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Sample records for bmp-chordin signaling network

  1. Patterning of the dorsal-ventral axis in echinoderms: insights into the evolution of the BMP-chordin signaling network.

    Directory of Open Access Journals (Sweden)

    François Lapraz

    2009-11-01

    Full Text Available Formation of the dorsal-ventral axis of the sea urchin embryo relies on cell interactions initiated by the TGFbeta Nodal. Intriguingly, although nodal expression is restricted to the ventral side of the embryo, Nodal function is required for specification of both the ventral and the dorsal territories and is able to restore both ventral and dorsal regions in nodal morpholino injected embryos. The molecular basis for the long-range organizing activity of Nodal is not understood. In this paper, we provide evidence that the long-range organizing activity of Nodal is assured by a relay molecule synthesized in the ventral ectoderm, then translocated to the opposite side of the embryo. We identified this relay molecule as BMP2/4 based on the following arguments. First, blocking BMP2/4 function eliminated the long-range organizing activity of an activated Nodal receptor in an axis rescue assay. Second, we demonstrate that BMP2/4 and the corresponding type I receptor Alk3/6 functions are both essential for specification of the dorsal region of the embryo. Third, using anti-phospho-Smad1/5/8 immunostaining, we show that, despite its ventral transcription, the BMP2/4 ligand triggers receptor mediated signaling exclusively on the dorsal side of the embryo, one of the most extreme cases of BMP translocation described so far. We further report that the pattern of pSmad1/5/8 is graded along the dorsal-ventral axis and that two BMP2/4 target genes are expressed in nested patterns centered on the region with highest levels of pSmad1/5/8, strongly suggesting that BMP2/4 is acting as a morphogen. We also describe the very unusual ventral co-expression of chordin and bmp2/4 downstream of Nodal and demonstrate that Chordin is largely responsible for the spatial restriction of BMP2/4 signaling to the dorsal side. Thus, unlike in most organisms, in the sea urchin, a single ventral signaling centre is responsible for induction of ventral and dorsal cell fates. Finally

  2. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  3. Retrograde signaling: Organelles go networking.

    Science.gov (United States)

    Kleine, Tatjana; Leister, Dario

    2016-08-01

    The term retrograde signaling refers to the fact that chloroplasts and mitochondria utilize specific signaling molecules to convey information on their developmental and physiological states to the nucleus and modulate the expression of nuclear genes accordingly. Signals emanating from plastids have been associated with two main networks: 'Biogenic control' is active during early stages of chloroplast development, while 'operational' control functions in response to environmental fluctuations. Early work focused on the former and its major players, the GUN proteins. However, our view of retrograde signaling has since been extended and revised. Elements of several 'operational' signaling circuits have come to light, including metabolites, signaling cascades in the cytosol and transcription factors. Here, we review recent advances in the identification and characterization of retrograde signaling components. We place particular emphasis on the strategies employed to define signaling components, spanning the entire spectrum of genetic screens, metabolite profiling and bioinformatics. This article is part of a Special Issue entitled 'EBEC 2016: 19th European Bioenergetics Conference, Riva del Garda, Italy, July 2-6, 2016', edited by Prof. Paolo Bernardi. PMID:26997501

  4. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  5. Bioinformatics analyses for signal transduction networks

    Institute of Scientific and Technical Information of China (English)

    LIU Wei; LI Dong; ZHU YunPing; HE FuChu

    2008-01-01

    Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioin-formatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.

  6. Protein evolution on a human signaling network

    OpenAIRE

    Purisima Enrico O; Cui Qinghua; Wang Edwin

    2009-01-01

    Abstract Background The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling networ...

  7. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this...... metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  8. Organisation of signal flow in directed networks

    CERN Document Server

    Bányai, M; Négyessy, L; Bazsó, F

    2010-01-01

    Structure of signal flow in the network is mapped using the notion of convergence degree (CD), introduced in N\\'egyessy et al 2008 Proc. Roy. Soc. B, vol. 275 p. 2403. We refine the definition of convergence degree and complement it with the notion of the overlapping set of an edge. Definitions of the edge measures are based on the notion of shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality. Properties of CD are explored on two model graphs, convergence degree values are calculated for regular oriented trees and its probability density function for networks grown with preferential attachment mechanism. Based on the node-reduced convergence degree representation of the network we distinguish nodes according to their local and global signal transmitting and processing properties. In case of real-world networks, local and global signal transmitting and processing properties differ, exhi...

  9. Signal Propagation in Cortical Networks: A Digital Signal Processing Approach

    OpenAIRE

    Rodrigues, Francisco A.

    2009-01-01

    This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of l...

  10. Organization of signal flow in directed networks

    International Nuclear Information System (INIS)

    Confining an answer to the question of whether and how the coherent operation of network elements is determined by the network structure is the topic of our work. We map the structure of signal flow in directed networks by analysing the degree of edge convergence and the overlap between the in- and output sets of an edge. Definitions of convergence degree and overlap are based on the shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality and demonstrate the crucial role of chordless circles. In real-world networks the flow representation distinguishes nodes according to their signal transmitting, processing and control properties. The analysis of real-world networks in terms of flow representation was in accordance with the known functional properties of the network nodes. It is shown that nodes with different signal processing, transmitting and control properties are randomly connected at the global scale, while local connectivity patterns depart from randomness. The grouping of network nodes according to their signal flow properties was unrelated to the network's community structure. We present evidence that the signal flow properties of small-world-like, real-world networks cannot be reconstructed by algorithms used to generate small-world networks. Convergence degree values were calculated for regular oriented trees, and the probability density function for networks grown with the preferential attachment mechanism. For Erdos–Rényi graphs we calculated the probability density function of both convergence degrees and overlaps

  11. Signal processing devices and networks

    Science.gov (United States)

    Graveline, S. W.

    1985-02-01

    According to an axiom employed with respect to electronic warfare (EW) behavior, system effectiveness increases directly with the amount of information recovered from an intercepted signal. The evolution in EW signal processing capability has proceeded accordingly. After an initiation of EW systems as broadband receivers, the most significant advance was related to the development of digital instantaneous frequency measurement (DIFM) devices. The use of such devices provides significant improvements regarding signal identification and RF measurement to within a few MHz. An even more accurate processing device, the digital RF memory (DRFM), allows frequency characterization to within a few Hz. This invention was made in response to the need to process coherent pulse signals. Attention is given to the generic EW system, the modern EW system, and the generic receiver function for a modern EW system showing typical output signals.

  12. Signalling in voice over IP Networks

    OpenAIRE

    Moreno, José Ignacio; Soto, Ignacio; Larrabeiti, David

    2001-01-01

    Voice signalling protocols have evolved, keeping with the prevalent move from circuit to packet switched networks. Standardization bodies have provided solutions for carrying voice traffic over packet networks while the main manufacturers are already providing products in workgroup, enterprise, or operator portfolio. This trend will accrue in next years due to the evolution of UMTS mobile networks to an “all-IP” environment. In this paper we present the various architectures that are proposed...

  13. Regulation patterns in signaling networks of cancer

    Directory of Open Access Journals (Sweden)

    Kannabiran Nandakumar

    2010-11-01

    Full Text Available Abstract Background Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. Results We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance. In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. Conclusion Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed.

  14. Neural networks for intelligent signal processing

    CERN Document Server

    Zaknich, Anthony

    2003-01-01

    This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN. Contents: A Brief Historica

  15. Quantitative phosphoproteomics to characterize signaling networks

    DEFF Research Database (Denmark)

    Rigbolt, Kristoffer T G; Blagoev, Blagoy

    2012-01-01

    Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for...... analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify and...... quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments in...

  16. Qualitative networks: a symbolic approach to analyze biological signaling networks

    Directory of Open Access Journals (Sweden)

    Henzinger Thomas A

    2007-01-01

    Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

  17. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  18. Global Optimization for Transport Network Expansion and Signal Setting

    Directory of Open Access Journals (Sweden)

    Haoxiang Liu

    2015-01-01

    Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.

  19. Using Artificial Neural Networks for ECG Signals Denoising

    OpenAIRE

    Zoltán Germán-Salló; Katalin György

    2010-01-01

    The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG) signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1)th sample from n previous samples To train and adjust the network weights, the backpropagation (BP) algorithm was used. In this paper, prediction of ECG signals (as...

  20. Application of Neutral Network by EEG Signal Classification

    Directory of Open Access Journals (Sweden)

    Michal Gala

    2008-01-01

    Full Text Available Analysis of long-term EEG requires that it is segmented into piece-wise stationary sections and classified. Neural network architecture is introduced for the problem of classification of EEG signals. This paper deals with basic signal classification into two classes. This work is a ground towards creating an algorithm to sleep status analysis. Signal is first worked by signal segmentation and then is used a neural network to classification into two class.

  1. RMOD: a tool for regulatory motif detection in signaling network.

    Science.gov (United States)

    Kim, Jinki; Yi, Gwan-Su

    2013-01-01

    Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod. PMID:23874612

  2. RMOD: a tool for regulatory motif detection in signaling network.

    Directory of Open Access Journals (Sweden)

    Jinki Kim

    Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  3. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  4. Defining a Modular Signalling Network from the Fly Interactome

    Directory of Open Access Journals (Sweden)

    Jacq Bernard

    2008-05-01

    Full Text Available Abstract Background Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Results Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Conclusion Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome.

  5. A fuzzy neural network for sensor signal estimation

    International Nuclear Information System (INIS)

    In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

  6. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

  7. The EEG signal prediction bz using neural network

    OpenAIRE

    Babušiak, B.; Mohylová, J.

    2008-01-01

    The neural network is computational model based on the features abstraction of biological neural systems. The neural networks have many ways of usage in technical field. They have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots. In this paper is described usage of neural networks for ECG signal prediction. The ECG signal prediction can be used for automated detection of irregular heart...

  8. An approach to evolving cell signaling networks in silico

    OpenAIRE

    Decraene, James; Mitchell, George G.; Kelly, Ciaran; McMullin, Barry

    2006-01-01

    Cell Signaling Networks(CSN) are complex bio-chemical networks which, through evolution, have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. From a computational point of view, modeling Artificial Cell Signaling Networks (ACSNs) in silico may provide new ways to design computer systems which may have specialized application areas. To investigate these new opportunities, we review the key issues of mod...

  9. The EEG Signal Prediction by Using Neural Network

    OpenAIRE

    Branko Babusiak; Jitka Mohylova

    2008-01-01

    The neural network is computational model based on the features abstraction of biological neural systems. The neural networks have many ways of usage in technical field. They have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots. In this paper is described usage of neural networks for ECG signal prediction. The ECG signal prediction can be used for  automated detection of irregular heartbeat – extr...

  10. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  11. Subsurface Event Detection and Classification Using Wireless Signal Networks

    OpenAIRE

    Suleiman, Muhannad T.; Sibel Pamukcu; Liang Cheng; Ehsan Ghazanfari; Suk-Un Yoon

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for even...

  12. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  13. Interleukin-7 Receptor Signaling Network: An Integrated Systems Perspective

    Institute of Scientific and Technical Information of China (English)

    Megan J. Palmer; Vinay S. Mahajan; Lily C. Trajman; Darrell J. Irvine; Douglas A.Lauffenburger; Jianzhu Chen

    2008-01-01

    Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor (IL-7R), activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstaik, shared interaction domains, feedback loops, integrated gene regulation, muitimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology.

  14. Decoding signalling networks by mass spectrometry-based proteomics

    DEFF Research Database (Denmark)

    Choudhary, Chuna Ram; Mann, Matthias

    2010-01-01

    Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system-wide characteriz......Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system...... perturbation. Current studies focus on phosphorylation, but acetylation, methylation, glycosylation and ubiquitylation are also becoming amenable to investigation. Large-scale proteomics-based signalling research will fundamentally change our understanding of signalling networks....

  15. Defining a Modular Signalling Network from the Fly Interactome

    OpenAIRE

    Jacq Bernard; Guénoche Alain; Angelelli Jean-Baptiste; Baudot Anaïs; Brun Christine

    2008-01-01

    Abstract Background Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Results...

  16. An extended signal control strategy for urban network traffic flow

    Science.gov (United States)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-03-01

    Traffic flow patterns are in general repeated on a daily or weekly basis. To improve the traffic conditions by using the inherent repeatability of traffic flow, a novel signal control strategy for urban networks was developed via iterative learning control (ILC) approach. Rigorous analysis shows that the proposed learning control method can guarantee the asymptotic convergence. The impacts of the ILC-based signal control strategy on the macroscopic fundamental diagram (MFD) were analyzed by simulations on a test road network. The results show that the proposed ILC strategy can evenly distribute the accumulation in the network and improve the network mobility.

  17. Processing of seismic signals from a seismometer network

    International Nuclear Information System (INIS)

    A description is given of the Seismometer Network Analysis Computer (SNAC) which processes short period data from a network of seismometers (UKNET). The nine stations of the network are distributed throughout the UK and their outputs are transmitted to a control laboratory (Blacknest) where SNAC monitors the data for seismic signals. The computer gives an estimate of the source location of the detected signals and stores the waveforms. The detection logic is designed to maintain high sensitivity without excessive ''false alarms''. It is demonstrated that the system is able to detect seismic signals at an amplitude level consistent with a network of single stations and, within the limitations of signal onset time measurements made by machine, can locate the source of the seismic disturbance. (author)

  18. Detection of Gaussian signals via hexagonal sensor networks

    OpenAIRE

    Frasca, Paolo; Mason, Paolo; Piccoli, Benedetto

    2009-01-01

    This paper considers a special case of the problem of identifying a static scalar signal, depending on the location, using a planar network of sensors in a distributed fashion. Motivated by the application to monitoring wild-fires spreading and pollutants dispersion, we assume the signal to be Gaussian in space. Using a network of sensors positioned to form a regular hexagonal tessellation, we prove that each node can estimate the parameters of the Gaussian from local measurements. Moreover, ...

  19. Hybrid digital signal processing and neural networks applications in PWRs

    International Nuclear Information System (INIS)

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications

  20. Signaling Over Protocols Gateways in Next-Generation Networks

    OpenAIRE

    Akinwande, Gbenga Segun

    2009-01-01

    In this thesis, I examined various signalling both in wired and mobile networks, with more emphasis on SIGTRAN. The SIGTRAN is the protocol suite applicable in the current new generation and next-generation networks, most especially as it enables service provider to be able to interpolate both wireline and wireless services within the same architecture. This concept is an important component in today’s Triple-play communication, and hence this thesis has provided a broad view on Signalling an...

  1. Crosstalk between pathways enhances the controllability of signalling networks.

    Science.gov (United States)

    Wang, Dingjie; Jin, Suoqin; Zou, Xiufen

    2016-02-01

    The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability. PMID:26816393

  2. Enhanced photoacoustic signal from DNA assembled gold nanoparticle networks

    International Nuclear Information System (INIS)

    We report an experimental finding of photoacoustic signal enhancement from finite sized DNA–gold nanoparticle networks. We synthesized DNA-functionalized hollow and solid gold nanospheres (AuNS) to form finite sized networks, which were characterized by means of optical extinction spectroscopy, dynamic light scattering, and scanning electron microscopy in transmission mode. It is shown that the signal amplification scales with network size for networks comprising either hollow or solid AuNS as well as networks consisting of both types of nanoparticles. The laser intensities applied in our multispectral setup (λ = 650 nm, 850 nm, 905 nm) were low enough to maintain the structural integrity of the networks. This reflects that the binding and recognition properties of the temperature-sensitive cross-linking DNA-molecules are retained. (paper)

  3. Signal propagation through feedforward neuronal networks with different operational modes

    Science.gov (United States)

    Li, Jie; Liu, Feng; Xu, Ding; Wang, Wei

    2009-02-01

    How neuronal activity is propagated across multiple layers of neurons is a fundamental issue in neuroscience. Using numerical simulations, we explored how the operational mode of neurons —coincidence detector or temporal integrator— could affect the propagation of rate signals through a 10-layer feedforward network with sparse connectivity. Our study was based on two kinds of neuron models. The Hodgkin-Huxley (HH) neuron can function as a coincidence detector, while the leaky integrate-and-fire (LIF) neuron can act as a temporal integrator. When white noise is afferent to the input layer, rate signals can be stably propagated through both networks, while neurons in deeper layers fire synchronously in the absence of background noise; but the underlying mechanism for the development of synchrony is different. When an aperiodic signal is presented, the network of HH neurons can represent the temporal structure of the signal in firing rate. Meanwhile, synchrony is well developed and is resistant to background noise. In contrast, rate signals are somewhat distorted during the propagation through the network of LIF neurons, and only weak synchrony occurs in deeper layers. That is, coincidence detectors have a performance advantage over temporal integrators in propagating rate signals. Therefore, given weak synaptic conductance and sparse connectivity between layers in both networks, synchrony does greatly subserve the propagation of rate signals with fidelity, and coincidence detection could be of considerable functional significance in cortical processing.

  4. Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

    OpenAIRE

    Omerhodzic, Ibrahim; Avdakovic, Samir; Nuhanovic, Amir; Dizdarevic, Kemal

    2013-01-01

    In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (delta, theta, alpha, beta and gamma) and the Parsevals theorem are employ...

  5. Social multimedia signals a signal processing approach to social network phenomena

    CERN Document Server

    Roy, Suman Deb

    2014-01-01

    This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social me

  6. The EEG Signal Prediction by Using Neural Network

    Directory of Open Access Journals (Sweden)

    Jitka Mohylova

    2008-01-01

    Full Text Available The neural network is computational model based on the features abstraction of biological neural systems. The neural networks have many ways of usage in technical field. They have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots. In this paper is described usage of neural networks for ECG signal prediction. The ECG signal prediction can be used for  automated detection of irregular heartbeat – extrasystole. The automated detection system of unexpected abnormalities is also described in this paper

  7. Human Identification with Electrocardiogram Signals: a Neural Network Approach

    Science.gov (United States)

    Wan, Yongbo; Yao, Jianchu

    2009-05-01

    This paper presents a neural network developed to identify human subjects using electrocardiogram (ECG) signals collected from an "in-house" wearable electrocardiogram (ECG) sensor. In this project, noises were first removed from the raw signals with wavelet filters. ECG cycles were then extracted from the filtered signals and decomposed into wavelet coefficient structures. These coefficient structures were used as input vectors to a 3-layer feedforward neural network that generates the identification results. In the current study, 61 datasets collected from 23 subjects were utilized to train the neural network, which thereafter was tested with 15 new datasets from 15 different subjects. All the 15 subjects in the experiment were successfully identified. The testing results demonstrate that the neural network is effective.

  8. Amplified Signal Response by Neuronal Diversity on Complex Networks

    International Nuclear Information System (INIS)

    The effect of diversity on dynamics of coupled FitzHugh–Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system's parameter. The complex network is constructed by randomly adding long-range connections (shortcuts) on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance. Furthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks. (general)

  9. Structural permeability of complex networks to control signals

    Science.gov (United States)

    Lo Iudice, Francesco; Garofalo, Franco; Sorrentino, Francesco

    2015-09-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met.

  10. SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES.

    Science.gov (United States)

    Strychalski, Wanda; Adalsteinsson, David; Elston, Timothy C

    2010-01-01

    Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast. PMID:24086102

  11. Prioritizing Signaling Information Transmission in Next Generation Networks

    Directory of Open Access Journals (Sweden)

    Jasmina Baraković

    2011-01-01

    Full Text Available Next generation transport network is characterized by the use of in-band signaling, where Internet Protocol (IP packets carrying signaling or media information are mixed in transmission. Since transport resources are limited, when any segment of access or core network is congested, IP packets carrying signaling information may be discarded. As a consequence, it may be impossible to implement reachability and quality of service (QoS. Since present approaches are insufficient to completely address this problem, a novel approach is proposed, which is based on prioritizing signaling information transmission. To proof the concept, a simulation study was performed using Network Simulator version 2 (ns-2 and independently developed Session Initiation Protocol (SIP module. The obtained results were statistically processed using Statistical Package for the Social Sciences (SPSS version 15.0. Summarizing our research results, several issues are identified for future work.

  12. Identification of melanoma biomarkers based on network modules by integrating the human signaling network with microarrays

    OpenAIRE

    Chunyun Huang; Youyu Sheng; Jack Jia; Lianjun Chen

    2014-01-01

    Background: Melanoma is a leading cause of cancer death. Thus, accurate prognostic biomarkers that will assist rational treatment planning need to be identified. Methods: Microarray analysis of melanoma and normal tissue samples was performed to identify differentially expressed modules (DEMs) from the signaling network and ultimately detect molecular markers to support histological examination. Network motifs were extracted from the human signaling network. Then, significant expression-c...

  13. Signaling network dynamics investigated by quantitative phosphoproteomics

    NARCIS (Netherlands)

    Giansanti, Piero

    2014-01-01

    This thesis describes the application of proteomics technologies to get insight into several aspects of phosphorylation signaling dynamics. The core tool in all performed experiments is mass spectrometry (MS)-based phosphoproteomics. In Chapter 1, a general introduction is given into proteomics and

  14. Techniques for labeling of optical signals in bust switched networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Koonen, A. M. J.; Zhang, Jianfeng; Chi, Nan; Holm-Nielsen, Pablo Villanueva; Peucheret, Christophe; Olmos, J. J. Vegas; Khoe, G. D.

    2003-01-01

    We present a review of significant issues related to labeled optical burst switched (LOBS) networks and technologies enabling future optical internet networks. Labeled optical burst switching provides a quick and efficient forwarding mechanism of IP packets/bursts over wavelength division...... multiplexed (WDM) networks due to its single forwarding algorithm, thus yielding low latency, and it enables scaling to terabit rates. Moreover, LOBS is compatible with the general multiprotocol label switching (GMPLS) framework for a unified control plane. We present a review on techniques for labeling of...... optical signals for LOBS networks, including experimental results, we discuss as well issues for further research....

  15. Network Non-Neutrality through Preferential Signaling

    OpenAIRE

    Hanawal, Manjesh Kumar; Altman, Eitan

    2013-01-01

    One of the central issues in the debate on network neutrality has been whether one should allow or prevent preferential treatment by an internet service provider (ISP) of traffic according to its origin. This raised the question of whether to allow an ISP to have exclusive agreement with a content provider (CP). In this paper we consider discrimination in the opposite direction. We study the impact that a CP can have on the benefits of several competing ISPs by sharing private information con...

  16. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    controls directional cell migration as a physiological response. The ciliary pocket is a membrane invagination with elevated activity of clathrin-dependent endocytosis (CDE). In paper I, we show that the primary cilium regulates TGF-β signaling and the ciliary pocket is a compartment for CDE......-dependent regulation of signal transduction. Upon ligand-binding and activation in the cilium, TGFβ receptors accumulate and are internalized at the ciliary base together with Smad2/3 transcription factors that are phosphorylated here and translocated to the nucleus for target gene expression. These processes depend...... migration. A number of central Wnt components localize to the fibroblast primary cilium, including the Wnt5a-receptor, Fzd3, and Dvl proteins. Inversin-deficient MEFs have an elevated expression of canonical Wnt-associated genes and proteins, in addition to dysregulation of components in non-canonical Wnt...

  17. Emerging connections in the ethylene signaling network

    OpenAIRE

    Yoo, Sang-Dong; Cho, Younghee; Sheen, Jen

    2009-01-01

    The gaseous plant hormone ethylene acts as a pivotal mediator to respond to and coordinate internal and external cues in modulating plant growth dynamics and developmental programs. Genetic analysis of Arabidopsis thaliana has been used to identify key components and to build a linear ethylene-signaling pathway from the receptors through to the nuclear transcription factors. Studies applying integrative approaches have revealed new regulators, molecular connections and mechanisms in ethylene ...

  18. Autonomous Traffic Signal Control Model with Neural Network Analogy

    CERN Document Server

    Ohira, T

    1997-01-01

    We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the length of cycle time period of traffic lights at each signal is autonomously adapted. We find a self-organizing collective behavior of such a model through simulation on a one-dimensional lattice model road: traffic congestion is greatly diffused when traffic signals have such autonomous adaptability with suitably tuned parameters. We also find that effectiveness of the system emerges through interactions between units and shows a threshold transition as a function of proportion of adaptive signals in the model.

  19. Optical Performance Monitoring and Signal Optimization in Optical Networks

    DEFF Research Database (Denmark)

    Petersen, Martin Nordal

    2006-01-01

    The thesis studies performance monitoring for the next generation optical networks. The focus is on all-optical networks with bit-rates of 10 Gb/s or above. Next generation all-optical networks offer large challenges as the optical transmitted distance increases and the occurrence of electrical......-optical-electrical regeneration points decreases. This thesis evaluates the impact of signal degrading effects that are becoming of increasing concern in all-optical high-speed networks due to all-optical switching and higher bit-rates. Especially group-velocity-dispersion (GVD) and a number of nonlinear effects will require...... enhanced attention to avoid signal degradations. The requirements for optical performance monitoring features are discussed, and the thesis evaluates the advantages and necessity of increasing the level of performance monitoring parameters in the physical layer. In particular, methods for optical...

  20. Detection of Gaussian signals via hexagonal sensor networks

    CERN Document Server

    Frasca, Paolo; Piccoli, Benedetto

    2009-01-01

    This paper considers a special case of the problem of identifying a static scalar signal, depending on the location, using a planar network of sensors in a distributed fashion. Motivated by the application to monitoring wildfires spreading and pollutants dispersion, we assume the signal to be Gaussian in space. Using a network of sensors positioned to form a regular hexagonal tessellation, we prove that the parameters of the Gaussian can be reconstructed from local measurements. We study the impact of the distance among sensors, and we show how a consensus algorithm can fuse the local estimates, effectively compensating additive errors in the measurements.

  1. Signal processing techniques for synchronization of wireless sensor networks

    Science.gov (United States)

    Lee, Jaehan; Wu, Yik-Chung; Chaudhari, Qasim; Qaraqe, Khalid; Serpedin, Erchin

    2010-11-01

    Clock synchronization is a critical component in wireless sensor networks, as it provides a common time frame to different nodes. It supports functions such as fusing voice and video data from different sensor nodes, time-based channel sharing, and sleep wake-up scheduling, etc. Early studies on clock synchronization for wireless sensor networks mainly focus on protocol design. However, clock synchronization problem is inherently related to parameter estimation, and recently, studies of clock synchronization from the signal processing viewpoint started to emerge. In this article, a survey of latest advances on clock synchronization is provided by adopting a signal processing viewpoint. We demonstrate that many existing and intuitive clock synchronization protocols can be interpreted by common statistical signal processing methods. Furthermore, the use of advanced signal processing techniques for deriving optimal clock synchronization algorithms under challenging scenarios will be illustrated.

  2. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

    Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  3. Perturbation Biology: inferring signaling networks in cellular systems

    OpenAIRE

    Molinelli, Evan J.; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo

    2013-01-01

    Author Summary Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of...

  4. Radar signal design problem with neural network processing

    Indian Academy of Sciences (India)

    C Krishnamohan Rao; P S Moharir

    2001-06-01

    Binary and ternary sequences with peaky autocorrelation, measured in terms of high discrimination and merit factor have been searched earlier, using optimization techniques. It is shown that the use of neural network processing of the return signal is much more advantageous. It opens up a new signal design problem, which is solved by an optimization technique called Hamming scan, for both binary and ternary sequences.

  5. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    Directory of Open Access Journals (Sweden)

    Andre Terzic

    2009-04-01

    Full Text Available Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7 are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network.

  6. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  7. Subsurface event detection and classification using Wireless Signal Networks.

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  8. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  9. Signalling network in dental apoptosis: focused on caspase-3

    Czech Academy of Sciences Publication Activity Database

    Matalová, Eva; Sharpe, P. T.; Šetková, Jana; Míšek, Ivan; Tucker, A. S.

    Vancouver: Keystone Symposia, 2006. s. 45. [Signaling Networks 2006. 30.01.2006-04.02.2006, Vancouver] R&D Projects: GA AV ČR KJB500450503 Grant ostatní: Royal Society 2005/R1 Institutional research plan: CEZ:AV0Z50450515 Keywords : dental apoptosis Subject RIV: EA - Cell Biology

  10. Classification of Electroencephalograph (EEG Signals Using Quantum Neural Network

    Directory of Open Access Journals (Sweden)

    Ibtisam A. Aljazaery , Abduladhem Abdulkareem Ali, Hayder Mahdi Abdulridha

    2011-02-01

    Full Text Available In this paper, quantum neural network (QNN, which is a class of feedforwardneural networks (FFNN’s, is used to recognize (EEG signals. For thispurpose ,independent component analysis (ICA, wavelet transform (WT andFourier transform (FT are used as a feature extraction after normalization ofthese signals. The architecture of (QNN’s have inherently built in fuzzy. Thehidden units of these networks develop quantized representations of thesample information provided by the training data set in various graded levelsof certainty. Experimental results presented here show that (QNN’s arecapable of recognizing structures in data, a property that conventional(FFNN’s with sigmoidal hidden units lack . Finally, (QNN gave us kind of fastand realistic results compared with the (FFNN. Simulation results show that atotal classification of 81.33% for (ICA, 76.67% for (WT and 67.33% for (FT.

  11. Multiplexed Signal Distribution Using Fiber Network For Radar Applications

    Science.gov (United States)

    Meena, D.; Prakasam, L. G. M.; Pandey, D. C.; Shivaleela, E. S.; Srinivas, T.

    2011-10-01

    Most of the modern Active phased Array Radars consist of multiple receive modules in an Antenna array. This demands the distribution of various Local Oscillator Signals (LOs) for the down conversion of received signals to the Intermediate Frequency (IF) band signals. This is normally achieved through Radio Frequency (RF) cables with Complex distribution networks which adds additional weight to the Arrays. Similarly these kinds of receivers require Control/Clock signals which are digital in nature, for the synchronization of all receive modules of the radar system which are also distributed through electrical cables. In addition some of the control messages (Digital in nature) are distributed through Optical interfaces. During Transmit operation, the RF transmit Signal is also distributed through the same receiver modules which will in turn distribute to all the elements of the Array which require RF cables which are bulky in nature. So it is very essential to have a multiplexed Signal distribution scheme through the existing Optical Interface for distribution of these signals which are RF and Digital in nature. This paper discusses about various distribution schemes for the realization in detail. We propose a distribution network architecture where existing fibers can be further extended for the distribution of other types of signals also. In addition, it also briefs about a comparative analysis done on these schemes by considering the complexity and space constraint factors. Thus we bring out an optimum scheme which will lead to the reduction in both hardware complexity and weight of the array systems. In addition, being an Optical network it is free from Electromagnetic interference which is a crucial requirement in an array environment.

  12. Phosphoproteomics-based systems analysis of signal transduction networks

    Directory of Open Access Journals (Sweden)

    Hiroko eKozuka-Hata

    2012-01-01

    Full Text Available Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

  13. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    Science.gov (United States)

    Coyle, Scott M

    2016-07-01

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems. PMID:27128855

  14. Novel links in the plant TOR kinase signaling network.

    Science.gov (United States)

    Xiong, Yan; Sheen, Jen

    2015-12-01

    Nutrient and energy sensing and signaling mechanisms constitute the most ancient and fundamental regulatory networks to control growth and development in all life forms. The target of rapamycin (TOR) protein kinase is modulated by diverse nutrient, energy, hormone and stress inputs and plays a central role in regulating cell proliferation, growth, metabolism and stress responses from yeasts to plants and animals. Recent chemical, genetic, genomic and metabolomic analyses have enabled significant progress toward molecular understanding of the TOR signaling network in multicellular plants. This review discusses the applications of new chemical tools to probe plant TOR functions and highlights recent findings and predictions on TOR-mediate biological processes. Special focus is placed on novel and evolutionarily conserved TOR kinase effectors as positive and negative signaling regulators that control transcription, translation and metabolism to support cell proliferation, growth and maintenance from embryogenesis to senescence in the plant system. PMID:26476687

  15. Model calibration and uncertainty analysis in signaling networks.

    Science.gov (United States)

    Heinemann, Tim; Raue, Andreas

    2016-06-01

    For a long time the biggest challenges in modeling cellular signal transduction networks has been the inference of crucial pathway components and the qualitative description of their interactions. As a result of the emergence of powerful high-throughput experiments, it is now possible to measure data of high temporal and spatial resolution and to analyze signaling dynamics quantitatively. In addition, this increase of high-quality data is the basis for a better understanding of model limitations and their influence on the predictive power of models. We review established approaches in signal transduction network modeling with a focus on ordinary differential equation models as well as related developments in model calibration. As central aspects of the calibration process we discuss possibilities of model adaptation based on data-driven parameter optimization and the concomitant objective of reducing model uncertainties. PMID:27085224

  16. The statistical mechanics of complex signaling networks: nerve growth factor signaling

    Science.gov (United States)

    Brown, K. S.; Hill, C. C.; Calero, G. A.; Myers, C. R.; Lee, K. H.; Sethna, J. P.; Cerione, R. A.

    2004-10-01

    The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'

  17. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  18. Signaling pathway networks mined from human pituitary adenoma proteomics data

    Directory of Open Access Journals (Sweden)

    Zhan Xianquan

    2010-04-01

    Full Text Available Abstract Background We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins, comparative proteomic data (56 differentially expressed proteins, and nitroproteomic data (17 nitroproteins. There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system. Methods The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses. Results For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a

  19. Early-warning signals of topological collapse in interbank networks

    CERN Document Server

    Squartini, Tiziano; Garlaschelli, Diego

    2013-01-01

    The financial crisis marked a paradigm shift, from traditional studies of individual risk to recent research on the "systemic risk" generated by whole networks of institutions. However, the reverse effects of realized defaults on network topology are poorly understood. Here we analyze the Dutch interbank network over the period 1998-2008, ending with the global crisis. We find that many topological properties, after controlling for overall density effects, display an abrupt change in 2008, thus providing a clear but unpredictable signature of the crisis. By contrast, if the intrinsic heterogeneity of banks is controlled for, the same properties undergo a slow and continuous transition, gradually connecting the crisis period to a much earlier stationary phase. This early-warning signal begins in 2005, and is preceded by an even earlier period of "risk autocatalysis" characterized by anomalous debt loops. These remarkable precursors are undetectable if the network is reconstructed from partial bank-specific inf...

  20. Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks

    Directory of Open Access Journals (Sweden)

    Ateke Goshvarpour

    2013-05-01

    Full Text Available The aim of this paper is to investigate the performance of time delay neural networks (TDNNs and the probabilistic neural networks (PNNs trained with nonlinear features (Lyapunov exponents and Entropy on electroencephalogram signals (EEG in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy are analyzed. To evaluate the performance of the classifiers, mean square error (MSE and elapsed time of each classifier are examined. The results show that TDNN with 12 neurons in hidden layer result in a lower MSE with the training time of about 19.69 second. According to the results, when the sigma values are lower than 0.56, the best performance in the proposed probabilistic neural network structure is achieved. The results of present study show that applying the nonlinear features to train these networks can serve as useful tool in classifying of the EEG signals.

  1. Signal Processing in Periodically Forced Gradient Frequency Neural Networks

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W.

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

  2. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  3. Image and signal processing for networked eHealth applications

    CERN Document Server

    Maglogiannis, Ilias

    2006-01-01

    E-health is closely related with networks and telecommunications when dealing with applications of collecting or transferring medical data from distant locations for performing remote medical collaborations and diagnosis. In this book we provide an overview of the fields of image and signal processing for networked and distributed e-health applications and their supporting technologies. The book is structured in 10 chapters, starting the discussion from the lower end, that of acquisition and processing of biosignals and medical images and ending in complex virtual reality systems and technique

  4. Distributed Signal Processing for Wireless EEG Sensor Networks.

    Science.gov (United States)

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center. PMID:25850092

  5. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    of the articles that are part of this PhD thesis (part II). In part III, we illustrate with an article that has been submitted recently, how next-generation sequencing data and mass spectrometry data can be combined to uncover genome-specific signaling networks. In part IV, I describe the two computational......) based on the integration of these cues; this integration and consequently the cellular decisions taken by cancer cells are arguably very distinct from the decisions that would be expected from non-cancer cells. Since cellular signaling networks and its different states are the computational circuits......Amongst the unique features of cancer cells perhaps the most crucial one is the change in the cellular decision-making process. While both non-cancer and cancer cells are constantly integrating different external cues that reach them and computing cellular decisions (e.g. proliferation or apoptosis...

  6. Neural Network AE Source Location Based on Extracted Signal Features

    Czech Academy of Sciences Publication Activity Database

    Chlada, Milan; Blaháček, Michal; Převorovský, Zdeněk

    Brno : VUT Brno, 2005 - (Mazal, P.), s. 55-62 ISBN 80-214-2996-8. [NDT in Progress. Praha (CZ), 10.10.2005-12.10.2005] R&D Projects: GA ČR(CZ) GA201/04/2102; GA MPO FT-TA/026 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source location * neural network s * signal features Subject RIV: BI - Acoustics

  7. Neural Network Nonlinear Factor Analysis of High Dimensional Binary Signal

    Czech Academy of Sciences Publication Activity Database

    Húsek, Dušan; Řezanková, H.; Snášel, Václav; Frolov, A. A.; Polyakov, P.Y.

    IEEE, 2005 - (Chbeir, R.; Dipanda, A.; Vétongnon, K.), s. 86-89 ISBN 2-9525435-0. [IEEE SITIS 2005. International Conference on Signal & Image Technology and Internet Based Systems /1./. Yaoundé (CM), 27.11.2005-01.12.2005] R&D Projects: GA MŠk 1M0567; GA MŠk 1M0554 Keywords : neural networks * associative memory * Boolean factor analysis * text mining Subject RIV: BA - General Mathematics

  8. Feasibility of RFID signal denoising using neural network

    OpenAIRE

    Vojtěch, Lukáš

    2010-01-01

    Radio Frequency Identification signal denoising can be a perspective method for the future intelligent Radio Frequency Identification readers with high reading distances capability. This paper deals with the Group Method of Data Handling neural network denoising filter experiments. Capability of the probability learning of the Group Method of Data Handling filters is an effective instrument in more exacting applications in comparison with classical Finite Impulse Respo...

  9. Wireless sensor networks for monitoring physiological signals of multiple patients.

    Science.gov (United States)

    Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C

    2011-08-01

    This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time. PMID:23851949

  10. Seismic signals detection and classification using artiricial neural networks

    Directory of Open Access Journals (Sweden)

    G. Romeo

    1994-06-01

    Full Text Available Pattern recognition belongs to a class of Problems which are easily solved by humans, but difficult for computers. It is sometimes difficult to formalize a problem which a human operator can casily understand by using examples. Neural networks are useful in solving this kind of problem. A neural network may, under certain conditions, simulate a well trained human operator in recognizing different types of earthquakes or in detecting the presence of a seismic event. It is then shown how a fully connected multi layer perceptron may perform a recognition task. It is shown how a self training auto associative neural network may detect an earthquake occurrence analysing the change in signal characteristics.

  11. Monitoring Breathing via Signal Strength in Wireless Networks

    CERN Document Server

    Patwari, Neal; R., Sai Ananthanarayanan P; Kasera, Sneha K; Westenskow, Dwayne

    2011-01-01

    This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call "BreathTaking". We show that although an individual link cannot reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.3 breaths per minute (bpm) RMS error. Use of directional antennas is shown to improve robustness to motion near the network.

  12. Modeling of cortical signals using echo state networks

    Science.gov (United States)

    Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

    2009-10-01

    Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

  13. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

    Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.

  14. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  15. NT2 derived neuronal and astrocytic network signalling.

    Directory of Open Access Journals (Sweden)

    Eric J Hill

    Full Text Available A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality.

  16. Analysis on Design of Kohonen-network System Based on Classification of Complex Signals

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.

  17. Identification of melanoma biomarkers based on network modules by integrating the human signaling network with microarrays

    Directory of Open Access Journals (Sweden)

    Chunyun Huang

    2014-01-01

    Full Text Available Background: Melanoma is a leading cause of cancer death. Thus, accurate prognostic biomarkers that will assist rational treatment planning need to be identified. Methods: Microarray analysis of melanoma and normal tissue samples was performed to identify differentially expressed modules (DEMs from the signaling network and ultimately detect molecular markers to support histological examination. Network motifs were extracted from the human signaling network. Then, significant expression-correlation differential modules were identified by comparing the network module expression-correlation differential scores under normal and disease conditions using the gene expression datasets. Finally, we obtained DEMs by the Wilcoxon rank test and considered the average gene expression level in these modules as the classification features for diagnosing melanoma. Results: In total, 99 functional DEMs were identified from the signaling network and gene expression profiles. The area under the curve scores for cancer module genes, melanoma module genes, and whole network modules are 92.4%, 90.44%, and 88.45%, respectively. The classification efficiency rates for nonmodule features are 71.04% and 79.38%, which correspond to the features of cancer genes and melanoma cancer genes, respectively. Finally, we acquired six significant molecular biomarkers, namely, module 10 (CALM3, Ca 2+ , PKC, PDGFRA, phospholipase-g, PIB5PA, and phosphatidylinositol-3-kinase, module 14 (SRC, Src homology 2 domain-containing [SHC], SAM68, GIT1, transcription factor-4, CBLB, GRB2, VAV2, LCK, YES, PTCH2, downstream of tyrosine kinase [DOK], and KIT, module 16 (ELK3, p85beta, SHC, ZFYVE9, TGFBR1, TGFBR2, CITED1, SH3KBP1, HCK, DOK, and KIT, module 45 (RB, CCND3, CCNA2, CDK4, and CDK6, module 75 (PCNA, CDK4, and CCND1, and module 114 (PSD93, NMDAR, and FYN. Conclusion: We explored the gene expression profile and signaling network in a global view and identified DEMs that can be used as

  18. Biasing vector network analyzers using variable frequency and amplitude signals

    Science.gov (United States)

    Nobles, J. E.; Zagorodnii, V.; Hutchison, A.; Celinski, Z.

    2016-08-01

    We report the development of a test setup designed to provide a variable frequency biasing signal to a vector network analyzer (VNA). The test setup is currently used for the testing of liquid crystal (LC) based devices in the microwave region. The use of an AC bias for LC based devices minimizes the negative effects associated with ionic impurities in the media encountered with DC biasing. The test setup utilizes bias tees on the VNA test station to inject the bias signal. The square wave biasing signal is variable from 0.5 to 36.0 V peak-to-peak (VPP) with a frequency range of DC to 10 kHz. The test setup protects the VNA from transient processes, voltage spikes, and high-frequency leakage. Additionally, the signals to the VNA are fused to ½ amp and clipped to a maximum of 36 VPP based on bias tee limitations. This setup allows us to measure S-parameters as a function of both the voltage and the frequency of the applied bias signal.

  19. The Ballistocardiogram Signal Monitoring System Based on the GSM Network

    Institute of Scientific and Technical Information of China (English)

    WANG Chun-wu; WANG Xu; LONG Zhe; ZHANG Ke-xin; YANG Dan

    2015-01-01

    Ballistocardiogram signal monitoring system based on GSM network was put forward in this paper. The system included a BCG signal acquisition module, a data processing module, a display module and a GSM module. The STM32F103VB microprocessor was used as the controlling core of the signal acquisition module. BCG signal acquisition, amplification, filtering and A/D conversion were completed by the resistance strain sensor and high precision A/D conversion chip of TM7708; VB6.0 software was used to realize the BCG signal analysis and processing;the SD card and LCD completed data storage and waveform display; the BCG data remote transmission and alarm function were realized through the GSM module. The system cannot only real-time monitor the changes of heart rate of patients by non-contact means, and can process data automatically, timely detection of arrhythmia and automatic alarm. The system is particularly suitable for heart disease patients receiving long-term home care;therefore, it has a broad application prospect.

  20. Regulatory Roles of Metabolites in Cell Signaling Networks

    Institute of Scientific and Technical Information of China (English)

    Feng Li; Wei Xu; Shimin Zhao

    2013-01-01

    Mounting evidence suggests that cellular metabolites,in addition to being sources of fuel and macromolecular substrates,are actively involved in signaling and epigenetic regulation.Many metabolites,such as cyclic AMP,which regulates phosphorylation/dephosphorylation,have been identified to modulate DNA and histone methylation and protein stability.Metabolite-driven cellular regulation occurs through two distinct mechanisms:proteins allosterically bind or serve as substrates for protein signaling pathways,and metabolites covalently modify proteins to regulate their functions.Such novel protein metabolites include fumarate,succinyl-CoA,propionyl-CoA,butyryl-CoA and crontonyl-CoA.Other metabolites,including α-ketoglutarate,succinate and fumarate,regulate epigenetic processes and cell signaling via protein binding.Here,we summarize recent progress in metabolite-derived post-translational protein modification and metabolite-binding associated signaling regulation.Uncovering metabolites upstream of cell signaling and epigenetic networks permits the linkage of metabolic disorders and human diseases,and suggests that metabolite modulation may be a strategy for innovative therapeutics and disease prevention techniques.

  1. Microglia Control Neuronal Network Excitability via BDNF Signalling

    Directory of Open Access Journals (Sweden)

    Francesco Ferrini

    2013-01-01

    Full Text Available Microglia-neuron interactions play a crucial role in several neurological disorders characterized by altered neural network excitability, such as epilepsy and neuropathic pain. While a series of potential messengers have been postulated as substrates of the communication between microglia and neurons, including cytokines, purines, prostaglandins, and nitric oxide, the specific links between messengers, microglia, neuronal networks, and diseases have remained elusive. Brain-derived neurotrophic factor (BDNF released by microglia emerges as an exception in this riddle. Here, we review the current knowledge on the role played by microglial BDNF in controlling neuronal excitability by causing disinhibition. The efforts made by different laboratories during the last decade have collectively provided a robust mechanistic paradigm which elucidates the mechanisms involved in the synthesis and release of BDNF from microglia, the downstream TrkB-mediated signals in neurons, and the biophysical mechanism by which disinhibition occurs, via the downregulation of the K+-Cl− cotransporter KCC2, dysrupting Cl−homeostasis, and hence the strength of GABAA- and glycine receptor-mediated inhibition. The resulting altered network activity appears to explain several features of the associated pathologies. Targeting the molecular players involved in this canonical signaling pathway may lead to novel therapeutic approach for ameliorating a wide array of neural dysfunctions.

  2. An ancient chordin-like gene in organizer formation of Hydra

    Science.gov (United States)

    Rentzsch, Fabian; Guder, Corina; Vocke, Dirk; Hobmayer, Bert; Holstein, Thomas W.

    2007-01-01

    Signaling centers or organizers play a key role in axial patterning processes in animal embryogenesis. The function of most vertebrate organizers involves the activity of secreted antagonists of bone morphogenetic proteins (BMPs) such as Chordin or Noggin. Although BMP homologs have been isolated from many phyla, the evolutionary origin of the antagonistic BMP/Chordin system in organizer signaling is presently unknown. Here we describe a Chordin-like molecule (HyChdl) from Hydra that inhibits BMP activity in zebrafish embryos and acts in Hydra axis formation when new head organizers are formed during budding and regeneration. hychdl transcripts are also up-regulated in the head regeneration-deficient mutant strain reg-16. Accordingly, HyChdl has a function in organizer formation, but not in head differentiation. Our data indicate that the BMP/Chordin antagonism is a basic property of metazoan signaling centers that was invented in early metazoan evolution to set up axial polarity. PMID:17360633

  3. Signal integration in the galactose network of Escherichia coli.

    Science.gov (United States)

    Semsey, Szabolcs; Krishna, Sandeep; Sneppen, Kim; Adhya, Sankar

    2007-07-01

    The gal regulon of Escherichia coli contains genes involved in galactose transport and metabolism. Transcription of the gal regulon genes is regulated in different ways by two iso-regulatory proteins, Gal repressor (GalR) and Gal isorepressor (GalS), which recognize the same binding sites in the absence of d-galactose. DNA binding by both GalR and GalS is inhibited in the presence of d-galactose. Many of the gal regulon genes are activated in the presence of the adenosine cyclic-3',5'-monophosphate (cAMP)-cAMP receptor protein (CRP) complex. We studied transcriptional regulation of the gal regulon promoters simultaneously in a purified system and attempted to integrate the two small molecule signals, d-galactose and cAMP, that modulate the isoregulators and CRP respectively, at each promoter, using Boolean logic. Results show that similarly organized promoters can have different input functions. We also found that in some cases the activity of the promoter and the cognate gene can be described by different logic gates. We combined the transcriptional network of the galactose regulon, obtained from our experiments, with literature data to construct an integrated map of the galactose network. Structural analysis of the network shows that at the interface of the genetic and metabolic network, feedback loops are by far the most common motif. PMID:17630975

  4. RECEIVED SIGNAL STRENGTH INDICATION MODELING IN INDOOR WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Edson Taira Procopio

    2013-01-01

    Full Text Available This study aims to identify mathematical models that represent the relation between Received Signal Strength Indication (RSSI and objects in an indoor Wireless Sensor Network (WSN. Using the Least Squares Method, four linear models have been identified: The first one relates uplink RSSI and objects; the second one relates downlink RSSI and objects; the third one relates uplink RSSI and obstacles and the fourth one relates downlink RSSI and obstacles. The obtained results, characterized by small residual values, attest the validation of all four models.

  5. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  6. A modular analysis of the auxin signalling network.

    Directory of Open Access Journals (Sweden)

    Etienne Farcot

    Full Text Available Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF and Aux/IAA (IAA transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants.

  7. Automated Measurement and Signaling Systems for the Transactional Network

    Energy Technology Data Exchange (ETDEWEB)

    Piette, Mary Ann; Brown, Richard; Price, Phillip; Page, Janie; Granderson, Jessica; Riess, David; Czarnecki, Stephen; Ghatikar, Girish; Lanzisera, Steven

    2013-12-31

    The Transactional Network Project is a multi-lab activity funded by the US Department of Energy?s Building Technologies Office. The project team included staff from Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The team designed, prototyped and tested a transactional network (TN) platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). PNNL was responsible for the development of the TN platform, with agents for this platform developed by each of the three labs. LBNL contributed applications to measure the whole-building electric load response to various changes in building operations, particularly energy efficiency improvements and demand response events. We also provide a demand response signaling agent and an agent for cost savings analysis. LBNL and PNNL demonstrated actual transactions between packaged rooftop units and the electric grid using the platform and selected agents. This document describes the agents and applications developed by the LBNL team, and associated tests of the applications.

  8. Pattern recognition for electroencephalographic signals based on continuous neural networks.

    Science.gov (United States)

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2016-07-01

    This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results. PMID:27131469

  9. Weak signal transmission in complex networks and its application in detecting connectivity.

    Science.gov (United States)

    Liang, Xiaoming; Liu, Zonghua; Li, Baowen

    2009-10-01

    We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size. PMID:19905385

  10. Computational study of noise in a large signal transduction network

    Directory of Open Access Journals (Sweden)

    Ruohonen Keijo

    2011-06-01

    Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

  11. Seismic Signal Classification with Offshore/Amphibious Networks Using an Artificial Neural Network

    Science.gov (United States)

    Williams, M. C.; Trehu, A. M.

