dataesr/scientific-paragraphs-categorization huggingface dataset:
Genic Content of the Sunflower Genome Using the annotation program, AUGUSTUS  [65] , 2467 and 758 genes were predicted in the unmasked and the repeat-masked datasets, respectively. After manual verification and elimination of predictions without the start and stop codons, we obtained a final predicted gene count of 2321 (unmasked) and 643 (repeat-masked). These predictions were verified by BLASTN (using megablast) against the Helianthus EST database and BLASTX against the nr database at an e-value threshold of 1 × e -15. A summary of the genes and genic features are shown (Table  3 ). Gene densities in the gene-rich (maximum gene density) and gene poor (minimum gene density) regions of the genome were computed by plotting the actual distribution of the genes in OmniMapFree  [83]. A greater than 3-fold difference in the average gene density between the repeat masked and the unmasked datasets was observed.
Gene families are abundant in both the datasets with as many as >100 genes/family in the unmasked dataset (Supplementary Figure  S8 ). Differences in the distribution of gene family size between the unmasked and repeat-masked sets also indicate the presence of large TE-related gene families in the sunflower genome. Table  3. AUGUSTUS  [65]  predicted genes and their genic features in the sunflower genome. Both the repeat-masked and the unmasked datasets were used with the following parameters: Alternative scripts = none, Allowed gene structures = only predict complete genes; Training set = Arabidopsis thaliana.
Gene Ontology Annotation of the Predicted Genes Predicted genes from the repeat-masked and the unmasked datasets were provisionally annotated through BLASTX searches against the NCBI non-redundant database and functionally classified using Blast2GO. Of the 2321 total genes in the unmasked dataset, 1475 had blast hits, of which 1405 were mapped to gene ontology (GO) terms and only 460 (~20% of the total) were annotated. Similarly for the repeat-masked dataset, 366 genes out of 643 had blast hits, 345 were assigned to GO categories and 245 (~38% of the total) were annotated. Based on the association with gene ontology terms, 70% and 93% of the total GO categories for the biological process and molecular function, respectively, were comprised of housekeeping genes (Figure  5 ; Supplementary Figure  S9 ).
While the largest GO categories were similar in the repeat-masked and unmasked datasets, there were significant differences (p < 0. 01) in the number of annotations for several GO categories (Supplementary Table  S3 ). As expected, the repeat-masked set had significantly fewer annotated genes associated with the integration, multiplication and transposition of transposable elements. Interestingly, GO annotations associated with response to stress, biotic and abiotic stimuli, and endogenous and external stimuli were significantly higher in the repeat-masked dataset, suggesting a role for transposable elements in the regulation of stress-related genes. The higher proportion of GO annotations in the repeat-masked dataset can be attributed to TE or TE fragment insertions into the promoters, introns and/or UTRs of the associated genes, which, when masked, result in an accurate gene prediction and thus increase the fraction of GO terms for those genes.
Beyond establishing the role of TEs in gene regulation and genome organization, this analysis demonstrates the importance of repeat identification for better gene identification and annotation, as a part of the ongoing sunflower genome sequencing project  [8]. Figure  5. Gene Ontology (GO) annotations of the gene predictions from the repeat-masked dataset in the "Biological process" category using BLAST2GO (B2G)  [66]  using an alpha score of at least 0. 6 and an ontology depth level of 3. * denotes significant difference between the masked and unmasked datasets at 99% confidence interval.
Transcriptional Activity of the Repetitive Elements in the Sunflower Genome To identify the transcribed portion of the genome and presence of TE-related transcripts in the sunflower transcriptome, both the repeat-masked and the unmasked datasets were screened against the ESTs of four plant species, two from within Compositae (sunflower and lettuce) and two outside Compositae (rice and Arabidopsis). Higher percentages of hits in the unmasked dataset compared to the repeat-masked dataset (Figure  6 ) indicate the representation of TE-related transcripts in the EST databases. Similar results were observed in all the four species indicating a conserved set of such transcribed TEs, also previously reported in maize  [21]. Overall, 2. 6% of sunflower ESTs show homology to transposable elements from our custom repeat library, also indicative of the presence of transcripts from transposon-related genes.
