dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Statistical analysis was performed using an online statistical environment (R version 4. 0. 0) (26). Additional packages "sp"  (27)  and "Rcpp"  (28)  were used to examine and describe the data. All reported P-values are two-sided, and the level of assumed statistical significance was P ≤ 0. 05. Descriptive statistics were used to summarize the results from the questionnaire data, with frequencies reported using numbers and percentages. Continuous datasets were first assessed for normality using histograms and the Shapiro-Wilk test; methods of statistical assessment and the reporting of summary data depended on the distribution of the dataset in question. In this respect, normally-distributed data were compared with either the paired Student's t-test or the 2-sample Student's t-test), or one-way ANOVA (with Tukey post-hoc comparisons), whilst results are expressed as mean, standard deviation, minimum and maximum.
Data not following a normal distribution were assessed with the Wilcoxon signed-rank test (paired data), the Mann-Whitney test or the Kruskal-Wallis test (with Dunn's test used for posthoc comparisons), whilst results are expressed as median and interquartile ranges (IQR). Further, repeated-measures ANOVA was used to assess the multiple heart rate measurements (Tukey test used for post-hoc comparisons). Relationships were explored using either Pearson's correlation (normally-distributed data) or Spearman's rank correlation (data that were not normally distributed). Prevalence and 95% CI were calculated using an online tool (https://epitools. ausvet. com. au/). Relationships between all owner-observed changes reported in the questionnaire and findings on clinical investigations for azotaemia, hyperthyroidism, musculoskeletal disease and dental disease were made using either the Chi-squared test or Fisher's exact test (where small values were present).
Physical and Physiological Parameters A complete clinical assessment was possible in 176/206 cats (85%). Abnormalities in the orthopedic examination and dental disease were the most common findings, followed by heart murmurs (Table  1 ). There was no association between age (P = 0. 10) or BCS (P = 0. 10) and the number of morbidities determined in those cats. A summary of data on weight, BCS and physiological parameters is presented in Table  2. Three cats (1%) were underweight (BCS < 4/9), 86 (42%) were in ideal weight (BCS 4-5), 93 (45%) were overweight (BCS 6-7), and 24 (12%) had obesity (BCS 8-9). Respiratory rate was measured twice during the consultation in 197 cats, with the second reading (RR2) being greater than the first (RR1; P = 0. 01). Heart rate was measured on three occasions during the consultation in 175 cats, with the first (HR1) being greater than both the second (HR2, P < 0.
The lesser prevalence of heart murmurs found in our cohort compared to this study could either reflect a decreased prevalence in this population, or be due to the environment in which the cats were examined. Whilst the cardiac auscultation was performed up to three times during the appointment, the environment was a first opinion veterinary practice based next to a busy road. This might have had made it difficult for an optimal cardiac auscultation to be performed. The prevalence of azotaemia in this cohort was 10%. In cats, a diagnosis of CKD requires evidence of sustained functional or structural kidney damage of at least 3 months' duration  (23). As data were collected from a single time point, not all azotaemic cats necessarily had CKD. It was also not always possible to collect a contemporaneous urine sample for measurement of specific gravity and, therefore, true prevalence of CKD in this cohort is not known.
Seventy-one percent of owners in the current study were either graduates, post-graduates, professionals or had vocational training; these findings are consistent with other studies whereby positive associations between education level or professional occupation cat ownership have been reported  (73, 74). The study has several limitations that should be acknowledged. First, data were only collected from a single point in time from this cohort and, therefore, cannot provide information about progression of disease. Related to this, examination at a single time point makes it difficult to diagnose some diseases (e. g. , CKD and hypertension) with confidence. Additionally, associations only could be determined between owner reported cat behaviors and clinical findings, no causal link could be established.
To address this, the cohort will continue to be monitored during the aging process, to enable such longitudinal data to be gathered, following cats that were disease free at enrolment to determine the timeframe between behavioral changes and the occurrence of disease. A second limitation was the fact that 206 cats were recruited, which was less than the 385 cats that the sample size calculation suggested and for some disease numbers were small meaning our prevalence estimates should be interpreted with caution and the power to detect associations was low. Furthermore, these cats belonged to only 147 owners and there is likely to be clustering of some management practices within owners. Finally, diagnostic imaging was not performed on cats with abnormalities on OE or a heart murmur and, as a result their relationship to underlying disease processes could not be determined.
