Endoplasmic-reticulum-associated degradation (ERAD) is an important protein quality control system which maintains protein homeostasis. Constituents of the ERAD complex and its role in neurodegeneration are not yet fully understood. Here, using proteomic and FRET analyses, we demonstrate that the ER protein membralin is an ERAD component, which mediates degradation of ER luminal and membrane substrates. Interestingly, we identify nicastrin, a key component of the γ-secretase complex, as a membralin binding protein and membralin-associated ERAD substrate. We demonstrate a reduction of membralin mRNA and protein levels in Alzheimer’s disease (AD) brain, the latter of which inversely correlates with nicastrin abundance. Furthermore, membralin deficiency enhances γ-secretase activity and neuronal degeneration. In a mouse AD model, downregulating membralin results in β-amyloid pathology, neuronal death, and exacerbates synaptic/memory deficits. Our results identify membralin as an ERAD component and demonstrate a critical role for ERAD in AD pathogenesis.

Land cover change detection (LCCD) based on bitemporal remote sensing images has become a popular topic in the field of remote sensing. Despite numerous methods promoted in recent decades, an improvement on the usability and performance of these methods has remained necessary. In this paper, a novel LCCD approach based on the integration of k-means clustering and adaptive majority voting (k-means_AMV) techniques have been developed. The proposed k-means_AMV method consists of three major techniques. First, to utilize the contextual information in an adaptive manner, an adaptive region around a central pixel is constructed by detecting the spectral similarity between the central pixel and its eight neighboring pixels. Second, when the extension for the adaptive region is terminated, the k-means clustering method is applied to determine the label of each pixel within the adaptive region. Finally, an existing AMV technique is used to refine the label of the central pixel of the adaptive region. When change magnitude image (CMI) is scanned and processed in this manner, the label of each pixel in the CMI can be refined and the binary change detection map can be generated. Three image scenes related to different land cover change events are adapted to test the effectiveness and performance of the proposed k-means_AMV approach. The results show that the proposed k-means_AMV approach demonstrates better detection accuracies and visual performance than that of the several extensively used methods. INDEX TERMS Adaptive majority voting, k-means clustering, land cover change detection, remote sensing images.

Variations in many genes linked to sporadic Alzheimer’s disease (AD) show abundant expression in microglia, but relationships among these genes remain largely elusive. Here, we establish isogenic human ESC–derived microglia-like cell lines (hMGLs) harboring AD variants in CD33, INPP5D, SORL1, and TREM2 loci and curate a comprehensive atlas comprising ATAC-seq, ChIP-seq, RNA-seq, and proteomics datasets. AD-like expression signatures are observed in AD mutant SORL1 and TREM2 hMGLs, while integrative multi-omic analysis of combined epigenetic and expression datasets indicates up-regulation of APOE as a convergent pathogenic node. We also observe cross-regulatory relationships between SORL1 and TREM2, in which SORL1R744X hMGLs induce TREM2 expression to enhance APOE expression. AD-associated SORL1 and TREM2 mutations also impaired hMGL Aβ uptake in an APOE-dependent manner in vitro and attenuated Aβ uptake/clearance in mouse AD brain xenotransplants. Using this modeling and analysis platform for human microglia, we provide new insight into epistatic interactions in AD genes and demonstrate convergence of microglial AD genes at the APOE locus.

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Abstract:In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors' knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.

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