Inhibition of In Vitro Amylolysis of Wheat Starch by Gluten Peptides.

The effect of gluten peptides (GPs) isolated from a gluten proteolysate on in vitro amylolysis of gelatinized wheat starch was investigated. GPs in a pepsin hydrolysate were fractionated into fractions with molecular weights (MWs) of 500-3000, 3500-7000, 10-17, and 35-48 kDa. The fractions containing peptides with MW > 10 kDa had a strong inhibitory effect on enzyme activity and amylolysis of starch, whereas GPs with MW <10 kDa had no inhibitory effect. Binding constants estimated by surface plasmon resonance showed that peptides in the fractions with MW > 10 kDa bound more strongly to α-amylase, in contrast to peptides of MW <10 kDa. Significant correlations were observed between digestion parameters and equilibrium binding affinity. We conclude that peptides with MW >10 kDa in a pepsin digest of gluten have a strong inhibitory effect on in vitro enzymatic hydrolysis of starch due to their strong binding affinity to α-amylase.

Automatic cataract grading methods based on deep learning.

BACKGROUND AND OBJECTIVE: The shortage of ophthalmologists in rural areas in China causes a lot of cataract patients not getting timely diagnosis and effective treatment. We develop an algorithm and platform to automatically diagnose and grade cataract based on fundus images of patients. This method can help government assisting poor population more accurately. METHODS: The novel six-level cataract grading method proposed in this paper focuses on the multi-feature fusion based on stacking. We extract two kinds of features which can effectively distinguish different levels of cataract. One is high-level features extracted from residual network (ResNet18). The other is texture features extarcted by gray level co-occurrence matrix (GLCM). Then a frame is proposed to automatically grade cataract by the extracted features. In the frame, two support vector machine (SVM) classifiers are used as base-learners to obtain the probability outputs of each fundus image, and fully connected neural network (FCNN) are used as meta-learner to output the final classification result, which consists of two fully-connected layers. RESULT: The accuracy of six-level grading achieved by the proposed method is up to 92.66% on average, the highest of which reaches 93.33%. The proposed method achieves 94.75% accuracy on four-level grading for cataract, which is at least 1.75% higher than those of the exiting methods. CONCLUSIONS: Six-category cataract classification algorithm show that Multi-feature & Stacking proposed in this paper helps achieve higher grading performance and lower volatility than grading using high-level features and texture features respectively. We also apply our algorithm into four-level cataract grading system and it shows higher accuracy compared with previous reports.