[Objective] This study aimed to investigate the genetic variation of g E gene of an epidemic pseudorabies virus（PRV） strain and its pathogenicity to piglets. [Method] By serial passage in Vero cells, a PRV strain wa...[Objective] This study aimed to investigate the genetic variation of g E gene of an epidemic pseudorabies virus（PRV） strain and its pathogenicity to piglets. [Method] By serial passage in Vero cells, a PRV strain was isolated from the brain tissues of stillborn fetuses delivered by sows with suspected PRV infection and preliminarily identified by PCR. g E gene of the isolated PRV strain was amplified and sequenced for phylogenetic analysis. In addition, the pathogenicity of the isolated PRV strain to 6-week-old piglets was evaluated. [Result] A PRV strain was successfully isolated and named PRV N5 B strain, which could proliferate in Vero cells and TCID50 of the 15 thgeneration virus liquid reached 10^7.125/0.1 ml. Specific bands could be amplified by PCR. g E gene in the isolated PRV strain was 1 740 bp in length. A phylogenetic tree was constructed based on full-length g E sequences, which showed that PRV N5 B strain and PRV strains isolated since 2012 were clustered into the same independent category and shared 99.7%-100% homology of nucleotide sequences. Compared with related sequences published previously, there were insertions of three consecutive bases at two loci. Animal experiments showed that intranasal inoculation of 6-week-old piglets with 2 ml of PRV N5 B strain（10^6/0.1 ml） led to a mortality rate of 100%. [Conclusion] In this study,genetic variability of g E gene in PRV N5 B isolate and its pathogenicity to piglets were analyzed, which provided a theoretical basis for the development of new vaccines to prevent and control porcine pseudorabies.展开更多
Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of ble...Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.展开更多
北斗B1频点信号采用二次编码结构,而二次编码使用的NH(Neumann-Hoffman)码会发生比特跳变,导致接收机捕获性能降低,对此,提出一种基于伪码与多普勒频率分离的北斗信号捕获算法.该算法将接收信号分为两路,利用延迟差分运算和测距码的双...北斗B1频点信号采用二次编码结构,而二次编码使用的NH(Neumann-Hoffman)码会发生比特跳变,导致接收机捕获性能降低,对此,提出一种基于伪码与多普勒频率分离的北斗信号捕获算法.该算法将接收信号分为两路,利用延迟差分运算和测距码的双极性分别对两路信号进行处理,消除了NH码比特跳变带来的影响.在此基础上使用匹配滤波器捕获码相位,使用快速傅里叶变换搜索多普勒频移,实现了码相位和多普勒频移的同时捕获.仿真结果表明,提出的算法成功捕获到了NH码跳变的信号,且在相同虚警率下灵敏度相比部分匹配滤波和快速傅里叶变换算法约有1. 03 d B的提升.展开更多
基金Supported by Natural Science Foundation of Jiangsu Province(BK20131334)Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu Province[CX(13)3069]~~
文摘[Objective] This study aimed to investigate the genetic variation of g E gene of an epidemic pseudorabies virus（PRV） strain and its pathogenicity to piglets. [Method] By serial passage in Vero cells, a PRV strain was isolated from the brain tissues of stillborn fetuses delivered by sows with suspected PRV infection and preliminarily identified by PCR. g E gene of the isolated PRV strain was amplified and sequenced for phylogenetic analysis. In addition, the pathogenicity of the isolated PRV strain to 6-week-old piglets was evaluated. [Result] A PRV strain was successfully isolated and named PRV N5 B strain, which could proliferate in Vero cells and TCID50 of the 15 thgeneration virus liquid reached 10^7.125/0.1 ml. Specific bands could be amplified by PCR. g E gene in the isolated PRV strain was 1 740 bp in length. A phylogenetic tree was constructed based on full-length g E sequences, which showed that PRV N5 B strain and PRV strains isolated since 2012 were clustered into the same independent category and shared 99.7%-100% homology of nucleotide sequences. Compared with related sequences published previously, there were insertions of three consecutive bases at two loci. Animal experiments showed that intranasal inoculation of 6-week-old piglets with 2 ml of PRV N5 B strain（10^6/0.1 ml） led to a mortality rate of 100%. [Conclusion] In this study,genetic variability of g E gene in PRV N5 B isolate and its pathogenicity to piglets were analyzed, which provided a theoretical basis for the development of new vaccines to prevent and control porcine pseudorabies.
文摘Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.
文摘北斗B1频点信号采用二次编码结构,而二次编码使用的NH(Neumann-Hoffman)码会发生比特跳变,导致接收机捕获性能降低,对此,提出一种基于伪码与多普勒频率分离的北斗信号捕获算法.该算法将接收信号分为两路,利用延迟差分运算和测距码的双极性分别对两路信号进行处理,消除了NH码比特跳变带来的影响.在此基础上使用匹配滤波器捕获码相位,使用快速傅里叶变换搜索多普勒频移,实现了码相位和多普勒频移的同时捕获.仿真结果表明,提出的算法成功捕获到了NH码跳变的信号,且在相同虚警率下灵敏度相比部分匹配滤波和快速傅里叶变换算法约有1. 03 d B的提升.
