In mammalian cells, tumor suppressor p53 plays critical roles in the regulation of glucose metabolism, including glycolysis and oxidative phosphorylation, but whether and how p53 also regulates gluconeogenesis is less clear. Here, we report that p53 efficiently down-regulates the expression of phosphoenolpyruvate carboxykinase (PCK1) and glucose-6-phosphatase (G6PC), which encode rate-limiting enzymes in gluconeogenesis. Cell-based assays demonstrate the p53-dependent nuclear exclusion of forkhead box protein O1 (FoxO1), a key transcription factor that mediates activation of PCK1 and G6PC, with consequent alleviation of FoxO1-dependent gluconeogenesis. Further mechanistic studies show that p53 directly activates expression of the NAD + -dependent histone deacetylase sirtuin 6 (SIRT6), whose interaction with FoxO1 leads to FoxO1 deacetylation and export to the cytoplasm. In support of these observations, p53-mediated FoxO1 nuclear exclusion, down-regulation of PCK1 and G6PC expression, and regulation of glucose levels were confirmed in C57BL/J6 mice and in liver-specific Sirt6 conditional knockout mice. Our results provide insights into mechanisms of metabolism-related p53 functions that may be relevant to tumor suppression.

Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem, which aims to obtain an optimal or near-optimal flight route with the threats and constraints in the combat field well considered. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV two-dimension path planning problem. Then, the UCAV can find the safe path by connecting the chosen nodes of the two-dimensional coordinates while avoiding the threats areas and costing minimum fuel. Conducted simulations show that the proposed method is more competent for the UCAV path planning scheme than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of final solutions.

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The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

This paper studies the problem of partial topology identification of tempered fractional complex networks. By tempered fractional calculus theory and pinning controlling techniques of network synchronization, we propose a strategy that can greatly reduce the expense of the partial topology identification. This method can also identify the whole topology of tempered fractional complex network. Sufficient conditions are given to guarantee partial topology identification of tempered fractional complex networks by designing suitable controllers and parameters update law. This method can also achieve synchronization between the drive network and the response network. Finally, some numerical experiments are presented to verify the validity of the method.