    2011-12-01

    The amphibious Central Oregon Locked Zone Array (COLZA) of seismic stations was deployed from 2007-2009 to record earthquakes occurring in the seismogenic zone offshore central Oregon. This array included two year-long deployments of ocean bottom seismometers (OBS's) from the NSF OBSIP. In addition to local and distant earthquakes, the OBS array recorded thousands of impulsive local signals, which are not easily filtered out by a standard STA/LTA detection algorithm. Many of these signals are likely of biological origin (informally referred to as "bio-bumps"). These signals have a wide range of amplitudes, can mask local earthquake phase arrivals, and make automatic detection more difficult. We show that signal characteristics derived from 3-component seismic data at each station can be used to filter out event detections that are unlikely to be earthquake-generated. A decision-making algorithm is run using a joint set of signal characteristics to identify possible local events and classify detections that are likely to be "bumps". We present results on the effectiveness of this classification technique using various combinations of input parameters applied to the onshore/offshore COLZA array dataset. The classification algorithm is a multilayer perceptron (MLP) artificial neural network, trained through backpropagation using human-identified examples of both earthquake phases and impulsive "bumps". The effectiveness of a neural network is highly dependent on the data space consisting of the inputs calculated for each signal, which represent its main characteristics and differentiate it from other events. As inputs to the neural network, for each event detection, in addition to the STA/LTA value, we determine three signal characteristics from 3-component waveform data: the variance of the power cepstrum calculated from a portion of the signal spectrum, the rectilinearity of particle motion, and the ratio of particle motion orthogonal to the principle direction of

  12. Traffic Signal Synchronization in the Saturated High-Density Grid Road Network

    OpenAIRE

    Xiaojian Hu; Jian Lu; Wei Wang,; Ye Zhirui

    2015-01-01

    Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essen...

  13. Signal detection, modularity and the correlation between extrinsic and intrinsic noise in biochemical networks

    OpenAIRE

    Tanase-Nicola, Sorin; Warren, Patrick B.; Wolde, Pieter Rein ten

    2005-01-01

    Understanding cell function requires an accurate description of how noise is transmitted through biochemical networks. We present an analytical result for the power spectrum of the output signal of a biochemical network that takes into account the correlations between the noise in the input signal (the extrinsic noise) and the noise in the reactions that constitute the network (the intrinsic noise). These correlations arise from the fact that the reactions by which biochemical signals are det...

  14. Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

    Directory of Open Access Journals (Sweden)

    Shi Zhiqiang

    2003-01-01

    Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

  15. 47 CFR 73.4157 - Network signals which adversely affect affiliate broadcast service.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 4 2010-10-01 2010-10-01 false Network signals which adversely affect affiliate broadcast service. 73.4157 Section 73.4157 Telecommunication FEDERAL COMMUNICATIONS COMMISSION....4157 Network signals which adversely affect affiliate broadcast service. See Public Notice, FCC...

  16. A fuzzy neural network combining wavelet denoising and PCA for sensor signal estimation

    International Nuclear Information System (INIS)

    In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

  17. Research on Characteristics of RC Polyphase Network for Quadrature Signal Generator

    Directory of Open Access Journals (Sweden)

    Hu Yi-ming

    2013-12-01

    Full Text Available The quadrature signal generator is widely used in devices such as the quadrature modulator and image rejection frequency converter. An RC polyphase network is commonly used as a quadrature signal generator because of its simple structure and perfect linear performance. This study derives explicit transfer functions for the RC polyphase network based on the complex signal properties and the matrix theory. Moreover, build-related parameters are determined to analyze the I/Q amplitude and phase balance characteristic of the RC polyphase network, thus providing a theoretical guideline for the design of a high-balance quadrature signal generator.

  18. Spatiotemporal Properties of Intracellular Calcium Signaling in Osteocytic and Osteoblastic Cell Networks under Fluid Flow

    OpenAIRE

    Jing, Da; Lu, X. Lucas; Luo, Erping; Sajda, Paul; Leong, Pui L.; Guo, X. Edward

    2013-01-01

    Mechanical stimuli can trigger intracellular calcium (Ca2+) responses in osteocytes and osteoblasts. Successful construction of bone cell networks necessitates more elaborate and systematic analysis for the spatiotemporal properties of Ca2+ signaling in the networks. In the present study, an unsupervised algorithm based on independent component analysis (ICA) was employed to extract the Ca2+ signals of bone cells in the network. We demonstrated that the ICA-based technology could yield higher...

  19. Research on Characteristics of RC Polyphase Network for Quadrature Signal Generator

    OpenAIRE

    Hu Yi-ming; Yu Guo-wen; Zhang Yuan-fa

    2013-01-01

    The quadrature signal generator is widely used in devices such as the quadrature modulator and image rejection frequency converter. An RC polyphase network is commonly used as a quadrature signal generator because of its simple structure and perfect linear performance. This study derives explicit transfer functions for the RC polyphase network based on the complex signal properties and the matrix theory. Moreover, build-related parameters are determined to analyze the I/Q amplitude and phase ...

  20. The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain

    Institute of Scientific and Technical Information of China (English)

    田允; 黄继云; 王锐; 陶蓉蓉; 卢应梅; 廖美华; 陆楠楠; 李静; 芦博; 韩峰

    2012-01-01

    Autism is a highly heritable neurodevelopmental condition characterized by impaired social interaction and communication. However, the role of synaptic dysfunction during development of autism remains unclear. In the present study, we address the alterations of biochemical signaling in hippocampal network following induction of the autism in experimental animals. Here, the an- imal disease model and DNA array being used to investigate the differences in transcriptome or- ganization between autistic and normal brain by gene co--expression network analysis.

  1. Genetic and logic networks with the signal-inhibitor-activator structure are dynamically robust

    Institute of Scientific and Technical Information of China (English)

    LI Fangting; TAN Ning

    2006-01-01

    The proteins, DNA and RNA interaction networks govern various biological functions in living cells, these networks should be dynamically robust in the intracellular and environmental fluctuations. Here, we use Boolean network to study the robust structure of both genetic and logic networks. First, SOS network in bacteria E. coli, which regulates cell survival and repair after DNA damage, is shown to be dynamically robust. Comparing with cell cycle network in budding yeast and flagella network in E. coli, we find the signal-inhibitor-activator (SIA) structure in transcription regulatory networks. Second, under the dynamical rule that inhibition is much stronger than activation, we have searched 3-node non-self-loop logical networks that are dynamically robust, and that if the attractive basin of a final attractor is as large as seven, and the final attractor has only one active node, then the active node acts as inhibitor, and the SIA and signal-inhibitor (SI) structures are fundamental architectures of robust networks. SIA and SI networks with dynamic robustness against environment uncertainties may be selected and maintained over the course of evolution, rather than blind trial-error testing and be ing an accidental consequence of particular evolutionary history. SIA network can perform a more complex process than SI network, andSIA might be used to design robust artificial genetic network. Our results provide dynamical support for why the inhibitors and SIA/SI structures are frequently employed in cellular regulatory networks.

  2. Stochastic effects as a force to increase the complexity of signaling networks

    KAUST Repository

    Kuwahara, Hiroyuki

    2013-07-29

    Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects - called deviant effects - in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

  3. Detection of Emergency Signal in Hearing Aids using Neural Networks

    OpenAIRE

    Lakum, Vamshi Krishna; Gubbala, Arshini

    2014-01-01

    ABSTRACT The detection of an emergency signal can be estimated by the cancellation of surrounding noise and achieving the desired signal in order to alert the automobilist. The aim of the thesis is to detect the emergency signal arriving nearer to the automobilist carrying hearing aids. Recent studies show that this can be achieved by designing various kinds of fixed and adaptive beam formers. A beam former does spatial filtering in the sense that it separates two signals with overlapping fre...

  4. Proteomics, pathway array and signaling network-based medicine in cancer

    Directory of Open Access Journals (Sweden)

    Xu Hong

    2009-10-01

    Full Text Available Abstract Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc. and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

  5. Energy Efficiency of Distributed Signal Processing in Wireless Networks: A Cross-Layer Analysis

    Science.gov (United States)

    Geraci, Giovanni; Wildemeersch, Matthias; Quek, Tony Q. S.

    2016-02-01

    In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: (i) hybrid, where the signal processing operations are shared between nodes and APs, (ii) centralized, where signal processing is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.

  6. Impulse-Induced Optimum Signal Amplification in Scale-Free Networks

    CERN Document Server

    Martínez, Pedro J

    2015-01-01

    Optimizing information transmission across a network is an essential task for controlling and manipulating generic information-processing systems. Here, we show how topological amplification effects in scale-free networks of signaling devices are optimally enhanced when the $\\it{impulse}$ transmitted by periodic external signals (time integral over two consecutive zeros) is maximum. This is demonstrated theoretically by means of a star-like network of overdamped bistable systems subjected to $\\it{generic}$ zero-mean periodic signals, and confirmed numerically by simulations of scale-free networks of such systems. Our results show that the enhancer effect of increasing values of the signal's impulse is due to a correlative increase of the energy transmitted by the periodic signals, while it is found to be resonant-like with respect to the topology-induced amplification mechanism.

  7. Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

    Directory of Open Access Journals (Sweden)

    F. Bagheri

    2013-02-01

    Full Text Available The Electrocardiogram (ECG signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

  8. Digital Signal Processing and Control for the Study of Gene Networks

    Science.gov (United States)

    Shin, Yong-Jun

    2016-01-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

  9. Digital Signal Processing and Control for the Study of Gene Networks.

    Science.gov (United States)

    Shin, Yong-Jun

    2016-01-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

  10. ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks

    OpenAIRE

    Iman Moazen; Mohamadreza Ahmadzadeh

    2009-01-01

    In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decom...

  11. Elimination of spiral waves and spatiotemporal chaos by the synchronization transmission technology of network signals

    Institute of Scientific and Technical Information of China (English)

    Zhang Qing-Ling; Lu Ling; Zhang Yi

    2011-01-01

    A method to eliminate spiral waves and spatiotemporal chaos by using the synchronization transmission technology of network signals is proposed in this paper. The character of the spiral waves and the spatiotemporal chaos in the Fitzhugh-Nagumo model is presented. The network error evolution equation with spatiotemporal variables and the corresponding eigenvalue equation are determined based on the stability theory,and the global synchronization condition is obtained. Simulations are made in a complex network with Fitzhugh-Nagumo models as the nodes to verify the effectiveness of the synchronization transmission principle of the network signal.

  12. Elimination of spiral waves and spatiotemporal chaos by the synchronization transmission technology of network signals

    International Nuclear Information System (INIS)

    A method to eliminate spiral waves and spatiotemporal chaos by using the synchronization transmission technology of network signals is proposed in this paper. The character of the spiral waves and the spatiotemporal chaos in the Fitzhugh—Nagumo model is presented. The network error evolution equation with spatiotemporal variables and the corresponding eigenvalue equation are determined based on the stability theory, and the global synchronization condition is obtained. Simulations are made in a complex network with Fitzhugh—Nagumo models as the nodes to verify the effectiveness of the synchronization transmission principle of the network signal. (general)

  13. Speech Subvocal Signal Processing using Packet Wavelet and Neuronal Network

    OpenAIRE

    Luis E. Mendoza; Jesus Peña; Luis A. Muñoz-Bedoya; Hernando J. Velandia-Villamizar

    2013-01-01

    This paper presents the results obtained from the recording, processing and classification of words in the Spanish language by means of the analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop). In this work, the signals were sensed with surface electrodes placed on the surface of the throat and acquired with a sampling frequency of 50 kHz. The signal conditioning consisted in: the location of area of interest using energy a...

  14. An integrated signal transduction network of macrophage migration inhibitory factor.

    Science.gov (United States)

    Subbannayya, Tejaswini; Variar, Prathyaksha; Advani, Jayshree; Nair, Bipin; Shankar, Subramanian; Gowda, Harsha; Saussez, Sven; Chatterjee, Aditi; Prasad, T S Keshava

    2016-06-01

    Macrophage migration inhibitory factor (MIF) is a glycosylated multi-functional protein that acts as an enzyme as well as a cytokine. MIF mediates its actions through a cell surface class II major histocompatibility chaperone, CD74 and co-receptors such as CD44, CXCR2, CXCR4 or CXCR7. MIF has been implicated in the pathogenesis of several acute and chronic inflammatory diseases. Although MIF is a molecule of biomedical importance, a public resource of MIF signaling pathway is currently lacking. In view of this, we carried out detailed data mining and documentation of the signaling events pertaining to MIF from published literature and developed an integrated reaction map of MIF signaling. This resulted in the cataloguing of 68 molecules belonging to MIF signaling pathway, which includes 24 protein-protein interactions, 44 post-translational modifications, 11 protein translocation events and 8 activation/inhibition events. In addition, 65 gene regulation events at the mRNA levels induced by MIF signaling have also been catalogued. This signaling pathway has been integrated into NetPath ( http://www.netpath.org ), a freely available human signaling pathway resource developed previously by our group. The MIF pathway data is freely available online in various community standard data exchange formats. We expect that data on signaling events and a detailed signaling map of MIF will provide the scientific community with an improved platform to facilitate further molecular as well as biomedical investigations on MIF. PMID:27139435

  15. The cost and capacity of signaling in the Escherichia coli protein reaction network

    International Nuclear Information System (INIS)

    In systems biology new ways are required to analyze the large amount of existing data on regulation of cellular processes. Recent work can be roughly classified into either dynamical models of well-described subsystems, or coarse-grained descriptions of the topology of the molecular networks at the scale of the whole organism. In order to bridge these two disparate approaches one needs to develop simplified descriptions of dynamics and topological measures which address the propagation of signals in molecular networks. Transmission of a signal across a reaction node depends on the presence of other reactants. It will typically be more demanding to transmit a signal across a reaction node with more input links. Sending signals along a path with several subsequent reaction nodes also increases the constraints on the presence of other proteins in the overall network. Therefore counting in and out links along reactions of a potential pathway can give insight into the signaling properties of a particular molecular network. Here, we consider the directed network of protein regulation in E. coli, characterizing its modularity in terms of its potential to transmit signals. We demonstrate that the simplest measure based on identifying subnetworks of strong components, within which each node could send a signal to every other node, does indeed partition the network into functional modules. We suggest that the total number of reactants needed to send a signal between two nodes in the network can be considered as the cost associated with transmitting this signal. Similarly we define spread as the number of reaction products that could be influenced by transmission of a successful signal. Our considerations open for a new class of network measures that implicitly utilize the constrained repertoire of chemical modifications of any biological molecule. The counting of cost and spread connects the topology of networks to the specificity of signaling across the network. Thereby, we

  16. Model-based design of self-Adapting networked signal processing systems

    NARCIS (Netherlands)

    Oliveira Filho, J.A. de; Papp, Z.; Djapic, R.; Oostveen, J.C.

    2013-01-01

    The paper describes a model based approach for architecture design of runtime reconfigurable, large-scale, networked signal processing applications. A graph based modeling formalism is introduced to describe all relevant aspects of the design (functional, concurrency, hardware, communication, energy

  17. Multiple-failure signal validation in nuclear power plants using artificial neural networks

    International Nuclear Information System (INIS)

    The possibility of using a neural network to validate process signals during normal and abnormal plant conditions is explored. In boiling water reactor plants, signal validation is used to generate reliable thermal limits calculation and to supply reliable inputs to other computerized systems that support the operator during accident scenarios. The way that autoassociative neural networks can promptly detect faulty process signal measurements and produce a best estimate of the actual process values even in multifailure situations is shown. A method was developed to train the network for multiple sensor-failure detection, based on a random failure simulation algorithm. Noise was artificially added to the input to evaluate the network's ability to respond in a very low signal-to-noise ratio environment. Training and test data sets were simulated by the real-time transient simulator code APROS

  18. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume is to be...... published by IEEE following the workshop, and we are pleased to inaugurate a new CDROM electronic format, which maintains the same standard as the printed version and facilitates the reading and searching of the papers. In recent years, the field of neural networks has matured considerably in both...

  19. Experimental Demonstration of Mixed Formats and Bit Rates Signal Allocation for Spectrum-flexible Optical Networking

    DEFF Research Database (Denmark)

    Borkowski, Robert; Karinou, Fotini; Angelou, Marianna; Arlunno, Valeria; Zibar, Darko; Klonidis, Dimitrios; Guerrero Gonzalez, Neil; Caballero Jambrina, Antonio; Tomkos, Ioannis; Monroy, Idelfonso Tafur

    2012-01-01

    We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks....

  20. Experimental Demonstration of Mixed Formats and Bit Rates Signal Allocation for Spectrum-flexible Optical Networking

    OpenAIRE

    Borkowski, Robert; Karinou, Fotini; Angelou, Marianna; Arlunno, Valeria; Zibar, Darko; Klonidis, Dimitrios; Guerrero Gonzalez, Neil; Caballero Jambrina, Antonio; Tomkos, Ioannis; Monroy, Idelfonso Tafur

    2012-01-01

    We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks.

  1. Methods for the Analysis of Protein Phosphorylation–Mediated Cellular Signaling Networks

    Science.gov (United States)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

    Protein phosphorylation–mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

  2. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks.

    Science.gov (United States)

    White, Forest M; Wolf-Yadlin, Alejandro

    2016-06-12

    Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks. PMID:27049636

  3. Experimental and computational tools for analysis of signaling networks in primary cells

    DEFF Research Database (Denmark)

    Schoof, Erwin M; Linding, Rune

    2014-01-01

    Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differ...

  4. Traffic analysis and signal processing in optical packet switched networks

    DEFF Research Database (Denmark)

    Fjelde, Tina

    2002-01-01

    /s optical packet switched network exploiting the best of optics and electronics, is used as a thread throughout the thesis. An overview of the DAVID network architecture is given, focussing on the MAN and WAN architecture as well as the MPLS-based network hierarchy. Subsequently, the traffic performance...... into an MI. Moreover, logic XOR is demonstrated in an MZI at 10 and 20 Gbit/s with good results. Using an MI, the excellent performance of a novel scheme for MPLS label swapping exploiting logic XOR is demonstrated at 10 Gbit/s with a negligible 0.4 dB penalty. Finally, three novel schemes are described...

  5. Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

    Directory of Open Access Journals (Sweden)

    Korf Ulrike

    2011-07-01

    Full Text Available Abstract Background Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. Results We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. Conclusions The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

  6. Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae

    OpenAIRE

    Chennubhotla Chakra; Wu Chuang; Farkas Illés J; Bahar Ivet; Oltvai Zoltán N

    2006-01-01

    Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR) mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate l...

  7. A comprehensive map of the toll-like receptor signaling network

    OpenAIRE

    Oda, Kanae; Kitano, Hiroaki

    2006-01-01

    Recognition of pathogen-associated molecular signatures is critically important in proper activation of the immune system. The toll-like receptor (TLR) signaling network is responsible for innate immune response. In mammalians, there are 11 TLRs that recognize a variety of ligands from pathogens to trigger immunological responses. In this paper, we present a comprehensive map of TLRs and interleukin 1 receptor signaling networks based on papers published so far. The map illustrates the possib...

  8. Energy-Efficient Distributed Signal Processing in Mobile Wireless Sensor Networks

    OpenAIRE

    ARIENZO LOREDANA

    2009-01-01

    Wireless mobile sensor networks are important for a number of strategic applications such as surveillance, fire detection, outlier detection. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-efficient signal processing during system operation. We present an overview of statistical prediction frameworks for tracking dynamic targets in range-based signal processing. The single mobile target algorithm has been evaluated...

  9. Microglia Control Neuronal Network Excitability via BDNF Signalling

    OpenAIRE

    Francesco Ferrini; Yves De Koninck

    2013-01-01

    Microglia-neuron interactions play a crucial role in several neurological disorders characterized by altered neural network excitability, such as epilepsy and neuropathic pain. While a series of potential messengers have been postulated as substrates of the communication between microglia and neurons, including cytokines, purines, prostaglandins, and nitric oxide, the specific links between messengers, microglia, neuronal networks, and diseases have remained elusive. Brain-derived neurotrophi...

  10. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  11. Frequency Sensitivity of Signal Detection in Scale-Free Networks

    Institute of Scientific and Technical Information of China (English)

    LU Fa-Ming; LIU Zong-Hua

    2009-01-01

    @@ It has been recently reported that scale-free topology favors the detection of a weak signal because of the higher amplification at the hub node than that at other nodes [Phys. Ref. E 78 (2008)046111]. We investigate the corresponding synchronization behaviors and find that the favorite detection depends not only on the coupling and noise strengths but also on the frequency of the external signal. We reveal theoretically and numerically that the amplification effect of the hub node will decrease monotonously with the externai frequency, which is useful to understand the high sensitivity of animal visual and auditory systems to weak external signals.

  12. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    DEFF Research Database (Denmark)

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.;

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission in...... astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  13. Dynamical patterns of calcium signaling in a functional model of neuron–astrocyte networks

    OpenAIRE

    Postnov, D. E.; Koreshkov, R. N.; Brazhe, N. A.; Brazhe, A. R.; Sosnovtseva, O. V.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission in astrocytic–neuronal networks. We reproduce local and global dynamical patterns observed experimentally.

  14. Modeling of inclined fracture network and calculation of fracture effect on seismic signal spectrum

    International Nuclear Information System (INIS)

    The paper considers modeling technique for the medium with a network of inclined fractures and calculations for completing seismic tasks for such a medium. The network of plane-parallel fractures has controlled dip angle and fluid saturation. The time section for a model with water-saturated fractures is produced. The comparison of incident and transmitted signal spectra is made

  15. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan;

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...

  16. Stability of multispecies bacterial communities: signaling networks may stabilize microbiomes.

    Directory of Open Access Journals (Sweden)

    Ádám Kerényi

    Full Text Available Multispecies bacterial communities can be remarkably stable and resilient even though they consist of cells and species that compete for environmental resources. In silico models suggest that common signals released into the environment may help selected bacterial species cluster at common locations and that sharing of public goods (i.e. molecules produced and released for mutual benefit can stabilize this coexistence. In contrast, unilateral eavesdropping on signals produced by a potentially invading species may protect a community by keeping invaders away from limited resources. Shared bacterial signals, such as those found in quorum sensing systems, may thus play a key role in fine tuning competition and cooperation within multi-bacterial communities. We suggest that in addition to metabolic complementarity, signaling dynamics may be important in further understanding complex bacterial communities such as the human, animal as well as plant microbiomes.

  17. Array signal processing in the NASA Deep Space Network

    Science.gov (United States)

    Pham, Timothy T.; Jongeling, Andre P.

    2004-01-01

    In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.

  18. Estimation of azimuth and slowness of teleseismic signals recorded by a local seismic network

    Institute of Scientific and Technical Information of China (English)

    靳平; 潘常周

    2002-01-01

    A new method that is applicable to local seismic networks to estimate the azimuth and slowness of teleseismic signals is introduced in the paper. The method is based on the correlation between the arrival times and station positions. The analyzed results indicate that the azimuth and slowness of teleseismic signals can be accurately estimated by the method. Average errors for azimuth and slowness measurements obtained by this method using data of Xi(an Digital Telemetry Seismic Network are 2.0o and 0.34 s/(o), respectively. The conclusions drawn from this study indicate that this method may be very useful to interpret teleseismic records of local seismic networks.

