Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

Ultrasound-Switchable Nanozyme Augments Sonodynamic Therapy against Multidrug-Resistant Bacterial Infection.

Abstract: Ultrasound (US)-driven sonodynamic therapy (SDT) has demonstrated wide application prospects in the eradication of deep-seated bacterial infections due to its noninvasiveness, site-confined irradia...

Soluble TREM2 induces inflammatory responses and enhances microglial survival

Abstract: Triggering receptor expressed on myeloid cells 2 (TREM2) is an innate immune receptor expressed in microglia in the brain. A soluble form of TREM2 (sTREM2) derived from proteolytic cleavage of the cell surface receptor is increased in the preclinical stages of AD and positively correlates with the amounts of total and phosphorylated tau in the cerebrospinal fluid. However, the physiological and pathological functions of sTREM2 remain unknown. Here, we show that sTREM2 promotes microglial survival in a PI3K/Akt-dependent manner and stimulates the production of inflammatory cytokines depending on NF-κB. Variants of sTREM2 carrying AD risk-associated mutations were less potent in both suppressing apoptosis and triggering inflammatory responses. Importantly, sTREM2 delivered to the hippocampi of both wild-type and Trem2-knockout mice elevated the expression of inflammatory cytokines and induced morphological changes of microglia. Collectively, these data indicate that sTREM2 triggers microglial activation inducing inflammatory responses and promoting survival. This study has implications for the pathogenesis of AD and provides insights into targeting sTREM2 pathway for AD therapy.

Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation

Abstract: Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an efficient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not discriminative to semantic categories, which are abandoned by a graphlet selection scheme as they make no contribution to the subsequent segmentation. Thereafter, we use a Gaussian mixture model (GMM) to learn the distribution of the selected post-embedding graphlets (i.e., vectors output from the graphlet embedding). Finally, we propose an image segmentation algorithm, termed representative graphlet cut, which leverages the learned GMM prior to measure the structure homogeneity of a test image. Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets. Besides, our approach performs competitively to the fully-supervised segmentation models.

Distributed Average Tracking of Multiple Time-Varying Reference Signals With Bounded Derivatives

Abstract: We present a distributed discontinuous control algorithm for a team of agents to track the average of multiple time-varying reference signals with bounded derivatives. We use tools from nonsmooth analysis to analyze the stability of the system. For time-invariant undirected connected network topologies, we prove that the states of all agents will converge to the average of the time-varying reference signals with bounded derivatives in finite time provided that the control gain is properly chosen. The validity of this result is also established for scenarios with switching undirected connected network topologies. For time-invariant directed network topologies with a directed spanning tree, we show that all agents will still reach a consensus in finite time, but the convergent value is generally not the average of the time-varying reference signals with bounded derivatives. Simulation examples are presented to show the validity of the above results.

Overactivation of glycogen synthase kinase-3 by inhibition of phosphoinositol-3 kinase and protein kinase C leads to hyperphosphorylation of tau and impairment of spatial memory.

Abstract: Neurofibrillary tangles (NFTs) consisting of the hyperphosphorylated microtubule-associated protein tau are a defining pathological characteristic of Alzheimer's disease (AD). Hyperphosphorylation of tau is hypothesized to impair the microtubule stabilizing function of tau, leading to the formation of paired helical filaments and neuronal death. Glycogen synthase kinase-3 (GSK-3) has been shown to be one of several kinases that mediate tau hyperphosphorylation in vitro. However, molecular mechanisms underlying overactivation of GSK-3 and its potential linkage to AD-like pathologies in vivo remain unclear. Here, we demonstrate that injection of wortmannin (a specific inhibitor of phosphoinositol-3 kinase) or GF-109203X (a specific inhibitor of protein kinase C) into the left ventricle of rat brains leads to overactivation of GSK-3, hyperphosphorylation of tau at Ser 396/404/199/202 and, most significantly, impaired spatial memory. The effects of wortmannin and GF-109203X are additive. Significantly, specific inhibition of GSK-3 activity by LiCl prevents hyperphosphorylation of tau, and spatial memory impairment resulting from PI3K and PKC inhibition. These results indicate that in vivo inhibition of phosphoinositol-3 kinase and protein kinase C results in overactivation of GSK-3 and tau hyperphosphorylation and support a direct role of GSK-3 in the formation of AD-like cognitive deficits.