dataesr/scientific-paragraphs-categorization huggingface dataset:
An analysis by Wolf-Meyer et al. in the general population 30 showed 6 that the gap in hypertension control between North American and European countries was 7 more pronounced for the BP threshold of 140/90 mm Hg than for that of 160/95 mm Hg, 1 1 2 including patients from 7 4 mm Hg in systolic and diastolic BP, respectively, for each 10° increase in latitude, became close to one. Adjustment also substantially reduced heterogeneity in prevalence ratios across European cohorts, with albuminuria the most important confounder. 3 5 independent of patient characteristics and baseline dialysis prescription. More recently, a 6 study from the International Database of Ambulatory BP in Renal Patients (I-DARE) 7 collaborative group showed wide variations in 24-hour BP profiles in patients with nondialysis 8 F CKD from 7 studies in 4 countriesdifferent countries even after adjusting for age, sex, eGFR F 21 22 23 24 25 26 9 and diabetes.
J. McIntyre, A. Shardlow, S. D. S. Fraser, P. J. Roderick, R. J. Fluck and C. W. McIntyre. The RRID study was funded by a Research Project Grant (R302/0713) from the Dunhill Medical Trust, a joint British Renal Society and Kidney Research UK fellowship (to NJM) and an unrestricted educational grant from Roche Products Ltd. No pharmaceutical company had any input into study design, data collection or analysis. The data are wholly owned by the investigators. Study Eligible patients (n) BP Age Gender Ethnicity Education Diabetes status History of CVD Obesity status Smoking status eGFR Albuminuria CanPREDDICT 2411 0% 0% 0% 0% NA 0% 0% 0% NA 0% 0% CKD-JAC KNOW-CKD 2686 1. 4% 0% 0% 0% 16. 1% 0% 0% 9. 5% 15. 0% 0% 8. 4% CKD-QLD 1674 0% 0% 0% NA NA 0% 0% 0% 8. 5% 0% 1. 2% CKD-REIN Introduction 2626 2.
CT Hounsfield values for bone, MAT and HBM tissues were calculated. We used protocols previously validated in animals to quantify yellow/red marrow and bone using a single energy CT (MicroCT) and image analysis using Slice-O-Matic software  [43]. Previous publications on the tissue thresholds were consulted, and final threshold ranges used for quantification of these parameters were further refined visually, using an image histogram  [43] [44] [45] [46] [47]. Thus, the following resultant CT number thresholds were applied: MAT ≤450; HBM 451-1900; and bone ≥1901 Hounsfield units (Figure  3 ). Ratios of tissue volumes/total tissue volume (i. e. bone%, HBM% and MAT%) and ratios of HBM/total marrow% and MAT/total marrow% volumes were calculated.
Cy3-lebeled RNA (600 ng) was hybridized on a microarray slide. After washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan control A. 8. 5. 1 software and the fluorescence signal was extracted using Agilent Feature (extraction software v10. 10. 1. 1 with default parameters). One sample from lean controls didn't pass the quality check and was excluded from the analysis. Microarray data and experimental details are available in the Gene Expression Omnibus (NCBI-GEO) database (accession GSE111719).
Statistics Results are expressed as Means ± SEM. The significance of differences between groups was evaluated with Graph-Pad Prism (GraphPad Software). We first checked that the data for each group were normally distributed and that variances were equal. We then carried out two-tailed Student's t-test, Two-way ANOVA with Sidak's post-hoc test or Pearson's correlation. Energy (Kcal/100 g) 315. 0 505. 8.
05, t ¼ 2. 862, df ¼ 13) [20e27], and 2 for molecules with anti-inflammatory properties (Nfil3, P ¼ 0. 08, t ¼ 1. 889, df ¼ 13; ppp2Ca, P < 0. 001, t ¼ 4. 921, df ¼ 13)  [28, 29]  (Fig. 3-A ). The most of identified genes were tightly related to immune cells (Table  2 ) and encode for cytokines, receptors, transcription factors or enzymes (Fig. 3-B ). As shown in Fig. 3-C , CVP from DIO mice displayed a pro-inflammatory transcriptomic pattern as compared to what was found in lean controls. To further gain insight into the identified genes, a transcriptomic network analysis was undertaken using the Ingenuity Pathway Analysis (IPA) software which combines a molecule interaction database (i. e. ingenuity knowledge base) and a network generation algorithm. This bioinformatics analysis generates the optimal gene network for a given set of transcriptomic data.
6-B ). LPS mice had a similar body mass growth than controls, suggesting that this LPS treatment did not elicit a change in their energy balance. Unexpectedly, LPS-treated mice displayed a similar preference for the oily solution than controls subjected to the vehicle alone, despite a two-fold rise in plasma LPS levels (Fig. 6-C ). The IPA software combines a molecule interaction database (i. e. ingenuity knowledge base) and a network generation algorithm. This software analyzes the transcriptomic data according to ingenuity knowledge base and generates the optimal gene network specifying the connectivity of each gene present in the dataset. Central nodes correspond to genes displaying with the higher interconnectivity between genes of the network. A-Network Relative expression changes between CVP from diet-induced obese mice and lean controls are shown in red (up-regulation) and green (down-regulation).
Acknowledgements This study was supported by grants from the  French National Research Agency :  SensoFAT-2 project  ( ANR-12-BSV1-0027-01  to P. B. ) and  ANR-11-LABX-0021-LipSTIC  (to L. L. ). AB was a postdoctoral fellow from  ANR-11-LABX-0021-LipSTIC. The authors thank  Jean-Christophe Blanchard  for the animal management,  Guillaume Maquart  and  Jean-François Merlin  for the technical assistance and  Jean-Paul Pais de Barros  from the  Lipidomic platform  ( Univ Bourgogne-Franche Comt e ) for LPS assays.
In addition, it is noteworthy that up to 4% of the OTUs observed in the undecayed Ascophyllum sample (T 0 ; week 0) and 41% in the decaying samples were unclassified. These unassigned OTUs are likely to represent macroalgal associated bacterial populations that are as yet unknown or not present in the SILVA version 123 database  [35]  used for taxonomy assignment. Table  1. Observed OTUs and species richness and diversity estimates of Ascophyllum-associated metagenomic communities obtained using MiSeq sequencing of the 16S rRNA gene from the intact seaweed (T 0 ) and each of decaying Ascophyllum nodosum samples collected at three phases of the decay period (week 2, week 4 and week 6) at 18 • C (2_18, 4_18, 6_18), 25 • C (2_25, 4_25, 6_25), and at 30.
In week 6, eleven distinct genera were   Members of the bacterial phyla Proteobacteria (54%), Bacteroidetes (27%), and Firmicutes (19%) were identified in the bacterial population cultured at the end of the decay process (week 6) from the 18 °C incubation flask. These phyla were also present at 72%, 7%, and 21% relative abundances, respectively, in the 30 °C bacterial population (Figure  2d ). In week 6, eleven distinct genera were identified at 18 °C, the most abundantly represented being Paracoccus, Algoriphagus, Celeribacter, Bacillus, and Primorskyibacter and four genera including Celeribacter, Bacillus, Pseudozobellia, and Paracoccus being observed in the 30 °C microbial community. The bacterial community associated with the seaweed decaying at 25 °C in the late decay phase differed from the bacterial communities isolated from the 18 °C and 30 °C microbial populations in this phase, with Proteobacteria (96%) dominating the dataset and three genera; Celeribacter, Bacillus and Roseobacter being isolated at 25 °C.
Proteobacteria dominated at the phylum level in the three datasets, comprising 93%, 92%, and 100% in the 18 • C, 25 • C, and 30 • C bacterial culture populations, respectively (Figure  2e ). Low relative abundances of Actinobacteria and Firmicutes were observed in the 18 • C and 25 • C populations, with both phyla not being identified in the 30 • C bacterial population. The majority of bacterial isolates cultured from the 18 • C and 25 • C samples, which belong to the phylum Proteobacteria; were further classified as α-Proteobacteria, with only 9-11% recruiting to γ-Proteobacteria. In contrast, γ-Proteobacteria were found to dominate the microbial community isolated from the seaweed incubated at 30 • C, with a relative abundance of 98%. Thirteen distinct genera, including Celeribacter, Paracoccus, Vibrio and Marinobacterium were present in the total bacterial population isolated using the ichip device (Figure  S1e ; Supplementary information).
The use of a next-generation sequencing approach (Illumina MiSeq) supplemented the 16rRNA gene-based approach employed on the cultured bacterial isolates. Given that the NGS approach circumvents the difficulties associated with the cultivation of bacteria from environmental samples and allows the identification of both cultivable and non-cultivable bacterial populations, it is not surprising that some phyla observed in the metagenomic communities of the macroalga were not identified in the total cultivable bacterial population. In particular, considering the isolation agar (SYP-SW) and the culture condition (72 h at 28 • C) employed in this study, it is highly unlikely that most phyla, including Planctomycetes, Spirochaetae, and Verrucomicrobia, which were found in the NGS dataset, would be recovered.
Scythe (v0. 994 BETA)  [63]  and Sickle  [64]  programs were used to quality trim raw reads and remove adapter sequences. This service was provided by Macrogen Inc (Seoul, Korea) as part of a next generation sequencing package. Trimmed paired end reads were merged (using the join_paired_reads. py script with the fastq-join method  [65]  ) in QIIME version 1. 9. 1 (QIIME. org) [34]  and processed using standard QIIME version 1. 9. 1 protocols (http://qiime. org/tutorials/illumina_ overview_tutorial. html). Briefly, a further quality step was applied by excluding reads with a Phred score less than 20 using the split_libraries_fastq. py QIIME script. The USEARCH algorithm  [66]  was used to remove chimeras and assign sequences to OTUs based on the SILVA database (version 123) (Max Plank Institute, Bremen, Germany)  [35]  at a threshold of 97% identity.
Singletons were identified and filtered from the OTU table and the OTU table was CSS (cumulative sum scaling) normalized  [67]. The taxonomy identified from the dataset was then represented through bar plots. Species diversity and richness within samples were calculated using alpha and beta diversity analyses (Chao1, Good's coverage, Shannon indices and principle coordinates analysis) using QIIME (version 1. 9. 1) (http://qiime. org/tutorials/illumina_overview_tutorial. html) scripts (alpha_diversity. py and beta_diversity_through_plots. py) [34].
PCR products were analyzed by gel electrophoresis on a 1% agarose gel and purified using a QIAquick PCR Purification Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Sanger sequencing was performed on the amplified PCR products by GATC Biotech, (Konstanz, Germany) and Macrogen (Amsterdam, The Netherlands). Low quality 5' and 3' sequence ends were trimmed using FinchTV (http://www. geospiza. com/finchtv) depending on the data set. The BLAST program (NCBI) (https://blast. ncbi. nlm. nih. gov/Blast. cgi) was used to compare trimmed sequences against the GenBank database and closest relatives to the bacterial isolates were identified. 16S rRNA gene sequences were checked for chimeras using USEARCH algorithm  [66]  and the data sets were de-replicated using the Fastgroup database  [73]  and Avalanche NextGen Workbench version 2.
Accession Numbers The metagenomic sequencing data (raw reads) was deposited in the European Nucleotide Archive (ENA) under the accession numbers ERR2608102 -ERR2608111. The 16S rRNA gene sequences for the bacterial isolates were deposited in GenBank under the accession numbers KY224981-KY225289, KY327837, KY327838, MG693225-MG693716, MG760723-MG760725, and MK480287-MK480325. Bacterial isolates IC18_D7 and ANT 0 _A6 with enzymatic activities and applicability in EAE approach were deposited in DSMZ culture collection bank under the accession numbers DSM 107285 and DSM 107318, respectively.
Statistical Analysis Prior to performing statistical analyses on data obtained by enzyme-assisted extraction (EAE) of total phenolics from F. vesiculosus, tests of normality were carried out with the Kolmogorov-Smirnov test for normal distributions and Levene's test for homogeneity of variance. A one-way ANOVA and post hoc Tukey's pairwise test was performed to assess significant differences (p < 0. 05) between commercial enzymes and control (50 • C); and a t-test was applied to assess significance differences (p < 0. 05) between EBS and control (28 • C). All data treatments and statistical analyses were performed using IBM SPSS Statistics V22. 0 (IBM Corporation, Armonk, NY, USA).
C, the most abundantly represented being Paracoccus, Algoriphagus, Celeribacter, Bacillus, and Primorskyibacter and four genera including Celeribacter, Bacillus, Pseudozobellia, and Paracoccus being observed in the 30 • C microbial community. The bacterial community associated with the seaweed decaying at 25 • C in the late decay phase differed from the bacterial communities isolated from the 18 • C and 30 • C microbial populations in this phase, with Proteobacteria (96%) dominating the dataset and three genera; Celeribacter, Bacillus and Roseobacter being isolated at 25 • C. Phylogenetic trees representing the bacteria cultured from both intact and decaying Ascophyllum nodosum samples using the maceration method are shown in Figures S2-S5 (Supplementary information).
identified at 18 • 18 C, the most abundantly represented being Paracoccus, Algoriphagus, Celeribacter, Bacillus, and Primorskyibacter and four genera including Celeribacter, Bacillus, Pseudozobellia, and Paracoccus being observed in the 30 • C microbial community. The bacterial community associated with the seaweed decaying at 25 • C in the late decay phase differed from the bacterial communities isolated from the 18 • C and 30 • C microbial populations in this phase, with Proteobacteria (96%) dominating the dataset and three genera; Celeribacter, Bacillus and Roseobacter being isolated at 25 • C. Phylogenetic trees representing the bacteria cultured from both intact and decaying Ascophyllum nodosum samples using the maceration method are shown in Figures S2-S5 (Supplementary information).
Molecular Modelling, Docking, and Dynamics of Vitamin K and VKORC1 Human Protein Vitamin K 3D structures were downloaded from ChemSpider website (www. chemspider. com). The VKORC1 model was used and the molecular dynamics (MD) system was built and prepared as in our previous paper  [32]. Molecular docking of vitamin K with respect to VKORC1 was carried out using AutoDock 4. 2 software  [40] ; 100 runs of molecular docking were carried out for each type of vitamin K, using the Lamarckian Genetic Algorithm. Molecular dynamics (MD) simulations were performed using GROMACS 5. 1. 2 package software  [41] , and the CHARMM-36 force field was applied  [42]. Each vitamin K-VKORC1 complex was previously embedded into DLPC membrane  [43]  using the membed function of GROMACS software. Solvation, energy minimization, 100-ps temperature equilibration, and 1-ns pressure equilibration were carried out using GROMACS routines.
MD Analysis and Figures Distance measurements between the vitamin K carbon-2 atom and C135 α-carbon atom in MD simulations were performed using the dist function of GROMACS. Binding free energy (BFE) on vitamin K-VKORC1 or vitamin K-membrane complexes and the VKORC1 residue contributions to BFE were determined using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method  [44]  implemented for GROMACS software  [45]. Graphics were drawn using Grace software  [46]  and 3D figures were prepared using PyMOL software  [47].
Background: Program decision-making to prevent and treat acute malnutrition in an emergency can be hampered by a lack of accessible and relevant overviews of directly available robust research evidence. There is often evidence from related settings such as from low-income countries, but this is dispersed across many databases, may be inaccessible and requires assessment of its relevance to the humanitarian setting. We describe a process whereby a multi-disciplinary, international group of specialists worked together to build relevant and effective collections of available systematic reviews on acute malnutrition, published and disseminated as online collections, to improve access to the evidence and concise, synthesised, relevant up to date evidence for programming. By describing this process, we hope to inspire other professional groups to take part in similar multi-stakeholder, multi-disciplinary projects.
Objectives: This project was designed to make the evidence from relevant systematic reviews about malnutrition as accessible as possible to support evidence-based decision-making and to guide future research on the prevention and treatment of acute malnutrition in humanitarian emergencies. Methods: Between March 2017 and March 2018, a large group (21 volunteers and stakeholders) with different backgrounds collaborated to review and curate collections of systematic reviews of interventions for the prevention and treatment of moderate and severe acute malnutrition relevant to humanitarian emergencies. The methodology loosely followed general guidance for overviews of systematic reviews with a pre-defined question (formulated using the PICOS format) and search strategies applied to multiple databases.
We checked the methods of non-Cochrane systematic reviews before inclusion for quality (e. g. at least two online databases had to be included; each review should include a quality assessment of included studies). Stakeholder consultation took place on the relevance of selected reviews and the final list of reviews for inclusion in collection was generated (including determining the relevance to contexts, such as lower-and middle-income countries, or conflict and disaster-affected countries). The structure of the collections was discussed by the whole group, and one volunteer (BS) prepared brief summaries of all included systematic reviews; these summaries were subsequently translated into French and Spanish. Before publication, one author (SvdK) categorised each review into a topic area based on what was identified.
Search results Searches (see Additional file 1 for detailed search strategies per database searched) were carried out in 12 databases (see search yields per database in Appendix 1), in the week of 15 September 2017. These searches yielded a total of 4646 potentially eligible papers after de-duplication. At this point, Cochrane reviews (n = 463) and non-Cochrane reviews (n = 4183) were separated and handled by different teams to compile the linked collections: Evidence Aid collection, consisting of relevant non-Cochrane reviews, and the Cochrane Special Collections, consisting of relevant Cochrane reviews. We did not search initially for Kwashiorkor (oedematous malnutrition), but a follow-up literature search and sub-analysis for Kwashiorkor showed that one paper had been omitted from the original search results, which was then included for screening.
with a pre-defined question and search strategies applied to multiple electronic research databases. Firstly, the question was formulated using the PICOS-Population, Intervention, Comparison, Outcomes and Study design format). The PICOS question was used to develop eligibility criteria and detailed search strategies to be implemented in both Cochrane and non-Cochrane databases. Databases searched were the Cochrane Database of Systematic Reviews, MED-LINE, Embase, CINAHL, Global Health, LILACS, ScieLO, PubMed, TRIP, HealthEvidence. org, HealthSystemsEvidence. org, WHO Nutrition and Epistemonikos (see detailed search strategies in Additional file 1).
