dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The major disease-associated STs have emerged independently We first analyzed the position of five representative isolates belonging to the major disease-associated STs within a phylogenetic tree containing a total of 32 previously sequenced L. pneumophila genomes. These represented the most distantly related STs in the database at the time of their selection (Supplemental Table  S1 ;  Underwood et al. 2013). This analysis showed that the five major disease-associated STs are found in separate major clades of the species tree with the exception of ST23 and ST62 that share a closer phylogenetic relationship (Fig. 1C ). This suggests that the dominant disease-associated STs have evolved independently from within a genetically diverse species. To investigate the evolution and diversity of each of the five STs, we separately analyzed 71 ST1 (59 ST1 and 12 "ST1-like isolates"), 37 ST23, 72 ST37, 122 ST47, and 35 ST62 isolates (Supplemental Tables  S1, S2 ) by mapping sequence data to a selected reference genome of the same ST (Supplemental Table  S3 ).
Furthermore, 21 isolates recovered between 2003 and 2012 from distant regions of the United Kingdom have no SNPs at all, and a further 17 isolates are just one SNP different from these 21 isolates. No SNPs are homoplasic, and no recombination events were detected using Gubbins, which uses high SNP density as a marker for recombined regions. This is an expected result given the sparse distribution of SNPs, as visualized using the SynTView program (Fig. 2A ;  Lechat et al. 2011). Recombination is the major driving force of diversity within STs 1, 23, 37, and 62 In contrast, the ST1 isolates were recovered between 1981 and 2011 from 14 countries over four continents (Europe, Asia, North America, and Africa). We included isolates within this data set, here termed "ST1-like isolates," that are nested within, and thus evolved from, ST1 isolates.
The SNP analyses showed that the maximum pairwise SNP differences between isolates are 12,964, 13,776, and 12,842 within the ST23, ST37, and ST62 lineages, respectively (Supplemental Table  S3 ), slightly lower than that observed in the ST1 lineage. However, when we analyzed the origin of these nucleotide variants using Gubbins, we found that 96. 3%-99. 0% of SNPs in STs 1, 23, 37, and 62 had been acquired by recombination (Table  1 ). There was a high level of concordance between these results and those obtained using an alternative recombination detection software, BRATNextGen  (Marttinen et al. 2012) , which uses a hidden Markov model (HMM) to detect SNP patterns in an isolate that are more similar to those from another phylogenetic clade than the isolate's own clade. Overall, >90% of SNPs identified as recombined by Gubbins were confirmed by BRATNextGen to be within horizontally exchanged regions.
The major disease-associated clones emerged very recently The small number of de novo mutations within each of the five major disease-associated STs suggests that these are very recently emerged lineages. To estimate their emergence date, we attempted to date the most recent common ancestor (MRCA) of each lineage using linear regression of root-to-tip distances against time, as well as with a Bayesian coalescent model as implemented in the BEAST software  (Drummond et al. 2012). Only the ST37 lineage showed some temporal signal in terms of SNP accumulation using Path-O-Gen (Supplemental Fig. S2 ). Using a relaxed molecular clock model in BEAST, we estimated that the ST37 clone emerged in about 1979 (95% highest posterior density [HPD] intervals: 1968-1985) (Fig. 3 ), which is 3 yr prior to the earliest ST37 isolate recorded in the SBT database.
Gene content analysis De novo assemblies were generated using an in-house Sanger Institute pipeline that uses Velvet  (Zerbino and Birney 2008) , SSPACE  (Boetzer et al. 2011) , and GapFiller  (Boetzer and Pirovano 2012). Prodigal software was used to predict genes in the assemblies, which were then clustered into orthologous groups using BLAST+ (Blastp) and the micropan R package  (Snipen and Liland 2015). Custom Python scripts were used to identify "accessory" genes present in STs 1, 23, 37, 47, and 62, but not in other STs.
Identification of core genes with high nucleotide similarity in the five STs The core genome of the ST representatives, with the exclusion of the distantly related STs (ST336, ST154, and ST707), was defined using Roary  (Page et al. 2015). For each core gene, a nucleotide alignment was generated using one representative from each of the five STs-Paris/ST1  (Cazalet et al. 2004 ), EUL00011/ST23_3, EUL00132/ST37_69, Lorraine/ST47  (Gomez-Valero et al. 2011 ), H043540106/ST62_2-and excluding all other ST representatives. The nucleotide diversity (π) value, a measurement first described by  Nei and Li (1979) , was calculated for each of these alignments using the R package, "pegas," and custom Python scripts. To test whether each core gene possessed significantly higher nucleotide similarity (or lower diversity) in the five major disease-associated STs than would be expected, these values were compared to those derived from testing all possible combinations of five STs from the set of species representatives, after adjusting for phylogenetic distance.