7 MY. Our results are comparable to those obtained from the random sampling of genomic data  [8] [9] [10] , suggesting that our dataset is indeed representative of the genome as a whole. We report a custom sunflower repeat database that can be used as "reference repeats" for Compositae generally and Helianthus specifically. Of a total of 6956 repetitive elements belonging to 682 repeat families, our results indicate that preferential amplification of only a few repeat families in the genome (6% and 19% of the total families in terms of bp coverage and copy number coverage, respectively) account for 50% of the entire repetitive content of the genome. Besides polyploidization, differential amplification rates of Class I LTR-RTs is the primary cause for genome size variation among different plant species  [84].
Gene predictions in the unmasked and repeat-masked sequence suggest that roughly 5. 2% of the genome is protein coding. We observe an excess of genes associated with the integration, multiplication and transposition of transposable elements in the unmasked sequence; and stress responsive genes in the repeat-masked dataset, emphasising the potential regulatory roles of TEs. Transcripts of TE related genes were found in the sunflower transcriptome, even though their transcriptional activity did not depend upon their copy numbers in the genome. Such a pattern has also been reported in maize where the rare retrotransposons in the genome are more abundant at the transcript level  [82]. These results suggest distinct roles for TEs based on their state of activity and/or amplification in the genome.
Conclusions Through detailed sequence analyses of a representative set of 96 Bacterial Artificial Chromosome (BAC) clones, we provide the first report on the overall structural organization as well as sequence composition of the sunflower genome. The assembled BACs will also be useful for assessing the quality of several different draft assemblies of the sunflower genome, and the repeat database reported here will aid in annotation of the sunflower reference genome. Research is ongoing to further characterize the sunflower genome. As more genomic information accumulates, we hope to address some unresolved questions including the (1) origin (e. g. , horizontal transfer), evolution, and function of TEs, as well as their fate following polyploidization; (2) impact of TE location and genomic organization on their proliferation and regulation; and (3) the role of transposable elements in gene regulation.
Introduction In drug design, the characterization of binding pockets is a key issue often addressed using molecular dynamics (MD). MD simulations unveil the evolution of large and complex biomolecular systems over time. With increasing computer power, MD generates increasingly large datasets with more frames and particles. The majority of existing programs are not optimized for such large trajectories, so analysis of pocket evolution requires improved software tools that are able to process pocket data in a reasonable time. To tackle this issue, we developed Epock, a software for efficient tracking of protein pocket volume throughout MD trajectories. We demonstrate Epock's functionality on the ligand-gated Gloeobacter violaceus Ion Channel (GLIC) homologue of the human nicotinic receptor  (Laurent et al.
Epock program features The Epock program takes a topology and MD trajectory as input. A configuration file specifies parameters for each cavity to be characterized, including a maximum encompassing region (MER). The MER provides spatial bounds for each cavity using a combination of simple three-dimensional objects (spheres, cylinders and cuboids) to determine a complex final shape. Epock is therefore intended to follow a priori determined cavities over time, not for cavity identification. Our implementation extends the method proposed by  Durrant et al. (2011)  in the POVME program. The Epock Tcl/Tk plugin for VMD  (Humphrey et al. , 1996)  offers an intuitive way to choose and position shapes to define the MER (see Fig. 1A  and B ). The Epock command-line tool natively reads xtc trajectory format used by GROMACS (www.
gromacs. org). Using the VMD plugin, a variety of 13 input trajectory formats can be processed with Epock. For each pocket, Epock calculates the space accessible to a probe, called 'free space', which is the set of all grid points where distance to protein exceeds the user-defined probe radius (typically, 1. 4 A ˚). The number of grid points that overlap each cavity residue is stored and can be outputted as 'residue contribution'. The volume of the free space is then calculated at higher precision using a finer grid. In addition to pocket volumes, Epock outputs pore profiles by calculating the radius of the largest disc that fits the previously detected free space along the Z axis. Epock produces similar results as the Hole software frequently used to characterize macromolecular channels  (Smart et al.
