUe+cntG3gyyeXSCLHslRcfWqHJNZi/OQN8yri3pERwn87wo2volRgUScUjZA1r17psbeXksSKByfd55Iqnn7fBZQSqIExMOgbdsYCqagawQ8rJZAFawLLsosLqWE4MCSRX5/XSimax9zWT2LCu3Pt7qZESZ+27WGtTJTfKC5glSe1DTZyLMuYp64l5Oc2WPHV2bLC0DsvE4JlEThdWorIAHvMoyHLMlnr5y+riDujpwA4OZHkdVYYTj/4I1w+c5Fi+xJ/sL/PR25k3Jme5c37okXfy9T6vi1rvYbSjEaWKFLsL3vowXNMlxlGKyZbCcHDvROJEWkCTz01eIhUHNjd6+HedxXnPHtnByTJSsXP0vYNWqfcuDHEGMX2KLBfTNawqFFfJiRRl78ksQbsummwaHqUjaXf1+ycm1A3ga+UT5HahkWhuXxtmzjRJJGXBkETczCT2Nn6HVzfoPG0wdIGw7xOuXxZcoV+33S5AvT7cPP9lzFaMRjGa8jloC+fVwjiHSEyzBFxZFmWiuFYkAeDQYfO0IGoMz19kit8a+sd+66fudhi0kBZR7ROcf8o8OrrMs7q9RKsVVw9r/iu83eJVcUb+XlevS8W3Q9Lyq6UKOYLpCuvNHdUCqTrhLyfwXSp+VwhI/jZUuTH7h54fnHxAlH0Ar9/cI9L/5//G8vb93nv973I4unvZpls8Wpxlbsnqdig13TSW7C7LV3vrz0wvHp3LGO+FK5czljmnldfrzBG8fT1mOcvlTivOVnG5JViPLBcPr/1EMZeAtf2SExTJr2GK4PD9fv+4psp3otJ087IoRGCkiLQeuE0hCAmI3dPM1qn2NuB0XDEbO74J/9M4EdXryd88D1JR14UcvThzPLSyyWLecXdCwMeXDgrKimNFGD9FN53tSR7quG1wyGf+XxO23quXx/w/I20C7gBDez151wytzC+5aX6WX7r1WQtfSiOrKI4oLVZJwMg5Eyl3q628FuMAfXvzTHgu2ldv6iIU0PdSpG6f+T58iueKNKMx33SRHN+J/ChS0ckuua4HnKUZ2sDvbJT7hFXXE35/2fvz2N2y/K7PvSzhj0/0zufuaaurm73ZLsH09gyONgmBJLLhehGFoIkspDuleEKnPtHkKIk8I+FEITcKxMkhJEVCdmxAjjE106guTbdgLHdxnbb7R5qrjrzOz7THtda94/f3s/znuq2faptt6qLs6SjqvOc95n2u9dv/YbvUAUeXmjA8HKladpYTLNyiQ0nc8390yk+QFkJZ2m+gDcfPIfRz/EHRwu++Tf/Ad39+5gXPsj5sx9gFU358vw6t0+iTVHu+0PqYEf24Wt34fNriWOTEeztSHL95n2JcbeuwNVpSes052VMWWvYNTxzQ0iLYq4kB/nOVGMNHE4d75ndIWPFa9UNfv31TAQVJjAteqlSJR34yGqSSBxHVxV8ucxxHsYjKN4z5nze8dM/u8I7z3PPR3zr+w1WB7R2opw2N/za5y6Yn65448aMW7cmjzTVRjl8+JmS3Da8+HDKZ39tTdc5Xnh+zIffMyjBybU4GJXcyO6hg+PXz5/hF35DVOPEZEmRJFoU2qxhnIWNg7vWgun27eMpijyJCe/O9fR1xWik+8mS4mQOP3836c+UHGMUV/YC33LzjEi33F7u8PpDOUxE+ls6y23jKcuO83OFcxZjFG/oDKUyjIE8gSyGh3PDndN9mW63AIH7J4H/5fwjaP0RvrN4yHP/7G+yev0u3/LJj7N4z8dZpbu8tL7J3XPJFVaV5ApGw+Gu7MOX72h+oxz1pn1w9dBysQh8/ksVxig+/L6Yj9yc0zjL/UXGfK3ZnVje89Ssb0humxDxWBqVe+OWp8cPSVTJa+U1fvPNDOdERe1o1mG08CS0DlyUMVobgSt28GCVUbea2VQRRRPK0vFT/7IjeLh+I/DB90a98aKj6iJOFhFfeLlluWh4YzflcP9A3KD76eQogw89XZHbhldPx3zuiw1t63nqZsoHnstQSmSOlQrsZkuuRhITfnP1LL/6kqXrArrnf1gbONizWGMpUkhjiQmdE77B2yoInsSFzXrHFgRZ1BEnHZExNJ0oBlycVzL6r8XgozuMKcKctF6Q2X20SvmtjgbnoK6F1NN1ohgkOEJNbHjE0a/tpHIfFEu6zvMttzJWb97j/q+9QX60RzHdI5oekOfXUEowrIOz5tDNdl4ch5fLjiTR7M4McQRVrahr13coYgpb03nL0lpsJyTegfxbNULOEyv0QBx5xnHDrHtI3Cy5aw9YrkS7dzY2vYa5bHKjAp33GC0Sq+vBtCNIkm0NnM8DJ/fntE3L9ZtjRulWFcl7ISSvVw3Lecl8FHM+ztB6i9+1RlNENVO7II0L6lpw1NbAJOse8WUYRWtGi/to15Hkz9F1MU3rcf14TyvBRSZR+IqJndpywx5vGfNVxoD/fm7yd9PKY0ecemKnaFpF1wXOT9fYyOB9SpoZDmaGwqzI3JLaxixtwuDaDcMUUfUJRKBtpdu2WDnKdUeaWrLEElk58BdLJz4DQSA8Xec5Pa3pOsdHno3p7t9n8eJrzMYjxtmIaHxIbK7gQ7SZ0IWwdQJVTlQ/Tk8riiJiOolJInq3Y5FINVqRmgarLGsT0fXqQvFGEcn0zuzymrGFPG6ZumPS6oI4vsK6klg3HelNITAoe1gTSGNRV1mWYlo2FOVaw/kcTh/MaeuWp56ZMk5FhrF1AqdoWoFtLc6X5OOUs4uYONIbk7QsVeS2YWoXJNGYqmwlLliYJDUBRdWJfHQe1RTNGdo7rHmKunJCJnQaY0RzPIlEcSWyfiOlalTAK8VjD/efxIR35cojT56IeVfnNQ+c5eKiARAYmlHsTSNGZkka1pxFY4z+rbtLzgnvbvAsCAHSVJPGikhDW8PZhfiHDM7AbRt48LCmKjs++kzG6vW7PPj110kPdxmNd4lmS/LsCpBuIErOgU3kXO+coqoCpyc1WRExHWuyGOYKynXb+6jEjO2aWsec25TIQBxJgayVTDrna9VP10SRbNTnCkk951501OcKsDvRxNZjlN/kCon1JJHZ8I2qRngZcQTkmrJ0nDxY4FrH0ZWcURp6J2iBRjadYrVsWc4rrNVEUdI3XkKfF70lV6g6mtYRRelGdREkTo2jFePyIcp1pPYpus7QdgF6CGSeGSaFxL3YBkxfDA7oEP0kV/ia1ju2IJgmK9IkpokslbOkSbYx0GibwSQnJu7WxPWcYlwyykYbZaG2F54YjwzO6Y3sZghC1quqDh8sk5HAVupGHIiDh+nUMBtrgQecVFyclbx+vsuH/uC3kR+JgUTzy/+W+MYNsm/9FqpmvOkEgiTaWSKb3LkgpOcoYX/qmWQdWsWcnsoNF1lRN/HBE4J89knmuTJeEKmOeZszr2QsOUhy5bZidPYmdnVBdO2DnBxXQpDcH/WQJcHgOsQoJNEd3iuO55Z7x0JgOtxRFKnjfGFwbUe9lonFOJFAet5JV7Kqoak7XOuoyo75wmGMFEtd5xkVMYluycOSUdKR5RE2EujSKGrQamt+lqkSU69R3hGPOqKon1z0yZbSovSURh4XtnJkbzWEe6w1uDK99bEn6xt6zdKKJEslKXWGODZ45+mAtnForQBD7hZk1Tl5PiY2RY+fN9Kpt4rRyACmT2ABLz4ZXetx8RZbVDeB8/MW7/zGNZ3+vcpVzcPFCPPCB5mNxZfEvPx5isMz8hsfoHOpGAy1w+uJY7FWfeOhcVBETHKY5g4wXMzlHlW9aohTothTNYrZyHM4WmKVJ4kKjBZVlQF6kNmO0eIednmCufIhTk+lmbG3k/VmQ2xeO7GexIpK2MXKcj7viCPNaF9TpIGLzGCMptOKJFYUkcSFxqU0nYgCKK2wkSUE4SN41xsXaRjltv/GoYdnKYwR59HcNsJNCvK7ilSHdWIiZbQkWaE3YxOzItFpj6yINgxKMq0TN+e6e8x9/SQmvCvXwWjJKO/1853lTTsS9UEkuTdGYbRl0p2SVmdM8n2KNL2kRCjn7mhkSRJDFCniSBpndeMp1x1KRRgtcLXzhebkpMa5wLVrGftTxaJU3LktnIHbF1f41k9+nPRwF7yn/qWfJ7l5nfxjH6bpxnROiv+B05PGAqEd1ArjSLM38czylqZLuBMPBocBTd8J94rWwaTwXCkWRLrlPB4RWYFEDQ2AUVQxOn0DuzrHXv8Q9+5VQjDeG20Kax80oTcI3Rt3dF5xujA8PJdcZjaC2ShQN7KnvQ+kqWKWiTTrvE4oGyP8xd7AsG0cVSU+SVUpBX6SpETakYUVaeTFeNYH8gSmcbmZ8HgUmSqJ6iXKO5KkJY5TwNN20uQAyRWypFd361WIhDMpfx57PYkLm/WOLQjGzMmtpg0xlUlI4qyvNMXVU2v5jcfNiqick0/XjFJH3YlueNv2+NoMQDZ83Uj3v209q2UrMJQgl6BuhNwLsLszYn/qONWGctVwfPuE1+7usXz+4xTTPZpf/re88s9+haMPHpN9rKQUuW/pPA56u7GncSJztlo2TGcJe6Oag/SCptvntczgXeiViRxa6Y0XQhZ3XFF3SNoVeXpEYqY4b2i9wQVFYVZED98knJ8Q3XCcPKzpWkf93AjbX5eAHJqRCsSmo1Mi7fXgQcVkEnPzULOTNeRZRtc6mrKWsWhUi0qBT3rJsl76y3mqsmE+F9xx14kra70XEeuWrLqgiBuKUUrbSFKR22pTEAAkbo1p1qiuIzEtcSSwDef8ZpNnsSOPO+rObLwixI30bW5yY+XPWx97sr6h1zhakcURLhhqF5HE442DbdM6saFXEVl9QbI6Ic32iXt98mFCYC2kiRAKh/tKoHlBOlatFA4yJfScn67xzpOkE5JY4ZyibTpWi4qH54rz5z7AOBthXv4888/9JsVTc7KnapwTdbCud96UUXYAFM4H6qolhJRx7tjJS6q22HTUNu6cGJquNyTUgb3ojNhX6OwAHyZ9Z06UxVLbEJ3dhbOHRNccZydrmsZx62a2KRqGFWlHbByd1ThvOT+rRZs90kzzjjyN0EajlRQEua3wQXNBStUoOhdQWjr4zgXqqqPtL7DWUDdW4JPKX5rICOQxMTU+aLpejtXqDtNVaNcSJQ5tHo0xggmWDubQjQxBZFjbTtE9bkHwJCa8K9eOPWccSaJcmRRrRtRVR/CBKJHCVmvI1sfE8weMx0+TJTPqVrMs1UZqc1w8OmtyHi66wHrdEsUGow2RDTRd4Pj+guADN65n7I47lLJUZcv5wwteu3eFxfMfZzTepf6ln+fl/+OXOfrgA4pPrGjaA9pu8EcQ3mBs/aZgDl4gtLtFw34652K0Txzr3jm8L7CRyV7bSWG/rx+QdCui5Aow2yTHgSFXeAN/doK96Xl49wLvPOvnRptivfMyfo+No4gaWm94cJ5z927NeBxxdU8+z8UqRSuJT1mimMYrfNCclynLUrNcB9raiRRw46gqUQmaX9RUZctkEmOUI20XJLbDGI13It86MqJ+5ILFoSVXqFcQPIlpxdg0aJrG0bUSB7LEM0okVxhiYOee5Aq/m/WO/dYtMU1IaIOl7VUrBBYwqGYEmg6qdIoKjiYkPRP9Uetq1fsEKM/Gq6AoDNampKkR3dtL58lg7w2yAaPYkuZSda+SHaLpAfGNGxx98JjixhFNWHPryFE1mvOlouvlNMWavFc0iEwvb6Y2mvqRVbj+hHbB4ILZkJwAnI5wRiYDWgWC8oARF0Kf0u1ewSQpjbNkhXRJtZKumVaB2MoYUBE275vGsLOTUORiyKRUILYw2inQVkiJzouc6MbITCtGExn9JWnUd1xkFKtjkXltfEQdj2gbg9GKYIUQrNTgzSCJfWUL6skhKnjKbkuKFA6BJraqn4I8algy/NzbKtqfKAe8K1ftY4yPxLuiV65J0gil5EBVSgrIOh6hs4ZGp/igH7mXhjXcU8O+K3KDVn1ciAYTP3ldAG0EvxrHijSL6MYCFVxFU6LxIcXhGcVTc8zBAYluuLrnWVWah2cyKby8Iiv7yVrVEwTlPQYJwBCgDZZueLwn13XBonVEcIpIOxTSxXde0XpDNz3EmojaRaSZFmM2w0bVzGq/+d7SNOgdnCcxWWY200VrxXPARgZrofMWj7yPUmCNIi9igs9JswjVB9E41j28AVpvqHwqEMXY9J3aXjEEtVEwq11Cme2ivaOpJYYMr+OcxKWBNDjokb+d836znsSEd+VqQ0IT5Dxpe6KZjfRmohy8KFLV6VTusbAlFocgk2fV3xqDDOmwxIcnk5zBCBdOK4XSmuC3/hdKQZxasqKPCanAhJKb16UYuH6Id2tu7HcsKzEWbfrJoVYBjTQsrNWbz+BCn/dYvXEmbpFmiOxBKRC8MnjdF9fKo1A0fa5Q+wS3c4hORA44K8A7LzHBiwiK0X6TK7iwjQmzWdRLuV/KFaaZxAQjn88HjWfgZUExilFaEce9OpAPxLGovCWJovOWOhrhStnfIdIbGGMIijZYfNBUpqCaHEEIm1zBaIhjhdKGJFZEJmymHEMhMPwunuQKX9t6xxYEr66OSMO0N6OQRHtvPxMVgGVD0zgenHh+bff95FHLahFRtXrTTVuXgSSWG3s4TJ2F1Cjecw2mWUvrHIs6om4Fuz4s5wNtJ5jjm7cKprOEJFa8WD5Fnl8j+9ZvIftYSRPW7J58me89+zSLWx/hX4SPcfeklx/tjcGyVMvzE82qjrB6RN1pRoVs8s4FzmtRA7E6MMo8PihO3D5GO3AQqQ6tDfMmZVVbymafu8UfQ48C5xcRH/mwvFZk4cFFxCjz3EzX5LZi3aWc1zkhwFMHJe89cnjksG6dZmfs+dgnrtC0gf2p4qzK8EE6b0bDzkQx+SbpdJ6eOx7cLwHNtWsp+zPB+99fTzizBQ/mKUkCSQLW+M0Gv6gzysZyhwlwExfg4r6QruNIMS6EBDTKAmnUkWjHOkSUdc936DkZbwvW9wQX+K5cr57NyBsxJnN9kX/95gQfAt6J3OZiDV9snydLWtbrmHVje3jJFtY3kN1AOtpxBNcPAqPE03lYVmLyV+Sa3f0c5zyTsaXIENOxZ0d0XUGewpfn14nNFfIbHyB7qibRDXv1bb7X/BoPbr6Pn+2e5sGJ35CLAaZjg79aUBSaqoGL3itg1HcpOxe4qLPNoZ0lEgfvV3sYLcV+ETV0QdP5lBbFveWIRfofoLPA6UXMe9/bu6XHooKSRJ6dvCM2rlcFiQgBrux0XNlRhOBpnWZVG9IYXnj/DOeETPmwFEhU6yQuzEbw3vdOqJsxde2pKpnO7EwtRa5IYzgtU1ZtzLrS7O/LoZ5EgTZEtN6wbMRo8aKc8roS4vbZQlMUAZBD31oRLhilLbFx4uze6g1EU2tJ5h5rPYkJ78r1xnKPnMlGHadzl3MFkeO9exz4lcmHybOW1TLuOXISA5ouEFtFnG7jQuckJjx15JmkAnGrO9sbk0GaRQQvxGPvBQr49FMjkRbNFS+tb5JnV8g/9mGKT6zwbs3OyYt899lnWNz6CD+rP8HdE7UxUvWIAtd4GpMkinUToXVO5xTTaSSwvFbxsJKcKDaB2UiK+3O/g9VjnDektqHzmrKLKFvD64s97k/+BGoSOD5L+MAHe7WvGM5WEUXiORotSE3Dqsu4qFOcV1zfrXnmwMmEvi/CZyPHhz44EfGBPHBaFXReb8wWpyPF5AXJNe49dNx+Y4HSihs3R+ztmE0cmduU40VMUciEwmqPQ5SIzuoxZWfp3Iy6u4X3AscKQYxKR4UUI+MsMIrFVb1s7eZnkkigRHTht7lj3rKexIXNescWBA8vItLWXhrnC8avrj1lqQHHYtHx8r2YJIo2RN4Bn9e5gHFqIy84uArHFq6MF1xVt1mnY17nCOcjtFbS5XJCHBySjdlEU2QxUQR3zlKUSqmaMWUtZmbfe/Zp7v+jn+LKf1SS3fzWDV/BI1OCJIY8N8SRYF1XTYT3ikSa/yKp1dj+cwZSLR26iybDqEBmazIjJDzXY4lXleb4TNG2gaMDzfU9GUucLCzzFcRWkZiWgiWNjml6974rxQUH/i6NybjXHjF3KXnseOpKzznoNdNDL1WolLhDj1JxcnUu4t69gAqByUhxNGvpnOZ8HdF2scAabBBS5DAd8JplbVmsDYsSTk4FT5imijRVxEb1KgGeLPYk2mG06yEBcn2MhuiS1fljLW3kz1sfe7K+odf9M0XRbF1GtYLZ1IhBVU+iLevA7dOMyKab6d8wRvYhoPpJ4tBF0r3M59FozUF8wtrn3GGHZSVJaZYZQjCkiUBeUg2TXA6ztqNXE4p6zsCYq3ue7zW/hvq3/z+OPtaRxk9vigF530CaKKYTmUR0Dla1TDGSaMASw6qxm88ZWzHrOq9iNDDNasZRiQmm7/pr5ivDS29qmjawO9Nc3ZWKZ1UJXFCrXmZQS0FQtdKN3MtLptGKxsfcWUxY14bYBq7sDt22wKLsOQFKhBmyJBD1043TueFhJxygIlfsjqUwW1eaZTA0nUiLyuHvRPnMG9aNoaw1q0pIml0XSBJPEotq0iiXAiKNPYntsMpTK4FQDddFq22X8HdcT2LCu3I9nEcU/WRggN2OCyNmlmVHEwIXFy0v3YtJoliIqJFMm1xP7sUOjaewKRYkJiy5ou5QmhF3ywMWLsIYNh1wJSkDWgd2p5pREWE03D0XJcOmG9O0B9zY7/jus89w7x//NFf+WMnk2W/hoY0fmUhYIw1EayRX0I2IEqTJViVwWcWbPZgbIdgv21RIwaYl1g3oqFdA0pxVigenmqYJ7O4oru3LBL5qNOtajNwi7ch0SamEC+ADHORLDu1DWhVz3OyyamOyyHFjP/QIDMWijmQy2UP2sjgwKzq0CqzKmKpsMEYzHhlu7AuP8WJt8V5kQ5OYfmrocEHTBcuyiViUhouV4s69lqbxjMeWIpdJ5TgT3kAaCZTIaN8ToOUaJpFwjSL7JFf4WtY7tiAoMk+W+c2o3xpFFSmS2OB8TJIYOaj7al7MyIbnwijXG/16OTDkpvUB1m3CebpH3SXUndkk6AcHMu6bjBRJJJbZnRvMcrYdxaHLVzWaxa2PcOU/KmmvPcvNyQWj5/LeoGPL0B+PDEk82J/LhssTeR1REbKYnlBrTaBVitb1ZGdnWOpUxn+dYCGtkc661iKDlsdSECwjs5l0VC7G2BFVF9P2Rcq8ydHxEc4bSieExIFgCH13ZYPXV711umAcI+MY5RHTWYrRSkzDlASXzokakdYw6bWDO684q4u+Myvvr5VU+cZo8kyIx5EVS/Q0cigF8yaRgmhtWZWDqouijhTl+m3MAfVXqfr/Pd3k76Z1MAvkxTbYV404kwuBXx4LXmQ5TTvAceTeSyIo0u1jl//rPCyaBKtn1M7S9kW0NZBn8v+TAmYjeZO228Jnhm61c/LfVaV5cPN9HH2sYzW9xtNhyf4koe7kkHZ9skG6nUwPCW7Si5+sKsX50qD1oMdPT0YWdaR5FdM4gQW0rvc/0QK/80HiwjiVgNh0MXWrNu8j+H1N28m+XJhEYFVBXjuNfa/pHXqTNLlmnZfPtK4lfs1GDqsDTWdZlfJFBrlg6bLqjXb7pGCDVx4motCLKhi1GfNHkcgzx1akUrMeG153liqIB8TDc1GJM0aKnOpx48KTmPCuXHnqN+RSmcyLSlcUKbouJo4NeS6/Z+ehbNjshzSWmDB4dAxrSNTXbcJ5skfTxZSdTBojKxMIee8t7DC6lE0NjczOSdNgWRnJFf5YSXf1GY7yOemNgqozlK2hc4rYwmQkr985Rdl33vOUTdJbt+JflPW5gjWazopx6FLFaCV7q+pMryom12H4rnnSk6296pEMULsIozJqF22K7UWTYvXeZtrQ9eakkZHr7IPCBmhcLyHcSSNSK4ElFhns7I8wRjxQjA6C9nBqI406+Ax0TnPejOiCpun0NhYmGm3Ek2CICWksoiOKwLxJt7nCWhTjgpd8o1y9DcjPk7iwWe/YguCgKMlH8QbT1jhD22kapxjllqa1vTqNkIXrJlDXnjTVPH/Tc5CXVM5yto5p2u2B4z3cm6fMq3ij3tE5xSQPHExVj7PtsNoTUJuOQduJ/OcgLaoVnC8V/yJ8jOzmt3JzcsF7Tz6DWZzw8JlP8svnz8u4r/DsjOh9CjRVIzCbWeFwAV65q7n9ZkmSWq5diRnllw/uPgg0cmjujCV5DkE6cc4rZoVjL10QgqLMLHUr04azKmepE9ZNxLLU/WsV3Na5dNytxyiIraOIRfKrcpa6k2So7WC5CiSRIo8acttwNLN0TjKWSeGIrZPg04jx22wE+5NWxpgry4NzKbCG6Yw1W+LWdCTKKtZ4RnFDYlrmTcarD8T8abX2vYOjqEQorajL9vFvoCdV/7tyPb93Sj6SjpIPivMqY17KQeaDojaSmJ+eiyxgXYt3QFFEfPA9iqvjJbWLOK9iwRW3euOpcf885qGO5VAzvTJWHDAzObiuzGr20iWNt5yWOava0KI20qKDFO/DM8XPdk+Txk/zdFjykeWnsRcPuPfMt/MLD56j7URCc5z7fuonevqRle5X5xRv3IPXXrkgTi03b46YjCTJsT3H4HShqRuRRt2bBPLEkUTSXTda4sJhdkFAUXc7rCoJ9dKJ09StYb4WeMViHaFURGxhZ9QxzVoS0zGOSrTy1D6mdZZFE/OFY8Xrry24eWvMUwcN07jEmjF1K3FhlHnyuKPpDKtKmjSTPLBTSFw4XUY8OBdn92kxfF/Tk6mhyBR7E+kAHuRLxnbNeTvm1ZMR87Xi7v2ON187p2sd2srzmmrxeDfPk5jwrly7RU0xqjccwjyxtE7Rdppxbqhb+R0PCfpqHVitHHmuee+twGFRUnaW8/LRXMH1ucJZFG864c4rijTwvmd0n3B7kb3VgTzx+FjO+VUleULTCoH4wZniZ/UnmDz7LRzlc547/0XM+pw7Nz/JL917irqV190bixjJshTCc5aIt5DzitfuKt68XZIkhitHCUUmn9X09/CqhFXpsUaxN1MUvYR4nipcLLnIbq8MFEJG54T4f1GnrLuYRR2xqiSe1W3KPZOilXTbjRbBj3FUo5WnCxrnDVoZTpxhtZaJodUyzdufOt7znHimzEYeqz0tMqksazFO3Js4jJLp6MlCzFmHBonRMlUMAcYFTHJPZAOTtCEzLfMm5fWHsRjOLhzzeUsIgbNeZrYum8e/gZ7Ehc16xxYEsemIdbchrcAgYafJYoVW0pWu6q3DYN144lgRG09mRfpHq/grCCZSBJhNZxCkO1Akops/LBdEvzto6NRW/hIEt9p1cPcEnNOMnssxixP8m69RXH0BqwOdFt1wazx1pzcBSbpoHtVzI5aLmq7zNF20mYgMhce6DCyXjiTRjHMJQkpJYrBN7J0UL9pv+BJ1p3FaRo8DXKJupYNiDUxyjYrkyxgV0EqIRVZ7OiVdxq63DDdKFE9i40jjnrTVm43B9rooBbFxfTCNWJVhY8g2BFnb33GRFeKz1Z5Iu406UllLMVBVjqZxBB/otCiU1NWWxPU7rWAM4S1V/1v//mR9461cr8l1RIfFBUNsY6yxQmrr9fsHHXHnAmXpqKoWazVWG1IjB0VsLCFoOifTw8ENc5hGjjIpCrQWuI7Wcm8nWp5/OaYMUwYYJgVhwxnYnyTYiweEN1+luPZNPWl+aDyER15DIDWhH/cHFudrsiKhbXNcL6wwvG/V75MkVsxGw7Su50sZiS+xavCYR6B2w7jf9TGm66Dp92+Wwg4Qad/H3xZDR9Cqh+5F1I1nOS+p6wKrPJFqRbHIRr35WnhEGGCA/KXW9X+PWK2F30UhsWeIDQNRMjbSiUxMS6xqjMppOkXdwHrdsZyXdG2HMQZtNG1dPta98yQmvDtXoh2RdhuyekAkfJXyOC8Fctt36gliVlrXDmuFt5faBs+juYJW2wl+0z56j8RWOu1ahf68liepnhyse/lM57YCJ00buHuieGhj0hsFZn2OenCb/Or80oTBS5MNs8kVACIjcrvOK9bLmq6z1E1EFGm02uYmq9JzcdERJ5rJyD6yp1Q/AbE9HHeQJwcRIglIwTPwKrp6MFNUjDNQ0UCA9gLT8YqgPEZvuZDey3XQ/WfutViIrPxWYDtNHZAJSoErjfigaJliXFaDG663fPaAHd4/SK6wXDmqytG2kis4J7LOddk99v3zJC5s1zu2IKi6GOWiTdU/r2KRE3VQVrJZBjKgUkpksCaCPZ2XisbNNl12awJ1q2gvNZgvq9doJWP+VRWjlYwg08jTOtHjrZpBcWRrOjY44XVOkufWGx4+80mKqy+gXcN3+n9OlxV8PvoWvnRvRGTkdScm9LwBgSoVueLW05PeYRXGmWDtBh3+ItWMC9ncwyhxMDnyQUaRb9I7LJeWpoPIyObuRQpIotB3IqUoGAqJ1Aqe996iwAfZoHG/SaWjLx3Ek3XGwiacrSLOF70iQ19Bd166GOImDGfrrTLS7qQPEG/BcQM0rWJeRmgdegJVoOk0kwKyRFPWmrKQ9xgwxeX67Vb99isfe7K+odd5N6FpJxuVmosy5nQh0JTFSgoBawWfD+Ip0nUiClC1QoD3QfWjbun4Ne0WBhh6ouqQODetYlEOkKMExYTaWY7nEfM1PdSghwChNlCYgTNQd3D32e+guP4BVAh85/iztDblxfY9vH6S9fs69M+Hsp8WzCaKZ957QJoarh0aRpl/pAhJIk2a6H76JvtbHpfPs6oNr+tDmaKsbO8+qljWlqYTo8JxFnCJYlXBugqPJDZVF7NqBb4XGY9VInYwm1iuXJ+R55rTMqVxhtNVwrqXXq4aKbxckCnIEKdO18nmGs8misgI7lrcWgN5JvKh3gsB2jYa52fEZkzjDGkcMBNFCCnGHOJ6J2NtFHWZPd7N8yQmvCvXuotQnTSqnFcsKsuq0v1UXiaGkZVcIQLMTFPkCdbCqg7cVRMRLul5cwN/DbZQvkGJTClY12ozcUvjsDEWW5QSSzbnZzI8V/bV8PyqM9y5+UmyqwuiruJ78k/TRRkvqvfzyon4AxRpYJTJhikbiVWTkeLZ90yJIsX+VG1gUsPKEs2okOlbGrNJ0Ic9WNaa22Esin+NGIkJV1JjvezFNJbvXDbQNhJL0jiQxyLd/HAlnfzIiqGZ8z3JOpEsfl7FlMZysbYs1vQ/OzRfFXnCRujlYi3XUKvAbLwNbgPE+zLsalVJY6PpMqxOqTvFpIA8Nawrw6p/rThSWKuo1o9fEDyJC9v1tgqCH/qhH+If/aN/xBe+8AWyLOMP/sE/yF//63+dF154YfMzVVXxX/1X/xU/9mM/Rl3X/NE/+kf5O3/n73B0dPS2PljZWlQrH69zmtOF5uRcXPaaVkxwisJQ7Ghiu3Xx7Zzi+EKzWGlGuWJ/6jcEk6YTTLoPQg4c8LlKybjt9ExK8sN9w+5YDrf7x575vGU8tuzvmo3pWB6LFFfoFQLqTvPL589jdeA7/T/n+O/8HbLdMXv/j7/BG3dTZhPL4bRjmlSc1xkPLyJaB+MMdkaaNA7sF+XGtEdk/qDKIsrG4gI9lGeLvQWYrzV3T7aShVptu5yhxwTHkeDr6lYSp6hP/vOo5bRMef2+EBGv7lsOp80GMhFFMoW5dyYFyXIN5/Ou78T2xk4aRpmoniwrw4Nz+Sw7o8DeuJPg078vvbRaCFKYlI3gPYfkKY1hZ+SJjKdqNatKOiCjTDgG6+XjQ4aCNoS3bOq3/v3J+r1ZX8+48HA5olA5Rgu/52RueHi6hQc5F5hMLDtjgeAMh7kPgVWlOFsKqbBIe9fySyZB0B/mfYyAQNkIEV7+zdC5jKrR3D0WB/LRyHKwo3q34LDBIQ9Tg6bV/OL9ZzFaigH7T36EdFSw98f/X/ziaUqWKI52A1nsKRu9gffNxrA3jUmiwE5Rk9p2g/MPQVEkQgoe9ng3HP6RNBwkLsSbDr0xw2fSfcIQmOS9yZGzrNZbIQVFYNHE3D+3otgy8ewWkvHPxqCup1gLxxdwpoVTsFyJlnoaS2zQWmKk1tu4EMIWKqAR4QXXf748AdcTrM8XAIrj8wiIyFI4mDii3JMlhulIWo8yJQ2sV493jD2JCV+/9fWMCcsqIkR2k8yfLzWnF5IrOCdmppOxYZSpHv4SeviwYr5WHJ9bklhU7kT1h03z0Jttgj90rstaFPcA9naGXEHx4ERgrpOJ5XBX94T4ID4Dl7g4ZWv4pXtPoRR8T/EZVn/v/0M8zjn483+Nf31cMC4Utw5airhlUcecLkRcZVoEDmeiypPH7QbPPxTyk0yUA70XZEDntjEBxJH8zkOJL1miiKLhO205SEUqni6t05R9vZHFjnHccFal3Dk1NC3sT3UPhR4KAvnZs6UGNMu1QHm0BmMM3otsq+QKsKw0pwv5fLvjwG7RSqOm0/3nDhu0RNMN/FBpGjgvPNG9iSMygarVrKuBb+FJrGO9fBsTgidxYbPeltjqz/3cz/EDP/AD/PzP/zz/7J/9M9q25Xu/93tZrVabn/nLf/kv80//6T/lJ37iJ/i5n/s57ty5w5/6U3/qbX+wxmkap6l73sBAOEtiRRJr0tT0TreXFER8b8gxEAWdkP/abktMtlYO7wFyMzxfK9G4TfquG/RjrViR57Z3zxwkyRSNEzJf179f5wSPXLWaNhmT7Y6JxjlWtSLFl0mSUTuBBele8Uj3XQMfEFKNtzRevBdab3riYtj8jJh0qc33GvgMl70UwubnLrv9SjI+BIHOidOr9xIYxPgDmk6uO0hX5TLpUWtRQUgSvRlDgnz31gmJ2mh6wyd5TALTVif4cuIV2+205XLAHQKc0b2LLL00W3gbpOLLOITLf97m+uEf/mGefvpp0jTl277t2/iFX/iFx3rej/3Yj6GU4k/+yT/5lo+lvuqfv/E3/sbb/mzvlPX1jAu1E3J91ZqNQ22WSrc8y4Q8mMR6k5Bfht8N/+2cdMubVu7J4T6LIukiWrPtTGkFaapJEtVLEg6HrCJNRflikCkc7nNRKZGfc16mYVWjaaIcOyrQaUJEy2ysKLJef7uXKh6mDUOyK6+hNjLBrTObvQbSzR8IggPXqW63cUEpmehdJk+3/Wcd4qXRsv9jKxO/2lmJC2Z7LeqeUGm0HP7D40PszFItSUbPvRhIyG2v2HKZw9UMcbLbusTKd2ETo6Ko14Xv44mnj0HqcsyQJEY/7in2exQT4Elc+J3W1zMmdJfu/6HLLzmCIk01eWaIB7+P/kz0/tHzSIy+1IZUa23/p7/XLt9jWovfRtxPrmEgwSryXPISH7axQD6f5AquV/CrW4k/bZQRj3NskWFDy85EbaA2bU/YFfLwMGEI/XfWtP0Z3npN6/Uj5+PW0HMbF9xlv4VL38dtrsk2jxIBkCFXUNS+zxUsG4XEtpPvpHuPguFaDNcv7eNybB/NFTqvNkaRQ0ytOyEUN91b85YB0dD/LvpJz+W8ZPidPHYceOt6kits3zOE4ZK+/fXw4UMODw/5uZ/7Ob7zO7+Ti4sLDg4O+If/8B/yn/6n/ykAX/jCF3j/+9/Pv/k3/4Y/8Af+wO/4mvP5nOl0yv/7n5yT5JON1vThzLGb12x0cYPiooy4d6Kpm7C5wY1WmwOr7YRA1LnAbKLZHQvJRimphOpOcb7UNN3Q0W6x2tM4Yf0bLbr4kXacrTNevW+oasGpeR/QWkmCHMtNOi08sQns5CV78RyrWlK3InI1Z9ERv3T7GvdOAoe7ipt7FUoFjhcpDy8evfkG2VMQ6b1xFvqRn3Ts6wYWS0fbBa4e2o3s6PnKsqoeNVYZDlmlpCs6OCifLTTrCvamcHN3jVWeu4uceycapWFaQJ74TaAEqb7HSYtSYeOEuKgiXr+vWa0duzPD1T3p/r1xbLj/QNyg81yk1FQPQ4os7E89e6MaDX3XU6ZCx3O7gTVtioF+glCu5vyl/+uMi4sLJpPJb3v/3Pk//2cmRf7ov63WXPveP/vbPv/y+vEf/3H+3J/7c/zdv/t3+bZv+zb+9t/+2/zET/wEX/ziFzk8PPwtn/fqq6/yHd/xHTz77LPs7u7yT/7JP9n827179x752Z/+6Z/m+7//+3nxxRd59tlnf8fP9I2wfj/jwt/6XyUuDCozV3c9+4VgyAe+0fk64c6xxIXOCb9Ia8W40KSJdJvmC5kqjEeG3YkcunksGN6qM5zMBSq4MwocTGRqVneiHmaUuIlH2nO8Snjtrjh6y4Eq5Lrp2JAmW2UMrQP7Rc1eekFESxZWxF35SFy4sqd4an+NVp5781y66pe8CyQBl7+Mc+E5SBEvSfW6gpPTjq7zXLsac3PfoVTgbGlZvAVmPxQLRsPO2DNOO5pOc//csipFzemp3RVWd9xfjrl9IrKhk1y6nqIsJnFhmnfMMnF5r7qIqjWUjeHOsWJdevZ2NFd3JOt/+a7hzdsl1mp2d+ONY7Tpk65hGjFMEADWTcTpQuCQ24nPtsgrV3P+8p/6rePC72VMgCdx4WtZv58x4e/803PibLJpWh1MHLOs6jH9ss6qlDePLVUNbScxwVrVJ+CBda04mwvkcHem2Z+K0pbtpwmtUyzWIqE7G3n2CyHXrlvR+7c6ME5EF/94lfPiHU1V9bweI8lvmm4L/iINRNZzWJQcJCfY0JJ2K6Ku5Dy7wi/efYp7p3C4o7i5u0YReLDMOJnrjYjBtvHZS/5m22JiKHaqBuZLkfTd3zVc3ZGYsCgNq6qHNz4SX2R/jXOZ/LedCBiUFRzuBJ7eW2K14/5yxJ1T24sDCMS66wUSQoBRKv4NAK0XVbNVbbj9ULMuPbszzbVdyVtun1ju3u8wRnxfbK+UOPw+D3cCh5MKzbYgWrcR98+jTa4w5O/DNSlXc/6ff/JJrvB21++KQ3BxcQHA7u4uAJ/97Gdp25bv/u7v3vzM+973Pm7duvVbbvK6rqnrevP3+Xwur70MxJ2naQJJonn2qONmcqd35tMENLfNEa/dzVisHG3je317y42rlmnhOVtoTk5byrJjVOTsFg2R3hJTl03MyVw2brbneaq4j1UdJ+0Op1VBHrXcSO9S1Ge8OX6OF+/MWC47VsuW1bLBRobpLCHPDeORYWckZL4v3Rvxxt2Unanl//bcv2P8r/4xxYe+jX++vMaXvnBK8uE9PnRtjVUdp6uE5SrQNIHlUnR3RT4roLTi2rWcuN8gQzGwXHke3C/pOs/R/oT9fIUi0LoxZW1woSdbu16hR0llvTfx7OUl8ybhpTfh3r2K0ftzrqUPyLoFZ9H7uffQEUea3bFmkorJybqxvTZxyVHyEIVn4Sesu5SysTw8bjh+sCJ9YcZOVqMIfLHMeflLx9jIsH803kxZRMJRUSSOg/QCTcD3krAn1ZTXSst86ckzkRoDUTuqak+1fhukYm0Jb8EFvvXvv9P6W3/rb/Hn//yf57/8L/9LAP7u3/27/NRP/RQ/8iM/wn/9X//XX/U5zjn+zJ/5M/zVv/pX+fSnP835+fkj/37lypVH/v6TP/mTfNd3fde74tAf1u9rXFh4bO1oW0+SaN57reNW/CY6OIISctwb+jovvllwcdFSlR1V1REnluRmxihXrEo4Oampyo746VEve+uZxDWpbbhoMu6dZixXnqu7cKu4j6XlYbvPw1VBFnVcz48p/Bylnubz64TT04q2cdRVS5JG+KuF8BhSGOceqwOvn2T84mnKbKz4E9f/HdNf/j/IX/hW/lV7jTt31uxOC3aTOZaOYyPv3zSB1aqjrh0hBJzzaKW4cjUnspLulLXAni7mjnt3FtRVy+HBIQfFSvgA7ZhFqTeyqD6IbOfAt7iy47mSnzNvc758u+D2myWjPOcwOSbrFpyY93HvQSCOFdNCM07bjYFZCIrdrOJ6dBuAB+YIH3KWleHhccPpSUmeTTcFw2KZ8uKv3yZOY9pn95nOkl5qVBNFMEo6rqYnaOU3JNFjPeONhyPmS0+aaPI+LpS1fIf1+vF6Wr8XMQGexIWvZf1+xoR1LcTatg1EkeI9RzVPxW+gg0MHhwoBmz/FS82M83lHue5Yr1vyPGJnkjJKPU1nOD9vWa87JqOcadYSaY/R0uBadxGLtaFzME47nkrfxPiWe/YaD/yIPG65md1hXB5jRi/wuXLM6UmN83KWW6sZT2OyVDMZafbG0nx45WTEvz4u2Jko/i9HP0/66f+Nqx/+OKvqaV55ZcE4H7MTL7DKcbJOOZ/7R3IF7/ymIDg4yDnYM490ytdl4OHDmrZxHO6NOBothMgbxqzrLXl5iAd1EzAGdsayr+d1wskZ3L+3ZpSPuBLdJ2vnzKNv4nwujb69iaj/tE4DgoDYL9Zcie6jCFyEGas2o+0yTs86jh+uKfIJu33R9uXbI17+8jFxbDm4MqYobN/xF0RHHnsO0gssHUY5FIFjvcurdcT53Mt0cpBmraFuPNX6UX7Fb7ee5Arb9TUXBN57/tJf+kt8+7d/Ox/84AcBqWjiOGY2mz3ys0dHR19R7Qzrh37oh/irf/WvfsXj40KRZHrTCVw1lrv2isBnesb6vIoxRiBESSyVqTFyUPigqRsYjy2jkSW2sKojtLYb0m7nhEQjBjuKB/UuRgXWXSQ6wFgemn0W0YSTdYFWAimKooTpLEH1cILIqt5nQLgEkYGdqSVP4Sw6ovjwJ1lMr/Ns1FFke0wLz0UjRLi2U2J4FAXiOMa5vgvWb4jJSHCPom4kiXOaKo6uSEWbJbBqEwY/gCEYJHGvFXwJktN2itMypXWa3akmTXKKNHDRTVnpgs4rdqYi5Vc1cLpKNuM7EI+AyOygCZQuonHCbZhOI6AgihRnZYJGIBVXb+5I1V9Y4lhhjdp0SapWXAkHHkZAsWwiogiK3kNimHSIY6nBqsfH9QX1VXCBb+P5TdPw2c9+lr/yV/7K5jGtNd/93d/Nv/k3/+a3fN5f+2t/jcPDQ77/+7+fT3/607/te9y/f5+f+qmf4kd/9Ecf+3O909fvd1yYjjVxamg6UQ9Z1nA3uirdQBXQBBZ10nfkDFGsKUYR2ojqx3ItXfadnRg3ickzkSeUEXtKZGLq1pBE8l7Oex7Uu1jlWbaJdME6y1k7ZW1yFnWMtYqiiKCIgBRrNUWhN6PtLbwH8kzu/9PoCtn7PspydIWrusN+oGBn7Fi2sq/bThJkpRTGRDhne3ihmLJNRnoD2xm6Y1mmuXJtjPOBPIV1J7P9Aec7/PygqjSM7etOc1xNqDvDOFdcv5FRpIGlG9EYaTnu7VpR72jheLF9XYBlG3Nm9wAou3jjzzAaWSBDa3EtBtEWv3JrnygxkiBlGnMpRpWt4biZifoQQzcwJrICSzLmksN0DyONo8cb7/9uYwI8iQtfy/r9jglFCnEGPpHzb9VE3DVXRTJTdSgdWJTCe8kz8epJUyvk0xpOFkbkcSe2h/xIviEKWEJW7rwijmTS2DrN/e6w59qkArPrLCfdHuu04HyVS0wYRaJuZmXPRpHaTMgbp/AYtAqS60Rwll3l6ke+TbxLxjVZMmaSOc4bcQmvW73JFayN8T70MBLJF4pMk/ZwnqaT4j+KFAcHify/hWUr+3BwXN6gCewAg1Q9DzEwrxOaTrM70yRxIbmCn1FGBV2tmY57r6RWc1Ymwl3o/R0WTUJqdgBYdYm4PHvFaKSBHGNkaiNwI8WV6zOMUUwmEUmseqig/D7rTnHejDYxQfexOLLiEWPt5VwBrNHo8Pj4oSe5wnZ9zQXBD/zAD/Drv/7rfOYzn/ldfYC/8lf+Cj/4gz+4+ft8PufmzZvc2G8pRsLs6Zzi9YeWX7gTo42i6J1/rRX8e5po8gSyxFM1mpfe6Hh4f83BUc43PWsYpR0Xa8XtE0PbQVl5ysozmxjec71jnDTcvcj4zG+I3fnBnmE2CtSt5TfOI5H3S0Q+c3dmNnCXEBSrOuqlPbfjsjz1HE6Flf9Lt6/xz5fXeMZ0fGf4F8SLL/Hg5nfzf772PubLQJGLv4A1kKeB2Ah7v4galAqsu5h1E9F04iwYAuxOFDefqUlNx0WdcPs03WChB7zeQJxKY08WidTY7dOYLx/DdKL50M0V+8kFx/WUz93ZoWpgNgp809MdVat55a7my6+0xIlmXEiRcLGKeN3MNu8zkDafvR7QNyxnS/j8yxIQjvbhOz9mcWGrs657h8UQhHz05sNiM+ID2czTIrAzGpQceifascCX1svHr/qlonqrtrDe3GOXV5IkJEnyyGPHx8c4576C4HZ0dMQXvvCFr/qWn/nMZ/j7f//v8yu/8iuP9RF/9Ed/lPF4/DXhZt+p6/c7Ljx7pSbNmx5Dq7j90PDZz2cYrSgKszHsS1MockMaC2SnaRVv3vc8eFCzuxvz3qdkSrWsdK/Hr1itFVWtGBWGW0eOUdLxcB7zmd/I8T6wOzNMi8DSK948HlM3stcmI5hOYiY5jHO3GdUP2PiqNxiyJnA4kwL4s7ev8q/bq1xVHR/Nfo2iucPdnQ/zi3dusZJmOkXWx4VexSuNxLPjMlSh7cTbRCk42FF8+FlPajvmleLuebqBXI6zrSypUoHEBpLLceH1mHGheeFGzU6yZtUlvHS2S+cUo9Tx4adEq/2Lb0TcvVeT5ZbdmZAxyzrmTb2/ISvHPXHz6WuKEESN6TdekgRlf1fxh759Cmx5RbCFONw5Mbxyt2++xFsMd54EilTiwrJXLxkgWY9tSvq7jAnwJC58Lev3OyYczRqKkcBTnFe8cRzxb+8VYoqVaeJIDEKLFCa5QilRrKkaxRt3HS++XLG3l/LC05pR6rhYa+72uULbBpo2MBlrnr3SUkQN9+Y5n/ryCOdgb9ewMwosvOHlezPpsGvFqJCGgkDgml5ZMNqQfZel5ApFGrh1IHnOL929xap6iltFwyfrTxGdvsT9b/oefuaVF5gvA9Ox4mAWemPS0EumtptcYdUmrBtL3WnOFpqyEb+f917vSK3jvIp55YGYeQ1xEba5Qmy2MeHhPObF25ZRDu+/vmYar7hoCn7j/j51K+Ifz1+taJzhtQcRJ2dSfBS5FBrLMuYNtSfczx4OaHTgmWsBrTQXK/j8q7IXD3bg2z8q7srtJQ7BABU+X2qOL0aPKMENJPBpETZu50pJDpMlnmr5+GiCJ7nCdn1NBcFf+At/gf/9f//f+Zf/8l9y48aNzeNXrlyhaRrOz88fqfzv37//FeOPYf1WgbeIGkaxYElrb1itDa+/fIIxmsluTppGjMcRh/vSzctTzyjpUMrS1J7z4yXTWcooDeykJetmxHINTeNZLDrKdUtkMxLrGEcld0LG3bsr2toRRROKVFM1cPdexdnxip39gls3M+IIJlnHQXqBDxqrR6wawc2KvwFMTGCaVNQu4t6J5UtfOCVP94iXX+L0Z/8Vk5vvZ758gZPTliiKmY0CSeSZpWLQlZqGsVmg8JypXZrO4LXadALTOLCXLsj1msodsSgjnJMEYnAKFe30QBY5RlFDFzRtF3N6WhNFKZN4zUH1OuvoBY7Pc5ZLxzi3TJOKSEfUdczp8Zo0jzA6JY4VZcWGOzEuNFmqyOLAuOhIbceyzDk7awghcPNKxkGxwgfNhU2p2m3F7ryiWihOTrvNSBVgNovYn4riSucMq34iEluRPQvx741ywM2bNx95/L/77/47/vv//r9/7Nf+amuxWPBn/+yf5e/9vb/H/v7+Yz3nR37kR/gzf+bPkKbp7+q93ynr6xEXRlFNFosSV+0sq1LxxsunaGuYTFPixDKZbOPCKBN8/LqR8fhyUTObRczyjnFc0bkRJ52hrANnFx3LRQOkJNYzimru+YR7d1c0jcPaMaNMjMxOzhyLRcd4bNnb6ScKuWMnL+WeVwmretDoFpJhbCGLRc741buaO3fW6G8qKJo7mN/8LJPpdRbrW5zPPeNi6+Q9zhyp7cijlmm0RBE412Nal+F6XwOQuHCQLch0Sef3uXMqcqNFCnEshUBkA0YFsqijuBQXHjyoCAcpRdSwrx/gzBEXq5yqgVEKs2RJpDPqxnDycMV4mjEuTO8HI9hrrRW7U4XORFJ0lDqMDizKiNOTGh/g6mHO1WmFC4pFHdH0CmQiTCAQh5OTRvhZmSGOFUWm5TtEnro1Gx6FVtJtbR/zFPt6xwR4Ehe+HjEhsy151GxyheXK8uZr5yitGE1SosiwuxszvqLFvM8Kfn9pDHXtOHu4ZDSOGWeeWSK5wroUQ9B16agrhzEJkXYUUU3TFbz5xpK26TBmxjiTCcP9By3zecNsJ+HqoSWLYZa37KdzXDBonaObqFcTk1xhlAWKWCB4904tr7yyIP2WMdHZS5x95ucZP/VhTs+f5+ysIU9T0ql4dAx8hdQ0TMwchSfSO3R+1CuPCWTYGsVOuia3Fet2j/lKeEl2KrLAQyGg+piQRy0uKO75mPnCEUWGSbTm0N2hNk9xcpGzWnvGmWIcV5RdTN1EnJ01pJnFWkuwEhPqOmyESLIE0lixU7TE1rEsM87PW7wPXN1POCzWuKBYNgl1J0pJTS+asqoU5xeuN6IVpcjp1LAzFtfitjel1EiTsog7VPx7o0j471uu8LYKghACf/Ev/kX+8T/+x/zsz/4szzzzzCP//tGPfpQoivjUpz7Fn/7TfxqAL37xi7z++ut88pOffFsfrHGGdRv3LHnN7lTxoW+RCiyKxJhMKcHOOi9jotjIoX94EBNFh+zMLI1zzJsUowNHu6LSMR4Z6iYmSxTHC82i2qFqFDduFHgPh3uace6IrObG9YzpNJZR5NpT1QqtYppOfpHDzeuDSJ8mUS99Vmc4rzjcVSQfFpjQg1vfzeTm+7kY3+AT711Rd5bTVeBsKVJ9ZZ0S2YQs9kzTFKs88yZlWdteuaSXSK0Ub8x3iMyUqjPsjsNGWch78IqN8VHTaU7abCNLuLMTk8SKk2oM6XOsmoSr+4puxzIrJOFWShyFd/dzisKwvysJz2DDrpQUHpEV2MGDi5gQYsoGDg8lYNctvHEumsd1K8HP9WQo36uFXD2yG5Kg1tJJEA1kLc/p8/+zpWJRxpSr+LHvH68M/i1jv+Hvb7zxxiNEoa92yOzv72OM4f79+488/lsdWC+99BKvvvoq//F//B9v3693sbPW8sUvfpHnnntu82+f/vSn+eIXv8iP//iPP/Z3eqeur2dcWHcxro1Fns4rdqea933woFcJUpsx8yBtK8Y5lraT7tXVayPGI4MYWaa4IAlzGitiG1FOLFmimJeauivoHFy7nuM9zCYiJWi0yA2Oep+MupE4BIaqFZ1uud97dbR+kgZbTfEre4rdacHexHF358NMpteZx3t84MaK2lkezmMenveQntYSW8soi2hzIyPzJmFVG3zfWReopOLN+YzITqg7zSQPG2hQ3UrnLum79603XNQpnRfo0eFhSpFr5nWCVleoupi9icN7RWIdlYupvSFLNXsHBZNJxGwiDYGmVzwb4oJSgbZTLEu7UV86uiKeC52DN04FLjlMBS7HhTxTjG72cMxYmgFKhd4kSoqHgWi9KuXal+vHu3d+tzEBnsSFx11fz5hQO4tqYjovZ/FsonjfBwTCFlnVS1/CqpJ7KE8CWSL7c28vwto9dmaWtnOsdCyTvF29mfo3bUQSC4R22UR0Dp55dkIIQaC3sXTtrx5FzGaW4OFiEZgraLqEi5HkCsMZDgL1HdaiFnngwx3FOB8zzR33r34P46c+zMXoGt/xgZKyizhbBR5eaIzWLBIjzuZxxipNMCqwaBLmpd14FUWRQH/fuJgQmzFVq9mfSiDSPYRYDBclPlWdpWwF2tN2AofWCk6qET69SdkkHO0GuqlmnLW9jKpM8ooioigMO2NFEklMGOBDlxWI7p3LGV63cHS4/f9XT0YbWdZBanRdDbAnONwzG2XCYdoQAlRtr0zkwEEvlpJQrh4/cX6SK2zX2yoIfuAHfoB/+A//IT/5kz/JeDzeYP2m0ylZljGdTvn+7/9+fvAHf5Dd3V0mkwl/8S/+RT75yU8+lmrA5VU2EbWKWNeCabuy0/HC1QofNGUrEngXa8v9k8BqHTBaY41gz5+64nj2qqLzAiFaV5pR6rm+KySWurO0nWZVa964D6uVZ29X8/xNMeGJbUdkHJNUMc0NndM8uDC8+EpNXTtOTw2v9VjEUSEqQ3kCs8JhjWfdGB5eRGgNN/cqPnRtzaLJepjQC3zivSu+9fj/C97xq0d/gi+/nuP6CZcPgXFh2J2OsGYraTgcnMbAYgX3jzXOKW5cMdzaF4zB2TpmsdZoLe7CkfHMS8vxxZaLMWysN05SbquUce54Zm+J1Z24PPbOxLMx+BAzzuHqTkNsHU1nKFuzkQUF0Tt//Y5jtWo5PEh4+qoEl3snijfueJQWCThjFFXlWSykcn/umZRnjhqs9qSmw2jHqk14ME+oGpnOdF3AeTi7cDS1f1t25L9d1T+ZTH5H5YA4jvnoRz/Kpz71qY0cmPeeT33qU/yFv/AXvuLn3/e+9/G5z33ukcf+m//mv2GxWPA//o//41d0Gv7+3//7fPSjH+UjH/nIY3+nd+r6esaFeRljfLSFycwcz11pCEFt1MFWteFkLgVl28LSCNZ1OoL9mdwDy0qzrGIiG5gVoryxNxZPkarRnMwVVaUZFfDsdXp1MiEYqjgw7r2wzleWN+976sZzMVeiKGKQuBBJgpwlQioefAasgaf21+wmc5Ztzi/eucVifYsP3FjxoebfAvBLk2/nS68ZvOtxwhomY8NylhNZgUDVg1Z6L823WMO9Y0UIhqsHmmu7sl/OVhGLtSKJFCqVgmDdGBZr0zcy4Kmr0tR4OI95cBEzKRxHxYpIO2oXsWoTGmeYFBCupoxyOJrJZLD1pk9CRFJQXNEFjlGWjv39iGeuSVy4/VDxxu0GYxTjsSWOFM7LhEEpxbUDxZVphdWBxLQYLXjhe+cpZSPfWRSGAvOFp2k9dfl43cDfbUyAJ3HhcdfXMyasaktFxKqSXOFg4njPkUB6Wy+J/fk64o37AgGaTjTOC2zo2j7cPBRNq7pTNC4mjx03d79SIefNB5rVGvZ34ZuedkTGb5JYgJ2ReHjcPYv5/JcqynXLndiIPKnVTKcRaaLI062GftloThcWawI3d9fsxAsu2oKfeeUFTs+f5zs+UPLRh/8byjk+e/VP8vkXxeAvjoRPk2WG6WjUy4NuYYogsJp1Bbfvi2/TzWuGZw63ucJ8LZKgxJJgX6wtJ3NJrrWCIlebXOGukVzh1o6oDPmgcF4MCPMEdmYCLzqYbmNC2+mNUlgIsFgbXr3dsVy0XL2S8ux1MVt87Z7hiy/WGw5BFEmucHEhv4P3PJtx66AhMo5RVJPohlWXcfuiYF3JxFaUlwIXc4nFjxsT4EmucHm9rYLgf/qf/icA/vAf/sOPPP4P/sE/4L/4L/4LAP6H/+F/QGvNn/7Tf/oRs5G3uzqv0H1HWmtxr4u0w4dAq0VNxBph1w9E2mFjGiVOer7TdE5kujxi6EH/X3Hx1XRdoG19L3ElxkJGeYwSA/QB9y7mPmqj9+9dwKltMPBBTHZUr3HcOoiVdMusklb3fBk4OW2pOwveodqGWHekiRiD1Y3Ig3UubLqLl0mDAwa4pU+WnRgr+aBQhEfkRofr4XxfPfeVdtQTcNpOHosjMTsyqI3xkevdQ5NYRvKXl3yWrWmIdCCF9Kz0o1rJzgf0JY7AQH6C3negv85aiXGLUdsvoDVYq1A+bJwe3876vTAb+cEf/EH+8//8P+djH/sYn/jEJ/jbf/tvs1qtNkoCf+7P/TmuX7/OD/3QD5Gm6YYwN6xhFP7Wx+fzOT/xEz/B3/ybf/Ntfqt35vp6xoW6UxgtieEgHxxpx+BorpV4lxitcMNeDRITTB9HxABH/lE0xgfjIHk9owNdp2jaAAip3/YdqdB3xbQWErPRoZcVHKTy5DnwqPb2ZegQgFYei8SFVQXnc0/teudO1xHHjjyL6bqwMWLs+j2r+tcZ4sPl9/I+iKyi2+qSX44L7pI+ufODk7tMMVw/nm87iQttZqTb33sgDETEAZoIooe+ff/QJwpbjXfvB5zypbjgwuazDs0FrXv5UR36a/uVfKFBkzyJVe+h4AGNvwRH/O3W75UB0ZO48Duvr3eu4Hvp3eF8tsqDknNfIefZcI6ES7eW6c/8Qa8/BPCRQisxHnVB9c65knA2jcQBqz1We5lKDPudLU9HGgO9rKcf5DDDJk45rzBq8AvoNecJWCWxbL4MnJ01lF2Ecg76XCHL8r5RFvAdRD15WF/KEeBRXyLfSy87Dy5otNruO9h6fAzeTd6DiQb3denWN51MUV1QmADOG7ogMWbwcBmmAC4MMSGggwi4hCAxK3h6BUWJybr3WvFO9vLl+DlcP0ERSH5glJffDZdyBUXvi6Romke/++OsJ7nCdr1tyNDvtNI05Yd/+If54R/+4a/5Q4F0niOvNhbit08i7pupSGia7cG+OwFQvUtoT0JZGVpnNiZEciYZQMY9y9JQ1vJvaQpRJKfb/TODNYZpYRglHU1nuH9uWKwDSQzPPxNjtHQVB0fSzvWjq0bxyl1N10llPc7kRj5epJyukg1kIYpiTleBXz36E8S6w+qO//CD91h2GV9+OOP4XDqZR7P2kQ6ED4p5aWlaqch3J3LDrmv4tZfjr9AhXlYGcVYV8yMQh+Wkd3Oer2X8/uBM8cb9nBBgZ6KYFjKiV0oIvk2reOV+vJkwDJu+bsT52Rp46polsoJXPlvK581SeO5W9EiA0tpge0eREAL3LoaxXsZgwJLG/pFxqhQvUtitV9Fj3z/hq4wB366iyH/2n/1nPHz4kP/2v/1vuXfvHt/8zd/Mz/zMz2zIQ6+//jr6a3BD+bEf+zFCCHzf933f237uO3F9PePCm/cDceo3ChudM1zkI6wRMllsPIn1HM620DSQZPx0oVhXlwy7FEjCL4YvA/l9A4tL5MllrXvojtrEk4HwFlu4dUWIhAOZX5L/bYL9xj1o2sBsosTpV8G9ec6xyWg7uX/GhebhPOaXJt9OHDum8Yo/9ZH7rHzBl072eXAuwgmjTLxUhrvOBYlnVQN5CuNc7vFVCb/yZZEgnE0URSqf+fjC4LzZ+CPIGF6miXVPUF6tA2cXgS+/JvuzKDR5KkooB9OO8W7DRZnw0h3LuvTMJprZSL73ohSukdZw9dCgtcThu6eK4IUI/PyzybYp4WCUK/bGHdYE6cSeZL2xE4+IJaQxzIqOnbTaFCqd16we08H89yImwJO48Djr6xkTFmtNlG7dhe+eGR7OpWs+QPw6B3szKeYHV3HnJc9o30JNc95uiv9lpSl7bmBkZUrXdULEN5qNZHHrNCdz8fZJE/jw+2K0jqVhoCVWDEaIVQOv3ZXCeVwoZiP5mQfLjJN1St1qpmNFnqacrQKfvfoniXXHxC75vm++z0pN+PzxEfdPNZMCjmbSPRf1REnSl5URVaIYdsZRD7mDX38lkmljIf4LArvWG/PWcS/Hn0SBOBLlwqFJcPdE88odgURORopJHvp8LLA3kRzo5buWzoly0GBgNkCNIwvP3bJYI4arp0vZe6Mc3v9CviEgSxzVaC2JgPeBe+fy/0Znm/dMI08WSzPncvPFBc16+fZUhp7kCrJ+V8Zkvx9rMIv46//LOVk+YTDjWa08ZemwVjGbCc43S2A2clgdNm6Fbac4uVAsVn4j06eUHDqjTBLMswUsV/Jao0LGZmUt+v5KCYdgb+JYV5ovveY4O6l46umCb362orA1VnuMcrhgOK9z1o3lwYXl819Ys1zU3Lg14bmbAlM4XSiWK9HP3hmLOsDFSnF86kgTzX/4wXs88/LP0M2u8O+KP8QrD3N2R44r4wWxbjeSnK03nJQFy8oSR55R3KIUfOF2yq/8u1MA3vu+Ha4fDJhh+a6XjZHSSGy9685wPBcTswcPO178zQe0TcfNZ/d56qmcJBZDpjxxHM8tX3q5YbGoGY8TplOpIVerAQ4Q85HnOnbSNfeXY168I6SlawdwMK4JKDov5Mo06pjFYrLy2nyHV+8a2i5QVTIZ2d2xPHetYxS3GO2ItHukE7BczvkjH3/6scxGXvr5TzEeFY/822K54rk/8EfelgnRk/XOWMPv9f/+198kK2a9v4YQT5NEHDGv7oshTmQ9me02HWkQI5svvRlx/0GD7qWKjVEbEyKQxGJdP9pBjKJtN7ysoaxFw79p/MYY8H3X1qSm6TtYjjZYLupMpJJPDL/yK2csztc8894D3vecJAlnS4kLUaQoMnEJnq/g7KIjzwx/6iOvcu0X/hfYv8Lnb/0nvHw6I7KSfGgVSIzsj9objhcp87VIKO+OWrQKfOGNmF/55Qcorfngh/a4dUXUOO6dCFFyf8dybd8TGd8nLQIjeuWu5uLCcXJc8vqX79LWLdeeOeLqzSk7U8vHni+5nt7n1dVVfvozngf35tx8epdnbsnpf3wmog2zWcTzNz2TtOXuecqXX+0IIfDMzYgbezWt0xzPI9Y1HO04npmdEqmOL58f8puvKOrGs1y01HXHzk7KMzcjRpnn1mzOrfAKxjV4bQjKsFguee8n/tDvaEz2JCa8u9YmV/jxc9I+V/BBcoWqckSxZmdqSROB9E5zh9ZhIztet5oHZ4rF0qGUqBYKf26bK5zOt7lCkYkUcFnBai3POdr/ylzhuedGfOK5C8Z2jUbOsJaIh9WUZRXz4MLwG19Ys17WPP3slBeekm76g3PD+VxyhYOZGAA+vNDcvtuSZYbv++Yvs/fPfxR7/Qa/8cL38YUHO+yOWq6NzolVS0CS4TZEHJcj5qUlsYFRKrHp82/m/PIvn6K14oX3z7h1FB4h72q95eykkSeNHs0V7j9o+dJv3KdtWm4+d8itWzlZotifinDDg4uYz32hYnFRMZ6m7OzEIkvaDMZoER96umInWXFvNeHLtyOcF9jW4USgTK0z+KDIo5ZJtEapwKsXu7z0pqZzQlTuOs/ebsQLNxrGcUNkOlJdo1TA4FF4losFH//WDzzJFd7m+l0Zk/2+rrCFywyjPu9lKtC2YUMebFqN71nyRgc6JVX44ESY9br3Wm9JbPK6Cq1VL58pI7FhXLWFwvQkxb6r7bym8xYfPFppXJCbdxgTJqntzdGEaCSTCUXThI39eBJ5jDaSVLSBZZfRza7QxgX7yQXRkduMyxsfbcb9nbdCGu6hB40TLkDwECdWvo+Sz+wvfU/oR3eqH6024h0gEoEQx4oosYQQ0EZf6qhC0+keaqRJ0whrt6+tlCKKhK8gvy7BX2ulcP3o1OqAJ9D1OM7WGVadVPpNq0Ux4KuUox5QQdP6AcLhHukCPNbto/RXVPlBvf0K/cl6Z60o2mJylZaiwDnpyJeNRmtN7Af4UOhH+9ubTPaq2vhiCNF1gPRA18nesHbrijtweASrrzaqWKHfC63TWGVxKqCQUfpQBGsNcWrJioQ0NSTR9rlNI1MOgezI+w7j/ZUvYP8KPp8ws+c8vatonWXZxhID/TYmXXYabZzu9dMhzWKMFefxIXa8dQn3QoGTSVxsRb51vY5I8xRjDHEakcSip944w8oXNM4QxYq8SLB2Kx9qjCJO9Ob6DcsYteE6GO1xXvUNG/lsZZfQaFEdGnpUEtMUund6NRpcMFTRCGO2U4HWPO6E4ElMeDcu58M2V2ALk/NOJEOVkvunanXPBQqbvT3AZGyvwz/ckwNnD7a5QtTDaNr+FgpvOb+GXEH2oaXWUiQrguQKXvV5CiSJoeuEQzNAmX0fE6JIcgUp1mV/d11gpSYcXb9BGM3Ysac8fyB8vxAUdYiFA4gSfyA/cHqg7uymsZbmEVoJxHFw+h4mcQPEWfW5QtkIwVgmLeIhkqTSHbFWb6aizitRQgwQx5o0j4iiLfxH99d/uK5dMBvIMfSwLeWl8ckQxwxrlW44Xc6zURm6HMc8EgNbFaFCIKgOo8Tj4XHXk7iwXe/YgmDY4HGv2lNaweQFH1gsHSvlSFND28pBNS0C01xmf12nWa06ptOI/VmvYd9r1Q6HdJ4JGXicC4zGebkhBly+mBWJ2Y9zMVopzlYxS2s3eDjdJ71aB/IkcO1KTNNFXN2D/aKkC5r7ZxnLZYu1MXkamKUNZS2j+LrxfPnhjGr3D7GfXPDMyz+De+VF1Ae+lc/tfQ/ndba5Hs4rysbQdoqm3eoY+wBXrhYblZVhMjAUP8AG3jRfGVaVwH52xp6dkScEy9nRmKrselMQue6LUrEoJWgc7Fn8rqVugrgjelEgKArDKJcJwLqLN8ZoWiuSyBGbjs5rlp2maoUEXjWxqIq0PZZQ0Ru3SNDo+sKh8mqT3IyTlszWX7V4+K2W1wb/FhzgW//+ZH3jrZ2dhPE0IUtkDy5XntWqo+uVJqJIEVlNFAl5d2cUmOYtnZP7K4o1eWY42BHowCBr570Q8OpGuDOTRKZ5q0qxWMlBlCUCv+uc7EEIVDWclzFrE9H5LTdh0PZOIrh5c0Tb5lw5MOwUNc4r7p3GrFYdWlvyRDPOHHUrhX3Ter50sk976z9hZs+59tLPcuXN1/Af+Di/kP0RLtbJJrEQhR61gUWcLeQetxaeeW4iU5RE1EbabsDgCyHRB9lvFyvNYi1FyWwMR7uBcZEChzR1x+FRxv6uwRq4d55wvDhgXSsO9gM7OxHWqF5lCfJUUeSWNAbTwzqNDqSpHLBx1B/+RpFEHu9F9OHFWjq88xWAQLCyzPTu5oY07kUS6oTK3cAoIR3HumNJ/lj3zpOY8O5cbSt8uiTueUF9p98H6eRXlaKsFKtScoWdUWCSO7yRZLppPVFkmI0FKtO5rSqe7c2v0kRiSRp5wHCx2HIDfK/gl2WarpMm3v1FxrlNNyT7Qd5T60CWBK4cJdRNxN5Mkcet+Kp0ZpMrxJGce4vECM/PBz5/fET5wvexY0+59vn/k/2XX8J+88f51cP/iNMy23AdnBdhBYEoKU7mUlS0Hdy6NUKrnnBcbyFMXSfQnaJH8Z4vDfOV5F97k8A0l++9emqHuurY20sY5ZIDlbWirMXF+egwxvuYph24mYEkUWgt+VbZWpRKKRvZd1IsSOPGBTF9azrN+cqyKBM6JzG564I0LZXaNBs6r2m9ofIRXZ2hCORRS2Jalu7xScVP4sJ2vWMLAh/CI1hfrUFpJR20xuGcVIqDA+AokxvLqP6QbBwQUaSOPHZ0LmLhtwx63XflRJfYExm5ATbkmn6zR1bciUE2kO3MI9jWUeZJe2LSKO9xgVlHbhsaL5dXkmgxHUtMS2QTfJCb/PgcOpcTHTncKy/y8k/+K96TZYS972VVm03H3nlEXquv6OtWKuYQYDwyvbzhVmVgOwkR8o5HnjNfbkeSedxSpIY8l0QkSfSG9Nu2whGIrRiyGQ1zFGUp/26tEgdhQ0+ilg5IfxmFxKkcTsnkoHMCuTg+k/Hh0KHVSrqHIQjRaJjMNE5vsNtZJApIA9nose6f30ZK7Mn6xl1pqsgzwcSHION758Lmj+rt7gcVjiTSjDMhw4EcQHGkyBLhq6wrvTkYB6K+4IylUVDWhqZPOLJEbYh2Im0qj5e1prNidFQ1slfkZ6XTNxmJIsck96S2pe2bD3XtKApLZAOp7YitRfVTggfnmhBmPL2ruPLma9z+Z/+Wm5Mp6oU/0hcd207oZXOvy7K+g5PoMOVwA/HfbKVZpQMHi6UjyyQuTLOWzsXMZhF1LfKqw/VellA38nxRIVGbQ1srRZJKF3WYhAxuxnGk8CFc6tBKQaO1xKXFSkibAwlZRAU02gTpohrhTjSdYVmLa/I4NSSmY93Vj3XvPIkJ787VdXLPDGfPkCsEH2ib0Itw6A1qoEj1hrzuQ8A70ctPY4kJZS1SliEMbthbY8AkcthLucLQrZbzV85QH4JwIA20PX7eGpiNPHl/HxeZTNjzRArd4fWGXMHqQGw68Q0x4Du4f6qp2x2eP1Dsv/wSX/xf/zUfmE3p9v8Ei9Js9vrAVxjee7UWvkQSKyYjtcmrBlWith32ntrALKsGLubbmJDHLaNMizJYoskzgVqHAGUjr2GtCA5oBYu12nA6rFFEkTQtWqep+xxquG6DiEAIZiO+sK4VD44dbev7CeF2emOU2kx3vFfUnaFs9CVeGLT+8VPbJ3Fhu96xBcG1fZhMPLH1Mr6qDA9WlUjUNQ7vA2lm2Z2K6cUoG2Al0inIiog80+RxQx41lJGQbKGvOF0A9ObAKhs4Pq4JIbA7KygSzzJozs4FT7u3nzEdiTzYJPNkvUnWQPpt1faAFoyi/BFYgYwtY+NITUMWe8aFoXOB6Qh2RwITUh/4Vp4vCji8ynvbX+Pp6YgTe8RpPaF1mmUjI3WtA3Hf9X84N9w/loBiRpo4Gkh4Q5CTz+q8whoh+FkL46RlJ16xTGKZinSeIldc321onebN1lIuPCERAzKtoKoDJ8clPgQmk4SisGilscaT2Za1NdCP6qzxxLrDqMAotlhtiSORhhU40pYEprSMFvOEfhrQceZT6lbedx0JOXndvR0fAo1/y9jvrX9/sr7x1o3DwGTiSKyQ4y+WlqrqNjAegNlOysGuEOqGuGC1CANkmRh+TbKOxDo6F6OU7NPOifyltVsoQN3CyUmN95ClqRDtO8EdX5xXWJvDrsGawGzkN2pEXa/ko3uI0rB8EK30QXUEJNHIo5ZRFjEZC5xQ3Ik9rbP4D34bt2Y7uIPrPJ+8zI1rI06bGSdl3h+I8l6m5wkpJXKox+fynlkKSQ9Jiqy89ygT4zCZmGraLpABo6RjmpSUjTQJACYFXNspaTrDqopZrR1xrDcNgVUZOD+XeDgeW0a5FCyzIvSwh62ykNGBxHRY5Wlig9WGOlIYLYTudaVYrUUbRrQelGDA444s6pi7mHVl+msZkUaGdfV4muNPYsK7c928YphMIY0dnVPSsV61PdTG4V1gtpNyeBD3MWGrahdbgbhlqWaUduRxSwgxq8pIE65lozbWOGliLUu4d29N8DCbjoWQ3xrOzjuOH67ZP8jZnVjSODDpG46DApFWAWu2hfqQgIcequz76j61XZ8rZDIp6+hzhRajAvZbPsEHZlPU0XXeHz7Hs/sFJ/qIh+VEJvaNpW4VJoP9qTRXL1aKi6U0WweRgMEJXSlFGstnlVxBCqwkVsyymt10Rdlaaf41nlEO13YqyRVOYi4qybvSuFdCbEUlKfhAMYpIU4NWmth68qhjHZlNfImNJzM1ThvGiSEyHmssYGg70/u8hE289F4mHANyoPM5VSOp7FCsrdsnucLXst6xBcF79k6YjksiWuqQcO/0kPlFRdt0NFWLc57dvYwb+y2TuN7gdqX6hnpsmY5glohLX9VFWCMKAetSYAbORexNFc6KzNftV44J3nPrVs40q2lcyr07C179zTeoX7jBreu7xDZwZbzgirqD0xEnbp+LJqN1etOt67zqMf8SVIKXKr+IxIF4mqbsTkc4rziateznKwA+t/c9hL3v5b3d51D/2/8Mp3Oe+54/Qvr8f0DpM2IzYm0jxnHDfnKKpeU3eJovvdSKI2mekkRSBMyyhtg4ITpqh/OGYyubxBrYTZYchjvMs5yu0zR1x+448J7xbcqQce/sCufnLZNJxO5U1EqWy47Xv3yfEAJ7V2bMdnOMSYmNYxRVrKMIrXosoXHkZo0PGpMK0dJ5TV1EuKA4WSY8vJBETPWSg6PMs5cuyXRJ2R2wqmxP8DQ0TrFef3WzoK+2nlT97871ws4DJmNx661Dwu2TQ5bziq51dK3DO89sJ+Xpw5pxXIlkZt+RH2VysM9Gnr1sRWJaWjflhFi6ak1gve6wNgIEdrBYBd585RTnPLPZVaIDhWoUZycl9944wdojnrmRE5vA4WjJXnRGFyz3qz3OqxjfH65D90oaBQIBcM5jjGIUN0yjJW0uPgPO95PHyLNsY34h/S7U89/Fe9JXOfiln8TN51z56B/iy+OP03jLuo1onGEUN1xJj7Gh5Yv6KV5+Q+NcIDqwjDOZVmSxw5pAYmWK6bzhroloG4EE7KRrjvQ9qixCKVH72Z90PJu8TpkWvHF8g9OTmvE0ZmdiRZCh9Lzx2gUAh1dGdLOY2UTkDVPToXW8abwk1jMya5zRaOVp+jg5yQRX/OAioqoH6Id0F0cZTJOKzNYs6pizheDGk1ITW025erxj7ElMeHeu9x+dMBlXWOWofczD871NrlCuarq2Iy+ucn3fMU7abRIepFgeF4bZCPYyyRVaN+M4SK6wWg+QRMu0EI7d8anjlc/fxnWOa9ffTxo5VrXh9hsXvP6FN3EffIr3PDUjTxxXigX7+gFeGc79Dss2pbMaow09unAjATpMObVWFJE4EK/ShOloJIap04aj/IIQFL968Mfo9v447wu/jv7H/wCOz3n+j/9R4me/h8qnXNiCdWsZxQ2HySmRavhN+xSvvCHvER/FjDLJFXbyhtR0uKDEudwZIisogCyBg2zOYXebMnuetkupa8fu2PFc+hqlKrh7doPz85bx2LLbmzdWtefNV04IITDbGzEaJxiTktqOSbxm3VrotdIS6xirOV4bbNLRxZYmtezmMZ1XHC8Tjs/Vhh/pQ2CUBXaTObles+oSluWAmDC0iWJdPZkQfC3rHVsQJNQkymAQuIgQ7jzOyUhNtGwVkXZEpsM7ucEGmIzt3fGMCmjChpRqzFYL//LqukBbtxucWqTl4HTO05Q1Xec3XcNIdSTtCmdijHYbEt9lOUIPG1OOgfyolFBvrfKbJCEynli3ND7ivM5Y1YanZiPU6ZzzVx8wOz8h6xYoG1iblNpZjHYkvsT6Bmv8JpAMREqtBqfivjPBlnQ9YPWt7jBN0+stD139QORrOiUOwt75R8aiXRdomxbXOZzzooXsB/Kl/6qkX0XoNdfdRnO4CxprhJfh9SAjK+9vdYdV3YYw7TbEJ1GRetzl+Sqb/G0QjZ6sd+aKVUXc/149MiaX+9/TtW7TRYqNI+lVutr+9z5AD8UDI6DxvT751i3b9ETjYYUAbdPheudAa+TnvQ+4vgAZllWe2FdoHQkRnqEDqC693pZMN7itD7raWnlxNXaqfx/B4F+sE5pOcf1qgZvPqe89JFudkc0qtIo38puRdsS+IvKihOacmH4NqklKCWQn0g7be38EJR18pWUsb7XH+Barfe//It85dhVtJAV51/lLr9mbAvUuac4J7Me57Xt+NV1wjXRorRLlABseJR/Ktd5KREbaYZRMe5wXT4bhdZvuK1//q60nMeHduVJVkSiLVh7fQ0s2uULncG2/d3sYzuC3A2wgq7onu2vlMf1ZNjSJlR5ETdgQfJuyJvTusvI8ufebsqLr3GYaEOmWpFvhtcXqseQKbAuSy34ASrPxVRpyBTNA6xRExhGrljrEnJYZi9LwzMEIdXzO2SsP2D09Jn9midKB0iRUnSHSjoSKqKv6mCD7d+uHIj4uVnfgbQ83lPcc9l+kOoxrMHbremYUkiv0TcbBy0n1Uw+xTmgJPtC17rfNFVR/TXRw4sOAItIKb7oegSC5nharAjQ9hFA5rBcFpcFrqXUQPckVvub1ji0IzrsJbSNyT42zNB2MZxnBC+4PIEs1x0vLshHm+6BZ33aSTJY1HJcFqU3FZTP3hKDYGWtCiDdju7IxWAt7V2copTjcCexEF6gi8Ox7dlDqOQ6ORoQgWrvzNidPRV8WB5mtqZ1oga/LQJFqqizCeSWjtWs507Fi3cWcqV3mTbo17umNjsKlDXpqDnnue/4Is/MTsJa9L/wcfrpHeeO7eLgq8CGjja6jgdNlRJptCbqDbvC6FpJRGgfyWBLsIvE8c02RxY7aJZzFV2hKy8F+TFFY6lbzenujVymA2U5Cksj19B7iRHNwYx+t4OjahNksYjISItCqzajb7SZa1BH3zR7QwyQYVJoEprWq5HVNP7LMEk8ei5Rr0/tFFFlP7Poa9qaMAd9a9f/7OQZ8N63TbkbdToUM6C2dg+lO9ghkKEk09+cZ8zjeyBE7PxCBxU/gYTki0o6qNYyyQJHC/kyjld4Uz94LT+bg2gyAKweGo9GCLEp4+pkxSWqZ7SSsawBDEhXo7IDgRP1jmtXMq5jThaaqhc9QJNIJH+eKK1dzZhPNuo0412OWTULTqo1BWmIcrdebouKsnXL1o3+IbHUGSvHUvX9Nl014bfIRqm7Gqo153d9AqcDxIsZaj+kFEOpWCM9lrYGIPPWMe8WQaeF437MRaeypXcSxvULZRMwmAqUoG89ryTPUZYTzMJ1JXOicYI2tVexfEQLzdBozKgxJLHjhdRvROeFtAMxLyx2zu0kIBuf4VS2eKVUjsCatYTaSuFAkDo9MXZUS4nLXbZs7jxsfnsSEd+d62Oywrici7+kNbQujSYpzgelOQQiBPLcczy3LqpAC1wQaJyIcTSviAcfrgqTPFWYjSXAPpgqwG3fsstbEceDgxgHaapEdTRYYFXjmuR20eo79wxF1C8vKch6PiJIrgJh5JaZlqWJWJaxKTxpr6lzUCotMcXCQU2SaVZsQ6R0WTbLB23u/ae9tGpwn6pD3/vE/yu7pMRjDwa/+NOwc0D713TxY5ZIr+GsoFThbRSSJJ4qlcVq30DlN3eY9n2cLRR5ljuKaIo06Sp9ykl5nXcXsTC1JrFk3itf90zSVxODxuHcYbiQHM8axezglhMDOXsF4HFHkWlTK2oyqHURRAufrhHtG8qnWG3zQtF5TteKEvFhr2t6IclKIn9KoRx1UKicEtckVkuiyx8zjrSdxYbvesQXBySpnSb7pDHcdTCaC941jkQGzVnF8rlDabORDB9yf6wuCh/N4QxAcpx1We0ZxTWZq1l3K7fmIZamJLOwfjrCR5mDSMO1OsHHHszfGJPHORlpvXSvmVUxiptIBUB2ZqVnqlLoJLJeOUW57DG5gnAlOMbKBdRNtSHGd28pwhbeQZU+qKenz/wFZt2DvCz/Hyb/4DKObR4xufIy6O6RsDA9aUetZrqUwGvwWmk6IRPOlp2kC04lhfxoRR4FZ1nA0qggoahex7naoO8PeTDEdG+oWXnow2hQrO1OB/9TNlmB9dHWMjTRXDiJmY+nedV6zaGLqru+uBrhYGdou38iYQc+t8EI0XNe9I6IRc5dJKhAnFwx1kGlKloSNPvLQxX3c5dF49Fc89mR9Y6+T9YgFI/yQ6LttXEgS3ZvnKe6dKrQSku5AohumclWjeNhFKBWRRIEiEe+CnXRNYUtql/BgPWJVG9IEDo5yjFYczTr241MyU3Dryh5pItrVVS17xOgIHyZE2gk8MCppnKFuLKu1kPmr3lV3lAUiK0pIZWtonUwHBwdmkM4dyEQD4KTM+fL442SzSoqBn/+XRHu7TL/jFveYsWwizpcpbSexz9otr6luRUmkrEXMYDLWNKNtXLg2XuCDpnQRq3ZC3RmmRcBnsK41L9WTjSzrdCLXtelfM44UBwcpSikmI02W0hMqNSFEdE4wv84J2bCsUyILReqIjMjFni6kwAiDHnoEk0IgHlZLctYi8ol5Ai7aTi4fuyB4EhPelevePCN3osjnvXAEi5E0/NLUYPsC88GpdL6LXDPKhFBb1b2kZwn3zyOSyJIlArm1OpBHNamuWXUZr52NWVUiw33lxgwbaa7utMw4JUo6nrlxjSjaJUnEyGu+VkQ2AWZY5UltQ6wbtMpZlZ6Li45REdM6uQfzBA72DGkM68bSefESGHIFFzQu6E2+oBUcl2OSZ7+H/JklB7/609z+yU8xfeaI8Z/9OGV9xNIb7ndiTFZWbNS+QPZv28k1cC5Q5OKVFNnA/qhmlpS4II3MZUhZNxHTERSZpmrgxYeiDNZ0YtimlLwWCCdhdz9HKcXOTsSoEE5X3WnmJJS13gjDnC01Pow2hGgQ5bSq6XOuShQZk1gzygTiNOQKFb15Yho2Qi9DQ+dx15O4sF3v2ILAXRqnDRCgNJXIb3qbamO2XaIhuQY5TIwRRZBN0h1EHhOkCrUqousttge4wKBv7kOgNQltiDdjQhg0tPvP5w1BebQ2osEfhHA0KPW4IJbpw3O9F11/r7cW6UCvKW42PgMDB6H0GcoG/HSP0c0jor1dVJCipm4NTWe2UwW9DRCXq+PL0KgQJKB03gqcKWjENn2wUpfvtvUukGt7+axtWo2N9Gbjdo5+nCnwpMgG0rjXNLahVw/oVQSATtETLYNcq/hRu3PxJVCgttAKH+TNgtqqqTzOEosS8xWPPVnf2Cv03XN0QPdFahTJjX65K6QvjfwHl21pGoTtfYXcxy6AcqIE5IL4CPhLkILB80SrsOnQXfb5GApW3+8nhaYLGtP7lERWyHmyv6TYHZ47TDWdluJ3eMwFqL3Beb0p0DsvUxGtYrpsIjFhPMGEjlHUADHznsMDW/jTphuvt583ePm8nRP5PhekG3cZwrC9lqH3ZNm+htGyf3W/p7kk9LPRhNeByHriSJHGW6nmDZzCBBLraPvxfugbBJGW39Vbj2TNoFy0VVMb4tDjrCcx4d25Lk/XgxLVwTSVBHVoHg7QoGE/DHstiYVUH/W5wjCxl1zB03lDezlX0JKLRLH4ofig6HRE27sbD+vyvnNBoXqDTnS0yRXipD9L/XbPDWd23cn5fBn64oOiDdHGZ0BkgzWVT0WYY+eA6TNHZFcOKAmMM0fTaZZe47rt57r8+X6rJlvnNY23fY4gsWErhsCl/KX3B9F9vqCHfE1h7XYHD4qIGra5QiomprHdyjRr5I/EELVRTwyJCKYMhZEPfSwOqs9ntoprSqkNZ+mx7p8ncWGz3rEFQes0hQ7ksetHZJY01hu98K4T6a7dsWBgq0akqqyBg1230fZdN3Yj2Vk1ooJzpqLNxpDOt7Duh87Xqlbcjq+zamMenivOzxum04jdieqt0ENPVDQ9/EdRd1Jdj3MJLm0n2r9lrXqMq3y+IRgN2sTz0gLFxmeg6RTLJhICsUkpb3wXoxsfQwWPCR0fTL/ARbHPS+qQRSk/P3QCba8kgoVRoUWtJB0kEhVnq4jTEIlcatoRGS/+AEsZm07HiiKVnd72EqeRFR8HpQJaG9alEIGXK09VK3YmiqNpxySqRCkljjYdDB8Ek5yaDqs7yi7hvBK/gizxmAED7BQX64g80aRWeA0+iOu0wD/lB8v67Wxy9RWTl7f+/cn6xluR9RRJS6Rdr9iT4i8RAMvKU+SGIpNic11D1cneOJi2zJKSxlvmlcCJqkZzvpTgXzYFcbT1/rAm9LwCOWTqTrN2Ocs25XSuOD5tKXLDzlT2vFaBptN0WtH5FKs9rdPsTQKz0XDohd7IR1HW24mFUkMBLO+9LA1tl/b64WrjN7BuI1pveG3yEabfcQsTOqxreE/4Tc6KKyyq69JxjCUBgm0jQynIM0XwouDTOUlGzlYRi0oU1MZJS9L7hwx67HkSyOJOoIGVOIbmmeJox5Fax92zmLML4XXVscSjNIZJ2pDbhkls2MklYao6S9OJ2shhsSQzFadmwvky79WPemUopNNXtpYs6ogi4RD4oJivBuUXWdX68bqBT2LCu3OJ34fvPQJAKUueiqBH3Yh62OVcYV1Lh9sauLHfkff3tkzuZW82XW9PzqNCFkUqUMJ2J0IpRdUGHjQHLNuE0zksFuItEo/7XKFP+BsMZe9RUPVT+cnI9l1z1Sfe8h5NB2cL/UiTUytYVgatRo/4DKwby4UtKE1C+9R3y2SgL5w/NH6RC3Z4zeyxrAwhKOpGXi+OxJjUWYkTzoma1+BZdL6OOFtJrjAIEVStZlVK3BgXAucLAUxfUMVWpv3CGzKsS0n46zrQNA6tDHncMkkqEtMxSmPJv4zHaOFLxLpDK8+qS1AqoXPiMRWbgEdi4NkqYpRq8qjGaIf322kC9FKo5ZNc4WtZ79iCwPUutbGVwyGkoLVsAumwywRglDohy/gIV8pj06xmFi8pXULnC5qeVzDojbctmzH2tAhEVmA9aSo3QdXAyVpG+POFY73uer1wiHtZPxdkKrCqLdWgy91vhs6p/hCXYmDYhF/RfdeiF7xU9pGDv2k1axtRO8vDVUHdHTJOpRiYHr+E3u1Ioz3hPpit/vKm4tey2UPoO239vb2qFGUlusrWGLQKtJ2irDxtGxgXZhMQALQXKbJx2qJVYJkYIivOy1Ulbbk0iYi0IzMVkRaMpEwihOhotaewJVFPFF63Fo0miRyZbemC5nSV9MWcdEUCqnc23rrEhsDG/Ohx1pMx4LtzWe2JjSjkhKBYp5Z1HQlmdxU2muRJJPu6bkXRRynFKK7Zjc8pvaiCVZ2lDLAq2eDsrRH4YZ56MRJS2wlc5xS1l325rgKrZdtL6bKZRg6QuBaFUqJ3nicDGVbR+a0r8nA/D82Jyx22qpHPA5enkuIU3HlN1c24x4xRJMXA6Phl/KEhja6y1NvJ5/D6SoEKjz7mPDjoNdcliR/3xZZWYdO40DqQRq5/HWk0WLNN+M/iCD94QfTKagCZaclNRTCKzMokddmmrFREGnWMzJoizKltgjU5TSeqJ5NUfrdla2mdIrFKhAn6gqCqPXV9qSAoH290+CQmvDuXGFuJcpYGQibnW9spLvrOdhKJ1HBkHM4nLEvZt9O0Zi+Zs3IZnR9RoWlaRdmoDdSubSVX2BkHYhtIY0XWQ2/ajo0YyGrtqKuOrjC9+7g0AAYEQdkamlY4QZIrsIU+XnLu9V5k0J0LRJFM4gDqVjNHuvaDz0DdKtatpeoMD1Y5ZX3EOHN8aPwiO6cvEU1v8DCeSrxr1GbCJzErbDrp3mwhlfBorqDGmkz5rUmYC4wLvUnSB1n1NPYUSYdRnmUshmpN62laL+TikSE2fa6gOhLTPpJ4axVIdU2kJDCubIRWmiLpyCNRRHu4TFhVitjqzfVyQW88FYbH2scUGoAnceHyescWBPujmmkWkZgWHzTHq4T7Z5qug6oWc6vZWGTyEt3i/IRVFaN1b3vdpdQu2jgFdj2BaHh+XcvIqkgF39900vX2HqzRaG1YVYqqcjR1hzEwy1vGcUNuKwqzovIpZbPPqpLgMoy5Lo8O60ZeN00VuxNFGm/dT1sENxhHfuN+2jk5gMdxI9VvyCgbQ90aLop99G6H0xFPF3e5kVteml/h/rHYhBeZIY4e7ZaZ3jQt9KPQsu4Z+zoQW4dSEXXjaWqBP41TUXW6WFvaTkhHVZOgVeBiKQYmIQj2L/SW8aLY4qh9xklZEIJIiaWmQxHwQdNhSHTDlUImPosm46JKaJ3mfKlZVwCaaRYLqchtN+TQOfWP1wgEeiJz0F/x2JP1jb1GcUVurZjeYVjVhgenUgiUladtHEkccTipyGxHYjOMFhm9urOc64mQkf1AautH7y5QN0IcTBO9Mc2rW5HbBThdxKRRwbIyrEtH2ziiSHE4lYlkZjtS29B6w73liPlK5HqT6FFVDecV60qMf9JUc7grcaGsFYu1/Eyebov6rRFiYBQ3RNqxauNeTCHmrLiCPzS0JuXZyQNujCPeXOzw0h2L84FxrjZku2E0P6wQRJ98sRJjxW7SJzpOSye+C0xyyG2L0Z7IWoyRuPZgnhLbhNOFoutNxbou0HVqA8lQKrBqs41nQmwdiXVEWkiBpSrQym80zavOcLxINxMRaQooUpsR647WicmRUmqjrDZMSH+n9SQmvDvXblEzTaONgtDpOuHhuRSu60ruyd2JZpasSU1D0xnm61gERbqIhcmpungDiZGzWJLKspJcIcs041zMBodcIYRAHBmMtiLksW6oqg6tU/bGLaO4YRRJrlD7hNcXe5xVvZqXeXQfAhtRkihSTEfChaoa+rNR4kFiA93QdOhApWxigg8ZS29oOs0FO0TTGzhteTq/jcstL8XXeHAqPifjvIdB6kDUZ4GXc4ULp4T07DVqQm+eZqhq4SbqXS2eDSiaNqZzsK40TSuTlflaUBzhUq0+KBAZPCsf8XBVCCE4acltAyrg0XRYEtNyNFpK07VNOC9TWqe5WEmuEBlNmSdfkSvAk1zhd7PesQXBM9lt8nSGw1D5lLPFmM/92jkASWrRRnPzSs5N9Rrp+hxdvI9lvY/3ikUVsaztBoPng1Tcy5XczKenNct5xWw3Z3eakiXSJbx7t6RrPXWTUc8sZeU5P6uYn6+xz465NTpm1j38/7P359G2Zdd5H/Zba+1+n/a2r2+qrwJQAAECBbARIZASSNsUGVKKRDEizXBIyXDoOIT8h5ERS5QjmcxQIsGOTNJSKMrxMEyJsjUUWlYkEjRARgJEEEKPqkJVoaped++777an2/1a+WPufc59hYavQAIqlt4c44773rnn7LPPPnvNNZtvfh+9oxv4d25Qr51hJ/0+9o+ERSNN1LJa2GGVp7OGvdsZ22cSLl4tWI+mXJ+Mub2vW0dl6AUVZWOYZZqiEonzjfCQ0GZU/nn2qoCyNrygtoj8da6kO5z/xD+k2d/He+//no8cnaGqGjbWUgZxjVZu2VlpWjxkYxV1o5lOa8AjMJbEKzE6Ynoijix8IOBMekJtPabZiOlCxMgOD0uqskEbvYQhONdVEFtaUVVzUsR85gVxOA9fMlwa5W03RbDZY++QjcPnULbhi6Nv4/M3YvLccTKpyLKG8TggCkKSwGdRajqlyNpJItfcI1YY7mf9r1fb0nvEpqJyAZmL2TvWfOGzeyitiWKfIDD0E3gk+hJJfsSd0WXScIOyMRxnAbsnkfDw+12Frk0GCsf+nZyT44zhKCYKE7QWHYK93Rl1bSnLHgdHAWUlYoV5VpLEfR4a3WLY7NOb7uIf7VAPt5hG7+GFG7pV9BbF7dCHwJeu3MFhze6tKdtne7z5QcdmPOXGZMTuvsJaRz8xrPUqykZzNDVUtQiYnYn2CWzONXuB41nERBmm+Xki/ywPDPa4+vn/kebOHr0/+hP8zucuUBQN8aWINBLRtLQd0u0wwZU17B353Lg+ZzgKObNmGISKRWG4casgz2vOriesB0cULmQvijg2isnMcmu3piwbnBUqUKMVRWFaJiAZftQ47sxT/tVnRV3+zY8HPLJ5BMCijpi4hKE/5wnvCyhj+bR6kk8+LUKJnQ2HPnWTEoeiIttPuk6qEoHJe8QL3/cJr0+7Gl4njsZULiB3EYfTPp//whQAzxfCjQvbCRfUNaL5MS59nOPFBtbCycJvYbur6nhRwfHEtuJaBYtZwWAUszaMiAKJFW7vLqhrS1HEzBceedFweJAxPV7wwAN9rvTvSKxweB3/znWa8Ra3B/8ee4ca31ck0SpW6Ip4k5nlzp2C9fWQR87XjKMF108G3LxtsY1j3PfpRSVF7XEw0cwXjo2hYys8JCSnsue4XfvMrOZls86dYMiV5CZnf+cfUu3dwf93/3d8dH5F9tphJHS+RiCYnrJSuGu7kFWl2d8vGQx8Lm1D4pccKp/j44o8r3jwUp/NeELtPI4Xfls8dZxMasrSytyGWa3Lju5c6INrjvOYzzyvqGrHY1dDLo/Ldk7Ko1GGgZlwaf4Sylmejt7OZ1+KKEpLltdUpaUofeIwJA78Fg519xypezUJwX2/sLTXbEIQ1TPCJqA0EZ7yJSufZC0ncIQfysbjVxl+OSfolZhOJbRRSwXhzpyj5eAVpeMyr6iq5u7hvlLEjapSnEFZOpratsJiikAVBOUMb36COz7AhBG6J+qmWtOqhd6dnVa1azUMRKQn0Qt8MxSO8Fa9s2vpd+cI4FHh2VKC+naocJoZstJwIfFo9veZvniDpJ4AZ3DWtaqoMuAbalE/rqwsFqtMe3yp5HWDfUq5JaWa0RCqolUO7gIly3xeUuY1UewTxSsFUxmmVEuYT1EbJpNaHGW1Ug9t2r8r5/CyCaoq0WPLInNkmaUoGqqqoawseelhtECuoB36bCTxaF5F2t8gjAyvfOy+/eE2vynwbEWtfZRyVBUspjnaCCGA57fBdzUnyCeESU5g6nZ412OeQ+jLUJ/RXaerZeIpGrJZQRQHLb6etuptqYqKbFHh+7rVPBC/4HkQMyfKT/BmB3B0B8/46NhRVq4dulO4lvygY0Kra0uRi5pq5NXEOsP3BjhnqFrYU6emCrIOlALPVfi2kM9ey+NVo5lpzYW+T3Nnj8VLN0iqSbt23N0DvqZezl9UyrTwPEeR1xSFiIR1eOaqlC4IgKdqLKZl75COwOSkoMgrPN8QBAarFEs10ZbIoXGaRak5PpqLb61DNBaHJCNlY8CHKD9G2QbtO2bTkrJq8DyDVhCGhkVhAJlrkE7HqYHve+QYvO8TXp8WVjOCJsYag3aWqob5TKbco9hvk4JVrOD36jZWUBSV7F9aC4RGZnxarYFKOo5FXlHX4V2QnrKNFbKsIQw1RWFFt6eNFUKVERYTvPkx9ugAHUaogcQUAE3QzhOeij+62MQ5iLyGxMsJTB/bSJfDtt146bqvSDZ8VeLX+XIvb2qZN6gaTZN4VHt3mH7pBklxvLxmy2q9doStDoEQjhi0leNUVUNdy37faS0555ZMYL6ql5CfpmnpW2cVZdUQhh5haJb6Jp3GQodUKGvDZFJRV5a8TFpYlcbhUM6hTYOXz9BNhYqcwLEKS1lKLFVVjryU77UbvO6QBO7eEIRLu+8XVqacezW51DfeJpMJw+GQ3/zdl0jTQctJa3hhL+XlWzVaKdJEKm/DHpwZlXjaMskDjmYytR+HgvUTXuu2mlSv6DPLSoLiMNAMe4I3LiqhwhTl3BXWtluwwxTOjvLlhuq1Kps3T1IOp5o4gHGvxjOOWW6YLPSSocg5afdtDGoir2ZW+hxNzZIto65X7+Uc9BLYHIiQ1/Hc41iKHS32H7ZHNY8Mb5LUEzKvz7TpU1nDcZEwzT0C4+hFLbSgDDhe+FS1Yp4LLVgYwPbYkgQyPFi1MxlJUBP7NWVjuLYfcnjilvLl5tT6UEqgTuEr4Eknc8X+kazG8VAzTFd/dw76ccNmOkfhuHY84MVb4hTXhjLMXNUiC1/VIn1eN63gSCPJQL6Y8HN/YYOTkxMGg8HXvH8+8okX6fX6d/1tNpvyXW+7+jVff99em9Z9r//iXz9HnI4kmLQez+70eP7FAq0VcSwUgxtjzaXNktA0HOcBk7m5i+Gmg86ADPTOM9lcu3Z4FGk2xzIUOFkoDo9FDDFNhIYU2iTdwqgP58YZkakxSuaZisa/yy+M0gbfs3f5hbKWdZ9EcGYs667zC4LVFbiOVmo5J9SLYWMg8zwHU5/9E3FSHVvX9qjm0aEUCWbeiONqQG09jvKIWW4Ifct6khOaimkZsT8T3YPjmXRPk1hzYcsyjCuB77S6IoOoZBDmZHXA0zcTbu1WhKGml4p+S11LMGOMYtRniY/ufOjBRLOz12Cd4+yWx9aoaa+hPCHyLb1AcMMv7ce8eL3G8xTntg3jnqWoRFulbiEgs5nFOrecWyiyCf/3/8PWV13X933C69O67/XDv/siUTqiaVXJX9qPub7TLJkDfU+xNoBz4xxPO+7MQvZPJJhMo7v3sY4IZLpoBffqVaywNpDO/6JQzBYsu2Ld3tjh88c9x5lRTqgbTBsrFI3P9eOUg4nM6gxTmVGaF5pZtmLgAikaDNOG0LPMco+T+d0Ds12RzDnh5d8cSoJzNPM4nNztE86MKx7tvUxSHjML1zlphpSNx0GWMFmYJe1wYBrRQ1mIbsgsg/nCEQaiy5SEzZLpDCD2G2JfhrFvHUUcTlZ0wUavBEWNlmscBbY9f/msJ3PF/mGDtbA2Nox7dxdHk8AyiiXJuX6Y8vKO+Jf1kaIfO8o2nqkbmfEoK4Eyy3WRWOH/+r9dvx8rvEp7VR2CX/iFX+AXfuEXeOmllwB4wxvewF/6S3+J7/u+7wMgz3P+4l/8i/zKr/wKRVHw3ve+l5//+Z9ne3v7VZ/Y83sjev0+vic3Sho5nnjAoLUIbRntuHUY8Ov/smY+KxmtCd9tGGhpzXuWxupWkEdac4NUFkkvtkReI5i0hUdRKUa9hke2MwAOs4jjuUcUWM4P56Rexl425PPXImZzy8F+zsGdGXGqefOTmvPrwlSwHk0xquEma+wcCEbxypmajWTOog64cRAzzTzW+o5LGznWKT7zpYBnnj4iCD3OnE3p9wy39y1ffKGiaRxR7IgjvdRdMAZu72s+crQNbPNn/uiCR3/rAyjf5/q3//v8i4OL7ec34ME099g9kG7EoKfYXpPrOc81k0XApY2Mx83TBOWcl+I38PTtNYoKpnPZ5McDzcNnFqReQdH4ZLWH1o61cEaq59wp1/nwZ1Nu3Zyzth5xdlsYnCYzx63d+q5uiWzgElFFkSVJNP1U88TZCWe9HXbqs/z2030ODgrBB7eVii5nzTsw5T2YYMNfiQv8t5M54Btt30y/sDMbkTBYdgAHieOxh8IldbBWjsMJ/MbHLGXR4HkFfmAIQ8PFcx5rfZnX6QgGnIMolE3o7Loi8h2Ns8tBv82h5dyabZOIVsFXSbfP6IbDLOEL12IWuUDrjg4WRLHikUc0Z9dkIH8rPiFQJdfU1tIvXNqS5HhRB+wcR9zKPQaJ49xaiXWKTz1nePYL+0RxwNUHBwz7moMTePGG8Hd7nm39gfxoBS/c8vidz53HuvP8e08tePMX/zvQhmcf/SE+cbyJEHdKEj4rfa7tSqdibaR54ILQAQNMc58z/TlXo+fxmoJb4QNcm6wxyw13DhqOjnLOnUt54lLBIMiYVRGT3McoGCcZPS/nqEz57Esxh0c1vZ4E90qJbsrTL4ofS2ON78NsrrizL5CitFcz6BuGfcVbLhxyxt1gT53jd17e4OjEcnxccnBnvlSqV0pR5vN7unfu+4Rvnn0zfcKzt8ek/cFSRTwJHY9e0WgttLaecdw+9vnN39UsFhVRVBGGpp0h1MRBQ20VRSXJuu/BuC8+4XSsMMtlUHncs1zZFKaQaeEzXRji0HJ+MKPnL9jLRjx9I2Y2t+zu5tzZOSFOHW94Y49zGxJcr8UZnm646frcuqNRGh46Z9nuTZlVES/uRUzmmo2h4upWTuM0n3vR55mnj4gSn0uXegx6ipMZvHhdEuMwtESRJDpFKQnK3qHho/PLwGX+xFMznvj0L6E8ny++6Uf42MEWYa0IPQ+H4njhL2OFtaFi65yIwGaFZlF4XFzPecJ7Gr/OeDl8jC/eGVPVimkmAmOjVHF1c0HslSyqkGkhscBmMmPgzdgr1vj/fS7k1s054/WYs9vCYjSZOW7vSVW0Qx9UlSXLWh8/qOn3PQY9xeNnp2wHe9wut/jYcz0Oj2psi3qQhKBlO8uKr3a7fJnd9wsre1V9kQsXLvBzP/dzfOITn+B3f/d3ec973sMP/MAP8PnPfx6An/7pn+bXfu3X+NVf/VU+8pGPcOvWLX7oh37o6zoxUdFbMQoJ7aVb8dW2m1fT3ggdpr2z0zy5XQup48w/baep/oQuy2La9zGajnVcjmWlWl3XlrpqpEV4FyxpBZ85bd3rG7vK7LvHl/LoLdf5SsSL5c2tXtESl5aZDDtX1qB8GeSJ3ZytYc0orVsWEX0XH+9d3MhtBi8cyAqnhOHH2rv51ZVqz7OFF931WVEtd7lbBu36FMXh8rm2De5ty0TSdM/tPrOT9+f0uSoZflYdH7FC63u/XbtBoVf+vFr7r/6r/4orV64QRRFPPfUUv/M7v/NVn/v5z3+eH/7hH+bKlSsopfjABz7wZc/5mZ/5meXn6X4ee+yxV31eryX7pvqFWlPUgnXtqlWBJ8NwRrslj7dt7FL45pVN0K4Td9pf6LaFrpat8RXksPMHne9YPu80L34LDSzL5i78OwiFncUs2bIE/rNaTwJNuhv3qjUorTHeSnQQVt2y04xlnTXWURQNeSZ+AW1AKxK9YH3QMIhlyL+xuh2gdC1DyIoStePybpzCaoNVZoktbqxqedilE9N9fqWEXrH7PB0/uDt17U93DDpfbd2KgrlpHLaxaKWWeOPlsV7hF4zR7TxT9/Pqhop/vz4B7vuF38u+mT6halZrqFtbvmfxjGuThHadNrb96WKGu9ectauu1ekuYmevjBWETIPWN6zuf3kv+bGNXf50rzt9vK5i3v3tNB26u8s/SawgmgrqLqHOLiaxXwHs0TSQZQJtKq2H8nxQmkQv2B43jHoNRou4qFy/1TGW69m1FMWtT+hihc5XCFS53as57SNX8ZZjFV/I57mbDnl1TbpYzlHXDU173brP2x1fHlt9V1qfWjtatdDye7P7scLKXlWH4Pu///vv+v9f+2t/jV/4hV/gYx/7GBcuXOCXfumX+OAHP8h73vMeAH75l3+Zxx9/nI997GO8853vfFUn1g2JhL5sVEWlqVol3Hluljfaow+nNE263OQ9rxXwaOT5p4XFQBb8wcRQ1eauTWqWGSDBKGmL+Z4EEzvTHkqlVLVmfQijvmFro0fxoCjr+R4cTD1mviGLPTxtOVl4SyrQ47lH1fSpG9nkRGJbcbQQDt40Vjzy2FiO5YuIkelp0iRaXoPuPL2W2jCNDRtrKVrBcV5y/dv/fWI3Z/325/nunf8Ze+4qn1n7Hm6epFgLw94q451lq0UEsD8L+VzvCYy2zGeiXNrRLIaBQLD2ZjGBiSiblVDKoQkxekxRadZGijQZ4PurBT7oKXqJf1d7M/DlpxM2E0YnuDXpcxQkTDOPKFSsr4ftPdA58q4NeDcn9Ncy6yRZeeVjr8b+/t//+7zvfe/jF3/xF3nqqaf4wAc+wHvf+16effZZtra2vuz5i8WCBx54gD/1p/4UP/3TP/1Vj/uGN7yB3/iN31j+3/Nes6M892TfTL8wzw3K18vAv25OM9rI7ziCq1eS5T1mnbT2lRIO8roRyE63vjwj9+Ms08wyvfQlQjuoKAN1iq6z2/DC5fuvDWDY06yPYy5djDFGIILzXFPWAUU9xrTwP9NiaY9mHnnVXwbZaST359FcuMpHA8Ub37SO0hCH8v5xBP6m14qKrc6/W3P9RBFfilAKjrOaZx/9IRK9YPPkef744W9Srl/gaf0Ud+YpeakY9FZQhVmm79qYb9qUafw4WjnyzJC3Q/7rY0MvTQgD2JuEHJqwZWlq/UueYnRKWSvSGMJAihUdxWoUQhLdLfoz7CuGfZk5Ol3IuT4ZchQkzEofrURhNgwDxmO//Q7kO80X95oQ/P59Atz3C/di30yf4LcUn12scFrQq6rlXgs8x0NXIxobUZRCnxn47XMafRcsTShDaTuJBudW92vnQyBs53jEERWV4vpxH6X6WKvYGAqz0dZ6j8WDvaVPyEvdUozGGO3IS0UcCsZ+mhkaJ7FCFIA3FN8gsYJikCoefXy0FAVUStbT2e1g6Q+6cwxaeHE/UYyH4hMOM48vvulHJBm48zku3PrH1Geu8LnxH2Vn2gPk+d18wsncnEoG4M4k4DO9x/G0JZ8J3Mgzkgz4njzn1kmynM/o4q68GuDpPkWt2NrQDAeDVttFXtNPJVaAu2M13frYzi9pDbfn6ZJhLY5gc92X+cJ29rK7BvmivOf7536ssLKv++hN0/Crv/qrzOdz3vWud/GJT3yCqqr4nu/5nuVzHnvsMS5dusRHP/rRr7rIi6KgKFbtnclkApwaotOrLLWsO1YQ+cLSCM6vC9Z+sjBMFopuOLdr+S+rge2N4hyczByTaUPgawY9jecJ5VfdSJIQBY4osMIGMtVkhQTy64MGr60WdqJDeyc+k7kophaVbPhlfZr7H7LCtENLwjRiLUwX4kiSEPqxfJ6i1UcIfHmebjUNynq18WslrASDWGBT09zjXxxeZGtY8907/zOf/Ln/nkf/xLcQ/cgfYZb18YzMVMi5KPJCApLAkwV2MlccTGThpZG0SKXioag9uX7HM33XRu0cFKUMFoaB4IY3hnK9ikpek4QrpqVOmTkOLaNEMNDzwmeWy5DgnRNNVUnbVJKGLmlZVSGchcDcHUh8LfuDoBL7G3/jb/Dn//yf5yd+4icA+MVf/EX+yT/5J/zdv/t3+U/+k//ky57/9re/nbe//e0AX/HvnXmex5kzZ17VufxhsW+0X8hLMKXojcBq4L4zKSIIRldoQ7vBwU5r4O5q4rIS7WBRCh61O44MrEkV79Q70KnjWgtxKHhfo1pFbuWoG80kM2QFbaAhJ2tbJV7rYJrBtOVC78eOIJDnTtvgNo1grS884eKbIPTkudB9LjmjLiEIfcHva+2YF4ZPXN9kfdDwxw9/k5v/7f/Ame94M+bdb+dwJsO6koTIsfN2/+wSrXmuuLkn553E4muVEoiWSluBoJmiaSR46hTHs0JRVQ7fh1EPerEEPZMW1TNIBf7ZWPkMdQP9yNFrz3tRGKYtVOD2kWHXrQo3SUSL2VZ3JS9Z9Oo6BK987NXafb/w6uwb7RNM2wnwjUD7Ol2NLlG1VvbMs2vCwHc48zmerjj5O92grBAijTBQrZinQNyyXOB5vZZFUHyIPlWsa5nDTpTQ9KaK7VFN4Am8UCEV+KO5z6JQLfLBa4UMRSQQZM0tCtEwkBhEjjtZxgqOUU+6a3IcWfO9VkuxqCTxlhhGkqDTw9KTheFjB1tsjxsu3PrHfPqvf5CHvvfN9H70KYpKMPQd1XFRyWfv5imVgsOJYvdQfMLaQGDWGKEV11poUA8mInLWxTAgMUbdSMwx7kn3Jis007Y4KdTrp7qlTi2F5iQBMywK8QnHM8NxqyIctu8hDIp33yvB19EheOVjr8ZeLz7hVScEn/3sZ3nXu95Fnuf0ej3+0T/6RzzxxBN86lOfIggCRqPRXc/f3t5md3f3qx7vZ3/2Z/krf+WvfNnjGwPLIJZhPAkqjQgIWRkgEdiJphcrAuSG9b2uFbVqU4dtpuydiiW1UvieYPJ9X27UuhE6MaWF/7ezpqUldE4tZbeXGaty9GJL4K2q7s6Bb1YS39J+Z/l3zzicFtGvzuwroDpGyxCOVuIkqjYhwGMJ8tLKtSJNjjiQ9lxz/kEe/cG3kVy9xJa7xZvPw6RM2J+HS9Ye1bbp5LqsEg4Qh5MEDXUjldJ5Jl2JJJRAYZ7DPHPLwevOodpW50HUoOVc4zapck6qIl2CNi89NLAo9TJ5CFqVw9OV2W6wsmNGaizk+Ze3RL+afS31wW4j6SwMQ8Lw7u5DWZZ84hOf4P3vf//yMa013/M938NHP/rRez6Pr2TPPfcc586dI4oi3vWud/GzP/uzXLp06fd1zH/T9s3yC2t9S9Kzy3tllmkWbYzQtdjDoGWfaTd10/7uuvXdBufcCuLm3N1Dx763ek7nF6JA7utORKh7DZzuHEhiEPr2y9hvylrud9X5hWYlBtTxgXfJh7WQFdKtqOrVgJ48XwKJum7XoZJjKiV84StxQU1gLOX6Bc7+kW/BnDnHhtnn8TMwKWNun4Q07bG75Cho1c6tEzYmEL8QBeIrTuaioeC1xQ0VtHztxeraRpFaDhZSqzZQ6c5J4XsW3UKQoNVlKMWZZIX4ECGHkA5x56OslUClKxB090C++Kq30V32+/UJcN8vvBr7ZvmEUVKTRg1KOGpo7EqEtNMTSGMl7GJa4oOwZfkRPQDpiocBmEYtxTwttBC5Nl5o44jGyl4IrTaAf/f5dHAjo+xd/09Di+9167u79+8uSpyGMYlfYFn8UEpe18F9u25GFyvUjQiwaiUKxNJ9dK0SsPitsI0D6rNXeejf+RbiyxfYqG7xxm04LAbcPIpXgohm5QuNlmN6LUojCR2x39BYxbQ2zLJVvNX5hOlihdroihYSuOu7OruecSSBXSoRi+6KIiuFDCIvxYdIEcMR+KIWXbYIkKIRspRV0dLdjxW+TnvVCcGjjz7Kpz71KU5OTviH//Af8uM//uN85CMf+bpP4P3vfz/ve9/7lv+fTCZcvHiRx8a3MOkW0yoWobHCZ2+/oiobFouKqmjYOpPSj31sIBWCQdJ1E+TLDDx3VyWx28h9H6JQEwYysR74jv0Tzc5tKZOZ8wG9WI5T1yK97RLZyHwjQa5DRHYuRgtCU5E3AUd5QlELVMkzbQB8itnHaNdm7Ss6wVm+yn67amfgyeS/VMxiJjNZOb1UE7WqhYHXEOoGFTkCT6ponx2/h+hPfweb7LL+if+J9PpNzHe9l4947yUr5EJ03MeDtCHymiXLEMAgqugHOYsq5MXdmOs3MtbXQ7YuQxLUzPOAW7dynHVsbEYM+gIpylpV1UUO84UVEbeeYj3JaKzhiIC8FKXEg4mhrleBfhgottcsg7imrFeDW1kBh0c1jXXUlW3ZRO59UKj5Cm3A7v8XL1686/G//Jf/Mj/zMz9z12P7+/s0TfNlQ27b29s888wz93wer7SnnnqKv/f3/h6PPvooOzs7/JW/8lf4zu/8Tj73uc/R7/d/7wO8Ru2b5RceHe9g0oZ5HYrQ2Czi9p7Qd9aVUNL1ej4bayJGFvosiQm6zXYFs1m1tjv8sHWyRtJI1uvhVLF7R0rxl8/7DBIZQGRhlgGFOVWEACkajJN6mVA0Tjpsoo2iT83vrBIWzzgC40hDR+MU+yeGwxM5Zgcz8owiDmRIMis0WavWm8RqWQhJgprA1KS+wsWC839aP4X5I29n3Tvg7HMfYXvvFou3fg8fMk8tYRJdctJPLEkguOLIq9FKGLm1skzKiGdeNHzpuQPOnB/y1jcEDOKKm4cBO7elqnB2W0gT6gammRJa2Nwxm9Uopdgce/TCWoIV5VHWiqxQ3DlumcVqS1U7kthwft2ymc6ZVSE7RyGLGo5OLIeHJXVtybOKqqwp8+k93VO/X58A9/3Cq7Fvlk+40ruNl1rmVUxhDfulz50joaY8OSkpiob19YjAM4S+zAb2k06FXH5HgRT2TvsC6WoLh37gSbfLM46DiebmboWzjrPbAXEI2kkArRuFbyAwDYFplvM3RsvAsK8bisbnpBChLaU0oNuOGctKt9HiDwLjIJDkpKy0FALajl5VSbFunAijYFEl5O0W2c3V+B6kYUWoGwLjEXoeRjs+l76b3o+8g41qh8HH/wnB9Ztc+uM/wG3zXqZZh0QQOFan2g5tkoQwj/WDjHkV8vzU59r1jM3NkIcvSqzw0l7Ayy9Lpn7mbMzaUM5nUcjvopICn+cp4lDWee000yIkrwzzXEgUOiFU54Rc5exaw3q8IGt8DufCklaUijsHVUtaIuKy92OFr89edUIQBAEPPfQQAG9729v4+Mc/zn/xX/wX/Ok//acpy5Lj4+O7Mv/bt29/zZbHV6vE9JsjlEnJm4DaycBLNheO29mkoCoqeoOQsvbxva5tLTettPMlO/aMDP4U9aoiZbQsGK8VEAs9uc3zrA1A62B5Hk3LRW6dDLQY5WgQZ2GUE9ViZhivx0yHNFpBW+mTqqFqhxvVshLotVm7BCfmrspAV7UMTLNkNCnLVufg1EByt0n7Wr7FxmpunqTMsj5Pnlf0rt/kxV//FI8/+gjeZXlRN5hjNISebRMCi1bSPolMTahLamOoa8dsUjAYBPhGhM6cg2xRyfWw4bKyYa2Ih5WV6BZ0Q4GhqamVw9P+smI4XziqytINShsj8xqJX2KUR17p5bBVUciAZl3LMFjZYSTuwaxVyyrM6ccArl+/fheV2Fe6/75R1rFsADz55JM89dRTXL58mX/wD/4BP/mTP/lNO48/aPtm+YWePcaYhLrF9VonQ3NynzQ4C8ZoykoCdql4y/1/ev373kqYTObW1HLIzejVoDIo8qxuGa98fGNbKlC9xCmf7g50vwPTLPn+aycDdWVt7tIpsc4tjsstjgAAyNpJREFU/YRRHZGBVPIaK2rIILM83QbvGYevG8CnrsUvuFODkJ62+LppiQDEL9yZpxzODI+fgTN7tzj6xOcYXX0M47/jFXAoEUUMvUaSCq9Aq9VkY9H4LBaOg50j+qOYwPdI/AqjA/JcAn6jPSLfUijVVvFkvZelXQUpur2G2mGNfC/zuaUohce9aRyBr/E9S+pl1E6jtdwLVeWYTQuqsiabFZR5SVXcW0LwWvUJ8Pr0C98sn5DaE7TukesA0ybceS7302xaki9KksSnrE1b5XcE5u4BX6NW8UNVK2xbJdd6hTDwjMP3pFudzSsa6yhraQ/odq/vrCv6OeRxoxyRKYl1hlExizrAofCsW2p7QDuIbLpO5Wrg37n2vNoOQV3T6pUoIlPj6Vr22BaO0DQK26IiPGXxdE1gpBJeW83OtEdR9XnjNgTXb/LSb36WJ978FvRm65NaP6jbWCHwVn5AK0fklQS6pNSerMk2Vgg8R+JXQMBsKkF5vRmJFouDsoVa1m1nUlmJhyJTUjmPhQ7QWq7xIrOU1arT6vsSNyVejkNhdIBS0iXIs3oZJzgLZX4/Vvh67Pc9oWCtpSgK3va2t+H7Ph/60If44R/+YQCeffZZrl27xrve9a5Xfdzb+jxhNaSypg3uVwrFAHXlMxgEDFNL3EJTRAhHICpVLe28yLd42lI23rL1XpQSvGotSULdCor1+gGNlWn+LhtvrFtijmurJYDWDr/FBi7qiFIH5HXAovQp6lWQ0A29GaOWrb3YbyhrzSTzWuzbCg8tr5PndQIkUeAYDmRlJ9EqwGmcCPssKp9pLsfqhnSnZYz5rvfy+KOPYIfrvIOPUm72+EL9BJ97OVoGJL5uyOuAw5kvSdRAoZUlbzySWLGxnZImmrxygDi+/iDEOcnsu+QlCbuqq6KuJXgRkZSOIUocZBKCG0qwU7XVEK0FF5iX6VI63fccg9SQj32apmtdKvLF3ewtX8sa9xXERtr/DwaD35NbeGNjA2MMt2/fvvu+/D02rVdro9GIRx55hOeff/4P7JivBftG+YU9dY6oGlJaj7KRheP7cs+FoayT4dBjbSBwE89IR85atWw9hz5EPnjGsmgMWaGpGshyGTaMItl0ZWBOhLGck4rc0dwXhdO5IiscaawIfC3qx14bjCuoWk702mqKytA4CXyjwOE1orLbDcKFnhNOb2tYlIbGik/YGMvnM62oWRo5Qq/GU5Yksgz6Gmel47lkIWop9BaVz6z0l59bKzguEhZv/R5GVx+jSsd8e/JZ8o2UZ6aXefplD60UFvFdiyrgaCHg5F5U0vMLLLC+HnD50XOsrcUscg1Eck3ba1+UMMnMkkTA9xRaGcrStV2O1jci1UZrFaEPG2uGpjFkhaMsBbO9P/Gp6g2atms6TME5gzG9VvBRuodFlt7TvfP79Qlw3y/8fuwb5RPuqLNE9WApqteZ0YpePyAIPYZDj2EquHRr1fLeWxQSK4S+ox9L962qzXLWqKxXYqEghbjAk1ikY8DpYC5ZIVCVMNCcZIH4A2MJjCTo8zomUyFF4zMtRBuoE0UDCcSrSoadAyM+Ia89Thbe3cVCJVpFtvUnjVNghQ48TeR+jsLOf7m2S+EtdQZOs6sdFgMu/fEf4Ik3v4VmtMF39j5BMU75Yv4Az9yICAMYJlJoKGrDJJNzsT2hKa6sIYkV65sJcdzFChI0D0YxzknRIiva5KIlHslNB5dSbdxmloO8GpmhWBtp6rrrqDoCT3Gy8LFuLFAk7YhDy7BnKDbDJfkIQL6o7/n+uR8rrOxVJQTvf//7+b7v+z4uXbrEdDrlgx/8IB/+8If5Z//snzEcDvnJn/xJ3ve+97G2tsZgMOA//A//Q971rne9atYAgOcPxqzZHr2gonGiPhrHhiDUhJHBWcf6mmEtzYm9ikXlsyh9wZ4VIJT1irWeJfJqskoWeV2LAFGWW0DgKd2U/HjktYwkglsrW4GsxooqaNUojDaEum7VTxXHRUJZa+EpzuTmDX2BIUnFr8MqiphHzy85qGL2TzRNI8PKaSQ4xg5+EAfCca4QCMDGsK1CtAM+npEhJedYcgeDsAnFoWN/HvIR7714lx3v4KMsfulv4azjwf/TX+VfTh+irjVq0xGaiiMbsbPvyHNLYz20irFO0Yvh4jmp7GeFIivk/dbWpHsS+NJe9TX0ExFR8T2PumVdMNrSWEPj1LL9GXqWfjsANcsN04U40r1Def+1keGBsxWRXwMBtMNDcSiY7MXs3hMC4RZWX/bYvVoQBLztbW/jQx/6ED/4gz8or7eWD33oQ/zUT/3UPR/n97LZbMYLL7zAn/tzf+4P7JjfbPtm+oUXDkaMmx6x3yy/zyDUMhfkq6V4zZlRQWgkyK4aTVkb8hKOp45RX7Hel4167gzTTIL9riLVWMMgUW1CAWnqUdeORe64fSjD9NN5TVlYqqFHHGiiQO7vwDQ4FIvKJ680Va2ZLCTB6MeOQdJgrWKyMFSVawfoGlK/5KSImC5EsDAK3FLYbylYGDUkXolWjn7kU/Z8rOswt3J9rFVUyrA/C7m2KxvkoCdDwXsnAR/ynsL47+Dbk88S/OovEFjLw/+b/zOfLC5iW2iTBiaZx5duClzy6oWYy+vyBuc3oZdIu3qygMnCkBfim0ESqoMTwWOPeq4daNQU7YyA0EDaJXOLdVIA6SfSyT1ZGCYLgRVe37U8XzjWxh6XzzTEfkMaGtYHesmOpJRjMX8FiPur2O/XJ8B9v3Cv9s30CS8dj1hDYoVuhxBRMkUYBigF6yPNWpoR6oas9sgqj9qJANlkZhkNdBsrNMyV+Iq6lsJhXcu8ohxXZn3SVBJYraTIUFQwn1uyrAE8tDaEvmG9X5P6JY1TnBQRWWlaUS0p4EWBEAF0kOauIBH6DYlfkVVeO6gL/WQVK8iQv/ic2sl6CIxj3CJJZDaiixWE9niSSaxgnaOfCJPRzaOY2+a96E3Hd/Y+QfN3P4Cxlof+g5/hX00fIgo12yNFYGqmhby+rByNDfC0pbaaXixMR54nLIZdrLC5GS47FpOZJY40454j8i1GS8G1m8GqrFkO8orWlCUcS5E3LxVZKddr91Bxo/EYpIrz6xWpV6EJ8IwUIaQABIv5vQ8F348VVvaqEoK9vT1+7Md+jJ2dHYbDIU8++ST/7J/9M/7YH/tjAPzNv/k30Vrzwz/8w3eJjXw9lhdKqvTGLOE2QSAT7B0EoBsUlgyz5SZvvvyLrV/Bx+/7CqUMYbAaapPHpf3etfXlBpMKoDHt+7TtpVrp5QYq78OSrqtZcny3yYC3GlysO07vU5t4h2MEqZp1kIGuZRj4HWeyWn1eq7BKEppu0XU3sWDw5astN3sCd6gbwnrBoG8IW0SUVDBX16VuWKqTGu2I2+vTdRS0FtVi51bV0yVsqH3vjhXLOUXRVmwaK50b7xTWWroBHQuUWw4On64SdN9v0MK6Gu9VJASnhhZPP/Zq7H3vex8//uM/zrd+67fyjne8gw984APM5/Mlk8CP/diPcf78eX72Z38WkOGiL3zhC8t/37x5k0996lP0er1l6/w//o//Y77/+7+fy5cvc+vWLf7yX/7LGGP4kR/5kVd1bq8l+2b7hbzUmG7oT7X0uFrWtdFSXe/4qoVdSO4/e2qt1o1Gq3ZA2LbDc75CKb1kuWrax8OgFQXUK8YyrRTaCDd0t+7rRlF7K47ujlavUyNvwtPVS+46pvgFvfQj3YCwc6rd0DuSAoNrYTyB75bwyKb9e2UNDmFNqWuZqeiO6dphS+cU+UZKYC22qglsvlQddk5RWb18fdM4UXlvpAvpe4607aDkpVRPxUetPltjV4PXy896iqihbDzptraDhUYL/FIGGKUSqBXMl8fqII8Oz1g6YdlO+8Deo1/4g/AJcN8v3It9U31CqaUo5+lT+1AHsVNLVj1o99dTscJpusoOAeDciqAg8FX7077eqpYiXITPujkh6GDIaglFrBuhMK9O7YOnNVC6mUHb7u1atd3A5eyRWsYKK2rllVYBSKxQNSsa9m5eqjPnFGVj0FaICFaxwopWeZq1+/U4xVhLU9aE9YJ+zxD48p4dUsO2eP66URS1xGZGO6LwlFaK7VSLFdZbQZ2XdOPtuXWxQ+OgtJ5g+U/Bd4wCqyRO6IhfYAU3WsKntVvCvUSPBmpz93X4WnY/VliZcq9U7fk3bJ2c9P/zHx8TJYPlpuwZGXCBFStG4K0ksY9nmuOpwxgY9xVxKDdtXq5unq6NfXkjZz2asagjrh+nzDK54TrqK6EFW7H9eMa1C1oWzqKQLkQUwOXNjLVwxqRMuHaYMs9XbCVGS3W7ozCdLKQ9Cav23zB19CIZVJzMpYvR8SB3zARJ2yU5mvvMc7Vk4XklD/krSE1EEXUj58HoZcJ6gV9nmDpnkWzwmexxbuzL/EUSStWhu4aBL3Ll/Viqmd38RccmBLQcxB0tmgwPhoEMYYG0CLOC1nmuGEyi4G6Bs6aFNHSMCUkoEI+OCk5w4PLYYj7hx7577Z7kyP/+Rw5Ienc/ZzGb8Ke/62vLmb/S/tbf+lv89b/+19nd3eUtb3kL/+V/+V/y1FNPAfDud7+bK1eu8Pf+3t8D4KWXXuLq1atfdozv+q7v4sMf/jAAf+bP/Bl+67d+i4ODAzY3N/mO7/gO/tpf+2s8+OCD93Q+/7Za973+5//9EVEi351Win4qlfduUzDKLXnFO8rOjmq3a1d3A3wd3EaUvR2bg5JBkJHVIbeOI+a5Ig4hjWQnygpNVsrQYBxafM9RVkKftxTYsnK8M+OaYZQzLUJuHvhkxYpO2GgZ1As9S1FrTubiFzzTsnQoSEJL5DeUjeZoKhXLDs9stFCd9sNqSWfYDQcXpQTRUshYaZjIPMFqU726XfHw4CaBXal/z9SQz9w+w86BJEFpJK9dFDIYHHhCANCPavLaMJmbpQp8F4h0nVXnhOHFuVZ7oPULi0KgWdB+FtMxpXRFAtngJViR6yIzHys8dRdIdMHRYjbhz/7Rr76u/6B9Atz3C68F677XX/ifJFboEv6Onru7X07P9jgHk4XiZCr3UZqolrry7jgBZL1e3crYjo6Z1THXjgdLvY6uWFXVK+hrGklHrGPc66jSO3rQ82ui7D0tI24cRksYjdcmAd0c5Io2XY7fqQ6Pe7In11a1MNvTsQL04pVPOF6sfEJVtfHSKSrQr0TXfGUz56G4jRWqBV61YJFu8ansCa7v+0taY6WENn26kPdeH4j6snOi6WDbeYeOraijP61qxSwTvxAGK6jxomh9tJJzlPXOcgD8dBLUFRFWlK+rIqx89/KaxXzCv//HxvdjhVdpr1nlk8bCdOE4OWlQGi6f89gcVijcssWzKAyHU01RwuFxzcF+RpL4bI4iNnoFh/OQl29Z5vOGft9jNNCEPlxNb7J965PM1q+wY97KPFMEfRglldzsucci14S+48xgQc/LmVQJt45j8lKxu2/Z28sZj0Me2W7YtDvoYJubOpFMu8XH+z6s9y3rScZhFvHcPhweFozHAVvrHQuKJfQabOkxz6W11lUDjFFcPafY7uXU1uPQ+WS5YBWn05qmcWxuBGyvyYKZZSudgc7JfO7liH85fYhB3/Bj53+Tyd/+edbf9gTNW/8Szz6fcelizAObwpQ0WYy4cX1OnPhsrwWMo5zCGlTh39VJUAoiryHwGo4XIQdHjqOjkrNnQ9YHEvDfuqO59vIMz9esr0ckscb3FWms8I0wC8SBDE33Wh7xstZMFnop4tY53boBGkVZ3XvW/rUGhV6N/dRP/dRXbft1C7ezK1eu8Hvl17/yK7/yqs/hvq1sMmmYzEryvMb3DW96LOL8cI7RDVoJ1d+dRY9b+wHzTKCBedYQhoZL5z1GvYbjmWHndkU2rzh3Lma8Df245sHkOhuHz3EyusSufoLpwtFP4Fx/iqcth3nK8cIn8i1ne1MSk7FfDJksUuYZHE9qjo9kuO7MWDH051inUUpwu4vcMV9IsL09qjmTHHNQ9HnuWsjeXs7WVsTls1KF76gCaYS+bzprqGpHVVqUVjz2gM+5/pTGGaa5h3OK6dxx4/qcIq+5dGXAAxekejfLNHnZdjDbhODplz0+WVyklxp+8NFnWP/tX2Hjwcf5BH+aLz5zzKUrA65uN4Rew9PXI5774oS0F7C9FjKOFmR1SFlFaK2WgYhzMMkD5rlQwR6fNCwWNefOhlzckCG/3QOPF547wfMN6xsxSaLxjKKMhJltrW8ZxeVdxY1F6bF3IjNgSahIIttWUVt9iXtc139QPgHu+4XXktUNTOYwmTZoDee3zTJW6HQCTjKf3QNNUTqOTiqOD3Oi2OPRh2LGffEJN3ZrskXNcBiwNtIkoeOB8Bpb1z7BfOsBrvEODk8c6yPF5kCge0fzgJO5JBXnR3MG/oJZlbA3j8lLzZ1j2NkpGI18rm42bHl38PQ6u0Zm+aoSsrYwcb7fsBbnTIqQ5296TKYNUaRJk1XBMvIbstJjMoeTSSMJtSdrJz2nGIUZpfU4mkusMM8s+/slVdVw8ULC1jnZn0/mhtlCEvIuCH/mRsS/mj5Ev2f4sTO/ztEv/jwbb30c/a6f4cWXCy5eCLm6mROaiqcXQ15+eUGc+myODBvJnMoa5mUgqAwjjIhKCVQ68mqOspDru4qjo5Iz2xIrAFzfhZdfPMEPPNbWI+LYkMSaQaqWOkpJ0CxJExSORelzZ2IoK0keAq8reijALdEX92L3Y4WVvWYTgiQC7QtrRWez3Jyq1K+q1b4nsIE0DYhi006ztwOGfUMQrGAA1sLUDuiPLzIL19EzyVYbK3j8LuPsBHqmhdAbZpUn2amDMFQMBgFpooUOzMQ01iyDWKPBd/K7bBSTMqRqNMOBxvejJQdy9xmKWoYOPSN0qJ63Ok4cCCbZ0h7Xp5Xl9rDWLeE/S+5ivarGd9CnutaEASySDdbf9gT6/CXW4ozHHu4zTC0ORWk9Qh+2tmM8T6rzh1mnHqowSpia8rJVNG1nAawT3vHBwJeNvZbKbRQq1tYjPE/T7xl8f5WkQNfqVEu2oa51KuwubbWzJQrwWtaiV7NIu4rPKx+7b3+4bTz28MOAovCXrDUnRbjc+EESeutEzj7wZfA2DPUyiPQMjIceSWyIIkXdOPJKM3FDguEFplqUhZNIY63jKBf1zaI2y0r4pIrIGp9JES67mIGv6fUD4tjgnKW0AY2TzTyOWPquwGuT3yqhqD36qcZtRuJPWvhRUQusoao1vgdxrNslZzBaEQUW6zSNU3jGEQXQSxTDUUhReCSxXl6PLrg+nWRrJcPHniedgY0HHycfnmXc1Dz8yJC1oUKrGudkzmn7bErga/JSszfv0bTsSEGr6DrJ/ZZJSS3hgGmi8TyfwIeyEYcaBorRWozna4ZDQxyuFE2tlSpiXotoU8fl3uma+C3EcJa1WOO2SpiV94YXvu8TXp+WRA7tg9ErsaF5cbeIZVaI8KXnKeJIU/cDgqAlKGkErjPoG+JIE556fKqG9NcuMIvWUXOWhANHc4kVylot19S0CKmsoag9ikpgyp6Bft8njgVKWKmA2uoW/rZ6rWkr6JMipKw1vQR83yzv8e58ilpgvoEvPiEMpIvpexD5NU1L5NHFCpHVDAY+de0RBgL5oYM06g6a2MGBJf4IfFikW2y89XG8i5cZhAUPXe0zSFwLQfIIfaEel1gBjvJkea2VEqjUPJdYQRgRpfMRRYp+38fzVmJiUQTrmymep+j35Tw7iFYXI5WNRgPOrPx85xO6jm/HKgdfDgH6WnbfL6zsNQsZ+uf/6ga9fl9U/pzm+ds9XrohtKBV2WAtjEY+2xvmrpYfrILONHJs9HNC3XBnEbNzYNohthVbTxxajIJbh4bnv5ThrOPqlYQLWw1ZoXnxplS/+32fzbaqL2IkAhkYRCWxqcgan5MsaDdwgQPUjeb2seHwxLI21Dx6ds4gWHCQ97l+EImIyCmqwzgUeFI/rFgLZ3i6pmiElaB2mnnpk1cGrR1Bq8qYlcLZe5rbPPRFZyBssbVLPHW7UNbijEeLTxLvv8Tk3BN8qnySae4xiGsGQU7W+HzhWsyt3ZLx2OfB8444aLh54PPSDYnStzZ8xn25hl07Pys0k1YkaHPoGKel4LdbjF7VaBaFXqrEvrJlGQWO9V6J0Zbd44iXd8SJDHqaOIJsPuGn/sTontqA/82HjkjSV7QB5xN+/Lu/dhvxvr02rfteP/y7L9Jv/UJlDV/cG/HC9RrbCDtN0zjSnsfG2OO0yrvA/2iDZ8swLvGU5WARcfvI0AkEdaJ9g7QhMJZbhz7Pv1jgnOORByOubOYsSo8vXjccHlVEkWE4EL9wWmE8CoRO7zQ8Dlab5f6JYTKX4b4rWwWpXzIpQu5MAspa8PlFKTCdUV+gi72wZhwthPGj8ckbb1klB8FAZ5W5azN0Dua5pqjE5/UT4RTvZhKkoykFgHGv5k2DFxhOb3DSv8AL+WWK2pD4NbFXMK9DPv5syIsvTNg+2+OND2v6Uc3Nw4CXb1QopTi77bE+sMuBRhCM92ypSrryuUKeYJnkAbuHq2pfR/MorDCiieAbiR6u7cc896LoEESRRxhq8sWE/+wnvjqU8L5PeH3a6Vgh7Q2WWPdrBwm37ljqWmiwm8bR63lsrumlACasBDCtE9jh1qAgMA0Hi4jdQ9M+voK5dvfz7qHhhRczmsZx5XLC+U1R3r2+azk5KUlTn9FQ8PdJuNrXB1FF7FVktc/hPKCqhSUoDhrqRnHr0OPgyDIeap64sGDgLzjIe1w/iCjru+f2Ql9gksO4Yis+wVc1mY1Y1AGNNUwLITU4DZsqK71Ube9QDEGr6t7FCksa5FZ1fRAWPGifIZnscLz+IJ9fPMyi9EjDmsQvyWufZ2+E7NwuWV/3efh8Q+zX3DiM+NI1iRW2t3zW+t3soFx7UXAXfzhMHYP4blagvDLMMmF/60wrlkQPgW/phxVGOXZOIq7tChvkoK9JQokV/oPvvx8rvFp7zXYI+v6csd8QUFAS8oLqsXd7Tl01VGXdUn6NOLMpVbu4lS8XNU1Nlou40EY4IdELCnuGXRVTlXBwKK3s8djnkUsi3FHXhp1rRzSNZftMTOhZFrnm9u6CWy/ts3VhjUF/0IoWNYzjUhxQo5k0kQzXKFC+MBUkfiWCarlhdzcnChM2whM282sQPchNFS0xvV2SMu7JvMA4mLPlbmHKkqPgDIt6jHOq5UAXTYCObeSQhMkiODWg3AY9ngiP+bohNBWVNXzu5oBnn5fOwFvcS5z8899g+F5NPX4Ls0xzblBy3txg6o/5ZBlx/aVDrB1z5ayPVo6igoM7EvHHUY/ANyShYq1XEXkNd1y4ZDzqbdecSw5pnGZex5Qt7Zk4pFbwre0AdFWQ0IfIqwhNjdYR80WDbRxh4OP7K2XDezH3ioSje+y+/eG2LbXLyB6jbUXpJTynRuzcmFKWDcWioK4ats6P2Rj3RZ/EdOxcbpmchp5lMzohUjllc4ZrpSEvLJNJzWJRs7ER8uRDjn5YUNU+117Yp64azp29RGBqFnjs7OZc/9I+m2dH9Hv9JYZ4mEhVfV4YFoVpk48KX9uWbtNRNB4vZ4abNzKBLF1csKH30OoMeycBVS16HfN5Q5oattdk4x+GGdt6F2Mr9r0zzCvZqEJTt9oEMAhlfS1qn2nuL1XSoZ1dCAQGpJSw+lRW89JuwBefOebhR4Z8x6UbeB//Tdbf9A5eHl5k3vgM0gVnm2scRdtMZ5u88JkXaZrLvOmREYHXUFZwe2eGUorxaCTVSSMbtq8b7hCzdySd242h5UxvJhVSJUxq1grJg7WCl86cJEJRIPBC31hSr2jFHGMODzKqsibthUSxR5GV93Tv3PcJr09L/YxxAJ6qqFzAdZVweFhSlpbFTHzC9rkBZzYCAl9w/r6x1FaEMrMMBglsxFMSvaCy29xyhqJ0TKaOorQM+oarZ6VYd72NFeq6YWsrIjCODNjZmbP78gHrZ0b4/gASzajnWEslVrBWMa+kQ2A0KF9YBPtBSWENWe5xe3eB76cMgzlbzS1sdJEdIwlBVUPeFvyGicQKa9Gcrfompik5iM4zc9GyaxgroT0VjR/HYRazKLxlzNHNHwbGEXhCzx60zGzP7cS8+LJ0Bt6U7GA/9mFGTzls/AjzXLORVpwLbzP1B3wy3+Dai4c4t8YD56RgWVRweCDDQnFsCH2BEA2TitBrOJwHHE4kYRls1JzrHbdEJD6NNWgVMMuCZdLWqbLbtuPqe5D6hfgXE5LlFtuIeFnjv7oK/32/sLLXbEKwaEL8podWIkKURpaHHxkIbWguYlW+rzk4dhxrgQyFwaptFPZFfOi47DHXMUXtkbSttV5iAEPggXOWRekRhXD10U2cdYwHnUqh5YnHe1y+klJVjsm04XjiOJ4akjgm8GDct0t8W+DJplxbzcEiwjnF+hB6jyekkWO/GLLwH2VWRPSThijQ7B4q9g8qwlBjrUcvNkzCgJMoxeiGYuFRNlL1ExpDhVI+SkVoBUnYcGUjo3GKO9OQk7mwJ3SMCXkdcGQjaivUqpcuxgxTy8nwjQzfqylH2zwxeIliEFE5n323zbSIaSz0BrHMXiQVgyDj7JrH4sEBWsH5LVjvFeSVYecoIC/l2g96EuCfLDyea7ZODUiJIunRVBiFklhozzolxK5CcbCIcQ4OJmqpUHx0UjNfaPJF8zXumLut60q88rH79ofbcp0y89sOgQtIQsuVB4ZUlWM+r6gri+dr9o9qYRwKhI60q7SnkazVaZWyUBFlY0hj8R+DXoB1QYt9rSlqjyiAiw+sA7A5coSmZhCVPP5Iwvb2BeracTypOTqGk9SQRD7GyHB94EmlLTRCU5zXAdMywFrF5sjRS8QvzOuQxmyzqANREA80RyeOg/2MxcKnn0bUTUBWeuSxj1GOrPAoasEbdBCEqhGlb2thlNac7c+onWbHpUJz6Gh50WsWVcAk86hqRRgoLl0ZsDZUnPQvsP7kU1TxgAf8F6nCkEoF7OuzHBV96soSpjG9QcQoLhkHc86sBVy+OsQYOL9p2ewtKGqP3Um0nPsZ9eT7m2WGF4sBvufohTWB13A4D9k/bMhyy3DgMeoLDGJ7mDMKFhSNz6SMKWrD4cSJQnFR0TSO+UxR5ot7unfu+4TXp+WNz6xJ0C2aIAktly5G1DUssoi6Ft2co6njZCb3u+/pZUA86kvleV7FlManajT9WOZVzFCYxzoaz7z2SKJVrLAx1mhdk0aWNz7R5+rVXuuLGuaLhqL0OJlHBB6MekKdq5CuAEiAe5RHWCtEHr2kRxo5josehXeZrAzbWEGxc6CXsQIYerEmqzzm8cN4xpLlQr1u21hBIDmGQyX4mzS0XNrIsFaxNwk5nCiaUxFgUUtnoW5kJuLihZBhajlee5DRO6FMxjyWvkw1CChsyFG9xqQSWHFvENPrefTDkoGfsz0OmF2VRX/pjGOzl5HVHrtHPovCxzMwSCVmmGQeebV+13c6WWjuHAnla5po0ljiulFakwR1e90S6kZxONVtkdhxdFwzX6j7scLXaa/ZhOA4T8h0n9oqNCLPfe4hwcjNq4Ci1uwcGD7/hSnZvKA3iEh7gt999Ipmo59T1B43j+Wm8T1HP2nwjWMc5SReTtaE3JqkHM08erHjHW/Q7US73HCDqOKxqzskbsYziwf4/3wo5+D2hKaqqauG3jjlW99xhstnhBYzDSQT352mXLut8X1448UF56I9Tuohn701Zv844eyG4ur6jMYpXt5NeOGZO/ihx9F2nyTxaBpH3Qb+mxsB6yNJUE5mkLUKjNOTEuvgO98e8Wj8NKD4XO8JDiahVBMaEQQ6nPns7Eu6e/mM44HNBQ7Fp4s3Uo+f5PHhy5z92K/STCbM3v2n+MjRW5hliqaxrK3HbKwZtuIDBuqEeFywnvbRyrIdHjAo7nAzeoDf+t2EF75wi0fedJ53POnjacfztzQvv5ShWnEYz1PkWcPJcS6wrAcHbA4VcdBwtjdl4E24Xazzr7/U4+ikJs8K8lw6QUcHc4q8orxHRVJgKSb3ysfu2x9umzR9inJMaQUuM4xL3v4oLbOGSNnv7Fue+cIhxaIgTEKSNKDXDxg9FjGOS4rGsDtNl1WkUU9a+qOoJPYKSusxLUKmhU8/sXzLG0KMdmz3F6Qmo29mnLm8j8LxzNEF/umH5xzuTTBGo40m7Uc8+viIM2vgG0ffzwh0xbwKuX3s4Rt4cHPGVrjPrOnxwtEaJ/OE9UHDdirDec/WEdee2yFKImCL0chvhQ5jtIbRQC91CjrmjckcbtwqqMqGb//WiCvRC1hjmEaPc/22R+grAtOQegVHi5gv3YS6tpzfNlzdbtCq5oX8Mi8PLnLVf4mNf/1PsLMps2/7E/zO4ls4nBmqckFv1GM8DtmMD9iwu5hRQ/LoGK0c2/ExQ47Y1Wf4ly/5XH/5hIceGfHmB2uMdjx7I+T5Ly2IIo8L50J6ieiQvPSlE6n6P7HBxlC6C1eimwxnt9jvX+HZ25fYO3Ts7GTMTxbUVU21P5Hf5eye7p37PuH1adMypsxSqlr2735UsXGxxjpN0Qg15q1Dny88s2CxqAgjjzA0xInHQ5c8NgcCbb3TFqOUgnGvxjOOcbQg9TKyJmJ32meWG0Y9y1NvlPk3rYSIZC0peGJtn8ROeTZ/kH/yWzUHe7JfaaXoDWPe9MYBFzakO9H3RQX8zjzl1qFADh8/O+WMf5sTO+Lztzc4OInZXnNcGkus8OKtlC9+/jZh5DO/PKbf90R3pJagfDz0GPZamGAmkMO8sBwfVzjn+La3hjxunsH6hqr3OLuHHl6tljSgk8xb6hRc3LJc3cxxTvH5xcPY6BEe7V3j7Ef/Ps1kwvzdf5LfnryVea4Bx+ZWwuaaZj2aMlAneMOaQdTDKMf56DajxQ4304f47X894oVnbvPoG8/w9jdIJ+O5mz4vvpRhjKLX9wl8zXxecnBngXOOBx8ecW7DkYYN59NDhhxxx27zuy+POTqxzBcVi0WNbSxHBwvKsqHM78cKX4+9ZhMC51q+/3bgx9OO2CuonQhYaGXwjKGuGsqypshrPE/j++IUfN1QtsJjRdVxXcuUum9qIp1TOa+lyhLIThzUMrNghRPcKEdERlIcE3k1TW2pyopiUVBmBdrTlJVb8RArEd2xTgRNlFJ4yhLXUxY6IS9hNmuox55IjSOBflXWOOfIsxqlhP+7LARTl6Yew75gnMvKUVWOsrDkeY21DogIyjlOaYz+8ru4bqSjAqL2G5pKAp7cY5ZpHhyENJMJxf4hXlNg7YoS1fO04HlVg7EVgS6JTImnLVEzxy8XGL8hzysWkzlV2eBpD89Yqkoxm+RoozFaEUYeRdEsJcWFOkwqqL6uCGyOpyxl5cgW9ZIv2VnkO86lKniv9gfFOX7fXlvWOLXk7HdtxdtTlsoaosAAGq0Vtm6oigptNIVRRJG4Oq0sYJbUgIHn8D2Lpx2BqYmMwE+si5ZsV1Egf/dbsUCtLJFdYGxF6DfYxlIVFbVWaKXwfLPk59etTzDIeu4G6TxdE9dTShMu6ZGtVa3Ssdz7VVFhjKEsaopiJeSlFcSRxraD/Z0wWVk78ryWGStHu55XHOXd59etjkFRSLdOKwg9oQ0sasO88anDADubUh+f4DVlq7QshAbGaGE2UQ3a1gSmJPYqGTJWJV4jhZGysmTzQiq02rZ0wpAvZB0XZUAYKKrKUpU1VSFwK9P6e98WeNUCjRX6xcJS1w3OWmxjqauauihpqnuDDN33Ca9PW86pOVAojLaEpsKh0MrDOo1v/Hb+cIVV9zzprPmmoaxNm1grfM9ijNyzga4JVUGtxH9YC6aF+ijlaNpYQStL5BYk+RGRV+EsNFWDtcLb7/nmbu58ZZf7ddkOxHq6Ia4m5H4iQmcLSz3UeLqho9Wvynbt5DVBqKlKS1GIUwkDTRrrFcymcZSlI8+rVlchxK8znNJ4uosJ7h76lXhGuiGhqSgbj0UZMs81VT+QWOHOIV5dAPJ5zKlYwagG7RoCVRGbCqMtUT3DK+f4scRpi8lC1MiNnEfdOLJ5ifE0nickEEUh0HBhRhMfpZXDVxVBlaGNpawgyxqqsllqGXXQ0aoo7v3+ue8XlvaaTQg20xn9ntxg1mluLwbcOFqnbmCeC7cuwCOPDYEhuh3F931ZCMctQ85aXxxA1SgWpWZeaI7m8hrhCZaE42im2DkQtpJRT9R3j7KAO7MHce4hJgvF5asR5y/2MUZhjAgZbQzlZqobRd54mFY98OyGJBs704Qj/3Fqqxj1HP3EY5TWwhzkNOOB4uIDG2gjbABhKDRja32ZiSgqvUxohn1FPzWigvhA0LIQVbwUvwHnFPOZTxrJENQgqohMjR0oGiu4weMZTBYjQh82ByXnBiWV85m9+0/hNQW6qfi+9H9hvrnJb/pv4jNPFxxPDLfzEZkfcbyI2Z+FaAWHaUovvMDxPObqVY/h6DEGfY+9E1FzXhvC29+xcdd3mhcB07VQZMh9xeFUmAiMHpGFIbMqZGOkSOKIydRyeFiKtHk/wg88yvze0/b7zAGvTxt5U/qhQ2OpncdLs21evhNQVXAybSiKGm0Uj71xE61XlZ4wbCk4ywCtHGupbBiL0udkIfj2w2kfkHmATn/keGbYO7SA4uq5PvVQ01jD4SIkL0Vx9MGHfS5fHRIGqhUx6zi2ZW6hsAFOKzxtWR+I8NHtWZ8D8xggCsS9SILyovGprCFNNeeubhNEPlvbMb3UMEhhYyCVy6y0LAoJaDrtjkECZ9eF7WMUF9wKH8A6TZ6ZJUSv4+PpRSVXL8SUtfCAP309Io3h6mbGIF1QqYDZt/0JvKZENTXvNh9mcvE8J7NHuf6lhtms5nY+ogo99hd9bhwKhDEf+4zDHrMq4upFn7XxGZJIsXNspJI6gHe+Y3RK48WitaGq1ygLi+cpbh9rJmGMv36Z8XjMtEoY9y2hb3AuYXK4oCprgiigaRqqVzDKfDW77xNen7YRzxj0xSdYNHeyAS/sD6kbRVaukvCHH0qBlKYNlgNPKCpneYDWjn5UY5QlqzxmuYRGx4sxSo2XA/haw3Rh2D2Ue27UagOc5CH/ev4wjX2YWaY5f8GxfSYhihRx2LINtSw9ti1qOKsIvIaNoazK27MeE/8J6kIz7ln6saIfV23CYxj0FBcf3MLzNOvrIUksbERr/QajYFFKYcE5RJ8l1eg1zYOXxKclQcnL4WMiTDgzrA0USSjEKJFXYnuKxgbCrrSApxdDQt+x3q/YSCtKFzB/95/EqwtMU/DHvd9gfnab3/TezKdvVxz1NHfyIYUfclLGHM5FJTrrPcigf5ajPOXqlZjh6CGGA8PBVAaeN0eK8dtHwGrgO8s9hsOAxjrCUGKFvPQJzTp5EDGrIrbGjjT2OTj2uL2b4YwiSQPC0KPMXw1k6L5f6Ow1mxCsqX3W7TFhMaHyY67X38LnnpV2eJ4LZOfCpQHf8qgjCSryymNRmqWgjcCALGf6cwJds5+lHLYCPweHDSfHBWnPZ3vLJwnh8Njy8osTAB58eEgcipDYZ78wZ+/mMRcfWOctT4T0Iks/LEn8gsZqjvKYeSFUYEXtSVXBs2wNS8pa8/Jtj907DeOh4YkrNcNQBm1EXVkxTB2XLyet4qpU5s+vlTzUv4lvC65VF3hhryf0f5EkCf2o5kx6QqgK9oo1nr69hrXCU9yLZaahH+SEumwrgjF5ZXjhpuLG9Tlb2zEPbuScNzfYd9t85OgtWAvfl/4vLP7ff4fhw5fZ/vb/jPms5OjEY/c4YhIFHEwMt/dF/TSOA8IgJPThyhnLIxdk0d7ck5X08CW4OJxgnWZWhRS1oaw186GhbrnVD44svq9wLmCaeGglAQ/ALeMxXxjqSuH5GpsGFFn9lW+Wr2D324CvT+tXB6zNjwnyCVWQ8kx9js99YUqZ10yPF1RFxQOPn+HNj/j0w4qs8sgrvaS1nWTCpjWOFniqZseOmGWGrHAcHtVMTwqGo5DHHvDox5bJHJ75wiG2blBqi8CPmcwNn3k64/atEy5dXeNtb/DoRxWpX5J4ohlyJ+sxzboOpPz2lGUtLShqj5du++zuOdbXPJ68nDEKZ+RNwLwSiuIkUpy9OCQMNBtrhjSCc+OMB8JrBE3Oy+FVXigGWKeIg5rIb0i8ivXgCE/VHNZjrk3WqK1QBaeRiJ113YGeX3B53VI0Hp99KeC5L07YPpvyxNmCs801DvUZfmfxLTQO3m0+TPU//ndsPHyVcw//X6irmvmsZPc4JU89bh36XL9VozQUVch04OMby+WNHG/LsTeNeGlHvr/HLzdcHhxg0WS10DSGXoDRvlRFM8funYYgUFjbY5DGeNoxTkrGCczziJ1+JF3VNnoo8698r7zS7vuE16eN1BEbzQlhfkLjhezYt/LFlxuq0jKfV1Rlw6XLPd7ykDDjzMuARenRWCEhmWSGXtywHuf4uqG2Kfu5R17C0YljMqno9z0untEkkWXvSPGlF+c463j4oR5pKMf41OfFJ5y/NObJxwJ6kWMUZwyDOY0zHOYp08LHcyvFcd9Yxr2aqtbcOvQ4nmiGfc3DZ3P6Qb5MBmqnGSSOS5cSjFb0EsHUnx2JmJhvC67ZKzx/R4gG4lDYxJKgYjOe4Kua/WLIF++MW4FWt5xp6AcZgRaSEk9bitrw/C2fl19esLEZcXGt5Fx4m6N6jd+evBWAP+79BpP/5v8lscK7H2M2LTiODXuTiEXkczj1uHMkKun7aUIUJIS+48qZmofPW47mmtuHArt6+HzFufRINFWqmLIxZKXHqG+WBeCjiWPmKZSKmMU+Rjs2egUbPXAu4s4djXYQBwalFEVwb11DuO8XTttrNiGYMSD0A2rtU+sAuxD4TBUITtc2ljTVhF5JqBsqLZy9p1k1GgtZ7VNrTVmbpXy27yvSnlTjXcv0Y4yiNwhxVtpTWaEpK2mFxWlIGBo8s6Iec462yq+W/OJVramVWkIFGtfKpvvCgZxXGl/7MszU/V05wkC6Gx3fcNVoMhdTK4+y8b4sW3UoauuhtKNsRN3YtbzrRjvqRrOoQmpjyBtvKece+Io4EQ7grPGZ+mOmRcwsE+jSfHOT4cOXMVvbxF7FhYs94liEwrKWLtTzTtEctlRoSglFWSdX3p1l40wrv66Xwmah7/C9Tg1aIBaNFWpCGdzq1Ilh0Peoa7cU8Mg7cuJ7sPtZ/+vTjrwz6NQnDvtUOsQdw3AUUZUNfuBRlSIslAQ1sSeduMYJxr6o2k5eZZhVEZ6y5LVphwsVYaCxvYAwFHo+z0gVcDCMaBppSR9MDFku8LskDYkig9dS9AFYp7Gs2s21VUxLCXi7ddjY9h5v131We/g6prCGsjHLob7x0GtFh1rl39qQRSmVH1JkviikKvEX8hkshQuxGPLaZ5YLGUGHi64bxaSMKBp/iRturAxcp71AsLt1yFG0zWHZ53AmMIfJxfNsPHwVtXWWUVLx6JvO0+97bTAl3ccw1Es15EWhW/VnjUYqdR3dIEDjDBZN0fgULWFCErqW5EHRNKIZIeRBhsB3S12COISz53rUtUAznXUU2b3dO/d9wuvTZvSJfB+rNLUOoIZ+aigDjdKKqmxIY41vhHXP6LspZHTLALSoAzwtPqHTFvF9RZJ4BMFKgdcYSFN/WWSYFyL853matBcRxaalzpWbyzqNbWGOjW059ZXBaL0caO10hjwjvqFsDFkdLAuH1snjcdjFFy1tqtVkKqX2AsrcW56Tcwi1MIraeaI11AiEurGtHolxNFYxr0JK7VFZQ2011gkBSZxKrJDXPlN/wKSKmOdSXJmf3Wb48GW8M9skXsWly32SWAv0uZJrJcU+ucZ1Iz62u95GC0pgqcfkPJr2GtVWYN+hb/FMi8CoBb4pXZ82VvDdMlYYjfwVTFPfjxW+XnvN6hD89V89pj8YLLl3h6klDQVjmtcGaxVJUDOKMuETz1MOZ/5y8xPeXcVkvlINDjy5WfqxTPlnpWH3UJEXjkFPsTGUu+D2kebOQY3vKUZDQxJKIJtEdhnIW6uWDuK0zLjtMHVtF1vO31FUisOJoygc/VQz6q/4gbWSc5xmiqoS9UAJtFcS4yLOw5Lzv9NamGaK6Vz+3UuE57usFSczqGth8+nFq/dSSs75ZCZDR41tMXoazm5otkeCB94KD+k1x9xRZ/nUzQ2Opo40VqSCxCIrRJZduM1l8TZ2dV066wKxxkIvXukMdB0doYlVZLl8R0nY0iNGljQUzLZrZy0Wswl/8o9s3hO38N/4H06IX8EtnM0nvO+Hh//WcQu/Hqz7Xv+Pf3OXwXhMEhvCQHFxG84MFm2SLJtZ4lX0/TlGWaZ1wrSMhKFmKgqfjYW8EEXwJBYGC6ETtoSeY15obt0RDO/6muHceoNziqdfdDz3zAFpP+LBh/qsDYRHvBc1eFoE/mAlulc3ilmm2d0XIoDRwGvX/Wo9FpXieApFaYkjzSCVcxmlNf2wpGwMu8chs2xFEyjvsaLi62h7fU+6iEY7DqeavX1Rbl0fGwaJY54rdvaEcnl9PeD8JkuhNZCk/NYdy3TWUFeWqmxQWnHuXMS5DVFyf2P8LKP959nbfhP/9PmH2LldsbXhc3a9ZQmbaCYzKXKsD0WvpW5E5wBkXUe+paw1e8ea+cKxtaa4vD4nMDXTMuIk88lLzc09gQ2mqWjARAGcWyu5mO6jaajwpQM5m/Ltb33499QhuO8TXl/Wfa//t39wzGg0IG51RgZJQz8scU6R1ZK4xn5NP8hRCg6ylP2JBIzCa+/IS8XhRFHWAiUKA1qYj+xDRa3ZP5HiWz+GUdpggRt7hp3bJWFo2N6UTl7oW+JAlMaXeyKyvqpaLecWnVtpbmglej4dTPh4pihKQQ0k4YpOvDtmVogIaKdLABJDdI/Jecja7maSskIzzcRfpJF0CapacTSFqro7VvAMaC1/PzhRLPKVnzAattcV28OCxKs4G+wyyPY4SC7wydsXOZ7JcXqxFAOmC8OikFihF9tlUbWzrmjRCRtaKx2OUVKhVVv0rOXancw180z0E9JIrkkcikaLwtG4NmGZTfjh79y6Hyu8SnvNdgieeeaEKJFKXBR7vPNbQi6lewBSBXO6lSYXXB5I9ti1wzzjmGWKW7sli0XF+nrE1oYh8mCjn7MWzjgueuwcxMznDWfWPa6MTgA4mIzZ38sYDEMeuazY6udUjSavZao/KzTzXN7TtGp/RQWzuaNuRIHQ9yXL3h5bBlHJ4Tzki/sVh/sL1jYSrJOBumHqSMKGstZMM3FI2VSYAWxjGY1DxkODMa2CshVlwOlCcJDCSORapUFN7SlmC7h+I2M2KdjYTrl4zicOFGv9mnGUc5hFPP285fpLh/QGMWvrMZ6n+czTBfNZyYWLPX78LcesffL/S/z4O/mE/U5OThqSSOYfAJzzlthM23ZHAt8SR5K0Hc58jmaKplnx/EaBBGuhqYiMR+J7ZLXH8cxnOhOYgLOinjruWc6mx8sZEodixr0zB3RDkK987L794bbf+fVPkQzWCKKQ/rjPuR++zAPelzC2xrZZuFWGBg+LoTIehfHbIB2ywjGbNezemlLkFZeujhlc9IkCx1Y/ZxTMuJMNeO5ln9u7c7Y3Bzw42gfgc89v8OJnv8T6+S3e+pYBD2wKI1DZSGWtrDV5qZaBu9aORQHXXp4ym2RsnxuiLsREoQj39aOKg1nAzm7BwZ0565sp7qxg+ftrJeej28xtyv50k6JUzBcNhwcFdW0ZjkKGA3Hf4gekkHBsFKCYTGsODzM8z9BLE1QqSfyXnjvgYOeIy4+eo5f0SSPF1rBkHC3Ym/f4Fx+b8cJnXhRq0VEPYzTXv9RQVzWPvuk83/Ho89z6+b/DxR/9QYr6QXaun7A2XmerL4wkB5OY42MRbAt8QxWI0nIS2aWy61HhsShg53bNbFqSJgmDYEGq5wS6j68TJibgi3PLrevHDEYJSqWkqebxsxmXdj+KLjJsEOG8gMn8HmlH7/uE16U9+/Qx6VAR+IY49XnnmxQXoh0cmsr51HjLKn3jOlhxW2QLhYN/svC4catgPi/Z3EzY3pQq/0ZasB6dMCl73DnuMV84NodwaXiIc4qb+xvcvHbMeKPHEw9GXBhOqax06BqryEoR2DpdJOt0RurGEYWaKITAU/TjhkFUcpSFHBzB0ZEkw+ORh+/D+gD6US2dwsLI3FTuOD6uqCpLv+8z6BsaLcUBpVQLuZF9WARBHUZLbKK1YpbBtesSK6xvJpzdDohCxZlxzUYy5yhP+NwXHddelFhhcyvB8zSfvl0xmxZcutznzz+2R/CRf8y5t34bn1YXmc5lnmktzqUzUcVyDpa2Q6FIAksaVjgHd6Yhh1PVDj6L+Z4i8ioCXdN4htrXZLXP/knAyaTB96WT6PuKtV7DheQORjVt99EwcffGPAb3/cJpe80mBJtbKVGa4HuKINBEfrNc2Is6EihN20qzTlrXRSXZ9iCu6QUlSRCgVEBeBsuqvTBpeCxMtIQLBIFUtbPGRyOV+I2tmDQxeMbSOEVWeexPPFERLaTCqLViPFAttl8R+q3oVpupdxV+qVxCEGqixCdNDf1EFm1ZKfZLb6kyHHgKF8qAsXPSircODK08eSAKyHkhKoxpqhkP9LJrIK06WF8PGQwC0kQvKwjWKgorQdN47GOtUJdtrInzO54Yjk484lhzR50lfvydzON13nBuwtWtgKJ2LErpznhGVFatZbnYN4aKflqgcGSVYZoZjF4xKxQVXD9Ol3MQ/UAGO4eph3Nm2cHQSoSdduajpcoywHx+75P/1rqWhenux+7bH267+qYH6A3X2nUU0IsaShPjPEXuEirnUVuPrBaBm7yWH+ckyRymkBUe49GYsnKkiVreo3ntsdCiTRBFmv5ABujndYxSjrWR4ZG3PcJwHBMHooo6L3zuTERlt6pZFgRGPQk2khAuXupTFClJopfqyXUjyt3OQZx49Icxg4FPLxHhoZMspGrOirhhIcOMQaDpDwOcbSE6WtrwSSywoqKEyUwUWoNAce5citdWOzuow5nzQ/qjmLU1oShqrHRcszqkcYrtsz2a5jJpP2RtLcLzFLOZzAz0+x5722/i4o/+IItzj/GkLji3sU4UyCBmBzVYX/NRWq5H08D6UIa4jXJM8oCpNQSeYjzyiCK5Bi8erxH5I0KvJjI1aag4ux2h1AZBoIhjTeAp9hcp1888tSwUWAyz6N4KBfd9wuvT1rdSBsOEMNREoSL0Cuq2ezRtelSNR2ENRdstmGaCTfc9SMOGYZjj6ZDqSkhWhPgeS59QNIZFHZE3nsQKvqKxjkUtOkNR0K6pvodnxCcsqoCDqU9ZK7ICZguHMTDsKeJAOhC+p76Myaa24hOslYJiFHukqaGXSOySl4q9KsC2LEKeB2Gg6PcFKuS3EBxjpBofBSKumueu9XWaUaqWcULVxiibmxIrxLH4pw7GU7Wxwvq6j3NrS7Vnz8BRT3McG5JYc5Bc4Nxbv42iv8lj0R6XxhKfdYUSrR29WGKFRSGxQuApYSbDMQt9guxuYoCiUtw4klhhEEusEHk1o56PdWbZGVEKprnHTb2xhFwDzPN7IxqA+37htL1mE4KHrxjGI0kEtGroByWV9Skan91pymQhuL3pXL5M3Wa9/RS20yln3A3qIODhQUTtPPaKNa4dJDQWpplHXiUUlRYRoZaq6ziLMNoxSBve8KDGaEfkWxkennl84Ys5i3lJWdTUtaU3CBk80acXNQTeShHwYBGzeySXtqoVi1IcUT81GB2xsWY4O5ahlxdvB1y/WeL7ms11jziCOFKsDc1y0RbtfEwSWvpRRV6GHB6WzOcljz3a5+EzCxSOvVnM8UwTB/DoZeFAzytHVkjCU9QKVfg4p3jwvOPKWZ9RUrEVH+Cphtv5iN1joVv81M0NPmG/kzecm/CWF3+FZvcWJ9/+Q/zTwycpKji7ZhnGBSdZwI3bUs3oPRGxGRzguYoivsB0EQv9YdsS3TtSfOZzc+ra8va3Dtg+f0LqOcy4YbPvkdce00xwjrcPFV94XgIKzxOaw3xx74vcfYVBIWe/8nPv2x8e++4/usn6Wo9eWGO0JfFyTuyIwvrcmvSZLDSzBeztV9S1I46FsWeQKt5y4Ygz3KDWAeX5iNr53ClG3DyKl3j4RSl85uOBIk0kONidpRgFD5ytePh8r8Xp1ixKj1uHhk9/5oTFNEdphdKaJA145JGBQA57DZc3SzxlOcwi9k+6eQDFvBD8/NrIo58aRgPF9kg6cC/c8rh2rcAPFJsbkriEgWK87AoIXWEYwPZYKot7k4hbuzWTk4KHH+7zxCVJzvcmIUczUfh86xsCAt9jkWsmC8hLmMwNZRWhlOONDyve9MiIYfxKv5BSN4p/+vxDFPWDPKkLvm3/f4Sdaxy/7fv48P6TFJVikFi2hpZ5Ybi2KwJN22uaS/Euvi24qc+TVymegWEqHd5bB/DPPzKjKmve+dQ633rpDqmXEV+qePy8dBGPZsK29vTLht/e36BpHEFg0EZRZOE93Tv3fcLr0x696rE2luF6T1v6QU5hQ3IbcmvaZ7owzDK4c9BQ15Y4dsSRxAub8YxtdYsm9bjaj6mdz+18xLV9Eeec5h6NTSlqKazFkezpu9MUrR3D1PKWJwI844QlrPa4Mwl45ksV81lFXTdURUPaCxg8mjBK6+XwLsAkDziaSdEvLzXgU1SKNFF4nse4r9gcik/40o7H9Rs5QaDZ3gpIY6FLX2sLgnkpxUqBDAnEuqx8TiY181nF+KGEq5sSK9w6STiYyKzSwxdFRDGvBFUBMpc0LwMAHj7f8MA5Qz8sWY+mGNVwJx+yN4lwzvHJ2xf5tLrIY9EeD33yv6W8cRP7nh/gI9V3CqFBaOkPSqa5x84NmExq0ihgvHaCp2pK65EVKw0IpWDvSPHCCxIrfOu3DHjL+QmxAT22bPYl2ZjlHmUtgm2fe87DOUcYCvV8vrg3nwD3/cJpe80mBL2wIQ3rJb+10U07ICMDf3kpbbeTSU1dWZLEg1jjnMJXFWExw1eGwCxwyjD1eigllHxlLZSfVUs56pkWctRm7IGxhB4oBLOncJQ1zKYFs0lGUzU0jcUYyXqNFi7jxCsxqmHqhcssG1ZUWsaIcqoM3AnepmlgOi2IIh+75gnUoB2MAZa6AHBqeFc5qrKhzGvBA3qFcICbaLmokqBu38MnK+RgjVXUVmEUxEGDVo5+kDNQJxhbkfnCJpQVmv0DODlpuLoV0OzeYvL550jecgeQxSK6EBUL41EUkC0qrI3wbYFvC4yWz9fxB2sNVe04PphTlTVFNcCoBoMlNtWSDnGhDRXSgTk6yrGNaxMCTZHdO7fw/UGh16dt9EvW04yel7e0ngIDKBuPea6ZzOH4pGFvV+6z0VoChISBIdAlyfwApwy1F2G1R+ZHKCXV8q6dbS1tS10216KSIbZBVC8ZQ46KhLrxpCp/OGd6PMPzPYzROJtQlH3BCGvHMMjwVUXZGI60Wd6HHdQxDES8KA4ckVdTW80is+ztTkjSkPFYRMm8tvLXbf51Lesr8sT3BF4oPNx5hefBIJBp20MT0jSgAxjEFYlfARGThegllLXABwJP/h54DaNgwYbdRduaKvTIU49J5rHzUs3O9RPObazDzjVOPv0Fksfe1rKhyExCGlbUVtE0ojEAWrjIG/ELriVTiPwG31icNRzsnpDNMhZvXseoBo8a5Tmsp/F1zMwzlLViNmu4/qUD6qohiHz80L9nEaL7PuH1ab3QLmMFT4suiUXTWE1ZSeFwtnCcHAvcDsKW/EKJBk65wClFoHOs0ky9BK1iqhbikiuZdTs9w1O2Q66+50iCZkkk4hBI72xaMpvkAumtapRWLamJJAOhV6OBzHiAXsJqOz58z4DzFKEvPqFxmrpxTE9yosTH2gDdnk/oS2Jd1XJyElQ7TMsoVpaWspJzjD2pLhrtaBqFCiRWEJ8QkhVa0AqO1SCzXwutsS+xgnYNhR+yiHzySrN/opnOnXQGbtxk/9PPc/Zb91BJNydhibyazBjKEhERswGeqkVrRNm70AFKOaoKjltB0rwcoJVFIarvRsn81aIlkilKONjPhP0w8QkCs9Q7uhe77xdW9ppNCF7YMYzzgDgMCDzHuVFOGp3g+TVX1xuqseHOLKGxHnluMUbaPItccWO2RhaHNE5YdhqrmZfekmWoYwTpJtJ9D9b7DVeG+xhlubVYY+coJAocl8YT+t6Cen2dnfM9TnoBeVaTZyVh5HN43NA0Pr3EZ3vkEZiGo7nPbCHH3hxZNpOMaRlwMg/I8m74R6rdxkC/H+J5iqJ0TJAh58m0xjYO39cEgaKsNFobZqHhZAbaaKJYBqOKxkcpR9mIM1kU8NJesLyWS6ExI8lIUSv2TnzyAs5teMTjglAXHC9iDibSTk1jSCKPonacfPsPkXzLPlZ5/ODWv6TRPsfRNrOmh29CgsDg+ZpFAXt2G081XD9MeeGaUBH2U4PvK6y1XLgyBmSwu2hCLIqTImZeStVy70hRlALHunq117JAyZzEq8n6beOwrwACvvL/9+0Pnz17M2Rt0SONUgLfcba/YD04IjGGcKui3PDYOUkpq5T5TFRJG+tYZI5r03Xy9C00VpM1PrZSLOZ+K67j2gTAwSmWoHGv5kL/SBL9KuX2YoCnLalfMg4XFPWQs5fGJH2pljnrCCKforAcTgxF5WF0n8A0HM5D5pms+e1RzXqSMS1DsiKgrIUMoGvTjwaai1fWWpYhIRuYZ44sszSNwAMCXwaXbx4GHIV+i8MVEaS6hllb9bdOfFxVw83DAK2CJfSxY+noxc3yWHkJZ9dCzLghMCX7iz63DoXVaGvDZ2NtgyhoOPrWf4f0sbdRBynvST9NZUIO3QaTKlnBDirLPNcc6i0803Btf8AzL4q/HvZ9wkDIDa4+uoW1sDGUJK8gZFImLCqfWeGxcyAwySjSPP7kmbvuiXvtHN73Ca9Pu75vmNarWOH8cE7fmxL6BeF6RTU23J71qaqIRdbgtYKis4Xi+nSNRRzJPdeSlcxK2Vf99rZaFvTaZGBzWHE+PcQoy2425vZJSBxazvZn9L0FjV3n+lqE52kp3JUNQWDYvdMwzwLiCLZGDb5xnCw8ZguB/2wNLRvpgmkZMsu+3CcEnqI/jPB9Of/pQlFVjkVm20JggzEKz1OAxAqThRQiw1Din0Ule6hzisAXnyCxQtA+LrFC6CtqI+yAtxc+eQnb4wBvVOOrWnQGph7WQS+GQYLAuN/zv+LM2/dogpjvMh+hGUYcJuc5rodknocxQi9eVDBpBmgs149TvviS+IQ0aWMF57j4wBogncTK+lgUkyJiUfrMC82dY4mZtIIHHuzJ+bfJTL64d5ah+35hZb+vhODnfu7neP/7389/9B/9R3zgAx8AIM9z/uJf/Iv8yq/8CkVR8N73vpef//mfZ3t7+1Ud+7lnjxmMDXHikyQeo9TjQpzhuZItt8DYkv74AQ4mmxwrTV2LKl/TWL54wycMRhQlTGcNjXWMBoa1gdzsHdZMK8H7WgUb6YJLk8+gnONO9J3c3LNsjDWDzTlb2UvYnmL37Bn6vZjJtGEy8Wgax97tjN1dx3AUUTc+UeBzPIXjSU0cafphxXZ4h9CMeNkEWOuoasgqwcx7BoZDD2slk88yyXavPXebqqzYvLDB9tm+BNyZYA+r2mGMYAyVEtpCEFyydRI43LqVky0q+oOQtTUZYI7bHCEvNS/dqDi4syB7cMB62iMyAfuzkNv7Ig50blMxSmsWpeGfHj4JwA9sf4zJ/+M/R2nNhR//s3zp3B8h8hp838f3DYvccXPSB+DZFxue/dwunm/YODMgSXx6PY8rFwNCX8SYFnVAZQ13JgEnc8V07tjZWVCVDVev9nj4vFQ1JgvDPNdk0b1Dhu5n/f9m7BvpEwC+8Lkj+iO595PEY/gWnyvmCNOUbLUiWoONx9g93OLY11jncBayzPLsNcNLwYiidMwXlqZxrI0N60OWlMIrSkKp6K3HC66efBJlG54ZvosbBwG92PHE5glrdo96pLl1YUiv71MWliKXamCeN9ypHfNEU9Y+geezKGCeCZvQKM45593i2Fvjht6kqtySsUzEER3epaBVKBcV4uPjmusvn1AVFRtnBmxuCuXX8UQ2tLp2IvoXGOraMcl9TEs36hkZKt65XZPnNWFoiGND0JIf9MOKSe7z8o2K2zszTq4OScMRsVdx4zDi+q2aMNQ8fAm2+jlZ5fGRO2/Cuif5o73PkP7aL6F8n+S7/yRPqzcBwl5mG9FyuJMPAfjiy47P/eubeL5hbWtAnPiMRj6PPBgRB46tQYF1mtJ67M9jDiaa6cJx+3ZOWVouX0p46HwtzG21UCIv7nG26L5P+Ddn30i/8PLLCw6OQ6LYEEea9Z5H3MzwbMVWMUE3JYPRo9zZuMDxRFFWjqpyTOqG5296XA8GlBUsMou1jn7PMOqd7tKrlj3QCcNOMuHq/r8C55itfRdPTyJGfcNoPGEju06VemxtbOL7IXluyfOapnHcvD4lz0rGGz3qBxOSUMhBJtOGKNIMopIz/m0iM+a6WqcohJ2wqlsazgDG4wDnnKh7VwLVvfHiAVVZsbY1ZG1Dhn4XmSbwJS5SWhGG0pmcFkJ7bp3MKk0Xcv1m04LBKGZzU7onoS/zEfNc86VrFYcHOdMrPYZxj9hUHM4D7hw5fF9xabthLc4pG8NvVd+OiuGPeL/N4pd/UVjK/uT/mmr7nSy8EGMkVlnksJ9JEP/CdcsXPr2DH3psnRmQ9iRWeOiyT+g7Rmm9FGzcn4WczBWTmeP27YyiqJexgtGOSeaRFYrMvzefAPf9wmnTv/dTvrJ9/OMf57/+r/9rnnzyybse/+mf/ml+7dd+jV/91V/lIx/5CLdu3eKHfuiHXv2JGeEQ7qyxwqfbaA+ntPy4uykuuwVc1Y6iFBnuxspmaU+xq0og7pbP70yOK++5hOlgUVagLcIcIhU+pRTGKLTRGKOXHLvActhPOPWdHEO59vUd9+6Ko7d7v9M4NuccTd0IpMkXXm5noSxX2WzH2a+1KJV2x3FW+Lk72fS7Po9aJUTQdUwsnrYtt/HqNdAOIldSTWy0h2ovmsrn9N0JiV9gNNjGUte0yspaeMIb+akrS1VJANa9f+OEa7lsecjv+h5OvX/XzXm11vHGv/LnG2WHh4f86I/+KIPBgNFoxE/+5E8ym31tpoO//bf/Nu9+97sZDAYopTg+Pr7r7x/+8Ifb7/jLfz7+8Y9/wz7L12vfaJ8AfNmara2m0iG1CWm0jzNCPABdC1q0QEA6TVkhA3Z1bWmsW7bx9bJdvWpdL4fxjYc13rKt35lTuoUVitaI54k/UEq18wSr53Z+SmuFfsVe9ZX8wivJoLW6+0Wn7wXXUgdbKwwi4pMEGqi+wvpR6ss3y472b3VcuS6qvTbqVCGlU1vtoJu1DlC+VORMnZP6BbHf4HlqCZUQ6kCBKKnWN8qxXPv5Wq0EqymsT2m/XH9l9Z0KjFMvz/vLn/eV7L5P+Ddj32i/oLRCa7XEglunqHWwjBUAHCtxwtP3S9NIga6u24S6WdF8S8Fw9XN62Tilcdq0WkTymFYSK+g2VjBm5QOclcDctGuzsw4e3PmEjkSjQy9AqyfQwgvFP61e79o9vvu9XFcWynbPXV4jtfpcX8mck/XQxUpfKVYwymG0XSYV3bXsaJ/zUrPINdYEy/hNZ1P69oien6O1XK/GOqpGy08thQNbW+raUpb2rnVZN0KGUll9F4TaWbfE+p/226/W7vuFlX1dHYLZbMaP/uiP8nf+zt/hr/7Vv7p8/OTkhF/6pV/igx/8IO95z3sA+OVf/mUef/xxPvaxj/HOd77znt/jkUeHRHEKyIaZV5Zb+QZGOSJT4vmW2/M+k5m00X1f2Ii0EvEspSBNZAivwwJ3i6EXNcR+Leq5lU9h4aSIuD14BKUc2dRj0NPEoTiSyo8pnShpVpV0Iura4nmac+ciBj2pvg/SBk87PGMwWph7rFVM7YBFHQhnfyqiPSBJTlHCfN4IvWokQUV/GLJ+ZkTTWLbPDTiz6WMdzOaWPG/ahSg3bBLCWjjDOcWhCSlKcUIbmxHWChQp8Fv8se+IvAZiaf3HUY/zW7AdHhA1cw7TlLhtI2SFUIt6xnF2zeJpx0m4xYUf/7OofA7HB2z89n/P2hvezsejf5f5rGQ285ku/KUTidOwDQgcZVEznyvuHMr5zBPDNDRLfuVxXyBN83lAUUg142Aqt+c8F1hBnt37Im2sJIOvfOwbZT/6oz/Kzs4Ov/7rv05VVfzET/wEf+Ev/AU++MEPftXXLBYLvvd7v5fv/d7v5f3vf/+X/f3bvu3b2NnZueux//Q//U/50Ic+xLd+67f+gX+G3499M3wCwKOPjUl6/SXGf5ZbnuFqW70T7u+9w4iTSUOeN0SRIY1X8DyQ4L2fCktPErLk3FZKgkzPiOYIwKSMeC55KxrHcZ4wTIVoYNFEYDaYltHy2N1mHASa8dCTQWB/xb2dl5ooUPgG8tpnz2yT1QFR4FgbynNrK8H2NFMcHsucUhIpwgj6fY+tMz2axjEcBgx6EjEUQatmWjuKwuCcY9iDcSIzBLM8JSskIDi77WG0zD4UpayHvFRM8oCyVpzd9hiPRpzftGzHxwSqJB/7FFUoa3ICB5MY34NBYvE9x6HbIPnuP4mpc0xd8vDRR9lYe5Ab64+R5z6NdRxMxemliePqI1vS4YyMJFEa7hxJwDXpBRzG/hK+tT6whL6mqgKKQoK2mwctVLKUzkm+uNcOwX2f8M22b4ZfuHQxJukFy+A6Kz12vDMY3ZDEG/iq4uZinYNj0djoknfPU0SRrDsXKnppB81ZcfH3I8HXl43hcB6KnlCesrMpyc3RNMG0pBm186n8WAqXVvboPGuYnBQEgeHCxR79niEOYdSTteN7BqUNgQeV1Zy4EfM6xDcQR5qgjdCallmoKC1aKcJQoIRpz2e03qOuGsbrKeNxuzYKR1mtMmqjRRtgM5HAM68GnMxb5rGzMfVmtExAmkaGjGO/QQPbWz5xbLh0xnE+uk1Uz8h6D7KfykzmdGEoqxitHWlo8YzlMDnP/7+9P4+27LrKu+HfWmt3p79N3Vt9SVWSLLmTMTbIogkB68M0CZ2/Nw74TYxJ8xFsBhj/gRkZYMMYDBMChCZA8n0QM5LxggMZcQghMRh3xI5wIyxbxrJRX6VStbc95+x+rfX9Mfc+55Yau0qWymrOM8YdKp177977nLvX3HPN+cznOfT//gfobAyb51m+7/9h6WW38rHO32Oym7M7jtieBo1JmWO02peiiFbUtWOaWi5sCSNiJw6Iw6BRivSsDKTwMZlEBGFNXcPmWNgSadGoQF6eErG830VcmOFJbQje9KY38Z3f+Z3cdtttlyzyO+64g6qquO2222av3XTTTRw7dozbb7/9cRd5URQUxXxYdHd3F4Cja5YglpaZ9zLcc35XVIDiIEZrz+Y4IMssRWEJgmDmfNdWtJJIpAZD4yhq2b0qBXFg6Yc5SsVoLQtoWhjOhysoJa3oTiKyXR6FNRG1E9194bNL5VtHmn1Liv1LYqARBXbWcmllND1qJpMaBnN1A/kZeaBlmSUMNb2emC11O4alla5QnZZClgYSDPKi3f17nGvnHzw9PcWjMHq5aRHCcKBn0mlzszQ/G2ZeHohO+Gq/YFhcICxT+vER4iimrmXQsLYw6CpGnYJOUDGuB9x/6O8w8Dvs+99/wIP//SMcDwLiw99JNi1I05g0D2f84SiRz9Y5+czyvJYKqVEUpSHvaMIQ1kbQT2q8F5k1Y2QTsTuVv31RSos0zy6/j+fc4/ACn6ZFfvfdd/Pe976XT3ziE7PF9xu/8Rt8x3d8B7/0S7/EoUOHHvf3fvzHfxyQ3f3jIYoiDhyY86WrquKP//iP+dEf/dHHrfJ+JfFUxgR44rhwZM3R7bcOoJDmmo1d2cS2a3934plOK+rakSSypvSeilYcCe+1pQhJsWBeATTKEzTuuGlp2JqO5PdC8Qwx2lNYkTXNKlnMuukI6KYy2OsqVgZSWexGQnEJtBQKlPLklcH5buMU7NEduQZpX4tR33hcE8WaXjcQnn9XUS8J7bDfM3SSZmgvUI1RmZq9h37H0Q/yJi70qCpPksg1JaFjNzNs7IiEYlkLNaBNwI2Gfb2MEVsEtmQ57jMehqSF5pELonu+uhKyPhIt8d2qy93qpfS6BTds3U5wz6dZuc4y6r1g5gY/TuXvkySKQweFxyyJlAx8bm9XWOvJsoCdROQjD+0TrwalAqZ9QxZKBffilnCmi0I6j0V2eQOEi5hw9XE1coX1ZUfc8XN561pxftoh0J4kTDDacWE3Yne3Ik1r9CAkSQKR7Qzlq50haivMzkuRoBtWDKOUrI7ZUhHWyeb5tF4DYJKbWbHRoiVXaLwOnEOee1mF1orVZcORfW0skCFZ+Tkz645Nqw5FLUXMTjyXSvdezToZ0lEUDf4kMfQHMdZ5BoOQfk+LUEBp8c293XYqksgxDCZ4NIEeNO7BsDJSGC0SqbsTN5sjSAJRRFoZaOJQs9rPWErPEJRThoODJFGX2koSPs0V/Y5iMCxJgprtekS1/1UM3BbL9/0/3P/HH+X6KCI6+vfIs5LppGaSBdLR1NAbzOcDrfUUuWXbCTU6bDZwcaw4uArd2FI30tAQzHIFkFyhrPwiV3iSceGKNwTvfve7+eu//uvHbUOcPXuWKIpYWlq65PX9+/dz9uzZxz3eO9/5Tn72Z3/2Ma87J3SbMGC2IahKaZ3njfuvdbA0MjhnZk7EIIlsa8RRWwXohp8rx50UAdZ1qaxuHALFSXM7k2pfXuqZTfa5fIlxIG6nvQTCQNPvRRSrYTOk66mtbob35IOvnZrRDcZ5yLQMmvcDnUicBndTaWFGgegAGw3dTmMYojTGJDgHw74kDEbD8lCRxOElbXKtPOeLfTiErhNHTZuunZPQEDYdkqpWbKfxjA/ZjRV5ZXg4vo6gV7M97TQKSHv/DrCTRUx1SBRYkiAhCxNWXnILJ6IIe+LFrEeWm1++3gw0ye8tL4f0esIbbikIxoh04l4FJt9oEzsv0qzDvgwW7VVaCgKFtYZAX4G28OO0/dpF3z5IWsRxTBxf/sDyo3H77beztLR0yU78tttuQ2vNxz72Mb73e7/3SR97L/77f//vbGxs8MY3vvEpOd5Thac6JsATxwWtPUFTzbdezMamadsWn1NrVlZkk5DEik7zpy0bXXyYU/Ra35B2ZkDOIQ9Po8Rwb5xKm3vQkcS/doqsTBo9cEW/C72Oot8NKMqg+X2a1rgICLTXq7UkAdPCMMlNo1ziZ66grau31rC0FBIEcixRElEw1DP+b9i4mSfNbJBz0E3akoRjq+zN3D/DkCamQd7QJ+Jo7hKaFtKhi0JPaBylNZyxB9HKM6liQuMkeYo0SWJQWpKhVplNZplCVleuZ+V6Tzo8wLItOXEkpqwUWdmaE86Vklq35ihUGBOK87Ka/32yQqNVKJ9xR4wNhb6osNYTBEbMiczlPcYWMeHq4mrlCtCKAQhqqygqebbnjUKYdYrV1ZDRKJS5mXD+jKytxIEAj1dtEU3WxW4RUznTeIZIp71snqPQKpDJOjqfLTENO4zLhH5HcoVO0mE0iogiRTdu5hGcotXVs42nD0hMKOoOzimRPG+e5ePUzIQB9q1IwS2OGqrhnlyh19UkkVy/MYaqauNL2x11nC9W8F5R1Gq2DmE+TNxJhGVR1bCVxTOvhCSCvAp4uH8DYadmK+8Rh1Kpry0zH6VxHpBqQxKGpEHMNOyw9FVfx/VJgr/+JazFlptfvl/WfLORGg0MnU53Rv9xXq7XNDRKMT71aKVICym21laxNFTYnkjHN56Usxwj2MvX/BJYxIU5rmhDcOrUKX7sx36M973vfSRJckUneiL81E/9FD/xEz8x+//d3V2OHj1KUSu6WuQxnVM8MjWcvyhP8/YmXls13HhEdqRZFZDXomG/PdGUVWPUo2T3a1278GFrbCgrTSdWrAw9/Y5jZ6p58BEJAssjcRCe5oq/fCBkvOM5ejTmpdcKPzbWFZGuKF3IuXTIdioPrbx56HXiuWrHyXOaCxdLhsOA6454Br2a8zsRJx+xOO+55lDA9YekwlU73Zh+OSIjA7WV1WLuoTz7RzWhthjl0crhveLU9oAPf7aL956VJcXSQBZQVjYJQgyDrnxu57cNG1tSKTy67lnpV5zZivjLT3bI84rjxwOuPSDzDkWlZ6ZjD5+TFmQUGcIwxOiETyTfTnzw29kXWV66/CCvSi5yOrqOu87so6zh+P6a1c604RqqGQfSo7BecWHS5dyWpqqFLlDXnuWR4pr1ik5Qk9UB06KhemiPUZBO7GXfV4/HA2z//+jRo5e8/va3v513vOMdl33sR+Ps2bOsr69f8loQBKysrHzRpPdK8bu/+7u85jWv4ciRI0/ZMb9cPB0xAZ44LgRaulyhceIOXBkuXCwvmTvZvz/mRccsSVDPbO9rq5hkIkGoFE1LX1HWonjh3by61O1o1pc1SeTYmSpOPSLV6/3rEWtKkxaKk6drdndLrjnW5ebjGd1Gzs97UQXZzBLSXFNbzTSX60oiTzdy1E5x+oLiwsVSBu0PKfqJZZIFnDpj8R4OrhuOrgtX1zScX6P9jP/b8m+19gyTko6RGOKRtXZu0uWuB0Xbu9eBpT6NB4vIqkahDC57Dxe2Fds7ll5Xszr0DOKK8+OE//PZkLJyHD8acs2+nCjQFKNIHIhrOHVOpAu9p0nQAx5evZFR7waWbcnxzsO8RG9xOjzBp0+vUtawNrTs66Uyt6Qk1pQ2YFLFVFazMQ65sC3x4PymcJ9HfcU1+3I6Qc1OEbMxFi+Vlgp62UPFi5hw1XA1cwVJ1D1x4LBOcW4asLHt9szkKdZW4KXXlsSmJq3CmTfQOJMks5Uf35sgO684vxVQlIYkVqwtebqxY3OsOXdRjr9vRVRwslLxvz8TMN5xl+QK8ry21C7gQtZnJ5XzZoUkvt09ucLpC5rNrZp+X3P8kGe5V3F2O+LB0zXewXXHAl5wKJfNchVQWU0UiKSnUsxinQa6UdXkEJ6wkfo9tT3gI5+NcQ7W92mW+76p8CtKK0WG5b6okm2NFafOCqXqyBqMuhXntkM+8qkRRV5z/NoO1x4Qf4SslIJrWijOPAxlKRsSYwxaJ3yi851Eh7+TtdjykuWT3Nq5yCPhce46s4/Kwg2HS9Y7knhXPpjlC3XjLn1hknBhW3KFi1uOshQxiOsOlHTDavb3nA1+L3KFJx0XrmhDcMcdd3D+/Hm++qu/evaatZa//Mu/5N/+23/Ln/3Zn1GWJdvb25fs/M+dO3dJO2MvnmjH1Q6ahsbhtHBk07TGez9b6PtWDKM4o2tyQi164xrhpbdUmaqGWs3/31qYTC3TqaXqB6wMFZGRhbG9XWKtp9ftoJtq2tnTE86f3qTXP0rn+opRMKbrJ3TyHYqoz1bQo6pFxi/NmWmYG+VxSjFNLRfPT4EeWgUkQY33EdNp1fxswHKSNtSiiMoaOkElAzjKMa06jMuIQDuGYU7H5GgsgarxKE4x4JHTU7z39LpD9o1cUyFR1M1wVBw4nFczVYLhUHTNk8CSl3Df5x4h3Z0yWrqJFxxR86SjFk3ira2SLK0IQk0YGpx1TCcl2bTgJV+1ztclFxk8eCdrN3QJg1WcVywlOfvNORQeqwKcMs1mwGAJmEYRWktpU5QYLINeRC8sGYQZRidYlzRdFBl6proCbeEvsshPnTrFcDi85B58PLztbW/jX/2rf/VFz3P33Xdf9jV9OXj44Yf5sz/7M/7wD//wqpzvcvF0xAR44rggnFNpuftmPReFxe2ZQNUqYZTk9IKikd6MqJQmN/MBPu9lWE94uUJFyTJHUVggoB5KRbAoYTqpsM5TlLLxLyvY3MzYPDdm376YblAyCsYy6KocuUuYVhETLxK+RcWsG6i1R3lRNNncyIAO3ocYLTEoy2xjtGgYJnK/i8a/IjSOxNQo5UmrEO9DwsDRDUq6Jm9mIDzWa86rDptbEi/jKKTf8VBLDChr6YpGgawH7xVpWhMEjdyituSl4tRDO2TTgpXlAwTrHo0lDh1VJLSE6dQ2aidCoRR1pZA41hw/HPMSvUX/4v2MjqwQmFVqC52oph+kGOUIlDiVVjrCaEftDGlpMFpTO99s0By9TkA3rOgFGbXXTCOD94owkNkmXV/ew38RE64ermauAJd2nKyDNHVzQRHnWVmKWY5TOiYn1D08HYpaQyYSvW21uu1et7FlZ9cyHlcMhyErQ0OgHbWVZ6J3nuGg28wKwrkzU86d2qDXP0r3eokJHT8lqcYUYZ/dIMG5QFQGC/mdOASjwGuJCRcvpHjfQSszo/dOxpXICRvDcjyl9galEora0A1rhlE6yxV2iSVXiCVXMDiMqvFe8ZCXXAFgNBwSBuIRAjR+SpCEDgeUlWFrq2QwCGFNEweWvAy57/PnSHdTRkvXc8NhJ55IVuKTdWI6lqb1JdSryW5OnpW89Kv2c2vnIsOHPkV5/TxXGMUZa17475VJJF9AU3mhX2VxwHYgDs1Z7phOaoYDwyAqGYRTjOpRO+l6hkZyBR8tcoUnExeuaEPw6le/mrvuuuuS1974xjdy00038ZM/+ZMcPXqUMAx5//vfz2tf+1oAvvCFL3Dy5EluvfXWK7qwopRht0AbtPYcW6s5ui+gtNIByAvhAe8UHVITsZtHjFNpka0NLYdXLJXVTHLpGlRWNgdaiTOx8NKkAl5UwgO89liCUjDsepkf8Jpj1w4ZLSd0Oob7Lw5Ioh79uKYXlVSl4fxuQl42Lf7+PKhM8taFFJIblwhDxdYEJlmXrIT1tRilm0AyGTTJibQT08CQhtIaLCpDUSsxQAkrQm0oXIedokNRG3ZTxcqqVGDCUGhBZa1Ic1FZMloRNjyeOIKDB2MCIxWKC14M1F7w0sNUpWU4CNgcqxllIgod+0aK/osSnEtIC0hzLxrnk5A0jQkDxenoOtZu6GJ1yNceuB+HYWL7nK4Oo5Qn0BajHJUzFFaqe72o5KVHCnIbcPJih41tTRIL7cCjmJYhadG4NTeV0LS4fG3hR6slta8BDIfDSxb5E+Gtb30rP/iDP/hFf+bEiRMcOHCA8+fPX/J6Xddsbm5+0aT3SvCud72L1dVVvuu7vuspOd5ThasZEwAmRYApApJQHmTXHqg5sh5T1YqNXU2aewY92MkTpjpiNwvZmUqrf21Uc2y1pLSGcS7JvXXSQdQKwoFBDQ1BIBuFaS6zRNdfJ7r6/Q70EovRmmPH+iwvJySx5t4LI+JwMJthaGcbylpa2cOuUP6cl7jgHKwuK7qdEVrDbgrjTHT+9+1r527gzHYzsKzbDgFoLcOTtZVWfRQqhpHBG8W06nBh2iMthebU78/v/7wUuk2ayzyOVgalhBqZxHDoYEwUQl56LiBGbde/YIm69nQTxfmxXIttCh6rI8/+FQ3omSGcb9RDfGPodDo8wejICh7NrYcfwKHZKJc5OV4l0I5RnBObiqyO2SliaqsZdSr2n6gobcCZ7YTtsTivp1VI7TQ7WcxuqptkyhAGnrS4PCrhIiZcPVzdXEGRl1IZ19pzdF/F0X1QWs32xJCXsgZ3yg5THbOVxWyN5Zm/2uQKZW2Y5LqJCbL+lYLR0NDpaKKwUdSyIhZy/QlZI71EzplEiqPHBoyWEvr9gAc3JVdIQjEhs5nm4jhimksXYtBtNzEwyTXOS67Q6w4xBnamMMk6FBUcPJCgtBRJz06HMmNUSrEhDQ1pJQO1eaXJCk0YiIFXqGqmLmQ771DWhp2pYnm1I2pBRih5UjwUk0OhYktcjSM4sL/xR6pgcxqhNdz4kgOUpWM0NGxNdTN8LZ3PKFD0kgjnoibWSDzYHUdMJzVxpHgkPE55fRerAl554CEcmtwlPOKkEm+aam9pA9I6wnkpXL7o0JjcBiRRh41tQzcRuXWlukyqaJYrBEauaZErPLm4cEUbgsFgwEte8pJLXuv1eqyurs5e/yf/5J/wEz/xE6ysrDAcDvnRH/1Rbr311itWExmnnqijUErTiR0vWDrH/sm9lL0+p5ZOsJV3KSrRsK8t7E7Fc2DQN7xgfcxhTjI2y5wK15kUAXmpmTa8wFFfEoCyhu2xaBIfWtfccCAl1JbSBlROkwSK5LihqmMu7Cg+dVdKUVg63ZBeP8FoRRxDEHiGPcW+obTpttKI89vysD24alnuFGxlMZ+7X7G1VbK+HnPtQUkStiaKex/RaKVmkqZg0Gr+kGt5wv0oJDYVG1mPz9xn2N2t6fcdB/eHs/Z5UckCnqaOonDUtaauZfPT78DqUIYIN3YVZzeEs/+1N4cEOuD8juL0eUcUKo7uV3SSmkGvYC3aIHQFF906p3aHFJVmnIakuXCc7zqzjzBY5WsP3M/+D/8nsJaz3/JDfPyhfWgFywNHN7KkpWFnKhzFlx+5wImtT1LFfeL1lxMFPZQSHeFd5O+VlzSLXP5e2fQKfAi+yK7/crG2tsba2tqX/Llbb72V7e1t7rjjDl7xilcA8IEPfADnHLfccssVnfPx4L3nXe96F//4H/9jwvDyA93VwNWMCQDbEwWBoYiE0vPC5dMc2PwcVW/IQys3cTHrk1WGCzuBPMi2HZubOSsrMS8+NOF49Xkm3VVOmsNMy5DaBmS5lLNGfejEjqpW7KaKaQbry54jBycE2lLYgNIG9GNFPwmoneHcluKOz6SzwUGlFGFk2Lcvpt8zDHuw3KtIAstmGnN+W4QNDi5bljo521nCXffB5kbB/gMdjh+SuHBmU/HwmbpR45GExFo/65xGoSKOhFe/3A3oBIaNrMvH7rJsb01Z29/lyAFJFMpK4mNZeSaTWpxLS09RagKjWF9RHN1XUVrNxR3D+S3RYX/ZdTWBdpzZNjx4RuLL2rKimzhWegXHOmdJ6glbZo3z2RKlNWyMQ8apUBY/fXqVwKxy6+EH2P8XvwPOU/+/3swH7+oTR4ZrDgYsdUp284gzm5KIvOrEDi/Y+Ah13OOe9VdwMpKH8W4uFcLtiWZjW5KGOJLByuwpoAxdLhYx4fJwNeNCWkBUKLTSRKHjxqWzHBj/LVXS45HRCXbKHlkVcGEs8yybu7C5VTEaBtywPuWofohds8rJyT7SMmjkdFWjpCVqQ3kJm7ui8nNoTXPjwQmBckyqmEkR0EscN10LELKxq7jrCyVFXs9lyY2i1xMvgX5PsTq0xIFjJ5UinNFweLVmpZOzlSd87kHD9nbF/vWIE4dlnmlzYrjndHiJbKoMjDbSqg11L0k0/SQiNhUXpj0+c69id7diOISD+8NZN6SlD+Z5K2Ywd2DuxpIr1BZ2porNXc2wB1/zYkVgNBtjONcoBh5YsfTiik5QsbyyQ6Bqdu2Qi1mfymq2p0EzQDzPFV554CEO3/77eGs5+w0/wKdOX4dWQm+OA8e0MGyO5X29/Ng2N6R/TRX2CA+8mFNRj8B4JmVIVgm1eJI1TtB6kSt8OXHh8icvLhP/5t/8G/7e3/t7vPa1r+Xv/J2/w4EDB/iv//W/ftnHVTi0r9Fu3h52SCXN7tGm1Qo0Hu0sWklLy6h55f6JBq6fSMta9IDlm3XtqMqaqrJUpaOu28qduqTluPeYGmaa2fKanx1XKeHwtjrie+H8/KtdrEK50bN2Zl23DoVzHe+Zq6KRAZu9msdyXn+JVq9WNFKpbjYI3Z7fe7n2wFeErkArt+c48/cqPGyFw4C1+KoicKUMPrayaU5Ml6pGwch5Dd6hvPyd5u1aNfvs2r+rc/PP4nJhrXvcr6cDL3zhC/m2b/s2/tk/+2d8/OMf56Mf/ShvfvOb+Yf/8B/OVANOnz7NTTfdxMc//vHZ7509e5Y777yTe++9F4C77rqLO++8k83NzUuO/4EPfIAHHniAf/pP/+nTcv1PN57KmPBo3XmFRzmL2hMX9t47sGeovdUJ9/PY0G7Cjbl0Hfkvcb+pRqMfoK4tVVlTV5a6mtOXHi/mPJ5ufnt/z39equzezzXB3eMEKGvn69Qj3UVrPXU1n7V6tC5365/yxWbu2lO1w86PjpmtHnnoCgJbYHCX/F77+ZW1JFIOaY94awlURZIowrAVYJAqZVUJI3Cmt+4vXasytDgf/HZPYikvYsIzC09HrqCZ+wG0mv7QeBM01X/X6PWrVl0MocO197vR7cDufD0+eg0/Gm080Fo4+FXlKCvb5AqWunYzYQ2jmeUkj3eclrLbKt3szUGsu9QnYC+cl3N7N48Jok7kqSs3owWZR619kWBl1il99LH3xrHACF2zpWftnblQeAJVS67AXgqnn+VHlW1zBY1vcoXQFiSRJwz8bOawtkLXrCpmYi1tHqX13F9lr2+L8/N88EqW9SIuzKH8o3slX2Hs7u4yGo341fdss75vwHJfpK9aHm1Wak6dV+yOLcujgEP7HHHgG/OOOWc+0MKbb9UC8sqQl3KjbU+kgh6FimFfKgHTHHZ25SZYW9GsDh1poTl93rGzU5MkhuFA2oW9DvQSWfyB8RjlqZ20La2bc/BFTQjSzBOFokYSBcyMvgA6CXRjWQhxKJWAdqgYxFlwZ2qIAs/asKQXlmR1yOY0oqg0k0xkFpUSRaJuPJdQaxet0R7vhVKxtetJYsWBFU8/qdlJA85siL/CykizPJDPQOhLUjFd7pQYbTm12eMLD0hwo/m8l5dDXnxtzVKS0zEFAzMmcCX9fIMo3aJKhjzcvZGtYsC4CDm3LfKt60uO1V5OZTWPbCVsjWHYg4PLJUlg2c0jNici3xYYCUTpdJd/8ppldnZ2nrCN194//59feJg4ufRninyXf/+2I1/0958sNjc3efOb38yf/MmfoLXmta99Lb/+679Ovy9ujA8++CDHjx/ngx/8IH/37/5dAN7xjnc8rmrGu971rkvajz/wAz/AQw89xEc/+tGn9JqfTWj/rv/+T7dZXe0zSOoZDa220kJ/5KJiPLGMhoaDqxIXWmjt6UYVoZZ15b3CIxKCG+Ng5ghc1bI5iBtVnrQQ/w+Afk/TS5gNt2WZ+BwM+3omYRg2GuZxKGpItVMz6WSQh5tzUm0cTyxRpBj2NWHQzjPIzyVxq4cu/iGtRGocNP4IWcA4nVOhBnFJVgVsTUOqWlR92hjTHgvmD/B2TQEz5884UuwbiQdDmms2d4WWtDKE5b4MELYD2r3YstrNMMpycnvI3z4kcxi9riZJxOV0bWhlZiAsGIUTAlXRr7aJ8x3KeMBJdZztoss4D2ZDg2tLMkxZO8257YDxVOLtvtG8cjjJRGkp3BMX/um3PXFcWMSE5ybav+tv/49t9rUxQUsXzSMx4eELmsnUMRpq9i874kDEOFr2fGDc7HntkcXRSpTXVjFORRY3ChW9jqyjcQbbOxJH1lcNywNHXmpOnXXs7FT0egHLI1E+7MbyDDWNOprMCmmmxTxXkLkHxSQTSl9gFIOurNGigqnYiYiaWTKXStbaExlHHMjw8HYaszUR74IDSwWDKGdaxVwYx+Sl0ITHUznfoKcaDxZPJ5ZZnHYzbn0rQOJEJngfDDs1u1nA+S1FbT1rS8KI8B7SUmjZkisUBMpxarvHfaccVe1n0qKjgeGGwyWjOKNjCvp6TGgLhuPTRNvnqPvLnF59GRfLJXbyhNMbAVUF+5Y8q/0S6xQXdiMmmQxyH1rKiUzNdp5wfkc6J2Ewjwk/9K2LXOFK8aR8CK4Gugn0E0cvLHFec/fpDvfel5LnNRvndinSgutfcpgTBwNGSUFsKiJdk9uI+y4OObfh6fcU+5ctSaNK4kJp12e55+KFnNFSzOF1zahb87kHA+64/SFsZXnxK44Rn4jYncK9f7vNxUe2OPHCg7zwRMKwU9MPS7pB3lT4JfhsFT3ObydMM8/KULE6qKms5p4HLff/7UUOHl3m77wyYK035dT2gFOPyNDTdcdC1gaFqKeYGqMska7pGnHWOGdWqepuU3kQN8DE1BxbEumSBzZHPHJWHtb9bkgYyPzDajcjNnWzkTIUzvDwhQ4nH5qwsprwwqOOQ91N7rHrPPRgxmQ352u+dh9HR7tYbzi51WdrohhnhnEqfMn7TtZ84bNncdbR6cVESUinM2K1M2W/Ocfp6jAff2gfcQjf1/lTNn7ndxidOMTBv/+PKcIbqZzGaBmqevCs4vN5gveeqq5x1hMEIb2opB/kWK9mQTMOxdjJlVegLfw4u3z3NO36AVZWVr6osci11177GJ7iO97xjstSLPhix32+IY48vdgyijOsM3z+wYTPfnabqqzJpgVVUXHDSw7xgsMwinP6YUqPCQUJ92wf5JFNw7DrObycEZuKJDR0YyOGQzuera2KXs9weL+hlzgeOe+582OnqMqaozfs59ChHnnuePihbXY3J1z3ooPcfL1hGBd0g5LYFFKV88J53y66nN+OmKaepaFQBcpK8/DpnHs/e5r9R1f5u9+wxMFRzqnNDqdOi7DBDSdijqwWGD1X44lNTb+JC4+YFbJCZp7KWjNVIXFgecHaFhrHgzvL3P2A+Hl0E0Mv8YSBox/XhNo1lUhHaQPObiTcd88OSysdjq4HHOhPeLAccu/9U/K04lVfu8Q1ww2sN5yeLLFVBIydIa9E1vQLDzruuuM0SiuOv2Bd5hEC2NdL6QcpJ8erfPCuPkmieMOxh5j+/36D4XVHWP+uH2G7uEbmN5on0b0nHZubEs+MqVBKsb4ec2DZk4R1IxMtdACNVAqNvbyh4kVMeG4iCj2dyDGISqxX3H2uxz33ZRRFztaFCdk054YXH+LYmmGUFOKkrXMyl3D3uRUeueAZ9hWHV2s6YY034IJGdnTiOXs2Z3k5Zn1ZcoWzGwGf+N8PYGvLV3/9dSz1NVkB99+7zZkHz3P9S4/y4us6jDoloyijb8QMzBJgvWa77LMx7jHNYGmgWOlVFLXmngc9999zkQOHl/j6V4Ss91Ie3OjzhXsLnHW88MYu68OcQEnBUysnRTi1i8Jz1uzH+f7MpBGgG5Rcs1ziUTy40efMuXmuEIXC/V/rTUlMifWGyhlKF3DyXJeHHthhda3HTUcVh/rbFPUKDzxYkE1Llr9miUO9LWofcH+xxOZYEWWGrOigFNzzkONv7jyDs2I61hvEdDpd1ju7rPkzPOKO8qnT15FEnu/K/prP/8L/l7WbDnDgh36YvHczZRgQGNkQnDwLn89kzYeBRWtFJ9Z0w4K+SamcIQpEnSiJZNOnqstXGVrEhTmesRsCo+cteWhaVo2pVRgHOOsImwQfwHpD6aByBq2k2tVq00oiLS11qXyLjfjelrrSEEYhum2pK3nNGI0OtNiOa6Ed6ObLo6h8gHUiAQhzHXTvpT2plCIIRfnIeoXzol6iNOimo+FRODy101ilRKHI6z025r6h9riZocnelp7z8//OFBKcoW42LLaR8dIKglATBBrn/Yx+JJ9rozbgtVynE0pCOwypm3ZnEBpc4yh4yd8GfwkdqUqGjE4cIlpbpVKaWJfEJiSJIkA6N7bRf3ZN/9/oSzls8/cifw/rL48rDE8NL3CBZx6iwBFqi1Yer5zQfQKNtRqlFDow87az15QuQusehRNFqziUypttNspCWZlLEmuj0HsogEGgME22GscBva7coUFo0IFBazVrz8/WNyI9WjmD83KsuHEobu/vINBESUTYlO7be7ul+PlmmN46hTfiyRIohzUa3ax/8SzxIj0Y1vPuB63vBzMdcetAO9VUJRWKRs/bzd9PEGqMkmMGxpMkcm1GC+3HoSlrLZztQOKrUn4WF1o6UgutnKgJaUccGQKjKOMBw+uOEO3fj/aWfljivPCSawtRpOh0GrM3IwPf8tnJ371o5ovabrDRnry+PObrIiY8NyH0nj3dwJaaYzRBaAijUO7tJqeonSYnprTilBuFqjEAU008aIeLFeDn+YCany9KIqy1l3xPzhc0+vnCHGipRCKjaah9QO3lfm1pe63MpjEQRUFjzNnmC+2amuc6bazx3mC1wWmD9nb23lupYq08NLRk5/UlOUMLB9Rez+Q+2+e/UhBGAUEwz2eg+VyDxkPBmyaHUDMPgRmFWSN5Wu3QTVLwKBbgTO2t7i+zdtMBeofX8U2ukJiaTtTkL05h2/xKS07VUsNn76PJWZyTXK/1R7kcLOLCHM/YDUEcyENJkmUZql1ZiSmrkDgOqErL8nJIaCqU8ozLmLSUHUASeQ6stjw8RWoNZSU6ubWVm6rbDYhj3XD0Fd1EceDoMt55lpbEVTgOFb1+RDbs0u0Gj+HTWm/YKTpMioCikiHiTiO5nFfygOt2Nfv2D+j3Q/JSsxMkFJUijvSMRlA78RqY1MK860cBJhGec7uQtfYkpqYXZM2iNU1QkGN4Jy178UPQbBER6HD20LROzFhWVxMGfYN1jmndEdOfQYRpFu2kiqmdntEcWsqOVp5eV7PvwJC6co38qwRL5xVWBQTazihHD3dv5ODf/8dUStwbV/wFwrimHBjyOqCs5Ry1Veymhjz3M4Mlj5pdgyg+NE6K+ZWYjbjH7PKfzl3/AlcHy52cfqQJVY3Whl4Cy6sditwShIaqrAkCTVpqIGYr7VBaeYAkoeXQijw4K2uorGGcBeymamaK1e0Yuh1NHAo9p98LWN0/xDnP0SMdjh+omeSGyaSHc54olmJAXgdYr6id/HdSRrN4NOp56MkadkgSvbISUZ3Yx2AUCTWhCLEOBoMA57zQknZFLCAOZb6njEyTZMui7yWW0HjWexOplPmAtE5kI+IUvY6eFQtkLSm0Cgia6wBZW4GB1X0dRiNDJyoJlGWQ1Bw5FFOUEUnkyOqYwoac39acOVezvBQw6nmS0DLsh6ysS1s9SUxjKiQbmEBVjOKcaw4GgOekOs76d/0I2luUdxwLHmIjXGeSrwKacFmzMpo7T8vnN1ck2ko7nD4vjvFaS7KUp5f38F/EhOcmkkAoOQ55dnRiMSYsS08YGsqiZnkUEJoSDYzLhKySdZpEnsNrDW3HQ1YZ8lLPcgWlFL1eSJK0GwoZCj507SrWitqO0VacxIcxo9UB/UFEoOccd+sDKh+wVQyYlCFl3ah7Nc+7staUtbibrx0YMBwK7W9SxngPw2GI957QNHELGqM0GMSGIK4JlG0KEFJ0iHRNogscmtoFeFrOv2pmk1rDL8W4iEkbCXCQfCgKYWU1YTAIgJrCiTpgfxA2BUUYVx3xgqkvLQ5qJeai6weGMjvR5BbOi89AZRKMcwy64rV0eullHPihH8YrTRn1GdmLqMiRDkOhevf0Hl8koVUGxtOURqmd0A1rKxuDoDaXrTwGi7iwF8/YDUGgfWPtLTdTYMT2PggM3kEdGzqxwjSDrmWtGadSiR92LHEosqOSmIsUZ1HNnUqDQM+Ga8QMDPqDCO/a7oIjMKIYEiUhYahn1b29Ay1ZGcycBNuqosgCzh+23W5AFEny224U2kpau0u2bv69QEsACbCzB7cGAi1SYjUG682M89iiTeDbTcGcZ+ixzfV1O5owlPOVLsB7JbbgTTWwqMV99NJBbZlJCENFtxvKwFRR75klUDhlMErUhKxTbBUDivBGYl2y4i8QlxOqJKYbDtDaExpDaCSZKmuNtfOOTvte9prJifzo5d8/sut3j3ltgWc34kCogW2HLgo9nUSLY2ZTjTJGnMcLrZlkhkkmRlyHViyDqKByRmQsG7+OvJjfG+2AXcvZjwLo9iK894z6MEoKlIrpdg1xEhIGqhliU4DcwNZppoWZSQB2YkniJTmQ95HEitFSTJLoZn6hcS4PpVporcwvKAXOafFP0NLObylEofHEgaVjcnp+l0z12PVdSts4m4bz4dtWqr+sFa7pHLRJgTFSuOjECtN0IENj6XeZxUKRDDZMU89kXJIkurkGRxxBpytJSxCoS4cx8cSmYqlTzihU28U19MOSY8FD9NIL5IMeYbAiMT7ys8RL4pmYtsWmIlA11sN4KqIOxkjHOE+vxIdgEROeawiMdKLmuYLEBHEoDogimWsxujXCM2xPRYGr37F0I9skts2Aey1eI22uEEWKMFAzEZAogOEwwlpRDWor4nFsSLriStx2BhwKixhsZXXAODN7hvbl2Vo35n5BoOj1AuJIYkpRN9r6oYKm69fORLbXGhpHHQVSOG06D+1gdagqagKsMqjHGRV1jUt7Xom0eyvbKk7J0OkY4qjtMEpsi0I9q/SX1jRmqvNjtus+jhS9fkhZOurazdZZWzyEuT/SxXKJvHczsS4Z2YvE5YS406UbVEIRNJok1DMzxmqPo7lr2AatW7Jq8oW6vvz7ZxEX5njGbgjaarwMC7fJs1h6d7tCEVgZevZ3t0lUTmkPcLbR400iJe3pWrMzlRuorGRw1jooS2kRlZVnmmtcY2oxHASiSetgZyqylwBRHNDradZ6Y/phSkdlJPWELBjwCEPGWet0KD9vFYAsamOUeB6YvcPGYsYl79OQhDWVNeykAVkhi8460d63jXtxrSCrxTcg1iXLwSbaOy52ejPzlSiUASbrRDbNOhlqigNHoCSQhU17tLKatAqZ5oo8sxSFJS8iyqb93u+ItnJRwfktUSpwztHvy2c0HivyvJ63FBuaRFrKkCdA5TSxCQnjmiqJiWzGC2rRpn6kex0P7a7KZ1G2Q96anSKmaKziWxt2aNxlr0BFy9UOV7vHvLbAsxtZFWOqDgpP7eWhmMSyyY2iGGtjDh8wHF/dIdYVZ8MRRRWKFF1pgISiKR7UVu69qvYzyT7nPHkucSMPZQ0tL8uGICvg4a0uaaGo6po4NvR7hrV+RjcsCFVNoGsKG7OTjZjmIplb1WY25G8aw0OtmrXYROD2gWabkn6/qxj1ZHOd5hKL4lBoTmgo6oC0MJS1JjJDsiAhUJZROIUQ8nqVyVSufzRQDBLpEmaFauZyxGvEaLnGMJQNwm4e4ZxicxpzbkPkSbU2xEGEdSJR2ut28R5OXwAwFKVnaUkWp9bzIczChiQ6IqtjdnPphGjtG68GxcVgP9mgj8Lz0uUHccuGs8U+Tm/3qGrRfRelohCtBsSN0MLB9eCSJCSbXt5jbBETnptoFXhausss4TaKbsdgI3HgPpBsEauMrD5MWkhMiAIp9BW1Yjed5wp5Idr0ZSmu2Vo7xqme+X/0euInUtewsWvIcihLh1ISj1Y6Ewbh9JJcobZLbE9Uo9bTXr3Qklo5Ya2ZeQ44J8/fNlcwWtMNq9mGZporAhNQJgGhVlROHIO9h2ktBlqxqRiaXbSxnI/7YiLYxJg4dM0ws1T8Wyd1EDpit6PFm6QyaBUxyTSTSUlZWrI8ICsDlJKCRxhIceXcpnRbnffzXGFiKXLbrPuGemhFLlQ2QwllFJCYGiJIOl0im3Gj/QwAp3vX89DOqohH5CLyEIWazV6XThhR1DIj1aoRtayGy8UiLszxjN0QtNP/vuHRgSSFYajodmRq/fByytGduzBFys7qiGkmfJ1urIiMGOacvWDJ2uS73VV6cM0OcHtXMQ2lRba2LDvxcQrnN0RW1HtPFIuj8RFziv74HKZIMWVKd7gOHGVj0xKGikFP1EbaboHo4srCShI1C1xVDeNxhdZipLEUpUzrmLyMuLjl0FqzrycP2NrpWQVyO49Iq4ADPcu+zXsIsl3Ora9jbYy1siFY6lZMy4CNXanm+ZFi0JFkJIk8vY5U8NJCKDk7E9jZzinzivFKzHRkiEOZ6u8GFae2e3zms1O2N6YcvmaZ48eipiIiXOsoVDMH4sKGsw2Y6A4HJFFEOTB0wwE32L9B/+nvU26POfZ//d+cVN8smu9jx+ZmSV2HBCYkiULi0DHqyja/DfLqMh1JAax77KCQfTJahQs8o7CVxqQks6pabUVdSwoBBqM9x1d3uDH9JKbMCPa9kq3JfspasTPVbE00WQ6b25aycgSBVP+A5uEvSXBtZROfJHBoXQoG5zcd9z8oVYIo0vT6IasjONI5S6/cIrAlps7JOiucVCO2duZSg0JHEmUPkKq8VDHbTphI8laVUGFWBzVHh1tkdcy9xZBJKrFi2DEEXjEtDFsTiVc70y6B6XJoOedFwedI8m224ldx4aLBWs9okNBPHGmpubAN06lj34ph0BWecxLN2/5nNw1KGS5uWh68f4eqrLFuBaNDurHnmtUpwyjlge0V/vzDEzbO7nDNDevcdEOCVnBhy7O9XWFMyLSKCHSHnSLmzKamqppZikDeyyRfIQyWecnySQ585Pept3fof+vrebB+OZMMLmxYdndL+oOQtIhIIhj1LDcezGZc8NppppPysu6dRUx4biI0Qj2pnG4cc+esgk4idNPDSxOObH4aXUzZWF9lZywLUfjtmt1Ucea8Jc9tI3U9N6sSKUvDxW153gUBrC5J4Wxn7Lm4JR2rsmgGdnuKg+FZBtkFwmKCKaYkw/0U9THOnKuIY02/JwaI7bO97e6Ljr7w4ctalIF2dkrphumYYZiSqoRxFnP+ogUMK90IZ+qZkqLILMdMg5D9/QnHpg8S5BNGgwNkmaKuLVrFJKEjKw2bu+KSvLKkiZdFEjUOxVsJkI1AFnFu07N5MaUqa0ajiKWBIQ4dS92KJKh4eKvH/fdLrnD0xArXXxMCkidsO+keWq+ofERaR2yONUUJu5EMEHciTzoM6QZDbrSfQf33/0SxPeaaf/gG7vffQl4qtnYsm5sFRREBEUkU0UvcTAWtZYS46vLX9SIuzPGM3RC0ELqIdAaiUM2G6YwWnpyuCnSZopSnqufVp7a9Zy1UpcW04r8I375tOdXNxiAKFXEo/54qRV17rPMyKBRAFHiMq9G2Fo3jukZ5h/XiB2AeJe7bVilkHrEdWmp2/o/qRrXDw9ZBXYsZiG2GItsA17YWm5FCuYaqfMwwlVbS+qtr0UJuW31A0+Jv+HyOmUaxd+355dzy+Tpp0xs/818AGcpUSj4vbebDvpZgNrisGspUVcu7y+tgthkqt8ekF3bo5xPigSUMhA7hrGzAqsZGPjDt0LcHB7ZpmV7+ffPE7oMLPHtRWgUNtc43G+8kmm94A+1JTElQTNFlRsC8d9zqU9cWyspRlfJAbTcE85+TyqBtaIrdeM65zdIKYxRRFBEEQkvQ3orvia3ky1mcl7XcUl+UgrpW2HDP0F04LxxUtvUiaIb/jSdUNaUWOb2q9rNBR63UrFLZao6XtXT9lHEzTwZr/YwLOytGVJ6idFhrGpWehu9vmiSkat5n3niuFDVl4Sgq6TBGpqanpyThkig7TUQTsRPNdcmtFTfzympqZ6jtPDZWzZ9D9vayIXLLmnp7h+LCJlE5IQzkeqz1VKWlLMxMQnXUk6qnVo5aBVTKUJvLKxQsYsJzE60WvTxb1WxQWKS89+YKObrM0fiZh5AUHFuaiad8AiU778TAtKX2xHu61VVDiwEZzjcamZFpcoV2kTqnKEuHNntm//YIgWgtx9Z6rqn/6LxUaEjN87Vy1FYovlrpRiihff7Ka84rlHdoW81yovmxmuuvPWUlJqZC//XNM3gem9rPp50/aHOFlvkQ6XqWKxR5BTDLp8JgLjbgvKb2Zlbk1YpLPEikG+FlnmB7zPTsNsN8Qtx1ZK3wiXVUlZtLNEdz9oVRLUPjSu6fRVxo8YzdEGynIS4IZ/q9h5dSblzLqZxhXCbktfDmLqy9EO0d58YD8twRx5peYlnp5AQmpqhC0nzO22tNPbxj9tAurEgCrg+LxlsgkVkFLw+wthL5ufJG4u51RP2a2FRkdczOOSNJQ0cz6kMYeMrGLVgp2Ddy9GJLUWs2x4Z8LA/8644nTTLgeWh3mbKSCprMGsDGJCYwEdNcVD0CIzq/cWgZlx3+dunr0MuOhzaHJIlr3htMi5B0psIhhj+TXCqncCmlqK6lann8uiHOSZI/TiWYDDsBiQkYJDVf89VDimpIL5H5DOth2jUUzdDkhUmXaRTRi0pefuQCzmse3F7mwbMSoMtaExrDI90TXPN//d/08wleG27J/oJssE54zVdjbUK3o+l35DOULsalfb/yMtVEQFp+1izagM81TFJFt5nVCQ1csy/lQHIR6wMmtktlA6w3PLL+VQDct3OA0xdkk3ton2fUrZgWAUkcku+xt1cK6Mm/rfPkuTwkujGsD3IZ8HddgqAvA8qJ8GzLCj67fZzAXIPRnjCWBHZrrIljRxgqeh0pCLhmM2I0rA5FAjSrDI9sGNLM0+0oDq1J8WNaGO6p1ykrze5UKpbTTHF+R5xG81JimDHiQJ5Ejrw2fFrdjA49py/E9Po1WknykRaGrJA5C+88WeHZSU2TfMPKQByad6ZiCDQaBvRetG+WAE0z6daOy4RID4iDmlfdskr6slX2jTzrw4LaaXb7EVkmj5WNcUhaGkadiled2MF7xT0Xl7j3pBNe9rImiTxn830MvvX1RKXEhW8Zfpzp+gqf7F/Hvae6xLFi0JMNSVVrLkx7M+UWB6R5xOVgEROem9hKI0odzXKFI8sp/fWc2hvSKqawzXNq/0tQ3vHIzpA0s0Shpt/xrA9zkiiitgFpZmYbBLjUeEs2uiI2sjYsm1mXmCCIJEGupVPvPdw9PUESXEMcV7NcYZwpBoOAJNaz+7m2spk3WlzRu5GjqBXbE800FyGQ6090mlkiz4M7K+Slpig92shavTiJCYxnnGrSXGLdqOfpxTXTKubu5GtQiefURo/hsJ69r7SQjoL3Hq1UQ99VTTHO04mlCFG1NKmu5roblmR2IlZMc9mEjboGGxiGnZpXvnxIXg4Z9TxLvZraKnbigDDUWOe5MEnI4oAksLz82DbOKx7cHHLybDNH2dOERvNI7wTX/MM3MMwn2KjDq9RHSddWSaIX83nTJUkUw95crnicB7P5SesURbXIFZ4MnrEbgp2Jwhsx2ejEjv3JBgcv3kUd9zjXv56deoB1hoeyQ1inOb0RkuclQaDoxzXL0S6h6VItDWbDurWd049AHqobW46icIRGs5JMCZQsGK3jS3bTk0zz1/fG1HVEGErrECRQJImYloy6liiw7GYhWamIDKz2C9aSHbaKAafO99jYrDm4P+D4/hKjHGd3Eh48Y3BeKvWBkST+wo5UAqtaEok4kt1vJ6jYyWP+5uEOaSbBp9vVaC0VvkneqPM0nY/a0hgYKfodRydyVFaRFYaikurq2kgCwOZYsbHlqK0iHRi6YcAgKth/eAejLIWNSeuI0hrGsSHvyKI7t6XROuKlRwpObH0SvGPc/2Y+nyfYxpkwNJqTuyucUt9MPLDckv0F6Xv+kO6RA1z3bUc5u3SAJJIhr0B7stIwzdWMfqUVFOXlb/2dtbhH6ZM/+v8XePZhPPUQQCcGHcLh5ByHHvwoLumxuXYj42iZcd3lb8eHGmMiOHcupz8Iuemo42jvIpOkS6AlLhTN5h1aSVJPXiou1FAU0nVYTXYIqfDLijAQWmIrYbixa/j43ziK3IrwgBHZwV7PE0eaTiLJf2Q8u5lhewwqhJVewcFkg4vlEg+c6bGxUdI/GnNglKO15+GNDqfPu6ZSJWs5zRxF2XZJpZIYalkzw6Tk4jjhU3fL0G9/UDMcmFllbpyp2byEtVIJ3U0NUaBYHjiWOiV5HTDNAzLvWRoo9o3EKPHctlAvrdXsZCGh7pKYmlceu4BRVnjBXlO4kM1OyE4iymgXtj1Ga/afqHjBxkcAuNj7NjY3azqdgJWRJHGP7HR5qH45YeD5luHHif783fT2rXLjrf+MSba/GTZ2zUZIcW7a+BA0rqvZ9PIIw4uY8NzExo6mVppOJHz2A8lFDlz8LDbqcHF4grEdULqAB/IjlLXh9IYhz0qMDuknltV4TGI61LZH3tUzQQuY04yLCjZ3pHMYh7CvM8WoVuoz2sNfV6QFfPq+gLo2RFEyG8z1XtR3OgkMu+KtM83NrHi4PsxZS3bYLvtc3OmzvWNZXzUcWxMmwNntmPse1jMmgdGKsvJc3BYZ8KqConT4WBMZTzcs2c4S7nowYZpatPYMBsHsPaWFxL62Gm6dl7wlgE7s6UaW0mqmuaGuxSDw0D4p1m2OFVu7nrpWFENNHWoGUcFXHd5FK0flQgobUjhDHIp8qbVwYVuzHUS86NCYG9K/BmDc/3o+nxmsFTWhJNQ8tLPK/f5biLuOV6mPov/iPSyvr3H9retsTfYTBdBLHFp78lIzTvUlzIuyuvz7ZxEX5njGbghaWlDbci99RBUPqMMOpY+wzpDbgGkRUjY21y2cbyf7pYLkmpZ9uMe5VI4rCjtVw5+tvUHR8GpDR6sFPG/5S2tNWpR6Rp2JTMuLbQxDtEe09puJf/ysXWcbulKg5/KB7VDjXl8E78BpqQAaMx+SqX0jc5h7sszR6WgGPd3MI7SbHuHqCu+wleNijzPiPLEODXQisW2f5powbCkJkNVye/QCj8Hh9uirt5QH7+Y0gNwGVHEf5W0ji9boL1vVaKqLzFkYaLLBOt0jBwiWl+mojMOr9UyFpbKSqOXl3ANBKciKy79/rHXoxygHPD93/c8ldDsiK9g6UloMtjvERh0K06Wwwk/dSQPyErLMUpWWuukoQkNDhFl7vXXxjUMx7/JeE4UK2/SeaxeAhkA5BkktMzNuHheclVa597K2okgD4moeBZCEjtA4glJDcw2tEojohQvPVuKCb+SG5eEOzJw+298FSYajUI6vmfue1LWjrCxBoBgN1MxHxDfrqNsxRKEmCKRD2KqctfQ8uWZxGh7EFYH27MYdokg6m3mp2TURvVjRC4SSVRJRumAWF5JYzSgGtfOUNqCOeyjvZr4HLVp6wySTLuh0fYXevlX0YERXpxxcFnWivJI5h7RQjFPfdEfkc8nSy7t3FjHhuYkolOeYbvKF2ofUcQ8bJFQ+om7m2yZ5dEmuoPbcii3tqF0LLd1FchCPUvJsbPPE2ovKllbi7O1mynwyXCv0Vw9IPDF6LiIQBZKLtM7FIOfWQEDd+BAJ3Vk6oXaWK9TWN/49ovwnFB6FbmJZHMkgsMQ3EecoSil69vsBw36j4a/nMaGTaMLQEwVq1sWEJibQPH+1XHcvFg+YvAyZBBJfqlqR1SFJUNMxQoFucwXr5L3H8Z4Cp29yhbCHwmP3yJY611ANrbAYMqNJ11ZZXl9DD0Z0dMahlWqWU4gilGzC9vocLHKFJ4dn7Ibg4HJJ1KkpGprI6ckKW+ErqL0mnwSUVrM7NZzd8FSVo6rrWTWsqAzTusOkjBmnhrwUy+9BRxLfOLAE2jEtQ1Eg0YqyhnOTPqFxdMOK9d4E6/SsIp4FutnlSlCwVgYSBz1FL5Hdaj8qCbUlLUPh/zX8eoeaBxwnO+zEiHQidGYqAkHDtVNajNMCIypBvYYSVFpR/9ieaHZ2K4rCcvhAwosO7qKU55HdARd2pKuyf8URBp7tieH8ptAf4lDTT/YkFQq6ieNgf0yoK4xewvsI62BnqtiehIx6AWbZ0jEVO0WHC7uiNhIYz9pIqgwXtjx57jh5sUO8/nK0cjxyPhEHYieVyLIWpZTdsZPNxDVfzXXfdpSOylgZP8Q3p3ews/4C/s/WSzi3qZlmjp2deqaIAFBcwSp3tcMt2oDPOdx4KCXqyJoEOJvvY3v571C7gJ3dmLwybOxqTj1SkWWWLK3Is5Ig0EyLhKybMK1jtieGrBC6zbAnD9xuVBOZmjwOqW1MGssm9uHxEqFx7OtMWR9cpPIRG+WQtIqIQ6QdbnVDKRA5zDhS9LsyBLvWnRCbCuuWuLjdxAYkeVCIdGGnY0gi4ceDPJQnY3Hq7XSMzCsENJKoiqW+XLfMAXiyKpgVNoLAcGi/4auObAJwanfEuS1DJ4bDq44wcFzcDTl11jEFBo3ZmtGeUc+RRIr9o5xrk9OEriBcvQbn+mQlnD7v+Nup4+D+hM6xChV4dsoeF6cdUVXTnkP7PFmhOb8JRek5s51wz/orADh3OsCYajZ/5L08vC9sWKz1fLJ/HTfe+s/o6pTV3Qc4uPtxdvbfxEe3X8rFbcX5ixVnTo9FYrYJZWU+vqx7ZxETnps4sloQdyVJVArO58uMk6/CesN4GoskeWY4v6UoSkeaSq6glaJ2ULiIrA7YTUVwYGXoWe5VBNoRGUuoLdMqwrqEaVPtv5j2CI2jH5YsDVMZbq9jytpgXdgMK8umoCwtUaTo9wyDjnQdh0lJoBxl3aGuW9EUj1G2yQuE3hwF0A8LjHIY3aEoPK6h+MykNx2gYdiDfkeEDKpacWESszPVZHlNWTpWlxQvPDhG4Tk37bE9EXWegyuW0Dh20pCzm3LQTqwItMMbiEMpri71ag73NglVRWxWUUrEHXammos7EUv9EL3siE3NbpFwcRLjnBRuDq6KjPLFLUeWO5KoQ3jgxWjgwm7UOBCLz0Bl5e+wtSM7kyR6Mdffuk5HZ6xMTvEN6afZ3nc9f7X5Qs5va3bHju1tcXj3TmY/i2x62ffPIi7M8YzdEIziHBXVWC964ZuTkDOVcEXbzsHWGM6dzSgKS6cb0ulIhaq0InmX102VsIBeIsZERju6QUmsKxQdkiikbAywtqdiSNZbKhmYCZUOqRs338AE8wXofTMwqGft7CRstLKbDoFr2o6PN5sire7GbbWppAGNQ6FUDHTTLejEjmFSYr1mJw1JC0WaN5XPytKNPQeDM3il2Yq6VJVsCIadmm5Ykpc98lx0gOtGGaGtgiglkqTDYJfI5WRxzLgrTqAXt2E8sXhvWBsEaGBaBuxM5UNYHkA/qXE+oK49eW7Z2NZEQW/2t2mVnPLcY61imoqakFiFJ5xdOsDh1ZpvTu+g+Mv3s/QNFhu+lJ2xZTyu2dnOxTSkUXwo88tf5NY59KPafs9X5YDnEtaiLUwSMqnEgGsri5lkXZyTtr51sLFlOXN6TJlXs5mhooioaqlalbVsBtLM00sUnVAKBL2wINYVRnn6nRClRGN7YxwQh54DvZrl9BHKsEcWyPkD45uNvMZa29zbkrzHoacTWgZBSqQK4mAAhLPWtjgKN1X5SIlwgZZ71DooCpkBECqSdB+CphvZiR2DWDYPWRVQWTXz6dBKzLwO+IfxKLaiLme9IQo8a70pvSCjqvdxbyEPz6opuhglcSw0ilGUMZo8QlClLC8vM+x1AMP9myWPnNpGqX288HCACzRZHbKxK8dYHToGSYXRARe0pqwc22PNyWgIwHgqs02tEaL3UozZ3S2pSsu9p7pMsv0cXC45uPtxiv/zl4y+Dohfynjq2NzIOfvQBeqqxjfruS4nl3XvLGLCcxPDOCOMOqSVUHd284iLNm5mAIUmvD2G8xdKirxGG904GbdCH5rKmSZXkMXZa4p7iSmJVIXRXXpJhG0kyie5IQo0ozhjZHawxgBDjIqYBMGsOFnXnrpyKC0dw07sSEJHx1QY7Qh0IhvpdmaBxn/JScVba5EZ18o1LAXpHgYBmEaNyHmPRtGJPctdyRW2piIpnuYy9Oy9Z9Dx7I/OAzCtIrYxRKFntZPSDXKcX+ZhG4hsqJNrmXcNoRvVjNgiqjLyKGHSCclKzeauZmfX4ppcwShPWobsTKUIujLwdGMRcChLz3RSs7FtOBX1RKY4o+mQSvemqkVadHOzwFnH502Xrcl+Dq1UfEP6adwn/jfLr3RY9aLZZmDzYirCJJXFWkdZXF6RABZxYS+esRuCtI4IrThutsMiLde3TbKt84yWZJCn29V0EmmX1dazm0dUVtOJhRscBp6iNhgtk/SlDsjqRos4lIrc5q5U6ftJQsf0KF3ARtohzbVw60LQSs9ajVGg6Hc8nUgW627ZAWT4VZRP5GG9kY+YlCFxBEtLIaGBaRVjlLgxryzLn8E0GsRJNOcYdiNLZCzOe7qxJjBCQlpejkQppVacqQ/iUYyzANVQBMpaY1SA0Z6VJYN1Qikq67nmcKuUcK5YJVCOSRXPFU5C1RisQN5Qh9JcC4cbqVJ6H1BbxfJIMehFJHHbXpVqRRCEMxWYwIi/Ql2H1LUXKdZIEoKd9Rew9A2WdPUaDqmUbpRwZivmdCRVV6Vlg1Rkl3//LNwHn5uYui6Rjai9VK3KSjPN5QFWlHNDmdW1nrhkKqGVdLoydDYp41lcCBrKSVoajBLli8KEM2Mvo4VTO57Kz24MB/S7Byl8wvnJYKYFHseSYFgrQgRhKF3DpNH03q4GGNWlqIU/rBWkZchFvURaRQQGeh2hIEwqkUM0GpaXE3SjpR6FiiSWjkYUeHqxbGIAOmFNHAg1bzQKiWNDVSvOq0N4FJNSHMutU+JE7jXWw8pyIEpqGtIyaOgJQm8qbcDFwbVoHOOqS6A9Uejp9UKGS12iSJHVAaHuMM4Dxql87nGoUUriwqiv6HUCOntmfnsdWF+PiSPFqOfoJ5aiCukPQsrCEMfSGa2dZmf/TYy+DrKVoxwyKcmJhE4yoMjXqMoapTVaXUGHYBETnpPIqhgXBkIVdoqiVjNn26Kae3z0+wG9nhFfoEBotbV1bBcd8krTS8TrIwwcRR1gtdBjAyUu3SCJcVHB7lS6+d2oQ8f0qFzIhbTPJJfZtyDwdDvSxRQqj5LuQCjGf7tlgvfif9DryHpPq5CLeoVJFRNHMBqJGMi0Ft+VwHhWV8LZMbWS2cJ+xxMYWUttrtBPNFEgA7pFGVJVnrJWnCvXAZiUchznFJkNZRviYdhrlYokqQeIQkcYCBX7gtuPNo5JlTTqZCJ3HoaNyII1KAXTQrM7aWlXStSQrGJl2TAcGLqJ5BlKeUY96MR6lnsIFVtLEadyJIlqBrAV2/uuZ/mVjunSEY7pCaNuwumNhAcCPVN6AiiyhVPxk8EzdkNwcRLT9TIAY520m8ZT31SjHdZ5Bn3D0YOim9+JHZ1Q+Kab44CN3YBBB/YNKyJjySox8hE5L3nbIk8q3YPzm56TJ6UC7V0fv39AVhrue9izvV0wHIasr4pEYTf2M959EtbE2rJbxjx4PiYrJBle7ssNdXE34KEsIAxl8n/fSLoC53flwZ9EjusONfzAZvMziCtWkwmBrrG+cSX2iiQo8V4x6kQkUUxeBkxz+N93D+RYsWxa2gpGXoks4omDFc4r0sKwm2qpFA5LkqBiK+3w1/f3KSvPviXFvmGNUopubPBO5iTGmSHV0nI9c0YIu9NpRK9nGPbhmvWKXliS1SG7mciPHlwu6UXlzN0ZYKeICUxIVUti028Ui/7P1kuw4Us5pFJe6O8idls8fPxl3Nk5SFmLvXwUeNLJ5S9yW1u0to95bYFnN85PB3RVB63EzGdn2gzC156isNS1YzgMuf54LPx6LQ+ZVpbwka2EMPCs9C2BcUxy8eyQLmAjGajFtCcKPRd3FA8+NBWpYt2n2H+EaWG49xRsbkpcWFs1xI3hX2DkIdlPKuKgpqgDHtzoz+7jtaG4j2+OA05d6MtmIPH0EtnIn92WoeUogONH2w21PPC7Uc0ozgm1bWiI8pmEodAMkkAGI9PCMM3h4w/tA5h1G4sKzmzFaB0TBZ5rDsh6yArN+R3RAT+wVNELCnbLDl84d4yqhuWBY7krg41rqyFK9eh0xNNhEhjObGjOncsBqKqIad/QS+CafTndsCKtQnZzoSLuG1kOLIvD8SjOm65qn2kekxcw6DWKSZXmo9svhfilHDIpN9rPENVbHLvpa0jidYpSkqHAQDaN+E+Xce8sYsJzE+fGCQOiZp0rprlmkjaCGhNLVTkGg4CDa5o4FM5/JxTTv4u7Iee3ZTOwPpJcobRGDPo8s9mAVp63Ezs2dzX33CObUOcHcGCJtAz4/IOwcTFjMIpYWw0Y9pBcIXaExtOPRHFot0w4eSGa5QqrQ7kHz22HPFiEhIEk+csDiVund0T+LAkdNx6Rgb266VQM4oqVeJdAWSovCmseRTcshI7XjenEMXkphZO/uqePVopOIoXSshYastGRqDmuVngPu1nAhV1DJ/LsHxX0woKtvMsnH1qmrEQRaV+/QIWeXiIDwUbDJA9IteHCtuLcuQzvPJNJRJJoloaK6w6UDKKSrA5mm5K1fkE3LNDN7AHAZq8LRBSlfEa9xFFZzV9tvhCrXsQxPeHF09sJJhucufZV9DvXUlaqiZWebHr5LqaLuDDHM3ZDUJSKMFZ7HubSfnOumRmoHIO+oRN7kkgWeDcsZWfvAtJM5MFC44iDWmy2rSQFbcXAaBlOlOQC8qzCOU+ai8pNWigmk4rdnYI4NuLYGcouvxvVYtGtLUaLmUmai+NuJ9aExjUDRrA7cXQ7muU+dCJHVmryUlLlTgz9qMIBlZUqZSeo6eiMQNWUxBRegZIhxNaivBuFGK3YnWo2NoRbv7oai1FY0yr1XjojSXipaUdgVOMzUGM9bO3UZGlNtyPJiAQ/aUvqho9YIUOOVWnx3lMUomDSTQydoGYQZngUu4h7YRJY+oEkCe0wZ+EMSRQ2n7sMVVVWc25TszO2dKOE2G0RXjzFcHSEYXedyiqSsOFyuiuYIVgoBzwnkZYG3Wx0nRO6SVk5XKOc46xDa0UvkQdxoD2BlmHWrUlAWkAXRdhzhNqilKGoFN5BbVrxAen+Ge1xDvK0wlrHZOrYngYy2DouGe/kxIkhDAxJ5AkDTxw48UwxlkA5cg+7qbTCzVARdl3jG2DYnTiSWKR2o9CRl9KJ9F66ap1YlHVCI93CTljTCQqMstQuoCJAI7zjQFkiXdOJhVC8M1Vs7UhRotfVdBMa+VH5HEc96ITtgGBEVTPzKDFKuqnnNz1F4YhDw3K39XqAXk8TBRLbylqRF3P99qLwZKG4nHeCml6QUTvdDDYr4sCShDWhbiiWqiYylqTpIkSNmERZKy5uK8ZTR3IiIaq3CDdOs7x8nNXhPopKE4cyJ5Vepg/BIiY8N5EXiijRyGQOjaIglJXkCmXpmjUlMaEX1fTCktwGWBcyTWWgNjKWTlCL4V0jtVnWqtHbl4270UKzm04KvHNM0z5ZZZjmmt3dgp3tjDAyBCaYKST241q0+k2N0W6WK0ymlk5sCJscp6hge1dyhVFP8oy80qR5kytEjkFUSq7ghD3RCQq6OiVwJbnqkpPgvcI0Ca7zmk4kHcJJptncklxgbTUkDmm8R4Su04kdvUBoGJM8oKwkJhjlCbVsoLZ2HFlm6XVC9vVpXJeFUaCUfF6iCOgpihrvIAhrIKDqitPyIJyiVJeskuJhZGr6RgqNHpm57IRiOgZNjqa9dD2bmYFRNyGYbKBOP8Ro33FG3SMUtaYTWmJjmbrLMyuERVzYiyvaELzjHe/gZ3/2Zy957cYbb+Tzn/88AHme89a3vpV3v/vdFEXBa17zGn7rt36L/fv3X/GFnbng6OVONHGbCfeVhgO/smQa2oso+0BrYCbOlWkBk4lw1pIoJg7DmT4t0FTy5v4C3it6Xbj2xAjfKPRsTWSxLC2F9HoBva6emZxYL1QgrTypF+OgnTRgmjry3JIVmqw0MiTUOBUHRgZwa2tmCjq6KZ8bbVFekztFUWusS0jr9ZnZRqu53VYFKqtJS5mwF9WBS5WDaitzE84pBj0DRGJWZqWtV1upRCqVsDlW5FlBXTl2dh2PNIFs0HWsDByT3HB2Q4axtFYcP94H2vkIjzGiRmR0wrQMyRsPhO0gwvpWpUXoHbVVxKFrzEwgK+WzmGaO8bjmzFbMqeNfxWh0hDQYcqi/M9tMAARVftn3j7MWu1jkVwVXMy589m8regPX8Pah24EDa0GziZWqUDf2jLq1CAgYS2RqsjrkVBZw9nxNt6spqpAwCGfJt242wa36hkjeKuIIXnDTCGulIPHA6SbJ7hl6vQGDvgwKhoFIdColncesCiiUYWMccuZczXRaYQ8ldOI5jSCJhbog8n+Gqp5rki/16pmwwU6RyAbCRoyLdo6q4RrvUTyqnSIrJJlJc8/2dolSijiOMFoS+K0d1xgsGXqxITCygenGok720IUO0GFzx3PmTNYUALpM84RODIdWSl50MGUj6/E3DwZMJpYk0Vx7TRcQGqdIDUtHsPaanUyEEGR2SYyHCoSO6ZxqaAOWUU98BtJCBAjOX6zY3MjpJAOO3vS1LK9s4JTh5WsPNYmD0D8n5nIpQ4uYcLVwdXMFy7hwDW1F1uygB6AYDWSNC214TgOxXuGcPIenUykMnI0Tkshd0hWIQz9TIWufbXEEN9y4gveeJFZs7EqxcXk5pNtdYjiQDlnUqBoWtax5SYBlVnF3LK7IaU86+SDrvpNITMiKRkGnlmsU6W1PaGqcV+R1SFEbatdlWsezTnz7Dm3zzC1rzbQQuk5ZyVyf0Aelw1pYKb46B6O+QROhmgJp2Dgpn9lJuGBiNseaaVpRlZaN7QDvkyZXsKz0LeM84MyGJi/kettcoa4lVwgDoUUZ1WNSRUyLtjMr81iiMihxt6gNvUQEDtrPvqzUbGbg9EbCmWtfxWjfcdJ4iWuSC42ypHyZ6DKlx1jEhb244g7Bi1/8Yv7iL/5ifoBgfoi3vOUt/Omf/il/9Ed/xGg04s1vfjPf933fx0c/+tErvrDTD4/p9KQKHUaGY0c7rA6lUhYZh1a+STK1KHYgCXNlNdPUs7NTUBQB3kdEoRYlj7h1wZW2UlUrpnljupHA/mU598Ud8ScIQ8XKSNGJaAzLWr1eNdtgZIVUKaeZZzKpKUtL1jOkhZ7JBXaSNsmAKSL3V9cyjAgQakvloLTSOdipFNM8wDvh3HZiP6v6t4ODLV2gqhqn4UaCrB3M2dyqKQpLsRyilZnJlgamoQ5c8ExTS1058lyqBltbJWlmGA4CDixbDva2OaOX+Ny9sLWVc+01fV5wVC5gYxywO5X3Ny2MbGIKGcyyDpw3ZIVuKh8SYJMIRt16ZooyzSXY7ezIAPHpSPPpzgGG3TUO9Xc4aE+ivKUMulQmxujLHxTybu7AvPe1pwubm5v86I/+KH/yJ3+C1prXvva1/Nqv/Rr9fv9L/q73nu/4ju/gve99L+95z3v4nu/5ntn33v/+9/PTP/3T3HXXXfR6Pd7whjfw8z//85esu2cCrlZcuPP2++j0lgjjkCiJePnXHuK6Q44oEMWPQNeNG2ZTVTMVHZOj6DGexpw+tUOchOwsJYShZmU5YH3Zz+OC9qSlZmssle+1ZcXx/RUexZ33GP72cxfpDhJuumnE6kioRf3EzuKRdU2i0chkXtj2PPzQNpPdjCBYZ3kQE5hW4UvWyiSVB3RrlhhHiuUk56h7gDzsk9kjTIqANDdsjeVnu4miG8vv704hbxKiQVcqdZOJY+PCFGM0y8vSTbQONjdLJuMCY/qsDjWhl5jYTRyTTHPvgyWbGxl5VjHdSfHOMd5OOTNIOHioz9ceucixs7dz6sAtfPjiPk7dv8ELbz7AdYekCnp6I+TiliMvFBvjkGkkNMWN7UZJzWjCwJCXmtPnYTy1HFwPuPFgRmwqLkx77EwN49Rz5vSYsw9doMjXSOI1VoervHztIQ7f/edQlfjBCJf02J1e3sN/EROuLq5WTHj45C5JTxOEmig0HL+2y/5li1GyCWg7hK1iofNqVmBLMy8D7VWAc8HMSHDQ8TO6YWA8ZaUZZzKnNOjCoeNy31zcgQubUqBYXdJNkVJYC0Z5bJOU11bNvECmqWd3t6KqLNNeQJrrmWTq3lzBeVE0dM38n1KQ6ILKh9RON0yDgEkmEqW9jnQxnBe/jvpRuUJRCsNCazlubSEv4MJGRZ7VFGsxgRGhBOvUbF7i5Fk/cy5P0xrvPefOZly4oFlaCvnaF9Yc6V7gtN7HZ+8J2LiYcfx4nxsOW5QSeuTuVCiVaRk0RVvDJFNNfhASBTIDUtU0+ZhnuS8xRWaUpNDbDhA/EGj6nWsZdY9wLLnIod3Pobwj666ShwOMujyhAVjEhb244ggSBAEHDhx4zOs7Ozv87u/+Lr//+7/Pt3zLtwDwrne9ixe+8IX81V/9Fa961auu6Dx1JTegbe2q98x4aCWtO+9UI98nr7cDrdbKZL8N51bbrdbvXtWfuWoQs4GW9mdERUh+T+u5PwC0VuOq6Uo0/+/ESbC9kVrt/L3YewzrQLnGfwCPRlr1rQmXd/Iz1jUW5l42A+0i12qPvbkH3Zy3vX5xTnTs3eiqR70H13QXrHWi660VdaOhrBoqgkIqfq1iUKtX3h7jkuM1X+3/t5VL69pOzPzv137u8h58o84ig0+VFVqU8hbjakJboPBEV0oZqq/erv/1r389Z86c4X3vex9VVfHGN76Rf/7P/zm///u//yV/91d/9Vcv6fC0+PSnP813fMd38C//5b/kP/7H/8jp06f54R/+Yay1/NIv/dLT8TaeNK5WXKiKAq1ynLWoxozPaN/I8olEYO2hthI3lPJohGPvvaeuLEFgZrLBLSR2zG9o79sBZaEdShJhqMqaurKzVrm5RMt87nHQxgVroa4sdVXPBp5hvk72YrYWmqq5sSXGVI1XgfyMrGuputlwTo0oCikGtFTI1ujQqfm69Y1PQVXWVLW/5Bra47ffryv5crb5+bIWqU8susgwyjZxVuiSbcdi72fnm8Srjd1trGp/praeqnSiaKJ8o6TSHKO51rqScxclFJWWjmFV4vMUZQzaWXR6eWoDi5hwdXE1c4W6srNnbzsP2voEaHXpvbkX1tE8e+Q+9G7+7AIPe8bW9uYPgZ4/w+raz3xCjJ6vVd90IdrnuJ/d+5KXPF7SOc9lxAPAIj5F7b2glEd5eT/t7WHtPFews+u/1E9Arr3Jh9yl52t9VKy99PNplQidm+cK7XPaG4Vu4tssV1Dzc0Br3nhp3tHGxcf+d+6BYJt855JrbHKs9hrEb6VhVHiN8g7lLIEtiXRG5C5fgWQRF+a4fH/nBvfccw+HDh3ixIkTvP71r+fkyZMA3HHHHVRVxW233Tb72Ztuuoljx45x++23X+lp8F4S1TKvKPKaNJMK1iQzZJWhqKXVlpfibqfwdExFHFjq2jPZzahrR7+nWRmKVF9RQVYKdca31by20m6lLSW24Eh1vXSNFJmYZFT1/KuopEsQGql8t0ZgSivCUFyBu7EEmclUHuKDLqwOPUkMaWaZTNxc8k9bGRDqFSz1LMOeDNPEIc2GZB7QnBNqQVFKMGo3PbPA0GyIRBNdOgy9xBEFcxmxYV+zuiJSrdm0ZLKbkef1PFg1swpAo4GuRb0pNY26ilQcaisLPwrk+IFpuzDMOL6mcRQFadW21C2zZ6PlnKgJJVGjxKA8ZdClNjFJtsnSxXtZunjvZd8/1jps0wqcfz09ygF33303733ve/md3/kdbrnlFr7hG76B3/iN3+Dd7343jzzyyBf93TvvvJNf/uVf5j/8h//wmO/95//8n7n55pv5mZ/5Ga6//nq+6Zu+iV/8xV/kN3/zNxmPL79bcjVwteJCC1tbbGWpKk9Ra0prsM40tEFDUQfkdSh8WiU+A87JfaG0ot8PGQyCRmGsXdeasvm33ZNcVFY6j3XtqYpKkubKz1r6Ra3FZ6MyTAupfrt2ENGADgzGNOomhtlsQlZIYp/EMOgqwkAJBz93whHWko3EpmKQVHQTRxyJS7oxj91UWOspK4kLrlXnauT8oDEEyiqySSFJjKKhOc2LGEkS0OvHBGFAVVQUWTFLtpyHihAXJTiviSJDlAg/WT4D08ROh20kE8PAEYc0rs1aDKRoEh6t5vKMTlM74RRr3ZiOKfDOobR0eOPQCU1oMEL1Bvg8xZ89jT//xdfY/PNZxISriasZE2Yb18KSF46saM3sZO2K1LCe035M1cz5+WZ2UKi9w77QaiVXUA1VRbp9QEO3ka74JDdMM0+WWdLUkRdC/8tLoe6lpZ79u6oVcQjDriiKBYFGG00cKbqJI4lktiAv5Bk/6nnWlhydGPLcMZlaofziCFQtXPykZtCVXKHfZaZsqGYbknm+IHkBTQHQX5L422ZD5L2fdUTaZD4KYDjQrCyLelmeVUwnxcxvRSmwXmN9E6tiTacr0sq7WcA4k7mtvblCOxPVFlrDoJFuj3xjOCnXLU7yQndq4Z0UIaCZCQktDk3WXaXsLBHluww3HmCw+dBl3zuLuDDHFW0IbrnlFn7v936P9773vfz2b/82DzzwAN/4jd/IeDzm7NmzRFHE0tLSJb+zf/9+zp49+4THLIqC3d3dS75atBWoqqwbbj7kTaWosvIAbxN0pSAyNaEWxZE8Fb37JBKzjsDIw7faI0PWLhjv2kqbJPpFKYNIVelkOKaSBLylCbUDR+1DLw49YQiqefhGgSIJLVEgmsF584Dsxo5hpxL5z0LmDeYVf08nKOhHBf2kFCWEWAYV93Yb2t1yXTeuhXuyAqk6NK7ETeVfKeHtJ6FtHJRbriL0e6JxXuQV2bSgLOpZ9cD7+TBwECjZENSeaS40q6KUgS1rJalo/RdaF8Q2sLRV1LYl6j2z2YJL35f8bhTIQCZAZWJqExHkE4ILpzAbl/fgB3C1fdyvpwO33347S0tLvPKVr5y9dtttt6G15mMf+9gT/l6apvzAD/wAv/mbv/m4lbSiKEiS5JLXOp0OeZ5zxx13PHVv4MvE1YwLWrX69R7nGn8NK9RB23BQaycbhKI2WK/QuLnZTy2zMEmi6XeljS3VNFnTshmYX0dbvbJONVU0qVrX1jfDi3Mn7qIS18yyEh6sblyIjVFoo9GmqSDqZqNRCVdXhg8bWeBKNhu103glD9lI18SmluH6QBzC2/mjvZuCtureOpMrpZqv+fersqbMS9FGb7oi7feVkgd60glkvVc1dVHO+LWyKdD4IMJh0EYRNtrFtRW5x7Ju44IkFYGWGBaGct2t1KDRMn+k92wIKmdwzGc5Wkj3VoQInNe4pIdPOvg8p7q4QbWxeVn36SImXD1czZigmqqStY66tpSldJqLSlM7WZulnRfzQGi6gXazDp73YhCYRHLvVTWUFTMKS/tcbFUP81IS/6IQ47GqcnLvN3NAbSwoKqEUV1bu/U7sSOL5hjcIFHHgiAPZEBSlXFMndow6FXHIzGnYeoXCYZQjNhWdoKITWjqxoxv72dpqn7Mw78J71+YH8wq+fN/POiPQeiS1XxKrurFsOKJIUZaWIi2weySdpSsqsSoMpVAAUkid5oq8kC7m3lyh7aS0ndY4EMPEtqAIElPKPbOf0M4otfRDmRHzXpGHA8qwiy4zgo1HCDbPXPa9uogLc1wRZejbv/3bZ/+++eabueWWW7jmmmv4wz/8QzqdzpUcaoZ3vvOdjxk+AiiLMcb6ZgLcUGSKbBpSB0KPsYHIUJWV3CxTU5LYKdOyoshCqmJMkWuyqSPEkWWaadpI8DlwlZhlZXmjShAAVVNFSx1FVuKtJk9DlJXEwYXAHjpPWwE02pOliiKrKHJLlpakE5H6zKYBeWoJlCGdOHxUk00jikym4NNpyCTZ3bOIFWkdkabxbGfcBqGsaAaDZt0AT55a8jRHa02exkTGkBeeIisoi4o8daQThw0caWUkWWnUWURBxVIWY6qioswdRVaTRyHpxDFhzHSqyFNDkRXkaUyWyGLPM0+eOQJtSCcWqoq0CMmmMvBN7XGlBLEs11RWKA6qto0agaYoFVkhDsRlPqXIIJ0YQlcQVDlGj4lcgZlMMGnGOG1Uix7d930cVMXuY9p+thZZ2b2bToA4jonj+DLv2Mfi7NmzrK+vX/JaEASsrKx80QfcW97yFr7u676O7/7u737c77/mNa/hV3/1V/mDP/gD/sE/+AecPXuWn/u5nwPgzJnLD3hPN65mXKirKUppqX4HNUW2SzrVWOMJbU5lKkobkpZiUjRxE+Jgh4mT+7gqxpS5I089xjea942almoegmWlyFOh4WSRJo0rGcDLvPx+YcnThCzWVAZc7NCIpvjMdbTp6uWposzHVEVGkXVIpwFaQzZVpKknChUhUAWQpVBkFa7STCcV42BCZSomdEnrgjRPyKYBZT0XRqgt5GmzFgMFThxYi6yizCcYo8hTRZYo8pTmWsYUWY90GuICNxuwzEo9i31lnlGVE2xVUhWGMhcfkMlkzO40ZZKMKbKYMh+Tp4a0MSHKmzgYmoB0qtC1JS0MWWNoGCuPsZa81uSpIk8t2TRgOimpjSXNI7KpIWuutS4nlPmYbBqRGsvEyPl1muGznCovGOcSS79UXFjEhKuHq50r+CZRdNaQp5p0YggCed4E2lNYTdao9UxNycRNmFYVeVpS5mOKzJJNK7SVhD4vG+puBTZscoViniu4Rt47Tx35NMfbYJYrzNUL5zQepYBaNsfZVFNkpTxr05p0IjN8kis4tNfkE4uKKrJpQpGJ8k860UzMGIdhYisqF5BWEWkezFyaRQVJkWXCx28pOSDP+iIrUFqRpyWRVuR5kyvklVzLVFMbTz2jOsnMo21zhXxMVRSUuaWISvIoZDqx7PoJ09yQp3FzrJAsbAaCUyQ+KU06sfhonitYJ/mcqiy1E2n0uolvrmqUyypNWdHkClPKYkyRGbJpyNSVmCjFqInQhKYpQZoxngplaJErXGFc8F8mXvnKV/q3ve1t/v3vf78H/NbW1iXfP3bsmP+VX/mVJ/z9PM/9zs7O7Otzn/ucBxZfi68n/Dp16tQT3k9ZlvkDBw484e/2+/3HvPb2t7/9cY/1kz/5k1/yWu6++27/8z//8/4FL3jBY35/bW3N/9Zv/dbjHvuP//iP/fXXX+/H4/HsNcC/5z3vueTnfvmXf9kPh0NvjPHdbte/853v9IB/97vf/YSfwTMBi7iw+LraX08UFxYx4ZmBRUxYfF3tr0WucGVx4cuSJZhMJtx33338o3/0j3jFK15BGIa8//3v57WvfS0AX/jCFzh58iS33nrrEx7j0Tuufr/PqVOn8N5z7NgxTp06xXA4/HIu83Gxu7vL0aNHn7bjL8719Jzrc5/7HIcOHXrCn0uShAceeICyfHwdYu8vlWgFnnDH/9a3vpUf/MEf/KLXdeLECQ4cOMD58+cveb2uazY3Nx+3vQfwgQ98gPvuu+8xbfPXvva1fOM3fiMf+tCHAPiJn/gJ3vKWt3DmzBmWl5d58MEH+amf+ilOnDjxRa/rK4mnKy587nOf40UvetHTer9drXv6ub5Or+a5Tp48iVLqCePCIiZ85bHIFRbnWuQKz4K4cNlbB+/9W9/6Vv+hD33IP/DAA/6jH/2ov+222/y+ffv8+fPnvffe//AP/7A/duyY/8AHPuA/+clP+ltvvdXfeuutV3KKGXZ2djzgd3Z2ntTvf6WPvzjXs/dcV4q2UvXJT35y9tqf/dmfeaWUP3369OP+zpkzZ/xdd911yRfgf+3Xfs3ff//9T3iun/7pn/ZHjx71dV0/5e/jyeJqxYWrcQ9crfvsubp2nqvnulIsYsIiV1ic67l/rivFMz0uXNGG4HWve50/ePCgj6LIHz582L/uda/z99577+z7WZb5H/mRH/HLy8u+2+367/3e7/Vnzpy5klPMsFjki3N9pc71ZPBt3/Zt/uUvf7n/2Mc+5j/ykY/4G264wX//93//7PsPP/ywv/HGG/3HPvaxJzwGj9MG/MVf/EX/mc98xn/2s5/1P/dzP+fDMHzMz3ylcbXiwmJDsDjXV+pcTwaLmLDIFRbnem6f68ngmRwXvuwZgqcLi0W+ONdX6lxPBhsbG/77v//7fb/f98Ph0L/xjW+8hPP3wAMPeMB/8IMffMJjPN4i/+Zv/mY/Go18kiT+lltu8f/zf/7Pp+kdPPOx2BAszvWVOteTwSImXB0scoXFub5S53oyeCbHhWeWteEexHHM29/+9i9rovsrefzFuZ6953oyWFlZ+aLGItdee+2XVDx4vO9/4AMf+LKv7bmCq3EPXK377Lm6dp6r53oyWMSEq4NFrrA411fqXE8Gz+S4oPyXOvMCCyywwAILLLDAAgss8JzFFTsVL7DAAgsssMACCyywwALPHSw2BAsssMACCyywwAILLPA8xmJDsMACCyywwAILLLDAAs9jLDYECyywwAILLLDAAgss8DzGM25D8Jd/+Zf8/b//9zl06BBKKf7bf/tvT+vxvPf8zM/8DAcPHqTT6XDbbbdxzz33XPF53vnOd/I1X/M1DAYD1tfX+Z7v+R6+8IUvXPIzeZ7zpje9idXVVfr9Pq997Ws5d+7cFZ/rt3/7t7n55psZDocMh0NuvfVW/tf/+l9P+XkeD7/wC7+AUoof//Eff8rP9453vAOl1CVfN91001N+ngWeXXi2xgR4fsSFpzMmwCIuLPD4eLbGhedDTIBFrvBsxDNuQzCdTnnZy17Gb/7mb16V4/3iL/4iv/7rv86/+3f/jo997GP0ej1e85rXkOf5FZ3nwx/+MG9605v4q7/6K973vvdRVRXf+q3fynQ6nf3MW97yFv7kT/6EP/qjP+LDH/4wjzzyCN/3fd93xe/pyJEj/MIv/AJ33HEHn/zkJ/mWb/kWvvu7v5u/+Zu/eUrP82h84hOf4N//+3/PzTfffMnrT+X5XvziF3PmzJnZ10c+8pGn5TwLPHvwbI0J8NyPC1cjJsAiLizwWDxb48JzPSbAIld41uKKnQuuIngc84Wn8njOOX/gwAH/r//1v569tr297eM49n/wB3/wZZ3r/PnzHvAf/vCHZ8cNw9D/0R/90exn7r77bg/422+//cs6l/feLy8v+9/5nd952s4zHo/9DTfc4N/3vvf5b/qmb/I/9mM/5r1/at/X29/+dv+yl73scb/3dH9+Czw78GyOCd4/t+LC1YgJ3i/iwgJfGs/muPBcigneL3KFZzOecR2Cq4kHHniAs2fPctttt81eG41G3HLLLdx+++1f1rF3dnYAMaEAuOOOO6iq6pJz3XTTTRw7duzLOpe1lne/+91Mp1NuvfXWp+08b3rTm/jO7/zOS44LT/37uueeezh06BAnTpzg9a9/PSdPnnxazrPAAo+HpzMmwHMrLlytmACLuLDAVxaLXOHyscgVnr14xjoVXw2cPXsWgP3791/y+v79+2ffezJwzvHjP/7jfP3Xfz0veclLZueKooilpaWn5Fx33XUXt956K3me0+/3ec973sOLXvQi7rzzzqf0PADvfve7+eu//ms+8YlPPOZ7T+X7uuWWW/i93/s9brzxRs6cOcPP/uzP8o3f+I189rOffco/vwUWeDw8XTEBnltx4WrFBFjEhQW+8ljkCpeHRa7w7MbzekPwdOFNb3oTn/3sZy/htD3VuPHGG7nzzjvZ2dnhv/yX/8Ib3vAGPvzhDz/l5zl16hQ/9mM/xvve9z6SJHnKj78X3/7t3z77980338wtt9zCNddcwx/+4R/S6XSe1nMvsMDTjedKXLiaMQEWcWGB5y6eKzEBFrnCcwHPa8rQgQMHAB4zfX7u3LnZ964Ub37zm/kf/+N/8MEPfpAjR45ccq6yLNne3n5KzhVFEddffz2veMUreOc738nLXvYyfu3Xfu0pP88dd9zB+fPn+eqv/mqCICAIAj784Q/z67/+6wRBwP79+5/S8+3F0tISL3jBC7j33nuf8ve1wAKPh6cjJsBzKy58JWMCLOLCAlcfi1zhS2ORKzz78bzeEBw/fpwDBw7w/ve/f/ba7u4uH/vYx7j11luv6Fjee9785jfznve8hw984AMcP378ku+/4hWvIAzDS871hS98gZMnT17xuR4PzjmKonjKz/PqV7+au+66izvvvHP29cpXvpLXv/71s38/Xe9rMplw3333cfDgwaf981tgAXhqYwI8N+PCVzImwCIuLHD1scgVvjQWucJzAF/hoebHYDwe+0996lP+U5/6lAf8r/zKr/hPfepT/qGHHnpajvcLv/ALfmlpyf/xH/+x/8xnPuO/+7u/2x8/ftxnWXZF5/kX/+Jf+NFo5D/0oQ/5M2fOzL7SNJ39zA//8A/7Y8eO+Q984AP+k5/8pL/11lv9rbfeesXv6W1ve5v/8Ic/7B944AH/mc98xr/tbW/zSin/53/+50/peZ4Ie5UDnsrzvfWtb/Uf+tCH/AMPPOA/+tGP+ttuu83v27fPnz9//ik9zwLPLjxbY4L3z5+48HTFBO8XcWGBx8ezNS48X2KC94tc4dmGZ9yG4IMf/KAHHvP1hje84Wk5nnPO//RP/7Tfv3+/j+PYv/rVr/Zf+MIXrvg8j3cOwL/rXe+a/UyWZf5HfuRH/PLysu92u/57v/d7/ZkzZ674XD/0Qz/kr7nmGh9FkV9bW/OvfvWrZwv8qTzPE+HRi/ypOt/rXvc6f/DgQR9FkT98+LB/3ete5++9996n/DwLPLvwbI0J3j9/4sLTFRO8X8SFBR4fz9a48HyJCd4vcoVnG5T33j+VHYcFFlhggQUWWGCBBRZY4NmD5/UMwQILLLDAAgsssMACCzzfsdgQLLDAAgsssMACCyywwPMYiw3BAgsssMACCyywwAILPI+x2BAssMACCyywwAILLLDA8xiLDcECCyywwAILLLDAAgs8j7HYECywwAILLLDAAgsssMDzGIsNwQILLLDAAgsssMACCzyPsdgQLLDAAgsssMACCyywwPMYiw3BAgsssMACCyywwAILPI+x2BAssMACCyywwAILLLDA8xiLDcECCyywwAILLLDAAgs8j7HYECywwAILLLDAAgsssMDzGP9/5Fkw3Yin9voAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphics.plot_state_prob_and_covariance(init_stateP, TP, state_means, \n", " state_FC, cmap='coolwarm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting individual traits from an HMM in split data \n", "When separately training and testing the predictive models on an HMM, you can use the functions `train_pred` and `test_pred` from the `prediction` module. The main difference to the full function with built-in CV (`predict_phenotype`) is that you need to assign additional outputs from the training step and pass them on to the test function. By default, the functions will standardise the features (when