dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Nicola Bellomo  acknowledges the support of the  University of Granada , Project  Modeling in Nature MNat  from micro to macro, https://www. modelingnature. org. Juan Soler  and  Nicola Bellomo  acknowledge the support by the  MINECO-Feder (Spain)  research Grant Number  RTI2018-098850-B-I00 , the  Junta de Andalucía (Spain)  Project  PY18-RT-2422 ,  A-FQM-311-UGR18 & B-FQM-580-UGR20. Youshan Tao  is partially supported by the  National Natural Science Foundation of China  (No. 12171316 ), and he also acknowledges support of  Shanghai Frontiers Science Center of Modern Analysis. Michael Winkler  acknowledges support of the  Deutsche Forschungsgemeinschaft  in the context of the  Project Emergence of Structures and Advantages in Cross-Diffusion Systems  (No. 411007140 , GZ:  WI 3707/5-1 ).
The aim of this study was therefore to provide the first published estimates of annual abundance of the Pyrenean brown bear population, while minimizing bias due to heterogeneity in detection probabilities among individuals. We used cross-border non-invasive sampling data collected from 2008 to 2020 in France, Spain and Andorra, for which individual identification was possible through genetic analyses or visual evidence combined with PCRD CR modelling. The development of new methods to estimate population abundance is timely, since it gives the possibility to compare the estimates obtained with the PCRD CR modeling approach with those from census approaches (MRS and MDS counts).
e. with births, deaths and temporary emigration), and repeated captures in a short timeframe (called "secondary occasions") between which the population is assumed to be closed to population changes. Data from secondary samples within each primary period are analyzed using closed models to derive estimates of detection probability and population size. Apparent survival and temporary emigration are estimated using open models by collapsing data from the secondary periods. Here, temporary emigration refers to some individuals that might temporarily emigrate to areas where foraging conditions or breeding success are better, or that might be temporarily unavailable for capture because they are in dens  (Henle & Gruber 2017). The population was assumed geographically closed, i. e. no emigration or immigration could occur between this population and another one outside the Pyrenees.
We used the Akaike Information Criterion corrected for small sample size (AICc) to perform model selection  (Burnham & Anderson 2002). These analyses were performed with the 'RMark' package  (Laake 2013 ) that allows calling the Mark program  (White & Burnham 1999 ) from R software (RCoreTeam 2013). Because we ran into boundary estimation issues, we then used a Bayesian approach to estimate annual population abundance, relying on the best supported model from the frequentist approach. PCRD CR models were first formulated in a Bayesian framework by  Schofield and Barker (2011)  and later by  Rankin and collaborators (2016). However, it is only in the few last years that a Bayesian implementation of PCRD models has been made possible without ecologists having to code their own potentially complex sampling algorithms  (Rankin et al.
2016; Riecke et al. 2018 ). These analyses were performed using program Jags  (Plummer 2003; and Riecke et al. 2018  for PCRD models in particular). The rationale in considering both frequentist and Bayesian frameworks was to use the advantages of each of them : the Frequentist framework allows model selection via AICc without prohibitive computation time, and the Bayesian framework allows for obtaining interpretable estimates, improving estimation when sample sizes are low, and having access to full posterior conditional probabilities of model parameters. Data and codes are available at https://github. com/oliviergimenez/pyrenean-brown-bear-abundance. We compared PCRD estimates of the annual Pyrenean brown bear population abundance with both MDS and MRS counts. Note that MRS for 2020 is provisional and will be reassessed in the future (see above).
Moreover, to use the PCRD CR framework, we excluded signs that were difficult to date, and those that fell outside of the secondary occasions (May to September), which left some individuals identified by MDS and MRS out of our database. Furthermore, MDS and MRS counts performed so far always included the individuals that were found dead in their yearly counts, while a PCRD CR model would only include them if the death occurred after the end of the primary occasion from October to December. Despite these limitations, our results suggest that the PCRD CR method provides reliable estimates of the size and trend of the Pyrenean brown bear population, while minimizing bias due to inter-individual heterogeneity in detection probabilities and quantifying sampling uncertainty surrounding these estimates.