Data access Raw sequence reads from this study have been submitted to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) under accession numbers ERP002503, ERP003631, and ERP010118. The assembled genomes used as references for STs 23 (EUL00011/ ST23_3), 37 (EUL00132/ST37_69), and 62 (H043540106/ST62_2) have also been submitted to ENA under accession numbers FJBI01000001-FJBI01000031, FJFB01000001-FJFB01000024, and FJLN01000001-FJLN01000039, respectively. The scripts used for the diversity analyses are in the Supplemental Material and are available at https://github. com/sophiadavid1/diversity_analysis.
(B) The percentage of clinical and environmental isolates from each of the five STs from the total number of clinical and environmental isolates submitted to the SBT database. Data are based on a total of 6116 epidemiologically unrelated clinical and 2826 unrelated environmental isolates (i. e. , including only one isolate from clusters/outbreaks) submitted to the database. (C) Maximum likelihood phylogeny, generated by mapping sequence reads to the Corby reference genome, of representatives from each of the five dominant disease-associated STs together with an additional 27 isolates that are representative of the species diversity. The size of the alignment was 3,576,470 bp. Generally, the five STs are found in separate major clades of the species except for ST23 and ST62 that share a closer phylogenetic relationship.
Acknowledgments Work in the C. B. laboratory is financed by the  Institut Pasteur, the Institut Carnot-Pasteur MI , the  French Region Ile de France (DIM Malinf) , the  Agence Nationale de la Recherche (ANR)  Grant No. ANR-10-LABX-62 - IBEID , the  ANR-10-PATH-004  project, in the frame of  ERA-Net PathoGenoMics , and the  Fondation pour la Recherche Médicale (FRM)  Grant No. DEQ20120323697. We thank  M. Tichit  and  M. Hunt  for their technical support. The  Plate-forme Génomique is a member of "France Génomique " consortium ( ANR10-INBS-09-08 ). Work at the Sanger Institute is funded by The  Wellcome Trust  Grant No. 098051. Author contributions: S. D. , C. R. , and J. P. , C. B. , contributed equally to this work. M. M. , C. G. , S. J. , and T. G. H. contributed to sample collection and epidemiological analyses; M.
They provide quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets  (Cauli et al. , 1997; Karube et al. , 2004; Ma, 2006; Helmstaedter et al. , 2008; Karagiannis et al. , 2009; McGarry et al. , 2010; DeFelipe et al. , 2013). However, more robust classification methods are needed for increasingly complex and larger datasets. As an example, traditional cluster analysis using Ward's method has been effective, but has drawbacks that need to be overcome using more constrained algorithms. More recent methodologies such as affinity propagation  (Santana et al. , 2013 ) outperformed Ward's method but on a limited number of classes with a set of interneurons belonging to four subtypes. One important aspect of classifiers is the assessment of the algorithms, crucial for the robustness  (Rosenberg and Hirschberg, 2007) , particularly for unsupervised clustering.
Data Preprocessing The datasets were preprocessed (Supplementary Figure  S1 ) in order to deal with missing values, often encoded as blanks, NaNs or other placeholders. All the missing values were replaced using the mean value of the processed feature for a given class. Categorical features such as morphology types were encoded transforming each categorical feature with m possible values into m binary features. The algorithms were implemented using different normalization methods depending on the algorithm used. They encompass for scaling feature values to lie between 0 and 1 in order to include robustness to very small standard deviations of features and preserving zero entries in sparse data, normalizing the data using the l2 norm [-1,1] and standardizing the data along any axis.
Hardware and Software The algorithms were implemented in Python 2. 7 3  using the Scikit-learn 4  open source python library. Scikit-learn is an open source tool for data mining and data analysis built on NumPy, a package for scientific computing with Python 5  and Scipy, an open source software package for mathematics, science and engineering 6. Morphological features 7  were extracted for each neuron using L-Measure tool  (Scorcioni et al. , 2008)  allowing extracting quantitative morphological measurements from neuronal reconstructions. The pipeline described above, featuring extraction with l-measure and classification algorithms, was implemented and tested on Power 8 from IBM (S822LC+GPU (8335-GTA), 3. 8 GHz, RHEL 7. 2). The code of the pipeline is avalaible in GitHub at https://github.
Classification Assessment For supervised algorithm assessment, accuracy statistics have been computed for each algorithm: accuracy = corrected labels total samples. (1) The accuracy ranges from 0 to 1, with 1 being optimal. In order to measure prediction performance, also known as model validation, we computed a 10 times cross validation test using a randomly chosen subset of 30% of the data set and calculated the mean accuracy. This gives a more accurate indication on how well the model is performing. Supervised algorithms were tested on neurons gathered by layers and m-types in young and adult population, by layers and m-types in young population, by m-types in young and adult population, by m-types in young population, and by layers of Pyramidal Cells in young population. We also performed the tests by varying the percentage of train to test the ratio of samples from 1 to 80%.
We provided the respective standard deviations. We included in the python code not only the accuracy code which is shown in this study but also the recall score, the precision score and the F-measure scores. We also built miss-classification matrices for the algorithm providing the best accuracy for each of the categories with the true value and predicted value with the associated percentage of accuracy. In order to assess clustering algorithm, the V-measure score was used  (Rosenberg and Hirschberg, 2007). The V-measure is actually equivalent to the normalized mutual information (NMI) normalized by the sum of the label entropies  (Vinh et al. , 2009; Becker, 2011). It is a conditional entropy-based external cluster measure, providing an elegant solution to many problems that affects previously defined cluster evaluation measures.
On the other hand, neuron classification is an example of data increase rendering classification efforts harder rather than easier  (DeFelipe et al. , 2013). Nowadays, different investigators use their own tools and assign different names for classification of the same neuron rendering classification processes difficult, despite existing initiatives for nomenclature consensus  (DeFelipe et al. , 2013). Recently, quantitative methods using supervised and unsupervised classifiers have become standard for classification of neurons based on morphological, physiological, or molecular characteristics. Unsupervised classifications using cluster analysis, notably based on morphological features, provide quantitative and unbiased identification of distinct neuronal subtypes when applied to selected datasets.
, 2003). Neuronal complexity makes their study challenging as illustrated by their classification and nomenclature  (Bota and Swanson, 2007; DeFelipe et al. , 2013) , still subject to intense debate. While this study deals with morphologies obtainable in optic microscope it would be useful to know how the electron microscopic features can also be exploited. Integrating and analyzing key datasets, models and insights into the fabric of current knowledge will help the neuroscience community to face these challenges  (Markram, 2012 (Markram, , 2013;; Kandel et al. , 2013). Gathering multimodal data is a way of improving statistical models and can provide useful insights by comparing data on a large scale, including cross-species comparisons and cross datasets from different layers of the brain including electrophysiology (Menendez de la  Prida et al.
The identified five missense mutations are private, while the deletion c. 886(-10_-31)del was found in 5 out of the 8 families. The mutation c. 886(-10_-31)del accounts indeed for about 37. 5% (6/16) of the total alleles. Since this mutation is located in the upstream vicinity of the 3′ -splice site, it was predicted to break the putative branch point sequence causing a negative effect on the recognition of the acceptor splicing site of intron 7 by the following softwares: BDGP (the score decreases from 0. 88 to 0. 60 when passing from the wt to the mutant allele), - - - - - - - - Aminoaciduria - - - - - - - - Table  1. Clinical characteristics of seven probands with familial renal glucosuria. a FPG, fasting plasma glucose; b 2hPBG, 2-hours postprandial blood glucose; c eGFR, calculated by CKD-EPI formula; d FUG, Fasting urine glucose; e PUG, postprandial urine glucose NetGene2 (confidence decreases from 0.
The PCR amplification reaction was performed as follows: in 50 μ L volume, 2 μ l of cDNA, 5 μ l of Expand High Fidelity buffer 3 (Roche, Mannheim, Germany), 1 μ M of each primer, 0. 8 μ M dNTPs, and 2. 6 U Expand High Fidelity enzyme mix (Roche, Mannheim, Germany) in a 9700 (Applied Biosystem, FosterCity, CA, USA) thermal cycler. Thermal conditions were 30 cycles of 95 °C for 30 seconds, 58 °C for 30 seconds, and 68 °C for 1 minute, preceded by 2 minutes at 95 °C, and followed by a final elongation step at 68 °C for 10 minutes. The PCR products were separated by electrophoresis on a 3% agarose gel and each band signal was quantified by Quantity One software (Bio-Rad, Richmond, CA). All transcripts were analyzed by sequencing. RNA analysis. cDNA was reverse transcribed from total RNA extracted from peripheral blood leucocytes.
