From b0f57a777c6bedaa5223cd66ba7a39852802ed80 Mon Sep 17 00:00:00 2001 From: amandalin047 Date: Fri, 15 May 2026 06:10:23 +0800 Subject: [PATCH 1/6] added machine-learning-with-nilearn.ipynb --- machine-learning-with-nilearn.ipynb | 8602 ++++++--------------------- 1 file changed, 1766 insertions(+), 6836 deletions(-) diff --git a/machine-learning-with-nilearn.ipynb b/machine-learning-with-nilearn.ipynb index ef685fe..0d977ef 100644 --- a/machine-learning-with-nilearn.ipynb +++ b/machine-learning-with-nilearn.ipynb @@ -1,21 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0bfaba8c", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "# Let's keep our notebook clean, so it's a little more readable!\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, { "cell_type": "markdown", "id": "5884385c", @@ -32,6578 +16,718 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 154, + "id": "23e55e22", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from nilearn import datasets\n", + "# from nilearn.input_data import NiftiLabelsMasker <- deprecated in version 0.9\n", + "from nilearn.maskers import NiftiLabelsMasker\n", + "from nilearn.connectome import ConnectivityMeasure\n", + "\n", + "from sklearn.model_selection import (train_test_split,\n", + " StratifiedKFold,\n", + " cross_validate)\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import (accuracy_score,\n", + " precision_score,\n", + " recall_score,\n", + " f1_score,\n", + " classification_report,\n", + " confusion_matrix)\n", + "\n", + "from IPython.display import clear_output" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "7df94dab", + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = \"/Users/jowanglin/brainhack-ntu/machine_learning/nilearn_data\"\n", + "RANDOM_STATE = 123" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "1d0a5067", "metadata": { "tags": [ "hide-output" ] }, + "outputs": [], + "source": [ + "# fetch the data\n", + "development_dataset = datasets.fetch_development_fmri(data_dir=DATA_DIR,\n", + " reduce_confounds=False)\n", + "\n", + "data = development_dataset.func\n", + "confounds = development_dataset.confounds\n", + "\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "id": "c1ab79cd", + "metadata": {}, + "source": [ + "How many individual subjects do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8f15348b", + "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
[fetch_development_fmri] Dataset found in nilearn_data/development_fmri\n",
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\n" - ], - "text/plain": [ - "\u001b[1;34m[\u001b[0m\u001b[34mfetch_development_fmri\u001b[0m\u001b[1;34m]\u001b[0m Dataset found in nilearn_data/development_fmri\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "length of data = 155\n" + ] + } + ], + "source": [ + "print(f'length of data = {len(data)}')" + ] + }, + { + "cell_type": "markdown", + "id": "432ee641", + "metadata": {}, + "source": [ + "### Get Y (our target) and assess its distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0871ebe4", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "
[fetch_development_fmri] Dataset found in nilearn_data/development_fmri/development_fmri\n",
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participant_idAgeAgeGroupChild_AdultGenderHandedness
154sub-pixar15526.00AdultadultMR
122sub-pixar12327.06AdultadultFR
123sub-pixar12433.44AdultadultMR
124sub-pixar12531.00AdultadultMR
125sub-pixar12619.00AdultadultFR
\n", + "
" ], "text/plain": [ - "\u001b[1;34m[\u001b[0m\u001b[34mfetch_development_fmri\u001b[0m\u001b[1;34m]\u001b[0m Dataset found in nilearn_data/development_fmri/development_fmri\n" + " participant_id Age AgeGroup Child_Adult Gender Handedness\n", + "154 sub-pixar155 26.00 Adult adult M R\n", + "122 sub-pixar123 27.06 Adult adult F R\n", + "123 sub-pixar124 33.44 Adult adult M R\n", + "124 sub-pixar125 31.00 Adult adult M R\n", + "125 sub-pixar126 19.00 Adult adult F R" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# Let's load the phenotype data\n", + "\n", + "pheno = pd.DataFrame(development_dataset.phenotypic)\n", + "display(pheno.head())" + ] + }, + { + "cell_type": "markdown", + "id": "859c97bf", + "metadata": {}, + "source": [ + "Looks like there is a column labeling children and adults. Let's capture it in a variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f2e0e48", + "metadata": {}, + "outputs": [ { - "data": { - "text/html": [ - "
[fetch_development_fmri] Dataset found in nilearn_data/development_fmri/development_fmri\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1;34m[\u001b[0m\u001b[34mfetch_development_fmri\u001b[0m\u001b[1;34m]\u001b[0m Dataset found in nilearn_data/development_fmri/development_fmri\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['adult', 'child']\n", + "Length: 2, dtype: str\n" + ] + } + ], + "source": [ + "y_ageclass = pheno['Child_Adult'] # equivalent to pheno.Child_Adult (see below)\n", + " # true for Pandas DataFrames; not true in general for dict-like objects\n", + "print(y_ageclass.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c0e6baf8", + "metadata": {}, + "outputs": [ { - "data": { - "text/html": [ - "
[fetch_development_fmri] Downloading data from https://osf.io/download/5cb46e2f353c58001b9cb2f1/ ...\n",
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\n" - ], - "text/plain": [ - "\u001b[1;34m[\u001b[0m\u001b[34mfetch_development_fmri\u001b[0m\u001b[1;34m]\u001b[0m Downloading data from \u001b[4;94mhttps://osf.io/download/5cb46e2f353c58001b9cb2f1/\u001b[0m \u001b[33m...\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "print((pheno['Child_Adult'] == pheno.Child_Adult).all())" + ] + }, + { + "cell_type": "markdown", + "id": "e1b2acf3", + "metadata": {}, + "source": [ + "Let's have a look at the distribution of our target variable" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c627d412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Child_Adult\n", + "child 122\n", + "adult 33\n", + "Name: count, dtype: int64\n" + ] }, { "data": { - "text/html": [ - "
[fetch_development_fmri]  ...done. (2 seconds, 0 min)\n",
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[fetch_development_fmri] Downloading data from https://osf.io/download/5c8ff3dc4712b4001a3b55f0/ ...\n",
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participant_idAgeAgeGroupChild_AdultGenderHandedness
154sub-pixar15526.00AdultadultMR
122sub-pixar12327.06AdultadultFR
123sub-pixar12433.44AdultadultMR
124sub-pixar12531.00AdultadultMR
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" - ], - "text/plain": [ - " participant_id Age AgeGroup Child_Adult Gender Handedness\n", - "154 sub-pixar155 26.00 Adult adult M R\n", - "122 sub-pixar123 27.06 Adult adult F R\n", - "123 sub-pixar124 33.44 Adult adult M R\n", - "124 sub-pixar125 31.00 Adult adult M R" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's load the phenotype data\n", - "import pandas as pd\n", - "\n", - "pheno = pd.DataFrame(development_dataset.phenotypic)\n", - "pheno.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "859c97bf", - "metadata": {}, - "source": [ - "Looks like there is a column labeling children and adults. Let's capture it in a variable" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4f2e0e48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['adult', 'child'], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_ageclass = pheno['Child_Adult']\n", - "y_ageclass.unique()" - ] - }, - { - "cell_type": "markdown", - "id": "e1b2acf3", - "metadata": {}, - "source": [ - "Let's have a look at the distribution of our target variable" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c627d412", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Child_Adult\n", - "child 122\n", - "adult 33\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.countplot(x=y_ageclass)\n", - "pheno.Child_Adult.value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "e751fc60", - "metadata": {}, - "source": [ - "This is very unbalanced -- there seems to be many more children than adults. It is something we can accomodate to a degree when training our model, but it is not within the scope of this tutorial. So let's select an arbitrary subset of the children to match the number of adults. As the 32 adults are at the beginning of the frame, this is easy to do:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8ed051b0", - "metadata": {}, - "outputs": [], - "source": [ - "data = data[0:66]\n", - "pheno = pheno.head(66)\n", - "y_ageclass = pheno['Child_Adult']" - ] - }, - { - "cell_type": "markdown", - "id": "555b5f3f", - "metadata": {}, - "source": [ - "### Extract features with nilearn masker\n", - "\n", - "Here, we are going to use the same techniques we learned in the previous tutorial to extract resting state fMRI connectivity features from every subject. Let's reload our atlas, and re-initiate our masker and correlation_measure." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "019cf275-61db-4a34-9444-9e396304ed09", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_atlas_basc_multiscale_2015\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdata_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mresolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sym'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Download and load multiscale functional brain parcellations.\n", - "\n", - "This :term:`Deterministic atlas` includes group brain parcellations\n", - "generated from resting-state\n", - ":term:`functional magnetic resonance images` from about 200 young\n", - "healthy subjects.\n", - "\n", - "Multiple resolutions (number of networks) are available, among\n", - "7, 12, 20, 36, 64, 122, 197, 325, 444. The brain parcellations\n", - "have been generated using a method called bootstrap analysis of\n", - "stable clusters called as BASC :footcite:t:`Bellec2010`,\n", - "and the resolutions have been selected using a data-driven method\n", - "called MSTEPS :footcite:t:`Bellec2013`.\n", - "\n", - "Note that two versions of the template are available, 'sym' or 'asym'.\n", - "The 'asym' type contains brain images that have been registered in the\n", - "asymmetric version of the :term:`MNI` brain template (reflecting that\n", - "the brain is asymmetric), while the 'sym' type contains images registered\n", - "in the symmetric version of the :term:`MNI` template.\n", - "The symmetric template has been forced to be symmetric anatomically, and\n", - "is therefore ideally suited to study homotopic functional connections in\n", - ":term:`fMRI`: finding homotopic regions simply consists of flipping the\n", - "x-axis of the template.\n", - "\n", - ".. nilearn_versionadded:: 0.2.3\n", - "\n", - "Parameters\n", - "----------\n", - "\n", - "data_dir : :obj:`pathlib.Path` or :obj:`str` or None, optional\n", - " Path where data should be downloaded.\n", - " By default, files are downloaded in a ``nilearn_data`` folder\n", - " in the home directory of the user.\n", - " See also ``nilearn.datasets.utils.get_data_dirs``.\n", - "\n", - "\n", - "url : :obj:`str` or None, default=None\n", - " URL of file to download.\n", - " Override download URL.\n", - " Used for test only (or if you setup a mirror of the data).\n", - "\n", - "\n", - "resume : :obj:`bool`, default=True\n", - " Whether to resume download of a partly-downloaded file.\n", - "\n", - "\n", - "verbose : :obj:`bool` or :obj:`int`, default=1\n", - " Verbosity level (``0`` or ``False`` means no message).\n", - "\n", - "resolution : :obj:`int`, default=7\n", - " Number of networks in the dictionary.\n", - " Valid resolutions available are\n", - " {7, 12, 20, 36, 64, 122, 197, 325, 444}\n", - "\n", - " .. nilearn_versionchanged: 0.13.0\n", - "\n", - " Default changed to ``7``.\n", - "\n", - "version : {'sym', 'asym'}, default='sym'\n", - " Available versions are 'sym' or 'asym'.\n", - " By default all scales of brain parcellations of version 'sym'\n", - " will be returned.\n", - "\n", - "Returns\n", - "-------\n", - "data : :class:`sklearn.utils.Bunch`\n", - " Dictionary-like object, Keys are:\n", - "\n", - " - maps: :obj:`str`\n", - " Path to Nifti file of the brain parcellation.\n", - " Images have shape ``(53, 64, 52)`` and contain consecutive integer\n", - " values from 0 to the selected number of networks (scale).\n", - "\n", - " - 'description' : :obj:`str`\n", - " Description of the dataset.\n", - "\n", - " - lut : :obj:`pandas.DataFrame`\n", - " Act as a look up table (lut)\n", - " with at least columns 'index' and 'name'.\n", - " Formatted according to 'dseg.tsv' format from\n", - " `BIDS `_.\n", - "\n", - " - 'template' : :obj:`str`\n", - " The standardized space of analysis\n", - " in which the atlas results are provided.\n", - " When known it should be a valid template name\n", - " taken from the spaces described in\n", - " `the BIDS specification `_.\n", - "\n", - " - 'atlas_type' : :obj:`str`\n", - " Type of atlas.\n", - " See :term:`Probabilistic atlas` and :term:`Deterministic atlas`.\n", - "\n", - "References\n", - "----------\n", - ".. footbibliography::\n", - "\n", - "Notes\n", - "-----\n", - "\n", - "If the dataset files are already present in the user's Nilearn data\n", - "directory, this fetcher will **not** re-download them. To force a fresh\n", - "download, you can remove the existing dataset folder from your local\n", - "Nilearn data directory.\n", - "\n", - "For more details on :ref:`how Nilearn stores datasets `.\n", - "\n", - "For more information on this dataset's structure, see\n", - "https://figshare.com/articles/dataset/Group_multiscale_functional_template_generated_with_BASC_on_the_Cambridge_sample/1285615\n", - "\u001b[0;31mFile:\u001b[0m ~/.local/lib/python3.10/site-packages/nilearn/datasets/atlas.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "datasets.fetch_atlas_basc_multiscale_2015?" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "466c6df9", - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
[fetch_atlas_basc_multiscale_2015] Dataset found in nilearn_data/basc_multiscale_2015\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1;34m[\u001b[0m\u001b[34mfetch_atlas_basc_multiscale_2015\u001b[0m\u001b[1;34m]\u001b[0m Dataset found in nilearn_data/basc_multiscale_2015\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nilearn.maskers import NiftiLabelsMasker\nfrom nilearn.connectome import ConnectivityMeasure\n\n# load atlas\nbasc = datasets.fetch_atlas_basc_multiscale_2015(data_dir=data_dir, resolution=64)\natlas_filename = basc.maps\n\n# initialize masker (change verbosity)\nmasker = NiftiLabelsMasker(labels_img=atlas_filename, standardize='zscore_sample',\n memory='nilearn_cache', resampling_target=\"data\",\n detrend=True, verbose=0)\n\n# initialize correlation measure, set to vectorize\ncorrelation_measure = ConnectivityMeasure(kind='correlation', vectorize=True,\n discard_diagonal=True)" - ] - }, - { - "cell_type": "markdown", - "id": "02cd96ea", - "metadata": {}, - "source": [ - "Okay -- now that we have that taken care of, let's load all of the data!\n", - "\n", - "**NOTE**: On a laptop, this might take a few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "923ade0e", - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "finished 1 of 66\n", - "finished 2 of 66\n", - "finished 3 of 66\n", - "finished 4 of 66\n", - "finished 5 of 66\n", - "finished 6 of 66\n", - "finished 7 of 66\n", - "finished 8 of 66\n", - "finished 9 of 66\n", - "finished 10 of 66\n", - "finished 11 of 66\n", - "finished 12 of 66\n", - "finished 13 of 66\n", - "finished 14 of 66\n", - "finished 15 of 66\n", - "finished 16 of 66\n", - "finished 17 of 66\n", - "finished 18 of 66\n", - "finished 19 of 66\n", - "finished 20 of 66\n", - "finished 21 of 66\n", - "finished 22 of 66\n", - "finished 23 of 66\n", - "finished 24 of 66\n", - "finished 25 of 66\n", - "finished 26 of 66\n", - "finished 27 of 66\n", - "finished 28 of 66\n", - "finished 29 of 66\n", - "finished 30 of 66\n", - 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It is something we can accomodate to a degree when training our model, but it is not within the scope of this tutorial. So let's select an arbitrary subset of the children to match the number of adults. As the 32 adults are at the beginning of the frame, this is easy to do:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8ed051b0", + "metadata": {}, + "outputs": [], + "source": [ + "data = data[0:66]\n", + "pheno = pheno.head(66)\n", + "y_ageclass = pheno['Child_Adult']" + ] + }, + { + "cell_type": "markdown", + "id": "555b5f3f", + "metadata": {}, + "source": [ + "### Extract features with nilearn masker\n", + "\n", + "Here, we are going to use the same techniques we learned in the previous tutorial to extract resting state fMRI connectivity features from every subject. Let's reload our atlas, and re-initiate our masker and correlation_measure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "019cf275-61db-4a34-9444-9e396304ed09", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "finished 63 of 66\n" + "Help on function fetch_atlas_basc_multiscale_2015 in module nilearn.datasets.atlas:\n", + "\n", + "fetch_atlas_basc_multiscale_2015(data_dir=None, url=None, resume=True, verbose=1, resolution=7, version='sym')\n", + " Download and load multiscale functional brain parcellations.\n", + " \n", + " This :term:`Deterministic atlas` includes group brain parcellations\n", + " generated from resting-state\n", + " :term:`functional magnetic resonance images` from about 200 young\n", + " healthy subjects.\n", + " \n", + " Multiple resolutions (number of networks) are available, among\n", + " 7, 12, 20, 36, 64, 122, 197, 325, 444. The brain parcellations\n", + " have been generated using a method called bootstrap analysis of\n", + " stable clusters called as BASC :footcite:t:`Bellec2010`,\n", + " and the resolutions have been selected using a data-driven method\n", + " called MSTEPS :footcite:t:`Bellec2013`.\n", + " \n", + " Note that two versions of the template are available, 'sym' or 'asym'.\n", + " The 'asym' type contains brain images that have been registered in the\n", + " asymmetric version of the :term:`MNI` brain template (reflecting that\n", + " the brain is asymmetric), while the 'sym' type contains images registered\n", + " in the symmetric version of the :term:`MNI` template.\n", + " The symmetric template has been forced to be symmetric anatomically, and\n", + " is therefore ideally suited to study homotopic functional connections in\n", + " :term:`fMRI`: finding homotopic regions simply consists of flipping the\n", + " x-axis of the template.\n", + " \n", + " .. nilearn_versionadded:: 0.2.3\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " data_dir : :obj:`pathlib.Path` or :obj:`str` or None, optional\n", + " Path where data should be downloaded.\n", + " By default, files are downloaded in a ``nilearn_data`` folder\n", + " in the home directory of the user.\n", + " See also ``nilearn.datasets.utils.get_data_dirs``.\n", + " \n", + " \n", + " url : :obj:`str` or None, default=None\n", + " URL of file to download.\n", + " Override download URL.\n", + " Used for test only (or if you setup a mirror of the data).\n", + " \n", + " \n", + " resume : :obj:`bool`, default=True\n", + " Whether to resume download of a partly-downloaded file.\n", + " \n", + " \n", + " verbose : :obj:`bool` or :obj:`int`, default=1\n", + " Verbosity level (``0`` or ``False`` means no message).\n", + " \n", + " resolution : :obj:`int`, default=7\n", + " Number of networks in the dictionary.\n", + " Valid resolutions available are\n", + " {7, 12, 20, 36, 64, 122, 197, 325, 444}\n", + " \n", + " .. nilearn_versionchanged: 0.13.0\n", + " \n", + " Default changed to ``7``.\n", + " \n", + " version : {'sym', 'asym'}, default='sym'\n", + " Available versions are 'sym' or 'asym'.\n", + " By default all scales of brain parcellations of version 'sym'\n", + " will be returned.\n", + " \n", + " Returns\n", + " -------\n", + " data : :class:`sklearn.utils.Bunch`\n", + " Dictionary-like object, Keys are:\n", + " \n", + " - maps: :obj:`str`\n", + " Path to Nifti file of the brain parcellation.\n", + " Images have shape ``(53, 64, 52)`` and contain consecutive integer\n", + " values from 0 to the selected number of networks (scale).\n", + " \n", + " - 'description' : :obj:`str`\n", + " Description of the dataset.\n", + " \n", + " - lut : :obj:`pandas.DataFrame`\n", + " Act as a look up table (lut)\n", + " with at least columns 'index' and 'name'.\n", + " Formatted according to 'dseg.tsv' format from\n", + " `BIDS `_.\n", + " \n", + " - 'template' : :obj:`str`\n", + " The standardized space of analysis\n", + " in which the atlas results are provided.\n", + " When known it should be a valid template name\n", + " taken from the spaces described in\n", + " `the BIDS specification `_.\n", + " \n", + " - 'atlas_type' : :obj:`str`\n", + " Type of atlas.\n", + " See :term:`Probabilistic atlas` and :term:`Deterministic atlas`.\n", + " \n", + " References\n", + " ----------\n", + " .. footbibliography::\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " If the dataset files are already present in the user's Nilearn data\n", + " directory, this fetcher will **not** re-download them. To force a fresh\n", + " download, you can remove the existing dataset folder from your local\n", + " Nilearn data directory.\n", + " \n", + " For more details on :ref:`how Nilearn stores datasets `.\n", + " \n", + " For more information on this dataset's structure, see\n", + " https://figshare.com/articles/dataset/Group_multiscale_functional_template_generated_with_BASC_on_the_Cambridge_sample/1285615\n", + "\n", + "None\n", + "\n", + "==================================================\n", + "\n", + "(data_dir=None, url=None, resume=True, verbose=1, resolution=7, version='sym')\n", + "Download and load multiscale functional brain parcellations.\n", + "\n", + " This :term:`Deterministic atlas` includes group brain parcellations\n", + " generated from resting-state\n", + " :term:`functional magnetic resonance images` from about 200 young\n", + " healthy subjects.\n", + "\n", + " Multiple resolutions (number of networks) are available, among\n", + " 7, 12, 20, 36, 64, 122, 197, 325, 444. The brain parcellations\n", + " have been generated using a method called bootstrap analysis of\n", + " stable clusters called as BASC :footcite:t:`Bellec2010`,\n", + " and the resolutions have been selected using a data-driven method\n", + " called MSTEPS :footcite:t:`Bellec2013`.\n", + "\n", + " Note that two versions of the template are available, 'sym' or 'asym'.\n", + " The 'asym' type contains brain images that have been registered in the\n", + " asymmetric version of the :term:`MNI` brain template (reflecting that\n", + " the brain is asymmetric), while the 'sym' type contains images registered\n", + " in the symmetric version of the :term:`MNI` template.\n", + " The symmetric template has been forced to be symmetric anatomically, and\n", + " is therefore ideally suited to study homotopic functional connections in\n", + " :term:`fMRI`: finding homotopic regions simply consists of flipping the\n", + " x-axis of the template.\n", + "\n", + " .. nilearn_versionadded:: 0.2.3\n", + "\n", + " Parameters\n", + " ----------\n", + " \n", + " data_dir : :obj:`pathlib.Path` or :obj:`str` or None, optional\n", + " Path where data should be downloaded.\n", + " By default, files are downloaded in a ``nilearn_data`` folder\n", + " in the home directory of the user.\n", + " See also ``nilearn.datasets.utils.get_data_dirs``.\n", + "\n", + " \n", + " url : :obj:`str` or None, default=None\n", + " URL of file to download.\n", + " Override download URL.\n", + " Used for test only (or if you setup a mirror of the data).\n", + "\n", + " \n", + " resume : :obj:`bool`, default=True\n", + " Whether to resume download of a partly-downloaded file.\n", + "\n", + " \n", + " verbose : :obj:`bool` or :obj:`int`, default=1\n", + " Verbosity level (``0`` or ``False`` means no message).\n", + "\n", + " resolution : :obj:`int`, default=7\n", + " Number of networks in the dictionary.\n", + " Valid resolutions available are\n", + " {7, 12, 20, 36, 64, 122, 197, 325, 444}\n", + "\n", + " .. nilearn_versionchanged: 0.13.0\n", + "\n", + " Default changed to ``7``.\n", + "\n", + " version : {'sym', 'asym'}, default='sym'\n", + " Available versions are 'sym' or 'asym'.\n", + " By default all scales of brain parcellations of version 'sym'\n", + " will be returned.\n", + "\n", + " Returns\n", + " -------\n", + " data : :class:`sklearn.utils.Bunch`\n", + " Dictionary-like object, Keys are:\n", + "\n", + " - maps: :obj:`str`\n", + " Path to Nifti file of the brain parcellation.\n", + " Images have shape ``(53, 64, 52)`` and contain consecutive integer\n", + " values from 0 to the selected number of networks (scale).\n", + "\n", + " - 'description' : :obj:`str`\n", + " Description of the dataset.\n", + "\n", + " - lut : :obj:`pandas.DataFrame`\n", + " Act as a look up table (lut)\n", + " with at least columns 'index' and 'name'.\n", + " Formatted according to 'dseg.tsv' format from\n", + " `BIDS `_.\n", + "\n", + " - 'template' : :obj:`str`\n", + " The standardized space of analysis\n", + " in which the atlas results are provided.\n", + " When known it should be a valid template name\n", + " taken from the spaces described in\n", + " `the BIDS specification `_.\n", + "\n", + " - 'atlas_type' : :obj:`str`\n", + " Type of atlas.\n", + " See :term:`Probabilistic atlas` and :term:`Deterministic atlas`.\n", + "\n", + " References\n", + " ----------\n", + " .. footbibliography::\n", + "\n", + " Notes\n", + " -----\n", + " \n", + " If the dataset files are already present in the user's Nilearn data\n", + " directory, this fetcher will **not** re-download them. To force a fresh\n", + " download, you can remove the existing dataset folder from your local\n", + " Nilearn data directory.\n", + " \n", + " For more details on :ref:`how Nilearn stores datasets `.\n", + "\n", + " For more information on this dataset's structure, see\n", + " https://figshare.com/articles/dataset/Group_multiscale_functional_template_generated_with_BASC_on_the_Cambridge_sample/1285615\n", + " \n", + "/Users/jowanglin/brainhack-ntu/machine_learning/mlenv/lib/python3.11/site-packages/nilearn/datasets/atlas.py\n", + "\n" ] - }, + } + ], + "source": [ + "import inspect\n", + "\n", + "# In jupyter (ipynb environment), you can do the following shortcut, using the question mark:\n", + "# datasets.fetch_atlas_basc_multiscale_2015?\n", + "\n", + "# But the \"pure Python way\" is to call the native function help(), or import inspect and call type() and attribute .__doc__\n", + "\n", + "f = datasets.fetch_atlas_basc_multiscale_2015\n", + "print(help(f)) # get both signatire and doc string\n", + "\n", + "print(\"\\n==================================================\\n\")\n", + "print(inspect.signature(f)) # function signature\n", + "print(f.__doc__) # function doc string\n", + "print(inspect.getfile(f)) # get file location\n", + "print(type(f)) # get type\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "466c6df9", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "finished 64 of 66\n" + "(cov_estimator=None, kind='covariance', vectorize=False, discard_diagonal=False, standardize=True, verbose=0)\n" ] - }, + } + ], + "source": [ + "# load atlas\n", + "basc = datasets.fetch_atlas_basc_multiscale_2015(data_dir=DATA_DIR,\n", + " resolution=64,\n", + " verbose=0)\n", + "atlas_filename = basc.maps\n", + "\n", + "# Initialize masker \n", + "masker = NiftiLabelsMasker(labels_img=atlas_filename,\n", + " standardize='zscore_sample', # <- standardize=True will throw FutureWarning: \"The 'zscore' strategy incorrectly uses population std to calculate sample zscores...\"\n", + " memory='nilearn_cache',\n", + " resampling_target='data',\n", + " detrend=True,\n", + " verbose=0)\n", + "\n", + "# Initialize correlation measure, set to vectorize (flatten the matrix into a vector)\n", + "print(inspect.signature(ConnectivityMeasure))\n", + "correlation_measure = ConnectivityMeasure(kind='correlation',\n", + " standardize='zscore_sample', # <- default standardize=True will throw FutureWarning: \"The 'zscore' strategy incorrectly uses population std to calculate sample zscores...\"\n", + " vectorize=True,\n", + " discard_diagonal=True)" + ] + }, + { + "cell_type": "markdown", + "id": "02cd96ea", + "metadata": {}, + "source": [ + "Okay -- now that we have that taken care of, let's load all of the data!\n", + "\n", + "**NOTE**: On a laptop, this might take a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "923ade0e", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "finished 65 of 66\n" + "subj155 of 155\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "finished 66 of 66\n" + "/var/folders/n3/nqqvqq1n0jx25dfd3nyxjxmh0000gn/T/ipykernel_87219/3669076329.py:7: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.\n", + " time_series = masker.fit_transform(sub, confounds=confounds[i]) # extract the timeseries from the ROIs in the atlas\n" ] } ], "source": [ - "all_features = [] # here is where we will put the data (a container)\n", + "import time\n", + "\n", + "total_num_of_subjs = len(data)\n", + "all_features = [] # here is where we will put the data \n", "\n", - "for i,sub in enumerate(data):\n", - " # extract the timeseries from the ROIs in the atlas\n", - " time_series = masker.fit_transform(sub, confounds=confounds[i])\n", - " # create a region x region correlation matrix\n", - " correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n", - " # add to our container\n", - " all_features.append(correlation_matrix)\n", - " # keep track of status\n", - " print('finished %s of %s'%(i+1,len(data)))" + "for i, sub in enumerate(data):\n", + " # will still get DeprecationWarning with masker.fit_transform:\n", + " # From release 0.14.0, confounds will be standardized using the sample std instead of the population std.\n", + " time_series = masker.fit_transform(sub, confounds=confounds[i]) # extract the timeseries from the ROIs in the atlas\n", + " correlation_matrix = correlation_measure.fit_transform([time_series])[0] # region by region correlation matrix\n", + " all_features.append(correlation_matrix) # add to list\n", + "\n", + " print('subj%s of %s' % (i+1, total_num_of_subjs)) # keep track of status\n", + " clear_output(wait=True)\n", + " time.sleep(0.01)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "2381e86c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(155, 2016)\n" + ] + } + ], "source": [ "# Let's save the data to disk\n", - "import numpy as np\n", "name_connectome = 'connectomes_developmental_dataset'\n", - "np.savez_compressed(f'./{name_connectome}', a=all_features)" + "\n", + "# Using numpy.savez_compressed because we have a LIST of arrays, not one single array\n", + "np.savez_compressed(f'{DATA_DIR}/{name_connectome}', a=all_features)\n", + "\n", + "# You can manually stack it before saving though\n", + "all_features_stacked = np.stack(all_features, axis=0)\n", + "print(all_features_stacked.shape)\n", + "np.save(f'{DATA_DIR}/{name_connectome}_stacked', all_features_stacked)\n" ] }, { @@ -6616,34 +740,29 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "09cac636", "metadata": {}, - "outputs": [], - "source": [ - "feat_file = f'{name_connectome}.npz'\n", - "X_features = np.load(feat_file)['a']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ab18a5cd", - "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(66, 2016)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "(155, 2016)\n" + ] } ], "source": [ - "X_features.shape" + "feat_file = f'{DATA_DIR}/{name_connectome}.npz'\n", + "feat_file_stacked = f'{DATA_DIR}/{name_connectome}_stacked.npy'\n", + "\n", + "X_features = np.load(feat_file)['a']\n", + "X_features_stacked = np.load(feat_file)['a']\n", + "\n", + "print(np.array_equal(X_features, X_features_stacked)) # just showing that they're equal\n", + "\n", + "print(X_features.shape) # sanity checking shape, should be (n_samples, n_features)\n" ] }, { @@ -6658,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "ddd8d1d0", "metadata": {}, "outputs": [ @@ -6684,7 +803,7 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "# import matplotlib.pyplot as plt\n", "\n", "plt.imshow(X_features, aspect='auto', interpolation='nearest')\n", "plt.colorbar()\n", @@ -6708,28 +827,7 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "a338511e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(66,)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_ageclass.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 135, "id": "1c9df452", "metadata": {}, "outputs": [ @@ -6737,34 +835,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "training: 52 testing: 14\n" + "train: 124 | test: 31\n" ] } ], "source": [ - "from sklearn.model_selection import train_test_split\n", + "# Split the sample to training/test and stratify by age class, and also shuffle the data\n", "\n", - "# Split the sample to training/test and\n", - "# stratify by age class, and also shuffle the data.\n", + "assert y_ageclass.shape[0] == X_features.shape[0]\n", "\n", - "X_train, X_test, y_train, y_test = train_test_split(X_features, # x\n", - " y_ageclass, # y\n", - " test_size = 0.2, # 80%/20% split \n", - " shuffle = True, # shuffle dataset\n", - " # before splitting\n", - " stratify = y_ageclass, # keep\n", - " # distribution\n", - " # of ageclass\n", - " # consistent\n", - " # betw. train\n", - " # & test sets.\n", - " random_state = 123 # same shuffle each\n", - " # time\n", - " )\n", + "X_train, X_test, y_train, y_test = train_test_split(X_features, # x\n", + " y_ageclass, # y\n", + " test_size=0.2, # 80% / 20% split \n", + " shuffle=True, # shuffle dataset before splitting\n", + " stratify=y_ageclass, # keep distribution of ageclass consistent\n", + " # between train & test sets\n", + " random_state=RANDOM_STATE) # same shuffle each time\n", "\n", - "# print the size of our training and test groups\n", - "print('training:', len(X_train),\n", - " 'testing:', len(X_test))" + "# print the size of our train and test sets\n", + "print('train:', len(X_train), '| test:', len(X_test))" ] }, { @@ -6777,13 +866,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 136, "id": "ef4e8516", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -6825,16 +914,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "834a4209", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SVC(class_weight='balanced', kernel='linear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "SVC(class_weight='balanced', kernel='linear')" ] }, - "execution_count": 28, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.svm import SVC\n", - "l_svc = SVC(kernel='linear', class_weight='balanced') # define the model\n", - "\n", - "l_svc.fit(X_train, y_train) # fit the model" + "l_svc = SVC(kernel='linear', class_weight='balanced') # define the model\n", + "l_svc.fit(X_train, y_train) # fit the model" ] }, { @@ -7276,25 +1874,55 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 138, "id": "0108a5de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train score (accuracy) = 1.0\n", + "test score (accuracy) = 0.967741935483871\n" + ] + } + ], "source": [ - "from sklearn.metrics import classification_report, confusion_matrix, precision_score, f1_score\n", + "# get model predcitions on training data\n", + "y_pred_train = l_svc.predict(X_train)\n", "\n", - "# predict the training data based on the model\n", - "y_pred = l_svc.predict(X_train)\n", + "# get model training score (defaults to accuracy)\n", + "acc_train = l_svc.score(X_train, y_train)\n", + "print(f\"train score (accuracy) = {acc_train}\")\n", "\n", - "# calculate the model accuracy\n", - "acc = l_svc.score(X_train, y_train)\n", + "# evaluate model on test set\n", + "acc_test = l_svc.score(X_test, y_test)\n", + "y_pred_test = l_svc.predict(X_test)\n", + "print(f\"test score (accuracy) = {acc_test}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "65ff2e3a", + "metadata": {}, + "outputs": [], + "source": [ + "# get model training precision, recall and f1, all in one convenient report!\n", + "cr_train = classification_report(y_true=y_train,\n", + " y_pred=y_pred_train)\n", + "# get a table to help us break down these scores\n", + "cm_train = confusion_matrix(y_true=y_train, \n", + " y_pred=y_pred_train,\n", + " normalize='true')\n", "\n", - "# calculate the model precision, recall and f1, all in one convenient report!\n", - "cr = classification_report(y_true=y_train,\n", - " y_pred = y_pred)\n", "\n", - "# get a table to help us break down these scores\n", - "cm = confusion_matrix(y_true=y_train, y_pred = y_pred)" + "# same for test set\n", + "cr_test = classification_report(y_true=y_test,\n", + " y_pred=y_pred_test)\n", + "cm_test = confusion_matrix(y_true=y_test,\n", + " y_pred=y_pred_test,\n", + " normalize='true')" ] }, { @@ -7307,29 +1935,57 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "13cc7101", + "execution_count": null, + "id": "c5d2ab58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "accuracy: 1.0\n", + "TRAIN\n", " precision recall f1-score support\n", "\n", " adult 1.00 1.00 1.00 26\n", - " child 1.00 1.00 1.00 26\n", + " child 1.00 1.00 1.00 98\n", + "\n", + " accuracy 1.00 124\n", + " macro avg 1.00 1.00 1.00 124\n", + "weighted avg 1.00 1.00 1.00 124\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEST\n", + " precision recall f1-score support\n", + "\n", + " adult 1.00 0.86 0.92 7\n", + " child 0.96 1.00 0.98 24\n", + "\n", + " accuracy 0.97 31\n", + " macro avg 0.98 0.93 0.95 31\n", + "weighted avg 0.97 0.97 0.97 31\n", "\n", - " accuracy 1.00 52\n", - " macro avg 1.00 1.00 1.00 52\n", - "weighted avg 1.00 1.00 1.00 52\n", "\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -7340,22 +1996,26 @@ ], "source": [ "import itertools\n", - "from pandas import DataFrame\n", "\n", - "# print results\n", - "print('accuracy:', acc)\n", - "print(cr)\n", + "def display_results(cr: str, cm: np.ndarray, mode: str, cmap='RdBu_r'):\n", + " print(f'{mode.upper()}\\n{cr}\\n') \n", "\n", - "# plot confusion matrix\n", - "cmdf = DataFrame(cm, index = ['Adult','Child'], columns = ['Adult','Child'])\n", - "sns.heatmap(cmdf, cmap = 'RdBu_r')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Observed')\n", - "# label cells in matrix\n", - "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", - " plt.text(j+0.5, i+0.5, format(cm[i, j], 'd'),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\")" + " cmdf = cmdf = pd.DataFrame(cm, index = ['Adult','Child'], columns = ['Adult','Child'])\n", + "\n", + " sns.heatmap(cmdf, cmap=cmap)\n", + " plt.xlabel('Model Prediction')\n", + " plt.ylabel('Ground Truth') \n", + "\n", + " # label cells in matrix\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j + 0.5, i + 0.5, format(cm[i, j], 'f'),\n", + " horizontalalignment='center',\n", + " color='white')\n", + " plt.show()\n", + "\n", + "\n", + "display_results(cr_train, cm_train, 'train', cmap='Blues')\n", + "display_results(cr_test, cm_test, 'test', cmap='Greys')\n" ] }, { @@ -7370,77 +2030,111 @@ "\n", "![](https://sebastianraschka.com/images/faq/multiclass-metric/conf_mat.png)\n", "\n", - "HOLY COW! Machine learning is amazing!!! Almost a perfect fit!\n", - "\n", - "...which means there's something wrong. What's the problem here?\n", - "::::\n", - "\n", - "::::{admonition} Solution\n", - ":class: dropdown\n", - "\n", - "The model was not cross validated. \n", - "\n", - "Scroll down and unfold the cells to see the answer!\n", - "\n", - "::::\n", "\n", "### Fit the model with the training data and cross-validation" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "c44adfdd", - "metadata": { - "tags": [ - "hide-input", - "hide-output" - ] - }, + "execution_count": 169, + "id": "81b9ae7a", + "metadata": {}, "outputs": [], "source": [ - "from sklearn.model_selection import cross_val_predict, cross_val_score\n", + "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE) # 5-fold CV\n", "\n", - "# predict\n", - "y_pred = cross_val_predict(l_svc, X_train, y_train,\n", - " groups=y_train, cv=3)\n", - "# scores\n", - "acc = cross_val_score(l_svc, X_train, y_train,\n", - " groups=y_train, cv=3)" + "y_train_arr = y_train.to_numpy()\n", + "cv_res = cross_validate(SVC(kernel='linear', class_weight='balanced'),\n", + " X_train,\n", + " y_train_arr,\n", + " scoring=['accuracy',\n", + " 'precision_weighted',\n", + " 'recall_weighted',\n", + " 'f1_weighted'],\n", + " cv=skf,\n", + " n_jobs=-1,\n", + " return_train_score=True,\n", + " return_estimator=True,\n", + " return_indices=True)" ] }, { "cell_type": "markdown", - "id": "bf5325ff", + "id": "520b132e", "metadata": {}, "source": [ - "We can look at the accuracy of the predictions for each fold of the cross-validation" + "We can look at the model train and validation scores within each CV fold" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "b4b68819", - "metadata": { - "tags": [ - "hide-input", - "hide-output" - ] - }, + "execution_count": 170, + "id": "122eb1ff", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Fold 0 -- Acc = 0.8888888888888888\n", - "Fold 1 -- Acc = 1.0\n", - "Fold 2 -- Acc = 0.8823529411764706\n" + "\n", + ">> FOLD 1\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n", + "\n", + ">> FOLD 2\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n", + "\n", + ">> FOLD 3\n", + " train valid\n", + "accuracy 1.0 0.960000\n", + "precision_weighted 1.0 0.961905\n", + "recall_weighted 1.0 0.960000\n", + "f1_weighted 1.0 0.958266\n", + "\n", + ">> FOLD 4\n", + " train valid\n", + "accuracy 1.0 0.880000\n", + "precision_weighted 1.0 0.896364\n", + "recall_weighted 1.0 0.880000\n", + "f1_weighted 1.0 0.864390\n", + "\n", + ">> FOLD 5\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n" ] } ], "source": [ - "for i in range(len(acc)):\n", - " print('Fold %s -- Acc = %s'%(i, acc[i]))" + "train_scores = np.stack([cv_res['train_accuracy'],\n", + " cv_res['train_precision_weighted'],\n", + " cv_res['train_recall_weighted'],\n", + " cv_res['train_f1_weighted']]).T # shape (n_splits, 4)\n", + "\n", + "valid_scores = np.stack([cv_res['test_accuracy'],\n", + " cv_res['test_precision_weighted'],\n", + " cv_res['test_recall_weighted'],\n", + " cv_res['test_f1_weighted']]).T\n", + "\n", + "for fold, (i, j) in enumerate(zip(train_scores, valid_scores)):\n", + " print(f'\\n>> FOLD {fold + 1}')\n", + " data=np.vstack([i, j]).T\n", + " df = pd.DataFrame(data=data,\n", + " index=['accuracy',\n", + " 'precision_weighted',\n", + " 'recall_weighted',\n", + " 'f1_weighted'],\n", + " columns=['train', 'valid'])\n", + " print(df)" ] }, { @@ -7448,60 +2142,35 @@ "id": "3c9c1fb1", "metadata": {}, "source": [ - "We can also look at the overall accuracy of the model" + "We can also look at the overall (aggregated) out-of-fold scores of the model." ] }, { "cell_type": "code", - "execution_count": 34, - "id": "32d8eccb", - "metadata": { - "tags": [ - "hide-input", - "hide-output" - ] - }, + "execution_count": 176, + "id": "42ec7e2d", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy: 0.9230769230769231\n", + "CV, OVERALL (OUT-OF-FOLD AGGREGATED)\n", " precision recall f1-score support\n", "\n", - " adult 0.96 0.88 0.92 26\n", - " child 0.89 0.96 0.93 26\n", + " adult 1.00 0.85 0.92 26\n", + " child 0.96 1.00 0.98 98\n", + "\n", + " accuracy 0.97 124\n", + " macro avg 0.98 0.92 0.95 124\n", + "weighted avg 0.97 0.97 0.97 124\n", "\n", - " accuracy 0.92 52\n", - " macro avg 0.93 0.92 0.92 52\n", - "weighted avg 0.93 0.92 0.92 52\n", "\n" ] - } - ], - "source": [ - "from sklearn.metrics import accuracy_score\n", - "overall_acc = accuracy_score(y_pred = y_pred, y_true = y_train)\n", - "overall_cr = classification_report(y_pred = y_pred, y_true = y_train)\n", - "overall_cm = confusion_matrix(y_pred = y_pred, y_true = y_train)\n", - "print('Accuracy:',overall_acc)\n", - "print(overall_cr)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "59b1d3b7", - "metadata": { - "tags": [ - "hide-input", - "hide-output" - ] - }, - "outputs": [ + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -7511,15 +2180,22 @@ } ], "source": [ - "thresh = overall_cm.max() / 2\n", - "cmdf = DataFrame(overall_cm, index = ['Adult','Child'], columns = ['Adult','Child'])\n", - "sns.heatmap(cmdf, cmap='copper')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Observed')\n", - "for i, j in itertools.product(range(overall_cm.shape[0]), range(overall_cm.shape[1])):\n", - " plt.text(j+0.5, i+0.5, format(overall_cm[i, j], 'd'),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\")" + "def display_overall_cv_results(cv_results, cmap='RdBu_r'):\n", + " estimators = cv_results['estimator']\n", + " valid_indices = cv_results['indices']['test']\n", + "\n", + " ground_truths = np.concatenate([y_train_arr[valid_idx]\n", + " for valid_idx in valid_indices])\n", + " predictions = np.concatenate([est.predict(X_train[valid_idx])\n", + " for est, valid_idx in zip(estimators, valid_indices)])\n", + " overall_cr = classification_report(y_pred=predictions,\n", + " y_true=ground_truths)\n", + " overall_cm = confusion_matrix(y_pred=predictions,\n", + " y_true=ground_truths,\n", + " normalize='true')\n", + " display_results(overall_cr, overall_cm, 'cv, overall (out-of-fold aggregated)', cmap=cmap)\n", + "\n", + "display_overall_cv_results(cv_res, cmap='Greys')\n" ] }, { @@ -7532,7 +2208,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 152, "id": "4807c3f1", "metadata": { "tags": [ @@ -7545,15 +2221,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "accuracy 0.9237472766884531, average permutation accuracy 0.4946732026143792, p value 0.009900990099009901\n" + "accuracy 0.968, average permutation accuracy 0.7414300000000001, p value 0.009900990099009901\n" ] } ], "source": [ "from sklearn.model_selection import permutation_test_score\n", "score, permutation_score, pvalue = permutation_test_score(\n", - " l_svc, X_train, y_train, cv=3, scoring=\"accuracy\",\n", - " n_jobs=2, n_permutations=100)\n", + " l_svc, X_train, y_train, cv=skf, scoring=\"accuracy\",\n", + " n_jobs=-1, n_permutations=100)\n", "print(f'accuracy {score}, average permutation accuracy {permutation_score.mean()}, p value {pvalue}')" ] }, @@ -7584,17 +2260,271 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 172, "id": "25e22a18", "metadata": {}, "outputs": [], "source": [ - "# Scale the training data\n", - "from sklearn.preprocessing import StandardScaler\n", - "scaler = StandardScaler().fit(X_train)\n", - "X_train_scl = scaler.transform(X_train)" + "# scale the training data WITHIN EACH CV FOLD \n", + "scaler = StandardScaler()\n", + "pipe = Pipeline([('standard_scaler', scaler),\n", + " ('linear_svc', SVC(kernel='linear',\n", + " class_weight='balanced'))])\n", + "\n", + "cv_res_scaled = cross_validate(pipe,\n", + " X_train,\n", + " y_train_arr,\n", + " scoring=['accuracy',\n", + " 'precision_weighted',\n", + " 'recall_weighted',\n", + " 'f1_weighted'],\n", + " cv=skf,\n", + " n_jobs=-1,\n", + " return_train_score=True,\n", + " return_estimator=True,\n", + " return_indices=True)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45cf2578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + ">> FOLD 1\n", + "all feature mean close to 0: True\n", + "all feature std close to 1: True\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n", + "\n", + ">> FOLD 2\n", + "all feature mean close to 0: True\n", + "all feature std close to 1: True\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n", + "\n", + ">> FOLD 3\n", + "all feature mean close to 0: True\n", + "all feature std close to 1: True\n", + " train valid\n", + "accuracy 1.0 0.960000\n", + "precision_weighted 1.0 0.961905\n", + "recall_weighted 1.0 0.960000\n", + "f1_weighted 1.0 0.958266\n", + "\n", + ">> FOLD 4\n", + "all feature mean close to 0: True\n", + "all feature std close to 1: True\n", + " train valid\n", + "accuracy 1.0 0.880000\n", + "precision_weighted 1.0 0.896364\n", + "recall_weighted 1.0 0.880000\n", + "f1_weighted 1.0 0.864390\n", + "\n", + ">> FOLD 5\n", + "all feature mean close to 0: True\n", + "all feature std close to 1: True\n", + " train valid\n", + "accuracy 1.0 1.0\n", + "precision_weighted 1.0 1.0\n", + "recall_weighted 1.0 1.0\n", + "f1_weighted 1.0 1.0\n" + ] + } + ], + "source": [ + "train_scores = np.stack([cv_res_scaled['train_accuracy'],\n", + " cv_res_scaled['train_precision_weighted'],\n", + " cv_res_scaled['train_recall_weighted'],\n", + " cv_res_scaled['train_f1_weighted']]).T # shape (n_splits, 4)\n", + "\n", + "valid_scores = np.stack([cv_res_scaled['test_accuracy'],\n", + " cv_res_scaled['test_precision_weighted'],\n", + " cv_res_scaled['test_recall_weighted'],\n", + " cv_res_scaled['test_f1_weighted']]).T\n", + "\n", + "estimators_scaled = cv_res_scaled[\"estimator\"]\n", + "train_indices_scaled = cv_res_scaled['indices']['train']\n", + "\n", + "for fold, (i, j, est_scaled, idx) in enumerate(zip(train_scores,\n", + " valid_scores,\n", + " estimators_scaled,\n", + " train_indices_scaled)):\n", + " print(f'\\n>> FOLD {fold + 1}')\n", + "\n", + " s = est_scaled[\"standard_scaler\"]\n", + " x = X_train[idx]\n", + " x_scaled = s.transform(x)\n", + " means, stds = x_scaled.mean(axis=0), x_scaled.std(axis=0)\n", + " print(f'all feature mean close to 0: {np.allclose(means, 0)}')\n", + " print(f'all feature std close to 1: {np.allclose(stds, 1)}')\n", + "\n", + " data=np.vstack([i, j]).T\n", + " df = pd.DataFrame(data=data,\n", + " index=['accuracy',\n", + " 'precision_weighted',\n", + " 'recall_weighted',\n", + " 'f1_weighted'],\n", + " columns=['train', 'valid'])\n", + " print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "50853fe5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CV, OVERALL (OUT-OF-FOLD AGGREGATED)\n", + " precision recall f1-score support\n", + "\n", + " adult 1.00 0.85 0.92 26\n", + " child 0.96 1.00 0.98 98\n", + "\n", + " accuracy 0.97 124\n", + " macro avg 0.98 0.92 0.95 124\n", + "weighted avg 0.97 0.97 0.97 124\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_overall_cv_results(cv_res_scaled, cmap='Greys')" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "67989419", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">> FOLD 1\n", + " Train predictions euqal (scaled vs not scaled): True\n", + " Train decision functions equal (scaled vs not scaled): False\n", + " Valid predictions euqal (scaled vs not scaled): True\n", + " Valid decision functions equal (scaled vs not scaled): False\n", + ">> FOLD 2\n", + " Train predictions euqal (scaled vs not scaled): True\n", + " Train decision functions equal (scaled vs not scaled): False\n", + " Valid predictions euqal (scaled vs not scaled): True\n", + " Valid decision functions equal (scaled vs not scaled): False\n", + ">> FOLD 3\n", + " Train predictions euqal (scaled vs not scaled): True\n", + " Train decision functions equal (scaled vs not scaled): False\n", + " Valid predictions euqal (scaled vs not scaled): True\n", + " Valid decision functions equal (scaled vs not scaled): False\n", + ">> FOLD 4\n", + " Train predictions euqal (scaled vs not scaled): True\n", + " Train decision functions equal (scaled vs not scaled): False\n", + " Valid predictions euqal (scaled vs not scaled): True\n", + " Valid decision functions equal (scaled vs not scaled): False\n", + ">> FOLD 5\n", + " Train predictions euqal (scaled vs not scaled): True\n", + " Train decision functions equal (scaled vs not scaled): False\n", + " Valid predictions euqal (scaled vs not scaled): True\n", + " Valid decision functions equal (scaled vs not scaled): False\n" + ] + } + ], + "source": [ + "estimators_scaled = cv_res_scaled['estimator'] # actually already defined, but for clarity putting it here again\n", + "estimators = cv_res['estimator'] # same comment as above\n", + "train_indices_scaled = cv_res_scaled['indices']['train'] # same comment as above\n", + "train_indices = cv_res['indices']['train']\n", + "valid_indices_scaled = cv_res_scaled['indices']['test'] # same comment as above\n", + "valid_indices = cv_res['indices']['test']\n", + "assert all([np.array_equal(i, j) for i, j in zip(train_indices, train_indices_scaled)]) # same skf, fixed random_state, should be equal\n", + "assert all([np.array_equal(i, j) for i, j in zip(valid_indices, valid_indices_scaled)])\n", + "\n", + "for fold, (est, est_scaled, train_idx, valid_idx) in enumerate(zip(estimators,\n", + " estimators_scaled,\n", + " train_indices,\n", + " valid_indices)):\n", + " print(f\">> FOLD {fold + 1}\")\n", + "\n", + " x_train = X_train[train_idx]\n", + " x_valid = X_train[valid_idx]\n", + "\n", + " y_hat_train = est.decision_function(x_train)\n", + " pred_train = est.predict(x_train)\n", + " y_hat_train_scaled = est_scaled.decision_function(x_train)\n", + " pred_train_scaled = est_scaled.predict(x_train)\n", + "\n", + " y_hat_valid = est.decision_function(x_valid)\n", + " pred_valid = est.predict(x_valid)\n", + " y_hat_valid_scaled = est_scaled.decision_function(x_valid)\n", + " pred_valid_scaled = est_scaled.predict(x_valid)\n", + " \n", + " print(f' Train predictions euqal (scaled vs not scaled): {np.array_equal(pred_train, pred_train_scaled)}')\n", + " print(f' Train decision functions equal (scaled vs not scaled): {np.array_equal(y_hat_train, y_hat_train_scaled)}')\n", + " print(f' Valid predictions euqal (scaled vs not scaled): {np.array_equal(pred_valid, pred_valid_scaled)}')\n", + " print(f' Valid decision functions equal (scaled vs not scaled): {np.array_equal(y_hat_valid, y_hat_valid_scaled)}')\n", + " \n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "421a52bc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbee43f0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdd2c6e1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43d01579", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 38, @@ -8566,7 +3496,7 @@ } }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "mlenv", "language": "python", "name": "python3" }, @@ -8580,7 +3510,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.5" }, "source_map": [ 16, @@ -8663,4 +3593,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From cca118bcee623ba11b3bbe82a00895052a53ef65 Mon Sep 17 00:00:00 2001 From: amandalin047 Date: Fri, 15 May 2026 06:32:54 +0800 Subject: [PATCH 2/6] need to fix markdown --- machine-learning-with-nilearn.ipynb | 494 +++++++--------------------- 1 file changed, 125 insertions(+), 369 deletions(-) diff --git a/machine-learning-with-nilearn.ipynb b/machine-learning-with-nilearn.ipynb index 0d977ef..caae4ae 100644 --- a/machine-learning-with-nilearn.ipynb +++ b/machine-learning-with-nilearn.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 25, "id": "23e55e22", "metadata": {}, "outputs": [], @@ -33,7 +33,8 @@ "\n", "from sklearn.model_selection import (train_test_split,\n", " StratifiedKFold,\n", - " cross_validate)\n", + " cross_validate,\n", + " permutation_test_score)\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.svm import SVC\n", @@ -49,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 3, "id": "7df94dab", "metadata": {}, "outputs": [], @@ -60,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "1d0a5067", "metadata": { "tags": [ @@ -89,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "8f15348b", "metadata": {}, "outputs": [ @@ -115,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "id": "0871ebe4", "metadata": {}, "outputs": [ @@ -228,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4f2e0e48", "metadata": {}, "outputs": [ @@ -250,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "id": "c0e6baf8", "metadata": {}, "outputs": [ @@ -276,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "id": "c627d412", "metadata": {}, "outputs": [ @@ -293,16 +294,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -314,27 +315,7 @@ "source": [ "print(pheno['Child_Adult'].value_counts())\n", "\n", - "sns.histplot(x=y_ageclass)" - ] - }, - { - "cell_type": "markdown", - "id": "e751fc60", - "metadata": {}, - "source": [ - "This is very unbalanced -- there seems to be many more children than adults. It is something we can accomodate to a degree when training our model, but it is not within the scope of this tutorial. So let's select an arbitrary subset of the children to match the number of adults. As the 32 adults are at the beginning of the frame, this is easy to do:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8ed051b0", - "metadata": {}, - "outputs": [], - "source": [ - "data = data[0:66]\n", - "pheno = pheno.head(66)\n", - "y_ageclass = pheno['Child_Adult']" + "sns.countplot(x=y_ageclass)" ] }, { @@ -349,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "019cf275-61db-4a34-9444-9e396304ed09", "metadata": {}, "outputs": [ @@ -610,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "466c6df9", "metadata": { "tags": [ @@ -661,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 12, "id": "923ade0e", "metadata": { "tags": [ @@ -675,14 +656,6 @@ "text": [ "subj155 of 155\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/n3/nqqvqq1n0jx25dfd3nyxjxmh0000gn/T/ipykernel_87219/3669076329.py:7: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.\n", - " time_series = masker.fit_transform(sub, confounds=confounds[i]) # extract the timeseries from the ROIs in the atlas\n" - ] } ], "source": [ @@ -705,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "2381e86c", "metadata": {}, "outputs": [ @@ -740,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "09cac636", "metadata": {}, "outputs": [ @@ -777,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "ddd8d1d0", "metadata": {}, "outputs": [ @@ -787,13 +760,13 @@ "Text(0, 0.5, 'subjects')" ] }, - "execution_count": 24, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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2HNCORttQOA5BWZB4kJnXLF+SkD9m41Y1jQFB8aSmNiIQCWeXy06BtgXVDQn9d8H0DQrZstCA1d3G25vFOnPNkXHapse20bqrTzL92VW0egXZWc3KJo1Q4KxI2gMxaNi+9QSP7B0DoHjEwq1oGoPi7PUpzkDm4mVqVZ9Xb32Yz9/1PHoeguqoxL1imfbuDvITmu7Xn2LyH1cRZyE7q0lcQViEdn9Ceb/EbsPidk3upKQ5qOl+ROPUFZMvEhSOWlQ3xpT3W1SuCEALhv9e0uizyf3sDDMP9xOVEtwVi3i4Tf6hDO0uTccRTbssSbx0n8gIZAiNsQRnRSLbgmRzg3jJx65Lrnr+Ph7+u21Ez6siHy4SljRxb0jmqEfYpRCxIC7F2BWb/ClodQuSjEZoyE4KtAVBOd33tQ0x7rxF2JMgAomMBFYAmTmBkhB4bY5/5FZWVlYolUrPyPdatVqlVCpx8sHVFAtP/a5MtaZYteMElUqFYrH4NLbw/LqgH+WEYciDDz7Ie97znrPzpJTceOON7Nq16wmXCYKAIAjO/lyr1dLlfB8rG2N5PtLXWK5A2CAzFpZrY3kCS4HlgXI0lieQvgJfIVXaMZGZBBn/S8fEcjWWJ1E+4GksBElWIy2Fm3ewHR/OdEwsL71gSF+jz3RMrECifIXlSWRWpW0M0y9+6afvbSmQGYnlu1gC7JyHzPi4eRfp+5CxsDwPmdVo30Oq9LNkJsHyrHRZz01/9s/87LrIjAT7TMfE99BZkE6Ck3ORWR/L8/9l+/gO2tdYrkb6Iv1/L+2YyGyC5fnYOQ/L89PXZNKOieWlHROZVei2h+VbWG66DRCAA5aXdkxsJ8ZyLSwfLPWvtlXGQgY2ViixNCS+Prt+MhJYDmlbMhLpn+mYKIGVgO2kHRPtSixfoGyN1U73q5QCSwnwdfr6nIf0/bMdE9vRyKzGyiXYnsZ2fCzvTPu8M2070zGxYokFKF8jlEA5GpmJkVmBzPjIrMJyfSzvzL7NJFhu2jGxPIEVg/QVVlue3X6PbSvLPXN8nDlOhAtS8rj9+1jHRJ45vrUEmQHhu2e2jYXlOmjvzOdEZ46RM9v4sfMi3b+PvbeNzIDtpB2T5Mz+lRmwshKpXSzfJ8koJAJLC6ysh/b89LzRQEal2yDy0vfzz7zWTfe77aTHVrrOCdI6cx5oNz1msxormyB9HzfvYGV9Et/HygbIjI/lWkhfY9sS7Vhn2pxuF9tJ0uMp1mhXIFwQFmgfrOBMx0SAlY2wXB/lCmxbYHl+ekwk6TLqTMfksTbjcba9lmefuQaIs/+KBIQDytVYbtoxkZkE2wGZUUidXj9klnSbn7nmSCtt02PbyMm5Z4+ZdL+kHZN0f6YdEyfnIjPpLXzLtc624bHrk/bBynrI2MfLO+n15cz5YmX/5Xx97NzVZ9YNV6RtyiRY7plr0GPnvK+xHI3tKGRGnDkG03NXZgRoge1ILNdOr1W+n+7btvUv63zmPSxXgkd6XErS4zGTINsSSwt0NkG2fGQscfMuluejsiHyzDU8vbZ5SD/tmMhMnF4rzuwr7acdE8sV6T70zmzrTHzmWpggpERa6blguQJhgfTS746fxKP/fEGQLzz1z1E8O8MTLujg14WFBZIkoa+v73Hz+/r6mJmZecJlPvShD1Eqlc5O/3Z8EsMwDMO4ECRanfP0bHRBd0yeive85z1UKpWz05MdJt4wDMMwfpIU+pynZ6ML+lFOd3c3lmUxOzv7uPmzs7P09/c/4TKe5+F53k+ieYZhGIZhPM0u6DsmruuyY8cOvvWtb52dp5TiW9/6FlddddWP9V7OiqQxn6XVq4mLipUdAa1+DYlgcUdCVNC0+jS5aU3YnSBjcJcl3cMroAR2Q+AuWsgwfUYtI2j2SYJOjbI1MkxjC1RoIY5ncUTC/CWS6mpJY0hgNzRhWRN1JNhNgbsiCXoSSofTgDrraIbstKCxKknfK58Q5SDOQ/6wmwbmljWnZjqxGpK9iwPIoSbuKTeN1xAa2dfGrgvCEpRXr6TrUAgJuhSF8TTwNHfIJc5A7rCLu2ihlcBfFOglDzWZZSSzTPlrOdo9SRoP0UwDLJ2GoDEg6TigUZk0RiQsauK5DEGnZriwQljSRDmBU5H0DK7Q7tLULmuTLHo4Pa0zMR7psv33JPQ+qEkurxF0aE69PN2m8do2zcGEOK9RnsJecCgek1itNOgwHgzxljSjm2ZxapLKOkl9lQIF/rzAWxJkZsFqQatTEuYkiQ/uMiCg40jM4F0af0MFbxm8JUGwJiBREm9REhcS/HnB4laB0oLW13vp8hsERUm7Oz02aqsVnY9CdkpiNyRD32kzeGcDBGmMgQLRkoSLPtiaqOmydEVEVNTkTwmshkS+dAG7BfFFdZoDmiSrSDyNFWmcZUn5iCLOQZhPY15QEBXSwOkon74m8TTujIM7a+PO2VgnfMKSTuMnZj28RUl2VmMVIpQL7Z70r6son74m6IC4lNCez+DfMI+20m0kQkGc1YwNLRDmJY0Bi/lD3SQZGO1fophtI5bTgOrMjASZxkc0TpRo9SpEAnYjXU/Xi3EXLdrdCqsl6HlYEXQI3KpmaZNDuy+muga6R1ew64I4tPEmHZqDGuY9mm0PbUOgbD6y/TMUxiXtA2V6d6WxSWuvOUllzGXmeRZBt0JEadtrw1YasxFBaTzCXdGUTiRkZzSNEUVzUFFdq5CPFFi4DBrD0Op2QED5EBRPKNr9MV17NcrRtHoE9bUxcV5TH4HsSZt2lyYsCdwqzF+hcGrgL2qK42ksU6tXUF2rcJYlxQPL9N1pkRmtgdDEKy6JmwbKtlZFtHsVQkHheHpJ3j8xQGMYrDbkp2IAZCQINrYQmYTO3RZKp9ckZ1nScTiktlqgZRrgmZnXKFfT2t2JZSsKVhuVUShHEBU07dBBuZrkZ5Y4eKqfgX+uERU0peMhQUd6rPXtEmSWFHEGZCCobQ8YvHiGKCdYWWOjfUXzmjrFwzaNIRCLLoVHPNodFrUxOL2vH6EAV2E1BZaTUF2fEHYoYi89BkQM3nIaJlVfk6BtRfHYmWSCI1mcZUlmVjAf5PEXNBt65kl8yJ8WiKpDnNeISKCG2jhLNkJDbTWorXWEAndjlcYL6rR6NU4D2t2ajRsmiTNQOGLjDjSw2qBc0nixUHPTK+47x2+uJ089Df89G13Qd0wA3vnOd/LGN76Ryy+/nCuvvJKPfOQjNBoNbrnllvPdNMMwDMN4yhKtSc4hMfZclr2QXfAdk9e97nXMz8/zvve9j5mZGbZv387Xvva17wuINQzDMAzj378LvmMC8Na3vpW3vvWt57sZhmEYhvG0OdcAVhP8ahiGYRjG00ahSUzH5Ptc0MGvT7e3XfMtlAOjG2bpvcPFagpkIOnZZWG30kqYzV6Bs2zR6tXIEBaX8hTGRVpM69+w/qWOG/5C+u8bduzCrQhWoiy5SfAq4NSh3ZMWwum92wLAbkL2lEVlgyYzp/GWBa3ra1hNSc/V01j5iJ5HYoJOjd2Em15zD+7aKsX7/TSo7Z96Geys0n3lLLU1Ca/d/BAqESgPcpMa21IoGyxLUT4oqF4UkptOsIK03YXrZ4lH24iqQ1DW5E5Lcqckf/fQDlrdaXGj/Mm0WNOq0QVaAzH5KZVWrF2WRBc10vW5P13vR6aGcCuCoCP9uXZfDwB9PRXKq1ZwnCT9xZl6QO0Oi6kbEu7Y+efExYTMhM3yS1oUv+tTPGKRnRJ4CxYMt1i5KGbLKw8R+4LiQx7LWzXbOqaJM+k+En1tSAQdN06TZDR2S1MbU2grDWpb3qxpjKZFmOYus6mOWtRn8/iLisJJhe3F/I/Nf4vdJD0jBNhNgW0n/PZb/g8H/9cmGiOC4o4FvHmLzr2CxIGul04S5xSnb/RYuCQLpMGWAE5NItsSEQguXXcSZFqsq7pO4VQl9ue60BKSmSx9l8ye3S7izGZyGhqrDY0RCDr0kzpTMzOCaDgkzmmcmqC1KqLjQIu3X/Jt5PVLrPpqQO60RPS34QXLCAXr1k+TH7dZ2dudBr8C/pyF0ODIhKVt4FY0IhZ0740JPtnPX2/5X9j1NMg5zqX7QFsauy7OVoFN2yNprGSQkaB4XBJ1KCZvUuk+uTghLIG7lH5oY1c3AN4xn64rZxEx/PeXfYbRzmU2PO8Er+x8mF++841U1yluvPFhljcKKhsT5v7vKpQDcTYNrlUZRWZOEF5XJTurSFyY+P9FNPsFyxssgnLavtIRwcA/a17+H3bhz0nirGZuh8RbgiifVqZ1Fy1qI5KkkBBnNSIU5LcsURyHOK/JzAvspqa+SqGzCV4lDZL1lxNaIxFRUWEFaXGvQ7/USWYxoa9YozBuYdctRJJWPc33NHjZdQ8C0HkowFsSfP7q2+h5WKEl2M0EVY7IzAjklM8nnv9Jopet8JLu/QTDIVFRs7DNJTOrKR9VVNenx75Q8Ok3/DFxYHPv8mpkPiLKpgHf//vyvwZgeaHAptEZKutzlI7A+H+waQ3H2E3B0lbBzFWC7kfSc73nWy5LjSzNPkGrL00cSKaz/MV/+VMAVFbh3TiPPlOY7G9f9T/PnvNv/I//hPdgnuyERf6EhR1o/OWE6sUhMtQ0V8U4yxLRthCvXqDrUc1vvfZz/MefuouBu2v8xdjnSTx45PAoVhu8FYX2FL0PKK66bh+XjE7w8y+/g/5LZ+jcB/9hw26sUKA1rOldTK+5jfQafmTPCFuuHKd+cZvLh08T9iT8+iu+SPMFdfq+u8xdn7ziR59sxjPK3DExDMMwjPPAPMp5YqZjYhiGYRjngcnKeWLPqUc5hmEYhmFc2MwdE8MwDMM4D9SZ6VyWfzYyHRPDMAzDOA+Sc8zKOZdlL2TPmUc5iQcfffB6nIbg9P5+aj9dQyRQPCJpd6XDxotEMPqNGl2Pavx5QZLReMd9KlsSnJrArQoK4xB2J0QlhbeiiPOasDMhur5C6Sj848QWgg7NfdOjxDnByGuOIxKN3Ugj+Ud+6QhRSaGeV8GpQeejAi0FtXUJ+W/kkRFUvjqAuy/L7OVp5H6zX/P1zz6PMHBoXVvHW5Zc/ro9nJzsYvmufkqHLf7+6CUw7+GuQPlYQH1XD62hhHwmYPmKiK5dDtVVFlEOKttDwr/vpfMOH9EZkJ0W1DdERAV4+9X/RH1TiD/hYoXpQX9yohvZltSHJUFZIkNB1HJQDnT+51PEWc3Pb7oHGUHfgxFuRRBvbBLnNLPHuqke7iCKLNDgr6SZPZmlGGfR5iOL11I4arP6xhPoyQzLlySUTsRYgcZbBnksQ2bSphm7QNr2rj2CI9UenLU1Bu5pIaXCWbapf6GfqKCprRaMfiPBX1KEBUF2SpKZkShXUz6sGPzOMtlTNnPP08y8QGMdylFTGapbI7KnbGqrFf6SxnESPrD3FXS9ZgLlaOYnykR5RZwT5OYSWp8aIMkr1n1sHK+aHjP+oiYzK5CBQBVjVFax93vrIJRYgSA7LVG2ZuFSTeeBBt68ZGqmg1xvA7eaZnQ4NUF9wCLJQM9DiuJxUMWYzJzAWxYUToK/IPAXBHFOs+ofW4S9MZWtMc6kS/4UxFlNdtzh2Gt8Pvzdm2g90sHx1zi0+jW5e7Pw3Q5kBFPfHKE+FuOuCOqjaSl5bxlEDOPfG6XnQY1XUyhX49QSLv61R/hPH/l1hu6KyE4LorIiO5W2ifUNiscg8TUdl81T3xwiWhZBZ0JYhK5VywgvYfRrdVZ/QbHms4uEAxFaQvcLpomKisHnTzB9tIewP+K373sVhw8Nsu/YELdPX8ObrvguI99IeHRpgLigGfv7GPHTi2gpQELpKGQmbfIvn4HdRRoDku49dXL3ZimeUAze3UIkkJkTVNdCdZXF3+65jHZPmtETd8aUj0eUjkd072lQOEFaon6Pg3ZAZxQrJ8osXJGgLWhd2aD/jnm6Niyy/q8i5p+X0N7QZv4SG5FJQKfXi8JxWPO3AbM7HE7OdtEY1I8bYqB1vMjXv3Y5iQfjP22T+PCGP/01FrdZODXwDk5B26LzYERmVnBnfRPR7g5qymfDx0O61i4x8pkTNPsF08/X5E9KorxAJPAz33kzSE0j8rBP+lgBtPoVr/76W7ECgT3vsLPzBG5dsXSpYv3/atG7K82UErFg459OsXBxjuIxaHcLmtN57DbIGGQuQgaCN3zmrTh1gUgE8yc7UHaaEfSWQ68nzmukm/CJf7iR1qVNGhtCgg5Nq0ty6hXgzjhYYXqNjsfa6HyM/nw30RsX+eBnXsvfHb8E2Qy5bXkn3btrXLrxBMVxRatH4pQCZndK7v3mVh45OczHd72AWtsjOxtxx/QGisc0/aUaU9VimgHnC8KhELoCTnxxDaX7fe7es4HsKZs/O/wC9HiOg28tsnJl+JP7XtLnPj0bPWc6JoZhGIZhXPjMoxzDMAzDOA9MjMkTMx0TwzAMwzgPFILkseqKT3H5ZyPzKMcwDMMwjAvGc6Zj4i4LdMMmymlUKSI5WEAoqK5XRIU0mCsqKE7eXGDmWoWMQQsIOhNIBK0+RVDWLF4b4s5bWE1J8VgLEQlKB22aVZ92l2DhWCdJRnPN0DidByIWP7oKLQSZeY27LHnw8GoQEB8oEnTA/M4EkWi0qwiLAi2hvlqBBJkIslOCcDCiNaDgVIa1vQuI7RWmW0UIJe1uhYg1lqWwBpvEGTjxcg9rxwqdj0jakQ2hpNUtyE8qWmtCckdc2l2CxrBAJ5LMogKp8Rc0dyxsQnoJaGj2CbwlibVkIxRkpzVBCcIOhX/CRUuY+PJq6A247d7rAFi42KGxOkHN+jg1gT9jkZ2SuPfnkZFg6jpAQWW1Q3ZG8IUvX0VtfczplTKZWQGWZv5iG6+iqF/VRHngLcH+UwPIRNP7bYfYh/lGjtZclla3C+M5+i+dIc6k5cZlCCvrHFbWW/Tf26RwWtHuTsv+L20VnHpZB1pAftzCqkvU5jqTUQe5Yw5RUaMzCTKC+kqG5kKWnV0n0mDTKQcAGWnaZQuhwFmymH3FGNmZiHaPZunymOagpr06xFlw8KfttNS70GgBrV6FTNL9PHVtHm2BDiRxLAmLmoWLHdo9isSHKAcLl0havYLiXhflQG1bQG0VtK+qU90SoTtDpq/Jku1qIkJBVEqDcwvjIDRQihgZm8dbFjjLEjQ0RtJS7U5D01wd4S1YtLa2KB9KS+pXtiS41TSwVShNmJNYLcHMlR6VKENtbcLJl9ooB/xpi5VtMSsbNeGKR1gUJB0xlft6IZCIUDC4YZ6gQ7N8oIt8scXhN7lMXudw4me76LzPwVsWzK4UKBy3OP3AEHZNUt7tIqd98uM2smYzmltmd3WY+pDN9GIJuyEY/w82iyc7KB8LKRy1iPKCoFOx8u1+enbHNIY0lfU5Oo5GxBlBbcTDX9JUtoeowTZRHqwZF9UbkpkXlHc7nLrJYmWty+LWHFFBEHRp4kx6DbGXbeyGpPNhi757FfpUlrlrewi+0cPEi3K48xZ9X3Nxa6Db6fERltKhLmqrPDLzml+79Jv4C4JkIMCtCvKnwVuQJB4UTmnseloev+dlE0Q5TZKB6VeN4Xa2WdrsIGO4fde1JJ7mS5MXs7g1x8JcEV0ukJnTdD0sUQ7kJ5I0kDkXUi43KLhtlAsygc69AuEnRLk0UHsxylEdtRGBYPyncixtAW8ZCic0rXU9xDlBfQRafZr8UJXCaUVmJv1LXdvpe+QmNXZVUt5v49UUMhL0ZWs8ljQiQ4Ge9fFPuag1LZyGJnvSJnHBrSv8KZv8gxmIJCub0iEtguGQ+kyeiZd2MdUuUxvLMdcsUBuRaAmcyJKdEshNdQZ6KuSPOLQCh5mdLrMTHQitObl7kOaRMiKB+iqFtejgZ0PiDFhtjVMKaPUpfCcm6o3wZ36yDxGUPvfp2cg8yjEMwzCM8yA5x0c557Lshew5c8fEMAzDMIwLn7ljYhiGYRjngblj8sRMx8QwDMMwzgOlBUqfQ1bOOSx7ITOPcgzDMAzDuGA8ZzomTgM2bpokMyvYuGomLTe/oul6WGAFYAVp5HjQnSAiiVCa0lEoHbboWrtExz6BFQhEw8apCxJfU12TwakLale2cCddaltDuh6W5E9I3tZzB9oSzF8mafeCtsCpw8jwIk5FUjgJ3gogwKtqrGyMSEDZoB1NayAm2tCkeCrGmXPo2CdIcoqdnSdoTec5cHgIEUpUMaa2GhpzOcZ6ltAS8qcFUmjanSK90achLGv85ZhL1p0mcSE/oSmc0IwNz5Odiynsd1nZpmhELtesO4aMwK1C0Knou1fjLUrmr1QkvkZGgvZQhEhgw6sO4x3K0D+0jN3SZGY13ryF1RLYjXQ7NQc0jW0BwZoAb94i7I1xGpp2F/zp6/+K/DGb9uES7R5N9y6b3t0RQVmSVF3ESJPqWoWOJPmphHa3QFuCNR2L5E7aVMYslKspem3a3ZrH7my2u9J/j/+Mz/RLI5yaQAbQtVfjViHJanIzCm1pVCJZiAr48zrNZgklYUlw0ZpJ+keXcESCt6wpnNS4q+uEJUFjUDB3OcRDAZ37W9RGXNyqoPe7NlpAZtxl3c6TxFnN2GUTZCactA2xwGoDlsZuQ7K5AUrw0+v3EvZHqLQaOFEeZAjFY4CC9pnsEHvOpXQEopksVs1ibGiBVo+mNZVH+wp3SeIvaPrunKd0LOEV2/bwyqE91NamWRod2xZQnqb3oRDlCGTLAi0o/7NPu1Pg1DWduyWZOc11l+8nKEmiPCCh82DC7m9tZPslxwGobgvJzGucStpopxhSH1Nkj7mE61pkT9mU1y0xOdVJ4URaQr95pExm3CU7k2arRHlBlIeobVPdEKNG2iCgNqawx+r89M99F3dJMtUqESuJjECczJA/lR7Xfl+Dxa0ujSGNW03LsVsBnPppnWYZxWkGmHJAWYLYF8iqjXs0g9CghtrkH/EJSyC0pnxA4NY12oawBImrceoadzk9sBJP0/u9ZSprLHoe1lhhmuUU9Cj670sISgItQDYtvGWBt6xp9QnK+2o0BwVHWn3kpjTPWzeOv6hZvkTh1CE7I6isIy0T3xS8vP9R8hPp/ug4HOI8nCfo0ASdYDUkCHjJwAH8igINp1/eRdApaL2iigzBqafDKqzpWSTvheyZHEQoaPUIVjaBM+mSDAQEXZr75lbhVTR2S9D7oEI7migPXlVRG3XxFzVuTZA7JVBK0uoUZJYUKrRw6hL5wiUgvcbVRzXL6y2UoxnMVFGFGCGh55ppvJE6dgv6/tZDC+g8lJCdFgQFSd+1U5SPxmBp+u5T9F87yaXrT7J1c3q9en7pEEFZMrNUxK1q6qsTos4YGWrayz5L9SxJBnKZgJ49MdmuJsubBP3b5lCewgrTYSKcmuSS/imUA4knsB/NUTwmcayEvjts2FpDt61n/PvoMY89yjmX6dnIPMoxDMMwjPMgQZKcw/2B5Glsy4XEdEwMwzAM4zzQ5xhjok2MiWEYhmEYxjPL3DExDMMwjPPApAs/MdMxMQzDMIzzINGSRJ9DjMmztCT9c+ZRTnB1jUMnBogKMH73KEk+DRsKi4LEg9hPo8qzUxalI5KVbYqVTbCyPaSyt4uwJIhzGm/OQiSQmZGERYFQUNyVofvyWWTFZmFnQn1U8YZHf56gJCkdhuJxTVBKs38mpjuJSorq2jNjTxy3qa6SJKFEeelYFuRjsqdt1LzP8gabrj2a2ipwlyz+174ryR+3uHH7fmRniH86zQZ5yWV7mavn0TZYgaY6UyDOQhDayLbEagtO3+Bw/ItrUY4m6BDM3xAyPtXN5HUOtfUx/rTF6vwSC+08/qImLIK7IlnaaqFcEFGaMeBUBNkTDuXD8NCxVaDhQxv+nvowCAXt4RBtQeKBSATK1djTLv4RL82GOe7gVRVWAL/x6KuJ8pwZi0ew/OIW8xc7VMfAXrFQE1lykxLRsljaaNPuTLMkDs734Vy9RG5a4S1Kxr8xRud+TVxUWFGaZeVWoHhUUL7fI86m2UGxn+5vd0UQewJ3RWIdzRAomzgrCDoE2QmLxIV9D69m9nAPvoxY3gJL2zThyTxhUaNc6NojyBz0kUGMcgANy5shNyXwr1jkwNEhCuMw96URtISooIm6YpSVbktvWfH6LQ9QPGjzuUd2kDvqpvuvJXCr6ZhEudmYqKApHkuXhzOZW4UYgJwTIhTIQGDlYhIXVjbA8f/Uy9RLE77xlSvYtbQGb8EizmnmT3XgzVvM7HQJC2A3BOGqgOradOyQ2hgsb0k/586DG1AOuDWNu7ZKq1OiNjR4+MBqnLqg4wGH5a2aqJSk4z8t+fizEn9Rk30kQ3MsYuVIJ7JqU12nUFdUUX0B2RmNjDT5CZW2PYIr153AaljYTkzia7SEoOHyma9di7cMvV6dy8qnqayDeDBAhuDPWrQWsvQ8EtB/r6IxKNAWtPo1I18R1MbAX4jITyVpBkZbozzwZyVRQZF4Gud4htqmiHZPOj5SnBEkTno85k7r9Dh2BYXTChlBbkJy5I0dZOY1YU7Q+cgy/poaI/+UMH2NRW1Nel6LBBIfljdrZAgq62DX4atHtjB3TcKuQ2upjkHuhEXiQW1jhLbBbkLia/58zwtAQ6tbYLXTa1V2GoKehCSnsFqCYXeJyhoL0bboejQiO6OxvlvCqWmWtjgsXppw+iurmTjYR39HjcymFbwlTTwQsvp5p7GnPGQkqLc9cjMR0UhA/lQTbyHN7FralI730+wTJC4EHWBJhdOAVrdE1G1kCPqbnbR6BZlZgVzVICpouvYqcnZAxwMOmWxA5R8HeOXaR2l3a6afL8jNJtQHLWQMyoVTh/qYeZ5F1302tVGLyQcH2T0+wqGpPrxlzZ0rm3BrGp0IKhs0+ZMWIpA0BwTEIh37LIalmRKnX6GQ95aICppG4NL5iMRbSjOekozmgdMjKE9TW61prQkJCzB9qJfEFcTH85T6az/x7yfj8cwdE8MwDMM4DxQCdQ73BxTPzlsmpmNiGIZhGOeBiTF5Ys+ZRzmGYRiGYVz4zB0TwzAMwzgPzj349dn5KOc5c8ckjiwIJWFHQtiV4BRDooKgtkaRndZk5jV2My2THRYgM2lh1wXWskM8GBB0avIn0gDWqKhxK9DuEuRPaRpDmsX7+pCBQETprbUXDBwlcQReVdHsE/hLGqemGR1cpHg4LXkcdiisNuSmNPasS2MkwVsU5PZ7rH/pMUQC+UlFlBFEowH50xoVS7QN060iScUh6EoQCu44upGV+TyZWU1tNTjLFnFOEQU2ylMgNO5KGvhpBYLqhgQdC4b7lolzGpEIkovr3P2Nizj08Cj1UWj3JYSr26BAW2m57zir0+C3TW1kBLpl0f1ozEenXkR+AmSiES0Lpy6wQujZnZAdqRH1RlhRGnSqLVhZcybwTZ/Zph0JtbUJKpI0xyLcqkAm4FYE7S7N+s2TuDWNvyiojwqak3kaBzrS4GVfI69YwQrBrkrCosYKQCiN0OAvK+xG+rlhSSA01DeFNPsFdivdD/XEI/HSMvBWAN6KRsRpkGm3XUM5mtJhARrirCZ/WuNVFIM3nub4qwt0PdrAqWninKYxrFmeKOEs2lghVLdG5CbSgM7SXgflglMT1EYlf/PgVdQ2JJQ76ygHnBpEJUXQkQZYB2WL7IwgLAt6HtIkOUVhIiJbarHt8nGm/vcYdkvwvKsPopZctJ2WFo+KCmfWIclo9s/0E3QoisfBn7UJ+hJ6H45x6mnAtzXn4i8Ikky6fqKvzcLlCi8Xouw0SDiKLBYvT/jNS74BtkaGAgT48xJcBf3pcANawtIlCY1VCcJPUFmF8hUqo2jP5hALLpklRdfeFoUTbfwFTW5S0+k2yU4LMl6EXU/fm7pN3BciI81KlCFvtfGWBTq0qGwEEUO+r46yBVoKMrNpKfXE18ReGni+ss7FbiaIGNxakgYhlzTK03hLAoRGNizsuqS2Kt0eSUbQ7hTEOUFutEqSAaepiHPpsADa1sxdE9MYEZx+WSfNyTyLWxyKRyAupmXWtYR2b0z+VHqJjTPpOV/ItcFREErKh9KhBqwQiAV2TSBjEEogpKJnd5M4p5l5XhYZgbIFdkWChvZgxAO1McKSRmcSnFpMWBDkJxKag4LO/RFOxaK+IcJdkfzO2i8TJ5I4I8gc8mhELomn0VKjNcxd5uKe8Ag7vPT4r6bDTzQGBMpJh6aISprqUg6nqchNJ2g/Ic5qEg8KpxXtHo3nRdgNgbIFn73nSoQC78slMguK3UvDoNOhP9xKxOCXJ3Armvoo5MctyofSYN/qxpionOBlI4qFJu1uwWy7gBbw8s2P0n+PJihrXvy8PWipyU7YZGcE/fcFyIZF+SGX/u818ecktSNlEj+9JuemNFZT0N9RQwP5UwK/GBAVNTIGLdN9m3HDn9j3Uhpjcm7Ts9FzpmNiGIZhGMaFzzzKMQzDMIzzQJ3jWDkmK8cwDMMwjKeNiTF5YqZjYhiGYRjngUKaOiZPwMSYGIZhGIZxwXjOdExUIrGKId6ChT9nk7kvR3Bpg8K4ZGWLJruQEJbTEtl2E/zFtOx40hVB1aHjoKbdA04d8qegslFRHFe0uwVuRaTR6QXF4Pp5dGfEzsIxOvfVUY6g74E2XbtmsEJNpCSVzTGdj2o6H5HUrmyhbFCeRudj4hzU10bsmxwgMydxGoqgS5Dd51NdB6P9S3hLmuV2Js2GKEdoAX4mZNXoAk4T8idh5IpJkryiXG4gsgnuiiA7k2aNJJ6mcMyis6/KxNFeikckmUmLJLbYfsMhtKuJhkIG7wTdsmmvCrHagtLJGOdMdosOLNy6QmRjpq6xODDXR3UMKmOSsc3T5CY1ygF/IWRTzywd9zvU16SZIB2HEryKptWjaZ4o4q4AUrPm7yLW/aVi7POa0X+YB6A1EuEvCI7uGSb2BfVVaSlxu7uNUxUkfpoBUcy0md8u8JYFTl0QZSHOCjKLCgTIGJJCQlhOM2+KXQ2SjKbdo9G5hC6nQW5GkfjQ6tG0ugXK1xAL7quN0fMALF0Ro/sDVCHBX0mY2SmZWiky+N2YyrosXiX968VbEohEIEOorgU7l5Yb7zgI9Z0tZAT2tipd+2MKnQ1IoCvXJHHTzC1/1iI/oXEamsqYpN2tibJQXS3Jj1vUBx2CwOHQHWtxfnaO9nDI3bs3QCnCWdVIM21aAn8hjdh/3caHKB2WZOcSgp6E8qOSUy+DwTtXQIEYbdD7QICIoXgcVCTpflCSJIL+e2r03LtM3HYY+qbggw/cjFcIsC9fpnF9g3Z/QnGvi1py2XrDYXr2xBQP2ehcTFdnHaecZqEU+2u89MpHKB6XTL1AMH1tlsnrsiReOizE8VoX9bUJfYUadkPgVCReXxPLS4iKgtf33sM35zcjEvCmHLRIS5mv75onKlhkp9Oy+rlJjSrE2G1F6ViLvm/PMr/dJTufoByB1YbOvZriIQu7qQnLitwpCRL6HkywQigdj8jOauympjFZIPFIM+zmJd5yum23bpwgymlWfWaSn7r6QYb/cYnsYkLPPRZeRXPJjmPkT9iUTsQUTyU49Qi7BZVaFtFOM3ScliYzn55P/oyN8tLy9e4yrOtbIM7Z9DyssJua+lhCVEizuYoDNfwpB09GyEDgTjnMb8/gL2kSTyBiqK62UTbgKNwK7GqspyPXonQyQlxRIUwsVFZRPqTZMXia3KQmOw3zl7g0hhVLWy30mdL8QZfCXZHpcV2zsduKxYssrJpF/z0J/qLGChVxTlGvZLBbYIUaPEV9FSw+L6bVJTl8eJC4NyQupcMpJD2lNPvH07R7NVEuHZIjd8LGKQVcOjhBreEz+rUqPX6d/Ok2u2bGaPRZ+IuCbzx4EW5V0NrYpvtnTjNxvYvKJtgtzdSvRRRPKjo3L5K8eBl0mpGnXE07tonLCXEWNvbOpVlEXRFRQeAuSWpt/yf2vZRocc7TU/Gxj32M1atX4/s+O3fu5L777vuhr19ZWeEtb3kLAwMDeJ7Hhg0b+OpXv/qUPvvJMI9yDMMwDOM8SM4x+DV5Co9yPvOZz/DOd76T2267jZ07d/KRj3yEm266iUOHDtHb2/t9rw/DkBe/+MX09vby+c9/nqGhIU6ePEm5XH7K7f5RTMfEMAzDMJ4jPvzhD/OmN72JW265BYDbbruNr3zlK3ziE5/g3e9+9/e9/hOf+ARLS0t873vfw3EcAFavXv2MtvE58yjHMAzDMC4kSstzngCq1erjpiAInvDzwjDkwQcf5MYbbzw7T0rJjTfeyK5du55wmS9+8YtcddVVvOUtb6Gvr49t27bxwQ9+kCRJnv4N8libnrF3NgzDMAzjB3rsUc65TAAjIyOUSqWz04c+9KEn/LyFhQWSJKGvr+9x8/v6+piZmXnCZY4fP87nP/95kiThq1/9Ku9973v5oz/6I37v937v6d0Y/8pzpmPieBEvXHuEOKsJ17VwK5qOr2bxVtJS682utJw2QNAJrR5B6ZjGyUTY3W1qoxK9tUZzQNPsE+hcjLYEtXUxQZcmKSV48xbTCyWKD3lYaJrDWaqrJNVVHiuX99HukswuFcmdtGkMSNrdAjHj0eoVKF+RPezh1AAB3R012hc3Wd5gkzigbEhczWIjS+/3Fpl/qA/RtGDZRXnwnzd8j+VmhtiHVr9g7mvDdDxiIb7YhdaQn1K0egWsa5CsbVFbmxDc3U1+qEr1qhbyyhVUIliVXUK207LVyxst+u+S+KddNr70CFPXWCQ+FF48Q99dFjLW6ERSOgqXDkzirQg6jigm7x6msgG698RM/JeYQ1/cwPDrx8metLFamsWtFmiwRhv0bprHX1Z0PmAzeZ3P6XckzO5wOfmqHnITINoWrV6N7ogIr61RPpCWKy8Xm3QcTgPvRCLIOhFROUHtrNDuVngV6Ho0xAo0VqjpPBiDrQjLCivU1E6WsNqCqDPGLwRYQqGFIDudlisXGkQoWLd2howV0RiSEAlU06ZwwEFGGm1BcymLtxiQmY+pD0sKxyWNNRGdj0gKly0ydtUpLh09TeIK8hMh8qSPSEA9VGL55+vEsQVacGyyh7AroTAZE21t0hgQ6ZAEE5p4dZvcVQuIM3+gLFwTkXk4i7cMvh2TOeGSP25Tus8nOplDS0iymrCsyU4J/ua+q6muV0y+UOLNW+RmFfn+OmFnhuI4vGz9fo6/1qLzUIh6+TI6kSRuGoDZHMzQXF3khZsOURu1ULEkCm0aNZ94OkvupEVUBBEJHhofpdFroTwgkiwf6iSqu4iWRTtwuH92lMaQprw/DdDMTWkaQ4LauoTDjw6TP25x8s5V2G3o3pugDuVJmjbi6mU+fOIlRIlFc0gTjAZ07If2qpCH942hpaDV66Ic6NzXRNZsFrfYtHs9VDFD1/6Y+qBFlJWUjkdpafeWpuNwiLdkUXjJDHFOUR2x6drXxqlGdN89m5aHD9NhC+rDFkG3orIloXASWr87SN99iuNvGOJLd13OwbcVqI7azF8X4jQVB2b7qW8IcSsx7ZKkMZQh6AQVWqDAKbdZWScpnFbULm2DTM/x8rGInj1tltsZZq5wWVlnkZ9OQKUB+QCeE+NcusyW7BR2M13OrWriLMxfDkJD4gq69gCh5NrXP4QjEiYnOqkP2rROFZifKoOlqayTxMoizqZByL0PBlyx8zDJxgZWMx0uYs3nW3gr4C1pCqsqzF/s4C2B8jXNX1lh6UVtFi6ysRoSseiSn1IUDyzztivvoHwI/NMOF71uP/2rF8ke8XDn0wiCoMtn4SIL5Wv6L50hzgjsRnqMRy2HR+f7yWVCpq8pEiQ27R6PpZUcCKivVthVC6sNXjZi+p9G8C5aQfgJ7R5BcynL8iZBK3QIH+nAX1GERQ0Clvd145QCMs9fYKGVI9lSp/iQR9+9DUa/ViHvPfHdhgvZ6dOnqVQqZ6f3vOc9T9t7K6Xo7e3lL//yL9mxYweve93r+O3f/m1uu+22p+0z/i0TY2IYhmEY54GCp5xZ89jyAMVikWKx+CNf393djWVZzM7OPm7+7Ows/f39T7jMwMAAjuNgWdbZeZs3b2ZmZoYwDHFd9ym3/wd5ztwxMQzDMIwLyWMF1s5l+nG4rsuOHTv41re+9S9tUIpvfetbXHXVVU+4zDXXXMPRo0dRSp2dd/jwYQYGBp6RTgmYjolhGIZhPGe8853v5OMf/zif+tSnOHDgAL/6q79Ko9E4m6Xzhje84XGPgn71V3+VpaUl3v72t3P48GG+8pWv8MEPfpC3vOUtz1gbzaMcwzAMwzgPzn2snB9/2de97nXMz8/zvve9j5mZGbZv387Xvva1swGxp06dQsp/ed+RkRG+/vWv82u/9mtcfPHFDA0N8fa3v53f/M3ffMrt/lHO6x2Tu+66i1e+8pUMDg4ihOALX/jC436vteZ973sfAwMDZDIZbrzxRo4cOXJ+GmsYhmEYTyOFOOfpqXjrW9/KyZMnCYKAe++9l507d5793Xe+8x1uv/32x73+qquu4p577qHdbnPs2DF+67d+63ExJ0+389oxaTQaXHLJJXzsYx97wt///u//Pn/yJ3/Cbbfdxr333ksul+Omm26i3W7/2J/VkW/xwMwI3ZfMYVmK+ohg8eYWS1vSEuUyAe0pRAx2K80YaPYLooaD/WiO5oDCvbdA4mvCTkX2mEu7QyBCid0QSD+m3Rvj+RHVywIebKzGailWfeoYixdrWt0St6KRpzIEZU2Uh7CsKR8UtHsUIpeWaa6vScicdFh6sBdrPIO/oLHboB1wVyRaC069spu/fN1fkDtlYbcEdh3+buJS6uMl2l3pgdq+rEl1Dbz8rXdhuYraqKQ0rlCJJGnbFA9btLa1aNR8it/LwPfKdHfVWAjyOFWJttKS37VX1xAx7PvuOnoe0uRPaoLP9TF7fczKGhtr2WZpe8KJaifl62cICoLi5fMknqY2bLPqfyjqm0NmPjlGc03E8jaNW4Puh6vkvp0jTiwWLhE0hmHgeyH5r+YpnlD4S5ra81vQEVI4CWLJIT6eR/7UIsPfClk81MXiNguhNAhN9VPDyLYkPFakfEigBZx+cZqdMf2qkNkrLTLjLiIS1G5snC21jYbsN/NcnDmFv5wQFtNS9O0tLfx5ydyXR9iUmUZZUDpo4yzY1NcmNLttyofTcvMnX5ZjaZMLAqqbYmTDovbiBsuVHFNfWcX+uTSo7PRLXNSqNvbOZcKSZuAPHIY6KmBrujrrWA2Lmec5OAeytAdjGkOCzGKMcyxD67vdRAVobG/hn3JpX9qk/rwmEw8PEnYorn7tw+ReMUNp4xKlowptadyKoLopZsemcYqHJdlJSenKOSZerHG/ViLxJN6K4v75Ufwpm8SVWF/uQNQtqmvh+HdXUVljs7jN5tsHN6IFXLRmEhY8xKyHtyBRNnjL4C1L7AmP2hgoC7InHJJigjPnsHbzFNFshoW5IlFvRKtHEJbS8yvOaMr7JFsuPoW3rClcOU/iweTNCXFO091fxftimZ1dJ/jtsS9TOgRyxaHVI7hm01E+9KLP0eyVFA+uoG3N5PU5ZFtQOJXu27jgMX2VRXZe0eyTLG9yaPUJgk5BbcQlyiuiT/cx8o20BP2x11ucfnGW8Z/rpz4i6LsX7Ab4CxpViik/Kqms10y80KW62sKtQOdeQfGgg0igsNejNmIRTubwT7nMX+wTdAka/Ralo4pSR4PCSJVSvo2WEBQFhYd82sMhhRMwv92hOuoxO1umNJ4+06+stkFCq1egbZg/3YH4dgezUQltQe/2WexAs7wlzdqRUXrNqw8LcDRfP7iZhyoj5A+7VMeg9z6wsjH+pINTh9lWgcYQWCFMvcDj/ns34N+fQ9uaxIeTL8vS9VMTNPsF9RMl+u8LyM4pLr7oBO27uln1KUnf/RHZWYG3KKmNSJqrS/zpPS+i1SOQEZz4o42s1LNpVuRAhAxisgdnKY5rZGfAxKFe4hwg0uuiaFlEkY1jJ3Qeiuj1a/jzAeVSg9x0Qt8uGNg+Q5wD+94CzcGEofdperurhDvqPFYQNQhsEk8TZQWFE6Ac2HntATKZkPgfu5mc6iSey1CYSBBKM31tiev6j/7Y3y9P1WN3TM5lejY6r49ybr75Zm6++eYn/J3Wmo985CP8zu/8Dj/90z8NwN/8zd/Q19fHF77wBf7jf/yPP8mmGoZhGIbxE3DBdrfGx8eZmZl5XIW6UqnEzp07f2CFOoAgCL6vCp5hGIZhXGiergJrzzYX7Fo9VoXux6lQB/ChD33ocRXwRkZGntF2GoZhGMZTobQ45+nZ6ILtmDxV73nPex5XAe/06dPnu0mGYRiGYTxJF2y68GNV6GZnZxkYGDg7f3Z2lu3bt//A5TzPw/O8Z7p5hmEYhnFO1Dk+jvlxC6z9e3HBrtXY2Bj9/f2Pq1BXrVa59957f2CFuh9maW8P3fkGcwd6yGUDnDqoZQ+7LbACzcJliv47JUEXODVY3K6or44RjqK1KqLrEUFQ1ggl6HhUoDzwVjTaU7RHQlTDwa5atKdy2DMu1TjD7E6Hk29cS9cjAqutCQsCd0MVb0UQFRSZWUF9FLJTErHgYrche8qiPZCgLcjMCISGKA9WG7StyfsBTg3+x8mbafUroo6YxId2bOOP1lAehGVFPtfGqQn+Ztc1yJM+TlUTZQTyeAZ30qG6IWG0b4liscXKxTFBp2Z+poRnxYRlRWskonO/orGYxQoh8TUy1kQFweIVCe5MOvy18jTenE3eDZi/vw9tQevOHhDQHNQkv19FrtgsvChAZmLypyStHk1zJIdy0tuQMgJvUbC83qU+IogzAmUJ8ndnsWY8ooJAKHBXBK1/7mZ5o0tx7Qqxr2l3SqK8pv7TVfKnJfkTAjQIlW7X7Iym5x89Skcg7FQUTggK38hRWrtM4ZjEqtpU18Jv7f0Z5i+xaQ5prLrEOZahuT6kOaA51u6luSrNmhIa7JrEbmuCkkBrwejXW2TnFTKCwpG0r2/tzaNnPWrrY5JEUt2YIGLIPJIhvrcDuylY3Jbl6OEB1m6aojPTRLma0tE0M6z/LondhPqATVRUtPoUQU8MCx6dBxVKCZKKCxq0Bd8+vp6pQ71UDnfSGJKISNAYUWQmbA4t9FJdr2j1axYf7cGbs2l3Ck6/2GL6+TB1qgvlaiZfKGl1C9ACLSG7fYmeh9v03R9w8dgE2VnFZLWIchVJZ0xrOEZoaHdBuyehc/s8VkugXNCXVXFnbOKiYny2C6ci8SYc7AUHf0kzcE9Mz+6IjgOQ+IJYSZYu0sxPlVEOFPe49GxcIE4k9WHB/3voSv5u+XKsEOymIDun2f3FLXzg/70ep6459YoucqfTy1nvQxqh0u0yf4nP+o9PURuRxBnQEmIf7CbYbY1yNe0uwfwlNpllRXG/Q9ejCf27QtwVWNoqkJGmOSDI73epbNCUDwn8xXTMJn9Js/SiNnZD0xzU6bhOEqymAAmZBYVIoO+eCvVhSWUlS62SYWGmSGZeE+UEtTUJ2WMu9VWgRZpNY3sxrS5JazjBbmqypy3ypzRBp0L4Ca1+zV89fA2dB2PyTkj5oXmsQJAZreFWNdk5RXMkxl5wuGLNSdqJg4ih61HN0mZBEli0R0LciqYnU6f/3pja2oTuPQkyFLT6NcqF0nFFZl4wfmgAbWtUMabR79Dokzw6OUB9Tcz8do+lzQ5hIb3WOjVNo89mx8YTKBsKz59j7nJJIZtmU+YPuEzcUODYLcO0uwSF72Wx2hIZQeXKNt27NZn+OupwnqVKjtkdDl85tI3FbRmCyGF5g0VtlWRiupPMrKaxLcBqSo6/roPFR3so/WPuTDsEI90r+IuCqCBo9guUrdl17yZa+8tUNif4hYDcaYuF1zeZvC6PV9F8ds+OH/8L6yl6ukYXfrY5r3dM6vU6R4/+S2rW+Pg4u3fvprOzk9HRUd7xjnfwe7/3e6xfv56xsTHe+973Mjg4yKte9arz12jDMAzDMJ4x57Vj8sADD/DCF77w7M/vfOc7AXjjG9/I7bffzn/9r/+VRqPBL/3SL7GyssK1117L1772NXzfP19NNgzDMIynRYIgeYpF0h5b/tnovHZMrr/+erTWP/D3QghuvfVWbr311p9gqwzDMAzjmXeuj2OerY9ynp1rZRiGYRjGv0vPmY6JHmkh0XTsEyR3dRJnoft+SW5SUx8ReEsWszdFaAErl4aUDll07LGQcx5ITZRPg/rUQJvGsMCpQlQQZCZtZN3GKobYTYHOKJxKentt+Nstgg6NskEkgITgcJHmSELH/rRsurage2+IDAUyJC1PHwqcqiAsgb+U4K6Ad80CakOD5/cfo90LV3WNgwCnGBLnNfOzJVrTeaw2+HOSajVDVFLgKKKumOWdYRqINg75CfCnLea/OYQQGn/GxlsSZE64fOfkOpy6wJtxWHx1AyxNYzgh6YyIchI0WDULtyqIs+D3N5AhHD7Vj/LS9alvCLGaAm9RsPi5YUrrlskc8PEOZmj2a4SGhW02yoXKni68FUGcA7eqKZzSeBVFZkmxcmmIHmoT5TVoQX5Sk3hQH4WV6SIyTgOQla9ormSwm5ramCbOCNpdkHhghenP89fEJMUEf1khNCwvFLACjYzTYMpWw8WtQpJT+IsCqw1OJqK4eZGFME9pv03QQTr8QJAGPgoFes6jOeDhrSQoGxrDClWKafclqHKMP2XjODFORWIFgkt/5lHCDo2M0qEPMhM2Rw8NcPjIIP6CRMYQdmhqI5KomAYb240zgZazNlYgiH2BXnIRsUC5mtzqCratyI7UyJ+UhIU0QDM7JXFqsL5rnq7dAqslkAGUjyiiomb4DkX+hIVVs8jMCvp3aawAEJrBuxMqlSytXhcZJKzNz6fbbbyDzpEVpJMgm5KgQ5Nk0ruec0e6Ccvpz8mBAnpdMw2YXPbQ65qUL59HJLC8PWb2CofaiE2rRxB0ao5O96I7Q2TdIiwpysdi5g93szJZJM5r3nXV1/nS/ouorIfS9gUaAwIRQ9CVEPvQvSfCWz7Tjh0CGaf/37+rxumfHcJpQNeBGKeW7vPGsCYsCi679BhhSRPnNItbLKIcVFdbLG5zifIgozRAffjry7R7Nf68pLIe4kxaer/r/gUcN6Zzf4vcRBpwaQWgbcif1FTWStrdmnZvFoDS/T7Z/T4jI4vkZhOagxqnKmn3KUpH0uXsFrxk3UG697YpP5peohurY6KcIHda4ucD4pxi7fA8ldU2Rw4OsXJZD8PfigiOFKmuTa95VkuCgke+vonVuSVq20LaZYnYWAehyR12ifKCZuzS6rLpelASZQRuReCupNejlbWSxpDGm7WQURpYnpuOyM4rfD9i9Ctpe2WQDvEQdirsNoQlwZ6JIRqbAhb39uBUBfOn0qDvxoiiZ3fE4HcDuvaHtHrS8y5xwR33mbla43+jSLyqjXMoDb6/ecM+vKqmMZ+lcEoRZ8D2I7QEpIbRFmFHQtwVoc48B9ASjh/tpzGSoCW0xkJKh9PzKeqLyA7WcZ2YKA/RiTzNrW2UA7Li/CS+kgBI+JfHOU9tena6YNOFDcMwDOPZzDzKeWKmY2IYhmEY58G5DsT3bB3E79m5VoZhGIZh/Ltk7pgYhmEYxnmgEahzSPnVJl3YMAzDMIyni3mU88SenWv1BJJYUnDbrGyCxkha9jsoC+ojEOcUiasRyw7+IjgLDsoGGUPSGSFsTX1UIWMQcx5xRhN0aoIyBB0KqyXQcz5hp6Kjr5qWWJcR9UGP/ClB+VhAZimNDI9LCblxi9oYtLs1/oJg4gYHGUFzQONWJFZb0BpOaI1GVEdt7Lam/JECmV15ds2N0blfUU88lK9JZjMoR5MttUBAWNaIBFw/xq5LCl0N7GWb/n9yWNqu0pLx3WnmQGlcUW/4WC2or4uIM5o13Ysk61r03xdR/occ5a46Tk3Se4dLfVhQGo/pOAD1tRHde2OiYwX6Hgi5bO1JrJaga28LOxsjFCDArWmWZ4poGxIvbVuc1fTsjpERRAMhUR6aa0PykyHz10Y0eyVBUSCrNknbwlsUrL3sNCsbwK2m2TBDqxdQNixerJGhxMlGJJ6gfAiq6xTtwZj8hKZz1xR9XztF4aCDVbFodUvanYLXX3ofUUGQeJorbnqU37z86yCgY09aGrs1nBDVXJYmylxePIHd1OSmNM3RGKcOmbkIf0mnEf4ZydS1dprRAmSOuWQG6whbkZ/QNI+VQEFuSnP3sbWIGMSlFZyWovsF08hCxNuf/w1aAzFRVhB3RhRPKKKSoveBJnZDkD8lycxqou6I6hqBDNIMKboDsp8r4dgJjpWQuXmWrn2a5asDtACnrsk7AXagibOaqKSYfUGCUxXUBy1y0yrNiHjRClpAfkohuwMqq21UZKFsaPd4fPHQxcxepcmfsOjL19FKUBhPLx/ZSUF20kJ0BmRnBCKBZG2LeMFHRALZlDCeZW68i7igsJdtrDZnhxpwqoItw9PYUx6qECMSweR1Fu5Qg61bTpN4mh3+CW698otkp2G5kkM5EGfB62/Sd08FK1DUh9O/HotbFmn2WEQZSW0sR35SEZTTTLCwLAg6NFE5QVlw+AsbiNa0ibpi8qc1+QlN+WhC54EI5aXnd24STv5UBx1bFwhLaRn73JSmtTFg+oYenPsKTF6fxQogKmqUBVt3Hof/sIhbSY9Xu5VmbeVeMUO8o4bSgsXNNlpCVEiPo9qqNFMvysNKlGH+Uh+nAT0P17FK6fAIURGGOioUjltc0XkS5cLY38YkriAqWBTGoXgM2l0SpybZfNU47dGQG8v7yJZalE5GqGN5cqU27W5NdWPM/sl+rFCz8PyI4vEWbhUyc5ryoTOZTfcoooImymmKXQ1qIy7Z2QjPiTj1swmdBwM6DwZkpyR992jiDCQ+KCXJHvaIi2dyRyxNWNLoXExUsAg6bGrDDtrSaEtjhRCtbdG5R7KySbNmYIGwpPCWNV/afQlRTlDsq5N4aWl5101oDAmc0+nYaKv/QWFnYhqDgsxphySj6RlZBplmPuYPumhLsPUFR9OhEf6xSHU2TzAQoVxN13c8vIrGHmj85L6YjCdk7pgYhmEYxnmgtEDpp/445lyWvZCZjolhGIZhnAfJOY4ufC7LXsienWtlGIZhGMa/S+aOiWEYhmGcB+ZRzhN7ztwx8XMheyYH0ZbGqQq0BdUtEWGHQihB116NtyAJS+AtCpIMtLsFbj6k1NFg4G6NjAQiEiQjbZRNGih3SKSl6C2NU5GUMm3qGyN63RqZ+YjiqZjFrT4y1PQ83OK1z7sPp66RoSA7I6itTei9X6FccCuC1mCMluB0t7ByMdkFRewLJq53aYwoMk5ElBF8Z2o92tLYtbRNzfkc2y86TuJqlAPJkTzhWJuBQg2ZQGNA0rdLEJYF2oHsnGZpiySXDchNa4r7HZSnWVeYp3BXJi0ZvkoSJRbekqA5ICieUJx6BSxcmZA94RBnJdZYncVtLj/T+zAyhrkdWQa6KpQPQ9Cp8ZcTeoZWsFoQDkUM7EpwVyR2M6E5oPHzIUGnQroJs1d4iIZN4gninAAJ7oyDV9HM1AoUxiHo0GQnBdMLJZK8IjstUb4imckQFqE2BtlpSWmfTcfBOjM3DTH5s6sIS5okrwg6IDet+OwdVxN0QHZKsuf/beMvjl5Lfiph+SJF/bIWg3eA8BNkW1JPfJyGpj4sEIGksTWgMeiwtBXCnpjSsSZDd8V0HI7QnkY70Kxk8DIRlQ1pmX67KaiNgnPcR0toLmRpdkumFktkHs1QibNkJ2zCkqDrXodGv6Rjj2Rpa4Y4q6lujohzgsy4i9UGhMZbsPCzIY1BSXUuT+Vkifn9PVTG0tPabkF1PTwwOcrcFZCZTYdeyB9xcGuQXVAsXCq4ZPNJ4t1l7JZmaYugs9QgKsA1m47iVhX1AYuucp3OPRL/hnlOr5QpPOyjnLTsd+Kn+yVXaBOUNcnqNmreBwE6H6P6AmQkyA9VyQzWsVuC8tGE7IxCS+h7oM1NPfuwm4LNa6eIuyOcmiA+nidIbEa/kTAZdzDiLGK3QAh9ZjgCaM9niDp8wrINEpQH1X1drFwWpsHX1YTaiKR0XKXDKbTAqafnTHW9pjGkcI9kcBZtGoPpcZ6baNLqtkGnQxA0+wXlI4qFxQL50wKnIlm4XOGecsnNJHS9ZAo0FCYi/AWBW9UcmOmjfVc3XkVTPgTu5ArZWY0tFeF8ls5Mk5Fv1tCOpnxAMHbVKcqH03Lu5aOKyUaJzLyiMSiYfGEB61iG4jjIEOZqedpX1WkqFxlB+78uI5TGbilys4rKC1sUJmKUpXn05CD5gy5/M30VUWjjrkRE5YTmZB6kpudei4uHJ9NA6VkHbQmaA5rGkGD2uoSOIwlT1wmUr/CXBFt7ZiicDpm+2mOgUIPAYuEij9krPNo9iqkXarQQ9N0X8F8v+zp2C9zONkLBjZfsx18QOHMOxYMrRFmJv6zwlgWZ7iYDd7dQbQsEDG2ZJWeHDGyZI3EFb7ryu9gtza9v+iesQJOdFTTrHomfXs9VIqiMOSTzPlYIwcYWcQZWl5YQsaA27BB0atyq5tB8L0lfyPIWDbbGWbBx6oLMUsLKOonn/uQKvSvkOU/PRs/OtTIMwzAM498l8yjHMAzDMM6DRAuSc3gccy7LXshMx8QwDMMwzgMTY/LETMfEMAzDMM4DfY6jC2tT+dUwDMMwDOOZ9ZzpmAQTeQq5NggIRkLaAwnOoo1TlWipafZKlKfJTWiyMxp0mnFw9apxhkoVWp2SxNMkGY17NEN2WhAW00lbIMO0FPdKM4NVsbg2d4jEt0h8gbIhzkrqwx5fOraNoEMQdCoqF0UUjlk0eyySnKLdpbErFm5V0FeuUSo2aHZLOo5GhH0xdlPQ4TUJOgUDhSpWMSTsjdNI9xWLh4+uwm79y60957TH+Hwn/oLAX9S0uiTF8YT+XSFaABqqtQyJB41RhfI1//DgpVivWMRbAuVC0HbIzirCkqbVLcmN2xQP2rQGE4TSqCQt4f6lhUvSDI0MTO7rY3kzdD6qafbYrDzSTX1tjDvlsLjVRjk6LWs9JbAshfYUzHuUjybYzXQ7igRKhwSd+zRL2zQd2RZuIy1pXR/VlEsNRDmk96E2si3Jj1UQCQx9OyQ7rUk8qK/K0vNgjYHvLFEYhzXrZhj5ZpPMQswLr93LyDdb5KYVlUtC/uaiTzF7ucRZkbDoMXOVQDdsVEZxvNVNq1tSHNeUjkj6/9Ehygp6HtJ4HW2CLg/lCFbWOhAL3GWQVZv2YgZ3WXDTNbuJipriOGm2STnBn0ozILrLddQVVaaCEs01Id6yprIOiqcSKhs1YUkw+vUW5d0OXfsitA1d+2JknG4ngMQFEsFllx4DJeg4nKCbNq1eTcd+aFV9eh6E+tqY/OUL1DdErGyNaXZLvEXB/l1rCDoVCxfZ5E9DwQtodyu25KfJHV/BX1HMTnYQdAgqD3cz1rlEdG2VoEOTmRVp+XFPU5suEA5GqAUPb1lCIaKvr4JYcgkGIgQQhTbZaU111KLZJ1EurKz3uHt5HSKGsttC1G2iTU3WX3mSHr/OqZdY1JTPrsZ6hIKo5hLlNW5Fkztl4yy3SRxBlNfYDZARdDzg4C8luCshblUze40mzqfHRVRQOFVJ126BFQpkBImvafco7CbM7iwQZ9LzX9vpsTh3OehIEuVBuZqOvRKnJqiuspj/9iDagdnLXRqjCSubIVrIEHRoGgOC6pgg6i+xcEVCI3TZsHmCpVaW2SsKDH9LsbJNMfP3q4h9QXBpg5kXKDJ2RHWVpOvRmPyEIhwOiX2BcuDKgVNoJeiwmzQHNN6HO1m4FKy2orrKwn00i7IEYXfClWtP4LxgkdHcMkoLnBNzrNswzfZLjpN0RtRHBI3Iw2kpop6I5c0ZBr8bUTqmGPmqIHEF6/9Xjdxwjfq6iNlWgaDDJjOn2bd/BKsuafVryscSlKfJj9t072sR5yx+/6GbiAoQNlwSD043yjSHFP6iYO55HSQ+zF0mCcqa+GiB+UszZE64RDlB7R8G2PvwGIu7+nEamn+a3UR9SPJ7f/caqmskQRnEoktmTtDYEKJXXIqnYrSXZnrpRQ+7BXvu2IBsCwoTUTocR4/Ac2LcUy7ZKYk36RB1JFhtgVNLsFtQm83/xL6XEsQ5T89G5lGOYRiGYZwHSp9bnIjST2NjLiDPmTsmhmEYhmFc+MwdE8MwDMM4D9Q5Br+ey7IXMtMxMQzDMIzzQCFQ5xAnci7LXsiend0twzAMwzD+XXrOdExkJFip5FCeQi6n2RNWS5A7nUbfh2WIM2AHaeS+t6IJi5rv7NnEvuNDtHoFpYsWycxIgt4kjTYvnBknpCshKcfICAp+gPI1fzF7PZU1NsvrLbQF7bIkKAvak3nirKZzj8CfdBAx1NYqtK1Jcookp/AXNPPVPLW9XchYs7LWwV6yCXoTHji+isagRqKxLIW7YKMt6Nk+izPjUDwGYYciHkrHJ4lqHlEeorwgzkF9yKI+5FAfFihH01luUF2TZhqgoWdohc5Mk8QFEYM4mWH2+QluRVA8meAvaNrd6WvtpkJaisSHlSCDW4XMXDqmUGZWsLBdUFkP1vo6MpB4S4KgQ9O5X7O8BeqjGn1/CRELnJpEOek4REJDY1BTXwW1EYmMBFOLJRYuEeRPpllAY+UlxIzH/CU+7oqkupQDoLLGZXkrRAVYWWcRdPkkBZ/GsODEnkGWNmdYWefyrYMbqY751Icl0k34/0/fhN0SODWBPyfxFiXukoVTbnOi1kVzQFNZn2bA1IckIoH6iETsK6Rj3/RYKBeKRyW1y9qgATvNZvrHhy9CS4hyAhmDyMYEvQky1izt7qG1kGXf0gCybtPqFlhtwcRLNMWjAn9eM7szS5yDlXVOms212ibqiAl6FM3FLOHWJvn+Osc+swHlKyprLKymxG4KKmsFwlJUV0nsikX9vm6cRZvsaRvlCsIOTTLcprCqQlTS1Ebh+MleCuOSjz94LVFPDqepwFbYDdh5wz72Hh0mPJlHaFAOBF0Kuy7AUciKTW5C0u6P0W2LhQPd9G+aA6npLdRJZjJYIel2UOAvarLzCbunhxAK7tm9ARkKkqrLgaNDeFZMdkryd7OX8c25TYT59C9ELQEBra0t6msKZ7PqooJGueCvaBYutgm6PWQEuXGLrr2a/KQCwF8QrGwA5aTneWZGUjwqiXJgNzSJJyic1GSnBVYAxaMCKxOT+DB4V0xtDBoXtamPKuK8Js5oZAROVVI6lF5zMnOC/EQ6f/aKDIUjNgvzBQ4dGWTyVBeNYc3ilnRMnuqOgOUt4OzJUTxoc/ihUdq9irAgySzEZA97NEY03jJ859h61GSGzx27lPwpwfirJL33Q7PXodWbjiMUFiRdD1rMt/I0HumkZLdwnAQcm9OLZfbctxZrycGpwsRKidqwjT/h4i8p5na4tHoki5ttZAzasXC/VgJHMblUoj5o0ewT4Gh0f0CcV9SGLKyGRNlQGfNpdVkgNMVxhX/KxVuBiZUy3qIkLGnijMBf1sSr2wB4SwJ/URF0KeIMNAc1sreNv5Buy8s6T5OfVAxdMUXngQSRgDPUIPGBQCI7QxYucsj2NLAb6bGVPwUbrhsnySmqqxyyG1ZwGprWg10oF8KyJlrTxq5ahJtazF3q0RhS5PvqP7Hvpccqv57L9GxkHuUYhmEYxnlgYkye2LNzrQzDMAzD+HfJ3DExDMMwjPNAcY5j5TxLg19Nx8QwDMMwzgN9jlk5+lnaMXnOPMq5eOdRhnpWEJFEexqhwG5B0CmwAoG3DElHRKtLsvT8gMyionQU8j0N7DkHf0mzsr+L5oaA7CmL1khEzyMxUUGTH7fIdbSwAggTi9xJi4V2nvLRiMycJspDbjaheCIGqbGbgsaIIMpp4iwMfzPBXbTIn7AQ5ZBWj0DvL5xte7tb41YEnQ9ZrBpYxF8UvKDrCEkiSVwIOjTNwGXLNcdp9gmKRwVCaoLOhGJPnXhLg/qwJn9aody0xHbia6y2YLmSI/E0KIFdl6wpL3L48CDtHo1Tg2SkjWxZNIYUQVHSGBKEfTEyPBOEeCRPlIN3jn6DxrCithqSvpDqxSGlw5CbgCiycKoCu5kGzTb600BYK4CopMmdtAiGQlrdEjHjERbT4EQ12qL7hikyc4J43scKBK1+TeEEHPjqBrJTkupFIUFXgnTS+uzKEeQmBD2PJMgIvMU2tVU+vQ9EeKN1mgMCt6bRLZvK2rSM+ZrBBb77yCbsBlhtQEOcTUsqvmTtIRaaWcqHQFtQ2xQBkF1IyE0qooJCxhplg7KhNqbQkUTbGq8YEOc0a9bMIpK0xH+7W1O830dnY9yaZuTKSa7ffoBECzr2CuIsaKkRsSA/nbCwM6G2JSTxwalpkBptAVrgDdeRdQvncJbmeJHGtXUoReSmFf6cxKlBMBBRKjeJCprsrEB5GmVrclNpm4vH0vWNHuwgOynITQJRGgSaK7VJPAtvMUI6iqgAsbKQboIMBU5dkGTS7aQtIJZkZyT1LQH5ozYvuuQApQ1LzO3p46rNx7i2+xj+aI36kKC6McGpa5QlaJct8pmAKK8ZXjeHFYIIJEMji8y38+SmNReXJvn1Vd9ITwiRBpYubdOsH5ojKEqESo/r/ElI8or5ywTde2PiTBqobLchcQQzL0zIn5KoF6yQnwBvURJ3xESX1em7t0K7T6EtiLNQHxFk5hWlccXy9gQpNHFOUx+26d+VkNvj4y1J7HoaSO6tpIHfYTk9NxpXpEHkdhN6H2oTZ9NtKnMxsm7RvUfT7lXkTluMDc/jLwiys2eCd8dqjH4jod0lsYKE1kBC/z0JUQ6SJY/CcUm77ZCfShCxQCiorZKo1S2y02kpfC0hUZLhqyZpK4d21aO5sRfxaIH8SYm7JKlujpFS4y8pisc0ixdZtLsVsZ8GkFqhot3js/S8EHveJQps/CVF/30RwkuwJnxEKaR7b4AebeEvafJTEdn5mKThUBuV2M00mPX6kaMk2+qEqwPy04rsbEBxVwZvRdDu1sRZQWZVjZ5HIrIzAjXrExahdKLNkLdC+VCDVuQgY43dhqDh0lgdUzhqY9kJiQftlkvnoQiVSUvTKy0Q5ZCgJMh9usTyNsWq604CYDUFOkn3le3EdB6KyczJn+hX/WOjC5/L9Gz0nOmYGIZhGIYBH/vYx1i9ejW+77Nz507uu+++J7Xcpz/9aYQQvOpVr3pG22c6JoZhGIZxHjyWlXMu04/rM5/5DO985zt5//vfz0MPPcQll1zCTTfdxNzc3A9d7sSJE7zrXe/i+c9//lNd3SfNdEwMwzAM4zw4H49yPvzhD/OmN72JW265hS1btnDbbbeRzWb5xCc+8QOXSZKEn/u5n+MDH/gAa9asOZdVflJMx8QwDMMw/h2rVquPm4IgeMLXhWHIgw8+yI033nh2npSSG2+8kV27dv3A97/11lvp7e3lP//n//y0t/2JmI6JYRiGYZwHj42Vcy4TwMjICKVS6ez0oQ996Ak/b2FhgSRJ6Ovre9z8vr4+ZmZmnnCZf/7nf+av//qv+fjHP/70rvwP8ZzpmJysdDK1WEJ0BmhbYbUEiQetwQS7KQg6QbQt4ix4x32W11tU1kNjsoDdFixfFZKdFoimTXNQ0bPLprLGxmpDY0QhpaLVq4kSSWN1wvhCF0HZIrOkaA9FNPosooJFfqRKVNK4FRA6LYtcXW2j7LTcuXc4Q1jWdFw5i1rdAqBwAvKTmuWtmpOH+mlta7G3PoRa9rBbkJsSNNsupyplrABavYLe7iod+yT6ux1EFY/sphWsUFPfFNIYEmn2QIciCdNMCn9esu3qo6zLzeNP25QPQmtAoysu+XGJ7GkjE01xXFPa4+BWJJU1DspKy9l/bvFKSocFpSMgFx3seYf6KogKAutYJi0BfpGie48m8aHv/oDE10R9Ia0+hazboCA7LcnMa6wAkrrDqQP9yABkd0DQmeAuC1q9guZITFiC4h4Xf87C8WLsncu0u86Uje+3qG0NOXVzkfnLYH67Q3y4QJRPy+GPjM2nWQeB4Ni+QV55+cPEOWgMp5kSPbsV0UjAuuwsv7B2F1ao8ecEPUMr1NbFtLosFi8WuBXJ9FU23XsaCAXOcIPyQy5IUEqArZmv5/AXBbF/ZviDEthzLlFGcHy8j+/s3gxAZSPkJzR2SyADwcI2m+GvCzInXBJXU12bHstBp6ZnZJl20wXSbJTRi6ZxHslD1aG6ShJeWseraIr7HWIlzw45EHYkKE/j1hVWO31PrQStgZjsnCIqCLzOFsHmFtGBIo0+h8pan9G+JXJTmkfn+7FO+8QDIWgIBuI0i2kwRPgJWoCo29TXR9xxYCPVfV3IAB75ymY+/ffXI+8t0e5PyB+3qK4FGWu0gDXlRbQF9cBFWVA6Ipma6SBIbJr9gqUox//H3p+HW3bVBf7/e609nnm481zDrXlIKpU5gQDGECAgDdo2+m2QVtu2iU0bf7aNj6K/9pGgNoK2AZ5Wwdan09DMQiSSgSRkTmpIzfNwb915OvPZ++y91/r9sUP85keBUFUkMdmv59lP1d13n3PWPXefc9Zd+zPMhzmUDc6MSW1DSGptjaMnB5GBxqkq8scFXreg9JwkSilCV+AXBAs3hhROBiBg1Zc0Xhf4RwvIOMEKd9qi8M0MyrWwKzIulx+B4UPoClo9kuwJE/NAhqi3g9XULG41aWyI77O13kcb0BwScTn0ELJDNZwDaSJXYFc1MlBYdWjOZ1g3OM/othnmrgFnUWI2of53Q2TPKfyiIHtOsb57nuUNFmZTo0yJTikWt5kYHTCaksqVHd69aS/N/vgtvLJWEmQ1xukUjWFBdkrjdQn8yGCulqMeupSftljYYdN1MCL11jmEgpFvwhtHjhM6AqOj6X+igyqE+OW4NcfclQYr602EoXEXBPknUhgdTSdvkMr4KAPcwymmb3Rg2sVd0SxtsfELBtnuJq1VAaELQVfI8VoP9q4sumkSugIUmG1NfXMHNeDhVDXh/gLzOywqW0J6NiySe908Wgh210bxu1z8wKRdNjDaGmkq8kdNxOtXeMe6/QQ5zZqBRWpjJs68QZgWHJnuw5hyiVIgA83azdMcPduPCCHIa9xMh+JR8GsOnZzEu7xFs+68ZJ9Ll+pSzuTkJNVq9YXtQx/60CUZX71e59/+23/LX/7lX9Ld3X1J7vOHkdQxSSQSiUTiX7B8Pk8+n/9nj+vu7sYwDObm5l60f25ujv7+/u85/uTJk5w5c4a3v/3tL+xTKu43ZZomR48eZe3atRc5+u/1mlkxSSQSiUTileSlDn61bZudO3fywAMP/NMYlOKBBx7guuuu+57jN27cyP79+9m7d+8L2zve8Q7e+MY3snfvXkZGRi76OTifZMUkkUgkEomXwcUWSbuQ295xxx28733v48orr+Tqq6/mE5/4BM1mk/e///0AvPe972VoaIg777wT13XZunXri25fLBYBvmf/pZRMTBKJRCKReI342Z/9WRYWFvjwhz/M7Owsl19+Offee+8LAbETExNI+fJeTEkmJolEIpFIvAxejhUTgNtvv53bb7/9vN976KGHfuBt/+Zv/uaCHvNH8ZqJMYmUIJfxSO9PIQIZZ4TkNe6sgdWAyIn7k5gtnu/FoSkeAbMh6axtU3jawS+CUZdYjbg3RW4ywmgLcqcl9aUMTkVgfKkLa1ly+5aHsFqKhcsNcscshIZWryTn+ph1gdGOsxGCcojVgLAQEbmgBUS5iIVDPRinUrQGBc0hQWSB2RYURyvYx1Ms+RlEvkNnzMeuaUq5Fpu75zA60LsrRGtBkBHkf3IWDE30RInlLQbmvEVrLMDogL0iQQmsuqZ84yxH713HI3PjeIMhQU5gVQWy5FPbHKCWHFY2CLwugbLA61ZkZiPCQkTuNEw0SkSOYGUTRKk4OMpZEQQ5TZjVVC/rUDhsYHTi7BC/bKIscDIdnCWJO1Invaiob+6gRdxXRrYMjLbA8DXdxQb9jwvCyxs4FY1V8ums8nAqmk5RoSJJ61iRzLQGEfc6yR20KR+KyExK2sMRrGmSnRSUDsLKfQP4XRHG8+n+y50MzgpxqhTg5yXprM+fP/BmHl5ej91QyBAW5vN07Y4j/t1FgdetGHgspLY6jVXXdGbT+CUwa5LUU1mMhkQ9WYp/13mNVROgwR6vkZmPEC0DI9+h5LZxFwSZuRBlQeG4wKnAwg5J5lz8HIZpTfqcQdcBjReYOCdcZL9H5GgiJfE2t7FWJKYHxuEsy1ugOaxonygw+KiK+x/VDJxFg8agQXpRERQj5GKcReSVBMUTERzOYR9P0ekOMToavyCYXCiBhjWlZYJSBI04k8yZtkhPSfK7HWiYaAnSk1grJtaMTZRWyI2N+Ge+fIXG+g5deyTpWcXgIyHNIUFtLTxzagxlaypni9hVgTKBhsnp2W4a6wLakcVH9r0Fs6Xx+0LcOZPmuRxGxUSZAq8s8Usi7g90a43CUYPlLQbKgOIeC6Oj8EuCylobw4t7RWkJ7f6ITkHR6hPUVrv0PxmQmQ3pea6DDKA1IGiMaPyyxt/Uputhm8q4RNlgz5lkJjXGskXXc4LMlKZ7f0Dtch//YBGzCV4PBFmBCCJq2zsIXzL71TFqnkPv0yAjqFwe4JcEkSMIU7C0TfDcxDCRA3ZDoQUU9ltoGfevuez64+BJnl4cAw3a0nQfCOndpcidBqsJS9vi82352V6Cg3mOVntZvjyi+7mAhSsk80t57BosXG4y3S5gtTUzNynmd9pkj9q4i4LyEUXpsMZd0hizDmioXd8mdCWtHoneW8BsCbSA9Fzcw2lpi0QZ8ThbDYeup02sK1Yo7TI5vVAmsiHV00ILaPfadAoCd8ImvS9FY1DS6Y7In1EY+YD5+QJzs0VW1jucrHbhlQ2K6TZ+UWC2IZ318bo1rSNFHp1bg9Bwaqab3LkQsalBel5h2yH2+hoygJUNBpOPDWM4EcqEwnFwrJBOTkBHMn8VpJ9KoxrWS/a5pLm4lGH9ko30pZWsmCQSiUQi8TJ4uVZMXuleMysmiUQikUgkXvmSFZNEIpFIJF4GyYrJ+b2sKyZ33nknV111Fblcjt7eXt75zndy9OjRFx3jeR4f+MAH6OrqIpvN8u53v/t7isMkEolEIvEvzcvRxO9fgpd1YvLwww/zgQ98gCeffJL77ruPIAi45ZZbaDabLxzz67/+63z961/nC1/4Ag8//DDT09O8613v+pEfqzKVp7KQxevSmDWJ0GA2RRwA16cJh3xypySNVQplaGrj4JcFVk1gH0/RfbCN4YFdExSPQmU9zF0l8bsV9avbIDXNDT611YLIhX2NYVbGTbLnNH1PtXBXFD27mzQ8B6cCjVXEgZZKsLQd0BAUFEFBISJBlFEERUVqTmM1iEucm5q86+P1hbhGgJxxMaccGsOCuekie2aGqK5TLG0xmZss0RxV1D0Ha8nE8OPA2rAQYS2bRHZcdvuy8Ulq45qp6TJdN80ghQYnojGqaA9HSKHJH7YQgaDTEyGDuKR67owkdAVv2HEYr0vwlv4DdPJQOhwHP6ZnBe6ixmgLzIbAnrWobopo9ht4PQqzrchMSmw7JEpp2jNZzJZi7EuC0nGfIAfSE9hVQXNY4AcmtTFJ+pEsYUqA0BSfcOjk4xdmsOJgr8RBk3ZNIzRkpxRCQ/FUSPakQbiUwi9BbbWguc0DCZELzkCLK/ITtPs0zrJEd3VQNnR8E3ewyU3lY9RGTPwiCENTXQf9jyyTWtBYDUFqukH+ZBMRgTtnYAQ8X6JcoU1Nc1WI1dCYLUF7MMJ4vhT60haT7FgVMZEiUAbN0YjGgIk31iGyBWEahh/0aQ4K3HmJ7vfj59RXNM8UkCFYdsg1P3GQ7lQDFUqcFUH5cEDujMZsClRK829+8lGCtMSuCoJSiDcUoI04wNdeMtACBh+UODVNY8BAaAjyivHxWbITbayWJmpadD29QNrs8N7rHyMzVMdqPF+2PQPtXk1pbIUopZERBMUIbcatBHryDbQJncCEUOJ1CRrDkupqi3C8TXpaIAyN1ZCQCwmzmk4etKkRQmMumURa8J4Nu1jZqsCKA8eRkJkSyEhjNTStsZAwA+H+AqUjPsPfbpM/G1I4HTD1egdnRVNfrUnNa5xFSWMU3AUDd0HSKWoaw5LljRaGH1FdZSFC8Da3CQsRQX+A8gwWrw/JTmp694QUj8bBq8rR8XmoYHmDRfagw6prJ6mvj7Dq0MmDUWlReM4mMyUxPE3rmW5KuxZojkR0PWkiAzA9TXtNh6AU8a7Nexl8vE0nJ1na5lC7Im7hUF8T8dwT6xAqPu8jR5A6Z1IfMvCKkk5BkFpQ5E9B5pwgGPUpXznPmtwSuBGdvEGQU+hQxq0R2rDnbFwkq7TfoO9pn8gCv6SZv0KgDUF9VBA5mna/QjVNstMdenc1yF67QOmIwvTi9xJ7uIlyNDKMy7/fvOEIAJ2OidcjeO/mp/HLCvbmMT1Ns8/ArmrypzSdgmbg0Rq54wb1MYk867JxbIb8PhuvS/DO4X1oCVPPDtIpQGUjNCoptIQoo6g+2kd6SuAeTLGy3kIfyuHnBf5kFrW7QPf+AL8Uvyb+y45vEeYjlnco2r5FfX2EyIaM3B9R2xQi0tEFfZ4lLp2X9VLOvffe+6Kv/+Zv/obe3l527drF61//eqrVKn/913/N3XffzZve9CYAPvvZz7Jp0yaefPJJrr322pdj2IlEIpFIXLTkUs75vaKCX6vVKgDlchmAXbt2EQTBi1o0b9y4kdHR0e/botn3/e9pAZ1IJBKJxCuN1uKit1ejV8zERCnFf/7P/5kbbrjhhVK3s7Oz2Lb9Qgnc7/pBLZrvvPPOF7V//nHV8k8kEolEInHpvWImJh/4wAc4cOAAn/vc5y7qfj70oQ+9qP3z5OTkJRphIpFIJBKXzsUUV/vu9mr0ikgXvv322/nGN77BI488wvDw8Av7+/v76XQ6VCqVF62afL8WzQCO4+A4zo97yIlEIpFIXJQkxuT8XtYVE601t99+O1/5yld48MEHWb169Yu+v3PnTizLelGL5qNHjzIxMXHeFs0/SN9jkl+++jukZwRBOUKEAm8gxK6DvSIQyzZ+CaJSSJjTyACCDBhXr9B1/Swn3y9JLcZlrJsDgv6nIgonYPARReExF/ucTeaIg97YAKnpd2q4S5owJWiMuFRXG5x9SxqlBa0BDQoyU5L0hEnubHxyOYvxr6NwyMAqemipiVxB6MLSFRHOssSQCulLnt07jhht8vY3P0V6VnPdppMEHZPsWYlVB3vBZOS+iObxIs6iiDM4GpA5a5I7A95Yh9bqgOeOjSJCAb5kdjnPTX3H+eDVD+AsSfJHDPIPpGlc2QYBVsUgfzZEbK5TXx1RXSOZaJQI8ppW5GA1IT0fZ3y88f95msY7a+idNdTaNmuvP8vgtyG1qBh6RBE5ksbGDreNHUQogXYjpm80mHmfz8SbHaQPSBi8eRKzDTcNn8Bd0rgrik4B/vXGPdTXxIlNsiMQ2ZDhmydovKGJMuMy2ZElSJ9rETmCTgEKBw3ClKZ4XGGdc8idNDA8EEJzTfoEA4+HZM9pcntcwrRAT6Zx7YA/3X0zVlPTdf0sxqyDsyKory+w8VcOEo55NNbmUbYR/w4rcTsDbWjsikREArNqULm2gzcYUBitYrShtZCJMwUOFFGOJlSSwYege1eNkb+XOBVN6II2BO3RgPZQhHnapVMQTL9OoqVm4HGPzX2z7Pr6VvacHCV1yqG+pYNXNmgMC9JzGukJ0rLD3DUgQigctBCepLYpoNUn0N8tH/7zlRcyXfyeiPSUpPp3w5y7OUOYEnQ9ZXL2Xb08941NPDCzgea5HK0hjbLj47sOaHJOB7MpCPo6fPB130KONUELAiXx17e5fuQ0VsUgM60wW2C2NflHXbQBkWfiLIHuxNlDfpcCQ1PMt8huWmFjZg5LRHTvkhhuiDY1xf2S5rCivHuZ9m01igdMmls9grxifqfD/OUpljdZtLtNhIbamjhTpTUoCDMaZ0nQHg4ICprI1RRPRBiepjbqYPggQzDPuLizJv33m7gFn/xBi8iF6Rvi5xji7KF2r6Y1IJARNMZDJh4ZpXDIoDkct2eYuaUPZ0XjX9FAS7CrcO7tfXTvktR+sklla8jsTRFdj1vkD5s8OreGkz9jk50OGXxwBcOOsOoCZ8nAWNUgNWXwvpEnKJ4KCbc2UZagNSAwPPDKEhFBq1+j2yb1tsvDp9eSPeRQWyUp75cUyw26DoUoE65bc5r0bAevW2AEivaqAKspyJ2JM/nWfHYCZ1mSmZRYyyazVztU16axP1tm9g0R1hsWCdMCv2URFCKG/34a/csLzLQL3PKBx1Ansmz8yeP83dffSOmwwPDBaiq6D7YZ+IXTeF2CgSdCFnfkSM8pvG6FVRekzQ5j/+oUpeMRadmhPiLp2TFH37Mhg4+EFPY4pOYF2tKM33KK7refo/8nztH7TIsrbj5Mcxi61i/xtnc9gdmMyJ0WDD3c4KPfegdDaxbRQrN1YIb0hEFXucHEWyW9jxn0feulK0mfOL+XdcXkAx/4AHfffTdf+9rXyOVyL8SNFAoFUqkUhUKBX/zFX+SOO+6gXC6Tz+f5tV/7Na677rokIyeRSCQS/6JdbADrqzX49WWdmHzqU58C4A1veMOL9n/2s5/lF37hFwD4+Mc/jpSSd7/73fi+z5vf/GY++clPvsQjTSQSiUTi0kou5Zzfyzox0fqf743oui533XUXd91110swokQikUgkXhrJisn5vWKychKJRCKRSCReMxOTyjrJ16e20ry6hVUxCEshzryB163xujVj3wgYub/Fqv8rGPuHEL8/JHv1Io3JPPN7+jBnbLyyIDehaY1GnPsJieFrJm+Ng+rC1R5Cwbq+BdJrqyx1spi+pveZJu0eSXpOYbYFjUqKwgkIs4ogSxwQVwQMjVUHEQgq20JsO2LgEYm7pOMy355E7aizqTBH7rQke8qgnG/xjW9eg9GB07UyxXyLxuUe1S0Rak2byZ8wiLo7RClwlxTd+zq4Sxq/JCjstUFq7HmT1LzAqhjoiTRfPb2dRuTi9SmcqmZ5uyL7bIrshKTvqbjcvT6cxfAk2oCJXUNoAYcaA8gAIleSPyH55onNWA8XMB/PI6Ri4t5VzFwvqI9IZq418AqS7sctvn5mKyKE8jMWqQVBp+oQpjTuskYNepx7ZAQRwnPLQygTlrYLsuc0D8ysZ83OSfJnQ8KMIpdvc2L/MKmnsgS5uBx94VSb6roMfl4iQ6hujjCbgtmbFHptkzAVl6T3ZjP8ffUKVsYtFq5R1DYGNAc1aNjYNc+WkRla/YLZ/X3YFYHRjsu5P/7oFlTHQJmC6niK+iqobopo9wiickB7OKRwFLSEoa+ZuNMWjcMlCmdCcCKUJbA21xh4VLMqt8z8TsnSjjzTN0oiB7KTmna3Re9jJulzBn3PRgx/fRbDE4hyh8aQzS3dhxARlLvrtFd3MFMhyhCEW5uYbcielTy5shpnKW7DUF8dB0+nzlqk5jXK1ChHU1nMxmXwXdCGxi9rFm4MCNMaZUFlA7TGO7TWBEyf6MFsSApH4zYJIhAsbRE0OzZWA0TL5K+O3kAwn8I5kGLhQC/GpMsjD20jPSVo9UlyUxFuRdEciku2Z4ptRATZExaNtQE9zwouWz9BT6ZB42gJhWCNM49QoAJJmNJYLcitq9BcW6T7r9MApI64ZCYlUQqUE7cGyE94ZCY1Xfvjczo9owkzitSSwp2y0EBqTlJdbdAcAWVBbS0gIMxqZCcOKO3JN6jv8Cgf8UnNx+ehXxIY+Q6GJzBb8XtN+qxJkFd4XVA8LHCXNN3PteNgWS3QpsC8eZH6uhC/KMg8msVaMcCIy7l3CjB7ugt3xmBlvcXy9iLOvjQyhMjW3Lr2MNlzmi/O7mTi7ZoolAQ5UHYcUNwpxIG7uTMwtmaeztE8N60+SWM8QAbgdQn6c3XmrjLwuhXPzQ3S6rexarAy7pI7bJGa09TWQicvmP6pUdTmBvV1EeGgT+/uDnM3KhZ2SJxZi+X5PJlZhXvMReYC5t44QOvv+zlwdpAv3XMDQTli/xPjhCnNyhZNcySik5csbk2x/8gIfllz7o0GfknglSQyELTGQg7N9dMOLabfCF84dwXagIWVHPVhg3P/NqC2VpFaUGArTty/hsV7hjm7d5CzH9BMNwsALFeyfOk71zB7rYvd0Jx7UxYRwdy+PgqHTXadGKM1EpF3PbInDRbf7LFw5Uv3uaQvsk/Oq3XF5BWRLpxIJBKJxGuNBn6IiIYfePtXo9fMikkikUgkEolXvmTFJJFIJBKJl4FCIC6iemtS+TWRSCQSicQlk2TlnF9yKSeRSCQSicQrxmtmYmI1QQpN+R9TGC2BuWwifUHhOJQOwcIOh6nXp5m/wqI2alE4YJH5ZAGdDVGWpu9phTagtlrQ+4TAnZcgoHjQxK4I7OMpWkOKg8eGiSLJmtQC7nJEZUMaZYHV0nQKmndt38PCNRHuvIGIoDEK5cMRwla0BuNS+OmzJkJoVjZKOjmBsyywahKvYXPv8U00hzTNK9rMzRXo9AfIUFNvu5TcNtaEQ+qcgTydwmwLhNS0B0IWbvNxFttxxogD1a0BqTM2O95wlCADaqyNGG2xqWeOz+y+AbMh8LoEFAOCXFye3y9ItASrLogcTbC2TWpDBW1qHBli1TUiAu91dbq/lMZqaNJziq2DMzTXxqXq03Oa1MYKYUZgtjVvX3UApwLtXkFzRJE6a1HeJ8jMRqQOpBARyAA6kYGWgjCtqI+BH5jM1nMoU+AsG5QzLdLnJFpAc4NP41/XUJakMSxp98SZNNrQlI4rRu4F1w0oHo+QARgNyabUNNoA2RYYDQNnRaCGPJa8DEvtNNkpRZSNCDOaMB1n2lgNAYEgPe1h+BrDE7izBpELBJLUlEmnINCmJnTjcuF6tM3stQaiaaJMge9ZdP7dMg89txGjIyic8iltXMYvC1Y2gww1QRrQMHOdweQ7+ykd1qTSHeav13x+6kqiFKwsZ8mWWnAuRf6sTzifYuFKRW19RN7y0FvriDBuexCVA3ITmuXLVNyOQICxbLF0hcLr1dgLBpGrGV89x+AjIUMPVGCoTd+3TWTTIDXQIMwpmkPxualSCrGhwdJijtagRktN2umwdvM0rXUdtl9zAntjjaErZsi8bZbstMIrSkJXkJ6JM93+147P0u7XvP09j5I+YzH/+pDDs32cWuiicBzWuzNESFoDArFio4ohQmkaR0u4c20ML87QMHzwejVef4i7qOk6GNDJWygLTE/j1BTLOxTaVTT7JYOvP0eU0rT7FF6PpnxAYzc0/U+GoCB7RuL1KCpbQyZP9mCfdWj32C+UoG+MKphK4fWHOCuaxsjzGWVdAZkZjbIFlU2a1oBDblIhpKa6IWJ5psCmP1uhvjZCCwgLivx+m4XrIrSpGVm7gBFAc1gjQ01rOCI3oYjSmnWpOeo/Vef9g48x9lXI5DzGvrJA8ZjCqSqEAqOjceqaqcUiua1LbM+eAwl2VdMpala8FO68wGoIbhk9ggw0tU0h3btrIMDrFgw+FuFUNLKjsXZncecM+nqrVMZtRCRIzQkiV2NUTPxC3PZCzLh43YLmiOYvb/hfpGcFmd4mhWNgDrWQvsBsSlp9kjADpb0mIhRoCf1Ptans7ND3tGLD+ineMHaCK8qTDN8Xh3h2HYy4YnSSyBYYJ1NcufM4aJB2FLdbOBUy/GBE6JssfHuQzkBA170u4//XI8holrdCkNOs2j79/NgBJdCO4vRUN+UjAfaJFFFavWSfSxeTkXOxxdleyZJLOYlEIpFIvAy0vsisnFdpWs5rZsUkkUgkEonEK1+yYpJIJBKJxMsgCX49v2RikkgkEonEyyCZmJxfMjFJJBKJROJloLRAJN2Fv8drJsZEXVtjZraEluCNBEQ5hVONo9cXr4nifhh9z0f1dwtq4xEz15lIS6FcTatXYvhgtqGyXuD1KrQQeF3g9UX4azyUq3jdtqOIpwt4ysIrGbR7BYVTEYav6Hta8fX7riF1ziRKaSJXE2Y0XkmiPQOzJYhScTST75ugoN0Lfgk6XRHWjE0h1yI9LRgfWECaCmPFivtLSMWZx0eQHYEMILNtmfSUwJp0MIodMs+kOHdzAcMD2QGRivDGfZ59Yj3BhjalfAs9mWb3xAhjQ4uk5gWNEQUVizCjKZxSaAF+WaNMcBck3d9yEQ+XcJYkvjKprxZ08hJ/OcX0GxVBVjB3U8SBx8ex50yyE/HpZv1DERFqli4TfPHo5dhVDZfVyJ+QtMcCGqOCpc0m4RV1RAStAU3LtwFw5wwKx+Fdq57DsUKcSoDZgrPTXfjdGm1C9pCD+UAxzpo6EZGZ1hRPRnSPVOhkBc1eg5zr45UlygLlaj52+GaCPIhQoGxN8YTCmHCZruWZX84zd6NC5gJKhzRGB0pHPQYe8xGeQX2VS3WtpHhS4fVGdIoKBFgNyE1G2MNNGiMSoSD3aJogp7CXJbVRg1y2zcK5IunuFmYLwpSB/loXWoLRgVavJH82xGpC726Nt6PFd2sqbdw8yanJHrzBAN02UUoQpRX1EYfMlMReMUhPGjx2eJz8PVkaY4r2uI9oGSxcpVj192GcSVHooFIK7SiyE4KgrOjeAydO9ON1GQD0lOq0uyTXXHWUVs1FuxFhVtPc0SZ1zqQzm8awI9zFuO9SZX83Jw8NIqTmwPQAzcU0kwf6WdjbR7ss42yKUGPXNfUxwX899W46XYrPH9xJUNDkD1rYdohfd1h+nc/B9jCfmbyB7DlF/8Z5Uqds/IIkzEfU1mZQpmDuGkgtauSaBqu/GGG2NamJOn5B0hwGZQoa/QbWiiR/2KKxJuTUiX5ypyXpGUn3Xk1mpoOzEiJDjdcDtfUh7oIkc8aEVJxBU1krcZYFhZMR+RMSsw19j8f9jeSqJo1hkAs2yzf6hCkwmwJ3MaDZL1GRxGhJjLpBdVsX7rxBkAPZkjTGFNayQeTA7HIeswE9uzVBWuDOGWgJ+WOSz09eSRhKJoMyE2+TdPYVmXxbD/URyey18TndyQlaPZKwalOtp3FkgGwatPsEytT0pJu0+zQ9exSVIE11rUHqnEljbZbCqYjcWUV9KP67VTyfpBI5moFMDWVC4YjB69/7DNrUuKvrBGlBe0AhNGRmNF37Nb+25z1U1yk8z2LpmpCdw5OEuYg110yQnlNkJxVeWZA/rbGrkqkbUxgrJrPXC04+M8q3HtrBlw7toLLWZF1hAb8gmW3maQ9ohBLsOj0av7e3TNbdepLJ2zQr6ywG/sHCvX6R3octquOCE78iCcqKgctmkWsbTMyXaYwq7JrGSgcYVYN0zufsO8FsgPRfMx+Lr1jJikkikUgkEi+DJCvn/JKJSSKRSCQSL4N4YnIxMSaXcDCvIMmaVSKRSCQSiVeMZMUkkUgkEomXQZKVc36vmRWTlB1w9brT+GWBsBRoQEHoCrInTVKLCrMuCNPQ7lVkJg0QYJ520aYmcgShC52dDeyKwOhroyWYTSjvk5hTDvaCwa7pEVpDEV86exl2Q5Ge1fh5iYg0qfkOv/3OLyE0oCE7CXZVEOQE2Ir0jEZLcJc0oW/iDQWUjiqKV82TmjJwlwQ3DJxGObC1OB2f1JbG645/xuHrpvC7FJEL/hNdNEY1ncEAHQm0AUFeU98Y0FrvU3jSJXXUIcoqyt9yMT5XJsoobl13iLlHhijeNk3utMTs8QhKIV5Z0hwWrP6Gx8ATHvqyOuV9VeQbllE2eJGJ9KFwvIkIBT1PGjTGFL3fMem9fA4ZClr9msaQIHLjcvepOUEUxsF+AIanyZyy6DoQkZ7TqJNZWqMh+VMwVlwhzEJ7OEQbcP/sRsqpFhO3OPhljWlHlPdrpA/yhhWUCZFjUBszWN4GS5sNvIe7aQwL7IZm5lgPzUEBl9VQmYhNPXPYlTjgVPqClfWSoBzRn69z19X/G2feQDVNGsOSzjV1jFYHZ7HNuq3nMNuarkMRoROX8I9yETIVUtsQsni5xFtxMbz4d14bV/Q8KwlymsiBrT0zYGha1RT+FQ0q6yy8LoHhQ88eRd93VljZaNEY0TSGJKFvEKQFuZTHxEoJUbNId7W4YfsxlJLoTMj89RGNNSHajEutb1k7RbtPkDsjMZYttKUpHDNoDtpxGf10B2fBoO9hg04ezK42tTWS0kCN0t4KaM1IrkJ6XvGxka/H5faLHvmTwIKDu6Rx5w0MM8JsalJzgtSsQKcizFkbe0+W1ISF2RTktiyhHGiMCYK0pD4i6DoYcXPvEYQv+LUd3yZ/Mg54jiLJLdsOYp11eFt+Lx8f/78sbhesPNpPkNW0Xt/AKnoABFmD9IzEriv85RSz1zjYDUVYdBEKUnMCuxYC4C4IIhswNdgKs61Jz2qqayQz17n4JZPaiEXxmKLnKSMOSNfgZDoYXlx6PbWgmb1W0hjRFI7DykZBYxWosxnMlmDk8mmyhTZmE4rHwOu2yE0q1LxLlFVEGcX8lYL2YEhqXqMNzfC343LyytZsHJxDKE19VOJWFF0HIqrjkk4RJs91Ucy1uT59nOIBSbjWo2dfB2dF0/usZtWXl6itgdagRnQk/eUaedlGpSPKRyLMlqDHaVC8bBGrHmLKiPSMxu9SZM80iWxBmBa0BjTVdbB0Qwct4zYMe8+MYLU0WsDXn7sMZWn8E3kaqxWyIygeBq9LYHqa64bP4C5IoqpN1zMm++cHQMCxyT5kCJEtECo+PtjQYuBJHzHokZmUhOWQzLoKm4dnMJuajOkjA83m0iz545CZ1qwaWCLIxh/Mzx0aY+B+A7uumX6Tgq93sbIJnBUY+LrFyDc1U/NFjD05Ng3Nkp6WiDAO6laOpjmXIXfEolPQbNw+8VJ9LKEvwfZq9JqZmCQSiUQikXjlSy7lJBKJRCLxMkgu5ZxfMjFJJBKJROLlcLHXY16l13KSiUkikUgkEi+Hi1wx4VW6YpLEmCQSiUQikXjFeM1MTGqHykw3CrR7NVYqQASC5rCmOSDIn1VYbU1nKMBd0CBABmD4gk5/iFE3yMwqZATBfIrmsMI8mMEvCaIUVDZpgnKE4cWz3/xxg3+39gnsSoBXFixcG5ekP/sWly/O7sTrVqSnRRxRruNsH2PZJEwJZEfglwVy3sFeNPGKkvp3ejE60BhV3Ht8M+XDIbUwhY4EylXYFWg1Xep+nN5i16A9FJGZFhR32bDgENmQPwk9QxWyxTadn6jClVVS3S2Wb/Go/6s6Zk1SD13E5VXmHxmk3RtnRqQmLCrbAuwKnHivwcmfM4iOZzn+84W414MCW0YAnPpXWRDQGhCkZiXagJnFAsrSiAhyk3Fp+559Ie1eTe7pFEvXhHhzGWQAzorG6GjavYIwp8CNsFqa546O4i7GWRRomFnJc2yin9SsIEwrxKk0rT5JmAb1eInCmZBmv0lqUdP1nKZ0LM5U8QZDWr2S9VvP4Q8EtOsORIJaxyVMg+EJshMSZWvSEyZBZPBQfRO505A9YeGXNJ25NGd+Ks/M64ucfnqE5c0GMtAsXaYxZh2cGQv7eIo147MEuXi8Xne85qpyEfVRQf6EJDOjObbSi/AM7EwHeSzzfJaCxl2Ms7lmbypjtDWGJ0ABoSRMC2bPdBEcziO7fdS+As88sAm/bYFn0P20Qf6IifQFos9nxUvR3OyTnYpQ3R1kNqCTA6E0qQUdl/ZfBq8ct10Iqg5WDf7Duu8QllIQaXZPjBCmBNd/4w4wNdHRHNV1z7+21mrCtCbwTVr9cQZYfU18PigDht98lvZwSLTGY2UpR2tA45cUQUYgI5i6GU60epGB4FS7hzAlCIshvmdx35FNFE7Afzn+M3ypupNV3/QQEZgtgTyUxbYjlBln+Bg+LG0xyJwx6XkuRHYUQd4iO+FRPBVQG7Pj9g4lsOpQ2m2C0IgQIhtKxxR+t2JpS/wzeCVJc0iQOw3loyHqVJbOlhZWXbN8Y4fcaUHPXk1lA4z+o8fgwyFWXdD/tE8QGTQqaXLTIanFiGa/RIYa0eshSz4YmsFHIjJnTcK0wK5IjHZEeec82TOSvOVhetAaUFTWGSxcbpCe0Xg9CiMVsniyzFOtcbQQIDT1YQsZwMIVgqlbuigcB3dRYLYFc8t5HqhsBiA93Say4cEDG2k/1MP8f/RY8LJELgw9rGisyrC0TdDuFoQZTfGoxli08cuazJRAt0zS8xFOVbNh9Qx9TwisuiB7VhJmIyqboHQkYOYGwUSzhLMCRkviFwSFlIe9ZIAWOMsB7R5B756AxpiilG8xd6WDeSyNDMDKdmh7FvPNLGFG8ODEemqrJCdr3bR7BWg4daKfICPYtG4KkQmpj0mWdiiMhiQzHxE5mnafpt0lWdxmktmboj0ckTV9grymsiludZE9HbddqK8LiVKaQ0eGX7LPpe9Wfr2Y7ULcddddrFq1Ctd1ueaaa3j66ae/77F/+Zd/yete9zpKpRKlUombb775Bx5/KbxmJiaJRCKRSLySfDf49WK2H9XnP/957rjjDn7v936P3bt3c9lll/HmN7+Z+fn58x7/0EMP8Z73vIdvf/vbPPHEE4yMjHDLLbcwNTV1sT/+95VMTBKJRCKReI340z/9U375l3+Z97///WzevJlPf/rTpNNpPvOZz5z3+P/9v/83//E//kcuv/xyNm7cyF/91V+hlOKBBx74sY0xmZgkEolEIvFy0OLiN6BWq71o833/vA/X6XTYtWsXN9988wv7pJTcfPPNPPHEEz/UkFutFkEQUC6XL/7n/z6SiUkikUgkEi+DSxVjMjIyQqFQeGG78847z/t4i4uLRFFEX1/fi/b39fUxOzv7Q435t37rtxgcHHzR5OZSe81MTKxqPLM0m3HQXO6UJHI1TgWsZsTsNZL++0zqa6B4RGB4mvS0BqlRacXCFYLWcAQ6DrzzeqO4FHt3HMxpLRsIBdmUT3VbwN76KNZiA6Fg3d/6OCfnseqCxVYGwxM4NUVtQ0iYVdh1GNw2R2OVIj0jCLKaqCsgHPNorNK0ByLq4yFmX4srRidp9hrcv3cza0YWEIHArml6yjVuGjhBalaSmVWkBxtUd/iM/OtTIKG9wceuK4J/6KH4dznE0wXCw3miIzkyz6RQzxXIn4Bqx+Wy/mn8LoU2QLVN+t4wRe6oRX4yhI6k53GTW35yN9kJgd8xCTKat3XvQyjInYXMGQO/qAnT0MkJVvUvUTwKytXUxuKy2nNXmnTt1zSubeFOWhBBdT00RmFxu0nvsz7kA/ANamMSmQopnvQYvNegOSi4fuw0xqyN0dGYTclVbzyMiKBnbwAKWt0Gdl3hLkcoS7C4XeJ0tbGXDLJTEYEyEG2DVC7+y8KPTAqnFKkFTXNI4y4I2hs98o7HI3PjZGZD1LVV3EWBKHXo3qfIzET07phj+P4mfkFSOCYIywG5sxq/rDg718X6v20i3Agt48DensfMuPz5xjio900Dx3BnDf7Nxl343fFxUSFk8Yo4mBYNhdMB/U8FBFkQXvz8WUWfyIl/7/1PdnAqAuoWZt1g6aYOhqdxlyH3WArXDDFmbYTWiCWb1IEUl912mOLhOpVNmuqJEsqC4skA2QFzxcRqau789m1Yyy2QkE77eF2CbVsm+KnL9qIMiFIKlYuwqxIkFItN7BoYbaAQ4BZ8VK/Pjd0nKQ1Wed3aE5gzNmZLYPiCIBe/NvNHDZ6dHSEzJbjnkZ2Eb6iCrVgzsMjvX/33LF6t+NT6u5n183hlm6CgiTY3yJ/StM5lcSsRnZwgyMRBrZELflHizLdJTTWYeEuKuZ0Whq9Jz2vsCnQKYDXBmrNp9QuWdkb4eYH0BdlJaA3GrQtaoyGRC4vbTLoumyf3WJrMnKL3PgurqVEGmJtqzNyQQgaKyNHM73BYqGYxZm3QUF1tMvT35xAaCrkWqd1pnGmLidugucHH8DTeYMjSNofGg33YNc3jz27A6xK485LykZAgp6hsgOxZiWVFuHMGnz15LXZDY5xMEWTjUvnpaYEM4tdRY0yx+qpJso+muX/PlrjFxlwVsaoJAppbPX5m7R4OzcZBpPNXGGgpSM8Kxr4090L5/agckDknKB316XrWoNVj0MkJTj8+SuNf1yhcP0f3Ph+zYRB2BTT7TXKnJP9h5GEiF1R3h+bqiIVqluwEuCcdtCFILWqmbzRx5yVLZ0q0Nvk4S3EpfWd3hlvHD1NvOxROR/zShsfo2Rdy6pmR+H3cA3vRoL4+ZOqrq+jrqaJM0JameFgw+VMRhWOCG954gOoGTWtdh/raELIBhxf76JQjnCVJrZZCaOgZXSF3wiTqCjDa//I+FicnJ6lWqy9sH/rQh34sj/PRj36Uz33uc3zlK1/Bdd0fy2NAUsckkUgkEomXxyUqsJbP58nn8//s4d3d3RiGwdzc3Iv2z83N0d/f/wNv+9//+3/nox/9KPfffz/bt2+/4CH/MP7lTQ0TiUQikXgVeKmzcmzbZufOnS8KXP1uIOt11133fW/3x3/8x/zBH/wB9957L1deeeUF/7w/rGTFJJFIJBKJ14g77riD973vfVx55ZVcffXVfOITn6DZbPL+978fgPe+970MDQ29EKfyR3/0R3z4wx/m7rvvZtWqVS/EomSzWbLZ7I9ljMnEJJFIJBKJl8tL3O/mZ3/2Z1lYWODDH/4ws7OzXH755dx7770vBMROTEwg5T9dTPnUpz5Fp9Php3/6p190P7/3e7/H7//+7/9YxphMTBKJRCKReBm8XN2Fb7/9dm6//fbzfu+hhx560ddnzpy5oMe4GK+ZGJNOUbPcTBOmNZ1CnHEiOwIRaTpZg/JBmLtBY3iwcl0HBHQKAgKJWZOYTYFVlehMRKc3RISCzLRCKNCmhjVNlAMLcwUypyx8ZTDzph7Sc4qJWzPM3DZC7+4OQmjCrGLuTSHujBlna1QUs3v6MVuC2pYAZYE9bcG8Q/ceTf6EQXmvgZrM0Aptlq4OGR+f5dxSEQQsvcGn0kjz5UOXo2wI0vHJmjnicPqeNWhTY0/Y1EcMmsOamevjMvKdwYBOX4g2wGpAmBYcnBlgz7c2oS1Nzx5Fub/K0jeHaKyNaPQbpCdMaqsFD35tJ90HPIQA0xM8uLKJMA3NIQjTYLYFzgqEGZjYPcT860JUJkJGkJrTlI4oOjlB5MVzY52OcBcFVk3QszekUzBxTro48waFUwoWHZY3uixcIZEhPHRwA2F3gF8UpOYEu+/ZjDag1WvSKcRlwhuDBsoWWC2NuyTozGRITwucasSp4/24i5LwRA6ciDW5JSrjkvooyBBMT9PdVefQuQF+avg5qmss9O4CygIx69DJSuqjBnPP9dEecHGqina/wKiYLF4dYTYFKhQc/ZUUOpTIjqAxBotv6LB0TUjuhEGrW/LFe28g3NLk/pkNiEDSyYG1aJI/Kel5VoCE5Y02C7/YIj2v0dkQLSCcT+EsSWYny0zebJG5eQ5nzkCE0Pcti8Yo1MYVrUHNmdkuZADLG02UqwhTcObPNzB7fYH0tMRdkCgb5nda2DWNGvYIsgKhBM01BdojOVoth65DAX1unXvuuwpla6yaJHvMQpkQlEMqx8u0BjRBHsxpB6UEP7npMH9z3xtYmc3znVNrCYd87Aqk5uKMj+w5heFrerMN2n2a6649QudoHsOJmK9n+b2H3kV5r+R/LLyJD/R+m9qYEbc3OJmh3SsQ5Q6tboP+R5fjF7qAIKdoDkoWrs6jHBN7JS6vvrJJUL+5SW1jiJagjfiPVaEhd8KkMQrd+zT5swE9e0La3YL0WROvKz4nl3f1YrY1i9sNWv2S9GJI/nSbnYOTDH6nxcp6BxnEP5c8mKV7n2buSgOvRxN151neaKCUpLndo9Md0fu4gXvGYfmKCNmWGG1NmCJ+7kNBmAKzDcqI2yR07YP6akV4Km778J41z7L4kx4yEPHznomfAsPXDD/YwV6WHD84RPP1Dd6ycx/CifBXdREsu/T0V+nrrfIPH7uJoXKV0vEAwxPYtYgwBYf/P2WUrVEWuGdt6uMRYdbA6xLkzoXkz4Z0yhHesQJSaGavdkitr7BqdAFtxO0O/ss//BytK9qIJRurIrHt8IU2Bn7JxK1EiEgQ5DUiFJSesKmti8idgTAFX39mB1Ek8fOSz0/sJEhL1LCHXYflzRCtaZM7ZlLf6TF7rhyfTydMtAEEEhnAYw9txVmS2FMW+aMmOpBUlrKIQICIsw6lDwuTJeyahkiQXVN96T6Y9CXYXoVeMxOTRCKRSCQSr3wXNDG59957efTRR1/4+q677uLyyy/n537u51hZWblkg0skEolE4tVLXILt1eeCJia/+Zu/Sa1WA2D//v38xm/8Bm9961s5ffo0d9xxxyUdYCKRSCQSr0rJpZzzuqDg19OnT7N5c9xG+0tf+hK33XYbH/nIR9i9ezdvfetbL+kAE4lEIpFIvHZc0IqJbdu0Wi0A7r//fm655RYAyuXyCyspiUQikUgkfoBkxeS8LmhicuONN3LHHXfwB3/wBzz99NO87W1vA+DYsWMMDw9f0gFeKmExZHvfNOlZwcjWWVqDmvxp0KagkxfIUCPLPs6KQPsSZQm8bo1V9BAK+p/q0OkPcHI+Q/8oKR0WBGlB9oykcNQgm/axq9DTVyVMa64vnCQzFxHZgtQClI51mLvaZiy/gtGSEAi8/hBtazIzAeGgj10VmCsmdkVgba5h+AIZgTJheUcEUuMaAWv+ryJUEhVJ7CWD/LMu/nyat2w4RP60ptUvkI8V6BQ0QVaj3bivj7usSc0KDE8QXFWHSNA9UCW8rkbf2yapbgr5yTVH8Vb5oKC2ymD5XJHG5R5kQiInzm7orPII05rKuMNoeQWzBafqXdg1WPO3s4gIEJCeV9hVHTfAVALRMkjPaKobNdlpn+p6zaqRBXJnNUbFxGpovB7FwuUmoSvweyP8gZDKuES5Cquh4/49dnx/9pxFa10HGUB7OESb0HxHjdS8ILUgSC8ocgeXyEy2yU5FUOoQZMFshuzcegqrQdz7aM7mTaXDADgVQf4E1Meg9mwPt64/xJRfisfWq2itCshMSWQYZ1CYTYHViPvxoOM+S9m+BnZNoJVgbGyBbLmFXQPpC0pddQYeMIhSoBxwFwRKC5brGay6oHwkIugLyJ8JURb0PtugfNsU/YU67W7BzvGzZCc0RlvgXLcEkUAPeMwf7SHIaYKuOGsncjXSF+RPwR1X3E96VmC0wV42UI5m9i0BuamITl7H50lOY9VgaYdGV23CFGzddhZlCsxWRCrVYWW9xXOLgyhXx+fQUIfchCJyNTs2nqG4bpn8KXCWNfaGGlEkmWvnsccaGNmAdNqnq9yg61CH0rGQgccaFI82aQ7B5eVzSF8w4FbJTkBUs1GPlgDwyoJ2ZBFoSfFkiOEJIlcjFAip6d5Tpbk6T3pWIzugLE3+dEThdIDf5dK7xyc1J8idBU5kQIPXF6EsiAZ82kMRjVFFal6QnfRw5ttkDy2SmVFEKY2zDCLS6PEm1fVxBltjLGLq5wO0JXnyoS1UxlMgwPAhyMLoTRO0eiXFY5A/CcILQcO6rgWox72hDF8TORoRCHQpoLJJUzyhKJwOAeh5LiRMQ6tX0ilAY1hQOiTYdt0Jgrxmm3uOkf9jEqU0/U9FZOYUmVlF5AgWdjhYTZDdPuFcmozhoxsmZiNAZEIsI6L9D300BwSj2RXslQ52BdyZBvKGFTaMT2N4AmUIep4LcRYNljeYtDb5tHrihXahBdrUTJ/qBgEZp8OZE304VYXVhE2Xn0VOxP1UtIAokmSmBJlzGncxQHY0dhWMtkAXA1oDAnfBoDoev3+4cyZyf46lyzSr88vURyVy2iWyQShw9qdp92t02wBTYTbAL2tKx33SXS1yUyGRo2mv6VA8Bo3VCqL4fchZNmiNBeR6G1Su7GA0JemFiNKzFo4ZvnQfTJeou/CrzQVNTP7iL/4C0zT54he/yKc+9SmGhoYA+OY3v8mtt956SQeYSCQSiUTiteOCYkxGR0f5xje+8T37P/7xj1/0gBKJRCKReC3QOt4u5vavRhe0YmIYBvPz89+zf2lpCcMwLnpQiUQikUi86iUxJud1QRMT/X2mab7vY9v2D30/n/rUp9i+ffsLLZuvu+46vvnNb77wfc/z+MAHPkBXVxfZbJZ3v/vd39OuOZFIJBKJxKvHj3Qp58///M8BEELwV3/1Vy/qLBhFEY888ggbN278oe9veHiYj370o6xbtw6tNf/rf/0vfuqnfoo9e/awZcsWfv3Xf5177rmHL3zhCxQKBW6//Xbe9a538dhjj/0owwbAqBscX+khM6NY+PYgaQ+01Fh1jV8WVN7gUci1WN5kIXyJX4TSEViy00TliHaXCVGE83iOpa0QZDW9uzStAUFqXlOtplGbAuSxLnKLgrLZILXQQXTZCC0IcgaFk4qn9o2TagiinMSsSyJbUxu1sU8L2n0K+n3aAwI9l8FWIANN/myEXzYoHdH89rv+gdtL/4koNDGPpQldTWsQbtp5iPtPr0evB7sSB8zKEPzuiOxRm05BEzlgdDRWQ2A+miMcUyyGRQYfFFRyObg64rbSXg4O93P2aD9hGtwZkzBtIFc3QTgUdizS9GyU7xJk4fQzI1DS/NLoo3zU+FlOvq8foTX+YMB81iR3RmBXwUsJEJrKBoFZF8xcmyJ7Bs5k+hiuK0obV+BQF1pC+XBEJysp7xE0h+JgzfSEiV+EyFGYTYHwJEZbIK0Iw9MYLYnXrVFnc4R9GmdZUDhUYe4NPdRXgzsv6H5QEqY0tdUuU/N95OcUdlWweHXEY7V1ZKY1lY1xaX7Q+D0h9+zfxi1bD9IpCHQqpLDfwq7H5c3TM1C92kM8JljaYuAuQpgS6KeKeNtbWGdTTM8OgAbbAacKK6dLDPzCPN7+Psw2yECQy3g0Ww4CqK4xSJ8wWNgB6VnNzA1Z6idc7CWDqFuxd2KEkgblaioTRcbWzzHzzAByfYN1vQtUvBTi6z0gJKVDcdn1IWuFymUBxb0W2oTsGTC31XCWc4BD6TAEOYGW0PsUtPoM3GXNoadX464X2FVJq6lZ+2ST4ruXWbBLqJzCPeXgleM/Ug59ex2b33ico13dcXBp00bMORyYWoNKKWRb4o8q6tM56jcaZCdBGy6L20y6n4v4QvkacjV4fH41zUHA0ITX1FlTqjJZKHFZbpKHmxupjZkExYjyXolTVRRurXLulgGKJ6IXysyv33qOo+kBCvtsiicCZq53MFvQGIFOT0j6tEVrbQd3OS6dH/QGAKQWTRZ2pCkf8hEFh+UtAhEJtAHNPpPMIxZWU+PUIoKsgZxNs3BZ/D5SXQ/Sj4Myvf6I08+MUF5QzF+nny9/XgIBzxxeg8gGULfIzPp0ci6VTQJVNykdFFRXC9KzAnukQW00R9ehkKVNJpGrcRcFnZyg2klhrwieaI4zc71JYdsS4XNlnJWQuassSkc0jWGBs6xx3QA9HGCJCJEJmbw5h3VO4w6FTG8OwdA0Q5tzb8rQXt3BqRXwDghO6yKDjwYgBM5iG7Etj7uoCdMdTN8iNdvCXszj9wfIpkH3/pBzm/PYZY/0jCTIWxzaP0rpNESOxOhomuOC4MYakdR0HilQPhKQnlPMjSvsVEDxqIVfEGhD0CkpCmtWuKp/gkMf3c6tb9lP5Z4e7E9VOfydNUSuJkprSgcFlY0G3ZtWaOR7iUY9amMuWnfo5A261i/R7li4lSzuvImzImi8voWfNTEWLdZvXuDgnhJsrVNdlcOuavzwJWwhd7EBrK/S4Ncf6Tfw3RgSrTWf/vSnX3TZxrZtVq1axac//ekf+v7e/va3v+jrP/zDP+RTn/oUTz75JMPDw/z1X/81d999N29605sA+OxnP8umTZt48sknufbaa3+UoScSiUQi8YoidLxdzO1fjX6kicnp06cBeOMb38iXv/xlSqXSJRtIFEV84QtfoNlsct1117Fr1y6CIODmm29+4ZiNGzcyOjrKE0888X0nJr7v4/v+C18ndVUSiUQi8Yp0sXEir9KJyQXFmHz729++ZJOS/fv3k81mcRyH//Af/gNf+cpX2Lx5M7Ozs9i2TbFYfNHxfX19zM7Oft/7u/POOykUCi9sIyMjl2SciUQikUgkfvwuaGLy7ne/mz/6oz/6nv1//Md/zM/8zM/8SPe1YcMG9u7dy1NPPcWv/uqv8r73vY9Dhw5dyLAA+NCHPkS1Wn1hm5ycvOD7SiQSiUTixyYpsHZeFzQxeeSRR87bE+ctb3kLjzzyyI90X7ZtMz4+zs6dO7nzzju57LLL+LM/+zP6+/vpdDpUKpUXHT83N0d/f//3vT/HcV7I8vnulkgkEonEK06SLnxeFzQxaTQa500LtizromM6lFL4vs/OnTuxLIsHHnjghe8dPXqUiYkJrrvuuh/5fnWvzw0Dp1jZIPG3tWiMKOqr4ih+s6kpPOLS3N2NM2PR87TEqUBtlcBdkFy17STNAUnqrIVQoCWotCJ0BGYbGqOgazYikKhcSPuGBk/V11JZ65Ka84lsEZdz7pO859onETuqlJ6TKAN0V4elHQptahjwYMZBR4K+sWXU6jbtsmR5o0F6WrC8WfDbZ/4VU2/SXNUzgd8TcdkNxwm6Q2wZUsq1MDxBc9U/ZSiUhqt4PXHJ8fSCojkMdlWTOxcRpRUiEhiBJr0QIVIh82GOhXoWsnEJbW84wKoLxNEMYQrCb3TT/dkMVh1SC4rVV00SdIX8xak34KxocmfBbAn6HzDJTEnMpsYbiOh9UmB4ktS8ID0n6D4QZ0IMrVqkPmSwfKyM1dRoW7O80UBL8EsCv6zIn4RNbz2G1w2pWYm7ALde+xzeuM/o/zaprYX1V0yQnhbkTksiB7QJ3mCOVr/AnY/bC6zc0sbrElTHJVoLWj2S2mowqwa3FA9QXxWfKwOP+7gLgtwJk1+88lGenF6Fu6wQTkS7V1NdB0FR4XWB9g38kvlCqfzGqMZd1ERVGy2ga58ms2UlDlJTkD1rUH2oH5VWpKfB8EAIzZWjEwQFhdmG1ILG8KCyUVM8EdLzpIGzIlCOJvVcisWrI1CgUxHX9JzBaAu8hRRHv7Oa2V39LG01yR+X1FcJVrZoDrWHwFJ0CqBGPCqbNIWPZpm72qUz1GHxjR3q17fo5KG6VtK4ss3KJtD9Hl2HQuy6ppBvMfGWNPvv30B2oAEdSVBQVLYoMucEfk/I67uO43UrZAB37Lwf5WhUWrF64wyZaUHYMXAWDbr3KTKzEVoKDA8Wdkg2bpmkNaDZWp7B8AWZExamGbGlOIO9N8MZr5sN7nTcUqDsUVsN7S7B8oMDFI9HVFcbVDZrUkuKuS+OUXjORnY0YVoSZDSGD31PR5R3mVgNSJ2xafVItt5wgsxxm9xBm+aAxCtDfcRm6vUu3c8pnJX49V5bG59TpqeJbEH/kx20gOxURO4MWHVB94GITkmRO2nAWIu51ykGHob8SYnVikvoD44sYcw43HTlIeauTFEfg+wZGbcKsOJS+80RwXC5gruiMBsRA094oCFy46yf2WqO1khINUxhNQT13V00ByTVNRbdz2k6WUH5cER9DQwUaiglmfPz2GcdUoua0mHNmtwSoiPJHrc4vNCH1QBZNzE9Te4M9DynqK626OQN5q/KEeQ1y9s14lAOpxJSXZdFbqlR3G+huzvUhw1U24QTGZa2pujkDJz+FsvXdWiMKZQl6HgW5uN55KMFrKYmcgWRI5C5gMA3CVOCTkHgLIO9Imm0HB6dXMPSFgOFZHlHmQPPrCZzDoYfVNhDTYKsQFma9j/20nUwJP9ECmVC/itZpm+OWDhbovQ32fj9ZGuL2uva5LJt3BMOI/eF7H1yHd5wQOdcBqup8boE9bp7UZ9hiYt3QXlR27Zt4/Of/zwf/vCHX7T/c5/73Atdh38YH/rQh3jLW97C6Ogo9Xqdu+++m4ceeoh//Md/pFAo8Iu/+IvccccdlMtl8vk8v/Zrv8Z1112XZOQkEolE4l++JPj1vC5oYvK7v/u7vOtd7+LkyZMvpPI+8MAD/J//83/4whe+8EPfz/z8PO9973uZmZmhUCiwfft2/vEf/5Gf/MmfBOL0ZCkl7373u/F9nze/+c188pOfvJAhJxKJRCLxypJMTM7rgiYmb3/72/nqV7/KRz7yEb74xS+SSqXYvn07999/PzfddNMPfT9//dd//QO/77oud911F3fdddeFDDORSCQSicS/MBdc4u5tb3sbb3vb2y7lWBKJRCKReO1IKr+e1wUFvwJUKhX+6q/+it/+7d9meXkZgN27dzM1NXXJBncpWSdTPHxuHG1qokocuCsDaIwIKhs11XEIx9ukFmDxFo/msMYbCWgPKJ7dtQ53WdMeC6htiLCrkJ4wKB5vkZ7RyLUNiEAWO5T7ati7svxc+UlyEx0q61LYDU3/Ux6tPo0Umo5vUV2vUa5C+wb9jwnQ4OxPoyWYczZzs0WiUKLsOIi1ujEiPSuYa2QpHjTZkJ7FWTDY9511FPdY1AOXSEk6BU1q2qC9yUOEgpVzBQwPyvsEdiXEbApWtkXMXi3BUqzffI7Zqw0WLjfZvmqKbyxeRhhKur9tgwAjExKlNXZFMPRQk8o1HSbeEa8fLu6AnO0hPElPuokywC8KnBVNZYMkNa8I0wKj5NPsl0SuIjcZEaahMRDPiafnirQGNZk1VZa2CbSlQECnIGjvaIHUeN2CXYdW4y5CayTECDT3Ht5MV3ed+R0WQXfIcjtNmAbD12SmBGYTOjlJ18EIoxO3F8hl26QWNMVjit58AxmC0RGYTcE9y5ehTDDagunXOSgL2v2az97/Bv5gy9dY2SCwz7iYLUHf0xHdT0syUxrZNMgfWiF/StPq02hL4xcF9pJB2Neh1S+pVtM4K5rIhdagIrJBtgXpRUX3LVOMlxepdFJIX9D/0DJBRlA6FpKak7TLBkJB6EJ60sC6cYnBBwSizydbbvHVY9tpr+lgFjtkti8TZhVWA3JTIZGjkYGgGqXIHnJwKprIN0jNSuavSGHVNNacjbQjVCApnFS0RwLMsy6pOUE666NMgVNV1BopshNQvnaWW8cOg9QoW5OeNAjTgIRvzm7FrkmCvOZPd92MTkdIT1D3HZqDGjnj0nX1HI0Bg5X1Jq3u+HzVBlxVPosYa1INUkSOxrlxka5Mi3roEqahYLb58uKVpGc0USRJzwiyMxFetyZ3rEJqQaNcjVeU1G9s4ZegcDogv3+J9JxARHGwpV3XdArQXtXB6xJM1kq0BiPafZra+pDMtCY/4dP3TMD8Tkn7miZhOn4NNgc181cK2l2STt4gzMaPF2QFyoRzb1aYDYlX1ogTaayKgVeWdPKQO1knN6F45/BzuMuCh3ZvAiBKaRqjGtmBMA3OisCdh+lKnsq4pLbKxi9ZICF/Nh7nz697FoBf7voO+TOKYLWH1dAEWUFzUFI4HZCa9TCbgpMHhjAMRTVwCdZ4tLsF5v8zz8OnxjG8uJ3GlQOTpOcU0hdEtiC9GNHsNbDrmtzJOoZH/No9IzEvq2Cv+CDgdaMnUQYMfN0icgSpCQuzIdBSsLRd8MEtD4JvoPt9Ojn4pcsfo74poL4uREtYWWfSGBYUHnfJHHDxyoLm2gC/CNe+ZT9XjJxjsFij7+kOI9YSxWNNtl51mpUrQiprTbyKixaQGmqw+l0naXUbNIc1CJi7KSJ31ELmAibfqlnYYeLsTxNVbJZnCvhlxZl3C/SAR/a4heEJiid8lAVSvnTXR75b+fVitlejC1ox2bdvHzfffDOFQoEzZ87wS7/0S5TLZb785S8zMTHB3/7t317qcSYSiUQikXgNuKAVkzvuuINf+IVf4Pjx47juP6VWvfWtb/2R65gkEolEIvGalNQxOa8Lmpg888wz/Mqv/Mr37B8aGvqB5eITiUQikUgkfpALupTjOM55C6kdO3aMnp6eix5UIpFIJBKvdoKL7C58yUbyynJBKybveMc7+G//7b8RBHH1TiEEExMT/NZv/Rbvfve7L+kAE4lEIpFIvHZc0MTkYx/7GI1Gg97eXtrtNjfddBPj4+Pkcjn+8A//8FKP8ZLQlqbRdBFKYBSCuLT8eBN3MS4vLxRELTPO3qrYZCYFoi1RhQC7Kli6TGOumLgzBvXxkCCnmb0ug1cW6GNZSqtXyD2RolLJoAz45NybmLjVYuGaiOWNksmfcJAhfO7glYQ1G9UdkD9mIBsGzX6JMqE1FoKIy1unTtsYMw4rl0VkZjS940ugoS/boDWo+fg9t+Gv8QizcRZLLXBZmC1QOAH5MwoAZwV++tpnMNqCKCU4/V5QJohsSGpegKE5sWcEBPglxYGn19Dv1hjvXWThDR0MD5wDKZQBIoKFnRmoWqAEjTGF0RbsPj0KAjbk5mj3C0wPGiNQvGaOxoikcqXPUHeF5nYPqyaZfr2g3aMJ04LmsMaYdTB8QX0ljbsgwNQEGY3Z0pTyLbSjaIxF5PsatPo1fY9JKhs1XeUGtT1dCAXp0xYLR7tRFkSuwPA0TlWxsOP501tDZT3of+widAXKFMzXsvhl6BQU1hUr7Jobxl0UZM9pCscVzdEIY1WD0W0z/M7Bn8JsCWQHWmsC5q40aPcJquOgUhHVLSVa/QIZAt0+zVURYVaTOuUQZME+47B0bYDZ1tgViV0DldL4OcnZY/0cWezFlhGGJ6hvLBBmoLraJMiA1dZ0cgIZgdetqZ4osbLBwDjjwhNFwsBA1E3ST6VZmctj9niYLc25WwRqzMNZFtyQPUZ7R4vKRo3phrRGQrxejV8WBMUIpl16e2o0RmT8/BcVZlvD40Xmr5BErkCeTbGyWdPuWHxndi3ujIWzZKCsOJskd9zEj0zMBqTmBJmcR+q0jQgEO3rOYTUF2Q0rLNczeN3QHFF0CgIRCUQEj8yPo6bS7HpsA52BgJWzJVqBxYqfxr5shVZkUw1cWv0CVbfoFGFlvUmUD6mvLzJ/bQRAmIHU7jRCQ2Wdzamf6wUNsgPV1QbzV8ZtAGTNREawOFEkPW0Q9XXIHzEJcoL6iMP8Tgt3SeA+m4mz09oCNeRhdKC6KSJ6/xLRsEeYFmTfNEeY0ZhVE2Vr7JrA3VZBCzB8UBacfmee2TeFfObw9TRWh6RmTEQIVk2iLU2U1vF5k4EgD62FDHYV2j2ChR2SoCdgcZuBVRc8vbKK8kiFD539VyxuF9huyNJVIQDNYcXSNovJWzLYNdC2ouObnK6UUb5BakHjhyYbB+eQASzd7PH017bR7pFEKUW7W7Ky3gQB1XHJ8rY8PY8vkD9hEKRBfKfI6XdkCdKCPZ+8nMZqhffeFcyWRoTg9SncFQUaHq2sI3XOhEWH9kDEXz71etxJC7NqICIwWxBtamK24wxJdyl+j/UGQp76+jaOLvaSszxavRZ/cvZWTv50ln1nh+h+0sSua5wZk8ysQinJ2UoJ3r3E2NXnWNmkyfY0qW/usG5onp7hCsrUGB1wFg36R5YRCkRHopomYQqcjVVmr3axGmAcT7+EH0xJE7/zuaBLOYVCgfvuu49HH32Uffv20Wg0uOKKK7j55psv9fgSiUQikXh1Siq/ntcFF1gDuPHGG7nxxhsv1VgSiUQikUi8xv3QE5M///M/59//+3+P67r8+Z//+Q88NpvNsmXLFq655pqLHmAikUgkEq9KyYrJef3QE5OPf/zj/PzP/zyu6/Lxj3/8Bx7r+z7z8/P8+q//On/yJ39y0YNMJBKJROLV5mKrt77mK7+ePn36vP//fu677z5+7ud+LpmYJBKJRCKR+KFdcK+cf86NN97I7/zO7/y47v6C/OzmXaSnNd2lOtqAoO6gbOh61kA5GiJBc0QjO4JOEQpHDDatmkGEgtxpSeacoFNUuHPxfC47pVBWfN+1eprmkOY9254lzGg2ZmcY/3yTDZ9pUzqqWHv3IgCWHeLOmuR3OWgD0jMSGYLVFBQHatxw7SGEAufKZcRoCzT4BUH0xR6CLJyrFpC+YPiyGTI5j+61y1S2B8w1coimQfX1HvURiTHtEL2+ygPn1hMUNK1+jTVlIxTkn3HpFCD/nENh/TJmS6DLAQjNFdmzjOcWMJwIp6IRV1WJsorWoEZEGsMTiFDgLMenjuWE8dcypHQ47gMD0PlqL35R455xWKhnyOyLKwT37BIYHWiMaMJiSFgOyZzTjI/OIxTgS6ymwOsS1PZ0Yc+bOMsGaaeD1RQ0hyRRWrG2tIi7rUJzVYjfpVDFANkhfr5KgjAlSM8IOhlJZi7CXRI0b2zGmThZgTeTwS8rnCVJq+Xw78cfpXePRycvqKyXpKcM/IrL3CNDfOOKv8SuQ3s0QNYNOr0hzpJGrGoiM3E2RPlQ/K9esdFuRP6ERIRxtoG1rYo1b9HJC5yVOEsj1d/Aamm6xlboyrRQz1cksBpxRgMCnGVY2hL3d5EB2LX4eS+eUFg1gTLi8yl3SlLbHNA/vIzthCgLnHkD45SL163529nrSe1OY1ckYd1CZELMhqB0LKI4UINBj8XD3fQ/1SZ3yGbdxinavYLVt51i5EEfuxax+upJUvOSlmdTcDwiR+MuxD+flpowBcv3DiIU1NYqGmcLpGc1pSPwrf1bGP52G31/meBMltQclA4ItAF+f1xyYOLAAMrWZCYF7oSNvSwxpMI2QuqzOTamZlidWcKqQ7q3SXuNT2peU9prkju4CBKsFUlmRtFYG5Kd0NhVTdeBCLOtafeKuGfUfoFf1rgLktScRnoSLcA+65Bais/f3KRP6WhEkIszTTp5jQzBPplChILMhEHuzhx933Cor1V4X+8jKEUMPxhi1QT9T7So11IYPshAEzma1V+u0f+Aid+22LTxHP76NkEWvMGQnmchTGn6HhW0xzpkpjXdwxXcFY27pMmfAmvOIn9aIxTsKE4C8b89ezXlXJM1n1PkJhQj3wpxn+8HlZ5VGPmAXLbNO0YPYKRCcudC2t/pZqGVAcA9lOKN79pFdirCnTfInYtwFzXKBr8rQks48h+7qe308LsVN75nN2u+WCFyBP47K1hVSc//16bdK+jZG9D7FNTH4veGo8u9CAVGfwurLhF2hLsEytYUTnVIz0f0fNll8QpFbbvP4nUhVkOQO2GiHE1lPseeI6sA+D/rvkD/U4qr154hvRDR7hX4vRHNAUna9ak3XVqPdSPR9D4D6pkiA98yOXp6gPozcW0twwctYW1hCbMlcOYNMqctwpSmsZBBmWA1NaWr5y7tB88PklR+Pa8Lnpg88MAD3Hbbbaxdu5a1a9dy2223cf/997/w/VQqxQc/+MFLMshEIpFIJF51konJeV3QxOSTn/wkt956K7lcjg9+8IN88IMfJJ/P89a3vpW77rrrUo8xkUgkEonEa8QFpQt/5CMf4eMf/zi33377C/v+03/6T9xwww185CMf4QMf+MAlG2AikUgkEq9GSfDr+V3QikmlUuHWW2/9nv233HIL1Wr1ogeVSCQSicSrXlL59bwuuFfOV77yle/Z/7WvfY3bbrvtogf142BVBX9/ZiuNMTCkQqUUhIJWv6a2FnqeAREJhh4OifIRZhMMX7MpP4vfG6EsaA5pZCBwF8BsCkJH0O6Py79Hyw65M9COLIKi4qbMEZrDaRYvz1IZlzTXlnBWBEJozGZc4r1TAL+skaHG649QD5XZ9fWtBDlNOdPCOJKheNBEhnEp+daagEYljXI0Z0/1sqV3lsWFPN1PmawuLuEONsk/6pI/q0jPCLJfytF8rgwaIgcy5+LnojmsyU5qEBA81E35UER+t0NqVjIXFADQSsS321fAnTPo2qdZuSIkMxUHv3r9ITIQiCNZcqcknzu0k4WdcWBrmNE03tQE4v/ftuYgzdUR7oIgssBoCQonIH3GotxfZfmy+DmsbguQviRzThM5cZCosbGOv8bDNUPyZxTSB3vRoGy3aB0pkpoy0RLoxKey1w2pRY0yoXQ8oHyghtlWtK9u4j6TIXI1RkdTGK2Smn0+gNeK8LRFZTwOSA6zCm3ApnVT/MxPP8wbHvwg3fvbpM5ZaAOMuoHpa+TRLHrZjoMruwzQ4M4bpE/aVLaEhOm4vP5IsQICCqcVA/fN0ylo9J4CTiUkjCTTywV+qncvhi+IXAECzJbG69FxwO+gwFnR9O4O8AYigpSg3adQNrx9/ADtfo1wFMGXe2lWXTr5+D669ynMpuCq4llag4rSUQVaIOcdwssbRLagspzBOpZGmRBkTTLTimMTfUSuph1aNAZtFrfaTDw8itUA2w6p+i7ZCahujvC7dBwALmD7Tx8iyIHsCN56/R5WNmuag4LUKZv5K1K0+zVWXRDkIEwJhILsyedLv89LjJbE64YgF7/ONpfmsGWE8CVdZoOHZ8ZxagopNeljDmEqLjUv/A4iis/XTk6QnjBZ/Amf6rig3S0JMnFJf3dFUV0H6RlBkNeEKcGOq07gd8UtKbyypDUcUVnrMP0TGmdJ4HVr8qeh3aeQHXAqcdD7iZ+3mL9SkJqRNEc0VsFn7koLbUJzyMWcdojG28gIikehNZYhM9Ph2rWnOf70GHLKRRuQPWFSeUcTd6zO7E+ElJ+2aPUJ1pUWCNLx89QcEOTOgNVU+F0KQyg6oclzlWFmbtTMLhZYvMyhMSSZuslC6DjIs9Ured3aE1iG4mtnt6FWHBoDJsqCvnQDbUJrNOSeA1uRocbvVlRXGQQZgZbQ86wkvRCRPynJ7nNxFyTf3L2NM+8sUTrq06i7ZCc03kcaeOs8pm8ymf2JEKseP/5ArkZrTQcpNetuOIN1zqF01Cd/QmKteFgtRXWNwdC3Qa5YIMDrjWhs89EGDI0ssW3DJGEadn77dqqrDfbPDjzfVgJkJiDIwvJcnkKuhV/WLLbSaIO4DcW4RNZMuq6djd+/BzXZSXj67BjWtvgP6NZIhAzgdduO0ikpGsMC/xu9P/4PpO9KYkzO60cqsPZdmzdv5g//8A956KGHuO666wB48skneeyxx/iN3/iNSz/KRCKRSCQSl8Rdd93Fn/zJnzA7O8tll13G//gf/4Orr776+x7/hS98gd/93d/lzJkzrFu3jj/6oz/irW99649tfD9SgbX/t1KpxKFDhzh06NAL+4rFIp/5zGdecWnCiUQikUi80rwcMSaf//znueOOO/j0pz/NNddcwyc+8Qne/OY3c/ToUXp7v3e16PHHH+c973kPd955J7fddht3330373znO9m9ezdbt2698MH/ABdUYC2RSCQSicRFehlK0v/pn/4pv/zLv8z73/9+AD796U9zzz338JnPfIb/+l//6/cc/2d/9mfceuut/OZv/iYAf/AHf8B9993HX/zFX/DpT3/6Igb//f3YCqwlEolEIpH48avVai/afN8/73GdToddu3Zx8803v7BPSsnNN9/ME088cd7bPPHEEy86HuDNb37z9z3+UrigdOF/9+/+3Q/8/mc+85kLGkwikUgkEq8ZF3kp57srJiMjIy/a/Xu/93v8/u///vccvri4SBRF9PX1vWh/X18fR44cOe9DzM7Onvf42dnZCx/3P+OCVkxWVlZetM3Pz/Pggw/y5S9/mUqlcomHeGl0ShrXisunL9UyOPMGVtXAWRFYdUF1XGKtSOavsMBUaAkygm988xrsZUnoQnYijvyvr1WYHjQHBb3PQDDcwapIWgOCfZUhCocMPnz6nQityZ8NQBBH6w8rwhM5gqvqAOTOaNwFgdkGZ8Gg3acJ0xpnRXBuqYjXF9Ia0DSHFX5JYC6Z5Iot0lOCTG+Tpw6vQXsG9TGBawR0zmQJMoJOTlAfjxAKlANBMcJdFPhFgb+hjR72UCa0+zT1rT5+QRJkobnd45r0STrKpFxs4tQ0ka3x1voYHY25ZNLu07hzEgSEaY1yNJ0iCMBeEYQpUOmIcCaNfL4k+5cevJauXZJOAWrjEOQ17R5BmNEsz+dJTRucmu4mNWGhSwHNYUFmWiPGWgihcU66rMot45VEnKnjCZ5bGkQ5Os6oiARG3UBLyJ3VeGVB5Ajmr7CobMrhlQzcZzOICMJCRGNE0Dxcork2oLW2Q2cqg4HGauo4+05Dp6g5uneULxzfQV9fhekbUvhdCrsqkB1BdY3E8ECEcbqe//zYwlRcPl62JUFR0bcr4uxyCU3cWuDsu3pxFwThliYz19s0DpeQUvO1+cvRAurDJq3RkNAVRK4mcsGuaUQEUzfFf0e0+gWpOYnZhKcWVlE6BHLRoj4G6bwXZylpWN5iELmaXdVR8iclrR4JGrSh6dTj3gHOhIM35oOhafaZzL5OYSzauIuCE6f7qI9JzJYmSmuq6xT12RztjsXKZRGyJdFSY7YEytJ0lIE2NO6S4B8Ob0G5itbqgPaaDu1eTZjSeIMhYRpaQxplgAihPh7RWu+TOxOXqFd9PkFWoxCcqZXRjqKjDdaXFmgMSRqVFACVLYrl13WoXjmIO22gnPi5CjMa3TTJndGY7TgDLsjGWTgIqK2LsKsCvwt2HV9FZkqSO6MJMpCaNhARlPcYdIpxq4jGKFg1QWtNh8iG5U2C3scMnOU4Cy0zJbD2Z0jParLXLtDqlYjVTcK2idmOM36kr5m91sWLTPRInK2TmYozmvSxLHp3ga4nLIKswKprnjy5mjAjKB0LyExrVrYraqsM8icEf3voatTTRfJ2m+JhiWqZ1FdHeL2a/qciQpc4c8WBx86spv5oLz2ZJtpUhCmB363Yd3YIZyl+PoSh8YoGWsbjSS0p0nMKvygIUxK7GrccQINsGXQdjGj1WYg5h6WrQypfH8Q+4xLkI4p7bQw/Pl9G0ytYCxYpJ6BotwlTmqmbbOqrNd5gmk7OAAGNAYPUvIRIYLQlxrxNpxQxv5Lj+HwPIoTR/mW8Hs3qrmWUKbBrwJKD2YKx0UWUkhgdWJ4u0BiStAcj/C6FM9Jg6mwXZivObFrZpgiqDuLRIp2SInPaAODxU2spHBcIDV75JUzBvURZOZOTk1Sr1Re2D33oQy/dz/BjcEErJudLFVZK8au/+qusXbv2ogeVSCQSiUTih5PP58nn8//scd3d3RiGwdzci/sBzc3N0d/ff97b9Pf3/0jHXwqXLMZESskdd9zxPdk7iUQikUgkzuMlrmNi2zY7d+7kgQceeGGfUooHHnjghdIf//+uu+66Fx0PcN99933f4y+FC1ox+X5OnjxJGIaX8i4TiUQikXhVejnShe+44w7e9773ceWVV3L11VfziU98gmaz+UKWznvf+16Ghoa48847AfjgBz/ITTfdxMc+9jHe9ra38bnPfY5nn32W//k//+eFD/yfcUETkzvuuONFX2utmZmZ4Z577uF973vfJRlYIpFIJBKJS+tnf/ZnWVhY4MMf/jCzs7Ncfvnl3HvvvS8EuE5MTCDlP11Muf7667n77rv5nd/5HX77t3+bdevW8dWvfvXHVsMELvBSzp49e1607du3D4CPfexjfOITn7iU47tklAUL83n0xgZhaFC8coGgGBGmnw+gDKF8UOOsQM/DNgjwi4LOQIA/0kE5muZwHOiXPSNpjMUBsIuXCaxzNp3uCLsCS800lSs6jOcWMRsR7S6TnudCpK/p2itgrAWHc6SWFEE2Lv3c7hFYdQh6AoKcpt2vcJ2A/GAdd17AYBysqlxNo+EitGa0tIKT95GeJMgrDKGJyiGtQcXKJo3ZkMzepFADXhyomtEYHcg9kyL7VAotBakZQfq4Q2NE4Jc02jO4p3oZEo0fGgTpODA4t9fBz8t4dq7A61OIdIjVEPTvmCVyNdetPkXxpCIzrSjtMTEHWuTOgLmxxrXXHaFyc5tOUZGaFUQpRW5CY7QFhhthdEAamk5BY52zERG4ywohNV7LJkpp5to50BCmwWzD7GIB3dXBXZBE3R2GHlb07gmIbIhS0OrXDDzhEdlxwLLXHQeSWssG2QlNNOyROmthzVtoSzPhx6X7rQYUjgnyJ2Fg0zx9hTpL1QxaxAGQYUYTlCP6dgWYHqzaPv18kHNEek4QdIUEeY3hCfLHDZY2maTvyZOZEqQXIuwqIMCyQwwf7HU1+gp1Du5ehd+lcJcUCI1d0/TshvRMHCjsdQnsiiA9aZCe07SGIhAghUZLyEzKuF2CHZCbVAR5RWdtG7MluDx/jupVPrUNISITYnjPB/dpULbGnLMxGhK/JMgfM5HDLerjEaXeOuXDEdnZKA6gnpdctfUkSgtEOsRsCXS/T2YqHsNCOwtaxCXBqxZGW2KumBAKDD/ecsdMiscV5f2a0X9Ywa5pSvsk2WIbLQXYisKTLlFGcWBxgNn9fci2ZDnM0got8mcVpa4GygFn3kAuWbS7JO6yJurzCd34d2RWDcTzJeqzM4r0NARpgbsocBcMOgVNeyBEmAqzqenkBYOPtolc6BQEIoLIjYPRzYYgNwGlXRbpWY3ZFize4hFmNO0ejf+GGjKA4kmfxuM9pBYVA6UaomkgVBysbHQUzdUBe/esJarZBAVFmBEoG6yGwN/UJsiKOGA8hF/Z8R0KpyPmr7SojkNmwqA1pFCWYKirSubGBebbORoj4BQ90lMGuVNQGzNxqhq/JPC6NCoy6OQ1J/cMU+yvY/gaXerAik1re5v166bJ5DzSiyHl5yT5M4r6qEQZ4vlWC4JWv6A9GNIaC1m1bZpmv4FflOROSfpGVmhd32TwsQ5m3aDVrykdaVM6KKgEKfInoNm2eXzXBtyxOmptG3dJsLTJorpGIkKorVO0exXFfSbugkAbmuxpk6vHzmJbIdqAfzP8DKkZwbGZ3ji4OILCUUHhVMTZyW6aLQd7c5VMT4vm+g6i2EF2BKYZYeaCuE3GGMjnz/0gG7dOiFzodEdYdki7V8Sv8/rL8xn1Urr99ts5e/Ysvu/z1FNPcc0117zwvYceeoi/+Zu/edHxP/MzP8PRo0fxfZ8DBw78WKu+wgWumNxzzz1orclkMgCcOXOGr371q4yNjWGal/TqUCKRSCQSr04vQ4G1fwkuaMXkne98J3/3d38HxJ2Gr732Wj72sY/xzne+k0996lOXdICJRCKRSLwafTfG5GK2V6MLmpjs3r2b173udfz/2PvvYMmu+7D3/a61Y+d0cpw5kxMGwCANCIAgATEpkpRtSVDdq2da8q0n0rbCH2K9erZcLhf9h2zqSZeSbT1ZunqXeixlM0gUAwSQSIPBgAAmxzNnTk6dw45rvT82NNd4HkoiQXEgYn+qdpHTp3f36u7duxf2+gWAP/qjP2J0dJSFhQV+7/d+7w3N/lKpVCqVSqW+Fd/Wuku/36dQKADwpS99iQ996ENIKXnggQdYWFj4jg4wlUqlUqnvWd+jVz3ejG/risnu3bv5sz/7MxYXF/nLv/xL3vOe9wCwsbHxdyrykkqlUqnU2953uY7JPxTf1sTkX//rf80v/uIvsmPHDu6///6bhVa+9KUvcdddd31HB/id4tQFViZEzecwrmZYv14lN2+SvwHOliR2NevHNZELW8cUmU2N4WnEwMBesXEaSaaGcayJXwUUZNcV+RtJaXaRScrH9z0bd8lmzSswGLHYvAe8ikH9gEVvQjBRa4HQrB+H3rRmMK6wW0k57Px5G52LsOsS73QZ72wZu6NxTmcpX1GYPUGp2Kc9B1kzIPAs7MkeylVc71QRhiK3JBl+GdjZI3fd5MjMCs66mZTYD8GvQlCE/rimsyemPxsioqRMuXBj2pHL00u7sIyY/qjAG1F0dipae8BuCsyBwBgIdM/EGMBmO0+U1ZzZHKc7KXHaiu4sxItZwrxg0HN4/tpOoq6FNpKMA6sl6cwIgooi9gxiG6KmTWZDYPYE+UVNc49BFBgYpiK2NfVBluZxH3MARgDmgovYtkFD5orD5h0mm3daRNkk6r6wAK2dDlY/ybhQMx79mRjDF/SmBIemVzF8MIIkC2BxUCFyBX4ZgqKg8aDP5kuj7CjUec+ui0QFjbKhcE1ibxsoU2AMNJvdHGY/Zun7NEOv+hgdA386QBvJ+6wcaO6D9r6Y7YMm3Yf6xA4Meg7VCzHeQoGF5RqqEKFtxWBIUj5t0TwAXjkpIR8WFbGTZJYNDg9oz4Ec8vGrmvVWgc5OQftIgDcT4D8zRP2gIK6GZF/NYPjwW8+9k/xrDqPPSkw7IrsmMOsWm3cLooLC3tPGbgk6eyK8IU3YcKi9JOmdrlK41MTsK1BQXFDYMuZH516BppVkrFx3qT8cEIyF7CjUETH0dkRM7NmkuKdBdlWQH+kRu5p3PfYKnUMBg5qkPSdpHiwxGBF4VcFkqUV7jyJT8BmMaayWpOfZMDnA7AmerO/nibET9Eckjc0CWmrCUtKOoLgQ0tkJudMunZ0k2V5B8jmW5iP8osAbEmgT+uNJBl5Yi3C2DITQdOZgMKYJiybBbNL8rD8uyC0KRk/GmH0w+5rODuhNJllebDuEZcXEMxGOFeE0NV7NIv/gJu2dkhsXRsktGWwfMuke9OlO2NReMnG2JVbZY3TXFr2JJJupNxNReDFDbl0RuySf2WsPsXUkKY8/++d9rI7G2ZIYvubGepXOyWHuqd6gdlozXmlTvRAhYvDLsHkPVK5EmD1BNucR5xW7716kuVqkdrqLuewgI9BNm8bvTdPruESupP5wQJRJsnn648nrzGwl5ye7bmA2DVafnkKZYARJluJWM4/1Wo61e5PvoogFaw9k6ewALzbxhgTRRobiZQNvYBM1bZxGkiEnQ+jORbgbEu1o4gyEBU35XDKGZ8/vptd3KF0LqEd5Rl4ZUMgPQCbfrd4UDIYkRsMkCgz0iTLBhSKjT5pYTkR+URAEJtHATD7zaR8EfP+xV/HGItwtgTce466aRKGBMQB3C9SjzdvwC5X6H31bSzk/+qM/ykMPPcTq6ipHjx69eftjjz3GBz/4we/Y4FKpVCqV+l51Owqs/UPwbef2jo2N/U+18u+77743PaBUKpVKpd4W0nThW/qO9cpJpVKpVCqVerPSamipVCqVSt0G6VLOraUTk1QqlUqlbod0KeeW3jZLOVFGEw4ssusCuyXILpp4w0m/Gr+qsdsCsyfpTyrcDYPYSaLjdSYmnAzwy1B/p0/4Whlla5x6EiHffmSAsy3RvoHZEzh2hLMNE5kWfkkw9DJ0ZpMsgdiFSEmstsDZlribArspk142WZ30UNmykHGSgWHs69DcB+62ZuNeCMZDShmPwnXBqYs7oG3hr2WRA0nB9hF1G7+qae6RhC0Hqw1nX5jDH046PgdFyC1q8oua2hlN8ZIBEgqLmuJViXXDYdpt8ODkPI1GHqsLqpT0xKmehbCoCYua4jVwV03CpJQN2VXJP9n5MpELIkp64MSVKPnStC1Uz0R2DbKrkqAIYUnhbmvMnsTOB3jDCpGPMPug7KR3UFjQGMsu0ZYLEmwjpvy8Q5RL+lyM37uKtpM+R2FRoxyNspL/gogdjV8WhDmBOVAUb0TELYvcRAfDh9yKTrKayhoRwfB0g7MbY7R3g9VP3ntrycFpCJ6dn+Opxd1IX2B4Ihl/WbHysMSvCuIXK0gvpvKaweo7HOKcSo63Uow5gPJFBTrJesovaUpPZogzIOo23XGD4f1bOLkAp+hTOm+iTejOaHJLAr8CmbpCBoL8UpLBYV/OICOB+1oWgAen55P+HhsW7oJNb3eI4QmsdRsZQX9CcfTAAp29EZv3QNizk+woPzm+s0sGg45LUNIUL5iYPYHRl3g1wZFHLrNxvEp/xMTsGrjbMcu9Er/70oO460nGiNURZM87mFsW236OoKLIXzVZrxcZK3TIv3eNvOtTvWOThW6VoWcthAY7SU7DbiXHfn2QJbcoGSl2CQuaYCLAsSIymYBgMuAnRk/wQncXIgZhKfKLSa8UOdtDG0kPnNhJMnKUrYkdKF2LiF1Bexd4w4rBqCauJL1X7I2kL0s272N2BEEtxisbZC46ZDYV2RVNUIKtIyZCQ5QRKEuDhjijmf3zCHfVYONui4Lrs30spjVn0HlxmMy6RsQCr6rJLyrc6w5GoGntAfdYnXBgsdkoIEMIKjG1bxiIx+q0ZyWZDU1zL1RKPYrzmvIVhYwUzcOK0lVFexfcv/M6xWuaqtmjMytZWKmx9FjSo6h8RVG4KhlUjaQfT2SQGe+y0i4iB5Lto3nijEZO9smsGmwf1WTzPq1dBsa6TWneo3gNhl8J8WqC7FqAUEk/GUiyZtzt5NewcGSbsWqbwT4fbUL+QIPhV2Imnukm33Ejwq9oZCjoTWm0Eggl6E7B0JkIw4PCVRO/qjHbEm9Y44/ENPdDVImYnd7CNGNWH3T4nTPHWXpXhsa1KjIE0wPpC2JbICcHaN8gs6GJ8gq/LDGMJMMpGFjkLtnJee4bLnEh5svX9mGVfQajCtmX2C2ItxxK8zGxC931/Hfzpyl1C+kVk1QqlUqlbof0isktpROTVCqVSqVugzTG5NbSiUkqlUqlUrdDesXklt4yMSb/4T/8B4QQ/Kt/9a9u3uZ5Hj/7sz9LrVYjn8/z4Q9/mPX19ds3yFQqlUqlUn+v3hITk5MnT/Jf/st/4Y477njD7T/3cz/H5z73Of7wD/+Qp59+mpWVFT70oQ99e08iQRhJQFt/UtGfjcgtSbqzChFDlIGJZyIq5wTZNU39rhgZg+gZiLpFUIkxlx2cRhKsF5QVrV0G9vksygarbhLlNe16jtahiK8v78LwoDcuMQaAAhHD8o0afk3j7fHo7IrwRuOkZPpfB+xdAhGBALzlPE5D4NWSoDHRNfAiE2XD40fOIwcSqylxtySnz8/gbkrspiCzDvmrFkiIRwPcjeTCmNNMSmoPhgWbx5Ly686KRf0gtO7zkJFgyq4TKJNMzqc3pRFSE9RitAHKBLOblHwvPbCBiME4VSDMa/5qcy9RVuNVDbzZAGvDojelyc8bWMWAzIbEL2tMD9wNg+5MEgRrvpIntywpl3vk1pOAUbulMbsCLUBnYwxPMAgtZAixq3G3NAvXh0FB/obE6iT39cYjggI42yIJhFVg+BrDU+Svm3RX8/RmI8Ks4MT5OeJMEjDbHTgIocmuCsIc9GcjRCSIslAu9hFCU7mokgDqosboSCa+rjD70J8NaezPog1BbGucDZPSyw7YCm9Yo0yBnvYQsWAwJIgygtyiRguIHWh2M0Q3coyUurR3x8QOaJGUQNcGdMcNzL6gPyqQIRSvafxaTFDSlC7Dcr8EQDgW4u0IEJ7EeD0wUFngrktWukWKF01kKMhesenMxQidBK5GGTDWbYwgOT4NH7IrksGo5pXFKQqLIcoQROWI5h6LG2tVALw9PnZb0J+KCcqa3Irg9LVJ3A2ZBBVezbBQr7Byo0brmVHWlyr4scnWfTHKgqAEg6okyoGMBJvrJcy+ZqubI7sicecdemcr7KpuY2zZPNXez1cW9yWBxLGgMwONw4ryF3JY7RCzB96IwmmCmPAoXIdBzcDsK4ZeTYK7R17SFF+zEXFyLAsNE8U2cUZjFEIGwxKnoRkMS5SVBGLLALwhzWBYYLckUS4pp96esYgzGqcO3T8bw94yiC0I9gwQGlQ5RM8MsPqa7KrGCDR2QzBa6GDYithLzhdmT9LaA/3TFdwtTZgXuNuCds+lMyPwS4LGgTy56wZeReLUBZteno0HYp7c2ofZB/u6i7tuoCV0ZiTIJFi3dFHAq0X6bZfxQgddCckvRdhTPfSNHINRheELbDNi9EWPOKNozrn0x6E7bqINWHpXhqAksLcNMpsCLTVeVeDWFc1WjtaTY+hY4O3w6Vwps3a/pLE/h9WUNIMMcV4R55I2AMaKw8gLgtwKeGWDwlJM52BA+UJy7KlpDxEKMhsCe8vEMSKUSgLho4GJX4uhEmD4GqujCfcNEAqi0EAMDLyqYNehFfwyiBdLeDUNXSvZv6AYDGvsTZMHZ+fhehYZCpSjCPMgKgH9EYk2k3P1d03aK+eWbvvEpNvt8sQTT/Bbv/VbVCqVm7e3Wi1++7d/m//0n/4T7373uzl27Bi/8zu/w3PPPccLL7xwG0ecSqVSqdSb99cxJm9m+1502ycmP/uzP8v3f//38/jjj7/h9lOnThGG4Rtu379/PzMzMzz//PPf9PF836fdbr9hS6VSqVQq9Q/DbQ1+/cxnPsPLL7/MyZMn/6e/ra2tYds25XL5DbePjo6ytrb2TR/zE5/4BP/23/7b7/RQU6lUKpX6zkqDX2/ptl0xWVxc5F/+y3/Jpz/9aVzX/Y497sc//nFardbNbXFx8Tv22KlUKpVKfaekSzm3dtsmJqdOnWJjY4O7774b0zQxTZOnn36aX/u1X8M0TUZHRwmCgGaz+Yb91tfX/6euxv8jx3EoFotv2FKpVCqVSv3DcNsmJo899hinT5/mlVdeubndc889PPHEEzf/v2VZfPWrX725z8WLF7lx4wbHjx//lp8vnAzQPRO7CaVdDUQgkkjwjCLOKjjSYeuIRXsnNA5qqt8w6E4JKEYIDfkFA7MrkO+qk12VONsSb0hTuK7R+7uEQyFjLygmJuoMvWgwXkxiW6yexuyDjJKS886qhbMtyJ5zMQYSq5FkQBSuy5tlxwGqd2xijvaxjtdxtzXOtqAwb/BPZk6Bgme+cBTDBxkK3G3Nu+88hzmA3s4YbyQpRd48GmJlQrypgCivae2Nifb36d3hkV15vUy+o6mch9wZF7+iWA9LjDstfN9CmxqkRvYlsQNqwsMfUvTHBOsrZZSj6e0OsJuCq2vDBEMx/TEJShDlkmynzp6IcrFPb2+A4Qv6oxojAGVrcosSb0QR20mp/rUHBP07BvSmBdoiuUypBFZL0DsxhLLB3UjKUGeqA6yWZDCiGUyHSdn6LQOzD71phdUDoTQiSkLsw3s6VF81cFdNlAOHdi+DEvgTIf7AQmtB60CMP6RAamJXM9gRcHRoBSE0G/e8nuXjCYSCxh4TGcDszk2EBq8Gw68onAa0DkbkL9jErqY3IdAaRCjQJgxGNZ05sMb6KBvUlTxxXiGFJrdk0J+MiYsRvUmBv9sjdpLjwa9pvLGY/P+6TPGSQTgc0toNV1ZHKM5rjIaJ6BloVxFnQEbJePtzIQ+OzYNOsr0ARCzwx5PUr8xGUj4/d9c2vWmN3dH0jw0oLAiK+QEbd9sgQASS3FryXs5Ob6HjJEvI2TYIxwJyqzG5kkdY0sQ5xdC962T+vEjuqoU2oXbSZOtLk4hMhLulk9LyWhMUNEJB9rLNYESgT5YISprsusbY0+WR2mVkBAezK4wX2+RXY+amN8muC2Qg6E4KtCUJ3t1CZ2J6U5qoZdMfE3RnBNuHLLbuFGgJvTFJUIKgrInHfYIiXHl1irCoiDsWQQl6k0lLAr8iCMqKKJtk4YXFZMzFq0l7gaAkyK4Iojw0jsbkj24jFMgll/6oIH/WQTUcWjuTDLTBkERLuLuyiAolxILaK8lriLMKvbtHay/0JpL3466pJZSVZAX5JUFQ1rT3KqwO3HhhCmfL4EBxLcmWszRxVhPlkiwrNLR3KZQp8MZihNRsdPNYizYb91gESzne/+6XKF2U6NkBjfUi9QMOFCOUDflF6I8l71l2PfksglpM51CAymi8YU1jrwFAf0zxniNnwTdwNyXR68cle3r847GTVE4nJ7Ty0S0QsP5ITJgTbB/VrD4oMdyY7fsigqGY6pdc7NE+YT7J/lvYrnDP9GKSMdY1by5b+GXB9v0RYtHFe7SDueRgtwRhUfPg0DWE5mabkT37lxm9dw0iQW5JEOU0J1dmQIO9r43dMLA7MD3aoD8OYV5TOWN8y78v37Y0K+eWbluMSaFQ4PDhw2+4LZfLUavVbt7+kY98hJ//+Z+nWq1SLBb52Mc+xvHjx3nggQdux5BTqVQqlfrOSWNMbuktXfn1k5/8JFJKPvzhD+P7Pu9973v5jd/4jds9rFQqlUqlUn9P3lITk6eeeuoN/3Zdl0996lN86lOfuj0DSqVSqVTq74l4fXsz+38vektNTFKpVCqVettIl3Ju6bYXWPtusZZtnNogKQN+vUxu0aA3rTF6ErMriebzeHf0GX5FY7UlQUEk5a/7SalnGYLpwa7qFlpCnNHYTUF7pyBazmI0TZq7DKYLTTqzAqXF6wFkgrAAQUngNECbSRl0ZUL+usTwBaWL0DoaUH/PgP77OoT3dai3coQNF/HFCq29msymxqtpfu2v3ovha7zxiOF71hl+xyqRK3hheZbwHW0ySwZhQaFchbVpErQdjJaZlHi3NVHTxlp0CMrJGDKbgsFoEjQnFGwEBZ5e303ctZLgvHUHpyFpHFEYiy4M+cnYL9mMnFJJYKSG8VqL7KJJ5IK1ZWIOe1TOg+xLGmeGkG0TGUJ2TdCbijF7Aq+WBBSGBU1nPY8506NU7OONh0gfKke2MFoGCPD3DghzIikPPgpBYBLlNMoGZ90knPYJJgOiLJQuSWIbtCHwhixaO2zUtTzdacitaFBw9sokVk9gtA3K5R6WEWO2JFZbkLlh4W4LzE2LxV6ZiWIbZ1ti9kXy3lpgDiDKw9LpMXrjSRuD7cNJ0Jy9bdDdHZJdloR5jRqYOFvJmAxfJAG8sSS/qHAPNnFXTVYbRfpjitwNA3fVSkp4rzrEGbC6JMF62wY78nW0AdamRf4G6A2Hrbs0hiewWhJz28TsQZTReEMaTMWzq3MoE4wgCaLUlkb0DYQiKccdC4ayfexG0q5Abdv4JRBCk1vVhFmBNjStnZLhWoeFhWGIkkBlNJibNvUDBr2NHCiwWgabjQK9qeTzetcPvEz9qKI3G1N51knK4HswGBHkFyGzoYnu7CJi6O8OUCbU7wvx6i7/+5Pfh72vzVP1fRRtj0FNcu3GCABxMaY/F9IfsZHPlyidsRCvlxOffmiRoKTo7YipvaqTINZM8h22mwJzxUELmDy0jqwFyL4kfyMJIrXbmqCsya5IgorCGAgq55LvvF8RSdD3nQNiNylXj6GxzRjDh9JVkuDu4y1kPzm9upuC3GoMEp5e3432DEQgMQKNNjW5Gwb6Rg4ZJMHV3pDm9No4hsfNYHh3SzDx9Ovnjp0DtIS/uH6QwbAmrEUYA0HkQmYzCd7N35DJsdOQiG2b7sUKxbu2GTkVolzFnz91jCgPcd3BKvgoU5C56GD2oTMLsZscd8oUWP3X31SRnC8zG4LYBV23sVuSlzemGX9KJgHWnkH1nCbzbJ5PXnw8Gb/QhJFBPO5jr5soCypnBfkbgrhrYtVNzJZBb0IQz+cx+xDlNPK1At9YmaJwDe64c57CvAFKUL4SY7QM8osCfa5AWIuIdnkAfObCMbSRnK9lCAfKaywuDDH8ksAbSt7fH5l7jaigUKdK+DM+3RnF4nqFzIbA6gp6Y9+96xBpuvCtvW0mJqlUKpVKpd760qWcVCqVSqVuh3Qp55bSiUkqlUqlUrfL9+jk4s1Il3JSqVQqlUq9ZaRXTFKpVCqVug3ebABrGvz6D5wMBIFnERY07qaBjCC3JMisSqyOwBgIVCQRSqNlEv0uNMk7JCAowmBEc+r0HFqA2U9KwStbY3Uk1myPoKg5eW2WsKhY2K7i1jWDUU2U00kWwqhG7OkSlDXulqazSyUR/BZY6xaFZ7ME8wWyT+YJ2zZmyyB2BU5d0psQWB0BscAcwOTOLdbOjLDxwjiDMU1/I4e3nqO/K0D6AnfNxOwJ8kO9JCPE1WSWTKyWAQpiNzmiO3tDzD54YxHa0FzsjLK+XcJsmnSnBXE1ZDATklswiDOa8tdcZABRFur7DETfYDCquX/4OoYHmU2N2RdYdsRgVCBigZjtIb2kxLXd0ohIkL8BytEIJTAHgux1C+ulAo21IkY3ycRpdTPoMZ/+VIRu2+SXkzL3TgOc01nsliQqRTgNgTQ09pJNWFL0JjUyTjJOIkcQu4KwHIOA7btj7LbGrFs49aQ0ft9z2F3dwuoIzJ4AkWQsxWMB1zZqxFqiTQgP9EGA3RKU5kPQSZT/xNf7AIQFRXeHSkqTdw2UCUOnNYXhLnEmKXPP0TadXTFx22YwJPF9i7CgCHo2KqNef39AG5BdFVhd6M4ojEDgj0a8+Ed30JvUWB3BYEyQ3dFGBsm4g6GYuXsXERrCSkxYiSmfcnh04jLZDY0WEOcURk9idQXST7I+tKG5tj6EjGEwqtCFCKFgttRg6LkNxp5rgqHp7YhYv1FlbKqO7Bs4mRBvPCK7kmSq7Ni1TmEBMqsCaagko8WHpxZ2Y/QkuRsGQSkpZQ9QOxMTO4L6XTHBtouMwC36xFmFsBTCUdhNiXetQKAMduc2ARAdk8GIpnrKRDgxQUEQZ5Lvp90SjD4juXZ6ksK8pHTWoLVb4tSTDLAoC/klTeE6FG5oFm8MYV/IYAQCbcD4M5rshiJ/Q+BuacyeQChBZ0YQTvsMnQkoXjRgy8GvaMRMD+FLer5NZ3/4eraRJgxMtJW0X/CGNFY3whjAcKaXXLqPBGsPK7Krgspjq8SjPoWFJEMtKGkemr6Gu62pH9UUFyOUCWsPSBBgmDHGQHDH6ApIyF638GuK/JJGmUk7gqAI3R0x0ayHKsTk9zXonRjCqfu46yZmXxAUNZllA9NUDJ1Oslr6o4LcMuSWNVZPM/TagNKpNTJLJvhJJld3WuNuJ+dUf5dHFEvW74OgpMldNzACjVcDPzRp7dMQC1pLJcrlHmZfkNnUxK5A6GTszv4WsZuU4tdTHmFeIwNB/vgmwWKO/oTgePUaMgC6Ju0Zg8y6pDel8cdD3BULxw2SbDUrxq+pm+/jy1vToKEzK4iyyTnvpfoM7ppBVNCY6zZmT2Isu3hVcOoa63Dru/a7lJakv7W3zcQklUqlUqnUW1+6lJNKpVKp1G2QLuXcWjoxSaVSqVTqdkjThW8pXcpJpVKpVCr1lpFeMUmlUqlU6jZIl3Ju7W11xURHkrCisJugBXR2Krr7QkwPKhcVct1BxGC3BX5VU70QIXMhWiT9L7ShMTsGbl0T5jVBSeBuCYJKjL+SS7JL8j6ZNYlhKAqLUdKXoy8w/KRHS7CRfb1fjiC7LOmPKzJ1Tf4G+GVw5toMxgRGPiIeC3C3NH5NUbihQcAPvOMUURY2mvmbmUMyBJGNQIFdCJj+SkCwa0Cc1YShgf1AneFT4O8fEI0HWIfaOHXB+KNLOOsmvQlN8aKJO9FjLr9FqdgjqoZkNjUogb1mEhY0uhpg+ElWjDfrv+FLUQ9y+FVN7ewAd0tTzg3QAvS4Rynvsef+BcwBbN0bIwNBbj0msyYpH9zG6oI3rIgdwNDE1RCnocm6AaptUbhi8oH7XqE/ImFvjzAH3ohChElUf29KwapDbjnpwWHvb5PZSAYnNPhVyF8xMTyB9CS9SUE0FNKfSPrGeFsZHijPoxzwq0kGlbJBbltM1lp0fIfivCbedsisSaLDXUSs0RKC4Yj6oQxRThDnFPkFibJB5ZMsoNacpLOZx90S2C2BcaLIyIuC/GiX6oWAA2PrxHlFodojt2DSHxdoE7JrGr+qGXrNJ7ckkT7kr5p094QgNP6hAd50QN71sbpJRgkKLl0Zp30gRPoSmQ9p3h3wWmMSryIQCtxVg9JFCIYiCssxdgeMQki86SLiJNPCsBWVyzGnlybpHB4iLLsYuQizY1Ac6zCea2O3JIOOQ/aGSW9SEzua64vDdGcgKEM8n8eZ6qJ394giSTwa0DvkERQ1nTkIypremEF/QmN0JXbDoLcjwh9YZNYMdM9kYqzBO9/3CnFecai4iq9MEGCODMiuQP2eCO0b2B2NX0l6GAVFzWBY3uxJ1ZlTjJwKsbua3JqidjqmNynwqgKhNKOTDUQEZleQqSu8siR2BOUrAd0ZQZTTRHlFdk1jz7sERQPxeB2GfYJaTPa5PDoT02lkGf+qgTckyC0JotBAuQq7rTF8weadLoNxxavnZ8mN9tCOIn/NpHUgpv9HY9y5cxE0VM7D6AnohC5+VZBdkXglA8OHoVeSzKrJaouwpClYHqWL4D64hd1MMnZiB7xaknFkDJLbxqbquHbIYM5n41ieKKNRe5JMMv/wgLsnlvArFoPJmOk/33q9v5egvRM6sw4XPjZOUNKM/5UkymiGX9ZkthR2S0LHwrVDCvOScDjCfKhOkJdEOcVgI4vdlIhYYG8ZNJZKhAWNNyQwvOS1CA3qxXLy3YxANWwqFzTeRMTWlRrTh9eono+51BslysF9d12mcikkKCbHXPGshTcZorUgmvU4OLJG8YpE6KQ3Ur2XTU5QIuljFDuaWEvCgsZqJT1x/OkAZST9r+yOxr9a/G79JL2ls3Lq9TpPPPEExWKRcrnMRz7yEbrd7t94/4997GPs27ePTCbDzMwM/+Jf/AtarW89y+ltNTFJpVKpVOot4y08MXniiSc4e/YsX/7yl/n85z/P1772NX7mZ37mm95/ZWWFlZUVfuVXfoUzZ87wu7/7u3zxi1/kIx/5yLf83OlSTiqVSqVSqZvOnz/PF7/4RU6ePMk999wDwK//+q/zgQ98gF/5lV9hYmLif9rn8OHD/PEf//HNf+/atYt//+//PT/5kz9JFEWY5t99upFeMUmlUqlU6jb46xiTN7MBtNvtN2y+77+pcT3//POUy+WbkxKAxx9/HCklJ06c+Ds/TqvVolgsfkuTEkgnJqlUKpVK3R7foaWc6elpSqXSze0Tn/jEmxrW2toaIyMjb7jNNE2q1Spra2t/p8fY2tri3/27f/c3Lv98M2+biYnVgaO7FtFCow3whpPS6ZkbFr1JxdadAiOAteMSs5cEu648LHGzAdqN6e4JyS1JckuC+rEIsydw6prurMJqSbKrksF4TPxSGXdbc8foCmFOUlyI0QLcpiYqaO4+ehW7LvFrSaCechS9UUnzgYDBXEAYmHijEbFnILcsepMClKAzK+jtDpjLbOI2NPmsz9DubfTBDpm1JIhraK5OtJIlyhqYN1yUBRknpD1fxi8JSs+42AsO/qUiuRXFarOIc0eT2hnN6Et9eLXIX1w6xHCuh9GwaO0BoiTAVwaC7PnkMbNbCmvdxq9ojL4kfx1ypo+WsHVHhsGwwP/jUWQI1a+63D+6wPzTO5J2AOsm8YTPoGoQFjT9Z4foj2lGDmziDSnyF20m/tzELwva82XMrkF3V8yzKzuTz+yVPPnl1wNPq+pmeXNnW9K4P6AzK/CvFHHaisqlGAC7CQjwK0lp96ASs3t2HaslcFdN8mNdPrtyB8rUFK9Bb0qRXUmCcAehxfpCldau5HNwtzTFr+aIXYlb18hMRPWChzHQyIHE7GustsBomXgjCjTYRZ+glJTIlyE09wril8qYXsyNVpnKRIuDw+sIRdIeYCKkPy4Iaor2rI1QYHegP64ojnYpXhWYV12sXECrl8EfihlMhZhtA0yNu2ShChGZ0xmcRZsfn3yRzu4YGYA62qGzE6yGiTIE3hDY5zNQCtESrI7AOZsBDVHPQsQaGSrMqy75BUGsJCvdErGtwTMA0JZOosk12E2BDCAe9xGniogrOZjPYS3aiLqNDAW5RTB7SYuC3FISNGzs65BZNHEyIcqGzIrJnvImr21PIGJBqAxG7DZ+WWC/kgMBzoqFs2pi+orK2aTUvQwE5mNbmF2J4WvGntcsvdugMy3pTEuiTHI/I4DNYzCS6+KNKrQBS+9TtHdDa6ekM2ORW9a4GxItwRtKXpcyBcELVczrLuVzJm5dIZ0Ye8WmcUASvqON09LsmdxgcscWaBAxWB1N5awgO9yj33GQnsTsQ+mcQZgVeLHFYFRg9V8PcM00KdxIWhRYA01/XFM/LCgsxsRKEtVC7sgv4dUEfvh6ewUTRr4RYLeSwE9taFTHYn2jxGi2izRVci5sC+6YWkYbUHra5YXrO2jNGRQvGaw/XKM3qQkLSbuF1pxk5CSojCJ2BFP713HaMWgYTIdJaf3FKp1dChFIhNDEDhgDwePHziIikL4gdjTSS9p/ZFeT0vl+FfpTcRKI2hbkVhUiFJi+BkvxzvvPcriySm/UIGf6FOdjFjtlto9YIEHbGtPTZGt91NkihRMZzn5hH+1diuyyJLMu8K4VKL9qEbuawfvaiBjur13HbgmK1xXGXBcCiQyT72dhoU9UjL67P07fAYuLi7RarZvbxz/+8Vve75d+6ZcQQvyN24ULF970eNrtNt///d/PwYMH+eVf/uVvef80xiSVSqVSqdtAaI3Q334E61/vWywWKRb/9myiX/iFX+Cnfuqn/sb7zM3NMTY2xsbGxhtuj6KIer3O2NjY37h/p9Phfe97H4VCgT/90z/Fsqy/dVz//9KJSSqVSqVSt8N3ufLr8PAww8PDf+v9jh8/TrPZ5NSpUxw7dgyAJ598EqUU999//zfdr91u8973vhfHcfjsZz+L67rf2gBf97ZZykmlUqlUKvW3O3DgAO973/v46Z/+aV588UWeffZZPvrRj/JjP/ZjNzNylpeX2b9/Py+++CKQTEre85730Ov1+O3f/m3a7TZra2usra0Rx/G39PzpFZNUKpVKpW6Dt3Ll109/+tN89KMf5bHHHkNKyYc//GF+7dd+7ebfwzDk4sWL9PtJsb6XX375ZsbO7t273/BY8/Pz7Nix4+/83OnEJJVKpVKp2+Et3MSvWq3y+7//+9/07zt27ED/D/Exjz766Bv+/Wa8bZZyBsf6XNwYwWoZOE1N+SKgQFmaoVfAaglimySS+10dgpGIkZMQXSiSWbQonrMwfM3Eh64jPEl4tIs2oHAteQtlkGQEDHYG9McEK70SflHiVSV2B4KcoHRBMN+sok2IshqrJyheNJGRJnveofiajXU6R+VVg0KtR2ZDJhk9W4Lsiqb8DZuvbB5gUJWYhqJxZohgYNHdoSmV+2xfrKENzeo7DKJZD6uTZOs425L2LjB8yKyDjKF+WOB7Fp12hvX74fJHTLyRmE/d92mKtscHH3uB6hlAQHtfRJTX9HZEhAXByvsirD1tsmuCqBbSOBbx1NJutAH51ZjY1XQe7+ENawajgi9d3UdQVpQuQ+WCgoZNlEkyOAb7PcyeoPP0KOZA0N0ZsXZc0J+JMDsCMd1HlAKkVNgtQexCmBWIapIFFLvgbEmUBc4Nh+r5JPOgudugOWdgdxUyTMqTG74grERkVg1ubFUIKhp/OKbbyN48TsKcoHxeoEzBO/ZdZfPSEE8cfx7DSzJJtu9WBAWBiKE9B9LQbB9wKS5ETD6tkoyUOzsgNdnlpDS2PJcndjVCg9lPvrjR4R7r92SYq2zTWC1y4uIcsQXF6zHWtklmXSP85HmCYlJmXAaCnZU63Wkw+wKlJF7XoXxWUrhoYbcFRiZChuAu2lhd0FLz6eX7cdcNgpLG385QvAZhKcYvC8wueGMxomkhIwhLiv5cQFCQDE80kYGmP+bgT4S09yhK2QGNTpbp+5aTLKQB5G5IZAzvPnSB+PUl5XK5R/mdaxj7O0STPmJfF7MvEDo5RkSclP/uzmoKl02sZ4oMZkKCxRwygMHOgKcv7eH+kQUySwaTToP5wTD5ZUVvb9IaoXJRkVuGyJFoA8KyIs5qnP+zSjTuo4WgPywpzEusbpLZ1Ngn6O6MsR7dYupJxfYgiy6H+FWFs2aSXwC7DVZX49UEwZE+dkti+K+3KYg05ustC/qjmvWHY4QAZSVZX9GVAnZXsdQssXlylN6EwO5AfiWiOyV4347zWIsO7nSH7qymO62JsnD+4hSZdU17VhK5gnqQY+O+5Ds7qCXfY7MraM8a7Cxugy+xREx3LiK4VMRpasKcYOuwjd3SGD7kliRWw0ArwXyjSjbvY3WT4+/lq7NYbUHzEY9ifsDQ6YDWwYjCYkSc0aCTrLfsusarSkQg2LpTs/XMOMuPmLR2SYy2gc5FiIGB3ZBklgx6r1UJcwLlwJn6GDNfaBGXYioXwN1Msl/cZozpJeXhiZOWHcrRDGoSbWn6Q8k59cXlGb546SBhAZ5f28nmXZKVlSpBQRMMRVAI6c6Aeq1E7Gr8Ggy/a4XiFUl0XwdvWMO4T+t+j+yyIP9nRYYObvGHF+8iymvWHlb49Qz2lkFUVFhdaOzPUTuZ/vf67ZZ+AqlUKpVK3QZv5aWc2ymdmKRSqVQqdTu8hZdybqd0YpJKpVKp1G2QXjG5tbdNjEkqlUqlUqm3vrfNxETcyCCERobgVwSRy+tBXoLWLpmUODY07pbGX8lhr5l0piVhXuONx0QZaByNuXxiFmfbIPN8HkgCEwFiB0QsqIx0iPKax8cu0NoL/TFBa39MUBaEeUG37yJiKFxLqngDlC8HDMYVyoLBqKI3CZ1GFm8oKZ+vJTT3J/97dn6C0nzI1vUqasLDWHeIHc10uYkMktLxmQ2BjgX5G5rmRgFvNMbqCrYeCjEHmtjV5BaTJ8+cd3G2JeWXbbSleb63h+lMgz968V6CosDeNnDXTeymoPyaifSh9pyFP19gMKqxCwHFsxamVFTPQnfcgJ19osDA7Al6OyKiwETlI8I8rL4vRIYQO4KgrNGegQyT0tS5GwLcGKGheMnEONAhXs0gVl0MqXHqGn8sJMqCkwnR1QCrA/2dIWaSsUZzl0SZmthO2g4YA0VuXZFd1TgNgbtsEWU1KpaYfQEKiAT7yutoMykfbgTQH9e8ML+TRx84w7XeEABRTlP7hmQwqvFLkvxC8pyl6xErD5usHTcwQk20kKd8TuDXNGEuCXQ2fEF+QWN3NHZTEG1kiG24sDmK7BkMj7aIcprOjIGyNVFOYA4EQUlgeklwdX5RcOlLu0BAfzrGOZPlnt3XQSTltGNXE3cs3Lomt5g8V5RL/pNqMBNidQUiFigLMDSt3UnQpjaTMuh2M7lvYahHmBc8MHqd3phJbAuIJDIQrCzWcJ2QxRcnMXtJGfruXNJ2wVcmQUmhJXT7DtGnR1Fnky+I9VIhGYuG7YMmmU1Nf0xgtQTdWUXsQu6ahd2WSUn6aza1apf//tpRzPsbfHnzIFWrh4iTv8VOUlK+uV8zGJa4zaQlgLspWH2XQjRsspsxsSvoTmu8IXAairCsGPu6oH9yiI27TSbzLUa+bFG6LCnMQ3s35NZjehOS2AU5n8Hsgd3WWD2ILUHwYAc9NSAsK8qvJlUto0JMZk1idpKA2ygyCCuKyuUYFPTGTLypkC984X5kKOi3MthNQWZT0J+NKF4w6U8IwqJGxrDUK1O6KHCbMUFRMPtFD7MPlSsRBdPDLAec60+Ao8isCzbeHSRB9TlwWwqrpzE8jbGnCwODwcDG9yzsrsIbi3lo32W0Cc6FDHtrmwQFg8Ilk/oBi+LlJKi5clbgNjRCaVRWUbwi8WsKqysIKgoRCxDJsZRd04RFTTAWUr0Qkl8QjOfaLL23hFk3KfzkMv7hAf1JxeadJmEuaXXhbsrkMYD+pMZqSFr7NKJr4ns2cTcJBI9iidkTyKZJZkNQftWidMIlfwNK928wd88i/k6PB4ev0d6lkC8XiGohTxx+EdOJ6E9oNh6OqL86zKHxVbSA4mUTnBh3WzC+e5PeEY/euKBx+Lt4GeI71Cvne026lJNKpVKp1G3yvboc82a8ba6YpFKpVCqVeutLr5ikUqlUKnU7aJ1sb2b/70HpxCSVSqVSqdsgzcq5tXQpJ5VKpVKp1FvG22Zi4tQFe4c3CYua7t0DBiOCH37XiyhLM/JylJSUDwXdGcHQy4KwovCGNIXZFmZLEjtQvGhSvpSUsXeamigjiDMaGSWZDlZL4FgRhWvw9OYeqmfAqUPlNUlmQ9HdFRG0HAwPwoIgLCTT3Y1jDspVBBWNsy0JqwrRMVGmTrIQNjSVc0kZ7CNzy2wcs9DZCDcbcOD+eWQgGHa6REMhMhAYnsa55tKdEhSGu0hfYPag9IpNax/EWUVnp8I5n0GbSXZSf1yTv2JSMDw2gzyjM3X8CgTDESJKMjb8CvSmNbErQAvspiAcWHR2x/ihiVcT9CYhCg2koRlMRsiB5ODMKrkrNiIG0THJrEn8WpIJcuzgPMoGbSsGYxp7yaZwTVK+EpH9YgFjok/+hqA7cPCrAiR094X4S3mMNYfytQizbhJUNMEOD6sP+UWJ3YaxFxStnRaDqqS9G3pTMTIEuy0Yr7XQUuOuGzhlj2G7y9gLMWExKUtvDgQqEiz1ypzfGsVuJSXtZZiU0o9cgdPSxIHB5lELqytQBgyGJZNPx/SmBGZXUJzXRJUIq51kaDX2S8Ii6ELE5FM9LCNGBoL91XUymwK/rBFRkqYQ7xzgVwAN+SWNESSlxmUIRlfiDStOnp2jPfd/HeeF0S7KTDIL+qMCqyt4/+hZjLZB8boiP9Wm/kCAzERk1wRmH5wNI8lQEjD2nCaOJYan+as/uJfiQkDh+gAsRfEKHNs/TzXXR5tQunuL3pRC+oI4q7jaqpFZk4Qljb6eoz0niPb0uXPHIr2DPu8+fhq/qigsKgYjgvyiwgjBHAisDhSvK6wOGAMw+9C4WOUHj7xGu56jaA/4zLlj+CXBYLdPZ0eSpWZ2BaVrIZEjsNsCu63J3jDJz0u0IcitKty6ILeiCfOCwlWD3phERpBb0Zw8N8dgSOKXwBsSOHVBe8bA7Glyy5rKeU3sgFcVxFaSTcbZAixnMAYSI9Rksj65BRNzANrgZoaYNjQbd0n8GmgzyTrKHG3gTYUQJNkoVlfjbBiodzYZPRmSWRfYpdX04AAAbYNJREFUXUXO8gHYvNPE7MPKvwiwupqtIybXezXs01k+9/V7mPqCQfUDy2SuOhgDMDzoTBps3SUQKvkuml2DiVoLw1Q4zQhtaF79g8OYfQgO9vnG0hR+UdKbUvTHNEioXIwYDAu6E0k5f6tuEJTAXZdkVzQ7/8xHRpC/aEMhxOppxFwPMxvRHTcR792mZHv0xxVRMabtuahtm7HnkvfVHGgqlyKiQz28kZjYhSijya3wegsAwd2zNyiOdnHaigND6wiVvL9OKzlfDUaTz6XVyxAoAx1JTtVnGPqGYLDPp/KSxe+9/ABaSaKcxlm1sJuCiUwbIxB4VU3lhI03pFldq1B60aVwQ93MEvquSLNybildykmlUqlU6jYQKtnezP7fi942V0xSqVQqlUq99aVXTFKpVCqVuh3SXjm3lE5MUqlUKpW6DdKsnFtLJyapVCqVSt0OaR2TW3rbxJgEZc3h0gqGJ3AuZgj39/nT03dhdQWbd5oEJU1UjEHD5iMhzmbS66WzXCS3LJJMmjxsP+ojYkH9jiSy3OoIsmuaoKpwmrC+WaJxRHHt9CTdqdd7icxAZ0aSv2YisxHahKCkMftJPxV3SyMCQTAa4u0IkJ7AGh2gsgojeL13y5igN63wIxN/SFGo9InOFzl9ZYrMhmAq00B2TayOwByAPxQTVBXV7ADDE3R3RxSWYoqXofaSQWZDEmc1/r5BkpGhwa9qstLnxI0drK+XsFtgr5vEWY2yxc2+K70JTVyM8KuaXdMb2HXJ9+24CApGTimGnnRgKUP2hklt7zZnz03jlzXekMDsSZQFsZ1kKpw6O4cMAEuRWwIEtPco/JJMskpezdM6GhBfKCADsLZMjLYBKsnmaM+YCED6gurXHIJi8tjKgv6wxAiSniyZdYHVlq9n5WhaAxdjICjc0ASrOf706h30awbulmYwkmRDCUOz2cvxQztOoywQEXg1gdPUWANNf0SC1NTORknGgKnpTSqae0y8iRARg18WWFsmWoLT1Ml72oR8pc/qQzma9RzGXJevn96HV9PYbUF+UeDVNLUvZkCDV9OEeWjthu7+gPwNMHxQ5ZBMbUDlHEmG1/4u/b5D614fhn16U4rsKvzvT38fuUVJd0rSvVHEWrORqy7aACSEOz0MPzkGGnsN+hs5zIHG7EFn2kZ6EfiSoCS4tD3M0maFeMxna7uAsy1xtiSZVYODlXUKSwq7Iagc2SKzocm9mOXVhSnGv2DyV18/girENPdIgpKmsT/JdFGmpnkkojUnEQ83iB0IC2BM9fnca0fJzNvcWVjiXbsuMxgVmBs2USEmtyTRJvSHTbpTgsF0iOGD1QHlgDKT753V0fTHBSKGoAy5NUVwuE9vSmDkIjp3e4y8cwWnmfRjKiwprD6YnsYvS4rXFZktTXioT5hNMojcrSQrzasK+teLhAWN1U2ypraOJb2YkEmWSVBWGF7ymTWXi+RqfcrjbawuKENg9gW9pQKdSROro9m4R+LFFpktRfmSQoYa9WqJ9h6onY0588oOvGGFNjVLj2uWT06gXz+TFxeS+4++qOhOw9HpJZSlWd4qEyzn6I9Y2A2Dzq4Yr6axLmSpFPoEZUH1jKB8Ken/1R8xcBqa7IYiyiTfVbOfZAbKCHoTDmFe090Z4V5x6U5Kck/lKD6VoTcpaF+sUrV6ZNYkciDZ2ixAMWLzmCTKCKKsIMoISl/OQjHE6gicRtKjpzCvKV2CVxaneOfUFer7DS7WhwnKGsMTxDaUroBy9M3Mo6VTE1ibFpfPTbLxUIS1aNPZAaYTkz2ZRcTgbkHswhdOHiV2NOGMT2u/JrsskJs22fUYZQm08b35Y/8PSXrFJJVKpVKp2yBdyrm1dGKSSqVSqdTtkAa/3tLbZiknlUqlUqnUW99tnZj88i//MkKIN2z79++/+XfP8/jZn/1ZarUa+XyeD3/4w6yvr9/GEadSqVQq9Z3x10s5b2b7XnTbr5gcOnSI1dXVm9szzzxz828/93M/x+c+9zn+8A//kKeffpqVlRU+9KEPfVvP8+H3P0uoDZQBzr11VN0hW/QYTMZoASKGnX8cY/gw8ZcmdgsQUJtt0NmpGOzzGUxGaN9IgqiqIZ1ZGIwqoozAridl6x/cfY3qa5JHHjjL0JmI5p0h2kwCxiqXIlTXwuxBMBkkga1eUmbdGEjGJhuMf8nE3dlhuNRF9iXdHTG9CYF3cIAuh/zszJOMvgDGlyu4h5u8/44zlK/GlIwB9957if7uAGWAdhQiFiyeHSMYihC+ZPuwQf3hgMZhjX6gReWuTQonMgRFjQwF7sEm3+jOMDPUwMqEBAUIhmKC4QgtweoIMhuCkZeTss1RLeTa6hDBDp8vfPle/CooS7D5QMzxh88SHOmzdbXKo3efRxvQ3xESlmIG4zEygmjSZ3i6QWZT49xwaDzkE+U0YsSjMyN5+Ie/wc73zlM4b3PokSv4NU00HuBsST706AmCkqJ0LURL+LEPP0Xvfd2kzHcPzL6mfndMpq6wemD4SaBcf0rR3Kfpdl0GBzw2HomY2LfBvznyeTo7IMoKsutJgKuUml8+8Dn+dP4OwiLc8fBlOrtjmgcUfknQm1EYpqI7aRC7IEZ8rLZkcF+PwiWL8I4eVlfzsR/+c3ozMdoQxE5yXI0UumgDMlccjG8UwExKn2uZBBdrE7bu1BSva+yWIHYFxWtQO2HRPKARSrDr9zSl3IDBqGDyazHqWp694xtkLjqojkXhmqT98IB/9vBTtA+F+BVNZWcjebysQhnQ3hNh2jHeTEDjUPIe4SiUKah+cIn6Ydh4oIgMJIMxhe9bPLrrMnLdYc9vRPR3hggNs/99m4vNEdo7JPllzf9919NJsPNjW9hXM2wckyhHk79sEdRiwoImGImwXi/fbvQkQkN7O4dfU/jDMdVij2K1R1DU3PCrvLo1gRaQ3dekdMGksyciHA2xewqhwF22aO6B7oymuyOicUAQlDWdHeAPJ20CANxGRPZEliijuWfHAmN/YbPxtdcDSAWEWfF6CXpBe7eiOyWpH9ZkTmXJbCvEww0Kj64TFjW9vQFjBzbY847rNA7p5DGGfPI5j+x1i/6YQESC7pSgMxcz+qxECE3vfIUoC0EJ7vjB87zr3rP4tWR8tdc0O/J1Ok+0Mb2kBYTVA6Mv8IuSiX0bqErIf3jPZ3A2TcLRkNjVOC3FyrsU3rBg+4BB9Zzm1JVZyrvrPLrrMsZYn+Yeid7VY3TXVlKWfTJk40oNgO27FHZb4zQ0mS2FXxY09gsGw4J4JACg/MA6mw/EbNwHOhsjchG/8BN/gtPQDN7d5c5/ehoZQVwJCbVBeEcP5Sru3zuPm/eRAbQOREQuNPcYtN/bI3fapXY2Jr+giTKw/X0enR0wXOnQDDMoG1wrKaWf3ddExGCEmtETiigLcj7DzD3LsKPHTzz8HHJgEJYVetrjrplFdv7IVdCC9v4YZWlGd9TZe/w6zjUXa6JHf0Lz8R/4U1Yfhs0HYrJLxrf1G/Nt+eusnDezfQ+67TEmpmkyNjb2P93earX47d/+bX7/93+fd7/73QD8zu/8DgcOHOCFF17ggQce+G4PNZVKpVKp1N+z237F5PLly0xMTDA3N8cTTzzBjRs3ADh16hRhGPL444/fvO/+/fuZmZnh+eef/6aP5/s+7Xb7DVsqlUqlUm816VLOrd3Wicn999/P7/7u7/LFL36R3/zN32R+fp6HH36YTqfD2toatm1TLpffsM/o6Chra2vf9DE/8YlPUCqVbm7T09N/z68ilUqlUqlvQ9pd+JZu61LO+9///pv//4477uD+++9ndnaWP/iDPyCTyXxbj/nxj3+cn//5n7/573a7nU5OUqlUKpX6B+K2L+X8j8rlMnv37uXKlSuMjY0RBAHNZvMN91lfX79lTMpfcxyHYrH4hi2VSqVSqbeadCnn1t5SE5Nut8vVq1cZHx/n2LFjWJbFV7/61Zt/v3jxIjdu3OD48ePf8mP/4VffwR+fvxOhoLVQonTBQDxXQmcjwpKifBnm/7Ekv6RYOw7t/RFmD7bWihieIHfWIX/FBA3aAHPdZuTlJBsgKIJfS0o3P391J15NINF0Jg3EwCAcDYhdWDtu4FQHKBvKJ5OSzlaH18toCzbPjLD6eES/7bK8WkEVI/LXDawu6Fgi6hb/8umfYPNOweCdHTrtDF++vJ/uuMH/5+p9vHxjmtxlG29I4K5YVM5CdllSuGQx9jwYHuRPO1gdQXi2yOalITo7FXYzKbMdvVThh6rfoOW5KJWUh87Pm2SvW4gY4kxSsrs7biC7BuaWhXM+Ay2LqBohIxBKI2LBM6cOMPxZF7speebaLsqXIDtv4WwZWG1JZkPgXHXpnBimtRuiPX0yF1wA5I0Mhgdfee4o565PoExYaFWw2gLdNxAK/ujlY+SWJKsPJWX4/4+nHkGfzyN9QEPsCiqvGhi+IrOl6I8KgrLCXZdUzguE1Ix9wab8isXmyVH+ZPMYyk4+i6AA/XGBnM/wCyf/MT+95zliV3P+L/aicxHFKwa5NYX0Bdb5LIXFCLsFqmEjQwhbDmhQKxn8iuCTX3k/Tt0gtqE/GxHmYfHFSYrziuBQH7+mePzQeQoLAqFez+A6R1JyXSbtFPxKkinhvb9N+YLA3YLFxx0A/LJm9bhBWIpp+y7lKzFmy0BG4JzJ8rWt3Uz9hQQNrYvVJPNGJ6+1fNbEfDUPvqR8UVBcUAA098G1a6NkNpJMEekLlKVxX8hTNAdkVwWX/heX4jmL2IHF708yO/rTMZ1pwVfqBxlMhXRfqxEWFEgwu5LeQZ/qK5LqGcHo1w2CQvJ9OnzPPMoEMTCwuoLCZYNmN0t7rYBQMIgtXDOifFnhnSmjJWQXTKx1C7sdoyXkljV2RxBXQpBJWwIApy4YfVZQOxNTuK65/gMGUS4pK39pe5i1d2jCgsbwICgIrJ4idqA3KbCbyWdQmJdELqzfD935Eq3nRkHB9GclG6+MsvVbswy/BFFW41zI0Os7xG7SpqB0CYbORGSXDDbek2S3hNWI7KrG7MNLX9/P2V8/jPRh+26FXxTcV7hGZ61Ac87AaeqkzYKjac/BnvImBJKFYIjqOcXUFwx2/lkXbQjMtoGznbzu5m4JsaBxrcqZ+hhh3ya/qNHXc6ytVrAbSSZTfqZNYTFGlAN6E5KgKGjNGeSXNLXTityqRm5bhAXYbBQonzWxG5LyKxY6lPz7r/wwdkdjnCrw4h/fQeViTP6izTMrc+S+niOzavLS83sJrxWIXSheMpFxkgkZNB20CfX9BvU7NN1ZcM9liPKaldUKz8/PYfiwfmaE/IKkvZUjU4/pjUlWH5SIGMKyYndxk7Dp8uzmHMMvQWbVoPiMSzd02BzksNsCd9WgckHT9Rzm/3InMoDAs7Bbgv/wuQ9itSRW3WBwcPAt/75825R+89v3oNu6lPOLv/iL/OAP/iCzs7OsrKzwb/7Nv8EwDH78x3+cUqnERz7yEX7+53+earVKsVjkYx/7GMePH08zclKpVCr1D19a+fWWbuvEZGlpiR//8R9ne3ub4eFhHnroIV544QWGh4cB+OQnP4mUkg9/+MP4vs973/tefuM3fuN2DjmVSqVSqdTfo9s6MfnMZz7zN/7ddV0+9alP8alPfeq7NKJUKpVKpb47BG+yid93bCRvLbe9wFoqlUqlUm9Lb7Z66/do5de3VPBrKpVKpVKpt7e3zcTE6AtUJDF7AnfDwGko/KqmcMZh6Buw+e6A4jmL7pTEGAjyV02chqY41CN3Rx3r4W1iBzILFl4tmaVuHzSI8wrDT54jKGuOzKygLJjKNHDamtEXIHvJYei1CGMg8DsOQRF6D/cwvKR/Q29CkF/SyBByl23MVRvDiRG2onPYp7NToWNB5bzgR+8+xcSzMVk3ie6PN11a+2KkVJjncvSnY4rXFXYr6bsSlDTdox6DapJlE2UhKCuyqwKVjVH5GBlDd2dMUNKc8yb56K6/wrJigrKmf2RAf3dAfjVGBgK/KrDbmiPH5hEKvPEYZ8tgenaLoKgJ8hJn00BEgq07BNkVODC5Rm9CEBY1hicIp31iN8lg8GsKd1ug1138mkIGgtySoHY2wPAEdjYgymm0FmS2NJllE8ODbCWJnDd7gtjR5JYksQ1BRdPeozA8TXEhQsSQ2Yxwt0DbyefWmYHDk6t0pyStgzHRTo+mn8HsC7SA2IXY0YR5zYcOvMJnFo+Bgv6ugOIrDp25pD/S+PMxc4/NE2UkZl+DAHdbYzUNvGFNZk1Smo8BUJbG7GumviRwNzVRURG5Aik0aiRg3SvQ2aEI86AF+GVBd1dMbIM5EOSWYfk9it52lsYjHn4JjIHgn+/8OlNPRVgdQfGSycqVYTY+6IOA5qEIZcO7hi9R/8kuuWVBnFe4m8n7ULqejM07MMDoS2JLsPpOheHEmH1BZaxNZkOT3VDUjmxSuGYw+yPX+O8X76C7M8ZdM+nOJH2TgpKm2c9QeVUSZzSr/SK5axbBRIA920VESeZZ7oxDmE/eZy0FpasKsye4+OQuwkLyHqKgM6fw2g6FsQ6GJ/hg9WUOVNbQBrCnh7utibIaNIR5IzmWqoLsmqZ42kYOJPkFQW4RnIYmcgQiBhlqcktJZpcMIWOHSQbHVQgLSY+k7FpAaV5RWNAEJYWMoL1HERU0oyegfEEgoiSrp7nLJBoK2fp+D2ugMXyBu6UJOzZCJ31vrIHG6iafxQcPv4I3sDm67wb1wxCUIcoqtu6GsADOpkGYF3x6+X5y102GXwuQ4euf16XkM4+1wGya+Mpi825Bfb/B9R/MI2JN5XzyOMXrCtMD0TPRjuKfzJzCaJhk6jFROeZ/OfY8YQE40iFrh7RnDVRoYPY0IobylYj6YehOGsgwOX9mVzVaCbwhqFxSeI92yF2yue/uy6y9O2Ywpugd8ln5oRBlwvumz9Mf1/gVxeydK2T2NnE3BcoEu6lx65rMksXg8ID+TIQ2dRILaoByFcf3XqNW7uI0NFN3rKFsMLcsFt8PmS1NPBSQXdNkx7t8bWE32QWT65dH6cxIwoKmeVhx4dw0za+NYbVhMBOyfVgwXOgSZTVhUZM579LbEYMGGQrKl0ANvnsLCWm68K2lSzmpVCqVSt0OaVbOLb1trpikUqlUKpV660uvmKRSqVQqdRsIrRFvIoD1zez7VpZOTFKpVCqVuh3U69ub2f970NtmKUeGkCt4aANiV9ObkEQ5TXcuxqtIyidsghLENkRTPt6wxhsSOFaE+koN/rzKYCaEu9pkVwXaSEqHi0jQn4yTUuVX4czyOCi4Mahi9RVOO6Z8VeFuBRg+iJ6BPxVQeCqLN6ygEBLmNGFeYO1r09sbEOUUlhXjXHHJXnbI35AMPWNRv0Nxb/4anUmD3qtVcgUPUfMpXTRwrQh/3wB7WxK5gv69fbof6PJD3/8C0tBk6orOrCbKaeyGgdtQEAtGvm5i9qF0wcBuCw66y0xaDbKujz8ck3slg1vw6Y0lQXCdoz7dGUErcMmuCIyeJNgz4M7aEjKErXf5eGMRVkuSWRd0HhnQC+2kLDkwmIgxLEXvsEecV7zz/rN4QzoJxB0KiKoRXhXWHrCJXU0x5+EPx3zf9EU274spzSvid7botzJEWRiMJ+XIi4+tERVjrLbA7ArCnAAN/WGT9fts4gzIgaR2PsJuC+4qLwKQu25wbMcN/p87Poc3EdLZqYhyijijsdqCP3jpXrzQxN0SICAswsTTmrVHY7YOm9xbWaA/ItECjJ5EG+Dsa5HZEMQ2NPYa7D2ySDAU0Z+A1g6DzhxoW+G2YsKmQ6nUpxM4oAWGB2rXgOAdHXLXDerHA0ScBMMWz1sQC6p/5RIe6jPYEVCQHvUDFkFZg4LcDQO17qImPIqXTPyhmLPdcR6YXCDKgcyHhAXByK5tuuMGQiWBxdZMj/YeRWbJJPNqhtI1RWOphDeUvO7WCyNYXc3p8zNoJShdNAgLmtyyxK/FiBgOjqzhVwXKgrbn0j/oITomcSwJqzH9uYCgpAmK0NkBgyFBd1oSuxpvPErKyi8YBEMxlbOCI7uXODy8hj8ScTUY4Up7mDAruHf6Br0JQWZDoGydBHlPhFgdzcYDMVEWtJE8D0BnJ2zdH7N2XLL+DoVX1XhDiv64xjZi+jMRjUOa/pimtRuWH8nQH5J0ZgXl3XVEBHZdEuUVYU7QOKSSz7mpiXJgNCxiz2TzTsnu77uGNgX2hol9Z4PehKR+ULB+zMXsQd70iSPJ6m/PMfSKxm7D1Fc1U0fWcLc11fMx5asxG508IoKtwzaDYUl/R0jsQmZDkzcDxu9cw1MW5Yuw8z3z2C2BESbnr8G4wvQ1IoKH7jtHdrjHHy7ejbI1/eEkMP2FrZ0gIJ/xmci36N05IDNv03zYSz7vOROzJ8huKLIbIYYvsHuaTDYgv5AEE+f+Mo+y4aXrsww/a1I+lwSrDz3pIGNY9UtULmoyG5LFzQrxiQpOXZNfUfTHBJ1ZMI81cM9myF03cTYNIAl+FYHAi03+t7mvIRSEKvmOFQ7WcVdNgoIgc9VBGzBYKBAGJv6QAlcx9FpEPO0x/IIkO95lMBYTVJLy++XD22gtCIZinP0t7Dbk5g2KB7cpX1bISFO4YN2On6jU/+BtMzFJpVKpVOqt5K+Xct7M9velXq/zxBNPUCwWKZfLfOQjH6Hb7f6d9tVa8/73vx8hBH/2Z3/2LT93OjFJpVKpVOp20N+B7e/JE088wdmzZ/nyl7/M5z//eb72ta/xMz/zM3+nfX/1V38VIb79urRpjEkqlUqlUrfDW7Ty6/nz5/niF7/IyZMnueeeewD49V//dT7wgQ/wK7/yK0xMTHzTfV955RX+43/8j7z00kuMj49/W8+fXjFJpVKpVOofsHa7/YbN9/039XjPP/885XL55qQE4PHHH0dKyYkTJ77pfv1+n5/4iZ/gU5/6FGNjY9/286cTk1QqlUqlboPvVOXX6elpSqXSze0Tn/jEmxrX2toaIyMjb7jNNE2q1Spra2vfdL+f+7mf48EHH+SHf/iH39Tzv20mJrGjeXjqGoYHsQODMYXVFdROSbo7kihxgMplBU0Lsy8w+9B8dYjelKY3DbWXTPzFPH4FsmuCzKbG6EnMvsTctvCGBD+w7wxI6EU22wdMWrMWMtJsHs3Sn4ixm5LxL5k0DivK5wXuZRdtQpSB/maO0fEm5sgAy4puljjvziq27o8xO5JP/OoTNI8ogqmAByfnGRtq0X5gwL3DNxBSIwNB4yCI6xlyX8rzJ2fvJPONDPWDAiOA3FJStrs7JbEqHr2JJIsiKCal6g00HZXhQG2D4kWT7JqCswWCAhQWFeaajbI01xeGad/jMfSqRrds/vLqASrnAQFW00Ac7FC6HhH1LN4zeh67KUHD0ElJNDDJnHeRnmAQW6DBaJvIDYfpmS3CksLdBl0N6PRdRC6iE7m46yZCAS+WGBtvIO9qMXQqaTMwCCwyqyYIGHpVERY1yhEMfWWeyacG9KYVuhKy9oDB0GsBF7ujoCG7pjl5ehcnB3NMfNXA2U5aEtS+IYh3DRidbNB9tUZQAiKBNxpjeIraiWQsfzx/lMqlgNJ8iLMt8KqCKDIIs6Bs8A/3uXh5gty8hfQFQVmTWRMULlhJqXEBzeUi1y+PIiMYPRWQzfpM/6qBu6URXZPCgqY/GcPrmT/9UUG87SCdGEtEZDZUUs7bBG9Is+PzIZWnXayuxq5L9uY2+KsL+5BJFwP8CqwvV+hNapoP+OhIMlZuk1lPSnlHWVh/QGO1DKoXQvrDkuJ1TZQRSE/yM0efoXlXQOlKUv5cRAJxoMt8s0ZuReNuCo6PzWNYCsoh1st5cGKMhoXVFeQXNdk1gdnXyACstsToSbKrgt5kkr2lJZxfHuNgYRUsRc3ocqSyQvlKwHOXdhE7YHqauBJROrVGZt6mNwXFiyYyBGfbQGgwfLDagsySSf66AEOTvyHQJpQvQi+wkfkQgNppTeF6ckwgIbes6XQz9CcVsauhEBK5gqknNf6+AYWlkOI1hVCQv2DjNATnn9+J01BJptA3KrQPhsQO+FWN09SEyqDwskt/NDnflK5FLH2foDVwaRyNqR8yiG3BdLmJjKG4EDP6Yg+0QJmCzhx0I5u1l8cYsjo0Dmo2enm0AfUDEkSSkRRbgtgBSyiic0WUFpRmWrgNhdGTXFsfIigrtjYLvDI/zY7fkVgdEIYmv6zoTiuGX43IbIRoI8lC3PwhD8uIiTKCzowgU1cMdgRMDDWxeorWXk24nGPoxS2UAT8+9AJbRwX9/T4jlQ75ZU1rH3RmJKYHmXVB/3IZq5u81+MvBJQvgruVHKevzE8z7w+jLMHe8ial6zFHR1aoXFCUrwSUrioKizEqF7N7fANzuscP3vEqW0dM5sa3CIqC986e54PvOIk3HjL5tEf3pSEWro1gVz3iWOJXobsrQinJ9hGBUFC+Fn73fpj+einnzWzA4uIirVbr5vbxj3/8lk/3S7/0Swgh/sbtwoUL39ZL+exnP8uTTz7Jr/7qr36778ZNaYxJKpVKpVL/gBWLRYrF4t96v1/4hV/gp37qp/7G+8zNzTE2NsbGxsYbbo+iiHq9/k2XaJ588kmuXr1KuVx+w+0f/vCHefjhh3nqqaf+1vH9tXRikkqlUqnUbSBUsr2Z/b8Vw8PDDA8P/633O378OM1mk1OnTnHs2DEgmXgopbj//vtvuc8v/dIv8c/+2T97w21Hjhzhk5/8JD/4gz/4LY0znZikUqlUKnU7vEWzcg4cOMD73vc+fvqnf5r//J//M2EY8tGPfpQf+7Efu5mRs7y8zGOPPcbv/d7vcd999zE2NnbLqykzMzPs3LnzW3r+t02MSSqVSqVSqb+bT3/60+zfv5/HHnuMD3zgAzz00EP81//6X2/+PQxDLl68SL/f/44/99tnYiLhanuI/s4QOdVHRAKrI2juTcpN+6MR/kjM1hGB3TAISor2vhgkuFsCLUlKKPcEcUbjVzRhViBDEBFYXUHsaupBFr+iaPkZ3LqmN6Wp7zPQRhLk50+EbB8R5K8beLXkcUuXoTcbYzUM1m9U0Qs51PMVvNGI9i6wmxJ3xSQcCyn88CpyINgxtcVXnjvK6loFa97l6aXdaJWMrXgVnKagcUgzN7GFN6KJHbBbgv6oJswl484+l6c/HRNlYOrxGxie4MutQ/zq/OM8f3UnYQEa+wVaJrNyryqxm8mYy8NdrEWH9fcFWA3J0cllGoegdMJFRODVXdbvNaiMtvmrzb2EB/tJaem8wF61sDsQF2NOnNuF2RcU99XJbAo2WnlqrwhyqzG0LLyGiz3v8sXzB7G64JcEhg8PjV6jv5JnMCKQx1o0lksU5xXecExn2qB8SbN51OT6/22O3qRD+bwgd9bBGAg6MxbXWjUQsPmAwqkNeLG5E78gsHrgbgu2jinUlsNQtod5oE1mQ+OsJWWzV95pMhhJxtHZzGO1A9bvswkLyfukLuZxWhDODTCuZfipB55l9PEl3LrGGCRBiQgQkSZ7w+RD97/E/+v7/k9EDN0JC+9MmYUPZDACzdRXNK1dArMrGQxr3E2JsqFwzSD/UobfW3uQMC/IrEnyywrpw/IjDgD1o4rx5yIWvQpWJkzK3XsG3mhE8ZxF8SrovonoGyxuVsgvKoQGbyrA3TTIH6rjlwwyW4rNexWl6xHOVJc/WriL/AUbvyIIyorsqoSzBfSf1mjug+7uiC9/7l7sM1nseZfCo+sY2xbGTA//aJ/+mGAwrJEReCMaZWsyqxK/qsmuSqQnUZbgnxw8RSd2k2NNxHz5+j4a+2wO71xGxNB4xKP2gkXr2Bjc2UYGAiPQDO4a4NdinIbG7mkmn+5RuqbIr8ZMflnS2h/jrku2jynqrRzOxQwjJ8FuxzSPKMKCwKvC9l0K42KO0RMw9JpGbNuERWjsNak85bJ63GbjePI69AOt5DQTgzYE7rYgqCjKr1oYAez4XJ+gKDjTniAoQnd3yMZ7AnqjBhRCxottzJbB6IkQvySQQmMMNH5J0tiXxVk36Y9polmPZ148iDLhUn+M6hlBvZlHy+Q81N4JxUsGrV1Jy42/evEQYVXxyNhV2p0Mhq+JRgIsO8IYCLKXHGq1Ls3dNlrC8OddNu8SZFcl9f0mqw85WL2IKKspfC1Da7GEX0te5+qDguwVm9XtEpt3S5y6pHhV0t9RJsppvjHYgeGDjgSbrTyDDzUpXEtOx4XFmJGX+8ipPlZXs3E85voPGjT3w2BEUz4nede+S3xu4TDV8z69yKY/JDm1NoXpKRr7bLrTEr8smZjZJlQGUWjyzMoc4897SDTagBObO/j8lcPYmyZbR1yUqdmxa51wM0N0qcBgZ4DRNWiuFxAa+qOSlUeM797v0lu4wFq1WuX3f//36XQ6tFot/tt/+2/k8/mbf9+xYwdaax599NFv+hhaa37kR37kW37udCknlUqlUqnbIO0ufGtvnysmqVQqlUql3vLSKyapVCqVSt0Ob9Hg19stnZikUqlUKnU7aOBNpAv/fcaY3E7pxCSVSqVSqdsgjTG5tbdNjEnswAND82QWLMTVLNrU9KdjRk4plKNx10xyEx2inCbOaCpnBWPPCNS0R1DQlC5DOBUQzXrYLZGUTa9rxN4u2oDoUJeRlxWr/RLlC4IPjn+DwmLE6EuK7Jpm5OU+dkuAhMJ1EDE4DU12TZPdiHHXDMJqxLFD10BDfzLGGEhyS4KgqMmua4aesbiztkTpkmCjnUc5ih87epLZdyxy3/gCyjew2knp6sGoIr8guXI56e449IqmNxujnKSMeOxC/8EuhSsGZh+uPz9NUIv56NDX6AU2P3PnM1QuKuy2YOyB1STzQED5Soy7JRjK95JspUgy9JrmQGGN0kUYfbGD3RYY+YjSFeierWIbMcaVDPlFMDxNMBHi1hVm3eTI3kUyG5p2J4uWwPkCpq+JXIE1MmBookXsaHZObCFDMDwoXo/5o5P3oC1FfknR286CpWj/SJfCNQPDA68iMXsw8fUB7laI1dUoE/yqIigmJewHIxptK7JuwPqgQJQVWD2NDKF8TiJiwb7COpXcAKHAuaOJ3YTYTjI9MpsKt+zR2p2jcF2jbNAmWAfbeFVQPYvSFfj84mGuXRrDaWm8EYXVg87ekK2jNtOPL/An37ibf/Xcj2E3BVZfU7l7k7EXYjbuSwooZVc11XMaqyOwH9ymel7Rm9AYj21TsHxkkLQuaOyXBGMRuWVN41GPuT8Ouf4hyBs+4VaSkSF8g+yiSeeoT5wRDJ8wcLYM4p5F44Bg6BWNkY3Irmh2V7eovrBGdi1AZ2L6QwZzQ9u8Y/wa3f0B/QMe1VcFYV7jjUWID26TXRXkrpuog10Ge32yd23jhyZqJCDo2bgvZ6lejKmd1Vg9Te01jeELcu/eIMrq17NLBN6w5nPXD7PXXaNzqcKZwRT/2/5nMAdw5tVZYldTOJmhMwvF801yXyhgN6F6wce86iIqAbEjEEoTFpK2B/nLLaxOTOGqQVDSjD4j2DW6RVBWNPYLZKypvSQZf7ZD5aKi9orEboBXFvRHJCMvgV/WKAN6UwKrA7kbBsFYSH85T3ZdEU771A+CN6xRrsJua0ZPxvg1h/yK4h3Vq4QFjbVtMvFZi6AsENs2WTPAbiZjqFzy6QQOblNjdzWmr3GaoCyY/rTJP3rkBZSteV/5NTbvizHmXbyDA4bORkn2UAxRXuNuC/IzbSZ2bbLQrzI53MRpBBhOzFChBxq8QwNq2R7lqwFRBgrXB1TOgbuVtEvIrGuW3pVBKIgdweiuLab/ssPUl1uIOGlnYTsh48/F9GdD2g8M2D5kYfiC373wAKMnY4gkx6YW6XVcytdCamcjOlMGQdnCsiNiJ2klMPNFhdURyEDQe1ePl9enmCk12Trs4MUW3RmYKLaJMpLKxQCzD05LMQgs1tsFMq9keGTyKtd/wGbt8zP4D3cYyvSIY4ndFvSmNYYneGTkCvl5g+JVyJ+zkTEY+ZDaaQ0a9HcxKSd1a+kVk1QqlUqlbgfNm4wx+Y6N5C0lnZikUqlUKnU7pMGvt/S2WcpJpVKpVCr11pdeMUmlUqlU6nZQgHiT+38PSicmqVQqlUrdBmlWzq29bZZyhIaK2aOwqMkf3aZ84f/qAVM7rUBDdyNHYV5itQXekKA/LBFLLmZf4FUFOpTIZZfBiCY/3cYINM7zBdxNAVqwcUzSDWwahzSuDOlOmHTHDHqTgs07s5h92LdjlaAkUDb0pqE/LmjtNDEHMLtzk20vh5jtk7thkFmXWB2NEUD9aEx7J3z+ubspLoSo0yUK4x2eXt/NUrNEpAwOzK1gdaB4IyKuRHRnFRM7tsiuCPpjktI5g5GTSRaSFhB2bRAQZUEGAkzFq8EY/499f84f37iT9fvA8GHzqQm8miYswPo9Endbc+XSOO3DASNPWSgTPvtb76Q/Jlh5uIBX0+hNJ+kJs6PP9iBLMOvT3gmNIxpn0aa5RzL7Fz5BbBBnBLFvsPcDl/HHQwY1wfYRQe1Ps2xtFoizivmlYVoHI7whgf7nm/z08a+x93d81h6PEE7M0FibQculcjmkP67RJpTmY4KyRXO3zcYDmv7ugNET0JtWtLdy6CkP2TNoNnI4RkRmW2H2NcPf6GF3NboaECPZfn6M4VNtutdKtA9EIOHahzM09gv8jSyxLZLeOQOB3QJ1qoQMYfeeVXrjgp+ae57jRy/TmZGoQkSYh9n/Du6mRvxckZ+69zk+88h/IShrVh5VbJ0bor7fZPJpzaBmkKlreuMSGUNjqUT9gETE0HutSqwFrb2gTECDs2LhVwRDf+ky/8M2w8+ZNMMszoaB1U+ykMKixly18apQP6IJdg2ovGxSmIfupESFkvo7fSp2nwsfHWP7kIuRiXE6iqU/2UlWBlRftHAvueRXQsy+4IfvfZlatocyoHY2YufwNrkzDs0bZZrzFfTAYGy8gT+kWbtf0top8UuC1pxEhtA8OYLQYDfh4H3zjD8b8r/uPsGj2StkVwWWiPnPFx5i+3GPf/TwCYrXoLAUUzujEQOfJ37xLxiMaTaPuhieYGasTue+Aa2dBmHeYP19AZf+aZnev2hRWI7JrQj6o5InJl+gsLuJMqE/YrL1cMi1D+Zp/eMO+ZUIBMgIMpuKwZCksABBRSMD6E8oRl7yEQMDnYvpD0t2/+eY6a8E1F7TmG2DzrQgszLA6kUYgeKduQsYPjgHWiw/pgnzmuKuJpe3hxns8+lMWXhDFj85dQK/KAhygsgVjD/ToXwJ1u+zOJabp7yzQV85FC8ln1v56y5WJxlDfi1m9ISiN6WYKTdxzIh/NPwS/MYwS+/Kknsxy91Diygb3HMZLp2bYu1+h8yWZv5HslgDTZQRRBlwmprJpz2yK4Lh1zzafZeFH8jT2lcguyIIC5p/vPsbbB8yMboGQoARQOVCzF/e/5u0dpjYmwar/SKVp1xQmu1DJrk1xdr9JvHZIpltRWdO4VUM/JrC2+0TbLuUMh5X6zXyq4qNXp7Zv/BYbpUYVCWdGYuh0wPasyatKxV217bQx1t8/uJhyhcE7Tt8xCsFVrpFxqpturtDSpfAbsPv/8UjDEY0rT3Jb8PoCcW7dl3G3QppHwopn3szlzBS3wnpFZNUKpVKpW6HNPj1ltKJSSqVSqVSt0M6Mbmlt81STiqVSqVSqbe+9IpJKpVKpVK3Q3rF5JbeNldMZACWiOlMC+rLZbaPxQC0dxps3SEx+2Btm0S5pFy8FiBDTTQS4I0q4gzY6yYySgJC/TNlshsBXk3jVzVh2wYNs8UGourzZGM/sSOIchBnNEag6Y9r1jsF/IrGq2mstiDYNUDZ0DkYsHpinOafTWK9lqO7OyLMa7qzArMrkJ4kGI45fHSB5XdaBMUksG002yE6X0Qh8GMTvwL9EQMiQeWsYPvEGO0jAYNhTWE5InYElXNJeWvhG/QmFYaXBPiVXrXJCp8j9hpThSbaTAJj+3MB2TVB6ZqidBViG/btW8Zes9g8pqkfFDz+T58HILuhkCGM7NukOw25rE8YG9CxkBG4GxJtJKX41+9zmc3XUQYc3bXIxS/uwWyYCAXjz8e0d0jcfIAuRMxMbGNvGXjDmu3nxrjQG2X+R3JUTloIqWl1Mkz+uUFzzkKbIH3oD0u8soGWAhEKRM/ALwry85LdO9dxzmSonBFYiw4/Nfkszb0SoWD50RyxJdCx5M7cDfxazPYdRVQuJn/FJLMmMQYCqysY2lHH7iqq50O0oTG8pKWBN6y4cnkcd1vzmxce4cSL+xAKpr6QlMzvj5hYA821Hy3zJ/NHWYtKAMz8ORieQEYwqEmymxHN3RLDB6utqb1sIEMQs30q5zU/OvQS1bOa1l6NUwe1r0vlUkzjIJhdwda9iqlMIyn7v0NitAzCoYgopzAHoEd92HRo7UsChg0ftBI4V1y+9OphSpcFcUZgXcoQ5CTxu5r8f1+5l9Zu8MZjrn9Q0tsZ8tlnjzGZbSEUbB02Wdiu0t0bIiLBrsPLGF2DtcUqhUPbVM4nQZXKEhQWkgDQsKQwBgJvCM6cnqW5y+al1iw/9NI/p3unR9XsMl1pMvlHFhfbo3R2CFo7DLaOCgZzNf7fv/cByhde/7Ifa7F0aoLCiQyZDc3aA5LhrzgMvwz6j4eo7zfoTWg6u2P+ZP0Y3qkq2tKsP6TY/X/E7PpMg5l/p2ntMBmMavpjSZBue1+M4SftHYZfDdEGbB110KbGXbDpTWku/1OL9qxNb1xS2F9n4jmP7o4cQdGkXzP42Lkf5//X3p2HWVEdCP//nqq6++17e9+goZsGGmQTMLTgQkZJxMRER59oHDLGjNGYaEyMJsZ3JhPjZMTEcbL9ErNMxGSS0UnyRpM37gtERQREEBFoaGi6m6b37ntv36XureX8/ijo2BFNjGC3eD7Pcx+aqlN1z1bVp6vOYhW7OC8U4x/WCfUIzK2lpNNBIjsDFGJeZ9fbf//3GCbYYa8j5r6PRMmXCKQGvx9YyPChOPf1LMEJwOAyCzskGJoVwIpBzxKN4SadWKtgcUkHZcEM/929lNRUg9pnTawodGRK0CwwT8oxtakH3YRAUhJvgUKRt3RGYBjycUHve4LkS2DwpCByW5xQr0AcHubqTwg2DtVjFUn8CY3400FCAy4DCzR+OzIXs0JSqHCI+AqkZkB6sp9Ym4sVFhgZQfEp/dghjdLtguFZgsrNEGwNIFyBrrlkUkHyRYLhkTCJ6UHMnB/N8Zas2HdhACMjCU4doT46iJnz4+R14m0FgtEC/hFIbC8nZxn4+wzSdYJcpSQwM4Vba2LkBOnpNskGnYzt5+AKPyVbDPIlb2PnV/cYfE5A6omJoiiKoowDNVz46N41T0wURVEURZn41BMTRVEURRkPqo/JUamGiaIoiqKMB1d6s3++leNPQOpVjqIoiqIoE8a7qmHycN9cfFkg4FD9R43m+a34k5JJz1i4Pm9kSvnLFsFBSbaxQGhIcvmiDYSmjBAckIS7BZot8Ke8XtuDc4Kj5/b3G1gxl6nhIVxLp9SfpXJLGl9aMmltgfIXUwCMtBbjhCT+hMDIAoMBYgcc9KBDcF6CkWU5rPkZLm1+nsrmHowsSAOMjKCibpiKQBqr3Gb6goPMj3exZ6CScI/gufYGinx5pAGJmVC20SDVAMweIdjhp2KrS+8pOqmpGqlpArPWxig1QQqyk1yyU2wKMfj98CK+1Xc2Lz8/HSMjvGnlpUA3JUMXZsjUCKoe66Q/EwG8qewj84f4v+uXMGldhr73FQDo7YsjG7MEfDaJkRDS53Uf1yyw4i4lLXmK9zrUBpKEeyV7H2kkV+0Q7RDeyBBNkJuTI98Txujzc2go5k2bjzdq6qyS3UgNSvYUcE0dK+fj0HshfsCmeqODHYXi1gLlz3RR+9t9lG8VxFt0rCJvOYA5xd1oNmQmCUoX99Ho62fqgyNEegre9lqBljL4+h8uZMnivfiykqKqNFVbTPQ8FB2Aqb8+RFEgT2+zxtAsH+DFZ+qDWeJ7BCLokJglMfcXUbRfw4pKDr5Pkq53yNQKrJDAiklYV8KmTCMAwzMNpAZmmcQ/Imn/MNSuzzEyzcWKCvKl3ogd2REm0mPxSGI+VkTgT2hUXdBB5JkoyWk6YloGbVaaUJfO2p6ZxA7YRLskbkCCJpEhl9JdNpomMbKCUI9GxQspjJwkGC0QSIAWsinbYSJsME5OoBckuWyApTP3I6tNlizci7AE/j4DGXDZ3FNH9JBD8T4XTXOpmjwMZXk0JFTnKa1JoglIrMyQOt0kuaBAtsYr0/huQWy/N525UWZStstk73AFyyYfQGiSsyJ7aO2uRCtIXto5FSvuEun2RoCF9g8SHJIMvMfFn5KUr4lgFzsUt1pEuy1q1zv0LbfoPwVi/9BF1eYCNRscpv+PSWcqTukuh9qnXab8AVov02m5opiDX4WSvQX06WnskMSKS6L7dQpFgnStRq7MoPHXOcI9LhV1w4R7JGXboXibn3yZILsoR25LGW3n+0lM1+g8TxLpdQgYNh89bQPmTBMjLciflSLe6jJ7cg8A2SVZhpsEZy5/mfK1nZTsLTB8kiQwKPClvZFXAc0mUpnh8tr1WDHJpId14h/opqjLxvVJb9r8uGR4kc1DnXM4lI5zSkk7qVNMhO1SKHEpuN7D8mCoQOf2GpwgCEcytDxP6Y4sI/UupbsKlOwtEN/vIKS336x2CPc59CyTFOKAgLa19fhGBHZYklyeo3h7AoCzIrspVNr4ik1a/9iANi2NFRUEh22CCZfSFpvY1yO4OgwtcrHrTYZOEmgWBHt19h+oxNcZIFclaKgYJFst8O0Kk5rmjZiMdGnYIYGZ8/Po/tlwKIg+6CNd60fXXYyMxCq1SewtBcA3Ana9ybn1O5EpP+Fub9kAqcPLfTUgYbi54N2X3y5HXuW8lc8JSL3KURRFUZRx8VYbFydmw+Rd9cREURRFUZSJTT0xURRFUZTxoEblHNW4PzHp6uriYx/7GGVlZYRCIebNm8cLL7wwul9Kyb/+679SU1NDKBRixYoV7N27dxxjrCiKoijHgCvf+ucENK4Nk+HhYU477TR8Ph8PP/wwO3fu5M4776SkpGQ0zDe/+U2++93v8sMf/pCNGzcSiUQ455xzME3zTX9f2CjgnJFEjBj0LHfZuq4Js1TQcY6Ba4AVkwzN8pGaDkW7/AzOFdyz4XRyWT+FmKAQBz0HjtfPkfRkr0MsQKHcwTei8evti6CgEdYK5EsD6HkYOikA4nAnv5lDGBlBoViSnuoSPqiRqdGRvQFG2uPMnXwIO2/w270L6DxUimZ5nbasUpfBvWWsbZlJ7RM6JcEsj3TOppA3SCzOM7kswZTIEE5QEu0U5KoE8b1gdUWwilwG52n4E4JAEvzDoGc0/NsiBKalcCMO/hITs8oh4/jZ2DsVJyCRGiAg0urDMCGwoQgr5rLrS5MYPuCVkTY5i/VMGTUz+hk4OUxwj9cheNLvfAS2RMisrQQpEJZX1YwMBPp1BucGSTTq3LP+dFKNguqzDxIY0MmXgn/EJZCwYCCAjDgUt4DrajhBr9NwuEfy3T1/hx236V4aQEsZzJzag57VSNUZDMwzMNKQLzHITa+g6yONuD68jsVRMHLwcOtJ2GGIHJT0tpZzw96L6T69iIG5QfwJiR2VBPo1Kub0c2rxfjRHYu4qpuMqBzvoTenf+okaOvpKmfpQHuGtcEDHOX72f9bL//DuAMF+Dd0UJGfbRA6CkdLRLK8u+LISX1qQXpzj/+49GfCmhPeNCMI9gkSjhp7WOfjeEP5hDSsmvXSVSnRT0Hm2n12JKlxdIBzY+8ok3PcNYxWBeyCCuydKIS6ZEe+nt9lH8e40MuxgDPgwBg0y1TqiPYScnkEK6FkWJ9EEVnuE1AwHORgAIDTg4rqCSHeBf5r3HNMiA8SfC7LtiVkIR+DLeB1gBZCq1xmp07hs5ib69pUhh/20PzsF6cJQd5zB9hJoi+DfFSLU5udwH0xiF3YzuNgl02gh9oXpfU+QgYPFHEiXInoCPJo+ifNnvUQ+roMAzRSk6jXy1TYj8ytJNkKwR8c14OBZGiKvYZgO/iGT4ZkGwU4/4YMa7Vsn0bfYT7LeoPUqg6DPJh/XGJ5p0LfYILLXT9VGiP1PEZkaH3ZbFOEK7BKbbK1LtkaSaXAwywXJxjCFIkF/ewlOQJAvFriG18E79FKIonZJ7R8lmgXVa3UyVTr9ySi/aTmZSMwkMCxha4zeMx3aHm3A8UP02TCuD3rNIswZVeTKDcKHNDINNsEhb2mMXrMIs62I3w0uJL4HulY69G2s9pZesAV2WFB0AKJ7fQwORelLRDmUL0bvCeCEDUSNya6D1V7Gb4njRB18aehfrGF0BWg/L0z4kMbQSX6cgEa2XMNszJMvFUi/i16QRA7qlC7tobjVBQHZyQ6uX+KM+Bhp8pZXuLblUkIdPmRHBN/8BLGIiatDYpoPreAt35CYGSZTKzDKcpQ/FkTq4IQgPzPHinm7mHH6Aao25Yn5TcI9kkjzAGXbJU5I4PjAKgI57KcilqbiRYkTcTHLBWJDnMHTCwRLTWL7vHtPZm4eBgK4UiDygkKxwIm4ZGtd8rvi1D5jM/2nDuYZI2/6d4tybI3rq5xvfOMb1NXVsWbNmtFtDQ0Noz9LKfn2t7/Nv/zLv3D++ecD8POf/5yqqioeeOABPvrRj77tcVYURVGUY0K63uetHH8CGtcnJr///e855ZRT+MhHPkJlZSULFy7kJz/5yej+trY2enp6WLFixei2eDxOc3MzGzZsOOo58/k8qVRqzEdRFEVRJhw1XPioxrVhsn//fu666y5mzJjBo48+yqc//Wmuu+46fvaznwHQ0+ON7a+qqhpzXFVV1ei+P7d69Wri8fjop66u7vgmQlEURVH+FqqPyVGNa8PEdV0WLVrEbbfdxsKFC7nqqqu48sor+eEPf/g3n/Pmm28mmUyOfjo7O49hjBVFURRFOZ7GtWFSU1PDSSedNGbb7Nmz6ejoAKC62uug1dvbOyZMb2/v6L4/FwgEiMViYz6KoiiKMuGoVzlHNa4Nk9NOO42WlpYx2/bs2cPUqVMBryNsdXU1Tz755Oj+VCrFxo0bWbp06Zv+vh09Nfgfj2FUmERbDZDe6Iqi/YdHjKS9aZ+1AhhZSaQLfPE8MuEnXyqxwxKrSOKEvMogJGRrvJ+DPd754sVZ/AM6DhpSE0R6bYQDZkUIgJF0CKlBcQsYGY18qSQfBzdm46vO8tL+yeh+h/xwEKPPT65KIlyQmiTUo+HrDJBo1NjUWo+UgqmVQ/h6/cwrOcQzXY34E96Ij4r3HqIQF+jVOZywS7BfYJiHR1j4IdinYVa4WJZOfIePQsaP9EnaUmVk8z4oKRDpFAQHIDPNYmChJNrl4pTayJCD9HudrqzhAIW4pCSYIzMJ/Ic7tHcvOzzyaFaB0ngGLeflsX9E4volUoBZKaluGCTaIclaPqQhseekSdXrdK4I4JuUobgsTXImzJ3UjWYJAsOS4XmSTC6AnjK86aMFdA4XExgWZGslZrlLIOmSbNTxpS00W5KrFIjOIIVyBysKVs6HkYFsjaBkh8aUoiGE7dWHkXpAQq6xQCbv58GeeYzU6VilNmJfmFCfJDVdUqi28e8IYxUZFB30huX40gJ9f4jylx1YkkQvwHvO2kXJ5CTDcySaA07YxchAoUhQvBs+u2gtnzrpGcCbRt+OSJwAxNtcQr0aSG8kUXDAS5+rQ3AA7KjLwa21pKdKfGmIHNTJ7C3GPwyuT+JPCiJdAk1I8uUOdtSPv8eHnodQv8AOCeTUHPJABNcHhTjYcYeiNg1fUiPUo5FqCCIkGE/HSTYE2D4yibiRJVchkLok1K15+RYAZ0MJ2VqvXvxo3VkQs9AsgTMji9EeJFSao3HWIaxiBysmCQ5JCjHv+ul6sQbfsMakRzTk9AyxAy5fOONRLqjZhmYJpgV62Z6YRKJJA10ifZLqjXmMIYOird0sOG0vuVobABmQyKBD/8lBhuYWoZvgT4ETgOCgwKx0saIQ3xxgeVUrTlCQbvSmc5cCshUa6Vqd5DQNJudwQhJj0MA/pGEXO1Q8r2GFQS9IUtPBSOtkTs1SKAazXGJFQdjQv7xA1wUWmXqHaFee4n15/nvJT9H2RHA3FWOWCTQHjGGD2Jm9mJUOThBKd8LuQ1UgINxjUfvHJCKvMTJFB+DSmk1olqA5tp9ot4Uetgn2C8xijcoXbTJ1LvE2C8cPH5j9Ck53mEPZOOFDglSdD701xNJpbYBX333DBoGEpGSnt/RFsE+gm15+Dc/wEelziW8OYGRABByCfXmKOlxST1XT/V4Xsz5PsEf36qorCHd7IyYb4wM4QYldbjEpnqS/swR/SlIoFozU6dhhjcEFkqoXCpQ/ECY9WVDcAlZEIocCtCQqcaUg2JnkwooXkToEDJv4viyVL+awI5JwjyRal6I2mqR/MUxqGKDyhRzuqUl8vX6aKvsov9B7ah7aE8BIC57ra8ANuZjlkki7TrBPIzp3CKkJ9l8YpNAdedO/W/5mkrfYMHn7ovp2GtdROddffz3Lli3jtttu4+KLL2bTpk38+Mc/5sc//jEAQgg+//nP8/Wvf50ZM2bQ0NDAV77yFWpra7ngggvGM+qKoiiKohwH49owec973sP999/PzTffzK233kpDQwPf/va3WbVq1WiYL33pS2QyGa666ioSiQSnn346jzzyCMFg8A3OrCiKoigTnJr59ajGfUr68847j/POO+919wshuPXWW7n11lvfxlgpiqIoynHmusBbmIvEVfOYKIqiKIqiHFfj/sREURRFUd6V1Kuco3rXPDFxAlBTnCJdB+7BMNlaF6tIUpiRY2SaixvwRtwEkpJCucPwAodUA1gZH1peED4k8KUF7rQcAFaRxKou4BoQ7hbkp5ne+WydonYYsYMMzjXIVhgkl5iMTDEwsgK7P4hvRFAoEjgBiZER6HnAFTidYbRhH7IrxPTpPcjJJr6UwIpA6KCBWSYRM9O4PpCuIG8ZtPeVYsUdDmaLSXbEyU52sCPQvr8SpDdqRjgCqXl50P1el/Q0m+CQxCmzMAwXxw8lm3wE+gyaKw5QHR9BZgxcP2ROTxPq8GFkBInpGqH9foIdfoSpIw3Q8hquH/oyUfwJb1STkRG4QW9UiPC5ZAs+AkMCf1LQf6qLZgucgDdypG93BalpkEiH0QqC0MYowobIQciPBMiafoy04KW9dUS6JNkqQaBfw7Z0nIgD3iAkjA0xgv0SrSAo2+aNOAkMSRIzI/jS3ho0wQFBpF331ihJ+rx81GBosc0rA9WYFd76QL60wAlLcKEimsF2NdJTXdC9tW0SJ0niLYJIqw99yTBSh3xcw/V5IwqcABw8x8XaGcPISLY8chL558so2q8R7hYEuw0ydS6GKUnMgv/vpfdyX/sp+FOCgQV+kKAVoO8UKGp3CfVJAsOSbK2kfmknoT5v3Sbpk4RnJvAnBcl5FlZUIqQ3WsSf0Ih2uuSLodiXRRqSzvf5KVTYOH6vLgSSkvjaEE5I4oQlRg7irxikp0hi+718DffZOD5Bao5FtlqwsWUaT/bNolDiXT9SA9fvlWXh5Ay6KcjWuJQ1DCNdQbBhBMfSKJR7o5b27ZhEbLdBpFMQGnC9taaEN1KpUGXT9T6Jb3uUgXmCH+4+nV+0L8Eqt/nGvnMZSEcI9Ur8sTzhQxq9SwKIqVm6PlzHzsdm4kvq5CoF0ucS7PYR6XbRLImRkbiGN4pOK4CwBGaNQ+Jki3u3LsHxg29IJ97qrZflBrywbkBi7AkT7RBoFuRn54juNxipF8T3uww3aQT7BcF+gTwUxI5I7HoTOyJJ1zvofpfwriChLh2z1M/A/CC/Ty7C9YEdlsQOuBRi3nUy+FIloW6dfIkkVyFoqBokNcVP/8IAg/NjyCKb+H4Hf1LytRfPQ88KHDQGZ/vR94dwApCpFWRqdIr2a6TqfCDhiYcWEz6kseOFBlJzLJyAd+2t3zkd3RSYNTb+mSkcH4xMEZS94ngjEx1JuFtStqvAyGSd5GzvWgu/EiQ7KYjjF6RnFdDTGsWlGaQB2RqXolaDxIwwSAjpFsIRYGp0p2IIWzBwqo0vBXZYkJqiE+rRSE7zMdykoZuQqfHuE5oNmpD0Z6J0/H0lWzL1ZKsFA6kIVszPwJwQTliSaIKR3igbt0/Hl9JIZEPs//sA2sY4uDBkhtm/fZI3si0D1pQ8qWyQSLuBVoBclUu+VJLOBOlephPfI7zRRW+XCTxceGhoiFWrVhGLxSguLuaKK64gnU7/xeM2bNjAWWedRSQSIRaLceaZZ5LL5d7Ud79rGiaKoiiKovx1Vq1axSuvvMLjjz/OH/7wB55++mmuuuqqNzxmw4YNrFy5kve///1s2rSJzZs3c+2116Jpb66poV7lKIqiKMp4cCVvaTKS4zQl/a5du3jkkUfYvHkzp5xyCgDf+973+MAHPsB//Md/UFtbe9Tjrr/+eq677jq+/OUvj25ramp609+vnpgoiqIoyjiQ0n3Ln+Nhw4YNFBcXjzZKAFasWIGmaWzcuPGox/T19bFx40YqKytZtmwZVVVVLF++nGefffZNf79qmCiKoijKeJBvcQG/w31MUqnUmE8+n39L0erp6aGysnLMNsMwKC0tfd0FdPfv9zqm3XLLLVx55ZU88sgjLFq0iLPPPpu9e/e+qe9/1zRM3IBLIhckMCSIdgqi7Rp6TqB3Bgl3adhhiZE93MHzoIGe0XCiLsLn4hTbZKa4WFGJ6whv2uYBjcqn/NjFDukpEmlp+FKCXFcUKyp4abCWcI/Eigom/9Yg1m6jWSAjDvkSieuHcI8g1C8J90owJE6pjRt20eoytO6tAbzOenr+cMc9G/x+G6nD1EmD5EYCSCkg6NAyUEll4yC+pIaWB3+/jhMEPauB8I6NHnSJHDCI7zTIVgk0w2VSSRLXB6kzc+RrLIYKEQbSEURBo2xnATtveB19awoElw5g5MCelcWXEkjADTs4EZfhHeXky7zOqUJC9TOCXKXEfyBAwHAoFEsy9Q6xFh0n4HUQjRzUkIY35b72YhGlux00C0paLYws+Pp9uAcihHsl0xt6SU8F1y8p6pC4WYPSbTpF7+uhenYfI402iVngT0JmkiBTKwgNuQSHHWJtOYyMJFfj4gQgMUsS7PeWA3D9Ej1i86npzxLfC0WdLnZEYqQFOIL9ByrpGigmfEhDTxjYISje7XUmzlW7pNvjSM3rrKfnQbgQ7RToURur2MWsENhhSahfUtTlkJruYtbY+FIawWGHQomDk/aha15HSCcAUgerCAJDGvliQaYO0nVghyR7X5lEvlTiywACAj4bOyyJ7vUhHEFgUBDud/EnYXi2wJ+Cfely/IM6pTsketJAcyDXZKIXJCNTwJcShLoFwQFJrlISGBYYpiRf5uIbsSh9aRgtZBNr9/4660rGcUMuek6Qq3W8joVpQXEsi29EUNwiGNpbSv3kAYSQCF1y2vw9lBdlCPVo5Mu8adsT03WEC0iQfkmow4eW1cjVOFilLlbBIG8ZCL9Dc8UBMjk/ZqnA/0IUIyvJVbrU/sJP9XNJqjcWCAwKgoMSY9ig/CWHcE+B4t0jRPocr+4fkhhZSajX63BbvNVHpMWPE4RIlyCQdAn1CWJtDsKBaDv4UuDqoBcEkRdDaBa4Brg+gdQhOOjV69pnXfSsQDvk3WMidSPIvgCBYUnFSxZWxFtO4b6di3Gn5rCLJIYpiRwSFOZmscps/Cko3SWxIlAVGsEsExR1OKSnCIw+H32LNFLTJfZQEN+iYdYPTyc12ybe6tWF4KAk1Qh6XuLLSZygJHZKP5XnHGRR816CxSahIa+jvx62vQ7hPQYXNW4jmHTJl7vkyjQGT7UZqYf+My2GZ/qJdjkE+nRCAy724hEcnyDca6H5HWqek/B4KaEeb5mAzGQXX04ifbC5dwr5WguCLtoTJfgSGlpWJ9lsUrLHpvzlPMFByfDJNmathZH1lmwobnXRLEFfKkpyexnRgy69+SIQYHVFSNb7GJnmEmv1OpzraR1fQsfIQn5vDDfkYgchckhwsKeEUI9GpMvr7B1sCZLLBMjMLFC/tBO3zMIpcohGTMpeloQGvcEA7zR1dXXE4/HRz+rVq48a7stf/jJCiDf87N69+2+Kg3t4TpVPfepTfOITn2DhwoV861vfoqmpibvvvvtNnUv1MVEURVGU8SDfYh+Tw09MOjs7xyxYGwgEjhr8hhtu4PLLL3/DU06bNo3q6mr6+vrGbLdtm6GhodddQLemxvtj+o0W5v1rqYaJoiiKoowH1/Ues/6tDvcxicViYxomr6eiooKKioq/GG7p0qUkEgm2bNnC4sWLAXjqqadwXZfm5uajHlNfX09tbe1RF+Y999xz/+J3vtq75lWOoiiKoih/2ezZs1m5ciVXXnklmzZtYv369Vx77bV89KMfHR2R09XVxaxZs9i0aRPgLR/zxS9+ke9+97v85je/obW1la985Svs3r2bK6644k19v3pioiiKoijj4Ri9yjkefvnLX3Lttddy9tlno2kaF110Ed/97ndH91uWRUtLC9lsdnTb5z//eUzT5Prrr2doaIgFCxbw+OOP09jY+Ka+WzVMFEVRFGUcSNdFvoVXOcdruDBAaWkp//M///O6++vr65FHaRh9+ctfHjOPyd/iXfMqx5fS+OyMdWgWfOEzvyLV5I2SqX3WJl8mKX1ZUP+HHLEDXkE7RQ5GmYnhd4i/5Kfh5C4AjHZv6mknIIm15fAP6GgWhNr8IODvlryCHYR/mvocyUZIzLFx/ILBOT4KcUl8mx/NhtgBh/KX8+RLBPF9WUJtfmZ/I0HjfTbV9waprR/AGfFhLsqSrnfI1tm4U0wqohmEhI7dVdx86sPYGR9lz/qJh0zylkF8L8TbbYT0pls3MhplL3rTxkudP03NbUPR5hBtL04m2iWJbA5jJAzeE2vjS7MfY+WybRSKdMrWBbDjDuGYCX8owzUgGjGx6gpoNlSv0/ENa9gxh/heb1SMFZX4r+hBz4OeE8SCJgjwDWsU77MQDgSWDDHSZHHPB39EbJ8Xn66zJYUi6FpuYBUJXJ9k4Wl7GFmR4e8q9lC+zSV8SNC/xOWy5ucYPi1P9g/V9CeizJvTQbhbYC5Lk6u1ibVJek4VuD7B8OwQw6flCXVrmJUOUx+0aD7vZdywixVzmVo1yLqhJuwwRHryhHoFZTski09q4w8rvoe+O0KoT7LstJ3UPpMn2QiGCf6kxnUrHiFTqaHZ3vTsojFDrlJS9FzIG62yeJj3nLEb3YTAkE3j/+aY/LjAv2iYoVk+ogcM0CWLyzsBqNpUQLhQtSlP7XoTKyLwn5TEmmYSPqRRul0jctIwvhFJ+SadzzT+kWC/wDci0fNgzs9hB72RUbopKN1VoCKYxpiVIleuYUxNE50/CCkf/ScLXD9YpS65KkloyKFQbeMEIFeuoddlOXR6mIMrS9E7g2QrNCJ7/Fw2YyPxVwxcv0QzNYSEkt2SoWTEm1K9XFA5qx/z7hqMJ4oJ7gzR/h9NDK6rITu9QLjbG9EUO+CSXZYBQI8VcAKS8m2CysZBJj8muWLuc9wx5zfIrEFjsA/H1jFMOH/VMwyfbKPZgt5TfIxMizIyyQdA5QtpZvx8COFKnIDGwMIYPc06+bggNOhgRQWZeodpv7aIdjtk6xwmP5UmNOAyME8n1O8S7ikQ7nNIzpQEByXF+22vTicl1mkpAguG6W920QowMlVglOU4+HcaRg4mndxNSYuNbesE+zVi7Ta5MoPgsEO2RuDkDLQDIarXS/pP1oh2OQRDBVaf+RvMCi/f/Sk4r+wlpAF2UGPS0yZ2hcWkPxaoel4i4gWyrXH+edJDTH5MMPQ+E6mD4xcUtUFyhkQKCPULgoZN97rJ7OqvQu6KkqnR0LOC+ZO7KMQl7uw0zw80AFDyimDwVItoi7eERNErfkbqXcI9efxJkJpgfu0hMrUa6Uk+Ys+HyJVpfOv6H5KZBHpa47Kznvbqnw3/Put+/L0GoZjJv1z3CyJd3ugtmTPwJyz6Tw6w8MrtRNp8BEpMUjMkhZikEBVUP+9QaI9y76XfoXRTPxX+NCW7Hc49fSuuT+DEvNGQ8b3e6D7XL0nPLvCJc5+i4X6X2jMPImzJqdPb+OBHnyMzGfJxgRPylqMIx3P4dIepkwYAWFjZRe85FkJC5Vbrbf3dpLyWemKiKIqiKONhAr/KGU+qYaIoiqIo48GV3iPsv9UJ2jB517zKURRFURRl4lNPTBRFURRlPEgJvJV5TNQTk3e8ZxIzsWJwy6YPE+w1kAb0LvFR3AIDSxzaPxAiOU2j5PQeyjYZBDdHAEjOs+hcP/k157OKfKM/+1OHv6OtkUi3pM43SKQLqp7V8GVcAkNeBUpP8f7NF2v0NAfIVbkcPCtCvinH8Lcl3cuCdF9S4FBHGdF9Bv6Xw0TbdUq36bi2oCacIrZPMnPuQe7acyaRsixDJ7v0DRdRGU1jRQUjtQbTzmgnX+7ywfOeJzkdrDCM1GkgIVslcYKQmulQNa+XQlSQmlfALnJIOiG+928f4eEt87FD3tTuRlKnsC9GYpYk1CfJbSsdvR5GpnhV6NxTtjM0XxJv9zqOdQ0UA5CdauO4GuGZCQB6mr22cODXxZQ/b3D5/7uabLUgvyhDbLdBoVhS+4xDvgT8wxpbn51J0WMRHuicT7JRJ7UsR/kLGvsyFZSvDWCdlWTJlA6mRQfITHHxbY0S7jRw/BDu1nB1iB6yCe0OIiT4khqJ6X6e2d9IUauO9Ev2t1bTPlKCLyMZnB0kNd0hW6mxZcc0rmu9hBln7cfISbb8v7l0nOMn3CtwfRDplHzv8ZWU7ciRnuzlQ9n9YQDMMmiY3U06GaLUn2Xg/SYH/87P3ssC9C3SMF8ppnpDhpKzu1kxdxc+4QCQaPTmwz54lp9Dy4JED7nkW+K4lkamqcDQGXmcp0tJzoT3fXY9X1t3AalZNq5PYBVJYkVZ8jENIysp6nDpXeJnuBDGfTlO5Ys58gMhMi+WEz2gExzwptI3Sk3CPYLUVAM9YVCYkUM4oOsuk59IUrtuBKvKIjToMvn97fTk4+QqJUZGIBzITLMYmifQ2kNIQ2IVSbKPVpGepJFbPkJuZp5Eo05utomv14cUgkiXN92+vsu7xip/HwRg4GTJnNIehmcYlBgZuqwSAv06WTeAm9fJ1rg8enA2wtSJHBToJsS39ZOYDf6UpHtZEYfOLqPzPIkV1XENvCnvdQESQgMSPasxNDvAyGSdYHWG/RdG6FkGoX5JqkHDihlkanQCgxq+nGRgroFmQ7IJxJYYrCsh1qJjF0mkJmF/BKMyBxIOvVCDFdZwbI3iM3sYmuVDsySaJanYVqCpoRurwubQ2d607YkZOu6mYr667UM4Ael1hh2U/KTzDIQLwpUMNQWIv+QnV+EjH9dwcwZOkcO67EwG5+jEng1iRbxr2peWlOwU2GFBqsmhc38FvlOGmV3RS83SQ0x6bBAjIyjx5wDwvRhl2AwxNEtneI5k2r0SaUC2xiVT5xIY1LCi3jXrGrCrv4qSPTYVT/eQq5QIB6596VIqtrnYZTa/fHA56UkCgG93vg+AXF+YG9d/hORpJrXPWtQ+peH6NGqfHuHpx+cT6ZJU3BvGl9SIHIRsleDgWQLflAyX3vs5cF1CusXAyRqPPrmIsldMKp4zEDZoFhSWjeCUWoi0wQuJKQzO8dO9bjLDSywmhRL8auMSL61Z76Z1+im7yA6EaXu0gfbd1Yi8xoaD9ZSv82PGNbpPe/v+XpeufMufE5F6YqIoiqIo40G6vLUnJsdvuPB4elc9MVEURVEUZWJTT0wURVEUZRxIVyLfwqico01wdiJQDRNFURRFGQ/qVc5RnfANkyMtSjdvUkgXcPImbs7EMcFFQl7gFCRuzsE1dWRBYGfyOAUTJy9wsyZuzsUxjde8+LItG9f0stDJC1xTIrMmTkEjM+J45yhIbMvBKRi45uFh63lwCkeOcXHyGm7WxAnmvfhlTdycg5N3cB2gAFpB4ubyWJkCTsH04pj14zg6bs7AzZqj8UaCncnjmib5tIVremmRNriOl27pCNycM3qMmytAXsNM24f/7+IUNC9uPgkauI7EKQgcU+Dm8ofj7s3QWkgXcE0T23JwTS8+mK/6jpyGa5reNaiBU5A4CNyc/FP68zqueTi/8gZIcA2JUwCZ/VPeOAVG88HJenlSsI6kU0OKw+fPC2zLQVguTt6BArimdz7v+zTcnO2VZSaPWzBxCsJLe17HzdnYmTyWz8K2Doc3vfNqBQmudz7b/tM+25K4pkBaXj1ycyaFtOWVqcloPUOAbXtlVjAK5I3D8S8crkcah+Pv4prCKx8pELqLk/e+N5+2cHNeeTt5w4tbNo8smFCQiIKXt1amgGOa2PbhemUaOHmQgj/V2byOkK/6f0HgZE1sB6Q8XE4FAzuTJ697cYXD9Sl3+DqQ4LqHr6m8wHXAOXL95CVuzjyc/wIn79VpJy+Q7p/yzdXk6HWaS9tIYeOYJmbaxs2ZCNPAyXr56uQ1HA1sJ38478DRvFlv3ZyFbWk4BQ3XBKcgsC0bp6Af/j+4jpdG1/ThukfiJbAtCyfv4BhgW4evT0N66ckLRN5bENY1JcLyZlTmVfXXsaR3Pfq9OutYLrbt1TPtcJ3AEbim7uWT7tVHaere9xU075rJmzgF16sTUuBYh3/O2eBCLm0fDiO9NOWFF14Ib5bnnIvU5Og1Yufy2I7AOXwvPHJfcI5cW6aLbVteWZkuCHDyGrZdwMm73jWVzWNbBraTxzmS54e3uTkX1zQOp0mO3oPcnAM4YLnYtu4db1sIp+CFLxzJZ330unVzzuGy8b7ryH3M1cG2839Kc+FPdUzkHK+u500cA9xcYfQacU3dK3PTu3d49cfx4uZ69cAp+LyyOFy3346nETbWW5pfzebEnKVWyBP1WdBh+/fvf9MLCCmKoijvbp2dnUye/NrRmMeCaZo0NDTQ09Pzls9VXV1NW1sbwWDwGMRsYjjhGyaJRIKSkhI6OjqIx+PjHZ2/SSqVoq6ujs7OTmKx2HhH529yIqQBTox0qDRMDCoNE8er01FUVMTIyAi1tbVo2vEbH2KaJoVC4S2fx+/3n1CNEngXvMo5UrHi8fg7+sIBiMViKg0TxImQDpWGiUGlYeI4ko6344/YYDB4wjUojhU1XFhRFEVRlAlDNUwURVEURZkwTviGSSAQ4Ktf/SqBQGC8o/I3U2mYOE6EdKg0TAwqDRPHiZKOE8UJ3/lVURRFUZR3jhP+iYmiKIqiKO8cqmGiKIqiKMqEoRomiqIoiqJMGKphoiiKoijKhHHCN0y+//3vU19fTzAYpLm5mU2bNo13lABYvXo173nPeygqKqKyspILLriAlpaWMWHe+973IoQY87n66qvHhOno6OCDH/wg4XCYyspKvvjFL46uy3G83XLLLa+J36xZs0b3m6bJNddcQ1lZGdFolIsuuoje3t4JE/8j6uvrX5MOIQTXXHMNMDHL4emnn+ZDH/oQtbW1CCF44IEHxuyXUvKv//qv1NTUEAqFWLFiBXv37h0TZmhoiFWrVhGLxSguLuaKK64gnU6PCbN9+3bOOOMMgsEgdXV1fPOb33xb0mBZFjfddBPz5s0jEolQW1vLZZddxqFDh8ac42hld/vtt0+INABcfvnlr4nfypUrx4SZyOUAHPXaEEJwxx13jIYZ73L4a+6nx+p+tG7dOhYtWkQgEGD69Oncc889xywdymHyBHbfffdJv98v7777bvnKK6/IK6+8UhYXF8ve3t7xjpo855xz5Jo1a+SOHTvktm3b5Ac+8AE5ZcoUmU6nR8MsX75cXnnllbK7u3v0k0wmR/fbti3nzp0rV6xYIbdu3SofeughWV5eLm+++ea3JQ1f/epX5Zw5c8bEr7+/f3T/1VdfLevq6uSTTz4pX3jhBXnqqafKZcuWTZj4H9HX1zcmDY8//rgE5Nq1a6WUE7McHnroIfnP//zP8re//a0E5P333z9m/+233y7j8bh84IEH5EsvvSQ//OEPy4aGBpnL5UbDrFy5Ui5YsEA+//zz8plnnpHTp0+Xl1566ej+ZDIpq6qq5KpVq+SOHTvkvffeK0OhkPzRj3503NOQSCTkihUr5P/+7//K3bt3yw0bNsglS5bIxYsXjznH1KlT5a233jqmbF59DY1nGqSU8uMf/7hcuXLlmPgNDQ2NCTORy0FKOSbu3d3d8u6775ZCCLlv377RMONdDn/N/fRY3I/2798vw+Gw/MIXviB37twpv/e970ld1+UjjzxyTNKheE7ohsmSJUvkNddcM/p/x3FkbW2tXL169TjG6uj6+vokIP/4xz+Oblu+fLn83Oc+97rHPPTQQ1LTNNnT0zO67a677pKxWEzm8/njGV0ppdcwWbBgwVH3JRIJ6fP55K9//evRbbt27ZKA3LBhg5Ry/OP/ej73uc/JxsZG6bqulHLil8Of/zJxXVdWV1fLO+64Y3RbIpGQgUBA3nvvvVJKKXfu3CkBuXnz5tEwDz/8sBRCyK6uLimllD/4wQ9kSUnJmDTcdNNNsqmp6bin4Wg2bdokAdne3j66berUqfJb3/rW6x4z3mn4+Mc/Ls8///zXPeadWA7nn3++POuss8Zsm0jlIOVr76fH6n70pS99Sc6ZM2fMd11yySXynHPOOS7peLc6YV/lFAoFtmzZwooVK0a3aZrGihUr2LBhwzjG7OiSySQApaWlY7b/8pe/pLy8nLlz53LzzTeTzWZH923YsIF58+ZRVVU1uu2cc84hlUrxyiuvvC3x3rt3L7W1tUybNo1Vq1bR0dEBwJYtW7Asa0z+z5o1iylTpozm/0SI/58rFAr84he/4J/+6Z8QQoxun+jl8GptbW309PSMyft4PE5zc/OYvC8uLuaUU04ZDbNixQo0TWPjxo2jYc4880z8fv9omHPOOYeWlhaGh4ffptT8STKZRAhBcXHxmO233347ZWVlLFy4kDvuuGPMo/eJkIZ169ZRWVlJU1MTn/70pxkcHBwTv3dSOfT29vLggw9yxRVXvGbfRCqHP7+fHqv70YYNG8ac40iYifg75Z3shF3Eb2BgAMdxxlQygKqqKnbv3j1OsTo613X5/Oc/z2mnncbcuXNHt//DP/wDU6dOpba2lu3bt3PTTTfR0tLCb3/7WwB6enqOmr4j+4635uZm7rnnHpqamuju7uZrX/saZ5xxBjt27KCnpwe/3/+aXyJVVVWjcRvv+B/NAw88QCKR4PLLLx/dNtHL4c8d+c6jxenVeV9ZWTlmv2EYlJaWjgnT0NDwmnMc2VdSUnJc4n80pmly0003cemll45ZLO66665j0aJFlJaW8txzz3HzzTfT3d3Nf/7nf06INKxcuZILL7yQhoYG9u3bx//5P/+Hc889lw0bNqDr+juuHH72s59RVFTEhRdeOGb7RCqHo91Pj9X96PXCpFIpcrkcoVDomKXj3eyEbZi8k1xzzTXs2LGDZ599dsz2q666avTnefPmUVNTw9lnn82+fftobGx8u6P5Gueee+7oz/Pnz6e5uZmpU6fyq1/96h17gf70pz/l3HPPpba2dnTbRC+HE51lWVx88cVIKbnrrrvG7PvCF74w+vP8+fPx+/186lOfYvXq1RNievGPfvSjoz/PmzeP+fPn09jYyLp16zj77LPHMWZ/m7vvvptVq1a9ZlXciVQOr3c/Vd45TthXOeXl5ei6/ppe1729vVRXV49TrF7r2muv5Q9/+ANr165l8uTJbxi2ubkZgNbWVgCqq6uPmr4j+95uxcXFzJw5k9bWVqqrqykUCiQSidfE70jcJlr829vbeeKJJ/jkJz/5huEmejkc+c43qvvV1dX09fWN2W/bNkNDQxOqfI40Strb23n88cfHPC05mubmZmzb5sCBA8DESMOrTZs2jfLy8jF1551QDgDPPPMMLS0tf/H6gPErh9e7nx6r+9HrhYnFYu/YP8YmohO2YeL3+1m8eDFPPvnk6DbXdXnyySdZunTpOMbMI6Xk2muv5f777+epp556zWPOo9m2bRsANTU1ACxdupSXX355zI3tyM37pJNOOi7xfiPpdJp9+/ZRU1PD4sWL8fl8Y/K/paWFjo6O0fyfaPFfs2YNlZWVfPCDH3zDcBO9HBoaGqiurh6T96lUio0bN47J+0QiwZYtW0bDPPXUU7iuO9rwWrp0KU8//TSWZY2Gefzxx2lqanpbXh8caZTs3buXJ554grKysr94zLZt29A0bfT1yHin4c8dPHiQwcHBMXVnopfDET/96U9ZvHgxCxYs+Ith3+5y+Ev302N1P1q6dOmYcxwJMxF+p5xQxrnz7XF13333yUAgIO+55x65c+dOedVVV8ni4uIxva7Hy6c//WkZj8flunXrxgyxy2azUkopW1tb5a233ipfeOEF2dbWJn/3u9/JadOmyTPPPHP0HEeGt73//e+X27Ztk4888oisqKh424bb3nDDDXLdunWyra1Nrl+/Xq5YsUKWl5fLvr4+KaU3PG/KlCnyqaeeki+88IJcunSpXLp06YSJ/6s5jiOnTJkib7rppjHbJ2o5jIyMyK1bt8qtW7dKQP7nf/6n3Lp16+iIldtvv10WFxfL3/3ud3L79u3y/PPPP+pw4YULF8qNGzfKZ599Vs6YMWPMMNVEIiGrqqrkP/7jP8odO3bI++67T4bD4WM2xPON0lAoFOSHP/xhOXnyZLlt27Yx18iRERLPPfec/Na3viW3bdsm9+3bJ3/xi1/IiooKedlll02INIyMjMgbb7xRbtiwQba1tcknnnhCLlq0SM6YMUOapjl6jolcDkckk0kZDoflXXfd9ZrjJ0I5/KX7qZTH5n50ZLjwF7/4Rblr1y75/e9/Xw0XPg5O6IaJlFJ+73vfk1OmTJF+v18uWbJEPv/88+MdJSmlNyzvaJ81a9ZIKaXs6OiQZ555piwtLZWBQEBOnz5dfvGLXxwzf4aUUh44cECee+65MhQKyfLycnnDDTdIy7LeljRccsklsqamRvr9fjlp0iR5ySWXyNbW1tH9uVxOfuYzn5ElJSUyHA7Lv//7v5fd3d0TJv6v9uijj0pAtrS0jNk+Ucth7dq1R60/H//4x6WU3pDhr3zlK7KqqkoGAgF59tlnvyZtg4OD8tJLL5XRaFTGYjH5iU98Qo6MjIwJ89JLL8nTTz9dBgIBOWnSJHn77be/LWloa2t73WvkyPwyW7Zskc3NzTIej8tgMChnz54tb7vttjG/9MczDdlsVr7//e+XFRUV0ufzyalTp8orr7zyNX8YTeRyOOJHP/qRDIVCMpFIvOb4iVAOf+l+KuWxux+tXbtWnnzyydLv98tp06aN+Q7l2BBSSnmcHsYoiqIoiqK8KSdsHxNFURRFUd55VMNEURRFUZQJQzVMFEVRFEWZMFTDRFEURVGUCUM1TBRFURRFmTBUw0RRFEVRlAlDNUwURVEURZkwVMNEUSYQKSVXXXUVpaWlCCFGp79XFEV5t1ATrCnKBPLwww9z/vnns27dutEF3wzjrS0Cfvnll5NIJHjggQeOTSQVRVGOo7d2x1MU5Zg6sgjismXLxjsqr+E4DkIINE09aFUU5fhRdxhFmSAuv/xyPvvZz9LR0YEQgvr6elzXZfXq1TQ0NBAKhViwYAG/+c1vRo9xHIcrrrhidH9TUxPf+c53Rvffcsst/OxnP+N3v/sdQgiEEKxbt45169YhhBizDPy2bdsQQowuVX/PPfdQXFzM73//e0466SQCgQAdHR3k83luvPFGJk2aRCQSobm5mXXr1o2ep729nQ996EOUlJQQiUSYM2cODz300PHOPkVRThDqiYmiTBDf+c53aGxs5Mc//jGbN29G13VWr17NL37xC374wx8yY8YMnn76aT72sY9RUVHB8uXLcV2XyZMn8+tf/5qysjKee+45rrrqKmpqarj44ou58cYb2bVrF6lUijVr1gBQWlrKc88991fFKZvN8o1vfIP/+q//oqysjMrKSq699lp27tzJfffdR21tLffffz8rV67k5ZdfZsaMGVxzzTUUCgWefvppIpEIO3fuJBqNHs+sUxTlBKIaJooyQcTjcYqKitB1nerqavL5PLfddhtPPPEES5cuBWDatGk8++yz/OhHP2L58uX4fD6+9rWvjZ6joaGBDRs28Ktf/YqLL76YaDRKKBQin89TXV39puNkWRY/+MEPWLBgAQAdHR2sWbOGjo4OamtrAbjxxht55JFHWLNmDbfddhsdHR1cdNFFzJs3bzTOiqIofy3VMFGUCaq1tZVsNsv73ve+MdsLhQILFy4c/f/3v/997r77bjo6OsjlchQKBU4++eRjEge/38/8+fNH///yyy/jOA4zZ84cEy6fz1NWVgbAddddx6c//Wkee+wxVqxYwUUXXTTmHIqiKG9ENUwUZYJKp9MAPPjgg0yaNGnMvkAgAMB9993HjTfeyJ133snSpUspKirijjvuYOPGjW947iMdWF89KM+yrNeEC4VCCCHGxEnXdbZs2YKu62PCHnld88lPfpJzzjmHBx98kMcee4zVq1dz55138tnPfvavTbqiKO9iqmGiKBPUqzucLl++/Khh1q9fz7Jly/jMZz4zum3fvn1jwvj9fhzHGbOtoqICgO7ubkpKSgD+qjlTFi5ciOM49PX1ccYZZ7xuuLq6Oq6++mquvvpqbr75Zn7yk5+ohomiKH8V1TBRlAmqqKiIG2+8keuvvx7XdTn99NNJJpOsX7+eWCzGxz/+cWbMmMHPf/5zHn30URoaGvjv//5vNm/eTENDw+h56uvrefTRR2lpaaGsrIx4PM706dOpq6vjlltu4d///d/Zs2cPd95551+M08yZM1m1ahWXXXYZd955JwsXLqS/v58nn3yS+fPn88EPfpDPf/7znHvuucycOZPh4WHWrl3L7Nmzj2dWKYpyAlHDhRVlAvu3f/s3vvKVr7B69Wpmz57NypUrefDBB0cbHp/61Ke48MILueSSS2hubmZwcHDM0xOAK6+8kqamJk455RQqKipYv349Pp+Pe++9l927dzN//ny+8Y1v8PWvf/2vitOaNWu47LLLuOGGG2hqauKCCy5g8+bNTJkyBfCGMF9zzTWj8Z05cyY/+MEPjm3GKIpywlIzvyqKoiiKMmGoJyaKoiiKokwYqmGiKIqiKMqEoRomiqIoiqJMGKphoiiKoijKhKEaJoqiKIqiTBiqYaIoiqIoyoShGiaKoiiKokwYqmGiKIqiKMqEoRomiqIoiqJMGKphoiiKoijKhKEaJoqiKIqiTBiqYaIoiqIoyoTx/wMSuTGSJWrTjQAAAABJRU5ErkJggg==", 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", 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" ] @@ -827,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 16, "id": "1c9df452", "metadata": {}, "outputs": [ @@ -866,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 17, "id": "ef4e8516", "metadata": {}, "outputs": [ @@ -914,14 +887,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "834a4209", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SVC(class_weight='balanced', kernel='linear')
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