Control Population For secondary objectives, group A was compared to data retrieved from a previous study (control group B)  [20]. Group B included 55 volunteer patients over 70 years of age residing in nursing homes who did not have a severe chronic disease or acute pathology in progress and were not malnourished. Therefore, it appeared particularly interesting as a control group for the acute hospital stress context encountered in group A. Exclusion criteria were as follows: uncontrolled or rapidly evolving diseases, dementia, type 1 diabetes, severe renal insufficiency (blood creatinine over 200 mol/L), severe functional limitation preventing exercising, long-term corticosteroid therapy, and age under 72 years. Details of the recruitment and methods have been reported previously  [20].
To further assess the associations between inflammation and energy expenditure, the difference in CRP levels between day 14 and day 1 (CRPdiff) was calculated for each of the 19 patients in group A, and partial Spearman's correlation coefficients between CRPdiff, REE, and TEE were calculated, adjusted for FFM, age, and sex. All analyses used Stata software, version 13 (Stata Corp, College Station, TX, USA). All tests were bilateral, and p < 0. 05 was considered for statistical significance.
Funding: This study was funded by a grant from the  French Ministry of Health  ( PHRC-R, 2001 ,  HCL/P/2001. 270 ). Acknowledgments: All isotopic dilution measurement methods were performed at the  CRNHL (Centre de Recherche en Nutrition de Lyon, Lyon France ). This study was promoted by  Lyon University Hospitals  ( Hospices Civils de Lyon, France ). Authors would like to thank  Jacques Bienvenu  and  Bruno Mathian.
Body Weight, Milk Yield, Feeding, and Energy Balance After each milking, cows were automatically weighted (software RIC version RW1. 7, Hokofarm Group, Marknesse, The Netherland). Only the morning live body weight (LBW) was used for weight analyses because the afternoon body weight was more variable. All cows were milked twice daily. At the entrance of the milking parlor, the cows were identified by an electronic collar, and the milk yield of each cow was automatically recorded (software Manufeed 500 pro, vc5 version 2. 011. 14, Manus-Delaval, Elancourt, France). As LBW is affected by digestive contents, the estimation of empty body weight (EBW) was corrected for the digestive tract content. A change of 4. 5 kg of digestive contents per kg of dry matter intake (DMI) was assumed  [12]. Variation of EBW (VEBW) was calculated day after day: EBW of the previous day was taken as a reference weight  [12].
Live body weight and VEBW as compared to one month before calving were measured from 4 weeks before calving until 16 weeks after calving in females. Dry matter intake was determined from the intake of fresh matter and the dry matter content of each feed of the ration. On average, there was one feeder for two cows. When a cow arrived in front of the feeder, it was recognized by a unique passive transponder attached to her ear tag. If the cow was allowed, the feeder opened, and the quantity of food eaten by the cow was automatically recorded (software RIC version RW1. 7, Hokofarm Group, Marknesse, The Netherland). Dry matter intake was calculated daily from calving to week 16 after calving. Energy balance (expressed in Mcal/day) was calculated from calving to week 16 postpartum and corresponded to the difference between net energy intake and net energy needs for body maintenance and lactation.
MALDI-TOF MS Analysis According to Piras et al. [14, 15] , MALDI-TOF MS analysis was performed by Ultraflex III MALDI-TOF/TOF spectrometer (Bruker Daltonics, Macerata, Italy) in positive reflectron mode. Briefly, for the external calibration, the standard peptide mixture calibration was used (Bruker Daltonics, Macerata, Italy): m/z: 1046. 5418, 1296. 6848, 1347. 7354, 1619. 8223, 2093. 0862, 2465. 1983, 3147. 4710), and to select monoisotopic peptide, MS spectra were analyzed by FlexAnalysis 3. 3 software (Bruker Daltonics, Macerata, Italy). After an internal calibration (known autolysis peaks of trypsin, m/z: 842. 509 and 2211. 104) and exclusion of contaminant ions (known matrix and human keratin peaks), the peak lists were analyzed by MASCOT v. 2. 4. 1 algorithm (www. matrixscience. com) against SwissProt database released 2019_09 restricted to Bos taurus taxonomy (561,176 sequences).