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Since the outbreak of novel coronavirus pneumonia (coronavirus disease 2019, in December 2019, it has rapidly spread to 187 countries, causing serious harm to the health of people and a huge social burden. However, currently, drugs specifically approved for clinical use are not available, except for vaccines against COVID-19 that are being evaluated. Traditional Chinese medicine (TCM) is capable of performing syndrome differentiation and treatment according to the clinical manifestations of patients, and has a better ability of epidemic prevention and control.The authors comprehensively analyzed the etiology and pathogenesis of COVID-19 based on the theory of TCM, and discussed its syndrome differentiation, treatment and prevention measures so as to provide strategies and reference for the prevention and treatment with TCM. Please cite this article as: Wang SX, Wang Y, Lu YB, Li JY, Song YJ, Nyamgerelt M, Wang XX. Diagnosis and treatment of novel coronavirus pneumonia based on the theory of traditional Chinese medicine. J Integr Med. 2020; Epub ahead of print.

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Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset 1 with millions of real comments and a humanannotated subset characterizing the comments' varying quality. Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.

In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursiveinput fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.

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