dataesr/scientific-paragraphs-categorization huggingface dataset:
Pangenome Analysis and Clusters of Orthologous Genes The composition of the core and variable genomes was calculated using a pairwise estimation of orthologous proteins in CDhit  (Li and Godzik 2006)  at a threshold of 80% identity on 80% of the protein's total length. We then modeled the progression of pangenome size with respect to the number of genomes included by randomly picking genomes and iterating the process 13 times, as described on the MAGE annotation platform  (Vallenet et al. 2013). R statistical software (R Development Core Team 2010) and the HEATMAP. 2 function of the GPLOT package were used to construct a heatmap based on the variable components of the pangenome. The Euclidean distance based on presence/absence was used to calculate the distance matrix between the variable genomes, and clustering was performed by unsupervised complete linkage.
Evolutionary and Phylogenomic Analysis The alignment of the nucleotide genome sequences was performed using PROGRESSIVEMAUVE  (Darling et al. 2010) ; from this, we extracted the core alignment by keeping only the regions where all genomes aligned over at least 500 bp. This core alignment was submitted to five independent runs of CLONALFRAME software  (Didelot and Falush 2006; Vos and Didelot 2009) , which consisted of a burn-in cycle of the MCMC (Markov Chain Monte Carlo) algorithm fixed to 50,000 iterations and a posterior sampling of 50,000 iterations. The prior iterations were discarded and model parameters were sampled in the second period of the run every 50 iterations, resulting in 1,000 samples from the posterior. Satisfactory convergence of the MCMC algorithm in the different runs was estimated based on the Gelman-Rubin statistic calculated in CLONALFRAME.
The genealogy of the population was summarized and the robustness of the tree topology was evaluated by concatenating the posterior samples of the five runs to build a 50% majority rule consensus tree using the CLONALFRAME GUI and MEGA6 software  (Tamura et al. 2013). From these runs, several measurements were also taken, such as q/h (relative frequencies of occurrence of recombination and mutation) and r/m (relative impact of recombination and mutation in the diversification of the lineages). A Bayesian approach, implemented in STRUCTURE software version 2. 3  (Pritchard et al. 2000; Falush et al. 2003) , was used to infer the lineage ancestry of the core genome by assuming that each strain derived all of its SNPs from one of the K ancestral subpopulations. The number of populations, K, was determined under the linkage model.
Results and Discussion Evolutionary Analysis Reveals the Existence of Two Lineages Within L. curvatus The phylogeny of L. curvatus was inferred using two different approaches. The first strategy was based on Bayesian inference with the coalescent model implemented in CLONALFRAME software  (Didelot and Falush 2006)  whereas the second strategy was to statistically estimate the probable number of ancestral subpopulations (K) within the genetic population of strains; this was performed using STRUCTURE with the linkage model  (Pritchard et al. 2000; Falush et al. 2003). The initial step of these two analysis consisted of the selection of the high-quality core genome using PROGRESSIVEMAUVE software  (Darling et al. 2010) , which took into account only the coding sequences (CDS) and the aligned regions with no frameshift between the 13 chromosomes.
NRIC0822. FIG. 1. -Phylogenomic clonal genealogy and population structure of Lactobacillus curvatus strains. On the left: Fifty-percent majority rule consensus tree inferred with CLONALFRAME software using the coalescent model (branch lengths are given in coalescent units). All branches are supported by a posterior probability of >95%. Strains are colored according to their lineage affiliation. On the right: Proportion of genetic material derived from each of K subpopulations as inferred by STRUCTURE (linkage model) and assuming K¼2 or 3 populations. Ancestral subpopulations are colored in red (lineage1), blue (lineage 2), and green (an unlikely lineage 3), respectively. Clade 2B of lineage 2 is colored in dark cyan to highlight its divergence from Clade 2A and a strong degree of admixture between the two lineages.
Acknowledgments L. C. T. was the recipient of a PhD fellowship from the  BecAR Program and Campus France Argentine. The  LABGeM (CEA/IG/Genoscope  and  CNRS UMR 8030 ) and the  France G enomique National  infrastructure (funded as part of  Investissement d'avenir program  managed by  Agence Nationale pour la Recherche  contract no. ANR-10-INBS-09 ) are acknowledged for support within the MicroScope annotation platform. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr) for providing computational resources and data storage.
