bluuebunny/crossref_metadata_2025 huggingface dataset:
A UNIVERSAL OPERON PREDICTOR FOR PROKARYOTIC GENOMES
Authors: LI GUOJUN , CHE DONGSHENG , XU YING
Published: February 2009

Identification of operons at the genome scale of prokaryotic organisms represents a key step in deciphering of their transcriptional regulation machinery, biological pathways, and networks. While numerous computational methods have been shown to be effective in predicting operons for well-studied organisms such as Escherichia coli K12 and Bacillus subtilis 168, these methods generally do not generalize well to genomes other than the ones used to train the methods, or closely related genomes because they rely on organism–specific information. Several methods have been explored to address this problem through utilizing only genomic structural information conserved across multiple organisms, but they all suffer from the issue of low prediction sensitivity. In this paper, we report a novel operon prediction method that is applicable to any prokaryotic genome with high prediction accuracy. The key idea of the method is to predict operons through identification of conserved gene clusters across multiple genomes and through deriving a key parameter relevant to the distribution of intergenic distances in genomes. We have implemented this method using a graph-theoretic approach, to calculate a set of maximum gene clusters in the target genome that are conserved across multiple reference genomes. Our computational results have shown that this method has higher prediction sensitivity as well as specificity than most of the published methods. We have carried out a preliminary study on operons unique to archaea and bacteria, respectively, and derived a number of interesting new insights about operons between these two kingdoms. The software and predicted operons of 365 prokaryotic genomes are available at .
CONSTANT TIME CLASH DETECTION IN PROTEIN FOLDING
Authors: BUGALHO MIGUEL M. F. , OLIVEIRA ARLINDO L.
Published: February 2009

Applications for the manipulation of molecular structures are usually computationally intensive. Problems like protein docking or ab-initio protein folding need to frequently determine if two atoms in the structure collide. Therefore, an efficient algorithm for this problem, usually referred as clash detection, can greatly improve the application efficiency. This work focus mainly on the ab-initio protein folding problem. A naive approach for the clash problem, the most commonly-used by molecular structure programs, consists in calculating the distance between every pair of atoms. We propose an efficient data structure that uses a three-dimensional array to store the atoms' position. We compare the proposed data structure with one of the best known general data structures for this type of problems (SAT tree) and with the naive approach. While the naive approach takes linear time to the number of atoms to verify if a new atom clashes with any previously-set atoms, the proposed data structure takes constant time to perform the same verification. The SAT tree takes logarithmic time for the same task. The results show that the proposed data structure surpasses the other techniques for any protein size. The proposed data structure takes near half of the time of the SAT data structure and close to a fifth of the time of the naive approach for the larger proteins. We believe that this data structure can improve the existing molecular structure applications by decreasing the computational cost needed for clash detection. The data structure presented in this work can be used for any protein structure clash verification, as long as the atoms that need to be checked are kept in the 3D array. This data structure is particulary useful when manipulating large sets of atoms, for example, in applications like loop prediction, structure refinement of large proteins, and protein docking.
EVALUATION OF INTER-LABORATORY AND CROSS-PLATFORM CONCORDANCE OF DNA MICROARRAYS THROUGH DISCRIMINATING GENES AND CLASSIFIER TRANSFERABILITY
Authors: MAO SHIHONG , WANG CHARLES , DONG GUOZHU
Published: February 2009

Microarray technology has great potential for improving our understanding of biological processes, medical conditions, and diseases. Often, microarray datasets are collected using different microarray platforms (provided by different companies) under different conditions in different laboratories. The cross-platform and cross-laboratory concordance of the microarray technology needs to be evaluated before it can be successfully and reliably applied in biological/clinical practice. New measures and techniques are proposed for comparing and evaluating the quality of microarray datasets generated from different platforms/laboratories. These measures and techniques are based on the following philosophy: the practical usefulness of the microarray technology may be confirmed if discriminating genes and classifiers, which are the focus of most, if not all, comparative investigations, discovered/trained from data collected in one lab/platform combination can be transferred to another lab/platform combination. The rationale is that the nondiscriminating genes might not be as strongly regulated as the discriminating genes, by the biological process of the tissue cells under study, and hence they may behave more randomly than the discriminating genes. Our experiment results, on microarray datasets generated from different platforms/laboratories using the reference mRNA samples in the Microarray Quality Control (MAQC) project, showed that DNA microarrays can produce highly repeatable data in a cross-platform cross-lab manner, when one focuses on the discriminating genes and classifiers. In our comparative study, we compare samples of one type against samples of another type; the methodology can be applied to situations where one compares one arbitrary class of data against another. Other findings include: (1) using three discriminating-gene/classifier-based methods to test the concordance between microarray datasets gave consistent results; (2) when noisy (nondiscriminating) genes were removed, the microarray datasets from different laboratories using common platform were found to be highly concordant, and the data generated using most of the commercial platforms studied here were also found to be concordant with each other; (3) several series of artificial datasets with known degree of difference were created, to establish a bridge between consistency rate and P-value, allowing us to estimate P-value if consistency rate between two datasets is known.
CLUSTERING ALGORITHMS FOR DETECTING FUNCTIONAL MODULES IN PROTEIN INTERACTION NETWORKS
Authors: GAO LIN , SUN PENG-GANG , SONG JIA
Published: February 2009

Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPI networks. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitate a fast, accurate approach to biological complex identification. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPI networks. In this paper, we review some representative algorithms, focusing on the algorithms underlying the approaches and how the algorithms relate to each other. In particular, a comparison is given based on the property of the algorithms. Since the PPI network is noisy and still incomplete, some methods which consider other additional properties for preprocessing and purifying of PPI data are presented. We also give a discussion about the functional annotation and validation of protein complexes. Finally, new progress and future research directions are discussed from the computational viewpoint.
RANK-BASED CLUSTERING ANALYSIS FOR THE TIME-COURSE MICROARRAY DATA
Authors: YI SUNG-GON , JOO YOON-JEONG , PARK TAESUNG
Published: February 2009

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course microarray experiments in which gene expression is monitored over time, we are interested in clustering genes that show similar temporal profiles and identifying genes that show a pre-specified candidate profile. Unfortunately, many traditional clustering methods used for analyzing microarray data do not effectively detect temporal profiles for the time-course microarray data. We propose a rank-based clustering analysis for the time-course microarray data.

Our clustering method consists of two steps: the first step discretizes the expression data into groups and then transform them into the rank data, the second step performs the rank-based clustering analysis. Our testing procedure uses the bootstrap samples to select the genes that show similar patterns for the candidate profiles. Simulation study is performed to evaluate the performance of the proposed rank-based method. The results are illustrated with the breast cancer data and the Arabidopsis cold stress data.
GENETIC NETWORK ANALYSIS BY QUASI-BAYESIAN METHOD
Authors: YUAN AO , CHEN GUANJIE , ROTIMI CHARLES
Published: February 2009

Genetic network analysis provides an important statistical strategy for the study of gene–gene interactions. Although existing methods work well in practice, several opportunities for improvement remain. For example, the regulation coefficients of some of the existing methods are not easy to solve, nor are the solutions they provide unique. Also, as genetic network analysis are typically applied to small datasets with large number of parameters, having prior knowledge about the parameters is valuable and should be incorporated into the analysis. The uniqueness of the parameter estimate and computational simplicity are also desirable in practice. To address these problems, we considered a quasi-Bayesian method for the analysis of gene regulatory networks by a multivariate linear model in which the data distribution is a quasi-likelihood, and the inference is Bayesian. This method incorporates prior information on the regulatory relationships; the set of regulation coefficients has a unique closed-form solution, and is very simple to compute. The model is evaluated by simulation and illustrated using a real dataset. This method is simple to use, permits information updating, is flexible to incorporate desired features, and has closed-form solution. Simulation studies show that the model fits the data quite well.
MINIMAL EXECUTION TIME OF SHOR'S ALGORITHM AT LOW TEMPERATURES
Authors: AVILA M. A.
Published: February 2009

The minimal time, T Shor , in which a one-way quantum computer can execute Shor's algorithm is derived. In the absence of an external magnetic field, this quantity diverges at very small temperatures. This result coincides with that of Anders et al. obtained simultaneously to ours but using thermodynamical arguments. Such divergence contradicts the common belief that it is possible to do quantum computation at low temperatures. It is shown that in the presence of a weak external magnetic field, T Shor becomes a quantized quantity which vanishes at zero temperature. Decoherence is not a problem because T Shor /τ dec < 10-9, where τdec is decoherence time.
QUANTUM MECHANICS EXPLAINED
Authors: MOHRHOFF ULRICH
Published: February 2009

The physical motivation for the mathematical formalism of quantum mechanics is made clear and compelling by starting from an obvious fact — essentially, the stability of matter — and inquiring into its preconditions: what does it take to make this obvious fact possible?
OPTIMAL EAVESDROPPING ON NOISY STATES IN QUANTUM KEY DISTRIBUTION
Authors: SHADMAN Z. , KAMPERMANN H. , MEYER T. , BRUß D.
Published: February 2009

We study eavesdropping in quantum key distribution with the six state protocol, when the signal states are mixed with white noise. This situation may arise either when Alice deliberately adds noise to the signal states before they leave her lab, or in a realistic scenario where Eve cannot replace the noisy quantum channel by a noiseless one. We find Eve's optimal mutual information with Alice, for individual attacks, as a function of the qubit error rate. Our result is that added quantum noise reduces Eve's mutual information more than Bob's.
MAXIMALLY ENTANGLED STATES AND BELL'S INEQUALITY IN RELATIVISTIC REGIME
Authors: MORADI SHAHPOOR
Published: February 2009

In this letter we show that in the relativistic regime, maximally entangled state of two spin-1/2 particles not only gives maximal violation of the Bell-CHSH inequality but also gives the largest violation attainable for any pairs of four spin observables that are noncommuting for both systems. Also, we extend our results to three spin-1/2 particles. We obtain the largest eigenvalue of Bell operator and show that this value is equal to the expectation value of Bell operator on GHZ state.
CONTROLLED DENSE CODING VIA GENERALIZED MEASUREMENT
Authors: LUO CUI-LAN , OUYANG XIAO-FANG
Published: February 2009

A scheme of realizing controlled dense coding via generalized measurement was presented. In this protocol, the supervisor can control the entanglement between the sender and the receiver and then the average amount of information transmitted from the sender to the receiver by only adjusting measurement angle θ. It is shown that when the quantum channel was a GHZ state, the entanglement and the average amount of information are determined by supervisor's measurement angle θ only; whereas when the quantum channel was a GHZ-class state, those are determined not only by supervisor's measurement angle θ but also the minimal coefficient of the GHZ-class state.
IMPROVING THE TELEPORTATION OF TRIPARTITE ENTANGLED COHERENT STATES IN CONTINUOUS VARIABLE
Authors: SUN YONG , SUN BEN-JIN , SHI MEI-LI , MAN ZHONG-XIAO , XIA YUN-JIE
Published: February 2009

We propose a feasible scheme for the quantum teleportation of tripartite entangled coherent state in terms of linear optical devices such as beam splitters, phase shifters and photo detectors. The scheme is based on the bipartite maximally entangled coherent state and the tripartite entangled coherent state with bipartite maximal entanglement as quantum channels. It shows that for an appreciable mean number of photons equal to 2, the total minimum of average fidelity for an arbitrary tripartite entangled state is 0.981684.
ENTANGLEMENT EVOLUTION OF A PAIR OF TWO-LEVEL SYSTEMS IN NON-MARKOVIAN ENVIRONMENT
Authors: HUANG X. L. , WANG L. C. , YI X. X.
Published: February 2009

The entanglement evolution of a pair of two-level systems is studied in this paper. The two systems without mutual interaction are independently coupled to different two-band non-Markovian environments. By comparing our results to others in the literature, we find that taking one of the Bell states as the initial state, certain non-Markovian effect protects the entanglement in short time scale, while on long time scale it leads to the entanglement sudden death (ESD), which never occurs for this initial state in the Markovian case. Finally, by analyzing the parameters in our model, a relation between disentanglement and decoherence is established and discussed.
QUANTUM DUADIC AND AFFINE-INVARIANT CODES
Authors: GUENDA KENZA
Published: February 2009

We construct new families of duadic codes and derive from them degenerate quantum codes. This provides a solution to a question raised by Aly, Klappenecker and Sarvepalli on the existence of quantum codes from duadic codes when the multiplicative order of q modulo n is even. We also characterize the affine-invariant maximal extended cyclic codes. Then by the CSS construction, we give a family of quantum code.
MULTISCALE MODELING OF STRUCTURAL FAILURE PROCESS DUE TO MATERIAL DEGRADATION AND MIXED-MODE CRACKING
Authors: SFANTOS S. K. , ALIABADI M. H.
Published: February 2009

In this paper, the multiscale boundary element formulation presented in Ref. 1 for modeling material degradation and fracture is extended to mixed-mode fracture problems. The case study is a spur gear tooth fracture. The inter-granular crack initiation and propagation at the microscale is considered to lead to the formation and propagation of macrocracks under mixed-mode failure conditions. Information from the microscale to the macroscale are transferred using averaging theorems. An integral nonlocal approach is employed to avoid the pathological localization of microdamage at the macroscale. Both micro- and macroscales are modeled using recently developed boundary cohesive element and stress decremental formulations. The obtained results are in good agreement with experimental findings and other reported numerical results.
AN INTEGRATED CRYSTAL PLASTICITY FE SYSTEM FOR MICROFORMING SIMULATION
Authors: CAO J. , ZHUANG W. , WANG S. , HO K. C. , ZHANG N. , LIN J. , DEAN T. A.
Published: February 2009

Based on Voronoi tessellation and the probability theory, a VGRAIN system is created for the generation of grains and grain boundaries for micromaterials. This system requires physical parameters obtained from microstructures of materials, such as the average, minimum and maximum grain sizes. Numerical procedures have been established to link the physical parameters of a material to the control variable in a gamma distribution equation and a method has been developed to solve the probability equation. These are the basis for the development of the VGRAIN system, which can be used to generate different grain structures and shapes that follow a certain pattern according to the probability theory. Statistical analyses have been carried out to investigate the distribution of generated virtual grains. The generated virtual microstructure is then implemented in the commercial FE code, ABAQUS, for mesh generation and micromechanics analysis using crystal plasticity (CP) equations for face-centered cubic (FCC) materials, which are implemented in the commercial FE solver, ABAQUS, through the user-defined subroutines, VUMAT/UMAT. FE analyses have been carried out to demonstrate the effectiveness of the integrated system for the investigation of localized straining and necking, encountered in microforming processes, such as extrusion of micropins, deformation of microfilms and hydroforming of microtubes.
A MULTISCALE FRAMEWORK FOR CHARACTERIZATION AND MODELING DUCTILE FRACTURE IN HETEROGENEOUS ALUMINUM ALLOYS
Authors: GHOSH SOMNATH , VALIVETI D. M. , HU CHAO , BAI JIE
Published: February 2009

This paper develops three components contributing to the overall framework of multiscale modeling of ductile fracture in aluminum alloys. The first module is morphology-based domain partitioning (MDP) as a pre-processor to the multiscale modeling. This module delineates regions of statistical homogeneity and inhomogeneity with a systematic three-step process that is based on geometric features of morphology. The second module is detailed micromechanical analysis of particle fragmentation and matrix cracking of heterogeneous microstructures. A locally enriched VCFEM or LE-VCFEM is developed to incorporate ductile failure through matrix cracking in the form of void growth and coalescence using nonlocal Gurson–Tvergaard–Needleman (GTN) model. The third module develops a homogenized anisotropic plasticity-damage model in the form of GTN model for macroscopic analysis. Parameters in this GTN model are calibrated from results of homogenization of microstructural variables obtained from microstructural RVE. Numerical examples elucidate the strength of components of the overall framework.
SOLVING PROBLEMS OF COMPOSITE FRACTURE BY MULTISCALE MODELING
Authors: BEAUMONT PETER W. R. , SEKINE HIDEKI
Published: February 2009

In critical conditions of variable stress-state, fluctuating temperature and hostile environment, where the objective is to design components and structures for longevity, durability, and reliability — structural integrity — then the balance between empirical engineering design based on continuum and mathematical modeling (sometimes called "distilled empiricism"), and physical modeling (sometimes called "mechanism modeling" or simply "micromechanics"), is shifted in favor of physical modeling. When combined with experimental evidence, physical modeling has the economic advantage of reducing the high cost of vast experimental programs of duration of many thousands of hours. Furthermore, existing empirical design methodologies at the higher (macroscopic) structural size scales can be supported and justified by fundamental understanding at lower (microscopic) size scales through the physical model. Armed with this information, together with knowledge of the mechanical behavior of the material over time, we follow the path of "physical model-informed empiricism", sometimes called "intelligent-informed design". Proof of identity of individual cracking processes based on their direct observation and an understanding of coupling between them is the first step in formulating a complete physical model of fracture.
MULTISCALE MODELING OF MULTIPHASE FLOW WITH COMPLEX INTERACTIONS
Authors: LUO K. H. , XIA J. , MONACO E.
Published: February 2009

This paper presents a variety of modeling and simulation methods for complex multiphase flow at microscopic, mesoscopic and macroscopic scales. Each method is discussed in terms of its scale-resolving capability and its relationship with other approaches. Examples of application are provided using a liquid–gas system, in which complex multiscale interactions exist among flow, turbulence, combustion and droplet dynamics. Large eddy simulation (LES) is employed to study the effects of a very large number of droplets on turbulent combustion in two configurations in a fixed laboratory frame. Direct numerical simulation (DNS) in a moving frame is then deployed to reveal detailed dynamic interactions between droplets and reaction zones. In both the LES and the DNS, evaporating droplets are modeled in a Lagrangian macroscopic approach, and have two-way couplings with the carrier gas phase. Finally, droplet collisions are studied using a multiple-relaxation-time lattice Boltzmann method (LBM). The LBM treats multiphase flow with real-fluid equations of state, which are stable and can cope with high density ratios. Examples of successful simulations of droplet coalescence and off-center separation are given. The paper ends with a summary of results and a discussion on hybrid multiscale approaches.
MULTISCALE GENERATION OF TURBULENCE
Authors: LAIZET S. , VASSILICOS J. C.
Published: February 2009

This paper presents a brief but general introduction to the physics and engineering of fractals, followed by a brief introduction to fluid turbulence generated by multiscale flow actuation. Numerical computations of such turbulent flows are now beginning to be possible because of the immersed boundary method (IBM) and terascale parallel high performance computing capabilities. The first-ever direct numerical simulation (DNS) results of turbulence generated by fractal grids are detailed and compared with recent wind tunnel measurements.
THE NEUROBIOLOGICAL INFRASTRUCTURE OF NATURAL COMPUTING: INTENTIONALITY
Authors: FREEMAN WALTER J.
Published: February 2009

Brains and computers are both dynamical systems that manipulate symbols, but they differ fundamentally in their architectures and operations. Human brains do mathematics; computers do not. Computers manipulate symbols that humans put into them without grounding them in what they represent. Human brains intentionally direct the body to make symbols, and they use the symbols to represent internal states. The symbols are outside the brain. Inside the brains, the construction is effected by spatiotemporal patterns of neural activity that are operators, not symbols. The operations include formation of sequences of neural activity patterns that we observe by their electrical signs. The process is by neurodynamics, not by logical rule-driven symbol manipulation. The aim of simulating human natural computing should be to simulate the operators. In its simplest form natural computing serves for communication of meaning. Neural operators implement non-symbolic communication of internal states by all mammals, including humans, through intentional actions. The neural operators that implement symbol formation must differ, but how is unknown, so we cannot yet simulate human natural computing. Here, I propose that symbol-making operators evolved from neural mechanisms of intentional action by modification of non-symbolic operators. Both kinds of operators can be investigated by their signs of neuroelectric activity. I propose that the postulated differences should be sought by classification of the spatial textures of the signs in EEG recorded from the scalp overlying those cortical structures unique to humans in the brain that I designate as koniocortex, while the subjects are engaged in elementary arithmetic operations.
MODELING CONSCIOUSNESS
Authors: TAYLOR J. G.
Published: February 2009

We present tentative answers to three questions: firstly, what is to be assumed about the structure of the brain in attacking the problem of modeling consciousness; secondly, what is it about consciousness that is attempting to be modeled; and finally, what is taken on board the modeling enterprise, if anything, from the vast works by philosophers about the nature of mind.
CORTICAL PHASE TRANSITIONS: PROPERTIES DEMONSTRATED IN CONTINUUM SIMULATIONS AT MESOSCOPIC AND MACROSCOPIC SCALES
Authors: WRIGHT J. J.
Published: February 2009

Continuum simulations of cortical dynamics permit consistent simulations to be performed at different spatial scales, using scale-adjusted parameter values. Properties of the simulations described here accord with Freeman's experimental and theoretical findings on gamma synchrony, phase transition, phase cones, and null spikes. State equations include effects of retrograde action potential propagation into dendritic trees, and kinetics of AMPA, GABA, and NMDA receptors. Realistic field potentials and pulse rates, gamma resonance and oscillation, and 1/f2 background activity are obtained. Zero-lag synchrony and traveling waves occur as complementary aspects of cortical transmission, and lead/lag relations between excitatory and inhibitory cell populations vary systematically around transition to autonomous gamma oscillation. Autonomous gamma is initiated by focal excitation of excitatory cells and suppressed by laterally spreading trans-cortical excitation. By implication, patches of cortex excited to gamma oscillation can mutually synchronize into larger fields, self-organized into sequences by mutual negative feedback relations, while the sequence of synchronous fields is regulated both by cortical/subcortical interactions and by traveling waves in the cortex — the latter observable as phase cones. At a critical level of cortical excitation, just before transition to autonomous gamma, patches of cortex exhibit selective sensitivity to action potential pulse trains modulated in the gamma band, while autonomous gamma releases pulse trains modulated in the same band, implying coupling of input and output modes. Transition between input and output modes may be heralded by phase slips and null spikes. Synaptic segregation by retrograde action potential propagation implies state-specific synaptic information storage.
PHENOMENOLOGICAL ARCHITECTURE OF A MIND AND OPERATIONAL ARCHITECTONICS OF THE BRAIN: THE UNIFIED METASTABLE CONTINUUM
Authors: FINGELKURTS ANDREW A. , FINGELKURTS ALEXANDER A. , NEVES CARLOS F. H.
Published: February 2009

In our contribution we will observe phenomenal architecture of a mind and operational architectonics of the brain and will show their intimate connectedness within a single integrated metastable continuum. The notion of operation of different complexity is the fundamental and central one in bridging the gap between brain and mind: it is precisely by means of this notion that it is possible to identify what at the same time belongs to the phenomenal conscious level and to the neurophysiological level of brain activity organization, and what mediates between them. Implications for linguistic semantics, self-organized distributed computing algorithms, artificial machine consciousness, and diagnosis of dynamic brain diseases will be discussed briefly.
FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES
Authors: OZTURK MUSTAFA C. , PRINCIPE JOSE C.
Published: February 2009

Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.
COHERENT STATES, FRACTALS AND BRAIN WAVES
Authors: VITIELLO GIUSEPPE
Published: February 2009

I show that a functional representation of self-similarity (as the one occurring in fractals) is provided by squeezed coherent states. In this way, the dissipative model of brain is shown to account for the self-similarity in brain background activity suggested by power-law distributions of power spectral densities of electrocorticograms. I also briefly discuss the action-perception cycle in the dissipative model with reference to intentionality in terms of trajectories in the memory state space.
CREATING NOVEL GOAL-DIRECTED ACTIONS AT CRITICALITY: A NEURO-ROBOTIC EXPERIMENT
Authors: ARIE HIROAKI , ENDO TETSURO , ARAKAKI TAKAFUMI , SUGANO SHIGEKI , TANI JUN
Published: February 2009

The present study examines the possible roles of cortical chaos in generating novel actions for achieving specified goals. The proposed neural network model consists of a sensory-forward model responsible for parietal lobe functions, a chaotic network model for premotor functions and prefrontal cortex model responsible for manipulating the initial state of the chaotic network. Experiments using humanoid robot were performed with the model and showed that the action plans for satisfying specific novel goals can be generated by diversely modulating and combining prior-learned behavioral patterns at critical dynamical states. Although this criticality resulted in fragile goal achievements in the physical environment of the robot, the reinforcement of the successful trials was able to provide a substantial gain with respect to the robustness. The discussion leads to the hypothesis that the consolidation of numerous sensory-motor experiences into the memory, meditating diverse imagery in the memory by cortical chaos, and repeated enaction and reinforcement of newly generated effective trials are indispensable for realizing an open-ended development of cognitive behaviors.
HIERARCHICAL SPATIO-TEMPORAL DYNAMICS OF A CHAOTIC NEURAL NETWORK FOR MULTISTABLE BINOCULAR RIVALRY
Authors: KAKIMOTO YUTA , AIHARA KAZUYUKI
Published: February 2009

Binocular rivalry is perceptual alternation that occurs when different visual images are presented to each eye. Despite the intensive studies, the mechanism of binocular rivalry still remains unclear. In multistable binocular rivalry, which is a special case of binocular rivalry, it is known that the perceptual alternation between paired patterns is more frequent than that between unpaired patterns. This result suggests that perceptual transition in binocular rivalry is not a simple random process, and the memories stored in the brain can play an important role in the perceptual transition. In this study, we propose a hierarchical chaotic neural network model for multistable binocular rivalry and show that our model reproduces some characteristic features observed in multistable binocular rivalry.
RATE AND GAMMA MODULATION IN ATTENTIONAL TASKS
Authors: BUEHLMANN ANDRES , DECO GUSTAVO
Published: February 2009

Experimental and theoretical work has related rate modulation and gamma synchronization modulation to visual attention. Here, we review briefly some of the influential experiments and our modeling work on the subject. We show that attentional modulation generally gets stronger along the visual pathway and that rate and gamma synchronization can vary independently of each other. Moreover, we show that in a model system, reaction times are faster in the presence of gamma synchronization. This suggests behavioral relevance for gamma synchronization.
LARGE-SCALE CORTICAL NETWORK COORDINATION: A PROPOSAL FOR THE NEURAL SUBSTRATE OF EXPECTANCY
Authors: BRESSLER STEVEN L. , RICHTER CRAIG G.
Published: February 2009

Optimal human behavior depends on the expectancy of future events based on perceptual analysis of an individual's present situation using knowledge gained from past experience. This article explores the proposition that the neural processes underlying perceptual analysis, knowledge retrieval, and expectancy are all integrated through the coordination of large-scale networks of the cerebral cortex. It is proposed that expectancy is created when local networks expressing knowledge of the likely future events associated with an individual's present situation are coordinated as part of large-scale networks expressing the totality of knowledge relations concerning the situation.
THE VISUAL SYSTEM: A "CHICKEN AND EGG" PROBLEM SOLVED
Authors: PARKER ANDREW R. , CAULFIELD H. JOHN
Published: February 2009

"What comes first: the chicken or the egg?" Eyes and vision were a great concern for Darwin. Recently, religious fundamentalists have started to attack evolution on the grounds that this is a chicken and egg problem. How could eyes improve without the brain module to use the new information that eye provides? But how could the brain evolve a neural circuit to process data not available to it until a new eye capability emerges? We argue that neural plasticity in the brain allows it to make use of essentially any useful information the eye can produce. And it does so easily within the animal's lifetime. Richard Gregory suggested something like this 40 years ago. Our work resolves a problem with his otherwise-insightful work.
SURFACE ROUGHNESS MEASUREMENT USING SHADOWS PRODUCED BY CIRCULATING LIGHT SOURCES
Authors: HIRATSUKA TOMOKAZU , HORIO KEIICHI , YAMAKAWA TAKESHI
Published: February 2009

We propose a new measurement method for a degree of roughness of a given object surface. This method is not to measure the degree of roughness of the object surface directly, but to estimate the roughness from surface images. Named as circulating light sources (CLS), its multiple light sources aligned in a circle illuminate sequentially, and produce images including shadow of the object surface. As the shadows on the images reflect a shape of the object surface, the shapes of the surface, concavo-convex shape, can be estimated by these shadows. In this paper, features of surface roughness are extracted by a Wavelet Multiresolution Analysis (MRA) from the shadow images produced by the CLS, and are classified by a Self-Organizing Map (SOM). A roughness of an unknown surface can be estimated by the SOM after learning.
NEURODYNAMIC CORRELATES OF HIGHER COGNITION AND CONSCIOUSNESS — EDITORIAL
Authors: KOZMA ROBERT , CAULFIELD H. JOHN
Published: February 2009

In spite of the explosive growth of experimental research in basic neurobiology and neurophysiology of brain components in the past decade, understanding the integrated functioning of the brain remains a significant scientific challenge. Essential for understanding human brain function is the detailed knowledge concerning the spatio-temporal dynamics of neuronal populations and their intricate interactions during cognitive functions. The aim of the present issue is to examine brain dynamics and cognitive functions from a multidisciplinary perspective and to introduce the most recent results in this research frontier. Topics relevant to the special issue include: (i) Modeling brain dynamics at the mesoscopic and macroscopic scales, including dynamical systems with distributed parameters; (ii) Applying tools of discrete mathematics, statistical and quantum physics, network science to describe the dynamics of brains; (iii) Experimental research on brain dynamics from various aspects, including fundamental neurobiology, evoked potentials, functional brain imaging, and cognitive functions; (iv) Clinical neuroscience issues for improved diagnosis of dynamic brain diseases and their potential therapies. This special issue is dedicated to Professor Walter J. Freeman on the occasion of his 80th birthday. Dr. Freeman produced breakthrough contributions to research on brain dynamics over the past five decades. The present issue covers all aspects of neurodynamics, starting from neural populations of high-level cognition and consciousness, as well as philosophical aspects and practical implementations on digital computers and hardware designs.
DISAMBIGUATING SOUND THROUGH CONTEXT
Authors: NIESSEN MARIA E. , VAN MAANEN LEENDERT , ANDRINGA TJEERD C.
Published: February 2009

A central problem in automatic sound recognition is the mapping between low-level audio features and the meaningful content of an auditory scene. We propose a dynamic network model to perform this mapping. In acoustics, much research is devoted to low-level perceptual abilities such as audio feature extraction and grouping, which are translated into successful signal processing techniques. However, little work is done on modeling knowledge and context in sound recognition, although this information is necessary to identify a sound event rather than to separate its components from a scene. We first investigate the role of context in human sound identification in a simple experiment. Then we show that the use of knowledge in a dynamic network model can improve automatic sound identification by reducing the search space of the low-level audio features. Furthermore, context information dissolves ambiguities that arise from multiple interpretations of one sound event.
ASKNET: CREATING AND EVALUATING LARGE SCALE INTEGRATED SEMANTIC NETWORKS
Authors: HARRINGTON BRIAN , CLARK STEPHEN
Published: February 2009

Extracting semantic information from multiple natural language sources and combining that information into a single unified resource is an important and fundamental goal for natural language processing. Large scale resources of this kind can be useful for a wide variety of tasks including question answering, word sense disambiguation and knowledge discovery. A single resource representing the information in multiple documents can provide significantly more semantic information than is available from the documents considered independently.

The ASKNet system utilises existing NLP tools and resources, together with spreading activation based techniques, to automatically extract semantic information from a large number of English texts, and combines that information into a large scale semantic network. The initial emphasis of the ASKNet system is on wide-coverage, robustness and speed of construction. In this paper we show how a network consisting of over 1.5 million nodes and 3.5 million edges, more than twice as large as any network currently available, can be created in less than 3 days. Evaluation of large-scale semantic networks is a difficult problem. In order to evaluate ASKNet we have developed a novel evaluation metric based on the notion of a network "core" and employed human evaluators to determine the precision of various components of that core. We have applied this evaluation to networks created from randomly chosen articles used by DUC (Document Understanding Conference). The results are highly promising: almost 80% precision in the semantic core of the networks.
Convergence to Compact Sets of Inexact Orbits of Nonexpansive Mappings in Banach and Metric Spaces
Authors: Pustylnik Evgeniy , Reich Simeon , Zaslavski Alexander J.
Published: February 2009

We study the influence of computational errors on the convergence to compact sets of orbits of nonexpansive mappings in Banach and metric spaces. We first establish a convergence theorem assuming that the computational errors are summable and then provide examples which show that the summability of errors is necessary for convergence.
Global Optimization for Sum of Linear Ratios Problem Using New Pruning Technique
Authors: Jiao Hongwei , Feng Qigao , Shen Peiping , Guo Yunrui
Published: February 2009

A global optimization algorithm is proposed for solving sum of general linear ratios problem (P) using new pruning technique. Firstly, an equivalent problem (P1) of the (P) is derived by exploiting the characteristics
of linear constraints. Then, by utilizing linearization method the relaxation linear programming (RLP) of the (P1) can be constructed and the proposed algorithm is convergent to the global minimum of the (P) through the successive refinement of the linear relaxation of feasible region and solutions of a series of (RLP). Then, a new pruning technique is proposed, this technique
offers a possibility to cut away a large part of the current investigated feasible region by the optimization algorithm, which can be utilized as an accelerating device for global optimization of problem (P). Finally, the numerical experiments are given to illustrate the feasibility of the proposed algorithm.
Endoscopic Findings in Loin Pain Hematuria Syndrome: Concentric Clot in Calyceal Fornices
Authors: Canales Benjamin K. , Windsperger Andrew , Lukasewycz Stephen , Monga Manoj
Published: February 2009

