dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical significance was consistently evaluated using. 05-level two-sided design-based tests. The Taylor series method  26  implemented in the SAS software system 27 was used to adjust estimates of standard errors for survey design effects. Logistic regression coefficients and their standard errors were exponentiated to create odds-ratios (ORs) and 95% confidence intervals (CIs). Design-based Wald χ 2 tests were used to evaluate significance of predictor sets.
Acknowledgments Funding/Support: The  World Health Organization World Mental Health (WMH) Survey Initiative  is supported by the  National Institute of Mental Health (NIMH ;  R01 MH070884  and  R01 MH093612-01 ), the  John D. and Catherine T. MacArthur Foundation , the  Pfizer Foundation , the  US Public Health Service  ( R13-MH066849 ,  R01-MH069864 , and  R01 DA016558 ), the  Fogarty International Center  ( FIRCA R03-TW006481 ), the  Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc. ,  GlaxoSmithKline , and  Bristol-Myers Squibb. We thank the staff of the  WMH Data Collection and Data Analysis Coordination Centres for Ali Al-Hamzawi, MD, Mohammed Salih Al-Kaisy, MD ,  Jordi Alonso, MD, PhD ,  Laura Helena Andrade, MD, PhD ,  Corina Benjet, PhD ,  Guilherme Borges, ScD ,  Evelyn J.
www. hcp. med. harvard. edu/wmh/. The  São Paulo Megacity Mental Health Survey  is supported by the  State of São Paulo Research Foundation (FAPESP)   Thematic  Project Grant  03/00204-3. The  Bulgarian Epidemiological Study of common mental disorders EPIBUL  is supported by the  Ministry of Health  and the  National Center for Public Health Protection. The  Colombian National Study of Mental Health (NSMH)  is supported by the  Ministry of Social Protection. The  Mental Health Study Medellín -Colombia  was carried out and supported jointly by the  Center for Excellence on Research in Mental Health  ( CES University ) and the  Secretary of Health of Medellín. The  ESEMeD  project is funded by the  European Commission  (Contracts  QLG5-1999-01042 ;  SANCO 2004123 , and  EAHC 20081308 ), (the  Piedmont Region (Italy) ),  Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain  ( FIS 00/0028 ),  Ministerio de Ciencia y Tecnología, Spain  ( SAF 2000-158-CE ),  Departament de Salut, Generalitat de Catalunya, Spain ,  Instituto de Salud Carlos III  ( CIBER CB06/02/0046 ,  RETICS RD06/0011 REM-TAP ), and other local agencies and by an unrestricted educational grant from  GlaxoSmithKline.
The Israel National Health Survey is funded by the  Ministry of Health  with support from the  Israel National Institute for Health Policy and Health Services Research and the National Insurance Institute of Israel. The  World Mental Health Japan (WMHJ) Survey  is supported by the  Grant for Research on Psychiatric and Neurological Diseases and Mental Health  ( H13-SHOGAI-023 ,  H14-TOKUBETSU-026 ,  H16-KOKORO-013 ) from the  Japan Ministry of Health, Labour and Welfare. The  Lebanese Evaluation of the Burden of Ailments and Needs Of the Nation (L. E. B. A. N. O. N. )  is supported by the  Lebanese Ministry of Public Health , the  WHO (Lebanon) ,  National Institute of Health/Fogarty International Center  ( R03 TW006481-01 ), anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from,  Algorithm ,  AstraZeneca ,  Benta ,  Bella Pharma ,  Eli Lilly ,  Glaxo Smith Kline ,  Lundbeck ,  Novartis ,  Servier, OmniPharma, Phenicia ,  Pfizer ,  UPO.
