{ "cells": [ { "cell_type": "markdown", "id": "54e73c60", "metadata": {}, "source": [ "# Functional connectivity with [`nilearn`](http://nilearn.github.io)" ] }, { "cell_type": "code", "execution_count": 1, "id": "ce1cef2f", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "69e37943", "metadata": {}, "source": [ "In this tutorial, we'll see how the Python library `nilearn` allows us to easily perform machine learning analyses with neuroimaging data,\n", "specifically functional magnetic resonance imaging (fMRI).\n", "\n", "You may notice that the name `nilearn` is reminiscent of [`scikit-learn`](https://scikit-learn.org),\n", "a popular Python library for machine learning.\n", "This is no accident!\n", "Nilearn and scikit-learn were created by the same team,\n", "and nilearn is designed to bring machine **LEARN**ing to the NeuroImaging (**NI**) domain.\n", "\n", "When performing a machine learning analysis, our data often look something like this:" ] }, { "cell_type": "code", "execution_count": 2, "id": "046ef3cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sitesubjectageage_residsexgroupfsArea_L_V1_ROIfsArea_L_MST_ROIfsArea_L_V6_ROIfsArea_L_V2_ROI...fsCT_R_p47r_ROIfsCT_R_TGv_ROIfsCT_R_MBelt_ROIfsCT_R_LBelt_ROIfsCT_R_A4_ROIfsCT_R_STSva_ROIfsCT_R_TE1m_ROIfsCT_R_PI_ROIfsCT_R_a32pr_ROIfsCT_R_p24_ROI
0ABIDEII-KKI_1292938.89315113.6428522.01.02750.0306.0354.02123.0...3.3622.8272.7772.5263.2023.0243.3542.6292.6993.179
1ABIDEII-OHSU_12899712.00000016.0817322.01.02836.0186.0354.02261.0...2.8093.5392.9442.7693.5303.0793.2822.6702.7463.324
2ABIDEII-GU_1288458.39000012.8662641.02.03394.0223.0373.02827.0...2.4353.3212.7992.3883.1483.1253.1162.8912.9403.232
3ABIDEII-NYU_1292108.30000013.6981391.01.03382.0266.0422.02686.0...3.3493.3442.6943.0303.2582.7743.3832.6963.0143.264
4ABIDEII-EMC_1298947.77275814.7724592.02.03080.0161.0346.02105.0...2.4282.9402.8092.6073.4302.7522.6453.1113.2194.128
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5 rows × 1446 columns

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" ], "text/plain": [ " site subject age age_resid sex group fsArea_L_V1_ROI \\\n", "0 ABIDEII-KKI_1 29293 8.893151 13.642852 2.0 1.0 2750.0 \n", "1 ABIDEII-OHSU_1 28997 12.000000 16.081732 2.0 1.0 2836.0 \n", "2 ABIDEII-GU_1 28845 8.390000 12.866264 1.0 2.0 3394.0 \n", "3 ABIDEII-NYU_1 29210 8.300000 13.698139 1.0 1.0 3382.0 \n", "4 ABIDEII-EMC_1 29894 7.772758 14.772459 2.0 2.0 3080.0 \n", "\n", " fsArea_L_MST_ROI fsArea_L_V6_ROI fsArea_L_V2_ROI ... fsCT_R_p47r_ROI \\\n", "0 306.0 354.0 2123.0 ... 3.362 \n", "1 186.0 354.0 2261.0 ... 2.809 \n", "2 223.0 373.0 2827.0 ... 2.435 \n", "3 266.0 422.0 2686.0 ... 3.349 \n", "4 161.0 346.0 2105.0 ... 2.428 \n", "\n", " fsCT_R_TGv_ROI fsCT_R_MBelt_ROI fsCT_R_LBelt_ROI fsCT_R_A4_ROI \\\n", "0 2.827 2.777 2.526 3.202 \n", "1 3.539 2.944 2.769 3.530 \n", "2 3.321 2.799 2.388 3.148 \n", "3 3.344 2.694 3.030 3.258 \n", "4 2.940 2.809 2.607 3.430 \n", "\n", " fsCT_R_STSva_ROI fsCT_R_TE1m_ROI fsCT_R_PI_ROI fsCT_R_a32pr_ROI \\\n", "0 3.024 3.354 2.629 2.699 \n", "1 3.079 3.282 2.670 2.746 \n", "2 3.125 3.116 2.891 2.940 \n", "3 2.774 3.383 2.696 3.014 \n", "4 2.752 2.645 3.111 3.219 \n", "\n", " fsCT_R_p24_ROI \n", "0 3.179 \n", "1 3.324 \n", "2 3.232 \n", "3 3.264 \n", "4 4.128 \n", "\n", "[5 rows x 1446 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# read_csv can read in just about any plain-text tabular data\n", "data = pd.read_csv('./data/abide2.tsv', sep='\\t')\n", "data.head()" ] }, { "cell_type": "markdown", "id": "12640014", "metadata": {}, "source": [ "For our purposes, what's most interesting is the structure of this data set.\n", "That is, the data is structured in a tabular format,\n", "with pre-extracted features of interest.\n", "This makes it easier to consider issues such as: which features would we like to predict?\n", "Or, how should we handle cross-validation?\n", "\n", "But if we're starting with neuroimaging data, how can we create this kind of structured representation?\n", "\n", "To understand why neuroimaging data needs some special attention,\n", "we will briefly talk about the basics of fMRI.\n", "\n", "## A brief introduction to functional magnetic resonance imaging\n", "\n", "Functional magnetic resonance imaging (fMRI) is a type of neuroimaging technique that measures brain activity.\n", "fMRI data is made up of a series of 3D images (or volumes) of the brain collected at a given frequency.\n", "Therefore, a typical fMRI file is a 4D image, with the spatial dimensions (x, y, z) added with the dimension of time t.\n", "We could, for example, acquire 1 brain volume every 2 seconds, for 6 minutes, which would result in an fMRI data file consisting of 180 3D brain volumes." ] }, { "cell_type": "code", "execution_count": 3, "id": "5266a20a", "metadata": { "tags": [ "remove-output", "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Added README.md to ./nilearn_data\n", "\n", "\n", "Dataset created in ./nilearn_data/haxby2001\n", "\n", "Downloading data from https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (0 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (0 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "Downloaded 121053184 of 291168628 bytes (41.6%, 1.4s remaining)" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "Downloaded 283607040 of 291168628 bytes (97.4%, 0.1s remaining) ...done. (2 seconds, 0 min)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Extracting data from ./nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..." ] }, { "name": "stderr", "output_type": "stream", "text": [ ".. done.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from nilearn import datasets\n", "from nilearn import maskers\n", "import matplotlib.pyplot as plt\n", "\n", "# change this to the location where you want the data to get downloaded\n", "data_dir = './nilearn_data'\n", "\n", "# Let's extract timeseries from one subject in a dataset\n", "haxby_dataset = datasets.fetch_haxby(data_dir=data_dir)\n", "haxby_func_filename = haxby_dataset.func[0]\n", "\n", "# initialise a masker\n", "brain_masker = maskers.NiftiMasker(\n", " smoothing_fwhm=6,\n", " detrend=True, standardize=True,\n", " low_pass=0.1, high_pass=0.01, t_r=2,\n", " memory='nilearn_cache', memory_level=1, verbose=0)\n", "\n", "# Apply masker\n", "brain_time_series = brain_masker.fit_transform(haxby_func_filename,\n", " confounds=None)\n", "# Function for visualising 3D voxel\n", "def expand_coordinates(indices):\n", " x, y, z = indices\n", " x[1::2, :, :] += 1\n", " y[:, 1::2, :] += 1\n", " z[:, :, 1::2] += 1\n", " return x, y, z\n", "\n", "def explode(data):\n", " shape_arr = np.array(data.shape)\n", " size = shape_arr[:3]*2 - 1\n", " exploded = np.zeros(np.concatenate([size, shape_arr[3:]]), dtype=data.dtype)\n", " exploded[::2, ::2, ::2] = data\n", " return exploded\n", "\n", "# Initialise a figure\n", "fig = plt.figure(figsize=(10,3))\n", "\n", "# Visualise the voxel\n", "ax1 = fig.add_subplot(1, 2, 1, projection='3d')\n", "ax1.set_xlabel(\"x\")\n", "ax1.set_ylabel(\"y\")\n", "ax1.set_zlabel(\"z\")\n", "ax1.grid(False)\n", "colors = np.array([[['#1f77b430']*1]*1]*1)\n", "colors = explode(colors)\n", "filled = explode(np.ones((1, 1, 1)))\n", "x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))\n", "\n", "x[1::2, :, :] += 1\n", "y[:, 1::2, :] += 1\n", "z[:, :, 1::2] += 1\n", "\n", "ax1.voxels(x, y, z, filled, facecolors=colors, edgecolors='white', shade=False)\n", "plt.title(\"Voxel (3D)\")\n", "\n", "\n", "# Add timeseries to figure\n", "# random voxel\n", "voxel = 1\n", "ax = fig.add_subplot(1, 2, 2)\n", "ax.plot(brain_time_series[:, voxel])\n", "ax.set_title(\"Timeseriese of a voxel\")\n", "\n", "plt.xlabel(\"Time (s)\", fontsize = 10)\n", "plt.ylabel(\"BOLD signal\", fontsize= 10)\n", "\n", "from myst_nb import glue\n", "glue(\"voxel-timeseries-fig\", fig, display=False)" ] }, { "cell_type": "markdown", "id": "889b6587", "metadata": {}, "source": [ "```{glue:figure} voxel-timeseries-fig\n", ":figwidth: 800px\n", ":name: \"voxel-timeseries-fig\"\n", "Illustration of a volume (voxel), size 3 mm x 3 mm x 3 mm, and the associated fMRI time course.\n", "```\n", "\n", "A 3D brain volume is formed by several thousand voxels, which are small units of volumes having a coordinate in x, y, z space.\n", "In fMRI, for each voxel of the brain, we have several points of measurement of the activity over time, which forms what is called a time series or time course.\n", "The time series reflects changes in neuronal activity over time indirectly through the blood delivering energy to activate the neurons, a mechanism called the haemodynamic response.\n", "This activity creates a contrast between oxygenated and deoxygenated blood around a population of neurons detectable by the magnetic field, called the blood-oxygen-level-dependent (BOLD) signal." ] }, { "cell_type": "code", "execution_count": 4, "id": "c3bb54db", "metadata": { "tags": [ "remove-output", "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# To get an impulse response, we simulate a single event\n", "# occurring at time t=0, with duration 1s.\n", "frame_times = np.linspace(0, 30, 61)\n", "onset, amplitude, duration = 0., 1., 1.\n", "exp_condition = np.array((onset, duration, amplitude)).reshape(3, 1)\n", "stim = np.zeros_like(frame_times)\n", "stim[(frame_times > onset) * (frame_times <= onset + duration)] = amplitude\n", "\n", "# Now we plot the hrf\n", "from nilearn.glm.first_level import compute_regressor\n", "fig = plt.figure(figsize=(6, 4))\n", "\n", "# obtain the signal of interest by convolution\n", "signal, name = compute_regressor(\n", " exp_condition, 'glover', frame_times, con_id='main',\n", " oversampling=16)\n", "\n", "# plot this\n", "plt.fill(frame_times, stim, 'b', alpha=.5, label='stimulus')\n", "plt.plot(frame_times, signal.T[0], 'r', label=name[0])\n", "\n", "# Glue the figure\n", "glue(\"hrf-fig\", fig, display=False)" ] }, { "cell_type": "markdown", "id": "a6994050", "metadata": {}, "source": [ "```{glue:figure} hrf-fig\n", ":figwidth: 800px\n", ":name: \"hrf-fig\"\n", "Hemodynamic response to a unit pulse of one second duration, following the model proposed by {cite:p}`Glover_1999`. The code generated this figure is adopted from a [tutorial](https://nilearn.github.io/stable/auto_examples/04_glm_first_level/plot_hrf.html#sphx-glr-auto-examples-04-glm-first-level-plot-hrf-py) in Nilearn, et la. The figure is licenced under CC-BY.\n", "```\n", "\n", "## Neuroimaging data\n", "\n", "Neuroimaging data does not have a tabular structure.\n", "Instead, it has both spatial and temporal dependencies between successive data points.\n", "That is, knowing _where_ and _when_ something was measured tells you information about the surrounding data points.\n", "\n", "We also know that neuroimaging data contains a lot of noise that's not BOLD signal, such as head motion.\n", "Since we don't think that these other noise sources are related to neuronal firing,\n", "we often need to consider how we can make sure that our analyses are not driven by these noise sources.\n", "\n", "These are all considerations that most machine learning software libraries are not designed to deal with!\n", "Nilearn therefore plays a crucial role in bringing machine learning concepts to the neuroimaging domain.\n", "\n", "To get a sense of the problem, the quickest method is to just look at some data.\n", "You may have your own data locally that you'd like to work with.\n", "Nilearn also provides access to several neuroimaging data sets and atlases (we'll talk about these a bit later).\n", "These datasets are great for developing your analysis workflow and testing new features!\n", "\n", "These data sets (and atlases) are only accessible because research groups chose to make their collected data publicly available.\n", "We owe them a huge thank you for this!\n", "The data set we'll use today was originally collected by [Rebecca Saxe](https://mcgovern.mit.edu/profile/rebecca-saxe/)'s group at MIT and hosted on [OpenNeuro](https://openneuro.org/datasets/ds000228/versions/1.1.0).\n", "\n", "The nilearn team preprocessed the data set with [fMRIPrep](https://fmriprep.readthedocs.io) and downsampled it to a lower resolution,\n", "so it'd be easier to work with.\n", "We can learn a lot about this data set directly [from the Nilearn documentation](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_development_fmri.html).\n", "For example, we can see that this data set contains over 150 children and adults watching a short Pixar film.\n", "Let's download the first 5 participants." ] }, { "cell_type": "code", "execution_count": 5, "id": "bd1b6ddf", "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dataset created in ./nilearn_data/development_fmri\n", "\n", "\n", "Added README.md to ./nilearn_data/development_fmri\n", "\n", "\n", "Dataset created in ./nilearn_data/development_fmri/development_fmri\n", "\n", "Downloading data from https://osf.io/yr3av/download ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff3df4712b400183b7092/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (2 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff3e04712b400193b5bdf/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff37da743a90018606df1/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff37c2286e80019c3c102/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (2 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5cb4701e3992690018133d4f/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5cb46e6b353c58001b9cb34f/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff37d4712b400193b5b54/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://osf.io/download/5c8ff37d4712b400183b7011/ ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. 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(1 seconds, 0 min)\n" ] } ], "source": [ "# change this to the location where you want the data to get downloaded\n", "data_dir = './nilearn_data'\n", "# Now fetch the data\n", "development_dataset = datasets.fetch_development_fmri(n_subjects=5,\n", " data_dir=data_dir, \n", " reduce_confounds = False\n", " )" ] }, { "cell_type": "markdown", "id": "6786f09f", "metadata": {}, "source": [ "Now, this `development_dataset` object has several attributes which provide access to relevant information.\n", "For example, `development_dataset.phenotypic` provides access to information about the participants, such as whether they were children or adults.\n", "We can use `development_dataset.func` to access the functional MRI (fMRI) data.\n", "\n", "Let's use [`nilearn.image.load_img`](https://nilearn.github.io/stable/modules/generated/nilearn.image.load_img.html) to learn a little bit about this data:" ] }, { "cell_type": "code", "execution_count": 6, "id": "d9b80d98", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(50, 59, 50, 168)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from nilearn import image\n", "\n", "img = image.load_img(development_dataset.func[0])\n", "img.shape" ] }, { "cell_type": "markdown", "id": "afc383a4", "metadata": {}, "source": [ "This means that there are 168 volumes, each with a 3D structure of (50, 59, 50).\n", "\n", "### Getting into the data: subsetting and viewing\n", "\n", "Nilearn also provides many methods for plotting this kind of data.\n", "For example, we can use [`nilearn.plotting.view_img`](https://nilearn.github.io/stable/modules/generated/nilearn.plotting.view_img.html) to launch at interactive viewer.\n", "Because each fMRI run is a 4D time series (three spatial dimensions plus time),\n", "we'll also need to subset the data when we plot it, so that we can look at a single 3D image.\n", "Nilearn provides (at least) two ways to do this: with [`nilearn.image.index_img`](https://nilearn.github.io/stable/modules/generated/nilearn.image.index_img.html),\n", "which allows us to index a particular frame--or several frames--of a time series,\n", "and [`nilearn.image.mean_img`](https://nilearn.github.io/stable/modules/generated/nilearn.image.mean_img.html),\n", "which allows us to take the mean 3D image over time.\n", "\n", "Putting these together, we can interactively view the mean image of the first participant using:" ] }, { "cell_type": "code", "execution_count": 7, "id": "40abeb41", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from nilearn import plotting\n", "\n", "mean_image = image.mean_img(development_dataset.func[0])\n", "plotting.view_img(mean_image, threshold=None)" ] }, { "cell_type": "markdown", "id": "95a6359c", "metadata": {}, "source": [ "## Extracting signal from fMRI volumes\n", "\n", "As you can see, this data is decidedly not tabular!\n", "What we'd like is to extract and transform meaningful features from this data,\n", "and store it in a format that we can easily work with.\n", "Importantly, we _could_ work with the full time series directly.\n", "But we often want to reduce the dimensionality of our data in a structured way.\n", "That is, we may only want to consider signal within certain learned or pre-defined regions of interest (ROIs),\n", "while taking into account known sources of noise.\n", "To do this, we'll use nilearn's Masker objects.\n", "What are the masker objects ?\n", "First, let's think about what masking fMRI data is doing:\n", "\n", "```{figure} ../images/masking.jpg\n", "---\n", "height: 350px\n", "name: masking\n", "---\n", "Masking fMRI data.\n", "```\n", "\n", "Essentially, we can imagine overlaying a 3D grid on an image.\n", "Then, our mask tells us which cubes or \"voxels\" (like 3D pixels) to sample from.\n", "Since our Nifti images are 4D files, we can’t overlay a single grid –\n", "instead, we use a series of 3D grids (one for each volume in the 4D file),\n", "so we can get a measurement for each voxel at each timepoint.\n", "\n", "Masker objects allow us to apply these masks!\n", "To start, we need to define a mask (or masks) that we'd like to apply.\n", "This could correspond to one or many regions of interest.\n", "Nilearn provides methods to define your own functional parcellation (using clustering algorithms such as _k-means_),\n", "and it also provides access to other atlases that have previously been defined by researchers.\n", "\n", "### Choosing regions of interest\n", "\n", "Nilearn ships with several atlases commonly used in the field,\n", "including the Schaefer atlas and the Harvard-Oxford atlas.\n", "\n", "In this tutorial,\n", "we'll use the MSDL (multi-subject dictionary learning; {cite:p}`Varoquaux_2011`) atlas,\n", "which defines a set of _probabilistic_ ROIs across the brain." ] }, { "cell_type": "code", "execution_count": 8, "id": "9f9f14a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dataset created in ./nilearn_data/msdl_atlas\n", "\n", "Downloading data from https://team.inria.fr/parietal/files/2015/01/MSDL_rois.zip ...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MSDL has 39 ROIs, part of the following networks :\n", "['Ant IPS' 'Aud' 'Basal' 'Cereb' 'Cing-Ins' 'D Att' 'DMN' 'Dors PCC'\n", " 'L V Att' 'Language' 'Motor' 'Occ post' 'R V Att' 'Salience' 'Striate'\n", " 'Temporal' 'Vis Sec'].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " ...done. (1 seconds, 0 min)\n", "Extracting data from ./nilearn_data/msdl_atlas/8eaecb9e05c478f565847000d9902a25/MSDL_rois.zip..... done.\n" ] } ], "source": [ "msdl_atlas = datasets.fetch_atlas_msdl(data_dir=data_dir)\n", "\n", "msdl_coords = msdl_atlas.region_coords\n", "n_regions = len(msdl_coords)\n", "\n", "print(f'MSDL has {n_regions} ROIs, part of the following networks :\\n{np.unique(msdl_atlas.networks)}.')" ] }, { "cell_type": "markdown", "id": "83ad0364", "metadata": {}, "source": [ "It also provides us with easy ways to view these atlases directly.\n", "Because MSDL is a probabilistic atlas, we can view it using:" ] }, { "cell_type": "code", "execution_count": 9, "id": "8cecaa59", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotting.plot_prob_atlas(msdl_atlas.maps)" ] }, { "cell_type": "markdown", "id": "c93bc7d4", "metadata": {}, "source": [ "::::{admonition} Different type of brain parcellation schemes\n", ":class: dropdown, seealso\n", "\n", "There are various ways of defining brain parcels.\n", "Largely we can classify them in two ways:\n", "\n", "- Functional vs anatomical\n", "\n", " - Anatomical atlases use structural land marks to separate regions. Example: [Harvard-Oxford atlas](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_atlas_harvard_oxford.html)\n", " - Functional atlases define regions based on the organisation of fMRI signal, such as functional connectivity. Example: [Schaefer atlas](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_atlas_schaefer_2018.html)\n", "\n", "- Probablility vs discrete segmentation \n", "\n", " - Probabilistic segmentation: use contiunous, non-zero values to define parcels.\n", " Example: [Difumo (functional)](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_atlas_difumo.html#nilearn.datasets.fetch_atlas_difumo),\n", " [Hammersmith (anatomical)](https://pubmed.ncbi.nlm.nih.gov/12874777/)\n", " - Discrete segmentation: parcels that have rigid boundaries. Example: [BASC (functional)](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_atlas_basc_multiscale_2015.html#nilearn.datasets.fetch_atlas_basc_multiscale_2015)\n", "\n", "It's important to understand the method used for constructing the atlas of choice.\n", "For example, using a anatomical atlas to extract signal from functional data might not be the best thing.\n", "To find out more, [watch this lecture from Brainhack School by Dr Pierre Bellec on brain parcellation in fMRI](https://www.youtube.com/watch?v=7uMVRebuDZo).\n", "::::\n", "\n", "\n", "### Applying a Masker object\n", "\n", "We'd like to supply these ROIs to a Masker object.\n", "All Masker objects share the same basic structure and functionality,\n", "but each is designed to work with a different kind of ROI.\n", "\n", "The canonical [`nilearn.maskers.NiftiMasker`](https://nilearn.github.io/stable/modules/generated/nilearn.maskers.NiftiMasker.html) works well if we want to apply a single mask to the data,\n", "like a single region of interest.\n", "\n", "But what if we actually have several ROIs that we'd like to apply to the data all at once?\n", "If these ROIs are non-overlapping,\n", "as in discrete parcellations,\n", "then we can use [`nilearn.maskers.NiftiLabelsMasker`](https://nilearn.github.io/stable/modules/generated/nilearn.maskers.NiftiLabelsMasker.html).\n", "Because we're working with probabilistic ROIs,\n", "we can instead supply these ROIs to [`nilearn.maskers.NiftiMapsMasker`](https://nilearn.github.io/stable/modules/generated/nilearn.maskers.NiftiMapsMasker.html).\n", "\n", ":::{admonition} Further reading on Maskers\n", ":class: seealso\n", "For a full list of the available Masker objects,\n", "see [the Nilearn documentation](https://nilearn.github.io/stable/modules/maskers.html).\n", "\n", "To learn more about the concept, `nilearn` provides a great [tutorial](https://nilearn.github.io/stable/manipulating_images/masker_objects.html).\n", ":::\n", "\n", "### Mask your data!\n", "\n", "We can supply our MSDL atlas-defined ROIs to the `NiftiMapsMasker` object,\n", "along with resampling, filtering, and detrending parameters." ] }, { "cell_type": "code", "execution_count": 10, "id": "6f463ef7", "metadata": {}, "outputs": [], "source": [ "masker = maskers.NiftiMapsMasker(\n", " msdl_atlas.maps, resampling_target=\"data\", detrend=True).fit()" ] }, { "cell_type": "markdown", "id": "fd84983b", "metadata": {}, "source": [ "One thing you might notice from the above code is that immediately after defining the masker object,\n", "we call the `.fit` method on it.\n", "This method may look familiar if you've previously worked with scikit-learn estimators!\n", "\n", "You'll note that we're not supplying any data to this `.fit` method;\n", "that's because we're fitting the Masker to the provided ROIs, rather than to our data.\n", "\n", "### Dimensions, dimensions\n", "\n", "We can use this fitted masker to transform our data." ] }, { "cell_type": "code", "execution_count": 11, "id": "70f1a8f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(168, 39)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roi_time_series = masker.transform(development_dataset.func[0])\n", "roi_time_series.shape" ] }, { "cell_type": "markdown", "id": "1c0c3ba3", "metadata": {}, "source": [ "If you remember, when we first looked at the data, its original dimensions were (50, 59, 50, 168).\n", "Now, it has a shape of (168, 39).\n", "What happened?!\n", "\n", "Rather than providing information on every voxel within our original 3D grid,\n", "we're now only considering those voxels that fall in our 39 regions of interest provided by the MSDL atlas and aggregating across voxels within those ROIS.\n", "This reduces each 3D volume from a dimensionality of (50, 59, 50) to just 39,\n", "for our 39 provided ROIs.\n", "\n", "You'll also see that the dimensions \"flipped.\"\n", "That is, we've transposed the matrix such that time is now the first rather than second dimension.\n", "This follows the scikit-learn convention that rows are _samples_\n", "and columns are _features_ in a data matrix.\n", "\n", "```{figure} ../images/samples-features.png\n", "---\n", "height: 250px\n", "name: samples-features\n", "---\n", "The scikit-learn conventions for feature and target matrices.\n", "From Jake VanderPlas's _Python Data Science Handbook_.\n", "```\n", "\n", "One of the nice things about working with nilearn is that it will impose this convention for you,\n", "so you don't accidentally flip your dimensions when using a scikit-learn model!\n", "\n", "## Creating and viewing a connectome\n", "\n", "Scientists have found that some cognitive functions involve the activity of neurons from an isolated region, and sometimes different regions of the brain interact together to perform a task.\n", "This functional integration leads to a description of the functional brain as a network.\n", "Formally, the co-activation of different time series is called functional connectivity.\n", "\n", "The simplest and most commonly used kind of functional connectivity is pairwise correlation between ROIs.\n", "\n", "We can estimate it using [`nilearn.connectome.ConnectivityMeasure`](https://nilearn.github.io/stable/modules/generated/nilearn.connectome.ConnectivityMeasure.html)." ] }, { "cell_type": "code", "execution_count": 12, "id": "c277cf6d", "metadata": {}, "outputs": [], "source": [ "from nilearn.connectome import ConnectivityMeasure\n", "\n", "correlation_measure = ConnectivityMeasure(kind='correlation')\n", "correlation_matrix = correlation_measure.fit_transform([roi_time_series])[0]" ] }, { "cell_type": "markdown", "id": "0b798832", "metadata": {}, "source": [ "We can then plot this functional connectivity matrix:" ] }, { "cell_type": "code", "execution_count": 13, "id": "3f945471", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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X/SoBx48fJzc3l1atWpmPs1/ZZsJgMFx3XGktIYQQQtRddWKGpyolJSXs3LmTrKws4OoGoQBnzpxhw4YNHDlyhPT0dAC+++47OnXqRMuWLfniiy/Iy8u7mU9bCCGEqBEyw1O1OpXwlG9AhrKkpvyEVl5eHqdPn6aoqKzqrVarpbS0lKNHj5r7Y+3cuZNJkyZx9913s2TJEo4dO0Z8fDzLli3jp59+4tixY8yePds8thBCCFFXSB2eqtXKJS2TycTRo0fZunUre/bs4d5778XKygobGxs6dSoreX/lcpRWq6VXr14kJyfj4+PD9OnT+eyzz/Dy8iImJobHHnuMhx56iKSkJAYOHMidd97Jhg0bmD59OocPHzYXNhwyZMhVY1cls83DlDo5VRmXXaxWqr467SLsLNR/KE+89rJybHCPVkpx++f+R3nMjAXLlGO7DJ2sHHtO8XW18W2pPGZhqXrCG1Cs3gIgtX5PpTjHhN+Uxyw5q14o07LLo8qxRoNaKxLjyT+Vx0xodI9ybHKeeisUL2tLpbiwdg8pj5lYrDYmwPns4qqD/tLIQ63sRbanemsL3x+nKcfaPv6mcmzBA28oxRVX42/jhpKGyrGti9RfA0dbZ6W4bzLclcc0qr8VcxK1liFN4zOVx9yj2DUmN0N5yP/3amXCs3PnTsaPH09oaCg+Pj40a9aMlStX8vvvvxMaGkpqaioGg4E5c+Zw9uxZoqOjadiwIfv372fu3LkMGDCAV155haSkJN577z0WLFjAjBkz8PDwIDk5GSgraGhpacnrr7+Or68vnp6et/iqhRBCiH9GRw320qpGsleX1MqEZ9u2bTRv3px3330XKFuy2r59O6tXr8bGxoa+ffuSk5PDzz//zOnTp7G3t+fHH39k9+7dAFhZWXHvvfeSmppKYGCguWaOvb29eZ9PgwYNcHJywsLCAk9PT3Jycti1axcdOnSQzclCCCHEbabW7eExmUw4OzuTkJDA//73P9auXYtGo+Gjjz7Cy8uLJUuW0LdvX+rXr09ERIS55o63tze5ubno9XoWLVpEly5d+PPPP/nwww8pKSkhMzPT3Dh006ZNFBYWMmXKFKZNm0arVq1o2bIlu3fvpqSk5Ba/AkIIIUT1aGtw/472Nt3DU+sSHo1Gw2OPPUZRURHvvfceX331FW3btuXMmTNYWFiYNyG7urri7+9PYmKi+WNLS0tycnLIyMjAYDCQmprK559/TmJiInv37uWxxx4jMzOTp556ip9++omHHnqId955hx9//JFTp07xxhtv4ODgcCsvXwghhKg2OaVVtVq5pGVtbc2qVavIyckhMTGRr7/+mp9++omioiLOnj1LaGgo7u7uODo6Eh8fT3BwMA4ODmg0Gnbs2MFbb73FqFGjaN26NcOGDWPy5MkEBQUB8Mknn5gfx2Qyme/PysqqUMjw76S1hBBCCFF31cqEB8qOhjs5OdGkSRN69+7NqlWraNy4MT/++CNWVlYYDAZcXFw4dOgQXbp0wcnJiQEDBhAUFISzszNz587F0vLaJy3279/P6dOnefDBBwFYunQpmzZt4v333zcvkf3d1KlTmThx4g27XiGEEOKfqtFeWrKkdfMUFxeza9cuFi9ezIwZM5g4cSL+/v589dVX5OXlMW/ePCwtLXnppZd48sknAXB0dOTee++lWbNmQMWj5enp6dxxxx0sXLgQgHfeeYe+ffuyceNGADw8PLC2tiYtLa3S5yStJYQQQtRWsqRVtVo5w2NlZUVycjJLly6lUaNGDB06lK5du+Lm5mZOWq6UlpaGl5cXUDYz1K5dOwIDA/nkk0/w8vJiz549xMXFmff7tG3blri4OL777jtcXV0JDQ3FZDJx/vx5QkJCrvmcpLWEEEIIUXfVyoRHo9HwwAMP8MADD1zz8/v27WPevHnmNhGBgYEMGDCA++67D09PT5o0acKPP/7Ipk2b6NevH2fPnsXb2xsHBweKiorw8vKiS5cuuLi4MH/+fEaPHo2zs7M5IRJCCCHqElnSqlqtXNLS6/VkZpZVpPx7OwmAQ4cOkZyczMSJEzl+/DjvvPMO3377rbluT0xMDC4uLpw5c4asrCx8fX0JDg4mPT2dS5cu0bBhQ9LT03nggQewt7fnk08+ITAwkKQk9aq5QgghhKg7auUMz7hx40hLS+Ozzz7DYDBgYVH2NMu7nIeFhRESEkJ4eDhQluCMHTuWUaNGUVRURHBwMP7+/jRu3JgJEybQtGlTHn/8cdasWUNKSgqNGjUiKSkJNzc3XnjhBTp37syQIUMqnMJSZbvifexsq17q8nzgGaXxNLnqJfWr0y6i/kL11g7xz/RTitNZqefLfezOK8dmfzVDOVaXfFEpzql5uPKY9nm5yrF6K/UWBI7HEpTiLhSp/wyc33ZaOTZgzzH12McHK8Vl7dqhPGaofdUtWMo1cFZvAWBIVivXv+P5d5THjP12lnJsSOop5dgS/95KcTnF6u1NEn7ZqxzbrId6K5Lc9/+rFBf62mjlMXuENVKOtd/3o3Ls2ofV2tG02L1VeUxtNSY5WvmrtbZo5Kj+fe0R5qoUdyHJmoWU1eGpqfo5UofnJoqNjTUvL2mueOHL/79evXpkZWVx/vzlP6JNmzbFzs6OEydOEBgYiNFopGvXrmzdupVFixbRu3dvLCwsSEpKwtXVFWtray5cuEBgYCCjRo1i3rx5nDp1SjqmCyGEqHM0Ok2N3m5HtTLhiY6ONhcYvFYjTz8/P3Q6HZcuXTLf5+npae6xFRgYSGlpKaWlpTz88MMEBgZiY2ODj4+PednK1taWQ4cOATB8+HDatGmDg4MDer36v66FEEIIUTfUyiWtoKAg9Ho9+fn52NvbV/icyWRCo9Hg4uLChQsXKC4uxtramoKCArRaLXq9HkdHRzw8PNi7dy+vvPIKCQkJaDQaHBwcSElJAeCRRx7Bz8/PPO7y5ctv6jUKIYQQNUWr06Ctzjrc9ca6TZe0amXCA2UzNvv27aN9+/bo9XqsrKyAywmPv78/hYWF6HQ6ACZPnszdd99No0Zla8SBgYGcOnWKTp06mY+av/DCC+YEql8/tX0q5aTSshBCCFF31colLYCIiAji4uIAzMkOXN7HExISwoIFC4iKiiIyMpK4uDiGDx+Oq2vZRq+PPvqIp59+usIpr7/PFlXH1KlTcXZ2Nt8CAwP/8VhCCCFEjdJp0dTQDV31U4PZs2cTEhKCjY0NMTEx7Nq167rxWVlZDB8+HF9fX6ytrWnYsCFr1qz5p1evpNbO8ERHRzNjxgzy8/NZuXIl7u7uTJs2zTxb06VLFwIDAwkICKiwNFXu7ye7/q6wsBBbW1uKiopYv349Fy9eZPDgyk+mjB49mlGjRpk/zsnJkaRHCCFEraDR1txmYw3VG2fp0qWMGjWKuXPnEhMTw4wZM+jZsycnTpwwFwW+kl6vp3v37nh5ebFs2TL8/f05e/bsdftZ1oRam/B07dqVxYsXc+rUKQYOHEi7du0qVEF2dXWlTZs2SmNlZWWxYsUKevXqhaOjI7169cLKyor169djYWFBQkJClUUHpdKyEEIIcbUPPviAoUOHMmTIEADmzp3L6tWr+fTTT3njjTeuiv/000+5dOkS27ZtM/e8rKzLQU2qtUtaLVu2ZP/+/cyYMYMhQ4aYa+5UJScnhzfeeIMNGzaY71u7di1PPfUUW7Zswd7eHgcHB44fP86XX36JhYUFwcHBlJaWkpWVdYOuRgghhLhxtDpNjd5U6fV69uzZQ7du3S4/F62Wbt26sX379mt+zapVq2jXrh3Dhw/H29ubJk2aMGXKFAwGw79+Ha6n1s7wXE9ubi579uxh79697N27l8aNGzNixAicnJwwGAx89NFHbNq0iaioKNzd3dm5cyft2rWjoKAAAH9/f9q1a8fSpUtp0KAB/v7+GAwGLly4cMOn1IQQQoi64O+Hc6610pGRkYHBYMDb27vC/d7e3hw/fu1Cl6dPn+a3337j0UcfZc2aNcTFxfH8889TUlLC+PHja/YirlBrZ3gADAYDycnJ5pYS5RuQJ06cyIsvvsi5c+fo3bs3iYmJ9OvXjyNHjuDq6sqjjz5KSkoKe/bsAcDS0hI7OzvzN8/Pzw9/f386dOjAqlWrOHPmDO7u7tJLSwghRJ2k0Wpr9AZlp52vPKwzderUGnmuRqMRLy8v5s+fT8uWLRkwYABjx45l7ty5NTJ+ZWrlDE9paSkWFhb8/PPP/O9//+O1114jLCzM3GbC19eXZ555huHDhwMwYMAAHnroIb788kv++9//4u/vT3h4OBcuXOC7774jKioKk8lEYmIiBQUFNG3alPXr1zNlyhS+/vpr5s2bx3333fePemk5tuuCo0PVp7+MWrWXWpur/hyCe7RSjlVtFwEQNu97pbiCzycoj2nMzlCP1Zeqx5aoxRalpSuPmb5PvVWAU4ivcqyqc3/EK8e6NVArPw+gtVL/dS+tp/az5dYpX3lMrlFEtDIGBw/1YUvUioU6B6uV/wco3rOh6qC/lOZkK8faBUcqxYXZqX9fk+p5KsemffupcqxBscVJacIR5TGdci9VHVTO9erNrpWJHdNL7fF1aq1oADZmOyjHdnIpUIrLQ/2aHHVqyzsOurIJgeouRV2P9q9Ny+fOncPJ6XJLmGvtY/Xw8ECn05Gamlrh/tTUVHx8fK45vq+vL5aWluayMlB2MjslJaVCGZqadtNneHJzc80zNkaj0fz/Vyo/YeXu7o6LiwsZGRX/WIaEhBAfH09iYqJ51ueBBx7g1KlT5OTkEBAQQEBAAJGRkUybNo1z587xyCOPkJqaSnp6OpGRkSQkJODk5ETfvn0xmUx8//335OdX481bCCGEuI05OTlVuF0r4bGysqJly5YV9s0ajUY2bNhAu3btrjnuHXfcQVxcXIW//ydPnsTX1/eGJTtwAxMevV7PiRMnKvSmevDBB+ncubO5B5ZWqzW3jijfX2M0Gvn888+JiIhg8uTJnD9/3pzwXNk8NCcnh/z8fPN9TZs25fz585hMJkJCQkhLSyM6OhqAXbt20aJFCwCSk5MJDQ2lqKiIs2fP4ufnx/Tp09m8eTMnT56sULdHCCGEqAtuZS+tUaNGsWDBAr744guOHTvGc889R35+vvnU1qBBgxg9+nKT2eeee45Lly4xcuRITp48yerVq5kyZYp51eZGuWFLWhMnTmTq1KksWbKE/v37k5iYiNFoJDAwkOTkZIKCgtizZw+vvfYaWVlZdO7cmdGjR1NQUMCXX37J2rVryc7O5sknnzRPlZUnN8HBwej1ejIyMsyVl0NCQoiLi8PBwYGwsDAyMzOxsLBg9uzZlJaWLXvo9XpOnTpF27Ztsbe35+LFi4SFhdGsWTO2bdtGo0aNKq3bI4QQQtRWZYlKzcxhaFDv6g5l20rS09N56623SElJISoqirVr15o3MicmJlboixkYGMgvv/zCyy+/TLNmzfD392fkyJG8/vrrNfL8K3PDEp7u3bszdepUtm3bRv/+/SkuLubChQvmvTIGg4Eff/yRBQsW4O/vz8KFC3n11Vd56623yM3NJTg4GIPBwOOPP27eW1P+grm6umJjY0Npaak5QZk5cyZdu3aluLgYHx8fLl68yMmTJytMqQ0ePJjQ0FAA/ve//5m/1mQy0bZt2+tej7SWEEIIIa5txIgRjBgx4pqf27hx41X3tWvXjh07dtzgZ1XRDUt4GjZsiKurK/Xr1+frr7+mS5cueHt7YzQayczMxGg0MmfOHPbs2cPFixcxGAx07dqV7Oxs6tevT2ZmprlNRHZ2Nrm5uTg6OmI0GtFqtbi6uvL111+zZs0aTpw4gcFgYObMmdjZ2WEymZg5c2aFCo8mk4m77rrL/HF5slNSUsLJkyextbU1J0PXMnXqVCZOnHiDXi0hhBDin7sRm5ZvNzdsD4+fnx82NjZ06dKF5cuXs3btWh5++GF0Op054QkICGDw4MFs2rSJP//8k2nTphEVFUViYiI7d+4EYP/+/Zw4ccK8rFW+yal9+/ZYWFjg5ubGsGHD+Oyzz6hfv755iatbt24VaupkZmYCkJ6ezrBhw/j888+BsirMs2bNYvPmzde9ntGjR5OdnW2+nTt3roZfMSGEEELcKDf0lJarqytWVlZERUXx6quvEhwcTOPGjcnMzESj0ZiPjltbW1NYWMjq1aspLi5mzJgxzJkzhwYNGmA0GunVq5d5Oav8BFfv3r2ZN28eo0eP5t5778XTs+xoptFoZP/+/eYlJ6PRyAcffICHhwfJyck4ODjw5ZdfMnnyZNLT0/H09CQgIICioqLrbli2tra+ase6EEIIURtoNJqyflo1cbtN97Le0Do8jRs3ZtOmTbzwwgtkZmbi5uaGVqslJyeH8+fPM2nSJHPhofz8fDp37kxUVBT33