dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses The descriptive results are reported as percentages, and continuous data are reported as mean (SD) for the demographic, prenatal and childbirth data. Responses according to the maternity centre (University Hospitals of Geneva vs. Clermont-Ferrand university hospital) were compared with t-tests for each item and each dimension of the FTFQ. Means and standard deviations were reported for each of the dimensions. Multiple linear regression analyses were conducted using a backward selection method to identify predictive factors related to the fathers' experiences of childbirth. The influence of the additional variables listed above on the three dimensions from the French version of the FTFQ was measured. For all of the analyses, a p value of <. 05 was considered significant.
Strengths and limitations Data reporting on fathers' experiences of childbirth are beginning to be better documented in the scientific literature but are still rare. This study contributes to increasing this knowledge, thus allowing better support for men throughout their fatherhood process. The experiences of 151 fathers were investigated, which is a good sample size for this type of study. A validated questionnaire with adequate psychometric properties was used, and each father's experience was evaluated at the optimal time for doing so after birth. The results of this study could initiate awareness among professionals and stimulate creativity to better support fathers throughout their fatherhood process. However, this study has some limitations. It relates to data collected 5-6 years ago and might not be completely up to date.
Acknowledgments We thank all the fathers who responded to the French version of the FTFQ. We thank  Chloé Barasinski  and  Marine Julien , the research midwives who collected the data for Clermont-Ferrand (France) and  Pierre Carquillat ,  Ophélie Boucher  and  Antonina Chilin , the research midwives who collected the data for Geneva (Switzerland).
Gridless sparse spike reconstruction is a rather new research field with significant results for the super-resolution problem, where we want to retrieve fine-scale details from a noisy and filtered acquisition. To tackle this problem, we are interested in optimisation under some prior, typically the sparsity i. e. the source is composed of spikes. Following the seminal work [1-4] on the generalised LASSO for measures called the Beurling-Lasso (BLASSO), we will give a review on the chief theoretical and numerical breakthrough of the off-the-grid inverse problem, as we illustrate its usefulness to the super-resolution problem in Single Molecule Localisation Microscopy (SMLM) through new reconstruction metrics and test on synthetic and real SMLM data we performed for this review.
The operator of acquisition is now Φ : M(X ) → H, the sparsity is enforced by a norm on M(X ) called the TV-norm. This variational problem is called the BLASSO (for Beurling LASSO): min m∈M(X ) y -Φm 2 H data term + λ m TV sparsity prior. (BLASSO) In this latter setting, the spikes can move continuously on the domain X : a comparison between the discrete and the off-the-grid reconstruction is given in Figure  1. The off-thegrid setting can be seen as the limit of the discrete case with a finer and finer grid  [11]. (a) Discrete reconstruction, which can be seen as spikes with support constrained on a grid (b) Off-the-grid reconstruction, the spikes can move continuously on the line. The red line is the acquisition y, orange spikes are the source (the cause we want to retrieve), blue spikes are discrete reconstruction constrained on a grid and green can move freely since it is off-the-grid.
One can see that the spikes are well-recovered as some non-zero red and blue particles cluster around the three δ-peaks. 12 The notion of retraction compatible with cone structure is central: in the Riemann context a retraction is a continuous mapping that maps a tangent vector to a point on the manifold. Formally, one could see it as a way to enforce the gradient evaluation to be mapped on the manifold. See  [24]  for other choices of compatible retractions and more insights on these notions. 13 See our GitHub repository for our implementation: https://github. com/XeBasTeX Conic Particle Gradient Descent applied for 2D Gaussian deconvolution, the red dots are the particle measure ν (k) (size of dot proportional with amplitude), the 3 white dots are the source measure, the image in the background is the noiseless acquisition y 0 and the black line are the paths of the particles ν (k) -all the paths constitute the gradient flow (ν t ) t≥0.
We can then clarify the forward operator Φ : m → R N 1 N 2 which encapsulates the integration over camera pixels  [13] , indeed with the evaluation of the discrete Gaussian kernel ϕ with standard deviations σ, for i ∈ {1,. , N 1 N 2 }: [ϕ(x)] i def. = 1 2πσ 2 Ω i e - (x 1 -s 1 ) 2 2σ 2 + (x 2 -s 2 ) 2 2σ 2 ds 1 ds 2. In the SMLM data set, one has the PSF standard deviation σ = 149. 39nm and N 1 = N 2 = 100nm. The reconstruction is performed by our implementation of the Sliding Frank-15 https://srm. epfl. ch/DatasetPage?name=MT0. N1. HD Wolfe in python 16 insofar as it is the more robust method available: indeed it works with Gaussian kernel, it has proven results in a noise regime, etc. The results are presented in Figure  9. The stack of 2500 images of 64 × 64 is qualified as high density with high SNR: the number of activated fluorophores is quite important, and the noise is not negligible 17.