The  Mexican National Comorbidity Survey (MNCS)  is supported by The  National Institute of Psychiatry Ramon de la Fuente  ( INPRFMDIES 4280 ) and by the  National Council on Science and Technology (CONACyT-G30544-H) , with supplemental support from the  PanAmerican Health Organization (PAHO). Te Rau Hinengaro: The  New Zealand Mental Health Survey (NZMHS)  is supported by the  New Zealand Ministry of Health, Alcohol Advisory Council , and the  Health Research Council. The  Northern Ireland Study of Mental Health  was funded by the  Health & Social Care Research & Development Division of the Public Health Agency. The  Peruvian World Mental Health Study  was funded by the  National Institute of Health of the Ministry of Health of Peru. The  Romania WMH study projects "Policies in Mental Health Area" and "National Study regarding Mental Health and Services Use " were carried out by  National School of Public Health & Health Services Management  (former  National Institute for Research & Development in Health ), with technical support of  Metro Media Transilvania , the  National Institute of Statistics-National Centre for Training in Statistics, SC.
Cheyenne Services SRL ,  Statistics Netherlands  and were funded by  Ministry of Public Health (former Ministry of Health)  with supplemental support of  Eli Lilly Romania SRL. The  South Africa Stress and Health Study (SASH)  is supported by the  US National Institute of Mental Health  ( R01-MH059575 ) and  National Institute of Drug Abuse  with supplemental funding from the  South African Department of Health  and the  University of Michigan. The  Psychiatric Enquiry to General Population in Southeast Spain -Murcia  ( PEGASUS-Murcia ) Project has been financed by the  Regional Health Authorities of Murcia (Servicio Murciano de Salud and Consejería de Sanidad y Política Social)  and  Fundación para la Formación e Investigación Sanitarias (FFIS) of Murcia. The  Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD)  study is funded by the  US National Institute of Mental Health  ( RO1-MH61905 ).
Participants All the 244 Lebanese civilian victims, who were aged 18 years and above, and were injured by cluster munitions were interviewed. To be included, participants must be mentally competent, have met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria for PTSD and present with symptoms that lasted more than 1 month (to rule out acute stress disorder). It is worth mentioning that data from interviews collected in 2006 were adapted to the new DSM-5 after its release in 2013. Military personnel, casualties presenting with injuries not caused by submunitions or those who recently had another traumatic event within a month were excluded. In addition, those who had PTSD symptoms resulting from other traumatic events that occurred in their life were also excluded from the study.
Demographic and outcome variables The data collected were as follows: ( The distribution of answers in the PCL in the short term and long term are presented in online supplementary appendix B and supplementary appendix C, respectively. Among the participants, more men (94%) than women (6%) were diagnosed with PTSD in 2006. The number of men diagnosed with PTSD decreased significantly from 224 in 2006, to 91 in 2016 (p<0. 001). However, the number of women diagnosed with PTSD did not change significantly in the long run (table  2 ).
Furthermore, we studied the nature and severity of the impairment along multiple dimensions to include work, family and social relationships. However, the instrument of measure used differs from one study to another and can lead to varying degrees of PTSD reporting. As many of the follow-up interviews were held over the telephone, we were limited by time. More demographical data on health, treatment and socioeconomic outcomes could have been gathered. In addition, information on comorbid mental health conditions like depression, anxiety, sleep disorders, eating disorders, substance use disorders and suicide could have been collected. More research is needed to study the interaction between trauma exposure, pre-existing psychological and biological vulnerabilities, and the post-trauma environment.
(2005). Spatial deployment of attention influences both saccadic and pursuit tracking. Vision Research. Harwood et al. (2008). The spatial scale of attention strongly modulates saccade latencies. Journal of Neurophysiology. De Vries et al. (2016). The saccadic size-latency phenomenon explored: Proximal target size is a determining factor in the saccade latency. Vision Research. ANR grant ANR-13-APPR-008 (CV, LM), NSF grant NSF:1232654 (MH) and PhD grant from the French Ministry for Research (CV).
Inertial measurement units (IMU) were attached to the right and left wrist, right and left foot, and hip as previously done by Seifert et al. [21]. An IMU was also placed on the climbing wall to locate it in the Earth reference frame. These IMUs include a combination of a tri-axial accelerometer (±8G), tri-axial gyroscope (1600˚. s -  ) and a tri-axial magnetometer (MotionPod, Movea, Grenoble, France) and sample at 100 Hz. Wi-fi transmissions to a controller allow recording using MotionDevTool software (Movea, Grenoble, France). These IMUs have been used previously to assess climbing fluency using the jerk coefficient of hip trajectory  [13]  and for automatic detection of activity states during climbing  [22]. Synchronization between the eye tracking system and IMUs was done by three handclaps during which the climber watched his hands; the hand clapping was detected in the scene camera of the glasses and through the acceleration peak of the IMU located on the wrists.
