dataesr/scientific-paragraphs-categorization huggingface dataset:
The platform also contains a fifth cluster only composed of one processor dedicated to task control at the platform level. Clusters each have 256 KB of onchip shared memory. The program memory is located onchip in a shared memory outside of the clusters area. An external memory is also available, offchip, with larger communication delays. This memory is accessible from the platform processors and from the host. All the memories are visible via a global memory map, which eases the possibility of inter-cluster communications. Each cluster also comes with DMA channels for inter-cluster communication and communication with the host. The work of design and implementation of the code generation toolchain was not dependent of a particular platform architecture. In order to decouple the problems of code generation and the adherence with a particular runtime API, we implemented a simple runtime library designed This runtime library provides a unified interface able to target POSIX Pthreads systems on one hand and MCAPI systems  [17]  on the other hand.
Tasks and ports The applications produced by our toolchain must be able to run on parallel architectures that do not present a central resource for task management and resource allocation (like STHORM). As a consequence, a primary concern for the code produced by our toolchain was to produce autonomous software entities, that do not require the need to access to a centralized service. In other words, a τ C application is only the aggregation of task objects, each of them only requiring access to its attributes, and all the application lifecycle, including initialization and termination, is done in a full parallel fashion. Figure  2  presents a UML overview of the types of objects that are executed at runtime. A τ C application is the composition of task objects. Each task object instantiated by the toolchain inherits from the generic task type.
A task is composed of port objects, and ports are instantiated by the toolchain from the generic port type. Task attributes are statically initialized during code generation. Task and port inheritance also allows us to introduce platform-dependent attributes. For example, MCAPI domains where associated to STHORM clusters. Each task must be associated to a particular MCAPI domain so that it can be correctly instantiated. On POSIX Pthreads platforms, we do no need such information.
It exposes a common way of working with τ C: all the processing code is held in a C library, and the main application loop is transcribed from C to τ C. The process is to take the C code chunks in the top level loop (listing 3), rewrite that function in a task (listing 4) and add the creation of the task in the main graph (listing 5). The end result is, in the current state of the prototype, compilable to our two targets (MCAPI on STHORM and POSIX Threads), including the offline instanciation phase, resulting in the production of the process graph in figure  3. Note that in that particular case, the width of the graph (the number of parallel processing threads over the video) is a command line parameter of the top-level process in the τ C code (and is written as you would read a command line parameter in a C program).
Conclusion We have presented the τ C programming language and model, and its implementation on two targets: POSIX Threads and MCAPI on P2012/STHORM embedded manycore. The language is a limited and simple set of extensions over C, with a parsing and generation of intermediate code in C, helped by an abstraction layer to isolate from the differences between the two targets platform. The parallel programming model is simple, has interesting properties both from the software engineering side and from the possibility of analysis offered by subsets of this model. Finally, this is well suited to platforms such as the P2012/STHORM embedded manycore, where the language and its compiler enables a high degree of isolation from the specifics of the architecture while maintaining a high degree of parallelism and efficiency.
Sometimes, a minimal implementation is done by simply editing datasheets stored on a computer. A better solution is to use a database that can be archived, updated and searched. Ideally, installing this database on a server rather than a local computer allows all members of the laboratory to use it as 'clients'. Commercial database softwares, such as FileMaker, have sometimes been used because they offer a user-friendly interface to develop the database and display information  (Nayler and Stamm, 1999). This solution is sometimes sufficient, but it requires to purchase the necessary number of licences so that all laboratory members can easily access data from various computers. Other companies offer storage services on the internet. This way, information about reagents can be stored and interrogated remotely from anywhere.
However, these services are sometimes technically limited, e. g. if fields are predefined and can not be modified easily, or if information can not be searched with sequence analysis tools. In addition, uploading research data on a private external server may raise important issues about security and confidentiality. Research institutions therefore often recommend storing the database on an in-house server. Dedicated commercial solutions, such as Lab-Collector (http://labcollector. com/), offer advanced possibilities. However, we consider that developing an open source alternative is important because of the following limitations of commercial softwares. First, users do not have the possibility to develop novel functionalities. Many biologists are familiar enough with programming to adapt database features to their needs.
, 2011). They are both well adapted to enter, edit and search items based on annotations or storage locations. OpenFreezer offers advanced possibilities to enter details about specific reagents, such as plasmid maps. However, neither of these programs provide any dynamic functionality that can retrieve information directly from DNA or protein sequences. Plasmid maps displayed by OpenFreezer are produced from static text files that must be edited by users, and stocks can not be searched by homology to a query sequence. We therefore have developed MyLabStocks, an open source software that stores information about laboratory stocks and uses sequence analysis tools to help users display, search and edit database records.
For example, when entering a new antibody in the database, a user can upload in this directory the product datasheet from  F. Chuffart and G. Yvert the commercial provider, a microscopy image he/she obtained with this antibody, a material and transfer agreement, or any other informative file. This way, any user can later download these documents directly from the item's page. For yeast strains, the item's page displays automatically the complete genotype of the strain by grouping information from all fields corresponding to genetic loci. For plasmids, MyLabStocks produces a plasmid map (see below) and displays it in the item's page (Figure  2 ). When displaying a plasmid page, MyLabStocks also performs a BLAST query search for homologies to oligonucleotides and plasmid features. This way, users instantly have access to the list of oligonucleotides matching the plasmid, and to the coordinates of the features displayed on the map.
Plasmid maps Every time a plasmid page is viewed, MyLabStocks dynamically generates a plasmid map that is displayed on the page. This map is produced by PlasMapper  (Dong et al. , 2004) , which interrogates a database of plasmid features and draws a map displaying the features that match the plasmid sequence. Users can update the plasmid features database directly from within MyLabStocks, by editing a table of features the same way they edit tables of materials. Note that MyLabStocks displays plasmid maps only to facilitate database browsing and information retrieval. It is not dedicated to the detailed analysis of plasmid sequences. Other programs provide extensive possibilities for such In this screenshot, the active table is the one storing plasmids information. Area 2 offers quick and easy tools to sort and search the table.
Searching the database using keywords and annotations The database can be searched in two ways. For easy and fast queries, a search option is directly available from every table of item. Depending on its nature, a field can be searched from queries entered in a text box or by selecting a value from a drop-down menu. If a search interrogates multiple fields, it returns the records matching all queries. MyLabStocks also provides a more advanced search option, where users can enter keywords in any number of fields, with OR and AND combinations (Figure  3A ). For yeast strains, this advanced option allows to search the global genotype instead of one genetic locus at a time.
Connection and privileges Users can connect to the database in any of four modes: view, add, edit and admin. The view mode allows users to freely navigate in any table and retrieve information without modifying it. The add mode allows user to add a record to a table. A single table can be accessed in this mode. For example, a specific login is necessary to access the table of plasmids in add mode, and this login does not provide access to the table of oligonucleotides or strains. The same applies to the edit mode, which allows to add and modify records. Preventing navigation across tables in add and edit modes minimizes the risk that users modify tables they did not intend to edit when they logged in. In addition, MyLabStocks displays a header band with a specific colour and warning message, inviting users to log out after their editing.
Implementation MyLabStocks is written in PHP/MySQL and is based on phpMyEdit (http://www. phpmyedit. org/). It can be used simultaneously by any number of clients using web browsers. Its sequence-based functionalities are performed by dynamic queries to BLAST  (Altschul et al. , 1990)  and PlasMapper  (Dong et al. , 2004). These two programs are free and must be installed with MyLabStocks. The database is structured in tables of materials (such as plasmids, strains, primers or pipettes), tables of annotations that generate dropdown menus (such as laboratory members or commonly used alleles), and a table of the sequence features that users wish to display on plasmid maps. Users can connect in four different modes, each one allowing distinct privileges on specific tables.
Back-ups All users can back up the content of MyLabStocks by clicking on one of two links proposed on the home page. The first link offers a full back-up option, producing an archive containing all the MySQL tables stored in MyLabStocks, together with all files stored in the items' subdirectories. The other link offers to back up the MySQL tables only, without downloading all the subdirectories which may contain voluminous files. These archives can be stored on the local computer of the user, who can then copy them on any safe storage device or system. The documentation explains how to restore the database from back-ups.
Editorial Cytoscape is an open source software platform for network visualization, data integration and network analysis  [1] [2] [3]. Cytoscape is most commonly applied to molecular networks and pathways in biology, though it is also used to interrogate the entire range from atomic and residue interactions in protein structures to chromosomal, cellular, organismal and social networks, as well as non-biological applications. Its success as a research tool largely comes from its flexibility. By maintaining an agnostic approach toward specific data types, file formats and network semantics, Cytoscape enables researchers to target any particular area of study they choose. This is accomplished through the development and use of Cytoscape apps. Cytoscape apps are software applications written in Java that customize and extend the functionality of Cytoscape.
This collection of Cytoscape app articles at F1000Research is intended to serve as a resource to both researchers and app developers. The author guidelines were customized for this collection to encourage relevant use cases and useful implementation details in a short article format. Researchers will find apps that connect to KEGG, Reactome and WikiPathways, perform identifier mapping, interface with R, GenomeSpace and GeneMANIA, perform network alignment and topological analysis, add pie charts to nodes, and export networks to the Web. App developers will find descriptions of Cytoscape API usage, including TaskIterator, CyNetworkReader, VisualMappingManager and CyCustomGraphics, as well as command interfaces exposed by other apps and links to their code. Every Cytoscape app published in this collection is free and open source.
Intracellular protozoan parasites are causative agents of infectious diseases that constitute major health problems for developing countries. Leishmania sp. , Trypanosoma cruzi or Toxoplasma gondii are all obligate intracellular protozoan parasites that reside and multiply within the host cells of mammals, including humans. Following up intracellular parasite proliferation is therefore an essential and a quotidian task for many laboratories working on primary screening of new natural and synthetic drugs, analyzing drug susceptibility or comparing virulence properties of natural and genetically modified strains. Nevertheless, laborious manual microscopic counting of intracellular parasites is still the most commonly used approach. Here, we present INsPECT (Intracellular ParasitE CounTer), an open-source and platform independent software dedicated to automate infection level measurement based on fluorescent DNA staining.
It offers the possibility to choose between different types of analyses (fluorescent DNA acquisitions only or in combination with phase contrast image set to further separate intra-from extracellular parasites), and software running modes (automatic or custom). A proof-ofconcept study with intracellular Leishmania infantum parasites stained with DAPI (49,6-diamidino-2-phenylindole) confirms a good correspondence between digital results and the ''gold standard'' microscopic counting method with Giemsa. Interestingly, this software is versatile enough to accurately detect intracellular T. gondii parasites on images acquired with High Content Screening (HCS) systems. In conclusion, INsPECT software is proposed as a new fast and simple alternative to the classical intracellular Leishmania quantification methods and can be adapted for mid to large-scale drug screening against different intracellular parasites.
Some open source solutions such as ImageJ software (http://rsb. info. nih. gov/ij) are very helpful for biologists with more than 500 plugins available. Nevertheless, mastering image processing concepts require specific competences and, to our knowledge, no versatile open-access solutions for biologists are available to automate intracellular parasite quantification. We present  INsPECT (INtracellular ParasitE CounTer) , the first open-source Java based software that uses fluorescent DNA staining and image processing framework to automate infection level measurement. This can be done through a user-friendly interface, where image files obtained from any fluorescent microscope and magnification, can be processed individually or as a batch, automatically or in a custom mode, without any experience required on image analysis.
Image processing and software development The image-processing pipeline for cell, parasite and cytoplasm detection is illustrated in figure  1  and detailed below. Acquired fluorescent images first need to be saved in tiff format, 8bit type and inverted to obtain dark objects in white backgrounds. These steps can be performed directly from the acquisition software, or lately by different image analyzing softwares. A simple procedure with ImageJ software is described in INsPECT user manual (File S1). 1. 1 Cell pipeline. Using Median Filter  [16]  raw input images are smoothed to remove possible noises. Kernel size is chosen small enough  (15)  to preserve cells' edge data. The next step is binary thresholding cells' areas. Since input images of this step still suffer from illumination inhomogeneity, the local mean adaptive threshold  [17]  is an appropriate method.
Parasites are then reassigned to their related cells and intra/extracellular parasites are discriminated for each cell. The software further automatically calculates all data required for most of experimental infection studies. INsPECT software is proposed as a free substitute or complement to the available quantification methods for measuring Leishmania infection level in vitro. It may be enlarged, however, to different intracellular trypanosomatids or unrelated parasites such as T. gondii. filter out random generated noises and keep real parasites alive. In order to accomplish this task, we execute the following steps: (i) Calculate the Global Mean Intensity (m) and Global Standard Deviation (stDev) of the input image. (ii) Pixels of the original image that have greyscale intensities equal to or below m+stDev are set to background (BG) in result image and also labeled in original image as BG.
To assign each detected parasite to its relevant cell, the euclidean distance between the center of that parasite to each cell is calculated and the cell with minimum distance is chosen to be the pair cell for that parasite. In the output image file, each parasite is therefore assigned with the same number as its corresponding host cell. By comparing each parasite's center with borders of extracted cytoplasmic regions, we then subdivide total parasites into intraand extracellular parasites. 1. 5 Software homepage and download. INsPECT software and the associated source code can be downloaded from the IRD bioinformatics homepage: http://bioinfo. mpl. ird. fr/index. php.
Images were captured on a Zeiss Axio Imager AX10 microscope equipped for DAPI (405 nm) excitation and 620, 640, 663 objectives. Ec50 was calculated using Graphpad Prismv5 software (sigmoidal dose-response model with a variable slope). 2. 2 HFF fibroblasts infected by T. gondii. T. gondii proliferation was analyzed as described hereafter. 5. 10 4 YFPexpressing RH parasites/well (48 well plate) were centrifuged on confluent HFF monolayers for 30 sec at 1300 rpm and incubated for 30 min in a water bath at 37uC for invasion. Wells were then washed three times with PBS to eliminate extracellular parasites and medium. After 24 h at 37uC in a humidified atmosphere containing 5% CO 2 , cells were fixed in 2. 5% formaldehyde (methanol free)/PBS for 30 min at room temperature. Nuclei were stained with Hoechst 33258 (2 mg/ml) for 20 min and then washed three times 10 min with water.
Software presentation Screen shots of the INsPECT interface are shown in Figure  2 , all frames are designed in such a way that a non-experimented user in image processing can easily work with them. A complete explanation of the software utilization can be found in the INsPECT user manual (File S1). The software comes with a basic functional image viewer that helps to see particles in images easily (Figure  2A ). Software can run either in an automatic or in a custom mode (Figure  2B ). In the former case, parameters and  options are set to some default values that are in agreement with the nature of most input images; however, end users can also adapt their intended parameters and options for cell, parasite and cytoplasm pipelines manually (see details in user manual). Users should then indicate input and output folders used for the rest of the analysis, and the nature of the images to analyze (Figure  2C ).
In parallel, random DAPI images were acquired with fluorescent microscope (406 objective) until approximately 300 cells were reached by condition (corresponding to 31 images and 1402 cells in total), together with their respective phase contrast images for cytoplasm detection (File S2). A batch analysis of all images was performed with INsPECT software (automatic parameters) and was completed in less than 7 minutes (Table  1 ). Direct comparison of output results shows that the number of detected intracellular parasites, and the subsequent calculated parasitic index (PI), is markedly higher with INsPECT software than with Giemsa method (Figure  3A ). Verification of the INsPECT analyzed images in the output folder allows us to confirm that the detected particles accurately correspond to true parasites, excluding therefore an overestimation of the parasite load by the software.
Conversely, the human operator naturally tends to select fields and cells offering the best visual resolution with Giemsa, generally excluding highly infected cells in which parasites cannot be well discriminated. The analysis of the ''Cell Parasites Report'' generated by INsPECT software confirms that highly infected cells are indeed frequently observed in untreated or low glucantime treatment conditions, and that this category of cells is mainly responsible for the differences observed between the two methods (Figure  S2 ). Once normalized to the non-treated infected control however, scaled PI values are very similar with calculated Ec50 of 73. 97 mg/ ml (95% confidence interval  [.
INsPECT software validation toward intracellular T. gondii parasite To further validate software robustness against unrelated intracellular parasite, we performed analyses on HFF fibroblasts labeled with Hoechst fluorescent DNA stain and infected by fluorescent YFP-expressing T. gondii RH parasites (File S3). All input images were obtained from HCS system designed for fully automated image acquisition and analysis (see details in Methods section). The analysis of representative images was performed with INsPECT software using only DNA fluorescent input images and choosing automatic mode. The size and distribution of Toxoplasma parasites strongly differ from that of L. infantum amastigotes, with tachyzoites parasites being mostly grouped in small aggregates inside cell cytoplasm. As shown in figure  4 , comparison of visual output annotated images to YFP labeled parasites confirms, however, a good correspondence between the two methods.
Discussion Monitoring intracellular parasite proliferation is an essential and quotidian activity for many academic or clinical diagnostic laboratories. The majority are still using manual microscopic counting, an unpopular and time-consuming task, that is further highly dependent on operator experience and objectivity thus leading to strong inter-laboratory variability. These facts make intracellular parasites counting a natural assignment for automation. In this study, we describe an innovative image analysis software allowing automatic detection of intracellular parasites and calculation of infection parameters such as the percentage of infected cells, the mean number of parasites per cell or the parasitic index. In a concept comparative study, we show that INsPECT software performs the complete analyze of 1402 Leishmania infected cells in less than 7 minutes, while counting the equivalent number of cells under microscope with Giemsa staining can take hours of effective work.
Accordingly, we expect INsPECT software to be as effective with any other intracellular kinetoplastid parasite as with L. infantum. Besides, we confirm that input images of T. gondii intracellular parasites acquired with HCS instrumentation could also be efficiently processed, even in automatic mode, proving therefore that INsPECT software is versatile enough to work with a variety of unrelated parasites and host cells. Regarding host cell nuclei in DAPI images, illumination variance was handled well by firstly, fitting appropriate noise model which removes noise efficiently while preserving precious cell portions' edge data and secondly hiring adaptive threshold with logical and automatic assignment of local window size. Overlapping cells were also detected and separated using state of the art quantification and segmentation approaches.