dataesr/scientific-paragraphs-categorization huggingface dataset:
Inquiry of the mouse aorta single-cell RNA-Seq data  20  showed that potential target genes were mostly expressed in VSMC and fibroblasts clusters. (Supplementary Fig. S6 ). Based on RNA-Seq dataset from GTEx, we found that FMD potential target genes were robustly expressed in tibial artery tissue, while SLC24A3 presented a lower expression in arterial samples of women, compared to men (P = 1 × 10 -5 , Supplementary Fig. S7 ). FMD risk loci and associations with blood pressure and hypertension. All FMD top loci were previously associated with at least one trait related to blood pressure, with the same alleles being associated with increased FMD risk and higher blood pressure/hypertension risk (Fig. 4a ). Hypertension is reported in a large proportion of patients with FMD in general, and in the majority of FMD cases in this study (51-85%, Supplementary Table  S1 ).
Discussion Our study is the most comprehensive genetic investigation to date dedicated to FMD, a non-atherosclerotic arterial disease primarily diagnosed in middle-aged women with few classical cardiovascular risk factors. Key findings from our study include: (i) identification of risk loci and/or genes using single variant GWAS and TWAS in arteries; (ii) through integration of newly generated and publicly available annotation datasets we narrowed down the list of causal variants in most risk loci; (iii) FMD risk genes showed consistent expression in arterial tissue, mostly in VSMCs and fibroblasts. (iv) We found an important genetic overlap between FMD and blood pressure, not only for the top associated loci and genes but also at the GWAS level, although hypertension is unlikely to confound the genetic association with FMD at the 4 loci described.
The phosphatase and actin-binding protein encoded by PHACTR1 regulates actin stress fibre assembly and cell motility  46  , functions highly relevant to the cellular disorganization that characterizes VSMCs in FMD-affected arteries  40. Further investigation, especially with in vivo models, is needed to more precisely define the function of PHACTR1 in the context of the genetic risk to a diverse panel of CVDs. Through genetic correlation analyses, we were able to globally position the genetic basis of FMD among the genetics of more commonly studied CVDs and traits. We showed that FMD shares a significant proportion of its genetic basis with hypertension and blood pressure-related traits, not only for the top associated loci and genes but also genome-wide. The look-up for  the currently large list of SBP loci in our meta-analysis of GWAS only revealed one additional suggestive locus near FHL5, potentially reflecting the limited power of our datasets to detect additional loci using the existing list of SBP loci.
Stroke due to subarachnoid haemorrhage shares several clinical characteristics with FMD, mainly a high proportion of younger (<55 years) and more women among patients, association with blood pressure and smoking  48. Finally, we observed an inverse association between FMD and CAD/MI both for the top loci and genes, and globally when FMD association is conditioned on the genetics of SBP. The elimination of the presence of atherosclerosis as the cause of stenoses and aneurysms is required for FMD diagnosis, which may have influenced this negative correlation with a disease where atherosclerosis is underrepresented. FMD is a disease Variants in LD (r 2 > 0. 5) with the lead SNP were used to query GWAS catalogue database (accessed on August 5th 2020), UK BioBank GWAS traits and specific meta-analysis of GWAS for CAD/MI, stroke, blood pressure and intracranial aneurysms.
c, d Genetic correlation results after FMD genetic association statistics were conditioned on systolic blood pressure. *Unadjusted P value of genetic correlation estimated from linkage disequilibrium score regression using a two-sided Wald test is below the multiple testing threshold for significance (1. 6 × 10 -3 for 31 tests). SBP), with all FMD loci established as having pleiotropic association with blood pressure and several other vascular diseases. Due to the need for angiographic imaging for the identification of FMD, it is possible that asymptomatic FMD was present in a small proportion of control subjects. We compared our genetic data to available expression datasets in tibial artery, coronary artery and aorta, but arterial beds most frequently affected by FMD lesions (renal or carotid artery) would be more appropriate.