The reconstructions of the gridless method and the Deep-STORM algorithm are presented in Figure  10 , where one can appreciate the reconstruction by off-the-grid on fine details. The reconstruction seems a small bit blurry compared to Deep-STORM, due to the plotting through a small spread Gaussian kernel. However, it is noteworthy that both comparison perform well to retrieve the filaments, in particular in the enhancing yellow circles: the off-the-grid reconstruction seems to better preserve the structure compared to the Deep-STORM's rough output. The quality of the reconstruction is notably interesting for off-the-grid reconstruction since it does not require any test sets to yield this reconstruction, on the contrary to Deep-STORM. The only data needed is the knowledge of (an estimation of) the forward operator, the off-the-grid reconstruction can be then performed from any input without having to train the model on different type and level of noise.
16 See our GitHub repository for our PyTorch implementation: https://github. com/XeBasTeX 17 See https://srm. epfl. ch/DatasetPage?name=MT0. N1. HD for more insights. Shorthand: we tested an off-the-grid method on both SMLM synthetic and experimental data set. The gridless problem is tractable thanks to the Sliding Frank-Wolfe algorithm, and yields compelling results. The results are all the more interesting since there is only one parameter, handy to tune and robust w. r. t. noise. Thus, it can be easily adapted to any other dataset with known acquisition operator.
Experiments with a culture derived from one positive subject revealed that bacterial growth depended on the amount of XG provided in the medium, demonstrating specificity for this nutrient (Figure  1b ). Attempts to enrich the causal XG-consuming organism(s) with additional passaging (total of 10-20 times) consistently yielded stable mixed microbial cultures that contained multiple operational taxonomic units (OTUs; between 12-22 OTUs per culture with relative abundance >0. 5%) (Figure  1c, 1d ). While these cultures had commonalities at the genus level, there was surprisingly only one OTU that was >0. 5% and common across all 21 enrichment cultures examined. This common OTU was identified as a member of Ruminococcaceae uncultured genus 13 (R. UCG13) in the Silva database  11  (Figure  1d , Supplemental Table  1 ).
Extensive work has been conducted to characterize the substrate-specificity of PULs, which is demonstrated by hundreds of genomes with characterized and predicted PULs in the PUL database (PUL-DB)  26. However, this database only harbored a single genome with a partially related homolog of the B. intestinalis PUL (B. salyersiae WAL 10018 PUL genes HMPREF1532_01924-HMPREF1532_01938), highlighting the diversity of polysaccharide utilization machinery that remains for discovery and characterization. Interestingly, neutral monosaccharide analysis from our XG-degrading culture showed a relatively stable 1:1 ratio of glucose:mannose in the residual polysaccharide in the culture over time, implying that lyase-digested xanthan gum was not accumulating as growth progressed (Extended Data 3). This could be due to fast depolymerization and cellular importation of the XG polymer following lyase removal of the terminal mannose.
UCG13 candidate xanthan locus. RuGH5a consists of an N-terminal signal peptide sequence, its main catalytic domain (which does not classify into any of the GH5 subfamilies), and 3 tandem carbohydrate-binding modules (CBMs), which are often associated with CAZymes and can facilitate polysaccharide degradation (Figure  3b )  30. The protein also contains a significant portion of undefined sequence and Listeria-Bacteroides repeat domains (pfam 31 PF09479), a β-grasp domain originally characterized from the invasion protein InlB used by Listeria monocytogenes for host cell entry  32, 33. These small repeat domains are thought to be involved in protein-protein interactions and are almost exclusively found in extracellular bacterial multidomain proteins. To test the activity of RuGH5a on XG we expressed recombinant forms of the entire protein (RuGH5a full), the GH5 domain only (RuGH5a GH5-only), and the GH5 domain with either one (RuGH5a GH5+CBM-A), two (RuGH5a GH5+CBM-A/B), or all three of the CBMs (RuGH5a GH5+CBMs, hereafter referred to as RuGH5a for simplicity).
