dataesr/scientific-paragraphs-categorization huggingface dataset:
The MCPyV genotype was identified by direct sequence analysis. PCR amplicons were sequenced with the automated ABI Prism 3,730 × l Genetic Analyser (Applied Biosystems, Monza, Italy). The resulting MCPyV DNA sequences were BLAST vs. MCPyV DNA from different viral strains present in the NCBI database (http://www. ncbi. nlm. nih. gov/blast/Blast. cgi). Specifically, MCPyV DNA sequences were aligned against reference sequences from North America (MCC350, EU375803. 1; MCC339, EU375804. 1), Japan (TKS, FJ 464337), Sweden, France, and Italy MCPyV isolates (MKL-1, FJ173815)  (22, 23). All experiments were carried out in laboratories with the standard biosecurity and safety procedures, according to institutional rules.
The terms defined in Equations (  6 ) correspond to the syntactic 5 ACGtk can be downloaded and installed from http://calligramme. loria. fr/acg/#Software. 6 These files are available at https://hal. inria. fr/hal-01417853/ file/quantification-and-frames. zip. [ 371 ] derivations of the sentence for which we want to provide a semantic representation. Their surface forms are given by Equations (  7 )-(  12 ).
The success of oncological surgery, the most widely used curative treatment for solid tumor whatever its histopathological type, is based on the accurate identification of the tumor's boundaries in order to achieve a complete tumor resection. For tumors of the central nervous system, the goals of oncological surgery are identical but their realization is made more difficult by their infiltrating character, especially the diffuse gliomas, within a highly eloquent organ. The main challenge of any neurosurgical oncological intervention is to define the limits of the resection while optimizing the onco-functional balance  1. Resection is based on the limits of tumor infiltration, which should be removed, and on the identification of eloquent brain areas, which should be respected. The actual identification of the tumor infiltration at the cellular scale is not possible intraoperatively and requires the development of an efficient and reliable intraoperative imaging tool, based on an imaging database of the main tumors of the central nervous system (diffuse gliomas, metastases, meningiomas, and healthy tissues).
The development of an intraoperative probe/device should be accompagnied by the construction of a large database of endogenous fluorescence response of tumor tissues. This intraoperative tool, in association with knowledge on collected optical response, will lead to an "optical biopsy" giving a real time result, and providing additional relevant morphological and physiological information during surgery, which may guide the surgical resection. The optical properties of biological tissues have a major importance in several medical applications for diagnosis and therapy  2. Knowing the optical properties of brain tissues results in quantitative information  3  , which allows the optimization of imaging techniques and the possibility of modeling the light path, the fluorescence distribution, the penetration depth and the possible interaction between fluorophores in the tissues.
Every spectra is the sum of emission spectrum of five fluorphores: NADH, FAD, lipopigments, porphyrins and chlorins. In addition, by calculating the integral under the curve of the emission spectra of NADH, FAD and the porphyrins, using a Matlab program developed by our team  17  , we obtained two different ratios under 405 nm and 375 nm excitation wavelengths  11, 18. At 375 nm, the calculated ratio has negative values because NADH is more present in brain tissues than FAD at the excitation wavelength  17, 19. As shown in Fig. 4a , the healthy tissues have a higher ROx than tumor tissues (p < 0. 001), which is inline with the literature  11. At 405 nm, the Fig. 4b  also shows a significative difference (p < 0. 001) tumoral and healthy tissues. Similar results were obtained on bladder tumor tissues  19.
To complete our study, we also collected data from a promising quantitative technique, the fluorescence lifetime. As previously done, we used two excitation wavelengths, 375 nm and 405 nm. We had a closer look at two molecules emitting endogeneous fluorescence. This choice was motivated by our previous study on a cohort of fresh samples that highlights the changes in NADH and our study on brain rats that shows a variation in porphyrins  17, 19. In Fig. 5a ,b we see that at both excitation wavelengths, the NADH presents a difference in lifetime between glioblastoma, meningioma, and control samples. Nevertheless, at 405 nm excitation wavelength, the control has a shorter lifetime than healthy tissue, where at 375 nm the control has a longer lifetime. The meningioma is very well distinguished from the control tissue (p = 0.
