Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm.

Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.

The application of electrocoagulation for the conversion of MSWI fly ash into nonhazardous materials.

This research investigated the electrocoagulation of municipal solid waste incineration (MSWI) fly ash at a liquid-to-solid ratio (L/S) of 20:1. The leachate that was obtained from this treatment was recovered for reutilization. Two different anodic electrodes were investigated, and two unit runs were conducted. In Unit I, the optimum anode was chosen, and in Unit II, the optimum anode and the recovered leachate were used to replace deionized water for repeating the same electrocoagulation experiments. The results indicate that the aluminum (Al) anode performed better than the iridium oxide (IrO2) anode. The electrocoagulation technique includes washing with water, changing the composition of the fly ash, and stabilizing the heavy metals in the ash. Washing with water can remove the soluble salts from fly ash, and the fly ash can be converted into Friedel's salt (3CaO·Al2O3·CaCl2·10H2O) under an uniform electric field and the sacrificial release of Al(+3) ions, which stabilizes the toxic heavy metals and brings the composition of the fly ash to within the regulatory limits of the toxicity characteristic leaching procedure (TCLP). Use of the Al anode to manage the MSWI fly ash and the leachate obtained from the electrocoagulation treatment is therefore feasible.

Basic characteristics of leachate produced by various washing processes for MSWI ashes in Taiwan.

Approximately 19.2% of Taiwan's municipal solid waste (MSW) that passes through incineration disposal is converted into ashes (including bottom ash and fly ash). Although bottom ash can pass nearly all of the standards of the toxicity characteristic leaching procedure (TCLP), its high chloride content makes its reuse limited; it generally cannot be used as a fine aggregate material in concrete applications. This research examined washing four types of bottom ash (BA) and fly ash (FA) with water to reduce their chloride content. The optimal water intensity for washing pretreated bottom ash was found to be 7-8L of water per kg of bottom ash, and the optimal water intensity for washing untreated fly ash was found to be 20-25 L of water per kg of fly ash. Based on regression analyses of the chloride concentrations of the leachates and their electrical conductivity (EC) values, each MSW incineration plant has its own ash characteristics as well as a specific regression line in bottom or fly ash leachate. Clearly, it is possible to monitor the EC values of the leachates online by estimation from regression equations to determine the chloride concentrations in the leachates.