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  • Matlab scripts used to analyze data associated with the manuscript entitled "A single cell atlas of the human liver tumor microenvironment". *please used Matlab 2019b to run the following m files. Files: inputData.mat: mat contains all raw and preprocessed data used in the study Create_Interactions_Network.m: Matlab script used to calculate Ligand-Receptor interaction score between different cell types. The script creates panels of Figure 3 and Table S5. Hepatocytes_Reconstruction.m: Matlab script used to reconstruct human hepatocytes zonation along the lobule axis. The script creates panel 'c' of Figure 4, Figure S4, and Table S7. Cancer_Cells_Spatial_Analysis.m: Matlab script used to calculate differential gene expression between malignant cells found at different zones (malignant border, malignant core, and fibrotic zone) captured by laser microdissection. The script creates panel 'd' of Figure 4 helperFunctions.zip: This folder contains required functions used by the m files.
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  • This contains the scripts we used while mining git repositories to quantify and characterize three different types of "Ghost Commits": MG 1, MG 2, and FG.
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  • # Installation conda create -n deep_texture python=3.6 source activate deep_texture conda install numpy pillow conda install keras-gpu conda install keras # if GPUs are not available pip install git+https://github.com/keras-team/keras-applications.git@d506dc82d0 # downgrade keras-application ## usage import deep_texture (prep, dnn) = deep_texture.setup_texture(arch = 'nasnet', layer = 'normal_concat_11', cbp_dir = '/tmp') dtr = deep_texture.calc_features_file("./test.png", prep, dnn)
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  • Summary: We clearly understand the difficulties involved in the complete installation of the BIOCOM-PIPE pipeline. So, we tried to answer this problem giving a Virtual Machine containing the BIOCOM-PIPE fully integrated into an UBUNTU system (LTS 20.04) with also the example dataset available. Background: The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. There exists a growing number of metabarcoding pipelines, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool. Results: BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (i) remove chimeras based on read abundance, (ii) align sequences with structure-based alignments of RNA homologs using covariance models or a post-clustering tool (ReClustOR), and (iii) re-assign OTUs based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) highlighted that BIOCOM-PIPE was better at discriminating land use groups. Conclusions: The BIOCOM-PIPE pipeline makes it possible to analyze 16S/18S and 23S rRNA genes in the same package tool. This innovative approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs. For advanced users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily.
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  • 1.0.0 - 2020-07-01 Feature Release This is a major revision of QSW_MPI. The focus of this release is the expansion of the simulation capabilities of QSW_MPI while focussing the scope of the package through the removal of features which are better supported through pre-existing alternatives (specifically file I/O and visualisation). Added Generalised support for quantum stochastic walks, including the non-moralising quantum stochastic walk through the qsw_mpi.MPI.LQSW and qsw_mpi.MPI.GQSW classes. Experimental support for sparse systems following the Gorini–Kossakowski–Sudarshan–Lindblad equation in its diagonalised form through the qsw_mpi.MPI.GKSL class. Support for MPI-enabled parallel output to HDF5 using H5Py via the non-user accessible module qsw_mpi.parallel_io. Additional operator types including the canonical Markov chain transition matrix, and those required for the demoralisation correction scheme. Changed All simulation types are now subclasses a generalised qsw_mpi.MPI.walk class. This breaks compatibility with version 0.0.1. qsw_mpi.MPI.walk.step and qsw_mpi.MPI.walk.series have been simplified, gathering of simulation results, or saving of the simulation results is now carried out through the qsw_mpi.MPI.walk.gather_result, qsw_mpi.MPI.walk.gather_populations, qsw_mpi.MPI.save_result or qsw_mpi.MPI.save_populations. Removed Removed visualisation module qsw_mpi.plot. For basic visualisation, direct use of Matplotlib and Networkx is recommended. Removed dedicated I/O module qsw_mpi.io. For HDF5 file operations, direct use of H5Py is recommended.
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  • Integrating data from multiple sources with the aim to identify records that correspond to the same entity is required in many real-world applications including healthcare, national security, and businesses. However, privacy and confidentiality concerns impede the sharing of personal identifying values to conduct linkage across different organizations. Privacy-preserving record linkage (PPRL) techniques have been developed to tackle this problem by performing clustering based on the similarity between encoded record values, such that each cluster contains (similar) records corresponding to one single entity. When employing PPRL on databases from multiple parties, one major challenge is the prohibitively large number of similarity comparisons required for clustering, especially when the number and size of databases are large. While there have been several private blocking methods proposed to reduce the number of comparisons, they fall short in providing an efficient and effective solution for linking multiple large databases. Further, all of these methods are largely dependent on data. In this paper, we propose a novel private blocking method for efficiently linking multiple databases by exploiting the data characteristics in the form of probabilistic signatures and introduce a local blocking evaluation step for validating blocking methods without knowing the ground-truth. Experimental results show the efficacy of our method in comparison to several state-of-the-art methods.
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  • No description provided.
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  • This cartography is specifically designed to look at conferences in a visual way. All the authors are arranged in the space according to a lexical distance, which implies that the more two authors are close, the more their dictionaries overlap. The lexical distance is calculated from the short abstracts, along with their title and keywords, using the TF-IDF algorithm. Such an algorithm enables us to find the most representative keywords for each scholar with respect to the entire scientific community. We believe that the lexical map represents a more democratic way to represent scholars. Relationships are not based on exclusive citations, which draw an academic hierarchy among scholars, but rather on public and sharable verbal units, the terms. The goal is the creation of an instrument in which the author can immediately recognize his/herself. Likewise, the author is invited to recognize his/her visual neighborhood, which should display co-authors and language-related peers. The cartography interpretation is completely subjective as well as its creation, the cartography is meant to be a visual instrument to stimulate reflection and foster interesting conversation. The cartography of 2020 was made possible by the outstanding contribution of May Ning, who brilliantly took care of the conference dataset, and Constance Crompton, who enthusiastically supported the idea to create a visual representation of the DH2020. More information about the visual method in the article titled "Mapping as a Contemporary Instrument for Orientation in Conferences" by Chloe Moon and Dario Rodighiero.
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  • COVID19 tissue simulator Version: 0.3.2 Release date: 15 July 2020 Overview This model simulates replication dynamics of SARS-CoV-2 (coronavirus / COVID19) in a layer of epithelium with an initial immune reaction. It is being rapidly prototyped and refined with community support (see below). In this model, SARS-CoV-2 (coronavirus / COVID19) infects a single cell, or a solution of virions is administered to the extracellular space. The virus is uncoated to explose viral RNA, which synthesizes viral proteins that are assembled into a virion. Assembled virions are exported to the environment, where they can diffuse and infect other cells. In the extracellular space, virions adhere to ACE2 receptors and get internalized through endocytosis. Internalized ACE2 receptors release their virus cargo and are recycled back to the surface. Resident macrophages ingest apototic cells and release a pro-inflammatory cytokine that recruits additional macrophages, neutrophils, and CD8+ T cells. CD8+ T cells chemotax towards cytokines released by infected cells and adhere. Cumulative CD8+ T cell contact time can induce apoptosis in infectd cells. Activated macrophages and neutrophils chemotaxis chemotax along chemokine and debris gradients and continue to phagocytose dead cells. Neutrophils also absorb free (extracellular) virus. The model includes a basic pharmacodynamic response (to assembled virions) to cause cell apoptosis. Apoptosed cells release some or all of their internal contents, notably including virions. Caveats and disclaimers: This model is under active development using rapid prototyping: It has not been peer reviewed. It is intended to drive basic scientific research and public education at this stage. It cannot be used for public policy decisions. It cannot be used for individual medical decisions. This model will be continually refined with input from the community, particularly experts in infectious diseases. The validation state will be updated as this progresses. Key makefile rules: make : compiles the project. make clean : removes all .o files and the executable, so that the next "make" recompiles the entire project make data-cleanup : clears out all simulation data make reset : reset to default settings (restores config file) More references Preprint: https://doi.org/10.1101/2020.04.02.019075 Model details: https://github.com/MathCancer/COVID19/wiki/About Homepage: http://covid19.PhysiCell.org Support: https://sourceforge.net/p/physicell/tickets/ Latest info: follow @PhysiCell and @MathCancer on Twitter (http://twitter.com/MathCancer) See changes.md for the full change log.
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