dataesr/scientific-paragraphs-categorization huggingface dataset:
The amino acid sequence of the β-glucosidase reference was obtained from Cucumis sativus. β-glucosidase 24-like is a sequence obtained from the predictive database BLAST program, access number XP_004154167. 1, with a 58,142 nominal molecular mass, similar to that identified in S. edule, which was calculated at 58 kDa. Table  2  compares the data for the homologous peptides found in both enzymes with a sequence of 10%. The low homology obtained from the BLAST database identifies just one member of the Cucurbitaceae family and is minimal. It is worth noting that β-glucosidase is a highly conserved enzyme in other families (as in the case of grasses), however, there is insufficient definitive information for this family. It is certainly a newly discovered β-glucosidase, which has not been reported previously.
The injected samples were first trapped and desalted isocratically on a LC-Packing PepMap C18 μ-pre-column cartridge (Dionex, Sunnyvale, CA, USA). After dissolving the samples with 0. 1% formic acid, they were loaded into an analytical C18 capillary column, connected online to the mass spectrometer. Instrumental operation data acquisition and analysis was performed under the full control of Mass-Lynx 4. 0 (Micromass). The 1 s survey scans were run through a mass range of m/z 400 to 2000. The acquired peptide ions were analyzed with the Mascot program (www. matrixscience. com) using both NCBI and EST databases. Parameters for the Mascot search required a peptide mass tolerance of 1 Da, and a MS/MS ion mass tolerance of 1 Da. Variable modifications included methionine oxidation and cysteine carbamido-methylation.
Bioinformatic Analysis A search for similarities (Blast) in conserved domains was performed with the highest rated peptides obtained from the MS on the MASCOT computer program (www. matrixscience. com) alignments, with β-glucosidase from Cucumis sativus. The sequence analyses were performed using ClustalW2 from the EMBL-EBI program (www. ebi. ac. uk/Tools/msa/clustalo/)  [53]. All experiments were made in triplicate, and a one-way analysis of variance was completed for each set.
Acknowledgments The authors thank  Fabiola Sánchez Esperanza  and  Teresa Hernández  for her skillful technical assistance,  Charlotte Grundy  for her support throughout the work and the  Instituto Tecnológico de Oaxaca  from the support. This work was supported by  Instituto Tecnológico de Oaxaca ,  Consejo Nacional de Ciencia y Tecnología  ( CONACyT  ( 0129932 ),  PAPIIT-UNAM IN20431 , and Prodep.
Characterization of microbiome composition The fecal microbiota of 69 samples (including 5 biological replicates) were characterized by sequencing of the V5-V6 region of the bacterial 16S ribosomal RNA with the Illumina MiSeq technology. The dataset was rarefied to 50,000 reads/sample (see S2 Fig) , and reads were clustered into 5039 operational taxonomic units (OTU) at 97% identity. The five biological replicates (sampling of the same individual few days apart, see S1 Table) allowed us to compare the microbial differences within individuals to those between individuals. We assessed differences in gut communities by calculating UniFrac distances, a phylogenetic based distance metric, which when weighted, accounts for relative abundance of taxa  [43]. Because both weighted and unweighted metrics capture different aspects of microbial diversity  [43] , we included both types of analyses in the manuscript.
To account for the effect of Entamoeba, we added it as a binary covariate to our ANOVA and identified 11 phyla that are significantly correlated with alpha diversity (FDR q < 0. 05, after multiple test correction; see S8 Fig). Although, as expected, the majority of these taxa increase in abundance with higher diversity, Bacteroidetes and Proteobacteria exhibit a decrease in relative abundance as alpha diversity increases. This negative relationship suggests that these taxa might be more competitive than others and drive down diversity. Predicted metagenome. We used the KEGG (Kyoto Encyclopedia of Genes and Genomes) database  [49]  and the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) pipeline  [50]  to predict abundances of pathways across individuals (see S9 Fig).
16S rRNA sequence analysis We obtained a total of 12. 65 million high-quality reads, resulting in an average of 175,784 reads per sample (+/-72,822). Raw Illumina sequences were demultiplexed and filtered using Cutadapt 1. 7. 1  [69]  to remove adaptor sequences (Read 1:CTGTCTCTTATACACATCTCC-GAGCCCACGAGAC, Read 2:CTCTCTCTTATACACATCTGCCGCTGCCGACGA), chimeras, sequences containing ambiguous bases, and low quality reads (Phred quality scores < 20). Read pairs were resynced using RISS-UTIL and matching paired-end sequences were merged using FLASH  [70]. Merged sequences over 250 bp in length (the maximum length of the V5-V6 region) were removed. The remaining merged sequences were analyzed using the opensource software package QIIME 1. 7. 0 (Quantitative Insights Into Microbial Ecology)  [71].
