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Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods
January 2025
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.
View Article and Find Full Text PDFApplication of bioelectrical impedance detection techniques: Cells and tissues.
Biosens Bioelectron
January 2025
College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China; Affiliated Dongyang Hospital of Wenzhou Medical University, Jinhua, China. Electronic address:
Pathological conditions in organisms often arise from various cellular or tissue abnormalities, including dysregulation of cell numbers, infections, aberrant differentiation, and tissue pathologies such as lung tumors and skin tumors. Thus, developing methods for analyzing and identifying these biological abnormalities presents a significant challenge. While traditional bioanalytical methods such as flow cytometry and magnetic resonance imaging are well-established, they suffer from inefficiencies, high costs, complexity, and potential hazards.
View Article and Find Full Text PDFDifferences in the distribution of steroids in sterol-containing liposomes: A study by liposome electrokinetic chromatography.
J Chromatogr A
January 2025
Department of Chemistry, Faculty of Science, POB 55 (A.I. Virtasen aukio 1), 00014 University of Helsinki, Helsinki, Finland. Electronic address:
This study was conducted to investigate possible differences in the interactions of some selected steroids based on their distribution coefficients with cholesterol- or ergosterol-rich liposomes. Structurally cholesterol and ergosterol have very close resemblance to each other and generally it is thought that they behave in a similar manner. In this work we will show that this is not the case.
View Article and Find Full Text PDFOut-of-Distribution Detection via outlier exposure in federated learning.
Neural Netw
January 2025
Department of Computer Science and Engineering, Inha University, Incheon, South Korea. Electronic address:
Among various out-of-distribution (OOD) detection methods in neural networks, outlier exposure (OE) using auxiliary data has shown to achieve practical performance. However, existing OE methods are typically assumed to run in a centralized manner, and thus are not feasible for a standard federated learning (FL) setting where each client has low computing power and cannot collect a variety of auxiliary samples. To address this issue, we propose a practical yet realistic OE scenario in FL where only the central server has a large amount of outlier data and a relatively small amount of in-distribution (ID) data is given to each client.
View Article and Find Full Text PDFBenefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation.
Med Image Anal
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Big DataBased Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory of Kidney Diseases, Beijing, 100853, China. Electronic address:
Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.
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