dataesr/scientific-paragraphs-categorization huggingface dataset:
MRI exams and parameters Diagnostic studies on MRI equipment evaluate the rate of examinations per 1000 inhabitants/year, productivity per equipment (tests/equipment/year), considering regional differences, differences in access, equipment usage, and waiting time for the consultation, with recommendations for the maximum displacement of MRIs. In Brazil, the Health Ministry (HM) Decree n o. 1631/2015 defines criteria and parameters for the planning and programming of health actions and services within the Unified Health System, better known by the acronym SUS (Sistema Único de Saúde)  [36]. The National Health System's database provides information to calculate MRI service capacity. We consider 40 min as the average time for performing exams on MRI  [37]. Since the number of equipment depends on the establishment, MRI capacity varies from each facility.
Acknowledgments The authors would like to thank the editor and referees for the comments and feedback that improved the quality of this paper. We also acknowledge and thank the technical support and discussions provided by  Francisco Carlos Cardoso de Campos ,  Rosiene Maria de Freitas , and  Sábado Nicolau Girardi , from the  Education Center in Public Health ,  of the Medical School of Universidade Federal de Minas Gerais, Brazil.
The government promoted it consistently under the popularized "3M" acronym (from the initial of each action in the Bahasa Indonesian language) and monitored public compliance thereof. This study aimed to evaluate compliance to the health protocol in public spaces between October 2020 and March 2021, thus including the entire second wave of the pandemic in Indonesia. Importantly, the data used here have been collected from the Indonesia National Task Force for the Acceleration of COVID-19 Mitigation by using the Bersatu Lawan COVID-19 (BLC) digital monitoring app. This represents the first time in which an app for digital health, introduced nationally, produced data able to be analyzed on a real-time basis and using an integrated approach. Importantly, the system uses observer-reported compliance, thus this app is able to minimize bias from self-reported data.
As the monitoring can be an entry from all Indonesian levels, the account given is generated based on the regional levelling access. The monitoring system was reported in real time using the BLC behavioral change app at public places, which tend to be crowded locations, such as markets, recreational areas, shopping malls, restaurants, places of worship, offices, train stations, bus terminals, airports, sport centers, schools, etc. Those locations were chosen based on the tendency or potential for crowds to become a place for clusters of COVID-19 transmission. Several studies have found the potential for transmission both indoors and outdoors, such as in transit places, restaurants, fitness centers, places of worship, schools, supermarkets, etc  [26, 27]. The reports sent include a photo of the monitoring results and an input data questionnaire by all personnel in the field.
Acknowledgments The authors would like to thank the  Indonesia National Task Force  for the Acceleration of  COVID-19 Mitigation  for providing access and insights to the Bersatu Lawan COVID-19 behavioral change app data and development process; the  National Military of Indonesia (Tentara Nasional Indonesia [TNI]) ;  National Police Department of Indonesia (Kepolisian Negara Republik Indonesia [POLRI]) ; and the community ambassadors who have supported the reporting processes.
Empirical results To test the theoretical predictions above we use the daily return and market capitalization series for all common US stocks from the WRDS/CRSP database, from 1963-04-30 to 2018-06-06. 9 We calculate daily return, CSV and EGR series for CW and EW market portfolios rebalanced daily, weekly, and monthly, and aggregate the daily series to obtain weekly and monthly series. 10 7 Eq. (  6 ) is a discretization of Eq. (  4 ) and both the EGR and the CSV of returns can be measured at any frequency without any further parameter estimation. 8 Hallerbach (2014) relates the EGR and the rebalancing return in discrete time. 9 At the beginning of each April we keep all the common shares (codes 10 and 11) without missing returns or market-cap data throughout each year. The number of stocks starts at 456, peaks at 6.
Implementation In practice, the refined Weissman estimator is computed using Hill estimator H( kH, n ) where kH, n := k H, n (ρ n , α n , k n ) = k n -ρ n 1 -ρn log d n 1 -d ρn n 1/ ρn (16) is an estimation of the intermediate sequence given in  (15). Here, we adopt the estimator ρn of the second-order parameter introduced in [20, Equation (2. 18)] and implemented in the package evt0 of the R software  [36]  which features a satisfying behaviour in practice. The resulting refined Weissman estimator is denoted by RW(k n ) = qn (α n , k n , kH, n ) = qn (α n , k n , k H, n (ρ n , α n , k n )). This data-driven choice of kH, n as a function of k n is illustrated on Figure  2  on data sets of size n = 500 together with its consequences on the estimation of the tail-index (left panel) and extreme quantiles (right panel).
Acknowledgments The authors would like to thank  Frederico Caeiro  for providing the R code associated with Algorithm 4. 1 and Algorithm 4. 2 from [26]. This work is supported by the  French National Research Agency (ANR)  in the framework of the  Investissements d'Avenir Program  ( ANR-15-IDEX-02 ). S. Girard also acknowledges the support of the Chair Stress Test,  Risk Management  and  Financial Steering , led by the  French Ecole Polytechnique and its Foundation  and sponsored by  BNP Paribas.
Similarly,  Hernandez et al. (2018)  find that the lower quantiles of oil returns have no significant effect on the lower quantiles of wheat but a negative effect on those of corn. Consequently, both commodities act as convenient hedges for coping with instability in oil prices. Exploring oil, gold, and bond returns, Raza et al. (  2019 ) investigate these assets related to optimal hedging effectiveness in US real estate stock returns under heterogeneous market expectations. To this end, they apply the wavelet transform (HTW) approach to decompose the dataset into different time horizons and estimate the assets' optimal hedge ratios with the ADCC model. They highlight the persistence of volatility, which confirms the existence of volatility clusters of different magnitudes, the asymmetric effect of negative shocks over positive ones, in which bad news of the same magnitude tends to increase volatility more than positive news.
Data and descriptive statistics In our empirical investigation, we use daily data from January 2000 to April 2019. These data relate to the Dow Jones Islamic and conventional emerging market indices (EMERG, EMERGC), gold prices, oil prices, the VSTOXX, and four CDS indices concerning industry in four emerging countries. All prices are set/estimated in US dollars, and the return series are calculated on a continuous compound basis, r i; t ¼ 100 *logðP i; t =P i; tÀ1 Þ, where P i; t is the closing price of asset i at time t. Table  1  lists the descriptive statistics based on the returns of these variables. Table  1  highlights the persistence of negative means for both the VSTOXX returns (À0. 01417%) and the CDS index returns in telecommunications (À0. 02909%). However, the highest average daily return is for gold (0.
study design The SPRINTT trial was a multicentre randomised controlled trial conducted from January 2016 to 31 October 2019 at 16 sites across 11 European countries. 8 The European Medicines Agency accepted the trial methodology and analytical strategy during an ad hoc scientific advice procedure that was completed in early 2015. A summary description of the protocol is available on ClinicalTrials. gov (NCT02582138). Details were provided in a dedicated publication  9  and are included in the supplementary appendix. The Università Cattolica del Sacro Cuore in Rome, Italy, coordinated trial activities. Trial sites are listed in the supplementary appendix. As part of the Innovative Medicines Initiative Joint Undertaking of the EU (www. imi. europa. eu), member companies of the European Federation of Pharmaceutical Industries and Associations gave in-kind support.
sample size calculation The size of the study population was determined to address the main requirements of the Innovative Medicines Initiative Joint Undertaking to evaluate whether a multicomponent intervention would reduce the risk of incident mobility disability in older adults with physical frailty and sarcopenia, and to characterise the physical frailty and sarcopenia condition and obtain information on intervention effects across its whole SPPB range (scores 3-9). To meet the first requirement, we performed a sample size estimation based on information retrieved from the Lifestyle Interventions and Independence for Elders (LIFE) study database  17  by running survival analyses for mobility disability according to different baseline SPPB score categories (<8 v 8 or 9). In LIFE, the hazard of incident mobility disability was observed to be significantly reduced by physical activity only in participants with SPPB scores <8 (hazard ratio 0.
Changes from baseline to 24 and 36 months in SPPB score, handgrip strength, and appendicular lean mass were analysed by mixed effect models with repeated measures. Models included the fixed categorical effects of intervention arm, the planned time point, the randomisation factors of site and sex, the intervention×time point interaction, and the continuous fixed covariates of baseline value and baseline value×time point interaction. For all analyses, a two sided P<0. 05 was considered to be statistically significant. We analysed safety data by intervention group in the two SPPB categories separately. Risk ratio with 95% confidence interval was used to estimate the probability of experiencing an adverse event. All analyses were run using SAS version 9. 4 (Cary, NC).
Differences in actimetry data between intervention groups were no longer evident 24 months after randomisation, when the number of observations was substantially lower. Overall, 78. 6% of participants completed full nutritional assessments, including dietary records over three days. Relative to baseline daily energy intake (23. 3 (SD 7. 4) kcal/kg/day), values increased by 6. 8% at 24 months (24. 1 (SD 7. 1) kcal/kg/day) and 10. 7% at 36 months (26. 1 (SD 7. 5) kcal/kg/day). A similar pattern was observed for daily protein intake, the values of which increased from baseline (0. 98 (SD 0. 32) g/kg/day) by 10. 9% at 24 months (1. 10 (SD 0. 32) g/kg/day) and by 14. 8% at 36 months (1. 15 (SD 0. 32) g/kg/day). Participants in the lifestyle education group attended on average 65. 9% (SD 26. 4%) of scheduled meetings, after medical leave and other circumstances that prevented participation had been excluded.
Flow of participants through study. *sum of individual items is higher than number of ineligible participants because screening was not always stopped at the first unmet eligibility criterion. some entries are different from those previously published 8 because of data updates after database cleaning. DeXa=dual energy x ray absorptiometry; sPPb=short physical performance battery; sPrintt=sarcopenia and Physical frailty in older people: multi-component treatment strategies on 24 June 2022 by guest. Protected by copyright.
759 Did not complete 24 months of follow-up Flow of participants through study. *sum of individual items is higher than number of ineligible participants because screening was not always stopped at the first unmet eligibility criterion. some entries are different from those previously published 8 because of data updates after database cleaning. DeXa=dual energy x ray absorptiometry; sPPb=short physical performance battery; sPrintt=sarcopenia and Physical frailty in older people: multi-component treatment strategies on 24 June 2022 by guest. Protected by copyright. 12 358 48 10 791 Not randomised Did not attend randomisation visit 48 BMJ: first published as 10. 1136/bmj-2021-068788 on 11 May 2022. Downloaded from Participant's decision Adverse event Lost to follow-up 177 Fig 1 | http://www.
Acknowledgements The authors are thankful to  Whelsy Boungou ,  Bernard Njindan Iyke ,  Thomas Walther ,  Harald Kinateder ,  Piotr Wójcik , and  David Vidal Tomás  for helpful comments and suggestions. Barbara Będowska-Sójka  acknowledges the support of the  National Science Center of Poland  under grant no. 2021/41/B/HS4/02443.
Study design The POLO trial was registered on 09/07/2014 with Clini calTr ials. gov as NCT 02184195. All study activities were performed in accordance with relevant guidelines and regulations, and approved by local ethics boards overseeing the study sites (see Supplemental Table  1  for the full list of IRBs and approval dates). Informed consent was obtained from all subjects and/or their legal guardian(s). The study consisted of three parts. First, reference values for the EORTC QLQ-C30 were derived from studies on mPC patients reported in the literature. Second, baseline scores from both the treatment and control arm of mPC patients in the POLO trial were calculated. Finally, the three sets of EORTC QLQ-C30 scores, i. e. , newly derived mPC reference values, POLO baseline scores, and general population norm data  [16]  were compared.
