Recently, Massive Open Online Courses (MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning, this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network (CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.
A practical strategy to reconstitute the Fc functions of nanobody was developed by nanobody C-terminal dinitrophenylation. The Fc functions are successfully reinstated as proved by the potent ADCC and CDC in vitro and anti-tumor efficacies in vivo.
Ac opper-catalyzed regioselective sulfenylation of indoles and ap yrrolo [2,3-b]pyridinew ith arylsulfonyl chlorides has been developed by using low loadings of ac opperc atalyst andt he cheap reductantP Ph 3 .T his method is effective for both N-protecteda nd unprotected indoles, providing as eries of structurally diversei ndole thioethersi ng ood to excellent yields with extremelyh igh regioselectivity.
Sortase A (SrtA) is a transpeptidase widely used to site-specifically modify peptides and proteins and shows promise for industrial applications. In this study, a novel strategy was developed for constructing immobilized-SrtA as a robust and recyclable enzyme via direct immobilization of extracellularly expressed SrtA in the fermentation supernatant using magnetic particles. Efficient extracellular SrtA expression was achieved in Escherichia coli through molecular engineering, including manipulation of the protein transport pathway, codon optimization, and co-expression of molecular chaperones to promote expressed SrtA secretion into the medium at high levels. Subsequently, a simple one-step protocol was established for the purification and immobilization of SrtA containing a His-tag from the fermentation supernatant onto a nickel-modified magnetic particle. The immobilized SrtA was proved to retain full enzymatic activity for peptide-to-peptide ligation and protein modification, and was successfully reused for five cycles without obvious activity loss.
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