The aim of this systematic review was to assess the effect of fructo-oligosaccharide supplementation on glucose homeostasis. The search process was based on the selection of publications listed in the Pubmed-Medline database until April 2016 to identify studies evaluating the impact of short-chain fructo-oligosaccharides or oligofructose on glucose homeostasis. Twenty-nine trials were included in the systematic review and the meta-analysis was performed on twelve of these papers according to the inclusion criteria. Fasting blood concentrations of glucose and insulin were selected as pertinent criteria of glucose homeostasis for the meta-analysis. The consumption of fructo-oligosaccharides decreased fasting blood glycaemia levels, whatever the metabolic status (healthy, obese or diabetic) and diet (low-fat or high-fat) throughout the experiment.
Meta-analysis The aim of the meta-analysis was to focus on the effects of dietary supplementation with FOS (scFOS or OF) on fasting blood glycaemia and insulinaemia, indicative of metabolic health, in the studies in rodents retrieved from the systematic review. Selected papers for the metaanalysis therefore contained, at least, data on the physiological status of rodents (i. e. healthy, diabetic or obese), on diet characteristics and on blood parameters. Selected papers presenting more than one experiment were separated into experiments that were individually encoded. Each comparison between control and prebiotic (scFOS or OF) was also individually encoded within each experiment containing more than 2 treatments. A total of 12 papers and 13 experiments were pooled in the database  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25].
Statistical analysis The interpretation of this database was based on a statistical meta-analysis  [26, 27]. The basic statistical model applied to the data was  [26] : Y ijk ¼ μ þ PRE i þ EXP j þ E ijk ; where Y ijk = basal glycaemia (or insulinaemia); PRE i = fixed effect of prebiotic; EXP j = fixed effect of experiment j, and E ijk = random residual error. Prebiotic effect was first tested qualitatively (control vs FOS) and then as a covariable according to the dose of FOS supplementation expressed as % in diet. Normalized residuals greater than 3 were discarded from the analysis. The parameters of interest were the least square means (control vs FOS) or the adjusted equation (covariance analysis), the P-value for the FOS effect, and the outlier treatment that presented normalized residuals > 2.
Systematic review Database We retrieved 29 studies evaluating the effects of a dietary supplementation with scFOS or OF on glucose homeostasis. Most of these studies was conducted in rodents (6 and 18 experiments in mice and rats respectively). The last 5 studies were performed in other animal species, namely veal calves (n = 1), horses (n = 1), dogs (n = 2) and cats (n = 1). The metabolic status of the animals was different between the experiments with either healthy, genetically obese or diet-induced obese (DIO), or genetically predisposed or streptozotocininduced diabetic individuals. In addition, the type of diet used in the selected experiments varied regarding the energy provided by fat, and we characterized it as "lowfat" when fat provided less than 20% of the diet's energy (standard diet), and as "high-fat" when fat provided more than 20% of the diet's energy (high-fat diet), generally at the expense of carbohydrates.
Structure Construction The 3D structures of both the hopanoid-containing and HOLA and HF-LA were built with PyMOL molecular graphics and modeling package based on the saccharide backbone of E. coli LPS retrieved from the PDB ID 3FXI. Atoms were modified and added accordingly and bond type and length were carefully selected and revised. The geometry of these two structures was further optimized with Maestro under the OPLS3 force field. The antagonist conformation of the hTLR4/MD-2 complex was assembled by merging the ectodomain of TLR4 from RCSB (www. rcsb. org) PDB ID 3FXI and MD-2 from PDB ID 2E59. The latter was aligned to the spatial coordinates of the MD-2 present in 3FXI and solvent, ligands, and ions were removed.
Molecular Modeling of Bradyrhizobium lipid a Binding to MD-2/Tlr4 We performed computational studies to identify the possible binding modes and understand the dynamic behavior of Bradyrhizobium lipid A (Figure  1 ) in complex with human MD-2/TLR4. Since the experimental samples contain different derivatives, we studied both hopanoid-containing and hopanoidfree Bradyrhizobium lipid A (HOLA and HF-LA respectively, Figure  1 ). Docking results were evaluated based on the predicted binding score and on the apparent degree of similarity with E. coli LPS and lipid IVA as known from their X-ray crystallographic structures accessible under the PDB accession codes 3FXI and 2E59, respectively. We considered both the insertion of the FA chains into the MD-2 pocket and the positioning of the disaccharide backbone.
