dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion With this study, we provide several different bioinformatics strategies that can identify biologically relevant biological molecules, pathways, and networks from multi-omics data sets in an unsupervised and supervised manner. The identified proteins, peptides, and miRNAs highlight modules relevant for CAKUT disease and can be used for future investigations. Finally, the application of open science principles in this study contributes to the reusability of data and workflows in, but not limited to, the rare disease field.
Multi-omics data sets The CAKUT multi-omics data set was obtained from a previously published study and reinvestigated in collaboration with the authors of the original study  6, 22. The initial study contains amniotic fluid samples from proteome and peptidome. Here we added novel miRNome data from amniotic fluid samples from the same patients as described below. In total 175 individuals were studied, of which 104 samples had a clinical diagnosis. This diagnosis consisted of antenatal diagnosis, amniotic fluid phenotype, and the postnatal outcome at two years of age. Non-severe CAKUT patients had normal GFR (glomerular filtration rate), moderately reduced GFR (60 to 90 ml/min per 1. 73 m 2 ), or reduced GFR (<60 ml/min per 1. 73 m 2 ) postnatal outcome, but are marked as CAKUT patients based on their antenatal diagnosis.
The severe CAKUT patients were diagnosed with severe renal failure, end-stage renal disease, or the renal phenotype lead to a termination of pregnancy. In total, the abundances of 7302 peptides were measured in the amniotic fluid samples of 162 subjects, 503 miRNAs were significantly detected from 150 samples and 1662 proteins were detected in 61 samples. Each of these three omics data sets includes non-severe CAKUT vs. severe CAKUT cases.
miRNome sample collection and analysis For miRNA analysis, amniotic fluid samples were collected in a prospective multicenter observational study focusing on foetal bilateral CAKUT as part of a clinical trial (https://clinicaltrials. gov/ct2/show/NCT02675686). The CAKUT disease severity was defined based on the renal status after two years of postnatal clinical follow-up. The total RNA was isolated using the Agilent RNA 6000 Pico kit protocol (5067-1513) and microRNAs were profiled using Agilent microRNA slides (Sanger miRBase release 21). The samples were labelled and hybridised according to the Agilent's microRNA Complete Labeling and Hybridization Kit protocol (5190-0456), followed by Spike-ins with the Agilent's microRNA Spike-In Kit protocol (5190-1934) and analysed using Aligent's High-Resolution Microarray Scanner GS2505_C.
FAIR Data Point and data deposition We used FAIR Data Point (FDP) to describe the CAKUT multi-omics data set. FDP is a metadata service that provides descriptions about resources  23. FDP uses Data Catalogue Vocabulary (DCAT) to capture the resources metadata. FDP serves descriptions of resources to both humans and machines, which makes integration of different data sets easier and allows reproducing results from previous studies. The human users who visit the FDP see the resource descriptions as HTML documents and the machine gets resource descriptions as a RDF document. We used the FDP [https://w3id. org/ejp-rd/fairdatapoints/wp13] created for the EJP RD project to describe CAKUT multi-omics data sets.
Multi-omics integrative analysis with mixOmics We used the mixOmics package  25  (version 6. 10. 9) with PLS-DA (Partial Least Squares Discriminant Analysis Discriminant Analysis) and sPLS-DA (sparse PLS-DA) supervised classification. The sPLS-DA method allowed for variable selection on each omics. As in principal component analysis, sPLS-DA projects large input data into a smaller dimensional space, with each component representing a different dimension. From the peptidome data, 53 samples were used for training and 51 samples were used for validation. The samples were assigned to each of the groups in the initial study  6. In line with this grouping, of the samples that matched between the peptidome and miRNome, 41 were used for training and 46 for validation. Nonetheless, when matching samples of the proteome to the other two omics data sets, there were only 23 and 10 samples for training and validation remaining respectively.
Thus, the proteome data was not used in the mixOmics analysis. For the sPLS-DA classification, a maximum of five components were chosen, where each component represents a separate dimensional subspace for data projection. The first component uses 50 miRNAs and peptides, the second component uses 20 miRNAs and 10 peptides, whereas all other three components use all miRNAs and peptides. Peptides are mapped to their respective proteins and multiple matches to the same protein are shown with the numbered suffixes. The exact peptide sequences can be found in the Supplemental Table  1. For the network shown in Figure  2C:  (i) the edges between miRNAs and peptides are based on their statistical significance, which was based on the multi-omics analysis of the training data. (ii) The edges between miRNAs and proteins are identified based on the known miRNA-mRNA biological links provided by the mirTarBase database, version 8.
Multi-omics integrative analysis with joint dimensionality reduction using momix We applied eight joint dimensionality reduction methods on peptidome, proteome, and miRNome data for 31 samples (18 non-severe CAKUT cases and 13 severe CAKUT cases) matched in the different omics data sets. We used the momix notebook  8. The methods we used are iCluster  26  , Integrative NMF (intNMF)  27  , Joint and Individual Variation Explained (JIVE)  28  , Multiple Co-Inertia Analysis (MCIA)  29  , Multi-Omics Factor Analysis (MOFA)  30  , Regularized Generalized Canonical Correlation Analysis (RGCCA)  31  , matrix-tri-factorization (scikit-fusion)  32  , and tensorial Independent Component Analysis (tICA)  33. We ran the methods to obtain two factors in the reduced dimension. We observed that nonsevere CAKUT and severe CAKUT patients could be separated by one of the two factors (Figure  3A-C ).
Among these top 5% molecules identified by the top three methods, we focused on the peptides and proteins identified by all three methods, and the miRNAs identified by two methods (RGCCA and MOFA), as there was no miRNA in common to all three methods. For robustness, we used the top features identified by multiple methods. We used the peptides and proteins common to three methods, and miRNAs common to two methods, as tICA does not have any miRNAs common with the other two methods. This resulted in 106 peptides, 18 proteins, and 13 miRNAs (note that for proteins, 16 features are selected but one feature is "P55285;Q9Y6N8;Q9ULB4", which correspond to three cadherins, CHD6, CHD9 and CHD10). The peptides were mapped to protein/gene using the UniProt Retrieve/ID mapping module (https://www. uniprot.
Figures Figure Legends Figure 1. Analysis samples. Specification of the number of samples from each of the three omics data sets that was used for the three bioinformatics strategies (mixOmics, momix, and pathway analysis). For the mixOmics and the momix analysis, the number of samples was reduced, since these methods required samples from the same patient that matched between the different omics data sets. For the pathway analysis methods, all samples with sufficient clinical data were used for analysis. The results of each strategy are highlighted. Figure  2. Integrative analysis of miRNome and peptidome data to identify combinations of variables from both omics datasets in comparison to the single-omics analysis using only peptidome data. Figure  2A : Multi-omics integration of miRNome and peptidome data using the block sPLS-DA method of the mixOmics package.
The colours indicate the quartile of the rank of the significant term for the specific dataset. Enrichment scores can be found in Supplementary Table  5. Figure  3H : Reactome enrichment analysis results of the features selected from different omics data by multiple methods (there is no enrichment result for genes selected from the proteome data). The significant results from different omics are filtered and integrated by orsum. The colours indicate the quartile of the rank of the significant term for the specific dataset. expressed proteins/peptides/miRNAs in WikiPathways pathway database on the combined miRnome, peptidome, and proteome data. Rectangular nodes represent protein products as determined from the peptidome and proteome, ellipses represent miRNAs indicated by their hsa-miR identifiers.
Acknowledgements The authors would like to thank  Matthias Haimel ,  Laura Rodriguez-Navas ,  Franz Schäfer ,  Nazli Sila Kara ,  Ugur Sezerman ,  Daphne Wijnbergen ,  Ana Rath , and  Chris Evelo  for helpful discussions, and the  anDREa  team for technical support. This work is supported by the funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  EJP   RD   COFUND-EJP N° 825575. OOs work is supported by the  Excellence Initiative of Aix-Marseille University  -A* Midex , a  French "Investissements d'Avenir" programme. JS and JBs part of the work was financed by the  French "Programme Hospitalier de Recherche Clinique" (PHRC)  number  N° 10 138 01 -N° RCB 2010-AO1151-38 , by grants from the  "Fondation pour la Recherche Médicale " (grant number  DEQ20170336759 ), and from the  European Commission Seventh Framework Programme  ( FP7/2007-2013 ) grant agreement no.
26 m 2 /kyr for a duration of t = 144 years (large circle and vertical gray line). Figure generated using the Python's matplotlib library. deviation expected between topographic profiles resulting from different slip scenarios -i. e. , ≪ 20% of the total scarp height (Fig. 3b, c ), otherwise the morphological signature of fault slip history remains hidden and undetectable. The model developed in this paper succeeds in reconciling the apparent paradigm 9 of young and old scarps associated with high and low apparent κ , respectively, by accounting for their contrasting slip history. We note that, due to the principle of superposition, a linear model of diffusion should lead to the least obvious effect of fault slip history on scarp morphology. This effect is, therefore, likely to be enhanced when considering more complex and potentially non-linear models.
Acknowledgements This study was supported by the  Agence Nationale de la Recherche (France) : project  TopoExtreme  (PI: R. C. -Grant  ANR-18-CE01-0017 ), project  SLOWDEF  (PI: M. S. -Grant  ANR-18-CE31-0008 ), and  IdEx Université de Paris  (Grant  ANR-18-IDEX-0001 ). This study has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( PI: P. S. -Grant 803721 ). We thank  Christopher Scholz  and the two anonymous reviewers for their constructive comments.
Petrographic Description Two thin sections corresponding to the surface (SS-0) and from 3 cm depth (SS-3) from the specimen were prepared and analyzed with a polarized microscope OLYMPUS BX51 with a color camera QImaging QICAM Fast 1394 at GEGENAA (Reims, France). The samples were permeated with blue resin to facilitate the image analysis of the porosity. JMicrovision software was used for the image analysis.
Ion Chromatography (IC) Anion analysis was conducted in 2023 to determine the salt-related ion content in the five samples. The samples were powdered, brought into solution in 1 mL of ultrapure water, agitated during 12 h, and centrifuged at 13,400 rpm for 10 min. The liquid phase was collected, and the process was repeated once to dissolve all the anions. The quantity of chlorides Cl -, nitrates NO 3 -, and sulfates SO 2 4-was measured with a Dionex ICS 2000 liquid chromatograph fitted with a pump, an eluent generator (EGC III) with a potassium hydroxide (KOH) cartridge, an AS50 injector, and a DS6 conductivity cell at GEGENAA (Reims, France). The separation of anions was performed on a 2-mm Dionex IonPac AS17 column coupled to a 2-mm Dionex ASRS 300 suppressor. Instrument control and data acquisition were performed using Chromeleon 6.
Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) In 2023, the concentrations of cations present in the five samples were measured using inductively coupled plasma-atomic emission spectrometry at GEGENAA (Reims, France) (ICP-AES, Iris Advantage, Thermo Fisher Scientific). The analysis was conducted through three measurements of 10 mL of each solution. The samples were the same five powders prepared for XRD analysis. 2. 3. 9. Thermogravimetric Analysis (TGA) Mass variation during heating was measured using TGA/DTA. The main purpose of this analysis was to determine the presence of gypsum and its original state of hydration. The device was a SETSYS Evolution 1750 with the software SETSOFT2000, located at LaSIE laboratory (La Rochelle, France). Three samples were analyzed, the two powders recovered from the surface (SS-P1, SS-P2) and the sample from the surface (SS-0) transformed into powder for this analysis.
According to the database, these peaks fit well with calcite compositions containing around 3%-4% magnesium. A further categorization was performed to facilitate a comparison with the results obtained by  Cardell et al. (2003)   [27] , based on pore size ranges: over 10 µm, between 10-0. 1 µm, and under 0. 1 µm (Table  2 ). These groups reveal that a significant amount, ranging from approximately 70%-85% of the porosity of Crazannes, falls within the 0. 1-10 µm interval. The surface samples (SS-0, CR-1, CR-M5) showed a lower quantity of pores over 10% in relation to the samples from deeper areas. Nevertheless, the differences between SS-3 and SS-6 do not allow for a conclusive statement regarding this variation. The category under 0. 1 µm was created to differentiate the fluid flow driven by capillary forces or by adsorption.
Micro Raman Spectrometry The objective of this analysis was to identify the presence of gypsum in the powdered samples and determine its hydration state based on the characteristic peaks associated with specific water molecules. The majority of observed grains were identified as calcite, exhibiting characteristic peaks at 155, 280, 712, and 1086 cm -1 , as documented in the RRUFF database. Although gypsum crystals were rare, their analysis confirmed the presence of the dehydrated phase, corresponding to gypsum crystals. The peaks observed at 413, 492, and 1008 cm -1 provided further confirmation of the presence of this mineral.
Chloride Titration The progression of chlorine penetration within the specimens is depicted in Figure  9. Across all samples, whether sourced from the upper or lower regions of the alveoli, the surface exhibits a higher chlorine concentration compared to the inner layers. In the initial samples, analyzed at approximately 0. 5 cm from the surface, consistently elevated values were observed, ranging from 2 to 2. 5 times greater than those found in the samples at 1. 5 cm depth. The deeper samples exhibited relatively constant values akin to those at 1. 5 cm. exhibiting characteristic peaks at 155, 280, 712, and 1086 cm -1 , as documented RRUFF database. Although gypsum crystals were rare, their analysis confirmed th ence of the dehydrated phase, corresponding to gypsum crystals. The peaks obse 413, 492, and 1008 cm-1 provided further confirmation of the presence of this mine.