4% females) had high-density genotyping data following QC. Controls (n = 1141) were participants without known FMD or atherosclerotic vascular diseases. The study was approved by the Mayo Clinic Institutional Review Boards (IRB # 08-008355). DEFINE-FMD Study. Eligible cases were females with an imaging-confirmed diagnosis of multifocal FMD and who fulfilled other accepted diagnostic criteria,  4, 16. DEFINE-FMD purpose was to establish a library of fibroblasts, DNA, plasma and serum from patients with FMD and unaffected healthy control subjects. FMD cases were required to have a clinical diagnosis of multifocal FMD that is confirmed by imaging [computed tomographic angiography (CTA), magnetic resonance angiography, or catheter-based angiography]. Healthy controls were matched to FMD cases according to age and sex, required to be receiving ≤2 blood pressure medications, have a body mass index <35 kg/m 2 and to be non-smokers.
Healthy controls underwent physical exam and those with bruits; unexplained hypertension or other cardiovascular findings were excluded. Exclusion criteria for all subjects included male gender, unifocal FMD, use of immunosuppressive agents, major comorbidities, diseases that may confound genetic/genomic analyses (i. e. , Crohn's disease, multiple sclerosis, etc. ) or any other form of heritable vascular disease (i. e. , Ehlers-Danlos, Marfan, Loeys-Dietz)  16. DEFINE-FMD study was approved by the Human Research Ethics Committee of the Icahn School of Medicine at Mount Sinai (Study ID: HS#13-00575/GCO#13-1118) and is registered with ClinicalTrials. gov Identifier: NCT01967511. Patient consent did not involve the possibility to redistribute biological material and any request should be addressed to DEFINE-FMD leaders.
8), and deviations from Hardy-Weinberg equilibrium (P < 10 -5 ). A total of 5,483,710 variants met these criteria and were kept in the final results. Association results were combined using an inverse variance-weighted fixedeffects meta-analysis in METAL software  60  , with correction for genomic control. Heterogeneity was assessed using the I 2 metric from the complete study-level metaanalysis. Between-study heterogeneity was tested using the Cochran Q statistic and considered significant at P ≤ 10 -3 Genome-wide significance threshold was set at the level of P = 5. 0 × 10 -8. LocusZoom (http://locuszoom. org/) was used to provide regional visualization of results. Statistical analyses and plotting were performed using R (3. 6. 1), through RStudio interface software (v1. 2. 335). Following packages were used: MatrixEQTL_v.
eQTL and colocalization analyses. We queried the GTEx database (v8 release)  61  with rsID of lead variants at FMD loci in arterial tissues for major associated genes (permutations q value < 0. 05). For each identified gene, variant-gene association was queried in all tissues using "eQTL calculator" function on GTEx website (https://gtexportal. org/home/). Uncharacterized non-coding transcripts were excluded from the analysis. For colocalization, all SNP Gene associations in the 3 arterial tissues were retrieved from GTEx to compare with the FMD GWAS metaanalysis result and the multifocal FMD GWAS meta-analysis result. We generated colocalization plots using locuscompareR package  62  and Bayesian posterior probability was calculated using coloc. abf function in R coloc package  63. The H4 coefficient indicating the probability of the two traits to share a causal variant was reported.
Gene-based and transcriptome-wide association analyses. Gene-based association was conducted using the MAGMA tool  13  , implemented in the FUMA platform  14. Locations of protein-coding genes were defined as the regions from transcription start site to transcription stop site (default option in MAGMA). TWAS was performed using FUSION R/python package  17. Gene expression models were precomputed from GTEx data (v7 release) and were provided by the authors. Only genes with heritability P-value < 0. 01 were used in the analysis. Uncharacterized non-coding transcripts were excluded from the analysis. Both tools used linkage disequilibrium information from the European panel of the 1000 Genomes phase 3. Bonferroni multiple testing correction was applied using the p. adjust function in R (v 3. 6.
