As one of the world’s few large-scale aquaculture countries, the mechanization, output and efficiency are low in Chinese aquaculture industry.with the development of industrial automation technology and computer image processing technology, aquaculture will be automated, scientific management transformation. As a specific application of computer recognition technology, underwater image recognition technology is important to promote aquaculture industry automation and intelligence. In this paper, first of all introduces the existing research of underwater target image recognition, mainly presents the underwater image recognition technology based on deep learning. And then the current problems of underwater image recognition is summarized. Besides, the future development of underwater image recognition technology and its application in wisdom fishery is also discussed, which provides other scholar with some theoretical help and a comprehensive reference.

The aim of the present study was to elucidate the effect of resveratrol on non-alcoholic steatohepatitis (NASH), and the molecular basis in mice and Hepa1-6 cells, in order to verify its therapeutic effect. c57BL/6J mice were fed a methionine-choline-deficient (MCD) diet to induce steatohepatitis and were treated with resveratrol. Mouse sera were collected for biochemical analysis and enzyme-linked immunosorbent assay, and livers were obtained for histological observation, and mmu-microRNA (miR)-599 and inflammation-related gene expression analysis. Hepa1-6 cells were treated with palmitic acid to establish a NASH cell model, and were then treated with resveratrol, or transfected with mmu-miR-599 mimic, mmu-miR-599 inhibitor or recombinant pregnane X receptor (PXR) plasmid. Subsequently, the cells were collected for mmu-miR-599 and inflammation-related gene expression analysis. Reverse transcription-quantitative polymerase chain reaction and western blotting were used to assess mmu-miR-599 expression levels, and the mRNA and protein expression levels of PXR and inflammation-related genes. The binding site of mmu-miR-599 in the PXR mRNA was verified by the luciferase activity assay. Mice fed an MCD diet for 4 weeks exhibited steatosis, focal necrosis and inflammatory infiltration in the liver. Resveratrol significantly reduced serum aminotransferase and malondialdehyde levels, and ameliorated hepatic injury. These effects were associated with reduced mmu-miR-599 expression, enhanced PXR expression, and downregulated levels of nuclear factor-κB, tumour necrosis factor-α, interleukin (IL)-1β, IL-6, NOd-like receptor family pyrin domain-containing protein 3 and signal transducer and activator of transcription 3. Administration of the mmu-miR-599 mimic inhibited PXR expression in Hepa1-6 cells, whereas the mmu-miR-599 inhibitor exerted the opposite effect. A binding site for mmu-miR-599 was identified in the PXR mRNA sequence. Furthermore, overexpression of PXR inhibited the expression of inflammatory factors in Hepa1-6 cells. The present study provided evidence for the protective role of resveratrol in ameliorating steatohepatitis through regulating the mmu-miR-599/PXR pathway and the consequent suppression of related inflammatory factors. Resveratrol may serve as a potential candidate for steatohepatitis management.

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Eye health has become a global health concern and attracted broad attention. Over the years, researchers have proposed many state‐of‐the‐art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely. However, most existing methods were dedicated to constructing sophisticated CNNs, inevitably ignoring the trade‐off between performance and model complexity. To alleviate this paradox, this paper proposes a lightweight yet efficient network architecture, mixed‐decomposed convolutional network (MDNet), to recognise ocular diseases. In MDNet, we introduce a novel mixed‐decomposed depthwise convolution method, which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low‐resolution and high‐resolution patterns by using fewer computations and fewer parameters. We conduct extensive experiments on the clinical anterior segment optical coherence tomography (AS‐OCT), LAG, University of California San Diego, and CIFAR‐100 datasets. The results show our MDNet achieves a better trade‐off between the performance and model complexity than efficient CNNs including MobileNets and MixNets. Specifically, our MDNet outperforms MobileNets by 2.5% of accuracy by using 22% fewer parameters and 30% fewer computations on the AS‐OCT dataset.

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