dataesr/scientific-paragraphs-categorization huggingface dataset:
The first inflation-adjusted French government bond, Obligations Assimilables du Tr esor index ee sur l'indice des prix a la consommation en France (OATi), was issued on September 15, 1998. In October 2001, the French government issued the first for Obligations Assimilables du Tr esor index ee sur l'indice des prix de la zone Euro (OATV i). At last, some bond debts have been denominated in other currencies (USD and GBP), but they represent at most less than 3 percent of the total outstanding amount. These specific segments of the French government bond market are out of the scope of the present research, because they deserve tailored investigations. We do not consider currently these bonds in our dataset for the purposes of this paper. The French OATs/BTANs bond market experienced a number of different periods since its onset in 1984.
Filters In fitting the curve, we impose the following filters, following GSW. 1. First, we confine our database only to regular bonds with no special features. We therefore exclude floating-rate bonds, inflation-linked bonds, and bonds that were denominated in currencies other than FFs, ECUs, or euros. In addition, following GSW, we exclude STRIPS of OATs known as the "certificats z ero-coupon fongibles" that are available on the secondary market. 2. We exclude BTFs from our investigation. Using the arguments of Duffee  9  regarding the variation in the U. S. Treasury bill yields, it is not clear how innocuous is the use of the BTFs. 15. We exclude the short-duration securitiesdthat is, all securities with less than 12 months to maturitydto prevent particular institutional details, unrelated to movements reflecting fundamentals, to affect the fit of the curve.
The AFT website https://www. aft. gouv. fr/index. php/en/tax-treatment-securities as well as https://www. aft. gouv. fr/en/tax-treatment-securities provide more details. Interested readers may also consult the "Article 125 A 00 of the "Code G en eral des Impôts". One can also see https:// www. legifrance. gouv. fr/that gives the long list of modifications that intervene during the period of our dataset.
Data 4  Bollerslev et al. (2009)  links the variance risk premium to time-varying risk aversion. We hand-collect data on all SPACs listed in the U. S. from the pre-IPO prospectuses, filed with SEC, from EDGAR. Our data set includes all SPACs since their introduction in the 3 rd quarter of 2003, spanning until the 4 th quarter of 2019. We end up with a total of 441 SPACs distributed over 66 quarters. In our principal analysis, we use two dependent variables. First, the SPAC share, defined as the quarterly SPAC IPOs scaled by total quarterly IPOs. We retrieve the gross number of quarterly IPOs from Jay Ritter's database. 5 Second, SPAC volume is defined as the number of quarterly SPACs. In our second analysis, we use the proportion of warrants held by sponsors (Sponsor share) as the dependent variable.
Materials: The CARTaGENE Study CARTaGENE (www. cartagene. qc. ca) is a population cohort consisting of 43,037 Quebec residents aged between 40 and 69 years at recruitment  (Awadalla et al. , 2013). Enrollment of participants began in July 2009 and was carried out in two phases in six metropolitan areas. On their enrollment date, each participant filled out a questionnaire about health, lifestyle, individual, and familial history of disease, and prescribed medication. Only the women were included in the breast cancer study  (n = 23,797). Linkage with administrative databases was included in the participant consent form: 1/the Quebec administrative health database MED-ÉCHO contains hospitalizations, claims, and date of death of insured patients (about 98% of Quebec residents) (data available from January 1st, 1998 to March 31st, 2016); 2/the Quebec Breast Cancer Registry contains information about the Quebec Breast Cancer Screening Program, such as mammogram results and breast cancers histological confirmation (data available from May 15th, 1998 to December 31st, 2017).
To comprehensively define women with a breast cancer (invasive or in situ), we used an algorithm based on a previous report from the Institut National de Santé Publique du Québec (INSPQ)  (Théberge et al. , 2003). Using the Breast Cancer Registry, we retrieved the incidence date of histologically confirmed breast cancers. Then, we selected all women having an abnormal mammography and retrieved, if available, the incidence date from the MED-ÉCHO database for women with at least two claims in 2 years or one hospitalization with the corresponding  ICD-9 (174, 2330)  and ICD-10 (C50, D05) codes. For the breast cancer study, only the women with genetic data available and who did not have a breast cancer before their enrollment in the CARTaGENE cohort were selected (n = 4,554).
