dataesr/scientific-paragraphs-categorization huggingface dataset:
Images were acquired on an epifluorescence Nikon microscope (Eclipse 90i Upright) with a Â 100 objective. Deconvolution was performed on stacks of images taken with a 100-nm z section, using the 3D deconvolution module of META-MORPH and the fast iterative constrained point spread function-based algorithm  47. Quantifications were performed using Image J. Phalloı ¨din staining was used as a mask for the cell. For endosomal and CD71 staining, foci were detected by applying a threshold and object segmentation by maxima. Analysis of lysosomal pH. AlexaFluor-488 OVA was coupled to pHRhodo Red SE, which fluoresces below pH ¼ 6. 5 and is quantitatively sensitive to acidic pH. Coupling was performed following the manufacturer's manual (Life Technologies). Overall, 10 6 DCs were incubated with 0. 2 mg of pHRhodo Red dye coupled to AF488-OVA in 1 ml of the medium for 1 h at 37 °C.
Acknowledgements We thank  S. Amigorena ,  N. Manel ,  Y. Bellaiche ,  S. Miserey-Lenkei  and  D. Obino  for critical reading of the manuscript. We acknowledge the  PICT IBiSA Nikon imaging centre  ( CNRS UMR144 ,  Institut Curie) , the  Curie animal facility ,  O. Malbec  and  F. X. Gobert  for technical support,  A. Chaillet  for his help with image analysis,  H. Ploegh  and  O. Bakke  for providing I-A b b-GFP mice and human Ii constructs, respectively. M. C. and M. L. H. were supported by fellowships from the  Fondation pour la Recherche Medicale  and the  Association pour la Recherche contre le Cancer , respectively. This work was funded by grants from the  City of Paris  and the  European Research Council  to  A. -M. L. -D. ( Strapacemi 243103 ), the  Association Nationale pour la Recherche  ( ANR-09-PIRI-0027-PCVI ) and the  InnaBiosantefoundation (Micemico)  to A.
-M. L. -D. , M. P. and R. V. , and the  DCBIOL Labex  from the  French Government  ( ANR-10-IDEX-0001-02-PSL*  and  ANR-11-LABX-0043 ). The super-resolution SIM microscope was funded through an  ERC   Advanced Investigator Grant  ( 250367 ) to  Edith Heard  ( CNRS UMR3215/Inserm U934 ,  Institut Curie ).
DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www. genomicscape. com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells.
genomicscape. com), which allows to quickly analyze the molecular changes during a biological process such as cell differentiation or disease progression. As an illustration, we report here, the molecular portrait of human B cell differentiation using GenomicScape. The knowledge of the differentiation of B cells into plasma cells (PCs) has greatly improved, mostly using animal models  [6] , but this process is less known in humans because of the difficulty to access lymphoid organs and to mimic in vitro this in-vivo process involving multistep cell trafficking, cell interactions and activations. In this study, we took advantage of our previous works about germinal center (GC) B cells and plasma cells  [3, 5, 7, 8]  with the generation of publicly available transcriptome data of human naïve B cells (NBCs), centroblasts (CBs), centrocytes (CCs), memory B cells (MBCs), preplasmablasts (prePBs), plasmablasts (PBs), early plasma cells (EPCs) and bone marrow plasma cells (BMPCs).
Supervised analysis of the gene expression profiling of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells, and mature plasma cells Publicly available gene expression data of the 8 B cell to plasma cell populations were GCRMA normalized and grouped in a "Human B cells to plasma cells GCRMA" GenomicScape file available at http://www. genomicscape. com/microarray/browsedata. php?acc=GS-DT-2. Running a SAM analysis using GenomicScape SAM tool (see S1 File) and starting from 10000 unique genes with the highest standard deviation (SD), 9303 unique genes were differentially expressed between the 8 B cell to plasma cell populations (SAM multiclass analysis, unpaired Wilcoxon statistics, fold change 2, 300 permutations, FDR = 0%). When different probe sets interrogated a same gene, the probe set with the highest standard deviation (SD) was selected.
They also overexpressed genes coding for cell communication signals in agreement with their close requirement of bone marrow environment to survive. This "Naïve B cell to Plasma cell" Atlas allows the user to quickly visualize online whether a given protein or pathway could be involved in the process of B cell to plasma cell differentiation. It should be useful for further understanding this differentiation, and in particular the abnormalities of these processes in B and plasma cell neoplasias. Of note, recent studies have reported gene expression profiling of human plasma cells purified from the bone marrow or spleen or of in-vitro generated plasma cells using Affymetrix or Illumina microarray platforms  [10, 11]. These publicly-available microarray data are available from GenomicScape, which can mine microarray data from various platforms.
The current analysis can be easily extended to these other studies. In particular, the current genes differentially expressed between B and PC populations could efficiently classify the B and PC plasma cell populations reported in the study by Cocco et al. [10]  using Illumina microarrays (see S1 File). In summary, GenomicScape is a flexible and free platform to easily extract biological information from genome-wide gene expression data. Additional tools for microarray data mining can be implemented, using the same aim: speed and easiness. Besides the current atlas of the transcriptome of the B cell to plasma differentiation, GenomicScape should be useful to stimulate the use of publicly available microarray data and lead to further understanding of normal and abnormal biological processes.
Overview of GenomicScape GenomicScape (http://www. genomicscape. com) allows users to easily visualize and analyze publicly available gene expression data, and to explore the landscape of gene annotation resources for genes of interest. The goal of this ongoing effort is to make available public microarray data and an easy-to-use web tool to quickly analyze them. To date, 50 publicly available gene/ncRNA expression datasets from over 5000 microarray experiments performed on different cell types and obtained with Affymetrix, Illumina or Agilent platforms have been uploaded into GenomicScape, which allows the user to readily extract information from these data. Standard tools to mine microarray data have been implemented on GenomicScape and a user guide is provided in S1 File. Briefly, the "Expression/Coexpression Report" tool allows the user to search for a gene using name or other identifiers, to visualize its expression profile in cell populations/tissues and to find genes whose expression in various samples are correlated to that of a given gene.
GenomicScape software architecture GenomicScape uses a MySQL database (version 5. 1) for storing microarray data and gene annotations from usual databases (predominantly NCBI and Ensembl). The application core functionality, was developed with PHP version 5. 3 utilizing R (http://www. r-project. org/) and Bioconductor (http://www. bioconductor. org/) scripts for data-mining. The web interface is implemented with PHPBOOST web framework and uses jQWidgets javascript library.
0 plus array (Affymetrix, Santa Clara, CA) in the Microarray platform of IRB (http://irb. montp. inserm. fr/en/index. php?page=Plateau&IdEquipe=6) using the same methodologies for probe amplification and microarray hybridization avoiding batch effect. GEP are publicly available from GEO database (http://www. ncbi. nlm. nih. gov/geo/, CBs and CCs: GSE15271  [4] ) or from ArrayExpress database (http://www. ebi. ac. uk/arrayexpress/, BMPCs: E-MEXP-2360  [12] , NBCs, MBCs, PrePBs, PBs and EPCs: E-MTAB-1771, E-MEXP-2360 and E-MEXP-3034  [3, 5].
Data analysis The publicly available data of these 8 B cell to plasma cell populations (38 samples) were normalized using GCRMA algorithm. They were grouped in a "Human B cells to plasma cells GCRMA" file under GenomicScape accession number GS-DT-2 available at http://www. genomicscape. com/microarray/browsedata. php?acc=GS-DT-2. Genes whose expression is differentially expressed between the 8 B cell to plasma cell populations were identified using Gen-omicScape SAM tool (http://genomicscape. com/microarray/webSam. php). The list of differentially expressed genes was used to perform a PCA analysis (http://genomicscape. com/ microarray/pca. php). The molecular pathways, encoded by the genes differentially expressed between the naïve B cell to mature plasma cell subpopulations, were pointed out using reactome FI (Functional Interaction) cytoscape plugin (http://wiki.
