The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography

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Although CEM is similar to conventional mammography it differs via an additional exposure with high energy X-rays (≥ 40 kVp) and subsequent image subtraction. Because of its special operational aspects, the CEM aspect of a CEM unit needs to be uniquely characterised and evaluated. This study aims to verify the utility of a commercially available phantom set (BR3D model 020 and CESM model 022 phantoms (CIRS, Norfolk, Virginia, USA)) in performing key CEM performance tests (linearity of system response with iodine concentration and background subtraction) on two models of CEM units in a clinical setting. The tests were successfully performed, yielding results similar to previously published studies. Further, similarities and differences in the two systems from different vendors were highlighted, knowledge of which may potentially facilitate optimisation of the systems. Contrast-enhanced spectral mammography contrast-enhanced mammography test object phantom system performance quality control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Breast screening using mammography is the only imaging modality that has been shown to reduce breast cancer mortality [ 1 ]. However, the sensitivity of mammography is significantly reduced in women with dense breasts [ 2 , 3 ]. Further, the presence of dense tissue is known to be a strong independent risk factor for the development of breast cancer [ 4 ], and since approximately one third of women older than 50 years have dense breast tissue [ 5 ] a significant number of women are likely to benefit from the addition of a supplemental or alternative screening modality to mammography. Much research has been conducted into the role of supplemental screening modalities for women with dense breasts, utilising modalities such as functional imaging (e.g. contrast enhanced MRI), tomosynthesis, and ultrasound [ 6 – 8 ]. The results of the DENSE trial [ 9 ] confirmed that the use of MRI in women with extremely dense breasts results in a significant increase in cancer detection rate, with MRI detecting an additional 16.5 cancers per 1,000 examinations after routine mammographic screening. The European Society for Breast Imaging recommend that women aged 50 to 70 years with extremely dense breasts be offered supplemental screening, ideally with MRI every two to four years [ 10 ]. The results of the ECOG-ACRIN 1141 trial showed that the use of MRI in women with heterogeneously dense breasts also resulted in a significant increase in cancer detection rates [ 11 ]. Unfortunately access to and availability of MRI are limited, costs are high and it is not able to be tolerated by some women [ 12 ]. Contrast enhanced mammography (CEM) is a relatively new, advanced functional breast imaging technique that is a suitable alternative to breast MRI. CEM has superior diagnostic performance to standard two-view mammography [ 13 ], even with the addition of ultrasound [ 14 ]. This is particularly true for women with extremely dense breasts [ 15 , 16 ]. Compared to the standard tomosynthesis protocol, the diagnostic performance is comparable but with reduced mean glandular dose [ 17 ]. A few studies have also confirmed equivalent or better diagnostic accuracy of CEM compared to contrast-enhanced MRI (CE-MRI) [ 14 , 15 , 18 – 20 ]. Recent studies have supported CEM as a screening modality for women with dense breasts with Coffey & Jochelson [ 21 ] noting "increasing data suggest that screening CEM in women with elevated breast cancer risk further improves the sensitivity and specificity of detecting breast cancer compared to digital mammography alone and to mammography plus US, with a diagnostic performance approaching that of MRI". A significantly higher overall patient preference towards CEM compared to CE-MRI has been shown, mainly due to faster procedure time, greater comfort and lower noise levels [ 22 ]. CEM is also accompanied by significant economic gain due to accessibility, reduced imaging costs and shorter study duration (30–60 mins for MRI, and less than 20 mins for CEM) [ 23 ]. CEM utilizes contrast media and a dual energy image acquisition technique: the standard two mammographic views of each breast are obtained two minutes after the injection of intravenous (IV) iodinated contrast material, using X-ray exposures at two different average beam energies, above and below the k-edge of iodine (~ 33 keV). This results in a low energy (LE) image (diagnostically equivalent to a standard mammogram) and a high energy (HE) image; the latter is not used for diagnosis but is used in a weighted subtraction process to produce a recombined image in which background features of normal breast tissue are suppressed and areas of contrast uptake highlighted, facilitating assessment of neoangiogenesis [ 20 , 24 ]. A CEM study is quick and easy to perform, and unlike MRI does not require specialized training to report. Like MRI, CEM can demonstrate lesions that are only visible following contrast injection which may require biopsy. CEM guided stereotactic biopsy is also now available and early reports suggest it to be a much faster and more easily tolerated procedure than MRI guided biopsy with lower cost [ 25 , 26 ]. CEM does have some disadvantages, which include (i) use of ionizing radiation when compared to ultrasound and MRI, or a higher radiation dose to the patient when compared to standard digital mammography [ 17 ], and (ii) possible adverse effects from the iodinated contrast agent. Recent surveys on the additional radiation dose burden of CEM compared to standard mammography indicate the magnitude of increase is a less than double (ranging from 20–80% increase) in mean glandular dose, and less than 50% increase in entrance skin dose compared to standard mammogram [ 21 , 27 , 28 ]. However, it should be noted that the overall dose remains well within the acceptable range for diagnostic mammography [ 29 , 30 ]. Adverse reactions to IV contrast material can occur but are usually mild [ 31 ]. Severe reactions are very uncommon (0.02%) and not dissimilar to the incidence following IV gadolinium contrast agent that is used in CE-MRI [ 32 ]. Mammography units undergo regular quality control (QC), which normally follows established protocols such as the RANZR program [ 33 ]. A QC program that ensures optimum system performance is essential given the importance of early cancer detection, and the fact that population screening results in a large number of asymptomatic women receiving a radiation dose [ 34 , 35 ]. Because of the differences compared to standard mammography, key aspects of the CEM system need to be uniquely characterised and evaluated. Although several studies have published test methods and technical data on CEM systems [e.g. 36–40], there have been few internationally acknowledged guidelines or standards for dedicated QC protocols for CEM [ 41 ]. Regardless, two key aspects of any QC program for CEM are linearity of system response with iodine concentration, and normal breast texture cancellation since these processes are essential and fundamental to the CEM process. In Cockmartin et al. [ 42 ] it was noted "imaging of the iodine signal, which may be influenced by factors such as breast composition and thickness, is of fundamental importance in this technique". Hence these tests are critical in any QC protocol for CEM units. In Cockmartin et al. [ 42 ] these tests were described and performed making use of several phantoms including the BR3D model 020 and CESM model 022 (CIRS, Norfolk, Virginia, USA). The aim of this study was to assess the utility of this phantom combination in performing linearity of system response with iodine concentration and normal breast texture cancellation tests on CEM units in a standard clinical setting. It should be noted that establishing pass/fail criteria for either of these tests is outside the scope of this study. 2. Methods 2.1 Equipment 2.1.1 Mammography units Two mammography systems were available for assessment, a GE Pristina and a Hologic Selenia Dimensions. The software versions were 3.4.51 for GE and 1.10.0.412 for Hologic systems. It is possible to set manual factors for both LE and HE for the GE Pristina, whilst only LE exposure factors could be manually selected in the Hologic Selenia Dimensions. Both units had the means to perform only a HE exposure. Details of the exposure factors for LE and HE exposures under AEC for each system are shown in Table 1 . Table 1 Available exposure factors on GE and Hologic systems for CEM procedure under AEC. Compressed breast thickness (cm)* Target/Filter Tube voltage range (kV) (kV p ) Model (vendor) LE HE LE HE Pristina (GE) < 4 Mo/Mo Mo/Cu 22–32 40–49 ≥ 4 Rh/Ag Rh/Cu 27–40 Selenia Dimensions (Hologic) < 5 W/Rh W/Cu 25–28 45 or 49 ≥ 5 W/Ag 29–33 *Breast thickness is accurate to within their tolerance levels. 2.1.2 Phantoms For all tests, the phantoms used were the BR3D model 020 and the CESM model 022 (commercially available from CIRS, Norfolk, Virginia, USA). The BR3D phantom consists of six 1-cm 50/50 breast-equivalent slabs, each with a mixture of 100% glandular and 100% adipose tissues in a swirl pattern. The CESM phantom consists of three 1-cm slabs consisting of 50/50 breast equivalent material, one of which contains two sets of four iodine inserts with varying iodine concentration from 0.25 mg/cm 2 to 2.0 mg/cm 2 arranged symmetrically and a 100% glandular insert at the centre of the four iodine inserts. The CESM phantom also contains one 2.5 cm thick slab, with 100% glandular and 100% adipose in each side. The photographic and mammographic appearances of these phantoms are presented in Table 2 . Table 2: Phantoms used in this study. Images are from CIRS website (www.cirsinc.com). 2.2 Test protocols 2.2.1 Linearity of iodine signal of varying concentration in image To assess the system response to changing iodine concentration levels, recombined images of BR3D and CESM phantoms with varying thicknesses acquired were analysed. The response was evaluated based on two related values: (i) mean pixel value (MPV), as per Cockmartin et al. [ 42 ], and (ii) signal difference to noise ratio (SdNR) for each insert with different iodine content, as per the RANZCR protocol [ 43 ]. Circular regions of interest (ROIs) were drawn over the inserts (1 × 100% glandular insert, 4 × iodine inserts with varying iodine concentrations from 0.25 mg/cm 2 to 2.0 mg/cm 2 ) as well as a background reading for each ROI. The background signal was taken as the average value of four adjacent areas surrounding the ROI (using an offset of 1.4 mm). Figure 1 shows the examples of these ROIs. 2.2.2 Background cancellation Quantitative assessment on background cancellation was carried out by (i) calculating the CoV from the background ROIs used in the SdNR assessment, and also by (ii) calculating the tissue cancelation efficiency (TCE) from the same images as per Cockmartin et al. [ 36 ]: TCE = MPVglandular - MPVbkg1 MPViodine(0.5) - MPVbkg2 where MPVglandular is the MPV from the 100% glandular insert, MPViodine(0.5) is the MPV from the 0.5 mg/cm 2 iodine insert, and MPVbkg1,2 are the MPVs from the background regions corresponding to the inserts used for the MPVglandular and MPViodine(0.5) measurements, respectively. TCE is interpretated as the relative visibility (contrast) of the 100% glandular insert with no iodine content to its background, compared to the contrast of 0.5 mg/cm 2 iodine insert to its background in a recombined image. Image signal due to anything other than iodine content should be eliminated in recombined images so ideally TCE should be close to 0. To gain more insight into the recombination process, the effect of mAs on TCE was also investigated. This was only performed on the GE unit since Hologic systems do not allow manual selection of both LE and HE exposure factors for CEM procedures. A 3 cm thickness phantom was used. For the effect of varying LE mAs, HE exposure was kept constant to 63 mAs while LE mAs varied from 10 to 300, and for the effect of varying HE mAs, LE exposure was kept constant to 32 mAs while LE mAs ranged from 10 to 300. 3. Results 3.1 Linearity of image signals for varying iodine concentrations The relationship between image signals within recombined images of CEM in terms of MPVs and SdNRs for varying iodine concentrations is presented in Fig. 2. The Hologic unit exhibited little difference in MPVs for iodine concentration of 0.25 mg/cm 2 to 1.0 mg/cm 2 although signal differences clearly exist in SdNRs at all levels of iodine. Since ideally the image signals (either MPVs or SdNRs) should be linearly related to the iodine concentrations, the linearity was evaluated as the coefficient of determination, R 2 , as shown in Fig. 3 . 3.2 Background cancellation Examples of corresponding LE and recombined images are given in Fig. 4 , which demonstrates substantial removal of the swirl patterns in recombined images for both GE (top) and Hologic (bottom). The recombined images were examined with a narrow display window, arbitrarily selected on each image to highlight any variations caused by non-iodinated materials in the background. An example of this is shown on the right column of Fig. 4 , confirming the disappearance of all non-iodine materials including the swirl patterns of the BR3D phantom as well as the 100% glandular inserts in recombined images. Manufacturer-dependent appearances were noted in these images as: (i) 100% glandular inserts were visible in GE; and (ii) gradient in the background pixel values is evident in Hologic. For the quantitative assessment, the CoVs of MPVs from each background ROI for the GE and Hologic were calculated to be 0.002823 and 0.000675, respectively. Although both CoVs are very small, about an order of magnitude difference between manufacturers was present, reflecting the relatively larger non-uniformity of the background MPVs for breast shape phantom in Hologic compared to GE. TCE metric results for both GE and Hologic units are shown in Fig. 5 . Although some inter-measurement variations are shown, general trends exist for both units that are opposite: TCE in GE decreases as the phantom thickness increases, whereas that in Hologic increases with thickness. Figure 6 shows the results for the effect of varying mAs on TCE performed on the GE system, where TCE appears to be low with low LE mAs and high HE mAs. 4. Discussion Contrast-enhanced mammography is a relatively new mammographic technique with unique features; it involves an extra exposure with higher energy X-rays than that for a typical mammogram and then combines the two images acquired at two different X-ray energies by a particular implementation of weighted logarithmic subtraction, which may be vendor specific. In this paper, a single phantom combination was used to conduct tests of key importance to the CEM process. In terms of utility, the phantom was easy to use, and yielded the required data which were consistent with those previously reported in the literature [ 42 ]. The authors feel it is suitable for assessing linearity and background cancellation for a CEM unit. The results of these tests also highlighted several differences between the two vendor systems included in this study. Although both systems exhibited R 2 > 0.95 for SdNR versus iodine concentration, MPVs alone from Hologic were not able to distinguish the differences in iodine concentrations between 0.25 to 1.0 mg/cm 2 , a result similar to Cockmartin et al. [ 42 ]. This appears to be due to higher variance in background MPVs in recombined images for Hologic than GE. Such high variance in background MPVs in Hologic recombined image (Fig. 7 ) is evident where the background around the inner breast (towards the centre chest wall) appears brighter than around the nipple edge, possibly due to a thickness correction overcompensating the inner breast region and introducing the variations in pixel values in uniform background. Based on the variation in the background tissue cancellation metric (TCE) depending on thickness of the breast where lower TCE values (closer to '0') would indicate the more effective removal of the glandular insert in recombined images relative to the iodine insert with 0.5 mg/cm 2 concentration, GE appears to be more effective for thicker breasts whilst Hologic shows the opposite. Combining all the observations (mainly results from TCE and MGD tests), it may be concluded that GE CEM is better suited for assessing the thicker breasts, and Hologic for assessing thinner breasts. A major limitation of the tests applied in this study was that system performance for varying glandularity was not assessed due to lack of available phantom materials. As an important future application of CEM may be in screening women with dense breasts, it would be important to thoroughly characterise the system responses to different breast compositions. Preliminary tests that could mimic the different glandularity were with 2.5 cm thick slab with half made up of 100% glandular and the other half with 100% fat was used, which showed very little differences in MPVs (< 1% of the average) in all inserts of varying iodine concentrations and 100% glandular for different background materials. 5. Conclusions A set of phantoms was used successfully to carry out fundamental QC tests for CEM units, demonstrating its applicability in CEM QC programs. The results of these tests also highlighted some key differences between vendors in the way system handles differences in iodine concentration. Declarations Competing interests The authors have no relevant financial or non- financial interests to disclose. Ethical approval The study did not require ethical approval. Consent to participate No human beings were participants in this research project. Consent to publish No human beings were participants in this research project. Funding This study was supported by a grant from the Royal Perth Hospital Radiology Research Grant. Author contributions All authors contributed to the study conception and design. Material preparation and analysis were performed by J-H Kim, M Kessell, D Taylor, M Hill and J W Burrage. Data collection was primarily performed by J-H Kim with assistance from those named above. The first draft of the manuscript was written by J-H Kim and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements This study was supported by a grant from the Royal Perth Hospital Radiology Research Grant. Support was also received from the Western Sydney Local Health District (WSLHD), NSW, Australia. 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Australas Phys Eng Sci Med 40(3):491–543. https://doi.org/10.1007/s13246-017-0583-x Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2024 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Editorial decision: Major revisions 29 Apr, 2024 Reviewers agreed at journal 17 Mar, 2024 Reviewers invited by journal 16 Mar, 2024 Editor invited by journal 14 Mar, 2024 Editor assigned by journal 14 Mar, 2024 First submitted to journal 13 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4091254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280306026,"identity":"3a80696a-b950-4370-b66e-a6de1460c895","order_by":0,"name":"Jung-Ha Kim","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1304-2851","institution":"Westmead Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jung-Ha","middleName":"","lastName":"Kim","suffix":""},{"id":280306027,"identity":"15396b37-870a-4d59-a1f4-d047f57e8ae8","order_by":1,"name":"Meredith Kessell","email":"","orcid":"","institution":"Royal Perth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meredith","middleName":"","lastName":"Kessell","suffix":""},{"id":280306028,"identity":"51981875-9f14-42a6-8dca-6666231d94b7","order_by":2,"name":"Donna Taylor","email":"","orcid":"","institution":"Royal Perth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Donna","middleName":"","lastName":"Taylor","suffix":""},{"id":280306029,"identity":"37957057-2b48-457b-b0c5-5f9323f90e55","order_by":3,"name":"Melissa Hill","email":"","orcid":"","institution":"Volpara Health Technologies","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Hill","suffix":""},{"id":280306030,"identity":"c7f3a8e4-ada3-4bcd-8c15-2c031dd5d115","order_by":4,"name":"John Burrage","email":"","orcid":"https://orcid.org/0000-0002-0104-3550","institution":"Wellington Street Campus: Royal Perth Hospital","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Burrage","suffix":""}],"badges":[],"createdAt":"2024-03-13 09:21:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4091254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4091254/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13246-024-01461-6","type":"published","date":"2024-07-02T00:31:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53117443,"identity":"bd57a4e5-ed73-4b22-8ddf-57473070ffde","added_by":"auto","created_at":"2024-03-20 19:49:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148273,"visible":true,"origin":"","legend":"\u003cp\u003eROIs used for analysis: the ten blue circles are the 2 × 100% glandular inserts and 8 ×iodine inserts with varying concentrations (2 at each concentration level). Black circles are examples of background ROIs for the 2 × pink inserts.