Xanthan utilization loci are widespread in modern microbiomes Next, we asked whether XG inclusion in the modern diet could have increased the prevalence of the R. UCG13 and B. intestinalis xanthan loci compared to other populations, such as hunter-gatherers, that are less likely to be exposed to this food additive. Using each locus as a query, we searched several publicly available fecal metagenome datasets collected from populations worldwide. All modern populations sampled displayed some presence of the R. UCG13 XG locus, with the Chinese and Japanese cohorts being the highest (up to 51% in one cohort) (Figure  5 ). The B. intestinalis locus was less prevalent, with two industrialized population datasets (Japan and Denmark/Spain) lacking any incidence. Where the locus was present, its prevalence ranged from 1-11%.
The three hunter-gatherer or non-industrialized populations sampled, the Yanomami, Hadza, and Burkina Faso had no detected presence of either the R. UCG13 or B. intestinalis locus. Although the size of the hunter-gatherer datasets is relatively small, excluding the possibility of a false negative suggests several equally intriguing hypotheses. Most obviously, inclusion of XG in the modern diet may have driven either the colonization or expansion of R. UCG13 (and to a lesser extent B. intestinalis) into the gut communities of numerous human populations. This is in concordance with the observations of Daly et al. who found that a set of volunteers fed xanthan gum for an extended period produced stool with increased probability and degree of xanthan degradation  39. Alternatively, the modern microbiome is drastically different than that of hunter-gatherers and these differences simply correlate with the abundance of R.
UCG13, rather than any causal effect of XG in the diet. Another possible hypothesis is that the microbiomes of hunter-gatherer populations can degrade XG, but through completely different microbes and pathways, a hypothesis that could be tested by culturing microbes from huntergatherer populations. To further probe the presence of the identified XG utilization genes in other environments, we conducted an expanded LAST search  40  of both loci in 72,491 sequenced bacterial isolates and 102,860 genome bins extracted from 13,415 public metagenomes, as well as 21,762 public metagenomes that are part of the Integrated Microbial Genomes & Microbiomes  41  (IMG/M) database using fairly stringent thresholds of 70% alignment over the query and 90% nucleotide identity. This search yielded 35 hits of the R.
UCG13 locus in human microbiome datasets, including senior adults, children, and an infant (12-months of age, Ga0169237_00111) (Supplemental Table  4 ). We also found 12 hits for the B. intestinalis XGOs locus, all in human microbiome samples except for a single environmental sample from a fracking water sample from deep shales in Oklahoma, USA (81% coverage, 99% identity) (Extended Data 10, Supplemental Table  4 ). XG and other polysaccharides such as guar gum are used in oil industry processes, and genes for guar gum catabolism have previously been found in oil well associated microbial communities  42. Since most samples searched were nongut-derived, this demonstrates that XG-degrading R. UCG13 and XGOs-degrading B. intestinalis are largely confined to gut samples and can be present across the human lifetime.
Metagenomic analysis Seven samples (15-mL) were collected at four time points (Extended Data 3; referred to as T1, T2, T3 and T4) during growth of two biological replicates of the original XG-degrading culture. Cells were harvested by centrifugation at 14,000 × g for 5 min and stored a -20 °C until further use. A phenol:chloroform:isoamyl alcohol and chloroform extraction method was used to obtain high molecular weight DNA as previously described  52. The gDNA was quantified using a Qubit™ fluorimeter and the Quant-iT™ dsDNA BR Assay Kit (Invitrogen, USA), and the quality was assessed with a NanoDrop One instrument (Thermo Fisher Scientific, USA). Samples were subjected to metagenomic shotgun sequencing using the Illumina HiSeq 3000 platform at the Norwegian Sequencing Center (NSC, Oslo, Norway).
0. 7 with default parameters. Contigs were submitted to the Integrated Microbial Genomes and Microbiomes system for open reading frames (ORFs) prediction and annotation  57. Additionally, the resulting ORF were annotated for CAZymes using the CAZy annotation pipeline  58. This MAG collection was used as a reference database for mapping of the metatranscriptome data, as described below. Taxonomic classifications of MAGs were determined using both MiGA 59 and GTDB-Tk  60. Human fecal samples (20) from a second enrichment experiment (unbiased towards the cultivation of Bacteroides) as well as two enrichments with mouse fecal samples were processed for gDNA extraction and library preparation exactly as described above. Metagenomic shotgun sequencing was conducted on two lanes of both Illumina HiSeq 4000 and Illumina HiSeq X Ten platforms (Illumina, Inc.
