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Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.
BMC Genomics
December 2024
College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge.
View Article and Find Full Text PDFDynamic Statistical Attention-based lightweight model for Retinal Vessel Segmentation: DyStA-RetNet.
Comput Biol Med
December 2024
Khalifa University, Abu Dhabi, United Arab Emirates.
Background And Objective: Accurate extraction of retinal vascular components is vital in diagnosing and treating retinal diseases. Achieving precise segmentation of retinal blood vessels is challenging due to their complex structure and overlapping vessels with other anatomical features. Existing deep neural networks often suffer from false positives at vessel branches or missing fragile vessel patterns.
View Article and Find Full Text PDFA comparison of traffic crash and connected vehicle event data on a freeway corridor with Hard-Shoulder Running.
Accid Anal Prev
December 2024
Global Data Insights & Analytics, Ford Motor Company, United States. Electronic address:
Police crash reports have traditionally been the primary data source for research and development projects aimed at improving traffic safety. However, there are important limitations of such data, particularly the relative infrequency of crashes on a site-by-site basis in many contexts. Crash analyses often require multiple years of data and the use of such data for short-term evaluations creates challenges.
View Article and Find Full Text PDFFully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python.
Skelet Muscle
December 2024
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada.
Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
Methods: We detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age.
Development and evaluation of an implementation strategy to increase HPV vaccination among underserved youth across Texas: a protocol paper.
Implement Sci Commun
December 2024
Department of Health Promotion and Behavioral Sciences, School of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA.
Background: All for Them is a theory-based and evidence-informed multilevel, multicomponent program delivered through schools to increase HPV vaccination among medically underserved youth across Texas. Given the potential logistical challenges of program implementation, understanding how to best support the implementation and sustainment of the program is critical. The overall goals of this study are twofold: 1) develop a multifaceted implementation strategy, Implementing All for Them (IM-AFT); and 2) evaluate the impact of IM-AFT on implementation outcomes for schools and healthcare providers to successfully implement All for Them in their respective settings.
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