Derivation of EORTC QLQ-C30 mPC reference values from the literature The targeted literature search was performed using the MEDLINE and Embase databases on 3 March 2020. The targeted literature review was aimed at identifying published EORTC QLQ-C30 values as reported by mPC patients before first-line treatment. A literature review of articles in mPC published since 2005, using the search string shown in Table  1 , was performed using MED-LINE and Embase databases (through OVID platform). Additional data sources included a review of the EORTC QLQ-C30 reference values manual  [15]  as well as trial data. The review of clinical trials was performed in Clini calTr ials. gov (Advanced search engine) with the following search strategy: Pancreatic Neoplasms (condition or disease), EORTC QLC-C30 (outcomes), Phases 2, 3, 4, and not applicable, and time limit was set to: 01/01/2005 to date.
EORTC QLQ-C30 reference values for mPC As shown in Table  2 , sample sizes for the new mPC reference values ranged between n = 466 (financial difficulties) and n = 639 (physical functioning, fatigue, pain) depending on respective EORTC QLQ-C30 scale. Data from patients with mPC yielded low scores (i. e. , poor HRQoL) for global health status/QoL (54. 3; on a scale 0-100) and role functioning (62. 5), whilst high scores (i. e. , good HRQoL) were observed for cognitive (81. 5) and physical (78. 2) functioning. For symptom scores, including financial difficulties, high scores (i. e. , severe symptoms) were observed for fatigue (46. 0), pain (41. 9), insomnia (41. 6), and appetite loss (44. 6). Lowest scores (i. e. , good HRQoL) were observed for diarrhea (13. 5), financial difficulties (15. 7), and nausea/vomiting (16.
Comparison of POLO EORTC QLQ-C30 baseline scores and EORTC QLQ-C30 general population norm data POLO patients reported overall similar HRQoL scores to the general population norm data, with none of the differences reaching the threshold of 10 points (Table  2 , Fig. 2a  and b ). Role and social functioning, and diarrhea and financial difficulties were marginally worse in POLO patients, whereas global health status/QoL, emotional functioning, pain, and dyspnea showed differences of between 5. 8 and 7. 2 points in favor of POLO trial patients indicating marginally better HRQoL in these domains among POLO patients. The remaining scales, with differences < 5 points, indicated that prior to their maintenance therapy, POLO patients reported HRQOL similar to that reported by the general population.
Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available to protect patient privacy, but are available from the corresponding author on reasonable request. Qualified researchers can request access to anonymized individual patient-level data from AstraZeneca group of companies sponsored clinical trials via the request portal. All requests will be evaluated as per the AZ disclosure commitment: https:// astra zenec agrou ptria ls. pharm acm. com/ ST/ Submi ssion/ Discl osure.
Sample and data collection We selected a sample of firms from Thomson Reuters Eikon which were from different countries around the world (for example, Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Kazakhstan, and Luxembourg) and represented diverse sectors of activity (Basic Materials, Consumer Cyclical, Consumer Non-Cyclical, Energy, Financial, Health Care, Industrial, Technology, Telecommunication Services, and Utilities). Our analysis used an unbalanced panel dataset including 16,893 observations from 5015 different firms for the period between 2014 and 2019. A two-step process was adopted to determine our final sample. First, we identified 5026 firms for which there was available data for all variables.
Dependent variables This study uses the environmental performance scores of ESG criteria retrieved from Thomson Reuters' Asset4 database as dependent variables. Measuring environmental performance is multidimensional in character, and some scholars have used emissions reduction  (Hartmann and Vachon, 2018)  or levels of consumption and resource efficiency  (Kock et al. , 2012)  as proxies for a firm's environmental performance. Following previous studies (e. g. ,  Qureshi et al. , 2020) , we opted for the performance scores from Thomson Reuters Eikon, defined as "a company's impact on living and non-living natural systems, including the air, land, and water, as well as complete ecosystems. " This index is generated from the weighted score of a company's strengths and weaknesses on indicators related to (1) environmental innovation, (2) emissions, and (3) resource use.
Independent variable The IMD World Digital Competitiveness (WDC) database was used to determine home country digitalization. The WDC analyzes and ranks the extent to which countries adopt and explore digital technologies, which lead to transformation in government practices, business models, and society in general. Home country digitalization takes a value on a continuous scale from "low" to "high" (0 to 100). The methodology of WDC ranking defines three main dimensions of country digitalization: knowledge, technology, and future-readiness. The knowledge dimension captures the intangible infrastructure necessary to discover, understand, and build new technologies. The technology dimension quantifies the overall context that enables the development of digital technologies. The dimension of future readiness examines the level of an economy's preparedness to assume and exploit its digital transformation.
Transition variable The institutional framework of home country was selected as a transition variable. This variable was extracted from the IMD World Competitiveness (WCC) database. The institutional framework was elaborated by aggregating several country-specific items, such as legal and regulatory framework, adaptability of government policy, government decisions, cost of capital, central bank policy, and country credit ranking. This variable also ranges between 0 and 100, with higher values representing a strong institutional framework for a specific country.
At the country level, GDP was considered in the analysis to measure the economic development of the home country  (Alam et al. , 2021). In order to measure the home country environmental culture, we selected the Environmental Performance Index (EPI) as used in other studies (e. g. , Leyva de la  Hiz et al. , 2019). The EPI is produced by Yale University (e. g. ,  Wendling et al. , 2018)  by aggregating several environmental items, such as water waste, energy, and other factors. It can be assumed that countries that rank highly on the EPI tend to invest more in environmental protection  (Singh et al. , 2016). This index ranges between 0 for the worst environmental value and 100 for the maximum environmental performance for a country. In summary, Table  3  reports the statistics and the correlations of our dependent, explanatory, and control variables for the complete dataset comprising 5015 firms and 16,893 firm-year observations.
Although home country has a strategic impact on a firm's behavior, ignoring other countries in which companies operate can be limiting. We analyzed home country digitalization since there was no data available at the firm level regarding the degree of digitalization. A future avenue of research could be to focus on firm-level data instead of home-country data. Specifically, studies can focus on the differences in the relationship between home country digitalization and environmental performance for more and less digitalized firms. Although we provide a multi-industrial perspective which takes into account industry effects, future works could additionally explore whether these findings are confirmed with respect to industry digitalization levels. Second, we obtained environmental scores from secondary data that was provided by Thomson Reuters Eikon.
Acknowledgments This research work is partially funded by the  Spanish Ministry of Economy and Competitiveness  (Grant No. PID2019-106725GB-I00 ), the  European Regional Development Fund  (Grant No. LifeWatch2019-10-UGR-01_WP6. 3  and Grant No. B-SEJ-398-UGR20 ), and the  Andalusian Regional Government  (Grant No. P20_00019 ). Dante I. Leyva-de la Hiz  is member of the  LabEx Entrepreneurship center  ( University of Montpellier, France ), funded by the  French government (Labex Entreprendre ,  ANR-10-Labex-11-01 ).
Dummy variable for each sector considered: industrial, communication services, consumer discretionary, consumer staples, financial, energy, health care, information technology, materials, real estate, and utilities. Thomson Reuters Eikon GDP This variable measures the economic development of a country. GDP is expressed in constant international dollars per person. Data are derived by dividing constant price purchasing-power parity (PPP) GDP by total population. The World Economic Outlook database EPI This variable measures the environmental performance of a country. EPI uses 32 performance indicators across 11 issue categories (e. g. , water waste, energy, air quality, and other factors) and it ranges from 0 (worst) -100 (best) Environmental Performance Index Source: Self elaboration from Thomson Reuters Eikon, the IMD World Digital Competitiveness database, the IMD World Competitiveness database, the World Economic Outlook database, and the Environmental Performance Index.
Methods Of the 12 models participating in the EMF-33 study, we use those that have Brazil represented as a separate region. We examine key results in primary energy, biomass production in agriculture, land-use change, the mix of biomass-based energy carriers, and emissions from both energy and AFOLU sectors. To assess how well the IAMs represent the Brazil region and their usefulness in providing regional policy advice, we compare the model results to available information on recent Brazilian trends. Current trends are assessed through a review of peer-reviewed literature and grey literature, reports from the Brazilian government and national research agencies, academic theses, and interaction with experts. Data sources to provide historical trajectories of key variables include national research institutes, government agencies and universities, and are listed along with the figures and analyses they informed.