dataesr/scientific-paragraphs-categorization huggingface dataset:
However, Zfh-1 has no known role in early SCCs, which support the first stages of germline differentiation. To elucidate genes regulating these events within early SCCs, we aimed at identifying a TF controlling early SSC behavior by screening our transcriptome datasets for TFs with exclusive expression in the soma. We excluded Traffic Jam (TJ) and Chinmo, two factors known to be active in CySCs and early SCCs  (Flaherty et al. , 2010; Li et al. , 2003) , as TJ is particularly critical for hub specification  (Wingert and DiNardo, 2015) , while Chinmo is also expressed in germ cells  (Flaherty et al. , 2010). One candidate was the Hox TF Abd-A, and expression analysis revealed Abd-A accumulation in hub cells, CySCs, and early SCCs of third-instar larval (L3) testes (Figure  1B ), similar to the CySC and early SCC marker TJ  (Figures 1I and 1J).
We called genes that had at least one enriched genomic region within 2 kb of the gene body in both DamID replicates as targets. For gene detection in TaDa experiments, we used high stringency parameters (FDR < 0. 01), which led to the recovery of 5,905 genes expressed in larval somatic cells  (Figures 3B and S3B). This list included zfh-1 and abd-A as well as many other genes known to be active in the testis soma (Table  S3 ). For the germline, we identified 2,199 expressed genes using the same strategy (Figure  S3B ). The Dam-fusion proteins were equally expressed in the soma and germline, while endogenous Zfh-1 and Abd-A are exclusively active in the soma. Thus, we analyzed our datasets for specificity by comparing active genes identified by germline or soma TaDa with the 2,686 genes that are targeted by Abd-A and/or Zfh-1.
We found only 6% (222/2,686) of the germlinerestricted active genes to be associated with Abd-A and/or Zfh-1 binding events, while 94% (2,464/2,686) of the genes expressed exclusively in the soma were targeted by either or both TFs (Figure  S3B ). This result demonstrated that Zfh-1 and Abd-A interacted with cis-regulatory regions preferentially in the somatic lineage, maybe due to permissive chromatin environments or availability of suitable cofactors. After having confirmed the specificity of our datasets, we excluded the germline-specifically expressed and TF-targeted genes from the analyses and combined the soma-specific transcriptome with the TF-binding datasets. Among the seven gene classes identified (Figure  3B ; Table  S1 ), we considered the somatically expressed genes bound exclusively by Zfh-1 (class 2) or Abd-A (class 3) or by both TFs (class 1) (3,366 genes in total) (Figure  3B ) to contain many genes expressed in CySCs and early SCCs in larval testis development (Table  S1 ).
A Comparative Approach Identifies a High Fraction of Stem Cell Regulators To select among the large number of genes the most significant ones for functional studies, we took a comparative approach by intersecting our data with published expression data retrieved from other stem cell systems. Our reasoning was that genes expressed in multiple stem cell types should regulate stem cell behavior with a higher probability. We selected data from Drosophila neural stem cells (NBs)  (Southall et al. , 2013)  and intestinal stem cells (ISCs)  (Dutta et al. , 2015) , as well as mammalian hematopoietic stem cells (HSCs), to delineate common and divergent genes and processes across functionally but also evolutionary diverse stem cell systems. These datasets were generated using diverse technologies, cell sorting, and RNAseq for ISCs  (Dutta et al.
, 2013b). Importantly, Wnt, MAPK, JAK/STAT, EGFR, and the Hippo pathways were significantly overrepresented in all four stem cell systems (Figure  S4T ). Several signatures were different in the various stem cell systems, including a high representation of stem cell-related processes in NBs, metabolic processes in HSCs, and the immune response in the testis soma (Figure  4B ). We next searched for individual genes common to all datasets; thus, we used orthology mapping to convert mouse gene identifiers into Drosophila gene identifiers. Intersecting wholetranscriptome profiling of fluorescence-activated cell-sorted (FACS) mouse HSCs  (Cabezas-Wallscheid et al. , 2014)  with the Drosophila datasets identified 716 transcripts common to all systems (Figure  4A ), with 21 of them encoding TFs (Tables  S2  and S5 ).
