dataesr/scientific-paragraphs-categorization huggingface dataset:
The phyloseq  [46] , vegan (v. 2. 5-6)  [47]  and microbiome (v. 1. 6. 0) [48]  R packages were used for the detailed downstream analysis. The α-diversity index was calculated with Shannon index using the phyloseq R package from OTU abundance table and richness was evaluated as the total number of OTUs present in each sample. The microbiome R package allowed us to study global indicators of the gut ecosystem state, including measures of evenness, dominance, divergences and rarity. All samples were normalized using the "rarefy_even_depth" function in the phyloseq R package, which is implemented as an ad hoc means to normalize microbiome counts that have resulted from libraries of widely differing size. The β-diversity of microbiota composition between groups and time points was tested through the Whittaker's index from OTU abundance table using the multivariate analyses of the homogeneity of group dispersion (the "betadisper" function of the vegan R package, that is, the distance of each replicates from the centroid of their dietary treatment).
05) in order to assess the community differences between groups. A differential abundance analysis was performed using the function "fitFeatureModel" in the metagenomeSeq (v. 1. 26. 3) R package at the OTU levels  [49]. To characterize a set of microbes consistently present in the cecal microbiota of broilers, a OTUs detection threshold of 0. 001% and a prevalence threshold of 99. 9% (e. g. , a given OTUs must be present in 99. 9% of individuals in each time point with a relative abundance of at least 0. 001%) was employed, using the microbiome R package  [48].
Bacterial Phylogenetic Composition and Cecal Core Microbiota in Broilers The raw sequencing data has been submitted to NCBI's Sequence Read Archive (SRA) repository (BioProject: PRJNA578628; Biosample: SUB6446261, accessions 13154251 to 13154370). Before quality control, a mean of 82678. 63 (S. D. = 22451. 57) of the raw read count was available for each sample (Table  S3 ). After quality control, a mean of 58606. 4 (S. D. = 16097. 52) was obtained (Table  S3 ). OTU counts per sample and OTU taxonomical assignments are available in Table  S4. One sample on day 21 belonging to group S (replicate 3) was removed from the analysis for having less than < 10000 post-quality control counts. Sequences across the whole sample sets were successfully clustered into 1183 OTUs and only (1/1183) 0. 08% of the OTUs could not be assigned to any phylum.
Abundances of OTUs Related to F. prausnitzii in Fecal and Cecal Contents According to Groups There were five DA OTUs annotated as F. prausnitzii in the whole dataset (OTU IDs 589282, 157308, 158981, 158632 and New. ReferenceOTU11002). Since at least one of these OTUs was found DA in most of the comparisons between the groups and sampling time points, we decided to further explore the global abundance of F. prausnitzii by adding the abundances of the five OTUs in each sample. The normalized global abundance of F. prausnitzii on day 40 clearly shows an increase of abundance according to the dietary treatment, with the highest abundances observed in the S group, either in the fecal or cecal contents (Figure  5 ). The ANOVA analyses show differences both in fecal (p = 0. 011) and cecal contents (p = 0.
Supplementation-Related Effects on Abundances of OTUs Related to Campylobacter spp. in Fecal and Cecal Contents There were five DA OTUs annotated as Campylobacter spp. in the whole dataset (OTU IDs 789621, 297260, 153941, 573432 and 1148151). Since at least one of these OTUs was found DA in most comparisons between groups and sampling time points, we decided to further explore the global abundance of Campylobacter spp. by adding the abundances of the five OTUs in each sample. The normalized global abundance of Campylobacter spp. on day 40 clearly shows a different abundance according to the dietary treatment, with the lowest abundances observed in the S group in the fecal samples and a similar abundance in cecal content among groups (Figure  6 ). On day 40, after the challenge performed on day 21, when animals resulted negative for Campylobacter spp.
Acknowledgments: The authors would like to thank the  Vanessa Demey and Danstar team  for product supply and technical advices,  Roger Coulombe JR  for being our real Mentor and for showing how to be a good human being and scientist. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources for the bioinformatics data analysis.
5 ml/min at 30°C. For SA10 column, 100% 1 mM NaOH was used to separate the monosaccharide at a constant flow rate of 0. 38 ml/min at 45°C. The retention time (RT) obtained with a single injection of each sugar was used to characterize the monosaccharides of the cell wall. Data acquisition and analysis were carried out using Chromeleon™ Chromatography Data System (CDS) Software (Thermo Scientific™). The sugars detected were fucose (Fuc), rhamnose (Rha), arabinose (Ara), galactose (Gal), glucose (Glc), xylose (Xyl) and mannose (Man). The amount of each monosaccharide was quantified against a calibration curve, which was obtained by varying standard concentrations  (1, 2. 5, 5, 7. 5, 10, 25, 50, 75 , 100 μmol/L). Significant differences in the yield of fractions and the amount of each monosaccharide among samples were determined at p < 0.
| Preparation of the libraries, processing of the reads, mapping, transcriptome assembly and RNA-Seq analysis Libraries were prepared, quantified and their average size determined as previously described  (Guerriero, Behr, Legay, et al. , 2017). The libraries were pooled (the 12 samples corresponding to the 3 stem regions) or run alone (the leaves, roots, calli, whole stem tissues) after normalising them to 20 pM and sequenced on an Illumina MiSeq. The pooled 12 libraries were sequenced in a total of 6 consecutive runs (5 MiSeq reagent kit V3, 150 cycles and 1 run of 300 cycles); the libraries obtained for the leaves, roots, calli, whole stem core and peels were each run using the MiSeq kit 300 cycles. Raw sequences have been deposited at the Sequence Read Archive (SRA) with the accession number PRJNA518071.
The parameters used for the de novo transcriptome assembly were: wording size was set to 20, the bubble size to 50 and minimum contig length of 300. The reads were mapped back to the assembly with a mismatch, insertion and deletion cost of 3 (stringent criteria), and a length and similarity fraction of 0. 95. The assembly was then annotated using Blast2GO PRO version 3. 0 against the Arabidopsis thaliana non-redundant database. For each of the 12 libraries, the mapping was performed with a maximum hit per read of 3, a similarity and length fraction of 0. 95, a mismatch, insertion and deletion cost of 3. The expression values were then calculated using the RPKM method  (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008). The expression values were subjected to an ANOVA statistical test with four groups (TOP, MID, BOT CORE and BOT FIBRES), each composed of three biological replicates and, subsequently, to a false discovery rate (FDR) correction.
| cDNA synthesis and RT-qPCR One microgram of RNA was retrotranscribed using the ProtoScript II RTase (NEB), following the manufacturer's instructions. Gene primers were designed with "Primer3Plus" ( http://www. bioin forma tics. nl/cgi-bin/prime r3plu s/prime r3plus. cgi) and checked using the software OligoAnalyzer 3. 1 tool from Integrated DNA technologies ( http://eu. idtdna. com/calc/analyzer). Efficiencies were calculated by RT-qPCR with a calibration curve consisting of six serial dilutions of cDNA (10, 2, 0. 4, 0. 08, 0. 016, 0. 0032 ng/μl). The list of primers is given in Data S1. The RNA extracted using the above-mentioned protocol was retrotranscribed into cDNA using the ProtoScript II RTase (NEB) and random primers, according to the manufacturer's instructions and as described in.
| GOE analysis of the cortical tissues at the bottom For C1, the GOE analysis revealed enrichment in genes partaking in the response to sugars (hexoses) and in the aspartate family amino acid metabolic process (Data S1). These are asparagine ASN1 is an important enzyme in plant primary metabolism, as it regulates N levels  (Hwang, An, & Hwang, 2011). The model plant thale cress has three genes encoding ASN; more specifically, only ASN1 is responsive to the endogenous sucrose levels  (Lam, Hsieh, & Coruzzi, 1998). Sucrose inhibits the expression of ASN1 via a cascade relying on the S1 class of bZIP TFs  (Curtis, Bo, Tucker, & Halford, 2018; Hummel, Rahmani, Smeekens, & Hanson, 2009). Notably, in our dataset, the bZIP9 gene showed an increase >4-and >3-fold when comparing FBOT vs. TOP and MID, respectively (Data S1).
According to our results, the nettle ortholog of AtASN1 was upregulated in the cortical tissues at the BOT, suggesting the presence of a C (sugar) depletion signal in these tissues. The expression pattern of other genes in this cluster corrob- played increased length of the primary root when grown in the dark and also showed an altered sensitivity to sugars. It was proposed that the xlg phenotype may be due to an altered glucose sensing, although a sucrose-specific mechanism was also invoked  (Ding et al. , 2008). In the dataset here presented, the upregulation of XLG1 in the peels of the bottom internode (fold-increase FBOT vs. MID > 3 and FBOT vs. TOP > 7) is likely related to a sucrose-sensing mechanism, as supported by the sucrose metabolism/transport-related transcripts induced in the same tissues (see below).
In our dataset AOS (At5 g42650) (represented by contigs 24544 and 26032) is expressed at the highest levels in the internodes at the top (expression ratios TOP vs FBOT > 3 for both contigs and TOP vs. CBOT > 65 for contig 24544 and >35 for contig 26032). Other genes intervening in JA biosynthesis are LOX1, LOX2 (At3g45140) and JMT (At1g19640) and appear in C5. LOX1 shows a FC TOP vs CBOT > 6, MID vs. CBOT > 3, FBOT vs. CBOT > 5. LOX2 is represented by four contigs (contig_16075, contig_16077, contig_32893 and contig_16844), with a FC TOP vs. CBOT > 10 for all of the LOX2 contigs. JMT, which catalyses the formation of methyl jasmonate, an important volatile compound signalling defence response  (Jang et al. , 2014; Seo et al. , 2001) , is expressed highly in the TOP and MID internodes (FC > 3 with respect to CBOT).
The formation of IAA engenders reactive oxygen species (ROS) which are known to be involved in fibre elongation (recently reviewed by  (Berni, Luyckx, et al. , 2018). Their role as a "double-edged sword" means that while they ensure the correct execution of developmental processes, their presence within plant cells needs to be strictly controlled, to avoid undesired oxidative damages to biological structures and macromolecules. Flavonoids are well-known scavengers of ROS  (Berni, Cantini, et al. , 2018)  and this capacity is linked to the structural features (geometry of the molecule, number and position of -OH groups) of this large class of polyphenolic plant secondary metabolites. In the dataset here obtained, an increased expression of genes partaking in flavonoid biosynthesis is evident at the TOP and MID: the transcripts encoding CHIL (chalcone isomerase-like), TT4 (CHS, chalcone synthase), TT5 (CHI, chalcone isomerase) and TT7 (F3′H, flavonoid 3′-hydroxylase) show high expression in young internodes, with a peak at the MID (Data S1).
Illumina Bead Array hybridisation, scanning and data processing Kidney gene transcription profiling was performed as previously described  (Kaisaki et al. , 2016)  using Sentrix ® BeadChipRatRef-12v1 Whole-Genome Gene Expression Arrays (Illumina Inc. , San Diego, CA), which contain 22,523 oligonucleotide probes (replicated on average 30 times) allowing quantification of transcript levels for 21,910 genes. Doublestranded cDNA and purified biotin-labelled cRNA were synthesised from 300ng high quality total RNA using the Illumina ® TotalPrep RNA amplification kit (Ambion Inc. , Austin, TX). cRNA concentrations were determined using a NanoDrop spectrophotometer and cRNA quality and integrity were assessed on an Agilent 2100 Bioanalyser (Agilent Technologies,.
Disease Models & Mechanisms • DMM • Accepted manuscript Waldbronn, Germany). 750ng of each biotinylated cRNA was used for hybridisation onto the arrays. BeadChip arrays were scanned on the Illumia ® Bead Array Reader (Illumina Inc. , San Diego, CA) and data were analysed using the Illumina ® Bead Studio Application software before undergoing comprehensive statistical analysis. Quality control parameters were: 0 ≤ G sat ≤ 1; Green 95 Percentile (GP95) for consistency between arrays (around 2000); GP5 background level in range of low 100 or below. Prior to array analysis, data from 757 Illumina oligonucleotides, which carry DNA variants between GK and BN  (Kaisaki et al. , 2016)  were removed. We also withdrew probes that detected only background signal (ie. Illumina detection score <0. 5 in >50% of samples).
Genetic mapping of expression QTLs in the F2 (GKxBN) cross eQTL analysis was performed using the R-qtl software package  (Broman et al. , 2003)  as previously described  (Kaisaki et al. , 2016)  based on genetic maps constructed in the cross  (Wilder et al. , 2004). The Haley-Knott regression method was used for genome scans  (Haley and Knott, 1992). We used sex and cross direction as additive covariates in our models to account for effects of sex and lineage on gene expression  (Solberg et al. , 2004). Permutation tests (n=1,000) were carried out to determine genome wide significance threshold for each transcript. QTLs with a genome-scan adjusted p-value <0. 05 were considered as significant. Regression model with sex and cross direction as additive covariants was as in  (Kaisaki et al. , 2016).
, 2016) , over 40% of probes with DNA polymorphisms gave significant eQTL signals (LOD>10), which deviated from the distribution eQTL significance when all oligonucleotides were considered (Figure  2 ). After filtering out probes with low intensity signal a total of 15759 transcripts were considered for linkage analyses. We identified a total of 2526 eQTLs at FDR<0. 05 (Table  1 ), including 2252 (89%) linked to transcripts localised to a single genomic position in the rat genome assembly (RGSC3. 4, Ensembl release 69) (Table  S1 ). Analysis of eQTL statistical significance showed that 509 eQTLs (20. 1%) can be detected with high confidence (LOD>10) (Figure  3A , Table  S1 ). Chromosome distribution of eQTLs showed that 11% of genes on average on each chromosome were regulated by eQTLs in the cross (Table  1 ).
, 2000; Marion et Disease Models & Mechanisms • DMM • Accepted manuscript al. , 2002). Of direct relevance to this study, our renal eQTL data can assist the identification of genes underlying QTLs for proteinuria detected in a GKxBN F2 cross  (Nobrega et al. , 2009). The 1. 5 LOD confidence intervals around the peaks of maximum linkage of these QTLs (approximately 77-120Mb on chromosome 5 and 60-112Mb on chromosome 7; RGSC3. 4, Ensembl release 69) contain a total of 69 renal eQTLs in our GKxBN F2 cross, which represent functional and positional candidates for these proteinuria QTLs in the GK rat. Among these, the 16 cis-regulated eQTL genes localised at the proteinuria QTLs (Abca1, Ambp,  Angptl3, Cdk5rap2, Echdc2, Fkbp15, Fktn, Inadl, Laptm4b, Ly6c, Mysm1, Nipsnap3b, RGD1311188, RGD1564131, Rraga, Slc22a22)  are particularly relevant since sequence variants at the loci alter their expression.
Tables The total number of eQTLs detected is given for each chromosome. Cis-regulated eQTL are those mapped within 10Mb of the linked transcript. Genetic effects were defined as transregulated eQTLs when eQTL markers and target transcripts map to different chromosomes. Chromosome length and number of protein-coding genes are based on ENSEMBL annotations of the rat genome (RGSC3. 4, Ensembl release 69). Full details of genes associated with eQTLs are given in Table  S1. Details of comparative analyses with published adipose tissue eQTLs mapped in the same cross  (Kaisaki et al. , 2016)  are in Tables  S4  and S5. Reciprocal genetic crosses between male and female Goto Kakizaki (GK) and Brown Norway (BN) rats used to produce individual (GKxBN) F2 rats are illustrated (A) and compared to the breeding scheme designed to derive recombinant inbred strains (RI-S) from spontaneously hypertensive rats (SHR) and BN rats (B).
Funding This work was supported by the  Wellcome Trust   Core Award Grant  ( 075491/Z/04 ), a  Wellcome Senior Fellowship in Basic Biomedical Science  ( 057733 ) to D. G. and grants from the  European Community `s  Seventh Framework Programmes  under grant agreement  HEALTH-F4-2010-241504  (EURATRANS) to D. G. and the  Fondation pour la Recherche Médicale  (FRM,  INE20091217993 ) to D. G.
2% and 0. 5%, respectively. This indicates that the quail genome, like the chicken genome, is highly contiguous and in terms of its expected gene content, is close to complete. 1 All species-specific assembly metrics derived from the NCBI assembly archive. 1 All species-specific gene annotation metrics derived from the NCBI RefSeq database. turkey aligned to same species Genebank (n=380). 2 Predicted proteins per species aligned to Aves known RefSeq (n=7,733).
2013 ). The generation of a high-quality quail genome has enabled us to perform a differential transcriptomic analysis of gene expression in quail infected with LPAI and HPAI, to better understand the response of quail to influenza infection. Lung and ileum samples were collected at 1 day post infection (1dpi) and 3 days post infection (3dpi). We also reanalysed previous data collected from duck and chickens  (Smith et al. 2015)  and compare this to the quail response. To provide an overview of the response to LPAI and HPAI in quail we examined pathway and GO term enrichment of DEGs (see Supplemental File S9, Supplemental File S10 and Supplemental Figures  S4-7 ). In response to LPAI infection, pathways enriched in the ileum included metabolism, JAK/STAT signalling, IL6 signalling and regulation of T-cells (Supplemental Figure  S4 ).
