Objective:To explore the difference of lymphocyte subsets in peripheral blood(PB)between aplastic anemia(AA)and hypoplastic myelodysplastic syndrome(hypo-MDS)patients,meanwhile to compare the clinical parameters obtai...Objective:To explore the difference of lymphocyte subsets in peripheral blood(PB)between aplastic anemia(AA)and hypoplastic myelodysplastic syndrome(hypo-MDS)patients,meanwhile to compare the clinical parameters obtained from PB and bone marrow(BM).Methods:The lymphocyte subsets in hypo-MDS(n=25)and.AA(n=33)patients were investigated by flow cytometry.Meanwhile,the differences in PB cell counts,biochemical indicators,BM cell counts and abnormal chromosomes between the two groups were analyzed.Results:The percentage of CD8^(+)T cells in AA group was significantly higher than that in hypo-MDS group(P=0.001),while the percentage of CD4^(+)T cells and the CD4^(+)/CD8^(+)ratio in AA group were obviously lower than those in hypo-MDS group(P=0.015 and 0.001,respectively).Furthermore,the proportion of CD4^(+)and CD8^(+)activated T(TA)cells,and memory Tregs in AA group was distinctly lower than those in hypo-MDS group(P=0.043,0.015 and 0.024,respectively).Nevertheless,the percentage of CD8^(+)naive T(TN)cells in AA patients was remarkably higher(P=0.044).And hypo-MDS patients had declined lymphocyte counts(P=0.025),increased levels of total bilirubin(TBil),lactate dehydrogenase(LDH),vitamin B12 and proportion of BM blasts than AA patients(P=0.019,0.023,0.027 and.0.045,respectively).Conclusion:In this study it was confirmed that the percentages of CD4^(+)and CD8^(+)TA cells,memory Tregs and CD8^(+)TN cells were significantly different between AA and hypo-MDS patients,which provide an essential basis for the identification of these two diseases.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
This paper deals with identification of subset autoregressive time series model. A simulated annealing based on approach for deter min ing the optimal subset of regression terms included in the subset autogressive m...This paper deals with identification of subset autoregressive time series model. A simulated annealing based on approach for deter min ing the optimal subset of regression terms included in the subset autogressive model is introduced. Numerical examples are given to show the effectiveness of the proposed algorithm.展开更多
隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cl...隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cloud-assisted multi-party PSI,TVC-MPSI).首先,TVC-MPSI应用星型网络拓扑结构,增加对单个云服务器的安全要求,仅利用密文交集基数和交集的多项式形式确保了交集的可验证性;其次,当客户端的集合包含多个子集时,引入了Pedersen门限可验证的秘密共享技术来实现对集合子集的匹配,从而实现细粒度的交集运算;除此之外,引入基于RSA的局部可验证签名算法(local verifiable aggregate signatures,LVS),保证云服务器端和客户端身份的不可伪造性;最后,通过正确性和安全性分析,以及全面的性能对比,表明协议在保证安全性的同时拥有较好的性能.展开更多
文摘Objective:To explore the difference of lymphocyte subsets in peripheral blood(PB)between aplastic anemia(AA)and hypoplastic myelodysplastic syndrome(hypo-MDS)patients,meanwhile to compare the clinical parameters obtained from PB and bone marrow(BM).Methods:The lymphocyte subsets in hypo-MDS(n=25)and.AA(n=33)patients were investigated by flow cytometry.Meanwhile,the differences in PB cell counts,biochemical indicators,BM cell counts and abnormal chromosomes between the two groups were analyzed.Results:The percentage of CD8^(+)T cells in AA group was significantly higher than that in hypo-MDS group(P=0.001),while the percentage of CD4^(+)T cells and the CD4^(+)/CD8^(+)ratio in AA group were obviously lower than those in hypo-MDS group(P=0.015 and 0.001,respectively).Furthermore,the proportion of CD4^(+)and CD8^(+)activated T(TA)cells,and memory Tregs in AA group was distinctly lower than those in hypo-MDS group(P=0.043,0.015 and 0.024,respectively).Nevertheless,the percentage of CD8^(+)naive T(TN)cells in AA patients was remarkably higher(P=0.044).And hypo-MDS patients had declined lymphocyte counts(P=0.025),increased levels of total bilirubin(TBil),lactate dehydrogenase(LDH),vitamin B12 and proportion of BM blasts than AA patients(P=0.019,0.023,0.027 and.0.045,respectively).Conclusion:In this study it was confirmed that the percentages of CD4^(+)and CD8^(+)TA cells,memory Tregs and CD8^(+)TN cells were significantly different between AA and hypo-MDS patients,which provide an essential basis for the identification of these two diseases.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
文摘This paper deals with identification of subset autoregressive time series model. A simulated annealing based on approach for deter min ing the optimal subset of regression terms included in the subset autogressive model is introduced. Numerical examples are given to show the effectiveness of the proposed algorithm.
