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VcaNet: Vision Transformer with fusion channel and spatial attention module for 3D brain tumor segmentation.
Comput Biol Med
January 2025
College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China; Zhejiang Institute of Optoelectronics, Jinhua, 321004, China. Electronic address:
Accurate segmentation of brain tumors from MRI scans is a critical task in medical image analysis, yet it remains challenging due to the complex and variable nature of tumor shapes and sizes. Traditional convolutional neural networks (CNNs), while effective for local feature extraction, struggle to capture long-range dependencies crucial for 3D medical image analysis. To address these limitations, this paper presents VcaNet, a novel architecture that integrates a Vision Transformer (ViT) with a fusion channel and spatial attention module (CBAM), aimed at enhancing 3D brain tumor segmentation.
View Article and Find Full Text PDFThe Role of Torsion on the Force-Coupled Reactivity of a Fluorenyl Naphthopyran Mechanophore.
J Am Chem Soc
January 2025
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
The unique reactivity of molecules under force commands an understanding of structure-mechanochemical activity relationships. While conceptual frameworks for understanding force transduction in many systems are established, systematic investigations into force-coupled molecular torsions are limited. Here, we describe a novel fluorenyl naphthopyran mechanophore for which mechanical force is uniquely coupled to the torsional motions associated with the overall chemical transformation as a result of the conformational rigidity imposed by the fluorene group.
View Article and Find Full Text PDFIdentification of possible drug treatment targets and related immune cell infiltration properties in acute myeloid leukemia utilizing robust rank aggregation algorithm.
Leuk Lymphoma
January 2025
Department of Oncology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, China.
In this study, we aimed to uncover novel biomarkers in acute myeloid leukemia (AML) that could serve as prognostic indicators or therapeutic targets. We analyzed AML microarray datasets from the Gene Expression Omnibus (GEO) repository, identifying key differentially expressed genes (DEGs) through the robust rank aggregation (RRA) approach. The functions of these DEGs were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.
View Article and Find Full Text PDFA Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach.
J Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.
Improving spatial disaggregation of vehicular emission inventories.
Environ Technol
January 2025
Department of Sanitary and Environmental Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.
Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories.
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