Tetrazole exerts anti-hepatitis effect in mice via activation of PI3K/Akt pathway, inhibition of cell autophagy and suppression of inflammatory cytokine expressions

Purpose: To investigate the effect of tetrazole on concanavalin A (Con A)-induced hepatitis in mice, and the underlying mechanism(s). Methods: Thirty 5-week-old, male BALB/c mice (mean weight, 30.5 ± 1.04 g) were used for this study. They were randomly assigned to six groups of five mice each: control group, hepatitis group and four treatment groups. With the exception of control group, hepatitis was induced in all mice with Con A (20 mg/kg) via their tail veins. The treatment groups received varied doses of tetrazole (1.0 - 6.0 mg/kg) within 1 h after hepatitis induction, while mice in the control group received an equivalent volume of normal saline in place of tetrazole. Serum activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined while expressions of interleukin-2 (IL-2), tumor necrosis factor ߙ) TNF-ߙ ,(and interferon gamma (IFN-ߛ (were evaluated by enzyme-linked immunosorbent assay (ELISA) kits. Expressions of protein kinase B (Akt), phosphoinositide 3-kinase (PI3K), nuclear transcription factor- ߢB (NF-ߢB), and autophagy-related genes were determined by real-time quantitative polymerase chain reaction (qRT-PCR) and Western blotting. Results: Con A-induced hepatitis significantly increased the activities of serum ALT and AST in the mice. However, after treatment with tetrazole, the activities of these enzymes were significantly and dose-dependently reduced in the treatment groups, relative to hepatitis group (p < 0.05). The levels of IL-2, IFN-ߛ and TNF-ߙ were significantly increased in hepatitis group when compared with the control group (p < 0.05). However, treatment with tetrazole significantly inhibited the expressions of these parameters. There were no significant differences in the levels of expressions of Akt mRNAs among the treatment groups (p > 0.05). The levels of expressions of LC3II and Beclin 1 were also significantly upregulated in hepatitis group, when compared with control group (p < 0.05). However, expression levels of LC3II and Beclin 1 were significantly and dose-dependently reduced by tetrazole treatment Conclusion: Tetrazole is effective in the treatment of hepatitis via mechanisms involving the activation of PI3K/Akt pathway, inhibition of cell autophagy and suppression of inflammatory cytokines expressions

Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines

We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.Comment: 11 pages, 13 figure

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.Comment: Published as a conference paper at NeurIPS 202

A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.Comment: 57 pages, 10 figure