dataesr/scientific-paragraphs-categorization huggingface dataset:
Metagenomic datasets This study uses the GMR metagenomic dataset 1 (SRA: PRJEB41102) that includes 5,230 samples of individuals of all ages. This dataset is partly longitudinal with 1,239 out of 2,081 individuals sampled more than once. Overall, the dataset contains 2163 infant samples, 1903 adult samples, 1057 elder samples. Individuals originate from the 13 following countries: Canada (n = 72), Finland (n = 925), Sweden (n = 470), Estonia (n = 206), Russia (n = 321), Denmark (n = 57), Luxembourg (n = 93), Germany (n = 264), Kazakhstan (n = 170), USA (n = 1604), Israel (n = 857), Italia (n = 135), Holland (n =56). These cohorts were selected because they gather mostly healthy individuals while accounting for some physio-pathological conditions that are known to be associated to microbiome perturbations such as preterm birth and antibiotic treatment.
Sequences were processed with the MATAFILER pipeline as described in Hildebrand et al. 1  and Bahram et al. 93  The Metacardis Body Mass Index Spectrum (BMIS) raw metagenomes were retrieved from Vieira-Silva et al. 17  (ENA accession number PRJEB37249) and processed with MATAFILER using the same settings. It is a cross-sectional cohort consisting of 888 samples. Metadata was retrieved from Vieira-Silva et al. 17  and Forslund et al. 94  We additionally gathered cohorts of individuals with a non-western (NW) lifestyle. The Indian cohort was obtained from Vishnu Prasoodanan et al. 33  (PRJNA397112). All other cohorts were retrieved from Valles-Colomer et al. 32  (Argentina, China, Colombia and Guinea-Bissau: PRJEB45799; Ethiopia: PRJNA504891  63  ; Peru and El Salvador: PRJNA300541  64  ; Tanzania: PRJNA529400  63  ; Ghana: PRJNA529124  63  ; Fiji: PRJNA217052  65  ; Madagascar: PRJNA485056  34  ).
Briefly, raw shotgun metagenomes were quality filtered using sdm v1. 63 with default parameters, 95 assembled using MEGAHIT v 1. 2. 9 with parameters ''-k-list 25,43,67,87,101,127 ''  74  and reads mapped onto assemblies using Bowtie2 v2. 3. 4. 1 with parameters ''-end-to-end ''  76  , genes predicted with Prodigal v2. 6. 1 with parameters ''-p meta''  75  and a gene catalogue clustered at 95% nt identity using MMseqs2. 80 MAGs (metagenomic assembled genomes) were binned using Metabat2 v2. 15  72  and combined in MATAFILER to MGS (metagenomic species), relying on canopy clustering. 96 Matrix operations were carried out using rtk. 71 The full MATAFILER pipeline is available at https://github. com/hildebra/MATAF3. MGS phylogenies were calculated de novo based on the amino acid (AA) sequences of at most 40 marker genes  97  present in each MGS and subsequently aligned using MAFFT v v7.
For a contiguous flattened (ravel function from the Python numpy package  103  ) matrix (whole dataset) or a vector (sample) x and its estimation b x: CS = P x i b x i ffiffiffiffiffiffiffiffiffiffi ffi P x 2 i p ffiffiffiffiffiffiffiffiffiffiffi P c x 2 i q The cosine similarity ranges from 0 to 1, higher values indicating greater similarity between x and b x. The optimal number of signatures was chosen by assessing the gain of cosine similarity (median) when increasing the number of signatures: when the gain no longer increases, the optimal number of signatures is reached. L1 regularisation parameter a applied to both W and H matrices was estimated by running the same procedure of bicross-validation with the a coefficients ranging from 0 to 100. The regularisation parameter was selected based on the method from Eng et al.
100  Briefly, we defined a threshold equal to the mean explained variance (MEV) value at a = 0 minus the standard deviation of the mean. We selected the a parameter value leading to the model whose associated MEV was greater than the threshold. We selected for ES computation an alpha value 1. 0, and regularization was performed on both H and W matrices. In order to reapply the GMR ESs to the Metacardis BMIS and non-western datasets, we reused the W matrix obtained from GMR together with the BMIS normalized relative abundance matrix as inputs to the Scikit-Learn 'non_negative_factorization' function, with the 'update_H' parameter set to 'False' and 'init' to 'custom'. Running the computation of H only consists in solving a non-negative least square problem. Prior to computation, we homogenized the taxa contents of the BMIS relative abundance matrix and W matrix by adding zero-filled rows for the genera that were absent in W or in the abundance matrix respectively.
Enterosignature analyses The enterosignature results were calculated using the whole dataset the parameters (number of ESs, regularization parameters) obtained with bicross-validation. The matrices W and H resulting from the NMF algorithm represent the weight of genera in enterosignatures and the presence of enterosignatures in samples, respectively (Figure  1A ). Normalizing W by its columns or its rows informs on the general composition of ESs in genera, and on the association strength of genera to each ES, respectively. 101 y normalizing the H matrix column-wise, we obtained the relative abundance of ES in each sample. We define as primary ES the ES of a sample with the highest relative abundance. We used the normalized H matrix to determine the number of ESs needed to best describe a sample.
5-7  91  on the normalized H matrix. We calculated the persistence of ESs in individuals sampled longitudinally using survival analysis. We distinguished the survival of the primary ES over time from the survival of the presence of an ES. For the former case, survival analysis per signature was performed after assigning to patients in longitudinal studies the most representative signature for each of their samples. For the latter case, we considered as present all ESs with a relative abundance greater than 1% and analyzed their time of disappearance in longitudinal samples. Persistence of ES and primary ES were calculated using the Survival R package v3. 2-7. 104 Statistical tests for the comparison of survival predictions between groups are log rank Chi ^2 tests from the Survival and Ezfun (v0.
Functional analysis and metabolic network reconstruction Consensus genomes of metagenomic species (MGS) and references genomes  106  corresponding to the GMR dataset were retrieved according to the data in Hildebrand et al. 1  Proteomes were functionally-annotated using EggNOG-mapper v2. 1. 6  89 based on eggNOG orthology data. 107 Sequence searches were performed using Diamond v2. 0. 14. 90 AZymes and Kegg Orthology (KO) annotations were retrieved from EggNOG-mapper annotation files. Higher-level categories associated to KO were downloaded from the KEGG database. 108 For each KO found in a genome, we incremented the number of the corresponding higher level metabolic categories and we ultimately compared the metabolic functions of genomes grouped by their ES assignments. CAZymes 109 were associated to their respective substrates according to Cantarel et al.
110  and The CAZypedia Consortium  111  and genomes were compared after grouping by ES. In both CAZyme and KEGG annotations, values by genome were normalized by the number of genes in the genome. Genome-scale metabolic networks (GSMNs) were reconstructed based on the annotated proteomes using mpwt v0. 7. 0 69 with Pathway Tools v25. 5. 68 Analyses of metabolic pathways in the networks relied on Metacyc v25. 1 database. 112 We retrieved the genera accounting for more than 3% of an enterosignature's activity and considered them as representatives of their respective enterosignature. This threshold was chosen because it allowed us to uniquely assign genera to an ES. Then, by grouping GSMNs assigned to genus-level representatives, a group of representative GSMNs was associated to each enterosignature.
Of 1,737 total species, 55, 81, 60, 149 and 43 MGS were stably associated to ES-Bact, ES-Firm, ES-Prev, ES-Bifi and ES-Esch, respectively. The metabolic potential and metabolic complementarity between enterosignatures were determined with Metage2Metabo v1. 5. 0. 69 The software simulates the qualitative individual and collective metabolic potentials of GSMNs provided a set of nutrients. A set of nutrients consisting of molecules described in a western diet  113  was selected. Three experiments were tested. For the first one, metabolic potential of each ES in isolation was calculated, running Metage2Metabo on GSMNs associated to each ES. For the second experiment, we computed the metabolic potential of pairwise combinations of all ES as well as all observed discrete combinations of ES in the samples.
