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( Yafeng Song ),( Jonas M. Nikoloff ),( Dawei Zhang ) 한국미생물 · 생명공학회 2015 Journal of microbiology and biotechnology Vol.25 No.7
The well-characterized gram-positive bacterium Bacillus subtilis is an outstanding industrial candidate for protein expression owing to its single membrane and high capacity of secretion,simplifying the downstream processing of secretory proteins. During the last few years, there has been continuous progress in the illustration of secretion mechanisms and application of this robust host in various fields of life science, such as enzyme production, feed additives,and food and pharmaceutical industries. Here, we review the developments of Bacillus subtilis as a highly promising expression system illuminating strong chemical- and temperatureinducible and other types of promoters, strategies for ribosome-binding-site utilization, and the novel approach of signal peptide selection. Furthermore, we outline the main steps of the Sec pathway and the relevant elements as well as their interactions. In addition, we introduce the latest discoveries of Tat-related complex structures and functions and the countless applications of this full-folded protein secretion pathway. This review also lists some of the current understandings of ATP-binding cassette transporters. According to the extensive knowledge on the genetic modification strategies and molecular biology of Bacillus subtilis, we propose some suggestions and strategies for improving the yield of intended productions. We expect this to promote striking future developments in the optimization and application of this bacterium.
Visual Attention Model Based on Particle Filter
( Long Liu ),( Wei Wei ),( Xianli Li ),( Yafeng Pan ),( Houbing Song ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.8
The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.