Diffuse Glioma 5 Glioblastoma 4 Meningioma 4 Table  1. Summary of the cohort used in the paper. parameters of sample, the laser beam and the integrating sphere used into account to find the optical coefficients desired. Noting that s μ′ is the reduced scattering coefficient, we will deduce the scattering coefficient µ s using the equation below /(1 g) s s μ = μ′ -According to the literature  2, 6  , the refractive index of human brain tissues is between 1. 33 and 1. 53. We have considered that the refractive index is n = 1. 4 for all samples 2. Spectral analysis. At the excitation of 375 and 405 nm five fluorophores are excited: NADH, FAD, lipopigments, porphyrins and chlorines. The measured spectra present the sum of the emission spectrum of these endogenous fluorophores. Spectral data are processed from a Matlab program developed previously by our team and already used in a previous publication [1].
Using this program we can determine the contribution of each molecule by adjusting the measured data to the equation below where: S total (λ): measured spectra, i: the fluorophore, S i (λ): emission spectra of the fluorophore I, f i : multiplying coefficient. NADH spectra is adjusted by a spectra obtained from a previous experiment done by our team on rats at 375 nm  17. FAD spectra is adjusted by a spectra taken from the literature  21. The other spectras of Lipopigments, Porphyrins and Chlorins are given by Gaussian adjustment. Lifetime analysis. The measured fluorescence decay curves obtained are adjusted by a mono-exponential fit using FluoFit software (Picoquant,Germany), which allows us to extract the fluorescence lifetime of the curve. Two criteria are taken into account to validate the fit: χ2 must be -1.
Whole-genome sequencing was performed on an Illumina NextSeq 500 platform using 2 ϫ 150 bp runs (Illumina, San Diego, CA, USA), at an estimated average read coverage of 130ϫ (Table  1 ). FqCleaner v3. 0 was used to eliminate adapter sequences (3), reduce redundant or over-represented reads (4), correct sequencing errors (5), merge overlapping paired reads  (6) , and discard reads with Phred scores of Յ20. Assemblies were generated with SPAdes v3. 1. 0 (7) , with different k-mer values ranging from 21 to 127. The draft genome sizes varied from 2. 93 to 3. 06 Mb, with a GϩC content of 37. 9% (Table  1 ). Multilocus sequence typing (MLST) and core genome MLST (cgMLST) allelic profiles were performed from the assemblies using the BIGSdb-Lm database (http://bigsdb. pasteur. fr/listeria), as described previously  (1).
Isolates belonged to MLST ST5 and ST155 (8) and cgMLST profiles were defined as new cgMLST types (CT2050 and CT2051)  (1). Gene prediction and annotation were performed by the RAST tool  (9). These isolates constitute the first L. monocytogenes genome sequences from Nigeria and will contribute to a better knowledge of the diversity of this species in Africa  (10). Accession number(s). The draft assemblies reported here were deposited at DDBJ/ EMBL/GenBank under the accession numbers provided in Table  1.
Objective A decade of microbiome studies has linked IBD to an alteration in the gut microbial community of genetically predisposed subjects. However, existing profiles of gut microbiome dysbiosis in adult IBD patients are inconsistent among published studies, and did not allow the identification of microbial signatures for CD and UC. Here, we aimed to compare the faecal microbiome of CD with patients having UC and with non-IBD subjects in a longitudinal study. Design We analysed a cohort of 2045 non-IBD and IBD faecal samples from four countries (Spain, Belgium, the UK and Germany), applied a 16S rRNA sequencing approach and analysed a total dataset of 115 million sequences. Results In the Spanish cohort, dysbiosis was found significantly greater in patients with CD than with UC, as shown by a more reduced diversity, a less stable microbial community and eight microbial groups were proposed as a specific microbial signature for CD.
Study population To study differences in the microbiome composition between IBD and healthy subjects and between inactive and active disease (remission vs recurrence), 34 patients with CD and 33 patients with UC were enrolled for a follow-up study in the Spanish cohort. Inclusion criteria were a diagnosis of UC and CD confirmed by endoscopy and histology in the past, clinical remission for at least 3 months-defined by the validated colitis activity index (CAI) for UC and the CD activity index (CDAI) for CD,  10  stable maintenance therapy (either amino-salicylates, azathioprine or no drug) and previous history of at least three clinical recurrences in the past 5 years. HC were without previous history of chronic disease. At inclusion and during the follow-up (every 3 months), we collected diagnostic criteria, location and behaviour of CD, extension of UC, and clinical data including tobacco use and medical treatment.
