dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding Open Access funding enabled and organized by Projekt DEAL. TP, GS and AN were supported by project funding (Grant No. 179158 ) from the  Swiss National Science Foundation (SNSF). AN was also supported by a  SNSF personal fellowship  ( P400PM_186723 ). GR was supported by the  German Research Foundation (DFG) , grant  RU 1747/1-2. DM is funded by the  European Union 's  Horizon 2020 COMPAR-EU project  ( No 754936 ).
C. Participants and data collection Participants were recruited from a pediatric rehabilitation center (Fondation Ellen Poidatz -Saint Fargeau). The inclusion criteria were: a clinical diagnosis of spastic CP, including hemiplegia, diplegia, and quadriplegia; age between 12 and 18 years; Global Motor Function Classification System (GMFCS) levels I-III; a minimum score of 2 on the Functional Mobility Scale 50m; ability to cooperate, understand and follow simple instructions to play the game; voluntary patient whose parents have given their free and informed written consent for their child's participation in the study. This study occurred between September 2020 and March 2021. Each participant wore the AR headset and followed the instructions given by the application.
D. Outcome Assessment During the session, raw data were logged (100Hz) in. txt format through the application and were available in the Windows Device Portal. Logfile contained time (s), position x, y, and z of the headset, step length, and distance traveled calculated with HoloStep algorithm  [23]. The outcomes were: Mean speed (condition MAX and condition INTER), percentage of time spent above the MAX target speed (condition MAX), percentage of time spent around the INTER target speed (± 0. 1 m/s) (condition INTER), time to reach the target speed (condition MAX and condition INTER). The target speed was not the same for conditions MAX and INTER (see Design study in section III. A. for more details). The threshold (± 0. 1 m/s) is the threshold for a change of outcome measure that has a meaningful effect for the patient  [33].
E. Data Analysis Data analysis was performed using MatLab version 9. 6. 0. 1472908 (R2019a) Update 9. Raw data (time, position, step length) were filtered with Butterworth filter design (Filter order 2, Cutoff frequency 4Hz) using filtfilt function. For each participant, trials were cut off three steps before the end, not considering deceleration. The instantaneous walking speed was calculated using position and refiltered using polynomial curve fitting (order 1, frame length 701). Statistical analysis was performed using R version 4. 0. 5. Mean speed, mean percentage of time spent above (condition MAX) or around (condition INTER) the target speed (with a cut-off of the three first steps, not considering acceleration), and time to reach the target speed were compared between scenarios. Moreover, the percentage of time spent above the target speed was analyzed according to the participant's target speed using generalized linear models (GLM).
Thus, Figure  5  was a GLM using linear methods to describe a potentially nonlinear relationship between predictor terms and a  response variable. In condition MAX, the mean speed relative to the target speed (µ Sn ) has been calculated for each Scenario (Sn): µ Sn = (µ Sn × 100)/(µ T S ) The different scenarios were compared using mixed linear models, including the patients as random effects to take into account the repeated measures. Parametric bootstrap tests based on the likelihood ratio test (LRT) statistic with 1000 iterations were used to perform hypothesis testing on the fixed effects. This strategy was preferred as it introduced less bias. Tests were implemented with the function PBmodcomp in the pbkrtest package (v. 0. 5. 1). Statistical significance was determined at the 0. 05 level throughout.
F. Ethical considerations Ethics approval was granted by the Ethical Committee of Ile-de-France 1 in France (IRB/IORG : IORG0009918). Additionally, all parents and participants from 12 years of age signed the informed consent before study initiation. All participants had a reflection period before the inclusion (minimum 15 days between information and approval). The National Commission guaranteed confidentiality and data access for Data Protection. A Data Protection Officer has been designated for all research studies conducted in the rehabilitation center. He assured that the data protection and the rights of the participants were respected according to the General Data Protection Regulation (European Union) 2016/679. This study was registered in the ClinicalTrials. gov database (NCT04460833).
