Long non-coding RNA nuclear-enriched abundant transcript 1 (Lnc-NEAT1) is a crucial mediator in cancer progression, which is associated with poor prognosis of patients with laryngeal papilloma (LP). Herein, we aimed to determine how Lnc-NEAT1 promotes LP development. q-PCR, MTT, EDU and Western blotting were performed to determine that Lnc-NEAT1 facilitates LP cell proliferation and hinders cell apoptosis. LncBase database, q-PCR, GEPIA online database, Dual luciferase reporter and RIP assays were utilized to confirm that Lnc-NEAT1 sponged miR-577/miR-1224-5p and negatively mediated CCNT2. Western blotting, MTT and EDU were used to confirm that Lnc-NEAT1 promoted LP cell proliferation and inhibited cell apoptosis through CCNT2. Lnc-NEAT1 was highly expressed in LP, and enhanced LP cell proliferation, and it was inhibited by Lnc-NEAT1 depleting. Concerning the underlying mechanism, it was found that Lnc-NEAT1 could functionally sponge microRNA-577 (miR-577) and microRNA-1224-5p (miR-1224-5p) and up-regulate Cyclin T2 (CCNT2) in LP cells. Notably, CCNT2 knockdown blocked Lnc-NEAT1-induced LP cell proliferation, and rescued cell apoptosis, which was specifically indicated by restoration of Bax, Cleaved caspase 3 and Cleaved caspase 9. Lnc-NEAT1 played a carcinogenic role in LP through mediating miR-577 or miR-1224-5p/CCNT2 axis, which may provide promising insights for the treatment of LP.

This article presents a novel method for optimal phasor measurement unit placement (OPP) for full power system observability in the presence of conventional measurements. The method considers eligible PMU placements that may be omitted by existing OPP methods and hence may provide a better solution and can guarantee that the PMU placement can restore network observability. This is achieved by creating a new observability criterion and a network transformation scheme. The new observability criterion uses injection and zero injection measurements to improve the solution and the network transformation scheme reduces the size of the OPP problem and is a prerequisite for applying the proposed observability criterion. The OPP problem is solved using binary integer linear programming (BILP) or binary integer linear programming (BILP). Therefore, the proposed OPP method can be easily combined with other OPP methods, considering other constraints/scenarios using BILP, such as OPP considering PMU current channel limits. The new method is tested using simulations of the IEEE 14-, 118-, and 2736-bus test systems. These results show that the proposed new method is feasible in terms of execution time, is free of solutions that fail to make the system fully observable, and is capable of identifying optimal placement solutions that may be overlooked by existing OPP methods.

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system.