dataesr/scientific-paragraphs-categorization huggingface dataset:
Step cadence and number of steps were recorded by the monitor when a participant was walking. The ActivPAL™ has been validated for use in adults to distinguish between sitting/lying, standing, and stepping activities  [44] [45] [46] [47]. Data event files from the ActivPAL™ were used to quantify sitting/lying, standing, and stepping time. In these files, the ActivPAL™ records each time an activity changes and the time that the activity changed. Sitting/lying, standing, and stepping time were calculated by summing the duration of each event and the number of breaks from sitting time were quantified as a transition from sitting/lying to either standing or stepping. Sitting bouts lasting longer than 30-min and 60-min were also used to test the effect of the conditions on the sitting bout length.
A customized R program (www. r-project. org) was used to convert the event data file to a second-by-second data file to estimate additional metrics of sedentary behaviors and time spent sitting/lying, standing, stepping. The following metrics of sedentary behaviors were computed over 24 h: total sedentary time (total time spent in sitting/lying events), total breaks in sedentary time (number of times a sitting/lying event was followed by a standing or stepping event), and time (minutes/day) in sedentary bouts ≥30 and ≥60-min. The same outcomes were also reported as percentage of waking time. Because sleep time was removed, we assumed that sitting/lying time mainly corresponded to sitting time during waking hours. The R package (PAactivPAL) is available for researchers to generate these metrics  [48].
Measurement of Physical Activity Intensity, Activity Energy Expenditure and Physical Activity Level Activity energy expenditure (AEE) and time spent in different activity intensities were determined using the ActiGraph GT3X tri-axial accelerometer (ActiGraph, Pensacola, FL, USA). Participants were instructed to wear the accelerometer during wake time by attaching it to their right hip directly above their right knee using an elastic belt that was provided. A sampling rate of 30-Hz was used. After each of the 3-day study conditions, data were downloaded using the Actilife 6. 13 software provided by the manufacturer and AEE per minute (J/kg/min) was estimated using the 'Freedson vector magnitude combination model'  [49, 50]. Total energy expenditure (MJ/d) was calculated as (AEE + RMR)/0. 9, where RMR was resting metabolic rate (MJ/d).
Physical activity level (PAL) was calculated as the ratio between TEE over measured RMR. Cut-points of <1. 5 and <3 METs and >3METs (metabolic equivalents) were used for very light intensity activity, light intensity activity and moderate-to-very vigorous activity, respectively. Minute-data during waking hours were summed to obtain data per day. Although sedentary behavior has been defined as activities with an energy expenditure below 1. 5 METs while in a sitting, reclining or lying posture  [51] , activities with METs <1. 5 were referred to as very light intensity activity in our study. By only measuring energy expenditure without recognition of the concomitant posture, we are including activities such as standing that are not sedentary activities. By choosing the term "very light intensity activity" we are more conservative and avoiding any misinterpretation.
Statistical Analysis Based on the diary log information, data were recorded on 43, 47 and 43 work days while in SED, ONE and MICRO conditions, respectively. Consequently, 23, 19 and 23 study days were non-work days when participants were in SED, ONE and MICRO conditions, respectively. The analysis of the working status effect (work day versus non-work day) was a posteriori analysis. This is why the number of work days and non-work days are unbalanced across the three conditions and the work status. If there was more than one measure assessed at different days per condition and work status, the mean value of the repeated measures served as outcome in the model. Linear mixed models were used to test differences in the two activity monitor outcomes, self-perceived challenge, vigor and fatigue, with sequence, period, condition (SED, MICRO and ONE), work status (work day vs.
Funding: This work was supported by the  National Institutes of Health  grant numbers  K99DK100465  (A. B. ) and  UL1 TR001082  (A. B. ),  NORC   5P30DK048520  (A. B. , D. H. B. ),  CTRC UL1TR002535  (A. B. , D. H. B. ),  5P30DK048520  (Z. P. ). This publication was also supported by Grant Number  T42OH009229-10  (A. B. ) from  CDC NIOSH Mountain and Plains Education and Research Center. C. S. (Cédric Sueur ) and I. D. are funded by a grant from the  French national Research Agency  ( ANR-15-CE36-0005 ) and by the  Grand Est Region (France). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the funding sources.
Participants The Research Ethics Committee approved the Study. Written consent for participation was obtained prior to data collection. Importantly, during the recruitment process, all participants were clearly informed that the laboratory session was very demanding and involved performing attentionally demanding tasks for prolonged periods of time. They were informed that they were free to withdraw from the study at any point. A total of 124 participants (85 females, M age = 22. 61, SD = 2. 51, range 19-37 years) were recruited and randomly assigned to the three experimental conditions: the control, the depleted inhibition and the intact inhibition conditions. Participants were tested in groups of two to eleven in a laboratory with separate computer stations. Nine participants guessed the true purpose of the study, and their results were excluded from the analysis concerning the frequency of involuntary task-unrelated thoughts, including IFTs and IAMs.
