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Background Ubiquitination is essential for many cellular processes in eukaryotes, including 26S proteasome-dependent protein degradation, cell cycle progression, transcriptional regulation, and signal transduction. Although numerous ubiquitinated proteins have been empirically identified, their cognate ubiquitin E3 ligases remain largely unknown. Results Here, we generate a complete ubiquitin E3 ligase-encoding open reading frames (UbE3-ORFeome) library containing 98.94% of the 1515 E3 ligase genes in the rice ( Oryza sativa L . ) genome. In the test screens with four known ubiquitinated proteins, we identify both known and new E3s. The interaction and degradation between several E3s and their substrates are confirmed in vitro and in vivo. In addition, we identify the F-box E3 ligase OsFBK16 as a hub-interacting protein of the phenylalanine ammonia lyase family OsPAL1–OsPAL7. We demonstrate that OsFBK16 promotes the degradation of OsPAL1, OsPAL5, and OsPAL6. Remarkably, we find that overexpression of OsPAL1 or OsPAL6 as well as loss-of-function of OsFBK16 in rice displayed enhanced blast resistance, indicating that OsFBK16 degrades OsPALs to negatively regulate rice immunity. Conclusions The rice UbE3-ORFeome is the first complete E3 ligase library in plants and represents a powerful proteomic resource for rapid identification of the cognate E3 ligases of ubiquitinated proteins and establishment of functional E3–substrate interactome in plants.

Arresting gear is the key equipment to ensure the safe and short-range landing of a carrier-based aircraft, which is a complex electromechanical-hydraulic system. Accurately and efficiently modeling of arresting gear system is critical to enhance its service life and safety. However, there is few researches reported that can achieve this goal at the same time, which hinders the quantitative analysis of the effects of various factors, such as velocity, mass, eccentricity etc., on the dynamic response of the arresting gear. Thus, this paper proposed an efficient model for solution of arresting gear dynamic responses based on BP neural network optimized by whale optimization algorithm (WOA-BPNN). And, based on this model a sensitivity study was carried out to quantitatively analyze the effects of various factors on the dynamic response of the arresting gear. First, a mathematical model of the arresting gear based on multibody dynamics and hydraulic system analysis was established, and its effectiveness was validated by the experiment data. Second, the dynamic response of the arresting gear under various factors was figured out by the proposed mathematical model, and a WOA-BPNN was established based on the obtained data, which could predict the dynamic response of the arresting gear with high efficiency and fidelity. Then, the Sobol global sensitivity analysis method was employed to evaluate the influence of each landing parameter on the dynamic response. Finally, the maximum cable force of the arresting gear was studied as an example of the dynamic response of the solution. According to the results, it could be concluded that the landing velocity and landing mass were the dominant factors affecting the cable force, and there were interactions among landing parameters. The proposed model could efficiently figure out the dynamic response of each component in an arresting gear and identify the most influential landing parameter, which could lay a foundation for enhancing the operational reliability of an arresting gear and optimizing the structure of the arresting gear.

The grid-stiffened structure is the key load-carrying sections of launch vehicles such as the storage tank and the interstage section. Thus, its load-carrying performance is directly related to the load-carrying efficiency of a rocket. In this paper, a structural optimization method based on the load-carrying efficiency was proposed for grid-stiffened structures. Firstly, a general 3D parametric geometrical modeling method for grid-stiffened structure was proposed, which could optimize the position of stiffeners and incorporate the key geometric features such as welds. Based on this method, the buckling load of the structure considering the internal pressure was figured out by finite element analysis, and the solution accuracy was verified. Then, a surrogate model was constructed based on the Particle Swarm Optimization-Least Squares Support Vector Regression algorithm for accurately and efficiently predicting the buckling load of grid-stiffened structures. Subsequently, the sensitivity of each structural parameter to the buckling load was evaluated using the Sobol method to identify the key ones. Finally, targeting these key parameters, a load-carrying efficiency optimization model was proposed, considering constraints such as mass and geometric constraints. The results showed that the load-carrying efficiency of the optimized structure increased by 11.20%. The methods proposed in this paper could efficiently establish a practical 3D geometrical model of the grid-stiffened structure, effectively improve its load-carrying efficiency, and maintain the given parameters within the specified ranges. This approach could lay a foundation for reducing the launch costs and improving the transportation efficiency.

The inner-surface damage of water conveyance tunnels is the main hidden danger that threatens their safety and leads to serious accidents. The method based on the principle of acoustic reflection is the main means of inspecting damage to water-conveyance tunnels. However, affected by the tunnel environment and equipment noise, the obtained acoustic point cloud model inevitably suffers from noise, which can produce erroneous results. Therefore, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the acoustic point cloud model of water-conveyance tunnels. The proposed method fuses the ideas of guided filtering and the unsharp masking technique and extends them to the 3D point cloud model by considering the position of the point. In addition, edge-aware weighting mean is also used to retain the edge features of the point cloud model while smoothing the noise points. The experimental results show that our method can obtain impressive results and a better performance in both the acoustic point cloud model of the tunnel and the simulated point cloud model than many state-of-the-art methods.

