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        Tissue-Specific CRISPR/Cas9 System of Cotton Pollen with GhPLIMP2b and GhMYB24 Promoters

        Lei Jianfeng,Dai Peihong,Li Jiyang,Yang Mi,Li Xiuqin,Zhang Wanqi,Zhou Guantong,WangzhenGuo,Liu Xiaodong 한국식물학회 2021 Journal of Plant Biology Vol.64 No.1

        CRISPR/Cas9 technology is a powerful tool for improving crop genetic traits. However, CRISPR/Cas9 system for efficient expression of cotton germ cells has still not been established. In this study, we developed a tissue-specific vectors to drive Cas9 expression with GhPLIMP2b and GhMYB24 promoters and established the effective method to transform cotton pollen by Agrobacterium vacuum infiltration. GhPLIMP2b and GhMYB24 promoters of cotton pollen were cloned into Cas9 expression vectors. The sgRNAs targetting to CLA1, ERA1 and GGB (drought-resistant negative regulation genes) were designed and constructed into GhPLIMP2b and GhMYB24 promoter vectors. Cotton pollens were tranformed by Agrobacterium vacuum infiltration with GhPLIMP2b and GhMYB24 promoter vectors. The results of clone sequencing shown that mutation types of the sequence were mainly base substitutions wth the frequency from 3.29 to 6.45%. Eleven potential off-target sites were chosen and two sites were observed. Our results indicated that GhPLIM2bP::Cas9 and GhMYB24P::Cas9 editing vectors achieved targeted edition of endogenous genes in cotton pollen, but there were a few off-target effects. This study provides an effective gene-editing system for the rapid acquisition of cotton mutants using pollen as a transgenic receptor.

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        UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

        Ning Wang,Jiyang Dai,Jin Ying 한국항공우주학회 2021 International Journal of Aeronautical and Space Sc Vol.22 No.6

        Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

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        Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

        Feng Ding,Dandan Meng,Jiyang Dai,Qishen Li,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2

        By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the information vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.

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