A genetic and spatial Bayesian analysis of mastitis resistance

A nationwide health card recording system for dairy cattle was introduced in Norway in 1975 (the Norwegian Cattle Health Services). The data base holds information on mastitis occurrences on an individual cow basis. A reduction in mastitis frequency across the population is desired, and for this purpose risk factors are investigated. In this paper a Bayesian proportional hazards model is used for modelling the time to first veterinary treatment of clinical mastitis, including both genetic and environmental covariates. Sire effects were modelled as shared random components, and veterinary district was included as an environmental effect with prior spatial smoothing. A non-informative smoothing prior was assumed for the baseline hazard, and Markov chain Monte Carlo methods (MCMC) were used for inference. We propose a new measure of quality for sires, in terms of their posterior probability of being among the, say 10% best sires. The probability is an easily interpretable measure that can be directly used to rank sires. Estimating these complex probabilities is straightforward in an MCMC setting. The results indicate considerable differences between sires with regards to their daughters disease resistance. A regional effect was also discovered with the lowest risk of disease in the south-eastern parts of Norway.     

交联琼脂糖珠固定化蛋白酶在纯化牛肺Kunitz抑制剂中的应用

本文介绍了珠状交联琼脂糖及以此作为载体,经氯代环氧丙烷活化后与蛋白酶(胰蛋白酶或糜蛋白酶)结合,制成固定化蛋白酶亲和吸附剂,进而用以亲和层析牛肺提取液中的Kunitz抑制剂的方法。纯化出的抑制剂在SDS-聚丙烯酰胺凝胶电泳上呈现单一条带,与参照物Trasytol(商品Kunitz抑制剂)具有相对应的电泳迁移率,其分子量也相符。纯化产品每毫克蛋白的抑制活力相当于16 000胰蛋白酶BAEE单位。纯化效果为90倍,收率约85％。     

使用线性微分方程构建初步的乳腺癌转移相关基因网络模型

随着基因芯片的技术的推广,越来越多的表达数据需要被处理和分析．利用这些表达数据提取基因调控矩阵从而构建基因网络是一个重要的问题．通过线性微分方程模型可以初步构建基因网络,了解网络结构,提取最显著的信息．然而由于分子生物学的条件限制或者数据来源的限制,导致实验数据不充分,使方程组无解．本文使用三次样条方法,对26例临床、病理资料完备的具有淋巴结转移的乳腺癌基因表达数据进行插值处理,使表达数据满秩,从而使用最小二乘法解出加权矩阵,构建初步的表达基因调控网络．通过对构建的基因网络的初步分析表明:乳腺癌转移的形成是由多基因异常引起多条传导通路异常,致使细胞恶性转化的结果,这与生物学上公认的看法是相一致的．因此,利用此线性模型方法对基因表达谱进行分析兵有一定可行性,在认识乳腺癌转移机制,乳腺癌诊断和治疗方面具有一定的理论和应用价值．     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

Quantitative assessment of the accuracy for three interpolation techniques in kinematic analysis of human movement

Marker obstruction during human movement analyses requires interpolation to reconstruct missing kinematic data. This investigation quantifies errors associated with three interpolation techniques and varying interpolated durations. Right ulnar styloid kinematics from 13 participants performing manual wheelchair ramp ascent were reconstructed using linear, cubic spline and local coordinate system (LCS) interpolation from 11–90% of one propulsive cycle. Elbow angles (flexion/extension and pronation/supination) were calculated using real and reconstructed kinematics. Reconstructed kinematics produced maximum elbow flexion/extension errors of 37.1 (linear), 23.4 (spline) and 9.3 (LCS) degrees. Reconstruction errors are unavoidable [minimum errors of 6.7 mm (LCS); 0.29 mm (spline); 0.42 mm (linear)], emphasising careful motion capture system setup must be performed to minimise data interpolation. For the observed movement, LCS-based interpolation (average error of 14.3 mm; correlation of 0.976 for elbow flexion/extension) was most suitable for reconstructing durations longer than 200 ms. Spline interpolation was superior for shorter durations.     

Spherical Boundary Conditions: A Finite and System Size Independent Geometry for Simulations of Electrolytic Liquids

The system size dependence of the thermodynamic properties of electrolytic systems in "Spherical Boundary Conditions" (SBC) have been examined in this work. Coulombic systems were simulated with different system sizes (N ) for a wide range of concentrations, different ionic charges and solvent permittivities. The effects of system size upon the mean internal energy ?U? and mean ionic activity coefficient ?γ _±? values were determined. Our results indicated that there was no dependence of the thermodynamic properties upon system size in the non-Euclidean geometry. Different methods of extrapolating the thermodynamic properties to infinite numbers were studied in detail. In contrast to the Euclidean geometry, the method of extrapolating ?U? or ?γ _±? versus N ^-1, N ^-2/3 and N ^-1/3 was not statistically justifiable nor advantageous. Therefore, SBC is well suited to simulating systems involving long-range electrostatic interactions because the results do not dependent upon system size.     

Geometric morphometrics as a tool for interpreting evolutionary transitions in the black fly wing (Diptera: Simuliidae)

A geometric morphometric analysis was conducted on wing‐vein landmarks on exemplar species of the family Simuliidae of the following genera: Parasimulium, Gymnopais, Twinnia, Helodon, Prosimulium, Greniera, Stegopterna, Tlalocomyia, Cnephia, Ectemnia, Metacnephia, Austrosimulium, and Simulium. Generalized least squares superimposition was performed on landmarks, followed by a principal component analysis on resulting Procrustes distances. Patterns of shape change along the principal component axes were visualized using the thin‐plate spline. The analysis revealed wing shape diversity through (1) the insertion points of the subcosta and R₁, resulting in the terminus of the costa exhibiting a trend towards a more apical position on the wing, and (2) the insertion point of the humeral cross vein, resulting in the anterior branch of the media exhibiting a trend toward a more basal position on the wing. Canonical variates analysis of Procrustes distances successfully assigned all exemplar species into their a priori taxonomic groupings. The diversity in wing shape reveals a trend towards decreased length of basal radial cell and increased costalization of anterior wing veins in the evolutionary transition from plesiomorphic prosimuliines to more derived simuliines. The functional significance of these evolutionary transitions is discussed. © 2013 The Linnean Society of London     

Estimation in independent observer line transect surveys for clustered populations

This paper introduces a framework for animal abundance estimation in independent observer line transect surveys of clustered populations. The framework generalizes an approach given in Chen (1999, Environmental and Ecological Statistics 6, in press) to accommodate heterogeneity in detection caused by cluster size and other covariates. Both parametric and nonparametric estimators for the local effective search widths, given the covariates, can be derived from the framework. A nonparametric estimator based on conditional kernel density estimation is proposed and studied owing to its flexibility in modeling the detection functions. A real data set on harbor porpoise in the North Sea is analyzed.