dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to evaluate the validity of the latter, Pearson correlation coefficients were computed for each reference Vicon index compared with corresponding Kinect indices. These two statistical tests were applied to: -CRP computed with both Vicon and Kinect data, testing hypothesis H1, -ILong computed with both Vicon and Kinect data (using the Kinect placed in front only) using 90 gait cycles to construct the Mean Gait Cycle Model to test hypothesis H2, -ILong when placing the Kinect behind the subject, considering 90 gait cycles to construct the Mean Gait Cycle Model to test hypothesis H3, -ILong considering Mean Gait Cycle Model built using gait cycles 1 to 90. This was applied separately for data given by a Kinect placed in front of or behind the subject to test hypothesis H4.
3-Results Figure  2  depicts CRP computed with Vicon and with Kinect data. As shown in table 1(a), CRP computed with Vicon data was able to detect asymmetry within the stride up to 95% of the time. However, CRP computed with Kinect data at the shank could only detect asymmetry for 73% (left deformation) and 52% (right deformation) within the stride. The results were similar when the CRP was computed at the thigh. Correlation between CRP computed with Vicon and Kinect data all along the cycle were 0. 45 and 0. 11 at shank and at thigh respectively, demonstrating that noise in Kinect data has a negative impact on the sensitivity and validity of CRP (H1). Correlation between ILong computed with the Kinect and Vicon data was 0. 92 based on 90 averaged cycles and the Kinect placed in front  [16]  as shown in Figure  3.
When using Vicon data, ILong detected that 96% of the gait cycle exhibits asymmetry, which was similar to results obtained with the Kinect (91% and 87% for the left and right asymmetrical gaits respectively), as shown in table  1(b). This suggests that noise in Kinect data has a low impact on the sensitivity and validity of ILong (H2). Correlation between CRP and ILong computed with Vicon data was 0. 09+-0. 43 for CRP at shank and 0. 04+-0. 50 for CRP at thigh, supporting the idea that these two quantities do not measure the same information. They both aim at measuring asymmetry, but CRP describes coordination (joint angle and velocity) within a specific joint for both body sides, whereas ILong measures the geometric difference between the two body sides. Results obtained with the Kinect placed behind the subject were very similar to those obtained when the sensor was placed in front, table 1(b).
For left asymmetrical gaits, 96% of the stride was detected as asymmetrical when the Kinect was placed behind, compared to 91% and 96% for the Kinect in front and for Vicon data respectively. For right asymmetrical gaits, similar results were obtained: correlation between ILong values computed with Kinect and Vicon data was 0. 92 when the Kinect was placed in front and 0. 96 when placed behind. This suggests that sensor placement has low impact on the sensitivity and validity of ILong (H3), and in fact positioning of the Kinect behind the subject may improve accuracy of results. The close correlation between ILong computed with Kinect and Vicon data is shown in Figure  3. In the two conditions (Kinect in front or behind) with a Mean Gait Cycle Model based on 1-90 cycles, the two techniques agree closely.
4-Discussion The poor results obtained with the CRP computed with Kinect data supports hypothesis H1: CRP was not reliably computed from gait data and this could be explained by two reasons. Firstly, the derivation of noisy joint angles delivered by the Kinect induces noisy derivatives. Previous work demonstrated a correlation of 0. 8 between joint angles delivered by the Kinect and an optoelectronic system  [21]. This could partly explain the low correlation between CRP computed with Kinect and Vicon data. Secondly, there is difficulty in correctly segmenting the feet and the treadmill belt separately during contact phase. This misdetection of the contact point may influence the computation of the shank angle, with a direct consequence on the associated CRP. This hypothesis is supported by the results: CRP based on the shank orientation computed with Kinect had the worst correlation with the one computed using Vicon data.
Consequently, designing a new index based on Kinect data such as ILong  [16]  is of great interest. In accordance with previous results  [16]  ILong computed with Kinect data was able to detect asymmetrical gaits across almost all asymmetrical cycles (H2). Moreover, the correlation between ILong computed with Vicon and Kinect data was 0. 92. This result has been obtained when placing a 5cm sole below one foot to simulate leg length discrepancy, as suggested previously  [16, 17, 22]. Further work is required to evaluate the actual sensitivity of the system for a wider set of perturbations to generate new types of asymmetry, such as placing soles of various sizes, or analysing the gait of patients with impaired locomotion. In previous work the Kinect was placed in front of the subject  [16] , which is not practicably feasible for some treadmills because of the anteriorly placed console panel.
Conclusion This study shows that CRP computed with skeleton Kinect data failed to assess GA, whereas validity and sensitivity analysis demonstrated reliable assessment when using the new GA index (ILong) based on depth images. This provides the clinician with a longitudinal spacetime GA index within the gait cycle for patients walking on a treadmill. We have shown that placing the depth camera in front of or behind the subject had no strong influence on the results and that even a small number of gait cycles would be enough to detect asymmetry in gait. Further validation of this method is required, using patients with various pathologies to complete the promising results reported above based on a single controlled perturbation. It holds the promise of easy, fast and low-cost within-stride gait longitudinal asymmetry assessment without requiring any specific technical skills.
Data collection and analysis Face-to-face semi-structured interviews were conducted with the seven senior doctors working in a single NICU in a university-affiliated general hospital in the Ile de France, between 1 June and 15 August 2012. The face-to-face interviews were made up of two parts: 1/ a free account by the participant of a situation in daily life in which he/she remembered having given apologies; 2/ an account of another case of apologies being presented in the professional sphere. The feelings about the meaning given to their apology in these situations were the subject of close questioning (similarities and differences between the contexts, intention, facility and verbal expression used). The meaning of the declaration of error and the way in which it was disclosed were explored, in its different aspects and implications (S1 Appendix.
Results Between 15 May and 15 July 2012, seven research interviews were carried out (average length 60 minutes; 6 neonatologists and 1 surgeon; 3 women and 4 men; aged 33 to 62 years, mean 46). Analysis allowed four main themes to emerge, which were present in the discourse of the doctors and/or the mother. Verbatim accounts have been added to illustrate the data (Dr1 to 7 for the doctors). Table  1  describes the situations chosen by doctors to report apologies in daily life and in professional life.
Surveys and study populations This study focused on Belgium and Brazil due to the data availability of independent, population-based, national surveys with similar questions about functional limitations and chronic conditions. The prevalence of risk factors for chronic conditions and the risk of premature mortality due to selected chronic conditions (cancer, diabetes, cardiovascular diseases, and chronic respiratory diseases) in both countries were very similar to the aggregated estimates of high and upper-middle income countries (Additional file 1), suggesting that these two countries are good representatives of the latter groups. Data from the following national health surveys from Belgium and Brazil were used: (i) Health Interview Surveys conducted in Belgium (HISBe) in 2008 and 2013; and (ii) National Household Sample Survey conducted in Brazil (NHSSBr) in 2008.
For Belgium, the data of the 2008 and 2013 surveys were pooled due to the small sample size of the 2008 HISBe, which did not allow fitting the multinomial model stratified by age and gender. Nonetheless, the prevalence of moderate and severe functional limitations was similar in the two Belgian surveys for most age groups (Additional file 2). In total, 4521 (HISBe) and 28,437 (NHSSBr) individuals were included in this study, respectively. For comparability purposes, the questions used to define functional limitations and the chronic conditions included in this analysis were common to the surveys in both countries. Functional limitations were defined based on limitations in three activities of daily living (ADL): eating, showering, and toileting (Table  1 ). These three ADLs cover the whole ADL spectrum: eating is considered the easiest task, which is generally lost late in life; toileting is an intermediate task, with middle loss; and bathing is a difficult task, with an early loss  [22] [23] [24].
The present analyses had some limitations. A first one is that the characteristics of both the work environment and cardiovascular health were self-reported and may therefore have been relatively imprecise. Concerning the risk factors, the potential issue is underreporting with a rate that varies substantially from one factor to another, as was previously shown by a comparison with medical diagnoses obtained from the sickleave database of the company  (36). It is particularly the case for obesity, the prevalence of which has been found to be underestimated by approximately 20%  (37). Nevertheless, it is unlikely that this imprecision was a major concern in the present study, which only investigated the existence of prospective associations without attempting to accurately determine prevalence or incidence rates.
ACKNOWLEDGMENTS Author affiliations:  Laboratory of Medical Informatics  and Knowledge Engineering in e-Health (LIMICS),  National Institute of Health and Medical Research (INSERM) ,  Mixed Research Unit 1142 ;  University of Paris 6 (Pierre and Marie Curie University ); and  University of Paris 13, Paris, France  ( Pierre Meneton); Center for Psychiatry and Neurosciences, INSERM, Unit 894; University of Paris 5 (Paris Descartes University ); and  Paris University Hospitals West, Public Assistance Hospitals of Paris, Paris, France (Cédric Lemogne ); Mixed Service Unit 11 and Mixed Research Unit 1168, INSERM, and  Paris Descartes University, Villejuif, France  ( Eléonore Herquelot ,  Sébastien Bonenfant ,  Marcel Goldberg ,  Marie Zins );  Nutrition Service ,  University of Paris 5 (Paris Descartes University), and Paris University Hospitals West, Public Assistance Hospitals of Paris, Paris, France (Sébastien Czernichow ); and Clinical Investigation Center 1418, INSERM;  University of Paris 5 (Paris Descartes University ); and  Paris University Hospitals West, Public Assistance Hospitals of Paris, Paris, France (Joël Ménard ).
The GAZEL cohort study was funded by  Electricité de France ,  Gaz de France , and  INSERM  and received grants from the program  Très Grandes Infrastructures de Recherche , the  Agence Nationale de la Recherche , and the  Agence Française de Sécurité Sanitaire de l'Environnement et du Travail. The present analyses were funded by the  Association Robert Debré pour la Recherche Médicale. We express our thanks to  Electricité de France  and  Gaz de France , especially to the  Service Général de Médecine de Contrôle , and to the  Caisse Centrale d'Action Sociale du Personnel des Industries Électrique et Gazière. We also acknowledge  Mixed Service Unit 11 of INSERM  and the  Université Versailles St. -Quentin  for management of the GAZEL database. C. L. has received honoraria from or has served on advisory boards for  AstraZeneca ,  Daiichi Sankyo, Lundbeck , and  Servier.
6 11 udy design Criteria for eligibility for the study: ► all infants under 6 months on admission ► with acute bronchiolitis and no other serious condition ► breast fed (at least partially) on admission ► discharged home. All parents signed a consent form on admission allowing extraction of data from medical charts. Parents were also informed about this study during hospitalisation if possible (preinclusion) or received information either by mail or by email when reached on the phone for the survey itself. We included all patients whose parents spoke French, had been informed about the study and accepted to answer a standardised survey on the phone after discharge. Parents were contacted 3 months (range 0. 5-6) after discharge. We did not include parents that could not be reached on their phone.
statistical analysis Data are expressed as median values (with minimum and maximum values) for continuous variables and number for binary or categorical data. Student's t-test was used for qualitative (categorical) variables. For comparison of non-parametric means, Wilcoxon's test was used if necessary for quantitative (numerical) criteria, while χ 2 and Fisher's exact tests were used to compare qualitative criteria. p Values <0. 05 were considered statistically significant. Analyses were performed with Biostat TGV software.
Methods In the discovery stage, we combined three GWAS datasets (EU-RLS GENE, INTERVAL, and 23andMe) with diagnosis data collected from 2003 to 2017, in face-to-face interviews or via questionnaires, and involving 15 126 cases and 95 725 controls of European ancestry. We identified common variants by fixed-effect inverse-variance meta-analysis. Significant genome-wide signals (p≤5 × 10 -⁸) were tested for replication in an independent GWAS of 30 770 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages. We did gene annotation, pathway, and gene-set-enrichment analyses and studied the genetic correlations between restless legs syndrome and traits of interest. Findings We identified and replicated 13 new risk loci for restless legs syndrome and confirmed the previously identified six risk loci.
[10] [11]  Given these genetically driven advances, we did a metaanalysis of GWAS on restless legs syndrome in people of European ancestry, followed by replication in an independent dataset. We hypothesised that this approach would enable us to detect novel risk loci and pathways associated with restless legs syndrome that would provide further insights into the molecular mechanisms underlying the disorder, and yield possible novel therapeutic targets or avenues for the repurposing of existing drugs.
Study populations and phenotype definitions For the discovery meta-analysis, we combined three GWAS datasets. The EU-RLS-GENE consortium GWAS includes cases and population-matched controls recruited in eight European countries, Canada, and the USA. People with restless legs syndrome were recruited in specialist outpatient clinics for movement disorders and in sleep units. Restless legs syndrome was diagnosed in face-toface interviews by an expert neurologist, based on the International Restless Legs Syndrome Study Group diagnostic criteria. 1, 12 A subset of the samples had been used in previous GWASs for restless legs syndrome (appendix p 10). 4, 6, 7  The INTERVAL study  13, 14  included whole-blood donors recruited in England, enrolled between 2012 and 2014. The validated Cambridge-Hopkins Restless Legs Questionnaire  15  was used to identify patients with restless legs syndrome and to exclude mimicking disorders.
Probable and definite cases were combined in one group. The 23andMe GWAS dataset comprised samples drawn from participants of the customer base of 23andMe (Mountain View, CA, USA), which is a genetic testing company. The restless legs syndrome phenotype was determined with one research question, "Have you ever been diagnosed with restless legs syndrome?", which had three response options: yes, no, and not sure. We include respondents who answered "yes" as cases and those who answered "no" as controls, and we excluded those who answered "not sure".
Added value of this study We did a meta-analysis of three GWAS datasets, which yielded a total sample size more than one order of magnitude larger than any previously published restless legs syndrome GWAS. We discovered 13 new risk loci for restless legs syndrome, taking the total from six to 19. Assessment of these candidate genes will enable more granular dissection of the pathogenesis of restless legs syndrome, which could improve determination of shared genetic architecture with other neurological phenotypes and the prospects of developing novel and more effective treatment options. Our results strongly support the link to neurogenesis, changes in neuronal circuit formation, synaptogenesis, and axonal guidance, thereby strengthening the concept of restless legs syndrome as a neurodevelopmental disorder.
Meta-analysis procedures For the discovery stage, summary statistics from each individual GWAS dataset were subjected to further quality control with EasyQC, version 8. 5, and we did a fixed-effect inverse-variance meta-analysis with METAL, release 2011-03-25 (appendix p 5). To address heterogeneity between studies in the identified association signals, we did a random-effects meta-analysis with METASOFT, version 2. 0. 1 (appendix p 5). Genomic control was done in each study separately before meta-analysis by calculating the inflation factor λ and adjusting for it. 16 o define independent genome-wide significant signals (p≤5 × 10 -⁸) in the discovery meta-analysis results, we used a two-step procedure (appendix p 5). Briefly, we first assigned variants to clusters with the "clump" command in PLINK software (version 1.
Heritability, partitioned heritability, and genetic correlation analysis Heritability and partitioned heritability were estimated by LD-score regression (with LDSC, version 1. 0. 0) and using the summary statistics of the discovery metaanalysis without genomic control correction. Partitioned heritability analysis used publicly available partitioned LD scores for 52 functional categories based on the phase 3 dataset of the 1000 Genomes Project, as precomputed by the developers of LDSC. For the genetic correlation analysis, we used the LD Hub database (version 1. 2. 2), which provides access to summary-level GWAS statistics of more than 200 traits. The analytical procedures and settings are detailed in the appendix (pp 5-6). To estimate the variance explained by genomewide significant association signals, we calculated Nagelkerke's pseudo-R² in the EU-RLS-GENE dataset with tenfold cross validation.
Results The discovery stage association study was done in 15 126 cases and 95 725 controls: 6228 and 10 992 from EU-RLS-GENE; 3065 and 24 923 from INTERVAL; and 5833 and 59 810 from 23andMe. After quality control, 6 864 281 SNPs and indels with minor allele frequency 1% or greater were available for statistical analysis. We identified 20 independent association signals meeting genome-wide significance (figure  1 , table 1, appendix p 13). No secondary independent association signals with genome-wide significance were found in these 20 regions by standard conditional analysis. We assigned the 20 independent association signals to 19 independent genomic risk loci (two mapped to the same gene, PTPRD, and are <500 kb apart; appendix p 65). Lead SNPs rs1836229 and rs62535767 of these two signals were not correlated (r²=0•0002, EU-RLS-GENE dataset).
Genomic control showed negligible inflation of the median test statistic (λ 1000 =1•004, rescaled to adjust for the large sample size), which LD-score regression revealed was mainly due to polygenicity (intercept 1•018), as is expected for a common complex disorder, rather than being caused by population stratification or other confounders. We observed between-study heterogeneity at some loci, but all signals kept genome-wide significance in the randomeffects meta-analysis (appendix p 14). Association data for all 20 signals were obtained from the replication dataset, which included 30 770 cases and 286 913 controls (appendix p 10). All 20 association signals of the discovery stage were replicated, three at a Bonferroni-corrected level of p<0•0025, and 17 meeting genome-wide significance (table  1 ).
In the joint analysis of discovery and replication stages, all loci but one had genome-wide significance in the fixed-effect meta-analysis (table  1 ). All loci in the joint random-effects meta-analysis had genome-wide significance (appendix p 15). Overall, 13 new risk loci were identified and all six known genomic risk loci for restless legs syndrome were replicated (figure  1 , table  1 ). The MEIS1 locus on chromosome 2 was confirmed as the strongest genetic risk factor for restless legs syndrome (table  1 ). The lead SNP, rs113851554, located in a putative regulatory element in intron eight of MEIS1, is a lowfrequency variant with odds ratio (OR) estimates of 1•82-2•16, which clearly distinguish it from the other risk loci for restless legs syndrome. The amount of heritability of restless legs syndrome attributable to all SNPs available in our dataset in the LD-score regression was 19•6%.
Moreover, the specific features of the restless legs syndrome phenotype, such as disease severity or preponderance of motor or sensory symptoms, might have varied between the studies. Detailed clinical data were scarce, and the restless legs syndrome phenotypes in the INTERVAL and 23andMe datasets were not clinically validated and, therefore, we cannot further address the underlying causes of heterogeneity. Studies including detailed assessments of restless legs syn drome symptoms are needed to dissect the specific roles of and possible interactions between genetic variants in the risk loci and the resulting phenotypes. Together, the 20 association signals accounted for 60% of the SNP-based heritability of 19∤6% estimated in this study. Our dataset was limited to SNPs and small indels with minor allele frequency of 1% or greater.
50 Our pathway analysis was designed to have high sensitivity to avoid downward bias, such as falsenegative results due to incomplete information provided by the annotation databases. To avoid upward bias (ie, falsepositive enrichment results), we corrected by sampling and null-GWAS permutation (phenotype label permutation). Significance of results in the tissue enrichment analyses might have been hindered by a lack of appropriate samples from relevant anatomical regions or developmental stages in the input datasets. Nonetheless, neuronal tissues were consistently prioritised, and nominally significant regions, such as midbrain structures and spinal cord, have previously been implicated in restless legs syndrome. 51 t present, major concepts in the pathophysiology of restless legs syndrome address alterations in the dopaminergic neurotransmitter system and in brain iron metabolism, and are supported by evidence from animal, imaging, and human post-mortem studies.
Contributors BS and JW designed and oversaw the study. BS, CZ, AVS, KO, BM-M, and JW did primary interpretation of the data. BS, CZ, KO, and JW wrote the paper, and SB and DAH contributed substantial edits. BS did the EU-RLS-GENE GWAS. CZ did the meta-analyses. ET and BP participated in quality control and statistical analysis of the EU-RLS-GENE GWAS. BM-M supervised statistical analyses for EU-RLS-GENE GWAS and meta-analyses. YD, AS, BH, WPo, DK, KS, CGB, WPa, CT, WHOe, MH, MT-L, AM, GMH, OP, IF, OAR, ZW, WGO, KB, and JW recruited patients for the EU-RLS-GENE GWAS. RPA, CJE, LX, JM, and GAR recruited patients and controls for the EU-RLS-GENE GWAS. MP, PV, CD, AF, LT, AFRS, SHS, CG, and AP provided control samples or genotype data for the EU-RLS-GENE GWAS. JW acquired funding for the EU-RLS-GENE GWAS and all meta-analyses.