dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic and phylogeographical studies based on sequences of ribosomal genes, revealed the existence of distinct species and, within them, clades in relation to geographical distribution potentially representing new or cryptic species. The morphological variability of Ostreopsis complicates the identification of species; thus, molecular analyses of isolates or field samples can be helpful. The aim of this study was to improve the characterization of Ostreopsis species and investigate the geographical distribution by using large dataset composed of both new and old sampled isolates. To determine if mitochondrial genes can be used to identify Ostreopsis species, we designed new primers sets then amplified and sequenced representative regions of the COI (cytochrome c oxydase 1) and cob (cytochrome b) genes.
Mitochondrial gene primer design A new set of genus-specific primers was designed based on Ostreopsis spp. and other related dinoflagellate COI and cob consensus gene sequences available from GenBank. The sequence alignment was performed with ClustalW v. 2. 0  (Larkin et al. , 2007)  and primers were designed using OLIGO Primer Analysis Software ver 6. 65 (Molecular Biology Insights, Inc. , Cascade, CO, USA). The genus-specific primers for the amplification of 715 bp of COI were OstrCoxF (5 0 -TTG GGA TCT CCG GTA CA-3 0 ) and OstrCoxR (5 0 -AAA TAC AGC TCA TGG CAA AG-3 0 ), while the genus specific primers for the amplification of 730 bp cob were OstrCobF (5 0 -TNA TAN ATC AGA TCT TAN TTC AGC AT-3 0 ), and OstrCobR (5 0 -AAR GKA AGA CNN NCA AAG ATG TTA G-3 0 ). The primers were synthesized by Eurofins MWG Operon (Ebersberg, Germany).
The sequences of mitochondrial and ribosomal genes obtained from new Ostreopsis spp. cultured isolates were listed in Tables  1  and 2 , respectively. All sequences were deposited in GenBank and EMBL-EBI-ENA. The other ribosomal sequences from Ostreopsis spp. isolates included in this study are listed in Table  S1. Supplementary Table  S1  related to this article can be found, in the online version, at doi:10. 1016/j. hal. 2014. 10. 004.
Phylogenetic and molecular analyses The COI and cob mtDNA and ITS-5. 8S and LSU rDNA sequences obtained as described above were aligned using ClustalW v. 2. 0. Ambiguously aligned positions and divergent regions were The jModelTest v. 0. 1. 1  (Posada, 2008)  was used to determine the evolutionary model that best fitted the data according to the corrected Akaike Information Criterion. For the Ostreopsis ITS-5. 8S rDNA alignment the most appropriate evolutionary model was found to be a HKY with a proportion of invariable sites (I), while for LSU ribosomal gene a general time reversible model (GTR) with a gamma distributed rate of variation among sites was selected. The most appropriate evolutionary model for Ostreopsis cob gene sequence alignment was F81 with a proportion of invariable sites (I), while F81 with a gamma distributed rate of variation among sites (G) was selected for Ostreopsis COI alignment.
The shape parameter a of the gamma distribution (G) and the proportion of invariable sites (I) were estimated from the dataset using default options. Neighbor-Joining (NJ) analyses were performed using Phylip v. 3. 69  (Felsenstein, 1989). The robustness of NJ trees was tested by bootstrapping using 1000 pseudo-replicates. Maximum-Likelihood (ML) analyses were run with PhyMl v. 3. 0  (Guindon et al. , 2010). Bootstrap values were calculated with 1000 pseudo-replicates. Bayesian analyses (BI) were performed using MrBayes v. 3. 1. 2 (Ronquist and Huelsenbeck, 2003)  with the following settings: four Markov chains were run for two millions of generations with a sampling frequency of 100 generations, resulting in 20,000 sampled trees. Each analysis used default priors and was repeated twice starting from independent points.
, 2011; Amzil et al. , 2012; Gorbi et al. , 2013). Therefore, it is urgent to accurately define species within genus Ostreopsis and to determine their geographical distribution. Molecular phylogenetic analyses proved effective to delineate species boundaries. Insofar this approach has been almost exclusively based on ribosomal markers. However, for most of the organisms the markers of choice are mitochondrial genes, especially those used in the socalled barcoding, that is COI and cob. To assess the applicability of mitochondrial genes in dinoflagellates, we provide the first data on COI and cob Ostreopsis mtDNA gene sequences. A total of 131 mitochondrial sequences were analyzed. The Ostreopsis isolates were collected from several localities, including the (i) Mediterranean Sea, (ii) Brazilian coast, Caribbean Sea, Canary Islands and Portuguese coast in the Atlantic Ocean, (iii) western coast of south China Sea (Vietnam), and (iv) Hawaii Islands in the Pacific Ocean.
Despite the greater length of COI and cob genes compared to the ITS regions and LSU D1/D2 gene, the absolute amount of inter-specific variations within genus Ostreopsis, compute as p-distance, are significantly higher for the ribosomal genes, in particular for the ITS-5. 8S regions than for mitochondrial ones (Kruskal Wallis Test, p < 0. 0001). Thus, in Ostreopsis spp. ribosomal nucleotide dataset including the ITS-5. 8S and LSU regions we analyzed, it seemed to be evident higher inter-specific genetic variation; while in the mitochondrial gene sequences inter-specific genetic variation is extremely low. Indeed, the mitochondrial gene inter-specific divergence almost equals to the intra-specific variation.
Acknowledgments Financial support was provided to A. P. by  Italian MIUR (Ministero Italiano della Universitae Ricerca) PRIN (Progetto di Rilevante Interesse Nazionale)   2009JS5YX9_005 ;  Italian MIUR PNRA (Programma Nazionale Ricerche in Antartide)   PdR 2010/ A1. 06 , and  EU (European Union)ENPI (European Neighbourhood and Partnership Instrument) CBCMED  ( Cross Border Cooperation in the Mediterranean )  M3-HABs II-B/2. 1/0096. Financial support was provided to S. F. by  IEO  with  Spanish CCVIEO-4 (Coleccion de cultivos de microalgas nocivas del IEO -4) Project. We'd like to thank  Dr. Maria Chiara Chiantore ,  Dr. Maria Grazia Giacobbe ,  ARPAL Lazio ,  ARPAT Toscana ,  ARPAP Puglia ,  Dr. Antonella Luglie  `for providing us field samples and  Dr. Wayne Litaker ,  Dr. Ana Amorin ,  Dr.
Materials and Methods We conducted a cross-sectional study using data from a prospectively managed database. The structure of this database has already been detailed and published elsewhere  [3]. Briefly we included all nonpregnant patients under 42 years old who were operated on (by laparotomy or operative laparoscopy) in our institution between January 2004 and November 2010. For the present study, we excluded from the cohort patients with cancer, non-Caucasian patients, and patients living outside Paris area (i. e. , outside the region Île de France). Indications for surgery, sometimes more than one for each patient, were the following: (i) preoperative assessment of endometriosis by magnetic resonance imaging and/or ultrasound; (ii) pelvic pain, defined as the presence, for at least 6 months, of dysmenorrhea and/or intermenstrual pelvic pain and/or dyspareunia of moderate to severe intensity; (iii) infertility defined as at least 12 months of unprotected intercourse not resulting in pregnancy; (iv) pelvic mass (benign ovarian cyst, uterine myoma, etc.
0 (data from  [9] ) between cases and controls, a type I error of 0. 05, and a power of 0. 80. We calculated the sample size to 320 (with a case-control ratio of 1 : 1). Regarding the Rhesus factor we assumed an OR of 0. 6 (data from  [10] ) between cases and controls, a type I error of 0. 05, and a power of 0. 80. We calculated the sample size to 626 (ratio 1 : 1). Therefore, with 633 participants, our study appeared sufficiently dimensioned. Statistical analysis was performed using SPSS 13. 0 (SPSS, Chicago, IL). Continuous data were presented as mean ± standard deviation (SD). Student's t-tests were carried out when necessary. The chi-square and Fisher exact tests were used for categorical data. We used OR and corresponding 95% confidence intervals (CI) to compare the distribution of ABO groups and Rhesus factor in the entire study group (all endometriosis patients) and in the different forms and stages of endometriosis, as compared to the control group.
