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The detached leaves of three Turkish grapevine varieties (cv. Karasakız, cv. Müşküle and cv. Tekirdağ Sultani) and three hybrid individuals for brined leaves (119, 154, 200) with different resistance levels to downy mildew (Plasmopara viticola) based on sporulation area (mm2) were examined for sporangia number at 7 days, progress of the pathogen and stilbene production at different times after inoculation. A significant positive correlation was determined between number of sporangia on the lesion and sporulation area. The lowest number of stomata infected by the pathogen at 48 h after inoculation (hpi) was determined in extremely resistant 200, followed by highly resistant cv. Müşküle and 154. Vesicles of the pathogen were observed in leaves of all tested plants at 15 hpi; however, in 200 the vesicle primary hypha, which developed at 72 hpi, did not elongate into intercellular spaces of the mesophyll after that time. Elongation occurred in 119 (resistant) at 48 hpi and in cv. Müşküle and 154 at 72 hpi, but development of the pathogen was restricted to 72 hpi. Pathogen mycelia covered the intercostal fields in sensitive (cv. Karasakız) and highly sensitive (cv. Tekirdağ Sultani) varieties at 72 hpi. The amount of resveratrol was found to be high in resistant types, while the highest amount of ε-viniferin was determined in 200 at 72 hpi. Cv. Müşküle and 154 contained higher amounts of pterostilbene than 119. These new results underline the role of ε-viniferin in 200 and pterostilbene in cv. Müşküle and 154 in high resistance toward P. viticolaand the importance of these grapevine types in downy mildew management.

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC,BOD-Tand COD-Twere correlated and the regression analysis showed that COD, TKNand NH4-N explained BOD and BOD, NH4-N, Tand TSSexplained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANNwas the best prediction model for COD, the Decision Tree for BOD and the ANNand Decision Tree for NH4-N.