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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.
    Data Types:
    • Software/Code
  • 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.
    Data Types:
    • Software/Code
  • # 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)
    Data Types:
    • Other
    • Software/Code
  • 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.
    Data Types:
    • Software/Code
  • 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.
    Data Types:
    • Other
    • Software/Code
  • An Open Source, Parallel and Heterogeneous Finite Element Analysis Framework
    Data Types:
    • Software/Code
  • Python Reliability Library Version 0.5.1 Released on PyPI 08 July 2020
    Data Types:
    • Software/Code
  • An Open Source, Parallel and Heterogeneous Finite Element Analysis Framework
    Data Types:
    • Software/Code
  • This is the first release, including codes and data associated with Passaro et al. at the time of submission. Associated pre-print and publication to be linked when available.
    Data Types:
    • Software/Code
  • This release of nichetoolbox includes functions for downloading and curating occurrence data, obtaining and transforming environmental data layers, selecting environmental variables, exploring relationships between geographic and environmental spaces, calibrating and selecting ellipsoid models, evaluating models using binomial and partial ROC tests, assessing extrapolation risk, and performing geographic information system operations via a graphical user interface.
    Data Types:
    • Software/Code