dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural effects of the A-loop mutations in position 816. Previously we have shown that the D816V mutation induces a local destabilization of the short 3 10 -helix at residues 817-819  [40]. A similar effect of mutation D816H is evidenced when comparing the crystallographic structures of KIT WT and KIT D816H bound to the inhibitor sunitinib (PDB ids: 3G0E and 3G0F  [30] ) and was also observed in a recently reported in silico study  [74]. Inspection of the clustered MD conformations from our simulations of KIT D816H/Y/N again put in evidence complete or partial unfolding of the adjacent 817-819 3 10 -helix in A-loop (Figure  5 a, b ). Considering the perfect agreement between these results, previous in silico studies and available experimental data, we interpret the structural modification in this region as principally induced by mutation in position 816.
Nevertheless, the KIT D816H auto-activation rate is significantly higher than that of KIT D816N  [25]. The KIT D816Y/N mutants showing moderate mutation-induced effects are only rarely detected in clinic as primary pathogenic mutations. V560G/D mutations represent the majority of KIT mutations found in GISTs  [14]  and melanoma  [111]. The impact of V560D/G mutation on JMR is also more pronounced than D816V/H mutation, consistent with a higher activating potency of this substitution  [15, 30]. Such relationships of the structural effects and the in vitro (and/or in vivo) activation rates (and/or  WT and KIT D816N/Y/H/V mutants. 2D and 3D graphs were drawn with Gephi and PyMOL modules incorporated in MONETA. Left panel: 2D graphs of a global topology illustrating the inter-residues communications in KIT WT and KIT mutants.
Transcriptionally active stages show global depletion in nucleosome levels Nucleosome dynamics were analyzed in synchronized P. falciparum cultures that were harvested directly after invasion of red blood cells at the ring stage, and at the trophozoite and schizont stages, as well as at the sexually mature gametocyte stage. The amount of chromatin used for immunoprecipitations was equivalent for all stages of the asexual cell cycle. However, DNA yields obtained from ChIPs consistently showed a reduced recovery at the trophozoite stage (Figure  1B ). These results are consistent with data from a wide variety of experimental approaches, including western blot analysis  [23]  and mass spectrometry analysis  [24]  (Figure  1C ), that showed reduced histone levels at the trophozoite stage, which is known to be the most transcriptionally active stage during the asexual cell cycle  [25, 26].
While detection of histone proteins decreased at the trophozoite stage as compared to the ring and schizont stages, mass spectrometry data showed an increased abundance of RNA polymerase II  [24]  (Figure  1C ), further validating a potential link between histone levels and transcriptional activity. Next, we determined the number of nucleosomes and the average nucleosome level at each stage using the nucleosome positioning tool PuFFIN  [27]. PuFFIN was designed in our group specifically for paired-end sequence reads. Extensive experimental results showed that PuFFIN outperforms other available nucleosome detection tools in terms of accuracy and number of nucleosomes reported along the genome. The number of reads mapped to each nucleosome was used to estimate nucleosome levels. To   [56] ) showing genome coverage for a section of chromosome 4, with the gene model indicated in the top.
C. Total spectral counts for all histone proteins (left) and all annotated RNA polymerase II proteins (right) obtained by mass spectrometry analysis of nuclear fractions from ring, trophozoite and schizont stage parasites  [24]. Abundance levels of histones decreased during the trophozoite stage, while the amount of RNA polymerase II increased, consistent with high transcriptional activity in this stage, but not the ring or schizont stages. Spectral count data were obtained from PlasmoDB (www. plasmodb. org). compare nucleosome levels between different stages, we normalized nucleosome scores using the total number of reads mapped to both human and P. falciparum genomes (see Materials and methods). In addition, we validated our nucleosome positioning data using a publicly available nucleosome positioning tool NOrMAL  [28].
