dataesr/scientific-paragraphs-categorization huggingface dataset:
1 and 4. 3 times more at the MID as compared to the BOT): these TFs activate genes involved in xylan and lignin biosynthesis and therefore the secondary cell wall biosynthetic program  41. Three laccases, i. e. LAC4 (IRX1; contig_10035), LAC12 (contig_15462) and 2 contigs annotated as LAC17 (contig_19910 and contig_17371) were also highly upregulated at the MID with respect to the TOP (Suppl. Dataset File): these same genes were shown to be upregulated at older stages of development in the hemp hypocotyl  22  and in the stem of adult hemp plants they may be associated with the peculiar lignification of the bast fibres. Bast fibres are hypolignified, however condensed guaiacyl (G) lignins were immunologically detected in the middle lamellas, cell wall junctions and S1 layer of phloem fibres in another fibre crop, flax  42.
In hemp bast fibres the presence of a similar condensed lignin was reported  43  ; hence the laccases here detected may participate in the formation of condensed lignins in the cell wall of bast fibres. In the "hemicellulose metabolic process" ontogeny there are genes involved in glucuronoxylan biosynthesis (the principal hemicellulose in dicots' secondary cell walls  44  ). IRX8/GAUT12 (irregular xylem 8/galacturonosyl transferase 12), IRX9, IRX10/GUT2 (glucuronosyl transferase 2) and PGSIP3/GUX2 (plant glycogenin-like starch initiation protein 3/glucuronic acid substitution of xylan 2) were upregulated 3. 2, 4. 5, 4. 8 and 5. 4 times respectively at the MID as compared to the TOP (Suppl. Dataset File). IRX8 belongs to the glycosyltransferase (GT) family 8 and is involved in the synthesis of the reducing end tetrasaccharide of glucuronoxylans, while IRX9 is a GT43 involved in xylan backbone elongation  45  ; IRX10 is a GT47 also involved in the elongation of the xylan backbone  46.
34; Suppl. Dataset File). IBH1 is a negative regulator of cell elongation  50  and its higher expression at the MID may indicate a function in the shift from elongation to thickening in bast fibres. A contig coding for a PLATZ TF also peaked at the MID (MID vs TOP FC > 13): PLATZ TFs were proposed to act as negative regulators of cell proliferation  51  and may contribute to the transition of bast fibres from a phase of active division to secondary growth. Among the contigs showing the highest FC increase between MID and TOP is a putative acid phosphatase (AT1G04040): notably, the corresponding protein in thale cress was found associated to the cell wall fraction  52. This phosphatase may be involved in cell wall-related processes in bast fibres during the transition from elongation to thickening and is therefore an interesting candidate for further analysis.
Gene ontology enrichment analysis of the BOT region. The BOT region is characterized by ontologies related to phytohormone and non cellulosic polysaccharide biosynthesis, as well as secondary metabolic processes (Fig. 5 ). Mapping against the Finola transcriptome confirmed these data (Suppl. Dataset File) and additionally showed the enrichment of auxin metabolism-related genes, among which, WAT1 (WALLS ARE THIN1), an auxin efflux transporter involved in secondary cell wall deposition  53, 54. In the "gibberellin biosynthetic process" ontogeny there are genes involved in the three steps required for gibberellin (GA) synthesis, i. e. formation of ent-kaurene from geranyl geranyl diphosphate, conversion of ent-kaurene to GA 12 and synthesis of C 20 -and C 19 -GAs  55. GA1/CPS (ent-copalyl diphosphate synthase) involved in the formation of ent-kaurene was expressed at the BOT > 7 times with respect to the TOP; KAO2 (ent-kaurenoic acid oxidase 2), which codes for a CYP88A member catalysing the conversion of ent-kaurenoic acid to GA 12  56  , showed an increase in FC > 4 as compared to the TOP; the genes encoding 2-oxoglutarate-dependent dioxygenases GA20OX2 and GA3OX1 showed a FC > 3 and >4 as compared to the TOP, respectively (Suppl.
Dataset File). The expression GA2OX2 also peaked at the BOT (expressed 12-times more as compared to the TOP): this gene encodes a gibberellin 2-β-dioxygenase responsible for the conversion of GA to an inactive form and participates in GA homeostasis. In addition to that, a contig annotated as a CYP714A1 (contig_19239), which participates in GA deactivation  57  , was also expressed at higher levels at the BOT with respect to the TOP (FC > 7). In the BOT region of the hemp stem, characterized by bast fibres with a thick tertiary cell wall, elongation has ceased and the homeostasis of GA levels by GA2OX2 may represent an important regulatory mechanism. Transgenic tobacco plants in which the gene GA2OX2 had been silenced were taller than GA20OX1 overexpressors (the GA 20-oxidase catalyses the rate limiting step in GA biosynthesis); these results therefore show that deactivation is the key factor in the maintenance of GA homeostasis  58.
Concomitantly with the activation of GA2OX2, genes involved in abscisic acid (ABA) biosynthesis increased in expression at the BOT. ABA inhibits stem elongation and in our previous study it was found in higher abundance in the hemp hypocotyl at older developmental stages  22. In our dataset, the ABA biosynthetic genes NCED3 and 5 were both upregulated at the BOT (FC > 5 and >11 respectively, as compared to the TOP); the gene XERICO encoding a RING-H2 zinc finger protein and involved in ABA homeostasis  59  was also upregulated at the BOT (FC > 4 with respect to the TOP; Suppl. Dataset File). Additionally, three contigs annotated as CYP704A2, which was proposed to be a candidate for ABA 8′-hydroxylation (i. e. catabolism 60 ), were expressed at higher levels at the BOT (FC BOT vs TOP between 28 and 135).
Although the LM5 antibody recognizing β-1,4-galactans does not label hemp bast fibres  22, 63  , it may be possible that pectic galactans in hemp fibres are masked and therefore do not react with the LM5 antibody. Genes involved in lignin biosynthesis, i. e. PAL1 (phenylalanine ammonia lyase 1) and one contig annotated as OMT1 (caffeic acid/5-hydroxyferulic acid O-methyltransferase; contig_2172) showed, respectively, a FC > 11 and >9 as compared to the TOP (Suppl. Dataset File). Likewise, one contig encoding IRX12/LAC4 (contig_13683) and a cytochrome P450 involved in lignification (CYP71A20)  64  showed a FC increase in expression > 28 and >35 at the BOT with respect to the TOP (Suppl. Dataset File). These results are in agreement with the presence of secondary fibres in the BOT region; these fibres, notably, show a positive signal at the level of the middle lamella after phloroglucinol staining  43.
Among the genes encoding TFs upregulated at the BOT, it is worth mentioning a LOB-domain containing protein 4 (LBD4), which showed a BOT vs TOP increase in expression of ca. 218 (Suppl. Dataset File). LOBs are key regulators of plant organ polarity which play also a role in plant secondary growth  65  ; their role in bast fibre development is to our knowledge not yet studied, however we believe that LBDs represent interesting targets in phloem fibre development. A contig annotated as NAC047/SPEEDY HYPONASTIC GROWTH (SHYG) peaked at the BOT (BOT vs TOP FC > 78): the ortholog in thale cress was shown to be associated with hyponastic leaf movements upon waterlogging via modulation of an enzyme involved in ET biosynthesis, i. e. ACC OXIDASE5  66. ET may play a role during fibre thickening: several genes encoding ethylene-responsive TFs were indeed upregulated at the BOT, notably ERF1, WRI1, ERF106 (Suppl.
Dataset File). Interestingly SHYG acts on expansin and xyloglucan endotransglycosylase/hydrolase genes to induce expansion of the petiole abaxial side. Its role in bast fibre thickening is yet to be unveiled, but it may entail a suite of cell wall-related events. A contig encoding a RADIALIS-LIKE 6 TF was upregulated at the BOT ca. 18 fold with respect to the TOP: very recently a RADIALIS-LIKE TF was found upregulated in flax bast fibres and a role in phloem fibre development was therefore suggested  14. RADIALIS-LIKE TFs play a role in organ (floral) symmetry; hence, together with LBDs, they may regulate aspects related with positional cues.
Raw sequences have been deposited at the NCBI Gene Expression Omnibus (GEO) with the accession number GSE94156 (available at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?token=itwfckosdjwdpiv&acc=GSE94156). Processing of the reads, mapping and RNA-Seq analysis. The raw sequences obtained were uploaded in CLC Genomics Workbench 9. 0. 1. Sequences were filtered as follows: sequences > 35 bps, the sequence quality score was left as default value (0. 05), the maximum number of ambiguities was set to 0. Adaptor trimming was performed using the Illumina adaptor sequences, then a hard trim of 14 bps at the 5′ end and 2 bps at the 3′ end was additionally carried out, resulting in a final sequence average length of 59 bps. We had previously published a de novo assembly for the variety Santhica 27  22  and proven its validity by comparing the results generated with our de novo assembly and with the Finola transcriptome  23.
We decided to merge the reads generated in this study with those previously obtained on the hemp hypocotyl  22  to get a better assembly of the transcriptome of the variety under study. We therefore uploaded in CLC Genomics Workbench 9. 0. 1 the reads obtained previously for the hypocotyls and those obtained in the present study for the fibres from adult plants. The parameters used are: 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 Viridiplantae and A. thaliana non-redundant database. However, in Suppl. Dataset only the annotation against the Arabidopsis database is shown, as it was used for the subsequent Gene Ontology term Enrichment analysis (GOE) in Cytoscape (vide infra).
A total of 3268 contigs was obtained (Suppl. Dataset File). Primer design. Primers were designed using Primer3Plus (http://www. bioinformatics. nl/cgi-bin/primer-3plus/primer3plus. cgi/) and verified with the OligoAnalyzer 3. 1 tool from Integrated DNA technologies (http:// eu. idtdna. com/calc/analyzer). Primer efficiencies were checked via qPCR using a serial dilution of cDNA (from 10 ng to 0. 0032 ng/µl). The primer sequences, amplicon length and Tm, amplification efficiencies and R 2 are indicated in Suppl. Dataset File.
Bioinformatic analysis. The annotation of the putative transcription factors (TFs) in the de novo assembly was carried out with PlantTFcat 70 (http://plantgrn. noble. org/PlantTFcat/), which gave a total of 2484 TFs (Suppl. Dataset File). The ICA was performed with the on-line program MetaGeneAlyse v1. 7. 1  71  (http://metagenealyse. mpimp-golm. mpg. de/). The Gene Ontology term Enrichment analysis (GOE) was performed as previously described  72  using Cytoscape (v3. 4. 0) with the ClueGO v2. 3. 2 plugin  73  (p-value < 0. 05, Benjamini-Hochberg enrichment, gene ontology from level 3 to 8, kappa score set at 0. 6). RNA-Seq RPKMs were log 2 transformed and loaded for clustering and expression profile analysis in a data analytics software developed in-house. The software includes a Web-based user interface providing interactive data visualisation in the form of a parallel coordinates plot synchronised with 2D scatter plots of PCA projections; the user interface is backed by an R server providing the necessary statistical analyses, in particular correlation clustering and PCA projection of multidimensional data.
Acknowledgements The authors acknowledge the  Fonds National de la Recherche, Luxembourg , (Project  CANCAN C13/SR/5774202 ) for financial support. This work was (partially) funded by the  FNR  grant  BLIZAAR INTER/ANR/14/9909176. The authors thank  Aude Corvisy  and  Laurent Solinhac  for their technical support. The authors gratefully acknowledge  Dr Fintan Mc Gee  for fruitful discussions on Visual Analytics.
Results and Discussion Genome-Scale Sequencing of Wild Brown Rats Drawn from across the World In the present study, to systematically explore the geographic origin and dispersal routes of the brown rat, a total of 117 Rattus norvegicus samples with a global representation and 1 black rat (R. rattus) were collected for genome sequencing. The species status of these R. norvegicus was initially determined by morphology and further confirmed based on cytochrome b (cytb) sequences by Sanger sequencing. Additional exploratory data analysis based on whole genomes found that the species status of seven individuals was ambiguous, as they displayed a closer relationship with the black rat, which might be attributable to genetic introgression between different species  (Teng et al. 2017 ). These seven individuals, along with the black rat were treated as outgroup.
This reaction was carried out using Phusion high-fidelity DNA polymerase, universal PCR primers, and the index primer. Finally, PCR products were purified (AMPure XP system) and their library quality assessed using the Agilent Bioanalyzer 2100 system. Clustering of index-coded samples was performed using a cBot cluster generation system and the HiSeq 2500 PE Cluster Kit (Illumina) following the manufacturer's instructions. Subsequent to cluster generation, library preparations were sequenced using an Illumina HiSeq 2500 platform and 125-bp paired-end reads were generated. However, because raw sequencing data contain numerous low-quality reads with adaptors, we applied filtering strategies to obtain high-quality data. This procedure entailed: 1) Removal of read pairs containing adapters; 2) Removal of read pairs generating a single sequence where the N content is >10% of the read length; and 3) Read pairs generating a single sequence where the number of low-quality bases (i.
e. , Q < 5) is greater than half the reads. Read Mapping, SNPs Calling, Filtering, and Imputation After filtering of reads using Btrim  (Kong 2011) , qualified reads were mapped onto the reference R. norvegicus genome (rn5) (ENSEMBL version 72)  (Gibbs et al. 2004 ) using the program BWA-MEM  (Li 2013). SNPs were detected and filtered using the Genome Analysis Toolkit (GATK)  (McKenna et al. 2010). Duplicate read pairs were first identified using Picard tools (http://picard. sourceforge. net/) before being realigned and recalibrated around putative variants downloaded from dbSNP (ftp://ftp. ncbi. nlm. nih. gov/snp/organisms/rat_ 10116/VCF/). We chose high-quality genotypes by filtering the SNPs data set with QUAL > 30, and removing sites where more than or equal to ten individuals were not called.
This criteria (QUAL > 30) narrows the false calling rate of each SNP to <0. 1%. Further, since the genome sequences included in our data set were of low depth, we performed ungenotyped marker imputation using the software BEAGLE  (Browning and Browning 2009) , after removing triallelic sites and filtering the raw vcf file. We finally generated a total of 24,977,888 autosomal SNPs.
We randomly drew one simulated phylogenetic tree to display as an example (supplementary fig. S10 , Supplementary Material online). This indicates that two migration waves out-ofsouthern East Asia can also cause the observed branching pattern in figure  1A. In order to understand the relationships between different geographic population, we computed a PCA (fig. 1B ) using the software GCTA  (Yang et al. 2011 ) after pruning the SNPs by plink with -indep-pairwise 50 10 0. 1 parameter to get the relatively independent sites  (Purcell et al. 2007 ). Population structure was then deduced using the software ADMIXTURE (supplementary fig. S6 , Supplementary Material online), a tool for ML estimation of individual ancestries from multi locus SNP genotype data sets  (Alexander et al. 2009 ). We evaluated different K values (from 2 to 10).
To avoid bias from imputation, we also compared the SFS before and after genotype imputation, and got very similar SFS (supplementary fig. S21 , Supplementary Material online). We calculated the likelihood function for different demographic scenarios using the software fastsimcoal2  (Excoffier et al. 2013). For each scenario, 100,000 coalescent simulations per likelihood estimation (i. e. , -n 100,000 -N 100,000) and at least 20 expectation-conditional maximization (ECM) cycles (-l20), up to a maximum of 40 (-L40), were used as the command line parameters for each run. At the same time, to avoid getting stuck on local optimum, 400 runs to 2,000 runs were carried out, whereas the Akaike information criterion (AIC)  (Akaike 1974 ) was used to compare different models. In this case, AIC ¼ 2kÀ2ln(MaxEstLhood), where k is the number of parameters estimated by each model, and MaxEstLhood is the ML function value for each model.
Moreover, when searching for a ML value, fastsimcoal2 may reach a local optimum instead of a global optimum. Thus, we repeated each step at least twice, to ensure we were not ending in a local optimum, thereby getting better estimates of the global optimum. In order to obtain confidence intervals (CIs) for final estimates, 100 independent DNA polymorphism data sets were simulated as joint SFSs conditional on estimated demographic parameters. ML analysis was then applied to each joint SFS over 30 ECM cycles and 30 runs. Overall, 100,000 coalescent simulations were used to calculate likelihoods, giving empirical estimate distributions and 95% CIs. The 100 simulated polymorphism data sets were further used to generate averaged simulated SFSs and to calculate simulated genetic diversity under the given demographic parameters.
IRD, CBGP), B. Sicard (IRD, IMBE), S. Traore(Institut d'Economie Rurale), C. Goarant (Institut Pasteur de Nouvelle Caledonie), S. Piry, Y. Chaval, N. Charbonnel (INRA, CBGP), C. Gotteland (CNRS/UniversiteLyon 1), Alexey P. Kryukov, Mr Erwan Lagadec, and Dr Pablo Tortosa (CRVOI-PIMIT). Authorization to use rat samples from the Seychelles collected during a CRVOI-IRD mission (SBS authorization dated 24. 05. 11) was provided by the Ministry of the Environment, Energy and Climate Change of Seychelles (special thanks to M. Alain de Comarmond, Principal Secretary, and M. Ronley Fanchette, Director of Conservation). Samples hosted at CBGP are stored at the collection platform (http://www6. montpellier. inra. fr/ cbgp_eng/Platforms/Collections-platform), and included in the small mammal database (http://vminfotron-dev.
Acknowledgments This work was supported by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences , Grant No  XDB13020600 , and the  National Natural Science Foundation of China  ( 91731304 ). N. O. O. thanks the support of  CAS-TWAS President's Fellowship Program for Doctoral Candidates. We thank the collaborators who kindly shared brown rat samples:  J. -M. Duplantier ,  L. Granjon ,  K. Bâ ,  C. Brouat ,  M. Diallo ,  M. Kane ,  A. Sow Zeng  et al. doi:10. 1093/molbev/msx276MBE.