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/7e01be290c3448f701ae1c2e.png"},{"id":53117442,"identity":"f809329b-30b1-4442-93fd-dc904b7d38de","added_by":"auto","created_at":"2024-03-20 19:49:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126943,"visible":true,"origin":"","legend":"\u003cp\u003erelationship of image signals – MPVs (top) and SdNRs (bottom) for varying iodine concentrations for GE (left) and Hologic (right) units. Each data point contains data collected over 3 days, and the spread is shown as the error bars. Different colours indicate different thickness of the phantom.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/30ca94e9bc8252c32e69cdeb.png"},{"id":53117448,"identity":"a6ca8b7f-01fd-4c34-bd9b-93bf0c4cdd1e","added_by":"auto","created_at":"2024-03-20 19:49:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53937,"visible":true,"origin":"","legend":"\u003cp\u003eLinearity fit test (R\u003csup\u003e2\u003c/sup\u003e) in MPV or SdNR for varying iodine concentrations in GE and Hologic units as labelled. Different colours indicate different thicknesses and the variations in the measurements on three different days are indicated as the error bars.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/203078cd2bd14167e0ecf24d.png"},{"id":53117444,"identity":"afc687c3-46d4-4f0e-a367-cbab83f26d8a","added_by":"auto","created_at":"2024-03-20 19:49:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":308806,"visible":true,"origin":"","legend":"\u003cp\u003eLE (left) and two recombined (centre and right) images of the BR3D + CESM phantom with 4 cm thickness for GE (top) and Hologic (bottom). Left and centre column images are displayed with default window settings by the manufacturers, whereas the right column images were displayed with an arbitrarily selected narrow display window used for visual assessment of the residual signals in the background.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/cdadcc2458987322f6d1284a.png"},{"id":53117447,"identity":"eb66bde2-83c9-4bd3-8704-08bad31f4d28","added_by":"auto","created_at":"2024-03-20 19:49:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":69069,"visible":true,"origin":"","legend":"\u003cp\u003etissue cancellation effectiveness (TCE) as a function of a range of phantom thicknesses measured on 3 different days for GE (left) and Hologic (right) units.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/51bc3ae3e8af1e404e9b0f80.png"},{"id":53117445,"identity":"782908a1-39f3-4269-a888-236506599b90","added_by":"auto","created_at":"2024-03-20 19:49:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42439,"visible":true,"origin":"","legend":"\u003cp\u003evarying TCE with varying mAs in GE system.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/2445d316fa1f9530207b267c.png"},{"id":53118357,"identity":"a676da13-191a-47d9-89c4-f8e37d7f8435","added_by":"auto","created_at":"2024-03-20 19:57:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119573,"visible":true,"origin":"","legend":"\u003cp\u003eRecombined image of the BR3D and CESM phantom (4 cm thickness) from Hologic. MPV from background region (yellow circles) are 2094.6, 2089.4 and 2073.8, from left to right.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/6751b8c10d39ed3c714e6ce7.png"},{"id":59532018,"identity":"def6a78d-0c09-49e5-ae0b-dd048a8fde92","added_by":"auto","created_at":"2024-07-03 00:31:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1310326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4091254/v1/a4807d13-73e6-401d-8b5e-d55c10abfce1.pdf"}],"financialInterests":"","formattedTitle":"The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast screening using mammography is the only imaging modality that has been shown to reduce breast cancer mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the sensitivity of mammography is significantly reduced in women with dense breasts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Further, the presence of dense tissue is known to be a strong independent risk factor for the development of breast cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and since approximately one third of women older than 50 years have dense breast tissue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] a significant number of women are likely to benefit from the addition of a supplemental or alternative screening modality to mammography.\u003c/p\u003e \u003cp\u003eMuch research has been conducted into the role of supplemental screening modalities for women with dense breasts, utilising modalities such as functional imaging (e.g. contrast enhanced MRI), tomosynthesis, and ultrasound [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The results of the DENSE trial [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] confirmed that the use of MRI in women with extremely dense breasts results in a significant increase in cancer detection rate, with MRI detecting an additional 16.5 cancers per 1,000 examinations after routine mammographic screening. The European Society for Breast Imaging recommend that women aged 50 to 70 years with extremely dense breasts be offered supplemental screening, ideally with MRI every two to four years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The results of the ECOG-ACRIN 1141 trial showed that the use of MRI in women with heterogeneously dense breasts also resulted in a significant increase in cancer detection rates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Unfortunately access to and availability of MRI are limited, costs are high and it is not able to be tolerated by some women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrast enhanced mammography (CEM) is a relatively new, advanced functional breast imaging technique that is a suitable alternative to breast MRI. CEM has superior diagnostic performance to standard two-view mammography [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], even with the addition of ultrasound [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is particularly true for women with extremely dense breasts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Compared to the standard tomosynthesis protocol, the diagnostic performance is comparable but with reduced mean glandular dose [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A few studies have also confirmed equivalent or better diagnostic accuracy of CEM compared to contrast-enhanced MRI (CE-MRI) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recent studies have supported CEM as a screening modality for women with dense breasts with Coffey \u0026amp; Jochelson [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] noting \"increasing data suggest that screening CEM in women with elevated breast cancer risk further improves the sensitivity and specificity of detecting breast cancer compared to digital mammography alone and to mammography plus US, with a diagnostic performance approaching that of MRI\". A significantly higher overall patient preference towards CEM compared to CE-MRI has been shown, mainly due to faster procedure time, greater comfort and lower noise levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. CEM is also accompanied by significant economic gain due to accessibility, reduced imaging costs and shorter study duration (30\u0026ndash;60 mins for MRI, and less than 20 mins for CEM) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCEM utilizes contrast media and a dual energy image acquisition technique: the standard two mammographic views of each breast are obtained two minutes after the injection of intravenous (IV) iodinated contrast material, using X-ray exposures at two different average beam energies, above and below the k-edge of iodine (~\u0026thinsp;33 keV). This results in a low energy (LE) image (diagnostically equivalent to a standard mammogram) and a high energy (HE) image; the latter is not used for diagnosis but is used in a weighted subtraction process to produce a recombined image in which background features of normal breast tissue are suppressed and areas of contrast uptake highlighted, facilitating assessment of neoangiogenesis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A CEM study is quick and easy to perform, and unlike MRI does not require specialized training to report. Like MRI, CEM can demonstrate lesions that are only visible following contrast injection which may require biopsy. CEM guided stereotactic biopsy is also now available and early reports suggest it to be a much faster and more easily tolerated procedure than MRI guided biopsy with lower cost [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCEM does have some disadvantages, which include (i) use of ionizing radiation when compared to ultrasound and MRI, or a higher radiation dose to the patient when compared to standard digital mammography [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and (ii) possible adverse effects from the iodinated contrast agent. Recent surveys on the additional radiation dose burden of CEM compared to standard mammography indicate the magnitude of increase is a less than double (ranging from 20\u0026ndash;80% increase) in mean glandular dose, and less than 50% increase in entrance skin dose compared to standard mammogram [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, it should be noted that the overall dose remains well within the acceptable range for diagnostic mammography [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Adverse reactions to IV contrast material can occur but are usually mild [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Severe reactions are very uncommon (0.02%) and not dissimilar to the incidence following IV gadolinium contrast agent that is used in CE-MRI [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMammography units undergo regular quality control (QC), which normally follows established protocols such as the RANZR program [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A QC program that ensures optimum system performance is essential given the importance of early cancer detection, and the fact that population screening results in a large number of asymptomatic women receiving a radiation dose [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Because of the differences compared to standard mammography, key aspects of the CEM system need to be uniquely characterised and evaluated. Although several studies have published test methods and technical data on CEM systems [e.g. 36\u0026ndash;40], there have been few internationally acknowledged guidelines or standards for dedicated QC protocols for CEM [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless, two key aspects of any QC program for CEM are linearity of system response with iodine concentration, and normal breast texture cancellation since these processes are essential and fundamental to the CEM process. In Cockmartin et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] it was noted \"imaging of the iodine signal, which may be influenced by factors such as breast composition and thickness, is of fundamental importance in this technique\". Hence these tests are critical in any QC protocol for CEM units.\u003c/p\u003e \u003cp\u003eIn Cockmartin et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] these tests were described and performed making use of several phantoms including the BR3D model 020 and CESM model 022 (CIRS, Norfolk, Virginia, USA). The aim of this study was to assess the utility of this phantom combination in performing linearity of system response with iodine concentration and normal breast texture cancellation tests on CEM units in a standard clinical setting. It should be noted that establishing pass/fail criteria for either of these tests is outside the scope of this study.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Equipment\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Mammography units\u003c/h2\u003e \u003cp\u003eTwo mammography systems were available for assessment, a GE Pristina and a Hologic Selenia Dimensions. The software versions were 3.4.51 for GE and 1.10.0.412 for Hologic systems. It is possible to set manual factors for both LE and HE for the GE Pristina, whilst only LE exposure factors could be manually selected in the Hologic Selenia Dimensions. Both units had the means to perform only a HE exposure. Details of the exposure factors for LE and HE exposures under AEC for each system are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAvailable exposure factors on GE and Hologic systems for CEM procedure under AEC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompressed breast thickness (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTarget/Filter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTube voltage range (kV) (kV\u003csub\u003ep\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (vendor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePristina (GE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMo/Mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMo/Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u0026ndash;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRh/Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRh/Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelenia Dimensions (Hologic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW/Rh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW/Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45 or 49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW/Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026ndash;33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Breast thickness is accurate to within their tolerance levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Phantoms\u003c/h2\u003e \u003cp\u003eFor all tests, the phantoms used were the BR3D model 020 and the CESM model 022 (commercially available from CIRS, Norfolk, Virginia, USA). The BR3D phantom consists of six 1-cm 50/50 breast-equivalent slabs, each with a mixture of 100% glandular and 100% adipose tissues in a swirl pattern. The CESM phantom consists of three 1-cm slabs consisting of 50/50 breast equivalent material, one of which contains two sets of four iodine inserts with varying iodine concentration from 0.25 mg/cm\u003csup\u003e2\u003c/sup\u003e to 2.0 mg/cm\u003csup\u003e2\u003c/sup\u003e arranged symmetrically and a 100% glandular insert at the centre of the four iodine inserts. The CESM phantom also contains one 2.5 cm thick slab, with 100% glandular and 100% adipose in each side. The photographic and mammographic appearances of these phantoms are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable 2: Phantoms used in this study. Images are from CIRS website (www.cirsinc.com).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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vL++NhEoaZ6UVH9tolY+e+1JXPkcF++VR8Aj4BH4OwRKWlxLqv87g77gmkSgpHlSUv01CVo57bQnceV0YLxbHgGPgEfARcC2qNytpIupd2X9+bWLgJ9HFWvsPYmrWOPpe+MR8Ah4BDwCHgGPwDWCgCdx18hA+256BDwCHgGPgEfAI1CxEPAkrmKNp++NR8Aj4BHwCHgEPALXCAKexF0jA+276RHwCHgEPAIeAY9AxULAk7iKNZ6+Nx4Bj4BHwCPgEfAIXCMIeBJ3jQy076ZHwCPgEfAIeAQ8AhULAU/iKtZ4+t54BDwCHgGPgEfAI3CNIOBJ3DUy0L6bHgGPgEfAI+AR8AhULAQ8iatY4+l74xHwCHgEPAIeAY/ANYJAiSTOfbuzP//ko9QeB4+DnwN+Dvg54OeAnwN+DnyZc6A0PLREElcaI17GI+AR8Ah4BDwCHgGPgEfg8iJQIon7Mlmmt+3/F+PngJ8Dfg74OeDngJ8Dfg78/RwoDR0skcSVxoiX8Qh4BDwCHgGPgEfAI+ARuLwIeBJ3efH2rXkEPAIeAY+AR8Aj4BG4JAh4EndJYPRGPAIeAY+AR8Aj4BHwCFxeBDyJu7x4+9Y8Ah4Bj4BHwCPgEfAIXBIEyjWJO3LkiIq72XH16tWfgvDoo48WKXvPPfd8KmcnYduurSpVqhRpC3++rMPtA/5djsPavFztXYo+MVaMgztmRdm1cS6NbFE2vmi5tW1zuCg748ePD/pTVL0v9wh4BDwCHgGPQCQEvjxGEqm1L1gGCYNYuQdlLI4sgO5BGcTEDiMpYSLn2jMZ0yG3hdUlN+6i7Lbh6n3R85IW+i9qtzg9l6y6/SxO50rXGYEDr5KImTteJcl+Gf3CR8M1PC+tPZtn1PvDI+AR8Ah4BDwCF4PAVbFyRCJxdBIS4pIxyiItlmE5W1gNKFvs3XJbXN0ykzfyc6mIAT7n5eWZ+cuaF9fPy+rIRTRmRK40+NvYlkb2IlwoURRcw3OzKCUbg6LqSyrn7yPSPC1Jz9d7BDwCHgGPwNWNwFVN4iA+4YWyNCQuPGQs8OFInS2skRZHIxFhnbDd0lwXRVBLoxspegMmF0NYiutnaXy4EjKGf2n6eaVIHJHa8NwsCisbg6Lqiys3LCLN0+L0fJ1HoLwi8N777+v9Dz4or+55vzwC5QqBq5bE2cIX3tYMkzhIDWVFLfgsfpGiYGa/qMURm6VdpIsbcfOX3FJRbUayg47J409R/YykS1lJ/YykR3vmKzk27MAHxoRkMshbO5SFx8x0wzkEF3lylzAbcXH7SrvWHrlhYr6aDnXhcTNdfHTbCfvjXrv2sBnGwPUl0vxybRk2lJleWMfF0/w3vUg61ifqXLyxSx+t3vwO23KxxS90rB03t35E8o86w4lxsDbNf9MNt23l5DZ+1qb1xexSbr6GZdC3NmmjtGPrtu/Pv1wEPv74Y7351ps6un+fNi9bqM2rlmvrtu3ac/CIXjr/mj76+K9frgOX2LrNQZ9/tp55LIrHoqxT8KohcZEmgv14uyCE5Yr74XYXHvTcwxYWIwNuHecsDuHFKCxT0rUtRO6C/UXs2kJVlK/F+VFSP8O6tqga9oYhcuYHWNpia2U2Dq582LZ7jX3TMR+t3nBzfaAdOzg3XfPXHV/ODXP8wT4HOlZutiLl1r7VmX9mh3Lsuj6ZbKTc9M1Hs2/jiS2SHfhofoZlkaFd07X+0wY6tEGyeuSpc32lLWRcfM035LETlo/kn/nmtun6Y23bWHHNudk2WfPV7FlbVm9+oo+u1ZPbmJR2bLHhj0uPwIcfvK/33r6gN149rxP7d2vb0kXauHiBduzYqW3bd2r1qlWaO2WSpjw/WOP7P6XJA/pr1ugRWjFnpvZv36q/XHjr0jv1JVi0ue7z4omLx+czfMo6DT/PXMpq7UvSd3/YrQlb+OwH38qZHPYjTp0tdlYfKce+q4eM2bcFJKyHfLjtsExJ19aGK2cLlbswufWRzu0P4mJ0zI75UFQ/Tc5ysCIVdbiLKDLIumNg7RWlb+XF4VBcnWFhPkZa6M0H6pgr6FzMQX9sjpke7bnzgXr32uQi5eaP1YV9xo71y82RNyxs/OzalePc8MB3O7f2sI8P7oGcjRvydo5M2N+L8Q99/LH2sBv2lWv6Q7thX93xCuOEbXyxsXFl3b758y8fgXffvqA/v3xOb1x4Ry+/+poO7NyhjYvna9nkcZo3fLCmPv24pg3op4XTp2r5/HlavmCulsyepQVTJmr2qOc1Y9gAzRw5RLPHjNT88aO1bukinTt75st3vIwtRJrLvuwzwuKx+HssyjjldHGrV1lb+4L6/JDz4xw+7EfaFgTqmST2I24/8nYd1nevWUxcOVuobHF0ZW2hDC8wrkxpzq0NV9Z8po3SHPTXDvpQWj3TMR8i9dNk3Jw2iuu3u4iihyw6dlh7dl1cjp790bttGv5uX00Oe66PkfA0feuz6ZLjX0lHuI/I21w0Xa4jzVmrd/MwJmGf8ct8dfU4D/cFWy7eYXkXG6uL1G/wNv/DbYTH9GL8o023Pdpwx9F8Io/kq2EFHmGc0AmPDW1ZKs3Yuu3789Ih8PFf/6oLr7+qV44f0UvHj+nlV1/X8ePHtH7GZC0Z94LWLV+hbdu3a9u2bdr84nqtX75Uy2dN04JRz2v2oP6aOfhZzXjuKc0a0FezB/fXrIH9NGtQX80dPkiLJozRkinjtWjSOC2bMkG71q7Qu+9cKJ1jV0DK5prPP/u781gUj0VZp+lnDKCslr5EfXdBcZuxH3T3x5kJE4mMFbVQmD3acO2Y7UiLJwsF7USqM3ulySMtQlZWkm2TC7fDwuf2I1wfvi6un2FZrsMLeFgmvIiG5a29sF5x19hwx9VIhY1peLF3rw0nk6UdfMDP8GHj6sqGZbhGjjbcI0zawteubPg8jEnYZ9pDJtJhWNh8setIspS52JgM2IKxe4THzcYAWZJ7XIx/6KFv/cEf9+/VtUsdtt3DxSqME3LIR7JHOe2WNLZuW/788wj89a8f66MPP9R771zQn8+d1UuHD+roti3aPHWs1rwwSBsmj9beTS/q1LlXdPbPb+rokaPavGSRFj//rBY8/5zWzJqunZs2aNf2rdqwcI6WvjBY8wf21fxB/TWr70Oa3fdPmvPME5r3zGOa++wTmtP/Mc179jEtGNhPc555XNMf+71mPvlHrZ0yTm+/+cbnnSsnV/b34fPiiYvH5zN8yjp1P/9rXFZrX5I+C0j4x9wWKyaDe3Ad/hG3BcgWDleec2yF7dtiYYsjcrZo0EZRtsK2S7oOL/b4SrpcR6R+Fte24e5ijA1bHMOLKH1xCY+1V1wb1GHPxYFza9N8sDax77bB+JiujZnpYtsdP8rdscR/s1uUj9YH1yZ6rh3qwnOqJHtWbz6bH9jCZ3cuWv8MC+rMH9p18cCuyVNu59ae2Xf9pz1rn3KzbTpubvol+Wc6Lv6GpbWFDPawZX1z/cV/8wWZSLYoQ4bk9qk0Y2s++lz64N139cZLZ/T6S2f16ssv6fTubTq4YqF2zpupTVPGadOk0do4dbyW9/+TFvf9o9YM7a89a1fpzEvn9dLZszr38nmdfem89mxYp0X9HtKSAU9o1/rVOrx3t3avXakNMyZq7bjhWj95nNaMH6XFzzyqRc88rlWjBmnLwrnasXaVtq9cqi1L5uvFOdO1atJoLRk5WPMHP61tS+bro3L4BCtzzyePwcXMgbL+1nyeAZXV2iXWtx/pogBxF0l+sMNyrjtu3e9+97vPyYYXPOy68u6526ZrvyznLFLWhrtglcVmaXTpt7VLbotjSbq2uJqu+WzX5JS59sEtPEbuoh9ukzZcXGyMwm1zHS4zPdMJzyPk7cAnk78YDIx8WJ/DZMHKS7IZxqQou66P2HTbs7YilVkd/XXntWHj4mCy5GGM3Do7xyc7IvkXqS+mS27zLYyBlWM7PLZuHfVuu5zbPKMOWbc+rGu++/wTBN554w29euyQTu/eoQOrl2rLtHFaObivVgx6StvmTtPeFYu1fdpYbRk/QhtGD9X64f21efIYbRwzVKsH99WawU9r49gR2r18sfYtXaDD61frzOmzOnnipDaMGag1w/rr8N49On3smI7u3Ka9K5do65xp2jpjorbOmqKVzz2qJZDB4QO0d80KnTp9VmfOntOJkyd19OhRHdy3V7u3bNGWFcu1fs4MvXL6RLkbOnd++3NP5kozB8o6ics1iStr57y+R8AjUHYEIGORCLcnRWXH9kpY+OiD9/WXt97U66dO6MzuHTq4epl2zJ6iHfPnBKTqxSnjtXbMMC196g+a8+DPNOeB/9PaoU9r98I52jVttHZMHKltE0dpy9gh2jpxhLZOHKmNI57TuiF9tXZQX20YOVBbxg3T/kVzdObQIb1y7rz2LZunTcOf1tE9u3X+/Gs6uWeX9i2Zr11zp2nLxFHaPm28No0ZqjUDn9TqQX21ZeILOrB5s47u2aNDmzfo6IEDOnr8lPZu26pN82YGkbmjO7frr38tX68gKc2i7WU8uXPnQFl/AzyJKyuCXt8jUIERsChmmMRxTZTLH+UbAQjbmy+d0StHj+jM/l06/OJq7V4wUxvHDdP6UYO0/oUhWjdyoFY+84iW9n1IKwf304qBfbWC6ycf0NKHf6EVT/5WW8eP0J7p47Vr/CDtmvyC9k6foN1Txmr7+Oe1Y8IIbRs7VJuf76eNw5/R5pHPaeuoAdo5ZYyOLF+ss7t26tzB/Tq6bJ4OL5unMwf26sz+fdo/b7r2zZmqPTMnaef0cdo5c3JA/tYNfVprB/XTupGDtGniC9o6dZz2r16hE0dP6NCevVo3aoBWDntGh3fvEu+ZK0+Huzj7c0/WSjMHyjp/PYkrK4Je3yNQwREIb4vyw/Rl3FZQwWG8LN1775139NYbb+rVl87q5N5dOrh1s7bPm6qVg57Qkn4PadmAJ7Xq+ae1buQAredhhBcGav2QJ7X6mYe04qnfa83Ax7Xu+We0fkhfbRzaV9tGPqO9k0fpwOxJOjhzrPZPH6N908dp7+Th2j1xhHaMGawdowZq+8hntW1YP20fN1S7pozWHurGDtGu0YO0a8LzOrhwpk5tXKcz2zbopT1bdWbHFh1eNEt7p47Rgb+RuR2TRmn7+GHa8sJAvTikn9YO7qd1w/pr4wsDtXPWZB3dulmnjhzToRfXaNPo57T/xdV6//3y9WWH0izaXsaTO3cOlPWHwZO4siLo9T0CHgGPwGVG4OMPP9QHH7yvN155RSd2bNGeVUu1mfvKIGBTJ2nP1q3av2WTdq9Zrp3LF2n7ornaNGWUVg9+Usv6PagV/f+k1UP66sWRA4Ot0I0jntWGEc9p85gh2jpumHZMGK4944dpHwRu+lgdnD5Wh2aM06GZ47V/0gjtGTNAu8cO1K5Rz2r7sL7aOfJp7Rj1jHaOG6LdE0dq7+SR2jdllPZNeUF7J43Q3gnDgrLDC2bq1ItrdHbLep1etzwgcgdmTdT+mRMCvZ0TRmj7uCHaOPQJbRz2tDaNGqjNowZo8+jB2jFxhHbNnqLD69do1/JFWjhtkt9O9Q9SfO6+bpccXS3nZf3p8CSurAh6fY+AR8AjcJkQePfdv2jLujU6sXundm9Yq60rl2n97OlaMXakFg96WnMe/o3mPvpbrZs+Tft27NLxAwd0bOdWHd22Wcf37NKJvbt0dMNq7ZkzUZtG9NfaZx/Wmuce04aRg7R1yljtWTBTB5fO1+El83R4yWwdXjxLRxbN0pHFs3R00UwdnT9dh2dP0uFZE3Rw9kQdmjVB+2eO1YEZY3RwzgQdmjdVRxfO0okVC3RqzTKdXr9SZzau1ZkNq3Vy9RIdXzJHx+dO1bFFM3Rq5UKdXLlIx+ZP0f4pI7R77GDtGNFf24f317YRT2v72MHaNXWsdk0do52TRmj76AHaPKy/do0foud+/TMN6Nf3MqFe+mauFuLg/Sw/0cDSz67Ikp7ERcbFl3oEPAIegXKHwLowtM0AACAASURBVIULF9S7c3tNGvSMdq1Zqe2rVmjHqmXaveFF7Vq3TluXLtGLM6boxSmTtHPVKp08elyvnD6tlw7t15k9O3X20CG9dv41vfnaa/rzyWM6t329Ds+frB1jBmrLsP7aOnpwsB26b+5UHVw6V8dWLtapdSt15sXVOvPiSp1Zt0xn1izR6dVLdGLlIp1cvkAnli/Q8eXzdXw5pGyJTq9dpnMb1+r8tg16dcdmvb5rq97Yt1NvHtyjt44c0Bv79+rlTWt0ZsV8nV46VycWTNPReVN0YNpo7Rw9UNtH9NPWYU9py7C+2so27chntHV4f+2ZPkFH50zS+N/+WHlxMRo2aFC5G5+LIUdf+cpXVNr01a9+VeH0j//4jypN+iJ6YZ3S+omcq2t6VlYaf5ExectLqxdJ12wUl5ufF5sXZ5M67DEnLI80P8o6iT2JKyuCXt8j4BHwCFwmBN599121atZYTVITNezh3+nAxnU6tm+fDm7bpsPbt+oY0bZ9e3XqwAGdOXZc518+r7cuvKsLr74SkLZX9u/UuQN79fq5l/XuO++KbdkP37mgd14+o3ObVuvA9NHBtuiWoU9q1wsDtG/8UB2YMlJHZo3X8XmTdXLhDJ1aPFMn50/R8TkTdHTGmICEnVwyS8dmjdOxGWN0dMZYHZk2WoenjdSx2eN1auksnVu/Qq/u2Kg39mzXO0cP6v1zp/WXsyf1xp6tOr9+uV5atUhnVi/SiaVzdHThDB1dPEuHFs7QnskjtPnZB7VzeF/tnj5Bw397v1omxymubm3Nmjb1MqFe+mYiLdKRyljgv/GNb+jb3/52kG688UZVrlxZNWrUCNJtt92mW265ReSU1alTR3Xr1g1SvXr11LBhQyUkJCgxMTFInMfHx39alpSUJFJcXJyioqKCe1ixX6tWLTVo0ECNGjX6VBZd5CjDbnR0dNBe7dq1A3n08O+mm27SDTfcoO985zv67ne/q5tvvjkov/7660Wi/tZbbw36gd+kSpUqBeXcQ4v/2Mb/+vXrB36QU0a7nOMribarVaummjVrBnrUxcTEBDrIc+5eU2aJdsCLhD740W9sYtvaxA+TQwa86Zv1h7Hh/LrrrtP//M//BOf0GyzoO/3DR1L16tU/HTtrkzLk/vVf/7XYLd/Sz67Ikp7ERcbFl3oEPAIegXKHwEcffaR7+9yhqNtuVVajBvrDD+/RphXL9Oq5l/XSiZM6eWC/Tu3brZf279H5owf15stnxRbshx9+oA/ef09vv/aKzm3boJMb1wVPjb5x9iV98O57+ujdd/XhhQu6cOyATi2dqQMTh+jQxOd1YNwgHRo/UIcmDNbBsQN1aPyggLgdnzleJ2aO1bGpo3V48gidWjhVZxbP0MmZo3Vs8lAdn/GCTi6erhMQvWmjdHz2RJ1cPEunVy3Wy+uX6fXt6/X+2RP66/vv6YPXz+svxw/prQM79frOLTq/fbNe3btLL61friMTB2n/6Oe0+Lkn9Nvbu6pxbH3FR9VSaoP6Wrt6Zbkbn0iELVIZC3tycrI6duyo7t276//+7//Ur1+/4P2PEydO1HPPPadHHnlETz31lEaOHKnJkydr5syZmjFjhmbPnq2FCxdq6dKlQVq8eLHmz58fJMq5XrJkiZYtWxbIjR07Nnhn4k9/+lP95je/0ZNPPqlBgwYFNtGbN2+epk+frkmTJgXtDxkyRI899ph++9vf6le/+pX+93//V3feeWfwNPrtt9+unj176o477lCfPn2C886dO4ty+vDzn/9ctPOjH/0ouL7vvvsC/d///vdB/2gb2/Tr8ccfF+9sJX/++eeDsgceeCDw8Ze//KXuv/9+cW04gMmzzz4bJPwfPHiwBg4cGCTqnnnmGfXv319PPPFE0F/asdS3b9+gHr2hQ4cG7dHm8OHDNW7cOL3wwguBLH2lL3fffXfQX/rZrVs30ccuXbrorrvu0i9+8Qv94Q9/CPzG94ceekj078EHH9TDDz8cJOrpG/YgkJHmgJWVdRJ7EldWBL2+R8Aj4BG4jAj86XcPqE6VyoquUU3xNauqW6t8jXjuGZ08dlTvvPOu3n7zLZ0/cUJn9uzQmZ1bdG73Np0/tFdvnj2ld/78ml4/slfHl87VofkzdXTxHJ3ZsFbntm7Sua0b9Maxw3pr73ZdOLhHb584opfXLtXR6SN1ZNoLOjRpqA6OG6JDk4br+LRROjFzTJAfGzdI55bO0J93b9EraxfrpYWT9NqmVfrLyUN69/zLunBor86vXqiXVy7UuTVL9PKaxXpp6ZxA9i+nj+njv7yjv773F338zgV9+PJZfXD2pN47fkivb1iuraMHqN/PfqDuTbOVEVVDydF1FFevtnKSE7R//97LiHrpmrKFuaT83/7t35SRkaFevXrpBz/4QUCqli9frp07d2rXrl1as2ZNQKymTJmiFStWaOvWrdqxY0dQR/3u3bu1Z8+eIHHNd2m3b98eyCBHwhZp48aNAfGD5EAuIB2Qoblz5wZ11K9fvz5I69atC8ggRA4S8rOf/UwQKogRhAmiCSHiGkIIcYPocU05pAn76JBoj3Lag8BxDTGjDqJH39Gln5CrP/3pT58SIc55F+XTTz8d4APhIkG4JkyYEJBOCC/kE5JLGXUQNfwk0ecBAwYEhI8+kSB/do69adOmBWQXkgyxg5jRD8gjpAyfwQFfOUeHNkmjRo0KCDd9g5jSFjjRX86xQbS0uPlQuplVtJQncUVj42s8Ah4Bj0C5Q2DkiGGqW6OqGtSoqpgaVRQfXVdZCbHq1b5QIwYN1L59+/Tmmxf00fsf6q2XzurU1s06tHR+QNo+eeBgabBluX/mRO2fPk77p43TwTlTg4cLeFDh1LL5en3XNr135qTeO3tabx7YpTPL5ujY9FE6PHFoEKE7OnWkjs8co5Ozx+r0zLEBSXtj+0a9vm6xzq+cq9fWLNKbW9bo7f079N7pE3pn92a9vGKOTs6dpNPzp+j0opk6t2Kezq9dqlc3rtYb2zfo45OHpPPn9ObJ49q6ZIGe/c39urOwtZqnJCq9bnWl1a2ljIRGSoiuq/ysDL366mvlbmyKW6zdOkhcVlZWEPH59a9/rTFjxgREAsIGoYLQUTZs2DDNmTMnKDMiR75ly5aAtEHeNm/eHFxTDpEjUb9p06agjnrsEnGCVBDhgowQoVu5cqXWrl0bJMgc5I9ryBCRpR//+MdBju6CBQsCgsc5pAUSB2GDKBlxgvT88Y9/FH0ikgeBgzAhAyGjjkgWkb1OnToFUUjaoT1ID0SIaBoJIkSEzaJt5EbIIFIjRowIopTo0pepU6cGZJWcekgchAySiL9G6vAF27SBf9jEBkSS1ynhL+3iC3qQSIu4kaMPEYR04hP12KGc8SJRN3r06KCd1NRUT+LK3V+qd8gj4BHwCFwhBBbOn6+omtUUG1UriEqlJCcqsWGMEurUUmajGHVo0Ux/euA3mjZ+gratW6OX9u3Wy5te1LE5kz95gIAX7NqrPyYN094JQ7V3wvPaP2GYDk4brYOTh+vIzHE6s2yuzm9YqQtHD+nCscP6857tOrt4ho5NGa4jk4bp2LQXdHr+JL28bJbOr1ms82uW6NVls3RuxQKdXzZHr65epPMQtRXz9PraJXqdaNzSmXp13VK9sXmN3tmyRu9sWqW3Nq7SwZkTtHTI05rY/0n98f/uU7f8PLXNzVKTlCRlRtdRer1ayk6IVVZSfLCd2rpJ43L3ol+mg0vUijv/93//dzVr1iyIYkEuIBGQB0gB55AQCAOkCEJA1AxyBTmDrBmhg6C9+OKLAcnjE3XIQN4sukYZ265EmSAVEBoiZ0S5aIetV+rJIXTIsyXLFi4yP/nJTwIf2G6lHj8gP0TTIIIWcTLCBEkkQe4gbJAg+kR/IFokyA6RLbYp2ZrFDjIQJ8gR0TBIIeSKMiNc5OhCmixRhgyy6BGRY4sYPyFk1BNVA0vksEtbyGMDoobvYI9vRNbQMftG2JCn3xBRSCcJfLCLDSOD9BM/SJBw7HCvYXFzoaw/Iz4SV1YEvb5HwCPgEbiMCGzauFExtWsorn49JTZqpJSUZKWlJCk5tkFAcLhnLCM2Rs1SktSzoLnu79NLT93/E0147I9aNbivdo4fFry77eDUUZ88mDBrnE7NnaQz86cFkbKTs8frxOyJwX1uLy2fo/Prluq1Tav1+rYNOv/i8uDetxOzx+vo5OEBoTsxe4JOL5imM/On6BS6M8fr9IyxOr9kll5fvUhvrV2st9cu1ltrF+nNdQv1l307dOHMKe1bv1qzBz+t5375Yz1wR3fd07aFuuZlq3VqgpolNlKL7CzlxNZXVnRt5TSKUU5SvDJiGyi2VnX17t7tMiJe+qaKW6zdOkhcq1atAqIDyYDIECGDYHENWeD8hz/8YRC5gkxwPxwRtQ0bNgREDSIHYYO4cf8bBAbiYPfPQdzYbjRSBFExYgRJmzVrVkDeyNFBFmIH0YF82D1dkBWiVNjDBtf4C1kzsmPbj5Aa6u3eOM4hj4sWLQpIIBFG2qM/3C/GFiW6RsaMtFEPOaIOmxAtfDcSRj2kitxIHHVE59hWJRpHsu1OiBt9hmRBFGkPm5AvI2DgBGHGro0B9sGEhB7l3P8GNiT6Z1vJ2EQGjIyMQsJ5eMId+/B56WdXZElP4iLj4ks9Ah4Bj0C5RGD//v3KTEoItlMTo6OVGBOt+Lo1FVenphLq1lZiVE2lNaivjLhGykmIU26jBmoa10DtMlODe8vubdNCf7irlwb94oea/KffaOlzT2jjyEHaPW2MDsyZrGOLZ+ml1Yv16ovL9eaG5Xpn6xp9sHuTPj6wUx8f3KGPD+zQB/u26d1t63Rh3RL9ecVsvcoTqAum6hhbsxOf1/7xQ7V/6pjgZcG7p4zRhnHDtXjYQE3q95gG/OG3+v1Pf6Sf9Omtuzu2UY9WzdWlaY46Nc5SYWaqWiTGqlVOppqlpyonpn7wOpHchFhlN4pRSv26io+up+5du+js2bPlbnzCC3RR1//5n/8Z3CQPAWF7kRvpv/e97wX3ikE4ICQ8INC7d+9Pt1wp5/4tiBD3rhGBW7VqVUDEeNjBCBbEAgIEGYGocA6pgIhA8oiq2dYoxA3iwhYgJAYdSAnkgygcfmGPeggb5xAbCAt2yYm2QWggdfjINVup2IBEQeCI8OErUT/uJUOG7VgiWmYHuxA2i25BmGgPO/hEvZEk8KFt/CWBI22TOKeefiFv5I5IGyQPTCxiSI4v6IAfCbKIT/QJ/9BnCxl9zqnDH/wjNzJIOYlrS0QseUq1qHlAeVmPslsoqwde3yPgEfAIeARKjcDJkyfVNr+poirdpNia1RRHVK5mVcXVqq744LyKUqLrKjMxXk3T09QsNVl58TFqEtdQzRJi1SIpTm0yUtQ+K02dctLUIy9T97Zurvu7d9Lv+vTUYz+8J3iZ7rA//VajHvyVJjz+B03p95im939Ss4cN1oLxozV/1DDNGvSMZjz9hKY8+bAmPP6QRv/xAQ399U818Bc/Uv+f/VCP/+g+PfT9u/Tr7/XR/d+7Sz++o5fu7dROt7dsos456eqYmawO6QnqkpOuLk2y1LFxllqnJ6tlUpxaZKQoPylOeQkxyotvqJzYGKXHRCsjKUHJCfHqUNg2iLj8+c9/LjVul0OwuMXareP1ItzYD8ngvrMePXoEDzkQeYNUQAaIUvF9Yh4eQBZSAdEiksXTp2xvQt4gGEZOID1sZUJOiDBBdCA2RKGIUBFxg8RBBiFmyEK4kIFwGVnjwQPa5t41ZLDLFiikxogSRAViRxnyEByedOUePqKGnEM48RPSaIQRW9wzx3YrRAgfIVzYxR5kjYT/LkGCXNF/iBakC/KGLrIWEaSMcyNRZs9wwEeinETTaB8Shi36DYbYZ0yQR4Y+Mx4QOItU4is+4K/5aeQTe+Y3PhBN5RUt7tiHz8s6Lz2JKyuCXt8j4BHwCFxGBCAufXr1UM0bv63oyrcopuptasSTqrWrKbFWdSXUqqqk2tWV1qCeMhs1UE58I+UlxKlFapJapaeqdVaa2malq016itqkJ6lNWpIK05JVmJGiwvQktc9IUqfGGerWukCd8rJVmJYYlHVpmqfubVqrV2Fb9WhdoG7Nm6pb08bqkZ+n3q2a6s62LXRXYYHuad9G93Rsr+9166J7b++lu3v20B2FrdQ9L1NdMlPUOT1RHVPj1SktQe1T49SOlJmi9tnpapuRqoLUJLVITlCzpDg1SYgNtlLT6kUps1FD5ebmKKFhjLp2aB8sxpCDjz/++DKiX3xT4QW6qOv/+I//CF5bATHjtRW8woIEibNIFOQKIkEkiu1JiBx13OvF1iZ9Z8sQ0gFZcckDZAbCwvYh5IQcogIRQhYig02IGnYhJGyZQvAgKRBL/OIVIRAuCCUJQgc5sagTRAhZIk4QQ7tnjy1fI5m0S3tE3tgyRp7oI/0ze/iL//QPIgRx4tzImEW86CvJ5OkLCTlyI1VGhLGJr2AIYUSGOs7RgYxBcIlSujiBB/59//vfD8YALC2KSSSRc3DCD2wzTpBTfKYN5CGHkEDeLVfUPKC8rEfZLZTVA6/vEfAIeAQ8AqVG4P3339dP7vueqn37OkXfWkkxVSorpmplxVa5RfHVqyihTk0l1amh1KjqyoipryapScrPzFB+eoqap6epVVam2uRmq012RhCRa536CYlrl5GsdhmpapeVpsLsdHVs1lTtGucExKowN0uFTRqrsHFWQADbZaWrfU66ujbNUfcWTdSrdb56t26uOwsLdGfHdrqnS0fd3amj7ihsre7NctQlK0WdM5PVOT1ZHSGKaYnqSDQuJ0OdctLVPis1aBsiSTSuRUqimiUnBA82ZCfEKSWqhjJjo5WdkaHYGtXUq1vXgACwSJ86darU2H3ZgsUt1m4dJI73jkEwIDa8m4z3rhHVghxwHxnkgnOICSQJQkFOtMiIHBEvSBJkxIga11YGOYHoQdCwBekiskeEjQcLeEcdRANiBDEkeoZ9iB0ybKeS8A85yBQkxcgRBA+/KOfhCO7T4+laIoS0B0m99957g76RI8u9cJzzDjZIJKQO2xA7cq4t+gg29BmSRHQRYgTBIlIGgTKSZ5E26iiDTEHW8NWIm5VRjx3wgowRySQnGZnDHn1jTNjmZmsb4gdGPDhBVBPMsAWmRCIhqRA6/KKOdunrf//3f1d8EnfkyJGgk+ybX4uH9d/+yPPy8j4HA3+wVsd5pAPs3Dr+OEyHPIwtb9CmnLy4Az3k8NEfHgGPwJVH4K9//av+8Ktfqsp131QDyFu1KmpUrYpiq9+m2GpE5aoqoVY1pURVV2aD+mqekabWeblq0zRPbZrlqbBJnto0zlGbvCy1zclUWyJg2WkqzMlQYXaG2mamqW1Gsto2zlbbnCwVpKUEZK9NZnJwX13H7DR1zstSt2Y56tkyX91aNVPX5nnq3jJfPVu3VI/WzdWjRVN1b5qrLrlp6pKToi5ZqeqalRpE4si75WUFBLBL46xga7V9VorapyerTVqimifGqkl8Q2XH1FPjxEbKy8xQSt2aSqlXS6kx9RVd5Rb1ub13QBBYQLnPqrwc7m9ucec82NC2bduA/BDxIcrFPWgQD0gFpAsiB6GiDGIDkYLUEOWhnMgRUSHkIXxE2SAPEAy2RiETlLO9CfGA2KEPeWzZsqXy8/PVoUOHgIBAWmgPckNbXbt2DfwrLCwMXkgM4YLUQKSIqpFDrCBlEB3IKO1AAmmHbUTegde+fftgqxgyAzGEEGIbu61btw5sc98fpA4ZIn9EAHn9CPXkkCgjc0TuLCpHDmnCHyNsRuCQI0G8qMM/SBzyJEgohA+8wJMcTDm36BrEjT7gA/3AL3TYkoagWlsQVcaO/oGRReTIIcJf//rXP7cWh+dFWefuFY/EuQQmTDTK2rmrRR/CVdQBMXOJFucuWTM91waYMrGLOlwbYfuujhE4T+JcVPy5R+DKIzCoX1/VuP76gNA0rHab4mtUV0LtGoqvVVXxtaorsU7NIKXUq63cuFjlp6WodW622jVvqg4FrdSxoEAdC1qqc6uW6twyX52aN1HHZnnq2CRXHfNy1LFJjjrmN1HnFvmBTJc2BepS2FpBXtBCXQuaq2vLfHVt3kzdmxJpS1Intkpzs9Qtv4m6N2+invm56t40Uz3yc9SjeV6w7dq9Saa656ape+MMdc1OV6fsNHXI/GQbt2VSIzVPiFV+YmzwdGrjuBhl1a+j7LgYpdWPUnKtGkqsXV11b7lZ99x1V7BgsoBCPj744IMrPygX8YqRf/7nfw4+dQVBgcBBhiAwEAdIBgTAnvCkHBLB06wQHqJVRKooJwIEBpAc1gDIhD3pyjXlEC7sQc7atWunnJwcpaSkBJ/dyszMDEgVfkCkICzUp6enKy0tTdnZ2QGZgsSx1Wu+Is99fLwmhaADRI6IGf7zFQr0aIN34bVp0yaI+kHcGjduLN6bhm3OIZP4xFYyhBIZ+okMn9Xi02FNmjQJbIABCVkILQlCSDTRri3HNxJ4QaTwz40qIgc+9ANyRs4YkCPLq0/oR4sWLZSbmytwoq/UQ7rBlb6CN+MAPvhhbWIfG+j9y7/8S8UmcfzlGZH7skgcRCUS8SkPf/UXQ7jwtyjS5ZI49zzcx0j6LqkLyxuRw88rfYQjlFfaH9++R+BKITB14gTVq3yr6le+WTFVblXc30hcQu1qn2yl1qujtOh6SqkfpdS6NZXVqKGapCSreVqKCjLT1a5Jnjo2z1fnglbq0ra1uhS2VTfudWvfTj3at1f3Dh3Uo3Mn9ezSST26dFGPzh3VvUM7dYXMtchXl/zG6pSXpSAql52qLo0z1bVZY/Vo0UQ9WzRVz+aN1Ss/V71a5KlXQb56tW6uXpQ3yVbXxpkBeeuYnRFso7ZNT1ZBSnzwapGCNO7bS1bz5ATlJcYpq2GD4CW/6dFRSomqpfia1VT/tlvV58479PNf/CLYwiKKcuHChSs1FJ9rNxxlKeqal/3ye0bkhsRWIpEcSBIRKSMSEAMiZwUFBQGZ4hyiANmC8EBijLBwDnFgm5Q6CA/kApIB+YDMGNEwmxApInLIowfpIUF48AWyQlTNoldEs4hwQWBojz5A1CA7+AQRg5hxDXnDB8gQRI1yCBkkER3IEfq0T//ImzZtGpA7vmYB0TPCxzWECHLJOTbQ59y9pow6SCS2IV7WJjltUE+/mzdvHvhJGXLk+IjfREnxGf/pE30FS9sOBk8SBBDCZmOCHjawRft8E/aa+Hbql03i+EMqryQOwmV/6GESa7i4BCpSGX0zXavHJpMofNBemORFKjO98kLiiCy6EUnzz+cegWsRgTVrViu2Ti3VhcTxYEPNakEELqE2UbgaSqlbR+kxDZTeqIEyG9RVdmx9Nc3IULPUFDWJj1XThFi1TElSy/RktUpPUWFOljo2bazORN+aN1WXFs3VuWVzdWnRTJ3zm6pTfp46Nc1T5yA1VqcmuerCdilRuGY56pbfWN2aZgcPL3TPTVe3nDR1y01Xj6bZ6poHwctV9/w8dWuaq045GeqYlaIO3BOXmxncG8eTsjzc0DYzRS1T45XXqL6yY6OVFVNP6fVrKy06Ssl1ayu2dg3F1Kqh23v30k//9kkotsDefvvtcjEN7Le8pPxrX/vap/fEESUjmkaEh/uqiMDZPWEQKIgUhAHixBYiW55s5RFhs6gd28roEhkiogZxQ5ffTYgXsiTkSNx7R0QPYgcRYWuU7US2cbmfjW1RewKWV4RwTTlPaHKPHroQRsgg5AZ/icTRBveCse3IliM2IdmUsbVJlBC/kMdP2ocEYYOtS0gmpM+IE/YhUJYsWgfRssS2K+SJBBklWR3kEFIFYSMnQdiop00IM2QVvEhEGy3iSOQSX8GVxPhAuBkPSDf1YGe+4yOkD/JHHyDF+HvjjTd+usZHmhdlnbiXZTvViAWEgIXYOmLOu/UQj3A9cq4e9ejYwTVExiVE1Jtds0duhyvrllNPHX4YgTH7rr1IBMlsX0xu/cB/2nHtWplrz3ww0mb+mozZ49owc2UpC2+1Wn/NhpsbBpaHfbRybFKHfQ7zkzKS60MYe/pph9kBB86xE5YP+2+6PvcIXCsI7Ni5Uylxsap9y01qUL3y3+6Dq/63p1NrKL5GleAJ1YzoKGXFNlBWw/pqzOtFcnPUNCVZjRvWV9O4GLXOSFEnnkJt01rtWzRXYbM8tcnJUhvuo0tLDp5g5R65wuy04OnRDjmZ6pibERCvDjkZ6pCVpvY81ZqWqML0RHXgAQbul2vZTN0LWqhnm5bq1aaFuuXnfHIPXJNsdcnNCCJ3nXPT1SUvW50bZ6p9Zkpwv13LlHjlxdZXbsN6yuJzYg3qKL1+HaU2qKukenXUoHoVxdSNCiIfLKIsprz+obw8oWq/dyXl3CcFYYHccL+WJe7fguwQ7YJ8QYDsHi3uNYNMcQ8gX1XgnBvteXABDCB0RPMgcuijB9njHJIIUYNoQUJsG5CoG4SKByMga7w4mK898IACn+/iixD8dkPguM+Oe8nQx3cib5AYiBn63JuHL9wbh3/4yUuIec0IOWWzZs0KiCL+QvLoP7/nEEsIEmOKb7SBrxBccCDRDnVECY38kUNwjYzRf8gudiiHGBrxg+wRIYPQ4T9E1h6QgJjaQxP0kTEg5z8I9M3ah8jhF1gblsxBCCCEE0zwCZIL3mx3X/Uv+w0v5kYymORGWFwZW+ypt8WdQTZywI805yzsHO4fi8m79SZjdVyja/pc4wd2rM5smn9GIswHIy7ma6B4Cf4xHMxXcvPLzJuMtc212xeTs5w685syzsMkCBnrq+lZbn01G9Y+OlaHj64PyJg8dqwflJsO5xyuf5SZH2G58Bww/3zuEajoCPAgw5tvvhk8hcm3LVkchwwdqviG0apduZIa1qqp6Ntu6m83KgAAIABJREFUEe+KIxIXX7um4nnxLw831Kmh9PpRwXZqFk+qJjZSs8wMNY5rqLzYemqdlqBu7QvVu/ft6tmjh7p26hQQuk75TdWlZX6wjcrXEbp36qAubQuC++Q65DdTp1Ytgm1Ytlv79Oqpe7mv6M471Kd7F9195+26u3dP3dmhMHhilS3W4EGG3Ax1bZyhHkTuiOA1zv6ExOVlq2OzxmrftInYTuXFxLkx9ZXdIEqZMfWUEcO2cG0l1KujqMqV1CCqTrA4s1ATKYF0lJfD1o6Scp5YhDzxhCREi983EuSAnIgbBI77/YxI8CQqDyjMmjUriIhBhHiIASLHk5WQCUgVuUXqiDJB1Ih2sRXINSSIaxLykB5IC2SFm/YtAsfLeXlNCF+JgDDyOw7ZgWxBWoikQWogY/aABQ9b8DAFfkH8eA0K9rDBbzo2OWcOY5O+0Cb+Q1LpM5jwlCikELvUcw4OEFIIEonoGEQJzCCDkDBIGbm9SsXk6Sf30llkju1P9PDdnlK1p1PxAyyIIoI/bRvhhGxD7mgP4gm5Q9YePoFEgiW6JGSuehLHHxeLM5OaQbTDXdgj1UciG+jaH4dLGijDnh0QgaLqrS1y98AGfzwc6BqZ4Np8NXmz4fbH6sqa44P5Hm4X2+G2kSnJD/pm/Y2Ea7i/bh+w7epTh4+UcRRXj4yb3DFCF4ypN8JnfQvLWZsmFzTs//EIVHAEIG/8TRAhIcLB4mKL0+NPPKGGUbVUnUhcVJTqVauiBpUrKb5mdSXUqa2EqDpKrFXjk/vj6tdRVlzDTyJyMXWVl9hIjRPilR0dpWaN6gXEjIhFr9691b1bt4DUdS9srd7dOuuO23vqrrvvUZ+7+uiO3j3Uq3MnUdejY3v17tlTd93VR/f97//qhz/8ke695y7d1aOr7undU3d376I7O3VQLx6AyMtSl5wMdWucGdwz171Fnjo3zlKXvBz1bNdWvbt3V0/u/erYSQWZaUEULju6jjLr11JmbAPlJCcoJaauGtaoqirfuV4JjRoF5IMFk2hTeXrhr/t7V9z5ddddF0SWIDkQBcgBhINoEwQBYsDvIAQG8sL2JCQDUgPxgdChRzn1bGPa050QG+xBttjSIzoEcYOsWaQOAkR7yEF6IB5WBhFjWxWSSPQNEkMOQYK0sd0IgSaSBenCF3yCuKELAWPblYgc/SMCR4LMkShHh/fKkZClr7RHH8ntHFuQIXxkrCG3hhN9hkRBoPANLMjtnGvILH1DFsIP8YTAEYmDCFKOXf62jIAamYOMUmbkEjn8YGywDQnEH0ggOOAHf5/gZS8GZiyqVav2uXUwPC/K+jP22f5iWS0Vo2+Ls0s23MU/Un2YbFjHaaY4klZSvduu6zLtGXkKkxomGO3bEclfqytrjn/mh7VDbkfYf5O1+kg5eJkN5MM6YTxdG+H2qHPxiFSPfSPEri07N/KGLnIuOUPXxho5O8JyVu5zj0BFRoB3oLGYWASAhYL0xJNPKjaqlm65/puKqnab6tWsoQa1aii2fpTiG9RVfL06SqhdU0l1ays1ul4QicuOa6icuGg1T0tSm2ZN1KpxtlpmpqptbrY6F7ZRN+4/6tRJXdu2Vo9O7dW7RzfdccftuvOue9Tn7u/pzj536M7ePXV7107q0bGdevfqpTuJwN33fd173326s1sn9encQffdeYfu7dNHfbp1VY/mROGyg09r9WxToG4FLT55L1xGsjrlZej2Hl3V56571LN3b3Vu30H5KQnKql9bGQ2igm3UtIb1lZmUqMT6Uap143d02w3fVvNmzYLFGJLCdh9kt7wc9ttVUv6tb30rIGxEuiA4ECDG1YgRRAEyAomxcwgFpAcdXqZLVA4Zth55EIJtPO6ng1wYaWFLEdJlW4AQINqBqEC8aJv/JEAOIURE8aiHRBF9wh8IG5E32oD8QAwhjLQDwYGw8JJgIoIkSJoROKJtfIyeqBx1+I9tIz6QIaJ7FkmzyBh9RQYCBzFCBllIlBE5I1FExsABGxA2EmXoWWQPwos97LBFy9wBE0gz1+jwNwYhBhsjlfTP2sUmiWuLzBkB5JoEgQMLSDB/txDHCvHFBiMjLNp2uEQgUr1L4sIkI3zNHwz27Ciu3kiHK4+e2x5EAht2uL5SFslfky1rDllxfcMv95pz8w0/iiNL5otLktB3r5Hh2h0b0yM3vGjLDto0H0qqNx3Lw2QsfO3KMa5GOIuSM3mfewQqIgIffvih9u3bFywcRuT4W2CbJjMlRdf/57+ryneuU51qVRVdp7Ya1q+j2Ab1FB9dVwlRtZVYr07whGpmHB+QT1DjpHi1ys1Su8ICdWjXVu0LWqogK1283qOQhxY6dFDnDu3/9jqRlurcprU6FxSoS9s26ta+nbq1bxNE4rq3afUJ2etQqF6dOqpXx3bqUZCv3u3b6M7O7XV7+7bB/XBdiMJlpah3+7a6vUd3dSto+ck9dClxap+VrF6dCnX77XeoS+fOatc8P/hCQ3aDusqMrhuQuKS6tZQQVUt1b7lJt3zrG2oYXTeIpBBRgiC899575WrYSyJvVs+3UyFGkAsICzmkg3ElegTZgGBwDrGAgBCNhXDxm8s9ZkTHiCZxLxY31EOyiJRBxiA46EPCICmQOggIJIi2eDHvmjVrgu+v8g1W7lujDYgN84t1AiICMYKw2U37tENkj99liBB2IX3YhcRYXyCftEOCFEFuSJQjgyx9g5Chz9w2Ikd/IV/IQOLIKSNxTYLYUg7Jsnrs4Q/+01dkIIxEMy3qB4YQZggWurQD1sjjh+GNTfykLfqGDPVGECGNyKLHOWOHf+jRLkSPPkGgb7755k8DEzb+bl7WCfxZeKmslorRN9JjCzKiEAfA5rB6l0i49RAGIw3IA4Bri2uX6CAbqR4Z2qIeHSMmRkQCZ67gdip+uP3EnzDpwm/DCfysD+Z7OMeeyVsd2Bpe2Ai3aXLkho3Jh8fK6l0/TMbG1+xggzL6YAdt4w8Heq4vyNo4cm5yrl2z43OPQEVFACLH54xYLFikiKYQMenRvYfqVKumb3/t31Tlxu+oduVbVK96VdWvXlUNqldVw+pVFVezmhJr1wiIXEZsQzVJT1XrFvkqbNtKbVu1UMvMdDWLa6CWaYnq1K6turKt2alj8L64dsFnsFLUNnjAIVltM5NVmJkcfHO1Y25W8JoQHk7olJOqTrnp6tw0R91a5qlH88bBi4B75OcGW6kd0xPUtUmWenbtoq6tC9SGz3/FR6tDZrK6t2+jrjzR16ypWmVlKC+hoTLr1Q4IXHKdGmpU7TZFV6usyv/9X6pxy81q06Z1QCpYlN96661yN+T8tpUm8cUGyBBjCkmAMEDKWPwhDJRBxiACkALIkG1Rzpo1K5CBBNorLbDFTf/8NkI2SESJSBBDCBe2aIMoGb/bkDi+rmCfyILUEDnCBr/VRNCInkHuKGO7lQTRs61Y7ENg8BF9+mFEDT0S5STOjXAZMTIfubYy+k8ycmSRMHL8t/vkyC1SCQHER4s0Yhd9ErZoF1kIHKQOgmr61JGwDynDDuQSfyBm2ICwWZSPOrDkGrIM1lwjQ0KfB0mIihK5rBBfbLBFnQXZJrgtzkYCrJzJY+fkyIVlzA6LuisLeG6ZEQJys2V/9VZm+pHKsYVNkyEP+8f1Fz0MF7Nv/obtuT647RmGrrxhYzbBLtJh9UaMIslYWRh//OYIY+G2FdZx+2Ztk7v+Yte9dn1z7Vn75p/PPQIVHQE+tcXTgiwsLKKQOG6M79ihg2pXrazvfOPruu0716nGd69XrZtvUFSlm4L3yDWsXkVx1SsrofqtSqpVXWn16ignvqGapSerSXKicmLqqWl8tNq1aqluPXp8cg9VQStB4Ph6QsvkOBUkNlKrpEZqnRKnwtREtQ2eRE0ST6byySw+n9UpJ1NE3bo2ywk+s8X3VIOt1Ga56pCdrsKURuraqoW68+qFNq3Us0Ohbu/cPtiW7ZjfVG2zs5SfkqishvWCz4Ul1a6phlVvVXTVyqp+0w2qfMP1atK0qe7/+c81f9FCvf3OO+VyyN3ftuLOeU8cr7mAOEBu7B4wi1JBjCAE5JAPSByykBVIAw8p8CoNnrjklRY8pMC8gExYRAtihQ0IHE9JQkbY6uOhAu5NY4uTaCYPGxDZo20id5BH/IHsUc6DCETu2L5173Hj3K7NJtuzkD/agSQRfYN4QeIgStYv+mBRLPpFRIxkRA4Shu/0hXMjdYaXRfjADpv4DFnlPzlgwLW9BJm+Uw4hwxd08QtSTE5UDjtE3Qxjw5B2sccaDDZuZNO9pm10sEG/aJMxgmT/13/91+c4RHhelHUifxYSKaulYvSNrLiLfDHivsoj4BHwCHgEQgjwVYLDhw8Hiy0REbbSeNqwc+dOiq5TSzd84+u6+RtfV9XvXq86lW9V1K2VVO/Wm9Wg8s2KrXqL4qvdpqSatym1bi1lNKgXvHYkJ7ahmiXGqXWTxurQtq06tmmrds2aqLBxtgoykpWf1EgtEhupIDlOrdM+IXBt0hJUmJmqDtkZ6pibrY7By3shcmnqzOtD+NA9ZZA73geXkaTC1Ebq1qYgeL/bnXf0VJ87eqt3z27qVtgm+D5ry/QU5TSsH/iWVK9W8PmwWjd+W5W+fZ1qV6+qjm1a65EHfq15Y1/Qyb279fFHH4bQKR+X4QW6qGtIHAQMYgPBYfE3IgH5gpiQIBiQIQgXMhATEtvJPHXKTfq8p4xrtk2NfCALaYFc8EADOtiCtEG8sEdUCjmIDOfIE1lCB0JFtApSBsmziB1RO54GZguWSB5rukX0eD0J9+tBENn2NTIHWYIU0hZ9MlJkW59EtsxvSJz1HUJlpI56SB2EjjLDDRmIFqQQe5A3CBcPFBCZhEjxd0K/LFpoETrwhtSRG8k0ogkRoy3axS5b05Bh/vMEQSPnXkTKiF6yjU3/INy0RRSOesanQkXiPIkrHz803guPgEfg6kSA96G9/PLLwULJYsFN59yz1L17NyUnxOnmb/6Xbvx/X1O1Sjer1i03q/aN31G9W24MvurQqMqtn7wIuAEv0K0TELncpCQ1S0lSfnKSWqSnqiA7SwU5GSrgm6mZacGXE1qkJKllWqpapiWJ97i1SktQ69Sk4D1ybdOT1Db9k3fEtSOH4BGxS4lT28QGapvcSO3Tk1SYFqdu7drq9tt76U4IXO+e6tGlkwqbNlZ+apKyExoFnwmLvq1SsC1c5frrVPvm76pT4yw98ePva2q/x7Ry1GBtHDNEm6eM1b7lC/TG2dPlbhCLIm3hcj7FxFcFLGoECYB8QFQgEJAHCArEgB0PomBEkIy8QxCIxLJzwZOnEBX0ITQkCAhbi9yTZQ8hYAfyRoQNokW0DMKGDPqQE3IIIVEmyBT3s0HmaJ9oGwmCBhnEFlu7kDXKIHwWrSNyR7ltW9I2ZAmiiG8WOaPfECH6RZv0wQiUETrrE9igxzYmNvAPQsY5OiRkIIUQWogc9sHBJYyGr9kFZ0grdrBHuW2dkkPgsEWkE2JoLynmoRH+/oxAWx/5u6Q9iCWyVz2Jc7fBmMh01B8eAY+AR8Aj8MURePfdd4N7SFkkWYh/8MMfBpGZFvn5qlejqm6+/luqdP03g4ceat30bdW95UZFV7pBsdWrKCGqppLq1FRqvdrKjGuk3NQUNU1NUtOkuOCbpflJccpPTVR+cnxwv1yTuGg1iWugpvExahbfUPkJMWqe2FAtSEmxQSqAtKUnB9uw7bNSg5f/tk2NV4fcLHXMz1O7zOS/PQHbUZ3bF6ptfhM1S0tWekw9xdaspgY1qimuZhWlRlUL3iX30J1dNewn39OsR36vVYP6ad3zz2rtkKe1emg/rRz4lFYNekrrRz+vIxvX68P33//iQF5izTBZK+r6q1/9qipVqhTcF8f9bHxFgMgckRte9wJBIMIDgYBIQOogOrzrjJfV8n1REl8g4BNXbK0SkYPU8U40tlfZZuXltuQQOdsKhORAViBMfHWBz1vxKSq+MsBDErwcFz+whR7EDj8gJhAiziFH+AMB45wy7JNDYoycMjeJAhpZs0gWkSrII23gK+2R83CG2y7ytAGBIrcX+UJiKSOBk8nQlvnFOfXYgGyBKQnyizyklXLO8Y9+GpnFPuf2Xj0wBV+wAXPeN8c5eBERxQcIJGQPPa7JGZtr4rNbl/jvyJvzCHgEPAIVHgFercE3Qw8ePBhEVYhSsOhCCnKyMlWvVnVV+va3dNM3vq4q1/0/1fzu9ap/y02KrfHJS4H5PFdS7epK4bNcyQlqnJqsxklxyo2LCd4j1yShkXJj6ionpo5yG0YprxFkLkZNE2LULClezRJi1CS+QfBy3haJsWqTk6kOBS3VoXm+2malqXV6ktrl5aqwSa5apSepRVaamqYmKic2RslRtRRNZLBWdRWkxuve9gXq95P7NPPJB7Vq+HPaMn2Stk8br81jhmr9iAFa+/yzWj2kn9YMI+8bkLgVA5/S6uf7a//KxXrnKvvs1le+8hXxmhE+8A6J4mPxROYgZowfDy1AgGwbkXNIBN8PjY2NDfTse6LoJyYmBjY4t4/XUxYfHx+U801R+/QUZNFeeossH5pPTk4OvjHKvctml++bkiiDuOAbpAQ7fHuUxLdO7SsIlFtCzpJ9vxRZzq2caxLfNKUNztG3etqDgEJceToWwkTOwwIQXUgf18hAYiFV9p1WnqJFj0Q9du17q5xTT+K+RPtMF+2CjeFDHWX0D7/xEfwZJ/MVH7HD2PCKF/ONawgyurwTsCgyT3lZj7JbKKsHXt8j4BHwCHgEvjACkDm+HXry5Mng/iTu7/nZ/T8LFrjGOVlqUKeWbrnum7rxv74W3C8XddstiqlRRbHVqii2yi3BJ7pS4xoqMzVV6fGNAoKVzgMQiQnKTUlSTlKcsuNjg2+ZZjeMVnajhsptFKMcyF9MXeXGRKlJbHSwJducp13T09QkOUFNkuLVBP3EeKXFRAdbubHVKiuu6q0qSGqkn3Up1ID7f6Bpjz+o1c8/qw2jh2jzWNIwbZ48VpvGj9LaYc9o1cCntOLZx7TsmUcFcVs+8Ekt7f+olj/7mJYPeEIrhw3QlhmT9fLh/V8Yw0ulWNxi7db90z/9k2rXrh0QE6JPRIwgbHaPGtuUbF3yUAHbkpTbPVZErYgQoUMEifu+ICoQBlI4UgTJgGyQICjUG9kyIkUdN+EjCxkih8BAXCAw6EB2IJj2uhHatGgUZMnIEHYgQCTsok87+GZ+YNslbFYHuUTPiBT26S8Yudu+RLyIuLG9SXSNaJgRO+Txkz6QsGdtQbKIxIElUT1wtOglJIx34OEj/tE28rTNljU57SBnOvynCTsWuSPqZ+NCRJJoJyTVHfvweVnnnidxZUXQ63sEPAIegXKCAA8/vPHGGzp69Ghwk/ngIUP0i5/fr+5duyo3M0PRUXVU7dZKqlrpBlW74XpV42XBlW5QXHQ9JSbEK6F+lGJvu1Xx1SortVFDZaalKTMlWRlxcUqrF6WUurWUwguE69VRap1aSouqqbS6NZRRr44yeb9bw2hlxMZ8QtqiagevOomrW0e5qcnBU6l//P5dGvfHX2he3z9q5aAn9OLwp7Vh+NPaOOxprR/8uNY+96hWD+6rdS8M0boxw7R6+KCAsC18/Pda8MhvNO+RBzTvkd9o7kO/0ryHf6P5j/9eSwc8peXDB2nt+NE6tn2TPv7ooys2GuEFuqhr7okjasa2Ivdo8bABLy7mm6W8TsZybkfivjTu8WJ7kGgr95XZwwjciM92oG1JQjAgFpAb2yZka4/7tiAsEB4iWhAuI1uQMUgPdbSBP2zhomORLuq5v4uIoD0YAOnk/jHuKaMMPbZc7V40CCbkBpIJAYJkGbkiYsY5vljEDPIFaaLMymmfiBb6+EOfIEe0R/u0h8+2zWoPFJBDqCinfcgX0TFkeQqX98WReOACDK0P4AxBww/0wRr8uR+RByPAny1l5DjnXjoejuChEYg213avHu2gS1+KmgeUl/Uou4WyeuD1PQIeAY+AR+CSI8BrSfjm6unTpwNiwI3sz/R/Rr/85S/UvVtXNc7KUMOo2qpd5VbVql5NNatV123fuV63fOPrqn79N4N763i5bsO6UcH3WRtWq6KGVSt/EsGr/rdIXrUqiqlaWQ2q3Kpo3lNXuZJq3XSDale6SbmJ8bq7e1c9+YcHNX/GdO3euEGHtmzUia0bdHzjGh1Zt1wHVyzWvqXztG/JPO2dP007p4/T1gkjtOmFgVo7rL9WDXxSy/o/rMVPPqgFj/5G8x9+QPMf/Z3mPvyA5kLqHn9QC/s9oiXPPR6kVcOe1YkdW8QDIFfiKG6xduu4T4ooFySI+8TXrVunXbt2iW/j8iqZbdu2BWVE4SAG3CQPkeDBAR5MQAeCwf1tEBMIB6QJUsO9Wdykj217EpSIEDYgHxArSB4JkkNEC3JGO3wOixf/8qACRBEdCBHRK3R50tQecODhBV5BgjwPSpB44IF3+NnrOyA2kDuInUXNIGQQRraMIViQThIkjXvUaIuoG/UQR4uCQVS5Nw072LP78ywihw76+Mv9eWCBz5A+7tUDJ84hYzyNS7JXu/DQBXiRsI0dSCL9oV/0lcR788AFcsb75vibIucJYsgcuEOAIZkmB0l1xz58XtZ56klcWRH0+h4Bj4BHoJwj8NFHH+kv774bkLozZ85o1+5dQaSOaMSggQP16COP6Kc/+Yl69eiuwpbNlZeRprTkRCUlxKtR/bqKrlFN9apWVt2qt6l+1SqqX62qYmrWUKM6tZXUoL6ykhLULDtLnQpa6qd3/X/23gNIzurM169bm8oba2vr7t31/ncX25gklFDOOSAJEMEITA7GYKLIAgWUw+TUPd09oSf05JxzT890T84zSiMJARIiZ2wwwb9/PZ987k5xQWvXCKf9VHXqfOGE97zdo37q955wj5x7d6omJ1OH20N69cghvXnymN55+UW9dfK43jh5XK+fOKbXjh/R6aOHdOrwiE4dGtYrI/16qb9HJ7s7dKzVr0N1FRooyVFPXro6s1PV5nWpxRGhpqhd8sftV8ATp9YUh1pSExRKS1RnTpp6i3M01FitN1956ffyiXz1B/qb7oE4QpRABAAACABowJPZm43PhhWagBjgwWpQIAIwQjlC6eE50EYZygIjwAMQRZuAH88pY+AO+OM5IEgZoBAlkH7ZToTtQ9guBIAZuzUI/dE/NlLHwIuxnz7ZYgOYAXKoa2xEnQKQsJv+yc3YULRMAkqBIAAM8AS6gDXAClgzYVADoKiMvDMLKyjDggUSMEcCUIFRM34Da0AagMk9tnBNwpfk+BJIA/YYG/4wMMe1UfIYJ2WxGZimPz4jYBZ/oUB+0/eA5+P9N/4WxmuBXd/2gO0B2wO2B36nHmAeHSdBcGwViyPeeustS7E7duyYhoeHrL3AWqw9v+pVXVWl0pJiFRXkqyA/T8WFhdaPW1lpqcrLylRTXa1mf5N62tt1sL9Xrxwe0Tsvv6QPXn9VH771ut5/+w2988brevPVV/X6qVd05qWXdfr4CZ08eEgn+np0NNSqQ411Gq6t0HBNhUYaanS4xa8jbUGNdrbraEeHjnR26HCwVUP1teotL1R/TbmG/I0aaW3WcDCggy0BjbS2aLilWYP+Bit/98yrv1Of0tm5fqzHvmOfOOZcAUOoXyg4gIxRjwAZQASlCoUJZQlgA0ZQ3AAhYAngoCz3QAWQwV5tJPZvA8wACeAKdYjyAJQBOOCEhA3UB+6AM6M6ASMADjlgRlkgkf7oF8gixy4DW4AY5emD1dPmyCuugSL6oR36ohyJkCZKIEAFQBnoBLTon/4AQd4BdyhsgBsAh8LGNf5jfPjQqJNGZeQdPmbs+IH2aQu7Kc97njEWrrGHcQJolCMBc8AunxnPzf5+hFLpF/UOyMRe6jJWwJxQ7tjP/qvX4/2S2hA3Xg/a9W0P2B6wPfAn5AEA78tfQx5z7AjLAnufkH/+hXVawntvv6V333lH733woT78+Bf6+S9+rk9//rF++enP9ekvP9Onn3yuDz74WG+ceV2vHjumF3u6dSjQpKHaSvWV5Ksn26uOdLc6013qzkpRT47Xyrt8yerITFanL1UdWanqzMtUV1G+usqK1V1Woq7yUnVXVaiLVFmuzvJydZaVqLO4UO352WrLTlNrRrJaUhzqykvXB2++/jv9ZL76A/1N9yhxTLbnxx5oAh4IKTIvjDlUTLAnfIjSBLAY0DBKFOUBO+aIARBABeoQCyGAN8KwwBt7t5k94YBAyhpIoV/qcQ8YAj6AI0oS0Ed97KMONpADN6hrgAr1ADdjH/AG7ABJlAEyKQ+AAVDAF+PAdq5JYxUy2qScgSvaBdKwDZtMHfqlHNBHAr6MGkfbJOCS8fCchK94Tj36oQ720j/PsYtrUw+YNPZTH7/gP+MT4JI2yKnH58CcQ2ASqMN/+Bb1lPl13/Q94Pl4/42/hfFaYNe3PWB7wPaA7YE/eA98/O5bOnN4WCdCzTrW0qCTHa06c3hI775yUh++85Y++uBdffT+O/r4/Xf18w8+0ocffKy333xHr710SieHh3Q4GNJwfY16i/LVmZ6kkCtSQWeYOpJi1ZnBRr5J6sxMVntGstrSPQp5nWpNSlCrJ14BV5SaXHFqSkpUID1V/rQUNbgTVJ8QpUZHhBqd0apPjFZDYrSaXPFqcieo0Z2gkaZ6ffHFZ78z357rx3rsu7/4i7+wtvUATIAcgIJ5YkziR7lh0j854AQUEK4DWLinDoCCQgc8ABIoTLw3pzsAHMAK96hHqF+UoR/UKVZQAilACW0y6R+IBMqAJNpCQQPigBzKAzo8B9CwGYihLsCDmoayRpuUwSYgjLAnc9tYLMBcOOa3ATvAKLYDSPQJPBlIox42Ml/PLMYgB2iBMcqhyqEMMj7swUZ9FiJUAAAgAElEQVRADxtJlKMNwpvMtWNs2GLmDTJG6gBgBuQMlBr1jrExRupQ3wAa/VEHO81KVD4L5vJRx/gAG/EfizjGfvZfvR7vl9OGuPF60K5ve8D2gO2BPzUP/Er65MMP9N7pl3RqsEdHW5o0XFFo7d02UFKgwfISHayr0miwWSe7Qjo10KMzB/v1xuiI3j5xVO+8dNyaA/fOqZf19ulX9Nbp03rj1Ct67aWXrFDqy4eP6kR/vxUyHWmo1UB5iXoLstWbl6HuvAz1FOaor7RA/VUVGqiv1WBjvQYaG9TXUK9eUn2dempq1FVVpc7yUrUV5lurU1vTPQqkOuX3xKrRGWkB3qlDQ7+zT+erP9DfdM/qVPZjAwSACX78UXsABoAGoAN6eA4IEO4kPAe4AEhsJEsZAAhYA85oi2vCfoAEiTqAGqFN4IMQIAACqABfwB3ARyiQZ8AiyYQTgTOgiH5Q2ABKynNNAtiANxMqRd2jPn0AUNjIYgRADhCjH7MIwyhlwBEJCCMBRoAXoWQWNpADdJTHDiDOhIQJy2Ij/eE3fIC9lMEnlMev9AuIYQfAxVw5+sT3KH9GAaQeYIkNtEW7wCarfqlHOROaphzt0D7tco3/8TEAh418BmxZ8k3fA56P99/4WxivBXZ92wO2B2wP2B74g/DAZ59+qvfeeF2nhgd0sKFafaV56i3J00BFqYary3U06NfJoQG9fPCQTh1hYcKoTo8e1aujozpz/NjZBQssWhg9pjOjh/W6lY7o9eOjen30iPXszDHyozo9elinDx/UKyODemmwTy/2detEb4eOd3XqRHenjnd16Fh3p451duhoZ7sOt4U0EmjWQHWpeoqzNVBbpaGWFo3wPBTUSDCkoUBAA0BedYW6K8rUUVqkgx1t+tWXv/qd+PdcP9Zj3wFxbD7LDz2gxo8/MAF4kKMaAUBADfAFxAFiKEMAHGFXAId7QIr6AAaARxgPeDPz0QA1Mzl/bDgQCAMKAQ3aIRnFj/aMcgfsADdGhSM319QHMmkf+7jGXuwASIEnFDTAinECRbQFINEuihk5QER5gAiAA4rIASOem/e0RRn8AoSSADEDbwbK6J/xAHbUpTzXABlQif+AQ8AVm/AdbVHPACE20x6fBVudoFYChUAcIEuf2MbnAXDTNgoloIsPAFrKspXM2M/+q9fj/WLaEDdeD9r1bQ/YHrA98EfsgS8+/1wfvv+B3njpZZ0aGdKLfV0abQ/qUKtfh/11Gg216MW+Pp06fEhvvPKK3n37HX1kzYP7TB9/8rk++uQzffyLT/XRxz/Xhx9+bM2Fe+/dj/Tuu+/rnbfe0Vuvv6E3z7ym1195Ra+dPKEzx0Z1+uhhawHEy8ODOjnQb0Hcyf5uvdjdrhPtLTra3KCDNWXqL8pSd4ZL7d4EhVLj1eKOVXNC2NmNfxPC1ZIUp7Z0l9p9HnXmeC3gHKwp13BTnUYCjTrY6tdIsFnvv/nm7+QT+uoP9Dfds9kvpySYSf1AExCB2gQUoPqYsKMJbwJdQA8hVzbFBUYAFYDBhDKN+gO4GfUNkACuUK14RlkgBLWIxLVR14AZE2oEWIAoEkBDGQAFlQ61CfjjHnDBdmwB1ABRA1bAE/e8x37ap4wBubFhz7HhUwAVPwBgwBcJdQwoBG4JywJ6qGvYN7Yf41P6ox9ADfXPgCh9AnFsBIwP8TngjI0GNLEZP1AHu5ijyFxFwJIxMGYDpLSNUkeIGH8C0fiZzwBfzZgxw4a438lfn92J7QHbA7YH/gd44IvPfqkP33pTrx07qOPtAR2sq9BIU61GAk06yKrQ9hYd6+vRiyMjenF4QCeCTTrSWKHRUKNeGuzWmSMH9cbJY3r71VPWqtP33nxN7736it4+eUxvHjuk146M6MzBQZ0e6tUrfZ062dWiE8E6HW2s0OHqAo2U5Wi41Keh4kwNFGaovyBNg4WZGizKVH9+qnoy3epMS1B7Sqxak+IUSIqX3x2jpoRw1cfsVV3UHtWF71RtxC7VRO5WXfRe1cUeUAOnOjjDraO42jLc1qKInrJ89VQV6+TwwO/kk/0maPvqc85OvfTSSy1IARZQmIAB4IV9xZgMD2iQABVAAqUHsAHgUHfIgQpAwwAUsAPQ8AyFi3a5BzgAFXIDWLxjHhf9AjYoXPSFQgVAoi4BLthCOdozcEMbJNoGXnhPHVMXu3huQqZGTQMMATPAiv4ZD2WZM8dCDmAIsCIZkKMc46dtyjFXkHlmnOpADtQZxc7AHHailHGP3wA+6gNpgC6QzKkM+BAgxE58h530xz0+4R5wBJzZFBlFDlsYO+UZD+Pkc8J26gBugDIqJf7mZI6vfv5j78f7xbSVuPF60K5ve8D2gO2BP3APfPnlr/Te66/q5aE+jdRXqd2XqpaUeLUmxajFE61gSpxCGW51ZKeoKzfF2putn5BlabZ6fInq8ESqzROlTm+CenOSNVCSrZHyPB0sy9VIaY6GAbLcFPX5XOpJT1B3Sow6kiLP1nOHK+QOV9AZrmBimNUOgNaeHKe2ZPIYtSfFKOSJUqszXC3x+9SSGGEdcN+ena6Q16Wm+DDVhu9Q1d7N1mkNnNhQtvs5le3apIo9m1UdvlMNCRHypyaqmfLuWNVF71F91E7VRe9QW4ZHv/jgg2/9Uxr743yuayDuggsusIAECAGWOB6KI6o4m5OjmjiblGuesZLVHEW1YMEC67xTzkbluKqxJx/QDluXoDIBHZzMABQCPkAZCVgCSigHyNAuZc0RWeZYLvpm3h5hX/rBPnPSAu3TLlDDc96b47WwlcRRW0ASbdMXahZ2kABD7klcMwbsoG9jjznmi33WsJexmTq0ie2U5+QJ7MIeYAooI9EPuQFTrgEywAxApC42Yj9lAUd8Qxv0R6JNxopNZnyU552BTuziHT7APoAWEAQeadc+O/Vb/7OzO7A9YHvA9sCfngc4teC9N86IMOVQQ6U68jLUnBSnxoRw1cXuU33sftXH7VND3F41xe9Xc2KkWpJiFUyKtU5LaEsCrqLVnhyrtuQYtbmj1ZoQrtb4fQomRijkjlKIPDFCLc6ws+8SDlhnnQaTEhRMdSuU6lZHGsqaS10+j3pzvOrL96ovL8267spOVrfPo26fS11ZSb/eZiRJHZmkFGulaijNpRZPnPyOCDUAZpG7VR+zX3Ux+1QTsUvVYTtVF7lH9XH71eCMVpMnTk2J0apnjHGk/aqJ2WeFV/Wrb3du3LnAbew7VqcCYSg5Rs1hzhVwAlyYM0aBIQDBJN4BLwAD15Q3wAYIGQUPuDKQZQDFKFyAHKoRcGTKAy7AiAFC2gSOSPRBbvo2MIMdgBY5MGTs4toAKHVomz5RwlDdUOwAKsZLAoZI2AkEYRdlAS7mo6F8kVPPABnt0S7lsZvEPUod9VD4qMM1CiJz+JjvR9galRL1EXgG5ujLhK95hh34BfDEz8bXjHWsv7jGVmMDOXDHMwPLQOw//dM/2Urcn95/r/aIbA/YHrA9cP498PP339PrLx7ToVBAHUVZCmalqsWXokC6W83JcfK7otTsiFCzK0p+d7QFbn7n2YPkmxOjFHBFKoASFr/PgrR2T5QFciFPtLUlSEv8XrXG71drUrQ62OMtl1MSctVfXqS+yjL111ZroK5G/WXF6mWPtwy3ejNd6s9L1WBhhgYLfVbotD8/Q725qerNSVFXToq6s5PVY0FeknpzktSXk6z+snwN1teqr7xQHZkeBZPj1eKKVsAVbUFdwBMrf2K0/Anh8idGKuCOUYgtSgpzFczPUTAzWaE0p1q9TvmTYpSzd5u1Qvb8e/2/WhwLaue6ZrNf1CMWILBNBoDBpHoUHCAAiAKoxkIdahBgAbAAEIAC0AGcAUXACGFD7oEZAycADfPJCPURJkQhItEXYUjCqGbyPzkJtcqEFqkPDAE8wCE2YQc2MgauecY1KpUBUOwElpgnxrw85ooxj45wJiBF3wbWsAFYIxGmZP4d5UnMccM3zMvDbsKX2APQkYA7EoBIfyakSjuMkTByWVmZOIeWzY/Zu80s5iDcSdvMccNOQrC0b8K8QKYBM9onjAoIkphzBxxSF3uxk8T4eM/cOcLGEyZMsCHuv/5E7CvbA7YHbA/YHhjrgZ9/+KFOHzmkkeZ6teWmqcYRoeqECNXEh6nBFS0/R1N5E9WSmWTlQFCzM/z/Km+tnmi1orKlJKglOU4tqGkJYWpzRVsA15YSp3avU+3pLnX6ktWTl6H+8kId9NdrtKtDo/0DOtbXq0PBZg1WFasny6OO1Bi1pcSrI82hnnSHutPi1ZvusKCtrzDL2qoENa4vN1k9mYnqSnOqOzNRPRlOdaU51JkcY+0Z15Obpo6UeAU9sQqlOBRMSVQwKU4tnli1JieoxR2tFvfZeXMBd5yCSfHqzEpRZ2GO2tITFXRFWmVaPVFyP/uY6vOyx7ruvF+fC9zGvvvrv/5rSw0DLDjGipWkAADz44AboAx1ClgzShiQBOChogFvAA/QYhLAwjWAA9RwDZQwPwxQAToAFZJZYUp/gB1QBYywYIGFEiSjXAFDqFmUA+RQqlCuAChsNGFIlChjKwDKe2CGjYfNuaqs2GScqI+8ZywAHDYCXzwHiFiEAXixKhfgAoiwkbKMz6hzqG3AFdDFOwAM/wGi2Mw1IMWYWWjAua/4nE2ROU0BiDYLPlj8QTnqUZ96tE+bAC1+MAtHgEvqsgcfnx/j48iy9vZ268xbNlzmPFzGQGh87Gf/1evxfgntOXHj9aBd3/aA7QHbA79DD3Ciws8/+lCnjh7WUEO12gty1JSeohp3vGoSY1STGKv6xBg1oLZ5PWrN9Ko1NV5tOWlqLylSe0GWQqkONcftsQ6XZ8Vn0B2j1uRYBVPjFUpJUFuKQx1ep7qyU9RblKOBskINludruKLYCkse6+vTicFBjXYEdai6WP25SepIBaJirba7slPVW5hlncTQlRIvQrNtnmh1pMarJy9NAwU+9eV61ZPpUpc3Xp0p8erKSFRnplvtqQ4FYveoMXKHGiO3qyFqt/zxEWp2RKnFE6NWV4xa3DEWcAKkAWekUBEb4sJVH71PNWHbVRexXbWRhFm3qzk+zILU6I0PKO6ZjXrvWzzF4as/0N90b5Q4lCGggmOuzER4wAvFCZBDzQLeCI0CTChtABoQA2QY8AF+gA/uUawAOMowkR8YA9BY/ABUMfEeZYwElKBGAS9sEwKYcKwUiWvABqgCsmiX9umHtgBAA42AFaoaSh0hRFQ64AoY9Pv9FuSwYhOAwxaAzIRIASaz6ALQBJBQJwE4Em2ggAGRgBrjpj42AVbk1MMm3pkQKT7EPrN4g7L0D1QCWOaMWOxjvNhHG4zPqJbU5x4/YbeBOLO1CgsYAGLaBQZpm1MygEVAkXEwp/Cbvgc8H++/8bcwXgvs+rYHbA/YHrA9cE4PfPbpJ3r/zdf18siQeqpLFPAlqzE5XjXOSFU7olUVF67quDDVJESowR2npmSHFT4NpDisif4BT5RCHHNVVa6uuip15GcokHhA9Qe2qjFqt1oTwtTqiVV7drI6c1LVkZag7qxk9ZeXaKiyWINleRquq7D2azvW262jzXUaKspQt1HNvA71ZKWqv8CnwZIcDZbkaqA4R7156epIiVNr/F4FYnapJX7/WZDLSlZfXrp6s1PUjbqXlaLenDQBf+2ohnEHLLsI/aK6Bd2xFogFPTEKpSaozZto5cBoMDXBCrP648PUGHtAdVH7VB2xS+V7WASxSfVRe9TsitX2n96j7ffcot76ynP6ejwvz/VjPfYd+8QxbwxwQK1h/zZ+8FHCgAWgBoAwahyKFdADyBAaBWbIASsgBqgB/ozyBtQBLyhIQBigAawBXqhaRomjP7YJQSEDQIwtqFQAJkCCbcAKAIOiRwL6SEAPdqCEYRt9AnAockAd/QCoACHjMis3ybEFe8mN/fSBLSiTKGf4hDYYJ/2gNjI2A6MAHidLYDdjwWeol6iCwC4+ww6usZH6+It2sQuABubMUWXAGKBKXwaI8ZeBNlRBs10LbfAcHwCijB/f8Az/oc7hIzucOp6/KLuu7QHbA7YH/kg98OWXX+iNV17UkfZmdVcWqqXAp8bMZNWlJKrWFa+ahHDVOiJU64xRnSvWgroGd6waXTFqdEefnfOWkqCA163WzCS1ZngU8rnVluNVR266QoRXmWfmTRSrQNsLstXuS1Zr4gFL6eoqyNIApyYAZA3VOhgK6lBzgwYK0tSVGnt2TlyOV/15Xg0U+jRcVqCR8gINl+VYK1W70hItVS3ENiGOcAXi9qvFGaG2RFa6xlhKXzdz5nwpFuz152VYix04T7XdAjWn1Uenz6OeQp/6qso0UFup/tpy9ZUVqpOVtFkp6i7OU3dJvroKsy21MehNFPPlWK2KMtcUt1/VMfv1yIbr9dw9t6kq1a3PP/t2juIaC2rnumafuLlz51qhQiAEmOPHH4hAVTLAA3CgLgEnwAvAgCoFNAAZKG2AgwE0YMeAHaBH2JPywBEwYoCP3MAcYIIKB5QAawa0AC+AymwUbECOuuypRpvAFLagEAKM9A1wsgiAuWT0jUIFiNEfqiAQxFiBS55RhzGinNEu5ekXiEOVA4SAPDNHj3EzXsYOuOE/bERpA3pRApmXh3pJqBfbCN0SWiU0CtChtDEeQqGhUMgCOaOI0reBaezmM8En2ME1NpnPh77xG/YAiUApZRkDEEw73//+920l7o/0/2DbbNsDtgdsD/xWHgAuXn/phIb8tWrOSlV59G6Vhm1TbU66/LXVCjU1qKOlVW1+v1oqSuXPyVC9J17AW3OaW01JCWrOSFGotEgdNVXqDTSrr6lePfU16qwuVzDdoSbHXgWSYiwFK+h1qJ05ZCUF6szPUotjv/zOMIXSk84ef1VepIHGOg35GzRQUaCu1Dhra5Au4I25bb9eqDBcmquRikIdqijQSEmWerOT1OGOVKszQq2pTrWyGMEZZc2t68rLVF9FsYZqqzVYV2PNj+vOdKsrg9CqU+0pCWr3OtSR6lBHcrx1rip9H24LapSjuro7NdxYrY4sVq96NFBdpkMdbTrU26uR9nbrtIb2TI+l3DW7otXqiVH+ni26cdFcPXHjeqXtel5vv3r6t/pcftPC5wK3se+AuEWLFlmT7FFtACkUMSABYAFIgAGUJu4BJZ4BRSbcBzgAFoAU8GMgDWUL0KIcyhgwh7JHWcoBg4AQQGhgBFgByihj1Lax74ARkikDjJEATtonhAloAmQoX4AUYWAULdrBHgOn5MYWyqOOoZgZO/EFEEQi5Et9xkRbQJhJhHBRvxgbYwXWCD8DcKzqZZEFcwiBNhNSBd6oB9ThT+AZkEOJ6+zsVG9vrxVqBcBQA/EXsEhZbCREzWeAPTwHSIFNxoQN5MZ+lE3q/9u//ZsNcb/pH5BdzvaA7QHbA39sHvjo/fd06thR9dZVqC41UaUx+1VwYJtydj8n35aNyt/xtKpSXWoqL1aovkZt9XVqrSpToCBH9amJqordr7LInaqI3q3qhDA1Zyaro6pMva0BHewb0KGhIQ319qi7oVbNSdGqidiqRrYFcUVZW4oAWYQkA+5INXviFEx3qyM3TT015epv9mugoVa9+WlqZ7+3pBh15WSoLz9d/fleDRRlabg0TwfL83Wogn3j8jRcXqD+wkx1OPcqEL/XWmzAliT0157mUFeeT33F+RpEWaupVF+OV52pCbIUOK9DbczJS45XiPlvzNVLcaiNuXKEbrOS1Z3jFZAW4LD7+HAFU5zqIBRbkK1OFMYMlwJJcdbeck1xBxR0Rsr97KNaNvkyPX7tlXI8eq9e+ZbOUx0Laue6BuLYsNcoYAAVUGZCn4AbsAAEoO6MBTgUK1Qp4AXAMWDFNUBHW0AG9QAj1DjqGAWNfmgfWDN1uKY/EvXpF9ACKiljwIt7bEaBQsmiDPBibAKSACZW0rItCWBHHQOk2IdtJMaEfQAViWtjF4BI+9SlD8bKmAE4FDWAyoRL6Zu6KGE8Y+EFc/MIqaIKApXYBCxiH2WBMUCWflitGggELIjr7u62VDnms9E/5Y3iSf9mCxN8glLJnD1ADp8yHhRGwrS0B8QBnvYWI39s/yPb9toesD1ge+C38EBDnk8lCRHK2rddGZs3yrflSWXv3KTcvZuVu/s55e16VoX7tqss7oCqXLHWAobaJIdqPQmqSIhQedRulUbtVlnULlXFhakx1akWwqI5XgXzMhQsyFQgK0VNSXGqi9ur2rh9anJFqikx0tpypMkRpia2HmF+WZpbHYU56m2o02CLX321lerIdCno2GNtDNyZl6me/Az1+JzqyXKrrzhLw6hvFfka4rrAq+HyfA2VF6gzOVrNUdusla5s4tvM/nLuaGsPuL6qUg23BDTcUKf+3DR1pznVR9i2rvpsqDQzSW1ep6UIBtM98jvDVR++VY1x+6zVpgFXjFDZmhKj1OyKUSAxxlrcUB+9S7VhW9SQEK4GT4IaE2MVcEZr6923asGEi/X0hvWKuf9WHe1q+y0+od+86LnAbey7P/uzP9Pll19uQROAA2ABCwAL8ADsAFTAC0CFUgYo8AwAojzKG+WAOKCC3EAc5YAKIMTMCzNKGQoSbdAe1ySux7aLTQCUOWZrrDqH0kRIkXcGOrEb+KNPoBEVjHlxQAyQQ8gRVQ2bSYyL/gAjAMsAmVHZUNiwi37NGAE8gBFVEmBjXIAZEEd5VD3apAygRluobrTPNX3xDhsZL/7CNmCLUGpXV5elwrHKlHmA2GjgD19QFpjFDq4BvaqqKkstNXYBccyFY7EEoIuNf//3f28rcb/5n5Bd0vaA7QHbA39cHshNTFDS5idUGBuu3PB98u14Tr5tTylrx7PK2bNZuXu2KP/AThVH7VM5ixjcCapJcak2meRQlTteFYmxqkt2qJHTCjKS5U9LVlOqW00pDjW4YlTjCFdN3H7VxYepLuGA6uL3qY6Nfp2Rakp2qiXdrTZfkjrLitTX7Fd/U5N6yovUlhJrgViAExSyM9RdkKUun0sdbAGS5lQPe7oVZFjz5voJreZ4NFSUqZHqcvWX5qvdEyN/zC41xu5SY/RONccBcjHW/nIDVSUaqChUT1aSetIcGijNsQ65P9LerqG6ajEnD5Dj9AZ//H5LWWt2xqjF2hvugPzxB+TnHNX4A2qM3a8mR7gaWNwRsUN1ETtUH7vH2j+uJi5ct61epqVTL9WWOzco7sHbNRxs/Fa+JGNB7VzXnNhw4YUXWvBBmBJIA1qAIWCH3NyjfBFWJQwKADFnC8gg3AhEATnkRrWiHqAB1FAHyBm7h5oBDmAG2KMcuVHfaA97aJME0AGJvAeqgDUAh2tACNtI1KMtgAyAI5wKOAF8QBxt0QY5dU3fwCvlSMyNM0ob17SHL+jfKIuAEVusAIuUBdQMoAJZ2IQPGDtwZ9oGfPG1URJZDAGEsU2I2XrErFTFRsCPRRJsIow/KU+YFUgjp45ZsGGgkfEB4YyZNpiT93d/93c2xH0rf212o7YHbA/YHvgD8EBTZYWeveMmFcfsU2linIrdThXGRyk/fKelxmUDcvu3K+/AThVF7VOZI0oVrjhVOmNU7YxWpSNSFY4o1XkS1JCSqMaMZDWmnV0AUeOOU7Ujygq5VsbsVU3sftU5wlSfFCd/RpJaslIU8qWoPd+nvvpaDQZbLIDrLC1UMDlWDRFb1Bi3V61sopvjVYijupwHxOa/HZludWc51Z3lUl+hz1qNOlCQqYHsJA0Vpmu4okj9JXnqZKPh+H2qD9+i+shtaorepeaEcGvFaRtHb3li1WGtbvWon/NTS/M1UJqv3sJsq49QcrzaUh1qTUpQqyfO2mLEn3DA2nqkLmqX6iJ3ibwpfp/8jkg1JUSqKT5CKIwBV5RStj2jVTOnav3cadpz/+1ybvypjnSGvpVP/lzgNvbd//pf/0vf/e53LRgxIbuxsAGAoGIBMYAHEMG8M4AHFQiIAyBQw4yKZmAHwALQDMjRDuBD4hr1CiUL8DGhSHL6NEBnlDyAj8Q7A10GAoEsIAk4Aprok3YAF6PEAUImNGqUP0AHIKO+sYdyJKAM9cuceACsYTfhT66Z48aCCTbhJUxKGJb+eI8d+ADQxU+Mk0Qf2GzmqzEPDgBmzhtgxpxEQI6VuKhy+JexsMWL2feOBROMn/IodcCfUUxpm7LYxPiN7/ENYeXvfOc7NsR9K39tdqO2B2wP2B74A/DAyWOjumXpAjmef0Ll8ftVnhitqvRkVfm8qkxPVVmKR6XuBBXHR6ok5oBKYvarNDZMlYBbssOaR1ef4lCtK1aV8ZGqckSp2hGpameEql0odC41pLrl5/SGfJ+ChTkK5fusVartOWnqqSzTYGtIwx0dGvA3qKukyDr9oPbAZtVFbVeztXo1WWz6G4jfr0DcXrHVR1emR50ZiermdIWiLA2U5WmgJE8DzJfLdmswL1XD5bkaqiqxQqVsGOyP26P68G2qC99i7QPXFLPbWrlq7WOX4VJbmtMCNqDN2j7EHW1t2MvihFY2+U1OsObstaYnqdkTryZXtBpREwkHJzvVnJIov+vsSQ6c4FAeuVPP33urFk6drNtXLlbMo/cp6amHdbS7/Vv55MeC2n93/Q//8A9asWKFtZLTnH6AesV8LhKT9M3WIszFogw50AA0ASUAG/coQQZWgAdgCIABlIA1lDgz94wFAIAP4AEYktMuz01YE3WLOiSgCpAC/AAt7g08AS7AFXVpB8BiZSrbp7DJLeMAsKhLn6YPrpm7hsrFOIElgIfEogTqL1261NpjjeO8WCjBEV4kToigHmBFf6xCNQsYsJc+eM6Gw7zjGtsYG+NiDPgKeAb8UB2BYRMeRmk0YWjaYnyMk2ue43uAGyUQkKR9bF61apU1DtQ7xkb/06ZNE6rrub4L4/0i2vvEjdeDdn3bAx1+GREAACAASURBVLYHbA+MwwOffPIL7XziMf3k6tUqdMSrOilBFYkxqk5xqi4zVfW5GarP9ak+K00NzG3LyVCgOF+hump1NDerozWgrma/2qsq1JLnU8DnVSDLq5aCLLWWFCpUXqL28hK1lRWovShb7bnplqrWWVaovkCzhrp7NNTRqf6mRnWUFFiwVBe2RXUxe+X3uhXKSVUo3Xn2VIeEcLU4WOAQa81t6/Ilqzv77P5wAyX5Giwr0EBpnvoKfBrITf11aLVYh5pqdchfr8HqEvXkeq294oKuCDXH7lMT4daonWqK2aMAZ7MmRolTJKyNfd2xarGOA4tSMDlOwVSndbRWW5ZXwYwkBdNcCqYlqpU8PVmt6W5rUQPHcxEuTnjqQW1YtUTL5s3W47dukOepR5T23JNqq6vRF1+e/3NUz/VjPfYdStw///M/W+eNAmeACQBHAmqAKsAMNQ2oABy4Rs1CaSIcCcDxzlwDdYQ+Ue64RlUD6EhGrUMpAvCAKgNlQBZwY0AN6KMM/VMXBYpwJooabRM2NfPa6APlaWzoEnCkTaDJACKwA9gAVYyXRQ+odcAZAASccU8yPiCnLDDENT4CarGP/vAH10CTOeOV8viPRQ3k9AdIAlpAHzaQ0w4+BkbxAzYbJZScceFz/DsWlukPXwG/1EcxpH1WxAKtnCvLWABZ+gdEOSd37Gf/1etx/NdhVbUhbrwetOvbHrA9YHtgnB4Y7O3WvevXaeuDD6ipvFz+8nI1FhWqqaRIzeUlaq2pUrCuVqHGRnUEAuoOtamnvUM97SH1drarr6tHA9096m9rU0+gWT2N9epqqFZnbYW6qkrVVV4koK27okQ9ddXqD7ZqqKdXQ/0DGmhvU3ddjRVS9SdGqD5qpxoTo9XiS7X2XWvLcFuh1VZ3lAIcdJ8Yoba0RHXm+tSZm2EdxdXtS7IWPPQV5qq/MNuCuP7ibA0W52i4ONvaeuRwU7WOBv06FmzWaGuTDvnrNNRQa+3/1pmXYa1IbXNFKeSJsk54IHSLMsfedbzvLs5VV3Guukvz1VNWdHYstVXqq61SV3mpOovz1FHgU1u2VyyGSH/hGT1z201aNmOyrlmyQPuffVLZ+3bIt2uzCnwZ+uDDD8f5qf2/1b/6A/1N9yacCmygHhlwAnyACuZTEbJj7hXhPraxYEGBWXAATHHP/DhWV3LiACFByrMyktAgc+sAHRYCACMmzIr6BIiZxRAGUkw4FJgzKhzQR3n6IwRJu/RBYmI/OXPDWJHJO7brIGEzIV8m+mOHWRTAOFHNTJgS2AHAADRAC9jDXmzFRsKj2GfCu4yfdumLREgT2ATkgCZUMxRIwrUk2gLQGIcJ3TI++jEJZRF1jnbIgTSjspEbf1CesrSFX40/GRNgaMYEcFIPuAaG//Ef/9GGuP/3T8V+YnvA9oDtgT8dD3z++WdKjY/T7auWKfaFbWoqK5G/pFhNJcXyl5aopaLCArnW2hqFairVDnTV16q9rlqdDXXqbmpUt79JPa0B9YaC6m1vU29bSH2hoPqDIQvuBjo7NdTVraGuLg12das/FFI3e8/lZyqQmmCt9GzyxKnF51Vbvs/aZqQt3aW2VKfaMpIVyslUMMOjoBvQilVHWqI6fB5r24+ODLd1SH1XVqq6c9Kt81V7c9PVl5+pgYIsDRZmaYi95KqKdLS+UqOBeo22t+hYd5dGe3t0tLNThwJ+DdWUqa8011LrOjNc6uTcVZ9bPfle9ZXkqK88XwNVheqvKFRvWb46LWUxQ50F2eopK1R/ZYn6q8tU4ojV7gfv0U2rl2nxrBm6d/1aOZ97VPmxYcqNj1SyK1EnT54871+gb4K2rz4H4v7lX/7FUnJQfMziAICJkB3QZMCIOVyoYKhfwBjgArgBS0zEZ44WqyvNUVLcA3VAnlkMgHLFyk3aAKgAROoCYMAf7QFphBYpC8iYECRwx3OgDzvZVoO+zWkHZpNc2gIgOT+0p6dHfX19/3fFJ2WYTwaQAaooZqhXRn0D5FC0gB/giMUH2IgPyFnpiR9YIEH/BlrJgUQUQ5QxgAvVEXsZK7CFDxgD48fXvOc54yQUzfw53gFd2Ec9nhM+xSbgzOx7B2DTNjYY2/AbkEn/qIbAKP1hK6oeYfOvfv5j78f7JbSVuPF60K5ve8D2gO2B8+CBI4cO6q6rV+uutSsU9cxjqkp2qjLZpYokp6pTXarLSlNjfrYaC7Llz8tSc2GOFVZtKcpVa1GegqVFClWUKFRWrBDh1uI8tZUWqr2McGqROsqK1Vaar7bCLLVme9WcnqSmVKf86SxwSFMwP1vtRTlqL8hUu8+jkNdpKWEhX5La83xqz89SGxCXHHf2CCzOQ02NV4fPbcEc56x256art8Cn3oIs9eSlW2ev9uSmqq/IZy1WGCzLt7YjGSkv1MGqIh2qKdPhhiodaa7TIX+DDjbVWytTB6vL1VdeaJ3b2p3vU2d+hjqyvVZqy0xWKM2tYConM8SrmY2O3XFqTXEplOZRYcRuhT/+kB66cb0WTb5UVy6Yoxee2CjfgV0qOLBdGTFn55OxpcT5/jf2x/m/uyacSjiO1ZT84KMwsaoR0ACYWLgAuKDKsfIUuKEsk+8BNhKb1HJsFBvVMh6ugSnKo0AZZQ8oAV5QsgAfygFaQBeqGc+ALCb7A0XAIpAD4NAvNpDzDGABdIAX+gLozIpZ1DnsAOD6+/utzXM5EYHngA/tAkeAkQmjAnMkVDlUOhQxFDjapw424QfsMrBLDqQCpPiO+WwG5AjlAmn0ZcLIXNMm46ce42EcRn2jLsof48C/+B4fouQBsyh9gC3PUAANwJp95fABnw1KHcl8hiiDf/u3f2tD3Pn+Q7Pbsz1ge8D2wB+aB37xi1/ovpt/pBuXzNc916xR9LOPWytRy+IjVBq9R5XuGNX6vGooyFVDfq7qs1LVmJ0uf2G+AsWF1ua/fl+qGpNZpeqwthqx5sb5UhXwpSjgS7VUttasNLVkp6sVZS3PpxBbeeRlKpTtVUua0zrNgVWdbCvS7IlSwPNfpzsEk+MV4KiupAS1eOLVwsa8nPrg5fB6z1loK8xUb2m++suL1FecY5380JWTYkFdf1GuhsoKNFReqOGKQuuYroMVBRquLNRwZYlGKks0WFFkbT0yUFZwFuIKferKy1AXEAfAeV0KpTgVTHGoNcVpwVtbqktBT7y1Lcven92tp2+7SVcvXaQl0ybr7tWLlLD5aRW4HCpzOeQO36fYuHhLSfn888/P69fgvwM38x4l7l//9V+tOVqAEVBCyBRg4RpgASQIRwIeQAdKEbACMABhBtoACYAMkEMFA5goB6CY8B9zvAAvlCZULuCNUCjtAzhABzllTBiTHAhCVfrqRH/ABljiPcoU7aIQ0i7whk2ADmofShp90gdKG+FGFjCgbpnFDFyTADnmqxESZbwAFzAHXNEG0AbokrjGfq6BPGwHWlk0gSKH3YCnGZeBOHzDNWOiPPPiUOvwPTYDx/gGhZJ2sRuYY1UtoG3US8oY9ZPPgrFiD23jO9Q9bPmbv/kbG+LO61+Z3ZjtAdsDtgf+QD3w/BOPa92cafrxyqX6yfq12vPIT1UQE2ZBXGnULlWmOFWbk6X6ggLV5vnUkJ8tf1GRmgpz1ZDuVm1ipKoTY1SX7FSDN1FNqQ75vQ41pyWeTey7lupQi5eVnAkifMqmv43sHRe3T7XRu61UF7tXDQkHrJMdWAXa6k2UNTfO61RrGic8ONXKth9eh3WsFnDXmhxnrSglxNqdn2lt2ttfWaz+0kL1FGZbe8N1Zydb56T2FedpoLxIQ+XFGqoo1GA5YHcW7gYrCjRI2LSiQAPlhRrgBIiyAvWXFlirXHtKctRTlKvewhz1FOaIBRVt2WlK3/m89tx/pzbdvkE3rlqu5YsX6cfXr9f+xx+Sd9Mjyt3/gvI9DsWGnd0TDeUJcD6f/wyk/Xc5Kxb/8z//0wrBAVv86AMrBlQACiAOmCOUigIEKAE1wAXABrgBc1yTE0ZFVQM8UPKAGNpGkQOIaIt2gBjCp0AQShsKE+UAPcoBK4ALcMl8L+aA0QYKGlBCYrUmiwRIQBdQx5wzQBR1ELUKoEGdIkRMGBKVjbApqhtKHGFH2kHlMqcrcM/8MhRK7AKuGAu24g8SQGf8g1IHWKEk8gw/AmdmcYKZS2eUOQCL9oA71DdAlLHzHDgGgrEfGCVnTzggERBkHNRlXIC1uQZSAWfqAHjYZ1RLxvFXf/VXNsSdzz8yuy3bA7YHbA/8oXog3ZuqFdOnaN3safrxisW6c90KPXPXrfLueFYFO55WSfh2VaS4VJ+brbqiAtXn56g2I0WVcfutM1bLYvepKjlRDenJ1pFcDckJamLFJqtVs9IVyEpVIDNZgYzks9fZGWr2pao53aMmr1uBtCQF0txqSXepJY2UqFBGkrURcJvPrWAGK0EBuISzZ6KmszrUpRZPnJpZDJEUp1avyyrP0VjWMVmEWFns8GsI6y0i3OpTf3GuBXJ9FUXqK8m3VDdgr7co52z5omxLyRsoy9dgeZH6mfNWkqv+0lz1FuVqoDhfA0X5akxJVMKmJ7TjJ3fo6R/foBsXz9GiKZfrutUrtH3Ts8r1pqjYGa3C3ZvkemGzwsPDLCDiR//9998/r1+F/w7ezHsg7oc//KEFN0AS0ARcocIBbYACwAYo8AyVCzWOd4DL2EPbgQfmZwEPlAfezCR+JtYDarRFPUARKKEsUAjEMGEfpQngoh/6BoYAIcDNrODETtQuyhlI4R3z21iRCYwBOoActrJIAOBjOxDgDaXNhE0pS78AFIn+UfTwAX3QNxCHoka/jAmQAjABTaMYYif1DOQxRq5ph3HgO8oDqIAcdnOPv7EVSOQdvgByzfiZg4dPgTJgn/HgUxZ8AH+obOQ8wy4+F8AO3+Bv/EJizJzOYT73r8vH+wW058SN14N2fdsDtgdsD5wnDxw+dEgb1qzS8mmTtGrGVEuRu2XlUj104zU6cP+dytnyqMrjI1SZkarqbJ8qk5wqDtum/K1PKH/PZpUlcKIDe8bFqCYhXLWJ0WpIdVnh1Nb8bAWL89VWWqSOihJ11laq29+o3oDfOiO1p7lR/S1N6ufe36i+pjr1NdSov6ZSvZWl6i0vVg951a9XguZmKORLVhDgS4qXn5MUEqPPKnPpboW8CZYy15bhUnu6ywq39uana6CyREMN1RquKdNQVbEGa8vVX1OhnvJideb51JGVpvacDLXlnN0KpYOzVVkFm5uhLubZ5aVbK2FDmSnKD9+tsEce0HN33Kwnf3yDNixfpKVXTNTq6ZP0yPVrlLx9k0pTPaotyFNRfIQin39aYWFnD2MHZN57773z9MmdbebrfqS/7pk5sQHQAQYACUKTwAWqEGBi5nMBByhoAAKQBqAAGIAc6hsKEkAKSFAH1Qz4ISQJuNAWgAKIADG0Q1neEU5kuwzUK8oBPSQgCbtoBxhBIaM9IMcofcAQEIOKBpyxwhRoBIiwg2uzapP5byRgDvUOGMJOQAowY3yobCh4KI1AFONGSWM+H7ZQHmUQVY9E+yYH8hgXgApEos6hkAF1+AvQArywCx/gZ54xBnKeYYsBNdrmnrHiX+bJ0T5gCdjiL+ATEOUeH6JIojYCqGYfuzlz5pwT4PhujPff+FsYrwV2fdsDtgdsD/wP9wAwcejQITU2NempRx/SNYsXiPlcK6dP0YZli3TziiW6Y+1qPX3TeqXv3KRKb5Iqk50q2rtVOc89rJztz6o4NkzljiiVRe1V2f4XVBa2TRVxB1TrjFK9K0pN3kQ1Z2eotSBHbcX56mCbjtpK9TXWqr+lUQOtTRoKBjTc1qqRznYd6u3R4YEBHRka0dHBEY0ODml0eERH+wc1HGxRL+ej5njVlumxDqQP+TxqB7aKctSZnaZQmkMhL+exEn5NUIsryjo7tTsvXUNNtTrc2aaDLX4N1pSrtyRHnTmpVki2MXaPGmL3q9EVo6b0JAWy09SSmXJ2L7hf7wlXnhCphE1P6oX77tQzt/xIj16/VhtWLNGKRfN1zeqV2njvXUrevVWFYS9YCmW5O17e6HDt2XYWHFBwAJ8PPvj9KHHMibvgggssJQxQASTIAQdsAzJQoEw4EdAx0ME7QAWIY04WkAEAoUBRBhABDoEf7gEygA3gYaI9OfcoRkAcz4ASo/SxMpZ6qGMkYAVQoQ5ARJgVaGG+Hc9ZkUl4FIihLP0CR0Ah9tM+gIcqRc4qToCMcQBv2M3iBD4Pk4AwAAyIAjgBQ/oHJIFKFD7aIYxLDhRiK/2a7VBM+JX2sQW/mnAqQEab5PgWwAME8RuQyXgAMfyCekmYmnb4TBgPPgYsgT1sMKttGSNz/miD59OnT7ch7n/4/+328G0P2B74E/XAxx99pBdffNGae1NeUaHMrCw5HQ49+egjumrZEl2zYolmX/ZDLZw0QdctWagfrViuG5ct1r1XrdKuh+9X+ranlLd5owp2bVJB5F5ZCyAidqpg5yblb39GRQe2qSI+TFUJ4ap2hKvOFaOGFKf8aR41ZyZb4dW2gmx1VhSpq6Zc3ZUl6irNVUd+ujpyUtSRm6KeIp+llAF1Rw8f1onjJ3Vi9LhGh4Y0HAyor7JU3dbCg0z1VhRpJBjQod5eHe7s0FB9jXqLs9WZk2ztK0eotcXJpr3x6spP00BlsYYaazXU1Kj+ihK1Z5w9nqs2fJsq9z2n8r3PqSJ8u+oJ02akqjXXp2pvkpL2vKDdD/1Uz95xszZuuEb3XrlMV82ZoSWzZ+qqVSu08aGHlO5xWeFmf0GO6t3RynzhaYVv3qTt27YpMiLS+kHnR/6jjz46r9+ur1Pdvu6Z2SeOcKYJ/QFbJgEJqGBAEJAB8JBQkoAH6qDCAXLM2zL7splwKkADlABcBsKAEmADpchAHn0AYmZxBaCCmgXcAE0koIwcqEKRwg4UMUCMhQoocWzFwVhQFAEfYJR2ADDuUaxQqbABVY96QCRQRTk+C+a1mXAoyiHzA3nOM1RTxox/6BebmW8GOJJzT/+0CaxRlrGQaAuwY0xAHrYxXhKwCswBcYRwgVrsxE+ohrSPz7EFW/EXcMZngB/wCyDH+NkuhYUZZpsRxjlx4kQb4s7rX5jdmO0B2wO2B37PHvjkk080Ojqquvp65eXnKyU1VWH79uvpjY/o1uuv1ZIZV2jmhEu0bukirZxxhaZ97z8089KLtGb+XEuhWz9vjq5bOFd3rVmmbXdsUOqO51WZGKny2D0qObBNBbueV9Hu51R8YJuq4vapOj5M1QBcslONaR41pHnUmOpSI/es8Mz2qhMYa25Sb2uLepobLKiztiLJTFILJyWkJai7NEdDbUEdPXhYx0ZP6PihozraP6DhthYN1VVqsLZCh4J+HR8Z0YvHX9TJY8c0OtCrQ4RoLdUuzYK51pR4K9TK0VrtbEvCyRGVZeoqKbDOcfW7Y1QXt181sftUG7NXtbH7VOpyKMPjVtSBMG158H5tvOk6/ezqVfrx0vnWHMKFUyZq9axpuv/6qxW/fYtKfRlqrq5SqLFelckOubY+ra1PPq4Xtu+wQmf8IBO2+/TTT8/rt+HrgO2bnrEJLIoN4AWoAUskQnbADgoTyg/PgAagA3jiOfABUDEGQAflisUKKFvAGxAHlKBcASaAG1DC3DVWhAIYwIcJlwKLhC+BHzP/zgAZ/eMvQJE5YvSFYoZ6hsIFcKKOAVeEJQEpEsqegUHawB6AEiWQRHn6ZSy0Q98AHcCEAkcf9MkYyYE8+sRX9AkIGkUOVZCxAmj0C7QxHqNq4hNsoB4+xMeUB8QoA2gCgyiFgBgQR86JD/gJ+MN2AI8FGdRFOeUz4blR76iHigfUUfaSSy6xIe68/oXZjdkesD1ge+D37IHTp0+rpLRU6Wlp8nrc2rttq+688Ue6dvlSLZ81Q/MnT9T0i3+ouVMmadXs6Vpy2UW64vv/oakXfk8LJk/UqtkzdOWcGVoze7quXzRX911/rbbcd4dczz2mov1bVbp/m8pZABF7QOWJcapyxqjGGa16T7wa2RsuK01+X5qaMlLU6HWpOTVRLRnJaisrVG9LswZ7+zQ8OKSR/gENsZ9YQ63a8tIV8DDfLU49ZQUa6e7U6NHjOnHipI6NHtPoyIiO9vZotLtDx/t79dLoqE6dOq1Tp1/VSydOanSoXyOBBvWW5qk9K8VaDNGaFG8BYmu6R+3ZqWrPzbQ27+0oyFGII8Y88cqOOiBXxAFFR0Zr95692vzc83r01g26c+UirZ01XUumTdHiqZfrqtlT9dD6VYp+6C6lP/+4CsJ2qTrTq8qCPGXHhWvfpqf01JNPWD+6JtTH6sMvv/zyvH4bvgnYvu45xzHNnDnT+rEHFvjxJ61atco6rol5Zqg6BiwI0zGvjPNWmWsGkAAfgB7QBhABKGNVKsqhlJkzSa+88krraCj6QWVCVQKEgCpAEbAyoEPYEBDhPe3iN1QtVDHmrAFZwBawBPABNChaAA6QRB0UPyCH97zDRmymH8rxjmSuyYEtE1YGxoAyQJcc4KIf2sV2xsZJDUCpmWdH+6YPyuEPIAuQZTz4BBAD2hg3feILQqEcm4WP8DP3gC9AjS8AMwPBlKc/FEau8efatWut+hy/RT3K2xB3Xv+87MZsD9gesD3w+/fAm2++qWyXQykR+5XuTFD83l26/9abde2yxVozb46WAnJTJmnGhIu14IopWjlrulZNn6p5l12sKy6+UDMuu0QLr5ii5TOna/XcWVq3cJ6uWTjfmtT/wHVrdODBe6wjp0qi9qk+KUH1njhVJ0RailytO1aNXo/YT465Zs25mdZ+cYHMFLVkJonwandDrXWqwxFLcTumI0eOarivX1311WrJ8MjvDLPOUu1tqNLh4REdP/myXn75tF556ZReevElvXziRZ16+WW9+uoZvfbGmzrzxts6/frreunYcR3t69ZgY7W6S/LUnpOmtswUhTKTrNMe2jIS1Zgcp5KY/Yp75nFtu+92bX38Eb2wY4e2vrBDz256Tg//7AHduGKJlk6fbIVPV86bow2L5+nZW66T88kH5dv2tPJ2EE5+Suk7NsmX6FDCjm166Kf36ZlnN1kwgZJDmG94ePi8fxm+Dta+6dmf//mfa8KECRYsAGyoOAAJsAFkAAGABFABIHANRJAAOuAKBQo1jQQ4cU8CMEiocAAOCRBBMeM9ZQEYyvCc94AOwELbZg4b/XIAPZDCe4CH8CLqGQlfAlgobqh1QJYJV6J+kYA3wqCUpQ7qF1AHrBmYA/gIh2IXOfBl4JSce55jO+MGxgzcmjAm/sJ+Eu/xIQnQM+FRchIqG/40qhuQC8CRGLMZt7mmLMlAHuUBbD4vEu0AbibhQ9KkSZNsJe68/5XZDdoesD1ge+D36IFf/vKXStizU6nh++RzOeRzOXVgy3O6+4brdO3SJVo3b46WzZiq+VMmasbky7Vg1kxdtXSJrl6+RMvnz9es6Vdo2uSJmjFxouZNmqDlM67QyvlztXLODK2cM03XLlmou66/Ws/ce6fCn9motL0vqDwuXE2EVF1xakw6u+2IPytFgexUteZkKJiXpdbcDAVz0hUsyFJXdbkGgq06ODCgo0dGNTp6QocOHraO6morzpPfEyW/K1ydhVka7mzT8WMn9Mqp13T6zFt67fV3dOaNd/T6W+/pzXc/0Fvvf6i3PvhIb7z7vl4985peGj2q470dOtJQaW0t0uB1Ky98t+Kff0pb771VD16zSvdcuUz3rluhR+66xVLQNm58XLffcYeuWr3SCjevmD3T2kLkwdtu0f6HfqKkpx9Rzq6tKgzfqYKwHUratFEJTz+siM2b9Phdt+iB+++3gAHAADYI2Z05c+a8fwu+Cdi+7jn7hy1btswCE0CF+VUmvIcCRWjSLDwgpAfwADtcE5pEGSMEyfYhqFRAEvO7TA5YkQAoygJYjJ17EwqkHQAJkEOhAn6AIQAEcAMuUaUAR+ASWATAaIcQKABH4pq+6AMbyHmObSh2rBQlLErIl/Avz1D0WDSAmkcIF5WPnJAqyWwRQl/4hvEDcoAo9qKkoYKRm4Q6ZxZdYCtjo56BRSAUeCUkDYSNBWNg1STGy9jJATT8Qh9AIX7AV/RDHyiX+ITPC8DFt4ArAIvC+pd/+ZfnBLnxfgnt1anj9aBd3/aA7QHbA7+lBxwH9ir6hS3yedzKTU9VusuhPc8+rdvXX63rli3RVYsWaMXcOZo/c5pmTpmsRbNna93K5bp27RqtW7VKixbM18zp03XF5ZdqxmUXa970K7Rk5nQtnT1TqxbO19qVy3T12lW6fv063X7TDXrgjlv09L13KWzjg8rYu031KU5rv7iWnFQFc7zqyM9QV3GeukoL1VWcr86SfPXUVKi/NaCDPT0aHR7WsaOjYguUwZ5uddZUyJ8cp4a4fWr2OtRdXaLDA316+eVTOvPWu3rzvY/13kef6oNffqkPPv9C7//yc73zi1/q7Y9+ruPHj6mhpFDeyH06sHWztj75hB676w7dc+1V1t54G+ZP14b5M/XjJfN0+zVX6tZbbtbVa9do2fz5WjF/ntYtWqA716/Tjo0PKSX8gPIj9qpo92YVh+9RbthuuTc/pZgnHtaO++7Q3evX6t4b1mvjYxu1c+cOC2YADuZYne+NfvkKfB2sfdMzjmPiRx7AMnvBAW9mbhgQBHQCRwAOoGZAAbBh0j4ARF3eEdZEYWReHIlr8w5wo65RyYBGgAgIIQFGQIpR5AAVbDOKFeoV8EO/ABinFXC6gdnihL3UgDIgDDsMPAKl3DOPjgUY5rxR5vMxxw2447Mg555yXLPVCNf4A5uBMPoHYrkmpMp4AFaAkcQ1iecofmbM3POetoBD/Ia/UAKBPmB1LLSh7AGxKHioegAfZWmTz4qx0RaQCYQSWsZ2fMA9nx/v6RN/2pv9/pb/KpwA4AAAIABJREFUOdrFbQ/YHrA98IfugcqSQj1443VKObBXGR6XctJTleFO1A5A7vprdcOVK7V+xTKtXrxAC2fN0MwpUzR/xgytWrJY16xeo2tWX6lVS5do4exZFuRNvewSXXHJRZo1dZIWLZin5cuWasWKpVq5cpmWL12sZfPmaPH0qVo+dYLWzZ6iW69arY333q1dzzyh+F0vKDNir4oTIlSZ5FB9eoqaczLUXlKgvvpKHeoI6cTQoE4dP6bTr5zUq6dP6aXjx3S4K6Se4hy1JMWqyR2l1ky32svyrXl1/b09GugfUFdXr/XjnZeVKUdUpDW+R27doHvWLtddVy7RnTderztvu0233bhBG1Yu0/r5s7X2iolaOflSrZwxWasXztPqFUt15fIl1jFad163Xs8/8rAcO7YoNzZcFalu1XjdKo/aI++2TYp+4kFFPv6gXrj3Nt24fJFuW7NCD9z+Y0uRQS3hR54f2cHBQf3qV78671+TbwK2r3vOweioOcAACegCeFCvuOcaWw0YGJUHpQtYAkYMLBlFDDgBUk3iOeCBOsT4ScwXM4oUYISyR878LqAD1YnQKTlqlYEaVC3sYXEDp0RwtBarY4EuFhyYVaDGFnJACvADagBOwIzVpiY3izK4N/PrGDfwCQAauwApVEkgDvt5B6jiK/wASGEHcEhb3AOVlGHsqGUkFDLGAHBRDhsJ4QKsRpVDiQRwUdLwORDI2CgPcJpNgAFZEv3yedAP8/toz4SpFy5cKFYif93nb56N90toK3Hj9aBd3/aA7QHbA7+FB1gRWZifp3VzZ+rAz+6WZ/vz8sZGypfkltfl1PbNm3XXbbfqR9deo+uuXqe1q1dq0cL5mnnFFM2adLmWz52tNYsWad2ypVqzBECbrXnMobvkIk3+4fc09bKLNW3KJM2ceYXmzZyuOVMmWqtbp/3gPzTzwn/X3EmXafH8+Vq5cqXWrlmjq9et1fpVK7Vh1XLdcfUa3XfDtXrklpu06b679cKjD2jvM48rYtvzituzS67IMKXExSgpPlaO8H2K2vKs9jx8n174yW3afOfNeub2H+npO27Sk/feoYfvvF333nCtbl93pW5ZvUw3L1+iHy1ZoPVzZ2rNjMlaO+1SXb94rn509VW64eprdOXC+Vo0eYLmT7pUS2ZN1+rlS3XNVWu04br1uveOW7X58ceUuGub8pzxqkz3qtbrUWOWV6Vuh1zPP6l9P/uJdv/0bj13z626Ycl8XbVwnh6+8Vo9fP9PtXXbNusHGegBIt5+++3f4hP7zYuaH+bfJAfiWGEJvAAiwBowAJhwDcgZJQ34YuEBahChYCAEGAFmAFNgBXAD8AAnEtckIIRy5EAJcGJWrwI2qHLAEvPNADmACVWOuWeED818L1QpAApbCY0CTAAXKhVwZUK9tAXM8Bxowj5AEltQwhibUa8IrxroApQoh8rGWI1SyLVZdAF80g99kGM7K2Hxi4Fe0wd24heUPMoyHhK2me8BAEZ5/AC8mYUM3APV2Mn3BdgcqzTSF58Tnxe+Rs0kxErCh0AcCyrmz59/ToDjezLef+NvYbwW2PVtD9gesD3wP8QD7EtGWIkfnnUrlll7nCU8crcSnviZErc8paS9O5S4d5e2P/247rz5Jl1/FarbSq1eulgL58zUFRMu0fSLfqD5ky7X0ulnFz2snjNTa+bM1NJpUzR/4qXW+ykXXqCJF35PE773n7r4u/9HF/3rP+uy7/4fXXHJhZo3e5aWLFmipcuWacnixVo0f57mT5uieRMnaMHEy36dJlhHVy2aMlGLJ1+uJVMmaPm0iVo5c6q1YvbKuTO1es4MrZ51hVbPnqYrZ03VlTMnazX5nOlaO2+21i2Yo7XzZmnt3OlaN2eGrpo7S1fNm209WzZjmhZc+n0tvOwHWjx9ihbMnqX5c2Zp6cI5unLlMl1/3XrdeuuP9ciDD2r/rh3K8DhVlZ8nf1G+Arnpas7PVXVastLD9ihm01Paef9d2nrHTXr42rVWf0uvmKR7r16th2+5UU88/rj27D273xqKUF9fn7744otv5Rv3m8CbKcPB6EASoAYooOqwiS/3AAg5gMB3BaAhBzhQwjirk2vADEgyypuBNgNxvKOMATkgjvlltAVkkZgXZ4AJVQ4lCdWLnLlnRo0DcAgxAihAJXBEXVac0h8JaKNto0jRngl9Ak6AD3YDRCYECQhhPyCGmgVkEjIFvIBPPjMURmzHXt5hG7AEBAN0xg5sMmOnPewEWLGDMQFb3NMufWIHQIpNACJhZMYL7NE/Y6MsfTM26tIG7fGea6AXwAXasJ3PDdAGAlEzWYVsPvOvy8f7RbQhbrwetOvbHrA9YHvgN/QAJzOgRPCjc9edd2j17BnaedeNSnz0HsU+fKfiNv7UArqYxx/Qlrtv0W2rl2jt7GlaMX2SFk++THMuvVBTL/h3Tb7g3zTzogutjYCXTJ2k5dOmaM3saVo3e7qunDFVSyZfptmXXKjpF12oqT/4ni779+/qon/937rsB9/TlEkTrZ3kZ86coRkzpmnm5Ms189KLNevSi6w2Z170A03/4fdFPvPiCzXr4gs1++IfaO6lF2nehEs097KLNH/CRVpw+SVaOPlysUfbkismavEVl2vJtElaMmOqls+YphWkmdO0fMZULZ02VQunTNaCSRM0b+Klmn35BE2/7BLNmnip5qMMzpqhNSuX68brr9VP7r1b27ZuVqIjTiUFOQo01qiruV79wWb1tQYUKMxVZtR+RW/don1PPKZtP71LT9x8nW5fPl/Lp1ymhZdfrB8vn6/HfnSVHrjzDm3eskURkRFWWA/l59133/0NP63fvtjX/Uh/07PvfOc7lvKDqkM4klAd3w1ADBABtoxKRigTkEAR4pQGzksFfngGZABIqE7Mw6IuIUzAhxyAoy0AAygBqr4KcShPJCCFd5ShLP0bdY5FBKzEZL4YkAM80Tc+NYfFYx8QQ1+AoYFP7DDKFQojkApAUR4FjnHQN/0yBmxG9QO0qEd9oIzngCljRLUjN8+5HvuO95Q3IMdYGBNtYgNKJrYCzqiKwCJ+BuQYI/MAGS9jZRzYh48AVOylbXwAiBN+pm18QYgZyOYa2LQXNvz2f0d2DdsDtgdsD/xBeuCzzz6zfvD4UX3iySe1bsVS3bxikTybn5DniZ8q/rGfKOGpR+R47knFP/OYXrjnFv1k7XKtnT1Fy6dersWTJ2jeZRdp6vf/QxP/8//T1B9coNmXXmSpZ4snXmxBzLoZU3Tt3Om6YeEcrZk9Q4unTLTAa+bFP9CMiy/U1It+qKmXXKSpl16syRf/UBO/f4EmAHr/P3vvAZ7VcaZ/7ybZ/2avxIm9boB676igXkEgEEX03qsoKggVhBBVopneO4heRW/uju1kncRx3JJdO4k3n5NNuT6nbBwnjuv9/e959eDx+aSXImG/wHOua5hzps89R+/58czMOYH+CPNuZ2AvvMNDiPLpYOqI8/NBQpA/EkOCkBgcYBzLSo0IQWpkBNKiI5AWFYHUSIJgiKmDEMi6khkWE4mU2Bikxscio2O0SU/4y01PQ8+uuSjIycDQ3GwUTRiHZYsXomH3Dly5dAH/8d3v4JWXfog3f/IKfvryi/jOpXNo3L8XW5YtQX3FTCwsLcHcyeMxrX8v9ElJQE5MCLIigzAgLR6zBvZE4YBemFVWhmXLlpuHO6HnjTfeaPN3w9k3WkvA1lz417/+dbOgngBGkCNYECQIA4QFAgUtPYQGXhNWOPX4/e9/31jjCG20DjGc5wQSrvEipNAKJBYsghbL5D1HoCGk0dlQRyihFYwQJIBFeGLbCJUskz5BiOvmON1KSxTrJVC+8soreOmll8w5QZTtYrv5nxW2j22j/iyT/aTlSyCK7RQLIttPuGMZPGceWrUEADn1TOClYztZDvNTP/aZ9dAnwLJ8sWgSDGmlY1tYlkyRsnxCJDdrsB7G07onmzr4GhOuW6R21IL6sG+sk2PB+mgZ5Dgxnps9CNkEOLaVlky1xNl/IXquCqgCqsBtrgBf9mt+/BcswJRJE9ElPgbl48fg4PLF2FtTip2zS7B9XhV2zq/G1tmlWFUyFeWjBmNE12z0SklE9+RO6NwpAWkdIxEfEogYXy8QtJKCfJEWFojMiBB0iQ5HfqcYDO2cgfE9czGuR2cM5Tqx9BR0S4xDdly0ealwemwM0poAK7ljRyRGRSAuJAgx/n6I9PNGZIAvovx9EB3kZ6ZmI/18THh0gB9iAgPQMcAPHQP9EBPgh9ggf/NC4k603tHCxhfxsr6cLHTPzUHP3M7olZOFPplpGJSdgnG9+Y3TSairnIl1ZdOwe/E8nD96GN/7zpN4+Uffx6sv/9D4j148j/27tmP9iqVYNn8uFpQVY860KSgbOwoj+MH7uEgDb7kdQzEsOwWVIwaguKA7poweYdbCEWL4kKe16FbsSLVvx+ZgraUw7lrkKy0EtthGWo/E4kNLFOGL4EXAojWKa7h++MMfGsuRWJlogSJAEHroCDC8vwgnLJP9J3jQEUZYJgGOZfKa4ML8TMe8tAgSRLhDlJDDeglGPGcawiUtTJzaJBAR4l5++WUDctytSrBiuWw/20jQYzvYTsIiQYjtZFlMw7YQ4thewpVMF3Oak1Ou4th3whEhT4CX7WLZrIPQJoAo8EcrG4GPgEbHc1rdnnnmGbNBgZDH/rHfBFW2iVBG6xtBjo4gxnazXyyDaakX2yHX7A9BUKykBDxCHGH3q1/9qk6n2n8keq4KqAKqwO2sAN8Tx4cdH6Jz5lRjQK98dE9OxMKSGTixYRX2z6/E7uoS7KypwO55Vdg1rxqbqyvMuq+iAb0xtHM6eqclIY9WtsQEpMZEGpCK9umAjr5eSAzwRVKgL1JD/JAdEYqucdEYmJmCSQX5KB7cH6VDB6Bo2EBMGVBgdqkO7dMT/fO7o3duDnpkZaBrShJy4mORFdcR2YmdzA7YnLRUZCUnIzMpEZmJichKSkJWYhKy4mORGRtpplk5Vdo5viNyExOQn5aCvrmdMbRfAcZw+m38eJQVTkb1tClYNKMQK2ZMxKayQjQsnIMTm1bj/IblOL96MS7s2oJHTzXi3KmTaNizE2tWPYIldYuwcF4NameXY05JEYrHjsKogl7olZ6MnI4R6BoXiT5JMZjYPRPzJo/BnEljMaFfL1RWVBiY4EOaQPDOO+/c8tumJWBrLpwP9/j4eAMgfODbFikCj4AFrUh0BBaupySMElho6aJljMBEyCCMEUIIgownYNAncDCOEMd7ThwtU4QswgtBiDqxHYQoWpS4eYLhhCyx3tH6xvVgvCa4EIgIla+99prZrUp4YZlcX8bNCKyD0CbARkDilCnbwLbzFR9cS0aYY/8IaewjwZB/I/IaE0KXvHaEMEVIFasewY3QxnBCGR37QcgiWNGxXdxVyjJfeOEF02aWzXSiCzUkmLLtstaNa9o4XUrrHNOxTmrKvhM6ec1xY52ETY4Ny+X9Rv24Xk53p97yPzutQBVQBVSBL1aBX//61+ZhsHhxHYqKZqBrRgoKMtOxoroCjVs34nB9LRrmlmHX3ArsWVCDvQvmYFdNOTZUFmPR9MmYNqQfBufmoFd6Kvji25yEeCSGhyLKzwsR7R9AtE97xAf5ISnENYWaGh6CzMgw5CcmmFdvTOzXG7NGDUHdzOnYsLQea5ctwZK51agtKTJWwelD+2NSv94Y168PxgwejDFDhmBk/wEY0jsfA/NyMah7Lob27IERPbtjRH4uRvbojHE9u2JK/95mR2jl2JFYOG0yVtXOwZYVy7F34zoc3rYJJ7ZsQOOGVWhcsRCNdTU4taQWh+rmYPucYiybPALzJozE3LKZmF1VYVxVZTlmV5ajoqwUJVMnY/SAvuYbsl3iYpAbF4H8xBgMz01H5fD+WDxhBOYXFWLaiKEoLpyCpUuWGqDhDsPf/OY3t3QaVe6e5mDNXRinVLlZgKBFGCPcECboE55oleOUpGwQILzQakXw4botAgbPaRkjeHDqj1YkggjLoxWPjuWwDK7hoiWNPtd5EapomROwYjsIbgQvgho3MbA8TqFy5yUtS8zL9hFeaDkTCxlhinUxPXd6EtDYLsIa4YjlsT7Wz52w3AzA13pwwwRhhyDH+glGBFDCF9fMiTWN4ew7wVDAle1kmKyH4zXP5ZpxhFNa6ghshDmCJ62NbDvhmDqwPewX20FN2VcCHDXmO+TYTraXGrOd7As1YH/ZPwIr4VWmZjkdTH34ehZ348+41h6tL6G1LdD8qoAqoArcZQpwbRytArRQ8CEwZuQIs0Ggf2YKllbNwundO3BszVLsn1eBPXNnoWF+NfYtnoN9yxeiYd0qbFu9Cktr56Bk0gSM6FeAPl07m1ePZMbHgtOZnAoN9fFCZKBrmjMuwNdAXUJYCFKiIpEeFYbc+BgUdM7CqEEDUDR5EuZWlGHlwnnYvqweu5Yuwva6edi2eC62LavHznXrsHP9WmxfXmfW6m2qmIFttVXYsaAaOxfVYE/9POyrm4sD9XNxaPlCHF25BI1rV+DM1g04v2s7LuzcgvPbNuDkupXYNX82Vkwfj5ph/VDSNw+FPbtgQvdsDM9KxIhu2Zg0aiSmFk7CjBnTMH3GNEyeNAHDhg5G7+55yO+cjZ7Z6eiblYaRedkoHzEQq2ZOxYppYzBnzBAUjxyGwnFjzGYGassH989//vMvBOB4C1/rgd1cPBe+d+jQAe3btze+t7e3OX/44Yfx0EMP4cEHH8S///u/g9eMo3/PPfcYd9999+H+++83aR544AHce++9V8N5zfwst127duac11Imy2VZEs82sHz6DJd6WSbrYXlM6+vri8DAQISEhCAsLAyhoaEICgoy4ayHaZmH7WJdLM/f39+koe/l5WXCme5b3/qWScuymc7Pz8+UHRwcbMpn2ayHjnUy3sfH56rjdUBAgCmfZcs5w+kYRme3l98zpWPbmZ59YjvZdvaZbafebD/byFfB0DGc8UwvWklfqJf0k21lu1kvx6m5MbfDWvvTpxDXWgU1vyqgCqgCN6HA73//e2NloMVldnU1BhX0QUZEEPqkJWDxrFKc2rcXJzetw6HFc7FvbhkaastxoK4Wh9eswJHtW8x75fZs3YI19UtQWVSEUQP7o1dmqtnIkBYVhoSQQANzYV4PI7TdQwjv8DAiAv0RExaK+JBgs34tPjjQWPDS4mPMDtG8rHQzrUor2/gBBZg2dJCxzNWWlmLpvLlYtWAu1lSUYHXRRKytKMHGmnJsrq3AxrlV2FRTgQ1VJeZluyvLpmPJjMlYUDgB1eNHo3TYAEwq6I6RXbMwJDsFA9ITUJAUg95JUeiXkYSBOenolxKHAV2yMHLIIIwcMRSDBw1An/w8dOucjR55XdGnTy8MGdAf40eNQFVJEbatXIbDG1ZjzawZqBzcC4UF3TF55HBj6Vm5aqWx5nAjA4H5izrsh7Oe/9M1AUY1aj2Ctb6EL+qvQ+tRBVQBVeAOUuCTTz4xn7Hi2idO0XDqqneXHLOWrSAtEbVlJTi2dzfONuzG0VVLcGB+JRpqStEwrxz76mtxaPUKHN26yXzpYSe/hzl/HsrHDsfQLhnIS0lEl8QEZESHIykkALF+Pma3aUj7hxHYoR1C/PwQGRiA6EA/dPT3RccAH8QG+iKOmxNCg9ApItS8coTTsBnhYciIiUROQpwpk68RyY2LQl5iHHqkdjKvNumRHI/uSXHo0eTyEzuie6dY9EiKN2F5nToiLz4S3eIizA7avPiO6J4Qg/zkePTpnIk+XbLQPTEGuclJ6Na1M/K6dkFeRgo6x4ab15QM7N0TE8aOQXXFLGxatxZnTh7H2SOHsHbeHJQP64+J3bIwbkBfsw6OUMzpuJ/97GdfKMDx1lQoUXC70XugtT9pCnGtVVDzqwKqgCpwkwq8//775r1SXMNDkCspLkZeRirSwoPRp0sOiieOx+4N63Dh6GGc3rEZh1cswr7aCuyqmoGdVcXYM68CDUvmY+/qZdi9agW21C/EI7OKUDF2BEb3yUffzBTkJXRENl9NEheDlOgoJIQFI8LPFyG+Xgj28UKIjzfCvL0Q5tMeEV7tEOXnjShOwwZ4o6MvN0t0QIxvO8T6tjMbJ+L9vZEQ4I1OQf7oFBSApOAAJIf6IynEHymhAea9culhgUgLD0Im4Y/vkUtJRNf0FHRNS0GXpDhkm5cKRyC7UzyyUpKQwZf/JieiW5ds9OqZh7698zGgey76p8ZhZOc0lI8bhfXLluDYvn24croRBzatQd2sEpSNGY5xeTkYVdATs8pmmnVk3IH41ltv4aOPPrrJUbn5bDf6ANf0Cn03f7e5cirEtVZBza8KqAKqQCsU+N///V+zm40Lo7kAfca0acjPzjTvVOvVOQfD8rti+dwqnDq0HxeOHMKJzetwaOk87K4uxvaKadhROQM7q0vNh993LZqLnYtqsW1+NVZXlWHBlAkoHtIfY/K7oX/nLOTzlR9JnZAd1xHpHaOQGhmO2JBARPn5IMyrPYLbP4SgDg8hxLs9Qjq0Q3C7BxH00P0IbfcA+O44A3k+Xoj28zKvNon190VcoB/i6fx9QcCL8/cxLySOD/BBAqdroyOR3CkBKYl8sXAMEiNC0SnQF4mhAeArTnLTUpCf2wUFPXticL8CjBwyEFPGjkHljGlYMGUMVk0dgx1VRTi2sg6HN63DqjkVKBvWF1MH98eYnnkY1a8A5U0AxzVwv/jFL74UgOMtoFCmUHaj90ArfjpMVoW41iqo+VUBVUAVaIUC/BD77373O7NzjjvvFi1ajJKiIvTMzUFGbAxyEmLQKyUO04YPxtZVj+DK6ZM4t28vjqxYjJ1V07GzagZ2zS7GrupS7K4pw+7aCvPqjoalC7Cnbj6211ZgXWUp6oqmoHzMSEzo3xuD87qgT1YmeqQmo0tSArJiI5EeEWy+0pAYEmCmVfn+N8JapM/DBuC4ri6k/YMIbfcgQjo8hFCvhxHm64VwvlMuwB8RfKdcgB+iA/wRHeiP2GA/sy4vMTwYSREhSAkPRnJYANLCA9A5NgLd0pLRJ6+bAbfRQwejcORQlI8eYsBt3ZxKNKxchhNrl+HEkhocqK3A6pnTMGfCKBT2zceY3AwMy8vF6EEDzRQqdwkePnQYb775JvgKly/ruNEHuKZX6GvtvaoQ11oFNb8qoAqoAq1UgOvjfvub3xiQW7N2LRbX1ZlvPfbt2QNp/ExVWDA6x0Sib1YKKqZNxoGtm3Fm91acWlln1srtW1yD/XUud6CuBoeWzceR1UtxdP1KHFu7AodXLcWBR+qxd8lCbFk4F6vmlJvXccwcPQwT+/bEsC7pKEjvhJ5c25YQgy6xUcjuGIHMqDBkRoaYlwincqo0xA+pEUFIi41GVnISstPTkJWRhpyMDHROS0HnlETkJCYgK55TuBHIiQkzrnN0KLrGhiG/UxT65aRhVL++mDpuHMpLSlE3twZrF8zBtjml2F1VhH2zi3BiYTVOLZuPkyvmY8OsaZg9dhSmDx+GCX16YEhGEgZkpmDciBHmtRCPrFplXuLKXahf5CaG5oZcoUyh7EbvgebuoxsJU4i7EbU0rSqgCqgCt0gBA3K//a15aej69RtQv2QJKisrMHzwYDPt2CnQH6kh/ujSMcrs5qyYOArbFtWg8ZFFOLl6CY6tWopjq5fi+PpH0Lh9E07v243TDbtwascWnNy0BsfXPoLj61fh2MZ1OLphNQ6sqMPuRXOwpXomVleUoG5mEWomjkbJwF6Y1LMLxnTLwoicNAxOT0T/1Hj0S09A/+x0DMzrikG9e2NI//4YMqAvhhT0xKD8rhiUm4UBWWkYkJ5kPvs1KDMJw3JSMLZrBqb06oLiIf1QXTgBS2qqsemRldi7aT0Ob91gXvZ7ctVCnKivwqn6apxdOhf75pRizfTxqBwxCBN75WF0j64Y0asnBmSnY0DXbEydOMG8moWvEeH7vn75y19+6QDH2+JGH+CaXqGvtT8nCnGtVVDzqwKqgCrQRgoQ5PjqEb7xnS8u5YtY59XWYtL48WZTQKxvByT4tkdygBcywwJRkJ6IkiEF5jUbJ9Yux7mt63B+z3ZcPHoIF44fw4XD+3F2z3ac2rYBjZvWmFeWnFi/EifXLMWJNcvM9fGtG3B00zocWb0Ee+dVYEfFdGwuK8SGskKsnVmIlSWFWF42AytmV2B57VysmFeLZTXVWFJeikXFU7Bg8hjMHz8EtWMGYv6YwVg4bgjqJw7HiunjsKZsKrbUlGNn/Tw0rFmJI5s3oHHbBpzZshZnNz2Ci5uW49LGpbi0cRkurF+GhgWzsXjSKBT174nRXbPNZ8MIhb2T49ErKxMjB/VHWanrDf98kStf2soX+X788cdtNAKtK0ahTKHsRu+B1t1xgEJcaxXU/KqAKqAKtKECBLk//elP5juM/BQR3wzPN8KXlZZiSP9+SA4LRkz7hxDr3Q7JIf7IjAlHr7REjOrZFTWTx2PPinqc3bsDjx09hMcON+Dizi04tWkNTqxdihOr6nBizRKc2rYeZxp24/yBvTi3ZxvObF6JU6tp0atH4/oVOLllvbHmNe7aipM7t6OxYQ9O7d+H0/v34tSOzaaMI3WzcXBBJQ7Mr8DBxVU4WF+Lw4/U4ejaFTi+aQ0at63HqR2bcGbHFpzbuQUXdmzE+S2rcGX7ajy+ewMe27UJF7atw/E1y7ChZhYqRg3B2PxcDMpORd+MZPRJ7YTu8VHoGheFgtwcFE6abD41xbf189NH/BD8H/7why/sRb7XM8Q3+gDX9Ap913NfuUujEOdOHY1TBVQBVeBLUoAfa//pT39q1nvx25d8/1ntvHmYOnkK8rvkICEkGNG+XkgIDUJqdATSoiORExuD/JRETCzoifmF483LeA8sXYBza5bg/PoV5qsJ5/fvwcXpcMrBAAAgAElEQVRjh3Hl+BFcOXYQVw7txZWGrbi4ezMuHtiLi0cO4tKxQ7h0+CAuHtqPiwcbcOngXlw6sBuX9m7H+R0bcXbTSpxavxynNq7C6Z2bcbZhF84f3IeLRw7jyrGjePT4UTx69CAePdiAK/t24LGGnXhy/w48uW87Tm9Zg4bli7C2ugJzJo7FuF7dMTA7Hb1SEtAjMQ55ibFmyrhzbBR6ZqVh3IhhqGr64Dqtk5w+5TvgqA83hXjSoVCmUHaj90Br71+FuNYqqPlVAVVAFbhFCnCh/m9/+1vzUXJ+03LV6tXmu42zq2djwrix6JmXi8SoCHQMDkB8aDA6BQUiIcAfCQG+SAzyRW5cNIZ264ypQ/tj0axi7FizGucOH8AzF87i2Uvn8eyVS3j+ykU8e+EMnjl9Ek82HsXjxw7i8cP78NihvXj08D48emQfHju8H1foDu3Do4cP4PLRg7hy7AguHz+GxxpP4rFTjXjidCOePH0STzWewFPHj+DpIwfx1KG9uLhzM/avXIq11bNQM3kcpg0dgBH5eSjIykB+Sid0TegIAlt2TAQyIkKRFhWKHtkZGDV0MGbNLDW7dbn2jR855wfGf/+7331prxC5RcOsxaoCN62AQtxNS6cZVQFVQBW49QrQ2vTee+/hv/7rv3DhwgXzQe9Vq1aZj4oT5gonT0afnvlI7BhtvsAQ2v5BhD30ACI6PIgYX290igxDUkIcMtNT0S07E71yMjGkRzeUThiLR+bXYteG9TiycztO7dpupkrP7t6Kiwd248rhA3js+FE8fvI4nmg8adyVk8eNBe8S19wd3I8L+/bi7K7taNyyDsfWr8ChR+qwY9FcrCwvQfXEMZg8sC+Gd8/DgK6d0SsrHV35JYmUJORwZ2tiPDI7RiMlPARJYcHIiI5Ez5xsjB8zChXls8wUMqeSd+3ajSeffBJvv/22R1rfbv0doDWoAi0roBDXsjYaowqoAqqAxyjALxD88Y9/xEsvvWS+ubp9+3YQ5pYsWYKamhrMmD4dQwcORHZqkoE5//vvRcCD9yEiOBDRUZGIiYpAZHAgQrzaIeTh+81nuJLCQsyXEnJzMtE7LxeDC3pi3OABKBwxDNNHDUPxmNEomzAOFYWTUVk4BeUTx2HmmOEoGjYI0wb1w9T+vTA2vyuGdM5EQXoSeiTGo1tiPHI7xSI7LgYZMVHIiI5Ceky02WGb1jEaqdGRSAwLMd9uTY4MN99s7d8rH4UTJ6K6ejbq6uuxZs0a0PJ48eJFM6XMFyJzraAeqoAq8HkFFOI+r4deqQKqgCrg0QrwZbbvvPMOXn755aswx5cEcyfrwgULUVVZYaZa+/fphazUJMRERiDQPwCBfn7w79AOPvfdA+97vgnfe+9BMC12gf6IighBVGQ4OkZFIDYyAnFhQYjlp7f86XwRH+iP+OBA13RtoB8SAviVBl/z5YWEQB8kyFcbggPNWr2E0BAkhAaa9XqdggMRFxSIuEB/xPj7gHGdU5JAcBs3erTZsDF/3nzTfq7947TpU089ZeCN0PplfD7Lo28AbZwqYCmgEGeJoaeqgCqgCtwuChDmuDvz9ddfN68kOXDggHktCa1zS5cuxYIFC8x75qZNm4pRI0ciL68bEmJjzOe0fO/7Nry/9Q14fesb8H3oAfj5eCHQ3wfBfj4I8vVGsLcXQjq0R6hXO4R1aI8wr3YI92qHSK/2iPDugAhvfsnBC5HmvD0ifDogzLsDwpmn/cMmfaSvF2KC/BEbGoSU2I7I65yD4YMHYeqUKSifNQvz5s837Vy9Zg22b9+B48eP47nnnjPTpn/7298U3m6XG1Hb+aUqoBD3pcqvlasCqoAq0DoFaKl69913zQtvf/CDH5jdm7t378bGjZtACx13tdbV1ZnXcxCepk0txMhhQ1HQowc6Z6QhNbETEuLjzZRrWKA/Amite+B+eN9/H7z//dvw+vdvwes+um/D67774PPAvfB78H4EPvQggto/hFCfDojiZ7YiQpHUMQaZSZ3QLScLfXv3xMhhwzCV07BlZWbKl+1YvmIF+J3Ybdu24fDhw3jsscfw6quvmk+PEd502rR194PmvrsUUIi7u8Zbe6sKqAJ3qAKEn3/84x9m3Rw/QfXiiy/i0qVLZnqS6+cITrTS8TujXEe3cOFCA3aVlZUoLS1FYWEhxo8bi1HDhmHIoAEY1L8v+vftg359ehkg692zJwp690a/gt4Y2LcvhgwciKGDB2LUiGFm+pawVlJcjMqKSsytnWvKr6+vN/XxU2J8Oe+ePXvQ2NiI73znO8aC+Otf/9oAKHfhetrrQu7Q20S7dYcpoBB3hw2odkcVUAVUAX7BgNOttNDxFSV839yzzz6L8+fP48iRI2hoaADBju9dW0u4W70KfIkurXaciiV81dUtxqLFiw2MLVy0yOwWdfmLsXjRIpOG6ZYuXWbyMT83JHBd25YtW0Br4KFDh3DmzBmzxu3HP/4x3nrrLTMFzHe80YKo4Kb3qirQOgUU4lqnn+ZWBVQBVcCjFSAoEZjef/99/PnPfzZQ99///d8G7F544QU8/fTTxmJ3+vRpA3j7DxwwkMfdoTt37cKOHTvM1CenP3m+c+dO42hV27d/v8lz6tQps5P0iSeewPPPPWd20L7xxhv41a9+ZaCN06S0tulUqUffKtq421ABhbjbcNC0yaqAKqAK3KwChDrCFMGO068ErL/85S8Gtmi1I3jxg/K0mnFaljBGSx7dm2+8gTfffNOEM57vbmMe7pbla0D++te/GlikFZDWQIW2mx0lzacKXJ8CCnHXp5OmUgVUAVXgjleAgCeQRwAjiNER+OjkWgBN0t/xwmgHVQEPVUAhzkMHRpulCqgCqoAqoAqoAqqAOwUU4typo3GqgCqgCqgCqoAqoAp4qAIKcR46MNosVUAVUAVUAVVAFVAF3CmgEOdOHY1TBVQBVUAVUAVUAVXAQxVQiPPQgdFmqQKqgCqgCqgCqoAq4E4BhTh36micKqAKqAKqgCqgCqgCHqqAQpyHDow2SxVQBVQBVUAVUAVUAXcKKMS5U0fjVAFVQBVQBVQBVUAV8FAFFOI8dGC0WaqAKqAKqAKqgCqgCrhTQCHOnToapwqoAqqAKqAKqAKqgIcqoBDnoQOjzVIFVAFVQBVQBVQBVcCdAgpx7tTROFVAFVAFVAFVQBVQBTxUAYU4Dx0YbZYqoAqoAqqAKqAKqALuFFCIc6eOxqkCqoAqoAqoAqqAKuChCijEeejAaLNUAVVAFVAFVAFVQBVwp4BCnDt1NE4VUAVUAVVAFVAFVAEPVUAhzkMHRpulCqgCqoAqoAqoAqqAOwUU4typo3GqgCqgCqgCqoAqoAp4qAIKcR46MNosVUAVUAVUAVVAFVAF3CmgEOdOHY1TBVQBVUAVUAVUAVXAQxVQiPPQgdFmqQKqgCqgCqgCqoAq4E4BhTh36micKqAKqAKqgCqgCqgCHqqAQpyHDow2SxVQBVQBVUAVUAVUAXcKKMS5U0fjVAFVQBVQBVQBVUAV8FAFFOI8dGC0WaqAKqAKqAKqgCqgCrhTQCHOnToapwqoAqqAKqAKqAKqgIcqoBDnoQOjzVIFVAFVQBVQBVQBVcCdAgpx7tTROFVAFVAFVAFVQBVQBTxUAYU4Dx0YbZYqoAqoAqqAKqAKqALuFFCIc6eOxqkCqoAqoAqoAqqAKuChCijEeejAaLNUAVVAFVAFVAFVQBVwp4BCnDt1NE4VUAVUAVVAFVAFVAEPVUAhzkMHRpulCqgCqoAqoAqoAqqAOwUU4typo3GqgCqgCqgCqoAqoAp4qAIKcR46MNosVUAVUAVUAVVAFVAF3CmgEOdOHY1TBVQBVUAVUAVUAVXAQxVQiPPQgdFmqQKqgCqgCqgCqoAq4E4BhTh36micKqAKqAKqgCqgCqgCHqqAQpyHDow2SxVQBVQBVUAVUAVUAXcKKMS5U0fjVAFVQBVQBVQBVUAV8FAFFOI8dGC0WaqAKqAKqAKqgCqgCrhTQCHOnToapwqoAqqAKqAKqAKqgIcqoBDnoQOjzVIFVAFVQBVQBVQBVcCdAgpx7tTROFVAFVAFVAFVQBVQBTxUAYU4Dx0YbZYqoAqoAqqAKqAKqALuFFCIc6eOxqkCqoAqoAqoAqqAKuChCijEeejAaLNUAVVAFVAFVAFVQBVwp4BCnDt1NE4VUAVUAVVAFVAFVAEPVUAhzkMHRpulCqgCqoAqoAqoAqqAOwUU4typo3GqgCqgCqgCqoAqoAp4qAIKcR46MNosVUAVUAVUAVVAFVAF3CmgEOdOHY1TBVQBVUAVUAVUAVXAQxVQiPPQgdFmqQKqgCqgCqgCqoAq4E4BhTh36micKqAKqAKqgCqgCqgCHqqAQpyHDow2SxVQBVQBVUAVUAVUAXcKKMS5U0fjVAFVQBVQBVQBVUAV8FAFFOI8dGC0WaqAKqAKqAKqgCqgCrhTQCHOnToapwqoAqqAKqAKqAKqgIcqoBDnoQOjzVIFVAFVQBVQBVQBVcCdAgpx7tTROFVAFVAFVAFVQBVQBTxUAYU4Dx0YbZYqoAqoAqqAKqAKqALuFFCIc6eOxqkCqoAqoAqoAqqAKuChCijEeejAaLNUAVVAFVAFVAFVQBVwp4BCnDt1NE4VUAVUAVVAFVAFVAEPVUAhzkMHRpulCqgCqoAqoAqoAqqAOwUU4typo3GqgCqgCqgCqoAqoAp4qAIKcR46MNosVUAVUAVUAVVAFVAF3CmgEOdOHY1TBVQBVUAVUAVUAVXAQxVQiPPQgdFmqQKqgCqgCqgCqoAq4E4BhTh36micKqAKqAKqgCqgCqgCHqqAQpyHDow2SxVQBVQBVUAVUAVUAXcKKMS5U0fjVAFVQBVQBVQBVUAV8FAFFOI8dGC0WaqAKqAKqAKqgCqgCrhTQCHOnToapwqoAqqAKqAKqAKqgIcqoBDnoQOjzVIFVAFVQBVQBVQBVcCdAgpx7tTROFVAFVAFVAFVQBVQBTxUAYU4Dx0YbZYqoAqoAqqAKqAKqALuFLgmxP3TP/0T1KkGeg/oPaD3gN4Deg/oPaD3wBd3D7iDN4m7JsRJQvVVAVVAFVAFVAFVwL0Co0ePRmxsLOLi4hATE2Ncx44d0RZOyqUv5/Hx8Z+rS+pk/baTPEyfkJBg4pKTk0H3b//2b7e1seaf//mf8bWvfc304eGHH76qNfuamJiIpKQk8LxTp06mv2lpaSaccbZjOuqRkpJiHM8lnnkZbztJl5qaCpZJX8LEZ5g4pmGZPXr0cH8T3UCsQtwNiKVJVQFVQBVQBVQBdwqUlJSguLgY06ZNw/Tp043jubgpU6Zg0qRJbt3UqVPRnJMyCwsLwXLoeM60Uhd9pmO47STNjBkzUFRUBLaztLQUs2fPxgMPPHBbQxytg1/5yleMI2Sxr+zfzJkzUV5ejoqKCpSVlZnzyspKVFVVobq6+nOOYRJHTRg/d+5c1NTUYM6cOcbx2ulqa2sxb948zJ8/3/i8FifhjBPH8jgubXUoxLWVklqOKqAKqAKqwF2vAOGBkEbAIizRnzx58lU3YcIEjBs3zq1rCfJYDsuzyxg/fry5duZhGttJmwQOCXOEOLb3wQcfvGMgjlZG9luglUAt/WRfxc2aNctAHSGPjpDHdPQF+Ah2AncEQbkWX6CP104o5DXhj9AmjtfMw7Foq0Mhrq2U1HJUAVVAFVAF7noFCA20tBCuBLhsmLqe8+YgT2BN8tvXEydOdGvZIzQwDZ2AIK11hEyCy3333XfbQxynVGmNE4gTq6VAHK1yAnMM47XtBN4Id3QCbYQunsu1ABx9G+JovRMLHgFOzgXgxGc4AbOtDoW4tlJSy1EFVAFVQBW46xUQSxxhS6xjhCdClziGE6Z4zTV0dDwfM2aM8QXiJD2veS4Ax+uxY8detcCxLNbBeMlDn/UQJAkNTCP18pxhhDi6e++997aHONlwwbWH7DNBmrBGOLNhTc4ZTliTeAE3whrhzIY3gTinzzTMR0serWycamUYyyTkEdwE5sRSx3COQ1sdHg9xR48e/dzNxfn+t956y8z/05eBa8l//vnnr2rFczsdI1gej0ceeeRqXI9rLDoMCgq6mpb5WnM4+2C3tzXl3mxe6iOa3GwZX2Y+0ZP3zfUc7O/1pr2e8m40jX1P8r661sF783Yen2v1T+NVgdtdAYKDQBXBig9sgSuClwCanDNOwpzpBMicEMdwCZM66NuOZbmDOFqq2FZC57e//e2rzzT7GXk7nkdFRV3tt1jcxAJH34Y4ATiGCYzJ9Cp9J7Q1dy1lSJyAIfMT2AhxAnIEPZ7fNRDHBxZvIj6Y5RCosx9kPG/uAci08oBu7uHOsp355KZtCabsh660qTW+Xb+AZGvKu9m8oiv7b2t7s+V9GflkjNkHGXd37ZCxvp607sq52ThpL/PLubu2yN/D7To+N6uT5lMFbicFnBBHsBLooqWNjpa3UaNGGZ8wN3z4cAwePNhY13gtgNcSxBHQxNlpBNwIkbRG2c5pibvTIY79leligqoAHKGLjrAlwGVfSzh9G+h4bsdJvMAbfSmb5xJPkBOYk2nWuwLiBGj4cHMefNDZ1rKWIM7Ox/JsYJI4uxyG8ZrpnOF2esYRAFp7OPsmD3JneGvruZH87PvtDAmioTsYsvW4XuCz87TVOe/Jlu6zlupg+tt5fFrql4arAneKAk6II1gRtAhmBLiRI0caSwytMosXL8aCBQsMLDDdiBEjrlrlxNImAEifaegIAXQ8ZzqBQ14TXjhVSkijz2lFATjm4TndnQ5xhGd78watbQJZAlgCZhLu9J3QZlvx5JyWNY7hwoULzfSpABzBjdDG6VUb5O4aSxwfru4ecPaDrDmI40PchiFeX88Dm3VKWqc1juUxXgCzrX90WJ/dr+spn3ma0+lmIVMh7npUb5s0HOvmxs5d6Ux/o/eIu/I0ThVQBdpWAVp9aAGzQYtwJQDHh399fT3WrVuHjRs3Gn/t2rVYsmSJASta6JhWrHHMK86eLuU5wwlxAnwMk/VgBDiBOIYJvAnQyZoxWqtu9+lUbmqg43MvMjLSaM9+8rdSpowJaDaUiQWtJZCzQc+2yDGcY8xxJLwtX77cjOGmTZtAt2rVKgNurFfqoC/WuLsC4mTKkrB0PQcHymllY5gNcSyHaTjI7gBHHqpM43xY8pqAdysgTgDxevrrTEO9pP8sx13/nHmd162BOGon+oqOLJ96sVxpG9PI2Ep6ab+zPS1dyxiwHp7LWEsdHCc5WLbUQ1/SMp7XTGunkXz0pU+sw3k/2OmaO5f/DEjdHCc5JEz86y2b7ZH7UPLafWX50mbGO3VtLj/bJX9zzONsix3HeGd9MhbSHvFFZ6cOooGMFcu32yzx9CWNlMm6eNht4jkPZxqGybiyDc5+mUz6z00r8Omnn+Ljjz/GRx99hA8//BAffPgB/vGPf+CDDz4wYQz/5NNPb7r82zWjE+LEYkY4I2QR4FauXGl+t+jzoU9/zZo15j1jBDgBOcIZQa05eBOwk3ixsskUKiHNdjbIMS2vCRr8u7gTII47U/kbIBDHvtPaSEjlmNgAx3MBLAE0ZzzDJY34TMNwTs3SwsZxW716NQjh69evx4YNG7BlyxbU1dUZyGM+Keeugjj50Zcf7Gv9MfMmlB9w25eHiJ3fTut8wDEdHyY85MFkl8Gy7Thz0Qb/SF3S9pspUh52zfXpRspj/pt52AkcsC5pC8fR7pu0zQ5zpr+etrJ8GSd5mMs42XWzLNYl9fLa2T/RXPIzXtJL+5sr51rtZF4pR/KzLgEOhlFn6ce1ypN4pmc5MkbOMhjPPsth90fy2vkljD4P+dsTPURfKU/i6TeXnu1h+XIwnbSVYTxnm2ScmJZOdOG5tF/SONvijJe8LJ9lSzx9aadTJ2mf+p8p8Mknn+IfH3yAv7z7F/z6N/+D1157Hc8+9xzOX7iIg4cPY8vWLVi9eg3qlyzBvHm1mD27yjwYS0u53qgE02cUYer0GU1TWCWYOasMlVVVBkzqltRj1epV2L59Ow4fPowz587hueefw2uvv45f/8//4N133zUAeCdAnxPiCEu0qhHMCAF84PPepKMVx/YJBAQPJ8TZwNbcOSGPlqeWAI5Ac6dDnFjiIiIijCVOppQJqoQuJ6QJmLUEcQJsEk9fLHC06tEKR3DjeHLcZEwZRisrIY/WOtbDc1rg7po1cfKgoCjXc8iDwU7LMPnxt8PlnD/28gCRMPryMOM541kODxkgOWdcWx+sy36I3Uj58rBtbbuoi/T5euuXultKT+1YrhyS3h4ftlseuJKuJV8e7s2ldxfHOujs/jnrlbbRl/vQbmdLbXKGs7/O9vHesu8vtsO+dpbR3DXT2+23tZW22/mcerjLz3xShvSZ6Z1/h6xfxtOuv6X8orvts3xpG+uUg+VKfazH7ivTMI7l8LhWfjutlK++S4F3//pXvPHzn+Opp5/GkaNHsXrtWlRUzcboceOR36sXsnI6Iy0tHckpaUhNS0daegYy0zORnZGFzIx0ZGSkIT2dLgMZaenISHelSU3LMOkzMjKQmZmJjMwsZGXlIDunM3JyOiO3S1fk5nZD167d0C2vO/Lz81HQtwDDhg/HjKIZWLhoEbZu3YrG04144fsv4P95+238/e9/v62GzQlxtHrRusbNDHyg8wHPe5MAR6sc3dKlS801rXFcx8V1c8wjljjZvCC+WN/EQieWNYIagU2mUe8WS5z92xIeHm4gjjDM3w+Ox81AnFjQBPboM0w2SHA9o0AcrakrVqww40qg4z3MNY+EPVkTR4C7a3anyo+z8we8pb9kppOHiqThA5TlyMFr+2HBcHlI2w9bPrTkYLm8OVgOy5fybuXDobmHprSnJZ/9ststD7mW0rsLZz+vV3cpR3SUa6dPvezxcYIC07PN9jg4y3Bey9gwn913uXfssphGNGFau3/OeiW/3Ctst+RnP673cJbLfKzX1oHXdtuvp2xn+21tWxoHtkXa7i4/63eODdsreaV9vGaZPJx62e1hPPOLlpJffGdeSS/1OdvKeOkj814rP9PL2DU3HtKOO93/45/+