dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENT The authors wish to especially thanks  Dr Ashour Hazem  for his contribution and participation to the validation of the tool in Hebron. The authors wish also to thank all of the participants in this study, and the  MSF  field teams and advisors. This study was funded and supported by  MSF, Operational Center Barcelona. Epicentre  receives core funding from  MSF. The funder participated in the design of the study and participated in the revision of the report and manuscript.
g. ,  Petitmengin et al. , 2006) , rare attempts have been made in sports. In an exploratory study, we combined brain activity using EEG (third person data) with first-person qualitative analysis of pre-reflective experience emerging during a standing split task in dance. We expected that brain activity signatures underlying the consciousness of various efficient ways of being-and-acting in this task would be identified. Data was collected through (a) video tapes of the athletes' behavior while performing the sporting task, (b) video-recorded and transcribed verbalizations and commentaries elicited post-action during self-confrontation interviews, and (c) 64-electrode EEG recordings made during sport performance. Qualitative analyses were performed on the data from the self-confrontation interviews using the course-ofaction method, which consists in identifying elementary units of meaning and their components (involvement, focus and general feelings) (e.
patients with established COVID-19. Searches of PubMed and Embase Central databases were carried out from December 2019 until July 2020. Predefined search terms were: 'COVID-19' OR 'severe acute respiratory syndrome coronavirus 2' OR 'coronavirus' OR 'SARS-CoV-2' OR 'coronavirus disease 2019' OR '2019-nCOV' OR 'novel coronavirus' AND 'renin-angiotensin system' OR 'angiotensin-converting enzyme inhibitors' OR 'angiotensin receptor blockers' OR 'RAS blockers' OR 'RAAS blockers' OR 'ACE inhibitors' OR 'ACEI' OR 'ARB'. Selection was done by two independent reviewers (M. K. and A. F. ). Inclusion criteria were defined as follows: (1) published studies including patients with established COVID-19; (2) comparison between RAS blockers and no RAS blockers; (3) studied endpoints included all-cause mortality; and (4) articles written in English.
ACE-I: angiotensin-converting enzyme inhibitors; ARBs: angiotensin II receptor blockers; ACE2: angiotensin-converting enzyme 2; COVID-19: coronavirus disease 2019; ICU: intensive care unit; NCT number: ClinicalTrials. gov identifier; RAS: renin-angiotensin system; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SOFA: sepsisrelated organ failure assessment.
Method Participants The Quebec Longitudinal Study of Child Development (QLSCD) is an ongoing population-based cohort that includes 2120 participants born from 1997 through 1998 in Quebec. The QLSCD is conducted by the Institut de la Statistique du Québec. 19 This cohort study was approved by ethical committees of the Québec Institute of Statistics of the CHU Sainte-Justine Hospital Research Centre and participant written informed consent was obtained. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. 20  the 1680 individuals contacted at the 20-year data collection point, 1235 responded, and 1174 provided information on social support at age 19 years and MHPs at age 20 years (56% of the original 2120 participants). Similar to previous studies that used this cohort, we selected comparison variables that have the potential to identify the most vulnerable participants and, in turn, those most likely to be lost at follow-up.
Statistical Analyses All statistical analyses were conducted using IBM SPSS, version 26. Prospective associations between perceived social support and MHPs in emerging adulthood were estimated using linear or binary logistic regressions. The first model was adjusted for sex only, whereas the second model was additionally adjusted for adolescent confounders, including MHPs (depressive, anxiety, oppositional/ defiant, inattention/hyperactivity and conduct disorder symptoms and suicidal ideation or attempts) and family characteristics (socioeconomic status, family functioning and structure). No sex-based differences were found in social support interactions across prospective associations; thus, we present regression results with sexes combined. Missing data for confounders ranges from 6% (MHPs) to 18.
Thus, testing two experimental groups of 10 individuals each requires 25h i. e. 3-4 days of full-day testing. Second, there is high degree of animal-experimenter interaction during the test (need for experimenter to install/remove cotton swab) which is known to be disturbing for animals performing the task, potentially affecting the obtained results  (Hänell and Marklund, 2014). And finally, the test is relatively labor-intensive and prone to subjectivity of experimenters as direct measures need to be live-performed by using a stopwatch. So, the first improvement we made was to automate the setup (data collection, data analysis) by using a videotracking software. Then we carried out a series of experiment which aimed at validating and implementing our improved setup. First we assessed reliability to see the extent to which our device yields consistent results each time the measurements are performed under the same experimental conditions.
2. 5) Data analyses and statistics Data were first computed as the total time spent sniffing over the three presentations of each odor. This cumulative duration has the benefit of highlighting robust changes in independent variables of interest but can be misleading when subtle effects are at work. In this case, we analyzed and depicted data as originally described in the literature  (Brielmaier et al. , 2012). This more detailed analysis has the advantage to emphasize discrete processes such as odor habituation and response to novel odors which could be very informative depending on the issue raised by the researcher. Such an analysis strategy is completely in accordance with our goal to not only achieve higher data density but also explore different behavioral dimensions from a single behavioral test.
This demonstrates that each lab, interested in using the setup, can easily install it at low cost. Non-social odors were purchased at very cheap prices from a private company specializing in food flavouring development. Social odors are completely free of charge as they were collected from mice reared in our facilities. For data acquisition, we used webcams also obtained in hypermarkets. The only significant investment to be done is related to the acquisition of a Video tracking software. However, this is crucial for the Refinement process to be efficient. Indeed, automation of data acquisition and data analysis through the use of such a system provides significant benefits such as objectivity of the scoring procedure, reduced work effort and reduced time spent performing the experiment, avoid animal experimenter interaction during testing and allow simultaneous tracking of individuals.
Interestingly, this mice model also displayed high level of anxiety-like behaviors. However, such an assumption has to be addressed in detail. The link between this olfactory behavioral trait of Balb/cJRj and anxiety was considerably highlighted by the fact that injection of the well-known anxiolytic diazepam completely reversed these alterations, not only demonstrating that anxious features can be assessed in our system but also that efficacy of putative anti-anxiety treatments can be studied. Our data also show that emotional reactivity can be assessed in this system by rendering environmental conditions during testing more challenging for mice. Indeed, sawdust removal is known to be stressful for rodents, inducing both behavioral and physiological changes  (Laviola et al. , 2002). In this aversive context, mice interest for odors was reduced depending on strain and odor types.
-Cognitive impairments Our data also indicate that learning and memory processes are likely to be evaluated in the system. Indeed, we were able to show that mice exposed to non-social and social odors during two consecutive sessions with a 24h inter trial interval can recognize all the odors presented before, irrespective of their ethological meaning. Such findings are in agreement with other studies showing that C57BL/6JRj mice could remember social olfactory cues 24h after a previous exposure  (Arakawa et al. , 2008). Importantly, it shows that short-term memory impairments can be assessed in the apparatus. In order to evaluate olfactory memory duration, the interval between the two sessions was increased to 5 days. In this case, data indicated that mice were able to remember only the social odors indicating that duration of long-term memory depends on the significance of the stimuli.
The mean score of the utterances conveying positive prosody was 4. 63 (SD = 0. 25), compared with 1. 26 (SD = 0. 22) for the utterances conveying negative prosody, t(16) = 37. 85, p <. 001. The authors then checked the prosodic contrast of the 12 utterances on the activation (high vs. low energy) dimension by conducting an acoustic analysis with PRAAT software  (Boersma & Weenink, 2004). Particular attention was paid to F0, the main parameter reflecting the activation of an emotion in prosody  (Bänziger & Scherer, 2005; Liberman & Michaels, 1962; Quam & Swingley, 2012; Scherer, 1986). A low, monotonous F0 pattern is characteristic of negative emotions in their depressed form (e. g. , sadness, cold anger, disgust) whereas a high, wide-range F0 pattern is characteristic of positive emotions in their excited form (e.
