【目的】建立一种猪A群轮状病毒(porcine rotavirus group A, PoRV A)快速检测方法,用于PoRV检测和流行病学调查。【方法】参考GenBank中猪A群轮状病毒(PoRVA)VP6基因序列(登录号MT025937.1、OP978242.1、PP566178.1)设计特异性引物和探...【目的】建立一种猪A群轮状病毒(porcine rotavirus group A, PoRV A)快速检测方法,用于PoRV检测和流行病学调查。【方法】参考GenBank中猪A群轮状病毒(PoRVA)VP6基因序列(登录号MT025937.1、OP978242.1、PP566178.1)设计特异性引物和探针,优化反应体系中引物和探针的浓度,建立Taq Man RT-qPCR检测方法,并通过特异性、灵敏性和重复性的结果以及临床应用对该方法进行评价。【结果】该方法可特异性扩增PoRV核酸,最低检出限度为27.0 copies·μL^(-1),灵敏度高于普通RT-PCR 100倍;与猪流行性腹泻病毒(porcine epidemicdiarrheavirus,PEDV)、猪德尔塔冠状病毒(porcinedeltacoronavirus,PDCoV)、猪传染性胃肠炎病毒(transmissible gastroenteritis of swine, TGEV)核酸均无交叉反应;组内和组间变异系数均小于1.10%,重复性好。151份疑似PoRV的临床样品使用RT-qPCR进行检测,结果显示检出率为42.38%(64/151),优于常规RT-PCR的检出率(33.11%,50/151)。【结论】本研究基于猪A群轮状病毒VP6基因,建立了适用于PoRV A检测及其流行病学调查的Taq Man实时荧光定量PCR检测方法,具有灵敏度高、特异性强、重复性好等优势,为猪轮状病毒检测和流行病学调查提供了技术手段。展开更多
It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly eval...It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.展开更多
文摘【目的】建立一种猪A群轮状病毒(porcine rotavirus group A, PoRV A)快速检测方法,用于PoRV检测和流行病学调查。【方法】参考GenBank中猪A群轮状病毒(PoRVA)VP6基因序列(登录号MT025937.1、OP978242.1、PP566178.1)设计特异性引物和探针,优化反应体系中引物和探针的浓度,建立Taq Man RT-qPCR检测方法,并通过特异性、灵敏性和重复性的结果以及临床应用对该方法进行评价。【结果】该方法可特异性扩增PoRV核酸,最低检出限度为27.0 copies·μL^(-1),灵敏度高于普通RT-PCR 100倍;与猪流行性腹泻病毒(porcine epidemicdiarrheavirus,PEDV)、猪德尔塔冠状病毒(porcinedeltacoronavirus,PDCoV)、猪传染性胃肠炎病毒(transmissible gastroenteritis of swine, TGEV)核酸均无交叉反应;组内和组间变异系数均小于1.10%,重复性好。151份疑似PoRV的临床样品使用RT-qPCR进行检测,结果显示检出率为42.38%(64/151),优于常规RT-PCR的检出率(33.11%,50/151)。【结论】本研究基于猪A群轮状病毒VP6基因,建立了适用于PoRV A检测及其流行病学调查的Taq Man实时荧光定量PCR检测方法,具有灵敏度高、特异性强、重复性好等优势,为猪轮状病毒检测和流行病学调查提供了技术手段。
基金supported by the National Natural Science Foundation of China (12072365)the Natural Science Foundation of Hunan Province of China (2020JJ4657)。
文摘It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
