dataesr/scientific-paragraphs-categorization huggingface dataset:
Since HSI is not able to penetrate into the surface, extravasated blood present in the image is identified as a hypervascularized tissue class (blue color) in the TMD map. Table  3  shows the execution times obtained using the HELICoiD demonstrator to acquire and process the validation database during surgery. To assess the processing times obtained using the hardware acceleration in the spatial-spectral supervised classification stage, Table  3  also shows the processing times obtained when the whole algorithm is implemented in the CPU, i. e. , sequential time results. The total processing time required in the accelerated version is computed taking into account the maximum time obtained between the spatial-spectral supervised classification (PCA + SVM + KNN) and the unsupervised clustering (HKM).
In summary, when the hardware accelerator Table  3  shows the execution times obtained using the HELICoiD demonstrator to acquire and process the validation database during surgery. To assess the processing times obtained using the hardware acceleration in the spatial-spectral supervised classification stage, Table  3  also shows the processing times obtained when the whole algorithm is implemented in the CPU, i. e. , sequential time results. The total processing time required in the accelerated version is computed taking into account the maximum time obtained between the spatial-spectral supervised classification (PCA + SVM + KNN) and the unsupervised clustering (HKM). In summary, when the hardware accelerator is not employed, the spatial-spectral supervised classification is the most time-consuming stage.
An HS brain cancer detection algorithm, based on unsupervised and supervised machine learning approaches, was developed and implemented in the system. The supervised algorithm was trained by employing a labeled dataset composed of more than 300,000 spectral signatures, extracted by medical doctors from 36 different HS cubes captured with the acquisition system from 22 different patients from Spain and UK. In this preliminary study, only one multiclass classifier was generated for the supervised part of the algorithm, employing all the types of tumors available in the training database to distinguish mainly between tumor and normal tissue, without identifying the different types of tumors. The implementation of the algorithm was partitioned between the control unit and a hardware accelerator, where the higher computational tasks were implemented in a many-core platform to achieve intraoperative processing (~1 min).
The demonstrator was validated using seven HS images obtained in four neurosurgical operations. The TMD maps demonstrate that the system did not introduce false positives in the parenchymal area when no tumor was present and it was able to identify different types of tumor that were not present in the training database. Currently, further investigations are being carried out by the research team in order to enlarge the training database and the validation database with more patients and types of tumors. Additionally, the fusion of both types of HS images (VNIR and NIR) is being investigated in order to investigate if the NIR information could help to more accurately distinguish the boundaries between the tumor tissue and the surrounding hypervascularized normal tissue. Furthermore, an extensive clinical validation of the system must be carried out.
Cortical thickness analysis (CoTks) A registration-based method (Diffeomorphic Registration based Cortical Thickness, DiReCT) was employed to estimate the cortical thickness  (Das et al. 2009 ) from the T1-weighted imaging dataset. The first step of this method consists in creating two-voxel thick sheets, one that lies just between the grey matter and the subcortical white matter and a second lying between the grey matter and the pia matter. Then, the former is expanded to the latter using diffeomorphic deformation estimated with ANTs  (Avants et al. 2007; Klein et al. 2009; Tustison and Avants 2013). The registration produces a correspondence field that allows an estimate of the distance between the outer and inner boundaries of the grey matter ribbon, and thus cortical thickness. This approach has good scan-rescan repeatability and good neurobiological validity as it can predict, with high statistical power the age and gender of the participants  (Tustison et al.
Entropy analysis (rs-CoEn) First, T1-weighted gradient-echo images were skull stripped using Brain Extraction Tool (BET) as part of the FMRIB software package (FSL, http://fsl. fmrib. ox. ac. uk). T2*weighted images were subsequently registered to the anatomical (T1-weighted) image using affine deformations. Skull-stripped T1-weighted gradient-echo images were registered to the MNI152 template (http://nist. mni. mcgil l. ca/?p=904) using affine and diffeomorphic deformations (http://stnav a. githu b. io/ANTs)  (Klein et al. 2009; Avants et al. 2011). The latter deformations were applied to the T1 registered T2*-weighted images. Since the resting-state fMRI signal can be heavily affected by motion, even following motion correction between temporally adjacent volumes  (Van Dijk et al. 2012) , we estimated the signal fluctuation associated with motion and regressed it out from the fMRI data prior to the calculation of entropy.
