dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Implementation The checkpointing scheme proposed above was implemented in the StarPU task-based distributed runtime system. The support for distributed execution of StarPU resides in its StarPU-MPI layer, which is based on the MPI communication interface. The support for checkpointing was implemented on top of this layer, it is composed of about 1500 lines of C code, and initially did not require modifications of the core of the StarPU runtime, the existing data acquisition core primitives were sufficient to implement it. It should thus be easy to transpose this checkpoint proposal to other runtime systems. Some optimizations described below however required some extensions. Besides, this checkpointing support is essentially independent from the MPI standard, and would be easily ported on top of other communication standards, provided that they have support for error reporting and failure recovery.
VIII. ACKNOWLEDGMENTS This work has received funding from the  EU 's  Horizon 2020 research and innovation program , under grant agreement No. 801015  ( EXA2PRO , http://www. exa2pro. eu). Experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr/).
Two-stage ratio We visualized the result of the two-stage ratio using sequencing data from panel 2 (see S1 File for details). This gene panel, designed by Ion AmpliSeq designer software, includes 1,206 amplicons on 70 genes. The run presented here was performed on an Ion Proton device and included seven patients. A deletion on chromosome 13 was shared by three of the seven patients (verified by karyotyping). Examples of the visualization obtained for two of the patients (patient 1 normal and patient 2 "deleted") are presented in Fig 2. Without the two-stage ratio, the region in nondeleted patients was disturbed and a false positive duplication event was detected by CovCopCan in both (highlighted by a vertical red rectangle, as for patient 1, Fig  2A ). The two-stage ratio improved the stability of the values so that no false duplication event was detected by CovCop-Can, thus increasing the specificity (Fig 2  , compare A  and B ).
96. Comparison with other tools. We compared CovCopCan with three other tools: IonCopy, DeviCNV, and ExomeDepth. IonCopy and DeviCNV are designed to analyze amplicon sequencing data without a control set. ExomeDepth uses a robust model for the read count data and to build an optimized reference set. We used the shiny version of the software IonCopy (v. 2. 1. 1), with the gene-wise analysis mode and default parameters. DeviCNV (v. 1. 5. 1) was launched with the recommended parameters, detailed in the manual. ExomeDepth (v. 0. 1) was also launched with the default parameters. We tested these tools on the same dataset, already described, containing the 22 CNVs. We only considered CNVs supported by at least three amplicons for all the tools. The results are presented in Table  2  as the number of CNVs detected.
CovCopCan, IonCopy, DeviCNV, and ExomeDepth each detected 22, 20, 18, and 19 CNVs, respectively (Table  2 ). Only CovCopCan detected all CNVs for a sensitivity of 1. Ion-Copy, DeviCNV, and ExomeDepth showed sensitivity of 0. 91, 0. 82, and 0. 86, respectively. It was impossible to verify all the other CNVs found by the various tools. Thus, we could not calculate specificity based on these data. However, a small number of CNVs would be expected, since the data correspond to germline samples. Thus, with only seven CNVs detected in addition to the 22 controls, CovCopCan must have had the best specificity for this dataset.
Performance on cancer data Low cell fraction. CovCopCan can also process cancer data. The main difference between germline and somatic data is that a cancer tissue sample may simultaneously contain both healthy cells and cancer cells. A low proportion of cancer cells may interfere with the detection of CNVs. We estimated the minimum proportion of cancer cells required for CNV detection by simulating the complete deletion of a gene covered by 80 amplicons using panel 1 (2,394 amplicons). We used a coverage matrix containing the data of 16 patients sequenced by an Ion Proton Sequencer. The deletion of the entire gene was simulated following this method: SRC i ¼ RRC i � 1 À CancerCellProportion ð Þ þ RRC i = 2 � CancerCellProportion SRC i is the simulated value of the amplicon i, RRC i the Raw Read Count of the amplicon i, and CancerCellProportion the proportion of cancer cells (0 < values < 1).
5% cancer cells (Fig  6 ). These results show that Cov-CopCan can detect CNVs within a heterogeneous sample if the cancer cells make up at least 15 to 20%. Comparison with other tools. We compared the performance of CovCopCan against IonCopy, DeviCNV, and ONCOCNV. First, we used these three tools on the deletion of the ATM gene described above. Like CovCopCan, both IonCopy, and ONCOCNV correctly detected the CNV with 19. 5% of cancer cells, but not DeviCNV (Table  3 ). In addition, we used another dataset obtained using panel 2. We sequenced the DNA of 54 patients in eight runs. Eighteen patients had a partial deletion of a chromosome arm, whereas two had a complete deletion of this same chromosome arm. The partial deletion was covered by 21 amplicons, whereas the entire deletion involved 39.
Availability and future directions CovCopCan sources are available on GitHub: https://git. unilim. fr/merilp02/CovCopCan/tree/ master. Pre-complied binaries can be downloaded from this page of the GitHub repository: https://git. unilim. fr/merilp02/CovCopCan/tree/master. CovCopCan offers a wide range of features to interpret data from amplicon sequencing to detect CNVs. This tool works on data generated from Ion Designer (Life Technologies, CA, USA) as well as that from Illumina DesignStudio (Illumina Inc. , San Diego, CA, USA). The user-friendly interface associated with our 2D visualization facilitates data exploration and manipulation allowing complex analyses such as those from cancer data. CovCopCan also offers the possibility to export the results in VCF format  [16]  or graphical output for publications.
The suggested system will be equipped with apps by which a physician may examine patients anytime from anywhere. It will be also possible to work on emergency scenarios and give a warning to the doctor with the current status of the patient, and complete medical records. We model the whole system by relying on the standard modeling language UML including class, sequence, and use cases diagrams. Further to ensure the privacy of users (practitioners and patients) we develop a role based access control (RBAC) for the proposed health system. To fulfill the healthcare policies and the well correctness of the reinforcement as well as the functionality of the system, the proposed approach relies on Alloy analyzer in order to check automatically the requirements. Alloy  [8]  can express specifications of a system's structure and behavior as an abstract model to evolve and expand.
Alloy language powered by Alloy Analyzer tool can be used to edit, build, and test its specifications. The analyzer simulates the specification and shows the system's flaws by checking the correctness and find the counterexamples that help to maintain the system. As well, it has the capabilities to present solutions in graphical format with customizing option. Depending on all these benefits, Alloy has been used for the analysis of many systems including security and software architectures. The proposed solution models first the health care system as well as the reinforcement mechanism using UML and express the related requirements in Alloy. It takes as input the system requirements as assertions and the reinforced model including UML diagrams and RBAC model. The results showed the effective correctness of the modeled system and its related reinforcement mechanism.
-Rahman et al. [18]  discuss the complexity of distributing services in many IoT devices as well as the challenges facing the system requirements for each level: devices, services, and applications. The proposed model assures a quality attributes such as update, interoperability, security, functional appropriateness, availability and adaptability. However, the verification process suffers from many limitations at the evaluation step. -Gupta et al. [6]  design an IoT-based health monitoring framework to reduce the neglect of healthcare. Their solution uses the second generation Intel Galileo board, an Arduino IDE that is explored to program the brain, and Xampp database. This solution provides support emergency medical services by collecting, integrating, and inter-operating of IoT data flexibly.
However, they require to overcome protocol distinctions among technologies and unify every aspect of the network. -Mainak et al. [13]  designs an agent based model which predict the response time of emergency service by taking into consideration the characteristics of road segments and driving behaviour of emergency vehicle drivers. First, they collecting real time driving data by Fire emergency service of Allahabad city using GPS logger HOLUX M1000C. Then, they analyze collected data in GIS along with road network, population density and land use data. Based on the analysis results, they model a fire emergency vehicle (FEV) service. For validation, they compare the theoretical response time with the measured one by simulating scenarios that have 80% matching segments. -Hussein et al. [7]  propose a coordination emergency responses framework using agent-based modeling.
The user interfaces builtin RESTful services which allow user to communicate with the HSN through the 2-way Proxy. -Molano et al. [17]  propose an architecture of IoT applied to the industry. First, they present a metamodel that generates industrial cases by extending cyber physical systems to cover covers sensors and actuators to monitor manufacturing. However, safety of data and system accuracy, standardization of technology and interoperability of systems within actual deployments are not considered. -Cristian et al. [4]  review the artificial intelligence-IoT fusion with a focus on four important fields. They presents AI basics and the general concepts of computer vision and Fuzzy Logic, and their link with IoT. Besides, they present natural language processing to facilitate the humain-machine interaction.
HMS Validation In terms of system specification, Alloy is a modeling language including a formal syntax and semantics. A specified model in Alloy can be in ASCII format as well with a visual representation. Generally, Alloy targets the formal specification of object oriented data models that can be used generally in data modeling, that also can be displayed graphically. Also for systems analysis, Alloy is a verification tool that automatically analyze the properties (requirements) of alloy models. After checking the properties, Alloy might generate counterexamples in case of the property violation. Alloy consists of predicates, facts, relations and signatures. Signatures represent the different entities of the system. Relations specify the relations between them. Predicates and facts define constraints, which apply on relations and signatures.
Each Alloy model begins with the module declaration. The first step is to declare the signatures using the keyword sig. Then, we define the relations (fields) which associate atoms. To define a subset, we should use the keywords extends or in, also the multiplicity keywords such as one, lone, set, some, etc. Facts correspond to the constraints which must always hold. Finally, we define the predicate using the keyword pred and run it. For the validation of HMS, we describe its classes and relations in Alloy specification respecting its syntax and logic. Listing 1. 1 shows a fragment of the transformation of HMS into the input language of Alloy where sigs refer to classes, extends to associations, and the multiplicity to the class diagram relations. p : one P a t i e n t } s i g T r e a t m e n t { / / R e f e r t o t r e a t m e n t c l a s s p : one P a t i e n t , r : one Room , s : some S t a f f } For the effectiveness of the proposed framework, we show the correctness of the proposed model through checking and simulation.
Conclusion This contribution proposes a concrete model for healthcare system, especially for emergency service in order to facilitate the integration and communication of IoT measures, devices and management systems. First, we proposed UML modeling for the structural and behavioral description of the specific domain of emergency. Further, we proposed Alloy for ensuring the correctness of the model by expressing it safely into its input language. With respect to the obtained results we ensure the correctness of the proposed model. Regarding the obtained results, we target to extend the work in different directions. First, we generalize a meta model from where we can instantiate the model of different services rather than medical and health care. In addition, we have to automate the generation of Alloy code and prove its soundness.
The simulation of diffusion MRI arising from realistic neuron models can help investigate the cellular microstructure 1, 2. However, ensuring correct connectivity between the distinct compartments comprising the system while minimizing the computational burden is one of the main challenges in our community 3. The design of accurate and efficient simulation algorithm and the construction of high quality neurons meshes are two difficulties in addressing this problem. Our previous work includes a MATLAB Toolbox called SpinDoctor that is a diffusion MRI simulation pipeline based on solving the Bloch-Torrey partial differential equation using finite elements and an adaptive time stepping method 4.
The first version of SpinDoctor focus on the brain white matter whose biophysical models subdivide the tissue into compartments described by ellipsoids, cylinders, and the extra-cellular space. In this study, we present a new module of SpinDoctor called the Neuron Module that enables neuron simulations for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho. Org 5.
METHODS: The key to making accurate simulation possible is the use of high quality meshes for the neurons. For this, we started with the morphological reconstructions published in NeuroMorpho. Org 5. These surface descriptions contain many self-intersections and proximities. We then used licensed software from ANSA-BETA CEA Systems 6 to correct and improve the quality of the neuron surface descriptions and produced new surface triangulations that are ready to be used for finite elements mesh generation. The new surface triangulations are passed into the software GMSH 7 to obtain volume tetrahedral meshes. To further study the diffusion MRI signal of neurons, we broke the neurons into disjoint geometrical components. As an illustration, we show in Figure  1  the spindle neuron 03a_spindle2aFI volume tetrahedral mesh broken into sub-meshes of the soma and the two dendrite branches.
These finite elements meshes are used as input files in the Neuron Module, where they can be further refined if required using the built-in option in SpinDoctor. Currently, the spin exchange across the cell membrane is assumed to be negligible in the Neuron Module. Through the Neuron Module, these neuron and components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor to provide the diffusion MRI signal attributable to spins inside neurons.
9. https://github. com/jingrebeccali/SpinDoctor/tree/NeuronModule Synopsis: The simulation of diffusion MRI arising from realistic neuron models can help investigate the cellular microstructure. However, ensuring correct connectivity between the distinct compartments comprising the system while minimizing the computational burden is one of the main challenges in our community. The design of accurate and efficient simulation algorithm and the construction of high quality neurons meshes are two difficulties in addressing this problem. The Neuron Module can perform HARDI simulation of realistic neurons at various b-values and diffusion sequences with high accuracy. The computational time per gradient direction is around 30 seconds which is much faster than some Monte-Carlo simulators. The linear relationship between the diffusion direction averaged signal and one over square root of b for tubular structures is validated by our simulations.
