dataesr/scientific-paragraphs-categorization huggingface dataset:
The smoothing was performed on the variable transfer in function of treatment. We analyzed early, late and total brood size data (data in supplementary informations; Table  A. 3) by considering the effect of different treatments on the number of laid eggs using Kruskal-Wallis rank sum test with R software and the stats package  (Team, 2013). Then, a post hoc pairwise comparison with Holm correction was realized between each treatment. We analyzed egg-laying delay as a function of treatment using Spearman's rank correlation between early and late brood size. For the reciprocal-transplant experiments, we analyzed hatching success and brood size (early, late and total) using Generalized Linear Mixed Model (GLMM) with Ade4 package  (Bougeard and Dray, 2018; Dray and Dufour, 2007)  and glmmTMB package  (Brooks et al.
, 2008; C. elegans: Buisset-Goussen et al. , 2014). As for population growth rate, under irradiated conditions the realized fecundity fluctuated significantly over time, suggesting transgenerational effect of the C. elegans reproduction  (Yue et al. , 2021). Moreover, this result seems logical since the calculation of both indexes is based on the same data set. Offspring survival is essential in the maintenance and growth of a population over the long term. We, therefore, measured hatching success during the experiment in parallel with population growth rate. Our results showed a lower hatching success in the high irradiation treatment compared to the control treatment. A decrease of the hatching success of about 20% has already been observed with an acute gamma irradiation of 50 Gy of the L4/young adult stage with the N2 strain  (Dubois et al.
Acknowledgements We are grateful to  Henrique Teotónio  and his team for their invaluable help and for providing the A6140 nematode population. We especially thank  Carolyn Hall  for linguistic corrections. Thanks to  Sandrine Frelon ,  André Gilles ,  Morgan Dutilleul  and  Patrick Laloi  for ideas, valuable discussion and comments during this project. This study has been funded by the  French Institute of Radiological Protection and Nuclear Safety (IRSN).
Acknowledgement MS and LL gratefully acknowledge the  Leverhulme Trust  for funding (Project  RPG-2019-235 ). MS and ARF gratefully acknowledge funding from the  EPSRC Programme Grant Crystallization in the Real World  (Grant  EP/R018820/1 ). AB and MP gratefully acknowledge the support by the  French National Research Agency (ANR)  under grant  ANR-21-CE30-0001  and the  Swiss National Science Foundation.
Nutritional intervention results in only partial recovery from SAM-induced cecal dysbiosis We observed a significant decrease in the weight and size of the ceca of SAM and Int mice compared to Ctrl mice (Figure  S3A ), likely indicative of microbial changes in the cecum. 28, 29  Indeed, unweighted PCA and ) at the species level in Ctrl (n = 9) compared to SAM group (n = 13) in the terminal ileum, 3 independent experiments. Among decreased species in SAM mice, arrows 1 and 2 pointing to (1) Candidatus Arthromitus (SFB) and (2) Akkermansia, are shown as box and whisker plots (right) that were generated after volcano plot analysis by combination of fold change (FC, threshold 2. 0) and t-test (nonparametric, Wilcoxon rank-sum test) analyses using MetaboAnalyst 5. 0 software. 86 *p-value<0. 05, **p-value<0.
Flow cytometry analyses After autoMACS separation, CD11c + (positive fraction) and CD11c À (negative fraction) cells were pre-incubated on ice for 10 min with the 2. 4G2 antibody to block Fc receptors. Cell mortality was evaluated using LIVE/DEAD Fixable Blue Dead Cell Stain Kit (Life Technologies). Intracellular staining was performed using eBioscience Intracellular Fixation & Permeabilization Buffer set according to manufacturer's instructions (Invitrogen). Multiparametric flow cytometry was performed using a FACS LSRII UV. Data were analyzed with Flow Jo software. Three panels were designed to study the phagocyte population in PPs, T and B cells in PPs and SI.
