dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  N. Buchet  and  N. Delsaut  for sample preparation. We thank two anonymous reviewers and  Jorijntje Henderiks  for the helpful reviews. This work has been made possible thanks to partial support from the  European Science Foundation (ESF)  under the  EUROCORES Programme EuroCLIMATE , through contract No. ERAS-CT-2003-980409  of the  European Commission, DG Research ,  FP6  (project  MERF ), from the  Agence Nationale de la Recherche  under project  ANR-12-B06-0007  (CALHIS) and from the  European Community 's  Seventh Framework programme  under grant agreement  265103  (Project  MedSeA ). Edited by:  J. Bijma.
ACTIVITY RHYTHMS We monitored visually the activity rhythms of two sympatric nests, one of F. cunicularia and the other of C. aethiops. Both were located in the Güllapoglu Arboretum of Trakya University, Edirne, Turkey (latitude: 41 • 40 0" N, longitude: 26 • 34 0" E). After defining an imaginary circle of 25-cm diameter around each nest entrance, we counted inbound and outbound worker ants on a 20 h basis (from 5:00 AM to 01:00 AM of the next day) during Frontal (upper row) and lateral views (lower row) of the heads and compound eye of Camponotus aethiops majors and minors and Formica cunicularia. The scale is indicated in each case. (B) Drawing of a frontal view of a left compound eye to illustrate the calculation procedure for the interommatidial angle ( ϕ) and the ommatidial diameter. Using the Image J software, three points (black dots) were defined on the outer visible perimeter of the eye and a circle (in red) was drawn to connect them.
, 2004)  as: A = π Cl 2 × Cw 2 To this end, length C l and width C w of the compound eye were measured using the Image J software (National Institute of Mental Health, Bethesda Maryland, USA). To quantify the mean interommatidial angle ( ϕ), which describes the cornea sampling density and the mean ommatidial diameter (D) (μm), which provides a measure of the sensitivity to light  (Land, 1997) , we used frontal photographs of compound eyes (examples frontal views are shown in Figure  1A , upper row). On these photographs, Image J software allowed us to define three points on the outer visible perimeter of each eye (left or right) and draw a circle that connected them (see example in Figure  1B : the three black dots are connected by the red circle line). The perimeter of this circle included the eye's outer perimeter (Figure  1B ).
ACKNOWLEDGMENTS We thank two anonymous reviewers,  J. G. Hildebrand  (Tucson) and  J. Spaethe  ( Würzburg ) for valuable comments and corrections on previous version of the manuscript. This study was supported by a  Turkish (TUBITAK) - French (EGIDE) cooperation program  " PIA  Bosphorus  109T588. " Martin Giurfa  thanks the  Institut Universitaire de France , the  French Research Council (CNRS) , the  University Paul Sabatier of Toulouse  and the  French National Research Agency  (project  MINICOG ) for support.
5× magnification. Images were acquired with Fire software (Leica), and BV/TV was analyzed using ImageJ software (National Institutes of Health). All quantifications were acquired with a fluorescent microscope (DMLB; Leica) equipped with a charge-coupled device camera (DXC-390; Sony) at 40× magnification. Analysis of the parameters was performed using OsteoMeasure Analysis system (OsteoMetrics).
We thank  Dr. P. Ducy  for valuable suggestions and critical reading of the manuscript,  Dr. H. Kronenberg  for generously providing the Ppr fl/fl mice,  Dr. T. -P. Yao  for kindly providing us with the anti-HDAC4-S246 phosphoantibody, and  Dr. R. Bassel-Duby  and  Dr. E. Olson  for generously providing the Hdac4 fl/fl , Hdac5 / , Mef2a fl/fl , and Mef2c fl/fl mice. This work was supported by grant  AR045548  from the  National Institutes of Health (G. Karsenty). A. Obri  acknowledges the  Fondation pour la Recherche Médical  for financial support. The authors declare no competing financial interests. Submitted: 31 March 2014 Accepted: 20 May 2014.
Genome assembly A genome assembly of the doubled haploid B. oleracea kale-like type TO1000DH was generated from approximately 94× coverage of the estimated 648 Mb genome (Figure  S1  in Additional file 1). The sequence data comprised previously available Sanger sequencing reads (0. 01 Gb) and newly generated Illumina (56. 33 Gb) and Roche/454 (0. 31 Gb) sequencing data (Table  S1  in Additional file 2). A hybrid assembly approach was taken using a de Bruijn graph-based short read assembler to generate initial contigs and scaffolds incorporating all available sequence data  [8]. The software tool Bambus  [9] , which provides a hierarchical approach to defining contig adjacencies, was used to generate scaffolds taking advantage of large insert mate-pair libraries (8 and 17 kb) and paired bacterial artificial chromosome (BAC) end sequences.
