dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and methods: We conducted a multicenter randomized controlled trial to compare information on EMA delivered by an animated film versus a face-to-face consultation. Women requesting EMA (≤9 weeks' gestation) from abortion clinics in Edinburgh (UK), Paris (France) and Stockholm (Sweden) were recruited. The primary outcome was women's recall of prespecified key information on EMA. Secondary outcomes were acceptability of mode of information delivery, clarity and helpfulness of information rated on a Likert scale. The study was prospectively registered with clini caltr ials. gov, ID number: NCT03417362. Results: 172 women completed the study (Edinburgh = 50, Paris = 78, Stockholm = 48). There was no statistically significant difference in recall scores between the animation and standard arms in Edinburgh and Stockholm sites.
| Randomization At the Edinburgh and Stockholm sites, the random allocation sequence was generated by an independent statistician using SPSS random number generator and block sizes of 10. Sequentially numbered, opaque, sealed envelopes were prepared by the statistician, who was not directly involved in the research team. Envelopes were opened with each participant once consent was obtained. Due to the nature of the intervention, the study was not blinded. At the Paris site, participants were quasi-randomized and allocated to study arm by year of birth-evennumbered years to animation and odd-numbered to standard care.
| Ethical approval Ethical approval was sought at each site. In Edinburgh, approval was granted by the South East Scotland NHS Research Ethics Committee, reference: 18/SS/0014 (26 February 2018). In Stockholm, approval was given by the Stockholm, Regional Ethics Committee Dnr 2018/1042-31 (7 June 2018). In Paris, the study was internally reviewed by the governance team of the Department of Obstetrics and Gynecology and deemed not to require full ethical review. The study was prospectively registered with clinicaltrials. gov and assigned registration number NCT03417362.
Identification of patients and controls The BALISTIC project (COPD in dairy farmers: screening, characterization and constitution of a cohort; ClinicalTrials. gov Identifier: NCT02540408) was conducted from 2011 to 2015 at the University Hospital of Besançon, France, in collaboration with the French national social security system for agricultural workers (Mutualité Sociale Agricole, MSA) and the federation of community health practices of Franche-Comté (Fédération des Maisons de Santé Comtoises, FeMaSaC)  (Degano et al. , 2012; Soumagne et al. , 2019). Subjects with COPD and matched controls were recruited through two parallel screening programs (the screening phase of the BALISTIC study) as described elsewhere  (Soumagne et al. , 2019). Inclusion criteria in the screening programs were: men or women aged 40-74 years, with no previous diagnosis of asthma or hypersensitivity pneumonitis, and who were either dairy farmers (dairy farmer subgroups) or unexposed to any of the occupational hazards that have ever been associated with COPD (non-farmer subgroups).
Statistical analysis Data are presented as number (percent) and mean ± standard deviation for qualitative and quantitative variables, respectively. Quantitative variables were compared with the Student t test. Qualitative variables were compared with the Chi-square (χ 2 ) test. To identify factors associated with pulmonary function, exercise capacity and symptoms, linear regression analysis and logistic regression analysis for all candidate factors were performed. In addition, in order to take into account the confounding effect of tobacco smoking, a subgroup analysis between NF-COPD and non-smoking DF-COPD was performed. A p value < 0. 05 was considered statistically significant. Analyses were performed using SAS software (version 9. 3, SAS Institute, Inc. , Cary, NC, USA).
, 2017). High resolution computed tomography (HRCT) was however not available for emphysema quantification, but is planned to be performed in the upcoming second part of the BALISTIC project (NCT03468101). Nevertheless, these results are in line with those found in COPD induced by exposure to biomass smoke, where emphysema is rarely found  (Solleiro-Villavicencio et al. , 2015; Olloquequi et al. , 2018). COPD is an inflammatory lung disease associated with systemic inflammation. It has been shown that levels of inflammatory markers such as TNFα, fibrinogen, IL-6 and CRP are higher than in non-COPD smokers, regardless of the severity of airway obstruction. The mechanisms of systemic inflammation are multifactorial and are still not completely understood. One hypothesis is that chronic inflammation arises from the spillover of inflammatory molecules from the lungs into the systemic circulation, while other mechanisms have been proposed including a direct effect of tobacco smoke on the activation of neutrophils or passage in the circulation of toxic gases or inhaled particles.
