Identification of a novel a-L-arabinofuranosidase gene associated with mealiness in apple.

In order to investigate the genetic bases of the physiological syndrome mealiness that causes abnormal fruit softening and juice loss in apples, an integrative approach was devised, consisting of sensory, instrumental, biochemical, genetic, and genomic methods. High levels of activity of a-L-arabinofuranosidase (a-AFase), a hydrolase acting on the pectic component of the cell walls, were found in individuals exhibiting the mealiness phenotype in a segregating population. The expression levels of the previously uncharacterized apple AF gene MdAF3 are higher in fruits from plants consistently showing mealiness symptons and high a-AFase activity. The transcription of MdAF3 is differentially regulated in distinct genomic contexts and appears to be independent of ethylene. Thus, it is likely to be controlled by endogenous developmental mechanisms associated with fruit ripening. The use of integrative approaches has allowed the identification of a novel contributor to the mealiness phenotype in apple and it has been possible to overcome the problems posed by the unavailability of near-isogenic lines to dissect the genetic bases of a complex physiological trait in woody perennial species.

Texture for Script identification

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.

Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.