) at the NSC (Oslo, Norway), and reads were quality trimmed, assembled and binned as described above. Open reading frames were annotated using PROKKA 61 v1. 14. 0 and resulting ORFs were further annotated for CAZymes using the CAZy annotation pipeline and expert human curation  58. Completeness, contamination, and taxonomic classifications for each MAG were determined as described above. AAI comparison between the human R. UCG13 and the R. UCG13 found in the two mouse samples was determined using CompareM (https://github. com/dparks1134/CompareM). Extracted DNA from a second enrichment experiment on XG using the original culture was prepared for long-reads sequencing using Oxford Nanopore Technologies (ONT) Ligation Sequencing Kit (SQK-LSK109) according to the manufacture protocol. The DNA library was sequenced with the ONT MinION Sequencer using a R9.
4 flow cell. The sequencer was controlled by the MinKNOW software v3. 6. 5 running for 6 hours on a laptop (Lenovo ThinkPad P73 Xeon with data stored to 2Tb SSD), followed by base calling using Guppy v3. 2. 10 in 'fast' mode. This generated in total 3. 59 Gb of data. The Nanopore reads were further processed using Filtlong v0. 2. 0 (https://github. com/rrwick/Filtlong), discarding the poorest 5% of the read bases, and reads shorter than 1000 bp. The quality processed Nanopore long-reads were assembled using CANU 62 v1. 9 with the parameters corOutCoverage=10000 corMinCoverage=0 corMhapSensitivity=high genomeSize=5m redMemory=32 oeaMemory=32 batMemory=200. An initial polishing of the generated contigs were carried out using error-corrected reads from the assembly with minimap2 63 v2. 17 -x map-ont and Racon 64 v1.
4. 14 with the argument --include-unpolished. The racon-polished contigs were further polished using Medaka v1. 1. 3 (https://github. com/nanoporetech/medaka), with the commands medaka_consensus --model r941_min_fast_g303_model. hdf5. Finally, Minimap2 -ax sr was used to map quality processed Illumina reads to the medaka-polished contigs, followed by a final round of error correction using Racon with the argument --include-unpolished. Circular contigs were identified by linking the contig identifiers in the polished assembly back to suggestCircular=yes in the initial contig header provided by CANU. These contigs were quality checked using CheckM 56 v1. 1. 3 and BUSCO 65 v4. 1. 4. Circular contigs likely to represent chromosomes (> 1 Mbp) were further genecalled and functionally annotated using PROKKA 61 v1.
13 and taxonomically classified using GTDB-tk 60 v1. 4. 0 with the classify_wf command. Barrnap v0. 9 (https://github. com/tseemann/barrnap) was used to predict ribosomal RNA genes. Average nucleotide Identity (ANI) was measured between the short-reads and long-reads MAGs using FastANI 66 v1. 1 with default parameters. Short-reads MAGs were used as query while long-reads MAGs were set as reference genomes. Short-reads MAG1 showed an Average Nucleotide Identity (ANI) of 99. 98% with the long-reads ONT_Circ01, while short-reads MAG2 showed an ANI of 99. 99% with the long-reads ONT_Circ02 (Supplemental Table  2 ). Phylogenetic analysis revealed that ONT_Circ02 encoded four complete 16S rRNA operons, three of which were identical to the aforementioned R. UCG13 OTU.
2. 1b. Reads were pseudo-aligned against the metagenomic dataset using kallisto pseudo -pseudobam  70. Of the 58089 ORFs (that encode proteins with > 60 aa) identified from the metagenome of the original XG-degrading community, 7549 (13%) were not found to be expressed, whereas 50540 (87%) were expressed, resulting in a reliable quantification of the expression due to unique hits (reads mapping unambiguously against one unique ORF).
