应用酵母双杂交系统筛选与NAP1相互作用的蛋白质

用NAP1作为"诱饵"蛋白,通过酵母双杂交系统筛选人淋巴细胞cDNA文库,鉴定阳性克隆;再利用酵母双杂交和免疫共沉淀验证相互作用. 结果表明,通过酵母双杂交系统筛选人淋巴细胞cDNA文库,阳性克隆的鉴定,发现了与NAP1的相互作用蛋白质―蛋白酶体α亚基3(PSMA3). 免疫共沉淀(Co-IP)结果证实外源表达的NAP1蛋白和PSMA3蛋白在293T细胞中有特异性的相互作用. NAP1作为FDC分泌的一种多肽,与PSMA3有特异性的相互作用,这种相互作用如何实现对蛋白酶体降解靶蛋白的活性调节,其作用机理有待深入研究.     

Cross-Entropy Loss for Recommending Efficient Fold-Over Technique

Due to the limited resources and budgets in many real-life projects, it is unaffordable to use full factorial experimental designs and thus fractional factorial(FF) designs are used instead. The aliasing of factorial effects is the price we pay for using FF designs and thus some significant effects cannot be estimated. Therefore, some additional observations(runs) are needed to break the linages among the factorial effects. Folding over the initial FF designs is one of the significant approaches for selecting the additional runs. This paper gives an in-depth look at fold-over techniques via the following four significant contributions. The first contribution is on discussing the adjusted switching levels foldover technique to overcome the limitation of the classical one. The second contribution is on presenting a comparison study among the widely used fold-over techniques to help experimenters to recommend a suitable fold-over technique for their experiments by answering the following two fundamental questions:Do these techniques dramatically lessen the confounding of the initial designs, and do the resulting combined designs(combining initial design with its fold-over) via these techniques have considerable difference from the optimality point of view considering the markedly different searching domains in each technique? The optimality criteria are the aberration, confounding, Hamming distance and uniformity. Many of these criteria are given in sequences(patterns) form, which are inconvenient and costly to represent and compare, especially when the designs have many factors. The third innovation is on developing a new criterion(dictionary cross-entropy loss) to simplify the existing criteria fromsequence to scalar. The new criterion leads to a more straightforward and easy comparison study. The final contribution is on establishing a general framework for the connections between initial designs and combined designs based on any fold-over technique.     

An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

A uniform experimental design (UED) is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs, limited resources and unknown underlying models. A UED enjoys the following two significant advantages: (i) It is a robust design, since it does not require to specify a model before experimenters conduct their experiments; and (ii) it provides uniformly scatter design points in the experimental domain, thus it gives a good representation of this domain with fewer experimental trials (runs). Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are needed. Constructing large UEDs using the existing techniques is an NP-hard problem, an extremely time-consuming heuristic search process and a satisfactory result is not guaranteed. This paper presents a new effective and easy technique, adjusted Gray map technique (AGMT), for constructing (nearly) UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to (nearly) UEDs with 2^t?1s four-level factors and 2^tn runs for any t ≥ 0 using two simple transformation functions. Theoretical justifications for the uniformity of the resulting four-level designs are given, which provide some necessary and/or sufficient conditions for obtaining (nearly) uniform four-level designs. The results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large (nearly) UEDs with four-level factors.