The cross-entropy method for network reliability estimation

Consider a network of unreliable links, modelling for example a communication network. Estimating the reliability of the network-expressed as the probability that certain nodes in the network are connected-is a computationally difficult task. In this paper we study how the Cross-Entropy method can be used to obtain more efficient network reliability estimation procedures. Three techniques of estimation are considered: Crude Monte Carlo and the more sophisticated Permutation Monte Carlo and Merge Process. We show that the Cross-Entropy method yields a speed-up over all three techniques.

Control of ion transport in mammalian airways by protease activated receptors type 2 (PAR-2)

Protease-activated receptors (PARs) are widely distributed in human airways. They couple to G-proteins and are activated after proteolytic cleavage of the N terminus of the receptor. Evidence is growing that PAR subtype 2 plays a pivotal role in inflammatory airway diseases, such as allergic asthma or bronchitis. However, nothing is known about the effects of PAR-2 on electrolyte transport in the native airways. PAR-2 is expressed in airway epithelial cells, where they are activated by mast cell tryptase, neutrophil proteinase 3, or trypsin. Recent studies produced conflicting results about the functional consequence of PAR-2 stimulation. Here we report that stimulation of PAR-2 receptors in mouse and human airways leads to a change in electrolyte transport and a shift from absorption to secretion. Although PAR-2 appears to be expressed on both sides of the epithelium, only basolateral stimulation results in inhibition of amiloride sensitive Na+ conductance and stimulation of both luminal Cl- channels and basolateral K+ channels. The present data indicate that these changes occur through activation of phospholipase C and increase in intracellular Ca2+, which activates basolateral SK4 K+ channels and luminal Ca2+-dependent Cl- channels. In addition, the present data suggest a PAR-2 mediated release of prostaglandin E2, which may contribute to the secretory response. In conclusion, these results provide further evidence for a role of PAR-2 in inflammatory airway disease: stimulation of these receptors may cause accumulation of airway surface liquid, which, however, may help to flush noxious stimuli away from the affected airways. ©2005 FASEB

A cognitive genetic algorithm for power distribution system planning

Power systems are large scale nonlinear systems with high complexity. Various optimization techniques and expert systems have been used in power system planning. However, there are always some factors that cannot be quantified, modeled, or even expressed by expert systems. Moreover, such planning problems are often large scale optimization problems. Although computational algorithms that are capable of handling large dimensional problems can be used, the computational costs are still very high. To solve these problems, in this paper, investigation is made to explore the efficiency and effectiveness of combining mathematic algorithms with human intelligence. It had been discovered that humans can join the decision making progresses by cognitive feedback. Based on cognitive feedback and genetic algorithm, a new algorithm called cognitive genetic algorithm is presented. This algorithm can clarify and extract human's cognition. As an important application of this cognitive genetic algorithm, a practical decision method for power distribution system planning is proposed. By using this decision method, the optimal results that satisfy human expertise can be obtained and the limitations of human experts can be minimized in the mean time.

Mechanisms of axon guidance in the developing nervous system

The human brain assembles an incredible network of over a billion neurons. Understanding how these connections form during development in order for the brain to function properly is a fundamental question in biology. Much of this wiring takes place during embryonic development. Neurons are generated in the ventricular zone, migrate out, and begin to differentiate. However, neurons are often born in locations some distance from the target cells with which they will ultimately form connections. To form connections, neurons project long axons tipped with a specialized sensing device called a growth cone. The growing axons interact directly with molecules within the environment through which they grow. In order to find their targets, axonal growth cones use guidance molecules that can either attract or repel them. Understanding what these guidance cues are, where they are expressed, and how the growth cone is able to transduce their signal in a directionally specific manner is essential to understanding how the functional brain is constructed. In this chapter, we review what is known about the mechanisms involved in axonal guidance. We discuss how the growth cone is able to sense and respond to its environment and how it is guided by pioneering cells and axons. As examples, we discuss current models for the development of the spinal cord, the cerebral cortex, and the visual and olfactory systems. (c) 2005, Elsevier Inc.

Specializations of the granular prefrontal cortex of primates: Implications for cognitive processing

The biological underpinnings of human intelligence remain enigmatic. There remains the greatest confusion and controversy regarding mechanisms that enable humans to conceptualize, plan, and prioritize, and why they are set apart from other animals in their cognitive abilities. Here we demonstrate that the basic neuronal building block of the cerebral cortex, the pyramidal cell, is characterized by marked differences in structure among primate species. Moreover, comparison of the complexity of neuron structure with the size of the cortical area/region in which the cells are located revealed that trends in the granular prefrontal cortex (gPFC) were dramatically different to those in visual cortex. More specifically, pyramidal cells in the gPFC of humans had a disproportionately high number of spines. As neuron structure determines both its biophysical properties and connectivity, differences in the complexity in dendritic structure observed here endow neurons with different computational abilities. Furthermore, cortical circuits composed of neurons with distinguishable morphologies will likely be characterized by different functional capabilities. We propose that 1. circuitry in V1, V2, and gPFC within any given species differs in its functional capabilities and 2. there are dramatic differences in the functional capabilities of gPFC circuitry in different species, which are central to the different cognitive styles of primates. In particular, the highly branched, spinous neurons in the human gPFC may be a key component of human intelligence. (C) 2005 Wiley-Liss, Inc.

Meaningful Interaction in Virtual Learning Environments

There is still a great deal of opportunity for research on contextual interactive immersion in virtual heritage environments. The general failure of virtual environment technology to create engaging and educational experiences may be attributable not just to deficiencies in technology or in visual fidelity, but also to a lack of contextual and performative-based interaction, such as that found in games. However, there is little written so far on exactly how game-style interaction can help improve virtual learning environments.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (