Polypeptide folding on a conformational‐space network: Dependence of network topology on the structural discretization procedure

Mapping the conformational space of a polypeptide onto a network of conformational states involves a number of subjective choices, mostly in relation to the definition of conformation and its discrete nature in a network framework. Here, we evaluate the robustness of the topology of conformational‐space networks derived from Molecular Dynamics (MD) simulations with respect to the use of different discretization (clustering) methods, variation of their parameters, simulation length and analysis time‐step, and removing high‐frequency motions from the coordinate trajectories. In addition, we investigate the extent to which polypeptide dynamics can be reproduced on the resulting networks when assuming Markovian behavior. The analysis is based on eight 500 ns and eight 400 ns MD simulations in explicit water of two 10‐residue peptides. Three clustering algorithms were used, two of them based on the pair‐wise root‐mean‐square difference between structures and one on dihedral‐angle patterns. A short characteristic path length and a power‐law behavior of the probability distribution of the node degree are obtained irrespective of the clustering method or the value of any of the tested parameters. The average cliquishness is consistently one or two orders of magnitude larger than that of a random realization of a network of corresponding size and connectivity. The cliquishness as function of node degree and the kinetic properties of the networks are found to be most dependent on clustering method and/or parameters. Although Markovian simulations on the networks reproduce cluster populations accurately, their kinetic properties most often differ from those observed in the MD simulations. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

基于加权相似性的BIRCH聚类算法

BIRCH方法是一个集成的层次聚类方法.它克服了凝聚层次聚类方法所面临的两个难点:可伸缩性和不能撤销前一步工作的问题.基于BIRCH聚类的多阶段聚类算法思想,结合基于权重的欧式距离度量和基于划分的K-means算法,提出了一种基于加权相似性的BIRCH聚类方法,并将方法应用在时间序列的气象数据分析中.     

Effect of particle agglomeration and interphase on the glass transition temperature of polymer nanocomposites

In this article, we utilize finite element modeling to investigate the effect of nanoparticle agglomeration on the glass transition temperature of polymer nanocomposites. The case of an attractive interaction between polymer and nanofiller is considered for which an interphase domain of gradient properties is developed. This model utilizes representative volume elements that are created and analyzed with varying degrees of nanoparticle clustering and length scale of interphase domain. The viscoelastic properties of the composites are studied using a statistical approach to account for variations due to the random nature of the microstructure. Results show that a monotonic increase in nanofiller clustering not only results in the loss of interphase volume but also obstructs the formation of a percolating interphase network in the nanocomposite. The combined impacts lead to a remarkable decrease of T_g enhancement of clustering nanofillers in comparison with a well‐dispersed configuration. Our simulation results provide qualitative support for experimental observations that clustering observed at high nanofiller concentrations negatively impacts the effects of the nanofiller on overall properties. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2011     

基于SOM网络的机场天气聚类分析

机场范围内的天气状况对机场运行有着较为显著的影响.然而,天气在发生时间、影响范围等方面的随机性给机场容量评估和流量调配带来一定的困难.运用SOM网络对2014年上海浦东国际机场的天气进行聚类分析,并运用DB指标和Silhouette指标对聚类结果进行评价.结果表明:SOM网络在机场天气聚类上有很好的精度与鲁棒性,2014年浦东机场的天气可按影响程度分为典型的五类,即:无影响、影响小、影响极大、影响大、影响中等.为实现航空天气聚类分析及空中交通流量管理自动化提供了有益参考.     

灰色聚类白化函数探析

为进一步探析各类白化函数对综合评价结果的影响,先构建经典三角、修正三角1、修正三角2、经典聚类和指数型这五种不同类型的白化函数,再分别应用单一型数据、混合型数据、单指标单调型数据和跳跃型数据对这五种白化函数进行比较验证.结果显示两端等级的白化函数、零权重问题和等级区间宽度对综合评价结果影响较大.     