  19. A network map of Interleukin-10 signaling pathway.

    Science.gov (United States)

    Verma, Renu; Balakrishnan, Lavanya; Sharma, Kusum; Khan, Aafaque Ahmad; Advani, Jayshree; Gowda, Harsha; Tripathy, Srikanth Prasad; Suar, Mrutyunjay; Pandey, Akhilesh; Gandotra, Sheetal; Prasad, T S Keshava; Shankar, Subramanian

    2016-03-01

    Interleukin-10 (IL-10) is an anti-inflammatory cytokine with important immunoregulatory functions. It is primarily secreted by antigen-presenting cells such as activated T-cells, monocytes, B-cells and macrophages. In biologically functional form, it exists as a homodimer that binds to tetrameric heterodimer IL-10 receptor and induces downstream signaling. IL-10 is associated with survival, proliferation and anti-apoptotic activities of various cancers such as Burkitt lymphoma, non-Hodgkins lymphoma and non-small scell lung cancer. In addition, it plays a central role in survival and persistence of intracellular pathogens such as Leishmania donovani, Mycobacterium tuberculosis and Trypanosoma cruzi inside the host. The signaling mechanisms of IL-10 cytokine are not well explored and a well annotated pathway map has been lacking. To this end, we developed a pathway resource by manually annotating the IL-10 induced signaling molecules derived from literature. The reactions were categorized under molecular associations, activation/inhibition, catalysis, transport and gene regulation. In all, 37 molecules and 76 reactions were annotated. The IL-10 signaling pathway can be freely accessed through NetPath, a resource of signal transduction pathways previously developed by our group. PMID:26253919

  20. Neural network committees for finger joint angle estimation from surface EMG signals

    Directory of Open Access Journals (Sweden)

    Reddy Narender P

    2009-01-01

    Full Text Available Abstract Background In virtual reality (VR systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models. Methodology SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects. Results There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion. Conclusion Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

  1. Integrating in silico resources to map a signaling network.

    Science.gov (United States)

    Liu, Hanqing; Beck, Tim N; Golemis, Erica A; Serebriiskii, Ilya G

    2014-01-01

    The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

  2. Insights into biological information processing: structural and dynamical analysis of a human protein signalling network

    International Nuclear Information System (INIS)

    We present an investigation on the structural and dynamical properties of a 'human protein signalling network' (HPSN). This biological network is composed of nodes that correspond to proteins and directed edges that represent signal flows. In order to gain insight into the organization of cell information processing this network is analysed taking into account explicitly the edge directions. We explore the topological properties of the HPSN at the global and the local scale, further applying the generating function formalism to provide a suitable comparative model. The relationship between the node degrees and the distribution of signals through the network is characterized using degree correlation profiles. Finally, we analyse the dynamical properties of small sub-graphs showing high correlation between their occurrence and dynamic stability

  3. Signaling-based path-segment protection in mesh optical networks

    Institute of Scientific and Technical Information of China (English)

    Yueming Lu; Chao Zou; Qiushi Wang; Yuefeng Ji

    2011-01-01

    Path protections have become increasingly important for current mesh optical networks because fast restorations in generalized multiprotocol label switching (GMPLS) networks are uncertain. However, setting up additional disjoint paths to protect connections leads to more path setup blocking and signaling collisions. We analyze signaling collisions, path blocking and blocking probability, as well as calculate node-to-node blocking probabilities. A signaling-based path-segment protection (PSP) is proposed, which integrates segment protections and path protections as well as enhances the performance of path protections and ring protections. The setup of PSP connections causes less blocking probability than the setup of path protection connections in the simulations.%Path protections have become increasingly important for current mesh optical networks because fast restorations in generalized multiprotocol label switching (GMPLS) networks are uncertain.However,setting up additional disjoint paths to protect connections leads to more path setup blocking and signaling collisions.We analyze signaling collisions,path blocking and blocking probability,as well as calculate node-to-node blocking probabilities.A signaling-based path-segment protection (PSP) is proposed,which integrates segment protections and path protections as well as enhances the performance of path protections and ring protections.The setup of PSP connections causes less blocking probability than the setup of path protection connections in the simulations.Recently,with the emergence of new protection and restoration methods,mesh topologies have gradually replaced ring topologies,especially in optical transport networks (OTNs)[1],automatically switched optical networks (ASONs)[2],generalized multiprotocol label switching (GMPLS) protocol networks[3,4],and packet transport networks (PTNs)[5].However,fast restoration methods currently face some challenges in China.

  4. Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data

    Directory of Open Access Journals (Sweden)

    Yachie Nozomu

    2010-05-01

    Full Text Available Abstract Background Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches. Results We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF. We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled. Conclusions The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics

  5. Integrating In Silico Resources to Map a Signaling Network

    OpenAIRE

    Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

    2014-01-01

    The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can qui...

  6. Signaling and Transcriptional Networks in Heart Development and Regeneration

    OpenAIRE

    Bruneau, Benoit G.

    2013-01-01

    The mammalian heart is the first functional organ, the first indicator of life. Its normal formation and function are essential for fetal life. Defects in heart formation lead to congenital heart defects, underscoring the finesse with which the heart is assembled. Understanding the regulatory networks controlling heart development have led to significant insights into its lineage origins and morphogenesis and illuminated important aspects of mammalian embryology, while providing insights into...

  7. Signaling and transcriptional networks in heart development and regeneration

    OpenAIRE

    Bruneau, BG

    2013-01-01

    The mammalian heart is the first functional organ, the first indicator of life. Its normal formation and function are essential for fetal life. Defects in heart formation lead to congenital heart defects, underscoring the finesse with which the heart is assembled. Understanding the reg-ulatory networks controlling heart development have led to significant insights into its lineage origins and morphogenesis and illuminated important aspects of mammalian embryology, while providing insights int...

  8. AE SOURCE RECOGNITION BY NEURAL NETWORKS WITH OPTIMIZED SIGNAL PARAMETERS

    Czech Academy of Sciences Publication Activity Database

    Chlada, Milan; Převorovský, Zdeněk

    Krakow: University of Technology Krakov, 2008 - (Kanji, O.), s. 250-255 ISBN 978-83-7242-478-5. [European Conference on Acoustic Emission Testing EWGAE /28./. Krakow (PL), 17.09.2008-19.09.2008] R&D Projects: GA ČR GA101/07/1518; GA ČR GA106/07/1393 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source recognition * artificial neural network Subject RIV: BI - Acoustics

  9. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

    Directory of Open Access Journals (Sweden)

    Kirouac Daniel C

    2012-05-01

    Full Text Available Abstract Background Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID, PANTHER, Reactome, I2D, and STRING. We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. Results We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless, we find a multiplicity of network topologies in which receptors couple to downstream

  10. A Signaling Network of Thyroid-Stimulating Hormone

    OpenAIRE

    Goel, Renu; Raju, Rajesh; Maharudraiah, Jagadeesha; Sameer Kumar, Ghantasala S.; Ghosh, Krishna; Kumar, Amit; Lakshmi, T. Pragna; Sharma, Jyoti; Sharma, Rakesh; Balakrishnan, Lavanya; Pan, Archana; Kandasamy, Kumaran; Christopher, Rita; V. Krishna; Mohan, S. Sujatha

    2011-01-01

    Human thyroid stimulating hormone (TSH) is a glycoprotein secreted by the anterior part of the pituitary gland. TSH plays an important physiological role in the regulation of hypothalamic-pituitary-thyroid axis by modulating the release of the thyroid hormones from the thyroid gland. It induces iodine uptake by the thyroid, promotes thyroid epithelial differentiation and growth, and protects thyroid cells from apoptosis. Impairment of TSH signal transduction pathway leads to thyroid disorders...

  11. Mapping Complex Networks: Exploring Boolean Modeling of Signal Transduction Pathways

    OpenAIRE

    Bhardwaj, Gaurav; Wells, Christine P.; Albert, Reka; van Rossum, Damian B.; Patterson, Randen L

    2009-01-01

    In this study, we explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signaling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomization of ...

  12. Patterns of human gene expression variance show strong associations with signaling network hierarchy

    Directory of Open Access Journals (Sweden)

    Ram Prahlad T

    2010-11-01

    Full Text Available Abstract Background Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV of genes and their relationship to cellular functions and physiological responses is poorly understood. Results To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Conclusion Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

  13. On the propagation of diel signals in river networks using analytic solutions of flow equations

    Science.gov (United States)

    Fonley, M.; Mantilla, R.; Small, S. J.; Curtu, R.

    2015-08-01

    Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical experiment to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for two cases: uniform and nonuniform velocities in space over all river links. We then use our analytic solutions to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, our results are consistent with empirical observations that delays are more significant as low flow decreases.

  14. Spatiotemporal properties of intracellular calcium signaling in osteocytic and osteoblastic cell networks under fluid flow.

    Science.gov (United States)

    Jing, Da; Lu, X Lucas; Luo, Erping; Sajda, Paul; Leong, Pui L; Guo, X Edward

    2013-04-01

    Mechanical stimuli can trigger intracellular calcium (Ca(2+)) responses in osteocytes and osteoblasts. Successful construction of bone cell networks necessitates more elaborate and systematic analysis for the spatiotemporal properties of Ca(2+) signaling in the networks. In the present study, an unsupervised algorithm based on independent component analysis (ICA) was employed to extract the Ca(2+) signals of bone cells in the network. We demonstrated that the ICA-based technology could yield higher signal fidelity than the manual region of interest (ROI) method. Second, the spatiotemporal properties of Ca(2+) signaling in osteocyte-like MLO-Y4 and osteoblast-like MC3T3-E1 cell networks under laminar and steady fluid flow stimulation were systematically analyzed and compared. MLO-Y4 cells exhibited much more active Ca(2+) transients than MC3T3-E1 cells, evidenced by more Ca(2+) peaks, less time to the 1st peak and less time between the 1st and 2nd peaks. With respect to temporal properties, MLO-Y4 cells demonstrated higher spike rate and Ca(2+) oscillating frequency. The spatial intercellular synchronous activities of Ca(2+) signaling in MLO-Y4 cell networks were higher than those in MC3T3-E1 cell networks and also negatively correlated with the intercellular distance, revealing faster Ca(2+) wave propagation in MLO-Y4 cell networks. Our findings show that the unsupervised ICA-based technique results in more sensitive and quantitative signal extraction than traditional ROI analysis, with the potential to be widely employed in Ca(2+) signaling extraction in the cell networks. The present study also revealed a dramatic spatiotemporal difference in Ca(2+) signaling for osteocytic and osteoblastic cell networks in processing the mechanical stimulus. The higher intracellular Ca(2+) oscillatory behaviors and intercellular coordination of MLO-Y4 cells provided further evidences that osteocytes may behave as the major mechanical sensor in bone modeling and remodeling

  15. Traffic signal synchronization in the saturated high-density grid road network.

    Science.gov (United States)

    Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

    2015-01-01

    Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN. PMID:25663835

  16. Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Chennubhotla Chakra

    2006-10-01

    Full Text Available Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. In these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets. Conclusion As transcriptional regulation represents the 'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the TR network control robust integrated responses to complex stimuli.

  17. Multimodal signalling in the North American barn swallow: a phenotype network approach.

    Science.gov (United States)

    Wilkins, Matthew R; Shizuka, Daizaburo; Joseph, Maxwell B; Hubbard, Joanna K; Safran, Rebecca J

    2015-10-01

    Complex signals, involving multiple components within and across modalities, are common in animal communication. However, decomposing complex signals into traits and their interactions remains a fundamental challenge for studies of phenotype evolution. We apply a novel phenotype network approach for studying complex signal evolution in the North American barn swallow (Hirundo rustica erythrogaster). We integrate model testing with correlation-based phenotype networks to infer the contributions of female mate choice and male-male competition to the evolution of barn swallow communication. Overall, the best predictors of mate choice were distinct from those for competition, while moderate functional overlap suggests males and females use some of the same traits to assess potential mates and rivals. We interpret model results in the context of a network of traits, and suggest this approach allows researchers a more nuanced view of trait clustering patterns that informs new hypotheses about the evolution of communication systems. PMID:26423842

  18. Method for analyzing signaling networks in complex cellular systems.

    Science.gov (United States)

    Plavec, Ivan; Sirenko, Oksana; Privat, Sylvie; Wang, Yuker; Dajee, Maya; Melrose, Jennifer; Nakao, Brian; Hytopoulos, Evangelos; Berg, Ellen L; Butcher, Eugene C

    2004-02-01

    Now that the human genome has been sequenced, the challenge of assigning function to human genes has become acute. Existing approaches using microarrays or proteomics frequently generate very large volumes of data not directly related to biological function, making interpretation difficult. Here, we describe a technique for integrative systems biology in which: (i) primary cells are cultured under biologically meaningful conditions; (ii) a limited number of biologically meaningful readouts are measured; and (iii) the results obtained under several different conditions are combined for analysis. Studies of human endothelial cells overexpressing different signaling molecules under multiple inflammatory conditions show that this system can capture a remarkable range of functions by a relatively small number of simple measurements. In particular, measurement of seven different protein levels by ELISA under four different conditions is capable of reconstructing pathway associations of 25 different proteins representing four known signaling pathways, implicating additional participants in the NF-kappaBorRAS/mitogen-activated protein kinase pathways and defining additional interactions between these pathways. PMID:14745015

  19. Adaptive coded spreading OFDM signal for dynamic-λ optical access network

    Science.gov (United States)

    Liu, Bo; Zhang, Lijia; Xin, Xiangjun

    2015-12-01

    This paper proposes and experimentally demonstrates a novel adaptive coded spreading (ACS) orthogonal frequency division multiplexing (OFDM) signal for dynamic distributed optical ring-based access network. The wavelength can be assigned to different remote nodes (RNs) according to the traffic demand of optical network unit (ONU). The ACS can provide dynamic spreading gain to different signals according to the split ratio or transmission length, which offers flexible power budget for the network. A 10×13.12 Gb/s OFDM access with ACS is successfully demonstrated over two RNs and 120 km transmission in the experiment. The demonstrated method may be viewed as one promising for future optical metro access network.

  20. Drastic disorder-induced reduction of signal amplification in scale-free networks.

    Science.gov (United States)

    Chacón, Ricardo; Martínez, Pedro J

    2015-07-01

    Understanding information transmission across a network is a fundamental task for controlling and manipulating both biological and manmade information-processing systems. Here we show how topological resonant-like amplification effects in scale-free networks of signaling devices are drastically reduced when phase disorder in the external signals is considered. This is demonstrated theoretically by means of a starlike network of overdamped bistable systems, and confirmed numerically by simulations of scale-free networks of such systems. The taming effect of the phase disorder is found to be sensitive to the amplification's strength, while the topology-induced amplification mechanism is robust against this kind of quenched disorder in the sense that it does not significantly change the values of the coupling strength where amplification is maximum in its absence. PMID:26274239

  1. Neural Network Analysis of Acoustic Emission Signals for Drill Wear Monitoring

    International Nuclear Information System (INIS)

    The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

  2. Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

    CERN Document Server

    Goela, Naveen

    2012-01-01

    A model, called the linear transform network (LTN), is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes transmit subspace projections of random correlated signals by applying reduced-dimension linear transforms. The subspace projections are linearly processed by multiple relays and routed to intended receivers. Each receiver applies a linear estimator to approximate a subset of the sources with minimum mean squared error (MSE) distortion. The model is extended to include noisy networks with power constraints on transmitters. A key task is to compute all local compression matrices and linear estimators in the network to minimize end-to-end distortion. The non-convex problem is solved iteratively within an optimization framework using constrained quadratic programs (QPs). The proposed algorithm recovers as special cases the regular and distributed Karhunen-Loeve ...

  3. Molecular signaling networks in regulation of immunity and disease

    DEFF Research Database (Denmark)

    Laursen, Janne Marie; Jensen, Stina Rikke; Sørensen, Morten;

    The gut microbiota, host tissues, and the immune system form a complex network where extensive crosstalk and molecular interactions substantially impact the overall state of the system. Concomitantly, modulation of host immune function is recurrently a result of the interaction of complex and...... dynamic microbial communities with the immune cell compartment in the gut, and therefore the interaction between components from different gut bacteria can efficiently shape the phenotype of the immune response. A specialized antigenpresenting cell present at mucosal surfaces, the dendritic cell (DC......), plays a crucial role in shaping the nature of the adaptive/memorybased immune response after encountering inflammatory compounds. In the gut, the DC is continuously exposed to microbial and dietary components that are recognized by its innate pattern recognition receptors, and the phenotype developed in...

  4. The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

    International Nuclear Information System (INIS)

    In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work, and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well

  5. Clustering of ECG Signals Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm

    OpenAIRE

    Elaheh Sayari; Mahdi Yaghoobi

    2014-01-01

    Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Whereas clustering of electrocardiogram (ECG) signals will help to identify the heart diseases as soon as possible. In this regard, neural network and fuzzy logic have been used in many application areas while each of them has the advantages and disadvantages. Thus, the present paper utilizes the proposed fuzzy neural network (FNN) with initial weights generated by...

  6. Regulation of rhythm genesis by volume-limited, astroglia-like signals in neural networks

    OpenAIRE

    Savtchenko, L P; Rusakov, D. A.

    2014-01-01

    Rhythmic activity of the brain often depends on synchronized spiking of interneuronal networks interacting with principal neurons. The quest for physiological mechanisms regulating network synchronization has therefore been firmly focused on synaptic circuits. However, it has recently emerged that synaptic efficacy could be influenced by astrocytes that release signalling molecules into their macroscopic vicinity. To understand how this volume-limited synaptic regulation can affect oscillatio...

  7. Power network transient stability electronics emulator using mixed-signal calibration

    OpenAIRE

    Lanz, Guillaume; Fabre, Laurent; Lilis, Georgios; Kyriakidis, Theodoros; Sallin, Denis; Cherkaoui, Rachid; Kayal, Maher

    2013-01-01

    The emerging field of power system emulation for real time smart grid management is very demanding in terms of speed and accuracy. This paper provides detailed information about the electronics calibration process of a high-speed power network emulator dedicated to the transient stability analysis of power systems. This emulator uses mixed-signal hardware to model the dynamic behavior of a power network. Special design allows the self-calibration of the analog electronics through successive m...

  8. Design of Transparent Distributed IMS Network: Security Challenges Risk and Signaling Analysis

    OpenAIRE

    Hamid Allouch; Mostafa Belkasmi

    2013-01-01

    The IP Multimedia subsystem (IMS) based on SIP as mechanism signalling and interfaces with otherservers using OSA (Open Service Access) and CAMEL (Customized Applications for Mobile networkEnhanced Logic).Is responsible for the interconnection of IP packets with other network, IMS support datacommunication services, voice, video, messaging and web-based technologies. In this work we present adistributed design of architecture that turns up some challenges of transparent mobility on the secure...

  9. Chemokine Signaling Directs Trunk Lymphatic Network Formation along the Preexisting Blood Vasculature

    OpenAIRE

    Cha, Young Ryun; Fujita, Misato; Butler, Matthew; Isogai, Sumio; Kochhan, Eva; Siekmann, Arndt F; Weinstein, Brant M

    2012-01-01

    The lymphatic system is crucial for fluid homeostasis, immune responses, and numerous pathological processes. However, the molecular mechanisms responsible for establishing the anatomical form of the lymphatic vascular network remain largely unknown. Here, we show that chemokine signaling provides critical guidance cues directing early trunk lymphatic network assembly and patterning. The chemokine receptors Cxcr4a and Cxcr4b are expressed in lymphatic endothelium, while chemokine ligands Cxcl...

  10. Using neural networks to enhance the Higgs boson signal at hadron colliders

    International Nuclear Information System (INIS)

    Neural networks are used to help distinguish the ZZ→l+l--jet-jet signal produced by the decay of a 400 GeV Higgs boson at a proton-proton collider energy of 15 TeV from the open-quote open-quote ordinary close-quote close-quote QCD Z+jets background. The ideal case where only one event at a time enters the detector (no pileup) and the case of multiple interactions per beam crossing (pileup) are examined. In both cases, when used in conjunction with the standard cuts, neural networks provide an additional signal-to-background enhancement. copyright 1996 The American Physical Society

  11. Common corruption of the mTOR signaling network in human tumors

    Science.gov (United States)

    Menon, Suchithra; Manning, Brendan D.

    2013-01-01

    Summary The mammalian Target of Rapamycin (mTOR) is responsive to numerous extracellular and intracellular cues and, through the formation of two physically and functionally distinct complexes, plays a central role in the homeostatic control of cell growth, proliferation and survival. Through aberrant activation of mTOR signaling, the perception of cellular growth signals becomes disconnected from the processes promoting cell growth, and this underlies the pathophysiology of a number of genetic tumor syndromes and cancers. Here, we review the oncogenes and tumor suppressors comprising the regulatory network upstream of mTOR, highlight the human cancers in which mTOR is activated, and discuss how dysregulated mTOR signaling gives tumors a selective growth advantage. In addition, we discuss why activation of mTOR, as a consequence of distinct oncogenic events, results in diverse clinical outcomes, and how the complexity of the mTOR signaling network might dictate therapeutic approaches. PMID:19956179

  12. Neighbor Discovery in a Wireless Sensor Network: Multipacket Reception Capability and Physical-Layer Signal Processing

    CERN Document Server

    Jeon, Jeongho

    2011-01-01

    In randomly deployed networks, such as sensor networks, an important problem for each node is to discover its \\textit{neighbor} nodes so that the connectivity amongst nodes can be established. In this paper, we consider this problem by incorporating the physical layer parameters in contrast to the most of the previous work which assumed a collision channel. Specifically, the pilot signals that nodes transmit are successfully decoded if the strength of the received signal relative to the interference is sufficiently high. Thus, each node must extract signal parameter information from the superposition of an unknown number of received signals. This problem falls naturally in the purview of random set theory (RST) which generalizes standard probability theory by assigning \\textit{sets}, rather than values, to random outcomes. The contributions in the paper are twofold: first, we introduce the realistic effect of physical layer considerations in the evaluation of the performance of \\textit{logical} discovery algo...

  13. Research of Crossbar Switch of High Performance Network of Signal Processing System

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The new type of embedded signal processing system based on the packet switched network is achieved. According to the application field and the characteristics of signal processing system, the RapidIO protocol is used to solve the high-speed interconnection of multi-digital signal processor (DSP). Based on this protocol, a kind of crossbar switch module which is used to interconnect multi-DSP in the system is introduced. A route strategy, some flow control rules and error control rules, which adapt to different RapidIO network topology are also introduced. Crossbar switch performance is analyzed in detail by the probability module. By researching the technique of crossbar switch and analyzing the system performance, it has a significant meaning for building the general signal processing system.

  14. ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks

    Directory of Open Access Journals (Sweden)

    Iman Moazen

    2009-09-01

    Full Text Available In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT and Probabilistic Neural Network (PNN. The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decomposed into sub-classes using DWT. A PNN is used to classify ECG signals using statistical discriminating features which are extracted from ECG and its sub-classes. Experimental results on five classes of ECG signals from MIT-BIH arrhythmia database show that the proposed method learns very fast, low computational complexity, and a very high performance compared to the previous methods.

  15. Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

    International Nuclear Information System (INIS)

    For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

  16. Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models.

    Science.gov (United States)

    McBride, Joseph; Sullivan, Adam; Xia, Henian; Petrie, Adam; Zhao, Xiaopeng

    2011-06-01

    Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. We have developed new techniques of signal reconstructions based on iterative retraining and accumulated averaging of neural networks. The effectiveness and robustness of these techniques are demonstrated using data records from the Computing in Cardiology/PhysioNet Challenge 2010. The average correlation coefficient between prediction and target for 100 records of various target signals is about 0.9. We have also explored influences of a few important parameters on the accuracy of reconstructions. The developed techniques may be used to detect changes in patient state and to recognize intervals of signal corruption. PMID:21566268

  17. Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models

    International Nuclear Information System (INIS)

    Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. We have developed new techniques of signal reconstructions based on iterative retraining and accumulated averaging of neural networks. The effectiveness and robustness of these techniques are demonstrated using data records from the Computing in Cardiology/PhysioNet Challenge 2010. The average correlation coefficient between prediction and target for 100 records of various target signals is about 0.9. We have also explored influences of a few important parameters on the accuracy of reconstructions. The developed techniques may be used to detect changes in patient state and to recognize intervals of signal corruption

  18. Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

    Directory of Open Access Journals (Sweden)

    D. Hämäläinen

    2008-03-01

    Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

  19. MONITORING NETWORK STRUCTURE AND CONTENT QUALITY OF SIGNAL PROCESSING ARTICLES ON WIKIPEDIA

    OpenAIRE

    Lee, Tao-Chun; Unnikrishnan, Jayakrishnan

    2013-01-01

    Wikipedia has become a widely-used resource on signal processing. However, the freelance-editing model of Wikipedia makes it challenging to maintain a high content quality. We develop techniques to monitor the network structure and content quality of Signal Processing (SP) articles on Wikipedia. Using metrics to quantify the importance and quality of articles, we generate a list of SP articles on Wikipedia arranged in the order of their need for improvement. The tools we use include the HITS ...

  20. Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting

    OpenAIRE

    Joonyoung Jung; Kiryong Ha; Jeonwoo Lee; Youngsung Kim; Daeyoung Kim

    2008-01-01

    We developed a ubiquitous healthcare system consisted of aphysiological signal devices, a mobile system, a device provider system, a healthcare service provider system, a physician system, and a healthcare personal system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We propose a scanning algorithm, dynamic discover...

  1. Deciphering the hormonal signalling network behind the systemic resistance induced by Trichoderma harzianum in tomato

    OpenAIRE

    Martínez-Medina, Ainhoa; Fernández, Iván; Sánchez-Guzmán, María J.; Jung, Sabine C.; Pascual, Jose A.; Pozo, María J.

    2013-01-01

    Root colonization by selected Trichoderma isolates can activate in the plant a systemic defense response that is effective against a broad-spectrum of plant pathogens. Diverse plant hormones play pivotal roles in the regulation of the defense signaling network that leads to the induction of systemic resistance triggered by beneficial organisms [induced systemic resistance (ISR)]. Among them, jasmonic acid (JA) and ethylene (ET) signaling pathways are generally essential for ISR. However, Tric...

