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Xiaozhan Li,Wenming Zhang,Mingke Wu,Fengxue Xin,Weiliang Dong,Hao Wu,Min Zhang,Jiangfeng Ma,Min Jiang 한국생물공학회 2017 Biotechnology and Bioprocess Engineering Vol.22 No.5
Succinic acid is a platform chemical with potential for bio-based synthesis. However, the production of bio-based succinate is limited because of insufficient succinate efflux capacity in the late stage of fermentation. In the present study, three different transporters, which have been reported to be responsible for C4-dicarboxylates transport, were employed for investigation of the transport capacity of succinate in Escherichia coli. After engineered strains were constructed, the fermentative production of succinic acid was studied in serum bottles and 3 L of fermentor. The results demonstrated that engineered strain showed better efflux capacity than control strain under high concentration of succinate. The highest production of succinate was 68.66 g/L, while the NCgl2130 transporter may be the best candidate for succinate export in E. coli. Further research showed that the expression levels and relative enzyme activities involved in the metabolic pathway all increased markedly, and the maximum activities of PPC, PCK, PYK, and MDH increased by 1.50, 1.38, 1.28, and 1.27-fold in recombinant E. coli AFP111/pTrc99a- NCgl2130, respectively. Moreover, the maximum level of intracellular ATP increased by 23.79% in E. coli AFP111/ pTrc99a-NCgl2130. Taken together, these findings indicated that engineered transporters can improve succinate production by increasing key enzyme activities and intracellular ATP levels. To the best of thew authors’ knowledge, this is the first report on a mechanism to improve succinate production by engineered transporters. This strategy set up a foundation for improving the biosynthesis of other C4-dicarboxylates, such as fumaric acid and malic acid.
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Xiuzhi Luo,Benxue Ma,Wenxia Wang,Shengyuan Lei,Yangyang Hu,Guowei Yu,Xiaozhan Li 한국식품과학회 2020 Food Science and Biotechnology Vol.29 No.4
The surface texture of dried jujube fruits is a significant quality grading criterion. This paper introduced a novel visual feature fusion based on connected region density, texture features, and color features. The singlescale Two-Dimensional Discrete Wavelet Transform was used to perform single-scale decomposition and reconstruction of dried Hami jujube image before visual features extraction. The connected region density was extracted by the two different algorithms, whereas the texture features were extracted by Gray Level Co-occurrence Matrix and the color features were extracted by image processing algorithms. Based on selected features which obtained by correlation analysis of visual features, the accuracy rate of the optimized Support Vector Machine classification model was 96.67%. In comparing with Extreme Learning Machine classification model and other fusion methods, the optimized Support Vector Machine based on selected visual features fusion was better.
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