dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Integration and Discriminant Analysis Omics data integration was performed using the R package MixOmics (version 6. 3. 2), which offers a whole range of multivariate methods to explore and integrate biological datasets  (Lê Cao et al. , 2009; Rohart et al. , 2017). Our datasets consisted of two sets (the AG and the SP sets) measured on the same samples with three independent biological replicates. The AG dataset included 352 proteins present in all samples and in the 2018_12 version of Uniprot protein database. The SP dataset included the quantity per grain of total glutenin subunits and gliadins, the total quantity per grain for each glutenin and gliadin classes i. e. , HMW and LMW, α-, γ-, and ω-gliadins. These two datasets (values were scaled) were explored according to the workflow presented (Figure  1 ).
We first investigated independently each dataset by Principal components analysis (PCA). Then, both datasets were analyzed conjointly with an unsupervised method, the sparse Partial Least Squares (sPLS) regression  (Lê Cao et al. , 2008). The PLS algorithm searches for the largest covariance between orthogonal components, which are linear combinations of the variables from both datasets studied. Sparsing leads to the selection of variables, which are the most involved in the relationships between the biological datasets. We kept 15 variables for each dataset (i. e. , all the variables for the SP set). The plotIndiv() function was used to project the samples onto the PCA and sPLS components. For sPLS, two plots can be obtained because a set of PLS-components is calculated on each dataset. Mathematically, it corresponds to two subspaces spanned by the variables in each dataset.
A mean subspace can result from them. In this third subspace, sample coordinates were averaged from the subspaces spanned by each dataset. The plotloading() function was used to visualize the loading vector of the sPLS analysis and represent the contribution of the variables kept for each component. Then, we used the block. splda() function from DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies)  (Singh et al. , 2019)  to identify correlated (or co-expressed) variables measured on our two datasets, which also explain the categorical variable of interest used to supervise the analysis. This latter analysis was supervised by crossing the developmental stage of the grain (four stages) and the nutrition (four nutritions) resulting in 16 combinations.
Statistical Validation A statistical validation of AGs involved in the network was undertaken by linkage mapping. The coding sequence of each AG in the network was retrieved from Uniprot and then blasted against the wheat pseudomolecule (IWGSC RefSeq v1. 1 database 2 ). The BLAST results were analyzed to find the coordinates of the bread wheat homologs on the pseudomolecule. Taking into account flanking sequences could be relevant because of linkage disequilibrium range in wheat and their role in gene expression. Therefore, gene regions were increased by 1,500 nucleotides upstream and downstream. Single Nucleotide polymorphisms (SNPs) located in these increased regions were extracted from the set developed by  Rimbert et al. (2018). Their genotyping data were retrieved for the 196 lines from the agronomic part of the INRA worldwide core collection  (Bordes et al.
, 2011) , which was phenotyped for grain SP composition  (Plessis et al. , 2013). Association study was performed with the mixed model  (Yu et al. , 2006)  implemented in the R package GWAS from rrBLUP  (Endelman, 2011)  comprising the kinship matrix (K) that accounts for relatedness among accessions to limit spurious associations. The Leave One Chromosome Out (LOCO) approach was used to construct different kinship matrices by testing each chromosome and leaving out the SNPs on the chromosome 2 https://www. wheatgenome. org/ being tested  (Yang et al. , 2014). Each off-target variants (OTV) was recoded to create two biallelic markers (a first one with the two nucleotide variants and a second one coded as presence for the nucleotide variants) or absence (for no call). Markers with a minor allele frequency < 0.
Data Structuration We used PCA (Supplementary Figure  2 ) and sPLS (Figure  2 ) to observe the structuration of our datasets. Individuals were projected in the subspace consisting of the AG and SP sets and then in a subspace in which coordinates are averaged from the two precedent subspaces (Figure  2 ). Roughly, grain developmental stages were discriminated in the AG subspace with the first two principal components explaining up to 54% of the total variance, while nutrition conditions were better discriminated in the subspace defined by SPs with 95% of the variability explained by the two first principal components (Supplementary Figure  2 ). The developmental stages and nutrition conditions are rather well discriminated in the mean subspace resulting from sPLS, which integrated both AG and SP datasets (Figure  2 ).
Network Analysis Reveals Albumins-Globulins Involved in Grain Storage Protein Accumulation We analyzed the results of the two-block analysis performed with the block. plsda from DIABLO, supervised by the nutrition × stage combinations, where the AG and SP datasets were quantitative variables with three independent biological replicates analyzed. Fifteen AG proteins were selected on each component and connected to variables present in the SP block. On the network (Figure  5  and Table  1 ), positive and negative correlations > |0. 8| between variables from 18 AG to 7 SP variables from datasets were simultaneously represented. Three sub-networks were identified. The main sub-network includes 16 AGs, which were connected to two SP variables (gliadin to glutenin ratio and the quantity of HMW glutenins per grain).
DISCUSSION From a dataset of 352 AGs, we were able to take into account the effect of the combination of stage of development and N and S supply to identify the most discriminating proteins FIGURE 5 | Network produced by the block. plsda in DIABLO. Each color represents a type of variable (green and bright yellow for AGs and SPs, respectively). The correlation cut off applied was 0. 8. The optimal components were considered in the network, which is bipartite i. e. , only links between two variables of different types are drawn. The color of edges indicates the sign of the correlation: red and gray correspond to positive and negative correlation, respectively (Table  1 ). The Uniprot identification is used for AGs. HMW and LMW indicate the quantity per grain of HMW-and LMW-glutenins. Alpha, gamma, omega and Gliadin are the quantity per grain of α-, γ-, ω, and total-gliadins, respectively.
The statistical validation carried out using genetic association analysis confirmed the putative role of 5 candidate AGs out of the 18, D0PRB4, I0JTT9, M7YBY6, M7Z2B3, M7Z7R6, in the synthesis of SPs (Figure  6 ). M7YBY6, annotated as an endochitinase, and M7Z2B3 (involved in the reticulum stress apoptosis) are identified by both integrative methods and significantly associated with SPs. Altogether, the integration of AG and SP datasets provided firm and consistent results on the relationship between some AGs and SPs. A few central components have been highlighted. Therefore, biological interpretation of the network could be helpful to improve understanding of the SP synthesis.
, 2015)  and are particularly sensitive to environmental conditions. The multivariate projection method used in this work revealed variables from each dataset (AGs and SPs) mainly involved in discrimination according to the combination of N and S supply and grain developmental stage (Figure  4B ). Eighteen AGs and seven SP variables important Accessions and protein names given by Uniprot were indicated. Only the positive and negative correlations values (>|0. 8|) obtained between AGs and SP with the DIABLO approach were given. Storage proteins expressed by grain (mg N grain-1), GlitoGlu, gliadin to glutenin ratio. a * Indicates function assigned by BLAST for uncharacterized proteins. in discrimination were highly correlated as shown by the network. Based on the principle of "guilt per association, " these AGs could influence the synthesis of SPs in relation to developmental stage and nutrition as confirmed for a few of them by linkage mapping.
The latter is overexpressed in S-deficient conditions. This enzyme contributes to increase the gliadin to glutenin ratio by decreasing the HMW glutenin quantity per grain. The down-accumulation of this enzyme might increase HMW glutenin quantity and then the baking quality when the level of fertilization is low. However, the role of this AG must be functionally validated. Thus, we have shown the interest of multivariate statistical approaches to extract the most significant variables from large datasets in response to abiotic constraints. Interestingly, these methods are able to consider the whole dataset instead of using only differentially expressed variables because the hypotheses rest on similar expression patterns across a set of samples indicating functional relationships. This has an important biological meaning because variables (AGs), which are not differentially expressed during grain development or in response to N or S supply, may be important actors due to interactions with other proteins (which may be differentially expressed).
FUNDING This work was supported by a Ph. D. grant from the  French Ministry of Higher Education and Research  to TB and the  French Government  managed by the  National Research Agency (ANR)  in the framework of Investments for the  Future  ( ANR-10-BTBR-03 ),  France AgriMer  and the  French Fund Supporting Plant Breeding (FSOV) , and the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the Grant Agreement Number  FP7-613556.
Instrumentation Nuclear magnetic resonance ( 1 H and 13 C NMR) spectra were recorded on a Bruker avance III 400 spectrometer equipped with a BBFO probe operating at room temperature using CDCl3 or DMSO-d6 as solvent. Differential Scanning Calorimetry (DSC) was conducted on a DSC 8 MC Mettler Toledo using aluminium pans. Scans were conducted under nitrogen atmosphere with a heating rate of 2°C/min in the temperature range of -100 to 150°C. FT-IR (ATR) spectra were recorded on a FT-IR 4000 Jasco in a range of 600 to 3800 cm -1. Number of scans: 32 or 64. Viscosity measurements were carried out using a Physica MCR 301 (Anton Paar) rotational rheometer equipped with a double gap measuring system that can regulate the temperature in a range from -40 to 200°C. Measurements were monitored by Rheoplus software.
