dataesr/scientific-paragraphs-categorization huggingface dataset:
| Procedure Participants were seated in a sound-attenuated, magnetically shielded recording room, at a 50-cm distance from the screen. The response device was an index-operated joystick that participants moved either towards them (when instructed to pull) or away from them (when instructed to push). All stimuli were delivered using Presentation software (Neurobehavioral Systems, Albany, CA, USA). All sounds were presented through airconducting tubes using Etymotic ER-3A foam earplugs (Etymotic Research Inc. , USA). First, the auditory detection threshold was determined for the two target sounds differing by two semitones (512 and 575 Hz), for each ear, for each participant using the Bekesy tracking method (Von  Békésy & Wever, 1960). Second, participants performed a discrimination task during which they were randomly presented with the target sounds equiprobably in each ear (four trials per pitch) and were asked to categorize the target sounds as either highor low-pitched sound within 3 s.
| Behavioural data analysis For behavioural analyses, we excluded incorrect trials and compared group performances using unpaired t-tests. We tested the group effect on global performance measures (median reaction times [RTs] and percentage of correct responses) and on three specific measures of the CAT (see Figure  3 )  (Bidet-Caulet et al. , 2015; ElShafei et al. , 2020; Hoyer et al. , 2021; Masson et al. , 2020; Masson & Bidet-Caulet, 2019) : i. CUE BENEFIT: Estimated as the difference in median RTs between the uninformative and informative NoDIS conditions. This measure reflects the deployment of top-down processes based on the cue information to faster discriminate the target. ii. AROUSAL BENEFIT: Estimated as the difference in median RTs between the NoDIS and DIS1 conditions. Because distracting sounds trigger both a long-lasting increase in arousal resulting in an RT decrease and a strong short-lived attentional capture (orienting) effect associated with an RT increase  (Bidet-Caulet et al.
| Brain recordings Simultaneous EEG and MEG data were recorded. MEG data were acquired with a 275-sensor axial gradiometer system (CTF Systems Inc. , Port Coquitlam, Canada) with continuous sampling at a rate of 600 Hz, a 0. 016-to 150-Hz filter bandwidth and first-order spatial gradient noise cancellation. The EEG data were acquired continuously at the same sampling rate from seven scalp electrodes placed at frontal (Fz, Fc1, Fc2), central (Cz) and parietal (Pz) sites, and at the two mastoids (M1, M2). Electrode placement was chosen in order to capture the well-established event-related markers of attention such as CNV, target P3 and P3 complex. The reference electrode was placed on the tip of the nose, the ground electrode on the shoulder. Moreover, eye-related movements were measured using diagonal EOG electrodes (bipolar montage).
Head position relative to the gradiometer array was acquired continuously using coils positioned at three fiducial points; nasion and left and right pre-auricular points. Head position was adjusted at the beginning of each block to control head movements. In addition to MEG and EEG recordings, T1-weighted three-dimensional anatomical images were acquired for each participant using a 3T Siemens Magnetom wholebody scanner (Erlangen, Germany). The processing of these images was carried out using CTF's software (CTF Systems Inc. , Port Coquitlam, Canada). In the sensor space, MEG and EEG data preprocessing were performed using ELAN  (Aguera et al. , 2011) , data analysis and visualization were performed using FieldTrip  (Oostenveld et al. , 2011) , while statistical comparisons were carried out using R (R Core Team, 2014).
| Sensor-level analysis Event-related potentials (ERPs) and ERFs were obtained by averaging filtered MEG and EEG data locked to three events: cue-related responses (cueRRs), target (targetRRs) and early distracting sounds (dis1RRs). For cueRRs and targetRRs, a baseline correction was applied based on the mean amplitude of the À100-0 ms period before the cue and target onset, respectively. For disRRs, surrogate distractor ERFs/ERPs were created in the NoDIS trials and subtracted from the actual distractor ERFs/ERPs. The obtained distractor ERFs/ERPs were thus free of cue-related activity.
| Source-level analysis Segmentation of the individual T1-weighted MRIs was conducted using the FreeSurfer software package F I G U R E 3 Schematic representation of behavioural effects triggered by distracting sounds in the CAT. A distracting sound triggers a long-lasting increase in arousal resulting in RT decrease (behavioural benefit, green shaded area) and a strong short-lived attentional capture (exogenous orienting) leading to RT increase (behavioural cost, red shaded area); with the behavioural net effect of distracting sound varying according to the time interval between the distracting and the target sounds (blue line)  (Fischl, 2012 ; http://surfer. nmr. mgh. harvard. edu). Segmentation was visually inspected and then imported (15,002 vertices) in the Brainstorm toolbox  (Tadel et al.
, 2011 ; http://neuroimage. usc. edu/brainstorm) with which further source analyses were conducted. The white matter-grey matter boundary segmented by FreeSurfer was used as a source space for subsequent weighted minimum norm estimation. A noise covariance matrix was computed on the resting state session of the experiment, which had been preprocessed with the same pipeline than data from the task session. The forward model was computed with the OpenMEEG software  (Gramfort et al. , 2010 ; https://openmeeg. github. io/). ERFs and MEG sensors positions were imported in Brainstorm. After co-registration between the individual anatomy and MEG sensors, cortical currents were estimated using a distributed model consisting of 15,002 currents dipoles from the time series of the 275 gradiometer signals using a linear inverse estimator (weighted minimum norm current estimate, signal-to-noise ratio of 3, whitening PCA, depth weighting of 0.
ACKNOWLEDGEMENTS We would like to thank  Sebastien Daligault ,  Denis Schwartz  and  Claude Delpuech  for technical assistance with the acquisition and analyses of electrophysiological data. This work was supported by the  French National Research Agency   ANR-14-CE30-0001-01  (ABC), by the  Rhone-Alpes Region  (HE) and by the  LABEX CORTEX  ( ANR-11-LABX-0042 ). This work was performed within the framework of the  LABEX CELYA  ( ANR-10-LABX-0060 ) of  Université de Lyon , within the  programme 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  French ANR.
Funding information French National Research Agency , Grant/ Award Numbers:  ANR-14-CE30-0001-01 ,  ANR-11-IDEX-0007 ; LABEX  CELYA, Grant/Award Number :  ANR-10-LABX-0060 ; LABEX  CORTEX, Grant/Award Number :  ANR-11-LABX-0042 ;  Rhone-Alpes Region.
We explore the use of different multi-state structures when studying antidepressant medication use patterns among women diagnosed with invasive breast cancer (n=18313) and agematched cancer-free women (n=92454) from the Swedish population, using the information in Breast Cancer Data Base Sweden 2. 0 (BCBaSe 2. 0). Results: While each structure has its own utility, the more complex structures -bidirectional and recurrentare able to take into account the intermittent nature of the prescribed medication data and deliver estimates about the total probability and total length of stay of an individual in an antidepressant medication status or discontinuation periods during their follow-up. Sensitivity analyses over different de nitions of the medication cycle and different approaches when modelling the transition intensity rates (Markov, semi-Markov or mix of the two) show that the estimates of the complex structures are quite stable.
However, longitudinal data regarding the long-term psychological state of the individual are rarely collected, and the long-term impact of the BC diagnosis has not been extensively studied. Yang et al  (7)  studied the effects of tumor related and unrelated factors on the long-term risk (standardized incidence and hazard ratios) of rst occurrence of depression, anxiety and other stress related disorders. A diagnosis of depression and other stress related disorders is usually made in primary care, but routine primary care information on diagnosis is often unavailable. For this reason many researchers turn to administrative drug prescription databases of in order to study indicators/proxy measures of mental health on a population level  (8) (9) (10) (11). Prescription data offers a readily available, affordable, quanti able, population-wide measure of drug use.
0 (BCBaSe 2. 0), a linked research database that includes data on dispensed drug prescription from the Swedish Prescribed Drug Registry both before and after a diagnosis of BC, with this information also being available for an age-matched control group of healthy women 18. This provides the opportunity to investigate the association between a diagnosis of BC and long-term antidepressant medication use patterns. The aim of this study is to serve as a guide, describing alternative multi-state model structures for deriving probabilities, using exible parametric survival models (FPSM)  (17)  when modelling the transition intensity rates, while also focusing on the potential and limitations of each structure and different modelling assumptions.
Study Cohort And Inclusion Criteria Breast Cancer Data Base Sweden 2. 0 (BCBaSe 2. 0) was created to provide a register-based research resource with data on an unselected cohort of women (and men) with BC in Sweden(18). The BCBaSe 2. 0 database includes information on individuals diagnosed with BC between 1992 and 2012 (n = 68450) identi ed in three Swedish Regional Clinical Breast Cancer Registers(19) and age-matched controls (ratio 1:5) without a history of breast cancer (n = 343200) at the end of the year of diagnosis of the index case, with the controls living in the same county as the case and born in the same year. The mean age at inclusion was 61. 8 years (range 19-102) and the cohort has been followed up until 31 December 2013. By means of record linkages to national demographic and health care population-based registers, information on dispensed prescribed drugs is available.
The Prescribed Drug Register was initiated on July 1st, 2005, with unique patient identi ers for all dispensed prescriptions in Sweden. The register includes, among other information, dispensed item (substance, brand name, formulation and package), date of prescription and dispensing, dispensed amount, dosage, de ned daily doses (DDD). The initial sample of this study consists of 113296 women from the BCBaSe 2. 0 database that have been diagnosed with invasive BC (n = 18904) and their healthy-matched controls (n = 94983), with diagnosis date (or start of follow-up for the healthy-matched individuals) between July 1, 2006 and December 31st ,2012 with no prescribed antidepressants for at least 12 months prior the start of followup. Out of the sample, 591 women who were initially controls became cases during follow-up.
Antidepressant Medication Cycles Antidepressants are not exclusively prescribed for depression. In a research letter, Wong et al  (18)  show that during the period 2006-2015 in Canada, 55. 2% of antidepressant prescriptions were indicated for depression, 18. 5% of prescribed antidepressants were given for anxiety disorders, 10. 2% for insomnia, 6. 1% for pain and 4. 1% panic disorders. Using prescription data of all women in the BCBaSe 2. 0 research database who have lled a prescription for antidepressants, it can be expected that some individuals received antidepressants for indications other than depression while others may have experienced depression but never received pharmacological treatment. Despite this fact, antidepressant use can be a useful proxy of depression, and in general, of psychological distress  (12).
However, complex multistate structures can be used to answer more composite research questions. The total probability of being in a medication cycle, including recurring cycles, can be studied via a bidirectional or a recurrent multistate structure, with limitations in each case. Synthetic example datasets with the multi-state data structures used in this study are provided in the Supplementary material. The robustness of the estimated probabilities derived from the different multi-state structures for different de nitions of being in an antidepressant medication cycle was qualitatively assessed via a sensitivity analysis, showing that the complex multi-state structures (Recurrent multi-state, Bidirectional, "Emulated bidirectional") are relatively insensitive to alternative medication cycle de nitions.
In addition, the probability of being in the medication cycle given that the individual entered the medication discontinuation period" can be estimated via the use of a recurrent multi-state structure, allowing for studying the susceptibility for anti-depressive medication use in women who have experienced such medication in the past. Other measures derived under a MSM framework may be of primary interest such as the length of a medication cycle or transition rates and rates ratios for experiencing the next medication cycle among individuals with different pro les (covariate patterns). Due to space limitations, estimation results regarding these measures and their interpretation under each multi-state structure are presented in Additional File 1. A pseudo-dataset is supplied in Additional File 2 k th k -1 th which can be used by the code in Additional Files 3 and 4 to create the multi-state structures and run the multi-state models discussed in this study.
Methods/design Design and funding. This is a prospective cohort study. Thus far, data have been collected at 2 points. The first part of the study was organized between 2015 and 2016, and the second part was organized between 2020 and 2021. Before the second part, the study design was a cross-sectional design. The cohort design beaun in the second part. This is the reason why we submitted the protocol study at this time. At its initiation, the EVAL-PLH study was supported by the French PREPS (Programme de recherche sur la performance du système de soins, Direction Générale de l'Organisation des Soins, 2013) and the French Institut National de la Santé et de la Recherche Médicale (INSERM, 2013). The study is registered in the ClinicalTrials. gov database (Trial registration: NCT02400528; registered 27/03/2015).
This committee is in charge of the general organization of the study: coordination, regulatory issues, and finding solutions for any dysfunction. All study partners (the physicians and the scientific, administrative, and logistical actors) constitute the EVAL-PLH group [see Additional file 1. The EVAL-PLH group]. Data were collected from the first 2 evaluations. The first wave was organized between 2015 and 2016, the second wave was organized between 2020 and 2021, and the third wave is planned for 2025-2026. Each wave is organized according to the following steps: 1. Persons with polyhandicap: The list of eligible patients is established by a dedicated clinical research assistant in each center and checked by the patients' referring physicians. Clinical data were collected from the medical records.
The list of eligible patients is updated at each wave (notification of deceased persons, identification of new cases, and investigation of patients who switch centers). The data are collected by a (permanent or not) doctor of the center. The doctor was experienced with persons with polyhandicap, and was encouraged to question the healthcare team in charge of the person. 2. Familial caregivers: A maximum of 2 familial referents are systematically identified for each included person. Contact information is updated whenever possible. A survey booklet is sent by mail to each familial referent. To optimize participation, a prepaid return envelope addressed to the coordination team is included to the mail. In the case of nonresponses, a recall system is planned. 3. Institutional caregivers: The members of the steering committee hold meetings with the institutional caregivers in each center to explain the objectives and modalities of the study.
Data collection. Various sources of data are used. An additional details file shows this in more detail [see Additional file 2, Data collection details] 1. For persons with polyhandicap, the data are collected from the medical records: sociodemographics, severity and stability, associated handicaps, comorbidities, neurodevelopmental patterns, medical devices, rehabilitation procedures, and medical treatments. 2. For familial caregivers, the data are gathered into a booklet, including the following: sociodemographics, nature of their relationship with the person with polyhandicap, marital status, number of children, educational level, occupational status, financial status, presence of the person with polyhandicap at home, personal health data (chronic diseases; use of health resources), psycho-behavioral data (anxiety, mood disorders, coping, quality of life, and burden), and specific information about the social environment and satisfaction with health care.
3. For institutional caregivers, the data are gathered into a booklet, including the following: sociodemographics, marital status, number of children, educational level, financial situation, notion of a handicapped person living at home, personal health data, professional situation (job categories, work schedule, experience in disability care), and psycho-behavioral data (anxiety, mood disorders, coping, quality of life, and burn-out). Data quality, data monitoring, and statistical aspects. The data quality is ensured by the dedicated clinical research assistant of the study (AB). The data entry plan and the data monitoring plan have been approved by the operational committee (including the clinical research assistant (AB), the statistician (IH), the referent epidemiologist (KB), and the physician experienced in polyhandicap (MCR)).
Results At this time, only data from the first wave (2015-2016) of the study were explored. A total of 875 persons with polyhandicap, 440 familial caregivers (390 parents), and 362 institutional caregivers were included. These findings were provided across more than ten international publications. The main findings highlighted the following: (1) The variety of clinical statuses: for the first time, the health status of the persons with polyhandicap, aged 3-70 years, was described in terms of etiologies, comorbidities, associated handicaps, behavioral disorders, treatments and devices, severity and stability  9, 18  ; (2) The evolving nature of the health status across the lifespan: for the first time, we showed the complex and specific needs of persons with polyhandicap in terms of care at every period of life, in particular the transition process from childhood to adulthood 10 ; (3) The adequacy of the care management: we showed that the adequacy of care management (in terms of care structures, nature of care provided) from objective indicators (such as the health severity and the age) seems less satisfactory than the perceived adequacy of the physicians 8 ; (4) The considerable impact on the quality of life for parents  19  and for health care workers caring for individuals with severe polyhandicap  16.
Readers may refer to the respective references for more details. The data from the first wave were also used to validate two new major tools for the assessment of individuals with polyhandicap. First, the polyhandicap severity scale  7  provides the first reliable and valid measure of the health severity status for children and adults with polyhandicap that can be easily applied in research and clinical practice. Second, the PolyQoL questionnaire is a short proxy-reported questionnaire assessing the quality of life of persons with polyhandicap (Hamouda I. Quality of life of polyhandicapped persons: initial validation of the PolyQoL. SOFMER 2021; October, Lille, France). The enrollment of participants in the second wave of the study is now closed, and we will proceed to the quality control of the data.
The research was approved by the French ethics committee (name: Comité de Protection des Personnes Sud Méditerranée V; postal address: CHU•HOPITAL DE CIMIEZ, Nice, France; website: https:// www. cpp-sud-medit erran ee-v. fr/; approbation date: 20/10/2014; reference num- ber: 2014-A00953-44; check-in number: 14. 041). All experiments were performed in accordance with relevant guidelines and regulations. pendent audit was planned. The database is securitally stored in the dedicated department of the sponsor (Epidemiologie department, the Service d'Epidémiologie et Economie de la Santé, Assistance Publique des Hôpitaux de Marseille, France. Prof. Pascal Auquier (PA) is responsible for the department and the referent epidemiologist for EVAL-PLH is Dr Karine Baumstarck (KB). The security of the database and the confidentiality are based on the appropriate French guideline (French National Data Protection Committee (CNIL) published new reference regarding data processing in health research to adapt the existing framework to the EU General Data Protection Regulation (GDPR)).
Design and sample A cross-sectional analysis of data from the observational Feelings and Psychological Impact of the COVID-19 Epidemic on children, adolescents and their parents in the Grand Est Area during the rst wave (PIMS2-CoV19) study was conducted. All children, adolescents enrolled in grades 3 through 12 in a school in the Nancy-Metz academy (Grand Est region, France), and their parents were eligible to participate in the study. A random selection of schools strati ed on the level (primary school, middle school, high school), the school sector (general, technical, professional or mixed) and the status of the school (public or private) was carried out among an exhaustive base of schools of the Academy of Nancy-Metz (Grand Est region, France) communicated by the rector ship. Parent and child-adolescent pairs were recruited electronically by their email address and they completed an anonymous online survey from May 26 to July 6, 2020, at the end of the rst lockdown.
Socio-demographic data, living and learning conditions during and after lockdown Children-adolescents completed a self-administered questionnaire that included sociodemographic data (age, gender, educational level) and living (frequency of exiting home) and learning conditions (time spent on schoolwork at home, focus at home for homework, di culty isolating at home, tensions and con icts at home and with neighbors, noises inside and outside the residence). Parents completed a selfadministered questionnaire that included sociodemographic measures such as gender, living arrangements, home location and living conditions such as having a person infected with COVID-19 at home.
Crude Odds ratios (CORs), Adjusted Odds Ratio (AORs) and 95% con dence intervals (Cis) were estimated. For each model, the goodness of t was assessed by calculating the model determination coe cient (R 2 ) and the percentage that was predicted correct by the model. Analyses were performed using SAS 9. 4 (SAS Inst. , Cary, NC, USA).
Characteristics of children/teenager's sample A data set of 471 child-parent pairs was obtained. The sociodemographic, learning and working characteristics of the 471 children/adolescents are presented in Table  1  for the entire sample and by school level groups. The children's mean age was 12. 9 years old (SD = 2. 9) with 53. 5% of females. Of the 471 children/adolescents, 35. 7% found it di cult or very di cult to focus at home for their homework and 27. 8% found it di cult or very di cult to be home-schooled. Most child and adolescents reported spending more than 2 hours per day on their homework (74. 2%) and 80. 1% reported not being afraid (or a little afraid) to go out and contract the COVID-19. Almost a quarter of the sample (22. 2%) reported never leaving home. Of the 471 children/adolescents, 2.