dataesr/scientific-paragraphs-categorization huggingface dataset:
Inbreeding coefficients from SNP and WGS data Inbreeding coefficients were calculated for each of the dogs across 154,230 SNPs from the chip genotyping dataset and 1,510,327 SNPs from the pruned WGS data using the 'heterozygosity' function of PLINK v1. 07  (Purcell et al. , 2007). The within-breed means of the individual dog inbreeding coefficients were used to represent breed-specific inbreeding coefficients for each of the SNP analysis breeds, as well as for the WGS breeds for which more than one dog of a given breed was sequenced (Bernese Mountain Dog, Chow Chow, Flat-coated Retriever, Greyhound, Irish Water Spaniel, Rottweiler, Scottish Terrier, West Highland White Terrier).
Statistical analysis The cor. test function of the Hmisc R package was used to calculate Pearson correlation statistics and significance values between SNP-based inbreeding coefficients, RoH and LnH, WGS-based inbreeding coefficients, RoH and LnH, and pedigree-based inbreeding coefficients. Correlation analyses utilized all breeds shared between each pair of data acquisition methods. Because F-values were calculated per individual within the WGS and SNP chip analyses, breed values were determined by averaging all contributing dogs of the same breed for each genotyping method. As such, a total of 11 breeds were represented in the pedigree data, 80 breeds in the SNP chip data, and 80 breeds in the WGS data. Fifty breeds were common to the WGS and SNP data, nine breeds to the WGS and pedigree data, and 11 breeds between SNP and pedigree data.
Acknowledgements We thank  Cord Drögemüller ,  Vidhya Jagannathan  and  Tosso Leeb  for providing select dog genome sequences. We also thank the many dog owners for contributing DNA samples for research. Thanks to the following groups and individuals for providing the extensive parentage data utilized in the pedigree analysis: Nicholas at http://www. papillonpedigrees. org, Karen Berggren  and the  Portuguese Water Dog Study Group  at http://www. pwdinfo. com/psgdb/, Eric Johnson  and Tollerdata at http://www. toller-l. org/tollerdata, Blair Kelly  at http://www. shakspernorwich. net/pedigreedb/, Amy Raby at http://www. k9data. com,Claire and  Jim Trethewey  at http:// www. alfirin. net/cdl/search. html, Katherine Branson  at http://www. acdpedigree. com, Gary Gulanas  and Bernergarde at http://www.
Data availability Whole-genome sequence data produced for this project has been submitted to Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra) under BioProject PRJNA318762. Accession numbers for all genome sequences used in this study are available in Table  S2. Raw SNP array data is available on Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under BioProjects GSE83160 and GSE90441.
html). The XY co-ordinates of the two poles and of the foci were recorded manually and processed automatically with Excel (Microsoft) software. The travelled distance (given in μm) was estimated over a period of 5 minutes by adding up the absolute values of the distances for all 10 sec interval as described before  [5]. The (x,y) coordinates of the foci at every time point were recorded and the distance travelled in the 10 s interval calculated. For the mobility measurements at the home position, 30 foci were analyzed (15 cells with two foci for markers). To measure the mobility of markers, the distance travelled by various foci was recorded when markers were at home position. In the growth conditions used, at home position, markers of Ori, Right MDs as well as markers from NS regions were segregated in the two halves of the cells.
Acknowledgments We are grateful to  Nancy Kleckner  for the kind gift of hupA-mCherry fusion, to  Stéphane Duigou ,  Olivier Espéli ,  Christophe Possoz ,  Isabelle Vallet-Gely ,  Virginia Lioy  and former members of the  Boccard laboratory  for helpful discussions. We also thank  Matthew Mc Neil  for his valuable comments on the manuscript.
Data analysis Image analysis and base calling were performed using the HiSeq Control Software and Real-Time Analysis component. Demultiplexing was performed using Illumina's sequencing analysis software CASAVA 1. 8. 2. The quality of the data was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequence Analysis Viewer). Reads were mapped to the Apis mellifera genome (Amel_4. 5) using the eland_rna module of CASAVA 1. 8. 2. NCBI annotation file (seq_gene. md. gz; 2012-12-17) was used to generate splice junctions automatically. Reads were also aligned to a set of contaminants, including the ribosomal RNAs, the PhiX genome (Illumina control) and the Illumina adapters. Reads mapping to contaminants were discarded. Gene counting was performed with HTSeq count (union mode).
As the sequencing was strand-specific, the reads were mapped to the opposite strand of the gene. Before any statistical analysis, genes with b15 reads summed across all the analyzed samples were filtered out. Differentially expressed genes were identified using the Bioconductor R package edgeR 2. 6. 2 and the Upper Quartile normalization method  [17]. Genes with adjusted p-value b 5% (according to the Benjamini-Hochberg FDR method  [18] ) were declared differentially expressed. Functional analysis based on GO terms was performed using the online platform DAVID 6. 7  [19]. Overlap tests with previously published brain transcriptomes upon parasitism with Varroa mites and N. ceranae  [10]  were performed using a hypergeometric test.
SK1-induced secretion of TGF-β1 stimulates macrophage polarization To study further on the molecular mechanisms that control macrophage polarization, we analyzed an RNA-seq dataset obtained from a collection of 472 human cutaneous melanoma tissue samples (TCGA). We observed that the expression of the immunosuppressive cytokine TGF-β1 was slightly, albeit significantly, lower in samples displaying a lower SK1 expression (Figure  5A  ; p < 0. 0001), supporting a positive correlation between SK1 and TGF-β1 expression in human melanoma tumors. Moreover, given that SK1 expression has been reported to correlate with the levels of TGF-β1 in different inflammation-based experimental models  [21, 22] , we next explored whether the effects of For all panels, significant differences were evaluated using Student t test.
Quantitative RT-PCR RNA from cells or tumors was extracted (RNeasy kit, Qiagen) according to the manufacturer's protocol and treated with RNase-free DNase (Qiagen). RNA quality was assessed by automated gel electrophoresis (Experion, BioRad). One µg of RNA was reverse transcribed (SuperScript II, Invitrogen) and the cDNA used as a template for qPCR. The reactions were performed in duplicate on the StepOne instrument (Applied Biosystems) using SYBR Green PCR kit and primer assay (QuantiTect, Qiagen). Results were quantified using the StepOne system software. mRNA of 18S and b-actin were analyzed for normalization. All the primers used were listed in Supplementary Table  S1.
Statistical analyses Results are expressed as means ± sem, and group comparisons were performed with an unpaired twotailed Student's t test for comparison of 2 groups, or oneway ANOVA followed by the post hoc Tukey test for comparison of experiments that consisted of ≥ 3 groups. The Mann-Whitney U-test was used to test statistical significance of differences in mean tumor growth between independent groups after treatment. Spearman's rank correlation test was used to determine the correlations of tumor size (Prism 6; GraphPad Software Inc, San Diego, CA). Pearson correlation was performed using GraphPad Prism. A p-value less than 0. 05 was considered statistically significant (*, p < 0. 05; **, p < 0. 01; ***, p < 0. 001).
ACKNOWLEDGMENTS AND FUNDING Financial support by  INSERM, Paul Sabatier University, Ligue Nationale Contre le Cancer  ( Equipe Labellisée 2013 ) and the  French and Lebanese ministries of foreign affairs (MAEDI)  and  Higher Education and Research (MENESR)  ( PHC CEDRE ,  30750VL ) is gratefully acknowledged. C. I. is a recipient of a fellowship from  Ligue Nationale Contre le Cancer. We thank the staff of the animal (Anexplo platform, Toulouse, France) and the flow cytometry (Inserm  UMR1048  and  UMR1037 ,  Toulouse, France)  facilities as well as the team of  Dr. I. Maridonneau-Parini  for technical assistance. We also acknowledge the assistance of  Drs. A. Dupuy  and  J. Bertrand-Michel  from the lipidomic facilities.
This allowed for the direct comparison of the effect of a manual centrifugation and an automated non-centrifugation process step upon hiPSC growth and characteristics by minimising the variability associated with fully manual culture processes. The differences in process steps between these processes are illustrated in Fig. 1. The CompacT SelecT automated cell culture platform (TAP Biosystems, Royston, UK) (Fig. 2 ); which utilises an incubator carousel to store cell culture flasks, multiple peristaltic pumps to dispense cell culture reagents, and a robotic arm to replicate many of the process steps involved in manual cell culture; was utilised in this study. This platform also has an automated cell counter incorporated; which utilises Trypan blue exclusion and automated imaging software to determine viable cell density, viability, and aggregate rate.