In contrast, spermatogonia in c587 > Vha13 RNAi and c587 > VhaAC45 RNAi testes were unable to exit the   S7A-S7C ). The observed phenotype was not due to a loss of interaction between developing germ cells and SCCs, as Vha13, VhaAC45-depleted, and wild-type SCCs labeled by F-actin enveloped the germ cells equally well  (Figures 6D, 6G, and 6J) , which was also the case for Vha16-1 (Figure  6M ). V-ATPases has been shown to control various signaling pathways  (Gleixner et al. , 2014; Petzoldt et al. , 2013). Importantly, we found the TOR signaling pathway to be enriched in our datasets (Figure  3C ), and network analysis revealed an interaction of V-ATPase subunits with the TOR (Figure  6A ). TOR signaling was implicated in the regulation of various mammalian and Drosophila stem cell systems  (Murakami et al.
, 2016) , and our results suggest that they may play a similar role in controlling the transition of continuously active adult stem cells toward differentiation. The next challenge will be to unravel how variations in the composition of an essential and basic protein complex like the NPC causes differential responses of cells, in particular in stem cells and their progenies. Our datasets in conjunction with the versatile and easyto-use analysis tool allowed us to identify a substantial number of stem cell regulators for detailed mechanistic characterization. Importantly, our analyses have shed first light on processes and genes shared between diverse invertebrate and vertebrate stem cell systems and uncovered functionally relevant differences. Owing to its flexibility and the option to include datasets from any species, our online tool represents a valuable resource for the entire stem cell community.
ACKNOWLEDGMENTS We would like to thank the  Drosophila community  for providing us generously with fly stocks and antibodies, in particular  Dorothea Godt  for the TJ antibody,  Ruth Lehmann  for the Zfh-1 antibody,  Margaret Fuller  for the c587-GAL4 fly lines, the  Developmental Studies Hybridoma Bank  ( Iowa State University ), the  Vienna Drosophila Resource Center (VDRC) , the  Bloomington Drosophila Stock Center , the  NIKON Imaging Center at Heidelberg University , and the  Genomics Core Facility at EMBL, Heidelberg. We would like to deeply thank  M. Fuller , as part of the characterization of the Abd-A phenotype was done in her lab. We also thank all the members of the  Ingrid Lohmann lab  for critically reading the manuscript. We apologize to all whose work was not cited due to space limitations.
ResCap design The ResCap capture library is a homemade core reference database (which will be available upon request) that comprises both well-known and hypothetical genes encoding resistance to antimicrobials (antibiotics, heavy metals, biocides) and genes coding for relaxases, enzymes involved in the process of DNA mobilization and transfer of conjugative elements that use to carry antimicrobial resistance genes (plasmids, conjugative transposons). The core reference database was built by downloading sequences associated with non-redundant antimicrobial genes available in the curated databases Arg-ANNOT  [27] , CARD  [28] , RED-DB (http:// www. fibim. unisi. it/REDDB/Default. asp), ResFinder  [29] , and Bacmet  [30]. These antibiotic resistance databases were combined within a non-redundant set.
Proteins were clustered in protein families by homology, using CD-HIT with parameters of 80% identity and 80% coverage. Each protein family was aligned by MUSCLE v. 3. 7  [31]  with default parameters and a hidden Markov model (HMM) was built for each family with hmmbuild function of the HMMER3  [32]  using default parameters. Hmmer search function (hmmsearch) was used against UniProtDB for each HMM profile to search homologous proteins for each family of proteins that confer antibiotic resistance. Manual curation of the database, which consisted on reviewing the annotation and the score of the search of datasets, allowed removing false positives. The proteins of the final data set were translated to a DNA sequences using ENA accession numbers associated with each UniProtDB entry. The final ResCap-targeted sequence panel consists of 78,600 non-redundant genes (81,117 redundant genes) with a target space of 88.
