dataesr/scientific-paragraphs-categorization huggingface dataset:
Quasi-experimental design was chosen rather than a randomized, controlled experiment since random sampling is impossible in French community gardens, where new membership and plot renewal in community gardens are decided by local authorities or private managers. Baseline sample size was estimated based on a priori power analysis considering household supply of F&V as the primary outcome. The hypothesis was derived from our knowledge of the food supply content of community gardeners vs. non-gardeners, based on data collected in a cross-sectional study previously conducted in the south of France  [17]. Assuming a potential attrition rate of 30% and a correlation of 0. 6 across repeated measures, a total of 160 participants is required to detect a 30% increase in F&V supply in the new gardeners group (namely one portion of F&V) for 80% power at the 5% alpha level.
The exclusion criteria are: past experience of at least one household member in community gardening, and never shopping for home groceries. Data collected from new gardeners will be compared with those from a group of residents who have never participated in a community garden. This control group will be selected using frequency matching from a subsample of persons who participate in a survey on food behaviors undertaken as part of the SURFOOD-Foodscapes project ("Mont'Panier" survey), and who have no experience in community or home gardening. This survey is being conducted in 500 households of Montpellier from May to December 2018 using quota sampling based on age and household composition. Frequency matching involves the selection of an entire stratum of reference subjects (control group) with matching-factor values equal to that of a stratum of index subjects (natural experiment group)  [29].
Data collection steps A first home visit will be scheduled to provide participants with data collection materials (a food supply diary, a hip-worn triaxial accelerometer and an online questionnaire), together with instructions on how to use it. A second and third home visit will be set up approximately 10 days later to collect the accelerometer and check that participants are filling out the food supply diary properly, and 1 month later to retrieve the food supply diary. The interviewer will be reachable at all times during data collection to respond to any concerns. For the control group, the food supply diary will be completed as part of the "Mont'Panier" survey, with the possibility of receiving it in printed form or online. For the accelerometer and the questionnaire, home visits will be scheduled according to the same protocol as for the experiment group.
When grocery and supermarket till receipts are available, participants will be asked to collect them in an envelope at the back of the food supply diary. To facilitate data entry, all family members will be encouraged to help fill out the diary. All food items purchased will be classified into 9 groups and 29 subgroups to see the share of major food groups in the household supply. The nutritional composition of each food item will be determined using the national food composition database and completed by recent market foods and recipes. Two indicators of nutritional quality previously described by Vieux et al. will be used to estimate the nutritional quality of 2000 kcal of food supply: the mean adequacy ratio (MAR) and the mean excess ratio (MER)  [30]. The MAR is an indicator of good nutritional quality and represents the mean percentage of daily recommended intakes for 20 key nutrients.
Physical activity Physical activity (PA) will be measured by triaxial accelerometry using an Actigraph activity monitor (wGT3X-BT or wActiSleep-BT, Actigraph, Pensacola, FL, USA). The Actigraph is a motion sensor that measure acceleration from body movements in three orthogonal planes (anteroposterior, mediolateral, and vertical)  [32]. The Actigraph will be set to collect data at a sampling frequency of 30 Hz. Participants will be instructed to wear the Actigraph fitted with an elastic strap on the right side of the hip for 9 consecutive days, except for bathing and performing activities in water. The Actigraph is accompanied by an activity logbook in which participants will be required to record time awake and sleeping daily, and time and duration of any strenuous exercise or device removal.
Physical activity energy expenditure will be estimated directly from raw triaxial accelerometry data using a previously validated model that combines an automatic activity-recognition algorithm with an activity-specific count-based model  [33]. The automatic activity-recognition algorithm relies on four signal features from time and frequency domains to classify 6 s consecutive time spans into six posture/ activity categories: a) lying down, b) slouching, c) sitting, d) standing still, e) moving on foot (sweeping, treading, walking, running, etc. ), and f ) cycling  [34]. In addition, usual physical activity in four domains (leisure, work, commuting, home) during the past month will also be assessed with an online questionnaire using the physical activity Recent Physical Activity Questionnaire (RPAQ).
