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.

Genome-Wide Analysis of Basic/Helix-Loop-Helix Transcription Factor Family in Rice and Arabidopsis

Abstract: The basic/helix-loop-helix (bHLH) transcription factors and their homologs form a large family in plant and animal genomes. They are known to play important roles in the specification of tissue types in animals. On the other hand, few plant bHLH proteins have been studied functionally. Recent completion of whole genome sequences of model plants Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) allows genome-wide analysis and comparison of the bHLH family in flowering plants. We have identified 167 bHLH genes in the rice genome, and their phylogenetic analysis indicates that they form well-supported clades, which are defined as subfamilies. In addition, sequence analysis of potential DNA-binding activity, the sequence motifs outside the bHLH domain, and the conservation of intron/exon structural patterns further support the evolutionary relationships among these proteins. The genome distribution of rice bHLH genes strongly supports the hypothesis that genome-wide and tandem duplication contributed to the expansion of the bHLH gene family, consistent with the birth-and-death theory of gene family evolution. Bioinformatics analysis suggests that rice bHLH proteins can potentially participate in a variety of combinatorial interactions, endowing them with the capacity to regulate a multitude of transcriptional programs. In addition, similar expression patterns suggest functional conservation between some rice bHLH genes and their close Arabidopsis homologs.

Induction therapy with autologous mesenchymal stem cells in living-related kidney transplants: a randomized controlled trial.

Abstract: Key Science Research Project of Fujian Province, China [2009Y4001]; Key Laboratory of Fujian Province, China [2008J1006]

Metal-Free Carbon Materials for CO2 Electrochemical Reduction.

Abstract: The rapid increase of the CO2 concentration in the Earth's atmosphere has resulted in numerous environmental issues, such as global warming, ocean acidification, melting of the polar ice, rising sea level, and extinction of species. To search for suitable and capable catalytic systems for CO2 conversion, electrochemical reduction of CO2 (CO2 RR) holds great promise. Emerging heterogeneous carbon materials have been considered as promising metal-free electrocatalysts for the CO2 RR, owing to their abundant natural resources, tailorable porous structures, resistance to acids and bases, high-temperature stability, and environmental friendliness. They exhibit remarkable CO2 RR properties, including catalytic activity, long durability, and high selectivity. Here, various carbon materials (e.g., carbon fibers, carbon nanotubes, graphene, diamond, nanoporous carbon, and graphene dots) with heteroatom doping (e.g., N, S, and B) that can be used as metal-free catalysts for the CO2 RR are highlighted. Recent advances regarding the identification of active sites for the CO2 RR and the pathway of reduction of CO2 to the final product are comprehensively reviewed. Additionally, the emerging challenges and some perspectives on the development of heteroatom-doped carbon materials as metal-free electrocatalysts for the CO2 RR are included.

Non-local Deep Features for Salient Object Detection

Abstract: Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4×5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.