DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject.

An adaptive weighted attention-enhanced deep convolutional neural network for classification of MRI images of Parkinson's disease.

Background: Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed.

Integrated analysis identifies the IL6/JAK/STAT signaling pathway and the estrogen response pathway associated with the pathogenesis of intracranial aneurysms.

Objective: We intended to identify the potential key biomarker and pathways that correlated with infiltrating immune cells during the pathogenesis of intracranial aneurysms (IA), to develop a diagnostic model, and to predict therapeutic drugs.

Methods: Three datasets containing intracranial aneurysm tissue samples and normal artery control samples from Gene Expression Omnibus (GEO) were included. Gene-set variation analysis(GSVA) and gene set enrichment analysis (GSEA) were conducted to find the significant differentially expressed pathways in IA formation.

Bioinformatics analysis reveals potential biomarkers associated with the occurrence of intracranial aneurysms.

To better understand the molecular mechanisms of intracranial aneurysm (IA) pathogenesis, we used gene coexpression networks to identify hub genes and functional pathways associated with IA onset. Two Gene Expression Omnibus (GEO) datasets encompassing intracranial aneurysm tissue samples and cerebral artery control samples were included. To discover functional pathways and potential biomarkers, weighted gene coexpression network analysis was employed.

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands.