U.S. Geological Survey in cooperation with U.S. Environmental Protection Agency, USDA Forest Service, and other Federal, State and local partners (see dataset specific metadata under Data_Set_Credit for details). See dataset specific metadata. Unknown vector digital data Reston, Virginia U.S. Geological Survey withheld CDOW.DBO.WaterbodiesNHD_USGS NHD Waterbody data was downloaded for the State of Colorado, reprojected and clipped to state boundaries. Edits were made to incorporate known missing reservoirs. This data will continue to be edited by CDOW GIS Staff as errors are identified and these edits will be passed back to USGS for NHD updates. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all. en 2008 See dataset specific metadata. In work Irregular -109.274330 -101.870297 41.069716 36.756648 140423.076200 763122.860700 4075447.043000 4546496.281900 0.000500 0.000500 U.S. Department of the Interior, U.S. Geological Survey, 1999, Standards for National Hydrography Dataset (http://mapping.usgs.gov/standards/) FWHYDROGRAPHY Hydrography Stream / River Lake / Pond Canal / Ditch Reservoir Spring / Seep Swamp / Marsh Artificial Path Reach Code U.S. Department of Commerce, 1977, Countries, dependencies, areas of special sovereignty, and their principal administrative divisions (Federal Information Processing Standards 10-3): Washington, D.C., National Institute of Standards and Technology. US None None. Acknowledgment of the originating agencies would be appreciated in products derived from these data. Earth Science Information Center, U.S. Geological Survey mailing address

507 National Center

Reston VA 20192 USA 1 888 ASK USGS 1 888 275 8747 ask@usgs.gov 0800-1600 Eastern Time In addition to the address above there are other ESIC offices throughout the country. A full list of these offices is at URL: http://mapping.usgs.gov/esic/esic_index.html See dataset specific metadata. ESRI ArcCatalog 9.3.0.1770 Feature Class Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach. This statement is generally true for the most common sources of NHD data. Other sources and methods may have been used to create or update NHD data. In some cases, additional information may be found in the NHDMetadata table. Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close. The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reaches codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through http://mapping.usgs.gov/standards/. This statement is generally true for the most common sources of NHD data. Other sources and methods may have been used to create or update NHD data. In some cases, additional information may be found in the NHDMetadata table. Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features. This statement is generally true for the most common sources of NHD data. Other sources and methods may have been used to create or update NHD data. In some cases, additional information may be found in the NHDMetadata table. Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map. This statement is generally true for the most common sources of NHD data. Other sources and methods may have been used to create or update NHD data. In some cases, additional information may be found in the NHDMetadata table. The processes used to create and maintain high-resolution NHD data can be found in the table called "NHDMetadata". Because NHD data can be downloaded using several user-defined areas, the process descriptions can vary for each download. The NHDMetadata table contains a list of all the process descriptions that apply to a particular download. These process descriptions are linked using the DuuID to the NHDFeatureToMetadata table which contains the com_ids of all the features within the download. In addition, another table, the NHDSourceCitation, can also be linked through the DuuID to determine the sources used to create or update NHD data. Unknown Converted NHD data to geodatabase format. Conversion included assignment of FCodes, FDate, and Resolution attribute values; assignment of reach codes to associated features; replacement of branched reaches with linear reaches; merge of area features with identical classification that adjoin or overlap; split of large area features that exceed 100/25 sq. km. (depending on feature type) at subbasin boundaries; reduction of feature classes, feature types, and attribution to simplify data; merge of network flow features at vertical relationship locations; conversion of artificial paths along coastline to coastline feature type; addition of M coordinates and values to network flow features; addition of Z coordinates, but not values, to all feature geometry; addition of value added attributes; switch to primary flow navigation by network features (NHDFlowline, route.drain) in place of reach features; and elimination of metadata boundaries. 2004 NHD for all Colorado Basins was downloaded from USGS NHD site. Waterbody feature classes were appended to create one consistent waterbody class for all basins. Data was then clipped to State of Colorado and reprojected into NAD 1983, UTM Zone 13. Grant Wilcox Colorado Division of Wildlife Aquatic GIS Analyst 970-472-4352 grant.wilcox@state.co.us Metadata imported and edited. withheld 20071120 12495900 Dataset copied. withheld 20071121 06515300 Metadata imported. withheld 20081016 09061400 Vector Simple FALSE 0 TRUE FALSE North American Datum of 1983 Geodetic Reference System 80 6378137.000000 298.257222 GCS_North_American_1983 NAD_1983_UTM_Zone_13N coordinate pair meters 0.000100 0.000100 National Geodetic Vertical Datum of 1929 0.000100 meters Explicit elevation coordinate included with horizontal coordinates SWAP_HWYS_Intersect Feature Class 0 OBJECTID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. OBJECTID OID 4 0 0 Shape Feature geometry. ESRI Coordinates defining the features. Shape Geometry 0 0 0 ComID ComID Integer 4 0 0 FDate FDate Date 8 0 0 Resolution Resolution Integer 4 0 0 GNIS_ID GNIS_ID String 10 0 0 GNIS_Name GNIS_Name String 2147483647 0 0 Elevation Elevation Double 8 0 0 FID_AllSWAP_Buff500m FID_AllSWAP_Buff500m Integer 4 0 0 ReachCode ReachCode String 2147483647 0 0 FType FType Integer 4 0 0 DOW_NAME DOW_NAME String 50 0 0 WATERCODE WATERCODE String 10 0 0 ALT_WATERCODE ALT_WATERCODE