dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral Analogies between Neutral Buoyancy Underwater and Microgravity As the present study is part of a scientific program studying human motor behavior in different force fields, the same participants were previously tested underwater in the same task for comparison purpose. Specifically, they were immersed in a prototypical submersible simulated space suit (AquaS environment;  Macaluso et al. , 2016) , to apply neutral buoyancy at the level of body limbs. As in the present study conducted 2 years later, we did not find any significant evolution in the reported variables across the successive trials performed underwater. Rather, we observed immediate reorganizations at the earliest onset of exposure excluding the presence of adaptive processes during the experiment. As in MicroG, AquaS environment also implied initial exposure before data acquisition related to the installation of participants on the pointing structure.
Material& method EMG on erectors spinae were recorded by two surface electrodes on muscle and placed according SENIAM recommendations. Zero-wire electrodes (1000 Hz) connected to motion capture system (Vicon, Oxford, RU) were used. A band-pass filter of 20-500 Hz was set as SENIAM suggested. Median frequencies (Mf) and RMS were computed for each muscle during hip flexion-extension (R2012, Matlab, USA). This movement was decomposed in three phases: flexion (phase1-from standing up to maximal hip flexion), maximal hip flexion (phase 2), hip extension (phase 3-from maximal hip flexion to standing up). EMG variables were computed for each phase. No normalization of EMG was realized since variables computed are in relation to density spectral power  (Halaki and Ginn 2012). A 3D motion capture system (ViconT10, 100 Hz) recorded data for 34 passive markers (14 mm) to define a plug-in gait model.
We then assessed the impact of USV emission on the animals' respiratory rate. Our data in adults confirmed previous results  [31] [32] [33]  showing that the emission of USV strongly alters the respiratory signal primarily through a lengthening of expiration (Fig. 2B ). Here we show that it is also the case for juvenile 30-kHz and infant 40-kHz USV emission. In contrast, the emission of 66-kHz infant USV does not alter the respiratory signal. Further comparison of the USV and expiration durations at the different ages highlighted that, while infant 40-kHz USV lasted for the whole expiratory cycle duration, as seen in adults and juveniles, 66-kHz USV were shorter in duration than expiration (correlation shown in Fig. 2C ). As a result, in adult and juvenile rats, for which USV are essentially observed during freezing, the emission of USV induces a lowering of respiratory rate below that observed during silent freezing (Fig.
The aversive event applied at all ages consisted of a 1-s mild (0. 4 mA) foot-shock delivered through the grid floor. During the first 10 min of the conditioning session, animals were allowed an adaptation period of free exploration. This was followed by 5 foot-shocks delivered with a 4-min inter-shock interval. Vocalizations, respiration and behavior were continuously monitored throughout the session. The animals were returned to their home cage after the session. Data acquisition and pre-processing. The respiratory signal collected from the plethysmograph was amplified and sent to an acquisition card (MC-1608FS, Measurement Computing, USA; Sampling rate = 1000 Hz) for storage and offline analysis. Using whole body plethysmograph setup, natural breathing signal appears as a periodic phenomenon showing alternating negative (inspiration) and positive (expiration) deflections.
The detection of respiratory cycles was achieved using an algorithm described in a previous study  78. This algorithm performs two main operations: signal smoothing for noise reduction, and detection of crossing zero points in order to define accurately the inspiration and expiration phase starting points. Artifacts were eliminated by determining a cut-off value for signal duration (see  32  for further description). Momentary respiratory frequency was determined as the inverse of the respiratory cycle (inspiration plus expiration) duration. The video signal collected through the four video cameras was acquired with homemade acquisition software using the Matrox Imaging Library and a Matrox acquisition card (Morphis QxT 16VD/M4, Matrox video, UK). The animal's freezing behavior was automatically detected using a LabView homemade software that had been validated by comparison to hand scoring by an experimenter blind to the rats' condition.
Data analysis. Shock induced USV. The number of USV recorded when the rats received shocks was expressed per minute and compared between groups (Foot-shock vs. No Foot-shock) and age (Infants, Juveniles, Adults) using a two-way ANOVA with independent factors, followed by post-hoc comparisons when allowed by the ANOVA results. Significance was set at p < 0. 05. USV Characteristics. For the 1-dimension Probability of Density Function (PDF) analyses, the frequency and duration of the vocalizations were analyzed by defining their PDF for each animal using a bin of 2 kHz for the frequency and 0. 1 s for the duration. The PDFs were then averaged within conditions and the standard error determined for each point. For the 2-dimension PDF, the USV of all individuals of the same age were pooled for analysis.
