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Cadmium exhibits both toxic and carcinogenic effects, and its cytotoxicity is linked to various cellular pathways, such as oxidative stress, ubiquitin-proteasome, and p53-mediated response pathways. The molecular mechanism(s) underlying cadmium cytotoxicity appears to be complex, but remains largely unclear. Here, we examined the effects of cadmium on the protein catabolism using two surrogate markers, DNA topoisomerases I and II alpha and its contribution to cytotoxicity. We have found that cadmium exposure induced time- and concentration-dependent decreases in the protein level of surrogate markers and therefore suggest that cadmium may be involved in proteolysis system activation. A pharmacological study further revealed the novel role(s) of these proteolytic activities and reactive oxygen species (ROS) in the cadmium-induced acute toxicity: (i) Proteasome inhibition only partially relieved the cadmium-induced proteolysis of topoisomerases; (ii) Moreover, we report for the first time that the activation of metalloproteases, serine proteases, and cysteine proteases contributes to the acute cadmium cytotoxicity; (iii) Consistent with the notion that both ROS generation and proteolysis system activation contribute to the cadmium-induced proteolysis and cytotoxicity, the scavenger N-acetylcysteine and aforementioned protease inhibition not only reduced the cadmium-induced topoisomerase degradation but also alleviated the cadmium-induced cell killing. Taken together, acute cadmium exposure may activate multiple proteolytic systems and ROS formation, subsequently leading to intracellular damage and cytotoxicity. Thus, our results provide a novel insight into potential action mechanism(s) by which cadmium exerts its cytotoxic effect and suggest potential strategies to prevent cadmium-associated acute toxicity.

The many subfields in the wireless sensor networking literature include data fusion, data aggregation, remote environmental monitoring, sensing (temperature, pressure speed) and various military applications. The distance between sensor nodes can be measured by a Received Signal Strength Indicator (RSSI). This study proposes both average and adaptive fuzzy logic algorithms for computing temperature in a monitored area. The main advantages of these methods are their simplicity and accuracy and better than the standard Manadni fuzzy logic method. Finally, comparison of the two methods in terms of root mean square error shows that the adaptive fuzzy logical algorithm with RSSI is better than average fuzzy logical algorithm for computing monitoring area temperature.

Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

Wireless Sensor Networks have attracted many researchers all over the world. Wireless sensor networks have become a kind of sub field including data fusion, data aggregation, remote environmental monitoring, sensing (temperature, pressure speed) and a variety of military applications. The Received Signal Strength Indicator (RSSI) is used for distance measurement between sensor nodes. This paper proposes both average and adaptive fuzzy logic algorithms to compute monitoring area temperature. The main advantages of these methods are simplicity and accuracy in area temperature monitoring. We compare the mean square of error of these two methods. Using the RMSE results, it is shown that the adaptive fuzzy logical algorithm with RSSI is better than the average fuzzy logical algorithm at computing monitoring area temperature.