DNA was stained with Sytoxblue revealing both eukaryotic and bacterial cells. Spectral confocal imaging projections of ileal villus and Peyer's patches from 49-day old Ctrl, SAM and Int mice stained by FISH for all bacteria (Eub-338 probe) and specific probe to identify SFB (SFB-1004 probe) were used to quantify the distance between the microbiota and the intestinal epithelium. Only Eub-338 specific signal that was not identified as positive for SFB-1004 specific signal was used to measure the proximity of the luminal microbiota to the epithelium (top of villi or follicle-associated epithelium, FAE) in each diet group, using ZEN Lite 2. 3 SP1 software line tool (Carl Zeiss Microscopy).
Quantification of fecal IgA by ELISA Stools were weighted and diluted in an inhibitor cocktail at 100 mg/mL containing PMSF 0. 35 mg/mL (Sigma-Aldrich), EDTA-Na2 0. 75 mg/mL (Sigma-Aldrich), pepstatine 1 mg/mL (Sigma-Aldrich) and aprotinine 2,000 times diluted (Sigma-Aldrich). After mechanical disaggregation, stools were incubated for 20 min in the inhibitor cocktail at 4 C. Stools were then vortexed and centrifuged at 14, 000 rpm for 10 min. Supernatants were collected and conserved at À20 C for IgA quantification. Total IgA detection was determined according to the manufacturer's protocol (IgA mouse uncoated ELISA kit, Invitrogen). The NUNC MaxiSorp high protein-binding capacity 96 well ELISA plates were used. Colorimetric reaction was measured at OD = 450 nm by microplate reader Infinite M1000 PRO using the i-Control software (Tecan).
The reads were first demultiplexed according to their dual barcode by samples. Quality of the raw reads were assessed using FastQC v0. 11. 9 toolkit  92  and low-quality reads were trimmed using Trimmomatic v0. 39. 93  V3_V4 primer with overhang adapters (FwOvAd_341F and RevOvAd_785R) were removed using cutadapt v3. 2. Amplicon Sequence Variants (ASVs) were produce using DADA2 94 in QIIME 2. 95  We obtained 6475 ASVs with a length R 400 nt to avoid non v3-v4 ribosomal sequence. A Naive Bayes classifiers trained on the Silva (release 138) 99% OTUs fulllength sequences were applied to obtain the pre-trained taxonomy classifiers used for the taxonomic assignation of the features with the classify-sklearn method. 96  The table was rarefied to 7500 sequences and filtered to exclude samples with less than 7500 sequences.
Raw paired-end reads were subjected to the following process 1) quality filtering with the PRINSEQ-lite PERL script  99  by truncation of bases from the 3 0 end not with quality <30 based on the Phred algorithm, 2) paired-end read assembly using FLASH 100 with a minimum length overlap of 30 bases and 97% overlap identity and 3) the search and removal of both forward and reverse primer sequences using CutAdapt, with no mismatches allowed in primers sequences. Assembled sequences for which perfect forward and reverse primers are not found are eliminated. Amplicon Sequence Variants (ASVs) were produce using DADA2  94  in QIIME 2. 95  We obtained 1549 ASVs with a length R 400 nt to avoid non v3-v4 ribosomal sequence. The following analyses was realized as described for terminal ileum content.
Data processing and molecule identification All the raw data generated by the UPLC-HRMS were converted to mzXML by ProteoWizard (Version 2. 0), and then processed by MZmine 2. 26. The identification of the metabolites was performed by using an inhouse database referencing more than 800 metabolites with their chromatographic retention time and their exact mass and MSMS spectra obtained in positive and negative ionization modes, including their adducts and neutral losses. Statistical analyses were performed using MetaboAnalyst 5. 0 software. 86.
In the Figures  3C  and 3D , Linear discriminant analysis (LDA) effect size (LEfSe) was applied to discern significant differences in the relative abundance of microbial taxa among all the groups, which included the non-parametric Kruskal-Wallis test and LDA2. 102  The groups were compared in pairs. Taxonomical features with p-value <0. 05 and LDA effect size >2 were regarded as significant microbial signatures. In the Figures  4E  and 4F , Spearman's rank correlation analyses were performed in the R environment using stats package and cor function. Heatmap where generated with gplots-3. 0. 1. 2 package and heatmap. 2 function using dist and hclust arguments to compute the distance and the hierarchical clustering based on pairwise distances calculated from Spearman correlation values.
ACKNOWLEDGMENTS We thank  Sylvie Me  ´met for helpful discussions and improvement in the manuscript. We acknowledge the  PICSL imaging facility  of the  CIML (ImagImm) , member of the national infrastructure France-BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ), and the cytometry core facility of the  CIML  for expert assistance. This work was supported by institutional funding from  Centre National de la Recherche Scientifique  and  Institut National de la Santeet de la Recherche  Medicale, by the  Agence Nationale de la Recherche   ANR-20-CE15-0016 , and by  Fondation pour la Recherche Medicale (FRM) , grant number  DEQ20170336745. The project leading to this publication has received funding from  France 2030 , the  French Government program  managed by the  Agence Nationale de la Recherche  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX.
Data Raw fastq files 16s rRNA sequencing data have been deposited at Zenodo and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Workflow data on the parameters used for the microbiome profiling of the terminal ileum and caecal contents have been deposited at Zenodo and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Code Continued.
Among decreased species in SAM mice, arrows 1 and 2 pointing to (1) Candidatus Arthromitus (SFB) and (2) Akkermansia, are shown as box and whisker plots (right) that were generated after volcano plot analysis by combination of fold change (FC, threshold 2. 0) and t-test (nonparametric, Wilcoxon rank-sum test) analyses using MetaboAnalyst 5. 0 software. 86 *p-value<0. 05, **p-value<0. 01, ***p-value<0. 001, ****p-value<0. 0001. See also Figure S2 and TableS2.
FTIR NCA polymerization was monitored by Fourrier-transform infrared (FTIR) spectroscopy on a Thermo Scientific Nicolet iS5 spectrometer equipped with an ID7 ATR module. Data were processed using OMNIC 9. 7 software. Polymerizations were stopped when NCA-associated carbonyl bands at 1850 and 1778 cm -1 had disappeared, corresponding to the total NCA consumption. Purified polymers were also analyzed by FTIR in solid state.
1 H NMR 2. 2. 2. 1. 1 H NMR at 80 MHz. Proton Nuclear Magnetic Resonance ( 1 H NMR) spectroscopy was performed on a 80 MHz MAGRITEK Spinsolve 80 Carbon, with the following parameters: NS = 64, repetition time = to 30 s, pulse angle = 90, by dissolving the product in deuterated water (D 2 O) or dimethylsulfoxide (DMSO-d 6 ) at a concentration between and 50 g. L -1. Data were processed using SpinSolve and MestReNova softwares. 1 H NMR was used to determine degrees of polymerization (n) of the PSar block and composition (x + y) of the PGluOBn block. Calibration was performed on the signal at 0. 84 ppm of the 9 protons of the tbutyl chain-end of the neopentylamine initiator. Signal of the methyl protons of PSar segment at 3. 10 -2. 67 ppm was used to evaluate n, and signals of the benzyl and the methylene protons of PGluOBn block respectively at 7.
1 H-DOSY NMR at 600 MHz. Diffusion-ordered spectroscopy was subcontracted to GreenPharma and performed on a Brüker Avance DRX-600 MHz spectrometer equipped with a cryoprobe-head. The product (10 mg) was dissolved in DMSO-d 6 (600 µL). Spectra were acquired with the ledbpgp2s pulse program from Bruker Topspin software with the following parameters for PSar 66 -b-PLGluOBn 23 : ns = 8, p30 = 1. 9 ms, p19 = 1. 0 ms, d20 = 180 ms, d21 = 5 ms, d16 = 200us, 1_TD = 32, GAB d1 = 10 s, G = 6. 44 Gauss/mm. The same parameters were used for PSar S1 except the following ones that were adapted: p30 = 1. 5 ms, d20 = 100 ms. Data were processed using MestReNova software with the peak fit algorithm, 65,536 points in the F2 dimension, 128 points in the F1 dimension and an auto decay component (multi-exponential processing).
