dataesr/scientific-paragraphs-categorization huggingface dataset:
Monosaccharide Composition of Pectin The monosaccharide composition was determined using a high-performance anionexchange chromatography-pulsed amperometric detector (HPAEC-PAD). WRP monosaccharide composition was analyzed by HPAEC with an ICS 3000 system (Dionex Corporation, Sunnyvale, CA, USA), equipped with pulsed amperometric detection (PAD) and an AS 50 autosampler. Twenty-five µL of samples was injected in the system and eluted into a pre-column CarboPac PA1-column (4 × 50 mm) and an analytical CarboPac PA1column (4 × 250 mm), equilibrated for 15 min with 18 mM NaOH. Data were collected and analysed using the Dionex Chromeleon 6. 80 software (Sunnyvale, CA, USA). L-rhamnose, L-arabinose, D-xylose, D-galactose and D-GalA were used as standards.
46r. The FC and FS were determined by measuring the ratio of foam height to total height immediately after foam formation and after 35 min, respectively. Regarding the kinetics modelling of foam stability, the empirical models (Equation (  2 )) were used to correlate experimental data of the foam ratio, which was expressed as the foam height to the total height ratio from t = 0 to t = 35 min. The half-time (t 1/2 ) that represents the stability of the foam was calculated as shown below (Equation (  3 )). The kinetics parameter, k 1 (foam volume per minute), and constant term, V r , were determined through non-linear regression using the curve fitting toolbox of MATLAB R2008a (Te Mathworks, Natick, MA, USA). V r = V r0k 1 t 0. 1 (2) t 1/2 = V r0 2 × k s 10 (3).
Based on these criteria, we classified four of the 20 strains as corresponding to MAGs positively associated with WLZ, including P. copri, and eight strains as corresponding to MAGs negatively associated with WLZ (Supplementary Table  1b-d ). As noted in the accompanying paper  1  , liquid chromatography-mass spectrometry (LC-MS) analysis of glycosidic linkages and polysaccharides in MDCF-2 and RUSF disclosed that cellulose, galactan, arabinan, xylan, and mannan represent the principal non-starch polysaccharides in MDCF-2. Gene set enrichment analysis (GSEA) of fecal microbial RNA-Seq datasets generated from children in the MDCF-2 and RUSF arms of the clinical trial disclosed that MDCF-2 produced a meta-transcriptome that was enriched for components of metabolic pathways involved in the utilization of arabinose, aarabinooligosaccharides (aAOS), and fucose.
2a, b ). Marker gene analysis allowed us to further subdivide the enterocytic lineage into three clusters: 'villus-base', 'midvillus' and 'villus-tip'. Pseudobulk snRNA-seq analysis, which aggregates transcripts for each cell cluster and then uses edgeR to identify differentially expressed genes in each cluster  18, 19  , disclosed that a majority of all statistically significant differentially expressed genes (3,651 of 5,765; 63. 3%) were assigned to the three enterocyte clusters (Extended Data Fig. 2c ; Supplementary Table  10b ). We initially used NicheNet 20 to evaluate the effects of the P. copri-containing community on intercellular communications. NicheNet integrates information on signaling and gene regulation from publicly available databases to build a "prior model of ligand-target regulatory potential" and then predicts potential communications between user-defined "sender" and "receiver" cell clusters  20.
After incorporating snRNA-Seq-based expression data from both sender and receiver cells, NicheNet computes a list of potential ligand-receptor interactions between senders and receivers. The ligand-receptor interactions in the resulting list are then ranked based on their effect on expression of downstream genes in their signaling pathway (i. e. , more downstream genes are expressed in a 'high-ranking' interaction). After this ranking step, an additional filter is applied, with ligand-receptor interactions having firm experimental validation in the literature designated as "bona fide" interactions. Finally, NicheNet uses information generated by Seurat 21 from a snRNA-Seq dataset to identify altered "bona fide" ligandreceptor interactions. We designated six of the epithelial cell clusters (crypt stem cells, proliferating transit amplifying (TA)/stem cells, villus base, mid-villus, and villus tip enterocytes and goblet cells) as "receiver cells" while all clusters (both epithelial and mesenchymal) were designated "sender cells".
We subsequently applied the Compass algorithm  23  to our snRNA-Seq datasets to generate in silico predictions of the effects of the consortia containing and lacking P. copri on the metabolic states of (i) stem cell and proliferating TA cell clusters positioned in crypts of Lieberkühn, (ii) the three villusassociated enterocyte clusters, and (iii) the goblet cell cluster. Compass combines snRNA-seq data with the Recon2 database  24. This database describes 7,440 metabolic reactions grouped into 99 Recon2 subsystems  24  , plus information about reaction stoichiometry, reaction reversibility, and associated enzyme(s). Using snRNA-seq data, Compass computes a score for each metabolic reaction. If the metabolic reaction is reversible, then one score is calculated for the "forward" reaction and another score is calculated for the "reverse" reaction  24.
As in the previous experiment, animals in the arm containing P. copri exhibited significantly greater weight gain between P23 and P53 than those in the no P. copri arm [P<0. 05; linear mixed-effects model (see Methods)] (Extended Data Fig. 5b ). Mass spectrometric analysis of host metabolism -We used targeted mass spectrometry to quantify levels of 20 amino acids, 19 biogenic amines, and 66 acylcarnitines in the jejunum, colon, gastrocnemius, quadriceps, heart muscle, and liver of the two groups of mice. Additionally, we quantified the 66 acylcarnitines in their plasma. The results are described in Supplementary Table  12  and Extended Data Fig. 5c-e. Consistent with the previous experiment, citrulline, the biomarker for metabolically active enterocyte biomass, was significantly elevated in the jejunums of mice belonging to the w/ P.
The absolute abundances of community members were quantified in cecal contents harvested at the time of euthanasia on P53. While the absolute abundance of P. copri Bg131 was not significantly different between the two diet groups (P>0. 05; Mann-Whitney), there were statistically significant differences between the abundances of 11 of the 19 community members (Extended Data Fig. 6b, c ). Nonetheless, we proceeded to test whether the increased weight gain phenotype associated with the presence of P. copri in the community was evident in the Mirpur-18 diet context. To do so, we repeated the dam-to-pup microbial transmission experiment where all animals were weaned onto the Mirpur-18 diet but where one group had received P. copri Bg131 (n=8 animals) and the other had not (n=9). All animals were euthanized on P53.
0 and Sequencing Primer v4 (Pacific Biosystems)]. The resulting reads were demultiplexed and Q20 circular consensus sequencing (CCS) reads were generated (Cromwell workflow configured in SMRT Link software). Genomes were assembled using Flye 36 (v2. 8. 1) with HiFi-error set to 0. 003, min-overlap set at 2000, and other options set to default. Genome quality was evaluated using CheckM 37 (v1. 1. 3) (Supplementary Table  1a, 4,  and 13a ). We applied Prokka  38 (v1. 14)  to identify potential open reading frames (ORF) in each assembled genome. Additional functional annotation of these ORFs using a 'subsystems' approach adapted from the SEED genome annotation platform 39 was performed as described in the accompanying paper 1. We assigned functions to 9,820 ORFs in 20 isolate genomes using a collection of mcSEED metabolic subsystems that capture the core metabolism of 98 nutrients/metabolites in four major categories (amino acids, vitamins, carbohydrates, and fermentation products) projected over 2,856 annotated human gut bacterial genomes  [40] [41] [42].
1. 3) to extract and align the amino acid sequences of 43 single copy marker genes in each isolate or each of the two MAGs, plus an isolate genome sequence of Bacteroides thetaiotaomicron VPI-5482 (PATRIC accession number: 226186. 12). Concatenated marker gene sequences were analyzed using fasttree  43 (v2. 1. 10)  to construct a phylogenetic tree using the Jones-Taylor-Thornton model and CAT evolution rate approximation, followed by tree rescaling using the Gamma20 optimization. The tree was subsequently processed in R using ape  44 (v5. 6-2)  to root the tree with the B. thetaiotaomicron genome and extract phylogenetic distances between genomes, followed by ggtree  45  (v3. 2. 1) for tree plotting. The similarity between the genomes of these strains and MAGs was quantified by calculating the ANI score with pyani 46 (ANIm implementation of ANI, v0.
2d-f , pseudocounts for each transcript in a given cecal sample were first normalized by the absolute abundance of the corresponding strain (Supplementary Table  3d ) in order to minimize the confounding effects of differences in strain abundance  57. The rlog transformation in the R DESeq2 package (v1. 34. 0) was then applied to this dataset for variance stabilization  58. To obtain relative expression of metabolic pathways, only transcripts corresponding to complete mcSEED metabolic pathways (i. e. , pathways with binary phenotype scores of 1; see above) were retained and transformed expression values were averaged across genes within a given pathway in each strain. The resulting aggregated pathway expression dataset was then centered prior to singular value decomposition with NumPy 59 (v1.
21. 5). Principal component analysis of samples was calculated by projecting the centered pathway dataset onto right singular vectors. Sample projections along the first two principal components were converted to a Euclidian distance matrix, and PERMANOVA was used to test for the significance of separation of samples by experimental group  60. Pathway PCA loadings were calculated by projecting the transpose of the centered dataset onto left singular vectors. Metabolic pathways were subsequently ranked within each strain by their corresponding loadings, and directionality was resolved such that more negative left singular vector 1 projections corresponded to higher pathway expression in the P. copri colonization context (Fig. 2f ). The PC1 contributions for each pathway were represented by a dot plot, while the box plots were generated using the boxplot function of the seaborn 61 (v0.
12. 1) with default settings such that the box represents the median and quartiles and the whiskers represent the range excluding outliers falling more than 1. 5x the interquartile range from the box edges. For differential expression analysis of microbial transcripts described in Fig. 2g , the filtered version of kallisto pseudocounts was imported into edgeR  18. For each community member, 'within-taxon sum-scaling' was applied by calculating the trimmed mean of M-value library size corrections based on the total pool of RNA reads from that member. This organism-scaled transcript set was then used for dispersion estimation and fitting of a generalized linear model (GLM). In addition to 'experiment arm', we included the absolute abundance of the organism in each cecal sample as a covariate in the GLM to reduce false discoveries due to differences in the abundances of community members.
A likelihood ratio test (edgeR  18  ) was then used to detect differential expression between samples obtained from members of the w/ P. copri versus w/o P. copri treatment groups. Transcripts with statistically significant differences in their expression were identified [q-value < 0. 05 (adjusted P value < 0. 05)] after multiple hypothesis correction was applied to the entire set of transcripts from a given organism via the Benjamini-Hochberg method. To analyze PUL expression discussed in the experiment described in Fig. 4e  involving P. copri strains BgD5_2 and BgF5_2, kallisto TPMs were generated by mapping reads to the entire collection of bacteria present in the cecums of P. copri-colonized animals. Due to the high fraction of genes with 100% nucleotide identity between P. copri BgD5_2 and BgF5_2, the kallisto index was generated to represent the set of unique genes between the two isolates by including the entire P.
Histomorphometric analysis of villus height and crypt depth Jejunal and ileal segments were fixed in formalin, embedded vertically in paraffin, 5 µm-thick sections were prepared and the sections were stained with hematoxylin and eosin. Slides were scanned (NanoZoomer instrument, Hamamatsu). For each animal, 10 well-oriented crypt-villus units were selected from each intestinal segment for measurement of villus height and crypt depth using QuPath 63 (v0. 3. 2). Measurements were performed with the investigator blinded with respect to colonization group. A twotailed Mann-Whitney U test was applied to the resulting datasets.
4U/µL rNase inhibitor (Sigma) and 0. 4U/µL Superase inhibitor (ThermoFisher)]. Approximately 10,000 nuclei per sample were subjected to gel bead-in-emulsion (GEM) generation, reverse transcription and construction of libraries for sequencing according to the protocol provided in the 3' gene expression v3. 1 kit manual (10X Genomics). Libraries were balanced, pooled and sequenced [Illumina NovaSeq S4; 3. 23×10 8 ± 1. 39×10 7 paired-end 150 nt reads/nucleus (mean±SD) from jejunal samples]. Read alignment, feature-barcode matrices and quality controls were processed by using the 10X Genomics CellRanger 65 5. 0 pipeline with the flag '-include-introns' to ensure that reads would be allowed to map to intronic regions of the mouse reference genome (GRCm38/mm10). Nuclei with over 2. 5% reads from mitochondria-encoded genes or ribosomal protein genes were filtered out.
Analysis of snRNA-seq datasets--Sample integration, count normalization, cell clustering and marker gene identification was performed using Seurat 4. 0  21. Briefly, filtered feature-barcode matrices outputted from CellRanger were imported as a Seurat object using CreateSeuratObject (min. cells = 5, min. features = 200). Each sample was normalized using SCTransform 66,67 and integrated using SelectIntegrationFeatures, PrepSCTIntegration, FindIntegrationAnchors, and IntegrateData from the Seurat software package. The integrated dataset, incorporating nuclei from all samples, was subjected to unsupervised clustering using FindNeighbors (dimensions = 1:30) and FindClusters (resolution = 1) from the Seurat package, which executes a shared nearest-neighbor graph clustering algorithm to identify putative cell clusters.
