dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects Forty-seven healthy subjects (37. 0 ± 1. 1 years) were included in this study after providing their informed written consent. The study received the agreement of the Cochin-CPP Ile de France IV (Paris) Ethics Committee and of the French National Agency for Medicines and Health Products Safety (ANSM) (Ile de France IV) and was conducted according to the principles expressed in the Declaration of Helsinki of 1975, as revised in 2001. The study has been recorded in the clinical trial base (clinical trials. gov ID: NCT03859882). Subjects were free from medical, psychiatric, and sleep disorders. Subjects had not travelled between time zones within 7 days prior the study. Exclusion criteria included physical or mental health troubles according to (I) Hospital Anxiety and Depression scale, HAD ≥16  [35] ; (II) significant medical history; (III) Epworth Sleepiness Scale, ESS >11  [36] ; (IV) Pittsburg sleep quality index, PSQI >8  [37] ; (V) morningness-eveningness questionnaire <31 or >69  [38] ; and (VI) habitual time in bed per night <6 h.
For the 4 SNPs identified as having the largest effect on both performance tasks and KSS score during prolonged wakefulness, we conducted a combination analysis using SNPstats R software  [43]  to assess a potential synergistic combination of the mutations on the values observed at 26 h of prolonged wakefulness. The mean results for each allele combination, the mean difference, and the p-value of the comparison with the most frequent combination were calculated using a Bonferroni test.
This allele was shown to affect the duration and intensity of deep sleep and to reduce vigilant attention in humans  [16, 59]. The allele frequency distribution in our sample is in accordance with the expected data from the 1000 Genomes database. We illustrated the influence of the ADA polymorphism on the temporal vulnerability profile of the TSD sleepiness score despite the low percentage of the mutated genotype (6. 5%) to show that carriers of the rare T allele are below the resilient profile, and thus should be discarded in future studies, especially those including small numbers of subjects, on the genetic determinants of neurobehavioral vulnerability to sleep loss. It should be noted, as a limitation of our work, that the number of subjects remain limited. In any case, we must remain attentive to the multiple co-factors that modulate the neurobehavioral vulnerability to sleep loss and are sometimes more influent than the genetic impact, such as physical activity >2 h/week or coffee/energy drink consumption  [60].
Data Coding We video-coded spontaneous MRB using the all-occurrences sampling method  (Altmann, 1974)  and a coding scheme adapted from  Povinelli et al. (1993)  and implemented in BORIS version 7. 9  (Friard & Gamba, 2016). Behaviors included, e. g. , holding, manipulating, or exploring the mirror, social responses to the reflection, watching the reflection, contingent movements and SE (see Table  S2  for details). We coded the onset and offset for each MRB, so we could calculate both the frequencies of MRB and their respective duration. Furthermore, we coded the time at which an individual entered and left the range of a mirror where MRB was possible. An individual was considered as being within range when located within a predefined three-dimensional area (2 x 5 x 2 m) in front of a large mirror (adapted from  Povinelli et al.
Data Analysis For data analysis, we used statistics software R version 3. 6. 3 (R Core Team, 2020) with the additional packages car, glmmTMB, exactRankTests, sfsmisc and ggplot2  (Brooks et al. , 2017; Fox & Weisberg, 2019; Hothorn & Hornik, 2019; Maechler, 2020; Wickham, 2016). Due to skewed distributed data, we report medians (Mdn) and interquartile ranges (IQR) unless indicated otherwise. We used a GLMM with Beta distribution, logit link function and zero-inflation (R function glmmTMB,  Brooks et al. , 2017)  to investigate possible influences on the proportion of time spent on MRB in the total time of a session (conditional component) and on whether MRB is demonstrated at all (zero-inflation component). Into the model, we included Condition (Large mirror/Small mirrors) as the test predictor and controlled for Session (first/second), Age and Sex (fixed effects) as well as Individual and Group (random effects).
