dataesr/scientific-paragraphs-categorization huggingface dataset:
| Dietary assessment and estimation of AGE intake In EPIC, country-or center-specific validated dietary questionnaires (DQ) were used at baseline, accounting for the usual food intake during the previous 12 months. 20 In Umeå (Sweden), Denmark, Norway and Naples (Italy), semiquantitative food frequency questionnaires were used. Malmö (Sweden) and the United Kingdom used semiquantitative food frequency questionnaires in combination with 7-day and 14-day records, respectively. Netherlands, Germany, Northern Italy and France used quantitative DQ. In Spain and Ragusa (Italy) the quantitative DQ were interviewer-administered and structured by meals. Harmonization of food groups and portion sizes for quantification was carried out centrally at the International Agency for Research on Cancer. 23 reference dietary AGEs composition database was used, which contain CML, CEL and MG-H1 concentrations (in mg/100 g of food) What's new? Advanced glycation endproducts (AGEs) are proteins or lipids with sugars added to them, and they can form in foods during cooking.
They have pro-inflammatory and prooxidative properties in the body. These authors investigated whether dietary consumption of AGEs affect risk of liver cancer, using the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Participants filled out food questionnaires to determine AGE consumption. Higher intakes of AGEs were associated with reduced risk of hepatocellular carcinoma (HCC) and increased risk of gallbladder cancer. However, it's still not entirely clear how much AGEs in the diet contribute to circulating AGE levels. obtained using ultra-performance liquid chromatography-tandem mass spectrometry analysis of 190 food items commonly consumed in Europe. 11 Foods from the reference database were matched to DQ foods by name and descriptors, particularly those pertaining to preparation and processing whenever applicable.
Third, to evaluate potential residual confounding by smoking and  (Continues) heavy alcohol consumption, we excluded in turn current smokers and participants reporting heavy alcohol consumption at any point in time. Fourth, we evaluated whether dietary misreporting may have biased our estimates by adjusting for plausibility of dietary intake reporting based on Goldberg's cut-off points. 28 Fifth, we compared results obtained using a missing value indicator with those using a complete case analysis after excluding all subjects with missing values in any of the covariates (total n = 11. 1%). Sixth (for hepatocellular cancer only), we used data from the EPIC nested case-control study to adjust associations for hepatitis virus B and C (HBV/HCV) infection status (measured by HBsAg and anti-HCV assays) and liver function biomarkers (gamma-glutamyltransferase, GGT; alanine aminotransferase, ALT; aspartate aminotransferase, AST; alkaline phosphatase, ALP; total bilirubin and albumin) available for a subset of participants.
Abbreviations: BMI, body mass index; CEL, N ε -[1-carboxyethyl]lysine; CML, N ε -[carboxymethyl]lysine; MG-H1, N δ -[5-hydro-5-methyl-4-imidazolon-2-yl]ornithine. a Residuals were computed by a linear regression of the log transformed intake of AGEs, energy and center. b Mean ± SD, all such values. c Refers to grams of daily intake per 1000 kcal. d Ranges from 0 to 18 points, zero showing no adherence to the Mediterranean Diet Pattern. e Percentages using the nested case-control dataset with n = 204 cases and n = 205 matched HCC controls.
As a negative control, HEK293T-Gα16gust44 cells were mock-transfected with the empty vector. Twenty-four h after transfection, cells were washed with C1 buffer (130 mM NaCl, 5 mM KCl, 10 mM HEPES, 2 mM CaCl 2 , 5 mM sodium pyruvate, pH 7. 4) and placed in an automated fluorometric imaging plate reader (Flexstation 3, Molecular Devices, San José, CA, USA). Cells were stimulated with different concentrations of brazzein, and calcium responses were monitored by an increase in fluorescence at 510 nm after excitation at 488 nm. Experiments were performed in duplicate and repeated 4 times. For data analysis, the recorded calcium levels of each well that received the same stimulus were averaged, the response of cells transfected with empty vector (mock cells) was subtracted from that of receptor-transfected cells, and net signals were normalized to background fluorescence (∆F/F0, F0 fluorescence light before stimulus application).
Participants Participants were recruited by the researchers via social networking, personal mailand professional mail databases (University of Thessaly, Larissa's Medical Association, Pharmaceutical Association of Larissa, Secondary Education, Larissa Primary Education Parents Association, Second Chance School, Hellenic Air Force, Airport of Volos), and the electronic questionnaire was promoted using the avalanche method. From the total number of 1402 participants, 124 did not answer the income question and were excluded from the survey. The final number of participants was 1278. Data collection was conducted from April 2021 to June 2021. Quotas were used based on gender (49. 0% male, 51. 0% female) and age, which was categorized as follows: from 18 to 30 years old, from 31 to 50 years old and over 51 years old.
Data Students were asked to assess their stress with the Stress Numerical Rating Scale-11 (SNRS-11). SNRS-11 is a scale ranging from 0 to 10, with 0 being the lowest and 10 the highest level of stress that individuals can imagine. The SNRS-11 scale has demonstrated acceptable validity and reliability in assessing momentary perceived stress levels in adolescents and adults, as well as strong correlations with longer, commonly used measures of anxiety, such as the State-Trait Anxiety Inventory  (Karvounides et al. , 2016). During the simulation-based intervention, the stress levels of the learners were assessed on three separate occasions for both groups: -Before the training; if they had previously experienced a clinical critical event, they were asked to recall their stress levels during that situation.
Statistical analysis Data were expressed in mean and standard deviation (SD) or percentage when appropriate. In order to compare the different stress levels of each group (control and prior exposure), a repeated measures ANOVA using the free software Jamovi 1. 1. 9. 0 was performed. An independent sample t-test was performed to compare the stress levels of the students at 4 months after the training. A Spearman test was carried out in order to establish a correlation between the values of stress levels at each time. The value of p ≤ 0. 05 was used as the threshold of significance.
Data collection and laboratory procedures Dietary assessment was performed using validated centre-specific dietary questionnaires  [35, 37]. To record diet history, most centres used an extensive self-administered food frequency questionnaire (FFQ) while in Spain and Italy it was completed during an interview. Malmö (Sweden) combined a FFQ, a 7-day dietary recall and an interview. Total energy intake, some food groups (total, red and processed meat, fruit, vegetables and legumes, and dairy products), and some nutrients that modulate iron absorption (fibre, calcium, vitamin C and heme iron) were assessed. Daily consumption of each food and nutrient was calculated from information collected by validated dietary questionnaires and, then, dietary variables were expressed as nutrient density per 2000 Kcal ([g/Kcal]*2000).
Statistical Analysis Median and interquartile range were used to describe iron biomarkers (SF, serum transferrin, serum iron and TIBC) given their non-normal distribution. The remaining continuous variables were expressed as mean and standard deviation, and categorical data as percentages. Natural logarithm transformation was applied to normalize the distribution of SF. Continuous variables were compared using Student's t-test and ANOVA while chi-square was used for frequencies. Information on dietary intake, HFE genotype, iron status, and sociodemographic and lifestyle characteristics was described by country and geographical region. A multivariate linear regression and logistic regression were performed to assess the association between possible determining factors and SF levels and excess iron, respectively, in the overall sample and by sex.
Also, around 60% of Spanish participants were blood donors that could lead to SF levels being skewed; however, similar studies in the general population in Europe  [4, 5]  and Spain  [42, 43]  obtained comparable results. Furthermore, we reached similar findings from sensitivity analyses excluding Spanish subjects. The high median age of the participants imply that results should be extrapolated with caution. In addition, biochemical analyses were performed using samples collected at baseline, which prevented us from monitoring iron status over time. Finally, haemoglobin data could have been useful for further verification of the association with HFE genotypes but they were not available.
We thank the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer  for the financial support to the coordination of EPIC study. The national cohorts are supported by:  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Ministry of Education and Research (BMBF) ,  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany) ; the  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) , the  National Institute for Public Health and the Environment-Bilthoven (RIVM) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra , and the  catal Institute of Oncology (Spain) ;  Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  ( 14136  to  EPIC-Norfolk ;  C570/A16491  and  C8221/A19170  to  EPIC-Oxford ),  Medical Research Council  ( 1000143  to  EPIC-Norfolk ,  MR/M012190/1  to  EPIC-Oxford) (UK).
