Virtual environments such as the CAVE and the ImmersaDesk, which are based on graphics supercomputers or workstations, are large and expensive. Most physicians have no access to such systems. The recent development of small Linux personal computers and high-performance graphics cards has afforded opportunities to implement applications formerly run on graphics supercomputers. Using PC hardware and other affordable devices, a VR system has been developed which can sit on a physician's desktop or be installed in a conference room. Affordable PC-based VR systems are comparable in performance with expensive VR systems formerly based on graphics supercomputers. Such VR systems can now be accessible to most physicians. The lower cost and smaller size of this system greatly expands the range of uses of VR technology in medicine.
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J Biomol Struct Dyn
November 2024
Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), UCAM Universidad Católica de Murcia, Guadalupe, Spain.
Classical Molecular Dynamics (MD) simulates the dynamical evolution of biological systems at the atomic level. Using MD in conjunction with high-performance computing (HPC) architectures, we can evaluate the possible interactions between a ligand library against one protein target to find a drug that can influence a protein target to cure a disease. Simultaneously, we can also obtain information about their dynamic evolution.
View Article and Find Full Text PDFJ Chem Phys
August 2024
Department of Physics, University of South Florida, Tampa, Florida 33620, USA.
Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon.
View Article and Find Full Text PDFBMC Bioinformatics
August 2024
Department of Ecological and Biological Sciences, University of Tuscia, Viale dell'Università s.n.c., 01100, Viterbo, Italy.
Background: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation.
View Article and Find Full Text PDFSci Rep
August 2024
Advanced Laboratory for Geostatistical Supercomputing - ALGES, Advanced Mining Technology Center - AMTC, University of Chile, Santiago, Chile.
This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources.
View Article and Find Full Text PDFJ Cheminform
August 2024
Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden.
One challenge that current de novo drug design models face is a disparity between the user's expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists' implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback.
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