目的构建大鼠程序性细胞死亡因子4(programmed cell death 4,Pdcd4)基因在体干扰模型,为进一步研究该基因的功能提供条件。方法通过给予抗原诱导肺部炎症模型大鼠,鼻腔滴入Pdcd4的干扰质粒,以下调其体内Pdcd4的表达。收集大鼠支气管肺...目的构建大鼠程序性细胞死亡因子4(programmed cell death 4,Pdcd4)基因在体干扰模型,为进一步研究该基因的功能提供条件。方法通过给予抗原诱导肺部炎症模型大鼠,鼻腔滴入Pdcd4的干扰质粒,以下调其体内Pdcd4的表达。收集大鼠支气管肺泡灌洗液细胞,提取总RNA,RT-qPCR检测Pdcd4基因mRNA的表达水平。结果在模型大鼠鼻腔滴入Pdcd4的干扰质粒,大鼠支气管肺泡灌洗液细胞中的Pdcd4的mRNA表达明显下调。结论成功构建了Pdcd4基因的在体干扰大鼠模型,为进一步研究Pdcd4基因的功能和作用机制奠定基础。展开更多
We develop a cosmological model in a physical background scenario of four time and four space dimensions ((4+4)-dimensions or (4+4)-universe). We show that in this framework the (1+3)-universe is deeply connected with...We develop a cosmological model in a physical background scenario of four time and four space dimensions ((4+4)-dimensions or (4+4)-universe). We show that in this framework the (1+3)-universe is deeply connected with the (3+1)-universe. We argue that this means that in the (4+4)-universe there exists a duality relation between the (1+3)-universe and the (3+1)-universe.展开更多
Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4(YOLOv4) model, a new YOLOv4 model combined with Kalman filter real-time hand tr...Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4(YOLOv4) model, a new YOLOv4 model combined with Kalman filter real-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network(CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43% at speed of 41.822 frame/s, achieving superior results than other algorithms.展开更多
文摘目的构建大鼠程序性细胞死亡因子4(programmed cell death 4,Pdcd4)基因在体干扰模型,为进一步研究该基因的功能提供条件。方法通过给予抗原诱导肺部炎症模型大鼠,鼻腔滴入Pdcd4的干扰质粒,以下调其体内Pdcd4的表达。收集大鼠支气管肺泡灌洗液细胞,提取总RNA,RT-qPCR检测Pdcd4基因mRNA的表达水平。结果在模型大鼠鼻腔滴入Pdcd4的干扰质粒,大鼠支气管肺泡灌洗液细胞中的Pdcd4的mRNA表达明显下调。结论成功构建了Pdcd4基因的在体干扰大鼠模型,为进一步研究Pdcd4基因的功能和作用机制奠定基础。
文摘We develop a cosmological model in a physical background scenario of four time and four space dimensions ((4+4)-dimensions or (4+4)-universe). We show that in this framework the (1+3)-universe is deeply connected with the (3+1)-universe. We argue that this means that in the (4+4)-universe there exists a duality relation between the (1+3)-universe and the (3+1)-universe.
文摘Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4(YOLOv4) model, a new YOLOv4 model combined with Kalman filter real-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network(CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43% at speed of 41.822 frame/s, achieving superior results than other algorithms.