Data analysis Gaze behavior. Gaze behavior during the preview was analyzed using SMI BeGaze eye tracking analysis software (www. smivision. com). Visual fixations were automatically detected from spatial and temporal characteristics of gaze data, under the assumption that fixation points generally occur near one another  [23]. The maximum spatial dispersion was set at 100 pixels. Since fixations are rarely less than 100 ms, and often in the range of 200-400 ms  [23] , we set the minimum fixation duration at 90 ms. The dispersion-based algorithm identifies fixations as groups of consecutive points within a particular dispersion, or maximum separation. It uses a moving window that spans consecutive data points checking for potential fixations. The moving window begins at the start of the trial and initially spans a minimum number of points, determined by the given minimum fixation duration (90 ms) which is equivalent to three frames for the sampling frequency (30 Hz).
Cluster validation. The Calinski-Harabasz (CH) index was used to estimate the number of clusters that best fitted the data and thus best classified the participants in each cluster  [31]. This index takes a form of Index = (a Ã Separation) / (b Ã Compactness), where a and b are weights  [32, 33]. In particular, CH (k) = (B(k) / (k-1)) / (W(k) / (n-k)) where n represents the number of participants, k denotes the number of clusters, and B(k) and W(k) denote the between and within clusters sums of squares of the partition. The maximal value of this index indicates the optimal ratio between inter-cluster distance (i. e. , inter-cluster separation) and intra-cluster distance (i. e. , intra-cluster compactness) and in turn, an optimal number of clusters  [28, 30, 33]. The CH index was calculated from two to ten potential clusters.
Gaze behavior during route preview The eye tracking data quality was 84%, i. e. 84% of samples were captured. Average duration of route preview was 105. 4±37. 2 s, out of the maximum allowable time of 3 min. The total fixation duration was 54. 6±27. 2 s, which corresponded to 50. 3±10. 6% of the preview duration. On average there were 2. 7±1. 2 scan paths with an average search rate of 5. 9±0. 6 fixations per second. They had an average of 314±140 fixations with 233. 4±106. 2 fixations on holds (74±7. 5% of the total number of fixations) and 80. 6±41. 8 fixations on the wall (26±7. 5% of the total number of fixations). Next, we explored the distribution of fixations on each of the 40 holds that composed the route. Across all participants, 5755 fixations were recorded; 1512 on the wall, 4243 on holds.
This project received the support of the  CPER/FEDER , GRR " Logistic, Mobility and Numeric "  RISC 1880 -FEDER 33172  for (LS). This project also received the funding of the  French National Agency of Research  ( ID :  ANR-13-JSH2-0004  DynaMov) for (LS) and  Edith Cowan University  for (JLC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Materials and methods The manuscript was drafted following the PRISMA checklist  [19] , provided in S1 Checklist. A common protocol for the seven umbrella SLRs was registered and is available on PROSPERO (Record ID: CRD42015010616), the international prospective register of systematic reviews  [20]. Review title, timescale, team details, methods, and general information were recorded in the PROSPERO register prior to completing data extraction.
Search strategy and eligibility criteria SLRs and MAs investigating the determinants of PA across the life course were systematically searched on MEDLINE, ISI Web of Science, Scopus, and SPORTDiscus databases. The search was limited to SLRs and MAs published in English, between January, 1 st 2004 and April, 30 th 2016. SLRs and MAs published before 2004 were not included to avoid duplications of the earliest individual studies included in the SLRs and MAs. According to the literature  [21] , Table  1  shows the MEDLINE search strategy, and Fig  1  summarizes the process of the literature research, common to the subsequent 7 umbrella SLRs. Thus, Table  1  provides the overall list of searched terms, whilst Fig  1  shows the count of the overall excluded/included reviews, related and not-related with psychological determinants.