Background: No evidence exists to date on which to base the selection of outcome measures for assessing nutritional interventions in critically ill patients. We conducted a systematic literature review to describe the outcomes used in recent randomised controlled trials (RCTs) assessing nutritional interventions in critically ill patients. Our objective was to set the foundation for the development of a core set of outcome measures for use in future RCTs. Methods: We searched the PubMed/MEDLINE, Cochrane Central Register of Controlled Trials, and ClinicalTrials. gov databases for RCTs of nutritional interventions in critically ill patients aged 18 years or older, published and/or registered between January 2000 and August 2018. Outcomes were divided into six categories (mortality, length of stay, duration of organ dysfunction, complications, functional outcomes, and others) and analysed according to the study characteristics and publication year.
Data collection For each study, the data were extracted by two of us (GT and MM, EP, or FT), who worked independently of each other. The data were recorded on a standardised form that was pre-tested by one of us (GT) on ten randomly selected articles and modified as needed. The following data were recorded: study characteristics (population, study design, blinding method, source of funding, number of centres, and geographic region); main study topic, recorded as clinical nutrition strategy, composition of nutritional support, or nutritional supplementation (see examples in Table  2 ); and primary and secondary outcomes. Outcomes were classified using predefined categories including mortality (at any time, ICU mortality, in-hospital mortality, day-28 mortality, day-90 mortality), length of stay (in the ICU and in hospital), duration of organ dysfunction (times on mechanical ventilation, catecholamines, renal replacement therapy; organ failures; and antibiotic therapy), ICU-associated complications (infections, metabolic complications, feeding intolerance), functional outcomes during the study period (muscle strength/walking distance tests, quality of life, physical function), and other (metabolic concentration, feeding measures, tube placement success rate, tube placement time, contamination of packs and glass bottles).
Characteristics of selected RCTs Among the 170 selected RCTs, 142 (84%) were published in 36 different journals and 28 (16%) were registered on ClinicalTrials. gov. The median date of publication was 2010 (2004-2013), and most RCTs (n = 89, 52%) were conducted in Europe. Table  1  reports the main characteristics of the selected RCTs. Published trials collectively randomised 30,003 patients (median, 78 (37-157)). Only 54 (32%) RCTs involved multiple centres, with a median of 11  (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18). The duration of the intervention was reported in 107/170 (63%) RCTs; median duration was 7 (5-14) days. Table  2  depicts the distribution of the various topics of the selected RCTs. The most common topic category was clinical nutrition strategy (69/170, 40%), with strategies to optimise delivery and minimise the risk associated with EN being the most common example (n = 36).
Most of the trials with complications as the primary outcome included fewer than 500 patients, whereas most of the large trials used mortality as the primary outcome and found no significant effect of the tested nutritional strategy. Figure  2b  depicts the distribution of topic categories by primary outcome according to the number of included patients. The studies including the largest numbers of patients mainly used length of stay and mortality, with clinical nutrition strategy as the topic. The use of complications as the outcome was fairly well balanced across topic categories. Figure  3  shows the primary outcomes in 28 protocols of future or ongoing (i. e. , unpublished) RCTs registered on ClinicalTrials. gov, according to population size and nature of the nutritional intervention, with the goal of predicting the forthcoming results.
ISS-WB procedure for discrimination between classical scrapie and BSE (performed at ISS: Istituto Superiore di Sanità) To discriminate between classical scrapie and BSE, the ISS discriminatory Western blot procedure (ISS-WB) was used. This test uses 0. 2 mg mL -1 PK for digestion and antibodies P4 (0. 4 µg IgG mL -1 ) and SAF84 (0. 8 µg IgG mL -1 ) for detection. Data were collected with a chemo luminescence imager (VersaDoc, Bio-Rad) and quantified as in TSE EU Reference Laboratory manual. Two decisive cut-off values for BSE are applied: in BSE samples the P4/SAF84 ratios should be < 0. 5 and molecular mass of the non-glycosylated PrP res band (N, see footnote 2 for the triplet terms N, M and D) based on SAF84 < 0. 5 kDa compared to that of internal control (sample I11)  [41, 42].
