dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion This paper evaluates GOM's feasibility to model industrial workers and subjects' dynamics and select the joint angles that best represent the gesture vocabulary, and predict their joint angles' trajectory. The statistical analysis conducted on the GOM models permitted identification of the joint angles that contributed most to the execution of the gestures of each vocabulary. For validation, these joint angles were then used for gesture recognition. These results demonstrate the potential of the selected set of sensors for a posture-based ergonomic analysis. By only using the data of the selected sensors, it was possible to discriminate accurately between different professional gestures and motion primitives where various postures of the spine, arms, and legs were assumed. The recognition of these changes in posture are clearly useful for ergonomic analysis of professional gestures.
The most significant joint motions for each gesture vocabulary were selected based on their statistical significance in the GOM models. The selection was then validated by achieving a high recognition performance of gestural time-series, which was modeled using continuous HMM. Four datasets were created for this work that contain professional gestures recorded under real conditions in factories and in a laboratory environment. The forecasting performance of the models was evaluated by comparing the simulated gestures with their original values. According to the Theil inequality coefficient and its decompositions, the performance of the models can be considered accurate. Analyzing the response of the models to external disturbances and identifying the body joints to enable tracking for ergonomic monitoring could be useful for faster and more efficient evaluation of workers' gestures.
To date, predictive simulation methods have been used to investigate pathologic gait patterns in cerebral palsy  30  as well as in poststroke  31  and crutch-assisted walking  32. However, few studies using complex 3D muscle-driven models compared predictions to experimental pathologic gait data and the clinical application of these methods still presents challenges such as inter-individual variability  33, 34  , which is greater in stroke patients than in healthy individuals  10. We have observed different gait patterns and responses to FES among stroke patients with drop foot pathology in our gait laboratory. The FES device provides us with an opportunity to characterize gait in our patients with the device deactivated or activated  22. Both gait conditions were analyzed using ID for a patient presenting with considerable gait disturbance.
Methods Experimental data. We investigated a post-stroke patient (female; age, 47. 2 years; height, 1. 60 m; body weight, 63. 8 kg) treated with the ActiGait FES device. The patient suffered a left hemispheric hemorrhagic stroke 10. 3 years prior to, and received FES treatment approximately 1 year prior to the measurements. The study was approved by the local Ethics Committee at Hannover Medical School (MHH) under reference no. 2489 and the patient provided written and informed consent to participate. All methods were performed in accordance with relevant guidelines and regulations. Overground gait was performed at a self-selected gait speed. Motion capture data were obtained with an optical infrared system consisting of 12 MX cameras controlled by Nexus v1. 8. 5 software (Vicon Motion System, Oxford, UK) at a sampling rate of 200 Hz and with the retroreflective markers attached to the subject in accordance with the Plug-in Gait model  35.
3  36  to obtain joint angles, moments and velocities, and muscle forces. The 3D lower body model gait2392 was used and the metatarsophalangeal joint was locked. Model scaling was performed in OpenSim using the static trial. The results obtained in OpenSim for each condition are referred to as ID results in the comparisons (ID-DF and ID-FES). Optimal control problem formulation. Parameter estimation, tracking, and predictive simulation were formulated as an optimal control problem. The frameworks used in this study were developed by Falisse et al. 29, 30. The optimal control problem was transcribed into a nonlinear programming problem using CasADi  37  and the resultant optimization problem was solved using IPOPT  38. Both software were implemented in MAT-LAB (R2019a). Direct collocation with a third-order Radau collocation scheme was used for transcription  29.
(1) J Track = tf ti W T1 q -q R 2 + W T2 Mj -Mj R 2 + W T3 (Fr -Fr R ) 2 + W T4 (Mr -Mr R ) 2 + W T5 a 2 + W T6 (q 2 + ȧ2 + Ḟ m 2 ) dt, (2) M pass = K pass1 exp K pass2 q -θ pass2 + K pass3 exp K pass4 q -θ pass1 -0. 001q, (3) J Pred = tf ti W P1 a 2 + W P2 Ė2 + W P3 q2 + W P4 (ȧ 2 + Ḟ m 2 ) 1 Dist dt, Data processing. We used three metrics to evaluate gait abnormalities. For knee hyperextension, mean knee extension (MKE) angle from the initial contact to the end of the terminal stance phase of the gait cycle was calculated. For SKG, the peak of knee flexion (PKF) angle during the swing phase was calculated. The drop foot was represented by the mean ankle dorsiflexion (MAD) angle during the swing phase. Knee flexion velocity at toe-off, ankle push-off work, leg circumduction, and early and late braking impulses  45  were also computed.
Early and late braking impulses were calculated as the areas of the posterior component of GRF curve (i. e. , negative anterior-posterior GRF) preceding and following the propulsion, respectively. Circumduction was calculated as the maximum lateral displacement of the calcaneus body origin (tracking and prediction results) and of the ankle marker (ID results). Since the ID and tracking simulation results were based on kinematic and GRF data collected during gait analysis, each condition had four trials; the mean and standard deviation were calculated after normalizing the gait cycle. Each predictive simulation condition yielded a single result that was also normalized. The ID and tracking results were filtered using a low-pass second-order Butterworth filter in MATLAB. All simulations were performed on the same workstation (3.
The different gait speeds showed a linear relationship between the increases in PKF, ankle push-off work, and knee flexion velocity, which has also been described by others  2. The developers of the framework used in our work validated that the model could predict the relationship between gait speed and metabolic cost of transport during healthy gait  29, 34. Using a similar model, personalized muscle-tendon parameters allowed the prediction of the crouch gait pattern of a child with cerebral palsy  30. Nonetheless, the validation of predictive simulations is, in general, still limited  33, 34. A limitation of the present work was the use of a generic musculoskeletal model that did not include subjectspecific characteristics. As neither magnetic resonance imaging (MRI) nor electromyography (EMG) data were collected, it was not possible to create a personalized musculoskeletal model.
A qualitative metasynthesis collects only partial data from the participants and the interpretations of the researchers, which are the data given in the initial articles. Moreover, although the review assembled articles from diverse cultural areas, English-speaking countries are overrepresented as we restricted our selection to articles in that language. That's why the conclusions of this study might be restricted to this cultural area. Finally, the results of the studies included in this metasynthesis were redundant from a methodological perspective. More participatory research methods are needed to involve professionals and patients so to reach more original and relevant results.
Studies Qualitative studies, based on a well-known qualitative methodology and using specific data collection tools and data analytical procedures (Table  3 ). As for exclusion criteria, we excluded studies including healthcare providers who were specifically trained in smoking cessation interventions or working in tobacco-related settings. Studies that focused on the perinatal period were not included because that would require a metasynthesis on its own (Table  3 ). Search Strategy involved screening from four databases: MED-LINE, PsycInfo, CINAHL, and SSCI from April through November 30, 2019, with an update in October 2020 (Supplementary Table  5 ). Preliminary research had identified several articles from which we selected keywords. Extensive lateral searches also sought to identify papers that might have eluded our algorithms.
These stages were carried out with the help of N'Vivo-12 software (QSR International). Progressively, each researcher conducted a crosssectional analysis of all of the data analyzed thus far, by regrouping similar categories and excluding of them. After this analysis, the three researchers met with the rest of the research groupwho had read and become familiar with the data during this timeto share the categories uncovered. During these two-hour meetings, the group performed the work of translation, that is comparing and assembling the categories obtained by the analysis of each article to develop the key themes that captured similar ideas from the different articles and then to develop overarching concepts about the research question. In practice, the group had to: (1) regroup the categories into themes, a reorganization that uncovered the framework of the participants' experience; (2) determine the key themes, that is, the most significant and relevant themes.