( IRD, CBGP), B. Sicard (IRD, IMBE), S. Traore(Institut d'Economie Rurale), C. Goarant (Institut Pasteur de Nouvelle Caledonie), S. Piry, Y. Chaval, N. Charbonnel (INRA, CBGP), C. Gotteland (CNRS/UniversiteLyon 1), Alexey P. Kryukov, Mr Erwan Lagadec, and Dr Pablo Tortosa (CRVOI-PIMIT). Authorization to use rat samples from the Seychelles collected during a CRVOI-IRD mission (SBS authorization dated 24. 05. 11) was provided by the Ministry of the Environment, Energy and Climate Change of Seychelles (special thanks to M. Alain de Comarmond, Principal Secretary, and M. Ronley Fanchette, Director of Conservation). Samples hosted at CBGP are stored at the collection platform (http://www6. montpellier. inra. fr/ cbgp_eng/Platforms/Collections-platform), and included in the small mammal database (http://vminfotron-dev.
New scientific and regulatory developments on these three topics are described. Considerations regarding the practical implementation of those developments in the risk assessment of GM plants are discussed and, where appropriate recommendations made to supplement previous guidance documents. Briefly, for non-IgE-mediated adverse immune reactions to food detailed risk assessment considerations are provided to determine the safety profile of the protein or peptide under assessment with regard to its potential to cause celiac disease. This assessment will include available information on the source of the transgene and on the protein itself as well as on data from in silico and in vitro testing, as and when appropriate. For in vitro protein digestibility tests, the EFSA GMO Panel considers that additional investigations are needed before any additional recommendation in the form of guidance for applicants can be provided.
To this end, an interim phase is considered necessary to evaluate the revisions to the in vitro gastrointestinal digestion test, proposed by EFSA, which are presented in an Annex to this document. For assessing endogenous allergenicity of GM plants and to support the practical implementation of mandatory requirements in Implementing Regulation EU (No) 503/2013, this guidance document provides further information on: (i) relevant crops subjected to such analysis; (ii) relevant allergens that should be quantified; (iii) methodology to be used for quantification; and (iv) principles to be followed for data interpretation and risk assessment considerations. During the development of this document, EFSA involved stakeholders and the general public at different stages, strengthening new means of engagement in its scientific process.
Objectives This guidance document is designed to assist applicants in the preparation and presentation of a well-structured application to demonstrate the safety of the GM plant under assessment, with respect to the allergenicity risks. Recommendations are also provided for the correct interpretation of the data in the risk assessment process. EFSA will continue to review the state-of-the-art in science and in the light of experience gained from the evaluation of GM plant applications, updating the guidance document, as and when appropriate.
If concerns are raised, additional tests will be required (Figures  1  and 2 ). Known CD peptide sequences are listed in Table  A. 1 as well as in publicly available celiac peptide databases (see Annex A-1). Following these searches for sequence identity, four outcomes are possible: • if the Q/E-X1-P-X2 motif is not present and no concerns are raised during the identity search with specific known CD peptide sequences, the probability of a T-cell epitope is unlikely; • if the Q/E-X1-P-X2 motif is not present but concerns are raised during the identity search with specific known CD peptide sequences, further investigation is required; • if the Q/E-X1-P-X2 motif is present and considerations on adjacent sequences can be used to eliminate concerns, the probability of a T-cell epitope is unlikely; • if the Q/E-X1-P-X2 motif is present and considerations on adjacent sequences cannot be used to eliminate concerns, a potential T-cell epitope is detected and further investigation is required.
In line with IR503/2013, the OECD allergen list should be taken as the starting point for the collection of 'potential allergens' (Figure  4 ). In addition, this list should be complemented with searches in the scientific and medical literature, and in various updated databases (EFSA GMO Panel, 2010; Appendix 3. 13, Table  I  for a list of relevant databases). It is noted that on the one hand the OECD list of allergens might not be complete at a given point in time, e. g. it might be out dated and/or miss relevant (more recent) entries. On the other hand, not all 'potential allergens' listed in this OECD consensus document can currently be measured due to technical reasons (e. g. amino acid sequence not available) and/or their clinical relevance might not have been demonstrated. Once a comprehensive search of 'potential allergens' in the literature and databases is conducted, the relevant allergens selected for quantification and subsequent comparative analysis should be justified.