dataesr/scientific-paragraphs-categorization huggingface dataset:
Margins of stability Data was processed with a custom made Python program. The centre of pressure (CoP) was directly obtained from the two force plates. A nine-segment model  (Tisserand et al. 2016 ) was used for the computation of the centre of mass (CoM) and extrapolated centre of mass (XCoM). XCoM corresponds to CoM position plus a certain ratio of its velocity (representative of the human inverted pendulum's eigenfrequency)  (Hof et al. 2007). The medial-lateral MoS on both prosthetic and sound sides, corresponded to the distance in the medio-lateral direction between XCoM and CoP at the time of the contralateral toe-off (Figure  1 ).
Acknowledgements Hubert Curien Partnership  ( French Ministry of Foreign Affairs ), PHC FASIC #  39370PM. The authors wish to thanks  Prof R. Brånemark  from  Massachusetts Institute of Technology, in Cambridge, USA , as well as  Drs K. Hagberg  and  E. Haggstrom  from  Sahlgrenska University Hospital, in Gothenburg, Sweden  for their contribution to the data collection.
13 14 Direct measurement and observation are expensive and time-consuming, potentially limiting their application to larger groups of workers. 15 16 lternatively, JEMs can be constructed using self-reported exposures, which make use of workers' knowledge of their jobs. Reported exposures from all workers are then pooled and exposures assigned at the job level. Use of a JEM to combine self-reported exposures at the job level reduces information biases due to individual variation in reporting. The use of self-reported physical exposures provides an efficient method to estimate cumulative exposure. 2 Although this approach has been used in a few studies of work-related psychosocial,  3  17 physical 2-4 and chemical exposures, 5 there are fewer general population JEMs built primarily from self-reported data for a large range of physical risk factors.
JEM data source Physical exposure data were obtained from the Cohorte des consultants des Centres d'examens de santé (CONSTANCES) project, a large (expected n=200 000) prospective French cohort study investigating occupational and social determinants of health in the general population. 18 CONSTANCES was designed to create a representative sample of French salaried workers. Detailed information on CONSTANCES is available at: www. constances. fr. CONSTANCES participants answered questions estimating 27 different physical risk factors in each participant's current job. Exposure questions were patterned after the Samarbetsprogram mellan Arbetslivsinstitutet, LO, TCO och SACO (SALTSA) criteria  19  and other sources. 20 Overall intensity of physical workload was assessed with the Borg Rating of Perceived Exertion Scale, ranging from 6 (no effort at all) to 20 (exhausting).
JEM development We used data from the first 81 425 CONSTANCES participants. Reported job titles were assigned a French 4-digit Profession et Catégorie Sociale (PCS) job code using the SiCore automated coding system. 21 The PCS classification system involves three nested levels of classification, from the 1-digit socioprofessional job categories (table 2) to the 4-digit PCS job code. This assignment resulted in 418 PCS job codes. Participants who were not currently working (n=35 466), those who did not report a job title or who were not assigned a PCS job code through automatic coding (n=10 396), and those who had missing exposure data (n=30), were excluded. To produce reliable estimates, we required a minimum of 10 valid responses for each risk factor within each PCS job code. PCS jobs with fewer than 10 responses were grouped with other similar PCS jobs to create adequately sized groups (a minimum of 10 valid responses for each exposure for each PCS code).
JEM participant inclusion: full cohort versus asymptomatic cohort We conducted preliminary analyses to determine whether exposure data from both symptomatic and asymptomatic workers should be included in the JEM. Since workers with symptoms of MSD may overestimate physical exposures compared with asymptomatic workers,  24 25  reporting bias is a potential concern. Symptomatic workers were defined as those reporting a pain level of 6 or more (on a scale from 0 to 10) in one or more of six body regions in the previous 7 days. We first used linear mixed models to compare self-reported exposure levels between symptomatic and asymptomatic individuals. Separate models were produced for each of 26 risk factors (the variable work outdoors was not analysed, we expected this risk factor was unrelated to physical pain).