String 10 0 0 FCode FCode Integer 4 0 0 EDITED EDITED String 5 0 0 ED_SCALE ED_SCALE String 10 0 0 ED_BY ED_BY String 10 0 0 ACRES ACRES Double 8 0 0 WC_CHECK WC_CHECK String 5 0 0 NOTES NOTES String 50 0 0 ED_DATE ED_DATE String 10 0 0 Playa 361 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 361 FCode 36100 Playa FCode Coded Value Default value Duplicate Integer Ice Mass 378 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 378 FCode 37800 Ice Mass FCode Coded Value Default value Duplicate Integer SwampMarsh 466 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 466 FCode 46600 SwampMarsh FCode Coded Value Default value Duplicate Integer Estuary 493 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 493 FCode 49300 Estuary FCode Coded Value Default value Duplicate Integer STKCODE Stock Code String 4 0 0 BIOLOGIST BIOLOGIST String 50 0 0 AREABIO AREABIO String 50 0 0 CATEGORY Category String 3 0 0 OBSOLETE Obsolete SmallInteger 2 0 0 LakePond 390 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 390 FCode 39004 LakePond FCode Coded Value Default value Duplicate Integer WC_INT WC_INT Integer 4 0 0 INPUT_ID INPUT_ID String 255 0 0 INPUT_DATE INPUT_DATE Date 8 0 0 EDITOR_NAME EDITOR_NAME String 255 0 0 EDIT_DATE EDIT_DATE Date 8 0 0 Reservoir 436 ComID 0 Resolution 2 Resolution Coded Value Default value Duplicate Integer Elevation ElevationRange Range Default value Default value Double FType 436 FCode 43600 Reservoir FCode Coded Value Default value Duplicate Integer OBJECTID_1 OBJECTID Integer 4 0 0 WaterId WaterId Double 8 0 0 FREQUENCY FREQUENCY Integer 4 0 0 FIRST_WaterId FIRST_WaterId Double 8 0 0 TSN TSN Integer 4 0 0 HUC8 HUC8 String 2147483647 0 0 fshID fshID String 2147483647 0 0 cpID cpID String 2147483647 0 0 Type Type String 2147483647 0 0 Origin Origin String 2147483647 0 0 UniqueAtribute UniqueAtribute String 2147483647 0 0 Status Status String 2147483647 0 0 GeneticStatus GeneticStatus String 2147483647 0 0 AverageDepth AverageDepth String 2147483647 0 0 MeasAvgDepth MeasAvgDepth Double 8 0 0 MaximumDepth MaximumDepth String 2147483647 0 0 MeasMaxDepth MeasMaxDepth Double 8 0 0 PopulationDensity PopulationDensity String 2147483647 0 0 PopulationTrend PopulationTrend String 2147483647 0 0 NonNatives NonNatives String 2147483647 0 0 NonNativesReclass NonNativesReclass String 2147483647 0 0 Stocking Stocking String 2147483647 0 0 StockingReclass StockingReclass String 2147483647 0 0 Connectivity Connectivity String 2147483647 0 0 Qualifier Qualifier String 2147483647 0 0 DiseaseRisk DiseaseRisk String 2147483647 0 0 HybridRisk HybridRisk String 2147483647 0 0 LifeHistory LifeHistory String 2147483647 0 0 NonNative_Stocking NonNative_Stocking String 2147483647 0 0 OwnerReclass OwnerReclass String 2147483647 0 0 Own_Name Own_Name String 2147483647 0 0 Mang_Type Mang_Type String 2147483647 0 0 Unit_Nm Unit_Nm String 2147483647 0 0 Loc_Nm Loc_Nm String 2147483647 0 0 State_Nm State_Nm String 2147483647 0 0 Wilderness Wilderness Double 8 0 0 ShapeArea ShapeArea Double 8 0 0 BUFF_DIST BUFF_DIST Double 8 0 0 ORIG_FID ORIG_FID Integer 4 0 0 COMMENT COMMENT String 1073741822 0 0 ALT_CODE ALT_CODE String 10 0 0 WATERNAME WATERNAME String 50 0 0 rid rid String 2147483647 0 0 fmeas fmeas Double 8 0 0 tmeas tmeas Double 8 0 0 ShapeLength ShapeLength Double 8 0 0 FID_RoadsHighways_CDOT FID_RoadsHighways_CDOT Integer 4 0 0 ROUTE ROUTE String 4 0 0 REFPT REFPT Double 8 0 0 ENDREFPT ENDREFPT Double 8 0 0 SEG_LENGTH SEG_LENGTH Double 8 0 0 REGION REGION String 2 0 0 TPRID TPRID String 2 0 0 COMMDISTID COMMDISTID String 2 0 0 CITY CITY String 20 0 0 FIPSCITY FIPSCITY String 5 0 0 COUNTY COUNTY String 14 0 0 FIPSCOUNTY FIPSCOUNTY String 3 0 0 POPULATION POPULATION String 18 0 0 FUNCCLASS FUNCCLASS String 40 0 0 NHSDESIG NHSDESIG String 30 0 0 ACCESS_ ACCESS_ String 3 0 0 TERRAIN TERRAIN String 1 0 0 DESCRIPTIO DESCRIPTIO String 130 0 0 Shape_STLe Shape_STLe Double 8 0 0 HWYSIGN HWYSIGN SmallInteger 2 0 0 ROUTETYPE ROUTETYPE String 10 0 0 ROUTESIGN ROUTESIGN String 25 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. NHDWaterbodyToMeta The National Hydrography Dataset is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of water, paths through which water flows, and related entities. The information encoded about features includes a feature date, classification by type, other characteristics, a unique common identifier, the feature length or area, and (rarely) elevation of the surface of water pools and a description of the stage of the elevation. For reaches, encoded information includes a reach code. Names and their identifiers in the Geographic Names Information System, are assigned to most feature types. The direction of flow is encoded for networked features. The data also contains relations that encode metadata, and information that supports the exchange of future updates and improvements to the data. The names and definitions of all feature types, characteristics, and values are in the Standards for National Hydrography Dataset: Reston, Virginia, U.S. Geological Survey, 1999. The document is available online through http://mapping.usgs.gov/standards/. The names and definitions of all feature types, characteristics, and values are in U.S. Geological Survey, 1999, Standards for National Hydrography Dataset High Resolution: Reston, Virginia, U.S. Geological Survey. The document is available online through http://mapping.usgs.gov/standards/. Information about tables and fields in the data are available from the user documentation for the National Hydrography Dataset at http://nhd.usgs.gov. The National Map - Hydrography Fact Sheet is also available at: http://erg.usgs.gov/isb/pubs/factsheets/fs06002.html. Earth Science Information Center, U.S. Geological Survey mailing address

507 National Center

Reston VA 20192 USA 1 888 ASK USGS 1 888 275 8747 ask@usgs.gov 0800-1600 Eastern Time In addition to the address above there are other ESIC offices throughout the country. A full list of these offices is at URL: http://mapping.usgs.gov/esic/esic_index.html Downloadable Data ArcGIS Geodatabase 8.3 tar and uncompress 20081029 Earth Science Information Center, U.S. Geological Survey REQUIRED: The person responsible for the metadata information. mailing address

507 National Center

Reston VA 20192 USA 1 888 ASK USGS 1 888 275 8747 nhd@usgs.gov 0800-1600 Eastern Time In addition to the address above there are other ESIC offices throughout the country. A full list of these offices is at URL: http://mapping.usgs.gov/esic/esic_index.html FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998 local time http://www.esri.com/metadata/esriprof80.html ESRI Metadata Profile en 20220829 09115200 FALSE 20260312 10443600 20260312 10443600 SWAP_HWYS_Intersect 002 140423.076200 763122.860700 4075447.043000 4546496.281900 1 Projected GCS_North_American_1983 NAD_1983_UTM_Zone_13N <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'><WKT>PROJCS[&quot;NAD_1983_UTM_Zone_13N&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,500000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-105.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26913]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>26913</WKID><LatestWKID>26913</LatestWKID></ProjectedCoordinateSystem> Linear Unit: Meter (1.000000) 1.0 FGDC CSDGM Metadata Esri ArcGIS 13.5.3.57366 SWAP_HWYS_Intersect -109.274330 -101.870297 41.069716 36.756648 1 0.000000 0.000500 -109.27433 -101.870297 41.069716 36.756648 1 002 SB40 SWAP HWYs Highways within 500m of potential SWAP species waters ISO 19115 Geographic Information - Metadata DIS_ESRI1.0 dataset 002 withheld Feature Class File Geodatabase Feature Class NAD_1983_UTM_Zone_13N EPSG 6.11(3.0.1) 0 20260312 iVBORw0KGgoAAAANSUhEUgAAAMgAAACFCAYAAAAenrcsAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzt3Xew7WdV8PFDTEIIiSQBQpFQBGmiYu/YsWFvoAgCCjqWGctY ZvxT/cPRPxBRx8EGgigKVrBXFBErKPZCUAMCBsQAIZD7zueZ/d2zsufcm8x79uXu9537zJw55+z9 +z1lPas/a63nwlOnTp06OovtuO5vd7vbHfvszTffvH5fcMEF6/eNN954dPvb3/5W+3/nO9+53um9 d7zjHev3hRdeeKtz8m7P+dvc6sdzp5vr+Xbumv21L+/2bu92i8/Pxn4dj0F7bLc24ZA7xLTIFoo4 QubT9XMcoCaS31qb73qv8SPY3b7Pt3d9sw/2ZBdPwp3a2WBmZ51Abq1BwMnRLXISza4EOh0QJve4 NUDNfuezu0R1njjOfjsT1++7KdG1/r/pppu2hHNbGeJBEchc/OTMNd/1M9+ZALktC69vnKb++ux0 788xd1Wr8+1w2s0337zdl11CijDOphp8VggkQtjlDiHu5PT97zebgz2QTfD2t7/96OKLL77VsY4j tHTV24r0k3h3baHz7ey1MyG373b3cDKz4yT8vvdu7wRynKTQTqf+zM8Rw1zYrRHHcf3Nz0+nSu22 CegpdWzO6Qz98+1d0y666KIzquTHtX36nc7a7jfJPEra5PS7Ks5c/Jvf/Ob19x3veMetx2L3/d2m jxA6hN9V3Y57b0opOu1xm3K+nf2G80/uPxmlffFzhzvcYbuHqV5vfetb137FyHyWUb8P1evCsyk9 /A0BJ4JPvbHfCMLf7/7u7370v//7v0f/9m//tpD2AQ94wAJMRAHxj9M5GycV7dJLL90C6tZE7X// 938fXXHFFeu5G264YY0RgZyXIO+aZp8wuAhk1xiHH/bGPl122WVrn+0NL+db3vKWRThT0u+q8gdD ICYUJ/B39sTb3va2NWHIhwCuvPLKhZhXXXXVeg8x/NIv/dLRXe5yl6NP+IRPOHqf93mfoz/6oz9a ALn73e++nrn++usX4usvooPIEYHP9bMLmNe97nULwPe///235yq///u/f/ShH/qh6339v/GNb1xj /c3f/M3Rwx72sO27k7jM2+b813/919qs+93vfkdvetObVt9/+Id/ePRFX/RF6znfkXzPfe5z13fG ed/3fd9bzAs8LrnkklsQ4J/8yZ+s9ZuPZ/WR4yGJ6H9jmuuufYapmJ8+NIzFXLX3eI/32K79X/7l X47uec97rvFr5mPeL3/5y48+7uM+7ui66647uvOd77z6b47GvdOd7rSef/3rX7+esU/GMb/2ftcu AH9zvetd77reM3bzm5IarP1vLm94wxvW/KxTgyfwxf5bp77ac2vurOx//ud/1v/TA3pSIjkxgWRE hUyTywMYEWhzLr/88oVkITGq710L/6zP+qxtn959+MMfvpAkQCZpACPE0Hcb0iYiPp/rX9/et/mv fe1r1zP+v8997rP6sRn6NI/Uq9e85jVrfve+971vgYAAb/4veMELjp7ylKdsx7zb3e62JQ594GjG /eiP/uijP/iDPzh65StfefTQhz50Ias5mZ/NR/D+NmcI5Jkkn+Zzz8Ulfe5/P1MdqSEMz1mPfiJ+ jAZC9TwkjzisH9Le6173Wv9/5Ed+5JrLPe5xjwWviy++eK1bI93nWDG3kDyCjaDsQSpQxNbegJP/ vQum3jFne2mvrr322rUH5mENnjVH38Mlz4C7Bj4ve9nLjh74wAcuRmee3jVGRHTOJYiJEJEtALAQ xL//+78vRDNJCA7oAOt5yKJ5HqAs1DNxc8RhA9uACOoWk7/wwgWMENm4mvc8D0g2R9840Hu+53su SQUZPePz3/iN31jIbIMgy3/+53+ujdFsnrn+8i//8tHnfd7nbTmbddggc6xBaPOAjHFthEjSmKc+ rc8YEAyyaK961atWXxAx5AbHuLWxjGuuV1999ZZgPA8e//qv/3r0Xu/1XgtO1gzummf09YEf+IHr O3PDYcEjaeJ/a/yP//iPta4IS7NPpzYOizixZz1nfgjOWrwzmVYS3mfete4Q1jogvHl633rNl5T5 53/+5+3Y4Opz0iYGGFGDS3AnxfQP5j43v/AwRnzSdmICiUITrzYNgAIAhDdRG3rNNdfcAqkADSAt zN+QZlfsxlEtOFGqX/30LK706le/eiFlc5kq3J/+6Z8uAP/jP/7j0cd//Mcvbm6eIQZA41BsHggH yCSJzYRM+oVMf/3Xf330iZ/4iYsr20B9adZng7yD8OKUH/7hH76+D7HAiooAQcCKFMEtEchUYWIS 3oOYCCoENDd9g6e+EIcGNsHHnHseUoK7hrsa09gID5wQzN///d+vsSGbsVInd1scGfzbUwQClvqy fkjd2KmbWs/o29rsN1iZa5qFOYDdrpo21clUbg1hmfPf/u3fbjWEcAAMze+cS5BsgHS91IK4B04G mH43+TYagscN82TNuChAye9tnPld6pnP9ON/APM3QNscBKBvQGdfsEPo4DiO/wHbdzYMwkOUf/qn fzr69E//9LV5xrGWD/uwD1vcEgenNukTQb3whS88etSjHrUQ1s+LX/zi1edHfdRHrY1HCOYRsunv gz7og9bf5gs5P+3TPm2tjSRpU80Dcfk7YtLiiPrUIJ9xSD2SSkNw1mQ9EAtxYBb6tubP+IzP2BKL z3NM6Dv7xbgYwqlTp9b32Q3+RhAZydaaPWVtxsNswDdvYiqXPcw+8GzqLYKFFxPxaxH6VCf105j2 mXr1KZ/yKWu+CDSplP1y0nZiAonDh7yT+uPgFgRZLdSiPdNzFkX18T8A553wXOJcA9Q8HS3eu56x 2dQmKp0+AB3AIY7f7/3e771+EId39YHzmh81CAeCrAEWRyORHvzgBy+pwXA3L+uwTu8gFsb5P/zD P6xNhGwYQhwd8pqH+aXSGNf8IuqcFz6H4H/+53++xoEwNlq/3vdcejp4649kQAh/9Vd/tR0zZuMZ zCIJbJ1+qLvZM+YL8f/yL/9y/Y/gp9cvj+Jb3/rW7X5CQvPQRz8kwYMe9KD1/X3ve99l6FsfWJOK 1mVc/TT/9t9z3o2BZJ+0hiRi6lWSJGbsGbaH/0l+zMAe5szIZjvnNkg6apTtf5wMImkWijtlexRD gyOls1t83hANMKdojcsBIkJJUkEWhjBktsk2zf8Q9CEPecjRK17xivU+hNSnz71HzcLt/G9OHVL6 22eQD4EhmJe85CVLl0eQpJA52Fz9IBDfUc8yEONi+oiwswmoeZ7LMJ7Mwnys30bXlxaRTXc2wkX8 7//+77/W5jtSAjzBl8Qzp6myklII27pSgc3J3xDfntkncwCDyy67bAt3z1AF9Zt6Cwnz+lmfNUNY c6T+8cyRxtYTU0PY5of7I5ya/sE+21TzXHhkDvArVbt4PXtiLvbavFPrDkaCaHklphGNe1pYuqeW nu2zDn1sQJ4kCJVKltu2/kMk3wOKvj1vHFxIXz7HUX1uk6hDH/ABH7AMcRLmz/7sz44e8YhHrGcg j2eMjZumlkC61AeN5IN45gDBiHMI5m/qCi4LqSB3tgmC0SCx+X3ER3zEQhiSoQjlqZ9rEM97uLr3 IHKeO33GTcEAwlGXzBux/sRP/MRCVKogWIcwYJV6o+k7Qx7i+ftDPuRD1hh/93d/tzyHpDCu7/fV V1+93iNlvANmniWN7A1kt27rsTbwRMyYDzWTG5hU9VmeRQSpTcL1XtIO4pNU1olJ/PzP//zRF3/x F69+7Esqt+9T2WOQuYqnJ20S3Dk/B8mOADBAaoI+jzC0KSK1edAD4WxGrswO/OjlgISLQV4IiiBw YkhITUtqsCM8j5vp+7M/+7MXQtD3cSG2QqoL1ev5z3/+4jgAjmtCPOMgBL/ZDTYWUvid18TG6Md4 xrahkMraccd5pmJ+kFCziUUJtG4qXpHMEDT3Nlj0N0nGNuHx8YPISBPEPU+S6zfpgDj1g5CTyuYH hlQhZx+YwO/93u8tdfMLvuAL1vtvfOMbF0GQUsahdmrgZm8RNebkt7nk0gXLDvDM23fWBS/MKYlU M58OeRGmeYKP8dh4eSc7TdfCLX1hflTKYKifmPBJ295ULC2Ob5MALndez1hgXq4M+wAI4QAJcNrg 1AD/Q1wA0HAufTuEszk2xKbpD5JCBhsPCYyDIyGqdHwInyoCSTo0hEi4bNytuesT4thcG2gtOHpu ZAjNi2JOuOw0aM3JRpvjH//xHy/O6rk2GDFN710hNvWRi9aYH/zBH7zWAjak0u/8zu9s3enc0Frn KyGkNYMd1Q4zmBIccZOoxsm2wonzkl29kWKYDVjm+NAPeHqGJENIGEBhQfru3MR8O5OaUbkxU/uE ydm/pKPnMAOE6W/PZFuCF0amT3iQuuf7GUe3r1SFvRDI9IHXUlFClA7i0lUT/RGNZmPygPne7+nX j5NCypCX+Hf6jqN6n1iHSAAGcQEUZ8QVzalTYtII4kLoCMNGAKz52nQbB7H026EeZCGlSLr3e7/3 W6fyHVjOk3ebpx+IoG+EiGlAQBIKxzMXxKmBQR6ljFabnws0Lx5CTJXQ9GMcRmpeuYiE1KRCmZ9x /V/f+iUd8yZCOuMZI4fKne50p4W45mlewQvh5OpNDbPe/tZHnqokyZRs+raXGGIOBp9bi/02H5LS 8+but/naE/93luZvjGc6cmYc3kGcpDeZ1dnmpLpswP426VJbTTqjsk3vkAwi2FwIFYfNAwQQPrNZ GdIQw7mElrSAxMaDjG0gbvubv/mbRx/zMR+zdQpo/e096o//bQRiQ2gQ1ncd5GnWBIEQoUaNy67A /ZKUcV+ckG6P+Dz3F3/xFwvJIYA5ez6X6jQsZ+Bl5xKkZcaxOZoXu+VXf/VXFyHnvTM/8EHgGAU9 3im59T396U8/+pzP+ZwFaw4Mz+T98QOhOSKMd8kll2zPUJI8uZMROQLwLKlvXRFIe25/5mFvTDEp mXub5PKM8WJMOUa8b8yeB0N/91ytPUiFPJhYrOyEmf01OWl6YB6qKNxGWHwn3bn/Ui/SNy0WouLy EML/CEVfGXydi8R9bTxg4zQ4K0TEIc2RSsDz9IVf+IWLI3JNWgMOC/HMA3Fo5mJMRj1OD/EgIOLz rDn436bkHDCPJCrEplLpn55vTQVHZreYm81u/knMYAs2iAGnNQ54UdWSgoXhgIFnEIr+SDfzoH5x VIR8H/uxH7ukLnss16hmL2IEnbPUzI3KlERjm2EMPsewIjANXDAb8MqzGY7MTNHOUgpExDSMDTaY mfdnqIsWA0mi2N8YaRG90yl0EARS2zW8a4m9CCMCKLRES3UCTJzK/526AhBAFfIMKN4FCEBOzCIW ROe7PFypCQUYegY3tykIBXJ2yIbDQxCIgBiNU8gCZMtgtwkzLgmSITJIi4Nm9EJMkgEiTemqdeKc Ia0/6oUxcGPIX8jG9Pt7x5oRuHgw3JeDgXQgRazfXI3NgcBJ4J32BOIhFozBXARRatkkiNcaID34 3XjjjVvjWd/gaU0kWfFs7St4UbMwIcjeSXnnR7n/4/L68Yy+ETW8MDYmRCvIrjUvBJ3xD3ZgBG72 NEdNwbAzZ/1gDgp3w8oDQod/WmIPIUQQ6arpwdkiJeT7LA5h441joxLZngE0CAFIgPbbv/3bi6PZ 4J/+6Z9eXBYiQHob+mu/9mtHj370oxfxQYRC6jPOQwRE5Ttqizl0tgOJbBZCoF5AFNLjK7/yK5et Y3NtKCnk3ewy3NvmGSc9G4KDT27sghk1sDAWCVVgnuY9RBeCkIQO5x7zmMeseZCeiKBYKe8jPg1X fsYznrGIKzWYJAMv/WY4G68Dt9ttiNN3wUnjigYHalt2pd+ep8rqLwdDLuppQJfCYJ2kN2cC+JG4 7UHSA3GlVgejyXhnJMdtyQF6l0uQ1Iom3eIjgPmcSdswHKcwiqI4kwypKPOcAHfCOWwErgb5NIhC fcKFqBjFR0E8SKNlpOqfXpv+7nMqQd42XI0uTeowbiFoofX+tnEF25EaqS021twQB45mLGtCJDii OXfeYw7+j5CtFSIXV1R8GtgZy7wKGykq+nM/93MXl/acQEpeOX2RZuwtsCQhzcM72VrW+9jHPnbN O2SF8B2cIjz7YwywePvb377WheAxA5/ndPBdqQtJWgThcBAhFoFQBEEn8kmSTtJ977tsRn2CM2J3 5gS+GFWMpkDSnAJ+jFsAZYw1aXVSb9ZevVhNpnyQXK++p4JwBeI6AJwHK9vDb4ApYK/NgAhtYiqH 52fsTqfPJAlksLlxYu7ZIm+NgcN6to3CpSC1jfA5hNIPAsk97Fkb8+u//utLUlFRzE2ffhjc5hfH 1mx03iHEa228X8ai2lH1CsWB0JCqs5ky4oILZOhwrsNWazY//XbekjoHSY1v3C/90i/dSijPe7e0 gMJXEAdCSDI7YBXhrFln3j+wComz3czPnPWtXxKVIwBB5fK2rvJb9KNPaiVYpFYVuZvUQRwl0k3v aKpTDoHs0KRNhH+wOem1dEHAhDwQF1AmRadu+azwgThloeblMiCYPF3leRSbVYxPXo3clRqkLLzD Z/ncvQ/pnX5DBojjGX/nOUKcEEEzdtImZwAEK4SF6oY7Iy5z1V8hFwgAh/Q84mAfQBTuaY1KBMFK D4Awxil4EIJRx8zlV37lV5YUhMTmZB0kBwQzf4egxmIok6ZUQeN2+DrjkzqVDtmsT8MsMIFrNoGO kJKdhgGYl7WDoZb3L5sP3DoFN04h6O2XfQYz38cMwad9nHFU02FRK/QlNXxGCpyNdmICOU7PmyV0 2gwcsL97h2ilKsURU8G03IxUj9y+ATCvzgxXsKG4lpYxrL30pS9d3Bky4cSQrKy0XIYhJm5szPz9 1oC7ehZhkoD6sDGQ0GaZH+RJz4dsqRMlXhkzd7UxSDFq0nd913ctewih6r/koEJpQh6w0Yc1eP6Z z3zmmgfC0i/jmzoFTn4XtAiJqVoiCTSIRx3koi6PA2JKBzA2giwnxvtXbYIcfY8IwSQvYt7JxvM9 YnKIKP6qMJA8l4XAFHYyE+2mu7aMSS2NoaiKXWLoTOVMtQoOgkC0Obk54USgv6dXBhJDqhC1c5Hc nRnxIZwNseHe8Twkyx0LSYot6oBR0xfiYFc4lcWRIF+JS7g2/d27uZFtUKpBCFFMlPFIFVzR+377 HIFwIrTOogJm2dTiyApV8S7ENbeyG63NeOnWpd5qJAOk48FqHoiGdHziE5+4/idlSB/rJ53AmBRB UOKZnIc4cTcHTEZ/ngeHbDBhG85JzOUdmxQCRKofjMF4CAmcvJu62z4WyZD2ENOx963F/zHRYKLZ Z4SPQAq+NFbSaqb2TgfRZMj7JpITE8iZJtR3If2MvykeKalSJG1qS4uHvBCOocguwG19DnCdYNsY nCwDrqaPXJZsEeoW4Gc/IAqbbFMzelMBeXcQSGcsZS5SO3wHIcwXh8XBIR5p0ykzJCkgMXc1pIIM 5tMPxIEkuLT5+yE9IKt5NTY7x1qsHRJBZGpdqaikB2kpdVlasHd/9md/dhEuuCFo0QQhYw4Cv3/m Z35mSRVMBEyLKTu1SQTzmbF4mMAZc2Lv2JPpVp+R2kmLVNLJIAtG1WZmqb+z4fRTBHhMah4LZNue 7XZWCaSWDaEVsm6jOugpR32elwQIag0EEqla+ADg2hxI6n/PTMBCINzHJvlNZzeO2CUbCZlwU++Z V4hSjnZxSkmBgvDaYERVeLvmHAJHLn3UvMptz5CvGQOxMtKpOw7unGWYq78hOYTTN7Wlk2sNMUAa 6zMfczQXqo0+C29HJAhMfxhMdgcCJiX93SGieX/yJ3/ykibsMQQJ1toNN9ywJBy7zDx43nJTgwtD OnU5b+XM45iEMD1n1QBIqmSYa8EqQtotTN53tVy8B6ti3ZZWHH8NYAp3L14pAHi2v9kniCiupxXz HzJkh/i8GCQNYVBBsksQaa5bRPCZn/mZa6NTSfxm11BT8uCU81xEMkL1LgnBb+89iI44ISSD2P8l RRVvVI5+RrK5/fiP//jRE57whEXoDhZ5yCBoCFWgYp4ixjAk9b54MKH7kJW9xM4xZwSAUEgqEgCh IVTS0fsFNJaObN6+BzOEgzlRoRB5+SCXbxibNSAceyGLDxMAK/BN8vZshjo4xRwnESQ1ciWnUhmX xApW/vddIUq7Zx1pJgdtpN+WZqFFwJbolDTYPfwrRTXPFwDYRH/bZBtS1GgSwoYANARxDjAP2TQI 4zveI0iDWxZA+ZznPOfoUz/1U5fqoJEqkATyIQ7lexCT520WFyoiZsiWPovT6xdxaOZmnXmhrAVB UXcQjDVQkTTrl69C3as6CokFQUmK3/3d312EXSiMscox0b85vOhFL1o/bBNwMnawg5QklTOhVD0w htzmok/GvmaNxjKn3MnXXHPNsn+oV/oq3MUaSZ8KYBTQWT+pRsfll+e6NT64ZWNU96oD48r71CZB eMfeH5eq+/8EgZTcYkE4+TztLI4/X/88tc0mKcwBlylTkIcEkXTSm52iD8Asxxly47KVldHKJ8FZ Syd1GMV4dUbjfxwS0ZAkuLnnIC1VJI+Pw0CSxTwhCtsBZzcvfacCpf4Vgl36MMmB44sC1hdE/aRP +qSjZz3rWVvYFVVQxh2k0SfkKthRngRbgD1kbogeI2GrOFkvpwSsvuZrvmYbOqP5zHvWV6RtB63W 5jsS6dSpU2uNiKNUW0wGnOxt+e3TdTzzeGaUrblMty/4zODG1LLdyAstQ76DZ/3qa6rVZZnuGvAn PQ85q+cgeRxmMGMt5MklO0XlDJVOvYrjA2qcKt/5L/zCLxx9yZd8yfJYkRQQiMFp0xGF8SEjYxrH qqyOXAheIcSWexMx+41DT+BX8wqideiIIKuY0mGd/xmwCBWxzJggxOBZxAGRIYugwUJTyqGv2F4x W5DGmnFsXJYEskbP4/CegbSQl81hzSQdpLYOBrY5+zvOTvIUqkPaFnYOpvOQ8MJNvTHvIKik5Ny3 Ajd9XwSFlpMCbK0dwds/TE/fRWt7v6hua81GzR6Z+TkVcshNXCRCBUJ2c5AOWsWKQHarUszfMzSl NusaMVqr8FFIAWngHMLGQkLqQ37/vC+dXkNCz9kAun7uYwUXGNc2EfctTso7NsyGUjfo9IgCclKD IHRFAmwKwoP03jEOxOMM8LsQC5vmeV40zyNmCK5GF/VOrFg2BVXJZyRYYTMaAxmRWJt+SD1zMS9E gkkgAM+QAqSEcA9S0rpTj6wLMWAKQsoxBBLSGqks5gmm2UtaNkGpxxA87q914DdvfkKoOT4QA8TO vswz1lmM/wsRyT0ccRX02Hedl2W/2EsSrxoCGOBxBUQOVoIc9zOfOV0rgZ8nhlTIYwSYlfVJx8cF bQggApxNT+3yHm5KokAOP4CJ+z/72c9eAPWZZ/WBy/nJBrDJncDTt3N9Vk8Kx6fqQOoihyFwBqvN 86zDM4TDlqgAAgJFnA7zICZEhcgRR+pEaqPvi1y2nh/6oR86etzjHreI8/M///MXwjCgC9sg1azD +jQSiXQgSalNpEHlfiqmQBKFYO/YROJWlgkyx/U1e1CmX+oghuIZzMPnGI71k5CTORojZ8E8L/K5 OadqFc5f6wwlNTQHQTbfDGSdav05J5BdFWoe3NyaGy5jPX196o3p19QQqovNYfT7e1b2rlRlZyFV a6R2IBicFrJC8grVcW/yAnWGwIhmSyCGQuGrwxsC+R9RhRSFRCDMClVAxHR6ffKQaenJM6HJnLzz cz/3c4tgIG5xXSUdIWgwAQMN9zYP/WMA5my91mo8SO0sROiLkBSSkF3C84dYRNumGoFFVUfan1Ka X/3qV29L+phzSWlpBblozY90RQiIEpMohz/7K09gxn/JcYgEHKqtVRhPBDJxpNAa48wjgYIxZxpC SXoHEax4ujZPOE/XOgmdC8n/7btULdwu75QNzSODWPwNiX2POyIYqkzBj7ia96gakMxzfnBbSIbQ hHBAMBvOjiC5IBrkDVnzpPmsU3wNItlghGzeP/zDP3z0pCc9aX1nbhWPpioWJ1Y0K6RFJKl/1CMb nXTU0t9JCeEpznVIE6oZwrQ+z5gTbxkEpXZaK2KorKoxfuqnfmp9B7YdVmIEZRRqlSd9yUtesg25 0Ui5Ahw9ExHo255Aas+QCtZtbphP6Q2l3dY8h5lkwM+ojJwivksda9+yfwqcBK/Or2Y0xUGrWNpx EmM3NOU4yTIJKy9V+eq4TSfVENqmVOGPiuTwSqMv08E9C2EQEw9SBQVwRt/737MZ8zhpYd82vRPd GsJBBCQSgvF3sUTGQpDyLahf5m6s1BNSBYedIexVI4RIEN08EIz/C98oLkt5H2O1+VVqJxXZNKQE puDEvPAb0gOcGPYQ1llIB5693zmNVhIaJLv3ve+9nROCwBwKQsyrVUUS87EH5gzW+uYyr55Vqk/j VGq24nja9FKFK+XJeK6q9rNgdhHRcACRZA/tVo8/mPtBzqTz5bnqby1C6LupZvVdYQgZeZAT0pSb kZoF+A7cymnAuakaSSGbB5Gqo0uP9zliwaGyPZpPB5FlOJbzbT4QZYr01uLnt37rt7axWdVngjQQ uYqRpFd57bk9fWdtnbHo66lPfeoKbESs3NwVjtAnhOdsKFQeEnc1AWbwHd/xHYujd+ayuxekrnUV TBnnBhuwe8hDHrKIQx9gAHak1KxWWA0x8+5Q1hy87wxnFogrXEerkHX2RkRRBmPqWaf0nqEFmIf3 5sFj2kSeQ223+v3B1ObNJWpB6dyV2rSAcgqqipiBdjobJVdvB0yAUolQkgEHwa1sJA5a5plxbYxD voLiiihFRB0eUlcgHMmgZTh6B0LoKySLW5lPNac8R62hinRmA/Fb44wZyqgEF8+QXOnm3jUfxjmk ZzvwSj35yU/enjzjyF0uhPvyZFUOtZpb5mnMr/u6r1vzq5BEsN3drzj/RHr757zlYRvXdfq+A0VS