Data extraction A predefined data extraction form, developed by the DEDIPAC-KH and checked by two authors (KA, AP), was used to extract data for each included review. In reporting data, authors agreed to define as 'reviews' those SLRs and MAs found eligible for the umbrella SLR, and as 'primary studies' those studies included in the eligible SLRs and MAs. Moreover, authors agreed to consider all of the terminologies and forms of PA, including unstructured (i. e. , PA linked with daily life) and structured (i. e. , Ex and sports), independently from their frequency, duration, and intensity. Sedentary behavior was not included in the DEDIPAC umbrella SLRs. The following information was extracted from each included review: year of publication, type of review (either SLR or MA), number of eligible primary studies included in this psychological umbrella SLR over the total number of studies included in the review; continent/s, primary study design, overall sample size, age range or mean age, gender proportion (% female), and year of publication of eligible primary studies; PA outcome details, type of determinant/s, aim of the review; overall results (qualitative or quantitative), overall recommendations, and limitations as provided by the review itself.
Three reviews measured time-specific PA: school break time PA and after school PA  [34] , recess PA  [32]  and PA changes across transition to retirement  [26] , which were considered as overall PA. Three reviews measured PA intention and/or behavior  [27, 29, 36] , and two the combination of PA and Ex (overall PA/Ex)  [35, 42]. Lastly, one review measured a specific Ex referral schemes adherence (ERS)  [43] , which was considered as overall Ex. For all data (outcome, determinant, review aim, overall qualitative and quantitative results, limitations and recommendations) of the included reviews see the S1 Table.
Study population As described in detail elsewhere  [20] , data were drawn from the Gazel cohort, an on-going epidemiological study set up in 1989 on employees of France's national electricity and gas company (EDF-GDF, Electricitede France-Gaz de France). At the start of follow-up, a total of 20 625 employees (15 011 male and 5 614 female) aged 35-50 years old gave written informed consent to participate. In January of each year since 1989, participants have completed a selfadministrated questionnaire on their lifestyle, health, and occupational situation. In addition to the annual questionnaire, a comprehensive update includes data from the human resources department, the firm's medical insurance program and the department of occupational medicine. The attrition rate in the study is low (0.
5% in 2008 at the 20-year follow-up), approximately 75% of participants respond to the study questionnaire every year  [21]. For the present analysis, exposure measures were drawn from the period between 1990 and 1996 and outcome measures between 1996 and 2012. The period 1990-1996 constitutes the measurement period of the exposure: Body Mass Index (BMI) and metabolic status. BMI was calculated from self-reported weight and height. Height was self-reported in 1990 and data on weight were collected through annual questionnaires from 1990 to 1996. All participants who reported their weight at least once over this period were included in the analysis. Body mass index (BMI) was calculated by dividing weight (in kilograms) by height (in meters squared) and categorized using the WHO classification  [22] : <18.
5 kg/m 2 (underweight), 18. 5-24. 9 kg/m 2 (standard weight), 25-29. 9 kg/m 2 (overweight) and !30 kg/m 2 (obese), with the under 18. 5 category (N = 192) removed from the analysis. Mean BMI over this period was used in the analysis. Metabolic status was defined as reported physician diagnosis and treatment of hypertension, type 2 diabetes, or hypercholesterolemia over the period 1990 to 1996. These conditions were assessed using the following question "Do you suffer or have you suffered from these disorders during the previous 12 months?". One positive response over the exposure window of 1990 and 1996 led participants to be classified as being metabolically unhealthy and no positive response over the period as metabolically healthy. We used this definition alongside data on BMI to create six phenotypes: metabolically healthy-normal weight (MH-NW), metabolically healthy-overweight (MH-OW), metabolically healthy obese (MHO), metabolically unhealthynormal weight (MU-NW), metabolically unhealthy-overweight (MU-OW) and metabolically unhealthy obese (MUO).
Covariates Analyses were adjusted for age, sex, marital status, occupational position, physical activity, fruit and vegetable consumption, alcohol intake and smoking status. Data on age and sex was obtained from company human resources. As described in detail previously  [20] , the measure of occupational position was taken from the employer's records of grade of employment at age 35 (representative of mid-career status) and categorized as executives, intermediate profession, employees and manual workers. Marital status (single or married/cohabiting), physical activity (competition level, regular but not competition level, occasionally, and none), alcohol intake (none, moderate: 1-21 units/week in men and 1-14 in women, heavy: ! 22 units/w in men and ! 15 in women), smoking status (non-smokers, current smokers and ex-smokers) and fruit and vegetable consumption (< 1, 1-2 and > 2 times/week) were self-reported in 1990.