  2. Regulation of the BMP Signaling-Responsive Transcriptional Network in the Drosophila Embryo.

    Science.gov (United States)

    Deignan, Lisa; Pinheiro, Marco T; Sutcliffe, Catherine; Saunders, Abbie; Wilcockson, Scott G; Zeef, Leo A H; Donaldson, Ian J; Ashe, Hilary L

    2016-07-01

    The BMP signaling pathway has a conserved role in dorsal-ventral axis patterning during embryonic development. In Drosophila, graded BMP signaling is transduced by the Mad transcription factor and opposed by the Brinker repressor. In this study, using the Drosophila embryo as a model, we combine RNA-seq with Mad and Brinker ChIP-seq to decipher the BMP-responsive transcriptional network underpinning differentiation of the dorsal ectoderm during dorsal-ventral axis patterning. We identify multiple new BMP target genes, including positive and negative regulators of EGF signaling. Manipulation of EGF signaling levels by loss- and gain-of-function studies reveals that EGF signaling negatively regulates embryonic BMP-responsive transcription. Therefore, the BMP gene network has a self-regulating property in that it establishes a balance between its activity and that of the antagonistic EGF signaling pathway to facilitate correct patterning. In terms of BMP-dependent transcription, we identify key roles for the Zelda and Zerknüllt transcription factors in establishing the resulting expression domain, and find widespread binding of insulator proteins to the Mad and Brinker-bound genomic regions. Analysis of embryos lacking the BEAF-32 insulator protein shows reduced transcription of a peak BMP target gene and a reduction in the number of amnioserosa cells, the fate specified by peak BMP signaling. We incorporate our findings into a model for Mad-dependent activation, and discuss its relevance to BMP signal interpretation in vertebrates. PMID:27379389

  3. Signal Network Analysis of Plant Genes Responding to Ionizing Radiation

    International Nuclear Information System (INIS)

    In this project, we irradiated Arabidopsis plants with various doses of gamma-rays at the vegetative and reproductive stages to assess their radiation sensitivity. After the gene expression profiles and an analysis of the antioxidant response, we selected several Arabidopsis genes for uses of 'Radio marker genes (RMG)' and conducted over-expression and knock-down experiments to confirm the radio sensitivity. Based on these results, we applied two patents for the detection of two RMG (At3g28210 and At4g37990) and development of transgenic plants. Also, we developed a Genechip for use of high-throughput screening of Arabidopsis genes responding only to ionizing radiation and identified RMG to detect radiation leaks. Based on these results, we applied two patents associated with the use of Genechip for different types of radiation and different growth stages. Also, we conducted co-expression network study of specific expressed probes against gamma-ray stress and identified expressed patterns of duplicated genes formed by whole/500kb segmental genome duplication

  4. A wireless sensor network for monitoring volcano-seismic signals

    Science.gov (United States)

    Lopes Pereira, R.; Trindade, J.; Gonçalves, F.; Suresh, L.; Barbosa, D.; Vazão, T.

    2014-12-01

    Monitoring of volcanic activity is important for learning about the properties of each volcano and for providing early warning systems to the population. Monitoring equipment can be expensive, and thus the degree of monitoring varies from volcano to volcano and from country to country, with many volcanoes not being monitored at all. This paper describes the development of a wireless sensor network (WSN) capable of collecting geophysical measurements on remote active volcanoes. Our main goals were to create a flexible, easy-to-deploy and easy-to-maintain, adaptable, low-cost WSN for temporary or permanent monitoring of seismic tremor. The WSN enables the easy installation of a sensor array in an area of tens of thousands of m2, allowing the location of the magma movements causing the seismic tremor to be calculated. This WSN can be used by recording data locally for later analysis or by continuously transmitting it in real time to a remote laboratory for real-time analyses. We present a set of tests that validate different aspects of our WSN, including a deployment on a suspended bridge for measuring its vibration.

  5. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  6. CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK

    Institute of Scientific and Technical Information of China (English)

    薛建中; 郑崇勋; 闫相国

    2004-01-01

    Objective This paper presents classifications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.

  7. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  8. Transduction motif analysis of gastric cancer based on a human signaling network

    International Nuclear Information System (INIS)

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs

  9. Effect of signal noise on the learning capability of an artificial neural network

    International Nuclear Information System (INIS)

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  10. Towards convergence of wireless and wireline signal transport in broadband access networks

    DEFF Research Database (Denmark)

    Yu, Xianbin; Prince, Kamau; Tafur Monroy, Idelfonso

    2010-01-01

    Hybrid optical wireless access networks are to play an important role in the realization of the vision of delivery of broadband services to the end-user any time, anywhere and at affordable costs. We present results of experiments conducted over a field deployed optical fibre links we successfull...... demonstrated converged wireless and wireline signal transport over a common fibre infrastructure. The type of signal used in this field deployed experiments cover WiMax, Impulse-radio ultra-wideband (UWB) and coherent transmission of baseband QPSK and radio-over-fibre signals....

  11. A network signal amplification strategy of ultrasensitive photoelectrochemical immunosensing carcinoembryonic antigen based on CdSe/melamine network as label.

    Science.gov (United States)

    Li, Jiaojiao; Zhang, Yong; Kuang, Xuan; Wang, Zhiling; Wei, Qin

    2016-11-15

    Taking advantage of CdSe/melamine network as label and Au-TiO2 as substrate, this work developed a novel kind of signal amplification strategy for fabricating photoelectrochemical (PEC) immunoassay. The melamine, a star-shaped triamino molecule, was firstly used for readily capturing CdSe QDs and forming a CdSe/melamine network, which was formed through strong interactions between the carboxyl groups of TGA-stabilized CdSe QDs and the three amino groups of each melamine molecule. In this strategy, the primary antibody (Ab1) was immobilized onto Au-TiO2 substrate, which made the photoelectric conversion efficiency increase significantly. After the formed Ab2-CdSe/melamine network labels were captured onto the electrode surface via the specific antibody-antigen interaction, the photoelectric activity could be further enhanced via the interaction between the Au-TiO2 substrate and CdSe/melamine network. Due to this amplification of PEC signals and the special structure of the label, the fabricated PEC immunosensor was applied for sensitive and specific detection of cancer biomarker carcinoembryonic antigen (CEA), and displayed a wide linear range (0.005-1000ngmL(-1)) and low detection limit (5pgmL(-1)). In addition, the immunosensor was performed with good stability and reproducibility, and the results to analyze human serum samples were satisfactory. PMID:27281106

  12. Neural network approach of active ultrasonic signals for structural health monitoring analysis

    Science.gov (United States)

    Kral, Zachary; Horn, Walter; Steck, James

    2009-03-01

    Maintenance is an important issue for aerospace systems, since they are in service beyond their designed lifetime. This requires scheduled inspections and damage repair before failure. Research is in progress to develop a structural health monitoring system (SHMS) to improve this maintenance routine. Ultrasonic testing, utilizing a system of piezoelectric actuators and sensors, is a promising concept Measured wave signals are compared with signals for previously scanned states. Changes to the signal could be the result of damage to the component. This paper focuses on analyzing the differences of states, using artificial neural networks. Neural network analysis has the potential of creating a SHMS of greater ability and processing. Experiments were performed on a thin, flat aluminum panel. Ultrasonic actuators and sensors were installed and a baseline scan was performed on the undamaged panel. Simulated damage was introduced in specific areas, and scans were conducted for several damaged states. Neural networks were created to assess the changing conditions of the panel. The system was later tested on a lap joint specimen to confirm the abilities of the neural network. This form of analysis performed well at locating and quantifying areas of change within the structure. The neural network performance indicated that it has a role in the SHMS of aerospace structures.

  13. Spontaneous calcium signals induced by gap junctions in a network model of astrocytes

    Science.gov (United States)

    Kazantsev, V. B.

    2009-01-01

    The dynamics of a network model of astrocytes coupled by gap junctions is investigated. Calcium dynamics of the single cell is described by the biophysical model comprising the set of three nonlinear differential equations. Intercellular dynamics is provided by the diffusion of inositol 1,4,5-trisphosphate (IP3) through gap junctions between neighboring astrocytes. It is found that the diffusion induces the appearance of spontaneous activity patterns in the network. Stability of the network steady state is analyzed. It is proved that the increase of the diffusion coefficient above a certain critical value yields the generation of low-amplitude subthreshold oscillatory signals in a certain frequency range. It is shown that such spontaneous oscillations can facilitate calcium pulse generation and provide a certain time scale in astrocyte signaling.

  14. Applications of autoassociative neural networks for signal validation in accident management

    International Nuclear Information System (INIS)

    The OECD Halden Reactor Project has been working for several years with computer based systems for determination on plant status including early fault detection and signal validation. The method here presented explores the possibility to use a neural network approach to validate important process signals during normal and abnormal plant conditions. In BWR plants, signal validation has two important applications: reliable thermal limits calculation and reliable inputs to other computerized systems that support the operator during accident scenarious. This work shows how a properly trained autoassociative neural network can promptly detect faulty process signal measurements and produce a best estimate of the actual process value. Noise has been artificially added to the input to evaluate the network ability to respond in a very low signal to noise ratio environment. Training and test datasets have been simulated by the real time transient simulator code APROS. Future development addresses the validation of the model through the use of real data from the plant. (author). 5 refs, 17 figs

  15. Signaling and Gene Regulatory Networks Governing Definitive Endoderm Derivation From Pluripotent Stem Cells.

    Science.gov (United States)

    Mohammadnia, Abdulshakour; Yaqubi, Moein; Pourasgari, Farzaneh; Neely, Eric; Fallahi, Hossein; Massumi, Mohammad

    2016-09-01

    The generation of definitive endoderm (DE) from pluripotent stem cells (PSCs) is a fundamental stage in the formation of highly organized visceral organs, such as the liver and pancreas. Currently, there is a need for a comprehensive study that illustrates the involvement of different signaling pathways and their interactions in the derivation of DE cells from PSCs. This study aimed to identify signaling pathways that have the greatest influence on DE formation using analyses of transcriptional profiles, protein-protein interactions, protein-DNA interactions, and protein localization data. Using this approach, signaling networks involved in DE formation were constructed using systems biology and data mining tools, and the validity of the predicted networks was confirmed experimentally by measuring the mRNA levels of hub genes in several PSCs-derived DE cell lines. Based on our analyses, seven signaling pathways, including the BMP, ERK1-ERK2, FGF, TGF-beta, MAPK, Wnt, and PIP signaling pathways and their interactions, were found to play a role in the derivation of DE cells from PSCs. Lastly, the core gene regulatory network governing this differentiation process was constructed. The results of this study could improve our understanding surrounding the efficient generation of DE cells for the regeneration of visceral organs. J. Cell. Physiol. 231: 1994-2006, 2016. © 2016 Wiley Periodicals, Inc. PMID:26755186

  16. The Signal Extraction of Fetal Heart Rate Based on Wavelet Transform and BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-hong; ZHANG Bang-cheng; FU Hu-dai

    2005-01-01

    This paper briefly introduces the collection and recognition of biomedical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI, the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women' s signals of FM were collected successfully by using the sensor. The correct of BP network recognition is about 83.3% by using the above measure.

  17. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Directory of Open Access Journals (Sweden)

    Siddhartha Jain

    2014-12-01

    Full Text Available Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  18. Multitask Learning of Signaling and Regulatory Networks with Application to Studying Human Response to Flu

    Science.gov (United States)

    Jain, Siddhartha; Gitter, Anthony; Bar-Joseph, Ziv

    2014-01-01

    Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem PMID:25522349

  19. DMPD: The interferon signaling network and transcription factor C/EBP-beta. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 18163952 The interferon signaling network and transcription factor C/EBP-beta. Li H..., Gade P, Xiao W, Kalvakolanu DV. Cell Mol Immunol. 2007 Dec;4(6):407-18. (.png) (.svg) (.html) (.csml) Show The... interferon signaling network and transcription factor C/EBP-beta. PubmedID 18163952 Title The interfero

  20. Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network

    Directory of Open Access Journals (Sweden)

    Ali Reza Mehri Dehnavi

    2011-01-01

    Full Text Available Background: Various techniques are used in diagnosing cardiac diseases. The electrocardiogram is one of these tools in common use. In this study vectorcardiogram (VCG signals are used as a tool for detection of cardiac ischemia. Methods: VCG signals used in this study were obtained form 60 patients suspected to have ischemia disease and 10 normal candidates. Verification of the ischemia had done by the cardiologist during strain test by the evaluation of electrocardiogram (ECG records and patient′s clinical history. The recorder device was Cardiax digital recorder system. The VCG signals were recorded in Frank lead configuration system. Results: Extracted ischemia VCG signals have been configured with 22 features. Feature dimensionalities were reduced by the use of Independent Components Analysis and Principal Component Analysis tools. Results obtained from strain test indicated that among 60 subjects, 50 had negative results and 10 had positive results. Ischemia detection of neural network using VCG parameters indicates 86% accuracy. Classification result on neural network using ECG ischemia detection parameters is 73% accurate. Accumulative evaluation including VCG analysis and strain test indicates 90% consistency. Conclusions: Regarding the obtained results in this study, VCG has higher accuracy than ECG, so that in cases which ECG signal cannot provide certain diagnosis of existence or non-existence of ischemia, VCG signal can help in a wider range. We suggest the use of VCG as an auxiliary low cost tool in ischemia detection.

  1. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  2. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals

    Science.gov (United States)

    Marcon, Luciano; Diego, Xavier; Sharpe, James; Müller, Patrick

    2016-01-01

    The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems. DOI: http://dx.doi.org/10.7554/eLife.14022.001 PMID:27058171

  3. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    Science.gov (United States)

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  4. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals.

    Science.gov (United States)

    Marcon, Luciano; Diego, Xavier; Sharpe, James; Müller, Patrick

    2016-01-01

    The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems. PMID:27058171

  5. Plant morphogenesis, auxin, and the signal-trafficking network incompleteness theorem

    Directory of Open Access Journals (Sweden)

    Karl J. Niklas

    2012-03-01

    Full Text Available Plant morphogenesis (the development of form and function requires signal-trafficking and cross-talking among all levels of organization to coordinate the operation of metabolic and genomic networked systems. Many if not all of these biological features can be rendered as logic circuits supervising the operation of one or more signal-activated metabolic or genome networks. This approach simplifies complex morphogenetic phenomena and allows for their aggregation into diagrams of larger, more "global" networked systems. This conceptualization is illustrated for morphogenesis in model plants such as maize (Zea mays and Thale cress (Arabidopsis thaliana from an evolutionary perspective. The phytohormone indole-acetic acid (IAA is used as an example for a well-known signaling chemical and discussed in terms of the logic circuits and signal-activated sub-systems for hormone-mediated wall loosening and cell expansion as well as polar/lateral intercellular IAA transport. For each of these phenomena, a circuit/sub-system diagram highlights missing components, either in the logic circuit or in the sub-system it supervises, that must be identified experimentally if each of these basic phenomena is to be fully understood within a phylogen

  6. Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis.

    Science.gov (United States)

    Newaz, Khalique; Sriram, K; Bera, Debajyoti

    2015-01-01

    Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these

  7. Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis.

    Directory of Open Access Journals (Sweden)

    Khalique Newaz

    Full Text Available Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC to protease-resistant isofrom (rPrPSc. Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are the prime network pathway(s that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that

  8. Characterisation of eddy current signals using different types of artificial neural networks

    International Nuclear Information System (INIS)

    Eddy current testing is one of the important techniques in nondestructive testing. Automated characterisation of eddy current signals (ECS), either in the form of lissajous patterns (figure-of-eight) or individual voltage vs. time signals is an area of growing interest. This is particularly relevant in environments where the signal-to-noise ratio (SNR) of ECS are very poor. Intelligent, timely and precise interpretation of resulting data, is the key for improving the efficiency of NDT and E. A comprehensive study has been undertaken by the authors for the characterisation of ECS having poor SNR, using three types of artificial neural networks (ANNs). The types of ANNs used in this study are [a] the error-back propagation model, [b] the binary Hopfield model and [c] the Kohonen's self-organising maps model. Eddy current signals, acquired from different types of defects such as holes and notches on stainless steel type 316 sheets were used in this study. (author)

  9. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  10. Coded Single-Tone Signaling for Resource Coordination and Interference Management in Femtocell Networks

    CERN Document Server

    Yang, Chunliang

    2011-01-01

    Resource coordination and interference management is the key to achieving the benefits of femtocell networks. Over-the-air signaling is one of the most effective means for distributed dynamic resource coordination and interference management. However, the design of this type of signal is challenging. In this letter, we address the challenges and propose an effective solution, referred to as coded single-tone signaling (STS). The proposed coded STS scheme possesses certain highly desirable properties, such as no dedicated resource requirement (no overhead), no near-and-far effect, no inter-signal interference (no multi-user interference), low peak-to-average power ratio (deep coverage). In addition, the proposed coded STS can fully exploit frequency diversity and provides a means for high quality wideband channel estimation. The coded STS design is demonstrated through a concrete numerical example. Performance of the proposed coded STS is evaluated through simulations.

  11. Emerging roles of microRNAs in the Wnt signaling network.

    Science.gov (United States)

    Schepeler, Troels

    2013-01-01

    The Wnt signaling network is known to regulate many cellular processes and is of crucial importance during development and in pathological conditions, including cancer. Small noncoding RNAs from the microRNA family (miRNAs) are important elements in the post-transcriptional control of gene expression. In this work, I review the cross talk between miRNAs and the canonical Wnt signaling pathway in various biological processes with particular emphasis on carcinogenesis. Because alterations of miRNA activity and aberrant Wnt signaling are each intimately linked to tumor biology, deciphering the complex interplay between these two regulatory modules is essential to advance our understanding of the integrated functions of miRNAs in signal transduction cascades and develop rational treatment regimens against cancer. PMID:23614621

  12. RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

    Science.gov (United States)

    Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

    2008-10-01

    A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

  13. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  14. Information Feedback Strategies in a Signal Controlled Network with Overlapped Routes

    Institute of Scientific and Technical Information of China (English)

    TIAN Li-Jun; HUANG Hai-Jun; LIU Tian-Liang

    2009-01-01

    We investigate the effects of four different information feedback strategies on the dynamics of traffic, travel-ers' route choice and the resultant system performance in a signal controlled network with overlapped routes.Simulation results given by the cellular automaton model show that the system purpose-based mean velocity feedback strategy and the congestion coefficient feedback strategy have more advantages in improving network utilization efficiency and reducing travelers' travel times. The travel time feedback strategy and the individual purposed-based mean velocity feedback strategy behave slightly better to ensure user equity.

  15. dNSP: a biologically inspired dynamic Neural network approach to Signal Processing.

    Science.gov (United States)

    Cano-Izquierdo, José Manuel; Ibarrola, Julio; Pinzolas, Miguel; Almonacid, Miguel

    2008-09-01

    The arriving order of data is one of the intrinsic properties of a signal. Therefore, techniques dealing with this temporal relation are required for identification and signal processing tasks. To perform a classification of the signal according with its temporal characteristics, it would be useful to find a feature vector in which the temporal attributes were embedded. The correlation and power density spectrum functions are suitable tools to manage this issue. These functions are usually defined with statistical formulation. On the other hand, in biology there can be found numerous processes in which signals are processed to give a feature vector; for example, the processing of sound by the auditory system. In this work, the dNSP (dynamic Neural Signal Processing) architecture is proposed. This architecture allows representing a time-varying signal by a spatial (thus statical) vector. Inspired by the aforementioned biological processes, the dNSP performs frequency decomposition using an analogical parallel algorithm carried out by simple processing units. The architecture has been developed under the paradigm of a multilayer neural network, where the different layers are composed by units whose activation functions have been extracted from the theory of Neural Dynamic [Grossberg, S. (1988). Nonlinear neural networks principles, mechanisms and architectures. Neural Networks, 1, 17-61]. A theoretical study of the behavior of the dynamic equations of the units and their relationship with some statistical functions allows establishing a parallelism between the unit activations and correlation and power density spectrum functions. To test the capabilities of the proposed approach, several testbeds have been employed, i.e. the frequencial study of mathematical functions. As a possible application of the architecture, a highly interesting problem in the field of automatic control is addressed: the recognition of a controlled DC motor operating state. PMID:18579344

  16. Simulation study on effects of signaling network structure on the developmental increase in complexity

    Energy Technology Data Exchange (ETDEWEB)

    Keranen, Soile V.E.

    2003-04-02

    The developmental increase in structural complexity in multicellular life forms depends on local, often non-periodic differences in gene expression. These depend on a network of gene-gene interactions coded within the organismal genome. To better understand how genomic information generates complex expression patterns, I have modeled the pattern forming behavior of small artificial genomes in virtual blastoderm embryos. I varied several basic properties of these genomic signaling networks, such as the number of genes, the distributions of positive (inductive) and negative (repressive) interactions, and the strengths of gene-gene interactions, and analyzed their effects on developmental pattern formation. The results show how even simple genomes can generate complex non-periodic patterns under suitable conditions. They also show how the frequency of complex patterns depended on the numbers and relative arrangements of positive and negative interactions. For example, negative co-regulation of signaling pathway components increased the likelihood of (complex) patterns relative to differential negative regulation of the pathway components. Interestingly, neither quantitative differences either in strengths of signaling interactions nor multiple response thresholds to signal concentration (as in morphogen gradients) were essential for formation of multiple, spatially unique cell types. Thus, with combinatorial code of gene regulation and hierarchical signaling interactions, it is theoretically possible to organize metazoan embryogenesis with just a small fraction of the metazoan genome. Because even small networks can generate complex patterns when they contain a suitable set of connections, evolution of metazoan complexity may have depended more on selection for favourable configurations of signaling interactions than on the increase in numbers of regulatory genes.

  17. Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

    Science.gov (United States)

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

    2013-04-01

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles. PMID:23530221

  18. Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

    Institute of Scientific and Technical Information of China (English)

    LI Yan; FAN Xiao-ping

    2008-01-01

    An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

  19. Wireless Network of Collaborative Physiological Signal Devices in a U-Healthcare System

    Science.gov (United States)

    Jung, Joonyoung; Kim, Daeyoung

    We designed and implemented collaborative physiological signal devices in a u-healthcare(ubiquitous healthcare) system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We show several collaborative physiological devices and propose WBAN mechanism such as a fast scanning algorithm, a dynamic discovery and installation mechanism, a reliable data transmission, a device access control for security, and a healthcare profile for u-healthcare system.

  20. Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting

    Directory of Open Access Journals (Sweden)

    Joonyoung Jung

    2008-12-01

    Full Text Available We developed a ubiquitous healthcare system consisted of aphysiological signal devices, a mobile system, a device provider system, a healthcare service provider system, a physician system, and a healthcare personal system. In this system, wireless body area network (WBAN such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We propose a scanning algorithm, dynamic discovery and installation, reliable data transmission, device access control, and a healthcare profile for ubiquitous healthcare system.

  1. Wnt-signalling pathways and microRNAs network in carcinogenesis: experimental and bioinformatics approaches.

    Science.gov (United States)

    Onyido, Emenike K; Sweeney, Eloise; Nateri, Abdolrahman Shams

    2016-01-01

    Over the past few years, microRNAs (miRNAs) have not only emerged as integral regulators of gene expression at the post-transcriptional level but also respond to signalling molecules to affect cell function(s). miRNAs crosstalk with a variety of the key cellular signalling networks such as Wnt, transforming growth factor-β and Notch, control stem cell activity in maintaining tissue homeostasis, while if dysregulated contributes to the initiation and progression of cancer. Herein, we overview the molecular mechanism(s) underlying the crosstalk between Wnt-signalling components (canonical and non-canonical) and miRNAs, as well as changes in the miRNA/Wnt-signalling components observed in the different forms of cancer. Furthermore, the fundamental understanding of miRNA-mediated regulation of Wnt-signalling pathway and vice versa has been significantly improved by high-throughput genomics and bioinformatics technologies. Whilst, these approaches have identified a number of specific miRNA(s) that function as oncogenes or tumour suppressors, additional analyses will be necessary to fully unravel the links among conserved cellular signalling pathways and miRNAs and their potential associated components in cancer, thereby creating therapeutic avenues against tumours. Hence, we also discuss the current challenges associated with Wnt-signalling/miRNAs complex and the analysis using the biomedical experimental and bioinformatics approaches. PMID:27590724

  2. Cutting force signal pattern recognition using hybrid neural network in end milling

    Institute of Scientific and Technical Information of China (English)

    Song-Tae SEONG; Ko-Tae JO; Young-Moon LEE

    2009-01-01

    Under certain cutting conditions in end milling, the signs of cutting forces change from positive to negative during a revolution of the tool. The change of force direction causes the cutting dynamics to be unstable which results in chatter vibration. Therefore, cutting force signal monitoring and classification are needed to determine the optimal cutting conditions and to improve the efficiency of cut. Artificial neural networks are powerful tools for solving highly complex and nonlinear problems. It can be divided into supervised and unsupervised learning machines based on the availability of a teacher. Hybrid neural network was introduced with both of functions of multilayer perceptron (MLP) trained with the back-propagation algorithm for monitoring and detecting abnormal state, and self organizing feature map (SOFM) for treating huge datum such as image processing and pattern recognition, for predicting and classifying cutting force signal patterns simultaneously. The validity of the results is verified with cutting experiments and simulation tests.

  3. Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

    Science.gov (United States)

    Navarro, Robert

    2006-01-01

    The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

  4. Novel scheme enabling broadcast signal transmission in WDM passive optical network

    Institute of Scientific and Technical Information of China (English)

    Xuejiao Ma; Chaoqin Gan

    2011-01-01

    @@ We present a novel method for providing broadcast signal transmission in a wavelength division multiplex-ing passive optical network (WDM-PON).An unmodulated optical carrier for downstream transmission and a pair of unmodulated single-side band subcarriers are utilized for broadcast delivery and upstream transmission, respectively.System performance at 2.5-Gb/s downlupstream and 2.5-Gb/s broadcast trans-mission is also investigated.%We present a novel method for providing broadcast signal transmission in a wavelength division multiplexing passive optical network (WDM-PON).An unmodulated optical carrier for downstream transmission and a pair of unmodulated single-side band subcarriers are utilized for broadcast delivery and upstream transmission, respectively.System performance at 2.5-Gb/s down/upstream and 2.5-Gb/s broadcast transmission is also investigated.

  5. A new cellular nonlinear network emulation on FPGA for EEG signal processing in epilepsy

    Science.gov (United States)

    Müller, Jens; Müller, Jan; Tetzlaff, Ronald

    2011-05-01

    For processing of EEG signals, we propose a new architecture for the hardware emulation of discrete-time Cellular Nonlinear Networks (DT-CNN). Our results show the importance of a high computational accuracy in EEG signal prediction that cannot be achieved with existing analogue VLSI circuits. The refined architecture of the processing elements and its resource schedule, the cellular network structure with local couplings, the FPGA-based embedded system containing the DT-CNN, and the data flow in the entire system will be discussed in detail. The proposed DT-CNN design has been implemented and tested on an Xilinx FPGA development platform. The embedded co-processor with a multi-threading kernel is utilised for control and pre-processing tasks and data exchange to the host via Ethernet. The performance of the implemented DT-CNN has been determined for a popular example and compared to that of a conventional computer.