In addition, for a smaller group of genes with annotated transcription start sites, promoter regions of highly expressed genes showed decreased nucleosome occupancy (Figure  3C ). Inside coding regions of highly expressed genes, we observed an opposite correlation between nucleosome occupancy and transcriptional activity as compared to the promoter regions. Highly transcribed genes were on average bound by more nucleosomes and at higher levels (Figure  3D, E ). A similar, albeit slightly weaker, association between nucleosome occupancy and transcript levels was observed when nucleosome maps were generated using the nucleosome positioning tool NOrMAL  [28]  (Additional file 1: Figure  S7 ), indicating that this result is not an artifact of the PuFFIN algorithm. In addition, the more open chromatin structure at promoters of highly expressed genes was replicated in sonication ChIP-Seq data obtained using either anti-H3 or anti-H4 antibodies (Additional file 1: Figure  S8 ), suggesting that this observation is independent of ChIP-Seq methodology.
Libraries were sequenced with a HiSeq 2000 (Illumina, San Diego, CA), generating 50 bp paired-end sequence reads. Sequence reads are available through the Short Read Archive under BioProject numbers SRP026365 (nucleosome positioning data) and SRP026367 (steady-state mRNA data gametocytes). Steady-state mRNA sequence data from ring, trophozoite and schizont stages are available under accession numbers SRS417027, SRS417268, and SRS417269, respectively.
Sequence mapping The first 5 bases and the last base were systematically removed from the sequence reads using FastQ Trimmer, part of the FASTX-Toolkit (http://hannonlab. cshl. edu/ fastx_toolkit/index. html). Contaminating adaptor reads were removed using Scythe (https://github. com/ucdavisbioinformatics/scythe). Reads were then trimmed for bases with a quality score below 30, and reads containing any Ns as well as reads shorter than 18 bases were discarded using Sickle (https://github. com/ucdavis-bioinformatics/sickle). The trimmed sequence reads were first mapped to the human genome (HG19, downloaded from ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/), and all non-mapped reads were subsequently mapped to P. falciparum 3D7 genome v9. 0 (downloaded from www. plasmoDB. org) using BWA  [52]  with default error rates, allowing a maximum of 1500 bp distance between read pairs.
Nucleosome positioning and normalization strategy Nucleosome positions and score were computed using our novel nucleosome positioning software tool PuFFIN  [27]. PuFFIN is a multi-scale peak-calling method that replaces each nucleosome-enriched paired-end sequence read with a Gaussian curve. By summing these Gaussian distributions for all paired-end reads, PuFFIN creates a smoothed coverage function, in which each peak represents a candidate nucleosome location. By changing the width of the Gaussian curves, PuFFIN can capture candidate locations at different resolution scales. The number of reads mapped to a nucleosome (expressed per 10 7 of total mapped reads) was used as a score for nucleosome strength. To compare nucleosome levels between time points, nucleosome scores were normalized by first calculating the ratio between the number of reads mapped to the P.
Gametocyte-stage specific expressed genes were defined as all genes among the top 30% expression levels at the gametocyte stage and the bottom 50% expression levels in all other stages. Analysis of previously published data sets P. falciparum 3D7 nucleosome positioning single-end sequence reads  [20]  were downloaded (SRX026772/3) and trimmed using the same strategy as used for sequence read libraries generated in this study. Sequence reads were then mapped to P. falciparum 3D7 genome version 9. 0 (www. plasmodb. org) using BWA with default error rates. Based on Giemsa-stained blood smears provided with the publication, the sample obtained 30 hours post-invasion was determined to represent the trophozoite stage. For the generation of nucleosome maps, single-end reads were extended to 110 bp and nucleosome positions were subsequently determined using the PuFFIN software.
The score for each nucleosome was normalized as described in the section "Nucleosome positioning and normalization strategy". Saccharomyces cerevisiae nucleosome positioning sequence reads  [31]  were downloaded (SRA001003) and were mapped to the S. cerevisiae BY4741 genome version Toronto_2012 (downloaded from http://www. yeastgenome. org/) using bowtie2 with "-very-fast-local" setup to account for possible adapter contamination. An MNase-Seq nucleosome positioning data set for the human cell line GM12878 was downloaded from the ENCODE database (DCC Accession Number wgEncodeEH000922)  [33]. Nucleosome positions and scores were determined using PuFFIN. Analysis of nucleosome positioning in relation to transcription levels were performed as described above for P. falciparum, using a previously published RNA-Seq expression data set for S.
