Tables and Figures Clause Samples

Tables and Figures. Where graphs or tables are used, all source data files must be supplied at the initial stage. Tables and Figures should be of reproducible quality, should include captions and should not duplicate material presented in the text. All Tables and Figures must be cited in the text. Tables should be numbered consecutively in the order of their first citation in the text. The table number and caption should be placed consistently throughout the report (either above or below the table). The default style is for the table number and caption to be placed above the table. The same guidance applies to numbering of Figures. Where figures have been imported from other software packages (e.g. graphs imported as pictures from Microsoft Excel) care must be taken to ensure that legends and axis titles are large enough to be legible in the printed report – see guidance on line artwork below. Explanatory notes should be placed in footnotes, not in the table or figure heading. Explain in footnotes all non-standard abbreviations. Contractors should check with the DCLG/WG/SG what the publication route is likely to be (web or hard copy). This may influence the format and file size of any pictures and images either embedded in the report or provided separately. For printed reports, all pictures and illustrations (line drawings, diagrams, graphs, boxes, photographs etc) must be suitable for high-quality reproduction at their intended size in the final publication. DCLG/WG/SG will not accept sub-standard illustrations, such as photocopies, pencil sketches, rough drawings, distorted or damaged material, or photographs that are too light, too dark or out of focus. Images scanned from printed media or downloaded from the Internet are not acceptable. Please supply each illustration both as an electronic file and in hard copy form (the latter should be a good-quality printout). You may also embed low resolution copies of the illustrations in your main document to give COMMS an indication of size and positioning. If you do this, please supply a separate text-only document, indicating within it the position of each illustration. Figures, pictures etc provided as electronic files should be identified with a file name that matches the number given to the figure, picture etc in the report e.g. Fig04.04.tif or Fig12.01.wmf. Printouts should be labelled with the corresponding electronic filenames. Note that all illustrations – drawings, graphs, photographs etc – should be identified as Figures...

Tables and Figures. Figure 1: Conceptual Framework of ▇▇▇▇▇ Caused by 4 Conscientious Objection to Abortion Provision Figure 2: Map of Uruguay 18 Figure 3: Scale Model of Gynecologist Decision-Making 43 Regarding Abortion Provision in Montevideo, Uruguay Table 1: Quotes Supporting Gynecologist Positions toward Abortion in Each Category 44 In October of 2012 Uruguay enacted Law 18.987, one of the most liberal abortion policies in Latin America, decriminalizing (removing criminal punishments for) first-trimester abortions for any reason, up to 14 weeks gestational age in cases of rape or incest, and at any time for fetal malformations incompatible with life or to save the life of the mother (Chamber of Representatives, 2012). Although this law was meant to expand access to abortion and reduce maternal health complications associated with clandestine abortions, several potential barriers to increased access have been identified. One serious barrier to abortion access is provider’s conscientious objection to abortion. Under Law 18.987 gynecologists and health care organizations with religious or moral objections to abortion have the right to abstain from providing legal abortions. According to Uruguay’s Ministry of Health, approximately 30% of gynecologists nationwide are registered as conscientious objectors to abortion provision. In some rural regions, 100% of gynecologists have registered (Presidencia de la República del Uruguay, 2013). High levels of provider conscientious objection can seriously impede patients’ access to abortion services by delaying provision of services and creating other barriers such as the need to travel or to use more expensive private clinics (▇▇▇▇▇▇▇ et al., 2013). Despite widespread use of conscientious objection to abortion and its potential impact on access to abortion services, little is known about gynecologists’ decision-making process or rationale behind abortion provision in Uruguay. The purpose of this study is to address this knowledge gap in Montevideo, Uruguay in order to mitigate against issues arising from conscientious objection and to determine gynecologist attitudes towards legal abortion provision. Abortion is highly restricted in the majority of countries in Latin America. In part due to this restriction, the region’s unsafe abortion rate is the highest in the world (32 per 1,000 women) (Guttmacher Institute, 2012). Legal restrictions on abortion are known to cause high levels of unsafe abortion and there is a proven link between ...