MJcooggUAAAgAElEQVRulfvLrqTSUYW9T2qBKUKonhYCrhjFdMHOM48Ziq9hBElihDEljHEr6KDl qg72BxPNOw9kAubM6ppl7adXokVkoB+nZkVwM1SlQEWeoOraQjBGd/1V95YK4TvIxVWqHwgH8Xxu DmyO1tJZB+LVv8/KDOzwD3H94i/+4uKwpJD/2QQ4JhXPmNbK8EeAhWW3sUX2IkDqHDXIejtjQGR5 1zzLKPc+daerEHIekCqQFqwQDQYEWX1WFcXOm7IDtGwBhICoOn8wDmKveuS11167xoHc1lSmX+7W rnbwHrh5r8DGKuhn68xQIPNyBlWS1+lSZq0Hg8pNbQ7eI/Xs6XFt4tM5r+5+unacgdQiM8Bm5O5x rfgcQEsdKB/D5jFSuU6pFDw23KWQswQcHI50KC+akQxpKoLWCXUGru8gZzFYkAz3w9Wr+Jgu7Adh 2HR/W4vncE4b10U0wki4gCFKNo13ipWqtrB3IkCf5y3TEHLRzvqoCj3i8745Q2TSkmFtLRHpjH49 Th0sZbXzCVKsfP2rN5EBxUNZizUVkm6NpCAJ+shHPnIrYamXJLH5YgTg6xlh9JDbGkoLOJNaHhFW vjRnTnkzXaYTQ4457zM+68QEsut9mq14q5KjZoRsmYRnAlB1rXYjRUsTBRyqSrklAOf70i8rLZpr mGqF+xWh6/yC31/uREUQvGtDGPA2EoFAgqJg45qkFZUOsqso4h1nHZCmeloI0fOQ2fuFWVhTAYF0 dxIEgsfliwIu8Yp64Tv9FNpOcnmG1IKYvFXWSQXTF/WD23iG72idYSS9QzbN3DqVrtr9BRdcsJAa XMCafZLqqF/zYm+AU94t3D2vU5EB5dBkN87LWc+EA0UVVN2xuL3waFeVyiu6r7bXc5BphO+2Chun bs2Q6NruLbMWGrJDuq460CAJce4HEcR9PaefbkuCVEQ0JMcVpaxyd5YcVXpnoQ4QUCg8wnna0562 PFIVjIBIcVf6Lk4NqahantEHlaUibzgpyZDXbaqiFXI2ZxIiZjKz6qar01rEhVEHHSQaH6GZJ0Ki nunnq7/6q7e5NtMoDoZdLzBtopwQRQGnEt10003bm54wgqqaIBLrI5GtP1cwoq9ye44JY2ICHCFV bSz92Z7MSO3TOXGqopgNWajJvKuyNp0+aSgHVRfrOGN7t6pimz5rPvmuw6FSK/1kNO/GbQEyzpIN oOUu1SBL/9sERizPEFsBhyT2Z+54/ZIKVSGkNkCEOHhr8U7OAxunX2oOGyedmlTwLI5ekpXfxmCA I1LfM4RJNmNmrBorz8yEZ7dLGQvh5T6tRi7bo1upUgeLg4Oo5jzz9atL1Wn5DB/Rz9s2aQoQn5Sg JmEsiBtx6LP8la498I6xqaclUll39QTAFeJWp/g4HCrQNZU6D960RaunVZ2ybpOapUf3Ubhhrym3 pxNtccDyiiF0XAvyRxAtfgIqxJwV9kIeiALxhW9QuTqUmm5YY+XrpwoBdFUEjVNNLRtC0lRMzbro +HnIZuuOjYrZlVfiJy7IoDaOc4cic4s34t3pOXMgBebFmkmzTuOLbLaevG7sFgSKKOLK1shO4P1q XbmlZ6XK1le4BqIJ5mUy2pvrNpVMIKi/vVvYjTkUVAqWJHOn+PaC4Y9ojYs4EHQFqf0fcaRqT2Qu MLQgTdLLeiqgAbYkLLu0MyYtuzYGcLDBitOLYAHzGrIZi1P2XzevTnEI0JAbx+lwqMs5p1gmussX iXDiPhme+foVd4PwM6whAxcCI9bpqoQsELQ7EbU2oxTTxjEnm24jST1IiTgQcLntGsKqhA8YdAoO gSqS0OGlsZ2p4M4VL4AsiNycypfIren78jYQ3fOe97x1aOjzwtora1RkgM9INJKJfTYP8h784Aev 3+ZcFX7MAXGwNfxUdR8TIWV45qq03vXP1FN75BlwKvoB4Rk3J0rBraUhl6CmX/BoblTaWfGxKptg oo/uejmpB2vvKbdVSdwVdcXqFN1ZbD8gdgHLbgMwyAKYVSGvFfEZ0XWYV3V2CNR8GI/eh7QAnWtw xurEvQp96H7EbI8M5jht7krzRlwl7ziww9X0YYPYI51oN26VWjK2jQUxdy/UMSdzpnrNK8isE9GV p++7KhPmyi5sRrQvYrD+1pjNgWCqk0XNs4bUs+oav+xlL9tG2hqDhM2+6B6PclukDpBeRSlYT3lA JEdxcu0jXBBEag/Nr+snSMSupiaJCu4UZIkQ0hQQhv2uNliJcNaHUHbvkT/nB4W7et80sky21Nrd A6kZzTkJzHuANqsClqqrTQIslgeCFPYecABNP7Pwce5CP961GZUgqpyPjfB3RNSFN3RuyND1bfVf y/PUHBjVVTKxJkgGiSp0h5gQdPp9NoxnGLcQslpbYOpvEofq1DUNCDlE9dszVByIUhiMcTuom3nu ms9Ja7Cz7qqJXHPNNes3QmVDsTuoTQgKAbZG8ydlMIhu8eUhTDXFTCq35LuK583aWGCMOLoWrmJ9 9sd8UsX1FWG0rwVU5p3MIbKPtjcbRNuN2q0wmWdsaOHRuzZH/cy6WLttuvRKpsqbAbg2EUftpidi WKvKYgQ1b8otYLDknRk0OUuppmvrmw6txQ0hNvdnp9HWSG2CWJBtxl5pbIi4W54q72Y3lH5aam7p yojXHH3WASg16nu/93uXlOEocM5AukAmITa+J72oeMWgQfDW1LlFjpAcD1WbvPvd774ORHnrur/d +BDWOqvO3r0jbKZuwcUMqo01K6lUE6v7HhEu2HT1NRjqK9WOZCidASxIFLCl6iXRrK2ytanuXapz UilyVmKxdpOhMrQBp3zk4zIMkxyTwHJFTi5dZXDvpFpoDGziOWPdxoeYSbbmEgeCzBAYp+1UOFUj Yik8vrghyOqzcqpDsLIAuwbO+QoJYnMjNghcETocVfgG7g0ZbToEgagQxuaTPNYIuStiXa7Fj/7o jx594zd+43pfX/qMcPWV6mJ9XdfWWVKS3Fg+x5WtETeubOqLX/zixflzHORksY9Jje5khLwlsenf uszD2ruurcJ04IBQ2Xpd3ONzz1pvhbSLpPae/bEnxd5pCAfcdrNSS4M4CBVrt6r7cQlRM084FSlj LE5dKMZ0+86YmqRLp8IZYd1EZIMAMs7mmRCqXIL6r3KGsakFdOTuyeDmhTAQgIpT8lGqgIw/p/YO 6EgTG02dKXMQhzMv42k8TDi5PqhCuaEhIemSYVlAprl3b0lXt1WtvXI/VRtha3SYikAqRVRGISI1 DkQrkNA6K5QdrMEMAnZLV9LgLZvsSvnv1Lfu6wBnISfWYiwE7JDUgSLVqSLd5ZyAT8hqz41lzFRG hFUxuhwfaRKde/kuL1bXKiShKt4BVnnh9pELclbqYs3/Z0yUlkivCvd0OU4jPCmUOlRYeHFUqUO1 whE8E1GkOuX607JrtMazUQXleY46QTXCoSFVV8TlrjQuqZNNYkzIhFggi2eyI6zL57ggNQ9yV4Gl NNSy4WIEECG7rDMF73RKrXWxp5Rdh4fsAecqXcNG//cMRIwRQCAcv8J2BYsiQgRozSQQhOU9g9RX XnnlNmUAg9A3Ndb34GtdpDYC1I91g4W1dehqzsHcOqzVPMC24t55+kgf33Wx0HSWkC6pytm12Zs+ 8z8pWN3f44Iwz7mKNSm2QyNAiTMWe1NuR638hhY/wwW6jLLCcvO7GeHbs8YsIG6GrCe5Esc2NCdA 7uM4f8Wx40r+hijmjWu6Y4P7lPrke5yt/IbyOSARzuq376hejOOIHSJCuBwE+f0reZRqqeWty0Yw L2tlt2AIpJg1lJZLKooINhcuWTFiEWJlRnO7+ruAQmuv8B1Y3PnOd17SQwMr7+QRq4hGBboLqe9s ArLrP8lcveAqTGICXXlhL8ChpDfPalzGiKOiGDlVcgx1b2W4pT/j9f1BqFjHlVYJUbsmC9BtFE5V VRBthrtPG6McgAiuZ8pJ8HmhH/VjQyE5ToNrpnJMr5WWNCvgMJ99xOD5IlczOP22QfpOZbAWz3T3 h83B/SAXA1lhBhw9Lpo3iWrGNmFUF+inLwSIE4MnhMFZcVEIDY44f2H/FcXLmK1KvPfAGoGKFcPt S1911pEnjUoDjv6fdzZWm7ebbF/5yldubwz2HIK2Jn1A5k7HMYHq8IIJZG0/M8pJXevMzjOnTtNb 10LIDZMC58o02Ut7mKetwETztV7z8V22qP/3IT3WfE7awUyjTCWqnGgGl0202bNKXxeuaBnykCM/ PVENqPzfFhrQu7C+ezSmd6vkJ82zAA2gBUrOMkKdKkO2ggdLQy12StE44+LIERpC8bzbq7qyuOw+ 6pT+IT9uHhEQ+ZAW4lEt2Dy4OoLq5F6/0oXNt9KfJANELVaMe1e/SaxyS0gNKiFi9j3YcEdX+bFS p54ppD9vGXWsUqdggmC7efehD33oNqfEOhAVKYIYqKIFgVYJHoxyp2MK5utdnyMi41sz1QxRU63A zNrZP4UUZVvUPGdd3bo7NQ1MsCzI7iecBdIPIharyc6LNDPCX/jCF65wb5PGfUwYN03yxME7Q7HQ 1CQIlk1RTaSKVUMMwC4UOuSBSDhLBaD1G1GU01zSlQYpcCgbWxh63zGCtSJ02Ri4NMQprooK4MDL 9+ZbeSFIiaOpamiONrJ7FBnyuWpTFwumFLIeA0Ec6e25NFM5k56QwNrNCaF0FUF3ESpgzTYwT/Px 7Pd///dvi+f5zJ4U1tJZxrzR6sYbb1wMDhLHwKxP3od1heCeSV2yLohuT/Xhc/MWjmKd9lhufM6X VDTzs+cIT3/G6rqGIgByMHgX3LpciRTpLCSnykGpWLlOiyStYkWb2T0YldqHlF3QGSeO8rtPJGSB vL4HEIADsA68fFbuhOfn4VM6dRmFtdS1DNlqxGqFnnS6bXzP2hBh5Th74SNCQVRGhGykCLWE4czV i1MqHoHo9EktVP+KhOg+vs4kUvmoZ8buvCWDFkfOo5ODIYlYTeGicBWvjnmQNsY1L4ivD8STEyC3 NHjbk6okWvf111+/9RwFM/1bP6OeVPJb8yym0V0vU13Sb5JEMzaC5QkDz3n+1blGEQv2psQ4zxi/ usXWZ53U92oFYwad6eQWPggjvfOLWdXO/xYAmTpbwDUTg7UZ6g2oJcdMJLe5OIkNrqph+dM4EpFf CEfVRdpkzSZ0Y279+oxrEjBtdj787i/PVoJI5VD7HGcXCt/ZhfMISKoUp3l4njrF3kDEirpVYsgz 3/3d373Ng9dsuHVxWX/f933fgpd54Ijdd1hxuqlSZiiXklpYCWKsykeHlhgBTk/1gtRgCabe9Wze K6H0hZU8fFNJJndzdXv1BxnBuVRlc4Pw1mDtJWJVLQXcqJAI3554VygM/IATuXgz8K2jaGOtqprW jiG2N96vrnJS3foxzOkZPQgJAgH7O2MMt/rBH/zBo2/91m9dn3ffx64+mWGulUiVDlkxZG3GFFVy BrBxKQAuwSo3b1Khw64kVhxIvJTnEBw1w2aW113mIjUEondYRuVwFoLbG8/3AhPp85Cn66JxR5ds ftmXfdk2hRcxf/M3f/NCvJCjswvcUSE4feQZqvQpeEy1AZJYS5fhgAvEg6SadRtTWExFE4TV47CQ 1HmOcc2dx8v6KneE0SCG66+/fklOCGi84uKy6xC2c6OCEructQs+q+tlbT6DtLlszR2Spx5hdOWc 5PgouUubqdrhQBG+9qSgVvs471c55wQyT8XzFCUO4+bf9E3ftPR0E7dZgtoYaWXP7WYlzjOO8pAz vjvwi/t2v58NIw0g2cw46xBuRnYmsXyP4+vfnEkEwLdZNhqyzbMaBFhdXUGBkBABUaNsrvE1/VVI TkUSm85jQ/Xpdt08LbgtGBXWTXqQaF0dkJNDPStXPmuFxiRJIDNk7RoB76laX1g5FQQiVTSvsqq5 pM3fPHxmXaQH7n7jpg4uKWTvzKt1CW/RcHvjIV5EhLDNAeOBoPqudi9VkU2E4KqjBVYkVHn6edZy BpRK0GVFlSItRyfvIMdH8Wma72cO/P81fh/toe0eFhZ+nFoDISAiEWvBdPbyFXYzCePcuXlLkgnB u0bNJtOFAZjKIA5puhfp3ObAKPRuoSBr0YNYbArJQZ3IG2MOPs/wNQfeHUhSXSwIYl0+t6FUE5vu