GENCODE 29 was used as reference annotation to quantify gene and long non-coding RNA (lncRNA) expression. Sequencing reads (fastq files) were mapped to GRCh38 human reference genome using STAR aligner (version 3. 6. 0c) with default mapping parameters  64. Low counts were removed by keeping genes where the count per million is greater than 1 in at least two samples. After filtering zero and low counts, an average of 34 million reads was retained per sample. R package Matrix eQTL v. 2. 1. 1 was used to compute cis-regulated eQTLs in the cohorts of cases, controls and cases plus controls, respectively  65. Age and the first five genetics principal components were applied as covariates. Case control status was accounted as a covariate effect for the case control pooled cohort. All cisregulatory SNPs were located within 1 Mb of the gene by using the hg19 genome (Ensembl Gene annotation build 75) and statistics were calculated by a linear model.
29. 0) annotate function. The 95% credible set of causal variants was retrieved from LocusZoom (http://locuszoom. org/). Full list of peak files used is available in Supplementary Table  S14. Analysis of SNP enrichment among ATAC-Seq peaks was performed using GREGOR  19. The lead SNPs from loci associated with P value < 10 -4 were used as reference for FMD-associated SNPs (Supplementary Data 1). We included in the analysis SNPs in LD with lead SNPs (r 2 ≥ 0. 7 in the European subset of the 1000 Genomes phase 3 reference panel). We used Integrated Genome Browser (IGB, v9. 1. 4) to visualize read density profiles and peak positions in the context of human genome  74. Overlap between FMD loci and other traits and diseases. We queried the GWAS catalogue database  75  , UK Biobank GWAS summary statistics made publicly available by the Neale lab at the Broad Institute (http://www.
All analyses were performed with the ldsc package (v1. 0. 1, https://github. com/bulik/ldsc/). We conditioned FMD association on systolic blood pressure genetic association using multi-trait-based conditional and joint analysis (mtCOJO) tool from GCTA pipeline  78. The resulting summary statistics were then used to calculate genetic correlation between FMD, conditioned on systolic blood pressure, and the previous traits. Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1200 Brussels, Belgium. 30 Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia. 31 St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia. 32 These authors contributed equally: Adrien Georges, Min-Lee Yang, Takiy-Eddine Berrandou. 33 These authors jointly supervised this work: Santhi K.
All variants within the 95% credible set in the FMD association study were tested for overlap with open chromatin regions in carotid/coronary artery-derived primary cells and dermal fibroblasts described in this study, open chromatin in coronary artery tissue21   and histone marks in artery tissues (aorta, coronary or tibial arteries) from ENCODE database. N SNPs indicate the number of SNPs tested at each locus. P indicates the GWAS association P value of the SNP in the reported association. Posterior probability of each variant for being a causal variant is indicated (Post. Prob. ). + indicates overlap of the variant with at least one dataset of the corresponding category.
Acknowledgements We thank all patients who participated in these studies. We thank  Dr. Antoine Chédid  for collecting and managing clinical data of patients in ARCADIA protocol. We thank  Patrick Bruneval  for his scientific input and exchanges about arterial pathology in FMD. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. This work has benefited from the facilities and expertise of the high throughput sequencing core facility of  I2BC (Centre de Recherche de Gifhttp ://www. i2bc. paris-saclay. fr/). ARCADIA-Pol investigators thank  Ewa Rudolf ,  Elzbieta Pazio  and  Małgorzata Lewandowska , who were responsible for all administrative work. The Steering Committee of the WOBASZ study expresses special thanks for participating in the implementation of the study to: all their co-workers from research teams at six academic centres, to nurses, doctors, and analysts from field research centres located in 16 voivodeships.
This study was supported by the  European Research Council  grant ( ERC-Stg-ROSALIND-716628 ) to NB-N and  National Institute of Health  grant ( R01HL139672 ) to S. K. G. The ARCADIA study was sponsored by the  Assistance Publique-Hôpitaux de Paris  and funded by a grant from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique 2009 ,  AOM 08192 ) and the  Fondation de Recherche sur l'Hypertension Artérielle. Genotyping of French study  was supported by the  French research agency  ( ANR-13-JSV1-0002 ) to N. B. -N. The genotyping of controls from the  Three-City Study  ( 3C ) was supported by the non-profit organization Fondation Alzheimer (Paris, France) to P. A. ARCADIA-Pol study was supported by the grant no. 2. 40/III/19  of  Institute of Cardiology, Poland. The  WOBASZ II Project  was financed from the resources at the disposal of the  Polish Minister of Health  within the framework of the " National Program of Equalization and Accessibility to Cardiovascular Disease Prevention and Treatment for 2010-2012 ".