In fact, Saccharomyces presents different exo-1,3-β-glucanases, encoded by the genes EXG1 and its paralogs SPR1 and EXG2, and the activity of these enzymes has been related to glycoside hydrolysis  [72]. In addition, according to the Saccharomyces Genome Database, the EGH1 gene encodes a β-glucosidase with a broad specificity for aglycones, which has been related to hydrolysis of flavonoid glucosides  [76]  and could have a role in aroma improvement. Microorganisms responsible for malolactic fermentation, especially the species Oenococcus oenii, have also the potential to improve wine aroma  [14]  (Table  1 ). This type of fermentation is characteristic of red wines originating in cold regions, where the acidity of grapes is higher. Several potential genes encoding glycosidases have been identified in Oenococcus and in other lactic acid bacteria (LAB), such as Lactobacillus and Pediococcus, as well as a gene encoding a β-glucosidase  [14].
Statistical analysis Data are expressed as means § SEM. Comparisons between sets of data were performed using one-way analysis of variance (ANOVA) followed by the Duncan test (all analysis were performed on parameters measured at sacrifice and are statistically independent). When statistical analysis did not show a dose-dependent effect of citrulline, we analyzed the global effect of citrulline supplementation versus no citrulline; results of all supplemented rats were pooled and compared with results of unsupplemented resected rats (controls). Statistical analyses (ANOVA, Duncan test, correlation, quadratic analysis, linear regression, etc. ) were performed using Statview Software (SAS, Cary, NC, USA). Threshold of significance was set at P < 0. 05.
Acknowledgments: T. B. received a grant from the  Japan Student Services Organization (JASSO)  and a grant from the  Centre National pour la Recherche Scientifique et Technique (CNRST) ,  Morocco. We thank  Nathalie Martins  from the  "Service de spectrométrie de masse" of the "Institut de Chimie de Toulouse " for her help in the LC-ESI/MS work.
Data extraction procedure All documents (n = 12) were reviewed fully. We developed a matrix to extract and tabulate action descriptions from each document across the action themes. Each column corresponded to an action theme and each row to a document. As possible, we (i. e. , LR and SB) retained terminology as originally presented in the documents, followed by synthesis of the content to bring forth how the descriptions aligned with the theme(s) to which they were assigned. Each analyst tabulated content from different documents and consulted with the other after completion of each document and when uncertain about where to assign descriptions. In cases of disagreement, additional coauthors from the working group were consulted to obtain consensus.
Data management and analysis Content in each document assigned to each of the action themes was unpacked by explicit focus on the food environment and food choice. This unpacking identified how recommended food-system strategies aim to modify food environments and food choice and helped diagnose where actions were prioritized. We developed a guide (Table  2 ) building from existing frameworks to characterize actions as those aimed at modifying the food environment and food choice. Food environment and food choice concepts were selected based on their presence in multiple frameworks and applicability to the action descriptions. To select food environment concepts, we primarily considered a framework of food systems for diets and nutrition (HLPE, 2017), a framework depicting external and personal domains of the food environment  (Turner et al.
Image Processing The µ-CT acquisitions were reconstructed in 3D volumes using PyHST2 software  [33]  and using the phase contrast Paganin filter  [33]. The resulting volume of analysis was: 2048 × 2048 × 2048 vx (corresponding to 1. 3 mm side). The three constituents of the frozen model sponge cake: air, ice and starch are represented by well separated grey levels in the 3D images as shown by the histogram in Figure  4. The segmentation of these three phases was then simply realized by grey level threshold. By performing a 3D closing operation on the starch phase, it was then possible to create the closed starch phase. This intermediate phase is a "filled" version of the starch phase (Figure  4 ). The structuring element connects the objects that are closer than its radius into one continuous object.
