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Abstract: Grape anthracnose, which is caused by Elsinoë ampelina, is a disease that negatively affects grape production. This study aimed to investigate the effects of aeration, temperature, light, and preculture period on the formation of E. ampelina conidia and conidial germination and virulence. The colony morphology on potato dextrose agar (PDA) plates was more diverse than that in PDA bottles. The assessment of different culture methods, temperatures, light conditions, and preculture periods revealed that optimal conidial production occurred on 25‐day‐old colonies grown in PDA bottles at 21°C for 24 hr in the dark. The cultures in PDA bottles consistently produced approximately 5.0 × 106 conidia under these conditions. No conidial formation occurred when the cultures were kept at 25°C in the dark. The highest germination rate of E. ampelina was 80% at 25°C after 24 hr, whereas no germination was observed at 17°C after 12 hr. Pathogenicity tests revealed that symptoms of the disease were observed 4 days postinoculation (dpi) on leaves of Vitis vinifera cv. Red Globe. New conidia were observed on the lesions at 8 dpi. This study provides an effective method for the conidial production of E. ampelina that may also be applicable for other Elsinoë fungal species. [ABSTRACT FROM AUTHOR]

Supersecondary structures (SSSs) are the building blocks of protein 3D structures. Accurate prediction of SSSs can be one important step toward building a tertiary structure from the specified secondary structure. How to improve the accuracy of prediction of SSSs by effectively incorporating the sequence order effects is an important and challenging problem. Based on a different form of Chou's pseudo amino acid composition, a novel approach for feature representation of SSSs is proposed. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are incorporated to represent the compositional features of proteins. Each supersecondary structural motif is characterized as a vector of 36 dimensions. In addition, we propose a novel prediction system by using SVM and IDQD algorithm as classifiers. Our method is trained and tested on ArchDB40 dataset containing 3088 proteins. The highest overall accuracy for the training dataset and the independent testing dataset are 77.7 and 69.4%, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011 [ABSTRACT FROM AUTHOR]

On the basis of the features of protein sequential pattern, we used the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to predict beta-hairpins motifs in protein sequences. Three rules are used to extract the raw beta-beta motifs sequential patterns for fixed-length. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are combined to represent the compositional features. Eighteen feature variables on a sequential pattern to be predicted are defined in terms of ID. They are integrated in a single formal framework given by IDQD. The method is trained and tested on ArchDB40 dataset containing 3088 proteins. The overall accuracy of prediction and Matthew's correlation coefficient for the independent testing dataset are 81.7% and 0.60, respectively. In addition, a higher accuracy of 84.5% and Matthew's correlation coefficient of 0.68 for the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nucleic Acids Res 2005, 33, 154), which contains 2088 proteins. For a fair assessment of our method, the performance is also evaluated on all 63 proteins used in CASP6. The overall accuracy of prediction is 74.2% for the independent testing dataset., (2009 Wiley Periodicals, Inc.)