dataesr/scientific-paragraphs-categorization huggingface dataset:
IMPROVE was set up for the study of cIMT measures as predictors of incident coronary events, and enrolled n=3711 subjects with at least three independent CAD risk factors. Detailed descriptions of IMPROVE, including the protocols for carotid ultrasound measures and SNP genotyping on Illumina CardioMetabochip and Immunochip arrays, have been reported  24. In the present study n=3378 subjects were used for genetic association analyses. To study the impact of PCSK6 variants on plaque histological characteristics, we used data from the AtheroExpress biobank (AE, www. atheroexpress. nl). The methods of genotyping and imputation in AE have been described previously  25. Briefly, in two experiments n=1858 patients were genotyped using the Affymetrix SNP 5. 0 (AtheroExpress Genomics Study 1, AEGS1, n=836) and Affymetrix Axiom CEU arrays (AtheroExpress Genomics Study 2, AEGS2, n=1022); 1443 remained after community standard quality control  26.
Statistical and bioinformatic analyses For genetic analyses, all SNPs in the region ±200 000 kb around the PCSK6 gene present on the Illumina CardioMetabochip and Immunochip were analysed, after filtering out those with minor allele frequency (MAF)<0. 03 and linkage disequilibrium pruning for pairs with r 2 >0. 8. Linear regression analyses were performed between SNPs and different cIMT measures using PLINK (v1. 07)  30  , assuming an additive genetic model adjusting for age, gender and population stratification, and results were corrected for multiple comparisons according to Bonferroni. All cIMT variables were logarithmically transformed before statistical analysis because of skewed distributions. Functional information about the variants was extracted using Haploreg v4. 1 software (Broad Institute).
Expression quantitative trait loci (eQTL) analyses from healthy tissues were performed using the public GTEx software (www. gtexportal. org). To enumerate the relative abundance of pericyte cell population in the plaque tissue, we applied the deconvolution strategy to BiKE microarrays using the Cibersort web software (https://cibersort. stanford. edu), based on recently published human plaque data  31. Briefly, resident cell populations were deconvoluted using a signature markers file generated from single-cell RNA sequencing of atherosclerotic human coronary arteries. This gene-by-cell type matrix file contained expression levels for the top 26 genes specific for each of the major detected cell types. Pearson or Spearman's rank correlations were calculated to determine the association between mRNA and protein expression levels from microarrays and LC-MS/MS, for parametric and non-parametric data respectively.
A functional coupling network based on extended, experimentaly validated PCSK6-MMP14-MMP2 protein-protein interactions was constructed using FunCoup software (http://funcoup. sbc. su. se). Gene set enrichment analysis was performed using GeneMania (www. genemania. org). Functional data about the genes was extracted using GeneCards (www. genecards. org) and PubMed literature search. Network matrices were constructed from the rat carotid injury microarray dataset by calculation of the expression correlation coefficients using the Pearson method and p-values were corrected for multiple comparisons using Bonferroni. Clustering was performed with the Morpheus software (Broad Institute), where the distance matrix was created from dissimilarity index (1-gene correlation). Multiple sequence alignment of the PCSK6 amino-acid sequence was plotted using the CLUSTAL and PRALINE softwares, and evolutionary conservation of the protein domain organization using the ENSEMBL database.
PCSK6 is enriched in plaques from symptomatic patients and localized to SMA+ cells in the fibrous cap and neovessels Next, we extended our previous findings demonstrating upregulation of PCSK6 mRNA in human carotid plaques  17  , using transcriptomic data from additional subsets of BiKE patients, as well as public microarray datasets (Figure  2A ). Upregulation of PCSK6 transcript was confirmed: in carotid plaques (n=50) vs. normal arteries (n=5) from BiKE (mean difference with SD=0. 64±0. 12); in a public dataset (GEO accession nr. GSE40231) comparing coronary and carotid lesions (n=40) with normal arteries (n=40) (mean difference with SD=61. 82±8. 38), as well as in a public dataset (GSE43292) comparing carotid plaques with matched adjacent tissue (n=32 patients, mean difference with SD=0.