Docking Calculations and MD Simulation of Bradyrhizobium Lipid A Into MD-2/TLR4 Model In a second approach, docking calculations of HOLA were performed into the MD-2/TLR4 model in the antagonist conformation reported by us somewhere else  (50). Interestingly, when compared with the structure of the MD-2/TLR4 complex in the agonist conformation (PDB ID 3FXI), among the non-bonded interactions between the two proteins, a loop of TLR4, composed of amino acids 263-266, protrudes into a MD-2 channel (Figure  S2  in Supplementary Material), located approximately between Asp161 and Tyr118. This protrusion is further amplified by the side chain of Arg264 that goes as far as to hover over the MD-2 hydrophobic pocket. This impingement of TLR4 over MD-2 diminishes the space available for ligand interactions in the MD-2/TLR4 complex compared with MD-2 alone.
We superimposed TLR4, based on the X-ray crystallographic structure (PDB ID 3FXI), to the docking results from the MD-2-only study and noted a steric incompatibility between the TLR4 protruding loop, and either a lipid chain or the hopanoid moiety from the docked ligand (Figure  6 ). This observation could point to the fact that HOLA and HF-LA carry on their antagonist activity by preventing or impairing the formation of a proper MD-2/TLR4 dimer essential for TLR4 activation. The hydrophobic interactions taking place inside the hydrophobic pocket were essentially the same as the ones described in the case of MD-2 alone. However, the presence of TLR4, reducing the space available for ligand binding, resulted in very few poses respecting the sugar orientation criterion mentioned above. Two poses of HOLA, in good agreement with lipid IVA, were selected for MD simulations to further investigate interactions with the receptor and the overall stability of the MD-2/TLR4/ligand complex (Figure  7 ).
These two poses are rotated 180° one to the other: in the first one, the HOLA lipid A is oriented as lipid IVA (PDB ID 2E59) and, in the second one, it is oriented as E. coli LPS (PDB ID 3FXI, Figure  S2  in Supplementary Material). Along the simulation time, the TLR4 presents important deviation compared to the crystal structure in relation with MD-2, as shown in the RMSD plot (Figure  S4  in Supplementary Material). TLR4 displays a tendency to break apart from MD-2 indicating that the presence of the ligand destabilizes the TLR4/MD-2 complex (Figure  8 ). This observation backs-up the hypothesis that Bradyrhizobium lipid A act as antagonists by either preventing complex formation (cf. protruding loop mentioned in the docking study above) or by disturbing the complex stability. In addition, Phe126 remains in its open conformation all along the simulation (Figure  S4  in Supplementary Material), this stability was previously associated with antagonist ligands  (51).
FigUre8| Evolution of the myeloid differentiation protein-2 (MD-2)/toll-like receptor 4 (TLR4)/HOPA complex (blue/violet/yellow) over the MD simulation. MD-2/ TLR4 structure from PDB ID 3FXI, represented in semi-transparent cartoon (gray), was superimposed for comparison purposes. On the left: system at t = 0 ns, from the docking calculation. On the right: system at t = 100 ns of MD simulation.
The meeting was financially supported by L'alliance nationale pour les sciences de la vie et de la santé (AVIESAN), the Institut National de la Santé et de la Recherche Médicale (INSERM), the Fondation ARC pour la Recherche sur le Cancer (ARC), and the University of Paris Descartes. C. E. is funded by the Deutsche Forschungsgemeinschaft (ES103/7-1 and ES103/9-1). G. H. P. is funded by the National Institutes of Health (ES028244). D. H. S. is supported by P42 ES07381 and a grant from the Find The Cause Breast Cancer Foundation.
Acknowledgments: We gratefully acknowledge all meeting participants whose presentations, posters, and lively discussions at the meeting contributed so much to its success and inspiration. We apologize to those we could not mention due to lack of space. The title of the abstract by  David Shepherd  inspired the title of our meeting report here. The 5th AHR meeting is slated to be held in 2020 at  Pennsylvania State University, USA , hosted by  Gary H. Perdew  and  Andrew Patterson.