ACKNOWLEDGMENTS R. A. , E. O. , and F. H. were supported by European Research Council H2020 StG (erc-stg-948219, EPYC). C. F. , R. A. , E. O. , and F. H. were supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Food Microbiome and Health BB/X011054/1 and its constituent project BBS/E/F/000PR13631, Gut Microbes and Health BB/ r012490/1 and its constituent project BBS/e/F/000Pr10355, Core Capability Grant BB/CCG1720/1 and the work delivered via the Scientific Computing group, as well as support for the physical HPC infrastructure and data centre delivered via the NBI Computing infrastructure for Science (CiS) group. J. F. was supported by the UKRI Biotechnology and Biological Sciences Research Council Norwich Research Park Biosciences Doctoral Training Partnership BB/T008717/1.
Some experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Universitede Bordeaux, Bordeaux INP, and Conseil Regional d'Aquitaine (see https://www. plafrim. fr ). C. Q. was funded through the MRC Methodology grant ''Strain resolved metagenomics for medical microbiology'' MR/S037195/1. S. M. W. was supported by an SNSF Early Postdoc. Mobility Fellowship (P2ELP3_155365), an EMBO Long-Term Fellowship (ALTF 755-2014), and grants from the Research Council of Norway ( B Materials availability B Data and code availability d METHOD DETAILS B Metagenomic datasets B Non-negative matrix factorisation B Enterosignature analyses B Functional analysis and metabolic network recon- struction B Enterotype calculation d QUANTIFICATION AND STATISTICAL ANALYSIS d ADDITIONAL RESOURCES SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.
Body condition score and milk production Body condition score was performed at calving, at 43 ± 3 DIM and at 70 ± 3 DIM using a scale of 1-5 with 0. 25 increments  (Edmonson et al. , 1989). Cows were classified as BCS ≤ 2. 5 and BCS > 2. 5  (Carvalho et al. , 2014)  and BCS 'loss' was attributed when ≥ 0. 5 points were lost between measurements. Fourth week milk yield (W4MK) and cumulative milk yield 30 days after calving (MK30) data were retrieved from the dairy farms management software (DairyComp 305; Valley Agricultural Software, Tulare, CA, USA) and were considered as covariates in all the regression models.
Statistical analysis Individual cow data were obtained from DairyComp 305, and exported to Microsoft Excel (Version 2011, Microsoft Corporation). Statistical analysis was performed in R (R Core Team, 2020). For the three treatment groups, with the 'pwr' package in R it was estimated that a sample of 330 cows per group would detect a 3. 6-10. 9% difference in P/AI between any two of these groups, with 95% confidence and 80% power, when using a two-tailed z-test  (Cohen, 1988). Multivariable logistic regression models, as used in the final analysis, will have increased the precision further and allowed detection of even smaller differences between the groups. An additional 30% more cows were enrolled to account for cow losses during the observation period. Fisher's exact test was used to compare distributions of cows for categorical risk factors of reproductive strategy, farm and parity.
Descriptive statistics From a total of 1491 dairy cows that were enrolled in this trial, data from 1037 cows were available for analysis. Reasons for exclusion for 454 cows (ED-Ov, n = 152; ED-PR, n = 145; DO, n = 157) were sold (51), DNB (78), UI (100), other reasons (53), missing data (74), non-compliance with protocols (71) and cows that died before AI (27).
Acknowledgements The authors thank the participating farms owners and employees for their willingness to participate. We thank  Ceva Sante Animale  ( Libourne, France ) for co-funding this study in conjunction with the  University of Glasgow  and the donation of the  Ovarelin®, Enzaprost® , and  PRID® Delta  devices. We also thank the residents of the  Farm Animal Department, University of Glasgow  for their help with the data collection during the course of this experiment. P. T. Pepler  was funded by the  Scottish Government Rural and Environment Science and Analytical Services Division , as part of the  Centre of Expertise on Animal Disease Outbreaks (EPIC).
| Statistics All statistical analyses were generated using GraphPad Prism 9 (GraphPad Software) and all data are expressed as means ± SEM. For the comparison between two or more experimental groups, statistical significance was assessed via Student's t test or two-way ANOVA with an alpha level of 0. 05. *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. The significance of the time-dependent variation of human IL-10 concentration in mouse plasma was assessed by simple linear regression and the correlation coefficient with a threshold level of 0. 05.
ACKNOWLEDGMENTS We thank (i)  Julien Hermet ,  Mehdi Djelloul-Mazouz  and  Yoann Delorme  (animal facilities of  INRAE institute, Clermont-Ferrand ) for animal care and technical assistance, (ii)  Silvère Baron ,  Philippe Mazuel  and  Sandrine Plantade  (animal facilities of  UCA, Clermont-Ferrand ) for animal care and technical assistance, (iii)  Christophe Ferrand  and  Marina Deschamps  ( INSERM, Besançon ) for technical advices and (iv)  Miltenyi Biotec  society for the initial loan of MacsQuant. We also thank  Isabelle Papet ,  UNH ,  INRAE  for the critical reading of the manuscript.
Bioinformatic Analysis For sequences homology and conserved domains analysis, NCBI BLASTN (https://blast. ncbi. nlm. nih. gov/Blast. cgi?PAGE_TYPE=BlastSearch, accessed on 15 January 2023) and Conserved Domain Database (CDD) (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi, accessed on 15 January 2023)  [19]  were used. For further proteomic analysis, the nucleotide sequence was converted to an amino-acids (AA) format by the EMBOSS Seqret tool (https://www. ebi. ac. uk/Tools/sfc/emboss_seqret/, accessed on 15 January 2023). The ProtParam tool (https://web. expasy. org/protparam/, accessed on 15 January 2023) was utilized to analyze the physicochemical properties of the rVP1 and CVB4 VP1-VP4. The properties include the relative molecular weights, the atomic composition of VP proteins' AA, molar extinction coefficient, theoretical isoelectric point (Pi), hydrophilic parameters, instability index (II), and so on  [20].
predictprotein. org/, accessed on 15 January 2023) websites. Secondary structure prediction is useful for 3D structure prediction  [26]. Protein modeling was determined by the automatic mode of SWISS-MODEL (https://swissmodel. expasy. org/, accessed on 15 January 2023), and model structures were visualized using RasMol 2. 7. 5 Software (http://www. rasmol. org/ software/RasMol_2. 7. 5/, accessed on 15 January 2023). BepiPred-2. 0 was used to predict β-cell epitopes of the proteins (http://www. cbs. dtu. dk/services/BepiPred/, accessed on 15 January 2023).
After that, the DNA from the same colony was extracted, attesting to the presence of pUC19-VP1 (Figure  3B , Lane 1). Furthermore, a restriction analysis showed two bands, each corresponding to pUC19 and the cloned VP1 (Figure  3C , Lane 1). DNA sequencing confirmed the success of the cloning process. The nucleotide sequence has been submitted in GenBank under the Accession Nb. MW193767. the cloning process, a PCR colony was amplified from the transformed colonies (Figure  3 ). As expected, the amplification of negative control (Figure  3A , Lane 1) and non-transformed colonies (Figure  3A , Lanes 2 to 6) did not show the target fragment. A fragment of approximately 850 bp was found predicting the presence of the cloned DNA fragment in the recombinant colony (Figure  3A , Lane 7). After that, the DNA from the same colony was extracted, attesting to the presence of pUC19-VP1 (Figure  3B , Lane 1).
Furthermore, a restriction analysis showed two bands, each corresponding to pUC19 and the cloned VP1 (Figure  3C , Lane 1). DNA sequencing confirmed the success of the cloning process. The nucleotide sequence has been submitted in GenBank under the Accession Nb. MW193767. the cloning process, a PCR colony was amplified from the transformed colonies (Figure  3 ). As expected, the amplification of negative control (Figure  3A , Lane 1) and non-transformed colonies (Figure  3A , Lanes 2 to 6) did not show the target fragment. A fragment of approximately 850 bp was found predicting the presence of the cloned DNA fragment in the recombinant colony (Figure  3A , Lane 7). After that, the DNA from the same colony was extracted, attesting to the presence of pUC19-VP1 (Figure  3B , Lane 1). Furthermore, a restriction analysis showed two bands, each corresponding to pUC19 and the cloned VP1 (Figure  3C , Lane 1).
The exact identity of the expressed protein was established by subjecting the recombinant E. coli DH5α cells to Western transfer analysis, along with negative control (non-transformed E. coli DH5α). Mouse anti-enterovirus clone 5-D8/1 (DAKO) conjugated with anti-mouse Ig-Peroxidase (SIGMA) directed against VP1 viral protein picked up a band at approximatiely 30 kD regions from the lanes loaded with cell lysate from the host cells transformed to express the recombinant VP1 viral protein. However, no such signal was produced in lanes from non-transformed cell lysates (Figure  4C ). The NCBI BLASTN tool showed that the nucleotide sequence of the rVP1 highly matched the target nucleotide sequence of the human coxsackievirus B4 strain E2 variant (GenBank Accession Nb. AF311939). The nucleotide sequences shared 96% identity with each other.
Bioinformatic Analysis of the Nucleotide Sequence of rVP1 The NCBI BLASTN tool showed that the nucleotide sequence of the rVP1 highly matched the target nucleotide sequence of the human coxsackievirus B4 strain E2 variant (GenBank Accession Nb. AF311939). The nucleotide sequences shared 96% identity with each other. Moreover, CDD analysis indicated that the rVP1 belonged to the rhv-like superfamily and contained a conserved domain pfam00073 (Figure  5A ). As expected, the gene expression leads to a coxsackievirus capsid protein.
rVP1 and VP1 have 47. 65% and 45. 42% of exposed AA, respectively. The protein structure homology-modeling was determined by employing the SWISS-MODEL server. The selection of templates depends on the similarity and coverage of AA sequences. Based on evaluation parameters, high standards models were applied as the tertiary structure of the VP proteins (Table  2 ). Model structures were observed by using RasMol 2. 7. 5 Software (Figure  6 ). The prediction of Linear B cell epitopes of rVP1 and VP1-VP4 capsid proteins was determined by the BepiPred server (Table  3 ). Among the potential linear B cell epitopes, VP4 included 2 epitopes with over 10 AA residues, respectively located at AA 5-19 and AA 29-54; VP3 contained 3 epitopes, respectively situated at AA 7-39, AA 55-67 and AA 139-150; VP2 included 5 epitopes respectively situated at AA 5-27, 38-60, 66-92, 130-165, 223-244; VP1 included 7 epitopes with over 10 AA residues, respectively located at  5-31, 33-50, 82-96, 127-138, 152-165, 199-209, 251-280 ; rVP1 6 epitopes 18-63, 83-93, 130-140, 155-168, 202-213, 258-273 (Table  3 ).
Glucagon-like peptide-1 receptor agonists reduce albuminuria and may stabilize the estimated glomerular filtration rate (eGFR) in people with type 2 diabetes (T2D). In this post hoc analysis of the SUSTAIN 6/PIONEER 6 trials encompassing 6480 participants at high cardiovascular risk (semaglutide, 3239 participants; placebo, 3241 participants), we investigated the effects of semaglutide versus placebo on eGFR decline. Pooled data by treatment were evaluated for annual eGFR change (total annual eGFR slope in ml/min per 1. 73 m 2 ) from baseline to end of treatment and time to persistent eGFR reductions of 30%, 40%, 50% and 57% or more, including subgroup analyses by baseline eGFR (30 to under 60 or 60 and over ml/min per 1. 73 m 2 ). In the overall population, the estimated treatment difference (ETD; semaglutide versus placebo) in annual eGFR slope was significant at 0.
