An alpha-amylase gene from Bacillus stearothermophilus under the control of the promoter of a major rice-seed storage protein was introduced into rice. The transgenic line with the highest alpha-amylase activity reached about 15,000 U/g of seeds (one unit is defined as the amount of enzyme that produces 1 mumol of reducing sugar in 1 min at 70 degrees C). The enzyme produced in the seeds had an optimum pH of 5.0-5.5 and optimum temperature of 60-70 degrees C. Without extraction or purification, the power of transgenic rice seeds was able to liquify 100 times its weight of corn powder in 2 h. Thus, the transgenic rice could be used for industrial starch liquefaction.
Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, amplitude variations, and nonlinear measures. The highly correlated vocal parameters are combined by using the linear discriminant analysis method. Based on the probability density functions estimated by the Parzen-window technique, we propose an interclass probability risk (ICPR) method to select the vocal parameters with small ICPR values as dominant features and compare with the modified Kullback-Leibler divergence (MKLD) feature selection approach. The experimental results show that the generalized logistic regression analysis (GLRA), support vector machine (SVM), and Bagging ensemble algorithm input with the ICPR features can provide better classification results than the same classifiers with the MKLD selected features. The SVM is much better at distinguishing normal vocal patterns with a specificity of 0.8542. Among the three classification methods, the Bagging ensemble algorithm with ICPR features can identify 90.77% vocal patterns, with the highest sensitivity of 0.9796 and largest area value of 0.9558 under the receiver operating characteristic curve. The classification results demonstrate the effectiveness of our feature selection and pattern analysis methods for dysphonic voice detection and measurement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.