A Dual Inhibitor of the Proteasome Catalytic Subunits LMP2 and Y Attenuates Disease Progression in Mouse Models of Alzheimer\u27s Disease

The immunoproteasome (iP) is a variant of the constitutive proteasome (cP) that is abundantly expressed in immune cells which can also be induced in somatic cells by cytokines such as TNF-α or IFN-γ. Accumulating evidence support that the iP is closely linked to multiple facets of inflammatory response, eventually leading to the development of several iP inhibitors as potential therapeutic agents for autoimmune diseases. Recent studies also found that the iP is upregulated in reactive glial cells surrounding amyloid β (Aβ) deposits in brains of Alzheimer\u27s disease (AD) patients, but the role it plays in the pathogenesis of AD remains unclear. In this study, we investigated the effects of several proteasome inhibitors on cognitive function in AD mouse models and found that YU102, a dual inhibitor of the iP catalytic subunit LMP2 and the cP catalytic subunit Y, ameliorates cognitive impairments in AD mouse models without affecting Aβ deposition. The data obtained from our investigation revealed that YU102 suppresses the secretion of inflammatory cytokines from microglial cells. Overall, this study indicates that there may exist a potential link between LMP2/Y and microglia-mediated neuroinflammation and that inhibition of these subunits may offer a new therapeutic strategy for AD

Arctic-North Pacific Coupled Impacts on the Late Autumn Cold in North America

The Pacific Decadal Oscillation (PDO) is known to bring an anomalously cold (warm) period to southeastern (northwestern) North America during the cold season of its positive phase through a Rossby wave linkage. This study provides evidence that the remote connection between the North Pacific and the downstream temperature over central North America is strengthened by the warm arctic conditions over the Chukchi and East Siberian Sea, especially in the late autumn season. The modulation effect of the Arctic manifests itself as an altered Rossby wave response to a transient vorticity forcing that results from an equatorward storm track shift, which is induced collaboratively by the PDO and the warm Arctic. This observational finding is supported by two independent modeling experiments: 1) an idealized coupled GCM experiment being nudged toward the warm arctic surface condition and 2) a simple stationary wave model (SWM) experiment forced by transient eddy forcing

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

How Does the SST Variability over the Western North Atlantic Ocean Control Arctic Warming over the Barents-Kara Seas?

Arctic warming over the BarentsKara Seas and its impacts on the mid-latitude circulations have been widely discussed. However, the specific mechanism that brings the warming still remains unclear. In this study, a possible cause of the regional Arctic warming over the BarentsKara Seas during early winter (OctoberDecember) is suggested. We found that warmer sea surface temperature anomalies over the western North Atlantic Ocean (WNAO) modulate the transient eddies overlying the oceanic frontal region. The altered transient eddy vorticity flux acts as a source for the Rossby wave straddling the western North Atlantic and the BarentsKara Seas (Scandinavian pattern), and induces a significant warm advection, increasing surface and lower-level temperature over the Eurasian sector of the Arctic Ocean. The importance of the sea surface temperature anomalies over the WNAO and subsequent transient eddy forcing over the WNAO was also supported by both specially designed simple model experiments and general circulation model experiments

Mouse genetics: Catalogue and scissors

Phenotypic analysis of gene-specific knockout (KO) mice has revolutionized our understanding of in vivo gene functions. As the use of mouse embryonic stem (ES) cells is inevitable for conventional gene targeting, the generation of knockout mice remains a very time-consuming and expensive process. To accelerate the large-scale production and phenotype analyses of KO mice, international efforts have organized global consortia such as the International Knockout Mouse Consortium (IKMC) and International Mouse Phenotype Consortium (IMPC), and they are persistently expanding the KO mouse catalogue that is publicly available for the researches studying specific genes of interests in vivo. However, new technologies, adopting zinc-finger nucleases (ZFNs) or Transcription Activator-like Effector (TALE) Nucleases (TALENs) to edit the mouse genome, are now emerging as valuable and effective shortcuts alternative for the conventional gene targeting using ES cells. Here, we introduce the recent achievement of IKMC, and evaluate the significance of ZFN/TALEN technology in mouse genetics. [BMB Reports 2012; 45(12): 686-692]

Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain