dataesr/scientific-paragraphs-categorization huggingface dataset:
2. as follows: genes shared by all species are called shared by all; genes shared by at least nine species among the ten studied species without phylogenetic logical are named "shared by some"; genes shared by only Hymenoptera species and without orthologous gene in D. melanogaster are considered as "Hymenoptera specific"; genes shared only by Microgastrinae are named "Microgastrinae specific"; genes shared only by Cotesia species and without orthologous genes in any of the outgroup are considered as "Cotesia specific". Synteny analyses. The different synteny analyses were performed on the orthologous genes identified by OrthoFinder v1. 14  74  and by reciprocal blastp from BLAST + v2. 2. 28 on the annotated proteins (e-value below 10e -20 ). The correspondence between the genes, the localizations on the scaffold and the figures were realized thanks to a custom R script (R Core Team 2013).
Then, featureCounts program from the Subread package  86  was used to determine fragment counts per genes using default parameters. To analyze gene expression, the raw fragment counts of ovaries and venom glands samples were first converted to counts per million (CPM) using the edgeRimplemented package 87 (R-Core Team 2017). Expressed genes were filtered based on a CPM > 0. 4 (corresponding to raw count of 15) in at least two of the libraries incorporated in the analysis (Supplementary Fig. 9A ) and subsequent normalization was performed on CPMs using the edgeR TMM method for Normalization Factor calculation  88  (Supplementary Fig. 9B ). The reproducibility of replicates was then assessed by Spearman correlation of gene expression profiles based on filtered and normalized CPMs (Supplementary Fig.
Acknowledgements We thank  Paul André Catalayud  for providing the pictures of C. sesamiae and C. flavipes  Germain Chevignon  for the pictures of C. congregata nymphal stages and  Juline Herbinière  for TEM images of C. congregata ovaries. We thank the  ADALEP (Adaptation of Lepidoptera) network  for the involvement of its members and access to bioinformatic facilities for genome annotation. C. sesamiae individuals used in this study originated from the icipe, under the juridical framework of a Material Transfer Agreement signed between IRD, icipe and  CNRS  ( CNRS 072057/IRD 302227/00 ). The authors thank  Bruno Le Ru ,  Gerphas Ogola , and  Julius Obonyo , which made the insects accessible for the study. C. congregata, C. sesamiae, C. flavipes genomes sequencing were funded by  French National Research Agency ANR  ( ABC   Papogen  project  ANR-12-ADAP-0001  and CoteBio  ANR17-CE32-0015-02  to  L.
Data availability The datasets and genomes generated during the current study are available from the European Bioinformatic Institute (EMBL-EBI) and National Center for Biotechnology Information (NCBI) at the following BioProject ID: PRJEB36310. Genome database (genomes and annotated genes) is also available on the web site BIPAA (Bioinformatic Platform for Agrosystem Arthropods) https://bipaa. genouest. org/is/parwaspdb/.
Acknowledgements. We thank the two anonymous reviewers for their meticulous work that helped to improve this paper a lot. This work was granted access to the HPC resources of TGCC under allocation 2019-A0090102212 made by  GENCI. Financial support. Julia Bres ,  Nicolas Viovy  and  Nicolas Vuichard  are supported by the Commisariat à l'énergie atomique et aux énergies alternatives (CEA). Pierre Sepulchre is supported by  Centre national de la recherche scientifique (CNRS). Review statement. This paper was edited by  Akihiko Ito  and reviewed by two anonymous referees.
Code availability. The code is available as part of the IPSL-CM5A2 Earth system model, which has been made available by Sepulchre et al. (2020). The code can be retrieved through svn, with the following command lines: svn co (http://forge. ipsl. jussieu. fr/igcmg/svn/modipsl/branches/ publications/IPSLCM5A2. 1_11192019/,  Ghattas, 2020). See also https://gmd. copernicus. org/articles/13/3011/2020/#section11 (last access: 25 June 2020; Sepulchre et al. , 2020). The ORCHIDEE code, which has been modified for this study, is available as well through svn: svn co (https://forge. ipsl. jussieu. fr/orchidee/browser/branches/ publications/ORCHIDEE_IPSLCM5A2. 1. r5307, Ghattas, 2018). The login-password combination requested at first use to download the ORCHIDEE component is "anonymous" and "anonymous".
More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In its current implementation, the method takes as input a phylogenetic tree together with mapped discrete evolutionary events on it and then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power when compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that even with datasets of fewer than 100 species, the method performs well in parameter estimation and in the selection of evolutionary scenario.
The rate matrix is defined by 12 parameters, or 8 when double mutations are forbidden. More complex models, like 2 sites with 20 amino acids each, depend on more parameters (on the order of a few hundred). Consequently, these methods need larger datasets and expensive computation time. Here, we propose a likelihood framework that is based on a minimal model of correlated evolution. The framework generates a series of nested models with 2 to 8 parameters that correspond to the mutation rates of each process and interactions between them. We show that these parameters are core values that characterize patterns of correlated evolution. The current implementation of the method assumes that the tree is correctly inferred and that the events are correctly placed on the tree. Using maximum likelihood, the method can (i) test for independence between two evolutionary processes, (ii) find the most likely type of correlated evolution between them (obligate or prefer-ential sequential order, reciprocal synergy, incompatibility, etc.
Analysis of correlated evolution between Watson-Crick pairs We applied our method to detect correlated evolution between Watson-Crick pairs of nucleotides in bacterial sequences of ribosomal ribonucleic acid sequences (rRNA). We downloaded 55 sequences of rRNA 16S from the SILVA rRNA database  (Pruesse et al. , 2007; Quast et al. , 2013). Of those, 54 are sequences of gamma-enterobacteria and one from a beta-enterobacteria, used as an outgroup. Next, we aligned the sequences with Muscle v3. 6  (Edgar, 2004)  and trimmed all poorly aligned positions using Gblocks v0. 91b with the default parameter values  (Castresana, 2000; Talavera and Castresana, 2007) , resulting in 1,233 aligned nucleotides. We next inferred a phylogenetic tree using PhyML  (Guindon et al. , 2010)  with a generalized time reversible substitution model.
epics- the alignment filtering procedure and the monomorphic positions in the alignment. Indeed, out of the complete set of 477 WC pairs, only 349 WC pairs (0. 05% of the 759,528 unmasked possible nucleotide pairs) have both their positions unmasked after Gblocks. Furthermore, the alignment contains 422 polymorphic sites, reducing the dataset to a maximum of 106 WC pairs that could be detected among a total of 88,831 polymorphic possible nucleotide pairs. Most of the 106 WC pairs show evidence of correlated evolution for a risk of 5%, but only ∼ 30 very strong evidence of correlated evolution (p-value < 10 -6 ). - * i /µ i = ν * i /ν i ), we estimated the reciprocal induction rate of mutation of one site onto the other. The rates are colored in black if there is statistical support for correlated evolution (at a risk of 1%) from the comparison between the model with five parameters to the model of independence with four parameters.
The comparison of performance with a selection of four previously existing methods demonstrates that the current framework is more specific at detecting WC pairs and can additionally estimate the strength of their reciprocal induction. As the model has a small number of parameters, the computation time of likelihood maximization is fast enough to analyze correlated evolution in a very large number of pairs. In the 16S analysis, we had to maximize 2,304,091 likelihood functions (finding all possible orders of events for all pairs). The total computation time on a 8Ghz laptop, We report the nature of the 75 best pairs (the ones with the lowest probabilities) among all nucleotide pairs, according to five different methods: our previous method with co-occurrences (epics-Id), the LRT based method (see legend of Figure  5 ) using our likelihood framework, the CoMap method based on co-occurrences on the tree,the MIp method based on Mutual Information, corrected for phylogeny, and the BTDiscrete module.
Pairs are colored in black when they are Watson-Crick, on dark grey when they are closer than ten Å in the 3D structure and in light grey otherwise. Below their rank, we also report their associated p-value. (b) Relative structural positions on the stems-loops for the nucleotides of the 75 best pairs, according to the five methods. without any parallelization, is 11 minutes for the 2-parameter model of independence (µ 1 and µ 2 ), 71 minutes for the 4-parameter model of independence, and at most 240 minutes for the full 8-parameter model (that was not used in the analysis above). This suggests that MCMC Bayesian surface exploration, that typically requires millions of likelihood estimations, could be run to analyze cases on a dataset of a reasonable size. Computation time remains, however, orders of magnitude larger than in the methods based on counts we have developed before  (Behdenna et al.
similar to TreeWAS. the work. This work was supported by a state grant from the Agence Nationale de la Recherche through the Investissements d'Avenir program ANR-16-CONV-0005. AB, MG, PP, AL and GA thank the Center for Interdisciplinary Research in Biology (CIRB, Collège de France) for funding. The authors have declared that no competing interests exist. Initial state Intermediate state Final state Initial state Intermediate state Final state (a 1 , a 2.
Sampling sites environmental characterization Climatic and edaphic conditions were obtained for each plot. Below, we briefly describe how they were measured or derived from climatic models but more details can be found in published studies that have already used the same data  (Yashiro et al. , 2016 (Yashiro et al. , , 2018;; Buri et al. , 2020; Seppey et al. , 2020). As we were mainly interested in documenting variation of protist community composition across the elevational gradient, we intentionally included elevation as a covariate, along with edaphic and climatic factors in our models. However, the biological variation observed along the elevational gradients results from climatic and edaphic parameters co-varying with elevation. The climatic and edaphic factors were selected based on two criteria: they must (i) be known a priori to have an impact on soil microbial community composition, and (ii) be relatively uncorrelated with each other (Spearman rho <0.
Fig. 2 ). Finally, we also included plant community type (phytosociological alliances) and plant richness as they can also influence belowground communities  (Delarze et al. , 1998; Fierer, 2017)  (Additional File 2). Plant richness was derived for 145 sites from the existing database  (Dubuis et al. , 2011)  where all vascular plant species were exhaustively inventoried in each site.
Bioinformatic analysis Reproducibility of the pipeline. The whole bioinformatic pipeline described below has been written in R (version 4. 0. 2) (R Development Core  Team, 2015)  with the use of the tidyverse (version 1. 3)  (Wickham et al. , 2019)  and the phyloseq packages (version 1. 32)  (McMurdie and Holmes, 2013). The associated R code is available on github: https://github. com/FloMazel/Protist_Altitude_alps. Sequencing data processing and taxonomic annotation. Raw reads were demultiplexed and barcodes and primers were trimmed using cutadapt version 2. 9 (with parameters e = 0, no-indels, m = 100)  (Martin, 2011). Reads were then quality filtered using the filterAndTrim dada2 R function (with parameters maxEE = 6, truncQ = 2, truncLen = 0)  (Callahan et al. , 2016). Amplicon sequences variants (ASVs) were inferred using the dada function and reads were merged using the mer-gePairs function (with parameter minOverlap = 8).
Chimaeras were removed using the removeBimeraDenovo dada2 R function. Triplicates were then pooled according to their respective samples (data are provided in Additional File 3). ASVs were chosen instead of cluster of sequences based on an arbitrary threshold (e. g. 97%) because they: (i) represent finer taxonomic units, (ii) allow comparison between studies and (iii) have become the unit of choice in environmental DNA analysis, including protist sequences  (Oliverio et al. , 2020). We assigned taxonomy for each ASV using the naïve Bayesian RDP classifier, as implemented in dada2 (function assignTaxonomy, parameter minBoot set to 50) with the PR 2 database (version 4. 12. 0 -file 'pr2_version_4. 12. 0_18S_dada2' provided on the dada2 website, data are provided in Additional File 4 along with bootstrap values for each assignation).
, 2017; Knight et al. , 2018). We focused on the log-transformed ratio between consumers and parasites read counts as these two groups are the most abundant ones. To measure the univariate correlation between elevation and this log ratio, we used a Bayesian linear model using the R package MCMCglmm (version 2. 29)  (Hadfield, 2010)  with 100 000 total iterations and 3000 iterations as burn-in. To further measure the correlation of other environmental factors on this log ratio, we built a linear model using elevation, soil pH, TOC, C/N ratio, mean annual temperature, bulk soil water content (%) and plant alliance and richness as covariates and tested the correlation of each factor using marginal sum of squares and ANOVA with F-values (i. e. testing the correlation of one factor given the correlation of all the other ones, using the drop1 R function).
We suggest that future studies could directly sample host tissue (e. g. roots) to provide (i) more direct evidence of the interaction as well as (ii) a better idea of the host identity. (iii) Current amplicon approaches based on one unique primer set provide limited information on rare parasites (e. g. the very low abundance of endomyxea and cryptomycota prevented us to analyse them statistically here). We suggest that focusing on multiple parasitic lineages at the same time using different sets of PCR primer targeting different lineages (e. g. as in  Bass et al. , 2018)  represents a valuable approach to better document the breath of parasite diversity. bioinformatic pipeline code is published on github: https:// github. com/FloMazel/Protist_Altitude_alps.
