Dynamical characteristics of bacteria clustering by self-generated attractants

Motivated by the recent work on Escherichia coli bacteria clustering [Park, S., Wolanin, P.M., Yuzbashyan, E.A., Lin, H., Darnton, N.C., Stock, J.B., Silberzan, P., Austin, R., 2003. Proc. Natl. Acad. Sci. U.S.A. 100 (24), 13910], we have conducted a computer simulation of E. coli chemotaxis induced by a self-excreted attractant and investigated how bacteria clusters interact through a self-excreted attractant. By modeling the variation of tumbling frequency in the context of phosphorylation rate change, we have investigated the dependency of clustering behavior on the sensitivity of cells to the attractant. We have found that there exists an optimal sensitivity leading to bigger clusters and that the geometry surrounding the cells also plays an important role in localizing the cluster formation. This result suggests that bacterial cluster formation can be reduced by making bacteria more sensitive to attractants, which is opposite to an instinctive way (making them retarded to attractants). In addition, we have studied the effect of an initial cell distribution on clustering.     

Anomaly detection in a mobile communication network

Mobile communication networks produce massive amounts of data which may be useful in identifying the location of an emergency situation and the area it affects. We propose a one pass clustering algorithm for quickly identifying anomalous data points. We evaluate this algorithm’s ability to detect outliers in a data set and describe how such an algorithm may be used as a component of an emergency response management system.

Variable neighbourhood search for colour image quantization

^** Email: nenad.mladenovic{at}brunel.ac.uk Colour image quantization is a data compression technique thatreduces the total set of colours in a digital image to a representativesubset. This problem is first expressed as a large M-medianone. The advantages of this model over the usual minimum sum-of-squaresmodel are discussed first and then, the heuristic based on variableneighbourhood search metaheuristic is applied to solve it. Computationalexperience proves that this approach compares favourably withtwo other recent state-of-the-art heuristics, based on geneticand particle swarm searches.     

Unsupervised Image Segmentation Using Hierarchical Clustering

In the analysis of a medical image database aimed at formulating useful knowledge for image diagnosis requires an unsupervised image processing technique without preconceived knowledge. In this paper, we propose a method for unsupervised image segmentation, which is suitable for finding the features contained in an image. A small region around each pixel is considered as a pattern vector, and the set of pattern vectors acquired from the whole image is classified using the hierarchical clustering technique. In hierarchical clustering, the classification of pattern vectors is divided into two clusters at each node according to the statistical criterion based on the entropy in thermodynamics. Results of the test image generated by the Markov random field (MRF) model and real medical images photomicrographs of a colon tumor are shown.     

复杂系统的泛系聚类方法

本文利用半等价算子ε_i和相等化算子δ_i研究了复杂系统的聚类问题;阐述了分析运筹复杂系统的δ(1,3)解耦原理、对偶转化原理和大系统分解原理;详细讨论了复杂系统的连通性与解耦性并提供了有关定理.最后,利用泛系聚类方法讨论了复杂系统的层次分析.     

Finding the needle in a haystack: educing native folds from ambiguous ab initio protein structure predictions

Current ab initio structure‐prediction methods are sometimes able to generate families of folds, one of which is native, but are unable to single out the native one due to imperfections in the folding potentials and an inability to conduct thorough explorations of the conformational space. To address this issue, here we describe a method for the detection of statistically significant folds from a pool of predicted structures. Our approach consists of clustering and averaging the structures into representative fold families. Using a metric derived from the root‐mean‐square distance (RMSD) that is less sensitive to protein size, we determine whether the simulated structures are clustered in relation to a group of random structures. The clustering method searches for cluster centers and iteratively calculates the clusters and their respective centroids. The centroid interresidue distances are adjusted by minimizing a potential constructed from the corresponding average distances of the cluster structures. Application of this method to selected proteins shows that it can detect the best fold family that is closest to native, along with several other misfolded families. We also describe a method to obtain substructures. This is useful when the folding simulation fails to give a total topology prediction but produces common subelements among the structures. We have created a web server that clusters user submitted structures, which can be found at http://bioinformatics.danforthcenter.org/services/scar. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 339–353, 2001     

Continuous Time Random Walk with Correlated Waiting Times. The Crucial Role of Inter-Trade Times in Volatility Clustering

In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of these types of stochastic processes and strong empirical motivation, models with a long-term memory within the sequence of time intervals between observations are rare in the physics literature. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by assuming that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model, applied to high-frequency stock market data, can successfully describe the slope of decay of the nonlinear autocorrelation function of stock market returns. We achieve this result without imposing any dependence between consecutive price changes. This proves the crucial role of inter-event times in the volatility clustering phenomenon observed in all stock markets.