dataesr/scientific-paragraphs-categorization huggingface dataset:
58%, Sphingidae 4. 41% Saturniidae 6. 02%)  [32]. The interspecific divergence distribution overlaps the intraspecific divergence distribution, resulting in the absence of a perfect gap between the two, making it impossible to define a species distance threshold. However, we detected an ''imperfect gap'' in the distribution (between 0. 7% and 2. 6%) in our dataset. This made it possible to define an optimal threshold minimizing assignment errors, between these values. The usefulness of this gap is debatable, but its presence, by contrast to the continuous distribution observed for congeneric and intergeneric divergences, suggests that levels of COI variation can be used for species delimitation, but not for genus delimitation. This may be due to the rate of evolution of COI and/or the fact that species delimitations are more consistent than the definitions of genera in aphids.
Disentangling Species Groups in Aphid: Barcoding and Morphology are Subject to the Same Limitations Overall, 77. 8% of the species represented by multiple specimens clustered into distinct clades on the NJ trees for COI. These clades were separated from their nearest neighbors, indicating that specimen assignation to species by DNA barcoding should be correct. About 19% of the nominal species appeared to be polyphyletic or paraphyletic with respect to other recognized species (including B. helichrysi and M. coryli, as previously discussed). Situations in which the distances between congeneric species are extremely small are problematic. We detected 14 polyphyletic clades of nominal species poorly discriminated by COI barcodes. These clades included a total of 50 aphid species belonging to four (Aphis, Dysaphis, Brachycaudus, and Macrosiphum) of the 87 genera represented in our dataset.
Only one of these groups, the D. crataegi group, is represented by several species in our dataset. Unsurprisingly, all these species were grouped together to form a clade with species displaying an overlapping barcode. This group has been studied in detail in Western Europe, first by Bo ¨rner  [59]  and then by Stroyan  [55] ,  [60] ,  [61]. It remains a matter of debate whether these taxa should be treated as species or subspecies of D. crataegi: this classification is somewhat arbitrary, as it is not based on valid biological criteria  [51]. The last genus containing poorly discriminated species is Macrosiphum. Four of the seven species present in our sample form two pairs of species, M. cerinthiacum/stellariae and M. cholodkovskyi/euphorbiae. With the exception of the little studied M. cerinthiacum, these species have been recognized as belonging to the morphologically similar M.
This led him to conclude that new species have probably been described erroneously. Intensive host-plant transfers have also been conducted in the Aphis  [85] ,  [86] ,  [87] ,  [88] ,  [89] , Dysaphis  [90] ,  [91] ,  [92] , Macrosiphum  [63] ,  [93] ,  [94]  and Brachycaudus  [95] ,  [96]  species groups. Conflicting results have been obtained between different investigations, suggesting that host plant associations may be inconsistent over both time and space. These inconsistencies are probably intrinsic features of the structure of these species complexes, rather than reflecting experimental shortcomings  [15]. DNA barcode database users must accept that species definitions are established on the basis of traditional taxonomy, which may be imperfect. It must, therefore, be borne in mind that many formal species are not monophyletic.
In addition, due to morphological homogeneity, incorrect species identification may occur more frequently in some species, contributing to the high frequency of polyphyletic species. Misidentifications with the use of GenBank as a barcode database have been reported  [97] , but the rate of misidentification in the construction of barcode databases has never been evaluated. The use of barcode databases built in collaboration with a taxonomist decreases the risk of misidentification, although mistakes may still occur, particularly for challenging taxonomical groups. All these factors increase the error rates for barcode-based identification and it is thus the traditional way of delimiting and describing species that requires re-evaluation. In this context, trying to identify the perfect gene for barcoding may be pointless.
Conclusion Our study contributes to the assembly of a DNA barcode library for the world aphid fauna. The addition of our dataset to those from North America  [1]  and Korea  [27]  results in the coverage of only 15% of the described species with published barcodes, this percentage being only slightly increased by the inclusion of recent taxonomic studies (i. e. [25] ,  [105] ). More efforts are therefore required for the barcoding of this group of economically important families and model systems for evolutionary biologists. The geographic scale of the available samples and the relatively well known taxonomy of this group of insects make aphids ideal for the testing of several issues relating to DNA barcoding, such as the impact of geography or taxon coverage on the accuracy of species assignment.
