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http://dx.doi.org/10.1080/10618600.2019.1689985 | DOI Listing |
Commun Eng
January 2025
THz-Photonics Group, Technische Universität Braunschweig, Braunschweig, Germany.
New applications such as the Internet of Things, autonomous driving, Industry X.0 and many more will transmit sensitive information via fibers and over the air with envisioned data rates beyond terabits per second. Therefore, the encryption has to be simple, fast and spectrally efficient, so that the power consumption and latency are low and the scarce bandwidth is not wasted.
View Article and Find Full Text PDFCommun Biol
January 2025
Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, 30602, USA.
In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States.
Gold nanoparticles can exhibit unique physical and chemical properties, such as plasmon resonances or photoluminescence. These nanoparticles have many atoms, which leads to high computational costs for density functional theory (DFT) calculations. In this work, we used the FLARE++ (fast learning of atomistic rare events) code and incorporated an active learning algorithm to construct force fields for gold thiolate-protected nanoclusters.
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