dataesr/scientific-paragraphs-categorization huggingface dataset:
Demographic characteristics of the families included in the current study as compared to the entire sample recruited at wave 1 are reported in Table  1. All data were collected during home visits through interviews, questionnaires, and standardised measures. Additionally, all data for the MCS is publicly available and can be requested through the UK Data Archive. Informed consent was obtained in writing from primary caregivers at each wave of data collection. Ethics approval for the primary MCS study was obtained from the South West Multi-Centre Research Ethics Committee and the London Multi-Centre Research Ethics Committee who also approved of the consent procedures.
08  (MacCallum et al. , 1996). Cross-lagged models were estimated using Mplus version 7. 4  (Muthén & Muthén, 1998 -2015). Standardized betas (β) are provided for each model as an estimate of effect size. Due to the large study sample we adopted a more stringent statistical threshold: alpha =. 001 instead of. 050 (see  Johnson, 2013). Missing data, regarding covariates from non-response, were treated using Full Information Maximum Likelihood (FIML), which has been found to be an unbiased and advantageous approach in handling missing data  (Enders & Bandalos, 2001). Two cross-lagged models were examined: a model without covariates and a model with covariates. Due to saturation of the first model when the six paths were included, the concurrent path at age five was removed. Overall fit for the first model of prosocial behaviour and expressive language was good, x 2 (1) = 17.
Strengths and Limitations The current study utilizes one of the largest and most comprehensive population-based cohorts on infant and child development sampled from 4 different countries across the UK. Further, the types of assessment utilized in the current study (i. e. , parent report for prosocial behaviour and a standardized assessment for expressive language) limit the potential of introducing bias that could arise from shared method variance. Despite these strengths, a few limitations should be noted. First, the data collected on children's social behaviour relied exclusively on the parent-report version of the SDQ. The assessment of prosocial behaviour can prove challenging due to the necessary inferences required (e. g. , is considerate of others), however parents may be at a particular advantage in this regard given their intimate knowledge of their children.
Fourth, given that no measure of expressive language was collected in the fourth wave of the MCS (at seven years of age), we were unable to look at the associations across a longer period of development. Having only two waves of data also limited our ability to test a mediation model rather than just controlling for covariates in the model. Finally, due to only having two assessments of prosocial behaviour and expressive language in the current study, we were limited in our ability to test all six paths of associations without saturating the first model. Relatedly, we were unable to regress both age three and age five prosocial and expressive language variables on covariates in the second model. However, stationarity (i. e. , when the causal structure remains the same over time;  Cole & Maxwell, 2003)  could possibly be assumed given the covariates that were used.
Acknowledgments: Permission to use the U. K. Millennium Cohort Study given by the ESRC Data Archive at Essex is gratefully acknowledged. The authors also thank the participants and their families for their long-term commitment to this study. We would also like to acknowledge X for his statistical expertise. Note: All variables collected when infants were 9 months. Item non-response occurred in both groups for variables presented. The academic qualifications in the UK system are as follows; GCSE grades D-G refers to secondary education and is approximately equivalent to having less than a high school diploma in the US education system; O level/GCSE grades A-C refers to completion of a secondary exit qualification; A/AS/S refers to further education and is comparable to the US equivalent of a high school diploma; Diploma in higher education is approximately equivalent to a two year college degree; First degree is approximately equivalent to a four year bachelor degree; Higher degree is approximately equivalent to a professional/graduate degree.
Statistical analyses Analyses were performed using Sigma-stat software (Jandel, France). Data distribution normality was confirmed using the Kolmogorov-Smirnov distance test. For each session, the effect of ethnicity on the Pmax-VJ relationship was studied by multiple linear regression analysis between the dependent variable (VJ) and the independent variable (Pmax) plus a dummy variable E corresponding to ethnicity (E = 1 for Afro-Caribbean and E = 0 for Caucasian) according to the following model: VJ = a + bPmax + cE + ε where ε is the error. The effect of practice on the VJ and Pmax was analyzed using a two-way analysis of variance (ANOVA): session (session 1, session 2, session 3) and ethnic group (Caucasian, Afro-Caribbean), with repeated measures on the first factor. Additionally, the practice effect was studied from reliability indices using the test-retest correlation coefficient (r), the standard error of estimation (SEE) and intraclass correlation coefficients (ICC) between each session.
Blue: this study; gray: GBR (gravel bed river) data set  (Church and Rood, 1983; Parker et al. , 2007; King et al. , 2004; Ashmore, 2013). etry of braided and meandering threads (Fig. 3 ). We treated the threads of braided rivers individually, based on the wetted area at the time of measurement (Fig. 1 ). We measured the cross section geometry, the discharge, the grain-size distribution and the slope of the threads from as many rivers, spanning as broad a range in discharge, as possible. We chose the sections at random, according to their accessibility, our purpose being to collect a statistically significant data set. To measure the cross section and the water discharge of large rivers, we used a 2 Mhz acoustic Doppler current profiler (ADCP, Teledyne-RDI StreamPro). The instrument was mounted on a raft and cross sections were performed from which we extracted both the geometry and the discharge of the threads.
Regime equations Figure  6  compares our measurements to four other sources. Three of them, the compendiums of  Parker et al. (2007) ,  Church and Rood (1983)  and  King et al. (2004)  include measurements from single-thread rivers. The fourth one corresponds to measurements on individual threads of the braided Sunwapta River  (Ashmore, 2013). These sources are hereafter referred to as the GBR data set. The Bayanbulak threads are widely dispersed in size (0. 6 ≤ W ≤ 35 m) and discharge (0. 002 ≤ Q ≤ 51 m 3 s -1 ). On average, they are smaller than the GBR threads. The median grain size of the Bayanbulak threads d 50 0. 013 m is finer (the standard deviation of the d 50 is σ d 50 ∼ 0. 008 m). Our data set therefore extend the GBR ones towards smaller threads with finer sediments. We now consider the empirical regime equations of individual threads (Fig.
7 ). To facilitate the comparison between the GBR data set and our own, we use dimensionless quantities, namely W/d 50 , H /d 50 , S and Q * = Q/ g d 5 50 , where g is the acceleration of gravity. Not surprisingly, the geometry of a thread is strongly correlated with its water discharge: its width and depth increase with discharge, while its slope decreases. At first sight, these trends are similar for meandering and braided threads. They also compare well to the GBR data set, although the Bayanbulak threads are slightly wider than the GBR ones on average. The measurement uncertainty, although significant, is less than the variability of our data, except for slopes smaller than about 5 × 10 -3. Despite considerable scatter, both our measurements and the GBR data sets gather around straight lines in the log-log plots of Fig.
New computational tools bolster the utility of epigenome data for biology and medicine As of today, IHEC has generated over 7000 multi-dimensional datasets, which are publicly available through several channels. For specialized analyses, the raw data files containing personally identifiable data can be obtained under the controlled access scheme from dbGaP (NIH) and EGA (EBI). For common analyses not using any personally identifiable information, pre-processed data can be obtained from the unrestricted GEO (NIH) and ArrayExpress (EBI) repositories. To guide new users, IHEC has made a substantial investment into dedicated data access tools. The IHEC Data Portal (http://epigenomesportal. ca/ihec/) provides a comprehensive overview and single point of entry for accessing all IHEC reference epigenome data  (Bourque 2016).
From a practical standpoint, one can use maps of long-range loops between enhancers and promoters to determine which gene is regulated by a disease-associated noncoding variant. For example, maps of long-range contacts in 17 primary human blood cell types exhibited systemic variation across cell types and identified over 2,500 potential disease genes when combined with a database of disease-associated variants  (Fraser 2016). Similarly, chromatin contact maps in 21 primary human tissues and cell types yielded a large compendium of candidate genes when combined with known disease-associated noncoding variants, and also revealed thousands of Frequently Interacting REgions (FIREs) with unusually high levels of long-range chromatin contacts (Ren 2016). Together, the studies in this section play a crucial role in using epigenetics to fill in the gaps between genotype and disease phenotype.
Acknowledgements We would like to thank the trainees, research and administrative assistants who are not listed in this overview but without whom the IHEC would not have achieved this work. We also thank collaborators who are not members of IHEC for their valuable contributions. The views expressed in this article are solely those of the authors and may not necessarily reflect those of the  National Institutes of Health (USA)  and the  European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of the presented information.
Results and discussion Reproducing early CD8 + T cell immune response in murine lymph nodes In order to refine the parameter values of our model, previously obtained in  [40] , we assessed its ability to reproduce two types of data generated at two different scales within 72-120 h post infection (pi) time frame: 1. Molecular data available through the ImmGen database (Fig. 1a ) and 2. Cellular data consisting in the count of F5 transgenic cells in the lymph nodes of mice infected with Influenza virus (Fig. 1b ). Regarding the molecular behavior of our model, we observed that the average concentrations of IL-2 and IL-2R in the simulated cell population increase sharply in few hours post-infection (IL-2 and IL-2R curves in Fig. 1a ). Around 78 h pi, a high level of IL-2•IL-2R (IL-2•IL-2R curve in Fig.
Due to asymmetric partition of T-bet between daughter cells, effector phenotypes appear soon after the first T cell division (see simulation movie Additional file 1). Around 96 h pi, effector CD8 + T cells dominate the population in the simulations (Fig. 1c, d ). Cell death appears sporadically following the emergence of effector cells and becomes frequent at later simulation points (see simulation movie Additional file 1; 120 h pi in Fig. 1d ). Overall, our model succeeds in reproducing the expected dynamics of CD8 + T cells in murine lymph nodes, at both the molecular and cellular scales. Importantly, it explains the cellular phenomena by generating in silico kinetics of the molecular species that match the in vivo data (from the ImmGen datasets). In addition, the model also makes some predictions such as the evolution of the proportion of the different cell types in a draining lymph node (Fig.
Funding This work has been supported by  ANR  grant PrediVac  ANR-12-RPIB-0011 , the  Finovi Fondation , the  Rhône-Alpes Complex Systems Institute (IXXI) , the  Université Claude Bernard Lyon 1 , the  Fonds Europeen de Developpement Regional , and institutional grants from the  Institut National de la Sante Et de la Recherche Medicale.
Availability of data and materials The multiscale model's simulation files supporting the conclusions of this article are included as Additional file 7. Note that CompuCell3D  [41, 42]  must be installed to run the files. The molecular datasets supporting the conclusions of this article are freely available on http://www. immgen. org, as mentioned in the section "Methods", subsection "mRNA profile". The CD8 + T cell count dataset supporting the conclusions of this article (Fig. 1b ) is available in the Open Science Framework repository https://osf. io/n9k32/.
Breath-bybreath cardiopulmonary data (Vyntus, CareFusion, France) were measured at rest, warm up and incremental exercise testing. Before each test, O 2 and carbon dioxide (CO 2 ) analyzers and flow mass sensor were calibrated using available precision gas mixture and a 3-L syringe, respectively. Minute ventilation (V' E ), V'O 2 , carbon dioxide output (V'CO 2 ) were recorded as concurrent 10 seconds moving averages, as was determined ventilation anaerobic threshold (AT) by the V-slope method. Ventilatory reserve was calculated as (MVVpeak V' E )/MVV * 100 where MVV is maximal voluntary ventilation estimated as FEV 1 multiplied by 35. Peak values were averaged over the last 30 seconds of exercise. Patient effort was considered to be maximal if two of the following occurred: predicted maximal work is achieved, predicted maximal HR is achieved, V' E /V'O 2.
statistical analysis The statistical analyses were performed with SAS software (SAS Institute Inc. , Cary, NC, USA; version 9. 3). A twosided significance level of 0. 05 was chosen for all tests. Descriptive statistics, with tests of normality (Shapiro-Wilk test) for values showed free distributions. Values were expressed as medians with interquartile ranges (IQR). Patients were divided according to GOLD classification. 30 onparametric analysis (Kruskal-Wallis test) was used to test for differences between classes. Post hoc comparisons were determined by Wilcoxon test with Bonferroni correction. Relationships between quantitative variables were assessed by the Spearman's correlation coefficient (r). Bivariate comparisons for categorical variables were performed with Chi-squared test (with Fisher correction as appropriate).
Locally estimated scatterplot smoother SAS procedure showed a broken-line trend between CMI or HR max and IC dyn changes. Linear mixed model procedure was used to analyze the change of CMI and HR max versus a broken function of IC dyn that took 0 before the knot and subject's IC dyn after the knot; the knot position was fixed by the smallest Akaike information criterion (AIC) using Gauss-Newton method. 31, 32 Thus, mean and 95% confidence interval of optimal knot, intercept (no change), and slope (increase) were estimated by nonlinear mixed model. As these two variables are used interchangeably in the literature to define CI, we took the middle of the overlap area of the IC dyn knot from these two relationships. IC dyn converted to DH as dichotomous variables indicating IC dyn , optimal knot (DH=1) or IC dyn $ optimal knot (DH=0).
Participants The National Child Development Study (NCDS) includes all children born during one week in 1958 (N = 18558) in Great Britain. Data collection was carried out on cohort members between birth and 50y. At age 44-45y a biomedical survey was conducted including a self-reported questionnaire, physical measurements, blood and saliva samples  (Power and Elliott, 2006). The sample used for this study is described in Fig. S1.
, 1994). We ran a Principal Components Analysis (PCA) to sum-up the information of the above self-reported variables using nonimputed data and deriving one single component. The PCA included three groups of variables: (i) the first group was created using seven dimensions of the SF-36 scale of HRQoL questionnaire (physical functioning, role-physical, bodily pain, general health, vitality, role-emotional, mental health). The social functioning subscale was excluded under statistical criteria when running the principal component analysis. (ii) Four items of the Sleep Scale of the Medical Outcomes Study included as ordinal continuous variables. (iii) The malaise inventory score ranged between 0 and 9. PCA show an adequate fit to the data (Component 1 eigenvalue = 5. 3; Overall Kaiser-Meyer-Olkin measure of sampling adequacy = 0.
Statistical analysis To control for possible bias due to missing data, twenty imputations were conducted using the multiple imputation program ICE in STATA V14. We took a missing-at-random (MAR) assumption. Each covariable with missing values was imputed including all confounders used in the models as well as variables from other sweeps correlated with the variable to impute but excluding the exposures variable (AL) and the subjective health index (Supplementary material). To observe the unadjusted link and better appreciate the evolution of the link between AL and later subjective health, the first model of the multivariate linear regression took only into account AL. Subsequently, and to mimic life-course experiences, we sequentially adjusted the multivariate linear regression resulting in four models: 1.
, 2006; Karlamangla et al. , 2006; Seeman et al. , 2001). However, previous studies have concentrated their analysis on specific dimensions of health mainly using elderly population based studies, and though have a lack of prospective data concerning early life, adolescence and early adulthood circumstances. The main novelty of our study is to focus our analysis on the basis of a global effect of stress on health, using an integrative measure -that is not centered on the dichotomy absence/presence of disease-in middle aged adults that have been followed since birth. This allows us to take into account a variety of material, psychosocial, educational and life styles exposures from early life and across the life course. Here we analyzed health as an integrative measure in middle aged adults using a life course perspective allowing for a better  understanding of the causal sequence of events.
Acknowledgments We thank the cohort members who are part of the 1958 National Child Development Study. C. B. S. is supported by the  University of Costa Rica  and funded by the  European Commission (Horizon 2020  grant no. 633666 ). The present work is part of the  Incorporation Biologique et Inégalités Sociales de Santé  project supported by funds from  Agence Nationale de la Recherche  ( ANR-12-DSSA-0004 ).
Other typical checks are 'C12', 'C15' or 'C18', which correspond to checks formed at age-1 at ,20, 50 or 80% respectively of the expected annual growth of the otolith for that age. The formation of checks C12/15 was studied along with evaluation of edge formation for age-1, noting the time of year when those checks were already recognisable. The age compositions of catches were obtained by applying the respective ALK to the length distribution, usually on a halfyearly basis (but occasionally quarterly). Usually, more than 1000 age determinations support every ALK, along with considerable sampling for length distribution (ICES 2014), well above the minimum standard sampling requirement established in the European Union Community programme for the collection of data in the Fisheries sector (see https://datacollection.
Acknowledgements The authors thank  Patrick Prouzet  and  Patrick Grellier  for their discussions, which helped refine the ageing method described herein. In addition, the authors thank  S. Junquera  ( Oviedo University, Oviedo, Spain ),  A. Astudillo  ( IEO ) and  J. Masse ( IFREMER ) for providing some otoliths for the original validation study. Many of the measurements presented in the second section of the Supplementary material were provided by  Aitor Astoreka (AZTI) , within a study funded by  European Union (EU)  project number  044132  AFISA (Automated Fish Ageing). The surveys and catch at age composition and mean lengths at age were produced by the  National and Regional Institutes of Spain (IEO and AZTI) and of France (IFREMER)  with the support of the their respective fishery departments (including the  Fishery Department of the Basque Country in Spain).
The importance of setting up a registry The exact prevalence of HPP is not known, particularly that of the odonto-HPP form. The dentist/dental surgeon can contribute to a better understanding of this dental anomaly and associated rare diseases by registering their patients in the HPP interactive biomedical database, D [4] (Diagnosing Dental Defects Database)/ PHENODENT (www. phenodent. org, Strasbourg Cedex, France). On 11 September 2001, the Comit e Consultatif sur le traitement de l'Information en mati ere de recherche dans le domaine de la Sant e (CCTIRS), a French consultative committee on data processing in health research, issued a favourable opinion of this registry, which was authorised on 18 May 2009 by the Commission Nationale Informatique et Libert es (CNIL), the national commission on data processing and protection (registration number 908416).
Global efforts are underway to set up an international registry with the aim of providing a comprehensive, real-life, longitudinal profile of patients with HPP (ClinicalTrials. gov: NCT02306720)  65. Data concerning patients' treatment and clinical condition will be collected as available from medical records at registry entry (baseline) and a minimum of every 6 months thereafter. The types of information collected will include demographics, HPP disease history and diagnosis, HPP clinical history at baseline, genotypes, biochemical, laboratory and radiologic phenotypes, disease status at follow-up visits, and patientreported outcomes (such as quality of life, pain/symptoms, and physical function) at baseline and follow-up visits. Patient confidentiality will be maintained throughout. As of 1 June 2015, eight patients have been enrolled.
Development of a specific database It is necessary to collect precise information on different aspects of HPP and to have a single tool for recording information such as genotype (type of genetic defect), phenotype (whether bone or teeth related), the course of the disease, any treatment that may have been given, and quality of life. Currently, these data are fragmented for each individual patient; they may be collected in some cases, but generally focus on one specific problem or area. The idea of such a database is to allow information to be shared between specialists in all areas of HPP (e. g. , growth, metabolism of bone, phosphorus and calcium, genetics, oral cavity, pain). A quality-of-life module and the ability to include data provided by patients are very important for assessment of the impact of the disease and its course.
Often, healthcare professionals may only be able to focus on their area of expertise, possibly due to various constraints in healthcare systems, and may not have access to information and resources on other aspects of the disease. As a result, they might not be able to provide a holistic approach to the management of HPP, and different specialties may need to be involved in the care of different manifestations of the disease. For the healthcare professional, therefore, the ability to access all the patient's data in a central registry will be extremely important. Orodental issues are closely linked to disease severity; therefore, this information is important to share. Limited data are available on the course of the disease in HPP, hence the importance of patient follow-up. With regard to teeth, little information is available to allow a comparison of primary teeth history (e.