Ocean University of China

About: Ocean University of China is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Population & Sea surface temperature. The organization has 27604 authors who have published 27886 publications receiving 440181 citations. The organization is also known as: Zhōngguó Hǎiyáng Dàxué & OUC.

The NLRP3 Inflammasome in Alzheimer’s Disease

Abstract: Innate immunity and inflammatory response plays an important role in the pathogenesis of Alzheimer’s disease (AD). As the major resident immune cells in the brain, microglial cells constantly survey the microenvironment and are activated by and recruited to senile plaques. Subsequently, they can phagocytose amyloid-β (Aβ) and secrete pro-inflammatory cytokines that influence the surrounding brain tissue. Recently, a wealth of information linking the microglia-specific activation of NLRP3 inflammasome to AD pathogenesis has emerged. We review here the activation mechanisms of NLRP3 inflammasome in microglia and several downstream effects in the brain, demonstrating that toxic Aβ peptide can light a fire in NLRP3 inflammasome and eventually induce AD pathology and tissue damage. More importantly, it has been demonstrated that inhibition of NLRP3 could largely protect from memory loss and decrease Aβ deposition in AD transgenic mouse model. So, we further discuss the recent advances and challenges in targeting NLRP3 inflammasome for AD therapy.

Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet

Abstract: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy $c$ -means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.

Stabilization of Singular Markovian Jump Systems With Generally Uncertain Transition Rates

Abstract: This note is devoted to investigating the stability and stabilization problems for continuous-time singular Markovian jump systems (SMJSs) with generally uncertain transition rates (GUTRs). In this GUTR singular model, each transition rate can be completely unknown or only its estimate value is known. In terms of a set of coupled linear matrix inequalities (LMIs), a sufficient condition is established to ensure the systems to be regular, impulse-free and stochastically stable. Moreover, the corresponding sufficient condition on the existence of a mode-dependent state-feedback controller is derived to guarantee the closed-loop systems stochastically admissible by applying the LMI technique. Finally, a numerical example is presented to illustrate the effectiveness and efficiency of the proposed method.

High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities

Abstract: Concerns regarding the rapid loss of endemic biodiversity, and introduction and spread of non-indigenous species, have focused attention on the need and ability to detect species present in communities at low abundance. However, detection of rare species poses immense technical challenges, especially for morphologically cryptic species, microscopic taxa and those beneath the water surface in aquatic ecosystems. Next-generation sequencing technology provides a robust tool to assess biodiversity, especially for detection of rare species. Here, we assess the sensitivity of 454 pyrosequencing for detection of rare species using known indicator species spiked into existing complex plankton samples. In addition, we develop universal small subunit ribosomal DNA primers for amplification of a wide range of taxa for detailed description of biodiversity in complex communities. A universality test of newly designed primers for the hypervariable V4 region of the nuclear small subunit ribosomal DNA (V4-nSSU) using a plankton sample collected from Hamilton Harbor showed that 454 pyrosequencing based on this universal primer pair can recover a wide range of taxa, including animals, plants (algae), fungi, blue-green algae and protists. A sensitivity test showed that 454 pyrosequencing based on newly designed universal V4-nSSU primers was extremely sensitive for detection of very rare species. Pyrosequencing was able to recover spiked indicator species with biomass percentage as low as approximately 2 center dot 3x10-5% when 24 artificially assembled samples were tagged and sequenced in one PicoTiter plate (i.e. sequencing depth of an equivalent of 1/24 PicoTiter plate). In addition, spiked rare species were sometimes recovered as singletons (i.e. Operational Taxonomic Units represented by a single sequence), suggesting that at least some singletons are informative for recovering unique lineages in rare biospheres'. The method established here allows biologists to better investigate the composition of aquatic communities, especially for detection of rare taxa. Despite a small-scale pyrosequencing effort, we demonstrate the extreme sensitivity of pyrosequencing using rare species spiked into plankton samples. We propose that the method is a powerful tool for detection of rare native and/or alien species.