Cell type assignment was performed manually based on expression of reported markers  33, 68, 69. Cross-condition differential gene expression analysis was performed based on a "pseudobulk" strategy: for each cell cluster, gene counts were aggregated to obtain sample-level counts; each pseudobulked sample served as an input for edgeR-based differential gene expression analysis  18, 19. For NicheNet-based analysis 20 (v1. 1. 0), all clusters in our snRNA-seq dataset were used as senders for crypt stem cells, proliferating TA/stem cells, villus base enterocytes, mid-villus enterocytes and villus tip enterocytes, plus goblet cells. We used the nichenet_seuratobj_aggregate (assay_oi = "RNA") function with its default settings to incorporate differential gene expression information from Seurat into our NicheNet analysis and to select bona fide ligand-receptor interactions.
𝑠 %&&' = H I𝑛 ( -1J𝑠 ( " -(𝑛 ) -1)𝑠 ) " 𝑛 ( + 𝑛 ) -2 𝑑 = 𝑎 ( -𝑎 ) 𝑠 %&&' If both 𝑎 ( and 𝑎 ) are non-negative numbers, a positive Cohen's 𝑑 indicates that the mean of group 𝑗 is greater than that of group 𝑘, whereas a negative Cohen's 𝑑 means the mean of group 𝑗 is smaller in that comparison. The magnitude of Cohen's 𝑑 represents the effect size and is correlated with the difference between the means of the two groups. Because the mean of the net subsystem scores as well as the net reaction scores could be negative, we made the following adjustments to Cohen's d in order to preserve the concordance of sign and the order of group means. The adjusted Cohen's d represents the metabolic flux difference 𝑚, and is defined as: ) is a Bayesian probabilistic model for detecting 'statistically credible differences' in the proportional representation of cell clusters, identified from snRNA-seq datasets, between different treatment conditions  26.
This method accounts for two main challenges when analyzing snRNA-seq data: (i) low sample number and (ii) the compositionality of the dataset (an increase in the proportional representation of a specific cell cluster will inevitably lead to decreases in the proportional representation of all other cell clusters). Therefore, applying univariate statistical tests, such as a t-test, without accounting for this inherent negative correlation bias will result in reported false positives. scCODA uses a Bayesian generalized linear multivariate regression model to describe the 'effect' of treatment groups on the proportional representation of each cell cluster; Hamiltonian Monte Carlo sampling is employed to calculate the posterior inclusion probability of including the effect of treatment in the model.
Acknowledgments: We thank  David O'Donnell  and  Maria Karlsson  for their invaluable assistance with mouse husbandry,  Martin Meier  for his essential role in generating shotgun sequencing and microbial RNA-Seq datasets,  Janaki Guruge  for her help with culturing and maintaining bacterial strains, plus  Su Deng  and  Justin Serugo  for oversight of biospecimen archives. This work was supported by grants from the  NIH  ( DK30292 ). the  Centene-Washington University Personalized Medicine Initiative  and the  Bill & Melinda Gates Foundation. Y. W. is the recipient of a career development award from the  NIH  ( K01 DK134840 ).
Results were expressed in comparison to control animals. Statistical analysis. Data are expressed as a mean ± standard error of mean (SEM). Shapiro-Wilk normality test was performed on each data set, followed by ANOVA, in case of normal distribution, or nonparametric Kruskal-Wallis test otherwise, followed by Tukey's or Dunn's correction respectively, for multiple comparisons using Graph-Pad Prism version 8. 4. 2 software. Corrected adjusted P-values for multiple comparisons <0. 05 were considered statistically significant.
The 50 municipalities investigated in the study were implemented in the model as random effects. The GLMM (Equation  2 ) was generated from 850 observations, which correspond to the time series from 2003 to 2019, for the 50 municipalities, with the following structure: 𝑌 𝑖 |𝑎 𝑖 ~𝑁(𝜇 𝑖 ,𝜎 2 ) 𝐸[𝑌 𝑖 |𝑎 𝑖 ] = 𝜇 𝑖 μ𝑖 = 𝛽 0 + 𝛽 1. 𝑇𝑦𝑝𝑒_𝑟𝑖𝑣𝑒𝑟 𝑖 + 𝛽 2. Year 𝑖 + 𝑎 𝑖 𝑎 𝑖 ~𝑁(0,𝜎 2 𝑀𝑢𝑛 ) (2) The annual parasitic incidence of malaria (𝑌 𝑖 ) in the municipality i = 1,. , 50 is explained by the fixed effect 𝛽 1. 𝑇y𝑝e_𝑟iver added to the year of occurrence 𝛽 2. Year, and by the random effect 𝑎 𝑖 , which is added to the value of the fixed intercept of the model, 𝛽 0 to form the intercept of the municipality i. The analyses were performed in the R software (version 4. 1. 0) in the RStudio development environment (version 1.
Results The GLMM (Equation  2 ), developed in the R software and reproduced in Equation 3, comprises 850 observations, which correspond to the time series of the 50 municipalities investigated. lmer(𝑙𝑛(API) ~ 𝑇ype_𝑅𝑖ver + 𝑏𝑠(Year) + (1|𝑀𝑢𝑛), 𝑑𝑎𝑡e = 𝐵𝐷)) Where ln(API) is the logarithm of the response variable annual parasitic incidence of malaria; Type_river is the color of the waters of the rivers that cross the state of Amazonas, this being a categorical variable, and which are classified as white, black and mixed; bs(year) indicates the smoothing of the variable year, which expresses the year of occurrence of the case of malaria and Mun corresponds to the 50 municipalities that were incorporated in the regression model as a random effect. Table  1  shows the results of the estimation of the regression coefficient (𝛽), confidence interval (CI) and p-value (with accepted significance up to 0.
Acknowledge The authors would like to acknowledge  Fundação de Vigilância em Saúde do Amazonas (FVS)  for providing access to the malaria data. We also appreciate the support of the  Instituto Leônidas e Maria Deane) (ILMD -Fiocruz Amazônia) , the  Universidade Federal do Amazonas (UFAM) , the  Institut de Recherche pour le Développement (IRD/França)  and the  Pós-Graduação em Clima e Ambiente of  the  Instituto Nacional de Pesquisas da Amazônia and Universidade do Estado do Amazonas (PPGCLIAMB (INPA/UEA)).
Funding The authors declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Agence National de la Recherche (ANR) project ANR-21-CE14-0040-01. SL was supported by the Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3. 0 unported license.
Autolytic activity The autolytic activity of bacteria is of great importance in cheese ripening because it leads to the release of intracellular proteolytic enzymes, particularly peptidases. These enzymes hydrolyse peptides in cheese and produce amino acids, which are in turn responsible for the emergence of flavouring elements  [35]. The autolytic activities of the collected LAB isolates were found to be different depending on the isolate and were evaluated as poor, fair, or good. The data in Table  2  show that the number of good Lactobacillus isolates was 8 (24. 3%), fair autolysis isolates was 11 (33. 3%) and poor autolysis isolates was 14 (42. 4%). These results agree with those obtained by Ayad et al. [18]. Meng et al. [36]  found that 56% of lactobacilli isolates were rated good autolysis, 32% were rated fair autolysis and the remainder were rated poor autolysis.
E The organoid survival has been monitored by measuring the percentage of viable organoids according to the time culture. F The organoid growth has been studied by measuring the organoid surface according to the time culture. G Oxidative stress has been monitored into the epithelial cells from the colonic based crypt of mice perinatally exposed or not to TiO 2. H The quantity of cytosine methylated of DNA from the epithelial cells from the colonic based crypt of mice perinatally exposed or not to TiO 2. Data are expressed as mean ± SEM and were analysed by Student's t-test. *p < 0. 05; **p < 0. 01; ***p < 0. 001 and ****p < 0. 0001 vs control group. At least n = 8 per group cells at days 30 and 50 after birth. However, perinatal exposure to TiO 2 increased DNA methylation, as evidenced by elevated 5-Me-dC in colonic epithelial cells at day 50 but not at day 30 after birth (Fig.
5 µg/ml, Hepes 1 mM, NAC 0. 5 mM, Y27632, noggin 100 ng/ml, EGF 50 ng/ml, Wnt3a medium ½, DMEM/F12 (final volume 1 mL)]  [61]. The crypts of each colon were plated in fifteen wells µ-Slide Angiogenesis Ibidi ® plate. Organoid stem cell survival (number of organoids formed), and growth capacity (organoid area (µm 2 )) were followed three, six and nine days after plating with a wide field transmission microscope (Apotome Zeiss, 10X lens). The organoid size was monitored by measuring its area using the ImageJ ® software, while survival of crypts was studied by counting the number of organoids formed in each well.