We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

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An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure related to the sequence with high accuracy. The updated prediction system is developed using data from the new version of the 3D-ALI database of aligned protein structures and thus is giving more reliable and more detailed predictions than the previous DEF system.

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Abstract--An algorithm for efficient learning in feedforward networks is presented. Momentum acceleration isachieved by solving a constrained optimization problem using nonlinear programming techniques. In particular, minimization of the usual mean square error cost function is attempted under an additional condition for which the purpose is to optimize the alignment of the weight update vectors in successive epochs. The algorithm is applied to several benchmark training tasks (exclusive-or, encoder, multiplexer, and counter problems). Its performance, in terms of learning speed and scalability properties, is evaluated and found superior to the performance of reputedly fast variants of the back-propagation algorithm in the above benchmarks.