Gf1Yr/wTm+//vFVVSERkuDebBGIXQ6ZBBnZB4ekQKZWlrLyKu5lLVzPoqwITflsThAdvNg/pAQYk ndtvrVG/pAR18Mu//Mu39knlQ0+dOrUIv8NSczRXBGku2W72qisa0vtJChHBVWKk3tlzKqoxMQsa gPkXIlOyVF7QilvHqHLxznMi8+IgKaNRy145adubDTLDSeb5RJl0lcu3ibgMjtYJekF6ABblA6DP uiu8IgCAUDEznBfAAbBrwyrYkK5b9mIJUQAMiQFYf8Ym9qkZNnhmMWo2L/eu+ZZBl3fNmNSVqjmW lquVT9J1ZxFaXpyqueei7CC19XaTE5jyBAa3ksB2D1N95jyl4D8w9AwVDDF2zRy1C+Eauz7ZJNXa 8jmpc7eNtDPPDgBx6ipE+r8rCKxpIdSmPpn4LO5Z/UphLgLafhUNUX4JhC99oSKBEUzZjOX1gAXm AJaIq3gzLXunoMXdeyLPGYF0blD4c+HJHexAwBAK4HAWHAvwcFPvUqFaTPYAgrKpNoWuWxBbpXdI Ee/lVrVZXI+4e3cEQg6AMpb3qTAlTRWi4Kwl5Jkh+SQTLgSZIIi+uEFtDn0d1/IO9ZG9sZsRCSk6 7Q82JAmiMQbEVJWFuhlhUOsQdxXnO/CaB18kW0F8PkdMpZ1WHbIbdYu4NS6YYBL2AiFwLoCZ962b ZLE2a81beM/NlWvVFybZwLULc7ouuvJE9rNi4xwTiMLvznLMC/y7GgKDwnBC8nJVtBhN+2JO1SkG V2uaTqEM9zSEg0iY6g7uJlm8k8UUINfFNhAE9QM8AOHOkNlGtqEaYNrETqdtoGcBrhwJfc1ncBpA qwJ7aomfzmCKAMZNSbaCFSFbp9apdQXT4bBUE4hQfgNitGYInLvTuqwRYlRlHpHrJy5YuEznId5H HLh+VU8QR+pHVRirhWXtvqtqY6fuucebn1Nz84JUkNOc8iAVz6QvRI1hGN9+dUBp3Z2iv/a1r13E UdE4xFd94g4RtS5HBYsCN60926rbqEhqTHJW6+9AtCILnVflzGmtqZXmFBFFFIXwFBB7MLV55yUr NtiE8xwBkDYLI2tdoAJwcduqFeqndNY8VTgxD4x3vv3bv30hDUnBHrBBKqN7DwLk8y9IjhdJPBEk Fb6NaDtrKTyiYECAR8yVsZmHcpAi24C6heB+5Ed+ZHmoFJUu8A9B6c/G2iDZgrOaYHkscX2t8wNz R+xtvr5muU8cEyJbq2f1693OPDrzwRQ8hxEUuo4BsN3K3/Cd93HzClhXAtT3YHnRpkJ+5ZpSc/JA +rHHCIcKGDf3bBcJzSsqSFrPcdJU6KE0ArCuNBCinV6sCKc7RrKDdk/Jk8L7uFlqbwTSHedRO+TH 8Yj7qNviATHPEeS1KYBmMTZWs0G4FbFfdUNcB4d7/OMfvxC72JuuL6u4NPuh0pSVAGKY4kyVHGKE MjgheKV6KuOPAN1b7j36NzXIOLxWXSZqbVQCazOGCu4Q3mZ2HcI838lt3R1+lR8Cpwxzn+UAwFn1 a0765QkqPgwsPG8++kKU5qkviEU98gxi4jGDZLmOvVdIDeahWovPnM8gDNIh1zTHAg9WzOSKK65Y DKPzlu51B7sYDBUXIYNd10GQRvqyJgTtPeNiDMJszL0C11UoyZvXFXfdNhyOed68uz5bm2Wkdi/O 2QehnJhAcqXhJNSWoi7jTgAMuTrM0WzWvOSkhCTI1a2pkApQC3hEHN7rLkLAq/QNVSH1SZ+pGj7X OrnHeTpx95656dMP4iAB9F+qrUZ6zXKnENJG4O5JGy1mUGQtVaONKoK1AtyF5xdBDMl4e/The8Tp HYhrTHPyPjtMv2DCa4bR6ANTKXe7w042Hg5d2m3nPvanom765dotbk2/ni2//qKLLloMoSrzRTlX vseekNB5nMAYwZWtWNafORdZ4P3czjPCApNAWHk3vQevync3DmIx3wItPYspzZKusx1EVROLiAun G5ceSvxbKKSpRtUsAp3uHEeyceUeQBCImscjjwak9n9hCDbV3xCLcak/xNpZRqV/KjPTtV6A3RXJ nusmVw2XLVCvw7Fq30LYEo5IsGK4qoNbgTStkkG+8651dXAGkSEUwpNsVMg51REsOADAj7pDIkA2 yE0C6g+sn/nMZy7CprZAausvvyN7gj0CdtPdjbjNk8TVP+5NQiNU8zXPMh+vu+667XlV5T4xGTaO 95/4xCeukj/mBvHNswJ6VNsqwliTPiq3VIQFoz4PFg9hsAUPc6UBmAMi63oDP9zX5jELzRWycnD3 g1SpBPAsssIHRKzziUrExKVynSKMAgRnbaMiddNPNX0DHMSKg2l5diBICTc2y7Pd2No1AwjHD8BD tGwC3M27HdwVOJe65hn92KTUJO9AkGp8FaAZx7VB5uDZ4smscV5A2llPc7CxENU9INlsxiRVIb6x EGhFJHBhRNY5iDZz9Y3hfYygO0fAe67Pc4gJMZNcENb/pPPFF1+8vaKAdw0BFvVQzWMMhm3UGVD3 B4KHOYM1Arc2RN/9gxWp8+4P/MAPrJt4xbWR+p7vGjseTNKRxEwjMLa/MbJZ9yAC2VfRuL0QSBVL KkpNPObSFX5R2Z+qWfjOc4VJhzhapXXSK2fmYCUtAaW01UrqdCkl4EMaSBmi6x+hEtM2LiKrEiBi 1g+iQlwRXOEoGfxVYTRGha0xA3YIzpa6WJUTa4UE1euylqqykGqYRYF/uGSX1thgV1PnzcnVDKm4 hHFdSN+NVQiDhyhd3bq8U9JT1RrBA5JxGkQYxgUvNl4Bj9aJO7PxTm3SnjGaInitHRFZi3U419JH 5Zv0we5AiAU9sov8X85N3rwah0DErG9zB3M4Yo3WKsq5qIKiKsC3FGxrDX77Io69EEh6ZBuR2IuK yzVHRF3BnMsOQnaaXp3WrtSq4kgEAjnTm22277kuAb9q7EWf4rSAiIMJt+ftijhKdU00Q+5KfM5L 6St2UHppt8Yaix7sc9yRXUBfh6RapXWmkRh3r4BboeVaxda6namznqSZsb1fYTkIV/Yl5JRVOBlJ yILL4ryewSg832FnpXU0v82/Yt7gl7v9hhtu2FY5yU6wz364j31vjBASfMynCGSN54rkKUzEONZV 1XjvwgF7Anak/cxfT1XKjVuWKJWt2s2kGxUSHItjM/6ssniQVU20AgEzwACqnGqEwPiCcBY27xjR OgzsAnriuNIzPmMzpHrh2J0KIyZjMuC4YT2LEOqbBMGdIALu1T0kGeYzrqyKjoANwRB5F3GaBwmm vy6x9J0Nth6bk8phPpW1iTgySG084oFYCK78jWpHQVKuUcjQxUDgQgKwWSB4ahWJpJ+iihGHtYKP /QFHzMMaSRTP2Q9roLZkn1jXVVddtZCOxPY5WGAi3X5b7d9sJWdaRSp090dS1ByKxCUFMTMER5UC l86drLVs1OzFiLJQIc3fmEu4lZQ2/iwOeFChJruVJAprL9wEhyK6tZLqEUcekZKZNJwGFySuO3G1 +DiRNtNFISTiMBYCyXlQif4OKrsg01ipJfn0J2Es4Fx44VJX0pcRVPeJkBj6QpzmhIN1CDfvBrfJ RRN0zVk55nHO5lCGok3HdY1dgQcw8HzISQpZDySEaFyt5guBzYutlEPA+qkq4OfzDiZxe83cIFnu bRwZM7jooosWLKmSkDrVtbvlK+RtjogWkvo+5PY84srLhQh9TqqVqFZKcDk+YFvk7wxZ6jymA9Dc 3+Zd2Vr45fN9pdsuHNhLLxtJMf3Ohasnhgu9dpKKKGxKocoz90MDZBvIqOuzrlwDaBwSt660TAdM FR2r/A+uhhsCXsinzSxFP4ixQhK7IhlxFCqPC7dJFZQu600/3f9h7ZC0otHGz8AtGC8VtDmBR+5f iIqzF06iD54pBBjyQe7K/5c4BFl9Z33G6y5DUiDO7nNEU0hLpVVzSORwwPGvvPLKBRMSiru1kkad kldgAuMAazbSV3zFVyzVFpGX245QciFriI9ULdqB3QFWOH8pCyXWlVocYZTPnvRFZDmI7HVJZPtI tz0rKlZEUrhAp8U2EtBsioVCWIvp3o9CN7SiVrWy+mwWQHsGADtt1idPR9UbK4FTJGrzmRxlEnKh +YXIFCI/ny23RB/mUMpsXDLPEQQtv8GG4dbdIVIIy7yhtesLEB54VOXDOJAmF7M5xDErvJcknRl8 5mRMyAjxfYdr52XqEDdbrHMX4xWgidBIEQh5h3FDFOSDjJibxutlHDDvXvQqoBiru1fKC5lVZayx w1zrQjAQPlf13HdwLZymiOxs3a6f7ncp2bmNd+sdn1MCKclJm0Z6OmIFFwJ8hnvGYtzc5qfS2DQ/ xDvAlGVX7kFh8AxGn5ddBhF8X8KRTa5WU/kh5Vs3j8JaIsxOr+np3eVhXsR4btKcA8aqskbpwz6f d6lbW5lw07EBOXHC1mfMKphU0Nn33eCa+5fxS3+3Jvp+SV7ZQV3x3IFmBILTkz76LPPPWiFftafi 7FpSO3VYv4gCt08NznNXhXVjmhMvJmSFDzkvwJuKx35rLRnsGjjPkqGkX9Xdq2hfkQbMA0NA9OZp DDYctbzLXE/a9ppymzqU6tBPHLYgvFpGV9cRVNYyCWLTiu/pHr04/yxG3b0bVLZCx3E9oRSSlHZF bQePEfW8xL6W6lXaqQ2H8BwDiNY8cCicz8Z11VhrqXmmCi5FAXgX1+vgrSsBKgznWYjaplcuNGlb PFRetyo7ZvOl0kjf7U5B45YOkCQrQiGm4Nlu3IL4F1544VqrhsAwiwoz5ODIa1jfpR2UvFVNtPw0 /7YAACAASURBVO5x0UiM6WHqWrU8eEmbHAH63XX5d81F4T2IyPPc2N3peBBerKmu7HoN5nVjGuLI xVlhBA3h4Do4Ym7MDn3SwX0GGbsarBCNYqQgSP796ilp02CbxJehPG+6mrZB+m8lThP95od7IxzP 4sQ8R7meqSLTrgjpy8m3BoZ1pYjyuEFAnqrUUkRDf9c3Lil+CZy0TsZrSekaeENaSVEQvPsytAIB K5nU/SJdI1CgZQR4/82tVd7JqNdyZTup71wCDPRZHkkn5tZmzyuW15lFRL1bJ61YNHuUd6o9ntX0 01TAyvcxO/ArovhgavNGrf2eYrLKhVpVzwGu0p2QGUcVjwTYlY2xuUXlAoBN7sL4DhnLuCvcGrBw uBJmcLjmZA4dyGmpdJqN6DpnHK6rytrIeQEQjp3hjTOWE98JOg4+78fDja2rCvGQzbysg5SD+A4H jaHvgj6tpSgArdDvDvBaQzFoMZHOTTo4028hNV22aZ4xEXMt5sv+5L26ecP8zNnedB101dnNkWuX V8r4qZRVXDQfh4vlixQQ2X7Yv6R4kRVFCgTvooGrVKJ1sFxYSn32joNhjoWDIJC8Nrv3MsxSQInJ qnjjJp282jCil5cmLouTShOdbmNcIe9Ifu8kS7WyfPZjP/ZjR094whO2KaGpbiQUbqffJErnH+ZM JUOQnSlocy3mGqF3QmyuOLKxChws7KYMQghLCuiLhExqlnTE2CxNtoNUiGC9Nj0bpOjWDNAkbAgM EbsFSt63GC19gBWYVTMM4Xi2sBf9+T0PSXN5X7AJJPWsuae+VRqoM5FspuLjIG4ePswmL2HnXzMi Oq/TDMcxLoJG6F2ToU9w7M4Sa+teE8QMTtaRmnUw0byV+omT7V5cYvMS24CEOCKW6jABrM3zTC6+ 4rE6Byhpv6A5xrLFA1ShJxCBQY3AZAk2P80mMDAhT/WjvPeTP/mTiyjEjCVNcq/OPOhULgicZEgC 5W7WbFh2VkXycLm+rzZuGYBJvgzQEDYVwfyF7XfHeqExcVHqF84e7BC5sqfGKw3W2p///Ocv+wlC Q3bnOFWSzE3d5T9VYHnTBvl8XiBpRf1K6bU+KuO8m6Nbfs2hMk9dje0z87WPVXrUN3U06WutVTzR OkeZFSVztvBa2r8YT3jTDcIHoWJlRKbHl+DfVWylyBbLVAXuuK/3u1qrNF22imfmYVEnqyUA4SBx nnmnBuKAVOVXd9UZDiYojv1TtOr3fM/33ELt6oRfK+svAkg1maEQWu7fXLAhLomQREmN6ILM0kbz rkGoqdqkHph7YRueRwCFkVMlGMoYh4tuXAHdoSRkAmvPV02maukRRi7pogWyhcCgqxBuvyngoFXZ UUP03unA1HNlU85cde7qbMLU5uy+ikbUd5LZ2sowbL2ad1KZq/rChT2lRAynvTzpafrevFizwnqh