In particular at day 5, when the expression of Pcsk6 was the lowest, positive correlations were restricted to Mmp14 and Mmp2, whereas that with Mmp12 was negative (Figure  4A ). Next, we constructed a functional network module connecting PCSK6-MMP2-MMP14 proteins via their direct and extended interaction partners  38. This network contained 33 nodes (Figure  4B ) and pathway analysis showed an enrichment of proteins involved in ECM organisation (FDR=6. 8E-50), collagen degradation (FDR=2. 4E-31) and growth factor binding (FDR=2. 6E-13), but also i. e. blood vessel development (FDR=0. 049) (Supplementary Table  VI ). Correlation matrices were then built from rat intimal hyperplasia transcriptomic datasets and clustered based on the similarities of the gene expression profiles (Figure  4C ).
Figure2. PCSK6 transcript and protein are upregulated in atherosclerotic lesions and localised to SMA+ cells. PCSK6 mRNA expression was upregulated comparing plaques and normal arteries in the BiKE microarray dataset, which was validated in public microarrays from 2 additional cohorts comparing plaques with adjacent vascular tissue or normal arteries. Plots showing log2 mean±standard deviation (SD). In the proteomic analysis by mass spectrometry of n=18 matched patient samples (BiKE), PCSK6 was also enriched in plaques (central) compared with matched adjacent arterial tissue (distal samples) and in plaques from symptomatic vs. asymptomatic individuals (A). By immunohistochemistry, SMA (red) was continuosly expressed in lesions from all stages of atherosclerotic disease (modified American Heart Association grading I-V; IEL-internal elastic laminae, arrows).
001 ) and the  FP7 EU project CVgenes@target  ( HEALTH-F2-2013-601456 ). Prof Humphries  is supported by a grant from the  British Heart Foundation  (BHF grant  PG 08/008 ) and by funding from the  Department of Health's NIHR Biomedical Research Centers funding scheme. Ljubica Matic  is the recipient of fellowships from the  Swedish Society for Medical Research and the Heart and Lung Foundation , and acknowledges research funding from the  Swedish Research Council ,  Swen  and  Ebba Hagberg ,  Tore Nilsson ,  Magnus Bergvall  and  Karolinska Institutet Foundations, Sweden.
Images taken with 20x and 40x objectives. Figure 4. A network connecting Pcsk6-Mmp2-Mmp14 is activated in rat carotid intimal hyperplasia. Expression correlations of Pcsk6 during all time-points in the rat carotid artery injury model were significantly positive for Mmp2, Mmp14 and Mmp12. However, on day 5 after injury only correlations with Mmp2 and Mmp14 were positive, while the correlation with Mmp12 was significantly negative (A). The functional network constructed from direct and extended protein interaction partners of PCSK6, MMP2 and MMP14 (B). Network weighted for closeness in biological function based on publically available data. Expression correlations of the genes in this network were examined in rat carotid artery injury datasets and presented as matrices where the nearest neighbours were clustered based on the correlation index (C).
Another important limitation is that we only included data from subjects of European ancestry, since GWAS data for other ancestries are currently too small to permit reliable projections for most cancer sites. In addition, several cancers (e. g. , lung, ovary, glioma, and breast) consist of etiologically heterogeneous subtypes that were not considered in our analyses due to lack of adequate sample sizes for appropriate subtypes for most of these cancer sites. Further studies of ancestry-and subtype-specific genetic architectures are needed to address these limitations. In our projections, we assume standard agnostic association analysis of SNPs without incorporating any external information ) level (dashed curve). Colored dots correspond to sample size for the largest published GWAS and those for doubled and quadruped sizes.
Methods Description of GWAS studies. We analyzed summary data from GWAS studies across 14 cancer types. For select cancer sites  26, 28  , we downloaded publicly available genome-wide summary-level statistics from the latest consortium-based analyses. For others, we obtained access to data through collaborative efforts with individual consortia. Details about individual studies, including the number of cases and controls, are provided in Supplementary Table  1. x : Total sample size assuming 1:1 case:control ratio (in thousands) y : Relative risk for people at 99th centile compared to average risk of the population Fig. 4  Projections of relative risks for individuals at or higher than 99th percentile of PRS as sample size for GWAS increases. Results are shown where PRS is built based on SNPs at optimized p value threshold.
Linkage disequilibrium (LD Quality control for summary GWAS data. Across all cancers, we applied several filtering steps analogous to those used earlier for estimation of heritability  45, 46  and effect-size distribution using summary-level data  15. First, we restricted analysis to SNPs within a set of reference ~1. 07 million SNPs included in the HapMap3 and that had MAF > 0. 05 in the 1000 Genome European Ancestry sample. Second, we excluded SNPs having substantial amounts of missing genotype data: sample sizes <0. 67 times the 90th percentile of the distribution of sample sizes across all SNPs. Third, we excluded SNPs within the major histocompatibility complex region (i. e. , SNPs between 26,000,000 and 34,000,000 base pairs on chromosome six), which is known to have very complex allelic architecture and can have uncharacteristically large effects on some traits.
The relative risk for those estimated to be at the 99th percentile or higher of the distribution of a PRS (compared to the average risk of the population) was calculated using the formula  12 expðÀ h 2 2 þ Φ À1 0:99 ð Þ ffiffiffiffi ffi h 2 p Þ , where h 2 is the population variance of the PRS. Absolute risk projection. For each cancer site, we projected the distribution of residual lifetime risk (up to age 80 years) for non-Hispanic white individuals in the general US population according to PRSs, which could be built from GWASs of different sample sizes. For any given age, we first obtain the distribution of residual lifetime risks based on a model for absolute risks developed using the iCARE tool that we have described earlier  12, 29. The iCARE tool uses projected standard deviations of PRS at different GWAS sample sizes and age-specific cancer incidence rates available from the US National Cancer Institute-Surveillance, Epidemiology, and End Results Program (NCI-SEER) (2015) to obtain absolute risk distributions.
In deriving absolute risks, we adjusted for competing risk of mortality due to other causes using the age-specific mortality rates from the Center for Disease Control WONDER database (2016). We then weighted the projected residual lifetime risk distribution at different baseline ages (in 5-year categories) based on the US population distribution of ages within 30-75 years, as observed in the estimated 2016 US Census. For cancers of the reproductive system, weights were based on the age distributions among males or females, as appropriate. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability The data that support the findings of this study are available by application from the participating consortia: BCAC (bcac@medschl. cam. ac. uk), BEACON (P Gharahkhani), ColonCFR (M Jenkins), GECCO/CORECT (U Peters), ECAC (TA O'Mara), GenoMEL (M Iles), GICC (R Houlston), ILLCO/INTEGRAL (C Amos), InterLymph (S Berndt), OCAC (PDP Pharoah), Oral Cancer GWAS (P Brennan), PanC4/PanScan (LT Amundadottir), PRACTICAL (Data Access Committee/http://practical. icr. ac. uk/), Renal Cancer GWAS (MP Purdue, P Brennan), and TECAC (KA McGlynn). For breast and prostate cancers, summary GWAS data can also be downloaded from http://bcac. ccge. medschl. cam. ac. uk/bcacdata/oncoarray/gwas-icogs-and-oncoarray-summary-results/ and http://practical. icr. ac. uk/blog/?page_id=8164. Public Health, Boston, MA, USA.
Acknowledgements The research was supported by an  RO1  grant from  NHGRI  ( 1 R01 HG010480-01 ) and the intramural program of the  National Cancer Institute. 39  Department of Neurological Surgery, School of Medicine, University of California, San Francisco ,  San Francisco , CA,  USA. 40Department of  Radiation Sciences Oncology, Umeå University, Umeå, Sweden. 41Case  Comprehensive Cancer Center ,  Case Western Reserve University School of Medicine, Cleveland, OH, USA. 42Institute for Clinical and Translational Research, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA. 43Lunenfeld- Tanenbuaum Research Institute, Sinai Health System, Toronto, ON ,  Canada. 44International Agency for Research on Cancer ,  World Health Organization, Lyon, France. 45Division of Hematology and Hematological Malignancies, University of Utah School of Medicine , Salt Lake City,  UT, USA.