Data availability The GWAS data generated this study have been deposited in the GWAS catalogue database under accession code GCST90026612. Summary statistics data used for genetic correlation were acquired from the respective consortia and are detailed in Supplementary Table  S15. The ATAC-Seq sequencing data generated in this study are publicly available on the Sequence Read Archive under accession code PRJNA690001. Other genomic datasets used in these studies were retrieved from public databases and a full list of files with accession is available in Supplementary Table  S14. Fibroblast eQTL dataset generated was looked up for identified genes only and cannot be made fully available to protect patient's privacy. Data accessibility will be provided upon reasonable request to DEFINE-FMD leaders.
+ All variants within the 95% credible set in the FMD association study were tested for overlap with open chromatin regions in carotid/coronary artery-derived primary cells and dermal fibroblasts described in this study, open chromatin in coronary artery tissue21   and histone marks in artery tissues (aorta, coronary or tibial arteries) from ENCODE database. N SNPs indicate the number of SNPs tested at each locus. P indicates the GWAS association P value of the SNP in the reported association. Posterior probability of each variant for being a causal variant is indicated (Post. Prob. ). + indicates overlap of the variant with at least one dataset of the corresponding category.
Food provides humans with more than just energy and nutrients, addressing both vital needs and pleasure. Food habits are determined by a wide range of factors, from sensorial stimuli to beliefs and, once commanded by local and seasonal availability, are nowadays driven by marketing campaigns promoting unhealthy and non-sustainable foodstuffs. Top-down and bottom-up changes are transforming food systems, driven by policies on SDGs and by consumer's concerns about environmental and health impacts. Food quality, in terms of taste, safety, and nutritional value, is determined by its composition, described in food composition databases (FDBs). FDBs are then useful resources to agronomists, food and mechanical engineers, nutritionists, marketers, and others in their efforts to address at maximum human nutrient needs.
In this work, we analyse some relevant food composition databases (viz. , purpose, type of data, ease of access, regularity of updates), inspecting information on the health and environmental nexus, such as food origin, production mode as well as nutritional quality. The usefulness and limitations of food databases are discussed regarding what concerns sustainable diets, the food 'matrix effect', missing compounds, safe processing, and in guiding innovation in foods, as well as in shaping consumers' perceptions and food choices.
Introduction Food databases (FDB), or more correctly food composition databases, contain detailed information on the nutritional composition of foods and on other relevant compounds (e. g. , polyphenols, phytic acid). Food components primarily determine nutritional features and, in some cases, quality aspects. For example, polyphenols, which are abundant in plants, are often associated to bitter taste and astringency sensation of foods  [1] , while acting in favour of food safety by inhibiting foodborne pathogens and spoilage microbes. Polyphenols can be intentionally added to foods for their bioactive properties  [2] [3] [4]  or they can be key natural components, as happens in table olive fermentation  [5, 6]. During the spontaneous fermentation process, olive's polyphenols help to select the suitable microbial populations, resulting in taster and safer foodstuffs.
Besides whole food composition databases, some specialised ones, generally concerning one class of compounds, are accessible to researchers and other interested parties. In this scope, two classes of compounds have emerged recently: bioactive molecules (such as polyphenols) and microbial metabolites (e. g. , butyric acid, accumulated during food fermentations and found to be beneficial in the gut). We open, herein, a parenthesis to categorize both types of compounds, because they have been increasingly noted in innovative foods that highlight health-related aspects. by. In the words of Biesalski et al. [23] , a 'bioactive compound' is a 'compound that occurs in nature, part of the food chain, and that can interact with one or more compounds of the living tissue, by showing an effect on human health'.