Tables and Figures. Table 1.Characteristics of patients and information for the 10 provinces in 2004 – 2006 Province Year Population (*million) Average of age Total cases No. of new cases（%） No. of previous treated case（%） No. of Male cases（%） Jilin 2004-2005 27.1 45.58 349 276(79.1) 73(20.9) 249(71.4) Beijing 2004 15.4 45.99 108 91(84.3) 17(15.7) 66(61.1) Zhejiang 2006 47.2 41.09 97 72(74.2) 15(25.8) 53(54.6) Fujian 2004-2006 35.1 49.00 479 323(67.4) 156(32.6) 319(66.6) Henan 2004-2005 97.2 49.37 98 65(66.3) 33(33.7) 65(66.3) Hunan 2004 61.6 45.98 102 47(46.1) 55(53.9) 74(72.6) Xinjiang 2005-2006 19.6 46.85 186 125(67.2) 61(32.8) 112(60.2) Gansu 2004-2006 26.2 39.12 220 193(87.7) 27(12.3) 134(60.9) Sichuan 2004-2005 86.5 45.98 106 56(52.8) 50(47.2) 78(73.6) Guangxi 2006 28.5 51.63 209 140(67.0) 69(33.0) 147(70.3) Total 2004-2006 444.4 46.49 1954 1388(71.5) 556(28.5) 1297(66.4) Any resistance to INH 217(15.6) 205(36.9) 422(21.6) Any resistance to RFP 184(13.3) 192(34.5) 376(19.2) Any resistance to SM 237(17.1) 153(27.5) 390(19.9) Any resistance to EMB 106(7.6) 117(21.0) 223(11.4) Single first line drug-resistant 168(12.1) 51(9.2) 219(11.2) Resistance to INH only 53(3.8) 21(3.8) 74(3.8) Resistance to RFP only 25(1.8) 10(1.8) 35(1.8) Resistance to SM only 72(5.2) 14(2.5) 86(4.4) Resistance to EMB only 18(1.3) 6(1.1) 24(1.2) Multiple drug-resistant 116(8.4) 164(29.5) 280(14.3) INH+RFP 22(1.6) 33(5.9) 55(2.8) INH+RFP+SM 32(2.3) 40(7.2) 72(3.7) INH+RFP+EMB 4(0.3) 21(3.8) 25(1.3) INH+RFP+SM+EMB 58(4.2) 70(12.6) 128(6.5) Multiple drugs resistant 91(6.6) 38(6.8) 129(6.6) INH+SM 36(2.6) 13(2.3) 49(2.5) INH+EMB 6(0.4) 4(0.7) 10(0.5) INH+SM+EMB 6(0.4) 3(0.5) 9(0.5) RFP+SM 29(2.1) 5(0.9) 34(1.7) RFP+EMB 10(0.7) 7(1.3) 17(0.9) RFP+SM+EMB 3(0.2) 4(0.7) 7(0.4) SM+EMB 1(0.1) 2(0.4) 3(0.2) INH 0.29 1.85 1.03 4.59 5.1 2.94 8.6 3.18 0.94 7.66 RFP 0.29 0 5.15 0.42 1.02 2.94 1.08 5.91 1.89 2.87 SM 0.29 8.33 2.06 2.3 3.06 0.98 5.38 19.55 0.94 2.39 EMB 0.29 0 15.46 0 1.02 1.96 2.15 0.45 0 0 SDR-TB 1.16 10.18 23.7 7.31 10.20 8.82 17.21 29.09 3.77 12.92 INH+RFP 0.57 1.85 0 2.71 0 5.88 2.15 0.91 15.09 4.78 INH+RFP+SM 1.15 1.85 1.03 6.68 2.04 5.88 2.15 2.27 11.32 1.91 INH+RFP+EMB 0.57 0.93 3.09 1.88 0 2.94 0 0.45 2.83 1.44 INH+RFP+SM+EMB 2.01 0 8.25 15.66 6.12 9.8 1.61 0.91 9.43 3.35 MDR-TB 4.30 4.63 12.37 26.93 8.16 24.51 5.91 4.55 38.68 11.48 Male 140（10.8） Reference 193（14.9） Reference Female 77（12.5） 1.18（0.88-1.57） 86（14.0） 0.89（0.69-1.16） New cases 141(10.7) Reference 112(8.5) Reference Previousl...

