dataesr/scientific-paragraphs-categorization huggingface dataset:
Genes responsible for fat phenotype are expressed at similar level in KO mice under STD and in KO mice under HFD, indicating that PPARg mediated FFA uptake and TG synthesis and transport pathways were not upregulated in KO under HFD mice. AskOmics analysis of integrated serum metabolomics and liver transcriptomics data. S11 Fig in  S1  Appendix specifies the data-driven model integrated in Askomics. Metabolomic and transcriptomic data were linked using KEGG and Reactome databases. Fig  6  describes the filtering process on KO/WT under HFD data at Week 30. Before the last step, no threshold was applied on fold changes. Considering only metabolomic data, 109 metabolites were found significantly modulated. Among them, 31 were identified as varying significantly and were linked to 5595 genes. Among them, 30 were linked to 265 genes that varied significantly themselves in expression levels between the same groups.
For 10 of these metabolites, the ANOVA highlighted a significant genotype effect. Metabolites can be linked to genes varying because of diet or genotype. Filtering out genes varying exclusively because of diet, 154 genes remained, linked to the same 10 metabolites. Finally, applying a fold change threshold of +/-1. 5 on these metabolites, we kept eight of them. Corresponding metabolites and genes data are displayed in S2 Table in S2 Appendix. Five of these eight metabolites are related to the bile acid metabolism. Among them, cholate is a primary bile salt (fold change KO/WT = 2. 3), taurocyamine is a by-product of taurine which is involved in the conjugation of primary bile salts (fold change qPCR analysis of bile acid transporter and regulator genes in ileum. To evaluate the expression of genes involved in fatty acid binding, transport and esterification (FATP4, CD36, FABP6, FABP2, ACSL3, ACSL5, MGAT2, MGAT3, DGAT1, DGAT2), bile salt transport and regulation (SLC51A, SLC51B, SLC10A2, FGF15, FGFR4), cholesterol transport and regulation (ABCA1, CYP8B1, CYP27A1, HSD17B10), RT-PCR was performed on RNA samples isolated from ileum samples.
Acknowledgments We are grateful for the help of many investigators, cores, and lab personnel, including  Aimo Kannt  and  Andreas Herling  for their advices in Diet induced Obesity studies,  Pascal Paul ,  Karine Florin ,  Dorothee Tamarelle ,  Valerie Martin ,  Mathieu Boucher  for their help in the statistical analysis, the histopathology department, the in vivo research support groups, the veterinary services and the  Global Transgenic Model & Technology group.
The workflow is implemented in the freely available SolvHybrid package. We first assess the adsorption of a water molecule at the Pt/water interface, which turns out to be a stringent test. The intrinsic error of our QM-MM hybrid scheme is limited to 6 kcal•mol -1 through the introduction of a correction term to attenuate the electrostatic interaction between near-chemisorbed water molecules and the underlying Pt atoms. Next, we show that the MMsolv solvation free energy of Pt (-0. 46 J•m -2 ) is in good agreement with the experimental estimate (-0. 32 J•m -2 ). Furthermore, we show that the entropy contribution at room temperature is roughly of equal magnitude as the free energy, but with opposite sign. Finally, we compute the adsorption energy of benzene and phenol at the Pt(111)/water interface, one of the rare systems for which experimental data are available.
SolvHybrid: Principles and Workflow The SolvHybrid package aims at computing the adsorption free energy of a molecule M on a surface S in a solvent using a hybrid scheme. This quantity, named here ∆ a G solv , can be decomposed in two terms: ∆ a G solv = ∆ a G vac + ∆ a ∆ h G (1) where ∆ a designates the free energy difference of adsorption of M on S and the superscript solv and vac refer to the process in solvent (here water) and vacuum or gas-phase, respectively and ∆ h collects all solvent effects (h stands here for hydration). ∆ a ∆ h G formally also includes the change of adsorbate-surface interaction due to the co-adsorption of solvent molecules and the polarization of the adsorbate due to the presence of the solvent. The first contribution (∆ a G vac ) is the Gibbs Free Energy of adsorption of the molecule M on the surface S computed in the gas-phase.
Molecular mechanics simulations The MUSIC module for AMBER,  39  implementing the GAL17 forcefield, was used to describe water-Pt interactions along with the set of published parameters. 34 A second version of the forcefield, dubbed "sGAL17" for soft GAL17, was also used. It consists in Figure  2 : Distance dependence of the electrostatic interactions, renormalized by the partial charges q i , q j. The green line corresponds to the bare Coulomb operator ( 1 r ), while the orange curve is the shifted electrostatic interaction according to Eq. 3. The blue line results from the short-range damping of Eq. 4. r cut , σ and r 0 were set to 8. 0 Å; 25 and 3. 3 Å, as used for all our computations. exactly the same functional form, but all parameters with energy as a unit are divided by four. Water-water interactions were computed via the TIP3P water model.
All geometries are available in the supporting information. The VASPsol module  21, 61  was used to assess the effect of an implicit solvent for comparison. The default settings for cavitation energy are used, and the plane-wave energy cutoff was increased to 600 eV. When a Pt slab was present, a dipole correction was applied in the out-of-plane direction,  62  to avoid solvating a macroscopic dipole. 18.
Adsorption of benzene and phenol at the Pt(111)/water interface The adsorption free energy of benzene and phenol are studied to quantitatively evaluate the accuracy level of our QM/MM hybrid scheme compared to the experimental estimates from Campbell et. al. 68  This provides us also the opportunity to compare MMSolv solvation energies to the one obtained with the widely used PCM, implemented in VASPsol. 21  a start, we compare the solvation energies of molecules in solution to their experimental counterparts, taken from the Minnesota database of solvation free energies. 71 e experimental standard solvation free energies of water, phenol and benzene in water are reported to be -6. 31, -6. 62 and -0. 87 kcal•mol -1 , respectively. The implicit solvent, which is fitted on this kind of reference data, is in qualitative agreement, yielding -7.
Conclusion Alchemical transformations are powerful to determine solvation free energies at the molecular mechanics level of theory. As we have demonstrated herein, the near-chemisorption interaction of water with a prototypical Pt(111) surface necessitates adjustments of the "standard" thermodynamic cycle in order to reproducibly avoid hysteresis when computing the solvation free energies at the Pt/water interface. In our approach, this involves an intermediate state with weak interactions between the water solvent and the metal surface. Furthermore, the electrostatic interactions between the strongly adsorbed water molecules and the metal atoms is damped in order to avoid nonphysical contributions due to double counting. The resulting scheme, implemented in the freely available SolvHybrid software package that currently relies on the combination of VASP and AMBER, reliably determines solvation free energies at the Pt/water interface.
2017 (Krasny et al. , 2018) )  and with available data  (Maceri et al. 2012 (Maceri et al. , 2013;; Marino and Vairo 2013; Morin et al. 2018; Khamdaeng et al. 2012). Values of parameters are summarized in Table  1. The proposed integrated computational approach has been implemented via a parametric code developed in the Python environment (Python Software Foundation), exploiting finite element solver-core libraries of ABAQUS (ABAQUS / Standard; SIMULIA Inc, Providence RI). In particular, the code, solving the structural analysis at each discrete time instant t n by means of the ABAQUS librar- ies for mechanics  (i. e. , part, sketch, step, interaction, load, mesh, job, odbAccess) , drives the enforcement of constitutive relations and the update of the reference systems  for next time step t n+1 via a post-processing of the results obtained at the previous step.
We have developed a tool named Soteria, to automate our proof methodology. Soteria analyses the specification to detect concurrency bugs and provides counterexamples. The contributions of this paper are as follows: -We propose a novel proof system specialised to proving the safety of available objects that converge by propagating state. This specialisation supports modular reasoning, and thus it enables automation. -We demonstrate that this proof system is sound. Moreover, we provide a simple semantics for state-propagating systems that allows us to ignore network messages altogether. -We present Soteria, to the best of our knowledge the first tool supporting the verification of program invariants for state-based replicated objects. When Soteria succeeds it ensures that every execution, whether replicas are partitioned or concurrent, is safe.
Proving Invariants In this section, we report our invariant verification strategy. Specifically, we consider the problem of verifying invariants of highly-available distributed objects. To support the verification of invariants we will consider a syntactic-driven approach based on program logic. Bailis et al. [2]  identifies necessary and sufficient run-time conditions to establish the security of application invariants for highlyavailable distributed databases in a criterion dubbed I-confluence. Moreover, they consider the validity of a number of typical invariants and applications. Our work improves on the I-confluence criterion defined in  [2]  by providing a static, syntax-driven, and mostly-automatic mechanism to verify the correctness of an invariant for an application. We will address the specific differences in Section 7, the related work.
In this sense we say that the pre-condition of merge for a given invariant I, is also an invariant of the system. It is this abstraction step that makes the analysis performed by Soteria to be syntax-driven, automated, and machine-checked. The fact that Soteria is an analysis of a program is in contrast with I-confluence  [2]  where no means to link a given program text to the semantical model, let alone rules to show that the syntax implies invariant preservation, are provided. In other words, Iconfluence  [2]  does not provide a program logic, but rather a meta-theoretical proof about lattice-based state-propagating systems. Our previous work  [21] , provides an informal proof methodology for ensuring safety of Convergent Replicated Data Types(CvRDTs), which are a group of specialised data structures used to ensure convergence in distributed programming.
Food contamination by staphylococcal enterotoxins (SEs) is responsible for many food poisoning outbreaks (FPOs) each year, and they represent the third leading cause of FPOs in Europe. SEs constitute a protein family with 27 proteins. However, enzyme immunoassays can only detect directly in food the five classical SEs (SEA-SEE). Thus, molecular characterization methods of strains found in food are now used for FPO investigations. Here, we describe the development and implementation of a genomic analysis tool called NAuRA (Nice automatic Research of alleles) that can detect the presence of 27 SEs genes in just one analysis-and create a database of allelic data and protein variants for harmonizing analyses. This tool uses genome assembly data and the 27 protein sequences of SEs. To include the different divergence levels between SE-coding genes, parameters of coverage and identity were generated from 10,000 simulations and a dataset of 244 assembled genomes from strains responsible for outbreaks in Europe as well as the RefSeq reference database.
, 2018). When genome assemblies are available, the BLAST method is faster than mapping and, with public databases, assembled genomes are more accessible than raw data. Therefore, the objective of this work was to develop a bioinformatics pipeline called NAuRA (Nice Automatic Research of Alleles) to detect genes coding for SEs in S. aureus genomes. NAuRA can screen every new genome for its toxin content and toxin allele profile. Moreover, NAuRA can create an allelic database to harmonize protein variant nomenclature of SEs, and to facilitate information sharing between outbreak cases. NAuRA was implemented to study enterotoxin-coding gene families in S. aureus strains responsible for FPOs in Europe between 2005 and 2017.
Bioinformatics Development An in-house workflow NAuRA was developed in Python to automate and harmonize the screening for genes of interest in a collection of genomes (Figure  1 ). Based on BLAST, NAuRA detects the presence of genes/proteins from a user-defined query list in the user-defined collection of genomes and provides an allele identifier when a matching gene/protein is found. Every allele-type sequence having a new identifier is added to the database of queries. Furthermore, the workflow offers the possibility to perform a quick clustering analysis using a neighbor-joining approach based on allele sequences. NAuRA is compatible with both nucleic and protein sequences and is available on github 1. From genomes in GenBank format and a list of query genes of interest in FASTA format, NAuRA converts GenBank files to FASTA nucleic acid (fna) or FASTA amino acid (faa) files where each sequence represents an open reading frame (ORF) previously predicted by Prokka.
, 2011)  to construct a phylogenetic tree using the neighbor-joining method implemented in Phylip  (Felsenstein, 2013). Node support values are calculated using the bootstrap method. The consensus tree is obtained using the sumtree. py script from the DendroPy Library  (Sukumaran and Holder, 2010). To analyze a gene family with an unknown level of divergence, a second Python script was developed, NAuRA_BPF (Figure  1B ). To optimize the setting of threshold values, BLAST analysis was performed with a series of coverage and similarity thresholds randomly chosen in uniform distribution. The best analyses corresponding to specific threshold values were selected using a monophyly test, which assumes that all alleles belonging to a given gene family form a monophyletic branch on a phylogenetic tree.
