The Ganges Brahmaputra Meghna Delta (GBMD) is a large and complex coastal system whose channel network is vulnerable to morphological changes caused by sea level rise, subsidence, anthropogenic modifications, and changes to water and sediment loads. Locating and characterizing change is particularly challenging because of the wide range of forcings acting on the GBMD and because of the large range of scales over which these forcings act. In this study, we examine the spatial variability of change in the GBMD channel network. We quantify the relative magnitudes and directions of change across multiple scales and relate the spatial distribution of change to the spatial distribution of a variety of known system forcings. We quantify how the channelization varies by computing the Channelized Response Variance (CRV) on 30 years of remotely sensed imagery of the entire delta extent. The CRV analysis reveals hotspots of morphological change across the delta. We find that the magnitude of these hotspots are related to the spatial distribution of the dominant physiographic forcings in the system (tidal and fluvial influence levels, channel connectivity, and anthropogenic interference levels). We find that the anthropogenically modified embanked regions have much higher levels of geomorphic change than the adjacent natural Sundarban forest and that this change is primarily due to channel infilling and increased rates of channel migration. Having a better understanding of how anthropogenic changes affect delta channel networks over human timescales will help to inform policy decisions affecting the human and ecological presences on deltas around the world.
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http://dx.doi.org/10.1038/s41598-020-69688-3 | DOI Listing |
BMC Neurol
January 2025
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.
Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.
Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).
Cell
December 2024
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94148, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA 94115, USA. Electronic address:
Three proton-sensing G protein-coupled receptors (GPCRs)-GPR4, GPR65, and GPR68-respond to extracellular pH to regulate diverse physiology. How protons activate these receptors is poorly understood. We determined cryogenic-electron microscopy (cryo-EM) structures of each receptor to understand the spatial arrangement of proton-sensing residues.
View Article and Find Full Text PDFPLoS One
January 2025
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Optical Coherence Tomography (OCT) offers high-resolution images of the eye's fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an, Shaanxi 710127, China.
The full-dimensional potential energy surface (PES) for the photodissociation of HNCS in the S(″) electronic state has been built up by the neural network method based on more than 48,000 points, which were calculated at the multireference configuration interaction level with Davidson correction using the augmented correlation consistent polarized valence triple-ζ basis set. It was found that two minima, namely, and isomers of HNCS, and seven stationary points exist on the S PES for the three dissociation pathways: HNCS(S) → H + NCS/HNC + S(D)/HN(Δ) + CS(Σ). The dissociation energies of two lowest product channels H + NCS and HNC + S(D) calculated on the PES are in good agreement with experimental results, validating the high accuracy of the PES.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran.
Intelligent reflecting surfaces (IRS) are valuable tools for enhancing the intelligence of the propagation environment. They have the ability to direct EM Waves to a specific user through beamforming. A significant number of passive elements are integrated into metasurfaces, allowing for their incorporation onto various surfaces such as walls and buildings.
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