dataesr/scientific-paragraphs-categorization huggingface dataset:
The surface does not have to be part of the geometry, as the propagation takes into account both the geometry and the user-defined surfaces at the same time. Currently plane and cylindrical surfaces may be defined, as well as surfaces formed from the combination of the two. Volume: the surface of one of the logical volumes of the geo- metry is reached. The user may choose if the track is to stop only when entering the volume, exiting the volume, or in both cases. Track length: a given track length is reached. This package was implemented following GEANE  [238]  from GEANT3, which was based on the equations developed by the EMC collaboration  [239]. Users may implement the example provided with the GEANT4 distribution, at examples/extended/errorpropagation. The geometry in this example simulates a simplified typical high energy physics detector consisting of an air-filled beamline, an air-filled central detector, a copper calorimeter, divided into four sections, an aluminium calorimeter, divided into ten sections, and an air-filled muon detector.
Introduction Analysis tools based on AIDA (Abstract Interfaces for Data Analysis)  [240]  have been used in GEANT4 examples since release 3. 0 in December 2000, but until 2010 no analysis code was provided in the GEANT4 source. Several AIDA-compliant tools are available, including JAS, iAIDA, Open Scientist Lab and rAIDA  [15]. However some of them have not been maintained, some do not implement the AIDA interfaces completely and some are not always easy to install and use. A new analysis package based on g4tools  [241]  was added in GEANT4 release 9. 5 with the aim of providing users with a "lightweight" analysis tool available as part of the GEANT4 installation without the need to link to an external analysis package. It consists of the analysis manager classes and also includes the g4tools package.
Multithreading Like all other GEANT4 components, the analysis code was adapted for multithreading. In multithreading mode, instances of the analysis manager are internally created on the master and worker threads and data accounting is processed in parallel on worker threads. The migration to multithreading requires no changes in the user's client analysis code. HBOOK output is not supported in multithreading mode. Histograms produced on worker threads are automatically merged on the call to Write() and the result is written to a master file. Ntuples produced on worker threads are written to separate files, the names of which are generated automatically from a base file name, a thread identifier and eventually also an ntuple name. No merging of ntuples is performed in order to avoid an associated time penalty.
Testing tools Within the release process, integration of new or modified code into reference tags is performed. This requires regular nightly testing of new or modified code against the code base on a number of different platforms. A platform is a specific combination of hardware and operating system version, a specific version of compiler, and a set of GEANT4-specific build options. This regular testing was migrated from a custom setup to using the CMake tool suite  [246]. There is a continuous testing cycle and nightly test runs. Testing on a specific platform is performed using the CTest tool of the CMake tool suite. For each platform, the source code corresponding to the set of tags to be tested is checked out using SVN, CMake is run to configure and compile the code, and CTest runs the tests.
More than 80 unit test codes are applied, each testing a specific part of GEANT4. In addition most examples and several benchmarks are exercised during testing, bringing the total number of tests run to more than 200. The exact number varies with platform, as optional software required by some tests may not be available on every platform. CMake takes this into account by checking the availability of optional software during configuration, and skipping tests where software is missing. Finally, a summary of test results is uploaded to the CDash web site. In addition to nightly testing, continuous testing is performed on a small number of platforms using a restricted set of tests. This regularly checks for changes in the status of tags, including also tags proposed for testing, and starts a test run when needed.
Results are then collected and pushed to the validation database. The end user, typically a developer, is able to run on-demand analysis tasks through an interactive web application and produce plots. The current grid tools implementation is based on the DIRAC workload management system which is used to distribute the jobs. A system was developed to support different GEANT4 tests, examples and applications that are or will be executed on the grid. GEANT4 libraries and executables are made available to the sites through the GEANT4 CernVM-FS  [248]  repository. Finally ROOT is used for the results format, merging of split jobs and ondemand analysis.
Quality assurance Each software release of GEANT4 is validated against memory management issues, in particular run-time memory errors (overwrites, memory corruption, etc. ) and memory leaks, through use of the Valgrind  [253]  tool. Verification for run-time memory errors is completely automated as part of the system testing nightly builds, so that results for each test included in the testing suite can be retrieved directly from the testing dashboard and monitored along with the normal development process. Specific sessions of Valgrind runs are executed periodically on a selected set of tests covering a rather wide range of physics configurations to verify the absence of memory leaks during the execution of a simulation run for multiple simulated events; a summary of the results is distributed to developers by the release coordinator, together with hints on where problems may originate.
Memory leak checks are usually performed on candidate releases as part of the software release process. The Valgrind tool is also used for detecting errors in multithreaded simulation applications, by means of its DRD  [254]  module. DRD allows the identification of potential data-races happening during run-time among threads, when one or more threads try to access the same memory location without proper locking or protection. It also allows easy identification and debugging of lock contention cases, when a test program may get stuck in execution. Static code analysis is regularly applied at every development release, by instrumenting the entire source code with the Coverity  [255]  tool. The list of defects identified by the tool are first analysed and then assigned to the responsible developers for further inspection and correction.
Distribution Releases and beta previews, including all data sets, are available from the GEANT4 download page  [256]  as compressed source code files and in compiled form for Linux, MacOS and Windows. The source code and libraries for Linux are also available from AFS in /afs/cern. ch/sw/lcg/external/geant4 and in CernVM-FS in /cvmfs/geant4. cern. ch/geant4. The accuracy of the GEANT4 physics models is benchmarked regularly using thin-and thick-target tests. Thin-target tests allow a detailed study of specific observables from a single physics process for a given configuration of projectile particle type, projectile kinetic energy and target material. A large set of published thin-target data, collected over several years, is used roughly once per month to validate each internal development release.
The ability to store images allowed the initial population of the database with the available collection of existing test results. The alternative method of storing raw data points allows more interactivity, and can be used for example, in performing regression testing and model comparisons. As shown in Fig. 23 , the physics validation repository consists of a PostgresSQL database, a Java API and a JSP web application. The PostgresSQL  [257]  relational database stores collections of tests, such as images, tags, descriptions, references, and images with meta-data or raw data points, both experimental and simulated. The Java API is based on the data access object (DAO) design pattern, which provides an abstract interface to the database. Using mapping application calls to the persistence layer enables the DAO to provide some specific data operations without exposing details of the database.
The web application  [258]  is based on the Enterprise Edition (Java EE)  [259]  of the Java Platform, deployed on a GlassFish Application server  [260]. This allows tests to be viewed and modified, new tests to be uploaded, and provides security and authentication to grant access to functions and data internal to the GEANT4 collaboration. The PrimeFaces JSF (java server faces) Framework  [261]  is used to create interactive, modern-looking web interfaces and charting tools based on JavaScript are used for plotting. general biasing methods, improved and expanded physics models, expanded validation and testing, reference physics lists, the addition of rudimentry analysis methods, and improved and expanded visualization tools. As a result the toolkit is more versatile, easier to use and makes more efficient use of available CPU and memory resources.
Acknowledgments The authors gratefully acknowledge the support of this work which has come directly and indirectly from many sources, too numerous to mention. Acknowledged as well is the generous support of experiment collaborations and universities who have allowed their members to contribute significant time and effort to the development, testing and validation of GEANT4. The authors also express appreciation of the users of GEANT4, many of whom have contributed code, spotted and fixed problems and disseminated GEANT4 throughout many disciplines.
Phialocephala subalpina holds a large and feature-rich genome The genome of P. subalpina strain UAMH 11012 was sequenced at 25 x coverage using the Roche/454 GS FLX technology. Sequence data was assembled into 204 scaffolds (excluding rDNA repeat and mtDNA) with a genome size of 69. 7 Mb and an average GC content of 45. 9% (Table  1 ). The complete rRNA repeat (part of this assembly) and the mtDNA (http://www. ebi. ac. uk/ ena/data/view/JN031566)  [38]  were assembled manually and validated by Sanger sequencing. Data sets are accessible at http://pedant. helmholtz-muenchen. de/ genomes. jsp?category=fungal. The genome and annotation data was submitted to the European Nucleotide Archive (ENA, http://www. ebi. ac. uk/ena/data/view/ FJOG01000001-FJOG01000205). Mapping of reads data against the assembled genome did not reveal any significant deviations from the average 25x coverage except for the mtDNA (2,629x) and rDNA repeat  (1,422x)  indicating that no repetitive sequences were collapsed in short scaffolds leading to an underestimation of the genome size.