Funding: The meeting was financially supported by L'alliance nationale pour les sciences de la vie et de la santé (AVIESAN), the Institut National de la Santé et de la Recherche Médicale (INSERM), the Fondation ARC pour la Recherche sur le Cancer (ARC), and the University of Paris Descartes. C. E. is funded by the Deutsche Forschungsgemeinschaft (ES103/7-1 and ES103/9-1). G. H. P. is funded by the National Institutes of Health (ES028244). D. H. S. is supported by P42 ES07381 and a grant from the Find The Cause Breast Cancer Foundation.
Approximately 70,000 cells were collected using a NovoCyte flow cytometer (ACEA Bioscience, Inc. ) and analysis was performed using NovoExpress ™ Software (Version 1. 2. 1, ACEA Biosciences, Inc. ). Samples were first inspected in all light scatter patterns and fluorescence channels to confirm quality and abnormal cells (dead cells, aggregates…) were excluded. Each acquisition contained unlabelled samples, single-stained cells and/ or an FMO strategy for reporting percentage of positive cells and compensation.
Statistical analysis for humoral and cellular analysis Non-parametric tests were performed using the Mann-Whitney t test (comparison between specified groups), the Kruskal-Wallis test and Dunn's multiple comparison post-test to compare all the groups together, or the Wilcoxon sign rank test (comparison of different treatments/ Assays for patients in the same group). A p value < 0. 05 was considered to be significant. All statistical analyses were performed using GraphPad Prism version 5. 01 for Windows (GraphPad Software, San Diego, CA, USA).
Data processing and statistical analysis Data processing workflow and statistical analyses were performed on the open web-based platform workflow-4metabolomics (W4M: http://workfl ow4m etabo lomic s. org), a collaborative research infrastructure for computational metabolomics  [27]. Automatic peak detection and integration were performed using the matched filter algorithm in the W4M pre-processing package (including XCMS software). All raw data were manually inspected using the Qualbrowser module of Xcalibur, while the Quanbrowser module was used for peak detection and integration of internal standards. To remove analytical drift induced by clogging of the ESI source observed in the course of analytical runs, chromatographic peak areas of each variable present in the XCMS peak lists were normalized using the LOESS algorithm (W4M package).
Features generated from XCMS were filtered according to the following criteria: (i) the correlation between QC dilution factors and areas of chromatographic peaks (filtered variables should have coefficients of correlation above 0. 7 to account for metabolites occurring at low concentrations and which are no longer detected in the most diluted samples) (ii) repeatability (the coefficient of variation obtained on chromatographic peak areas of QC samples should be below 30%), and (iii) ratio of chromatographic peak areas of biological to blank samples above a value of 3. Statistical analyses were performed with W4M (multivariate and univariate statistical tests), Simca P (multivariate PLS-DA models) or Prism (univariate tests) software tools. The discriminant metabolites were selected by combining multivariate variable importance in the projection (VIP) obtained from the PLS-DA model and univariate p values (nonparametric Mann-Whitney statistical test).
Metabolite annotation and LC/ESI-MS-MS validation Feature annotation was performed considering a ± 10 ppm mass tolerance and using our in-house spectral database  [26, 28] , as well as the publicly available databases KEGG  [29] , HMDB  [30]  and METLIN  [31]. To be identified, ions had to match at least two orthogonal criteria among accurate measured mass, isotopic pattern, MS/MS spectrum, and retention time; and to those of an authentic chemical standard analyzed under the same analytical conditions, as proposed by the Metabolomics Standards Initiative  [32] : level 1 (identified): based on accurate mass, column retention time similarity with a standard and MS/MS spectrum. Level 3 (putatively characterized): based on accurate mass, and interpretation of MS/MS spectra. Metabolite identification was further confirmed by additional LC/ESI-MS-MS experiments, performed using a Dionex Ultimate chromatographic system combined with a Q-Exactive Plus mass spectrometer (Hilic) or a Fusion mass spectrometer (C18), under nonresonant collision-induced dissociation conditions using higher-energy C-trap dissociation (HCD), at normalized collision energies (NCEs) 10, 20, 40 and 80%.
Cells were labelled and analyzed by flow cytometry using a NovoCyte flow cytometer and analysis was performed using NovoExpress ™ software (Version 1. 2. 1; ACEA Biosciences, Inc. ). Percentages of ILC1; ILC2, or ILC3 cells among parent cells (CD45 + CD127 + ) and grandparent cells (Lin -SSC low ) are indicated in colour and in brackets, respectively. Lin: mix of labelled anti-human CD3, CD11c, CD14, CD16, CD19, CD56, FcεRIα , CD1a, CD123 (See figure on previous page. ) patients when compared to IgE-CMA patients, but less clearly compared to IgE-resolved ones (Fig. 8b, c ).