Triplex-WB procedure (performed at WBVR: Wageningen BioVeterinary Research) PK was used at 0. 05 mg mL -1 , and WB performed with a mix of antibodies 12B2, Sha31 and SAF84 on a single membrane at concentrations of respectively 0. 2, 0. 1 and 0. 5 µg mL -1 IgG. Calculations on resulting fluorescent antibody signals were carried out with ImageQuant software exactly as before  [44]. The fluorescence of the mAbs with respective Zenon labels Alexa647, Alexa488 and Alexa555 (InVitrogen) was normalized to 1 based on recombinant ovPrP ARQ on each gel. Molecular masses of PrP res bands D, M and N (see footnote 2 for triplet band nomenclature) were estimated with Gel-Pro analyser software (Media Cybernetics) using as reference SeeBlue dye markers which are visible at 647 nm. Samples were analysed in triplicate.
Parameters calculated from the Image Quant software figures such as molecular masses, 12B2/Sha31 ratios, M/D ratios, D-, M-, and N-fractions, and SAF84/Sha31 ratios at the 24 kDa PrP res fraction yielded per sample standard deviations below respectively 4, 27 16, 5, 12, 23 and 17 in percentage of the average.
Statistical analyses With statistical software (GraphPad Prism ® 8), one-way ANOVA compared three or more unmatched groups, based on the assumption that the populations were Gaussian. When P values were ≤ 0. 05, means were considered to be derived from non-identical populations. In that case, one-way analysis of variance was used to establish whether differences between groups of data were greater (P ≤ 0. 05) than expected using Bonferroni-Dunn t-test.
Acknowledgements We want to pay our respect for the very skilled contributions of both WBVR engineers  Jo Erkens  and  Jorg Jacobs  who did pass away, JE in 2015 and JGJ in 2017. We thank  Dr. Gabriele Vaccari  ( ISS, Rome ) for his advises in our goat research,  Dr. Human Rezeai  ( INRA, Jouy-en Josas ) for providing rec-ovinePrP, and  Dr. Thierry Baron  ( ANSES, Lyon ) for supplying a sheep CH1641 field sample.
We next sought to define the diet-specific microglial phenotype by assessing patterns of gene co-expression using unbiased network analysis software, Miru  40. The utility of identifying gene expression patterns and transcriptional networks underpinning common functional pathways in microglia has been previously described  41. Using a Markov clustering algorithm to non-subjectively subdivide our data into discrete sets of co-expressed genes, we found three major clusters of genes that were differentially regulated in n-3 deficient mice (Figure  2I-J ). The mean expression profiles of these three clusters showed that clusters 1 (microglial homeostatic signature) and 2 (fatty acid uptake, transport and metabolism) contained genes whose expression was relatively lower in the n-3 deficient group as compared to n-3 sufficient group.
Cleavage of C3 not only releases the opsonin C3b, but can also release the anaphylactic peptide C3a, which modulates microglial phagocytosis by acting on the receptor C3aR 48. Our analyses also revealed a significantly increased density of C3aR immunostaining in the whole hippocampus of n-3 deficient mice, corroborating microglia transcriptomic data (Table  3 ). (Figure  5C ,F; Table  3 ). The C3aR-positive cells were uniformly distributed throughout the hippocampus, including the CA1. Expression levels of Cx3cr1, Cx3cl1 and Tgfb, all involved in microgliamediated synaptic pruning  24, 25, 49  , were found to be increased in the whole hippocampus of n-3 deficient mice (Supplementary Figure  6 ). Finally, we quantified the C3 protein on freshly extracted hippocampus synaptosomes from n-3 deficient and n-3 sufficient mice using an ELISA assay.
ACKNOWLEDGMENTS Funding for this research was provided by the  Institut National pour la Recherche Agronomique (INRA) , the  Bordeaux Univ , the  Foundation for Medical Research (FRM)  ( DEQ20170336724 ), the  French Foundation  (FDF, # 00070700 ). CM was funded by the  French Ministry for Research and Higher Education , CBB was supported by the  French National Agency for Research  ( ANR-12-BSV4-0025-04 ) and Agreenskill, JCD was supported by the  Nouvelle Region Aquitaine  ( 2011. 1303003POST-DOCTORAT ), QL was supported by the  Region Ile de France (PICRI , the  Ceberal Palsy Foundation  # 13020605 ), AT was supported by the  French National Agency for Research  ( ANR-2010-BLAN-141403 ), ADG was supported by  Agreenskill and Marie-Curie European Grant , CL was supported by  Idex  grant.