Coverage plots Genome browser plots were obtained using Artemis  [56]. Coverage plots were prepared by extracting the read counts for the region of interest for all genes included in the analysis, and subsequently calculating the average value for each nucleotide position. Coverage profiles were smoothed using overlapping 150 bp sliding windows, and were subsequently plotted using bioconductor R package ggplot2. For coverage heatmaps, read counts per nucleotide were divided by the total number of mapped reads (× 10 7 ) and subsequently multiplied by the stage-specific normalization factors (Additional file 1: Table  S2 ). Nucleotide positions with zero read coverage were replaced with the lowest overall read coverage (i. e. 0. 001). Read counts were then converted to log 10 values, and were finally plotted in R using the pheatmap package.
Since low amounts of DNA were obtained using the anti-H4 antibody, we concluded that this antibody was not well suited for ChIP-Seq experiments and was therefore not used for further analysis. C. Total spectral counts for all histone proteins (left) and all annotated RNA polymerase II proteins (right) obtained by mass spectrometry analysis of nuclear fractions from ring, trophozoite and schizont stage parasites [24]. Abundance levels of histones decreased during the trophozoite stage, while the amount of RNA polymerase II increased, consistent with high transcriptional activity in this stage, but not the ring or schizont stages. Spectral count data were obtained from PlasmoDB (www. plasmodb. org).
Acknowledgements We thank  John Weger ,  Rebecca Sun , and  Glenn Hicks  ( Institute for Integrative Genome Biology, University of California Riverside ) for their assistance in the library preparation and sequencing process. We thank the  ENCODE Consortium , the  Snyder laboratory at Stanford University  for generating the nucleosome positioning data set and the  Wold laboratory at the California Institute of Technology  for generating the RNA-Seq data set. The following reagents were obtained through the  MR4  as part of the  BEI Resources Repository, NIAID, NIH: Plasmodium  falciparum strains  3D7 (MRA-102 ) deposited by  D. J. Carucci  and  NF54 (MRA-1000 ) deposited by  Megan Dowler ,  Walter Reed Army Institute of Research. This study was financially supported by the  Human Frontier Science Program  (grant  LT000507/2011-L  to E.
Analysis of Paired-end Illumina Reads One library or two multiplexed libraries were loaded on each lane of a HiSeq 2000 (Illumina), and 110 bp paired-end reads were generated. Reads quality was evalued by FastQC v. 0. 10. 1 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and trimmed off using the paired-end mode of Trimmomatic v0. 30  (http://www. usadellab. org/cms/index. php?page = trimmomatic). Trimmed reads were mapped along S288C chromosomes reference sequence (GenBank NC_001133 to NC_001148, PLN 06-DEC-2008), plus the two SUP4 alleles (SUP4-opal and sup4-(CAG)) using the paired-end mapping mode of BWA v0. 6. 2  [50]  with default parameters. The output SAM files were converted and sorted to BAM files using SAMtools v0. 1. 18 [51]. The command IndelRealigner from GATK v2.
5% of white colonies) as compared to glucose (66. 7%). This suggests that even in repressing conditions (glucose), the GAL1-10 promoter shows some level of leakiness which is, associated to multicopy plasmids, apparently sufficient to induce TALEN expression. In support of this observation, we noticed that when crossing two haploids strains containing a stable trinucleotide repeat and one of the two TALEN arms, none of the diploids obtained contained a repeat longer than 30 triplets, strongly suggesting than even in repressing conditions, leaky expression of both TALEN arms occur to a level high enough to induce repeat contractions when both plasmids are in the same diploid cell (Figure  S1 ). Quantification of steady-state TALEN transcripts in glucose and galactose media confirmed a low but detectable level of transcripts in glucose (Figure  S2 ).
Out of six remaining SNPs in coding regions, two are synonymous (third codon base) whereas four are non synonymous and encode point mutations in five different genes (Table  S2 ). We concluded that expression of a TALEN targeted to a specific CAG/CTG trinucleotide repeat has no effect on other triplet repeats nor on the overall mutation rate of the yeast genome. Since deep-sequencing cannot reveal reciprocal translocations that could be induced by the TALEN, as a last control experiment, a PFGE was run on the heterozygous SUP4-opal/sup4::CAG strain. DNA from two colonies grown on glucose and 20 colonies grown on galactose was prepared embedded in agarose plugs and loaded on a PFGE. All karyotypes were normal, showing no evidence for aneuploidies, large segmental duplications or translocations (Figure  3C ).
Clone GFY6162-3D/2 was crossed to GFY6162-3C/1, and diploids were selected on glucose SC-Leu plates supplemented with G418 sulfate (200 mg/ml). Right: Molecular analysis of four diploids shows that two of them (#3 and #4) exhibit a large contraction of the (CAG) 75 repeat, that occured during or right after the cross, even though cells were crossed on glucose medium and the split-TALEN is not active. The two haploid parental strains are used as an additional size control (labeled ''C''). In this particular cross, diploids #3 and #4 were selected for further induction experiments of the split-TALEN. (PDF) Figure  S2  Steady-state level of TALEN transcript in repressing (glucose) and inducing (galactose) conditions. Strains GFY621 (SUP4-opal/sup4::CAG) and GFY622 (sup4::CAG/sup4::CAG) were grown overnight in liquid SC -Leu glucose medium supplemented with 200 mg/ml G418 sulfate.
Cultures were washed twice with water, diluted to ca. 10 7 cells/ml and grown in 20 ml SC -Leu glucose or galactose medium supplemented with 200 mg/ml G418 sulfate, for four hours. Total RNAs were extracted and analyzed by Northern blot as previously described  [73, 74]. The full left TALE arm purified from pCLS16715 was labeled by random priming and used as probe. Blots were stripped in boiling 0. 5% SDS and rehybridized with a randomly labeled actin probe, covering the ACT1 yeast gene. Membranes were exposed and signals were quantified on a Fujifilm FLA-9000. Relative amounts of TALEN as compared to actin transcripts are shown on the graph, in both growth conditions. There is a 10-32 fold increase of TALEN transcripts in galactose as compared to glucose, depending of the strain. In GFY622 the level of TALEN transcripts is lower than in GFY621, in both conditions.
Visualization in Everyday Life People are now accessing information in a wide variety of situations such as in social situations, while travelling, while shopping, or while looking for a restaurant. It seems to make sense to make use of what we have learned in visualization to empower us in our everyday lives. Taking visualization out of the workplace and into everyday situations will also introduce new challenges in developing appropriate interactions in a social context. We are starting to approach this from a domain-specific perspective, focusing on specific data that people need and use, identifying the important features of their information, understanding the specific nature of their tasks, and looking for factors that arise from their everyday social context and routines. One example of this new direction is the Visits project.
g. , when implementing dynamic binding), and so the respective testing configurations; and (iii) ideally, the testing services should be reusable, composable in larger testing artifacts, and traceable by themselves. In this paper we present PASCANI, a framework that assists testers in the specification and execution of automated, composable, and traceable tests, which are compliant with the Service Component Architecture (SCA) specification  [1] , for service-oriented systems. Our framework comprises two subsystems. The first is the PASCANI analyzer, a mechanism for analyzing the possible service configurations to test, which generates a test specification template from a set of selected components and services. The second is the PASCANI language and its implementation, a domain specific language for specifying tests through the definition of test modules.
A test module specifies the components and services to test, the test cases for verifying the system's functional and extra-functional requirements, and a test strategy, which specifies how to execute the test cases. For the composability, the language allows to import other test modules and compose them in the test strategy through a set of compositional operators. From a module specification, PASCANI generates a configuration of testing components, and deploys and executes it using FRASCATI  [2] , an execution middleware for SCA-compliant systems. To evaluate our approach, we integrate PASCANI into a service-oriented online shop retailer (OSR) application. Our results show the applicability of the framework by generating, deploying and executing configurations of composable tests on the services of the service provider, and the compositions of the service integrator.