BACKGROUND: Unintentional leaks (ULs) are frequent adverse effects in CPAP-treated patients. We previously published a novel methodology for analyzing the determinants of UL using polysomnography. We now propose a simplified recording system using a type 3 polygraphic device (Somnolter; Nomics S. A. ). RESEARCH QUESTION: (1) To describe individual UL determinants provided by the Somnolter software in automatic-CPAP-treated OSA patients; (2) To subsequently describe the clinical consensus of four physicians on how to manage each individual UL situation.
STUDY DESIGN AND METHODS: Somnoler recordings performed under nasal automatic-CPAP were automatically analyzed with APIOS software. For each polygraphic recording, APIOS provided the OR and the CIs for potential determinants of UL: mouth opening, CPAP pressure, body position, and mandibular oscillation. Based on these results, each of four physicians was asked to choose one of four strategies: (1) increase or decrease therapeutic pressure; (2) change nasal mask for oro-nasal mask/chinstrap; (3) favor a nonsupine or supine position; (4) no action for individual leak management. Subsequently, a meeting was held to determine a consensus choice for each individual case. RESULTS: Seventy-eight consecutive patients underwent home-polygraphy with Somnolter. Fifty recordings were analyzed (16 females; 65 [57-75] years of age; ).
Data analysis strategy We used a confirmatory factor analysis strategy using structural equation modelling to test the discriminant validity between proactive goal generation and proactive goal striving. Structural equation modelling is a multivariate statistical analysis technique that is used to analyse structural relationships between observed measured variables and latent constructs. This method is preferred by researchers because it estimates the multiple and interrelated dependence in a single analysis. The analysis was conducted with the software MPlus  (Muthen & Muthen, 2004).
Participants were informed about the research aims in a letter. A short questionnaire was sent to the entire sample of supervisors at two different times, with the instruction of providing an identification code to allow the researchers to match their responses to a second survey at time 2 (six months later). At the end, 73 managers participated in the research at two different time points (response rate: 86%). 92. 6% of the respondents were male. The average age was 40. 7 years. Most of respondents had a job tenure of over 12 years (62. 5%). Participants were primarily employed in the production departments (30. 2%), maintenance and logistic departments To establish that the sample size provided adequate power we conducted a post-hoc power analysis using G*Power 3  (Faul, Erdfelder, Buchner, & Lang, 2009).
Data analysis strategy In order to verify the role of proactive goal generation as mechanism by which the organizational antecedents affect proactive goal striving we statistically tested a mediational analysis model with MPlus software  (Muthen & Muthen, 2004). In psychological sciences, mediation analysis is a statistical technique that is used to assess the relative magnitude of different pathways and mechanisms by which a set of independent predictor variables may affect a criteria outcome. In the present study, we set up proactive goal generation as unique mediator variable of a fully mediated model. A mediator variable is the variable that explains the relationships between the dependent and the independent variables. Therefore, we assumed that the organizational antecedents (perceived job control; supervisor and co-worker support) affect proactive goal striving only indirectly, through the mechanisms of safety envisioning.
Methods We conducted a multicenter, prospective, simulation study, in France from September 2017 to March 2018. The study was approved by the local French ethics committee (Comité de Protection des Personnes Sud-Est VI de  Clermont-Ferrand (IRB00008526; No. 2016/CE87). A consent form was collected from all participants (patients and intensivists) after they had been informed orally and received a written information form. The study was registered on the ClinicalTrials. gov website under number: NCT03013530 in January 2017 and complied with the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)  [18].
The number of patients participating in the study was fixed according to the feasibility of answering 20 questions per patient for each round and the case-mix. The sample size of intensivists was fixed to assess how ADs affected the change in variability between intensivists in the DFLSTs. We calculated that at least 120 intensivists evaluating eight patients were necessary to show a 20% relative difference of variability (sample size for a twosample standard deviations F test), for a two-sided type I error at 5%, a statistical power of 90% and an intra-class correlation coefficient at 0. 5, i. e. , 960 DFLST scores per round. All statistical analyses were performed with Stata software (version 15, StataCorp, College Station USA, TX). Continuous data were expressed, according to statistical distribution, as mean and standard-deviation or median and interquartile range.
Statistical analyses The statistical analyses were carried out with the Statistical Package for Social Science, version 25. 0 (IBM SPSS Statistics for Macintosh, Armonk, NY, USA: IBM Corp. ). For each of the questionnaire employed, Cronbach's alpha was calculated as a measure of internal consistency and scale reliability. It was calculated as a function of the number of test items and the average inter-correlation amongst them. A reliability coefficient ≥ 0. 6 was considered acceptable. Indices of asymmetry and kurtosis were used to test for normality of the data. Values for asymmetry and kurtosis between -1 and +1 were considered acceptable in order to prove normal univariate distribution. First, planned independent t-tests or Fisher exact tests were used to compare sociodemographic and work-related variables between completers and dropout participants.
Longitudinal studies are needed to better clarify the effect of alexithymia and empathy on the levels of professional QoL over time. Thirdly, although the use of univariate analysis to select variables for multivariate analysis has been widely employed in the previous literature, it could be a source of error, causing for instance the rejection or inclusion of inappropriate variables in the multivariate analysis model  [29]. Moreover, only MPs were considered for the present study. Comparing results in the MP population with those of other radiation oncology professionals may help to better understand the association between professional QoL and personality factors in the different specialists working in the field. Finally, no specific data with respect to the individual working environment of MPs were collected (research vs clinical duties; small vs large departments; MP vs radiation oncologist as supervisor).
Data analysis Data were analyzed off-line with custom-made Matlab algorithms (R2015b, MathWorks, Natick, MA, USA). Force plate data were recursively filtered with a 15 Hz second-order low-pass Butterworth filter. Gait events were determined when the vertical ground reaction forces crossed the 50 N threshold. Gait parameters were averaged over the first and last five steps of each phase, late slow baseline (BL), early adaptation (EA), late adaptation (LA), early post-adaptation (EP) and late post-adaptation (LP). EMG data were high-pass filtered using a 30 Hz second-order Butterworth filter, full-wave rectified using an absolute Hilbert transform, and low-pass filtered (10 Hz second-order recursive Butterworth filter). EMG data were down-sampled to 1000 Hz to obtain similar sample frequencies for the EMG and force plate data.
EMG and force plate data were manually synchronized by aligning the moment of heel-strike of the left leg with the peak in TA EMG activation of the left leg. EMG data were time normalized with 100 data points per gait cycle, 60 data points for the stance phase and 40 data points for the swing phase, starting at heel-strike  (Den Otter et al. , 2004). Amplitude normalization was done by dividing the EMG signal by the maximum amplitude of the fast baseline EMG for each individual muscle. EMG was averaged for the first (EA, EP) or last five strides of the phases (BL, LA, LP). EMG data of one young subject was not included in the analysis.
Principal component analysis Principal Component Analysis (PCA) was applied to study differences in amplitude or timing of the overall muscle activation pattern, by reducing the original individual EMG signals to a lower number of principal components (PCs)  (Daffertshofer et al. , 2004). The input in the PCA was the combined EMG data of all phases and all participants, for the fast and slow leg separately. For each participant, an EMG matrix (X m,c ) was created by concatenating the phases (c) of each muscle (m). Then all subjects were included in one matrix (X EMG ; formulas see Appendix). Based on X EMG , the covariance matrix was estimated. The eigenvectors and eigenvalues were calculated and sorted in descending order of eigenvalues. The temporal projections were determined by projecting the original EMG data onto the eigenvectors (Appendix).
Gait parameters Two repeated measures ANOVAs were performed for the dependent variables SLS and DSS, with within-subjects factor Phase (BL, EA, LA, EP, LP) and between-subjects factor Group (young vs. middle-aged adults). Post-hoc testing was done with dependent t-tests with Holm-Bonferroni correction for the Phase comparisons: BL vs. EA (effect of perturbation), EA vs. LA (adaptive change), BL vs. EP (aftereffects) and BL vs. LP (return to baseline). Statistical analysis was performed in SPSS 24. 0 (IBM, Armonk, NY, USA). Level of significance was set at p < 0. 05.
Acknowledgements We would like to thank  Linda Wolthuizen  and  Tessa Huisjes  for their help during the measurements,  Lotte Knol  and  Timon Louwsma  for their help with the manual synchronization, and  Wim Kaan ,  Dirk van der Meer  and  Emyl Smid  for their technical support. Lastly, we would like to thank the participants.
Methods: We evaluated the activities carried out by the Saint-Louis Hospital CTU and pharmacy. Process mapping was established to trace all the steps of the circuit and to identify potential risks or failures. The risk analysis was performed according to failure mode, effects and criticality analysis. The criticality of each risk (minor [Mi], moderate [Mo], significant [S] or major [Ma]) was scored, and corrective actions or preventive actions (CAPAs) for Mo, S and Ma risks were proposed. Results: We identified five Mo, six S and no Ma risks for the CTU part of the process. The most frequent risk was traceability failure. To reduce its frequency, we developed and validated software dedicated to ATMP activities. Another S risk was non-compliance of CAR T cell-specific steps due to the significant variability between companies.
Our CAPA process was to implement procedures and design information sheets specific to each CAR T-cell program. In addition, critical steps were added to the ATMP software. Our CAPA process allowed us to reduce the criticality of identified risks to one Mi, seven Mo and three S. For the pharmacy part of the process, five Mo, two S and one Ma risk were identified. The most critical risk was compromised integrity of the CAR T-cell bag at the time of thawing. In case of unavailability of a backup bag, we designed and validated a degraded mode of operation allowing product recovery. In this exceptional circumstance, an agreement has to be signed between the physician, pharmacy, CTU and sponsor or marketing authorization holder. The implemented CAPA process allowed us to reduce the criticality of risks to three Mi and five Mo.
The impact is critical in cases of product inversion. To solve this issue, after reorganization of the CTU facilities, these two areas were extended and totally separated. This corrective measure, by reducing the O score from 4 to 1, decreased the criticality of this risk to moderate (RPN = 16). (ii) Non-compliant traceability of received PBMCs (Table  3 , Pc2R1, RPN = 64): this risk is significant and the most frequent. The root cause was due to the non-electronic traceability system for ATMP activities at the CTU. To address this issue, we developed software dedicated to ATMP activity and included it in the CTU QMS. This software allows the recording of data regarding PBMC receipt, QC operations, manipulation (when necessary), storage and shipment to the manufacturing site. This CAPA, by reducing the O score from 8 to 4, decreased criticality, though it remains significant (RPN = 32).
To this end, a tanker of vapor nitrogen was dedicated to the storage of PBMCs, and the location was handled by the ATMP software. This CAPA reduced the occurrence of this risk from rare to exceptional (O = 1). (ii) Non-compliant traceability of PBMC freezing (Table  3 , Pc6R1, RPN = 64): refer to paragraph Non-compliant traceability of received PBMCs and Table  3  risk Pc2R1. (iii) Deviation of storage temperature not declared to CAR T cell manufacturer (Table  3 , Pc6R2, RPN = 32): the temperature of all nitrogen tankers is monitored at all times with an electronic traceability and alarm system. However, it was observed that not all deviations were declared to the CAR T cell manufacturer at the time of shipping. To solve this issue, we implemented systematic temperature traceability in the ATMP software to provide a full recording of the storage temperature applied both during the storage period of PBMCs and before they are shipped to the CAR T cell manufacturer.
This preventive measure made the probability of detecting this risk systematic and immediate (D = 1) at the moment of CAR T cell receipt and control by the pharmacy staff. CAR T cell storage in the cryobiology area of the CTU During this process, we identified one moderate and one significant risk (Table  4 , Pp3 and Pp4): (i) Error or lack of CAR T cell storage traceability (Table  4 , Pp3R1, RPN = 64): this significant risk may appear because the pharmacy staff performs the storage in the CTU facility without any common traceability platform between the departments. To correct this risk, our aim was to implement traceability of CAR T cell storage in the dedicated ATMP software. This corrective measure has allowed the reduction of the criticality of this risk to moderate (RPN = 8). (ii) Deviation of storage temperature (Table  4 , Pp4R1, RPN = 16): this risk is moderate, but its impact is significant if the storage temperature does not conform to the requirements of the CAR T cell sponsor or marketing authorization holder.
Establishing a secure ATMP circuit in French hospitals is important to guarantee their safe use. Our FMECA allowed the detection of numerous causes of potential circuit risks and the reduction, thanks to identification of relevant CAPAs, of their criticality, resulting in better control of the CAR T-cell circuit. This analysis appears all the more important, especially in the context of insufficent security and efficacy data of these products, in guaranteeing the best use of such an expensive (price hinders the circuit) but innovative product. The effectiveness of implemented CAPAs is continuously assessed, in particular through audits in both the CTU and pharmacy departments. These measures may be adapted in accordance with regulatory or medical advancements as well as in response to new requirements of pharmaceutical companies.
2012). Despite the increase in effective population size (Ne) Roma had in the generations after the bottleneck (SM Figure  9 ), which might be explained by the extensive gene from Europeans  (Font-Porterias et al. 2019) , the degree of genetic homogeneity in the Roma genomes are comparable to populations of Southern India who are known to follow cultural practices of marriages between close relatives  (Mondal et al. 2016; Nakatsuka et al. 2017). At its lower point, the Ne of Roma was even lower than the Ne of Southern Indian populations (SM Figure  9 ), making Roma more prone to carry deleterious alleles at higher frequencies (SM Table  8 )  (Kalaydjieva et al. 2001). It is noteworthy that the Mendelian associated variants found in the present dataset are located in genome tracks of European origin, pointing to the extensive very recent admixture with Europeans.
Samples DNA samples were collected from 40 volunteers, self-defined as Roma that belong to four main migrant groups: 10 Balkan, 5 Vlax, 10 Romungro, and 15 North/Western Roma, and coming from five countries (SM Table  1 ). All volunteers declared that their eight grandparents were self-defined as Roma. We tested for relatedness using Vcftools  (Danecek et al. 2011 ) --relatedness and retained all the individuals who were less related than third degree cousins. Geographical positions of these samples are shown in Figure  1A   (Kahle and Wickham 2013)  and detailed in SM Table  1. All samples were collected with informed consent from the participants under the approval of the IRB of the CEIC-Parc Salut Mar 2016/6723/I. Whole genome sequencing data were obtained using Illumina HiSeqX sequencing platform, at average coverage of ~30X.
We merged our data with other samples of Roma (6 additional Romanian Vlax individuals) and non-Roma (109 individuals) origin (see SM Table  2 ). After quality control (FastQC, Brabraham Bioinformatics), all the individuals were mapped against the human reference genome GRCh38 using BWA mem (version 0. 7. 15)  (Li and Durbin 2009). After filtering according to the GATK Best Practices guide  (DePristo et al. 2011) , variant calling was performed using GATK  (McKenna et al. 2010). We performed an extra filter on coverage using a home-made python script to deal with differences in the average coverage of the samples (individual filter for coverage from half to twice the average coverage) to retain only those variants that had coverage between half and twice the individual average coverage.
Funding This work was supported by the  Spanish Ministry of Economy and Competitiveness  (grant number  CGL2016-75389-P  ( MINEICO/FEDER, UE)  and  "Unidad de Excelencia María de Maeztu " (funded by  AEI -CEX2018-000792-M ) to DC and FC; and  Agència de Gestió d'Ajuts Universitaris i de la Recerca  ( Generalitat de Catalunya , grant  2017SGR00702 ). NF-P was supported by a  FPU17/03501  fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.