Tables and Figures. Figure 2.1. Acculturation Conceptual Framework Figure 2.2 Literature Review Eligibility Flowchart

Tables and Figures. Figure 1. Screenshot of Diabetes App Lite by BHI Technologies, Inc. Figure 2. Screenshot of Glucose Buddy by Azumio Figure 3. Prevalence of functionalities found in selected glucose tracking apps Table 1. Survey results, all respondents (n=1601) Number (%) Country Do you have diabetes? Smartphone platform Table 2. Survey results among patients reporting a history of diabetes (n=588) Number (%) Country Diabetes type Do you use insulin? Do you use insulin? (type I Do you use insulin? (type II only, n=408) Do you use a diabetes app? Table 3. Characteristics of app usage among diabetic respondents reporting use of diabetes apps (n=18) Number (%) Language in which app is used How much did you pay for the app? Proportion of respondents reporting frequent use of the following documentation functionalities Proportion of respondents reporting frequent use of the following reminder features Information sharing

Tables and Figures. Table 1 PALOMERA partners 6 Figure 1 Project management and governance chart 7 Figure 2 PERT chart 8 FIGURE 3 CONFLUENCE ▇▇▇▇▇▇▇▇ PIMS 12 Table of Acronyms ALLEA European Federation of Academies of Sciences and Humanities AMU Université D’Aix Marseille CC 0 Creative Commons Public Domain Dedication CC-BY Creative Commons Attribution International Public License CoNOSC Council for National Open Science Coordination CRAFT-OA Creating a Robust Accessible Federated Technology for Open Access DACH Deutschland (Germany), Austria, Confœderatio Helvetica (Switzerland) DARIAH The Digital Research Infrastructure for the Arts and Humanities DESCA Development of a Simplified Consortium Agreement ▇▇▇▇▇▇ ▇▇▇▇▇▇▇▇▇▇ Institutional Open Access Publishing Models to Advance Scholarly Communication DoA Description of Actions EC European Commission ED&I Equity, Diversity & Inclusivity ERA European Research Area ▇▇▇▇ European Research Infrastructure Consortium ESF European Science Foundation EU European Union EUA European University Association ExCom Executive Committee FAIR Findable. Accessible. Interoperable. Reusable. IBL PAN Instytut Badań Literackich Polskiej Akademii Nauk IPR Intellectual Property Rights JISC Joint Information Systems Committee KER Key Exploitable Result KPI Key Performance Indicator LIBER Ligue des Bibliothèques Européennes de Recherche – Association of European Research Libraries M# Project Month <number> MWS ▇▇▇ ▇▇▇▇▇ Stiftung NB Nota bene OA Open Access OABN Open Access Book Network OABT OAPEN OA Books Toolkit OAeBU Open Access eBook Usage Data Trust OAPEN Open Access Publishing in European Networks OASPA Open Access Scholarly Publishing Association OATP Open Access Tracking Project OBP Open Book Publishers OER Open Educational Resource OPERAS Open Scholarly Communication in the European Research Area for Social Sciences and Humanities OS Open Science PEDR Plan for Exploitation and Dissemination of Results (while in this document it will be referred to as Dissemination, Outreach, Engagement, and Exploitation Plan) ▇▇▇▇▇▇▇▇ Policy Alignment of Open Access Monographs in the European Research Area PCT Project Coordination Team PESTLE Political, Economic, Social, Technological, Legal and Environmental factors PIMS Project Information Management System RFO Research Funding Organizations RPO Research Performing Organizations SPARC Europe Scholarly Publishing and Academic Resources Coalition SSH Social Sciences and Humanities ▇▇▇▇ ▇▇▇▇▇ August Universität Gö...