, 1993 ; Supplementary Fig. S1 ). Epock output files include the computed trajectory of free space over time, a feature inspired by the trj_cavity software  (Paramo et al. , 2014). This trajectory is readable by VMD to accessibly illustrate the relationship between pocket volume and protein conformation. Results for pocket volume, residue contribution and pore profile can be plotted in VMD using our plugin or the provided Python scripts (see online documentation: http://epock. bitbucket. org/docs).
, 2011)  and MDpocket  (Schmidtke et al. , 2011)  (details in Supplementary Discussion). Epock was the fastest program tested, analysing the 800-frame GLIC trajectory in 6 s. While absolute pocket volumes may differ among programs, volume variations over time are strongly correlated indicating qualitatively identical results (see Supplementary Fig. S2 ). In a second application, we used Epock to analyse the volume of the ligand binding domain of the HSP90 based on a range of different conformations and complexes captured by crystallography (as discussed in the original MDpocket paper  Schmidtke et al. , 2011,  see Supplementary Discussion and Supplementary Fig. S4 ). Epock processed this dataset of 86 X-ray structures in PDB format in a matter of seconds, producing output that allowed correlation between the pocket volume and movements of a particular helix formed by residues 100-110, as reported by  Wright et al.
The main scientific challenge is to differentiate between men who will benefit from screening and men who will not, reducing overdiagnosis and overtreatment while maintaining benefits (ie, lower mortality). There is no international consensus on targeting screening at men at higher risk. There have been a limited number of studies of screening in men with a family history of PCa  [16] [17] [18]. Most of the studies support the use of targeted screening; however, methodological differences make it difficult to draw conclusions from these data  [16, 17, [19] [20] [21] [22] [23] [24] [25] [26]. The IMPACT study (Identification of Men with a genetic predisposition to ProstAte Cancer: Targeted screening in BRCA1/2 mutation carriers and controls; www. impact-study. co. uk) is an international, multicentre study evaluating the role of targeted PSA screening in men with BRCA1/2 mutations.
These data suggest that lowering the PSA threshold for biopsy in BRCA2 carriers could potentially detect early-stage disease, thus reducing the need for more toxic treatments and ultimately reducing PCa mortality. However, this lowering would need to be balanced against the risk of potentially life-threatening side-effects of biopsy  [41, 42]. In IMPACT, men will be offered a prostate biopsy at the end of the study (at the centres with the capacity), which may provide evidence for the optimal PSA threshold for detecting clinically significant PCa in this cohort of higherrisk men. The observed differences in PPV may also reflect the higher incidence and grade of PCa previously reported, particularly in BRCA2 carriers. The higher PPV in BRCA2 carriers suggests that PSA may have a higher specificity in this high-risk setting.
Conclusions The first screening round of IMPACT demonstrates that targeted screening for PCa in men with a genetic predisposition detects clinically significant disease. Using a PSA threshold of 3 ng/ml results in a low biopsy rate (8. 0%) and a high PPV, particularly in BRCA2 carriers, for the detection of intermediate-and high-risk disease. Although the observed differences in PCa detection rates between carriers and controls was not statistically significant, the trend is clear. With larger numbers of PCa in the follow-up phase (5 yr), these differences, if sustained, are likely to be significant. Future screening rounds will determine the optimal frequency of PSA testing, determine the utility of PSA screening in BRCA1 carriers, and provide further data on the value of annual screening in BRCA2 carriers.
Methods Sequencing and genomic alignment. The study involved the use of human samples, which were collected after written informed consent was obtained. Samples and data were obtained and managed in accordance with the Declaration of Helsinki under protocol 08/H0308/303: somatic molecular genetics of human cancers, Melanoma and Myeloma (Dana Farber Cancer Institute). The same protocol was approved by RES Committee East of England-Cambridge Central. We sequenced the protein-coding exome of 84 tumour samples from 67 patients with MM. DNA was isolated from CD138-positive myeloma cells purified from bone marrow, and constitutional control DNA originated from peripheral blood mononuclear cells. Purity of the CD138 þ fraction was assessed by anti-CD138 immunocytochemistry post sorting, and only samples with 490% plasma cells were sequenced.