Acknowledgements We thank all the keepers, curators and directors at the participating zoos for their extraordinary support for this study, especially  André Schüle ,  Christian Aust  and  Ruben Gralki  at  Zoo Berlin ,  Karoline Albig ,  Sven Zeidler  and  Kerstin Gribbe  at  Zoo Halle ,  Sandra Reichler ,  Anke Fuchs  and  Dominik Winkel  at  Zoo Heidelberg ,  Daniel Geissler ,  Stefan Leideritz ,  Nico Schenk ,  Evelyn Kanzler  and  René Berger  at  Zoo Leipzig ,  Kai Perret ,  Susann Paelecke  and  Julia Schrage  at  Zoo Magdeburg ,  Severin Dressen ,  Matthias Schmitz  and  Julian Kusak  at Zoo Wuppertal as well as  Daniel Hanus  and  Hanna Petschauer  at the  Wolfgang Köhler Primate Research Center. Many thanks go to Isabella O'Neal ,  Pieter Nyssen  and  Peter Niehoff  for data collection and coding, to  Raik Pieszek  for manufacturing the small mirrors, to  Cédric Girard-Buttoz  for statistical advice and to all colleagues within the  Evolution of Brain Connectivity  project for valuable input and discussion.
Methods: The observational study (#NCT04367337) enrolled 6,064 adults residing in Australia, Canada, China, France, Gambia, Germany, Israel, Italy, Malaysia, Poland, Portugal, Romania, Singapore, and Switzerland. Data on cross-situational handwashing adherence were collected via an online survey (March-July 2020). Individual data were matched with the WHO daily reports of COVID-19 and indices of containment-and-health policies. Country-level human development index and sociodemographic variables were controlled. Results: Multilevel regression models indicated that as the total cases of COVID-19 morbidity and mortality grew higher, handwashing adherence decreased. As increases in recent cases of COVID-19 morbidity and mortality occurred, handwashing adherence increased. Higher levels of containment-and-health policy index were associated with lower handwashing.
Procedure This preregistered observational study (see Clinical Trials. Gov, #NCT04367337, rst registration date: 29/04/2020) was conducted in 14 countries: Australia, Canada, China, France, Gambia, Germany, Israel, Italy, Malaysia, Poland, Portugal, Romania, Singapore, and Switzerland. These countries had different COVID-19 morbidity and mortality trajectories within the study period and differed in cultural context that may affect handwashing. Data collection was initiated on March 24 th , after obtaining ethics clearance (following the institutional regulations in each study country) and preparing 8 country/language versions of all study materials. Data collection continued until July 22 nd 2020. Individual-level data were collected via web-based survey using Qualtrics platform. The questionnaire took approximately 15 minutes to complete.
' The Hebrew language version included an explanation that the questionnaire addresses handwashing for hygienerelated purposes and it does not deal with ceremonial or religiously motivated handwashing. Next, information related to sociodemographic variables, selected social-cognitive variables, and adherence to WHO handwashing guidelines across situations was collected via self-report. Daily rates of COVID-19 morbidity and mortality from 20th January 2020 until 22 nd July 2020 were obtained from the WHO Situation Reports [8] and indicators of containment-and-health policy responses to COVID-19 were obtained from the Oxford COVID-19 Government Response Tracker database  [15]. COVID-19 and country-level containment-and-health policies data were matched with participants' data using dates and countries of data collection.
Responses were provided on a scale ranging from 1 (strongly disagree) to 4 (strongly agree). Participants were asked if they encountered the following situations during the previous week: caring for someone sick, taking care of an infant, caring for an animal, or treating a wound or a cut. If a respondent indicated that they did not encounter one or more of these situations in the previous week (e. g. , they did not care for an infant), the respective item was removed from the mean item score value for the participant. The mean item scores for handwashing adherence are reported in Table  1. Sociodemographic Variables. Data referring to country of residence, age, gender, marital status, education, and perceived economic status were collected. Perceived economic status was measured with one item, 'Comparing to the average situation of a family in your country, what is the economic situation of your family?', with responses ranging from 1 (much above the average) to 5 (much below the average).
Assessment of Country-Level Variables Human Development Index. Values 2019 country-level indicators of Human Development Index (HDI), capturing overall development, health, and educational situations were obtained for the 14 countries participating in this study  [24]. COVID-19-Related Containment-and-Health Stringency Policies (Country-and Week-Speci c Data). Containment-and-health policy index data were extracted from the Oxford COVID-19 Government Response Tracker database  [15]. This index combines 'lockdown' restrictions and closures with measures such as testing policy and contact tracing, short term investment in healthcare, as well as investments in vaccination. In contrast to other indices (e. g. , overall government response), the containment-and-health index refers directly to health-related policies and excludes economic measures such as income support and debt relief.
Data Analysis Of the 6,397 potential respondents who provided their consent, n = 333 (5. 2%) provided sociodemographic information only and withdrew from providing further data. These cases were excluded from any analyses. The excluded subsample did not differ (all ps >. 05) from the nal sample (N = 6,064) in terms of age, gender, education, and economic status. Data for the basic sociodemographic variables (age, gender) and handwashing adherence were missing completely at random, Little's MCARχ 2 = 6. 26 (df = 3), p =. 098. Missing data analysis accounting for all individual-level variables were not missing at random, Little's MCARχ 2 = 165. 53 (df = 21), p <. 001. Missing self-reported data in the nal sample were accounted for by using the full information maximum likelihood procedure. Preliminary analyses (correlations, descriptives) were conducted with IBM SPSS 26.
Individual-level predictors that were signi cantly associated with the handwashing adherence index were included in the main analyses (multilevel regression). Separate models were t for each of the 6 COVID-19 morbidity and mortality indices, due to multicollinearity issues. The multilevel regression analyses were conducted using the lme4 R package  [25] , R version 4. 0. 3 [26] and restricted maximum likelihood estimation procedures. Each of the 6 models accounted for 8 predictors. Owing to the complexity of the model and limitations of the estimation method, there was no convergence when the country-level variables (COVID-19 morbidity, mortality and policies) were modelled as random effect predictors. Thus, predictor variables were assumed to represent xed effects, with the nal model assuming random effects only for the intercept for each country.
" It allowed the jaw to move naturally in the vertical dimension but prevented lateral and horizontal motions. The probe was positioned under the chin, between the arches of the mandibular body. The tongue surface contour was captured in the midsagittal plane. No additional head-to-probe stabilization was employed to maximize the naturalness of speech and make the recording more comfortable for children. Trials during which participants moved were discarded after visual inspection of the data (see  Noiray et al. , 2020  for a full description of the experimental platform). The acoustic speech signal was simultaneously recorded using a Shure microphone (fps: 48 Hz). Reading proficiency was assessed with a standard battery of German reading tests (SLRT I -Salzburger Lese-und Rechtschreibtest -  Landerl et al.
, 2019a ) the tensor smooths were set for each level of the C 1 variable (i. e. consonant type was integrated as an interaction factor in the model). Data visualization, an essential feature of GAM statistical modeling, was performed with the fvisgam function of the itsadug package  (Van Rij et al. , 2020). For a full description of GAM procedures and functions we recommend  Wieling's (2018)  tutorial.
Selection criteria for the questionnaires among countries A roster of 2,000 health professionals in psychiatry (HPP), matched according to age, gender, experience, and geographic location, will be compiled using snowball sampling from the networks of ad hoc professionals and researchers mobilized in March 2020  [11]. Thus, 23 experts will provide a database of HPP to whom to send the questionnaire or will relay the questionnaire directly to the professionals, according to the target number of respondents per country, i. e. , a total of approximately 2,200 professionals. After discussion with the scientific committee, we decided that the number of professionals participating in the study would be based on the population threshold of the involved countries with an overestimation of 10% after considering the number of psychiatric hospital beds per country  [15] , the number of psychiatrists per 100,000 inhabitants  [16] , and the heterogeneity of this data (Table  1 ).
Data Sources and Searches A search strategy was developed with the support of a research librarian (MDL). Appropriate key words were selected according to Subject Headings, terms used in existing studies on cognition and PMD use, and the "Mental functions" chapter of the ICF. The search strategy generated 147 terms related to cognitive functioning. The search was conducted in online databases including MEDLINE (Ovid), CINAHL (Elsevier), PsycINFO (Ovid) and Web of Science (Clarivate). Search strategies are available in the Supplementary Materials file. The search was performed from the inception of each database in February 2019 and was updated in March 2020. Reference lists of the included studies were hand searched to ensure coverage of the literature available.
Study Selection Inclusion and exclusion criteria: Scientific peer-reviewed studies including PMD users (inclusive of ages and diagnoses), reporting cognitive functioning and PMD use (capacity and/or performance) outcomes, presenting original data, and published in English or French, were included. Editorial, commentaries and theoretical papers were excluded. Selection procedure: Identified studies were exported to Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia), where duplicates were removed automatically based on the title of the references. Remaining duplicates were deleted during the abstract and title screening. Eligibility was determined through abstract and title reading and by reading the full-text articles. Screening and eligibility were carried out independently by two authors (A.
Data Extraction and Synthesis Data were extracted independently by one reviewer (A. P. ) into study-specific extraction tables. The same data extraction approach was applied across all the studies following a standard data extraction template, but with flexibility according to various methodologies. The studies were organized according to their purpose and by age (children, adults, older adults). Studies were organized by group. The first group included studies evaluating the predictive or correlational associations between PMD use and cognitive outcomes; cognitive assessments tools were summarized. The cognitive functions identified in each outcome measure were classified according to the ICF  [13]. The items of each outcome measure were examined to determine which cognitive functions were assessed.
Discussion This scoping review explored the relationship between cognitive functioning and PMD use. Research aiming to surpass mobility limitations for people with dual cognitive and motor impairments is increasingly important due to the ageing population  [43]. Seventeen studies met the inclusion criteria. However, including multiple moderate-quality studies, this scoping review provides only a moderate strength of evidence and highlights that this area of research has not been deeply investigated, as previously mentioned  [44]. Among the seventeen studies, samples of children, adults and older adults were represented, allowing the consideration of cognitive functioning in relation to PMD use through a large age range. However, as cognitive resources and data processing are different for children, adults and older adults, further research is necessary to determine relationships between cognitive functioning and PMD use through the lifespan.
Limitations While comprehensive in search, this scoping review has some limitations. First, it is possible that the combination of keywords may have missed some studies despite consulting with experts on the keyword choice. Moreover, data extraction was not conducted by two separate individuals in duplicate. However, transparency was enhanced by regular discussions and team meetings, presentation of emerging findings, and sharing of findings with authors. All steps and decisions were recorded in a logbook. Second, in relation to the evolution of PMD and clinical practice, the large range in publication dates represents a limitation. Although studies reported on small sample sizes (thus limiting generalizability), the search and synthesis spanned all age groups. Despite differences in cognitive impairment and age between participants, this broad synthesis of a heterogenous group was justified at this early stage in research on PMD and cognition to provide a comprehensive overview of existing literature.
Stimuli We used 168 faces and 168 objects images. Faces were drawn from the Productive Aging Lab Face Database  [28]  and from FACES Database  [29]. They were selected to cover a large range of ages (19-75 years), different eye and hair colors, as well as both genders. They had a neutral facial expression, to avoid trivial stimulus-driven valuation, as would be obtained for instance with smiling faces. Objects were drawn from the Boss database  [30]. They were chosen to cover a large variety of preferences. In a pilot behavioral study, 12 participants rated the estimated price of the 168 objects. Object images were square while face images were in portrait format (ratio = 1. 25). Images were sized to cover one third of the screen in width dimension. On average, luminance was higher for objects (207) than for faces (127).
