dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  NIAID Tetramer Facility (Emory University, Atlanta, GA)  for supplying CD1d tetramer and  Dr. T. Camou  ( National Reference Laboratory, Ministry of Health, Montevideo, Uruguay ) for the gift of the S. pneumoniae serotype 1 clinical isolate  E1586. We also thank  Hélène Bauderlique  for her help for cell sorting ( BICel Cytométrie Plateform ,  Institut Pasteur de Lille, France). A special thank to  Gwénola Kervoaze  and  Eva Vilain  for their technical support, and  Jean-Claude Sirard ,  François Trottein ,  Isabelle Wolowczuk  and  Sandra Weller  for their critical reviewing of the paper. This work was supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Centre National de la Recherche Scientifique (CNRS)  and the  Conseil Régional du Nord-Pas de Calais  [ StreptoCOPD  project; grant number #  13005300 ].
The sequential extraction procedure was conducted in clean room conditions to avoid contamination by Pb. Soil samples were analyzed for trace and major inorganic elements by ICP-MS and ICP-AES respectively. The extracted fractions were then analyzed, after purification, by Thermal Ionisation Mass Spectrometry (TIMS) for the Pb isotopes. We focused here on the 208 Pb /207 Pb and 206 Pb /207 Pb ratios to be comparable with the data from the literature.
Lower-Limb Region of Interest and Pelvis Zone The asymmetry index presented in this paper focuses on lower-limb movements. This lower-limb region is called the leg zone. This leg zone is defined as the volume between the lower leg zone parameter at 0. 22 H and the upper leg zone parameter at 0. 47 H, where H is the height of the subject. These parameter value estimations were done posteriorly with our dataset in order to maximize the correlation between Kinect™ measurements and ground truth obtained with reference to motion capture systems. In the same way, to ensure correct comparison of left and right legs at various times in the gait cycles, we need to displace the pelvis location (origin of the kinematic chain) to a unique position, i. e. , the same distance to the camera. To this end, the pelvis zone is defined by a 20-cm square centered on the pelvis, the position of which is estimated at 0.
Asymmetry Index Based on Skeleton Information As previously mentioned, we assumed in this work that skeleton information based on Shotton's method  [14]  would not be reliable enough to obtain an accurate asymmetry index. To evaluate this assumption, we applied the method described above with the MSCM to skeleton information provided by the Kinect™ software. We also applied this method to the skeleton information from the reference motion capture system in order to have a ground truth. The computation follows the same scheme as previously presented. Firstly, steps are detected with the distance between knees at a constant estimated knee height, as described in  [19]. Each pose is then associated with one of the 10%-long interval i of the step. Secondly, the skeleton in each pose is laterally and longitudinally registered thanks to the root joint position, as described above for depth images.
The analysis was automatically carried out on the last 120 gait cycles using the proposed method. Spatiotemporal gait parameters, such as step length and duration, were measured following the Clark et al. [11]  method with both Kinect™ skeleton data and motion capture skeleton data on the last 120 gait cycles. Step duration and length were used to compute the reference gait symmetry ratios (SRlength and SRduration), as suggested by Patterson et al. [4]. This reference value from the motion capture system will be used to evaluate the relevance of the index introduced in this paper by assessing the existence of an asymmetry and to evaluate SRlength and SRduration computed with the Kinect™ skeleton.
Asymmetry Index Based on a Skeleton Estimated with a Kinect™ The results for Ilong(i) computed at 10 intervals (i = 1,…,10) of the step cycle when using skeleton data delivered by the Kinect™ software are presented in Figure  5c. There is no statistical difference between right-deformed gait and normal gait from 50% to 70% of the step. There is no statistical difference between left-deformed gait and normal gait at 50% and 80% and of the step. There is a significant difference for Ilong (p < 0. 001) between the left-deformed gait (resp. right-deformed) and the normal gait, as shown in Table  2. Comparison between the Ilong(i) computed with the Kinect™ skeleton and the Ilong(i) computed with accurate motion capture data leads to a correlation coefficient of 0. 796. The mean value of Ilong was on average 22.