Trial design The trial designs for SUSTAIN 6 (NCT01720446) and PIONEER 6 (NCT02692716) have been reported previously. 8, 9 In brief, adults with T2D at high risk of a CV event were randomized to semaglutide or placebo in addition to standard-of-care treatment. Having a high CV risk was defined as being aged $50 years and having established CV disease or CKD, or being aged $60 years with CV risk factors. Exclusion criteria for both trials included kidney failure treated by chronic hemodialysis or peritoneal dialysis; PIONEER 6 additionally excluded those with an eGFR <30 ml/min per 1. 73 m 2. 8, 9 In SUS-TAIN 6, participants received s. c. once-weekly semaglutide 0. 5 or 1. 0 mg (median follow-up of 104 weeks), whereas participants in PIONEER 6 received once-daily oral semaglutide 14 mg (median observation period of 15.
Subgroups In this post hoc analysis, data from the SUSTAIN 6 and PIONEER 6 trials were pooled by treatment (semaglutide [once-weekly 0. 5 and 1. 0 mg s. c. , once-daily 14 mg oral] or placebo) and analyzed overall or in subgroups based on eGFR at baseline (30-<60 ml/min per 1. 73 m 2 or $60 ml/min per 1. 73 m 2 for efficacy, and <60 ml/min per 1. 73 m 2 or $60 ml/min per 1. 73 m 2 for safety). Data were also stratified by renin-angiotensin system (RAS) inhibitor use (an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker [yes/no]) at baseline. Data from participants with a baseline eGFR <30 ml/min per 1. 73 m 2 were included in the analyses. However, as baseline eGFR <30 ml/min per 1. 73 m 2 was an exclusion criterion in PIONEER 6, only estimates (including results from interaction analyses) pertaining to eGFR $30 ml/min per 1.
Outcomes Change in eGFR was evaluated over time, in the overall population and in the 2 eGFR subgroups, both in a pooled analysis and in separate SUSTAIN 6 and PIONEER 6 analyses. All available data points from the 2 trials were used until week 104 for SUSTAIN 6 and week 83 for PIONEER 6. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation. eGFR, evaluated as a treatment effect on annual eGFR slope, has been shown to be predictive of kidney failure endpoints, based on a metaanalysis of 47 clinical trials evaluating CKD treatments in 60,620 participants. 21 In SUSTAIN 6, visits occurred at weeks 0, 2, 4, 8, 16, 30, 44, 56, 68, 80, 92, and 104. In PIONEER 6, visits occurred at weeks 0, 4, 8, 14, 26, 38, 50, 62, 76, and 83. Annual eGFR slope was assessed post hoc in the overall population and in the 2 eGFR subgroups, both in a pooled analysis and in separate SUSTAIN 6 and PIONEER 6 analyses.
As differences in the treatment effect of other glucose-lowering medication classes (e. g. , sodium-glucose cotransporter-2 inhibitors) have been observed according to whether patients were also receiving RAS inhibitors,  22  the mean annual eGFR slope analyses (overall population and eGFR subgroups) were also evaluated in 2 subgroups according to use (yes/ no) of RAS blockade at baseline. The change over time in estimated urinary albumin-to-creatinine ratio (UACR) was also assessed post hoc in the overall population of the SUSTAIN 6 trial, and in the 2 eGFR subgroups. These data were not collected in the PIONEER 6 trial. Safety was assessed by incidence of adverse events (AEs), serious AEs, severe AEs, gastrointestinal (GI) AEs, severe hypoglycemic AEs, AEs leading to premature treatment discontinuation, and acute renal failure (acute kidney injury).