Genome Sequencing, Assembly, and Annotation Genomes were sequenced using Illumina NextSeq system (ICM institute, Paris, France) and the Nextera XT DNA Library Prep Kit was used to construct libraries. The sequencing data were presented as reads of 2 * 150 bp. Before assembly, reads were normalized using BBnorm 2  to reduce coverage to 100x and trimmed using Trimmomatic  (Bolger et al. , 2014)  to remove bases having a Phred score of less than 30 and to remove reads smaller than 50 bp. Assembly was performed in three steps. First, the de novo assembly was processed using Spades with default parameters. Then, the nearest complete public genome of S. aureus was found using Mash  (Ondov et al. , 2016)  and used to perform the scaffolding step in MeDuSa  (Bosi et al. , 2015). Finally, a gap-closing step was processed with GMcloser  (Kosugi et al.
, 2015). Genome assemblies were evaluated using Quast  (Gurevich et al. , 2013)  and scaffolds smaller than 200 bp were removed. Genome annotation was performed using Prokka  (Seemann, 2014). Raw reads and assembly data are available in the European Nucleotide Archive (ENA) under the study number PRJEB36867. Publicly available genomes were also added to the dataset: 101 assembly genomes available from the RefSeq database in NCBI. They were re-annotated using Prokka to ensure a homogeneous dataset. Multilocus sequence typing (MLST) was performed on each sample, the sequence type (ST) was assigned using the MLST program 3  , and the database PubMLST 4  and the scheme of seven housekeeping genes (arcC, aroE, glpF, gmk, pta, tpi, and yqiL) developed for S. aureus  (Enright et al. , 2000). Thus, our total collection included 101 genomes from public databases and 143 genomes sequenced in-house.
For strains sequenced in-house, they were isolated in Europe; 103 were potentially responsible for an FPO, 14 were reference strains, 15 were isolated from self-testing and 10 had been used in a previous study  (Titouche et al. , 2019). The quality of sequencing, assembly and annotation was robust: the coverage of reads was on average 329x (Supplementary Table  S1 ). Regarding the assembly, the expected size of S. aureus genomes was found (on average 2. 71 Mb) and the number of contigs varied between 1 and 69. The dataset annotation was harmonized using the same software (i. e. , Prokka), and the number of annotated genes varied between 2248 and 2751.
Screening for Genes of Interest We screened for the previously described 26 enterotoxinsand TSST1-coding genes using protein sequences as queries on the 143 S. aureus genomes isolated from food matrices and on the 101 publicly available genomes (Supplementary Table  S1 ). The accession numbers of these genes are given in Supplementary Table  S1. Reviewed protein sequences available in public databases were chosen. For the other SEs for which no reviewed sequences were available, their genomic localization, and for two new SEls, sel26, and sel27, the protein sequences were taken from the literature  (Zhang et al. , 2018). Because these genes are genetically close, parameter adjustment was performed to discriminate all alleles of each gene using the Python script NAuRA_BPF. Coverage and similarity parameters were tested for values between 80 and 100 to perform 10,000 simulations.
Among these simulations, monophyletic groups were found for each gene in 3072 simulations, the other 6928 analyses were removed. The best analysis was selected to maximize allelic diversity for each gene. For each gene, a distribution of the number of different alleles was established using the 3072 selected simulations. A random choice among analyses providing a number of alleles between the first and fourth quartile of the distribution was performed. This procedure conserves the diversity of alleles and reduces the stringency of the selection parameters. Nucleotide sequences were extracted from GenBank files, and using BLAST best thresholds result obtained by NAuRA for the best analysis.
Determination of Thresholds for Detecting Staphylococcal Enterotoxins NAuRA is a Python pipeline to detect sequences of interest in genomes with a BLAST approach. It was developed to mainly detect SE genes. With the different divergence levels, simulations were performed to optimize filter parameters of identity and coverage. Accordingly, among the 10,000 simulations, 64 were conserved after the monophyly test (Figure  1 ). The values of the parameters used for the 64 simulations covered the whole uniform distribution between 80 and 100 for identity and coverage parameters, except for the four following genes: sea, see, sep, and selv. This means that the 64 selected simulations were based mainly on these four genes; for this reason, for the final analysis, the lowest threshold to maintain the allele monophyly was 80% of identity and coverage for the 23 SE genes, 90% of identity for selv and 84% for identity for sea, sep, and see.
