YTHDF3 phase separation regulates HSPA13-dependent clear cell renal cell carcinoma development and immune evasion.

Insufficient understanding about the immune evasion mechanism leads to the inability in predicting current immunotherapy effects in clear cell renal cell carcinoma (ccRCC) and sensitizing ccRCC to immunotherapy. RNA binding proteins (RBPs) can promote tumor progression and immune evasion. However, research on RBPs, particularly m6A reader YTHDF3, in ccRCC development and immune evasion is limited. In this study, we found that YTHDF3 level was downregulated in ccRCC and was an independent prognostic biomarker for ccRCC. Decreased YTHDF3 expression was correlated with the malignancy, immune evasion, and poor response to anti-programmed death ligand 1 (PD-L1)/CTLA-4 in ccRCC. YTHDF3 overexpression restrained ccRCC cell malignancy, PD-L1 expression, CD8+ T cell infiltration and activities in vivo, indicating its inhibitory role in ccRCC development and immune evasion. Mechanistically, YTHDF3 WT was found to have phase separation characteristics and suppress ccRCC malignancy and immune evasion. Whereas YTHDF3 mutant, which disrupted phase separation, abolished its function. YTHDF3 enhanced the degradation of its target mRNA HSPA13 by phase separation and recruiting DDX6, resulting in the downregulation of the downstream immune checkpoint PD-L1. HSPA13 overexpression restored ccRCC malignancy and immune evasion suppressed by YTHDF3 overexpression. In all, our results identify a new model of YTHDF3 in regulating ccRCC progression and immune evasion through phase separation.

Cross-View Approximation on Grassmann Manifold for Multiview Clustering.

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

Bidirectional Probabilistic Subspaces Approximation for Multiview Clustering.

The existing multiview clustering models learn a consistent low-dimensional embedding either from multiple feature matrices or multiple similarity matrices, which ignores the interaction between the two procedures and limits the improvement of clustering performance on multiview data. To address this issue, a bidirectional probabilistic subspaces approximation (BPSA) model is developed in this article to learn a consistently orthogonal embedding from multiple feature matrices and multiple similarity matrices simultaneously via the disturbed probabilistic subspace modeling and approximation. A skillful bidirectional fusion strategy is designed to guarantee the parameter-free property of the BPSA model. Two adaptively weighted learning mechanisms are introduced to ensure the inconsistencies among multiple views and the inconsistencies between bidirectional learning processes. To solve the optimization problem involved in the BPSA model, an iterative solver is derived, and a rigorous convergence guarantee is provided. Extensive experimental results on both toy and real-world datasets demonstrate that our BPSA model achieves state-of-the-art performance even if it is parameter-free.

Adaptive variable impedance control of dual-arm robots for slabstone installation.

The conventional dual-arm cooperative control method employed in the construction field cannot adequately meet the requirements of high precision and adaptability because of the use of large objects, heavy load, unstructured environment, and constraints related to the complex operation processes involved. Dual-arm robots are highly complex multi-degree-of-freedom system, and when using this robots for slabstone installation, it is necessary to consider not only the position constraint and force coupling relationship of the closed chain system, but also the force/position control while the slabstone contacts the wall. To solve this problem, this paper presents slabstone-installation model and motion element decomposition for dual-arm robots. Moreover, a control strategy is proposed by combining an adaptive variable impedance tracking controller for the force/position control of the slabstone and a tracking controller for the end-effector trajectories of the dual arms; the controllers proposed meet the requirements of position tracking and contact force tracking of the slabstone. Finally, different slabstone installation scenarios are simulated to verify the effectiveness of the proposed algorithm. The results show that the algorithm is compliant for the slabstone installation process and can meet the requirements of force/position control.

Practical Prescribed Time Control of Euler-Lagrange Systems With Partial/Full State Constraints: A Settling Time Regulator-Based Approach.

Many important engineering applications involve control design for Euler-Lagrange (EL) systems. In this article, the practical prescribed time tracking control problem of EL systems is investigated under partial or full state constraints. A settling time regulator is introduced to construct a novel performance function, with which a new neural adaptive control scheme is developed to achieve pregiven tracking precision within the prescribed time. With the specific system transformation techniques, the problem of state constraints is transformed into the boundedness of new variables. The salient feature of the proposed control methods lies in the fact that not only the settling time and tracking precision are at the user's disposal but also both partial state and full state constraints can be accommodated concurrently without the need for changing the control structure. The effectiveness of this approach is further verified by the simulation results.

The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist.

This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified.

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis.

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

An efficient estimation of distribution algorithm with rank-one modification and population reduction.

As a type of model-based metaheuristic, estimation of distribution algorithms (EDAs) show certain advantages over other metaheuristics by using statistical learning method to estimate the distribution of promising solutions. However, the commonly-used Gaussian EDAs (GEDAs) usually suffer from premature convergence that severely limits their efficiency. In this paper, we first attempt to enhance the performance of GEDA by improving its model estimation method. The new estimation method shifts the weighted mean of high-quality solutions towards the fitness improvement direction and estimates the covariance matrix by taking the shifted mean as the center. Theoretical analyses show that the new covariance matrix is essentially a rank-one modification (R1M) of the original one. It could effectively adjust both the search scope and the search direction of GEDA, and thus improving the search efficiency. Furthermore, considering the importance of the population size tuning in GEDA, we develop a population reduction (PR) strategy which linearly reduces the population size throughout the evolution. By this means, the exploration and exploitation ability of GEDA could be balanced better in different search stages and a more proper utilization of limited computation resource can be achieved. Combining GEDA with the R1M and PR strategies, a novel EDA variant named EDA-R1M-PR is developed. The performance of EDA-R1M-PR was comprehensively evaluated and compared with that of several state-of-the-art evolutionary algorithms. Experimental results indicate that the R1M and PR strategies effectively enhance the global optimization ability of GEDA and the resultant EDA-R1M-PR significantly outperforms its competitors on a set of benchmark functions.