Current Uses, State-of-the-Art, and Future Challenges of Food Composition Databases The reference FDBs that were once tables on paper and later on physical digital supports are nowadays easily accessible online, holding and managing large quantities of data and metadata that can be inspected and downloaded. As previous versions, online FDBs mostly detail the composition of fresh produce as well as branded foodstuffs, discriminating energy sources and macronutrients into their components (e. g. , amino acids, sugars, starch, fatty acids), as well as minerals (e. g. , calcium, iron, sodium) and vitamins. Often, information on other features, as the content of dietary fibre and relevant bioactive constituents (e. g. , carotenoids, polyphenols) is also included, and recently, more and more information has been made available, in pace with the development of convenient interfaces to access and use it.
FDBs have been evolving in adapting new ICT tools. A trend in establishing connections between different databases can be observed, thus expanding the available information while allowing the access either by specifically designed algorithms or by individual discrete users making simple searches. Connections between FDBs complement information about a certain food or about the food sources for a certain compound; for example, bioactive compounds are included in the eBASIS database, in the US isoflavone database and in the French Phenol-Explorer database, all linked to EuroFIR and to FoodData central, as detailed below. FDBs' interlinkage adheres to agreed international standards and guidelines, which are of the competence of InFOODs, the International Network of Food Data System from the FAO (UN, Food and Agriculture Organization).
It acts as a network of regional datacentres with a central coordination, as well as a forum for the international harmonization and support for food composition activities. InFOODs aims at linking agriculture, biodiversity, food systems, health, and nutrition to achieve better nutrition worldwide. The network regularly issues publications on food composition and other food-related aspects, and its webpage provides access to searchable FDBs  [12]. The standardization and harmonizing of food composition data from different countries with distinct metadata are essential to ensure efficient data linkage and the retrieval of information. Hence, tools and procedures have been developed aiming to guarantee interoperability between the databases. Langual is such a tool  [26]. It is a food description thesaurus that stands for 'langua alimentaria' or 'language of food' and provides a standardised language for describing foods, specifically in classifying food products for information retrieval.
Each of their over 40,000 foods is described by the means of numerical attributes on food composition (nutrients and contaminants), food consumption, and legislation. Langual establishes a correspondence between these food attributes (descriptors) and common language terms in different natural languages  [26]. This important tool facilitates the linkage to many different food data banks from different countries, interpreting distinct designations and resolving ambiguities to ensure the correspondence between food and their attributes, thus contributing to coherent data exchange  [27]. The food indexing system of Langual already considers food source (e. g. , animal or plant species), food preservation (e. g. , fresh, frozen), cooking, packaging, etc. However, the next generation of this European FDB thesaurus is even more complex and comprehensive.
This global initiative under development-FoodOn-deals with a very comprehensive semantics encompassing descriptors for food safety, food security, agricultural practices, culinary, nutritional and chemical ingredients, and processes  [26, 27] , as can be overviewed in Figure  1. ardised language for describing foods, specifically in classifying food products for information retrieval. Each of their over 40,000 foods is described by the means of numerical attributes on food composition (nutrients and contaminants), food consumption, and legislation. Langual establishes a correspondence between these food attributes (descriptors) and common language terms in different natural languages  [26]. This important tool facilitates the linkage to many different food data banks from different countries, interpreting distinct designations and resolving ambiguities to ensure the correspondence between food and their attributes, thus contributing to coherent data exchange  [27].
Experimental science advances are based in data, including from FDB, and such figures are commonly fed into models, producing results from which conclusions are withdrawn. Nowadays, these processes can be easily automated by using a bot/API to download data from FDB, which can then be analysed with the assistance of an AI, allowing for instance rapid identification of patterns and trends. With more or less automation, the ability to provide reliable and significant results rely on the research's rigor and methodologies, as much as on the rigor and detail of the semantics and structure of the database from where the information was withdrawn. Specially developed apps may provide insights on more obvious relationships (e. g. , between dietary intakes and health) or less obvious relationships (as between food composition and climate change).