dataesr/scientific-paragraphs-categorization huggingface dataset:
mansoni genes automatically from primary reference; (2) recording of contextual, life cycle stage, and citation information into a customized database; and (3) programmatic correlation of this data with existing database annotations. This resulted in annotation of 96. 6% of S. mansoni sequences, greatly exceeding existing annotation from any single database, which topped out at 62%. NLP annotation further extended this coverage with life cycle stage and characterization information not readily available in database annotation. Next, the Life Cycle/Tissue Filter refines interactions for likelihood of in vivo interaction based on biological context derived from NLP of the component proteins in each interacting complex and their expression in each of the four life-cycle stages of S. mansoni in different human tissues (Materials & Methods).
Materials and Methods The initial predictions of S. mansoni-human were generated based on a protocol described in  18  , briefly reviewed here. First, genome-wide S. mansoni and human protein structure models were calculated by MODPIPE  46  , an automated software pipeline for large-scale protein structure modeling  47. MODPIPE uses MODELLER  48  to perform the canonical comparative modeling steps of fold assignment, target-template alignment, model construction, and model assessment. High-scoring models were deposited in MODBASE  49  , a publicly accessible database of comparative models. Next, resulting models were aligned to SCOP domain sequences, and if a model aligned to a SCOP sequence with more than 70% identity, it was assigned that SCOP domain identifier. These annotations were used as the basis for a search in PIBASE, a database of domain-domain interactions.
Application of Biological Context Filters. Interactions that pass the Network filtering are then filtered for biological context using the following methods. Natural Language Processing (NLP) & Enrichment Filters. S. mansoni Protein Annotation: In preparation for applying the Life Cycle/Tissue Filter, a Natural Language Processing (NLP) protocol was created to automatically identify from the literature which S. mansoni proteins were expressed in different pathogen life cycle stages. S. mansoni protein database identifiers and their amino acid sequences were extracted from the GeneDB  20  , National Center for Biotechnology Information [NCBI], TIGR  51  , and Uniprot/TrEMBL databases  52. A literature search identified experiments indicating proteins expressed in different S. mansoni life cycle stages and categorized each literature reference into corresponding life cycle stages.
All literature was then mined using NLP to derive accessions and context information. Accessions were derived from the text with regular expression searches corresponding to the specifications of the database (for example, a word in the text matching the regular expression form [A-Z][0-9]5 indicates a Uniprot Accession). Thus, for each paper, a list of protein accessions was obtained. All protein accessions were then mapped by comparing sequences to Smp accession, Uniprot accession, and NCBI accession, in that order of priority. Thus, the final result of NLP processing was a list of all accessions of proteins expressed in life cycle stages of S. mansoni. S. mansoni protein sequence data from these initial interactions were enriched from biological annotation obtained from MODBASE  49  , GeneDb 20 , NCBI, Uniprot  52  , and primary reference in literature.
The annotations included protein names, links to referenced resources, and any available functional annotation. Human Protein Annotation: Human proteins were annotated for tissue expression (GNF Tissue Atlas)  53  , known expression on cell surface, and known immune system involvement (ENSEMBL)  54. Functional annotation for each protein was obtained from Gene Ontology Annotation (GOA)  55. Human protein sequences were correlated with predicted interacting sequences to determine involvement  17. Life Cycle Stage/Tissue Filter. Next, the Biological Context Filter was applied to S. mansoni and human protein interactions in the four life cycle stages associated with pathogenesis and infections in humans. S. mansoni proteins were filtered by life cycle stage, known expression and excretion, using NLP and database annotation.
Targeted Filter. The final step in the Biological Context Filter uses a targeted post process analysis based on NLP and database annotations using two additional data mining steps. For each of the interacting protein complex pairs, three parameters were analyzed: pairwise expression in both known human tissue target and S. mansoni life cycle stage as indicated by the Life Cycle/Tissue Filter, expression or involvement in known human immunogenic responses, and S. mansoni protein expression or involvement with human proteins targeted by other parasites. Parameters for additional data mining in the target analysis include the following criteria: investigator-selected proteins of interest and NLP derived key terms that were used to target annotation data in protein names and functional annotation (Uniprot 52 , GeneDB 20 , Gene Ontology [GO] 56 ).
