RAS is frequently mutated in human cancers and has opposing effects on autophagy and tumorigenesis. Identifying determinants of the cellular responses to RAS is therefore vital in cancer research. Here, we show that autophagic activity dictates the cellular response to oncogenic RAS. N-terminal Apoptosis-stimulating of p53 protein 2 (ASPP2) mediates RAS-induced senescence and inhibits autophagy. Oncogenic RAS-expressing ASPP2 (Δ3/Δ3) mouse embryonic fibroblasts that escape senescence express a high level of ATG5/ATG12. Consistent with the notion that autophagy levels control the cellular response to oncogenic RAS, overexpressing ATG5, but not autophagy-deficient ATG5 mutant K130R, bypasses RAS-induced senescence, whereas ATG5 or ATG3 deficiency predisposes to it. Mechanistically, ASPP2 inhibits RAS-induced autophagy by competing with ATG16 to bind ATG5/ATG12 and preventing ATG16/ATG5/ATG12 formation. Hence, ASPP2 modulates oncogenic RAS-induced autophagic activity to dictate the cellular response to RAS: to proliferate or senesce.A ctive mutations of RAS, one of the first oncogenes identified, occur in about 20% of human tumors (1). Oncogenic RAS can transform cells and promote tumorigenesis, although it can also induce senescence and suppress tumor growth (2). Senescent cells are arrested and incapable of responding to mitogens, although they are viable and metabolically active. Senescence is characterized by dramatic cellular remodelling, which is energetically demanding. Autophagy, a genetically regulated process responsible for the turnover of cellular proteins and damaged or superfluous organelles, is a stress response involved in energy homeostasis (3). It was shown that autophagy is a critical mediator of oncogenic RAS-induced senescence, suggesting a negative role of autophagy in tumorigenesis (4). In contrast, a number of recent studies showed that active RAS requires autophagy to maintain its oncogenic function in tumorigenesis, arguing for a positive role of autophagy in tumorigenesis (5-8). The underlying reason for the conflicting observations remains unclear.RAS activation inhibits autophagy by activating the PI3K/ AKT/mammalian target of rapamycin (mTOR) pathway (9), and rapamycin, an mTOR inhibitor, is a potent inducer of autophagy. RAS also inhibits autophagy by reducing Beclin-1 expression in intestinal epithelial cells, an important mediator of autophagy (10). However, RAS was reported to induce autophagy by increasing the expression of key components of the autophagy machinery, such as ATG5 (11) and Beclin-1 (12). These reports suggest that RAS signaling performs a finely regulated balancing act to control autophagy. Identification of switching molecules that determine the cellular responses to RAS is thus needed urgently.The tumor suppressor p53 is one of the most well-established pathways by which RAS mediates cellular senescence. A recent study also showed that p53 is able to regulate autophagic activity by inducing the expression of LC3 (13). However, it remains unknown whether the abilit...

APF (Active Power Filter) is widely used in power system harmonic control and reactive power compensation, has been proven as an effective method to overcome various power quality issues such as unbalanced source current, large reactive power harmonic and neutral currents due to the proliferation of nonlinear loads. Optimizing the performance of APF using conventional ip-iq detection method based on instantaneous reactive power theory is quite difficult because of the complex coordinate conversion, what’s more, the presence of low-pass filter will cause a certain delay. This paper proposes the implementation of BP neural network to extract specific harmonic, it can optimize the APF performance for load compensation under distorted supply voltage condition and sudden load fluctuation. Weight adjustment using the BFGS quasi-Newton algorithm, which can accurately detect the fundamental and harmonic component of the phase amplitude . Matlab simulation results demonstrate that the performance of BP neural network algorithm is superior compared to conventional method, in terms of both convergence rate and solution quality.

The title compound, {[Ni(C(9)H(4)O(6))(C(14)H(14)N(4))]·0.41H(2)O}(n), exhibits a three-dimensional hydrogen-bonded supramolecular framework. The Ni(II) cation is six-coordinated in a distorted triangular prism defined by two N atoms from two 1,3-bis(imidazol-l-ylmethyl)benzene (bix) ligands and four O atoms from two 5-carboxybenzene-1,3-dicarboxylate (HBTC) dianions. The bix molecules and HBTC dianions both act as bidentate ligands, linking the Ni(II) cations to form a one-dimensional coordination polymer. A two-dimensional wave-like net is constructed by O-H...O hydrogen bonds linking adjacent chains. Partially occupied solvent water molecules fill the cavities and link these layers to form a three-dimensional supramolecular structure via O-H...O hydrogen bonds. The title compound was also characterized by powder X-ray diffraction and thermogravimetric analysis.

Abstract. Nowadays,data mining is a hot topic in all sorts of fields. Potential science applications include, Telecommunications companies apply data mining to detect fraudulent network usage. Companies in many areas of business apply data mining to improve their marketing and advertising. Law enforcement uses data mining to detect various financial crimes. Given the well known complexity of Network engineering processes and artifacts, it is perhaps not surprising that data mining can be applied there as well. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest [1]. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. In this paper, discussing methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, describing methods for learning both the parameters and structure of a Bayesian network.