diff --git a/.gitignore b/.gitignore
index 616f591..305bab6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,7 +11,6 @@ dist/
docs/build/
examples/**/models/
-*.png
*.npz
*.npy
*.tif*
diff --git a/LICENSE.txt b/LICENSE.txt
index f002eb1..a6aaf3d 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,8 +1,8 @@
-License for Noise2Seg
+License for DenoiSeg
BSD 3-Clause License
-Copyright (c) 2019 JugLab
+Copyright (c) 2020 JugLab
All rights reserved.
Redistribution and use in source and binary forms, with or without
diff --git a/README.md b/README.md
index 1713c38..9cb16c7 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,93 @@
-# DenoiSeg
-Microscopy image analysis often requires the segmentation of objects, but training data for such a task is hard to obtain.
-Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.
-We achieve this by extending Noise2Void, a self-supervised denoising scheme that can be trained on noisy images, to also predict dense 3-class segmentations.
-The reason for the success of our method is that segmentation can profit from denoising especially when performed within the same network jointly.
-The network becomes a denoising expert by seeing all available raw data, while co-learning to segment even if only a few segmentation labels are available.
-This hypothesis is additionally fueled by our observation that the best segmentation results on high quality (virtually noise free) raw data are performed when moderate amounts of synthetic noise are added.
-This renders the denoising-task non-trivial and unleashes the co-learning effect.
-We believe that DenoiSeg offers a viable way to circumvent the tremendous hunger for high quality training data and effectively enables few-shot learning of dense segmentations.
-
-## How to run
-### Conda-Env
-You can either install the necessary packages into a conda-env with the setup.py file by typing `pip install .`
-from within this cloned git-repo or you use a singularity container.
-
-### Build Singularity Container
-1. Install Singularity
-2. `sudo singularity build noise2seg.sim singularity/noise2seg.Singularity`
-
-### Run Singularity Container
-Now you can run a jupyter-server with this container:
-`singularity run --nv -B singularity/user:/run/user -B ./:/notebooks singularity/noise2seg.simg`
-
-### Run Singularity Headless
-1. Install PyInquirer
-2. `python3 run_noise2seg.py`
-
-For the headless to work you have to set following parameters in the file `run_noise2seg.py`:
-```
-singularity_path = "/projects/Noise2Seg/singularity/"
-base_dir = "/projects/Noise2Seg"
-gitrepo_path = join(base_dir, 'Noise2Seg')
-base_path_data = join(base_dir, "data")
-base_path_exp = join(base_dir, "experiments")
+![Teaser: DenoiSeg](resources/teaser.png)
+# DenoiSeg: Joint Denoising and Segmentation
+Tim-Oliver Buchholz\*,1,2, Mangal Prakash\*,1,2,, Alexander Krull1,2,3,
+and Florian Jug1,2,^
+
+1 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
+2 Center for Systems Biology, Dresden, Germany
+3 Max Planck Institute for Physics of Complex Systems, Dresden, Germany
+^jug@mpi-cbg.de
+* Equal contribution (alphabetical order).
+
+Microscopy image analysis often requires the segmentation of objects,
+but training data for this task is typically scarce and hard to obtain.
+Here we propose DenoiSeg, a new method that can be trained end-to-end
+on only a few annotated ground truth segmentations.
+We achieve this by extending [Noise2Void](https://github.com/juglab/n2v),
+a self-supervised denoising scheme that can be trained on noisy images alone,
+to also predict dense 3-class segmentations. The reason for the success
+of our method is that segmentation can profit from denoising, especially
+when performed jointly within the same network. The network becomes a
+denoising expert by seeing all available raw data, while co-learning to
+segment, even if only a few segmentation labels are available. This
+hypothesis is additionally fueled by our observation that the best
+segmentation results on high quality (very low noise) raw data are obtained
+when moderate amounts of synthetic noise are added. This renders the
+denoising-task non-trivial and unleashes the desired co-learning effect.
+We believe that DenoiSeg offers a viable way to circumvent the tremendous
+hunger for high quality training data and effectively enables few-shot learning
+of dense segmentations.
+
+Paper: [https://arxiv.org/abs/2005.02987](https://arxiv.org/abs/2005.02987)
+
+## Installation
+This implementation requires [Tensorflow](https://www.tensorflow.org/install/).
+We have tested DenoiSeg on LinuxMint 19 using python 3.6 and 3.7 and tensorflow-gpu 1.15.
+
+#### If you start from scratch...
+We recommend using [miniconda](https://docs.conda.io/en/latest/miniconda.html).
+If you do not yet have a strong opinion, just use it too!
+
+After installing Miniconda, the following lines might are likely the easiest way to get Tensorflow and CuDNN installed on your machine (_Note:_ Macs are not supported, and if you sit on a Windows machine all this might also require some modifications.):
+
```
+$ conda create -n 'denoiSeg' python=3.7
+$ source activate denoiSeg
+$ conda install tensorflow-gpu=1.15 keras=2.2.5
+$ pip install jupyter
+$ conda install nb_conda
+```
+
+Note: it is very important that the version of keras be 2.2.4 or 2.2.5, hence the explicit installation above.
+Once this is done (or you had tensorflow et al. installed already), you can install DenoiSeg with one of the following two options:
+
+#### Option 1: PIP (current stable release)
+```
+$ pip install denoiseg
+```
+
+#### Option 2: Git-Clone and install from sources (current master-branch version)
+This option is ideal if you want to edit the code. Clone the repository:
+
+```
+$ git clone https://github.com/juglab/DenoiSeg.git
+```
+Change into its directory and install it:
+
+```
+$ cd DenoiSeg
+$ pip install -e .
+```
+You are now ready to run DenoiSeg.
+
+## How to use it?
+Have a look at our jupyter notebook:
+* [Example: DSB2018](https://github.com/juglab/DenoiSeg/tree/master/examples/DenoiSeg_2D/DSB2018_DenoiSeg_Example.ipynb)
+* [Example: Fly Wing](https://github.com/juglab/DenoiSeg/tree/master/examples/DenoiSeg_2D/FlyWing_DenoiSeg_Example.ipynb)
+* [Example: Mouse Nuclei](https://github.com/juglab/DenoiSeg/tree/master/examples/DenoiSeg_2D/MouseNuclei_DenoiSeg_Example.ipynb)
+
+## How to cite:
+```
+@inproceedings{BuchholzPrakash2020DenoiSeg,
+ title={DenoiSeg: Joint Denoising and Segmentation},
+ author={Tim-Oliver Buchholz and Mangal Prakash and Alexander Krull and Florian Jug},
+ year={2020}
+}
+```
+
+## Reproducibility
+The current release and master is a refactored version of the code used for the paper.
+This refactored version produces the same number as reported in the paper, but if you
+wish to use the exact code used in the paper, please continue [here](scripts/reproducibility/README.md).
+
+Further results (qualitative and quantitative) can be found on the [wiki](https://github.com/juglab/DenoiSeg/wiki).
diff --git a/noise2seg/__init__.py b/denoiseg/__init__.py
similarity index 100%
rename from noise2seg/__init__.py
rename to denoiseg/__init__.py
diff --git a/noise2seg/internals/N2S_DataWrapper.py b/denoiseg/internals/DenoiSeg_DataWrapper.py
similarity index 99%
rename from noise2seg/internals/N2S_DataWrapper.py
rename to denoiseg/internals/DenoiSeg_DataWrapper.py
index 8260916..232aaaf 100644
--- a/noise2seg/internals/N2S_DataWrapper.py
+++ b/denoiseg/internals/DenoiSeg_DataWrapper.py
@@ -2,7 +2,7 @@
import numpy as np
-class N2S_DataWrapper(Sequence):
+class DenoiSeg_DataWrapper(Sequence):
def __init__(self, X, n2v_Y, seg_Y, batch_size, perc_pix, shape, value_manipulation):
assert X.shape[0] == n2v_Y.shape[0]
assert X.shape[0] == seg_Y.shape[0]
diff --git a/noise2seg/internals/__init__.py b/denoiseg/internals/__init__.py
similarity index 100%
rename from noise2seg/internals/__init__.py
rename to denoiseg/internals/__init__.py
diff --git a/noise2seg/internals/losses.py b/denoiseg/internals/losses.py
similarity index 79%
rename from noise2seg/internals/losses.py
rename to denoiseg/internals/losses.py
index 4c008db..ebd931b 100755
--- a/noise2seg/internals/losses.py
+++ b/denoiseg/internals/losses.py
@@ -29,25 +29,25 @@ def seg_crossentropy(class_targets, y_pred):
return seg_crossentropy
-def loss_noise2seg(alpha=0.5, relative_weights=[1.0, 1.0, 5.0]):
+def loss_denoiseg(alpha=0.5, relative_weights=[1.0, 1.0, 5.0]):
"""
- Calculate noise2seg loss which is a weighted sum of segmentation- and
+ Calculate DenoiSeg loss which is a weighted sum of segmentation- and
noise2void-loss
:param lambda_: relative weighting, 0 means denoising, 1 means segmentation; (Default: 0.5)
:param relative_weights: Segmentation class weights (background, foreground, border); (Default: [1.0, 1.0, 5.0])
- :return: noise2seg loss
+ :return: DenoiSeg loss
"""
- denoise_loss = noise2seg_denoise_loss(weight=alpha)
- seg_loss = noise2seg_seg_loss(weight=(1 - alpha), relative_weights=relative_weights)
+ denoise_loss = denoiseg_denoise_loss(weight=alpha)
+ seg_loss = denoiseg_seg_loss(weight=(1 - alpha), relative_weights=relative_weights)
- def noise2seg(y_true, y_pred):
+ def denoiseg(y_true, y_pred):
return seg_loss(y_true, y_pred) + denoise_loss(y_true, y_pred)
- return noise2seg
+ return denoiseg
-def noise2seg_seg_loss(weight=0.5, relative_weights=[1.0, 1.0, 5.0]):
+def denoiseg_seg_loss(weight=0.5, relative_weights=[1.0, 1.0, 5.0]):
class_weights = tf.constant([relative_weights])
def seg_loss(y_true, y_pred):
@@ -69,7 +69,7 @@ def seg_loss(y_true, y_pred):
return seg_loss
-def noise2seg_denoise_loss(weight=0.5):
+def denoiseg_denoise_loss(weight=0.5):
n2v_mse_loss = n2v_loss()
def denoise_loss(y_true, y_pred):
@@ -80,10 +80,3 @@ def denoise_loss(y_true, y_pred):
return weight * n2v_mse_loss(tf.concat([target, mask], axis=channel_axis), denoised)
return denoise_loss
-
-def seg_denoise_ratio_monitor():
-
- def seg_denoise_ratio(y_true, y_pred):
- return tf.reduce_mean(tf.reduce_sum(y_true[...,2:], axis=-1)[:,0,0])
-
- return seg_denoise_ratio
diff --git a/denoiseg/models/__init__.py b/denoiseg/models/__init__.py
new file mode 100755
index 0000000..b242b51
--- /dev/null
+++ b/denoiseg/models/__init__.py
@@ -0,0 +1,4 @@
+# imports
+
+from .denoiseg_config import DenoiSegConfig
+from .denoiseg_standard import DenoiSeg
diff --git a/noise2seg/models/n2s_config.py b/denoiseg/models/denoiseg_config.py
similarity index 93%
rename from noise2seg/models/n2s_config.py
rename to denoiseg/models/denoiseg_config.py
index 97c41b6..b5c5d83 100755
--- a/noise2seg/models/n2s_config.py
+++ b/denoiseg/models/denoiseg_config.py
@@ -7,10 +7,10 @@
# This class is a adapted version of csbdeep.models.config.py.
-class Noise2SegConfig(argparse.Namespace):
+class DenoiSegConfig(argparse.Namespace):
"""
- Default configuration for a trainable segmentation (Noise2Seg) model.
- This class is meant to be used with :class:`Noise2Seg`.
+ Default configuration for a trainable segmentation (DenoiSeg) model.
+ This class is meant to be used with :class:`DenoiSeg`.
Parameters
----------
@@ -21,7 +21,7 @@ class Noise2SegConfig(argparse.Namespace):
Example
-------
- >>> n2s_config = Noise2SegConfig(X, unet_n_depth=3)
+ >>> denoiseg_config = DenoiSegConfig(X, unet_n_depth=3)
Attributes
----------
@@ -52,7 +52,7 @@ class Noise2SegConfig(argparse.Namespace):
train_reduce_lr : dict
Parameter :class:`dict` of ReduceLROnPlateau_ callback; set to ``None`` to disable. Default: ``{'monitor': 'val_seg_loss', 'factor': 0.5, 'patience': 10}``
train_loss : str
- Switch between seg- or noise2seg-loss; Default: ``noise2seg``
+ Switch between seg- or denoiseg-loss; Default: ``denoiseg``
n2v_perc_pix : float
Percentage of pixel to manipulate per patch. Default: ``1.5``
n2v_patch_shape : tuple
@@ -61,7 +61,7 @@ class Noise2SegConfig(argparse.Namespace):
Noise2Void pixel value manipulator. Default: ``uniform_withCP``
n2v_neighborhood_radius : int
Neighborhood radius for n2v_old manipulator. Default: ``5``
- n2s_alpha : float
+ denoiseg_alpha : float
Factor modulating the contribution of denoising and segmentation. alpha * denoising + (1-alpha) * segmentation: Default: ``0.5``
.. _ReduceLROnPlateau: https://keras.io/callbacks/#reducelronplateau
@@ -117,7 +117,7 @@ def __init__(self, X, **kwargs):
# fixed parameters
self.n_channel_in = 1
self.n_channel_out = 4
- self.train_loss = 'noise2seg'
+ self.train_loss = 'denoiseg'
# default config (can be overwritten by kwargs below)
@@ -141,13 +141,13 @@ def __init__(self, X, **kwargs):
self.train_checkpoint = 'weights_best.h5'
self.train_checkpoint_last = 'weights_last.h5'
self.train_checkpoint_epoch = 'weights_now.h5'
- self.train_reduce_lr = {'monitor': 'val_seg_loss', 'factor': 0.5, 'patience': 10}
+ self.train_reduce_lr = {'monitor': 'val_loss', 'factor': 0.5, 'patience': 10}
self.batch_norm = True
self.n2v_perc_pix = 1.5
self.n2v_patch_shape = (64, 64) if self.n_dim == 2 else (64, 64, 64)
self.n2v_manipulator = 'uniform_withCP'
self.n2v_neighborhood_radius = 5
- self.n2s_alpha = 0.5
+ self.denoiseg_alpha = 0.5
# disallow setting 'probabilistic' manually
try:
@@ -189,7 +189,7 @@ def _is_int(v, low=None, high=None):
ok['n_channel_in'] = _is_int(self.n_channel_in, 1)
ok['n_channel_out'] = _is_int(self.n_channel_out, 4)
ok['train_loss'] = (
- (self.train_loss in ('seg', 'noise2seg'))
+ (self.train_loss in ('seg', 'denoiseg'))
)
ok['unet_n_depth'] = _is_int(self.unet_n_depth, 1)
ok['relative_weights'] = isinstance(self.relative_weights, list) and len(self.relative_weights) == 3 and all(
@@ -223,7 +223,7 @@ def _is_int(v, low=None, high=None):
ok['n2v_manipulator'] = self.n2v_manipulator in ['normal_withoutCP', 'uniform_withCP', 'normal_additive',
'normal_fitted', 'identity']
ok['n2v_neighborhood_radius'] = _is_int(self.n2v_neighborhood_radius, 0)
- ok['n2s_alpha'] = isinstance(self.n2s_alpha, float) and self.n2s_alpha >= 0.0
+ ok['denoiseg_alpha'] = isinstance(self.denoiseg_alpha, float) and self.denoiseg_alpha >= 0.0 and self.denoiseg_alpha <= 1.0
if return_invalid:
return all(ok.values()), tuple(k for (k, v) in ok.items() if not v)
diff --git a/noise2seg/models/n2s_standard.py b/denoiseg/models/denoiseg_standard.py
similarity index 74%
rename from noise2seg/models/n2s_standard.py
rename to denoiseg/models/denoiseg_standard.py
index bd8e04d..ac96fb9 100755
--- a/noise2seg/models/n2s_standard.py
+++ b/denoiseg/models/denoiseg_standard.py
@@ -14,21 +14,21 @@
from scipy import ndimage
from six import string_types
-from noise2seg.models import Noise2SegConfig
-from noise2seg.utils.compute_precision_threshold import isnotebook, compute_labels
-from ..internals.N2S_DataWrapper import N2S_DataWrapper
-from noise2seg.internals.losses import loss_noise2seg, noise2seg_denoise_loss, noise2seg_seg_loss, seg_denoise_ratio_monitor
+from denoiseg.models import DenoiSegConfig
+from denoiseg.utils.compute_precision_threshold import isnotebook, compute_labels
+from ..internals.DenoiSeg_DataWrapper import DenoiSeg_DataWrapper
+from denoiseg.internals.losses import loss_denoiseg, denoiseg_denoise_loss, denoiseg_seg_loss
from n2v.utils.n2v_utils import pm_identity, pm_normal_additive, pm_normal_fitted, pm_normal_withoutCP, pm_uniform_withCP
from tqdm import tqdm, tqdm_notebook
-class Noise2Seg(CARE):
+class DenoiSeg(CARE):
"""The training scheme to train a standard 3-class segmentation network.
Uses a convolutional neural network created by :func:`csbdeep.internals.nets.custom_unet`.
Parameters
----------
- config : :class:`voidseg.models.seg_config` or None
- Valid configuration of Seg network (see :func:`SegConfig.is_valid`).
+ config : :class:`denoiseg.models.denoiseg_config` or None
+ Valid configuration of Seg network (see :func:`denoiseg_config.is_valid`).
Will be saved to disk as JSON (``config.json``).
If set to ``None``, will be loaded from disk (must exist).
name : str or None
@@ -44,10 +44,10 @@ class Noise2Seg(CARE):
Illegal arguments, including invalid configuration.
Example
-------
- >>> model = Noise2Seg(config, 'my_model')
+ >>> model = DenoiSeg(config, 'my_model')
Attributes
----------
- config : :class:`voidseg.models.seg_config`
+ config : :class:`denoiseg.models.denoiseg_config`
Configuration of Seg trainable CARE network, as provided during instantiation.
keras_model : `Keras model `_
Keras neural network model.
@@ -59,7 +59,7 @@ class Noise2Seg(CARE):
def __init__(self, config, name=None, basedir='.'):
"""See class docstring"""
- config is None or isinstance(config, Noise2SegConfig) or _raise(ValueError('Invalid configuration: %s' % str(config)))
+ config is None or isinstance(config, DenoiSegConfig) or _raise(ValueError('Invalid configuration: %s' % str(config)))
if config is not None and not config.is_valid():
invalid_attr = config.is_valid(True)[1]
raise ValueError('Invalid configuration attributes: ' + ', '.join(invalid_attr))
@@ -178,13 +178,13 @@ def train(self, X, Y, validation_data, epochs=None, steps_per_epoch=None):
# Here we prepare the Noise2Void data. Our input is the noisy data X and as target we take X concatenated with
# a masking channel. The N2V_DataWrapper will take care of the pixel masking and manipulating.
- training_data = N2S_DataWrapper(X=X,
- n2v_Y=np.concatenate((X, np.zeros(X.shape, dtype=X.dtype)), axis=axes.index('C')),
- seg_Y=Y,
- batch_size=self.config.train_batch_size,
- perc_pix=self.config.n2v_perc_pix,
- shape=self.config.n2v_patch_shape,
- value_manipulation=manipulator)
+ training_data = DenoiSeg_DataWrapper(X=X,
+ n2v_Y=np.concatenate((X, np.zeros(X.shape, dtype=X.dtype)), axis=axes.index('C')),
+ seg_Y=Y,
+ batch_size=self.config.train_batch_size,
+ perc_pix=self.config.n2v_perc_pix,
+ shape=self.config.n2v_patch_shape,
+ value_manipulation=manipulator)
# validation_Y is also validation_X plus a concatenated masking channel.
# To speed things up, we precompute the masking vo the validation data.
@@ -197,14 +197,6 @@ def train(self, X, Y, validation_data, epochs=None, steps_per_epoch=None):
validation_Y = np.concatenate((validation_Y, validation_data[1]), axis=-1)
- # Add alpha-scheduling
- cross_point = self.config.n2s_alpha
- def scheduling(epoch):
- return np.clip(1 - epoch/(self.config.train_epochs - 1) * 0.5/cross_point, 0, 1)
-
- alphaScheduling = AlphaScheduling(self.alpha, scheduling)
- self.callbacks.append(alphaScheduling)
-
history = self.keras_model.fit_generator(generator=training_data, validation_data=(validation_X, validation_Y),
epochs=epochs, steps_per_epoch=steps_per_epoch,
callbacks=self.callbacks, verbose=1)
@@ -259,6 +251,80 @@ def prepare_for_training(self, optimizer=None, **kwargs):
from csbdeep.utils.tf import CARETensorBoard
class SegTensorBoard(CARETensorBoard):
+ def set_model(self, model):
+ self.model = model
+ self.sess = K.get_session()
+ tf_sums = []
+
+ if self.compute_histograms and self.freq and self.merged is None:
+ for layer in self.model.layers:
+ for weight in layer.weights:
+ tf_sums.append(tf.compat.v1.summary.histogram(weight.name, weight))
+
+ if hasattr(layer, 'output'):
+ tf_sums.append(tf.compat.v1.summary.histogram('{}_out'.format(layer.name),
+ layer.output))
+
+ def _gt_shape(output_shape):
+ return list(output_shape[:-1]) + [1]
+
+ self.gt_outputs = [K.placeholder(shape=_gt_shape(K.int_shape(x))) for x in self.model.outputs]
+
+ n_inputs, n_outputs = len(self.model.inputs), len(self.model.outputs)
+ image_for_inputs = np.arange(
+ n_inputs) if self.image_for_inputs is None else self.image_for_inputs
+ image_for_outputs = np.arange(
+ n_outputs) if self.image_for_outputs is None else self.image_for_outputs
+
+ input_slices = (slice(None),) if self.input_slices is None else self.input_slices
+ output_slices = (slice(None),) if self.output_slices is None else self.output_slices
+ if isinstance(input_slices[0], slice): # apply same slices to all inputs
+ input_slices = [input_slices] * len(image_for_inputs)
+ if isinstance(output_slices[0], slice): # apply same slices to all outputs
+ output_slices = [output_slices] * len(image_for_outputs)
+ len(input_slices) == len(image_for_inputs) or _raise(ValueError())
+ len(output_slices) == len(image_for_outputs) or _raise(ValueError())
+
+ def _name(prefix, layer, i, n, show_layer_names=False):
+ return '{prefix}{i}{name}'.format(
+ prefix=prefix,
+ i=(i if n > 1 else ''),
+ name='' if (layer is None or not show_layer_names) else '_' + ''.join(
+ layer.name.split(':')[:-1]),
+ )
+
+ # inputs
+ for i, sl in zip(image_for_inputs, input_slices):
+ layer_name = _name('net_input', self.model.inputs[i], i, n_inputs)
+ input_layer = self.model.inputs[i][tuple(sl)]
+ tf_sums.append(tf.compat.v1.summary.image(layer_name, input_layer, max_outputs=self.n_images))
+
+ # outputs
+ for i, sl in zip(image_for_outputs, output_slices):
+ # target
+ output_layer = self.gt_outputs[i][tuple(sl)]
+ layer_name = _name('net_target', self.model.outputs[i], i, n_outputs)
+ tf_sums.append(tf.compat.v1.summary.image(layer_name, output_layer, max_outputs=self.n_images))
+ # prediction
+ denoised_layer = self.model.outputs[i][..., :1][tuple(sl)]
+ foreground_layer = self.model.outputs[i][..., 2:3][tuple(sl)]
+ foreground_layer = K.cast(K.greater(foreground_layer, 0.5), tf.float32)
+
+ denoised_name = _name('net_output_denoised', self.model.outputs[i], i, n_outputs)
+ foreground_name = _name('net_output_foreground_threshold.5', self.model.outputs[i], i, n_outputs)
+ tf_sums.append(tf.compat.v1.summary.image(denoised_name, denoised_layer, max_outputs=self.n_images))
+ tf_sums.append(tf.compat.v1.summary.image(foreground_name, foreground_layer, max_outputs=self.n_images))
+
+ with tf.name_scope('merged'):
+ self.merged = tf.compat.v1.summary.merge(tf_sums)
+
+ with tf.name_scope('summary_writer'):
+ if self.write_graph:
+ self.writer = tf.compat.v1.summary.FileWriter(self.log_dir,
+ self.sess.graph)
+ else:
+ self.writer = tf.compat.v1.summary.FileWriter(self.log_dir)
+
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
@@ -273,9 +339,7 @@ def on_epoch_end(self, epoch, logs=None):
val_data += self.validation_data[-1:]
else:
val_data = list(v[:self.n_images] for v in self.validation_data)
- # GIT issue 20: We need to remove the masking component from the validation data to prevent crash.
- end_index = (val_data[1].shape)[-1] // 2
- val_data[1] = val_data[1][..., :end_index]
+ val_data[1] = val_data[1][..., 3:4]
feed_dict = dict(zip(tensors, val_data))
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
