With the broad accessibility of high-performance computing resources, the significance of a molecular dynamics simulation is now rarely limited by hardware and/or software availability. Rather, the scientific value of each calculation is determined by the principles that underlie the theoretical model. The current review addresses this topic in the context of simplified models applied to large-scale (∼20-100 Å) dynamics in the ribosome. Specifically, we focus on applications of the "SMOG" class of structure-based models, which can be used to simulate spontaneous (i.e. non-targeted) conformational rearrangements in complex molecular assemblies. Here, we aim to provide an entry-level assessment of the methods, which can help bridge conceptual and communication gaps between the experimental and computational communities. In addition, inspecting the strategies that have been deployed previously can provide guidelines for future computational investigations into the relationship between structure, energetics and dynamics in other assemblies.
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http://dx.doi.org/10.1016/j.ymeth.2019.03.023 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
Background: This mixed methods study identified needed refinements to a telehealth-delivered cultural and linguistic adaptation of Meaning-Centered Psychotherapy for Chinese patients with advanced cancer (MCP-Ch) to enhance acceptability, comprehensibility, and implementation of the intervention in usual care settings, guided by the Ecological Validity Model (EVM) and the Practical, Robust Implementation and Sustainability Model (PRISM).
Methods: Fifteen purposively sampled mental health professionals who work with Chinese cancer patients completed surveys providing Likert-scale ratings on acceptability and comprehensibility of MCP-Ch content (guided by the EVM) and pre-implementation factors (guided by PRISM), followed by semi-structured interviews. Survey data were descriptively summarized and linked to qualitative interview data.
BMC Health Serv Res
January 2025
Institute Patient-Centered Digital Health, Bern University of Applied Sciences, Quellgasse 21, Biel, 2502, Switzerland.
Background: Hospital at home (HaH) care models have gained significant attention due to their potential to reduce healthcare costs, improve patient satisfaction, and lower readmission rates. However, the lack of a standardized classification system has hindered systematic evaluation and comparison of these models. Taxonomies serve as classification systems that simplify complexity and enhance understanding within a specific domain.
View Article and Find Full Text PDFSci Rep
January 2025
Research Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.
Maze tasks, originally developed in animal research, have become a popular method for studying human cognition, particularly with the advent of virtual reality. However, these experiments frequently rely on simplified environments and tasks, which may not accurately reflect the complexity of real-world situations. Our pilot study aims to transfer a multi-alternative maze with a complex task structure, previously demonstrated to be useful in studying animal cognition, to studying human spatial cognition.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Civil Engineering, Damascus University, Damascus, Syria.
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy.
View Article and Find Full Text PDFPhys Med Biol
January 2025
CREATIS, INSA de Lyon, Bâtiment Blaise Pascal, 7 Avenue Jean Capelle, Villeurbanne, 69621 Cedex , FRANCE.
Compton cameras are imaging devices that may improve observation of sources of γ photons. We present CoReSi, a Compton Reconstruction and Simulation software implemented in Python and powered by PyTorch to leverage multi-threading and for easy interfacing with image processing and deep learning algorithms. The code is mainly dedicated to medical imaging and for near-field experiments where the images are reconstructed in 3D.
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