dataesr/scientific-paragraphs-categorization huggingface dataset:
VYTOP calibration with physiological and metabolic data To calibrate VYTOP, we performed physiological and biochemical measurements (Figure  2 ) on 90 tomato plants. The growth of 90 plants was monitored on an automatic phenotyping facility, which allowed an automatic monitoring of plant transpiration rate. Sampling of 6 plants per day was performed during 10 days, starting at 28 days after seedling. Xylem metabolite concentrations and organ weights were determined on sampled plants. The biomass composition of each organ was measured in another experiment performed in the same environmental conditions, with the same tomato variety and a similar plant age. Weight dataset of sampled plants was used to measure the growth rate for each organ, the organ dry weight ratios (leaf:root, and stem:root) (Figure  2a , Figure  2b ), and the fresh:dry weight ratio for each organ (Supplementary Figure  1 ).
The data obtained was completed with literature data for lipids, minerals, (hemi)cellulose and lignin to generate accurate organ specific biomass equations (Supplementary File 1). For each organ, the biomass equation yields 1 g of dry weight biomass (Supplementary File 1). In comparison with  Yuan et al. model iHY3410 (2016) , our updated biomass equation for leaf had a C:N ratio closer to the one observed from experimental data (6. 5) in high CO2 level, high N provided as a NO3 -:NH4 + mixture  (Royer, Larbat, Le Bot, Adamowicz, & Robin, 2013): 5. 48  in VYTOP vs 18. 52 in iHY3410. Quantification of metabolites concentration in xylem sap (Figure  2 ) was performed using NMR. Glutamine was the major metabolite in xylem sap (4. 019 ± 1. 419 mM). Fifteen other organic molecules were detected: 11 amino acids, 2 sugars, ethanol and fumarate, whose concentrations were much lower than that of glutamine (<0.
250 mM). Metabolic fluxes in xylem were computed using these concentration data and the transpiration rate of plants (see Materials and Methods). We also gathered data from literature to choose which compounds is present in the phloem compartment  (Hijaz & Killiny, 2014)  and to estimate photosynthetic activity in stem. Former experimental results indicated that photosynthesis in stem is limited due to less exposed surface to light  (Hetherington, Smillie, & Davies, 1998). According to this study, the leaf:stem ratio of contribution to photosynthesis is around 7. 7 (considering here the petioles as stems), and this value was added in the model as a constraint. Th Ammonium:nitrate uptake ratio can be optionally constrained in the model to take into account different fertilizer conditions. Ssince the fertilizer used for the experiments contained both ammonium and nitrate and we did not measure the uptake rate of each nitrogen source, the ratio was let unconstrained for our simulations except for use case 4.
The web tool SAMIR (Semi Automatic Metabolic Identifier Reconciliation)  (Peyraud, Cottret, Marmiesse, Gouzy, & Genin, 2016)  was used to find the appropriate BiGG IDs for metabolites and reactions. The correspondences between MetaCyc and BiGG IDs are provided in the final metabolic network in table format (Supplementary File 1). For some metabolites and reactions, no BiGG ID was found even after manual researches in BiGG database, probably because they are specific to plants. New BiGG IDs following the usual nomenclature were created for 1094 reactions and 758 metabolites (see Supplementary File 1). The SBML file  (Hucka et al. , 2003)  was generated using Met4j (https://forgemia. inra. fr/metexplore/met4j) and ModelPolisher  (Römer et al. , 2016). The resulting metabolic model was tested on autotrophic growth and heterotrophic growth (using FBA, see Simulations section) on different organic substrates before generating a multiorgan metabolic model.
lycopersicum. 21 reactions, linked to 34 genes were added to the model iHY3410. 15 reactions had a GPR complete with an e-value superior to 5e-131 (Supplementary File 1), 6 had none. After this second curation step, we computed the mass balance (on carbon, nitrogen, phosphate, sulfur and oxygen) and manually curated the reactions with incorrect carbon, nitrogen, phosphate, sulfur and oxygen balance. We generated a final tomato metabolic network Sl2183. The model is available in MetExplore  (Cottret et al. , 2018)  https://metexplore. toulouse. inrae. fr/metexplore2/?idBioSource=6237, BioModels  (Glont et al. , 2018)  under the ID xxx and in a github repository https://github. com/lgerlin/slyc-metabolicmodel/ [once the paper is accepted].
VYTOP construction To generate VYTOP (Virtual Young TOmato Plant) (see flow chart in Supplementary Figure  5 ), we built organ-specific metabolic models for leaf, stem and root. To this end, organ growth rates and organ-specific biomass equations were determined from our experiments and implemented as constraints in the simulations. We generated an in-house script to parse the metabolic network Sl2183 (SBML file) and replicate it in three to represent three organs. In the generated model, each organ is represented as a "metacompartment": metabolite and reaction Physiological constraints were added to impose different physiological roles in organs. i) Uptake of minerals and water is not allowed in leaf and stem, while it is allowed with no limitation in roots (assuming they are not limiting factors for well-watered plant and adequate nutritional supply in the soil).
Acknowledgments We thank  TPMP  ( Toulouse Plant Microbe Phenotyping ) platform (Castanet-Tolosan, France ) and its staff  Nemo Peeters ,  Felicià Maviane Macia  and  Fabrice Devoilles  for their technical support in plant cultures and imaging, as well as HiTMe (High -Throughput Metabolic phenotyping) platform (Villenave d'Ornon, France ) for biochemical analysis of organ metabolites. We acknowledge MetaToul (Metabolomics & Fluxomics Facitilies, Toulouse, France, www. metatoul. fr) platform, which is part of the MetaboHUB-ANR-11-INBS-0010 national infrastructure (www. metabohub. fr) and its staff  Cécilia Berges ,  Edern Cahoreau  and  Lindsay Peyriga  for access to NMR facilities. We also thank  Sophie Colombié  for helpful discussions and feedbacks on the manuscript.
Funding information LG and AE were respectively funded by a grant from the  French Ministry of Higher Education and Research  and by the  French Laboratory of Excellence TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ). The study was funded by the  French Laboratory of Excellence (LABEX)  project  TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Transcription factors from the nuclear receptor superfamily are natural candidates, as they can sense fluctuating levels of nutrients and xenobiotics and promptly adapt hepatic metabolism by modulating gene transcription. Here, we took a global approach to identify potential hepatic effectors of gut microbial signals by performing transcription factor enrichment analysis on previously published mRNA expression datasets from germ-free (GF) vs. conventional (Conv. ) mice. We identified PXR as a nuclear receptor whose transcriptional activity was strongly affected in the absence of gut microbes, in accordance with previous work that identified gut microbial metabolites activating PXR in the intestinal epithelium  [26]. We used Pxr +/+ vs. Pxr -/-C57BL/6J mice treated with antibiotics (ATB) to elucidate metabolic pathways controlled by the microbiota-PXR interactions and demonstrated that, in physiological conditions, this interaction affects both hepatic fatty acid and xenobiotic metabolism in a sexually dimorphic way.
Gene expression Total RNA was extracted using TRI Reagent® (Molecular Research Center, Cincinnati, OH, USA). RNAs were quantified using Nanodrop (NanoDropTM 1000; Thermo Scientific). Two micrograms of total RNA were reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems TM). The SYBR Green (Applied Biosystems, California) assay primers are presented in Additional file 2. Amplification was performed using an ABI Prism 7300 Real-Time PCR System (Applied Biosystems TM ). Quantitative real-time polymerase chain reaction (qPCR) data were normalized to TATA-box-binding protein mRNA levels and analyzed with LinRegPCR (version 2015. 3).
Microarray Microarray experiments were conducted on n = 6 mice per group (3 per cage). Total RNA was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Gene expression profiles were obtained at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France) using Sureprint G3 Mouse GE v2 microarrays (8 × 60 K; design; 074,809; Agilent Technologies, Santa Clara, CA, USA) following the manufacturer instructions. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus (Edgar, Domrachev, & Lash, 2002) and are accessible through GEO Series accession numbers GSE123804 and GSE156837.