Statistical analysis We confirmed the Gaussian distribution of the data for the three groups using the Shapiro-Wilk test  (Shapiro and Wilk 1965) , as well as the homogeneity of variance with the Levene test  (Levene 1960). Statistical analysis was performed with SPSS 20 (SPSS, Chicago, IL, USA). Two consecutive repeated measures ANOVAs were employed to assess differences in CoTks and rs-CoEn between the three groups. Cytoarchitectonic areas were considered as between subject factors and hemisphere as within subject factor. Post-hoc independent-sample t tests (Bonferroni corrected for multiple comparisons) were performed when statistically appropriate. The relation between age, visual deficit duration and the cortical thickness and cortical entropy for the entire brain and for each cytoarchitectonic ROI was analysed through linear regression in SPSS 20 (SPSS, Chicago, IL, USA).
Acknowledgements We would like to thank the team of the " Centre d'investigation clinique" of the  " Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts ", particularly  Saddek Mohand-Saïd MD  and  Céline Devisme PhD  for their invaluable help in patients' recruitment and the members of " Forschungszentrum Jülich GmbH"  for providing us with the cytoarchitectonic maps of the occipital lobe.
Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks. BioLQM provides core modeling operations as building blocks for the development of integrated modeling software, or for the assembly of heterogeneous analysis workflows involving several complementary tools. Based on the definition of multi-valued logical models, bioLQM implements import and export facilities, notably for the recent SBML qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case.
Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements various updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.
These effects are further formalized as logical rules (also called logical parameters or logical functions), specifying the target activity level of each component according to the current levels of its regulators (a subset of all model components). Interactive software for model definition such as GINsim  (Naldi et al. , 2018a)  or The Cell Collective  (Helikar et al. , 2012)  enable the definition of regulatory graphs and logical rules. However, these logical rules are self-contained and can be used to recover signed regulatory interactions. The relative simplicity of this formalism enables the definition of large models with dozens of components, without requiring precise knowledge of kinetic parameters. A formal definition of logical qualitative models is provided in Appendix 1 in Supplementary Material.
The CoLoMoTo consortium was recently founded to facilitate model sharing and foster cooperation in the qualitative modeling community, building on the introduction of the SBML qual exchange format  (Chaouiya et al. , 2013; Naldi et al. , 2015). The bioLQM toolkit presented here reinforces this effort by implementing a collection of model modification, format conversion, and dynamical analysis operations in an extensible architecture illustrated in Figure  1. On one hand, format conversions enable the integration of several software tools in complex analysis workflows. On the other hand, the core data structure and model modifications provide building blocks for the development of integrated modeling tools, which can add their own model edition and visualization capabilities. BioLQM is notably embed in the popular GINsim software  (Naldi et al.
LOADING AND CONVERTING LOGICAL QUALITATIVE MODELS The increasing use of qualitative models to study biological systems led to the development of various software tools for the logical formalism  (Albert et al. , 2008; Garg et al. , 2008; Müssel et al. , 2010; Terfve et al. , 2012; Naldi et al. , 2018a)  and related qualitative approaches  (Batt et al. , 2012; Paulevé, 2017; Stoll et al. , 2017). Most software tools use their own file format for the definition of models, hindering the delineation of analysis workflows combining different tools. The SBML qual exchange format  (Chaouiya et al. , 2013)  has recently been proposed to improve interoperability between modeling tools. However SBML support is often missing from existing software and may not be a priority for newer ones. To ease model exchange between software tools that do not all support the SBML qual format, the bioLQM toolkit provides an extensible list of format handlers connected to the internal model representation.
e. , its evolution over time) is given by transitions between states of the model, controlled by the updating calls (i. e. , by the logical rules of the model) and by updating modes which define the synchronization between concurrent updating calls. Various types of updating modes have been introduced, with most software tools focusing on a specific subset. BioLQM aims to provide an extensive choice of updating modes in a single toolkit. In the following subsections, we further distinguish deterministic and non-deterministic simulations and provide an overview of all updating modes implemented in bioLQM. While stable states, which have no transition toward other states, do not depend on the updating mode, reachability properties and cyclical attractors can be strongly affected by the choice of updating mode as illustrated in Figure  2.
Stochastic Simulations Stochastic updaters enable the computation of a single successor, which is selected randomly among multiple possibilities and can thus change between calls. A stochastic updater can be derived from any non-deterministic updater by assigning identical probabilities to all transitions defined by the original updater. Alternatively, a custom updater can be constructed by defining individual probabilities. BioLQM provides the random tool to compute single random trajectories using the above stochastic updaters. This tool is limited to the construction of individual trajectories and does not provide a complete stochastic analysis. As listed in Table  1 , bioLQM enables the conversion of Boolean models to the format of the MaBoSS software, which uses the Gillespie algorithm to estimate the probabilities of Boolean states of a continuous time Markov process, and provides a collection of scripts to further analyze the simulation results  (Stoll et al.