High-throughput DNA sequencing For profiling microbiome composition, the hyper-variable region (V4) of the bacterial and archaeal 16S rRNA gene was amplified by PCR. On the basis of our analysis done using Primer Prospector software,  12  the V4 primer pairs used in this study were expected to amplify almost 100% of the bacterial and archaeal domains. The 5 0 ends of the forward (V4F_515_19: 5 0 -GTGCCAGCAMGCCGCGGTAA -3 0 ) and reverse (V4R_806_20: 5 0 -GGACTACCAGGGTATCTAAT -3 0 ) primers targeting the 16S gene were tagged with specific sequences as follows: 5 0 -{AATGATACGGCGACCACCGAGATCTACACTATGGTAAT-TGT}  12  {GTGCCAGCMGCCGCGGTAA}-3 0 and 5 0 -{CAAGCA GAAGACGGCATACGAGAT} {Golay barcode} {AGTCAGTCA GCC} {GGACTACHVGGGTWTCTAAT}-3 0. Multiplex identifiers, known as Golay codes, had 12 bases and were specified downstream of the reverse primer sequence (V4R_806_20).
Sequence data analysis For microbiome analysis, we first loaded the raw sequences into the QIIME 1. 9. 1 pipeline, as described by Navas-Molina et al. 14  The first step was to filter out low quality sequence reads by applying default settings and a minimum acceptable Phred score of 20. Correct primer and proper barcode sequences were also checked. After filtering, from a total of 2206 faecal samples, we obtained a total of 115. 5 millions of high-quality sequences with a number of reads ranging from 1 to 223 896 per sample. We used the USEARCH 15 algorithm to cluster similar filtered sequences into Operational Taxonomic Units (OTUs) based on a 97% similarity threshold. We then identified and removed chimeric sequences using UCHIME. 16 Since each OTU can comprise many related sequences, we picked a representative sequence from each one.
Representative sequences were aligned using PyNAST against Greengenes template alignment (gg_13_8 release), and a taxonomical assignment step was performed using the basic local alignment search tool to map each representative sequence against a combined database encompassing the Greengenes and PATRIC databases. The script make_phylogeny. py was used to create phylogenetic trees using the FastTree programme. 17 To correctly define species richness for the analysis of between-sample diversity, known as β diversity, the OTU table was rarefied at 6760 sequences per sample and kept for further analysis a total of 2045 samples and 115. 5 millions of reads. Rarefaction is used to overcome cases in which read counts are not similar in numbers between samples. The summarise taxa table was used to classify taxa from the Domain to the Species level.
4%, respectively. Of the 34 patients with CD, the median duration of the disease at sampling was 6. 5 years. For four patients, the diagnosis of the disease was made the same year as the sampling, and the algorithm was able to detect three of them (75%). We validated our method with several unpublished and published data. To evaluate the sensitivity of the markers, we analysed a cohort of 54 patients with CD recruited at the University Hospital Leuven (Belgian CD cohort). Microbial DNA extraction, 16S rRNA gene amplification and sequencing and data analysis were performed in our laboratory in Spain. We generated about 5. 2 million high-quality sequence reads for the 187 samples. We applied our algorithm to the whole cohort and identified an overall sensitivity of 81. 8% of the samples as being CD (true positive) (table 2).
The plot was performed using an R script on relative abundance of the eight bacterial genera. The gradient of colours for the bars corresponds to white=absent, clear blue=low abundance and dark=high abundance. (C) Unweighted UniFrac Principal Coordinate Analysis representation of the various groups of subjects: HC=unrelated healthy controls, CD, Crohn's disease, Significant differences were found between CD and HC, UC, IBS and anorexia (NPMANOVA test, p<0. 001). NPMANOVA, non-parametric multivariate analysis of variance. developed postoperative recurrence (with a Rutgeerts score of i3 and i4, n=28) harboured a higher relative abundance of Streptococcus ( p=0. 002; FDR=0. 17) than those who remained in remission (with a Rutgeerts score of i0 and i1, n=26). This result suggests that the presence of Streptococcus in stool samples before surgery is a predictive marker of future recurrence.
To evaluate the specificity of the markers to detect CD versus UC, we analysed a cohort of 41 patients with UC enrolled at the University Hospital Vall d'Hebron (Spanish UC cohort). The study was part of a European project (MetaHIT; http://www. metahit. eu) and included patients with UC in long-term remission. Clinical information is shown in table 1. We extracted and sequenced the faecal microbiome at baseline (ie, collected before any intervention), generating 1. 5 million sequence reads and tested our algorithm on this dataset. We obtained a specificity of 95. 1% for the detection of CD versus UC (table  2 ). We also tested the specificity of our algorithm on several non-IBD published datasets, namely on IBS, subjects with anorexia and healthy subjects. IBS and CD may present common symptoms, including abdominal pain, cramps, constipation and diarrhoea, and a simple method that distinguishes CD from IBS could also help reducing unnecessary endoscopies.
