Objective\ To understand the transcription of BamHI L DNA fragment from genome of strong virulent GA strain of Marek′s disease herpesvirus (MDV) in lymphoblastoid tumor tissue induced by oncogenic strain Beijing 1 ...Objective\ To understand the transcription of BamHI L DNA fragment from genome of strong virulent GA strain of Marek′s disease herpesvirus (MDV) in lymphoblastoid tumor tissue induced by oncogenic strain Beijing 1 (a specific local strain in China) of MDV. Methods\ Two oligonucleotide primers were synthesized according to the reported sequence of \%meq\% gene an ideal oncogenic candidate and our previously determined sequence of BamHI L fragment of Marek′s disease herpesvirus (MDV), respectively. Reverse transcriptase PCR(RT PCR) assay was performed by using these primers and the mRNA as a template which was isolated from visceral lymphoblastoid tumors obtained from chickens artificially infected with strain Beijing 1 of oncogenic MDV. Southern blot molecular hybridization was further carried out to detect the product of RT PCR with digoxigenin labeled nucleotide probe from BamHI I2 and L fragment in the gene library of MDV strain GA, respectively. Results\ Two probes could simultaneously hybridize this cDNA amplified by RT PCR with a length of about 730 bp. Conclusion\ It is suggested that \%meq\% transcription could extend from the right hand end of BamHI I2 to the adjacent BamHI L, and the BamHI L region was likely to be transcribed in MDV induced lymphoblastoid tumors.展开更多
为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹...为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹采集数据。为提升动力学参数辨识的准确性,在加权最小二乘法基础上进行改进,提出了IRLS算法对动力学参数进行辨识。最后以六自由度机器人为试验对象,进行了参数辨识试验。结果表明,基于IRLS算法的辨识方法与加权最小二乘法相比,前3个关节力矩误差的均方根(Root Mean Square,RMS)值降低了13.28%,后3个关节力矩误差的RMS值降低了28.57%,6个关节力矩误差的RMS值平均降低了17.15%,证明了基于IRLS算法的辨识方法的有效性。展开更多
现代谱估计方法能够反演基于几何绕射理论(geometric theory of diffraction,GTD)的模型参数,但不能处理非均匀不完备的雷达散射截面(radar cross section,RCS)数据。此外,通过暗室测量获取完备的RCS数据也需要较大的时空开销。针对上...现代谱估计方法能够反演基于几何绕射理论(geometric theory of diffraction,GTD)的模型参数,但不能处理非均匀不完备的雷达散射截面(radar cross section,RCS)数据。此外,通过暗室测量获取完备的RCS数据也需要较大的时空开销。针对上述问题,提出一种基于迭代加权最小二乘(iteratively reweighed least squares,IRLS)的跳频模式下GTD散射参数提取和RCS重构方法。该方法将稀疏重构理论与GTD散射模型相结合,能够在RCS数据非均匀不完备的条件下反演散射参数和实现RCS重构。仿真数据和电磁计算数据用于验证所提方法的有效性,实验结果表明该方法对降低暗室步进频率RCS的测量成本和扩增雷达RCS数据具有重要意义。展开更多
基金Project supported by the Natural Science Foundation of Chonging(CSTC-2005BB1154)Agricultural Wild Plants Protection Financial Project of Department of Agriculture of China(2005076)
文摘Objective\ To understand the transcription of BamHI L DNA fragment from genome of strong virulent GA strain of Marek′s disease herpesvirus (MDV) in lymphoblastoid tumor tissue induced by oncogenic strain Beijing 1 (a specific local strain in China) of MDV. Methods\ Two oligonucleotide primers were synthesized according to the reported sequence of \%meq\% gene an ideal oncogenic candidate and our previously determined sequence of BamHI L fragment of Marek′s disease herpesvirus (MDV), respectively. Reverse transcriptase PCR(RT PCR) assay was performed by using these primers and the mRNA as a template which was isolated from visceral lymphoblastoid tumors obtained from chickens artificially infected with strain Beijing 1 of oncogenic MDV. Southern blot molecular hybridization was further carried out to detect the product of RT PCR with digoxigenin labeled nucleotide probe from BamHI I2 and L fragment in the gene library of MDV strain GA, respectively. Results\ Two probes could simultaneously hybridize this cDNA amplified by RT PCR with a length of about 730 bp. Conclusion\ It is suggested that \%meq\% transcription could extend from the right hand end of BamHI I2 to the adjacent BamHI L, and the BamHI L region was likely to be transcribed in MDV induced lymphoblastoid tumors.
文摘为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹采集数据。为提升动力学参数辨识的准确性,在加权最小二乘法基础上进行改进,提出了IRLS算法对动力学参数进行辨识。最后以六自由度机器人为试验对象,进行了参数辨识试验。结果表明,基于IRLS算法的辨识方法与加权最小二乘法相比,前3个关节力矩误差的均方根(Root Mean Square,RMS)值降低了13.28%,后3个关节力矩误差的RMS值降低了28.57%,6个关节力矩误差的RMS值平均降低了17.15%,证明了基于IRLS算法的辨识方法的有效性。
文摘现代谱估计方法能够反演基于几何绕射理论(geometric theory of diffraction,GTD)的模型参数,但不能处理非均匀不完备的雷达散射截面(radar cross section,RCS)数据。此外,通过暗室测量获取完备的RCS数据也需要较大的时空开销。针对上述问题,提出一种基于迭代加权最小二乘(iteratively reweighed least squares,IRLS)的跳频模式下GTD散射参数提取和RCS重构方法。该方法将稀疏重构理论与GTD散射模型相结合,能够在RCS数据非均匀不完备的条件下反演散射参数和实现RCS重构。仿真数据和电磁计算数据用于验证所提方法的有效性,实验结果表明该方法对降低暗室步进频率RCS的测量成本和扩增雷达RCS数据具有重要意义。