For each sample, a sequencing library was generated by addition of sequencing adapters. The detection of the sequencing fragments was performed using MiSeq Illumina technology. The targeted metagenomic sequences from microbiota were analyzed using the bioinformatics pipeline established by Vaiomer from the FROGS v1. 4. 0 guidelines  [31]. Briefly, after demultiplexing of the barcoded Illumina paired reads, single-read sequences were cleaned and paired for each sample independently into longer fragments. Operational taxonomic units (OTU) were produced via single-linkage clustering and taxonomic assignment was performed in order to determine community profiles. The PhyloSeq v1. 22. 3 R package was used to provide a set of classes and tools to facilitate the import, storage, analysis and graphical display of microbiome census data (including alpha and beta diversity analysis).
Functional and gene set enrichment analysis Gene expression datasets comparing hepatic signatures of GF vs. Conv. animals were found on the Gene Expression Omnibus data repository accessed in September 2019. Only data obtained in wild-type mice were considered. Data obtained at different ZT were pooled to increase sample size. Five datasets were available and used in the present analysis (GSE31115, GSE5390, GSE71628 and GSE114400 (males and females)). Differentially expressed genes were calculated using the GEO2R tool for microarray data and using BioJupies  [33]  for RNA sequencing data. Genes with adjusted p values < 0. 05 and Ifold changeI > 1. 2 were considered differentially expressed. Analyses of transcription factor enrichment were performed with Enrichr  [34] , by interrogating the TRRUST Transcription Factors 2019 database and reporting adjusted p values for mouse transcription factors while discarding human transcription factors.
Microarray data analyses Microarray data were processed using R (http://www. rproject. org) and Bioconductor packages (http://www. bioconductor. org, v3. 0). Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath) and normalized using the CrossNorm method  [35]. The linear model was fitted using the limma lmFit function  [36]. Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using the Benjamini-Hochberg procedure for false discovery rate (FDR). Probes with FDR ≤ 0. 05 and |fold-change| > 1. 2 were considered to be differentially expressed between conditions. The gene set list of sexually dimorphic liver genes is based on the comparison between Pxr +/+ Cont males and Pxr +/+ Cont females.
Sequencing data analyses The operational taxonomic unit (OTU) files were formatted for linear discriminant analysis effect size (LEfSe) analysis using the per-sample normalization of sum values option, using the R package Phyloseq v1. 22. 3. The linear discriminant analysis (LDA) effect size (LEfSe) was run using default values (alpha value of 0. 05 for both the factorial Kruskal-Wallis test among classes and the pairwise Wilcoxon-Mann-Whitney test between subclasses, threshold of 2. 0 for the logarithmic LDA score for discriminative features) and the strategy for multiclass analysis was set to 'all-against-all'. LEfSe cladograms from the LDA effect size data were generated with Bacteria as the tree root.
Multi-omics analyses Bidirectional correlations between caecal OTUs and hepatic transcriptome were investigated using Nintegration discriminant analysis with DIABLO, an algorithm that aims to identify a highly correlated multiomics signature discriminating several experimental groups  [37]  using the R package Mixomics v6. 10. 9  [38]. OTUs with a minimal presence of 1% in all samples were kept. Samples were excluded from the microbiotadata if they were not measured in the microarray analyses (i. e. integration was performed on n = 6 samples/ group). DIABLO models were fitted in males and females separately; we used 2 components in the models, and for the estimation of model parameters, the crossvalidation procedure (CV) method was used. For the Circos plot and correlation networks, only correlations with a Spearman's rank correlation coefficient > 0.
Absence of gut microbiota alters PXR-dependent gene expression in the mouse liver To investigate the biological functions impacted by gut microbial signals in the liver, as well as transcription factors that could be potential sensors of these signals, we first interrogated previously published microarray and RNAseq datasets comparing the hepatic transcriptomes of GF vs. Conv. mice (Fig. 1a-g ). Process and pathway enrichment analysis using genes significantly up-and downregulated in GF mice (Fig. 1b  and e , respectively) highlighted xenobiotic metabolism as significantly affected by the absence of gut microbes in most studies. Only one study used both male and female GF mice (GSE114400). Interestingly, in this dataset, gene ontology (GO) terms related to xenobiotic metabolism were significantly enriched in the list of downregulated genes when comparing male GF vs.
Conv. males but not when GF vs. Conv. females were compared (Fig. 1e ). Looking for upstream regulators of these genes, we found several transcription factors whose biological activity was significantly perturbed in GF mice, among which was PXR (official name NR1I2  [39] ), a master regulator of hepatic xenobiotic metabolism (Fig. 1c  and f ). Again, results from the GSE114400 study illustrate sexually dimorphic results since PXR (NR1I2) was predicted as a significant upstream regulator of the genes that are both over-and under-expressed in GF compared with Conv. male mice (p = 0. 01 for both up-and downregulated genes), but not in females (p > 0. 05) (Fig. 1c  and f ). Accordingly, in all studies in males, the expression of cytochrome P450 (CYP) family 3 mRNA was decreased (Fig. 1g ). This result is consistent with reduced PXR activity in GF mice, since PXR is a well-known regulator of CYP3 expression  [40, 41] , as confirmed here using WT vs.
The gut microbiota-PXR interaction controls the host's hepatic xenobiotic metabolism Another interesting finding from our microarray data was the strong enrichment of genes related to xenobiotic metabolism. ATB-treated Pxr +/+ , but not Pxr -/-, males displayed lower hepatic expression of genes involved in phase I xenobiotic metabolism (Fig. 4d ). In order to test the functional consequences of the downregulation of these CYP genes, the regio-selective oxidative metabolism of [ 14 C] testosterone in males was measured, using hepatic microsomal subcellular fractions. Testosterone metabolites with peaks at retention times of 10. 5 and 14. 7 min were significantly lower in ATB-treated vs. control Pxr +/+ males (p adj = 1. 5 × 10 -3 and p adj = 0. 014 resp. , Fig. 4e ). The 10. 5 min peak was formally identified as 6β-hydroxytestosterone (Additional file 13), a product of 6β-hydroxylase activity performed primarily by CYP3A11 in mice  [16].
Acknowledgements We thank all members of the  EZOP (Experimental Zootechny)  staff for their careful help with this project. We thank the staff from the  Genotoul: Anexplo ,  Get-TriX ,  Metatoul-AXIOM  and  Metatoul-Lipidomic  facilities. Pxr-/mice are a generous gift from  Pr Steven Kliewer  ( University of Texas Southwestern Medical School, Dallas, TX, USA ). Colony founders were kindly provided by  Pr Urs A Meyer (Biozentrum, University of Basel, Basel, Switzerland ).
 depends on the rate of body mass gains which scales allometrically with body mass.  is a dynamic response variable that depends partly on the ratio between fluxes of gains and losses and covaries negatively with . The model can be parameterized either from existing large databases of animal growth models or directly from experimental results. The model was applied to experimental results on three ectotherms and one endotherm and compared to the results of the simpler and widely used time model. IsoDyn model gave a better fit with relatively little calibration. IsoDyn clarifies and expands the interpretation of isotopic incorporation data.
Simulations and parameter estimations One of the major advantages of our model is that it can be parameterized and fitted readily with available information data or data that can be gathered in DSE. Describing dynamics of body mass require three parameters that are ri, ro and , and only two parameters if we consider  following a common framework (e. g. =2/3 in the case of the von Bertalanffy growth model). Two additional parameters are needed, Δi and Δo, for simulating the incorporation dynamics of stable isotopes. Simulations presented here, were generated following a numerical integration algorithm under R software v. 4. 0. 3 (2019) using the package DeSolve  (Soetaert et al. 2010)  to solve eq (8). Because fitting the four parameters (ri, ro, Δi and Δo) from data on isotope incorporation alone ( 15 N values over time) is not possible, the parameters can be estimated in two ways which can be called simultaneous and sequential and by using dynamics of body mass in parallel.
In simultaneous estimation, the parametrization of the four main parameters (ri, ro, Δi and Δo) can be done using both the dynamics of body mass and stable isotopes incorporation. For simplicity, we chose to perform the calibration considering that Δi and Δo as opposite values but equal in absolute value (Δi = -Δo). This means that the same isotopic discrimination was applied to gains and losses, an assumption also done by  Flynn et al (2018)  in a mechanistic simulation model. Parameter estimations were performed using the Nelder Mead function of the lme4 package which allows to set boundaries for the parameters (ri and ro must be positive). The function minimizes the sum of two symmetric bounded loss functions (hereafter named the cost function) which accounts for the difference between predictions and observations for dynamics of body mass and stable isotopes respectively.
This cost function is ideally suited to fit several models to several dataset  (Marques et al. 2019). Local minima can be found during the optimization process. In order to ensure to detect the global minimum, the initial starting values of the parameters were randomly selected and the procedure is performed twice first (N=2). Then, the process continues (up to N=20) until the value of the cost function for the last set of parameters is lower than the best set by 5%. Parameter sets in which some parameters stuck to the boundaries were systematically deleted. Interval estimates of parameters were evaluated using a bootstrap method (N=500) by adding lognormally distributed scatter (mean coefficient of variations of observations) to the predictions with replacement of the original data sets  (Marques et al.
, 2018)  or the ontogenetic growth model  (West et al. 2001; Hou et al. 2011)  or studies on the selected species. Then, the estimation of Δi and Δo were done in a second step by implementing the values of the three previous parameters in Eq  (8). R code for all analyses, figures and tables is available from GitHub (https://github. com/Sebastien-Lefebvre/IsoDyn).
As a result, the proportion of kg explaining  differed noticeably from one case to another. In mouse and Pacific yellowtail, kg/ ratios were 10 to 20% lower respectively in IsoDyn estimates, whereas it was 60% higher for the sand goby. As for the specific estimates of the IsoDyn model, ri ranked according to the maximum body mass of the species (Pacific yellowtail > mouse > sand goby) and ro was higher for mouse (endotherm species). The predicted maximum body masses (as calculated with eq(7)) were 331. 5 g, 23. Case study: calibration using the sequential parameter estimation To determine the suitability of IsoDyn model when intra-specific variations of ri and ro occurs due to different ration levels, we re-analyzed the data from Gaye Siesseger et al. (2004). In this DSE, the growth of Common carp (Cyprinus carpio) was manipulated by changing food availability through different feeding levels.
We estimated parameters in a sequential approach because dynamics of body mass were restricted to start and end values preventing a reliable calibration of ri and ro. First, we used parameters from the DEB "Add My Pet" data set to calibrate the model (see supplementary material 2 for more details), and then adjusted the scaled functional response (f), an Holling type II function ranging from 0 to 1, that controls the rates of body mass gains (ri) and of body mass losses (ro) to fit the observations of body mass change over time (Fig. 5 ). The f values were estimated to be 0. 16, 0. 29, 0. 53, 0. 82 from the lowest feeding ration levels to the highest ones (Table  2 ). This allowed estimating values of ri and ro for the four treatments according to ESM2 (Table  3 ). Then, independent values of Δi and Δo were simultaneously fitted using δ 15 Nb-δ 15 Nd values and giving Δi = 1.
Like DEB-dependent growth models, DIB models are species-specific and each case requires calibration with a high number of parameters (over 22 in the case of Emmery et al. 's (2011)  application), although DEB models can be potentially parameterized with values from a huge database (Marques et al. , 2018). Moreover, their application involves computationally intensive calibration and expertise in DEB that is not common among ecologists. We venture that for this reason,Pecquerie et al. 's (2010)  model has not been applied widely. As far as we know, Emmery et al. (2011)'s study is its only empirical application to stable isotope studies. Olive et al. (2003)   and Martinez del Rio and Wolf (2005)   suggest a decreasing relationship between  15 N and . The generality of this result is unknown because both Olive et al.
Design This article is a secondary analysis of a cross-sectional study, and the objective was to analyse fathers' responses from a clinical obstetrical point of view. The data initially were collected for the cross-cultural validation of the FTFQ into French. However, during that transcultural adaptation and its testing, the collected data were only used in a psy-chometric approach  ( Capponi et al. , 2016 )  and not analysed for their content.
, 2012 ). Two reminders were sent, if necessary, up to six weeks post-partum. They could withdraw from the study at any time. Participants who received the questionnaire by mail were allowed to not send it back. Participants who received receiving the questionnaire by email had the option to click on a link that would remove them immediately from the mailing list for this study. Most of the participants wished to complete the questionnaire online (96%). We used the Lime Survey software package ( https://www. limesurvey. org ), which is an online survey program commonly used for scientific research. The procedure lasted approximately seven months, from July 2014 to February 2015.