using the summary metrics approach) or center the kernel (when using the Fisher kernel approach). To keep the separation between training and test set, we fit these estimators only in the training set and then pass them on to the test function. The additional outputs depend on whether or not your routine involves deconfounding:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Separate training and testing with deconfounding \n", "When using deconfounding, we also estimate the parameters for deconfounding only on the training set. We need to assign them as output to the function `train_pred` and later pass them to `test_pred`. We here use a similar routine to the one in [*Predicting from an HMM*](./Prediction_tutorial.ipynb), so you can compare the syntax. \n", "\n", "We first specify the options and then train the predictive model using only the training set:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "np.random.seed(123)\n", "\n", "options = {}\n", "# general options:\n", "options['optim_hyperparam'] = 'GridSearchCV'\n", "options['nfolds'] = 10 # number of folds for inner CV loops\n", "options['group_structure'] = twins_train # group structure that inner CV should take into account, in this case the family relations. This option makes sure that related subjects are never split across CV folds\n", "# when not using group/family structure, simply don't specify this option\n", "options['confounds'] = confounds_train # confounding variables (here sex and head motion)\n", "# when not using confounds, simply don't specify this option\n", "\n", "# Fisher kernel options:\n", "options['shape'] = 'linear'\n", "options['incl_Mu'] = True # include state means\n", "options['incl_Sigma'] = True # include state covariances\n", "# the initial state probabilities and the transition probabilities\n", "# are used by default, but can be excluded by setting options['incl_Pi']\n", "# and options['incl_P'], respectively, to False\n", "\n", "# train the model and assign outputs:\n", "# model_train: the trained model\n", "# scaler_x: the feature_standardiser or kernel_centerer\n", "# CinterceptY, CbetaY: the estimates of the deconfounding\n", "model_train, scaler_x, CinterceptY, CbetaY, behavD = prediction.train_pred(hmm, ts_train, target_train, T_train, predictor='Fisherkernel', estimator='KernelRidge', options=options)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using the Fisher kernel, you need to pass the time series, session indices, target variable, and confounds for **both the training and test data** to `test_pred`, and then specify the indices of the examples that were used for training. This is necessary in order to construct the kernel (similarity between the examples in the test set to each of the examples in the training set)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "ts_all = np.r_[ts_train, ts_test]\n", "T_all = np.r_[T_train, T_test]\n", "target_all = np.r_[target_train, target_test]\n", "confounds_all = np.r_[confounds_train, confounds_test]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "options = {}\n", "options['confounds'] = confounds_all\n", "results = prediction.test_pred(hmm, ts_all, T_all, model_train, scaler_x, CbetaY=CbetaY, CinterceptY=CinterceptY, behav=target_all, train_indices=train_indices, options=options)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of predicting Y in the test set using the Fisher kernel approach is rho=0.78\n" ] } ], "source": [ "print(f'The accuracy of predicting Y in the test set using the Fisher kernel approach is rho={format(results[\"corr_deconf\"], \".2f\")}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can interrogate the results by looking at the deconfounded version of the predicted variable in the `results` dict:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(target_test, results['behav_predD'], s=5, color='tab:blue')\n", "minval = min((min(target_test), min(results['behav_predD'])))\n", "maxval = max((max(target_test), max(results['behav_predD'])))\n", "plt.xlim((minval-1),(maxval+1))\n", "plt.ylim((minval-1),(maxval+1))\n", "plt.gca().set_xlabel('True Y')\n", "plt.gca().set_ylabel('Predicted Y')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In contrast, when you use summary metrics for the prediction, while the syntax for training the model is the same as above, you should only pass the test set to the `test_pred` function. You may need to adjust your session indices if you have split the time series, as we did above. In our case, that means we need to reset the first index of the session indices of the test set to 0:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "T_test_adj = T_test-T_train[-1,1]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# train the predictive model using summary metrics\n", "np.random.seed(123)\n", "options = {}\n", "# general options:\n", "options['optim_hyperparam'] = 'GridSearchCV'\n", "options['nfolds'] = 10 # number of folds for inner CV loops\n", "options['group_structure'] = twins_train # group structure that inner CV should take into account, in this case the family relations. This option makes sure that related subjects are never split across CV folds\n", "# when not using group/family structure, simply don't specify this option\n", "options['confounds'] = confounds_train\n", "model_train_summ, scaler_x, CinterceptY, CbetaY, behavD = prediction.train_pred(hmm, ts_train, target_train, T_train, predictor='summary_metrics', options=options)\n", "\n", "# test the model only on the test set:\n", "options = {}\n", "options['confounds'] = confounds_test\n", "results = prediction.test_pred(hmm, ts_test, T_test_adj, model_train_summ, scaler_x, predictor='summary_metrics', CbetaY=CbetaY, CinterceptY=CinterceptY, behav=target_test, train_indices=None, options=options)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of predicting Y in the test set using the summary metrics approach is rho=0.62\n" ] } ], "source": [ "print(f'The accuracy of predicting Y in the test set using the summary metrics approach is rho={format(results[\"corr_deconf\"], \".2f\")}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Separate training and testing without deconfounding \n", "\n", "When not using deconfounding, the functions will by default standardise the target variable. In this case, you will need to assign the estimator `scaler_y` as output (instead of the deconfounding estimates `CinterceptY` and `CbetaY`)." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# train the model without deconfounding\n", "# (remove options['confounds'] or set them to None)\n", "np.random.seed(123)\n", "options = {}\n", "# general options:\n", "options['optim_hyperparam'] = 'GridSearchCV'\n", "options['nfolds'] = 10 # number of folds for inner & outer CV loops\n", "options['group_structure'] = twins_train # group structure that CV should take into account, in this case the family relations. This option makes sure that related subjects are never split across CV folds\n", "# Fisher kernel options:\n", "options['shape'] = 'linear'\n", "options['incl_Mu'] = True # include state means\n", "options['incl_Sigma'] = True # include state covariances\n", "model_train, scaler_x, scaler_y = prediction.train_pred(hmm, ts_train, target_train, T_train, predictor='Fisherkernel', estimator='KernelRidge', options=options)\n", "\n", "# test the model: pass the model, scaler_x and scaler_y on to test_pred\n", "# remember that we here need to feed both the training and test data as input since we are using the Fisher kernel\n", "\n", "options = {}\n", "results = prediction.test_pred(hmm, ts_all, T_all, model_train, scaler_x, scaler_y=scaler_y, behav=target_all, train_indices=train_indices, options=options)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of predicting Y in the test set using the Fisher kernel approach without deconfounding is rho=0.83\n" ] } ], "source": [ "print(f'The accuracy of predicting Y in the test set using the Fisher kernel approach without deconfounding is rho={format(results[\"corr\"], \".2f\")}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same with the summary metrics approach. Remember that we here only pass the test set on to `test_pred`:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# when not using deconfounding, leave out options['confounds'] and pass the scaler_y from train_pred to test_pred\n", "# train the predictive model using summary metrics\n", "np.random.seed(123)\n", "options = {}\n", "# general options:\n", "options['optim_hyperparam'] = 'GridSearchCV'\n", "options['nfolds'] = 10 # number of folds for inner CV loops\n", "options['group_structure'] = twins_train # group structure that inner CV should take into account, in this case the family relations. This option makes sure that related subjects are never split across CV folds\n", "# when not using group/family structure, simply don't specify this option\n", "model_train_summ, scaler_x, scaler_y = prediction.train_pred(hmm, ts_train, target_train, T_train, predictor='summary_metrics', options=options)\n", "\n", "# test the model only on the test set:\n", "options = {}\n", "results = prediction.test_pred(hmm, ts_test, T_test_adj, model_train_summ, scaler_x, predictor='summary_metrics', scaler_y=scaler_y, behav=target_test, train_indices=None, options=options)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of predicting Y in the test set using the summary metrics approach without deconfounding is rho=0.61\n" ] } ], "source": [ "print(f'The accuracy of predicting Y in the test set using the summary metrics approach without deconfounding is rho={format(results[\"corr\"], \".2f\")}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classifying from an HMM in split data \n", "\n", "Analogous to `train_pred`, and `test_pred`, the functions `train_classif` and `test_classif` allow classifying from an HMM. We have simulated timecourses for two groups of subjects, where the first 50 subjects belong to group 1 and the second 50 subjects belong to group 2. As in the regression problem above, we have simulated the effect to be in one of the state means (but making sure that the difference is small enough that the state does not occur exclusively in one of the groups). As opposed to the simulated data above, there is no additional between-subject noise, so the group difference should be fairly obvious. \n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|████████████████████████████████████| 109M/109M [00:06<00:00, 16.3Mbytes/s]\n", "usage: osf fetch [-h] [-f] [-U] remote [local]\n", "osf fetch: error: Local file ./data_prediction/T_forpred.csv already exists, not overwriting.\n", "usage: osf fetch [-h] [-f] [-U] remote [local]\n", "osf fetch: error: Local file ./data_prediction/Y_forpred.csv already exists, not overwriting.\n", "usage: osf fetch [-h] [-f] [-U] remote [local]\n", "osf fetch: error: Local file ./data_prediction/confounds_forpred.csv already exists, not overwriting.\n", "usage: osf fetch [-h] [-f] [-U] remote [local]\n", "osf fetch: error: Local file ./data_prediction/family_forpred.csv already exists, not overwriting.\n", "100%|████████████████████████████████████| 408k/408k [00:00<00:00, 5.60Mbytes/s]\n" ] } ], "source": [ "! osf -p 8qcyj fetch Prediction/tc_forclass.csv ./data_prediction/tc_forclass.csv\n", "! osf -p 8qcyj fetch Prediction/T_forpred.csv ./data_prediction/T_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/Y_forpred.csv ./data_prediction/Y_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/confounds_forpred.csv ./data_prediction/confounds_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/family_forpred.csv ./data_prediction/family_forpred.csv\n", "! osf -p 8qcyj fetch Prediction/hmm_class.pkl ./data_prediction/hmm_class.pkl" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "data_c = pd.read_csv('./data_prediction/tc_forclass.csv', header=None).to_numpy()\n", "twins = pd.read_csv('./data_prediction/family_forpred.csv', header=None).to_numpy()\n", "group = np.ones(100)\n", "group[50:100] = 2 # create classes: the first 50 subjects are group 1, the second 50 subjects are group 2\n", "T = np.repeat(1200,100) # we have simulated 100 subjects with 1200 timepoints each\n", "T_tc = glhmm.auxiliary.make_indices_from_T(T)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "data_c_preproc,_ = preproc.preprocess_data(data_c, T_tc)\n", "del data_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we now have a structure where the first 50 subjects are group 1 and the second 50 subjects are group 2 (i.e., the classes we are trying to recover), we will use a different split to above:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "train_indices = np.r_[np.arange(30), np.arange(50, 80)]\n", "test_indices = np.r_[np.arange(30, 50), np.arange(80, 100)]\n", "\n", "# separate time series for training and test set\n", "T_train = T_tc[train_indices,:]\n", "T_test = T_tc[test_indices,:]\n", "# to get time series indices, use aux functions:\n", "ts_train = glhmm.auxiliary.slice_matrix(data_c_preproc, T_train)\n", "ts_test = glhmm.auxiliary.slice_matrix(data_c_preproc, T_test)\n", "\n", "# separate target variables for training and test set\n", "target_train = group[train_indices]\n", "target_test = group[test_indices]\n", "\n", "# separate group structure for training and test set \n", "twins_train = twins[train_indices.reshape(-1,1), train_indices]\n", "twins_test = twins[test_indices.reshape(-1,1), test_indices]\n", "\n", "# for this purpose we will not need the full dataset anymore, so delete it to save memory:\n", "# del data_c_preproc, group, twins, T_tc" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "T_train_adj = T_tc[np.arange(60),:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we can either train a separate HMM only on the training subjects or load the pre-trained HMM (fit to all subjects)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "np.random.seed(123)\n", "T_train_adj = T_tc[np.arange(60),:] # adjust T_train so timepoints are in reference to the training set (rather than the full dataset)\n", "hmm_c = glhmm.glhmm(model_beta='no', K=6, covtype='full')\n", "hmm_c.train(X=None, Y=ts_train, indices=T_train_adj)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# to load pre-trained HMM instead:\n", "# hmm_c = io.load_hmm('./data_prediction/hmm_class.pkl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train a classifier that we want to test on a separate test set later, we need to specify the options for the kernel and classification, and then save both the model and the scaler (feature normaliser or kernel centerer) as output. Estimating the scaler only on the training set and then passing it on at the testing stage avoids leakage of information between the training and test set." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "np.random.seed(123)\n", "\n", "options = {}\n", "# general options:\n", "options['nfolds'] = 10 # number of folds for inner & outer CV loops\n", "options['group_structure'] = twins_train # group structure that CV should take into account, in this case the family relations. This option makes sure that related subjects are never split across CV folds\n", "# optional outputs:\n", "options['return_scores'] = True\n", "options['return_models'] = True\n", "options['return_hyperparams'] = True\n", "options['return_prob'] = True # for classification, you can optionally also return the probabilities for each class, but note that this will take longer\n", "# Fisher kernel options:\n", "options['shape'] = 'linear'\n", "options['incl_Mu'] = True # include state means\n", "options['incl_Sigma'] = True # include state covariances\n", "\n", "# use HMM trained/loaded above and standardised timeseries to classify sex using Fisher kernel method:\n", "classif_train, scaler_x = prediction.train_classif(hmm_c, ts_train, target_train, T_train_adj, predictor='Fisherkernel', estimator='SVM', options=options)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As explained above, when using the Fisher kernel, you need to pass the time series, session indices, target variable, and confounds for **both the training and test data** to `test_classif`, and then specify the indices of the examples that were used for training. This is necessary in order to construct the kernel (similarity between the examples in the test set to each of the examples in the training set). When using the summary metrics approach, you can pass only the test set to `test_classif` and set `train_indices` to `None`." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "ts_all = np.r_[ts_train, ts_test]\n", "T_all = np.r_[T_train, T_test]\n", "target_all = np.r_[target_train, target_test]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then test our classifier in the test set by providing the trained model and scaler:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "options = {}\n", "results_classif = prediction.test_classif(hmm_c, ts_all, T_all, classif_train, scaler_x, behav=target_all, train_indices=train_indices, options=options)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of classifying Y in the test set using the Fisher kernel approach is 0.8\n" ] } ], "source": [ "print(f'The accuracy of classifying Y in the test set using the Fisher kernel approach is {results_classif[\"acc\"]}')" ] } ], "metadata": { "kernelspec": { "display_name": "glhmm", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "nbsphinx": { "allow_errors": true } }, "nbformat": 4, "nbformat_minor": 2 }