dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion To our knowledge, this is the first study investigating associations between exposure to greenspace and age at menopause. It was based on prospectively collected data of a well-established representative cohort including 1955 women living across Europe who were followed over 20 years, together with time-varying measures of greenspace over the course of the study. We observed that higher residential surrounding greenspace was associated with an older age at menopause and these findings were robust to a number of sensitivity analyses. Due to the lack of other studies it is not possible to compare our findings with those of others; however, our observed associations are plausible because of several potential underlying mechanisms: Stress in humans is reflected by a high level of cortisol  (Kudielka and Kirschbaum, 2005) , which is reduced by exposure to greenspace  (Tyrväinen et al.
Acknowledgments We thank all participants, fieldworkers and coordinators of the  European Community Respiratory Health Survey  for their efforts as well as the  Research Council of the European Union , the  Norwegian Research Council , the  Spanish Government  and the  University of Bergen  for their financial support. Further, we are very grateful to  Ersilia Bifulco  and  Sandra Suske  for the hormone analyses carried out at the  Core Facility  for  Metabolomics at the University of Bergen.
Funding  Kai Triebner  has received a postdoctoral fellowship from the  University of Bergen. Payam Dadvand is funded by a  Ramón y Cajal fellowship  (Grant:  RYC-2012-10995 ) awarded by the  Spanish Ministry of Economy and Finance. The present analyses are part of a project funded by the  Norwegian Research Council  (Grant:  228174 ). Coordination of the ECRHS I was supported by the  European Commission as part of the "Quality of Life" program  and the authors and participants are grateful to the late  C. Baya  and  M. Hallen  for their help during the study and  K. Vuylsteek  and the members of the  COMAC  for their support. Coordination of the  ECRHS II  was supported by the  European Commission as part of the "Quality of Life" program  (Grant:  QLK4-CT-1999-01237 ). The coordination of the ECRHS 3 was funded through the  Medical Research Council  (Grant:  92091 ).
Methods The aim of the present study was to explore the relationship between physical activity and body-esteem among 15-year-old adolescents, in association with the factors highlighted in earlier studies (i. e. gender, weight status, maturation, self-esteem, family wealth, and ethnic group). Empirical data of Finnish adolescents were obtained from the Health Behaviour in School-aged Children (HBSC) study. This World Health Organization collaborated, cross-national survey uses ndings at both national and international level (i) to gain new insights into young people's health and wellbeing, (ii) to understand the social determinants of health, and (iii) to inform policy and practice with a view to improving young people's health. Physical activity has been one of the research areas in the HBSC study since the rst survey in 1984.
Further details on the HBSC study procedures can be accessed elsewhere  [33, 34]. The nationally representative cross-sectional data for the present study were obtained from 15-year-old pupils in Finland in the spring of 2014. The schools (N = 122) were chosen from the Finnish school register via a cluster sampling method, and sampling was adjusted to take into account the province, the municipality, and the size of the school (with probability proportional to size, PPS). The participating class/group was selected randomly in each school. Participants responded voluntarily and anonymously to a standardized paper-and-pen questionnaire during a school class, after being informed of the con dentiality of the data, and of the fact that only group-level results would be reported. Data collection took place in the classroom environment under exam conditions.
Statistical analyses The effects of different categories of MVPA on body-esteem were analysed via mixed effects linear regression. Regression coe cients with 95% con dence intervals (CI), p-values, and R squares (R 2 ) were calculated. The association between MVPA, weight status, self-evaluated family wealth, self-esteem, and bodyesteem was examined by a mixed effects linear regression, adjusting for the potential modifying effects of maturation and ethnic background. In addition to β-coe cients, there was calculation of 95% CIs, pvalues, the R 2 for the individual variables, and cumulative models. The statistical analyses were conducted separately for the girls and the boys, and were performed by SPSS statistical software (version 24. 0) and Stata (version 15).
Acknowledgements HBSC is an international study carried out in collaboration with WHO Europe. The International Coordinator of the 2013/2014 survey was  Joanna Inchley  ( University of Glasgow, Scotland ) and the Data Bank Manager was  Oddrun Samdal  ( University of Bergen, Norway ). In this study, only national data of Finland was used. The Principal Investigator of national HBSC Team Finland Jorma Tynjälä have given us permission to use Finnish national data. For details on HBSC, see http://www. hbsc. org.
Availability of data and materials The dataset supporting the conclusions of this article is available in the HBSC Data Management Centre. The HBSC Data Management Centre coordinates the work with the international data le and the trend data, and is the Data Bank for the HBSC study. The centre distributes data in accordance with the HBSC data access policy. The HBSC Data Management Centre is based at the Department of Health Promotion and Development in the University of Bergen, Norway. Professor Oddrun Samdal is the Data Manager of the HBSC study. Email:dmc@hbsc. org, Web: http://www. uib. no/en/hbscdata.
TeleDiab-2 was a 13-month randomized controlled trial evaluating the efficacy and safety of two telemonitoring systems to optimize basal insulin (BI) initiation in subjects with inadequately controlled type 2 diabetes (HbA1c, 7. 5%-10%). A total of 191 participants (mean age 58. 7 years, mean HbA1c 8. 9%) were randomized into three groups: group 1(G1, standard care, n = 63), group 2 (G2, interactive voice response system, n = 64) and group 3 (G3, Diabeo-BI app software, n = 64). The two telemonitoring systems proposed daily adjustments of BI doses, in order to facilitate the achievement of fasting blood glucose (FBG) values targeted at ~100 mg/dL. At 4 months follow-up, HbA1c reduction was significantly higher in the telemonitoring groups (G2: -1. 44% and G3: -1. 48% vs. G1: -0. 92%; P < 0.
| Study design and treatment After giving informed consent, the participants were randomized using a predefined block size (with stratification by centre  4  ) into group 1 (G1, control group), group 2 (G2, IVRS group) and group 3 (G3, Diabeo-BI app software) (see Section 3 in the File S1). BI doses were gradually adjusted based on capillary blood glucose (BG) levels. Details of the algorithms used for the adjustment of BI doses with IVRS and Diabeo-BI are presented in the File S1 (Section 4; see also Reference 5). The study lasted 4 months, with a 9-month extension period (Figure  1 ). During the extension period, IVRS was discontinued in the G2 arm and G2 patients were mixed with those from the G1 arm (Diabeo-BI was continued in the G3 arm). Patients in G2 and G3 had telephone consultations every 2 weeks during the initial 4-month period, and all patients had faceto-face visits at 4 and 13 months (Figure  1 ).
To our knowledge, TeleDiab-2 is the first long-term (>1 year) randomized, controlled study evaluating the impact of a "basal calculator" (Diabeo-BI app software) uploaded to a smartphone to initiate BI therapy in T2D patients, without prior dose validation by a healthcare provider (HCP). Indeed, many of the current, web-based self-management systems for insulin titration require dose-validation by HCPs. This is the case for the PREDICTIVE 303 algorithm,  8, 9  the LTHome system  10  and TeLiPro. 11 Studies with the Diabetes Pal smartphone  12  and the MyStar DoseCoach glucometer  13  included patients already treated with insulin and failed to show superiority in terms of HbA1c reduction. The Welldoc system 14 has shown some metabolic efficacy in subjects with particularly unbalanced T2D, but it does not propose automatic adjustment of insulin doses.
The study was approved by the ethical committee [Comité de Protection des Personnes (CPP); Committee for People Protection) of Ile de France VI. All procedures were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines and in accordance with French privacy law (Informatique et Libertés) when processing personal healthcare data (Act of January 6, 1978, amended by Law No. 2004-801 of August 6, 2004). The trial was registered with ClinicalTrials. gov identification number: NCT00937703.
Data collection Qualitative data came from semi-directed separate faceto-face interviews of both caregivers and patients. The same person, a public health resident, trained in qualitative research and one of the study's investigators, conducted all the interviews. They were held in each patient's and caregiver's home, except once, when the caregiver wanted to meet away from her home, and lasted from 15 to 115 min. The design of the questionnaire was based on a review of the literature and was validated along with the protocol by a group of HAH professionals volunteers during a weekly staff meeting. This reunion included a dozen of HAH professionals (doctors, nurses, assistant-nurses, psychologist). Their feedback mainly concerned the need to not only target patients ongoing supportive care but also suffering from a wider spectrum of diseases, and to take into account every step of HAH (from hospital release to HAH discharge).
One test session of interviews was realized in order to validate the content of the questionnaire and the process. One interview of a patient and one of each of his two caregivers were realized during this session. The data from the test interviews was included in the final data set. All the interviews began with a very general question concerning both patient's and caregiver's current quality of life and then moved on to more specific questions exploring four main themes: 1) their participation to the decision of transfer to HAH and motivation(s) 2) their opinions on the advantages and barriers of home-care provided by AP-HP's HAH (including questions on quality, simplicity, coordination, reactivity, self-participation and relationships with healthcare professionals), 3) the presence of an impact on various health parameters since the transfer of the patients and 4) the relationship between caregivers and patients during the time of the care and its evolvement.
Data Collection Semi-structured interviews were conducted in the Health and Occupational Medicine department provided by the occupational physician upon the agreement of the participants. On average, the interviews lasted for 38 minutes, with a standard deviation of 12 minutes. The purpose and context of the interview, including its recording and anonymity, were explained in a standardized way. Fours general questions were asked: "Can you tell me how you perceive the work in psychiatry? What does working in psychiatry mean to you?". And then: "indicate, in your opinion, what are the main resources at your workplace? "Indicate, in your opinion, what are the main constraints at your workplace?". The interviewer encouraged the participants to speak freely by rephrasing any questions that were not answered sufficiently.
Data Analysis and Use of IRaMuTeQ Software All interviews were transcribed, and analysis was performed using «R Interface for Multidimensional analysis of Texts and Questionnaires» (IRaMuTeQ) software (Pierre Ratinaud, LERASS, Toulouse, France). This program allows different processing and statistical analysis of texts. It is based on the R Software (R Core Team, Vienna, Austria), and it is coded in the python programming language. In 2009, Pierre Ratinaud developed it for the French dictionary, but in recent years, many other full dictionaries in other languages have become available. The IRaMuTeQ software provides five types of analysis: classic text statistics, specificities of research groups, descending hierarchical classification, similitude analysis, and word cloud  [47]. This program employs the ascendant and descendant hierarchical clustering method on discourse.
Conceptual definition of the construct As there is no consensus about the dimensions that make up the concept of safety culture, a literature search was conducted to identify the dimensions and items that constitute the safety culture construct in the industrial segment, armies, transport organizations or the health sector. The literature search consisted of two stages. Firstly, the 20 most cited articles published between 1980 and 2006 with the terms "safety climate", safety culture", "safety behavior" or "safety performance" in the title, abstract or keywords were extracted from the Scopus database. Among them, 8 articles were screened out because of the absence of questionnaire. A similar search strategy retrieved 11 non-redundant articles for the time period 2007-2012. As a result, a total of 23 papers were identified as being of sufficient quality and relevance to merit data extraction  (Cooper and Phillips, 2004; Cox and Cheyne, 2000; Dodek et al.
Data Collection and Validation Measurements were taken 6-hourly onboard the ship. These measurements were made manually by officers of the ship at the compass deck, which is located at the top of the ship. The readings were taken from 6 directions: east, west, north, south, upward, and downward directions. Measurements at Tokyo Port were also manually taken at 00 UT and 06 UT hours during the working day. The readings were also taken at six directions as those onboard the ships. In order to test the importance of sensitivity of measurements as a function of distance from the ground, two additional measurements were taken: at 50 cm and 10 cm above the ground at the attic of the office  (Wang et al. , 2019). 3 Results In the first two months after 11 March 2011, the highest monthly radioactive dose rates measured at Tokyo Port office were at 0.
Data availability All raw data files reported in this work are available for public downlad at https://doi. org/10. 6084/m9. figshare. 975769 (Wang et al. , 2019) , and the calibrated data files are available at http://doi. org/10. 5281/zenodo. 3468896 (Wang, 2019). The data we submitted is reachable with one click (without the need for entering login and password), and a second click to download the data, consistent with the two-click access principle for data published in ESSD  (Carlson and Oda, 2018).
Analysis of BTEX BTEX were desorbed from the activated charcoal by using 5 mL of carbon disulfide under ultrasound for 15 min. The extract was transferred into a 2 mL vial then analyzed by gas chromatography (CP-3800, Varian USA) coupled to a flame ionization detection as reported 1. 4. Urinary metabolites 1. 4. 1. S-phenylmercapturic acid (S-PMA) Urinary S-PMA was analyzed using a Waters Acquity ultra-performance liquid chromatography (UPLC) coupled with a tandem mass spectrometer detector (Waters XEVO TQS Waters, Manchester, UK). The analysis was performed at 40 °C using a gradient elution of ammonium formate and acetonitrile (Table  S1 ). Results were collected by TargetLynx 4. 1 software (Waters) (for more details see supplementary material).
1-Hydroxypyrene (1-HOP) Determination of 1-HOP was performed according to by  Li et al. (2005)  using a Waters Acquity ultra performance liquid chromatography (UPLC) coupled with a detector Waters XEVO TQS tandem mass spectrometer (Waters, Manchester, UK). Results were acquired by TargetLynx software 4. 1 (Waters) (for more details see supplementary material).
Statistical analysis Data processing was performed with the statistical analysis software SPSS for windows, v. 22. 0. 0, Oct. 2016: p-values less than 0. 05 were considered significant. One-factor ANOVA followed by Tukey tests were used to compare means values obtained between the different groups. Moreover, Spearman correlation analyses were used to evaluate the relationship between BTEX and urinary biomarkers. Data from multi-pollutants measurements between interior and exterior of dwellings are reported as mean and standard deviation and U nonparametric test Mann-Whitney was used to determine the statistical differences.
HRV analysis. The RR data recorded was downloaded via Polar FlowSync software for Mac version 2. 6. 4 (Polar, Kempele, Finland) and exported for analysis with the Kubios HRV Standard software v3. 0. 0 2 (Biosignal Analysis and Medical Imaging Group at the Department of Applied Physics, Kuopio, Finland)  [10]. For the analysis, the last 5 minutes window for each position was used. All the ectopic beats were filtered with the artifact correction option of the.
Load of exercise analysis Each individual exercise daily load was calculated using the training impulse score (TRIMPS) method  [12]. HR and GPS data were monitored continuously during each stage with the HR monitor. HR was divided into five zones, i. e. 50-60%, 60-70%, 70-80%, 80-90% and 90-100% of the individual maximal HR. Work load quantification was derived from the duration spent within the five HR zones  [12].
Statistical analysis Data are presented as mean ± SD. All analyses were performed using SPSS v. 21 for Mac and STATISTICA v. 7. 1 for Windows. Normal Gaussian distribution of the data was verified by the Shapiro-Wilk test. Analysis of variance for repeated measurements was used. A Tukey's posthoc test was used to identify where the differences lie. In addition to the day-to-day effects on HR and HRV, the Tour was divided into 3 periods: period 1 (stages 1-9), period 2 (stages 10-15) and period 3 (stages 16-21), with rest days following stages 9 and 15. Pearson's product moment coefficient was calculated to assess the relationships among RPE, TRIMPS, the mileage of the stages, and the HR and HRV parameters. Significance was set at P<0. 05.
Gut microbiome diversity in healthy donors To characterize the bacterial diversity of the gut flora of the 1000 healthy donors, we performed 16S rRNA gene sequencing on standardized collections of fecal samples. From this cohort, we obtained profiles for 858 individuals and we normalized the data for sequencing depth   S1  (see the "Methods" section). A total of 8422 operational taxonomy units (OTUs) were detected, corresponding to 11 phyla, 24 classes, 43 orders, 103 families, 328 genera, and 698 species. On average, we detected 193 species per individual (standard error 1. 9, standard deviation 55. 1), with a minimum of 58 and a maximum of 346 species. Inter-individual variability was already marked at the phylum level. Figure  2a  presents the relative abundances of the 8 phyla observed in more than 10% of study participants.
Discussion We investigated the potential influence of demographic, environmental, clinical, and genetic factors on the fecal microbiome composition in 858 unrelated healthy individuals of French descent. The Milieu Intérieur cohort is particularly well suited for such a comprehensive assessment  [30]. The study participants have a homogeneous genetic background, live in the same region, and are evenly stratified by sex and age, which provides an excellent opportunity to search for unique determinants of gut microbiome diversity. First, we used the rich data collected through questionnaires that gathered detailed medical history as well as lifestyle and socio-demographic information. We also considered laboratory results that could indicate underlying physiological differences (e. g. , levels of hemoglobin, glucose, hepatic transaminases).
Gut microbiome diversity estimates Based on OTUs, we calculated two types of microbial diversity indicators: αand β-diversity indexes. As estimates of α-diversity, we used Simpson's diversity index, observed richness, Chao1 richness estimate, and ACE (abundance-based coverage estimator). We applied Yeo-Johnson transformation with R package VGAM  [52]  to normalize these phenotypes. The histograms of raw and transformed distributions are shown in Additional file 1: Figure  S1A  and Additional file 1: Figure  S1B , respectively. As estimates of β-diversity, we used Bray-Curtis (weighed), compositional Jaccard (unweighted), and Unifrac (weighted) dissimilarity matrices. All diversity indicators were generated on non-rarefied data using the R package vegan  [53]  that was corrected for sequencing depth prior to indexes' computation  [48].
All pairwise correlations with a Spearman's ρ > 0. 6 or ≤ 0. 6 and a false discovery rate (FDR) < 5% were considered colinear; one variable from each pair was removed from further analysis, resulting in a final set of 110 variables (described in Additional file 2: Table  S1 ). Of these, 39 had some missing values (< 1% in 25, 1-5% in 10, 5-10% in 4 individuals), which were imputed using random forest method in the R package mice  [54]. We evaluated the effects of various clinical measurements within their normal healthy range, such as those of BMI (mean ± SD: 24. 26 ± 3. 26 kg/m 2 ) and C-reactive protein (CRP; mean ± SD: 1. 99 ± 2. 58 mg/L). Several symptoms of depression, such as lack of interest in doing things and poor self-image, and potentially relevant personal and family medical history information (such as route of birth delivery, immunization history with several vaccines, and familial occurrence of diabetes or myocardial infarction) were investigated.
Human DNA genotyping As previously described  [56] , the blood was collected in 5-mL sodium EDTA tubes and kept at room temperature (18-25°) until processing. After extraction, DNA was genotyped at 719,665 single nucleotide polymorphisms (SNPs) using the HumanOmniExpress-24 BeadChip (Illumina). The SNP call rate was > 97% in all donors. To increase coverage of rare and potentially functional variation, 966 of the 1000 donors were also genotyped at 245,766 exonic variants using the HumanExome-12 BeadChip. The variant call rate was < 97% in 11 donors, which were thus removed from this dataset. We filtered out from both datasets genetic variants based on a set of criteria detailed in  [57]. These quality-control filters yielded a total of 661,332 and 87,960 variants for the HumanOmniExpress and HumanExome BeadChips, respectively.
Genetic relatedness and structure Relatedness was detected using KING  [58]. Six pairs of related participants (parent-child, first-, and second-degree siblings) were identified. Of those, four pairs had both genotyping and microbiome datasets and one individual from each pair, randomly selected, was removed from the genetic analyses, leaving in total 858 individuals with both genotyping and 16S rRNA gene sequencing data. The genetic structure of the study population was estimated using principal component analysis (PCA), implemented in EIGENSTRAT (v6. 1. 3) [59]. The PCA plot of the study population is shown in Additional file 1: Figure  S13.
Genotype imputation As described previously  [57] , we used Positional Burrows-Wheeler Transform for genotype imputation, starting with the 661,332 quality-controlled SNPs genotyped on the HumanOmniExpress array. Phasing was performed using EAGLE2 (v2. 0. 5)  [60]. As a reference panel, we used the haplotypes from the Haplotype Reference Consortium (release 1. 1)  [61]. After removing SNPs that had an imputation info score < 0. 8, we obtained 22, 235,661 variants. We then merged the imputed dataset with 87,960 variants directly genotyped on the Huma-nExome BeadChips array and removed variants that were monomorphic or diverged significantly from Hardy-Weinberg equilibrium (P < 10 -7 ). We obtained a total of 12,058,650 genetic variants to be used in association analyses. We used SNP2HLA (v1.