dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the difference did not reach statistical significance (P = 0. 08; Mann-Whitney U test) (Dataset S5), this finding is consistent with the significantly lower butyrate levels in DR versus FF cecal samples. The representation of the mcSEED "bile acid transformation" subsystem was significantly lower in the transplanted DR microbiome (P < 0. 05; Mann-Whitney U test) (Dataset S5), suggesting a reduced capacity to deconjugate bile acids. Consistent with this observation, LC-QQQ-MS analysis of cecal contents revealed that the major conjugated bile acid present in the mouse gut, taurocholic acid, was present at statistically significantly higher concentrations in mice colonized with the DR microbiome (P < 0. 05, Mann-Whitney U test) (Fig. 5I  and Dataset S5). Notably, plasma levels of a major conjugated bile produced by pigs, glycohyodeoxycholic acid, were also significantly higher in DR pigs than in their FF counterparts (P < 0.
05, Mann-Whitney U test) (Dataset S2). In addition, the representation of pathways for branched-chain amino acid and tryptophan metabolism were significantly reduced in the DR compared to FF microbiomes (P < 0. 05, Mann-Whitney U test) (Dataset S5B), as were the levels of these amino acids (P < 0. 05, Mann-Whitney U test) (Fig. 5J ). As in DR pigs, mouse recipients of the DR pig microbiome exhibited significantly higher levels of serum triglycerides compared to mice harboring the FF pig microbiome (P < 0. 05, Mann-Whitney U test) (Fig. 5K  and Dataset S5). Targeted MS also disclosed that the DR donor microbiome recipients had significantly lower hepatic levels of C14:1-, C16:1-, and C18:1-acylCoAs, plus medium and long-chain acylcarnitines (P values varying from <0. 05 to <0. 005, Mann-Whitney U test) (Fig.
5  L and M and Dataset S5) as well as significantly higher levels of lactate in their gastrocnemius muscle (P < 0. 05, Mann-Whitney U test) (Fig. 5N  and Dataset S5). However, in this controlled feeding study, there were no statistically significant increases in alanine, glycine, and urea cycle intermediates in sera collected from fasted mice containing the DR compared to FF microbiomes (P > 0. 05, Mann-Whitney U test) (Dataset S5). Nevertheless, our findings support the hypothesis that the microbiome is causally related to selection of alternative energy substrates. In both the pig and mouse models, the DR microbiome was linked to reduced hepatic fatty acid metabolism (β-oxidation). In pigs, the reduction in fatty acid catabolism was linked to an apparent increase in amino acid utilization, whereas in mice, the alternate fuel appears to be glucose, as suggested by the significant increase in gastrocnemius muscle lactate levels.
Materials and Methods Experiments Involving Pigs. Husbandry. Protocols for breeding, feeding, housing, sampling, and killing animals belonging to the FF and DR groups were approved by the North Carolina State University Institutional Animal Care and Use Committee. All studies were performed under the supervision of a veterinarian. The diet treatments followed procedures described in ref. 21. Briefly, four 6-to-8-mo-old gilts (mixture of Landrace and Yorkshire genetic background) were divided into two groups. Two weeks before artificial insemination, MATRIX (Altrenogest; Intervet) was given to all animals (16 mg in their daily feed) to synchronize estrus. Gilts received semen from a single sire (Duroc genetic background). Gilts in the FF group also received 2. 5 kg/d of a gestation diet, supplemented with a micronutrient mixture and docosahexaenoic acid (DHA), from the time of estrus synchronization to the time of insemination, after which the amount of diet was reduced to 2 kg/d during farrowing (Dataset S1).
