Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence

Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

Structure and dynamics of a gene network model incorporating small RNAs

As advances in molecular biology continue to reveal additional layers of complexity in gene regulation, computational models need to incorporate additional features to explore the implications of new theories and hypotheses. It has recently been suggested that eukaryotic organisms owe their phenotypic complexity and diversity to the exploitation of small RNAs as signalling molecules. Previous models of genetic systems are, for several reasons, inadequate to investigate this theory. In this study, we present an artificial genome model of genetic regulatory networks based upon previous work by Torsten Reil, and demonstrate how this model generates networks with biologically plausible structural and dynamic properties. We also extend the model to explore the implications of incorporating regulation by small RNA molecules in a gene network. We demonstrate how, using these signals, highly connected networks can display dynamics that are more stable than expected given their level of connectivity.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

The International Consortium on Mental Health Policy and Services: objectives, design and project implementation

The concept of the burden of disease, introduced and estimated for a broad range of diseases in the World Bank report of 1993 illustrated that mental and neurological disorders not only entail a higher burden than cancer, but are responsible, in developed and developing countries, for more than 15% of the total burden of all diseases. As a consequence, over the past decade, mental disorders have ranked increasingly highly on the international agenda for health. However, the fact that mental health and nervous system disorders are now high on the international health agenda is by no means a guarantee that the fate of patients suffering from these disorders in developing countries will improve. In most developing countries the treatment gap for mental and neurological disorders is still unacceptably high. To address this problem, an international network of collaborating institutions in low-income countries has been set up. The establishment and the achievements of this network-the International Consortium on Mental Health Policy and Services-are reported. Sixteen institutions in developing countries collaborate (supported by a small number of scientific resource centres in industrialized nations) in projects on applied mental health systems research. Over a two-year period, the network produced the key elements of a national mental health policy; provided tools and methods for assessing a country's current mental health status (context, needs and demands, programmes, services and care and outcomes); established a global network of expertise, i.e., institutions and experts, for use by countries wishing to reform their mental health policy, services and care; and generated guidelines and examples for upgrading mental health policy with due regard to the existing mental health delivery system and demographic, cultural and economic factors.

Rheological Properties of Organoclay Suspensions in Epoxy Network Precursors

This paper deals with the evolution of the state of dispersion of organically modified montmorillonites in epoxy or amine precursors. The epoxy prepolymer is a diglycidyl ether of bisphenol A (DGEBA) and the curing agent is an aliphatic diamine with a polyoxypropylene backbone (Jeffamine D2000). The clay dispersion is evaluated at the platelet scale (nanoscopic scale) from X-ray spectrometry [wide-angle X-ray diffraction (WAXD) and small-angle X-ray scattering (SAXS)] and at the aggregates scale (microscopic scale) from rheological analysis. The organoclays used form gels in the monomers above the percolation threshold if no shear is applied and present a mechanical gel/sol transition when shear stress increases. Gel strength and viscosity at high shear rates are linked to the nanometric state of dispersion and reveal the existence of two different organizations depending on organoclay/monomer interactions: (i) When the clay shows good interactions with the monomer, a significant swelling of the clay galleries by the monomer is obtained. These swollen particles lead to formation of weak gels which after shearing give high relative viscosity fluids. (ii) When the clay develops poor interactions with the monomer, the clay tends to reduce its exchange surface with the monomer and leads to a strongly connected gel. Shear breaks down this physical network leading to a very low relative viscosity fluid composed of nonswollen particles keeping a high aspect ratio. (C) 2003 Elsevier B.V All rights reserved.

Small regulatory RNAs in mammals

Mammalian cells harbor numerous small non-protein-coding RNAs, including small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs) and small double-stranded RNAs, which regulate gene expression at many levels including chromatin architecture, RNA editing, RNA stability, translation, and quite possibly transcription and splicing. These RNAs are processed by multistep pathways from the introns and exons of longer primary transcripts, including protein-coding transcripts. Most show distinctive temporal- and tissue-specific expression patterns in different tissues, including embryonal stem cells and the brain, and some are imprinted. Small RNAs control a wide range of developmental and physiological pathways in animals, including hematopoietic differentiation, adipocyte differentiation and insulin secretion in mammals, and have been shown to be perturbed in cancer and other diseases. The extent of transcription of non-coding sequences and the abundance of small RNAs suggests the existence of an extensive regulatory network on the basis of RNA signaling which may underpin the development and much of the phenotypic variation in mammals and other complex organisms and which may have different genetic signatures from sequences encoding proteins.