13 Mb, not yet reaching the 200 Mb target capacity offered by the custom SeqCap EZ library format (NimbleGen, Madison, USA). Probes targeting the antibiotic resistome include 47,806 putative antibiotic resistance genes and 7963 functionally characterized genes which are designed here as "canonical, antibiotic resistance genes. " Probes targeting the metal and biocide resistome include 30,794 putative resistance genes and 704 canonical resistance genes. The platform also includes probes for 2517 relaxases genes of the ConjDB database  [33] , which are used for plasmid identification and classification)  [34]. In addition to the 8667 genes that confer functionally proven resistance to these antimicrobials (canonical genes), the platform also includes targets for 78,600 resistance gene homologs (47,806 for antibiotic and 30,794 for biocide and metal resistance).
Bioinformatic analysis Reference-based workflow Analysis of sequence data from metagenomes constitutes a challenge because of the inherent variability of the samples analyzed, and the limitations of current bioinformatics' methods for unequivocally identifying specific alleles from short-length reads (100-150 bp). To overcome such limitations, we developed a novel approach to define variables suitable for inferring gene abundance and gene diversity and, in our case, to perform quantitative analysis of antimicrobial resistance genes. Moreover, we suggest a workflow of variable normalization in relation to the information content of the targeted variable that would make it possible to compare different samples from various hosts. These tools were developed for ResCap but could be implemented for any other metagenomic sequence dataset.
Shotgun metagenomic sequencing allowed assembling the sequences into contigs to infer the functionality of the sequenced metagenome. Figure  1  shows the workflow that illustrates and defines the variables used. Fig. 1  ResCap analysis workflow. Processed reads are mapped against the reference database. SAM files are parsed to extract the reads unequivocally mapped and those ambiguously mapped to determine the genes unequivocally detected and to form the allele network. The allele network is built using all the study's SAM files. The MGCs determined from the allele network were used to perform the statistical analysis of abundance and diversity. Finally, a differential analysis was performed with the abundance data.
Raw data processing Reads were mapped against our database, comprising ARG-ANNOT  [27] , BacMET  [30] , and ConjDB  [33]  databases independently, using Bowtie2 software  [36]. Bowtie2 was set up to retrieve all end-to-end possible alignments and to suppress both discordant alignments and mixed alignments. The output SAM file was parsed to get the fields of Query template NAME, Reference sequence NAME, 1-based leftmost mapping Position, MAPping Quality, Position of the mate/next read. Reads with unavailable information (field Query Template NAME equal to '*') were suppressed. Subsequently, a homemade Perl script was used to count matched reads per gene. Using the SAM parsed file and the length of the reference genes, the Perl script generated a table with the following fields: (i) the number of reads per gene mapped ("RPG", gene depth coverage); (ii) the number of reads per kb of gene ("RPK"); (iii) the number of the reads that were mapped unequivocally to a given gene ("unique"); and (iv) the percentage of coverage of the gene sequence ("horizontal gene alignment coverage") of each mapped gene.
To quantify the MGCs in each sample, the highest value shown by an allele (node) within a given MGC is the occurrence of such an MGC (abundance). This criterium is used to avoid an over and underestimation of data that would occur when using the mean or median of the reads corresponding to alleles with high homology (thus, sharing a high proportion of reads) but present in different proportions. Therefore, the MGC approach builds a unique variable for each set of possible detected alleles. Figure  2  shows the 839 MGCs of our sample (237 for AbR, 283 for biocides and metals and 319 for relaxases). A homemade Perl script was used to build the allele network from the SAM parsed files, taking the mapped genes as nodes and searching the ambiguously mapped reads to create the edges. The Perl script calculates the edges' weight as the number of reads that map the linked nodes at the same time.
Data analysis The resistome of a given experiment was analyzed in terms of gene abundance and gene diversity according to the methodology described above. The abundance and the diversity of genes in a particular resistome are the (dependent) variables that define this resistome, which are measured as the number of RPKMs per MGC and the number of MGCs per million reads (MPM), respectively. This MGC system builds a set of normalized variables that allow analyzing abundance and diversity within and between samples, and thus the comparison of datasets from various sources. The MGCs of the antibiotic resistance gene database were divided according to antibiotic families  [27]. The MGCs of the relaxase database were organized in known different relaxase families  [33, 41]. The MGCs of the biocide and heavy metal resistance gene database were classified according to susceptibility to specific compounds  [30].
