034) was the only statistically significant predictor associated with unfavorable visual outcome.\n\nCONCLUSIONS. In this study, we attempted to find risk factors related to poor visual outcome

in patients with ocular sarcoidosis. The results suggest that only the presence of cystoid macular edema was significantly associated with worst visual outcome.”
“BACKGROUND: Nitrogen is an indispensable element for fruit metabolism and low or excessive N levels can affect the accumulation of the most important components that contribute to the flavour and aroma of the fruit. Among them, sugars, acids and volatile compounds can be considered quality markers. The objective of this selleck screening library EPZ-6438 cell line study was to evaluate the effect of N fertilization on these quality markers of the fruit at two harvest dates.\n\nRESULTS: Strawberry plants were grown in a hydroponic system and N was applied as

Ca(NO(3))(2) at concentrations of 0.3, 3 and 6 mmol L(-1) in the nutrient solution. Total soluble solids, soluble carbohydrates, amino acids and organic acids and volatile compounds of the fruit were analyzed. The fruits produced at 3 and 6 mmol L(-1) N had higher contents of esters, soluble carbohydrates and amino acids. The hexanal content increased with the 6 mmol L(-1) dose. The effect of fertilization was more marked at the second harvest date.\n\nCONCLUSION: The availability of N in strawberry plants affected the accumulation of quality markers. The fruits expected to have the best flavour and aroma, with both high levels of soluble carbohydrate and esters

and low levels of hexanal, were obtained with 3 mmol L(-1) nitrate in the solution. (c) 2009 Society of Chemical Industry”
“Cluster analysis is frequently used by the plant breeders in grouping germplasm collections into a few homogeneous groups in order to identify accessions with specific property of potential relevance for their plant improvement programs. The set of descriptors for the germplasm Ulixertinib accessions consists of both numerical and categorical descriptors. In such situations, the standard principal component analysis will not be appropriate for feature extraction of data using all descriptors because it deals with only numeric variables. In this paper, nonlinear principal component analysis was used to analyse the descriptors of lentil accessions which can handle mixture of measurement types. The first two nonlinear principal components were used as input to fuzzy c-means algorithm in grouping 518 lentil genotypes into four clusters based on their agronomic and morphological traits. The study demonstrated that the proposed nonlinear principal component based fuzzy clustering has a promising potential in agriculture as a tool for evaluation and efficient grouping of germplasm collections.