Model Conversion Enables Interoperability As discussed in section 2, the analysis of complex models can combine several software tools. After running the following command, the new p53. sbml file will contain the functions defined above in the SBML qual format. This format is suitable for use in several other tools, or for submission in the BioModels database  (Chelliah et al. , 2013). $ bioLQM p53. mnet p53. sbml.
Definition of Complex Analysis as Scripts More complex analysis tasks can use the integrated scripting interface. Based on the java scripting engine, it supports scripts written in javascript (as part of the java platform) or in another supported language by providing additional libraries (including python and lua). The following sample script generates all possible individual knockout perturbations, and saves each modified model. This script can be launched using the -s flag, followed by the script name. Additional arguments can be used to adapt the behavior of the script. In this example, we specify the name of the original model file.
SUMMARY AND DISCUSSION The increasing use of logical models of biological regulatory networks led to the development of multiple complementary software tools for their analysis. The recent introduction of the SBML qual format  (Chaouiya et al. , 2013)  and the formation of the CoLoMoTo consortium  (Naldi et al. , 2015)  aims to facilitate the exchange of models between tools. The bioLQM toolkit enables the use of additional software tools through conversion to their native formats. It provides model conversion operations in the CoLoMoTo notebook  (Naldi et al. , 2018b) , enabling the delineation of analysis workflow involving a series of different tools. BioLQM can also be used to apply various perturbations to the converted models, enabling the study of model variants emulating a knockout, an ectopic activity, or the loss of an interaction.
Model modifications include the booleanization of multi-valued models for analysis with tools restricted to a Boolean formalism, as well as model reduction, decreasing the number of components to ease the analysis of complex models. Finally, bioLQM provides several internal tools for the dynamical analysis of logical models. Two of the included tools allow the construction of deterministic and stochastic simulation traces, based on a comprehensive collection of updating modes. BioLQM also implements non-deterministic updating modes, which can be used as core components of complete simulation engines, as done by the GINsim  (Naldi et al. , 2018a)  and Epilog  (Varela et al. , 2013)  software suites. Two other tools enable the efficient identification of stable states and the approximation of most complex attractors.
The features described above are organized in a flexible architecture to facilitate the addition of new modules (file formats, model modifications, analysis tools) and to provide a consistent API. In the next version, the configuration API of analysis tools will be further improved to improve their use through python scripts in the new CoLoMoTo notebook. Hardware requirements strongly depend on the size and structure of models and the operations performed. The complexity of individual logical rules can be a limiting factor: components with tens of regulators could have intricate rules with high computational cost. Fortunately, such rules are seldom used in biological models. Any desktop computer should be able to load and convert most models, including large ones. However, detailed dynamical analysis of models beyond 30 components can rapidly fill the available memory.
Such behaviors could be emulated in logical models through the addition of synchronizing components. Proper support for this use case would require extensions of the SBML qual specification, as well as changes in the internal data structure. Like most modeling tools, bioLQM is currently centered on logical rules, however a complete model may contain important additional information, such as annotations and graphical layout information. Model annotations are supported in SBML core (without additional extensions), however annotations can be defined in any format, hindering interoperability. Further discussions are needed within the community to delineate best practices and ensure that annotations can be shared efficiently. Graphical layout information can be stored along with SBML qual models using a dedicated extension.
This information is currently supported by JSBML and GINsim, it will be integrated in future versions of bioLQM. JSON "sidecar" files could then be used to facilitate the integration of such additional information with file formats which do not support it directly. The reproducibility of model analysis relies on sharing both the model itself and the definition of simulation parameters, in particular initial states and updating modes. A single initial state can be defined in the SBML qual file. Additional initial states and simulation parameters fall in the scope of the Simulation Experiment Description Markup Language (SED-ML) format  (Bergmann et al. , 2015) , which does not yet support qualitative models. Ongoing discussions should lead to extensions of the SED-ML format and the Kinetic Simulation Algorithm Ontology  (Courtot et al.
