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Centromere protein M (CENPM) is essential for chromosome separation during mitosis. However, its roles in lung adenocarcinoma (LUAD) progression and metastasis remain unknown. In this study, we aimed to explore the effects of CENPM on LUAD progression as well as the underlying mechanisms. We analyzed the expression of CENPM and its correlation with clinicopathological characteristics using GEO LUAD chip datasets and TCGA dataset. We further investigated the impact of CENPM on LUAD in vitro and in vivo. In silico analysis and qRT-PCR revealed that CENPM is upregulated in LUAD compared with that in normal lung tissues. Via gain/loss-of-function assays, we further found that CENPM promotes the LUAD cell cycle, cell proliferation, migration and invasion, and inhibits cell apoptosis in vitro. The in vivo study showed that loss of CENPM inhibits the growth of A549 xenografts. Furthermore, we found that CENPM can promote the phosphorylation of mTOR rather than directly affect the mTOR content. Inhibition of mTOR activity abrogates the promoting effects of CENPM on cell cycle progression, cell proliferation, migration and invasion. Taken together, these results show that CENPM plays an important role in the growth and metastasis of LUAD and may be a promising therapeutic target in LUAD.

In this paper, we propose an inertial proximal splitting method for solving the non-convex optimization problem, and the new method employs the idea of inertial proximal point to improve the computational efficiency. Based on the assumptions that the sequence generated by the new method is bounded and the auxiliary function satisfies the Kurdyka–Łojasiewicz property, the global convergence analysis with a more relaxed parameter range is proved for the proposed method. Moreover, some numerical results on SCAD, image processing and robust PCA non-convex problems are tested to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.

Numerous attempts have been made to develop efficient methods for solving the system of constrained nonlinear equations due to its widespread use in diverse engineering applications. In this article, we present a family of inertial‐based derivative‐free projection methods with a correction step for solving such system, in which the selection of the derivative‐free search direction is flexible. This family does not require the computation of corresponding Jacobian matrix or approximate matrix at every iteration and possess the following theoretical properties: (i) the inertial‐based corrected direction framework always automatically satisfies the sufficient descent and trust region properties without specific search directions, and is independent of any line search; (ii) the global convergence of the proposed family is proven under a weaker monotonicity condition on the mapping E$$ E $$, without the typical monotonicity or pseudo‐monotonicity assumption; (iii) the results about convergence rate of the proposed family are established under slightly stronger assumptions. Furthermore, we propose two effective inertial‐based derivative‐free projection methods, each embedding a specific search direction into the proposed family. We present preliminary numerical experiments on certain test problems to demonstrate the effectiveness and superiority of the proposed methods in comparison with existing ones. Additionally, we utilize these methods for solving sparse signal restorations and image restorations in compressive sensing applications. [ABSTRACT FROM AUTHOR]