Correlations between ratings of fatigue and task-unrelated involuntary thoughts Some previous studies have reported positive associations between increased cognitive fatigue and levels of mind-wandering (e. g. ,  McVay & Kane, 2009 , 2010; Zhang & Kumada ,2017). To investigate this relationship in our sample, we calculated correlations between the ratings of perceived fatigue (i. e. , general, physical and mental tiredness) and the number of IFTs, IAMs, present-oriented and atemporal thoughts. Given that the three conditions did not differ in the number of these thoughts, the correlations were calculated on the data pooled across the three conditions. None of the correlations between the different ratings of fatigue and types of thoughts reported were significant (all p > 0. 309).
Acknowledgements The present research and writing of the paper were supported by a grant from the  National Science Centre, Poland  ( 2015/19/D/HS6/00641 ) to  Krystian Barzykowski. We thank  Marcin Draszczuk , for IT help in preparing the IMP, and  Justyna Konieczny ,  Karolina Golik ,  Jakub Dubis ,  Julita Kielińska ,  Karolina Janur ,  Dominika Główka  and  Monika Sarwa  for their help in collecting data. We also wish to thank  Elizabeth Warden  for her generous help in editing the revision and two anonymous reviewers for their constructive and helpful comments on the earlier version of this manuscript.
e. , to the left or to the right of W1). In the second block, W1 performed two dyadic trials with both remaining walkers in random order. In the third block, each of the 8 triadic formations was repeated, crossing from the opposite side as the first block. Once all 18 trials of a role configuration were completed, the participants were assigned new roles and the process was repeated. In total, we recorded 432 trials: 96 triadic trials (8 formations, 6 role configurations and 2 sides) and 12 dyadic trials (3 role configurations, 2 sides and 2 repetitions) for each of the four experimental sessions. No data points for any included trials needed to be interpolated. One trial (formation [90.
Kinematic Analysis We first post-processed the raw kinematic data. The medio-lateral sway movements that occur during gait were removed with a 3rd order low-pass Butterworth filter (cut-off frequency = 0. 5 Hz). All kinematic analyses were performed in MATLAB R2015b (The MathWorks Inc. , Natick, MA, 2015) 1. We expressed specific interactions based on the walkers involved; we labeled the interaction between W1 and W2 as I12 and the interaction between W1 and W3 as I13. Additionally, any outcome variable that has the subscript "12" or "13" specifically refers to I12 or I13, respectively. We addressed the research questions using three different timeseries variables. Each outcome measure is explained below in the context of the specific research question.
To account for these 1 The code for the kinematic analyses can be found in the supplementary material and on https://github. com/Rens88/PW_to_Multiple_Public. The 'exampleRun. m' can be used to run the code with some mock-data. Data can be made available upon request. 2 SPM (Statistical Parametric Mapping) is a commonly used open source statistical tool in neuroimaging (available on http://www. spm1d. org), specifically, through the application of Random Field Theory (RFT) that makes inferences of topological brain image features that are continuous functions of space or time  (Adler, 1981; Pataky, 2010 Pataky, , 2016)). The methods are based on a General Linear Model (GLM) of analysis; in its simplest expression, SPM runs an analysis per time point, grouped as a continuous statistical process and tested for probabilistic behavior through RFT using univariate probability and spatial covariance.
Creating one of the most important emotional stimuli database,  Lang (2005)  rated emotional stimuli in terms of "approach-avoidance" behavior -indexed by the subject vs. target distance -induced either as "appetitive" or "defensive". Within the framework of motivated social interactions, embodiment appears to be a central process. This process quickly occurs in the dynamic of psychological processes involved in motivated social interactions and we want to underline with this paper that embodiment's modulation of the postural correlates of emotional information processing appears when considering the temporal dimension of the response. Posturography is a suitable experimental method to assess postural control by determining the body's Center-of-Pressure (COP) displacements  (Gurfinkel, 1973).
, 2004)  or freezing responses  (Hillman et al. , 2004; Azevedo et al. , 2005; Lelard et al. , 2013; Stins et al. , 2015a). Most of these studies assessed posturographic correlates of emotional processing by calculating mean postural responses induced over a time period of 6 up to 50 s. Similarly, one decade ago, the use of modern neuroimaging techniques to explore the neural correlates of human male sexual arousal was still emergent and most studies considered recorded data through classical subtractive addressing the question of the more activated brain areas in response to visual sexual stimuli as compared to neutral ones. In new studies, we recorded simultaneously cerebral hemodynamic responses by functional neuroimaging and erectile responses by volumetric penile plethysmography  (Moulier et al.