Study design This monocentric, double-blind, controlled, randomized trial was conducted in 2012-2013 in the Department of Neonatology of the Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milano, Italy (NCT01611649). The study was approved by the local Ethical Committee and conducted in accordance with Good Clinical Practice and the principles and rules of the Declaration of Helsinki. Parents or legal caregivers provided written informed consent prior to enrollment of their infants in the study. The study protocol has been previously published  [14].
Objectives and outcomes The main objective of this study was to investigate the effect of formula D on the RBC membrane total Omega 3 FA content as compared to formulas P and PDHA and to BF reference group. Total Omega 3 FA levels included ALA, EPA (eicosapentaenoic acid, 20:5n-3), DPA (docosapentaenoic acid, 22:5n-3) and DHA levels. The secondary objective was to evaluate changes between 4 months and enrollment in whole blood FA contents in infants consuming formula D in comparison to formula P and PDHA and BF. Impact of formulas on growth, body composition and gastrointestinal tolerance was also investigated but these data are under publication in another article.
Statistical analyses Statistical analyses were performed using SAS software (SAS Institute Inc. , Cary NC, USA) by Soladis, Lyon, France. Continuous variables were expressed as mean and standard deviation. Differences among groups for FA levels were analyzed with an analysis of variance with 3 fixed factors (group, sex, gestational age). Pairwise comparisons were made using post-hoc Tukey's test for multiple comparisons. A p-value < 0. 05 was considered significant.
Materials and Methods: Cow and human milk miRNome expression datasets of cow and human milk lipids at 2, 4, and 6 months of lactation periods were preprocessed and predicted for their target genes using TargetScanHuman. Enrichment analysis was performed using target genes to extract the immune-associated gene ontology (GO) terms shared between the two species. The genes within these terms with more than 50 different miRNAs of each species targeting were selected and reviewed for their immunological functions. Results: A total of 146 and 129 miRNAs were identified in cow and human milk with several miRNAs reproduced from other previous reports. Enrichment analysis revealed nine immune-related GO terms shared between cow and human (adjusted p≤0. 01). There were 14 genes related to these terms with more than 50 miRNA genes of each species targeting them.
Equivalent biological influences of milk miR-NAs among breastfeeding species were implied by several studies  [1] [2] [3] [4] [5]. Furthermore, cross-species milk miRNA absorption between human and another breastfeeding mammal was even demonstrated  [8, 9]. Considering such mutual effects of milk miRNAs, the insight into such conserved orthologous genes should render us with novel knowledge for both animal evolution and pediatric sciences. Since milk miRNome analyses are most conventional among cow and human miRNA studies, miRNome expression profiles of these two species are hence considerably available in the public database -making the cross-species analyses between them priorly suitable and profitable. Milk miRNAs significantly contributed their roles in neonatal immune system  [6, 10].
Sample datasets Cow and human milk miRNome expression datasets were acquired from milk lipids of nursing mothers and dairy cows at approximately 2, 4, and 6 months of lactation periods. All cow and human milk miRNome expression datasets with their corresponding sample filename IDs used in this study were retrieved from the sequence read archive (SRA) database (https://www. ncbi. nlm. nih. gov/sra) (Table  -1 ). Further details of each sample file could be attained from SRA database.
Data preprocessing The miRNome expression datasets were extracted and checked for sequencing quality as previously described by Chokeshaiusaha et al. [15]. Nucleotide sequences of annotated non-coding RNAs applied in this study were retrieved from the Ensembl database (https://asia. ensembl. org/info/data/ftp/index. html). Quality trimming and adapter trimming were performed by Cutadapt v1. 14 program  [16]. Only sequencing reads with length between 18 and 30 nucleotides and having Phred score ≥20 for at least 50% bases were selected for genome alignment. Alignment was performed with the selected reads corresponding to the species' genomes -UMD3. 1 for cow and GRCh38 for human. Any sequence reads aligned to rRNA, tRNA, snRNA, snoRNA genes, or acquired more than five positions aligned were discarded.