Core Genome Definition and Phylogenetic Analyses The core genome of the 244 strains, which is defined by all genes shared by all genomes in only one copy, was determined using Roary  (Page et al. , 2015)  applied on the GFF3 files obtained in Prokka. Based on the pBLASTn approach, Roary extracts the pan-genome and aligns all its ORF sequences using MAFFT  (Katoh et al. , 2002). To remove the misaligned regions, the Gblock program  (Castresana, 2000)  was used on this alignment. To obtain a robust phylogenetic tree with the maximumlikelihood method, iQtree  (Chernomor et al. , 2016)  was run. The node support values were calculated using the bootstrap method. Bootstrap values were calculated with 1000 replicates and a bootstrap convergence test was carried out to check if this number of replicates was sufficient.
Description and Justification of the Dataset Based on the core genome, the dataset was representative of the known diversity of S. aureus species. Furthermore, MLST analysis was performed on all sequences and 62 different STs were found in the collection, although it was not possible to determine the ST for 22 strains. The clonal complex was estimated for these strains. Among the 62 different STs, the 10 most frequent STs found in 40,000 genomes of S. aureus  (Petit and Read, 2018)  were the following: ST22, ST8, ST5, ST239, ST398, ST30, ST45, ST15, ST36, and ST105. However, some STs frequent in food, which were not detected in  Petit and Read (2018)  study were also identified, e. g. , ST1 and ST389. Strains involved in FPOs formed a paraphyletic group (starred in Figure  2 ). In the maximum likelihood tree, strains responsible for FPOs were distributed throughout the tree and were mixed with strains isolated from humans or environment (Figure  2 ).
For example, strains occurring in France in 2017 (17SBCL13STA, 17SBCL08STA, 17SBCL09STA) constituted a monophyletic group with genomes from the RefSeq database and also with strains responsible for FPOs in Bulgaria in 2017 (17SBCL580, 17SBCL585, and 17SBLC586) and in Belgium in 2013 (13SBCL319STA and 13SBCL320). This pattern suggests that strains are not structured according the country of isolation or year of isolation. For instance, strains isolated in  1997, 2011, 2013, and 2015  belonging to ST8, are monophyletic with genomes from the RefSeq database.
aureus. Regarding genes located on plasmids, several toxin profiles were found within the same strain group. For example, the strains 11CEB284STA and 47A clustered together, but only one of them had the sed, selj, and ser genes and the other had seb, sek, and seq. No difference in toxin profiles was observed between strains responsible for FPO and the other strains. Strains from the RefSeq database had the same toxin profile as FPO strains. For example, 15SBCL1507STA and GCA_00262835 both showed the egc cluster and the SElX-coding gene; the 13CEB176STA and GCA_001350955 strains had the genes coding for SEB, SEK, and SEQ. As no toxin profile can be shared by environmental strains and FPO, this could suggest that food contamination occurs via environmental strains. A large number of toxin profiles were found (71 profiles, of which 34 were found only once) (Figure  3 ).
Enterotoxin Sequence Analyses The number of variants differed according to the SEs (Figure  4 ). The number of protein variants varied between 1 and 11, except for the SElX-coding gene for which 31 protein variants were found (Figure  4 ). For the egc cluster comprising seg, sei, sem, sen, and seo, the same magnitude of protein. A similar number of variants in SED/SElJ pair localized on pIB485, and the SEC/SEL pair localized on SaPIbov was observed. It is also possible for genes localized on the same plasmids to have a different number of variants. For example, one protein variant was found in the dataset for SET, whereas four variants were found for the other toxins localized on the same plasmid (SElJ, SER and SES). In all, 16 SE genes (sea, seb, see, seg, seh, sei, selj, sem, sen, seo, sep, ser, set, selz, sel26, and sel27) had one dominant protein variant in the dataset (at least 50% of strains), whereas no dominant protein variant was observed for the 11 other SE genes (sec, sed, sek, see, seq, ses, selu, selx, sely).