dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources: We developed and published a protocol, applying the Navigation Guide as an organizing systematic review framework where feasible. We searched electronic bibliographic databases for potentially relevant records from published and unpublished studies, including the WHO International Clinical Trials Register, Ovid MEDLINE, PubMed, Embase, and CISDOC on 30 June 2018. Searches on PubMed were updated on 18 April 2020. We also searched electronic grey literature databases, Internet search engines and organizational websites; handsearched reference list of previous systematic reviews and included study records; and consulted additional experts. Study eligibility and criteria: We included working-age (≥15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (<15 years) and unpaid domestic workers.
We considered for inclusion randomized controlled trials, cohort studies, case-control studies and other nonrandomized intervention studies with an estimate of the effect of exposure to long working hours (41-48, 49-54 and ≥55 h/week), compared with exposure to standard working hours (35-40 h/week), on alcohol consumption (in g/week), risky drinking, and alcohol use disorder (prevalence, incidence or mortality). Study appraisal and synthesis methods: At least two review authors independently screened titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage, followed by extraction of data from publications related to qualifying studies. Two or more review authors assessed the risk of bias, quality of evidence and strength of evidence, using Navigation Guide and GRADE tools and approaches adapted to this project.
Results: Fourteen cohort studies met the inclusion criteria, comprising a total of 104,599 participants (52,107 females) in six countries of three WHO regions (Americas, South-East Asia, and Europe). The exposure and outcome were assessed with self-reported measures in most studies. Across included studies, risk of bias was generally probably high, with risk judged high or probably high for detection bias and missing data for alcohol consumption and risky drinking. Compared to working 35-40 h/week, exposure to working 41-48 h/week increased alcohol consumption by 10. 4 g/week (95% confidence interval (CI) 5. 59-15. 20; seven studies; 25,904 participants, I 2 71%, low quality evidence). Exposure to working 49-54 h/week increased alcohol consumption by 17. 69 g/week (95% confidence interval (CI) 9.
Types of exposures We included studies that defined long working hours in accordance with our standard definition (Table  1 ). We again prioritized measures of the total number of hours worked, including in both of: main and secondary jobs, self-employment and salaried employment, whether in the informal or the formal economy. We included studies with objective (e. g. , by means of time recording technology) or subjective measurements of long working hours, whether, including studies that used measurements by experts (e. g. , scientists with subject matter expertise) and selfreports by the worker, workplace administrator or manager. We planned to prioritize objective measurements for studies that have presented both objective and subjective measurements. Studies with measures from any data source, including registry data, were included.
Types of outcomes We included studies that defined alcohol consumption, risky alcohol use and alcohol use disorders in accordance with our standard definition of these outcomes or for which re-analyses of individual participant data were feasible. We included studies that assessed absolute measures of alcohol consumption in grams of alcohol consumed per average week (outcome 1), risky drinking (outcome 2), and/or the prevalence of, incidence of or mortality from any alcohol use disorder, as defined by the ICD-10 codes F10, G72. 1, Q86. 0, and/or X45 (outcome 3) (Table  2 ). For alcohol consumption, we considered for inclusion studies that measured the outcome using validated tools (e. g. AUDIT-C)  (Bradley et al. , 1998)  or other self-reporting by means of questionnaire. For alcohol use disorder, we considered for inclusion studies with documented International Classification of Diseases-10 (ICD-10) diagnostic codes and studies that applied methods to ascertain the diagnosis of alcohol use disorder per ICD-10 criteria.
Electronic bibliographic databases We searched the seven following electronic bibliographic databases: The Ovid MEDLINE search strategy was presented in the protocol  (Godderis et al. , 2018a (Godderis et al. , , 2018b)). The full search strategies for all databases were revised by an information scientist and are presented in Appendix 2 in the Supplementary data. When we neared completion of the review, we conducted a top-up search of the MEDLINE database on April 2020 to capture the most recent publications (e. g. , publications ahead of print).
Organizational websites The websites of the seven following international organizations and national government departments were searched on June 2018: 1. International Labour Organization (www. ilo. org/). 2. World Health Organization (www. who. int). 3. European Agency for Safety and Health at Work (https://osha. eur opa. eu/en). 4. Eurostat (www. ec. europa. eu/eurostat/web/main/home). 5. China National Knowledge Infrastructure (http://www. cnki. net/). 6. Finnish Institute of Occupational Health (https://www. ttl. fi/en/). 7. United States National Institute of Occupational Safety and Health (NIOSH) of the United States of America, using the NIOSH data and statistics gateway (https://www. cdc. gov/niosh/data/).
Extracted data A standard data extraction form was developed and trialled until data extractors reached convergence and agreement. At least two review authors independently extracted data on study characteristics (including study authors, study year, study country, participants, exposure, and outcome), study design (including study type, comparator, epidemiological model(s) used, and effect estimate measure) and risk of bias (including source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest, and other sources of bias). A third review author resolved conflicts in data extraction. Data were entered into and managed with Microsoft Excel. We also extracted data on potential conflict of interest in included studies.
Methodology used to analyse individual participant data (IPD) from unpublished studies We obtained data of relevant datasets from IPD collections with measurements of working hours and alcohol consumption. We considered working hours/week as hours worked in all current jobs including hours worked at home, if specified in the data set. Participants who worked <35 h/week at baseline or during follow up were excluded, as well as participants younger than 15 years old. The categorization of hours/week was as follows: 35-40 (reference), 41-48, 49-54 and ≥55 h. Alcohol consumption was defined as absolute measures of alcohol in grams per week. Alcohol consumption was calculated from the questions on frequency of alcohol use and on the amount of drinks over a time period. A standard drink was considered to contain 12 g of pure alcohol.
Assessed strength of evidence Our systematic review included observational epidemiologic studies of human data only, and no other streams of evidence (e. g. , no studies of non-human data). The standard Navigation Guide methodology  (Lam et al. , 2016)  allows for rating human and non-human animal studies separately, and then combining the strength of evidence for each stream for an overall strength of evidence rating. However, the Navigation Guide also allows for rating one stream of evidence based on the factors described above (i. e. , risk of bias, indirectness, inconsistency, imprecisions, publication bias, large magnitude of effect, dose-response and residual confounding) to arrive at an overall rating of the quality of evidence as 'high', 'moderate' or 'low' (see above and the protocol).
Alcohol consumption in g/week Seven studies with a total of 33,734 participants from three WHO regions reported estimates of the effect of exposure to long working hours on alcohol consumption (in g/week), compared with working standard hours (35-40 h/week). All studies could be included in a quantitative meta-analysis because we generated analysis from raw data using our pre-specified parameters. Compared with working 35-40 h/week, exposure to working 41-48 h/week increased consumption by 10. 40 g/week (mean difference (MD); 95% confidence interval [CI] 5. 59-15. 20 g/week, 7 studies, 25,904 participants, I 2 71%; Fig. 5 ). Exposure to 49-54 work hours/ week increased alcohol consumption in grams per week by 17. 69 g/ week (MD; 95% CI 9. 16-26. 22 g/week, 7 studies, 19,158 participants, I 2 82%; Fig.
Acknowledgments We thank  Paul Whaley  ( Associate Editor for Systematic Reviews, Environment International ;  Lancaster Environment Centre, Lancaster University, United Kingdom ) and  Tim Driscoll  ( University of Sydney, Australia ) for the editorial guidance and support. We thank  Marianne Virtanen ,  Markus Jokela  and  Mika Kivimäki  for having kindly facilitated access to or shared individual participant data for reanalysis. The authors alone are responsible for the views expressed in this article, and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.
Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.
6% of the variance of the movements; (2) the number of PMs that represented 94. 6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.
Instead of processing the multiple DOFs individually, the motor system controls a reduced set of compositional elements called synergies (i. e. , patterns of muscle activations)  [7] [8] [9]. These synergies are combined and, depending on their respective weights, are used to generate different movements. With the progress of motion capture (mocap) systems, converging evidence has been provided across various movement contexts that motion datasets could be properly defined using a reduced number of synergies. For instance, Principal Component Analysis (PCA) has been shown to be effective in extracting synergies, by decomposing motion data into uncorrelated principal movements (PMs). Troje  [10]  first used PCA to disentangle the motion patterns of human gait. Similarly to "eigenfaces"  [11]  or "eigenvoices"  [12] , the whole movement of the walker was decomposed into simpler one-directional PMs (i.
For instance, dense mocap datasets could be decomposed using PCA before more complex machine learning procedures, notably considering the crucial role of proper pre-processing steps in improving the performance of deep neural networks for automatic SL recognition  [39]. Again, such recognition models could complement the prior work made on the automatic classification of ASL alphabet letters using PCA  [30]. Furthermore, taking advantage of the inter-segment coordination with PMs could open up promising perspectives for automatic SL generation via virtual signers (or signing avatars), as previously shown for robot control problems  [5] , such as control of artificial hands  [7, 40, 41]. For instance, animation models could use dense mocap datasets to produce realistic movements by processing only a reduced subset of PMs while keeping most of the information about the original movements.
In the present study, we used PCA in order to determine the extent to which upper-body motion of French Sign Language (LSF) could be described within a low number of PMs. Moreover, we aimed to quantify whether these PMs were shared across various signers in spontaneous discourses. For that aim, (1) common PMs were extracted from a mocap dataset containing spontaneous LSF motion of six signers; (2) individual PMs were extracted from the separate data of each signer; (3) the consistency of the principal movements across signers was assessed.
Motion capture corpus The data used in the present analyses were taken from a previously reported study  [42]. In brief, each of six deaf native and fluent signers (3/3 males/females) had freely described the content of 25 pictures using French Sign Language (LSF). The signers had been selected so that they were all deaf, fluent in LSF and working in professions that require them to be comfortable expressing themselves in front of a camera (e. g. , teacher, journalist, story-teller, translator). They all gave informed written consent (translated into LSF for better accessibility) before the experiment, in accordance with the ethical standards of the Declaration of Helsinki. Using a motion capture system equipped with 10 cameras (Optitrack S250e), the data consisted of the upper-body movements recorded at 250 fps, in three dimensions.
Data pre-processing All the data processing was conducted in Python (Python Software Foundation https://www. python. org/) using custom code, which is publicly available at the following GitHub repository: https://github. com/felixbgd/SL-PMs_Bigandetal_2021. The 3D positions of all markers were defined in reference to the pelvis (used as the origin). From each of the 24 original recordings, one mocap recording unit with the duration of 5-second was extracted from the beginning of the utterance, irrespective of the semantic content. Each mocap recording unit was thus related to a different SL utterance. This resulted in 24 mocap examples per signer, of 5-second duration each. The movements of each individual signer (i. e. , the concatenation of their 24 mocap examples) were described in a matrix containing 30,000 posture vectors (rows) defined by the 3D coordinates of the 21 markers (columns) at each time frame t: pðtÞ � �! ¼ ½x A two-step normalization was then applied to these data, in order to allow extracting common PMs across different signers while reducing the effects of anthropometric differences, as defined in prior work  [23].
Similarities in principal movements across signers Individual PMs (i. e. , specific to each signer) were computed from the mocap data of individual signers. As shown in Fig  4 , most of the overall variance was explained by the first eight individual PMs. Combined, the first eight individual PMs explained 95. 7% (SD = 0. 6%) of the cumulative variance. By comparison, the first eight common PMs explained 94. 6%, and the first seven individual PMs explained 94. 3% (SD = 0. 9%). Therefore, although the common dataset contained a wide variety of unsynchronized movements performed by six different signers, the common PMs explained a similar amount of the movements variance, compared with PMs computed separately for each signer. Visual animations of the first eight individual PMs seemed highly similar to the common PMs for most signers, although they may sometimes be ranked in a different order.
This observation was further confirmed by the cosine similarity measures, as show in Fig 5. For instance for Signer 4 (Videos 9 to 16 in S1 Dataset), all the first seven PMs reported high levels of similarity (sim > 0. 7  [47] ) and PM8 reported a level of similarity near the latter threshold (sim = 0. 69). All the PMs of this signer were similar to common PMs of the same order (i. e. , individual i th PM is similar to common i th PM), except for PM3 and PM4, which were ranked in reverse order. Of the total number of 48 individual PMs across signers, 25 PMs reported a similarity level above 0. 7, and 36 reported a similarity level above 0. 5. The amount of similarity could vary depending on the signer. For instance, although some signers also presented consequent levels of similarity (e. g.
Discussion In the present paper, we used PCA to decompose the upper-body movements of spontaneous LSF into a reduced set of simpler, elementary, movements. The original motion data were transformed into a new space spanned by principal components, called principal movements (PMs). The first eight common PMs (i. e. , computed on the whole dataset containing all signers) accounted for 94. 6% of the variance in the movements, which suggests that the control of spontaneous SL motion can be limited to a low number of dimensions-far fewer than the multiple dozens of DOFs of the upper body-, as previously demonstrated for full-body movements, such as gait  [10] , diving  [20] , skiing  [18]  or juggling  [21]. This outcome is also in line with prior work on the control of hand gestures, such as grasping  [31, 48] , piano playing  [29, 49]  and in particular the production of SL letters  [30] [31] [32] [33].
The PMs extracted from our motion dataset are thus more likely to reflect potential synergies used by signers in real-life conditions, which is crucial notably to conceive efficient real-life communication tools. Although it is now well known that PCA is efficient in decomposing human motion into low-dimensional subspaces, the fact that it is confirmed for unconstrained continuous SL is still an intriguing result, considering the variety of analyzed movements in the discourses (e. g. , gestures were neither necessarily consistent in structure nor synchronized in time across signers and examples). By contrast, all studies investigating the synergies of SL in hand gestures  [30] [31] [32] [33]  were limited to a specific, well-defined, set of linguistic forms: letters of the alphabet. As precisely outlined by one of the latter studies  [30] , the common synergies extracted across signers producing ASL letters may be expected because of the hand postures that were targeted to conform to the same forms defined by the alphabet.
First, the number of PMs needed to account for most of the variance in the movements was nearly the same for individual PMs as for common ones. Although the number of PMs retained is a simple metric, it was not trivial that it would be the same across the common and individual datasets. For instance in prior work on full-body synergies, the PMs common to all participants did not necessarily represent the original movements as well as the PMs computed for each participant  [18]. For a comparison with prior SL-related work on hand gestures, a similar synergistic structure common across signers has been reported but it was more likely to occur, given that all signers were constrained by the common forms of an alphabet  [30] , by contrast with the free discourses used here. Given prior outcomes outlined on postural mechanisms related to gesture expertise  [21, 29] , we may also note that the similar number of PMs retained across the six signers of the present study could reflect that they had a similar level of expertise in SL gestures, all being fluent signers.
Furthermore, although PMs of lower variance (i. e. , PMs > 8) can be deemed to be directions of noise  [19, 20] , intriguing results including recent work on ASL hand gestures  [31, 50]  have shown that high-order PMs can be highly structured, which calls for further research investigating whether the high-order PMs (e. g. , accounting for less than 1%-variance) of upper-body motion in spontaneous SL could be under volitional control rather than being related to noise. Beyond the similar number of synergies, PM subspaces of the present study quantified consistent motion patterns across signers. Indeed, we showed that a high number of individual PMs (i. e. computed separately from the mocap data of each signer) were similar to common ones, although they were sometimes ranked in a different order or expressed as combinations of the common PMs.
In the present study, common PM4 (i. e. , trunk rotation) can be described in the transverse plane but it was clearly visible from a frontal view. PM3 was best visible from a sagittal view. Still, the patterns quantified by most PMs were occurring in the frontal plane. These findings interestingly complement prior SL-related work on the synergies of hand gestures  [30] [31] [32] [33]. Indeed, the mocap data used in the present study include upper-body trajectories by contrast with its predecessors, which have investigated the kinematics of the dominant hand only. Inversely, these prior studies have applied PCA to precise recordings of finger gestures, while our mocap data were recorded on various upper limbs but only included global motion data of the wrists and hands. Both approaches provide fundamental insights into the complex structure of SL motion.
Taken together, these findings could be of particular interest for improving automatic SL processing models, which requires relevant representations of both upper-body movements and finger gestures, in particular for automatic recognition  [39, 52]  and generation  [53] [54] [55] [56]. Further work examinating larger mocap datasets and across a higher number of signers could be of interest in order to refine the definition of common PMs, which here were common to six signers only. The successful application of PCA to the complex movements of spontaneous SL provides potential contributions to SL research for both fundamental and application purposes. First, PCA allows resynthesizing PMs in the original 3D space, which enables researchers to visualize these directions of high movement variability.
For these reasons, PM decomposition has been widely used to better understand the coordinative structure of complex movements and could shed light on the motor control of SL movements, as previously investigated through common laws of motion  [43, 57]. Furthermore, it allows for dimensionality reduction. This has a significant potential impact on machine learning procedures used in automatic SL tasks, in the same way as some past studies aimed to drastically lower the frame rate  [58]  or the number of markers  [59]  to reduce the bandwidth in SL telecommunication. Following the present results, the dimensionality of dense mocap datasets could be considerably reduced using only a subset of PMs while keeping most of the information. For instance, it could significantly reduce the dimensionality of the input of deep learning models used for the automatic recognition of SL and, more importantly, exploit knowledge from the structure of SL motion to improve their performance.
Common PMs were computed from the mocap dataset containing the 24 examples of the six signers. As shown in Fig 2, most of the overall variance was explained by the first eight common PMs. Combined, the first eight common PMs explained 94. 6% of the cumulative variance. The first eight common PMs (Videos 1 to 8 in S1 Dataset) are shown in Fig 3 and are described in details in Table.