目的 基于分泌卷曲相关蛋白2(secreted frizzled-related protein 2,SFRP2)基因,评价荧光定量法(Methy Light)、微滴数字PCR(droplet digital PCR,ddPCR)、核酸质谱、靶向亚硫酸氢盐二代测序4种甲基化检测方法。方法 对4种甲基化检测方...目的 基于分泌卷曲相关蛋白2(secreted frizzled-related protein 2,SFRP2)基因,评价荧光定量法(Methy Light)、微滴数字PCR(droplet digital PCR,ddPCR)、核酸质谱、靶向亚硫酸氢盐二代测序4种甲基化检测方法。方法 对4种甲基化检测方法的检测限(limit of detection, LOD)、定量限(limit of quantitation, LOQ)及稳定性方面进行比较,其中稳定性用变异系数(coefficient of variation,CV)来评估。结果 SFRP2基因在Methy Light、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的LOD分别为1.2500ng/孔、0.0625ng/孔、0.0625ng/孔、1.2500ng/孔,在LOD方面,ddPCR和核酸质谱的表现较好;SFRP2基因在MethyLight、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的LOQ分别为0.800%、0.032%、4.000%、0.032%,在LOQ方面,ddPCR和靶向亚硫酸氢盐二代测序的表现较好;SFRP2基因在MethyLight、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的变异系数分别为2.72%、0.68%、0.73%、0.15%,在稳定性方面,靶向亚硫酸氢盐二代测序的表现最好。结论 ddPCR的甲基化检测在检测限、定量限稳定性和经济性方面具有优越性,能够稳定地检测出肿瘤细胞的痕量游离DNA,在液态活检中具有广泛应用前景。展开更多
The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of hete...The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of heterogeneous reservoir cores relies heavily on high-resolution CT scanning images.However,this approach requires a considerable amount of data and is associated with high costs.To solve this problem,a method for predicting core permeability based on deep learning using CT scan images with diff erent resolutions is proposed in this work.First,the high-resolution CT scans are preprocessed and then cubic subsets are extracted.The permeability of each subset is estimated using the Lattice Boltzmann Method(LBM)and forms the training set for the convolutional neural network(CNN)model.Subsequently,the highresolution images are downsampled to obtain the low-resolution grayscale images.In the comparative analysis of the porosities of diff erent low-resolution images,the low-resolution image with a resolution of 10%of the original image is considered as the test set in this paper.It is found that the permeabilities predicted from the low-resolution images are in good agreement with the values calculated by the LBM.In addition,the test data are compared with the results of the Kozeny-Carman(KC)model and the measured permeability of the whole sample.The results show that the prediction of the permeability of tight carbonate rock based on deep learning using CT scans with diff erent resolutions is reliable.展开更多
为探究本课题组获得的2头体细胞克隆猪隐睾症形成的可能原因,本研究利用相对荧光定量PCR技术检测了WT1和FGF9基因在睾丸组织中mRNA表达量变化,同时利用亚硫酸氢盐测序法分析了其启动子区CpG岛甲基化状态。结果表明:WT1和FGF9基因在2头...为探究本课题组获得的2头体细胞克隆猪隐睾症形成的可能原因,本研究利用相对荧光定量PCR技术检测了WT1和FGF9基因在睾丸组织中mRNA表达量变化,同时利用亚硫酸氢盐测序法分析了其启动子区CpG岛甲基化状态。结果表明:WT1和FGF9基因在2头克隆猪睾丸中表达量均高于对照组,其中克隆猪C1中2个基因的表达量与对照组相比差异明显(3.49、9.83 vs 1.00)。亚硫酸氢盐测序结果显示,WT1基因启动子区在克隆猪C1和C2中的甲基化程度没有明显变化,而FGF9基因启动子区2个CpG岛在克隆猪C1中甲基化程度高于对照猪N(94.54%vs 18.18%,71.11%vs 26.67%),而在克隆猪C2中不明显。综上表明:WT1基因的异常表达可能是引起克隆猪发生隐睾的原因之一,但其甲基化水平不是影响该基因异常表达的因素;克隆猪C1睾丸组织FGF9基因启动子区发生超甲基化,这可能导致其mRNA表达异常,从而成为诱导克隆猪隐睾发生的可能原因。展开更多
文摘目的 基于分泌卷曲相关蛋白2(secreted frizzled-related protein 2,SFRP2)基因,评价荧光定量法(Methy Light)、微滴数字PCR(droplet digital PCR,ddPCR)、核酸质谱、靶向亚硫酸氢盐二代测序4种甲基化检测方法。方法 对4种甲基化检测方法的检测限(limit of detection, LOD)、定量限(limit of quantitation, LOQ)及稳定性方面进行比较,其中稳定性用变异系数(coefficient of variation,CV)来评估。结果 SFRP2基因在Methy Light、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的LOD分别为1.2500ng/孔、0.0625ng/孔、0.0625ng/孔、1.2500ng/孔,在LOD方面,ddPCR和核酸质谱的表现较好;SFRP2基因在MethyLight、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的LOQ分别为0.800%、0.032%、4.000%、0.032%,在LOQ方面,ddPCR和靶向亚硫酸氢盐二代测序的表现较好;SFRP2基因在MethyLight、ddPCR、核酸质谱和靶向亚硫酸氢盐二代测序中的变异系数分别为2.72%、0.68%、0.73%、0.15%,在稳定性方面,靶向亚硫酸氢盐二代测序的表现最好。结论 ddPCR的甲基化检测在检测限、定量限稳定性和经济性方面具有优越性,能够稳定地检测出肿瘤细胞的痕量游离DNA,在液态活检中具有广泛应用前景。
文摘The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of heterogeneous reservoir cores relies heavily on high-resolution CT scanning images.However,this approach requires a considerable amount of data and is associated with high costs.To solve this problem,a method for predicting core permeability based on deep learning using CT scan images with diff erent resolutions is proposed in this work.First,the high-resolution CT scans are preprocessed and then cubic subsets are extracted.The permeability of each subset is estimated using the Lattice Boltzmann Method(LBM)and forms the training set for the convolutional neural network(CNN)model.Subsequently,the highresolution images are downsampled to obtain the low-resolution grayscale images.In the comparative analysis of the porosities of diff erent low-resolution images,the low-resolution image with a resolution of 10%of the original image is considered as the test set in this paper.It is found that the permeabilities predicted from the low-resolution images are in good agreement with the values calculated by the LBM.In addition,the test data are compared with the results of the Kozeny-Carman(KC)model and the measured permeability of the whole sample.The results show that the prediction of the permeability of tight carbonate rock based on deep learning using CT scans with diff erent resolutions is reliable.
文摘为探究本课题组获得的2头体细胞克隆猪隐睾症形成的可能原因,本研究利用相对荧光定量PCR技术检测了WT1和FGF9基因在睾丸组织中mRNA表达量变化,同时利用亚硫酸氢盐测序法分析了其启动子区CpG岛甲基化状态。结果表明:WT1和FGF9基因在2头克隆猪睾丸中表达量均高于对照组,其中克隆猪C1中2个基因的表达量与对照组相比差异明显(3.49、9.83 vs 1.00)。亚硫酸氢盐测序结果显示,WT1基因启动子区在克隆猪C1和C2中的甲基化程度没有明显变化,而FGF9基因启动子区2个CpG岛在克隆猪C1中甲基化程度高于对照猪N(94.54%vs 18.18%,71.11%vs 26.67%),而在克隆猪C2中不明显。综上表明:WT1基因的异常表达可能是引起克隆猪发生隐睾的原因之一,但其甲基化水平不是影响该基因异常表达的因素;克隆猪C1睾丸组织FGF9基因启动子区发生超甲基化,这可能导致其mRNA表达异常,从而成为诱导克隆猪隐睾发生的可能原因。