Method Searching, retrieving, and selecting the literature In October 2012, we searched the Medline database via the PubMed search engine following a search strategy using the keywords "Cambodia" or "Cambodian" (Additional file 1). The timeframe was from 2000 up to and including the date of the search. Since most Cambodian physicians were, until recently, taught in French at university, we complemented this search by performing a French-language search of the Directory of Open Access Journals (DOAJ) and scientific journals on tropical medicine in January 2013. Eligible articles were 1) related to research conducted in Cambodia and 2) related to human health or the health system in Cambodia. We excluded articles solely presenting research findings on pure botany, chemistry, physics, or zoology.
Data extraction and management Article identifiers were downloaded from the sources (year of publication, journal, name of authors, name and country of first author's institution). All eligible abstracts were downloaded. The information on articles' topics, type of research, type of data, level of evidence and content, were independently extracted from the abstracts by two researchers using a standardized form which was pilot-tested on 200 abstracts and refined accordingly. Discrepancies between the two researchers were systematically explored, and a consensual decision was reached in all cases after team discussions. Data were entered into an in-house EpiData database format.
The lowest class was given to expert opinion. Following the approach adopted in a recent Indian study  [24] , we assessed the adequacy between publications and the burden of diseases by comparing the proportion of articles addressing each disease with the country-wide burden as estimated by the Global Burden of Disease Study (GBDS)  [19]. Because more recent GBDS data was not available for Cambodia, the 2004 Study was used. Abstracts were screened in search of recommendations to policymakers, healthcare providers, researchers, and patients. We then classified those messages as 1) "Actionable messages" if they were action-oriented and specified the actor and/or the target; and 2) "Implications" if no actionable message was clearly stated but a need for action was identified. Accessibility was systematically checked via the PubMed Central repository, via the Health Inter-Network Access to Research Initiative (HINARI) website (a WHOsponsored website providing free access to some articles for LMIC) and via the journal websites, or the Google Scholar search engine.
The Whitehall II study is a prospective cohort study of British civil servants (government employees)  [17]. The target population was all London-based office staff, aged 35-55 years, working in 20 civil service departments on recruitment to the study in 1985-88. At baseline 73% (10,308 employees) participated. Since then, 8 follow-up examinations have taken place approximately every 2 to 3 years. Diabetes cases were identified from 1985 to the end of 1997 (study phases 1-5), for which data on sickness absence for follow-up were available. We detected 279 cases of diabetes who had at least one year of follow-up time (196 men, 83 women).
Risk Factors In all cohorts, comorbid conditions included cardiovascular disease, hypertension, asthma, rheumatoid arthritis and depression or psychological symptoms. In FPSS, data on comorbid physical diseases were based on entitlements to special reimbursement, and data on psychological symptoms was based on the General Health Questionnaire (GHQ) 12-item version  [19]. In GAZEL, data for all comorbid diseases were derived from survey responses to a checklist of doctor-diagnosed chronic conditions. In Whitehall II, information on coronary heart disease was based on clinical examination, medical records, and hospital records. Information on comorbid physical diseases was based on self-reported longstanding illnesses, and information on psychological symptoms was based on the GHQ 30-item version  [19, 20].
In FPSS and GAZEL, self-reported height and weight and in Whitehall II, height and weight measured at the study clinic were used to calculate body mass index (BMI) from which overweight (BMI = 25-29) and obese (BMI30 kg/m 2 ) participants were identified. Low physical activity was measured through the harmonized measure of the large European-wide Iindividual-Participant-Data on Working Conditions study  [21]. Participants who reported none or very little moderate or vigorous leisure-time physical activity or exercise were coded as having low physical activity. Smoking was categorized as current smoker versus non-smoker in all cohorts. Average use of beer, wine and spirits consumed per week (FPSS, Whitehall II) or day (GAZEL) was requested and transformed into units of alcohol per week. Risky alcohol use was defined as 22 units/week (men) or 15 units/week (women)  [22].
