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Channel dimensions (width and depth) at varying flows influence a host of instream ecological processes, as well as habitat and biotic features; they are a major consideration in stream habitat restoration and instream flow assessments. Models of widths and depths are often used to assess climate change vulnerability, develop endangered species recovery plans, and model water quality. However, development and application of such models require specific skillsets and resources. To facilitate acquisition of such estimates, we created a dataset of modeled channel dimensions for perennial stream segments across the conterminous U.S. We used random forest models to predict wetted width, thalweg depth, bankfull width, and bankfull depth from several thousand field measurements of the National Rivers and Streams Assessment. Observed channel widths varied from <5 m to >2000 m and depths varied from <2 m to >125 m. Metrics of watershed area, runoff, slope, land use, and more were used as model predictors. The models had high pseudo R-squared values (0.70 to 0.91) and median absolute errors within ±6% to ±21% of the interquartile range of measured values across ten stream orders. Predicted channel dimensions can be joined to 1.1 million stream segments of the 1:100K resolution National Hydrography Dataset Plus (version 2.1). These predictions, combined with a rapidly growing body of nationally available data, will further enhance our ability to study and protect aquatic resources.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631553 | PMC |
http://dx.doi.org/10.1111/1752-1688.13116 | DOI Listing |
J Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
View Article and Find Full Text PDFPLoS One
December 2024
Sporecyte, Orem, Utah, United States of America.
This study explores COVID-19 communication between medical experts who upload YouTube videos related to health/medicine (hereinafter medical YouTubers) and their viewers. We investigated three specific elements: (1) how medical YouTubers' use of words related to analytical thinking is associated with their viewers' engagement, (2) how medical YouTubers' use of different types of emotion is associated with their viewers' engagement, and (3) the emotional alignment between medical YouTubers and their viewers. We collected 194 COVID-related video transcripts from five YouTube channels and 375,284 comments from those videos.
View Article and Find Full Text PDFNanomicro Lett
December 2024
UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, The University of New South Wales (UNSW), Sydney, NSW, 2052, Australia.
Membrane desalination is an economical and energy-efficient method to meet the current worldwide water scarcity. However, state-of-the-art reverse osmosis membranes are gradually being replaced by novel membrane materials as a result of ongoing technological advancements. These novel materials possess intrinsic pore structures or can be assembled to form lamellar membrane channels for selective transport of water or solutes (e.
View Article and Find Full Text PDFJ Zoo Wildl Med
December 2024
Durrell Wildlife Conservation Trust, Les Augrès Manor, La Profonde Rue, Jersey, JE3 5BP, Channel Islands.
The aye-aye () is an unusual lemur with a small population in human care. Cardiac pathologies, but not normal size parameters, have been reported in this species. This study aimed to determine whether radiographic cardiac scaling systems commonly used to evaluate heart size in domestic mammals have potential clinical application in aye-ayes.
View Article and Find Full Text PDFFront Oncol
December 2024
College of Computer Science and Software Engineering, Hohai University, Nanjing, China.
Introduction: Hybrid architectures based on convolutional neural networks and Transformers, effectively captures both the local details and the overall structural context of lesion tissues and cells, achieving highly competitive segmentation results in microscopic hyperspectral image (MHSI) segmentation tasks. However, the fixed tokenization schemes and single-dimensional feature extraction and fusion in existing methods lead to insufficient global feature extraction in hyperspectral pathology images.
Methods: Base on this, we propose a multi-scale token divided and spatial-channel fusion transformer network (MT-SCnet) for MHSIs segmentation.
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