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您的检索式：作者名="Xingwen Xia"
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| 1 | Infective viruses produced from full-length complementary DNA of swine vesicular disease viruses HK/70 strain显示文摘The full-length cDNA clone of swine vesicular disease virus HK/70 strain named pSVOK12 was constructed in order to study the antigenicity, replication, maturation and pathogenicity of swine vesicular disease virus. In vitro transcription RNA from pSVOK12 transfected IBRS-2 cells and the re- covered virus RNA were isolated and sequenced, then indirect hemagglutination test, indirect im- munofluorescence assays, eleectron microscope test, 50% tissue culture infecting dose (TCID50) assays and mouse virulence studies were performed to study the antigenicity and virulence of the recovered virus. The result showed that the infectious clones we ob- tained and the virus derived from pSVOK12 had the same biological properties as the parental strain HK/70. The full-length infectious cDNA clone, pSVOK12, will be very useful in studies of the anti- genicity, virulence, pathogenesis, maturation and replication of SVDV. | ZHENG Haixue LIU Xiangtao SHANG Youiun WU Jinyan BAI Xingwen JIN Ye SUN Shiqi GUO Huichen TIAN Hong FENG Xia YIN Shuanghui GUO Jianhong CONG Guozheng LIU Zaixin CHANG Huiyun MA Junwu XIE Qingge | 2006 | Chinese Science Bulletin2006，51，17： | 6 |
| 2 | Attention-Based Multi-Scale Prediction Network for Time-Series Data显示文摘Time series data is a kind of data accumulated over time,which can describe the change of phenomenon.This kind of data reflects the degree of change of a certain thing or phenomenon.The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction,but they are not enough to mine the periodicity of data.In this article,we perform periodic analysis on two types of time series data,select time metrics with high periodic characteristics,and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data.A loss calculation method for traffic time series characteristics is proposed as well.Multiple experiments have been conducted on actual data sets.The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods(ARIMA,LSTM,etc.)and 3%-5%increase on MAPE. | Junjie Li Lin Zhu Yong Zhang Da Guo Xingwen Xia | 2022 | China Communications2022，19，5： | 0 |
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