Effects of carbohydrate headgroups on the stability of induced cubic phases in binary mixtures of glycolipids

This paper is part in a series of papers, investigating the influence of carbohydrate headgroups on the mesogenic properties of glycolipids. While previous papers focussed on the synthesis and mesogenic properties of the pure compounds, we will discuss here our results obtained with binary mixtures. Mixtures of compounds, one forming a lamellar phase and the other one a columnar phase in their pure state, displayed always an induced cubic phase. The stability of this induced cubic phase depends significantly on the structure of the carbohydrate headgroup of both components. Thus it was possible to derive structure–property relationships by comparison of the phase diagrams that have been obtained, if the carbohydrate headgroup of one component was changed systematically. We observed an interesting effect of galactose headgroups which might be of great biological importance. Furthermore, the observed kind of kinetic of the S_A→cub transition might also be of great biological relevance.     

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.     

Time‐Dependent Predictive Accuracy in the Presence of Competing Risks

Summary Competing risks arise naturally in time‐to‐event studies. In this article, we propose time‐dependent accuracy measures for a marker when we have censored survival times and competing risks. Time‐dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event‐free subjects at any select time. Time‐dependent (dynamic) specificity (1–false positive (FP)) can be based on the marker distribution among event‐free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause‐specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause‐specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time‐dependent predictive accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337–344) and Heagerty and Zheng (2005, Biometrics 61, 92–105).     

Under-smoothed kernel confidence intervals for the hazard ratio based on censored data

In cancer clinical trials, it is often of interest in estimating the ratios of hazard rates at some specific time points during the study from two independent populations. In this paper, we consider nonparametric confidence interval procedures for the hazard ratio based on kernel estimates for the hazard rates with under-smoothing bandwidths. Two methods are used to derive the confidence intervals: one based on the asymptotic normality of the ratio of the kernel estimates for the hazard rates in two populations and another through Fieller's Theorem. The performances of the proposed confidence intervals are evaluated through Monte-Carlo simulations and applied to the analysis of data from a clinical trial on early breast cancer.     

Wing morphology and flight behavior of pelecaniform seabirds

The selective pressures associated with flight are significant factors in shaping the morphology of volant forms. Tropical seabirds are of particular interest because of their long foraging bouts, which can last hundreds of kilometers in search of unpredictable (spatially and temporally) resources. Here, we contrast wing loading (WL), aspect ratio (AR), and planform shape among five pelecaniform seabirds and correlate morphological diversity with known differences in flight strategies. Overall, WL and AR scaled with body mass. The Great Frigratebird had lower WL than that predicted, whereas the Red-tailed Tropicbird had higher WL than that predicted. The tropicbird also exhibited a lower AR than that predicted. Visualization of planform shape was accomplished by using Thin-plate spline relative warp analysis (TPS/RWA), and three major regions of variations were discovered: wing base, mid-wing, and distal wing/wing tip. As expected, the three boobies were more similar than either the tropicbird or the frigatebird. The tropicbird had a broader distal wing and more rounded wing tip, associated with its greater use of flapping flight. The frigatebird showed the greatest deviation in the distal wing and wing tip associated with the high maneuverability required for aerial pursuit and kleptoparasitism. By using TPS/RWA, important differences were detected in planform shape that would have otherwise gone unnoticed when using only WL and AR. These differences correlated strongly with parameters such as maneuverability, flapping, and soaring flight.     

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．     

基于几何形态测量学的五种稻蝗前后翅的形态变化研究

【目的】定量分析5种常见稻蝗属昆虫前后翅的形态变化规律。【方法】运用几何形态测量学方法对5种稻蝗雄性前、后翅进行量化分析,并结合主成分分析法(Principal component analysis,PCA)和薄片样条(Thin-plate spline,TPS)分析法探讨稻蝗前后翅的大小和形态变异。【结果】5种稻蝗前、后翅的大小和形态差异都比较显著,前、后翅的相似关系和5种稻蝗的系统发育关系一致。前翅差异的部位主要在缘前脉域、前缘脉域和臀脉域;后翅的差异主要在亚前缘脉域、前缘脉域及轭脉域。【结论】稻蝗前、后翅形态变化的几何形态测量学分析结果与稻蝗系统发育关系一致,可用于稻蝗属种的分类。稻蝗前、后翅发生变化的部位是其飞行时的受力部位,这些部位可以作为稻蝗物种分类特征。     

A geometric morphometric analysis of the shape of the first upper molar in mice of the genus Mus (Muridae, Rodentia)

Phenotypic variation in the shape of the first upper molar among 595 mice, representing nine extant and three extinct taxa of the genus Mus , was studied with thin-plate spline analysis. The reliability of classification of individual specimens into known groups based on their molars varied from 75 to 100%, depending on group and method used. Including 13 sliding semilandmarks to the analysis improved the detection of different kinds of size and shape variation as well as visualization of shape differences between studied groups. Correlation between phylogenetic and morphometric distances suggested about 80% contribution of phylogenetic inertia to the molar shape variation; moreover, the importance of localized versus global shape changes was similar in the detection of phylogenetic signals. Finally, shape changes along individual evolutionary lineages were revealed, suggesting a few cases of reversals, convergence and/or retention of ancestral shape. The evolution of mouse molars has thus been driven by random effects of drift together with stabilizing selection and convergence.     

Classifying antibodies using flow cytometry data: class prediction and class discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.