Exploring Risk Factors of Alzheimer’s Disease Using Mouse Models

Mouse models of Alzheimer’s disease (AD) that accurately recapitulate pathology and molecular changes are crucial for understanding disease mechanisms and subsequent therapeutic development. We examined five commonly used mouse models of AD (5xFAD, J20, APPNL-F, APPNL-G-F, Tau P301S) and compared their whole- and phosphoproteomes with human AD (the integration of three published datasets) to study whether they can mimic protein/RNA expression discrepancies, molecular changes, and enriched pathways found in human AD cases. The mouse models especially 5xFAD and APPNL-G-F show proteomic signatures similar to human AD but lack human-specific AD progressions, such as dysregulation of synaptic pathways and networks. Integration of large-scale turnover profiling of over 10,000 proteins in 5xFAD and wild-type mice with multi-omic datasets demonstrated discordant mRNA/protein expression of amyloidome components, suggesting an interaction with β-amyloid (Aβ) may decrease protein degradation and trafficking

Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods

Reconstructing a high-resolution (HR) hyperspectral (HS) image from the observed low-resolution (LR) hyperspectral image or a high-resolution multispectral (RGB) image obtained using the exiting imaging cameras is an important research topic for capturing comprehensive scene information in both spatial and spectral domains. The HR-HS hyperspectral image reconstruction mainly consists of two research strategies: optimization-based and the deep convolutional neural network-based learning methods. The optimization-based approaches estimate HR-HS image via minimizing the reconstruction errors of the available low-resolution hyperspectral and high-resolution multispectral images with different constrained prior knowledge such as representation sparsity, spectral physical properties, spatial smoothness, and so on. Recently, deep convolutional neural network (DCNN) has been applied to resolution enhancement of natural images and is proven to achieve promising performance. This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which mainly include spectral reconstruction CNN and spatial and spectral fusion CNN. Experiment results on benchmark datasets have been shown for validating effectiveness of HS image super-resolution in both quantitative values and visual effect