dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and participants This study was based on the CHRIS study, a single-center, population-based study designed to investigate the molecular, behavioral and environmental determinants of human health, whose baseline assessment was carried out between 2011 and 2018  [9, 10]. Blood was collected from CHRIS study participants following overnight fasting. After immediate pre-analytical sample processing, sample storage at -80°C was performed as described in  [9, 10]. Health-related information was collected through either self-or interviewer-administered interviews based on standardized electronic questionnaires. Participants were requested to bring any boxes of medications taken in the preceding week to the study center. Drug information was retrieved via scanning the drug box barcodes, automatically classified according to the Farmadati database (https://www.
farmadati. it/), and stored in the CHRIS database. At the time when the present study was set up, the CHRIS study included N=6075 participants. Budget limitations allowed to measure the RAAS biomarkers on a random sample of 800 samples. Taking into account that the smallest treatment group included 100 individuals, we sampled 8 age-and sex-matched groups based on the AHD treatment: 1) normotensive; 2) untreated hypertensive; 3) participants taking other drugs not prescribed as AHD (referred to as non-AHD); 4) participants on ACEi monotherapy; 5) participants on ACEi combined with diuretics (ACEi+diuretics); 6) participants on ARB monotherapy; 7) participants on ARB in combination with diuretics (ARB+diuretics); and 8) participants on beta blocker monotherapy treatment (Beta blockers).
The penalty level was tuned by selecting a penalty parameter ߣ using k-fold cross-validation (CV), with the aim to minimize the mean squared error (MSE). We set k=8 and the smallest MSE was observed at ߣ=0. 03, 0. 02 and 0. 01, for angiotensin I, angiotensin II and aldosterone, respectively (Supplementary Figure  3 ). The statistical significance level was set at 0. 05 in all analyses. All analyses were performed with the R software v4. 0. 5 (https://www. r-project. org), using the packages stats v4. 2. 0 and cluster v2. 1. 2 (https://CRAN. R-project. org/package=cluster) [23]  for cluster analysis and glmnet v4. 1. 3 and glmmLasso v1. 5. 1 (https://CRAN. R-project. org/package=glmmLasso) [24]  for penalized regression analysis.
To better understand the origin and functions of PCs in BOS, we investigated the cells from cluster 1, identified as potential progenitors of PCs in the BOS lung graft. Cluster-1 B cells expressed a number of transcription factors: Zbtb32, described in memory B cells, but absent in PCs; Zbtb20, expressed in B-1 cells and peaking in PCs  (22) ; and Bhlhe41, recently unraveled as a marker of the B-1 population, which regulates B-1 cell differentiation, homeostasis, and function  (23). On the basis of their first 50 marker genes, cluster-1 B cells were defined as an innate B-1 cell population, according to the Immunological Genome Project (ImmGen) database  (24). In addition, cluster 1 was characterized by the expression of the surface markers Cxcr3 and Itgb1, involved in lymphocyte homing and adhesion, respectively, and without any previously reported functional role in B-1 cells (Figure  6A ).
IgA, mostly produced around the bronchial epithelium in steady state, is considered protective, because it is the first line of defense against airborne pathogens  (47). IgA, therefore, is unlikely to contribute to rejection after LTx, although in some rare cases, IgA has been found capable of contributing to autoimmunity, as in the case of IgA nephropathy  (48). We sorted CXCR3 + ITGB1 + PC progenitors from mouse lung grafts and demonstrated that this population specifically contributed to the increased production of IgG 2c. The IgG 2c is mainly generated in response to carbohydrates and proteins  (39)  and provides protection against bacteria and viruses (49, 50), but its contribution to chronic rejection was hitherto unknown. Using the ImmGen database (  24 ), we determined that PC progenitors identified in rejected mouse lung grafts display a B-1 phenotype.
scRNA-Seq analysis of murine lung grafts Preparation of murine lung grafts as single-cell suspensions. Single-cell suspensions of murine lung grafts were used for the scRNA-Seq analysis, flow cytometry, and FACS. The graft lung tissue was digested and separated into single-cell suspensions using the Lung Dissociation Kit, Mouse (Miltenyi), according to the manufacturer's instructions. scRNA-Seq. Single cells were captured using the 10x genomics chromium platform. A total of 10,000 cells were loaded and single-cell libraries were generated using the 10x genomics 3′-end gene expression kit. Libraries were sequenced to a depth of 650 million to 1 billion reads, resulting in 94. 2% to 97. 9% sequencing saturation. scRNA-Seq analysis. Sequencing data were processed using the Cell Ranger pipeline from 10x Genomics with the mm10 genome assembly to generate unique molecular identifier (UMI) count matrices for each sample.