ACKNOWLEDGEMENTS We would like to thank  Jo Ann Cahn  for translating the manuscript. This work was supported by  IReSP Institut de recherche en santé publique  within the call for project to reduce and fight against smoking 2018. (Nb:  APA19002HSA , reference  TABAC-V3-09 ) as part of the research project " Influence des références théoriques et du sTAtut taBAgique des médecins et des Infirmières sur le sevrage Des patiEnts fumeurs (TABACIDE) ". The ethics evaluation committee of  Inserm , the  Institutional Review Board  ( IRB00003888 ,  IORG0003254 ,  FWA00005831 ), has reviewed and approved the research project.
Study Design We used data from the EDEN mother-child cohort, which was designed to assess preand postnatal determinants of child development and health  [18]. In brief, between 2003 and 2006, 2002 pregnant women (<24-weeks' gestation) aged 18-45 y were recruited in two university hospitals located in Nancy and Poitiers, France. Exclusion criteria were multiple pregnancies, known diabetes before pregnancy, not being able to speak or read French, and plan to move outside the region within 3 years.
Study Sample Of the 2002 recruited mothers, 95 (4. 7%) withdrew from the study during pregnancy, mainly for reasons of convenience, leaving 1907 children with a known birth date (Figure  2 ). Children were included in our analysis if their parents had completed the child question-naire, as well as either the mother or the father self-reported questionnaire at year 5 (n = 1190). Complete data for all EBRBs was an additional criterion, which led to a study sample of 978. data used are provided in Additional Text S1 ("Family socio-ecological factors definition").
Study Sample Of the 2002 recruited mothers, 95 (4. 7%) withdrew from the study during pregnancy, mainly for reasons of convenience, leaving 1907 children with a known birth date (Figure  2 ). Children were included in our analysis if their parents had completed the child questionnaire, as well as either the mother or the father self-reported questionnaire at year 5 (n = 1190). Complete data for all EBRBs was an additional criterion, which led to a study sample of 978. The study sample was compared to the EDEN population not included in this analysis (that is, live born children for whom any of the EBRBs was not available at year 5, n = 929) for parental demographic characteristics and SEP, as well as infant birth characteristics. Within the study population, we used t-tests to examine sex differences for continuous behavioral data, among children with all EBRBs available at year 5 (n = 978).
Participant Characteristics The study sample was compared to the EDEN population not included in this analysis (that is, live born children for whom any of the EBRBs was not available at year 5, n = 929) for parental demographic characteristics and SEP, as well as infant birth characteristics. Within the study population, we used t-tests to examine sex differences for continuous behavioral data, among children with all EBRBs available at year 5 (n = 978). After handling missing data, i. e. , imputing contextual data and weighting respondents (see the Handling missing data section below), we described contextual variables according to sex.
Handling Missing Data Missing data were taken into account using a combined inverse-probability weighting and multiple imputation (IPW/MI) approach: IPW accounts for missing outcomes (i. e. , lifestyle patterns) data whereas MI deals with missing data on contextual factors for these respondents (n = 978)  [27]. As suggested by Seaman et al. [27]  and Varsh-ney et al. [28] , we conducted a comparative sensitivity analysis to assess the impact of missing data. More information is available in Additional Text S2 ("Handling missing data-detailed procedure"). All statistical analyses were performed using SAS ® 9. 4 (SAS Institute Inc. , Cary, NC, USA). The significance level was set at 0. 05.
This includes potential differential bias such as social desirability  [59]  and impaired precision. Some have suggested the use of accelerometry in studies examining combination of movement behaviors in children  [60] , given that these objective measurements undeniably provide more precise and complete data on the intensity and duration of total physical activity. However, they do not distinguish between different activity types and the various contexts in which they take place  [60] , which are valuable knowledge to be translated into preventive interventions. Finally, we included as many paternal factors as possible in the models provided they were relevant to our socioecological framework and available in the EDEN cohort (i. e. , education level, physical activity and interactions with their child).