The Ki67 expression was not performed systematically in all study centers before 2016; for this reason, data concerning this proliferation marker could not be collected and analyzed. Histological examination was used as gold standard. [ 18 F]Fluorocholine positron emission tomography/computed tomography [ 18 F]Fluorocholine PET/CT images were acquired using two PET/CT scanners (Discovery 710; General Electric Healthcare and Biograph 20 mCT Flow; Siemens Healthineers) between 45 and 60 min after intravenous injection of 3 Mbq/kg of [ 18 F]fluorocholine. PET/CT was conducted from the upper neck to the abdomen. [ 18 F]fluorocholine PET/CT images were acquired for 2 min/bed position for Discovery 710 and at a speed 0. 4 mm/s for Biograph mCT Flow. PET images were reconstructed with non-contrast low-dose CT images for Biograph mCT Flow and with contrast low-dose CT images for Discovery 710.
Semi-quantitative analysis Secondarily, quantitative measurements were performed by a nuclear medicine physician who was blinded to histology, laboratory results, first-line imaging, and the result of the first overall visual analysis. Images were analyzed using the syngovia software (Siemens Healthcare, Erlangen, Germany). Four criteria were measured or calculated, namely, maximum SUV max , liver ratio, thyroid ratio, and size ratio. The performance of each criteria was represented by the area under the receiver operating characteristic (ROC) curve (AUC) and Younden index, positive and negative likelihood ratios (LR+ and LR-), and accuracy. For each lesion, the following parameters were measured: (i) SUV maxbw (standard uptake value maximum body weight): if the exam was considered normal, without target lesion, the mediastinal background noise was measured and taken account for the ROC curve analysis; (ii) SUV meanbw ; (iii) SUV peakbw ; (iv) SUV max liverbw : measurements on the liver were normalized: ROI drawn in the right liver, at the limit of segments VII and VIII, with a standardized size of 3 cm in diameter.
Statistical analysis A statistical analysis was performed using commercial software (GraphPad Prism, La Jolla, CA, United States). Receiver Operating Characteristic (ROC) curve were performed using Wilson/Brown method. An ideal criterion would have an area under the curve (AUC) of 1, and a criterion with no discrimination utility has an AUC of 0. 5. A correlation test between two outcomes was carried out by Mann-Whitney test. For all the tests, a P value of 0. 05 or less was considered statistically significant.
Study design and participants A retrospective cohort analysis was conducted. All active French military personnel aged 18 to 64 years who received outpatient care requiring reimbursement between January 1, 2019, and April 30, 2021, were identified in the database. The selection was conducted as followed: (1) individuals affiliated to the National Military Social Security Fund (CNMSS), which is compulsory for military personnel; (2) aged 18 to 64 years (which is the age limit for active military personnel in France); (3) being the principal beneficiary of the health insurance; and (4) being an active service member, which means military personnel currently in service (Fig. 1 ). All individuals who met these criteria were enrolled in the study, whether at the Fig. 1  Flowchart for the identification of the studied population in the SNDS database beginning of the 2019 period or at the end in 2021.
Outcome measures We collected data relative to the number of reimbursed packs of different drugs per week during the COVID-19 crisis, in particular psychotropic medications, known as "support" drugs or drugs of interest to the French military, between 2019 and 2021. We identified the three main psychotropic drug codes in the SNDS database using Anatomical Therapeutic Chemical (ATC) classification: anxiolytics (N05B), hypnotics (N05C), and antidepressants (N06A). For each category, the number of claims per week was collected over the study period, and rates were counted per week.
Statistical analysis We performed negative binomial interrupted time series (ITS) regressions  [22]  to study outcome variables before and during the COVID-19 epidemic using previously defined variables. We then estimated the corresponding rate ratios with their 95% CI (CI). The pre-epidemic period spans from January 1, 2019, to January 23, 2020, and the epidemic period from January 24, 2020, to April 30, 2021. To describe trends, segmented regressions were performed with a breakpoint on January 24. All time variables are described by week. Age category and sex were included in models as adjustment variables. Sensitivity analyses were performed to take into account a seasonal effect given the nature of the data used: the seasons considered were winter, spring, summer, and autumn. During the epidemic period, we also identified 3 phases: (1) the first lockdown, extending from week 12 to week 19 (from March 17 to May 10, 2020), (  2 ) the second lockdown between weeks 44 and 51 (October 30 to December 15, 2020), and (3) the last period (curfew) from weeks 13 to 17 (April 3 to May 3, 2021).
