目的研究细胞膜角蛋白8(CK8)和乳腺癌耐药蛋白(breast cancer resistant protein,BCRP)作为多靶点治疗逆转耐药的可行性。方法共转染特异的CK8-siRNAs和BCRP-siRNAs至人乳腺癌多药耐药细胞MCF-7/MX,用Western blot方法检测siRNAs对CK8和...目的研究细胞膜角蛋白8(CK8)和乳腺癌耐药蛋白(breast cancer resistant protein,BCRP)作为多靶点治疗逆转耐药的可行性。方法共转染特异的CK8-siRNAs和BCRP-siRNAs至人乳腺癌多药耐药细胞MCF-7/MX,用Western blot方法检测siRNAs对CK8和BCRP蛋白表达的抑制,荧光染色用激光共聚焦显微镜观察细胞膜表面CK8表达量的变化,并用Sulforhodamine B的方法检测转染前后细胞对多种化疗药敏感性的变化。结果MCF-7/MX细胞导入CK8-siRNAs和BCRP-siRNAs后,CK8和BCRP的表达水平均明显降低,且细胞表面CK8染色也明显降低,同时对米托蒽醌等化疗药的敏感性明显提高,耐药表型明显逆转。结论CK8和BCRP在MCF-7/MX耐药细胞中共同高表达且在乳腺癌多药耐药表型的形成中起重要作用,共同抑制CK8和BCRP的表达可以有效逆转乳腺癌的多药耐药,为治疗肿瘤多药耐药提供了一条新的思路。展开更多
In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing ...In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.展开更多
To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise...To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity展开更多
A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model(IMM) filter.In this approach,the actual innovation,based on a mismatched model,can be formulated as sum of the theoretical in...A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model(IMM) filter.In this approach,the actual innovation,based on a mismatched model,can be formulated as sum of the theoretical innovation based on a matched model and the distance between matched and mismatched models,whose probability distributions are known.The joint likelihood of innovation sequence can be estimated by convolution of the two known probability density functions.The like-lihood of tracking models can be calculated by conditional probability formula.Compared with the conventional likelihood estimation method,the proposed method improves the estimation accuracy of likelihood and robustness of IMM,especially when maneuver occurs.展开更多
文摘In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.
文摘To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity
基金Supported by the National Natural Science Foundation of China (No. 60736045)the Fundamental Research Funds for the Central Universities (No. 103.1.2.E022050205)
文摘A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model(IMM) filter.In this approach,the actual innovation,based on a mismatched model,can be formulated as sum of the theoretical innovation based on a matched model and the distance between matched and mismatched models,whose probability distributions are known.The joint likelihood of innovation sequence can be estimated by convolution of the two known probability density functions.The like-lihood of tracking models can be calculated by conditional probability formula.Compared with the conventional likelihood estimation method,the proposed method improves the estimation accuracy of likelihood and robustness of IMM,especially when maneuver occurs.