Results Of the 20 625 participants recruited to the study in 1989, 6 150 were excluded for one or more of the following reasons: BMI<18. 5 kg/m 2 (n = 192), missing data on BMI-metabolic status phenotype (n = 3 100); a further 3 266 persons did not have complete data on CESD over the follow-up. The final sample consisted of 14 475 participants (10 814 men and 3 661 women). Compared to those excluded, participants included in the analyses were older (43. 9 years vs. 43. 5 years), more likely to be men (74. 7% vs. 68. 2%), married or cohabiting (86. 7% vs. 81. 7%), physically active (67. 4% vs. 60. 7%), and to have a higher occupational position (15. 1% vs. 9. 7%), all p<0. 01. Baseline characteristics of participants included in the analysis by metabolic status and BMI categories are presented in Table  1.
However, a pooled analysis of eight cohorts  [19]  found an increased risk of depressive symptoms among MHO individuals compared with metabolically healthy non-obese participants and concluded that the phenotype MHO was not a benign condition regarding the risk of depression. The inconsistency of these results may be due to several factors. First, we assessed metabolic health status based on self-reported data from questionnaires, while in the above-mentioned reports it was based on measured data on clinically assessed blood pressure, triglycerides, HDL-cholesterol, CRP or glycated hemoglobin blood levels. However, this is unlikely to have unduly affected our results as a previous study has shown strong correlations between measured and self-reported weight and height  [26]. Secondly, the duration of follow-up was either short in previous analyses  [18]  or analyses were cross-sectional  [19] ; our results are based on longitudinal assessment of depressive symptoms over 16 years.
Finally, observational data even when drawn from a longitudinal study cannot rule out reverse causality or residual confounding. Thus, the association between abnormal metabolic status and depression could be due to unmeasured hazardous behaviors such as poor diet or medical adherence  [20, 31]. The main strengths of this study include a large sample size, longitudinal design with repeated measurements of BMI, metabolic risk factors and depression data, and a long follow-up period of nearly 16 years. We were also able to take into account several confounders. However, this study also has some limitations. First, since the data were self-reported, participants may have underestimated their weight and overestimated their height that would have the effect of lowering their BMI. Second, depressive symptoms may also be under-reported although previous analyses show that participants of the Gazel cohort reported similar levels of depressive symptoms as the French general population  [32].
Third, we were not able to exclude participants with depressive symptoms occurred in the time window when exposure and covariates were measured (1990-1996) because depressive symptoms were assessed for the first time only in 1996. Finally, we assessed depression by the CES-D (23) which does not allow a clinical diagnosis of major depression. However, it is a widely used instrument for the identification of depressive symptoms, which has been shown to be reliable and valid across varied cultural and sociodemographic settings. To our knowledge, this study is the first prospective study using repeated CES-D measures to examine the associations between the MHO phenotype and depressive symptoms. In summary, our data suggest that the metabolically healthy obese phenotype is not associated with higher risk of depressive symptoms.
Acknowledgments " Dr. Guy-Marino Hinnouho  is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. " The authors express their thanks to  EDF-GDF (ElectriciteDe France-Gaz De France) , especially to the  Service General de Medecine de Controle , and to the '' Caisse centrale d'action sociale du personnel des industries electrique et gazière ". We also wish to acknowledge the Population-based Cohort Unit,  UMS 011 INSERM Versailles St-Quentin University responsible  for the GAZEL database management. The GAZEL Cohort Study was funded by  EDF-GDF (ElectriciteDe France-Gaz De France)  and  INSERM  ( Institut National de la Santeet de la Recherche Medicale ), and received grants from the  'Cohortes Sante´TGIR Program' Agence nationale de la recherche´(ANR)  and  Agence  francaise de securitesanitaire de l'environnement et du travail (AFSSET).