Acknowledgements We thank  Jan van der Meer ,  Olivier Bronnimann  and  Christophe Seppey  for help during the initial part of the project. F. M. thanks  Mathieu Chevalier ,  Olivier Bronnimann ,  Sabine Rumpf ,  Antoine Adde  and  Pierre-Louis Rey  for initial feedback on the results of this study. Open access publishing facilitated by the  Universite de Lausanne , as part of the  Wiley -Universite de Lausanne agreement via the Consortium of Swiss Academic Libraries.
1b ). These data suggests that ADIPINT is a promising candidate and further study was devoted to this lncRNA. ADIPINT is intergenic and located on chromosome 9 (Fig. 1c ). None of the three transcripts in the ADIPINT loci have protein coding potential according to CPAT, RNACode, phyloCSF, and sORF riboseq coding prediction softwares. ADIPINT is not conserved between phylogenetically distant species at the sequence or genome synteny level. RNA-seq and CAGE-seq 18 in human adipose-derived stem cells (hADSC) from subcutaneous WAT revealed that transcript 2 of ADIPINT is the dominant isoform expressed in mature adipocytes (Fig. 1c ). Transcript 2 is 4. 8 kb long and contains two exons (Fig. 1c ); the junction was validated by qPCR and Sanger sequencing (data not shown). Upregulation of ADIPINT expression during differentiation in hADSC and enrichment in mature adipocytes isolated from WAT were confirmed by qRT-PCR (Fig.
1d, e ). Analysis of RNA expression in different human cell types in the FANTOM5 7 database demonstrated that ADIPINT is exclusively expressed in adipocytes (Fig 1f). Subcellular fractionation studies and RNA-fluorescence in situ hybridization (FISH) revealed ADIPINT to be predominantly localized in the cytoplasm of adipocytes (Fig. 1g-h ). Thus, ADIPINT is an adipocyte-specific cytoplasmic lncRNA linked to insulin resistance, fat mass, adipocyte size and thereby lipid metabolism.
CEL files were preprocessed and analyzed using the Affymetrix Expression Console (version 1. 4. 1) and standard SST-RMA method. Affymetrix probe IDs were mapped to the FANTOM-CAT gene model 7 , 54,980 probe sets mapped to 40,590 unique gene IDs. Qlucore Omics Explorer (http://www. qlucore. com) software was used to compare arrays as detailed previously  17. Genes annotated 'lncRNA' were selected for analysis and a paired comparison was made across time points with the effect of sample eliminated. qRT-PCR analysis was carried out as described previously  9. Probes targeting ADIPINT, PC, GAPDH, 18S, B2M, MALAT1, NEAT1 and MT-ND5 are listed in Supplementary Methods Table  2. The ΔΔ CT method was used for normalization for all experiments apart from RNA analysis in whole of fractionated WAT where the Δ CT method was used.
After hybridization and amplification steps with pre-amplifier, amplifier, and linked labelled probe, the probes were detected using Alexa® Fluor 650 dyes, according to the manufacturer's instructions. Cells were stained with 1:2500 BODIPY 493/503 and Hoechst 33342 for 20 minutes before mounting with ProLong Gold Antifade Mountant (ThermoFisher). Images were obtained with a Nikon spinning disk confocal microscope system (Nikon) equipped with a Nikon ECLIPSE Ti inverted microscope and a 1. 4 NA 60x oil immersion objective (Nikon), under the control of NIS-Elements AR version 4 software.
1% formic acid) in 55 min and up to 95% of solvent B in 5 minutes at a flow rate of 300 nL/minutes. Mass spectra were acquired on a Q Exactive HF Orbitrap mass spectrometer (ThermoFisher) in 350 m/z to 1600 m/z at resolution of R=120,000 (at 200 m/z) for full mass, followed by data-dependent higher energy collisional dissociation (HCD) fragmentations from the 17 most intense precursor ions with a charge state 2+ to 7+. The tandem mass spectra were acquired with a resolution of R=30,000, targeting 2x105 ions, setting quadrupole isolation width to 1. 4 m/z and normalized collision energy to 28%. Acquired raw data files were analyzed using the Mascot search engine v. 2. 5. 1 (Matrix Science Ltd). Maximum of two missed cleavage sites were allowed for trypsin, while setting the precursor and the fragment ion mass tolerance to 10 ppm and 0.