The data presented here confirm that COI barcodes are a potentially useful tool for aphid identification. This approach simplifies identification for 80% of the species, including some species that are difficult to identify on the basis of morphological characters only. However, our work also highlights identification difficulties in Aphis, Brachycaudus, Dysaphis and Macrosiphum, genera including a large number of pest species. This may be the stumbling block for the actual use of the aphid barcoding tool, particularly in agricultural management programs, which are likely to be the principal users of this tool. These problematic groups of species have been studied by taxonomists for a very long time. Barcoding cannot replace a comprehensive taxonomic analysis. Detailed genetic, morphological and ecological investigations are required to define species boundaries, and this is the job of taxonomists.
However, systematics studies take much longer than barcoding  [106]. Such long-term work is incompatible with the urgency of societal demands for a powerful, user-friendly identification tool. One possible pragmatic solution would be to mimic the procedure used by aphid systematics specialists: assigning specimens to a group of species, returning their names with information about the host plants of the different nominal species included in the group, and then allowing the user of the system to identify the specimen on the basis of the available information. This procedure, together with the different assignment methods, will require evaluation in future studies before the use of aphid barcoding databases as accurate identification tools for applications in pest management and plant quarantine.
This work was based on financial supports received from the  BIOFIS  project (number  1001-001 ) allocated by the  French Agropolis Fondation (RTRA -Montpellier)  to JYR, the project  @-Speed-ID included in the network ''Bibliotheque du Vivant '' funded by the  CNRS , the  Museum National d'Histoire Naturelle , the  INRA  and the  CEA (Genoscope) , the  SDIPS  project allocated by  INRA  to EJ and the  ANR JCJC-05 (Brachycaudus)  grant awarded to EJ and ACDA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Clinical trial Details on the design, protocol and conduction of the clinical study are provided elsewhere  [1]. In brief, non-obese, healthy male smokers in an age between 30 and 60 years and smoking 10 and more cigarettes per day for at least 5 years were included in the trial. The study was approved by the Medical Ethical Committee of the Maastricht University and Academic Hospital Maastricht, The Netherlands (ClinicalTrials. gov, NCT00742287) and conducted according to the World Medical Association Declaration of Helsinki of 1975, revision 2008. All subjects provided their written informed consent to participate in this study. For the present investigations blood samples of 13 men were available before and after an intervention of 8 weeks with a daily intake of two capsules containing 100 mg MOF from Vitis vinifera L.
Microarray analysis Fifty ng of total RNA extracted from each sample were amplified and fluorescently labelled to produce Cy5 or Cy3 cRNA using the Low Input Quick Amp Labeling two-color kit (Agilent, USA) in the presence of spike-in two colours control. After purification, 825 ng of labelled cRNA/sample were hybridized onto G4845A Human GE 4x44K v2 microarrays (Agilent, USA) according to the manufacturer's instructions. The G4845A Human GE 4x44K v2 microarray contains 27,958 Entrez Gene RNAs sequences. After hybridization, microarrays were scanned with Agilent G2505 scanner (Agilent, USA) and data were extracted with Feature Extraction software (Agilent, USA) using Lowess normalization. Statistical analyses were performed using the free R 2. 1 software (http://www. r-project. org) after controlling the variance of each gene.
Data were analyzed with a Student's t test to detect differentially expressed genes and the probability values were adjusted using the Benjamini-Hochberg correction for multiple testing at 0. 1 to eliminate false positives. Genes selected by these criteria are referred to as the ''differentially expressed genes''. Microarray data have been deposited in accordance to the MIAME guidelines in the public Gene Expression Omnibus (GEO) database (http://www. ncbi. nlm. nih. gov/geo/), accession number GSE54325 and GSE54643.
Biological interpretation Gene ontology (GO) annotations of biological processes for differentially expressed genes were conducted using MetaCore (https://portal. genego. com) and BioMart data mining tool of Ensembl database (http://www. ensembl. org/biomart/). To extract maximum biological information of differentially expressed genes, together with GO, genes were also classified according to their role(s) in cellular or metabolic pathways using Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www. genome. jp/kegg/pathway. html) and MetaCore (https://portal. genego. com) databases. Potential transcription factors involved in the regulation of differentially expressed genes were searched using network algorithms for transcription factors development in MetaCore software.
