dataesr/scientific-paragraphs-categorization huggingface dataset:
Data preparation The processing of fMRI data followed standard procedures. The volumes acquired represent the BOLD signal in 2. 5 x 2. 5 x 2. 5 cm 3 voxels of the brain. Each volume contains 54 slices of 84 x 84 in-plane voxel, and is recorded in 1. 205 seconds. Preprocessing entails a correction of temporal synchronization of the acquired slices, a realignment of the volumes of each session on the first one, and a correction of the deformations due to the local distortions of the magnetic field and participants' movements. Normalization uses the DARTEL procedure  (Ashburner, 2007)  to put the imaged brains of all participants in the standard MNI space. Several nuisance covariates were computed to eliminate motion artefacts, potential blood pulse and respiration artefacts, which were highly relevant in a paradigm involving speech, as well as global grey matter signal, white matter activity, and cerebrospinal fluid activity to control global signal fluctuations unrelated to the task (TAPAS toolkit,  Kasper et al.
Data analysis Facial emotions were extracted automatically from the videos of the human and robot interlocutors using a freely available machine learning algorithm  (Arriaga et al. , 2019)  available at https://github. com/oarriaga/face_classification. The algorithm uses a Convolutional Neural Network to classify facial expression within 7 classes (Anger, Disgust, Fear, Happiness, Sadness, Surprise, and Neutral). According to its developers, it can achieve an accuracy of 66% on a dataset of manually labelled 35887 grayscale images. The recordings used for this classification, with 640 by 480 images of full-screen faces of the human or robot interlocutor looking directly on the screen, were particularly well-suited for this automatic classification, in contrast to the more variable images used for the development of the algorithm.
The probability predicted for each emotion was extracted frame by frame, then summed over all frames of a trial, therefore providing one score for each emotion per trial. Statistical analyses were performed in R with the package lme4. Multivariate models were used as the effect of each predictor (level of facial happiness, type of agent) is evaluated while holding constant the effect of the other predictors on the dependent variable. We introduced the happiness score obtained for each trial as a predictive continuous variable for all regions evaluated, with the nature of the agent the categorical factor of interest. Identity of the participant and sessions were used as random variables. The models were selected in a backward process based on the restricted maximum likelihood.
Conversely, Gotham et al. found that a lower educational level of mothers was associated with more internalized behaviours in their children  5. The paucity and heterogeneity of the findings in the literature on the association between parents' socio-economic status and anxiety disorder in their children with ASD also merit further study  6. One of the strengths of our study was that it used data from a prospective cohort involving a large number of children with a confirmed diagnosis of ASD and that it includes a standardized clinical record of anxiety. This study must be interpreted in light of certain limitations. First, although the sample size was quite large, missing data limited the multivariate analysis. However, this potential limitation can be mitigated by the stability of multiple imputations of the missing data.
Parental socioeconomic status (SES). SES is a composite variable based on the mother and/or father's professional background. When the parents were living together, the highest SES in the household was considered; when separated, the SES of the parent with whom the child lived most of the time was considered. The SES was scored as high (business owners, professionals, executives), middle (farmers, supervisors, skilled craftsmen), or low (farm workers, laborers, service employees, unemployed). Caregivers completed questionnaires electronically on a web database, including the Child Behavior Checklist (CBCL), the Sensory Profile, the Aberrant Behavior Checklist (ABC), the Hospital Anxiety and Depression Scale (HADS), and their socioeconomic status (SES).
Acknowledgements We warmly thank the contributing families, the  ELENA consortium , and the ELENA cohort staff ( Ela Miniarikova ,  Flore Couty ,  Lee Audras-Torrent ,  Mathilde Bérard ,  Myriam Soussana ,  Julie Loubersac ,  Laetitia Ferrando ,  Christelle Vernhet ,  Noémie Fourgassie ,  Laura Fragey  and  Marion Sevens ). Special thanks go to Elena Study Group:  Pr Amaria Baghdadli ,  Dr. Catherine Chabaux ,  Dr. Clarisse Chatel ,  Pr David Cohen ,  Dr. Emmanuel Damville ,  Dr. Marie-Maude Geoffray ,  Pr Ludovic Gicquel ,  Pr Renaud Jardri ,  Dr. Thierry Maffre ,  Dr. Alexandre Novo ,  Dr. Roxane Odoyer ,  Dr. Marie-Joelle Oreve ,  Dr. Lisa Ouss ,  Dr. Didier Perisse ,  Pr Francois Poinso ,  Dr. Julien Pottelette ,  Dr. Laurence Robel ,  Pr Catherine Rolland ,  Dr. Marie Schoenberger ,  Dr.
Methods From January 2017 to December 2019, qualitative and quantitative data were retrospectively collected from the NPPC patient's active le and medical records (at the Children's Hospitals of Nice CHU-Lenval): 1) Potential traumatic events (DSM 5), 2) DSM-5 diagnoses, 3) Request of new consultations and 4) Number of follow-up consultations. The study has been carried out per the 1964 Declaration of Helsinki and its later amendments and declared to the French National Commission on Informatics and Liberty (CNIL). Ethics approval was obtained from the University Côte d'Azur local Ethics committee (CERNI; n° 2021-064).
Discussion This paper aims to present data related to the activity of the Nice Pediatric Psychotrauma Center (NPPC) three years after the unit's inception. 866 children and adolescents were treated at the NPPC. For the great majority, the traumatic event, which led to seeking consultation, was related to the terrorist attack on the 14th of July in 2016. Patients with other types of traumatic events (road accidents, mourning, migration, etc. ) were also treated at the NPPC. The primary diagnoses recorded were PTSD (71%), but other diagnoses such as acute stress disorders, major depression disorder, and anxiety disorder were also recorded. In fact, patients with PTSD had a high comorbid rate such as sleep-wake disorders, anxiety and neurodevelopmental disorders. These results are coherent with the literature.
Acknowledgement " 14-7 " Program is funded by  Fondation de France ,  Promenade des Anges ,  Conseil Départemental 06 ,  Société Française de Psychiatrie de l'Enfant et de l'Adolescent et des Disciplines Associés and Rotary Club. This work has been supported by the  French government , through the  Université Côte d'Azur "Joint Excellent and Dynamic Initiative" Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. ".
Results In order to test how behavioural data from individuals can be used to infer a dynamical probabilistic variable internal model and assess the contribution of inductive biases to the internal model, we used an experimental paradigm that could fulfil a number of key desiderata. First, the paradigm relies on across-trial dependencies; second, as in everyday tasks, the state of the environment cannot be unambiguously determined from the observation of momentary stimuli; third, the structure of the task is new to participants; fourth, the complexity of the task is relatively high, i. e. an a priori unknown number of latent states determine the observations; fifth, behavioural measurements during task execution are continuous, which ensures that rich inferences can be made. In the alternating serial response time task (ASRT,  [35] ) a stimulus can appear at four locations of a computer screen and the sequence of locations (untold to participants) follows a pre-specified structure (Fig  1A ).
Our proposed model class, iHMM, is appealing because it can accommodate highly complex statistical structures, including the ideal observer model or the Markov model. The flexibility of the model comes at a price that more data is required for inferring the model. This motivated the choice to infer one model per session and track learning by comparing inferred models across days. Our choice is motivated by the revealed multi-day learning process that seemed to be characteristic of all participants. The approach has a limitation too, that early in the training (and especially on the first day) faster changes in the internal model cannot be captured. Alternative model classes, such as a hierarchical version that can more effectively perform chunking, can be more effective in learning from more limited data and can be used to explore the evolution of the internal model in more detail  [50].
e. average) over the response time predictions of model samples. 3. We evaluate model performance by computing the R 2 explained variance measure of the predicted response times on the response times of the test dataset. In any given session we train the model on one set of blocks and predict response times on a distinct set of test blocks. During optimizing our model and algorithm, we concluded that using ten consecutive blocks for training provides the best results for the CT model. We also found that using ten blocks for the test set decreases variance of the R 2 estimator sufficiently to have individualised learning trajectories. Note: since actual beliefs depend on past beliefs, one can think of the belief sequence as the path of a light-ray in a large dimensional fog (representing the state uncertainty).
Prediction of the next stimulus is computed by marginalizing over the latent state posterior distribution: pðy tþ1 jy 1 ; y 2 ;. ; y t Þ ¼ ¼ X s tþ1 pðy tþ1 js tþ1 Þpðs tþ1 jy 1 ; y 2 ;. ; y t Þ ¼ X s tþ1 �s tþ1 y tþ1 pðs tþ1 jy 1 ; y 2 ;. ; y t Þ ¼ X s t ;s tþ1 �s tþ1 y tþ1 pðs tþ1 js t Þpðs t jy 1 ; y 2 ;. ; y t Þ ¼ X s t ;s tþ1 �s tþ1 y tþ1 ps t ;s tþ1 ŝt Throughout the execution of the task, the internal model of the participants is continually updating. We do not directly model the computation of the participants that estimates the current π and ϕ parameters. That is, within a given train or test dataset (10 consecutive blocks) we hold π and ϕ fixed. We do allow, however, for these estimates of π and ϕ to change between sessions. For a summary of when each parameter is allowed to change see Table  1.
Note, that in order to avoid assigning probability to negative reaction times, we use a truncated Normal distribution. The response time parameters are jointly inferred along with the internal representations (dynamical model, observation distribution, latent state inference). Validation on synthetic datasets. In order to validate our behavioural model as well as our inference method, we looked at how well we can recover subjective probabilities on a synthetic dataset. We chose to constrain our analysis to the recovery of subjective probabilities instead of the generative model structure due to the unsupervised nature of our method: the objective of inference is to learn the distribution of data (which is in direct relationship with the predictive probability of upcoming stimuli). This is in contrast with more supervised methods where the emerging representations can be gauged by performing tasks that rely on the latent variables.
The standard deviation of the response times is an appropriate measure since this can be also computed for data obtained from human participants. We generated the response times for 10 ASRT blocks (the same number we used for inference on human data). Standard deviation of the resulting response times (symbol colours on S3(B) Fig) arise from the interaction of all parameters. Different combinations of the response time parameters resulting in the same standard deviation are marked by identical colours. Then, we used the CT inference method to generate a second set of (posterior) internal model samples. We computed the same model performance measure as for human data (response time prediction performance) and compared it to the prediction performance of that of the original internal model of the participant (S3(B).
Train and Test Datasets. For the reason described in the above paragraph, for each day (out of 10) of the experiment, out of the 25 blocks each day, we selected blocks 11-20 as a training dataset and blocks 1-10 as test datasets. The main reason for this choice is that on each day in the initial few blocks participants may be engaged in a warm-up phenomenon which fundamentally alters their behaviour in the task. If we use the first 10 blocks as test data, the performance metric may be influenced by 10-30% depending on how many blocks include altered behaviour. However, if we used this part as training data, the whole internal model inference would shift fundamentally, since our inference algorithm assumes a fixed model the entirety of the 10 blocks. During model inference (train dataset) and performance evaluation (test dataset) the first five random trials and all incorrect response trials' response times are not considered.
Then, participants play the model forward into the future (open purple circles). Finally, they generate predictions for the upcoming stimulus (Bb, squares in grey boxes) by summing over the possible future states (open purple circles in grey boxes). Participants use previous state beliefs and the new stimulus to update latent state beliefs. In this particular example, at trial t + 1 only one of the possible states can generate the observation, hence there is only one dark purple disk. Again, they play the dynamics forward and predict the next stimulus. C Predicted response times against actual response times are shown for individual trials for an example participant (dots). After training our inference algorithm on a training dataset of 10 blocks, we predict response times of another 10 blocks on the same day.
This research was supported by the  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 , D. N. , G. O. );  Hungarian Scientific Research Fund  ( NKFIH-OTKA K K125343 , G. O. ;  NKFIH-OTKA K 128016 ,  D. N. ,  NKFIH-OTKA PD 124148 , K. J. );  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (K. J. );  IDEXLYON Fellowship  of the  University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) (D. N). B. T. was supported by scholarship by  Budapest University of Technology and Economics  as well as by  Mozaik Education Ltd. (Szeged, Hungary). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Criteria for discontinuing or modifying the allocated interventions {11b} Subjects may be withdrawn from the study at the discretion of the investigator or sponsor due to safety concerns. In addition, a subject must be withdrawn if one of the following applies: • Subject chooses to withdraw from the study at any time. • Major violation of the study protocol. • Other circumstances that would endanger the health of the subject if he/she were to continue his/her participation in the trial. Reasons for withdrawals and discontinuation of any subject from the protocol must be recorded. The evaluation of protocol violations and patient removal will be produced only at the blinded data review stage. Main major violations are: • Participant did not complete a minimum of 50% of the program's modules, including the follow-up visits.
Assignment of interventions (for controlled trials) Allocation Sequence generation {16a} The randomization will be done with Ennov-Clinical software (CS-random module). The randomization will be centralized, available online, and stratified by minimization based on centre and on two age classes (6-11 and 12-15 years) and medication status (according to major classes of psychotropic), balanced with a 1:1:1 ratio. Each subject (i. e. , here duo or trio of child and parents), who meets the eligibility criteria and signed informed consent, will be randomly assigned to one of the three study groups by the study investigator.
Child behavior checklist (CBCL) The CBCL is a standardized form that parents complete to describe their children's behavioural and emotional problems  [32, 38]. We will use the problem items for ages 4 to 18 years that can be completed in approximately 10 min. The data obtained with the CBCL are summarized on a profile that displays the parents' ratings of each item. The profile also displays the child's standing on syndromes of problems that were derived from statistical analyses of CBCLs in large numbers of clinically referred children. In order to capture changes, the CBCL is completed for the preceding month (instead of 6 months).
In the event that an included subject is withdrawn, the investigator should make every effort to perform a final study visit and complete the assessments and procedures outlined in the Table  1  for the follow-up visit. Data management {19} Source documents are original documents and patient records from which patient data are reported in the e-CRF. The investigator must commit to allow direct access to data sources in the study during inspections, audits or inspections. For each participant there is an e-CRF, developed from the software Ennov Clinical to control data quality at entry. This software complies with the FDA recommendations on computerized systems for managing clinical trials ("Guidance for Computerized systems Used in Clinical Trials"), electronic signature ("21CFR part 11") and other domestic and international norms (CDISC, ICH, BPC 2001/20/ CE…).
Pseudonymization of data In order to preserve anonymity, participants will be identified on the e-CRF by a unique identification number, the first letter of their first name, the first letter of their last name, their gender and their year of birth. A list of names of participants will be kept in the investigator's file. The investigating team will ensure that the anonymity of each participant in the study is guaranteed. The information will be collected for each participant in accordance with the study data collection form by the investigator(s) or the clinical study technician(s) in charge of the study.
Statistical methods {20a} A statistical analysis plan (SAP) will be prepared by the sponsor. Statistical analyses will be performed with SAS ® version 9. 4 or higher (SAS institute, North Carolina, USA). Data will be analysed under the responsibility of the Unit of the Clinical research of CHU Montpellier with the SAS ® software (SAS Institute, Cary, NC, USA). The statistical analysis plan (SAP), covering all the analyses to be performed on all data, will be written before database lock. Population sets  Four analysis, sets will be defined for this study: 1. The Enrolled Set (ES) will include all subjects who sign an Informed Consent form. 2. The Randomized Set (RS) will include all subjects who are randomized. 3. The Full Analysis Set (FAS) will include all subjects who are randomized and have a valid primary efficacy measurement.
Demographic and baseline characteristics {20c} To ensure comparability, patient demographic and baseline characteristics (including medical history and prior medications) will be summarized by group using descriptive statistics. For continuous variables, descriptive statistics (n, mean, standard deviation, standard error, median, minimum, and maximum) will be provided. For categorical variables, patient counts and percentages will be provided. Categories for missing data will be presented if necessary. In the case of non-comparability for one or more parameters, an adjustment will be made on this/these parameter(s) for analysis of the judgment criteria. A multivariate linear regression model may be performed to account for potential confounders. The type of missing data for the variables of interest will be determined.
Conditional rejection of the hypothesis NMAR (Not Missing at Random)-the assumption MCAR (Missing Completely at Random) against MAR (Missing at Random)-will be tested by studying the relationship between the variable status (missing or not) and the values of the covariates. If the missing data is type MAR or MCAR, a multiple imputation (Rubin, 1987) will be implemented for a sensitivity analysis. In order not to increase the database and to get a good estimate of the variance parameters, five imputations will be realized.
• Accuracy and Reliability of Data To ensure accurate, complete, and reliable data, the sponsor (or their representatives) will do the following: a. Provide instructional material to the study sites, as appropriate. b. Provide a start-up training session to instruct the investigator(s) and study coordinator(s). This session will provide instruction on the protocol, the prompt, and full completion of the clinical report forms, study procedures, and the transmission of data in a timely manner to the clinical database for statistical analyses. c. Make periodic visits to the study site. d. Be available for consultation and stay in contact with the study site personnel by mail, telephone, and/or fax. e. Review and evaluate Case Report Form data and use. f. Conduct quality review of database.
Auditing {23} The purpose of an audit is to confirm that the study is conducted as per protocol, International Conference on Harmonisation-GCP and applicable regulatory requirements; that the well-being and the rights of the subjects enrolled have been protected; and that the data relevant for the evaluation of the investigational product have been recorded, processed, and reported in compliance with the planned arrangements. The investigators will permit direct access to all study documents, drug accountability records, medical records, and source data.
Authorship eligibility guidelines and any intended use of professional writers {31b} We will list all the professionals that have participated in the RESIST study. • with substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; • Drafting the work or revising it critically for important intellectual content; AND • Final approval of the version to be published; AND • Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved We do not intend to hire any professional medical writers.
EPP0437 ESKALE study, a French real-world study of esketamine nasal spray for patients with treatment-resistant depression Introduction: Esketamine nasal spray has been developed to treat adults with treatment resistant depression. On Dec. 2019, EMA granted a market access approval in this indication. Objectives: ESKALE is a descriptive study of treatment resistant depression patients treated with esketamine in France. Methods: Observational retrospective study. 157 patients are included in 3 cohorts depending on their treatment initiation date. This abstract presents the second interim results of patients treated with esketamine and whom data collection ranges from Oct. 2019 and Sept. 2021. Results: 66. 7% of patients were females. Average age was 49 years old with 26 patients > 65 years old.