  6. Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling

    Directory of Open Access Journals (Sweden)

    Thanh-Phuong Nguyen

    2014-01-01

    Full Text Available Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs, the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI to model the functional network of NDs. The aim of this work is fourfold: (i reconstruction of a PPI network relating to the NDs, (ii construction of an association network between diseases based on proximity in the disease PPI network, (iii quantification of disease associations, and (iv inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration.

  7. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways

    OpenAIRE

    Breitling, Rainer; Gilbert, David; Heiner, Monika; Orton, Richard

    2008-01-01

    Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also a...

  8. International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking

    CERN Document Server

    Shirur, Yasha; Prasad, Rekha

    2013-01-01

    This book is a collection of papers presented by renowned researchers, keynote speakers and academicians in the International Conference on VLSI, Communication, Analog Designs, Signals and Systems, and Networking (VCASAN-2013), organized by B.N.M. Institute of Technology, Bangalore, India during July 17-19, 2013. The book provides global trends in cutting-edge technologies in electronics and communication engineering. The content of the book is useful to engineers, researchers and academicians as well as industry professionals.

  9. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    OpenAIRE

    Wei Zhang; Guozhen Tan; Nan Ding; Guangyuan Wang

    2012-01-01

    This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the ...

  10. Vestibular and Attractor Network Basis of the Head Direction Cell Signal in Subcortical Circuits

    Directory of Open Access Journals (Sweden)

    Benjamin J Clark

    2012-03-01

    Full Text Available Accurate navigation depends on a network of neural systems that encode the moment-to-moment changes in an animal’s directional orientation and location in space. Within this navigation system are head direction (HD cells, which fire persistently when an animal’s head is pointed in a particular direction (Sharp et al., 2001a; Taube, 2007. HD cells are widely thought to underlie an animal’s sense of spatial orientation, and research over the last 25+ years has revealed that this robust spatial signal is widely distributed across subcortical and cortical limbic areas. Much of this work has been directed at understanding the functional organization of the HD cell circuitry, and precisely how this signal is generated from sensory and motor systems. The purpose of the present review is to summarize some of the recent studies arguing that the HD cell circuit is largely processed in a hierarchical fashion, following a pathway involving the dorsal tegmental nuclei → lateral mammillary nuclei → anterior thalamus → parahippocampal and retrosplenial cortical regions. We also review recent work identifying “bursting” cellular activity in the HD cell circuit after lesions of the vestibular system, and relate these observations to the long held view that attractor network mechanisms underlie HD signal generation. Finally, we summarize the work to date suggesting that this network architecture may reside within the tegmento-mammillary circuit.

  11. Nitric oxide:A potential key point of the signaling network leading to plant secondary metabolite biosynthesis

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The endogenous signaling network of plants plays important roles in mediating the exogenous factor-induced biosynthesis of secondary metabolites.Nitric oxide (NO) has emerged as a key signaling molecule in plants recently.Numerous studies demonstrated that the main signaling molecules such as salicylic acid(SA),jasmonic acid (JA),reactive oxygen species(ROS),and NO were not only involved in regulating plant secondary metabolite biosynthesis but also interacted to form a complex signaling network by mutual inhibition and/or synergy.The recent progress in the signal network of plant secondary metabolite biosynthesis has been discussed in this paper.Furthermore,we propose a hypothetical model to show that NO might act as a potential molecular switch in the stgnaling network leading to plant secondary metabolite biosynthesis.

  12. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    Science.gov (United States)

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. PMID:26691180

  13. Cognitive sensor networks for structure defect monitoring and classification using guided wave signals

    Science.gov (United States)

    Jin, Yuanwei; O'Donoughue, Nicholas; Moura, José M. F.; Harley, Joel; Garrett, James H.; Oppenheim, Irving J.; Soibelman, Lucio; Ying, Yujie; He, Lin

    2010-04-01

    This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification.

  14. Bifurcation mechanisms of regular and chaotic network signaling in brain astrocytes

    Science.gov (United States)

    Matrosov, V. V.; Kazantsev, V. B.

    2011-06-01

    Bifurcation mechanisms underlying calcium oscillations in the network of astrocytes are investigated. Network model includes the dynamics of intracellular calcium concentration and intercellular diffusion of inositol 1,4,5-trisphosphate through gap junctions. Bifurcation analysis of underlying nonlinear dynamical system is presented. Parameter regions and principle bifurcation boundaries have been delineated and described. We show how variations of the diffusion rate can lead to generation of network calcium oscillations in originally nonoscillating cells. Different scenarios of regular activity and its transitions to chaotic dynamics have been obtained. Then, the bifurcations have been associated with statistical characteristics of calcium signals showing that different bifurcation scenarios yield qualitative changes in experimentally measurable quantities of the astrocyte activity, e.g., statistics of calcium spikes.

  15. Application of computational approaches to study signalling networks of nuclear and Tyrosine kinase receptors

    Directory of Open Access Journals (Sweden)

    Rebaï Ahmed

    2010-10-01

    Full Text Available Abstract Background Nuclear receptors (NRs and Receptor tyrosine kinases (RTKs are essential proteins in many cellular processes and sequence variations in their genes have been reported to be involved in many diseases including cancer. Although crosstalk between RTK and NR signalling and their contribution to the development of endocrine regulated cancers have been areas of intense investigation, the direct coupling of their signalling pathways remains elusive. In our understanding of the role and function of nuclear receptors on the cell membrane the interactions between nuclear receptors and tyrosine kinase receptors deserve further attention. Results We constructed a human signalling network containing nuclear receptors and tyrosine kinase receptors that identified a network topology involving eleven highly connected hubs. We further developed an integrated knowledge database, denominated NR-RTK database dedicated to human RTKs and NRs and their vertebrate orthologs and their interactions. These interactions were inferred using computational tools and those supported by literature evidence are indicated. NR-RTK database contains links to other relevant resources and includes data on receptor ligands. It aims to provide a comprehensive interaction map that identifies complex dynamics and potential crosstalk involved. Availability: NR-RTK database is accessible at http://www.bioinfo-cbs.org/NR-RTK/ Conclusions We infer that the NR-RTK interaction network is scale-free topology. We also uncovered the key receptors mediating the signal transduction between these two types of receptors. Furthermore, NR-RTK database is expected to be useful for researchers working on various aspects of the molecular basis of signal transduction by RTKs and NRs. Reviewers This article was reviewed by Professor Paul Harrison (nominated by Dr. Mark Gerstein, Dr. Arcady Mushegian and Dr. Anthony Almudevar.

  16. Design of Transparent Distributed IMS Network: Security Challenges Risk and Signaling Analysis

    Directory of Open Access Journals (Sweden)

    Hamid Allouch

    2013-01-01

    Full Text Available The IP Multimedia subsystem (IMS based on SIP as mechanism signalling and interfaces with otherservers using OSA (Open Service Access and CAMEL (Customized Applications for Mobile networkEnhanced Logic.Is responsible for the interconnection of IP packets with other network, IMS support datacommunication services, voice, video, messaging and web-based technologies. In this work we present adistributed design of architecture that turns up some challenges of transparent mobility on the secured IMSarchitecture. We introduced the architecture with clustering database HSS and automatic storage of datafiles that give a secure access to database. This paper gives an overview of classification of security in IMSnetwork and we show delay analysis comparison in signalling interworking with and without securingGateway (SEG in the registration of any UE in access network based IMS. We show that there is a tradeoffbetween the level of increasing system security and the potential delay incurred by mobility in AccessNetwork .we conclude that this architecture is suitable for operators and services providers for the newbusiness models delivering ,the services based IMS Everywhere, anytime and with any terminals.

  17. Real-time synchronization of wireless sensor network by 1-PPS signal

    Science.gov (United States)

    Giammarini, Marco; Pieralisi, Marco; Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Fioravanti, Matteo

    2015-05-01

    The use of wireless sensor networks with different nodes is desirable in a smart environment, because the network setting up and installation on preexisting structures can be done without a fixed cabled infrastructure. The flexibility of the monitoring system is fundamental where the use of a considerable quantity of cables could compromise the normal exercise, could affect the quality of acquired signal and finally increase the cost of the materials and installation. The network is composed of several intelligent "nodes", which acquires data from different kind of sensors, and then store or transmit them to a central elaboration unit. The synchronization of data acquisition is the core of the real-time wireless sensor network (WSN). In this paper, we present a comparison between different methods proposed by literature for the real-time acquisition in a WSN and finally we present our solution based on 1-Pulse-Per-Second (1-PPS) signal generated by GPS systems. The sensor node developed is a small-embedded system based on ARM microcontroller that manages the acquisition, the timing and the post-processing of the data. The communications between the sensors and the master based on IEEE 802.15.4 protocol and managed by dedicated software. Finally, we present the preliminary results obtained on a 3 floor building simulator with the wireless sensors system developed.

  18. Adaptive Wavelet Neural Networks for Signal Detection in DS-CDMA System

    Institute of Scientific and Technical Information of China (English)

    WANGLing; JIAOLichengt; TAOHaihong; LIUFang

    2004-01-01

    The Multiple access interference (MAI) is the major factor that limits the performance and capacity of a nonorthogonal Direct sequence Code division multiple access (DS-CDMA) system. By using the adaptability of highly parallel structure neural networks and the excellent approximation ability of wavelets, two kinds of Adaptive wavelets neural networks (AWNN) signal detectors are proposed in the paper, in which the inputs of detectors are the received signal vector corresponding to a single interesting user sampled at the chip rate, named by AWNN single-user detector, respectively, and to all or partial active users sampled at the bit rate after passing through a matched filter, named by AWNN multiuser detector and partial users AWNN multiuser detector. The complexity of the multiuser detectors only depends on that of wavelet networks. The performance analysis of the proposed detectors compared with the matched filters under single-user and multiuser systems and the multiuser detector based on multilayer perceptrons are carried out by Monte Carlo simulations. Results show that the adaptive wavelet neural networks multiuser detectors are superior to other detectors mentioned above.

  19. FPGA implementation of a ZigBee wireless network control interface to transmit biomedical signals

    International Nuclear Information System (INIS)

    In recent years, cardiac hemodynamic monitors have incorporated new technologies based on wireless sensor networks which can implement different types of communication protocols. More precisely, a digital conductance catheter system recently developed adds a wireless ZigBee module (IEEE 802.15.4 standards) to transmit cardiac signals (ECG, intraventricular pressure and volume) which would allow the physicians to evaluate the patient's cardiac status in a noninvasively way. The aim of this paper is to describe a control interface, implemented in a FPGA device, to manage a ZigBee wireless network. ZigBee technology is used due to its excellent performance including simplicity, low-power consumption, short-range transmission and low cost. FPGA internal memory stores 8-bit signals with which the control interface prepares the information packets. These data were send to the ZigBee END DEVICE module that receives and transmits wirelessly to the external COORDINATOR module. Using an USB port, the COORDINATOR sends the signals to a personal computer for displaying. Each functional block of control interface was assessed by means of temporal diagrams. Three biological signals, organized in packets and converted to RS232 serial protocol, were successfully transmitted and displayed in a PC screen. For this purpose, a custom-made graphical software was designed using LabView.

  20. The network of P(II) signalling protein interactions in unicellular cyanobacteria.

    Science.gov (United States)

    Forchhammer, Karl

    2010-01-01

    P(II) signalling proteins constitute a large superfamily of signal perception and transduction proteins, which is represented in all domains of life and whose members play central roles in coordinating nitrogen assimilation. Generally, P(II) proteins act as sensors of the cellular adenylylate energy charge and 2-oxoglutarate level, and in response to these signals, they regulate central nitrogen assimilatory processes at various levels of control (from nutrient transport to gene expression) through protein-protein interactions with P(II) receptor proteins. An examination of the phylogeny of cyanobacteria reveals that specific functions of P(II) signalling evolved in this microbial lineage, which are not found in other prokaryotes. At least one of these functions, regulation of arginine biosynthesis by controlling the key enzyme N-acetyl-L: -glutamate kinase (NAGK), was transmitted by the ancestral cyanobacterium, which gave rise to chloroplasts, into the eukaryotic domain and was conserved during the evolution of planta. We have investigated in some detail the P(II) signalling protein, its signal perception and its interactions with receptors in the unicellular cyanobacteria Synechococcus elongatus PCC 7942 and Synechocystis PCC 6803 and have performed comparative analysis with Arabidopsis thaliana P(II)-NAGK interaction. This chapter will summarize these studies and will describe the emerging picture of a complex network of P(II) protein interactions in the unicellular cyanobacteria. PMID:20532736

  1. Co-evolution of Hormone Metabolism and Signaling Networks Expands Plant Adaptive Plasticity.

    Science.gov (United States)

    Weng, Jing-Ke; Ye, Mingli; Li, Bin; Noel, Joseph P

    2016-08-11

    Classically, hormones elicit specific cellular responses by activating dedicated receptors. Nevertheless, the biosynthesis and turnover of many of these hormone molecules also produce chemically related metabolites. These molecules may also possess hormonal activities; therefore, one or more may contribute to the adaptive plasticity of signaling outcomes in host organisms. Here, we show that a catabolite of the plant hormone abscisic acid (ABA), namely phaseic acid (PA), likely emerged in seed plants as a signaling molecule that fine-tunes plant physiology, environmental adaptation, and development. This trait was facilitated by both the emergence-selection of a PA reductase that modulates PA concentrations and by the functional diversification of the ABA receptor family to perceive and respond to PA. Our results suggest that PA serves as a hormone in seed plants through activation of a subset of ABA receptors. This study demonstrates that the co-evolution of hormone metabolism and signaling networks can expand organismal resilience. PMID:27518563

  2. Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow

    CERN Document Server

    Buibas, Marius; Nizar, Krystal; Silva, Gabriel A

    2009-01-01

    An optical flow gradient algorithm was applied to spontaneously forming networks of neurons and glia in culture imaged by fluorescence optical microscopy in order to map functional calcium signaling with single pixel resolution. Optical flow estimates the direction and speed of motion of objects in an image between subsequent frames in a recorded digital sequence of images (i.e. a movie). Computed vector field outputs by the algorithm were able to track the spatiotemporal dynamics of calcium signaling patterns. We begin by briefly reviewing the mathematics of the optical flow algorithm, describe how to solve for the displacement vectors, and how to measure their reliability. We then compare computed flow vectors with manually estimated vectors for the progression of a calcium signal recorded from representative astrocyte cultures. Finally, we applied the algorithm to preparations of primary astrocytes and hippocampal neurons and to the rMC-1 Muller glial cell line in order to illustrate the capability of the ...

  3. ICE: a scalable, low-cost FPGA-based telescope signal processing and networking system

    CERN Document Server

    Bandura, K; Cliche, J F; de Haan, T; Dobbs, M A; Gilbert, A J; Griffin, S; Hsyu, G; Ittah, D; Parra, J Mena; Montgomery, J; Pinsonneault-Marotte, T; Siegel, S; Smecher, G; Tang, Q Y; Vanderlinde, K; Whitehorn, N

    2016-01-01

    We present an overview of the 'ICE' hardware and software framework that implements large arrays of interconnected FPGA-based data acquisition, signal processing and networking nodes economically. The system was conceived for application to radio, millimeter and sub-millimeter telescope readout systems that have requirements beyond typical off-the-shelf processing systems, such as careful control of interference signals produced by the digital electronics, and clocking of all elements in the system from a single precise observatory-derived oscillator. A new generation of telescopes operating at these frequency bands and designed with a vastly increased emphasis on digital signal processing to support their detector multiplexing technology or high-bandwidth correlators---data rates exceeding a terabyte per second---are becoming common. The ICE system is built around a custom FPGA motherboard that makes use of an Xilinx Kintex-7 FPGA and ARM-based co-processor. The system is specialized for specific application...

  4. The signal synchronization transmission of a spatiotemporal chaos network constituted by a laser phase-conjugate wave

    Institute of Scientific and Technical Information of China (English)

    Li Wen-Lin; Li Shu-Feng; Li Gang

    2012-01-01

    The signal synchronization transmission of a spatiotemporal chaos network is investigated.The structure of the coupling function between connected nodes of the complex network and the value range of the linear term coefficient of the separated configuration in state equation of the node are obtained through constructing an appropriate Lyapunov function.Each node of the complex network is a laser spatiotemporal chaos model in which the phase-conjugate wave and the unilateral coupled map lattice are taken as a local function and a spatially extended system,respectively.The simulation results show the effectiveness of the signal synchronization transmission principle of the network.

  5. The signal synchronization transmission of a spatiotemporal chaos network constituted by a laser phase-conjugate wave

    International Nuclear Information System (INIS)

    The signal synchronization transmission of a spatiotemporal chaos network is investigated. The structure of the coupling function between connected nodes of the complex network and the value range of the linear term coefficient of the separated configuration in state equation of the node are obtained through constructing an appropriate Lyapunov function. Each node of the complex network is a laser spatiotemporal chaos model in which the phase-conjugate wave and the unilateral coupled map lattice are taken as a local function and a spatially extended system, respectively. The simulation results show the effectiveness of the signal synchronization transmission principle of the network. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  6. Food-Sharing Networks in Lamalera, Indonesia: Status, Sharing, and Signaling.

    Science.gov (United States)

    Nolin, David A

    2012-07-01

    Costly signaling has been proposed as a possible mechanism to explain food sharing in foraging populations. This sharing-as-signaling hypothesis predicts an association between sharing and status. Using exponential random graph modeling (ERGM), this prediction is tested on a social network of between-household food-sharing relationships in the fishing and sea-hunting village of Lamalera, Indonesia. Previous analyses (Nolin 2010) have shown that most sharing in Lamalera is consistent with reciprocal altruism. The question addressed here is whether any additional variation may be explained as sharing-as-signaling by high-status households. The results show that high-status households both give and receive more than other households, a pattern more consistent with reciprocal altruism than costly signaling. However, once the propensity to reciprocate and household productivity are controlled, households of men holding leadership positions show greater odds of unreciprocated giving when compared to households of non-leaders. This pattern of excessive giving by leaders is consistent with the sharing-as-signaling hypothesis. Wealthy households show the opposite pattern, giving less and receiving more than other households. These households may reciprocate in a currency other than food or their wealth may attract favor-seeking behavior from others. Overall, status covariates explain little variation in the sharing network as a whole, and much of the sharing observed by high-status households is best explained by the same factors that explain sharing by other households. This pattern suggests that multiple mechanisms may operate simultaneously to promote sharing in Lamalera and that signaling may motivate some sharing by some individuals even within sharing regimes primarily maintained by other mechanisms. PMID:22822299

  7. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Science.gov (United States)

    Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

    2015-01-01

    Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors. PMID:26571415

  8. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    Full Text Available Aberrant activation of sonic Hegdehog (SHH signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs. To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays. We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

  9. Synaptic network activity induces neuronal differentiation of adult hippocampal precursor cells through BDNF signaling

    Directory of Open Access Journals (Sweden)

    HarishBabu

    2009-09-01

    Full Text Available Adult hippocampal neurogenesis is regulated by activity. But how do neural precursor cells in the hippocampus respond to surrounding network activity and translate increased neural activity into a developmental program? Here we show that long-term potential (LTP-like synaptic activity within a cellular network of mature hippocampal neurons promotes neuronal differentiation of newly generated cells. In co-cultures of precursor cells with primary hippocampal neurons, LTP-like synaptic plasticity induced by addition of glycine in Mg2+-free media for 5 min, produced synchronous network activity and subsequently increased synaptic strength between neurons. Furthermore, this synchronous network activity led to a significant increase in neuronal differentiation from the co-cultured neural precursor cells. When applied directly to precursor cells, glycine and Mg2+-free solution did not induce neuronal differentiation. Synaptic plasticity-induced neuronal differentiation of precursor cells was observed in the presence of GABAergic neurotransmission blockers but was dependent on NMDA-mediated Ca2+ influx. Most importantly, neuronal differentiation required the release of brain-derived neurotrophic factor (BDNF from the underlying substrate hippocampal neurons as well as TrkB receptor phosphorylation in precursor cells. This suggests that activity-dependent stem cell differentiation within the hippocampal network is mediated via synaptically evoked BDNF signaling.

  10. Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals

    Science.gov (United States)

    Johnsson, Roger

    2006-11-01

    Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.

  11. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Joaquin J Torres

    Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  12. Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

    Directory of Open Access Journals (Sweden)

    Azadeh eRahmani-Badi

    2015-04-01

    Full Text Available Many bacterial pathogens use quorum-sensing (QS signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF, is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized as cis-2-decenoic acid (C10: Δ2, CDA that induces biofilm dispersal by multiple types of bacteria. Furthermore, CDA has been shown to be involved in inter-kingdom signaling that modulates fungal behavior. Therefore, an understanding of its signaling mechanism could suggest strategies for interference, with consequences for disease control. To identify the components of CDA signaling pathway in this pathogen, a comparative transcritpome analysis was conducted, in the presence and absence of CDA. A protein-protein interaction (PPI network for differentially expressed (DE genes with known function was then constructed by STRING and Cytoscape. In addition, the effects of CDA in combination with antimicrobial agents on the biofilm surface area and bacteria viability were evaluated using fluorescence microscopy and digital image analysis. Microarray analysis identified 666 differentially expressed genes in the presence of CDA and gene ontology (GO analysis revealed that in P. aeruginosa, CDA mediates dispersion of biofilms through signaling pathways, including enhanced motility, virulence as well as persistence at different temperatures. PPI data suggested that a cluster of five genes (PA4978, PA4979, PA4980, PA4982, PA4983 is involved in the CDA synthesis and perception. Combined treatments using both CDA and antimicrobial agents showed that following exposure of the biofilms to CDA, remaining cells on the surface were easily removed and killed by antimicrobials.

  13. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

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    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  14. Secured operating regions of Slotted ALOHA in the presence of interfering signals from other networks and DoS attacking signals

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    Jahangir H. Sarker

    2011-07-01

    Full Text Available It is expected that many networks will be providing services at a time in near future and those will also produce different interfering signals for the current Slotted ALOHA based systems. A random packet destruction Denial of Service (DoS attacking signal can shut down the Slotted ALOHA based networks easily. Therefore, to keep up the services of Slotted ALOHA based systems by enhancing the secured operating regions in the presence of the interfering signals from other wireless systems and DoS attacking signals is an important issue and is investigated in this paper. We have presented four different techniques for secured operating regions enhancements of Slotted ALOHA protocol. Results show that the interfering signals from other wireless systems and the DoS attacking signals can produce similar detrimental effect on Slotted ALOHA. However, the most detrimental effect can be produced, if an artificial DoS attack can be launched using extra false packets arrival from the original network. All four proposed secured operating regions enhancement techniques are easy to implement and have the ability to prevent the shutdown of the Slotted ALOHA based networks.

  15. The Limitation of Primary Signals Entering DVB-T On-Channel-Repeater Working in SFN Network

    Directory of Open Access Journals (Sweden)

    Marek Dvorsky

    2013-01-01

    Full Text Available This paper considers an issue of signal coverage in uncovered places using a broadcasting device called a Gap Filler. The main focus is placed on the analysis of potential negative effects while signals from two and more primary transmitters simultaneously enter to the Gap Filler. In particular, in the measurements the impact of reception cross delayed signals received by the Gap Filler from two adjacent primary transmitters operating in the Single Frequency Network was analysed. The influence of different receive signal levels from two adjacent primary transmitters was also examined. In the conclusion, based on the experiments, the limiting factors useful for individual transmitters in the Single Frequency Network were determined. The analysis and finding the limit parameters can help bradcasters in further setting and debugging of the Gap Filler network. Finally, the described laboratory experiment was also verified under the real SFN network condition in border region Vsetinsko to verify the laboratory findings.

  16. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Science.gov (United States)

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  17. Interaction between Calcium and Actin in Guard Cell and Pollen Signaling Networks

    Directory of Open Access Journals (Sweden)

    Dong-Hua Chen

    2013-10-01

    Full Text Available Calcium (Ca2+ plays important roles in plant growth, development, and signal transduction. It is a vital nutrient for plant physical design, such as cell wall and membrane, and also serves as a counter-cation for biochemical, inorganic, and organic anions, and more particularly, its concentration change in cytosol is a ubiquitous second messenger in plant physiological signaling in responses to developmental and environmental stimuli. Actin cytoskeleton is well known for its importance in cellular architecture maintenance and its significance in cytoplasmic streaming and cell division. In plant cell system, the actin dynamics is a process of polymerization and de-polymerization of globular actin and filamentous actin and that acts as an active regulator for calcium signaling by controlling calcium evoked physiological responses. The elucidation of the interaction between calcium and actin dynamics will be helpful for further investigation of plant cell signaling networks at molecular level. This review mainly focuses on the recent advances in understanding the interaction between the two aforementioned signaling components in two well-established model systems of plant, guard cell, and pollen.

  18. Information theory and signal transduction systems: from molecular information processing to network inference.

    Science.gov (United States)

    Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H

    2014-11-01

    Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. PMID:24953199

  19. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  20. Modeling propagation of infrasound signals observed by a dense seismic network.

    Science.gov (United States)

    Chunchuzov, I; Kulichkov, S; Popov, O; Hedlin, M

    2014-01-01

    The long-range propagation of infrasound from a surface explosion with an explosive yield of about 17.6 t TNT that occurred on June 16, 2008 at the Utah Test and Training Range (UTTR) in the western United States is simulated using an atmospheric model that includes fine-scale layered structure of the wind velocity and temperature fields. Synthetic signal parameters (waveforms, amplitudes, and travel times) are calculated using parabolic equation and ray-tracing methods for a number of ranges between 100 and 800 km from the source. The simulation shows the evolution of several branches of stratospheric and thermospheric signals with increasing range from the source. Infrasound signals calculated using a G2S (ground-to-space) atmospheric model perturbed by small-scale layered wind velocity and temperature fluctuations are shown to agree well with recordings made by the dense High Lava Plains seismic network located at an azimuth of 300° from UTTR. The waveforms of calculated infrasound arrivals are compared with those of seismic recordings. This study illustrates the utility of dense seismic networks for mapping an infrasound field with high spatial resolution. The parabolic equation calculations capture both the effect of scattering of infrasound into geometric acoustic shadow zones and significant temporal broadening of the arrivals. PMID:24437743

  1. Identifying Network Motifs that Buffer Front-to-Back Signaling in Polarized Neutrophils

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    Yanqin Wang

    2013-05-01

    Full Text Available Neutrophil polarity relies on local, mutual inhibition to segregate incompatible signaling circuits to the leading and trailing edges. Mutual inhibition alone should lead to cells having strong fronts and weak backs or vice versa. However, analysis of cell-to-cell variation in human neutrophils revealed that back polarity remains consistent despite changes in front strength. How is this buffering achieved? Pharmacological perturbations and mathematical modeling revealed a functional role for microtubules in buffering back polarity by mediating positive, long-range crosstalk from front to back; loss of microtubules inhibits buffering and results in anticorrelation between front and back signaling. Furthermore, a systematic, computational search of network topologies found that a long-range, positive front-to-back link is necessary for back buffering. Our studies suggest a design principle that can be employed by polarity networks: short-range mutual inhibition establishes distinct signaling regions, after which directed long-range activation insulates one region from variations in the other.

  2. The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response.

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    Cole Johnson

    2014-03-01

    Full Text Available The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans.

  3. Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashwani Kumar Narula

    2015-06-01

    Full Text Available This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

  4. Network Analysis of Epidermal Growth Factor Signaling using Integrated Genomic, Proteomic and Phosphorylation Data

    Energy Technology Data Exchange (ETDEWEB)

    Waters, Katrina M.; Liu, Tao; Quesenberry, Ryan D.; Willse, Alan R.; Bandyopadhyay, Somnath; Kathmann, Loel E.; Weber, Thomas J.; Smith, Richard D.; Wiley, H. S.; Thrall, Brian D.

    2012-03-29

    To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

  5. Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data.

    Directory of Open Access Journals (Sweden)

    Katrina M Waters

    Full Text Available To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

  6. Low-dose radiation affects cardiac physiology: gene networks and molecular signaling in cardiomyocytes.

    Science.gov (United States)

    Coleman, Matthew A; Sasi, Sharath P; Onufrak, Jillian; Natarajan, Mohan; Manickam, Krishnan; Schwab, John; Muralidharan, Sujatha; Peterson, Leif E; Alekseyev, Yuriy O; Yan, Xinhua; Goukassian, David A

    2015-12-01

    There are 160,000 cancer patients worldwide treated with particle radiotherapy (RT). With the advent of proton, and high (H) charge (Z) and energy (E) HZE ionizing particle RT, the cardiovascular diseases risk estimates are uncertain. In addition, future deep space exploratory-type missions will expose humans to unknown but low doses of particle irradiation (IR). We examined molecular responses using transcriptome profiling in left ventricular murine cardiomyocytes isolated from mice that were exposed to 90 cGy, 1 GeV proton ((1)H) and 15 cGy, 1 GeV/nucleon iron ((56)Fe) over 28 days after exposure. Unsupervised clustering analysis of gene expression segregated samples according to the IR response and time after exposure, with (56)Fe-IR showing the greatest level of gene modulation. (1)H-IR showed little differential transcript modulation. Network analysis categorized the major differentially expressed genes into cell cycle, oxidative responses, and transcriptional regulation functional groups. Transcriptional networks identified key nodes regulating expression. Validation of the signal transduction network by protein analysis and gel shift assay showed that particle IR clearly regulates a long-lived signaling mechanism for ERK1/2, p38 MAPK signaling and identified NFATc4, GATA4, STAT3, and NF-κB as regulators of the response at specific time points. These data suggest that the molecular responses and gene expression to (56)Fe-IR in cardiomyocytes are unique and long-lasting. Our study may have significant implications for the efforts of National Aeronautics and Space Administration to develop heart disease risk estimates for astronauts and for patients receiving conventional and particle RT via identification of specific HZE-IR molecular markers. PMID:26408534

  7. Dorsoventral patterning in hemichordates: insights into early chordate evolution.

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    Christopher J Lowe

    2006-09-01

    Full Text Available We have compared the dorsoventral development of hemichordates and chordates to deduce the organization of their common ancestor, and hence to identify the evolutionary modifications of the chordate body axis after the lineages split. In the hemichordate embryo, genes encoding bone morphogenetic proteins (Bmp 2/4 and 5/8, as well as several genes for modulators of Bmp activity, are expressed in a thin stripe of ectoderm on one midline, historically called "dorsal." On the opposite midline, the genes encoding Chordin and Anti-dorsalizing morphogenetic protein (Admp are expressed. Thus, we find a Bmp-Chordin developmental axis preceding and underlying the anatomical dorsoventral axis of hemichordates, adding to the evidence from Drosophila and chordates that this axis may be at least as ancient as the first bilateral animals. Numerous genes encoding transcription factors and signaling ligands are expressed in the three germ layers of hemichordate embryos in distinct dorsoventral domains, such as pox neuro, pituitary homeobox, distalless, and tbx2/3 on the Bmp side and netrin, mnx, mox, and single-minded on the Chordin-Admp side. When we expose the embryo to excess Bmp protein, or when we deplete endogenous Bmp by small interfering RNA injections, these expression domains expand or contract, reflecting their activation or repression by Bmp, and the embryos develop as dorsalized or ventralized limit forms. Dorsoventral patterning is independent of anterior/posterior patterning, as in Drosophila but not chordates. Unlike both chordates and Drosophila, neural gene expression in hemichordates is not repressed by high Bmp levels, consistent with their development of a diffuse rather than centralized nervous system. We suggest that the common ancestor of hemichordates and chordates did not use its Bmp-Chordin axis to segregate epidermal and neural ectoderm but to pattern many other dorsoventral aspects of the germ layers, including neural cell fates

  8. Signal Quality Outage Analysis for Ultra-Reliable Communications in Cellular Networks

    DEFF Research Database (Denmark)

    Gerardino, Guillermo Andrés Pocovi; Alvarez, Beatriz Soret; Lauridsen, Mads; Pedersen, Klaus I.; Mogensen, Preben Elgaard

    Ultra-reliable communications over wireless will open the possibility for a wide range of novel use cases and applications. In cellular networks, achieving reliable communication is challenging due to many factors, particularly the fading of the desired signal and the interference. In this regard...... schemes must be complemented with macroscopic diversity as well as interference management techniques in order to ensure the necessary SINR outage performance. Based on the obtained performance results, it is discussed which of the feasible options fulfilling the ultra-reliable criteria are most promising...

  9. Recognizing the Taste Signals Using the Clustering-Based Fuzzy Neural Network

    Institute of Scientific and Technical Information of China (English)

    HUANGYanxin; ZHOUChunguang

    2005-01-01

    A fuzzy neural network system model for recognizing 11 kinds of mineral waters by its taste signals is proposed. In the model, an entropy-based clustering algorithm is used as structure learning to partition the fuzzy input space and extract fuzzy if-then rules, and the Gradient Descent optimization algorithm is used as parameter learning to refine the fuzzy rule parameters. Experimental results show that the system can obtain perfect interpretability, robustness, learning capability and the cor-rect classification rates by choosing two system parameters properly.

  10. Circadian period integrates network information through activation of the BMP signaling pathway.

    Directory of Open Access Journals (Sweden)

    Esteban J Beckwith

    2013-12-01

    Full Text Available Living organisms use biological clocks to maintain their internal temporal order and anticipate daily environmental changes. In Drosophila, circadian regulation of locomotor behavior is controlled by ∼150 neurons; among them, neurons expressing the PIGMENT DISPERSING FACTOR (PDF set the period of locomotor behavior under free-running conditions. To date, it remains unclear how individual circadian clusters integrate their activity to assemble a distinctive behavioral output. Here we show that the BONE MORPHOGENETIC PROTEIN (BMP signaling pathway plays a crucial role in setting the circadian period in PDF neurons in the adult brain. Acute deregulation of BMP signaling causes period lengthening through regulation of dClock transcription, providing evidence for a novel function of this pathway in the adult brain. We propose that coherence in the circadian network arises from integration in PDF neurons of both the pace of the cell-autonomous molecular clock and information derived from circadian-relevant neurons through release of BMP ligands.

  11. Circadian Period Integrates Network Information Through Activation of the BMP Signaling Pathway

    Science.gov (United States)

    Beckwith, Esteban J.; Gorostiza, E. Axel; Berni, Jimena; Rezával, Carolina; Pérez-Santángelo, Agustín; Nadra, Alejandro D.; Ceriani, María Fernanda

    2013-01-01

    Living organisms use biological clocks to maintain their internal temporal order and anticipate daily environmental changes. In Drosophila, circadian regulation of locomotor behavior is controlled by ∼150 neurons; among them, neurons expressing the PIGMENT DISPERSING FACTOR (PDF) set the period of locomotor behavior under free-running conditions. To date, it remains unclear how individual circadian clusters integrate their activity to assemble a distinctive behavioral output. Here we show that the BONE MORPHOGENETIC PROTEIN (BMP) signaling pathway plays a crucial role in setting the circadian period in PDF neurons in the adult brain. Acute deregulation of BMP signaling causes period lengthening through regulation of dClock transcription, providing evidence for a novel function of this pathway in the adult brain. We propose that coherence in the circadian network arises from integration in PDF neurons of both the pace of the cell-autonomous molecular clock and information derived from circadian-relevant neurons through release of BMP ligands. PMID:24339749

  12. Investigations of Escherichia coli promoter sequences with artificial neural networks: new signals discovered upstream of the transcriptional startpoint

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Engelbrecht, Jacob

    We present a novel method for using the learning ability of a neural network as a measure of information in local regions of input data. Using the method to analyze Escherichia coli promoters, we discover all previously described signals, and furthermore find new signals that are regularly spaced...

  13. On the use of a crystal detector for a phase calibration of the large signal network analyzer

    International Nuclear Information System (INIS)

    This paper introduces a crystal detector as a new calibration standard for large signal analysis [1–4]. By means of the detector as a reference element one can calibrate the phase distortion of the large signal network analyser (LSNA) on a dense frequency grid. In this paper we estimate and validate a parametric black-box model for a crystal detector

  14. Deciphering the hormonal signalling network behind the systemic resistance induced by Trichoderma harzianum in tomato.

    Science.gov (United States)

    Martínez-Medina, Ainhoa; Fernández, Iván; Sánchez-Guzmán, María J; Jung, Sabine C; Pascual, Jose A; Pozo, María J

    2013-01-01

    Root colonization by selected Trichoderma isolates can activate in the plant a systemic defense response that is effective against a broad-spectrum of plant pathogens. Diverse plant hormones play pivotal roles in the regulation of the defense signaling network that leads to the induction of systemic resistance triggered by beneficial organisms [induced systemic resistance (ISR)]. Among them, jasmonic acid (JA) and ethylene (ET) signaling pathways are generally essential for ISR. However, Trichoderma ISR (TISR) is believed to involve a wider variety of signaling routes, interconnected in a complex network of cross-communicating hormone pathways. Using tomato as a model, an integrative analysis of the main mechanisms involved in the systemic resistance induced by Trichoderma harzianum against the necrotrophic leaf pathogen Botrytis cinerea was performed. Root colonization by T. harzianum rendered the leaves more resistant to B. cinerea independently of major effects on plant nutrition. The analysis of disease development in shoots of tomato mutant lines impaired in the synthesis of the key defense-related hormones JA, ET, salicylic acid (SA), and abscisic acid (ABA), and the peptide prosystemin (PS) evidenced the requirement of intact JA, SA, and ABA signaling pathways for a functional TISR. Expression analysis of several hormone-related marker genes point to the role of priming for enhanced JA-dependent defense responses upon pathogen infection. Together, our results indicate that although TISR induced in tomato against necrotrophs is mainly based on boosted JA-dependent responses, the pathways regulated by the plant hormones SA- and ABA are also required for successful TISR development. PMID:23805146

  15. Gene network homology in prokaryotes using a similarity search approach: queries of quorum sensing signal transduction.

    Directory of Open Access Journals (Sweden)

    David N Quan

    Full Text Available Bacterial cell-cell communication is mediated by small signaling molecules known as autoinducers. Importantly, autoinducer-2 (AI-2 is synthesized via the enzyme LuxS in over 80 species, some of which mediate their pathogenicity by recognizing and transducing this signal in a cell density dependent manner. AI-2 mediated phenotypes are not well understood however, as the means for signal transduction appears varied among species, while AI-2 synthesis processes appear conserved. Approaches to reveal the recognition pathways of AI-2 will shed light on pathogenicity as we believe recognition of the signal is likely as important, if not more, than the signal synthesis. LMNAST (Local Modular Network Alignment Similarity Tool uses a local similarity search heuristic to study gene order, generating homology hits for the genomic arrangement of a query gene sequence. We develop and apply this tool for the E. coli lac and LuxS regulated (Lsr systems. Lsr is of great interest as it mediates AI-2 uptake and processing. Both test searches generated results that were subsequently analyzed through a number of different lenses, each with its own level of granularity, from a binary phylogenetic representation down to trackback plots that preserve genomic organizational information. Through a survey of these results, we demonstrate the identification of orthologs, paralogs, hitchhiking genes, gene loss, gene rearrangement within an operon context, and also horizontal gene transfer (HGT. We found a variety of operon structures that are consistent with our hypothesis that the signal can be perceived and transduced by homologous protein complexes, while their regulation may be key to defining subsequent phenotypic behavior.

  16. Application of signal processing techniques for islanding detection of distributed generation in distribution network: A review

    International Nuclear Information System (INIS)

    Highlights: • Pros & cons of conventional islanding detection techniques (IDTs) are discussed. • Signal processing techniques (SPTs) ability in detecting islanding is discussed. • SPTs ability in improving performance of passive techniques are discussed. • Fourier, s-transform, wavelet, HHT & tt-transform based IDTs are reviewed. • Intelligent classifiers (ANN, ANFIS, Fuzzy, SVM) application in SPT are discussed. - Abstract: High penetration of distributed generation resources (DGR) in distribution network provides many benefits in terms of high power quality, efficiency, and low carbon emissions in power system. However, efficient islanding detection and immediate disconnection of DGR is critical in order to avoid equipment damage, grid protection interference, and personnel safety hazards. Islanding detection techniques are mainly classified into remote, passive, active, and hybrid techniques. From these, passive techniques are more advantageous due to lower power quality degradation, lower cost, and widespread usage by power utilities. However, the main limitations of these techniques are that they possess a large non detection zones and require threshold setting. Various signal processing techniques and intelligent classifiers have been used to overcome the limitations of passive islanding. Signal processing techniques, in particular, are adopted due to their versatility, stability, cost effectiveness, and ease of modification. This paper presents a comprehensive overview of signal processing techniques used to improve common passive islanding detection techniques. A performance comparison between the signal processing based islanding detection techniques with existing techniques are also provided. Finally, this paper outlines the relative advantages and limitations of the signal processing techniques in order to provide basic guidelines for researchers and field engineers in determining the best method for their system

  17. Phytochrome and retrograde signalling pathways converge to antagonistically regulate a light-induced transcriptional network

    Science.gov (United States)

    Martín, Guiomar; Leivar, Pablo; Ludevid, Dolores; Tepperman, James M.; Quail, Peter H.; Monte, Elena

    2016-01-01

    Plastid-to-nucleus retrograde signals emitted by dysfunctional chloroplasts impact photomorphogenic development, but the molecular link between retrograde- and photosensory-receptor signalling has remained unclear. Here, we show that the phytochrome and retrograde signalling (RS) pathways converge antagonistically to regulate the expression of the nuclear-encoded transcription factor GLK1, a key regulator of a light-induced transcriptional network central to photomorphogenesis. GLK1 gene transcription is directly repressed by PHYTOCHROME-INTERACTING FACTOR (PIF)-class bHLH transcription factors in darkness, but light-activated phytochrome reverses this activity, thereby inducing expression. Conversely, we show that retrograde signals repress this induction by a mechanism independent of PIF mediation. Collectively, our data indicate that light at moderate levels acts through the plant's nuclear-localized sensory-photoreceptor system to induce appropriate photomorphogenic development, but at excessive levels, sensed through the separate plastid-localized RS system, acts to suppress such development, thus providing a mechanism for protection against photo-oxidative damage by minimizing the tissue exposure to deleterious radiation. PMID:27150909

  18. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

    Energy Technology Data Exchange (ETDEWEB)

    Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

  19. Ionospheric Sounding Opportunities Using Signal Data From Preexisting Amateur Radio And Other Networks

    Science.gov (United States)

    Cushley, A. C.; Noel, J. M. A.

    2015-12-01

    Amateur radio and other transmissions used for dedicated purposes, such as the Automatic Packet Reporting System (APRS) and Automatic Dependent Surveillance Broadcast (ADS-B), are signals that exist for another reason, but can be used for ionospheric sounding. Whether mandated and government funded or voluntarily constructed and operated, these networks provide data that can be used for scientific and operational purposes which rely on space weather data. Given the current state of the global economic environment and fiscal consequences to scientific research funding in Canada, these types of networks offer an innovative solution with preexisting hardware for more real-time and archival space-weather data to supplement current methods, particularly for data assimilation, modelling and forecasting. Furthermore, mobile ground-based transmitters offer more flexibility for deployment than stationary receivers. Numerical modelling has demonstrated that APRS and ADS-B signals are subject to Faraday rotation (FR) as they pass through the ionosphere. Ray tracingtechniques were used to determine the characteristics of individual waves, including the wave path and the state of polarization. The modelled FR was computed and converted to total electron content (TEC) along the raypaths. TEC data can be used as input for computerized ionospheric tomography (CIT) in order to reconstruct electron density maps of the ionosphere.

  20. Two time-delay dynamic model on the transmission of malicious signals in wireless sensor network

    International Nuclear Information System (INIS)

    Highlights: • Role of time delay to reduce the adversary effect in WSN is explored. • Model with two time delays is proposed to analyse spread of malicious signal in WSN. • Dynamical behaviour of worm-free equilibrium and endemic equilibrium is shown. • Threshold condition for switch of stability are obtained analytically. • Relation between stability and the two time delays is also explored. - Abstract: Deployed in a hostile environment, motes of a Wireless sensor network (WSN) could be easily compromised by the attackers because of several constraints such as limited processing capabilities, memory space, and limited battery life time etc. While transmitting the data to their neighbour motes within the network, motes are easily compromised due to resource constraints. Here time delay can play an efficient role to reduce the adversary effect on motes. In this paper, we propose an epidemic model SEIR (Susceptible–Exposed–Infectious–Recovered) with two time delays to describe the transmission dynamics of malicious signals in wireless sensor network. The first delay accounts for an exposed (latent) period while the second delay is for the temporary immunity period due to multiple worm outbreaks. The dynamical behaviour of worm-free equilibrium and endemic equilibrium is shown from the point of stability which switches under some threshold condition specified by the basic reproduction number. Our results show that the global properties of equilibria also depends on the threshold condition and that latent and temporary immunity period in a mote does not affect the stability, but they play a positive role to control malicious attack. Moreover, numerical simulations are given to support the theoretical analysis

  1. Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Jeremy Worley

    2016-02-01

    Full Text Available The Target of Rapamycin kinase Complex I (TORC1 is a master regulator of cell growth and metabolism in eukaryotes. Studies in yeast and human cells have shown that nitrogen/amino acid starvation signals act through Npr2/Npr3 and the small GTPases Gtr1/Gtr2 (Rags in humans to inhibit TORC1. However, it is unclear how other stress and starvation stimuli inhibit TORC1, and/or act in parallel with the TORC1 pathway, to control cell growth. To help answer these questions, we developed a novel automated pipeline and used it to measure the expression of a TORC1-dependent ribosome biogenesis gene (NSR1 during osmotic stress in 4700 Saccharomyces cerevisiae strains from the yeast knock-out collection. This led to the identification of 440 strains with significant and reproducible defects in NSR1 repression. The cell growth control and stress response proteins deleted in these strains form a highly connected network, including 56 proteins involved in vesicle trafficking and vacuolar function; 53 proteins that act downstream of TORC1 according to a rapamycin assay—including components of the HDAC Rpd3L, Elongator, and the INO80, CAF-1 and SWI/SNF chromatin remodeling complexes; over 100 proteins involved in signaling and metabolism; and 17 proteins that directly interact with TORC1. These data provide an important resource for labs studying cell growth control and stress signaling, and demonstrate the utility of our new, and easily adaptable, method for mapping gene regulatory networks.

  2. Identifying early-warning signals of critical transitions with strong noise by dynamical network markers

    Science.gov (United States)

    Liu, Rui; Chen, Pei; Aihara, Kazuyuki; Chen, Luonan

    2015-12-01

    Identifying early-warning signals of a critical transition for a complex system is difficult, especially when the target system is constantly perturbed by big noise, which makes the traditional methods fail due to the strong fluctuations of the observed data. In this work, we show that the critical transition is not traditional state-transition but probability distribution-transition when the noise is not sufficiently small, which, however, is a ubiquitous case in real systems. We present a model-free computational method to detect the warning signals before such transitions. The key idea behind is a strategy: “making big noise smaller” by a distribution-embedding scheme, which transforms the data from the observed state-variables with big noise to their distribution-variables with small noise, and thus makes the traditional criteria effective because of the significantly reduced fluctuations. Specifically, increasing the dimension of the observed data by moment expansion that changes the system from state-dynamics to probability distribution-dynamics, we derive new data in a higher-dimensional space but with much smaller noise. Then, we develop a criterion based on the dynamical network marker (DNM) to signal the impending critical transition using the transformed higher-dimensional data. We also demonstrate the effectiveness of our method in biological, ecological and financial systems.

  3. A RECURRENT ELMAN NEURAL NETWORK - BASED APPROACH TO DETECT THE PRESENCE OF EPILEPTIC ATTACK IN ELECTROENCEPHALOGRAM (EEG SIGNALS

    Directory of Open Access Journals (Sweden)

    Mr.S.Sundaram

    2014-10-01

    Full Text Available Epileptic attack persons are detected largely on the analysis of Electroencephalogram (EEG signals. The EEG signals recordings generate very bulk data which require a skilled and careful analysis. This method can be automated based on Elman Neural Network by using a time frequency domain characteristics of EEG signal called Approximate Entropy (ApEn. This method consists of EEG collection of data, extraction and classification. EEG data from normal persons and epileptic affected persons was collected, digitized and then fed into the Elman neural network. This proposed system proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn as the input feature. Approximate Entropy (ApEn [1] is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the Approximate Entropy drops sharply during an epileptic attack[2]and this fact is used in the proposed system. Type of a neural network namely, Elman neural network is considered in this paper. The experimental results portray that this proposed approach efficiently detects the presence of epileptic seizures[3] in EEG signals and showed a reasonable accuracy.

  4. The Brassinosteroid Signaling Pathway—New Key Players and Interconnections with Other Signaling Networks Crucial for Plant Development and Stress Tolerance

    Directory of Open Access Journals (Sweden)

    Damian Gruszka

    2013-04-01

    Full Text Available Brassinosteroids (BRs are a class of steroid hormones regulating a wide range of physiological processes during the plant life cycle from seed development to the modulation of flowering and senescence. The last decades, and recent years in particular, have witnessed a significant advance in the elucidation of the molecular mechanisms of BR signaling from perception by the transmembrane receptor complex to the regulation of transcription factors influencing expression of the target genes. Application of the new approaches shed light on the molecular functions of the key players regulating the BR signaling cascade and allowed identification of new factors. Recent studies clearly indicated that some of the components of BR signaling pathway act as multifunctional proteins involved in other signaling networks regulating diverse physiological processes, such as photomorphogenesis, cell death control, stomatal development, flowering, plant immunity to pathogens and metabolic responses to stress conditions, including salinity. Regulation of some of these processes is mediated through a crosstalk between BR signalosome and the signaling cascades of other hormones, including auxin, abscisic acid, ethylene and salicylic acid. Unravelling the complicated mechanisms of BR signaling and its interconnections with other molecular networks may be of great importance for future practical applications in agriculture.

  5. The brassinosteroid signaling pathway-new key players and interconnections with other signaling networks crucial for plant development and stress tolerance.