We performed both open-and closed-reference Operational Taxonomic Unit (OTU) picking at 97% identity against the May 2013 Greengenes database  [72]  such that OTUs were assigned taxonomy based on 97% similarity to the reference sequence. Non-bacterial 16S rRNA sequences removed and those that did not align were clustered to each other prior to taxonomic assignment. The average percent of mapped reads per individual was 83% (SD = 7. 5%) and did not vary significantly between populations (Welch's t-test, p > 0. 2). All summaries of the taxonomic distributions ranging from phylum to species were generated from the non-rarefied OTU table generated from this analysis.
Diversity analyses To characterize diversity across individuals, rarefaction plots were generated for each sample using the phylogenetic distance metric for diversity  [73]. Samples were rarefied to 50,000 reads, the maximum depth permitted to retain all samples in the dataset. All diversity analyses were conducted on rarefied OTU tables containing 50,000 sequences per sample. Measurements are based on the mean values calculated from 100 iterations using a rarefaction of 10,000 sequences per sample (20% of the total 50,000). Alpha-diversity was calculated for each sample based on phylogenetic diversity, Shannon's index  [74]  and the Simpson index  [75]. Beta-diversity was assessed based on both unweighted and weighted UniFrac distance metrics  [43]  using the Greengenes phylogenetic tree  [72].
Multivariate analysis of composition data Intergroup differences in microbiome composition for subsistence, location, population, BMI, sex, ancestry, age, dietary factors, and parasitism were assessed by PERMANOVA  [76]  using non-rarefied OTU abundance data and implemented using the "adonis" function of the vegan package in R  [77]. To identify taxa significantly associated with each covariate of interest, we normalized the distribution of each OTU and used an ANOVA, FDR corrected for the number of OTUs in our dataset. For the ANOVA, OTUs with identical taxonomic identifiers were combined. In parallel, we also restricted the merging only to OTUs names defined at the family, genus or species level. Both results are reported in Supplementary Material ("merged OTUs" versus "partially merged OTUs").
Metagenomic predictions We used PICRUSt v1. 0. 0 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to generate taxonomy-based predicted metagenomes for each sample  [50]. Counts from the rarefied OTU Table  (50, 000  OTUs per sample) were normalized by the predicted 16S rRNA gene abundances and functional predictions of Kyoto Encyclopedia of Genes and Genomes (KEGG)  [49]  pathways were determined using pre-computed files for the May 2013 Greengenes database  [72]. Relative abundances of the functional predictions were calculated. We also compared the predicted metagenomes of individuals to determine which functions were enriched or depleted across covariates (subsistence, population, location, ancestry, BMI, age, sex, dietary components, and parasitism phenotypes) for abundant (! 0.
HTRF signal was collected in a PHERAstar microplate reader (BMG LABTECH). K i values were obtained from binding curves using GraphPad Prism software (GraphPad Software, Inc. , San Diego). The expression level (B max ) of GHS-R1a expressed in HEK293T cells was determined by radioactive assay using 125 I-His 9 ghrelin as described previously  (39). Inositol Phosphate Assay-Inositol phosphate accumulation assay was carried out 48 h after transfection on adherent cells on a 96-well plate at a density of 50,000 cells/well. IP1 production was measured using the IP-One HTRF kit (Cisbio Bioassays Ref. 621PAPEC) as described previously  (39). Briefly, cells were stimulated for 30 min at 37 °C with the ligand to be tested in 70 l of IP1 stimulation buffer. An anti-IP1 antibody labeled with Lumi4-Tb (15 l) and an IP1-d2 derivative (15 l) were added to the cells.
Bias factors of GHS-R1a ligands Data were analyzed using the operational model with ghrelin as the reference ligand as described under "Experimental Procedures. " Quantification of bias was only possible for ghrelin, MK-0677, and JMV 1843. Values are mean Ϯ S. E. of three independent experiments performed in triplicate. Statistical analysis was performed using a two-way unpaired Student's t test. NC means cannot be determined. **, p Ͻ 0. 01; *, p Ͻ 0. 05. BF ϭ 10 ⌬⌬log(/KA). sors. To confirm the results obtained with the BRET sensors, we then used a totally different assay. This assay consisted in measuring GTP␥S binding to purified G q and G 13 proteins in the presence of purified GHS-R1a embedded in lipid discs. As expected, the agonist MK-0677 promoted an increase of GTP␥S binding at both G q and G 13.