USV versus Behavior. The behavioral data corresponding to each USV call were extracted and their category (Freezing or Movement) examined. This allowed us to determine in which behavioral state the USV occurred preferentially. Differences were tested using Student t-tests. USV versus Respiration. The respiratory signal corresponding to each USV call was extracted and analyzed. Specifically, the duration of expiration phase during each call was examined and correlated to the duration of the call. The PDF of the respiratory rate was analyzed while the animal was silently freezing and moving, or vocalizing using the same analysis protocol used for USV characterization. Maternal Interaction experiment. Testing paradigm. On the testing day, pups were brought to the experimental room (warmed at 28-30 °C) in their home cage with their littermates, the dam being taken to another cage before transport to avoid any stress transmission to the pups.
Acknowledgements This work was supported by  CNRS-PICS program  (JBB, AMM, RMS),  Partner University Fund Emotion & Time  (JBB, AMM, RMS),  LIA CNRS-NYU LearnEmoTime  (JBB, MRC, AMM, RMS),  NIH-R37HD083217  (RMS). This work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  (JBB, AMM), the  Emotional Brain Institute at Nathan Kline Institute and New York University School of Medicine Child & Adolescent Psychiatry. The authors gratefully acknowledge  Ounsa Ben-Hellal  for taking care of the animals, and  Claudia Veralli  for her help proofreading this article.
Even when objectives were not achieved the workshop provided a valuable forum to analyze the reasons for the failure and to initiate new directions. 2. Develop care pathways for some of the common child and adolescent psychiatric disorders and improve flow through the psychiatric services  [16]. By developing care pathways and ensuring a smooth flow through the service, this project group managed to reduce the waiting list from 6 to 9 months at the start of the training to 4-6 weeks by January 2015. This achievement did not come easily. It required a full review and gap analysis of provincial child and adolescent psychiatric services. This was followed by the design and adoption of referral pathways by participating institutions. Work is ongoing towards the establishment of a single comprehensive database with accurate data collection.
Acknowledgements Mental Health Leadership Working Group  Jannes Bornman ,  Francois Petersen ,  Wendy Vogel ,  Zaida Damons ,  Charl Prinsloo ,  Emily Smith ,  Elmarie de Lange ,  Rene Nassen ,  Anbrenthia Moos ,  Bernard Janse van Rensburg ,  Craig Bracken ,  Salome Mashile ,  Florence Makobonyane ,  Zamo Mbele ,  Jacqui Naude. Daniel Gerard , for his support and belief.
Dropbox had the dual advantage of offline use and online syncing. Dropbox provides additional free storage for the referee if a person creates a new Dropbox account based on a referral. The principal investigator ensured during training that all those involved in data collection created their own Dropbox account by sending them the Dropbox referral request. The CamScanner application was linked to the district's Dropbox folder. The purpose was to scan all the completed forms rapidly in the field and generate quality (aligned, high resolution and in focus) jpg/pdf files for sharing with the project investigators.
Parts I and II of the questionnaire The DCs and the district-level supervisors were expected to complete the data collection for Part I (a sin- † Available from the corresponding author on request. gle-page questionnaire filled out using record review) and for Part II (a three-page questionnaire filled out to read: filled out using structured close-ended patient interviews) of the questionnaire within respectively 1 and 2 months of the date of enrolment. Scanned copies of the paper forms were added to the Part I and Part II folders in Dropbox. Each interview was audio recorded using a smartphone, and the audio file was added to the Audio folder of Dropbox. All the scanned.
Monitoring data collection and quality control Indicators pertaining to the timeliness and quality of the data collection for Part I and Part II were followed up by the district and USEA supervisors, respectively, in the 'Case-wise monitoring tool' Excel sheet. Ten per cent of the audio recordings were randomly assessed for quality by the USEA supervisors. Repeat interviews were conducted if the data quality was suboptimal. If any project staff identified that an enrolled study participant had to be excluded from the study (mostly due to initial misclassification), this was flagged in the comments column of the 'Casewise monitoring tool' Excel sheet for review and decision by the principal investigator. As it is a remote-control application, the TeamViewer software was helpful for trouble shooting, training new staff or retraining existing staff.
The principal investigator required a large amount of storage space in the Dropbox account, as the scanned files and audio records of all 18 districts were stored in his Dropbox. This potential problem was circumvented by using the Dropbox referral feature. There are some limitations to this system. Without laptops, internet or smartphones, it would be difficult to implement the model. Telephone calls were also the principal mode of communication, especially when the project staff were in the field. As interviews were audio recorded, participants might have become self-conscious and responded accordingly. In conclusion, we describe an innovative model for data collection and sharing in a multicentre OR study using open-access tools. We hope that this information is helpful to others who are undertaking or preparing to undertake similar ventures.
Income per consumption unit A socioeconomic questionnaire, including total household income and number of individuals living in the household, was submitted to each participant. Some missing values (20%) were reported for household income. In this case, to estimate household incomes, individuals with missing information were matched to individuals with complete income data according to their sociodemographic (age, gender, marital status and number of children) and socioeconomic (socio-occupational status, educational level) characteristics, and living standard variables (home owner or not, home equipment) using the Kohonen algorithm  [34, 35]. Income per consumption unit (hereafter "income") was then calculated as self-reported household total net income divided by the number of consumption units in the household.
Dietary data Dietary intake was assessed using a 7-day food record on the study sample of 1719 adults. The foods declared as consumed by the participants (n = 1,314 foods and non-alcoholic beverages, including water) were placed in 9 food groups, 25 food sub-groups and 54 food categories in the national food nutrient composition database (CIQUAL 2013  [37] ) associated with the survey. Food group and sub-group quantities, and energy and nutrient contents (macronutrients, sodium, free sugars, saturated fatty acids, essential fatty acids, fibre, vitamins and minerals) were calculated for each individual diet.
Diet cost Diet cost was calculated by multiplying the quantity of each food in the diet by its mean national price. Diet cost was expressed per day or per 2,000 kcal (i. e. energy cost). As previously described  [34] , mean national prices were expressed in euros per 100 g of edible food, and were obtained beforehand from the 2006 Kantar-World Panel purchase database  [38] , which gives the annual food expenditures of a representative sample of 12,000 French households. Briefly, for each food in the food composition database, mean price was first calculated, in euros per 100 g of food as purchased, by dividing annual expenditure by the amounts purchased for all the food products corresponding to this item in the Kantar-World Panel database. Given that these prices were paid by a representative panel of consumers, the mean national prices were more likely to represent the most frequently purchased forms of each food.
An additional analysis, based on GLM and non-parametric chi-squared test accounting for survey design was conducted to compare the sociodemographic and socioeconomic characteristics of individuals in the final study sample (n = 1,719) with those excluded from the analysis (n = 905). The Operational Research and STAT packages of SAS version 9. 4 (SAS institute, Cary, NC) were used to run linear programming models and perform statistical analysis, respectively. To ensure robustness in conclusions, a conservative approach was used, with an alpha level of 1% for all the statistical tests.
Also, modelled diets were generated regardless of the usual combinations between foods (e. g. bread and butter for toast), and some normally combined foods could be individually removed (e. g. butter), undermining the realism of a few optimised diets. Nevertheless, diet modelling is well-recognized by the scientific community as a flexible, robust approach to translating nutrient recommendations into realistic food choices  [74]. Another limitation is the use of mean food prices instead of real food expenditure to estimate diet cost. It assumes that prices are constant, ignoring variations in time and space. However, in the present study, the food price database and dietary intake database were concurrent  (2005) (2006) , and so we can assume that price variations due to inflation were contained.
We are aware that both dietary data from INCA2 and food price data were almost ten years old, but they were the most recent open data available for the French population at the time of the study. Although merging individual dietary data with average price databases was the best tool available for diet cost analysis at the time  [15] , further studies exploring actual food purchases merged with nutritional composition of real food products are now needed. In conclusion, the present study shows that to improve diet quality, dietary recommendations can be roughly similar in terms of food groups and sub-groups for all individuals whatever their income. However, the results suggest that to reach nutritional adequacy without changing energy intake, low-income individuals would have either to adopt costlier food choices or to accept wider deviations from their habitual diet.
5, 7, 16 urthermore, the available evidence is limited because of a restricted range of occupations, crude measurements of effort-reward imbalance in some studies, and, importantly, a clear underrepresentation of employed women. Thus, larger, better characterized studies are needed. In this study, we set out to test prospectively the association between effort-reward imbalance and later coronary heart disease in a uniquely large multicohort dataset with individual-participant data from 11 cohort studies. In doing so, we use predefined harmonized assessments of effort-reward imbalance, job strain, and coronary heart disease events  3, 17  and took into account the role of job strain in potentially generating a link between effort-reward imbalance and coronary heart disease.
Study Population We used data from 11 independent cohort studies initiated between 1985 and 2005 in Denmark, Finland, France, Germany, Sweden, and the United Kingdom. All studies were part of the "individual-participant data meta-analysis in working populations" (IPD-Work) consortium that was established at the four centers initiative meeting in 2008. 3 The aim of the consortium is to provide a large-scale harmonized data base for the longitudinal estimation of associations of predefined psychosocial working conditions with disease outcomes. The participating studies comply with the Declaration of Helsinki and were approved by the local review boards. Informed consent has been obtained from participants. Details of the design and participants in the studies included in the present analysis are provided in eAppendix S1; http://links.
lww. com/EDE/ B193. Studies are briefly identified, and their names spelled out, in Table  1. Our analyses were based on participants who were employed at the time of the baseline assessment in each cohort study. Participants with missing data on age, sex, effort-reward imbalance, or incident coronary heart disease and those with a prevalent coronary heart disease diagnosis at baseline were excluded from the analyses (N excluded = 6,047; eAppendix S1; http://links. lww. com/EDE/B193). This resulted in an analytical sample of 90,164 employed people, who were followed up for a mean of 9. 8 years.
