基于微流控技术的细胞水平高通量药物筛选系统的研究进展

药物筛选是新药研发的关键步骤,创新药物的发现需要采用适当的药物作用靶点对大量化合物样品进行筛选。高通量筛选系统能够实现数千个反应同时测试和分析,大大提高了药物筛选的实验规模和效率。其中基于细胞水平的高通量药物筛选系统因为更加接近人体生理条件,成为主要的筛选模式。而目前发展成熟的高通量细胞筛选系统主要基于多孔板,存在细胞培养条件单一、耗时费力、试剂消耗量大等问题,且较难实现复杂的组合药物筛选。微流控技术作为一种在微米尺度通道中操纵和控制微流体的技术,具有微量、高效、高通量和自动化的优点,能较好地克服多孔板筛选系统的不足,为构建细胞高通量药物筛选系统提供了一种高效、可靠的技术手段。微流控系统在细胞培养材料、芯片结构设计和流体控制方面均可灵活变化,能更好地实现对细胞生长微环境的调控和模拟。文章综述了基于微流控技术的细胞水平高通量药物筛选系统的研究进展,按照不同的微流体操控模式,对基于灌注流、液滴和微阵列的3种类型的微流控细胞筛选系统进行了分类介绍,并分别总结了它们的优缺点,最后展望了微流控细胞水平高通量药物筛选系统的发展前景,提出了该领域目前存在的问题以及解决问题的方向。     

专家系统中证据的合成,传播与修正

在专家系统中，由于证据的不确定性和推理规则的不确定性，推理也相应地发生变化，需要对证据进行合成、传播与修正。有许多文献进行了研究［２］，［３］，［４］，但已有的方法大都是针对不同的不确定性推理给出不同的方法。“本文旨在给出不确定性推理中证据合成、传播与修正的一般公式。     

包含度理论

不确定性是复杂系统的特征。在人工智能与专家系统中，不确定性的研究越来越具有重要意义。Ｚａｄｅｈ［１］于１９６５年提出模糊集，把经典集合扩充到模糊集合，从而解决了“对象”的不确定性。本文引进了包含度的概念，解决了“关系”的不确定性。同时指出，包含度不仅是各种不确定性推理方法的概括，而且解决了知识获取与矛盾规则排除两个重要问题。     

Relationships of exponents in multifractal detrended fluctuation analysis and conventional multifractal analysis

Multifractal detrended fluctuation analysis (MF-DFA) is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis (DFA), which was developed for detecting the long-range correlation and the fractal properties in stationary and non-stationary time series. Although MF-DFA has become a widely used method, some relationships among the exponents established in the original paper seem to be incorrect under the general situation. In this paper, we theoretically and experimentally demonstrate the invalidity of the expression τ(q)=qh(q)-1 stipulating the relationship between the multifractal exponent τ(q) and the generalized Hurst exponent h(q). As a replacement, a general relationship is established on the basis of the universal multifractal formalism for the stationary series as τ(q)=qh(q)-qH'-1, where H' is the nonconservation parameter in the universal multifractal formalism. The singular spectra, α and f(α), are also derived according to this new relationship.     

关于加权全最小一乘的探讨

设p（v;a;μ）为v∈Rk到超平面μTv+a=0的垂直距离,其中μ∈Rk,a∈R1.本文研讨的是寻找适当的a和μ使达到最小,这里{v1,v2,…,vn}(?)Rk而qi>0,i=1,2,…,n是给定的权.本文采用一条全新的途径来研讨上述加权全最小一乘问题的求解.文中导出了要使Q（u0,μ）达到最小,μ和u0应满足的若干本质性必要条件,而满足该条件的（μ,u0）只有有限多个;进而提出了一个求解加权全最小一乘问题的有限步终止算法.     

Statistical inferences for varying-coefficint models based on locally weighted regression technique

1. IntroductionIn recent y6ars, some progress has been made towards increaJsing the flexibility of linearregression models. One of the obvious extensions in this direction is the sthcalled varyingcoefficient regression models in which the regressiOn funtions are llnear in the regressors,but their coefficients are allowed to change with the value of another factor. Specificallysuppose that we have a response variable Y and regressors X1 t X2,' t Xv as well as anothervariable V. A varyingco…