Acknowledgements We would like to acknowledge all field co-workers from the  Brown Bear Network (ROB)  and from  Spanish and Andorran teams. We thank the editors and the three reviewers,  Tim Coulson ,  Romain Pigeault  and  Susannah Woodruff , for their constructive comments and questions pertaining to the manuscript. We are very grateful to  Tim Coulson  and  Marci Johnson  for thorough English language editing and checking. We finaly acknowledge the recommender  Nicolas Bech  and the Managing editor of  PCIEcology Marjolaine Hamelin.
Long term research need #1: The social ecology of SARS-CoV-2 and other pathogens An understanding of what pathogens (e. g. , viruses, bacteria, eukaryotes, prions) circulate in ecosystems and how and why they succeed in crossing species barriers is required to develop and implement effective mitigation and prevention strategies. This calls for a better knowledge of the ecology of microbes and their transmission routes. A much larger number of wildlife and livestock samples need to be analyzed for various pathogens, including viruses from high-risk viral families, which could feed into risk ranking frameworks and web tools such as Spill Over: Viral Risk Ranking (https://spillover. global) (Grange et al. , 2021). For that, we need field observations for data acquisition. Analysis of this data needs to be supported by complex risk and data analysis platforms that implement foresight methodologies in order to be able to capture and quantify the resulting environmental health risk 1 an EU H2020 programme that includes 24 partner institutes across Europe and hundreds of collaborating researchers and wider community stakeholders D.
g. , wastewater-based epidemiology through sewage surveillance or surface water monitoring at the mouths of rivers)  (Larsen and Wigginton, 2020; Trottier et al. , 2020) ; and (iii) to share data and cobuild projects between local communities, administrations, other stakeholders and multidisciplinary teams of scientists, veterinarians and medical doctors to anticipate and mitigate epidemics. The latter would warrant the employment of formal foresight methodologies to address the societal, environmental and ecological dynamics and interactions that underpin the emergence of novel pathogens and the respective public health risk management options within a single, integrative framework. With the objective of discovering novel viruses with zoonotic potential and anticipating future disease emergence, observatories should collect (with dedicated campaigns), archive, share (through open access databases) and explore metagenomic data for viral sequences.
Social ecology is not only a technical science dealing with technical problems that can be managed by instrumental decisions  (Boer, 1984). Social ecology and health may help policies and laws adapt when facing evolving socioenvironments such as EIDs by acknowledging different values, ethics and responsibilities  (Morand and Lajaunie, 2019)  2021), but these cannot be traced back to the host(s) from which the virus passed to the human species. Screening wild fauna for viral genomes (metagenomics) and implementing open-access databases are necessary steps to trace the thread of transmission and viral evolution within the intermediary hosts of SARS-CoV-2. As the number of sequences increases, we should be more successful in identifying the origins of SARS-CoV-2, new variants and other emerging viruses.
Acknowledgments The authors acknowledge the  HERA  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  825417. DDG and JB received support from the  Laboratoires d'Excellence (LABEX) Tulip  ( ANR-10-LABX-41 ). JFG has benefited from one " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ). The  National Institutes of Health -National Science Foundation Ecology of Infectious Disease Program  Grant  NSF-1911457  provided support to  J-FG. J-FG  is also supported by  IRD ,  INRAE , the  French School of Public Health (EHESP)  and  University of Montpellier. S. M. is supported by  French ANR FutureHealthSEA  ( ANR-17-CE35-0003-01 ) and the  Thailand International Cooperation Agency (TICA)  project " Animal Innovative Health ".
AVR System Description and Modeling The basic role of an AVR system is to keep the generator's terminal voltage constant via the excitation system. A basic AVR system consists of four major components: an amplifier, an exciter, a generator, and a sensor. These components are represented by Energies 2022, 15, 8973 3 of 20 transfer functions  [9, 12, 15, 46]  and modeled in the MATLAB/Simulink environment to study the dynamic performance of an AVR. A first-order transfer function with a gain and a time constant is used to represent each component of the AVR system. Table  1  shows the transfer functions of the aforementioned components. The closed-loop block diagram of the AVR system without a controller is shown in Figure  1  along with the transfer functions. Amplifier G a = K a 1+T a s 10 < K a < 400 0.
AVR System Description and Modeling The basic role of an AVR system is to keep the generator's terminal voltage constant via the excitation system. A basic AVR system consists of four major components: an amplifier, an exciter, a generator, and a sensor. These components are represented by transfer functions  [9, 12, 15, 46]  and modeled in the MATLAB/Simulink environment to study the dynamic performance of an AVR. A first-order transfer function with a gain and a time constant is used to represent each component of the AVR system. Table  1  shows the transfer functions of the aforementioned components. The closed-loop block diagram of the AVR system without a controller is shown in Figure  1  along with the transfer functions. To compare the results fairly, in this work, the AVR system's parameters are   = 10,   = 1,   = 1,   = 1,   = 0.
Funding: This work is supported by  PHC Maghreb (Program Hubert Curien) MELINA (Mastering Efficient Lighting In North Africa)  granted to L. C. via  Campus France  (Grant No. 43981ZG ) and funded by the  Ministry of Europe and Foreign Affairs (MEAE)  and the  Ministry of Higher Education, Research and Innovation (MESRI) in France , and by the  Algerian Ministry of Higher Education and Scientific Research (MESRS). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
METHODS This study employed a quantitative approach. We developed an online, self-administered questionnaire based on extant literature using a five-point Likert scale (1: "strongly disagree"; 5: "strongly agree). The data for this research were collected from April to June 2021. Translation/back-translation techniques were used to translate questionnaires for non-English speaking samples into their native language; issues regarding understanding and translatability were raised and addressed throughout the process. Additionally, we conducted a pilot test with 30 respondents and checked the reliability using Cronbach's alpha. Results revealed a Cronbach's alpha greater than 0. 8 for all constructs, indicating acceptable reliability. We retrieved a total of 742 and 685 completed questionnaires from Iran and France, respectively; after initial cleaning and data screening, the number of usable questionnaires dropped to 714 and 664 (Table "S1" in the Online Supplementary Document).
Data analysis The SmartPLS 3 software was used to perform PLS-SEM. PLS-SEM was selected primarily due to its ability to analyse the models including both formative and reflective constructs  [54]. This method has become a useful technique in research because it enables to analyse complex models with formative and/or reflective constructs with non-normal data and small sample sizes  [54]. We employed a combination of PLS-SEM analysis and multi-group analysis (MGA). We used PLS-SEM because it is more appropriate when conducting MGA  [55, 56]. In order to assess the conceptual model across two different contexts using PLS-SEM, this study evaluated the measurement model -by gauging the reliability and validity of reflective constructs -and the structural model -by evaluating R2, path coefficients, and the standardized root mean square residual (SRMR) values -as an approximate model fit for PLS-SEM  [55].
Acknowledgments V. B. , B. C. , and A. M. have received the support of the Chair " Modélisation Mathématique et Biodiversité " of  VEOLIA-Ecole Polytechnique-MnHn-FX. The authors have been also supported by  ANR  projects, funded by the  French Ministry of Research: B. C. by  ANR MESA  ( ANR-18-CE40-006 ), V. B. by  ANR ABIM  ( ANR-16-CE40-0001 ) and  ANR   CADENCE  ( ANR-16-CE32-0007 ), and A. M. by  ANR   MEMIP  ( ANR-16-CE33-0018 ).
Acknowledgements The authors are gratefully indebted to  Camille Pouchol  and  Tommaso Lorenzi  for their constructive remarks about the model. JAC would like to thank the funding received from the  Volunteer Lecturer Program  of the  IMU Commission for Developing Countries  to give a numerical analysis course at  Universidad de la Habana in March-April 2019. The research reported in this paper started during this period. JAC and FEAB thank the  Facultad de Matemáticas y Ciencias de la Computación de la Universidad de la Universidad de La Habana  for their hospitality. JAC was supported by the  Advanced Grant Nonlocal-CPD (Nonlocal PDEs for Complex Particle Dynamics: Phase Transitions, Patterns and Synchronization ) of the  European Research Council Executive Agency (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
Background: Natalizumab and fingolimod are used as high-efficacy treatments in relapsing-remitting multiple sclerosis. Several observational studies comparing these two drugs have shown variable results, using different methods to control treatment indication bias and manage censoring. The objective of this empirical study was to elucidate the impact of methods of causal inference on the results of comparative effectiveness studies. Methods: Data from three observational multiple sclerosis registries (MSBase, the Danish MS Registry and French OFSEP registry) were combined. Four clinical outcomes were studied. Propensity scores were used to match or weigh the compared groups, allowing for estimating average treatment effect for treated or average treatment effect for the entire population. Analyses were conducted both in intention-to-treat and per-protocol frameworks.
Data source This study is a result of a collaborative project  [11, 17]. Longitudinal demographic and clinical data were extracted from MSBase on 15 th of May 2018  [18, 19]. The Danish MS Registry cohort included all patients treated with natalizumab or fingolimod from 1 st of July, 2011 when fingolimod became available in Denmark, until 1 st of March, 2018  [20, 21]. The OFSEP cohort included data from 27 French university hospitals extracted from the European Database for Multiple Sclerosis (EDMUS) software in July 2014  [22]. No patient from OFSEP was recorded in MSBase. Some Danish patients who were recorded both in MSBase and Danish MS Registry (2% of Danish MS Registry) have been excluded from MSBase and only considered in the Danish MS Registry.
Causal contrast of interest Intention-to-treat analysis retained all matched or weighted patients in the group as initial treatment allocation regardless of their following exposure, until either the last data entry or the study outcome. Per-protocol analysis retained all matched or weighted patients until the date of treatment discontinuation (or the date of last data entry if it occurs earlier). Pairwise-censoring was used as a technique of censoring after matching. In each pair, study follow-up of both patients was censored when the follow-up of one of the two patients was censored. This approach prevented imbalance due to differential duration of follow-up in the matched groups.
Patients' characteristics after propensity score balancing procedures (matching and weighting) The distributions of PS showed a good overlap between the treatment groups, except in the tails (Fig. 1 ). The use of three caliper values for PS-matching led to three similar matched datasets (Table  2 ). The characteristics of the matched groups were comparable to the characteristics of the overall sample. The excluded patients tended to experience less disease activity. Table  4  presents patients' characteristics by treatment group. Overall, 35% of patients treated with fingolimod had an EDSS score < 2 at treatment start while it was 22% in the group treated with natalizumab. The matching procedure improved the Table  1  Overview of the analytical approaches used in the present work according to the outcomes a Analyses were conducted in intention-to-treat, on treatment and pairwise-censoring (matching only) frameworks b Two type of weights were considered (inverse probability weighting and weighting by the odds) c Three values of calipers were considered (0.
Discussion In this empirical study conducted on a complex chronic neurological condition, with long-term follow-up data, several non-linear outcomes and well powered dataset, most of the methodological choices (PS matching/weighting, caliper values, weighting on IPTW vs. odds, and pairwise censoring) resulted in consistent overall conclusions, in accordance with two of the three original studies  [5, 6] , the pooled analysis  [11]  and a recent French head-to-head prospective study  [36]. In a longitudinal observational study conducted over the long-term in the presence of frequent changes of therapy, an intention-to-treat causal contrast tends to be associated with more variability in the observed effects than a per-protocol contrast. Importantly, violation of the positivity assumption demonstrated the most pronounced negative effect on the consistency of reported results.
Limitations Through consistency and exchangeability assumptions, it is assumed that there were no unmeasured confounders. Nevertheless, our study was limited by incomplete MRI data, while MRI activity is a known prognostic factor in MS  [49]. Reassuringly, two of our three previous studies that accounted for MRI at treatment start showed results consistent with our primary analysis  [5, 6]. In addition, heterogeneity of data in multisite registries (with potential differences in therapeutic practices, health care systems and treatment access) may increase variance of the associations between treatments and outcomes  [50]. On the other hand, heterogeneity that is representative of clinical use of the compared therapies extends generalizability of the results. We have mitigated the potential heterogeneity in the present dataset by including country as a random term in the PS modeling.
Funding OFSEP was supported by a grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ", within the framework of the  "Investments for the Future" program , under the reference  ANR-10-COHO-002 , by the  Eugène Devic EDMUS Foundation  against multiple sclerosis and by the  ARSEP Foundation. ML has recieved travel grant from  ARSEP  foundation for this project. The  Clinical Outcomes Research unit at the University of Melbourne  received funding from  NHMRC  (grant number  1140766 ,  1129789 , and  1157717 ) to support this study. The MSBase Foundation is a not-for-profit organization that receives support from  Biogen ,  Novartis ,  Merck ,  Roche ,  Teva Pharmaeutical Industries and Sanofi Genzyme. The Danish Multiple Sclerosis Registry did not receive any funding to collaborate in this study.
Availability of data and materials OFSEP: The individual data from the present study can be obtained upon request and after validation from the OFSEP scientific committee (see website: http:// www. ofsep. org/ en/ dataa ccess). MSBase: MSBase is a data processor, and warehouses data from individual principal investigators who agree to share their datasets on a project-by-project basis. Each principal investigator will need to be approached individually for permission to access the datasets. DMSR: Anonymized data will be shared on request from any qualified researcher under approval from the Danish Data Protection Agency.
Our findings can help improve COVID-19 vaccine acceptance among French healthcare and welfare sector workers in the perspective of a long-term strategy beyond the mandate, but also in other population groups, for other vaccines and in other countries. Regulation (GDPR). Study participation was anonymous without possibility of indirect identification. Data including on self-reported vaccination status and self-assessed presence of a risk factor for severe COVID-19 were thus not considered sensitive personal data and no written informed consent was required. Persons visiting the study website saw the study information and had to agree to participation before starting the questionnaire.
We take advantage of the joint release of daily information on conflict events by the Armed Conflict Location and Event dataset, on the one hand, and on government policy responses by the Oxford Covid-19 Government Response Tracker, on the other hand, to estimate how restriction policies have affected dynamic of conflict outcomes. We use a difference-in-differences strategy that exploits the cross-country variation in the timing of restriction policies. Studying how restriction policies shape conflict behavior is subject to its own methodological challenges. First, estimates are likely to be biased because the covid-19 pandemic dynamics, and how people responded to it, can be systematically different between countries that implemented restriction measures and the non-implementing countries (Goodman-Bacon and Marcus, 2020).
19 Would-be autocrats are using Covid-19 as an excuse to grab more power, The Economist (April 23th, 2020, url). 20 How Maduro Is Using Covid-19 to Silence His Opponents Even Further, Americas Quarterly (July 21, 2020, url). low voter turnout. 21 President Maduro led coalition won an overwhelming majority in the disputed elections and has strengthened its grip on power. The preceding four vignettes, of course, do not constitute an exhaustive catalogue of Covid-related conflicts. Still, they help us to envision different channels through which Covid-related restrictions could impact conflict events worldwide. We explore these linkages more formally in the subsequent empirical analysis. 3 Data and empirical strategy.
Data and stylized facts Conflict. We use conflict event data from the Armed Conflict Location and Event dataset (Raleigh et al. ,   2010, acled, hereafter), which we extracted from the acled website on September 12, 2020. The data contain daily information on conflict events with specific details on the nature and the actors on both sides of the conflicts. Events are compiled from various sources, including press accounts from regional and local news, humanitarian agencies, and research publications. 22 Our analysis focuses on 134 countries, for which Covid-restrictions data is also available, from January 1st 2016 to September 1st 2020 (see Online Appendix Table  A1. 1 for a full list of countries). We do not consider the last ten days of data (September 2-12, 2020), as it is likely that reporting of events for this period was still incomplete when we retrieved the data.