dataesr/scientific-paragraphs-categorization huggingface dataset:
THE BrainVISA SOFTWARE ENVIRONMENT BrainVISA 1 is an open source software platform dedicated to the analysis of neuroimaging data  (Geffroy et al. , 2011). It comes with a comprehensive library of image processing functions and tools, that are accessible either by calling executables (compiled for each operating system) or through wrapper libraries callable from different programming languages such as C, C++, and Python. Moreover, the BrainVISA environment offers several characteristics that directly meet some of our specifications. First, its data management strategy relies on the use of a database to index data which is stored in the filesystem. 1 http://www. brainvisa. info. The ontology of the database is customizable (as described below in 2. 2) and BrainVISA insures the equivalence between the database organization and the architecture of the filesystem (name and organization of directories, subdirectories and files).
The current version of BrainVISA uses SQLite to organize and manage its database, allowing for a standalone use (i. e. , without any server) and direct access from Python. Second, BrainVISA supports several file formats that are commonly used in the neuroimaging community, including the recently developed Nifti1. 0 file format. The Nifti format has been designed to provide a unique data format that would be supported by all neuroimaging data analysis packages, which allows for easy exchanges between different software platforms. Although Nifti was thought to store magnetic resonance imaging data, it is perfectly adapted to store data acquired in functional optical imaging experiments. We therefore decided to use the Nifti1. 0 format to store all the data that needs to be manipulated in Vobi One.
Third, BrainVISA comes with an application programming interface (API) that makes it possible to easily develop GUIs, as illustrated on Figure  1. Indeed, constructing a GUI with the BrainVISA API consists in writing a Python script that mainly describes the inputs and outputs. Following this format, a view is automatically generated for the user to fill in the input parameters; the outputs are automatically named according to the filesystem architecture that matches the ontology of the database. A panel is also created to allow the user to follow the execution of the process. On top of these three features which directly meet some of our needs, Vobi One will directly benefit from two extra capabilities offered by BrainVISA. First, it is possible to define specific viewers for each type of data handled by BrainVISA; in particular, BrainVISA comes with a companion software, Anatomist, which is dedicated to visualizing imaging data.
VOBI ONE : A BrainVISA TOOLBOX A BrainVISA toolbox is a set of Python 3  programs divided into three components, each living in a subdirectory: types, hierarchies, and processes. The toolbox defines the GUI for each analysis component, as well as the customized database ontology used to store and retrieve data files. The different files that need to be manipulated by the toolbox are defined in the types subdirectory. Precisely, three different object types are defined here: FileType, Format, and HierarchyDirectoryType. A large list of these three types of objects is provided by the BrainVISA environment. The Format entries defined in a toolbox like ours allow defining new file formats which are not recognized by BrainVISA, with their associated file extension. In the case of Vobi One, we defined several proprietary file formats used by the optical imaging companies that are included in the acquisition set-ups in functional optical imaging experiments.
The HierarchyDirectoryType define directory types as used in the toolbox. Finally, the FileType entries define the categories of files used and created by the toolbox; for instance the type "OI GLM Denoised" was created to identify the files containing the result of a denoising using a general linear model (GLM). All these entries are defined in an objectoriented fashion and can therefore inherit properties from their father FileType, in particular from the ones natively defined in BrainVISA. The hierarchies files define the way the data is stored on disk in correspondence with the SQLite BrainVISA database. Two types of entries are therefore described in the hierarchies: (1) the architecture and naming conventions of directories and sub-directories, which start from the root BrainVISA database directory, and which match the HierarchyDirectoryType defined in types; and (2) the naming conventions for all the files stored in these directories, which match the FileType entries defined in types.
Importing routines In functional optical imaging experiments, the acquired data is composed of timeseries of two-dimensional images (2D+t data). Each experimental trial is stored in a single file, in a format, often proprietary, provided by the CCD camera vendors. For now, Vobi One includes importing routines for two of these formats (. blk files for Optical Imaging Ltd. , and. rsd files for SciMedia USA Ltd. ), which are the ones used in the setups at our institute and that are also widely used by the community. All importing routines have the same functions: (1) they allow to convert data from the input format into the standard Nifti 1. 0 format; (2) they populate the BrainVISA database so that all the following processes can efficientely retrieve the data; and (3) the Nifti files stored in the database all follow the same naming convention, regardless of the input format; this name includes all major informations about the experimental protocol (the date of acquisition, the experience number within that day, the trial number, and a number that encodes the characteristics of the stimulus for this trial).
Visualization The visualization tools available in Vobi One are defined as BrainVISA viewers (see Section 2. 2). They are directly accessible in the user interface of any of the processes described above as soon as a viewer is available for the file type associated with an input or output parameter. Otherwise, they are accessible as standalone processes in the Viewers category. All image types are directly associated with Anatomist, the companion software of BrainVISA dedicated to image data visualization. Moreover, several viewers have been developed for the graphics produced in the session-level postprocessing routines (some of them are shown on Figure  5 ).
SCRIPTABILITY AND THE OIDATA PYTHON MODULE As described above, the Vobi One toolbox sets up the computing environment around the processing workflows. One still needs to write the code that actually performs the different operations. In order to do this, several options are available: (1) include the code directly in the Python files that define the processes of the toolbox; (2) create executables that will be called from BrainVISA; (3) design a dedicated software library. The first option is definetely to be avoided because, for example, it would be impossible to share functions across different processes, resulting in duplicating code. The second option is the one used in most of the toolboxes that are included in the main BrainVISA platform. The developer can then choose its favorite programming language.
The main advantage is that the executables can be used outside of BrainVISA. But an important drawback is that one has to go through a compilation process which can be cumbersome when attempting to offer a truely cross platform tool. We therefore opted for the third option, i. e. , to develop a software library that would be linked to the toolbox code. Since the toolbox itself is written in Python, the easiest choice was to develop a dedicated Python module. We named it oidata, which stands for optical imaging data. We will now describe the advantages and drawbacks of this choice. The main negative aspect when implementing some data analysis procedure in Python is the computational efficiency, or potential lack thereof compared with executables compiled from C or C++ source. But since most computations in Vobi One consists in linear algebra operations performed on matrices, its computational efficiency will be guaranteed by using numpy 4  methods on numpy arrays  (Van Der Walt et al.
, 2011). On the other hand, developing a Python module offers several advantages: (1) no compilation is necessary to install Vobi One; the installation process simply consists in unpacking the pure Python module and the toolbox Python files into the main BrainVISA installation directory; (2) it seamlessly becomes possible to fully operate Vobi One by writing scripts in Python and directly calling the methods offered in this module. Moreover, we imposed an extra constraint when designing the oidata Python module, by forcing that the core of each process in the Vobi One toolbox (as defined in 2. 2) consists in calling a single function of the oidata module, as illustrated on Figure  1 ). Therefore, there is a perfect correspondance between using the GUIs and writing scripts. We hope that this can ease the task of users who start using Vobi One with the GUIs and want to start writing scripts because the structure of the scripts they would have to write would intuitively correspond to what they are used to do with the GUI.
INTEROPERABILITY Another major feature of Vobi One is its possibility to interact with other softwares. It is indeed crucial that one can easily perform some operations with Vobi One, and some others, which for example are not available in Vobi One yet, by using other software tools. This interoperability capability is insured by the fact that all image stacks in Vobi One are stored in the Nifti format. Indeed, standard input/output libraries are available to manipulate Nifti files from many languages including C, Matlab and Python, and the Nifti format is also natively supported by numerous other software suites dedicated to neuro-imaging. Various analysis results provided by Vobi One, such as denoised timeseries averaged across all trials of a sessions for a given list of experimental conditions, can therefore be easily imported in other software environments.
Dataset The data provided with Vobi One consists of a subset of the data that was used to validate the linear model in  (Reynaud et al. , 2011). The reader is referred to this article for a full description of the experimental protocol, but we here briefly describe the characteristics of this dataset that are relevant for the present paper. The data was recorded on an awake behaving monkey (Macaca mulatta) in a VSDI experiment. The CCD camera produced images with 512 × 512 resolution at an acquisition frequency of 110 Hz. For each trial, the beginning of the recording was triggered on the heartbeat of the animal as soon as it achieved correct fixation on the red dot located in the middle of the screen. A visual stimulus consisting of a drifting grating was then displayed on the screen. Six different contrast levels were used for the grating, changing from trial to trial.
DISCUSSION Our decision to develop a software package dedicated to the analysis of functional optical imaging data was initially motivated by the need to provide support for users at our institute. In practice, this meant replacing a set of Matlab scripts that had been developed and customized by several users, by a unique software package for which we would offer centralized support and that could also serve as the central piece of inhouse collaborative research. We recently decided to make Vobi One available to the community because it now contains enough functionalities to be an asset to the few groups using this modality within the neuroscience community, as we described earlier in this paper. In particular, we find it important that our central methodological contribution, the construction of a linear model to analyze single experimental trials, can be easily used by researchers in our community, which is now the case thanks to Vobi One.
ON-GOING DEVELOPMENTS IN VOBI ONE The development of Vobi One is still on-going, because all the features that are needed by the researchers are not yet available. At present, the way our local users process their data is by using Vobi One for all available operations and complementing it with their own code and tools for all unsupported tasks, thus demonstrating the inter-operability capability offered by the toolbox. We here want to briefly describe the main features that are currently under development. First, although several processes are available to visualize timeseries, and compare them across regions, experimental conditions or processing methods, the visualization capability of the associated spatial maps still rely on a proficient use of Anatomist, the companion software of BrainVISA that is dedicated to image visualization.
DISTRIBUTION AND COLLABORATIVE DEVELOPMENT Vobi One is available through our website 5. This site is also our internal software development plateform, which uses a Trac 6 system based on Mercurial 7 for version control. The software package is distributed as a tarball that just needs to be unpacked in the BrainVISA installation directory. It comes with detailed installation instructions and a user's guide that takes the reader step by step into the BrainVISA environment and each of the Vobi One processes. A full dataset is also provided, which is one session of the data that served to benchmark our linear model in  (Reynaud et al. , 2011). We strongly hope that researchers will benefit from using Vobi One and that developers will be interested in further contributing to its development. Short term objectives for such collaborative development would obviously be to support additional data formats to better answer the needs of the community, and also to add other data processing techniques such as those based on source separation algorithms for instance GIF  (Yokoo, 2001) , or TSCA  (Blumenfeld, 2010; Omer et al.
CONCLUSION We have described Vobi One, the first open source software suite dedicated to the analysis of functional optical brain imaging operating at the mesoscopic scale. Vobi One is a toolbox of the BrainVISA neuroimaging software environment. As such, its developers can directly make use of BrainVISA's image processing library and its API, while its users benefit from its graphical user interface, its built-in database support and its interoperability capability. Advanced users can also write Python scripts to fully operate the toolbox, thus facilitating the production of reproducible research. Two main types of analysis methods are available in Vobi One: (1) the standard method based on divisive normalization to baseline level followed by subtraction of blank trials; and (2) a framework based on a thorough modeling of all noise sources and a multiple linear regression, as described in  Reynaud et al.
The powdery mildew population crashes during the winter due to a lack of living host tissue, but reinitiation of the epidemics in the following spring is enabled by the germination of sexual resting structures named chasmothecia. Epidemiological data. Small-scale data were collected during the summer 2011 within one Pl. lanceolata meadow (ID 609) located in the Eckero part of the A ˚land archipelago (Neither the host plant nor the pathogen is protected species and Finnish legislation (Jokamiehenoikeus) allows the sampling of wild species to everyone). This Pl. lanceolata population (approx. 2400 m 2 ) was visited weekly between July 18 and August 25 (week 29 to 34), except on week 33 (five observations in total). Pattern of infection within host populations is known to be highly aggregated in this pathosystem  [22].
Regression Model for the Analysis of Strain Contributions Consider a host population covering a spatial domain divided into I similar grid cells. The cells are labelled by i [ f1,. ,Ig. Two types of observations are made: epidemiological observations and pathogen genetic observations. The epidemiological data are pathogen intensities Y i (t) in cells i [ f1,. ,Ig at times t [ ft 1 ,t 2 g. The pathogen genetic data observed at time t 2 are J samples randomly collected in the grid cells and classified into a set of S strains. The label j[f1,. ,Jg is used to identify the samples. The label s [ f1,. ,Sg is used to identify the strains. For each sample j, i j is the cell where j was collected and s j is the strain of j, and I ~fi j : j~1,. ,Jg5f1,. ,Ig is the set of cells containing genetic samples.
Application to Powdery Mildew of Plantago lanceolata The ranking method was applied to the field data of powdery mildew epidemics displayed in Figure  3. Among the 44 collected samples, 40 represented single-strain infections, leading to the identification of five different strains within the pathogen population. Among the four mixed-genotype infections, three could be attributed to a mix of previously identified strains, whereas one remained unattributed and was removed from the dataset. For the analysis, we used the intermediate bandwidth b~2 to decrease the risk of detecting false positive differences. In addition, since there are five strains, the total number of unilateral tests is 10 and the rejection threshold is of the order 0. 05/10, using Bonferroni's correction. However, this threshold value is certainly very conservative (tendency to under-reject the null hypothesis of coefficient equality when this hypothesis is wrong).
The classification of transposable elements (TEs) is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system.
It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats), rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus. Availability: PASTEC is available as a REPET module or standalone software (http://urgi. versailles. inra. fr/download/repet/ REPET_linux-x64-2. 2. tar. gz). It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque), and the other of which is not.
This tool can classify class I elements into LTRs and non-LTRs and, where possible, into SINE or LINE elements. However, TEClass cannot distinguish between the various orders of class II elements. The SVM (Support Vector Machine: supervised learning model) classifiers in this tool were trained with RepBase Update, a database of eukaryotic repetitive sequences  [6] , using different class lengths and vectors of tetramer and pentamer frequencies. This approach is useful when TEs show no clear similarity to known repeats. REPCLASS can classify TEs into classes I and II, and into the orders LTR retrotransposon (grouping LTR, LARD, TRIM, DIRS and PLE from Wicker's classification), DNA transposon (grouping TIR, crypton and Polinton from Wicker's classification), LINE/SINE and helitron. In some cases, this tool can also identify the superfamily of the TE.
REPCLASS consists of three modules. The first is based on homology and has the greatest impact on final decision. At this step, the software carries out a tblastx analysis against the RepBase databank. The second module (in terms of order of priority for classification) searches for structural charac-teristics: terminal repeats, such as LTRs and TIRs, tRNA, polyA signals and SSR. Finally, the third module searches for target site duplication (TSD) around TE copies within the genome. Neither TEclass nor REPCLASS can classify TEs at the level of detail given by Wicker's classification (see Table  1 ). We therefore developed PASTEC (Pseudo Agent System for Transposable Element Classification), a modular tool able to classify TEs automatically to order level, for all nine orders of autonomous TEs defined in Wicker's classification plus three orders of nonautonomous TEs (LARD, TRIM and MITE).
If PASTEC cannot classify a TE to order level, it classifies it to class level. PASTEC is also designed to filter out false-positive repeated sequences identified by de novo approaches. It can therefore distinguish SSRs, rDNA sequences and potential host genes from real TEs. Moreover, PASTEC can classify and recognize incomplete TEs and potentially chimeric TEs (detection of an anomaly in the evidence used to select the classification, or several classifications possible). In this last case, PASTEC chooses the best classification if it has sufficient evidence to make a decision. All evidence is written to the output file, to facilitate the manual curation of potential chimeric TEs. We tested this tool by comparing the results of these three tools on three different datasets.
Materials and Methods PASTEC was developed in the REPET package  [7]. In this context, we used PASTEC to classify the consensus TE sequences found de novo in a genome. PASTEC uses several features of TEs to classify TE consensus sequences. It searches for structural evidence and sequence similarities stored in a MySQL database obtained during a preprocessing step. The structural features considered are TE length, presence of a LTR (long terminal repeat) or TIR (terminal inverted repeat) detected with a custom-built tool (with a minimum length of 10 bp, a minimum identity of 80%, the taking into account of reciprocal orientations of terminal repeats and a maximal length of 7000 bp), the presence of SSRs (simple sequence repeats detected with the tandem repeat finder (TRF) tool  [8] ), the polyA tail and an ORF (open reading frame).
The blastx and tblastx routines are used to search for similarities to known TEs in Repbase Update, and the hmmer3 package  [9]  to search against a HMM profile databases (TE-specific or not), after translation in all six frames. Sequence similarities are also identified by blastn searches against known rDNA sequences, known host genes and known helitron ends. The databanks used are preprocessed and formatted. The Repbase Update for PASTEC can be downloaded from http://www. girinst. org/ repbase/index. html, whereas the HMM profile databank formatted for PASTEC is available from the REPET download directory (http://urgi. versailles. inra. fr/download/repet/). PASTEC classifies TEs by testing all classifications from Wicker's hierarchical TE classification system. Each possible classification is weighted according to the available evidence, with respect to the classification considered.
TEs are currently classified to class and order level. PASTEC can also determine whether a TE is complete on the basis of four criteria: sequence coverage for known TEs, profile coverage, presence of terminal repeats for certain classes, presence of a polyA or SSR tail for LINEs and SINEs, and the length of the TEs with respect to expectations for the class concerned. We designed PASTEC as a modular multi-agent classifier. The system is composed of four types of agents: retrievers, classifiers, filter agents, and a super-agent (Figure  1 ). The retriever agents retrieve the pre-computed analysis results stored in the MySQL database. They act on the requests of the classifier or filter agents, filtering, formatting and supplying the results. The classifier and filter agents are specialized to recognize a particular category.