Effect of ganoderic acid on diethylnitrosamine-induced liver cancer in mice

Purpose: To investigate the hepatoprotective role of ganoderic acid A (GAA) on liver cancer induced by diethylnitrosamine (DEN) via Nrf-2/HO-1/NF-κB signal pathway in mice. Methods: Sixty male C57BL/6J mice were randomly divided into 4 groups: (1) control group, (2) DEN (25 mg/kg) group, (3) GAA (20 mg/kg) + DEN group, (4) GAA (40 mg/kg) + DEN group. The protective effect of GAA on liver was evaluated by determining malondialdehyde (MDA), superoxide dismutase (SOD), inflammatory cytokines including interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and the expression of heme oxygenase-1 (HO-1), nuclear factor erythroid- 2-related factor-2 (Nrf-2), IκBα, p-IκBα, p65, p-p65, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in serum. Results: The results demonstrate that GAA treatment significantly suppressed the generation of MDA, proinflammatory cytokines, and restored the activity of SOD in the serum of DEN-induced liver cancer in mice. Western blots analysis revealed that GAA significantly restored Nrf-2/HO-1/NF-κB signal pathwayrelated protein levels in DEN-induced mice liver cancer model. Conclusion: This research reveals the anticancer activity of GAA in liver tissue, and suggests that GAA counters DEN-induced liver  cancer through Nrf-2/HO-1/NF-κB signal pathway. Keywords: Ganoderic acid A, Nrf-2/HO-1/NF-κB pathway, Liver cancer, MDA, GAPDH, SO

Efficient Offline Policy Optimization with a Learned Model

MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/sail-sg/rosmo.Comment: ICLR202

The OX40/OX40L Axis Regulates T Follicular Helper Cell Differentiation: Implications for Autoimmune Diseases

T Follicular helper (Tfh) cells, a unique subset of CD4+ T cells, play an essential role in B cell development and the formation of germinal centers (GCs). Tfh differentiation depends on various factors including cytokines, transcription factors and multiple costimulatory molecules. Given that OX40 signaling is critical for costimulating T cell activation and function, its roles in regulating Tfh cells have attracted widespread attention. Recent data have shown that OX40/OX40L signaling can not only promote Tfh cell differentiation and maintain cell survival, but also enhance the helper function of Tfh for B cells. Moreover, upregulated OX40 signaling is related to abnormal Tfh activity that causes autoimmune diseases. This review describes the roles of OX40/OX40L in Tfh biology, including the mechanisms by which OX40 signaling regulates Tfh cell differentiation and functions, and their close relationship with autoimmune diseases

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity