CircZKSCAN1 Suppresses Hepatocellular Carcinoma Tumorigenesis by Regulating miR-873-5p/Downregulation of Deleted in Liver Cancer 1.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-associated mortality worldwide. CircZKSCAN1 (hsa_circ_0001727) was reported to be related to HCC development. The present study aims to elucidate the potential role and molecular mechanism of circZKSCAN1 in the regulation of HCC progression. METHODS: CircZKSCAN1, miR-873-5p, and downregulation of deleted in liver cancer 1 (DLC1) in HCC tissues and cells were detected by RT-qPCR. Correlation between circZKSCAN1 expression and overall survival rate was measured by Kaplan-Meier survival analysis. The effects of circZKSCAN1, miR-873-5p, and DLC1 on proliferation, migration, and invasion were analyzed by CCK-8 and transwell assays, respectively. CyclinD1, Matrix metalloproteinase (MMP)-9, MMP-2, and DLC1 in HCC cells were detected by Western blot assay. The binding relationship between miR-873-5p and circZKSCAN1 or DLC1 was predicted by the Circinteractome or Starbase, and then confirmed by dual-luciferase reporter assays, respectively. Tumor volume and tumor weight were measured in vivo. RESULTS: CircZKSCAN1 was downregulated in HCC tissues and cells. Kaplan-Meier survival analysis suggested that there was a positive correlation between circZKSCAN1 expression and overall survival rate. Functionally, circZKSCAN1 blocked proliferation, migration, and invasion of HCC cells. MiR-873-5p was a target miRNA of circZKSCAN1, and miR-873-5p directly bound with DLC1. Rescue experiments confirmed that miR-873-5p overexpression or DLC1 knockdown attenuated the suppressive effects of circZKSCAN1 on HCC tumor growth in vitro. Besides, circZKSCAN1 inhibited HCC cell growth in vivo. CONCLUSIONS: This study firstly revealed that circZKSCAN1 curbed HCC progression via modulating miR-873-5p/DLC1 axis, providing a potential therapeutic target for HCC treatment.

Optimal competitive hopfield network with stochastic dynamics for maximum cut problem.

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm.

An annealed chaotic maximum neural network for bipartite subgraph problem.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Frequency Measurement of Pure Rotational Transitions of D(2)O from 0.5 to 5 THz.

Frequencies of pure rotational transitions of D(2)O were measured in the region 0.5-5 THz with a high-precision far-infrared spectrometer using a tunable radiation source. Measured frequencies of about 150 spectral lines, 30 of them being newly measured lines, provide an excellent frequency standard for the far-infrared region together with our previous measurements on H(2)(16)O, H(2)(17)O, and H(2)(18)O. Molecular parameters of Watson's A-reduced Hamiltonian have been obtained to reproduce the observed frequencies. Copyright 2001 Academic Press.