Biocide and heavy metals resistance genes that belong to more than one functional category (e. g. , genes conferring resistance to various metals or genes encoding resistance to different biocides) contribute equally for any of them. Descriptive statistics were performed using the dplyr  [42] , tidyr  [43] , and ggplot2  [44]  packages of R  [38]. A statistical analysis of gene abundance, analogous to that used for comparing the abundance of mRNA among samples in differential expression analysis  [37] , was performed to quantify the relative abundance of the MGCs. Differential analysis was performed using the DESeq2 package  [45]. Tables containing the original abundance data obtained by ResCap and MSS data sets were used as input for the DESeq2 package. Normalization and statistical analysis were performed with the default parameters of DESeq2.
Reference-free workflow ResCap includes probes for approximately 78,600 genes that are homologous to known resistance genes, with different degrees of sequence identity, which might be involved in antimicrobial resistance. This ensemble of genes has also been considered in the definition of resistome  [17]. Assemblies were performed by MegaHit software with default parameters  [46]. Prodigal  [47]  was used for gene recognition and translation with the specific parameters for metagenomic sequences. Quality assemblies' quantification was performed by Quast software  [48]. Predicted genes were first annotated against the ResCap database by Best Blast Hit approach using blastn software  [49]. To identify only the genes belonging to the ResCap database or their homologs and to minimize the false positive ratio, Blast hits were filtered by e value of 10 -100 and 80% of reference coverage.
Genes with identities higher than 95% and coverage higher than 80% were cataloged as belonging to ResCap database (ResCapDB). The remaining sequences were translated to proteins. Proteins were compared against UniProtKB database by blastp. Again, hits with higher identity than 95%, coverage higher than 80% and e value lower than 10 -100 were considered as UniProtKB known proteins. The set of remaining proteins that did not accomplish this threshold were cataloged as novel proteins. Additional file 2 shows this analysis workflow.
ResCap achieves better recovery of target genes than MSS An average of 1. 9 × 10 7 paired reads was obtained from the MSS and ResCap datasets (0. 92-3. 2 × 10 7 ). The ontarget average (the number of reads mapping on the target genes relative to the total read number) against the selected databases (see "Methods") was 0. 11% (0. 07-0. 18) for MSS data and 30. 26% (20. 27-41. 83%) for ResCap data, which represents an enrichment of 279-fold (Table  1 ). The analysis of gene abundance, expressed in reads per kb per million reads (RPKMs), demonstrates a better recovery of gene coding for resistance to antibiotics, heavy metals, biocides, and relaxases (plasmid genes), when using ResCap compared against MSS. Figure  3a  represents the RPKMs inferred before (MSS) and after capture (ResCap) for all the samples analyzed, and Additional file 3 shows the gain plots for each sample.
Comparative analysis of resistomes from various samples In order to further demonstrate the improvements of using ResCap over MSS, we compared the resistomes of human and swine samples to estimate the relative abundance of MGCs by using these two methodological approaches. Volcano plots in Fig. 7a  show a general view of the relative abundance of MGCs for each dataset (AbR, metal and biocide, and relaxases) and each method. MGCs for antimicrobials were more abundant in swine samples than humans while those for relaxases were predominant in human resistomes (represented as positive and negative p values, respectively, expressed in log 2 in the x-axis). The figure also reflects the higher number of MGCs detected by ResCap than by MSS and the improvement of the p values. Figure  7b  represents the distribution of the MGCs in different hosts by using MSS and ResCap.
Reference-free evaluation Assembly statistics and coverage show that the information obtained with the ResCap platform only covers the small portion of the metagenome to which the platform has been designed (Fig. 8 ). The genes detected were classified in the ResCapDB, UniProtKB, and novel categories (see "Methods" for definition of each category). ResCap in comparison with MSS, improves not only the number of the detected canonical genes included in known databases (Arg-ANNOT  [27] , BACMet  [30] , and ConjDB  [33] ) as shown in previous sections, but also the number of detected ResCapDB, UniProtKB, or novel homologs as presented in Fig. 9  (ResCap UniProtKB 752 ± 237 genes vs. MSS UniProtKB 237 ± 107 for humans, and ResCap UniProtKB 441 ± 71 genes vs. MSS UniProtKB 82 ± 46 for swine; ResCap Novel genes , 79 ± 38 genes vs.