, 2012). Rausch and colleagues have developed DELLY that integrates short insert paired-ends,   ; pop. , population; ind. , individual; ref. , reference; seq. , sequencing; ins, insertion; del, deletion; inv, inversion. long-range mate-pairs and split-read alignments to accurately delineate genomic rearrangements at single-nucleotide resolution  (Rausch et al. , 2012b). In our studies, we used SVMerge  (Wong et al. , 2010) , a pipeline that integrates structural variation calls from five existing software, and validates breakpoints using local de novo assembly. Unbiased exploration of next-generation sequencing data is laborious, however it is essential for deciphering the true complex nature of structural variants. Toward this goal, we visualized read mappings to the whole of mouse chromosome 19 as well as a random set of regions on other chromosomes using the shortread visualization tool LookSeq  (Manske and Kwiatkowski, 2009)  in 17 inbred strains of laboratory mice  (Yalcin et al.
sanger. ac. uk/. Data sets described in this review are also available under accession numbers "estd118"  (Yalcin et al. , 2011) , "estd185"  (Yalcin et al. , 2012a) , "estd200"  (Wong et al. , 2012) , and "estd204"  (Simon et al. , 2013)  from the Database of Genomic Variants Archive (DGVa). The project website (http://www. sanger. ac. uk/resources/ mouse/genomes/) provides tools to automatically search for structural variants by location, gene, strain, type, and functional impact. A workflow of the procedure is explained in Figure  6A. Results can be exported as TSV and CSV format. Specificity and sensitivity of automatic SV calls are described in detail in  Yalcin et al. (2011). To access and query the data manually, visualization of alignments (both at base-pair and read-pair levels) can be done using LookSeq (Figure  6B )  (Manske and Kwiatkowski, 2009) , a Web-based tool to visualize paired end reads NGS data or using the Integrative Genomics Viewer (IGV)  (Robinson et al.
I. INTRODUCTION Recent years have seen a substantial increase in the importance of open-source software: the Linux operating system is used on platforms ranging from embedded systems to supercomputers, Android has captured a majority share of the smartphone and tablet market, and applications such as Firefox, Eclipse, and LibreOffice are used across the desktop computing spectrum. Furthermore, the open-source developer community is becoming increasingly professionalized, with large organizations investing time, effort and money in opensource development. The increasing reliance on open-source software implies that it is important to investigate potential weaknesses in its development process. Years of software engineering research and practice have shown that adequate testing is critical to the development of reliable software  [1].
These easy-to-collect metrics can help characterize projects in which testing is insufficient, and thus can help developers and managers assess when more testing effort for their software may be required. In this study, we investigate over 300 open-source Java projects from the GitHub 2  hosting site and Debian  3  Linux distribution. GitHub hosts millions of software projects including some well-known projects, such as Hadoop  4  from Apache and Aether 5  from Eclipse, whereas Debian is a popular Linux distribution and contains projects such as the Jetty 6  server from Eclipse and the Netty 7  client-server framework. We examine the adequacy of testing of the selected projects by analysing the code coverage reported by executing the test cases present in these projects. We also investigate the relationships between various project characteristics and code coverage.
B. Sonar Sonar  10  is an open-source platform to manage software quality. It is designed as a web-based application that can be installed standalone or can be integrated into an existing Java Web Application Container such as Tomcat. Sonar integrates seamlessly with build automation tools such as Maven and Ant. It supports analysis of design, architecture and object-oriented metrics for Java applications. Sonar combines various existing tools, such as JaCoCo,  11 for getting code coverage, and Surefire,  12  for executing unit test cases. Sonar can also compute the various software metrics that we use in this study, such as lines of code, cyclomatic complexity, number of test cases etc. Sonar considers each method in a test case file as one test case. In terms of code coverage, Sonar gives information about line coverage, branch coverage and overall coverage.
C. Maven Maven is a build automation tool that primarily supports projects implemented in Java. Projects using Maven can be built using information stored in the project object model (POM) file, i. e. , pom. xml. This file provides information about the project, its dependencies on other modules and plug-ins and the order in which the project's components will be built. Maven dynamically downloads all the dependent plug-ins and Java libraries and adds all these artefacts to the local repository. Sonar leverages Maven's project directory structure to gather information such as the number of lines of code, the number of test cases, the number of packages, and the number of classes and to run test cases to collect the coverage level of the project.
D. GitHub & Debian GitHub is a web-based project-hosting platform based on the git 13  revision control system. GitHub has become one of the largest collaboration networks of software developers, hosting more than 3,000,000 users and over 5,000,000 repositories. GitHub hosts project repositories ranging from gaming, to operating systems to web servers, written in various languages. This makes it an interesting source on which to conduct an empirical study. We clone the git repositories of projects that use Maven. Based on this criterion, GitHub provided an initial set of 757 projects. Debian is one of the most popular Linux distributions. It provides a wide range of open-source software, some of which use the Maven build system. We cloned the repositories of projects that use Maven when possible using the URL provided in the Debian metadata.
A. Methodology For our empirical study, we downloaded the projects from GitHub and the projects distributed by Debian. Our dataset includes projects developed by well-known organisations such as The Apache Software Foundation and The Eclipse Foundation. We clone projects that use Maven as the project management tool. Out of 945 projects, 872 projects contain test suites. We analyse these projects and run test cases. For project set-up, we run the command mvn clean install, which clears any pre-compiled files of previous builds, builds a dependency tree for all the sub projects specified in the pom. xml (the root POM) and compiles all the. java files. Then, we run Sonar using the command mvn sonar:sonar, which performs dynamic analysis by running test cases and then creates reports based on the results.
B. Statistics We now present some statistics describing the data collected in our empirical study. We compute these statistics to characterize the projects in our dataset and assess the suitability of these projects as representative samples to answer our three research questions. These basic statistics also describe the range of values of the various metrics for the projects in our dataset. We analyse the correlations of these metrics with code coverage in Section IV. a) Lines of code (LOC): We used Sonar to count the lines of code of projects in our dataset, excluding comments, blank lines and test cases. Figure  1a  depicts the distribution of the number of lines of code of the projects in our dataset. 90 projects have between 1 and 5,000 LOC, 56 projects have between 5,000 and 10,000 LOC, 129 projects have between 10,000 and 50,000 LOC, and 25 projects have between 50,000 and 100,000 LOC and 27 projects have more than 100,000 LOC.
The mean size of the projects is 31,120. 71 LOC and the median size is 11,484 LOC. The largest project in our dataset is Apache Hadoop, which contains 454,137 LOC. b) Test Cases: We use Sonar to collect the total number of test cases for each project. Sonar also gives information about the number of test cases that failed and the number of test cases that were skipped. Test cases can be skipped due to compilation errors, missing dependencies, etc. Figure  1b  shows the distribution of test cases across projects. 147 projects have fewer than 100 test cases, whereas 41 projects have more than 1,000 test cases. 96 projects have between 100 and 500 test cases and 43 projects have between 500 to 1,000 test cases. The number of test cases varies from 1 to 31,414. The mean number of test cases per project is 563.
97 and the median value is 141. c) Cyclomatic complexity: Cyclomatic complexity is a measure of the number of linearly independent paths through the source code of a software program  [16]. Cyclomatic complexity is particularly useful in approximating the number of test cases necessary for independent path testing  [17]. A program with low cyclomatic complexity is typically easier to maintain  [18]. Figure  1c  depicts the distribution of cyclomatic complexity of projects in our dataset. 86 projects have complexity between 1 and 1,000, 140 projects have complexity between 1,000 and 5,000, 46 projects have complexity between 5,000 and 10,000 and 34 projects have complexity between 10,000 and 25,000. 21 projects have complexity above 25,000 with the highest complexity value being 114,045. We can observe that most of the projects have complexity below 10,000.
VI. THREATS TO VALIDITY As with any empirical study, we also have several threats to validity. Some of these threats are described below: Threats to external validity relate to the generalizability of our results. We have investigated over 300 open-source projects of substantial size from GitHub, which is one of the most popular super-repositories, and from Debian, which is a popular Linux distribution. Projects in GitHub are not restricted to a particular domain. Debian also includes projects from different domains. Thus, we believe there is little threat by only analysing these projects. All the projects that we analyse are written in Java. It is unclear if the results would be the same for projects written in other programming languages. In the future, we plan to reduce these threats to external validity by extending our study to also analyse projects written in various programming languages.
Threats to internal validity relate to the conditions under which the experiments are performed. We use Sonar to find software metrics such as lines of code, number of tests, cyclomatic complexity and code coverage. Sonar relies on Maven, implying that non-Maven projects are not taken into account, and that projects that do not fully conform to the Maven directory structure may be interpreted incorrectly. The latter issue could lead to incorrect calculations of some software metrics such as lines of code. Similarly, during dynamic analysis, i. e. , running of test cases, there may be cases where Sonar is not able to find and run all of the test cases, leading to a wrong coverage value. We have, however, manually checked some of the projects, and these appear to fully conform to Maven directory structure.
For all the projects in our dataset, we use the latest revision of the repository, rather than a released version. We do so to check whether project is regularly tested by the developers before it is available to the users. We calculate the number of developers for a file as the number of people who have committed at least one change that modifies the file. It is possible that a developer only makes non-essential changes to a file; still we include that developer. We also only run the Junit tests that Sonar identifies; there could be some test files that are missed by Sonar and these might impact the coverage levels. Threats to construct validity relate to the suitability of the metrics that we investigate in this study. We use standard metrics, e. g. , code coverage, cyclomatic complexity, lines of code, etc.
A. Studies on Testing & Code Coverage Numerous studies have investigated the importance of testing and code coverage on the overall quality and reliability of the software. Mockus et al. study two industrial software projects (Windows Vista from Microsoft and an application from Avaya) to understand the importance of test coverage on test effectiveness and analyse the required test effort with different levels of test coverage  [20]. The results of their study show that increased coverage leads to a reduction in the number of post-release defects but increases the amount of test effort. Cai and Lyu study the relationship between code coverage and fault detection capability using coverage testing and mutation testing  [23]. They analyse a large project to understand the relationship under different conditions of coverage metrics and testing profiles.
g. , size, complexity, etc. ) with code coverage. Our study shows that despite the importance of testing, for many projects, the code coverage is low. Gopinath et al. investigate the correlation between test suite coverage and its effectiveness in killing mutants  [15]. They start with more than 1,000 GitHub projects but need to remove most of them due to compilation, etc. errors. They end up with around 200 GitHub projects for their analysis. Most of the projects analysed are small (less than 1000 lines of code). They find that there is a correlation between test suite coverage and effectiveness. Our work complements this work by addressing a different set of research questions. We also study a larger set of projects and most of them are of larger sizes (more than 10,000 lines of code). Inozemtseva and Holmes also investigate the correlation between test suite coverage and its effectiveness in killing mutants on 5 large Java programs  [26].
B. Studies on GitHub In this work, we investigate over 300 projects from GitHub and Debian. Many other studies also investigate GitHub albeit for different purposes. Dabbish et al. examine the value of transparency of large-scale distributed collaborations and communities on GitHub  [31]. They interviewed several GitHub users to understand how a user infers the technical goals and vision of others based on the code they edit. Pham et al. conduct interviews with several users of GitHub to understand the impact of transparency on the testing culture  [32]. They present several strategies and challenges faced by collaborating developers and project managers. Further, they give suggestions to mitigate these risks to improve the testing behavior of people working on the same project. Bissyandé et al.
Project Distribution across Coverage LevelsCoverage indicates the amount of code touched by the test cases, but does not ensure that the program runs correctly on the tests. We thus next calculate test success density as the number of test cases that are executed successfully out of the total number of test cases. Figure2depicts the test success density of all the projects in our dataset. We observe that 254 projects have test success density greater than or equal to 98%, out of which 200 projects have 100% success density. 45 projects have test success density between 75% and 98%, and 6 projects exhibit success density between 25% and 50%. Only 9 projects in our dataset show a success rate below 25%.
Cloning and sequencing PCR product cloning was carried out using TOPO TA cloning kit (Invitrogen) into chemically competent Escherichia coli top 10 cells, and positive clones were tested according to the manufacturer's instructions. Plasmid extraction and 16S rRNA gene sequence were performed as described in a previous work  (Artiguenave et al. 2000). Bacterial 16S rRNA gene sequences were deposited in GenBank under accession numbers JN093309 to JN093512.