RAD captured a greater portion of the genome (Figure  1a ) and a higher percentage of the potential sites were tagged and sequenced (Figure  1b ). RAD mapping was performed using a doubled haploid population derived from a cross between TO1000 and Early Big (var. italica, broccoli type), identifying 67,567 potential nucleotide variations (both SNPs and insertion/deletions or indels) segregating among the progeny. These data were compressed based on recombination breakpoints to provide 2,299 anchor points that were used to generate a high-density linkage map for B. oleracea (Table  2 ). Based on the linkage analyses, 66 scaffolds were found to be mis-assembled and were separated according to the SNP positions and available genome synteny data. In regions of low recombination the order and orientation of the scaffolds were assigned based on their syntenic relationship with Arabidopsis thaliana and/or B.
Genome annotation of Brassica oleracea A combination of homology-based and de novo gene prediction was used to annotate 59,225 gene models in the B. oleracea genome  [13]. Complete protein coding genes were predicted for 54,475 models. The gene models were annotated based on homology to proteins in public plant databases (Table  S3  in Additional file 2). Orthologues for 94. 6% (56,082) of all gene models were identified within the various UniProt databases, of which 52,774 (94%) were found in species from the A sliding window of 500 kb was used and the trend line is based on the mean of 10 windows. Brassicaceae (Table  S3  in Additional file 2). RNASeq data generated from four tissue types provided evidence of expression from 62. 7% of the gene models (Table  S4  in Additional file 2). The number of B.
Single base resolution of the B. oleracea methylome Sequence data from bisulfite-treated DNA extracted from TO1000 leaf tissue was analyzed to provide a picture of the cytosine DNA methylation landscape across the genome (Figure  3 ). From the almost 69 million trimmed sequence reads, 47. 7% were uniquely mapped to the B. oleracea genome, providing an average read depth of 7. 29 (per strand) at 93. 5% of the cytosines in the assembled genome. Methylated cytosine bases were determined by applying a binomial test to data extracted from reads at each cytosine, using unmethylated lambda DNA as a control. Cytosine methylation can be divided into three types based on sequence context, where mCG and mCHG (where H is A, C or T) possess symmetry and mCHH is asymmetric. The level of DNA methylation observed throughout the B.
Bambus  [9]  was used to overlay all the 454 MP information and the BAC end sequence data onto SOAPdenovo scaffolds to improve scaffold lengths as described in  [34]. In short, all 454 MP reads and BAC end sequence reads (Table  S1  in Additional file 2) were aligned to the scaffolds using a genomic mapping and alignment program (GMAP)  [36]. The output from GMAP was used to create a Bambuscompatible GDE formatted contig file that indicated scaffold links. Redundant or multi-mapped mates, mates where only one read mapped, and those where both mates mapped to a single scaffold were considered invalid links. Each link was considered in Bambus in ascending order of their length, with scaffolding parameters including a redundancy level of 3 and link size error of 5%. Any potentially ambiguous scaffolds were resolved using the 'untangle' utility of Bambus.
These scaffolds were split and the correct position of each of the fragments was determined based on marker and collinearity information. Final scaffolds were renamed as 'Scaffold' and numbered sequentially based on their length from longest to shortest. The order and orientation of scaffolds within each pseudomolecule were determined based on marker order within each scaffold, and marker contiguity pattern between adjoining scaffolds. Scaffolds with too few markers were ordered and oriented using collinearity information. The final version of the draft genome representing nine pseudochromosomes and 32,919 unanchored scaffolds was collated using a custom Perl script (Additional file 3), and the ordering and orientation information of scaffolds within each pseudochromosome was compiled in AGP files.
Transcriptome sequencing The whole plant transcriptome of B. oleracea based on Illumina RNAseq data was characterized for four different tissue samples (leaf, root, flower, and pod; Table  S4  in Additional file 2). Total RNA was isolated using the RNeasy plant mini kit (QIAGEN, Toronto, ON, Canada), including on-column DNase digestion, according to the manufacturer's instructions. The integrity and quantity of total RNA was assessed using RNA 6000 Nano labchip on the BioAnalyzer (Agilent). Sequencing libraries were constructed following the standard TruSeq RNA sample preparation guide (Illumina), and PE sequencing was performed using the Illumina Hiseq 2000 platform. A total of 12. 5 Gb raw RNAseq data were generated. Prior to read mapping, all reads were filtered for adapter contamination, ambiguous residues (N residues) and low quality regions.
Gene annotation For accurate annotation of gene models, an integrated computational approach based on two major genome annotation pipelines, Maker  [13]  and PASA  [40] , was adopted. Maker provides a simplified process for aligning expressed sequence tags (ESTs) and proteins to the genome, and integrates this external homology evidence with ab initio gene predictions to produce polished gene annotations with evidence-based quality statistics. Inputs for Maker included the repeat-masked B. oleracea genome assembly (masked against 'te_proteins. fasta' in the Maker package, which contains a generic list of common transposable elements (TEs)), PlantGDB ESTs from B. oleracea, B. rapa and B. napus, and Uniprot (SwissProt + TrEMBL) plant protein database. Ab initio gene predictions were made by Fgenesh  [40]  and Augustus  [41].
Maker gene structure annotations were further updated by PASA using evidence from Sanger ESTs and multiple de novo RNA-seq assemblies. Post-Maker processing included splitting potentially fused genes, extending genes, resolving internal rearrangements, trimming overlapping genes, and removing proteins of less than 50 amino acids and with no BLAST match to A. thaliana. The final annotation set contained a total of 59,225 gene models. Protein names are assigned using AHRD pipeline  [42]  with names extracted from BLASTP hits in TAIR v10, Swissprot and TrEMBL databases (queried in March 2012).
Repeat annotation A TE database constituted from de novo analysis of B. napus (Chalhoub et al. , in preparation) was merged with databases of TEs previously constructed from analysis of B. rapa  [1]  and B. oleracea  [43]. The TEs were classified into major subclasses and superfamilies (Table  3 ) based on their structural features. Inside each superfamily, elements sharing more than 80% sequence identity over more than 80% of their length, and at least 80 bp, are considered as belonging to the same family  [44]. The merged TE database was used for comparative repeat masking  [45]  of the B. oleracea and B. rapa genomes.
Ligation products equivalent to 450 bp were purified using the BluePippin DNA size selection system (Sage Science). Bisulfite conversion of this DNA was performed using the QIAGEN EpiTect Bisulfite kit. The libraries were quantified using a BioAnalyzer (Agilent) and KAPA library quantification kit for Illumina (KAPA Biosystems), and sequenced from both ends (paired-end) for 100 cycles on either an Illumina HiSeq 2000 or Illumina HiScanSQ. BSMAP (v2. 74) and an associated python script (methratio. py) [52]  were used to analyze cytosine methylation ratios. All reads were trimmed for quality and adapter sequence, potential PCR duplicates were removed and only those reads mapping uniquely to the genome were retained. Sequencing of cytosines in lambda DNA included in each library provided an error rate for non-conversion; this rate was used in a custom Perl script to identify methylated cytosines using the binomial probability distribution at a false discovery rate of <5% (Additional file 4).
Perl was used for text manipulation to assign pseudochromosome coordinates to gene features, 5′ promoter, 5′ UTR, gene, exon, intron, 3′ UTR and 3′ regions, and incorporate these annotations in a custom. gff file. A combination of Perl and R was used to perform statistical analyses on these data. Graphics summarizing the observed statistical relationships were developed using ggplot2, RGL and corrgram in R  [47]. All relevant Perl and R scripts are open source and freely available upon request.
Acknowledgements We thank  Jacek Nowak  and  Faouzi Bekkaoui  for project management support. Work in Canada was supported by the  Canadian Canola Genome Sequencing Initiative  (http://canseq. ca), Genome Alberta , industry partners and the  Agricultural Flexibility Fund (Agri-Flex, AAFC). Work at the J. Craig Venter Institute , the  University of Missouri  and the  University of Georgia  was supported by grant number  0638536  from the  US National Science Foundation. Work in Australia was supported by the  Australian Research Council  (projects  LP0882095 ,  LP0883462  and  LP110100200 ). Support is also acknowledged from the  Australian Genome Research Facility (AGRF)  and the  Queensland Cyber Infrastructure Foundation (QCIF). Work in the UK was supported by the  UK Biotechnology and Biological Sciences Research.
Accession numbers All the raw data used in the assembly of the genome, the RNASeq data used in the expression analysis, and the bisulfite sequence data have been deposited at DDBJ/EMBL/ GenBank short read archives under accession numbers SRP040796, SRS586190, SRS586272, SRS586273. The whole genome shotgun project has been deposited at DDBJ/ EMBL/GenBank under the accession JJMF00000000. The version described in this paper is version JJMF01000000.
We confirmed the expression of all the ZF-Gal4 fusion proteins by western blot analysis (Figure  S3 ). The assay revealed that the TA domain of mouse Zfy1 has a similar activity to human ZFX and ZFY. Strikingly, the mouse Zfy2 TA domain is 5. 5-fold more active than that of mouse Zfy1, and is ,10-fold more active than that of mouse Zfx. The TA domain of the putative ZFA protein proved to have a very weak TA activity. A single nucleotide deletion near the beginning of the ZFY/ZFX open reading frame of Zfa actually makes it very unlikely to translate a protein that includes the zinc finger DNA binding domain, which would preclude binding to target genes. Zfa is now flagged as a pseudogene in Genbank (accession no. NR_037920).
Acknowledgments The authors thank  James Turner  for help with some RNA FISH analyses, A ´ine Rattigan and  Obah A. Ojarikre  for assistance with genotype analyses and mouse colony management respectively, and  Dr. Radma Mahmood  for assistance with histological analyses. NIMR Procedural Services  generated the transgenics and Biological services provided help with mouse breeding. MJM thanks Frederique Lembo and  Jean-Paul Borg  of the  Marseille Cancerology Research Centre (CRCM ) for help with the transactivation assay in yeast. PSB thanks  James Turner ,  Julie Cocquet ,  Peter Ellis , and  Dirk de Rooij  for helpful discussion.
Experimental 2. 1. Crystal data  Hydrogen-bond geometry (A ˚, ). (Farrugia, 2012) ; software used to prepare material for publication: SHELXL97 and PLATON  (Spek, 2009). The amine under investigation, 2-amino-5-(methylthio)-1,3,4-thiadiazole, belongs to the larger family of heterocyclic compounds with potential medicinal importance and broad spectra of biological activities. Among these types of organic molecules are several isomers of thiadiazole  (Demirbas et al. , 2009). 1,3,4-thiadiazoles represent one of the most biologically active classes of compounds with a wide spectrum of activities. A large number of 1,3,4-thiadiazoles have been patented in the agricultural field as herbicides and bactericides  (Wei et al. , 2006). In the present investigation, we report the synthesis and crystal structure of a new organic-inorganic hybrid compound prepared from the reaction of the title amine with the hydrochloric acid in aqueous medium.
Inheritance of Wing Melanisation A total of 829 butterflies (403 males and 426 females) on 3 000 harvested eggs over three generations were photographed. Adult size was estimated by averaging the area of left and right forewings (i. e. 'size' variable). Total black area was also averaged over both forewings for each side (i. e. ventral and dorsal, see Figure  1 ). As ventral melanised area was correlated with dorsal melanised area (Pearson's correlation test, r 2 = 0. 87, t = 51. 21, df = 827, p, 0. 0001), we used dorsal melanised area for subsequent analyses. We estimated wing melanisation heritability from the pedigree data (829 butterflies over 3 generations) fitting animal models with the R package MCMCglmm  [43]. The animal model is a mixedeffect model that allows assessing the genetic and non genetic components of phenotypic variation.
Acknowledgments We thank  Marielle Peroz  and  Sandrine Chertouk  for rearing maintenance. We also acknowledge  Charlotte Joreau ,  Lucas Auer  and  Christine Li  who helped us in pictures' acquisition and  Gabriel Ne  `ve ( University of Provence ),  Vincent Debat  (MNHN) and Marc Thery (CNRS) for their advice in picture analysis. Pierre-Yves Henry (MNHN),  Celine Teplitsky (MNHN)  and  Thomas Tully  provided us with statistical advice. Finally, we thank  Lhorine Francois  ( University of Bordeaux ) for her English corrections.
MB and DL are funded by the  Agence Nationale de la Recherche , via the programs (1) open call DIAME, (2) 6th extinction MOBIGEN, and (3) open call INDHET. MB and DL are part of the  Laboratoire d'Excellence TULIP  ( ANR-10-LABX-41 ). SD was funded by a post-doctoral fellowship from the  Fondation Fyssen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Sanger sequencing After filtering against multiple databases, sanger sequencing was used in all available members from family 025 and 727 to determine whether the potential mutations in causative genes cosegregated with the disease phenotype in these families or not. Direct PCR products were sequenced using Bigdye terminator v3. 1 cycle sequencing kits (Applied Biosystems. Foster City, CA) and analyzed using a ABI 3700XL Genetic Analyzer.
Targeted high-throughput sequencing Approximately 133K bp of exons and adjacent intronic regions of the 30 genes known to be responsible for ADNSHL were captured and sequenced. The average sequencing depth for target region is about 4156, and 99. 44% of the average coverage for targeted region is more than 206, which is satisfied with the requirements for calling SNPs and InDels (Table  4 ). For the proband of family 025, a total of 484 variants were identified, 47 of which were nonsynonymous variants, splice acceptor and donor site mutations and coding indels that were more likely to be pathogenic mutations; only 3 of the 47 variants were with the allele frequency which were less than 0. 01 in dbSNP137, HapMap, 1000 human genome and local dataset; and a missense variant, c. 823T. C (p. W275R) in exon5 of KCNQ4 (NM_004700), predicted to be ''Damaging'', ''Probably Damaging'', ''Deleterious'', ''Disease_causing'', ''Conserved'' and ''Conserved'' by SIFT, Polyphen2, LRT, Mutation Taster, GERP++, and PhyloP respectively (Tables  5  and 6 ).
We introduce PEPPER (Protein complex Expansion using Protein-Protein intERactions), a Cytoscape app designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. PEPPER identifies connected subgraph by using multi-objective optimization involving two functions: (i) the coverage, a solution must contain as many proteins from the seed as possible, (ii) the density, the proteins of a solution must be as connected as possible, using only interactions from a proteome-wide interaction network. Comparisons based on gold standard yeast and human datasets showed Pepper's integrative approach as superior to standard protein complex discovery methods. The visualization and interpretation of the results are facilitated by an automated postprocessing pipeline based on topological analysis and data integration about the predicted complex proteins.
, 2012). However, these algorithms were not developed for use in small-scale AP-MS experiments (e. g. using only a single bait protein) and are unable to integrate experimental data with repositories of PPI. We reasoned that although not all the protein partners may be detected in a given AP-MS experiment, these proteins may have been previously identified as interacting with either the bait or some of the preys of the experiment. Based on this hypothesis, we developed PEPPER, which addresses the problem of finding protein complexes by combining the experimental results of a single AP-MS assay with the available information from protein interactions in a global PPI network. PEPPER solves this non-trivial problem by using a multi-objective evolutionary algorithm  (Elati et al. , 2013) , which was tested to demonstrate the relevance of our integrative approach.
To do so, we used publicly available AP-MS datasets for yeast and human species and compared PEPPER's results with those of state-of-the-art protein complex discovery methods. Our findings highlight the relevance of integrating PPI repositories to the analysis of AP-MS experiments. We propose PEPPER as a Cytoscape application to further refine protein complex predictions through functional and topological analyses. contain as many interactions as possible. These objectives are often conflicting, and thus, no single solution can be considered to dominate over the others. Instead, the optimal solution is a Pareto optimal set with multiple solutions. SPEA2  (Zitzler et al. , 2001) , a popular Multi-Objective Evolutionary Algorithm, is used for the simultaneous optimization of the two objective functions and to identify solutions approximating the set of pareto-optimal solutions.
CASE STUDY We assessed the performance of PEPPER and two network clustering algorithms for protein complex discovery-MCODE  (Bader and Hogue, 2003)  and ClusterONE  (Nepusz et al. , 2012) -on a benchmark dataset of 135 yeast and 9 human single-bait AP-MS experiments and using a set of hand-curated protein complexes as gold standards. For network clustering methods, performance was assessed for each AP-MS experiment by selecting the predicted complex which best matched the seed (details in SI performance comparison section). For each experiment, the reference complex from the gold standard best matching the seed was used as the ground truth in a binary classification task. Compared with both of the clustering methods tested, the complexes predicted by PEPPER scored higher in all of the performance measures for both organisms (details in SI performance comparison section) with notably an average increase of 16% of the geometric accuracy in human and 12% in yeast.