dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The goal of MoPc is to predict relevant miRNA-targets integrating information from miRNA, mRNA and proteome profiles. The predicted targets are validated with three reference databases (miRDB, TargetScan, and miRTarBase), both for the MoPc method and for the bivariate correlation between mRNA-miRNA and protein-miRNA. As reported in Tables  2, 3 , and 4, MoPc predictions are enriched in independent database targets for the three cancer datasets analyzed. Except for the enrichment of glioblastoma in miRDB, all enrichments are statistically significant. The miRNA-target pairs predicted from the bivariate mRNA-miRNA and protein-miRNA correlation are also statistically enriched for each cancer dataset and database, unless for breast cancer in the bivariate protein-miRNA correlation in the mirDB database.
An essential aspect to compare is the total number of miRNA-target interactions predicted by MoPc and bivariate mRNA-miRNA and protein-miRNA correlation. Analyses of glioblastoma and lung cancer datasets benefit from the results obtained through MoPc. Indeed, MoPc returns 17. 20%, 18. 26%, and 18. 49% of miRNA-target interactions for breast, glioblastoma, and lung cancer. Although the bivariate mRNA-miRNA and protein-miRNA correlation are generally more enriched in the three external target databases, they return a higher percentage of miRNA-target correlated pairs, typically greater than 36% for both mRNA-miRNA and protein-miRNA. This aspect is of uttermost importance, as the number of investigable gene-miRNA pairs is not clinically useful if it is close to 30-60% of all gene-miRNA pairs in the input dataset.
MoPc proves to be an effective method in identifying key miRNAs within a dataset, reducing the number of targets while maintaining the statistical significance of the enrichment. After the MoPc computational validation in the three external databases, we focused on specific miRNAs known in the literature to be associated with specific diseases. Indeed, the enrichment in miRDB, TargetScan, and miRTarBase verifies that the miRNA-target interaction is known or predicted in the literature. However, such enrichment does not consider whether the predicted miRNAs are key in the specific disease (breast, glioblastoma, lung cancer). Therefore we compared MoPc miRNA-target pairs with disease-associated miRNA in the literature  [22] [23] [24]. Regarding breast cancer, MoPc detects the main miRNAs involved in the cancer genesis, including miR-126 and miR-140 (with anti-angiogenesis and anti-tumorigenesis function) and miR-200b and miR-200c (promoting metastasis and invasion).
Conclusion Identifying key miRNAs is of fundamental importance for the early diagnosis and treatment of many diseases, including cancer. However, in the literature, various tools deal with providing a list of relevant miRNAs without exploiting the information coming from the expression of proteins. This paper presents MoPc, a novel method for predicting key miRNA-target pairs by exploiting the computation of the partial correlation between mRNA and protein expression conditioned by miRNA expression. The method is applied to three TCGA/CPTAC datasets: breast, glioblastoma, and lung cancer. The results show enrichment for each dataset in the three databases, miRDB, TargetScan, and miRTarBase, except for breast cancer in miRDB. Furthermore, MoPc successfully identifies key miRNAs in each cancer type.
Funding This study was funded by the  European Union 's  Horizon 2020 research and innovation programme DECIDER  under Grant Agreement 965193. This work was supported by grants from  ITMO Cancer AVIESAN (National Alliance for Life Sciences and Health) , within the framework of the Plan Cancer 2014-2019 and convention "2018, Non-coding RNA in cancerology: fundamental to translational ( 18CN039-00 )".
This last example is the perfect illustration of how cell-based models combined with quantitative datasets can reveal emerging properties at the population level that will directly influence the rate of cell death, notably here the role of homoeostatic density. Combining these cell-based models with simulations of the dynamics of all -or parts-of the apoptotic pathway as well as relevant spatio-temporal feedback and cell-cell interaction could help in the near future to explore the relationship between local stress, cellular neighbourhood, caspase activity, cell extrusion and emerging rate and distribution of cell elimination (Fig. 4 ). This task could be progressively democratised thanks to the numerous libraries allowing user-friendly implementation of Potts and vertex models combined with ODEs  [186] [187] [188].
Identifying new relevant modes of feedback and predictive parameters While these approaches outline emerging properties coming from mechanical inputs as well as biochemical reactions, they require prior knowledge on the relevant signalling cascades, relationships between cell shape and signalling as well as relevant spatiotemporal feedback. Identification of novel feedback and non-cell autonomous factors regulating epithelial cell death requires large datasets with extensive characterisation of the number and the distribution of dying cells through long-term live imaging. The detection of cell death events can rapidly become an important bottleneck for this type of analysis. This may rapidly be solved thanks to the recent development of methods to automatise cell death detection through deep learning.
