Functional Characterization of the Paeonia ostii P5CS Gene under Drought Stress.

With persistent elevation in global temperature, water scarcity becomes a major threat to plant growth and development, yield security, agricultural sustainability, and food production. Proline, as a key osmolyte and antioxidant, plays a critical role in regulating drought tolerance in plants, especially its key biosynthetic enzyme, delta-1-pyrroline-5-carboxylate synthase (P5CS), which always positively responds to drought stress. As an important woody oil crop, the expansion of Paeonia ostii cultivation needs to address the issue of plant drought tolerance. Here, we isolated a PoP5CS gene from P. ostii, with an open reading frame of 1842 bp encoding 613 amino acids. PoP5CS expression progressively increased in response to increasing drought stress, and it was localized in the cytoplasm. Silencing of PoP5CS in P. ostii reduced drought tolerance, accompanied by decreased proline content, elevated reactive oxygen species (ROS) accumulation, and increased relative electrical conductivity (REC) and malondialdehyde (MDA) levels. Conversely, overexpression of PoP5CS in Nicotiana tabacum plants enhanced drought resistance, manifested by increased proline levels, reduced ROS accumulation, and lower REC and MDA contents. This study isolates PoP5CS from P. ostii and validates its role in regulating drought tolerance, providing valuable genetic resources and theoretical insights for the development of drought-resistant P. ostii cultivars.

An Individual Prosthesis Control Method with Human Subjective Choices.

An intelligent lower-limb prosthesis can provide walking support and convenience for lower-limb amputees. Trajectory planning of prosthesis joints plays an important role in the intelligent prosthetic control system, which directly determines the performance and helps improve comfort when wearing the prosthesis. Due to the differences in physiology and walking habits, humans have their own walking mode that requires the prosthesis to consider the individual's demands when planning the prosthesis joint trajectories. The human is an integral part of the control loop, whose subjective feeling is important feedback information, as humans can evaluate many indicators that are difficult to quantify and model. In this study, trajectories were built using the phase variable method by normalizing the gait curve to a unified range. The deviations between the optimal trajectory and current were represented using Fourier series expansion. A gait dataset that contains multi-subject kinematics data is used in the experiments to prove the feasibility and effectiveness of this method. In the experiments, we optimized the subjects' gait trajectories from an average to an individual gait trajectory. By using the individual trajectory planning algorithm, the average gait trajectory can be effectively optimized into a personalized trajectory, which is beneficial for improving walking comfort and safety and bringing the prosthesis closer to intelligence.

Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series.

Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual's kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective.