It is difficult and significant to realize the aim of “one‐pot” and “nonenzyme” for traditional colorimetric detection of blood glucose. The synthesis of nanomaterials with 2D morphology is also a challenge for the bovine serum albumin (BSA)‐directed method. Here, the BSA‐directed synthesis avenue for metal oxide with 2D nanomorphology is developed. MnO2 nanoflakes (NFs) with controllable morphology can be obtained by changing the synthesis conditions. Fortunately, not only is the glucose oxidase (GOx)‐like nanozyme (MnO2 NFs) discovered, but MnO2 NFs also show dual enzyme activities (GOx‐like activity and peroxidase‐like activity) in similar pH range. That is to say, a “tandem nanozyme” (nanomaterial with tandem enzyme‐like characteristics) is presented here. Further, the one‐pot nonenzymatic strategy is proposed for the colorimetric detection of glucose, where the oxidation of glucose and the colorimetric detection of H2O2 are simultaneously conducted under the catalysis of the single nanozyme (MnO2 NFs). The method shows high sensitivity, low limit of detection, and short detection time, due to the proximity effect and in situ reaction. The as‐synthesized 2D tandem nanozyme expands the species of nanozymes, and the proposed strategy breaks traditional colorimetric detection process, accomplishing the purposes of “one‐pot” and “nonenzyme” in the true sense.

Surface charge of biological and medical nanocarriers has been demonstrated to play an important role in cellular uptake. Owing to the unique physicochemical properties, charge-reversal delivery strategy has rapidly developed as a promising approach for drug delivery application, especially for cancer treatment. Charge-reversal nanocarriers are neutral/negatively charged at physiological conditions while could be triggered to positively charged by specific stimuli (i.e., pH, redox, ROS, enzyme, light or temperature) to achieve the prolonged blood circulation and enhanced tumor cellular uptake, thus to potentiate the antitumor effects of delivered therapeutic agents. In this review, we comprehensively summarized the recent advances of charge-reversal nanocarriers, including: (i) the effect of surface charge on cellular uptake; (ii) charge-conversion mechanisms responding to several specific stimuli; (iii) relation between the chemical structure and charge reversal activity; and (iv) polymeric materials that are commonly applied in the charge-reversal delivery systems. Graphical Abstract

Large pre-trained language models such as BERT have shown their effectiveness in various natural language processing tasks. However, the huge parameter size makes them difficult to be deployed in real-time applications that require quick inference with limited resources. Existing methods compress BERT into small models while such compression is task-independent, i.e., the same compressed BERT for all different downstream tasks. Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks. We incorporate a task-oriented knowledge distillation loss to provide search hints and an efficiency-aware loss as search constraints, which enables a good trade-off between efficiency and effectiveness for task-adaptive BERT compression. We evaluate AdaBERT on several NLP tasks, and the results demonstrate that those task-adaptive compressed models are 12.7x to 29.3x faster than BERT in inference time and 11.5x to 17.0x smaller in terms of parameter size, while comparable performance is maintained.

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Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.