ACKNOWLEDGMENTS This research work has been motivated by questions raised by the CARTaGENE cohort at the  CHU Sainte-Justine. The authors would like to thank all the CARTaGENE participants for their generous investments in health research. The authors would also like to thank the  Régie de l'assurance maladie du Québec (RAMQ)  and the  Commission d'accès à l'information (CAI)  for their support in obtaining the data relevant to the study.
DATA AVAILABILITY STATEMENT The data analyzed in this study were collected in the context of the CARTaGENE cohort. The data are available upon official request and ethical approval. The dataset cannot be made publicly available but can be obtained by interested researchers upon request to the CARTaGENE team at CHU Sainte-Justine. The requests can be made using the CARTaGENE online application at sdas. cartagene. qc. ca, by contacting the CARTaGENE team at access@cartagene. qc. ca or by calling 514-345-2156.
Acknowledgements This study has been carried out with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Program, within the Cluster of Excellence COTE (ANR-10-LABX-45) and the Bordeaux-Waterloo joint grants. The author are thankful to Alexandre Pryet for his constructive comments on the original version of the manuscript, the two reviewers and the invited editor for their comments and suggestions.
Data collection and processing Several different publicly available data sources were used in this study. The data on case numbers and fatalities per country covering the period of Jan. 22-Jun. 22 2020 were downloaded from the John Hopkins-maintained server (https://github. com/CSSEGISandData/COVID-19). [21]  Data from different regions within the same country were added together. Data on mitigation dates were obtained from publicly available news sources as shown in Table  S1. Data on SARS-COV2 testing were also obtained from publicly available sources as shown in Table  S2. Indicator data were downloaded from the World Bank Open Data portal (https://data. worldbank. org/indicator) [22]  as EXCEL files and batch processed with custom Matlab scripts. Hourly weather data was obtained from Synoptic (https://download.
Acknowledging that most oncologists are not trained, have restricted or no time or they do not have established tools to support or protect cancer patients from internet hazards, who should really support and 'e-protect' patients with cancer? It could be argued that recognised 'approved' medical organisations should provide patients protection by imparting recommendations directly via the internet. Unfortunately, this is not the case. In 2018, only a minority of official medical societies provided updated and structured recommendations to patients and family members on their websites. 7 In a web survey of 370 oncology/anesthesiology societies, the scope and content of recommendations for physicians regarding cancer-related pain and cachexia were remarkably inconsistent. The proportion of societies providing up-to-date on-line evidence-based recommendations to physicians for cancer cachexia was only 2.
Data Collection As indicated, we built our analysis on data from several sources (see table  3 ) to improve our study's reliability and validity  (Eisenhardt and Graebner 2007). We collected 260 documents published between the setting up of our four brokers and 2018 to trace the broker organizations' evolution and interplay with the cross-sector collaborations over time. We specifically focused on these brokers' series of annual reports, board meeting minutes, and strategy and governance documents. To juxtapose insights from these documents with external sources, we identified academic articles and partneror donor-commissioned external evaluations (see table  S1  in the Supporting Information online). These time-embedded data sources were the primary basis of our analysis. In addition, we conducted 38 semistructured interviews   between 2016 and 2018.
We selected the interview participants to cover the internal and the boundary management positions within the broker organizations (see table  3 ). We did so to capture their perspective of developments and perceptions about the status quo, which the archival data covered to a limited extent. We asked the interviewees about their organization's strengths, weaknesses, and development; the challenges they faced in their work; their relationships with the collaboration partners; and how they coped with emerging challenges. We recorded and transcribed all the interviews and asked follow-up questions by phone or email when we required clarification.
Data Analysis To analyze our data, we followed a three-step process in which each broker organization and its collaboration formed a separate case. We examined the broker organizations-namely, their choices, behavior, and the consequences at the collaboration level-as the central unit of analysis. As a first step, we followed  Eisenhardt's (1989)  suggestion to learn more about each case individually. To this end, we examined the multiple data sources to gain a better understanding of each broker's context and, specifically, of its evolution and effects on the collaboration. We did so by chronologically mapping the respective insights emerging from the different data sources in detailed, longitudinal case descriptions (see table  S2  in the Supporting Information for an overview). We enhanced the qualitative rigor by arranging the data into a more structured form.
Some prior PCSK9 inhibitor cost-effectiveness analyses have either underestimated the background cardiovascular event rate in the everyday clinical practice or do not take into consideration the benefits of averting death  (7, 12). With a very high-risk population and longer duration of follow-up than previous trials of PCSK9 inhibitors, fewer deaths were observed with alirocumab than with placebo in the ODYSSEY OUTCOMES trial. The trial therefore provides a relevant dataset to project the effect of PCSK9 inhibition after ACS on long-term survival and cost per QALY. A limitation in previous models is the way death was modeled. For example, 2 prior analyses  (10, 12)  used the cardiovascular disease policy model to determine the cost-effectiveness of alirocumab or evolocumab based on overall trial hazard ratios for selected endpoints.
Department of Medicine III, Goethe University, Frankfurt am Main, Germany; s Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado; t Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, Université de Paris, French Alliance for Cardiovascular Trials, Institut National de la Santé et de la Recherche Médicale U1148, Paris, France; and the u National Heart and Lung Institute, Imperial College, Royal Brompton Hospital, London, United Kingdom. Supported by Sanofi and Regeneron Pharmaceuticals, Inc. Editorial assistance was funded by Fondation Assistance PubliqueÀHôpitaux de Paris (Paris, France). Sanofi and Regeneron Pharmaceuticals were involved in the study design, collection, analysis, and interpretation of the data, and in the development and review of this manuscript.
Dr. Schwartz has received research grants paid to the University of Colorado from Resverlogix, Sanofi, The Medicines Company, and Roche; and a coinventor of pending U. S. patent 62/806,313 ("Methods of Reducing Cardiovascular Risk") assigned in full to the University of Colorado. Dr. Steg has received grants and nonfinancial support (as cochair of the ODYSSEY OUTCOMES trial, his institution has received funding for his time devoted to trial coordination, and he has received support for some travel related to trial meetings) from Sanofi; has received research grants and personal fees from Bayer (Steering Committee of MARINER, grant for epidemiological study), Merck (speaker fees, grant for epidemiological studies), Sanofi (cochair of the ODYSSEY OUTCOMES and of the SCORED trials; consulting, speaking), Servier (chair of the CLARIFY registry; grant for epidemiological research), and Amarin (Executive Steering Committee of the REDUCE-IT trial; consulting); has received personal fees from Amgen, Bristol-Myers Squibb, Boehringer Ingelheim, Pfizer, Novartis, Regeneron Pharmaceuticals, Lilly, and AstraZeneca; and has a European application number/patent number, issued October 26, 2016 (number 15712241.
ACKNOWLEDGMENTS The authors thank Sophie Rushton-Smith, PhD (MedLink Healthcare Communications, London), for editorial assistance, which was limited to editing for style, referencing, and figure and table editing. The authors wish to acknowledge the following sponsor employees, who contributed to this analysis: Andreas Kuznik (Regeneron Pharmaceuticals) and Alex Cockerham (Sanofi). harvard. edu. Twitter: @DLBhattMD, @gabrielsteg.
, 2015; Rozenblat, Pumain, & Velasquez, 2018). Geographers were very soon interested by the developing science of complex systems  (Dauphiné, 2003; Banos, 2013). They have experimented new tools for modelling and predicting land use changes at different scales of analysis as cellular automata, especially in Belgium and the Netherlands  (Engelen, White, Uljee, & Drazan, 1995) , even by developing a dedicated software named Spacelle  (Guermond, 2005)  or using multi-agent systems for simulating diffusion processes  (Daudé, 2004). Many solutions for improving the modeling methods in spatial analysis and spatiotemporal processes have been proposed  (Mathian & Sanders, 2014; Sanders, 2001). Among the most continuous efforts in theoretical building with the help of modelling is through introducing in urban studies dedicated dynamic simulation models.
A few scholars also developed land rent theory, with contributions coming from different perspectives and cross explorations: it encompasses planning theory and practice (Paul Lacaze), public finance (V. Renard), economic-marxist perspective (Lipiez), land economics (Derycke, Sallez and Granelle), and political science  (Guigou, 1982). Many efforts have been devoted to applying fractal models for the description of spatial urban forms. The pioneer work by  Pierre Frankhauser (1994)  has been considerably developed and enriched by Cécile  Tannier (2017)  who participated in the elaboration of the dedicated software Fractalyse, and the Belgian geographer Isabelle Thomas who undertook international and multiscale comparisons of fractal measurements  (Tannier, Thomas, Vuidel, & Frankhauser, 2011; Thomas & Frankhauser, 2013; Thomas, Frankhauser, & Biernacki, 2008).
At week 32, a follow-up phone call was made to collect adverse event and concomitant medication information. Q2W ¼ every 2 weeks; R ¼ randomization. STATISTICAL ANALYSIS. The primary efficacy endpoint was analyzed in the intention-to-treat population; missing data were accounted for using a mixed-effect model with a repeated measures approach using all post-baseline data available within the week 4 to week 12 analysis windows. Further details on the determination of sample size and the statistical analyses of efficacy and safety endpoints, including the hierarchical testing procedure for key secondary efficacy endpoints, are provided in the Supplemental Appendix.
blind, placebo-controlled, parallel-group, phase 3 study (Study in Participants With Homozygous Familial Hypercholesterolemia [HoFH] [ODYSSEY HoFH]; NCT03156621) that evaluated the efficacy and safety of alirocumab 150 mg every 2 weeks versus placebo in adult patients with HoFH. METHODS The ODYSSEY HoFH study was performed across 27 sites in 13 countries, and was designed to evaluate the reduction in LDL-C with alirocumab 150 mg every 2 weeks in adult ($18 years of age) patients with HoFH in comparison to placebo. A full list of all.
Early initiation of statin therapy has been shown to reduce occurrence of cardiovascular events in patients with heterozygous familial hypercholesterolemia (25). A trial of alirocumab in pediatric patients with HoFH has recently completed (An Efficacy and Safety Study of Alirocumab in Children and Adolescents With Homozygous Familial Hypercholesterolemia; NCT03510715). CONCLUSIONS Treatment with alirocumab resulted in significant and clinically meaningful reductions in LDL-C in patients with HoFH, as well as significant reductions in other lipoprotein and lipid parameters. Alirocumab was generally well-tolerated, with no clinically significant differences between alirocumab and placebo groups with regards to safety findings; no safety concerns were identified from the open-label data. ACKNOWLEDGMENTS The authors thank the patients, their families, and all investigators involved in this study.
The BHS provide appropriate treatment to the patients according to the treatment guidelines outlined in the PEN protocol. They prescribe the following medicines: Metformin 500mg, Gliclazide80mg, Amlodipine 5mg, Enalapril 5mg, Atenolol 50mg, Atorvastatin 10mg and Aspirin 75mg  [14]. These data are recorded in the clinical record form during every follow-up visit. Patients with 10-year CVD risk >10% are expected to visit every three months or more if needed. The details of the project are outlined in Fig 1. Reporting: On the last week of each quarter, BHS use tally sheets to abstract data from the screening register, NCD disease register and clinical forms, to prepare a quarterly report that is sent to the township health facility. Reports from the township level are collected and compiled by the state/regional health departments and sent to Central NCD office in Nay Pyi Taw.