Work Disability Outcomes In the FPSS, data on work disability were derived from registers of the Social Insurance Institution of Finland (sickness absence) and the Finnish Centre for Pensions (fixed-term and permanent work disability pensions). Absences were tracked from 2005 to 2009 (mean follow-up 4. 7, SD = 0. 9 years). In GAZEL, data on sickness absence and disability pensions were obtained from employer records from 1990 to 2009 (mean follow-up 3. 9, SD = 1. 5 years) and absences were tracked from the first occurrence of self-reported diabetes. In Whitehall II, information on sickness absence was obtained from Civil Service (employer) records from January 1986 to 1998 and data on retirement on health grounds from survey responses (mean follow-up 4. 6, SD = 3. 6 years). In all cohorts, shorter follow-up was due to old-age retirement or death and in Whitehall II and GAZEL, also due to leaving the organization.
In FPSS the follow-up time for all participants begun in Jan 1, 2005 after the baseline survey in 2004. In GAZEL and Whitehall II the follow-up time begun from the phase when diabetes was first detected, and risk factors and covariates were derived from the survey at that phase or if data were not available from the most recent survey. We used two outcome variables: (1) absence duration, as measured by the number of absence days (named 'work disability days') and (2) absence frequency ('work disability episodes'). Both outcomes were based on sickness absences and disability pensions. An episode of work disability could include a period of sickness absence, or temporary or permanent disability pension award.
Covariates Socio-demographic baseline covariates were age, sex, occupational grade (based on occupational grade and categorised as high, intermediate and low), and marital status (married or cohabiting versus single, divorced or widowed). Data on occupations in each cohort were based on company records (FPSS and GAZEL) or self-reports (Whitehall II). In FPSS, high occupational grade included e. g. , teachers and physicians, intermediate grade e. g. , registered nurses and technicians, and low grade included e. g. , cleaners and maintenance workers  [15]. The GAZEL study comprises employees in executive and middle management positions (high grade), other non-manual workers (intermediate grade) and manual workers (low grade)  [16]. In Whitehall II, the corresponding occupational groups were administrative (high grade), professional or executive (intermediate grade), and clerical or support (low grade)  [23].
The study-specific estimates were pooled using fixed-effects meta-analysis. Meta-regression analysis was used to assess subgroup differences. All study-specific analyses were performed using SAS 9. 4 (PROC GENMOD and PROC LCA procedures) statistical software and meta-analyses were performed using fixed-effects meta-analysis in Stata (METAN procedure) version 13. Level of P<0. 05 was used to define statistical significance.
In GAZEL, diabetes was measured by self-report and in FPSS it was measured using medical records. Although the validity of self-reports and medical records of diabetes has been shown to be reasonably good  [33] , neither of the methods detect undiagnosed diabetes. Although a specific strength of our study is individual, daily-based register data on work disability measured as sickness absence and disability pension, in FPSS, sickness absence registers covered absences that lasted over 9 days only and in Whitehall II, sickness absence was register-based whereas work disability pension was based on survey responses. There were also some differences between studies in the measurement of risk factors. Furthermore, although we assessed several important risk factors of work disability in people with diabetes, we were unable to examine other features, such as duration and severity of diabetes, quality of diet, and adherence to treatment, which might also be important.
Research design and setting This study included two ongoing prospective study cohorts: The Finnish Public Sector study (FPSS)  [5, 13] , coordinated by the Finnish Institute of Occupational Health, is a prospective cohort of employees working in 10 towns and 21 hospitals. The Ethics Committee of the Hospital District of Helsinki and Uusimaa approved the study. The baseline for the present study was in 2004 (Fig. S1 ) when a total of 48 076 participants responded to a survey (response rate 66%). This baseline was chosen because complete sickness absence data for follow-up were available from 2005. In addition to the survey, health records between 2001 and 2004 (see description below) were used to identify diabetes cases, yielding a total of 1359 diabetes cases. Follow-up of sickness absence was from 1 January 2005 to 31 December 2009.