E00y16dWLkJZffnjnQZHYIBF7dCPjZdoM4MI22gcDPDjojgjQis/XehE6o+N/Pqv//p1ZYBcdw3S RFS5YJMOuTdLd13A2vGIVIVdywffupOoubG7D9D8ecTMiZQqbKWq5JVB6lwiAxXymROOLbkLTHjo fM+1SXImcatDXHXI1NwQp0uD9G8s8KN6FTKSZ+j1r3/9NlkMXCoLy/4AK0wGnLv5KfXXeN0EzJnh 8+K/SnUIflVQzHXbhaDm34WtpTtD/hLmPDdrLSdVY6AZ7AeRk56RVMDiPGirFGm1VfP5+4x4zBvj 1Lyi19U+QgAVb84e6cAtTwvAII7uhiivW4NQfmyKTaenRhzNY6o4RdAWcauV31yrQN2MIC11GHKw Zay17EauUWvMwDYGqVDYeyEmiIaksL5p1Key5dqF8JBOM2fw8p5n5zUSRU8Xkt+lonLgfTfvKaz4 Q+rRvFTnHpvkM31UOWXuvf47gZ9XqWkxi+r7IkwwMV/jhNzBIpXQfpGObAk4VdnSHAnd7lVwayf6 YF8w45znObdBLHoGlXXgNd1wkD2dkc4KsYrALBQkkR1X6Xqz7umDtFUjKawjpMEBn/3sZ6/IXcRS EWWcRn+pL+WaE9VxGP0XpoFrdp4xXZCdYicpSrryM0/h/Z9k81y2UJzXMyFdB40ZsLmFC8nQZ8Ub rIF08H3RvuDoc+9lP1SaVUwaFTZiM38eJ5/zupWgZn1Jq9Q/ja348pe/fJu70e28ucmto0s9u6vd HpQIVSG71hTDg+C5nLPHIihNCAxGRqNAlArvpZ63di2PV1dApL6BSXu26/4+JwSSjRFg80J0lpEf u/pFBS/GwUIAKaKQO900dcW7cTZ2CeQpADB7x/8Ii7SxUWXj2ZxEusIOCNLmafPekPRlAK0cTQQR kZf3YtxyO8x1GrIaSeWk2NhV/NA6MZ+HiEmV6gcXu1Vs1JxnFWEqWiC8BmLimlzB+krVzCVuToij i1OtD2NIr08ydg9hLaJ60KZ+cNI7dXVXlck75j2cm91RlDKmUDbm9BAGN20WLy+u7hu+4RuWHZn0 03JcJOXSLMwDzrT/YFCkxEnbXmyQWSbfjw0s7bEymxZTvVhABEyLrSZSpSxtlv8ttFAVQACogL5b 1qXQ6kpeftu3fdvRd37nd26Lo2nd8YErVk+pEPHOOiZAq8PbWUwBcSX+eLbS/p0YFy4CWVIfNJ/L XcG5qX+FXPRMZy9FwlYHK05YuMwsXI17IwTMQIO8MR3EI/xm3ttSLk21AjAUhFXBBBKjAzqEkdp4 yUZia52tQOByeSq+Bxb21W+2YBfcyE60dntvrsWbzXMlkhQzKTTe+2Dqf15Hab1ah4gRdmWRwCdY gkFq1z7aXk/SIdy870MDQAsBmLLuilUqT6ADtg6XOiQLKF2ngCtXhSMg4FCIKh2XxEAAEL6roCFF agvkSDr0XYdWrSd7yjPdYmQsfRS+3fnKDGdILZvEoWUXydSDiEWbVgRBjJP+iiZOLelA0HfznCRp TWJYK9gmxcDbu9JYSWcn8NQg/ZYCi1AZ4xX0Kz+lPPOK4r1uc6lQsLI/FXazJg4U+TaYSGWOqFHZ F0la88wmqGypfTQna+o8xHdJDEwSXDA+4fZU1eYxr4zuQBaMuvx0SviDuuU2l+g00kuGmcZuJ81a 1Sl4l77qq75qu3i/4xA4FkQBfFysSzZtCJXJ5wBbnnZ2T4duVUYsZme6dGfVwmLFcC2RqV2cGVJW Jih35Fx/Kkdu3RCrwgw+d6+hd2fIi3lrni8fI4IPlkXYgmNnSpAG1/W99XXFBDhDbioNFazKhaSE Z/Rt3G586manzkO0xrrrJt9m1vWqQmJuWBmbiDSPHuS2/s6S7BUPFwLpTvTSluPy5tytwEUwBFsw L5qgwhpdnZdtGbPEwLI77NlB2CDzsHCeEWglDs2cD23mXeCMFun023PyRSyUvVCSEeBmo2iJ1MaK ADN+q/pXjJF5JJ3ayDxXeUDipvqCRJ1p4I4VLijtM2KfJ/31GeFHOCEBjt3ttxGH+XVDUqEzvF4F Q1K7PAfRqCFU12wqKlaSJM9VNbXMASPJpuruD2vLrZ1ruUQyBNR+VVP4xk3htiRTxTYwEqpsl3FW FqiCHManVpEA+qBmxTw69K2WWCpjFWYQsb3IwLeOnkUonZsVNlQozULmjfMg3DsYGyR9XSsPRCsv ITXBgjslbqNxdF4LCNhFNunCni3XG1dkayAeHg7AB4QSsELgOG8JPRqg563pLg7vQhR/dxJtTHM1 BxtNZfOdvqosOK9DiDhSj0KQxLt1VIm9YgrWnTpZrntZdZiCPvWHQ6d++l4/3anRGlPBypngIIBk +s1bRBp6H0ElybzTlXLgWX0psDafkPjaa6/dxlFVJaVDXswInDCPLjMK3rne5+1i0/2rdSLfeRMm WKxV9uUMOIUz3q8ypHkVF5a00vahVt0Ct0/aQfZGyALwnYZHNEmPGbJQ3JbNoQpUfRFQSn4BqEII 9Ff1DJtTacu4f3kWIdW8gzCbwLgQtCQjAPZsoS9sBIeLGfd0d3ZM4fAdUmpxqiRkHrqCJLUudCmi tw33fU4CBjWEIy26cjq4JhE186jfUnRzU6daeLd3qGXWjXl0Y5Z3KkPkObCHmBVZaHx/G+tem5Rg c4WEncpD7AI2tWANbrn6wdlYHTR25mT/qrfs/6rKF0sF4f22FxVfsPa8Y6lrFcgDO9JMKnd2zIzX O6mxvjcCiWt2DlK2GsBWS8l3HSalltmoJE4X7nBdVpxaH8LLcdeC+wAuLxIAI6xOgAuHiGDThwsL mWcFPVOuOXFuk33v/4oYQBSI390UqU8RS4hSLFLfebZi2ji5cqFxfM2zjE+Og1ROhJLRqlXPSsSs zyHIzG4MqfVRiEexYL4zf1yaBE49TCpCPnsAhk7nRS+wExByeSiXbIgQbPVXym8XBtmTUmW9a5+6 HjvGU7q0elfsrBDY8xgdAjA/yFx6MwnKwQAXEJU5dUDqb+usen33zxf4ak3TjXzOjXRcptqvVULU pvszf7WW7pkPvULWABUiVFAZclIPPNfpdoFrWpU7qCCFZADQzGQswK0UVIA174qbVSnEOzYkfzyx XZKTcToDaW0Rvpb3Ls6YA0CDKN3YVDmjmX5LElJvICziqYiCeUI+4yAkcwvpIEAqS2vDXPIW5jWq htaMd4rTavr0PpujOrzWmnPhuuuu2xabA1tw1g8ihPT2rNpe5ggeMatugOrkG9HJUSmWSkUbRjfY ge28Ni3OjxAwRc9X8CKYFyNX8Q3wqx7zPkLd92aDhOwVlwbouG2qRyqBDey2JRuJwzHQcHpAnYv2 PbFq86qNVGqsv31XjjTi7LCu8qWaTYIseUmya/TpeWoGrleoRCV3OlPosLLDuxC7KiwFWeZGzXUb AkKWvmcfdEakj6Su561dZDG36YRl1UPMwzOzTljPQT7IUR1ba9K6YyRVtzsREUU1ipO45eAgvA5F b9i4j1PbcHyMyfezGqQ9iBEWSNrdHlplXpP6peCKOi5URwObGIA5Vqd5Ieomn6c9rSqm/YyorNMR QGdI+yjcsBcCgVA2hfgEANylMIjUgFSIGbrhs8rzdL+f84LClAuFyABPpYFoPiOWq9da4QNuRzE8 BbIBUEacGrL6Z7RTyxBBGXPpv3micM5q6XaNQ4lgzS0jW6sAWmV/qIXmN8Owrav7vyFr12Br3qve LySOCAuDyei3lmy5iul512eIQ71eofvdXFs1lxAvHT94zgsziwzApbNV7rIx7IsEMK+qr8d4vItR 2P9qlkWgzkrEfyGiJGcEG06YT9ELGeB5F1NVKyARww2m85wGvEpI0w6mqglugHsCWBc+Vp816s47 04JtYBGh9G6fQ9jclLlyp0HbYSHEsLHUMdIo9+tzn/vc7bUF8+AvDwebApDp206aERM9l/RJ6nhX v0R/Zxp5nGYllWmTtOmpkQgYMYaMXe1WdqRnwUr/k5sjXDCrD+stb1wzt1kLLOeABrGKITPPeV8j ZK8yJFibf9HB5ZJ3NXNxdBHhpZdeuq3GH+K3bvPrummqEALXl/n6HNMTZdxBb3ALjojR//OajGLn uixH6/R9FtUoDk/LCVPo/nTpn7TtLdydrgv5ICOuV6hJPv2uDshIra6qPGeABFyGJslTJfi4Zxyi uB5EBagQSwYhUY1zAY6NRqyepTp1+Y3NL2UXUQCg/zu5TWplmOZCDPlnveAFuM1GFT3aMzbOvOjj EB5SONTrBikqSkRVlHHXJ3tGIhgY+BtikjSIIuTMME2CVRTC31SsRz/60dvrtXNbB3/zMW5XNrSW Ul8r31NA5R023+fUMFa2WLZelegxFX3YL5LGHhbJkG2RTTodOtRd8yrws9CXeV9kh5az4swMDZqH tf3OLX1SW2Rv4e5FhAKGDQAgSOsz3yUmc8laIMRVogUyd/mLBsGcmpIonoU0neICakXYeEWUltGn 8QASYRmLZ8Z5CZ20ahoQRl/islyr/JjHPGYRFE9RkcUQk4qW/prqMRNvAvoMM6nkapKItDLfTqhV LqcClb/BkOw9TKUUV8hWtG6X7HQo6XfcskPQEA8HR5SF9cSJEVnOA/PBEJK6Mzy9awmKXvD99Zvc /1kkQd/mGzwiApIJYZmTps+uoAiBi+PScnh0r32XtHpm1kirDFLSIJgXrKolzfzOkC+J7yDuSa/i YOVvcMT0+CRK9W1xUZtXBUDIAoktiv7s1BUH7QIXhrjnujOik/Aq9VV1vfpJnVBrIVP+cOEsvEmM TcSRChSgZ6mY6SlJvUuazKsatHkDFATscwRp3WwBBEOSmBPGAAbleUgBYBtVl7ZoXOulOmE0SVww LSA0FcPzhdoorlBeS1G0mv5So7TSE/KEdXsv6dsh5f1GzkjqUKHvFWbo/g+ERgJhcsXSNb8itEvo Kg03G0fLRdv5S/DtFtwYVlIzWyPbJZU3nNyH/bH62kcn5UKXUlt50DL0AN7/qQflhmhdhEnNYsy1 oaQCYqO68ZXb4AqIFY9UeIY+fSe8gQ1CIlCjkhy5DBUyeMELXrCkVtcbQ5A2qWjXvD2TWCKYmXM/ RX56dAg0626V384GQFw8d9ydeVsqsRr3nkZuuSDWbDyfFRtWnawqOmpU1gp+p14yepNmNQdxqZ3l s2BCqXYI8fLLL98eclbzKtuuLMWipT2bS7yCeBqblApVBRpw7fQ8W0sf7VUu/w5+K7iNqZFSSem8 ijN4s7kVwHhQdbGIUkAtYLEDq0KpIUk6Ic4BSF0kb4MDfnWUCmHAiQrH0CC0Z6vm3qbYfOclyugU HZvXqfghm+ZcIdHvd9XXq1ronTjcPPircF3nNqkYM3K5CzELfMwQ74xGAKTvqHAR4SxKUbxSyFUp m0I9tPkdrl5QZqH5qTGdSZFMuV+F5YBRFSiLt4q4Ku4GDiT4m9/85rUmEqy12OPmg3iouaRG6nKn 7Ult/WF23qUNkOCyBaUMl4vTmVV5KNmpiMLfvrMm+4WI0wzAtVx7rT3p94z/O+cHhWV4ASYAQHKb Xyg1pAZMky8LDSABBWe1cVQRYr0zA4TRIZIGECqWQDCLp44ZX3+8Tr5ne3TzEiLIdYlj4tSdmxRo B7FSJ3INF2pStmRIGfK2oTOZSesKOBtb8QNICqk8SxJSJ43rmeoHc4XijlSb4qtK6a3yPdjOq467 wEZjVyF8xaPlqXun0HpI1Ql1V8yR1iQsA3mGs6RuGZcUfuADH7gdI1c5uBSlbJ72wFoQh5YkNabP wQZMEVhpyI973OO2AawdKiKs7jmMMXZ5qD4R7JTihbBk+M+W5DlpyZ8Fi5N2EFKRCF3iCIG7KIWx TeybLG6Sjs5QZCi7n6970KtsmDekxVbKHxEW0TtzpeOAuB2RbiOnOuFdm2Iz8nBI7WQ0ZyBSIaqf O291jTgKXSgyeF7V0PkJrolw5sWSzbXko8rbIOo8SoijOy3mZoNtJ9/ZABC2MO+kY/kX4MyFjQjB oAzCsghFRCMaxIFY7UGqTwe0naukw79zcziqzRwLsEA0VcXEHELs7nhEEPChA8C8Tklg8Cq/xnhd Y5ALGv6QgOXHpF4jJH1UaOO4tg/psRcCKby5qw4suPBoQAeU7t+oOkYeLYarv2cBs5lFV7hHZWUC bFUwqFNPecpTlmSwOaRRd7KTAt7p0krSKo5pbBtggytVY1zP4fxxz7imDeyA0KZYS8lEiBeC4nDm