Tables and Figures. Table 1. Population characteristics for both leprosy patients and healthy controls. Figure 1. Distribution of log transformed levels of IFN-γ, TNF-α, IL-10, IL-4 and IL-17 in leprosy patients and healthy participants with and without schistosomiasis infection. Table 2. Analysis of Covariance (ANCOVA) of group differences in IFN-γ distribution controlling for age, sex, and vitamin D deficiency Table 3. Analysis of Covariance (ANCOVA) of group differences in TNF-α distribution controlling for age, sex, and vitamin D deficiency Table 4. Analysis of Covariance (ANCOVA) of group differences in IL-4 distribution controlling for age, sex, and vitamin D deficiency Table 5. Analysis of Covariance (ANCOVA) of group differences in IL-10 distribution controlling for age, sex, and vitamin D deficiency Table 6. Analysis of Covariance (ANCOVA) of group differences in IL-17 distribution controlling for age, sex, and vitamin D deficiency

Tables and Figures. Metabolite 250 µL, 2 hr 500 µL, 2 hr 1000 µL, 2 hr 500 µL, 4 hr Average RR Calc. Conc. RR Calc. Conc. RR Calc. Conc. RR Calc. Conc. MBzP 0.13 1.71 0.14 1.81 0.13 1.72 0.13 1.72 MEHP 0.32 2.56 0.30 2.22 0.30 2.08 0.27 1.60 MEOHP 0.17 2.86 0.20 3.33 0.19 3.23 0.19 3.19 MiBP 0.57 3.06 0.73 3.99 0.81 4.40 0.78 4.23 MBP 0.64 16.56 0.78 20.39 0.79 20.58 0.78 20.23 MEHHP 0.24 4.12 0.28 4.84 0.26 4.45 0.27 4.63 MEP 0.13 1.71 0.14 1.81 0.13 1.72 0.13 1.72 MECPP 0.30 4.90 0.31 5.09 0.29 4.71 0.29 4.81 MEP Linear 0.5 S2 MECPP Linear 0.5 S3 MEHHP Linear 0.2 S1 MBP Linear 0.5 S2 MiBP Linear 0.25 S2 MEOHP Linear 0.2 S1 MEHP Linear 1 S3 ΣHMWP (MECPP) 0.48 0.66 0.91 0.922 ±1.048 ΣLMWP 2.02 5.94 10.45 12.531 ±33.693 MiBP 0.31 0.31 4.56 6.332 ±27.618 MBP 0.15 0.27 1.85 1.677 ±3.359 MEP 0.35 1.49 4.19 4.161 ±10.556 MEP 0.89 (0.81-0.97) MBP 0.47 (0.17, 0.75) MiBP 0.49 (0.21,0.77) MECPP 0.68 (0.46, 0.89) MEP=194g/mol Sample conc. (ng/mL) = nmole x 1000mL = ng/mL 194 1mL 1L ng/nmole MEP= Sample conc. x1000= nM 194 MiBP=Sample conc. x 1000=nM 222 MBP= Sample conc. x1000=nM 222 MEHP=278g/mol Sample conc. (ng/mL) = nmole x 1000mL =ng/mL 278 1mL 1L ng/nmole MECPP=Sample conc. x1000=nM 303 26 y = 0.3369x + 22.982 25 y = 0.5624x + 23.026 24 23 22 y = 0.0687x + 23.301 MECPP MEP MBP Linear (MECPP) Linear (MEP) Linear (MBP)

Tables and Figures. None ABSTRACT: Prescription opioid abuse has reached epidemic proportions in the United States. As a result, states have implemented policies to help reduce prescription opioid abuse and misuse through prescribing rules enforced by state licensing boards. In at least one state, the medical board has mandated the use of treatment agreements for any patients receiving opioid medications from their physicians. These agreements require physicians to urine or saliva test patients annually for drug abuse and to engage in pill counts or other methods of determining drug abuse and allow for those findings to be turned over to law enforcement, if necessary. Treatment agreements, particularly those containing these provisions, should not be adopted by state medical boards. The negative effects on the physician-patient relationship and trust in the medical encounter and the lack of evidence to suggest agreements will be effective in reducing prescription drug abuse do not support their use as a population-based strategy to prevent prescription opioid abuse. KEYWORDS: Ethics, Informed Consent, Pain Management, Opioids Introduction