Brain maps, great and small: lessons from comparative studies of primate visual cortical organization

In this paper, we review evidence from comparative studies of primate cortical organization, highlighting recent findings and hypotheses that may help us to understand the rules governing evolutionary changes of the cortical map and the process of formation of areas during development. We argue that clear unequivocal views of cortical areas and their homologies are more likely to emerge for 'core' fields, including the primary sensory areas, which are specified early in development by precise molecular identification steps. In primates, the middle temporal area is probably one of these primordial cortical fields. Areas that form at progressively later stages of development correspond to progressively more recent evolutionary events, their development being less firmly anchored in molecular specification. The certainty with which areal boundaries can be delimited, and likely homologies can be assigned, becomes increasingly blurred in parallel with this evolutionary/developmental sequence. For example, while current concepts for the definition of cortical areas have been vindicated in allowing a clarification of the organization of the New World monkey 'third tier' visual cortex (the third and dorsomedial areas, V3 and DM), our analyses suggest that more flexible mapping criteria may be needed to unravel the organization of higher-order visual association and polysensory areas.

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 (

Classical simulation of quantum many-body systems with a tree tensor network

We show how to efficiently simulate a quantum many-body system with tree structure when its entanglement (Schmidt number) is small for any bipartite split along an edge of the tree. As an application, we show that any one-way quantum computation on a tree graph can be efficiently simulated with a classical computer.

The contagion of international terrorism and its effects on the firm in an interconnected world

International trade and investment economies are highly integrated and interdependent and can be exploited by organized, international terrorism. The network of inter dependencies in the international economy means that a terrorist attack has the potential to disrupt the functioning of the network, so the effects can reverberate around the world. Governments can control the distributed effects of terrorism by auditing industrial networks to reveal and protect critical hubs and by promoting flexibility in production and distribution of goods and services to improve resilience in the economy. To explain these network effects, the authors draw on the new science of complex networks which has been applied to the physical sciences and is now increasingly being used to explain organizational and economic phenomena.

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 (

Spontaneous combustion and simulation of mine fires and their effects on mine ventilation systems

The structure of a comprehensive research project into mine fires study applying the Ventgraph mine fire simulation software, preplanning of escape scenarios and general interaction with rescue responses is outlined. The project has Australian Coal Association Research Program (ACARP) funding and also relies on substantial mining company site support. This practical input from mine operators is essential and allows the approach to be introduced in the most creditable way. The effort is built around the introduction of fire simulation computer software to the Australian mining industry and the consequent modelling of fire scenarios in selected different mine layouts. Application of the simulation software package to the changing mine layouts requires experience to achieve realistic outcomes. Most Australian mines of size currently use a ventilation network simulation program. Under the project a small subroutine has been written to transfer the input data from the existing mine ventilation network simulation program to ‘Ventgraph’. This has been tested successfully. To understand fire simulation behaviour on the mine ventilation system, it is necessary to understood the possible effects of mine fires on various mine ventilation systems correctly first. Case studies demonstrating the possible effects of fires on some typical Australian coal mine ventilation circuits have been examined. The situation in which there is some gas make at the face and effects with fire have also been developed to emphasise how unstable and dangerous situations may arise. The primary objective of the part of the study described in this paper is to use mine fire simulation software to gain better understanding of how spontaneous combustion initiated fires can interact with the complex ventilation behaviour underground during a substantial fire. It focuses on the simulation of spontaneous combustion sourced heatings that develop into open fires. Further, it examines ventilation behaviour effects of spontaneous combustion initiated pillar fires and examines the difficulties these can be present if a ventilation reversal occurs. It also briefly examines simulation of use of the inertisation to assist in mine recovery. Mine fires are recognised across the world as a major hazard issue. New approaches allowing improvement in understanding their consequences have been developed as an aid in handling this complex area.

Examining the role of networking capability in small and medium firm internationalization

While research on SME internationalization has increased, there remains a lack of relevant theory on the SME internationalization process. The literature reports that small firms overcome their resource poverty-based constraints to internationalization by developing network relationships. Networking enables SMEs to acquire much needed internationalization process knowledge, and knowledge for the development of innovative products and services for this internationalization. However, networking activity has not yet been conceptualized and measured as a competitive capability in internationalization research. Drawing on the capability-based theory of competitive strategy, this paper conjectures that internationally entrepreneurial SMEs build and nurture distinctive networking capabilities, enabling them to acquire new knowledge. These learning capabilities enable them to pursue innovation thereby facilitating nternationalization. Data from Australian firms largely supports the conceptual framework. Implications for theory and practice are presented.