The Seurat package was used to perform normalization, clustering, and to generate UMAP projections  (67). Cell barcodes containing fewer than 200 UMIs and genes detected in fewer than 3 cells were excluded. The R package SoupX was used to remove free RNA contamination (68), using Hbb-bs, Hba-a1, Hba-a2, Hbb-bt, H2-Ab1, H2-Aa, H2-Ab1, and H2-Eb1 as markers of erythrocytes; Scgb1a1 as a club cell marker; Cd74, Ighg2c, Igkc, Ighm, and Ighg3 as markers of B cells; Sftpc as a marker of type II cells; Apoe, Lyz2, and Psap as markers of macrophages; and Ly6c1 and Ly6a as markers of other lung structural cell populations. Principal component analysis was performed on z score transformed data using 30 dimensions for the full data set and 20 dimensions for the structural lung cells and B cell subsets.
R E S O U R C E A N D T E C H N I C A L A D V A N C E JCI Insight 2022;7(17):e156648 https://doi. org/10. 1172/jci. insight. 156648 using resolutions of 0. 1, 0. 3, and 0. 1 for the full data set, structural cells, and B cell subsets, respectively. The R package clustifyr was used to aid in assigning cell types to clusters  (69). The Tabula Muris droplet mouse scRNA-Seq data set was used to define a reference data set for calling cell types  (12). Average log-normalized expression values were calculated for cell types defined in the cell_ontology_class category. Genes differentially expressed in each cluster compared with other clusters in each tested comparison were determined with a Wilcox rank-sum test from the Presto R package  (70). Heatmaps were generated using ComplexHeatmap or Seurat  (71).
Pseudotime values were calculated using the CytoTrace algorithm, using the UMI count matrix as input  (21). RNA velocity vectors were computed and plotted using the velocyto Python package  (72). Data and code availability. Sequencing data, UMI count matrices, and cell-level metadata are deposited in the NCBI's Gene Expression Omnibus database (accession # GSE166386). Analysis scripts and an interactive University of California, Santa Cruz, cell browser instance are provided at a GitHub repository (https://github. com/rnabioco/LTx-rejection-map; commit ID 02944e1).
Statistical analysis Normally distributed data are reported as mean (± standard deviation), non-normally distributed data as median (interquartile range), and categorical variables as frequencies and percentages. All analyses were performed applying an intention to treat approach. Differences between baseline characteristics of patients with and without diabetes were assessed using Student's t-test, Wilcoxon rank sum test, or χ 2 test. Changes in eGFR over time were examined by repeated-measures mixed-effect models. All available data at randomization, at 4, 16, 32, and 48, and every 24 weeks after randomization, up to week 192 were included, with no imputation for missing data. The model was adjusted for randomized treatment, time, and the interaction between randomized treatment and time as fixed effects, with patient-level random intercepts and slopes with respect to time since randomization.
01. Gene ontology enrichment analysis was done as described here  [60]  and simplified using GO slim terms. rMATS v4. 02 (Multivariate Analysis of Transcript Splicing (MATS): http://rnaseqmats. sourceforge. net/; accessed on 24 June 2021) was used for differential splicing. The arguments used were: -nthread 4 -read-length 120-t paired -libType fr-firststrand. The gene annotation GTF file described here  [46]  was used as a reference annotation. Differentially spliced events were defined with FDR < 0. 001 and |IncLevelDifference| > 0. The UpSetR and eulerr R packages were used to compare the sets of DEGs or DAS. The PheatMap package was used for hierarchical clustering, and clusters were extracted using the cutree function. The log2 fold change (log2FC) values of the DEGs and IncLevelDifference of differentially retained introns were used to cluster DEGs and DAS.
To determine the role of RS33 in regulating pre-mRNA splicing, we analyzed the genome-wide RNA deep sequencing data to identify altered splicing events in the rs33 mutant compared with the WT. We identified ~600 differential splicing events between the WT and rs33 and found a high frequency of intron retention (IR) events, followed by alternative 3 splice sites (A3 S), alternative 5 splice sites (A5 S), and exon skipping (SE) events (Figure  1c ). nt deletion and a 1-nt substitution (Figure  1a )  [24]. The rs33 mutant did not exhibit obvious growth or developmental defects compared to the wild type (WT), except f subtle reduction in seedling growth (Figure  S1 ). To identify RS33-regulated genes, we performed a deep RNA sequencing analys seven-day-old WT and rs33 seedlings. A differential gene expression analysis between WT and rs33 plants revealed a reduced expression of ~150 genes, while ~100 genes w upregulated (Figure  1b ).
To determine the role of RS33 in regulating pre-mRNA splic we analyzed the genome-wide RNA deep sequencing data to identify altered splic events in the rs33 mutant compared with the WT. We identified ~600 differential spli events between the WT and rs33 and found a high frequency of intron retention events, followed by alternative 3′ splice sites (A3′S), alternative 5′ splice sites (A5′S), exon skipping (SE) events (Figure  1c ). To further validate these differential splicing events, we analyzed the IR events set of genes whose splicing is regulated by RS33 using reverse transcription PCR ( To further validate these differential splicing events, we analyzed the IR events of a set of genes whose splicing is regulated by RS33 using reverse transcription PCR (RT-PCR). We observed the IR of OsRS29 transcripts specifically in the rs33 mutant as compared to the WT (Figure  1e ).
Data Availability Statement: The RAW data and processed data files were deposited in GEO (GEO; https://www. ncbi. nlm. nih. gov/geo/; accessed on 24 January 2022) under accession number GSE194283. The following secure token was created to allow a review of the record GSE194283, while it remains in a private status: etopyiycdhcllgb. Please note the following points: This token allows anonymous, read-only access to GSE194283 and its associated accessions while they are private. Treat the token as you would a password and realize that the token provides access to GSE194283 to anyone who uses it.
In environmental metagenomic experiments, a very high proportion of the microbial sequencing data (> 70%) remains largely unexploited because rare and closely related genomes are missed in short-read assemblies. The identity and the potential metabolisms of a large fraction of natural microbial communities thus remain inaccessible to researchers. The purpose of this study was to explore the genomic content of unassembled metagenomic data and test their level of novelty. We used data from a three-year microbial metagenomic time series of the NW Mediterranean Sea, and conducted reference-free and database-guided analysis. The results revealed a significant genomic difference between the assembled and unassembled reads. The unassembled reads had a lower mean identity against public databases, and fewer metabolic pathways could be reconstructed.
To reconstruct rare strains from complex assemblages thus requires sometimes an enormous dataset with a very high coverage depth exceeding sometime 1000x  [4]. The approach described by Nielsen  [5]  allows, however, the reconstruction of any species with an adequate sequencing depth (~ 50x according to the simulation) and permits the binning of some rare members with the rarest having 0. 02% relative abundance. However, a minimum sequencing depth is often needed, but not always sufficient for accurate contig assembly. Globally, assemblers perform poorly in the presence of multiple similar genomes from closely related species. In that case, unassembled reads can also belong to the flexible or accessory genome of the main components of the community. For instance, members of the wide spread marine Prochlorococcus genus have a huge pangenome, with ~ 1000 common genes (core genomes), and a 'flexible' genome, which is found in only one or a few of the Prochlorococcus genomes  [6].
Assembling Raw paired-end Illumina reads were preprocessed by removing Nextera adapters with the bbduck program from the BBTools package (12. 10. 2015 release) (http://jgi. doe. gov/data-andtools/bbtools/). Reads were then trimmed and filtered using Trimmomatic v. 0. 33  [25]  based on their quality generating a read length of ca. 85 bp. A total of 34 to 112 million reads per sample remained after filtering (Table  S2 ). For each metagenome, high-quality reads were assembled into contigs with IDBA-UD  [26]  with the default iterative k-mer assembly and k-mer length increasing from 20 to 100 in steps of 20, the correction option, and with both pair-end reads (-r entry) and single-end reads (--long entry). Two kinds of reads were discriminated by mapping all the reads against the built contigs (Fig.
Community composition, functional abundance table and OTU abundance table inferred from assembled and unassembled reads The composition of the unassembled and assembled read fractions were compared to each other with MetaFast  [28] , which allows a direct reference-free comparison of shotgun metagenomic data. The Bray-Curtis dissimilarity matrix computed by MetaFast was used to construct a non-metric multidimensional scaling (NMDS) ordination with the vegan package in R  [29]. An OTU abundance table based on 16S rRNA gene was built for assembled and non assembled reads separately. The 16S rRNA gene were identified by comparing all preprocessed reads to the SILVA database  [30]  with BLASTn (identity ≥ 90% and length > 80 bp). An abundance table was built by clustering reads at a 97% similarity against the SILVA sequence collection.