Therefore, we applied our algorithm to the faecal samples of 125 subjects previously diagnosed with IBS. The sequence data were obtained from a recently published study. 32 Of the 125 patients with IBS, the algorithm identified seven as being CD, thus showing only 5. 6% of false positives and a specificity of 94. 4% (table  2 ). The algorithm was then tested against a set of 1016 faecal samples collected at King's College (London) from a cohort of 977 healthy twin individuals  23  and against 158 faecal samples obtained from HC and patients diagnosed with anorexia. 33 omprising healthy female adult twin pairs from the UK, the former study was originally designed to evaluate how host genetic variation shapes the gut microbiome. Our algorithm detected 75 out of 1016 samples (7. 3% of false positive) as being CD, thus showing a specificity of 92.
7%. The second study was designed to address dysbiosis in patients with anorexia compared with HC and to evaluate the shift in the microbial community after weight gain in patients with anorexia. 34 As shown in this study, anorexia is associated with an alteration of gut microbiome composition. In order to evaluate whether changes occur in the gut community as a result of a condition other than IBD, we tested the algorithm on this anorexic cohort. Our tool detected 9 false positives out of 158 samples, thus showing a specificity of 94. 3%. Figure  4B  illustrates the profile of the 8 microbial markers in the whole dataset of 2045 faecal samples from the various conditions: HC, CD, UC, IBS and anorexia. The results clearly confirmed that CD is characterised by a different abundance profile of the eight markers compared with the other groups, as also shown by a separate clustering based on the unweighted UniFrac PcoA representation (figure  4C ).
Our method showed an accuracy of 64% for the prediction of CD versus UC (60% sensitivity and 68% specificity) and of 77% for the prediction of CD versus HC (60% sensitivity and 94. 8% specificity), respectively. Moreover, we noticed that this dataset does not carry any sequences belonging to the genus Collinsella and a very low abundance of Methanobrevibacter, which in our algorithm allow the differentiation between UC and CD.
Acknowledgements We thank  Julia Goodrich  for sharing information on her published twin study from  UK ,  Santiago Perez-Hoyos  for his advice on statistical analysis and  Andreu Schoenenberger  for his statistical analyses. Contributors CM: Study concept and design; FC, NB, FG and SV: acquisition of samples; AS, DC, GS, KM, EV and HS: acquisition of data; VP, MP and XM: analysis of data; CM: interpretation of data; CM: drafting of the manuscript; FG, HS, SV and CM: critical revision of the manuscript for important intellectual content; VP, MP: statistical analysis; CM: obtained funding). All the authors contributed to manuscript revision.
Sequence Data Processing and Polymorphism Detection Illumina adapter sequences were removed from raw reads and remaining reads were assigned to individual lines using barcode sequences (one specific barcode sequence for each line) with the GBS barcode splitter tool 1. Barcode sequences were trimmed, unassigned reads were removed and remaining reads were trimmed to 90 bp. The STACKS software v. 1. 28 (Catchen et al. , 2013)  was used to identify single nucleotide polymorphisms (SNPs) in the 180 RIL population and their two parental lines. First, reads of low quality (below an average phred score of 10 with a sliding window of 15 bp) and reads with uncalled bases were removed using the process_radtags program. Remaining reads were merged into one single file per individual line using a a Classification using the Phylotype-Sequevar nomenclature  (Fegan and Prior, 2005).
, 2006; Treangen and Salzberg, 2012). Thus, highly polymorphic loci (>3%) were discarded to avoid potentially false positive SNPs. Missing genotyping data were imputed using a classification method called "random forest"  (Breiman, 2001; Poland et al. , 2012)  implemented in the missForest package  (Stekhoven and Bühlmann, 2012). The package also provided the proportion of falsely classified entries (PFC) for imputed data.