Ethics The study was reported to the relevant authorities within the French Military Health Service. The French Military Health Service has permanent access to this database, made possible by the monitoring of specific training for the benefit of doctors and statisticians in charge of the analyses. This data is delivered thanks to an official French decree (Décret n°2021-848 du 28 juin 2021, relative au traitement de données à caractère personnel "système national des données de santé"). All data were anonymous. No individual consent was necessary.
It is likely that a method of this type this could be applied successfully to military personnel as well  [35]. The present study draws on comprehensive nationwide health care reimbursement data from the SNDS. It was therefore possible to precisely describe the changes in healthcare consumption of active military personnel in the civilian healthcare system thanks to the exhaustiveness of the database (not sample data). Nevertheless, we recognize that there are limits to this work. First, though all drugs delivered by pharmacies are taken into account because they are systematically collected, we did not have access to data for drug consumption during hospital stays in the SNDS database. The SNDS database does not allow for the identification of military personnel in operation or the different armed forces (army, air force, navy, etc.
), and the reimbursement database for outpatient care includes limited clinical data or data regarding lifestyle. However, the SNDS database includes information about certain comorbidities thanks to input from other databases including the long-term diseases database, the hospital information database (PMSI), and the drug dispensing database. Military personnel are subject to strict medical selection and surveillance. They are therefore rarely affected by the chronic comorbidities in the long-term diseases database because these are most often incompatible with the exercise of their functions. Additionally, while the database specified the delivery of drugs, actual consumption and thus compliance cannot be assessed, even though these are important factors for the study of psychiatric conditions.
Availability of data and materials The database used in this study was transmitted by the National Health Insurance Fund -CNAM (Caisse nationale de l'assurance maladie) (responsible for the extraction of SNDS data). The use of these data by our department was approved by the National Committee for data protection. We are not allowed to transmit these data. Data are available for researchers who meet the criteria for access to these French data from the National Health Insurance Fund: training that opens a personal accreditation, approval of the protocol by required authorities (Expert Committee to researches, studies, and evaluations in the health field-CEREES, and National Committee for data protection-CNIL). Contact: Caisse Nationale de l' Assurance Maladie, 50 Avenue du Professeur André Lemierre, 75,020 Paris https:// www.
Acknowledgements We gratefully acknowledge  TotalEnergies  for funding and providing the material for this study. We would like to thank  Ali Raeini  for his help with pore-network extraction and fluid occupancy mapping. We also would like to thank our colleagues at  TotalEnergies  for technical discussions. We would like to thank  Alexandra Klimenko  for reviewing the manuscript. Branko Bijeljic  is also very grateful to TotalEnergies for funding his Senior Fellowship.
DNA extraction, bisulphite conversion and DNA methylation data processing Methods relating to DNA extraction and bisulphite conversion, and DNA methylation data processing have been described previously and are detailed in Additional file 1 and are the same as in our previous pooled analysis  [10]. In brief, the MCCS, EPIC-Italy and EPIC-IARC measured DNA methylation using the Illumina Infinium 450 k BeadChip methylation array, and PLCO used the llumina InfiniumEPIC 850 k BeadChip methylation array. The pipeline for normalization of the methylation data was the same across the four studies (Additional file 1). β-values were calculated for each CpG site for each sample using the R package minfi. β-values were calculated for each CpG site for each sample using the R package minfi. Methylation measures with a detection P-value higher than 0.
Methylation-based measures Epigenetic aging We used the normalised DNA methylation data to calculate the epigenetic measures of aging developed by Horvath  [7]  and Hannum et al. [8] , as well as PhenoAge  [9] , and GrimAge  [20]  (composite biomarkers enriched for adverse phenotypes) as these have been shown to be accurate predictors of chronological age, and their deviation from chronological age (i. e. 'age acceleration' [AA]) was consistently found to be associated with risk of disease, cancer and death. These measures are calculated using methylation data at 353, 71, 513 and 1,030 CpGs, respectively, and were obtained using Horvath's online calculator https:// dnama ge. genet ics. ucla. edu/ new  [17, 19, 20]. Their respective age acceleration measures, defined as the residuals of the regression on chronological age, were also computed using the online calculator.
9 years, compared with > 6 years for all studies we included). We nevertheless did not observe that OR estimates were larger when blood was collected closer to diagnosis (within 5 years: OR ~ 1. 01, 0. 98, 1. 01, 1. 04 for AA-Horvath, AA-Hannum, AA-PhenoAge and AA-GrimAge, respectively). The study of Durso and colleagues  [35]  compared Horvath and Hannum age acceleration measures between 233 Italian women who developed breast cancer (mean age at recruitment: 52. 4 years, mean time to diagnosis: 3. 8 years) and cancer-free controls and found no evidence of an association. A study of multiple health outcomes using Generation Scotland data included 83 incident breast cancer cases, diagnosed over 13 years of follow-up in women aged ~ 51 years at baseline  [36]. A tendency for risk associations to be positive was observed: per SD, AA-Horvath, HR = 1.
01 (P = 0. 95), AA-Hannum: HR = 1. 24 (P = 0. 07), AA-PhenoAge: HR = 1. 36 (P = 0. 01), and AA-GrimAge = 1. 19 (P = 0. 16), respectively, in age-adjusted models. The literature to date therefore includes, to our knowledge, approximately 3,550 breast cancer cases and is consistent with a weak (of roughly 8% increase per 5-year AA for AA-PhenoAge) or null association between epigenetic aging measured in blood and breast cancer risk. There has not been to our knowledge any study examining methylation-based predictors of lifestyle-related factors with risk of breast cancer. A handful of studies have examined risk of overall mortality  [33] , survival from oropharyngeal cancer  [37]  and risk of several types of cancer in the Melbourne Collaborative Cohort Study  [38]. Another study used the Cancer Genome Atlas datasets to develop lifestyle predictors based on tumour DNA methylation  [39]  and found that the BMI-associated methylation signature was predictive of shorter breast cancer survival.
Funding This research was partially supported by the  Intramural Research Program  of the  National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics. The work performed by the  Epigenomics  and  Mechanisms Branch  at  IARC  was supported by grants from the  Institut National du Cancer (INCa, France) , the  European Commission (EC)   Seventh Framework Programme  ( FP7) Translational Cancer Research (TRANSCAN) Framework , the  Fondation ARC pour la Recherche sur le Cancer (France) ,  and la Ligue Nationale (Française) Contre le Cancer  to ZH. The MCCS methylation work was supported by the  National Health and Medical Research Council  (grant numbers  1011618  and  1164455 ) and the  Victorian Breast Cancer Research Consortium. The work performed at Imperial College London was funded by  Breast Cancer Now  and supported by the  Cancer Research UK Imperial Centre , the  National Institute for Health Research Imperial Biomedical Research Centre  and the  Ovarian Cancer Action Research Centre.
Funding declaration The present study was supported by the  Hellenic Foundation for Research and Innovation (H. F. R. I. )  under the " First Call  for H. F. R. I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant" (Project  ALAS -"ALiens in the Aegean -a Sea  under siege"; Project Number:  HFRI-FM17-1597 ; Katsanevakis et al. 2020b). FaCr was partially funded by the project  PO FEAMP CAMPANIA 2014-2020  (DRD n. 35 of 15th March 2018). XT obtained partial funding from project  MARGECH  ( PID2020-118550RB ,  MCIN/AEI/10. 13039/501100011033 )from the  Spanish Government. AR received grants from the  University of Catania  through " PiaCeRi-Piano Incentivi per la Ricerca di Ateneo 2020-22 linea di intervento 2 ".
Literature and clinical trial searching process Systematic literature searches of articles published between 2000 and October 2021 were performed in the electronic PubMed database from the American National Institute of Health and the Google Scholar database. The searches were conducted using medical subject headings(MeSH: "colorectal cancer" or "colon cancer" or "rectal cancer" combined with "resveratrol" or "sulforaphane" or "quercetin" or curcumin" or "capsaicin") and free-text words and limited to articles published in the English language in indexed and peer-reviewed journals. A clinical trial search was performed using the clinicaltrials. gov website. To select international studies of interest, "colorectal cancer" was selected as the condition of interest, and either "resveratrol" or "sulforaphane" or "quercetin" or curcumin" or "capsaicin" keywords were used in the "other terms" section.
during FolFoX-based (Phase i) Chemotherapy in Patients chemotherapy and with inoperable Colorectal continued for the Cancer. duration of the chemotherapy course. a Phase 1, double-Blind, 9 Patients with resveratrol 5 g of oral srt501 (or • safety, tolerability nCt00920803 randomized Clinical study colorectal placebo) once daily and pharmacokinetic to assess the safety, cancer hepatic for 14 days before profile of srt501 Pharmacokinetics, and metastasis surgical removal of Pharmacodynamics of hepatic metastases srt501 (resveratrol) in subjects with Colorectal Cancer and Hepatic Metastases Phase i repeat-dose study 20 Patients with resveratrol oral administration • Pharmacodynamics nCt00433576 of resveratrol in Colorectal diagnosed 8 days before of resveratrol Cancer Patients: tolerability, chirugicaly colorectomy (dose • Concentrations of target tissue levels and removable and posology not biomarkers in Pharmacodynamics colorectal given) tumoral and healthy Cancer colorectal tissue (Ki67 and CoX-2 expression, M1G concentration) resveratrol for Patients with 11 Patients with resveratrol resveratrol tablets at 20, • test the hypothesis nCt00256334 Colon Cancer diagnosed 80, or 160 mg/day that resveratrol colorectal during 14 days before modulates wnt Cancer and surgical resection signaling in vivo in planned for colon cancer and surgical normal colonic resection mucosa Phase i Biomarker study of 30 Healthy patients resveratrol seedless fresh red • define the minimum dietary Grape-derived low and others grapes at 3 different dietary achievable dose resveratrol for Colon polyphenols doses: 1 lb/day, amount of Cancer Prevention 2/3 lb/day, and 1/3 lb/ resveratrol-rich fresh day red grapes which are effective in inhibiting wnt signaling in human colonic mucosa.
Microscopy. Fixed samples. IF images were taken as Z stacks with the spinning disk confocal microscope Andromeda TILL-FEI (electron microscopy charge-coupled device [EMCCD] camera iXon 897 and α-Plan-Apochromat 63×/1. 46 Oil Korr M27, WD 0. 1-mm objective). Image acquisition was performed with LA, FEI software and analysis with OA, FEI software and with ImageJ. To analyze SUN2 expression, Z stack images were taken of the cell height with 0. 2-μm step and were processed using maximum-intensity Z projection on ImageJ software. The following formula was used to calculate corrected total nuclear fluorescence (CTNF) of SUN2: CTNF = Integrated Density À ðNuclear Area Ã Mean fluorescence of background readingsÞ: To analyze cortical/cytoplasmic actin ratio, images were taken on the midsection of the cell.
75, WD 610 objective and coupled to EMCCD camera iXon 897 to image HeLa Kyoto EGFP-α-tubulin/H2B-mCherry cells and HeLa Kyoto EGFP-LaminA/H2B-mCherry cells; fitted with a Plan-Apochromat 40×/1. 4 Oil-DIC M27 WD 0. 13 mm and coupled to EMCCD camera iXon 897 for SiR-actin labeling; and fitted with an α-Plan-Apochromat 63×/1. 46 Oil Korr M27, WD 0. 1-mm objective and coupled to EMCCD camera iXon 897 to image HeLa Kyoto Mad2-LAP/H2B-mCherry cells. The microscope was equipped with a temperature-and CO 2 -controlled incubator box (37 °C and 5% CO 2 ) and images were taken every 2. 5 min for 15 h minimum. Acquisition was performed with LA, FEI software and analysis with OA, FEI software and with ImageJ. Quantification of the duration of mitosis phases in HeLa Kyoto EGFP-LaminA/ H2B-mCherry cells (Figs.