To reach the largest possible portion of the population, interviews were carried out from Monday to Friday between 10 am and 8 pm and on Saturday between 10 am and 5 pm. According to the French legislation, the survey protocol and processing of data collection was subject to a declaration to CNIL, the French National Agency responsible for ethical issues and protection of individual data collection under no. 2043940. All the information was collected anonymously, and the participants were informed of their rights to access and rectify personal information.
Questionnaire and measurements Data were recorded using a standardized questionnaire administered by phone. Aside from socio-demographic variables, collected data were grouped into four general categories: 1) environmental variables and exposure to mosquito, 2) perceptions of the illness and risk of contracting ZIKV infection, 3) perceptions and practices of protective behaviors promoted by the public health authorities to control the spread of the disease and 4) self-reported frequency of protective behavior in response to the zika epidemic.
Data analysis Statistical analyses were performed using STATA12 software (Stata Corp. , College Station, TX, USA)  [26]  and SPAD8  [27]. Primary sampling units and strata were considered for calculating estimations according to the design effect. Post-stratification weights were used to correct potential biases due to misrepresentation of demographic characteristics, including gender and education level. All estimations were obtained using STATA12 "svy" commands. Bivariate analyses were conducted using Chi-square tests to compare proportions, and linear combinations tests and regression combined with Wald tests were used to compare mean scores of risk perceptions. The level of statistical significance was set to (p = 0. 05). Data were adjusted according to gender and level of education. In addition, a multiple correspondence analysis and a hierarchical cluster analysis were performed to determine the natural groupings of observations regarding the level of knowledge about zika disease and its issues in order to cluster the population in different groups according to their literacy.
Research/Clinical Data Evidence-based clinical data are published continuously. Therefore, frequent review of the literature to update guidelines and safety information was acknowledged as being highly important. In addition, clinical data from each hospital database/registry are also relevant to understand how efficient the management and treatment of agitation is. Without proper record forms of patients with agitation (electronic format is highly recommended), there will be a lack of real-world evidence to make decisions and progressively optimize and update guidelines locally. The expert group agreed as well that clinical data (from clinical trials or from centers' databases) are also relevant to address issues related to comorbidities or other patient characteristics (e. g. , age, gender, medical conditions, and so on), which would allow a more personalized treatment protocol.
FUNDiNg Supported by an unrestricted educational grant from Ferrer International. Conflict of Interest Statement: GDS has received honoraria (speaker/advisory board) from Angelini, Otsuka, Janssen; RF has been speaker for some workshops/seminars for Lilly, Otsuka, Lundbeck, Janssen, and Bristol-Myers-Squibb; MG has received funding for research projects and/or honoraria as a consultant or speaker for Ferrer, Janssen, Lundbeck and Instituto de Salud Carlos III; GG has received honoraria from Laboratories Bago, Otsuka, Laboratories Baliarda, Roemers, Casasco, Ferrer, Bayer, Baguete Engineering in Anesthesiology and Pfizer; KGK received speaker honoraria by Eli Lilly, neuraxpharm, Servier and Trommsdorf; JK has received financial support for IIT studies from Medtronic EuropeSARL (Meerbusch, Germany) and he has occasionally received honoraria from Lundbeck, Otsuka, Servier, Janssen, Trommsdorff, and Schwabe for lecturing at conferences; KP has received honoraria from AOP Orphan, Eli Lilly, and Ferrer; FR from Ferrer for the work under consideration for publication (non-financial support); AR has received financial compensation from Ferrer, Lundbeck, Janssen-Cilag S.
CONFLICT OF INTEREST The present work was supported by grants from the French National Agency for Research (ProgestVentil; ANR-15-CE17), Association Française pour le Syndrome d'Ondine and Chancellerie de l'Université de Paris (Legs Poix 1504). Fanny Joubert was supported by the "Fonds de dotation pour la Recherche en Santé Respiratoire 2012".
ACKNOWLEDGEMENTS We thank  William Hempel  ( Alex Edelman & associates ) for editing the English text. Camille Loiseau performed animal experiments, analyzed data, discussed the results and their significance, generated figures and wrote part of the manuscript. Florence Cayetanot wrote part of the manuscript. Fanny Joubert performed animal experiments, analyzed data, discussed the results and their significance and generated figures. Anne-Sophie Perrin-Terrin performed animal experiments, analyzed data and discussed the results and their significance. Philippe Cardot,  Marie-Noëlle Fiamma  and  Alain Frugiere  discussed part of the presented results and their significance. Christian Straus performed clinical observations, analyzed data and discussed the results and their significance. Laurence Bodineau  designed and supervised all animal experiments, obtained funding, shaped, and interpreted the data, discussed the results and their significance, and wrote the manuscript.
To do this we used the peer victimization data to generate trajectories from ages 14 to 19. We then related these trajectories to brain regions previously shown to be sensitive to stress and maltreatment and hypothesized chronically victimized adolescents would show larger decreases in volume over time. We also incorporated measures of stressful life events and childhood maltreatment in our analyses to elucidate whether observed effects were specific to peer victimization or related to stress in general.