Awareness or, do we find neural correlates of awareness in animals? According to the criteria for reaching the state of awareness derived from brain activity data in humans as mentioned above (ERP peak latencies, selective attention, local gammaband activity in EEG recordings from higher sensory processing areas), we searched for correlates of awareness in animals. Since animals have brains of various sizes and, possibly, various levels of signal processing, ERP latencies of animals may differ from those of humans, e. g. , animals with smaller than human brains may have shorter ERP latencies associated with signaling awareness/consciousness  (Siegel et al. , 2003; Woodman, 2012). On this background, we selected animal data which allowed a separation of brain waves by our criteria into those that could be addressed as awareness response and those suggesting conscious perception.
General discussion Besides humans, mammals and birds may perceive stimuli with awareness and consciousness By making a distinction between awareness and consciousness in defining these terms (see Introduction), we could separate NCCs of these attributes of brain function in data of brain activities related to stimulus perception and responding in humans. Valuable NCCs were found in recordings of ERPs and EEG gamma-waves from which correlations mainly between latencies of EEG peaks, latencies of strong gamma-wave occurrence locally or globally, and behavioral indicators of awareness and consciousness could be extracted. Remarkably, these correlations were not only consistent among numerous studies from humans but could also successfully be applied to data sets from animals. Thus, our comparative analysis identified measures of brain activity that can be used as neural markers indicating potential awareness and consciousness while perceiving sensory stimuli, at least in mammals and birds.
Design The study design was a two-group non-randomized controlled trial with self-selection sampling. The first group of subjects downloaded and used KANOPEE, which provided interactions with a virtual companion to obtain personalized recommendations for improv-ing sleep over 17 days. A second group of subjects downloaded My Sleep Diary to obtain access to an electronic sleep diary and a repeated follow-up on insomnia complaints, which was considered as the control condition (no intervention). Both apps were developed by our research lab, are downloadable for free on Google Play Store and Apple Store and are available only in France. A specific authorization from Google and Apple was obtained to put our non-profit apps online during the COVID-19 crisis. The study was approved by the ethical committee of University of Bordeaux and was registered at the US National Institute of Health (ClinicalTrials.
Criteria for not participating were: (1) not owning a smartphone; (2) not being located in France; (3) having completed the first insomnia questionnaire outside the inclusion period; or (4) being younger than age 18. Recruitment was performed on Google Play Store and Apple Store by downloading one of the two apps. Baseline characteristics of users were collected at the registration step after acceptance of informed consent obtained directly on the apps, in accordance with General Data Protection Regulation (GDPR) and the Commission Nationale de l'informatique et des libertés (CNIL) requirements. Both apps collected the users' information about age (in years), gender (male/female), educational level (middle school/high school/less than 5 years of university/more than 5 years of university), initial insomnia complaints (Insomnia Severity Index (ISI), see later outcome measure) and depressive and anxiety symptoms (Patient Health Questionnaire-4 (PHQ-4))  [20].
Data Collection and Validation process 2. 3. 1. Participants and Methods A total of 120 children and adolescents aged between 8 and 18 years old took part in the study. These children and adolescents were recruited through advertisements sent to different academic, associative, cultural, and sports networks. Participants were classified according to age as children ((8-11) years old) or adolescents ((12-18) years old). The study was entirely detailed and explained to the participants and their legal representatives, and written informed consent was obtained. This study was conducted in accordance with the Helsinki declaration and was approved by the Ethics Committee CPP Sud Est VI (reference: 2020/CE 27). After inclusion, participants were asked to complete the CAPAS-Q for the first time. Adolescents above 11 years old filled out the questionnaire themselves while children were assisted by their parents.
MVPA was calculated as the sum of MPA and VPA. The ActiGraph data were downloaded using the software provided by the manufacturer (version 6. 0, ActiGraph, Pensacola, FL, USA) and imported into SPSS v21 (IBM, Chicago, IL, USA) for data processing and screening. R package 4. 0. 2 accelerator (www. datahunter. es, accessed on 25 January 2022) was used to identify wear time between 8 a. m. and 10 p. m.
