Reduced and oxidised glutathione (GSH and GSSG) contents, and glutathione reductase, and glutathione S-transferase activities were studied in the livers, muscles, and blood/erythrocytes of male Sprague-Dawley rats exposed to intermittent hypoxia (6 h.day-1) at a simulated altitude of 7,620 m for 1, 7, 14, and 21 days. Significant decreases in GSH and increases in GSSG contents were observed in the muscles and blood of hypoxia-exposed rats in comparison to unexposed rats. Significant declines in GSH content by 43% and 45% respectively in muscles and blood were observed in the group exposed for 1 day which tended to recover on subsequent exposure. Glutathione reductase and glutathione S-transferase activities were decreased in the livers and erythrocytes of hypoxia-exposed rats, but were increased significantly in muscle. Lipid peroxidation was also increased in the livers and muscles of exposed rats. The changes were indicative of an increased production of reactive oxygen species and an impairment of drug and xenobiotic metabolism during exposure to high altitude hypoxia.

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

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This paper proposes Flow Permissions, an extension to the Android permission mechanism. Unlike the existing permission mechanism, our permission mechanism contains semantic information based on information flows. Flow Permissions allow users to examine and grant per-app information flows within an application (e.g., a permission for reading the phone number and sending it over the network) as well as cross-app information flows across multiple applications (e.g., a permission for reading the phone number and sending it to another application already installed on the user's phone). Our goal with Flow Permissions is to provide visibility into the holistic behavior of the applications installed on a user's phone. In order to support Flow Permissions on Android, we have developed a static analysis engine that detects flows within an Android application. We have also modified Android's existing permission mechanism and installation procedure to support Flow Permissions. We evaluate our prototype with 2,992 popular applications and 1,047 malicious applications and show that our design is practical and effective in deriving Flow Permissions. We validate our cross-app flow generation and installation procedure on a Galaxy Nexus smartphone.