| 1 | Analysis of the distribution characteristics of enterovirus types based on environmental surveillance from 2013 to 2021 in Fujian Province, China显示文摘Environmental surveillance (ES) is a useful approach for monitoring circulating viruses, including polioviruses (PVs) and non-polio enteroviruses (NPEVs). In this study, the results of nine years of ES from 2013 to 2021 at six sampling sites in three cities in Fujian Province, China, were summarized. It showed that the sewage samples contained abundant viruses, but the positive rate was affected by different sampling sites. From the 520 samples, 431 PVs, 1,713 NPEVs, and 281 human adenoviruses (HAdVs) were isolated. PV isolates had been markedly affected following the adjustment of the immunization strategy. All but one PV isolate were Sabin-like strains without wild PVs. One isolate was vaccine-derived PV type 3 with 10 variation points in theVP1 region. After May 2016, PV type 2 was no longer detected, and PV type 3 became a superior serotype. Of 1,713 NPEVs, 24 serotypes were identified, including echovirus11 (E11), E6, coxsackievirus B3 (CVB3), CVB5, E7, and E3 were the predominant serotypes (37.65%, 20.96%, 11.50%, 8.87%, 8.23%, and 7.06%, respectively). The temporal dynamic of the six common serotypes was inconsistent. E3 was frequently isolated, but the number of isolates was low, with no obvious peaks. E6, E7, and CVB3 exhibited periodic changes with a high peak every three to four years, and E11 only had one high peak lasting four years. Summer-fall peaks of the echoviruses and spring-winter peaks of CVB were observed in the monthly distribution of virus isolation. The infectious isolates of various serotypes of different species identified from the sewage samples showed that ES is an essential part of pathogen surveillance. | Xiuhui Yang Shaojian Cai Xiaoqian Wu Yong Zhang Dong Li Yahong Chen Qianjing Chen Shuangli Zhu Dongmei Yan Wenbo Xu Hairong Zhang Zhifei Chen Suhan Zhang Yong Zhou Mengping Zhang Ningxuan Zheng Na You | 2023 | Biosafety and Health2023，5，4： | 0 |
| 2 | Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network显示文摘In this study,a differential amplification convolutional neural network(DACNN)was proposed and used in the identification of wheat leaf disease images with ideal accuracy.The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output,which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability.Firstly,since there is no large-scale wheat leaf disease images dataset at present,the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images,and five kinds of data augmentation methods were used to expand the dataset.Secondly,DACNN combined four classifiers:Softmax,support vector machine(SVM),K-nearest neighbor(KNN)and Random Forest to evaluate the wheat leaf disease dataset.Finally,the DACNN was compared with the models:LeNet-5,AlexNet,ZFNet and Inception V3.The extensive results demonstrate that DACNN is better than other models.The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%. | Mengping Dong Shaomin Mu Aiju Shi Wenqian Mu Wenjie Sun | 2020 | International Journal of Agricultural and Biological Engineering2020，13，4： | 0 |
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