After hybridization, free DNA was washed away and the BeadChips were processed through a single nucleotide extension followed by immunohistochemistry staining using a Freedom EVO robot (Tecan). Finally, the BeadChips were imaged using an Illumina iScan. Illumina data have been deposited in accordance to the MIAME guidelines in the public Gene Expression Omnibus (GEO) database (http://www. ncbi. nlm. nih. gov/geo/), accession number GSE 54690.
Illumina data analysis Raw Illumina data were filtered by Genomestudio software and R packages Bioconductor Minfi and Rnbeads to remove low quality data using a detection P-value threshold of 0. 05. Crossreactive probes (i. e. targeting several genomic locations) and probes containing SNPs were filtered out using the extended annotation provided by Price et al  [33]  (see  [34]  for a detailed description). bvalues were computed using the formula b-value = M/[U+M] where M and U are the raw ''methylated'' and ''unmethylated'' signals, respectively. Beta-values were corrected for the type I and type II bias using the peak-based correction  [34].
DNA CpG pyrosequencing Bisulfite modification of 1 mg of gDNA was performed using the EpiTect Fast DNA Bisulfite kit (Qiagen) per manufacturer's protocol. Forward, reverse, and sequencing primers were designed using the Pyromark Assay Design Software (Qiagen Inc. , Valencia, CA). Bisulfite converted DNA was diluted 1:50 with water. A touchdown PCR program was used to amplify the DNA using the HotStarTaq kit (Qiagen Inc. , Valencia, CA). PCR cycling conditions were as follows: denaturation at 95uC for 30 seconds, annealing (temperature varied depending upon primer and replication cycle) for 30 seconds, extension at 72uC for 1 minute for 45 cycles followed by a final extension at 72uC for 10 minutes. Samples were then stored at 4uC. The following primer sets were used for DNA CpG pyrosequencing: CCR6 forward primer TTGTGTTTGGAGTAGTGTTTAGT, reverse primer Biotin-CCTAAATCTTCCACCTCCTCTTTATACCAC, sequencing primer TGTTTGGAGTAGTGTTTAGTA; MAFB forward primer AGGGAATTGAGGGAGGAGAG, reverse primer Biotin-ACCCACCCCCCTACCCTACAAA, sequencing primer GTGATTTGGTTTAGAGGT; PCDHB4 forward primer AGATTTTTGGTTAGATGGGTTATAAAG, reverse primer Biotin-ATTCATCCAAACAAACAAATATTAACC, sequencing primer ATGGGTTATAAAGGAGT.
32 to 1. 72. To identify the biological function in which these genes are involved, gene ontology, pathway and gene network analyses were performed. Gene ontology revealed that differentially expressed genes are involved in different over-represented cellular processes (table  S2 ). Among these processes are those regulating macrophage-derived foam cell differentiation, chromatin organization, cell cycle, chemotaxis, regulation of cell-cell adhesion, regulation of metabolic processes, regulation of lipid storage and regulation of cytoskeleton organization. To further refine the biological functions in which differentially expressed genes are implicated, the genes were placed according to their role(s) in cellular or metabolic pathways. Pathway analyses of the differentially expressed genes using MetaCore software identified 59 significantly over-represented cellular pathways (table  S3 ).
Among these 59 pathways, 19 are involved in the regulation of inflammation, cell adhesion, cell cycle and cytoskeleton remodelling (figure  2 ). Pathway analysis using KEGG database also revealed pathways such as cell adhesion, MAPK signalling pathway, regulation of actin cytoskeleton, cell cycle, chemokine signalling pathway, focal adhesion and leukocyte transendothelial migration. Among the genes present in these pathways are CXCL12, SCRIB, PDGFRL, FERMT3, ICAM1 or VCAM1. As for MetaCore, KEGG database pathways also indicated cell adhesion, chemotaxis or regulation of cellular cytoskeleton as significantly altered pathways upon MOF supplementation. Moreover, bioinformatic analyses were performed on the differentially expressed genes with the aim to identify transcription factors that could be modulated by MOF and which are potentially involved in the regulation of the expression of the identified genes.