hJdffdVYv1atWYPCadPRu6AvsnK6ICk5FckpKUhMSkKnRJdLTExGUpLrm5rJSalITUkzQJeRnoGszExkZWYhMzMDGXQZWcjIyDTAlkk/I9Ncp2dkGngzAJfdGZ075yKnc2d07pKL3Nyu6No1F3l5ecjr1h3du/dAfn5P9Mzvgd59eqFPn94oKOiD/v37YtiI4SicOhWLl9Tj4KGD5l7671/+Eh99/LHHDhuhQdafEa4IYrLGjXF84NMJxHHqbdmyZVchgOBFSxytd4Q2ghrLsB0hjmXTSV02wBHimruWtPRZz50ynWr/nYeGhl6FOAKxbYmzrWqEKwEzhtPyZq+bc0Ic09MxjVjiZCrcnlblOadV+Y44lnFXQhz/OuUhzR/q5g7+wMvhfDhKOH3GsQz++MvDwY53PqTscuUhwTQsRw77ISZhbeWzHhtArlUu09ptlvS8qVvSTtI05zv72lwaZ5jzwe+Mdz7Ym0t/sw9ZGQsZHxkz0ZDaSBzb5bx21ittc/aB+ZxpnWnsa6a162Ucr+2xcl7b+Vs6d7bf1pZ9Zr1OaLLb7S4/65T+y73D+8FuM9PYdco16xAneRnHvEzf3CFjZbfX/ntkXl7bh/SRYdfKb+djOc1pY6e5E89f/NGP0LugH1IzM5GUmmpgjR/07pTED3snoVMnF7glJScjKTkFyeYD3qlIS01HWmoaUlP4YW9+wJsgRz8FKcmpSEzshPCwEISGhCIsJBShIcHGDw8JQXhIGMJCwsCHKf3oqBikp6cjM4MQmGUscjldctG1Wx6698hHj5490btPAXr37oM+BX1R0K+vAbm+fQvQt19fDBjQD0OGDsKQoUMwbNhQjBo9Ct959jmPHS6CGiGLoEQAk6lRghyhixsY+PAXcBMLDh/83OEou1jFCscyCHPuII7A5oQ2ATVapAgzjGeYpGP4nQJx8rUG/o0HBAQYnWU6VUCOkEZoI1RxalOgTABOdpvaICfnTCPp6LMcOv62CZRzWpUbG2iJ43dSme6unU6Vv86WfngZbv/w82Ho/LFnGTbgyI8/fTkkzPnQkXj6LNv548+BY1hbH+xTc/1o63rclcf62ecbPZjP2XYpx/nQd4IC66Ke9ti4q5/jZYMFy5e65MEuZTGdnZb1SFqp175mH1geD5Yh57xmOVKuSeDmH+Zz9onXzvvWbpub4q5GMb3dXtZj685z1iMH22vHXyu/c2yYn+XZOrA80YHp7fZIveJLfrvfLIvjJGNlx7FsqUvaYpfP9ku85Je2SF3SXqaTOLaHZdt1SRvvZP/S5UtIy8hAIiEtxQVpySmpLmBLSTF+YlIiOiV2Mp8rio2NQ0x0DKIiohAZHo7w0FCEG0gLQ2hwCEKDghFMFxCEYH9/BPj6IsDHF4F+/sYF+fsjyD8Agf7+CPQPQLB/AAJ8/RAcGIgQ5ifkhYUhMjwSURGRiImORlxsLDp16mQAMSs7B3ndOc3aHb379EHffv0xYMBADBkyyLxHbfDgQeY1HDt27PLYYSMYEboIbLYVje+C4zQpwWDRokXmPuaDnxDAaVTCHdMPHTr0c8BGeGN59lo4+5p5bKucDXQytSqWORviGHanQBz/5mVNnL+/v9FDplNlY4MNZAJwBDGxwF0L4piWmxPosyxOq9LaxnHjOHKanI7T45KGPp2A412zJs7+6+QPOAfIdvzx5iE/4nac81x+xOkzvbM8KYs/+HZe+bFnvP2gtdPwXB4odpuv99x+6LAsu57rLaOt0skD0+7fjZbNh6Qzv1NvZ5+d19ejp3McWS8Phtv1s2xnv6Q9ttbOPNJv5pf0TMPzGzlauqdYhl0nz1nXtQ5bX7bfbhvLkMNOJys30oAAACAASURBVNowzg5vLr9zLORvwBlut9UZJ/2y9XXqwGvnuDjLEa2d6Zz3h60Bz9lHSUPfjpdw0elu8B999FFkZucgOS3dQFynxETExcUjKioaEeERBtJCg4MREhiEoMBAA2ACYkF+BDJ/BAUEIMg/0PjBAS4w43VIQKABtwA/fwNrJo0Bt0AwHfPSD/QjxLGOYIQGhSDs//4ngC6UYBfI82CEBgfBtCM4BCHBwQgLC0V0VDTiYxOM5S87Oxs9uncHrXMEuar/+21KT10rR2ggeBGsCF4CXHwHHCGOljmm4TQq18JxbRU/10TAYpx8gotlyEuBacWzHa11dGKdYx2EOQE6lkVIs+GNYTbE3UmWOP7uCMT5+fkZ7fm3L1Y4WRdHUJMNC4QrWssIZDbACewJ3El6scBJHkIcHQGN41dbW3v1PXESzjVwdz3E3Q0/tNpHVeB2VYCQJbBn94FQJv9BssP1/ItV4Nz5c4iLj0d4eISBI1rHjNXM1x+ELxeoEboCEeIfaGCM5+JCCG2Mo+UtwAVygSZdEEKbIC7QrwnyCHr+AaYc+q4yAkwdhDWCXFgwIY5+sAvmggPNuWsaNgRhISHm2kzPBoca6KP1LywkGOGhwYiMiEBCfLxZU/fOO+98sWJeZ20CcQQqATgCHYGLUCebFgTwmIZAxnCeS1qGEepowRs2bJhxtNLREQgJdYQ8+lK2DXICcQQ3G+7uxOlUJ8SxvzbEEaoE5ATSCGMCaoQ4ppE4hkucDXFMxzR0hDP6tGZyzCWvgKLkE6vfXbU79Tr/VjSZKqAKfMkK8MezOYizLXFfchPv6upPnzqJ4MAABPhxWpM+wc1lTSPM8ZxgR4AjsBlACwj6PNDRouZPUHNZ10w+ghyhrqkM+izHWN8IcH4BxlIXYqAx4KrVjUAWaixx4geZKVpa4TjV6oI7WuaCzTQup3IJfvQjw0IRFRaGqPBQdM3tjD/96U8eObZ8oIsVjqBGoKAvjsAlAEYIExATSxuvCWr0hwwZYqaR+/Xrh4EDB5rzAQMGoH///gbqxLJH4COc2fVKfQJw9OVcLHIEG8LON77xjf/f7IBY1W8Hn1Y4scT5+PgYGOZUMvtGyBKIE5CzYUygjGkYTuiSPBxL5qFjmIAa0xLiBPQkXKx1jOPUqwAcz+U9cfQ5Dm11fDYH01YlajmqgCpw1yjgnO6UH/y7RgAP7+iF82fNlCYhTqDNWMkEzGhFMy7QgJeZEjWWNVrSXPAWLBY7P1rqAs16NzOtyilTG+IMHPqZtXJiiQsK4Fo5Al2QC+qaplBDObUaSIBzQVxIUKABN2Opo7WuCdwizEaJEISHhiHCAFw4osLDMHr0KLz//vseqf71QJxMg4pPCKMTkKOljVY4whrBjJseGhsbcebMGbN4nrDRt29fY5FjOgIb09EJrDUHcZLmToY4b29vY5kkxMmU8fVAHEGM069MS/ijY34pQ8CPPh2BzekIb+IE3Ahyck5rnEKcR/7ZaqNUAVVAFfA8Ba5cuQSCFDcXfAZxhLEmK1wTwAX6cX0bQe8za5pY3kxas9ZNLHKu9XDBfizTD4G+fsbCZ/JyfVwT2LnyuaZsXVOrfggNCECIrIUza+QCXWvimqZYw4ODEC5WuNAQRIQS4EINwBHkuNkiKjzc7C70PLVdLXJCHGGK06TieC2gRZ/XTpgjxBHgCBKEt2effRZPPvkkHnvsMTz99NO4dOmSAYk+ffoYixzLlnIJaHS8ljDWQ4CTOJlSJbAQXL75zW/e1pY4+c8j/Q4dOhggZh85pWxb02xLnMCY+LS0EdiYnnrxnADGzQiENV4TDGl9o24CZgJtTl/gTXyml12xHJe2OtQS11ZKajmqgCqgCniYAo8//piZ5gzw9Uegrz+CfP1c1jNe+/nD38+1s9S1No6QxmlSfxdsmbVvLnBzwZ1rGpZwJlOpfr6+8PfxhT9BzpTN8l3TqWZzQxPouSDO31jjOL3L3a0uS1wwwgLF8haMCGOB41QqAS7UuDDCW2g4Iulzw0NkuNkN6GFSX22OE+IE1ASqbJgiVPGaECYgR59r4DhtevDgQXz3u9/FlStXcPbsWWOJu3z5Mp577jmcOHHCWO4GDx5srHbMx7oE0KS+lgCOgEOAYXvvueeeOw7iqC2hi5AqU6I2xBHEnI56ENaYbufOnTh//jy4OYja79mzx6TneDEfoY2AZjsb5ATynBDHNBzvtjoU4tpKSS1HFVAFVAEPU+DJJ58wa9n8fQhZLouca2o1AEEG7PwMgAng0YrGdWzceWo2NfBVIcZa57Komd2qfn6u9XO+/vAjwAnE+fl9DhJpyQv08zX1Ew5Nuf6uDRK0yHFjhAE5Y5Fz7U4NCw5ybWIICUVESKhrGpVTqU3WOFrhoiMicPLkSQ9T+rPm0KJDgOLDngDFaVICFsPo8wHOcHE2bDEtrXAFBQXGivTMM8/g3LlzBt5OnTplrHK8fuKJJ8xaVFqFuG6OU6pijSO8yPo4tkGuGeZ0BBxCy50AcbIm7qGHHjLrCW2I45gIyBHACGvOa2oh6+gOHTqExx9/HBcvXjQgR4im5oRqmWoVkLOnVAXYbJizIU/OOeZtdSjEtZWSWo4qoAqoAh6mwHPPfse1Js7H9T43Px+XRY5WOIIbnYE7E04rWpOlTl4VQui6uqnBD0H+fk1r4vzh7+MDf29XuZ+3xDVZ/Jp2wtIKF9q0Jo7gRjgMCRRHkOP6OG50cDnXdCqtccFmOpUwx2lVWRMX1zEa3/ve9zxM6c+aQ2AgUBEi+LAmOBHYGEaoouO57ZhOgI9WOK534wOf06gECa6FI7jS8ZzWOFqIOD3HDQ9cS8fyWI7UIT7b0RzIMYxAQovct771rdveEicQ9+CDD16FOHfTqTbEcT0cYZaa8ZUvhDbC8unTp43m9C9cuGDGgi9kFuueDXA8V4j77O9Az1QBVUAVUAVaqcB3v/u8gSNa4ozFjH7T1KeBuCa4C/RxgZeBO65p8+cGBdeuVYE4TqPyJb6ujREBpjy+6DegqUyujXNBoB+CaYFrAsUgAhxfMdI0DWvKJcjRGsf1ceY1Jk1pmjY70CJnII6vFjGbG1xTq9zUkJyUjLff/lUrlbl12QXiCASEKlrfxBJH0JJr27chjq8U4Xo4ggWnTQlsAhM2xNFKR0scd67SgueEOLH0Ccw5rXC8FkvcnQhx7B/BjONBq5kNbc1Z4piO48AXL3PtIWH5+PHj5h2enLrmNa1zfDkz4ZfpCW4tWd0kXKxvts962upQS1xbKanlqAKqgCrgYQr84AcvmI0BtLb5GZATmPM1a9gCfHxcX1wg2DVNuXKzwufWyNmvFjFTq65dqczr7+2yxtEP8PZGoEChr69rw4OPa0MFp1UN5PkyL8vnNGvTy4QJjAGcxnWttTPr5YK44UE2OQjIuT7p1bVLV4/dmcrhF4gTS5xzOpXXthPAE0scIY7r3DiteuTIEQMUtALRMkRHqOMmB0IFXzEiX3hwQhxBQeCQINccxHE9HIHk29/+9h1liaNl8lq7U51QRy2oF18KLmsQCc0EOW4u4bo4TqnyBc3UUqZTbTiTdXACcPTteDlXiPOwH0ptjiqgCqgCnqjAj196EdGREQagaIFzWeO4jo3w5ueCMH42iwBH4OIuVtn8cPXVIlzrJtOvTVDm6w9/b2/4efnAz9vbdd4EdH7ernpckOftqpvA1wR4ZqesqYfWOm6scFnwOO1qrHNmrRwhrulFwE1WuXBueggNxfjxY/Hxxx97otymTQIDMoVKmCNE8Vp8J8QJgBHouL6NAEcLGx/6BDhCGwGCFiKec8E9rUlcO8f3yQkoshzWwzrpC8TRZxgdAYSAI1ONPL+VlrivfOUr5h1u9rvceP7Vr361TcFRplMfeOABown7KX0ksBGu6VM3WjmdEEcoo2aMp+ayoYFWUOpNKxzDOSYsl4BGaGsO2JoLE4CjrxDnsX++2jBVQBVQBTxHgVdefRkxUdHG6sYpVT+uYTOOljMXWMnmBIIcLWkukBPLWRPYGchrytsEZH5ehDgv0KcljmXTuaxyPvA3cV7G0udaP+eq03xr1dcHgU3WOvPqEwFHf5dFzmx6MNOwXCfnssqFhQQhPCwECxfO9xyBm2kJpygJAwQqgSqZ0qTPB7g9lcpzSctzAhktcfKiX65727dvn5lSpSVux44dZhq0d+/eBlakDuaTcxscBeZYtw1xnGokcDLsVkIcYY0gJzBHX8LsV4O09tyGOIIw+0VAlfVr14I4eU8cdVq5cqV5jQuhmVY5Wj+5NnHFihWmPAIfp7IJZPa6OBve5NyGNzlXiGvmD0eDVAFVQBVQBT6vwE9+8jpio6MMMHETgkAWz3153bQ5wTUtSqgT59qwYICvyYImU6ZiYaMVztfLG75NIOdHkGsCNxPeoQN8O3hdnbZlnS5HmBMgJCS66nJNt7reM2fWzzXtZOUrSfhuOX5fNSIsFLt27fp8Jz3simAksMaHNSHKdgSta0EcrWtMw6lSWuS42YFlMZyvHmE4p105bUh4EzCkL7BGiKHjNeuXa7HEfVEQZ1vgCGoCW62FNmd+KZeWOELcjVriaIEj6BHQOMXMDQ681/gJwe3bt2PBggXGAsd4Ah/T2gDX3DlBTsDN9jlObXXomri2UlLLUQVUAVXAwxR4443/RHxsR7OZwQVZLvAyMNc0FcpwF8R5w9/H21jOArz9jKWO69xc06WfpQnw8YYvwzsQ4JrK8/K6CnMENzqfDh3g3aGDa61dEyyyLpNX1uI1bYwQCyDfYyebIlxfg3Ctmwvhu+u4Ri4sDJcuX/IwlT/fHD7cCUw2uPGhLU4gjvAlTqxxYomzv9xAgBs0aJDZ7EDrnHySi2lYB/PQt61wAmq0RPFcAI6+xH0RECdgJSBn+04Ia+211CUQx77LxobrmU4lmBHQmJb5BALFgkc4Fwsc0xL6aI3juTheiwVOfBve5Fwh7vN/M3qlCqgCqoAq0IwCP/v5G0iIi3OtfzMg5ZpCpUWMMBZoAM4Fb4ESf9Vi5uuaKvXyckEefS/XRgZOobqsbS5LnA+BrgnqfBhHiGvvssTJOjxOo7osfbJmzrUOz/UOO07ffjaVG8Bzs9GCX4Pgujlf8565jtHRePHFF5vpqecEEQJo/RILmA1vBC2CG8GL8QJZXGPFa7GsCegxjPm5Ro5xcs38dNzYQF+scYQ0lkUIoTWJjtesRwBOfMYRUAgrt3I6la/88Pf3N+vfOJUaExNjNlK0NdDZEEdd2Gf2keNB+BIYE/himEAb0xDEmIbxkoY+p2PpmFbgjVY3njOM51wbR0CTz2oJrDFOzm1fIc5z/l61JaqAKqAKeKwC//3Lt5DcKcF8F5WQJEAlU6P+Zl2ba00bwzg1ShhzrXdzwZhZ88Z1b1edj4E073Yd4NW+Pbzbt4dXey94t+9gnDlv1x4+HeiaAFA2NRgLXNOOVnlRMOO4u7XJKmdgz+x2bQJOE+5jNkCkpaTgf37zG4/Vmw3jg52WMUIEoUsscnxwC3wRLmip4UO/pqYGc+fONVDAeAKcOAHA6/EJZ2ItEnChzzBCCOMJd/TZLknLdt4qiOPaN0JiSkqKqZswyvfa5efnG6hjvMBXayxxdhn333+/sVayX9JHATiBNcKVABfPBdxsi5pY2egzDR3HjD7TsSzmmzdvntmxyulXOl7PmTPHgB3T2vDGc5ahEOfRf8LaOFVAFVAFPEOBX/36baQkJoKWLRekuSxorqlQTnm6QI3gZmCsXXu0J5wZ1x4d2rnAjIDm1b4DOjxM3xvhQcGIDg5FdEiIcZFBwYgICkJUUCCigoIQExKCyMCAJsjrYGCOU6ve7Wmhc023sk5CnnFNlj3XdCs3Rbimcc3Uq5e3gTzuYs3r1hUfefDOVI46IY4PacILAU6sZwQzXvPhT8sNIYLf5eSDnQ/92tpaAwWELIE5G94E7Ojb4TxnubS4CawQXngu16xTLHJsF51Yl24lxN13332oq6tDUlIS+JLcbdu2ITc316xZI7QRvmidaw3ASTlShjuIk+lOwpXAM335PiqhjIDGdC1BHOOZjvGEb/Zv+fLlWLJkiVk3V19fj/nz5xtYcwLcHQtxb731lhnE559/3jN++b7gVkj/5Sbs0aPH51rAhZUSx/PmDmpnx/F/epKHvlPboKAgE0//Vhwsl+/b0QOQ8bXHx6kLx5xjpkfzClyPhnZO3vPu9LbT3snn77zzewzt3R25KUnolpqCbmmpyM/KRO/O2eiRkYruqYnokZaKnhmp6JWVht7ZGSjonI2+XXLQr0s2+nfNwYBuORjYrQsGd8vFwNwcDM7NxuyRQ7BowkgsGj8SC8cNx4KxdEOxYMxgLBo7FIvHD0ft6MGYVNATE/rkY1LfXpjSrxcKB/RG4YC+mDqgANMG9sW0gf0wtX9vTB/QG8WD+6J46ECUDB2E0qGDUMLzYYMxc/hgzBo5FHMmjMGmRXPx6SefePSQEZgIWoQxApb4BDMCEx/kBDZCHK0y8m4xhhEo+DsgwOaENbkW6574BEWBMtbPMnhNiJNpQLHGyXQrrXKMu5UQ9/Wvf91AUffu3UG4Ydu4UYO7b/k3alvQ7OfVjZ7b5bQEcdSCWguwUXuOhehPKHMHcUxPxz4wHcePfVq6dKkBOFrhCKqEOYId65E6bJhjGMexrY4vfWOD/Dhz0Jyg0Vad9PRy3D28+SCyQYvnzT2c7DKoqTuAsstwlt8WWrF8jqe7NrRFPXdKGQQ46mWP4Z3St7boh/0b0dy976xDHgDXk9aZ9067/uDv7+HE2kewrbYaG2eXYf2sYqwtm4HVpYVYUzQeq6ePw6oZk7CqaCLWFE3E2qKJWFc8CetLJje5SdhQwmuX21A8CRuKx2HtjPFYN30s1kwfi9XTxmLNtDEuN3Uk1k4djfXTx2Jj8QRsmTkF22YVYkf5VOysmIpdldOxq6rI5WYXYWdVMXZUFmFnZZHxd1S5/J0VM7CjYgZ2zS7G3pqZOLCgCmdWLMTTO9bjvT++49HDRFgghIkFjlYvPrS5bo3gJBY4mZ4jRBAg+HCnRU7yN2dxsyGO5YtjHcxHuKDPeghtAhysi+EESv7O0H0REMe/RU6lRkREmCnbf/mXf0FeXh78/PzMb97Xvva1NgG564E4akGwIihTa9FcwI5hNwJxLIcAR5DjzlVCOCGOAMdwTquyDhvgeH7HQRz/GuVH+lZBHH/MPfUH/UaAi1qxHzbUya+ZDQD2ucSL31x+G+okXWt9lukJEMc23Kr7qjUaOdulljj3aspvxPX+HfPhcb1p3dd8e8d+8Pe/4cLWjdi/qAZbq0qwsWyagbO1MyZizYzxWF80HhuMI5xNwEaeF4/HpuJx2FQ0FhvtNDPGYdOM8dg0YxzWT6cbg/XTRmHdtDHGbZg2Fhunj8SGaSOxafpYl5sx1qTfXDweW4rHYXPJRGwrmYitpS63pXQyXG4Sts6cjG1lE7G9bDJ2zJqC3RXT0VBdhIO1M3FkQRUa6+fiypp6/P5nP/XoQZHpVFreBLoIT3ztBaGLIEFYI8zJQ50gwXOG8ffbaYkTi5uUx2sBOPqEOE6h2o7tILwRTOgTYlg2LXFMT59pCHv33nuvgSr5D1Bb+/aUKdfBtXX5dnn8+gQtfeyrDbICbdSY+hOmqLmAHc9lKpV62U7SMo3tCG20wC1atOhzIEewI+SxTimH5+I4vm11fOmWOHZEfqBv1cOWA+ypP+i80eQGdPZfdKEvR3Nh7JvklXiWSTBwHqyPzj6aC7Pjb+bcEyBOtBBtbqYftyJPc+1SiHOvtGh2vX/Hnvw3776nbRv74ft/x6Uta3Gkfh5215RjR1UJtpdPx5ayqdg8cwq2zpyEbTMnYXPpFGwxbgI2l07CFuMmYksJz13+1pIJ2FrqcltKx2NLyQRsM3A2HptpdSsai80zRmEz/aLx2Fo01oAb820vnYAdMydgR9lk7CKglRdi16yp2FUxDbsrpqKhcjr2V83A/tkzcGBOMQ7NnYkj88pxfGEFTi6qxKn6OTizbD6e3LAcb73wbNuK1MalCcTJjlECFyGO73jjOSGAIMGHO88JEXS03NAnXNkQZwMbzwlydphAHC1tYnkTYCGUEEBYF3/naX1j+ZxC/SIhTp5xX4TfEsRRC+ogljeBMY4FdadOkkbAS3yBOPq249o3gpw4WuHs6VS7PI4Jr+nfdhAnP8B8mPLhLgMpfzt2PB9mznims/Mxnnnk4DV/3HmTSl7GS7kSRl8OO60dznjGsR1sr+Rl+XZ5zQGSlH0jvvSD5bMuu1wJs8uTNthgwvbKIeXxWjSz0zYHV9JfKaMtfKnHHk9n22iNEn2ljfa42FpIvyU9y5fDjmM5YuWStPTt9JLvZnyWL+VK++SavvRRwphe8tjnEi/tYlnsu4w543ne1oe0Req365A49oHtYhppn7TDbh/j2/qQe4J68Fz0lDG228s00g/6bL8cvGZa6cetaKvU5cn+B+//DVe2rsPJ5QtxeNEc7K8tx945Jdg9uxjbzZTlNOwsn4Yd5dMN3O2cNdVMfW4vL8SOWVOxc1YhGLazvBC7y6dg96wp2MmpURM/BTtpNSubbGCwYe5MnN+yCqfWLMPxZfNxaNFsHJhXjv01JdhXXYK9VTOwp7wQe8unoKFyGvYT3GYXYX91MQ7VlOLI3Jk4WjsTx+bPwsmFVTi1uApn6qtxbkkNLiyrwcVHFuDJdUvx+sVTniy5ASk+pPlKEPpiMZPNCrQOEQS4KF7WwTk3NjgtboRAOpblBDiGMY6ARkucWN5YB4GS0EC4I7jRMR1/a+i+KEuc/Xd6q89bgjj2lRBFXQhuhDmxgDKMwGZDlwAcfcZLGjucEEcrnMCbAJxsbLDLE4hjGO+Ftjra/lfY0TL58ZWBkx9lXstD0E4jP8SMlx9s/pjbDxOe8wbkIeXa6e14SSNl8VpuYFMAYNrB/BInZUr7mJ5h0gZ52ElbpZzW+qKDtJW+tEvKljRSN69FC0lj+4yTdjOc59TTPphG+mqHt+ac9bDt0k554LJMiXP2je2Qvks66Rvzy7loIGkZxzDJI/2TdNIGk6CN/rH7xiLt9vFaAIR1M62dvrl2iT7SR/oMa8tD2iJlyv1FX+KkrWyjtFN0pi/tYxk8t+8tKfdmfdYnfZb2MIxHc21hW+VgPsnLMLsfvGY727KtUq+n+39/7694bPt6nFu5GCeXzMPRRXNwaH4F9tfORMOcMuytLsXe6mLsJWTNLsGeqiI0VBWjoWoG9lZNN7BF4Npj3AzsrZyOPZXTsbeSFrTp2EWgK5sMTpceX7kYL144g++fP4UfnDuJ759rxAtnjuO7Jw7hO0ca8PSBnXhi71Y8vn0dLm16BOfXLcbpFfPRuKQGJxfPxsnFVcadWFiJxoUVOLOoEmfrZ+PCsrm4vKIWl1fMxxNr6/Fy40F8+qnnbm6gRYwPafnqAiFLoEymWAlShC3CBKfdCHGEDMYT0sQJoHH60wY5htuO6QmMhDSCHMsiuNHZeXkusEeYvFsgTvQQwBULKPUnnNmAZUOanAvEMZ3txKpKGOe6OLGmMj1hrTmIYxtopW2r47NfwbYqsZly5AeYP8xyyAOEcc3F8wdXHsaSh778ONsPE4bJg4Zp+GPeUrzURd8+WIbUx7z2A0HaKumlDLs/Etdan22QtjvrZdnOupnmWu1g36S/zenq7G9r+8D8znqk3TJOznjmkbG1faazD3m4M42UxbGyx0vSS53X0kfS34jvvMd4zTbJIfcSr6XN0o7m2uUsj/mdfZeyb9ZnHXa7WA7HXuqRdsq9wninzvbYyLmd/mbbxnyii4yrXZa7OLafbWH/5LDbzTDpm4yBpLvT/Q8/+hDPHdmLi6uX4NSyBThZX4Pji6txdGElDs2rxIHaWdhfU4qGOaXGQkeL2WeuCPtmu6Y46e+ropvuAjzCXPl0A3Bbi8fikYlDcXrTKvzo0hn84MJJ/ODCCeO/eL4RPzx/Ej+8eBIvXmzEixdPGfejS6fx0pUz+NHlU3jp4gn86NxRfL/xIF441oDvHdqF5/ZtxjO71+Gpbavw/P6t+MGJBnz/yB788OgevHa5Ee/9+Q/48B9/xycff+Rxu1UJR4QqATkBOPoELXFMQzDjtCYdoYxxAnBML+diQaPPNPZUraRheoE+pqNjuQJ/cs2/ebaRjpBHwOGrQOx1ay29+oNp7E0E/DvjtaTnRoV//dd//Zz7P//n/4AbGr75zW+CX1Pg+jueMx3Dne+Kk7Lk96W5a4mT+lkO0/GaZfPLFoRVOvaTYE14omsJrhhOQBNwa86XKVjxpSzJR1/CpC6WI+DHOGrOe6Otjs+eOm1VYjPlyA+w/QMqP6qMay6eP8z2A0cGjcU7H3iMs3/43cXb9dpNZX0CT06oYdmsQ47m2itxrfXZPmmH1ENfDmf7Ja3EN+dTDymD6Z15nHo1V8aNhjnHj/ntcXLGS19bqkf6zbY6y5I4uUekr1Im49v6sO8J1iPX9Hlt37vSPmlHc+1yjsGtgDin5tSE9ci9Le1k++Rwjpn0QeLb2ue9KeMoY806RDPqKwf7w7SMYz47vd1uO/+tbr+0zZP8X736Yzy6dhnOLF+AM0vn4tSSuThRNwfHF1TiyPwKHJpXbtagHZxbigM1pWb680BNcdP5TByYU4IDc4qwr9ploeP6tb3caTprCrYWj8fqSUMwb1hvPLpnK1559DxevHQSL146hR9dPImXLjUa/0fGP42XLjK8ES9dOoUfXz6Ll6+cxSuXz+CVy6fwyuNn8Prj5/D6k+fx+tMX8PpT5/H6E+fx+pMX8PrTV/CTZ67gp09fMnE/efYS/vP5R/HmC8/gl6/8B/7npy/jD2//wkDdl60970WCFuFJvsBA6LJdc+F8sIsTMLN9lsl4gTL6DBNQE8ucECRXlAAAIABJREFUxNt5ec5wWuLoCHSEG8II20vwEQji346AEwHLhjvG8ZqvDuG0JaGsffv28OFLnAMCEBwcjNDQULMbNSoqynydITIyEtHR0WZXKvvNb77yhb+9evUy74xLS0tDXFyc+c/kQw89ZMoVKLPrljaxDbZjuL1Z4hvf+Ib5vqxs8hBQJazaTqDO9gXAbOiSMPpOsGOYM7/kdQId62Za6k4rbVsdn5FJW5XYTDnyA2z/gMpDj8mbi7cfOM4HnPOaA2r/uLuLlweVnZ5tsOtzPhDstrbU3ma6fVNBfKjabWO77Guey8OKutmw0FKFLEMO5revGc5re2wkbWt8lmm3TcaYvtRpxzOM49hSO5xj7LxmfqmDcfZ1S2WaRK34R9rA+4WH3DfUWPrJcLnnpB3STrlmGuc9S22c42QqacU/LE/uHSnGrkfaabdd+ihtdI6ZlNPWPuth3aKtaCZ/C6K11Ou8ttvNNNI3SX83+X/94zt4fPNqXFy5GGeXL8DppbU4VV9j1pydXFhpYO7Y/HIcXTDLQB03FBydV4bD82bhcG05Ds+dicNzS3CophgHq4twoJoWuanYUz4ZO0rGYuXEIagbNwjPHd+HVx6/gJeunMNLl8/g5cunjPux8U/jx5cbm8JOu8Dtyhm88uhZvPbYObz62Bm8+vgZvPb4Wbz2xFn8pAnefvLURfzkKQLdRfzk6UsG4n76zCX857MX8Z/fuYw3n30UP/vu4/jFfzyOX3zvCfzyh8/h7dd+gN/97FX84e038Zff/Rp/+9P/i3+8+2d89Pf38MmHHwCffnpLh59TabT8EKoIWM1Z4GxLGdM4ncSLb1vYCGEEMtYh5/SlDLFA8W/CdoQHgg3z0RFuCCGEC36xQSBOYEkASqDtnnvuQYcOHczvEj+dlZycjOzsbHTr1g19+vQxO0L5uSuCpQ2NAqwrV67E5cuXcfr0aRw7dgwNDQ3mw/Lr1q0zmwEIlOwHv+rA36nExEQDhbQSyqtIBNgELqXN/HuXc1r6WIYAm8CXWM/ElzVxTl82OnCzA89txzDb2XE8Z1ky9UqfdTnX3RHkOH5tdXyhEMcbSg77IS8/0PZDzY7ngNoPH/vHneU5f7CZ1q5L4uXhyniGycPK+QPPvHZ9zMf0cjTXXolrjc922PWyLNZtP8zZDtGJDzrpQ0v1sjxJL2lYnjwMWYazTknXGp912OXy3B4Te3ylHsbbOjNc8jBcAELGS/rAskUHGRvmlXOml7xSV1v4bBudlC3tsvvNeiScPo/m2uXUh2Xa494W7ZX7WNrLMu17QdopWjKeuovOkl/6wXiWZadvTTtZjq0dy5bxF82kLQy39eG5nZftlrzST7vfrWnnF5H3k08+xscffYiPPvgAH77/Pv7xt7/h/ffew9/ffRfv/e+f8dc//RHv/uEd4zilaB98Ge7HH7ryfvT++/jg/ffx0tkTeHLjI7i8Zikura43QHdxxUKcXz4fZwl0ddU4VTcHjXU1aPz/2Hvv4Dqy7L7/H9uyZK2rbJfLkiVv2ZJl6y9nS7JWa1nesvVbSyuttNrJOSdyyGEY5pwDEpEzmNMwgkTOmQCYkUFyODMcZnI4iRxyiO+vPrffwbRaD8QDCHI43H5Vl7f79k19+j30h+fcc+6Cae7cHc+fpm3zpro1atvnTNTW2TgfTNDGGeO0burrypnwkla99JiWvPS4Gndu0tGyvTpUskuHS3brUMkOlw4X79aRItJOHS7aoSPFO1x+uBhw26UjpbsH4e1Y2W4dL9vrIO54xR51VBQ6jRwg11FZqM7KfeqsKlRn1X45mKsqVEc1ab+6q/ert77Eg7mmSp1qqdDptmp90F6j3qYy9TdX6VR7nT443KSPOtp1rveYLp3u07XzH+mLq5f11efXdOurL3X71s27WnMHIP3VX/2VAxu0TcRFA3QsEfiWbadIP/7xj51Ginr+xHfZn6jLdWCJckJoEDSXcvqnH8qtT87/7M/+7O8k2v/kJz9xiTn+xV/8hWuL9orfDMlACW0YAPX973/facoANvpnbIARmGEdGAv72bFg1apVAshWrlzp/i4sWbJk0FOTNWM7d+7URx99pPfff9/9zejt7VVXV5c6Ojp06NAh1dfXq7S0VLt27VJubq7rDzAiNAv3+B//438U+7AyL8DSoM0/b8p++Zd/2QEmMjA5cM/Iy59++tOfKlpCLiS0hXY8VE6dOyXaMSZyNrkzJ8rH6vMNmYxVj1H6sT/A/FE1gdsfWHtxWLm9KOycesE61g9/uK0eOX+k/WV8qfmQc93G9JdZe5u21aWcvujT6pAH52cvFWs/ktzkYv3bfIN9+OfgH89/P9bGZGN9+l+4VofcrnOP9+rjfxb+F6iNTR68B7/8uW7z98uAOtY35SQ797fhvqzcP/5Y3a99F3iO9mE8/1jB767dj39edszcubfgM7S+xyK3OTMWyb5P0cqtDrndE3m08rGYG3L03zty4RP8nTDXYJm1sza088/T7nMs5jmSPgCq218DZLcckN366oaDsptffqkbn3+u6599qi+uXdMXn3yiL65e0bULF3Xl4zM6Ulmqpl071LTzPTVs36r6rZtUu2WDajesVfXaPFXl56p2ba7q1ubo4N6dOlq0V4f37dKRwh06vHu7Du3crPZtG9S2db1aNhWoIS9DtemJqkmPV3VqnKpSV6sqeYUqkpaqBKeBhZNVtPBd7cORAI/QhdO0a9EM7Vo0XbsXT3f5TsJ8AHvzJmvrnHe0ecY45U96RfGvPK7FLz6mpl3rHWgdLdsjL+3y4Kxkt9O2HS3eqaMlu3SkxNO+oXU75gBut46Z9q1sj45X7Nbx8j06Xgm47VVnZaG6ADgHb4XqqipUd/U+dZGqOCcvUlfNfnUBczVF6qotVndNqbprS9SN2bW+RH0N5eprLNPJ5gqdaqnS+wdqXDrVVqP32xt04kCl+lqqdOpgvU4fbdVHHW0613dsJI/b1f3Zz37mQAOgIAEcpjlCo2THfD+tjv+76i+36+S05RogU1JSovz8fGd6pIw+/WOxDs3fNnjMnGxepuWiHwCJHQ9+53d+x4HbH//xHzvQMWgDzPgbgFYNWFu2bJkLdkuIDbxtATu0T5gS0fSZSRPNXFZWltra2lw6ePCgAzfgjXT06FEHc93d3err6xOAd+zYMR04cEDl5eVOa8d4aBMBIky1mF4BNuZtCTlwP3Zv/vu2Opb7r3FsMvEfW13/c7N60XJ/n9bGymxO9Plf/+t/HfH3aqgG9xXi7AU21GTC8lACoQRCCXzXJHDr5k1dPfuxTh5qU0dlqY6Wl+pI6X4dKtqrQ/v36FDhLrXu2qmWHVvVvG2jGjdvVP3GAtWsy1d1fpaqctNVnpWm8kwvVWanqyonQ9U56arKTlVlVqqqslLccRXHmSmqykxWVcYaVWckqSE3TU156WrMTVVTXqoac9LUlJuippxkNeWsUWNWkmozEjx4S4lXdfJKVaUsV+WapapMWqLy5TNVsmiqihdOUdHCKS7fO2+Sdi2YqsIlM1W4dKYKl8zQ3sUztBeoA+bmT9H22ROcNm7Na09pwTN/rfrt69RVU6zjlYDYHg/EHNBhHt2tY6V7XH4UcHMaN7RulANzO532raN8rzorSIVeXrlHXZX7vFS11wGbB3CFDtrQvgFvmFY57gbkSLX71Q3IAXG1pepxMBeButoS9dSVqqe+3KXuhjJ1N5Squ75UPY0V6muqUG9zhfqay5wWb6Tfx0ceeWQQuAwoeJEbaPESt2MDBoOFoXJbI4apsKCgYHBK7733nn7rt37LQQyQQHv6ZNyh+qKc8a0+OebU3/iN39Dv/u7v6gc/+IHTLmEGxhRI2IzVq1e7BMShecObFlBD64j5FrMvJl9Mqaz3wqzK2rdHH31UQC3ghaauuLhYRUVFLgdE0byRl5WVqbq6Wg0NDWppaVF7e7sDOzR1AB1g19PT4/5Tv379egeRAKL9h5752z3bffnvP1qZ/zrHftiy+n/4h3+o//Jf/ou7Fqwf7dz64HmRkLPV8z/rEOIGv8LhQSiBUAKhBL49CZw8eljVm9aqJCNFJakJKk+JUwWAlZ3m4KsiK8XlgFg5KTNFFZnkySpNT1ZJepJK0hJVnJbkUmlaksozSGtUnp6kiowkVaSvUUV6koM2wK0qI0nVmUmqBuIyk1RLykjwUmaS6jKTVJ+ZpIbMBNVnJKo2PV61aatVm7pKVSkrVZ28QtXJy1W9Zqmqk5aqfOUclS6ZptJFU1W66F2VLpyqffMnOUjbt3S2ipbP0v7ls1W0bJb2LZ3pwdzCd7Vj7jvaNP0NZYx7VvMe/3OVF6Sqt7bMmTrRnmEKPY5TAo4K5WjXPKcFD/B2qwOtW9kunWwu1cdH6nSyuVydlZhQ0cABb3vVXbVHPZV71VO1T91V+9RbBayhhWM9nAdtgFt37T711OxzWrju2v3qiUAcGjlST12x08h11wF1HsQBbd31ZequK1eXOy5RZ32xOuuK1FlbrI6aIvU2lo34ywW48AI3MAC82GYK7RgvdTRdeGbaS92AwV720XLq4ESQk5Pj5vP111/rdmQPWYDnj/7ojwY1SdHaU+Yfh7GBHhwacEoAVDDzAmFo0zCPmlkULRtmTdZ4AU4Am8EaoIYnKKCGaRJYw2SL6RfTK31iCv1f/+t/uU3ht2zZ4rT/rInjeOvWrS5t27bNrZXbvXu39u7d6xKAV1NTo8bGRjU3N+vw4cNCU3fy5EkdP37cgV92drabL+P/5//8n52M/LJH3n5gHUo2lNvzQC4c8xzPnDnjtIH//t//+0EYu1MfjGUAhynaxkd7av3S/jsFcUFz0rdl1hjxLzFsEEoglEAogTtI4NOrV7QnLVn70xK1LyVRhfGrVBi3TPvXrFZxSryKUhJVnJrgIK00LUFl6WtUmp6ksrREl5emr/GO0xJUmpqk0rRElZOAtwjAVQFvpLREVacnqTo9waWaDEyjCarJSFBderxL9RnxItVlJKrBJc7jVJce5yCuJmW1qlNWqiZluWodxC3zIG7VPJUtnaFyl6apfPE0p417b/Z47V86UyUrZqlkxWyVREBu/9JZKlwyXbsXTNHWWeOVO+EFzX/yJ9qwfLb66su99WqsXRsEOc/D9LiDtj0ReNvr8s6K3brY1awvzhzV5f4D6qn2AK4DrVtloXqq9+n0gSp90FalE/Ul6nEatyKndeupieSRMpwcempK1Es54FZTou6aEnVhTo0kB3GAXF2xuupKPVOrOy9VV12JOmuL1FGLabZYHdXF6mkYOcSxZsxA4ld+5VecVotYcAAQXppoqv7Fv/gXDhR4sd8JCvzXcCTIyMjQvn37ZBD35ZdfqrKy0u0YAORRHwAh+dtybBDB3ACMf/2v/7WDN9ZqMTdgDTMpWje2jUILh3MAmja8YnEWMGBjnVcQ1v73//7f+pM/+ROXMMP+8Ic/dHBJ/gd/8AfOzIoWERBlzRuODZiEKVu7dq3WrVsntGykDRs2OMjDCWLPnj3OIQKzKpo6TLKYX830CuQBhMwZLSieroCyQavBWVAewXOAy+TG8oyLF7/Zo7ewsNCZsYNtguf0wbg4ZZgM0BYiK5xBrP53CuLu8DcwvBRKIJRAKIHvrATOnDqp7fGrtDclXvuSE1SYsEL745epaE28g7iSlHgVpyaqNDVRJclxKk2JU3lqvCrQ2KUmqiw1QRVpCapMS3BlFWlx3nGkrCo9QV5KVFVagqrT4tyattqMeLe+zYO3BKdpq89IUEN6nBoy4tWQkaD6dLRwHMepPj1O9WmrVZe6ymnj6lJXqDZ1pWpTljugq4qbr6oVs1S9YrYql89U5bIZKlvyrnbNmajipTNUhqZuxWyXgLni5bNUvGyG9ix6V9vnTNDayS9r+XM/0+o3n3Fm0K7aImfeBOJwRMDD1GnXKvY6bdzx8l3qLNurjnJMpnvUV7tfpw9UqL+hWF0Ve1xCI0f73tr9+uhwnT4+3qjTrdXqripyJlO0cEBbf32JPmyr1fusZ6srcgDXi6YNiIuk3poS9TntW5F66rjmaeMws3bXoKkrcqDXVVvizLAdNfudowTr7IC9kX5Y6G5wxsbvABDaLWCIhPaKxe280A2s7OU+VA5coL2jPmE8Ll++rIGBAd26dct5mBLyw9bBGUgAEwYl9Msxa8h+8zd/U//9v/93t+AeeMNEmpCQ4ACOYLXmPYm2jbAoaLnMSQIgwUEDWMPRwUANsyMb3QOa9A3EoN0jASw4JWB6TU9PV0pKikupqakipaWluXKuAamsnTOw8wPd9u3bB4GuqqpKtbW1bj0dZlbWyLLOjjqYe9GiAU1oGrl3A7qh5GvywSzLbgs3btxwj920nZwAltaf1Y/WH88I71NgHZMzWlJkyLpAex4hxI30VxXWDyUQSiCUwBhLoKv1gPasidfupDgVrlmlwvil2p+wXMUpq1WSEqfilAQVJyeoLCVepZaS41SWstqZXctTV6k8NU4VafEO0gA1UmVaoqrSE1XNOSCXgcYtYhZ1ZtNE1WUmqg5YQxOXkejMpg2ZQNtqD+IyE9SQmahG4C4jzgEeIFefukr1qatVF4G6upQVqolboJpV81Szao5qV81RzcrZDup2z5ukkqUzVLlqrspXzlbZyrkqdUA3SyXLZ2rfkunatWCyNk17Q8mvPaU5T2BSTVZvQ5m6qovVUbVfnYQDcTDnrXXzYG6Xg7fBdW8Ve9w55legr792v0s9ODBU7VNP7T711Zeot7ZIPdVF6nWmVE8Ld+ZQra5/3KXPzxzTh+3V6qktUq8BXE2R+hpLHeD1N5Wp129SZU2cM60CexyzXq7MaeA6a4ocxDH/nrqRa+LQVpkmjjVmhNgAjDBHEkKDFzqmRgCAF34sgGFgRhv6RjtlHxb//9qv/Zrrz18PYKB/2lCO9g+HAAASBwFb64bplDVuAB3wwXy5B7RtaOkwjQJumEQBN9bMoVkD1IARkgEbWjDgifQf/sN/cIljNJBAXHJy8t9JBnWWA3WZmZkO5gA6Eto7wG7jxo3O/IqGDrMra+kwubKOztbPYXplrSDaT+aPs5M9D4Mo5GHHBmKc44mLFg4NIZDM5+rVq24nBmAcGVKfusH2Vo7M6QOYB94BSmTJGkNrE0KcfXvDPJRAKIFQAt+SBJqLCrVj1RLtWLlIe+KXac/qxSpKWKaS5FUuoXkD2MpSPC1cafJqlSWvVGnyikGQq0iOU2UqEBenKgdzq1XpvEcTVOXKPU2bW/PG2jZnOsWEmujWwAFx9ZmYTz3NW6MzqQJ1CeKYdXEN6fFqSF+t+rRVakhb7RJA15C2SnXJy1QbN99L8QtUl7BAdXFzVbt6nnbNm6ziZbNUtXqBKuLmqyJunipWz1XFKg/o0MjtWzJN782ZoPyJL2n+k3+u9CmveqbOutKIt6h5j+KU4HmampYNYOPYeZ5ieq0s1PvN5bp2+qA+++CgPmir9CAOmKoqUk9Vsdc3oUSqAbr9OlFfpLPH6nWus0EnmkrdOjl3zYFciU4frNX5rhZ9fKxJ/Q3lQivXiwYOjVxdkXrMg9Vp54ojZtR9Ol5VqGOV+9RbP3KIA9JMK4Y5lZc/mihe3Kw/Q7tFvDVe6NFgwqDCn1PXr1kDTq5fvz4IGoT24LoBoeW0Q7sETP7oRz9ygIbZMTEx0WnggAw8T5kjoTwIfYHpz9axYRJEkwS0oWXjHlh7xv0YnAFowKElzv3J6g0FcdHAbs2aNQ72ADvT1gF2wByAhfkVzRhr6QhJgrMEmjm0cThCsG4OsyuaPOAU8MTcbDI0mPLLmGdh52j07FNRUTHoBWvPweoFc+sXOWFeRu5oY1kvCNTaGCHEmXTDPJRAKIFQAt+CBDCzlG1cq02LZmnbkjnatXye9q5cpP1AXMpqlWI+ReuWFqey1NXOlFq6ZpXK16x0IFeRvEqVyatVQUqNU2XKKlWmEPoDqFut6rR41aR5oIYWzta9OYhz2jdbB+cBm2c2jZhSAThMq8Aba+PS4785d8fxqk+L82AuaZHq4xeoMX6B6hMXqiFpseqTFqkucbF2zZ2okhVzVJWwyKXK+AWqiF+oyvj5Kl89T6Wr5qp4+UztXjRVG6e/ocRXHtPcJ/9MRVmr1N9Y7syZLtxHdbFzROiMOCQQFsQDOC9EiPMqJd5bVaGDsku9zbp2qlUftpU7hwZg7RsN3D4HcH1o5Go8ZwaudTmnhn060Vjq1s+dwsO0FnNrqT5ordX7LdXqrSt1ZZ6mDpMq3quEIvFSVw1mYNbC7XNaxCMVhS7u3Ei/XqyJM4jjJY9jA6ZQjjF7GkyYlsxe/EEg8J+bxs4ghP6BGD5ojG7evOk0XUACdajPmHicAgzMCecEnBVM84Y2DnhjHRmaIkymwBvwgZmUnRQM2tCwASGAGjswBBOgYsASvDZSiAPgkpKSZCBnkGdAh9kVhwbW1LGODu0c0AXMsXYNL1cADo/WU6dOufhzcXFxDmCZfzAsiV/OHKN5ZK2hfVgbSLnJldxgLNjW6nH9n/7TfzoIhexugTaQttQJIc6kG+ahBEIJhBL4FiRAWJG9OelaN3eqNi6Yoe2LZ2n38nkumC6aN2dCTYt3697QyLmUvErlyR7ElSevkgO5FDRvq1WVQlrlIK4mdbVq0uIcyNWmJ6g2DWAzBwbPC5U1bwZ2aOG8FO/Mp5hQGzOS1JgZr6aMBDVlxqsxMwJ16XFqjEBdY+py1SfM9+AtcZGa1ixWU/ISNacuV0PKCu2aN0nl8QtVnbRYVUmLVZ24VFVJy1SVuESVCYucdq5s1VztXzpD782bpLxJL2vhU3+huLceV9ueDeprqHDhPYjXBiDhUdoFcJG72G77dLKpRGePN+nMkQb11hepm3VwAJoznXoeqJhBnZkU7ZtzXij21r7hPUq9iHcqa+vOd9Xrxrmjuna6XaeaPc0ba+NYJ4cGjn68xPo34sntVycpMj+29fLMwMWeJm4Ujg1mTgXODNB4qfvBjhc6oOWvEw0IrIyXP4k+DAQAK9aC2efTTz91sdoMIIAqvEPRtKF9A2RY74VGDLMpmjfgzq95A9xY24apFI2bmUTJgTE/oJnmLQh2AJ0l6hvEETfOgMyfA2v+xDUzrdq6OQM408qRA3No5wA6TK3mDAHMsTMEwYOPHDmi/v5+t3YOT1i2zcIsbI4lJl/LkRnx6cyUSgw7thHjOnLneSFfS9bOcupEe65WbvVCiLNvbZiHEgglEErgW5DAzZtfaW92qvJnTtS62VPcXqQ7l87W3uXzVJK00jkwlDuv00jYkeQ4lSdH1sIlr3SaN6eBi2jegLhqp4Hz4K3GaeI8bRxA53md4nmKxynr4EjxzmkBZwZCiaCNc2vgMhPV5JIHcB7EAXYJEahDU7daDUlL1JC4WPVrlqhpzTI1pyxVU8oyHcxJUvHy2Up/82mniatNwQki4giRvExVa5a6VJm42GnmSlfNV+HSWdoy+22lvvWM5j3151o3Z5w6ynY7kPPgiTVnOCWUqNvBmKddA7TOdbToXGeL06KhpeupxmyKJ2qxPjpUryv97TrfdUAnGsqdRs5AjryvrkSsd8OpAZh7v7VcF3uadb6rQSdMG4hp1ZlPyXFWAOiIJYcGzgsSjBYQcy4JUyqBhrurivVBe/2Iv12AES9yXth+SDOgs3J7oY8mN5DD/HnhwoXBObJODM9MoItwH2iRMLUCcQAMa7VY88YcMcniqICTAo4JOCRgJjVgA778oMaxwVm0HGALAp0f4pgL8OUHOD+wGbiNJschAo9XzKyELwHYCFeCKbS1tdUFEkYzx/o5AhTjaUt8PZMjUMbaObR4fHAYwVyNM4I9L6s7muflf/a0DyFu8CsbHoQSCCUQSuD+S+D2wG2VbVqr/BkTlT9zgjbNmeT2I921eIb2r1wg1r95nqhxqkhZPZgAOc4ridmWZuvfADiAzUu1aQnOC7UmnfAgES0cDg0cR5wZvDVwiZ7zQiZatwSXPHhLUmPWGjVlr1FTVpKashLUnJWo5swENWcmqhlNXcpSNaxZrAbgLXmpWlKXqSVthVoy4nSoIF3pb7+oZ370h5r813+q5Def0s75k1QRP191yUsja+pWqs55uK5QZfJSlcYvciC3cebbWv3K41r+8s/13urZ6qooVH99hXrrypw2rK8GbVixeqvL1FNd6qCsr75I/UCWM5HuVy/7obJ2DYhrr9XlvnZd7mtzmjXCinhQ6Dk6nDveoE9OHtSFrhb1NeB56sWIw2SK9q2/oVQnMa02lEU8UxkfRwZv/ZvzTq0uEiFNALfj1Xt1rGqvjlbu0fuN5Trfe2TEX66hIG40L/+h2phmhxyN2gcffOBMqiymB8JYdwe4EWCXrbEIFYLplEX2wBuOCv71bjgm+AHO1rQFNW/+c44N5qw8CH2cmyYOc65BnB/UKBtp8re3YxwicIKwsCWYWnFwIMAw3qyYWM2LlTpoI4n/htmZ+wD2+JhHKmBo25EBeXcDccHnGELciH9WYYNQAqEEQgmMrQRaSvYpb+ZE5bz7ptbNmKDNgNyCadq9ZJZzcMCEWpES7wUAdg4OniaOdW/OkYEtsCJm05qIowJx32oykr6BN8ymznmBAL54pXqODE7zlpWopix2ZIgAm0EbeXaKmrKT1Zy9Ri3Za1zenA3QJakxdaUakpeoMXmpmpKXOfPpgbQVak1bqYN5KWrKiNP6pXOVt2qp0hfNUebyhcpeNldJU95Q5tTXlT/lVW2b+Zb2Lpqs4uUzVL5qjlsjh8fq7vmTtH7qq0p6/TFlT3tF+1KWqrN8r6eRqwekgLlS9dWWqc+BVsRU6sKCAGbFLhwIeb9zQgDEyp1WrbeuRH1uXZsXQoQ6Zw7WOcC70NOs/sZS9aKRc5o2tHSlOnO8SRf7Durj400RkCsbhDjMqp6ZN5LxUcsJAAAgAElEQVRXAXNFOlaBSXWfg8bPLn+j5Yr123M/IA4o8Gv5WMeGtyneo4QxYe0bMd8stAk7KKC1Y90bAIezAo4KBm/mnGB5NI0aoBaEtCC8Gfz5c9PsMRdAC+jyQxtlI00Gbpabdo9+zczKmjlMrKyXQytHnDk8eTGvdnZ2unK8hjEbE4wXLZ198H5lWy/kjOl7KPNpEM5iPQ8hziQd5qEEQgmEEviWJNB/7Iiypk9U5qQ3lPfuOK2fOV5b5k3WjoXTtXfZXJUmrnDauEriwRFKBNNpJJxIdUaiSB60JajG7bSQqNosQockqS5jjctrcWpw4MYODGvcbgyEDmkAxoC3QYBjiy2gje22vC23WnKS5U9o4jyAW66G5OVqSl6u5tSVTgPXnL5SbVnxastdo4O7t+jKhQuD64JMvJiYrly6pO6jh9VcXqSyreu0KytJm+MWad3iacpfOE35i6cpc/ZExU9+XSmz39HWlbO0L2WJjhZtV39DhfP27K0vlUt1mEEBr3KX+usq1F9PWbn6Ab06A7oSFx6krwbwK/e2zAIGgTq0bfXlQpuHhg/zKuVOE1dXrPfbq3Sus1lnjjWqr4GttrzrxIojCDBBfb09V1mrVyTWxB2vLNQHLRX66NgB3b79td1+zPn9gjhMtiQW6rMjBABHXDLWvi1YsGAwpAmOC5hWWR+H6RRPTUynBnBAlsGbQZlp2ILQ5ocza2N1OEfrFkym4QPiACw/wHEMwFE+kkQ7AziDQgNB64f1cmZixUsVkCMkCRsQEI4ErRxgh6aSNYCsDbxy5YozyeJ8AiT7NZ6AXKyQNly9EOJi/jmFFUMJhBIIJXBvJPDpJ1e1bsk8pb/zqrKmvKa108Zp46wJ2jZvinYunKnC5fNUlrTKxX0j9ltluqUEt+cpXqf1mWtUm5moWgAtixTZRisCbIQWYRst20qrPmuNSA1ZyWrMTnbaNqd1A+ByU9SSk6KW3GS15K5RS26KDkTKMKM2pSxXY/IyNSQvc2vfOG/GhJq6Qgcwo+alqH1Trm5e/8YzL1bJYYJiFwF/oqyjpU5bVs7W7sR5OrCjQCfqK9TfCMyVq7eh3GnZPAgrd2ZXdnzora9QX12F+uoBOaCrzHmJGvj1AmL1QJwHfF7OOfUBxEgbA736IgeNfXVoAsvVTfgTktuSy3NqOI5HajXr4fbqRF2x0+h9fuVSrLf/t+rdC4jzr6kyuAAw2HkBCCPuHBvPE7TXnBcwqWI+xduS8CLAG04LwJuBmx/KDOBM42a5QRq5v36sx0AdIDdjxgwHcEHoMvgymMOMaSBmudXhnOuc+2HQrls5Y6Cdoz6OD5hP2S2KkCTseMHaN3Z9AOSAOrSYrA9EVngQA2F+mUc7Hw7U7nQ9hLi/9ZMJT0IJhBIIJfDtSKCucJeypr6ujIkvKXfqG1o7fZw2OZCbqp0Lp6lw+WyVJq1QZVpkr9OMNU7rVu3gDYDzpawU1WUlq24Q4IA3QA9tXNIgvDVkp6ghO1mNOZhNk9XsNron9yAOmHPaOK5lJagpfZUaHcAtVYMzoS5XU8oKZ0ZtTl3hrh/I8uLKXTzRO+aCPNVxWLuSFmnn6hmqyIl3Dg/9Td5G8ycbgTrPXEre11CuvsYyt2cpWjlPa1fmynsbPA0ecGflPU67BryVOejz4LDEbf/lASFQ6EFfD/uk1laou65MXcSKq/W8Zjuq2aFhnzOt9tcUq7N8ty6c6hu1HO4VxNm6LOCCxO4BgBZmUgLbsv6NrbMwp7Jw38ynmFpN+wY8AFQAWRDSOLfkBzf/cazgFqxnEGfaM9PA+eGLY4M0gzfLrZ5dt/NouWnpgDiuB+PL4fRgoUjwXj19+rTbm5XtxpAVIWHuBGBjcS2EuFH/vMKGoQRCCYQSGDsJfHL5ktYvm6v0Cc8r852XlTflTa2b/pY2zX5H251GbroKl81WSdwSF9S3JitFNVnJqkHjlsUG9skeyLnzZFcG2HkaOrRyngkVEyub2jeigXNauBQ15aY6gAPeHMhlp7i1bzg0NFI3bbVb90bst6akxWpMXuIgrjF5uRpT8EZlPdwKNRP4N3WVzhxtHzvBBHq6dOYDFeclaceKaSpMmKumLRnOm/RkS7X6W6rU11yl/uYqnWgiB+yq1d9UqT52WmgsVV8DkOatc3MA1wCoAXllMrhzGjxAsLHMmWXR9JnGjrV47MpA6qzzNrlnm63OGpK3t2pvdaGOFW3TR91HA7Mf2em9gDjAAc2bLa4n7hjARWBhAA6PS7bMIvYb22UR9w3tGyZW076ZWRPAMlgbKjdwC14Pwlks52jiCIcyc+bMMdHEGdgFcwM6gzi/xs/qosVjrRwerDt37nT7zhIgmODA5KwjRKv5ve99756CXAhxI/tNhbVDCYQSCCVwzyTQc+Sg0qe8odRxzypz4kvKm/K61k8fp82zJ+q9+VO1e9E07V2K1+p8lScudzsz1KCBy05WbXaKy82UWpeNNi5F9dnJqkcrl5XsgM4zo6KBS1Gj08SlqhENnNPIsR4uyQsrws4MKSvVsGapGgjam7RYdYmLVO+C+C5R/ZqlLmFWxZxKqktZqY+PH75n8rGOb1z/Uoer9mv76unauepdFa2Zp5atGW6T+5MH6nTyQL1OttTrxIEanWiu1anmWp1oqXFwB9ChvetvrHRr6wC1vqZy75xQIoBbY4X6GiudAwPQB9z1kKOBq8cZAhMqqcTtidrtYsfhFVuozpJtatu9UWe6j9t0R53fK4izNVnsAkE8M9a5YTpFAwfAET4EBwYADo0SzgsWsBeAA8hsrRs55wZr5H5g85f7j2OBNn8dAA7TLc4DzBHQ8sOVgZflpmkz6LI8eB0Y8yfqWR0/xJlZFa2claOZIxTJli1bnEaupKRk0HO1qanJOYUAwGZWHQvNW7CPEOJG/fMKG4YSCCUQSmDsJdBWVa6Ut19QypvPKHPCiw7kNkx/S5tnTdS2uZO0c/5U7Vk0TYVLZ2r/ijkqiVuosiSALs6ZV2uy1jgtXF12shzIAXMO5Fj/xlq5ZAd2zpSalRyBuSRvo/vUyPZZaxa5wL118Qvd9lm1iQtVm7hItYBcwkLVJS5UTYIHdpRXxy0QwYTPjgBcvN0kPflx7NKAt2uABUgdTrqXPv5QtdtztW3ZVG1dNFG7Vk1TTX68juzf5DRl/Q7eanWypUYnWuoc0FHW34R2rlr9zTXqj2jwTjTVuLI+AK8ZzR0gR0iRCmFqRWvn9kh1+6bizFCqbucIUaoeQons36yDOwrUvnerLp85PdzUY7o+VhCH1s3WZXGMlyRODMQzY/9THAXwQjUNHKFGADvb5xQNnJlPASvAjxQEuVjgzQ9ywx37IY5jQI51eAAnIAVQGchxDnwZrMWa+wGOY387gznG8EOcjUl9tu/C6QHvVTRybNtlnquEIlm8eLEzQVuAZnsOQRgb7XkIcTH9lMJKI5UA/5PjD0SsHxaF8iVmwei9+rDYlDTaT/CeWMTKnMnvxedu53sv5hT2ee8lAMC0lu9T+sSXlPrms8p8+wUXimP9tDe1ccbb2jLnHb03b6p2LXhXuxdP197F07Rv6UwVLZ+r4lXzXZy1sqRlqlizQlXJ7NzA1lurXAiSmjTiyK1SNQF3k1eoZs1yVa9ZpupEdlFYqNqE+aohxXupNn6+ahMWqiZxiWqSljhNXE089YC4xapYNV+VK+eqPjdFn1++qIHbt3X71i3dvnlTt766oVtffaWvv7rhzj89d1Yn66td6q+rUn9tlXqry3W6tUGfXPxA1y6d1ueXP9KXlz/S7WsfauDGFenrG7p9i3Rdt29+qYFbNzRw67pLnOv2DQ3c/EIfdbWq6b1sNWxNV9O2NLXtSFdH2UZ90Fah853NutDT6tLFnjZd6Dmoiz2tOt/TrvfbG9R/oFYnWmt0sqVW/cBec436mqp1orlSvU3V6m2sVA8wh3YuAnZswdVXs1+dxdt0eFeeWrdmqW5DuuvrxhdfjNmXZKwgzh/WAogA5P7Nv/k3DtKIucYaLpwFXnnlFaeBI0Yc22aZBs68TwEpP7gNdRzUzA0Ha0NdD0Ic58wFL1Cgjd0ZDKjuBcSZVs4gjjFINibXATk0cjg8mGkVz1Vixb3//vsu3AjmX+YNPPPOME3oaMHN3y6EuDH7uYUdPcwSAOD44YwETB9meYT3du8l0NXerOzp45T11lPKHPe0ct55UWunvqFNmFdnTdD2uZOdiXXnAhwfItq5xdNViLl12UwVLZstNpYvWTlHJSvnqnTlbJWunCO2tyojHtuKOSpbMUelK2apOnGR2tZlqG1tulrXZap9XZba1mWrfX2WDq7PUfv6yPEG8mwd3JCjgxvzI8e5OrJ9kzr27tCxnVt1bMcWHduxVUd3bHLlR7au18Et63Voyzod3lygQxvzdGhTvtpd+xx17dmiry70SJ/1S9f6pU97pc96pc97NXC1U7evdGjApeMauNLpjnW1SwNXOO/QwNUur821bu/4arcGrnRr4PJxDVzukj7pka71eumTPumTXunTbunLUzrfe9Bp6d5vrdep1joHcqdaanSyuVqssTvRjMauyjk69FQVqqt0h47sXa+2rdlq2ZiuxnXJqlmbpI7aYl27cG7MvxRjBXH87QLcTCP3L//lv3QBegncyy4MQBFODHihssE6JkDMp4CH7XWKFgzYAtCC8ObXwI3VMWM9KBBn8BbMTVNHjucqQYEBObbrAuTYagtnB8KPTJo0yWkvQ03cmP9Mwg5jlcDdaLFiHeNBrhfUxD3Ic73buS1fvvyeaRjvdm6/SO0vn/tY+zPjlTHuGWW+9ZSyxj+rgkkvaf27r2vTjHHagol1zjvaBtDNw9Q6STsXTNHuCNTtXfyuChdPU+GSd7VvyTTtJy2drv1Lpmv/4hnat3i69i1+V5UJC3VwU75aN+apFcByqUDtm/N1cNNatW8qUHskP7gxz4OxTcBYnto35Lp0eGO+A7TDm/Ic4AFrh4E9dz1HreuydKCAlKkDa7PUsjZDzXlpOrwlT5+fOixd6ZYud0qXj0uXOqRLx6XLHdIV8mPSpWPeNY4H09FIeafXhrau/THpIsedXr9XeqTLvV660iWRrp/Ux8er1bQ2Tm2bU9W+LVOHtmfp0I5sHXovWwe3Z6ltS4YObEhR07pENa2NV31evGpzVqsie7Wad67VifZGfX718j37So4lxAEPaILY65OtsYA2NHDz5s1zTgxPPfXUoAkVDRwaHsCNFASzaBA3lDbtbsofBIgzbZwf2KwMjRwmXfNexbRqIUgAOdbImbMDseXY2eHXf/3XB7dS82vURnscauLu2c/v4eoYgAkhbmQm4u/qN8BM2/fKTPxdlcu3NW9ipPW0N2vz0pnKePMJZb31pHLGP62Cd573YA7NHGbWmW+7PUe3zWbtHFA3WTvnT9YulyZp9/zJ2jN/kvYsmKzdC6Zor8s5nqzyuIVq25CnlvV5at2Yr7aNeR7IueO1at+Q5663A3nrAbeIJm4j5bnetQ0GdDlqo8468mwvcbw2S+1rM13eujZTrQXpOpCfpkMbcvRJT2tEewa8eQA3AIRdOKaBi0eli+TesXd+RLpwxF3jnGsO2i4e1wCw5+oely4ChZ0auNThkgd1XRoA7j7r1bUz7apIW6CK5PkqT5mvyrR5qkpfpKr0xarKWKzK9EUqTV2k6rwENW7J1qHi7frgWJs+vXheX9+6ec+/EmMNcWz9hGMCwLZ06VK3XotN7HFiYAstnBjY1P0P/uAPnAMBEAWE2Ro4YI5zIM7AjnODPX8+HLz521M3GrAFy6gHtBD+BK2YrYkzwDLz5p3WuVHHkplCrb61xzzqT1z3j0E9zoE4TLpm1qWexZJjjRxx5Gpra92uDgQFBvDY6cK24BotuPnbhRB3z3+G93cAzH32gAEv/8dMgnY9eA1Is3Ve1EEbw4dya0Nu69b8ZRz7P7TxQ59BYLT+aWflBg52DlDYPQVNmcwvOAfOaRPtQ/ugTPz3FuyfPuy6tfXXQQ7+8eycdjYvvwwo98/Z31cs86Uv5m/jMIY9C3/f1GMcvxz8z94/J+rZvdEfxzZ3O6dvG5PrHNtzijbvsOzeSOCr69fV29aoHXELlTH+aeWOe0K5459S/oTnVDDxBeWjoZvyqjZMe9OZXLfOHKdts952afvst+WlcXpvzlvu+L05b+u9OeO0fdYEt5YOOGvdkK/WDV7e5gAOoCtQ20ZP29a+PleDCQ3b+hy1GsStz3Gm17aI+fXg+lwHcJ4pNltt64C4CMgVZAqQa8nPVOu6bF061qSBK70S5tJLndJFoAs4O6qBC0c9mLsA1HHsAZwHcd/Am0Ec4DZwscPrA6gbhL8IyF3q1AAavk96dPuLEzrRXqfOulJ11OxXR22ReghP0t6gDzsO6sKpXl09+5G+uHZVt7++dW8e7B16HQuIM/MdWrh/+2//rYMIzKeEEhk3bpzbxJ41cITDQAPHjgN4gAJkwNu/+3f/btCJwcDLYI46QCH1/QkTrEGctbFzy02bxzmwhuepP9F3EOLoix0i2MvVv05tKPgyEAOuDNwsp4zr7JFKbmDGOY4K/gSYmSnV+gLirMxgkj5oT33Cj+C1iqNDQ0ODent71d7e7v424yiCo4P9reUZ2XOysljzEOLu8AP6rl3iBctLmY9BkL1seYn7AYYXOV8SPtFe8AYbrkKkjv/lTxv/OX3Z2Na3XR+uf5srfXDsP6cMGCFxbNBiUGGgYmPYfIO5wQn17MP8bM6UUYdkH65bfRvfrtv4Nj//OWV87D7I+TCWfzz6Nxm5Cr5/gvOlHv2SbE7kNh/mZ33ZuH7ZmNwYgja0ZS7Wp/+63avN29qQR7vmm3Z4eB8kgOPDuVP9atm7XVsWv6usCc8p802A7gnljntKueOeVt74Z7R2wjNa987zWvfOi1o/5SWtn/yy1k16UesnvaB177zgrlMn49VHtWX2RGdKPYDGbAPJ0655OccRbRtr4zhe52nYWt1aOW/NnLeGDu2bp4FjLZ2tpztIPbfOLktta1l7l+G0cq1rs9RakKlz7TUawOTpIA5NXMQ86jRrHR6UXTiuAQdyxxzUAW0Dl454WrdLHGOG5RoAhxYOADz2TQIMHdwBiselT7qkL/gP3/2Hs1i/JmMBcayDYyE92z+x1m3KlCnOE3XatGkuDtzPf/5zB3Zo4YALYAyYAq4ALTZ2N09UA7IgxAFt/gTYGaz52/iPHxSIA8oM6IA5AMwPcBxjJjVNnEGcQR8gZ44O1KHc+mGNHGbV0tJS5+jA38+Kigqxzyrbm/H3179W0f4ejyQPIS7WX9MDXo+XsL3Qg1O1Fy+5/8MXxaCCl7pBAHUMSqx+8LqVW9/+vrhGX/7+gu2D/Rt4GDjYuX/OjGGwwbz99xutvs3RcoMXzq2+XSO3e2GMaNe5B/+Ydg82Rzu3Pq0/+uJD22g/TqsfzP3z5RryZA728cvAxjL5WB3yaGPaffj7sDbWl83b+rDvitUL829fAl/fuiWC33bUV6l6U452rp6nvKmvKP2NJ5T++s+V/trPlf36o8p543HlvPmEst94VNmvPaKsVx5R+st/ozUv/KWWP/H/qeDdN4Sp9MDabLUOatdynQYOM6uXADmfidQdA3CsewPqPNhza+TQxjlzqgd11HHnOEuYNm5thtPGtean6cPGMg1cYs0apk7Wv/nMqhw7+EIT58EZgIbJ1IFcRNvmnQNxwB7wdsRB38B5D+RMU4eZ1kEc6+I+69PA7ZFvDXa/nvxYQBy/fwLOAmcvvPCCiwWHJo6/JWylhQYOEyqODNRBAwaAAVwGWsNBHNDmTwaB1o9Bn4EdufXN8bepiQO6ADM0aBYuxA9xaNQ4p54BmoEf5wZ3ppUD6KweQMheq4BcVVWVc3QgGDCx5dB+Emh5tBo4+7seQtz9+jXe43GivYxtSAMSgw0r90PCcJAVvG4veoMBcv9L/l5DnI1voHGn+492v0Hgsjr8MOgr2nVkYPdLfatjcrVz6ys4R/q2ulbnTrn/+VDvThDHdeZnP2wDaJvDUONEk5u1MdnSlnrWt18GQ/Ubln87Erh186auf/6prpz9SB90HlV3U43aS/aoZc9WNWxfr4Yd61W3fa2adm1WS+F7Olxdpu6DrTrR1qJDmwvUsi5XLetzdGA94JYvNG2tDs689W7eGjcfmPm0bW6d3IYcHXLmVIM782y1PFsHnfdrptrWeSbV1rx0vV+1T7cvdktXOpwzA+ZOB1rONOqtcXPgZubUi956OAd1Dtgi6+IuAHHfmF8dzJ1HI+eZVb36HEecJj7tkwYeTogzOMBshxmVrbMwQ86fP9+ZUS2YL7HgADjMqACcwRW5ac7GAuKCIGfjfNsQZ9qzaBo44M0gzsDNzK92bnBHPwAcplWAzvqlD9bH7dixw+2zilmVvVYJrIz379/7e3/PaePs7+tI8xDivp2/tWM+ahAg/AMYxFHH//GDVxDSgv0Fr/vb0mfw/F5DHGP6wYIv/nCAxBy5Dz52f35QoZx+uGbX/X3Slj7sE6xj53Y9CEO0pU6sH/98aTMcxFm/Jhe7V+4peJ/+uv57ojw4b6tLbvdokOi/Fh5/dyVw9dwZHd66Vs14jTpNHGvjPLMpIOetc7P1cDne+jZnNs0d9D51YUc2cJ7nQM6dO8jLdVq4dkywThNnIUyyhDn1QF6a+op36uYFvEbxJu3w1qyZedRp3HBqANwiZlLnzGBauYCDwwVvHd03plTPrDroJOGcJY55Hqqf9Wtg4PoD++BGq4nDRAccYEbFGxVHhYkTJyouLs5tbv/ss886qPu///f/ukC0v/d7v+e0cGjEDK78uV+DFu0YzRvmVLRxHJs2j7oGgv7cX84x49ImmIJr4jhnrmwyb5ov04SRA1aYMv3JYMtygy6rGzwPwhzauSC4WV+W0wfj25xsjRzXrb+NGzc6R4fGxkb3NxYTKx7Cv/EbvzEY+sUAzkLB2Pmd8hDiHtif78gnxoP2v1x5GfNC50M51w1KDOxslCCk2cs62nX69AOGvfRtLBvPP5fh+rf5GGzYuc2XPpm/QRDXDVJsjsPl/jlTl3P6tA99+4EmKE/OSVbHZGRztHPrz+Ri92RwZfWpd6d7CM4Xefrr23OgH/r0y5trVpfcf5/+cf19RJs31/nYPXPMffrHsnZh/t2VwOWzH6t1U4GaCzLVsi5brRtynFkVZwQXGoQy55WKs4Jdy1bbWlv3lqN2Z1Ll3AO7QxvJvXOcGzzHhkwdXOvFoGtdS/ssteSnqWvPZt042yXiv7mwIs6bNOJpegnNGsBmWjYcGzyzqaed49jMp0c14DRvhzRw/kgkGcR5ucGd81T9HIj76oF9cKOFOPtb9Uu/9EtuPRvr3vBGNTPqY4895tbB/cmf/MngfqhmAvXDmx1HAzc/iAFftDeAA7bsuvVBjkaPxDo7KzcNXTRgi1Y2FMQBUMCUgZXlBmmWG/RZXc7tmNzChOBJSkKTZlBIn1bX+rcyyg3kzOmCMlsfh1mVHR1YE3fs2DF1d3c7OMScHdyWy7So9hzvlIcQ98D+fEc3Mf/D9r946Y0Xr/+6jeAvpw0vbn89AMEAhXI+/nPaWB8Agx1Tl2P/ebT+8eDxj0fcIv+5fyzKmV+wzOozVrQP41odgxvq+cs59n8MwvztrE5wfLatsXrkweuc8zGgsrpW7h+XY/+8aOM/5x6D/TBXf5nN0/r1PwPGNgi2eZD7PzaeQZy/b39dK2f88PPdlcCls2dVXZCtloIMtaAdI7m4bl5MNzxJgTCgC2/SVta7DWrWPKcFQA3g8xwXPHMpdQ457Rzm1EgA4XWEGclUWwEpQwdyU3Vs21p9+eExL1ivxYq7fMwLDeI0cTgwAHMWTgQTK1DmmUY9L9ZjcuvfHLwdjsAcdYA+AzjWyQF9HR4wXj8tDXz9wD640UAcWhx+o2jhfu3Xfs3tvAC8AXGEEyGgL1tq4chAvDhzZACYACqDK3/u16JFO/ZDnv+69QG48Tfld37ndxzEUZ9rfjOtvw87jgXiTPsVDeIMrAzcyA2wqG/QRU5dElo3wM0SmjQDNqtj5/7crlmf1r+VA4QEAt6zZ4/q6+vV39/vcrY6Y/eM0To4hBD3wP58w4ndSQJAiGm4/PWAohAo/BIJj0MJDC+BS2c/VnF2mhrzM9RckKPmtdlqWZupA0CWCweCF6nnSUru4r05mMtW69oI0AF++elqzUtVS16qM5MCaTgyOE9VdntwXq1Zancgl6E2YsXlpunwxhwX8Nd5qOLcwG4MThsHyEUC/ToHhiOROHBe+BFPI/eNJu42zgznj0mR5Dk3eODmgC8CgW693Wc9GrhxJrJr6/Ay+jZqjBbi0OSwuT3mzeeff14rVqxw+6K++OKLok/MqIQTQauFFs3Ay6ArmBtUDZXT3tqYts3O0bqR0NIBZRyzRs8fuoS69BHsPxaIA5osGTD5c7tmcGVx3cjN7GnARR3a+ttwbNo2K/fDG8do26yOjU1d+rUEDOLQgNKiuLjYBQEG5DCz/vmf/7nbyxbwHokWDlgPIe7b+GWGY961BPjyRoO4oTRxdz1g2EEogYdYAlfOn1NhVprKMxLV7EAuUy1udwU8SNHKZTvNXGsB0IZG7ZvkmUUJ3pupA/kZLogvmjbOWwsIJ+KtgTu4PgJvrItziTVxGQ78CEHySfcB6SrODZ5JlW2z3K4NaOJYH+cgztPEeQF/vQDAnkYuEkokYkLV+aPSeS8kCWFFBtC8Oa0dDhMRk+1Xp6Wblx7opzoaiDMt3Pe//30Ha2jh2ISdmHAE+SXYLN6oxFuzeG4GXEPlQbgK1jMAC8KgmU1/8IMfOC/YH//4xy5nbCCO6/Rt7f39UhaEOOqyvg+tYlCjBjQZQFlOGfXIExMTXY6jAuDEf/g5NkeEIIRZHz0eP7IAACAASURBVORBaBvunDaMSTKIowyQM7NqdXW1+vr6dODAAWfmRh4AHGkkzg4hxD3QP+FwckNJAIDjj1UwDVU/LA8lEEpgaAlcu3xJ+7LTtDt5lWoyk9XC2jg0b04bh0nV22nBgMyZUl2oEGDOM7WisXMmUuLARfZfbXcx4by27QVeSBHPuYH1cABfhlrz0x3oXTpSH9kiK7LLgttuy4M3D+Bs1waDN/LIrg7OXHrEaeA8kypr49DQETi4SwPkLsYcENfpmVJvn9XAjXu3ZdbQ0o79ykghziDgV3/1V523KZo3gvqyATtmVPrjP7po4fCMvNM6OD9QxQpxVg/4ov1v/dZvOW0f4AjA4QnL8V/+5V86GAPigjBnIEhf0SAOT1ruKRaIA7aAKAO1bdu2OS0YTgUktsVCM0Yd+jP4Gg7Shrtu/fghjmPaFRQUaOvWrS4IMMF/2cmBeHI8m3/8j/+xe6fZcwy+36KdhxAX++8prBlKIJRAKIGHUgI3v/pKpevytCspTjtXL1VtVrKaC7Iia+S8dXFuHZvturDOi/uGWdRtoTWomfOAzmnr0MD51r5hWmXNnAv6CwBG1sQBcaSzLVUauNzjPFPdtli2/ZYzpx51gX29LbYia+OcaRRwA9gwmdqxrY1jvVwE2i542jgX7Bet3rUe6euz0o2rD/TzHC3EscH9//t//8+ZUNFavfnmmy4uGWXmzGDmTWDJD2zRjg2sLA/WsXLL6dPgDHjDXPh//s//cevwCDgMyAF0mF4BPXIDQH8+FhAHOKGFY40bm9IDbuxjSuIY0ybmUMyrBl/DQdpw162fIMRxzjzQxm3fvt05OfT09Ki5uXlQG2egBsjZ8Z3yEOIe6J9wOLlQAqEEQgncHwnU7dmpnQmrtGn5Ar23YpHqs5I8iMNMGvFStd0YvJAjkcC/zsEhchwJBuzMpZhdSZhgC75xePB2bojAn+2hmpum03VF+vpSZF9TFycuEvDXQowMeqACaxHPU2LCGcSdP+y26GKbLm/9GyZU4C2yjVckd2FMrvVJNz+Wbn52f4Q7ylFGCnG87FkLh5kULRyOSdOnT5eFFAGcMG3y4jdAAryCUBY894MVx8Hr9OGvY+viKEfzR2JsPDFxqOAcmAP0qMtcaG9zGiqnzkg0cab9SkpKcp6mANvevXudlyieohyzvylr1dDGAV/DAVos14eCOLR9OE6YkwNjt7W1OW0cseTQUOJRHCvA8bxDiBvljytsFkoglEAogYdJAic7j2vzysXatGyBNiyarZ3LZqs2I1GNeZlqMm/ViDMDgX3ZioudGNrQyhHgF8eFDdleWBE8Ut0+q5FrkZ0aDqKxc2vjbE0dzhPpaslLU3/5bt0CtC53eTs2sHk9OzVEttpycMbuC4Pep575FCeGb7xSI16oboutLg3QxwVyHCWIQRfZbosxrp2Ubj24gX75bo0G4tDC4biACdU8Ugkpwgb3f/zHf+zMm3ikAkpBGOPctGn+3A9oHPuvcey/zjn9YColMSYaQMCNeaGR++lPfzpoTqWtQZy/n2jH9A2E4qgRiznVYAotG6DGPqaFhYUO3gA4jgEpzJl+54QgqHFtJIn2AKSN7z/mGmvjgDa0gZWVlS7kCPurAtz/6l/9q5g0cKadCyHuYforHN5LKIFQAqEERimB6198rp2p8dq0ZJ7WLZihdXOnaNuiaapMWaWmgiwHcs0F2W5rLreuzQGZt4eqaeY8mAPovKC/BwE9ggZv8Moc/LktuCJ7qQKHBZlqzktTz77tukmsuCto40wLFwkhYt6m547o9tnDun3WiwPnaeEM7NDO4czAHqkAoJcGCCLM8dUet+m9izdH/1dPSbcf3H1TeYzDQVxw7RQL4tFuPfnkk04LR/iKl19++W8F9mUtHJo6wAnIQhPmh7kglEWDqTuV0Z4+mQf9Aoxo4QA5NE3AJOeMTz3qxzom9X74wx+63Q5igTiDKTRxxH9D+4Y2DnDbv3+/O2ZLLOqZFg6NHOf+NBKA88OgQZz1bX1yjjYOkypgiTaOuHGUA6kWJsZA7U55CHGj/IMXNgslEEoglMDDJoGjDdVat2Cm1s6bpoK5U1Uwa7I2zZmo0oQlaspN94IBY17FDMp6uPVZbmsuB29O8+ZBnQspsh6tnAdxbj9VM7VGAv+6QMKRMCbNuek6vmujbnx01HNuwKx6yduo3vMuPaqBc4d1++N2fX2W1Kbb5w5qABMqmrhzx3TbmVW/gThAzrUdBLhu6fwhDXx8SPqkVwOffvzAP77hIM5ii9lLn704MTdOnTpVq1ev1oQJE/T0008PBvbFs9N2VQDEDN4MpEab+6GOPujXQA5QZEycKTChopkDIg0eRzIm4xjEmTMCOZou03YBQv7EdSAO6APk0H4BTiS0cXio2nUAjLYGW5aPFOIM5GhPf9HmhjYOD9k9e/a4SAuEGwEsn3vuuUEHhzvBm10LIe6B/xmHEwwlEEoglMD9kcCN619qR0q88ue8q7xZ7yh/xkTlTx+vtdPHa/fi6arLiFfLWk8bR3gQF9zXbXTvbc01qKHDgYHgvuziENHCYV71ggF7plSCBrvgwW7XhnQd3VagL94/pAEXYgSAwynB26nBg7XDun3ukG5/fEi3z7Tr9tmDun0uEtTXmVRZ/4bmrjPijdrp9fVJn8tvn2nT1x8e0MDHBP/t0u3rn94fod7FKMNBnGni0MBxjCmOQL54by5YsECvvPKK0+ah+QKi2B/Vb7oEoPwAdjfH1pdBGZBGcF+AjrhwHJvmz9bCcc3qxzI2c/+f//N/OkAFjACzO0Ec1/BMBaQ4NucFYI4EZJlGj/4M2oL5aCDO2vhBjjEsUY4ZF09VHC7YxQFvVcLBIDuDtOHyEOLu4gcWNg0lEEoglMDDJoEPeruVPWuycqePV/a745T77jjlvfuG8qa8qo0zxqk0bpGa81IHY8XhqODtixrxPLV1c+ybGgG4NrR0zqOVOHPejg14sAJ1LhVk6NDGHH3Sd0B4pgJvLrFLw/mjum2ODOc8jZzTyp0/otumibMYcMSDYx2dW//W4+LODZxv19cfNOrm+8269cEB6VK3bl95XwMDAw/8o4sF4njJEyTWtth64YUXHORMnjzZOTdgwiQu3O///u870yawZMDFMWAUTMMBlR+8CBaMps00fP7gwfRj4AaYmPZttBBHf2jy2AMWKIsF4qiDMwHQRBtyYA7tmwGWX3NGWTAZkMWaMx7J3y/wxviWAEs8VdHGmYMDceNYu8fawX/wD/5BTCAXQtwD/zMOJxhKIJRAKIH7K4FD1WXKmPyaMqe8qezJrylnymvKnfKqcie9pIJJL2v7nIkqT1iq5tzkSJw4ttzyAM1p2JyjQ57agTczqUa26voG4mxrLtbFeSB46XiDvkYD5xwTiPN2VDJww1yKh+r54w7sXDBfjge33orAGzs+XO7S7fOHdOuDBt08UauvTtTr5qkm3f7woG6fPa7bNz6/vwId5WjDQZyZUQG5f/bP/pkDtRkzZriF/wT3xZRKeA/Ah5e9xYXzQ1oQ4Dj3X492bBBGXda8YaZlCy92gEDbh7nUQM9MttHy4WAyODfqA6QJCQlD7rYAHN0pBQHtTnXtWrBNEOYM2iwPXuecvgA50/xxTn0cHFirV1tbK8KNEL+OdYw8T9PCmcbVzv15CHGj/HGFzUIJhBIIJfCwSgAdVd3ubUqZ8LIyJr6ozHdeVtY7ryj3nZeV986Lyn3nJeVPelnbZo1T6co5asxM9AIDYzKNgFvbxjwd3Fig9g35atuQL8+pwdvxwdsBwvZaxUOV3R1ydPZgrb5Gk0YQ34u2CwPgFjGbnvMgzoM5ttfywoi4HR3Q4F0+7tbM3TxVp6/6KnS9p1LX+2p1vb9eX51q1tcftunmJw/+Wjj7Xg0HcbzceaGbKRVgW7RokRYuXOhA4K//+q+dVyimVNsjFSgzwDKI8sNS8Lq/rh2jXQPKgEIgAi0fCYhjNwbG8vcTDeAos/Gp65/DUMfUv9cQFwS2aOdBSDN4szx4nXP6Adz8mjjK0bzh4IBJtaOjwyWeHzI2WLvTDg4hxNmvJcxDCYQSCCUQSmBQAre//lo1721U8rinlTr+WWW8/YIyJ7yorAkvKHvii8qZ+KJyJzyvvLef06apr2rvwqmqTlqqpuxkp5UD4to35Yscc2prJCxJ64Z8HVifqwPrSDk64DxUPS/Vj5orIhAHwKF5w+MUEyrpiHTOO3caOrcN13Hn0ODWyn3YopsnqnSjq0RfdpbpeleZvuyu1Bc91Q7ibp1u1VcXTzzQe6UOCj9yMBzEGcDxkgdwiA2HQ8O0adPcDg0E1SW4L5oytGOYOgEmf4oGTP7r0Y4BMMrpE2gzeCMH5qKtvYsGcmMNcdGAy7Rplker4y9jrRzODpZs7Zy/ThDSDN4sD163c/qwNXE2H8bBpIpTA0F/Mani9MDaPzOpmgOLQZ0/DyEu+KsJz0MJhBIIJRBKwElgYOC2mvfvVPL455Qy7jmlvfWcMsc/r8zxzypr/HPKeftZ5Ux4Trlvk55VwcTntHHqK9o9/x2VrZ6v+tTVbv2cC/5L2JFN+WrfvF6tm9fr4OZ1at+0Vq0b83RgvQdzp2qL9DXepJc7JXZqcNtqeQ4LgBsaOLxQcWq4daZVX71frxto3LqK9cXxffr82D59frxYn3WU6rPOcn3RVakve2ucKfXLs70aGPj6O/VkY4W4f/gP/6EDp0mTJjmIe/vttwf3SQUGAC20ZkAc0OYHs9FAHPBFOyDOAA6YYByAkdz6tbHGCuKA0qHMqUCSARM55wZLlvthLFifNmjGgCrbX5Xz4doYvFnun4P/2OYDyDEfcoANBwdMqlVVVcJLtby8XMT2+973vjds4N8Q4r5TP+lwsqEEQgmEErj/Euhpb1HWtLeU+uZTSn3jKaW/+Ywy3npKmeOedilr3NPKe/tZ5Y1/Rjnjn1Lu+KdUMOFZbXjnJW179zXtnjNexUumqTJugWqSl6s+I95p7Ajy25KfppaCNLXkpaq3eIe+Yp0bOzZcPK7bF47o1tlDuvVRi746Xa8bJ6r0RU+RPu8o1GdH9+izQzv1KenwHl07UqhPjxbp02NF+rSjRNc6y3W9t1qf9Vbry3O9Grh9+/4L7i5HjAXicGr45//8n7vwHQT3xZxqXqkE2GWtGrHhALig0wGAZbDlzw28hsqBOKCMNmjdzJRKDsRR5teyDdWPv9w//lDHOEaw60MQ4gCiIGjFeg5k0Z6cfU3ZEguossQ5Md3oD0iz3IAt1pz+aeuHSSCOcvrHS7WmpkbHjx/XgQMH9O6774rAzaZtNdO5XwvHcQhxd/kjC5uHEgglEErgF0EC1y5d0N7MBCW++pjSXn9cKa8/rtQ3HnfHGW88oexxT7nkIG7c08oZ/4zy3n5G+eOfjqSnVDD+aa0H7iY9r82TX9LWqS9p67uvaPu017Vt2muqTZ6vi+279Onxvfrs2B59fnSHrh3erk8ObdUn7Vt0tXWLPjlA2qarre/pcvt2XTm4U58cKtS1w4X65Oh+fXK0SJ8dL9GN3kp90lWj61c++s4+nlgh7jd/8zf185//3Gnh5syZo+eff94F+GV3BOLGsUZtLCEO+PKbVP2ODUAFnqpA3EhAbihw85ffC4gzsDKYigZxmDyBr7GEOPoziKN/HBzYy/Xw4cM6evSoC9aMjM2pIYS47+zPOJx4KIFQAqEEHgwJEJaj9+ABrVswWUkv/41SX31Mya8+qrTXHlPGG48p880nXMp680nljHtKOQ7mnlYuIPc2UIe27lnlY3oF8N5+WgWRlD/uKZWselenKwt0oWGtLjeu08XG9V5qWq+LpOZNuty8RRdbtulS63u62AbI7dTVg7t15fAuXT2yR593lOrK0X262n9At758sPdGHe6pxgJxrJsCbl577TXFx8drypQpbscGnBzYp9TWwxkMBeHKyi0H0KzOULlBHOMCh0Ai4EYfLMgnDdV2qHL6tDkMld8LiDOYAuaAKSDOn4ArQoEYcBnMxaqBs3pBTRz9WcgTxsVsS+DflpYW56WKdg7T8d//+39/0MEhqIULNXHD/YK+o9dRof/2b//2fZ+9/wvGmoLhPidOnPhbX07m/V35MNdY58sm1KN9HnfT1uQb7VkwH3tedXV1Yyb2119//Y73yliMy9xG+7E+ot0Xfdq9DSXzO123a0M9W57HUOOO9n6+i+1u3riuYzXlWjt3khJe+JlSX3lEKa8+otRXH1Haq48o4/UnlPkGQPekALqst55S1ltPKvutJxzcAXUe2AFzT2vdhGdU8PazKk2YqZOVeTpTu07n6tfqXD35Ol1o2KALDZt0vnGjzjdt1oWmLbrQvF3nD7ynS23bdbl9l64d3avzbXt0saNGX145852IAzfcs48F4n71V3/VmUvfeeedwfVwjz76qNvmyuLDsXYNSPIDmp1HAya7NlROG7REhBoxKOMcyCKZlm6o9tHKo80jWHavIA6YAs4M4gA3f8K5wYAL+DIwG0keDeIIN8K4QCLjsQUYf98INcLxz372M7He0f5WR8tDc+pwv6Lv0HV7afOgh3qB3avb8Y/Hi445DPey44X/Xfzwguf+hnrR++/JZOGXj//6nY7vpq3/uxB8DszbyjiO5T7uNM9Yrxl8IbvRQhzfGdoP9f1CznZv5EG53+k6fdt3knrI3/9h/sEy//VfxOOvrn+pnpZ67V6zTGtee0xrXv6ZA7nUVx51MJf+2qNKf/0xZZCjrXv9MWW+8Ziy3njcQR2autxxZm59WuWJc9RfkasPawC5fAdzH9eu1dm6DTpXv0HnGjbqXONmnW3cqLNNW3QZTVzbTp1p3K6LHdX64tKHwqv2YfnEAnFstYXzwrx581y0fzRytPvxj3/stqiyvVLRmAFPBl2WUxaEpeHO/f1EA7KxKgvOA4hjD1YL1hsM9os2baQJiLJEW4CKtXGWADvKqWN9jwTe/HUN5OgHGCTRL+Vo/9577z1VVla6MCPV1dVubeM/+Sf/xP29C82pD8uvOob74EUUfHnF0GzUVXi5BV/KjG8vxGgdU/+7/EIcCfxwn6N9HnfT1kDOoIbnYGXB5xXtGY1FGeP4vwcGcnczvt2D/76Y60ihjTY8F+uHY/tOMucg3AbPx0I+D0sft2/f1qUzH6hl/w5tWzFHqW89pRSALgJzaa/8jVIxv77yiEto69Jf9wAv843HncYOLV35mrnqKc3R+xU5Ol2Vow+qcvRRdY4+qsnXx/VrdaFxoy42b9P55i06XbdOH7UW6lJ/u65/ekl40j5sn1ggDqcGvpts1zR//vy/FR8OpwYzdRrEBQErCEqxnAf7uFfnwbmg4SNsCltp2X6naMj8QGRAZrmB11C51bPc+vLnXKM9sEXyg9lIjoeCOPoGGrdt2+Y8U9mCq7GxUXgb//qv/3oIcQ/bD3u4+xkNxNHGXmbWP+f+F7CVx5LzR+VObblmGhVe7N+1D/cX60v9bkDsbtpGg52xgKiRPKvg92Asxo92X8yJ71TwO+cv8x/bPfjL7gRxwX6tfZj/XQmgCbt6/mP1tjaooiBVGxdMUebE55X00s+V6sDub5T2ys+VAci9+qjT0mW9/qiy33hM1anz1V+WpdOV2fqwKk8fVaORy3PHJ6vydbpxq84cKtPV94/p+rWL+vrWzb87gYeoJBaI+/73v++cGNgvddasWWLbLbbaYr9UWw9HeBGACO0bwBWEo5GemxbvXubR5sl4rPVDA4c2LqiJ84MXxwZfQwFctHLa+PuxPu4G3gz0hoI4xkDjt3nzZrdjA84NbW1tztOYPWd5V4aauIfohz3crfDCGY3mh3amiSC/mxcXL+8gFPrnbZoY6vAFjRWIrA/uzyDQP0+DBuuXOoCDwQPn/vrW31C5jUF7fzvG8c/ZP15wDAMxfx2T81DjWrm1NXAJ9u2/L67552RtGJcP87f7sdzGiZb7ZUxf/vY2fysz2fjl4m/PeP7nQH92nXwkn+B9WVv6sXlZGfMymQx3nbp2H/66yM9kaP2GeewSAOrwbD3T362eAw1qL9qh2s05Ks6M0674hdq+fJa2L52h95bOUOPmdPXWbNGpxl36qL1E5zobdOXUUX12/rRuXLukr2/eiH3gh6DmcBBHEFicCJ588kmtXLlSBPl97rnn9JOf/MQ5NfjjwwFFaLIANvNUJR8pwFH/Xmnegv0G5wbEcW9o3wC4sYA4wCqYDLos9wMccd2sfKT5UBAHTLLujnVxxcXFam9v15EjRxyoAuD8/Qwh7iH4Qcd6C7yIRvpitL55CfKFCb4M7XosOS9Ze3HGWp8xY31R+l+wBjHkjEk/JHsZW5nNhzG4zhyH+9AH/fKhvfVp59Yn5/75B8cwmVp7ux7L/Vpbe57W1k0qYg60Z+WXBdejwY7VieX+6YM527w5D45PPzZ+UNbUD8rNxrdnYHM0OdNmuI+1CcrP/72wPpi7PafhrtOGeZGsDWPZ/fvnbvds44R5KIF7IYHhIA7PRcCHvTb5TuKZyn6pmBxxaghCHBAUBKPRnAdh616fM0czB6NlxJxKCkJcNC0aZf4U1L4FAW6oc4ANgIsF4qyePzfgs/5tTjYfII4Aw3v37nVx4jCp0uaHP/yhiAUYQty9+IU9oH3y0rGX/kinSFteYvbiGml76vMC5OU3kg9/gGIZkxf3ne4tCA30669vL+JY5ncnOTKOvej998l4BgI2RnAOJqNo7f19cRxsO9T8Da4Y24CI8Tn3w85Q7YPj2rn1Gzy3PsntPqkTlAvn/ucabfzgHG2sofJo90Xd4SBtuOvRxvPPnfZ2v345R2sXloUSGAsJDAdxv/RLv+Sg7I033hCBfidOnKjHH3/cBcRl0/uHDeKA0L/6q79y6+GGgjiDo6FygybLDaqGy8cK4gzmbHzLDeLYuaGpqckF/cXEyt/QO4UZCb1Tx+KX9oD1cSf4uNNUaed/OftfYHdq578GdBhE+MuHO6ZNLOMFoSbYbxAagvWjQUSwDzs3WOCFHXxpB2GFcahj98+xwU1wDvTPvdLHcJ9g2+D8eV6MRX82X5O/ndszZaxg++HG5zr9Wx/Mxz/34DMLyiX4PKKN7+8/lvlEuy/aMZdo87Gy4a4Hx7ZnSbmNabKNBoTB9uF5KIG7lUAsEEd8tvHjxzuII2e7Jn53eKzygjfTKdqysdLE0c/9SKbhM03cwwxxaO3wUN2xY4dzaujs7HTmVczHwDp/J6OlEOLu9lf2ALbnZcVLZiQffvT2orZ2nFMe64f6vPhG86FdcPxo/VCHL/JQnyA00K9fFtEgYqi+/OX24+FlzodxTDbBPoPnwTlY+1hkFWzr7zsIFkOd++Xqb++/vzsd833iXmlLX/4+gvfgl4vdp0EU5/62Niay9c/RyofK7T6DbTj3P2vac86YfIa77h+PNv57szGtL/r1X/e3DY9DCYyVBIaDOGKI4X1KjDg0cWjkHnnkEbFTwx/90R/9rZ0axhLiDK7uV/6LAHFoAgn4u337dvc3q7u72x2zE8cv//IvRwU4/naGEDdWv7YHqJ/RQNzdTp8XpP9lTX+85Oyld6f+qWNAdKd6do0vrr8+L1h7oVLunwfl/hc7Y9GeNsN96Mc/f387P6xYn1aXMf11g+fIiuuxfO40fwML+uNj/do8gtepY3ON5f5tftaGudgHmfoBycr9cqHMngfjMT/ryz8+srB7sH7ulEe7L6vPnKwv5sv4/s9w162u/ztkZdbWxjc52/UwDyUw1hKIBeJ4iU+ePFlLlixxuzbQBs9UCy9yLzRx9wvebBxbt4djBgFwCS/ybZhTzRQ6XO5fC2fHwTZmRrUciCPMiO2h2tXVpd27d+uJJ57QP/pH/yiEuLH+cT2o/fFC9Kf78aIxSPGPy7H/BcoLkMTHXoJW318vVrlaW3Lrl9zKDR7snNwgx8qGk41BgNU3OKBvK7O5+8sAALtuY/hlZPMd7l79/dBftPn76/iPExMTB+dgbf3XKbO5DzcPrlOfe7APx8H78MvA+rb7pm5w/sFzf/82TjC3/pjPUPdg14Lzs76Gu047P2RaO//3Npa5WrswDyUwWgnEAnGsewPiFixYoFdffdWtGbPwImZOBYKCQGRgFEtube937p8bYxPsl90oCC8CxOGl6o8T5w8NMtoQI8OtjeN6EMhGc04/BnCs36MPNHFbtmxRVVWVWxO3b98+523Mrhz2dyuYh5q40f66wnahBEIJhBIIJRBK4B5KYDiI+5Vf+RX9/u//voM4C/T705/+VD/60Y9cObs1WBgRAzA/GMV6fD/WvwXHYL7B+aGJY83fnSDOwM7gzkAp1jyEuHv4hQ67DiUQSiCUQCiBUAK/KBIYDuLQ0PyP//E/HMTNnTvXBfoF4tj4/vd+7/fcnqpjAXEGgPc7D0IcoIf3rT9GnMFaUAsXauJG/iuJbZHPyPsNW4QSCCUQSiCUQCiBXzgJxAJxP/jBDzR16lQZxBFHDYhDQ4fTw8MEcUAkgY392rYgxPlDi0TTvsWiaRuuTjTzKWvfopUHy/xr5MykGs2c2tHRof3794fm1F+4X314w6EEQgmEEggl8FBIYCiIs8Cv3/ve91ww2IULFzrHBtbEsfCfNXEEif1v/+2/6T/9p//kEqFIiP5v55ZTHkxcs/q0CV6nzJ/MeSJaWbRr1LNyy/1thzoG4p5//nkXcNdAyw9tABPx1khsZE+yc8utfCQ58drulPLz80W6Ux275q9r82PeXGedMHHiGhoa1N/fr4qKChe8meccXAtn55jMx+oTauLGSpJhP6EEQgmEEgglEEoglEAogfsogRDi7qOww6FCCYQSCCUQSiCUQCiBUAJjJYEQ4sZKkmE/oQRCCYQSCCUQSiCUQCiB+yiBEOLuo7DDoUIJhBIIJRBKIJRAKIFQAmMlgRDiVd0n7AAACzdJREFUxkqSYT+hBEIJhBIIJRBKIJRAKIH7KIEQ4u6jsMOhQgmEEgglEEoglEAogVACYyWBEOLGSpJhP6EEQgmEEgglEEoglEAogfsogRDi7qOww6FCCYQSCCUQSiCUQCiBUAJjJYEQ4sZKkmE/oQRCCYQSCCUQSiCUQCiB+yiBEOLuo7DDoUIJhBIIJRBKIJRAKIFQAmMlgRDixkqSYT+hBEIJhBIIJRBKIJRAKIH7KIEQ4u6jsMOhQgmEEgglEEoglEAogVACYyWBEOLGSpL3qB8212XT3BMnToz5CPRJ34wR6+e3f/u39frrr8da/VurxxyZ62g+f/qnf/pA3OP9nMfdjFVXV+e+R+T+j32/bNNn/7W7OY7lNzHS7/XdzCdsG0oglEAogW9LAiHEfVuSj2Fce1ndC4jzv2BjhTigiLk86BDH/JjnaCAOmHkQ7vF+zuNuxjKAQ2ZBiKPM/vPB8fLly2P41t99FcYixfq9vvsRwx5CCYQSCCXw7UgghLhvR+4xj2ogZy/DmBvGUNFAbiQvu4dREwf0+eV7N1qpGMQ+ZBUgxw9C93MedzOWgZx/7nynRgPRQwpnmAvM3/8JIc4vjfA4lEAogYdVAiHEPeBPNoS40T2gWM2pBiDfNsQZUPtB6G7AaqRSu5uxTIb+uQOk9wvi7Dfiv+cQ4vzSCI9DCYQSeFglEELcA/Bkzfzjz81kaS8oP2T463FsH3uZUpcXKNeCL1KrwzVetOSMYeNwTnuDCrtuY9CfzY0y64N6JM7tQz3ggBS8ZnX8OfWtH3LmZB+DDP88/depZ+PYHIP3bn1Z7u+L8Whv/TAX//XgWPRtc/3/2zsXG6mVIIqSC8EQC6EQCSIO4iAWnu5K5+2lZI89jMezRqelpd2f+vRpQ13NgOgz43upb6axxa7vJPPkvefMa3ncy34r1q17Jv/0aZ1Tc11i0n7zjK/m0HPxgU0/Z39+cu60POfO+p0Kf5sEJCCBf4nAuwL4l051obOk4FF4KPIpUjSEBAWI4sx6itUsfpnLfvwhQCiG7YtiF39zPXO9nvFSvrFL6+LaxZP1t00rv8zYsU+sNMRZcoFVr7OHNc6N/UrIt+kZF18zFgIv6/ELQ2LBeC0W+7AjLnfNerPi3Jwr/Z48si/556f9reW2deaZW99zbDlLx8qePfzbvvNL7s0UBsTOOo3fI4zTc358JBdY9z6fJSABCVyZwPufhFc+xYVzn8UqRZqClWNRoCj+HJXCGnuKE8W097b/+GZv/OCDQrdlH5sUw86PfFo49FyLDua3+tgk7xYBk0vOwTp5t9/kw3rPz2dsm9meWMmvf5aYdKyszz05Q3ykcRfJh/ZIHokV+73tVqz2Eb+cm3kYdu59P+y71ccn72H2Zdz593vb3LKX3yPtf/qb5+u9PktAAhK4KgFF3ItvbhbbWXwoUIgMij0CJT0FjmLK3hyt/fXerOGL4rllH5v4aDFCUY8PcgXpPBvza30KbfJNHlMEzCLc6zNu/Cc2jNbiZX7pzFuxsn5vmz5jT965B+4i+dCmzTzzrTz+hn1saB0rc1njXSJv9sKwc5/27F3rO9/4J0a45Hn6Ti409jJOT67MTZbM20tAAhK4MoH3PwmvfIqL556Cw08X0hyLApViljaFWI8ppuzN/i5mU4AhHBIjbcue+ORIbm/GlSvjLszMrfWz6M/xLMK9Th597sR+hoiD0do51uaT/8yHvGPDXbRYuXXmrTzuYZ/4t2J1ntk7x+TSuff9rDHpeXyEA+8X73Z8dcs47zVt5pP5fu8znufD1l4CEpDAlQm8/0l45VNcOPcUlxYf8ygUKPa0EKPwU+S6EOKnixnFj2LLmD3Yr63HZ8efuaX4dnG9R0iQC3mHS4ueWYSzv9cTN3toGeen97DWPWcOy/hM24oVnx0rNgiP9t3PxOl98UFM7jL7mHskj3vYb5156545W3ravB/mb/Xhmrzxg49mFvvM93tGfqylz3rmaZMl8/YSkIAErkxAEffi20vhQnB0n0JGcWI+hb7nEBOsd9/7Mo8wSEFkH8+z2LFOsWS9c6Wwzjlsez7Pexq26cktz+2LYtx74xsRxHzs742bc+6JlXjEod9zPsQONtwJtsTOPM/Zu3bmtTyynxh7GOyJ1Xv6bn78+PF/rMTMGXtv5uY5Oe/s551xp4i67Oed5HyZY1/mlt77zidsbBKQgAT+FQKKuBff5FqBW5t/cbqGl4AEJCABCUjggxBQxL3wIiLUlj4ZyHx/+vDCFA0tAQlIQAISkMAHJaCIe/HF9FdffEXkp3AvvhTDS0ACEpCABC5AQBF3gUsyRQlIQAISkIAEJDAJKOImEccSkIAEJCABCUjgAgQUcRe4JFOUgAQkIAEJSEACk4AibhJxLAEJSEACEpCABC5AQBF3gUsyRQlIQAISkIAEJDAJKOImEccSkIAEJCABCUjgAgQUcRe4JFOUgAQkIAEJSEACk4AibhJxLAEJSEACEpCABC5AQBF3gUsyRQlIQAISkIAEJDAJKOImEccSkIAEJCABCUjgAgQUcRe4JFOUgAQkIAEJSEACk4AibhJxLAEJSEACEpCABC5AQBF3gUsyRQlIQAISkIAEJDAJKOImEccSkIAEJCABCUjgAgQUcR/kkr58+fL78+fPi9l8//7996dPn95+8jzbrfVe+/nz5zR9G6/FnZtjTx57baYPxxKQgAQkIAEJHENAEXcMx7/28uvXr5vCKCKsBVOeW8jdWsd3esTcTDTicU/DV/by3Hns8eEeCUhAAhKQgASOI6CIO47lQ56+fv36h1jD2T2iLTYt6lq4Ibz607hv37797jExl/rs3Sv4yEORt0TSOQlIQAISkMAxBBRxx3B82MuSiEN4paf1XD8vrd8ScbGNMNvbkt89Ii5fuyri9tJ1nwQkIAEJSOB+Aoq4+5k9xWJJxLUIIyjCLZ+gba2zN/3ce68g4+/CpU+uafmUsOcTh5g9T+72EpCABCQgAQkcR0ARdxzLhzw9Q8QloXzahqDiq9PEithKi5hj/dYBYtPCL37n39VD3MVPfPpJ3C2irklAAhKQgAQeI6CIe4zfYdbPEnEzQT7By3xEFkJsirRpd2sdEaiIm9QcS0ACEpCABJ5HQBH3PLZ3eV4ScXw1yadmcRgRFtGUua31pQT607QWZvPr1mnbe1lDvGUcv4o4yNhLQAISkIAEnk9AEfd8xrsiLIm4GC7969QWYlvrHbztMt/C7F4RN0XbHPt1apP3WQISkIAEJHA8AUXc8Uz/yuOaiOuvPOM44oi/25bx1jrJZF/bTdsWdNh0P9cj2loUJq/soSHiErc/SWTdXgISkIAEJCCBxwgo4h7jd4h1BE//TLHV/zghomi2rfXYtMBq+8wTu+f7mXV6BCHj9PhB2KVnvn35LAEJSEACEpDAMQQUccdw1IsEJCABCUhAAhI4lYAi7lTcBpOABCQgAQlIQALHEFDEHcNRLxKQgAQkIAEJSOBUAoq4U3EbTAISkIAEJCABCRxDQBF3DEe9SEACEpCABCQggVMJKOJOxW0wCUhAAhKQgAQkcAwBRdwxHPUiAQlIQAISkIAETiWgiDsVt8EkIAEJSEACEpDAMQQUccdw1IsEJCABCUhAAhI4lYAi7lTcBpOABCQgAQlIQALHEFDEHcNRLxKQgAQkIAEJSOBUApsirv9/TJ///D9O5SEP3wHfAd8B3wHfAd+BZ7wDe9Tgpojb48Q9EpCABCQgAQlIQALnElDEncvbaBKQgAQkIAEJSOAQAoq4QzDqRAISkIAEJCABCZxLQBF3Lm+jSUACEpCABCQggUMI/AeFGgA+fBIadwAAAABJRU5ErkJggg==\"\u003e\u003cbr\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Test protocols\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Linearity of iodine signal of varying concentration in image\u003c/h2\u003e \u003cp\u003eTo assess the system response to changing iodine concentration levels, recombined images of BR3D and CESM phantoms with varying thicknesses acquired were analysed. The response was evaluated based on two related values: (i) mean pixel value (MPV), as per Cockmartin et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and (ii) signal difference to noise ratio (SdNR) for each insert with different iodine content, as per the RANZCR protocol [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Circular regions of interest (ROIs) were drawn over the inserts (1 \u0026times; 100% glandular insert, 4 \u0026times; iodine inserts with varying iodine concentrations from 0.25 mg/cm\u003csup\u003e2\u003c/sup\u003e to 2.0 mg/cm\u003csup\u003e2\u003c/sup\u003e) as well as a background reading for each ROI. The background signal was taken as the average value of four adjacent areas surrounding the ROI (using an offset of 1.4 mm). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the examples of these ROIs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Background cancellation\u003c/h2\u003e \u003cp\u003eQuantitative assessment on background cancellation was carried out by (i) calculating the CoV from the background ROIs used in the SdNR assessment, and also by (ii) calculating the tissue cancelation efficiency (TCE) from the same images as per Cockmartin et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTCE =\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPVglandular - MPVbkg1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPViodine(0.5) - MPVbkg2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMPVglandular is the MPV from the 100% glandular insert,\u003c/p\u003e\u003cp\u003eMPViodine(0.5) is the MPV from the 0.5 mg/cm\u003csup\u003e2\u003c/sup\u003e iodine insert, and\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMPVbkg1,2 are the MPVs from the background regions corresponding to the inserts used for the MPVglandular and MPViodine(0.5) measurements, respectively.\u003c/p\u003e \u003cp\u003eTCE is interpretated as the relative visibility (contrast) of the 100% glandular insert with no iodine content to its background, compared to the contrast of 0.5 mg/cm\u003csup\u003e2\u003c/sup\u003e iodine insert to its background in a recombined image. Image signal due to anything other than iodine content should be eliminated in recombined images so ideally TCE should be close to 0.\u003c/p\u003e \u003cp\u003eTo gain more insight into the recombination process, the effect of mAs on TCE was also investigated. This was only performed on the GE unit since Hologic systems do not allow manual selection of both LE and HE exposure factors for CEM procedures. A 3 cm thickness phantom was used. For the effect of varying LE mAs, HE exposure was kept constant to 63 mAs while LE mAs varied from 10 to 300, and for the effect of varying HE mAs, LE exposure was kept constant to 32 mAs while LE mAs ranged from 10 to 300.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Linearity of image signals for varying iodine concentrations\u003c/h2\u003e \u003cp\u003eThe relationship between image signals within recombined images of CEM in terms of MPVs and SdNRs for varying iodine concentrations is presented in Fig.\u0026nbsp;2. The Hologic unit exhibited little difference in MPVs for iodine concentration of 0.25 mg/cm\u003csup\u003e2\u003c/sup\u003e to 1.0 mg/cm\u003csup\u003e2\u003c/sup\u003e although signal differences clearly exist in SdNRs at all levels of iodine.\u003c/p\u003e \u003cp\u003eSince ideally the image signals (either MPVs or SdNRs) should be linearly related to the iodine concentrations, the linearity was evaluated as the coefficient of determination, R\u003csup\u003e2\u003c/sup\u003e, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Background cancellation\u003c/h2\u003e \u003cp\u003eExamples of corresponding LE and recombined images are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which demonstrates substantial removal of the swirl patterns in recombined images for both GE (top) and Hologic (bottom). The recombined images were examined with a narrow display window, arbitrarily selected on each image to highlight any variations caused by non-iodinated materials in the background. An example of this is shown on the right column of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, confirming the disappearance of all non-iodine materials including the swirl patterns of the BR3D phantom as well as the 100% glandular inserts in recombined images. Manufacturer-dependent appearances were noted in these images as: (i) 100% glandular inserts were visible in GE; and (ii) gradient in the background pixel values is evident in Hologic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the quantitative assessment, the CoVs of MPVs from each background ROI for the GE and Hologic were calculated to be 0.002823 and 0.000675, respectively. Although both CoVs are very small, about an order of magnitude difference between manufacturers was present, reflecting the relatively larger non-uniformity of the background MPVs for breast shape phantom in Hologic compared to GE. TCE metric results for both GE and Hologic units are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Although some inter-measurement variations are shown, general trends exist for both units that are opposite: TCE in GE decreases as the phantom thickness increases, whereas that in Hologic increases with thickness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results for the effect of varying mAs on TCE performed on the GE system, where TCE appears to be low with low LE mAs and high HE mAs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eContrast-enhanced mammography is a relatively new mammographic technique with unique features; it involves an extra exposure with higher energy X-rays than that for a typical mammogram and then combines the two images acquired at two different X-ray energies by a particular implementation of weighted logarithmic subtraction, which may be vendor specific. In this paper, a single phantom combination was used to conduct tests of key importance to the CEM process.\u003c/p\u003e \u003cp\u003eIn terms of utility, the phantom was easy to use, and yielded the required data which were consistent with those previously reported in the literature [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The authors feel it is suitable for assessing linearity and background cancellation for a CEM unit.\u003c/p\u003e \u003cp\u003eThe results of these tests also highlighted several differences between the two vendor systems included in this study. Although both systems exhibited R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.95 for SdNR versus iodine concentration, MPVs alone from Hologic were not able to distinguish the differences in iodine concentrations between 0.25 to 1.0 mg/cm\u003csup\u003e2\u003c/sup\u003e, a result similar to Cockmartin et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This appears to be due to higher variance in background MPVs in recombined images for Hologic than GE. Such high variance in background MPVs in Hologic recombined image (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e) is evident where the background around the inner breast (towards the centre chest wall) appears brighter than around the nipple edge, possibly due to a thickness correction overcompensating the inner breast region and introducing the variations in pixel values in uniform background.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the variation in the background tissue cancellation metric (TCE) depending on thickness of the breast where lower TCE values (closer to '0') would indicate the more effective removal of the glandular insert in recombined images relative to the iodine insert with 0.5 mg/cm\u003csup\u003e2\u003c/sup\u003e concentration, GE appears to be more effective for thicker breasts whilst Hologic shows the opposite. Combining all the observations (mainly results from TCE and MGD tests), it may be concluded that GE CEM is better suited for assessing the thicker breasts, and Hologic for assessing thinner breasts.\u003c/p\u003e \u003cp\u003eA major limitation of the tests applied in this study was that system performance for varying glandularity was not assessed due to lack of available phantom materials. As an important future application of CEM may be in screening women with dense breasts, it would be important to thoroughly characterise the system responses to different breast compositions. Preliminary tests that could mimic the different glandularity were with 2.5 cm thick slab with half made up of 100% glandular and the other half with 100% fat was used, which showed very little differences in MPVs (\u0026lt;\u0026thinsp;1% of the average) in all inserts of varying iodine concentrations and 100% glandular for different background materials.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA set of phantoms was used successfully to carry out fundamental QC tests for CEM units, demonstrating its applicability in CEM QC programs. The results of these tests also highlighted some key differences between vendors in the way system handles differences in iodine concentration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have no relevant financial or non- financial interests to disclose.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThe study did not require ethical approval.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eNo human beings were participants in this research project.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNo human beings were participants in this research project.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by a grant from the Royal Perth Hospital Radiology Research Grant.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation and analysis were performed by J-H Kim, M Kessell, D Taylor, M Hill and J W Burrage. Data collection was primarily performed by J-H Kim with assistance from those named above. The first draft of the manuscript was written by J-H Kim and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was supported by a grant from the Royal Perth Hospital Radiology Research Grant. Support was also received from the Western Sydney Local Health District (WSLHD), NSW, Australia. The authors also acknowledge the valuable contributions to data acquisition from the following medical physicists: Chris Leatherday, Anita Reed, Danielle Hudson (Royal Perth and Fiona Stanley Hospitals) and Lesley Maddox (Sir Charles Gairdner Hospital).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee CH, Dershaw DD, Kopans D, Evans P, Monsees B, Monticciolo D Breast Cancer Screening With Imaging: Recommendations From the Society of Breast Imaging and the ACR on the Use of Mammography, Breast MRI et al (2010) Breast Ultrasound, and Other Technologies for the Detection of Clinically Occult Breast Cancer. 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Australas Phys Eng Sci Med 40(3):491\u0026ndash;543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13246-017-0583-x\u003c/span\u003e\u003cspan address=\"10.1007/s13246-017-0583-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Contrast-enhanced spectral mammography, contrast-enhanced mammography, test object, phantom, system performance, quality control","lastPublishedDoi":"10.21203/rs.3.rs-4091254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4091254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eContrast-enhanced mammography (CEM) is being increasingly implemented clinically, providing much improved contrast between tumour and background structures, particularly in dense breasts. Although CEM is similar to conventional mammography it differs via an additional exposure with high energy X-rays (\u0026ge;\u0026thinsp;40 kVp) and subsequent image subtraction. Because of its special operational aspects, the CEM aspect of a CEM unit needs to be uniquely characterised and evaluated. This study aims to verify the utility of a commercially available phantom set (BR3D model 020 and CESM model 022 phantoms (CIRS, Norfolk, Virginia, USA)) in performing key CEM performance tests (linearity of system response with iodine concentration and background subtraction) on two models of CEM units in a clinical setting. The tests were successfully performed, yielding results similar to previously published studies. Further, similarities and differences in the two systems from different vendors were highlighted, knowledge of which may potentially facilitate optimisation of the systems.\u003c/p\u003e","manuscriptTitle":"The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 19:49:25","doi":"10.21203/rs.3.rs-4091254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-04-29T04:01:52+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-03-18T02:10:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-16T12:32:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2024-03-14T22:09:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-14T10:14:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2024-03-13T18:23:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"963b56aa-53cf-4f7d-bf76-e40209a667c0","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T00:31:03+00:00","versionOfRecord":{"articleIdentity":"rs-4091254","link":"https://doi.org/10.1007/s13246-024-01461-6","journal":{"identity":"physical-and-engineering-sciences-in-medicine","isVorOnly":false,"title":"Physical and Engineering Sciences in Medicine"},"publishedOn":"2024-07-02 00:31:03","publishedOnDateReadable":"July 2nd, 2024"},"versionCreatedAt":"2024-03-20 19:49:25","video":"","vorDoi":"10.1007/s13246-024-01461-6","vorDoiUrl":"https://doi.org/10.1007/s13246-024-01461-6","workflowStages":[]},"version":"v1","identity":"rs-4091254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4091254","identity":"rs-4091254","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}