HMP0dHR2Nvb4+xcee2M+Ph4jhw5QmBgoPlE1qZNm+jWrRtTpkzhP//5D1qtlp07d9KwYUOSkpLw9fUlKiqKiIgI3n33XcaPH4+Pjw8pKSnk5ORc9/GEEEKI2kir06KtoU3LWlOtrkn8j93Qq2rZsiU///wzbm5ujBs3jsaNG+Pq6oqfnx+5ubnUr1+fcePGsXr1ao4fP87cuXPx9/cHypbEKks+hg0bRuPGjRk0aBCff/65uUKjXq/H39+f3r17s2TJEhISEgBwcXHBysrKvAzl4uLCgw8+yPnz51m5ciVubm4UFxdz8aJ6USohhBBC1B03dIanQ4cOnDlzBigrIghl1RQjIiLMMY6Ojjg6Ol71tadOncLS0pKgoCDzclb5huWsrCzeeustHn74YQDWr1/Pq6++SkhICHfccQdNmzYlNTWVPXv2kJmZSdu2bcnJyeHIkSM88MADREREcOLECUaPHs0333xDfn4+9erVIyUl5bobl69FY+uIxq7qipwm66qrMQOUBDRTfuz9c/+jHKuzUs9tVSso2z2hFgdg+OMb5ViXbveqx6oGOrgpj2l4tL5yrP26OcqxlnfcrxTn90Sm8piliSeUYzVNuyjH5n8zTSkuZYd6lV23yHrKsY4NTirHpm3doxQXOXGs8pjbLBspx0b7qP1uAxj3r1aKMzXtqjzm+W1nlGNbjOqjHOv10ECluLNzZyuP6f+2emzp2vnKsWfXH1CKs3jQXXnMVn6WyrEnc68uyHctQVr1paK0IrXYjOKyuH9SP6cyGtPtuaR1Q2d4YmNjq+yNUV5t+e/7Z7Zv3862bdvQarXmz5VvWG7YsCEJCQmYTCZKSkro1q0bsbGxrFq1CltbW6ytrWnbti1arZZ3332X0tJSBgwYwLFjxwBo06YNGzdupFmzZrRr1441a9Zw5swZqbQshBBC3KZuykJdecJiMpkqlJI2mUzmassajYbS0lLzaaqDBw/y6quvEhoaysyZMyuMFxkZSXx8PEaj0byJuX379hw6dAgLCwtzo7OmTZvy66+/cuLECWJiYkhPT6ekpIRWrVqZl7e6d+/OwIED+eSTT8wVoIUQQoi65FZWWq4ramxJS6/Xs3//fo4fP85vv/3GwIED6dGjB3C55o1Gc3n3d3FxMdbW1hw6dIhVq1aRk5PDN998wz333MP48eOJiYnh0KFDTJgwgXbt2mEymczJTUREBN988w15eXk4Ojqi0Who1qwZ8fFlTRd9fX05duwYDRs2ZOTIkcTExODh4UFubi4JCQk0aNCAuLg48vLycHBw4K233uLSpUtERUXV1MshhBBC3DSyablqNXZVU6ZMoW3btpw4cYKGDRvSqFHZGnhBQQHbt29n3759nDlzho4dO9KqVSveeustoKwz+pdffmlOQvz9/fn888954IEHCAgIwNbWFqDCMbkGDRqQn59PTk6OeX/PsmXL6NixIwABAQGcO3eOgoICRo8eTdeuXbG0tKRJkybmnl3r16/HxsYGKGt+9sknn5hPegkhhBDi9lJjMzxRUVHce++9TJ482XzfrFmzWLlyJR4eHnTs2JELFy4wcOBA+vbty7Rp03jzzTcZP348vr6+tGnTBhsbG2JjY/n888+xsLDAZDKRlJR01cyLvb09dnZ2HD16lNTUVHbv3s3evXt55513APD09KRdu3bk5eVVqLa8YMECoGwprbIurpWR1hJCCCFqrZpcirpNNy3XWMLTokULnnvuOX788Ud27NiBu7s7jo6OpKamsn79egDq16/P/v37cXBwYPjw4Tz99NPodDrs7e3NMy+enp6UlpZSWFiIj48Pqamp5OfnY2VlhaWlJQaDAZ1OR3BwMCNHjiQwMJDAwEBefPFFmjVrZn6csWMrP5Hx96JKJpOJX3/9lZ49e1b6NdJaQgghhKi7amxJKygoiPz8fJYsWYK7uzvdunXD09OTjh07muvbmEwm0tLSgLJlJCcnJ7Kzs6lXrx6nTp0CymrkGAwGjhw5wtNPP83PP/+Mn58fq1eXHecsT1amT5/O8ePHWbduHZ9++indunWr8jnm5uYCcPz4cR555BHzJmWNRkOfPn3MTUqvRVpLCCGEqK20Gg1abQ3dbtNKyzW6Myk0NJRJkybx6quvEhUVhYeHB5aWliQnJwNlx8G//fZbADZv3kxISAiurq44ODiwf/9+AJydnenatStarZbQ0FDmzJlDZmYm999/f9kT/mvPTvnenmvR6/UcPXoUg8Fgvu/OO++kY8eOZGVlkZaWxtKlS3n99dfNSU+bNm04dOhQpWNKawkhhBC1lUanrdHb7ahGryoiIsI8EwPg5uaGpaWlOakYO3YseXl5REZGMmPGDLp0KSuENmzYMIYPHw6UzfA8++yztGjRAgAPDw9zknMtp06dYu3atWRkZJjve+WVV2jSpAl//PEHULb/prytRVJSEj4+Prz88svs2rWLZcuWAdCkSZOb3qpeCCGEEDdHjVZajoqKYvPmzYwcORIom625cn9OkyZNGD16NC+88AJ+fn5A2TJXdasbZ2Vl8dxzz7F9+3YCAgJwc3PDxsaGfv360a9fP8LDwwkKCmLHjh00bdqUhIQEgoKC8PDw4NChQ3Tr1g1vb2969+5NamoqP//8M7169WLevHk1+XIIIYQQN4VWp0FbQ5uWtcbbc0mrRhOeK1tJQNnx8AkTJlSI+fty0N83EOv1eg4cOMCBAwdITk42JzBXMhqN5v5cTz31FABvv/02X375JW3btiU8PJxmzZphZWXFkiVL6NSpE/Hx8Tz22GMcPnyYhx9+GL1eT3h4OE2aNGHMmDG899575t5b1VH05zqs7GyqjLNudofSeEbvBsqPnbFgmXJsHzv1oorG7Iyqg6heuwg6PKocql97/ercVzr/61alOLfwYOUxXWI7KcdejDurHOtY+r1SnLViCwoAXb2myrGGQ78px9pFqLU4CfbxUR5z3TPqrQL0RlPVQX/ZcalQKW5qH/WWJc2i1EtUlHz1tnKsVfM2SnEavdo1AcS8N1I5tvRisnJszsb/KcXZ+3sqj2lY/7ly7IlvNijHmgzGqoOAcAv1ti3aePX2Jg0L1ar0a/0bKo9psXe9UlxxRhZQw60lbtOEp0aXtP7eSuKftJifMmUKMTExnDt3jmPHjjFp0iRzQcHyKs22trY0aNCgwibj5557jtDQUH766ScaNmyIVqulbdu2rF27lpycHPz9/XF0dOTChQtA2XJbSkoKsbGxdOvWjc8++4y8vLx/c/lCCCGEqKVq3c6kqKgounfvzsSJE5k+fTrW1tZs374duJxAWVlZ4efnV6EVhJOTE0FBQcTFxeHn50d2djYeHh5ERUXx7LPPctddd+Hs7Ex+fj6lpaX4+vqSm5tLfn4+b775JkePHuXcuXPm5EoIIYSoK2TTctVuaLf0f6JFixbmI+q+vr6kp6fTpEkT4HLCo9Pp8PX1JScnh5KSEiwtLbG2tsZgMGBpaYlOp8PR0ZHk5GRGjhzJqVOnaN68OUajkeLiYpKSknBzcyMtLY1z584RHh7OmDFjaN68OTqd7pZduxBCCCFujFqX8AQFBVFQUMA333zDrl27KCoqokGDq/e1uLu7o9FoSElJITAwEIAffviBQYMGAWUFDA8dOkSnTp34+OOP8fT0JDk5mcjISHJzc4mOjmbMmDHUq1cPgB49eph7fwkhhBB1iVZHDW5arpFhap1al/BA2czOwoUL6dq1K1lZWcyZM4fnn38eOzs7jEYjWq0WFxcX3Nzc2L17N8XFxXz33XdERETQvXt3AFq3bo1Op8NkMuHp6Wked9KkSebHqU4tHWktIYQQorbSaDVotDW0abmGxqltamXC06hRI3r16sXgwYNp3749H3/8MU5OTgwbNswc4+joSFFREc888wxRUVHUq1ePF1980Twb9Oyzzyo9VlxcHIWFhTRtev3TLtJaQgghhKi7auXOpOjoaFasWAFA586d6dOnDytWrCArK8tchNDR0ZGPPvqItLQ0fv31V+bNm6fU7dxkqnjcNTExkS+++AKA/PzKjxZKawkhhBC1lVarRaurodt1iv3WZbXyqjp06IDPFfU9HnvsMdasWYOLi0uFOHt7+2qPrdFoKCws5MiRI2RnZ7NixQrmz59P+/btWbx4caVfJ60lhBBCiLqrVi5pxcbGEhsba/74eienyvf0lDOZTBXq/5w5cwYnJyfc3d3Jz8/noYceIj09HWdnZxYuXEjr1q3ZtGkT3333nbn6sxBCCFGX1GjhwRoap7aplQnPlUwmE0ajEY1Gc81ptvL70tLSyMnJoX79+gBs2rSJUaNGAWUtLd577z127dpFZGQkEyZMwNHREShrePrtt9+SmZmJn5/fVQmTEEIIUdvVZP0cqcNzExiNRkwmE1qt1px0aDSaSmd4CgoKGDNmDElJSZw9exZbW1vefvttOnXqxLRp05gwYQK9e/dmwIAB/PDDD0RGRnLgwAEGDRrEvffeS4sWLYiOjsbf35/du3cTGRlZ7WSn+FIORYXFVcZpju9RGi//11XKj91l6GTl2OyvZijHGvWlSnEu3dRL9VenXYT2LrUN5wC2+44oxTm376I8Zt7uLcqxxVm5yrFn5q5VimsVGqk85sXf1inHOgZ5K8da1VdrLWG4qH5+tct/+yrHWvoGKsfenZmuFHf6s8qXrP8uqI96CwZ9Nf44GLPUnqtx81LlMQ05Wcqx1XldMw6qFWENuKuj8pgaW/VtCE3+M6zqoL/kHfhTOVaVIai5cqzuwlG1MR08lMc06YvU4krU4sQt2MNz6NAhZs6cyZAhQ1i6dGmFTcRarRadTnfVktTkyZO56667mDdvHkVFl7+5dnZ2rF+/nqCgIHbt2sUTTzzB8uXL+f3334mIiMDa2hqAQYMGkZKSgo+PD99++y2vv/46W7Zs4Z133uHEiRM0btyYgwcPAmWNSYUQQoi6RKPV1ujtdnRTr+rChQtMmjSJI0eO0KFDBz799FOmT59Obm7Zv5I3btzI008/TYsWLfjuu+8A+OKLLzh//jyvvfYaPXv2vGpZq127dublqTvvvJOgoCC2bNlCvXr1zK0nnJycOH78OC4uLri4uNC2bVuGDx9OQEAA1tbWdO/end9++4077riDTZs23cRXRAghhBA3w01d0ho7diz169dnypQpQFlxwHfeeQd3d3eGDBnCsmXLCA8P56WXXqJRo0bs3LmTs2fP8tprr9G4ceNrjtmqVSv++OMPAJydnUlNTaVdu3aYTCZmzpzJE088wdGjR7Gzs8Pd3Z0PP/yQBQsW4ObmRvfu3QkJCQFg3bp1eHioTzcKIYQQtUX5kfKaGut2dNMSHr1ej5WVlbkwYFFREU2bNiUyMpJt27YRHR1NXFwcs2bNqvB1JSUl1KtXD71ej06nu2o/T2hoKK+99hopKSmcOHGCY8eOMXr0aGxtbYmLiyMqKoqgoCD++9//otVqeeKJJxg+fDg2NjYVxilPdirbtCyVloUQQtRaNdn08zZNeG7aVRmNRpycnDAayzY5WlpaAtCmTRsOHz6Mj48PCQkJQNlmZMCcHK1cuRIrKyt0Ot1ViUZ4eDjBwcG88MILjB07lt69e+Pm5oatrS2vvfYaBw8e5H//+x+RkWUbQd3d3bGxscFkMmEwGK56npVtWp46dSrOzs7mW3n/LiGEEELUfjct4bGyssLZ2Zn4+LKd/+WJj4ODA05OTnh7l50gOXz4MHZ2dgDY2try5JNP8v3335vbTPzvf/8zfy2UJTD+/v5MmjSJLVu28Mwzz1y+uL/2+xgMhgpfA1ef/io//l4ZqbQshBCittJoteaj6f/6dptuWr5pS1parZaOHTvyyiuvMHz4cPz9/QF4//336d+/PxqNhjfffJOpU6dy8eJFEhMTmTdvHl26dMHOzo6EhATq169PdHR0hY3LdnZ2WFlZcfjwYcLDwykpKTHPHpX7e2JTfvT9ShqN5rpH0q2trc2nvoQQQojapCZPV0nCUwM6duxIs2bNmDhxIi4uLhw/fhxHR0f69OkDlLWQaNiwIXq9nkaNGuHp6YnJZKJt27a0bdu20nE/++wz3N3dASokO8nJydjb21doA3GtxKa4uJiTJ0+yefNm2rZtS8uWLWvysoUQQghxi930woOLFi1i+fLlHD58mEcffZQuXbrg6elp/nybNm0qxJcnJ+XVlq81C+Pu7m4uWlgeo9FoWLx4MVFRUXTt2tUce+bMGTZs2ICXlxf33HMP+fn5TJo0id9++43u3bsTExNzg65cCCGEuDHKlqMqb8NUvbGu3t96O7gllZYfeughHnrooUo/f62TUiUlJWi1WvMMzosvvsjJkydZtGgR/v7+FZaoypOj06dP89lnnxEbG0vPnj3p0KEDzz//PB4eHlhYWPD777/z4YcfYmlpiYeHB9OmTbsxFyyEEEKIW+qWtZYwGAyV9sf6e7KzefNmevTowQ8//MDdd99Neno6Z8+epaioiJSUFPz9/Tlx4gSTJ08mJyeH/v37c//992Nra0tmZib9+/enadOm/Pzzz7i4uPDVV19x5swZnnzySc6cOUPr1q1JTk42P6/rNSv9O4cWMTja21UZp1UsqW7duE3VQX85V6yeheuSLyrHGksUW0sojwjnf92qHKvaLgLAc/THSnFr6rVSHjOhoEQ5ttv9DZVj/e8IU4rL2fmH8pgZB08rxyasO6QcG/XTK0pxloHXro91Ldr9vynHGhTbRQCU5OYpxWWezlQeM7CoQDl2+5TVyrFd9qq1AinQVv2eUs5u3RzlWMuG6sv1F098rRQX8kSQ8pi5u9XfB+zqqf2+AJxZo9a6p/nd2cpj5tp5KcfapaodYjGFVr414++sQiLU4mwzAOmlpeKWXZVOp7tmsnMt5ft61q0re7PIzMwkIyODTp06mU9LzZ8/n8jISEaMGMHSpUvZsGED06ZNw9HRkW7duuHt7U1GRgadO3fGYDAQHBxMSEgISUlJeHt7U1RURGFhYbWSHSGEEKI20Gq1NXq7HdWq5qGV8fDwwNnZGW9vb1avXk3r1q2xtrbG0tKSzMxMSkpK+O6770hISECn05Gfn8+qVavo3bs3JpOJhIQEQkJCcHJyIi4uDqPRiKWlJQcOHECn0+Hv749erychIYGIiAjpmC6EEELcZupEwmNhYYGrqytdu3blww8/pLS0lL59+3Lx4kUyMzMpLS0lKCiI8+fPExwcjL+/v7mujp+fH+fPnyckJIT77ruPZ555hunTp2NpaUlkZCShoaFotVoaN25MYWEhUHnxQSGEEKI2kiWtqtWZq2rZsiUeHh74+vry0ksv0bRpUxo1asSlS5fQaDR4eXmZl7z279+Pk5MTWq2Whx56iHfffZc777yTuLg4PvroIy5evEh8fDwjR47E29sbT09PJk2aRIsWLSp9/OLiYnJycirchBBCCAGzZ88mJCQEGxsbYmJi2LVrl9LXLVmyBI1Gw/33339jnyB1ZIYHymr4bN26lRdffJH4+Hisra0JCgpi+/btZGVl8dxzz7F48WK+//578vLyeOONNwB4/PHHadKkCZaWlrRu3RpLS0vee+89AI4dO8a8efOIi4vDzc2NV1999aqiheWmTp3KxIkTb9r1CiGEEKpu5QzP0qVLGTVqFHPnziUmJoYZM2bQs2dPTpw4gZdX5Zu/ExISePXVV+nQocO/fcpK6swMT0xMDOvXryc4OJiPPvqINm3aYGdnh1arJSEhgR49ejB27FhGjhzJ8uXLzft3yvtepaenM3XqVFauXAnA6dOn+c9//sPWrVupX78+Tk5OnDlzptLHl9YSQgghaiuNRmuutvyvb5rqpQYffPABQ4cOZciQITRu3Ji5c+diZ2fHp59+WunXGAwGHn30USZOnEhoaOi/vXwldWaGJywsjJSUFACCgsqOQTZp0oTp06ebY1xcXLC1teXbb7+lV69ehIeH88EHHzBjxgxiYmJo0qSJ+YV95pln6Nu3r7n3Vmlp6XV3pktrCSGEEP+f/H3rxrX+Dur1evbs2cPo0aPN92m1Wrp168b27dsrHfvtt9/Gy8uLp556ij/+UC/F8W/UmYTH3d2dtWvXAmUvsMFgwNbW1vz5devWMXToUBo2bEhYWBjLly/niy++ICAggJEjR/LKK5frily6dAl7e3uaNGkClO3PkWRGCCFEXXUjlrQCAwMr3D9+/HgmTJhQ4b6MjAwMBoO5AXg5b29vjh8/fs3xt2zZwqJFi9i/f3+NPF9VtT7hOXXqFPHx8bRq1QoPDw8ABg8eTI8ePRgyZIg5zs7Ojr59+/LSSy8REBBA7969Wb16NVFRUWzdupV58+aRlpZG/fr1iYiIwN/f35y9WllZ3ZJrE0IIIWqrc+fOVehFWRMTA7m5uTz++OMsWLDA/Df9ZqmVCU/5JuTt27cTEBCAm5sbNjY29OvXj379+hEeHk5SUlKFrylvF3H06FECAgIIDw9n//79PPnkk0ybNo1du3bRoUMHgoODcXV1xc7OjtOny6rUls/w/JPj6DqvIHSODlUHmoxK45WcUKsYCmDjq1411al5uHJsUZpilVsHN+Ux3cKDlWOd23dRjlWtoNzzzG7lMU8MekA5Nqir+vfAOixSKc5UqlceM8RF4WfvL0e/2qQcq/rzqs1Xr+BtcvGsOqh83GrEWpaqVcaOHKT+r1+bNj2UY5s/magcm6tYQTmrSL2KurNiRV4Ak079H3e+rUPUAv3Uq407NC1SjtVY2yjHhg/qrhRncFOvCn2xQK3iPICTv1pV6KOZ6r/bbvXV3gczbMv+Ft6IGR4nJ6cKCc+1eHh4oNPpSE1NrXB/amoqPj4+V8XHx8eTkJBA7969zfcZjWXvNxYWFpw4cYKwMPUq29Vx0xMevV7PgQMHOHDgAMnJyeYE5kpGoxE3NzfGjRvHU089BZSt93355ZfExsbSpk0blixZQmlpKRYWZZfg4uKCnZ0dmzdvJiMjg8TERIYNG0ZxcTFNmjThnnvuoVOnTkDZZqlmzZrxxRdf8Pzzz2NjU/aLtXfvXjw8PMx7hIQQQoi6QKvToq2hhKc641hZWdGyZUs2bNhgPlpuNBrZsGEDI0aMuCo+PDycQ4cqtrl58803yc3NZebMmVcto9Wkm35Ka8qUKcTExHDu3DmOHTvGpEmTiI+PBy5neba2tjRo0KDCLM5zzz1HaGgoq1atomnTpqSkpJCXd7mHTvmszZdffsnWrVu577776Nq1K87Oztja2nLy5EmMRiNGoxGdTsfjjz+OhYUF//nPf5g0aRKPP/44kydPlqKDQgghRDWMGjWKBQsW8MUXX3Ds2DGee+458vPzzdtOBg0aZN7UbGNjQ5MmTSrcXFxccHR0pEmTJjd0i8lNn+GJioqie/fuTJw4keTkZMaOHcv27dsJCwszJxtWVlb4+flx9OhR89c5OTkRFBTEqVOn8PPzo7i4mLS0NBwdHSt8zZNPPllhU5WFhQWOjo6kp6dTWFiIvb29uXXEzz//zCeffMKZM2fo0aMHsbGxNzS7FEIIIW4EjVaDpoZ6YGm01fuH/4ABA0hPT+ett94iJSWFqKgo1q5da97InJiYWCv6c930hKdFixacOnUKAF9fX9LT082npcoTHp1Oh6+vLzk5OZSUlGBpaYm1tTUGgwFLS0u0Wi3Ozs7k5uZWaPbp5uZGeno68fHxhIWFmZe8/P39yc7OpqSkpMLjaDQahg8ffjMvXwghhKhxt7q1xIgRI665hAWwcePG637t559/Xu3H+yduesITFBREQUEB33zzDbt27aKoqIgGDRpcFefu7o5GoyElJcU86/LDDz8waNAgALy8vFi4cCGrV69mx44d+Pv706lTJ3OiA5j39zzyyCP/+nkXFxdTXFxs/lhaSwghhBB1xy05peXr68vChQvp2rUrWVlZzJkzh+effx47OzuMRiNarRYXFxfc3NzYvXs3xcXFfPfdd0RERNC9e9lu/O7du/PFF19gaWlJ37596dChwzUTJxVffvklDRs2pG3btpXGSGsJIYQQtdWtnuGpC25JwtOoUSN69erF4MGDad++PR9//DFOTk4MGzbMHOPo6EhRURHPPPMMUVFR1KtXjxdffNGc1PTv35/+/ftX+7HLN0ZfuZ64a9cuTp8+fd2EZ/To0YwaNcr8cU5Ojuz3EUIIIeqIW5LwREdHs2LFCgYPHkznzp05e/YsS5YsoX///ri4uABlCc9HH33EokWLqj2+yWTCaDSi0Wiu2ih15cfls0mtW7euco1RWksIIYSorcp7adXUWLejW3JVHTp0qFCQ6LHHHmPNmjXmZKecvb19pWPo9XrS0tKAy7M25TQaDTqdzpzc6PVlxZ5MJhNr164lOjqaDh06mJOp+vXrk5ioXjxMCCGEqE00Oh3aGrpprjgMdDu5JTM8sbGxxMbGmj/WVfHils/WlM/IaDQakpKSmDNnDu++++5VszgZGRksXLiQ8+fPs2nTJjp06MCHH35IcXEx06ZNY968edSrV4/OnTvTokULQkJCKCwspKioyFyEUAghhBC3j1rXWsJgKCupfmUSVJ7QXHmf0Whk7ty52NvbU1xczJQpU8yfy8/P580332TevHm8++67DB8+nFmzZnHXXXdhY2NDo0aNcHZ25oknnmDLli0MHDgQZ2dnTpw4QfPmzav1fI2XUjDqFcrFG9VKxWus1BOuwlK18v8A9nm5yrHp+04pxRkera88pktsJ+XYvN1blGMTCtTaClSnXUSjL1eoP/4I9ROAFntOKMVln8lQHtO/vXpbgfy0fOXYrw6rPYeOweqP7+zeRDnWxUK9rL9eo1ao7Pikucpj6n6ovMvz3+WcV//davuK2qS6oyGv6qBy/o2UQ0sd1Ft2FF1UO4la6qbeNoa965VDLVp0U441ndinFrdzpfKY1i36KccWHVR7z3Lpqv73xSftgFJc6cWylQ7ZtFy1m35VpaWlHDlyxNw23mAwYDKZzJ/X6XRXzfjs2LGDtWvX8sQTTzB06FD0ej379u3D2tqa3Nxc7r777grLWv7+/oSFhREZGYmdnR1Dhw7lwoULbNu2jXbt2nH27FmgrKJzTk4OTk5OBAYGVih0KIQQQojbx01LePbv309ERARt2rTh+eefN28S1ul0aDQaioqKKCkpYenSpTz77LPMnj2b/Pyyf5Hef//9LF68mAEDBpCZmcncuXPp27cvMTEx3HfffbRv377CspaFhQWtWrXizJkzADg4OKDT6QgJCeHixYusXbsWKOsEq9frsba2xsXFhW3btt2sl0MIIYSoMeUzPDV1ux3VyJJWdnY2R44cYc+ePVhZWfHMM89cFWM0GgkODuaLL74wl5v+7bffOHHiBCtWrKBZs2Y0b96ctWvXcuedd/Lzzz+Tl5fH66+/TsuWLQkJCaFXr14UFBSwc+dOCgoKaNy4Mb/88gsdOnSo0EgUwNPTk2+//ZZHHnmExMRE0tPT6datG7a2tsyePZv69evTuHFjPvjgAwCGDx9eYaZJCCGEqCs02ho8pVUL2kDcCDWS8DRu3BhfX1969OjB5s2bKSkp4amnnsLW1tbct8rd3Z2QkBDOnj1LSUkJ+fn5bN26lV9//ZXnnnuOgQMHMmTIECIjI3n66acJDQ3lp59+4syZM3Tu3NncjsLPz4+SkhIuXLhAy5YtWbZsWdmFWFS8lHbt2rFixQr69+/P4cOHef3119FoNLRv356GDRui0Wjw9Ly8nh0cXI11aCGEEELUKTWS8ERFRTF06FDuv/9+vv76azZv3kx8fDxNmjQxJzwuLi4UFxdz9913ExoaSr9+/QgNDSU0NJQWLVoAEBERYd60HBoaipOTE4mJicTExLBy5UqgbH+OTqfj9OnTdOjQgc8++4xHH32UsLAw3n77bfPjNW7cmNDQUObMmYO7u3uF5+vl5XXN6yj/2muR1hJCCCFqK9m0XLUauarmzZub9+R4eXmh1+sJCgoqe4C/psasra2JjIzkueeeY9euXbz22mu0bdsWnU5n3nBcv3598zi+vr5s2rSJevXqUb9+fQ4cKNux7unpiVarJT09HT8/P95++226d+/Oo48+ClxuDOrt7c358+fNxQL/Xqun3JX3V5bsQFlrCWdnZ/NNqiwLIYQQdUeNJDxt2rRh48aNrF+/nlmzZuHn54eTk1OFZMLS0pKgoCBzsUCTyWSeacnIKDsCe9ddd2E0Gnn11Vd57LHHiIyMJCAgAD8/P5544gmKi4uxtbXl7bffNic4bdq04YknnqBRo4pHMz09PZkwYYK5oKBWq70q6Smv6wOQm5tLfHy8eYbp70aPHk12drb5du7cuX/7sgkhhBA1QqPV1NymZW3l//ivy2pkSat169YcPHiQZcuWERAQwJo1a7jvvvuIiYkxx+h0Ovz8/MjNzTVvMHZ0dKSgoIDExEQMBgN2dnZ88803LFmyhLZt29K1a1e0Wi0mk4lZs2aZx/p7ccDywoQajYasrCyee+45tm/fjpeXF+7u7gwfPpx7770XrVZLaWkp58+fx93dHUdHRz777DN++uknLC0tqV+/PmPGjLlmhWdpLSGEEKK2kk3LVauRhMff35+AgADmzi0r7DVz5kzGjh3L4sWLK+yXcXJyoqioiKSkJPMm4TfeeIPAwEBz7R0vLy9efPHFCuOXV1kuKSnh4MGDHDhwgOTkZPr160d4ePhV/bHc3NwYN24cTz31FFOnTuXbb7+lXr16TJ8+nR07dhAcHEzPnj156aWXcHV1Ze/evaxYsYLo6OiaeDmEEEIIUcvUWBpnb2/Pb7/9BsDIkSPR6XQsXry4wjJSREQEX331VYUTUVFRUVdtKi5v/lnhiWq1TJ06lZiYGM6dO8exY8eYNGkS8fHxwOW9OLa2tjRo0MC8lBUTE8OZM2cICAhgzJgxHD16lIkTJ7J06VJOnTpFixYtsLGxITo6mtJS9equQgghRG2h0epq9HY7qrHWEl26dCEv73I59O+//x4nJ6cKMZaWllhaWlY5Vvny1N9FRUXRvXt3Jk6cSHJyMmPHjmX79u2EhYWZ462srPDz8+PEiRMUFxdz7NgxgoODcXZ2Zt++fTzyyCPmGaMdO3bw+OOPo9frzRWXq0tr54DWrvImp2a2jkrjmYqLlB87oDhJOVZvVfXrXs4pxFcpzn7dHOUxL8adVY4tzlIv1d/t/oZKcUFdWyqPWZ12ESGzvlWONayaoRipVlIeIPO4+uvq4OugHPuk83mluMR3xyqP6TFokHKsPuGYcqyuQO3npWG/dspjOt7RUzk26ZvPlWM1Br1aoFb9rdl0er9yrKW72u82gM5GrWWHRbb6+5AmSO33FSD3f18ox6r+Hvi9M0p5TKtS9X0sFt2eUIpLylb8/gNe3mqvlaH0r99rra7sVhMk4bm+2bNnA5ePdv+T5KEqLVq0MNfj8fX1JT09nSZNyvrzlCc8Op0OX19fli9fzp9//klISIi5z9a4ceOYPXs2bdq0YdSoUZw/X/am7u3tzdGjR2nbtm2NP2chhBBC3Ho12jzUYDBU2fn83wgKCqKgoIBvvvmGXbt2UVRURIMGDa6K8/T0pHPnznz88cfmqs5QVjvn0qVL5tmd0NBQoCx52rNnD23btr1uLR4hhBCiVtJqy241NdZtqEav6kYmO+V8fX1ZuHAhnp6eZGVlMWfOHAoKCoDL+3icnJzw8PBg//79AOaCgW+88QYTJ06kZcuWdOzY0byc9emnn/L8888D16/FI4QQQoi6qUZneG6GRo0a0atXLwYPHkz79u35+OOPcXJyYtiwYeYYW1tb7O3tzbVyyvcN9evXj/vvvx9bW9sKY1pZVb1WLZWWhRBC1FYanQ5NDU061NQ4tU2dm7eKjo5mxYoVAHTu3Jk+ffqwYsUKsrKyzMfTXVxcmDhxIk8//TRwudqzhYXFVcmOKqm0LIQQotYq37RcU7fbUJ1LeDp06ICPj4/548cee4w1a9bg4uJivk+j0WBnZ6c0ntForLS68pWk0rIQQghRd9W5Ja3Y2FhiY2PNH6vuGyqv7fP3eK3i5iyptCyEEKLW0mpr8Fh6nZsLUVLnEh4V5cmNVqs1b0LWaDTmZMdkMmEymTh79ixnzpzh22+/pWnTpldVeBZCCCHE7aFOpnHZ2dls376d+fPns3XrVoAKy1LlyU15slNUVMThw4d54403mDNnDhqNhn379nH33XezbNkyIiIiSEhIYOTIkUDlndWFEEKI2qi8l1ZN3W5HdWaGZ9OmTYwfP56cnBzOnDlD48aNadeuHR06dADKlraKi4uxtrYmISGB+fPnc+HCBXbv3k2vXr3w8vJCq9Xyyy+/EBoaSqdOnXB3dzd3W7906RLR0dHMnDlTeZlLCCGEqBU0NbjZWHN7blqudQlPZmYmJ06cuKoIYFBQEOPHj8fFxYVZs2Yxbtw4QkJCSEhIoHv37mRlZXHHHXcwbdo0cnJymDZtGn/88QczZ84kMjKSSZMm8dprr/Hpp5+yceNGevbsiaOjI40bN6awsBA3NzdsbGyIi4ujfv36ys/3zNy5OCi0bQju10dpvLyjh5QfO7W+evl7x2MJyrGqLO+4X/3xS79Xjj0zd61yrP8dYUpx1mGRymNa7DmhHKveLgJ0fV5SivOzmK08Zk41Wnb4d3RRjjXp1VqcBD75lPKYhbs3KscWXVIv+2AX4FN1EHDy+9+Ux4y0Vz/Nae/jXnXQX3QntijFmUrUWxBc+mOTcqydl4ty7OlfTynF+fdcpzxmflyccqxqawuA7LMXleKCzx9UHtMtL0s51pCZphTXytNfecyMpT8pxWVn5VUdJIBasKT19+UjKysrxowZc1VcvXr1uPPOO4mOjsZgMHD06FFKSkqYPXs2H3zwAX/++ScGg4GPPvoIX19fwsPDCQwMxNnZmVatWuHvX/aDFhAQQF5eHkVFRdSrV4/Vq1ebj6o3atSI1NTUG3/RQgghRE2SY+lVumEzPKWlpcTFxeHt7Y2rqysmkwm4upJx+fJRWloaFhYW7N27ly1bttC+fXuaN2/Ohx9+aC4MWN66wtXVlfT0dC5dusS6devYt28fJpOJpKQkBg8ejKWlJY0bN+bEiRMEBQURGBjI8ePH6dGjBx4eHuh0Oi5cuECrVq2YN28eGo2GzZs3Exsbyx133HGjXhIhhBBC3CI1nvDs37+fRx55BFtbW3Q6HY0aNWLu3Lk4OJR1dC0oKCA3N9fc42r8+PH89NNPFBYW8v777+Po6EhISAjPPfccjz76aIWxy5MlX19fLl26RHx8PK1ataJJkyY89dRTODpe7kher149Dh06RPfu3WnQoAGHDx8GytpOlJSUEB8fT/369fH29iY8PJzGjRvTpUuXmn45hBBCiBuuJjcby6Zlyk5HHTlyhD179mBlZcUzzzxzVYzRaCQ4OJjPP/8cHx8foqKiWL16NT169ODZZ59l9+7dNGnShNGjRxMSEsKFCxeYNWtWhdo6rVq1orCwECibKbKwqPg0Q0JC2L9/Px4eHvj6+pKWloajoyO5ubkcOHCApk2b4ufnx9atWxk1ahRRUVFcuHDB/NzGjRuHj48PR48eJTs7m4ceesjcfqIy0lpCCCFErVWTS1G36ZJWtdK4xo0b8+KLL5KcnMxXX33FrFmzzIlJ+ZKVu7s7AQEBxP21Oa158+akpqYyf/58/P39iY+Pp0uXLnz77bckJiZiYWHBSy+9xMKFC/njjz8AaNmyJZs2lW3Eu7JQYPkMT/369Tl//jwWFhYMHjyYixcv0qJFC9q1a8f8+fOBsr5Zb731FgAdO3Zk2rRpaLVaLC0tzZWaGzRoQHR0NJmZmeYGpJWR1hJCCCFE3VWthCcqKoo333yTKVOm8Oyzz3Lw4EHi4+OBywmPg4MD7u7unD17lpKSEoqKinB1da3Qx6r8mHhSUhLvv/8+06dP5+zZs+blrQ4dOnDs2DEA8vPzzY9fnvAEBwfj6uqKXq+nfv36vPfeeyxfvpzDhw/z5Zdf4uzsTEBAAM2aNTN/7ZWbo8sLE1paWjJjxgyWLVvGBx98QG5ubqXXLq0lhBBC1FrllZZr5HZ7LmlV66qaN2/Oxo0bAfDy8kKv1xMUFFQ20F8vkIODA46Ojjz77LN06tSJ+vXr07t3b7y9vcnOzgbA09OTc+fOmbuad+jQgTfeeIOePXuSmZlJs2bN8PHxITo6mqlTp171PNzd3Zk5cybh4eFA2b6cevXqAddObMqrLpfTaDQVPnZxcaGkpISsrKxKr93a2honJ6cKNyGEEKI2KO+WXlO321G1Ep42bdqwceNG1q9fz6xZs/Dz88PJyalCkmFpaUmDBg3o27cv27ZtY/Lkybi4uNCmTRv+97//kZeXR0pKCidPnqRFixb8/vvvtG3blvbt27N161b69OmDjY0NixYtYv369UyePLnS51M+q1T+/5UlNlfeZzQaOXjwIIsXL2b79u0A+Pn5UVBQQHp6enVeDiGEEELUEdXatNy6dWsOHjzIsmXLCAgIYM2aNdx3333ExMSYY7RaLf7+/uj1evR6PVZWVhiNRho2bMikSZPo2bMneXl5jBw5Ei8vL2xsbPjuu+/MM0Xlyk9xXamgoIATJ06wf/9+mjVrRsuWLc2f02g0Vx15z8rKYuPGjRw/fpzo6Gh69uzJe++9x7p162jQoAHHjh1Dr9fTuHFjfvzxRzIyMqrzcgghhBC1g1Zbc0tRt+mSVrUSHn9/fwICApg7dy4AM2fOZOzYsSxevBgvLy9znJOTE4WFhSQnJxMcHGyeYXn88cd5+OGHK5yIunJ5yGg0XjNxAdi6dSsdOnTgwQcfpKSkhN27d3Px4kV69OhBfn4+e/bs4eDBg3h4ePDwww+TnZ3NhAkTOH/+PE2bNjXvH3r++ecZOXIkO3bs4JNPPuHSpUvMmjULS0vLf5TwBPbtg5O9XZVxmvB2SuM5hzVXfmzHBPXKsReK1Cu3nvsjXinO74lM5TGtq1GVuVWoelXknJ1/KMWZStWvP/tMdX4ODihHqlZQ1t49XP3Rm6nXjfIId1OOrd9vtFKczd4flce07v2ccqxtgfrPltHaXinOed1O5TF17mrVmwFsrWyUY4312yrFmXTqb802h3crx1r5ByvHNnmsjVKcZZPYqoP+4tTmHuVYTuxQDg1W7H9odFCvip3iqf5e7BluqjoI4GKC+pj9n1CKK05OhWmfK4/7/1m10zh7e3t++63sD+3IkSPR6XQsXry4wrJWREQEX331FcHBV/9yWVpampefrnoyV3Q3/7vg4GC6du3KsmXL+PHHH/H392fnzrI3sJ9++omZM2eSkZHBsWPH+PLLLzEajaxbt4533nmHMWPG0LFjRwDS09Pp3bs3M2fOpF69ehw7dgyNRoOLiwuZmZkVmpAKIYQQdYJUWq5StQsPdunShby8y707vv/++6s28FpaWl63rk1lszjX4+3tTXx8PMnJyfz444/8/vvvfP755wD06dOHHj16sHPnTr744gv+/PNPBg0aRL9+/Xj11VcJDQ2ltLSUDz/8kE8++YT+/fszdOhQDhw4wJYtW7h48SJ+fn6cOXOGrKws3N3V/xUghBBC3GoarQ5NDSUqNTVObVPtGZ7Zs2fTp08f84bhm3VaydLSkpycHPr27cvmzZvNTURzc3PR6/U888wzfPXVV7Rt25aCggLi4uKYMGECK1as4M033+TXX39l586d2Nvbk5iYyJo1a/jqq69ITU3l6NGj1KtXD3t7e0pKSm7K9QghhBC3i9mzZxMSEoKNjQ0xMTHs2rWr0tgFCxbQoUMHXF1dcXV1pVu3bteNryn/aGeSwWCo9gxNTejatSuLFi1i8eLFfP/996xatYqDBw+yZMkSIiIiWLx4MQMHDsRoNHLu3DlycnL49ddf2bVrF02aNEGr1TJ69GhKSkqYPHkyzZo14+uvv6ZZs2Z06tSJ//znP+aihEIIIUSdodFe3rj8b2+a6qUGS5cuZdSoUYwfP569e/fSvHlzevbsSVratbvIb9y4kUceeYTff/+d7du3ExgYSI8ePUhKSqqJV6JS/6iXlu4WndHv2LEjmzdvNtffCQ0NJS4uDn9/f/bs2cOCBQuIi4sjPz+fkydP4urqyscff4yPjw+PPvooLVu2xMbGhmnTplX7saW1hBBCCHG1Dz74gKFDhzJkyBAA5s6dy+rVq/n000954403ror/5ptvKny8cOFCli9fzoYNGxg0aNANe543rFv6jdC6dWumTJlCx44d+eKLL7CxseGOO+6gXr16ZGZmsmTJEvr378+TTz6Jr68vTk5OrF279rpjlm+e1lZxDG/q1KlMnDixxq5FCCGEqCk3Yg/P3/9hb21tjbW1dYX79Ho9e/bsYfToyyc7tVot3bp1M9e6q0pBQQElJSW4uamfJP0n6lTC06BBA9atW4e1tbW5o3r9+vUBGDRo0DUzw/Lmo0ajEZPJdNVJsCsTnWs1Ki03evRoRo0aZf44JydH+mkJIYSoHcpbS9TUWHDV37jx48czYcKECvdlZGRgMBiuqp3n7e3N8ePHlR7u9ddfx8/Pj27duv3z56ygTiU8rq6uFXprXcu+ffuYP38+hw8fJiMjg2HDhjFo0KAKJ69MJhMajYaioiI2b97MTz/9xJ49e3j66ad58sknrznutTJbIYQQ4nZ17ty5CgeTbsTfwGnTprFkyRI2btyIjY16Tat/ok4lPFA2fZafn4+rq6s5cQHMbSUOHTpEUlIS7733Hh4eHrz99tvo9Xoee+wxRowYQUJCAq1ateK1116jYcOGrFmzhj/++IM//vgDR0fHCmMKIYQQdcINqLSs0jfSw8MDnU5HampqhftTU1OrPAT0/vvvM23aNNavX1+h2feNUufqR48bN868tHRlkcDyJCUsLIzg4GACAgKoX78+rVu35sCBAzg6OjJ06FC2bt3Kgw8+yDPPPANA06ZNCQoKwtHR0VzpWQghhBBVs7KyomXLlmzYsMF8n9FoZMOGDbRrV3mHgXfffZdJkyaxdu1aWrVqdTOeat2b4YmNjeWjjz4CqJCclP9/SEgIubm55no6CQkJtGjRAicnJy5evMiDDz7IhQsXzMffwsLCzMlOVRuXr0VjYYnGovIii+VMVmrl7zUXE5Ufu+Ss2voowPltp5Vj3Rq4KsWVJp5QHlNXr6ly7MXf1inHZhxUu64QFwflMf3bRyjHZh4/qxybE6cWW512EZ0OblWO3Xf3XcqxDufU2hUUX0xRHtOitEg5lswLyqE6R7WNjkWZBcpjWkSotYIBMJ47phy7LUOtBUGJobjqoL/4bVZvb9LolU7Ksdnxq5TiPC3UlzmM9upFXXW2au+ZAFknzynFnTFe3aOxMueTc5VjfR3VXgN/x3rKY+oU//F9qcARoEa7nFd3nFGjRjF48GBatWpFmzZtmDFjBvn5+eZTW4MGDcLf35+pU6cC8N///pe33nqLxYsXExISQkpK2fuIg4MDDg7q79XVVecSnujoaE6fLvsjd60Exc/PD41Gw4ABAzAYDNSrV4/x48dz5swZVq9ezeuvv86dd96Jl5cXZ8+eJTg4GJPJRFJSkmxCFkIIUTfVZEuIao4zYMAA0tPTeeutt0hJSSEqKoq1a9eaNzInJiZW+Hs9Z84c9Ho9ffv2rTDOtTZF16Q6l/AEBQWZ9/HY21f8F0D5/hsvLy8CAwMZMWKEuanp6tWrcXFx4c477yQ9PZ2cnBz27t1Lz549KSws5NixYwQGBsoeHiGEEKKaRowYwYgRI675uY0bN1b4OCEh4cY/oWuocwkPgKenJ/v27aN9+/bo9XqsrKyAigmPhYUFjo6O5q9p164dO3bsICIignbt2nHHHXeg1+uxs7NjwoQJhISEAEiyI4QQou65hTM8dUWdTHgiIiKIi4ujffv25mQHLicrPj4+/P7772RkZBAYGIjRaCQkJIRXXnmFRx55hKZNm1aYHWrevPlNvwYhhBCipmi0WjQ1dEqrpsapbepkwhMdHW3eFLVy5Urc3d2ZNm2aeZame/fuREVF4efnB1ze6xMUFERQUNA/ekxpLSGEEELUXXUyjevatSsAp06dYuDAgRWWpAC8vLyIjIyssudXYWEhe/fuZcmSJezfv/+6sVOnTsXZ2dl8kw3OQgghag2N7vKy1r+9aWRJq9Zo2bJllQnK38XHx3Ps2DG6dOmCnZ0dGzduZOTIkbi5udGgQQPOnDlD06ZNK02SpLWEEEIIUXfVyYTnetLS0jh//jzR0dFoNBruueceEhMTcXV1xd3dnRYtWqDT6fjPf/7D7Nmzad++PVC2a9xkqrxGhrSWEEIIUWtpNKCpoUWb2/Twzm2T8Lz55pusXLkSOzs7jEYjQ4YMYfjw4WRlZTF9+nR69Ohhjl2yZAlt2rQhPDwcgJKSkgpLYkIIIUSdotHWYMJTJ3e7VKlOXFVaWpr5/0tKShg4cCC7d+82fwxlfTueeOIJdu3axfz58/n11185fPgwMTExHDlyhKKiIn766ScyMjLIysqitLQUV9eyisKWlpaYTKbrzvAIIYQQou6qlTM8+/btY968eRw5coT09HQCAwMZMGAA999/v7lR2ZEjR2jVqpU5SWnSpAkXLpSVo69Xrx6urq6cPn2arl278sgjj/DDDz/g6uqKv78/9erVY9WqVRQXF2NnZ4fBYKhyg3NlTEWFmBS+1CLrvNJ4xUd3KT+2ZZdHlWMD9qiXv9daqf1YaJp2UR7TcOg35VjHIPXy7wnrDinFHf1qk/KY+Wn5yrEOvtVoWdHRRSnOI1ytVQJUr11E9Jq1yrElJqNSXLUWeQsylUONbv7q4xoNVccAJzept21xW/Chcuz+hTuUYzueVPv9zitRe/0BPJ7orxxr8qmvHHvx1CWluFAHT+UxtYXZyrEaT/UTtbaeau1wgkvilMf08lVvMeObtFMp7qSVes8oLzu192HdX9MWJo0WUw3NzNTUOLXNLbkqvV5PZmbZm9+VsypGY9kv+aFDh0hOTmbixIkcP36cd955h8WLF/Pf//4XKKvDc/jw4QpjRkREkJiYSFFREZs2baKwsJC2bdvi6elJw4YN+eOPP1i1ahUtWrQwL2X98ccfAOZkZ8uWLTf2woUQQghxS9ySGZ5x48aRlpbGZ599hsFgwMKi7Glc2fE8JCTEnJjExMTw5ptvMmrUKIqLi2natCmLFi0CLicrzZo148SJE7Rq1Qo/Pz8ee+wxvLy8sLGxITMzE6PRaK7EHBwczEsvvcTIkSOZN28excXF7Nmzh+LiYtq0aVOhmKEQQghR68kenirdkoSnqo7n9erVIysri/Pnz5uLBzZt2hQ7OzuOHz9OWFgYGRkZ5q8xGAz4+Pjg6urKxx9/TOPGjc1jOjk5YWNjQ0JCAqGhoeb7e/TowaxZs3jvvfcoLS2lcePGPProo1haVt35XAghhKhVNJqaO10lp7RqTlUdz319fdFqtVy6dHkN2dPTk7S0NAwGA6GhoZhMJrKysnBxcTHHuLu7Ex8fT0REBCaTCYPBgKWlJU5OThw5cqRCwgNlBQzLixhWRSotCyGEEHXXLUl4VDqeu7q6cuHCBYqLi7G2tqagoACNRoNer8fR0REPDw/Wrl1LQEAAy5cvp1+/fri5uREXF4dGozHfAH755RecnJwqfT45OTmcPXuW4ODgSuOmTp3KxIkTa+5FEEIIIWqKVlt2q6mxbkO37KrKO55D2SbmcuWbmP39/SksLDTv0Zk8eTJ33303jRo1AsDNzY2hQ4fy9ttvk5WVhaWlJdOmTePll182j1We8Pw9iTEYDBgMBvMm6W3btjF79mxOnTpV6fMdPXo02dnZ5tu5c+f+7UsghBBCiJvklh1Lr6rjeUhICJMmTWLOnDmYTCaaNGnCpEmTzLVz5s+fz2effXbdxyjfqKzVaivsFfr7EfSQkBCcnZ3Jzq78yKRUWhZCCFFbybH0qt2yhKeqjuddunQhMDCQgIAA88blK5VvLtbr9ZWeqrrW/qDS0lK+++471qxZg5ubG9OmTcPPzw+TyUR6enrNXaAQQghxs8gprSrdsquqquO5q6srbdq0qZDsmEwm8zKU0Whk9+7d/Pzzz9ccPzk5ma1btzJy5EgeeOAB9uzZA8CJEyf48ssv6dWrF6GhofTr1w8nJyccHR25ePGiVFsWQgghbkO3bIbneh3Py9s8/H2G5sqNyFqtluTkZBYuXIilpSXe3t60bNnSHDtjxgy2bNnCI488QteuXXnsscf4448/WL58OV27duXRR8uqFH/yySecP3+ewMBAkpKSyMnJwdnZ+cZctBBCCHEjyAxPlW5ZwlNaWmpu7VA+q1Ke4FyZ2JTLy8vj4MGD/PHHH3Tp0oXWrVvzySefsGfPHiwtLRk1apT5hBdAo0aN2LdvH0888QQODg4sXbqUPXv2cP78eXr06EFBQQF2dnbUq1ePQ4cOERoayqlTp8jMzKxWwlOSkkCJrU2VcRb+YUrjWUd3Un5so0FfddBfAh4frBxbWk+t/Hn+N9OUx7SLaKYca1VfPTbqp1fUAhVbJQB8dThDOfZJZ7WWIQAmfZFSXP1+o5XHdDi3WzlWtV0EoPyGN7/dUOUho6J9lGN1VuqtXixs1d7G7jv6q/KYv19Ur8fV6fXKT4D+ndX2JUpxttE9lcdM/0O9Qrx+tfpr0GbFYqU4w+ZvlcfUOroox+aeOKIca/yrp2JVEp3ClccMyj6hHHvBP0YpLviXGcpjamzsqw4CbDOy/voCSXiqctOuymQy8cMPP/Dwww/Tvn172rRpw5YtW9BoNGi1WnOyU1payr59+/jss89ISkoC4Oeff2bw4MF89NFHJCUl8dprr3Hy5Ek+/fRT7rnnHubMmUP79u3RaDTm5MnR0ZF27dqZx4iMjOTs2bO0atWKLVu2kJubC5SdBsvLy8PV1ZX09HRzvBBCCCFuHzWa8FyvGJ9Go+Gnn37C3d2d+fPns3fvXnr27MnmzZv59ttveeCBB9i5cyeffPIJL730EuvXr+eVV17h4MGDNGrUiHPnzjF06FA++ugjPDw8WLduHb6+vtja2rJ582ag7Lh5ucDAQE6fPs2BAwfKLlSr5eDBgzz55JO4u7vzzDPP0L17dzQaDf369cPd3Z1evXoRHBxcky+JEEIIccOZNBrzSa1/f5NKy1WKjIxkzZo1NG3atMLyktFoRKvVEhoaSoMGDSq0fpg6dSqXLl1i/PjxODs7s2PHDiZPnkz79u0ZMWIEK1eu5Omnn6Zjx47k5eUBZb21yhMZHx8fc5d0nU5nnuEJCgrCaDTyzTffsGLFCi5cuMBHH32EhYUFo0aNYvXq1Xh4eJj3/fj5+fHAAw/U5MshhBBCiFqiRmd4mjVrxsGDBwGuedqpUaNGzJ8/n9jYWHOF5E6dOuHm5sbdd99Nbm4uPj4+eHh4ANCzZ09Onz6Nra0tTk5OJCcnA9CqVStOnjwJQHh4OGvWrGHx4sXs2bPHnGQ5OztTr1492rZty6OPPsoXX3xB8+bNAbC3t6d///506dKlwn6d653QKi4uJicnp8JNCCGEqBXK9/DU1O02VKNX1bx5c3bu3AlUTB6u7ILu5ubGmDFjSEhI4KGHHqJJkyZkZmYCZUlKcXGxuc9WYGAge/fuxdXVFScnJ/NMTtOmTdm7dy8Affr0ISwsjD179lRoU2Fvb49Op8PCwoJ77723wpF3KJt12rdvH4sWLWL48OFs3rz5qo3SV5o6dSrOzs7mW2Bg4L98tYQQQogaUt48tKZut6EaXdJq06YNM2bMuOr+8kTCx8cHR0dH6tWrh4VF2UO3bt3aXPAvNDSUtm3bMn/+fIxGIz///DNDh5adAvH29sZgMFBaWoqHhwcbNmzAZDJhY2PDJ598UuHxypfTWrdujY2NDUeOHOHEiROcPn2afv36ERwczC+//MIHH3xAaGgogYGB2Nramvt2Xcvo0aMZNWqU+eOcnBxJeoQQQog6okYTnhYtWnD27FkAc4HAK9s4+Pn5YWFhwblz54iMjATKEpnz58+TkZGBh4cHjz/+OFlZWXz77beEhYUxaNAgAB555BHzOEajkdatW5s/Li9IWN5CYufOnWRkZHDvvfcyefJkFixYQKdOnSgsLCQ+Pp6XX36ZNWvW8PDDD/PUU08pXZu0lhBCCFFrybH0KtVowhMUFERpaSlwufXDlTQaDUajkTNnzlRoCTFp0qQKJ6xeeOEFXnjhhau+vnzmprS0lLy8PNzc3Mz36XQ6jEYjGo2GAwcOcODAAe655x48PT3p378/7777Lnl5ebz55pssW7aMxo0bs3nzZsLCwtDpdObZICGEEELcfmq88KCjoyNvvvkmVlZW/Prrrzz44IM899xz2NraAjB8+HC8vLzMyY7JZOI///nPVeNcq/Fn+X/HjRtHWloan332GQaDwbw8Vv75Zs2asXfvXi5evEhkZCS//PILAA4ODhgMBmxtbXnwwQf5/vvvefPNN/H19SUjI4O3336bDh061PRLIoQQQtxQ0jy0ajWe8Nx9991s2LCBO++8k+eee45u3bqZkx0oW/a60pXFAq/cNHytxp/lYmNj+eijj676mvL/Dw4OpqCggOTkZBo2bMjFixf54YcfOHXqFOfOneONN97A29ub3377jfT0dI4fP84vv/zChx9+SGxs7FXd1K9Ho9WhUYgvTYpXGk8X1lz5sY0n/1SOzdq1QznWrVO+UlzKDvVKqME+6lV2DRfVKwJbBjauOgjQ5l9UHrNjcIRybOK7Y5VjA59UWz612fuj8pjFF1OUY6uzIKtaQXlI8n7lMc88P0A51trFQTk2+6za91aXm6o8pqttPeVYm0L1ny2dYsX1Emu1KrsA9v6eyrEW9nnKsbo8xWbK7uq/2xZeAcqxRdu2K8fmJqYpxaXnFCuP6e/irxybXWyoOgjwveM+5TFVl5Usk1Mvx1/n72a1SMKj5v33368y5soaPVAxaVERHR1tPsl1rcTI19cXCwsLkpKSaNq0KTY2Nnz88ce0atWKl19+GX//sh9ko9GIp6cnnp6elJaWcuTIEdLT0/Gpxh9nIYQQQtR+N6SXVmlpqbkf1rUSkuomOH8XFBSEXq8nPz+/wlF0uJxMubq6mttEuLq6MmzYMO68805zXE5ODvv27SMlJYW0tDRWrFjBE088IcmOEEKIukc2LVfphlyVhYUFOp3uustS/5anpyf79u0DQK+/3ESzfHksMDCQtLSyac769euzadMmDAaD+fSYk5MThw4d4quvvuLcuXOMGDFCKi0LIYQQt6lb1i3934qIiCAuLo727dubN0DD5dkjLy8v1q9fj16vp3nz5pw4caLCBmiAESNGMGLEiJv+3IUQQogaJTM8VaqzCU90dDQzZswgPz+flStX4u7uzrRp08wVlbt3705UVBQWFhb07dv3Xz9ecXExxcWXN7xJawkhhBC1hiQ8VaqzV9W1a1cATp06xcCBA5kwYUKF9hFeXl5ERkZed1lt1apVjBw5ks6dO/PSSy+Rm5tbaay0lhBCCCHqrjo7w9OyZUv2799fZVxxcTEbNmxg+/bt7Nu3j4ceeoiHH36Y5ORkli9fTpMmTRgwYMBVm5//TlpLCCGEqK1MGk0N1uGRXlp1RkZGBtOnT6d///7Y29szYcIEmjdvzgsvvMDUqVPR6XRkZ2fj6+vLa6+9pjSmtJYQQghRa8mSVpXqXMKj1+vNrSOSk5Pp168f4eHhV8Xl5+cTFxdHly5d6N69Oz169KBTp04cPnyYP/74g4EDB7J69WpGjBhBWFgYsbGxtGnT5l8fmRdCCCFE7VPn0rgpU6YQExPDuXPnOHbsGJMmTSI+vqyKcfmRc3t7e0JCQoiLi8PNzQ0bGxvOnTsHlB1nP3r0KHfeeSdDhgwhLy+P48ePy7F0IYQQdZdGU7O321Cdm+GJioqie/fuTJw4keTkZMaOHcv27dsJCwszz85YWVkREBDA77//jkajwdnZmS+//JKNGzeSkJDAu+++C8CAAQMYMGAAycnJ6HQ6IiIiSExMJCgoSPn5lBQUUfJX7Z/rsXJ0URrPlHZW+bETGt2jHBtq76Qcq1qe3C1Svfz+umfmK8d2+a/6qTrt/t+U4kwu6uX3nd2bKMd6DBqkHFu4e6NSnHXv55THtCgtUo6lIFM5NCparQBnddpF1PtkqXLshoYxyrE/nVc7MTlzhHorlKAWV88aV0YTr94CIXHxt0pxAQ/3Vx4zfd8p5VhjSalyrOP+zUpxe6cvVx4z+gX19yxrV/X2IqVFJUpx7SzV24uUbFe7foBGPmp/MwoOqrf4KUjLUorLzFJvF/L/XZ2b4WnRogWnTpX9gvv6+pKenk6TJmV/oMoTHp1Oh6+vL5mZmRiNRvz8/MjLy6N9+/Z88skn3HHHHcDlo+W+vr54eXnh4uJCZqb6HwUhhBCiVijfw1NTt2qaPXs2ISEh2NjYEBMTw65du64b//333xMeHo6NjQ1NmzZlzZo1//TKldW5GZ6goCAKCgr45ptv2LVrF0VFRTRo0OCqOA8PDwBSU1Px9vamU6dO9OvXD3t7e3P7ic2bN7Nt2zbz8thjjz12zf1AQgghRG12K7ulL126lFGjRjF37lxiYmKYMWMGPXv25MSJE3h5eV0Vv23bNh555BGmTp3Kvffey+LFi7n//vvZu3eveQLjRqhzMzxQNiOzcOFCPD09ycrKYs6cORQUFACX9/GU18uJj4/H19eXtLQ0duwom040GMo62wYEBODs7EyvXr348ssvmTBhgpzEEkIIIarhgw8+YOjQoQwZMoTGjRszd+5c7Ozs+PTTT68ZP3PmTO666y5ee+01IiIimDRpEi1atGDWrFk39HnWuRkegEaNGtGrVy8GDx5M+/bt+fjjj3FycmLYsGHmGFtbWxwcHDhz5gwtW7bk7rvvpl69sj0nFhZllx0VFUVUVNStuAQhhBCi5tyiY+l6vZ49e/YwevRo831arZZu3bqxffu197dt3769Ql07gJ49e7Jy5cp/9HRV1cmEJzo6mhUrVjB48GA6d+7M2bNnWbJkCf3798fFxQUAFxcXJk2ahJ2dHQAPPfTQv3pMaS0hhBDi/5O//527Vj26jIwMDAYD3t7eFe739vbm+PHj1xw3JSXlmvEpKSk18KwrVyeXtDp06ICPz+VTJI899hhr1qwxJztQtoG5PNm5ntTUVDZu3MjPP/9sXuq6FmktIYQQorYqq7RcczeAwMDACn/3pk6deouv8t+pkzM8sbGxxMbGmj/W6XRVfo1erzd3VTeZTJSWlvLUU0+xZ88eGjRogIeHBw0bNiQsLOyaXy+tJYQQQtRWJlPZrabGAjh37hxOTpdLmlxrj6uHhwc6nY7U1IpH/lNTUytMTFzJx8enWvE1pU7O8Kg6cuQIAwcOpFGjRnTt2pUVK1aQl5eHRqPhP//5Dx4eHhw5coSVK1cyZcoU3N3dKx3L2toaJyenCjchhBDidvX3v3nXSnisrKxo2bIlGzZsMN9nNBrZsGED7dq1u+a47dq1qxAPsG7dukrja8ptkfDs2LGDNWvWUFhYCJTN4BgMBj799FNCQ0M5cOAA77//PvPnz2fhwoUAZGVl0aJFC6Bsf055HR4hhBCirjGaTDV6q45Ro0axYMECvvjiC44dO8Zzzz1Hfn4+Q4YMAWDQoEEVNjWPHDmStWvXMn36dI4fP86ECRPYvXs3I0aMqNHX5O/qxJKWXq8nPz8fV1dXcw0dKMsitVotBw8e5PDhw7Rs2RJbW1s0Gg2///47J0+eZNGiReZCSEOGDGHZsmVER0fj4OCAm5sbcHmarnw8IYQQQqgZMGAA6enpvPXWW6SkpBAVFcXatWvNG5MTExMr/G2NjY1l8eLFvPnmm4wZM4YGDRqwcuXKG1qDB+pIwjNu3DjS0tL47LPPMBgM5mPl5YlP06ZNOXDgAHl5eeYXuKCggKSkJLy8vCguLsba2poWLVqwevVq0tPTMZlMZGdnA2WJjkaj+UfJjqGgiFJj1dmw1sNfaTyTVv1bkpynV45t4Fz5ct3fGRw8lOIcG5xUHlOv8BqVs/RV3xtlyExXitNWo7WEi4V6+X19wjHl2KJLaif7bKvRAoLMC8qhRje1n0EAnVXV++IArF3Uy/9Xp11E15M7lWN/c2ysFGcR2lR5zIxC9Z8BTwcX5Vjv9i2V4jS+VxdTrYy9r/rvtqW9jXKsxsJSKc6rqfq+i6L0S8qxH77yg3Js18Zq71n13UOUx9RYX79ScIVYT7XWEiX5G5XHNBQVVx0EGP46PWz661YT/sk4I0aMqHSGZuPGjVfd169fP/r16/cPHumfqxPTGbGxsSQmJgJU6GZe/v/BwcHk5eVx6dLlX6YGDRqQnl72x7B8s3JYWBjnzp3Dz8+PgIAAtm3bhsFgQKvVotFoiIuLk+PmQggh6hyjqWZvt6M6kfBER0dz+vRpgGvOwvj5+aHVarl48aL5vkaNGpGfn09qaioajcY8i3PhwgUcHR155ZVX2LRpE5988gmHDh3ixx9/5N133yUpKemmXZcQQgghbo46kfAEBQWZ9/FcOcMDZRuUAVxdXblw4QJFRWXdo7VaLf3792fMmDGkp6ej1Wr5/PPPad++Pe7u7lhaWrJ8+XISExN58skn+eKLL2jdujW+vr43/fqEEEKIf8NkMtXo7XZUJ/bwAHh6erJv3z7at29/VU0djUaDv78/xcXF2NjYkJ+fT3x8PM8++yy///47999/PxcuXMDPz4/33nsPPz8/jEYjDRo04L333lN6fKm0LIQQoraqyaUoWdK6xSIiIoiLiwMu78mBy/t46tWrx6xZs/D39yciIoIXXniB4uJiXn75ZRYtWsT+/fvZunUrsbGxmEymam9QlkrLQgghRN1VZ2Z4oqOjmTFjBvn5+axcuRJ3d3emTZtGSEgIAF26dMHPzw9/f/+rkpHw8HDz/xcVFWFjY4PJZKrW6SyptCyEEKI2u00nZmpMnZnh6dq1KwCnTp1i4MCBTJgwwZzsQFmz0LZt25qTkL/3xdq0aRMhISEsWLAAKJsZ0ul05mSnqjVLqbQshBBC1F11ZoanZcuW7N+/Xyl2z549zJ07l8mTJ+Pl5QXAwYMHad26NS+88AIAaWlpTJs2ja1btxIcHMx7771HcHDwjXr6QgghxA0je3iqVmdmeKqjUaNGeHh4MH/+fKCssKCbmxvp6ek88MADXLp0iX379nHhwgWWLVvGwoUL8fPzu8XPWgghhPhn5JRW1W7LhMfBwYG+ffvy1VdfAWUnrPbt28cTTzxBbm4umZmZ6PV6Dh06xPr160lKSrptv8FCCCGEqENLWtXVsmVLGjduzDfffMOjjz7Kzz//zKhRo1i5ciWJiYnce++9pKSkcPHiRZ555hliY2N57bXXrtsx/Vq01lbobKyqjDNmKBY09A+vOuYvXtZqpd8BDMnq7Qq0JWotK9K27lEec8elQuXYuxXbRQCU5OYpxVmWliiPqddU/f0spyvIVY61C1ArwW+0tld/fEc35ViMhqpj/mJhq/bWkH32YtVBf/npvHopB9V2EQCTc48qxWmSDiiPaVT/EcCYl6UcW5ik1grEOqpAecySfPXfraKL2cqxDvXDlOLSDqUoj+kZpd4yo3N9V+VYg17tZ1ujV39dUWytAaApURvXxlP997W0SO19WFdS1gbF+NetJtTUOLXNbTnDU27kyJEsW7aMffv20bhxY0pKSggLCyM+Ph6TycTQoUN59dVX+eCDDygsLOTcuXO3+ikLIYQQ4ga4rROezp074+PjwyuvvEJQUBDu7u4EBQVx8uRJLl26xJw5c7j77rsZNmwYHh4eREVF3eqnLIQQQlSbyVSzt9vRbbukVV6BefDgwTz44IOUlJTg4OBAaGgoiYmJ2NjYEBkZSWBgINHR0fj7q3eSFkIIIWoTOaVVtds24SmvwNyiRQvmz5+PTqcDoHfv3vTu3RuAjh07Ko8nrSWEEEKIuuu2TXjKWVlZce+99151f0FBAVu3buXIkSOcO3eOp59+moiIiErHmTp1KhMnTryRT1UIIYT4R2ryOPntemr5tt7DA2WJzd69e8nKyqpw/7Rp05gyZQoZGRmEhYVx8uTJ687ajB49muzsbPNNNjgLIYQQdcdtO8OTn5/P0KFD2bt3L05OToSHh/PCCy/QunVrli1bxuHDh1m+fDlubmXHBMt7bFXG2toaa2vrm/X0hRBCCGVyLL1qdX6GJyMjgzFjxrBjxw4ASkvLahJMnToVd3d3jh8/zq5duwgICODjjz8mMzOTQ4cO4ePjg5ubG4WFhZhMpusmO0IIIURtZqIGT2nd6ou5QWp1wqPX6/nzzz9ZuHAhkyZN4vjx49eMKygo4OTJkwBYWFhw6dIl0tPTueOOO8wxL7zwAn5+fixatIiGDRuSnV1WgMva2tq8wVkIIYQQt6davaQ1ZcoU3n77bcaNG8epU6eYNGkSb7/9NmFhYRiNRrRaLfb29oSEhHDmzBnz16WmppKVlUWDBpererq5udGgQQO2bNlCTEwMZ8+eBcq6qptMJvMpruoqTL2EhUKlZQfF8QwHNyo/dli7h5Rjdzz/jnKsc7CzUlzkxLHKY07tc/XG8cqc/myxcmzmabUK0pGD1HP745PmKsc27NdOOfbk978pxTmv26k8ZlGmeuXYk5sSlWPvO/qrUpwuN1V5zJkjjijHWoQ2VY5VraCs92+uPObF2M7KsT9dUKv2DXDfxjlKcUZrR+UxDYoVeQG826q/rnMGfqQU98Jv7yuPqXVSr2R/Z4++yrF/Dn1FLXD/L8pjahq3V44t3v6jUpxVB/X3bNdEtd+XgrQM4DOMJhPGGtpsXFPj1Da1eoYnKiqK7t27M3HiRKZPn461tTXbt28HLh87t7Kyws/Pj6Sky60bAgICyM/PJzf3ctl/a2trbGxssLS0JCoqilOnTnHkyBEsLS3R6XSYTCY2b96MXq/+5iGEEELUBqYavt2OavUMT4sWLTh16hQAvr6+pKen06RJE+BywqPT6fD19SUnJ4eSkhIsLS1xdHQkODiYXbt2ERMTg62tLQDLli3jvvvuw9HRkTfeeIO33nqLO+64g6ysLPbv388jjzyCpaV6/xQhhBBC1A21OuEJCgqioKCAb775hl27dlFUVFRhmaqcu7s7Go2GlJQUAgMDARg3bhx9+vTBzc2NPn36sGPHDgwGA23atAHg5Zdfpnnz5nz33Xf4+voyYsQIYmNjZT+PEEKIOkcqLVetVic8UDazs3DhQrp27UpWVhZz5szh+eefx87OzryPx9nZGTc3N3bv3k1OTg7Lly+nc+fOfP7550yfPp3p06cTEBDAyJEjadz4chfmLl260KVLl1t4dUIIIYS4GWp9wtOoUSN69erF4MGDad++PR9//DFOTk4MGzbMHOPk5ERBQQEPP/wwrVu3xsPDg9atW9OxY0fmzZuHhcW/v0xpLSGEEKLWqsmmn7fpDE+t3rQMEB0dzYoVK4Cy7ud9+vRhxYoVZGVlodWWPX1HR0dmzZpFcXExW7ZsYeXKlfTq1Qug0mTHYDAwf/58Hn/8cTp37swbb7xx3Q3LU6dOxdnZ2XwrXzoTQgghbjUjphq93Y5qfcLToUMHfHx8zB8/9thjrFmzBhcXlwpx9vb21/z6M2fOMG/ePEaOHMmIESNISEgA4Pvvv+ePP/6gW7duLFq0iMjISC5dulTp85DWEkIIIUTdVeuXtGJjY4mNjTV/XFm9nPPnz5OZmUl4eDiWlpYYjUby8/OZNm0a2dnZdO/eHZPJxIULFwgICODzzz9n0KBBDBw4EICwsLDrPg9pLSGEEKK2MtXgktZtWoan9ic8VTEYDOh0OkaMGMGff/7JypUrad26NVqtlnfffZfi4mKWLFlS4Ws2btxIcHAwLVq0qDCGEEIIIW5PtTbh0ev1HDhwgMOHD/Pnn38SHR1N3759cXV1rRCn0+nIzs6mRYsWxMXFsWLFClq3bo1er6e4uNh8KquoqMjcRsLOzq7C8pUkO0IIIeoyOZZetVqb8EyePJlJkyYxevRo6tevz59//slPP/3Exx9/THBwMCaTyVwz5+zZs2RkZDBp0iR++OEHtm7dSqtWrbCwsDAXEryyOWh5a4qEhATCw8MpLS3FwsLiH830uHfsgJO9XZVxhospSuOZDAblx04sVi+SGPvtLOXY4j0blOK2WTZSHrNZVLRybFCfZOXYwCK11go2bXooj6n7YbtyrOMdPZVjI+1t1R7f3afqoL9YRKi3tnBb8KFy7O8X1X62XG3rKY8Z1CJcOTajsFQ51lh1Zxegeu0i2m3bqBz7S71WyrH602rtAjL3fas8ZtAr45RjNbnpyrF391drcaKP7K48pm7bkqqD/nKmiXobhpZfL1SK+61Df+Uxuy7zUo7d9PJnSnGt9z+tPKaz7tq9I/9Ooy37myVLWlWrtZuWW7RowV133cXkyZMZNWoUEyZMIDw8nHfeKesJZbriO+Ls7MymTZt44IEHeOaZZ8xtKNzc3Mybiw0GA4a/kgl3d3e6du3KrFmzyMjIMJ/kWrRokRw3F0IIIW5DtTbhiY6O5tixYwCUlpbi5+fH/fffb+6lVX4kHcqahQYGBrJ27Vq2b9/OypUrOXz4MJ07d2bHjh3s378fnU6HTqfj+PHjHDx4kOeffx5/f39eeukl7r77bpo3b86OHTsoLCy8JdcrhBBC/FNyLL1qtXZJKygoCL1eT35+vvnIeWBgIBqNhgsXLuDn52eutLxr1y5OnDjBlClTiI6OZuDAgXz33XeMHTuWrl278sEHH+Dv709SUhJJSUmMGTMGgLlz57J+/Xr0ej3NmjWT2jpCCCHEbarWJjwAnp6e7Nu3j/bt2wOQm5tLWFgYFy9exM/PD4PBgFar5c8//zTX2YGyGaGHH36YOXPmMGnSJH799Vc2b95M9+7dadu2rbkfl0ajoXt39fVnIYQQojaSPTxVq9UJT0REBCdPnjQnPJ999hmOjo40bNgQAEtLS0pLS7G3t6e0tGyTY3FxMdbW1syePRsnJydMJhM9evSgRw/1TavXIq0lhBBC1FZGkwljDWUqNTVObVOrE57o6Ghmz56NRqNhzZo1GAwGJk6ciLW1tfmUllar5b333jMve5UXB/T29q5y/D///JPffvuNkydP4uXlxbhx47Czu/aJq6lTpzJx4sSauzghhBBC3DS1dtMylHUz1+l0nD59mvvvv58PPviApk2bApiPpGu12krbSgCUlJTw559/8tlnnzFp0iTi4+MBOHbsGP/97385f/48d999N35+fqSkVH50XFpLCCGEqK0Mxpq93Y5q9QxPq1at2LVrl3J8Wloarq6uWFpammvqTJkyhdWrVxMTE4OnpyfHjh0jLCyMcePG0aJFC/MG5qpq8EhrCSGEELWVLGlVrVYnPCrKl7Zee+01lixZwpIlS7jjjjvQ6XR8++23HDx4kF9++cVcobmkpIT4+Hjc3d1p2bIlcHnfjxBCCCFuT7V