Comparison of the genes revealed only 145 common genes in both studies (figure  S1A ). However, the pathways identified from differentially expressed genes by means of KEGG database in our study coincide with those reported by Tome-Carneiro et al  [37]  (figure  S1B ). Bioinformatic analysis revealed that the differentially expressed genes are involved in the regulation of different cellular processes. The most overrepresented pathways are involved in chemotaxis, cell adhesion, immune response and cell cycle. Chemokines and adhesion molecules play important roles in leukocyte adhesion, trans-endothelial migration and activation  [38, 39]. Among the genes involved in chemotaxis and leukocyte infiltration is CXCL12  [40] , which appeared to be down-regulated after 8 weeks MOF supplementation.
Tagged cDNA libraries were equally pooled and sequenced on 2. 66 lanes (same flow cell) of a HiSeq2000 (Illumina) in 100 pb single end reads. Quality control analysis of the raw dataset did not indicate any differences among lanes regarding the quality or quantity of the reads generated. The raw reads are available at NCBI's SRA repository (PRJNA221286 BioProject; accessions SRR1006118 to SRR1006133).
Read mapping and estimation of gene expression on RNA-seq data Raw reads were trimmed for low quality bases at the end of the RNA-seq reads (bases at 39 end nucleotides were successively removed until finding a base with a Phred quality score. 10 or until the read length became less than 40 bp long). They were further aligned to the Sus scrofa reference genome (Sscrofa v10. 2 sequence  [18] ) with TopHat v2. 0. 4  [19]  and using the gene annotation available at Ensembl v68 [www. ensembl. org]. The aligned reads were assembled into transcripts by using Cufflinks v2. 0. 1  [20] , which constructs a minimum set of transcripts per locus that best describe the reads in the dataset. This approach allows Cufflinks to identify alternative transcription and splicing events that are not described by pre-existing gene models  [20].
Estimation of transcripts and genes counts was also performed by using Cufflinks functions (e. g. Cuffdiff program providing the estimations of raw counts for every gene and transcript). In order to validate the steadiness of counts predicted by Cufflinks, two other tools were independently used to count aligned reads that overlapped with the porcine genes: (1) HTSeqcount (part of the HTSeq framework, version 0. 7. 1, in 'union' mode) and (2) Qualimap v07. 1  [21]. Linear regressions were performed with the 'lm' function in the R statistical environment for each animal and tissue independently. To analyse whether genes were ubiquitously expressed among the targeted tissues, a Venn diagram was plotted by using Venny, an interactive tool for the comparison of lists. Venny is freely accessible at: http://bioinfogp.
Statistical analysis of gene expression The estimated raw counts of each gene calculated with Cufflinks were analysed by using the Bioconductor edgeR package  [22]  in the R environment (version 3. 0. 1). To avoid inclusion of genes containing too few reads, only genes containing at least 1 read per million in 25% of the samples were retained. In order to discover biologically important changes in expression, the ''calcNormFactors'' normalization function of edgeR package was applied. This function normalizes the data by finding a set of scaling factors for the library sizes that minimizes the log-fold changes between the samples. The scale factors were computed through a trimmed mean of M-values (TMM) between samples  [23]. The differentially expressed (DE) genes were detected by applying a generalized linear model (GLM) likelihood ratio test, which is based on the idea of fitting negative binomial (NB) with the Cox-Reid dispersion estimates  [22].
Dispersion was calculated using the ''estimateCRDisp'' functions provided by edgeR package  [24]. Once negative binomial model was fitted and dispersion estimates obtained, the determination of DE genes using the GLM likelihood ratio test was performed. The GLM included the tissue type as fixed effect, and the piglet as a repeated factor to properly account for within-subject correlations. Tissue type was defined as (1) duodenum, (2) jejunum, (3) ileum and (4) ileal Peyer's patches. Each of the treatment groups was compared using the contrast argument of the ''glmLRT'' function of the edgeR package. The edgeR generated a fold change for each gene, P-values and the associated Benjamin-Hochberg false discovery rate (FDR) values. Genes showing a FDR,0. 05 were considered as DE. The MA plots, which show the relationship between the expression and fold-change across the genes, were obtained by using the function ''plotSmear'' in the edgeR package  [22].