Clustering Multivariate Longitudinal Observations: The Contaminated Gaussian Hidden Markov Model

The Gaussian hidden Markov model (HMM) is widely considered for the analysis of heterogenous continuous multivariate longitudinal data. To robustify this approach with respect to possible elliptical heavy-tailed departures from normality, due to the presence of outliers, spurious points, or noise (collectively referred to as bad points herein), the contaminated Gaussian HMM is here introduced. The contaminated Gaussian distribution represents an elliptical generalization of the Gaussian distribution and allows for automatic detection of bad points in the same natural way as observations are typically assigned to the latent states in the HMM context. Once the model is fitted, each observation has a posterior probability of belonging to a particular state and, inside each state, of being a bad point or not. In addition to the parameters of the classical Gaussian HMM, for each state we have two more parameters, both with a specific and useful interpretation: one controls the proportion of bad points and one specifies their degree of atypicality. A sufficient condition for the identifiability of the model is given, an expectation-conditional maximization algorithm is outlined for parameter estimation and various operational issues are discussed. Using a large-scale simulation study, but also an illustrative artificial dataset, we demonstrate the effectiveness of the proposed model in comparison with HMMs of different elliptical distributions, and we also evaluate the performance of some well-known information criteria in selecting the true number of latent states. The model is finally used to fit data on criminal activities in Italian provinces. Supplementary materials for this article are available online     

一般分布区间型符号数据的SOM聚类方法

区间型符号数据是一种重要的符号数据类型,现有文献往往假设区间内的点数据服从均匀分布,导致其应用的局限性。本文基于一般分布的假设,给出了一般分布区间型符号数据的扩展的Hausdorff距离度量,基于此提出了一般分布的区间型符号数据的SOM聚类算法。随机模拟试验的结果表明,基于本文提出的基于扩展的Hausdorff距离度量的SOM聚类算法的有效性优于基于传统Hausdorff距离度量的SOM聚类算法和基于μσ距离度量的SOM聚类算法。最后将文中方法应用于气象数据的聚类分析,示例文中方法的应用步骤与可操作性,并进一步评价文中方法在解决实际问题中的有效性。     

基于Tucker3分解的三路数据聚类方法

从两路数据聚类分析到三路数据聚类分析实质上是由平面分析到立体分析的过程。三路数据聚类方法研究的核心之一是如何把传统的两路截面数据聚类技术向三路数据聚类扩展的问题。本文基于Tucker模型的思路,提出一种先对三路数据执行矩阵分解,而后进行聚类分析的三路数据聚类方法。这种方法不但能够通过核心矩阵反映三路数据三个模式信息联系的强度大小,而且还可以在一个分解框架下对三路数据的三个模式同时进行聚类分析。实证分析结果表明,本文提出的聚类方法不但灵活、易于理解,同时也有着良好的判别性和实用性。     

Handling uncertainties in toxicity modelling using a fuzzy filter

A fundamental concern in the Quantitative Structure-Activity Relationship approach to toxicity evaluation is the generalization of the model over a wide range of compounds. The data driven modelling of toxicity, due to the complex and ill-defined nature of eco-toxicological systems, is an uncertain process. The development of a toxicity predicting model without considering uncertainties may produce a model with a low generalization performance. This study presents a novel approach to toxicity modelling that handles the involved uncertainties using a fuzzy filter, and thus improves the generalization capability of the model. The method is illustrated by considering a data set dealing with the fathead minnow (Pimephales promelas) toxicity of 568 organic compounds.     

Damage resistance of carbon fibre reinforced epoxy laminates subjected to low velocity impact: Effects of laminate thickness and ply-stacking sequence

A major concern affecting the efficient use of composite laminates is the effect of low velocity impact damage on the structural integrity [1–3]. The aim of this study is to characterize and assess the effect of laminate thickness, ply-stacking sequence and scaling technique on the damage resistance of CFRP laminates subjected to low velocity impact. Drop-weight impact tests are carried out to determine impact response. Ultrasonic C-scanning and cross-sectional micrographs are examined to assess failure mechanisms of the different configurations.It is observed that damage resistance decreases as impact energy increases. In addition, thicker laminates show lower absorbed energy but, conversely, a more extensive delamination due to higher bending stiffness. Thinner laminates show higher failure depth. Furthermore, quasi-isotropic laminates show better performance in terms of damage resistance. Finally, the results obtained demonstrate that introducing ply clustering had a negative effect on the damage resistance and on the delamination area.