The effects of 100 nm pure boron nanoparticles on the primary antioxidant system of Nile tilapia (Oreochromisniloticus)

In this study, the effects of 100 nm boron nanoparticles (B-NPs) on the primary antioxidant status of Nile tilapia were researched via analysis of enzyme activities and related gene expressions. This is a new study which focuses on the relationship between B-NPs and oxidative stress that contribute to the literature in terms of its scope. Fish (n = 15) for each group were exposed to three different concentrations as 5, 25 (n2) and 125 (n3) mg/L during 96 h to see the response of the primary antioxidant system. According to the results, SOD expressions differed in all treatment groups compared to the control group (P < 0.05). CAT expressions were different in 5 and 125 mg/L groups compared to control and 25 mg/L groups (P < 0.05). GPX expressions were only different in 125 mg/L group (P < 0.05). The changes in enzyme activities of SOD and CAT were significantly different in 25 mg/L groups. GPX enzyme activities were not significant (P > 0.05). TBARS concentrations in 25 mg/L group were significantly different from those in the control and 125 mg/L groups (P < 0.05).

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.