Search results for: Mümin Dizman

Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

Abstract:

Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

Procedia PDF Downloads 309

Authors: Saylav Ejder Bora, Arife Erdogan, Mumin Alper Erdogan, Oytun Erbas, Ismet Parlak

Abstract:

Objective: The aim of this study was to evaluate histological, electrical and biochemical effects of metoprolol, lipid emulsion and magnesium sulphate as an alternative method to be used in preventing long QT emergence, that is among the lethal consequences of amitryptiline toxicity. Methods: Thirty Sprague- Dawley male rats were included. Rats were randomly separated into 5 groups. First group was administered saline only while the rest had received amitryptiline 100 mg/kg + saline, 5 mg/kg metoprolol, 20 ml/kg lipid emulsion and 75 mg/kg magnesium sulphate (MgSO4) intraperitoneally. ECG at DI lead, biochemical tests following euthanasia were performed in all groups after 1 hour of administration. Cardiac tissues were removed, sections were prepared and examined. Results: QTc values were significantly shorter in the rest when compared to amitryptiline+ saline group. While lipid emulsion did not affect proBNP and troponin values biochemically as compared to that of the control group, histologically, it was with reduced caspase 3 expression. Though statistically insignificant in the context of biochemical changes, pro-BNP and urea levels were lower in the metoprolol group when compared to controls. Similarly, metoprolol had no statistically significant effect on histological caspase 3 expression in the group that was treated with amitryptiline+metoprolol. On the other hand, there was a statistically significant decrease in Troponin, pro-BNP and urea levels as well as significant decline in histological caspase 3 expression within the MgSO4 group when compared to controls. Conclusion: As still a frequent cause of mortality in emergency units, administration of MgSO4, lipid emulsion and metoprolol might be beneficial in alternative treatment of cardiovascular toxicity caused by tricyclic antidepressant overdose, whether intake would be intentional or accidental.

Keywords: amitryptiline, cardiovascular toxicity, long QT, Rat Model

Procedia PDF Downloads 176