However, in light of the rapid advancement of sequences now available across vertebrates and beyond, an up-to-date analysis of the evolution of this gene family is now required. Moreover, the complexity of understanding the cooperative ADAMTS biology in rodent models of development  [8]  means additional vertebrate models are required to further elucidate ADAMTS biology during embryogenesis. Zebrafish has emerged as a popular model to study vertebrate embryonic development with well conserved gastrulation, somitogenesis and organogenesis  [15] , which generates a body plan showing strong parallels to rodents and human  [16]. In this current study, we undertook a detailed analysis of the evolutionary history of ADAMTS through the three major rounds of whole genome duplications (WGD) across vertebrate species using up-to-date genomic DNA databases supplemented with additional sequences from other repositories.
Results The initial aim of this study was to understand the evolution of ADAMTS genes through the 3 whole genome duplication (WGD) events in vertebrates  [17] [18] [19] [20]. ADAMTS proteins share a common structure with distinct modules, including a propeptide region, a metallopeptidase M12B domain, a disintegrin-like and a thrombospondin (TSP) type-1 domain followed by a spacer (Figure  1 ). With the exception of ADAMTS4, which lacks additional C-terminal domains, all other members contain from 1 up to 14 additional TSP type-1 domains. These can form the Cterminus for ADAMTS1, -5, -8 and -15 or that can be in addition to other domains, those being PLAC for 11 ADAMTS (ADAMTS2, -3, -6, -7, -10, -12, -14, -16, -17, -18 and -19), a GON-1 domain for ADAMTS9 and -20, or a CUB domain for ADAMTS13 (Figure  1 ).
The adamts family is dynamically expressed throughout zebrafish embryonic development Based on the detailed analysis of the evolutionary relationship of the ADAMTS family across vertebrate species, it became of interest to scope the potential of zebrafish as a model to study roles of ADAMTS enzymes in development. Therefore, Q-RT-PCR was performed on RNA extracted from staged zebrafish embryos to determine the expression profile of the adamts family, that revealed dynamic and overlapping expression patterns (Figure  6 ). Notably, with the exception of adamts17, all members of the family detected during embryogenesis were maternally derived with amplicons representing mRNA expression found at the one cell-stage. With the consideration of maternal mRNA instability in mind however, it is important to note that full-length maternally derived encoding mRNA transcripts for each adamts gene detected were not confirmed at this stage.
In general, phylogenetic trees demonstrated very similar patterns of branching and branch lengths across ADAMTS genes. The first exceptions are teleost ADAMTS2a and ADAMTS2b that both have very long branches compared to the tetrapod orthologue ADAMTS2 indicating the former two are rapidly evolving possibly gaining divergent functions, a pattern of sub-functionalization. The second is ADAMTS20, which has a very distinctive branch in the primates, suggesting specific divergence. We previously reported the remarkable conservation of certain ADAMTS members (ADAMTS5, Genbank JF778846. 1; ADAMTS9, Genbank JF778848. 1; ADAMTS15a, JF778847. 1) at the primary amino acid level  [23]. Ancestral ADAMTS gene function has been previously reported in early evolution with C. elegans gon-1 shown to mediate distal tip migration during gonadal morphogenesis  [6].
Moreover, ADAMTS knockout mice show profound developmental defects demonstrating their unquestionable importance during vertebrate embryonic development. ADAMTS1 knockout mice have reduced rates of ovulation and impaired fertility as well as abnormally remodelled ovarian follicular basement membranes  [24] [25] [26]. We found a low abundance of adamts1 transcript in oocytes but did not investigate its abundance in follicular somatic cells in this current study, although its presence has been previously reported in teleost fish follicular somatic cells  [27]. Therefore it may have similar roles in the ovulatory process across vertebrate species. During limb development, reduced interdigital tissue resorption leading to soft tissue syndactyly (webbed feet) occurred in combinatorial ADAMTS5, ADAMTS9 and ADAMTS20 knockout mice and mesodermal Prx-1 Cre conditionally deleted ADAMTS9 (which is nonredundant in this developmental context)  [28, 29].