These have been successfully applied in vivo in Drosophila epithelia to detect extrusion through fluorescent live imaging  [189] , in cell culture using transmission light microscopy  [190, 191] , and to discriminate between various types of cell death through live imaging and immunostainings  [192, 193]. Feedback and spatial coordination can then be revealed from the spatio-temporal distributions of cell death using point pattern analysis, which can outline biases in distributions compared to a so-called Poisson process (where every event is independent in time and space). These include local and transient clustering or dispersive effects  [103, 180, 194] , as well as putative coupling between cellular events  [180, 195]. Alternatively, network representation of cell-cell interactions and tissue architecture can help to reduce the dimensionality of the imaging dataset, while encapsulating information about cell geometry and distances  [196].
Acknowledgements We thank members of  RL lab  for critical reading of the manuscript. We also apologise for relevant works that could not be quoted due to space limitation. TC is supported by a PhD grant from the international doctoral programme  PPU from Institut Pasteur. Work in RL lab  is supported by the  Institut Pasteur , the  ERC starting grant CoSpaDD (Competition for Space in Development and Disease , grant number  758457 ), the  Cercle FSER , the  CNRS  ( UMR 3738 ), the  ANR PRC CoE-CECa , and the  ANR-10-LABX-0073.
SNP datasets. SNP genotyping and Quality Control: All SNPs used in our analysis were taken from the custom house sparrow Affymetrix 200k SNP array based on the resequencing of 33 individual house sparrows  (Lundregan et al. 2018). SNPs on the array are distributed across 29 autosomes in the house sparrow genome (chromosome 16 was excluded as sequences on this chromosome are difficult to assemble due to containing the highly repetitive major histocompatibility complex). A total of 3,116 adults were genotyped on this array. For GWAS analysis, the original 200k SNP array was subject to quality control in PLINK v1. 90 (Purcell et al. 2007). Mendelian errors were masked using the flag --set-me-missing, and loci with a minor allele frequency of < 0. 05 were discarded. SNP data were further filtered in the R library GenABEL v1.
8-0  (Aulchenko et al. 2007 ) in R v3. 6. 3 with the check. marker function, retaining SNPs with call rates > 0. 9 and individuals with call rates > 0. 8. We did not filter SNPs that deviated from Hardy-Weinberg expectation. After QC, we retained 180,817 SNPs, which we hereafter refer to as the 180K dataset. The SNP density on autosomes was 1 SNP every 5. 1kb on autosomes, and 1 SNP every 206kb on the X chromosome. We investigated the linkage disequilibrium (LD) decay profile for each chromosome for all loci occurring within 500kb with each other using the flag --ld-window-kb 500 in PLINK v1. 90. LD decayed to r 2 = 0. 05 at a distance of ~100kb, with LD decay slightly faster for microchromosomes (Figure  S1 ).
SNP Quality Control for crossover estimation: When estimating recombination rate, any genotyping errors can cause problems when determining the grandparental origin of alleles. This can lead to false calling of double crossovers, resulting in an overestimation of recombination rate. Given that crossovers are inherently rare, false calling of double crossovers can have substantial downstream consequences on the analysis. For this reason, we conducted a second, more stringent quality control on the 200K dataset for crossover estimation. SNPs were filtered for Mendelian errors in plink and loci with a minor allele frequency of < 0. 05 were discarded. For each SNP locus and individual, we extracted the raw allele intensity data from the Affymetrix Axiom genotyping project. These data were used to calculate the combined SNP intensity, R, and the allelic intensity ratio, Θ: 𝑅 = 𝐴 + 𝐵 (1) Θ = ( 2 𝜋 ) × arctan ( 𝐵 𝐴 ) (2) where A and B are the probe raw intensity values for each SNP allele in alphabetical order (e.
g. C & T, respectively). These data generally cluster into the three genotypes separated by their Θ values (Figure  S1 ). For each SNP locus, we calculated the mean and standard deviation of Θ for each genotype (i. e. for AA, AB and BB). After extensive visual examination of SNP clustering, we discarded any SNP locus where the standard deviation of Θ for any genotype was > 0. 046. This threshold was selected as it ensured adequate cluster separation in the data and removed ambiguous genotypes occurring between clusters (see Figure  S2  for a visual example). The remaining 63,535 SNPs were filtered to remove SNPs on the X chromosome, and those of unknown position; 61,180 SNPs were retained. We hereafter refer to this data set as the 61K dataset. This analysis was conducted in R v3. 6. 3.
Linkage mapping. Autosomal linkage map construction and chromosome phasing was conducted using the 61K SNP dataset in CRI-MAP v2. 504a  (Green et al. 1990 ). To ensure unbiased phasing, a standard approach was used to quantify the crossovers occurring in individual gametes transmitted from parents to their offspring  (Johnston et al. 2016). Hereafter, focal individual (FID) refers to the individual in which meiosis took place (i. e. the parent). For each FID-offspring pair, sub-pedigrees were constructed in each case where the FID, its parents, mate, and offspring were all genotyped (Figure  S3 ), and then subject to the following quality control. First, Mendelian incompatibilities between parent and offspring genotypes were identified using the prepare function. Mendelian errors were then masked in both parents and offspring using the create_crimap_input function in the package crimaptools v0.
1 (https://github. com/susjoh/crimaptools, (Johnston et al. 2016 )) implemented in R v3. 6. 3. Sub-pedigrees containing parent-offspring relationships with more than 0. 1% mismatching loci were discarded. A total of 894 sub-pedigrees were used, representing 894 individual gametes transmitted from 368 unique FIDs. Sex-specific and sex-averaged linkage map positions in Kosambi centiMorgans (cM) were obtained for each chromosome using the map function, assuming SNPs were ordered as in the sparrow genome  (Elgvin et al. 2017; Lundregan et al. 2018). SNPs for which both adjacent SNPs were greater than 3cM away in initial linkage maps were removed as they were assumed to be incorrectly mapped (132 SNPs). The map function was then re-run on the remaining 60,352 SNPs to produce the final linkage maps.
Recombination rate calculation: The CO dataset was used to calculate recombination rates in FIDs using several approaches. First, we determined the autosomal crossover count (ACC) by summing the number of crossovers per gamete. Second, we calculated the rate of intra-chromosomal allelic shuffling, ̅  , which is the probability that two randomly chosen loci (or genes) are on the same chromosome are uncoupled in meiosis  (Veller et al. 2019). This was defined as: 𝑟̅ 𝑖𝑛𝑡𝑟𝑎 = ∑ 2𝑝 𝑘 (1 -𝑝 𝑘 )𝐿 𝑘 2 𝑛 𝑘=1 where for chromosome k, p is the proportion of the chromosome (or genes) from one parent, L is its fraction of the genome length (or gene count), and n is the autosome count. We define ̅  and ̅  as ̅  measures calculated with chromosome and gene proportions, respectively. To test the broader effects of chromosomes size, we then subset each ACC and ̅  measure as follows.
Determining Heritability of Recombination Rate. The proportion of phenotypic variance in recombination measures attributed to additive genetic effects (narrow-sense heritability h 2 ) was determined using a restricted maximum-likelihood "animal model" in the package ASReml-R v4  (Butler et al. 2009 ) in R v4. 2. 2. A genomic relatedness matrix (GRM) at all autosomal markers within the 180K SNP dataset was constructed with GCTA v1. 94. 1  (Yang et al. 2011). The GRM was adjusted for sampling error using the --grm-adj 0 argument, which assumes the frequency spectra of genotyped and causal loci are similar. Models were run for ACC, ̅  and ̅  in both sexes, and in males and females separately. The fixed effect structure included phase coverage from YAPP (the length of the genome that can be phased and therefore within which crossovers can be detected), parent sex as a fixed factor, and age of the FID in year of gamete formation (defined as the difference between offspring hatch year and parent hatch year).
Genome-wide association studies (GWAS): GWAS of recombination measures were conducted with the 180K SNP dataset using RepeatABEL v1. 1  (Rönnegård et al. 2016 ) in R v3. 6. 3. Models were run for ACC, ̅  and ̅  in both sexes, and in males and females separately. The package models the additive effect of SNPs, where genotypes correspond to 0, 1 and 2, and slopes and standard errors of associations are calculated. To account for inflation of test statistics due to cryptic population structure, a relatedness matrix accounting for family structure was fit using the van Raden method  (VanRaden 2008)  implemented within RepeatABEL. To account for any further inflation, we divided association statistics using the genomic control parameter λ, which was calculated as the observed median χ 2 statistic, divided by the null expectation median χ 2 statistic  (Devlin and Roeder 1999).
Acknowledgements We thank students and fieldworkers for help with fieldwork, and the hospitality of inhabitants at the study area in Helgeland who made this study possible. Sampling was conducted in accordance with permits from the  Norwegian Food Safety Authority  and the  Ringing Centre at Stavanger Museum, Norway. We thank  Cathrine Brekke ,  Mark Ravinet ,  Jarrod Hadfield ,  Alexander Suh ,  Anna Torgasheva ,  Anna Runemark ,  Homa Papoli Yazdi ,  Martin Stoffel , and  Anna Hewett  for helpful discussions on this topic. The.
Integrin expression vectors The sequence corresponding to integrin 5 (Itga5) mRNA (GenBank ID: KC439457. 1), including the 5' UTR leader sequence and the complete coding sequence, fused in frame to a flexible linker  64  and mCherry sequence was synthetized by GenScript and cloned using XbaI/XhoI sites in pCAGGS vector for electroporation (ITGA5:mCherry_pCAGGS). The complete coding sequence corresponding to the predicted integrin subunit 4 (Itga4) mRNA (NCBI Reference Sequence XM_040676057. 2) was placed after the ITGA5 5' UTR leader sequence, fused in frame to the flexible linker and eGFP sequence was synthetized by GenScript and cloned using XbaI/XhoI sites in pCAGGS vector for electroporation (ITGA4:EGFP_pCAGGS).
Statistics Statistical analyses were performed with Prism 6 (GraphPad). Datasets were tested for Gaussian distribution. Student t-test, or ANOVA followed by multiple comparisons were used with the appropriate parameters depending on the Gaussian vs non-Gaussian characteristics of the data distribution. Significance threshold was set at p < 0. 05. Box and whiskers plot: the box extends from the 25th to the 75th percentile; the whiskers show the extent of the whole dataset. The median is plotted as a line inside the box. Point-Biserial correlation analyses between parameters and extrusion rates from simulations were performed as described in section 5. 5 of the Supplementary Information. Enzymatic and drug treatments 10-somite long portions of the whole trunk at the level of the prospective forelimb region were dissected from embryos at stage HH12 as described previously  29, 38.
Acknowledgements We thank  Dr. Andreas Merdès  ( CBI ,  CNRS UMR5077, Toulouse ) for the kind gift of PCM1 antibody. ET is a research director at the  French National Research Center (CNRS)  and supported by grants from  ANR SingleCrest  ( ANR-21-CE13-0028-02 ), the  Association pour la Recherche contre le Cancer  (ARC,  ARCPJA22020060002084 ) and the  Fondation pour la Recherche Médicale  ( FRM AJE201224 ). CD is a senior researcher at  French National Institute for Medical Research (Inserm). BG is an assistant professor at  Université de Toulouse. SMA's research is funded by the  Austrian Science Fund (FWF)  through the project  F65  and by the  Vienna Science and Technology Fund (WWTF)  [ 10. 47379/VRG17014 ]. SP's research was funded by the  Vienna Science and Technology Fund (WWTF)  [ 10.
47379/VRG17014 ]. MAF's research was funded by the  Atmospheric Mathematics (AtMath)  collaboration of the  Faculty of Science of University of Helsinki , by the  Academy of Finland , via an  Academy  project (project No. 339228 ) and a  Finnish center of excellence  (project No. 346306 ), and by the  Centre for Mathematics of the University of Coimbra   -UIDB/00324/2020  (funded by the  Portuguese Government through FCT/MCTES). MAF was also partially supported by the  Vienna Science and Technology Fund (WWTF)  [ 10. 47379/VRG17014 ]. We are grateful to  Prof. Pierre Degond  and  Dr. Diane Peurichard  for fruitful discussions throughout the project. We thank  Drs. Benjamin Steventon  ( Cambridge University ) and  Valérie Lobjois  ( CBI, Toulouse ) for critical reading of the manuscript.
Three distinct groups of gut bacteria possess large reductase arsenals Previous studies have identified multiple respiratory reductases that use distinct electron acceptors. Characterized respiratory reductases are often evolutionarily related, sharing a level of sequence homology that places them within one of two enzyme superfamilies. "Molybdopterin reductases" (Pfam PF00384) have a catalytic domain that binds the redox-active cofactor molybdopterin and tend to act on inorganic respiratory electron acceptors (Figure  1A , Table  S1 ). 13, 14, [23] [24] [25] [15] [16] [17] [18] [19] [20] [21] [22]  "Flavin reductases" (Pfam PF00890) have a catalytic domain that binds the redox-active cofactor flavin adenine dinucleotide (FAD) and tend to act on organic respiratory electron acceptors (Figure  1B , Table  S1 ).