j0mUOZnqU5qvUHZrol4UnWyz82il15esVbkgcyv3vRP8GdqdxPO+wnhlNTqsxKC6eCfjG7Kx7cDD +GCUmppr2Lpf+tKXLqYGPv6vSGDcGyy6xk0rAJVny3cQm6YQcwHPzjjywBnXmLnc/W+u1o447FNp 2h1Ct/5y1LMR90UUezfS4zLdrAowBc7F3dgiNg5XgHw4aMk7VezLZsjVF/FB6rgYTtiZgoT+4nps rtteSRBqBWkBGaTyet4YOGuc0ecZ5zM3W1+ICYL3vZZEs54MU5uMUyISY5WfEHPIjikFoOhkY0Cs 4BGBzJTaWgbnjJYGg257QhwkIa6dsc1I57HrbhRjkFK5jvtdTd1i1bqyAHFRCx+wuQkqYsx7WLJa hd8ykql6JIn52T/IXSE4+8E71XlKlSM907lNKrHPY4hsKfgk6hmczK3UiarSt4e7RLIPNWsvRrrF xEW0auXanC6hBDjAEqAGIIA1cx4ANH13Xp8wI32rhwQxn/Oc5yxuaRz92wCA70JPRiNp5tmQLi4G wCF/xqSrzOjoxmbMUkNIlHllQ5GmcflUhsqOWgtkJNnYVOVpV0AvBkLSmTMiTLpoEVXnD7lztcL/ K+TWfX+IBIGCoR9/Qxp53HH6Dg2tuZpWhXR0+t8BpXFJFmu64oor1m8wsnf+rniEVmRxamHFNOxZ lR6NYZ7+rrif+WCY5kvCWEsV2XM2VNJJ3+ZjP6pZTMLAj6TJ6STIwRjp2SHpip0Sa2WGVauoEA8u XQjX3XcAMYvPaXGaCCZRbSNIlU6lSQ3qi8+Nj3B4VhwUUn28N92uxU91KmwO5oUQ9E8qQKRiomxC F05q00WtVbrf8366wcn4mAXJZW422Tt5Yjqo6wJT64fU1l3FkLyEU7KkyoSYmE+hKd0yW6ai/62f Oms+1sYp0N3wYGVs8GoekBZS3nHjSi0mzDv6K4lqZuyVLlDNZOpj1TXBtHTlKshXEbEktho4Y2Kl INhjjhXv5iTJk1gE9Nlse1GxTDq3JQov4jMDEDDpkLxYnezizMXyF0RXOugsWA15IXSnrJrv20TP F1VacKJm8yCmlgGaj10rfGNW58PlSB7zx1G7Ri3C6ZanbIt5wKiFzE60SQhu3Wc84xlLYlSYuzwG m48Dq5nLHulqge6UT20JMefNVMFUw505J0hjahYVjjqivxhTFWO6J130gbvYy5GfJ91FDkP266+/ fiFlHqhZA63QmiJrkyrmhThyF6cFZGT7HxxJMDhgbcYB96rSF+Lvh2THvMqPaY5JzONansCZ9XlO E6ZmEF0F1DQbCQC5ShldVS3hhqQDF2MTsAvPLvstF2+HcZXWqVRnBRK6l4JOXsnPQib6jcsWOdy8 CsgrvqdCA0JPbJw+qUFxSxubx0lLLZit+89tKrtInJKxHWS677ukHm7tQjcKEsyOSL3Q15QmqXeV AjLnbobyubkhbJyZ5EDsxmD3UGtT4R7/+MevcwnGOvcwOyZYY3b6vP2maETlfDRwy7M3c3wQqbG8 1z0vMRNzBVNz7TZbUrSazOZNohgrqVhkAuKofvE8NSfJqLOpreFicDoYAjEZiyxWKg+VVrAZQAsp Kb0SNyEBxCdBaMhILckP3y1Hx5WPbHNKwKmgWSfM6aepVDMXxeb6sZG4OnWDLky6QWSIkxHtXRuQ jVP5GsTSgVqu3pA5JJK0Q32BKJDY5ltf7tbioZ7+9KdvLx3N1ooYUt8Kc69GmPlCas85E9LYO8EM cncPCIbEE0WSzvixWtmOFUZonC4mut/GbkRAMcAKjGcDlSffjVlVuewKuDxUVcrsurjC/60TjGkX xrMGDDQ3vTm1x+2tZn5lq87ntKmanrTtpbJiqkrBYggmu6IQ5sq+4DCdcuNoz3ve85YqQAXBrVMD iHRI310Z2rxmrPxujarlf8iGS/PYVKCtd7TpM+8iS8jJsC/WqvBzHB8RlPNuk21gPn6tmK9Uv2wk 0k3ik/nn8kYckMOcwKFTZCfLs7xOrt4QJb9/qsk8oyCBq4oIxuBuPYjEXK3Hb4jkGf2AQSkFxW+Z H4SG2AjC+km9V7ziFctGYQOE/NkYbLSqZXZ/oZaHLQ8ZKdX6wRHsEG9VJ83B3O1x68x+hQcRVp68 1Fr7UXjKrrdqSpCTGuonJpDOL0qRtQCIUeZY+cKMQEiHk1blG8C+5Vu+ZS2eSlKodKVd/EQcuHmB drUOuABLv4iiOC2tyoea7xFQqaxxqM4uSh6qThXp5tanJFW5E5DaOmxW16llw3R1QRXVjVN8V/dY 5BlD1HIlUv+qvk6apYtrnSuBEeQlDQopIQW9H1xaR+92fUOHqqXGdkPutBv8XYVKEjKP36te9apt JmN3BnoeLPXDRrJHiMk6IK33fYaAwLd8F/+nUeg7e9U+lZcypWa5QLn4tWye8kh2C4Nou7bhOSWQ 7pGDLBnP0z1b8lLcnMGYW6+DKUCDMCXdlGDTolOhOql96lOfevS1X/u121Rf/RiHdFJfK4Nuitlc lKVqZhd5pturZt0pVeAhsw0k/tOvraF7ySGzuTGyETDu7Zl8/EkC68Ao2F04bQXwIJVIghpuCY6l DEACJ97G83+V6EuKKs9dKwDUc+ZGtRLR4NlnPetZW+O/hKaS0SpLNAtbJKUf9ahHbesHFIsWUSH8 InO9iwG6wYs3MccLfPCbNNJfxKkKid/V7tJ/bvd5Wl+yW+kUORTaw+zSSRydOc3iGufci9WB2Mxd mAktXXswk6DKtS6sBHEkRrsJttPtcgYgBURAZKkZiBPi4HQ+yzPSKThglWOtdU0BblwOewdhnaxr nrFRiCNDkMfJ99QH8+A6hRRdOOk76lqHXCRkp8cQhyEMIa0DlzUnn7NrIBwYFeqt6bfau9250ilz LtNyr6sIGWH5nBrk/7xqxi4Vt8IYfpOa5kpSGMdeBJsrN+mw1mAfMADfx/Sq+s6tbR36d4KPEdhX 74f8iK3YKmtInY2ZmgPmgCFUiCGHDcLuIFEr76VjgH70tQ/Vam8EUtCcxZT/YHERR5UtNMhfYeMC /nBumzS9F917oeXirRVC3/XEFVVj6HNv6hug25ROsrVcmHlnuj/E+YQAS8Zu9X4hgeeqKeX5gv08 C9ltMmJNXSs0PRggcn0hKC5V3+vfeYR1OTR98pOfvLVBWnN2SIQQR4ecnSngvmBh/qSMeSgrpIFf p/rgmxSAvBDZfmAqDjS5g33WfSvGAMu8fG/YVDnUzIEaVnkma7Qm33s+Ndh62R5Vj8l46Hw8uQAA CHtJREFUrmig9XRHYdKgOwdLtdVXFw3Bk1kVv7lMvJg2xz5DTk5MIFX1g7ABbSYXZcD7DWnLF64Y QtU0aoXKQ5J00i7gpI7oix6eTTALUUO6dOZaORazRGeHXwzT7BWcupCXok6twRhVU3H/iPrBVBfj QnTIx77hWcpFG6Ibi7rje7p94TMONqlHhWtAnBwcBT7OlF5/6ycEps7k4jU/TAdzgDBsHfBhDOPO IqUhbAXhICapCAG9VziQvfI8j1U5K/e5z31uUTBi5qh0ZVt56Z7VH4bQqTxYVEOgulkVkfD/vEVr qsTlvXQO5vvGL3EqJ1AwCt6p91XzPOdu3u6182PTbJANzzDsrKEw7dpMMip/uFueAMbiLZK7FPKK xi1ldPrVbUxBdJABwlShZJ5+d5pe0Tp6cglZeXkQsM1PPfO7PHEE6u9O/iFbamEIZF6Fd2jmwZYx Hu8aI13CFuO8y32q15vDAXI0bwyiOxO7Z9GYnfNAvmw4ahPCQMB5qcCouLZct+XPgD0YIHoEwgX/ ohe9aBGud/V5382lmYgzZCddOiPB4cGpW24xHP+X01G8mL2vYry+M8jNp1gzLRUJ8YKPeZWGPe0j rVCkXW9V0eDTFjnn0bxdz1WYhmYTisma1QRreW2i+qI8caBK9fie2jPvDgQ8iDovmim6lxGIYFKr 8oQVEVudqepG8Xo5S/C7vJYQ27lNa/E9lYZxmZTCsdkhshS1VICkUMlXeX6UxmR3QMqI+klPetJ6 Z14SQxrvRje3lnImaj6zHsyJ96ir6yrnWgSv+ZJa1lEeRuV9HMRZk+DGxz72satfzyO2mzYXF+nT +QsYdNsXIqm+WfkuqcoRhzWG2EXu5oAIiauLVZxVTLJSRhwhiKT6z108WsRzV3NP477D4YO5gq2Q As2Eu1u7ixZTbVIjovpa7xZA5x2ExdXawZ7fhVngXNQo3Mpmlo0GCY2dGzL9tpL6XfZSOq/PcfMq heetwXkhQh6fjHDSsVAVSMVIr4haz9gkyNDlpfpO7YPE8y6SiKpDxpLD4rzd+pRaWrhKlei9x1uF kOe1ZuaeXVSWnv7LBuwORw4Cfxfeb73mintb5zs30r+r4MAlr1Hey64y0CB8tZkjAv15p4xNz2AC WtEQOTLMudi94JL9M7WPmOU8lC5XPRzr/OkgghV3Eb7k+lrfTTHY3xm0EKwQivLA/U/0t1AAoOfa dGLa5hgfd6xcUHkKEcg07gDbjw0ttF7fuaBTZwplwdUKiNO3kjpclrxCnVfU7yyNUwh+hnpFHbpX ZN5tMTmfueY10kgMqlWuYvOeV9SZO6+Z35AyJ0l3yldITSMR89aBmzWVwWedPFDVzSIZwfgRj3jE gksFIiB78WkVbihM3tjdTpvnkKGuv8oqVdG9SOaquxQ5nA2SXVhlx9TPWSNgxtBll6SqV8n/IAgk r0SFusoKy/uSmnM6z8IsRlaeRcYb71Lfpxr4vySesv8Ay7u+q5pg6lsqQnkmxV7VZhhKnjHvMihx WDFLNoCnh8TAXblTIyxIlju5KxwgUeN3CIeIcU6xWMHNs0mUXOVasKJOIpAQMb0bN48Ys3fK3Z4h 4J2WQzDIW6hNBq6/iwK2vuyLIoxf85rXbDMwYxyNWxQum8x+5FDpUlVSgXSaBSwKASLh5pqm9Itx dELflQfF92VvJPGnl6xaYaUrH0TRhu7ECJFnQNkkjLj5bkKLxaWDl2VGbaAuVRqmOxATrV0iWbZf d+oV2lKxNf54od9VHOwsobKiIXAtQ1a/EBOC0L1JJ+7Q6jghyk7Ikxypcvnvc4Eaz9zp7/qqVGdc Obtn3qeYe3UWI8j9PTMJqwMMLvPujVmmKEnaVW1JM9IAEnNdd/eiVtFpev9lG+Zij8Eu6YYIwdj/ iN+7hf50h0vnSZ18F+tlrA6JS94qzH4h5KboRN/P/ZnIPiOqq9U8q+ZXlvWk7cQE0slqHL6qJrVq zO626XlIpAZUYjlimLVY/d35STcIFb9UqEsHVziMzWI3mCNpBIlnDFen/LlM22ifQRC6PVduZTgL bIRUs4SmlvHc2uOOfpNqEUmSDxGmJthcyNsJf6pXKkuccfdkOG6rpZqme8/IWN6lDt+qHZxh7yzE GUqldsAQAbD/HvnIR655+smmKnW6Q2Hrq+JJIev20Z6YS7k3WpHLee66p7Bzs4IfEeZUgVPjJx6l 4rV+7waPPGMHUZt3N4loEseZqmvPynnTM5HbuGeSSLNcpZZYnv7wkpUgN9cst6LNSI8OycptmHkQ EWdXkJlHxSXYHSSIjRP1CxGqiJjdMBG1AM7+r3YVlSNJUKh+zZw6EKz8qXcgbv9Pz1BnEdkYMaj6 nLnalQDKRVpYO9jqhyrpbMf3pIXfhftfurlwtOhmrX1qDrl62TWl2ZZRqpGG3plF4grW9GxqYgje /95LIlfoLjujmwKSKkUvVPxhX6fpJyaQiCDC6ICwHI9dA323VS4onb6UTe/PezTSK6fdEHElBWwm TmnzEMmuDtrJdC7E0mg74OtmWZ/PLLeSmSBBVw0cR/g2aEYMV0y5Cun+NgeSaRqZ5ljhhOYbQVRR cYZ7p2enPiUJtdaV6pHLVYshJIU9K3kqF7f+qaTTiaC1B6RkqhxXdeNMeBYL5rlwYqp/WqWDMsjn 4Z/WQV83gWWga0kTrf5z66aSVmhvH1dBn5hAdhFl9//bQsW7BaEnUs9ykrW4cwCanHNKl96ZcwpJ GnPXX36m+e7aLMd9P9s8+9kdZ863Oc0U1rm5x6kLvTM9g3Ndjb2r8s5x5hzm/HbPrC7ZfDcRvXFa 81Q3y+NpjrtwmWuZMG3c4wzrPouw5mfzConawdyTfr6db/8/t/MEcr6db2do5wnkfDvfztDOE8j5 dr4dnb79HyIUfYoEBeZbAAAAAElFTkSuQmCC