Conclusions Despite the limitations, and although the food intake decreased with the association of guarana and WBV exercise (GUA + WBV-E), it is possible to conclude that body mass did not changed. In addition, guarana extract promoted effects on muscle as compared with the control group, and the increase in the uptake of Na 99m TcO 4 justifies the alterations previously reported for natural products on the uptake of sodium pertechnetate in selected organs. Guarana also promoted modification in stool consistency and did not alter the body mass of the rats, reinforcing guarana as a functional food. This dataset and the knowledge of the association of guarana with WBV can give greater reliability concerning the effects of guarana in healthy animals, opening pathways to further investigations that involve this type of treatment in experimental models for diabetes, cancer, and Alzheimer disease.
, 2016). This study was based on the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, which includes one third of the Medicare healthcare users. The participants were aged 66 years or older, diagnosed with cancer from 2004 to 2011 and died of cancer within 3 years. The authors concluded that both pre-and post-cancer depression subjects received less high-intensity care and were more often referred to palliative care. Another American study on the same database confirmed these results on small-cell lung cancer in patients aged>67  (McDermott et al. , 2018). These results deserve replication in a non-US country to determine if the association between major depression and lower EOL high-intensity care was specific of the US care system. Moreover, the populations included in the SEERS database was not exhaustive, the studies were limited to elderly patients and depression was defined in the 12 previous months, including also isolated depressive episodes.
Study design and data source This is a population-based cohort study of all patients aged ≥15 years who died from cancer in the hospital in France between January 1, 2013, and December 31, 2016. The study was based on the French national hospital database (Programme de Médicalisation des Systèmes d'Information (PMSI)), in which administrative and medical information is systematically collected for acute (PMSI-MCO) and psychiatric (PMSI-PSY) care. This information is anonymized and can be reused for research purposes  (Revon-Rivière et al. , 2019; Rochoy et al. , 2019). The PMSI is based on diagnosis-related groups, and all diagnoses are coded according to the International Classification of Diseases, Tenth Revision (ICD-10)  (Boudemaghe and Belhadj, 2017). Patients were identified using the algorithm developed by the French National Institute of Cancer (INCA) specifically designed to identify cancer-related treatment in the French national hospital database  (INCA, 2013).
EOL patients were identified using the following criteria: i) in their last 3 months of life, ii) diagnosed with solid cancer in a metastatic stage or with brain or liver cancer (regardless of the stage of the disease, as these tumors are rarely metastatic and usually lethal at local regional stages)  (Rochigneux et al. , 2017) , and/or (iii) received palliative care (authorization of a palliative unit or bed care or the ICD-10 code for palliative care [Z515]). Data from patients who died in the community or in nursing homes are not available in the PMSI. As dementia may be a confounding factor and given that RMDD is associated with an increased risk of dementia, patients with a diagnosis of dementia were excluded (ICD codes: F00*, F01*, F02*, F03*, F051*, G30*, G311*). From this selection, we defined 3 populations: 1) Cases: The RMDD patients were defined by a specific ICD code for recurrent major depressive disorder (F33*) in the PMSI-MCO database and/or in the PMSI-PSY database during the 3 previous years before death after exclusion of F20*/F22*/F25* (schizophrenia), F30*/F31* (bipolar disorder), F603* (borderline personality disorder) diagnoses.
These diagnoses are associated with specific severity, treatment and relapsing characteristics; they have thus been excluded to homogenize the cases group. F32* (isolated depressive episodes) have not been included in the case group to avoid any confusion between RMDD and post-cancer/ EOL depression that have different impact on EOL care  (McDermott et al. , 2018). 2) Controls: The controls were patients from the PMSI-MCO database without psychiatric conditions according to the specific ICD codes (chapter F*) and without admission in psychiatry (PMSI-PSY). 3) Matched controls: Cases and controls were matched using a 1:4 ratio according to 3 criteria: age at death (+/-2 years), sex and primary cancer location (15 modalities).