@@ -425,19 +489,17 @@ def prepare_model(self, model, optimizer, loss):
if self.config.train_loss == 'seg':
loss_standard = eval('loss_seg(relative_weights=%s)' % self.config.relative_weights)
_metrics = [loss_standard]
- elif self.config.train_loss == 'noise2seg':
- loss_standard = eval('loss_noise2seg(alpha={}, relative_weights={})'.format(
- self.config.n2s_alpha,
+ elif self.config.train_loss == 'denoiseg':
+ loss_standard = eval('loss_denoiseg(alpha={}, relative_weights={})'.format(
+ self.config.denoiseg_alpha,
self.config.relative_weights))
- seg_metric = eval('noise2seg_seg_loss(weight={}, relative_weights={})'.format(1-self.config.n2s_alpha,
+ seg_metric = eval('denoiseg_seg_loss(weight={}, relative_weights={})'.format(1-self.config.denoiseg_alpha,
self.config.relative_weights))
- denoise_metric = eval('noise2seg_denoise_loss(weight={})'.format(self.config.n2s_alpha))
+ denoise_metric = eval('denoiseg_denoise_loss(weight={})'.format(self.config.denoiseg_alpha))
_metrics = [loss_standard, seg_metric, denoise_metric]
else:
_raise('Unknown Loss!')
- seg_denoise_ratio = seg_denoise_ratio_monitor()
- _metrics.append(seg_denoise_ratio)
callbacks = [TerminateOnNaN()]
# compile model
@@ -474,4 +536,4 @@ def _set_logdir(self):
@property
def _config_class(self):
- return Noise2SegConfig
+ return DenoiSegConfig
diff --git a/noise2seg/utils/__init__.py b/denoiseg/utils/__init__.py
similarity index 100%
rename from noise2seg/utils/__init__.py
rename to denoiseg/utils/__init__.py
diff --git a/noise2seg/utils/compute_precision_threshold.py b/denoiseg/utils/compute_precision_threshold.py
similarity index 100%
rename from noise2seg/utils/compute_precision_threshold.py
rename to denoiseg/utils/compute_precision_threshold.py
diff --git a/noise2seg/utils/misc_utils.py b/denoiseg/utils/misc_utils.py
similarity index 100%
rename from noise2seg/utils/misc_utils.py
rename to denoiseg/utils/misc_utils.py
diff --git a/noise2seg/utils/seg_utils.py b/denoiseg/utils/seg_utils.py
similarity index 100%
rename from noise2seg/utils/seg_utils.py
rename to denoiseg/utils/seg_utils.py
diff --git a/denoiseg/version.py b/denoiseg/version.py
new file mode 100755
index 0000000..7fd229a
--- /dev/null
+++ b/denoiseg/version.py
@@ -0,0 +1 @@
+__version__ = '0.2.0'
diff --git a/examples/DenoiSeg_2D/DSB2018_DenoiSeg_Example.ipynb b/examples/DenoiSeg_2D/DSB2018_DenoiSeg_Example.ipynb
new file mode 100644
index 0000000..ae74257
--- /dev/null
+++ b/examples/DenoiSeg_2D/DSB2018_DenoiSeg_Example.ipynb
@@ -0,0 +1,57147 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# DenoiSeg Example: DSB 2018\n",
+ "This is an example notebook which illustrates how DenoiSeg should be trained. In this notebook we use a refined version of the Kaggle 2018 Data Science Bowl (DSB 2018) dataset. We already split the data into train and test images. From the train images we then extracted 3800 training and 670 validation patches of size 128x128. The test set contains 50 images. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Here we are just importing some libraries which are needed to run this notebook.\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "from scipy import ndimage\n",
+ "\n",
+ "from denoiseg.models import DenoiSeg, DenoiSegConfig\n",
+ "from denoiseg.utils.misc_utils import combine_train_test_data, shuffle_train_data, augment_data\n",
+ "from denoiseg.utils.seg_utils import *\n",
+ "from denoiseg.utils.compute_precision_threshold import measure_precision\n",
+ "\n",
+ "from csbdeep.utils import plot_history\n",
+ "\n",
+ "import urllib\n",
+ "import os\n",
+ "import zipfile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Downloading and Data Loading\n",
+ "We created three versions of this dataset by adding Gaussian noise with zero mean and standard deviations 10 and 20. The dataset are marked with the suffixes n0, n10 and n20 accordingly.\n",
+ "\n",
+ "In the next cell you can choose which `noise_level` you would like to investigate."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Choose the noise level you would like to look at:\n",
+ "# Values: 'n0', 'n10', 'n20'\n",
+ "noise_level = 'n10'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a folder for our data\n",
+ "if not os.path.isdir('./data'):\n",
+ " os.mkdir('data')\n",
+ " \n",
+ "if noise_level == 'n0':\n",
+ " link = 'https://owncloud.mpi-cbg.de/index.php/s/1WXxLSqbK0ZIxF5/download'\n",
+ "elif noise_level == 'n10':\n",
+ " link = 'https://owncloud.mpi-cbg.de/index.php/s/dRc1AHcaH8mqeh7/download'\n",
+ "elif noise_level == 'n20':\n",
+ " link = 'https://owncloud.mpi-cbg.de/index.php/s/hy6xSq82kCoqqSH/download'\n",
+ "else:\n",
+ " print('This noise level does not exist for this dataset.')\n",
+ "\n",
+ "# check if data has been downloaded already\n",
+ "zipPath=\"data/DSB2018_{}.zip\".format(noise_level)\n",
+ "if not os.path.exists(zipPath):\n",
+ " #download and unzip data\n",
+ " data = urllib.request.urlretrieve(link, zipPath)\n",
+ " with zipfile.ZipFile(zipPath, 'r') as zip_ref:\n",
+ " zip_ref.extractall(\"data\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading of the training images\n",
+ "trainval_data = np.load('data/DSB2018_{}/train/train_data.npz'.format(noise_level))\n",
+ "train_images = trainval_data['X_train'].astype(np.float32)\n",
+ "train_masks = trainval_data['Y_train']\n",
+ "val_images = trainval_data['X_val'].astype(np.float32)\n",
+ "val_masks = trainval_data['Y_val']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of train_images: (3800, 128, 128)\n",
+ "Shape of train_masks: (3800, 128, 128)\n",
+ "Shape of val_images: (670, 128, 128)\n",
+ "Shape of val_masks: (670, 128, 128)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Shape of train_images: {}\".format(train_images.shape))\n",
+ "print(\"Shape of train_masks: {}\".format(train_masks.shape))\n",
+ "print(\"Shape of val_images: {}\".format(val_images.shape))\n",
+ "print(\"Shape of val_masks: {}\".format(val_masks.shape))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Small Amounts of Annotated Training Data\n",
+ "With DenoiSeg we present a solution to train deep neural networks if only few annotated ground truth segmentations are available. We simulate such a scenary by zeroing out all but a fraction of the available training data. In the next cell you can specify the percentage of training images for which ground truth annotations are available."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set the percentage of annotated training images.\n",
+ "# Values: 0.0 (no annotated images) to 100.0 (all images get annotations)\n",
+ "percentage_of_annotated_training_images = 0.5\n",
+ "assert percentage_of_annotated_training_images >= 0.0 and percentage_of_annotated_training_images <=100.0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Raw image size after augmentation (30400, 128, 128)\n",
+ "Mask size after augmentation (30400, 128, 128)\n",
+ "Shape of X: (30400, 128, 128, 1)\n",
+ "Shape of Y: (30400, 128, 128, 3)\n",
+ "Shape of X_val: (670, 128, 128, 1)\n",
+ "Shape of Y_val: (670, 128, 128, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Seed to shuffle training data (annotated GT and raw image pairs).\n",
+ "seed = 1 \n",
+ "\n",
+ "# First we shuffle the training images to remove any bias.\n",
+ "X_shuffled, Y_shuffled = shuffle_train_data(train_images, train_masks, random_seed=seed)\n",
+ "# Here we zero out all training images which are not part of the \n",
+ "# selected percentage.\n",
+ "X_frac, Y_frac = zero_out_train_data(X_shuffled, Y_shuffled, fraction = percentage_of_annotated_training_images)\n",
+ "\n",
+ "# Now we apply data augmentation to the training patches:\n",
+ "# Rotate four times by 90 degree and add flipped versions.\n",
+ "X, Y_train_masks = augment_data(X_frac, Y_frac)\n",
+ "X_val, Y_val_masks = val_images, val_masks\n",
+ "\n",
+ "# Here we add the channel dimension to our input images.\n",
+ "# Dimensionality for training has to be 'SYXC' (Sample, Y-Dimension, X-Dimension, Channel)\n",
+ "X = X[...,np.newaxis]\n",
+ "Y = convert_to_oneHot(Y_train_masks)\n",
+ "X_val = X_val[...,np.newaxis]\n",
+ "Y_val = convert_to_oneHot(Y_val_masks)\n",
+ "print(\"Shape of X: {}\".format(X.shape))\n",
+ "print(\"Shape of Y: {}\".format(Y.shape))\n",
+ "print(\"Shape of X_val: {}\".format(X_val.shape))\n",
+ "print(\"Shape of Y_val: {}\".format(Y_val.shape))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next we look at a single sample. In the first column we show the input image, in the second column the background segmentation, in the third column the foreground segmentation and in the last column the border segmentation.\n",
+ "\n",
+ "With the parameter `sample` you can choose different training patches. You will notice that not all of them have a segmentation ground truth."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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BVS2/9hQKhVuKiHw58BdU9YZ+iS4UCoVbRZl/CoVC4ZVPcdQUXjGIyIeKyB/LlsKPxbpy/ejdXq9CoXDvIyIPisgniIgTkQ/BHIZlfikUCredMv8UCoXCq4+brfErFF6O7GHlTg9hpWp/B/g/7+oaFQqFVwo11vbyj2DlAj+IWZQLhULhdlPmn0KhUHiVUUqfCoVCoVAoFAqFQqFQKBReJpTSp0KhUCgUCoVCoVAoFAqFlwlFqCkUCoVCoVAoFAqFQqFQeJlwakbNJ37m/6gSlWreo07oJx6/TlSHa1CFBP25hvZMwK8V1yUW91f0U2F6JeJXCURQD4v7AutzwuGHd+zeN+c/+4C387rJM3z85HF+q72fXzl+HQ/WB7ym2ucoTlmkhj2/pJLIM91ZVqkCYObXfPT0MQAu92fwkthzS1ZaMU8NP/7cR/Erjz2KPDFl+oxQzRW/xtZnGcEJKhAnplG51tY7LHoA1AlxFuh2PWGRCMcd1eVj2D+kf91DrO6riZWACNU8Ir0S5j2I0J0JSITqoKXfqzh6uGJ6JbL7e/t09+2wulijgzSmIAqTyy3SJ44enZIqYfpcDwr9jkMS+FWimvdUzx6TZjX9mYbkhVQ5lpcC7Z5QHylhlagPI65TujMeFSEsIhIVnJC80M/yMtuERJA+ocGhDiQqouCXEYmJVHvUC92eB6A6iqCAgAqoF9uOvH9TECTZU/3UESsb8zDv8YsWRJg/skM3E1YXHa5T9p6IAKRK8KtEvd+SGk8/9dQHLf5wDcGhXkiTCg2CW0dIioaNxhingeOH8tgKSNpsDwrNYaQ6zvtXBPV2W14MqIO9x1dIVPq9Kh8TCQRi7QjznurqgtXDZzh6uGLn6Z7Js8s8bkJqPMkLrlcQ6HYCqRbaHUdYJXbes6I7U3H0/hUkcHGz72MFGqDdE1INYQmuhbBQfKfURwlJEBsbW79Mtuxdh2+VapGQXnG9koJtUz9zJC+EVcJ19hpJ+fmf/lq5yTniZUF6+oNuWY3mpzz0+lu1qPeKn3ry7XflewuFbW7m+L+Vx6574Pfu6bnok93n3PJ68Ts1N9ytua9QuBGudR7czmP2n6cfvqfnIri189Ew/ndqnrjT31conMbJ+edax+WNvOdmud58dKpQI8nO/xQcKoADDUKqPBITQrKLzpjfq/kiOd8AE3QQu8gfbkAlkajCfqpZaYWT/F3qWGnFSgONBhyJRapZpYqJ616wfp36UaBZpYqjNOGwm5BaT4i2XmDCQqoEiW4jNmShQXJGj1YOkm2HRM3bk1fWO8Q5UuVMkFAgqS1TBLxAwm5qF+wkxbd2oazBmaCzSMSJECvB94r0Np44ZxfwqIlbbIQk1MQjbSpSHez+sO5JcRF8a98Fef8EE6MQsX3m7F8kb7sIGkD81nd4saGpHA6IjUODrStANexOAZw998J12exb1ymShnF3SB3y+/NwrvO2+3ysqIIzwUVd3gdJEVVi7UlT2+7hO4XNdw3r4qLa0Kftm47H57BvBCU2AQ2C74YxcyC23+39tj62HYpWHolKWNp7hnVBN8LPeLj7PEbD+A//qokyXWOijusYBS71NjbJ20Crh5TEjq+0Ob/s2MzP6fB9QpLN+kgEh75ApCkUCoVCoVAoFAqFwr3DqUKNW5vjYn0u2AUnILVdXPpVIiw6pE9Ux32+yFXC2oO40dGgwuggkATSOWJ07PkVi9Twy4sPolPPzLVEhMM05an2HPvdjEcmV5m5in8/v59FX/PBu8+y61eAiTT7ccZT3TnecfQAq1gx72qeuHweuVpRHQnVQlEHsYH1nsNNBd/aenUzhyQlzM1x0p6pcJ1S769xawhekCyyaOWR6YT2XGB13jG7HPErZf6ACQt+FfFdwrd2uZ5qj4vKzlMdkpT+zIRwtKb6w6us/8glFq+pqa72hHnH+kJDbBzVccSvE5PHrqDekaoLqBdcVGLj6d9vJ4sCGxHM9RAWML3cUR2uWd0/pZs6VmdNEQkrE0xSEBNVnKCi6ESItdBNhfpYqY8i3a6nb4R6bk6M1XlPrCEF+57mQHBdQjERyNwkQjczd0dYZNEu2fKkTawvNqwvBMLCBLJYm8i1+6QpaLERJJprKE4d8/sDk4PE5HKLW/Wwblm+7iyL+z3NfqJaJFx2r4R5B6rEqUlIYWnCRFjEfLypuU26aMKKCG7dI31i+UBDu+M4+9gKt+xZvWYKQHNljfQpC3GJ0EW0qeguTPGryNl3tqTaE3cq/LzDxUh3piJOXP4OG+vkbdxiBXESUG9juDorLB5M1AeO5nk7LlMFsQYNuiXMCC4oceJwLew+2eHbhGsTceKJE3ufOiE2ti/rw0RYRBOshHxOJvv+QVgqFO4hbvSXiuJUujHel19+hs+WsS4UCreKG5lPbucv2IXCvciN/j9czpUb471xdp18z51whZ0u1ERFNRFWjKUVkN0XQVDvNq4BByTBdYoXcx2kvHT15ihwvRKOHavjhve050eHzMy17PkVlVh5ipeEk5QdM4Hd0OJFWafAcZwwkY6ayMR1VBJZ9DXPr6ZcPdqhO66pWruQTfkC2Q0OC7XSE8lul+0SmcE9kRorG5JecTEhXUIrT9qbmptjcOqojkKQZGeFJEVFSLUbHS2SFHFCjDVEJVVudIPoMHYwuo102oylOeaW2HovG5cQ2Hjak5AqP4oFbus9m8/o+NnBpeFsuEmV7atUQd8ILgj9BFKdnUKiVjYVHf1kcLCY8NJn0WB0UImQvMPV2fHT2fKHkiQA6XV0hAxjIVGplrYPYuPwC8ElHZ064xhnEYaURbHscjKXiuKXPan29DOPXyWkT3ls7ZjU2o9jEBsbs1SZ+NTPwujEkT7hKk+qPLHxeaA0CyXmkPKr/kX7InkQzUKKF1aXKnPKjDsjizhNFmpqSCG7sFA0QZza+TUcZ6my88yEGROFBrcQZDdOtXXcYc4qFRsbilBTKBQKhUKhUCgUCvcMpws1bTJB4iCSak97vs7lLlhOSm15GBrMnUIyR0OYR9bnK7uAjlslLyvYfRyWq4ZfeM0HcrZZ8eDskA+cXeYDmmeYp4ZFajjrlziURaw5ihM+bPYUThK/dvgoy1TzwLk1FbDS55inhi56njvYRd+9Q9MKrs0ZIFOhek6pjxOaL5arwx7XK9VQshPtojwctaQmsL5Q4ddKOO7wqx637OguzGgfmJlTYT6UoCi7j83NtbBbm4jQJagc6z1z2qi3C3bXK3o+oDKxrJHOMkVk4iFnxrioaBAW739mzBpRZ2PnOqU6TtmVpC8QgYaL99jUJqwA1XzzXhK4lNAoYwmN6xW/FsLayqS6maObDu4MG7v2rDlDmn1TA44f8GgQ2jMm8NQH5gDppyZ6TIa8G49l5LhAvd9TH0TmDzXEWqiWuSSns+2tDiJaO7rdQJhHpk8t6c41rC5VhGXEJ3OJ1EduFNIkKtIlZB3BC/2ON8fKWgnLnvD8gvaBPY4fCjQHielziltHXBvp92r6iTfnVErMHzARJaxtG48eqXOGj5WUjUIYmItlN9Dumlg1E7FjSEycDIuIqJJCTcJykbod4eB1gl8KO09ZuZdbC+qV9iz0U0iThHQbJw0KqwsmZFWHJtatzptDy+xUdu5VeT+mZJ/pJ0LMTi4Tiaq8PltiYAG4e3XYhdO52f1xvV84CsatPM6Ls+b2cCfGtfy6evOUX7BvLe/LcV5yTW4fJ+eh2znGP/Xk28s+vEFu1lVW3GincyuO8ztxzpwq1HR7lV1U55IRt0726/yQXVO5UbgZQ02zeANDYGp2U6giKQsXLVzZ32U1s2yas9WSeWq43J/huW4PLwkvifesznHcNczOmuOmzhaQX1y+FoDH15f4nflr+P1nL9IdNEyWOT9G7DuqYwvZ9W3aZJwER/QmiqBQzXv7jBMkJpqrnWWzzIKJHZ3ZUCQpYaHjfbASJ9HsuEhKtUpoUuojl0WMjXvGBAol1Rbsqzng11Yq/yOQGslBwOYYGQQmDUJymDDQKq5NaO3sIjyLMmEezdESbLl+mbZyhoQYsoPE52yTYKG33Y4QJ+buGMSCQVyLEyEmcq5PXkcP3a6YkyM7cdKwLuSyqJAzVtRl545uXEuVA2eCi+b8FpyglR/HKjYed3Fv45bpt8u4Aq6NuZzLtt9FE6VwJhpWOZBXsxNlCP8dMntUTIiRaN8HUGX3jrmtdHx+2KZhX8jm6Ty+uSxMBsHEwoFjLfiluY+6HSFV4FshBbVyp0pRb24aHbNoTMyRmMuiku0L1WE7oV4ly8rxeb+vGV1HrstZO5kwjy9Y30KhUCgUCoVCoVAovLw5VahZXvKElaPaB+kSYW65F66N9Ls17bkaDXYR65cJv+ppz03oZs4yQzqly84Q120ya1wL8akp+7s1XfRMfMcHTc/wjvlDPDa/wOv2LnN/dcS/f/5+rh7O2K3WPDzZZ9evWaaa73vPn2DZV8zbiv39HZrfnVL34DrL+0iNiTS7T1p2jkS1C3mgPRNIwVwRrofqsMvlJR6/aPFPP0984DxHH7hnJSZdBLWQ3+qoRdYd/fkpsfF0Z6sxJ8R1SvPcEuki4WBFmtWsLk3AmbhQH/aE/TXr+6f050J2v2yF+ebcl27mkAjTZ1ZjXkq/U7G8v7aSmVpoDsxpknJeULVO+HXEH7fWvWi3BhHC4cpKfipPnFZ0e3l3Vyai9I2wuiisLqntR6+QxbTq0Man3bPVlJ4xLFo9rC8MwbtCPzUHkMsBySkIsYJ+Zi4cv0qEk2JUFCR6NJdEqRO6XVu/6jjSzzztuV1zq+QORhLVlukAacbj1DpYWUZSmgSkTUyftbI6dSbOJbayerx9XzXP3a9yBzPXWZcl15mTbBC5rAtXoN/1VlaWhRwkl3GhuDaSKsf6nNDPzJnkIkyuKrER1hcw99QK0h7EWUIrBa+oy6Jc75BeqPcd0kO3Z/lJQwA0YuLb9OkV/SzQng2EVSIcb7qZ+bWVe8WJByd2TLabEq3ChvLL3N3ndoz9pzz0+uL4oBzX9yLFsfTy4GbHv/yCfTrluL43uJPOmsK12T5XbqXbuOzLe++4PlWoaQ6s/W9qwuh6SN668MTGyjD8yi5SXZdQ7/ArBU24HNpbH6eti3HF9eZ6aedC6gOLdpff6jxJHfvrKUdrE2/+oDrP1YMdunnFuw8usd/OaKNnHQOXj3bpe0fsPWle4VsTElwWa3Rh4bLWxsjyPrrJxhHheqU5yF2FJn68OE91QC6eARFmT63RIPR7NbFxOei1wnvLYIHsWFFFem+Cyl6D9MnaUWMXzakSk2MU6yTUK2GdxrIaDeYKCUtzmEyuYiG5E9s11vkohzO7TQtsdSaA+LWNr+WtNGOXKFEl7jZoEFYXNm3BUyV0WUiIjZUukV0pEs3toQH6nZxtkz/nVF6QceM6GYWbWMPyoqM5UOoDC0V2vW1XP7E21TpkvzB0b7LOUuSOXKjtO0lYKVoXkWMdRQrLYXH0UxP+wjxuOiINnbb8xpXjV1YaNbh7tAnmAPNb3ZyGTli561U3DbhWaa5aO3pzlCl+HYkTz/qMzx3BNudInOYcGOrcscqe7/YU14PkcRi6XHU7SqryuDo1G08uX5Io9n5v6+TXuYwvDceN4lol5ZydsLSAYd8l+oknVdZO3K/MJaZOiDs17NQ3MBUUCoVCoVAoFAqFQuHlwKlCzeSZJeodcWpvczGZk2TXj511wn5PeH5J3GtIE0+Y94RjZWgN7VdDu6hcElM5XO9pz7hcyuFZHezwG4uH7W0CB0cznEv0Vyb4lfBUc5bL9S4xOjQKaV6N5ULhyOOXjCGwYWkZLUN3p+FifH3WLqgn+xG/StQHLSrC6jUNEqF+PpIaT3d2j/rqivr3niQ+cj/Hr53RN+ZAAQjBymhQpT5cI13ETytS5VlfqHCtMmn77MLpkeSJuCwmCK5NhGPMAeGEvrYckrC0C+7w9ByCY/HI7ljCJLppvz0E06qD6rjDLTvaSzP6xtGe8bnjj5U89VNPt+M4fNRb96CnEt1MWLzGynDixNpQq+Ryngjkkpy+GlwmYgJCtBIfC7g1V9SwD2IDi5mJCpPnEt50KlYXgrmNenlBGdEgJsVJ7pBUSefSDwsAACAASURBVC6PMueMU6gO17jDJTqtSU2gO1PnUi0TMibP2fEo6kZHUvIOGnN/+XmHNhYGnKaefuZGYalaRKRX+h1vpVU58Hh5wZxg06cjcVqZY2WZcDHR7ThWF4T60HKKhm3vdqxb1rivcrByfzblluA29tXC3End2bRplR4UVyW0d2gv0AvSmVjmEMIC3DqHX3dKvd+DQD/1uC5Z2V6vSEzoTqCfCs0Vxc3XSNuj3rN6aEas3Y3PCK9Cbqe6Xn5BfCH3yi8Y9zJ3YoyL8+P2civHt5xzN0Y5lm8ft2Ns77Vfxe9Fboezpuy3a1PceLef23ns3U4X2qlCTaq95X0crCxo96KVm7hWx/KR2Djcbk2aWFZL2s0OhuwKqOZWiuHXCXE5RDcpfsXoZghzIT5X4zrL8Oh2E/1EcUsrA0pXGzqXS22SheCKmpvCLwS/tvDWapFyaYvkIF7LcpEEdc5Mcb0F8K4vNhakO3XZmWFtntUJ7YUJcef9zKGwTITl0HFISY3LHZiEuFMhnTd1CWszjQir18yQqPg2WVeiytHWDj1XjYISuQtVNc9lNlFJ3tGfn47dtURzxooIeBNrwirRzRzH71dTzwN+NRm7KrleR3eIBqHbsRKvFCxLZv6AI1XkMidGkQbB8m88tl6tkCYJAiQVHLl1tM/73ZvI41dCvW/L18CYUTOUm1WLhOuddV+K5l7SYCVIKIRFQuqc5YO5rcj5O3Z0etK0MneRM7GoObJ9nGqXBTN7qwZHEogud2Rq7NglO2CGfJtUZadKspKk5Dci0tD2eghq9m0iBWF9vsL1yu6TJoBJsrGLjc/72dxJKQjrs0KcgnQ2GLEZysqEfkfRaYLsnKFzpChInZA64a8IYblJ/u12wFVQLbJIOMmCizBuv/Q2xkNXrfWlhvZ8bce92jHpV1sWoMJ1uR1/jBSM8kfH7aeM8SuPsk9vH2WOvv2UMX5lUITj20cRrm4/9/oYv6RQ49qIO1jA3ox2d4rvlOooomqtmVPt6GeVlQfVQrvn6BtBw8ZdEJYgy2gOAxg76wy5LGEJqFAdW7bM6pKj39m0LQ5X8wX5EJw7hK524Fu7VYvE9LmObjewPuNwWUhyQxtlsdIZMDfK6ry5KYayLFE/dkRa73ja3cDkINFc7W0M+sT6fEOcbnJl+mlAKnPCkCCsIv00sLwY8J3S7EfrkJXLjfqZEBZKWCl+bSJCddTh+kQ/q6yr0qzadOkZtpm8LetEfdixPjth+RqhW3jrULU0sWDIXInZAdTuZNdKZeJKv7MRuzToKNKoy2VAzgQGUUgzhUqhF1RNjBEFrSHVip7p0P2KyXND5ksWfXIos+siYSFj6ZCK0NU5ZNhb56uwjPR4cwmpoM5cUNLnzJbKEyeBfurHLlbVkdVfDfvBtYM4lbNxGgsTjr3DLy1XaRTCGgv6rXLr85iDnau5jXFscomSAElxayVOhdUZz2Q/Mnl6Tb8TxhbZmkvZJJljq59Ae9ZKm6Qzm1KaKCmB1JBmCTftSSsPvUfavAPqhA8J10JY5LbdAfqZ4mroD03YihO3acudzx3XCak3Qc9FZXXeE3NGU1grk2fX+PWJfu2FQqFQKBQKhUKhUHjZcqpQg0BqPKtHL6BeqBbJSi9mHlETNTS3SIaNMON68ItNi2P10J6rxrDcVJG765jAYp2DTAyIE/tekjltXNwSZlpza4TVJgsEchCvF7ug75XJ1Th29xk3dN6hIqSJJ3lh+lzODJk566Rz0OegW0e9jkyuWLvpfubxDug2Tp2hW5NfZR0lWllTP/Ggys5TLS4qftmTGo9MA5IE38rYlak+6AmLnvbcpq02uWvPcCEuKXf+qR3rPSFMhdQIYamce2fMQgPW4ajddImyDJZNd6IxWyaLIeqVOFHi7uYC3i08biWkWk0gqxTxCXUOUXPO4GB9Plm2SutQr6wuKq4Tc0hhuTPdjgX+1kcR1yb6adjkA3WWY4NYeLCGnGPkzd3k11bKEycB3atpzwTLuVknC9o9spDg5X0BdcL0Sm/jlQU815m4l7zgvMDYhh0TyNrcIrxXmoM4inkSlTOP2+c1OLRyqLcOW9MrlsGUKkeszaVUHZtLrNvzpHwWSYJqbvtE1ASz9X09NIl6tyUPAV0UtHf4vY7JtCUlISWhPZ9IjcOP3cvsuBIdhMlc/laZu8ivUg5w1rEdfKjNIWbbmki1N+dR4YYpvwLeOu7VXzAKhcIrkzK/Fwo3R/n//NZxr7s8CneOU4UazS6U9ozHRXMzpMrR71quR1hZWG6sBN9Zxx9J1iq5WlreyNAeuZ9IvhjP4aq5DXJ9FEd3wOq8p59uXC5htclCkaRUc3NiVMcW4ju0gO5mLgfqOsIiUh332fmQBZBkgceoomFiOa7zNnfKqU38OWrRytMFR1j2hP0V3fkp3cUKSc7cJEM+zHDFnbNdhm5WGgTplXp/ba6QPiHRBCpJDtdad6TUCL5L+EVH/+CEbseZgNBnp03OsxlaTycP/Y7k8iLH5PlIc2XN+nxDt+vwaxMN4sRll0VWLobVzA81l/UgSqoVv9ujCVQFlh7XQ5pYRo14K1XToTxtuSmXQoBowk2/q4RjE9XAulJ1M0esrbTJRx0dPkMobnXco0FY3F9b+dfCyrnijolYoubm6meefmoOLUmCxwKXUcy15XNJGjpmJrncdUo943FlY5DHluzYUQgLE6pSZU6V6dO5U5R3pJCDh1ulWvRjSV2qzInTRHNqdXt+bPctObvHiTlzAPBKmPY8fHGfeVtzuJgQgxJDYmdnxYNnDrk832G5rmG3pw8et/ZWGjWeiFkEjbpx8nTg2oSLJtTgQKNkwc7h2xwEXmXXTqFQKBQKhUKhUCgU7glOFWr6HY9EmDzfQ9LxV36f2xOP+Sliv/C7LtHs525GMogxdjEcFnYBHVaOmMUVNwbTmjMg1kIKjvpwKEfKy3JD15+cMxIcrjfHhjlr0uhCiVNHqmrrxhRzR6o2opUnBUec+tx22xwogxMl7lRWPtIlYuWI983MobBIY2tv16o5M+aWVRIWJv60Zy3fpjrsUS8sH5hZl555ny/unYlFSTfdkKaB5N3oiqGyi/0mOzeOHrZOW82RjXuzb66JvhHWZz2pmhCrTYgtCm5tzpT2jCc2QrsnxAl0u+awCXOxNtWNdSOK863dXyndHmhlrintTfyiTiSvHD+qSC/4hYkmQ7mReixbRnJ2TXYHuQjdnidO3Ci+Ta5GXFS6vco6N61sny8uhRzya524ujM1/dREO9crdafjfmjP1SAwe84EPnK7776x9uBhmRBv4suQhzQIHEP77TjNglabM4Uu5BDmpXV9qvJ+Uy/EqRCnNa614zvWWXR0wxiYcNPtCbGx0icNOfRZFLfw9FF4LF3Eh0hdR5yPpFqYLxrevbpIvw5o5/CzHtf0xPkUbcldoLKrqoFu18rEYi34SqmG0qsIfh2RLtk4dSbQaLNVKlUoFAqFQqFQKBQKhXuC0zNqKguS9QuzB2h2HkgcamryTXV83neWRTMIIi4mC3RVcIO7ITpivVmOJHDriF8HXAfVMuHX+WLTWV5H8tBPBDz0TqyiRc1l4PpNC2f1AgH8etOGmZRQH3LpUnaqiIDaxbc6sRbYipWRBCFO3FgiQ8pCTVR0EJZyDouKWOmSglv3pNo6LVl2iVlvVPJQDe2kc7CtihvbZm/n0qhAt2cdlqpFFlmWJhIMjo6xb3b+jGQhLWEtsePEBJlU2c1txZTo8F2Da0NAvYLfuqKPeaVDQryaYLN21AfV1jbkzlGJTW6KH44Ja9sdKxnbb4sCScdSOb9KUFuLcL+GaplDfRs3unAszyeZQyQp3W5F8tA8by2oTbywHB5bgbyN2fmjsnGTDO6TfseELMnb2Dcyttb2AmGx+cxQ6iZJoWPM8hn2m+Us5W1tIE6VVCla27iEIwvwSa5Cp0LT9DinuJBInSP2ATpnHbZ2E1UVWVaaO21tSvw254FtqyQbH/WDSCq4fPy6PIbJgzgpQk2hUCgUCoVCoVAo3EOcKtTEyoSN7myFWyeqwxZmFWnPjx10mv3EZL8lzgKpNoHCpUQ/M+eMShhFhLE8KFmOTKqF9qynmttz/czR7bJp56xDCZQ5ZpoD6y7U7rrxArafOhb3OcISJvtxfN4vI36drL34ToNb9YRlZ+sRBNdFSCa8aBBSFUgetHG5Y5QFI8dGqObJ2jRn5097NpACTK72uDYxeXadhaqERDeWc5mjAxODvIylWuqFat4RjjtEJ8RGxpIncwQJk+c0dx7K3Z7m0USaicuuIis3s7Be66zVnanop47VJaGfQKrJgbcmoCwfjqizDBe8IlVClx6/cMS9iJv16JWGsBRSpdbNqREU8HMr3XIt5lya6ChWhBVMrlgLaddj311BHJ1Q9u/yfmurXh1bl67ZZcVFpT62z213rep2HOuzwpkneur9jpi7ig0OpH7XW6ldBJ82eUnrcz7nz9iB0J618N9YweyZRDXvUF9t9k2bOPfOlXVxqhwuKmF/DcHh2gq3joTDFakOaO7yVC187g5Wb5VqbUQanDmWUEiNopVCE3FBURVmk5am6rl89Qxx4Uwgq5R+VdEtK6qFw63NkuZXwvRyIqyU6jjmbl6WhQRYSHXjmFRCdeystC4I1TwSIrkFeil9KtxZ7nbd9Xv7/SW34r2jjFfhXuNOHrN3e/57NVDGuHAvcjeyabbnvvd2Hizn2Y1xO/fr6WHCDDk1gkSXhYiNm0adtax2bSQ1HqptJ4KM5SP2RBZqcutjFxVNg2Mm53/4ocxJXxCrYdk3uYQlCjLdBOWqg1QL2ua2yTmrxNwr5pYhOHuuT0hMeaOGBSgkGcWZIX9GVHNZC4TVxhEiScdOTrG2blTVsrNAYW/j4VfJxJ/t0pscRDx8p/SKdBa2q+JyO2WlqwMqQj1P47hJVFxMiMrY5hphLMex12z9UzCRxkKZN2KKekUncVwmPocFi9+EFzuFCNKBiLmpYjPsA9m4O2TLlZOX53ImS/LZaVNtuk0Ntz7vN9fbuGggl5uZ28V1Om7D2PJb834Uv7Vv8nEkg5hn+UipIZfFWYtvE+DMWTIIZGwFTA9OML/s7DiY1basLma3lcetemSxwqWaRE3wJhLGiRvbom+PhyRBtyws6tW6WeXXY3RIpcyqDkTt/dnJpJ2z1t0do0vM9VYiFpbmKkpR8EHGYzV5MZGosueHsSMLhKnySNFpCoVCoVAoFAqFQuGe4VShpj4a+mnbP+pNrJlc7cYOOCTozjWQcheaLo33JQphGcf8GhULiY1Tx/GDFWGtNM/HsZOUb5Xpczkzph4UBoXaroRTtd1O2wQaF2Hn6WRulMacOG6dy1Emtnmi1snHRBkF72jPmwIR5n12KVhmTliYO8XKsZSwckwut4Tnl8QzDXESqA8jyQvdnqc9M3S8snBjv4pMHruCVoF4djq+1u/V9NNANc+dkGaebm/HRCYno9PItYnqOFE/OydNAotHdnIpUI1fJ8Ii4o576qPtMiUL/XVdIqzBRUfSXCoToD9jAo0svJXt+FzGkwJ4JZ7rIQnxsMaR3SETRauETHtcUDjT0iehPa7wx47p09a9q99V+hkcP2x1cKImEqVK6aeKBivLkU6YXhb8mlHsW521kGq/VvxKqQ46cwXtWbjy5IptXrdbESceDdDsR6RPhEUPIqzP16NgaJ2d0lguRWvlVb5xoxtpOZngWqXOLavjxNPtzsb95NqEdJV1/Jp6+ks17UfsMX0uMnl6jj9aEfaVNKuJjad9dEI/heoYwlKojkygWl3K3bHURC5deuLKE6mIZx2VTzhRYp1wxx63FtLExsW3Jl75pYk0rh9KwjzSJ5r9jn4axhBqRHKHMLFjZIk5hJocNL0stU+FwmkMv4Lci06R8otXofDy4Frn4qvxF+x7cR4tFF5pXOs8vFm38b04L93L677NqUKN6yxsNTYmkJDzLqRLOLKLILebdj3QmyNCgKFdslrWsIURy1ZAsLfXXNRNlx7sYnm0AAzOmNH9Qu5aZHctO8TElVRZyKvleiTr6OSyKJDdJjg3OirG1t05EHnojDSINKJWriUx5+/kTkxIzgFJSvLWhlqDIJ1u3BopQcruHYC8DZLyOPQJnVnQroXOyljm5TS3/1bLOJGYnSWyuVnujn3v6CwhO35yQLNtcxZlQg6UbXMbKWfODxIwdHKKFl6LAw1mHxIVcz0lxVUJwW1cOmMGDKRaNx2xxISeVCtpksArCWeaRXZN+WifTTXQWQ4NYGLa6EzJ+04sGHjs8NTH3JLaytrIGTHS8wLXk/RpbEst0XJb1OX9NxxTSfP25uykLh/jzsSzVLnxuIqN2PKijMExokNmjAmGxLwuMpSbydg1S7rNedWvA/N1TVKxsqdhvw6dooZjMW1n1ORuTzg0bu3v3EnLRc25SDkzaGAIAS8UCoVCoVAoFAqFwj3BqUJNddSRGs/6bI13+eIwl+AkwOUgXStXseDSOHFjPgtiGSEuKpPBwVA7VITmMCFxyC8xwSE21r67PlbCOuFXdsHt+iyUJHMJHD9c5y49FrLbXF2zvtCwfsDBPtQHSqotqyMsowUEVx4Nzi7uHVRDQHK+SK/3+5xZErPLIpBy6dbqvhp3thqFANdZqZGzChnLrllH/NyuxruHzttYjcHL1io5LCIpCN2eZckMrc0RaHL3qeMHAwisLl6wVtbzTTaOeqGf+rFNdD+xYOH6OI9V0ixOYfkotbXhJli5lzbJxJ9OTIyZJhMe1i6XpinaZFfH3Jlwc2xdlpK3gNvpoTlGFg+l0d00Oq6CmjBUJyQkSGKlPL11ijp+tEd6Yfcxj+tMgPIra7ve7jmWlxrL41npWAYXKxkFucEZlUiwU5FyxyXLAYokccTK4deCayN941mfC/h17kj23Aq36mgvmUupPupwfUL6OotP1l7dXFdCnDokws4z5gpbX5zQ7Vo3qvo44dZKtyPEqXU1A4hTEyGrQ0cKSr9nTh+3dqMYmFY184MKnUXcpEfuzwrNH06p5jKKbLEBROinVm41BBer8+M4AYgK1VFuSz+147w6aHF9oj3fEEtGTaFwQ9zLzpo7QRmXQmHD9X6pfV/Ok1fKr8C3izIuhcK1ud68876cMyf/Jirn3wu5E+Pykhk1lj9iQklqvGWxAFo5YuNeIFyQNIfxmrMFwA+/8meXzOh4iEN+ywvzToayJtnqUjR0ERped51unsvlVABhCX58bZNZg2p20ORW2QKuz6IFg6NhkycjacjfMffMICK5TreybWxcRtcOgyNERreOehCvSMforBjKnNQNjqTsOhpyeBJjx6Khi5KNlQUSD8+NTo08fhpAkVEgAzbOF5XszpGN2+JExgwOs/NkUUUDqLOyJXMEycbhIUqq0/hRyJ8XG0sJCReU1MlmG52C19Fxo96ECOu4lY+NvM+THwSxXDqkW46qfD/WbpN/NBw3421wbQ1uLyv1Gh1S2S2lXkiSXTxDRzJVpEtIpeM+9uuUnTMmrqWcEWMZM7oZ1+GYHN0wWfiB3KmJUazxa6GvhdQ7nKTR6UMyAVCiuXLsuMjjsn22Ss5yyueTeiE2WYzMotOYnbPVIKxQKBQKhUKhUCgUCi9vTm/PnQWQyZWOVDuWl+rxYjc2jm4mzJ7pmT69yEGtlv0SG8tq8V0iPL8EoD8/RZ3glxGpTeRxrVIdWMeotjLng2917P4TJ9Y1yrdZVMkX6jtPdaiDbi8QK+HokYbmMHLhNw6JOxXdmQrXWp4L+aI8ZWdGv+tRsa44rkv4eYcGR7dXWfegVY8Ga0PuV0pY9nS7gW7Hj6LJkMUT5qYsuC6iTljd1yAR6v0WrRztmWBdmVZ+HFMN5pBQYSxJEYVq3uOy6wgBv0yk2rG64G1c1taliiRIDw6lumqdrbrdQGyEdtcRm3xBL1sOl16Q1lE9n3NlzlguEHGTgiuznqrp6Z6eEeZC92BLaCL9fo1bO/zShKvujJKClQxRJVwTEac4UfrOo51DvOJcIonVvWmVS+J6yzRqz5ljJl7okOOARMfkqrL3B2vW5yuWFx3TK4nJ1dbGTIR+Zk6ioU338myNehPPfJtwbUSSUg/ldNNArJ2FEmdnVJyEUdyRpKzua6ycKJpgKH3Oy3n+COlm+DOVZb4ct/R7Ne3ZYOJN7rblemWyr/Qr8veAX5njKE5sV1bHjm43oQ+uSFHQzuGOA+FY8MceXZuKooDvh8+Qc3tMeAyLRKqF9Z7DtzB5PqKB0U0kEVbnPbEJlmnTKVAhMWRRsDhqCneW8uvLnaOMdaHw0txKt9ztcNK8kijOxELh2twOh8rtcNK8EniluIFeQqjZ/BSvYmVGm2Bha7Fsob5+zAsBxi5HKTnLhUkJaRPihjKnIT9F0NptZZAwvi4hZ2/0OZg4qQUMi7k9AAuMFRMnYi1o49HKbS5MHaiYKDI4MIblW8tih9QesltCFAi2PrES3NrcFSRAbV1cp9ZNSHL+iYJ6N3a5MrdJdvjMo4kAuWxpyLextuFb+SVqYoT6F5a4oFb6JHETrmzuJFAdgpMj7G4JENHKcFxvrctTncuZcv4MYqUyqtlJlLCw29bRizfBQoHO2UfWzlw1LlcyzZKVTeXyJh821ifnlURCkxDVjgfxCXVu426CsROVLD1+KWNrbtclC79dW+cmcuculzZZNfb57JKJil/mTmTZPdPPHGGV8AsdnTjJg9RCtxdwvR+PZ1EFHXKNdFy27kzRaWX7WR1pGlCx401iFj40l/flMR/OpO1cGURzeZgQ134sBRtKm6x0TjaOsiG7u7cuVr61lud+FRF1uGjtw4fv9NjxaK4dj+8Yhckhu4a+BNQUCoVCoVAoFAqFwr3EqUJNu+fHvJlqnpj9wRFaefrdmjCPNNhr7YXJeLHponXOiVNPnIDrGty6J+wvUO/pLs1yx6jcSriqc3ciGUOFLbfGskHCPFJfWSBdpL1/l9g42j1vbobLa9qzNXHiWavDPTgd113yRW0/88TGjSU4gF1kN4JGIVUV6mTMjHFdZd2ccuch15l7xreWA+LXkfWFmliLCVfJOgelXM6UvKDnAvVBz/TdV9Dg0WlNagJxVlEfdpCUNPEmDuUL75RdRpIUQeh3PH6V2HnXIXGvYfmgdSsKi5jzgcCvI37Rovc39BMx98Uaps8nkgfXevqZsD6vuT12Fmd6QYKJbdILfuXQTlAXcK0NUtg3caI+tMfr84k0S8zumyNibaadS3if6HtP33lC1eMaZXnUoKuA7PSEOtItwwvKb4aSqunjHr+yTKFqnpAuUh3mUrtogpg/7nGLNe44QHC0F6akSqgPetsvRyvUe9r7prR7nvkDnslVYfLsir4P1kq7FvqJuYnUwfRqMlfUOpdBpUHQEfqJJz5yhlQJ7Z4JTN2us05cV9uclaSs75vQ7vksxGguRcptykcxM4tECeqnq81jtddcC74TUmX7Z3BYuQ7CWqkWlk9UX1mRpoFulsVEsVyksIq4dcSteguwBtb37+RjJ+bcpPV4jBUKhcLNUH4ZLxRe/ItsOS/uHPfqr+GFwu3i5PxTzpE7x5106Zze9Sk7QQYhZUBUSd7lrkP2XMrCSzW34Fu/zE4LhwX5NtbyOAVzZ7jI6JZx3sJth842/cSWNXTB6feslfbgGhmcLSj4NlEfZmdBzrwZOjWpM9eJxLTJfcn0U4fWMpYg+TaZW0bMrVEtzEGTahOFquOIX/a4tkdiBYiF/Ea1AFcnFug7dGtKStqbkqYV/U6VRSjB9Q7prXTMHmvO01GLOBkyaxIgQtxr0NrKxDTA+kIYS2/6qSfV07EEaAyr9WyVBckm4kdMnNGhdClf9KvTHFJrQoOF8ZJdLCYiaLCL/a7zOKe4HC6dkkNECVVEVYjRIc5ae4cqEqpI10RUHG7pkU7wK8ylk50fvrXjbH1pkjN8cjZLVORMTZoMdhXodzypktwRyqHnTZzz60glMLlqQb/q7T2+1c2+X9kY+XXaOJdypI/l/Ng6uVVExOHXOjpThhwY8n5x60SNiZnJ5XK0BJDzYiZWdiZJIIJrxcrQqq0smm5wE9mBWR1jYtxax7we9cL6ftvG+ihZtk/tkPyZOA30O1UORdbRdSQxH/eVHztyFQqFQqFQKBQKhULh5c+pQo1fJQtQPZPdCDkslwRpkl0f6zSKBt1M8GslLBP++RZJie78lDRxaGgsOHZqLa19m/DLRH3QWulQEIh2Id2eqYgTN4b1Lh6akDw0+1ZKFJZ2USpqZSE7T7ZjS2XXJvw6Ehu7oK+Oe9y6t/IsZ4HBWjlW55ux7XJYKmcei1mAsovwydocCv1OIBx3+HmLrDqIEXd+gquF6rhDorK8rwZg9szagmiTkirP6sFdul3H6rzDdTnbJAtJQ3nWkMlTHfVIVPod6/pk5WOwur/B5Y5Rq0sVxw85JleU2eWe9flAP3WEVe6UFKx0p5+4PMZDCZXtTx1ab0+jleD01gErVUBQ1Cl+HaiOTLBJHuaPKGmiY8vubl4jVWIya0nJoQohJJqqY7muib23Vt5VYjptqUMkJaGrAv75gF8I9ZGtU6rs32qe6HYcxw9UuVTMHCW+hW5vCIpOoLC4ZMKD6zxRYX3WEdbK7ruOCfuJ5lln5Wu1x0Vz6gxUhy1u2RN369HBhIDm8Oh+asdcfZTwXcK3bgwzVhHixI9hxdVxhxwo3c4OKdj6ohCjCZv9WVNa/LGVjvlVDqjuc0jw/8/emzXJkVxZmt/VxczcwyMCQCaZWWTVVPXLvI1I/4v5zf0jRqQf5qVHZkq6FhbJXADE4ouZqeqdh6tqHiC7UCwySQKZekRCgIzF3UzNPJB6/NzvlKth42e7D+7+KRGOifmLWCvaIU+O41eO+Ayv/+9H8mFg+eVowOezcHkTubyye8Cv1zSS1Fru9x4jQwAAIABJREFUvB8+gC53df0l9bnPBn9O+nOsdU8MdP3Y9MfwU3qS5o/Tn8Kq6f9mdP2Y9afwU3qS5j+nH4JV89dc848aNc3YmN5l3Kq26QtWES1ZGd7XyIsY/BRspGPdB2PSZAPdaoDw/OKBKyPGBaEEB40dUxM3LivuKZn54m3EqDUYFalJDGGDw5ahbrjFIhJSrqmCdBNg54nPK/60GgsmOfw81JNkS12oc5ZWqO1PrQbbn8xE0hhgDDZWkpU8makyPCSsRjkgo41LlcpMgTrisuj2IS+4IQ2QXIIgXnCr1V5fkxF2HZphMb4zYyftzDTbwLarbo1PLl2vhf2MJYf8Iqw3jsvPrTLbrbK1C1nNOluKqcFxJQuyYsaOKJoVVdCd4Fwh+EIujtNlwPvC/ubCsgRKdpwvkbMOpDmgizNcUdTaxAThaLDc5a4ygXJjszTDqd2IEJ+UcMlMg2xjZlKwmuxVt9TNZkqINS6Fkij1vtLoKARLMwVXG6DYxpfic62QH30dlZPKJrreujaW5zbeUDxmXLLjb9XoblX8s0OKMLyTD1rMXAJ/gXhU4yqFCg6eLbWWd367L0QtWROPEC4FjQ5UGZ5yTYoZ/DhcGoT7RfrH2WurcZm6urq6urq6urq6urq6Pg993KgJZgTsf3WiRM/yeqBU3sf4LjF8c6TsI3kfCaeMX4Q8OtKN4OeAFGW+d9sIErV6WqXxacQqv6vSzpMnYXybCM8L692ADo74nEDZTB8pFcJaN6F5lG3sB9y1hrnAcudJkzC8n3GPJzR4iIF4nAzOmq7wY/VC2gmuNjvl0ZFHYQgOKYVyM1IGT3heQJXzLw+UKOz/+QgOnv/hgDoIF7fBkgHi2ZqJWouUm7ON7qhSRmvmWW/sUsTHta69s8mkrJRRmG894VK4+W1mvXGsN4JfIJ4LbqlV5DRDpjZdBRv/mb61xMf+tyunn0c0eEt2rNcK6FLbotxK5RI1o8bGdkRBsQanjLVke184TDPvjzvm55HD6xNf3T7zzdOB8yWyHgdYHTI7XLERKxuRs+cdH83Qurx2dUzpymdZ91ejxsajCuFxtvGewbHeWjJrfLuCKnlfTTsvGyfJLRl3zjAFVDw5Ohh9hVJDONb00+CgKPE5UaKzdqfm9yQjLm9tZ4PdE+21ER8TQ1FOX4/kwZ7bz8Lw4JAE+9/YyNp6kK0ZfXhSpreF+c5eK+FsRkwexUDUtXK7NUyNj9lG36JHFIb3a32B2n01HF+MirX0VIVlb9Dqrq6/onqy5i+nH/Jdo66uH6v+kLRH/331w+g/k6zpa971U9J/Ju3R/33+0/THJGs+hf93/fjo0zmDE85f75GidazIEiRSlPRqIt0E8s7Vd/jr17LiLxZD2H3vtw2/pVYspTO+z1v1cd5F1kMgHhPj2wxiG9Y8efJo5o+o1rERG0NpjT5uLQxPVo+dB1eTBEJYC+6SCVGQ7CjRI4cJOc2wmvHDC0NHw3UUqaUfwrkQnwwmXPaDtVMFYf75nuKFcDLDJd2Pm/EktanIkim1oaduvK3W3Fvy5kXLEFg9NwUzHAQkla2NqnFzXDKILDeONFraA63nLbX5qiZNRKGidKxdSGB+HVAP4/etdYvKoampn/M1+XF5o+S9kve16cs3B8D+mN9NLFMmZ0dKHj9m5jnwq3f35GxpoPF2RlVYHkZYq/G0gMuW5MnxxfjXCuGsW8JIsphpVCHEae+ByXhAoaanBOY30RqQLpnihbxzMBfcCmX05MkSNGUU/Lmmb27sWkE1RAoQhPNd2D7XkjFSLAHlcrm2d2EjUerh8rPBqsYHO554soSLS5bMChf7GT+3+7+mz14Yan6xkbjGXWrcInUNoGPGy3IfbTFqW5erI3RuNsNGsm4spGY2hVPpMOGuT0afwj96PxX9Mf8z0tX1U9OfMnLQ9Z9T/73f1fW/1g8xktn1h+lzM44/atSEcyZPnvMXHr8oN7/O+NOKf7qQ73fMb0bS3lli5akQ5kx8XJDzWqughaEoGhzpEDdYsFtheFhwl4ScZkr01vBzToTfPpC/uCXdDuRRSFOtvVbYvU1mJLjaXiS2uY3fnShDQA4DeedJO4cc1RqRvOCSt7TFfsCfZiTlyvAw2Os1ffJihEsgnBP+eUGjbfjbiNblTSBH4dX/uODmxOnvbsmTWNNSNX/aCIqkBjMW8MI6vWjvaS0/WRm/T5AK85sb2/A/ZRu3mtjMFhtxKpu5UrIdex7suVsKpFQGyjZ6lWy95tpitPu+sO6E9WCjN+rBH5VwrlBdD+lW0dcLPigiShwSqsK6evIl4L+PlMVx8QYW9iGzzoH1yeOmhI+Zu5sLU0j8avEUWkrFVeAzVrNezCQKs/FkNLCNQbFK5e7UqvFQ7wdnKSUVrg1gawFHNbAc/lwssVUTMFvdeimUAHmwUTfJSjhb6uX8uq7POzPg3FK2VihZC25O+FDH9Lyt7/m1Pb6fjRHjzxm/FKZvcq0Mt+MOpzqeV5NWxVeuTQUHNyNPso14AaRd2KrMSxDyvvWat3uMWmderP0pFdgFsheWgz3vzeXDUbuurq6urq6urq6urq6uT1sfNWrWW6sUjufasgTkm8jyerSK7dEMlHjSbfymRIdIRHIBEdZXo22YGz8lV9PgzUg8euKSKJNnvjdujDvtWF6NLPe2SfUrSG3pCadsI1C3YUuOlMGxvt7V0ZRiqZBkG2t1riZznDFfVFl/+XNrBCpaTQkbD4pnO648WioGNdZOjo5wzvjzShnMrInPBR+Fsgto9BsXZjqV2qRkiY1t5KS2OEkqxhw5Wa03QNobF+fy86mOhklde39NV6xKqJv4Ei29NDxd2SQNeNtqyOOxgCrLncFvp+9XSpCaqKmpG2MDM7xVhqOy3Ajr3hq5SjDzJD9FCrUp6h5L7zxFg9weMsSCs1uAnB26OmRxFA2U1fPdEhCnlHOAJPijJ5xh+q4e+3o1xeDKyWnGnEs2DiUF4jEjGTM+orDcWMoqnAt+tURJyUI8m5Eyvw6ESyEcMyUGM3n2loiKp0I8GiwbrElKndh9nK15qYF81dm1DOdCeBbWfSDtrRHKLbrBmP1q5+FWM038JVGip0yetLf7u51PHsyMmh4K8TGZqafKchspg92PbXwLAQYjQoeTXf+0dzVBBogZQI2tFI6J4Zw281HFXiNdXZ+SPtVxgx/yHfNPJcnSUwBdXX+a/pKvoU/hHdyurq6/jv6Q0ae/5O+Iv1ZJwV/iHD+X37UfNWry1EZTSq1/VtIUmF9Vrow2SK7VVIsq6mszFKDeNpUt2SHFUiFWwe2QEggV4GsNTI6yH0g3nnVvbT6tiltS3bhyHaECG/VIN6G2SCXQgs9lGx1SV5MlwRIUx7/xlAi7b4tBdAM4rRDfmn5ozT7qjaHj54KsGeccRcy4KWsFIbfm6GTjSyoGW6Yeo3oDJ/tFcSrXlM0Lo6Z4S4agbOZFGpxt1tc6cnbJdnyDJVLiuVabL6XWTRs4WZ0QH5d6/cwciN+d0CmQd548WP15ux7DUdn/eib//chyJ9bu5e183NnV5ItSbmzcx52dVanfZMTXGSsVVKlNUoLg0Kxw9mgNP5GFeBTCCcanvB077sorstGta2U65dqK5eZ6j92Y6ZAHDNb7XBk9SXHY/ZUmgya7pAyzRXfUmwmnDsYHawbzx9XMxNudJajma6oFseMpUUijICq4xVNG+3y42PFom4SqZpPLalXZa8aJ1FE8NqaQFCVHIY+WignHZEkYgLtIjoIPDtWCS8XG3uox+Ysl3NJOzAysvCaJV0cwPinuvBKLMW2Wu7i9/rq6urq6urq6urq6uro+fX3UqIlPuQJ2a03xarXLqK8bUsWfC+GSbbyiqI0zlcLl6xszCmbFX3TbfPs5VyBrYL1x6N/fgsLhVwn1wvmrCck25rSp+gHLK2N0+FlRr6x7Z2mKY+WHONvAqoDuzLRwSRkeEunGDJq7f6p8GqnjJIMlM4Z3F3AOtwzkyQym+FyIDwt5Fzj/7W01F2xsykwe2wCHY20Lqk1Nbi2WXIlCeM5W5Txn3LxSdtEAwpU9s//NDMD556ONIJ0NXOubF+aMU+MqZ0SSwmDP7bGNet55ihfSjaN4QWVA1MaFJCvx9c7W7ZwBz3rjiEdl/02u3BzH+L4Qzsrxa896Ww2ADGWs1dxVOihkcO8DZV/w08VQOcVRiuAWgRXg2ioVTvXPsxkpy43DjYJfzEwJ51JHjaQaIZZ+Er2OezVjqwSpzWCAQhodjDDfe/Jg41wuWYvS5ZVnvtuRR0u9qBOcF+PGSB0taiN0WQkna9zKk6WWxvfrtv6N8+JmJWrZjmt4ttElv7I1eql3nH95sCTQKeGyXUyXzPQcMcbO+HbGP82k1zvyrlbd18arPDrWgyWi/Dnj5kz49gl3v2e5D/hTJpyMy6PBob7eK4MHHSiT3RPAxkPq6ur6X+uvVR/7p9TXdnV1ff76S7yr+6m8g93V1fVp64eoof6Pft98LhDfT0UfNWrcUtAgyOi2JprWKiPFRkTcamNG1NEjWTNSipkdk2M6p2qi1HGbpIivY0dBWG888ZiJjyvrfWTdeeJzwc2WtoDKTXFXUyWspcJkZatWhuvYECIUb2Mu/mSJhbSzxMrwuCJJWQ8BsFEhVJE5gy+WmqhjS24t+ONMujFTyUZS6nPUESdbJzOVUm1ucquNxIjWNMjzgiyrfTiHeGfJowD+uEApuDcD2V/TE1pqGmOsoZU6Y9QgyuqwkSpVcj1XY9WY2dHgzWCVz22cpnFl/aoMD6sxXAa3tVKdv3S15rxybIOisUF+BfUG+vWLoIMgouiLWiEDGdvP+4tY0ui5GS61scvXmvWmojiti+ocJdRxrvLhPaciG5C3qQGU82iJmfW2Pa9Son3uemz2uAZ9lq39yVJOZiIWb01MrIo7W0W8W2x0rgTZzCNr7bK/hwZwLmwQYEv0WK24k0KsZpUkM51cNuaN5GzXbnSEk9W+p73fquGlgJ9rxfeyImu+Pu/zaqN9o403tVE+Hex41bW2rq6urq6urq6urq6urq7PRR8ffRoNehufEgikfbQK7cFGeeIxkSbPejvWhhxwh8ESJ6sSi0F0dbi27LjKtkmTJRuGh0QZHKdfTPi5ML7PzPcGML7/xwvh4cz5b2/Jo2N6u0C5Jmvik21sKdRacG8skacVXxk5bk6QbTwL55jrz4azjQxND4DC+e9uK6TXNsfhXDf1+wG/FsZ3yUwGdx3P8bOla/xsiRqtm+MG9W1jUOUXh99b2/Vg6xgPH9aY+4vBa0s0iHIbs4kPmTJ4e7wom+GQJm+NPxdFin2twWl335mBVOrmvZkWw5OxT84/H2pCxaqy/VxIk5D2ZnJoVPSQcKFQFg+lzfeYWSKLMH+3Q4rUMSXZkjiSrenIrde18NXYC5dqNtXU0Hpro2vhlHFzIdQ6bpcKafJWk35nJpR6u15mTgiXL+qx1BGttLPkynqw88i3GVks3TO8t9ErlxyhwoBFYfdNwl8S/nlGh0CeKpfo4Uy52zG/juTJkYdWA68b28hXNpPBe0GDQ1IxMLITLl8OGwTZr2a6uKTk4ki7AOwrb0k/qNKWogy1vnx+FVjuPPH2q82gK1FYbwfi00L47plytyPvA/PrSBqjGYRZCc8Z13rBu7q6gD8twfLneIenJ2u6un5a+kszH/7Qx+jvYHd1/fT0Q7z+f8jfIX9MlfaPVR81ajQ4Szss1qhTxhcJhHJtk7HxkFrtrM0owJIqDdr7Ih0DNmrS0hLqra3HrYKUYlyZWBM6cx2BErbkTktU+NXGhDTIxpfRmjJRan21CNTNPaWCagHKdbRGnY0NbSmOXMdtWkVyuTYnaU3x4IQWT9keszY56QtmiXohezNvWk05ajDZHAXZGQS21O9pH60hyx5LtjUowxXKrCKUAfxcx9KSs2RKrXT2c7bv2VtFujVgGdumjGKQZX9l6TTeSjOaUJDWsJXkelIC6rSO5bR1k63NqY08SbKPFumwVE9dSyBXU6sEcElq7fqH91erW8+D/dnWpFTOS7pR1Kn9vIMyaB0DgnxT4CZRvCVU8uhxyUweUdlqz7dmp1TAFftcrchu11C93ZOWahJKAl+qOZlfpH585dkktXap0dn90RqfKrNGMltTV2PsqADhxX2YFJH6fV6QvdvWskGOKQU5XZDdANnb/elBC2g11loKq6urq6urq6urq6urq+vT10eNmqXWOQcv20Y0Pid2/zpTdoF0iIRLZqgclzx6hnczbk6sr3eoE4bvjlBg/XIPAv64IkXZZ2V9PXH8RbQN/6qknbXqxJMyvs827nS/Y3hY0aNYRbZYGgYAJ6w7x+WVJ54Ku+9Wq1TOStl7S2NMZuAMD4n4nIhPdiJl8HWz78hRWO6E4Um5+/+O1w26E7SNFY1+W5c2arXcWZvQujeGzO6bFZy1C9kD8CEMORdrkpoqW6eClcHgwJaiWM1Quo+wQjjZua63kbxzLAfH+JgZ3yaWV2GD5kIF8DYYLrDe2HpJNo7O9N2C5IK7JNL9iH4xMJ4K4ZhYbwPrrceflfFtgwoLlzCgQQknG60pUwU6ezNkjKliH+Ek+LOxaPyim2FlIzhmaqjA+YvKbGkprGQJotQMJSesB79xhBoQuni4fGHmVBmgeKUEM2byPl9NLrHPA5AFd3LEJ1fXpNZ/J2H3jRlHz78Y8Evk8C98YEymLw7ghPic8IvDXxzLrSPthPEhEx8Ty6tI8cL0zkaSljtLSMWnjN/Go4oBqAdH2vktdRWOGb8We02smfPfHkg7x/7XM5ILly8n1MP4YCwhPxe7325DXRcHzkFK5F1keTUwvlvZ/TaTDgYRzpOnTet1df3U9aklabq6un5a+tSSNF1dXT9dfWpJmq7f18cTNbVZCWfv0EsxHo27LOjoDeq6ClT2h6RSjQ3bFIvqVkO9pVGih9Xqrl1NyNiTXZ9T1Bp18BWOCmZexJZYqD9TDZMGim3pnRIdpbZPtWTKltRYs/28FxC3HVtLw9AaePx1d7ulaK6ncv2LXJ//5fdv59yYKk4pXNelJU1KtONjMzIEcXpNQbRz3dI1XNf25fG1r4lc4bEtmSO12WrJdn6pIEvBX6z9yK21GUlsVMnP1Kr1mohBcLV+WsOL5A824tTW3S3VpLkofrVmJkvo1PtDG2+nncOHn8vTdZ2bmbGlilxl2wyQpzqa5WzcCV+fpykqfkpoFsrqt+dSV9NdUSg14dSSTCVY9XfjASmCjNVgSgWple4usyWHJJdtnRuzhvjhPecqG0hyQVJN2viXya7KrVnz9Xo6Sy+JKuRa152qAVeNq8bEUS8wjZVLU793yXZCImh4cT92dXV1dXV1dXV1dXV1ffL6qFHjF93MFrcWwimZGTNG8s7SHGnnkNeR+Jzx58z564k8CuP7jFsKy89vDBp88OQorPuBeFZufm1PffuvM8ttZL63Ou5YG5OWO084GxQ27xq/xYydVGunx7eLwV5XjyjksaZkBoefjbkyXrJttlersLZNbCGeFsoQWA83+AX231mTT7of65hMfYz280Wv41vOxqriU7aNvwTblCdze3wQymBJGxsJUvLgyaOwe5sZ3qfNuGosmvXgSSOUn8XKyDHGSToILkE4ZfysBG9jUstdRBIMj2YW5KkZEo0CbA1SeXIcvw6Es2N4NyPBUUaPuyT2//NMPoykW+MKxaOtfcr8Hog3HCsgeBHyoKTbUg0DwV+M/RKflXhq4GFFxZnZU00FV2vJ48mMjXi0Yy/R4LuX1xXYvFpteDgX0uRqnbsdT9prTdQY5JhDdZBO1dBz4MfM128eebqMnM8D6+JYs5lNbfQMrDJbvbL/tvJ5boJdixuHy5ZmklTwWJKlDEJ8zgwPSrhkqNcvR8FFwa3K8JhQgeU+2D36fqUEx+VnE+GUGd5eKGOwpEvlKrk145IxelA4/mJEFG7+5YzkQjoM9hq6DZTBeDfDsRCfEukmsvzvPyPvzKjJO1uH9S6QBzOGOk2466euzyVJ01k1XV0/Lv05fn/8OX8/9HfHu7p+vPohWp1+97H+nL8rOqvmP2p9Sh8mIVCgVgCrWEWyFKsnpujGN1GxTbU6M1S0JVawRERSYb2xjWwbVfKL4mcb78ijgVtbLfemFhTR6+ZTshLmCuANdeM8CH6px/Yi1aMi5F2sxk1Go9/Au241Jom17TjKIEhxuGRA19ag02qjwZI7KgavBeORtM178b+TYniJeFGFpDWU4g2lUxQRubYHbVDZ66hVa95qMNnWPqReNqDu9nTNXFosJePq19QLZfT2XKqUnY1iUT40VEqw9Q9Hh3qrI78yfKxZyi2Cv0A4wfBo17CNYYGNs4nayJKtv51WM2zaurTzbJ8XvY5MlWj8Ig2VExMgj4ruMsTCtF/JybFevP3gUIhDYgyJJ0ZytqiNBqWIGUvWXGXtUCXXOneRmlp6mYyqh1cTLbq+YNY4+/52z33A9eF6324NWvW1VKKv59KSN/V65GjflysL6eVt79oYXuPq1NeC1GOpNfFt7bWlf1JN/nRGTVdXV1dXV1dXV1dX12ejjxo14Zi3zeBm0jhHGWzzOzwkwnHFnRbyYSTvAv5ccKtw/sKjDm7/eSHMpZo9jrMPlD08/yJY69OTjeDsv1lw54SbE+luIu094ZSRXFCJVhPeAMDP9nhlMBDv/rdLbZ/ydYRF0KMgazFTabQGIrJy/mrcRkSkWGrCLwV/Tmh0rIewpXLUKSXEjZsSnxL+km0kyztOf2OskP235tQcv47bWIrLejWL6mbZVbhu8Y54WpDziu7iVv2slYeCGF9G1Nan1S4bRJgK17WL57Ser8eYKM0AUeMBeZEPwM159Myvohk2ftoSIeNjIR6zGRLb6I7y+n/YJv/8hdQGMEu0uMXhLzC9U2PmfLeQbgJ5d51BCg+WgJpf+c3QUMeWpEk7txmA4VyIz2xGWwkw3zmWe9manFQg3xbkbuGrLx85DAu38cLTOvGP6Ut8KLy5PzL4TJDCsgb03YC4ytYZCy5ma7BKgkvWALYcHCHWhI/Wxi+q4THbOrol4y+e889H5nvH9E4I58J8K+RJGB/NZGntZuFozlgZHH7ODP92It2OXL4at+vTzJ3LFwO8GRgeE+GSyWeDdpfRA54yug0eLFkZH+yeSPsrNyk+WYKt3SfDg92TbfSt68etP/Rdhs8hqfFDzkz/qfopvnvT1dX1oT6V3wOfw+/vrq5PSX/oa+ZTeY1/TD9E4vaHOs//9m///bNYsx+DPp6oyaWO5zg0gsFATHlwNpLkhSBiMOGpNdZYOsYYIq6hZHCrMtZRHZeujA192aDkGselcTxegHlXS8g05kb7U2rKxVIE1dDJ18+R6vcG21yXZGZAa7CyTbMdgz8XKI4S6mPkuqMWpcSWznC1BcgMhfXgjY0zVP6KQilS24wwwybbmtgIlRkmzldYsZNtk90+8iRItnrxD5g4tVHI0hLXpERLpLi1bCyZPNnlbQ1GLTnSNu/qrJ0pFBsz8qdEHqwJyq2Cvqh1Dmd9waWBeMQSULVy25I+sjVvgbUZuaUQLlZd3dJIrf0qXMrWUgRsbU3Go5ErlyZwTSQloVwCT+eJlD0O5bxGNFvyJ7iCf9lHXa+HJFvXIs5qxtVGofxSkz9ZrX67YCN2L9IqpUKsjeFj1xGMb+NXu1m3NJPYWJyf61hXsBRMGQMar/fydt1q4ktK+15n6a4XX/fnUlM49rXwvFIGG516me4q4erItPu/3bNdXV1dXV1dXV1dXV1dn4c+btSck20Ip4CK20aEwjmz3Huef+kYHh3DY9g22cNzwV+U3bcrYKwMFUc4F9xcuP9/LtUcUdLdyOmrwYDEUsHBg42GUHQbJVKhbqATUtSOx11HPsiAmtlAaUZRsQaf2XbQjT+y+9UzZOX0X+4oQTazowwON2fG3zyRDyO4yTbl50weLUWUd47lzpMHMxfyaJv1p7+zY/RLA+5eDQd/EYZHZXqovJLoKNGRDp7ihXApSFLik63X/MVImoT53uFniE/VgDln8mTJlHC2pqa0s7RFG72KT9YeNH9pSZn1Lhg35f1aa8zd1sCEWtImnKyRKL67IMcLGl6BBKhclnVvSZrpnTFp5ntrAtt9Wyok10wXdUIejctTgq1DPBrjp6V0ymAGV5ocvijjNxc0epb7aEyfXTXAPqjhBo2wTmYUxWdBT4HT+cBxLJy+jKTk0bMnixk1TpSC4H1Bh4I7evxZKIuZjgb/FeKTjWyND3aPnL+o6/VgxkgahLRz5GHErZa8CqdMOGdrExuF4akaj20sypk5Fx5mcJAPAzk61p/vLFW16MYnasZXOBmjZrmLlGgNZWa0GFtp/PZIGSP61Z5wTMRvntD9aDyl+jiXN5Hl4LbxM79YYiwPssGbu348+mPfyficGCh/zWPs7xR1df1x+jG9g/0p6XP4nd3V9dfWH8tg+ZwYKH/NY/yc1unHoo8aNWUXKMFVRgvAla0hBca3SjyrJSPauEy6/rwoxOcaNWijNzc2bqJiRks8mlFRosPlAlk3HoefjV+T9n7jedgDf3icLR2SR4evJobUpp1mTmiwxMH6erfxW9x65cGknSVc3DqiwR5HVC1REd3GzTFGSmWl7Oy/S2RL1DSOi6WJasJGLA2R40CYC265niNY+sHNtnAlTnbudWxqvQ1mDjwvlrxJtusug/F//JoowZJNLbqk3pItrjYRaRC0Alc0SE1/6JauKV5ItyOyj9s4mcsGFnLpRUpDzYxySYmnck2+eKs7l9JSNjV9shjAubiXjJnWsiSkw0AZ3JZIgg8ZOSqVsZJAJyhRKaOi7po4KcUhojBlwpARUeYUmFMg+szhyyPPYYdKvJ5HEmtgWnUzkdB67GsFTtc2sy3xJTYO15Iq/lLwl+v9R7033cXOOd/0pM/KAAAgAElEQVQNlqQJdu8hQAY/X9etmZA2ulZh0JW15ABZLKWU7nfgBKmNZLobbKSvXrsSDbjs8pWf1BqzmunV1dXV1dXV1dXV1dXV9Xnoo0bNchvR0MDAH4JWh6fCq3+cr0wUV1uMBjN2xAkkZfzmBKWQbyfy6Dl/2fgoMBwL0zczZfSkycMMrpRt5CR8vxj/Zry1jX5wNsLzu9wNZ408y41jfyrEd2fbCDtHGjxltMalPAjzqwGA6ftMWDIuF3J0zHcel6xaPJwT8eFCmaJxVyZH2hnLpQRrCyoDrAeD1Gqd0CpRkSTEJ6l105aeWO9huQjnWdj/Rth/l62iuY7luKy4x7OZAfGWHIV4Mojx+Y1jJ7D759m+vrMNetp5xrcz/uFM2Q8GRk5lY5mUINuoVaqNUBtkdlWKClJHi/LkmV8bmyceC3625Id4UF/PrwKh48naseJTIo/OuEDRXAHJyvCckVolHc6ZVkFuZpdsxlXxcP5ZpERYDrI1PTW4cxrtfnILeA/zpJTbxP7VGe8LT29voNiImfeFw6szQ0g4Uc5r4OFpz9dvHvmvX/yK/2v6W74Jd5RjRBaDILcacr+amSFFGR5z5RWtUMA/g47exvpGT965bZwrPq+484qRoIXlyz3qhfhwQb3j+e/3ILKNQEkBvxS7r4aADm5Lw6w31ubk1nptYgU4zxkNwuXNaObYUwIH6+sdrXo977y1i1Wjqak9dkt/dX3++iHfwfg/f/Ff+zu0XV1df7L+2N8j/Z3Zrq6uH0Ivfwf9EGnj/jup61PSR42ajc9CZYqcjd3RGo3SLuCWglsyxftr+0xWSyM4WF9PWw01IsTnQhmF+dbGeHLlf7jc2oysSYlSQajRW1pB2RIF+cbVlIONnMSHBbdYg5NbCmUXybtI3vl6fAV/ESS7jX2S9ja64i+WRmjNUe287MORJtlYNGlvEN8yQPHUdMeLNqdsx5nHynNxIKuNP7nEluDRWlBEPec8ONKXt1tSqI0U+axM722MK73eo3VtWlX08mrE74KtjSriLTrhFjNKXGXTILIlfMiV4VMUt7QLTeX5tATGFfzb4Mjh2GrAXf3T23kUS7y052vMn5ZGUrGxsRJla3VSb2Zbmmy9hmdL97Q6ab+YObjcih0z9jPilfNxNMbLWhlDojinRJ9RFb57vmFZAyU5ni4j//P4hsfTRLkEJAtShHAUwhkQJccrq0eynV/+am/HcUoGr17MNENrpfbOgRP8+KEDIlkp0xUO7eprpnghT5YcytPeDJnlRQpNq0FV1y/vr+wZO3fQAuFobJr1rrKHKu8HrlDs9ph5bGNkdKOmq6urq6urq6urq6vrM9LHGTVrMfgq1jY0PCQ0tGSEkPaOoLbhb6kJv9goU4Oxnl8bG6ZVcE/fzOR94HLvyVHIO4ckSxKYmWCGzsZUKdexGJcMPpsnt410DI+Z8PaIxoC/DDZidBOZXxuzY/+bleFo/JcyKGUwQ2C9MfBx9MZasVEne560c8x3jjxBmq6V2csdpJ1u9dF5UNsEOzXw79kOtNRiHwqERRge64hVTc+00TGhmVHC/OW4jahIbmBgJRwTJTrmN+MGnjWei9SkjGd4MjOqwZb9xcbN2lhXq48uQRBphoCtZ2tjstpt3ZgmDbZszBMYHtfK3xlQD+uNsWpaRbirI0NuqaNuTkh1lCpNdr8M57KdfwlQbmx8a3zMrHvH5bUjXAS/KGkvLK/BzXbvaVBcKOj7AUkCQdGgiIBzhTEmLkvk+WlCi6DZcTqN/JO+5vw0IRdnRk2G4QGGJ91q5Eu253DJ7tnLvSNclN33gr/Y2JmLDsmQdrb2JQh+uTogw6ON26WbUF8LZoiF55U8BdaDjc+te2F6V9h9VwHB1YQ0g6psCSQVYaz3UK5V7O7xDHc70n7c1r6l3OIxEd9dbCQqei4/G8jhatZ0fb76c7278znxan4q6tei63PQD3mf9newPy31a9H1uejPkcp7mTbur4W/vvq1+A+MGr+aeREuZmqU0dWNbtqAqG41jkx8rGbI2JIyvqYyZBuZkQx5X6G+b21EJlyycTYGt5kGUNtuLr6mMywYUqLf+Csq1hqEwOXv7i2FknQbwdogt75CijFjYvp+oQTH8y/NcPCLGUTLvd82v2lnldB5J6x7CGfwF0vS5P2LUadDQoJWY0BQ53CLEB8tEdFGm9YDm9kjxbgiwzZiZOunwdIy3tnxpJ1DRkV92Jq0mgGAQjzrB9dKg5CdbCaSrWH9Wj2O+Fxs1KmO8PjF0jr+YlXPOgm77xN+zpy/HMiDEOY6jlNH2ta9MWzisdT1KluLURk9eR/w52xjWKEZGo0JZD8jokgRpvd2XFZfTk1rmTHRjjnvlORBbhLjtHJhsIrzSSEU1tWTk2NZAusS0IcBDQpjJi+e47KD54A/OgssaW0cy1bQhNoInku6sYNGKbY2l4yb7VyQ6wigFAhzwZ8LeXLbKJpk3QwtakV7O+9wsfUenrkyiuTKV7LHt9dZOBvHZr2P29fUCWU/UoLbRtrs3nHW0OWM+ZNuAmUwx8/Pylj4cEyw67PRT/kfpp+aukHT9Tmo36c/XvV/b7o+F/XN+49f/Rpf9fHRp7Xgal2xeoP1hlPZGm0snlGNlfOKrIn05YEyBPJkTBewtEKD86a9jSgN7xbbnBeFyZNGNo6MFBC1Tb6kZtyYGVEq38ZaoAy8ev5ZJMxqBlI1Nayqu1ZGxwqBTYp/uoBz8MvBAKyrPcZyMHMnniGN1mCUJkg3BtT1F0vQ5H0BZ4bQcLvgfSGtnlIcWTxFA/Fo69dAw+lGtzSHFJszimdBso2rSFFKaeBhS1TMNzZTZQBha2dSZ0aJtT5dK6g30LL7oPkZfQGWlWTpnLT3pDshNDDwUnBzogwTWYXh+zPu4UQev2C98fgGxx1qW9VkoOMGenZLRr27ApcnS0j5VCroVvAXS9K4dK1Xt58t27gb1WBS/2FTUZ4g7wrjbmU3Llz0xtbQKXilrI4CaAkwe+KzI0+KThmdHbI4/NGSOlpHvFzSLdmD2tiQW8pmcrnkcGvBzRm3ZihlG99rDWRuVsLJGDJ5sPvFZXCXFUkFP+ftOog3Q0yS4ud8rdKuHBlrTBOk1pY3rs3ldW1Ta2DgyUxOtzZjyda1tEr3fdhayYZnu7ca8Lirq6urq6urq6urq6vr89DHW59Gv7UTWZJAkVSQnMnTyHoXCaeMvyQIDnVh45GU2gaUBzMowo2vdddm2OCFHGxjj7IlZ0RbEgGW+wD3gf2vL7gls95ZI1M8FyRBeF4NMEwwno5awsOt2eaKClsawuDCsL7Zg8D+t5YAslplRzxpBb5aoqWcbcQIYL1V1gOkXyzcvjrx/LBDF09aPTk7S9SUuukeC6dfUFuFjAuTByUcIZzFALazrSUCy11l9Mw2UlUGWzu/6GY6ubVt8KuxcFaG98vGggFLEaXbaGyU1Fg7boMAE6yuu329cVLAGpv8nHFrQaOn3O/reJYZXlKvB9LMlsqqKeAGt3FbrL66tWU5lnvPciOMj+BXRQZX4bluS1lZBbed1803hfNrx+lrqSaafag4Lt/vuPgJl4QSlHi7IE5Z3o/XyIhCui2oKJy9GTpqY1N5hPhkhpu/mEkVn6zu3arX/caNKaOw3gTyVxG/mEnmL4XDP59Z7gfSTR3xy2WDHxsgW8h7g1W3tW7JM3UwPGXcuxX2kRL81v5ko242ukZR0o2l0cy0VMZHM3nyLmwtZOq1AqMNbGzHZdDsEhpXSjdDraur69NTTyh0dXX9NdXfse7q6vpU1JM0v6+PGzX1nfoGI7VNKQb69ULae2NrXKiNTDXxEq4tUSXas+Sxmgy1rluDpTDWvbNK5Fmvhk1ltMx3Bv91c8I9XeB+rLXPZhj4OVOy4oNUk0c3M8kt5fdOro2oIDB+e7GkyE1Ei1wbc9rzt+purL2pjMrh1Ylf3D3y/55G8uLRCg/WUmdYBIiFfF8gOfyzMUJ0UDhJNR6M+WIJEqmtPEJMBcrLWmrdAL92TtUw0XruxwVUq0FVk0OTR53fDBj2roJytZpmV96QJYwKeBvZsvSUWjqmjmFZsqmBhuvatjWpNd6ahbRzLDfClMQYNVrvj0lIeyHM1TgIdk/Ndy01VeHIsZpPD4nLq4H1TnGzrZdUfgxPlcAsllQapxURZU07SAJeDVI8ZkgOme1c20iV1rRKONcK7mRjSpIL56+sEl2SmXkl2LHPr2zMKA7C/reF8P0R9Q4NcTMGURvjapc/jwYaXm69NXt52dhE5STImq/QmHZc9X5oY2TNpGnQaX/O1cSr91MAVWtWC5eMuyT09UiqJk3ZXq/WHCX5mr7q6urq6urq6urq6urq+rT18dGnUhMb5+voU9451p9Z4sLSNBlZM+l+tBaotRCfDQ5bvG3MXVb8xTbH6RA3bod9LzayE8S4NCvEY8afMxAtnTNFECEcV/zFsbyy5Mh6O5Anx+lngfGpsPv1hfUusvxitA35JROOCXdJZshEMy40wPLaiL+txcjPyvzK8fgPxuHxC6w3ZiKUSdGxcDkP/ErvCSHjbuvmV4X1HK2mPAsUwVWmj40cWR10OArhZGZQHo17I0WZvs9mGI1mSvnZTJU01vYrBXVuu1TTW/v+5Ys9LhfI16al8LwavHb04IXh/ZUlpK5t9G1Mpwyy8Uxejhr5xdI2ZlgplzfB0i3Jjjeey9bY5FcIa6mNQzbe5nJBVhuX2v/WMz46/MUMiLSva72YaRKPmTw6lkO9Lq6e43fCeoD1XqvZ0dhDV57N8/sdAG4200dV6jXwtuYXIR6F+MzWXhWflHC5nmPaeaT4yoSxdWlNYgHQR63rD/OrQAmvNuNkuQ+UGOs9APFo57jehu0xXDLmjktKfM6oE07/cPfiBWY/e3njySOMDx4/F4b31jaVB7eZddTxJ2Cr8fbnTLoJXN7YGJ+vY3wOG1f0lwxOUNdrnz4n9XcSfvzqSZquz0n9fv3xqf870/W5qactfrzq1/bf18eNGrWNql+MUdLGVPLorwyYYomOHB1p7xjeW123lPjhY9XvK7U9xy1shk2JDo3X72vcFD/b+IoGoai3tEZRIFbDxcyjtIcwG/NFvdRGooJb7XOyZqSED/gtJbprI5JCqA1Iy+uCWwR9Fsqg4CypQSiU4pjnQAgF7+2/S21zoqZqJAturiMvwVIxbmWr51Znm/fG9vGXhKRCGUYLxryoRN+gxQh5cBX8W8y82jlKqmNONU3kHpOd6+BR2BqYLAUiaOPgtJRJ46sM1xQUoqhTq2JX6tda+5MQToUSX3BxKvOFVMfI8nVcKlzyFfJMNWqq2eKr2VCCfa0ESNU0CycDOv+uSXO9eMDs63NLPR/jAKmAW60O3Z9heK9mtgUbO3PJqrAbHJuCtS0VRff2cnCL4kTNNKsmT4nCeuvrGJSBnde9q4ZMHQnLSqrV8b6lmurrx8+FtPOsB7d9f7sh8wjrjdSRODGI91JwyW3rvJ1/S50lxa0Z9aH+rMGK3aCUygCScm2F6urq6urq6urq6urq6vo89HGjJmkd7TAArHqBZJXDNjIj6OBIfiQdPOtOmL7JhPcXdqMnHDznL4zFkSd3rXLOZtBosPGNMgjLwW2bTfWCRk98P1uyYB+s4vjW3Bw3G0w1vr/g70curyakQN5Hwjlz+NdCnjzrwSM64gdr5PHnhD8nA8NO3ka7nDVJzXeeEiE+VkNqVEvCnISLgzJZlEME0uotwUFNcijgFLdPlHPAv3UGEh5rAiTbGI+fa0InsqVgyuAR77Z2Hz+XbVOeJmG+c8SzgWuljsWkG1vr3XeJ8LRw+WrHeuMYR2PNaDWBdLRzXA81SROFeC7svl3IoyftDEw8PJTKN7mmoKa3CTfnjXkSj8UguWshR7dBn9XLBgH2F8Uf15qu8qS9Pe/Nv55xS2Z+Y7fb+Ji3eyE+Ze6fM8td4PSzmma6wO4bZXwLp18Iy6vC9FtPOMN6Z8wfnKWW/KXxgOr4W6r3rkI4KmFWdKEaOWxraEaLHftwtHu8BNlq1KGaSYutfdp71hvHerA/zYy58nva6NP4NtGq0AGC2t8vX0SkWPOW1Pu/DI4ymEEDagDgc7Ha9V2wlikvpNHat3a/uVj9/M6jQVjvBiQpu+8yw9sL/nlm/vqW9dbX1jXH9O0ZWdIP9Oui68+p/k7Cj189mdD1Oanfrz8u9X9juj5H9bTFj1Mv/33p1/bf18eNmsq2kKIbtLYxU1Qqz8M7CNRERt0sl4JLBT87S5G8aK6RjKViikJle6irAN3KZtGa9nAXS8PoTR2Bim5jtEhWS4+sxUC12fglUpSwFDQ4Mxm8IBWWS1bc2SDCGiZkqz6um/faJqVeyQN4jMGCWHOPACJKUffCqMF26K0Aq1YHSVFLfpRm1rA1DTmu/72BjmGDBwtQko2BuZr+ALa10spRQaSCbC2RkkcBvAGf9UUiJ8j2PSrYaJKvDJes+LVQkqMEtTXBTDoDR4MT3WrYadyacmX4WEroRfOUN/Nt46yUeq3qz7R6aePC2IianxyizhqVkuKyjWy5uY6OXSxpY+dY4daAWyqs+mRr5dZ6/3hrp2qcnZfSer1KvN6XL9e43euNG+MviVLHkEptaPJrXaOWcKl/umwNUdUrq9fczMCWQpNU25rg2oC1ysYlUi/bPdVMMHUYUwiguAr4trFCt2RLTy3rBp1uY27Wa/+xV3lXV1dXV1dXV1dXV1fXp6SPGjX+ZK1KeQrbOIcUJU9h20RakqZCapNxP9AdaWetN7u3aRuPMXCswVbdksg+WjrAQZiVeMyEY00v3Ab2c0bmfK19ro3HJVZ+y5s96oXdtwm/FNwlVwaLJz6tjN9n0iGSJm8b7aJM707IsqKvd+TJcX7jNwMpj8L8plB2BXdYwSnFFaaYCb6QsqMUR4gZVVjngKoQdglVIT9GZHE28pSE6btrkiYebdRneDR2iL9kXAUI23pkENlGVYb3CwDTt8J6CJy+ivhZGZ4N1uvXyrI5DITnjL8o640lXcYHRSorSJ2ZIc08kQzrnTUT+Yvt4NNkLUPqYHjMW/tRvonM97XFK0hl95Rq/AguF8Ipk3eONDkuXwTm1wfCyZ57OTgD3EaPuyTGt6uZC0umjJ7j38Q6GhRwCQ7/ZmknBJaDI+9g/1tl/02rroLxnZlbbgEE0t5GlQ6/zpZ+uWTS5Fnu6nhebowaIQ/2ZzgXXIbh0Uyk/W8MzDx/ORhv5qmOjLlqpgH+khmLst540iTEp0w8JfLoUaEyleDy5QAC4WgJqBwcqDJ9v24GErAl1Eq0tAy1ar4EIVysAWr3K+t5X19NxgU6DNWdqoZoNTXT5Fn/9oD6A8NDIj6u5L1Veae7EX7HqOrqgv5u+cf0Q76D19e5q+vfV38n9Q9TX6eurj+//tu//ff+WvuI2tr8EP9f09f5D9NHjRqN/oO0B9T0S5Rt5APq2MtiaQZLBNS0RuXN2LjJtbGmFKEMAQ2u1n5Xzkm5phlEK18juI3n0pg5qJkbeXSVAdNakQrqfUO8QH0M9WIYkwIaQz3mK/vDapJtVKmMBaIxaHwoxGhjI0WFUhw5t/UQxIGgiOg2AiUVPivU1EyqzJPGqWk8k2LnqfX8tjRK44uopY5kKbjJb6wUFdukt+Yo6nmRirUo2cFWCG4dV5MX17AlbcA2/NmOpySbJmqjaXaKcl3LF+vfrntLAF3PW7bK6e2+ad8q8uLn6/HVx1ePsW2SvgBL1/tite9vRpKvCSOrUTfzxa1mOrnVDCrnBb+6D9IyLmlNbumWsLHkjuJmu8aSKutnLds9Ykmtek22a3dNTElWGwOsKRhRRbnye2zd6jmLGU6toaylnMLF7uE01WtVTZg2snR9zN95gRZ7bvsmNpNPctkSS3m4vn66urq6urq6urq6urq6Pn191Kh5/t/2+FnZ/eYEIqw742z4IAyPifjugoYdaXTsfz0Tvz+S7yZK9NY4g8FyS/Q8/d1AaYZOVtbDztp1zgUV26CDpQPCOSNPShk8ZfSgagkKbMMbnxMahMubASmKvxREBeZ64ArrbaS8Gbb6azM/rHVnG20BxsfCcnA8/72wHgpySFCE9RhZvbKEQFn9VgGNALX6+earI0NIvH9/gyYHUSkU3MWjXimDJWrCGYbnwvhupQQhT8YQAchTrTSvUOPh0QC8JbpqdGVUrFZaiqLBNvX+UojHhDsn0u1AGaxdKRRLfyDC+eeDpTQ2UDOEizK+S6yHwPmN5+Y3K8O3R8J+oIyetAukvSc+G+Q4XAyoO32/GuR5yZYicQOuMlo+MJ/Uzmm9CUgx/gtqhsJ6Y8mscbHGr+l9rmM9rUK8tlFNBtwdH3QbSWv16c1kaU1VLltqprFnlntr85Ks5MmRdsL0LjO+Xc0sdLDcB/Jg3J1mLqI2gmXGX95qz61G/mpYuqUwZKUMjnmyNZeszG9skff/dkZFOP/NhBSrHG+18G4p27Ux9oxjuRVrKDtn0i6SI4zJarURMzTPXw6IQnzO17FB+MBo8w/JkkrRm8FaTcjlzm+8nK6urv+cehqmq6urq6urq+uqnob5y+mjRg3UpMlgG9XQNsulckBGD0UrVLWmEMTamFqiQFJBxFppJMpmkljDUOVuBEcRQQdQV1txSrmmXipLprFp2uesXYitQUeDs4314Eg7Y9S4VOum6141j4LoNRHkF7Uxolph7byxQxBBnCJeIVXuTCi4oOTFIVlY5kDODnFq7VCzQ9KLTXFDioSaGpGW5Kl/35JCbGkJ9UKxp8etVO6JHWOptd1+VRyQY63tVjMQ2kiYKCi68Xda4qXVOrfK6WaSvExcbPyhynZpI2sNLF2CAy8b+6U1PLmltj0VtgapxppB6vhTUlR0a+xqI2dtBKvVkbdrIw0AJGwgYKlpn+LtByyp1KIr8oLrYm1Vba3BkjrNtDAujCW+VADfmC6WCFN3NfMkV1pwsAezx24cmZqSacDtup5aR5SKt3SQv1h1uUtlM36s9ez6mpCsOJGrEaNXNpSKcYCkcpxEgaR2Tk6Q4jdGlP3AlSXk8n/wIu/q6urq6urq6urq6ur6ZPRRoyacCghcfj7iZrW0gHdbo8zlZxN+LkzfLZYaeL0n3RibJo+WCBneFsJ55fafIE+B+bXxbVBrOPJrIQVHnoTlxqq2XfI13WDjJWUUVIThYYairHcRRIjP2Ta7c65tOMEqkG8sqZD2wu2/Zqa3C+tNsFajwTbRvm7wh/cJKYFwNmNHRXHBDIU4JvbTwrMbSS7w6vWR+92Ff9Y3lOdI/vWO7GD45dGSH78ZjIlSocR+BQTWO8Flh1vD1ipUom30/bkgpVC8bd7zZE6DS4powZ9XJBfC4FjuA+cvHf6ihIvCnY1+HX41E97OXL6+qbBdNtPEki6WSBkeDKTMixGaPDnS3WQNRME4RP6sG7jWn8s2UqPRsR7C1WCq6Y7G22njZyVO5NESK/5SyDuPZMf4W2OuzD/bU0bj2sRjYffPD5RdZHk94bIQzrUGfqgtVEEINbVj66XVmNAPWC1QR4h2jvn2OraVBzvu+XVtJntfiM+F+LDgUiFPwcy9vbcxI7HkTxnsHhu+P9dxLsd62DHfOUvx5GZcQnzMIDB/MV5NOWC9DQyPifFf3oFzaAyU1zvWgydc1NJjqpTR1qKN9WlwsCZElXi0Sva8s+Y0f7bEj5szy6uB5d5Tghk1+9+uxMfVoMYqDA8dUNP1++pJka6urr+m+juyXV1dn4p6s1TXp6qPw4TnukGMziqtR79t0lG9Njg5S8IUuaZfGjMj7yOSPFrHTloTjW9cmaXgQkGyJUV0rqZAEIp310TI7xJR9co5SYdYm3wsnRDmYskPMT6MRVTYNu6iZlwY26ZsCSEpkE4BHIhTUvJclkgpDlSY18CzG+vKWb04wLp6tAjuZYIliVVH1yYiP+vGSVF/PY1mNLgEqrq1asUKDV7e7K7HnZVw0i0h0cwCWa0dS1JBwvXBpSgO2UaUcI0xZGvjF0tslMFtDKGSrZVqcz5qu1e6qbdKqdeiJlfK9OHJSL1vorOUj8uFUp2hjcfjZWPtQGUhRTP/2pq0pqqW6jFzpn60pI+39TUAUW22inbcfrFEjhljSqkGlstXxo4OjuxtPVoKCJWN7ZMHwbX2pMYGWgthlmuSq96rbdTIEkHgsx1froYTQ6QMgXwYyLsXa8aV36M1JaQK8oLxo76af/lFcqwUG32qiTZUUG8cHA3u+joFcH30qaurq6urq6urq6ur63PRR42a+LSQ94Hj18amQYZtXESy4ufaDhTdizpgeDnedP5qsg1zAg2QRqtNDsdEOGf801w3qYJfhHiyzee6D1eDoqZQVEBejHeIWsvU+cuAS0o8Ffy5ML1fiU+ePLrrWI0z1gnYhnd8uxj/pY27OAP0xu8iJSr5ppBnx9mHzbQ4vdtxkh1uyLgpU7xCEfLDgBShDFpHeewchwdLvgzP2UC0q5J3bjMk7Nxa4uWaXPFLYfz1E+l+x/f/xx4/w/67tLUptZaleCz4c8afVlgTfm4OhC1Qq6qOR2tTak1cebLRpeG5gELa+20M6mW7ExXiW6KwHGwkbf+b1YwtZxXQ8+u4jVQ5MUMjPi4M32fKEMAJPhlnJ92OV8Ax10RKupvIO0/au82oyrXeO8ytjp3rOE8dW9LEVkOuviZn9rb207ts1dpzYX4dmW99vUfq+Bew3EW77usVOt3GjjTAurfkTBlDrSovxKeVcLGqd6kspDw6AwEDw5Pxd9z/z96bLEtyZGl6/zmqamY+3CEGDDkxq8jmokQoLf0E3FF6wTXfhE/BV6HwAVq45r646Q27u7ozgcwEAhFxJ3cbVPUcLo6q2Q2UVCSQFQBi0F8kJBM3rpubq5k7oL//5/uTIA0O80Ww33uyQ7wKOH/mEc4Kf5Z1zWzoWZ8AACAASURBVDkZLDj3tN6Hj69jPNiN292mMkZGULJ7yM0CPxk3qJpUae9WE6mack3vv36Ob3RakqapqemH6l02fHz/mE1NTU0/RN//HHqXnyEtSdP0vuvtjBpVIOvGpslbWxEX4Gm8DIgXbksYEKCglYVSAa3iCSqEcLYWqLwrgFbtASK4MYOXWlnMbzTcqNuSM9AC2lUzSWrTkBQ4azUnavUxyZbKcVFtH6540yzJit13irQnLBew1qCJkAdF3gng1UDChUEjke2fE4EioX/lbESrU3AihDuD/7poaQc3GT8m7UuN+WKg4XreLLryW/LAEEfQYJvz/q4Aeju2saTJDB0PM2n8mC2psu/LaBdDIwPVzCBdOSm8GGtFgoN4QHqGn8xwk9IqZIkoRS4pGDMFgHCWMo5V/kQt68qoTVzx0iENAd1tRnhIGyB658sxkj3Om2lGYumr3Btbx0+yGkOrmSOl1SujpLg2TlJlxBiTx+4/PxbuUDETQYA/5/LPdoxqYiyDX1M5NmKUATEmkGi9PsZoQufKvbjBqavBZ++HkuApCS1aBI7JjuUIy3UH6aikXzYoNGWr5dbyTrTWtDLut7f3hh/N1MkDr0k1l62dSj0h+61CnVHMw64Ymow30zVNTU1NTU1NTU1NTU1N77X+KkyYxBgZQNnooyYaBO5+xvy8x/kzRn+j6B503bi6SeAWQXh5BgRIT3YGJD5n5MFhfObAiZEHRrjPCK+nUt+sWJ7tjRdSTCHxBmXN3TYywlnhsY26iLff48QID7bZz71BYTnRmmhB2ezmwdpwONum/uqfIpZLh/vfFdNpAeYnjPk5kA8C6gR8ZvgTIw8KdXZcngmX/9lqnk9fMjgCxz/ndbzGTQI3JuS+w3zF6B4E/mRpE/HGQQExwsliFGnvID2QDx2gwOGrCengMT315fkygihkYfj7BTxF5GOPfNkjHZ0ZP8nZ9RktMVQTFuFmhvTeNve9w/SEsX+R4U/WAoUy/iNMQGH51LUevotrMxPEmpEqELcmXO5/63D6DeHwNWH/kuAmMy6mJ5bY6e7immKiR5XWeSjn95CwXASkY0lmyQZA5kVAScw0KXBjIrvutdKao8KfCreomBjiGd3rGe5hLpBfgg7eANlPrP2Jk4JmoH9ttWFS0inKdk+lnYN01fjDVklekl5QwBd2Did7ze68ALAEWu4I58+NSdPdi113T/APtvbL0w4p1DQRkAb7+/RkB4qC7rUl28bnfh3Rc7M1qsWLDungyqgYlftdV3PzccNZ06erlqRpamr6JdS+qW5qanpf9P3/FmqfT03vu95q1ORDgDheq6MplfSCJ6gXaHDgaDXKIGA5Mvq7DJ4F6eiQdwx/5yw1UGC1tRGnvy3jU4sBi5dnw7rJ5Sjo7gVxb6ZCbT7yZ1k3ykqw9EhJy6C2GDlj23BUdHe5sGtslIfk0XnATIfsa92SjSD5c+HEZCA8AABDb6yiOTwQeDY4sASAZwJnYL4mcAL6G0vQ1JYpKWZEHmyMyxWW73rewJoIyiXhU1MWlATqGfEyrI1bdWRHyxqqM+YLRwGQsFyV0abOUjmV5xLuEyQwxl/tVvPDLYpwstGm86/6zXxQ2JhaqSKvzVu558K52cZrgK2xSTpGd1LoX4DuwUbdKqMmnAW5I9z/fgAn4PD1BCzlGvaM5eiMl5MKgPpB3kiBSCCEuwSeEpZnO0hgcJSVz6Jsr1mdVW+7SRCScYpyz1DXIx07uDmDYkbeh1JnbuwgfzKgb7zsADHTjbK9Dk7G9qEMMMrIlystZllX0DUnAytTqSJfnu+hZHXrEhjxyMXoKsmbWO7VobQ1lRYtoLCDpKSbmOy8Fei6R2NMCixXXbmXtrYr4/Io/MnWZ7n2rZ67qampqampqampqanpA9JbjZp48Bu3RDaArP2MQYOHmzP617BGnSNheKXwp4Txs4DcE4ZvPDhmG+kJtql1i6B/vdjGNgvidY/pqYebbQyne73AnWak4YgceOWSdDfGlaEkkMFj/HKwdEYZfbFUjRlJbs7gOSPvvG3sZ0taoEBxK3xYugKZjRXiq+tmmCPQ3W3rwVkKuNZaqryVGGF+BvBCePYfk8GLtUCTPda2HiUyhgxhbZ4CyuRQGUNCgdVyNECwBof5ytIm3b1YoqlWUDvbxCszKFrrEkrCJPe0jhW5WdD/ZUS6HHD6soM/A4dvoq0zAfMlIz63dqBwLs+dACkQYS1rJR2/kXBKA8NPgv5VRO4tGdXdZXS3xXBQBS8ZvGR0bKmeF//WWEfHPwh4jAARomfEg1Vpu4VBSdHd5LUCWzpG7hg8RvDdGfp8Z7Xrtbp8scYvCYwcCPMlo3NAONPaPhaPhByA4TXDj9YAJZ4Q7hNcFLhThHrG+NyMrP4mgZKgu8c6amTtYkDalTTMaImxvPPIwcDRrLARuMAYnwe4RXH46mwpIHSrkVhTQjXtpFzSOeWecLOuHCAiAs0RbknotVabM9LOYX7iy1pv7Ge7QED3agTFjOX6qhk1H5jexcx0S9A0NTW9K33/s+htny/tG+qmpqafQv8SM+ttnzktQdP0oevtiZp+S5twVIR7446Is2/208HDTRnD/QLSHdxiQOF4GQrPBGaUrKMktG721ROQAYALmNgMkrRjgxZfBABWDz0/cSUl04FnQbhbABhcNQ0OOTiw1iYeM0c42uY0d2wAXxdWs4mTbuBdNhPFWnuomB2WmPGjortXSNlg13RO/9qOU9km/a29rjqiZQaJYvdigThLdRAbMNaMHli9dkk/UFbsvroHYoJc7qCekY8d1BP621zSTGLJm8FGwvyUjXXSO6hY0sWqq4vB4YuZA4b2BrTdv5DVAKhJHk7ldZ4E4T6bUbP+DpCO1vTlRkt1WLLGeD+UN9YPRymmSmHL6GbuVLNv961d+/lpDzcF+IcFbmb4sVRVn7KZQHtXRotqykcRn+7Axx7S2fPlgQEBXLku9f5yhVG0XNr9s7ZDCSEPvAKbOVm6Sj2Dot0L3Z3VaT1mHNWWLhJaEzGPG8TcKHAzAC5V5zu71+PeHpcOlt6pAGcuTVtK5fd7WivU/VnAuYx5qRoguGNo5+09OCZo7xBL0xanYk4GMytJCguKrG2NFwYlS+g0fXj61/zHSFNTU9NPpbbZaWpq+qXUjOOmT0lvNWrkEeCUkxp3gxnUOeTBIe8c/CnBvXpA5xmUgv28t7YclM0wgLW+uI53iGMQKUgtpeIWQRoYuTfYLRTobw0Cm3qP3AMuOriO4UcbYfH3C0gC5mtn4ypiyZt1g00E9QYZdlxNkVJbvdimXDoDA1djQslAt2kg+LPVZOeOkfutMrq/k5KIsM1xdxehTDh/XlITxajxL0fIPkC6wSqXBeuoFi+WyFAiS+p8/RfIaQT//jfQw4DlqlvHlqo5pp6QHcFPlhZSx1taoiR2OBPmK7eeK9R4NKRA/yraKFbPxXAwRg8mgj9Zo1GF4dYabWsNUrgpA2QjRqQKXsoImremJI6KPFiaByhjOENtd7Lf3b02Q2S5dHA9I9zN4DmbSTMK/CkiXnZIO15TJaQKVcJy+ehWLQDianphNWq2BqflaONibhRQMTJy2NIwNo5nxpkPluQJ93YdcxlHqpXdxjKyMJMUY6iOZvkpG+h6bwZN7szky4Otb9o5u27dNi5VlXozdEIFIM8CNyXwnMpjS6Ktc5ZOmmaI60vNub1WCVyMSatGF0cgNs4OiAo/6G/4ZGhqampqampqampqamr6RfRWo6ZyM2paZPpyb+aH20Cqee+BL64Qr2yDPbxawGNCuuhK1bNtTN1UIKhTLokCWjfFUjg03X0GvxIslx5pYIzPHZQ99t9l451E27hLx0DhlCgR+ru8bawdkErqQ6lusi0VA8/IFrRA3DNcVPTfLTb28pn9RXcSyGyb/O7BgLy5cG1cVNCsiHuGXPDKo6kJIzNGgP7ezI7xdxeQfhsTcmOBMSuMO5IU6eCROg/3b34HZMX8ZCjNR5ZGoVAMmsKI4UWQO0ba9VguHNKuJInyxrCZnpgR092XeuerAMj3LvxZ0N2kdbQo3EfwmCD7YONm2dgsrgCkUUahOCuQYW1Sgc0kKwklXhTDnJEHM3SglqpykxY+UOG5FIbP+bf7rQocQLzoIIELo8XMouXokAZCd5LVvAGwppu21FcxwmoFu1p6JTwk+NHutbR3yD2VKm3C8DKBo2C5ClvrUrKWKCrja24W7F5EpINDPDj4WYpBQpiv/QqzPvx5BkXB+MUAgMzIypaacYtg/01EPDqcP/cIo/GWwlkQxs30sWN5M6BQTD0C0t6Ddh647JEHtnG4XFuuBN2dIh4tnbOyjLSOrFEbffpI9L6mZtqI1o/Xj12zT3Wdmpqampqa3qb3NTXzr/n39vv6mn5q/dg1+xTW6a1GDdcq5GyGx3KxVVpz1FKnzNCLgDzYqAiPCe7mbCMlvVs3obwYCNidI8AEKX+Xu22j7c4J7hSRe4fcA3Fvx7z4Y4K/mQFnfI54DAa1fTR+YokKLvXJsHpiMQOFBGsldC612Lkn+EmxnzNEFOq61UhiR+BEcJM1DZFam5UZJYp8RchDMbLEjodHo0Rutmaf5cpZvXMZxQrFSLEaZ4PPVjNpfr4DAMQDr2NVnMp5r4kMlGSIVX3P14R4NF4LJUIoIOR0KJXaDwBIbZysAJkpYzV8/O0M2fmVNUQxQ9kAtbV+ejVGal12qWAnANJZ8sgttg7+LHDnBOUA5W1siWNJsAQHghZYMWG+cGaWvbYESR643G+6mhUSgLQDwmgA6NqyRNACkN5GmRTb2JWW43DMwGyGkYQeEtx6D1SGTtr3yF25ByOs8rzwmDiqrZMfoBfORolGQe49ck+IO3vOizmDzwv4aQ91sKYx2cy7cLcg7XaIR/u5Hy11xVGM3+RLCsxvr6lCgiWwpaoCFZPOrrkv9364W5CHHrrj9R61NwatdeJNTU1NTU1NTU1NTU1NH4beatT0r6ONAnUMjmY21ArimmDxUzLWyNlDC69EDgPSMdiYjJh5EO4WMz2yQFF+zxPSgREeBOF2gXaM5dkAdZayOHxr55F7B/nMjIz6vPXx1UyohgaANVlSR0Igiq60Pi2XfjMdsiLvbbzEj7KO0NgTbWNaZkqVJqeeVvPHj4Wtwzb+s/suQx0Qjw5uFhy+npH2DvHoEB4y3JgQLwPSYVv24cUCN81meDkGYCmNqYxzhZoQERRzhrFcmEGzwZ0BChurJA32mOkZgTJt16sD3KjYvVLkngvI1owtCT3cHEqlOcGXlMvDbzuIo3Xcy59r5bexfcJJ4EdBOCWIZ6SjAYPdKEgHRgp2PMq1OrtcL0fgaK1h3e0CCQ7p4A2mnATx4BH3jO5eMLx+ZNyUVI6tiSI8GLsn7azeOjwUzks0E3F+2m/XUu3802Br/PofdhBvrV2kwO4bhQRguu7gZmC4zcg94+G/Pxb+kIGf1RO6G4P7Lpd2rPHLAZQHUFL405ZuqqbJ9NkA6Qj9jcBPW+V4fb1Km6kSL4OtmeqWhJLaCCbgZKmg+ZKgZKBiykBfGEMAkA5mhFpV+Vs/A5qafpTe5TcYj4/1KaRG/ta1+5cgik1NTZvaN9g/Xu0b7Kamd6N3CS5+fKxP4T33t5ZovIvyjfddbx99WgTE9Ci1UPkuUtgztgGnJcGpQpfCo+kLI8UDCloBtaQweK/jR0Dbaq5kAwPvXGk/0rWqOvfW6lOZJHW8RdXSCUhbzTUpjI+DMlKiVrdcW5/8yCvwFyWdAqa1hUdC3dyawVNNGI5q7UyumENl9KSaIAYQLjybo5labjK+jHprurJmJlo3/YCtJy0JRL6sg9j6OCAzwa1mlDGD4sFMmnihZpxlrFXf5RDr+qSdPY5TSRn1tk7pBLAr9d71Wudy7eraAAXuTMiB4Oc3UxlS2rYqW4XnvF2DXNaF7HWIg8GeBWuttmYC+3JvxAxiu1YGTi6jO1xYP2NCDgb+rc+xVYnbPVpH9Oo60yIQV3gzZUzKTYURQ3ZO8xNC7oF0sJRP/8p+b7m0Rq/h1tY87bbUV71nXczgmOEDI2e2JBSMq2QpLF6vr/gCMiZbL06PQNKiQLYsUN55gwz3Vt/up5rAQlkbuxddVuTOQYIljrS0slHars/jdeJHXJympqampqampqampqam91uk+i9v4v7n//X/0FonDQBQbGM4i40+UTErwqsJfJoQv7hEOvi17Wd87iDOxowsjVOf2UaEwl3hpJT67gp7XY0SbBvN3LuV2yGBsBx4NQtqbbifBP5kqQ8bJdlGh5SBw18W8JRt/KqzGmVSS2KkPeP8nLF/Ibj4jy+Rnh8xft4b5HUWY5yU106iK6+nbtLD2cyBeOBiGgDhIaN7vdjzhUfGSFn3NLg3R1MKqFa9rXXcFf4Nm9ESD4R4CSyXYiYQ2wgQCEAy48afrJ5b+rLRj7DRq1Inrgz4M9DfCjiZ4TS8jPB3M8ZfHRCPmymW1/py3caUiomCalrMlhaiaONT+dBDBrcCdiujpvJk3CyFGcNrm5iZGW5dF54FLgrSzqq0u/sIioL5ab8lpQDkwSC9uz+doMFhue7NnEsFtOsJD792mJ5bExYYCHdWvT4/A/KgSDszEf0DreNslO13/AkYXkmpC7cUlS9Q4MrXoaQrd6lWsX9/ZAxq76Pc2bm7xV63P8WSpGFMz7vVpKkGC0fF8N0CylJMS4d4FZB2jLhn9LcZ/ato9wKRVY93hP2fRvCUMP76gNwz/p//63//oOef5C//Y3ObfiH93N9UfKypkXe5jh/yGvGX/98H/Vn0v/D/1j6L3hP9VO+Dj/nb2ap/7dp9DGv0f8v/+UF/FgHt8+iX1M9V/f2xp0b+w5/+8V/92j6GNfqXPo/e3vrU0WocAMYFUbbNO2fYht0TsicEVxIRjEew4c3MyH1JvxBKygWAWCICBWirDmvCxqqQSyV22Qjb5pfWCmItaZf6GKAkSLJaAMEBqSuA4YG25EkdvyrGUz1XlMeQKmixeREJxmBZ2SilsYmTlpTEdlxgG5dSJuSg8EzgJUGGYGDjpGUErPzizq8tVYCZJgDW1yfeTJHK3bFUT6mJ9gp1CngzbZAJmgmyWIW3hGJ6KAF5S+VUvk4e7ZpRfnTRyzhVmU4rAOfK4zHjgZLCRbEmqm67FpxLOiYbnIVnASVa263kSIX7spkXQGnT8uX6FwCOmyzRhV0ZRyv3Sk1mPa79rtwddVrO3e7J+vdpR4iXth7qLD3kZiANCukVGgQKIB14ZQPVFBPlbcRshfyW56g/qMwhYkX0zkbjFrwBmLYUzcbQsVSWAiJQ79dzrU1rUKzXrJo0Fc683udpS+dACHDbYylm0JLWa9rU1NTU1NTU1NTU1NT0Yeiv1HPb5ru7tQ2fegJ1DPG2QaSsyDtGGhgcBwTPgAD+lI2RwYT9i2TjHAVo6xaBmwTdyxF5H7A86QAF/GiJlgp1paxrygZEoGQ8D3GE5ak1NPX3edukM618mTSUuu5cTY9tc5wGt3J31GFrzzkXvgd5cFTMf/cMuWP4swBS+S/G+yDFmuABgF1ppepenqGdh/t8wFrBPZfHd4x44csoECGcLTXUvZpAWfDw90eknhDOZsjc/b2tV3enpVIb66gLKdYmI3WKfC3wQ4IP5rhMXQ+kMjIVGf7BDIg0GAA4XoiZPo4Q7oHuDjh/0YE+6+DPguFVgpsNopx2dov4MVkN9cGBmODuEogJuTeIM8gYRZQ6q+wG0H97Ak0RcjGYUeU8QGTA5jlj+GqG9g7Lk6F6GKupYK1RlqYB2b2n4ta6aUtM2bXLHeH1/3QJjjZ6FPeM6QkjD4S0A+YninQUSx8RMP42W7LmYkbnFPM5QIUgXeEUORvjc/cOaW9V3+EMdPeyjuSFuwg3Z0zPB8RDWM2c3JtxNJztvscjnpKN3inCfYZ/WOyeJELeeTP9SpKmMpC6m2SNZcFBd4RYkmqWJhIosSVrDoONfJWWK14y8r5DuuyR9o8q3JuafoQ+5G8m3if9FOv473/97z7oVE1T09+ids+/G72LdfwYvsFuavpb1O79d6N3uY6POX4f23V5q1GzjuEU9gukJltQNpSPxjrc1uS08mccgEXBAmhtoikwWYism9IKBLY/tD53TbFY0oaBxUZQKNdkiz6C/urK0LHnqaNWutWE63ZsCaVm/FGtNYmuFdq5d8Xg0TJ6RKuBVJk0gLFqSBScxRIMVNIfuqWK6liK8nYK6gAVWDKjVImDSuKlI+TOjlFZP7m3x4gvho2v8Q6AnMKHDOfs3N2QLFkTHVQVyls6SbytQ2XWuGlL7ZAonIOZPPW6JNmuG29wW+ltpI1qsooA8bymOkgVYAYcG5eoriFtf0+q0EcV8JTtvGpzlxQYNS9bwogU5THlST22ES3dWrTS3pq50lA4Lk6B8oe7DHaKYYjGhumMMyN4HI2qZlwxiag2ZmGtrX+ccKm/+0bqptwfa6JFHx3b8bq2yvU1b81hnIytVEfGJDByb+kuHjMAhiMtRllhJ4Wa7hFogTjX91BTU1NTU1NTU1NTU1PTh6G3w4QLqHZ6HkBZEc7GFqnjObk33kY/WZ1xPBgfo4JmAWC5dOBsDVKAJVpyx8DTvZ3AuUBoHYFnRUiWakiDgx8z6KSYnna2ESdrCTp+NQGiZgwV1kxtE+IoVgWeClRWFDwz3OJKS1UG1LgySsDwKpVkkAMlRbiP0MDIvVsrkasZ4aYMzqXpxzPCScqIkbXv+M5DOmcjNAK4Yr5QYbpwVrgHgZ8y4t7Wan7S21qrrff0lK1avIwj5cH+zE9lHcVJlxnuckE+BdDM6A8Lro9n3DzskRPj4jCBSHGeOsTZIz8wMhTS2Vp0dwQJingwILGbbdSIE7AcGbgAKNt1C/cGYV6uyz1wF5H3Hnf/XY/uJNh/Pa38nTw4MxNmMwtOv7e2JH8WqxUvvJs82LWIl50ZZXMGL3ZOae8Q9wQKluYavovwtzPSdY+0d2bQRYGbMuAI42CJrHDWdVxuORLGL2xMTxnQMgIWLmdcHCZrdidFKgDlp1cnzNHj5uURWBh879b7F6jGWBljSvW+DgCCNXPdG2dJH41DSeA3qsmppLKkYyxXHvIsYPdiQXg9AcA6niceOH6d4Mds1d29w/nL0gIlZt74uxngYt7sPNLOWEASCOIY5Bi0GOPH97w1mTU1/QD90t9GfCwNR7/0OjY1fSz6uT8LPtZvzD/0z9Smpl9Sv9Tnwvf/m+hD/1z6WF7Hz6W3GjW1PcjGnFCSIgo3Y4XdVuYKPG/jKygV2QrInm2UpCZS1gQMypjJxqRBSVLwXHgkUcrvK0hpbROiXJgnnjaOzXrShelR0hXICmaFLsZLUSLjr3zvXMST5SlKesOObf+fYOkPY7CIpSpcTQZZAw8FRbocbOynJG/cLGvCxxqKsI5jrYmbei7ZAihS16CYQ5wASeV3ARvfASyy4RTaCZwTMCmYxZIoanEQyQyVkjrR8lwlXQOtUORH6Q8FpDZYLVtD0prMoM1k8bNVk1feD+UtESWeAO82dhCXYFF57bzY9ZFHbCF1Bn6uYz+cH7FzvDGM8sD2nPLIRSnXUMs1nK8J8YKQdrImWWQQoM8gAmJ2CC5jZfwqYVwCUmYQG+dGOi2m2PfuUdl4QTWp4uYM5Lqm22t8zFra7ndaR6DcrBBHSMduTYlZy1lpCCv14iu/SK0FipeSVIIZVnauBid+Yy0f1aK71vrU1NTU1NTU1NTU1NT0weitRk3qCS4Cw3dxMxyyGTPLdYf5uTO+y0mhKmu1MyWgu42AKtKut/TE0X6XZ1nrsvPOY7ny8KMg3CeknUPaMbq7Bf5mLm1GBI4BgBizI1qqR3pnSR+BcWRgG2g3Azwm26gGc5ooCroxAaKYnw2QjuBnG72qzVBpx8V0csgDr81NdcyLY01+ZLjF6oM4mmG0HBkSgPGpg1sUw+sMf87ovnmAdh75YMkRN5nzkPtyXmnbxLtJbOyLbBPuT2YUuNlSLxIY0ivSXkGRkO8DaJ/QHSMcC5bkMXQRqoSHcw/JDvnkASFoL2WMqbZUKcI9YXi5GTArMFgVCkJ3a5yaePRm3IwZ0jEeftshnBWX//lk7kupQEcSuNnuj+lZwHIg7F/YOhh/5TGXZ7R746IHHEE6h9zzCvMNJynMFTHz5WmP+YlH7gj9bYYDQ/qybotahfZAWC4JD7+D8WieLGZSJUZ3OePqOOLmbo+Hmz3CLiKEDMcCUcLDdwdAAHcRwUMEXQJx9tCbAIllRE7tGkln94o/GX/Ivx5BcwLcBcQz/L2xZ2rrmHTW6pUHXlufdt8l9H95wPn3lzj9ulvvF17sPg53CwBgftIV5o0ijIrDH06Q4LA86eFPCe52AncOUIfuNoLPEfnYIe89zp8Z3+fqv05w50e93U1N77k+9G9927dETU0ftj629/CH/pna1PQp62NJoHwsr+Pn1luNmv62JEIK96OOGLnZ6q/fSA+UFAlRQdcwACHjxjzi2qx8GzY4r59sNERL0qAmcapJo99LXVSOiLhtBEs6Wjk3yoAWVg5UIYNH7hhu2Zg4lEsqgqzeWelRkmPJBRxsqZYKA1YyLosGXhMWuWMzkpIduLY/paFAlQ/9VhPOZHXWVF9XeVHFAIsXNjYknY3ZoIM9rzdeDcoYz8aOoZU90vmM3ifcTT1i9FBhaOX/FIAuBECuqQ+sDVzGhsGaiFF61L5FJQHEKPBlS+FwKnXo3kF6t46ZISkYUuqnyzXzBfJMAM+WSkpX/XZNi1FW1wmqltYSu4ls7UpCaTEuESWrSgfKeTsg7RhpT0h7gXRiCSxW0JDArIjJQYSN3ZMZmRXBlciOs9fofbb0lRLYKXLQdeTJ2EA21ldH+zgKZN8BQyj3G5D3we73KADRanxVVe5QPnQlLbO93yTY2qdjWI1R4VEevgAAIABJREFUzoowlkpzb+NU/j5akmnwpUVLIB1DQm/Jr0XA5R6PB4/8qBa+qampqampqampqamp6f3WW42aw3+5gfQB05d75B1jvrTUiZ94HcWgXCusBRQN/AqUTWewNqcKyiWxTSXYUiWcBf3LGXkwzgYJEB4yKCuk89vYTVHauzcAxOHeUh7TtYOLinDSwgDp4M8ZbkoYv/SYrhkcbdPb32S4RZB2ZrgsF2Y++VFASeEfFqC0G/mzINxH5N4SH/N12F5rhjVbEdA9VACxbeLnS7Y6cL+zKuuSDDGzBmtlOQCE2dbn9KVDPBbIsAPyTteXCtjrNehvAS4nQBJDhHDoFlz2E/7y6hL5FMC7ZIZZZ0YEOYUmBpIlVtzZRm1yB/gRZoCkjUkkwcwvybye+3ztQQoMrzIoCWTnkQaHeHQIJ0H3agLnDCxA5xhuseryVFrBSIHhxQQl4PZ/2IEE2H8b13vFOD9YTRuDHm8jUOHBTMPwkKBMePhtByXg4usICYTpGWG5Ush1sqTUzHDHhMNhwhI97k8D8sKAEHIqqaXdDMeCcWdJpMNuhghjnAOIFLRPECHksTZI8QqE5ijgOWH81Q65Z/SvIkiA6Zk1kg2vSgqtGpO6gafTwSEPO1uX4hUpAcslI/VkLWpZ4UcBT1jvyXgMCA8R4Q8voNcXWD4/2D17Thi/GLBcMC7+MCPcTAgXlvo6feHfMIOampqampqampqampqa3m+9vZ67D4C3TSNmgR/JDJGHbCyV0qZUq68tkWGPrU02/mRjF+lgT1Wrm0kUKBXavGT4wgJRz+V3GHnn1mphjrq2KVlcZOPLkOra3qS0NeiI5/JctSWopnIKD0TMbLLacEuexOvBeDWLlnQHraNBbha4xZI00tmxgNIoVEeHSMGJVsYMlaSRdoy845Wz4042AqZEgCP4yTg+6WBGSdrZg3kxnkx9rRxp5aS4IeOwWzAljzkfbNSHFDK7N66jigKzQ/+SQdlMGrcA4WTGGkdLIuW+cnksueLGYvRI4dmUtIYyMF+bIeHKOknnLCnkCGnvkAfaUlTlmklJdoQCF7a0Tqk9L8krcY/SNQDcSawafGW+WLzJj1qaq7g0PgGcCJjY3C0CJDKmYrqEkNH3EcxqJkxZm1jYNASFCCNmh+XcQTMBkeFGghttzaQ2UnExIn2BWFd2UhK4uvZaWEc9rfeORoBjrdRmuEXh72RtHeMEONK18am+pzjZiF29qnp1hOwsxZOODlKan2xdFeoZvAhCeSM2o6apqampqampqampqenD0VuNmnzsANioEyVGnwE/JvjXI/Kxt9aeVNqIklglNtVxFoML+9vRWpX6I7RAYVfQblZAxLg1Y0K+7BC78jsBmAqXZPcybWBeLc9FhNzzmlbgZOdW0y9Wyc0bX6aM++SOQEII92YgrSMms42PnL/s4GZd/z4HtgptVWtAyoLxywFpKMaBWhPT2joFA+2agWWbd14y9OixHK0pihdFuFvgTgvi8z1S59HdmWmSB7I67ctoQOCTM4MqmxFBMyEPBhHe7Wf86vIOX91eYTz3ZtQwQCdnbJpuA+r6O4fDV2YCxQvAjUB/UwHAQNzzWgEOBfyY4R4WAzx7hu+4GA4Z6eAxPnPwo2L3wlIxee/LeBzZsToznziZkUNqIGJKguFVKgaOmufmCJwEnNT4QHteDRH/EBFePEAud9aklAQEoL9JkMCIR0svkRiEOtw6SFDITqEzY5EO3WHBxX7Cry7u8Fn/gNfLDlMOeHE6Ima31ppnJSyLA27Der+EB0Z3b/eQNSuZYZMHgptcAfkCbkqgmBE8ryNj1srkwIugfzVv76vf7DBfMi6+FgzfjMj7AOkYfpRtvKvAoykraM5gNWCxdB7zlxfrseZrh/EzxuHPGbvvIpDNNKvJIzeW+7ep6a/ol54b/tA5Cj/n+n3oa9XU9EP0S9znv/Tn4LvUT7l+H9M6NTW9Tb8kW+U//OkfP/j32s+xfh8z/+atRg1gCYLcb5t0cYz588O6+VNn5kDqgm2sz1YrbDU/hPnLo1VpV05G4bVIKNwYYB1xEmcmjhaWCxfYrtULM7qbxeqwfdnIewP6Hv9km9LcO1BW+JOdA5Wqb/GlSSdtvJF0cGuLD0cD/ZIArrfz4qxWJb7j1SCqr4EXRR91HV/yZzOprKaarAWqpEkMJOvhFsHuu8KLYUCDM35Ob+mT5ZKQdlbPDAKQ2BqaejHTRQBNZImaoAAr5tnjm4cjAKDrI7JnSGbETEAmUCRASoJmquNNWkwfA/BWQ6J7ELiXgnh06znI4JF3Njrjp1xGmawKOpy1mBe8pn3suplB42Y148aTjfAsZY1zMW5E4U4LpPfQ676kUgQYN1PEmp4c8JmZfCCU5I4xc6xBqYwNXcEMGg9Ir9B9KmAhQIWQMuNm2mHJzv43ORApPAuEjenjWcC1VWt9AwCQknZZilnTbYkff8qW1oJd09pCVkMs1uSkyIM33lDvQBnYvxD4c17fQ1LbwlJ9P2BlMOXL7o31hYWsACaEUUDfltGwaKYgSUk4MSEPDvpX3+VNTU1NTU1NTU1NTU1N74vevoVj2xzmgcFR4e9LmuLzYKmTk6ymSiopCk5qJonaZvL8uYcSYbjJZTSKoB7IgaCTJSnEGaOkjpDkwTbpnBRIutZw85yMj3I52PM6wJ8E3VevIVd7jL86wJ8zwn0l2doolPiy0Z4E8cjr8YyZY8aBG6MlfXouJo+NPc0XDBdtNCd3th77bxP8mNbRKn+KhavjkHYOemXV1AbsJeSBEE4J/bczlqc7xAtnpo945J4Rd4z5mrZEC8HAvwCoz4ASVAiazMxSp4BTpMXjVvbY72fs+whRQIRxL4y8MGgM4EhwE+DPpUo9K/jBxpzSDpAyCrX/S0T/l3ssXxwRD96u+85jufbGA/r6DHWM5bqHEqG7N+NGOlphxHXkyI3W2jQ+D8iB0L+WYuBlS10pQEsCv7wD7QekY2fJnkXgcwKJGRs8OOSdQ7z0JYGFFXSceyprbMbeci0GBRZAe0G3j0iLg0weooSYHW7HAfdTj4fTAMmEq6szOp8RswMR4J1VnZvzVGf46viRNXAtx3Kd2BJB7mEBxYx86KGebSSKAFKDV7vZ1in3lq5ZLhj9Tcbh66m8x0qjV0crSLtWfVM2I3O5CGbOuNo+Jut4n3/I6F9HaxDLZtQgCRBcMdYKzLmp6T1WS4g0NTX9kvoYv4ltamr6MPUxJ0SafpzezqhxlmLwoxky83Nr6+nuZW0JctG+yfejNfTkjrFcB7hJ4JKiK4wXV6C5eV9SKaUxOA1u23SeM9yYIKGOXNnokPT2GA0OYC4jJYzzc4+wZ7jpAtJb0iLtHfJugJsEPGf0NwnhxPAPZqYA3dowBRTeTE+YP9sDamwQdYR08KCk2L9IjxqQLPESj4y07+DPUuCwwUaslvwGr6eO/FCydE48HCyR4QnxwoMOtnFPPYGXMt4UbaxmeQIzyuIjwAgBGgQUGTR6qFcIAw+JMfepontQXbLhW4ab7XEcbWSHzUWw9qZY+T2WUInPDtZkBSAe3Xrdao24UjHYHCHunbViLca3IW8mgj8Xo07U6rgTFdPH4kccM/jVyQysXz1F7h2kM3PMXp+DlGYsTgpJCqpJrUWQDt5eR0lGxb3xgvy5cImCQhMhjgE6M2hyyJlwmh26fUTXJTif4TzwZD8icMb92BufhgVx8fAnhj8R+husXCTj0hD6O0V/q3CTGhB7dgU+7UvzUkn5ZALEGp+oQJEzjMO0ekDOeEyUBG5iuDkDirUS3X7JxtAkMJbBro2bCv+mJM3ABNmXGI/05Z4T4//I9l5ranqbfon/MGgGzY9TW6+mT0n1s6iN8Lw/auvV9Knp+59DbYTn/dGnsF5vNWoqLNiNGWnvMF85+EnRv0rlG36yKuBzXMc/xt8cEA/OUhVzNpAp2+9Z85FtfLnAe632GWvFN88JJAaq5aUkBEpqR9exKUsoLFeWRujuunUUpaZxOiZ4Bfz9Ap4TUKrEfW+AYi4jVrmM0KQrg7t2t8nAvwPBnwX+PkKKmQA1eGw8WLsTLzb+k/euVIvLChWu1dkQM3+Wg8Ny5HXUKO1KEmkw7glHgBaFPwMpGsBXBUABBFtduQJegckgtyhjMNF5RNlSE9zZhn94qfAjkIdyPWvFN8q411yhzJZaWq7Ceh3TYBDa3TmZgQCssODcEdJgQGLKqzu0jTeVY/AioFwMFC4g5ESg0wj0HeZnwzo+ZiBkA1SLY3C20TXODCTAjQk8GZdGe4MiwwFpKK1jIyBdMY0SmUkz2zpJZmgipCDwPsN7AbPgqhtt9EkYOTGic8iJ0Z8J3R1w/FNG6gnLhV0rccBwkxHuEtLeldEuBpEZUbmvwGOY8Vbr2B8Bt7W0ftX3lxQgMc8ZXNYuHv0GdRZL0ACAeLeya7SM9JWlR9qxmWjl+vZ3j0Df8r1xrqampqampqampqampqb3Vm+HCZfxI3+K4FTadQDwpY32iCcslw4kPXbfzHC308bdYIPHLtfeoMKj8Wf618nSCZ010/hTKiMztklPlz261zP6P0VMv73A/NSv4F6gNBQVA+n4pwwlIF5sLUf+JNj9JRrMmAnxuodydSoMYuyiIPduHZ2p9dwAkHcFQDybkRMvw7rZdVOGW8g2zB5wUwYntdpwEFywBFI4C3JHOH8e4JYyIuaMB+MnAc+61nRzMiNjOWxcEjcThr845J0iXhhIhxcyI6IXaC+Ig4Am+7k7MXBicDQmCxjwM2H3MsPNini0TXzaAUh2DpTsuYzZQ6uJFkoiZv+tAX/TzkF3Hu6cwFPE8b8p8s5j/Cz8swYwXmTjEwHQo18NHxKCP2WAgPz5E4CxVp8v1x7iFcphrXLPxXHwU4Y/FaOic+BiGt3/Nhh42ZkRFS9lHRtTr0BQaJeQL0u6RYHjccLT/YjbccAcPf7xn34HTQwKAs0E+dMOfiL4M+AmtVTYBPS32FJSJT0DtTE9DQz1G4umQqXdnCGeEa8Mspx6gp8Eh28s3jI/6e3eywouDKT5eW+JK1dSPK6adXUkqvBsyqiUBDKjzxO6B0F/k63enLGmoCpsu6nph+rn/Nao6YeprVfTp6x3maz5mL95/SnV1q3pU9dPkaz5/mdae5/9MH0KSZqqv5KoAbQ0OhkLxDb1uWOIsyRDZdR0Nw5e1YCmMxnI1NkG1QwRgZstGVE3m1ZTvbXhpMsBae/RfXsCvbqF/v4Cy5GRBtu49jcWx1E2Lkp3SpCeMV270r4EuEUQXp2RLwekQzA2SE+FG7KN8uiubGizJUp4FmulGshMjFg5IAxKtpnmbMwcTlaNVCuZAawAWCotU7kjpL2lgFwsz58VvCj8lNcaapIyGhR4NXMQFd0dIWbj20CMM0NCyEzQncDtMrIEIBJ4ppVFw4a0gZthDVNRDCarjLQviY8KFRZdq7DVmUGgROAs8PcLkBXpsIfUauwk8A8n8LFHvHCWoDknUOeQUWrcCygYwGrmmVFTLjKRtYmpwk2pQKQBou9xVMjOJZwT+BwhgwcCrwmrtCfk3TbSJX1JHWV7LLGCO6vknucAWRyGkHDsZtyOA3Jm8IsOHAnxeQIyof+OrcFp3tJCbhHwlFczMR2MK8SLruDrujYEG3EisfcBnJmbqSfEo90H4XZBugiIF84Ms7y5KHFvDVZusZYyG7kr7WHV/KKajKK1Uj33QH9fRqQ6Lum0LS21lZE3NTU1NTU1NTU1NTU1ve96q1FDGeu38TxlHL9OQDFrfG018sbdyDvG9JsLuHOCPyVLHTjbdIKMZWIJDmtrAgPLlcf07ArdfUb3coT0DnlgTL86wl/vAAX6WwHJZgwBJdkglgiRYOfDSeEKeDhfDliuOsQLZwkNAP1ttmrpnUfaubVxKveWgqntOGko9eGeDXzMADMAZjNJGAilVUqZgMA4/PG0jlZp7zA/7cFZ0d8YpyXcJ9to57p5BvLRl3EqO7/uJOtIDEegu9XS1MSQAKSDmvny4KDOQbyimwhuAbobhZ8UadhguySK+drBzYz+JsI7govGd5mveD2PdUyrGFnLJQMXbI1aiyANtj7xogOoW9NLVLgnWpuwoiAePdLzAC5pne4uov9OkPfeTCndjA04QrzqAQX23y7Ipcpa2c4lB0uMxL3dotOzDrkj7L9ZwIvg6p8Sck9YLhh8JMwzrYkamgh0ZugXgl9f3+GfvnkG3Aa8GJ/gBV+DFgYlgp/N8Ov/7MEz4fhVYRSxXQNrPNuSVwb4LamsOYOiIO091G98meqJcBRosHvMRYV7bWky/+qEvL/CcmR0twn9izPS5YC8cwgngZ+3kSbpCerKemfF1X86Q3qH5SKAkyKcFJwYKdp9GS/8OvoUHmSFc2vzaZr+Bv2U3xp9jPopvuH5FNatqemH6sckaz6Fb1r/mt5FEqmtY1PTm/pbkjX/0nvwU3h/vcsk0qeUpKl6e+uTVpCqbbLDuTQd9cbVoCRWz/xofMbNBKpQXdQkxaOURa3W5roRL+NVnTf+iKumSTUAyngIaG2+ocdw1JIwqEaDtVR55MHSDJRLa89icGE9+nKeljaoFd3VcLKNLr2B9ajpBQmlejnasfLOQ0HgMQIpQ3sP5GKCFNCutfRkUBZQllLhzDbqVSuYFaBo61RTKC7arj88KNLe0jkcbSwHKMmXaKDY7t6MGiUzdZRt7CV3VK6TwolCz4K0Y8T9o1GybI1G1TCypM9WiV6Nh8oSmi8cOCn6u7yud+XyiCekwVqy7Dop3HkxxpAvY1mww4rjtU3M3Sdg8CtwuK55HakSb0mT1FsSi6Kgu1kgnYOEAAmAmwz0rK4AkjOQEoOh0EzghUCzs7G2VE1IWx832Z/uwZJjKxDY2U0lSlu1eTFskMv/Vg5NPW/aYMH19ZLUkTYBLXFl1ZAANEXgooeymY0q2zGUCfBWge5TgrsdQccBuCzw6qhQp+DahNWV56vvvbwlcZqampqampqampqampo+DL3VqPGjMWCWpxvjhZMi3Ke10ShL2cSXTfv0tAPQwY8CztYupM6SK0rAcmEmRa3MHl5lpD3j9T/s4WaFi9YM5E8J0+c94t426DVlwSXRg8KmyR0gHcC5AIt7wngMZhw5QlikNPQwxIfCACEsVx4cFd3NYumOg/1zOJXxrr6YHKrg2TbZae8KG2RrYlJPOP/d5Wqw1GOmg8f0JEAZcJMzfgxQTCQHjgI3K/xDBJJgeTZAyphWDoTlaM/f31qSBjBgbnevZbzGjBgpJlncG6tFHGF4LVt6iYDx884MmdmMo/5OSuV0qT4vJgFJWScHhPsEnjPCnbUTWVqDEEaryeZZVh7L2gA2MbwH/KSgpFiuA/RJt1a2d9+Zy3T+3QVyb0ZdeBDwaYb4Yqw9YqqQFpMuCfxkxshy6cA7xv5PoyWkDg5uIQyvgdQT5itG2gPLlSL8scN//ep3cA4rtBgEdK8JfkQZTStGzayrmRK+WZAOAQ+/6eAnRbjPcGOEf3lCenpAughIXVgNQiVCGtx6P0CL2WgTclA2aHTaO3T7ARwFu9cZvBRzj2xUqaaNOFoKjZJda3dOVv8+dMiDR9rxWhleR8tU7DC1VSpeOFByxQhsZs271l9z8z+mJMS7+NboU9K7/Naoqanpn+tT+jb1XaglkZp+Lv21e+xjur/a++rH6V0kkT7FdXz76FNhmNSNoR8Lf+MRl8VgvGXmQxQo9dM422Yd5Vt+a7vZTBYlrDwTcdaCRGKMGVrTHbbxfNyYI7Uxhx8ZFY5QmTEp2IZ/rdQuZkJtjAJK4qCkc0C1qUcLX0YAuJKqUDMlKnekgIdXuGtJPeSe19dHuiUaasuSOoJmAvGWtqgpH4oZECnHxQrThb0kGx0DkE/GTeFk/JI60qJUzrPWgpeUEGdARdfEkYBAHmsSg3QbHzNWj25/ly0txUkAEUsAXVoTkVWI69Y+VEaaUEyFN9I5hWFEqlurE5Gxj4rX9c8SH1pG2jxQoypKBo/mTOVal+fNZuQ4sXuNY7n2zpg9HG3N0g7IO12f0y0GC5Zg6SE/mRm2mk4xg8SX9f3ee0LVarfLaNbj8cB6/tv9XtrFlCxoRYB0vvB5SsKsq2Nh2/1UU0Hbc5ZjBqumr/eIFoOHhNbf4TqCV9I4qvjnL6KpqampqampqampqanpvdXbR59gaZnz54zwoNj/4WSb7gLZ1eAKsyODxwReMuKTAbl3CPc24pEO7g2DxJVNvpsEEqw1SoIlUTiaCZF7hoRQ4LqK7tZSHNMThxwMoKreEhEkpdr6TtG9mqDPBiwXfjM8Clekju74s8GEJdhYzcNve7hFcfh6NDhsSTJA3RvVxrJWlSu61xMoZky/voAEazPiZMkH9YzpudWFd/dSqrgdvJak0EOEv5sBthGwfLDf5SygyRqtpLZhZ2ujCg/A/hvBfO0xPmdQAlzc1nR4neHPGf5pQO4sDaQOOP5hAoki7W2saHriLDV0FmRv6Z1wEoSHjDxYk1C4ywb5dYw8eLgxAsDKqvEnWY2rtHOYnzj0txn9i8nq2AmIR4fcM/xoCRXATJuHv78AYEZPmAV+yqCkyJcDpHdwi8KfM8Ldgvlpb/dGR0hktdRultXoGr8Y3qhBl8HGrnJv5tbhT4AEM/P6G4BebiZheDBDSUe7F6//02yjbIO3NMr1APHWmmWLDCzXHZbrDn60tNfSd5agWlNmsRhbxShxbEDt3pXROjM749MBvAi6mxnpELBcd+Bo5mQ8WkKsjhxW1k99/1SIcDXvAGsY41mQDg65Ixy+muBvR0y/vrCf9Vv6q+lfrx/q5r/LlpL3RR/Ta/k51JJITT+HPqVvsJv+dWr3QtNPpR/677uPkTHyMb2Wn0MtifTj9FajRlxtbCr8l8B4A95SpFRTL9Z24x793ZpCqTvskphYR3MYJTmypQjEl4RBbbchAthSFtIB6YDCUSlhgQFwIyEdOwOwlmOuSQfanptSbTviLd3DgAQHYrG00KOUBym28aCS4KiMmVyrke8MLAvAmoeoJFsS1vEp9fToOQDUzXdBwdTXSUntEN0GsLW5ssKTiVi5O9uG3lJOljB5DAh+xEvRyljBej3tZwAvgtw92tATmbFFWBu8OOuW3kmlsckRKJeRH8draqkmiWpqhx7fMnVtakrEEQSPqtKzWsKoJJzE0fa8siVxaiMSV9Oi/A4n48m4WQujR0sVOSBhAwVzUriSeOEpg5MgHWlr4yoGTB3xW38eBe68wO18ue9LuiowhAg8p5KWKhXxZCaN8GPDzxJW6ip3iUryRUsLGNY0z/p6CZCOHrFpHvGBys8AMwORzDjkxfhG2ryapqampqampqampqamD0ZvNWrihQep4vi11Sg//H4PPxqDpY6IyD4gFQgsh9J+MyWkYwd1BDcK3ISV62KNTXWXbn/CWeBLwiaHbUQmDcayEWePTTtC2gHn35gp4h8I6aDof/eA19/tkQ493AT4Ubc0ggPSjtfETh2DOn9m6YnDnyPUE27+zYAwKg5fz5BuA90ysIKJu7sMlwTjr3ZIA2N6aqMvl//vLShl3P/bL4yBcpvWkajaEpQODvN1v06hDC8jfGGPQBTxqgMYCA8GbF4urHVKAkF2jHhghJPg6r/Y+UkwOG81N6RzZeO/JXmmz7p1HIuj4vDnaOM3fqvzDvcJ/n62ETdyyDtGOrjy/MBBADdn9C/jCgH25wj/X7+BuziA50toYMTLYOvUVwh0MZQUCLcLOGaEzkDK6WBA53gIa7qKROFGS8zkfWeNWLD0lHhL/rjFjCg7vpTH5rVNys0GMnaTJVxyb4kSLYBk4xRhTfr40RI9ICAdAsbnvrCNbBxq9zLBjbkAke047rt74PUthpsDtO8Qv7hEPHo8/MaSUftvE/xDRPfHl4BzkC+uDFRdrhdnSyLp0VuV/H3C/CQgDYT+1gyW+draucyYU/hTgnSM5TLALYruNkI6RtpzGS9z6O4j3MMC6Tzy5xfwpwh/ijj9br+OizX97fpbXf1//+t/19ISn7ja9W/6KfRD76uP8Rvspqam90M/9vPlcZqifSZ92mrX/4fpr8CEZQUI544BYqjX0shk40iUda2qllplrFg3yJzMiFjri4G18clSAfYzygCXFIp0xqdRJht92W+/V1uNwJYw0K4c1CniAYACfiwJBEcgV4776DyVN5YHZV15NvoofcKpJCBcYedEtRRNYNSGKTcZC0Yud2a4lMdaC5Kurx2P9smVJyMdI6uHGy2B4adcgMTGIdnaqnQd4eLyGqQ0IdUUhR24JIMc4KU+1pJHubM5sJqSeXw+0vE6fmVpoa3FCWxwXM2W9IEALmXj6nhvveVkKZRqHpnB8IiVsjJ3bFxOApfRrEdJj8JzkfpCCtfITaVaWjdOUL1+VHg+aee2tQYsraRlhE0UbhEb6+JimmUboarwZKhCA5d0EEBkDWP1+BZvopUfA+dAXQc4t/GNdEs51eurfQc4XnlDSmVMKoolakpaLe0d0mDjalYVX0DPxVzjRUtKx8FP7g1ItL1X7LEcHSiF9R62c3+zkaqpqampqampqampqanp/ddbjZrdH++Mo3LsAfVI+5Js2Tu4icBJ4KYENyriVY9YkhJ19AVE6whG/8d7QBUyBEjvkfalgSkQ1DAoCHcR7hQRnwyl0YkQj8D5NxkaFN0LZ+BUIahTpCcJIMV4MwCJsFwLOFl1uHQwIO3JdqrhnOBOEfOzARIY/Y2s4zSkBSi72MadlwyOgnSw1+xPxoBZrgLy0cNNim7K2H+ToES4+YcLKBH2L5KNThWToCYhcm+10P4scIuAo2B8HjA+87j4SuHvZnSvToB3uPuHJzZO9ZDfSFMokQFoVZEHh/nKWRJpFAiw1nHnYoBRAoaXI6RzuP+7AdER5kszgFzUdfwsHgNyCOhvBeFzd2pyAAAgAElEQVSUCsyWQPdbVbiSMYd4yeDTbObMs0vkQ4d4YVXoaaDNlFlH2wpseechvWK58silkr2uOaiagI/MOW9tYP3LGWFwyB2vo1LxyGV8ycbJzs+NX7N7mdd0kTpCvArgWeBmQbywhNDwOhtPKAtQ28oC1hGl7j5vkF6YSZV7B5TEkpmPDHcx4P9n792WJLnOK83v3wf3OOSpCiiAAEFJ7LaRzcXYzLzAPHnfzDzA3LWNjUwtqVsiiWNV5SkO7vvwz8W/3SNBjYoSCYqAuJcZjMiqzAgPD88kfOVa36rRg2tz4bmyfWvPHQ52MafPbi6Ps6w5zZXwNBuseM48//Urnj/zZoY5MzddcmzfFdykhJPxgsJ3T6gT9oct+Wrg/GbEz5YIOl1FTq8daRfxU2D3dSI+2WpVbef1e7Djrn+TfgjH/z8ir6arq+tPo9/350j/DXZXV9cPpT80qfcybdx/LnV1/cv6oFGjImtqwDguljzxZ2N9lE1Y2Shg6ZSQzEyog0OdthtjJV+P9jn6YrGnygsGi1oiJzqm14H5ypGuIV0p3CRCrNTHLVSl3iZcqHivKFCzQ1UsZRMgtzXxhdUiisGOjzPSpsaXSWn7PCUeLGVRR6sQ1SBrusTSIC84N8WYMKUliPzUYjRtcUla6iFfvaD1KGtFp0aHKxAmY/pQQXcjGv33uD3rAlGbz1YP6ToaELdBfVWEum0MnQXI3CpedbDzaTUkAzW/PB5ofJtix1aGtirU1qGkcnndLOfBgxdbK2r8IjcrgYvR4mfFT7qmOdzcklnJjtcnXfkrS9Jkub4sFWSgZ0tuvYRBX96DsjHDZoFTrwteypouAtdSJW1+/MV7vl4DAiW09E2DFZfRX5IoVVf+jZ+rmTf7ePn+WK7p9rhlY99SdTCek08VVfv6Gu1r3eRw7ZjCCepgy2duNpbOsvxk6RtFY0BjIF+P1MHhZ0tV5Tt7ruFJ27XVElKbQN7ZtRQO9fuMoK6urq6urq6urq6urq4ftT5s1Ize2BzNXJivhHiEMdX1Yze3G1zFFnvuz8iUyK/3qBPiVw8QPPf/60cgsHlnXJblptctgNtibI75ynF6I6QbJd1U6qby+af3bELm7x9+Bgp/+fO3jD5zypG5eA7TwFFG9OgtuXEnhAOE4zIZrfinM3L/hPv0qq1M1RVY7FJl+zhTR8/0KlIGR9oJ4ayEU13TDohVcPxUcaVy/HSketh/Ndt60egMQjsZo+f0JhDOyvDYkhpIY644wqkyTBV/SkgpTD+7Im8dZXhhMFUzcsTbUlXeOeYrx+a+sPtqIl1ZmuV86ykjXP86Ew6XVMh8N6C+LQOlC7cl3fjViPGnQjgX0j4Ym2bvqBGGp9pMA6syLWaVXg1tYtuBM0ZMaNDgsm1rT4diC1AtSeKfZ0TNOLKKkD1W2jVjBUvRlPGSxko7h+SwsmjS3jhB8ai4qkw3ZoJt3lulrg72GOu12+bbadPe8djqTmpmER7cZHwbq5opw7cHS9d8vFuNmgVwvaSs0s3AfBOsktSqaFRL39Qg5BtjH7m0QJcLItpSXo7jTUtCHQNuquy/zszXVhsLZzOTcjOhJBXIlXqzJe8jh88G/Kxs3iZObyLPP/fsv6rc/OOZ8+uB+apxa0JkvrGU0N3fPFsKqqurq6urq6urq6urq+snoQ/DhG83xstIVtmJh8arWUyLpOuNbNk6yuBwc8HXZTlJKK/2aLQak6hSveBa0sBPQjzKutwTTpYSKRtLA+S9xWHeP+8YY8bfzjhX2YTELsy82T5TVTiXwP8Ir3h3iCQn5K0wvnVIEYZng9SW2y2yGcgbTw0G0pWqlrhwYlWR6NaUxFINWl7jspJE4+/UKvj5+wtX6xT1NhiUd5Q1zaJLQqcZAJZOcnC7wZVqC0vZkijrIpITM8sEwrmsC0GuJVKMUVPtHGbBTbZaVUd7jVYFal+rND6MI28cflbc3BIbIiu3pUTrL4Wzvfa8tXMyvRqa4ZFt+eh5pu4i8+toaZ2sLYXU+ETucs5cqw9ZDaexcwK4NjHuiiLalqVaosY3I0SDoy7JnALDY7YZ7HZ+/KlYPW+0OXU/WdqkDPYY4VTWdFQZ3ffmqv3gWjrKkjZlP6ChfY5iZqKDvA/r2lXZXODEFVuXoigEY9aEY1ln3a32V6iAircqWDuOdf1sroSzUGqbDw9CmGwZLF8Ndo2KvXdhaqkkb8tWw6NdB9OrSA2WLtLGbpK2ZJ6vBtzw8irt6urq6urq6urq6urq+jHrg0bN+aOAT8r2qzP+pGze22/+095u9MOhrDfj041nvhbCMTZOi1WRTp9t2w0yK0SVIpZmSMZrsYSGGKNmKlS/Q6pjem3Gxun9llOo/NUX33E9TGx84jpM/O/X/8ToEhuZ+b+G/5n/836PD5U4ZM5ybStAXynDY2J6PTbGCN8D/gKUwTG3hIboUoOxitdivixwWkQoW7vZj4/ZoLkLz/iUqNEzvx6YrzxlK9SpTU5Ht1bH7DmFvHHI3oMq8bmlXg51hRWrl7VeZmyTsJoiGpop1lIYNTpbMSqVfBWtOtPMl/j+hA6B6aNNS+UIwxMM93U1f/wp4+ZCGXaIWirGzQX1kbxxHD+xNMz+a4g54d89ovGO02uPT0o4X6pOurVz5CcDTpdq62E1tITNXNs89aU+5JK9/qX24yczCPPuwqiRpAzfHZHjhMzXa4VOB0e69o0RUygbS/bE58z41TP5bst8E5nuPHkr6xrVshK1+WZCFNLdaKmYrWumGdTRka48Lyfb7c0X4wxnq6eVXashvZ+QUlHvzNw5Z/t8Fy2Z5kFHoQbP8JAJkxlwkh3n154ahfHevi+mV/ECZ05qa2Ji16ufKrtvlPnGcfjUMzwp4dTqVbFV2ipMryMQ/+0/Gf7M9cfoTHdWTVdX1++rH+LnRmdCdHV1/SH6IX+G/PZ/E/WfS11d/1wfNGqGRzNi8t7WZBauyFIn8alCsQTI+OhwxbdFI7+yXvy54ifFT0uXxG406+uxLTFpY5VAuo1MceD82jHdCmVTkaESxkwcMr+4es9NmDiUgdFnomQGyWxc4i4e2V+f8aI4VznuCulGOH7sUDeu7BYyK8ekLumJxnxZ1cyaOjimV4FwMHAr1GYONDZJvTyWeqFGjw6uVaBgvK+EsyWQlgRMHazeYxPi1RIrntXAGe5n1DtOnwyWWsoON+l6XjUIpRke8VlxU8EvNS4BvCOcMiU6jp9GXIHrs0GPbV2qsrm3laK884RjaSZNRpLVe1xujBht8OHGnFnSUzU66t0VGj2b+7ImWKTV2Nysl1Wluqw/2d/T6lDLKtfC5AknJT5lytaT9p68M0NwWXtaDEENDsZhvb6W92v79Wz/6q3WNjzan+e7LWVjqaR4rITJzBKA8cHmufP+8m0glfZa7bjdXIlPkHfOTJ5qn5NGUOco42iz4S29VAePVLfWvmQMjddkr394NjOlBmkJJ0+68mutS+rFvMyjtDSVJXvOr0MzEvne8YajvZ/rglr7HFEaDPpD3+VdXV1dXV1dXV1dXV1dPyZ90KgZ303UwXP+2PaSXVaWGWupiszVaii1Eh/NlAGoo7eKiAjDQ0JKNa5Ne6wahTxalWO4z3bzLsJ045luhfm2TXJvCmEoXF+duNlM/PX+G3Zu5u9PbxhdwokSpbB3Ex/FA2+uDlQVVIX7fSbdeM4fOWrw7L8uxKmu5oE2cyVvzQwY39taz2IouFQ4v9lwvnXsJ6uwSHHrrDIYgBfaqk8zEUp0q2mxeVfX2oq0iXAwcyIcM+F5Rt9sVrOIqoT3R3SMpF9uLE2RIYaKn7yBYge3clzCQZA5I8FeA15QwB8zbAKnT8xo2X11gT77ooRjIW/NIAjHgj9MloIqdTW09MWikVMhnGWtZNUgpFdbRJXx7Uy6jZx2Ad/OiUuVcMh2PHJxCUSBsoCE5VKVGoRwhvA0oX6DXl8mq8fHSjjWdb1Io3GTyuhWLkw4VeI/3sMQmT+5ws0Vf8yUbWC+i2viKT4XY8BszbgZ3p6Qohz+6orqhXioDRp8MZgM+Juo40DeGJjZZSXthbyxBSuXYPe24KrBqC8z9PbeL3BqP1f8qZL3ntIWz8rGrfydq98kwnNCvUMHabPqrEtW051rVTa9cIyKEpO+AChfTBrU4Np6aXt1dXV1/WD6fX4D+l/q7/6crq6urq6urq6fon7I5PwHjZrjz7frv6tYwkGqGkOjLQLN+4HamBsoxOeMS4UaBgjNpPBi5s1gN6VgBoTUdiMb7KbcJ2X7VpnvPOm64q8T292Ed0oqnv/69Dlbnxhchhr529On3IYTvxwhqWcbElMJTDkQYmHeFhAzMfLW6iZhahWjhwRemG88OTrS55aMGB4yTqA6vyYgwqHgTpm6DYC7QGiX1adzsYTIYJPNw+NlXkkKuGomiTT4bAiO80eB8lkkHq1eNdyboTV/ek0NwtgeQ7JVhcIxkVykBofLlrKog2P+aGc3/qPNd0tV5ptIDcLmO4PnTq/imvhYtMB307UH9oTnGTdZ/Sk0c2nhCUlRwtHSROnK4bIQnwrUS6rIZTNgShSCE/BC9d9n1UjVteZmvaGWAlKDBz//8ho/V6sEnVxLH5mZsaR1zm9stWupsPlzAy1/ckMdPPNdWM9ZGSwFYzweMztULuDf8yc7XFHiU2l/7izpMghuUob7eZ3g9qfK7ls7zjIK8aAMj41DUyA8F6SyMnDWylzj2UgDLlvapxKfpIGIFfVjgydbjS48nFDvia8DNbZrdBTma6tt1XA572V0lM3lfY0HxSXW17qe464fjXoFquunqh5N/4+hXjXo+o+m/v+nP131ClTXT12//fPnh/wl1ocZNXfGJRmfikFVR8HPBv1FjZOS9575yhHOrR6TK+6UcPtIWWaWmyFTR6tzuAJxtqUo3MJraTe/h4KKp+4quzGxH2dUhVwdv3m+ZQyZv7p6RwIep2umGvgs3gOw8YmqwkTA+4psCuqi1XxGQSKAI2BJIC2W5CmDtEUrJT4LFZA2PR2PFX/OuCmh0Sos7pyRatBeYJ3QXjgl8ZCp3lE2rs1Sm5kgpeKSoFJ4/mzL9Fq4/sdCPCj+eQJVTj/bUL0wPGSrEZVqy09TQXbxksw4mxFRt4752hIZgzPjYL4ys2Dz3t63+doh1QDB0GpHzaxYjAVXGjC3WJ2qRlt1WkwWP5kxNG0cLgnhuVgFzpm54Eqb5/b2nkKb324T51LMqLAZaTuPi5EA9v6kvbB5B7v3MzqZgTffBIMfA4gy7y2hEk4Vny6T2vPdQB1awiXTZrbtvbVZ8sbQaemWGqDceFxRdr85g9JWsoQS29T5OaOjR0ePnyrhWKhxIO2F4UnXBA7V1p1gMb7AzxioevkOW1a8SsUdM+6cV+5N2QUzc6oZa3KcEBHCaU9ysqZ38rYtcCFrvWm+hnSj63OoE8IJysbeC8nfr0p1dXV1dXV1dXV1dXV1/bj1QaNm93Ve/92JNlZGu7kfIe+t5hGPlkTxp0zeB+ZXQ6vyQHhKBgT+2QYV2H+V11nsOghzg8DGg67piM1bRX3gyJ7pJuJEcb7y8c0Bh/KUR/Z+5q/33/DF8I7/Y/v3/N/+5/zN86dUn6hR8H4LKuSdMr22lSeXLCGDwvEzq+7E50I4Ci6ZuzDdhXXSezEz4rNHnaMOnrLxzbxQpjszTuLozaBKlerFEiyZdmNvZkM4FMKhNpaNIx6Xas1SnTIjY6kKSQUE0j5YlWVnrJNwtvPkZnsssrFwWGDHCmNLrpTREhj7L1uty1+iFWlv9aLFTEn7QBk9062lc1xqnJ5oX1Pb1w5PlkjyUwXHZTGqKOnKk3ZWd7IkjMFxh0ebZC8bbwmbNgpmFS6hRvCTwY3D0Raq/CkhuVLGPWWwRIwUxW1A1ea2qTDf2vlxyda9wnMxEPA5k64i0qa0RWm1rmrAX4Xp9diWuASNwvTKjs9PzfgIi4nVWEzVkkWjawtVfkn0VNJNNKOogaaXepw6Y/zE50yNjun1iNTB2DKnbOms9rjL+tjhi08sQXMjq9FVNpD3jYPjzQCqg1Kj/e+iuULZSjPFQMeeqOnq6vr91H+z2dXV9WNUT9B0dXX9qfRDJGj+tfqgUROfUls58lB1rXbUjd2Iq4MwmXHgTxl/mJhfDaQrSyC42VgviKxz0cNjsorMxlOjVTrCua3+tJvheFDKgzDfeoqLlFhx8RILOOfI1ic+Dk98Ht7zF2HLr8sDo89kdQy+IA2oUiPkDbSN5PUx0l6QKuwezUjSYJDjtLXpY/8idVKjg+CsGhMdtS095Y2jRhB1uFnwTwlxxt/xorhSqUOwqeip3cA3eK5vk+TSeDm2krVAbG3KXEVWo0SiJTVc0lYDasybIPjJajfqgAatVWeVGadKeDLQrlW37FwsS0oLILoO0gwbR95CPGBmTfMAjLtjnBc7hgrVDA5XKsxttp3LOdYlYdMSNQuTZjVClrdEbKI7HgpubqZTrshpxqUtUq365MqF82PT5JBGbxPyczOQzlbfcidjvZSNv4CBS8Wlgn86Q8r4fVxBvzU48sZMkTV5tDB2XjBf3FwJJ1k5NAv7p2xsjt1m6KHUlpBp/7uklPLOrUkalbDuMckyTR4dh088ZWvXLoCboQz2sXgApQ5Qts0sczZvvhg6NUA4CxQog64Jp66urq6urq6urq6urq4fvz5o1Cxa5p/nF3yZcCrEp2TJAYfVgabM+G4iHAN5a6s850+3qNiNtajxU+xB7EZY1EyAMnjGRxjmagmEg7L/lVC+ixy/KNSrTK6OrI5zCbyfdvzXwxe8y1d4+W+8LVf8T7tv+NvjJ/z6+dZe3CaRd4FSrK4jCufXDjfD8GzVmRrNiDnf+ZW14ueW2Ggqg+P4xdX68QKjXealVRQNkHYBlypXvzrbzf9g5yucFRrTxs6fAX39LKvxMn+0w5XK1f/znYGVf3FnfJ9iJlZ8trTSdOtBfGOfNMMsOKvfTMVgvY21os7SMccvdus6lZuVeMi4rAzPdV3c8mclnDLhWIw9dG2vcfflhKRC3QYzLRpTBhHj2bw/Um42zHcjeetabagSHqcGszWDTr2sJhTQFqjM/VjMDHsvPHoV8FcRP41oW+Ryc7UE1FHWBSWD9AIIsrPjrT4wPFU2X5bVl3O5GTlTQVIhv9qh0Wp7NUozT2D/dV7NMXXCfNuqUKNb36f4nBkeZtxhgpRJn95YmibIBdqr4BuUORwNYIzIaoq5zDoRrk44fRzJW2dVKm08G7WqkwZLzaBWpyqjMn+WoQiSHBoUYkWeAv4k5KuKXlXcdx6fBX/ucZrfR70r3fXnrH7d//jU2VZdf87q1/2PQ50n0/XnrD/Fdf9ho+bF/DE0Y6VNavup4I4JHT012KoRC7+lLetogOpcW4myx6hjY5bUy3qUVWWwe/j2uS7D8KTUE0yvHXlwpOJIxa/LTu/mHU4q/zR8xKyBnZ8oKhyngVJagkV0OXx7/mhOg8us60YajK9iTBj7RwrQlpoM1uvWyWlVWYMj6/KTWFqEIrgp2wLUuMBasBv1ZlasaZhlslqxlawsxPsnxDtU7i7rWuWyVGUJH5Aqq3FiBkFLflS1mo6/VG/yKP8M6GspFDOYFjNFsuKT1d1sNlxwx4Sbkk09R6t/gQF5mYE5QR0po6zvs0uKpII/2yKVpYUuz48qUqUlZCylYu+DnUOrDbXrpgGYl1TM8r6sb2rzfmq02e0yGJRaWwLqe+9Te4w6esro23R1g/1W8E/GmcmL6TM4W2YalhRUuyamhBzPMM3ws1s7bmmz2O01+slqfLaKRkugvVzAulw3ZWj8mWyppSV1pV6NpbOpSBHjG3nw20KZnTGWnCJeW2oJO8+hosFfGDUvvwG6urq6urq6urq6urq6ftT68OrTJ4MZJo8ZPxW23xQkG1y3Rk+5Gcgbm+JO19H4H/cTMiXOr/fknXD1q4xU5fnzeKlKzRCfCqEqUh3xkBneT5RdIG+MnZI3sPvO5oylBubbyP1uz7SfUQXvK4c08G7acSoD0RWCFP7u4WPuv7pGzh4/CdsnafwTe15oS0xtYae2Ss/1r6zmVaPdDKcr1wwpMzRcLqSdo26c/VlRxge7sV/rSVUpg+Ppl3t8UuJjoXqD+fqk+LOs6z95ZyaAFLfyU1CoX3yynn/XTIp0FXj85bAaXPYa7LiX2k31oC6sSRN1QmgVo2U2XZ2lXfxU0CQGOG5GUNl65ttos9e+naOkEBzVRc5vNvb1jd2TrhzDQ2B3nCm7SNo5Nu8L1/9jQiY7L/7hhC8VvEO9R2Vn5s9c29KXHW98StTBk3cGr5bazJnywmEQLmkXb3Ugycru60TZOA4/s0vZJcij4/TZnjosJohDqic++QsD51zwZ3voGqR9bUVKJT6cKVcDzz8f12qWn8CfCmXjKZs98mbf3nsHFcZ7M7g0CG6qbP/uO3QzcPrLO9QvZhjE50rZCPPeEUVXL1QqHN9YlW6+MwPNTULIEI6eGpV0U8FDPQTwiuwzWszg000ltdchkyfdFNLNv/4HQdf/v/pvjbr+nNSv8x+/erKm689J/Tr/caona7r+nPSnvM4/aNSoF7SlF5bEhZsL7jjDzkwadS3V4YUqLcVQrJpTLXyxJg3WZI4FJl4kOyruOFtaQ16kDppREY+WLKiHyAkQB84XojdD4GHYGHAY5fk8WqplNoCw5PbPC3PGlQt4d01clMbyaKyP5e8W00IaE6V6wTlFqxi3Ry+HvHBiapCWzKkU3Pox6FoVU/dbSZhij1V3sZ2f9vjZnjdvpEFyl8TRAklZODMtsfHiNS3vmz/XtoLk1roN7rIyZC/MvrAGM578rGgF2rT4WvXSC3dmvUbaBDWquFNqCRoHbjnGZiA0k0pKBVwzZb5/HMvHa2JmCXW11NVaMXKCtEUsDS+YMVXX9I19fnvvkWYOOXwyOPAit85ULeeimUL+YsAt13GNZmQt55clRaOs14NP1ZJG3lJBLytjLlVq9GvF6eX3Rtk2xszQuDPpYsgRrAaFA2pL0jhFlzqZUzSCZLueGLDzL/RITVdXV1dXV1dXV1dX109IH4YJH2qDuFoiow4Od0zwzVvcq1vcGBjmy9/V6Jhe2ZKOn9TSDTurDF3/k8VZ8tZTB2G69VY3mdW+dmtgV5ufthvYvHXkTeOeZLj+20AZAtNrJW8rB2c1pGMeeH/e8t3DFbU43HVCZ4ccnN30BnDJbrq3723aOpyK3YwPluA5vhmQAsOhEo6VzXcz821kuvWMD4XwnPE7Rw2WWCkOu+EuyuatLRRpcLiWOvFTIdyfqWFnCZCkDE+J+TqSdw4/V8JpMQCU+GD1oqktZrmkLVFTCEfHeO/xyZa3hodMeJyYX21IN6E93wUy7GarSZUxWCLq7REdAunnW/LWUV8FwrkSnwrzdSDtHbuvEtvfHJCf7UlXjvnKjJTrt0/o4cSwjZRtYHoVCKfK7d/dtyvINVNObKr94x0uW+1nvrOEjp/qWnFyqeKmDCJEbwZGuoprCghVBDOVFjOPVoECm9xWBzy1i9RbMmj/TV4Xs+JzZvj6ifMvbjnfRsbHyvBUyVtH2jl2X822CnVr12o42FS2ekfZek6fbhqnpvGEzmYk5Z1fq1nj+2QrZ7toy16N6TO+zwZr/vwj1AvxOa9m1mp2JiWcHOGUkbmSdxvKIJw/Usptxj17pAh5bw6OVZ4WJ0ohNtMwefvYLyajQvL2+dnMuDrW75mfXb+f+m+Nurq6fkz67Z9FPXnQ1dX1761/KeHX/1upq+uH0YcZNUtiINuNv3pbP5LNBo3hBUOkUMaxwWBlTdlASxu0is73kx6sfJbqhbKPlMHSCi43uOzKLGkHe1DcLNRByNUxnwxMfNgOzDlQi6NWQYtrYFljobxMx9QgyCDU3B70xT3smvp5yT+BBgb2lgBa6kdLAkRfpGGW173wR6JHF9MlKfJbrJU1FQMssNnFIJCqxuLxDUg82WT4snak0RuHRRamjhk7LEwbJ2tNShsnyKULD0gquLkg1a8pFZVmlLTEjlTQGJBxuDBV3CWRQ5ss1zZXvcCDXX5xDtprkiWthMOLGJ9HlbosM6mu56P6l9DiBuBNrMwgVC4pomZChENZAc72+h3STJZwNuOsBll5OepkfS8W6LO2dbLlenO5sYzKi4SOf3Gu1iSQrMkmsJRR2X3/W2tN+mgzPmcs+eLkMkbmzHSp23q5bitoS2oZlwgoL67Ttha2qgpSBLwloqTI967xrq6urq6urq6urq6urh+3fkf1yQwFf5htSWjwzLcDvPpsBbPGR1vAyT/bcXzjiae2YrS3G8TttwmfKnkbVraJS8ru6xl1YpPYV47DZxv8ZItLw1MlnDLn1wN5K9TBbpDHxwqqhLOQt8IpjczXA18CIVSu9mceHnbIuwgO0o0Sny9pGnVw+Jl1jjbvKy7ZOo9Pyu7b0tgobdr6zkyg8bGQ9p7za29Jn6K2QpRtpchumO1r8t6vtaCyccwt7bL7NuNPBTdlpMT1efy5rOyVdB1sgWizVMl8W0JySNWVgQIw3UXS58NqIiwJHkm1mU12PH4yw2r6dIebK+PbibIN8CoSnzLxqwc0vmK+du3Yty0NVRkezFQ6//K1PWk7FpvwFp7/8629jmzmz/hg89guK/6YkVNiUIiDI+3ttU13YeXPoEqJLcHy2jM+VXa/OZGuIukuEJ8r4ZApWzOS4lNCUsHlYT2O0mDA4VQYfvUexoH5kz1555k+uiU+Fu7+36f1vPmDTXbn60jaBTbfngB4+OsrShSGZ3tfh4cM7jKNbteOrEZfDVC2DtG4LlD5szaosYN44d4s58gfLUW0QGlcqZStpww2C14juFkoyfHmL94z+MKvv3wFk4dkppucLjvbspg4wXg22lI1fhJLsgVsje3kevPpB1T/rTAR3x8AACAASURBVFFXV9ePUZ1d09XV9afSv5Tw6/+N1NX1h+mDRs0yIVw3wWoho2sJB8HPFX/MqHfU/Yhoq4jU1saYYVnOwS0rNm1dqVz4NH6q1FFe8EpaCiO4S7IhXRI2qIFdUSUc7ONp3DJ5hWAzxfG5mTvBEjh+bqmWjE0dY8aPFHv+JbkhpaWHBjOQXNYLcLfVtNy83JBfWDbG1mnJmordQHtpNR2x40i2/uOyrpPbi2EBUDZWefJJLwmLhTfT2CbAmiCxyk4hPpVL8ic6qizn+reSFMLKvHFzS4jsRlAIp5ZmWSoyylpfqq22Q3CNa9O4N3P951wYMQaMXEXc4G0NaUkZyYuUTptlpzF1linrpd5UvbR1K3dZSlJdn0eqrn+uoZkjvk2fn63SVjbtNTsHtS0vbTx1cC++1mbNl5RSjS8SU1WtTeTsWKQokiFoXQ2cMrg1CeUWk4zGDFr4NkvaKVdqcJTo27pYXc2fMti1gihShCkFVMUYNM74TC9TXCuXSICgqFckCTJLYzK19ShRY9d0o6arq6urq6urq6urq+snow8aNb7NJp8/NmZH3toNoJ+V+FyJ3zyRPr3h8NmG+Ji5/se0ckm27ywBMt1Z/Wd8rKzQ4WCJk3AqxPdnXBrW1SOwtMJ841ejZngyzsbzZ/YH178uxCOgVpWqX794Gc1IyHtLp8RnMyLi0YwaWOo77bHfGztnvhuslpKqrVjthXiEMFfCqeInYbhP+HPm+S93pK2tHElRplcBqbD5bjZQrkLZBdI+UkabXpYK8d0JtzzeKeNOGXew6aF0/Qopwvi0zGOb8bAAlV1jAQG4bH+3/c0J/w9fUb/4hHw3Mt8EyiCkvb248aHi54pkS8fUwWjAw1OiRMfpF9eEY2H35YmyCa0C1AyGZiTFYgbD9NHQUjp2/OM3R+P7XA24BvWdXw2c7zz6cUCdvW9+1jVdMt7nC5i4VYf8XNm8rWaY1WoG12AVqTrI98HCSwWpmVfqhBItVZM/vsKdE+G7J/x+g7otKjDfDoSWZjp/PDBfO0v/zJV0FRG11FcdHIefGch5XOpjs1I2nroNDI+FeH9Go83Rp9tIurJrVBR2vznjpkzZRjsve1kNLX8u+Mcz+mpHugn2Ws+XGlm6Eubbdk0WePxujwRFQoVNMT+wNsPKKTJUXKyMY0JEcU55/vKKeO9sWaw2A0uFsq0Xvk3XD67f57dG/TfeXV1dfyz132B3dXX9qfX7JPz6z66urn+uDxo1LlVjmojDFyVYU2SdLK43W6vmLEkS1baWA9ISJ76BbRf2h8tqC0jabtZDc0wql9qOLEyOy6KOyvJYF+hsODewb7uZt0UjS+74k004h7M2vok9hsuNeWKDUZSNnYIlAUE7xuG5IhlLmVQD/tpqkL8kXposhWH8EfWuQWeFeKyNK+JaCsQ1aG5L4QCiI1QzKmzVqYAq/pTR6EhXbXb6nKhDoI5WiQqnSh08/vWtJZ5kSaG8XNvSFykefbHeJGgwUDM0w2PjLimiqpRNQL3NVYvanPqymrQye8LCAnKIMxNvfKykvSOPsqZv8tbZWlbyuGSVJgBisMdqSZY6tLnuU12/1k1mVGl0FBfXGtWSFnJ5Ye80bs9+Q9lFS9lg12lRLN3j5WL6iJAaANifa5suX5JFSyJJqKNri1seKWNj5Fgy6SXA2R+tmrWwg+z7pLF0glB3g6V5hPae1HbNVcLJptrrAKijOKs0yVVdUzUoUAQ8hNHOX5oD0r5BRAUN9nqX7xdjSL14zV1dXV1dXV1dXV1dXV0/en04UXOyJR2XHG7KhK/v0c1I/mhP2QaOP9/hZiU+l7WaU6MlImq0G/fN2wTA9NrSCr5BcRdTpWwCGmRleUhVEo1LcrREhiUsIB6szmGTy8JwqGYAZFnhxHnjSHsYnpR4rGtqoYZm4Mx6qRQB852dgurBJ6hnhz8V4v1E3QbSLrT5aMgbDzubEHcLHFnAzfricxzPn0eGg7L/pxNx4wn7gD9X6iY0QLKQrgKiHr8NuFwJTxOSrQolqcC37+Duhul/eYObwD2e4HbH/MrWqcb3mXQdmV+9xs1mppRByKPdsEsFya1iNRWrMqVCHbylZ1pCKm88ot7es2Dnzc8Lf8czPGTcXBi/fkaj5/jFFeohX1lVqwabva7RlpDCl2dOn1/BrW9pHmW6tedS5xkOwvjdqRklkToIaecIUVbDb/NdaitiwvCYcMfE/GZL3jnOt36tArkM2++y1bCAOnrSzWDX4LhMbrP++2Um3a6F00eOMgjx4PDJ2EhSLnUvDcJ85ZlvzKBLe8fwVAiHQjgW0Ex4npE5IwdzMWUIOH9hyeDsa/P2YjC5orhTtppdELbvHOFsKSJLXznKqMg1iCi1JW3c7KixcH114vm4Id0P9hzNnCkbxZ8Fcru+xap/nSb8x1dPyXR1dXV1dXV1XdRTMl1df5g+aNTU0UNR/HG2+en9lrobyLvQILeXpaSy9ahYlMMnVtNlSaosHBA/1ZXx4nCQ6npjXJZUy8Iz0UtFCex5XLUbaqCBd21lqHqrU6kzPo5v89bFu5bkWcCurF/rCgz3+cK/cc10EIeGSBk9ZeNWRo2bK24yxoz6ZgBEIT5bBSpvfVswas+VDBbsGxBYfTN4TmVdbJKiUJSyNXjOkipy+5GytSUsFxWGaK91vqRNRCuaLqtEC+PHzZclKLAaVi2Km4wXJI33YutMl0SQCgyPxabLG1umjG0y/dhWpjxU51ZGz8LbGZ+zvY79sCZX3GS1n+3bQB4x422y1yoK4Vwo6imDog7Slb9wgdrXa7BFsGVNbDjUluiS1RxTL+R9WBe4fFH8+cLQSdeetHPrtLafDbg7PFlaJUxtMvtQLIWkQFZbZlIQ9QZ/no11UzYefy641F7L4HHRDLxyM6ypK1cVN1dqMNA0wmooSqnkq4G89+TR5s3jwaa407UlaurcLv7ZGWPHKRTh4Wlnq2VLpUkFSYI/CzWqAa0xVg3dp+nq6urq6urq6urq6vpJ6YNGTdoFwrngf3NAYyB/fEXeeubbYKmApCvgNO+sSuNnq+W4uaUTRoeK4M/1Ag8eHPNNq4ecrYZTxsuNtLE9vp98oc0guwzjdxMaHNNfjHbDf7IURLoy0LDVnQz+ymCmSjgvvBAzecoQkaKMXz5CLug4UK8Gpo9GdDRzZ0kHoZbY2R4L8Wk2OK1zPP6nPTXC5hsze86fjJQXBoacM64oodW1NDjcVPC5NvPBrfyY+XZo8F0zHtLuMj3tkqPuBlQgHC/rT2YiWB3MTACbI/ez1cuWlNN8E1pdyq0VKDNYdDXaXDLzZvPNEfd0RoeIjp7jL/aoE+Kz1ZRsqctSMD6p8X+eK/HLR/LHV8x3lmiRqoRjxj9P7KtVwpZZ7HRj5354PyFZKWOkDMJ0422Fa1LiwQyjsvGUdm2psySRT5US3TptXYNxefxsi03+nHHP83qa8u6GtBObFAfKZMe3/S7hcl25Ru6crO4UbS5ccsUfhOHered6vjVzxaUKqZlgQRAdUcFqb42BJDO4qaA+kHZ27S7LYTIVytZx+shbVU1gfFBUlOe/EHRQW3yq4M4N4j0YbLi+HdGhIpti7Jpqi1HhWUg3Stko4dgMzEA3arq6urq6urq6urq6un5C+qBRE86F6oXH/+1TRPVFKkbXRImnomo1m3Cu+JPNNJfRoaHxXbDpYEXa41TCUdbEBwCCGTxTJd0E8iiEyRgrftbGrLGKT76OLRmhaxrEJxieWFMUNRqzJUyV+HxhtIRDgqqEK4864fSXd3ZDnqrVW3au3VDXNQ0Uzko4WmrGgLzGabH6k1iKZqkaNbZODZDe7BpThzWN4p3gp9LMHLXUEhBOlr7JO08VcNk4PC7beS+7gMwVf0wtiWE8F9GF/2M35vForJuVd0N7bmcrRS5VYvvzJUUDrAmafDMiu2E14IYHM4bU22s2w01Qtxg1bY1qN6LeUlaLAZR3wapWjWuzVpSi4ATqGNaKky1BmUEXn8zg0mBGjFSr1yFinz8046Qq8Tk3gyuuqaU6epv3cktlLnP9a12vCanNqGomzXwbEVXGb4sZVwJlDOS9R7K274O2QtZMR5eN32PvkzlAIpYYU9eqXp6VVxOPS4pJqYMjf7TFTZXdN3k1KfPGqmt+ApyjRoPNuFmog6LbAknwTx45BXgfLj6mQLo1c9OfrcbGi3RNV1dX1+9Sn1Tt6ur6ManPznd1df2p9ds/h/49/xvpwzDhqVBuBt7/tccl2H9VCWe7aS6jI2/bTWo1c4CkhEPG5UrebaiDEI7GXanBmWHT0gvh2GAhL6aG/akSH87k/d6mmZOuk9muKr6ZHmnfKlazXmpA1VIsNbzg5HhLKQzvzm1CW/BPk4GID5aMeP48GEvnvlhFZeMI50o4qqVxmoE0vJ+og18no9W12o9qM2p0NQCWKtX5dWwslYtJQJtmhgW0a5WY8duzfbzZXtIuFTtPIqRdYMgJOU5wPVpNqNiN+VILCsd6WYkqij9ncw/abPZ8G5AqSKn4UnGTrKmU0qar59uwGjJurgxvz0it5JsNGmz1SaoZK25WwsnqQkvlaalUabU6XPass+pS22qTF4oT3GimS1ngwEpjHiVq9CsQWBQDEFfl/KmllsJZkVnxhxkVIQTXFsUM3qzOqmQqQnyaGb5+pm4jdQjk62jH1IzC6dYhCsN7m+s2uLLn+HFYja8yOsrG4c8GfpbU1rQWw87LmhyrUWDrG3S5GTWHZpq1BFDeRsb7xPD+bEtRo2f+xUDZCv5s54JdOyfZVpziNpE04ifBH23RDGe1v/MbJd8UwoPHzZCvFY2dItzV1dXV1dXV1dXV1fVT0weNmryzmeXdVxcGSolCvTUOi5+tTuTKi2RGXBZ+7Ia+hrae0/63fDzYvPPXR+rGbpqtvmRz0FQY7xN+0pa0WHgsWEpBZE328CINop41PWDJkrLWefLVYKEHJ+Qvrl8cD22228wnq7BYimT88on5kytqMD7K/GpcH88SELLydvLWZq1DSxNt7staEVIHZbR/L4PgskOqXxMs2pgxdfBrNYwZwmF5ba2WUyo1Ouaf31JGv6ZmpNCYOZdz4lvl6fzp1s5Hq5uVsfFZztnSQt5RxkjeXWIXSwIIsTRIerWxGlCyFSyNdv6H+9zqQZYQKXu3vg/hZBUzmxgXCA0cvXVIxs61F05vIn5Wrn4zUYMjbz0a4PDzLX6ya6vGtmQV45qWWlbDEFkXr9Tb5+WNLVeFU20LTcvC1kjex9VwMVMpmmk32fV7fjOuxp+KsLkvBr+e65qG8VNBFnD2aKym9Tpshpi2a11FqdFSTP5UqNFRR7caUiU69HokHBLheWY3OtLRI0Xsmnm285m3VnlKjwOSGidoVBJW59LGvvFPnuHBEjmuGK/GzVbb6+rq6vrXqidrurq6fkz6IZM1y2P9l/7fRl1dXf8G/TGSNb/rZ9qHYcKD3YRu35mJUYZ247hxlqw512YUtESBMyiqOMFlRYuSrzy1VYjUC2UAqYI7Ti0NMDSDwgCrAP6QcKfM9NHGkjhLTahVYfx0SWYsslnwpdaihGMlPCfyLlC2FyLxfOWbmaCIKuFs5oo/F1xbZAqHBG/vCbsB99rSFzV44qGsgNsahfhcoSrlptWXjqw38yU6agxU3153tNdeBsHNDg12zFINEKvRQbY0DKq4uaDeUbZtdWqu1F1YeTNr0ihbPUbLhcEi2YyH+dqgtuP95RjUYatS9WKg5a3xXWSZphYa90ZJ1x4pMNzPVodqVaRlEQzXHmPnVrMuHNSWpoJQ3eIkWJJEBIaHAqNjvg6MjxDentDRI3Vkuouc7xzjo9WKyuCowQxC0RdT4y2dU4MzU6QdRxnbc82Co03MB6G4QN566igtwWOfK9WMJ3XGuQFLM/mkxKe2lpXNdJRUL+tZG2MMqW/1rGUSOzQTEJoR12bhUwEHRRqEO1ebSI8Q78/I84nhYUCKUmKgjNbeKuNynIo7WMVOsb9T0YtRU8BnuwbDSdfzEU50o6arq6urq6urq6urq+snpA9Xn/ICfKm44ADXEi7GQhkebHobVaaPB+YrB3hQ2L4tFxZLSylQLBmAwPkv72yhZ7rcRdqUdEKHAMExvp8seQIgwuatJRTSlc13p63gkzLeF1v6ufBjjRNyFcnbtlrUJrTj0e5043MGgXRlMNjp1WVuOu8i/vUtZRvbElImPifcOUOulpzYeFxjrgxPFVElPhqMNrcJ77DMg3tBiiDVteqWGSMuqRkczt4Kl6odV1UDLG8C549jq4q1utdUKRurnQ2PijsXymAT5+FoxoI2o2D/q/PleAqMT7a6VLdxhRvTqjv+VKzSo3Y+4sEAyXOwCe3j55v1mpCihGjGzJIs2n05kfdhncLW4NqylbD9ZkZKpY6eMjiOn43UYOBnFNLHu/V983NleLZFrjLaY5vZoFB1rUO5Yh+XnV3CkivDWdn9aqJuIuk6Qm2JmqUW1UwaWwCrDI82kV2jpWBccg26bOmX+SbY80tZUzN58ECkDi3Z0pJZw0NuS2dWRcs735I9BRVhvh2ogyPtHPFQiWdFqyWyzp/u0M/2lK1bZ+JdsVl6ycrYDDGQds65rHUVS5DVaBWopQoXn8xUS3vpnJqurq7fS/+evzXq6urq+l36l34W/fbPl54G7Orq+mPpdyX8fvvnz4f+++d3Jfw+aNQssF8pimrFRaEiiFj6wk3ZGCdLqmLTEhsK+o7VpEHsBhYFX3TlzPi5Eg6lpTiW/siFq+HOGWpFx2iGwKki2TPdekqEMoKorFBdqq7Ho2I1HW3T3+r0ArqtlvhAgH0weOxG1tnnMtrK0lJr8VPFP54hZQPIzuMKv6WCPxuE1k+l3fQHqBcYswaH9zavrI1z6+dWsXEXro4skRTMaKrRzCir+rgGCLZ56jI0rkvlklYpVlEqm2DG0fsTOEfZ7tC27IQqNfr1PNmTmTEgS3rEOas6LakVj60mAeFsxkBpppM6M1P887QmqoB1wQoBf0rIOSNXA2ALSOosGbVweuz5LSkUzvVSCyuNCZMvV7B6WdM/pfFvfGPy+O8ekbsrUqvUiSqKrItVtipWcbNdA5Jr21oXqNpMqwwEagyICjpd3pcaLjWrpeq1fr9US0SJymXWPVWIzhhA8fsVvWUKPO/D+r0Dlohx2ZIyqMDB3odlhr5G1u8Zqx/a90JZHl8gTPa9l67oRk1XV1dXV1dXV1dXV9dPSB+uPkVnNRylGR7tnnYQ/CiUXbQb01TYvE3EZ3+B5grkrW9mjzBfW+VjeCiEueKPGR0caWdGCQLpZkv1O6sjTdUMkKpMH43UIGy/mZBc2by1pZ/6rs1Anws1OEqrar00fvy5EurlZjUsSZFXw+WGe/ErvBkjUh1lG9e1ojI6/H6EHJFaKdtA2TgKDirEY7b1oFc20SzZ+CrhOdnq0QZb75kuJtRiIMVDW01qpkfeB1vwubIDHp/MmFn+flm7gtr4K241XOy8e+a70FaerixRs7UEzvETRzwo+y/dCmBejIfzRxF1A7XxZPLOrZUZl2H/VV5rZTXI+rxlEDNars28Mq6MI4+sk9rTx1vcXAnHhD8kbv67Hfd87VtVzFJC00eB8bGw/fpsKZhoc+Yu1VYxuhgmrliyaYEUu7lSo2f+q48poydvHQFwc2X86hl5eCb94mPS7WBmlGKpG1iNPlds5n36bGsVr2IT7/b3ZsqhzrxEBZyuNafqBQbjM9Gqf1ZNq60GJ8SnwvXXR86f7nj+YmgcHm3XMMxXlqjZfZXwqXJ6M7RXa483PF/Mqry5VNb8rMiDmVzpyuDM4+PCaApm7HR1/cTU1z5+PPohfmvU1dXV9cdST9B0/bmoM+R+PPr3SPh90KgxkK0gWtHfiuQsAFdVAdqMcc1kNRjqogXoqs1AWXgz7pypEhpjoxk7bUlqqXRosARIGWWdoF5SCxRw7cZ9vYG/PK3dWNMSPaorJ0VKvcw4x0uKgXbzfWGNNNNHtXFYHOIFxa9snBrNKOCpzWS3P3dLGqXWtfLkUnuSdowLkHgFFDeTaXmdNRoENhwaH2h0LDRdqa3m9eK8LvDf6nRdvJLi1+M0Q8Xez7IxNgvKWs0qgz13bfxfS7OYCeAyhJPVhNSZa1QXDwEzwQzYux7iZZbc2/Opc/jJIbXgjwmX/Hr+RHU1faAlqTaByovXuLy/zvgyy3u8gKbBUjxl9O0ccnlvU0YPR/wpUTf+e8e2XJO065R2HKKX9NXlhZphoxXEiSF7lu+L9rHVvbB1s5fz81ityz0ecR9tjblTWFfBVkA2XNJF1cDJssy0z/WSHlOP+vb+LFDvVC3Npc7SSrkyHOqFmdPV1dXV1dXV1dXV1dX1o9eHV5/ab+yH+4xzstZlwhFQKIPj/DqQ9gYX9rO2G31h9+WEP864o4FjxvdbS73sjGNS32yhqtWZWlIDDNA6PGTi/RmNHvWuVXYg7zyilpZY5pzVC3m0uebh4ZL6kFyRUjl9vmW6CWzeF/zJqi6SKptvz9TRc/zUUjA+KeND4ea/HdHoKRvjkxhHxyaop1cjZesY3yf8sXL6dLxMUs+F7ZezAWKvBoPT3m3MlGoVGymVfD1aAmWuttQzFTMAtqHBae3cD0/FprxTBXWt5qVrukSkGUJFSWGZIzdnYqnRLItSi3kyPNnaUB6F+VqYXgl+Bjc3s0IbpHaZHi9COBroeXh04LHFpFb5cUnX2emlCoSD+Jjxp8Tps50xUkSoEQ6fj3bRnQzevPuHB+pu4PiFMWq2by0Fkq9H6uCMR7S3NMoCfV4YOJLbutOxoEGY7yJSlHjIZtbExaRyTL+4Q//qFeEpEZ5mnn65J+0c1/80GxR5cKvxKEXZfmvspcXMyTu/cpYsMWNpoZdprDErIpZoQWH7nc2Gz3d2fcXnatfEZ3f4U+bV32TSzWDJpQbkjgd7b4+fDK1KVwkvzKIlUQVm+sSv8poeE/2+KZT2DleE8V2ya6ir60+oP+S3CT1Z8+NVf0+6/hzUf4Pd1dX1x9Dv8/+hf4zloa4fVj/ke/I7EzW6JF4Aqlp4oy5JDrksOZVLQkOagYJzDehqCRoGb9Ue11g2RZC5XpIRWQlnq4tIUeoo1NhWcpQVJLskHvx5YbzIOk8tautRLhUkFaT81otyAu2GfFlHWkwfqYpM9gXSbvQdWNqi2sLOmuyBCxfnn523S2LDtY/FCSR7LvWCTODmYktXzVxyVFsFakyfZRJavbZUUDu3XEwEM6uMcyMiLdnDyioRffG+NDh03ljKpWxawinbY4ra49DYMrWBa+09tvWtOly4Nst7pg54scC1gqNbvcr+kHWSvA7gsoM5wRgpQ+PvpGb6DJc1pWVtLJzMPLIDbekhKl4XE6P9fXtfLQ1Du14MFhyeDTps1y3fr8kt10C7Fu3PpJ3rxRVkTUQt3xv2Pi2PtVSx2rl2L/gwxa7fGoRwLvhjbnycFvtxlqxRvfCAXFpWzNr328v0F5frdgFI11YPs/dxSZBd1tS6urq6urq6urq6urq6fvz6MEy42k3q/Np6LjUKki3J4eeKmwrqB9TZDHSN4JLdqD9/MaAysvt2Y4mU2iaDG6DXpJacaHegw8PM9tcT5Wokvdow34b1Jh5gvnYrIycelOv//kDdRfQvdkjFZrjP4HMzCubM5tsz8SkYzHV0JDeYMbLczNphIGppjdMvronHTLg/U7exgXkx7shUiUC69uhNIBwNcqtOKPvIdBdXM0KKEk6VMjrmW/vc+JTIO0/eOYb7mXB/RjdmXPljAif4Y4PVjt7YJc8zki1ts1Rp6uCYr9y6MnS+dZRR2LxvBle06hTNaMkb48wMj0reC+fXiks25RyfYHhS0hXkrbTZcJt6XoyR6uH8kV+NIpfUpsmhQXPNAPFzRbJSNmbISYH4VFZzZ/PWXuN83VhGd3vSzcj5lRAPEB/bAtPo8JMtYKkXXBH2//CIe/9M+ouPyfvAfONRccSjTcQPD9lMFgV/zoRjMsMsOtxzmzyfMqiy+S4RD47pVUBfB1y2hMr41oDI8+1gJou7JJP8pKuRomKVJHWw/82EP2fOH28aR8kWriRXCM5WpQbHdONahQ3CORBOxqgJp8p87SijY/Ou4A6FEiM12vXusrJ5ZwZWGe0aCIdCugmc7yK7bwrbr0/Mr0bSlTffR9VqYF5I12GtwHV1/Xvpj/Ebnp6s6erq+kP1h/z86L/B7urq+kP0Q7JLerLmz0O/06gBSySsf4bdsC6/1XdTJR4MbFqirSuBtBRMSzssPBvXeCRLokGNf4NcboiXVMLln1ZlquCC2MhRaWmONU0hdnPa1qckOmT0qMa1FqIrYHg5pnb8bT1oWYoSD3V2dhxVVyOmxmDHMFVq8GbGtMSIRoPILlWYhbEjRaHxUmoU0lVY2S929t26wLSkHrRNSdsHCyvnsmy1pGj8wjR5wahZzZnFYAiWkMl7xWXBJSFvaHwZxSWrJJXxksBY5p/LaF8vxea77RiW8yXfe06rpknjvFz4P7Qk1AVKrIhW3NbqQWUTqFHwkxlJCzMGLJEieQFKG7QZ3ybBXyBXahTceo3Y8y/1N/WO6h2u2Gy5PfAlpVK9WBJpSdC0v19NGn9h7azmYtU1keWhJaJqq45dkk/L++fnutaVjDVz4Rv5eTlee/3rdd7OcQ1LfEoRZH1cg3yzmkes1zfr9+bC6UH/OSunq6urq6urq6urq6ur68erDxo1fjYQaWoLQPFoqQl/zA0SGxgeZja/PnP8T3cc3wRyA8Te/I+Z+DSvBsr8agSxVEodHGnnCVoJD8WYNVtPuo38f+y9648dyXXlu/aOiMw8j3qQ7FZ3S7IB+w5wgcH8/3/I/TRjXPjaY7ekFskmWXUemRkRe98Pe0eeojVD2WNI6hZiAUI3q6tOnXwUoVy1zw/IdQAAIABJREFU1m/lu4Th04r0cQbpiDoGB6sqhid7rXyIIFHk13uUg809hxUIq9oa045BNfqDux1LMz8ap0YjA2zH2Fg7gHFZaA/wOoJzRThnzN/usbwKOPxmxfDjDC4TZPCECwHrXTRzItBWh2rw1/a91ztbOdr/XrD7YYEmxvpmh7IL0GCJEAC4fGPppf3vVygTljej11uMjyIjI14qxvcF+T6hHAI4AxoU6/1tMloCUHdA2SnydytQCetD3Ko5dSLIqMgPwFyBcLU0lIxAHRTlXqCsGN9GhJkwfrQUTjDkEGQg1OTLQ4taskWB4BwXcViuekXKZswLqCrGqpAhYP56RFgFj/9wQZ0i1oe4gXXDtSA+zYhPZpaUxwn12wPKwUyyeLYJ7/U+gINi+CjbmpMGgwrXfUTZM8YPAj4VYw+9XMlKPmt+sftLIjsM+GYctn9KJPCLeld6ruAqkCFAhmDfW8iTRMbg4bVi+OGKcBxRp/02R5+PxnUyA4gwfiifQYuH5+pAa69AFYWSIsyAjIT5dcDufcXdf/8IHSJ0iBv4eXjKoFVQfz1Z8uw5I8zlP/FXRFfXv19/jt/o9GRNV1fXv1d/ir8n+m+wu7q6/iP6U/6d0ZM1f9368jx347Fsv8m3xR/axY21YmtIVovhrBs3A3AWS1GDELeFnYKtQmO8Et5SIk1WWQkbTHd7UC4veDl0mw9P5/aA3l7gxlYJswF429S2PfzyZ+kN4AbTbQ/MDVobPI3QamCagoNuyf6bmygqLY1CKBOBlJH8vbSH6DDbfDV58MLO0YskSTsvwMaEsfrRv0lEqCc5qKV1rA4mww15ogGok0IjgOVF9YUBic5vqQDEeDsyWF2m7BV1VGgSgIE62oUqe0KYgXTR7T0AZghFUYRrBZhQdrbkRfUFJ0X8z810a3wbN3C2+6UlQNxIq4fhtuiUfCWrvEi/wFIlJIo6RfBaEa4ZBAaxbowbm/o2QLQMvsqk6omU2z0nQ3D2D26JqZbeKrpV5giNL6RANP6NJcZe3FAANLAZKdEByNXuZ87iCSdLDXEVSyN5moezTY9Xr9HV0YxSzgIw+70IyH6w+9HvyQaUDny7XzQxRHr1qaurq6urq6urq6ur6+eiLxo1G7R3UWgA1gMDB4DvbYkpXhV6nyCTGTXjk2xz3DIwchwQz8VaMW58tBli9qnn9SGCs08PexrFeDIBvFTwIlhej14VsYSKDNbzoGqrSXf/eEW5G3H9Om28ETORCPG0Ij4vWN/sUXeMsveHVsFWV2kMEpv+tupOvouQQTaAcTqb2SNpxPIYIRE4/E7AqyUrlAk0BZQDY34TUE+K4SOD1JaJ4qViememjW6QVwUoGPzXK1HxItAI5GNAWBXjj5asqTtj1qB4fSw48yQR8pGQj9j4MpawAfKDna/hbVtAAupOgfsCWRh8DeAFCBfC+qiohwK+zxiGgrxESGHUo0AmggyEeCEMz2SMosXOkwZbcZq+f8L8q3tcv44YzoIwK/LezAP2So+GZHybJ0vWpGdjr6gbX60aFxZBPkbUr4et5pTObnDMFUpWIwOA6d0KSYzr1wnpHLD/OANcwSUAdwl5z+AcQGIMoTIRxk8VYRFM7201aX2IqCMBsLSOvWebRg+rIJyzJW2SV7BUzRDKFfUwAmwpLgl+HzVzKVpqqk2vQ20dLF7az1UFfbbIpEB9YWImW0Fb7y21tP9dRhUgjoyyYzz//XGDGZfJoMtcghlHbvIsjwlcvvhj3tX1n9Zf4jc4PVnT1dX1v9Of4++F/hvsrq6uL+nP+XdET9b8derLjJoXSYc29czllmL4TM5hCc4KkcHAHuFyA9ACMMZHIqwHm/62SWJYhWfVbXIacD6Lto+RJwoIkm7fWwKh7hI02ES3shk54SoYPhrEuO4HN1ns4wbctWQFoTFJAIsJmYESvDYlyfkiVS0NI5aOaWtK9iac3VPN6IkX+xxLiHyeFpJIxqQp6nPbltoIlwKqVgvTDERn0tTRTJZtRYhtgUk8hROyQqIZMC1NQ+LvudgaVr737zFbKqlWM7o0qKeMrDoFBpgVzApVstknaUwgmI+QjFtTR4IE+7gMhPK4h0a7przYpDkXgz/b1Lfa9S23e2fjDFX11Sf7eNmHLWXF7XN9CQxVQQFurNzqQXExIwdeX6oN0rza1zfDjV76Inhxf7t5Zkkaun3Ni/SOEtkilwJ1l0BT2mbEW/osrBW8CsJc/Nwmv29bhandCAoJbGtgsHPbqmF1DBurqM2gN6A2V6tP1YmR93a+zcBkhKxIp4owV8Q9owp9Vv/r6urq6urq6urq6urq+unry0aNL9s0s4IzEGfB9G41BsiON4irJKv8hMWMmby3h/Thg9V04tW4GyRW65nfEMaPwP6HguVVwvUNY3hSDM+eSPC1HCUgXnzG2vkjktJWf5GBke+jrQQ9F+RjxPLAuP9hQfzH36L8/XdY3oy2aMSE8X0G54r1YdgmoA0WTLeH4VUQFkvQ1J3VdbgYN4XXagyWgcHVa0xQQAi8CqIo9l5jsmlxB9tG3kwkibdKVrha9SW+P4GK1YcAIHy4QPYjLn97ADt7RAaGDGZk1IEQF0G8CCQF5Dt3IJRseasSwpVQd8D46xOWawL/fxPAhJrNtNCkkEEhFdCoQBRwEIQgZtIUAq8GIQ6zJZjqRD4T7QmpYmmO868nxFkxPAviKVtyZAog9XukAukp3+aimYxJrAD5LHf0GfB8z+CCjVdDxa7Hdg+QcZNAQDoHcBaMHzLg7Ju6T1gfDP68rVONlm4K6wuzZqtGOaB5kQ2QrIFQ9mYU0oGdOWRmHERR7wJkoM10KpO9RjxlhEsGna5AstoTMSFkqzc1NhJgi1mSaJtij88LeC7Id0f7b76Wlc5yS2KtgvG0Yn01YbkPSFdBvFSkJ1u0ik8zaCkY0j3Krleeuv761ZM1XV1df0n132B3dXX9VNSTNX9d+qJRs96Z0bDVORxE24Cp8WI1lJoYvCqSCngRsDNZNAB1HyFVkY9hY9QoA+lk1SnyFAqvuK0aedpEiG61n8oI12Kf40tOYambYUNVt/pQuip0YOg3r0GqGJ4zSGxhSCNB2JgeylZvAd+WferE9v4vqwOJLbUhwdeC1oKQk7NoAkiBeLaH5DoywLRVelqagaqiRKursM92K5ODjwGqAeE0gV4cj+xH6Bg286gla8LSmD2Nq+KJpYUB57LUEQAp6l6gUTGfRujCvvCkoKlC3YiRSVCYoYN+xuwJQ0VlRWWgZkI8262yvAbCYjPtrXZTk9VuSMTNDoKGBstpiRyrtHFV8GzmXbhmq3DtIiTwltoK2WC98ZwhY4AE9urcbap9+lD9fpJbYiQQ6t4m0sMsny1lKRNECQh+XxIjXitiEegnNyKrsWHY7119sajVrqclZ8wkagYUYCYmAJR9hAZGVPXPs4nwsg/gRexekdu5a0taVAT5YQIeXtQEV6uMxWuxJJEY76nuB9TJa1qJUPbO/qkK0gm0NyeqVQC7urq6urq6urq6urq6fj76olEzv7KEzPEk9mDeFpzuAobnivRxRrkbUXfBZ7rFKjqwFIzBaa2mU3a3VA4pMH0QxKulJLgo4mKE3TbXvFWrGMhTsJrQUrc6FhQI12IP1tVmmDUaD4WfCsoUkH91h/H9jPTuAio7SwE5vLhMlmQIc/VqFqPsAuZXjHgl8KcLeEjgMaE8jqhjtPrNdQUdRgQmLI/JF4jMqJGRQUUxfJztLaZgAFrRbeln+lARzxXrQ0SZGHW0VE88TwjX4sYAoR4HaPTlJALqju1B/1JsqWh0tk1iUAHCDKhzXurBDJpwv0IrAe9GsBgwWHaC3XFBXiPKHIGokOkP+0DjZM5DuBdc5wR8f4QyMH9bEU6M9IRtBUoG2PLW4tPjjC0Z1BJFpFZvowqMq91P/DxD9yPW19PNFBEgzIrh3QX8w4+Qb98gP04oh2grRmcBr4Ld7y4AgHK0lSy4oVZ2AbwK0qnYua/iwF0GRoYq3RJiTwJeCsI52xx2MqMtzq32xJ64aaaKr4qNjLCIpX381EU30PJ9RM1+Dtq8/RgwPwYMJ0I828ITV4EWgIjtHiyCy3cjysQYTr6uNtuxxie7n2SMkDFifUieELMKmIaw1aOq/6zEi1WwNNJn8OWurq6urq6urq6urq6un7a+aNTs3ovVnS5evQgEJTVGycA4/+3RPpEs5SKJkZ6t9sIloG48FXugVzKgLRcD0AJAOSZfkzLeSpi9KkS0VXy4WHJBBgPqxqst7lCuIGbUyOBaQVdF3UWUg32dREJYBpAoLt+OyHvG7l2xh2wHE5djcg6OpxcWRR0J89+/2YC/ZQqQSFhfT+DjsB1vkxKAyJgfg6dBjI8io32dJKuxhFW3+lZbCAqemlA2Jo8lRJzdAgYGBYGgYpwXKmYIlKktDdmncSXQbP9eDwCCoi7BKkyjVXoQFZQEpTDEOSxhrIipoJQAKQxVoFbnuZCiVIYIb0tIGsWrQQxaFekqSFdg/AhwtVpWuAIBAhkJZWSkKsZkiQRqc9eJoa8PkMYhciMk3wWsRwb0gJEtbUNqq0uhrXIRsLyZPG1lYOvhwwrKiqhWZ1peDQ5xzmYGim6cIlvSAtbHBKoJw48zSOHpHTds2gqXKLiSmz8Zkhh1vCViNoNJb4kbibStOOU7hx5/qDax7uBtwK63DITCtqdOavdfOldQUUvKjAyqg5l3/rXpVEA1fL4y5YtjvBqrRxIDyWp8LP8LME9XV1dXV1dXV1dXV1fXT1JfNGqm99WqOmfbMG6/nVcmLK8iLr9gxCsQZ4UEMwvSibbpZVKgDvZgnq7VDIYd39aVGFvihtSqHtFTMs2MyHvC+GRrODKYkREvxQDDuZqBoAouAr6skDGg7BhlJNQBGE4MXiOubxjrPWH3I8CLzThrCrh8twMADE+WdAizouwI19cJ6apIp2ZMAMtDABAwPDu4ltqDuk1Ar3eEsAK7MfixBpQdIR9stccgxHqb4yb4RLQ6JJkRczXgMhvApTFwCH5eq2yJIA0vQbhAspAJZgBgvc1yJ+PPpKlYoqQGqz4BiKng/jDjdB2xagKUUCuDfeJZhKDSvpcCSaHJqkEkZuKFRRDmivU+Id8506cq6mCJobCSL1E5lIYBBaMcg713r4fxKpAYMb9mkEQAuw3gzFmgQhurZX2IqMkB1os6C0nBa4Y8jljvnUlzJXD2Ke2HwZbAsgACLI92foaPZqxUN9bazDsXYw+BbImKTyv4mACE7R7fZuerLW1JILD6f0uWKAuzYno3Q1Kw2mCxeqAGZ+Q0YLVPcMeTpZmW19ESYyVtqapwFYzvF5Amq+QlW/5Sght2vpp15zDlpW4pt66urq6urq6urq6urq6fvr5o1HCbkIavDbkBoWRJkP1b+029+m/zSwTyfYQMVg/RYMaFsiUNAEvSkAL5aEmI9FztgX5iZ3UoEGHWhAAhW1IDCsyvAkgCJrYFKI5WVZGhreTY94xXQbxYVUsS4frtiHRRq7REQjmmbVWn7GjjjCibqUKi2H2o4NUeeqVYBaaBk8NqXJT0VAAmlEOEBmD/zuswWaBD2FI06VK2CfKWxjGGir1XKpYSUQaWV6P/u/N2njPqFLA+BkhKiMEMiPFJbP56soSSMrA+ABIVfCXoGrclJw0KVEI+W1WLgoKiIO5XMCuua0LJAWrjQQCAdY7QytBCQGFodJjtKSCeGPFsZlvjqbR6U7yaCQE1E4eLHSPUVo4Qne1TbzPtXG6JjzArxo+K8UPF+OOC+asJ5cBbtUfIlrmm9xl1YJx+ZZvkMhhUeLsfyYHWPG7GUViq8V78OGV0dk1kEPlS1UobZLhObObJosh3EcvrB4SrYHqXUQ7BZrNnh2mPlgrbvc0gVeR7q2QNH+0eWV6PBqSe63YPULWfozZj3tI7dRfBRfy8mXmjRHYNImH+ZgdezLCpu4iyd4OJDdTd4MicBeG0brDmrq4/lTq0rqur66ekDhnv6ur6S6uDfbv+s/ojq08vZrgZ2wRxA6kOT7KlJiTCl5/YH35vn0eCLTUTFzFmzURI1RgxAJzHYpwa+8LGN/F/inFQACBdeKt92AMsQUHb9DVnf+heKi7fTViPjHQWW94hn3ZWY9q0ahUAf9AlS9I827T3lkZQsnRHlm2yOWarIS2vzQAZPhavaXlyhi0xkz5lcBFQEeOMDG5KCbbEg00yWwJDg01fx1kxvq+QIViChu0BnFSRLoI6EKoa2JcZWB8VMlqKBCtQJ19zAgAhUGab5IaAEzCOBSKEnIPVm9qsNykkByATaGVQvaVMwtUYPnERcIXzW9pykvrilJswWUC+htWMqFZ7Ir6lrto5VbZrkc7GmOHTDP1mh/Vo3BbjwVj1Lj4v4CEAZCaZJALpzXQD7GNrtNfkCkxvK+LzAk02/82LfS0CQWHvg2AJH4wBZW/3JFfFchcwvyIcv1ekpwX5fo8y0TavrmyJmPS0AGT8Ii6K8amgThH5ISBdBPHs3Bg/Xw2u3c4jyKbj0Sbt2z3oM+d1NDNx/ACEp8WPk7fjlcFcqjAb+4mWClrzf+xvha6urq6urq6urq6urq6/mL5o1Gigz9aN2FdkNHi6pKhVZMhrIr7qRKIYzvbEPH+VIBHbg2d6qmZikNU25q+GLUGSj8FgsNkeMtNFEHJLPVi6hqoao6N47cRTBIAZGHUwDolS8PlmweG3eVu/CcttJcjSGT73fGeg292PBexVnlZraomhxiFpL1B30Tk4ZjqkU7YVrF38jIWiiVGGYA/kI6NuRo3i+vVwW5RSuBmjSBebgxb/urCY2dBSQhIJ6aIYTmJQ4sHOZ74D6k4hgyC9ngEllB8nKCt0X5xMTKhLwEVGEAPMAqlWcQpRMKSCOgXUwMDCdhwChJUwfAJ4NdOuXSMlMlCwB2PW+4TqLCJ204bEjCegrRcB6zGAq5k/G3RYFOnkIOrXB3BRTB+qvbbCF5NaGkdw+G11o8bMu0BmCI6fBHU0Iy7OinC1+7Hu08aiCatACVheJWPTOIw6ncRTY1Z7W/bR7j9j+qLu7POVbOErXjLSKXhKJ0ISm4lTCWEf/ZoT4kxWa8PtnpJEGD9WTKLbPdGqT9dvrZYX5mqGkCqUIvRoM+/1YcL6mDA/hu1ct2tg62jA/Kvj/8nfC11dPxv135h3dXX9JdV/S97V1fVTUU/v/HXpi0YN4AmI0VIbcXWjANhSI1B7OLRVqNvX8epJGUpb2gDqCZJq6ZJyN2B9iFtiRiJBByDVljYwyGzZ01YFCtkMI+O4sE14e9rDUjXGxZFoqZThyepDZR+hka1mUxWaeHvvgCV+gk+OUxbwWiHRUjfwFJF/ExNhW5CK12qJmaUCyY2icEvqSCADx0ay9FGyGWoSOEvHKmIkVvOyNSoHOCf7HvTCoJBgXJJhFv/eZlDFa7BU00GAqDjsVuQaUAqBAkDROC9aAlAJIgGUBIhsLBZP1DApOIjxafyY2zVOp1uCBIAZZj7PzZ5+KiOh7IB4BZBv5lNYb6aNeJpJBKBKoEpg0hu/yOG5jRv0v5qZpqoYPmXIwMh3wUy16obcXAEKW2UtLF6LSmzppWDLZASgjD5dngghA2G26wMxE68Odg6aGdlgw6Rq4OxrtmsfyEycweDLAvvcli5T9uU03IDSjSMTrsXuTVXQXIDI2329Ha9XuJqhV8ewmXQkChTczERVr+XZOlhXV1dXV1dXV1dXV1fXz0NfNGriKUPGgOVxuM0QXyqmHy6ou4RyiJ44IbCYsWATx0C+S1vSpi3ktAd1iD2MRiKMbnSE04L8aod8Fw1Ou1QoW0UlnW/sEVKg7IKbGj7tfZWtbhVWtXSBp3J4FZuwFvu+630yEK6bDXEx5sz0+8XmtxW2JrVk0JhQdwmcCcoB4vyP9sCej5biGH9cwFlQD8a+oRdsnzoFzG+Ss1h0Wwiafr8gfrggfXVE3YWNKxJnq0KFudis8y9G1GQGT1NbGlrBqF6JUgLu/sWAzc+/DqhjwPI/X0MDkAZFHe29tPUnVAIyA0uAVEAHAw7Pn0bMGIFi9RtEhRAQL7epcNLbWpWtIJnpotVSNMNZMJyAdK4vuDEEWFPHV5wIYUefmXtKVoFDIks1ZcFyTCg7xvjB1rrWx4jKjLBEM9RytVRVikACJAbES8XwtCLMAfEStsnwOpqRtR4DJAJxZ2mhdLZ797NzP0VcfzGAi2L3tiLfBSz37NwbIJ4Fw0c7NtklhA8XM0ecoTN+bCkvS5IZgFpR9gl1YpSdGXq2iMYgDVh3A5Rvxt/+X87QwFjeTNvPkhIwfqhu2CimHzOm99iMpWbmrffJUlenut2LXV1/avXf5HR1df2U9G//LuoJvK6urj+3/nfMrP7/lbr+mL7MqPFlG2h7iPaPrwWc/u2v+82BIAVUrJoB8uqLV4BI1JIxAEgEWpqZUkHXFXwYQNVeV5luKQo144Nb+mVgqKozPXBbSYIZMFTNfDGz58bVAbCt+jAstWEcGoCXcnugLQKUCqTgqSF7D7f3JNtrWd1LABHIOPj6jlVcuCoqGn/E/1fte/NSQJcZ4Tra8Y22gMSrgKoARUDDbYWorTvh5tdsgGbADIF4toWk4T6hTuTGGWF+Q+AAyByMWRNt9YgqgTKBM6FCoGBQtnSLHyJ0tOOnQlvah7QlpPT23pigYkYCV6tttaloZQKxbpPmVBVEhJAby8a/R7vPyKfHK/ncNbavs3sDkMD2fbLBmEmwpVbscw1kvN2bfDtv29pSuxZXex2ePRmVKzCaSYhiBk45WAXNjveWgrEqoL+4WGXKjkm26wLziW4z7F6zEn+ttnJmAG6Aiy+jzcWg1y+WqNoyVDO4eBXwUj77OWxLVBpaCqfPc3d1dXV1dXV1dXV1df1c9EWjJt8PoKo4/usMSYx8jKgjY/7lnSVZLsU5LcGTHRHT2xVhLjj/eo86EHbvDKQrbWXpEBEigZcC2ScsrxLKPiDc2UpOmAXL64i8Z8RZt/oQFBg/2KJODQwl3h5IjUlzgxe35IFEW5NqvBgSxfDJZrglEmRgLI9mhvDXe8RrQXp3sQfn/bilYzTYAlBT2UeHHZspoZGhKWB5Ff0YGlDYkhrpqgitprSaESBjRP2bNxuDptVpcrRKVjuG9FztwX64mUotwdLEL4wKECFeLVUk6SXklnD8Z0vgLK8UEs144xXglTB8CNu6ltV87PWWRzMb9j8owgyMT2Kz6juCgMDF+TOL1+BeVMPynZ2PxhSqBzOj0qWA14rxnaU9wmXdDAYZI+ouYr2PuH4VjWGztgMF0pOvJkWCICCeVoS14PjPBZIC6s6uTX6c3OQim9YugvGHM+i6gv7uFfIxIJ3q7V5JjOVXk12/RVEHclgwGWRYDA7NWZHOFcPvz+BPJ5TvXqHuE8pXR+PdvDHmEntSKz2tqGPA+jiA14p4LpbMSnZcnAXpXMwk2gUICOPbC2ipWL8+oI7Gu2k/AyBAQkRYFPFa/TxE8Gz1q/WbA8reXB2DDzNEX7hUXV1/Bv05kjX9N+NdXV1/SfXfhnd1df1H9Kdco+uJ5r9OfdGokUCWBKgCxq16ISNDY4Oh2kOgpQJg9Q+xpIylA2wpiQGIMuqeQYW2lR9LSzjzozifhC1tUJPPVAs2w8TAHwC96MxQBQi3PyuRNVCCvy8icJFbAkMUymHj5qgvQVFhxMib8YIXiaLPRO19mUHSZsHbshXJLfFAG4D4xmohUa8MeXrIjYdmqpAFXrxiZABnUjt2S1MwAN7SNiQ3foqGF7UkT9zwaqZAOjlDZiJwBCSpGREFCAvAGVtihz3tkk7kbCFsCZh2zhvcGL5W1M6nLSEpZLBzEgKB9fPrQS2Q1VbFnElEQV6sZrFVoJYbl6UlnGzhy6+1KqgImAiaxc+tVfC2FNSL0EmroZHPiFsdzdg7m/HHnijb6kx+n3mFjrRFWswgrFPYuDPbhW/H5Utd9uGb8diSX+11qCqCHwtgFb86+TS8WjLLpr3be355TxLQkjrRQMlbKuoP8T5dXV1dXV1dXV1dXV1dP1F9efUpEoQtWcNZkD6tkClgeUxYHhjXrwjTe8X+nS05oRLqPgKBML5ftqoTFAinFTRErI8RJJ//ht/SCwEhqyd1BOmseP5VRDkQ9r8TxAys99F4Hx9Xe+3EZtoUqwuRKMrdgOUxuUlkD8NpFUz/+gS6zMjfvYKMVmniLBhO5JBfeyCW8Wg1lIEwfigY3p3Ba0SYXyRYfImqVcHWB+PxhNUevMd3MzQyyjFttaB8IKz3AXEO4EUho5kYjSGyHvkFY8eSP1wU8bRuSRFeq9W5AmEIjPmrAesdIyyKMFfkO5ubHj9YJWe9T4ACh982TpAZM/Hi73UG6qioI4GzpYPKZMdFYtPTj/+vLQ7lg6VAzt8y4hU4/uvqNSibFq8jgVeD9obZEjOXX1mqqu4CpKqZJwDK3kyN5YHBBUjnEWGRbe2I14r0ZK83vZ3BpxnrN3cou3CrfrlJUQ8JCIS8j+Bqs+waP09AUSFQZFx/fUQdGGEV8KooB3OLwiyIc8X+H95Ch4Tr372y9/GjVZnyMfoimN1jMjKWb47gVzuDPTNhebQkz/TOUl8tfYaH0V5rsXnxurObJs52n8e52ucOhN0Pi/FxjiPqFHD5JkISNjNJiRCvvkyWW+3LOD11ipD7ESBLEKXnAqqyQbS7uv4S+lP8hqcnabq6uv5P1X/b3NXV9ZfWnyJZ0/9u++vUl40aBqAEJee5VAEQNl4GF3/gX2RLIdjXERDYoSMtWuLpjuwplBQMHAvn2BRsiQyq6g/dgAbdEhBbg8OnkeGcFGoMDiKfWTajQZkQL1Y3QpXtc0kZwmxg3KKW9vDXroPxQdrkNvj2kNvmk8vudg5uJ+vFP/mWGGp1LGLL/EggUMQteQEHzlZ4KsWTKWwRzRlcAAAgAElEQVQplZd8HQSCDLcUDhd/n6qe5ri9rkFs4ZDcG9uFql0zzvZPS0VZWoZXOwBLBum2dGRrW5YsaYmfrZLmCQ+rofk1orbS5akbhytT1S2NJNGuoQT1ahptxpvxgPy1A9m618AQN4M244JtTaulZoTcRKNbckV9rntLz7SP8S2OElYBvUy2+FR4mKulc6IzYzK26/MyafNyEQoEKPzepBf3U3v97aK3c+dpHfH7Otp9aSkmNY6QeP1uxB+KYEZMeHGfOFOpcX+21FJXV1dXV1dXV1dXV1fXT15/BCZsD6TbFLeDeetAGJ4Fh98Jho8LwocLypsDyjGB/AH++osBysDhNwuoVFtEIsLwMVva49XoM9VAOgmGH2fUfbJJ5mLpg4d/mgEB6s7YJmExMO3yKtlaz6lYxWcIkCFsazpl4u2hNT0XxNMKHSMQg9W4loryYHPOcbYZ8TAbs2a9Cxg/Vuz+6SPkOKI8jKiemgjXDF4K1ocD8p6tYlSB4ZyhRFgfIiQR8tHWn+rks+Zne+BXr6XYHPcL8wLA8FTNYGgT3JEgIbyolsEXg25T3em5YviYsT4k5GMwbg0B5RhAFRg/WQWrnbcymXkS8h/WYYYnxfShOHwZmF/51PeOjcvyVBADgaonn7xiVKaAumPkPUFiAGeGJAbnYImlJ0V6WkFFILsIiYz13pI/cb5VxCQR8iFujKFyCMh7hsQJ4XHE/NqO7/ibjHCtWB8trSSV3SgyUyezzb3Hi5hpN9hUdzytCDNDPYlUBnaTUTH8cAJUsf76NTSSmXdrRXieQfvBZ8KtAhgvFWEuBntWRd0lgMnuscCYfzECBMRThUa2FNVVkJ4tLaTB7qUyGvl4MxOzWCLqPmF8tyB9mvHwYTYTjgj1kHD6m8mXtswICmwrV/rCXCKSzdBrZlv7mezq+kupp2C6urq6urq6um7qKZiuP6Z/V6Lm9jBtlZVbkqYac2Q3oO4iyo6RnisoC+JFNmaHDgFlspnkuFSQmGEBoS2looG3OtK2NgUADIRLsbSMz2O3OWoJBPJ0hPrHuSrS1R7SDeXiKZQxGqR3raAiCKt/TlsiaimNALSJZTsHzjhpaRUihFmhdGOOvEzHKJPzSpyp4ktYnAWotCU+/q3CUv0cMyCWUoIfo0YDItvn+bkR5/9UuQGU/T221Am/SP2Q6M2godtSk0QgBLWUzSoba4WcyRIWqwlxUah48ibrjZNCt0oV51tyRCPfUkfO6WmsolaT4xfsHptZr9vn22vpdn9wgZ1z0c8WnDiL3YPRANFmZBF0fZFwactfMLMlLI3nYykV2Q9Avd1DAKCJUe8mN25kW2+ilsxyFpO6KcKeEOIXHB+onw+1ylM7B7Rxa14ya27MHLuunpIiNuB0ZKSLL0n5aarpRb3LzyupJXS4CuDrWujNp66urq6urq6urq6urp+N/ohRQ24CWCIj3xmHI52sTsSroO4j1tcD5seAsiM8nCy9ED/OAOO2XLM3lsr49gJltuWnotDVjItyl8CrIMx1q2rk+wSNhP0/PQFrxvn//srWpcgqNTLwVu9o08nxYqyTOkUzdgJhfRyQj7audPePZ/AlI5KBkPNd8kqPPeiXkVBHgg4JMkRIsoSIDIQwBpAC4/sZI4DlzbTxbZopIAHIB/Z6ELbETJiNL4MiIBGUxwllClZxqmp8FlWsry01MXz/EYgB+asjcmKsd4zhWTC9ne01RRxizJbyWMUqWUxITxmkwPJ62AC+VI1702RpE7YEzJWQngvCXLA+RqwHdpCxYnq7gJdiVbXA0JOfb68ctYWj8Vqc/SLGXJnYEz6E9ImAWcHXAi0MPkYAltKRxCiHgHipGH7zBB0jdEzgLIgXW90yY0o31kozKLgo4tNiVas42RT5w+3cN1OpTgzStM1vp6cV8co4/XL0xNLejKHFTaZAKPuA5Z6RLorp3WqGWLmZULQWUBGUQ0Q+2nIYieLwmwUQRX4YAFWM7zPKIeL8XUI622u1VS3299deM55tkYyuxrkpD3uUKWB9iAiLYP/9xdhHh7SdtwZuVnGgtidowjmDcoUOscOEu7q6urq6urq6urq6fkb6slETAEFLsNwYM8Fngcshbh8zU8ISIXWf7AWINpbK7u0KKgpNAZICyi5asmURyMCoyaozJGFL1dSdJWjy671VZzxVEmd/eHeWB6/ijBlGWCrCue05WxVJg6ckCPbeWsKCCPFcoMnqOCBg96HaPPPDhDr6A7haRabNOL9c/bH/ZtBZJePP2Mdv60LbuQgEGZOnTSy1ogRf6+GtZgYCyi/ut+tgvB57CC/7BK6yTUZTFeeakHOCLNFDaktWUGC41C2toeyfF+2fLREjIyPzAF4VYxWU3YsYBpGlqXzuGs4Qatfe0jXGB9LRKmVUb4kdcYML8PTMIp50su8RrtXew/1um3GHrznJ4OeqKHixtFY7v1CrErXXgCh2PtPe7jt2wDMVM3rqwJBgNbTGrtm4O+vNqLGUjyemIkFjAMbgxqCi7vZb3a2MhJAtJZOP0RaaktXejHdUkc7GqmlmZ7x4aoY8wMW0IWYQ2XhGboY1+DHaYtbLFTF46sjvx3a/hyWCWposdKemq6urq6urq6urq6vr56IvGjV1sIfVW21EES4F6e0J+esjlq8H46Q8ZcSdpTvqRNAwoA6WMonninguSP/je1AMyH//LcohYnkMSCfC8GmFDAPKrpk1hDgLQrYlpDoQ6jDaNLKbH8OTMWHynS39xEuGBoZURXhawR9PgB7sICiiNoYH24M1HwLCbOmP9P6Mehyx/HrEcBYc/+ET6sOEy7fjZi7Ec0WcK2pi1Cls5sYGfb2Urc6igRDa+WpJEPIFLWUsbwasR8b4ydJDmoyxIlPYOCMyMD79XzuEVXH4fkFYKgbYQ/jy2paveFWk54xwzVbLcvaNREJzi/Le+DLxXLZEigxs6SKHLjeTbb0LkGhrTvF5weXXB9SB7LwOQDnEDfBr94IYLHi1xaG6czMn4FaVCnYPrfcRYeSNpTJ8XKFsSSfOgvHHBXWKmH8x2Ytrq1wJyt5YNfvfr4inDFoLUHUD79b9YEya5wXxSTH9ViBTRD0ONved63Y/58dxW/dqkOdQ/RysYudSDSBN1SDHUJhhlz5nC82vA/LBPgYAhx/smOc3cUt8xVlAuSLkit1SsT4MuH4dMX2oGH9cUadgxlF04ynb+5IpuuFmJtTwsdzm6f2eA7ym50ZlmyqvOzOjqCo4G7fpZTWvq6urq6urq6urq6ur66etL8OEKwBV5DuD05KozW9/fUS+T6gJ4IlRagQJttRHS98AVk8qiEhfv/LUx4sHR+eEaLA0RlwU/Hxbx4mzLe3E2aeNR69JOe8jXCtCtodh9aUh2ScA90CgzUzRYABeq7eoVVgUAMMSLils1af89R51DNuDPC/2VFxHY+yQM0c+O8Yp+Mf8sIolRpQA9tSGRIJMaUsFAQCYUA5WzwqLpT4Q4eDkZnbcTDJyloudVwfGVrWESEuAyAtA8cn5MmvdzKCWNFGBX9PbglNYPQUzJTPIRkK+j5ZGebEopJGwvBlva0d+r3AWZ8jYJ6ezIM6EeBW/FmZmyWDn97PEEeycykCoiRBG3qpUu4slaSQyQiYgEvLjZMfsCRs7F8ZlsQ8AZRcgj8mqYdeywZt19NUpdmB2EWPdMEGGaEBg5+QYzBdbNarBltPZ1rRsVv22KsW+jnW7/y3RUscAGehF1YluxssLJhJUUfZW+asj3yDBtSVw3GQqgng2Q8u+1j7e6m1lH5BfQLW7urq6urq6urq6urq6fh76olETskICsNwFg/ReBHWwJRtLupgBAjJWRpzlBZzXHirLRKAhgL+7MzMk0AaZNa6MJRwk2UrR+O6KcrSlHT7d+C4AIClBApx3Ixh+zOBcQbmCglVk8jECx+gP59kME3+Q5mqvRUU8yWLGTh1t1UcicP522FaVOBvgto6MOjLSqYAXT8HwrbpU9mGbWDbwraVJjKFjlZ06DVjvw21i3LUeGHUEpg/ks9T2CXGWrT5E6kDdLAAYdbRzzyODJGyQ5cZYaamL+FTAq4CuGRh9QYrc7FH97H3E1Y0AtvRMmRh1AJaHAC5AOldALPlRdozra/bUiF33eKoIiyDMBXVv607jj9kMGnagrb19Y/PAkiBcrTYGT+uUkbDeGXQ5rIS7/7lY6ul++qwWtbxOUAJ271bAl7+4EGi5JWjKMeD6OmD/ewLPdYMuNxYR4LWybPwZjcYsevrbgHhV3P1LQZsCD9eK+Lyg3I8ou4DxQzYw9TcT6uhQbJ8+B9n3ANoiGSMfefuZghpnR8KtpmXXzNlMd57gSYTQJsGLGN/IAdgGX87bsYoblcPbM+i6Yv2vX2G9Y59v/9JPeVdXV1dXV1dXV1dXV9dPSV80auK5biwVLgajNVZIgezilmx5maJovYycLL0Rry9mqMnMCyVCOpkxUJ37kc5WPzFuTEY8GxBYnRHTlp6Mf2KGRDlEkEZQHbZUDvnCkCSGhsGYHUSI1+qsElvwEbZ0z/wmgRSYPlSr6ow+98zwlR9LUrQ1ozrFz1IKliSqXsUKoErGsBEgXqt/Pzu2dLl9YUufpKtVjyCWxAjXggAgzsZSkYHta59X1NEXmSptq0HA7UG/MWeUaavHcGJAR2ji7VxosPcNxZZakcG+l02xA7t3BWA7H1AgeCqmpTlIwmZMSbRK2fQ+g5aMUAQh2KR0TcEgylU3cHO8Wn3pdk8ESLD0yHCyFI4ZH8aHya/3ED92vEwYtTsuemKlKjgyZAzIdwFl5BcT8wLOBlVOZwbnZigCy6PNvXOxRanj99UNSGypIXuvzjpiNwQRt2QQVX9PtV1fNWPKDZgwt6UoN17UTCJVYwVJIHBp9TeAodt70kiAsCWjxJk5IP9ZwgbDpqqQKYGCGafpoojn+llqqaurq6urq6urq6urq+unrT9i1BRb2vHqRzxl8GUFfXwGxgHxbmcgWwcEa2x1IwIOlh4Jq9VKWopFBgPNxnOFuhFAaomNVmPhywqsGbjfQcbo60ZWOWnVHQAoh7A9TDfjoYFuDax745G0tIcSWcom2APy9Q0jzsD+N1dIZODB6km1GU+q4EXARVCOaUvJGMzWH7jnCrDxYxABLgxa7fs1E8tWjLDBaAG4gSSbaQAG+FRAYoZV3SWU/QheAT4vAEYH8ypIPu+0KGM71mZYgYzbA6QbRLjBdtkNjIsinjPWOEASW1UrC6YPCyCC+Rd7m0ify5aOYQDxUsw8i4x8F5EPjPEjgeYMgiU96iGhjuy1IzHmCgHDR09BFYEOEfluMDNCLbkyzAYXhpoZJ/t0m81mSx5ZKgjbx6qDiWlk1ImxHvjGc/EkEHkFLF5s7QpuAM6v7T6Ksxkqh+9nA0zfJZtXL7e5+Hb+ys4qT8asaVBi/QNTRAm+oCWfGZZtiQswaHD1WlT7Xs0Ea++RmAARAGGrX6mn05QJ4SoIVSG7CAwOGL4I0lMGF0FXV1dXV1dXV1dXV1fXz0NfXn3yqkkdGcqKckgIBIS8g9xNWF9NzoiRDS5bJ1takugLP55iKQebum6AW16dKTPSxkopuwSJA4bnEeFSDGAbCPFiJs7wbOtOXDxhM1jyIp2KzxgHxKsgzt5XYWO9kAjKISDfRYSrJQyGDwskMiROngrxRMeHFeWYUF5F46XsoyUVRJEP9rHdb2eEa8b1u4OZUc8LwEB8vBkKkgj5mGxN6RAMuvxp3VIZ64OZGJwVYRav3zRIbkTdG1A2zLawdf2be18x8oduN4okEoanDKqC+asJdaLPzYLmNxFQE1n9a7k9uNeJIcNk5zXrZijk++FWyRFY3UvgEGJCOTq3qFhF7PC7jHAt0N1gwN/yooJ0sCUkXp1RExlI4TNAMVUgXqqZLvuIsFTQKrYiNvJmcMUnS+MsXw1QIoT5NtWNtpoFIHkiCgDSpYCuGbq7LW4pucmigv1bq+Q1eK/6/He8VtSJcf06YfxIGJeCeFoRrozkIODLLxLqABx/I9sUeDPFSNQNSULZWV0vna1ORVAsjwnr8fZ+xo+KOFeUKWzg5W3tShQ6JsgYP+MchVkRluI/g2rJo4ERn/O2CCZD+Hf+ddDV1dXV1dXV1dXV1dX1l9YXjZoG+5Vgf7BqTATvBtT9gHwXoZeKVHSbrFZfINqWoopVZmRwtkq6JSPaQz8RwMVWisrOEgsx0VZDij7fHOa6VXtaPYlEjT8ysK/y3B5ilQhhNZMnHweUkTBWM274soIBDDs3C4JVlsJ5hQyWBhJP3cATKnW0P/NcQKcrQAd7/7mYSVAUCF5F8mpVPjCWRwJnBr+tftxWQ6oj3WpV1Rei/L83jktYBHW0alE6V1uYCmbGNFAyz550ejUCcG6QX79/ey1VbvUcwAylsqMNXtxSH+VgnBRe9TPOjAQy82nPZq4sxqdJT6sxewabXQfq9n1ldDMl3ypE4qyj9nFb4aqb0UeFEUj8ezr/p1rah4oY64jbPeBVspZWKrc5c5I2613MqAm8GVCN/ZKuBRoYSxy2e7N9XdnZulOcne+zFPv+awSPEfLLhLL3emAV5GST7nUwvoy9Hpzn5BdEAKiiDoR8aDPhnsrJAmoz4C2l04DJkSFeYdsqUqtYYqnYPSSTXbfhR1vIKq/2llbq6urq6urq6urq6urq+lnoi0aNeLJgePYp6w8z6iHh/Hd3vrJkn2O8Dqt4tHrN+MlTAIkhniLhrNj9WLfFJUsc2INoyIKwBE/jWJUlnis4W5IFR2D4aFPU8+vkdSZLlFy/mzZ4a94RJA5WuVoUYangpWD8QEiJEa4VUMX113f24L3aolSZAgITeA2bqWRGAG9rSg3wev31AZz3vrSkWL+7hzJheYwbLDjOiundCqoRkowZIkPYzITxY0Z6Nu4IiSUh6kTbObfzZfWpcPEE0BiR79NmmvBi563cj9CHyc7JrJDBF6ecmdLqYru3GWUfcP42Ip0F0/sCzkC4mplSR0I8qwGBd40d5HPYZMaaRkaYBffvF0iylJJGRr4fEGYDOGsgUPJby+HDynYf2TqSgosgXuUGlo5kKZ42Mz0x6jiAKjA8VcRLBVWBjAGyjxu4WkZ7P+NvnyFjwvLNfjPw2hKVpAB9fUDdRTMbi4CLWu1uYIS3V7AWxDH4Nb9xcCzJZde5HBIiACorwJac2X2oGM52fM1cI3FoMGBz8FUxfjLjqkzhBQcoY//725ITYIm0l9/fFsqcj0ONt6PbClUdGfluskSRJ54AgJZsZuKrvSWYurq6urq6urq6urq6un4W+nKiBrCUSRbwUsFLhuyTpSl87lqSJUA2RoxzQcJSQcWhvv6cSNXSMY0PY3PGsr0+HAabjxES2yRyxfpgfx5UgYqt+hGvNpdcJjMmGuy2DrAFIPV1o+qpiqLbdHY5GOx49LSCsgFdNfKLtEb7px1Ym6hej754dBWbgd77Ss9A27GGVcG5IqyMsBokVgMbCFlhDBvIjVfjvJdWvwmLQY+pOAB5Lb5gFV8sawGoijrZohOpA2uVPl9lJgIVS17UkVEmICz+GqJgTzw1/gtEtjlw8rSUeqpKyQHL70+g4w75aOaUTOFWuWKGumFHis/4Qp+9rWIsFuMJGWOnLVw1/kq4iqWOLiuoCOpub8daFATdjC06X8HAVqVqaZoGJNZoBpwGGPOnCuoY7ZYVS6Nwli0NpkQg58i0paY2Jd/+Z/Bnm0AH4IwmbD8IGjxJMxvjp9UIQZbeis8rwmkxDhSzTY4n2rg2VG7GFtrLbutidh00WK2K/Vy244bYdVT6w/Pe1dXV1dXV1dXV1dXV9dPVl2HCJ+tuNEBwfnMAABy/N75LHRnLQ8B6zxg/KdLZHqpbDQUM8FqBQKiTmRXhWuzFydIjdQqouMFVOfu6EGh74OR8mxgmUUzv8u1BuVoCJCyCeFq35Ee+H5CPARoG0P2AfDCw8HCqzkOxNMf1qwQuiul9tlTM6xGSCOOTIJ4F6Tn7YhIbDJgIZc+QcKuzpOcCLm7UBOPzSCTMX02gqhh/zNDIWB+Sz4Ybf2czRwAMnzJ4Fax3Vr+ZfrQkSzkO2/WwxScgLjYTXo4J66thqyut9wEagN3vsydAWn3KVqnKMYFXwev/sSAfIi7fJMTFa0fFzqNGRj0OkGjmyfUXIwAgPZdtyYrXCsRg52W8AYrLLgA02gJTusFyGxMnXi1VsryxCfR4rs6nYYSlIp6yQYbXgvWboy1yJYNNQwCabQ0szAYCNqPPDD6dRmhkpKcMBIJEBhczAGUMkMg2ze73C9SZQ4mQX+8ABfLReETjB0sLrQ/JYL2+VJV+vGJ9s8f63Q7jR2PyxIunyu6NK5OeitXkBrb7vxDiqWL4/Qn1OKIO01aNs4uqbmyFW1WNAVQgvT1ZZWsaPIVUoYcJEneQxG5gktekDFhs11KQvz6Cvjra+Z9fdN26urq6urq6urq6urq6ftL6cqJmm3/2paRoCYtwKcAU7WHUV4YA3RIMbSEHcJPnxett6zmePNn4MNWmkdFSAS2d40kZVWPFKHzWWACNMCOnqlWIZjOBCADtk7F1HERbR0vMSCSwGDRXYMs9pHSbik63mlZYBGGpqERAULDCj9NXeNr782RLyAoRbImXOhLibOyXGv39AgZ5DQQZbpPi7VxtU8ue9KnxRYKjpUT8f0pmCIVi56CtOXFpXJNwe10GyhjAq4DPBeQsE/WKTrtuGsiqam6+iCc8QmJwFq+KKTQEaGhRqcblsXuhjjdGCxXnr2hjGJEbXEA84/NjytUqO9cFlPdbQkuCVb3A5CtaL+61tdi1i8HOXfX7LeCWoBqNGxPavelJq5ZMsdf32fIK0FJBzhG6bYADlKsZO3uyie+Z/HV0u+Yhy1ZBUiUwLJGGNYNy+uxnY/uxaCkdfpHmgSVittqT3zfIFWHx+yfxLX0jVn2yupSaERhslr6ltrq6urq6urq6urq6urp++vqiUZMfBmiAL9YoprezPRQXe1CsO8bwLBg/VKTnjDAXrK8nrPuE4WO2tAOwPfTD60QyBly/tiRInGV70JTA0GSfR1VR9gFo3I8KXL8etgdTzorx/QoEQt5H1MeE9b/sMT5VjO9W1J1VTIrxdc0YyILxR3ufEhkcCJOvA7UKTXqut+ljhi1A7QPKdJsIt1qTbhWbOrBxSD5YKmerQiWfjXYwbHoWDO/O4A8nyO5rVK+sALaMBFWkc72xVRweW/aM+ZExPit2v19RdhH1VUKYFcNTsfnlqtgV9YSK1X0k0gbglV3E9U0ANCC8iQirYve+IJ4r4vOC/DBZ5czrWwb+tdl0ZcL6EMCZsT/bxHl5tQMYGD4WZ/r4mldRqwIR7JwxMH4woPN6l6DROSpklbE2aV12EfkQMXxYkeYV4Zoxfgh27veMcj+Cx7glnmwJTBHJDAzKFTJGrI/Ddj/RRcBPV+SHEfPriOQ1JarRkzUV8eoQaADxZIZf+HQGcABgaSL1tBB9fbAJ8wLkBlvOL1JfVZHenoEqwNfHbWGKiqVmSATpVLYqE2WBBjOYlAnrfUQdCMnTXpf/8sbvddkMrXhaMfzjD+BfvcH8+oCQbQmMKm6ppl3A8tjeX9iMwK6urq6urq6urq6urq6fvr48zx0ssSGBwOHF0x7foKZUfNLZkwrqyQQAxjZJ4capUdpqRBJhCZUXqZ1mLrR0ghJZMqIlSJh9gQrbg6sxRtSXdYAyMuIU/HWwLSCRp3TUazFgS41s60nR0xquVtux9+qmh/NBtohQ+7MnSiiLpz58uvrF2VX2yfIUAAfttqUfqCdSiLbZ7bZM1Mwh+HFyFp9LZ2fneHIkVzMGmDYjpB379jFgS6gE+PTzWkGLGWoasbFktvfmZhRnS+2og2m3JSG162zpFXt/PDC00HaeyFNPlbxGNL9YaLLbwnguI0OmAN2Nzj3SLbUkgUDBGDNo54rs/tLAYCL/d7uOJLCFpyltoOnGHGqJLc7V0018OxYi+/6Jb2tXfn+UXbTUjbxIX4m/FXmR1vH7TduxMUGHZO+P24XVbQZcktXI7OfNzoXFmOxekA0yY7DrEAPAtCWC7B62a9wSO41hg9tl6Orq6urq6urq6urq6voZ6I9WnzgrhlIBsUWabTJ4qdh/n5HvR1u22QVQHTxB0Oo2hPUxWaoEZgLMrweAvFq0Wr1IosF968ioycwTrupmELb6R1gEHAjrgTcTgotg+LSCS4KyLebMX6XtEMaPFfFasDwm1IFw/qWlctLF0x/ZK0ReRbJaiy02ycCokXyJCti/LYjPGcubEXUkMy48JcRuaADYzI2wuJG1CNbHiPmBMb+OCOsRw3PF8DEjXDOgivXV9BmzRgZ7eK8jIWTF3fcF8VwQTovVbdSSTmUfcfznFfzpgut3b7AeGeMn8WSLnaRyTFACdu/rrX7mpoT9Wcx4iLSlYoaPGVQFl+92IFXc/z9vgRTx6b+9BlVL9sjAyHcBYdYtUWX1s8nMKjc64tnqSbQPiFfF/r//ABBh/dUryGipmTrYTLjEAfn4uN1/rYpGbmANT5bo4WqrT63i04wjuz8BiGJ5NaD8ckTIivHJYdLVrgcVQXheQKpYvz7YtR6s7lQmS6oMT9XuzZ3NrLe0SzqLJ48IMgBUaaut5a+OZsJEtvNzCCgHS4fJwCg73q5fvBpTpo68rZ01vg2TYve72ZaudvHGhHoz4vLdL+0aPVVf9lLkO0t9xdlSQ8d/ta8t+9Tnubu6urq6urq6urq6un5G+uOrT8BmQEg0NgtnAeS20GOwX/vtva3PeLrGKx2NMwPgNj0sltrgIgBswpudlyIBqIGtOqW6LfFwUQh8dtpXlGwT3P4cZ3WQL8DVqyLXgnApoDZr3ZI17MmFVUFt0UkBdtZxq/PUkW/JHNHbshLckFJ18K6ZBqT/P3tvuiNJkmXpnZjCyMUAACAASURBVHtFRBcz8yWWXKqqu9nFDQQIEPMW88x8C4I/CAIEprtRa2ZkRPhmZqoqIlf4414R9ahpRpM1RXQmRg6QqAyPcDM1VfVE6YlzvoNmPokxfGodyS/KsamvVc8HFdpXfHg3Jkrl9ABfpjUsWUMjm1ngQEN4dW7tuphRU69bkZ13Unhns/zr17y8ei2tFxXWFaL9GlsSijUdUhKD6roTv7r2udi1tJceAuAYMrgdhiv7uc8jNRONI/a57QLA1p5qaqiqLUaZsUdiiR3ilvyiYsfltIdXRqepF95Xw6hAa3E1UeT2JFWdxeZNUNihVM7z6+Owqlv9Gic7T6xmjlvVFGuGnN079c/We62mdorbzbu28jQRXCSQCIqllfRz2zUNmgTjeq92lnBXV1dXV1dXV1dXV9cvRv+vjJr6wJ1GpwkUBtggt7xljJ9sWScwwtMGWjNk8rZSVMB4VcOozQ+2lZpzBNs0dWV1nH8zYr0j+CvBRcHlnUcOhOOfI8Ii7bjS0e+mhRSMH9c2c82bclH46QpaN+C7GRKA6aN+PZ20Q6XmkJo+/low/ZQQT7qIJLbgFK5qshTPSMdgiY4Cf8kgKS0NdP5uwPBScP+/f4IcR5z//gDYg/jwmHD8l8XAzEA+DJq4OE0A6aoSbwXxRoGzfInA5IGi7yeeQIODjF4n088RadaJ8eX9AL4LoFIwnIstMwnc86LXb/DN0JHBWbqDkEaCvzo4tonrWuMyjhBFsQnygvzmCAhw+NOihtaWtJ2zaRpkmQPCCyOwLnzlQdk6hYH59wl8XkDvZ+SR8Pw/v29z3C4WDI8JuOq9kWeHeGS4pej0dRRl0NQFMKuJbW9GFLaUlRTI+KpqZctOVKyClkSrVwYVTrMaaHQfGj+Ic0F4jGrQBTJDTb8uuSCsgiFDU03XiO1+BMhZokpNx0Ja36rnkkSTSQpZZl2JerxCDgPyHCxJw2Z+6QoWld3o2u6Gdj04CsJTRLwNkOAgAYizw/wRCE8RzqbH40HTP3Wy25/1s3d1dXV1dXV1dXV1dXX9MvRvMGpeRQUKFMQq2GGt9jf8cDUtoOBZjMoKKX4H6fKm/JZssNriLMUCaE3EjAHKWi0pxLpQlAVu26s6dZkHQKsdiXc6z31NgGNQEV0XmjxQJtCkVSiO2MGqNcWRBeK09sKxKH+nwWE15UGys1sAmOFU2THUaiuApoHkOCIdA9JICh5OtngUXHsIV4iuIM11yto1hg4IyMeA4rk98LdVqtE306IuTlWeDCVqtSsAyMdRExVWQ6u1NbLEiYvG7DloPU0c4Oy1JLCubFVGi7O0TE2CjF5TNJZQ0YUk0pUlO0dVMnuAJz1fZEkXob2ChcqpIZvDRmPRtPRLYxNpKojafZWVe+RoT6LYtVceTQUcZ/0zvNfm6mdxq7QVrWKvjfqPlC/utzwyihvavV0TM4BeC/+isaA88hcQXyp6XDIH5Mkjz66laep5aO9bfy4aa0g/gzuvAAHDyWDVg9b1av2LEmwJzEw30G6OdnV1dXV1dXV1dXV1df0i9FWjJltKoS75hOdoZgmBooCXhHQ3It54jJ82uJcV27sD8mRrRlYloVwQXiKKY+RBOTZ5IHBkFM/Is8d67zE+JISnDfMfz5hhtQ/PGD8lBQ0bi2SvhahBEGfC+EQID9JYIdubEduNA5XBeDGC8SGpGTCymht1BtsxthtWZs6iCYfxc2oPzvHGaQoDaODiwlZzgaYYlO8CgICXfzggD4T1njB9BsLTBhkc1rejnj8ChocN/nHD8m7AdmTEo57rcNVIRfp+VDNlEatNZeTAiLfeoL17TcpfM9w1IZ00BVRXn5Z3I1CA8XPUdNGazLBxcEngDQ68fDu/YrAkuEUQb9RI4Kz1IxnUKUmn0AyXwtQMgzgTxs8F7rwhHwOSLXehAMs3I0hGTQVJ0ZQOjD9j5lRlBOWwG3l1/h3YU136C9jqkyayZPbI46DcoyUjTw7bWwe32sT6kuCeVxSeASa4qyah4klv//BZk0fROEG1lke5aNUvld0YuXEoDggXgVtKmyvPkwMlwfDPH/S1/uG9gqjr6pcUpNkhvxmQJ5soz1rjcwuaUdTqWUzIk90TLwJ3TaA/fUSYRtycb7C9O+DyfUA8ENabAdNjhr9oJSsPALCfx66urq6urq6urq6urq5fjr5q1HCyVMmmE9oyupZ8EE/g0SFN+uAabwLy6KwGUqz2otUUFIBi3hkvxiOpy0r19YqzNRzHKE5TNvVBsz40F9bZZ8BmsreCySoy+TBoTSaJQXz1Afv10k+dYqasZkG8Udhqndz+IqnhtJri1gLK0tat+NUMNhk0mBOQw57Y4VQwPujvtXQHsJsQs98/N1vaRzS1BAD5deKICXl0athcMiQw8sRtgaguCwH6/ung2sIRZezrSp6V92OrQo0hU3Qm3UVd3MqjfmZn0+mA1sy0VkYtyVQNKxRoNYwJ+WZENihyXSXS99FjB6weVHaeEW+CMjlLW1HjCzUujRSkk9P1sWQJmVqHsspcOItevyQgqUkfOzeOtYpnyRtX9HpStpTLIWjyJoomn+y8p4PTtSebGS/OYMtLXZWCnfPSGEz52ztA0AC+nESXobIAcFqjeikIr9hLdUHKVT6SJWm0dmb1vALQzRFymJoZ6raCNCnQWOznxMWCshD8ovezv2TjQHV1dXV1dXV1dXV1dXX9EvRVo8ZdLc1xVXMgnjyK14fC+uBaV3biW4Z4j8OPuk50/cYjTYT5E+Czpm90OnmyB2qDp7KlKYJVNHLResisKZbiCOFF0xMKKGast5p6mB4yhqeE4fcPkLsDrr86wF0zwkuEWzM4C7bbgDTtZk9NQPASIaPH+bczqADDk7QaC68Z/uECOU2QMMC/xH2qukKPxSozpWAQrQrRkVsqwl8Ew+cNefbY7j14UyOgpYBuHQBLP5AaV/XBGgTk0S6NmTBpJISnjPDhgu27I7Y3vpkWxVFLvADAdut2PlApgBliefIQWzYqpGaAAnsLhksCRcH2ZkA6Mg5/WpWT4wgyerz8/WSG0m6ulcYaKghnrY2t78yocYBbtLKVZ0YhwvC4AQCefnsAFWD+EJWls6S2iMRmsNX1qXq94q1vkGEItCYnaoIBwPRhNfNHlJfkADZwshqIpKaipXooZnDyEA9st8oFGj+tANSYzJPDeq/vGQ+76XR4zhg+bdjuB+SZ4ZZqpgjEEV7+8QQAGB6THv/Vklmppr0A/7TCfT4jvb9BvB2QTk7XzjY9Nhn0uKefNoU42+eI390h3gacvw/wS8HwnJEmhzTt8+t+EXAiDE8Zbs3wD8teR+vq6urq6urq6urq6ur62evrRk1dDRo0WVKnkmuipCS0pAhlwElBcQqE5Qj4UhdvABk8wIBfstVc6mS31m38onyb7d1kyRKBFzUh8qhgWkqaYnBbAWeDr0qB3B2QTgPyQADcFwaSu2a4ldrKEkppqR0QwUVNb/hLtjQFafohJkBEuSSTgYfznpyoE+VUCtKsny28qJlEBQ1wDNiyDwESnCVIKtTEEi8MpIlAA0DiANI6FZWC4pS/MzwkBeRKXXKqqSQYhFmNIAAYUJeSYA/+bq+KeTVp3CZqxFnKKU0OcvQoriapRFeFvAeKVsHKa2YNl1ZFUuOt2HFgT+JYkqdBcplQnC4qAWgwXQlaHfOLTaVb0kR5PmimGApjeErKLromgIEy6y1cF7iK8ZLCVcDV8NkyeMvwnlraRwb7PtF7slhdTk+uvo5fBFS4JWzqJLp8OzYWkJpS+gcK6T3S0luW3NGToPc7b6LnaR6RbgZs976loUDq8lWGTh4dSHhfe5oVAu3XYtPwhPFRMDwLKL+aMhdo7e/oMG8C3tLXfsy7urq6urq6urq6urq6fkb6evXJ4MDp6JURE/bqCexBXAb9urN6hng1A/wqKJtOJhcmyORBWcBLBnllzwBax/GPK9xPj9j+22/w8usR88cE95zgrE7z8ve6FhTOog/W9gDuz8pc2d5OOqVtx4Ija5VnUa4Orwn5GCCeG3emeKtmbVqZCc8bxDPyQSe2aY3tAVkGfW1/kWZeUS5w16jn4BtNm0yf1vZgXo2KQlqJgj1ED+cCt4lNY++m13pLKLa+VIgQjwCg3Js5EcJPFzVpsrT6GWBAZwLgGbREO67aoVEGzXY/NDOgQnrdIhh+OgOs12K7DVjuHYazqNESMygJZFSzZ/zxCjhCvB0boLbBe03iCSUwwiWBNoFMWlfy5wTKAhk10VNnqNNs12NWU8pfBHkkS4hAWS1mXrirHlf4vIDWqJ9t9Hvlp9a7mECpIDyriUfJFqAW4/OIU0iyfR9nAa8Z4hmX9xMKAX4V8GbmXSp7lYrVQMsjMDwV+FUgA6EINR5PTVuhAMhaxSqsrBoqahqBCPk4YLvzWN4w3GKVJa7nVQ3DPDNgRlIeHdY3umLlVoNTO03d+A/P2H59h/VtaGDi9U5TTcOT/3+eYO/q6urq6urq6urq6ur62enriZrzhjJ4xFudiNZ1HLSJ6ZracEtRQGogTJ91fWm7CSg6tmQrSlmNi+CAogZK8aSA3DcT8u2ANDmEi85Bl4FBq1aehqeM4kmnjktBOnqACPE27EyYTTA+VGOFwKuuUAGaZFFOSMF2GwDWWhdgiRSBJmGyaHVmcCi/easJmGwP+9jTEvHkDIxrq1FeUybxbtAk0MuGdAy4fhNeGQL7QpB4skUh/fccCMNzaSwZUEE4q8nltC2E5e9uWk2oeIZvnBRNkJSiwF+S0hao3Kqf0S3SjDaKAK1qlMS3B9QFK3/JOK7S+CjbuxmAJkZqLag4xnanqRtxasYNj8mqa9zmrNPsUQ77fRTvhrbWhFIQXjRVlY6MAjVG6gqXJnq0DpdmBjsCR4UaQ4A8B9BkvBwmNSGkQIJOpG/3fuffGOsmHfQ6iqVf3DUrxPccW32qeMbwmFrVr3hCPHireBHcokaRvzDyyPBXZSGlk0MeSYHApejaUy4YP0c1aUKdRLeq2qpT9nm08/pYGqvHrVo1zJNeA3fV85KNDTU+ZDODRJfSgk6h490Jbkk4/DFpvc0TivP7fdbV1dXV1dXV1dXV1dX1i9FXjRq6qktQ6y5uLfu0dn1YXwS8CtIhIA8Et2a4pw1kZkpNF1RORvGaeHAvK/JhgBw8ZCSkKcBfZV8E8qz1liRtbSp8uli16IA8Omz33mpLmnThNSNPHglOf71p+qTOhwNAOjJyIIx19trMBRjkmKIyTtIhNJAyZzVCKvw4HjT1wdHr+pJNSscjw3mCv2hN5fwrXR4KZ02+uNWqUIFs6rxYAgmYHjXZs91oTcmbKeZWQR4Y1/cOfmGEF01K+KvYtQDUbdHXpUL7QpWtDfGWUYQh3tlxCGRgbHeu1Wzqald8MyPNDuudzUc7rViFZ+W7xCO1Yy7MCE9k8+oEXvV8pVkTSM5YRGl8xcNJQHiIxjziBniuEGetRpU2ha0XhcG28iTGm8mjVYnsmsjASEeHy3uG24BwKS3x02pUtWGUC2gR8BLNbNHEUXiOWpM6r5DThPVNaN/rVk3zODN1NLlSEG+88mVSAbKuObkN4MumxtLNqMbM7FA2M3dmh3hSGHB4zu1a8SatLgZoog1EzWgKT7EZk3mEragxxAeMP17hPj+D70/Ic4AEbjyePs/d1dXV1dXV1dXV1dX1y9FXjRqILSjVClBjYOhkc4hZH9oDa32o1k9GBwn6QO+yQma3d7PWgCYzUZbYKhn+JWP8HBvgtiY04v0IiE57Uy6Ibw9tKae4fVEKBvPNo6Z1aj2prlABQHiJ4CXDXzx40NQNiJDGOiU+6RT1ZumOrImhPHNLJSxvvc6KJ2B8UsODU0F4UaiweOWZxJNWUO7/r2gmlMKPUbklYrybVDA+6zkk47YMz8pL0UUntNUfv6g5488J6eAg086L4U3aJLeQmQbQ1aJ6vVDq++OVYWGLUAWQwMBhACWBvwIyWlVKCFSgU9ak61RulWbCyKCfucKSOQncynb+GWUyho2gwYLDo74vJzWXrt94hHPB9GFV48HSOm7ZjYu6xiT2+/rFOvGtn9lfBXf/rCaPBGqJmsrmcZstMJmplW8mUMzwL5uaP4egqSQzY9xSQCk3YPP67dzSL0I6HS5+P58owPCkF3L59Q0oCZxxg9TQI2z3g9almMBRFNTtqVUBKRvUmCwtU4DhUZM/kKJT9m98g1XLoIaazB7gG2xvJuTZGa8J8K6Cfrq6urq6urq6urq6urp+Cfp6okYKStHEifBeg6Gs9Qt3TcDskSeyB1j9Pp3Xpn2SmgjxpEkI8QCgIN8qtwr80wKZg9aUAu+JCgDD4waKGfH91BI5ZGZKnXAWJl0F2gBeRUMmr6C0lHXpR+tAWl8pDro2xYR0dOCtINTpbdnNqVahmRnxAMyf1BSotSj/vKoBMHvI4BBvPNwqGP98QbofkcahJSGaYWJgYncVsKvHSXCL1bXG/eG6GmO60JTBiZEBg/qiVX2SrS2R1aXyYPPboskKt7zqwcgORkaxlNTAuqa0ZXC0ie9SF6MswWKMovCcDRLNrz4P9DzY8aZZj4Ebv8hqc5a0qiyWeFQWD79aRqoMIglqiKDeW2bS1WMvrGmiQmqc+ecVMgVsd0ENsSj6GkUrYJxE159s8pyZ4D9fFeh8M2hdaOQGhOZN4K4R8XZEOjqtQK3SgMev72MqOmleGMqLyYzxc9p/lhwhT9RW0+rxFee+aCi586bVvpOuY7mnuH92T9huNI3jLvZ1p9PjmQPSyel8fdnf8zVHqKurq6urq6urq6urq+vnra8aNWUaIGP4AtRLWQ2SPDAK6bezVWkKU0tEkChfRVke9aFaEzXhJcH99AQeAniddQVn8PoQX7QmRAQQa7Jke6PJmjypEYGBLMmSW1qEi4CE2sN1sSltZenook+hESBly4RnfYAOnoGi5kMeGNfvBvirYHiIyJNDIWfHbpPkrKtQlArSURMPnAcgF+SDRx4Yy70DJ0aaj9huGNf3jPGhYP6YdTI7Fp3INmZK8cByp5Wnwwc1fygrvybeMvwqGD9GgIFsK0dqCFgCaSTk2SM86SR0TWg4q1eFl4RCCkXmTeDP9oBPwPZ2wnrvEM46D82OwVnTGiDC+mZAqXdJUR4RCGparPrnysBIk1bR4kwYnwT+knH6XQRlQZ69pa64cXiUexPhDg7FebhNa0/+ki1xlcFLAh2CmUH2WZ62HXBdTQgB/FnZPes3B8QTY7lnjE8F4yc1TkBAmh2oMHjVmlnxhMwMPg1qXOUCZwDiPDKWd0ENo6sHR8H0cWtsHAUi6+uDCemg5h9s3UocAEdY3vk9QZV1tj1PjDQS4pGRx2H/eSOt8omtjFEuDZbNm5pQbs3wi9cEVXB2P6vhBU8Izxn+Is0IpFS+qFN1dXV1dXV1dXV1dXV1/bz1VaNGxgCZFB5bGjxYp6DhCAIDyKb68A01XRza3+i3B8ZSUApBsn4NKYMA8NVDBg+Z/b+aUICo+QNgX8XBnqTQP2iVojoTLWr0gNDYMnJwyAM3YKuCkQuKF33INaWJwMm+sdTKjf1+rS5ZhajObufAIG91mpGQJ0AyAcRIMyGP0Af3V7PNIG6sn8JkyR59Ta4P9jWFshF4TVpzGV0zFajo+RCn89ajLRzJSSnOlDQNxVc1McSTnVOtALE9wIu39416bQWMcI66WnS7A5Gr4SCOLClD4CVBOGhVZ9B0TLgazPi8ga4bCh2/THWo7wK36MS225waJ2aC8SpqhtmUeqtsFSiUmsiA0vU+sfQIgDwR0qjJlXwt7dgBoHhASGt6lAUFCiNua2CwezYJMKqZwk4POCRbhwoOZdB7j6WArNqUDsPOb6phKLJEUUZj8LR/RJNBmRU0jQLIgPZ1NQB3phHAO9h620HSlT9TV704CbCWZmC+/vxdXV1dXV1dXV1dXV1dP3991ah5/J9uQAIML7mlQABb5/EGcB31AX04C/xFGpD1/HcHpImwHZXPMjwbN2Yi5HlAvP01wktC+HBGPgYs3476pq8YKv6c4aLo+5IyRuqDLmCLSaPDeuvgl6ILRDNDgm+vpRwZ5Z3odLKaF/7zFWBg/fZoU90w7k1BDoTLrye41cC7Tn/v8CHZA7UCeykXEIB0cppYCWRJCa3yUAbCuWD+qHUZXnWtJ81OExLbXs86GpS4rkkNH67GMRlBSWe288FjvXNwsRiMGA1InMf9gXx9G1AYuPlPZ5AUbG8nAwMr94TM5ChM4E1w+sOmlSdjoMjA2O71ehRbkOLNjCNHEEuDUAGCpWWK1ZXCRc2uPASdab+McJcNWID07WzrUwphducIvib4s06i59mBsp7XePJIB8bwlOGvqaWHoGGgtrzlNk0opVMApMC/ZK2wXdWoyDODV53aXt94pImQZg/KwPEPC9yakY5BP5fXmW2XlXmkNT2C+NLm3FGUY0RJjaQ9LaTMHo4CKnrv6rVWczA8J/15OagpNS5WiSqAW3UGfLvT9JrMej6ruRNesqbEHIFixvRxs8UwvSfSgZuZOP20wS06R58DI956NQm7urq6urq6urq6urq6fhH6qlGTx50vommG0oC/gKY52vJQqfBbS2xgT4jU2eUqcYAcGJQcPLOmc7zVnl4HL6ImRECa6iEDuXK0apM3g8QTitPUjjAbYNYMpZq6qEDd6mcwNP3j2VabaiJBj7Ut+Yimh4Dd9FnHoDUwe806aa2Gjr2+JXo4AeEptUSIkKUcLD1S7IA4FpQEyMESGaWACu2m1OCMbVIXjMoX10Dc/tla8iga5Lh+nx1bwW7qUC7gJVvSxoC9nkBuTzG18wcANlfd3tupgUGWAHGbcohyIAUxA3BXAhW7J4gaGBiOUBx/kbapKREJhBzQrg2h7Ner1dCwp7WsKkfZEl5XnSTPA4FhqS4DOrfPVKD3ql2T4izRVWFL7STttbdap9OL8upmrUkp+7pOfAsK6yw3JWnsJa0PlnZP0CbG5wkGpaZWH2Si/WeCWQHcubTjkvaz8+p463HwvtDV1dXV1dXV1dXV1dXV9cvQVx/hbv9lRSGdQqYCuEtu08iUAFcKpk/5ixnsPHukG63e+KUgnPMX6ze1slQNiPRuBgDMPxoB11afwITxhxfQywX03T3yISAdXDMcOBYMD5tWmIpxXaKAggJJeNP1p9cmgBo5ympZ/7s7NVKM48KprvOoUWAjPJpAKWoG5IEhgXF945BmQrioKeCX0h6U3VZw+EE5LhViDNRKjK5NSSDkwQHQVEir8NgcNwlw+bsjxOsMeP09vxaMD+nVlLW9ZtB6VTzq5Tz8sKkpNOmvwyVpPcoWmsDA8BDhf3hEOc1IpwHilZETbzzySGZqFISz7DUrey9OBcc/6vXKs9P1opcIt+r50fNEiAfGeuPaQhcVwF8zwsOCEhwuvzlobS0BbtPXyJNHOjqMDxHH30fl/owO/kWntJfvZjUQk/KP5h+uQCmQ0e+T7VkTRzWhVI2V+YcVhyzIkzdQNKP4nRETj2xT5JqkGs713tBzHe9Cu//cVZqR6K4J67tRuU1OZ+zHP5/1floHSHCId/o+bi3IE2G7DfCL6LKUOIBJz+HC2E4DJAB+UTMpj9zgxTIwthvWn62XZLBoMxGjsnVkmOCuCWHNatL5Xn/q6urq6urq6urq6ur6pejrq09R1LGwv7CvaY6aNCGxaeiYAc/qVVhqg5POJHPUB31xbNPUBcgFvGXI4JAmq8GsWWsxzJqaIEtSWKKgzki3SWZ6xeWIVociS1SsWm+q5kmhPQVSUwytXnXN7dfFlqLaa7/6/roEVV+jPsBzqifLWCOvVCG21eyBoL32bn68SiSRhndes04qY6QwUOKr5Ant/9Rjqrwbt2SgaF0HvKeF6gpQrfDQGlEOoyVbNH3BtnpV6zJ/aaxV9hBnTVcVlHadNHWlhhdlQpqd8m8G/VCaYLFkjJk+KICP0r6u32usoWRz2nZtNb2jn5nrvWQGW/G7IUEZNtHO8NDXEEdwWUBrBgXXoMCv00WamHll7Ml+D9VU2f6b+mcrh0lnv2tkCiija0tVqKwd+766fqY/T+WLexkEW1XTNJZeM7QLXe8J8Qb0rgkrgVXkCEIADW7nQ0V0dXV1dXV1dXV1dXV1/UL0VaMm3gZ9aDe+yXrvMLwI5j9fAVFORz6N2N5OytmIgvC8ASLIpwFi9Zk8Mi7fBZAUTB8z/DWDXzbkQ8Dy1iNcNBUTTx7xwEhTrWvcYHia2gO4XzQhEI/KP5HvRri1YHiMyl+ZNXkx/umK9OaAaMkeJQrba7xEUNbpZ94y+J/+CBpHxH/8Fml2ut4zEOJMGF4Ew1NGnjQh4pPySeaPWeG9DxEowPXbAYWA8SGheMJy71oCJs6E87cO46Ng+pStqgLMnxL8OeH6qwlxpta2iTODMzA8JtSFKrwyZNKx9ZdancdtShXmpMYCr+oeLd8OzdTiqFPgCHZ8eYB/PEImTYnkSVeLDn+8wj2vWH5zg3TQ9AYJMH1OZsKoiZYmTdK4JUNGh+0u6K83QXja9N6YNRWkhkKFAhPS6fCqalaU5+II212AWwTjpxV59li+PyBcEvw5KkcmMIYn/WzxZJydwJDB4eXXIzgVjI8Z/iXCf760alL89gbrO53rdo6xvgvIAyM8ZzOc0JacqP16r+0JgOGSER5WyKhMmmocFjNsNNECuCWjBMbTf3+jy1aPGe6aMf35jHQzYn1vE+D1GtbXcMB25yGeMH+ILcUjXqftXdb0DomDBDW56qJUNRoVqKw/q89/P6I44PT7DW79Cwexq6urq6urq6urq6ur62err9Mr6l/yZwAo4Kwpj3QMcJcIvmw6VQNN37g1v+LT6J/Vyos9BNtqEAjIh2ArRPZer5IuZHwZhfa6neUhO8OmGAOm8kUUqMug7MFx1NTFcCKsFwAAIABJREFUq+pTq/040rcKaiKxM+ND9hUlFMBF+7WvaRoArK/TgLNW0RKPPXVi5ko1XuiVoVLXolCgqaFB55zr6g8V2HmqqZP9z9Or1E+9NBI0TVFNoZYUMmMqj9xqYhBYiom/OG/F65w68IpDA8Bdc7uO9fy08479Gn2xZGQppsLaHdOkVNGJdoMe1/uJqCCznatSIAZjdqtdC0vt6HtSu/5N9T7wyjhq6aaagklZGUbOAaz1qnb8Rc9RcZY+sXRTnTPXJJLymdqiFBPyIbSfiR2grCCkCoWWEbsRA03yUGCU4NRUqnDm+OrzEFrCpwV8yN6D8Z+p3l9/idJpn49gta89kdXV1dXV1dXV1dXV1dX1y9DXq09JH1Q5ZhQi+HNGOjo8/eOI458Yhx8fwdMANzqEz1fQ0xn5/R3yMSCePAoD44eLglRN7pqQJ4/zbyY1GK4CfxVwFEtk6JJPBcpu9x5uVRPAXzIqJJigvA8UTZmkmbHeMkg0LXL644bxzy9ATEApSN/cIM8e8aB8kvVOeSR35/coSfQzJjUxwkXaZ13vHPxV0yjpyMiBMH1KcJvg/KsBaazMnHrOBOGyw3vdVjB/LPDXYp9RJ5+3W4ft1hnjRhpgdvxpQXGM6/eTwXLVvOHN4LijPbkXaPpoBg4fBOFJV4XywLAcEbYbNRNOf4pwlwT/sAAA+I3VcljTKMqZKfBrthUjB//5ivBjQribUYJDnpSnU+oy0qJsIt4S2KarYTWceKPpF7cKwkuCf1hBIli/OQBMCA8rSnC4fjuqaSIA2MC/I8MtDnxNcC8b8hyQD17rXNeCeKuvXc9BnjxICg5/WtuilhotjHR/wPZ2ANtxUFLDyJ8zOHJbSxofNQ3mX2yp6eCAAoyfU7tvt3uP8/cew7MgvGRst84mygvcUhCPTqfZB2XkTJ/U6JKRIKNDOhyQJ+X2+KtgeJFmNlXDsk7Yp6PxggzGXE24Cm4G1AD1FyDN3Ba/qOy1smrkSGDk+f/7fxi6urq6urq6urq6urq6/n30VaOm8i/qw6fCeWErToRy1CdAtySU4IDbI2TyKJamUOYLa8ok7emIYms0Lha4RZStYdwWt4oBYHWOG6+SKXXtibIeNm8C0A64HR9lX+cBNMUwB02v1Id7BopXA4WTAXdlh9HqN1oKJmkixG1qIvkrwElfu5oFtXrECVaJUWNFvD6Ac66/X6HClnIor9I22Tg1IMjs7RzZ525rVfTFrymrocRRzYJS6ccE5MMAsBpZVLCfe7s2zlg3Mnljoti1CgwJxqCJAyi69rq8ZnAiRGcrXqSmjYxezQGyY2RLqLCZbY4gk1cTwha21neTXgdj+FTVhEhhAmwNqnhLoYzK+eFYwCmrYfUqZZMnPVad/2aUKehCUvUIa5qK9N5qK2UES1NZWisXvE4wVblFMAINivxFosWulzggD3qPtORXXaKKmiyD3R/1mJTjpNeI1wJmYxkxQCO+SFHV6882U8+56CT8ZNPrdv9SVsB1sWvxxTpVV1dXV1dXV1dXV1dX189aXzVqZFRDZbnTlZnjn3RZxi9aG1m/OyE8rXAfXxC/v1NOiVV3wnMEpCDPQY2aXKGvbMkPTc4MjxvEa7KDc4F7SYhHXR4aPm1w5xXpzQxxDPeyAkTwdwEoQHiOKIGxjgHhKWP84QX5OCCdBmXe3E9Y3gfEA2P+mOAWQbbqz/xTBElBvB0gTqekay2mrjz5a4ZfspoBAgTj26xvR6SDJnI4FUwfNU6z3XmF414yGMrm4U0wPGwotoaU7cHaX4vNNuv75ZFRBkIetEpWWCtRvBncN1RYrxpInArGjwm8JazvZ6Sj0wWrUnD5Xl9j+qisE46axlm+mQEC/EWvRbwdWlIpnjzSvNdv0tFZcsa4Mx8uBiY+2GISIY9eF5WqcZHUyHNR4FbRBamZkWc1EMTmss+/Uu7N4QdR04IByA6AViYRt4UuCYx4o27F6V8uoDVi/f6kXBmDUi/vg1aRDMfCeQIAhJcECfoa1bDQyhra+VLTRYG/arxJW4XSi1EwflrhPp2R7w9IpwF+EXA2Q9DXGWxCnugL6LM4wG/A8Hlt5k06BcSjR/EV/pzsHzRDrpqZFSpdGTSUBO4S27R4Hh0Ka7oqj4TwIvDXDLf8K72orq6urq6urq6urq6urp+9vmrU8KZGwsiWZIjaOXIbgTeBWxIkOJQ3R1BWQwKoXJWy/7toIqU4goyavnCbJhjiSZMP4gjhkuDOERDArQpsLcGpkeMJ6WbUA7NnUBldm4LOI0MOgyZx1mzVI4JbtFtTZ8bbuhMRCJZOcDqbzbYWxFFAmwADIw8O7prB0aosfq+fhHPWRaEKOz5nTVf4/fcr56QAAJF9bjFwraUr6qIV8EWKo56/AjQGDtd0RhSUgZHGsaU2AH3AT5MaDJOZTsrn0YSSznNTW+2idp0KqFB7L7cKeFXDhbJoYsqzJldqEsaMo3a/2FpTNT3aZPmqCZB4cuAEDI8FXBNBm34WBwAX2jk3niBkaZ5UUKJ9tlMAjc5MtfIF96Xed8VB0yXVmAmM4qgtkHHWilE1SvLMyIW/ZL7YqlSpDKTRgY4Tiuc95VS5QWZuUQGmT9LWmsSRJb7UFAPvaTIJZOkYvQdrWgys71WYMDwliCOkk9Mk0tuhJb1aYscB4bIbavWcc7bFrHoturq6urq6urq6urq6un4R+qpR4y9qTgzPRSe1kwClwHlS9sjjgvj+gOvbCcffneF+ekIZAhA88s2I4hgUNZFCWwKCQ74blANzFRQmrG8DUPTBfXgq4Icz+OxRnIMcgjJKRjVj0mEEFVsvgi7/5IGQRk1grDLCnxP8ywaZPIQdhueI8kLY7jzSzA3OK4FAheBfohpBQdMv4fNiiz4FyU/IA8NdEvgakQ+DPkQboHX4rOtGy3czUID5zxdIcLh+P8FtguGzGlewefDCQHjJ4CTIzTwwo0SAYnUhYIcIo1ZyDFzrFv1+ihnr+xnbrTF01tJSHfH05XVMs6ZjwnNsDJP28N7munfIL4gQnhLcZQOtGWAgH0fI6BCP3Ng5/ipwl9TMKWXW7AtD9TOEpw0UM/J4AAlw8wdNJlXYM0cBonJuitP6Uh7UhHObQqpJFHq8vgl2HvV70+RalaydR9pZPm3O2gFBDGod1bgTrwtKukqFlshRZpIClfPAkIEQ4SGebTpclIUklS+jr09ScPMvCygL0jEA3qqDIyFNtlQWtOrktoJw1tSWu0bwNWpVzzPyrSWi/vBsyZ4bpCPj+i1b4meHBuua2Ab3tIEvK9Lbo5pKSStP7hob4Lurq6urq6urq6urq6vr56+vV58GfdgVp3/77xd9CHcGtk33M3JQHo0MDnR70OoG1YSHKDeECHKjFSMZlFNSiMBbUQhu4Ja0wRAgc4AMXsG29qDcuDNE+3HZfHK4lpZckcEh3Y0Qp+aO26SxZigrnwawJMXAKLeD8j0mQmGGOwSD9mpqg1PRFEmYwWuGu0Sko4Mwa7ojl5awyMeg0GVbOSqOm9FSDYNC0AWmXCAA8qyJELcJ3KZw2MIEYYANDktSwIs0zksOjHLUmtn0KbUkRl2VCi/2GR0BCZh+uECmgOv3E0gUfluTGc3ICAo/1gSUpnDAA/I7p9fNgLV+kT1NwsD6foC/CPxzVBDvKeypjk0Nj8KEMvj2fRIIqGtERdMvHAW8JpSREA8eMlAz0wpxW1XSdan9Gqr5VjA8ZwUvR6vY2WoSPCEPsKodgZNOkRciZd68Mj2AHd5cryungrJq1S/eOPBa4Da7JhH6Wk7TWJSLTqPngnIKbX2JshqTEggRygjiVCADYRsUhuw8Q0YHqcDgUiBzsCUpNaKmz7vhUmHVmtxh4HZAPgaFPgeGs5/ZMvDO6enq6urq6urq6urq6ur62evrRo09HOeJ2sOxTi5n5NEhn/QhkKNY0mQGJWnJG0rFTBfG8tbbGs2eIHGbYPxwQboZsblBkxmDb0s/yUyMBtatjI+J2mtwMiCx/V4eGclpVQRmtFCxVEYiyKgP6SCt12xHrfKIJ7Ar4KTvu90wwrlgeEpIB4d1Ihz/KHDnDXini0zxpOtA/qqfNx68rQolnfIOWpOhKDbjTeBXU9gMYD065AAcPiRwFKSJrW4DreewnfNranyfNDukmXD4cYN/WJBPo1Z9jGMzPltNyxEcAP79B9B3b3H+X2ZwBKYHZQ3xVgzqq6aIGkZqUuSBkUfG9b1HHmBT68DN77ZWq9nuAi7vHA4FGH+MiPcT4tFbIgkIzwkcs9bFPLdqUgUB64nQa+YvGeM1QpyuMUkgZPs8bi1m1um5bkaNA9JM+pk+aGqHt2znySHPHsk7m24nyKIVu3hQc2V40nSSQnJegZpLhfDC7mUg3jhsR4Z3es541fraeheQB0K4ihpgy74UVazS56MgPGc1dSpcOBWkmZEmAmUP7wh5dm1OnTIhz2oappHBqeDw4wZxel1KrU9lu48PQQ2w6n9Vo8btPz9dXV1dXV1dXV1dXV1dP3991ahxi9hDnlZn/DmhBFZo8KaT0AraLZBJZ57dCoXAGojVv2zgleBnBwloD6LDUwIvWWskRZMu8cbv89Ni3A16BW2xhEke9QHVX7KaLKOCZ6up4a8Z673HdmJ76C062+wI7pqtZqRg4/oQy5ZyKGTH9yJwV60aSWDQQIgnDwlHTf8wIQd9qHcLwKLHDNI/VxedhEgTOZZ+yBMjT4zwrKZCeM7wnhCPDlQY4SWjOMLyZmfBiCewZ53+ftmANxMkeKz3AdvNDnDmVVegqsEGBtLkwL/9HjI6zB8LxAFpJDjWaI+/Cvw5YrsNSAeFHZeRMMYMTgXhLHCrTWcnNVnEThqVgvmTxjWuvzk2s4ekVpE8ODn4cwJfoq6DGYOFUoH/fEUZHbZ3M9yagVzgloThWQ2O4pQjIwNheEzgTZQ3w2jcHb+UtjBGgNZ8cq1yab1qeAbCFY19Ey5qNIVLUjMxsBmSO8OIoHDk4giwe9YvRdNDy16JGp6ywpdnNZfKpCtY6aScmfEpG4cmt2lzTVipKea2apjpMlqdaQfUCNNqlgGeJ5tzv+rqlXgHf8kInxfENxPS0QxKoCWC8sjdqOnq6urq6urq6urq6voF6d+ACWebXNaHSLckJD8gHhlDLnAvG0g0TSLTAXlUhgdDkGzpJ/yUwKXAbSOokC0hFfiHFZRz42dojYVQjmzmgMJsqZS9ulGKVWa8mhaPC2QKWI5qwogncMxwS0ZxAfFIyM8MdxWt0nhCeC5tKUheMWFqHQWweeNLUYjwlkHJgYRtwcjMDNY6TU3yQArIKkt50iSNW2FgW24z3GnSJIS/Cuha2irR5Ttdxxo/RoDRjJpCBLIaT1gS+PkCN3nw0WE7aSJjelRTyW8K/sXsIDYDXgbC9bsJlIHxISEdGNc32nEiIfCTwD0v8GY81OMDdDLaX8TSGTZLPtSOkMKmh8eEeONwfefNHLAFqAJlFGXAP0c9jxXIPHllF/3wEXyc4WarkBW9Nv4ZDby73WtixZ8j3OMV6e3Rlo7UvHCLNGMDFarM+3XkWOC2BBRdssoDa5JmzWoOiYA3ZRTlaWgVOzCBk0BIq1U6Ha/1P3dNSNOIPDKmTxtoE+RxQnFavQNZCiYXDA9Za11JIMlAxmYq+kX+MxhzvZeKI8Qj28y7nk+dfxdNV7FWt9yS4B5eILNXowlmWNZZboMhd3V1dXV1dXV1dXV1df0y9FWjZn0/gKNyZCgrb4ZyQXhRIwWOIGNAcYzt1iMeGWnWmsbwkMBbRnozA8XqQEB7EM4nm6F21AyeEqglG4ojXQtKBSVAq0P2+5rKYeTD0IC8tNp08ZLBl4j5A8Nf/L5UddV0Tk10pNm1VEOTAH7JjW8jgYGiKYbhKSFP+nWtzFhawRG2W4fCakzV2gwnQiGtfw1L0kqWwYzdVpd7Ckop4CwYHzSZUatIflHjaHhW2HE6OpAEDBc9b7wJhhfArwasnblNiddVoWoQjZ+SpT60vrO+JfgzwMlSGqcR4jQxMz6ZK0bVcAIoCkabZM8Tt7Uqkp3j4jZN60ggzD8lhOekiZCBkA++sVJIitbHmJF/+73WlEatJ63vR02IPG2QwbUEitsEefIodADFDJ9FDbOBsd3r9ZlzAV0KEJOeRs8Qgi56eULx+v5uEeRJ62PVdKprXfGkybHxQY2deDu01SdAK366EuXtOsp+Ha0+t70ZtA63ik6vi1al8qRGSpwZw7MgvCSko8N26yz1tfOT3FXNses7Blgh2yRqkuXRYb33du6B5ZsR25tv28/G8JjAa8b2ZlAjUb5c5urq6urq6urq6urq6ur6eeurRk08MNxaMHySZtQANrecRKeH7UE7D8oUEa/w1OmjcmHWtzqpPX5a9/RMcIinYNUWXUSq08j1gbcQQInAjbOhs9N1cQhAq0m15MSawWvSh/nnDbxacsZgvfr6dswDtcnq+nUqmiLR5SPWFabAoKTLRAqnVTOH1wzeHPLIWO6t1mWrQeECc2z02Gp9qjhq89D7LDZQBPDX3I6jMMGtaoDwNaEcNFXiBtaZbBjHpAgokTJd7DxWMG6tZrlIWjkKDAle17MmgCPp5/OkpogtJ7lrBueCePQoXo0N5QBl+7N2zpPodahw5FgaBNmtAv+0IB0VCpwHtuUlAmc1aoojbG8nvc5WCdtu1FgKjzaLXdeoklWdmBAeFyDq9RcHpIN9ljqbLrKvHNk8OVjPTzWX0qi/3o5adarcpDzoa3ESu9/c/jqyV+O0doW2ylWTK+L0Z4azpo24mTjUmD9aDSvwLxvScUaaCB4Ao7T7nletfaWjnt/hUd9LvF7TdCC4hTCcRTk3I1kiTA08d42Qb0bkkeCve1Ksq6urq6urq6urq6ur6+evrxo1hTUhEe8Gq+6U9tAK0sRJhf4e/rTCP6/Ix0HNjU0UImuslPXdpDPcSR9I09GZuaImEOWCwh5pJFuOJk08ELdJZX8BwPownB0jvWP4VTD+pAmM9c0Atzj4s2vVmWKsEwEaN0Y/C2tFRvYUDCU1Y2RgBSgnhxIF0BCLgYL3VEU8eoCA4w9RUy2TMnGGBz2e7c6jsK7wwEC47bw6AtnKj1Z4MgppiqkQIZwz3DXDPVxAcYKfdYVqezc3A6KoV9YSINu9hziP+aeI8BgxGieoBNZpaSkYngV3/0TgpIbK8BARPp4R3x6QTgH+knR9yThDw+OmjJ9j+ALsLDaZngeGWwXjQ9pXrQDEtzPEW2LlmkG54PIPoyWqDso1st+nosbG9DmDY0E+BqTJmTkDNUFEK2DxbgIIWG91WtutChv216T30HFCPo1Y3g/gTe8vAO2+q4YUx4LDj8nWm/ZEDAoa9yc8ay0tG8yX16znEsB265BHgls93CXBXwScqJktxQFChHUKiAfG9T0jPBccfsrK2jEDsS48kQBuKXZu1fT0F32deAQ4E8g4xXVFavwUW42qXo946xFP+mPtqlkk3ajp6ur6t/Uff/0f/qrv+1//+L/9jY+kq6urq6urq+vfX3/t/8f5a/8/1Wv9G0YNQVzRdRkBqK42tWEbbmaIu0bwT4+gdNNmhWsCp7AlZAqQWVM02Won7loM+prBydkyEAwcbLWTTdr0cvGs61CsCYZSExBBUzKUGWV4tez0agWnQB/qgbrwQ22JqKYOavKjMAGuoEh9HZv6lv3z7FWVbA/DXqHE12Tv4XW9yetDNiWtwWhsgtpDNlAnom3pCFDmTRZQ1IQQ5wKxGXGyqlG7CsamER+QB6sXrRllyyiOkWdvaR7AGWtFuSvQOemYNNlDlpSJeTe0kgC5WC2LGohZvJ6nPABus3WkTc+ljE45Mg6WiJG9kuV3Zk+drkYpCmO2io54bsyhJrLEjq0/Kb9HDRhOr6pkg0eeHNLM8GTz4PVlLG1E2Uy3q8Ks4QgCbqmgPBGwQM//qxlxKsZItnu+GT9UU11qwLWfH6fnKY2EeNTVLD3OPTn1ehq8Ko3cfj4AIE8EETWz1FS0j5PkL75V62A1ddZMmu7TdHV1dXV1dXV1dXV1/WL09erTUasg4aoTyBwFeWSs3zD8qqwaSoLxU9Z6yfs75OMAGZyyYQCDzgrmf35ACR6X394iTfrwCgA8OdBFob1uEYSgQF132bB8f0A8MPxlf281CTQRcfu7VU2FLHBSMG+i89WWxsgj6arSkrG8U2bH9EnXg/w560P5yGAB3DVBRoflnaYp3FV0GnneJ3PSQU/X9DHCXzP8VV8j3vjGMckTsN2d4BbB9MMV6WbA8tbDZ61PVfhwTfAMz2rqnP/hiBwI4aznLd56yMBwlyPyccB269RoWaVVaWoSSOtIjOFRkzDrfQDdBAwPG4pnLO88XCyYflwho8N6HxDOguHzijx5xP/mDeKNR5oIHAd4r+eweMLy7QzKBcPjBncucKO38+L0M37Uc15TKLzpnDVvgkKWWiLtCZ3+sLWEE2BLW5vAnxNk0uNyq8AtYiYdNKlia09ggGxW/PY/bYDBhgsR0lGvTTHwMic1ttLJtevnrgrvzaO+lgKvnXJ3StEJ7YFx+daDpwIZJk3lbHovxOAbG2d8yDgset82E6coILguTtUqnb8w3KYm5Hrv4BeGvyoIWgYgb3o+rm8Z6UBY3hf9+mzcomwJqKsZpkJY3jlcvpnhFiBcC8bPCcPnDZdfT9iOjOlBf578omtaXV1dXf+a/hZ/41Nfoydrurq6/hb69/wb7K6urq7X/w36L00b/5f8d+mrRk1d8HmtlqZI+u9cLOniCGX0KMZiKTXGUP9ni7BOk/76L58daeeRUBLQmtvaU2XZgGl/vWKrVCIAV1aN6DoRUZs1DkRArsyTPV1BSUD25yB7PaSmHDgLslV9asqnJTms4sNJANnTFZw0eZIH0pTJJYIHB84Gf61pIewP8vWc5KCz48NLsfey1x1c47PAeCe1eoairyFePzMlrZBpgkYrT7qOpEZJ+4zOzoOoAZFnB/F6rYpnWy6q15objLYlbMANUuuWBJDOqmvKSBM/nAVSWD8eAxCCWzLgCGn27fU4a3II0AROXTOCJUcUzFyMAUSgLOAs4C3pZ84B4LKnU+rc+isuS00uOdh1Za0J8bafDwiZuYSWoCpsnKGkLB99jx0szGtuqa/9vrLrarDfCrMezmSMGYVh1ylzcXq/FAbSkRAPQDoWyCTAlPUyR0bOhMJ63iGWJmPC8KiLZRXSLU5TS5y00oW/+Pnt6urq6urq6urq6urq+nnrq0bN2/9zaekNELVloOlBV4/IDJA8OoDcXjEBsN0GW6LRB+74q3sAmrQIlhDgWCyVAmxvlF9CuSDeDYi3QZkqTxnbjUNxHv6iT52U9YH08ptZ6zuxtHUeZwmNNA/toRiOEF4y/IV0GSkV44IIxo/K+ciHAAAYP8X2GSgXuGhg2E2QJ4figXhyiCcHf5HGOOFNl3wKE/jGIzwn8PMZYYtw14h8GJCOvjFXqBTQVrC9mTQRY/WZNOmsc3jR1463w54Q8YTtlozJIrYWVCCz8lI4G39l0xrQduvBqeDwxwVwhPWdTkrHuRpb+t7iCcOz2GdkxBtvJoyuKxWvC0j0infilmxcIQIvGdM5It0MiCeH8JxBm6AcdKlIVga/cgz8Oen5zwWwc59mhxzIgL2k60/PqRlZVJxW164JlATLtwc174zXQ1KMa0zwq05o6/cVLN9MWN64Zjidv9P00Lv/Y4E/RyzvJ1AB/PMKtzjMswMlnU6nLOAtIx8D5OAxfo46U38M2O6HVn0CKsNIa2R5tCQPWxXtKhhWgbtErO8nXN95xJOCgeOt3s/pWCwtZi7ZTQRxQRat4eEYIUIoiZE3RroyhifG4ceE7dbh+v4ACWosDg8R7nnB5R9ukce/6FZ1dXX9V6//P/7m+T/++j/0VE1XV9dfpb/Ffzv+Fn+D3dXV9V+v/pb/DXmdNv5rX+/riZoommKpKzc1rRD39IlKH7grZ6UQvgDP6tSzNy6K/e3/q2+tK0WVVSKOAEctnaCpnt0kqMkV8QSqjBdXkyWsaYZiHJfy+jjt3z1Am6312CpQXZDiVPaERF32AYzPUoCsKYh2/GYQUC6gmAHP+4JV8ICz6XFHNs+txySOgMBtrYgMDFxf1y0KImkrS7noR3MMZ7CUYtwYwKo3RCCUlgip55ek2CIUtZUlhR9rioWKJmU4qlFTl4lQyBgy2K9/TT7VNS6vpoZeA0ud1DlsqKlWSI+7VHhwnfbOuRlsr6/Rfm8UoP5e0VWjlkyydFM9PtTjqekroN1vHAv8UhrbpR4/lQKIgK0aJJO3xS3Re8PuFxmcLZPp61WodjXc2j0i2FNS9fTXBI4Zm3pNCekA5FHTL+Lt/g1A8QUwLhIRwFSAMYOowPmMnBySEOA1dSOB20JWZf6QwM6rGovlFTenq6urq6urq6urq6ur6+etrxo12/3QDJFqWlQOSZ4c0oHhr9KWdagAadZ6B2d7aLRExHavKY3wnCADcH3HCGcDugrAawZFASdBPAU1EYI+HM9/XsBRUw2FNW0hXvkjYgZIDvreKofjDxm3/3RBnj3ywEgHNSXSQdMp04cVAJBuRuTJ4fpeGTDhIjbhrVPbHHU6OtrEN9sMMgCEpw0cBfFmUFPAjK26vlP8mwY1vr4PuL5nDM8F4SzY7rymOM5Zk0KvzjNvBcMPzyjOYf3+CBY1GuLRI40GAY7Sln78RRDOgnjU+e14Ukjy9FMECRBvNC3klgy3EfxVz9ny1iFcxJJBduoszHF9a4tWf9zgojT47etJdBkY8cg2OY5mWGw3DrhxGJ4z3CUpM2gkXN9positBS4WhGduYF0A7TWKIzMgnBlHwPxThDtHPb+e4S+5nVtAjZc0MJY3Di4ywsg6Vb0Kxg8XzP+0Ir09Is8exx/TXhWagvGPHB7+hyPcVnD63RUNC5HTAAAgAElEQVQyOCzvBzNSCMOLYHiKugI1j+AoCOek9Scpmsiq5oxj5IkB0Sl3Er038uSwfHPAy/cO1+/LbqqY6ZSngjIKKGvtigXgIPgff/UjpBAe1wkP5xnpOahpNmds9x7nXwWEi61mJTWOtjcDyruhpdS6urq6gP43zV1dXT8v9RReV1fXv7d+rmm8rxo1vKnxUFMRyhqx3yTlZIgnULJFpFzgAbC3dR7aExksYlwWZY6EF31Yl7DDel0BkKrpU5o58JerODr5XZMdsFQImoHSJsRfqUJ4YYkSGRzIGC6F0F6LN10jqm9al4t0WQivqkX2HkUXq0AEmZXRUw2LCvyF1LWe0tg0/3d7b9YkyXFlaX5XFzNz94jIBQsJFruWmZ4Smbf+F/2b51+MzMtIj0hL11RzSBDILRZfzHS583DVzAOsYhIkABZA6BFJSUSmh7uZmnkk9Pi531Fha8KyRIviq9qYGaDRWyKipSG0vV6YdUv9bOda9NlY0tpkhaWc0GsldVYr1GrrsCYxEGtiWseg4FlqKbrGA6rgr8kNon2fX9Z2KDaTZ2vOslkk/FypRbbxrZVPtCaXrmmQ9TXF0ikt9SIq25o+N2e+cU+sf/7876TdP85ZskmNncPlyipaDRupdj+6YveGOrHkmMh2D9jjaa1g3sbezhlJFVGlOtfG2xrLJkC6aXyirOS9Zzk48gHy3tZsvX/VK7ovyFjQIohTdoeFMSaqCqU6SnXXN4oAaoaO8Xu4JobW+7n82/dBV1dXV1dXV1dXV1dX149bHzVqxq9PAKhzEBxlb5vQGl3bXBoIFXHEp0y4PyPnBSmV0z9/RjqsVdKV4esjiJBvR8Jj4tVvHsgvd5x+NW21zeMHabBaNngvAvkmIjls4Na8b2NKSbc0QsgQzvZa/mJQ2HQTN0Nj+vqCLJk6BjR6Tr8cUQfDQ0HUmnP8pTK+vaDeKr7TTWC59QwPpSWBzMiIT1aZvRoO4WmhDp7jf9rbcz2VNjLj7PyLEo8FKa7Bc5+llBrIOD7lZlyYuTB/fkC9WCNRtbEYl5XpTTJTpXFrQqrGi0kFbkIb+bLjWk2f2tgvLPa66cbas9zGGHKkvcGXw9kMi/GDJUGWF4FcHYd/PYET5lfGHsqTpUx2v7s0iLNSDpG8C9vYmwa7V4Y3RyRX1N9to1wAZbLjyKPgkzU81UGY7xzjfTHeS/FoMJNlHRMzWLRr5kS7CG3tVoiuPxdL3wyO5cWAu4m42RI+Xs2AyYeIDpY48efCzb+mrfnLz8r05kKZAvlw5dtocNRRSDu7F148JfxxIYvxf06/aM1gH1qD1Gdm3PmklEHIO6zV6dOEFjHI8iExDJmb3czgzW2JvvDPL74iV8//9eZXLNmzpIAqxEMiLx4922uVUVC/gqxtTcd32SrawxWA3NXV9fPVX+tTot4A1dXV9W30Q/6M+C5MiK6urp+P/hpJmuf8vj/3dT5q1Gj0G3NGg6MGt/FEXFLiUfFzbZXMSt0P+FRQ1WfQXHuu9Hq/jUEpUO4mS7UUcA2CIrUxSfw1HbGO20g0M4Pc+C5Cq2AG17itUoDG55B6HYexhh2HT5Z6KaO31AbfTFRsKRQvlOgal2Rt0Clb+1K6jaDBUkRZ8eeE+paEaYkQKRCfMtW3cSG16mbJa4uRa81RutUnPx8VW9kzK1zXnly3FiR1V4ZKGT0uiLFWZqjjylb5Ji/ILbJBi9dIiayNV+vXLY3jUn02lmPrdn28rW/1kG9tdEhSJU++cXha0qalfHQIqFcbWUtKurUL5udqbVleGvOmnXqhjS0VNDpKaNfJO4sWbYDhxi8SqOt6tVTMxkzSa9uWekdxgluaybbycXwza07tes661csbu8cMt+cNSuFiT7o+row27rSOHlmSzODAuAZVHiBPNuIkoSIeVIVpSowx4UQp7Tq46liqrflhWAjeFicXR84erbLd6zWY2eWy2+5jDULVqynW1dXV1dXV1dXV1dXV9dPQR42adDcCz8ZZ1jSEQjgXxq8vuFNCLjPpi5dcPpuYquJO6ZmhY9Xd9/80IAXu/t8FHYVTY9aEs210rRJbDTrrjfNiIyaQdpYqmb66IHNC0shaT63RkeTZaQjU0Rn75ZhYXgbmW098ssecPxvI05Wjg+M6JrJu5idPuguWhFgq/lxw5wSHaKyV154ywPRBW/LCXtqOX1huPON9Yfif78mf3fH06Y7hsRDvE34uyJLRIVCDa9XU9rpWE23nINkgxbRxoXUEyUatWg33ZIZTmTzgGd8nQqrMr0d0gLRrFdCDmTR+tlGicFbqAEWkmSKKH9jGj1xRfKqt1jxSg3B5PWxpFTNw7LmfvoiE1kKVd460E6YPleEh45MZH+nFCArjl4+ocywvX4BCvF8sLVPCBoOWCuFszV3u/oSMt+jOU0aPRN0ao6hmxrilmIm4t/Go+GTruT6XVMVlM5LSbbSEz32yhE8brct711I1GZcK47u6jTpJUcJTuo5RqZlau69n/HGxa+kc80trkpruS2vjAhlsbKqMUHZtzUdFD5kYC84p3lde7s/cDDNfHw8sOSCiBFf5/fmWfVj4p7u3nEvk6/MN7087Tvc7WBySHDgoE6Rq13n8YJwlS505/EX7+FNXV1dXV1dXV1dXV9dPSB81ajaOydrm49g262YWVHQK1H3cILPp5YjcDPbYZJtkMgxPwTbDl0yZAiXaC+j8jFEyg6CU0RkYWNnGkqRYVbXUaIkaGt8jtERE1StLh2vSBMxAKYMDtdNdq8FdeZaS0DUNY6ZQONXNPCg7D0ztOTAI8qV9j2drGQI73jBXM5i+eEkZnSVpiqWS0uhRP9r65WppE/lmygMFPxczopwgxV1hvvtw5dGoblwdUUj7sDF43AK+tTe50vg9LV1Tg5lg1QODUL0jj0KJMDyCP1dqcNCAylv9db0metxSt4an7dyrvY6fbRxLo6NO11RHHQ2C6+e6XVsas0jrs0TRmmyaInXw2+iWSnu9KtdK88FbequtXV2rqBukWB2EIPhLpQ6OMgjpJtjYV0vd+GYWLi+HjW+0GlbrqJ/kamkZ7D1QRo/KQHiosKau8nofycaR8YvdI3nlxmQ7vhAKpTiWxaN7a3d6ubtQquNxHlAV5hIo1XEpkVQ8x2UgV4eLhZpaxf0i+IvdzysnyMYR7fWcW2+qrq6urq6urq6urq6urp+CPj76JNiGeeVcNDhsOGYzFuZC+nTP/CpuIzHnT6x5aHwohHM12KrC7uuWWniYLcUwmZnjFzMgqmfbCKe9sNzZBtQvyv59QlLl+OuJGtiqlm0EiDa6Aj7XZnbYRn5NAK3NTXW0lIVflPhUrDb72fiLjahYW9Dw7kK+GdCDJx08y53f4KzT+4wU5fI6mOHRgMhrw058KuSd5/5/mYinyvTOKMc1CJdPIsutsP+6EB8LZWcjVsMH47zQxrb808xaHV4HgwqX0dqr/LlaW1Yx1kp8TLil8PCfb0g74eZ32eq9G9xXno3s1CAsh2ecmKGlY6Ktg18q8TGxvBiog424iSpuMePCJRt184+XzeQqLybmV5a4CUA4FfwxsXy2I++crXVV6j6iYn9PM2loY07wDCDdKrbLYaRMZq5oq0cvyZJFK99mq82uSo12buu4WhmFvBPikxAHIY/WIFWj35I7knVrc3r4x9GO72LmUTg/e46jY7i3e5kC+eDRW4+/ZNw54ZdngGfBRuecEE6tjr6ZSS5BUWEIheMSyIsnFY8T5R9v3uFE+b/f/4JzChyXwd4PxVGrY04BVSGOmfkSNpMmHhWXzOiqDfi8GmG6PJs/7PrO+mOzpZ3H8ZfpY7O6fU2/H3VOQ1dX149J/Wf7357+2DXt//78ZfrYe6Sv6fejH2vL0x/qo0bNcL9Qo8FYBYPtAuRDIKgSHsyw8Yu/phcuCrPiZt0As1JtrERyBW+tP8OxjdCIpR2GSyXdepbPo/39Q4OvTsLpc9uwnj6z8aD4ZN9La3oKl4pvxk0ZjBXiZ0tAhKdKwJIyxmexsSGf1hEZt6Uo/FyJHy5mjjTzxUZnzFMIT8Xao4IZB+GsaFDSjW9jN1bVvL6Wbfiv7UY02G04W+pBXwRrk6pK2bdL0QwMS9pAHYwTJEW3JEo421hX2QcKWAX5LrQGpsakkbXCGhuRGszEKBMsd2ZYrWNWK5AZgfnOAwNpbyNoLmlLsADJqrUBG90aPHkfqKOjTIK/KPEhI2rXvUbZGEAAy12rCZ8ruGuCxSUz2BTZGqPqPsDe2C7SkkuIcXiMC1SboWOcF7eYeecT20idHs208IuZS3Ft/WrtYyXaOS53lvSJR+PyXGHQdr2Gp3Y/q9oxPhuXK4fYWEu6NYupXw2TlvoSG3mSIrgEFGHJHnGKi5VShadlJEglOIMJe6dUFXJxPDzuqUXQ1BrFquCOHn8RwgnCye4zV3QzaKSsI2pcDaSurq6urq6urq6urq6uH70+Xs/9/gk3DqTb2JIiiTIF5lcBfy5IMqPGzZU62qbXUgp1S3HkvYcKu98+GfMkelBleCzbGI6fK8ObI8uLF5w/dezeVMbHwvHzQJmE885aoS6f6Da+I60Vyl/AvRF8snEVHORJcFmQSzXuyJLJtyN1cIQn45OwVn97Z81LBfw547+6R292lE9vtgSRtBGj+LDgn2byqz1l9ISzncPpF7Y++98t1tj0ydhMq9pSDlc48mqm5J2Q9sY0kQJ5b8macDYjqQ4eDULa25hOOGY0N2bKMeGOczM1HPngrbHpUvFnS5tIgOFD3pg9NTiWF0Lew/yyJZLSH1Raq5k46vw2PjM82lhVbXXcPNjIVR086TZy/vR6Cw0PhfHNmXw7kg+hGTXtLwXSjQMx46QGYX7pcRmG+xXAq9s4VRkbRyjb6NYKRJZqYOd4stTK8rKxhGbj1qwmTXxYzABa8nZ8kgqUiiRz3tKvXpEPoZlTdvzhUggfZuo+cvlswC1KOJdmgOhWJ76OaKX9Ok7XxvNu/ZYQW9NROKiT4maQi0OKkLNHRImxkNtYE1jb0/r7kgNLDpT7iCSHTzToNvjZ7v1wVoZjbVXf1+TMaqouL8IGae768/VtnfY/fFz/xPDj+jbr2tuDfprq1+uvo/4J9ver/gn235b6Nfth9Yfvl2+bNu7X5eP6NimPn0oSpOuq79JC91GjJn92B062yYk62ibUXywRUe/2libQK1y23lhEI5zqln4QhXIzokG4fDJsm3eXzLCRYo1ReWf1xafPHZdXtllfUwk1QL61BAvicElwMzDB+VNHOssGkI1tpAWgTp46+VZbbCYKCuP7BV1Nlgq7N1bNPP+vn7c0guLmglsq6TaQ9g4+m3B3Q2ulMuCx+nUUS8n7aLDeU9mAyCuU1mVbi7JrIzEXG63xzZgpY7iOKgFuKRTxlJ1DThV/tArwEhz5JqK3A65USzQ5kOyuyZRgxzV/MjaYrUF+885ah+q41por4Sz4s1Amayg6fyYsL2RrznLZ4Zd2EspWg12j2+rK18pxWMfQKuHUkjOwtXfBFe5rB3D9nrXBaOUChXMlPrZ2rNAYO4AkW6/5pUGOzSBhu6csdaSUKZDuHGUUxneJ8HCh7ger+PZuq09/flzx0WDPbslodFfmy+hbIorWZGYcpRpka5VaGTolro1k1sR0/lRIt7bWdk8o6hURxTn7FXzBu8opRVwO3I4zVYWvP9xQskcOGU0OfVr5RIJLYmbNDG7WzWzbzkftfouPeWsQ6/r2+q7/AHaT4d/XX7KufS1/OurX6IfTt13bvjH68/Rt1rVvjH466tfoh9Wf+174Yx9i9ev0Tf056/KH/0/U1/LHq+/jGv2J1icbVZFWH12D7Yb9Um1Tuo/oymfxlhypgbZRFYMDN25M3nnK5Dl9agyReGockGM2SO4UDGg7WIuNimxwVpfa8+8LxEoqET9DzEJx1qpTB3M5hidleDCTCNomuyUKVITl1mqxh0dL1MwvbBxl9zWUwdp74rEyvp1xxbgx6S7YqJL3yMFb6qUqeTLDxs+WnCmTa8mjbEaGv0YZrPa5GHRZLPGwQplXreNHAOSKRNfGmLC2q8YJyjurwR4/ZMKcILUa8dYgJXUd6XFbvXYZ2zqNSh0UjQpDpZSAP9l1q7vKIoLktZYcygdYq9OlNlZRaHXUrdVIq+DL6ki5lqhS5FSgQrkbqG3kzbrZzahYDUAzF4zmvI4OuaTEh4V0N1Ck1XJjY1LUStrb2kzvy8b2YTNMjDW03DrmO4eflfAAdQyUyZN3BiheUydg40L+kpFLhlxsRK5eE1zq2UbBpCglmpGIWrrGJWnvEdngy+qEdAd5p1cwt7MXEzHQr3cV72w95iWiwKf7I1WFdI5QhOF2IXuPnj1SWptVxoDRSS2hFN12/2yjWXVNA3VGTVdXV1dXV1dXV1dX109FHx99ypZoOP0i4BNMbw26mvfOTIbkNyPAzcp4KcwvbWzGGDK6AX1XLkg4G0tjeFiTJK21ZzUTdpbs0LD2I0M5VPCK22VEoL5I5CzUwQ5fvRIfHG5pYyHJcdkZ3yaebRxGW9X3mtDJk5ko431rTLrzSIbxQ258G2s+qoMQToX9XDejwV+yjb0cdpQBY4+gm4GzMmxY28xb+kaqI1yMc+MWSxJJsTGscDZDaQXt5hcjosrh/7sY9+Zmag1FmeoFNwiX1wH9LDLc28hO9W4zLABKNLZMOliaZnlhBo16tfWMhbp3JLXEEm2cDA9+FtwCNSgSrYrcJSUfvCU1TplwEeKTa9XV9prpdjCzwItVaLM2IlmVeQ3C6Ytx4+648qxlKSm1mtMgRVHvrPJ77wizMWBkMdZRuCh5gvlla+m66GaExKfK9PUZPw/4FPBzQaM38ywY18bG0Gq7L9u9OgaIHpcCGtw3R57caiLZ7z7ZuUlto2Gt/SqejSOkbdzNzRCwcTKrC7f0U0oeVZCoPJ1HahXmhxGy4/85D+YczWbMLHmye+zi8AuEJyGcbewJbLxwNZNW4PWaYko3YbsPu76dvs9PJ/7rr/5LTxnw/axpT9b8ZfprfOrWr8kPp++6tv1T139ff8m69rX8fvRD/Czv1+SH13cZ31j1/Nr3a/bdfqb0ZM130w+5ft/nc37UqLFUAKQboc4wvrONcHnGHtHWMhOOlXApLI33sW1gV5DpsxEgn9RaiVjZJ81ECYIGtdGc1VAQGF+fibGwLMGKkHyhVkfR1mjjlZwjZbRxkBKFdBDSjaU4Ym3w3lYvThsPQtsG31maxmsl3Fuawk4CNFgLlC/X9IU7JxABdmbItGjIakZtDVmw/fe60XdLsXaqbCM6NIaJS5Wqso1s5b3Hz5Vwf0ajNyhzMT7Lxv+ZDA4czoLOshle63rXYOedDs0A21fUtfSJU8Tb4tSpjWgprZ2ogW9LAyE7a32SaiM/XiuyVJwq/mi8mjJ5WMeJ1uahdhzumDczhiAsN9bcNDzqBr0VvYKbbQ6KzRipQWBua1UUcmmcG8e8d43Z0q7jKMQjuNOCd0KIDsmKhmZiiaWCtEGFXbpe1xodokpdy5tSS9Vou9ytTawMZiS59X6CLfnkmoGT2/211qL7yPV9UEFLM31USEugZIccAy5BYrTrk6QZNGIA4WR13Pa7pWlo/KC17cnpNakEVle+mkhdXV1dXV1dXV1dXV1dP359fPTpxoCwfrYNaT54XFJ2X6etTWYdVVnBt/GxEB8hnjIUpYZojUO3fqtT9slGMsroyQe3NUaBbUDLZIkaKbbZTbPVEr++O+JEefd4oLbWJxz4sVBuHJfPDCJ8+Kog74XxQRgeizUEtc2qZDMqVujrmqgorlVXv4ybqTTcJ4avj9Rd3NqXVCB/urdNfVHiSYmPxUZuvFDbZl4qxp9p0NkVkisXxV0y5TDY45pRkG4sFVEHMxbik0FZll8c7M+biYDamFcZHIffLcSnxMM/7Xn6wnP4qhJOlXjMlNGzvAwsL5T51wnxBoERp4hTNDvKU4QskNcElK2neqiDjTpJaxI6fWbg33g2UHR4uFCnQL4byZNdx3Cu+EsljTZaNL1NuEsxsPDOM7/0WzOYy8pwb/yUGoUa3FblPb6bSbcDy8vIcJ+ZvirbPVn3ESme/f+4R6Pn/KsbXKpMv3tCoye9nHCzPV6jp4x2f0l2+EshXgoqg12LBgdeXgSkwvT7Mzght6YySZU6espo5uPGqWmSYk1gNmYXrtBoLDUWZhjfm1nmkiXG8o0Br3k2+lSLoBdrgVInDF97XBHKYCZWOMmWdvLJ6rjD2cwpl9nSSqhBqfPkGN9Uqw6fw3Y/d31c/dOI7199Tf+21ZM0P6z6+n7/6mv6t6X+b8wPr57Y+P7V1/RvUz/Edf2oUbOyXVxqVb9eIBlrRYNQ1dnIDNgGvzU4udRgvtoSE0Us0dIgq5YqaOmTNpJU/QqwbbM7YptowVIHqjD6gsgf2XV6pewqNdpB+5YA8edqx1uuRo0K6ItA9eDL9nKApRO2DfkjsCSYAurdNkJSRmejTK2pyC+WvKjeGWzX2zlIO0eKQnRUkS3RUaOjjB4nZhisDVii4Kvi5owGRxlCW1uoYqmQtfrZXwr+/Qn9z3vyvjUNtXOUoJQB8g52dxdjoFwGg9j6SqqW0liZJ/aNbImbzTzb1qWtyamN1qSMRL+NeG2jQcL2/VIVt2Q4BNRfQbshXeukVYEGZS6DELxYO1O7Fi5X/DGhsUGAgzPA9dtHRIR4NxkT5+GIxIAfPFKqQYPbOdQgiDOjxmXjF1VHqyeX7R5cVYNYC1htVeHPEimrWWZrZoaTS8YbUrHHqZixIkUJl1bRHRq/qdWia3aUIBTvqMkjybFGecLJ+EyrUekvtqY1Gq/Jz/aeXE0aqQYOdlmtPWw9l5aukWtoqKurq6urq6urq6urq+tHro8aNYd/eaIOnvmTCWgba4GlQYYRCOeypSZKa1cqg8NHM2vGN2fUO9LhgDoYHs3UWF5ZHbGfDQyb9kKZoIw28uHUU18mwi7x+u5EcJWvHm7I2VGL8T38TaIunvr1tAUdymTpj/ikDE+VsnOUneP8OlAGGI5mOpXhep5S142vbukbM2yEerMjvZxYXgT82Uyo4T5BO4caxQyXpRLvL9QhUMaROgjzFDfGybq5P/1yRP201TmvaQ2XFb8o4Vjw50z47TsQIXzYWzJkP1B2gXzwBt2tyvJyIN+8YnisxKPiL7a2KzBXG+snhkLDsOBD5e5w4RwjF6+U+0h4cqSXBXeToApaHHLvcInWcgTT24pfLOnjloruR1Bl+PoIzjEFx/mXe55+PRDOSpgr6SaQd4FwzrisLLetKUnMlDl/Nljz10OGyRI1ee+QT3ZIUaavZtJd5PLJsAGb/Wzw4PR3r5sRWC0h9etPzNjarQ1Nglsq49uFfBPJOwe3kTJ5S77oysapTF/NaHQc/35voOunQo2OdBuaEWPmpJtLG9MTys6eR4paLXg7PmutsjEqWEfrhBohnCxhkw+edPDgRrLArpmXZdTGzjEzZveVfb3cGlfHGEXK7W8SwHaeZoBqq26v+FlIt5F0G1nurjDtrq6un6++z096eiqhq6vru+q7sGp6EqGrq+v70PfBqvkhE1IfZ9TMyTgkrZnIAK9rbXbbPM6t9SYrLNVqsAPUIri6wj0av6XKNnpURrdVVgMb6Hc1BqggXomxEFwluEopjlrd1pojmPngCg1gq5bMiEKUZyNH3tIIdYCyWJ302kJkm9jGSSnGilEVVFqbVXTXRp1vJBWu5suaIpFUcC1NoWIJF1FQtddcx1fKwPZ6a9pBWjLClTYuJbK91mpIGNS2GT/V1r94adydxkrxgo4G4K3RHp+Ls3NaExu+EL0jhULxwdZgrAxTJi2B2p5L2nmAmUhuqbhmlGj0dlzZWpjc2f68hsabKWuSBfRi94ilnK7nXFf4bav33v48CCEb0Hl7XDBgs6V2BB0NVry2GmlLMq3jR5boEUjPUj5BqHKt5NZ2D0m1ZM9a767r0rv12rQWq8ZskoqBkr1sY0zSUmIqslWV094XrlVq+0WJx9Uguo6baWiv1Q5IMg3YXEEMBF0RM3AWM6vM/HHbsa4JHqkKxe5VFdneS11dXV1dXV1dXV1dXV0/DX3UqKnTAMFtoxXh4YJGj9tF5k8ip888w+QYRsf0+xP+/ZH571+TboNtGJ3w9A8HEGzT2dpwahTS3jaew0PFZVqrj+BmId9Uyq7iRMnZ8+5pj4gyDBkRZYqZSwo8vjlAFepNucJ7ncPPxu+w2mLbwG9cjwZg1baZrwEcghRLy7g5o9kAwuqEugu4VImP1xGn5UVjnNR1vKltvmfjyqzpBg/bWJCfK/5cCCe38X0Axnczck6NDeNZPtuR9oHl5Rc2tjNcx25qsPEhn4xJ4hYbuZlfBauKbo95/E9CPij5ztIml3+9tdccK1nUjCNXGYZM/KxQPxE+vzlxExf++5efoefGZHk2DrQxhJJBfJdPRvLOsdw4pg+F3Zdn/KVy+LJsAF4bA4P5lTFfDv96QqoyfzKhwTg7/lIJT4s1LbUmppUxU4NjfDczvlHyjTF9VtPs8sqOcXrniE+Z4Tfv0N2I/vLWjrNULq8HHv5TZHyoxDVdtb82Y/mLR4tjfhlBYHhorztYq9n+y7kZZVB2gfRJ2FqVVkMyETbjb10f1jE7NQMKrUxvrdo7XJThaPdfPFbCOfP465HlVhjfs5k8rijxaNcvjwGpleFoqZ10CM2oke015Gzjcuw96q2pzOXK+K52Rk3XX139084fr76PT426un5q6vfuj1fPfxb94XXq/5Z0/S2ps2l+vPr3En5/eJ3+2L8jP+T1/BNGTWiwV4fTek13VG2f+tuGs4yNH+INsCvNqICWygBLyFRLVCDgWqvQyoa5Jl8slbEmFcASKfY7xlgRxUuDsqJXwKvyjJHCN9IGriV5pNgG2idrM7rW8AsAACAASURBVNoYO9Ie7xpktrb9/Npe1NgyiJknNQjh0pqIWtOTjuGaNGmvaSmPtk473yqplfqs7lmCg1wRsXExcWKjOnI9hzWB41UbvHaFyBZc8RQsTVIi5INS9moV52qcn1XiYAoZEY+q4J0SfGHwV2Dvumbos7VsiZUyejQYo8fGviBPQtnbQrpUtzTQ6g+o55oQWq91u95ry9SVp6JbNbqu61KuiZa6AqzbdVrZSDoNtvawNW+pa2yddlxlct+AAWtwVFqzE9dEj6VmFLeU1jrlqEOrCb8oLrGdv0vF0kpB2vc1zlBrNVNvHKBw0a0225JT4JaKpEqY1fg8s70/qucKd8ZMGyngFm2smjUKBIqAW49fN/iziiVqXL2+F7u6urq6urq6urq6urp+/PqoUbO8HqjBTINwqYRjNONAleHDQnxyLC8Cy43j/MUO/2rEz5X4mGwsBIi/ORmj5oVBYYZ3F9Q7wqm9tBrTIx1gfqWkz/L2+uIUESUEgwhfzvYctTpElJtXJ5YlsBwHSzFk28DmveAXt6VnpEJobUXqbQM7njIqML+04yiDRUZq8gZmbZt0VCiHQLrxhLONbpWhGSxPxi/R1tZ0/vWtmUKpmolyyWbuOOH0y5HjLzz7N5XxQ6YMZnakww5RGN8n3FwY3p2pU2C529s6P5QrtPicCfcX0qd7Lq8H/CkT3h0Zwy0uBc6fBBvv2it1v0YzFD20NS3Cbj/zv734mvfLjq9Ot9wMM3fDhd88vuTN08EeN1ZcDviFZmQJaSfk0XN55a3RKukGL057x/GLkfhkjVPVCxrcdUSrjR2dv9gZc+XgWsMVDEGYvgSkmV9nxT/O1Fc78t5gxS6ZweXnQr0JaLD2qdXISIfA/L+/tsTKqZix0pJU4YIxc87FqsOdXWdRSAeHy8L+N0eocPlib4yYSzEmzpKpNyPLy4H5pWN+6ZjeVqb3xerf94KfBZ8r8521dg1HIZwq+395sFaqv7sx3s7bRLrxzC88flbCpZL3nrKztqxwbukvJ0iDF2sb43Lr5ZvcBt6+VpnTRq/M7PIXq29Pt5asGT7INyrIu/76+i5z+F1X9fX7fvWXfGrU9dPV//Hb/7N/ivsd1dfvh1df45+H/uuv/ktPl3xH9fX7YfRjS/h91KgpY2NgtHSFCpacie6aaig2tmSf9DsbD9JrmsItBZWKvhoA28ADuFxb6kG2ZpprRXRLSlShZI/3FeegqmVDhmA71FQ8zilusFYndY4afDNjaNyOlrBoDUBSFFevG1eftDUDgahQvcNRGwvnWaJGr6mEtfFHGijYzTYmlfcNDFy+mWKwNqtrE5W2jbVUrk1Jq2rdmCgbd+TZqJZGb+yRqtTRU+9sVCrvHHmCMgka6raG4hQXDLxS2khObS/oWpwpV7d9XRaHnLwlpipbgsQSIQYBXq+Xy0o8NbZP0Y05JGL8mLXuW1Soz8a9XF7TVcZtWddXiiVh6hQslTK3dVgbmnh2TVdzooJgx4iyjUZZ85K9hiW13NbutI2/NTNHo7cWsnbNfLIUz3O5DL7Vim9fL2z3iMurWUJLu9i9rg04vHJ4gOv93tJV+uw+U1kTPWzHszFz2j3tysr6uf6+rU9LfbmkaKGlip7NsHV1dXV1dXV1dXV1dXX9qPVRoybtDHgazxU/G3NGB0e6Ca3dSRgeC7vfJ9JtsE/8z2Y8VG+JCjkviHc2LuQFqQMuVfwpI641JmXFX8CfBXfy1JuMGwp19tQqiCg+VLQI4pVf3j5SquNf370C4O72TFVBgafLHfVtS31c6jZaNb/01ACH3y24pbK8iKgTwtEafi6vPDVYQqYWZ+ZGkyVyViisEk61jVG1r9+ewQt5urGvn5KtwRi2kaF4LITTtTVICvhckbaJltQMmlzBtfWGjX9SJk+ZPKk1brlFuXwayb8erDFrFJYXUHaKxooExQ8FcRURKMVBdqTkuU8TpzxQVTilgXOOOFFuppmn9y+YvjbOj2TYf10Jp8r45mxg25toldWDI2bFn1qsQwTZAMDBODiLjcvl/WTmWTFzZ3qbzWBQRVKFSuMAJeroufxyz/BuYfztA/VmokztNm3mkFaD7ZqZUywBNWfKPnL+LNq1n6+jVGV0zK8jpdWAx1PFzZXlZaBE4fzLCbco8SGZeZIaoDi4LaXiL4XpDfYeEBjuC8OjJbHKThg/ZETN3EQhv5iogyfv3FZZv7KL1jGu4X7BP8ykT/eWrhms7l6dGUFrMm1uPB5JrZ1sqe0+svEpM8jsz+rgKKPbziXdxW9wjrq6fkj1T3Z+muoJmq6/VfV7u6ur6z9aPf3y09SP4Xp91KjxyTbG4VRt03cTjeFS2if27YN6bW05fq4bRLZGg2iUFzsA3Ky4xtJQL5YGCWZA5NHZRt6DeoXsqDOIV4gVHyrO1cbkgKdlBGAaEql4jqfROCEAxbgkZYSyc7jFNsfWjMSW4tlqs9dWqsYNubJ0rrEgkYrDteSCbmNda+tO8JbmWZt2yuhtDYLbuDdblXJj76jC8yiNBodWpY4RjWaCbY1Q63GsjJ/tm3iW4GBbQ1rDk/MVETOeANzOTJXfPL4kuEpsXBonyrl45hTwFyGcwaXrNVZvppO1PFliZk0oIbIBk1GhYhXZZXSMb2b8ORGPA640xg8G65XS7iu9spDWtJXVXAt1GqiD30aAtFVRo1YTLlVRb2aK+jWp1ZbGXxM/LrXriaNWtvSJvyjOt1E2J/iLR1Qp+4DLFf+wfCO1YnXqDh3WG/9qBq5Jm/V61NFTo9tSMXnnt1Ypa7pqCakpbPejPYcY8ydCHfy2HsjaStXMxIglyKLFjUQdxcuW3tJg7Wg1GvS4q6urq6urq6urq6ur66ehj27h4mPBpcrw9kLdRx7/YcLPxttwXnDZxkzy3uMvFXesxmsJjjIZhyTv97isjO8WANJNaH9uqZy0t+YlM1cUHSsyO+QckF9cOOxnvLOd9TxHUOGr+xtiLPzq7oGvjwdOv72BVnfsspBuFH8xtsn4WPHn9gszlYprposaF0W9jSW53EZ4UmseWkebvEND3ca9wDbZZbQK6PgUbWQnrgbGsBknklsKxF2ByRsIl7YJVxo/RVBv4Nq8k22chw2y2zgt3qFB2riRbNXWK0BZFkFxxJdmxJzngPPKq5dHjpeB3//mFePLC//46Ts7FxWO88DpaWR6FIb761qUKKjzm5EQjhlRtZTGBpK21JDzgk+V+YVnuXXsvqy494+MXqhTZHlh6Y5043FJiY/JRsbuohk+rc7aXwp1sAQRz9ZbWw22S5Xhtx8srfOLFzaO19YvnCwhVSYhHCvxcdnuZ7e3e08UcML4fkGd8PgPI7mxcGoQ5heO4bFy++ECWawyPVXcUqjDSB2kjYYZq8ktmXIzUBuXR0VINwEUwqVSg3D5JG6GzpqKqYOnjmZEiYI/V5wX5lsb30t3vjVFGb9mOTgbF7tk1AXKrYGKNQh1dJY4avdvmXx7b7qNJdTV9VNU/0S8q6vrP1I/hk9Vu7q6uqCnc35u+qhRo0GozpFeTdQozczQBjg1M0ODbZBda+YhqXFkqnFh1kYda8Vpm8ogGxA1nqw1qAYIZ6F+CNRB0ajUY+Rh9oTRDIfyZGM/cqN4X5lLoFSHekWKIM20sAcZd2ZrTVpTKS35sMJmtW1iw8USNFaz7DYosCxW65z3fkvYlMltzTyiVt2M2jiSVCWcMzU40o1tuEuQDWq8tg6VwZ4jniuSG8x4AMTbsRmEhTJ5pGpLbpgJ4EqF0kyZAmnvbdRqUOqo0M7pfBwQr4Sh4H01bExbkJwCXx8PDKEwhczT+z3hq0g8srF3AEsZqZL3rpkMzo5hUTue4MzYOq213LqxhvIhIi9vKDeDtUU5q6iOD8nOQbmmUdSer0ZHbu1YUq+JEJeMw7IyV+rtDopSo2/G1nWdXC647JBc0Wd8lm+wXlwzU0TwsxlppZlPobFoyn6gjp58MCOSluiRjbGDQbI1bn+3qgax9brUf3NfrvfQdv5LxZdyrSWPO2uUWuw1fDFzR4rVl9fWvLWm2MK5UEa7b0oDEVuKyN4DNfTRp66urq6urq6urq6urp+KPmrU5MlmapZb2zjHU7WRDRHcUvCPs8Fto1VzI7aRpFbkQ0GqMUo0eubPD9TRkVtzDdI27Y8Z1EZl4iO4JJw/h7ov+HuPS4F8W1GnhEdLCdRdJmfPcRnIxYG3Ha9LLS2hLV3SeB8AW2V2a8DJh9UQsRSCP7cq5ugMfOvsnMmVPFlbj8v2PHln5xCPFa224ZdiJoGfC+HdkXoYyfs9ZRDKIPikG3cGMf5PHcAnwWcl76Q1bNkx+cWOPR2sFWh8WLa0jSRjskg2I8O98mZMDUrdlc2R0PuBGit3XzzgnTJnvwGFy+z58OHAtF/Q3Uz8MnL339lGvlajyGWD3S63zoyCi/FQwrm05iY7vvj22OC53mC3Cuk2UIcD6WCGlUuKuyjTv7y1a/D5Harg5rKNm+Vp5PLKMxyNjZN3Qp4c40PBz7oloNKrHaJKnsyoubz0xHNluE9W511shCnt/+AWb6NJtrYBdTA8mkGy3JoZNzwWqJDuImVyLDeOeHJEYYMDu2JjcKdfRMooDI/VAMNzRbBadq0Qzsa7Mbiwpc3yzlvjVDHOUThW/GnBv3mAJXHIn1LHYCmr1ZirMLyvGyNqNUHDqRDfn6mf7an7QBmsDn731tJwJVoTWNef1g/1KcXPIRHSP9np6vr+9bwZ5ft4rp+Tfg4/d7u6/hp63lz5ff0c+bm00PX0S9d31UeNmpX34S7VEgejMV9cAh0c5XbcHrs23NhYj0eixTpqcGbKFEWfjQBBGyvKNmYjeU1LtERHctuGWpJVXJe9mSz6GFl84ENx1GopCOOkgJvFwMSzJVhqFPL+Wsm8cUKq4pY2ntRGh4z/URqXxUwbbgb8UpneQd5Z7fMKqh3vDWSb9y3NMAb87HDzSI2+MXuagTXbuIsGoUaYPpQteSJVmd611NBoj99qs9rvy8vxOpIFliSJ7pqW0DUpArRRMcVBUEp1lAopWcRIRjNztDhODxOn+x27s6BeKaOtp78ofhGG+2TpmF1s/B3BOfcNUHOJDnc32XUVGwuKpzbikyrxaNd8TTGVVwdLzMwFCc7SIKOnjFZXbY1Ytl6uuC2VBeDP7fyHa1JGio0HAVw+HfCXSnzK231WR6FEq/52qW7pruGhQYOdnXM8tvt8sPay8Jhx2W9coLLz2+iav8+4uXD4XbtvxzVp1Jg1yYycbZytjSflybUUUUu9rM1O3pH+7rWN1O3NiHGLpdQkq5lPqVAJxqjx6/idQ+4mW5tLbfXj0uDFleFYKUtP1HR1dXV1dXV1dXV1df1U9CeMGtsw+3OlDo75Zdscn82A0ei2xhkbURFYGSBqKY+1snp4SAan9VfWCLSK4qRWmSxCGTHzom0uNVhSRp1SXtiOOb4NqEBWsRpqAXW6mS7x2Fp/CpQobZzJjKEaWjroWBG1RMzWQJTNqKnBUfbh2qDzlIkfLugv95TBE+aKW5Th/Qy1UqY9dXAsB4cfhHiMUK2RyM7B2TkulSIeERjfL7hTYvlkQoMjvrvg5mQJJeegGV919ORdYH7piU/CeMobeLcG2aqXt7Eqp0hcjRoDMpfqUIWc7Fr4WKlFqLNHjoFwEvxsl6zsWlV5bc1D9zOSK+fPIzVoM5Iq4WztS2CjSOkmbveNrW8xoyFVwlxQJywvB2oU0ssJNxfCh4ulTaZAGT3zS78loIzjUpDikXaN1gYmUbbWLpfq1spVRuHyyhOPYiNCjf2yspD8UnBnJQ+2duObGZcr8+vxWQOYsLwIuCT4Y8ItBZcD6RBslK1xgAbFQMm/f0BK5fKPn1BGZ9XezVixqnHfxrnsXloOQjwpw1Nrq0o29qXBcfpiIk9W9e2yMi6WDJJUzKiZCw7ws4PRU7FE0xKbsXQuW0pMiiJLJT5VQmfUdP0E1T8R7+rq+o9U/xS8q6vrx6Kezvl56uOMGt+SJW2nF851a7eRqpvZsnJApKi120Rhercgi4FU1QnpYByX8UNBxZg1ltTwjUGCQWyTcP5cbIznk4QfCvlhQBbB39tx5EO1dijXmpmq4GZHPArxCOHYUgqy8j0sLSFFqaN8AwS8NgKtPJR025ZEbYxrrZguLVGytUEJXD63JMPKXhmeKn626vEaHWkft/RGGaGGYDXlQfBLwLexK62VcojUKeDPCakVraAhUKJrBlQ7TlUoClWod4HlxurNh0eQLFAFPZtJJkNFfCUlT8me8mgGkqjgTo7p0YDFfobxvR1/Ge34XLKkU9lFMxJam5A1NanxXWgppmoGysorQizJ5C+Y+dYajzS0dT1lW79f327JGW0pouHBxpdqNLPMJWV4rIRTMeOm3XPxmM0IaaNQazPUyn2Rqpb6GoVwqsQnvSZUiuKA5eWwQZqlmsHiyrUlqk6h3f/Gk/Hzs8p2VeoYKL98gXoh3TYGT/E4V3EXBdeMtKoM95kahHh0SG6m3WDQbTObqo3gJTGId1Hq0EbdRmd/d0zUXWB+Fa15bbkmjdKNpwwBvyjh8iypI9cEW9e3088lkvt96vv8H4hu0HR1XfV87KDr2+u7rFv/+d/V9W/1fBSzv0e+nf7w59Bfsm59zX/e+rhR05p21BsY1Z/rdQxFMVjrujlvbJgyGpNFsuIuGT95ymAwXpeV4Z017eS9td3UQRqHw8DCYVaWF44ETDczLw5nfp9eoDUQPljddbkrxqWpDdJbBLcIbgZ/MRhsjcbqkAI+WZ2zmwvL68E4Hm0EyrXUAph5tNzacYZzbdyZbKM5QzM/GhRYRVhujVUzPJmBFY9m7MiSER83EO427hUt4aPeRpykeFy2NS2Tb80/yVImz9Iqtj5cq7mr4tTOMe+EcLHNuStCqW1sDGAs1sxUHGVx+Cdn0OUK8VEY3+lWFz0+VIZHS2SY+XGt0t6AzJUtqZL3a8tWMz6WYocd15G1671TRrc1YhmnJqMtQeOyMr2tDWCMMVe+vCd/fse8H3CpmV+XjEu1Pb/gzi1ZtI827iVck1qKrV9r4hrOhfBkDVM0xoylvWydprcJaddBkuJT4wGNwcbLoo0r+dnSL9v7IzqW27iZW+toHVVobeXNvFH8KeGdoOe12lsNKB1b2quoQYq9Ek8ZirK8HJq5Ze1Pko3Jk/aOcFHiuWzV73kU0o0wvbfHGReHbSSwq6urq6urq6urq6ur66ehj48+pdaQFCxdc3ntt8RBuFTCCfLkKZMBZf1ctlaoMlr6ZR1/cUlb0sI1jg1WJ5zUGqRSJR0Cee8Y3ytSHJdyw5eHPYwVxkoZPVIgfAjGUzlcEw407kcdYH7ljLEyr61FZhjo0GDGXpjeGntlfhXMgGmtPGG2Y/VzRXJt51AsjRMFdW5LXITZzCD1Yp5RS3jU4bAlSyRDSHXblIdZN+6IeqGqQ9zKKbGxIMmV8LTgUrFxlsFgzSrWMLVWRftzZXTCfOfIOwhPguSAy0INyhI9OlT8WBCn1EmRBcJJWnWzML1Tdm8Lae84fR6IRxvHubxyNgJVvI15PVXUs0GVhwe71m62lIt6Y82sbUb+UsmHljLJrbFoNjMkvZ5sfOjBxqPCU8LNBbfYtZj//pW1YAnba+R92MwjUQhHkFKJ7y/o6CnjhBNLfYWzGTvpJpD2Dj8rfnZbvXpto0+7389WuT0G8Gb61ejILwa7fo6t8lrarzJZYiycCv6SGbNxaMrOztXPNtIkuSJAeII6es6/2llVe7IxwjLIlvyxN1lLDSUzLsVbagjYklxSKvGx8XeCUL01h9XoGJ4q8dzuY2fjdqiNqpXYEzV/rr6vTzD6p+DfXn2turr+uP7ShEj/FPbbqa9TV9ef1ndNiPR0yLfT85/zfa1+3vqoUSNrFTdQvBg/o9ieVqoZFtZk01IjsoJ6bfRF1OEuBbemEBpseE1cQGOhpIo/Z8pgiY9wthkW9Y58EZZfGHdFgyIq+LON0dThD8ZdWuFRGWgmzXVcSAWqX4HH4M+2Ca4hWkV3q4kO5xUufB0dkZYectmAyFINFGt8EXs+nIDXLUEiqttzSlYkWLW2FP1GMkMdW0sT2CiYS3aOdg0q4mRj0NTobOJrZfucK/WVo0w2xiSlGQBekOTamFm14/SKILhspoSGds7HQjoYxyWcbb3KwDam5XIbT/LCcnCAGVkuVdwlmYkUfQMlywbJrXszCeK5gYUXM77KziIe/mJwZFkyrprBkw+BdBeaMbHWYNtYWh1lq8ZWZ8kVPye0rbWNMRmEl2ay1UBLCbkt4VNDAyYfE25O1DFsLVcaLO211mi7pGgb81uTVxvAOVdctdpuaI1QazKn3fJmBHmWG4dfIJwNSp1HM+1cur7f3FKQ6tp9J9vY3fr8VhlfcMeZehipd2ODFTdY9alSxqshZXXzbBX0XV1dXV1dXV1dXV1dXT9+fdSoGe6XBvj1SHYMjV0STpU6CpfXgeG+cPjtQt75Ns6EjUDtjK8R2mjG5dOIn5Xb//aecjfx4Z8P+EUZHsr2vS4p4/vMcjNY5fE9DB8EdYEy2aiT1W7bJn760hI+fm7mSoZ4VMaH2lJA0gwaId9YGmdNcfjjAqrc/MbOb3kRqAEurzw+KeEi1nC1+GubVUvCXF7ZY3dvC+FYWnLEmCHqhTKAVLEmIsU22q3Bat3kW8311UhyDbh7+Wwk7wOXT263cSproVqbqATnBe/FkkiPhZvfQnnvePrCkw+Q91C9MrxzSPX4c2wJGmV8L7z4H4U8CcutmTs1WgNSfGrnWOH2f2ZoTV9ldIRi0N7pvdWYXz6NhGNlmjN18OSbiEuV8UMi3QTyweMvdq/kvaO0qmlpIz8uN9aPF8p+QIMjH8I2euWXa/LIJTOFEGfJFsUq0atxYNRJY/iwGWDlxjqphyf7Ot347X4YHq26+vQPB6oXG3OrAHb/1iitAUxbZXrY1mVNW+W9p+x2Dd67vr4S1Eyly+ejpb9OhRod4WzPNd/Z2NLuTdneZ3VwaBibWcTW9JV33gxAzDT1l2wQ6cNIOUTSbXNgxO4rFbcZTBuLJ3N9wq4/W9/1U6Ouj6uvU1fXn6dvm6zpn8J+O/V16ur68/XnJmt6kubbqa9T1x/qT4w+WYrCLViyJrkGKy2kECjR0hvulJDJf+OT++oFaTXX6iyNgwIpW0tUaGMabWQIAWptqQJ7Dr8Yt8MMBEuA2JMbODeeDEIcTtqSI8anGT8ka+k5rI1O7TW84p+KVVwXM0j8cYEacQffWpQABClX/s2WFGkpnRqhDAKquFKR4gzuWpQqmKHQWqSQxmpZUxHCdTPdOCJK46asKR4vpIPgkoFwgc3kWLkjNdh1cdnafsxYsETSKn82I2Z40GYQCfFJmd4tLHeR3Lg42rgtro3xqEC4tLrwwW1GkTVZWWqjDA6XrI5bg0F7JbcUFmZKWaNXhb1r1evXZJC6dv6sBpRBkw0+vSau1MZ4GsjYeDrNnPEW9Vprujc2TXu+svPg2Ebu1NuYGqupkpW0sxEkq4jXLZ3yXOoslUJrkFqTQXVwVG88JnXgFxu9U2ctY3lydrxrKq20xjNvJpCfVwNOtl/r9V/vj3XUS53geAYFdrKlw9bkEc8A3c/rzEUthdbV1dXV1dXV1dXV1dX109BHjZp0NyBFCY+zbS73Hj9XwuMC2MYbVeo+WiXzU7FUTVw31kp4stmOeGc74PnvX+Fy5dV/O1NGT7r1DPeZ+OZEeTGRbiPxVHHFzIoywe73uhk1tnE200JKq5A+aWN1WDJk+PIR+fQG9ZHhPuNPqcGAZdvEltsRsFESaKZCbuND1QyWcMyMb87k25F8CJvZsD4mH6x6eXy/2CZboA6W3AinwvjlE/nFxOXz0RI3xZI0a8W0OmF+FahBmN4IPlXGt4slVMaBMCvj2wsaHOk2NkOhmslUFY2OvI/kvXFTxntleFLSQzMPZm18njbidLE/m19F0s5Ro0FopZhhod4MB6nK5bUxV8bHgmRLGkmB/Ve1PZeZG8al8TZy0wwSyZaUWo2H4UPempFqsPEqlxVJzZETMcPr1ADJo/Fx8uTJk8MvkXAshGPeWDBbwqUlkRja60/GnymDmXh+WXlDupmG6caDeGuxmhtTaXDknVVjjx9sDG9+6awx7FQ3Eyc+ZeL7C8snO/Le4xdFHaSDmVFhct8wjcrktnt1bZ9SJ+SDxy3GQ6pRttTV9j0Vpt+f7T3z6dQYRxF/Ni6PWwp+drYuTwunv9szv/Ds3mTiMZNaOmkDfXd9J/25nxr9XNXXqavrr6M/fI/1T2L/ffX16Or64fTHEn7fR9vR35J6ErLrL9VHjZoaBNfYHGsttaURbOO7jqEYHFhhBfc+a0dagbxSLfVSRmORDB9OwEi69cZ/OV2oN0MzNBQWSAdLGvhF8UkbC0coUa9smtJMFoyV44oil2Vjyril4OYGZfWt/qYlLOT5BlbX9MGVLyIVa3AqsY3UyAZ2teQESDCo7dqAJblu4FmWhOThmqZR3QyGtaWoREvr1GjjQH7JZiitj21mxvqckurGyNHorP7bX80YyjoGYyYFArnBm62G2owu9Y2P82wkq8Z1ZMbqy9fKbKn29+tpUK/3A35NwLTkCUJoDUVr25BVkLfvFUH0mxyXNcWyPqc6qNjrSbtDw/Gb3KB1PdcEiejasmTHUeLVlKO1U22lWS29s173NdFlTWCWAqrBkjFr2ma99ySr3RPtue2YZVvTGvjGfaUOqIKgWxV5HWyczCVbC7uv2vdJS9fIcyPLflUvOMf1Hm3vL1ns/i6xXa+WDtpYOl1dXV1dXV1dXV1dXV0/GYmq/ulHdXV1dXV1dXV1dXV1dXV1dXX94HL/0QfQ1dXV1dXV1dXV1dXV1dXV1WXqRk1XV1dXV1dXV1dXV1dXV1fXj0TddQ6qhQAAAFhJREFUqOnq6urq6urq6urq6urq6ur6kagbNV1dXV1dXV1dXV1dXV1dXV0/EnWjpqurq6urq6urq6urq6urq+tHom7UdHV1dXV1dXV1dXV1dXV1df1I9P8D6B+itoiN2kgAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "