耐力训练大鼠心肌基因谱改变及壳寡糖预处理效应

研究大强度耐力训练大鼠心肌组织基因表达谱的改变,筛选与心肌损伤过程相关的差异表达基因;观察壳寡糖预处理效应,探讨壳寡糖作为抗疲劳强力因子保护心肌结构和功能的分子机制.分别从对照组、壳寡糖处理组、耐力训练组SD大鼠的心肌组织中抽提总RNA.Cy3、Cy5荧光标记,反转录分别合成cDNA探针后,与含有12 000个基因的SBC大鼠高密度12K基因表达谱芯片杂交.杂交信号经扫描后,Genespring软件分析表达信号.差异表达基因共616个,大强度耐力训练组差异表达基因157个,其中上调141个,下调17个;壳寡糖处理组差异表达基因459个,其中上调183个,下调276个,涉及细胞凋亡差异表达基因有36个,下调24个,上调12个.结论：耐力运动所引起的心肌结构和功能的积极适应甚或心肌损害都是由众多基因共同调控的复杂过程.壳寡糖能调控大强度耐力训练大鼠心肌组织多种功能类别基因群表达水平,从分子水平揭示了壳寡糖保护大强度耐力训练对大鼠心肌造成不良影响的作用机制.     

On a ‘Level-Sensitive’ Headcount Ratio: Revisiting Shorrocks’ Poverty Index

It is often argued that indicators of achievement or deprivation should be measured such that they display a property of ‘level-sensitivity’, whereby a given change in the indicator acquires a greater significance the higher (lower) the level of achievement (deprivation) at which the change occurs. In this note, it is shown that a level-sensitive headcount ratio of poverty can be derived from an application of the very useful graphical device called ‘a deprivation profile’ advanced by Anthony Shorrocks (in: Jenkins et al. (eds.) The Distribution of Welfare and Household Production: International Perspectives, 1996).     

功率器件有 JTE 结构的电场的数值分析与模拟

本文用 damped-Newton(DN)法求解由一般非线性方程获得的Poisson 方程。研究了阻尼因子λ对其收敛性和求解效率的影响。利用此方法计算了横向表面掺杂为 Gauss 分布的器件的归一化电场与掺杂浓度和掺杂层宽度的关系。     

Probabilistic Expert Systems for Forensic Inference from Genetic Markers

We present a number of real and fictitious examples in illustration of a new approach to analysing complex cases of forensic identification inference. This is effected by careful restructuring of the relevant pedigrees as a Probabilistic Expert System. Existing software can then be used to perform the required inferential calculations. Specific complications which are readily handled by this approach include missing data on one or more relevant individuals, and genetic mutation. The method is particularly valuable for disputed paternity cases, but applies also to certain criminal cases.     

Extending conditional likelihood in models for stratified binary data

The conditional likelihood is widely used in logistic regression models with stratified binary data. In particular, it leads to accurate inference for the parameters of interest, which are common to all strata, eliminating stratum-specific nuisance parameters. The modified profile likelihood is an accurate approximation to the conditional likelihood, but has the advantage of being available for general parametric models. Here, we propose the modified profile likelihood as an ideal extension of the conditional likelihood in generalized linear models for binary data, with generic link function. An important feature is that for the implementation we only need standard outputs of routines for generalized linear models. The accuracy of the method is supported by theoretical properties and is confirmed by simulation results.This research was supported by MIUR COFIN 2001-2003.     

Estimation and testing in generalized partial linear models—A comparative study

A particular semiparametric model of interest is the generalized partial linear model (GPLM) which extends the generalized linear model (GLM) by a nonparametric component.The paper reviews different estimation procedures based on kernel methods as well as test procedures on the correct specification of this model (vs. a parametric generalized linear model). Simulations and an application to a data set on East–West German migration illustrate similarities and dissimilarities of the estimators and test statistics.     

The Cox Proportional Hazards Model with a Partly Linear Relative Risk Function

The conventional Cox proportional hazards regression model contains a loglinear relative risk function, linking the covariate information to the hazard ratio with a finite number of parameters. A generalization, termed the partly linear Cox model, allows for both finite dimensional parameters and an infinite dimensional parameter in the relative risk function, providing a more robust specification of the relative risk function. In this work, a likelihood based inference procedure is developed for the finite dimensional parameters of the partly linear Cox model. To alleviate the problems associated with a likelihood approach in the presence of an infinite dimensional parameter, the relative risk is reparameterized such that the finite dimensional parameters of interest are orthogonal to the infinite dimensional parameter. Inference on the finite dimensional parameters is accomplished through maximization of the profile partial likelihood, profiling out the infinite dimensional nuisance parameter using a kernel function. The asymptotic distribution theory for the maximum profile partial likelihood estimate is established. It is determined that this estimate is asymptotically efficient; the orthogonal reparameterization enables employment of profile likelihood inference procedures without adjustment for estimation of the nuisance parameter. An example from a retrospective analysis in cancer demonstrates the methodology.     

A simultaneous variable selection methodology for linear mixed models

Selecting an appropriate structure for a linear mixed model serves as an appealing problem in a number of applications such as in the modelling of longitudinal or clustered data. In this paper, we propose a variable selection procedure for simultaneously selecting and estimating the fixed and random effects. More specifically, a profile log-likelihood function, along with an adaptive penalty, is utilized for sparse selection. The Newton-Raphson optimization algorithm is performed to complete the parameter estimation. By jointly selecting the fixed and random effects, the proposed approach increases selection accuracy compared with two-stage procedures, and the usage of the profile log-likelihood can improve computational efficiency in one-stage procedures. We prove that the proposed procedure enjoys the model selection consistency. A simulation study and a real data application are conducted for demonstrating the effectiveness of the proposed method.     

关羽兵器考

蜀汉大将关羽,以其忠义勇武成为当世名将,随着时间的发展更是成为后世崇拜的偶像,被尊为"武圣",和"文圣"孔子平起平坐。关羽冲锋陷阵使用的兵器也成为后世学术界积极探索的课题。