Bifurcation discovery tool

Motivation: Biochemical networks often yield interesting behavior such as switching, oscillation and chaotic dynamics. This article describes a tool that is capable of searching for bifurcation points in arbitrary ODE-based reaction networks by directing the user to regions in the parameter space, where such interesting dynamical behavior can be observed. Results: We have implemented a genetic algorithm that searches for Hopf bifurcations, turning points and bistable switches. The software is implemented as a Systems Biology Workbench (SBW) enabled module and accepts the standard SBML model format. The interface permits a user to choose the parameters to be searched, admissible parameter ranges, and the nature of the bifurcation to be sought. The tool will return the parameter values for the model for which the particular behavior is observed. Availability: The software, tutorial manual and test models are available for download at the following website: http:/www.sys-bio.org/ under the bifurcation link. The software is an open source and licensed under BSD

Design and User Satisfaction of Interactive Maps for Visually Impaired People

Multimodal interactive maps are a solution for presenting spatial information to visually impaired people. In this paper, we present an interactive multimodal map prototype that is based on a tactile paper map, a multi-touch screen and audio output. We first describe the different steps for designing an interactive map: drawing and printing the tactile paper map, choice of multi-touch technology, interaction technologies and the software architecture. Then we describe the method used to assess user satisfaction. We provide data showing that an interactive map - although based on a unique, elementary, double tap interaction - has been met with a high level of user satisfaction. Interestingly, satisfaction is independent of a user's age, previous visual experience or Braille experience. This prototype will be used as a platform to design advanced interactions for spatial learning

Evolution of Wurtzite Structured GaAs Shells Around InAs Nanowire Cores

GaAs was radially deposited on InAs nanowires by metal–organic chemical vapor deposition and resultant nanowire heterostructures were characterized by detailed electron microscopy investigations. The GaAs shells have been grown in wurtzite structure, epitaxially on the wurtzite structured InAs nanowire cores. The fundamental reason of structural evolution in terms of material nucleation and interfacial structure is given

Evolution of associative learning in chemical networks

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells

Polarity driven formation of InAs/GaAs hierarchical nanowire heterostructures

The structural and morphological characteristics of InAs/GaAs radial nanowire heterostructures were investigated using transmission electron microscopy. It has been found that the radial growth of InAs was preferentially initiated on the {112}A sidewalls of GaAs nanowires. This preferential deposition leads to extraordinarily asymmetric InAs/GaAs radial nanowire heterostructures. Such formation of radial nanowire heterostructures provides an opportunity to engineer hierarchical nanostructures, which further widens the potential applications of semiconductor nanostructures. ©2008 American Institute of Physic

Short-wavelength infrared photodetector on Si employing strain-induced growth of very tall InAs nanowire arrays

One-dimensional crystal growth enables the epitaxial integration of III-V compound semiconductors onto a silicon (Si) substrate despite significant lattice mismatch. Here, we report a short-wavelength infrared (SWIR, 1.4-3 mu m) photodetector that employs InAs nanowires (NWs) grown on Si. The wafer-scale epitaxial InAs NWs form on the Si substrate without a metal catalyst or pattern assistance; thus, the growth is free of metal-atom-induced contaminations, and is also cost-effective. InAs NW arrays with an average height of 50 mu m provide excellent anti-reflective and light trapping properties over a wide wavelength range. The photodetector exhibits a peak detectivity of 1.9 x 10(8) cm.Hz(1/2)/W for the SWIR band at 77 K and operates at temperatures as high as 220 K. The SWIR photodetector on the Si platform demonstrated in this study is promising for future low-cost optical sensors and Si photonicsopen0

Residual stress in laser cladded rail

To improve the fatigue life of components subject to loads with high surface strain gradients, it is possible to coat them with an alloy of higher durability. The present study focuses on the effect of cladding high value track components, made of a standard rail steel UIC 900A/grade 260, with a layer of a premium martensitic stainless steel to reduce wear and fatigue. The laser cladding process inevitably generates residual stresses in the clad and parent metal, which could be detrimental to the integrity of the component. Therefore, measurements to determine the residual stress state of cladded rail were performed using semi-destructive centre-hole and deep hole drilling and non-destructive neutron diffraction techniques. Subsequently, the effects of cycling loading and wear, representative of typical service loads, on the redistribution of the residual stress field were investigated. It was observed that laser cladding causes a triaxial compressive residual stress field in the clad and near the interface and a tensile stress field in the parent material. The stress field is shown to change when the first cycle of load is applied but reaches a steady state after only 10 cycles: After the 10th cycle there is no evidence that the clad continues accumulating strain which could indicate that there is low risk of ratcheting. Wear effect on residual stress redistribution was found to be local on the surface of the specimen only

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date