A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, "minimalist" models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid β2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.
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http://dx.doi.org/10.3390/ijms20163866 | DOI Listing |
Neural Netw
January 2025
School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China.
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Video frame interpolation (VFI) is a task that generates intermediate frames from two consecutive frames. Previous studies have employed two main approaches to extract the necessary information from both frames: pixel-level synthesis and flow-based methods. However, when synthesizing high-resolution videos using VFI, each approach has its limitations.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK.
Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become essential, guided by HR-structured inputs like RGB or grayscale images.
View Article and Find Full Text PDFNanoscale
January 2025
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
The highly anisotropic and nonadditive nature of nanoparticle surfaces restricts their characterization by limited types of techniques that can reach atomic or molecular resolution. While small-angle neutron scattering (SANS) is a unique tool for analyzing complex systems, it has been traditionally considered a low-resolution method due to its limited scattering vector range and wide wavelength spread. In this article, we present a novel perspective on SANS by showcasing its exceptional capability to provide molecular-level insights into nanoparticle interfaces.
View Article and Find Full Text PDFMaterials (Basel)
December 2024
Luoyang Institute of Science and Technology, Luoyang 471023, China.
Aiming at the problems of scarce datasets and the low identification accuracy faced in the field of weld-crack detection, this paper proposes an artificial-weld-crack preparation method based on the doping of dissimilar metal particles to augment the number of samples of weld-crack defects. Meanwhile, data augmentation methods such as random cropping, scaling and Mosaic are combined to further enhance the richness of the samples, so as to provide strong data support for the proposed weld-crack-defect detection model. Given the limitations of storage and computational resources in industrial application scenarios, this paper designs the lightweight detection network YOLOv6-NW.
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