The visualization of DE genes found to be ubiquitously present in the different type of tissues was performed by the VennDiagram package in the R statistical environment. Subsequently, we further explored genes exhibiting statistically significant differential expression between all intestinal tissues together and ileal Peyer's patches. In that case, the GLM also included the tissue type as fixed effect, and the piglet as a repeated factor to properly account for within-subject correlations. The tissue type corresponded to (1) joint dataset combining all intestinal tissues (duodenum, jejunum and ileum tissues together) vs. (  2 ) ileal Peyer's patches. In all cases, genes showing a FDR,0. 05 were considered as DE.
Functional analysis of expressed genes We used the web-based program CateGOrizer to batch analyse gene ontology (GO) classification categories  [25]  and to infer the main biological functions associated to the different tissues. The list of GO terms, as available in Ensembl database (www. ensembl. org) for the molecular function ontology that corresponded to expressed genes in each tissue was uploaded into the application. In CateGOrizer, the GOslim2 list was used to select a set of highlevel GO terms to describe the main features of the functional classification. The Ingenuity Pathway Analysis tool (IPA; ''http://www. ingenuity. com'') was applied for the functional analysis of DE genes (FDR,0. 05). Firstly, we uploaded into the application the list of human homologs that corresponded to the protein-coding genes DE between tissues.
Then, each gene identifier was mapped to its corresponding gene object in IPA. The DE genes between tissues were classified for the categories of molecular function, cellular component and biological process using gene ontology (GO) annotation. This approach allowed the identification of statistically overrepresented functional GO annotation, determining their up-or down-regulation, and tissue-specific transcriptional networks. All listed or reconstructed cellular pathways were derived from the expert annotated database that is provided by the Ingenuity Knowledge Base. The IPA annotations follow the GO annotation principle, but are based on a proprietary knowledge base of over 1,000,000 protein-protein interactions. IPA output included biological functions and signalling pathways with statistical assessment of the significance of their representation being based on Fisher's exact test.
Detection and quantification of gene expression in the four-targeted tissues Over 25M reads were generated per sample and ,90% mapped to the pig genome reference sequence (supplementary Table  S1 ). To identify eventual issues related to libraries or systematic biases, exploratory data analyses at the gene-level were performed before and after normalization. Built-in quality control methods were implemented in Bioconductor and associated packages. Upon rigorous examination of the resulting diagnostic plots (supplementary Figure  S1 , Figure  S2  and Figure  S3 ), all animals and tissues were retained for further analysis. Altogether, 24,924 genes were assembled by Cufflinks. The high reproducibility among read counts software tools was validated by linear regression, with coefficient of correlations $0.
Acknowledgments We are grateful to  S. Vincent-Naulleau ,  J. J. Leplat ,  S. Bouet ,  J. Lecardonnel ,  F. Andreoletti ,  F. Levenez ,  P. Pinton ,  A. M. Cossalter , and  N. Bertho  for participating in the sample collection during the  SUS_FLORA  project  (French National agency , project  ANR-10-GENM-016 ). We are also grateful to  Marco Moroldo  ( INRA ) who helped with the editing of the manuscript.
4C  and D ). Indeed, in the 3 shRNA clones with 2-fold lower EB1 expression, the doubling  time was increased by around 12 % and cell number was significantly decreased by around 15% at 96 hours. In contrast, EB1 overexpression decreased the doubling time in a EB1-dependent manner (-1%, -16. 6% and -25. 9% for U87 P16, P11 and P15, respectively) and significantly promoted cell growth (+6. 4 ± 2. 5%, +21. 6 ± 3. 0% and +39. 6 ± 1. 7% for U87 P16, P11 and P15, respectively) (Fig. 4D ). These data were confirmed using a trypan blue dye exclusion assay (data not shown). Moreover, down-regulation of EB1 significantly decreased viability of U87 P11 cells by 12. 5 ± 3. 3%, thus rescuing partially the normal U87 P0 phenotype (Fig. 4F ). Proliferation of astrocytes, that display undetectable staining of EB1, was also significantly lower than U87-MG and U251-MG glioblastoma cells (-59.