dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed the hand slapping behaviour -used by the baboon to threat or intimidate the recipient -is the most common and frequent visual gesture of the repertoire  23  produced intentionally and unimanually in a lateralised manner across similar agonistic contexts and similar emitter's postures  9. Hand use was recorded in a baboon when slapping or rubbing quickly and repetitively the hand on the ground in direction to a conspecific or a human observer at an out of reach distance. Handedness for manipulative actions was assessed among a sample of 71 baboons using the welldocumented bimanual coordinated "Tube task"  9. Hand use was recorded when extracting food with a finger out of a PVC tube hold by the other hand (see  7  for full details of the hand preference's data collection procedure). (which was not certified by peer review) is the author/funder.
Acknowledgments General: We are very grateful to the  Station de Primatologie CNRS , particularly the animal care staff and technicians,  Jean-Noël Benoit ,  Jean-Christophe Marin ,  Valérie Moulin ,  Fidji  and  Richard Francioly ,  Laurence Boes ,  Célia Sarradin ,  Brigitte Rimbaud ,  Sebastien Guiol ,  Georges Di Grandi  for their critical involvement in this project;  Leonard Samain-Aupic  for sulci labelling;  Ivan Balansard ,  Sandrine Melot-Dusseau ,  Laura Desmis ,  Frederic Lombardo  and  Colette Pourpe  for additional assistance.
Funding: The project has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  grant agreement No  716931 -GESTIMAGE -ERC-2016-STG (P. I. Adrien Meguerditchian ), from the  French "Agence Nationale de le Recherche"  ( ANR-12-PDOC-0014-01 ,  LangPrimate  Project,  P. I. Adrien  (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
ACKNOWLEDGMENTS This Research Topic was conducted as part of a research stay funded by  University of Nantes (FLCE)  in collaboration with the  Centre de Recherche sur les Identités, les Nations et l'Interculturalité research laboratory (CRINI). The authors are grateful for the funding received from the  Autonomous Community of La Rioja RIS3 program  (Ref. :  CAR -PID2019-105764RB -I00 ).
In contrast, a significant improvement three months after the end of the MBI was observed in both the high-and moderate-risk groups. Our study has several strengths. We used gold standard RCT design, recruited a relatively large sample of VPT young adolescents and analysed the data on an intention-to-treat basis. Nevertheless, theoretical and methodological limitations of this study should inform future research. First, the beneficial effect of MBI observed via parent-reported questionnaires might be questionable given the subjective aspect of these tools. 58 Future studies should consider the completion of questionnaires by multiple informants from different settings (e. g. parents and teachers) to give a more objective view of the changes occurring after MBI. 55, 59   ond, one of the main study limitations is the absence of an active control condition or a placebo condition.
ACKNOWLEDGMENTS We thank and acknowledge all participating young adolescents and families who made this research possible. We also thank the  Fondation Campus Biotech Geneva (FCBG) , a foundation of the  Swiss Federal Institute of Technology Lausanne (EPFL) , the  University of Geneva (UniGe) , and the  University Hospitals of Geneva (HUG) ; the  Research Platform of the University Hospitals of Geneva (HUG)  for their practical help; as well as  Mariana Magnus Smith  and  Françoise Stuckelberger-Grobéty  for their implication as MBI instructors.
Data collection All these data (academic performance and socio-cognitive assessments) were collected online via a dedicated platform built for the purpose of the study. Students completed the tests and questionnaires directly on the platform, which was made accessible from the school computer lab. Data collection was supervised by national education personnel trained for the purpose of the study. During data collection, each class was divided into two groups to guarantee a sufficient number of computers per student and minimize noise. Data collection spanned a maximum of thirty-seven weeks and varied depending on each Science and Technology discipline. At the beginning of the school year, all students completed the psychological assessments over a period of four weeks. Several weeks later (two to four weeks for Technology, nine to eleven weeks for Earth and Life Sciences and thirteen to-fifteen weeks for Physics-Chemistry), the academic pre-tests were administered.
Acknowledgments We thank all study site lead investigators ( Yannick Morice ,  Delphine Pailler ,  Florence Prost ,  Annabelle Restoy ) and the numerous teachers who participated in this research. Financial support was provided to P. Huguet by the National EFRAN research program on digital education for a project titled "e. P3C: digital technologies at the service of the Plurality of Contexts, Skills and Behaviours". The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Special thanks to  Marie-Danielle Campion  (Rector) and her successors ( Benoı ˆt Delaunay and Karim Benmiloud ) who strongly supported this project.
Financial support was provided to P. Huguet by the  « Ministère de l'E ´ducation et de la Jeunesse/Ministère de l'Enseignement Superieur, de la Recherche et de l'Innovation/Mission Monteil pour le numerique educatif/ Programme  d'investissements d'avenir, action EFRAN » for a project titled « e. P3C: digital technologies at the.
Working memory span scores were determined for each complex span as the total number of stimuli correctly recalled in the correct position  (Conway, Kane, Bunting, Hambrick, Wilhelm, & Engle, 2005). Scores on the three complex span tasks were standardized and then averaged to produce a single, composite working memory capacity score. Accuracy and median response times on correctly answered processing problems in the three complex span tasks were also retrieved for each participant. To ensure that participants performed both component of the complex spans correctly, the WMC IN GIFTED CHILDREN ! data from a subtest were excluded if processing accuracy scores were in the bottom fifth percentile of the sample, and working memory capacity was computed from the other two subtests  (Gonthier, Thomassin, & Roulin, 2015; Unsworth et al.
Data Analysis The data and R scripts needed to replicate the statistical analyses are available on the Open Science Framework platform at https://osf. io/y48uc/. A first series of analyses examined whether the gifted and nongifted groups differed in terms of overall working memory capacity, processing times and processing accuracy on the working memory task, and in terms of scores for the three attentional networks. These analyses were performed based on the general linear model, using between-subjects ANOVAs and mixed-design ANOVAs (with Greenhouse-Geisser correction when appropriate). Posthoc tests were performed using Tukey's HSD. Effects sizes were computed using eta squared and partial eta squared. A second series of analyses examined whether the difference in working memory capacity between the gifted and nongifted groups was mediated by the processing efficiency and attentional network scores.
These analyses were based on bootstrap tests for multiple mediation  (Preacher & Hayes, 2004 , 2008). Indirect effects were estimated using the lavaan package  (Rosseel, 2012 ; bias-corrected method with 20000 bootstrap samples). This approach provides 95% confidence intervals for direct and indirect effects; an effect is significant at the p <. 05 level if the 95% confidence interval does not include zero. Indirect effects were estimated based on all available data (i. e. using the full sample for all putative mediator).
Data Analysis Using the Lovibond and Lovibond  [34]  method, the depression, anxiety, and stress subscales on DASS-21 were, according to individual sum scores, categorized as normal, mild, moderate, severe, and extremely severe  [34]. These subscales were then grouped as normal/mild, moderate, and severe/very severe. We created two groups: medical professionals consisting of doctors and nurses; and nonmedical professionals, which included other jobs in health care, volunteers, nonmedical staff, and community health care workers. Descriptive analysis (including means, SDs, and frequencies) and inference statistics (multivariate analysis of variance [MANOVA]) were calculated using SPSS, version 24 (IBM Corp)  [37].
Acknowledgments This study would not have been possible without the support of European health care institutions that helped us distribute the survey link. We thank our Spanish supporters Fundación INTRAS; the  Regional Authority of Social Affairs  ( Castilla y León ); the nursing homes ACALERTE and LARES;  Hospital San Pedro en Logroño ,  La Rioja ; the  Fundación Rey Ardid (Aragón region) ; and the care organizations Pronisa, AFAVITAE, and  ACyLNP. In France , we would like to thank the  Marseille Public University Hospital System. In Austria , we thank the  Sozialdienste Wolfurt  for their support.
Data collection procedure The questionnaire was transcribed in Google Forms and e-mailed between July 1st and August 20th, 2020 to 250 GPs of Bouches-du-Rhône department who met our inclusion criteria. A follow-up reminder was sent 15 days afterwards (to non-respondents). The GPs had to validate the 38 questions in order to complete the questionnaire and to avoid missing data. The questionnaire remained anonymous with no personal data requested. The duration of each GP's participation to complete the questionnaire was estimated to be approximately five minutes.
Generally, the tools that support emotion detection are based on sensors devices, cameras, and microphones. Then, emotional data are analyzed by applying various AI methods such as machine/deep learning algorithms and data mining techniques. A series of recent studies have proved that basic emotions  [17]  namely anger, joy, fear, surprise, disgust, and sadness do not play a significant role in computer-based learning  [15, 21]. In e-learning processes, we study a subset of cognitive-affective states that are the most relevant to learning. These affective states are a combination of basic emotions and include Flow/engagement, frustration, confusion, and boredom  [22]. For this reason we present, in what follows, the Flow theory  [8]  as well as the cognitive disequilibrium model  [23]  that investigates the dynamics of affective states over time during learning activities.
Therefore, the proposed method is composed of three main steps as follows: 1. Trace recovery: this step collects players' data produced during a CMSG session. These data cover visual data (real-time video recordings from webcams) to capture facial expressions, and textual data (exchanged messages during playing) to detect emotions from text. 2. Basic emotions and stress detection: the previously collected data are analyzed according to the discrete model  [17]  to classify learners' basic emotions. Given the sensibility of crisis context to stress factor, we also detect the learners' stress state (which is not considered as a basic emotion) to study its impact on their actions during playing. 3. Learners' profiles construction: the detected basic emotions are then analyzed to produce individuals formative and summative evaluations of learners' emotional profiles as well as a collective emotional profile (group emotion) using a decision tree model.
iScen Platform Iscen 3  is a software platform aiming to create interactive scenarios and use them in multi-players game sessions on a local network or on the Internet. Developed by Eversim 4  society, this platform is specifically intended for crisis simulation, management, and training. It relies on the Client-Server architecture. The server is used by the moderator who creates and administrates the scenario and the game session. All the learners (participants) connect to the server through their client machines to join the session. IScen has several communication systems consisting of voice chatting, messaging, and a system of shared notes. The messaging system let us incorporate different types of content like text, images, videos, and questionnaires. IScen requires defining scenarios which the game session instantiates.
Discussion In order to test our method, the obtained summative evaluation results are compared to self-reported subjective descriptions based on the GEQ. After the collaborative iScen game session, the GEQ core module is presented to learners as an electronic questionnaire to complete. The descriptive statistics obtained from learners responses are reported in Table  5. As shown in Table  5 , positive feelings are much less severe and less frequently experienced compared to negative feelings (lower than the mid-value of the scale). In fact, participants reported the level of positive affect to be low (2. 00). More specifically, results analysis shows that immersion (reflecting how players felt strongly connected with the game) and Flow (indicating whether players lost track of their own effort and/or the passage of time during the game) receive respectively average degrees (2.