MALDI-TOF MS Matrix Assisted Laser Desorption Ionization/Time Of Flight (MALDI-TOF/TOF) UltrafleXtreme mass spectrometry (MS) was performed on a Bruker Daltonics mass spectrometer. Mass spectra were obtained in linear or reflector positive ion mode. The laser intensity was set just above the ion generation threshold to obtain peaks with the highest possible signal-to-noise (S/N) ratio without significant peak broadening. All data were processed using the FlexAnalysis software package (Bruker Daltonics). Polysarcosine samples were solubilized in methanol at 10 g/ L and diluted to 0. 5 g/L in the matrix composed of α-cyano-4-hydroxycinnamic acid (CHCA) at 10 g/L in an acetonitrile/water 1/1 v/v mixture. Three deposits of 1 µL of the diluted solution were made on the MALDI plate. A close external calibration was carried out using PEG (1000, 2000 and 3500 g/mol).
analyses in DMF in the presence of 0. 45 %w/v LiBr were performed on an Agilent 1260 LC equipped with a diode array detector (DAD) and a differential refractive index detector (dRI) and a threecolumn set of PSS GRAM analytical columns (100 Å, 8 × 300 mm, μm; 100 Å, 8 × 300 mm, 10 μm; 1000 Å, 8 × 300 mm, 10 μm) with exclusion limits from 100 to 1 000 000 g. mol -1. Analyses were carried out on samples prepared in DMF + LiBr at 45 • C using the same solvent as eluent at a flow rate of 1 mL. min -1 and filtered on a 0. 45 µm hydrophilic polytetrafluoroethylene (H-PTFE) syringe filter. Data were acquired and processed with OpenLAB Chemstation software.
SEC in DMF with RI and LS detectors. Another SEC chain in DMF equipped with a light scattering detector was also used to analyze the copolymers. SEC analyses in DMF in the presence of 1 g. L -1 LiBr were performed on ThermoScientific UltiMate 3000 chromatography system equipped with a DAD, DRI detector and a multi-angle light scattering detector (MALS) from Wyatt technology, and two Shodex Asahipak GF310 and GF510 columns (7. 5 × 300 mm, 5 µm) with exclusion limits from 500 to 300 000 g. mol -1. Polymer samples were solubilized at 5 g. L - 1 in DMF + LiBr for 24 h and filtered on a 0. 45 µm PTFE syringe filter. Analyses were carried out at 50 • C using the same solvent as eluent at a flow rate of 0. 5 mL. min -1. Toluene was used as a flow marker. Data were acquired and processed with Astra software.
SEC in H 2 O/ACN SEC analyses in water/acetonitrile/trifluoroacetic acid (H 2 O/ACN/ TFA) 60/40/0. 1 v/v/v were performed on a Malvern Panalytical Omnisec Resolve and Reveal equipped with a UV detector, dRI detector, RALS-LALS detector and a set of 3 Waters Ultrahydrogel columns (two columns 250 Å, 7. 8 × 300 mm, 6 μm and one column 500 Å, 7. 8 × 300 mm, 10 μm) with exclusion limits from 1 000 to 400 000 g. mol -1. Analyses were carried out on polymer samples prepared in H 2 O/ACN/TFA 60/40/0. 1 v/v/v at 38 • C using the same solvent as eluent at a flow rate of 0. 7 mL. min -1 and filtered on a 0. 2 µm nylon syringe filter. Data were acquired and processed with Omnisec software. The dn/dc of polysarcosine S1 (purity 98. 4 %) was determined at 0. 1545 mL. g -1 with this system by performing a calibration using seven different concentrations.
