| 2 | Identification and profile of phenolamides with anthracnose resistance potential in tea(Camellia sinensis)显示文摘Tea anthracnose is a prevalent disease in China that can lead to reduced tea production and lower quality,yet there is currently a lack of effective means for controlling this disease.In this study,we identified 46 phenolamides(including 27 isomers)in different tissues and organs of tea plants based on a developed workflow,and the secondary mass spectra of all these compounds have been documented.It was revealed that tea plants predominantly accumulate protonated aliphatic phenolamides,rather than aromatic phenolamides.The profile of phenolamides indicate that their buildup in tea plants is specific to certain tissues and acyl-acceptors,and this distribution is associated with the extent of phenolamide acyl-modification.Additionally,it was observed that N-Feruloylputrescine(Fer-Put,a type of phenolamides)was responsive to the stimulated accumulation of the tea anthracnose pathogen.The findings of anti-anthracnose experiments in vitro and on tea leaf demonstrated that Fer-Put was capable of significantly inhibiting the growth of anthracnose pathogen colony,effectively prevented tea leaf disease.Furthermore,it was observed that Fer-Put treatment can enhance the antioxidant enzyme activity of tea leaves.TEA002780.1 and TEA013165.1 gene may be responsible for the biosynthesis of Fer-Put in the disease resistance process in tea plants.Through these studies,the types and distribution of phenolamides in tea plants have been elucidated,and Fer-Put’s ability to resist anthracnose has been established,providing new insights into the resistance of tea anthracnose. | Wenzhao Wang Xingcui Xie Yuanyuan Lv Haonan Guan Lu Liu Qian Huang Yumeng Bao Jie Zhou Lu Bao Chunmei Gong Youben Yu | 2023 | Horticulture Research2023，10，9： | 0 |
| 3 | Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control显示文摘Using a brain-computer interface(BCI)rather than limbs to control multiple robots(i.e.,brain-controlled multi-robots)can better assist people with disabilities in daily life than a brain-controlled single robot.For example,one person with disabilities can move by a brain-controlled wheelchair(leader robot)and simultaneously transport objects by follower robots.In this paper,we explore how to control the direction,speed,and formation of a brain-controlled multi-robot system(consisting of leader and follower robots)for the first time and propose a novel multi-robot predictive control framework(MRPCF)that can track users'control intents and ensure the safety of multiple robots.The MRPCF consists of the leader controller,follower controller,and formation planner.We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments.The experimental results indicate that the proposed system can track users'direction,speed,and formation control intents when guaranteeing multiple robots’safety.This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration. | Zhenge Yang Luzheng Bi Weiming Chi Haonan Shi Cuntai Guan | 2022 | Complex System Modeling and Simulation2022，2，4： | 0 |
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