MRI DATA PROCESSING All additional processing was accomplished with the fiber tractography MRTrix3 (RC_3) software toolkit and iFOD2 algorithm  (Tournier et al 2012; Tournier et al 2019). The fiber orientation distribution (FOD) functions from the diffusion signal in each voxel were computed using the multi-shell-multi-tissue constrained spherical deconvolution (CSD). The individual FODs across the 24 subjects were used to create a population template within MRtrix3. A manually defined occipital lobe mask on the template was then used to produce occipital lobe specific connectomes. The safety margins between the occipital lobe and the exclusion border were large and included posterior aspects of the temporal and parietal lobe. The mask excluded the brainstem, the cerebellum. The anterior border of the mask was defined by a coronal line at the anterior aspect of the splenium of the corpus callosum that extended straight to the lateral convexity of the cerebrum.
ACKNOWLEDGEMENTS This article is based upon work from  COST  Action  CA18106 , supported by  COST (European Cooperation in Science and Technology). SC received funding via the  Helmholtz Initiative and Networking Fund  as well as from the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement  785907  ( Human Brain Project SGA2 ) and  945539  ( Human Brain Project SGA3 ). MTS is funded by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 818521 ). The authors would like to thank  Krista Bonnello Rutter Giappone  for her meticulous critique on language and style of this manuscript. Data were provided by the Human Connectome Project,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the  16 NIH Institutes and Centers  that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
Dejerine and A. Dejerine-Klumpke in Anatomie Centre Nerveux; Volume I (1895)    was fully translated (MB) from its original 19 th -century French to more universally accessible English and reviewed for its accuracy by the experts in this field (CB, MTS). The complete translation is available as supplementary material. SUBJECT SELECTION A template-based, probabilistic, fiber tractography study was conducted in 24 healthy and unrelated consenting volunteers (range 22-35 years, 12 males). The analysed diffusion MRI dataset was derived from the S1200 release of the publicly available WU-Minn Human Connectome Project (HCP) database (https://db. humanconnectome. org/). To ensure data homogeneity, the subjects selected had none of the following features/criteria: endocrine abnormalities including hyper and hypothyroidism, a handedness score below zero (people with a left-handed tendency), color vision abnormalities, illegal drug use, history of psychiatric problems, hypertensive individuals, alcohol detected by a breathalyser, data with quality control (QC) issues A or B.
Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets.
Here, we present ChromSCape, a user-friendly interactive Shiny/R application that processes singlecell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated to cell identity and breast tumor subtype.
Using these approaches, we can study the heterogeneity of epigenomes within complex biological samples, such as tumors  1  , and start appreciating the role of epigenomic diversity and the dynamics of chromatin in disease and development. Existing tools used to analyze bulk ChIP-seq experiments are not fit for the low coverage and sparsity of these single-cell histone modifications datasets, which is due to the inherent low number of copy of DNA molecules per cell -maximum two for a diploid genome. Several computational methods for the analysis of scATAC-seq have been developed to deal with the specificities of single-cell DNA-based datasets. They were recently benchmarked  9  , with SnapATAC 10 , CisTopic  11  and Cusanovich2018  12  being the top-three performing methods. These tools, initially dedicated to scATAC-seq and without graphic interface, require some scripting skills.
Biologists with limited computational training can manipulate and analyze scRNA-seq and scATAC-seq datasets using applications such as 'scOrange'  13  and 'SCRAT'  14. With ChromSCape (Figure  1 ), we propose a user-friendly, step-by-step and customizable Shiny/R application to analyze all types of sparse single-cell epigenomic datasets. The user can interactively identify subpopulations with common epigenomes within heterogeneous samples, find differentially enriched regions between subpopulations and interpret epigenomes by linking regions to associated genes and pathways. The pipeline starts from aligned sequences or count tables, and is designed for high-throughput single-cell datasets with samples containing as low as 100 cells with a minimum of 1,000 reads per cell up to 25,000 cells on a standard laptop.
ChromSCape identifies cell identities from scChIP-seq data To test the efficiency of ChromSCape (Figure  1 ) in identifying cell sub-populations based on their epigenome (H3K27me3), we generated an in-silico dataset with known ground truth, mixing 4 different human cell types: Jurkat B cells, Ramos T cells, MDA-MB-468 breast cancer cells and HBCx-22 tumor cells derived from a luminal breast tumor PDX model  1. Interestingly, Jurkat and Ramos cells were processed within the same microfluidics experiment, preventing the existence of any batch effect between them (see Grosselin et al. ,  1  ). We compared ChromSCape to methods specifically designed for single-cell epigenomic datasets (scATACseq) for their ability to identify cell identities. Based on a recent scATAC-seq benchmark  9  , we selected the top-performing methods, namely Cusanovich2018 12 , SnapATAC 10 and CisTopic 11.