In addition, a phylogenetic analysis was conducted based on unique clade-specific marker genes for assembled and unassembled reads with metaphlan2  [31] , and the list of taxa and their relative abundance was used with LefSe  [32]  to identify the taxa that best explained the differences between the fractions. A functional abundance table was built with a reference-guided approach based on the UNIREF (90 and 100)  [33]  and KEGG databases  [34]. Reads were compared against the databases using DIAMOND  [35]  with the blastx mode and the following parameters: -evalue 1e-5 --sensitive -max-target-seqs 1. Each function in these tables contains reads originating from multiple genomes. The generated abundance tables were characterized by zero-inflation. We removed all genes present as singletons only in the 80 samples (40 assembled and 40 unassembled), or detected in less than 20 samples.
Gene loss are presented in the Table  S3. Overall, we counted 846 16S rRNA OTUs, 6984 KOs, and 1,210,645 proteins (UNIREF90) in the entire dataset after applying strict filters described in the experimental procedures section (Table  S3 ). The statistical analysis were conducted with the ALDEx2 methods  [36]  that take into account the compositional nature of the data  [37]. Differences in abundance between the two categories of genes (derived from assembled and unassembled reads) were considered as significant (P<0. 05) when the Welch and Wilcoxon tests were convergent. The significant results annotated against the KEGG database were used to discriminate metabolic pathways between assembled and unassembled fractions with the "gage" and "pathview" functions implemented in R  [38, 39]. Multivariate analyses were conducted with the R MixOmics package  [40]  by using the "spca" function with centered log ratio transformation (CLR) after replacing zeros with the "cmultRepl" function and the "czm" option included in the zCompositions library  [41].
Binning covarying gene groups with assembled and unassembled reads The most common approach to reconstruct genomes from metagenomes is to build MAGs. MAG construction is based on mapping reads to contigs, but since we cannot obtain contigs from the rare reads, we chose an alternative approach to survey the potential genomic content of the communities. Co-Abundance gene Groups (CAGs) were built separately for the assembled and non assembled datasets, from the table gathering the functional abundance (UNIREF90) and OTU (SILVA) tables, with 3 different approaches: MSPminer  [42] , canopy  [5]  and Partial Least Squares regression (PLS) based networks. MSPminer and canopy bin covarying genes by a robust measure of proportionality or correlation between genes, and give a same weight to the proteins and rRNA genes.
In our approach, unlike in the original methods cited, we used the abundance of functions rather than a gene catalog. In addition, we introduce a new method to bin genes from abundance tables by associating a Partial Least Squares regression (PLS) and a bipartite network. PLS relates the OTUs (16S rRNA) and the protein tables. The goal was to predict the protein variations from the OTUs dynamics. The regression was computed with the "spls" function associated to the regression method in the MixOmics package in R  [40]. In a second step, a bipartite network based on PLS was built linking OTUs and protein genes. The edges with a weight lower than 0. 8 and orphan vertices were deleted by using the igraph package  [43]. A CAG was then defined by grouping all the protein genes associated to one OTU.
The quality (completeness and contamination) of the CAGs built by these 3 different approaches were checked with checkM  [44]  with the option "--genes". In a first step, 149 CAGs were defined and the taxonomy, completeness and contamination was assessed by checkM (Table  S4 ). The temporal dynamics of these different CAGs were assessed from the median of the gene counts at each sampling date, and a network was built based on Spearman correlations. CAGs were considered redundant if their weight (i. e. correlation) in the network was higher than 0. 95 to a CAG with the same taxonomy and amino acids identity >95%. This identity was computed with compareM (https://github. com/dparks1134/CompareM). These criteria were based on the histogram of the edge weight (i. e. correlations), manual inspection of the network cluster for the CAG taxonomy and the amino acid identity.