Markers were ordered using the default values of JoinMap. After ordering, the mean chi-square contributions of each marker were computed. A high chi-square contribution indicates that the marker does not display a good fit in the linkage group. Thus, markers with mean-square contributions over five were removed and the remaining markers were re-ordered. In order to compare our map with published Solanaceae maps, catalogs of our loci were aligned on the eggplant (Solanum melongena) genome (SME_r2. 5. 1; Hirakawa et al. , 2014) , the tomato (Solanum lycopersicum) genome (SL2. 50; Sato et al. , 2012)  and the potato (Solanum tuberosum) genome (PGSC_DM_v4. 03; Xu et al. , 2011)  using the Blastn program in the NCBI's BLAST+ v 2. 2. 28 software  (Camacho et al. , 2009). The cut-off values for a significant hit were fixed at 1 × 10 -20 for the eggplant genome and 1 × 10 -15 for tomato and potato genomes.
" Genotypic and environmental variance was calculated using ANOVA results to estimate the broad-sense heritability (h 2 )  (Hallauer and Miranda, 1981). The normal distribution of phenotype values was tested with Shapiro-Wilk tests (normality was accepted for P-values > 0. 05). Phenotypic data from assays with strains GMI1000, PSS366, and CMR134 were analyzed separately. They were also combined, insofar as the strain effect in the ANOVA model was not found to be significant by  Lebeau et al. (2013). For the purposes of consistency with previous work, data from strains GMI1000, PSS366, and CMR134 were considered together and named "grouping, " whereas data from individual strains analyzed across two seasons were named "combined. " According to their W and CI values, parental lines and progeny were attributed to the six phenotypic classes defined in  Lebeau et al.
(2010) : 1 = highly resistant, 2 = moderately resistant, 3. 1 = partially resistant, 3. 2 = latent infection, 4 = moderately susceptible, 5 = highly susceptible. QTL mapping was performed for Waudpc variable using the R/qtl package version 1. 39  (Broman et al. , 2003). R/qtl considers a RIL population as fixed (without heterozygote genotypes) and treats the heterozygotes as missing data. An initial scan was performed using Simple Interval Mapping (SIM)  (Lander and Botstein, 1989)  with a 1 cM step and a non-parametric model. All variables with a distribution close to normality were then analyzed with Composite Interval Mapping (CIM)  (Zeng, 1994)  by using a parametric model with the simple regression method  (Haley and Knott, 1992). The number of covariates was fixed according to the number of QTLs detected in SIM analysis.
Searching for Candidate R-Genes Underlying the Detected QTLs The ITAG2. 4 tomato annotated transcriptome (Sato et al. , 2012 2 ) and the annotated transcriptome of the susceptible parental line MM738 (Sarah et al. , 2016 3 ) were used to search typical R-genes belonging to the TNL (TIR-NB-LRR), the CNL (CC-NB-LRR), the RLP (receptor-like proteins) and the RLK classes. Transcripts of the MM738 accession were mapped on the eggplant draft genome using the "Exonerate version 2. 2. 0" software with the est2genome model  (Slater and Birney, 2005). RNA-seq reads of the resistant eggplant AG91-25 were produced, cleaned and assembled following the method published previously  (Sarah et al. , 2016). Cleaned reads of the two parents were mapped onto the MM738 transcriptome using the Burrows-Wheeler Aligner  (Li and Durbin, 2009)  and the variant calling was performed using the Genome Analysis Toolkit, version 3.
3  (McKenna et al. , 2010). Candidate R-genes found in QTL regions were further analyzed by comparing the parental line alleles. We also searched candidate R-genes in transcripts which are uniquely present in AG91-25 line. Sequences of cloned BW-resistance genes RRS1 and RPS4 (genes ID 834562 and 834561;  Deslandes et al. , 1998 Deslandes et al. , , 2003)) , RD19 (gene ID 830064;  Bernoux et al. , 2008) , RE-bw (Accession ID JQ429763. 1; Xi'ou et al. , 2014) , ERECTA (gene ID 817173;  Godiard et al. , 2003)  and wat1 (gene ID 843886;  Denancé et al. , 2013)  were aligned on tomato and eggplant genomes using the BLAST tool included on the SOL genomics network website 4.
Among the 867 SNP markers (The 90 bp sequences wearing the SNPs), 827 markers gave significant hits on the eggplant reference genome, 199 gave significant hits on the tomato reference genome and 249 gave significant hits on the potato reference genome. Among the 33,873 contigs of the eggplant genome, 595 contigs were anchored in our genetic map, of which 454 were aligned on the tomato genome SL2. 40 version. The final dataset, which includes the genetic map and the physical position of SNPs, is provided in Supplementary Table  S3. Overall, the data generated a dense new linkage map of eggplant from an intraspecific cross, with 14 linkage groups that have several regions anchored on the tomato genome and produce 12 chromosomes encoded from E01 to E12 (Figure  1 ).