Detection of molybdopterin and flavin reductases in prokaryotic genomes Genomes and metagenome-assembled genomes of common prokaryotes in the human gut microbiome were downloaded from the version 2. 0 of the Unified Human Gastrointestinal Genome (UHGG) in MGnify database on July 2022. 57  Only species representative genomes identified in more than five distinct samples (1533 total) were included in subsequent analyses. Gene prediction was done with Prodigal (version 2. 6. 3) 58 in single mode. Molybdopterin and flavin reductases were identified based on the PFAM accessions (PF00384 and PF00890, respectively) using the searching tool Hmmsearch (version 3. 3) (E-value cut-off 0. 001). 59, 60  SIGNALP (version 5. 0) was run to predict the putative cellular localization of the proteins using the parameter -org gram.
61  To extend the genomic reductase content analyses to the identified taxonomic families, selected representative genomes for Burkholderiaceae, Eggerthellaceae and Erysipelotrichaceae (Figure  S1A-C , Table  S3 ) were downloaded from the Genome Taxonomy Database on October 2022 (release 202 of April 27, 2021). The same protocol as described above was performed to detect the flavin and molybdopterin reductases.
Reductase pangenomics Pangenomes for E. lenta, S. wadsworthensis, and H. filiformis were calculated anvi'o 7. 1 (with parameters anvi-pan-genome --mcl-inflation 10). 67  Briefly, the program (1) performed an all vs. all NCBI BLAST to create a sequence similarity network, (2) used the Markov Cluster algorithm to resolve gene clusters, and (3) was visualized with "anvi-display-pan". 68  Subsequently, the species pangenomes were subsetted for gene clusters annotated with the Pfam PF00890 using "anvi-split".
Metagenomic analyses of human fecal samples Human fecal samples underwent shot-gun DNA sequencing. After undergoing mechanical disruptions with a bead beater (BioSpec Product), samples were further purified with QIAamp mini spin columns (Qiagen). Purified DNA was quantified with a Qubit 2. 0 fluorometer and sequenced on the Illumina HiSeq platform, producing around 7 to 8 million PE reads per sample with read length of 150 bp. Adapters were trimmed off from the raw reads, and their quality was assessed and controlled using Trimmomatic (v. 0. 39), then human genome was removed by kneaddata (v0. 7. 10, https://github. com/biobakery/kneaddata). 73  Taxonomy was profiled using metaphlan4. 74  Alphadiversity (a reflection of the number of unique bacterial taxa and their relative abundances) of fecal samples was estimated using Inverse Simpson Index.
0. 3). Specifically, reads were first trimmed at 210 bp for forward reads and 150 for reverse reads to remove low quality nucleotides. Chimeras were detected and removed using the default consensus method in the dada2 pipeline. Then, ASVs with length between 300 bp and 360 bp were kept and deemed as high quality ASVs. Taxonomy of the resultant ASVs were assigned to the genus level using the RDP classifier (v2. 13) with a minimum bootstrap confidence score of 80. Species-level classification can be provided using blastn (v2. 13. 0) and refseq_rna database (updated 2022-06-10). RNA extraction, library preparation, sequencing, and transcriptomics analyses E. lenta and H. filiformis were grown in 50 mL of media supplemented with 10 mM formate and 10 mM of the tested electron acceptor. A BHI baseline media was used for E.
Post cDNA synthesis, Illumina compatible adapters were ligated onto the inserts and final library quality was evaluated using TapeStation (Agilent technologies). Libraries were normalized using qPCR and then sequenced on Illumina's NovaSeq 6000 platform using 2x150bp read configuration. High quality sequence reads were mapped to the NCBI reference genomes of E. lenta strain DSM2243, (GCF_000024265. 1/), H. filliformis strain AF24-29 (GCF_003459085. 1/), and accordingly. The reads were mapped to the respective reference genomes using Bowtie2 v2. 4. 5 and sorted with Samtools v1. 6 using default parameters. 75, 76  All sequence data were deposited to the NCBI SRA database under the bioproject ID. Read counts were generated using featureCounts v2. 0. 1 with the flags -p, -B, and -C. 77  Gene expression was quantified as the total number of reads uniquely aligning to the respective reference genomes, binned by annotated gene coordinates.