Adiponectin regulates the malignant biological behavior of endometrial cancer cells via AMPK/mTOR signal pathway

Purpose: To study the mechanism by which adiponectin regulates the malignant biological behavior of endometrial cancer cells. Methods: RL95-2 cells were cultured and re-suspended in complete medium. A preliminary study showed that adiponectin (20 μg/mL) has the most inhibitory effect on cellular proliferation. The cells were divided into control group, 20 μg/mL adiponectin group, and 20 μg/mL adiponectin + 10 μmol/L AMPK inhibitor group, with 4 repeat wells set up for each group. Cell proliferation, apoptosis, invasion and migration ability, as well as the expression levels of MMP-9, Bcl-2, p-AMPK, p-mTOR and p-4ebp1 were determined using standard procedures. Results: Cell proliferation was significantly higher in the 20 μg/mL adiponectin group than in the 10 μg/mL adiponectin group (p < 0.05). Relative to the control group, apoptosis and p-AMPK expression level in the 10 μg/mL adiponectin group increased, while cell invasion and migratory potential, as well as expression levels of MMP-9, Bcl-2, p-mTOR and p-4ebp1 declined significantly (p < 0.05). The apoptosis and expression level of p-AMPK in the 20 μg/mL adiponectin + 10 μmol/L AMPK inhibitor group decreased, while cell invasion, migratory ability, and amounts of MMP-9, Bcl-2, p-mTOR and p- 4ebp1 were elevated, relative to the 20 μg/mL adiponectin group (p < 0.05). Conclusion: Adiponectin promotes apoptosis of endometrial cancer cells via AMPK/mTOR signaling pathway, and inhibits cell proliferation, invasion and migration. Thus, adiponectin has the potential to exert anti-tumor effect in humans

Causal Reinforcement Learning: An Instrumental Variable Approach

In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out. With the advancement of digital technology, decisionmakers constantly analyze past data and generate new data through the decisions they make. In this paper, we model this as a Markov decision process and show that the dynamic interaction between data generation and data analysis leads to a new type of bias -- reinforcement bias -- that exacerbates the endogeneity problem in standard data analysis. We propose a class of instrument variable (IV)-based reinforcement learning (RL) algorithms to correct for the bias and establish their asymptotic properties by incorporating them into a two-timescale stochastic approximation framework. A key contribution of the paper is the development of new techniques that allow for the analysis of the algorithms in general settings where noises feature time-dependency. We use the techniques to derive sharper results on finite-time trajectory stability bounds: with a polynomial rate, the entire future trajectory of the iterates from the algorithm fall within a ball that is centered at the true parameter and is shrinking at a (different) polynomial rate. We also use the technique to provide formulas for inferences that are rarely done for RL algorithms. These formulas highlight how the strength of the IV and the degree of the noise's time dependency affect the inference.Comment: main body: 38 pages; supplemental material: 58 page

Improving QCD with fermions: the 2 dimensional case of QCD with Sea Quarks

We study QCD in 2 dimensions using the improved lattice fermionic Hamiltonian proposed by Luo, Chen, Xu and Jiang. The vector mass and the chiral condensate are computed for various $SU(N_C)$ gauge groups. We do observe considerable improvement in comparison with the Wilson quark case.Comment: LATTICE98(improvement