This database provides estimates of the three indicators per gram of food for 212 commonly consumed generic foods, based on a hybrid method combining input/ output and LCA approaches, as previously described by Bertoluci et al. [41]. Estimates will be allocated to each food collected in the food supply diary by linking them to the closest food in the 'SUStable' database. Environmental impact of household food trips For each food purchase recorded in the food diary supply, participants will provide details of the trip made (origin/ where the trip started, destination/where the trip ended, and mode of transport). All locations will be geocoded using QGIS 2. 18 to compute the total distance travelled for each food trip. Information collected on the origin/ destination of each trip will describe the overall travel pattern associated with the food trips and tell whether food trips are integrated into multiple-purpose trip.
Planned statistical analysis Summary statistics (mean, median, standard deviation and frequency distribution) will be generated for baseline characteristics. Baseline characteristics will be compared between the experiment and control groups using one-way ANOVA or a Kruskal-Wallis test for continuous variables, and chi-square or Fisher's exact tests, for categorical variables. The magnitude of change within and between groups between t 0 and t 1 will be calculated. Data will be analysed to address the research questions, applying appropriate linear mixed models. Differences within and between groups in the outcome parameters will be analysed. Time, group and their interaction will be defined as fixed factors. Subject and group will be included as random factors. If imbalances occur between groups, the baseline values will be treated as covariates.
Besides, the automatic activity-recognition model used in the study to derive information on physical activity from raw triaxial accelerometry data was found to improve physical activity energy expenditure and total energy expenditure predictions from a hip-worn triaxial-accelerometer in free-living conditions, compared with traditional count -based prediction approaches  [33]. The study has some limitations. First, although several well-known confounders will be measured in the study and controlled in the analyses, the use of a natural experiment makes it difficult to control all extraneous variables, and so unknown and unmeasured extraneous variables may affect the results, threatening validity. Second, the natural experiment and control groups might differ in some pre-study characteristics because participants are not assigned randomly to the two groups, which is liable to bias the estimates of the experiment.
Funding This research was part of the  Surfood-Foodscape  project. This work/project was publicly funded through  ANR  (the  French National Research Agency)  under the " Investissements d'avenir " programme with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Foundation. This work was also supported by the  Olga Triballat Institute. The funders have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Patients All consecutive patients hospitalized in the department of Rheumatology in Hospital of Nancy since 2005 for suspicion of RA were screened. Only patients fulfilling the following inclusion criteria were selected for this study: patients fulfilling ACR 1987 and/or ACR/EULAR 2010 criteria for RA, patients fulfilling only ACR OA criteria of hand OA served as control group  (17). US exam and RX exam of hand and feet should be performed within 6 months. The only exclusion criterion was the presence of severe joint deformities that could prevent a complete appropriate US, RX and clinical evaluation. Finally, 122 RA patients and 46 OA patients were included. A complete assessment of their disease was performed (clinical, biological, radiological, and ultrasound evaluations). For RA patients, data collection included the Disease Activity Score 28-joint count (DAS 28) and treatments at the time of evaluation (bDMARD, sDMARD, corticosteroids, and NSAIDs).
Study Subjects The present study uses data from the MultiSensor sub-study  [29]  of the RECORD (Residential Environment and CORonary heart Disease) Cohort study. From February 2007 to March 2008, individuals that came to four of the IPC (Investigation Préventive et Clinique) Medical Centers for a free medical examination offered by the French National Insurance System for Employees were invited to enter the RECORD study. Eligibility criteria were age 30-79 years, residence at baseline in 10 districts of Paris (out of 20) and 111 other municipalities of the Ile-de-France region and sufficient cognitive and linguistic abilities. During the second wave of the RECORD Study (September 2013 to June 2015), after completing their medical checkups, participants were systematically invited to enter the RECORD MultiSensor Study whenever monitoring devices were available.
Anthropological and Biological Data During the screening visit, participants underwent a medical examination including anthropological measurements and a blood draw in a fasting state. Details about collection of anthropological and biological data can be found elsewhere  [30]. Anthropological measurements were made by trained nurses at the medical centers. WC (Waist Circumference) was measured using an inelastic tape placed midway between the lower ribs and the iliac crest, on the mid-axillary line. In this study, we used the following outcomes: serum glucose concentration, plasma triglyceride, high-and low-density lipoprotein (HDL and LDL), as well as body mass index (BMI) and waist circumference (WC).
Physical Activities and Postures Physical activities and postures were derived from two tri-axial Vitamove Research-V1000 ® (Vitabase v2. 0 B5, Temec Instruments, Herleen, The Netherlands) accelerometers. Participants were requested to wear one at the trunk and the other on the right upper leg during wake time for seven days, as they carried out usual daily activities in free-living conditions (except for water-based activities). In addition, they wore a small GPS device and kept a log of the places they visited and the wearing times of the devices. We invalidated days with less than ten hours of wear time, and subjects with less than four valid days out of seven  [31]. Twenty-two subjects out of 154 did not meet these criteria. The base software of the sensors, VitaScore ® , has a large nomenclature of physical activities and postures.
To simplify the analysis, we combined them into five categories: lying (trunk in horizontal or nearly horizontal position), sitting (trunk upright or nearly upright), standing, light physical activity (LPA, including slow movements) and moderate-to-vigorous physical activity (MVPA, including walking, running, biking etc. ). To identify bouts, we used a modified version of the function guideline. bouts from the R-package activpalProcessing  [32]. The minimum length of the bouts was set to 60 seconds and the threshold for the proportion of the behavior of interest was kept at 0. 8 (see Figure  1 ). For the sake of simplicity, we did not analyze partitioning patterns for each behavior, but on three broader categories: SB (including lying and sitting), non-sedentary behaviors (NSB) including standing, LPA and MVPA, and MVPA only.
Other Data Individuals that joined the study answered a questionnaire regarding socio-demographics, dietary and health habits, from which we used data about age, educational level and annual total income. As nutrition is thought to be correlated with both sedentary time and health  [33] , we also added to the models information regarding nutritional and health habits. These variables are described in details in Table  A2  in Appendix C. A discussion about the validity of the questionnaire is to be found in another paper devoted to the RECORD study  [34].
Other Data Individuals that joined the study answered a questionnaire regarding socio-demographics, dietary and health habits, from which we used data about age, educational level and annual total income. As nutrition is thought to be correlated with both sedentary time and health  [33] , we also added to the models information regarding nutritional and health habits. These variables are described in details in Table  C1  in Appendix C. A discussion about the validity of the questionnaire is to be found in another paper devoted to the RECORD study  [34].
Sequencing Exome sequencing and analysis was performed as previously described. 31 Briefly, genomic DNA was extracted from venous blood samples using a QiaAmp DNA blood Maxi Kit (Qiagen), or from saliva using Oragene saliva DNA collection kits (DNA Genotek) according to the manufacturers' protocols. Exome capture was performed using the Agilent SureSelect system (v4 or v5) and paired-end libraries sequenced on an Illumina HiSeq (2000, 2500, or 4000). Read alignment and variant calling were performed according to GATK best practices for germline short variant discovery (https://software. broadinstitute. org/gatk/). Variant annotation was performed using Variant Effect Predictor, with population frequencies from gnomAD and Bravo/TOPMed freeze 5 collections. 32 All putative causal variants were manually inspected in IGV.
Acknowledgments Supported by grants from the  National Health and Medical Research Council  ( NHMRC APP1107098 ), the  Ophthalmic Research Institute of Australia , and  Flinders University. KPB was funded by an  NHMRC Career Development Award , and JEC by an  NHMRC Practitioner Fellowship. DGM was supported in part by the  National Human Genome Research Institute , the  National Eye Institute , and the  National Heart, Lung and Blood Institute  grant  UM1 HG008900 , and by the  National Human Genome Research Institute  grant  R01 HG009141.
| Boldness Boldness was assessed for each individual independently in circular containers (30 cm in diameter) filled with 5L of dechlorinated tap water at 17°C and 500 ml of water from a tank containing conspecifics. The containers were surrounded by curtains to standardize light conditions and to hide the experimenter. A shelter (pipe, 7 cm length × 3 cm diameter) was added in each container to allow the fish to hide. After having introduced each individual into the shelter and after 10 min of acclimatization to reduce stress level induced by handling, the shelter was opened and each individual was filmed for fifteen minutes. Video footage was subsequently analyzed with the software "BORIS"  (Friard & Gamba, 2016). Boldness was quantified as the time spent outside of the shelter. The order and the containers in which individuals were assayed were randomly attributed.
| Genetic analyses Thirty additional adults from each of the thirteen sites were sampled for genetic material. For each of these thirty individuals, we collected and preserved in 70% ethanol a small piece of pelvic fin and individuals were then released in their respective sampling site. Genomic DNA was extracted using a salt-extraction protocol  (Aljanabi, 1997). Eighteen autosomal microsatellite markers were considered in this study: Polymerase chain reactions (PCR) and genotyping were performed as detailed in Supporting Information Appendix S1, resulting in a final data set of 357 genotypes. We checked for multilocus deviation from Hardy-Weinberg equilibrium (HWE) and for gametic disequilibrium using GENEPOP 4. 2. 1  (Rousset, 2008)  after sequential Bonferroni correction to account for multiple related tests  (Rice, 1989).
The presence of null alleles was then assessed at each locus by analyzing homozygote excess in five populations that did not follow HWE, using MICROCHECKER 2. 2. 3 (Van Oosterhout, Hutchinson, Wills, & Shipley, 2004). We discarded from further analyses any locus showing significant gametic disequilibrium and/or evidence of null alleles, resulting in the withdrawal of one locus (CtoG-075), for a total number of seventeen loci. We computed Nei's standard genetic distance  (Nei, 1973)  between each pair of populations using the diveRsity R-package (function diffCalc;  Keenan, McGinnity, Cross, Crozier, & Prodöhl, 2013). A hierarchical cluster analysis was then performed to uncover genetic relatedness among the thirteen populations using the functions hclust (R-package stats) and as. phylo (R-package ape;  Paradis, Claude, & Strimmer, 2004)  to convert the genetic dissimilarity matrix into an unrooted phylogenetic tree based on complete linkage method.
Finally, we estimated the overall level of genetic differentiation F ST among the thirteen populations using the hierfstat R-package  (Goudet, 2005). The resulting global F ST corresponds to the interpopulation variance component in allelic frequencies  (Yang, 1998) , and to the level of differentiation among populations due to genetic drift only  (Leinonen et al. , 2013). This value is directly comparable to the interpopulation variance component in quantitative traits (P ST , see below). A 95% confidence interval (CI) was computed for the observed global F ST value using a classical cluster bootstrap procedure with 1,000 iterations  (Field & Welsh, 2007) : CI lower and upper bounds were computed as the 95% percentiles of a theoretical distribution of 1,000 F ST values obtained from the random sampling of the thirteen populations with replacement.
| Heterogeneity in trait covariations among populations We tested whether covariations among the four traits (i. e. , syndromes) were different among the thirteen populations. We first synthetized and described, for each population, patterns of trait covariation using path analysis. Traits were scaled to the mean within each population (i. e. , each population displays a mean of zero with a variance of one for each trait), and a general path analysis linking each trait to the others (saturated path analysis) was computed for each population independently using the lavaan R-package  (Rosseel, 2012). These resulted in thirteen path models (each path model corresponding to a population's syndrome) and thirteen associated covariance matrices. Then, we then tested whether these path models (and hence trait covariations) varied among populations using a test of heterogeneity on covariance matrices among groups (metaSEM R-package,  Cheung, 2015).
In the case of covariations, among-and within-population variance components were calculated in a similar way but with the addition of a random slope, corresponding to the covariable trait (Supporting Information Appendix S2). This allows estimating among-and within-population variance in the covariation between each pair of traits (Mazé-Guilmo, Blanchet, Rey, Canto, & Loot, 2016). The generalized linear mixed models were run using the lme4 R-package  (Bates, Maechler, Bolker, & Walker, 2014). We applied a classical bootstrap clustering procedure with 1,000 iterations  (Field & Welsh, 2007)  to assess the 95% confidence interval for P ST. We then compared the CI of P ST for each trait and each covariation (i. e. , 10 P ST quantified in total: 4 single traits and 6 covariations among them) to the CI of F ST.
AR is financially supported by a Doctoral scholarship from the Université Fédérale de Toulouse. This work was undertaken at SETE, which is part of the "Laboratoire d'Excellence" (LABEX) entitled TULIP (ANR-10-LABX-41). CO N FLI C T O F I NTE R E S T None declared. AUTH O R CO NTR I B UTI O N S AR, SB, FS, and JC designed the study. AR and SB performed fieldwork, and AR performed trait measurement. GL carried out the processing of genetic samples. JGP analyzed genetic data and AR performed statistical analyses. All authors interpreted and discussed the results. AR and SB wrote the first draft of the paper. All authors corrected and improved the paper, and approved this version of the manuscript.
C Mean number of both TAF and non-TAF in neonatal rat cardiomyocytes at days 0, 3, 5, 10 days following treatment for 24 h with H 2 O 2. Data are mean AE SEM of n = 3. > 50 cells were quantified per condition. Statistical analysis performed using one-way ANOVA (Holm-Sidak method); **P < 0. 01. D (Left) Representative images of cH2AX immuno-FISH in H9C2 myoblasts 3 days 10 Gy X-irradiation (red-telo-FISH; green-cH2AX). White arrows identify areas shown in higher magnification panels. (Right) Mean number of both TAF and non-TAF in H9C2 myoblasts at days 3 and 5 following 10 Gy X-irradiation. Data are mean AE SEM of n = 3. > 50 cells were quantified per condition. Statistical analysis performed using one-way ANOVA (Holm-Sidak method); ***P < 0. 001. Scale bars represent 1 lm. Scale bars in single-plane images 500 nm.
E Representative time-lapse images of H9C2 rat cardiomyoblasts expressing AcGFP-53BP1 from 3 days after 10 Gy irradiation at the indicated times (min). Images are maximum intensity projections with a 6. 7 lm focal depth. Scale bar represents 1 lm. F Kaplan-Meier survival curves for AcGFP-53BP1c DDR foci in H9C2 cells 3 days after 10 Gy irradiation at 10-min intervals for 24 h. > 500 foci from 10 cells were tracked per condition. Gehan-Breslow test was used, P < 0. 001. G Schematic illustration showing 1-month-old C57BL/6 mice treated with 2 Gy whole-body X-irradiation, followed by a recovery period of 11 months before culling at 12 months of age. Mean number of TAF in a-actinin-positive cardiomyocytes. Data are mean AE SEM of n = 3 mice per group. Ninety a-actinin-positive cardiomyocytes were quantified per condition.
Each image throughout the z-series was examined to confirm nucleation state (Movies EV2 and EV3). However, < 25% of all EdU incorporation resulted in multi-nucleated CMs. We next evaluated the percentage of CMs positive for proliferation marker Ki-67 and found a similar increase following navitoclax treatment and in aged INK-ATTAC mice treated with AP (Figs  EV5H  and 8K ). Expression of Aurora B, a component of the contractile ring required for cytoplasmic separation during cell division, was only observed in CMs from hearts where senescent cells had been cleared, where it was located in mid-body between nuclei, indicative of cytokinesis  (Hesse et al, 2018; Fig 8H). Altogether, our data support that senescent CMs are involved in age-associated cardiac hypertrophy and fibrosis and that their clearance may induce a compensatory CM regeneration.
RNA sequencing Paired-end reads were aligned to the mouse genome (mm9) using a splicing-aware aligner (tophat2). Only unique reads were retained. Reference splice junctions were provided by a reference transcriptome (Ensembl build 67), and novel splicing junctions determined by detecting reads that spanned exons that were not in the reference annotation. True read abundance at each transcript isoform was assessed using HTSeq (Python) before determining differential expression with the tool DESeq2, which models mean-variance dependence within the sample set. Significance was determined using an FDR-corrected P-value ≤ 0. 05.