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.

Dysregulation of Ubiquitin-Proteasome System in Neurodegenerative Diseases

Abstract: The ubiquitin-proteasome system (UPS) is one of the major protein degradation pathways, where abnormal UPS function has been observed in cancer and neurological diseases. Many neurodegenerative diseases share a common pathological feature, namely intracellular ubiquitin-positive inclusions formed by aggregate-prone neurotoxic proteins. This suggests that dysfunction of the UPS in neurodegenerative diseases contributes to the accumulation of neurotoxic proteins and to instigate neurodegeneration. Here, we review recent findings describing various aspects of UPS dysregulation in neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and Huntingtin’s disease.

Flexible two-dimensional Tin+1Cn (n = 1, 2 and 3) and their functionalized MXenes predicted by density functional theories.

Abstract: Two-dimensional (2D) transition metal carbides/nitrides Mn+1Xn labeled as MXenes are attracting increasing interest due to promising applications as Li-ion battery anodes and hybrid electro-chemical capacitors. To realize MXenes devices in future flexible practical applications, it is necessary to have a full understanding of the mechanical properties of MXenes under deformation. In this study, we extensively investigated the stress–strain curves and the deformation mechanisms in response to tensile stress by first principles calculations using 2D Tin+1Cn (n = 1, 2 and/or 3) as examples. Our results show that 2D Ti2C can sustain large strains of 9.5%, 18% and 17% under tensions of biaxial and uniaxial along x and y, respectively, which respectively increase to 20%, 28% and 26.5% for 2D Ti2CO2 due to surface functionalizing oxygen, which is much better than graphene (15% biaxial). The failure of 2D Tin+1Cn MXene is due to the significant collapse of the surface atomic layer; however, surface functionalization could slow down this collapse, resulting in the improvement of mechanical flexibility. We have also discussed the critical strains and Young's modulus of 2D Tin+1Cn and Tin+1CnO2. Our results provide an insight into the microscopic deformation mechanism of MXenes and hence benefit their applications in flexible electronic devices.

Spectral-Spatial Constraint Hyperspectral Image Classification

Abstract: Hyperspectral image classification has attracted extensive research efforts in the recent decade. The main difficulty lies in the few labeled samples versus the high dimensional features. To this end, it is a fundamental step to explore the relationship among different pixels in hyperspectral image classification, toward jointly handing both the lack of label and high dimensionality problems. In the hyperspectral images, the classification task can be benefited from the spatial layout information. In this paper, we propose a hyperspectral image classification method to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated in a hypergraph structure. In the constructed hypergraph, each vertex denotes a pixel in the hyperspectral image. And the hyperedges are constructed from both the distance between pixels in the feature space and the spatial locations of pixels. More specifically, a feature-based hyperedge is generated by using distance among pixels, where each pixel is connected with its K nearest neighbors in the feature space. Second, a spatial-based hyperedge is generated to model the layout among pixels by linking where each pixel is linked with its spatial local neighbors. Both the learning on the combinational hypergraph is conducted by jointly investigating the image feature and the spatial layout of pixels to seek their joint optimal partitions. Experiments on four data sets are performed to evaluate the effectiveness and and efficiency of the proposed method. Comparisons to the state-of-the-art methods demonstrate the superiority of the proposed method in the hyperspectral image classification.