The membrane was blocked and incubated with rabbit pyruvate carboxylase (PA5-50101) or house-keeping α-tubulin (2144, CST) antibody overnight at 4 o C. The following day the membrane was washed with TBS-Tween 20 (0. 1%) and incubated with α-rabbit IgG, HRP-linked antibody (7074, CST) for 1 hour at room temperature. The membrane was washed in TBS-Tween 20, and the protein signal developed using the ECL™ Select Western Blotting Detection Reagent (GE Healthcare). Bands were visualized on a ChemiDoc MP Imaging System (Bio-Rad). Image J software was used to measure densitometry of PC and α-tubulin bands. 30 µL of the remaining lysate was added to 225 µL of PC activity buffer (80mM Tris-Cl (pH 8. 0), 2 mM ATP, 8 mM sodium pyruvate, 21 mM KHCO3, 9 mM MgSO4, 0. 16 mM acetyl-CoA, 0. 16 mM NADPH and 30 units of purified malate dehydrogenase (LMDH-RO Roche)).
Ca 2+ Signaling in Response to Fungal Challenge To observe the Ca 2+ signal and hemocyte aggregation behavior in response to fungal challenges  (33) , L5 larvae of S. exigua were injected with 2 µL Fura-8 (1 mM) and incubated for 30 min. 2 µL of fungal conidia and hyphae were injected. At selected time points post-injection (PI), hemolymph was collected and fixed on a slide glass by using 2. 5% paraformaldehyde. The Furapositive cells were observed under a fluorescence microscope (DM500, Leica, Wetzlar, Germany) at 400 × magnifications. Fluorescence change over time was analyzed using ImageJ software (https://imagej. nih. gov/ij). Each time point was replicated three times.
Statistical Analysis The data for continuous dependent variables were subjected to one-way analysis of variance (ANOVA) using PROG GLM in the SAS program  (40). Mortality assay by the leaf dipping method was analyzed by repeated measure ANOVA. All experiments were conducted in three biologically independent replicates and the means ± standard errors (SE) were plotted using Sigma Plot (Systat Software, Point Richmond, CA, USA). The means were compared with the least significant difference (LSD) test at a Type I error of 0. 05.
Research with eicosanoids is challenging because these compounds are produced and operate in very small amounts. Physiological quantities of PGs and other eicosanoids are often determined indirectly using radioimmunoassays and bioassays. Sensitive mass spectrometry now enables accurate direct determinations of eicosanoid quantities at physiological levels. Our data show TXB 2 occurred in the fat body of naïve larvae at approximately 0. 28 ng/g, which increased by 2. 2-fold to about 0. 61 ng/g at 16 h PFI. This chemical determination strongly bolsters our view that TBX 2 is present and operates in larval tissues. Injecting a dsRNA construct specific to SeTXAS led to reduced accumulations of mRNAs encoding SeTXAS at 24, 48, and 72 h PI. Treatments with the gene silencing construct led to increased larval mortality at 5 days PFI, which was significantly, albeit not completely, reversed in larvae co-injected with dsTXAS+TXB 2.
Warner-Bratzler Shear Force Measurement Steaks were thawed at room temperature by immersion in a circulating water bath for 4 h. After that, external fat was trimmed from the steaks, and they were cooked in open bags in a circulating water bath (Grant Instruments Ltd. , Cambridge, UK) set at 72 • C to reach an internal end-point cooking temperature of 71 • C. The cooked steaks were cooled down and stored in a refrigerator at 4 • C overnight. Shear force analysis was conducted following a modified version of the guideline of the American Meat Science Association (AMSA)  [6]. For each steak, seven cores were taken with a 1. 27 cm diameter parallel to the muscle fibre direction. The shear force was measured by an Instron 4464 Universal testing machine (Instron Ltd. , Buckinghamshire, UK), and data analysed using Bluehill 2 Software (Instron Ltd.
LC-MS/MS Data Processing and Protein Identification The raw files from the LC-MS/MS were aligned against the Bos taurus database (i. e. , ref_bos_taurus, 23,970 sequences) with Mascot V. 2. 5. 1 (http://www. matrixscience. com, accessed on 30 August 2020). The precursor and fragment mass tolerance were set up at 10 ppm and 0. 02 Da, respectively. The variable modifications included carbamidomethy-lation (C), oxidation (M), and deamidation (NQ). Protein identification could be verified when at least two peptides derived from one protein showed statistically significant identity. The Mascot score was 33 with a False Discovery Rate of 1%, and the p-value was adjusted at a given threshold (0. 0093). 2. 5. Bioinformatics Analyses 2. 5. 1. Protein-Protein Interactions (PPI) The protein-protein interactions between the putative protein biomarkers were analysed using the STRING web service database (https://string-db.