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24 results on '"CAI Bin"'

15. ATP1A1 mutations cause intermediate Charcot‐Marie‐Tooth disease.

Author

He, Jin, Guo, Lingling, Lin, Shan, Chen, Wenfeng, Xu, Guorong, Cai, Bin, Xu, Liuqing, Hong, Jingmei, Qiu, Liangliang, Wang, Ning, and Chen, Wanjin

Abstract

Intermediate Charcot‐Marie‐Tooth (CMT) disease is a heterogeneous group of inherited neuropathies characterized by progressive muscle weakness and atrophy of the distal extremities, distal sensory loss. There were still a large proportion of causative genes for intermediate CMT failed to be identified. Here, using whole‐exome sequencing technique, we identified two novel missense mutations in ATP1A1 gene, c.620C>T (p.S207F) and c.2629G>A (p.G877S), in two Chinese CMT families. Further functional analysis revealed that these mutations led to the loss function of the ATP1A1 protein. The two mutations did not affect the levels of messenger RNA but possessed a damaging effect on ATP1A1 protein expression and they downregulated the protein levels of ATP1A1 by promoting its proteasome degradation. Taken together, we confirmed ATP1A1 as a novel causative gene for intermediate CMT. [ABSTRACT FROM AUTHOR]

Published
2019
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24. Dynamic energy balanced max flow routing in energy-harvesting sensor networks.

Author

Cai, Bin, Mao, Shan-Li, Li, Xiao-Hui, and Ding, Yue-Min

Subjects
*ROUTING algorithms, *WIRELESS sensor networks, *ENERGY harvesting, *DATA transmission systems, *ENERGY consumption
Abstract

We propose a dynamic energy balanced max flow routing algorithm to maximize load flow within the network lifetime and balance energy consumption to prolong the network lifetime in an energy-harvesting wireless sensor network. The proposed routing algorithm updates the transmission capacity between two nodes based on the residual energy of the nodes, which changes over time. Hence, the harvested energy is included in calculation of the maximum flow. Because the flow distribution of the Ford–Fulkerson algorithm is not balanced, the energy consumption among the nodes is not balanced, which limits the lifetime of the network. The proposed routing algorithm selects the node with the maximum residual energy as the next hop and updates the edge capacity when the flow of any edge is not sufficient for the next delivery, to balance energy consumption among nodes and prolong the lifetime of the network. Simulation results revealed that the proposed routing algorithm has advantages over the Ford–Fulkerson algorithm and the dynamic max flow algorithm with respect to extending the load flow and the lifetime of the network in a regular network, a small-world network, and a scale-free network. [ABSTRACT FROM AUTHOR]

Published
2017
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