Thus, the interpretation of the results considers a lower RH% in the winter and the eventual possibility of the door connecting to the laboratory being open for a short time, which would create humidity circulation and also a decline in RH%. Due to the limitations of ECOS-RUNSALT in accurately calculating compositions that include complex ions such as Ca 2+ and SO 2 4-, the software suggests removing gypsum from the equation. However, since gypsum is one of the main salts found in the analysis and its thermodynamics are crucial for understanding the deterioration of Crazannes, two separate calculations were performed  [49]. Firstly, a simplified model was tested, including only Ca 2+ , SO 2 4-, Na + , and Cl -. This was carried out to understand the thermodynamic parameters of the main salts present (gypsum and halite) and the sequence of their crystallization.
annulata (TBL20). (B) Levels of miR-34c-3p are high in virulent macrophages and decrease in attenuated macrophages. (C) miR-34c-3p levels in attenuated macrophages increase following their transfection with miR-34c-3p mimic. (D) Endogenous levels of miR-30f are low in virulent (V) macrophages and high in attenuated (A) macrophages. Transfection of attenuated macrophages with the miR-34c-3p mimic (Am) did not significantly change miR-30f levels, demonstrating the specificity of the miR-34c-3p mimic. Data are represented as mean 6 SEM. n = 3. ***, P , 0. 001 compared to virulent macrophages; ###, P , 0. 001 compared to attenuated macrophages. miR-34c-3p levels were estimated using qPCR. Infection-Induced miR-34c-3p and Activation of PKA mSphere Month YYYY Volume XX Issue XX 10. 1128/msphere.
A total of 10 ng of RNA was used in the reverse transcription reaction with miRNA-specific primers. The real-time reactions were performed in a 7500 high-throughput (HT) fast real-time PCR system (Applied Biosystems). Data were analyzed using the comparative threshold cycle (2 2DDCT ) method. The threshold cycle (C T ) values for the selected miRNA targets were subtracted from the C T values of the endogenous small noncoding RNA control RNU6B (control miRNA assay; Applied Biosystems). qRT-PCR for mRNA. Total RNA was reverse transcribed using the high-capacity cDNA reverse transcription kit (Applied Biosystems; catalog number 4368814), as follows: 1 mg total RNA, 2 mL RT buffer, 0. 8 mL 100 mM deoxynucleoside triphosphate (dNTP) mix, 2. 0 mL 10Â random primers, 1 mL MultiScribe reverse transcriptase, and nuclease-free water to a final volume of 20 mL.
ACKNOWLEDGMENTS This study was supported by a  Competitive Research Grant (CRG)  from the  Office for Sponsored Research  ( OSR-2015-CRG4-2610 ) at  King Abdullah University of Science and Technology (KAUST) , awarded to A. P. and G. L. , M. H. , F. B. -R. , and Z. R. acknowledge  KAUST  for postdoctoral fellowships. S. M. and T. M. were supported by AP baseline funding ( BAS/1/1020-01-01 ) from  KAUST. G. L. also acknowledges  ANR-11-LABX-0024  and core support from  INSERM  and the  CNRS. FACS was performed with the help of the  Cytometry and Immunobiology Facility (CYBIO)  of the  Cochin Institute  ( INSERM U1016 ).
Subject category: New Taxa: Bacteroidota The Genbank/EMBL/DDBJ accession number for the 16S rRNA gene sequence of strain LLG6346-3. 1 T is OQ511313. The Genbank/EMBL/DDBJ accession number for the genome sequence of strain LLG6346-3. 1 T is SAMN33610405. Two supplementary figures and three supplementary tables are available in the online version of this article. The genus Zobellia belongs to the family Flavobacteriaceae (order Flavobacteriales, class Flavobacteriia), was proposed by Barbeyron et al. [1]  with Zobellia galactanivorans as the type species of the genus. At the time of writing, the genus Zobellia comprises 8 validly named species, all isolated from marine environments and mostly from macroalgae. For example, Z. galactanivorans Dsij T was retrieved as an epibiont of the red alga Delesseria sanguinea  [2] , Z.