Brassica sprouts contain abundant phytochemicals, especially glucosinolates (GSs). Various methods have been used to enhance GS content in sprouts. However, the molecular basis of GS metabolism in sprouts remains an open question. Here we employed RNA-seq analysis to compare the transcriptomes of high-GS (JL-08) and low-GS (JL-09) Brassica alboglabra sprouts. Paired-end Illumina RNA-seq reads were generated and mapped to the Brassica oleracea reference genome. The differentially expressed genes were analyzed between JL-08 and JL-09. Among these, 1477 genes were up-regulated and 1239 down-regulated in JL-09 compared with JL-08. Enrichment analysis of these differentially expressed genes showed that the GS biosynthesis had the smallest enrichment factor and the highest Q-value of all metabolic pathways in Kyoto Encyclopedia of Genes and Genomes database, indicating the main metabolic difference between JL-08 and JL-09 is the GS biosynthetic pathway.
RNA-Seq Reads Mapping and Transcript Assembly After removing those with only adaptor and unknown nucleotides larger than 5%, or those that were of low quality, the clean reads were filtered from the raw reads. Cleaned RNA-seq reads were then mapped to the reference genome http://plants. ensembl. org/Brassica_oleracea/Info/Index using Bowtie (http:// bowtie-bio. sourceforge. net/index. shtml) and TopHat2 (http:// ccb. jhu. edu/software/tophat/index. shtml; Kim et al. , 2013). Then the SAM (Sequence Alignment Map; http://samtools. sourceforge. net/; Li et al. , 2009)  files were generated by TopHat2 and subsequently transcripts were assembled by Cufflinks (http://cufflinks. cbcb. umd. edu/; Trapnell et al. , 2010). Fragments per Kilobase of exon per Million Fragments (FPKM) was used to measure Cuffdiff to describe transcript abundance.
Expression Annotation Gene function was annotated based on the following databases: Nr (NCBI non-redundant protein sequences), Pfam (Protein family), COG (Clusters of Orthologous Groups of proteins), Swissprot (A manually annotated and reviewed protein sequence database), KEGG (Kyoto Encyclopedia of Genes and Genomes database), and GO (Gene Ontology). The SOAP aligner (http:// soap. genomics. org. cn/soapaligner. html) was used to evaluate the coverage depth of reads. Differential expression analysis of two varieties was performed using the ratio of the FPKM values. Reads abundance with a P < 0. 05 were assigned as differentially expressed based on False Discovery Rate (FDR) control. The unique reads with the value of logarithms of radio ≥ 1 and FDR < 0. 01 were assigned as differentially expressed genes (DEGs).
Finally, in order to better understand biological pathways in B. alboglabra, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG;  Kanehisa et al. , 2008)  database to categorize gene functions with an emphasis on biological pathways. A total of 7563 unigenes were assigned to 105 pathways. The pathways with the largest numbers of unigenes were "plant hormone  signal transduction" (ko04075, 509 unigenes, 6. 73%), followed by "ribosome" (ko03010, 502, 6. 63%), "oxidative phosphorylation" (ko00190, 332, 4. 39%), "plant-pathogen interaction" (ko04626, 320, 4. 23%), "spliceosome" (ko03040, 319, 4. 21%), "protein processing in endoplasmic reticulum" (ko04141, 288, 3. 80%), "RNA transport" (ko03013, 280, 3. 7%), "purine metabolism" (ko00230, 251, 3. 32%), "starch and sucrose metabolism" (ko00500, 236, 3.
, 2001). In the Col-0 line, AOP comprises of two tandem genes, AOP2 and AOP3. However, no AOP expression was detected in Chinese kale sprouts. In Arabidopsis, PEN2, TGG1, and TGG2 are three myrosinases genes involved in degradation of GS. Among these, PEN2 functions to cleave indolic GSs. TGG1 and TGG2 have long been known as important myrosinases  (Gao et al. , 2014). Aliphatic GSs cannot be degraded in tgg1tgg2 double mutant while indolic GSs can be reduced slightly, indicating that TGG1 and TGG2 mainly degrade aliphatic GSs and have only slight effects on indolic ones. In the sequenced tissues, five orthologs of myrosinase genes (Bo8g039420, Bo00934s010, Bo2g155810, Bo9g022660, and Bo14804s010) were identified based on the NCBI database (Table  3 ). Only the expression of Bo8g039420 was decreased in JL-09 compared with that in JL-08.
ACKNOWLEDGMENTS This work was supported by the  National Natural Science Foundation of China  (No. 31401859 ),  Outstanding Youth Training Program in Colleges and Universities of Fujian Province  ( K8015060A ),  Fund for Young and Middle-aged Teachers in Science and Technology Research (Science and Technology Class A)  ( KXML2006 ),  Young and Middle-aged Teachers in Science and Technology Research in Fujian Province  ( K8014012A ), Horticulture Postdoctoral Funding from  Fujian Agriculture and Forestry University  (No. 132300155 ), and  Fujian Minjiang Scholars Program  ( MJJZ13-003 ).
Analyses based on murine immunological databases may not apply to other species, including bovines. However, a combination of immunoproteomics, immunoinformatics, conventional gene expression, and subsequent immunological confirmation provides an effective method for comprehensively characterizing antigenic proteins  [28]. This study was conducted to assemble a global antigenic profile for M. bovis using immunoproteomics and immunoinformatics and to identify promising candidate proteins in M. bovis using gene expression analyses and other serological methods. Among the eight identified M. bovis antigens expressed in E. coli, MbovP579 was confirmed as a conserved, sensitive, and specific antigen in both field isolates and the attenuated vaccine strain M. bovis-150. An indirect ELISA (iELISA) based on recombinant MbovP579 (rMbovP579) was able to effectively detect the M.
Analysis of whole cell proteins (WCPs) from M. bovis HB0801 Immunoproteomics revealed antigenicity of both membrane-associated and cytoplasmic proteins in the WCPs of M. bovis HB0801 cells. Analysis of M. bovis WCPs using 2-DE identified 639 well-resolved spots corresponding to 84% of the total number of coding sequences identified in the HB0801 genome (Figure  1A ). Among those, 32 spots reacted with pooled sera from experimentally infected calves 35 days after infection (Figure  1B ). No proteins reacted with the negative control sera. Mass spectrometry (MS raw dataset available at PRIDE repository-PXD003479) confirmed the presence of proteins in 21 spots (Figures  1A  and 1B ), corresponding to 16 different M. bovis proteins. Single spots identified 11 proteins, while five proteins were characterized by 2, 4, and 8 isoforms, suggesting post-translational modifications of these proteins (Table  1 ).
Antigens identified via membrane fraction analysis of HB0801 The membrane proteome of M. bovis was first fractionated with Triton-X-114 (TX-114) and then subjected to 2-DE to better resolve basic and liposoluble transmembrane molecules and MAPs. Most of the TX-114 soluble fractions were resolved at the basic end of a 17 cm immobilized pH gradient (IPG) strip [pH 3-10 nonlinear (NL)] in the 2-DE map (Figure  2A ). After Western blot analysis, protein spots that reacted strongly with antisera from experimentally infected calves 35 days after infection were excised from the 2-DE gel and subjected to MS. MS analysis (MS/MS raw dataset is available in the PRIDE repository -PXD003479) recognized the presence of proteins in 17 spots (Figure  2B ), corresponding to 29 different M. bovis proteins. Sera from the negative control animals did not react with any of the M.
bovis proteins. Eighteen proteins were recognized as single spots, 9 proteins were recognized in two spots, the variable surface lipoprotein [coding sequence (CDs) Mbov_0796] was identified in 4 isoforms, and cobalt/nickel ABC transporter ATP-binding protein (CDs Mbov_0594) was recognized in 3 spots (Table  2 ). Five of the 29 antigenic proteins were located in the cytoplasm, 10 were predicted to be MAPs, and the remaining 14 lipoproteins had unknown functions according to the PSORTb database (Table  2 ).
Interestingly, MbovP579 secretion was also predicted by a nonclassical secretory pathway with a high SecP score of 0. 84. In addition, the predicted template structure for MbovP579 according to ElliPro was an ADPribosyltransferase toxin known as Clostridium difficile toxin subunit "a" (CDTa) (PDB ID-2WN4) (Figure S-1A). CDTa is believed to have catalytic activity. Alignment of the MbovP579 and CDTa amino acid sequences revealed similarity of approximately 31% for amino acids residues 591-674 at the MbovP579 C-terminus (Figure S-1B).