6Scvwt6rDq1atYuzYsaSlpZnvKy9A6O3tzaVLl1i/fr35cydOnMDDwwNXV1cKCgowGo1YWlpSUlJSoVKzJDtCCCHqMqPJhKGGbjLDc5Pt2bOHuXPnMnnyZLy8ykp8nzlzhlOnTuHl5WVegtJqtVy4cIHi4mLeeust0tLSWLFiBQ888ADOzs5cvHgRAFtbW3OC4+3tjaWlJYmJiUDZrE/5WNWlsbBEY1F1GX6tarsArXpri/PZxVUH/SUk9ZRybGlOtlJctE/le6f+ruSrt5Vj9Tr178P2KauV4po/mag8Zs75XOXYpG8+V46193FXirO1sqk66C/Gc8eUY/cv3KEc2+l1J6U4m8KLymNq4tVbdng6uCjHGvOylOJ+upCnPGZ12kX0PLNbOVZ39DelOK/eYcpjavIvVR30D6QeSlWKa5B8UHnMgqQE5dgGDc4ox6Z9NVcpLmJAlPKY1Xkvdm/gphRnb6n+3mbITKs6CDBk35jv/41y6dIlXnjhBX766Se0Wi0PPfQQM2fOxMHBodL48ePH8+uvv5KYmIinpyf3338/kyZNwtnZuVqPXWtneBo1aoSHhwfz588HymZyHBwc8PDwALgqOfnll1948cUX6du3L/PmzQPK6vikp6ebv758xsjV1ZW2bduyYsUK0tPTsbS0RKvVsnbt2gqzR0IIIURdUNY81FRDtxv3PB999FGOHDnCunXr+N///sfmzZsZNmxYpfEXLlzgwoULvP/++xw+fJjPP/+ctWvX8tRTT1X7sWvtDI+DgwN9+/Zl4MCBvPnmmxQXF3P48GHuvPNO4PIpLQA7OzsKCwvJysoCYN26dWzfvp0OHTowefJkjh8/Tnh4WePCwsJCjh07xuOPP87q1asZP348NjY2xMfHYzAY+PDDD80zSkIIIURdUJOnq27UKa1jx46xdu1a/vzzT1q1KptF/fjjj7n77rt5//338fPzu+prmjRpwvLly80fh4WFMXnyZB577DFz429VtXaGB6Bly5Y0btyYb775BhsbG9avX0/z5s2vitu/fz/Jycm0a9eOsWPH0rNnT7777jtcXV15+OGHmThxIp9++ikzZsxgyJAhnDpVtryzZMkSOnXqhLu7O0OHDmXRokXmKsxCCCHE/2c5OTkVblcW3/0ntm/fjouLiznZAejWrRtarZadO3cqj5OdnY2Tk1O1kh2oxTM85UaOHMnMmTNp3LgxkZGRZGdf3l9S3ktr8+bNDB06lPHjx5s/98ADD/DRRx8xfvx4Vq1axbfffou3tzd9+/alS5cu5rgBAwYoPQ+ptCyEEKK2uhHH0v/eX3L8+PFMmDDhH4+bkpJy1QqKhYUFbm5u162Dd6WMjAwmTZp03WWwytT6hKdz584sXbqUV155hdatW+PufnnjZ/myVkpKirmGTkFBAXZ2drzzzjvm+/r06UOfPn0qfQyj0YhGo6mwTPZ3UmlZCCHE/yfnzp3DyenyAYbKTjS/8cYb/Pe//73uWMeOqR+wqExOTg733HMPjRs3/keJV61OeMqPjg8ePJgHH3wQo9HIf//7X/PMTnmCMnbsWGxtbQHMrSEiIyOvOZ7JZLpqw7PK6azRo0czatQo88c5OTnSXV0IIUStUH6kvKbGAnBycqqQ8FTmlVde4YknnrhuTGhoKD4+PlcdDCotLeXSpUv4+Fz/JHNubi533XUXjo6OrFixAkvLqk9H/12tTnjKE5oWLVowf/5883rd3xMUf3//q75227ZtWFtbEx0dbY6/1ixOcnIyu3btQq/X06VLlwozSFeSSstCCCFqKyPU2Omq6u5Z9vT0xNPTs8q4du3akZWVxZ49e8yFf3/77TeMRiMxMTGVfl1OTg49e/bE2tqaVatWYWOjXrrjSrV603I5Kysr7r33Xu66664K95fX37mS0Vj2rVqxYgW//voreXlltTcKCgo4dOgQc+fO5ciRIwD8+uuv9O/fn8WLF3P+/P+xd9ZhUW1fH//SClIqCooIilICAiaioIiC3YGFgYGKglev2OJV7Ebs7u4OsDFQxEBKaaQ7Z2a9f/Cbc50ryPFeDHz353nm0Zn5zt77HE6ss/baa8X954AsBoPBYDAYZWNkZARHR0e4urriyZMnePDgAaZMmYIhQ4ZwK7Ti4+NhaGjIlZXKzs5Gly5dkJeXh127diE7OxtJSUlISkr6IjlxRfzSHp6yEE9zbdq0CQsWLMDBgwfRvXt3iczJAGBubo63b99CSUkJW7Zswb59+2BnZ4cXL14gODgYixcvRnh4OOrVq4djx4590T6DwWAwGFUFoYggrCQXT2W1UxaHDh3ClClTYG9vzyUe3LhxI/d9SUkJ3r9/j/z8fABAUFAQt4JLX19foq0PHz5AV1eXd9+/lMFTXFyM4OBgBAcHIzExEQMHDuTy54gRiUSQkZGBUChEfn4+Xr58iTZt2nBTUWJjRVdXFwEBAcjJyUHTpk2RlZWF2bNnQ1VVFXPmzMHNmzfRpUsXbNu2DWPHjoWmpiasra3RvXv3H77dDAaDwWD8F6gSV2lRJbVTFjVr1sThw4fL/V5XV1eifzs7u0obzy9l8Cxbtgze3t6YP38+wsPDsWTJEnh7e6Nx48ac50VGRgZ5eXkAgOnTp0NOTg47duzA7NmzuWBmANDT00NhYSHi4uJgZGQEIoK6ujry8vLQqFEjhIWFwdnZGa9evUJ4eDgCAgIwc+ZMdOvW7Zs8PILkeAgUK55PlFZS5tWerJYe774NalfnrS2p35O3VrHhlwHfZSF6ya+sAwDIm7firRVlpvDWdgq6wUuXI63Iu802M/jP9EoJi3lrZd7f56UT6bfh3ebDVP4Xgg5hT3hr5R8d5aWTqc+/BELM4SO8tXVtrHhrC+ITeOl6+/vxbrM46g1vLd9yEQAgNO5UsQjAdEUj3m0uWOLEWxt1NYS31trXi5cucMwfvNusbVyXt1bPjP95UKf3AF66R+MX8G4z+hZ/bXEev+tA5jr++yo9NJqX7lNeAe82/7/zS8XwNG/eHA4ODli8eDHWrFkDBQUFPHpUWn/ncyOkoKAAjx8/xvjx4zFo0CCcO3cOgGQwc7169SArK4u4uDjUr1+fiwRXUlKCuro6CgoKkJqaipiYGGRlZaFatWro2LEjF/PDYDAYDEZVQUiV+/od+aUMHktLSy4LspaWFlJSUtCsWbMvdLVr18bz588hLy+PlJQUvH79GidOnOAClsXeoFq1anHJjDQ0NPDqVWmRu+rVqyM5ORnp6elYu3YtPDw8cPPmTYwfPx7Kyvw8MQwGg8FgMKoOv9SUlo6ODvLz83Ho0CE8efIEhYWFZZZ6iIyMRFFREaytraGvr4/evXvj9OnTXDZmoVAIWVlZ1KhRg0t2ZGhoiI8fPwIAWrRogSZNmkBPTw/r16//gVvIYDAYDEbl8z0yLf9u/FIeHqDUs7Nz505oaGggMzMTfn5+XLS2eAna/fv3MWzYMERFReHOnTs4ePAgpKWlsWfPHgB/T3/1798fQ4YMAQDs3bsXLi4uICJoamrCwMDgmxIXFRUVfVFXhMFgMBiMXwHxKq3Kev2O/FIeHgAwMDCAk5MTRo0aBRsbG2zatAkqKioYP348F6n9+vVrJCUlQU5Ojisl8XkdLXFJCVNT0y/aLysgOTk5GUVFRV/NnMxKSzAYDAaDUXX55Tw8FhYWOHPmDIDS5Wi9evXCmTNnkJmZyWVa/uOPP7Bu3ToAf5eSaNq0KZo2bfrVtgsKCvDs2TOEhIQgNzcXY8eOhYWFBWxtbXH48GFu9VdZeHl5ISsri3vFxsZWxuYyGAwGg/GfEU9pVdbrd+SX8/C0b98eHz584N4PHz4cI0eOlPDM1K1b8dLG9PR0ZGdn4/jx43j8+DFevnwJOTk5aGpqwtPTE1evXkX9+vWxbt06qKiooKCg4Ks1tVhpCQaDwWAwqi6/nIfH2toaW7du5d7LyMjwzouzdu1a2NjYoFGjRvDx8UF4eDhq1qyJ2bNno1mzZpg0aRICAgLQu3dvhIeHQyAQcMZO9erVoaCg8F0TLjEYDAaD8T1gy9Ir5pfz8JRHcXEx8vLyoK6uXmb5h9u3b+Pdu3fw9fVFs2bNuLpYDg4OAABHR0fcu3eP0w8bNgxeXl4wMDCAvb09zMzM4OjoyGV5ZOUlGAwGg1FVYKu0KqbKGDzz589HcnIy9uzZwy07B8BlV46MjERMTAzMzc0BANWqVYO0tDSKi4shLy8PMzMz7Nixg2uvffv22LVrFz5+/IiQkBCcOHEChw8fxt27d7/J2JGtrQVZpYqz+IoKy48P+hxhSjzvvrM0+BdOyy7iX/+2saI6Lx2Z2vNuU6qYfzZQ0d1jFYv+Rz7PDMqZhfz3lbLwG5JPSvM/haiEXzZWkuHfZomQf8Hb3BL+x0B1i678+ldQ4t2m9pBBvLVSWl+moygPheb5vHQiBf45tjJe8M8KXacn/2zTfDMor89/x7tN6ecXeGuFxSW8tVLy/CpSN7A14N2mqETAW4t6/NsVKPPL4Cws5n8dEJbw12Z94ncM1upgy7vN4pzLvHQ52fzuLYxfcEqrPKytrRETEwNAcqWV+P8ODg7Iz8+HlZUVJk2ahN27dyM2Nhby8vIASrM4Jycnc7+TlpaGkZERnJycMGvWLNy6dQvPnj1Denr6D9wqBoPBYDD+OyIRVerrd6TKGDwWFhaIiooCIFlC4vNioX5+fpg+fToaN26MgwcPol+/ftzKqxo1akBeXh5xcXHcb8VZnQHg4sWL6NGjBzcVxmAwGAwG4/ehykxp6ejocHE8Skplu9GbNWvGxe/88ccf0NXVxdOnT2FnZwcAaNiwIZ49ewZtbW0AwI0bNzBkyBAIBAIoKipi0aJF0NLS+lGbxGAwGAxGpSCqxGDj39TBU3UMHqC0HtaLFy9gY2PDxeZ8TmJiIurWrQsFBQVERETAwsJCIpuygYEBQkNDufd9+vRB586doa+v/9Ul6QwGg8Fg/MqwoOWKqVIGj5GRESIiImBjY/OFsQMAr169wooVK5CdnY2ioiKMGTMG7dq141Zd+fr6QlZWlntfr1493n0XFRVJTHex0hIMBoPBYFQdqpTBY2FhgfXr1yMvLw9nz55FrVq1sHz5cujq6gIArKyssGjRIjRu3Bj169fnfieO8xGv7Po86Dk5ORkhISGIjY3FyJEjy/X0sNISDAaDwfhVERJBWEmemcpq51ejShk89vb2OHz4MMLDw+Hs7Iy2bdtyxg4A1K5dGx06dCjztwUFBXj37h1evnyJhIQEzJ49GwsWLMDVq1ehpaUFTU1N9OjRA7Vr1y7z915eXvD09OTeZ2dnf7X2FoPBYDAYP4rKXF31u67SqlIGj5WVFV6+fFnmd58nCwwPD8f79+9hbW2NlStX4urVq0hKSkK1atXQsWNHdOnSBbdv30Z2djYuXbrEBSqLq7GXBSstwWAwGAxG1aVKGTzlITZ29uzZg82bN0NeXh6qqqrQ1dXFiBEj4OXlhdOnTyMlJQWzZs0CAJw7dw5SUlL4+PEjiouLoaysjJo1a/7kLWEwGAwG49sRovJWafFPuVi1qBIGT3FxMYKDgxEcHIzExEQMHDgQhoaG3PdSUlKIiYnBq1evsHz5cq6cRGFhIapVK80W2rBhQzx58gRhYWFo2rQp7Ozs8PbtWzg6OsLGxgZ169ZFkyZN4OXlxUpLMBgMBoPxm1ElDJ5ly5bB29sb8+fPR3h4OJYsWQJvb280btwYQqEQMjIySE5ORnZ2NpSUlJCbm4uCggJoaGhw3+vp6aGgoIBLRKiqqoo//vgDU6ZMwbNnz/DkyRMcOHAANjY2aN++Pe+xiQrzIZLhYVZb80urLxP+gHffWueW89Z+vBbEWxuvp8FLF/fwQ8Wi/9F61TTeWmF2Jm+t4g0/XjpVXX4p/QEA9fmntKeol7y16fcCeOmqvX7Gu816d4N5a2u78C/tkHLvPi+dUn1+xwoApLwIr1gkblerFm9tSR6/siXCQn6lPQBAZ8Z83lqpPP7Z2RcsceKl+5ZyESKrnry1EVPX8NaW5PArlxB+MbRi0f9ov3IEb23u2R0Vi8RjOB3IS9fh2HrebQoV+Xv8pQuzeOlEGckVi/6H9rhJ/ISJycCWk2xZOg+qhMHTvHlzODg4YPHixUhMTMTcuXPx6NEjNG7cmFtVZWpqCmtra3Tt2hWtWrWCgYEBGjZsiD///BMAuGSDaWlpXLtycnKQk5NDx44d0bFjR9y8eRMJCQk/fgMZDAaDwfgPsFVaFVMlDB5LS0uuDISWlhZSUlLQrFkzAH8vMVdQUMDIkSPRrVs3vHnzBsHBwdiwYQMMDQ3Ro0cPyMnJQUVFBSkpKRAIBJCVlcXbt2+hpqaGzMxMhIaGQlpaGsbGxj9tOxkMBoPBYHwfqoTBo6Ojg/z8fBw6dAhPnjxBYWEhmjT5spqynJwctLS0oKWlhc6dOyMyMhKvX7+Go6MjZGRkoKGhgQ8fPiAvLw+qqqrIy8vD3LlzER8fDx0dHUyYMAFGRt8w9cFgMBgMxi+ASEQQsmXpX6VKGDxAqWdn586dsLe3R2ZmJvz8/ODm5gZFRUUuyFhcHV1eXh4JCQlISEhAp06duOXkJiYmSE1N5abBLCwscPr0aRagzGAwGAzGb06VMXgMDAzg5OSEUaNGwcbGBps2bYKKigrGjx/PGTxFRUXw9vbGvXv3ULduXTg6OqJLly5cG/369ZNoU5x5mQ+stASDwWAwflWElejhqax2fjWqTMVMCwsLnDlzBgBgZ2eHXr164cyZM8jMzOQ8Nnp6elizZg1CQ0MREBAALy8vqKiolNtmYWEhACApKYkrG0HlBGv5+PhAVVWVe7EsywwGg8H4VRAbPJX1+h2pMgZP+/btoampyb0fPnw4Ll++DDU1Ne4zGRmZMg0cgUCAsLAwJCUlYfXq1ejRowdatGiBfv36ITIyEsrKyli7du1X8+94eXkhKyuLe8XGxlb6NjIYDAaDwfg+VJkpLWtra1hbW3PvZWRkytUGBwfj4sWLePPmDYKCgqCoqAhVVVX4+vri8OHDmDBhAgYPHow9e/Zg1qxZOHXqFBo3boyIiIgyg6EBVlqCwWAwGL8uQlHlTUUJRZXSzC9HlfHw/JOvxdCEhIRAKBTCyckJCgoKOHfuHO7cuQNjY2M0bdoUurq6UFNTg6OjI/Lz85Gamgo9PT28ePHiB24Bg8FgMBiMH0WVNXhMTEwQEhIC4Mu4m+HDh2PBggUYMWIEjIyM8PbtW+67Bg0aICIiAgDw+vVraGhoQF1dHU2bNuUMnvLieBgMBoPB+BVhMTwVU2WmtP6JmZkZXr16BVNT0zJjb0QiEaSlpaGtrY1Xr16ha9euAICWLVvCx8cHhw4dgkAggKenJ2RkZNCsWTMcPXr0m8eRHxkG2eoVT3XVqHaJX4MNTXj3XX3EPN5asy78078nH9nNS2fp2Yt3m4K0RN5aOS3+AeFyTa146UhGnnebghr8yyXI1dLirVWso8ZLJ1+/Ie82DWbY8taSpj5vbfGl67x0skq5vNsUlQh4a+WUqvHWFqbxS+tft40p7zalclJ4a7+FqKshvHTC4hLebX5LuYi2D/15awWnVvLSRftH8m4z5iK/8ioA0MCxLW9tk36teekKg/mVTAEAmVqaFYv+R3HiR146KXn+xzXvvtMyAbBVWnyosh4ec3NzBAaW1k8pyyMjXrllZGSE0NC/b/YGBgZQVVXFli1b8OTJEwwZMgQA0KJFC3z8+PH7D5zBYDAYDMYPp8oaPK1atcKrV68q1DVt2hQxMTHce11dXSgoKHC1tcTGUpMmTZCXl4eioiKWiJDBYDAYVQpRJU5n/a6ZlquswWNpaYno6GgApdNXQqGwTJ2enh7y8/NRXFxaKVlVVRVpaWlcXI/YuJGWloaCggLCwsJ+wOgZDAaDwag8hFSJMTy/aRxrlY3h0dHRgUBQGhMgJydXrq5mzZogInz48AEGBgYAgA0bNsDE5O9YmYKCAhw9epSb7ioLlmmZwWAwGIyqS5U1eABAWVkZ8+bNg7y8PK5fv45+/fph0qRJqF69OqdRVFTEjRs3oKSkBKB0Cqtdu3YS7Yg9RNOnT4eOjk6Zffn4+HDZmBkMBoPB+JVgQcsVU2WntACgW7duuHXrFgoLCzFp0iQMGzZMwtgRIzZ2AEjE54hEIu77cePGwc7Orty+WKZlBoPBYDCqLlXaw7N69Wre2rKWrotXcmVnZ0NWVhaKiorl/p5lWmYwGAzGrwrz8FRMlfbwAKV1sgQCARe0XF7SQCkpKQiFQrx9+xaxsbEoKirCggULYGpqCnt7e+zcuZPF5TAYDAajSiIQUaW+fkeqjMGTn5+PFy9eYM+ePXj+/Dn3uaysLGRlZbnaWmIvzqdPn5CV9XdCsl27dsHExATjxo3D/fv3IScnh4EDByIkJATHjx/H27dvcfLkyR+7UQwGg8Fg/D8iPT0dw4YNg4qKCtTU1DB27Fjk5vJLYEpEcHJygpSUFM6ePfvNfVeJKa0HDx6gffv26NevH0pKSvDs2TOkpqaia9euyMvLQ2xsLO7evQtjY2MIhUK4uLhAU1MT7dq1g4+PD6KiorBnzx6ukKgYJSUl9OjRA9HR0VBSUioz/ofBYDAYjF+dqjKlNWzYMCQmJuLGjRsoKSnB6NGjMX78eBw+fLjC365fv/4/5cmrEgZPw4YNYW9vz3lgli1bhqCgINjZ2WHmzJmIiIiAtbU16tWrh82bN+PGjRto3Lgxxo8fD19fXzg6OnIxOgKBADIyMpCSksL69esxdOhQDBs2DBcuXMDJkydRWFiIatX4p/+WlpOFtFzFu1FGi1+5gLyb/Mtb5PedzVubs3oFb62wsJiXrk5/Z95tZvtf5K1NfcU/VX3a+4O8dFotdXm3WZjGf2pTphr/khVR18N56ZoNb8W7zazI87y1aeHpvLWtzlR88QEAmVz+JRiUX97lrZWSLT/VxD+pod+Yl87PeSPvNrsNCuSt/RTyibfW2teLl+5bShCU5OTz1vItFwEAsv1n8dJZ6zTl3SYVF/LWivL4n4dRF5/y0hkOt+fdpox6Hd7a3NfBvHQqA/lfM6VSo3np5D59nzIo34N3797h6tWrePr0KVq0aAEA2LRpE7p164bVq1ejXr165f725cuXWLNmDZ49ewYtLf4lfT6nSkxp1a1bF5GRkUhMTMTWrVtx584djBo1CrKyslBXV0eNGjWwaNEi9OjRAxEREdDV1YWUlBTGjh2LV69eIScnBxoaGkhLS4OsrCxnId6/f5/bcWfPnkVkZCRbfcVgMBiMKsf3yLScnZ0t8fo8F92/4dGjR1BTU+OMHQDo3LkzpKWluVJRZZGfnw9nZ2f4+vpCU5N/jbN/UiUMHjk5OWRnZ2PAgAG4e/cu1NTUsHHjRohEImhqaqJt27ZcwHGdOnW4bMnZ2dlQVFREkyZNkJGRgdOnTyM1NRWnT59GRkYGVqxYAR8fHzRs2BC1atXCkCFDICtbJZxeDAaDwWBwCIkq9QUADRo0gKqqKvfy8fH5T2NMSkpCnTqSnjNZWVnUrFkTSUlJ5f7Ow8MD1tbW6N2793/qv8rc3e3t7bF48WIYGhoCAExMTNC9e3fo6+vjwYMHyMzMhIqKCrS0tHDmzBkYGxvj5s2baNCgAdTU1LBhwwZs374dGzduROPGjaGtrQ0HBweYmpqiTp063BJ1BoPBYDAYQGxsLFRUVLj35aVmmT17Nlas+HrYxLt37/7VGM6fP4/bt2/jxYsX/+r3n1NlDJ4OHTrg7t27nMHTuHFjvH37Fu3atUN2djY+ffoEHR0d+Pj4YO/evWjcuDFatmyJkSNHAig1kNauXcut5hLD1z3GSkswGAwG41flewQtq6ioSBg85TFjxgy4uLh8VdOoUSNoamoiOTlZ4nOBQID09PRy78W3b99GZGQk1NTUJD7v378/2rdvD39//wrHJ6bKGDwtW7bEsmXL0KFDB+zbtw8KCgro1q0bCgoKoKWlxQUaN23aFIsXL8ayZcu+aOOfxo6Y3NxcKCgofLUmFystwWAwGAzGl2hoaEBDQ6NCXdu2bZGZmYnnz5/DysoKQKlBIxKJ0Lp16zJ/M3v2bIwbN07iM1NTU6xbtw49e/b8pnFWGYOnSZMmuHHjBhQUFKCrq4tJkyahQYMGAErLPnzO1wwXMVeuXMGOHTsQERGBmjVrYs2aNdwfoCy8vLzg6enJvc/Ozub6ZzAYDAbjZ1IVlqUbGRnB0dERrq6u2Lp1K0pKSjBlyhQMGTKEW6EVHx8Pe3t77N+/H61atYKmpmaZ3h8dHR3o6el9U/9VxuBRV1dHXl7eN/0mMzMTAL5whSUnJ+POnTtwcHDA/v37IRAIuLpa5cFKSzAYDAbjV6UqGDwAcOjQIUyZMgX29vaQlpZG//79sXHj3ykjSkpK8P79e+Tn80+3wJcqY/BUhEgk4uplSUtLo6SkBHfv3oW8vDwcHR0B/F1PKzQ0FFFRUVi6dCnk5OQgEokgLS1dZr0tBoPBYDAYlUPNmjW/mmRQV1e33BJRYir6vjyqnMHzT8NGzD9XWZWUlOD27dsIDAyEv78/evToARsbGwBAs2bNoKWlhRYtWsDS0hJWVlawtbWFqanpD90WBoPBYDAqAyGJIKxgpuJb2vod+SUNntjYWERHR0NfXx+ampqcBwb40rABgNTUVLx9+xaXL19GSEgIZs6cibZt2+L169fIzc2FpaUljI2NOX3NmjUxd+5cmJubIzs7Gw8ePMDChQtx9+5dmJiYfNNYFZs2g6JS+VXWxZB8DV7tKfSbzrvvom9wRjWayS/DKwAIPr7hpYve6su7TaX6FQe0idF27MBbq+uiw09Yj382WEFNflmxAUA2K563tn7XG7x0cs2sebepIct/mrVRDf5/A+HdI/yEtfgnAQtac4q3to4p/3aTQ8rP3/E5U2+v5t1msYkDb22TxFe8tYFj/uCla2BrwLvN8IuhvLXR/vyzmPPNoEwt+/BuM3Mjv+zNAFCSV8Bb26hHS146WS1d3m0KUvif23JK/DJj557dwbtNGXl+t+eC9NIVw6JKnNISfccprZ/JL5N8JisrCxMnToSBgQEGDBiA7du3o6Cg9IAXGzkikQjBwcGYO3cuxowZg8jI0pP39OnT8PDwgI6ODiZNmoQJEyaguLgYu3btgqWlJQYNGoSaNWtK9KepqYlx48Zh4sSJOHLkCDp06IBbt2792I1mMBgMBoPxQ/ihBg8RQSgUlhkgrKioiIyMDKxatQqBgYHYv38/9PT0MHXqVMydOxfGxsYIDQ3FypUrISUlhQ4dOsDZ2RlxcXFo1qwZlJSUMGDAAPTo0QMqKioIDg5GtWrVIBQK8fjxY65/Menp6RAIBFwxUVlZWV6ruxgMBoPB+NWorLISlRn8/KvxXQ2efwYWSUlJQUZGpsxpKTk5OdStWxclJSVITEzE1atXAZRmWczOzsabN2+QnZ0NIsLMmTPh4uKCBg0a4OHDh1BUVESrVq3w4cMHAKVJBp8/f466detCVVUVGRkZXP9iEhISMH36dFhbW8PY2Bg1atTAwIEDv9euYDAYDAaD8RP51zE8xcXFePbsGYKCghAaGooZM2Z8sSb+nwZGSkoKrl27xk1LieNqxDE6TZs2xcKFCyEvLw85OTmYm5ujVatWqFu3Lre6ytjYGDk5OVBVVUXLli3x+vVr2NraQlZWFgkJCQAAKysrrhBZs2bNMGfOHFy9ehUuLi6wsLAAUBoJPmDAAIwePRrGxsaoXr36v90VDAaDwWD8VAQiQKqSPDOC3zNm+d8bPH5+fjh//jzMzMxQv359EBFEIhHnhVFXV0dAQACCgoIwffp0zJw5E5GRkRg6dCiMjIywYcMGODs7w9bWFkKhENLS0qhbty46d+6MxYsXQ1VVFQBgaWmJ0NDSoLymTZvi+fPnCAkJgba2NpSVlRESEsIZRBEREQAAMzMzHDp0CAAwZMgQVKtWDU2bNpUISK5Rowbs7Oy49xUtSWelJRgMBoPxqyIUEaSrQB6en8m/Mnhyc3Nx7949rF27Fubm5gBK62FIS0vDzc0NDRo0wIoVK5CdnY3IyEgkJyejQ4cOSEhIwLRp0wCUlmo4deoUbG1tuakvHR0d3Lx5E0lJSZzB07ZtW5w7dw4AYG5ujo8fP2Lz5s04ffo0oqOjsXHjRpSUlKBdu3ZQUlICANja2nJxO+rq6hg9erTE+DMzMxETEwNjY2Pk5+dj586daNSoEfr06VPuNrPSEgwGg8FgVF3+lcEjFApRr149nD59GikpKahduzaaNWsGAGjfvj1u374NoHTaSF5eHgkJCTA0NJSI6Wnbti1WrlwJ4O8aV3p6eigqKuIyJAOlU1JBQUEAACUlJTg7O6NatWrIzs6Gu7s7V0y0R48eX4xTIBAgNDQUISEhqFatGqSkpODu7o66detCQUEBrVq1wtq1a5GcnIwaNb6+bJyVlmAwGAzGrwrz8FTMvzJ4VFVV0bdvX3h6euLx48dQVFREUVERjhw5grZt22Lz5s0AgDp16kBeXh4xMTGwsbFBdHQ0wsLC0LRpU0RHR3PGitjgqVOnDgoLC5GWlsb1Vbt2bdy7d0+i/379+pU5LpFIhJiYGMyePRtxcXF4+fIlDA0NYWZmhgEDBiA5ORndunXD1q1bue1wc3ODjo4OsrKykJeXx3mJ/gkrLcFgMBgMRtXlX8fwdOzYES9evMDHjx8RGhqKY8eOYdWqVVi0aBFSU1MBAHXr1sXTp0/RoEED1KpVC9WqVcOGDRsgEonw9OnTMtNL+/n5QV1dnXtPRBJVVLOysvD69Ws8f/4c8vLymDhxIvedtLQ0atSogWHDhqF169ZwdXXFnDlzuN/fuHEDioqK+PDhA/T09KCnp4ewsDDo6uriwYMHyMjIKNfgYTAYDAbjV4V5eCrmP2da1tXVha6uLoKDg5GUlARZWVlYWVlh9uzZUFBQgEAgQGJiIoDSGB1dXV0YGxtj4sSJaNr0y0yenxs7QOlKr88Dio2NjaGlpYUuXbrg7t27EAgEGDt2LLfKqnbt2lzJ+IYNG+Lp06ewsrKCrKws1NTUICMjg4SEBBQWFkJHRwdaWlooLi5GcXExUlNToa2t/V93CYPBYDAYPxSWabli/rXBk5SUhNDQUCQlJSEqKgrXrl3DX3/9BQDYsmULdu/eDTU1Nfj5+XGxLurq6iAidO/e/Zv6+nz1VPPmzeHq6oo+ffrg4MGDuHv3LiIjI7kYIqA0dkdWVhYGBgaIjIzkfq+lpYWUlBT069cPJiYmGDRoECwsLBAaGgoi4jxT34JMzbqQqcHDK5SbVrEGgEx2Mu++b5XwL5fQpTH/VPUqOem8dPW9+ZeWEN7cy1srVZ2/ly3n2QNeuhqmhbzbRNBN3lIpnun3ASDvf6sIK0KlFf/zQ6RUi7dWuiCLv1ZZjZdOtg7/BwSLqfy3qzCF3zEIABrNm/DSSavw31cyD4/y1ubHf+StrW1cl5dOVCLg3Wb7lSN4a2MuBvDWUjG/c+ZbykWoua/krZV+cYm3lm8ZiE/X+ZV3AQCt/vzzsoWuOsBLZ+41hneb0cfO8tKl5FZ+VfHflX9t8KioqODatWsIDQ1Fs2bNsGDBArRp0wZAqdfH29v7i9/07dsXOTk5ACBRH+tbMDc3h7+/P/r06YM6deqguLgYOjqS9ZTEMUHNmjWDv78/ioqKoKioiBo1asDAwACWlpZwd3fn9FpaWpCVlUVKSso3j4fBYDAYjJ+NUESVloeHTWn9A0VFRfj4+JT7vTgvj7S0NOdhGTx4MPf9vzF2AKBVq1ZYtGgRbt68ic2bN6NZs2ZQUVGRMKDE/RkYGCAjIwPZ2dlQVFSEsrIyNDQ0EBsbC6B0tZmMjAyUlZWhpKSE/Pz8CvPxMBgMBoPBqHr859ISQqGwzPpY4jISlW08tGzZEq9evcLJkyehra2Ny5cvIzAwsEwDSlNTEwKBAPHxpe5OGRkZVK9enVuRJSMjwyU9zMjI4DxDDAaDwWBUJYgIJKqkFzEPT5n8aCOhfv360NbW5paWb9iwAXPnzsXhw4dRp04dTif2+DRq1Ai5ubnc53369MGQIUMgIyMDIoKMjAz279+PixcvYsCAAeUaaCzTMoPBYDB+VUQiqrRg4981aPmHVkuvLJSUlLjkhtOmTYOMjAwOHz4s4WUSe3x2794NW1tbid+KjTSxFdu6dWtcuHAB1tbW5fbp4+MDVVVV7sWSDjIYDAaDUXX4zx6en0GnTp0kvDYnTpyAiopKufry4nKkpKQgFArRpEmTCmOKWKZlBoPBYPyqEFXeVBSb0vqF8PUtXQ4tNmTKMnbE3+Xk5OD58+eIi4tDmzZtoK+vz30njjPiA8u0zGAwGIxfFXH8TWW19TtSJQ0e4O8VVmUhNmjevHmDyZMnAygtW3H9+nUMGzYMXbt2RV5eHj5+/Ijr16/jw4cPcHFxgaWl5Y/cBAaDwWAwGD+IKhnDA5QGS4tEIgiFwi/cb1JSUsjNzYWfnx969uwJf39/HD9+HF26dIGHhwcAYMeOHRg5ciRSU1Ohr6+PcePG/avEgwwGg8Fg/GzEQcuV9fodqVIGzz+XvktLS5e79F1KSgonT56Eu7s7RCIRBAIBhg8fDgB4/vw5GjZsiMLCQixcuBDu7u5o0aIFLly4ILESi8FgMBgMxu/BLzGlJRAIEBERgbp160JdXR0ikYiLsfkccWBxcnIyZGVl8ezZMxw4cAARERFwdXWFi4sLpykuLoaWlhYSExOho6PDeYEMDAwQFRUFDQ0NdO7cGVFRUTA0NESLFi2QkpKCoqKib4rVyfK/AlH1ivUq/Vx5tZejzD9Vf8tCIW+t0otzvLVQr1OxBoDg6nbeTb4/dIu3ttms8by1inqNeemkFKrxblPWsjNvbc7Ffby1MtXk+QnfP+bf5jeU4ZDS0KlY9D9y3r/hpSt8+Ih3mwrqNXhr1804zVtrp69esQhAxy4DeLf5oVl/3tomTT7w1uqZteEnrMe/FEzu2R28tQ0c2/LWivL4pd4oySvg3ea3lIsQWfAvRfLUthMvnekt/tch6eQQ3trmhw/y0pXc5H+9aNDNjpdOOjUDOHgFJAJIVLGeD5XVzq/GTzV4Xr58iaFDh6J69eqQkZGBgYEBtm7diho1Si+M+fn5yMjIQP369QEAI0eORL169XDnzh0sWLAABw4cgIODA3x8fKCkpCSx0oqIoK2tjTdv3kBHRwcFBQWoUaMGFBQUEBsbi3bt2iE1NRWvX7+GoaEh0tLSkJCQABUVFZZtmcFgMBhVCrZKq2K+m8GTlZWFN2/e4Pnz55CXl8eECRO+0IhEIjRs2BB79+6FpqYmzM3NcePGDUhLS8PPzw+xsbEwMTGBh4cH2rZti8zMTOTn5+P+/fuQk5PDli1b8PjxYzRv3hxNmkgWEKxZsybq1auHixcvwsnJCTVq1IBAIIBIJIKGhgZq164NIsKuXbsQEhKCBw8eYObMmQDAjB0Gg8FgMH4zvlsMj7GxMdzd3ZGYmIgDBw5g8+bNKCgodX2KrcdatWpBW1sbEf+rIt28eXOEhYWhVatW2L17N968eQN9fX3s3bsXANCiRQvUrVsXcnJyAIA//vgDenp62Lp1K4YPH44zZ84AAEpKSgAAY8eOxadPn7Bo0SL4+/tj2rRpqF27NkaMGIH8/Hy0atUKjRo1gpaWFhYuXAgHB4fvtTsYDAaDwfhusKDlivluBk/z5s0xb948LFu2DBMnTsSrV68QGRkJ4G+Dp0aNGqhVqxaio6NRUlKC4uJiqKurQ0tLC4cOHYKpqSnOnTuHxMREfPz4EZaWllwbAoEAHTt2xJw5c7Bt2zZuNRYAyMqWOq5atWqFTZs24dOnT1ixYgXU1dUxZcoUAICqqioUFRVRvXp1TJw4Ee3bt/9q8sGioiJkZ2dLvBgMBoPBYFQNvtuUlrm5Ofz9/dGnTx/UqVMHxcXF0NEpDZoUGxY1atSAsrIyJk6ciC1btsDGxgbjxo1DcHAwzpw5g5CQEOTn52P48OFISEiAmZkZ4uLiAIDLs7Np0yYkJycjJSUFO3bs4L4To6WlBT8/vy/GJyUlBT09PcjKyiI3N5eLGyoPHx8fLF68uFL2DYPBYDAYlQlLPFgx383D06pVK/j7++PmzZvYvHkz6tWrBxUVFYml5XJycmjSpAkGDBiABw8eYMWKFZCWloaKigo+fvwIAAgJCcGTJ0/w+vVr6OjoIDExEdnZ2ZCRkYGamhosLCzg7u6Os2fPwtDQsNzxiKu6i71LQ4cORcOGDTFmzJgKjR2gtLREVlYW94qNjf1vO4jBYDAYjMqisiqliwj4TQ2e7+bhadmyJV69eoWTJ09CW1sbly9fRu/evdG6dWtOIy0tjfr166O4uBjFxcWQk5MDEUFPTw8DBw6EoaEhtLW1MX36dJiZmQEAXr9+zZWSqF+/fpnB0MDf02Zib88/szInJibi5cuXaNq0KR4+fAg9PT1oaWmVu0KLlZZgMBgMBqPq8t0Mnvr160NbWxtbt24FAGzYsAFz587F4cOHUafO33leVFRUUFBQgMTERDRs2JD7fNmyZVi1ahXk5SVzl2hpaUm8F4lEEIlEXyQg/KfRIhAIEB4eDgUFBTRq1Ai2trbYvn07rl69iufPn6NTp04YN24cTExM2LJ0BoPBYFQpRESQqqTl5KLfdFn6d820rKSkhNu3bwMApk2bBhkZGRw+fFhiWsvIyAgHDhzgjB2xoaGkpAR5eXkQEUQiEZKTk7Fr1y64ublh+PDhuH79eukGSEtDVlZWwkDJzs7GkydPsGbNGjx79gzXr1+HlZUVpk6dil27duHjx4/o1KkTwsLCMHXqVAQHB0NbWxurVq0C8PvmIGAwGAwG4/8r3zXxYKdOnZCbm8u9P3HixBeVzeXk5Lhl5mUhJSUFIsLOnTsRFBQEJycnqKmpQSgUIiMjA/Hx8bhy5QoAYNKkScjIyMCCBQsQFxeHPn36ICUlBRcuXEBQUBA+ffqEJUuWYOnSpVi+fDkUFBRgYWEBIsKQIUNw4MABAPjqai0Gg8FgMH41iCoxaPk3fej/rgaPr68vgL+rl//T2OFLQEAATp8+jQcPHkjE0cTExGDdunUwMjKCqqoqxo8fj8OHD0NZWRna2tqYPHkycnNz0bdvXzx58gQyMjJo0qQJ+vXrh1q1akEoFCIuLg7a2tooKSlBnTp1eK3Y+pzUV5EolC/fYBOj1j2HV3uqxe95961cXZW39uqQpby11nOceOmibwbzbpOE/HOV5wY/5a39cPk5L53hSP45luj9C97ajNBo3tqs6DReuoYi/vsqM4x/8Hx1DX4lGABA9L9cVhWRE5PMu01BIb82AcDeuDZvrbCYX4mVp64zeLdpdXAnb23yga28tXV68ytvIVCuy7vN8NOBvLVN+rWuWPQ/oi7yOw8b9WjJu01BSjxvLd9yEQDQOuA2L52UoJB3myTP/z7At12ZWpr825SWqVgEQDpfAICt0uLDdy8tIRQKvwgY/lYSEhLQtGlTKCgooKSkhAtuPnXqFOrVqwczMzM8ePAAx48fx7Jly6CpqQkDAwMUFhaiWrVq0NXVxblz57gSFWIaNmwIHx8fTJs2DVu2bEGvXr1QvXr1/zRWBoPBYDAYvx7ffe7mvxo7AJCXl4d69eqhsLCQm/6SkpLCu3fvEBAQgGPHjkFNTQ0PHjyArq4uatWqhaysLOTk5EBWVhaNGzfGoUOHEBERgStXrmDjxo0AgAYNGiAmJgYrVqxAYmIiHB0dK2W8DAaDwWD8SESiysy2/LO35vvwS1RLr4hGjRohMDAQaWlpqF+/PvLz86GoqIiaNWti4MCBmDp1qoReW1sb79+/R3JyMjQ0NLB161bs27cPw4YNg6qqKvr27YuSkhJs2rQJKioqbEUWg8FgMKo0rHhoxVSJ6FwrKytkZ2fj1KlTAABFRUXk5OSgRYsWiIqKwpQpU7BgwQJ06tQJZ8+ehZGREerXr89NTzVo0ABz5sxBYGAgrl+/jkmTJkFOTg6qqqq8jR1WWoLBYDAYjKpLlfDwqKurw9nZmZuWSktLw8ePHzFq1CjMnj0bu3btglAoxJ9//glra2soKytjxgzJAMXyVl4lJycjLCwMQqEQtra25Y6BlZZgMBgMxq8KiUpfldXW70iVMHgAoG/fvmjRogUOHTqEli1bomXLllwpiTlz5lT4+8LCQsTGxqJBgwaoVq0aMjIyMGLECERFRcHAwACNGjVC48aNoa2tXebvvby84Onpyb3Pzs5GgwYNKmfjGAwGg8FgfFeqjMEDlE5NzZ49+5t+s3XrVmzevBnVq1eHpqYmPDw80KlTJ8yYMQMdO3bExYsXAZR6eqpVq1ZuO6y0BIPBYDB+VUQiglQlLScX/abL0qtEDE9FXL9+HaNHj4atrS1atmyJa9eugYgQFBSEa9euYd++fXj69CnOnDmDunXrIjs7G+Hh4ejYsSMAoLi4GHXq1PnXeYIYDAaDwfiZVFbh0MrM51MW6enpGDZsGFRUVKCmpoaxY8dKJCguj0ePHqFTp05QUlKCiooKOnTogIKCgm/qu8p4eMQR6FJSUlygsfj9pUuXQETw8/NDfHw8lixZAn19fezZswdWVlawsrICEUFaWhomJiYIDg6Gvr4+ioqKAICr18VqaDEYDAaD8f0YNmwYEhMTcePGDZSUlGD06NFc0uDyePToERwdHeHl5YVNmzZBVlYWwcHB31wV4Zc0eMTGDRFxeXE+N3Q+10lJSXHGS6NGjWBsbIzt27cjMDAQ8vLy3A4RiUTc/8VZnxMSEgCAS2b4b4ydRl4LoKLMIyMnz0ycoowk3n0fSq3FW2v57AFvrYoMv4zAsv34928om8Fb+y2Yd8vipRPW1OHdJgWe5a2t95dnxaL/0TDuFS+dqAb//fpBxD8jb8OSCN7aGBVDXrqU7CLebbaV+8Rbq19Ll7dWqjifn/DlNd5t3m4/iLfWaHBz3tpH4xfw0vHNHg0AHY6t560tDL7PW2s43J6XTlZLl3ebn67f4K01vXWLt5ZvpmOSLT9s4Z/ErOWfnX71+oe8dJvDj/JuM9v/Ai9dQXrpiuGqkGn53bt3uHr1Kp4+fYoWLVoAADZt2oRu3bph9erVqFevXpm/8/DwgLu7u0RIi4GBwTf3/9OntIgIQqFQoqColJQUpKWlOWOHiPDhwwfs3LkTmzZtQlhYGABwv9HV1cWnT58gLy+PlJQUVK9eHQoKCtDR0UF8/JepzPX19aGhocEVNhUnM4yPj0dqaup33V4Gg8FgMKoC/0zFIp4V+bc8evQIampqnLEDAJ07d4a0tDQCA8sukZKcnIzAwEDUqVMH1tbWqFu3LmxtbXH/Pn/jXcx3N3iKi4vx9OlT7NmzB25ubtixYwcyMkqf9sUeGhkZGQnXVHx8PI4fPw5vb2+4urri+PHjmDx5Mp4/fw4ZGRnIypY6psQGkYGBAd69e4f27dvD0dERmpqa6N+/PwwMDHDnzh0Akh6itLQ0jBgxAtevX8fJkycRHh6O48ePY926dRAIBN97lzAYDAaDUamIiCr1BZQuFFJVVeVePj4+/2mMSUlJqFOnjsRnsrKyqFmzJpKSyp7diIqKAgAsWrQIrq6uuHr1KiwtLWFvb4/w8PBv6v+7GzxLly5F69atERERAX19fTx9+hSjRo1CdHQ0pKSk8OHDB0yYMAFOTk5YvXo1ACAuLg5eXl4oKirCmjVroKamhtjYWPj5+cHNzQ2NGjUCAM6A0dbWhoqKCpydnXHnzh2sXLkSQKnlWLt2bfz111+QlpZGcnIyjhw5gpCQEDRs2BCnT5/G+fPnMWTIEJw8eRLNmjWDmpra994lDAaDwWBUKt8jaDk2NhZZWVncy8vLq8y+Z8+ezTkVynuFhob+q+0Sz+RMmDABo0ePhoWFBdatWwcDAwPs3r37m9r67jE8lpaWcHR0xNKlpfOhCQkJWL9+PZYuXYrt27fj+PHjUFFRwZw5c3DgwAGsXbsWkydPhra2NhwcHKCiogIbGxtoampi1KhRUFRUhKmpKdzc3Lg+FBUVoaamhsaNG3MrrYgIsrKy2L17NzZt2gRjY2PIy8vDwMAACxcuBBHB1NQU+/btY4HKDAaDwWD8AxUVFV6rl2fMmAEXF5evaho1agRNTU0kJydLfC4QCJCeng5NzbIryWtpaQEAjI2NJT43MjJCTExMhWP7nO9u8FhYWODdu3cASjesXr166NOnDyZOnAgA2LdvH+7evYvatWtDVVUVf/zxBzw8PKCoqAhlZWUAgJKSEm7cuIGcnBw8e/YM7u7u6NSpEwwNDblq7PLy8nj58iXat28PJSUlSElJgYigr6+PJUuW4K+//uLa+5xvKS3x+fwlKy3BYDAYjF8FokoMWv7GWloaGhrQ0NCoUNe2bVtkZmbi+fPnsLKyAgDcvn0bIpEIrVu3LvM3urq6qFevHt6/fy/xeVhYGJycnL5pnN99SktHRwfFxcXIy8vjYm8aNGgAgUDAGRD5+aWrLOrVq4dq1aohMzMTurq63PxcSUkJ3r59i7t37+L9+/do164dZ7yIDZbRo0djwIABUFRU5PoWf6eiolKmsQOU/mFFIlGFf2AfHx+JuUyWZZnBYDAYDP4YGRnB0dERrq6uePLkCR48eIApU6ZgyJAh3Aqt+Ph4GBoa4smTJwBK7+MzZ87Exo0bcfLkSURERGD+/PkIDQ3F2LFjv6n/H7JKS0NDAy9evODe5+TkoFGjRsjPz0fz5s25oqDBwcGoV68eVFRUoK6ujufPnwMoXUV19epVbNy4Ee/fv8eYMWNQv3790g34X7Czubk59PX1v+qxEa8I+xzxirCKPD1eXl4Sc5mxsbHfviMYDAaDwfgOkIggqqTX90w8eOjQIRgaGsLe3h7dunWDjY0Ntm/fzn1fUlKC9+/fc44QAJg+fTq8vLzg4eEBc3Nz3Lp1Czdu3EDjxo2/qe8fkofHyMgIYWFhsLGxAQDs2bMHysrKUFdXx6RJk3D8+HF07twZWVlZmDJlCmRkZNC7d2+JLIqenp4Stawq4vHjx0hPT0fHjh25quniFWHA3yvEIiMjcefOHeTn58PR0RFNmzYtsz1WWoLBYDAYvyri3HWV1db3ombNml9NMqirq1tm/7Nnz/7m0lL/5IcYPBYWFvD19YWUlBQuX74MoVCIRYsWAQDat28PPT09vHv3Ds2aNYOWltZX5/PEiKfJ1NXVJTIkixMMBgcH4+3bt7CwsED16tWRkpKC2NhYXLhwAZaWlujZsydevHiBP/74A4aGhjAxMeGm3BgMBoPBYPxe/JA7fKdOnXDy5ElERUWhT58+aNeuHXR1dbnvtbW1JaqU80kXPX/+fCQnJ2PPnj0QCoWcsSI2fMzMzPDixQvIyMjA398fzs7OmDRpEuLi4vD69Wtoa2sjLy8PSUlJuHnzJlupxWAwGIwqS1XItPyz+SEGT4sWLbgApMrC2toaGzduBCC50kr8/4YNGyIvLw/JyckwMjKCvLw8+vXrBxMTE6xatQrXrl2Dh4cH6tWrBxcXlzKXu/Oh6NEFFCnySFcuLcOrPXl9M959f8sxKf0N9px/Fo9SGQBa1JPj339kGG+tUMectzZHsU7FIgBp+fwTSipYDuStlRfw37E1czN56ZI0+G9/XGIOb20dLSPeWp2s9xWLANRXq8+7zZJHd3lrpRS+4Xohy+84lDK24d2k/Ul+xxUA3uc2AETf4llaooR/aQmhYk3eWplaZS/9LVOrzm8fCFK+zGZfHlr9+Z9b0skhvLUkz++a9S3lIuot28VbO+5Db166/Mf8y5tkvIvmpcvKKY11EYno224KX+F3rZZeZedwLCwsuAyMZXmE6tWrBzk5OcTGxqJZs2aQkZHhosD19PTw6NEjCAQC3LhxA9nZ2QgKCsLUqVPRsWNHGBnxvzEwGAwGg8H49amyBs/ny92VlJQkvhPH9KipqXG1tOrWrYtXr17B1tYW6urqkJKSwsePH6GkpISQkBDExcWhXbt2vJIsMRgMBoPxK0EiIUjE3zNYUVu/Iz+9eOh/4fPl7sXFxdzn9FkdEHFWx6ZNm+LRo0cAShMZZmdnIz09HTdu3MDGjRsRFhYmsdydwWAwGAzG70OV9fAApcvdIyIiYGNjA3l5ee5zcRxPnTp1cOPGDYhEIrRp0wYJCQkAABMTEyxcuBCamppo3749XF1dK+yLZVpmMBgMxq8K8/BUTJU2eCwsLLB+/Xrk5eXh7NmzqFWrFpYvX86tAHNwcEDz5s0hJSXFlbIgIigrK0tkXv58WXt5+Pj4YPHixd9tWxgMBoPB+LeQSFSJBo+oUtr51ajSU1r29vYAgPDwcDg7O2PRokUSy93r1KkDExOTMldxCYVCbuqLz5J0lmmZwWAwGIyqS5X28FhZWeHly5dffJ6dnQ0VFRWurLy4PP3niDMuCwQChIaGokmTJl/NpMwyLTMYDAbjV4WEQpCwkjw8ldTOr0aVNngA4O3bt7h27RrCw8MhLy8PS0tL5OfnY8yYMRJxPUBpYLP4szVr1mDXrl3o1q0bduzYgdOnT8Pe3p7X9BaDwWAwGIyqRZU2eD59+oRly5ZBQUEB9vb2KC4uhqmpKZdo8NmzZwgJCUFAQACuX7+OqVOnws3NDWlpaXj37h127twJLS0tPHr0CG/fvuWmyBgMBoPBqEoQVWLQMv2eHp4qHcMzb948aGlpYdeuXXB2doaLiwukpKQwffp0vHjxAvHx8di2bRs6d+6M169fIz4+HufPn0dERARKSkpgbW0NPT09ODs74+HDhwC+b9E0BoPBYDC+B+JVWpX1+h2psh6ekpISyMjIwNjYGACQn58PRUVF1KxZE7Vr10ZSUhKMjY3RsGFDNGvWDDVr1uRKXEyZMgXR0X+n7dbW1uayNn8r1czaoVoNpQp1Ip5lBQRJMbz7DoMeb22L+qq8tbZq+fz6z+Ef09S0II+3VibhLW+t4id+weMq9RvzbrPw1X3eWtnOLry1woxkXjoNQ/5Gt5Yy/7+BVnwgb21C/a8X7xWTVcT/wmigqcNbK6XxDdoSfsdr0aNzvNsM8NjDW1urCf/SDsV5xRWLAGR94rdNACBdmMW//8SPvLW5r4N56eSUeJTW+R+hqw7w1jY/fJC3VkpQyEu3ev1D3m3yLRcBAMZH+B1bsqn87zPV47fy08nzL/Hz/50q6+ERCoVQVlaG8H/BVdWqlZ50qqqqUFFRwcePH1GnTh3Iy8tzwcuGhoaIiIhA48aNIS8vj2vXSuuahISEICMjAwC/wqUMBoPBYPxKMA9PxVTZu7u8vDzU1NQ4z4xAIAARQVVVFbVq1UJiYiJUVVVRrVo1Ltuyrq4u4uLiUFJSAh8fH6xcuRIGBgYoLCyEsrIy4uLifuYmMRgMBoPB+E5UWYNHWloaHTp0wM2bNxEdHQ15eXlISUkhJCQE8vLySE9Ph0AggIyMDCIjI1FcXIzatWvD3t4eKSkpsLKywunTp/H+/Xt07NgRHTt2hKxslZ3hYzAYDMb/Y5iHp2Kq9B2+ffv2sLCwwF9//cVNY5WUlMDOzg4NGjRAXl4e3NzcoK6uzi1HX79+PQCgoKAAO3bswK5du1C9enWu1ER5sNISDAaDwfhVYZmWK6ZKGzwAsG3bNly8eBFBQUEYOnQo2rZtK1EA1MzMTEIvXoVVvXp1DB06FK6urlBVrTigl5WWYDAYDAaj6lLlDR4A6NGjB3r06FHu958nE/w8qWD9+vUhEokgFAohJSX11YBlLy8veHp6cu+zs7PRoEGDShg9g8FgMBj/DZFICFSSh0fEprR+bb5mtEhJSYGIuJe4rATAf1UWKy3BYDAYjF8VVi29Yqps0PI/kZGRkTBeUlNTMWfOHDx+/BgAOGPoc2MHAJ48eQJ3d3d06dIF+/fvB8CSDzIYDAaD8btRJT08xcXFCA4ORnBwMBITEzFw4EAYGhp+ocvPz0dYWBjatGmDN2/e4M2bNzh06BAaNGiA+fPno1q1ati/fz8aNmyI/v37o3Hj0uR0rJYWg8FgMKoSzMNTMVXS4Fm2bBm8vb0xf/58hIeHY8mSJfD29kbjxo0hEokgLS0NJSUlNGzYEImJicjKysLixYshEAgwceJEBAUFYdasWdi8eTNCQ0PRuHFjaGlpQVtb+5vHknn7EoTVKp7qUu85hFd7eQb863mZRmbw1hoo84+6z0UdXjodaf6GoXT9pry1whq1eWupURteurcZ/DLcAoCavTlvbXwW/3ZbaNSvWAQAaR95t1lfmX+27TD5Fry1Da+t56XTasc/G23+q8e8tSV5/ry11TT4ZTqWb9+fd5stX47jrVWS4+8oz1z3By9drQ62vNsU8czgDQBS8vyzIqsMdOalyz27g3eb5l5jeGtLbu7jrZWpVf4K28/ZHH6Ud5v5j6/x1vLNoCyo3Yh3mw/X3ualSxPwvwb9f6dKTmk1b94cDg4OWLx4MdasWQMFBQU8evQIwN/eGXl5edSrVw/R0dFQVVVFgwYN0LhxYzg5OWHy5Mnw9/eHsrIypk2bhoiICGzevBktW7ZEYmLiz9w0BoPBYDC+HaEQVEkvCJmH55fB0tIS4eHhAAAtLS2kpKSgWbNmAP42eGRkZFCvXj2kp6eDiFCnTh0oKyujoKAAampqkJKSQmxsLHr27ImePXsCALp3744HDx5gwIABP2fDGAwGg8H4FxBV3iqt37VaepU0eHR0dJCfn49Dhw7hyZMnKCwsRJMmTb7Q1a5dOjWSkpKC+vXr4+PHj8jKykL16tWho6ODkJAQFBcXY//+/UhOTkZhYSEaNeLvcmQwGAwGg1E1qJJTWkCpZ2fnzp3Q0NBAZmYm/Pz8kJ9fWmFYXCxUXEj0w4cP0NbWRkJCAj59+gQA0NfXx4cPHyAtLQ05OTm0bdsW27dvh6Wl5U/bJgaDwWAw/g3iTMuV82KZln8pDAwM4OTkhFGjRsHGxgabNm2CiooKxo8fz2mqV68OZWVlREVFwc7ODra2tqhTpzQgd/v27VztrHnz5lXYHystwWAwGAxG1aXKengsLCxw5swZAICdnR169eqFM2fOIDMzk8vHo6amhiVLlmDo0KHQ0tLC4MGDoaWlBQBfFAotKCj4an8+Pj5QVVXlXizLMoPBYDB+FVjx0IqpsgZP+/btJYp9Dh8+HJcvX4aamhr3mZSUFBQVFcv8fVFREby9veHk5AQzM7MKvTxeXl7IysriXrGxsZWyHQwGg8Fg/FdKp7Qq7/U7UmWntKytrWFtbc29/2cGZTGfPn3Cxo0bER4ejg8fPmDUqFGYPHky7t+/j5iYGMyaNQuWlpbcaq7ykg6y0hIMBoPBYFRdqqzB8zXS0tLQq1cvXL16FQkJCTh27Bi8vb1ha2uLtm3bomXLljh06BC6dOmCjh07AgCviukMBoPBYPyKUCUWD2VTWr8gFy9eRGZm5hefq6ioQElJCZGRkWjQoAHs7Oxgbm6O+vXrw8HBAcHBwbCzs8Phw4fRtWtXzJ8/HwEBAcjI4J+5mMFgMBgMRtWhSnh4RCIRpKSkuOkmcfmIQ4cOQVpaGt26dUNOTg6UlZUBAHJyclyenebNm6NGjRqIiYmBiYkJACAqKgrLly+HsrIyIiIikJCQgIkTJ6Jly5ZcAVG+lOQVoURYcbHRwqc3ebWn1pZ/6vfnMbyl6NJYnbdWWYafdZ9cyL+0hGwQv+0HACou5K2V1zXipaup34l3m5rJwby1deryL5mReuwCL53GIBfebcp8Q923Oor8T3epako8hfyfmfKTM3lrhYVFFYv+h6CQX2p99Zg3vNtUlQnlrRV+Q2mH9NBoXrrinMu829QeN4m39luQSuU3Vhl5/sdV9LGzvLUNutnx1kpJlx3S8E+y/fmdgwCQ8Y7f9gNA9fitvHR8y0UAQLePz3jp4uPjgSZNmIeHB7+Mh0dcodzZ2RnDhg1DWloa97m0tDSkpKQgFAoRFRWF/Px8pKenIzw8HMOGDUOPHj3w4sULifYMDAwQEhICoHS6ytPTEw4ODkhPT8fEiRMBAH379sXMmTOxdu1anDx5EsHBwUhO5n/xYjAYDAbjV0AkElbq63fkhxk8xcXF3JSR2Lj5HCkpKeTn50NGRgZBQUE4e/Ysp71y5Qr69u2Lvn37onfv3ti4cSNq1qyJAQMGwMzMDBcvXkSHDh0k2jU0NOTKT+jq6qJu3brYvn07jh07Bl1dXRARZ9yIV3MJBALOS8RgMBgMBuP34YcZPPPnz4enpycAQFhOYbK3b9+idu3amDp1Kk6cOAGhUAhpaWkkJyfj/v37OH/+PK5cuYIrV64gNTUVo0aNQkJCAgCgpKREYoWVvr4+UlNTAQB6enrQ19eHnp4e5OXlAZQaOefPn8eQIUNgY2ODbt26Yf78+ahevfr33A0MBoPBYFQ6JBRVWvFQEv6ey9J/mMFjbW2NmJjSoJPyln4rKirC398fEydOhJmZGfz8/ACUVkevV68eAEBDQwOGhoYICgqClpYW8vPzUVJSAjk5OYm2tLS0IBAIkJSUBE1NTXz8+BEfP34E8HfpiVatWmHw4MHYvn07Xr9+jSFDhnyPTWcwGAwG47tCVImJB3/T4qE/zOCxsLBAVFRUaafSZXeblZUFGxsbSEtLw8nJCTt27EBOTg50dXVRo0YNJCcnQ0FBAYqKikhKSgJQuiLr5cuXSE1N5WppiT8X18/S09PDvHnzUKtWLYn+zczM0LdvXxgbG5ebx0dMUVERsrOzJV4MBoPBYDCqBj/M4NHR0UFxcTHy8vLK9fDcvXsX586dQ6NGjeDu7o6PHz/i+vXrqF69OjQ1NfHmTekqC1VVVYSFhQEAZs+ejSlTpmDMmDGl0er/Q1paGidOnIClpSUUFBRgZ2dXbnxOSkoKXrx4Ue5UG8BKSzAYDAbj14WVlqiYH7pKS0NDg1tNVVz891JSsaHx/v17jBgxAmFhYQgJCcH58+dx7tw53L59Gzo6Onj+/DkAwNbWFm3atEFJSQmGDx+Ox48f4/z582jSpMlX+y8sLORifgBgy5YtaNasGbp06QJvb2+kpKSU+1tWWoLBYDAYjP9Geno6hg0bBhUVFaipqWHs2LHIzc396m+SkpIwYsQIaGpqQklJCZaWljh16tQ39/1D8/AYGRkhIiICNjY2XPAwUFoWQiAQoLi4GDo6Olxhz5YtWyIjIwO1a9fGggULuLpY9vb2FfaVmJiImjVrQlZWFqNGjUJkZCTy8vJQu3ZtrFmzBpqamrh06RIuXLgAPT09AJCohv5PWGkJBoPBYPyqkEgISP36eXiGDRuGxMRE3LhxAyUlJRg9ejTGjx+Pw4cPl/ubkSNHIjMzE+fPn0ft2rVx+PBhDBo0CM+ePYOFhQXvvn+oh8fCwgLr16+Hr68vHBwcMGTIEC6uJysrC2PHjkWPHj04vaKiIvr06YMWLVpAXV29TINDJBKVucy9X79+ePToEWRkZPDgwQP89ddfePXqFezt7bFjxw48fvwYampqUFIqTbBWWFjIDBoGg8FgVEmqwpTWu3fvcPXqVezcuROtW7eGjY0NNm3ahKNHj0rMvvyThw8fYurUqWjVqhUaNWqEefPmQU1NjZv14csP9fDY29vj8OHDCA8Ph7OzM9q2bYtGjRoBAGrVqsXVtfoa4izLAHD79m2oq6tLWHji742MjLhpJ2NjYy4HUOvWrXHkyBHUrVsXdevWhbm5OSwtLWFlZYW2bdvCycmJ17aIjazcIn5ZXpHPL3uwQs7XXXufU5RfsUZMzjcEWZMMvyWJOUX8s/zK89x+4BszLefy2wnfsv2KuXm8tYLqOby1OTwzAn/LMZAjw3+7ZL/h8UbA8+8l/y1j5bn9ACD8Bq2sDL8Nk8njf8LwzdwLAMK8At7a3OISXjrie10BkJ3D/3gt+obzUJ7neZBfwD8rNt/tB4DsfP77VRpyFYsAFHzDWHOK+I+Vb7bv/G8wJPgujMnJ+d81SFiCinP+80RYUuYY/utMx6NHj6CmpoYWLVpwn3Xu3BnS0tIIDAxE3759y/ydtbU1jh07hu7du0NNTQ3Hjx9HYWEh7Ozsvqn/H2rwWFlZ4eXLlxXqEhISUKtWLW7Hfl7F/PMVXiEhIUhPT0diYiL09fXRtGlTLnePnp4e3r9/D6DU4AkODsaAAQMQHBwMWVlZtG7dGk2aNIGzszOio6Nx/Phx3L17F9LS0ujatWuFYxQfZFYrv60URcV4V3J7pWz5Lq0yvguLtv/sEfymbPvZA/g+rDvys0fwfdh9/meP4OeiqflNcsHb45XafY0aNb5YnLNw4UIsWrToX7eZlJSEOnXqSHwmKyuLmjVrciuvy+L48eMYPHgwatWqBVlZWSgqKuLMmTPQ19f/pv5/mVpaYqNm06ZNWLBgAQ4dOoRu3bpBIBBAVlYWRISYmBj4+/ujSZMmsLa2xrt373D48GEYGBjAy8sLTZs25QwiY2NjLltzx44dMWLECFy9ehXq6uqYPXs2ZGRkoKGhAQ0NDbRo0QL9+/eHl5cXnjx5wsvgqVevHmJjY6GsrMwZY9nZ2WjQoAFiY2OhoqLy1d//bO3P7p+N9edrf3b/VWmsP7t/Ntafr/3Z/ZenJSKkpKSgdu3a5aZ8+Td87mgQU553Z/bs2VixYsVX23v37t2/Hsv8+fORmZmJmzdvonbt2jh79iwGDRqEe/fuwdTUlHc7P8TgKS4uRnBwMIKDg5GYmIiBAwfC0NBQQiMSiSAjIwOhUIj8/Hy8ePECLVu2hIaGBq5cuYJ169ahadOmiIiIgIaGBmRkZDB58mQIhUIsX74ctWrVAhFx+XRMTEywfft2FBYWwsjICLVq1cLTp08l+ktJSYGioiKUlZUREhKC169fY968eby2SVpaGtra2mV+p6KiUuGB+6tof3b/36L92f1/i/Zn9/8t2p/d/7do/7/3/y3an93/t2h/dv/fov3Z/ZelVVVV5fW778WMGTPg4uLyVU2jRo2gqan5Rb1KgUCA9PR0aJbj0YqMjMTmzZvx+vVrrgC4ubk57t27B19fX2zdyq9wK/CDgpaXLVuG1q1bIzY2Fu/evcOSJUsQGRkJ4O9YGBkZGeTllc4ZT58+HXJyctixYwcAQFtbG5mZmejZsyeuXr0Ka2trnDp1CoaGhigoKMC7d+8gFAolrNFGjRpBIBAgPj6eW4UlTkwojvNJTk7G0KFDYWJignHjxqF169Zo2bLlj9glDAaDwWD8FogrIHztJS8vj7Zt2yIzM1Mi2Pj27dsQiURo3bp1mW2L79v/9F7JyMhwVRP48kMMnubNm8PBwQGLFy/GmjVroKCggEePHgGQLDNRUFCAx48fY/z48Rg0aBDOny+dw23UqBEUFRXRpEkTCIVCmJiYIDY2FnJycqhevTpycnIkMiUTEeTl5SElJcW50YqKivDq1SuJPg0MDLBlyxa8evUKgYGBmDdvXqW6BBkMBoPBYJRiZGQER0dHuLq64smTJ3jw4AGmTJmCIUOGcOWj4uPjYWhoiCdPngAoLQSur6+PCRMm4MmTJ4iMjMSaNWtw48YN9OnT55v6/yF3d0tLS65yuZaWFlJSUtCsWbMvdLVr18bz588hLy+PlJQUvHnzBseOHYOSkhJq1KiB2NhYyMjIoFatWpCSkkJ2djYMDQ3x6NEjbNu2jauVJU5kaG9vz+XuuXXrFqysrAD8bfDIy8tDR0enwrISfFFQUMDChQt5RbH/bO3P7p+N9edrf3b/VWmsP7t/Ntafr/3Z/X+r9lfl0KFDMDQ0hL29Pbp16wYbGxts3/73Qo2SkhK8f/+e8+zIycnh8uXL0NDQQM+ePWFmZob9+/dj37596Nat2zf1LUVlJbH5DmhqamLNmjV48uQJ3r59i7Nnz3I5cMRERkaiY8eOICLo6+ujfv36KC4uhq+vL5YvX44mTZpg4sSJiImJwfTp0+Hu7o527dph7ty5CAoKgo+PD5uSYjAYDAaD8QU/zOCxsLCAmpoa7O3tce7cOQwePBhubm5QVFSEUCiEjIwM9u3bh9DQUHh7e3PVzwcPHoxWrVpBQUEBGRkZmD9/PgoLCxEWFoaGDRv+9GAtBoPBYDAYvz4/bFm6gYEBnJycMGrUKC67ooqKCsaPH88FLr9+/RpJSUmQk5NDfn4+FBUV4e3tDVlZWejp6XHxNdWqVYOZmdmPGjqDwWAwGIwqzg/z8KxYsQKPHj3icuPs27cPR48exZEjR6CmpgYA+PTpE2RkZFC7du0fMSTGN1JWXgYGg8FgMKoCP2xJUvv27SXW2Q8fPhyXL1/mjB0AqFu37i9t7IjLU/DhW5fL8aGgoOCLHAY/kk+fPv20vgEgNTUVWVlZEAgEvPRfKwZbVSkuLkZ8fDwvrTjojw98n3tKSvin24+Ojuat/RZiY2ORmZn5Xdr+/8zXahl9jkAg+C3PLcbvzw8zeKytrSUSBMnIyFQpb8GzZ8+wefNmXqUxLl68CAcHB943Jr7s378ff/31F96+fVuhdteuXXj27FmFuvDwcJw6dQoFBV+vW0NEcHZ2xoABA3iPtzLJzMyEi4sL9u3bh5SUlAr1cXFxcHBw4LUPPr/Z8zWm+BAXF4e7d+9WWnsAMHbsWGzbto1LsVAeubm52LRpE27cuFFhmyKRCBs3bkR0dPRXDZ+EhAQsW7YMcXFxFbZ55MgReHh44OrVq7yNKT4sXboU3t7eCAoK4mV8PXz4kFe7fn5+lf63+vDhA3Jz+dUae/PmDe92//jjD+zbt+/fDqtM/vrrLyxatAipqakVardt24Z9+/ZVqP0eD31iXr9+jYKCggqPLfGK3W8x/oGvPwCIt4uP0efv749jx47x6nP//v14+PAhiov511FjfBss6QxPZGRkkJWVhatXr3I5hMqCiGBnZ4dOnTrB1dUVL168+Gq7ZZ1Y//xM/N7ExAT169fHrl274O/vX26b0dHRiI6OxtGjR3Hr1q2v9n3r1i34+/vj9OnTX72ASUlJ4datWygsLIStrS2XJLI8xDfkkJAQ3L9//6taPhfGfv36oUOHDpgwYQK0tLQq1Gtra2PQoEHw8PDA6dOnv6oVG967d+9GQEBAuRc78cUzKioK4eHhCA4O5r7752+EQiGOHj2Ks2fP4sqVK1+9OYu3/8qVK9i7d2+5urFjx0JBQQHe3t5cxtHyyMnJQU5ODgICAnDu3DkUFpZfNHLYsGF4+fIl6tatW+5DiEgkwsCBA1GtWjUoKytLfPfPbb937x7++usveHt7o02bNhU+2Ij3q0gk+uqNZufOnbhy5QoWLVqETp06cQsbymPs2LHcb77GvHnzcPXqVWRkZFR4ExOP78WLF1/1eN69exeTJ0/G27dvKzTM9u7di3HjxiEiIuKrOjFdunSBj48Pli1b9sW4yhprRRw+fBjnz5+Ht7d3hR52IoK6ujpCQ0Nx4sQJfPjwoUzd50WeAwICcPv2bRQXF5d7rn/L+Ddv3oy5c+fiyZMnX31AES+Gyc3NxZw5cxASEvLV7focKSmpMscqrtX45s0b9OnTB2lpaeWOMzg4GG5ubpCVlZUwuMpq9+zZs1i2bBnk5eUhK/vLVHz6/SBGhYhEIiIi2rx5M9nb21O3bt3o6tWrlJeXV6aOiGjfvn3Uo0cPateuHZ04caLMdgUCARERpaSkUHR0NL148aJMXUREBPf/T58+0YYNG8jT05OOHj1a7pgjIyPJ19eXpk6dSkeOHClXV1JSQseOHaPp06fTli1bKDIy8gtNZmYm5eTkcO89PT1JX1+f3r59W2abmZmZNH/+fOrTpw81b96cYmNjy+3/czw9PWn9+vVffH7o0CHq3bu3xGef7+uvcenSJbKxsaFVq1aV+b24nXfv3tGgQYMoMTGxTJ1QKCQioo8fP5Kenh65ubmRsbExLVmypNwxffr0iTZu3EgzZ86kw4cPU0ZGRrnjjI6OJkdHRwoMDCzz+4cPH5KdnZ3EeMrbB+LPMzIyyNfXlzw8PGj//v2UkpLyhXbVqlUS+7akpISio6Pp48ePErqxY8eSm5ubRP9PnjzhjmHx/ikuLqZevXrR4cOHiejvY7y8sYq/JyJasGABhYaGlqlLS0sje3t7evnypUR/n4/nc+bMmUPdu3cv87vPOX78ODVv3lxifOWNVdzO5cuXqXPnzhQWFlamLiYmhqysrOj06dNfbYeo9O9qamrKbdfXePr0KT148IAiIiIoPT2dbGxsaMyYMRWO+969exQaGkqvXr364rv09HTq0KED+fv7c2N/8OAB/fHHH/T69WsJ7f379+nixYuUnZ1NDx8+JE9PT1qxYsVXxz527Fjq3r07NW/enGbOnElXr14t89gVv//48SPFx8dTSUkJEX35tzt9+jSZmZlRbGwsp6mIDh060NKlS7n32dnZZfZNRLR8+XIaOnQo976sYyc3N5datGhB+/fv596XpWnXrh13jRa3k5SU9IU2OjqamjVrJnFsp6WlUUJCwhf3GMZ/g3l4eCAlJYVr165h69atmDdvHho0aICbN2/i4sWLXNV0sQ4ANm7ciB07dsDDwwPOzs7YsWMHNm7cKNEm/a/uV1FREQYMGIB9+/bBw8MDBw8elNBduHABLVu2xOjRo+Ht7Y3w8HDY2trC3NwcoaGh2L9/P/eUExAQgKVLl+Ljx49QUlLC+PHj0apVK7x9+xabNm2SeHrdvHkzrl27hsLCQgwYMADDhg1DTEwMLly4gKCgIE63fv16qKurw9nZGQsXLkRgYCC8vb0xb948dO3aFffu3ftif6mqqsLFxQXPnz+HrKwsV3NM/CRfFmfPnkVkZCSmTZv2xXe1a9dG3bp1v/ibAKXTBp97psR9lJSUoLCwEN26dcOWLVtw7949uLu7l9m3uGyJUChE7dq1IRQKv3hqk5aWhkgkwqFDhzBz5kz4+vrixo0bOH36NP744w+JMYnHUKdOHUyYMAEWFhYIDAzE8ePHERsb+0X/IpEIO3fuxIcPH9CiRQvus8+Rk5NDUVER9zeUlpbm+nv79i03fSousSIUCqGmpgY3Nzd07twZz549w4kTJ7jknEQEoVCI4uJidOnShfsbzJw5E506dYK7u7tEMrB69eph7NixAEo9YS4uLhg4cCDMzc0RERHBPc3LycmhRo0anBdOPEYpKSkUFxdzJWXEiJN+jh49GvHx8TAwMACALzyINWvWRP369blj/fO/T2FhIe7fv899l5WVhZcvX2L37t0Avj5NmZGRgSlTpkBKSorzgonH/E9vj7S0NFJTUzF79mxs3LgRTZo0wdu3b3Hq1CkJ3YkTJ9CzZ0/07dsX+fn5ePDgAZYuXcpVmZaWlubGv3fvXri4uMDc3BzJycnw9/fHkCFDcOzYMcTExHBtPn36FKNGjYKfnx+mTJmCM2fOICAgAKmpqejatSuysrIkvBLi9i9cuAAPDw8cPXoUS5cuxaFDhyTGqq6ujrZt20JBQQFPnjzB3LlzsXTpUgQGBmLQoEGcN/vYsWOYMGECdu