dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Three groups of well-qualified experts were identified: (1) 18 dual-career experts from three universities with sports science programmes, two high-performance centers, Olympic bodies, sports clubs, an internet designer, and all members of the EMPATIA team. This group was entitled to rounds 1 and 2; (2) a sample of 79 parents (F = 47%, M = 53%; education: university = 69%, high school = 31%) of dual-career athletes (range: 14-24 years) competing in team (72%) and individual (28%) sports at national (highest championships: 53%) and international (European and World competitions; 47%) levels was drawn from the national databases of parents who previously contributed to the development of the EMPATIA framework on parenting dual-career athletes  (Varga et al. , 2021). This group was engaged in round 3.
Statistical Analysis Data were analyzed using the Statistical Package for the Social Science, version 24. 0 (SPSS Inc. , Chicago Illinois). Descriptive statistics was applied, and the frequency of occurrence (expressed in absolute values or percentages) was calculated for the questions for which a single response or multiple responses were allowed. Except for neutral responses, data reduction has been conducted by collapsing very positive and positive responses, as well as negative and very negative responses. Then, the chi-square tests were applied to verify the distribution of answers between parents and dual-career experts, with a level of statistical significance set at p < 0. 05 for all computations. Finally, the sum of positive or neutral responses >70% was considered a cutoff for agreement.
Developmental Phase During the initial developmental phase, an agreement defined the overall structure of the educational programme for guiding the users in their search and interpretation of information. It was deemed crucial to address the demands of dual careers and the role of parents, and to suggest resources for parents in search of supplementary information (videos, papers, books, online sites, etc. ). Then, the best-ranked statements/items included in the concept mapping of the EMPATIA framework were organized in four modules labeled after macro-aspects, and parents could ask about dual careers: "Why, " "What, " "How, " and "Where. " Finally, the online platform's planning and design were approved to ensure an effective data indexing to enhance the search operation. The implementation phase tested the developed system regarding the expected results and the defect-free requirements of the system.
, 2002). Fifteen years after the study by  Oatridge et al. , a pre-conception prospective cohort MRI study resulting in complete "pre-post pregnancy" datasets of 25 first-time mothers, 20 nulliparous control women, 19 first-time fathers and 17 nulliparous control men who were all scanned before and after their (partner's) pregnancy, or at a comparable time interval, showed that primiparous women undergo extensive and highly consistent reductions in regional grey matter volumes across pregnancy in comparison to all three control groups. The very large effect sizes and fact that all women could be classified as having been pregnant or not based only on the changes in their brain structure point to very robust and consistent neural changes. These changes in grey matter volume were mirrored by reductions in surface area and cortical thickness and the reductions remained for at least two years after delivery  (Hoekzema et al.
Acknowledgements JLP was supported by the  INCR (Institut des Neurosciences Cliniques de Rennes)  and  BAA (Bretagne Atlantique Ambition) ; EH is supported by the  Netherlands Organization for Scientific Research , the  Brain & Behavior Research Foundation  and the  European Research Council   ERC StGr 948031 ; JSL is supported by  NICHD  grant  HD097085 ; BL is supported by  NIMH  grant  MH117482-02.
Participants CONSTANCES is a French national population-based cohort that included more than 220,000 volunteers living in metropolitan France and aged 18 to 69 years at inclusion  (15). Participants were selected from French adults covered by the National Health Insurance Fund according to a random sampling scheme stratified on age, gender, socioeconomic status and region of France. All volunteers receive a health check-up at inclusion and answer annual questionnaires with information concerning, in particular, their socio-demographic and socio-professional situation, their health status and their lifestyle. Among the 182,022 volunteers included from 2012 to 2018, 119,562 participants were employed at inclusion. After excluding 8,292 independent workers and 453 participants older than 65 years, we made the linkage with the National Health Data System-NHDS (Système National des Données de Santé-SNDS) that was feasible for 99,058 participants.