MS1 scans were acquired at 60K resolution (AGC target=3x10 6 ; max IT=50 ms). Data-dependent collision induced dissociation MS/MS spectra were acquired using Top speed method (3 seconds) following each MS1 scan (NCE ~28%; 15K resolution; AGC target 1x10 5 ; max IT 45 ms). Proteins were identified by searching the MS/MS data against a database of all proteins identified in the original culture metagenomes using Proteome Discoverer (v2. 1, Thermo Scientific). Search parameters included MS1 mass tolerance of 10 ppm and fragment tolerance of 0. 2 Da; two missed cleavages were allowed; carbamidimethylation of cysteine was considered fixed modification and oxidation of methionine, deamidation of asparagine and glutamine were considered as potential modifications. False discovery rate (FDR) was determined using Percolator and proteins/peptides with an FDR of ≤1% were retained for further analysis.
2 to allow liquification of XG, which was necessary to extract RNA from these cultures. As before, cultures were harvested by centrifugation, mixed with RNA Protect (Qiagen) and stored at -80 °C until further processing. RNA was purified as before except that multiple replicates of DM-XG RNA were pooled together and concentrated with Zymo RNA Clean and Concentrator TM -25 to reach acceptable concentrations for RNA depletion input. rRNA was depleted twice from the purified total RNA using the MICROBExpress TM Kit, each followed by a concentration step using the Zymo RNA Clean and Concentrator TM -25. About 90% rRNA depletion was achieved for all samples. B. intestinalis RNA was sequenced using NovaSeq and community RNA was sequenced using MiSeq. The resulting sequence data was analyzed for differentially expressed genes following a previously published protocol  76.
Briefly, reads were filtered for quality using Trimmomatic v0. 39  68. Reads were aligned to the each genome using BowTie2 v2. 3. 5. 1  77. For the Bacteroides intestinalis transcriptome reads were aligned to its genome, while for the community data reads were aligned to either the B. intestinalis genome or the closed Ruminococcaceae UCG-13 metagenome assembled genome (MAG). Reads mapping to gene features were counted using htseq-count (release_0. 11. 1)  78. Differential expression analysis was performed using the edgeR v3. 34. 0 package in R v. 4. 0. 2 (with the aid of Rstudio v1. 3. 1093). The TMM method was used for library normalization  79. Coverage data was visualized using Integrated Genome Viewer (IGV)  80.
Looking for R. UCG13 and B. intestinalis. XG Loci in Metagenomes Available cohorts of human gut metagenomic sequence data (National Center for Biotechnology Information projects: PRJNA422434 81 , PRJEB10878 82 , PRJEB12123 83 , PRJEB12124 84 , PRJEB15371 85 , PRJEB6997 86 , PRJDB3601 87 , PRJNA48479 88 , PRJEB4336 89 , PRJEB2054 90 , PRJNA392180  91  , and PRJNA527208 92 ) were searched for the presence of xanthan locus nucleotide sequences from R. UCG13 (92. 7 kb) and B. intestinalis (17. 9kb) using the following workflow: Each xanthan locus nucleotide sequence was used separately as a template and then magic-blast v1. 5. 0 93 was used to recruit raw Illumina reads from the available metagenomic datasets with an identity cutoff of 97%. Next, the alignment files were used to generate a coverage map using bedtools v2.
29. 0 94 to calculate the percentage coverage of each sample against each individual reference. We considered a metagenomic data sample to be positive for a particular xanthan locus if it had at least 70% of the corresponding xanthan locus nucleotide sequence covered. The R. UCG13 locus and B. intestinalis XG locus were used as the query in a large-scale search against the assembled scaffolds of isolates, metagenome assembled genomes (bins), and metagenomes included into the Integrated Microbial Genomes & Microbiomes (IMG/M) comparative analysis system  41. Within the LAST software package, version 1066, the 'lastal' tool was used with default thresholds to search the 2 loci against 72,491 public high-quality isolate genomes, and 102,860 bins from 13,415 public metagenomes, and 21,762 public metagenomes in IMG/M.
Acknowledgments We gratefully acknowledge  Stephanie Theide  for growth curve analysis suggestions and  Tina Johannessen  and  Alexsander Lysberg  for help with Nanopore metagenomics. We thank the  University of Michigan Proteomics Resource Facility, Microbiome Core, and Natural Products Discovery Core  for their support in completion of this project. We are grateful for support from the  US National Institutes of Health  ( DK118024 ,  DK125445  to ECM and  UL1TR002240  in support of MPO) and the  Research Council of Norway  ( FRIPRO program , PBP and SLLR: 250479, LHH: 302639). We thank the  University of Michigan Center for Gastrointestinal Research (UMCGR) , ( NIDDK 5P30DK034933 ) for financial support with proteomics. The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported under Contract No.