Non-linear model equation for three-dimensional Bunsen flames

The non linear description of laminar premixed flames has been very successful, because of the existence of model equations describing the dynamics of these flames. The Michelson Sivashinsky equation is the most well known of these equations, and has been used in different geometries, including three-dimensional quasi-planar and spherical flames. Another interesting model, usually known as the Frankel equation,which could in principle take into account large deviations of the flame front, has been used for the moment only for two-dimensional expanding and Bunsen flames. We report here for the first time numerical solutions of this equation for three-dimensional flames

Flame front propagation IV: Random Noise and Pole-Dynamics in Unstable Front Propagation II

The current paper is a corrected version of our previous paper arXiv:adap-org/9608001. Similarly to previous version we investigate the problem of flame propagation. This problem is studied as an example of unstable fronts that wrinkle on many scales. The analytic tool of pole expansion in the complex plane is employed to address the interaction of the unstable growth process with random initial conditions and perturbations. We argue that the effect of random noise is immense and that it can never be neglected in sufficiently large systems. We present simulations that lead to scaling laws for the velocity and acceleration of the front as a function of the system size and the level of noise, and analytic arguments that explain these results in terms of the noisy pole dynamics.This version corrects some very critical errors made in arXiv:adap-org/9608001 and makes more detailed description of excess number of poles in system, number of poles that appear in the system in unit of time, life time of pole. It allows us to understand more correctly dependence of the system parameters on noise than in arXiv:adap-org/9608001Comment: 23 pages, 4 figures,revised, version accepted for publication in journal "Combustion, Explosion and Shock Waves". arXiv admin note: substantial text overlap with arXiv:nlin/0302021, arXiv:adap-org/9608001, arXiv:nlin/030201

Targeting Signal Transduction Pathways in Metastatic Breast Cancer: A Comprehensive Review

This review summarizes some of the key signaling pathways involved in tumor progression and some of the novel therapies that are in development for the treatment of metastatic breast cancer patients

Mammalian MicroRNA Prediction through a Support Vector Machine Model of Sequence and Structure

BACKGROUND: MicroRNAs (miRNAs) are endogenous small noncoding RNA gene products, on average 22 nt long, found in a wide variety of organisms. They play important regulatory roles by targeting mRNAs for degradation or translational repression. There are 377 known mouse miRNAs and 475 known human miRNAs in the May 2007 release of the miRBase database, the majority of which are conserved between the two species. A number of recent reports imply that it is likely that many mammalian miRNAs remain to be discovered. The possibility that there are more of them expressed at lower levels or in more specialized expression contexts calls for the exploitation of genome sequence information to accelerate their discovery. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we describe a computational method-mirCoS-that uses three support vector machine models sequentially to discover new miRNA candidates in mammalian genomes based on sequence, secondary structure, and conservation. mirCoS can efficiently detect the majority of known miRNAs and predicts an extensive set of hairpin structures based on human-mouse comparisons. In total, 3476 mouse candidates and 3441 human candidates were found. These hairpins are more similar to known miRNAs than to negative controls in several aspects not considered by the prediction algorithm. A significant fraction of predictions is supported by existing expression evidence. CONCLUSIONS/SIGNIFICANCE: Using a novel approach, mirCoS performs comparably to or better than existing miRNA prediction methods, and contributes a significant number of new candidate miRNAs for experimental verification