Pigs in the DR group received 40% less feed per day than the amount consumed by their FF counterparts (Dataset S1). The amount of food consumed by each pig was assessed by measuring feed disappearance each day. Piglets were weighed every week for the first three diet phases and every 2 wk during the last two diet periods (Dataset S2); the resulting dataset was analyzed by applying linear mixedeffects models built using R (v3. 5)  (45)  and the lmerTest package (v3. 1. 2)  (46). Biospecimen collection. Fecal samples were collected from pigs by swabbing the rectum with a cotton-tipped swab. Following sample collection, the tip of each swab was clipped and transferred to a sterile microfuge tube. Bulk fecal samples were collected into sterile cryocups on postnatal days 70 (fresh samples from a subset of animals) and 154 (samples from all animals).
7%) were subsequently removed prior to statistical tests of differences between treatment groups (outlier values are indicated with an asterisk in Dataset S2). Fecal bacterial V4-16S rDNA amplicon sequencing. Each fecal sample on a frozen swab tip (Dataset S2) was resuspended in 650 μL buffer A (200 mM NaCl, 200 mM Trizma base, 20 mM EDTA) by shaking in a Mini-Beadbeater-96 (BioSpec Products) for 20 s. A 500 μL aliquot of the resuspended sample was then transferred to a new tube containing 0. 1-mm diameter zirconia/silica beads, 500 μL of a phenol:chloroform:isoamyl alcohol mixture (25:24:1), and 210 μL 20% SDS. DNA was extracted by bead-beating, purified (Qiaquick columns; Qiagen), eluted into 100 μL Qiagen buffer EB (Qiagen), quantified (Quant-iT dsDNA broad range kit; Invitrogen) and the mixture was adjusted to 1 ng/μL.
Variable region 4 of the bacterial 16S rRNA gene was amplified by PCR using the following conditions: denaturation (94 °C for 2 min) followed by 26 cycles of 94 °C for 15 s, 50 °C for 30 s, and 68 °C for 30 s, followed by incubation at 68 °C for 2 min (4). Amplicons were quantified, pooled, and sequenced (Illumina MiSeq instrument; paired-end 250-nt reads). Amplicon sequence variants were generated from demultiplexed paired-end reads with DADA2 (50) and taxonomy was assigned based on the DADA2-formatted training dataset [GreenGenes Database Consortium, v13. 8  (34) ] in R (v3. 5)  (45). Shotgun sequencing of fecal community DNA. Fecal DNA was prepared and subjected to shotgun sequencing using methods described in ref. 5 , with minor modifications. Libraries were generated from DNAs isolated from fecal samples collected at the first and the last sampling point of each dietary phase (n = 181) (Dataset S2B) using the Nextera XT kit (Illumina) with the reaction volume reduced to 2.
5 μL (51). Libraries were then pooled and sequenced using an Illumina NovaSeq instrument (4. 14 × 10 7 ± 1. 01 × 10 7 paired-end 150-nt reads per sample (mean ± SD). Demultiplexed reads were quality filtered (Sickle; v1. 33) (52) and Nextera adapter sequences were trimmed (cutadapt; v1. 11)  (53). Reads assigned to the host genome (Ensemble Sus scrofa genome; Sscrofa11. 1) and the genomes of two dominant ingredients of the pig diets, corn (Ensemble Zea mays genome; B73_RefGen_v4) and soybean (Ensemble Glycine max genome; Glycine max v2. 1), were removed using Bowtie2 (54) yielding 3. 53 × 10 7 ± 1. 02 × 10 7 (mean ± SD) reads per sample (Dataset S2). Filtered reads were subsequently assembled into contigs (Megahit; v1. 1. 4) (55). The resulting contigs were annotated with Prokka (v1. 12)  (56).
Open reading frames identified by Prokka were clustered at 80% identity with MMSeq2 (-min-seq-id: 0. 8) (57). Genes were given CAZyme annotations using the CAZy database  (25). Additional functional information for each CAZyme-annotated gene was obtained by querying the National Center for Biotechnology Information nonredundant protein sequence database with DIAMOND (v0. 9. 19) (58) as well as by using a manually curated database developed by one of the coauthors (B. H. ). Genes were assigned to metabolic pathways by querying the mcSEED database using DIAMOND (blastp; e-value < 0. 001; percent identity >80%)  (59). The number of reads assigned to each gene was normalized by gene length in order to express gene abundance as read counts per million (CPM). To calculate the CPM value for each gene, we first divided the reads assigned to that gene by its length in kilobases, then divided the sum of all reads per kilobase (RPK) values for all genes in a sample by one million and, finally, divided the RPK value for a gene by the per million scaling factor.
EMMER. See SI Appendix, Supplementary Results for details of the EMMER algorithm. The Pandas (v0. 42. 2) (60) and NumPy (v1. 16. 4) (61) packages were employed for data processing and calculations of vNE. After selecting information-rich features, results were visualized with matplotlib (v3. 1. 0) (62). The Procrustes test (SciPy package; v1. 3. 0) (63) was applied to quantify the degree of similarity between PCA plots. Linear regression (sklearn package; v0. 21. 3) (64) was used to describe the relationship between the number of information-rich features and the dissimilarity score between PCA plots; the results were utilized to optimize the threshold selected for information-rich feature calling. PERMANOVA (scikit-bio package; v0. 5. 5) was used to test the statistical significance of differences in the projections of FF and DR pig microbial community samples on PCA plots.
26396-506), the tube was gently vortexed for 20 s. The resulting mixture was passed through a 100-μm-pore diameter nylon cell strainer (BD Falcon); the filtrate was subsequently mixed with an equal volume of PBS/0. 05% L-cysteine-HCl/30% glycerol, and aliquoted into 1. 8-mL glass vials (E-Z vials, Wheaton). Tubes were crimped with covers containing a PTFE/gray butyl liner (Wheaton) and stored at -80 °C. Just prior to colonization, the clarified fecal sample was thawed within the gnotobiotic isolator and a 200-μL aliquot was administered to each recipient mouse via an oral gavage needle (catalog no. 7901; Cadence Science). Animals were initially weaned onto an autoclaved sterile Teklad Global 18% Protein Rodent Diet (2018S; Envigo) and fed ad libitum. A finisher diet comparable to that given to pigs (Dataset S1) was sterilized by γ-irradiation (30 to 50 kGy); administration of this diet was initiated 2 d prior to gavage of pig microbiomes.
HMMER (v3. 1)  (65)  was used to determine how many of the 18, postnatal day 154, information-rich CAZymes could be detected in recipients of the representative DR and FF microbiomes. Each CAZyme gene, along with highly similar homologs identified by MMSeq2 in the pig shotgun sequencing dataset were aligned using Clustal Omega (v1. 2. 4)  (66). Each resulting alignment then served as the input for constructing an HMM profile specific for one of the 18 information-rich CAZyme genes. Mouse shotgun sequencing data were subsequently searched using HMMER and each of these profiles was used to establish how many of the postnatal day 154 information-rich CAZyme genes could be identified in the cecal and fecal microbiomes of DR and FF recipient animals. A CAZyme gene was deemed "present" if a homolog with a bit score greater than 50 was found.
Data Availability. Bacterial V4-16S rDNA amplicon sequencing and shotgun sequencing datasets in their raw format prior to postprocessing and data analyses have been deposited at the European Nucleotide Archive, https://www. ebi. ac. uk/ena/browser (accession no. PRJEB38446). The Python3 implementation of the EMMER algorithm can be accessed in GitHub (https://github. com/ HWChang/emmer/). All other study data are included in the article and supporting information.
ACKNOWLEDGMENTS. We thank  Maria Karlsson ,  Marty Meier ,  Sabrina Wagoner ,  Justin Serugo ,  Su Deng ,  Jessica Hoisington-López ,  Kaitlyn Walker ,  Kaylyn Goranson , and  Andrew Smith  for superb technical assistance and  Arjun Raman  and  Zachary Beller  for helpful discussions during development of EMMER (entropy-based method for microbial ecology research). This work was supported by  NIH  Grant  DK30292  (to J. I. G. ) and the  Bill & Melinda Gates Foundation  (X. L. ). H-W C. is the recipient of a  "Government Scholarship to Study Abroad Predoctoral Fellowship ,  Taiwan. "J. I. G. is the recipient of a  Thought Leader  award from  Agilent Technologies.
Compute the correcting factors R θ = θ s -θ 0 θ s -θ r (= 1 -S e,0 ) and R K = K s -K 0 K s (= 1 -K r (S e,0 )). 6. Compute the scaled air-entry water pressure head, |h * a | = | h a h g |. 7. Compute the square scaled sorptivity, S * 2 K (h * 0 , 0) = R K R θ (c p -2|h * a |) + 2R θ |h * a |. 8. Upscale to derive the square sorptivity, S 2 K (h 0 , 0) = S * 2 K (h * 0 , 0)(θ s -θ r )K s |h g |. As an illustrative example, let us consider the case of a loamy soil at water saturation with a slightly positive water pressure head at the surface (h 1 = 0) and an initial water pressure head of h 0 = -10 m (dry conditions). The loamy soil has the features of loam as defined in the database of  Carsel and Parrish (1988). Its WR and HC functions are described by the vGM model, with the following shape and scale parameters: θ r = 0.
Another improvement concerns the consideration of the residual water content θ r in the proposed scaling procedure. It would also be interesting to somehow derive the residual water content and not to assume it to be equal to zero as it might also alter the shape of the soil water retention function. The use of the scaled sorptivity and the proposed scaling procedure for these purposes is the subject of ongoing studies and will lead to implementations in many methods and tools for the characterization of single-or dual-permeability soils, including the BEST methods  (Fernández-Gálvez et al. , 2019; Lassabatere et al. , 2019)  and the SoilWater-ToolBox software developed for the characterization of homogeneous and heterogeneous soils  (Fernández-Gálvez et al. , 2021). Appendix A: Dimensionless hydraulic diffusivity functions, D * (S e ) In the appendices, for the sake of clarity, the notation of the shape parameters was simplified to λ, m, n, σ , in order to avoid heavy equations.
When Eq. (  B9 ) is combined with the capillary model, i. e. , Eq. (  37 ), the expression becomes (B13) The last term of A can be simplified easily: (B15) In this case, we need to assume that ml + m -2 > -1, i. e. m > 1 l+1 , to use the beta function. Such a condition corresponds to m > 2 3 , for the by-default value of l = 1 2. The two first integrals can then be replaced using the beta and the gamma functions, leading to the final expression for part A https://doi. org/10. 5194/hess-25-5083-2021 Hydrol. Earth Syst. Sci. , 25, 5083-5104, 2021 Code availability. Note all computations were done using Scilab free software. The scripts for the computation of Eqs. (  28 )-(  31 ) for the computation of WR and HC functions, Eqs. (  38 )-(  41 ) for the computation of the dimensionless diffusivity, and Eqs.
(  56 ) for the computation of the c p parameter can be downloaded online at https://doi. org/10. 5281/zenodo. 4587160 (Lassabatere, 2021). Note also that the computation of the proposed sorptivity was implemented in the SoilWater-ToolBox software that interrelates specific modules to derive the soil hydraulic parameters using a wide range of cost-effective methods, accessible online at https://github. com/manaakiwhenua/SoilWater_ToolBox/ (last access: 7 September 2021,  Pollacco and Fernández-Gálvez, 2021 ) (open source under the GP-3. 0 License). Data availability. No data sets were used in this article. Author contributions. LL established the question, performed the analytical developments, computed the numerical results, and provided the first draft of the manuscript. PEP performed the analytical developments and verified the numerical results.
