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Screening a Panel of Acid-producing Strains by Developing a High-throughput Method
Lijuan Zhu,Hui Zhang,Shiyuan Wang,Anqi Zhao,Lingbo Qu,Wenlong Xiong,Md. Asraful Alam,Wenlong Ma,Yongkun Lv,Jingliang Xu 한국생물공학회 2022 Biotechnology and Bioprocess Engineering Vol.27 No.5
Organic acids are natural cellular metabolites, which are widely used in food, pharmaceutical, and chemical industries. Among them, L-lactic acid is of special interest, because it is widely used in food and pharmaceutical industries and its monopolymer (poly (lactic acid)) is a green, renewable, biodegradable, and biocompatible alternative to the petroleum-based polymers. Currently, organic acids are predominantly produced by microbial fermentation. Their productions have been substantially improved by genetic modifications, metabolic engineering, and fermentation optimizations. However, the commonly used microbial producers still suffer from low acidic tolerance. Screening higher tolerant acid-producing microorganisms from the nature is relatively less explored. The traditional fermented foods are good resources for the screening of acid-producing and probiotic microorganisms. However, they are relatively less explored, especially those foods in developing countries. To speed up the acid-producing microorganism screening, we developed and validated a high-throughput method in this study. By using this method, we screened 1,296 colonies in 4 days and obtained a panel of acid-producing microorganisms. Among them, a Lacticaseibacillus rhamnosus showed the potential for organic acid production and probiotics applications.
Energy-harvesting Q-learning secure routing algorithm with authenticated-encryption for WSN
Li Cuiran,Wu Jixuan,Zhang Zepeng,Lv Anqi 한국통신학회 2023 ICT Express Vol.9 No.6
Wireless sensor networks are susceptible to a variety of network attacks. Due to the limited energy of nodes and selfish nodes in the network, the packet delivery rate is lower. To address these issues, we innovatively propose an energy-harvesting Q-learning secure routing algorithm with authenticated-encryption. The algorithm uses physical unclonable functions and optimized Q-learning to ensure that the transmission path is reliable. Meanwhile, we combine the LSTM-based prediction model to predict the energy value that the nodes replenish. In addition, simulations are performed to compare the performances of the proposed algorithm with other algorithms under different attacks. The proposed algorithm has greater improvements in the packet delivery rate, filtering selfish nodes, and reducing node energy consumption.