7cCRMTE8jKysLDwwNZWVk4d+5cmdOBly5dQkJCAi5evIjAwEDo6uri+PHj3FgBIC0tDTk5OZCSksLq1avRv39/zJ8/H71790ZkZKRE5vuioiIcPHgQ3t7eZdYxLOvv/ODBAygpKWHOnDm4cuUK3Nzc0LVr1zK99CdPnkRiYiKePXuGLl26ID09nTvvP+f+/fto3bo1unTpguPHj6Nr165wcXGRiK/MzMxEkyZN0K9fPxARtmzZgr59+8LCwuKLIptJSUlo1KgRzM3NkZ+fj1WrVqFTp04YN24c5s6d+9NjJ38nmMHDk/fv32Po0KGws7ODr68v9PT04OPjgx07dnAHpPgik5KSgilTpqBTp04YM2YMPD09ceDAAUyePJlrT3zCT5w4EQMHDkS/fv2QlpaG5s2bA/j7hqeqqgp9fX18+PABqqqq+OOPP3DgwAFs3LgRYWFh8PHxga+vL3Jzc7FkyRLMnz8f8+fPR9++fbFy5UrExcWhZs2aCAsLg6+vL9LT03H69Gm4u7tj5MiRWL16NWxtbZGYmIiSkhJkZWXh9OnTXKHVVq1aoU+fPlBUVERgYCDu3LkDc3NzFBUVQVFREba2ttizZ4/EmMXbt2bNGvTv3x8dO3bEkydPICMjg3fv3iExMVFi3+bk5ODevXt49OgRwsLCuM9DQkKQmJgIXV1dBAYGcqVGAHAxR7dv3+ZuOOICtHl5eXBzc0Pfvn2xZs0alJSUwNfXFwUFBejatSuys7O5sUpJSUFNTQ0bNmzgDENxfNk/L3SrV6/G/v37IRQKUVJSgnr16uHSpUvIzMzkpi3EbvT8/HwEBATg5cuXGDp0KPr27YvQ0FCcOnUKr169krhhC4VC9OrVCw0aNMD48ePx6dMn7qYYHx8PoVCIZs2aoXHjxhKxHuILfGpqKo4fP46ioiLOgJgwYQKmT5+O4cOHw87ODmPGjEFUVBQOHDjAufZlZGTg5OSEvXv3YsiQIfjzzz9Rt25drF27FsOGDUNQUBDXR/369TFlyhS4uLhg3bp1MDU1xbNnz9CrVy9Mnz4dJSUl3P5q1KgRDh8+LLEdQOmNfu3atXj//r3EAoDAwEC8e/cOmzdvxps3b+Dh4YE5c+bg6tWrnKawsBACgYCb8pORkeEMFJFIxE1JiftJSEjgplXl5eW5duLi4iSmWPPz83Hw4EGUlJSgWrVqAP4OzD516tQXBlpSUhKUlJQQEhKC2bNnY968eVizZg13E3vx4gWqVavGTU9NnToVq1evxosXL3Djxg107NiRM4hfv34Ne3t7FBYW4sGDB5g2bRq2bduGwsJC7N+/H0eOHAFQahRMnDgRa9aswf79+zFt2jRcvHgRAoEA586dg4GBAezs7PD+/Xtuf0tJSSE+Ph6LFi3CuXPnuH1y69YtbNiwQSJOpE6dOli5ciWGDBkCDQ0NzJ8/H3fv3sWwYcNw9uxZBAcHY9q0aThx4gTOnDmDGTNmwNnZGZqamhg9ejRkZGRw7do1XLt2jdu2vLw8XLt2De/evcOLFy8gLy8PNzc3CAQC7N+/n+t727ZtMDMzw40bN7jM+uvWrYOTkxOGDx8ucbwrKCigRo0aXJbhz42h4uLiMuOgTE1NUVBQgIYNG+Lq1auwtbXF+PHjJR7qpKSk8OLFC3h6emLKlCnw9/eHlZUVbG1tERIS8sWUrJ6eHsLCwtCnTx+8fPkSS5YsgaysLC5cuACg9LzU1NREfn4+tm7dCmtra9y5cwcdOnTA0aNHsXnzZolpdhMTExgZGWH06NFo3bo13r17h2nTpmHGjBlITEz84nrJ+A/8HMfSr4/YzSkUCkkgENDdu3fJ0tKSAgICOE3Pnj1p4cKFElMIRER79uwhJycnTldSUkJjxoyhRYsWSbjwiYi2bNlCr169oq5du9KuXbuIiOjRo0e0f/9+unTpEhGVTk9169aNNm/eTJGRkZSVlUX79u2jv/76i5o2bUo7duwgIqIPHz7Q0KFDafLkyfTo0SNat24d/fXXX2RqakqmpqYkJSVFd+7cISKivXv3kra2Np08eZJu3rxJy5cvp/79+5OJiQlJSUlR586dae/evUREFBISQuvXr6c///yT3r59S3FxcXTr1i1asWIF2dra0q1btyTcwkOHDiU/Pz/us23btpGlpSX5+PhQhw4dKDw8/Iv9kJ6eTn/++ScNGTKE7t27R4GBgWRjY8NNsW3fvp0mTJggMT2YmZlJzZs3p+vXr0u01bdvX1qwYAE9fvyYvLy8aOHChRQdHU35+fk0f/58io+P57S3b9/mpm/CwsKoefPmNH36dIlj4PO/44IFC6h79+709u1bKioqoujoaDIyMqKYmBgJbY8ePcjDw4OGDRtGU6ZMofT0dAoLC6OpU6eSj48PFRUVcX0GBwcTEVF8fDzNmzePXFxcKCgoiD59+kQGBgb06NEjIiKaOXMmdevWjd68eSOx/zp06EBbt27l3ov3Y2RkJLm7u5OhoSFFRUXR+/fvadasWXTv3j0aNWoUeXl50ZYtWyg4OJjmzZtHd+7c4Vzor1+/JnNzcxoxYgQ5OjrSx48f6eLFi7Rx40Z69+4dFRQUEBFRUFAQDRw4kJKTk7n9Kt7OzZs30/v377lx7dixgzp27EjZ2dlkZGRECQkJJBAIqLCwkDw8PKhp06bk6upKCxYsID8/P9q+fTtlZ2dz7aakpFDnzp1pypQpEvv6jz/+oKFDh1J2djalp6cTEdHatWtpxYoV9OnTJwltWloajRgxgnbv3k3x8fGUnp5O48ePp127dnG/FdO7d29uau5zdu/eTSYmJrR//37KysqiO3fu0Jw5c8jT05PmzJlDRES2trbUpk0bsrOzo8ePH3O/dXZ2poyMDBo2bBgtW7aMnj9/ThMmTKB27drRiBEjuCnNFy9eUI8ePSgjI4P8/PzI2NiYoqKiiKh0GrBr164S05/r168nLS0tietTXFwc3b17l4KDg6l58+YUFxdHK1euJEtLS1q5cqXE8R0ZGclN0Yk/nz59Orm7u9OhQ4eoT58+dPbsWU7fp08fbvo3NTWVNmzYQPb29rRq1SpasWIF5efn06tXr+jPP/+kGTNm0I0bN4iIyM3NjXx8fCT25/r166lBgwbUq1cvKi4upuLiYiIiWrRoEddnWloaEZVOwfbs2ZM738RTRSUlJTR+/HhuGu7u3bt0/vx5evPmDRUWFnLXPSKiQYMG0cqVKyXG8P79e256UNyms7MzGRgYcNeXCxcu0PHjxykqKorS0tIkQhBsbW3p7NmztGfPHpo+fTodOHCALl26RKtWraKZM2dSamoq5efnExHRwoULadu2bbRo0SLy8PCg5cuX06FDh+j48eO0ePFiyszM5NodMGAA7dmzhxiVww/Lw1NVWblyJbS0tNCtWzecO3cOb9++Rf369WFiYoJ58+bhwoULqFu3LkQiEby8vCAvL48lS5bAzc0NgYGBWL9+PQICAvD06VMcP34coaGhMDY2xrlz52BoaIhr167By8sL7u7uWL16NYRCIdq0aYPq1avj5cuX2Lp1K/r374/c3FxMnjwZqqqqWLBgAerXr8+NsWPHjnBycsK0adNQXFwMNzc31K5dG1OmTEHjxo0RFxcHkUiEdevWITAwEEePHoWOjg7Onj0LDw8P+Pj4YMiQIQCAly9fIiEhAZ6enkhJScHMmTMxffp0pKSk4NixY4iOjka/fv3QsWNHAKWu289TC6xatQoPHz7EmTNnAAARERFQV1dHVFQUNm3ahN69e6NPnz6cJ+bw4cOIjo7mpkvu3r2Lc+fO4cWLF1i7di369u2L3NxcJCQk4NKlSwgKCkJeXh4cHBywf/9+9OrVC15eXlz/WVlZmDBhAlauXAkdHR2kpqbizz//hJKSksS0okgkwrhx41BcXIxPnz5xT37Kysro0aMHFBQUcOnSJUhJSeHo0aPIy8uDiYkJ2rRpg02bNuHkyZPQ1taGnJwcmjdvjunTp3NP1nPnzkV+fj4WLVoEOzs7mJmZIS0tDevWrYO6ujry8vLQsGFD/PXXXwgKCkJRURHq1q2L3bt3o7i4GJs3b8bNmzdRUlICe3t7zJ49mxu3l5cXAgMDYWdnB5FIhODgYGhqasLPz4+bpho3bhwGDhyI7t27AwDWrVuHgIAAnD17FsnJyRgwYADatWuHRo0a4dq1a+jduzdGjBghcdw7OTnh5cuX8PPzw9OnT7Fv3z6cOHECbdu25TTFxcWwt7dHo0aNkJaWBqDUWzd//nyEhobi/fv3EAgEqF69OnR1dbF161ZcunQJGRkZ6NevH/bv349bt25hzJgxEAqFCAkJgZOTE+Tl5TFx4kQ8e/YMDRs2xNOnT9GtWzc0btwY9vb28PPzw+PHjzF48GDEx8cjMDAQjx49wvDhw2FlZYURI0bg/fv32Lx5Mzp37gwHBwc0adIEADBgwACEhoaiXbt2mDJlCoyMjLB37148ffoUBgYGsLGxQcuWLTF9+nR8+PCB8yrOnDkTiYmJaNGiBfr3748GDRoAKA1MHTNmDNq1a4fg4GAEBARw+yciIgKNGzfmPAQXL17EggULYGtrizdv3uD69esASj040tLSUFdX5367c+dOnDp1CpaWlhgxYgSuXbvG/f7ly5e4dOmShAcMKJ1qTExMxJw5c7Bx40a4ublBRkYG169fx8uXLzF79mycPHkSgYGBmDt3LlRVVcsMKC8pKYGfnx9OnToFY2Nj9O7dG9nZ2Xjy5AkMDAzw9OlTbhpWfMy7urri8uXLaNasGRQUFJCQkIDly5dDW1sbly5dwt69e9G4cWOIRCKcP3+e84SKefjwIVxcXODq6oqZM2cCADw8PCAjIwOBQAB1dXV07doVDRo0wLp166CgoID+/fvD0tISAODj44MHDx7g4sWLCA8PR5cuXdC2bVuoq6vD2NgYzs7OUFdXx4gRIyAQCHDw4EGJ/rOysuDs7AxnZ2cMGzYMALB9+3Y8f/6cO15nz56NUaNGYe/evZg/fz7GjRuHtLQ0jB07Fk2aNEGrVq2wfPlyuLu7Y/PmzejUqdMX01cPHz7EuHHj0KVLF7x79w5OTk5ISUlBQUEBVq1aJTGmo0ePYvXq1bhz584XCwUY/5Kfam79goifbsT/7ty5k0aNGkX79u2j+/fv040bN6hfv340cuRIunnzJvc7gUBADx8+pKlTp9KsWbNIIBDQjh07aNKkSTR8+HCKioqiuLg42rFjB40YMYJ0dHS4J8pVq1aRkZERrVmzhhwdHWnmzJl0+fJlMjc3pwkTJtD27du5J5wZM2ZQv379KDAwkIRCIWVmZlK7du2oQ4cOtGPHDi4o7s8//6QePXpwT4EikYh27txJ1apVI1dXV3r+/DkRlT6hm5iY0IwZMyT2w82bN6lfv340fvx4GjduHMXGxlJOTg5t27aN3N3dacuWLWXuL09PT9qyZQsdPXqU3N3dydLSkubPn8/to8+xs7OjGTNm0IQJE2jw4MF09uxZys/PJycnJ6pfvz7l5OSQUCik1q1b09u3byknJ4fevHlD3t7e5OvrW+bTN1HpE9Tw4cMpPDyciEo9JyNGjJAIvN67dy8NHDiQiIisrKyoW7duNH36dC5A/OHDh0RE9OzZM9LX1yd3d3eaMmUKrVmzhkpKSujmzZvUtm1bWrx48RfHzp07d6iwsJBGjhxJS5YsIZFIRB06dCBTU1POW3HhwgVq1aoViUQimjRpEmlqapKJiQkXWDxs2DAyMTHh2u7duzcXBHvu3Dk6cOAAzZ07l/bu3fuFJ2rjxo20ePFiysrKIiKioqIiGjRoEGVnZ9Pw4cNp9OjRRERUWFhImzZt4rwSRKVBq87OzqSpqUnOzs7c58uXL6cFCxZw23n37l3q1asXDRw4kHR1denx48eUnp5O69ato0GDBtHu3bvp4cOHdOXKFRo1ahRt27aNnjx5wrW3atUqcnJyIhsbGxo9ejS9efOGiEqfrr29vUlfX59at25NOTk59O7dOzpz5gyNHz+eJk6cSFlZWbRz507asGEDbd++nRITE6lPnz40a9YsiQDSGzdu0ODBg2nChAnUqVMnGjlyJOnr61O/fv2+OGZOnDhBXl5eZGBgQIMHDyZHR0fO0xAQEEDdu3enM2fO0F9//UXTpk2j8+fPU1FREbm5uZGbmxupqalRw4YNJY4x8d8lLS2N/P39qVmzZvTHH3+QkpISLVy4kD5+/EiFhYUS48jNzaVTp06RmZkZeXh4ULdu3STG2KFDBzIwMKAPHz5w+0vcz9y5c2nRokXk5uZGgwcP5oJ679+/T40aNaJly5ZRnTp16Ny5c9wxKD5GxBQXF9OpU6eoX79+1K9fP5o4cSLnhbt06RKNHz+eGjZsyPUvEAjo1KlT1KpVK+rduzcdPHiQiIh8fX3JysqK845s376dpkyZQn5+fhKBwGvWrKEjR45QYGAgxcbGkrm5OfXq1YtWr15NXbt2palTp1LPnj0pPT2d205/f3+aP38+de3alVxcXGjmzJnUvHlzysjIoIyMDFqyZAmdP3+eiEoDzL28vGjx4sX05s0b8vf3lzhffHx8aOvWrfT8+XOKiIggKysr8vDwoNmzZ5ORkRG9e/eO+vfvTwMGDKB79+4RUan3TV9fn2bOnElEpQsjwsPDqXr16nTlyhUiIkpOTiYjIyO6f/8+EZV6oy9fvkxmZmY0b948ql27NjeGBw8eUNeuXSk6OpqISmcKzpw5QwYGBpz3l1E5MIOnHMQHNxHRrVu3aOzYsbR06VLOoBBPSRARt7JEJBJRaGgoLViwgMaPHy8xdSLm+vXrpKamRqNGjaJXr15xF6Xr16/Ttm3buBVVIpGIpk2bRg4ODjRq1ChauHAh59L28fGRWJ2xc+dOatq0KY0aNYr++usvzjW9bds2iYtOfHw89e7dm0xMTKhHjx7cRSEjI4M6duxInTp1krhIu7u7k6+vL+3bt4+GDh1Kz549I6JSY2H79u1E9Lf7V/y7c+fOkYuLC3Xs2JHu3LlDz58/pwEDBkhMbRARBQcHk6OjI/f+woULZGVlRQEBAXT9+nVyd3enzZs3k52dHc2dO7fcv5O4/0ePHtHOnTspICCAAgICaNu2bTR8+HDasWMHde/enaZPn04bNmwgLy8v+vjxI504cYJiYmJo7ty55ObmRunp6WRqakoDBw7k/p65ubl0/Phx7iJ2+/Zt8vLyogULFlB8fDy9fPmS7OzsaNGiRVRUVEQBAQH05s0bEolEVFJSQlOmTOH+FhMmTKBJkyaRn58fXbx4kaKjo+nt27e0d+9ebpVUly5dSFFRkWJiYig0NJSsrKxo/vz55OHhwRkp/0RsREZFRdHly5fp5s2bFBoaSr169SJvb286ePAgubm50YABA4iIaNmyZdS9e3fu+N6yZQs5Oztzx2F2djZduXKFtm3bRjNnzqTNmzcTEdG8efO4C7x435w7d46mTZtGO3fulBjTjRs3qHXr1l91xcfHx9OiRYvo/v375OvrSw4ODhQQEEApKSl07NgxGjZsGD19+pTTl5SU0LNnz2jKlCl08eJFibYuX75M9vb2EseE+LiIj4+n169f0+7du+nmzZs0YMAAbiqipKRE4uabmppKOTk5lJWVxU2FPXr0iFq1akUnT54kotLpuu3bt5Onpyft3LmTwsLCqFmzZvT8+XPasmULaWtrSzwIERElJCTQiBEjaPbs2dS4cWPy9/cnLy8vmjFjBj1+/FjiWhIYGEjTp0+nOXPmkJSUFLet4r/z69evydHRkdatW0fJyckS/cTFxdHTp0/J1taW+0x8Xt6/f5/27NlD69atowkTJpCDgwN5enpKaD7f13/88QdpaGh8MSX49OlTWrhwIS1evJg7hs6cOUMTJ06k9PR0CQPq+PHjZGxsTPHx8ZSamkonTpygcePGcde4iRMn0rhx42j9+vVkampKJSUllJOTQ7169aLGjRvT1atXqUWLFtz06ecPTCkpKfTy5UtatGgR+fj40IkTJ6i4uJjOnTtHcnJy3IOIUCike/fu0Zw5c2jhwoUSbcyZM4c6dOhAmzZtIhsbGzpy5Ajl5+fT3r17afjw4bRp0yY6ceIEGRoaUvv27WnWrFncPs/MzKQmTZrQ7NmziajUwJk8eTJNmjSJM1K6d+/OrX4jKp1mu3r1KkVFRZGhoSHNmjWL+87a2pp7CM3Ly6OgoCDuesuoPJjB8w+EQiHl5uaSk5MTDR8+nHvKe/36NRkYGNCYMWMoLi6ORCIRCYVCCgkJIXNzczp48CCJRCISCAT05s0bcnBwoK5du5a5fPL58+e0adMmmjlzJjdHHRYWRr6+vjRr1ixuvjklJYUOHjxIAQEBNGvWLJo2bRoXD/DmzRuJJeSbNm2i+fPnk7u7O7m5uXGencuXL1NAQAC3FDM8PJw2b95Mvr6+ZGtry93QiIh69epFkydPpvDwcMrIyKDw8HCys7Pj5qaHDBkiEUPz+bL6tWvXkre3NwUFBXGeFaLSpfyOjo4SF/Xg4GDKz88ne3t7Onz4MLd/pk6dSs7OzhQTE0PBwcE0bdo0sra2llhO+/kFS/z/d+/ekbGxMY0bN44mT55Mbm5utHfvXrp8+TK5u7vT+vXrqU+fPjRp0iTq27cvde/enUJCQoioNKYgKCiIiIhcXV1p9erVRFR6Q+/duzdZWVnRqFGjuBvjs2fPyMvLi4tDCA0NpcOHD9PTp0/JwsJCIobC09OTXF1dqUePHqSvr0+dOnWipUuXkomJCbff//zzT9qwYQP3N+zduzddunSJnjx5QsXFxeTs7Ew1a9bk5v8/9wiIxyS++M6YMYOMjIxo/fr19OjRI1q7di0tWLCA3N3d6d69exQUFERCoZAOHjxIHTt2pPnz55OpqSm9e/eOiEpvfLt37+Zixy5dukSzZs2ifv36kaWlJdev+JwgIlq9ejUtWrToi7FdvXqVbG1tKSMjg4RCId2/f58uX77M7WuiUoO8WbNmlJaWRqdOnaLWrVvTiRMnSCAQUL9+/SS8Z2JWrFhB9vb2Ep6NwMBAGjVq1BdaotJlwJ8vx3d2dua27/M4vaKiIrp69SoJBAJa9vjByAAAPKJJREFUv349bdq0iZKSkig3N5e6dOlCtra23HGalJREu3btom3bttGFCxckYshOnjxJWlpanAdUzKdPnygqKoo7f7OysmjhwoU0adIkun79usRS6cuXL5OpqSn5+PhQx44dudg+MQkJCdSnTx8aO3Yst88/T6/QsWNHiWtOXl4ePXjwgPsbDR8+nDQ1NbnYv3+yY8cOatu2LY0cOZLGjBnzxXL2ly9f0tKlS2nWrFmUn59PN2/elDCyiouLufN98ODB3ANEQUEB+fv7U2FhIYWFhXHLv3v06EFr1qwhIuL+Vk+ePOG2Mzc3l2vv8weyqKgoEggENHPmTJowYQJnXNy4cYMaNWpEvr6+3JiCgoIk4uxu3bpFLi4uXH8vX76krl270pw5c6iwsJD27t1LPXr0oCZNmlBcXBydP3+e3N3dadeuXZx3i6j03BOPPTc3l9atW0fDhg2jLl26kKurq8R+y8zM5AzY3NxccnBwoF69elHv3r0l0lswvh/M4Pkf/8yPkJqaSm5ubtSjRw/O1Th16lRavHixxAFPRHTt2jVycHCgVatWcTemcePG0cWLFyWeIFesWEFHjhyh+/fvU0lJCfn5+ZGnpyeNHz+etLW1ycHBgRQUFMjY2Ji2b99Oy5cvp8GDB9OmTZuIiGjx4sXk4uJCz549IykpKTIzM6NNmzZRVFQUnT59mvbv30/Z2dnk6+tLI0aMoLNnz9LEiROpVq1aNHToULp//z69e/eOPD096cmTJxQREUHdu3cnd3d3SkxMpBYtWpCqqirNmjWLhgwZQkeOHKFdu3aRr68vFRQU0NGjR2nMmDFf5J2wsrKihQsX0tixY8nT05N2795N2dnZdP78eWrXrh19+vSJLl26RPPmzaPTp09Tp06dKCMjgw4dOkTe3t60detW8vPzI2VlZWrdujVpaGjQpUuXKC8vjxYtWkSLFy+my5cvfzElRlR64ejbty9niIWFhdHevXtp4cKFRFR6gZwxYwaZm5tzv/f09KT+/fsTEdG6detIQ0ODXF1dqW3btly7s2fPplWrVtGLFy/I3t6elixZwk1XhIeHSzzJFhcXk52dHV29epWISm/AmzZtoqysLDp9+jS5urqSmZkZ1//p06dp3LhxEv+fPHky6evrk4GBAY0bN4569OhBy5cv544la2trbrrtn0/ky5YtIy8vLyIqvRG4ubnR2LFjJcY3c+ZMGjt2LHdTuH//Pjk4OND48eO5NsUX+kGDBtGBAweIqPTG4+LiQuPHj5d42hcf10eOHCFLS0suYF98A05JSaGBAwdy07b+/v7k6upKnTp1Ijc3N87I2rVrFzeldunSJW58t2/fpmnTpnEeMzHZ2dnk7OwsYVzFxMSQubk5HTt2jPtMfB4eP36c1q1bx4139erVZGlpyd3ExYZBQkIC9ezZkyIjI+natWvk4uJCy5cv526Irq6u1KpVK87LmpeXR1OnTqWjR49ScXEx97BDVGrQiw1Q8T6xt7eny5cvS/RJVPpAMGbMGDpx4gQlJiZSQUEBTZo0idPevn2bOnbs+IXxJ/ZciPv8vE13d3caN24cxcXFERHRlClTJI6He/fu0ZEjR8jV1ZUzVolKp2qysrJo9OjR3DVu4cKF5OTkRP7+/hLXsoiICK59otKHpe7du3/hEWrfvj0XsCz+fWBgID169IgmT55Mo0aNkphOd3Nz4x6YcnJyqFWrVhIePfHf/ePHj7R27Vru/caNG2nUqFGcJ+7t27dkYWFBU6dO5X67ZcsWSkpKIpFIRN7e3tSuXTvauXMnZ2zGx8dT9+7dac+ePZSTk0PXrl2jwYMHcwHOt2/fphkzZtCKFSs4L05GRgb17NmThgwZwp0D+/btI2NjYwmDUrzwRXzNFjNjxgyqW7cu5zn6/GGCUfkwg4eIPDw8yMDAgCZNmkTh4eEST1ve3t7UoEEDcnFxIRsbG/Lw8KD69evT/v376fz58xI3wQEDBtD48eOpU6dOXHyImAkTJtDo0aNp48aNZGFhwR30Dx8+JD8/P3r48CGVlJTQ5s2bydLSkhYuXEh3796lFi1akIWFBTc1EhoaSgKBgObOnUvm5ubUtm1bOnDgAA0cOJDU1dW5G/3mzZvp4sWL3EnbunVr6tu3Lx06dIhsbGxIX1+fYmJiKD4+niZNmkQFBQUUFRVFzs7O5OrqSo8ePSJXV1fq06cPmZmZUXJyMuXn50usICAqXcElNh6IiK5cuUJdunThVhklJCQQUekJ7+joSLKystwTdnx8PO3fv5/69OlD8vLyNGTIECIiOnbsGOnp6VF+fj4lJCTQ2rVrydPTkw4fPvyF0RMSEkJ169aV2N+RkZHUtWtXioiIoOLiYtq5cycXV0JUOk0xbNgw7iZx5coViSmCU6dOkbKyMucl+/jxIw0cOJDz7v2T7OxsGjJkCG3YsIGcnZ1p4sSJZG5uTpMnTyahUEiLFy+mkSNHclM0z549IwcHB8rOzqbExEQ6efIkzZkzh5o0acJ59/z9/alv377c/vbx8SF9fX26du2aRN/+/v7UrVs36tq1q0SyQEdHR86bIjYYNm7cSC4uLtxNITY2lvr06UPTpk3j/k45OTl08+ZNGjlyJPn4+JBIJKLIyEhavnw5ubm5SbjoxUyaNIn09PQ4lzxR6fHXuXNnCV1JSQnFx8fT6NGjuWkVV1dXcnR05AwgMbGxsTRixAjy9PSkBw8ecAbmpEmTaNCgQV8cB3v37qVx48Z9kYyzdevWEivYRCIRLVq0SMLTQ1R6w/586jQ0NJQmTpxIf/75J+ehWLlyJdWtW5fu3r1Lnp6e1LNnT4n9+zlZWVlkZGRE06ZNoz59+tCMGTO+iNcRc/LkSerRo4fE1PPnvH//nvr160eurq5lejjT09NpypQp9Oeff9LOnTvp0aNH5OvrS3p6etwKMKLSh4GDBw/SrVu3iKjUmJ00aRJneHh7exPRl4n0du/eTR07dqSjR49KeGo/H0NGRgZNnz6dzM3N6eLFixQQEECjR4+mvn37EtHfxs779+/JxsaG4uLiaMaMGVS9enXunJo8eTL179+f1qxZQxs2bKBnz55xsU9iA1yMs7MzzZo1S8IIO3LkCA0fPpy2bdtGAoGAsrOzydLSko4cOUL79u2ja9euUUZGBhfDdPDgQXJxcaFTp05xMZKf/42KioroyZMnNHnyZJoxYwbl5OTQ69evacqUKZyHWLy/vLy8yN7enotFu3r1Ko0bN45mzZolcXwEBweTk5MT/fnnn5xRvnz5cjIyMuJCDBjfD2bwUKnl7uDgQGPHjiUbGxtuKbP4ZH7+/DmXqXbVqlWkqKjIXQzbt29PZ86c4Z4E7927R6dPn5a4aFy/fp0zChwdHWnevHnUs2dP8vLyooKCAkpLS5Nw9R89epTatGlD169fp4KCAgoKCvpiySxRqXegffv25OvrS6mpqTRjxgzaunXrF08JISEhtGzZMho8eDDdv3+frly5QmPGjKGXL19Sbm4uJSQkcCdwYWEhjR8/nqZMmULv37+njIwMevv2bbkZPxMTE8nMzEwiXmPp0qVcxuTPL0gXLlwgV1dXMjU1pbt373Kf79q1i/r3708HDhzgjJAePXpwN/D8/HzatGkT55b/J2/evKGOHTvSuHHjqLCwkD59+kRGRkackZibm0s3b96ksWPH0vTp08nKyopu3779xfjEFBQU0LRp08jMzIwzekpKSmjYsGF08+bNMm9wDx48IBcXFy5oMykpiXr27MkZLBs2bKCxY8fS8ePHqV27dhI3YaJST4ShoaHEceDg4CCxn/bs2cPdlD7n8uXL5ObmRlu2bOHm/Y2Njbnl7OXdFIqKiigvL48GDBhAp06d4jTiC/2UKVNo+vTplJmZSSkpKbRixQravXs3PXr06ItjbM2aNVSrVi0aMWIEDRw4kExMTMo0DsXExMTQnTt3qFevXqSsrEyOjo6cISbm06dPNHnyZBo3bhyZmpqSi4sLtWnThoqKiuiPP/6grVu3ctuWlZVFmzZtouHDh1Pfvn1p69at1KlTJzI1NSUzMzM6evQovX79moqKiignJ4e2bt1KJiYm5OTkRL169ZLIrvv5tMmff/5Jbm5u3LF37Ngx2r59O7Vt25bTxcTEUEBAABfX9jlbt26ViFWLiYnhpg0/n5KuKNNyamoqTZ48maytrSk1NVXiO2tra1qwYAFt3ryZfHx8yNXVlRISEujFixcUGhpK2dnZlJWVRc2aNeMe2MSenfj4eFqxYgU5Ozt/cVx/ftzcunWL7O3tafHixeUabgUFBeTn50ddunThYtbECAQCKigoIC8vL5owYQL3uTi1xtixY8nJyYlcXFyoV69etGTJEjIyMqK9e/fSxYsXydzcnMaMGUN+fn40btw4CWP683H7+/vTqFGjaPny5Zy3ae7cuTR48GAKCAigs2fP0pgxY2jHjh0kEono0qVLNGrUKNq4cSOnF4lEEtOd7969o/nz59Po0aMpMjKyzMzKQqGQfH19qW3btpx3LjAwkNzd3bl7g7jNlJQUGjp0KI0ePZqbZjt06BDZ2dmV6cVmVB7/7w0ekUhEaWlpNHHiRC7t+YsXL0hBQYG6du1Ks2fPlsg/QVRqkYtjMLZv307y8vI0cOBA6t27N0VHR3MuU7FbODc3lxITE2nLli2cJ2LZsmVkZmZG7du3p169epGlpSWZmppyc9zXr1+ndu3a0caNG7l+Dxw4QKtXr6aJEydyT5337t3jAu/EhIWF0c6dO8nb25uLD4mOjqalS5fS8OHDuXEVFhaSk5MTDRo0iDQ1NcnV1ZUOHTpERERLliyhXr16cauVxPtKfNK+fv2a3r9/T6mpqXTt2jVatGgRLV26lPLy8rgVY5+fvK9eveIMmHPnzpGenh6dPHmSzp49S71796YzZ87QrFmzaO3ateTs7MyVMeBbQiI5OZn69etHNWvWpF69enGBxuIxiG/iAwcOlJi6+nyMDx48oH379tGdO3coISGB9u/fT23bti23hMeyZcto0qRJNGnSJIlcK69evaI2bdrQrl27JG4ahw8fpj59+lCPHj0kbhribdy/fz/Z2dnRmTNnaPny5RIrdCraDzdv3iQPDw+ysLCgPn36cDEB/1xFR/T3TWHFihWcZ+efms8v9C4uLhQaGspNjU2YMIHu3LnzxYU/OjqaLl26RI8fPy6zRElZFBUVUXBwMO3du5eGDBnyRVxJcXExpaamcvlkMjMzKTAwkKpXr07169enfv360bp167jpxNevX9PatWvJx8eHdu7cSbNmzSItLS3q1asXeXp6Ur9+/ejx48eUmZlJ2dnZ9OzZsy+mzcTbL2b16tU0atQoOn78OBGVGtiDBw8mIqKLFy/S6NGjqXnz5tSqVSsyNDSkrKwsrr3nz5/TtGnTKDk5mbZu3UpDhw6l5s2bk62tLc2cOfObpjDEBsXn01fp6ekS8UsfPnwgT09PbqxiFi5cyE0dJiYmkrW1Nbm4uHDjFLcpEAjKLbPx9u1biSmwfy5EECM+LsRtfR5rN378eOrYsaNE3NObN28oLS2NfHx8JM7N/fv3k4eHBxGVTjlOmzaNlixZQj4+Ppx3pKxxhoaG0tChQ8nX15e2bdtGLVu2lCh9cunSJZo5cyYtXbqUOwYGDhxYrjFDVOpxXLJkCXc9Lm8fnT9/njp06MDpxOMUaz6/3nh4eHDHIxF94T1jVD7/7w0eMffu3aOmTZtScHAwTZ8+nQYOHEh3796lnTt3cqsuxCfNiRMnaMSIEURENGbMGJowYQLl5+eTu7s7vXjxggoKCrg6KLNmzeK8I4cOHaJ169YRUWlSr65du9LQoUMpJSWFioqKaMmSJdSuXTvOfRsSEkLW1tbk4+NDc+bMIXt7e+5JQ09Pj7Zv304lJSX09u1b6t27Nzk7O1NSUhKZmprStGnTaObMmWRqakp2dnYUHx9P+fn5tH79eho4cCCdOHGCBg4cSJ6eniQSiej9+/fk5+dHI0eO5Lwzfn5+El4O8fbfv3+fTE1NqWvXrjRt2jSaPXs2Xbp0iYYPH04DBw7kLlJiXF1dqXPnzjRgwAByc3OjsLAwCgoKIgcHB+revTvdvXuXBAIBF7fxzzpd4lU34rn9a9euccs9P6ekpISWLFlCHTp0kFjmLEYkElFYWJhELNSSJUuosLCQnj59Ss2bN6eVK1dSmzZtOI/V9evXycjI6Iu6SAcOHCA7Ozt69+4d2dnZUevWrWnZsmVEVDqdI/6/uF8xgYGB5OLiQhs2bJDwqom5c+cOde7cmQwMDDgDUbwN4riTpKQkztP0+faFhobS3LlzadasWXT37t1y6xWJtUOH/l97Zx4X1ZG9/SJOMks+xpnJTBxJ9OcYdw2CikZEdlEWEZQdZBFUEJRFNEJYJMpiUERxA4yARkGMOK4xEANqRMQlQ5Q2BjSKRpQWRdml6ef9g/fW3NvdQIMgLvX9C3q99/atqlPnPOccB4SEhODBgwcdTvR8g3rTpk2YP38+srOzBd4GrrBje72r2joeLpTm5uaGyMhIwWcUFRUJxPXA/zIFDx8+jMOHD2P48OFwcXFBdna2QDRfX1+P2NhYHDt2DFKpFPHx8ejTpw98fX2xcOFCVFVVyWnyuO/mL05JSUkCw9fIyAgTJ07EBx98gI0bN1LPnKOjo2CTUFZWBg8PDzg4OKB///5ITU1FZWUlHj58CE1NTfqbdnZnL5FIkJiYiKSkJKirqwt6xe3cuZOGU6VSKX7++Wc4OTlhypQpgv5YFhYW+PTTT7F3715kZWWhoKCgzeOQ7X/l6OiIjRs3Cn4niUQi+J//d3l5OcRiMerr67FmzRqEhobi6NGjNAOrubkZK1euxMKFC6mX8ttvv4W5ublAL8f/rRTdZ9wxPnr0CLW1tXB3d6fGMPddnIh+9erVCAwMhLOzMzQ0NGBubo64uDjBWOCfc3V1NRobG+k1OnfunEJv18WLFzF58mTk5eVRDy/fg8c/7i+//BJTp05FZWWl0ps7RtdhBg/+d1Pv27cPOjo6mDx5Mt15tdWgztfXF2+99RZmzJgheJwbDNXV1Thz5gz8/Pzg7e2N27dvo7i4GLq6unB2dsZHH32ETz75RFC3A2gNbfztb3/DkSNHALS69bdv347JkycLXnfo0CFoaGhQQV1lZSV8fX2hq6srV0XU2dkZQ4YMwf379yGRSJCWlgZjY2OMGzdO8Lra2lpkZmbC0dGRTiz89HzueBwcHFBYWIj6+nrk5eUhLCyMxsWrqqoEu5nDhw9DW1sbQOvuOyMjA97e3rhw4QKqq6vl3PNXr15FTEwMnRA5xGIx7O3t4e7uDk1NzTZ3lxKJBGFhYRg9ejR+/fVXhZNIeXk5oqKiYGBgQA3XwMBAXLp0CUVFRdDQ0EBtbS0kEglqa2vlmkRyOpDy8nIkJibC2toaRUVF0NTUpCLJ9oyNa9euwcHBAcuWLRNUiuVec+fOHcyZMwdhYWHU5f306VPs3bsXdnZ2GDFiBK1pI/s9t2/fxooVK2hKfHuLwvz586Gurg5zc3OsW7eu3YkekA+NzZs3D8nJyTTcqqWlhdDQULnvUxbOC7d06VL4+PjQsTd48GA5g6egoABTp06lC7iamhrs7e0RFhaGKVOm0E2GVCpFbGwsbG1tAQDu7u5wc3ODSCTC/Pnz4eTkRDV5R48elTP8ZD0w/Gtw6tQpgZYDALS1tenY5bhz5w7Kyspo8gN3XJqamti9e7egwrGyi15BQQHNjCorK4O2tjZ8fHxw7949zJgxg26s+Mfw+eefY+XKlYIxHRISAnV1dZiYmMDd3Z1qxGTLTfBZv349DdE3Nzfj/Pnzcs1m+e/97LPP4OTkhNGjR2Pt2rW4e/cubYCclpZGr7FUKkVcXBz1NI8fP56GOZ89e4aKigr07duXZkUBHTendXJyEoQa+eMtIyMDhoaGMDQ0hFQqxa5du2Bvb0+F6bLv4a67ra0tUlJSYG5urrAhL9BqcOXk5CAtLQ3Lly8XeK5kP1NZbyjj+WEGD49ffvkFenp6NKWUv9vhx3SB1p3jvHnzqGu2oaFBMOjmzJmDzZs34/bt24iJiYGjoyNu3ryJR48eQSQSITg4GK6urrh8+bKcEDgiIgJLly4F0OrKnTRpEtV88L/jp59+gqqqKtV5REVFYcKECfR5/mTt7e1NM3Kqq6upkJfTnPAxMTGBn5+fwmu0adMm/OEPf6D6kNraWmzatIkeb01NDVJSUmiH9PPnz8PFxYW+//bt2zA3N8fw4cOxfv16hdqg27dvY8OGDQgNDRW4rq9du4YPPvgAOjo69DH+b9TS0gJvb2+MHz8ehoaG7S7i69atw7Bhw6hA9Msvv4SnpycmTpxIBbTp6em0vcbatWuxefNmGnJ5+PAhxGIxTExMqHHo5uZGs6+445GFH8efN28eZsyYQcNKLS0tdJFvamqCi4sL7O3t6TW4efMmBg8ejPHjxys0+KRSKV0UfHx85K4R//yjo6NhY2Oj9ESv6Brm5+fDzc0NW7duhZGRkZxQX1kUhdJWrVoFd3d3DBs2TFAYkU9mZiasrKygpqZG77+Wlha5bthAa/ZPv379BGFCAHKaPB0dHRw+fFggopa9DrJekJaWFjQ0NGDRokVyv7/sQiyRSNDU1IQZM2YgPDwcCQkJ6Nu3r0CMzq8jpIjq6mosXLgQurq6dMEVi8WwtLTE0qVLaWuUAwcOICQkBDY2Nrh58yZKS0sRHx8PPz8/ZGRk4MSJExg6dCj9zujoaHh5eSn8Tu48OF1VZGQkLQZpamoKS0tLeixNTU2C7DxuU2hlZUUzspqbm5GamkpDz/zzTU9Pp5lPstfh6tWrGDNmjKC9COe5qampkbuHs7KyYGxsLPC6ccydOxcqKioCI93Pzw9Lly7Ff/7zH7l6ShxBQUHo06cP9arJerb4rYYsLCzw3nvv4eDBg3Kfw4115tl5cbxRBo8iPYMsp06dwtixY3HhwgUUFxe36+7fsGEDNDU1aVoqx/r162FmZkb/v3PnDpKTkzFr1ixBaCQhIQHu7u7Izs4WeEZ2795NJwapVIqNGzfCy8sL+/bto5MKZ8zY2NjQ2i83btzA7Nmz8dlnn9GFnBuIWVlZ1JsB/E/IqyiEkJaWJrdD5GhsbISfnx8++eQTavScOXMG+vr6ePLkCb744guoqKggKioKFy9eRGNjI3R0dARCRTs7O6xevRpWVlYIDAyk2VHcBMDpHTiPFPC/SeX06dNwcnKCnp4efd+FCxfw9OlTpRfx48eP4+OPP4a5uTkyMjJQUVGBGzduQFtbm06kv/zyC0aNGoWzZ88iPj4ekyZNQkpKClxdXQViXA8PD2RnZyMpKQl6enro27cv4uPj6fPt7UCbm5uxatUq6Ojo0GvJ9yiWlJQIqhMDrRqB3bt3w9bWllaaFovFNIzT0tLS4aJw4cIFpSb6tsaLrLdq3rx5sLCwkDu/9uhobJWXlyM0NFRwz8p+f319PQIDA6kouLGxkRa14983QGsWpYmJCfWmcNcDkNfk/fGPf4SDg4Mg8629OaOmpgYhISEwMzPr8LxKS0uxZs0auvkAWsX8qqqqVG/HJzMzE9XV1Vi7di22bNlCizxy4vMtW7Yo9BBcvXoVw4YNQ25uLi2wl5+fD4lEgo0bNyI9PR1JSUno06cPNQYaGxsxbtw4ODg4IDIykobfuLmhsLAQHh4e+PHHH2FgYABzc3Pk5eVBLBYLUtnd3NwQHh6OO3fu4NSpU9iyZQvCwsJgaWkJoFVgzmVM8uHf+6dPn4arqysSExMF4W2gtSSArq4uTE1N6TzY0tICDQ0NOeF7XV0drY7NNzp8fHzg5OSEkpISjBkzhm7wxo4di9mzZ8PU1BQGBgbQ0tLCgwcPBPNjfn4+IiIi8NFHH9HwPwB6r/AN4u+//x6BgYEICAjA8ePHqUdfkVHO6HneKIOno/hvS0sLnj17hi1btmDBggUYP358h+7+VatWyS1S27Ztw9///ndB6wOxWIyMjAycPXtWYWggJSWFpkbOmDED8fHxgoHD6YaSk5MF5zFjxgxBvZ/a2lp4eHjA2dkZ169fp4+fOHECdnZ2goleNoTAiebU1NQEWTuK2LlzJ1RVVeHi4gIrKyuaUvnbb7/BzMwMK1asQFxcHC5evIjq6moq7HRxcaGhNK7miLOzM91R19TUYOTIkThw4IDgOsfGxmL37t10UgkNDcXYsWPx1VdfYdiwYSgsLFRqEa+qqsLo0aNx4cIF/PLLL3B1dUVMTAyKi4tx8OBBLF68GBMmTICenh5SU1Oxd+9eaGho0M+cM2cOQkND4enpidLSUurlmDlzJgoLCzs0NsrKyuQaD6anp+PTTz8VeNvU1NQExcj279+PlJQUev5ZWVmwtbXFypUroa2tLWgkCXS8KCgz0fMrHbfn7ampqRHcVx3BeeE6GlttGQ38cXHy5En8+9//RlFREfLy8jB69Ggq6JfF398f06ZNo+/vSJPn5+cnSF1vz+vy5MmTducMPvzQFodIJMKIESMEnjlbW1tYWVkJDG4XFxdq6BcVFSEgIABxcXEoKioSGNepqamC2jvZ2dkYOHAgSktLBR7lw4cPQ1VVFdu2bYOXlxccHR0RHx+PhIQEjBkzhiYxPHjwAJGRkYJaR9z3xcTEYPr06fT6eXh4oLq6GlKpFGVlZbCzs4O2tjbV4qxZswZmZmb0/XwvL//3//nnn2FjY0OPQRZXV1eMHz8eT58+pTopRTx+/BgbN26Eubk5tLW14eHhAT09Pfr806dPoaOjgz//+c+0cjJnkMjO1ydPnqQe2WvXrmHUqFFUM2djY0MNmrq6Opw8eZKWatizZw/mzZuHgwcPIjAwUNC2hfHieCMMHr6rX5nd9+rVqzv0FPDdkXv27MGhQ4dw8uRJLFmyBGKxGHv27IGTkxO2bdtG38NX4SsKDWzZsoVWUJX9HqBVLOzm5oa1a9eisrISy5YtE+ys+a8NDQ2Fqakprl69ihs3bkBNTU2uwiwgH0LQ1NSkqc9816wijh49iilTpsDT01MwaUVGRmLq1KkICwtDaGgovv32WzQ2NkJTUxOGhobo37+/ICMsOjoaFhYWuHTpEgwMDATFwoDWgmFTp06Fu7s7lixZQosMci57Lg2UMzY62q3xRa2lpaVYvHgxIiIicO7cOTQ1NUEkEtGQ3JEjR/Dee+8BaPX6/Pvf/0ZaWhoiIyPxl7/8BVeuXMGzZ88E4sX2jA19fX25rLympibk5uZi2rRp2L59OxYvXow5c+bQ65+RkYExY8YgKCgIqqqqNIx66dIlLFmyRLDLlKW9RaG2tlbhRC+VSnH48OFOeauUpbOhtG3btsHb2xvh4eFISkqij/Pv9ZiYGFohOy4uDpaWlgIvHD8TiZ9lxEdWk9fZOaOrIUI+3H3j4OCAL774AlpaWtTg5r5vzpw5CAkJgZeXF8rKylBRUYHAwEBERkZSAyssLAznzp2Dvb09ioqK6L0ZFBSksLSDSCTC8OHDBf2dZFm3bh3+9Kc/0flMKpWirq4OOTk5mDx5Mh4/fowff/wRWlpacu/duXMnLCwsEBUVBV9fX6irq9NCrzU1NfDz85Orps6d78OHD9u9blFRUVBRURGE8hXB1eXJzMzElStXqNHHN545g5XrAg8IfzNvb29MmjQJEyZMQHJyMi1xYGFhgenTpwtCYNOnT4ehoSE8PT2xfPly1NbWIi8vDytXroSlpSUtIsp4sbwRBg+HMrvvysrKTsV1s7KycOPGDfzrX//Cu+++S93LVVVVtPbLkiVL5NI9AfnQgLOzM6ytrXH06FHqmgeEg7KsrAy+vr6YOXMmrUnChz9Ak5OToaenhxEjRnSYjVNeXo6wsDDao4v7nNzcXKxYsUKugir/uNXU1DBkyBCaCSORSLBr1y7s2rUL27dvh7+/PzQ1NbF48eI2M8K2bt2KIUOGyDV25LLJuAUuNTUVAQEBSEpKoh4xPm0t4gDkJnu+wDwkJARLlizBt99+K9A+SaVSXL16FQMGDED//v0FWS5c48G2kDU2RowY0WFWnrq6OgYPHkw/49atW5g1axZdEA4dOiRXNp9/rIroaFFQNNFz590ZvURHdHZsLViwADY2NtiwYQPi4uJgYGAg0HVwvx9f+H7//n0EBATIVfdta+y1p8kDlNOMdPa8OsLOzg59+vTB3bt3OzS4L1++jLNnz1KP4datW+mGITQ0FKtWrUJSUhKOHz+O//u//5MTWXPU19dDV1cXc+bMwbNnzxRqUlJSUjBkyBCquwFaxbncOCwpKREY1Px565tvvkFGRgbS0tIEGw5tbW1ERUXR/7mxKpuB2B7ff/99l0JEisZLdHQ03nnnHYEHFmgtUsjpm/Lz8+Hp6Yno6Gg6L/Lnov3799M+WdeuXUN4eDg8PDyokdMZbyije3mjDB5AufivsnFdLy8vqh9Yvnw5NDU1MXv2bMFu/9SpU4IB3VFooLKyEps3b6YZDJxBwxcyVldXIz4+Ht999x2qqqqQnZ0tqNfD/46cnBxMmzatSyGEO3fuYNSoUbQya1vs3LkTKioqGDt2LHbs2IHAwEAEBwfTLunm5uZ4//33BVoDRRlh/EmGO6709HSoqKgINEUHDx6Ep6cnTTdVRFuLuCz8a7VmzRqFhf2A1ol9+PDhgjoxs2bN6rAHDmdsDB48WKmsvMrKSkFWVFZWFiZPngxfX18aEigpKcGAAQME2Sod0dGi0NZE3xm9hDIoO7Y4jwmf3377DTY2NnBxcaFCbkU9urgwJ39hBTrWDfE1eZ29Bsqel7JwHhCJRNKmwR0bG0u1Q/fu3UNubi5UVFSoF+bBgwfYunUrPvvsMzg4ONCwc3uGxNy5c6Grq0tf8/XXX2Pr1q3UUMrNzcXw4cOpR42PWCzG+PHjqdAfAL3fT548iYSEBMEYzMvLo41zjx8/Dm9vb0yZMkWhwPhFws/Wa2lpQV1dHSwtLTF06FA6H4tEIixevBheXl64d+8eNdBKSkowatQoODo60kzPO3fuYMOGDTAxMaGeY0bv8MYZPBwdxX878hRs374d+vr69PXcbmbOnDnQ0tJCU1MTzpw5g88//1xptzj//8jISNjY2GDr1q2C1Ed+Ib2dO3fC1tYWQ4cOFWgN+J/1PK72zMxMmmHFDWipVCq3aAOgXq61a9fi4sWLMDQ0xMcff4ycnBzs2LEDpqamSmWEcefHX3QKCgrw0UcfUQMKgNzCrAhuEecbRrIpw4Dw/GWbFPJ59uwZzMzMsHz5csTFxQlaarRHTk4OQkJClMrKU1QHJS8vD8uWLUNUVBRNU6+rq2uz+nVXUZSWzaGsXkIZOhpbnMckPDycvoe7LiKRCGZmZtSYaatHV1paGlauXIn6+npB9p4ymry2eN45ozPI3n+KDO6ZM2di4MCB2LVrF30sMTFRLusLQJvVkRXBhQM3bdoEQ0NDREREYNy4cUhMTERzczNKS0sxcOBAWq2cT1ZWFjw8POTGuqGhoaCW0549e5CdnY0pU6Zg8ODBWLp0KTIzM5GamipXgqA3kO2tWFVVBVtbW5iZmVEdXWVlJdLS0uTqOOXn58PY2Bjr1q2jY5Tr7s7oXd5YgwdQLv6ryFMgkUgQGhpKd1J5eXkIDw/H2LFjER4ejjFjxmD06NEYN24cLl++3OnQwJo1a2Bubg43Nze4ubnhiy++EMR8+VVDp0+fjk8//RRnz56Vqwnx4MGDTrnaZQ2fkpISuLi44O7duzSklJ+fj/T0dIUL86NHjzB69GiqzeHc/cpmhHGPt7S0YOnSpbC1taXNUcViMSZPnkz7F3F0pCc5cuQI7ZY+b9482uVcls4Uy/P19cX777/faTd6R1l5/Dosy5cvh4+PD/z8/PDkyRP8+OOPCAsLQ2BgoMDYU/a4uwNl9RLK0p4XrqSkBGPHjqUVt4H/GT1GRkaCxbatHl1mZma4fv26UkZ/W/W2ZOnqnNEdqceyBvegQYOo+FUikcDQ0BCNjY3Izs7Ghx9+KPD6KktNTQ0VG1taWqKxsRExMTHQ09ODu7s7VqxYgfv377dZIZrrXTd//nzMnTsXKSkpsLCwEFR1vnLlikA0zO8hZWtrK1dH7EUj21uRX/gwMjISenp6NPTZXm9FKysr+Pv7C+4DRu/yRhs8gHLxX0Xu/pycHJibmyMgIADjxo3DypUrsWnTJsTHxyMuLg6//vqrwEhRNjTw5MkTGBkZ0Z3B6dOn4ePjA39/f7mMAaC1Ts/x48fh5OSE1NRUSCQSnDlzhmYHKOtq50/IAQEB2LdvHyoqKuDp6YmIiAgcO3YMZ86cwccff0z1EYpoampSWMenMxlhVlZWNI00NjYWQUFB1LNhbm7eae0I0KoV+Mtf/kIzZJ63jHtnjB1ls/I4IiMjYW5ujl9++QXW1tZwdHTEw4cP8dtvvyEyMlIQ1njRdFUv0RZthdIAxWMGAAwMDBQ2MQXke3T5+vp2q74G6Pqc0R1wBvff//53hIeHw9fXF/n5+bQGD4dIJMLf/va3NstLKMLf3x/m5uZYtGgRiouLcf36dZw/f57WvTp27BiGDh0q8N4oMuQ4nV5MTAwiIiIEiRsVFRXQ1dXFvHnzBNewubkZLi4uz+U57C7a660okUiQlZUFExMTNDQ0tNtbUSqVws3NDcHBwb19Soz/zxtv8CiLrLu/sbERBw8exLx587B3717qXRGJRDA0NJSrIMyhyC3OnzTq6uqgp6cnmKgyMjLw6aefClzXS5cuRUBAAGJiYlBdXY0LFy7AyckJK1euRL9+/ZCXl0df2xlXe1JSEnR0dKjBVVNTg7CwMPj5+cHe3p7G59vbsUqlUnh7e8PFxUWpjDD+a8RiMby8vOhOqby8HEFBQYJwFvdZHcHP9EhNTUVMTAwGDhwo2FG+qGyJjrLyuOcbGhrg7OwsqDLt7+8PAwMDAFDYRPZVp71QGtA6ZjQ0NFBdXQ0/Pz9BFmNbNDU10fBWd+trlKWj83oeuPG7Y8cOzJ49W5D2zXmr6urqlP7+8PBwzJ07F6WlpbC0tMSsWbPQ0tKCc+fO0dD9gQMH4ODg0Kn+X4D8WE1OTsbUqVORnZ0tEKArCje/aKTStnsrzpw5EyEhIdi3b5+gIGpbvRXt7OxQUVHRY/cAo/Mwg6eb8fDwELjhFcF3i3Nu+traWpSXl+P333+nxdy4Sp5JSUmCSf4///kPDA0NceTIEaxatQqOjo64fv06xGIxfvjhB0GHbT4dudolEgk8PT3pYis7gfKzC9ozeDhxKFf8UBbZjDDus3bt2oXNmzfDwcEB1tbW9PuvXbsGGxsbQRioI7jr+sMPP2DhwoX0nA8dOgRVVVVs374d3t7eCsWXz4uyBfu4rDyOnJwcJCcnY/v27fjss88E5fqdnJzkGia+SXBjhjP82kPRPdKd+pqXAdkWF1yNLlmhtuxrFVFUVIR33nlHECbU19enPa0sLS2hqamJMWPGUCNS2VAq/3W5ubn473//i6dPn+LcuXPQ09PDtm3bXsqspbZ6K3711Vc0iaOjOk4+Pj4oLi7utXNgyMMMnm6C6+ZrYmKi1Ou///57QWzY1NQUwcHBMDY2xqZNm3D+/HnMnDkTRkZGGD9+PBUSnjlzBra2tjQMVlFRgaSkJNja2gqKgrWFrKv9/v37ePToEfVQhYWFyWUqRUdHC0rtt0dni8px2R+lpaWYPn069ewEBwfDwsIC+fn5sLOzo+XyO8O9e/egoaFBwx+cm/nWrVuwtbXt0DDtKso2OKypqaHp5r///ju8vLzw3Xffoby8HIsWLcK6deuQnp6O4OBgGBkZ9cixvkrk5uY+dzitp/Q1vQH/3rp06RIWLlyI8PDwTotj79+/j/j4eLi6ulJtyqRJk2gXb6C1RQyXZdkV3diCBQtgbGwMT09PmJqaoqCgALdu3YK+vj5CQ0M73Ty1J+Huh+7orch4uWAGTzdRWVmJPXv2KMxg6oiAgABERETg9u3bUFNTE+w4KyoqBDUeuOJ8bm5udPKvrq7G119/rXS8nnO1BwUFYdasWdDT08OMGTPw1VdfoaqqCiNHjkRwcDBu376N9evXY8KECUqJOjubEebh4YHVq1ejpaUFbm5u0NfXR2FhIaRSKaqrq5GQkIAFCxYIsnU6szjt27cP1tbWuHbtGm394OTkBJFIROPxQOc7VbdFZ4vVhYSE0Pi+m5sb3n33XZq2evHiRSQmJtLms1yI8UWKlF9Xekpf09O01Z6Ee/zu3bvw8PCgrWY6Q1VVFTIzM+Hg4IAhQ4ZQ73Jnw1eKyM3Npc0zq6urcfLkScyePRvl5eWoqKiQa1D8svC8vRUZLx/M4OkFfv/9d8EubMeOHbSM+tq1awG0llU/dOgQFdc2NDTg4cOHePbsGWpra+Hq6gpPT0+6QHYm7RRojTVPnDgRDQ0NKCkpQX5+Pj788ENERkaioaGBekBmz55Nhb7tGQadLb4WFhZGO1gDrfV23N3dERMTI9DV8L+zI8NE1hgQi8UICgqiNWtOnjyJ9evXt1mqvrtQJisvPT0dWlpa1EBuamrC3LlzoaamRtPWud+eW3Repl3wq05P6mt6Au7ePnfuHNLS0pCSkkK9VC0tLfTeUDbbjINvRNXU1CAnJ4emVCt6TVe4deuWoCdaY2Mjli1bJtDS9QY92VuR8XLCDJ4XTHNzM4YOHQodHR0cO3YMTU1N2LNnD/70pz8Juorr6+vTnQUAWFtbw9LSkva6AVorqRobG3cqa0cqlaK5uRkODg7UCOEG6P379zF16lRkZWXRY+UWamU8C8qKQ4uKivD2229TYfW6deuQkJAAkUiERYsWITw8HEVFRZ2avPmT1oEDB5CYmIjTp0/j6tWrNFxXVVWFiRMn0lYUPUl7WXmJiYkYOXIkbST79ddf0y7bXEosv4/VqxpyYXQPnDFz7949DB8+HEFBQQgICIC6urrAG9zV+4Q/tpubm2mPLmdnZ7maUcqgqB2IpaWlQK/m6OjYqcKZPUFP9VZkvLwwg6cX2LBhA/T19RETE4Ovv/4aUqkU6enpGDFiBOLi4mBtbU0XQKC1Jgs3+RQXF8PDwwPR0dEAWmv2KKuv4WNra4vU1FT6P791A7+zeWdRRhzKaQa8vLywYsUKaGpq0t2qWCxGYGAgvL292+zh1R4JCQmYMmUKfHx84OPjg5CQEPz888+4f/8+zMzMEBsb2+Vz6wqKsvJ+//13eHh4YN26ddizZw80NDQEYYjk5GS8/fbbKCsrY8YOA0Crty84OFiQ4r1v3z6Ym5vj7t27nbpPsrKy8M0336CgoECh17ClpQVXrlxBYmJip0Oosu1AdHV1qVZu/vz50NbWhqurq1Li856iJ3srMl5umMHTC/z000/Q0tLCggULsGrVKmzduhWPHz9GUVERNm7cKEg/r6iowO7du2nti4a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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.fill_diagonal(correlation_matrix, 0)\n", "plotting.plot_matrix(correlation_matrix, labels=msdl_atlas.labels,\n", " vmax=0.8, vmin=-0.8, colorbar=True)" ] }, { "cell_type": "markdown", "id": "e6c221db", "metadata": {}, "source": [ "Or view it as an embedded connectome:" ] }, { "cell_type": "code", "execution_count": 14, "id": "d95bec25", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotting.view_connectome(correlation_matrix, edge_threshold=0.2,\n", " node_coords=msdl_atlas.region_coords)" ] }, { "cell_type": "markdown", "id": "1845a801", "metadata": {}, "source": [ "## Accounting for noise sources\n", "\n", "As we've already seen,\n", "maskers also allow us to perform other useful operations beyond just masking our data.\n", "One important processing step is correcting for measured signals of no interest (e.g., head motion).\n", "Our `development_dataset` also includes several of these signals of no interest that were generated during fMRIPrep pre-processing.\n", "We can access these with the `confounds` attribute,\n", "using `development_dataset.confounds`.\n", "\n", "Let's quickly check what these look like for our first participant:" ] }, { "cell_type": "code", "execution_count": 15, "id": "e0105860", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5 rows × 28 columns

\n", "
" ], "text/plain": [ " csf white_matter global_signal std_dvars dvars \\\n", "0 439.699409 451.645460 525.387206 0.000000 0.000000 \n", "1 439.471640 451.103437 525.155030 0.940416 14.937284 \n", "2 439.744498 450.981505 525.324735 0.885710 14.068359 \n", "3 440.772620 450.600261 525.606646 0.864385 13.729636 \n", "4 440.115442 450.678959 525.656775 0.830847 13.196932 \n", "\n", " framewise_displacement t_comp_cor_00 t_comp_cor_01 t_comp_cor_02 \\\n", "0 0.000000 -0.093142 -0.047009 0.110108 \n", "1 0.055543 -0.091190 -0.075796 0.084680 \n", "2 0.054112 -0.051778 -0.012647 -0.014665 \n", "3 0.057667 -0.025552 0.004938 -0.044833 \n", "4 0.051438 -0.032434 -0.021743 0.003173 \n", "\n", " t_comp_cor_03 ... cosine00 cosine01 cosine02 cosine03 trans_x \\\n", "0 -0.132180 ... 0.109104 0.109090 0.109066 0.109033 -0.000233 \n", "1 -0.026517 ... 0.109066 0.108937 0.108723 0.108423 -0.006187 \n", "2 0.003982 ... 0.108990 0.108632 0.108038 0.107207 -0.000227 \n", "3 0.077124 ... 0.108875 0.108176 0.107012 0.105391 0.002492 \n", "4 0.063508 ... 0.108723 0.107567 0.105651 0.102986 -0.000226 \n", "\n", " trans_y trans_z rot_x rot_y rot_z \n", "0 -0.076885 0.062321 0.000732 0.000352 0.000841 \n", "1 -0.078395 0.056773 0.000112 0.000187 0.000775 \n", "2 -0.069893 0.083102 0.000143 0.000364 0.000716 \n", "3 -0.074707 0.060337 0.000202 0.000818 0.000681 \n", "4 -0.084204 0.085079 0.000183 0.000548 0.000682 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_table(development_dataset.confounds[0]).head()" ] }, { "cell_type": "markdown", "id": "eba67dbf", "metadata": {}, "source": [ "We can see that there are several different kinds of noise sources included!\n", "This is actually a subset of all possible fMRIPrep generated confounds that the Nilearn developers have pre-selected.\n", "For most analyses, this list of confounds is reasonable, so we'll use these Nilearn provided defaults.\n", "\n", ":::{admonition} Never pass the full fMRIPrep confounds to the denoising function.\n", ":class: warning\n", "\n", "In the recent version of nilearn, \n", "we implemented function [`load_confounds`](https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds.html)\n", "and [`load_confounds_strategy`](https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html)\n", "to help you select confound variables based on existing literature and fMRIPrep documentations.\n", "User can select preset denoising strategies and the function will retrieve the relevant regressors. \n", "For more information, please refer to the\n", "[this nilearn document](https://nilearn.github.io/stable/auto_examples/03_connectivity/plot_signal_extraction.html#sphx-glr-auto-examples-03-connectivity-plot-signal-extraction-py).\n", ":::\n", "\n", "Importantly, we can pass these confounds directly to our masker object:" ] }, { "cell_type": "code", "execution_count": 16, "id": "8bcba60b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corrected_roi_time_series = masker.transform(\n", " development_dataset.func[0], confounds=development_dataset.confounds[0])\n", "corrected_correlation_matrix = correlation_measure.fit_transform(\n", " [corrected_roi_time_series])[0]\n", "np.fill_diagonal(corrected_correlation_matrix, 0)\n", "plotting.plot_matrix(corrected_correlation_matrix, labels=msdl_atlas.labels,\n", " vmax=0.8, vmin=-0.8, colorbar=True)" ] }, { "cell_type": "markdown", "id": "9c005976", "metadata": {}, "source": [ "As before, we can also view this functional connectivity matrix as a connectome:" ] }, { "cell_type": "code", "execution_count": 17, "id": "596d9901", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotting.view_connectome(corrected_correlation_matrix, edge_threshold=0.2,\n", " node_coords=msdl_atlas.region_coords)" ] }, { "cell_type": "markdown", "id": "57525969", "metadata": {}, "source": [ "In both the matrix and connectome forms,\n", "we can see a big difference when including the confounds!\n", "This is an important reminder to make sure that your data are cleaned of any possible sources of noise _before_ running a machine learning analysis.\n", "Otherwise, you might be classifying participants on e.g. amount of head motion rather than a feature of interest!\n", "\n", "## Exercises!\n", "\n", "\n", "1. Load different atlases and view them!\n", "\n", " - Try to load a different atlas availible through the `nilearn.datasets` module.\n", " - Try to show one region of interest. You might need `nilearn.images` module.\n", "\n", "2. Masker report!\n", "\n", " Nilearn has added a new feature to generate visualised reports for the masker!\n", " [Here](https://nilearn.github.io/stable/manipulating_images/masker_objects.html?highlight=masker+report#visualizing-the-computed-mask) is the relevant section.\n", " Try the report function on different type of maskers!\n", "\n", "3. Try different options in `plotting.plot_matrix`\n", "\n", " - In a new cell, try this command `plotting.plot_matrix?` to see the full documentation.\n", " - See all these options we didn't cover? Try them and see what changes in the figures.\n", "\n", "## References\n", "\n", "```{bibliography}\n", ":filter: docname in docnames\n", "```" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "md:myst", "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.11.5" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.18" }, "source_map": [ 14, 18, 23, 36, 42, 62, 137, 150, 176, 212, 221, 229, 234, 251, 256, 300, 307, 312, 314, 369, 372, 385, 388, 426, 431, 435, 439, 443, 446, 459, 461, 481, 489, 493, 496 ] }, "nbformat": 4, "nbformat_minor": 5 }