The annotation pipeline and manual validation resulted in 20,173 gene models. In addition, 91 tRNA genes coding for all amino acids and 20 5S rRNA loci were identified. None of the expected single-copy core orthologous genes found in eukaryotes (248 and 246 genes, see material and methods) was missing in the protein predications for P. subalpina indicating that the core gene inventory had completely been covered. This was supported by EST data as 28,045 out of 28,092 assembled transcripts (99. 83%) mapped to the assembly with high coverage (Additional file 1). 73. 6% of the 20,173 proteins showed an identity >30% with known proteins in the Similarity Matrix of Proteins database (SIMAP)  [39]. The remaining 5,313 proteins with less than 30% identity included 4,233 speciesspecific P. subalpina proteins.
No significant length difference was observed among the high identity and the low identity genes (Additional file 2A). Moreover, mapping of the 4,233 proteins against the 454 EST dataset and RNA-Seq data showed that 2,833 of these gene models were covered by ≥50% of the ORF length with EST/RNA-Seq data (Additional file 2B). A classification scheme of the gene models based on different analysis is given in Fig. 2.
In addition, repeat regions were generally rich in AT content as also ApA, ApT and TdT were more frequent in repeats than in non-repeat genomic regions (Fig. 3 ). The genome indicates horizontal gene transfer (HGT) events from bacteria co-occurring in the same habitat Twenty one out of 163 genes, originally with a non-fungal best SIMAP hit, showed a skewed taxonomic distribution or higher bit scores with non-fungal taxa in BLAST searches against the NCBI non-redundant protein (nr) database (Table  4 ). Phylogenetic analysis for these 21 genes showed that they likely result of HGT as the gene trees significantly deviate from the expected species tree (for three examples see Fig. 5b-d ; see also http://purl. org/ phylo/treebase/phylows/study/TB2:S20196). In contrast, the RPB2 gene which was used as a control in the analysis, showed the expected ascomycetous phylogeny (Fig.
Principal component analysis based on these two datasets showed that P. subalpina was either placed within the pathogens or close to the saprophytes (Fig. 6a, b ). Moreover, P. subalpina also shared the highest number of QuartetS clusters with the two mycorrhizal species (Table  7 , Fig. 6c ). The most frequent FunCat annotations for the pathogenand saprophyte-related clusters showed that the secondary and C-metabolism was most often included but also several putative orthologous proteins classified as virulence and disease factors were recognized (Table  8 ). A total of 6,556 InterPro accessions were annotated among the 14 species included in the analysis. A plateau of approx. 5,000-5,500 distinct InterPro accessions per        oxysporum. The genome of P. subalpina was especially rich in genes coding for glycoside hydrolases (471), glycosyltransferases (150) and redox enzymes (auxiliary activities enzymes, 157).
Whereas necrotrophic pathogens kill the host tissue by secreting effectors like toxins and/or proteins, hemibiotrophic pathogens grow intracellularly. and form specialized structures such as haustoria for nutrient uptake  [70]. Recent comparative genome analysis showed that there are only few genes associated with plant pathogens that are absent in non-pathogens  [33, 71]. However, domains overrepresented in necrotrophic/hemibiotrophic pathogenic species compared to saprophytes were identified  [71]. When our dataset was enriched for overrepresented InterPro accessions in pathogens only few of the accessions were in accordance with Soanes et al  [71]. However, a closer look showed that also of the accessions listed by Soanes et al  [71]  were also overrepresented in our dataset yet with a factor <2x.
Irrespective of which InterPro accession list was used for analysis, P. subalpina was placed close to the pathogenic species (Fig. 7b  and Additional file 7), indicating the robustness of the analysis. The list included genes with protease/peptidase domains, cutinases, pectinases, genes including a necrosis inducing domain or genes with chitinbinding modules. Many of these gene classes were shown to play a pivotal role in plant-pathogen interactions  [68, 70]. For example, PAMP-induced host chitinases can either be overcome by the action of secreted proteases  [72]  or the secretion of LysM effectors that may be coupled with a chitin-binding module  [73, 74]. The finding that P. subalpina includes the repertoire of pathogenic species fits well with the recent host-fungus interactions studies showing that PAC strains behave along the antagonism-mutualism continuum and are localized rather towards antagonistic interactions as the colonization results in reduced biomass accumulation of the host  [22].
Annotation of the P. subalpina genome The annotation strategy is presented in Additional file 9. In brief, a reference dataset of 1,089 gene models/proteins covered by full-length EST sequences was established and used as training dataset for Augustus  [98]. Ab-initio gene prediction was performed on the masked genome using GeneMark-ES  [99] , Augustus  [100]  and FGENESH (Neurospora and Ustilago matrices). Geno-meThreader  [101]  was used to calculate spliced alignments for P. subalpina ESTs and protein data from related fungal species. The program was run based on a P. subalpina specific splicing model trained using 454 EST dataset with the software BSSM4GSQ  [102]. For the training of the splicing model, ESTs were chosen that showed a high coverage >98%, a sequence similarity of 100% and had only one hit on the genome resulting in >4,000 intron/exon junctions.
Estimating the completeness of the P. subalpina genome and classification of gene models The completeness of the P. subalpina genome and annotation was assessed by mapping two separate highly conserved core gene sets including 248 and 246 proteins respectively  [106, 107]. In addition, the fraction of successfully and non-mapped ESTs was analyzed. Gene models were classified based on the identity against the best hit in the Similarity Matrix of Proteins database (SIMAP)  [39]. Proteins with identities ≥30% were considered as confidential gene prediction. The quality of the low identity genes (proteins with <30% identity with a protein sequence in SIMAP) was assessed by mapping the protein sequences against the assembled 454 EST dataset using GenomeThreader and analyzing the coverage of the gene model by ESTs.
In addition, RNA-Seq data was available following the genome sequencing/annotation  [108]  (http://www. ebi. ac. uk/ena/data/view/ PRJEB12610) was mapped against the genome using tophat (http://ccb. jhu. edu/software/tophat/index. shtml), discarding reads with bad mapping quality. Read coverage was calculated using coverageBed (http://bedtools. readthedocs. org/ en/latest/content/tools/coverage. html) for each exon within the coding sequence. The coverage graph coverage graph was calculated in R using the ggplot2 package  [109].
Presence of RNAi pathway and analysis for the presence of RIP mechanism A reference dataset of key proteins in the RNAi pathway of Neurospora crassa (ARGONAUT, DICER, and RNAdependent RNA polymerases (RdRP)) was used to search for similar genes in P. subalpina as described in Laurie et al. [110]. In addition, the presence of repeat-induced point mutations (RIP) in the genome of P. subalpina was analyzed. As RIP results in C-to-T transitions at repetitive loci  [45, 111]  we analyzed di-nucleotide abundances of all predicted interspersed repeats and of nonrepetitive control sequences using RIPCAL  [112]. Overrepresented dinucleotides were identified by determining the fold change of the dinucleotide abundance between repeats and controls. S. sclerotiorum was included as a reference in the RIP analysis.
Analysis of horizontal gene transfer from non-fungal species A total of 163 proteins showed the best hit with non-fungal taxa (124 with bacteria, 20 with plants and 17 with metazoa) when mapped against the SIMAP database in PED-ANT. The possibility of HGT for these genes was evaluated. In a first step, a BLAST search against the nr database (query coverage ≥40%; identities ≥20%; best 1,000 hits) was done and the taxonomic distribution and similarities of the hits were analyzed in R. Genes for HGT were selected as candidates if (i) they showed a biased taxonomic distribution of the hits (<15% fungal hits) and/or (ii) the fungal hits showed smaller bit scores in blast searches than non-fungal hits. Candidate genes were further analyzed using a phylogenetic approach. The full protein datasets of the ≤1000 best blastp hits against nr database for each candidate protein were downloaded.
Gene clusters enriched in saprophytes were searched using the same strategy. Both matrices were then subjected to principle component analysis using the vegan package in R  [120]  to analyze the position of P. subalpina compared to the other species. Moreover, FunCat terms  [121]  were enriched for these lifestyle enriched clusters by mapping P. subalpina geneIDs against the FunCat annotations and selecting the ten most frequent FunCat terms (any geneID/FunCat category was only considered once). In addition, putative orthologous gene clusters shared among the two mycorrhizal species T. melanosporum and C. geophilum with O. maius and P. subalpina was analyzed separately as the limited number of mycorrhizal species did not allow to properly defining lifestyle enriched clusters. In a second step, the non-redundant portion of Inter-Pro accessions per gene model, i.
Acknowledgments We thank  Marzanna Küenzler  und  Weihong Qi  ( FGCZ, Zürich ) for running the 454/FLX sequencing and  Thomas Wicker  for help in the classification and construction of the TE library. We also thank  Andrin Gross  and  Ottmar Holdenrieder  ( Institute of Integrative Biology (IBZ), ETH Zurich ) for helpful comments on a previous version of the manuscript.
Availability of data and materials The datasets supporting the conclusions of this article are available from the following repositories. European Nucleotide Archive: http://www. ebi. ac. uk/ena/data/view/JN031566 http://www. ebi. ac. uk/ena/data/view/FJOG01000001-FJOG01000205 Pedant Database: http://pedant. helmholtz-muenchen. de/genomes. jsp?category=fungal. CAZYme: http://www. cazy. org/ TreeBase: http://purl. org/phylo/treebase/phylows/study/TB2:S20196 In addition, datasets further supporting the conclusions of this article are included within the article and its additional files.
The analyses of these outcomes were carried out on 320 participants (out of 400 included in trial) corresponding to 1212 analyzable sexual intercourses between M2 and M24. The 80 participants with missing information were excluded and did not differ significantly from those retained for this part of the analysis, in terms of socio-demographic characteristics and sexual behavior indicators (data not shown). Overall, participants reported that 42. 6% of the total number of most recent episodes of sexual intercourses were protected by PrEP as the unique mode of prevention, irrespective of their sexual partner and sexual behavior. Over 28% of most recent episodes of sexual intercourse were without PrEP intake, 16. 7% during condomless anal sex (i. e. , high-exposure group), and 11. 7% during no condomless anal sex (i.
, 1998). Moreover, they were also available to meet participants between scheduled visits, if the latter desired. This individualized support might have had a positive impact on retention in the study, as illustrated by the low discontinuation rate observed (12%,  Molina et al. , 2015)  but also on the high level of adherence observed, as demonstrated by pill count, plasma drug level dosage  (Molina et al. , 2015) , and self-reported PrEP use (present results). Our findings are consistent with other studies showing a similar lack of disinhibition in at-risk practices after initiation of a biomedical prevention strategy within a clinical trial  (Baeten et al. , 2012; Grant et al. , 2010; Guest et al. , 2008; Liu et al. , 2013; Marcus et al. , 2013). The present results were obtained with data collected during the double-blind randomized phase of the ANRS IPERGAY trial, which may have impacted participants' social behavior since they did not know in which arm they were randomized.
Acknowledgements We would like to thank the study's participants for their time and their dedication to this research, for the benefit of their community. We also thank the  AIDES community advocacy group  and their community peer counselors who made this study possible, through their availability and continuous interaction with the participants and study staff at study sites and, most importantly, between scheduled visits ( J. M. Le Gall ,  S. Morel ,  V. Pechenot ,  S. Bagge ,  A. Djessima Taba ,  M. Danet ,  K. Moudachirou ,  B. Dos Santos ,  J. Lambec ,  S. Rouby ,  X. Teruin ,  N. Dumon ,  V. Coquelin ,  P. Brunet ,  L. Gilly ,  T. Cepitelli ,  R. Porion ,  D. Rojas Castro ,  B. Spire ). We thank our community advisory board for its continuous support during the study ( S. Karon  (secretary),  D.