III. THE PASCANI FRAMEWORK SCA defines a programming model to build software systems based on a component model underlying the SOA design principles. SCA provides specifications for the creation of components and their composition to develop complete applications. Components can rely on different environments and technologies, given that SCA is independent of implementation and communication mechanisms that are specified in the implementation and binding models  [1]. Figure  1  depicts a partial view of the SCA architecture, including integration testing components, for the OSR application described in Sect. II-B. This figure will be used in the following sections to explain the PASCANI framework. The legend in the figure presents the main artifacts of SCA. PASCANI comprises two subsystems: (i) the component and service analyzer, which generates a test specification template from a set of selected components and services; and (ii) the testing specification language, whose design and implementation includes a translation model (i.
e. , a compiler) and an execution model. That is, the framework assists users (i. e. , human testers) in both describing the system to be tested in terms of its components and services; and specifying test modules with the corresponding test cases and strategies for verifying the system's functional and extra-functional requirements. Naturally, the analyzer generates the test specification templates in the syntax defined by the PASCANI language. Finally, the framework deploys and executes the components that realize the test modules. PASCANI is a valuable testing framework for service providers, integrators and consumers, since it supports them in the cost-effective specification and composition of test cases and strategies (i. e. , test modules). Furthermore, in the case of service integrators and consumers, PASCANI is particularly useful for composing testing services, exposed by service providers, despite the technology used to develop them.
The current version of the PASCANI framework is available from its SVN repository at http://pascani. org upon request. This section focuses on explaining the elements of the framework that allow the specification of test modules from the perspective of the service provider. For this, we elaborate on the scenario of a third party service for matrix multiplication, as introduced in our running example (cf. Sect. II-B). In this case, the service provider uses Strassen's algorithm for implementing its matrix chain multiplication (mcm) service (cf. Fig. 1 ), and uses PASCANI for the specification and composition of its tests.
A. Language Specification The main unit for specifying tests in PASCANI is the test module. PASCANI defines two types of test modules: basic and composed. A basic test module does not compose other test modules and is intended only for two testing targets: unit and system testing. In contrast to basic modules, composed test modules reuse other test modules, that may have different testing targets. A test module specifies the system's components and services to be tested, as well as the test cases with their corresponding execution control flow. We also refer to this control flow as the test strategy of the module. Modules are the mechanism to both specify a deployable system and its test components, specify test strategies, and compose tests. For this, a PASCANI module defines two mandatory blocks named system structure and tests, and an optional block named control.
Control blocks are similar to main methods in programming languages such as Java and C++, thus they are optional in modules that do not specify a test execution strategy. Listing 1 illustrates the structure of a basic PASCANI module. This module tests correctness in the implementation of the classical Strassen's algorithm that is used as the basic solution for our case example (cf. Section II-B). System Structure Block. This mandatory block specifies the architectural configuration of the system to be tested (cf. lines 5-9 in Listing 1). For this, it defines variables that are used to represent the corresponding components (cf. line 5) as well as services and interfaces (cf. lines 7-9). The specification of the architecture to test can include two types of components: anonymous and known components.
test is a primitive type introduced by PASCANI. In PASCANI, the verdicts are values in the set V = {passed, f ailed, inconclusive, exception}. Lines 11-19 in Listing 1 specify the test case named correctness to verify whether the service strassen. mcm multiplies a pair of matrices as expected. The verdict of the test is returned through the variable isCorrect (cf. line 18). Control Block. This optional block allows the definition of test strategies, that is, the execution control flow that specifies (i) the test case invocations that will be executed by the module; (ii) their execution order; and (iii) the way of operating or composing their verdicts to obtain the final result. Moreover, control blocks may contain programming-language specific instructions (e. g. , Java statements) to prepare the test environment (e.