For all conditions, after the expressive meaningless utterance, the child had to answer the question, "So what do you think, how is Pilou feeling?" on two possible responses: Pilou is feeling good or Pilou is feeling bad. The child touched the screen to choose his/her answer (see Figure  1 ). Each participant was tested individually in a quiet room, where the experimenter settled him or her in front of the computer. The deaf children with CIs were tested in a clinical setting while the CA and HA participants were tested in a school setting. In the first of two sessions, participants were asked to judge the prosody embedded in a context. In the second session, the prosody was presented on its own. One week separated the two sessions. Data analyses. In this study, the prosody (positive vs. negative) was either embedded in a discrepant context or presented on its own.
Applying this procedure, we calculated diet specialism for each cuckoo species, regarding the complete set of avian species in the world  (Wilman et al. , 2014). The Gini coefficient for diet specialization was calculated using the package "DescTools" for R  (Signorell & mult. al. , 2019). Finally, the index was standardized between 0 (generalist species) to 1 (specialist species). The worldwide distribution range of cuckoo species was obtained from the literature  (Davies, 2000)  and from the section "data zone" in the BirdLife website (http://dataz one. birdl ife. org/). The data on distribution range refer to the extent of occurrence of breeding/resident of each cuckoo species and is provided in square kilometers (km 2 ) (IUCN & BirdLife International 2017). Additionally, we recorded overall body mass for each cuckoo species from the same publication used for the diet traits  (Wilman et al.
, 2003). When K approaches 1, trait evolution follows a mode of evolution that is consistent with Brownian motion, and if K > 1 close relatives are more similar than expected under Brownian motion, while if K < 1 closely related species are less similar than expected  (Blomberg et al. , 2003 ). Blomberg's K was estimated using the "phylosig" command of the "phytools" package for R  (Revell, 2012). Data on bird species cannot be treated as independent sampling units in comparative analyses, because species are evolutionarily related  (Harvey & Purvis, 1991). Therefore, we modelled interspecific variation in diet specialism index across a phylogeny, obtaining the phylogenetic relationships among cuckoo species from "www. birdt ree. org". We downloaded 1,000 phylogenetic trees from the backbone tree based on  Ericson et al.
(2006)  for the 119 cuckoo species that were the focus of this study (ESM, Table  S1 ). The consensus tree was obtained applying the 50% majority rule (i. e. , the proportion of a split to be present in all trees)  (Rubolini, Liker, Garamszegi, Møller, & Saino, 2015). In order to manage phylogenetic trees, we used the following R packages: "ape"  (Paradis, Claude, & Strimmer, 2004) , "phangorn"  (Schliep, 2011)  and "Rphylip"  (Revell & Chamberlain, 2014). Phylogenetic regression of the diet specialization index on the breeding distribution range, body mass and parasitism (brood parasitic/ nonparasitic species) was carried out using the "pgls" command of the "caper" package for R. A test of variance inflation factor (VIF) of candidate model was applied to check for potential multi-collinearity issues among predictors, using the "car" package for R  (Fox & Weisberg, 2019).
Acknowledgements. We would like to thank  Oliver Schülke ,  Julia Ostner  and  Gabriele Schino  for insightful comments on an early draft. We acknowledge the  Think Tank on Social Relationships  funded by the  Leibniz Science Campus Primate Cognition  at the  Leibniz Institute for Primate Research and University of Göttingen, Germany  for providing the inspiration to collaborate on this paper.
We calculated the power of that experiment using the SIMR package in R  (Green & MacLeod, 2016). With 28 participants and the same number of items per condition as the present study, the estimated power was between 98. 17% and 100% (95% confidence interval) to detect both the main effect of type of change and the type of change × lexicality interaction for accuracy on the "different" trials. We therefore judged that 31 participants would provide ample power in the present study to reveal the effects of interest. 3 Word frequencies were obtained from Lexique2  (New, Pallier, Brysbaert, & Ferrand, 2004)  using the Zipf scale of van Heuven, Mandera, Keuleers, & Brysbaert (2014), which corresponds to Log10 number of occurrences per billion. two random digits were presented for 500 ms and participants were instructed to repeatedly read them aloud during the whole duration of the trial.
Analysis Given the speeded nature of the task, we first performed RT and error-rate analyses using linear mixed effects (LME) models. We then applied signal detection theory (SDT;  Macmillan & Creelman, 2005)  to analyze discriminability (d') and bias (c) in making a "different" response. The statistical analysis of log10 transformed RTs and error rates was performed with R software (version 3. 5. 1), separately for "same" and "different" responses using the LME4 library  (Bates, Maechler, Bolker, & Walker, 2015) , declaring participants and items as random variables. We first tried fully randomized mixed models, including random slopes in addition to random intercepts  (Barr, Levy, Scheepers, & Tily, 2013) , but in several analyses (for Error Rates on "different" trials and RTs on "different" trials), the analyses failed to converge (that is, no solution was found within a reasonable number of iterations).
Acknowledgements This research was funded by  ERC  grant  742141. The authors thank  Julie Pynte  for her assistance in data collection. Open practices statement The data and materials of the experiment are a v a i l a b l e a t : h t t p s : / / o s f. i o / h k 9 q r / ?v i e w _ o n l y = 0d67482c526648adb730ffbf989a76e4.
Acknowledgments We acknowledge the support from the  Credit Agricole Loire/ Haute-Loire Foundation. We acknowledge  Patricia Morel , President of the  French Federation of Physical Education and Voluntary Gymnastics (FFPEVG) ;  Marilyne Colombo , Vice-president of research and innovation of the FFPEVG;  John Picard , National Technical Advisor of the FFPEVG; and all the participants of this study. We acknowledge the interns from  Gerontopole Auvergne-Rhône-Alpes and Jean Monnet University  who participated to realize this study. We also acknowledge  Dr Willian J Caldas  and  Mr Thomas Demange  for the translation of this article.
Participants Data from 6272 emerging adults, aged 18-30 years, participating in a larger cross-cultural and cross-country research project  (McCabe et al. , 2019)  were included in this study, of whom 68. 9% identified as female. The majority of participants reported being single (60%), and indicated that they were heterosexual (86%). On average, participants' Body Mass Index (BMI) = 23. 30 (SD = 4. 94). Participants were recruited from university research sites and using online forums located in each of the collaborating countries: Australia (n = 597), Belgium (n = 618), Canada (n = 768), China (n = 669), Italy (n = 661), Japan (n = 622), Spain (n = 821), and the United States (n = 1,516). In the overall sample, 86. 5% of participants reported being engaged in full or part time study, with rates varying from 73% to 97% across countries.
Acknowledgments The last author wants to thank  Alexandre J. S Morin  for his statistical advice, as well as the entire Academy for Body Image, Eating Problems, and  Health , and in particular  Drs. Lina Ricciardelli  and  Marita McCabe  for their vision and leadership. SOCIOCULTURAL MEASURES ACROSS COUNTRIES MPEGW, MPELW, MMGW scales (see models  12-6 ,  13-6 ,  14-6 ,  15-6 ,  16-6 ,  17-6 ); BMI = body-mass index; MFAGW = messages from father to gain weight; MFALW = messages from father to lose weight; MMOGW = messages from mother to gain weight; MMOLW = messages from mother to lose weight; MMGW = messages from media to gain weight; MMLW = messages from media to lose weight; MPEGW = messages from peers to gain weight; MPELW = messages from peers to lose weight;  PSI-BIBCQ = Perceived Sociocultural Influences on Body Image and Body Change Questionnaire ;  SE = standard error  of the coefficient; SES = socioeconomic status.
PARTICIPANTS AND METHODS Patients attending a specialized pediatric clinic (Pediatric Obesity Department of Romagnat and Tzanou, La Bourboule, France), were eligible to participate in this study. Patients were eligible if they had a Body Mass Index (BMI) above the 90th percentile according to the international reference values  15)  , were free of any physical contraindications (i. e. recent fracture, articulation issues, etc. ) and did not use any medication that could interfere with the principal outcomes of the study. The adolescents and their legal representatives received information sheets and gave their written consent for participation as requested by the ethical authorities (Committee for Human Protection: CPP Sud Est VI, Clinical Trial: NCT02482220). Following an initial cardiac test and a medical screen by a pediatrician to ensure the ability of the adolescents to complete the protocol, anthropometric measurements were performed, body composition assessed by dual-energy X-ray absorptiometry (DXA, QDR 4500 Hologic), their maximal aerobic fitness (VO 2peak ) evaluated and they were asked to perform a Spartacus run test.
e. ball and racquet games or recreational activities such as trekking or snowshoeing). The adolescents also attended nutritional education classes twice a month led by a dietician and received psychological support through individualized consultation with a professional once a month. During the intervention, the adolescents were submitted to a normo-caloric diet based on their age and gender recommendations  17). The statistical analyses were carried out using the statistical software Stata (version 13, StataCorp, College Station, USA). All statistical tests were conducted for a two-sided type I error at 0. 05. Continuous variables were described as mean and standard-deviation or median and interquartile range, according to statistical distribution (assumption of normality studied using Shapiro-Wilk test).
In France in 2015, the total direct and indirect costs of mental health problems was estimated at EUR 81 billion annually  [18] , and three out of four work-related mental disorders were due to depression  [18]. Previous observational studies on TRD have been primarily retrospective using claims databases or electronic health records  [7, 16, 19, 20]. However, it is difficult to correctly identify patients with TRD using claims records given the multiple definitions of TRD and subjectivity in detecting an adequate duration and appropriate adherence to treatment. With fewer prospective cohorts of patients with TRD  [21, 22] , there is a lack of data on this specific population. More real-world evidence is therefore needed to better understand TRD, to improve its diagnosis and management, and to inform cost-effectiveness studies.
Data Source The FondaMental cohort is a French multicentre, prospective observational study of patients with TRD  [23]. It is led by FondaMental, a public academic foundation created by the French Ministry of Research in 2007. The FondaMental cohort includes patients from 13 French expert centres specialising in the management and treatment of MDD. Inclusions in the cohort began in September 2013, and 450 TRD patients are planned to be recruited  [23]. An interim analysis was conducted on the cohort using a data cut-off date of 19 February 2018. Patients were included in the cohort if they met the following criteria: experiencing an MDE according to the DSM-IV-TR (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision) criteria  [24] ; failing at least two pharmacological treatments of two distinctly different classes (Stage II of the Thase and Rush staging model  [25] ); and experiencing moderate to severe depressive symptoms, as indicated by scores above 20 on the Montgomery-Åsberg Rating Scale (MADRS)  [26, 27].
Patients with bipolar disorders, psychotic disorders, obsessive compulsive disorders, eating disorders (with body mass index inferior to 15), somatoform disorders, and mood disorders related to substance abuse or misuse were systematically not included in the cohort. The data collection process has been previously described  [23]. In brief, data were collected through a web-based application, e-resistant depression©, which gathered all clinical monitoring, research and treatment data. Both self-administered (EQ-5D-5L) and observer-rating instruments (MADRS, Columbia-Suicide Severity Rating Scale (C-SSRS)) were used  [23].
Study Design This study provides an analysis of the prospective FondaMental cohort. The patients received an exhaustive clinical assessment at the date of inclusion in the cohort, which was assigned as the index date in this study. Follow-up assessments were performed every six months until the occurrence of one of the following: the last visit before death or drop-out; the last visit before data extraction; or the final visit at four years post-index date. Data were extracted on 19 February 2018.
Study Outcomes Demographic and medical data were collected at the baseline evaluation. The severity of depressive symptoms was evaluated according to the MADRS score. Suicidal behaviour was measured using the C-SSRS  [28]. The collection of treatment information relied primarily on patient-treating physician input, patient prescriptions, and patient records. In particular, antidepressant treatment use was analysed, as well as the use of ECT and repetitive transcranial magnetic stimulation (rTMS). Treatment proposals were made during baseline visits and each follow-up visit for patients who did not respond to the previously proposed treatment strategies. Healthcare resource consumption was recorded at each visit by the occurrences between two consecutive visits. Hospitalisations were assessed both aggregately and by cause, i.