JEM exposure metrics When reporting JEM-assigned exposure values, studies have used different exposure metrics. 29 30 MSD-focused JEMs have typically reported arithmetic means 1 and medians,  31  therefore we reported both metrics. We also corrected the JEM mean value using empirical quantile mapping (EQM) methods 32 to adjust the group-level data to better reflect the distributions of individual-level exposure estimates. Using EQM, JEM mean values falling within every 1% quantile range were adjusted to reflect respective 1% quantiles of the individual-level self-reported values; this adjusted JEM mean is referred to as bias-corrected mean. To compare exposure metrics, we calculated the within-job variance, between-job variance and r 2 values for these three  exposure metrics for all 27 physical exposures.
Exposure assessment When using job means, the mean differences were near-zero for all exposure variables (-0. 002 (repetition) to 0. 003 (drive car or truck)); job medians led to a mean difference ranging from -0. 27 (rest eyes) to 0. 40 (repetition) (online supplementary table  2 ). JEM bias-corrected mean ranged between -0. 05 (handle objects 1-4 kg) and 0. 007 (repetition and drive car or truck). The bias-corrected mean also led to lower variance differences compared with JEM median values. dIsCussIOn Assessment of workplace physical exposures is critical for the prevention of MSD and other conditions that may be affected by workplace physical activity. 33 34 The purpose of this study was to develop and evaluate a JEM using individual-level self-reported physical exposure data from a prospective general population cohort study in France.
After clustering the PCS codes into 229 groups, we found significantly higher between-job variance than within-job variance among all 27 exposures tested. Our MDS plot (figure  1 ) supported the interpretation that the CONSTANCES JEM created HEGs, with distinct separation of exposures between jobs and some clustering of exposures within broad job categories. We also found that using a bias-corrected mean led to the most favourable HEGs while best approximating individual-level exposure reports at the level of the job. The CONSTANCES JEM was constructed using self-reported data from asymptomatic workers. Symptomatic study participants reported higher workplace physical exposures than asymptomatic participants; previous studies have shown differential reporting of exposures by symptomatic workers due to higher perception of exposures  24  or altered work behaviours.
35 t is also possible that higher exposures were accurately reported by those with MSD symptoms, because of actual exposure differences between individuals within the same jobs. While using only the exposures reported by asymptomatic workers created more HEGs, this approach somewhat reduced the overall mean exposures estimated for each job. Future analyses will compare this JEM with other JEMs created from expert-rated exposure estimates or direct measurement, and internal comparison with a new cohort of CONSTANCES participants, to investigate the impact of excluding exposure data from symptomatic workers. Several metrics have been used to express the central tendency in JEMs. For example, median exposure values were used in a study constructing a JEM to study workplace psychosocial factors,  31  means were used in a JEM for shoulder disorders based on expert-rated job exposure estimates  1  and geometric means were used in a JEM for magnetic field exposures.
COnCLusIOn JEMs can be constructed using self-reported data; this method of obtaining data uses workers' knowledge of their jobs, while pooling this information at the level of the job reduces information bias. We developed a JEM using self-reported data for 27 physical risk factors. Our results demonstrated the ability of this novel JEM to create HEGs of physical risk factors that discriminated between different jobs. This JEM provides a potentially robust assessment method for assigning current or cumulative workplace physical exposures in general population studies. Although these preliminary results indicate that the developed JEM may be a promising tool for physical exposure assessment in epidemiology studies, there remains a need for further validation, including comparisons with other exposure assessment methods and demonstration of exposure-disease associations using this JEM.
Contributors Bae, aD, Yr and aMD designed the study, obtained funding, and reviewed and edited the paper. MY and Bae made significant contributions to the data visualisation, writing and formatting of this manuscript. SP-B was the primary data analyst and made significant contributions to the visualisations. JHa made significant contributions to the conceptualisation, and reviewed and edited the paper. Funding this study was supported by research funding from the american national institute for Occupational Safety and Health (niOSH r01OH011076). the French cOnStanceS cohort is supported by the French national research agency (anr-11-inBS-0002), caisse nationale d'assurance Maladie des travailleurs salariés-cnaMtS, and is funded by the institut de recherche en Santé Publique/institut thématique Santé Publique, and the following sponsors: Ministère de la santé et des sports, Ministère délégué à la recherche, institut national de la santé et de la recherche médicale, institut national du cancer et caisse nationale de solidarité pour l'autonomie, as well as institute for research in public health (ireSP, capacit project).
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program LIEPP  ( ANR-11-LABX -0091 ,  ANR-11-IDEX-0005-02 ).
Acknowledgements The authors send their thanks to  Prof. Boucar Diouf ,  Head of Nephrology Department (Dakar) ,  Prof. Abdou Niang , Nephrologist (Dakar),  Dr. Saoussen Krid & Prof. Rémi Salomon ,  Pediatric Nephrologists at Necker (Paris, France) ,  International Society of Nephrology (ISN)  and the entire pediatric team at  Aristide Le Dantec Hospital.
Patients and study characteristics Additionaly to the IADL questionnaire collected during the MC visit, we considered the following patients' characteristics collected using the eCRF of the MEMORA study during the MC visit. We focused on the following patients' characteristics: education level, marital status, cognitive status, aetiology, the MMSE score ranging from 0 to 30 and evaluating the overall cognitive performance, and the relationship between the caregiver and the patient. Details of the collection of the data in the MEMORA study are available in previous open-access manuscript. Additional information was collected at the second interview, either by in-person interview, either by phone in order to detect possible changes between the two measures: change of caregiver respondent between the 2 visits, change of nurse who administered the questionnaire, and the following events: entry in emergency department, hospitalization, occurrence of a fall, change of place of life, occurance of a death in the family.
Sample size The sample size required was determinated with the STATA software (SSQDL function). To obtain a Cohen's kappa coe cient of 0. 8 between the two methods of administration of the questionnaire. By assuming a proportion of patients dependant for at least 2 items of the IADL questionnaire at 50%, with a power of 80% and a risk alpha of 0. 05, the sample size required was 138 patients per branch. With an expected loss of follow-up or missing value rate of 30%, the total sampe size was estimated at 197 patients per branch.
Characteristics of study population Overall, 420 participants were selected and randomized (Fig. 1 ). Among them, the IADL could be collected at the rst visit for 365 patients. After the second visit, the IADL assessment was available for both modes of administration for 292 patients (80%). The majority of incomplete data at visit 2 was explained by unreachable participants in the branch 1 (in-person -phone) and by cancelled or postponed visits in the branch 2 (phone-in-person). Supplementary le s. Table  1  shows that patients with IADL measures with the 2 modes of administration had similar baseline pro le than those without IADL measure at the second visit in terms of age, sex, aetiology, MMSE, and IADL, except that they had a lower education level, were more frequently married or in couple, and were less often with no neurocognitive disorders.
Data were collected prospectively for patients visiting the MC for the rst time which should avoid recall bias. After selection of the patients, loss of participants occurred before each visit and the trial could nally be carried out among 69. 5% of the selected participants, either mainly because the visit was cancelled or postponed in branch 1, either because it was not possible to reach the caregiver in branch 2. These missing evaluations were expected in this population, the patient having at a least memory complaint and the caregiver possibly experiencing burden making its participation di cult  [12]. As we anticipated this loss, the sample size was corrected a priori, nevertheless it constitutes an attrition biais. It was obviously not possible to evaluate the agreement in patients with not double measurements of IADL but we compared patients' characteristics according to completeness of IADL data at the 2 nd visit and slight differences were highlighted in terms of education, marital status and cognitive status.