Chargaff's coefficient of the genomic DNA of strain LLG6346-3. 1 T was deduced from the complete genome sequence and expressed as the molar percentage of guanine + cytosine. The nucleotide sequence of the 16S rRNA gene deduced from the complete genome sequence of the strain LLG6346-3. 1 T and sequences of the 16S rRNA genes from all valid species of the genera Zobellia, Maribacter and some other related genera of the Flavobacteriaceae family were aligned using the software MAFFT version 7 with the L-INS-I strategy  [18]. The alignment was then manually refined and phylogenetic analyses, using the neighbour-joining  [19] , maximum-parsimony  [20]  and maximum-likelihood  [21]  methods, were performed using the MEGA 6 package  [22]. The different phylogenetic trees were built from a multiple alignment of 50 sequences and 1437 positions.
001). Sequences were aligned using MAFFT  [18]  and maximumlikelihood  [21]  phylogeny was reconstructed using RaxML  [27]  with 100 bootstrap iterations. Pairwise comparisons of 16S rRNA gene sequences were made by using the database EzBioCloud (https://www. ezbiocloud. net/identify)  [28]. Genomic relatedness was investigated by comparing the strain LLG6346-3. 1 T genome sequence with those of the type strains of other Zobellia species using the Average Nucleotide Identity (ANI; http://jspecies. ribohost. com/jspeciesws/#analyse)  [29] [30] [31]  and the dDDH via the online server Genome to Genome Distance Calculator 2. 1 (GGDC; http://ggdc. dsmz. de/distcalc2. php)  [32]. The results from GGDC analysis were obtained from the alignment method Blast+ and the formula 2 (sum of all identities found in HSPs / by overall HSP length) for incomplete genome sequences  [33, 34].
Exploration of carbohydrate active enzyme-coding genes in the genomes of the strains LLG6346-3. 1 T , Z. russellii KMM 3677 T and Z. roscoffensis Asnod1-F08 T was carried out via the online server Microscope from the French National Sequencing Center (http://www. genoscope. cns. fr/agc/microscope/mage)  [35]  and the CAZy database (www. cazy. org)  [36]. The best pairwise comparison score with 16S rRNA gene from the strain LLG6346-3. 1 T (1516 bp) were obtained with Zobellia russellii KMM 3677 T (98. 6 %) (Table  S1 , available in the online version of this article). Phylogenetic analyses by neighbour-joining of 16S rRNA genes from species of the family Flavobacteriaceae showed that strain LLG6346-3. 1 T belongs to the genus Zobellia and forms a clade with strains Z. russellii KMM 3677 T and Z.
Acknowledgements The  LABGeM  ( CEA/Genoscope  and  CNRS   UMR8030 ), the  France Génomique and French Bioinformatics Institute national infrastructures  (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform. We are grateful to the  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ) and the  Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr) for providing help for computing and storage resources. This work has benefited from the facilities of the Genomer platform, which is part of the  Biogenouest core facility  network. We thank  Gwenn Tanguy  and  Erwan Legeay  for sequencing. This work has benefited from the  CORSICABENTHOS expeditions  (PI:  Line Le Gall ), the marine component of the " Our Planet Reviewed" programme.
The DNA G+C content is 38. 28%. The type strain, LLG6346-3. 1 T (RCC 7657 T = LLG 32918 T ), was isolated from the surface of the brown alga Ericaria zosteroides (C. Agardh) Molinari & Guiry 2020. The Genbank/EMBL/DDBJ accession number for the 16S rRNA gene sequence of strain LLG6346-3. 1 T is OQ511313. The Genbank/EMBL/DDBJ accession number for the genome sequence of strain LLG6346-3. 1 T is SAMN33610405. distinct modes of action and exquisite adaptation to their natural substrate. J Biol Chem;288:23021-23037. DOI: 10. 1074/jbc. M113. 467217.
Parameters analyzed The AKIM-software was used for data acquisition. Blood samples were obtained during amenorrhea and were analyzed at the local ISO-certified Department of Laboratory Medicine, General Hospital of Vienna, Vienna, Austria according to ISO 15,189 quality standards: estradiol, follicle-stimulating hormone (FSH), LH, prolactin, AMH, testosterone, dehydroepiandrosterone-sulphate (DHEAS), and sex hormone-binding globulin (SHBG) were measured by the corresponding Cobas electrochemiluminescence immunoassays (ECLIA) on Cobas e 602 analyzers (Roche, Mannheim, Germany). On the same day, a vaginal ultrasound was performed with an "Aloka Prosound 6" ultrasound machine and an "UST-9124 Intra Cavity transducer" (frequency range 3. 0-7. 5 MHz; Wiener Neudorf, Austria). PCOM, the main outcome parameter, was defined as when the number of follicles per ovary exceeded 12  [2, 3].