AS was supported by grants from the  Spanish Ministry of Economy and Competitiveness  with  FEDER  funds to AS ( BFU2015-66689 ,  RYC-2013-12817 ), a  BBVA Foundation  Grant for Researchers and Cultural Creators to AS, and a  Basque Government  grant ( PI_2016_1_0011 ) to AS. PG was supported by  Inserm ,  Université de Paris ,  Investissement d'Avenir  ( ANR-11-INBS-0011 , NeurATRIS),  ERA-NET Neuron  (Micromet). MET, CL and KB were supported by grants from  NARSAD  and the  CIHR  awarded to MET. We thank  R. Van Der Wickt  and  Celine Lucas  for technical assistance. This work benefited from the facilities and expertise of the imaging platform Imag'In (www. incia. u-bordeaux1. fr),which is supported by  CNRS  and  Region Aquitaine  and from the  Bordeaux Imaging Center. We thank  Atika Zouine  and  Vincent Pitard  for technical assistance at the  Flow cytometry facility ,  CNRS UMS 3427 ,  INSERM US 005 ,  Univ.
Only closed MalE conformations Fig. 6. Integrated mechanism of maltose transport based on structural and biochemical studies. Cartoons A, D, E are directly derived from crystal structures (A, PDB: 3FH6; D, 3PUZ; E, 3RLF or 2R6G). EPR studies from Davidson and Bordignon's groups suggested that the main conformational route occurs through ABCDEB, or ACDEB if maltose is highly available  [68, 69, 86, 99, 101]. Step C is probably transient but is depicted for clarity. In detergent, the ATPase activity of MalEFGK 2 was insensitive to the presence of maltose, and EPR studies suggested that this was due to the increased ability of MalEFGK 2 in detergent to transition through H in the absence of maltose  [68]. Note that conformations I and G are also observed by EPR and cross-linking studies  [69].
Acknowledgements This work was financed by an  ANR  grant (No. ANR-17-CE11-0045-01  to C. O. ), an  ERC Starting Grant  (No. 638536  e SM-IMPORT; to T. C. ),  Deutsche Forschungsgemeinschaft  ( GRK2062  project  C03  to T. C. ; a GRK2062 visiting scholarship to C. O. ;  SFB863  project  A13  to T. C. ). T. C. was further supported by the  Center of Nanoscience Munich (CeNS) ,  LMUexcellent  and the  DFG excellence cluster Center for integrated protein science Munich (CiPSM).
Assessment of dietary intake and other predictor variables At baseline, information was collected on lifestyle, health status, socio-demographic characteristics, anthropometry and medical history. 13 Dietary intake during the year before enrolment was measured by country-or center-specific validated food frequency questionnaires (FFQs) or diet histories, as previously described. 13, 15 o correct for any systematic under-or overestimation of dietary intake across the participating centers, dietary intakes from the questionnaires were calibrated using a standardized, computer-based, 24-h dietary recall method in an 8% random sample of the whole EPIC cohort. In order to estimate the intakes of individual fatty acids, the EPIC Nutrient Database (ENDB) was matched with the National Nutrient Database for Standard Reference of the United States (NNDSR; developed at the USDA).
Study design and included studies The present study comprises secondary data analyses from 11 national and cross-country European studies (Austria, Belgium, Czech, France, Germany, Italy, Ireland, The Netherlands, Poland, Switzerland) and from New Zealand (for comparability with the other studies only the non-M aori population was included). Prevalences of malnutrition were calculated by the respective local data analysts represented in the MaNuEL consortium. Five crosssectional studies [17e21] and six longitudinal studies [22e27] providing data for cross-sectional analyses were included. Data collection for our analysis samples was performed between 2008 and 2016. The number of included participants per study in our analysis ranged from 114 to 1226. Participants were recruited from different settings: Community-dwelling  [22, 23, 26] , communitydwelling with home care  [17] , community including day hospital  [27, 28]  or nursing home  [21] , one primary care center  [19] , only nursing homes  [18]  and long term institutionalized care hospitals  [27, 28] , one geriatric day hospital  [20]  and acute hospitals  [24, 25].