Qualitative data were analyzed throughout data collection and used for immediate feedback to staff. A more formal analysis of the data was conducted during October 2018. EXPOSURES Multimodal hand hygiene improvement strategy with increased availability and accessibility of alcohol-based hand sanitizer, staff training and education, and evaluation and feedback in near real-time. MAIN OUTCOMES AND MEASURES Hand hygiene adherence before and after the intervention in 3 hospital wards, stratified by health care worker role, ward, and moment of hand hygiene. RESULTS A total of 686 preintervention adherence observations and 673 postintervention adherence observations were conducted. After the intervention, overall hand hygiene adherence increased from 32. 4% to 57. 4%. Adherence increased in both wards in Anka General Hospital (inpatient therapeutic feeding center, 24.
Context The pilot study was conducted from April 23 to May 25, 2018 in 2 hospitals in northwest Nigeria supported by MSF: (1) Anka General Hospital (AGH), Anka, Nigeria, where MSF works with the Nigerian Federal Ministry of Health in the pediatric ward, the inpatient therapeutic feeding center, and the isolation ward, and (2) Noma Children's Hospital (NCH), Sokoto, Nigeria, where MSF and the Ministry of Health work in preoperative and postoperative wards that care for patients with noma. 17 e HCWs employed at these sites are a mix of MSF and Ministry of Health employees (ie, nurses, physicians, guards, hygienists, and health promoters). Qualitative data were analyzed throughout data collection and used for immediate feedback to staff. A more formal analysis of the data was conducted during October 2018.
Study of the Intervention We conducted a prospective multicenter quality improvement study that evaluated an intervention for improving hand hygiene adherence among HCWs. We piloted the use of novel electronic data collection, analysis, and visualization tools for feasibility and ease of use in this lower-resource context. Infrastructure improvements and some training for hand hygiene were performed prior to implementation of this pilot study. For the study, we performed preintervention adherence measurement first, then educational group discussions second, TOT for supervisory staff third, and postintervention adherence measurement last. Discussion participants were sampled purposively, and all participants provided their verbal informed consent to participate in the discussion and for the discussion to be recorded and disseminated.
Quantitative Measures Hand hygiene observation data were collected using KoBoCollect (KoBoToolbox), an open-source and free mobile data collection application. 21 We designed a form in KoBoCollect called the Hand Hygiene Observation Tool (HHOT) (Figure  1 ). The form was uploaded onto mobile phones with Android operating systems (Alphabet) in AGH and tablets with Android operating systems in NCH. Each observer had participated in the TOT and was trained for approximately 10 minutes on how to use the tool, followed by a supervised observation period. Trained observers recorded data for each directly observed hand hygiene opportunity of HCW staff in the facility. A hand hygiene opportunity was defined as any of the 5 moments defined in the 5 Moments for Hand Hygiene: (1) before patient contact, (2) before an aseptic or clean task, (3) after bodily fluid exposure risk, (4) after patient contact, and (5) after contact with patient surroundings.
Quantitative Measures Hand hygiene adherence was defined as the number of correctly performed hand hygiene actions divided by the number of total hand hygiene opportunities observed. 22 We measured the adherence at the ward and hospital level and stratified by role level within each hospital. Using the KoboCollect application programming interface, a set of clearly defined methods for allowing the communication of data among different applications and platforms, we connected data from the KoboToolbox server to Power BI (Microsoft Corp), a partially proprietary online platform for automated data analysis with an interactive visualization display (dashboard). The application programming interface was used to populate Power BI, and the dashboard was refreshed daily. To evaluate whether the intervention was associated with hand hygiene adherence, we compared preintervention with postintervention adherence rates and calculated 2-tailed χ 2 and their respective P values to determine significance of the difference.
moments, thus facilitating the rapid identification of low adherence. This information can be used to rapidly target interventions and educational efforts for HCW groups or specific hand hygiene moments. The version of PowerBI used by MSF is a paid version, but a simplified free version also exists. Even so, data collected in the HHOT can easily be shared with any free or open-source data analytics system or program to generate automated, real-time, data visualization dashboards (eg, MSF's Github dashboard [https://github. com/MSF-UK/MSF-Dashboard]). The only costs associated with customizing an electronic dashboard would be from development work.