Bioinformatics Protein sequences of ADAMTS family members across Chordata were retrieved from 3 databases (DB): Ensembl (v70), Uniprot (SwissProt + TrEMBL) and nr-prot (NCBI). Sequences from representative species were aligned using MAFFT  [42]. Using ad hoc scripts and manual selection, multiple sequences in each species were trimmed, retaining the best-annotated sequences in those alignments. To root the phylogenies, when available and pending their ability to be aligned, the sequences from Ciona intestinalis, Ciona savignyi, Branchiostoma floridae, and from outside Phylum Chordata (e. g. Strongylocentrotus purpuratus, Saccoglossus kowaleskii, Crassostrea gigas or insects) were used. Phylogenetic analyses were achieved using the methods of Neighbor-Joining (BioNJ)  [43]  implemented in SeaView  [44] , or run under a WAG model with estimated gamma distribution parameter for the Maximum Likelihood (PhyML)  [45] , both of them with 1000 bootstrap iterations.
In addition to our phylogenetic analyses, the orthologous relationship to each of these genes in the vertebrates was verified using Genomicus  [45, 46] , which allows the visualization of orthology by synteny from the genomic information provided by the Ensembl database. BioNJ values (available upon request) corroborated those obtained by PhyML but were not added to keep clarity in the figures. We also looked for adamts proteins beyond Chordata and to discriminate them from other metalloproteinases such as those encoding ADAM genes, common protein sequences of each ADAMTS encompassing the metalloproteinase, the disintegrin-like, the first TSP type-1 and the cysteine-rich domains were selected. Each of these sequences was used in BLASTP (blosum45 matrix used) searching of the nr protein database at NCBI restricted to the non-vertebrate Opisthokonta species.
Statistics Q-RT-PCR results were analysed by one-way ANOVA with Tukey's post hoc analysis using the SPSS or Graph-Pad statistical software packages. P < 0. 05 was considered statistically significant. P values for comparative levels of gene expression across adult organs are available in Additional file 5. were analyzed using maximum likelihood (ML). Branch values show the bootstrap replicates. This large phylogeny is available as a Newick formatted tree that can be opened with any software displaying phylogenetic tree (e. g. Seaview  [41] ). The collapsed version of this Newick format tree is shown in Figure  4.
Acknowledgements This work was supported by a grant from the  Australian Financial Markets Foundation for Children  ( 2010-162  to DRM and ACW). CMD and ADS were supported by  Australian Postgraduate Awards. MJB was supported by a  Deakin University   Postgraduate Scholarship. CK was supported by a  Deakin University Molecular & Medical Research SRC ,  Postgraduate Scholarship and a Muscular Dystrophy Australia Postgraduate Scholarship. The authors would like to acknowledge the  Deakin University Animal House  staff for their efforts in animal husbandry. There are no financial conflicts of interest.
Validation and discussion In this section, we aim at validating the improved shear-lag model as extended in this work to predict impulsive and indicial response for a double lap bounded joint. First, we define the following constants: φ Sato ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi eG 0 =2Ee 0 p and φ ¼ φ Sato =θ, which stand for the absolute value of the indicial response after infinite time as obtained by simple shear-lag  [9]  and the improved shearlag (current work), respectively. We also define the constant Ψ ¼ G 0 c=Ee 0 θ 2. These three constants are used in order to depict dimensionless results. In what follows, the improved shear-lag model is compared to the simple shear-lag model and finite element method. The finite element model is undertaken with Abaqus software.
Ion Torrent PGM Sequencing and alignments Emulsion PCR was done according to the manufacturer's instructions. Ion sphere particles were enriched using the ES module and sequenced with the ion PGM on a 318 v2 chip (Life Technologies, Saint Aubin, France). As the sizes of the library fragments were different and because many fragments generated with the Haloplex library were superior to 200 bp, we used the Ion PGM Sequencing 400 Kit and the Ion PGM Sequencing 200 Kit to sequence the SeqCap EZ library. Libraries were pooled at a concentration of 20 pM. The platform specific pipeline Torrent Suite software v3. 6. 2 was used to perform optimized signal processing and sequence alignment onto the human genome reference hg19.
