Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.Comment: Follow-up to arXiv:0706.3375. Journal submission 9 Jul 2007. Published 19 Dec 200

The activating receptors 2B4 and NTB-A, but not CRACC are subject to ligand-induced down-regulation on human natural killer cells

Activation of natural killer cells can be mediated by different receptors. Stimulation of the receptors 2B4, NTB-A and CRACC, members of the SLAM-related receptor family, induces cytotoxicity and cytokine production. The surface expression of 2B4 and other activating natural killer cell receptors is down-modulated after receptor engagement, which results in a weaker response to consecutive stimulation. We tested whether this regulatory mechanism applies to all SLAM-related receptors expressed by primary human natural killer cells. After co-culture with target cells expressing the respective ligands different effects on receptor surface expression were observed. While 2B4 ex-pression was strongly reduced, NTB-A showed less prominent down-modulation and the expression level of CRACC remained unchanged. The expression levels of the receptor-proximal signaling molecules SAP, EAT-2 and FynT did not change after receptor engagement. Co-culture with target cells expressing the ligands for NTB-A or CRACC had no impact on subsequent NTB-A or CRACC-mediated NK cell activation

International Synchronisation of the Pork Cycle

International Relations/Trade, Livestock Production/Industries,

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

Termination of Integer Term Rewriting

Recently, techniques and tools from term rewriting have been successfully applied to prove termination automatically for different programming languages. The advantage of rewrite techniques is that they are very powerful for algorithms on user-defined data structures. But in contrast to techniques for termination analysis of imperative programs, the drawback of rewrite techniques is that they do not support data structures like integer numbers which are pre-defined in almost all programming languages. To solve this problem, we extend term rewriting by built-in integers and adapt the dependency pair framework to prove termination of integer term rewriting automatically. Our experiments show that this indeed combines the power of rewrite techniques on user-defined data types with a powerful treatment of pre-defined integers