To identity the pathogen that causes the mosaic and yellowing symptoms on Atractylodes macrocephala Koidz in Jiangxian, Shanxi province, biological inoculation, sequence-independent amplification (SIA),RT-PCR and other identification methods were used. The results showed that the chlorotic and necrosis symptoms occurred in the indicator plant Chenopodium quinoa after it was infected with the pathogen,and the same symptoms appeared after the reinoculation of healthy Atractylodes macrocephala Koidz; this reflected that the disease was likely to be caused by a virus. The results of SIA and sequencing showed that Broad bean wilt virus 2 (BBWV2) was present in severely mosaic Atractylodes macrocephala Koidz leaves. To further characterize the BBWV2 isolate from Atractylodes macrocephala (BBWV2-Am), the polyprotein partial gene encoded by BBWV2-Am RNA2 was cloned and sequenced. Sequence alignments showed that the nucleotide sequence identity of BBWV2-Am SCP and LCP genes ranged from 79.3% to 87.2% and from 80.1% to 89.2% compared to other BBWV2 strains,respectively; the deduced amino acid sequence similarities of the two gene products ranged from 91.2% to 95.7% and from 89.44 to 95.5%, respectively,compared to those of other BBWV2 strains. Phylogenetic comparisons showed that BBWV2-Am was most likely to be related to BBWV2-Rg,but formed an independent branch. This is the first report of BBWV2 in Atractylodes macrocephala Koidz.
Amino Acid Sequence ; Atractylodes ; virology ; Fabavirus ; chemistry ; classification ; genetics ; isolation & purification ; Molecular Sequence Data ; Phylogeny ; Plant Diseases ; virology ; Sequence Analysis ; Viral Proteins ; chemistry ; genetics

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.
Humans ; Exoskeleton Device ; Muscle Fatigue ; Muscles ; Algorithms ; Electric Impedance