Data collection and source of data Quantitative component. For objective 1, aggregate data (number of patients screened and number with various NCDs and their risk factors) were collected from the Quarterly NCD screening report that is available at the central NCD division at Nay Pyi Taw. For the second objective, individual patient level data were obtained from patient clinical record forms which are maintained at the RHCs/UHCs by the BHS. A structured health facility survey checklist was used to assess the status of manpower, trained staff, availability of medicines and equipment in 20 selected facilities. Qualitative component. A total of 22 KIIs with four cadres of BHS were conducted. Participants were recruited until no new relevant information pertaining to the major themes was being obtained.
Acknowledgments We would like to thank the  non-communicable disease control program , the  Department of Public Health  for sharing and the giving permission to use the dataset used in this study. This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and Medecins sans Frontières (MSF/Doctors Without Borders). The specific  SORT IT program  which resulted in this publication was jointly organized and implemented by The Center for Operational Research, The Union, Paris, France; The  Department of Medical Research, Ministry of Health and Sports, Myanmar ; The  Department of Public Health, Ministry of Health and Sports, Myanmar ; The Union Country Office, Mandalay, Myanmar; The Union South-East Asia Office, New Delhi, India; and  London School of Hygiene and Tropical Medicine, London, UK.
The technical advisors of MRD code the final diagnosis, according to ICD-10. The trained data entry operator in MRD enters the individual patients' details like age, sex, date of admission, date of discharge, ICD-10 code, and type of discharge into the CAMRS maintained by HMIS unit of Department of Public Health (DPH). The data entry operator downloads the CAMRS data into compact-disk (CD) and sends the CD to the HMIS unit at Nay Pyi Taw every year. At the HMIS unit, these annual data files from each of these hospitals are compiled and prepared in Microsoft Excel format.
Data variables, sources of data, and data collection The details like age in completed years, sex, dates of admission, hospital, and ICD code of final diagnosis were extracted from the HMIS database obtained from DPH. The principal investigator obtained the data files in Microsoft Excel from 2012 to 2017 during the month of April 2019. The admissions with ICD-10 code of final diagnosis corresponding to four major NCDs like malignant neoplasms (cancer), chronic respiratory diseases, cardiovascular diseases, and diabetes were grouped under each disease category. Within the major NCDs, each of the ICD-10 codes was labeled with the condition they represent. The classification used by the Global Burden of Diseases (GBD) was used to categorize the diseases (Supplementary Table  1 ). The common malignant neoplasms, cardiovascular diseases, chronic respiratory diseases, and diabetes-related admissions were used as outcome variables that were described across gender, age, and year of admission.
Data analysis and statistics Data obtained in the Microsoft Excel format was analyzed using EpiData analysis software (version 2. 2. 2. 182 EpiData Association Demark). The frequency and proportion of inpatient admissions with the final diagnosis of any of the four NCDs were calculated across the year of admission, hospital, age groups, and gender. The chi-squared test for trend was used to assess the increase or decrease of proportions of NCD admissions to the total over the study period. The line graphs depicting trend in the number and forward projections using linear regression were developed for admissions with any of the four major NCDs. Similarly, the trend in the number and forward projections was developed for admissions with any of the four major NCDs in each of the study hospitals.
The study has some strengths. We used routinely collected HMIS data in which ICD-10 coding was consistently used in all three hospitals during our study reference period and, thus, limited the potential misclassifications due to changes in the coding system. Also, the classification of diseases based on the widely used ICD-10 coding system enhanced the validity and comparability of the study findings globally. We included three hospitals with high bed occupancy rates and representing different regions of the country. Thus, it could have accounted for regional variations in the pattern of inpatient admissions. However, the generalizability is limited as only one tertiary hospital each from the three regions was included. We adhered to Strengthening the Reporting of Observational studies in Epidemiology (STROBE) for reporting the study findings.