Sickness Absences The systematic linkage of the CONSTANCES cohort to the National Health Data System-NHDS (Système National des Données de Santé-SNDS) provides access to objective and exhaustive data regarding duration and date of occurrence of sickness absences, from January 2009 to December 2019 at the time of study  (16). Based on this information, we computed the number of short (<7 days), medium (from 7 to 28 days) and long (>28 days) sickness absences that occurred over one year of follow-up since baseline.
Finally, we searched for dose-dependent relationships between the frequency of cannabis use and the risk of sickness absence by introducing the frequency of cannabis use as a continuous variable in the models instead of introducing it as a categorical variable in order to compute a p for trend. In sensitivity analyses, stratification for sickness absences in the 3 years before inclusion, i. e. , zero sickness absence vs. at least one sickness absence, was performed. We also examined whether the associations could remain similar while performing a negative binomial regression with count data for the dependent variables. Non-response biases were examined by comparing participants who reported their cannabis use at baseline with those who did not. We had complete data for the outcomes (i. e. , sickness absences) since data from the National Health Data System (NHDS) are exhaustively registered.
Third, the frequency of cannabis use was measured at inclusion as an overall measure of the frequency of cannabis use in the past 12 months, making it impossible to take into account for the roles of past exposures or changes in consumption during this period. Fourth, a complete history of sickness absences was not collected. Nevertheless, these data were available since 2009, i. e. , 3 years before the inclusion of the first participants in the cohort and sensitivity analyses showed that the associations remained significant in the subgroup of participants who had no prior sickness absences in the 3 years preceding inclusion. Finally, some potential confounding factors were not measured, such as personality and social environment. Overall, our results are in accordance with our a priori hypotheses as we found evidence that consuming cannabis increased the risk of sickness absences.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because personal health data underlying the findings of our study are not publicly available due to legal reasons related to data privacy protection. However, the data are available upon request to all interested researchers after authorization of the French "Commission Nationale de l'Informatique et des Libertés". Requests to access the datasets should be directed to MZ (rf. mresni@sniz. eiram) and MG (rf. mresni@grebdlog. lecram).
Each visit includes anthropometric measuring, evaluation of motor and speech development, guidance of infants' emotional and developmental needs and feeding, supporting the parents in their new roles, and screening for infant social withdrawal and maternal depression with the ADBB and the EPDS, respectively. All visits are reported in the electronic health visitor journals that also include birth information of the infants. Data were extracted from the electronic Danish Health Visitor Journals. Inclusion criteria were as follows: infants aged 2-12 months born in the municipality of Copenhagen during the study period with minimum one ADBB screening, and mothers had minimum one EPDS screening. Exclusion criteria were as follows: invalid personal identification number, stillborn (the infant's death date is the same as their birth date), infants with a death date before their birth date, infants born outside of the study period, incomplete ADBB and EPDS screenings and ADBB screenings prior to mother's registered EPDS screening were excluded (if they took place on the same date, they were included).
Statistical analyses All analyses were performed using SPSS Statistics 26 (IBM). To test hypotheses 1-3 and 5, multiple linear moderation analyses were used. ADBB scores at the three different waves were the dependent variable in the different models. Gestational age, mother's EPDS score and an interaction effect between them were the independent variables in all of the models for hypotheses 1-3. EPDS score was mean centred. For hypothesis 5, the independent variables were partner's and mother's EPDS scores and the interaction term between them. EPDS scores were mean centred. As the residuals in the linear regression analyses were not normally distributed, a bias-corrected and accelerated (BCa) bootstrap procedure was used, and 95% confidence intervals (CI) were used to evaluate these associations (if zero, that is, the null hypothesis, was not included in the interval, it was interpreted as significant).
Third, the ADBB and EPDS scores at the first wave may have been measured at the same day, meaning it is not possible to test for a causal relationship between the two at this time point. Fourth, assessing paternal PPD with the EPDS has not been validated in a Danish context; however, Swedish studies have tried to validate the instrument and tentatively concluded it may not be a sufficient screening instrument for PPD in fathers  (Carlberg et al. , 2018; Massoudi et al. , 2013). Future studies should try to validate the EPDS in a Danish context or use another measure for assessing paternal PPD. Our study also has a number of strengths. First, the sample size is the largest to date, making it possible to do subgroup analyses and analyses with categorical measured data (even though the majority of participants score low on the EPDS and ADBB) without suffering a major power loss.
Second, the data were collected by health visitors who visited all families with a newborn, resulting in most mothers and infants being included in the target population, minimizing the risk of selection bias. In conclusion, the present study finds that both maternal depression and prematurity are risk factors for infant social withdrawal during the first year of life. We were not able to confirm the role of partner depression on social withdrawal, and future studies should investigate further using well-validated instruments. The present study has identified important predictors of infant social withdrawal, but it has not investigated how infant social withdrawal may change for the individual infant during its first year of life as a function of age and change in parental depressive symptoms.
Mental health assessment Overall mental health, as well as depressive and anxious symptoms, were assessed using two validated instruments. Depressive symptoms in the past 2 weeks were measured by using the Patient Health Questionnaire 9 (PHQ-9; score range from 0 to 27), with a cut-off point ≥10. 19 For each participant who had up to 2 missing items with missing values, we imputed missing values with the mean score for all complete data for this score (n=57; 9. 6%). Anxiety level was measured by using the Generalized Anxiety Disorder 7 (GAD-7), which distinguished "moderate anxiety" (GAD score between 10 to 14) and "severe anxiety" (GAD score ≥15). 20 We also imputed up to 2 missing items with missing values with the mean score for all complete data for this score (n=30; 5. 1%).
Additional information To examine the main demographic characteristics of our participants, the auto-questionnaire also collected information on age, sex, living status, education level, and employment status. We also collected data from medical records on emergency triage classification, which ranks conditions from least urgent  5  to most urgent  1. 21 We also collected data on the reason for admission, and history of psychological or psychiatric follow-up. Data on substance use was collected as complementary data of the 17-items checklist.
Data and statistical analysis The GHU-checklist score We created a score based on participants' answers to the 17items on the checklist. A response was valid if a participant had answered "yes" to at least one of the items. Each missing value was considered as a "no" answer. Each "yes" response was given a value of 1 point. We then constructed a score by summing the points for all items except for the two related to feeling "calm" or "serene", which were subtracted (Fig. 1 ). Therefore, the score had a possible range of -2 to 15. Analysis Patients' characteristics were described for all included participants. Categorical variables were described by proportions (%); and continuous variables by means and their standard deviation. The age and sex distributions in patients Figure  1  Scoring system for the 17-items GHU-checklist score.
included and excluded from the study were compared using the t-test and the chi-square test, respectively. Receiver Operating Curve (ROC) plot curves allowed the calculation of an optimal cut-off point using the SAS macro %ROCPLOT using the Youden index,  22  which allowed the creation of a binary variable characterizing those at risk of depressive or anxiety symptoms (yes/no). Using the newly binary scale, we calculated sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) to assess the diagnostic capacity of the dichotomized score. Analyses were performed using SAS9. 4 Ò software (SAS Institute Inc, Cary NC). Statistical significance was determined at the. 05 level of confidence.
Participants characteristics Of the 1599 potential participants admitted to the ED on the days of survey administration, 608 (38%) were included in our study, with valid data for 593 patients. The study's flow chart indicating reasons for non-inclusion is presented in Fig. 2. Among eligible patients, 44. 5% were excluded because they did not understand French well enough to be informed about the study and to self-complete the questionnaire (the hospital is based in a very ethnically-diverse geographic location), 34. 1% due to either life-threatening or serious medical condition, or a condition which did not allow them to give informed consent and fill out the questionnaire, and 9. 3% for other reasons (including unreturned questionnaires for 6. 9% patients). Non-included patients were significantly older, with a mean age of 46.
Diagnostic capacity About 80% of all participants had completed data for the PHQ-9 (79. 8%, n=485), and the GAD-7 (80. 4 %, n=489) scales, and were therefore included in the tests assessing the diagnostic capacity of the GHU-checklist score. The optimal cut-off point on the GHU-checklist score calculated by the Youden index was different for the two mental health assessment tests. The Youden index was 4 for mild and major depressive symptoms (PHQ), and moderate anxiety (GAD), and 6 for severe anxiety (GAD). Therefore, we used the lowest cut-off point (<4) to dichotomize the score. Fig. 4  shows the results of the different diagnostic tests for the newly binary GHU-checklist scale. The capacity of this scale to distinguish patients with overall mental health problems, moderate and severe anxiety, as well as mild and major depressive symptoms was rather high, with sensitivity varying from 81.
Model-based systems engineering The Renaissance of MBSE can be attributed to the adoption of the Systems Modelling Language (SysML)  (Friedenthal et al. , 2014; OMG, 2017)  by the Object Management Group (OMG) in July 2006. SysML results from the creation of a Unified Modelling Language (UML) profile tailored to systems engineering. The creation of this new graphical modelling language led to the need for an MBSE method, that is, a systematic logically ordered sequence of steps to be followed by systems engineers to specify, validate, design, and verify a system. Numerous SysML-based MBSE methods have been proposed. IBM developed the Harmony for Systems Engineering (Harmony-SE) method  (Hoffmann, 2011). Though Harmony-SE was created as tool-neutral, today it is supported by the IBM Rhapsody software.
, 2012). However, SysML diagrams do not serve to predict the evolution of behavioural or structural properties of an engineered system. To do so, a few researchers took the initiative to couple SysML with a simulation language. For instance, Paredis  (Paredis, 2010)  worked out SysML4Modelica to perform bi-directional transformations between SysML and Modelica. However, such attempts remain very limited to some SysML diagrams, especially the parametric diagram, and their implementation are not integrated with a sound systems engineering method yet. Thus, to date, MBSE artefacts is a set of static descriptive semiotic systems often called graphical models or diagrams created with standardised notations so as to ease the communication among stakeholders without providing any simulation capabilities.
PMM is by definition language-and tool-agnostic as academic and industrial case studies have been implemented with various modelling and simulation languages and tools including VHDL-AMS  (Micouin, 2013) , Modelica  (Pinquie, 2016)  with the support of packages developed to create requirements models  (Otter, 2015; Nguyen, 2014; Buffoni and Fritzson, 2016) , or Matlab-Simulink-Stateflow  (Nilsson, 2014). Nevertheless, one should be aware that the quality of the models and simulations depends on the capabilities and maturity of the language and the tool preferred for the implementation. PMM was recently successfully applied to an operational Airbus Helicopter programme  (Becquet et al. , 2018; Fabre et al. , 2020). Fig. 2  shows that in the PMM method, the verification relies upon a top-down recursive co-simulation of specification models and design models stimulated by a model of the operational environment and scenarios.
html (accessed on 11/12/2021) 90°, the robot arm shall have an absolute overshoot no greater than or equal to 3 mm). These requirements serve to verify the design. A virtual lamp, on the top right of a requirement name (grey in Fig. 17 ), indicates the state of the requirement: grey if it is not tested, green if it is verified and red if it is falsified. All Unity models can be easily reused for another project including a robotic arm. Indeed, the definition of the kinematic joints for assembling parts is automated by a script based on the CAD model created during the design activities. The allocation of UDP communication ports associated with the controlled angular positions is also easy to automate with a pre-defined list of reserved ports on the enterprise network. Of course, if one wants to define an   V.
Interoperability alternatives including shared memory and data exchange standards such as Functional Mock-Up Interface (FMI)  (Waurich and Weber, 2017)  could also be considered. The simulation model should be updated to add "send data" functions to the outputs of any moving element. To visualise the state of the requirements, we also must modify the specification models by adding the same "send data" functions. Fig. 21  illustrates the exchanges between the computer running the co-simulation and the virtual environment. These two operations are manual but they could be automated with Matlab scripts. In the rendering engine, we must import the 3D model. The conversion of a B-REP CAD model in STEP format into OBJ format is automated thanks to our CAD converter. It also gets the relative positions of parts within an assembly.
An ad-hoc macro in Solidworks extracts the definition of kinematic joints from the native kinematic CAD model and a C# script automatically rebuilds the kinematic joints in Unity. The definition of text-based requirements in the VR panel is manual but the task could be automated with a macro parsing the requirements in the Simulink specification models. • Immersive Model-and Human-In-The-Loop Verification When adding end-user interactions with the system-of-interest, to perform an Immersive Model-and Human-In-the-Loop verification, human-machine interactions must be implemented in virtual reality. Some interactions can be reused from models in previous projects, but every model can be different leading to the need for new VR developments, making this step very time expensive, mostly for systems with complex interactions or with the use of other devices such as haptic arms.
Analysis of patients and endpoints The objective of the ATUc programme was to evaluate characteristics of patients with TRD receiving esketamine, as well as safety and patterns of use, in a real-world clinical practice setting. Data were collected from patient medical records. Data collected over the cumulative period of the ATUc included socio-demographic and clinical characteristics of treated patients at treatment request and initiation. Safety was evaluated as frequencies of AEs, serious AEs and discontinuations due to AEs. Details of AEs were collected throughout the ATUc period and determined to be either 'expected' (those which led to esketamine discontinuation in at least two patients (>0. 1%) in all Phase 3 TRD studies) or 'unexpected' (those which were not classified as 'expected' AEs in the Core Company Data Sheet dated 24 September 2019 (version 004) which was in effect at the end of the AE reporting period) (U.
S. Food and Drug Administration 2020). As suicidal ideation and behaviour are known symptoms of MDD and are exacerbated in TRD (U. S. Food and Drug Administration 2020), the ATUc SmPC included special precautions for patient monitoring. All AEs reported during the ATUc period were considered by the sponsor as related to esketamine. Standard pharmacovigilance practice was used, attributing AEs of unreported causality as treatment-related. The exploratory clinical efficacy endpoints (reporting of efficacy data was not mandatory) included the MADRS total score (raw values and changes from baseline), the response (defined by at least 50. 0% reduction in the MADRS total score) and the remission (defined as a MADRS total score of 12).
Statistical analysis Descriptive statistics were used to analyse the cumulative data from 23 September 2019 to 25 March 2020 using Excel (Microsoft Corporation, Redmond, WA) and SAS (version 9. 4, SAS Institute Inc. , Cary, NC) software. Mean, standard deviation (SD), median and range (minimum, maximum) were used to summarise continuous variables. Frequency distribution (number and percentage of a patient in each category) was used to summarise categorical variables. Remission analysis was performed using the SAS life test procedure (Kaplan-Meier method).
Treatments prescribed before esketamine initiation From the start of the current depressive episode, all 66 patients were prescribed at least two ADs from two different classes (mean [SD], 4. 2 [2. 1] ADs). Fifty-four (81. 8%) patients were Authorisation for Use cohort. a Rejected owing to treatment access criteria not met: 2 patients presented with bipolar disorder, 1 presented with psychotic disorder and 1 had a history of aneurysm. b Seven requests were still under investigation at the ATUc end date. c Five patients were included in the ATUc but were not treated, as the esketamine nasal spray pharmacy order was not received. d Esketamine nasal spray was ordered by the pharmacist, but no data were reported by the psychiatrist about treatment administration in these patients; however, treatment administration was confirmed through medical contact.