In all cases, the scale had 21 steps (10 values on each side of the center), but only three or four reference graduations were shown. Participants could move the cursor by pressing a button with the right index finger to go left or with the right middle finger to go right. The rating was self-paced (up to 8 s) and participants had to press a button with the left index finger to validate their responses and go to the next trial. The initial position of the cursor on the scale was randomized to avoid confounding the ratings with the movements they involved. Data are provided as mean (std). F. Vinckier et al. After the scanning sessions, participants were first asked to make a series of 336 choices between two stimuli of the same category and the same sub-list. Faces were paired by gender. Objects were paired such that the prices of the two items were approximatively matched.
fMRI analyses T2*-weighted echo planar images (EPIs) were acquired with BOLD contrast on a 3. 0 Tesla magnetic resonance scanner. We employed a tilted plane acquisition sequence designed to optimize functional sensitivity in the OFC. To cover the whole brain with a good temporal resolution, we used the following parameters: TR = 2. 29 s, 35 slices, 2 mm slice thickness, 1 mm interslice gap. T1-weighted structural images were also acquired, coregistered with the mean EPI, segmented, and normalized to a standard T1 template to allow group-level anatomical localization. All data processing and analysis was done using statistical parametric mapping software SPM12  [32]  (https://www. fil. ion. ucl. ac. uk/spm/) implemented in Matlab. Preprocessing consisted of spatial realignment, normalization using the same transformation as structural images, and spatial smoothing using a Gaussian kernel with a full-width at half-maximum (FWHM) of 8 mm.
ACKNOWLEDGEMENTS The study was funded by  Fondation Les -petits-trésors. B. F. A. was supported by the  Fonds de Recherche du Québec en Santé. F. V. was supported by the  Quebec Bio-Imaging Network  scholarship. The authors are grateful to  Maël Lebreton  for helpful insight and to the  Autisme-Rivière-des-Prairies-Hospital registry (Laurent Mottron, coordinator)  for providing access to participants.
Study design and participants Our study sample comprised participants in the ongoing internet-based Students' Health Research Enterprise (i-Share) project-a prospective population-based study on students' health which was launched in some French universities in 2013. Students were informed about the purpose and aims of the study through yers, communications in classes or social media. To be eligible students must be registered at a university or higher education institute, be at least 18 years of age, and be able to read and understand French. Volunteers provided an on-lined informed consent. The enrolment procedure has been previously described. 34 e i-Share protocol was approved by the "Commission nationale de l'informatique et des libertés" (CNILNational Commission of Informatics and Liberties) (number: DR-2013-019), which ensures that data collection does not violate freedom, rights, or human privacy.
At enrolment (i. e. , baseline assessment), self-administered on-line questionnaires collected sociodemographic characteristics, physical and mental health parameters, personal and familial history, living conditions, lifestyle habits, and substance use. One year later, students were invited by email to complete a follow-up questionnaire. Three reminder emails were sent at 14, 28, and 33 days following the invitation. For the present longitudinal study, we used data from a sample of students who were included in the i-Share cohort study between February 2013 and September 2019, who participated in the follow-up, and for whom data on STB were available. Baseline information was available for 15, 667 students. These students were solicited to participate in the follow-up, and 5255 agreed to participate (33.
This evaluation of prediction performance yields a measure termed the out-of-bag error, which represents the overall error of the algorithm in terms of outcome prediction. The out-of-bag sample is also used to calculate the relative importance of each variable for the prediction. To this end, the value of a given variable is randomly shifted in the out-of-bag sample, and any resulting change of the out-of-bag error re ects the variable's importance in the prediction. Finally, all individual decision trees are aggregated to create the nal predictor algorithm. To carry out these analyses we used the randomForest and caret packages in SAS and R. Missing data on the predictors (2%) were handled using the R missForest algorithm  44  speci cally designed to deal with missing data in random forest models.