AUTHOR CONTRIBUTIONS GD and AP processed the human milk samples, and the results of bacteriological analyses were verified in double-blind experiments performed by PL of CHU. GD worked with AP to write the first version of the manuscript; unfortunately, GD is now deceased. PL performed a double-blind bacteriological study on the HHP samples from GD at the CHU. LC and PM performed the experiments, analyzed the data and wrote their part of manuscript and participated in revising the manuscript. Technis and results of Bacteriology was revised by PL and AP. The technics and results of Lipids by LC. The technics and results of proteins by PM. CB wrote the manuscript.
ACKNOWLEDGMENTS The authors would like to acknowledge  Dr. Delphine Lamireau  and the technicians from the  Bordeaux Human Milk Bank. We also thank  LC ,  Carole Vaysse  and  Laurence Fonseca  for performing the lipid assay and the technicians from  ITERG, Bordeaux University , for performing lipid measurement. We thank PM,  G. Miranda ,  Z. Krupova , and  L. Bianchi  for performing the protein assay UMR1313 GABI,  INRA ,  AgroParisTech ,  Université Paris-Saclay , 78350 Jouy-en-Josas, France.
Differences in the composition of bacteria in saliva have been shown in overweight women as compared to NW subjects  12  and an association between microbiota found in subgingival biofilms and obesity has been reported in adolescents  13. Unfortunately, these data remain poorly predictive of a putative influence of oral microbiota on the orosensory detection of fat since oral microbiome is greatly heterogenous  14. Indeed, oral cavity encompasses multiple ecological niches (i. e. tongue surfaces, gingival sulcus, teeth, lip, soft and hard palate, saliva) colonized by distinct microbial communities displaying specific features (i. e. diversity and stability/variability -see Human Oral Microbiome Database, www. homd. org) influenced by local challenges (i. e. oxygen variation, nutrient availability, mechanical stress, salivary flow and composition)  15.
001). Nevertheless, ONT were more numerous than NWNT (n = 8 and 4, respectively -Fig. 1 ), suggesting that obesity increases the susceptibility to be poorly sensitive to oral lipid stimulation. Interestingly, a clear difference between OT and ONT was also found, confirming that LA detection threshold is not strictly dependent on BMI  22. Therefore, the microbiota and salivary data were next analyzed according to three complementary comparisons: NW vs O, T vs NT and OT vs ONT (Fig. 1 ). Microbiota ecology surrounding circumvallate papillae and body weight. To delineate for the first time the gustatory papillae microbiota ecology and whether this ecology is related to the subjects, body weight, swab samples were taken directly from the V-shaped row of the circumvallate papillae at the back of the tongue dorsa to collect the CVP associated microbiota.
The CVP samples were extracted and sequenced to obtain the16S rRNA gene profiles for NW and O subjects (n = 45). Similar to what has been found in feces, the CVP microbiome was found to be mainly composed of anaerobic Firmicutes and Bacteroidetes phyla (Fig. 2A ). Interestingly, the frequency of Proteobacteria, which reached ~10%, was higher than what was previously found in fecal samples. At the family taxonomic level, we observed a reduction of the frequency of Porphyromonadaceae in favor of a doubling of the Bacteroidaceae in the O group (Fig. 2B ). To more precisely identify the differential taxa at all taxonomic levels we performed linear discriminant analysis  23  effect size (LEfSe; http://huttenhower. sph. harvard. edu/gal) with the 16S rRNA sequence data  24  (Fig. 2C ). Only the abundance of the Deltaproteobacteria was slightly lowered in the O subjects (Fig.
2D ). The primary coordinate analyses confirmed that the two ecologies were similar (Fig. 2E ). We further quantified whether there were changes in the alpha diversity and found no difference at any taxonomic levels (Fig. 2F ). Therefore, we performed linear regression to identify the taxa that could be associated with the BMI as a continuous variable. We identified that the Bacteroidales (order), Enterococcus (genus), and Staphylococcus (genus) were positively correlated with the BMI (Fig. 2G-I ). In contrast, we found Prevotella (genus) and Butyricicoccus (genus) to be negatively correlated with BMI (Fig. 2J, K ). To gain insight into the potential molecular functions of the CVP associated bacterial microbiome, we used PICRUSt to predict the metagenomic contributions  25. PICRUSt predicts potential metagenomes by ascribing annotated genes within a known sequenced database, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), with respect to the presence or absence of OTUs in 16S rRNA data sets.
The predictive confidence can be assessed in PICRUSt by calculating the nearest sequenced taxon index (NSTI). The NSTI calculation quantifies the average branch length that separates each OTU from the closest sequenced reference bacterial genome with weighting for the abundance of that OTU in the sample. When NSTI number is low, it suggests that PICRUSt is likely to perform well in predicting the metagenome of the organisms in a sample. In this study, we used the KEGG database output for the reference OTUs and calculated a mean NSTI of 0. 17 ± 0. 02 s. d. , which is considered a mid-range quality value and reported to produce accurate PICRUSt metagenome predictions  25. The composite predicted metagenomes were parsed into KEGG derived modules for pathway and structural complex prediction. Pathway modules represent functional units in KEGG metabolic pathway maps and structural complexes often form molecular machineries.
Pairwise analysis was performed for NW vs O subjects. The cladograms show the KEGG pathway hierarchy represented by rings with the consolidated pathway modules (identified in the legend) at the outermost ring, and each circle is a member within that level. KEGG modules are shaded in color according to the group in which it is most abundant (P < 0. 05; LDA score 2. 0). The respective KEGG pathway cladogram legends are incorporated into the graphical representation (P,Q) of the relative predicted gene count for each differential feature. identified the Enterorhabdus genus as positively correlated and the Barnesiella genus as negatively correlated with LA tasting threshold (Fig. 3G, H ). PICRUSt analysis of pathways showed the highest differential orthologue counts for nucleotide and amino acid metabolism between T and NT subjects.
Seven taxa were found to be negatively correlated with the LA tasting threshold (Fig. 4K-Q ). Consistent with differences in the CVP microbiomes between OT and ONT groups, we identified several differential pathway and structural features in the predicted metagenomes from PICRUSt analysis. ONT subjects were differentiated by an increased count in modules for metabolic pathways associated with central carbohydrate metabolism and bile acid modification (Fig. 4R, T ). ONT subjects were also characterized by increased predicted structural components related to environmental information processing such as multiple phosphotransferase and simple sugar transport systems (Fig. 4S, U ). Therefore, the microbiota surrounding CVP from OT and ONT subjects displays taxonomic and predicted metagenomic profiles which could assist explaining the LA tasting phenotype.
Associations between selected biological parameters, salivary characteristics, microbiota composition and BMI or oral fat sensitivity. To further delineate the role of oral environment on the orosensory sensitivity to lipids, resting and stimulated saliva flux and composition were determined, and then were simultaneously analysed both with oral microbiota composition and a selection of biological determinants using a multivariate approach (MB-PLS-DA method) in NW vs O, T vs NT, and OT vs ONT groups. By merging data from different origins (blocks), this global statistical analysis is used to generate multi-variable predictive information on the functions of complex biological systems. Four blocks of predictors (i. e. selected biological determinants, resting saliva, stimulated saliva and microbiota composition -Table  1 , supplemented data) were analyzed with regard to two components, body weight (NW vs O) and sensitivity of the orosensory lipid detection (T vs NT and OT vs ONT).
At the gene prediction level, the CVP microbiota was also characterized by a broad range of metabolic activities regardless of the group classification of the subjects. Nevertheless, despite identification of a few positive and negative correlations between the frequency of some taxa and BMI, neither the taxonomic profile of the microbiological ecology in direct vicinity of CVP nor the corresponding alpha diversity were sufficient to discriminate clearly between NW and O subjects. Therefore, we analyzed the predicted metagenomes using PICRUSt software, since in human fecal microbiota the metagenomic functional differences are highly and unequivocally associated with the obese phenotype  30. The gene function analysis of the predicted metagenomes suggested that microbial pathways involved in energy metabolism (i.
Subjects. The subjects were a subgroup of the HumanFATaste study approved by the local ethics committee (Comité de Protection des Personnes Est-1) and registered at Clinical Trials (#NCT02028975). Only volunteers having performed the oral lipid detection threshold test and provided samples of salivary and oral microbiota have been selected. NW (BMI < 25 kg/m 2 , n = 21) and O (BMI ≥ 30 kg/m 2 , n = 17) age-matched, adult, white men participated in the current study. For NW subjects, eligibility criteria were no regular drug intake and plasma triglyceride and glucose levels <1. 50 g/L and <6. 10 mmol/L, respectively. Because type-2 diabetic subjects have blunted taste responses  53  , obese non-diabetic subjects were selected according to the following inclusion criteria: no hypoglycemic drug intake or surgical treatment of obesity, fasted plasma glucose levels <6.