Trochoidal machining could significantly improve cutting efficiency, enhance cutting stability, reduce cutting temperature, extend tool life, and reduce the cutting costs. However, in trochoidal machining, there are few studies focusing on modelling the instantaneous cutting power due to overlooking the importance of cutting temperature modelling. Also, instantaneous cutting power is an important basis for the optimization of trochoidal parameters and cutting parameters. In this work, we established a new and efficient method that could predict the instantaneous cutting power in trochoidal machining in high fidelity. First, the specific cutting energy of a given workpiece material, cutting tool, and cutting parameter in milling process was calibrated by cutting experiments. Second, the influence of the radial depth of cut on the specific cutting energy in milling process was quantitatively studied. Third, combining the obtained relationship between the specific cutting energy and radial depth of cut, the specific cutting energy curve in trochoidal machining was obtained. Then, a way to figure out the instantaneous material removal rate was proposed based on the acquired instantaneous 3D un-deform chip in trochoidal machining. Finally, based on the obtained specific cutting energy and instantaneous material removal rate, an accurate and efficient approach to predicting the instantaneous cutting power in trochoidal machining was proposed, and a practical application was demonstrated. The effectiveness of the proposed approach was validated by cutting experiments. The method proposed in this work could be adopted in cutting parameter optimization, tool-path optimization, and cutting temperature prediction in trochoidal machining.

Accurate direction-of-arrival (DOA) estimation is crucial to a variety of applications, including wireless communications, radar systems, and sensor arrays. In this work, we propose a novel deep convolutional neural network (DCN) called TADCN for 2D-DOA estimation using an L-shaped array. The network achieves high estimation performance through a triple attention mechanism (TAM). Specifically, the new architecture enables the network to capture the relationships across the channel, height, and width dimensions of the signal sample features, thereby enhancing the feature extraction capability and improving the resulting spatial spectrum. To this end, the spatial spectrum is processed by the proposed spectrum analyzer to yield high-precision DOA estimation results. An automatic angle matching method based on TADCN is employed for estimating the pairing between the estimated azimuth and elevation DOA sets. Furthermore, the overall efficiency is enhanced through the parallel processing of the angle estimation and matching networks. Simulation results demonstrate that the proposed algorithm outperforms traditional methods and deep learning-based approaches for various noise levels and snapshots while maintaining better estimation performance even in the presence of correlated signal sources.

The structural deformations induced by rocket launch vibrations, on-orbit thermal gradients, and gravitation fluctuations can lead to significant deployment errors for large-aperture, segmented space telescopes. As the size and number of segments increase in future telescopes, the optical-based methods for detecting deployment errors suffer from the range limitations of the millimeter scale and time-consuming processes of the month scale. To address this, we propose a new method for rapid-deployment error detection based on long-range, high-precision capacitive edge sensors. These sensors feature a measurement range of ±13 mm, with a precision better than 7.3 nm, enabling efficient and simultaneous error detection across all segments. This approach significantly reduces the time and steps required compared to traditional optical methods. Through experimental validation, the designed system demonstrated the ability to detect and correct large deployment errors and maintain co-phasing precision, meeting the stringent requirements for future space telescopes. The proposed sensor system enhances deployment efficiency, offering a viable solution for the next generation of segmented space telescopes.

High-precision direction of arrival (DOA) of wideband signals is a very important technology in the field of radar and communication. In this work, we propose an efficient support vector regression (SVR) architecture via a genetic algorithm (GA) for wideband DOA estimation, which exhibits high estimation performance and generalization performance. By adopting the two-sided correlation transformation (TCT) algorithm, the network is trained only from reference frequency data to increase the training efficiency. In order to reduce the redundant information in the array covariance matrix and lower the dimensionality of the input features, the array covariance matrix at a single frequency point is preprocessed according to its conjugate symmetry and elemental characteristics, and the dimensionality-reduced input features are obtained. Specifically, the dimensionality of the input features does not increase with the number of sub-bands when dealing with broadband signals or ultra-broadband signals, which can significantly reduce the training time of the model and the storage capacity of the system. The increased performance of the proposed algorithm is highly desirable in resource-constrained scenarios, and the experimental results demonstrate the efficiency and superiority of the proposed network compared with existing methods.

The direct collocation method transforms a trajectory optimization problem into a nonlinear programming (NLP) problem by discretizing both control and state variables. During the NLP solution process, repeated calculations of the first and second derivatives of the NLP and the values of the dynamic system at each discrete point are required, leading to great computational complexities. Therefore, this paper proposes the following method: First, the hyper-dual number method is introduced to accurately identify the sparsity of the second-derivative matrix of the NLP and to determine the locations of the non-zero elements. Then, a multi-core parallel approach is used to rapidly compute the non-zero elements of the first and second derivatives of the NLP as well as the values of the dynamic system at each discrete point. Finally, OpenMP is employed for programming calculation in the C++ environment to further enhance computational efficiency from the perspective of programming language. Simulation results demonstrate that the proposed method effectively improves the efficiency of trajectory optimization and its computational efficiency without compromising accuracy. 直接配点法通过对控制变量和状态变量都进行离散将轨迹优化问题转化为非线性规划(nonlinear programming, NLP)进行求解。在求解NLP时, 需要反复计算NLP的一阶/二阶偏导数和动力学系统在各离散点处的值, 计算量比较大。针对该问题, 提出如下解决策略: 引入超对偶数方法准确识别NLP二阶偏导数矩阵的稀疏型, 确定其中非零元素的位置; 采用多核并行方式快速计算NLP的一阶/二阶偏导数的非零元素以及动力学系统在各离散点处的值; 在C++环境下采用OpenMP方式进行编程计算, 从编程语言角度进一步提高计算效率。仿真结果表明, 文中方法给出的策略在不影响精度的情况下, 均能显著提高轨迹优化效率。

Objective This study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH). Methods Totally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set ( N  = 2678) and was validated using the remaining cases ( N  = 1105). Univariate and multivariate logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model is presented in the form of a nomogram, which is internally and externally validated with two independent datasets. The discrimination of nomograms was assessed by calculating the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in the training and validation sets. Results The final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902, 0.926, 0.851 in the training cohort, internal validation and external cohorts. The calibration accuracy and diagnostic performance were satisfactory in both the training and validation sets. Conclusions This predictive model based on a retrospective study was externally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution in people living with HIV.