Potential confounding factors Six sociodemographic, clinical or hospital characteristics were considered as potential confounders for EOL care for cancer and were included in our analyses. These factors were determined according to the variables available in the PMSI database that may have impacted the EOL care of patients: -Social deprivation assessed by an index validated on French data and based on the postal code of the domicile  (Rey et al. , 2009). The FDep09 index involves four socioeconomic ecological variables: percentage of high-school graduates, median household income, percentage of blue-collar workers and the unemployment rate. The FDep09 index was categorized according to quartiles, from the least (Q1) to the most deprived area (Q4). year of death (as the EOL practice may have evolved throughout time as mentioned in the rationale); time from cancer diagnosis to death; comorbidities assessed using the Charlson modified Comorbidity Index  (Quan et al.
Six confounding factors were included in the models: social deprivation (4 categories/quartiles from the most favored (Q1) to the most deprived area (Q4)), year of death (4 categories: 2013 to 2016), time between cancer diagnosis and death (days), metastases (yes/no), Charlson modified comorbidity index (3 categories: 0, 1 or 2, >=3 comorbidities) and hospital category (2 categories: cancer specialty vs. nonspecialty centers). These analyses were performed with a multivariable generalized linear model (logistic for qualitative outcomes; log-normal for quantitative ones) with the matched cluster as a random intercept using the PROC GLIMMIX in SAS (step 2). The statistical analysis was performed with SAS 9. 4 (SAS Institute). Statistical significance was defined as p<0. 05.
, 2011) , which may explain our longer RMDD length of palliative follow-up. These three levels of barriers should thus be captured with qualitative approaches and raise questions on the future organization on the EOL care of subjects with RMDD. In the present study, the RMDD subjects were already diagnosed before cancer onset. The differences in EOL care are thus not a matter of undiagnosed depression. Yet we don't know if RMDD subjects received correct psychiatric follow-up due to the absence of data on resistant depression status, outpatient consultations and treatments, which is a limit of the present work. To exceed this limit, we excluded isolated major depressive episodes diagnoses from the database to avoid any confusion between recurrent pre-cancer depression and post-cancer and EOL depression.
, 2018). Recurrent major depressive disorder diagnosis was defined on a 3-year basis (vs. only 1 year for  Doan et al. ) , which strengthens our group definition. Despite its importance, post-cancer depression has not been explored in the present study given that our database was a hospital database. Most of the acute depressive episodes are treated outside the hospital, which may have biased our analyses. Future studies should include ambulatory data to address this question such as the French reimbursement ambulatory database (Système National des Données de Santé/SNDS). Strengths. This study has explored a 4-year national database of >220,000 deaths from cancer with a validated method of patient selection  (Rochigneux et al. , 2017). This study is the first replication of  Doan et al. results , which suggests that the impact of RMDD on EOL is not due to the USA healthcare system while Doan et al.
study has included only a one-third subsample of medicare database  (Doan et al. , 2016). Doan et al. limited their  results to patients aged >66 while almost one third of our subjects were aged less than 60 years. The case and control groups have been strongly homogenized by excluding other forms of depressions (bipolar, schizophrenic, borderline and isolated depressive episodes including post-cancer and EOL depressive episodes). Our selection focused on people at the end of life requiring palliative care (see flowchart and inclusion criteria) and thus avoid including patients for whom death was not expected (and thus justified more high intensity care).
Major disturbance events can have large impacts on the demography and dynamics of animal populations. Hurricanes are one example of an extreme climatic event, predicted to increase in frequency due to climate change, and thus expected to be a considerable threat to population viability. However, little is understood about the underlying demographic mechanisms shaping population response following these extreme disturbances. Here, we analyse 45 years of the most comprehensive free-ranging non-human primate demographic dataset to determine the effects of major hurricanes on the variability and maintenance of longterm population fitness. For this, we use individual-level data to build matrix population models and perform perturbation analyses. Despite reductions in population growth rate mediated through reduced fertility, our study reveals a demographic buffering during hurricane years.