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http://dx.doi.org/10.1111/j.1740-8709.2011.00315.x | DOI Listing |
J Fish Biol
January 2025
Minderoo-UWA Deep-Sea Research Centre, School of Biological Sciences and Oceans Institute, The University of Western Australia, Perth, Western Australia, Australia.
The deep-sea demersal fish fauna is characterized by a prevalence of elongated-body forms with long tapering tails. Using baited camera landers at depths of 4500-6300 m in the Pacific Ocean, we observed multiple instances of backward swimming using reverse undulation of the slender body in four species: the cutthroat eel Ilyophis robinsae, abyssal grenadier Coryphaenoides yaquinae, and cusk-eels Bassozetus sp. and Barathrites iris.
View Article and Find Full Text PDFArch Microbiol
December 2024
Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India.
Klebsiella pneumoniae is a leading cause of anti-microbial resistance in healthcare-associated infections that have posed a severe threat to neonatal and wider community. The escalating crises of antibiotic resistance have compelled researchers to explore an innovative arsenal beginning from natural resources to chemical modifications in order to overcome the ever-increasing resistance issues. The present review highlights the drug discovery efforts with a special focus on cutting-edge strategies in the hunt for potential drug candidates against MDR/XDR Klebsiella pneumoniae.
View Article and Find Full Text PDFMethods Mol Biol
December 2024
Institute of Physiological Chemistry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Adenosin triphosphate (ATP)-dependent nucleosome remodeling factors sculpt the nucleosomal landscape of eukaryotic chromatin. They deposit, evict, or reposition nucleosomes along DNA in a process termed nucleosome sliding. Remodeling has traditionally been analyzed using mononucleosomes as a model substrate.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Amity Centre for Artificial Intelligence, Amity University, Noida, India.
Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction.
View Article and Find Full Text PDFSci Rep
October 2024
College of Science, China Agricultural University, Beijing, 100083, China.
In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount. This study introduces a novel DTI prediction approach-Multi-Layer Graph Attention Neural Network (MLGANN), through a groundbreaking computational framework that effectively harnesses multi-source information to enhance prediction accuracy. MLGANN not only strides forward in constructing a multi-layer DTI network by capturing both direct interactions between drugs and targets as well as their multi-level information but also amalgamates Graph Convolutional Networks (GCN) with a self-attention mechanism to comprehensively integrate diverse data sources.
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