Acknowledgments: The authors thank  Jo Ann Cahn  and  Alena Piatrova  for their help in preparing the manuscript and the EDEN mother-child cohort study group, whose members are I. Annesi-Maesano ,  J. Y. Bernard ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A. Forhan ,  X. Fritel ,  A. Germa ,  V. Goua ,  R. Hankard ,  B. Heude ,  M. Kaminski ,  B. Larroquey ,  N. Lelong ,  J. Lepeule ,  G. Magnin ,  L. Marchand ,  C. Nabet ,  F Pierre ,  R. Slama ,  M. J. Saurel-Cubizolles ,  M. Schweitzer , and  O. Thiebaugeorges.
The inclusion period of medical orders lasted 6 months from January to June 2020. Due to the fact that the dispensation of medical orders from hospital setting (MOHs) is less frequent than medical orders from ambulatory settings (MOAs), each pharmacist included the 10 first MOHs they dispensed. Each inclusion of a MOH immediately resulted in the inclusion of the consecutive MOA they experienced at the pharmacy. A total of 10 MOHs and 10 MOAs were included in pairs for each community pharmacy. This study was conducted in compliance with French legislation related to observational studies. Patient consent was obtained for all the subjects included in the study. Patients' records were anonymized prior to analysis. The database was constituted and collected in accordance with the reference methodology MR004 of the Commission Nationale de l'Informatique et des Libertés (no.
Discussion Our study has limitations. Among them, the drug names involved in the DRPs and PIs were neither collected nor reviewed. Additionally, the significance of the interactions was only assessed by pharmacy students and their mentor. The investigators did not perform a secondary analysis on medical orders or pharmacist interventions. Moreover, clinical data were not collected during our study. Nevertheless, to date, this study is the first to conduct a comparison of DRPs and PIs on MOs provided from both hospital and ambulatory settings considering clinical and organizational impact on patient care pathway and the workflow of the CP teams. Our study highlighted that MOs from hospital settings generate more PIs than medical orders from ambulatory settings. The students reported 8. 3% and 4.
Data Collection We examined the epidemiologic association between anticontagion measures and COVID-19 at the county level using data on jail populations, anticontagion policies, and COVID-19 cases. Jail population data (January 1 to November 15, 2020) were obtained from the Vera Institute of Justice and represent 1614 counties in the US. 41  To construct the dependent variable, we transformed county-specific time-series data on COVID-19 cases into first differences of their natural logarithm (specifically, natural logarithm transformation on cases plus 1), which is the per-day growth rate of cases. We used panel regression models to estimate how the daily growth rate of cases changed over time within a county with respect to changes in jail populations and policy interventions in that county.
The more than 220 000 staff who move in and out of jails on a daily basis are likely to contribute to jail-community spread of airborne pathogens, such as SARS-CoV-2. Had we been able to account for this movement, we expect that association of jails with community COVID-19 case rates would be higher than captured by our present analysis. Although jail population turnover (ie, releases and admissions) is relevant to the spillover of carceral outbreaks into broader communities, we are limited by access to data on daily jail populations without means of directly identifying detainee turnover between days. To enable interpretation nearer to this epidemiologic dynamic, we inferred daily jail cycling figures by imputing jail turnover statistics, matched to jail size, reported by the Bureau of Justice Statistics.
Estimated Relationships Between Growth Rate in COVID-19 Cases and Log Jail Population and All Intervention Policies in Multivariate Regression Analysis With County Fixed Effects and One-Week Lag Between Intervention and COVID-19 Growth Rate eTable 6. The Relationship Between COVID-19 Case Growth Rate and Jail Population and All Policy Variables When Accounting for the Effects of COVID-19 Daily Growth Rates Two Weeks Ago on Subsequent COVID-19 Daily Growth Rates eFigure 2. The Cumulative Plot of t-values Obtained From Regressions Dropping One State-Week (ie, One Week of One State) at a Time From the Panel Dataset JAMA Network Open | Public Health Association of Anticontagion Policies and Jail Decarceration With COVID-19 Case Growth Rates We Plot the Bivariate Correlation Between Log Jail Population and COVID-19 Growth With County Fixed Effects by Varying the Lag Periods Applied on the Log Jail Population Term (1, 2, 3 and 4 Weeks) eFigure 4.
JAMA Network Open | Public Health Association of Anticontagion Policies and Jail Decarceration With COVID-19 Case Growth Rates Our sample included 1605 counties with data available on both jail population and COVID-19 cases, resulting in 51% of US counties, 72% of the US population, and 60% of the US jail population. COVID-19 case data were obtained from The New York Times. 42 This study used only deidentified public sources and was deemed exempt from institutional review board approval under guidelines at Harvard University. This study followed the relevant sections of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. We merged data on policy interventions from the COVID-19 US State Policy Database 43 supplemented with additional county-level data aggregated by the Becker Friedman Institute, 44 Stanford Intervention Data, 45 and Keystone Strategy.
The Relationship Between Daily Jail Population and Growth in COVID-19 eTable 5. Estimated Relationships Between Growth Rate in COVID-19 Cases and Log Jail Population and All Intervention Policies in Multivariate Regression Analysis With County Fixed Effects and One-Week Lag Between Intervention and COVID-19 Growth Rate eTable 6. The Relationship Between COVID-19 Case Growth Rate and Jail Population and All Policy Variables When Accounting for the Effects of COVID-19 Daily Growth Rates Two Weeks Ago on Subsequent COVID-19 Daily Growth Rates eFigure 2. The Cumulative Plot of t-values Obtained From Regressions Dropping One State-Week (ie, One Week of One State) at a Time From the Panel Dataset JAMA Network Open | Public Health Association of Anticontagion Policies and Jail Decarceration With COVID-19 Case Growth Rates We Plot the Bivariate Correlation Between Log Jail Population and COVID-19 Growth With County Fixed Effects by Varying the Lag Periods Applied on the Log Jail Population Term (1, 2, 3 and 4 Weeks) eFigure 4.
Data sources We conducted an observational retrospective study based on the following open-source medico-administrative datasets collected over the year 2017. The regional observatory of EDs (ORU) participates in the collection and processing of emergency room summaries in order to improve quantitative and qualitative knowledge of the activity of emergency services, improve the conditions of patient management by these services and adjust the primary care offer to the needs of the population. In the Auvergne Rhône-Alpes (ARA) region in France, 77 EDs recorded more than 2. 3 million ED visits per year, representing 11% of ED visits in France  [19]. The National Institute of Statistics and Economic Studies (INSEE) is responsible for the production, analysis and publication of official statistics in France and provides quantitative data about the demography and the economy at the level of the territory.
Definition of catchment area With the ED as statistical unit, the medico-administrative databases have to be linked and organised to describe each ED. This step implied to define a catchment area per ED, reflecting its geographical attractiveness. We defined the catchment area of an ED as the most represented residential places of the patients admitted to this ED. So, for each ED, we extracted all the zip codes of residential places of admitted patients from the ORU database. Then, the mean number of visits per zip codes was considered as a threshold to define a catchment area. Consequently, the zip codes with frequencies above this threshold were selected in the catchment area. We estimated that the mean was as relevant statistical parameter due to its sensitiveness to low and high values.
Data selection The first level represented the territory of the hospital. The organisational density indicators corresponded to quantitative datasets extracted from the INSEE database such as the number of inhabitants, distribution of inhabitants by age and sex, distribution of inhabitants by socio-professional categories (farmers, craftsmen tradesmen and company managers, executives and higher intellectual professions, intermediate professions, employees, workers, retirees or without professional activity), number of general practitioners and nurses per inhabitants; from the FINESS database such as the number of housing project for dependant elderly people and the number of beds; from the IRDES database such as the localized potential accessibility to general practitioners (LPA indicator) and from the Corine Land Cover inventory such as the land use dichotomized in urban or rural if at least 50% of the territory is rural.