The efficacy of ligands to modulate GHS-R1a-promoted G q (A) and G 13 (B) activity was assessed by monitoring changes in the BODIPYா FL GTP␥S emission intensity. GTP␥S binding is expressed as raw values of fluorescence emission of BODIPYா FL GTP␥S. Data are from one representative of three independent experiments, and statistical significance between unliganded and liganded GHS-R1a was assessed using Student's t test (***, p Ͻ 0. 001; **, p Ͻ 0. 01). Kinetics of GTP␥S binding to G q (C) and G 13 (D) were carried out under the same conditions. GHS-R1a-catalyzed GTP␥S binding is expressed as the percentage of maximal MK-0677 stimulation. E, K act (min Ϫ1 ) values (mean Ϯ S. E. , n ϭ 2) were calculated from GTP␥S binding kinetics using GraphPad Prism software. molecules identified in this study that display a bias behavior similar to that of JMV 2959.
Pups were fed by breastfeeding. The PUFA intake of pups has not been assessed and thus represents another limitation of the study. Because of the small sample of control animals (n = 3 litters for the PUFA ω-3/O2, PUFA ω-6/O2, PUFA ω-3/air, and PUFA ω-6/air groups and n = 1 litter for the control/O2 and control/air groups), we have not be able to perform multiparametric statistic tests to study the variable "litter" and we analyzed data with one-way ANOVA and post hoc Bonferroni tests. The present study allows us to suggest that maternal feeding with PUFA ω-3 can prevent BPD injuries from developing in newborns with a high risk of prematurity. PUFA ω-3 supplementation has recently been studied in human clinics in the situation of severe preterm neonates with respiratory distress syndrome  [25].
Statistical Analysis Values were expressed as mean ± SEMs. The comparisons between groups were analyzed with an analysis of variance test followed by one-way or repeated ANOVA, and a Bonferroni test was performed for multiple comparison using software Graphpad Prism version 5 ® (Graphpad Software, Inc. , San Diego, CA, USA). Statistical tests were used to compare pups' growth and morphometric measures in each maternal supplementation group exposed to hyperoxia versus the ambient air. Differences with a p value of less than 0. 05 were considered statistically significant.
Results Inner ear cell type-specific gene clusters. To characterize the HC transcriptome in early post-natal auditory and vestibular systems, we used the Atoh1/nGFP transgenic mice expressing a green fluorescent protein (GFP) in all inner ear HCs  18  (Fig. 1a, b ). Auditory and vestibular epithelia from inner ears of postnatal day 1 (P1) mice were separated into HCs, epithelial non-HCs (ENHCs) and non-epithelial cells (NECs) by flow cytometry (Fig. 1c , Supplementary Fig. 1 ). Gene expression levels were recorded from the sorted cells using whole genome expression microarrays (Supplementary Data 1). Hierarchical clustering, applied to all genes detected as expressed, showed a clear division of the samples based on cell types, namely HC, ENHC and NEC (Fig. 1d ), demonstrating, as expected, that cell-type identity, rather than tissue of origin, is the major determinant of the cell transcriptome.
d. calculated over all genes assigned to each cluster). Expression levels of each gene were standardized (mean ¼ 0, s. d. ¼ 1) prior to clustering. This methodology enabled grouping of genes that share similar patterns, but not necessarily the same magnitude of expression. The entire set of clusters is shown in Supplementary Fig. 2. (f) Enriched GO categories with a P value o0. 01 (hypergeometric test) after false discovery rate correction for multiple testing. (g) Marker genes specific for cochlear HCs (top) and vestibular HCs (bottom) as detected in the expression microarray data set (left) and confirmed by the RNA-seq data set (right). Red represents enrichment, Green represents depletion. Data represent results from three biological replicates. (enrichment P value ¼ 1. 6E À 9; hypergeometric test), and statistically over-represented in the clusters of genes whose expression is elevated in HCs (29 genes, P value ¼ 5.
24), Wdpcp 25 , Tmie  26, 27  , Ush2a and Ush1c 28 , all of which have known roles in murine HC development. Consistent with the known role of RFX proteins as master regulators of ciliogenesis  29  , the 225 putative RFX target genes were enriched for cytoskeleton and cilium genes (Fig. 2b, c ). Overall, the expression of the putative RFX targets was significantly enriched in both the cochlear and the vestibular HCs, with the vestibular showing a greater level of elevated expression (Supplementary Fig. 7 ). We next analysed two recent expression data sets that also recorded gene expression in the inner-ear and defined sets of HC-enriched genes. One of these studies purified cochlear HCs using the Atoh1 A1GFP/A1GFP mouse  30  , and the other used Pou4f3-GFP mice to isolate auditory and vestibular HCs for expression analysis  31.
P=1. 27E-77 Figure  2  | RFX binding signature is enriched in promoters of HC-enriched genes. (a) De-novo motif analysis revealed that the promoters of genes whose expression is elevated in inner ear HCs are significantly enriched for a DNA motif that matches the binding signature of the RFX family of TFs (the known RFX motif is from TRANSFAC DB  70  ; accession number M00281). The enriched motif was detected in promoters of 225 HC-elevated genes (enrichment factor ¼ 2. 7; P value ¼ 1. 5E À 37). (b) GO functional categories that were over-represented in the set of 225 putative RFX HC target genes. (c) Expression pattern of selected putative RFX targets that function in ciliogenesis (n ¼ 3; error bars: ± s. d. ). (d) HC enrichment was defined per gene as the ratio between its expression in HCs and its average expression over the three probed cell types (HC, ENHC and NEC).
RNA extraction. For the samples processed by RNA-seq, TRIzol LS Reagent (Life Technologies) was added to the sorted cells (3 vol of reagent per 1 vol of sample) immediately following sorting after the completion of the post sort analysis. Total RNA was extracted from sorted cells using the Direct-zol RNA MiniPrep kit (Zymo Research). RNA integrity was evaluated with a RNA Pico Chip on a 2100 Bioanalyser (Agilent). For the samples processed by microarrays, RNA was extracted using the RNeasy Plus Micro Kit (Qiagen). All samples used for gene expression analysis had a RNA integrity score 48. 5. Gene expression profiling and analysis. Microarrays data analysis. Expression profiles were recorded from six cellular populations (three cell-types: HCs, ENHCs and NECs, isolated from two inner ear organs: the cochlea and the vestibular system).
These six conditions were probed in biologically independent triplicates using Illumina MouseRef-6 v2. 0 Expression BeadChips (Illumina, San Diego, CA, USA). These arrays contain 445,000 probes which collectively interrogate all RefSeq annotated mouse genes (426,000 genes) and B7,000 RefSeq predicted genes. Expression levels were calculated using Illumina's Bead-Studio package. Probes not readily detected in the data set were filtered out using detection P values assigned by Bead-Studio to each measurement and requiring that each probe be detected (P valueo0. 01) in at least two samples. This criterion left 23,051 probes, corresponding to 17,275 annotated and predicted genes for subsequent analyses. Arrays were then normalized using quantile normalization. Hierarchical clustering, using average-linkage method, was applied to assess overall similarity between the different conditions.
Differentially expressed genes in the data set were identified using two-way analysis of variance (ANOVA) analysis. 6,556 probes were significantly (false discovery rate o5%) associated with either factor (cell-type or organ) or showed significant interaction between these two factors (that is, different cell-type expression patterns between the two organs). Main expression patterns exhibited by the set of differentially expressed probes were delineated by cluster analysis using the CLICK algorithm implemented in the EXPANDER package  57. Prior to clustering, expression levels of each probe were standardized to mean ¼ 0 and s. d. ¼ 1. To define cell type-specific marker genes, we first examined the microarray data set for cutoff values that correspond with 'expression' compared with 'no-expression' of probes, and observed that that all probes flagged as not expressed (that is, detection P value40.
Adapters containing 6-nt indexes were ligated to the double-stranded cDNA. The DNA was purified between enzymatic reactions and the size selection of the library was performed with AMPure XP beads (Beckman Coulter Genomics). Libraries were pooled, and each received 0. 25 a lane of sequencing on an Illumina HiSeq 2500 with a paired-end 100 base configuration. Library construction and sequencing were performed at the Institute for Genome Sciences of the University of Maryland, School of Medicine (IGS). RNA-seq data analysis. Sequenced reads were aligned to the mouse and zebrafish genomes (mm9 and Zv9) using TopHat  58. Based on profiles of base-calls quality, the reads in the mouse data set were trimmed and only the first 50 nt were used for the alignment. Gene expression levels (read counts) were calculated using HTseq  59  based on ENSEMBL gene annotations  60.
The mouse and zebrafish data sets were then normalized using quantile normalization and then a floor value of 20 reads was set (to avoid inflation of fold-change estimates due to division by low counts). In the mouse and zebrafish data sets, 12,814 and 17,668 genes were detected as expressed, respectively. GO analysis. Enriched Gene Ontology (GO) categories in the clusters were detected by DAVID 61 using the set of all genes expressed in the data set as the background set. Analysis of cis-regulatory motifs. Over-represented sequence motifs in promoters were searched using AMADEUS  62. The set of promoters of all genes detected as expressed in each analysed data set was used as the background set. The promoter regions examined spanned from 1,000 nt upstream to 500 nt downstream the TSS. Mouse, human and zebrafish orthologous genes were mapped using ENSMART utility  60.
