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While bats are increasingly recognized as a source of coronavirus epidemics, the diversity and emergence potential of bat coronaviruses remains to be fully understood. Among 1779 bat samples collected in China, diverse coronaviruses were detected in 32 samples from five different bat species by RT-PCR. Two novel alphacoronaviruses, Rhinolophus sinicus bat coronavirus HKU32 (Rs-BatCoV HKU32) and Tylonycteris robustula bat coronavirus HKU33 (Tr-BatCoV HKU33), were discovered from Chinese horseshoe bats in Hong Kong and greater bamboo bats in Guizhou Province, respectively. Genome analyses showed that Rs-BatCoV HKU32 is closely related to BatCoV HKU10 and related viruses from diverse bat families, whereas Tr-BatCoV HKU33 is closely related to BtNv-AlphaCoV and similar viruses exclusively from bats of Vespertilionidae family. The close relatedness of Rs-BatCoV HKU32 to BatCoV HKU10 which was also detected in Pomona roundleaf bats from the same country park suggests that these viruses may have the tendency of infecting genetically distant bat populations of close geographical proximity with subsequent genetic divergence. Moreover, the presence of SARSr-CoV ORF7a-like protein in Rs-BatCoV HKU32 suggests a common evolutionary origin of this accessory protein with SARS-CoV, also from Chinese horseshoe bats, an apparent reservoir for coronavirus epidemics. The emergence potential of Rs-BatCoV HKU32 should be explored.

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Haze is a common atmospheric phenomenon that causes poor visibility in outdoor images, which greatly limits image application in later stages. Therefore, haze removal has become the first and most indispensable step when dealing with degraded images. In this paper, we propose a novel bright channel prior (BCP) model and a saliency analysis strategy for haze removal. First, we obtain a more robust and accurate atmospheric light by a superpixel‐based dark channel method. Second, we utilize the dark channel prior (DCP) to handle dark regions in hazy images. However, the DCP often mistakes white regions for opaque haze and thus causes serious colour distortion and halo effects. To solve this problem, a new BCP is proposed to accurately estimate the transmission of bright regions in hazy images. Third, we fuse the DCP and BCP using a multiscale fusion strategy with Laplacian pyramid representation to gain the correct transmission information for both bright and dark regions. Finally, a novel saliency analysis strategy for transmission refinement is proposed, so that the texture details can remain present to the greatest extent in the restored images. The experimental results illustrate that our proposed method performs well in restoring images containing bright objects.

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