These sequences were generated on the HiSeq2500 Illumina instrument. Long-reads used for gap filling were generated at 20x coverage on the same DNA source using a RSII instrument (Pacific Biosciences). The Illumina sequence reads were assembled using ALLPATHS2 software  (Maccallum et al. 2009 ) using default parameter settings and where possible, and scaffold gaps were closed by mapping and local assembly of long-reads using PBJelly  (English et al. 2012 ). The Illumina long insert paired-end reads (3 kb and 8kb PE) were used to further extend assembled scaffolds using SSPACE  (Boetzer and Pirovano et al. 2014 ). The draft assembly scaffolds were then aligned to the genetic linkage map  (Recoquillay et al. 2015 ) and the Galgal4. 0 chicken reference (Genbank accession: GCA_000002315. 2) to construct chromosome files following previously established methods  (Warren et al.
Gene Annotation Specific RNA-seq data for the genome annotation was produced from the same animal used for the genome assembly. RNA was extracted from heart, kidney, lung, brain, liver, intestine, and muscle using Trizol and the Nucleospin® RNA II kit (MACHEREY-NAGEL), following the manufacturer's protocol. The Coturnix japonica assembly was annotated using the NCBI pipeline, including masking of repeats prior to ab initio gene predictions, for evidence-supported gene model building. We utilized an extensive variety of RNA-Seq data to further improve gene model accuracy by alignment to nascent gene models that are necessary to delineate boundaries of untranslated regions as well as to identify genes not found through interspecific similarity evidence from other species. A full description of the NCBI gene annotation pipeline was previously described (Thibaud-Nissen et al.
251 million reads of 36 nucleotides in length were generated in total for quail. Reads were quality checked using FastQC and trimmed for quality using Trimgalore. Mapping was performed to the quail genome (GCA_001577835. 1 Coturnix japonica 2. 0), chicken genome (GCA_000002315. 3 Gallus_gallus-5. 0) and duck (GCA_000355885. 1 BGI_duck_1. 0) using Tophat2  (Kim et al. 2013 ) using default options. For quantification and differential analysis, the following pipeline was used. First transcripts were assembled and quantified using cufflinks  (Trapnell et al. 2010) , guided with the NCBI annotation for the relevant genome, and the multi-read correct option was used. The transcriptomes were merged using stringtie merge  (Pertea et al. 2015)  and cuffdiff  (Trapnell et al. 2010 ) was used for differential analysis using default settings.
To determine orthology between quail, duck and chicken genes, reciprocal BLAST searches were performed. For analysis of GO term enrichment the PANTHER overrepresentation test  (Thomas et al. 2003 ) was used and for pathway analysis Ingenuity Pathway Analysis software (QIAGEN) was used. For clustering analysis BioLayout 3D  (Theocharidis et al. 2009 ) was used using default settings except 1. 4 inflation for Markov clustering.
ACKNOWLEDGEMENTS The authors would like to thank the  Edinburgh Genomics sequencing facility (Edinburgh, UK)  for carrying out the transcriptomic sequencing, RNA sequencing of the reference individual was performed on the  GeT-Plage platform  (http://get. genotoul. fr/en/)and funded by the  INRA   Genetics Division (QuailAnnot program ). HSR and LSR lines sequencing was supported by the  French Agence Nationale de la Recherche  ( SNP-BB  project,  ANR-009-GENM-008 ). We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing help, computing and storage for these resources and to the  ITAVI SeqVol program  for financing gallo-anseriformes genome sequencing. KMM was supported by a  National Health and Medical Research Council Overseas Postdoctoral Fellowship.
DATA ACCESS All data generated or analysed during this study are included in this published article (and its additional files), or in the following public repositories. Data has been submitted to the public databses under the following accession numbers: genome sequence data, NCBI Genome [GCA_001577835. 1] (https://www. ncbi. nlm. nih. gov/assembly/GCF_001577835. 1/); transcription annotation data, SRA [SRR2968870 -SRR2968911] (https://www. ncbi. nlm. nih. gov//bioproject/PRJNA296888); RNA-seq data for infection studies, Array Express, quail [E-MTAB-3311] (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-3311/), duck [E-MTAB-2909] (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-2909/), chicken [E-MTAB-2908] (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-2908/); Sequencing of HSR/LSR lines, SRA [SRP047364] (https://www.
As noted earlier, NEAT can modify the topology of the neural networks during evolution. Every structural modification in the network is identified by a unique innovation number to enable alignment of genomes for recombination purposes. When implementing NEAT with the possibility to exchange individuals as described for robot-to-robot learning, care must be taken to avoid conflicting innovation numbers. In our implementation, we keep track of a centralized global innovation database. If this would not be possible or desirable, one can use a distributed systems such as odNEAT  (Silva et al. , 2012)  or Gene Clocks  (Fernández Pérez et al. , 2015)  Algorithm 1 summarizes the individual and robot-to-robot learning mechanism in pseudocode.