The water solubility of rice bran protein (RBP) was improved by deamidation under alkaline conditions. The degree of deamidation was found to be a major factor in improving the solubility of RBP. The decrease in molecular mass or the degradation of peptide bonds was not detected in deamidated RBP under the conditions used. The thermal property and secondary structure of deamidated RBP measured by differential scanning calorimetry and Fourier transform infrared spectroscopy indicated that the secondary structure of RBP was well preserved during alkaline deamidation. By raising pH and temperature for deamidation, the generation rates of constituent amino acid racemization and lysinoalanine increased. The highest solubility (~90%) of RBP was achieved by treatment at pH 12 and 120℃ for 15 _ 30 min by enduring side reactions. Moderate solubility (~40%) could be achieved by deamidation at pH 8 and 100℃ for 30 min to minimize side reactions.

Stack Overflow is one of the most popular Programming Community-based Question Answering (PCQA) websites that has attracted more and more users in recent years. When users raise or inquire questions in Stack Overflow, providing related questions can help them solve problems. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited since interaction information between two questions may be lost. In this paper, we adopt the deep learning technique, propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. We adopt the attention mechanism to capture the semantic interaction information between the questions. Besides, we have pre-trained and released word embeddings specific to the software engineering domain for this task, which may also help other related tasks. The experiment results demonstrate that ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in the duplicate question detection task of AskUbuntu, which is a similar but different task, proving its generalization and robustness.

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