The metagenomics workflow was used to classify organisms from a metagenomic sample by amplifying specific regions in the 16S ribosomal RNA gene and was exclusive to bacteria. Library construction and sequencing. The V3-V4 hyper-variable regions of the 16S rDNA gene were amplified from the DNA extracts during a first PCR step using Vaiomer universal 16S primers V2 (Vaiomer 1F 5′-CTTTCCCTA-CACGACGCTCTTCCGATCTTCCTACGGGAGGCAGCAGT-3′ and Vaiomer 2R 5′-GGAGTTCAGACGTGTGCTCTTCCGATCTGGACTACMRGGGTATCTAATCCYKTT-3′) and following the protocol described previously  56. The joint pair length was set to encompass 476 base pairs amplicon and included specificity for the 16S rDNA gene of 95% of the bacteria in the Ribosomal Database Project. For each sample, a sequencing library was generated by addition of sequencing adapters and multiplexing indexes during a second PCR step as described previously  56.
The pool was denatured, diluted and loaded onto the Illumina MiSeq cartridge according to the manufacturer's instructions using MiSeq Reagent Kit v3 (2 × 300 bp Paired-End Reads). (Illumina, San Diego, CA, USA). Bioinformatics pipeline. The targeted metagenomic sequences from microbiota were analyzed using the bioinformatics pipeline established by Vaiomer  56. Briefly, after demultiplexing of the bar coded Illumina paired reads, single read sequences were cleaned and paired for each sample independently into longer fragments. To obtain optimal results, the last 80 bases of the R2 reads were trimmed due to low base quality score. After alignment against a 16S reference database, sequences were clustered into operational taxonomic units  57  with a 97% identity threshold. Remaining sequencing errors were filtered out by eliminating the OTU with less than 3 sequences, and a taxonomic assignment was performed in order to determine community taxonomic profiles against the RDP database using the RDP Classifier tool.
An average of 59,434 raw pairs (118,868 raw reads) per sample were obtained by sequencing and 57,042 pairs (114,084 reads) were conserved after QC filters. To estimate individual CVP microbial alpha diversity, rarefaction curves were generated based on metrics and the number of OTUs present in the samples was determined (Shannon diversity index). The taxonomic output matrix containing the count data for OTUs per sample was processed with the processed with the online Galaxy interface for LEfSe (linear discriminant analysis effect size) algorithm using an alpha parameter significance threshold for the Kruskal-Wallis 58 test among classes set to 0. 05 and the logarithmic LDA score cut-off was set to 2. 0. The functional metagenome was inferred from the clustered 16S sequences using the PICRUSt software (version 1.
0. 0) as per the instructions provided for the Genome Prediction Tutorial for PICRUSt (http://picrust. github. io/picrust/tutorials/genome_ prediction. html#genome-prediction-tutorial) with recommended scripts and default settings  25. As described in the PICRUSt tutorial, the sequences previously grouped into OTU were processed through the QIIME closed reference OTU picking tool with a 97% similarity threshold to obtain a set of OTU IDs from the Greengenes reference collection (gg_otus_13_5. tar. gz) as input for prediction of corresponding metagenomes by PICRUSt. Through this inference process, the abundance values of each OTU were normalized to their respective predicted 16S rRNA copy numbers and then multiplied by the respective gene counts for metagenome prediction. PICRUSt was also used to calculate the nearest sequenced taxon index (NSTI) to quantify dissimilarity between reference genomes and the predicted metagenomes.
The resulting core output was a list of Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues and predicted gene count data for each sample. We used in house scripts to parse the output into KEGG module categories for functional pathways and structural complex hierarchies using the KEGG database (http://www. genome. jp/kegg/module. html). The output matrix containing the relative abundance of KEGG orthologous groups (KO) per sample was processed with the online Galaxy interface for LEfSe using an alpha parameter significance threshold for the Kruskal-Wallis 58 test among classes set to 0. 05 and the logarithmic LDA score cut-off was set to 2. 0. Respective cladograms were generated with modules at the lowest level. Quantitative plots of differential features were generated from normalized module level predicted gene data showing means with standard deviation using GraphPad Prism 6 software (GraphPad Software, La Jolla, CA, USA).
Statistical analysis. Statistical analyses (non-parametric Mann-Whitney's tests and non-parametric Kruskal-Wallis tests followed by Dunn's multiple comparison tests and Spearman's correlations) were conducted using the software PRISM v6. 05 and the software environment R version 3. 3. 1. Partial Least Squares-Multiblocs-Discriminant Analysis (PLS-MB-DA) was used to determine which physiological, microbiotal and/or salivary variables (i. e. X blocks = predictor variables, Table  1 ) were the most discriminant to characterize the subjects according their BMI and/or orosensory sensitivity to lipids (i. e. Y blocks = variables to be explained). Therefore, three independent analyses were performed to fully explore the differences between the subjects (i. e. NW vs O, T vs NT and OT vs ONT). Statistical treatments were performed using the free software R 3.
3. 0 (http://cran. r-project. org/). The main R package used for multivariate data analyses was ≪pls 2. 1-0≫. Statistical treatment requires an pre-processing step as described by Hassani et al. 61. In brief, all the variables (belonging to both X and Y) were mean centred, then variables in X and Y were scaled block-wise to balance the sum of square contribution for different blocks and finally, to explore the systematic variation patterns in X which predicts the systematic variation patterns in Y, PLS algorithm was applied. Relationships between X blocks and Y and weight of each block X for explaining Y were calculated as described elsewhere  62. Number of components in X that best explain the maximum of variance in Y were chosen based on the percentage of total variance in the Y block explained by the successive components as described previously  63.
Acknowledgements This study was supported by the  Agence Nationale de la Recherche  through the Grants  ANR-12-BSV1-0027-01  ( SensoFAT2 project  to  PB ) and  ANR-11-LABX-0021-LipSTIC  (to LL) and by a Grant From the  Région Midi Pyrénées  (to RB). We thank  Isabelle Robin-Simoneau  for recruitment of subjects,  Michael Chevrot ,  Géraldine Enderli  and  Marlène Gomes  for 3-AFC tests and  Matteo Serino  for technical support. We also thank the  Pr M. El Qannari (Oniris, Nantes, France)  who has kindly provided the R script used for PLS-MB-DA analyses.
Licking Tests The CB 1 R -/-(n = 9) and WT (n = 9) mice were deprived of food and water for 6 h before the test. The mice were conditioned to choose between a palatable (4% sucrose) and a control solution. Once the mice were conditioned, they were randomly subjected to two-bottle test, containing either control (0. 3% xanthan gum) or a test solution (0. 3% xanthan gum + 0. 2% linoleic acid, LA). The number of licks, motivated by each bottle, were recorded using computer-controlled lickometers (Med Associates, Fairfax, VT, USA). Data were analyzed for 5 min from the first lick.
NIS-Elements software was used to record the images. The microscope was equipped with Lucas EM-CCD (Andor Technology, Gometz-le-châtel, France) camera for real-time recording of 16-bit digital images. The dual excitation fluorescence imaging system was used to analyze individual cells. The changes in intracellular free Ca 2+ were expressed as ∆Ratio, calculated as the difference between F 340 and F 380. All test molecules were added in small volumes with no interruption in recordings. For Ca 2+ signaling experiments, the fatty acid was dissolved in ethanol (0. 1%, v/v) and added into the experimental cuvette. Anandamide (AEA, CB 1 R endogenous ligand), arachidonyl-2 -chloroethylamide (ACEA, CB 1 R synthetic ligand), LA, DB-cAMP, and U73122 were supplied by Sigma (Saint Quentin-Fallavier, France).
We decided to include participants with at least three recalls in the validity study to ensure i) reasonable intra-individual variations, ii) seasonal representation and iii) sufficient statistical power. Participants who completed at least three 24-hour recalls were more likely to be older than participants who completed the first questionnaire but did not complete three 24-hour recalls. They had higher energy intake and healthier dietary habits than their counterparts (data not tabulated). From the 214 participants who volunteered for the validity study, only 130 were interviewed for the 24-hour recalls. The main reasons were contact difficulties as they were mostly active people, and that this study was conducted by only one dietitian which prevented us from interviewing several participants simultaneously.