In this work, microcrystalline cellulose (MCC) was activated with ultrasonic waves. The influences of ultrasonic treatment on the changes of supramolecular structures and morphology structure were studied by WAXS and SEM. The accessibility of the MCC was characterized by water retention value (WRV) and specific surficial area. The influence of ultrasonic treatment on the reactivity of MCC was investigated through the reaction of MCC being oxidized into 2,3-diadehyde cellulose (DAC) by periodate sodium. The mechanism of the reactivity change of ultrasonically treated MCC was examined. The results showed that the degree of crystallinity of MCC decreased and the degree of polymerization showed little change after treatment with ultrasonic waves. The morphologial variation of the treated MCC was significant when compared with the untreated MCC, which contribute to the improvement of accessibility. The aldehyde content of DAC prepared from ultrasonically treated MCC was improved from 64.19 to 85.00%, indicating that the regioselective oxidation reactivity of MCC was significantly improved. The aldehyde content was found to first increase with time of ultrasonic treatment to a point, and then decrease as time progressed. In addition, the aldehyde content was found to increase with an increase in ultrasonic power.
Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as “feedback” to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.
Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pretrained Roberta variants and discover that general-domain fine-tuning does not help generalization, which aligns with the discovery of prior art. Proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a publicly available manual annotated queryproduct pair data.
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