5 moments, thus facilitating the rapid identification of low adherence. This information can be used to rapidly target interventions and educational efforts for HCW groups or specific hand hygiene moments. The version of PowerBI used by MSF is a paid version, but a simplified free version also exists. Even so, data collected in the HHOT can easily be shared with any free or open-source data analytics system or program to generate automated, real-time, data visualization dashboards (eg, MSF's Github dashboard [https://github. com/MSF-UK/MSF-Dashboard]). The only costs associated with customizing an electronic dashboard would be from development work.
MATerIAl AnD MeTHODs study population To conduct a case-control study we examined two distinct populations matched for age, fat mass and gender. Subjects were recruited at the Pitié-Salpêtrière University Hospital, Paris, and exposed to the same clinical examinations. Stool samples were collected by similar procedures. Between July 2007 and September 2015, 80 adults (age ≥16 years) with PWS were examined in the Nutrition Department (French Reference Centre for PWS, Nutrition department, Pitié-Salpêtrière Hospital, Paris). We obtained all authorisations of the Ministry of Research and the French Institutional Review Board (Comité de Protection des Personnes Ile de France 1, reference number 2014-mai-13577) to use the clinical data from our register for research purpose, including those performed with international collaborations as well as the analysis of faecal microbiota profiles.
They had an average BMI of 25. 5 kg/m² (BMI<25, n=10, 42%; 30>BMI>25, n=11, 46%; BMI>30, n=3, 12%). All patients and parents gave informed consent. If the patient did not fully understand the informed consent, the consent of the patient's legally authorised representative was required for participation. We also obtained faecal samples from subjects with common obesity that served as controls matched with the PWS patients for body fat mass index (BFMI) and percent fat mass (obese controls, OC). The OC group was selected among obese French subjects involved in the European project MetaCardis. 18 Subjects provided written informed consent. The MetaCardis study was conducted in accordance with the Helsinki Declaration and is registered in clinical trial https:// clinicaltrials. gov/ show/ NCT02059538 Gut microbiota composition as well as metabolic and inflammatory host phenotype are related to obesity.
1% and present in 20% of the samples (n=166), and genera (L6-features) with abundance of at least 0. 1% and present in 10% of the samples (n=66). Representative sequences for OTUs that showed significant differential abundance or correlation with metadata were blasted against the NCBI 16S ribosomal RNA sequences (Bacteria and Archaea) database to obtain a more specific taxonomic annotation. 16S data are available at European Nucleotide Archive (accession number: PRJEB33908; http://www. ebi. ac. uk/ ena/ data/ view/ PRJEB33908).
Acknowledgements We thank louise helldén, carina arvidsson, antonio Molinaro,  Manuela Krämer , robert  Jakubowicz  and anna hallén for technical assistance. We also thank  Dr hichem gahouti  (obese control phenotyping) and Valentine lemoine for their help in the clinical investigation of PWs and their parents (Pitié-salpêtrière hospital, ican and nutrition department). The clinical investigations were performed at the reference center of rare Diseases (nutrition Department) and at the human nutrition research center (crnh ile de France), Pitié-salpêtrière hospital. The computations were performed on resources provided by snic through  Uppsala Multidisciplinary center for advanced computational science (UPPMaX)  under Project snic  2018-3-350.
The Pixie-Net combines an embedded Linux system with XIA digital pulse processing developed on the Pixie PXI spectrometer series. Incoming signals are digitized at 250 MHz. Using a ZYNQ All Programmable System on Chip (SoC) with programmable logic (FPGA) and dual-core ARM processor on the same chip, the ADC data stream is captured and processed in the FPGA section and the ARM processor writes the results to a removable USB drive  (XIA LLC, 2018). Data are recorded in list mode 0x400. Run type 0x400 creates single-channel event records in binary format, and coincident events are reconstructed in post-processing treatment using the ROOT software toolkit  (Brun and Rademakers, 1997). The two Pixie-NET modules must be synchronized, which is traditionally accomplished by distributing clocks and triggers via dedicated connections.