Finally, the substantial capacity of the platform (200 Mb) makes Res-Cap extensible up to 2-fold of its current capacity, providing opportunities for updating the platform with probes for newly published resistance genes or for resistance genes added to resistance gene databases. ResCap updates will be publicly available through the GitHub repository (https://github. com/valflanza/ResCap) and the Nimble-Gene webpage. Nonetheless, the threshold of ResCap detection remains unknown due to the lack of a negative control that demonstrates the ability of ResCap to pick antibiotic resistance genes from quantified minority populations (e. g. , mock genomic populations). Although appropriate, the complexity and variability of the metagenomic samples makes it difficult to use a good negative control for this type of study.
The parameters to express "gene abundance" and "gene diversity" allow comparing the resistomes of various samples. Relative abundance parameters are widely used in computational analysis of MSS datasets but require specialized statistics, because these compositional parameters are influenced by the variability in metagenomes of different a b Fig. 5  Quantification of unequivocally mapping reads. The figure shows the comparative of the quantification of reads mapping on just one gene (or allele). First, the abundance of reads that are unequivocally mapped on one gene (a). Second, the number of genes (or MGC) that have almost one read that maps unequivocally (b). Box plots are differentiated for MSS protocol and ResCap protocol samples. The novel "mapping gene cluster" (MGC) concept allows to provide a set of normalized variables that can be measured in terms of abundance and diversity among samples.
8  Assembly statistics. Assembly statistics was calculated by Quast software. Statistic summary of the main assembly variables; the number of contigs (all and longer than 1 kb), number of genes per sequenced Mb, the size of longest contig, the length of the assembled metagenome per sequenced Mb, and the N50 (the shortest contig length at 50% of the metagenome). Coverage data were calculated as the total sequenced bases divided by the total length (without normalizing). Length per Mb and genes per Mb were normalized by the total amount of megabases sequenced by each sample fecal microbiota transplantation, to alleviate the adverse impact of antibiotic administration; and (vi) to analyze the effect of eco-evo drugs and strategies to combat antibiotic resistance  [55].
Funding This study was supported by the  European Commission ,  Seven Framework Program  ( EVOTARFP7-HEALTH-282004  for VFL, FB, JLM, AA, DE, ER, RJLW, WvS, FdlC, and TMC), the  Joint Programming Initiative in Antimicrobial Resistance  ( JPIAMR Third call, STARCS ,  JPIAMR2016-AC16/00039  to  TMC, RJLW, WvS) , the  Joint Programming Initiative in Water (JPI Water StARE   JPIW2013-089-C02-01  to JLM) and the  Ministry of Economy  and.
AbstrAct Objective the goal of managing axial spondyloarthritis (axSpa) is to improve and maintain patients' healthrelated quality of life (HrQol), mainly through targeting towards low disease activity. Here, we aim to gain insight into the joint evolution of HrQol and disease activity by identifying and characterising latent subgroups of patients with longstanding disease displaying similar trajectories throughout 8 years of follow-up. Methods Data from Outcome in ankylosing Spondylitis (aS) international Study (n=161) and groningen leeuwarden aS cohort (n=264) were used. Biennially, HrQol was assessed by aS Quality of life (aSQol) and disease activity by aS Disease activity Score-c reactive protein (aSDaS-crP). Bivariate trajectories of these outcomes were estimated by group-based trajectory modelling.
Patients Data from two prospective, multicentre, longitudinal observational cohort studies, Outcome in Ankylosing Spondylitis (AS) International Study (OASIS) and Groningen Leeuwarden AS (GLAS) cohort, were used for the present study. 18 19 OASIS started in 1996 and was conducted at several secondary and tertiary referral centres in the Netherlands, Belgium and France. At baseline, 217 patients were included, all fulfilling the modified New York criteria (mNYC). 20 In order to increase the sample size and create a database most related to reality with all types of disease activity and treatments, including anti-TNF-α treatment, which was not yet available at the start of OASIS, the sample was enriched with patients from the GLAS cohort. GLAS started in 2004 and was conducted in the north of the Netherlands.