Ascertainment of Coronary Heart Disease We ascertained information on incident coronary heart disease during the follow-up period from national hospitalization and death registries in all studies except Gazel and HNR in which hospitalization registry data were not available and nonfatal events were based on self-report in annually distributed questionnaires. All nonfatal events in HNR study were additionally checked against medical records. We used date of diagnosis, hospital admission due to myocardial infarction, or coronary heart disease death to define coronary heart disease incidence, coded using International Classification of Diseases (ICD) codes  19  or MONICA definition. 20 n the mortality and hospital records, we used only the main diagnosis. We included all nonfatal myocardial infarctions that were recorded as I21-I22 (ICD-10) or 410 (ICD-9) and coronary deaths recorded as I20-I25 (ICD-10) or 410-414 (ICD-9).
In additional analysis, we examined the associations of efforts and rewards (both dichotomized at median) with coronary heart disease. We pooled the study-specific effect estimates and their standard errors in a random effects meta-analysis, which provides a conservative estimate of an association. Heterogeneity in the effect estimates was quantified using the I 2 statistic, which indicates the proportion of the total variation in the estimates that is due to between-studies variation. As sensitivity analyses, we repeated the analysis after excluding coronary heart disease events during the first 3 years of follow-up (to explore reverse causality) and in samples stratified by sex, age, and socioeconomic position. We used the SAS 9. 4 (Cary, NC) statistical software to analyze study-specific data.
Thus, our data do not allow a determination of whether the covariates were likely to represent mediators or confounding factors. These limitations may have contributed to an over-or underestimation of the association between effort-reward imbalance and incident coronary heart disease. It is also important to consider that the dataset was restricted to European countries with high occupational safety and health standards including distinct antistress policies. The generalizability to other regions and cultures is therefore unclear. We have recently shown that the prevalence as well as the health effects of work stress may vary across different countries and between different types of welfare state regimes: countries with more generous social security systems and an active labor market policy had a lower prevalence of health adverse psychosocial working conditions  38  and showed a less pronounced association between work stress and depressive symptoms, a predictor of coronary heart disease.
A. S. is a British Heart Foundation professor. IPD-Work was also supported by the EU New OSH ERA research programme (funded by the Finnish Work Environment Fund, Finland; the Swedish Research Council for Health, Working Life and Welfare, Sweden; the German Social Accident Insurance [DGUV], Germany; and the Danish National Research Centre for the Working Environment, Denmark); the Academy of Finland; and the BUPA Foundation, United Kingdom. Funding bodies for participating cohort studies are listed on their Web sites. The study was conducted independently of funding agencies. None of the funding agencies played an active role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Results: We implemented a FHIR server over i2b2 to expose EHR data in relation with five FHIR resources: DiagnosisReport, MedicationOrder, Patient, Encounter, and Medication. The architecture of the server combines a Data Access Object design pattern and FHIR resource providers, implemented using the Java HAPI FHIR API. Two types of queries were tested: query type #1 requests the server to display DiagnosticReport resources, for which the diagnosis code is equal to a given ICD-10 code. A total of 80 DiagnosticReport resources, corresponding to 36 patients, were displayed. Query type #2, requests the server to display MedicationOrder, for which the FHIR Medication identification code is equal to a given code expressed in a French coding system. A total of 503 MedicationOrder resources, corresponding to 290 patients, were displayed.
Conclusion: We showed the feasibility of implementing a Java layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources. An important part of this work was the structural and semantic mapping between the i2b2 model and the FHIR RM. To accomplish this, developers must manually browse the specifications of the FHIR standard. Our source code is freely available and can be adapted for use in other i2b2 sites.
Introduction The wide adoption of electronic health records (EHRs)  [1]  has made a large amount of data available to various actors, including executives, physicians, researchers, etc. , for various purposes, including administration, management, clinical practice, and research  [2]. Many groups have promoted the secondary use of EHR data for clinical research  [3, 4]. Reusing EHR data can aid clinical research by reducing redundant data capture, providing a better understanding of real patient populations, testing hypotheses, verifying clinical trial feasibility, screening populations, recruiting patients, detecting safety risks early, analyzing treatment outcomes, and supporting post-marketing monitoring and long-term surveillance  [5]. Deriving benefits from the reuse of EHR data through data warehousing appears to be the best strategy  [6] , because operational databases relying on EHRs are difficult to query given the multiplicity of relational tables, which are optimized to facilitate patient management, but are not suitable for research purposes  [7].