Participants Participants were part of the IMAGEN project, a community-based longitudinal study of adolescent brain development and mental health. Participants were assessed at eight study sites in England, Ireland, France, and Germany. Individuals were included in the analyses if they had peer victimization data at ages 14, 16, and 19 as well as structural MRI data at ages 14 and 19 (neuroimaging assessments were not carried out at age 16). The comparison of observed characteristics between those included and excluded from the analyses can be found in the online Supplement (Supplementary Table  1 ). Questionnaires were self-administered on home computers using the Psytools software package (Delosis, London). The local research ethics committees approved this study and written consent was obtained from participants (and from their legal guardian at ages 14 and 16).
Voxel-based morphometry (VBM) All age 14 and age 19 MPRAGE data were preprocessed in SPM8 (http://www. fil. ion. ucl. ac. uk/spm/) using the VBM8 toolbox with default settings, including the usage of high-dimensional spatial normalization with an already integrated Dartel template in MNI space. All images were subjected to nonlinear modulations and corrected for each individual head size. Images were then smoothed with an 8 mm fullwidth at half-maximum Gaussian kernel with the resulting voxel size 1. 5mm  3. The automated anatomical labeling (AAL) atlas was employed to exclude the voxels outside the grey matter. For each brain area defined by the AAL atlas, the volume of the area was estimated by summing the grey matter volume over all voxels within that area. Total intracranial volume, used as a covariate of no interest, was estimated by the summation of the grey matter, white matter, and CSF volumes in native space.
Peer victimization trajectories We estimated peer victimization trajectories using longitudinal latent profile analysis (LPA) in MPlus version7 (Muthen & Muthen, Los Angeles, CA). LPA enables identification of classes of adolescents who may follow different experiential patterns of peer victimization (e. g. , high versus low levels at different ages). With LPA one can test how well the class model fits the data as well as confidence that the individuals are likely to be following the identified developmental profile. Peer victimization sum scores from ages 14, 16, and 19 were entered into LPA and we fit a series of trajectory models, progressing from a one-class model to a four-class model (trajectory criteria can be found in the Supplementary Information).
Acknowledgements This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the  Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways)  ( PR-ST-0416-10004 ),  BRIDGET  ( JPND: BRain Imaging, cognition Dementia and next generation GEnomics ) ( MR/N027558/1 ), the  FP7  projects  IMAGEMEND ( 602450 ;  IMAging GEnetics  for MENtal Disorders) and MATRICS ( 603016 ), the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  Swedish Research Council FORMAS , the  Medical Research Council , the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ; eMED SysAlc01ZX1311A; Forschungsnetz  AERIAL 01EE1406A ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-1 ,  SM 80/7-2 ,  SFB 940/1 ).
All these sources were analysed together to obtain a general view of acceptability, implementation and practicality from the point of view of the facilitators. Furthermore, to gain more insight into the experience of the receivers, all six potential receivers from one high school, whether or not they chose to participate, were invited to an individual interview. These interviews were also transcribed, added to the NVivo database and coded using the same coding grid as the analysis on the facilitators in terms of their experience of the peer intervention, but with an additional focus on the characteristics of the potential receivers, in particular the following two themes: friendship and other peers (definition and number) and health and overweight (definition and personal experience). A descriptive analysis was also undertaken on the information on facilitators and receivers collected by measures and questionnaires as part of the larger PRALIMAP-INÈS trial at the start and end of the school year.
Physical activity and sedentary behaviour were measured by the International Physical Activity Questionnaire (IPAQ),  35  the Eating Attitudes Test (EAT-26)  36 37  screened for anorexia and bulimia symptoms, the Hospital Anxiety and Depression scale (HAD)  38  screened for anxiety and depression symptoms, and the Kidscreen 39 explored perceived health and quality of life. Study sample characteristics (age, gender, school type, FAS, BMI) and health scores (physical activity level, total EAT-26 score, total HAD score, as well as individual scores for anxiety and depression, and the Kidscreen score) were described using percentages for categorical variables and mean±SD for quantitative variables. Statistical analyses were carried out using SAS V. 9. 4. The main research questions to assess feasibility and how each source of data collected and analysed contributed to answering those questions are summarised in online supplementary file 1.
Data Recording Participants' movements in the frontal plane were recorded by a KINECT device, located in front of them, on the wall above the TW, at a height of 1. 85 m. The KINECT captured the figure of the participant at a mean rate of about 25 frames per second. The information contained by each frame were accessible through a comma-separated values (CSVs) file. For each frame, the participant posture and the TW posture were recorded as well as the timing of the frame, the participant's and the TW's bars and head inclinations were measured in degrees (Figure  1C ).