DY, BL, DMF, SdP, and MR verified parts of the developments and numerical computations. JP and JFG helped with the use of the Kosugi model. RDS and MAN helped with the editing and the presentation of the results, in particular the writing of the application of the proposed approach for end users. All the authors contributed to the editing of the manuscript. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Financial support. This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-17-CE04-010). Review statement. This paper was edited by Nunzio Romano and reviewed by two anonymous referees.
For example, Xiong et al. [57]  found that the FI max of ultrasonicated samples decreased and their λ max shifted from 334. 8 nm to 338. 0 nm. Besides, fluorescence spectroscopy is a useful tool to determine protein hydrophobicity, as mentioned in Section 3. 2. Typically, two conventional spectra (fluorescence emission and excitation spectra) are sufficient for a basic single-fluorophoric model system. However, real food samples are complex with multiple fluorophores, therefore it is recommended with more advanced techniques, including excitation-emission matrix (EEM) fluorescence spectroscopy, synchronous fluorescence spectroscopy (SFS), and total synchronous fluorescence spectroscopy (TSFS)  [90, 91]. Their data analysis is performed by chemometrics methods such as principal component analysis (PCA), the partial least squares (PLS) regression algorithm, linear discriminant analysis (LDA), and others  [87].
smFISH validates mitral and tufted cell types To identify genes selectively expressed in OB projection neurons, we used the R-package glmGamPoi (Ahlmann-Eltze and Huber, 2020). We calculated the combined average raw expression of the top differentially expressed genes for each cell type and found it to be highly specific for each cluster (Figure  3A  and C ). We then selected a few specific marker genes (Figure  3B.
Given that a substantial fraction of isolated nuclei in our experiments was comprised of granule and periglomerular cells, in addition to projection neurons, we devised a novel computational approach to recapitulate the constituent cell types from bulk RNA sequencing data by simulating single nucleus expression profiles. Previous methods simulated the transcriptome of a single cell based on the overall distribution of gene expression levels in the bulk RNA sequencing data, producing many nuclei that were similar to each other and to the original bulk expression profile  (Konstantinides et al. , 2018). This worked well for clean bulk RNA-seq datasets with only one cell type, but for our mixed datasets, the simulated nuclei resembled an unrealistic average of the constituent cell types. Therefore, to capture the diversity contained within our bulk RNA-seq datasets, we used regulons as the unit of analysis to create simulated nuclei with more biologically realistic transcriptomes (Figure  6A  and B ).
We first compared simulated and snRNA-seq nuclei by using principal component analysis to project both into a shared low-dimensional space. We used these principal components as input to a UMAP projection to visually inspect relationships between simulated and snRNAseq nuclei (Figure  6B ). Consistent with histology and snRNA-seq analyses (Figures  1  and 2 ), simulations from both AON-injected and pPCx-injected bulk RNA-seq datasets contained cells types other than projection neurons, and this contamination was more pronounced in simulations from AON-injected bulk RNA-seq datasets. The dispersion of simulated nuclei throughout this space indicated that simulations successfully recapitulated the diversity of cell types in the bulk RNA-seq datasets, as each cell class from the combined snRNA-seq data had some simulated nuclei in its vicinity (Figure  6B ).
Single nucleus RNA sequencing analysis Raw sequencing datasets were processed using the Cell Ranger pipeline (10x Genomics). Count tables were loaded into R (version 3. 6, https://www. r-project. org) and further processed using the Seurat 3 R-package  (Butler et al. , 2018). For each of the three replicates, we removed all nuclei with fewer than 500 distinct genes detected or with more than 5% of unique molecular identifiers stemming from mitochondrial genes. After quality control, we merged the replicates and retained a total of 31,703 nuclei (median of 2,300 genes per nucleus; for each replicate median genes per nucleus: R1=2,266; R2=2419; R3=2,322). Principal component analysis (PCA) was then performed on significantly variable genes and the first 30 principal components were selected as input for clustering and UMAP, based on manual inspection of a principal component variance plot (PC elbow plot).
Clustering was performed using the default method (Louvain) from the Seurat package, with the resolution parameter of the FindClusters function set to 0. 3. Differential gene expression analysis on single-nuclei data was performed using the glmGamPoi R-package (Ahlmann-Eltze and Huber, 2020). Gene set enrichment analysis (GSEA) on the resulting log-fold changes was performed as described in  (Subramanian et al. , 2005).
Bulk RNA deep sequencing analysis The 'Trim Galore!' utility version 0. 4. 2 was used to remove sequencing adaptors and to quality trim individual reads with the q-parameter set to 20. Sequencing reads were then aligned to the mouse genome and transcriptome (Ensembl GRCm38 release-89) using RSEM version 1. 3. 0 (Li and Dewey, 2011)  in conjunction with the STAR aligner version 2. 5. 2  (Dobin et al. , 2013). Sequencing quality of individual samples was assessed using FASTQC version 0. 11. 5 and RNA-SeQC version 1. 1. 8 (DeLuca et al. , 2012). Differential gene expression was determined using the R-bioconductor package DESeq2 version 1. 24. 0  (Love et al. , 2014). Gene set enrichment analysis (GSEA) was conducted as described in  (Subramanian et al. , 2005).
Integration of single nucleus and bulk RNA deep sequencing data Nuclei were simulated from each bulk RNA-seq replicate using a weighted random sampling of regulons with replacement. A regulon's relative weight corresponded to the prevalence of its transcription factor in the given bulk RNA-seq sample. Each time a regulon was selected, the counts for its transcription factor and all target genes increased by one. The number of regulons expressed in each simulated nucleus was randomly selected from a list of how many unique transcription factors each snRNA-seq nucleus expressed (normalized expression > 2). Simulated nuclei were treated as raw count matrices and integrated with snRNA-seq nuclei using the SCT package in R (Hafemeister and Satija, 2019). To filter simulated nuclei, we trained 1000 linear discriminant analysis (LDA) classifiers with the python package scikit-learn  (Pedregosa et al.
, 2011). For each classifier, snRNA-seq nuclei were split into test and train datasets, with 75% of nuclei used for training and the other 25% used for test. Each classifier was trained to predict whether a nucleus was a projection neuron (whether it was selected for subclustering in the initial Seurat analysis) based on values for the 30 top principal components from the SCT integration. Each classifier was applied to the remaining snRNAseq nuclei for testing, and accuracy was evaluated using the Jaccard index calculated by scikit-learn  (Pedregosa et al. , 2011). The classifiers were then applied to the simulated nuclei. Simulated nuclei predicted to be projection neurons by all 1000 classifiers were designated as putative simulated projection neurons and selected for further analysis. Similarly, these putative simulated projection neurons were integrated with snRNA-seq mitral cells using SCT.
Regulons Atf1  , Creb1, Elf1, Gabpa, Hcfc1, Hlf, Max, Mxi1, Phf8, Pou2f1, Rcor1, Runx3, Smarca4, Srebf2 Bclaf1, Crem, Ctcf, Esrra, Esrrg E2f6, Fosb, Foxo3, Gata3, Maz, Myf5 Prrx1, Rax, Snai2, Sox10, Sox2, Tcf7l2 Etv3, Gabpb1, Gmeb2, Nr2c2, Rxrg, Sp1, Stat1 Atf4, Bmyc, Cebpb, Dbp, Dlx1, Dlx2, Dlx5, Egr1, Emx2, Erf, Fos, Hmgb3, Jun, Junb, Klf13, Klf5 Klf3, Nfe2l1, Nr2f2, Pbx3, Ppargc1a, Sox4, Taf7, Ubtf, Xbp1, Yy1  Unique targets  When simulating nuclei from bulk RNA-seq data, the probability of selecting a given regulon is determined by the abundance of its transcription factor in the bulk RNA-seq dataset. (A3) Nuclei are simulated from each bulk dataset through random sampling of regulons with replacement. This method maintains broad differences between datasets while accounting for heterogeneity within each dataset.