DLS Dynamic light scattering (DLS) measurements were carried out on a Zetasizer Pro (Malvern Panalytical) equipped with a He-Ne laser (633 nm), at 25 • C and a scattering angle of 174. 8 •. Software used was ZS Explorer. A low volume plastic cell of 10 mm optical path length was filled with 70 µL of sample. Viscosity of the dispersant was corrected according to the solvent or mixture of solvents used. Data were acquired on three different measurements with an automatic optimization of the number and duration of runs per measurement. Results are expressed as an average of these measurements. D h of the objects is the intensity mean for each population. PDI is calculated from the autocorrelation functions using the cumulant method. Procedure for the determination of PSar molar mass by DLS was adapted from literature  (Weber et al.
Zeta-potential Zeta-potential (ZP) measurements were carried out on a Zetasizer Pro (Malvern Panalytical) equipped with a He-Ne laser (633 nm), at 25 • C and a scattering angle of 174. 8 •. Software used was ZS Explorer. A folded capillary cell (DTS1070) was filled with 1 mL of sample diluted 1:100 in water. Data were acquired over six different automatic measurements. Zeta-potential (ZP) values and ZP deviations are expressed as an average and standard deviation of these six measurements.
UPLC Ultra-performance liquid chromatography (UPLC) measurements were performed on an Acquity UPLC H-class from Waters equipped with a reversed-phase column (Acquity BEH, C18, 130 Å, 50 mm × 2. 1 mm × 1. 7 µm, Waters) and a diode array detector (DAD) Acquity eλ from Waters. Data were acquired and processed with Empower 3 software. The gradient elution was performed with 2 solvents: solvent A was water in the presence of 0. 05 %v/v trifluoroacetic acid (TFA) and solvent B acetonitrile in the presence of 0. 05 %v/v TFA. Column was equilibrated for at least 30 min at a A:B mixture of 80:20. The sequence duration was 8 min with the following gradient of A:B solvents: 0 -2. 5 min 80:20, 2. 5 -4 min 5:95, 4 -8 min 80:20. Eluents were degassed by the machine. Flow rate was 0. 6 mL. min -1. Column temperature was 35 • C.
HS-GC Headspace gas chromatography (HS-GC) was used to dose the content of DMF as a residual solvent in formulations. HS-GC were performed on an Agilent 7890B GC system equipped with split/splitless injector, a flame ionization detector (FID), an Agilent 7697A autosampler and using a CP Sil 5CB column (50 m length, 0. 32 mm diameter, 5 µm film thickness). A known quantity of sample was introduced into a 20 mL crimp vial and dissolved with a few milliliters of a low volatile solvent (water, DMSO or NMP). The vial was crimped and incubated in order to reach an equilibrium of concentration of the residual solvent between the liquid phase and the gas phase. The headspace of the vial was then injected into the GC system via a split injector and the residual solvent was detected by FID. Data were acquired and processed using OpenLAB Chemstation software.
Cryo-TEM Cryo-transmission electron microscopy (cryo-TEM) images were recorded on a LaB6 JEOL 2100 (JEOL, Japan) operating at 200 kV. The samples were prepared by placing a drop of a nanoparticle suspension on a holey carbon grid (Quantifoil Micro Tools GmbH, Germany). Excess solution was blotted off with a filter paper. The grid was quickly immersed in liquid ethane, placed on a Gatan 626 cryo-holder and transferred to the microscope. The grids were observed at low temperature (-180 • C) and under low dose conditions (JEOL minimum dose system). Pictures were taken with an Ultrascan 1000 with a 2 k × 2 k CCD camera (Gatan, USA) and analyzed with ImageJ software.
Acknowledgments This work benefited from the facilities and expertise of the « Mass spectrometry team research » ( Vincent Guérineau ),  Institut de Chimie des Substances Naturelles (ICSN) ,  CNRS ,  91198 ,  Gif-sur-Yvette , France. This work also received the expertise of the  Transmission Electron Microscopy platform (Jean-Michel Guigner ),  Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC) ,  Sorbonne Université ,  CNRS UMR 7590 ,  Muséum National d'Histoire Naturelle ,  75005  Paris, France, and of the  Chromatography platform at LCPO ,  CNRS UMR 5629 , with the support of  Amélie Vax.
Parameters of gene co-localization describe the genomic architecture of the system at the level of each gene or of the entire system. Each gene corresponds to one HMM (hidden Markov model) protein profile to enable sequence similarity search with HMMER  (Eddy, 2011) , and different genes (thus protein profiles) can be defined as exchangeable if they have the same role in the system. The search engine screens a database of genomes for potential systems using HMM profile searches and the clustering of co-localized hits along the genome that match the systems' model. MacSyFinder has been used with success to annotate a variety of microbial machineries and pathways, including protein secretion systems  (Abby et al. , 2016) , CRISPR-Cas systems  (Abby et al. , 2014; Couvin et al. , 2018)  and other prokaryotic defence systems  (Tesson et al.
For more details, one can consult MacSyFinder's comprehensive documentation, including the User Guide, the Modeller Guide, and the Developer Guide, created with Sphinx and available at: https://macsyfinder. readthedocs. io/. Two datasets showing command line examples and expected input and output files are provided on the Figshare platform: https://doi. org/10. 6084/m9. figshare. 21581280 (Abby, 2022a)  and https://doi. org/10. 6084/m9. figshare. 21716426. v1 (Abby, 2022b).
To this end, we annotated with the corresponding models the completely assembled genomes of 21105 bacterial and archaeal strains retrieved from the non-redundant NCBI RefSeq database (as of March, 2021, see Dataset S1). We analysed the distribution of the scores for the hits for the different genes found in the detected systems, and attributed as GA score the minimal score observed to the corresponding profile. In total, 1000 HMM profiles from the four packages are available with GA scores. They now offer the possibility for gene-wise filtering with HMMER, ensuring optimal usage of the 104 macsy-models hereby updated for MacSyFinder v2.
Comparison of MacSyFinder v1 and v2 MacSyFinder version 1. 0. 5 and MacSyfinder v2. 0. 0 were run on the dataset of complete bacterial and archaeal genomes described above (RefSeq March 2021) to detect the TXSS and related systems. The models of TXSScan v1. 1. 1 were used with MacSyFinder v2 while the models published in  (Denise et al. , 2019)  for the TFF-SF and  (Abby et al. , 2016)  for the other secretion systems were used with MacSyFinder v1. The total number of systems detected in the dataset were compared between the two MacSyFinder versions for each type of system. The same was done to compare the median system wholeness (proportion of the model-listed genes detected) for each system.
Testing the performance of MacsyFinder v2 We evaluated the performance of MacSyfinder by measuring the running time and the RAM used. We measured the overall time of the run and the time spent in several parts of the software: the genes identification by HMMER (hmmsearch) and the resolution of the best solution. To assess the used RAM, we prefixed the macsyfinder command line by the "/usr/bin/time -v" utility and extracted the "Maximum resident set size" value. We ran the analysis on a sub-set of the above-described complete genomes dataset, consisting of one genome per bacterial species. This corresponded to 6092 genomes (6455 chromosomes, see Dataset S1). To analyze the behavior of the resolution of the best solution, we ran macsyfinder with these genomes as input and using the "ordered_replicon" mode with three macsy-model sets: TXSScan/bacteria, CasFinder and CONJscan/chromosome.
We also compared the novel algorithm based on graphs' usage (using NetworkX) to an implementation based on a Mixed-Integer Linear Solver (https://www. python-mip. com/) (Supplementary Figures 4 & 5  (Abby, 2023) ). To assess the time spent in each part of the program, we ran macsyfinder with the TXSScan/bacteria models in "gembase" mode on datasets gathering increasing numbers of genomes  (1, 10, 20, 50, 100, 200, 500)  selected from the 6092 genomes set. All tests were performed on the following setup: Python version 3. 11, linux kernel version 5-15. 80, AMD Ryzen 7 3700X 8-Core Processor (16 threads) for CPU, and 64GB DDR4 for RAM.