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.     

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.     

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。     

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…     

Performance of two-hop infrastructure based multi-antenna regenerative relaying in Rayleigh fading channel

Cooperative relaying is considered as an effective technique to enlarge the coverage area and enhance the system capacity for the future wireless systems. In this paper, an infrastructure based multi-antenna cooperative relay network has been investigated. Closed form expressions of outage probability and average error rate have been derived, when the relay and the destination perform selection combining of the signals. The relay is assumed to operate in the adaptive decode and forward mode. The effect of number of antennas installed on the relay and their placement has also been studied.     

琅玡山抽水蓄能电站厂房位置及轴线方向的优化选择

地下厂房位置及轴线方向的选择是一项重要的地质工作,本文在分析琅山抽水蓄能电站厂房区工程地质条件的基础上,就厂房位置及其轴线方向的优化选择,进行了分析和研究。     

论地质灾害防治工程的地质观与工程观

概括前人的经验, 结合作者个人的实践, 本文较详细地讨论了地质灾害防治工程的地质观念与工程观念。提出地质观的核心问题是地质灾害体的变形破坏力学机理分析, 工程观观与工程观运用的最佳耦合。文中还讨论了超前预测的作用, 监测反馈与工程效果检验的关系, 以及地质工程师与设计工程师的关系等, 最后以链子崖危岩体防治工程为例进行了说明。     

A step-by-step guide to building two-population stochastic mortality models

Two-population stochastic mortality models play a crucial role in the securitization of longevity risk. In particular, they allow us to quantify the population basis risk when longevity hedges are built from broad-based mortality indexes. In this paper, we propose and illustrate a systematic process for constructing a two-population mortality model for a pair of populations. The process encompasses four steps, namely (1) determining the conditions for biological reasonableness, (2) identifying an appropriate base model specification, (3) choosing a suitable time-series process and correlation structure for projecting period and/or cohort effects into the future, and (4) model evaluation.For each of the seven single-population models from Cairns et al. (2009), we propose two-population generalizations. We derive criteria required to avoid long-term divergence problems and the likelihood functions for estimating the models. We also explain how the parameter estimates are found, and how the models are systematically simplified to optimize the fit based on the Bayes Information Criterion. Throughout the paper, the results and methodology are illustrated using real data from two pairs of populations.