Measures In the FPSS, identification of diabetes cases was based on national registers of purchased diabetes medicines (oral medication or insulin) and entitlements to special reimbursements for their costs by the Social Insurance Institution of Finland which covers all permanent residents. To be eligible for this register, a patient's condition must meet explicit predefined criteria (diabetes which has not been responsive to lifestyle intervention and needs long-term antidiabetic treatment). Participants with diabetes were also identified from responses to a survey question on doctor-diagnosed diabetes. Data from all these sources were compiled to identify employees with diabetes. In the GAZEL study, participants with diabetes were identified from responses to a checklist of > 50 chronic conditions in annual surveys.
• Five trajectories describing disability level at the beginning of follow-up and its development over 5 years were identified: 'no/very low disability', 'low-steady', 'high-steady' and two 'high-increasing' trajectories. • The majority of employees with and without diabetes had low-disability trajectories. • Diabetes was associated with 'high-increasing' disability trajectories, although this affected only 10% of the population with diabetes. • Obesity and physical inactivity, irrespective of diabetes, and smoking among employees with diabetes were associated with adverse disability trajectories. scheme covers sickness absence of > 9 days. Work disability pension can be granted after ~1 year of sickness absence. For the FPSS cohort, we obtained data on sickness absence and work disability pension between 1 January 2005 and 31 December 2009.
In the GAZEL cohort, employees were covered by a company-run insurance scheme. The policy regarding long-term sickness absence was to grant a disability pension after 5 years of absence. We obtained sickness absence and work disability pension records (1 January 1990 to 31 December 2008) from Electricit e de France-Gaz de France. All these records included the first and last dates (if relevant) of all absences and disability pensions. For each employee, we computed the annual sum of disability days for the 5-year follow-up period. Socio-demographic baseline covariates were age, sex, socio-economic status (occupational grade) and marital status (married or cohabiting vs single, divorced or widowed). Comorbid physical diseases were obtained at baseline. In the FPSS cohort, data on comorbid disease (chronic hypertension, ischaemic heart disease, heart failure, rheumatoid arthritis, asthma or chronic obstructive pulmonary disease) were based on entitlements to special reimbursement for medication.
Statistical analysis We used group-based trajectory modelling, implemented in SAS version 9. 4, to identify clusters of individuals (trajectory groups) who have followed a similar developmental trajectory for work disability in their annual count of work disability days over the 5-year follow-up period. Groupbased trajectory modelling is increasingly being applied in clinical research to map the developmental course of disease and it enabled us to identify the number, shape and size (i. e. the percentage of the population following that trajectory) of different (latent) trajectory groups in the data  [18]. We used Bayesian Information Criteria to evaluate model fit. In the group-based trajectory modelling, the Bayesian Information Criterion is always negative and the maximum (the least negative value) indicates the best model  [19].
g. obesity)' to the regression model. Similarly, we tested the interaction by sex. A three-way interaction 'cohort*lifestyle factor*diabetes status' was tested to examine whether there were any differences between cohorts in these associations. A subgroup analysis among FPSS participants was carried out to examine the cause-specific distribution of work disability. All analyses were performed with SAS 9. 4 program package (SAS Institute, Cary, NC, USA).
Data collection Two groups of 6 healthy and voluntary older adults made up for equal proportion of women and men took part in the experiment. The first group was composed of 6 persons (69(±3) years) who had not fell in the preceding six months and were able to walk without assistance. The second group comprised 6 older persons (68(±4) years) who had fell at least one time in the last previous months and for whom the fall risk had an impact on their activity of daily living. We only present here the results obtained in the case of the TUG test. The image processing algorithm provides the spatio-temporal gait parameters (stride length (m), the stride frequency (strides/s), the step length (m), the step frequency (steps/s), the gait cycle duration (s) and the gait speed (m/s)) as well as the joint (hip, knee, ankle) AROM.