To generate the ChIPsequencing library, 10 ng of DNA were used as input for the ChIP-seq Sample Preparation Kit (Illumina). Sequencing was performed with the Genome Analyser II (Illumina) using the 36 Cycle Sequencing Kit v2. Sequences were derived from a single channel of the flow cell. Data were processed using the Illumina Pipeline Software package v1. 0. Immunohistochemistry on paraffin sections. Immunohistochemistry reactions were performed on 5-mm sections of paraffin-embedded inner ears from P1 wildtype ICR mice. Sections were deparaffinized with xylene and rehydrated with a decreasing ethanol gradient, followed by antigen retrieval with sodium citrate buffer (pH6). Sections were blocked with PBS supplemented with 0. 2% Tween-20, 10% normal goat serum and 1% bovine serum albumin for 30 min at room temperature before incubation with rabbit polyclonal anti-RFX3 (HPA035689, Sigma-Aldrich, 1:200) overnight at 4 °C.
Antibody labelling was performed with a biotin-conjugated anti-rabbit IgG (1:500, Life Technologies) and detection with Alexa Fluor 594-conjugated streptavidin (1:500, Life Technologies) for 1 h at room temperature. The nuclei were counterstained with 300 nM of DAPI (Life Technologies). Samples were then mounted in anti-fade medium (ProLong Gold antifade reagent, Life Technologies). Images were acquired with a Nikon Eclipse E800 coupled with a Qimaging Retiga EXi Fast1394 and analysed with the Volocity Image Analysis Software (Perkin Elmer). Immunohistochemistry on whole mounted samples. Sensory epithelia from cochlear and vestibular systems were dissected from mice at the ages specified in the figure legends. Tissues were permeabilized in PBS-0. 5% tween20, blocked in M. O. M. Mouse Ig Blocking Reagent (Vector Laboratories) for 1 h at room temperature and incubated with mouse monoclonal Anti-Acetylated Tubulin (Sigma) diluted in M.
(e) Two major clusters containing genes whose expression is elevated in HCs. Each cluster is represented by the mean pattern of its genes' expression levels (error bars: ±s. d. calculated over all genes assigned to each cluster). Expression levels of each gene were standardized (mean ¼ 0, s. d. ¼ 1) prior to clustering. This methodology enabled grouping of genes that share similar patterns, but not necessarily the same magnitude of expression. The entire set of clusters is shown in Supplementary Fig. 2. (f) Enriched GO categories with a P value o0. 01 (hypergeometric test) after false discovery rate correction for multiple testing. (g) Marker genes specific for cochlear HCs (top) and vestibular HCs (bottom) as detected in the expression microarray data set (left) and confirmed by the RNA-seq data set (right).
Acknowledgements We thank  Ferenc Livak  for his help with flow cytometry analyses and cell sorting, which were performed at the  University of Maryland Marlene and Stewart Greenebaum Cancer Center Flow Cytometry Shared Service; Ru-ching Hsia  for her assistance with SEM performed at the  University of Maryland Electron Microscopy Core Imaging Facility ,  Amiel Dror  for graphic illustrations; and  Jian Zuo  for the Gfi1-Cre mice. We thank  Zubair M. Ahmed ,  Tomoko Makashima  and  Michal Zalzman  for critically reviewing the manuscript and  Amal Isaiah  for technical assistance. This work was supported by the  Triologic Society Career Development  grant (R. H. ),  NIDCD/NIH   R01DC013817  and  R01DC03544 (R. H. ), DOD CDMRP MR130240  (R. H. ), the  Swiss National Science Foundation  grant  310030B-144085 ,  3100-06489  (W.
The computer program was designed to accept parameters and data obtained from in vivo studies in animals or human volunteers, such as the quantity and duration of a meal, the pH curves for the stomach and small intestine, the secretion rates into the different compartments and the gastric and small intestine emptying rates. The system is controlled by software named StoRM for Stomach regulation and monitoring  (Guillemin et al. 2010 ). To control the transit time of the chyme in each compartment, a power exponential equation for gastric and intestinal delivery is used f t = -( ) 2 1 2 t / / b where f represents the fraction of the chyme remaining in the stomach, t is the time of delivery, t ½ is the half time of delivery and β is the coefficient describing the shape of the curve, as described previously  (Elashoff et al.