Rice Xa21 gene encodes a receptor-like kinase that confers broad-spectrum resistance against Xanthomonas oryzae pv. oryzae (Xoo). Recently, a number of genes involved in the Xa21-mediated disease resistance pathway have been identified. Based on our previous data and the literature, we chose 16 candidate proteins and made corresponding antibodies. Using Western blotting, we systematically investigated the expression profile of the proteins in Xa21-mediated disease resistance response. We found nine proteins with altered expression. We further compared their expression in resistance, susceptible and mock responses, and found that GST expression was up-regulated during the resistance process, indicating GST is a positive regulator in resistance responses. ATPsB expression was down-regulated during both the resistance and susceptible response processes, although it was higher in the resistance response than that in the susceptible response. The total amount of MYB, GAPDH, CatB, Trx and NB-ARC proteins was lower in the resistance than in the susceptible response, but their abundance per unit bacteria in the resistance response was still higher than in the susceptible response, suggesting that these proteins might be positive regulators in the resistance response. In addition, expression of another ERF was induced by inoculation with bacterial blight pathogen, and expression of Zf-LSD1 was activated by wounding stress alone. Interestingly, most proteins showed similar altered expression patterns in the resistance and susceptible responses, but differed to some extents, implying that both responses might share common molecular mechanisms. This study revealed evidence of resistance-related protein expression, providing a foundation for better understanding of their functions.

In this paper, we design a technologically intelligent wheelchair with eye-movement control for patients with ALS in a natural environment. The system consists of an electric wheelchair, a vision system, a two-dimensional robotic arm, and a main control system. The smart wheelchair obtains the eye image of the controller through a monocular camera and uses deep learning and an attention mechanism to calculate the eye-movement direction. In addition, starting from the relationship between the trajectory of the joystick and the wheelchair speed, we establish a motion acceleration model of the smart wheelchair, which reduces the sudden acceleration of the smart wheelchair during rapid motion and improves the smoothness of the motion of the smart wheelchair. The lightweight eye-movement recognition model is transplanted into an embedded AI controller. The test results show that the accuracy of eye-movement direction recognition is 98.49%, the wheelchair movement speed is up to 1 m/s, and the movement trajectory is smooth, without sudden changes.

No abstract