Context: It is well established in the literature that healthier diets cost more than unhealthy diets. Objective: The aim of this review was to examine the contribution of food prices and diet cost to socioeconomic inequalities in diet quality. Data Sources: A systematic literature search of the PubMed, Google Scholar, and Web of Science databases was performed. Study Selection: Publications linking food prices, dietary quality, and socioeconomic status were selected. Data Extraction: Where possible, review conclusions were illustrated using a French national database of commonly consumed foods and their mean retail prices. Data Synthesis: Foods of lower nutritional value and lower-quality diets generally cost less per calorie and tended to be selected by groups of lower socioeconomic status.
METHODS The goal of this systematic literature review was twofold: to examine whether nutrient-rich foods and higher-quality diets in different societies generally cost more, and to discuss food prices in relation to the diet quality of lower-socioeconomic status groups on the basis of data from multiple countries. This review followed the meta-analysis of observational studies in epidemiology (MOOSE) guidelines for systematic reviews. The criteria used to define the research question are presented in Table  1 , using an adapted problem, intervention, comparison, outcomes, and setting (PICOS) strategy. The principal conclusions, based on the literature review, were illustrated further using a national French database listing the nutrient composition of commonly consumed foods and their mean retail prices.
Literature search strategy The search, conducted in 2014 using PubMed, Google Scholar, and Web of Science databases, identified publications linking food prices, dietary quality, and socioeconomic status. As shown in Figure  1 , the literature search led to 819 abstracts dating to the year 2000 and later. Letters, editorials, and commentaries were excluded on the basis of titles and abstracts, leading to 720 full-text articles. The list was further restricted to 130 articles after 590 citations that did not pertain to nutrition economics or diet cost were excluded after review of the full text. Twenty-one articles dating prior to the year 2000 were included in the review on the basis of expert consultations and bibliographies of papers published by researchers, including the present authors.
Data analysis To confirm conclusions from the literature, the relations between the nutritional quality of food and the price of food were assessed using a French nutrient composition table for 1117 food items consumed by a representative sample of 2624 adults participating in the 2006-2007 national dietary survey INCA2. 40 The national food price database for France was obtained from the 2006 Kantar Worldpanel, based on annual food expenditures of a representative sample of 12 000 French households. For each food item reported as consumed by INCA2 respondents, its standard price was calculated by dividing the annual expenditures by the amounts purchased for all the food products corresponding to this item in the Kantar Worldpanel and available on the French market. Given that these prices were paid by a representative panel of consumers, the standard prices were weighted by the most frequently purchased forms of each food.
After adjusting for preparation and waste, food prices in the database were expressed in euros per 100 g of edible portion. Median and mean costs of energy (i. e. , energy cost in E/100 kcal) provided by foods were calculated for each main food group. Nutrient quality of foods was estimated using the SAIN:LIM ([score of nutritional adequacy of individual foods] Ä [score of nutrient to be limited]) ratio, calculated as described. 41, 42 Correlation analyses were conducted to assess the relation between energy density (kcal/100 g) and energy cost and between nutrient quality and energy cost.
It was not until the late 1990s that data on the nutritional composition of food products were introduced into surveys on food purchases  62  and -conversely -data on food prices were introduced into surveys on food consumption. 63 In 1994, Australian researchers estimated the price of each of the 229 foods on the frequency questionnaire used in a food survey of the general population  25  using supermarket food prices. [64] [65] [66] [67]  In general, food prices were incorporated as a vector in the nutrient composition database and were treated as an additional "nutrient. " Both France and the United States have national food price databases that are available for research purposes. The French database, based on purchase data from the Secodip panel (later called the Nielsen and then the Kantar Worldpanel), was compiled in 1997 to analyze diet costs in the national INCA1 dietary survey.