Acknowledgments: We would like to thank  Alain Denis  from  UR FRISE INRAE  for his technical help. We acknowledge SOLEIL for provision of synchrotron radiation facilities on ANATOMIX and financial support concerning the proposal study number 20200227. ANATOMIX is an  Equipment of Excellence (EQUIPEX)  funded by the  Investments for the Future program  of the  French National Research Agency (ANR) , project  NanoimagesX , grant no. ANR-11-EQPX-0031. We would like to thank gratefully  Timm Weitkamp  and  Mario Scheel  for assistance and for their help on ANATOMIX beamline. CNRM/CEN is part of the  LabEx OSUG@2020 (Investissements d'Avenir , grant agreement  ANR10 LABX56 ). 3SR Laboratory is part of the  LabEx Tec21 (Investissements d'Avenir , grant agreement  ANR11 269 LABX0030 ). We would like to thank  Michaël Trichet  and  Alexis Canette  from  IPBS (Electron Microscopy Facility, IBPS, Paris, France ) for their technical help during Cryo-SEM imaging.
Zn and Cu were assayed at the ENVT laboratory in Toulouse by atomic absorption spectrometry. Plasma GSH-PXE was analysed at Iodolab (Marcy-l'Étoile, France) by colorimetry according to the  Paglia and Valentine (1967)  method with a RANSEL kit (RANDOX). The assays were carried out automatically on a BM6010 analyser (JEOL, Tokyo, Japan). Milk production and milk quality results were averaged per week and analysed using repeated data analysis with a mixed linear model in SAS® version 9. 1. 3 (SAS Institute Inc. , Cary, NC, USA). Body weights and BCS were analysed using ANCOVA GLM in SAS. No covariates were included in the statistical analysis, rather the data were tested (Bartlett's test) to ensure homogeneity, which was determined between groups at W0 only. No statistics were run on the DMI, feed efficiency and other related variables, as these were only available as group data.
Although unable to evaluate the data by more robust statistical methods, there were no differences in the proportion of pregnant cows between treatments (Table  7 ). Twenty-four cows were pregnant in the ORG group   whereas there were only 18 pregnant cows in the INORG group and 16 in the CON group after W10. Thus, the cows in the ORG group had the lowest number of AI services. These findings were in contrast with the data published by  Kropp et al. (1990) ,  Uchida et al. (2001) ,  Nocek et al. (2006)  and  Horchanok et al. (2019) , who reported improvements in reproductive performance in dairy cows fed organic forms of minerals, compared to inorganic control diets. These published benefits included reduced empty days, fewer AI services and higher pregnancy rates. The number of mast cells, lymphocytes and PMN were recorded from pooled, group milk samples at W0, W5 and W10 of the trial.
Data acquisition was averaged every minute during the constant mixing phase at 100 rpm and every 10 s for other conditions. Samples of approximately 15 mL were taken at the beginning and during the hydrolysis  (3, 6, 20, 24, 48 , and 72 h). The enzyme in samples were intermediately inactivated by increasing pH with the addition of 0. 1 mL NaOH 10 N. Samples were analyzed in terms of rheological, granulometric and glucose production.
Accordingly, risk of myocardial infarction, stroke, deep venous thrombosis and pulmonary embolism are increased, and geriatricians need to be aware of these complications. Because coronavirus uses angiotensin-converting enzyme 2 (ACE2) as its receptor, molecular mechanisms include derangements of renin-angiotensin-aldosterone system (RAAS)  [3]. Several existing cardiovascular drugs and other drugs have beneficial effects on endothelial function/RAAS and on the sequalae of their dysfunction. This offers opportunities to improve prognosis by re-purposing these therapies. At the moment evidence is collected from retrospective, observational studies, which despite propensity score adjustments used in many analyses are not equivalent to randomized controlled trials (RCTs). A recent analysis of trials registered on ClinicalTrials.
The situation may be different for cardiovascular drugs (e. g. , antiarrhythmics, anticoagulants, antithrombotics, colchicine) causing acute benefits by their effects on inflammation, thrombosis and coagulation. In the following we shall review available evidence of cardiovascular drug treatment, mostly from observational studies, and their relationship-benefits or harms-to COVID-19. Besides conventional cardiovascular drugs we also include a review of nonsteroidal anti-inflammatory drugs (NSAIDs, including colchicine), as well as vitamin 1 3 D, because observational studies have linked low serum 25-hydroxyvitamin D concentrations with increased CVD risk  [5]. Geriatric aspects are discussed where feasible, but age-specific data are usually lacking. Data were sought from clinical databases (PubMed, Embase, preprints in medXriv) using COVID-19 and specific key words for each drug class.
Acknowledgements All authors are members of the  EuGMS Special Interest Group of cardiovascular medicine. Dr. Grodzicki  reports speaker's or consultation fees from  Abbott ,  Sanofi ,  Servier ,  Pfizer ,  Boehringer-Ingelheim , and  Abbott. Dr. Strandberg  reports various cooperation (research, educational, consultative) with several companies (including  Amgen, Novartis, Orion, Sankyo, Sanofi ) marketing cardiovascular drugs.
Processed reads were then assembled using the SPAdes de novo assembler v3. 13. 1  (Bankevich et al. 2012)  testing assemblies with k-mer values: 21,33,55,77 using the parameter: -careful. Depending on the size of insert, From outer to inner ring-contigs (scale-x1Mbp), CDS on the for-ward strand, CDS on the reverse strand, RNA genes, homologous CDS to known antimicrobial resistance (AMR) genes, CDS with homology to known virulence factors (VF), GC content and GC skew scaffolds smaller than 350 bp were filtered out. Assembly quality was assessed using in-house script and QUAST v5. 0. 0  (Gurevich et al. 2013). We estimated genome completeness at 99. 59% and checked that no contamination was detected using CheckM v1. 1. 3 (Parks et al. 2015). After eliminating low coverage and small contigs (< 350 bp), we obtained a total of 38 contigs and 34 scaffolds resulting in a genome of 4,315,004 bp (NCBI accession number JADRJL000000000) and an average G + C content of 45.
87% which is close to other genomes from the Bacillus subtilis group  (Rey et al. 2004; Du et al. 2019). The N50 of the draft genome is 440,121 bp, with an average of 126,912 bp and a notable four gap region (Fig. 3 ). Species identity was confirmed by comparing the genome reference sequences of B. licheniformis and Bacillus paralicheniformis available in the NCBI database (https:// www. ncbi. nlm. nih. gov) using the BLAST service deployed on local servers with the genome obtained sequences and fastANI  (Jain et al. 2018). The draft genome was most closely related to B. paralicheniformis Bac84 (accession number ASM299392v1) living in sea environment  (Othoum et al. 2019 ) with an ANI value of 98. 94%, and shared only 94. 36% identity with B. licheniformis DSM 13 T (NC_006270. 3). B. aerius CCMM B940 was, therefore, reclassified as B.
paralicheniformis CCMM B940, placing it in the B. subtilis group; a group known for its wide array of uses in biotechnology mainly through species like B. subtilis and B. licheniformis  (Fan et al. 2017). Taxonomic identity of the CCMM B940 strain was also confirmed using the GTDB-Tk tool for genome classification  (Chaumeil et al. 2020)  with 98. 95% identity to the B. paralicheniformis KJ-16 genome (accession number ASM104248v2). The genome was then annotated using the default parameters of Prokka 1. 14  (Seemann 2014)  showing 4259 protein-coding genes, 51 tRNAs and 4 complete rRNAs. We further used eggNOG-mapper v2. 0. 1 (Huerta-Cepas et al. 2017) to assign proteins sequences into functional categories. More than 76% of the predicted proteins were attributed to functional subsystems. Proportions of genes with known functions (3047 CDS) were assigned to each category as presented in Fig.
2012). Hereafter, we focused on CCMM B940 glycoside hydrolases related to hemicellulose, cellulose and pectin degradation, comparing their protein sequences to the sequences of GH and pectate lyase (PL) of seven strains of B. paralicheniformis (A4-3, ATCC 9945a, BL-09, CBMAI 1303, FA6, MDJK30, ZAP17) available in the CAZY database  (Lombard et al. 2014 ; database release 22 November 2020). Blast results was obtained using default parameters. Best hits with almost 90% amino-acid identity and 50% length alignment were selected and illustrated in Table  2  (the corresponding sequences are provided in Fig  S2 ). Among 74 GH and PL sequences from CCMM B940 showed homology to other strains of B. paralicheniformis, 52 are dedicated to the degradation of carbohydrates in plant cell walls, representing 27 different CAZymes families according to the classification of B.