Two (n ¼ 13) or three (n ¼ 2) serial samples were available for 15 patients. Fifteen patients were sampled at disease relapse, including 14 with serial samples, and 52 were sampled before treatment. Karyotype and/or FISH data were available for all patients, and the Affymetrix Genome-Wide Human SNP Array 6. 0 (SNP6) was performed in 19/84 samples (Supplementary Table  S1 ). Genomic libraries enriched for protein-coding exons were generated by hybridization to RNA baits from matched tumour and germline DNA samples, using the Agilent SureSelect Human Exon Kit (Agilent, G3362)  54. The libraries, containing an average insert size of 200-300 bp, were analysed on the Illumina HiSeq2000 sequencing platform. Paired 75 bp sequencing reads were generated using the standard protocol. Paired-end sequencing reads were aligned to the human genome (NCBI build37) using the BWA algorithm  55.
Acknowledgements This work was supported by a grant from  NIH   PO1-155258  (N. C. M. , P. J. C. , K. C. A. , H. A. -L. and S. M. ),  RO1-124929  (N. C. M. )  P50-100007  (K. C. A. and N. C. M. )  PO1-78378  (K. C. A. and N. C. M. )  NIH RCA125711C  (M. A. S. ), by a  Veterans Administration  grant  I01--BX001584  (N. C. M. ), and by the  Wellcome Trust  (grant reference  077012/Z/05/Z ). P. J. C. is personally funded through a  Wellcome Trust Senior Clinical Research Fellowship. K. C. A. is an American Cancer Society Clinical Research Professor N. B. is an  EHA  fellow and has been supported by a grant from the  Lady Tata Memorial Trust. P. V. L. is a postdoctoral researcher of the  Research Foundation-Flanders. S. N. -Z. is funded through a  Wellcome Trust   Intermediate Clinical Fellowship.
INTRODUCTION The Large Synoptic Survey Telescope (LSST) will combine an 8. 4 meter telescope with a light-gathering power among the largest in the world and a camera made up of more than 3 billion pixels. Data on billions of galaxies will be used to create a three dimensional map of the Universe with unprecedented depth and detail. This map will then be used to tomographically map dark matter, chart the clustering of galaxies, and to characterize the properties of dark energy. In addition, a wide range of other topics in astrophysics will be probed using the 15 Terabytes of imaging data that the system will produce each night. Of particular importance in executing the LSST science mission is the fidelity of both the photometric and astrometric measurements that can be made using the data that the instrument produces.
PROTOTYPE LSST CCDS AND TEST FACILITIES 2. 1 Baseline CCD architecture In order to meet the various requirements outlined above, a nominal sensor architecture and design was established. The features and specifications of the baseline design are shown in Figure  1. The LSST project has worked with two vendors to produce candidate devices for testing in LSST laboratories. Those devices have been tested and work on their detailed characterization continues. In the prototype testing phase of the project a set of automated laboratory control and image data collection facilities have been employed and/or developed in order to increase the testing throughput. LSST testing laboratories at BNL and Harvard have benefitted from the use of a customized version of the RTS2 observatory automation software  3.
This has simplified the automated control of the optical test benches and collection of the many image files on which much of our testing and characterization work depends. Higher-level programs written in Python allow for collection of complete electro-optic test data sets with minimal human intervention. During the production phase of the program, further automation, with acquisition software based on the LSST Camera Control System (CCS) will allow for both automated data acquisition and archiving. The use of the CCS software will allow for the early integration of camera hardware and software components. In a similar vein, sensor electro-optical test data are currently reduced via a suite of software tools that is based on the developing LSST Data Management (DM) software framework, allowing researchers to begin to use the DM software as early in the project as is possible.
ACKNOWLEDGMENTS LSST project activities are supported in part by the  National Science Foundation through Cooperative Support Agreement (CSA)  Award No. AST-1227061  under Governing Cooperative Agreement 1258333 managed by the  Association of Universities for Research in Astronomy (AURA) , and the  Department of Energy  under contract with the  SLAC National Accelerator Laboratory. Additional LSST funding comes from private donations, grants to universities, and in-kind support from  LSSTC Institutional Members.