The loin pain hematuria syndrome (LPHS) creates a considerable burden, both for patients afflicted with the disease and for those involved in medical management and diagnosis. To date, the diagnosis of LPHS remains one of exclusion, with some speculation regarding the extent of actual pathology. We report ureteroscopic findings in 2 cases of LPHS. These findings provide objective confirmation of underlying pathology in a difficult-to-manage disease process.
The Human Vaginal Bacterial Biota and Bacterial Vaginosis
Authors: Srinivasan Sujatha , Fredricks David N.
Published: February 2009

The bacterial biota of the human vagina can have a profound impact on the health of women and their neonates. Changes in the vaginal microbiota have been associated with several adverse health outcomes including premature birth, pelvic inflammatory disease, and acquisition of HIV infection. Cultivation-independent molecular methods have provided new insights regarding bacterial diversity in this important niche, particularly in women with the common condition bacterial vaginosis (BV). PCR methods have shown that women with BV have complex communities of vaginal bacteria that include many fastidious species, particularly from the phyla Bacteroidetes and Actinobacteria. Healthy women are mostly colonized with lactobacilli such asLactobacillus crispatus,Lactobacillus jensenii, andLactobacillus iners, though a variety of other bacteria may be present. The microbiology of BV is heterogeneous. The presence ofGardnerella vaginalisandAtopobium vaginaecoating the vaginal epithelium in some subjects with BV suggests that biofilms may contribute to this condition.
Postulated Role of Vasoactive Neuropeptide‐Related Immunopathology of the Blood Brain Barrier and Virchow‐Robin Spaces in the Aetiology of Neurological‐Related Conditions
Authors: Staines D. R. , Brenu E. W. , Marshall-Gradisnik S.
Published: February 2009

Vasoactive neuropeptides (VNs) such as pituitary adenylate cyclase‐activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP) have critical roles as neurotransmitters, vasodilators including perfusion and hypoxia regulators, as well as immune and nociception modulators. They have key roles in blood vessels in the central nervous system (CNS) including maintaining functional integrity of the blood brain barrier (BBB) and blood spinal barrier (BSB). VNs are potent activators of adenylate cyclase and thus also have a key role in cyclic AMP production affecting regulatory T cell and other immune functions. Virchow‐Robin spaces (VRSs) are perivascular compartments surrounding small vessels within the CNS and contain VNs. Autoimmunity of VNs or VN receptors may affect BBB and VRS function and, therefore, may contribute to the aetiology of neurological‐related conditions including multiple sclerosis, Parkinson′s disease, and amyotrophic lateral sclerosis. VN autoimmunity will likely affect CNS and immunological homeostasis. Various pharmacological and immunological treatments including phosphodiesterase inhibitors and plasmapheresis may be indicated.
Reference Point Group Mobility and Random Waypoint Models in Performance Evaluation of MANET Routing Protocols
Authors: Jayakumar Geetha , Ganapathi Gopinath
Published: February 2009

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on‐demand routing protocols for mobile ad hoc networks: dynamic source routing (DSR), ad hoc on‐demand distance vector routing (AODV). A detailed simulation model with medium access control (MAC) and physical layer models is used to study the interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share similar on‐demand behavior, the differences in the protocol mechanisms can lead to significant performance differentials. In this paper, we examine both on‐demand routing protocols AODV and DSR based on packet delivery ratio, normalized routing load, normalized MAC load, average end‐to‐end delay by varying the node density, network loading, and mobility variations for reference point group mobility and random waypoint models. This framework aims to evaluate the effect of mobility models on the performance of mobile ad hoc networks (MANETs) routing protocols. Our results show that the protocol performance may vary drastically across mobility models and performance rankings of protocols may vary with the mobility models used. This effect can be explained by the interaction of the mobility characteristics with the connectivity graph properties.
A Differential Equation Model of HIV Infection of CD4+ T‐Cells with Delay
Authors: Yang Junyuan , Wang Xiaoyan , Zhang Fengqin
Published: February 2009

An epidemic model of HIV infection of CD4+ T‐cells with cure rate and delay is studied. We include a baseline ODE version of the model, and a differential‐delay model with a discrete time delay. The ODE model shows that the dynamics is completely determined
by the basic reproduction number R0 < 1. If
R0 < 1, the disease‐free equilibrium is asymptotically stable and the disease dies out. If R0 > 1, a unique endemic equilibrium exists and is globally stable in the interior of the feasible region. In the DDE model, the delay stands for the incubation time. We prove the effect of that delay on the stability of the equilibria. We show that the introduction of a time delay in the virus‐to‐healthy cells transmission term can destabilize the system, and periodic solutions can arise through Hopf bifurcation.