BpMADS12 gene role in lignin biosynthesis of Betula platyphylla Suk by transcriptome analysis

MADS-box transcription factors show highly diverse regulatory functions in a wide variety of organisms. In this study, we characterized a MADS-box gene （BpMADS12） from the white birch （Betula platyphylla Suk）. This gene is a member of the suppressor of overexpression of CO 1/tomato MADS 3 class of MADS-box genes. We generated lines overexpressing BpMADS12 and found that these had higher levels of lignin compared to that observed in nontransgenic lines. Transcriptome anal- ysis revealed numerous changes in gene expression patterns. In total, 8794 differentially expressed genes were identified, including 5006 upregulated unigenes and 3788 downregulated unigenes in BpMADS-overexpression lines. Differentially expressed genes involved in the pathways for lignin and brassinosteroid biosynthesis were significantly enriched and may have contributed to phenotypic changes. The results from a quantitative RT-PCR analysis were consistent those obtained with the transcriptome analysis.Our transcriptome analysis, in combination with measure- ment of lignin level, indicated that BpMADS12 promotes lignin synthesis through regulation of key enzymes in response to brassinosteroid signaling. These results suggest that this MADS-box protein is crucial to all subsequent structural events and provide a good foundation for studies aiming to elucidate the developmental mechanisms underlying formation of wood.

Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.

A Physics‑Based Seismic Risk Assessment of the Qujiang Fault:From Dynamic Rupture to Disaster Estimation

This study achieved the construction of earthquake disaster scenarios based on physics-based methods-from fault dynamic rupture to seismic wave propagation-and then population and economic loss estimations.The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry,stress field,rock properties,and terrain.Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved.The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment.Additionally,there are significant variations in human losses,even though the economic losses are similar across different scenarios.Dali Bai Autonomous Prefecture,Chuxiong Yi Autonomous Prefecture,Yuxi City,Honghe Hani and Yi Autonomous Prefecture,and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks,with Yuxi and Honghe being particularly vulnerable.Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault.Notably,although the fault is within Yuxi,Honghe is likely to suffer the most severe damage.These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.

A new denoising method for photon-counting LiDAR data with different surface types and observation conditions

Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.

Surface clogging process modeling of suspended solids during urban stormwater aquifer recharge

Aquifer recharge, which uses urban stormwater, is an effective technique to control the negative effects of groundwater overexploitation, while clogging problems in infiltration systems remain the key restricting factor in broadening its practice. Quantitative understanding of the clogging process is still very poor. A laboratory study was conducted to understand surface physical clogging processes, with the primary aim of developing a model for predicting suspended solid clogging processes before aquifer recharge projects start. The experiments investigated the clogging characteristics of different suspended solid sizes in recharge water by using a series of one-dimensional fine quartz sand columns. The results showed that the smaller the suspended particles in recharge water, the farther the distance of movement and the larger the scope of clogging in porous media. Clogging extents in fine sand were 1 cm, for suspended particle size ranging from 0.075 to 0.0385 mm, and 2 cm, for particles less than 0.0385 mm. In addition, clogging development occurred more rapidly for smaller suspended solid particles. It took 48, 42, and 36 hr respectively, for large-, medium-, and small-sized particles to reach pre-determined clogging standards. An empirical formula and iteration model for the surface clogging evolution process were derived. The verification results obtained from stormwater recharge into fine sand demonstrated that the model could reflect the real laws of the surface clogging process.