dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the imaging platform of the  Genetics and Developmental Biology unit at the Institut Curie (PICT-IBiSA@BDD)  for their outstanding support. We thank the animal facility of the  Institut Curie  for their invaluable help. We are grateful to the  Buchholz laboratory  for providing the Cdh1-GFP BAC construct. We thank  Yvonne Petersen  from the transgenesis platform at the  European Molecular Biology Laboratory ,  Takashi Hiiragi  and his lab for their very generous help in making the Cdh1-GFP mouse line. We thank the  Maître and Turlier labs ,  Yohanns Bellaïche ,  Lorette Noiret ,  Andrew Clark  and  Guillaume Charras  for fruitful discussions and comments on the manuscript. Research in the lab of JLM, who is supported by the  Institut Curie , the  CNRS  and the INSERM, is funded by the  ATIP-Avenir program , an  ERC-2017-StG 757557 , a  PSL "nouvelle équipe" grant  and  Labex DEEP  ( ANR-11-LBX-0044 ) which are part of the  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ).
, 2014; Mavruk, Saygu, Bengil, Alan & Azzurro, 2018) , providing new opportunities to overcome practical and budgetary constraint, especially in poorly studied areas. Here we accessed the knowledge of Mediterranean fishers, to reconstruct changes in fish distribution and abundance, altogether with their related spatial and temporal dynamics. We did so by: 1. Compiling a dataset of species that were perceived as increasing or new by respondents (hereafter referred to as increasing species); 2. Using this multivariate information to explore the structure of perceived change across different subsectors of the Mediterranean Sea; 3. Testing for the effect of spatial gradients on the overall number of increasing species; 4. Exploring the spatio-temporal evolution of increasing species.
, Sbragaglia, V. , Cerri, J. , Bariche, M. , Bolognini, L. , Souissi, J. B. ,. The final version, after peer-reviewing, has been published as: "Azzurro, E. , Sbragaglia, V. , Cerri, J. , Bariche, M. , Bolognini, L. , Souissi, J. B. ,. & Gianni, F. (2019). Climate change, biological invasions, and the shifting distribution of Mediterranean fishes: A large-scale survey based on local ecological knowledge. Global Change Biology. First published: 20 May 2019. doi:https://doi. org/10. 1111/gcb. 14670" Poisson distribution of the data  (Killick & Eckley, 2014). To quantify the intensity of this break, we also determined its jump, defined as the difference between the perceived abundance before and after the breakpoint. Since the breakpoint analysis was not sensitive in detecting the exact year of arrival of the 'new' species, we also extracted from each species-specific time series the year of perceived arrival, which corresponded to the year at which the perceived abundance changed from 0 (absence) to any of the other scores (i.
Acknowledgments The Mediterranean LEK initiative was initially conceived by the international basin-wide monitoring program CIESM Tropical Signals (funded by the  Albert II of Monaco Foundation ) and subsequently adopted by the projects  BALMAS (Ballast Water Management System for Adriatic The final version, after peer-reviewing, has been published as: "Azzurro, E. , Sbragaglia, V. ,  Cerri ,  J. , Bariche ,  M. , Bolognini , L. , Souissi, J. B. ,. & Gianni , F. (2019). Climate change, biological invasions, and the shifting distribution of Mediterranean fishes: A large-scale survey based on local ecological knowledge. Global Change Biology. First published: 20 May 2019. doi:https://doi. org/10. 1111/gcb. 14670".
Statistical analyses We assessed HBR, ER, PIR, EIR and PR using generalized mixed effect models (GLMM) with collection points and villages as nested random intercept. All models were fitted using the 'glmer' function of the package 'lme4'  [25]  run in the R software  [26]. For all models, we performed backward stepwise deletion of the fixed terms followed by Likelihood ratio tests. Term removals that significantly reduced explanatory power (p < 0. 05) were retained in the minimal adequate model. We used the post-hoc Tukey method to do multiple comparison among modalities of the fixed terms using the 'emmeans' function of the 'emmeans' package  [27]. We considered main effects and interactions of the fixed terms in all the models. We performed HBR and EIR analyses using negative-binomial models fitted on nightly counts of all Anopheles and of Anopheles individuals found to be infected by P.
Introduction In the past two decades, component-based software development (Cbsd) has emerged from existing software engineering paradigms as a challenging discipline for software engineers. Standards, such as Uml  [OMG07] , have evolved their specification to integrate components as first-class modeling elements, while in the past they were considered as simple deployment units. The proposal of methodologies, languages and tools has thus been a major step to make this promising way of building software concrete  [CSAC11, OMG07]. In this paradigm, applications are built by assembling several "prefabricated" software units, called components, which publish interfaces that make both the provided (served) and the required (used) services explicit. There are two major concerns in component-based software development that motivate our work: 1.
State of the Art In existing approaches, directories, repositories and registries are key concepts to leverage reuse in Cbsd  [ITV04]. We will provide the different definitions from our viewpoint to give the context of our approach. Component repositories provide stores where component implementations (code and configuration files) are saved to be later retrieved to build an application. Repositories are the place where the code of component types can be retrieved. The repositories provide some primitives to import the different component types. Component registries act as locator for component instances. They index the actual available component instances within an execution environment. Upon registries or repositories, component directories can be built to enable providers to publish information on available components, and users to know which components are available.
Compared to our approach, no work studies the automatic construction of component type hierarchies nor proposes concrete directory indexing mechanisms that could be implemented and used in component registries or repositories. Code repositories indexed with keywords or full text analysis: Most directories do not use classification and hierarchies. Code search engines, for instance Koders 2  , Google code search 3  , Krugle 4  and Merobase 5  , are derived from web search engines and use full-text indexation to build code repositories that can be queried by keywords. Merobase  [HJA08]  uses meta-information (syntactic structure of classes) and not only source code (considered as raw textual documents) to support more relevant queries: keywords can be associated with syntactic elements (classes, methods, variables, etc.
Test-based classification: In his dissertation  [Hum08] , O. Hummel addresses the discovery of components (source code, executable, etc. ). He goes further keyword-based searching (with general search engines) and regular expressions-based searching (like in Google Codesearch). He proposes syntactical queries based on different information including name and method signature. The approach is completed by steps where (1) it is checked whether the component can be compiled, and (2) it passes tests that define the desired semantics. Our context is not using the whole Web as a software repository as it is proposed in  [HA06]. In our case, we hypothesize a knowledge of a type hierarchy which is relevant to apply the Liskov approach, and a reduced vocabulary. There is also a certain normalization of the form of the signature.
Also we consider components that are not source code components, but described as in CBSE approaches by required and provided interfaces, containing operations described by their signatures which is specific to our problem. Libraries such as Fractal libraries are described under this form and do not need additional effort to be used. OWL-based approaches: Ontologies are used in  [YRZM10]  to represent concepts of the domain and types of components and their relationships. A query is then defined as a pattern graph which is applied to component library description in order to retrieve components. Components are described by meta-data in the form of an OWL ontology that represents the domain in  [GK13]. In these works, the kind of components that are dealt with is not specified. There is no syntactic description or organization (based on required / provided functionalities and interfaces) and no classification of the components is proposed.
Building a Hierarchical Component Directory Our three-step process relies on a specialization hierarchy of the data types 6  (Figure  4 ) involved in the functionality signatures of the sample components. For example, Route is specialized by both PubTranspRoute (public transportation route) and TouristicRoute. The initial flat (non hierarchical) set of components is easy to use for a given specified purpose: if a software architect wants to build a component assembly dedicated to touristic routes, she/he will use the TouristicRouteCalculation component (Figure 5) and connect it to a (required) component providing zoomable maps and to  What can be done if the TouristicRouteCalculation component fails? How can the architect rapidly determine which other component can replace it? How can architects build high-level assemblies with components that are general enough to be easy to replace rather than too specific assemblies that are difficult to repair at runtime? A suitable solution is a browsable component repository with the following characteristics: 1.
Classification Technique In our approach, the classification process relies on the definition of sound component substitution. This definition is obtained by first considering functionality and interface substitution. As a classification technique, we are using Formal Concept Analysis  [Wil82]  (Fca) which is a theoretical framework to analyse data. It extracts a partially ordered set (poset) of concepts (the concept lattice) from a dataset composed of objects described by attributes (the formal context). A concept is composed of two sets: an object set called the concept's extent and an attribute set called the concept's intent. These sets satisfy the following property: the extent is the maximal set of objects that share all the attributes of the intent. Reciprocally, the intent is the maximal attribute set that is shared by all the objects of the extent.
The DICOSOFT tool Dicosoft is a Java prototype tool that we developed in order to implement the component classification method. It analyzes repositories of Fractal components described in Fractal Adl 8  and implemented in Java with Julia (the reference implementation of Fractal) 9. Figure  17  shows the most important modules in Dicosoft: the LibraryParser and the LibraryClassifier. The LibraryParser takes as input all the following files: • Fractal Adl (Xml-based) files describing components, by specifying the interfaces they provide and require and the (nested) descriptors of their potential internal (encapsulated) components. • Java source and class files containing interfaces and classes implementing the components. If a flat structure of files is not available, Jar files can also be parsed by Dicosoft to extract interface and class files.
• An Xml file containing the type hierarchy (here, types are interfaces, classes and component types). This tool provides as output an intermediate Xml file containing a merge of all the parsed information. This enables to have a single file with all the information necessary for the classification. Besides, this allows us to decouple Dicosoft from the Fractal ADL syntax. Concretely, this Xml file is an Xmi (Xml Metadata Interchange) document that complies to the Cocola (Components in Concept Lattice Analysis) metamodel [AAF + 09]. The Cocola metamodel generalizes the Fractal component model by adding information about component implementations. It summarizes all data on component architecture descriptions. This metamodel has been implemented as an Ecore model on Eclipse  [Ecl09]. Thanks to the Emf (Eclipse Modeling Framework) plugin  [Ecl09] , we obtained an Api to parse and generate Xmi files that comply to the Cocola metamodel.
The LibraryClassifier takes as input the Xmi file generated by the first module and generates context tables, according to the classification variant selected by the user. It then uses an external tool developed by our team, named eRca 10  , to generate lattices or AOC-posets. Dicosoft includes a graphical user interface which enables end-users to choose a component library. It then generates classifications according to the selected classification method (variant). It provides views of the classifications (or exports them as images) and also makes it possible for the user to specify queries to analyze classifications. These queries are names of components, interfaces or functions. Dicosoft returns a list of possible substitutes for the chosen architectural elements. Figure  18  shows a screenshot of our prototype tool.
Experimentation on three Fractal component libraries We experimented the approach presented in our paper on three different Fractal component libraries of various sizes: Swing (smallest in terms of number of components), Rmi and Dream (largest). Figure  20  depicts the size of each analyzed library. In the next subsections, we highlight different operations offered by our classification approach such as component search for substitution or component refactoring, and we evaluate scalability of the classification approach using these three libraries. In the first subsection, we show how a software architect can retrieve a possible substitute for a failed component in a library classification. This library is Fractal Swing 11. We provide some metrics on the library and on the Galois Sub-hierarchies (AOC-poset)  obtained with the three different classification variants.
Background: Depth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next-generation sequencing pipelines, including the analysis of RNA-sequencing data, detection of copy number variants, or quality control procedures. Results: Building upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching >100× speedup over the state-of-the-art tools. The performance and scalability of our solution allow for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface. Conclusions: SeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.
Samtools and bedtools depth of coverage modules do not provide any support for a multi-core environment. Mosdepth implements parallel BAM decompression, but its main algorithm remains sequential. Sambamba, on the other hand, promotes itself as a highly parallel tool, implementing depth of coverage calculations in a map-reduce fashion using multiple threads on a single node. Regardless of parallelization degree, all of the aforementioned tools share a common bottleneck caused by using a single thread for returning results. Finally, GATK was the first genomic framework to provide support for distributed computations; however, the DepthOfCoverage method has not yet been ported to the current software release of the toolkit. We present the first fully scalable, distributed, SQL-oriented solution designated for depth of coverage calculations.
Implementation We have implemented SeQuiLa-cov in Scala programming language using the Apache Spark framework. To efficiently access the data from a BAM file we have prepared a custom data source using Data Source API exposed by SparkSQL. Performance of the read operation benefits from the Intel Genomics Kernel Library (GKL)  [15]  used for decompressing the BAM file chunks and from a predicate push-down mechanism that filters out data at the earliest stage. The implementation of the core coverage calculation algorithm aimed to minimize the memory footprint whenever possible by using parsimonious data types, e. g. , "Short" type instead of "Integer," and to implement an efficient memory allocation strategy for large data structures, e. g. , favoring static Arrays over dynamic size ArrayBuffers.
(B) Provided SQL API to interact with NGS data. The first statement creates a relational table read set over compressed BAM files using the provided custom Data Source, whereas the second statement demonstrates the use of the bdg coverage function to calculate depth of coverage for a specified sample. The presented call for coverage method takes sample identifier (sample1) and result type (blocks) as input parameters. bdg coverage is implemented as a table-valued function. Therefore, it outputs a table as a result, allowing for customizing a query using Data Manipulation Language, e. g. , in the SELECT or WHERE clause. For the purpose of this example, we assume that the BAM file for sample1 contains only reads from chr3. (C) Concept of distributed version of events-based algorithm. Assuming that we run our calculations in a distributed environment, the computation nodes do not work on the whole input data set (table read set) but on n smaller data partitions (slice1, slice2,.
Execution and integration options SeQuiLa-cov can be used as an extension to Apache Spark in the form of an external JAR dependency or can be executed from the command line as a Docker container. Both options can be run locally (on a single node) or on a Hadoop cluster using YARN (see project documentation for sample commands). The tool accepts BAM/CRAM files as input and supports processing of short and long reads. The tabular output of the coverage computations can be stored in various file formats, e. g. , binary (ORC, Parquet), as well as text (CSV, TSV). The tool can be integrated with state-ofthe-art applications through text files or can be used directly as an additional library in bioinformatics pipelines implemented in Scala, R, or Python.
Benchmarking We have benchmarked SeQuiLa-cov solutions with leading software for depth of coverage calculations, specifically samtools depth, bedtools genomeCov, sambamba depth, and mosdepth (results of DepthOfCOverage from outdated GATK version are available at http://biodatageeks. org/sequila/benchma rking/benchmarking. html#depth-of-coverage). The tests were performed on the aligned whole-exome sequencing (WES) and whole-genome sequencing (WGS) reads from the NA12878 sample (see Methods for details) and aimed at calculating blocks and window coverage. To compare the performance and scalability of each solution, we executed calculations for 1, 5, and 10 cores on a single computation node (see Table  2 ). Samtools depth and bedtools genomeCov are both natively non-scalable and were run on a single thread only.
Conclusions Recent advances in big data technologies and distributed computing can contribute to speeding up both genomic data processing and management. Analysis of large genomic data sets requires efficient, accurate, and scalable algorithms to perform calculations using the computing power of multiple cluster nodes. In this work, we show that with a sufficiently large cluster, genome-wide coverage calculations may last <1 minute and at the same time be >100× faster than the best single-threaded solution. Although the tool can be integrated with non-distributed software, our primary aim is to support large-scale processing pipelines, and the full advantage of SeQuiLa-cov's scalability and performance will be available once it is deployed and executed in a distributed environment. We expect that there will be a growing number of scalable solutions (Big Data Genomics project  [17]  with tools DECA and Cannoli as well as GATK4  [18] ) that can take advantage of reading input data directly from distributed storage systems.
Availability of supporting data and materials The Docker image is available at https://hub. docker. com/r/bioda tageeks/. Supplementary information on benchmarking procedure as well as test data are publicly accessible at the project documentation site http://biodatageeks. org/sequila/benchmark ing/benchmarking. html#depth-of-coverage. An archival copy of the code and supporting data is also available via the Giga-Science database GigaDB  [19].
Abbreviations API: Application Programming Interface; BAM: Binary Alignment Map; CPU: central processing unit; CSV: comma-separated values; GKL: Genomics Kernel Library; NGS: next-generation sequencing; ORC: optimized row columnar; RAM: random access memory; SQL: Structured Query Language; TSV: tab-separated values; YARN: Yet Another Resource Negotiator; WES: wholeexome sequencing; WGS: whole-genome sequencing.
For Ki67 staining, blood cells and lamina propria cells were incubated with antibodies for monocytes staining. BD cytofix/cytoperm kit (BD Bioscience) was used to do the cells fixation and permeabilization and next staining with anti-Ki67 and Hoechst. For Annexin V staining, cells apoptosis was quantified by the expression of membrane phosphatidylserine (PS) detected by Annexin V binding using Annexin V-APC Apoptosis detection kit as recommended by manufacturer (ebioscience) and analyzed by flow cytometry. Flow cytometry data were collected with the FACS LSR II (BD Biosciences) or FACS Canto II (BD Biosciences) and they were analyzed by Flow Jo software (version 7. 6. 5).
Statistical analyses. All tests involving multiple comparisons were adjusted using the Benjamini-Hochberg procedure controlling the false discovery rate  46   Survival analysis. We performed survival analysis using Cox's proportional hazard model; we assessed the significance of the hazard ratio between the reference and the other levels using Wald tests, and assessed the global significance of the model using the logrank test statistic (R package survival v. 2. 41-3). Kaplan-Meier and forest plots were drawn using R package survminer (v. 0. 4. 2). Note that three LCNEC samples from George et al. 14  had missing survival censor information and were thus excluded from the analysis (samples S01580, S01581, and S01586). DNA extraction. Samples included were extracted using the Gentra Puregene tissue kit 4g (Qiagen, Hilden, Germany), following the manufacturer's instructions.
The expected average coverage was approximately 120x after removal of duplicates (11 GB). DNA data processing. WGS and WES reads mapping on reference genome GRCh37 (gencode version 19) were performed using our in-house workflow (https://github. com/IARCbioinfo/alignment-nf, revision number 9092214665). This workflow is based on the nextflow domain-specific language  47  and consists of three steps: reads mapping (software bwa version 0. 7. 12-r1044)  48 , duplicate marking (software samblaster, version 0. 1. 22)  49 , and reads sorting (software sambamba, version 0. 5. 9)  50. Reads mapping for the targeted sequencing data was performed using the Torrent Suite software version 4. 4. 2 on reference genome hg19. Local realignment around indels was then performed for both using software ABRA (version 0.