Coverage analysis For our needs, we developed a specific tool, DeCovA, which is a command line Perl script using bedtools (http://bioinformatics. oxfordjournals. org/content/26/6/841. full. pdf+html) to extract depth from aligned bam files, and R tools to draw corresponding graphs (available upon request to thomas. simonet@chu-lyon. fr). DeCovA first determines the exonic target regions from either a bed file of genomic intervals or a list of genes or transcripts, by looking for overlap with annotation data from a RefSeq file (eg RefGene. txt or EnsGene. txt, from UCSC). Secondly, it runs the coverage Bed tool from the Bedtools suite to extract the depth of sequencing at each base of these regions, for each bam file to be analyzed (provided by a list of files, or directories where to find them).
Finally, these data are used to compute coverage of targeted regions, which are reported, for each gene or transcript and for each sample, as a graph highlighting with a color code the regions not covered at different depth thresholds (Fig. 2A ) and a text file containing the fraction of each exon that is not covered and genomic position of not covered domains (Fig. 2C ). Additionally, a global analysis, for all the targeted intervals taken together, is also outputted, as a bar plot of the fraction of region of interest covered for each depth bin. The script also prints a bed file reporting these uncovered intervals to quickly determine which exons would have to be Sanger-sequenced to achieve full coverage. DeCovA was conceived as a flexible tool: i) different depth thresholds can be set (up to 5), ii) length beyond ends of exons can be changed, either to a set value, or to reach the positions specified in the bed file, iii) the analysis includes or not non-coding transcripts, and iv) a file containing known mutations can be provided: the depth at these mutation positions will be reported and they will be plotted on the graphs.
Our tool can perform a combined analysis of a group of samples, which allows a quick comparison between them. In this case, a unique picture containing the analyses of all samples can be provided, for the graph per gene and the global bar plots, as well as a summary text file listing, for all the genes and transcripts, the samples not covered (Fig. 2B ). In order to allow for the comparison of different enrichment and sequencing methods, the script provides a bar plot of means and standard deviations of coverage from all samples (Fig. 2D ), and more importantly, a graph plotting the number of covered samples at each target position, above the gene scheme, for each depth threshold, which elicits the regions that constantly escape the method (Fig. 3 ).
Mutation identification Variant calling was performed by the Torrent Suite software V3. 6. 2, using germline parameters and low stringency settings. The vcf files obtained were annotated and filtered using the Ion Reporter Software 4. 4 with custom parameters (inclusion of variants located in exons, splice site and UTR regions, not synonymous, with a minimum coverage of 20×, and not reported as common SNP in the USCS website). We also tested the NextGene software (Softgenetics, State College, PA, USA) to realize a new variant calling from BAM files, using personal filter settings: inclusion of variants within coding sequences (± 12 bp) and intronic splice-sites with a minimum coverage of 20×, and discarding of synonymous single-nucleotide variations. Each mutation identified was visualized using Alamut Visual (Integrative Biosoftwares, Rouen, France).
Depth and coverage analysis DeCovA used these alignment data to provide a friendly graphic illustration showing the depth line, per gene included in the design and per sample, and the stretches of DNA not covered at 30×, 50×, or 100 × thresholds, colored over rectangles representing the bed intervals, above schemes of exons and intronic regions of all transcripts (Fig. 2A ). In our experiments, Haloplex data from patient 13 displayed insufficient coverage and was excluded for further analyses. Bar plots of means of coverage (Fig. 2D ) showed a similar coverage for both library-building techniques. For example, 84% ± 5 of the targeted regions were covered at 30× with Haloplex and 80% ± 11 with SeqCap EZ (but note that we performed deduplication for SeqCap EZ data, to avoid artificial increase of depth).
Mutation identification Data were first analyzed on a blind basis, with NextGene and Ion Reporter. We did not consider variants in the 5′-or 3′-UTR (except for the first 30 bp) as well as intronic variants that were not located within the acceptor or donor splice sites. With the filter settings we defined, NextGene analysis generated an average of 159 variants per sample for the Haloplex panel and an average of 82 variants for the SeqCap EZ panel. Detection of single nucleotide variation (SNV) analysis was the most efficient, allowing the identification of 10/15 of expected SNVs with the Haloplex panel and 11/15 with the SeqCap EZ panel (Table  1 ). The large number of potential indels detected by NextGene analysis (average of putative indels per patient = 151 (SD = 33) for Haloplex and 72 (SD = 10) for SeqCap EZ) made it impossible to select true indels from artifacts mimicking indels.