关于带宽极值问题的一些结果

本文研究的问题是确定f(p,B）的值，也就是给定顶点数p和带宽B，求满足最大度不超过B的连通图的最小边数，本文给出了一些f(p,B）的值及相应极图。     

On the exact distribution of linear combinations of order statistics from dependent random variables

We study the exact distribution of linear combinations of order statistics of arbitrary (absolutely continuous) dependent random variables. In particular, we examine the case where the random variables have a joint elliptically contoured distribution and the case where the random variables are exchangeable. We investigate also the particular L-statistics that simply yield a set of order statistics, and study their joint distribution. We present the application of our results to genetic selection problems, design of cellular phone receivers, and visual acuity. We give illustrative examples based on the multivariate normal and multivariate Student t distributions.     

Selection of the number of regression variables; A minimax choice of generalized FPE

Summary A generalized Final Prediction Error (FPE_α)_ criterion is considered. Based onn observations, the numberk of regression variables is selected from a given range 0≦k≦K, so as to minimize . It is shown that if α tends to infinity withn, the selection is consistent but the maximum of the mean squared error of estimates of parameters diverges to infinity with the same order of divergence as that of α. A meaningful minimax choice of α exists for a regret type mean squared error, while for simple mean squared error it is trivially 0. The minimax regret choice of α converges to a constant, approximately 3.5 forK≧8 ifn−K increases simultaneously withn, otherwise it diverges to infinity withn.     

On bandwidth sums of graphs

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Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes

The goal of present work is to analyse the effect of having non-informative variables (NIV) in a data set when applying cluster analysis and to propose a method computationally capable of detecting and removing these variables. The method proposed is based on the use of a genetic algorithm to select those variables important to make the presence of groups in data clear. The procedure has been implemented to be used with k-means and using the cluster silhouettes as fitness function for the genetic algorithm.The main problem that can appear when applying the method to real data is the fact that, in general, we do not know a priori what the real cluster structure is (number and composition of the groups).The work explores the evolution of the silhouette values computed from the clusters built by using k-means when non-informative variables are added to the original data set in both a literature data set as well as some simulated data in higher dimension. The procedure has also been applied to real data sets.     

Application of plug–plug technique to ACE experiments for discovery of peptides binding to a larger target protein: A model study of calmodulin‐binding fragments selected from a digested mixture of reduced BSA

To discover peptide ligands that bind to a target protein with a higher molecular mass, a concise screening methodology has been established, by applying a “plug–plug” technique to ACE experiments. Exploratory experiments using three mixed peptides, mastoparan‐X, β‐endorphin, and oxytocin, as candidates for calmodulin‐binding ligands, revealed that the technique not only reduces the consumption of the protein sample, but also increases the flexibility of the experimental conditions, by allowing the use of MS detection in the ACE experiments. With the plug–plug technique, the ACE–MS screening methodology successfully selected calmodulin‐binding peptides from a random library with diverse constituents, such as protease digests of BSA. Three peptides with K_d values between 8–147 μM for calmodulin were obtained from a Glu‐C endoprotease digest of reduced BSA, although the digest showed more than 70 peaks in its ACE–MS electropherogram. The method established here will be quite useful for the screening of peptide ligands, which have only low affinities due to their flexible chain structures but could potentially provide primary information for designing inhibitors against the target protein.     

Rapid Selection of Phage Se-scFv with GPX Activity via Combination of Phage Display Antibody Library with Chemical Modification

IntroductionReactive oxygen species(ROS) are known to de-stroy biomacromolecules and cause cell injury[1]. Un-der normal circumstances, there is a balance betweenthe production of ROS and their destruction. Many dis-eases, such as brain ischemia, tumor, v…     

饱和放大情形下光纤参量放大器的增益和带宽特性研究

利用龙格库塔法数值求解非线性耦合方程,研究了单抽运光纤参量放大器在增益饱和区的增益谱特性.通过比较考虑抽运光损耗与忽略抽运光损耗增益谱的差别,分析了抽运光损耗对增益谱的影响.此外,给出了在饱和放大区,信号光的增益谱与光纤长度、输入信号光功率、抽运光波长与零色散波长偏离之间的关系.发现在饱和放大区,增益的整体水平有所下降,增益谱的可用带宽相对于小信号放大有所减少,增益谱在可用带宽范围内出现了旁瓣.这些结果将对工作在饱和放大区的单抽运光参量放大器的设计提供有益的帮助.     

武汉大学"化学拔尖计划"的探索与实施

详细介绍了武汉大学"化学拔尖计划"的探索与实施,包括学生的选拔机制、培养模式和管理模式。学生的选拔依照"严入口、小规模、高水平"的原则进行;从课程体系、教学内容、教学方法和评价机制四个方面介绍了学生的培养模式;对学生的管理采用导师制和书院制并行的方式。     

基于iPLS的血清胆固醇、甘油三酯近红外定量分析

为了建立血清样品胆固醇、甘油三酯近红外分析最优模型,利用近红外透射光谱技术结合间隔偏最小二乘法（iPLS）建立预测模型。结果表明,胆固醇最优建模波段是1700—1798nm,最优预测模型的相关系数Rp、预测均方差RMSEP分别为0.984、0.198mmol/L;甘油三酯最优建模波段是1654-1746nm,最优预测模型的Rp、RMSEP分别为0.967、0.157mmol/L。采用iPLS建立血清胆固醇、甘油三酯定量分析模型,不仅可以提高模型的预测精度,而且模型更加简洁、数据运算量也更少,优选出的特征谱区还可为设计小型专用近红外分析仪器提供依据。