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Xu Zhanyi,Yan Ru,Qian Jiali,Chen Dongping,Guo Yirong,Zhu Guonian,Wu Huiming,Chen Mengli 한국곤충학회 2022 Entomological Research Vol.52 No.11
RNA interference is an efficient approach for gene function identification and a potential novel strategy for selectively controlling pests. The red flour beetle, Tribolium castaneum (Coleoptera: Tenebrionidae), is a major global storage pest, which causes great economic loss. Juvenile hormone esterase (JHE), a carboxylesterase, is responsible for the degradation of juvenile hormone. However, the knockdown effect of jhe on metamorphosis of pupae or adult in T. castaneum is unknown. In this study, we analyzed the expression profiles of Tc j h e in the larval stage, we found that Tc j h e was expressed throughout the whole larval instars and the expression levels were relatively high right after molting. Furthermore, we knocked down the expression of Tcjhe by injecting dsTc j h e , which significantly increased the mortality of adults and decreased the eclosion rate. In addition, abnormal developmental phenotypes, including wing deformitity, pupal–adult monsters and shrunken adults, were observed. Our finding indicates that JHE plays an important role in the metamorphosis and development in T. castaneum, suggesting that Tc j h e could be used as a potential target for the development of RNAi-based control strategies in T. castaneum.
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Chinese-clinical-record Named Entity Recognition using IDCNN-BiLSTM-Highway Network
Tinglong Tang,Yunqiao Guo,Qixin Li,Mate Zhou,Wei Huang,Yirong Wu 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.7
Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.
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