Dr Kentish-Barnes  reported receiving grants from  French Ministry of Health  outside the submitted work. Dr Azoulay  reported receiving grants from  Pfizer  and  Merck  and personal fees from  Alexion Pharmaceuticals ,  Sanofi ,  Baxter International , and  Gilead  outside the submitted work. No other disclosures were reported. Funding/Support: This study was funded by the  French Ministry of Health  to the  Assistance Publique-Hôpitaux de Paris  (grant No. PHRC 2020 ,  APHP200389 ). Role of the Funder/Sponsor: Neither the funder nor the sponsor ( Assistance Publique-Hôpitaux de Paris ) had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Related Work 1. 2. 1. Eye Movements An enormous effort by cognitive psychologists and neuroscientists has been made toward understanding ATCOs' eye movements. These studies implicitly or explicitly assume that eye movements, as an information-seeking behavior, is closely related to workload, conflict detection, and performance. Eye movements data were recorded and analyzed to identify their relationships with workload. For example,  Ahlstrom et al. studied  the correlation between eye movements and the ATCO workload. They investigated the relationship between ATCO saccades, blink frequency, pupil diameter, and air traffic flow  [10]. The results showed that the use of DSTs in supervision work can reduce the ATCO workload. Tokuda studied the relationship between saccadic intrusion and workload  [11].
The content, operation, and purpose of the simulation was introduced; Step 2: The participant sat in front of the screen, and adjusted the seat to his/her own comfort; Step 3: Eye tracking and BFB2000 devices were calibrated before starting the experiment; Step 4: The subject was required to be familiar with the simulation tool. Step 5: The simulation began. Eye movements data, heart rate variability (HRB), and skin conduction level (SCL), and EEG data were recorded; Step 6: The simulation was stopped. The participant completed the NASA-TLX form and 3D-SART form. Figure  4  shows a participant during a simulation exercise. The presented study only focuses on the eye movements and subjective measurements, while the EEG and cardiovascular data are reported in a separated study.
Methods: We conducted a search of PubMed, Scopus, and Embase databases. We included all study designs (qualitative, quantitative, mixed) reporting development, evaluation of efficacy, or acceptability of apps facilitating dialogue with a health professional or promoting health for people who have migrated, minorities, or tourists with a language barrier, using any outcome. Two researchers selected the studies independently. We collected general information about the app, information about health literacy and cultural adaptation, information about the development of the app, evidence on acceptability or efficacy, and information on app use. Data were collected by 2 researchers independently and results were reviewed to verify agreement and reported according to PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis).
Search Strategy, Databases, and Keywords We wrote a protocol prior to starting the systematic review and reported the results according to the PRISMA reporting guideline  [12]  (Multimedia Appendix 1). We conducted a search of 3 databases of scientific publications: PubMed, Scopus, and Embase. The keywords varied according to the database used. To determine keywords, we undertook a broad preliminary search and selected some articles (between 5 and 10) identified as meeting inclusion criteria determined by the authors. After selecting these articles, we searched for their related keywords, MeSH terms (Medical Subject Headings), terms in titles or abstracts, and EmTree terms. Then the selection of keywords was tested and different combinations were tested so that the total number of results was manageable and yielded relevant articles.
Study Selection A list of articles retrieved from all 3 journal databases was compiled. After excluding duplicates, 2 researchers (FT and SP) independently reviewed the title and abstracts of all documents for preselection. Articles were then reviewed in full for inclusion or exclusion; if an article was excluded, the reason was documented. We managed the selection of articles with Rayyan  [13]. Differences of opinion regarding the inclusion of an article were managed by a third researcher (ORT). We also.
Data Collection We used 2 data collection coding sheets (Google Forms): one for each article (type of publication, year of publication, and journal) and one for each app studied (general information about the app or electronic tool, information about health literacy and cultural adaptation, information about the development of the app or electronic tool, evidence about the acceptability or efficacy of the app or electronic tool, information about the use of the app or electronic tool). All articles were read by FT, and to improve the validity of results, we performed data triangulation by having a second author read articles independently. When articles presented additional sources of information regarding an app, such as gray literature or a website, we retrieved data about the app from this source and noted the references of this additional source of information but did not use them in the article selection.
Selection The database search was carried out in October 2019. We retrieved a total of 15,752 articles from 3 databases after removing duplicates, of which 15,618 were excluded on the basis of title or abstract content. We assessed the full texts of 134 articles and excluded 87, most because the electronic tool described did not meet the inclusion criteria. We subsequently added 14 articles, because they were found either in the original background search or cited by articles that had been initially selected. In total, we included 61 articles (Figure  1 ). The difference between the very high number of articles originally retrieved and the number of articles selected can be explained by the fact that the original search yielded a number of articles related to translational research. The 61 selected articles were all read and analyzed by FT, and 51 (84%) were read and analyzed by a second reviewer (35 by SP and 16 by AVY).
Strengths and Limitations We conducted an extensive search of the literature using 3 different databases of publications. The selection of articles was conducted by two researchers working independently; results were compared and differences were settled by a third party. Most articles were read and their data extracted by two researchers independently, and results were compared when analyzing data. This enabled us to have a higher quality of data and a reduced risk of bias. We extended the scope of our systematic review to both electronic tools designed for translation and those designed to promote health among people who have migrated with a language barrier. Although the electronic tools have seemingly different objectives, it is likely that, in the future, there will be hybrid apps that will be developed to integrate both objectives.
Acknowledgments We sincerely thank  Corinne H Miller , Clinical Informationist at  Galter Health Sciences Library & Learning Center of Northwestern University, Chicago, USA , for her help in the developing the search strategy and keywords. The study was funded by the  Agence Nationale de Recherches sur le SIDA et les Hépatites virales  (grant  ECTZ109287 ) and by  Office Français de l'Immigration et de l'Intégration  ( 20191104122918510 ), whom we thank for their support. The funders had no role in study design, data collection or analysis, manuscript writing, or decision to publish. JWG was supported in part by the  National Institutes of Health  ( R01MD010440 ).
Data collection Data were collected between September 2013 and February 2014 (for the variables related to breeding, growth, and telomeres) and until 2019 for chick survival. Unique numbered aluminum ring and color combination are attributed to each adult during the annual captures in winter and to each nestling before ledging, allowing individual identiication of each bird. During the breeding season, all chambers in the study colonies are checked every three days, allowing us to identify new clutches and estimate clutch size (in this study, clutch size: mean = 3. 5 and SD = 0. 57 eggs; brood size before ledging: mean = 2. 25 and SD = 0. 84 birds). All chambers are visited daily at the end of the incubation period (approximately 15 days), to identify the hatching date. The nestling period usually lasts 21-24 days  (Maclean 1973a).
For the telomeric sequence, the conditions were set at 95 °C for 2 min followed by 30 cycles of data collection for 15 s at 95 °C, 30 s at 56 °C, and 1 min at 72 °C. The 132 weaver samples used in the present study were part of 504 samples that were measured in duplicates on 5 diferent runs. Each run includes 1 plate of telomere sequence ampliication and 1 plate of control gene sequence ampliication, due to the diferences in qPCR temperature conditions. On each plate, we ran a dilution curve (20-0. 313 ng) to check that the eiciency of ampliication did not vary among plates, and a melting curve to check for ampliication artifacts (like primer-dimer signal, see ESM 2). The mean qPCR amplifying eiciencies were 99. 8% (99. 4-99. 9) for telomere and 99. 9% (99. 7-100. 1) for the control gene (100% relect a doubling of the DNA sequence at each ampliication step).
Efects of helpers on chick telomere length: model itting and selection For the tests conducted either on day 9 or day 17, we built separated mixed linear models (LMM) with 'nest' as a random factor nested in 'colony' to control for non-independence of chicks in a nest. Due to the relatively low sample size (n = 50), we decided not to include more than ive explanatory variables to avoid over itting  (Harrison et al. 2018). We applied a model selection procedure with the 'MuMIn package on R, based on the lowest Aikake's information criterion corrected for small sample sizes (AIC; v. 1. 43  Bartoń 2013). The procedure was adjusted to provide models containing no more than ive variables, including the two main efects we wanted to test, i. e. , prenatal presence and postnatal variation in the number of helpers.