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谷子NBS类型抗病基因同源序列的克隆与分析 被引量:1
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作者 瓮巧云 宋晋辉 张爱香 《湖北农业科学》 北大核心 2012年第12期2599-2601,共3页
以谷子(Setaria italica Beauv.)抗锈病植株十里香和感锈病植株豫谷1号为材料,利用抗病基因同源序列(RGA)技术克隆谷子核苷酸结合位点(NBS)类型RGA并进行分析。共获得了3个RGA,分别命名为RUS1-1、RUS1-2、RUS1-3(Resistance against Uro... 以谷子(Setaria italica Beauv.)抗锈病植株十里香和感锈病植株豫谷1号为材料,利用抗病基因同源序列(RGA)技术克隆谷子核苷酸结合位点(NBS)类型RGA并进行分析。共获得了3个RGA,分别命名为RUS1-1、RUS1-2、RUS1-3(Resistance against Uromyces setariae-italicae,RUS),它们编码的蛋白质均含有NBS类型抗病基因编码蛋白的共有特征结构域P-loop和Kinase2α。BLASTX分析结果表明,3个RGA的编码蛋白与水稻中含有NB-ARC信号传导结构域的蛋白质、NBS-LRR类型抗病基因等的编码蛋白同源性为47%~66%。聚类分析结果表明,3个RGA属于NBS-LRR类型抗病基因,且与番茄NBS-LRR类型抗病基因I2聚为一类。 展开更多
关键词 谷子(Setariaitalica Beauv.) 抗病基因同源序列(RGA) 核苷酸结合位点(NBS)类型
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利用SSR标记进行杂交油菜品种鉴定 被引量:8
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作者 张冰清 陆徐忠 +8 位作者 吴新杰 李莉 陈凤祥 马琳 张小娟 倪金龙 汪秀峰 秦瑞英 杨剑波 《中国油料作物学报》 CAS CSCD 北大核心 2014年第6期728-734,共7页
利用分布于油菜连锁群上的33对SSR核心引物,对12个育种品系和市场上的杂交油菜品种进行鉴定,探讨取样方案、检测方法和位点判读,将样品间SSR位点类型分为相同位点、疑似相同位点和差异位点3类。分析位点类型发现,12个油菜品种/系间的差... 利用分布于油菜连锁群上的33对SSR核心引物,对12个育种品系和市场上的杂交油菜品种进行鉴定,探讨取样方案、检测方法和位点判读,将样品间SSR位点类型分为相同位点、疑似相同位点和差异位点3类。分析位点类型发现,12个油菜品种/系间的差异位点数为4~22,其中同父异母杂交种(3个)间差异位点数为4~16,同母异父杂交种(4个)间差异位点数为7~18,市场抽样的品种间差异位点数为12~20。同一品种不同样本间的差异位点数为0~2,据此,将区分品种间与品种内的差异位点阈值设定为2,即在利用33对引物对油菜杂交种与对照样品进行SSR分析时,当两样品间差异位点数〉2时,判定为不同品种;当差异位点数≤2时,判定为近似或极近似品种。盲样鉴定实验及其与田间鉴定方法的对比实验,均证明了本研究确定的SSR鉴定方法可行。 展开更多
关键词 杂交油菜 品种鉴定 SSR分析 位点类型 阈值
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Enhanced Protective Efficacy of H5 Subtype Influenza Vaccine with Modification of the Multibasic Cleavage Site of Hemagglutinin in Retroviral Pseudotypes
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作者 Ling Tao JianJun Chen +5 位作者 Jin Meng Yao Chen Hongxia Li Yan Liu Zhenhua Zheng Hanzhong Wang 《Virologica Sinica》 SCIE CAS CSCD 2013年第3期136-145,共10页
Traditionally, the multibasic cleavage site (MBCS) of surface protein H5-hemagglutinin (HA) is converted to a monobasic one so as to weaken the virulence of recombinant H5N1 influenza viruses and to produce inacti... Traditionally, the multibasic cleavage site (MBCS) of surface protein H5-hemagglutinin (HA) is converted to a monobasic one so as to weaken the virulence of recombinant H5N1 influenza viruses and to produce inactivated and live attenuated vaccines. Whether such modification benefits new candidate vaccines has not been adequately investigated. We previously used retroviral vectors to generate wtH5N1 pseudotypes containing the wild-type HA (wtH5) from A/swine/Anhui/ca/2004 (H5N1) virus. Here, we generated mtH5N1 pseudotypes, which contained a mutant-type HA (mtH5) with a modified monobasic cleavage site. Groups of mice were subcutaneously injected with the two types of influenza pseudotypes. Compared to the group immunized with wtH5N1 pseudotypes, the inoculation of mtH5N1 pseudotypes induced significantly higher levels of HA specific IgG and IFN-y in immunized mice, and enhanced protection against the challenge of mouse-adapted avian influenza virus A/Chicken/Henardl2/2004 (H5N1). This study suggests modification of the H5-hemagglutinin MBCS in retroviral pseudotypes enhances protection efficacy in mice and this information may be helpful for development of vaccines from mammalian cells to fight against H5N 1 influenza viruses. 展开更多
关键词 INFLUENZA H5 subtype VACCINE PSEUDOTYPE HEMAGGLUTININ Multibasic cleavage site
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An Iterative Clustering-Based Localization Algorithm for Wireless Sensor Networks 被引量:1
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作者 罗海勇 李慧 +1 位作者 赵方 彭金华 《China Communications》 SCIE CSCD 2011年第1期58-64,共7页
In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most... In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance. 展开更多
关键词 wireless sensor network node localization iterative clustering model
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