Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.Peer reviewedFinal Published versio

Enhancing thermoelectric performance of Cu3SnS4-based solid solutions through coordination of the Seebeck coefficient and carrier concentration

Improving the thermoelectric (TE) performance of Cu3SnS4 is challenging because it exhibits a metallic behavior, therefore, a strategy should be envisaged to coordinate the carrier concentration (nH) and Seebeck coefficient (α). The coordination in this work has been realized through the Fermi level (Ef) unpinning and shifting towards the conduction band (CB) via addition of excess Sn in Cu3SnS4. As a result, the solid solution Cu3Sn1+xS4 (x = 0.2) has a moderate α (178.0 μV K−1) at 790 K and a high nH (1.54 × 1021 cm−3) value. Along with the lowest lattice thermal conductivity κL (0.39 W K−1 m−1) caused by the increased phonon scattering by carriers, the highest ZT value of 0.75 is attained at ∼790 K. This value is 2.8 times that of the stoichiometric Cu3SnS4, and stands among the highest for ternary Cu–Sn–S sulfide thermoelectrics at the corresponding temperatures. More importantly, this approach used in the case of ternary Cu3SnS4 provides a guidance or reference to improve the TE performance of other materials

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The pathogenic relevance in Alzheimer’s disease (AD) presents a decrease of cerebrospinal fluid (CSF) amyloid-ß42 (Aß42) burden and an increase in CSF total-tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study (GWIS) of T-tau/Aß42 ratio as an AD imaging quantitative trait (QT) on 843 subjects and 563,980 single nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain “missing heritability”. Linear regression method was used to detect SNP-SNP interactions among SNPs with uncorrected p-value≤0.01 from the GWAS. Age, gender and diagnosis were considered as covariates in both studies. The GWAS results replicated the previously reported AD-related genes APOE, APOC1 and TOMM40, as well as identified 14 novel genes, which showed genome-wide statistical significance. GWIS revealed 7 pairs of SNPs meeting the cell-size criteria and with bonferroni-corrected p-value≤0.05. As we expect, these interaction pairs all had marginal main effects but explained a relatively high-level variance of T-tau/Aß42, demonstrating their potential association with AD pathology