To passage these cells, after approximately 7 days and once 80 % confluent, the cells were pre-treated with 10 lM ROCK inhibitor solution for 1 h and an automated precentrifugation passage protocol was performed to dissociate the cells with Accutase TM (StemCell Technologies); agitate any non-dissociated cells; quench with mTeSR1; and obtain cell count, viability, aggregation and cell diameter data. The mother flask containing the dissociated cells was then ''outfeeded'', which refers to the temporary ejection of a flask from the platform and allows the flask to be re-imported and recognised by the CompacT SelecT software. The cell suspension was then centrifuged, the cells were resuspended in fresh mTeSR1, thoroughly mixed, and reintroduced into the mother T175 flask which was imported back into the CompacT SelecT.
A cell count was also performed, 8 9 10 6 cells were isolated, isolated cells were diluted, and the appropriate number of new barcoded Matrigel-coated daughter flask were seeded with 3. 5 9 10 6 cells. For each passage, identical pre-treatment, and noncentrifugation protocols were utilised. Similarly to the centrifugation arm, after the pre-experimental passage, the number of daughter flasks seeded from each mother flask increased to two. Due to a malfunction of the Cedex Automated Cell Counter, non-centrifugation granddaughter flask cell count data could not be collected for the fourth batch. After three passages, identical harvesting and downstream processing steps to those described for the centrifugation arm were utilised.
Replicates of each of the specific stain and isotype control tubes were generated, and all tubes incubated in the dark on ice for 30 min and washed with stain buffer. Control beads were also prepared with the appropriate antibodies. Prior to analysing hiPSC samples on the BD FACSCanto II (BD Biosciences) using the FACSDiva software version 6. 1. 3, unstained negative beads were analysed. A gate was then set around the singlet bead population on the FSC vs SSC plot; and the FITC, PE, and Alexa Fluor 647 stained control beads were analysed to ensure that the positive populations fitted on the FSC and SSC scales, and to calculate compensation. Unstained cells were then analysed, and a gate was set around the main cell population on the FSC vs SSC plot. Next, the isotype controls were analysed, followed by the hiPSC samples.
Statistical analyses Viable cell density, viable cell yield, population doubling time, viability, cell diameter, and aggregate rate data for pre-centrifugation, post-centrifugation and non-centrifugation hiPSCs across all passages was analysed using twoway analysis of variance (ANOVA) multiparameter analyses, through the IBM SPSS statistical software, to determine significant differences. Furthermore, one-way ANOVAs were utilised to assess the significance of differences in viable cell density, viable cell yield, and viability of pre-centrifugation, post-centrifugation and noncentrifugation of hiPSCs within the 2nd passage. One-way ANOVAs were also used to assess the significance of differences between the standard deviations (SD) of the viable cell densities, viable cell yields, and viabilities of pre-centrifugation, post-centrifugation, and non-centrifugation of hiPSCs, in each of the four batches, from the second passage.
Acknowledgments The authors would like to thank  Dr Robert Thomas  ( Loughborough University ) for advising on experimental methods and data interpretation. Finally, the authors would like to acknowledge the contribution of  Dr Forhad Ahmed  ( Loughborough University ) and  Dr Rachel Bayley  ( formerly of Loughborough University ) for providing assistance with the hiPSC Flow Cytometry analysis. Funding and support of the  EPSRC  for the  EPSRC Centre for Innovative Manufacturing in Regenerative Medicine  is acknowledged.
Molecular Modeling The sequence of LsTPST (GenBank AHJ26006. 1 or Uniprot W6FFX7) was used for fold recognition and homology modeling. The LsTPST sequence analysis and fold recognition were carried out with servers PHYRE (http://www. sbg. bio. ic. ac. uk/phyre2/), I-Tasser (http://zhanglab. ccmb. med. umich. edu/I-TASSER/) and FUGUE (http://tardis. nibio. go. jp/fugue/). The LsTPST monomer and homodimer model construction and analysis were carried out with program MOE 2013. 08  (Molecular Operating Environment, 2011. 10, Chemical Computing Group Inc. , 1010 Sherbrooke St. West, Suite #910, Montreal, QC, Canada H3A 2R7, 2013). The model of LsTPST monomer (residues 47-346) was built with the homology module in MOE 2013. 08 using as template the crystal structure of the catalytic domain (residues 52-353) of human tyrosylprotein sulfotransferase-2 monomer (TPST-2; PDB code: 3AP1; resolution 1.
Results cDNA Cloning and TPST Primary Structure An amino acid sequence of the L. sitkana TPST was determined by the methods of molecular biology. Total RNA isolated from L. sitkana liver was used for the synthesis of the first strand of cDNA. For amplification of cDNA fragments encoding the TPST degenerated oligonucleotide primers were used (Table  1 ). These primers were synthesized on the basis of the conservative sites of the amino acid sequences of human TPSTs and the partial sequences of TPST from marine mollusks, which were found in GenBank. The cDNA fragments, obtained through PCR, were cloned in plasmid vector and sequenced. Analysis of the nucleotide sequence of this cDNA fragments by BLAST2 had shown that a fragment ~750 bp in length, obtained with primers SU-F2 and SU-R4, was homologous to cDNA encoding the TPSTs of other animals.
Molecular Modeling The LsTPST amino acid sequence analysis using I-Tasser, FUGUE and PHYRE2 servers showed that the protein central part has a fold with high confidence similar to the hTPST catalytic domain. A model of the full amino acid sequence of LsTPST, which was constructed by server I-Tasser, included cytoplasmic and transmembrane parts, and additional 4 helixes at the C-terminus (data not shown). The I-Tasser model with high C-score had TM-score 0. 47, which showed the structural similarity between model of LsTPST fragment and catalytic domain of hTPST (3AP1) structures. The LsTPST sequence analysis by FUGUE and PHYRE2 servers showed with high confidence that the LsTPST fragment had folding similar to the catalytic domain of hTPST (PDB ID: 3AP1). The alignment of amino acid sequences LsTPST and human TPST-2 showed that LsTPST had 64% of identity and 77% of similarity with the catalytic domain of TPST-2 (Fig.
1 ). The theoretical model of the spatial structure of the LsTPST catalytic domain (residues 47-346) was obtained by MOE 2013. 08 (Fig. 2A ). The crystal structure of the catalytic domain of the human TPST-2 (PDB code 3AP1_A;  Teramoto et al. , 2013)  was used as a template for modeling LsTPST structure. Analysis of the obtained model by MOE program showed that the monomer structure of the LsTPST catalytic domain had the following proportions of secondary structures β-strands (7. 3%), α-helix (48%), loop and disordered structure (44. 7%). The structure of LsTPST was stabilized by 86 hydrogen and 19 ionic bonds, 131 hydrophobic contacts and two disulfide bonds. Monomers of LsTPST and human TPST-2 contained free SH groups Cys204 and Cys210 in the catalytic domain. Superposition of LsTPST and human TPST-2 structures showed that the value RMSD for Cα atoms of the theoretical model and template crystal structure was 0.
Discussion The primary structure of TPST from marine mollusk L. sitkana was determined by cDNA sequencing. The comparison of the LsTPST amino acid sequence with two human TPSTs and the TPSTs of other invertebrate species revealed 70-85% of structural homology. It should be noted that the maximal degree of homology was 85% with the protein from С. gigas (GenBank EKC35269). The degree of amino acid homology with TPSTs of vertebrates is also high: 74-81%. Analysis of the sequence of LsTPST using the InterProScan server indicates that it contains a catalytic domain of TPST-2 from Arg5 to Gly355. The multiple alignment of the amino acid sequences of TPST-1 (GenBank O60507) and TPST-2 (GenBank O60704) from human and some invertebratesinsects Drosophila melanogaster (GenBank AAM94031) and C. quinquefasciatus (GenBank EDS40555), clam C.
gigas (GenBank EKC35269), nematodes C. elegans (GenBank O77081) and sea squirts H. roretzi (GenBank AAM09087) is presented in Fig. 4. Their comparison demonstrated the conservative fragment PRSGTTLM. Amino acids of this fragment are involved in the binding of PAPS  (Teramoto et al. , 2013). The transmembrane region from Leu12 to Ser30 of LsTPST (Fig. 4 ) was defined with InterProScan server (http://www. ebi. ac. uk/Tools/pfa/iprscan/). It coincides with the transmembrane regions of other animal TPSTs  (Stone et al. , 2009). The multiple alignment of TPST sequences revealed conserved residues, which allowed to speculate on their potential functions of LsTPST. Five of the eight cysteine residues (Сys90, Сys150, Сys204, Сys219, Сys227) are conserved and four of them formed two disulfide bonds, suggesting that these bonds are required for the stabilization of the monomeric molecule of LsTPST.
There are suggestions that this region in addition to some conserved residues is responsible for the specificity of the enzyme  (Stone et al. , 2009). Taking into account that data on the tyrosylprotein sulfotransferases in marine mollusks are absent we compared the amino acid sequence of LsTPST with partial sequences from marine mollusks, which was found in GenBank. This comparison has   shown 66% of identity with uncharacterized protein of gastropod Lottia gigantea (GenBank B4BVQ4). As a result of this study a new source of TPST was found. We have determined the amino acid sequence of the TPST from marine gastropod L. sitkana. The studied enzyme has been identified as TPST-2, on the basis of the analysis of its primary structure. A high degree of homology between TPSTs from terrestrial vertebrates, insects and marine invertebrates indicates the importance of sulfation of tyrosine residues for the metabolism of invertebrates and vertebrates.
, 2003; Gonzalez, 2005). This hypothesis was confirmed by tafazzin gene analysis, which showed that the exon 5 sequence was present in all mammalian species tested, whereas the splice recognition site required for the incorporation of exon 5 into the mRNA was present only in primates. As CL composition is identical in primates (i. e. , with mRNA transcripts containing exon 5) and other mammals (i. e. , lacking exon 5), and the full-length human tafazzin was unable to restore the CL profile of taz yeast fully, it is important to determine the function of this full-length variant in CL metabolism. BTHS patients with a mutation in exon 5 ["Human Tafazzin (TAZ) Gene Mutation and Variation Database"; www. barthsyndrome. org] have recently been identified. Theoretically, these patients should have normal levels of the tafazzin variant lacking exon 5 if the mutation does not render the initial transcript unstable or triggers its degradation.
ACKNOWLEDGMENTS This work was funded by an  "Association Française contre les Myopathies " ( AFM 15137 & 15661 ) grant to PXP and by basic support from both the  "Centre National de la Recherche Scientifique" (CNRS)  and the  "Institut National de La Santé et de la Recherche Medicale" (INSERM). IMM is supported by a grant from the  Danish Council for Independent Research -Natural Sciences (FNU). AS held a  Marie S. Curie-Cofund "NEWFELPRO" postdoctoral fellowship  from the  European Community. We thank  Lawrence Aggerbeck  for critical reading of the manuscript and for the many suggestions provided. We also thank  Alex Edelman  and Associates for the English corrections.
In this context, resources refers to anything that could help in such a process, such as annotation formats, content sharing mechanisms, tools and services for text processing, knowledge bases and the accepted standards associated with these. As of 2016, PubMed, the most widely used database of biomedical literature, contains over 26 million citations 1. It is growing constantly: over 800 000 articles are added yearly 2 and this number has substantially increased over the previous few years  (1). This constant increase is recognized as a major challenge for evidence-based medicine  (2) , as well as other fields  (3). One of the tools for tackling this problem is text mining (TM). However, as many surveys have shown  (4) (5) (6)  the TM landscape is fragmented and, at its worst, it can be hostile to the uninitiated, giving rise to such questions as: Where should I look for resources? How should I assemble and manage my TM workflows? How should I encode and store their output? How can I ensure that others will be able to use the TM outputs I wish to share from my research? Moreover, one may be drawn to popular standards-while lesser known standards go unnoticed, yet may be more suitable.
Each of these levels must be addressed if we wish to promote a culture of true interoperability within TM. We have addressed several levels that are relevant to the text-mining life scientist as seen through the sections of this report. This paper has been split into the following sections to categorize and help understand the existing technologies: • In 'Content discovery' section, we start by explaining how and where to get the input text data, i. e. corpora of publications. We describe metadata schemata and vocabularies and discuss the most popular publication repositories. • In 'Knowledge encoding' section, we show how the original document is encoded with additional information, describing annotation formats. We also outline formats for knowledge resources that are frequently employed in the TM process and describe a few examples of such databases, especially from the area of life sciences.
Publications are usually stored in searchable structured databases typically called repositories. Although many repositories stand alone, an aggregator may connect several repositories in looser or tighter networks by aggregating publications, or information about them, from other repositories in the network. The internal mechanism of a repository relies on a set of structured labels, known as metadata. Metadata can generally be defined as data used to describe data, and as such metadata may themselves be stored and managed in repositories usually called metadata repositories (or registries, or simply catalogues). Usually, aggregators act as metadata repositories in that they harvest metadata from repositories and make them available to facilitate the search and discovery of publications. Metadata for scientific articles, e.