The results were interpreted according to the following rules of thumb  [39] : <0. 3: negligible correlation, 0. 3-0. 6: fair to moderate correlation, and >0. 6: moderate to high correlation. Analyses were conducted for all participants and then in subgroups according to age and gender. Cronbach and Lin coefficients were compared between subgroups  [40, 41]. Statistical analyses were performed using Stata software (version 15, StataCorp. , College Station, TX, USA). All statistical tests were carried out for a two-sided type I error at 5%. A Sidak's type I error correction was applied to take into account two by two comparisons concerning correlation coefficients.
Personalized treatment approaches based on clinical criteria for possible, probable, or definite autoimmune OCD could prevent undertreatment and chronification while also avoiding unnecessary immunotherapies that are not without (potentially very serious) side effects and are sometimes requested by worried patients and relatives because of "non-specific" findingsto this extent it is crucial that any proposed criteria are validated vis a vis their sensitivity and specificity in identifying immunotherapy-responsive cases of OCD. The first clinical trials are already investigating the effects of immunotherapies, such as rituximab, in autoimmune OCD subtypes (https://clinicaltrials. gov/ct2/show/NCT04323566). Based on the experience of patients with AE/AP and other neuroimmunological disorders, it can be assumed that immunotherapy in patients with clear autoimmune causes can lead to an improvement in prognosis  [42].
ACKNOWLEDGEMENTS DE was supported by the  Berta-Ottenstein-Programme for Advanced Clinician Scientists, Faculty of Medicine, University of Freiburg. MAS was supported by the  Medical-Scientist-Programme, Faculty of Medicine, University of Freiburg. MAS is a member of the  Obsessive Compulsive and Related Disorders Network (OCRN)  of the  European College of Neuropsychopharmacology (ECNP). JLC was supported by a  Gullstrand Fellowship, Uppsala University Hospital Uppsala Sweden. TP was supported by a  NIHR Clinical Lectureship.
Secondly, as outlined by Kujawa, Weinberg, Hajcak and Klein (2013)  [58] , EEG data in children are more likely to include noise and artifacts. These two considerations point to the need for a more refined analysis method to obtain more consistent ERP data  [58]. In this perspective, temporal-spatial PCA have been proposed as a useful and more reliable measurement data for separating sources of variability in components and differentiating latent components from unsystematic sources of noise in adults  [59, 60]  and children  [60, 61]. The aim of the current study was to assess the cognitive correlates of the processing of emotional stimuli in children with social anxiety and to provide support for the phenomenon of AB by recording evoked potentials and subjecting the data to a temporal-spatial PCA.
Task and materials The emotional spatial-cueing task was adapted from the protocol used by Bar-Haim, Morag and Glickman (2011)  [30]. The task was administered using E-Prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA) on an Asus X756U system (screen of 17 inches) in the Cognitive Psychology and Neuropsychology research laboratory of the University of Mons. Each trial began with a fixation cross appearing at the center of the screen for 500 milliseconds. Then, two rectangular frames (600 mm X 1000 mm) appeared on the right and the left side of the fixation cross for 500 milliseconds, and one of them showed a face depicting either a disgusted or a neutral expression. The pictures shown were faces of 10 different children (5 boys and 5 girls) from the Radboud Faces Database  [73].
EEG acquisition, preprocessing and analysis EEG data were recorded at a sampling rate of 500 Hz (V-Amp, Brain Products, GmbH, Munich, Germany: 0-500 Hz bandwidth, 24-bit A/D conversion) from 64 electrodes sites (AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, Fp1, Fp2, FT10, FT7, FT8, FT9, Fz, O1, O2, Oz, P1, P2, P3, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP10, TP7, TP8, and TP9), with an actiCap acquisition system arranged in a standard 10-20 layout (Brain Products, GmbH, Munich, Germany). One electrode (P4) has been excluded from further analyzes due to poor functioning during data acquisition. The reference electrode was located at FCz throughout the recording. The ground electrode was placed on the forehead between Fp1 and Fp2.
Electrode impedances were kept below 10 kΩ. Off-line analysis was performed using Brain Vision Analyzer 2 software (Brain Products, GmbH, Munich, Germany). We applied a band-pass IIR filter (0. 1 to 30 Hz) and then, to correct eye blink artifacts, we used a semi-automatic ocular correction function implemented in the Analyzer 2. 0 that used a simplified version of the independent component analysis (ICA) to correct ocular artifact specifically in the EEG signal  [74]. This method has been proven to be useful to separate neural activity from muscle and blink artifacts in EEG data (e. g. [75] ) and has been shown to present several advantages compared to other artifact removal methods (see  [74] ). All data were re-referenced to the average of all scalp electrodes. Finally, both for faces and targets processing, we created epochs by segmenting data from 200 milliseconds before and 500 milliseconds after stimulus onset.
Epochs were baseline corrected using the mean voltage calculated from the 200 milliseconds preceding the event. Each epoch of 700 milliseconds with at least one channel showing a maximal amplitude difference of 200 μV was excluded from further analysis. Stimulus-locked ERPs were averaged separately for faces and targets. ERP averages for each participant in each experimental conditions were submitted to temporal-spatial principal component analyses (PCA) using the ERP PCA Toolkit, version 2. 7  [76]  and respected the published guidelines for applying PCA to ERP datasets  [77, 78]. PCA analyses are used to extracts linear combinations of data that distinguish patterns of electrocortical activity across all-time points and recording sites  [58]. Separated analyses were conducted for faces and targets processing.
Firstly, a temporal PCA was performed to capture the variance across time points and to maximize the initial separation of ERP components  [79]. All-time points were used as variables, and we considered all subjects, conditions and recording sites as observations. Simulation studies previously conducted suggested that Promax rotations are the most effective for temporal analyses since they do not force orthogonality amongst the components  [76, 78]. Therefore, a Promax rotation was used, and following this first rotation, a parallel test  [80]  was conducted on the resulting Scree plot  [81] , in which the Scree of our dataset is compared to a Scree plot derived from a fully random dataset. The number of factors retained is thus based on the largest number of factors that account for a greater proportion of variance of the fully random data set (see  [76]  for more information).
Based on this criterion and on the resulting Scree plots  [81] , seventeen temporal factors were extracted for faces processing (total variance explained = 97. 3%), and eighteen temporal factors were extracted for targets processing (total variance explained = 91. 5%). Each temporal factor may be considered to be a virtual epoch and can be described both by its factor loading (which describes the time course of that factor) and its factor scores (which give that factor's value for each combination of subject, picture type, and recording site). Spatial information is preserved by temporal PCA; scalp topography can be reconstructed for any time point, subject, and condition by multiplying the corresponding electrode scores by the factor loading and standard deviation  [82]. A spatial PCA was then performed on each of the temporal factors to synthesize the spatial dimensions of the dataset.
Data collection During the 3 months following this preliminary session, participants attended three meetings with a psychologist, with whom they had no prior relationship; in addition, no subsequent relationship with the psychologist was expected. These meetings included two sessions devoted to psychological tests (drawings, projective assessment) and a third session consisting of an interview regarding the experience of bullying. These verbatim interviews constitute the corpus referenced by this study. The interviews were strictly individual and semistructured, and open-ended questions were used (Table  1 ). The interviews were audio-recorded with the participants' permission. They aimed at in-depth exploration of the participants' experiences of bullying and the meaning that they attributed to those experience, in which context participants were encouraged to openly convey their viewpoints.
Participants Interviews were proposed to 22 adolescents, 21 of whom were ultimately included in data collection (a 16-year-old girl wished to terminate her participation prior to the first interview without providing a reason). The basic demographic and clinical characteristics of the participants are summarized in Table  2. All participants experienced physical, verbal and relational bullying; six participants had also been cyber-bullied. In accordance with the sampling strategy, participants were of both sexes and displayed heterogeneity regarding age (from 12 to 17 years), socioeconomic status and level of education (ranging from low to high and from 7 to 11 th grade, respectively) as well as the duration of the bullying they experienced (from 2 months to 10 years). Both outpatients and inpatients were included, with a variety of psychiatric diagnoses and levels of symptoms.
Acknowledgements The authors thank all the participants and their families for their contribution to this project. They also acknowledge the help of  Elodie André ,  Véronique Ballet ,  Elisabeth Baranger ,  Violaine Chauvin ,  Florent Cosseron ,  Alice Delorme ,  Pierre-Jean Egler ,  Solène Hautot ,  Claudie-Corinne Senot ,  Lucie Tropato ,  Lucie Yvon , for data collection, of  Lucie Leroy  for data analysis, and of  Jordan Sibeoni  for advices in revising manuscript.
Methods Sample We collected data from 855 students (between the 3rd and 5th grade) with an average age of 10. 16 ± 1. 18 (57. 5% were females). Data was collected in 14 different public schools and a total of 45 classrooms (see Supplementary Methods 1 for a descriptive table for each classroom). We also collected administrative records for each student, including classroom attendance, gender, educational level of student's parent or guardian, and GPA. Most of the students in the sample have been members of the same classroom for more than three years, spending together around eight hours each school day. Our study period spans from July to December 2017. The experiment was run at the beginning of the first semester (July-August 2017). We also measured again academic performance and attendance in December 20217.
However, illnesses and family issues are not constrained to systemic configurations, and they directly impact class attendance; that is why we control for class attendance in both observed periods. Finally, our data comes from vulnerable public schools in a peripheral county of the capital region of Chile. Then we can assume a certain level of homogeneity in household incomes. Moreover, we controlled for the guardian's level of education as a proxy for household income. Therefore, we are also capturing income variations. SM 1. 2: Homophily Index for GPA Our proposed approach for mapping the social capital network in elementary classrooms unveils the non-existence of homophily between students regarding GPA. We calculate the homophily index [97] as follow: H r = corr   G r i , 1 N r N r j G r j   , (S1) where G ir represents the GPA of the ego student "i" and G jr represents the GPA of the alter student "j".
Aim: To systematically examine the effect of early motor interventions on motor and locomotor development in infants <1 year of age with motor developmental disability or at risk of motor delay. Methods: Pertinent literature from January 2000 to September 2021 was identified by searching the PubMed, Embase, Cochrane, Pedro and Web of Science databases. Selection criteria included interventions starting before 12 months corrected age. Methodological quality was assessed with AACPDM criteria, Mallen score and Cochrane risk of bias methodology. Evaluation procedure was performed using PRISMA protocol (PICO approach) and AMSTAR-2. This review was preregistered in PROSPERO (CRD42021286445). Results: Ten articles met the inclusion criteria; seven had moderate to strong methodological quality. The interventions included treadmill training (n = 3), crawling training (n = 1), "tummy time" (n = 1), physical therapy with neonatal developmental program (n = 1) or Bobath approach (n = 1), treadmill training combined with active leg movements (n = 2) or Bobath physiotherapy (n = 1).
Our strategy for the review was to use four keywords: Infants AND Motor AND Training AND Therapy. Despite searching for training strategies focused on gross motor and/or locomotor development, we decided to keep the more general keyword Motor. This strategy allowed us to evaluate whether the motor function stimulated in each study met our selection criteria. The electronic databases used in the review were PubMed, Embase, PEDRO, Cochrane and WEB of science. We also examined the reference sections of systematic reviews to ensure we did not miss any articles, but none were included in our systematic review because they were either published before 2000 or did not meet our criteria. Finally, in order to avoid repetition, we selected only original papers and not subsequent papers that expanded on the original ones with supplementary outcomes but used the same cohort and protocol (see the details in Appendix S2 and in the results section).