ACKNOWLEDGMENTS This work benefited from the feedback of members of the  CoLoMoTo consortium  (colomoto. org). Pedro Monteiro ,  Claudine Chaouiya  and  Denis Theffry  provided feedback for the integration in GINsim and  Epilog. Julien Dorier  and  Gautier Stoll  helped with the BoolSim and MaBoSS formats respectively. Loic Paulevé provided feedback and implemented the Pint format. Francisco Plana  implemented the block-sequential updater. Hannes Klarner  implemented the CNET and bnet formats. Céline Hernandez  tested the API and provided feedback.
Acknowledgements R. L. W. acknowledges support from  Welch Foundation  Grant # AX-1857. I. L. G. acknowledges support from  DGAPA-UNAM  under Project  PAPIIT IN108817 ,  CONACYT-México  under Project  285821 , and  DGTIC-UNAM Supercomputing Center  under Project  LANCAD-UNAM-DGTIC-049. H. C. W. acknowledges support from the  French Research Agency (Agence National de Recherche, ANR)  in the frame of the project " FIT SPRINGS ",  ANR-14-CE08-0009  and HPC resources from  GENCI-IDRIS  (Grant  2016-096829 ). X. L. L. acknowledges previous funding from  NSF -DMR-1103730  and  NSF-PREM DMR-0934218. This work received computational support from the  Laboratory of Computational Nanotechnology and UTSA Research Computing Support Group  through the  HPC clusters Antares3 and SHAMU , respectively, as well as the  Texas Advanced Computing Center (TACC)  at the  University of Texas at Austin.
Implementation Occupancy models formulated as HMMs can be implemented in the software E-SURGE  (Choquet et al. , 2009) , which allows for the decomposition of the observation and state processes in multiple steps, providing more flexible, and often more easily interpretable parameterization  (Gimenez et al. , 2014). We provide in Appendix A and Appendix B two detailed step-by-step procedure to implement occupancy models accounting for false positives with and without heterogeneity in E-SURGE. We also refer to the E-SURGE manual  (Choquet and Nogue, 2011)  as well as  Choquet (2008)  and  Choquet et al. (2009)  for additional details. We also provide in Appendix C and Appendix D the R (R. Core  Team, 2013)  codes to fit the models described above.
Other parameters were held constant: p A10 was set at 0. 1; the initial occupancy probability ψ 1 was set at 0. 8; probabilities of true positive p A11 and p B11 were both set at 0. 5 and b was set at 0. 7. For all simulation runs, we used 100 sites with 3 and 10 occasions. For each scenario, we simulated S = 200 datasets, and for each dataset, fitted both models. Simulation and analyses were done in the software R (see Appendix C and Appendix D). We compared the two models' performance using averaged AIC differences, calculated from the 200 repeated runs. We chose the AIC because it has been shown to be effective at selecting the number of classes in finite-mixture capture-recapture models  (Cubaynes et al. , 2012). For each model, we also calculated the relative bias defined as: ∑ - = S θ θ θ 1 ( ˆ) s S s 1 ( ) and root mean square error (RMSE) for occupancy probability estimates: ∑ - = S θ θ 1 ( ˆ)² s S s 1 ( ) We performed a second simulation study, considering the same form and levels of heterogeneity, but applied to the true detection parameter p 11 instead of the false positive detections parameter p 10.
Numerical Approach Simulations were performed using OpenFOAM R , an open source object oriented Computational Fluid Dynamics software written in C++  [25]. A second order steady state solver was used, specifically simpleFoam which is part of OpenFOAM version 5. 0. A finite volume approach is used for incompressible laminar flow, where pressure and momentum equations are solved iteratively until a specific residual criteria has been reached. A speed up of the solver is achieved by parallelization and by using the fast iterative Generalized Algebraic MultiGrid (GAMG) method for solving critical systems of linear equations.
Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure.
This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github. com/ LaboratorioBioinformatica/MARVEL.
, 2008; Koskella and Brockhurst, 2014; Braga et al. , 2018). Isolation is the gold standard for characterizing and assessing phage diversity, and many new phages are isolated every year from diverse environments such as oceans, composting, and human sewage, among many others  (Sullivan et al. , 2003; Kumari et al. , 2009; Amgarten et al. , 2017). However, isolation of viruses is constrained by the requirement of cultivable bacterial isolates as hosts. This hinders the prospection for new phages since the vast majority of the microbial species are uncultivable under laboratory conditions  (Solden et al. , 2016). In this context, tools for mining viral sequences in large datasets of metagenomic reads and contigs are crucial to retrieve information about novel phage genes and genomes  (Rosario and Breitbart, 2011).