Mitochondrial Genome Sequencing The 13 mitogenomes sequenced in this study are listed in table 1, together with the GenEMBL AC numbers. Except for B. schlosseri, the new mitogenomes will be described in detail in a distinct manuscript (Griggio F, Gissi C, in preparation). Ascidian samples were stored in 95% ethanol or RNA later until DNA extraction. Total DNA was extracted from the muscle of a single individual (for Styela clava and Halocynthia papillosa), from zooids previously separated from the tunic (for Aplidium coeruleum and B. schlosseri EA), or from a small piece of colony with the tunic (for all other colonial samples). For S. clava, A. coeruleum, and B. schlosseri EA, total DNA was extracted using the DNeasy Plant Mini kit (QIAGEN) following the manufacturer's protocol. Genomic DNA libraries were prepared with the blunt-end repair, A-tailing, adapter ligation, fill-in, and indexing steps  (Meyer and Kircher 2010).
Gene Annotations Mitochondrial genes were annotated by similarity to orthologous metazoan genes, taking advantage of the BlastN/BlastP service of the MitoZoa database (D'Onorio de  Meo et al. 2012). The start codon of protein-coding genes (PCGs) was defined as the first ATG or nonstandard initiation codon  (Wolstenholme 1992) , not in overlap with the upstream gene and maximizing the similarity to orthologous ascidian proteins. According to the existence of a tRNA punctuation model in the ascidian mt transcript maturation  (Gissi and Pesole 2003 ), incomplete T or TA stop codons were hypothesized only if immediately adjacent to a downstream tRNA gene, and then assumed to be completed by posttranscriptional polyadenylation. Transfer RNA genes were identified by their potential cloverleaf secondary structure using the programs tRNAscan-SE  (Lowe and Eddy 1997)  and ARWEN  (Laslett and Canback 2008).
tRNAs with unusual structure, such as those lacking an arm, were searched also using specific patterns designed with the PatSearch program  (Pesole et al. 2000). All the earlier-predicted tRNA sequences were checked through multialignment to orthologous tRNAs of other ascidians and deuterostome representatives. Therefore, the final tRNA boundaries were defined based on sequence similarity and on the presence of a conserved cloverleaf secondary structure. Ongoing Speciation and Ascidian Mitogenomics GBE Genome Biol. Evol. 6(3):591-605. doi:10. 1093/gbe/evu041 Advance Access publication February 25, 2014 tRNA genes were named according to the common nomenclature of mt tRNA genes, and to the MitoZoa database (D'Onorio de  Meo et al. 2012). Thus, only the isoacceptor tRNA genes for Gly, Ser, and Leu were named based on the recognized codons.
The 13 PCGs and the two rRNAs were initially aligned with MATTF v6  (Katoh and Toh 2008)  and then the alignments were manually optimized. Protein alignments were back-translated to the nucleotide level using TranslatorX (http://translatorx. co. uk/, last accessed March 11, 2014). tRNA genes were The nonsynonymous (dN) and synonymous (dS) pairwise substitutions rates were calculated with the codeml program of the PAML v4. 4b package  (Yang 1997) , using an advanced model of codon substitution accounting for differences in the transition-transversion rates and for the codon usage bias  (Goldman and Yang 1994). The codeml program was run with the options CodonFreq ¼ 2, icode ¼ 9 (ascidian mt) and runmode ¼ À2 (pairwise comparison). Pairwise uncorrected distances were calculated using PAUP*, separately for the first plus second codon positions (P12), and the third codon position (P3)  (Swofford 2003).
Analyses of Cox1 Sequences of B. schlosseri To further investigate the B. schlosseri intraspecies variability, we analyzed all 138 partial cox1 sequences of B. schlosseri available in GenEMBL at May 2013. A compilation of cox1 sequences belonging to closely related species was also considered (GenEMBL, May 2013), to select the most reliable B. schlosseri outgroup through preliminary phylogenetic reconstructions. Thus, the analyzed outgroup sequences were as follows: Botrylloides violaceus (18 sequences), Botrylloides leachii (3 seq), Botrylloides pizoni (2 seq), Botrylloides fuscus (1 seq), Botrylloides nigrum (1 seq), B. tyreus (a synonymous of Botrylloides tyreum, see the WORMS database at http://www. marinespecies. org/ (last accessed March 11, 2014); 1 sequence), Symplegma rubra (1 seq), S.