Amplicon sequencing Amplicon sequencing data were originally published in Lambert et al. [46]. Briefly, specific primer pairs 27F (5'-AGRGTTYGATYMTGGCTCAG) and 519R (5'-GTNTTACNGCGGCKGCTG) were used to target the V1-V3 regions of the bacterial 16S rRNA gene and sequencing was carried out with Illumina MiSeq 2 x 300 bp kits. The analysis of the raw reads was done by constructing amplicon sequence variants (ASVs) following the standard pipeline of the DADA2 package  [47]. Abundant ASVs were defined as the ones with a representation > 0. 01% within a sample, and rare ASVs as having an abundance < 0. 01% within a sample  [48].
The sparse principal component analysis (sPCA) based on UNIREF90 and KEGG annotated genes separated the assembled and unassembled fractions (Fig. 5 ). The multivariate analysis explained 31% (UNIREF90 clusters) and 36% (KEGG clusters) of the variance along axes 1 and 2. By comparing pathways (KO) present in the assembled vs. unassembled fractions, we identified two pathways involved in photosynthesis and flagellar assembly, which were enriched in the assembled communities (Fig. S6 ). The unassembled fraction was not significantly enriched in any of the pathways referenced in the KEGG database. This result is congruent with the previous statistical analysis showing few KOs enriched in this fraction (Table  1 ).
Thus, the overall seasonality of the microbial communities in response to the environment was mainly driven by the rare gene pool. A similar observation was made from coastal sands, where turnover in community composition was no longer observed when 50% of the rare species were removed from the dataset  [56] , and the Arctic Ocean where the rare biosphere was sensitive to environmental heterogeneity  [57]. Rare communities can be classified according to different patterns of seasonal abundance and activity  [17]. Within this classification, there is a group defined as rare taxa that never bloom but are active. It has been shown in bacteria, Archaea, and Eukaryotes  [14, 17, 51]. These rare but active taxa also have a temporal pattern linked to biotic or abiotic parameters. Even though our metagenomics approach does not allow to infer activity, the reproducible seasonal dynamics of the continually rare community that we observed could suggest that they are active.
Rare and abundant gene pools: many unknown functions This study showed that there was significantly more unknown genes in the rare fraction than in the abundant fraction  (Fig 4 and Fig S4). The microbial rare biosphere could thus be seen as a large pool of genes possessing known and unknown functions and considered a reservoir of "genetic novelty"  [20, 68]. Since the rare gene pool showed strong temporal dynamics, it indicates that this reservoir of rare functions plays a role in ecosystem functioning. Some of the rare reads could nevertheless be mapped against database references (UNIREF or KEGG). They corresponded to known potential functions, but the identity of these rare genes was significantly lower than that of the abundant ones. This suggests that the rare gene pool harbors different variants of known genes found in abundant microbes.
It should be noted that no metabolic pathways could be built from the identified rare KOs. The sequencing depth may have been too shallow to detect all the steps of the pathways present in the rare microbes, or some of the steps may be conducted by proteins coded by unknown genes. For the abundant microorganisms, the fraction of the mapped reads against the UNIREF databases (90 or 100) always represented a low proportion of the total clean reads (< 45%). This result at the short-read scale is in agreement with previous studies showing that 40%-60% of the coding genes cannot be assigned to a known function in the marine environment  [69, 70]. Even in the human gut microbiome, which has been extensively studied, approximately 40% of the genes have unknown functions, although the "mappability "of the metagenomes against microbial genomes reaches ~ 80%  [71].
33 39. 87 37. 23 Before cleaning k__Bacteria p__Proteobacteria Databases 7. 91 Features 3. 79 k__Bacteria p__Cyanobacteria 1. 72 k__Bacteria p__Bacteroidetes c__Gammaproteobacteria After cleaning Reads Features c__Prochlorales o__Prochlorales c__Flavobacteriia o__Flavobacteriales f__Prochlorococcaceae Reads f__Flavobacteriaceae g__Prochlorococcus s__Prochlorococcus_marinus ass-cano-CAG0089 47. 85 9. 63 k__Bacteria p__Proteobacteria c__Betaproteobacteria o__Methylophilales ass-cano-CAG0091 31. 27 0 k__Bacteria p__Actinobacteria c__Actinobacteria ass-cano-CAG0093 ass-cano-CAG0104 ass-cano-CAG0108 39. 47 43. 36 31. 99 SILVA 0 KEGG 5. 18 0. 54 k__Bacteria k__Bacteria k__Bacteria 6959 p__Proteobacteria 9826 p__Proteobacteria p__Bacteroidetes 1260545 4. 97E+08 c__Alphaproteobacteria c__Flavobacteriia 846 6984 o__Flavobacteriales 1191644 4.