    Science.gov (United States)

    Gruszka, Damian

    2013-01-01

    Brassinosteroids (BRs) are a class of steroid hormones regulating a wide range of physiological processes during the plant life cycle from seed development to the modulation of flowering and senescence. The last decades, and recent years in particular, have witnessed a significant advance in the elucidation of the molecular mechanisms of BR signaling from perception by the transmembrane receptor complex to the regulation of transcription factors influencing expression of the target genes. Application of the new approaches shed light on the molecular functions of the key players regulating the BR signaling cascade and allowed identification of new factors. Recent studies clearly indicated that some of the components of BR signaling pathway act as multifunctional proteins involved in other signaling networks regulating diverse physiological processes, such as photomorphogenesis, cell death control, stomatal development, flowering, plant immunity to pathogens and metabolic responses to stress conditions, including salinity. Regulation of some of these processes is mediated through a crosstalk between BR signalosome and the signaling cascades of other hormones, including auxin, abscisic acid, ethylene and salicylic acid. Unravelling the complicated mechanisms of BR signaling and its interconnections with other molecular networks may be of great importance for future practical applications in agriculture. PMID:23615468

  6. Neural network technique for identifying prognostic anomalies from low-frequency electromagnetic signals in the Kuril-Kamchatka region

    Science.gov (United States)

    Popova, I.; Rozhnoi, A.; Solovieva, M.; Levin, B.; Chebrov, V.

    2016-03-01

    In this paper, we suggest a technique for forecasting seismic events based on the very low and low frequency (VLF and LF) signals in the 10 to 50 Hz band using the neural network approach, specifically, the error back-propagation method (EBPM). In this method, the solution of the problem has two main stages: training and recognition (forecasting). The training set is constructed from the combined data, including the amplitudes and phases of the VLF/LF signals measured in the monitoring of the Kuril-Kamchatka region and the corresponding parameters of regional seismicity. Training the neural network establishes the internal relationship between the characteristic changes in the VLF/LF signals a few days before a seismic event and the corresponding level of seismicity. The trained neural network is then applied in a prognostic mode for automated detection of the anomalous changes in the signal which are associated with seismic activity exceeding the assumed threshold level. By the example of several time intervals in 2004, 2005, 2006, and 2007, we demonstrate the efficiency of the neural network approach in the short-term forecasting of earthquakes with magnitudes starting from M ≥ 5.5 from the nighttime variations in the amplitudes and phases of the LF signals on one radio path. We also discuss the results of the simultaneous analysis of the VLF/LF data measured on two partially overlapping paths aimed at revealing the correlations between the nighttime variations in the amplitude of the signal and seismic activity.

  7. Neuropeptidomics Mass Spectrometry Reveals Signaling Networks Generated by Distinct Protease Pathways in Human Systems

    Science.gov (United States)

    Hook, Vivian; Bandeira, Nuno

    2015-12-01

    Neuropeptides regulate intercellular signaling as neurotransmitters of the central and peripheral nervous systems, and as peptide hormones in the endocrine system. Diverse neuropeptides of distinct primary sequences of various lengths, often with post-translational modifications, coordinate and integrate regulation of physiological functions. Mass spectrometry-based analysis of the diverse neuropeptide structures in neuropeptidomics research is necessary to define the full complement of neuropeptide signaling molecules. Human neuropeptidomics has notable importance in defining normal and dysfunctional neuropeptide signaling in human health and disease. Neuropeptidomics has great potential for expansion in translational research opportunities for defining neuropeptide mechanisms of human diseases, providing novel neuropeptide drug targets for drug discovery, and monitoring neuropeptides as biomarkers of drug responses. In consideration of the high impact of human neuropeptidomics for health, an observed gap in this discipline is the few published articles in human neuropeptidomics compared with, for example, human proteomics and related mass spectrometry disciplines. Focus on human neuropeptidomics will advance new knowledge of the complex neuropeptide signaling networks participating in the fine control of neuroendocrine systems. This commentary review article discusses several human neuropeptidomics accomplishments that illustrate the rapidly expanding diversity of neuropeptides generated by protease processing of pro-neuropeptide precursors occurring within the secretory vesicle proteome. Of particular interest is the finding that human-specific cathepsin V participates in producing enkephalin and likely other neuropeptides, indicating unique proteolytic mechanisms for generating human neuropeptides. The field of human neuropeptidomics has great promise to solve new mechanisms in disease conditions, leading to new drug targets and therapeutic agents for human

  8. Three-dimensional WS2 nanosheet networks for H2O2 produced for cell signaling

    Science.gov (United States)

    Tang, Jing; Quan, Yingzhou; Zhang, Yueyu; Jiang, Min; Al-Enizi, Abdullah M.; Kong, Biao; An, Tiance; Wang, Wenshuo; Xia, Limin; Gong, Xingao; Zheng, Gengfeng

    2016-03-01

    Hydrogen peroxide (H2O2) is an important molecular messenger for cellular signal transduction. The capability of direct probing of H2O2 in complex biological systems can offer potential for elucidating its manifold roles in living systems. Here we report the fabrication of three-dimensional (3D) WS2 nanosheet networks with flower-like morphologies on a variety of conducting substrates. The semiconducting WS2 nanosheets with largely exposed edge sites on flexible carbon fibers enable abundant catalytically active sites, excellent charge transfer, and high permeability to chemicals and biomaterials. Thus, the 3D WS2-based nano-bio-interface exhibits a wide detection range, high sensitivity and rapid response time for H2O2, and is capable of visualizing endogenous H2O2 produced in living RAW 264.7 macrophage cells and neurons. First-principles calculations further demonstrate that the enhanced sensitivity of probing H2O2 is attributed to the efficient and spontaneous H2O2 adsorption on WS2 nanosheet edge sites. The combined features of 3D WS2 nanosheet networks suggest attractive new opportunities for exploring the physiological roles of reactive oxygen species like H2O2 in living systems.Hydrogen peroxide (H2O2) is an important molecular messenger for cellular signal transduction. The capability of direct probing of H2O2 in complex biological systems can offer potential for elucidating its manifold roles in living systems. Here we report the fabrication of three-dimensional (3D) WS2 nanosheet networks with flower-like morphologies on a variety of conducting substrates. The semiconducting WS2 nanosheets with largely exposed edge sites on flexible carbon fibers enable abundant catalytically active sites, excellent charge transfer, and high permeability to chemicals and biomaterials. Thus, the 3D WS2-based nano-bio-interface exhibits a wide detection range, high sensitivity and rapid response time for H2O2, and is capable of visualizing endogenous H2O2 produced in

  9. Energy Analysis of Received Signal Strength Localization in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    D. Girbau

    2011-12-01

    Full Text Available This paper presents the investigation of energy demands during localization of wireless nodes in ad-hoc networks. We focus on the method based on the received signal strength (RSS to estimate the distances between the nodes. To deal with the uncertainty of this technique, statistical methods are used. It implies more measurement samples to be taken and consequently more energy to be spent. Therefore, we investigate the accuracy of localization and the consumed energy in the relation to the number of measurement samples. The experimental measurements were conducted with IRIS sensor motes and their results related to the proposed energy model. The results show that the expended energy is not related linearly to the localization error. First, improvement of the accuracy rises fast with more measurement samples. Then, adding more samples, the accuracy increase is moderate, which means that the marginal energy cost of the additional improvement is higher.

  10. AUTOMATIC SEGMENTATION OF BROADCAST AUDIO SIGNALS USING AUTO ASSOCIATIVE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2010-12-01

    Full Text Available In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC, Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

  11. Efficient transmission of subthreshold signals in complex networks of spiking neurons

    CERN Document Server

    Torres, Joaquin J; Marro, J

    2014-01-01

    We investigate the efficient transmission and processing of weak signals (subthreshold) in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances - that naturally balances the network with excitatory and inhibitory synapses - and considering short-term synaptic plasticity affecting such conductances, we found different dynamical phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron and increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases and therefore for quite well defined and different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite ro...

  12. Using received signal strength indicator to detect node replacement and replication attacks in wireless sensor networks

    Science.gov (United States)

    Hussain, Sajid; Rahman, Md Shafayat

    2009-04-01

    With the advent of powerful and efficient wireless sensor nodes, the usage of wireless sensor networks (WSNs) has been greatly increased. However, various kinds of security breaches have also been introduced, which exposes the vulnerability of these nodes to defend the valuable data from some specific types of attacks. In this paper, we address node replication, replacement, and man-in-the-middle attacks. We analyze the feasibility of using received signal strength indicator (RSSI) values measured at the receiver node to detect these kinds of attacks. As RSSI value is readily available for every message received by a node, a successful utilization of this information in security breach detection can be a very important contribution.

  13. Efficient Signal Processing in Random Networks that Generate Variability: A Comparison of Internally Generated and Externally Induced Variability

    Science.gov (United States)

    Dasgupta, Sakyasingha; Nishikawa, Isao; Aihara, Kazuyuki; Toyoizumi, Taro

    Source of cortical variability and its influence on signal processing remain an open question. We address the latter, by studying two types of balanced randomly connected networks of quadratic I-F neurons, with irregular spontaneous activity: (a) a deterministic network with strong connections generating noise by chaotic dynamics (b) a stochastic network with weak connections receiving noisy input. They are analytically tractable in the limit of large network-size and channel time-constant. Despite different sources of noise, spontaneous activity of these networks are identical unless majority of neurons are simultaneously recorded. However, the two networks show remarkably different sensitivity to external stimuli. In the former, input reverberates internally and can be read out over long time, but in the latter, inputs rapidly decay. This is further enhanced with activity-dependent plasticity at input synapses producing marked difference in decoding inputs from neural activity. We show, this leads to distinct performance of the two networks to integrate temporally separate signals from multiple sources, with the deterministic chaotic network activity serving as reservoir for Monte Carlo sampling to perform near optimal Bayesian integration, unlike its stochastic counterpart.

  14. Clustering of ECG Signals Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Elaheh Sayari

    2014-12-01

    Full Text Available Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Whereas clustering of electrocardiogram (ECG signals will help to identify the heart diseases as soon as possible. In this regard, neural network and fuzzy logic have been used in many application areas while each of them has the advantages and disadvantages. Thus, the present paper utilizes the proposed fuzzy neural network (FNN with initial weights generated by genetic algorithm (GFNN for the sake of improvement testing speed, accuracy and for reducing the chance of the FNN getting stuck on a local minimum. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat and atrial fibrillation beat obtained from the Physio Bank databases were clustered by the proposed GFNN model. Model of evaluation results indicate that the proposed model can perform more accurately and it has less testing speed than the conventional statistical methods, a single ANN and FNN. The total clustering accuracy of the GFNN model is 98.23%.

  15. R2NA: Received Signal Strength (RSS Ratio-Based Node Authentication for Body Area Network

    Directory of Open Access Journals (Sweden)

    Yang Wu

    2013-12-01

    Full Text Available The body area network (BAN is an emerging branch of wireless sensor networks for personalized applications. The services in BAN usually have a high requirement on security, especially for the medical diagnosis. One of the fundamental directions to ensure security in BAN is how to provide node authentication. Traditional research using cryptography relies on prior secrets shared among nodes, which leads to high resource cost. In addition, most existing non-cryptographic solutions exploit out-of-band (OOB channels, but they need the help of additional hardware support or significant modifications to the system software. To avoid the above problems, this paper presents a proximity-based node authentication scheme, which only uses wireless modules equipped on sensors. With only one sensor and one control unit (CU in BAN, we could detect a unique physical layer characteristic, namely, the difference between the received signal strength (RSS measured on different devices in BAN. Through the above-mentioned particular difference, we can tell whether the sender is close enough to be legitimate. We validate our scheme through both theoretical analysis and experiments, which are conducted on the real Shimmer nodes. The results demonstrate that our proposed scheme has a good security performance.

  16. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  17. In silico evidence of signaling pathways of notch mediated networks in leukemia

    Directory of Open Access Journals (Sweden)

    Raghunatha Rao

    2012-07-01

    Full Text Available Notch signaling plays a critical role in cell fate determination and maintenance of progenitors in many developmental systems. Notch receptors have been shown to be expressed on hematopoietic progenitor cells as well as to various degrees in peripheral blood T and B lymphocytes, monocytes, and neutrophils. Our aim was to understand the protein interaction network, using Notch1 protein name as query in STRING database and we generated a model to assess the significance of Notch1 associated proteins in Acute Lymphoblastic Leukemia (ALL. We further analyzed the expression levels of the genes encoding hub proteins, using Oncomine database, to determine their significance in leukemogenesis. Of the forty two hub genes, we observed that sixteen genes were underexpressed and eleven genes were overexpressed in T-cell Acute Lymphoblastic samples in comparison to their expression levels in normal cells. Of these, we found three novel genes which have not been reported earlier- KAT2B, PSEN1 (underexpressed and CDH2 (overexpressed.These three identified genes may provide new insights into the abnormal hematopoietic process observed in Leukemia as these genes are involved in Notch signaling and cell adhesion processes. It is evident that experimental validation of the protein interactors in leukemic cells could help in the identification of new diagnostic markers for leukemia.

  18. Electrical signal transmission in a bone cell network: the influence of a discrete gap junction

    Science.gov (United States)

    Zhang, D.; Weinbaum, S.; Cowin, S. C.

    1998-01-01

    A refined electrical cable model is formulated to investigate the role of a discrete gap junction in the intracellular transmission of electrical signals in an electrically coupled system of osteocytes and osteoblasts in an osteon. The model also examines the influence of the ratio q between the membrane's electrical time constant and the characteristic time of pore fluid pressure, the circular, cylindrical geometry of the osteon, and key simplifying assumptions in our earlier continuous cable model (see Zhang, D., S. C. Cowin, and S. Weinbaum. Electrical signal transmission and gap junction regulation in a bone cell network: A cable model for an osteon. Ann. Biomed. Eng. 25:379-396, 1997). Using this refined model, it is shown that (1) the intracellular potential amplitude at the osteoblastic end of the osteonal cable retains the character of a combination of a low-pass and a high-pass filter as the corner frequency varies in the physiological range; (2) the presence of a discrete gap junction near a resting osteoblast can lead to significant modulation of the intracellular potential and current in the osteoblast for measured values of the gap junction coupling strength; and (3) the circular, cylindrical geometry of the osteon is well simulated by the beam analogy used in Zhang et al.

  19. Signaling Network of Environmental Sensing and Adaptation in Plants:. Key Roles of Calcium Ion

    Science.gov (United States)

    Kurusu, Takamitsu; Kuchitsu, Kazuyuki

    2011-01-01

    Considering the important issues concerning food, environment, and energy that humans are facing in the 21st century, humans mostly depend on plants. Unlike animals which move from an inappropriate environment, plants do not move, but rapidly sense diverse environmental changes or invasion by other organisms such as pathogens and insects in the place they root, and adapt themselves by changing their own bodies, through which they developed adaptability. Whole genetic information corresponding to the blueprints of many biological systems has recently been analyzed, and comparative genomic studies facilitated tracing strategies of each organism in their evolutional processes. Comparison of factors involved in intracellular signal transduction between animals and plants indicated diversification of different gene sets. Reversible binding of Ca2+ to sensor proteins play key roles as a molecular switch both in animals and plants. Molecular mechanisms for signaling network of environmental sensing and adaptation in plants will be discussed with special reference to Ca2+ as a key element in information processing.

  20. Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients.

    Science.gov (United States)

    Pflugradt, Maik; Mann, Steffen; Tigges, Timo; Görnig, Matthias; Orglmeister, Reinhold

    2016-02-01

    Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system's overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system's synchronicity is as precise as 30 μs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments. PMID:26479338

  1. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  2. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

    OpenAIRE

    Shuman, David I; Narang, Sunil K.; Frossard, Pascal; Ortega, Antonio; Vandergheynst, Pierre

    2012-01-01

    In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the...

  3. A blind hierarchical coherent search for gravitational-wave signals from coalescing compact binaries in a network of interferometric detectors

    OpenAIRE

    Bose., S; Dayanga, T.; S. Ghosh; Talukder, D.

    2011-01-01

    We describe a hierarchical data analysis pipeline for coherently searching for gravitational wave (GW) signals from non-spinning compact binary coalescences (CBCs) in the data of multiple earth-based detectors. It assumes no prior information on the sky position of the source or the time of occurrence of its transient signals and, hence, is termed "blind". The pipeline computes the coherent network search statistic that is optimal in stationary, Gaussian noise, and allows for the computation ...

  4. Regulatory Networks and Complex Interactions between the Insulin and Angiotensin II Signalling Systems: Models and Implications for Hypertension and Diabetes

    OpenAIRE

    Çizmeci, Deniz; Arkun, Yaman

    2013-01-01

    Regulatory Networks and Complex Interactions between the Insulin and Angiotensin II Signalling Systems: Models and Implications for Hypertension and Diabetes Deniz Cizmeci, Yaman Arkun* Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey Abstract The cross-talk between insulin and angiotensin II signalling pathways plays a significant role in the co-occurrence of diabetes and hypertension. We developed a mathematical model of the system of ...

  5. Constant change: dynamic regulation of membrane transport by calcium signalling networks keeps plants in tune with their environment.

    Science.gov (United States)

    Kleist, Thomas J; Luan, Sheng

    2016-03-01

    Despite substantial variation and irregularities in their environment, plants must conform to spatiotemporal demands on the molecular composition of their cytosol. Cell membranes are the major interface between organisms and their environment and the basis for controlling the contents and intracellular organization of the cell. Membrane transport proteins (MTPs) govern the flow of molecules across membranes, and their activities are closely monitored and regulated by cell signalling networks. By continuously adjusting MTP activities, plants can mitigate the effects of environmental perturbations, but effective implementation of this strategy is reliant on precise coordination among transport systems that reside in distinct cell types and membranes. Here, we examine the role of calcium signalling in the coordination of membrane transport, with an emphasis on potassium transport. Potassium is an exceptionally abundant and mobile ion in plants, and plant potassium transport has been intensively studied for decades. Classic and recent studies have underscored the importance of calcium in plant environmental responses and membrane transport regulation. In reviewing recent advances in our understanding of the coding and decoding of calcium signals, we highlight established and emerging roles of calcium signalling in coordinating membrane transport among multiple subcellular locations and distinct transport systems in plants, drawing examples from the CBL-CIPK signalling network. By synthesizing classical studies and recent findings, we aim to provide timely insights on the role of calcium signalling networks in the modulation of membrane transport and its importance in plant environmental responses. PMID:26139029

  6. Experiment on Synchronous Timing Signal Detection from ISDB-T Terrestrial Digital TV Signal with Application to Autonomous Distributed ITS-IVC Network

    Science.gov (United States)

    Karasawa, Yoshio; Kumagai, Taichi; Takemoto, Atsushi; Fujii, Takeo; Ito, Kenji; Suzuki, Noriyoshi

    A novel timing synchronizing scheme is proposed for use in inter-vehicle communication (IVC) with an autonomous distributed intelligent transport system (ITS). The scheme determines the timing of packet signal transmission in the IVC network and employs the guard interval (GI) timing in the orthogonal frequency divisional multiplexing (OFDM) signal currently used for terrestrial broadcasts in the Japanese digital television system (ISDB-T). This signal is used because it is expected that the automotive market will demand the capability for cars to receive terrestrial digital TV broadcasts in the near future. The use of broadcasts by automobiles presupposes that the on-board receivers are capable of accurately detecting the GI timing data in an extremely low carrier-to-noise ratio (CNR) condition regardless of a severe multipath environment which will introduce broad scatter in signal arrival times. Therefore, we analyzed actual broadcast signals received in a moving vehicle in a field experiment and showed that the GI timing signal is detected with the desired accuracy even in the case of extremely low-CNR environments. Some considerations were also given about how to use these findings.

  7. Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

    International Nuclear Information System (INIS)

    Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As2O3

  8. Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

    Energy Technology Data Exchange (ETDEWEB)

    Stueckle, Todd A., E-mail: tstueckle@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Lu, Yongju, E-mail: yongju6@hotmail.com [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Davis, Mary E., E-mail: mdavis@wvu.edu [Department of Physiology, West Virginia University, Morgantown, WV 26506 (United States); Wang, Liying, E-mail: lmw6@cdc.gov [Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Jiang, Bing-Hua, E-mail: bhjiang@jefferson.edu [Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107 (United States); Holaskova, Ida, E-mail: iholaskova@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Schafer, Rosana, E-mail: rschafer@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Barnett, John B., E-mail: jbarnett@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Rojanasakul, Yon, E-mail: yrojan@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States)

    2012-06-01

    Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As{sub 2}O

  9. A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Engelbrecht, Jacob; Brunak, Søren;

    1997-01-01

    We have developed a new method for the identication of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs signicantly better than previous prediction schemes, and can easily be applied to genome...

  10. DMPD: Glucocorticoids and the innate immune system: crosstalk with the toll-likereceptor signaling network. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 17576036 Glucocorticoids and the innate immune system: crosstalk with the toll-like...07 May 13. (.png) (.svg) (.html) (.csml) Show Glucocorticoids and the innate immune system: crosstalk with t...he toll-likereceptor signaling network. PubmedID 17576036 Title Glucocorticoids a

  11. Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling

    OpenAIRE

    Cho Kwang-Hyun; Choi Sun; Kwon Yung-Keun

    2007-01-01

    Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a fee...

  12. Improving Throughput and Delay by Signaling Modification in Integrated 802.11 and 3G Heterogeneous Wireless Network

    Directory of Open Access Journals (Sweden)

    Majid Fouladian

    2016-02-01

    Full Text Available Current trends show that UMTS network and WLAN will co-exist and work together to support more users with higher data rate services over a wider area. However, this integration invokes many challenges such as mobility management and handoff decision making. Vertical handoff is switching process between heterogeneous wireless networks in a 3G/WLAN network. Vertical handoffs often fail due to the abrupt degrade of the WLAN signal strength. In this paper, proposed a new vertical handoff method for to decrease the number of signaling and registration processes, to examine the Special conditions such as WLAN black holes and to eliminate disconnecting effects. By estimating user locations related to WLAN position, interface on-times will be reduced which makes the power consuming of the system decreased. To indicate the efficiency of proposed approach the performance and the delay results are simulated.

  13. A membrane protein / signaling protein interaction network for Arabidopsis version AMPv2

    Directory of Open Access Journals (Sweden)

    Sylvie Lalonde

    2010-09-01

    Full Text Available Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway compatible vector. The mating-based split-ubiquitin system was used to screen for potential protein-protein interactions (pPPIs among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases, 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 387 pPPIs between 179 proteins, yielding a scale-free network (r2=0.863. Eighty of 142 transmembrane receptor-like kinases (RLK tested positive, identifying three homomers, 63 heteromers and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

  14. Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications

    Directory of Open Access Journals (Sweden)

    Arjun Verma

    2016-07-01

    Full Text Available We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.

  15. Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications.

    Science.gov (United States)

    Verma, Arjun; Fratto, Brian E; Privman, Vladimir; Katz, Evgeny

    2016-01-01

    We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed. PMID:27399702

  16. RECEIVED SIGNAL STRENGTH ESTIMATION IN VEHICLE-TO-VEHICLE COMMUNICATIONS USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Zeinab Talepour

    2014-03-01

    Full Text Available Received signal strength (RSS is a major performance metric in vehicular communication system design. Experimental measurements of the RSS for vehicular communications are not cost effective. Therefore, off-the-shelf ray-tracing packages are deployed to substitute the costly measurements with RSS simulations. However, the simulation process is too time-consuming if the RSS is required over a long interval of time. We propose a new RSS estimation approach using neural network (NN to reduce the computation time. First, ray-tracing is used for simulating the RSS in some instances of time and training the NN. Then, the NN is deployed to estimate the RSS afterward. We apply the new approach to an antenna placement problem in vehicle-to-vehicle (V2V communications. The numerical results show that RSS computation time reduces significantly by using the proposed estimation approach, and the approach is as effective as ray-tracing in the RSS simulation for the antenna placement problem in vehicular communications.

  17. Exploring phospholipase C-coupled Ca(2+) signalling networks using Boolean modelling.

    Science.gov (United States)

    Bhardwaj, G; Wells, C P; Albert, R; van Rossum, D B; Patterson, R L

    2011-05-01

    In this study, the authors explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signalling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP(3)R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomisation of the Boolean operators ablates oscillatory pattern formation. Furthermore, knock-out simulations of the IP(3)R, TRPC3 and multiple other proteins recapitulate experimentally derived results. The potential of this approach can be observed by its ability to predict previously undescribed cellular phenotypes using in vitro experimental data. Indeed, our cellular analysis of the developmental and calcium-regulatory protein, DANGER1a, confirms the counter-intuitive predictions from our Boolean models in two highly relevant cellular models. Based on these results, the authors theorise that with sufficient legacy knowledge and/or computational biology predictions, Boolean networks can provide a robust method for predictive modelling of any biological system. [Includes supplementary material]. PMID:21639591

  18. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Science.gov (United States)

    Houli, Duan; Zhiheng, Li; Yi, Zhang

    2010-12-01

    We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  19. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Houli Duan

    2010-01-01

    Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  20. Statistical Modeling of Large-Scale Signal Path Loss in Underwater Acoustic Networks

    Directory of Open Access Journals (Sweden)

    Manuel Perez Malumbres

    2013-02-01

    Full Text Available In an underwater acoustic channel, the propagation conditions are known to vary in time, causing the deviation of the received signal strength from the nominal value predicted by a deterministic propagation model. To facilitate a large-scale system design in such conditions (e.g., power allocation, we have developed a statistical propagation model in which the transmission loss is treated as a random variable. By applying repetitive computation to the acoustic field, using ray tracing for a set of varying environmental conditions (surface height, wave activity, small node displacements around nominal locations, etc., an ensemble of transmission losses is compiled and later used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e., to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc..

  1. Digital Signal Processing for a Sliceable Transceiver for Optical Access Networks

    DEFF Research Database (Denmark)

    Saldaña Cercos, Silvia; Wagner, Christoph; Vegas Olmos, Juan José; Fagertun, Anna Manolova; Tafur Monroy, Idelfonso

    2015-01-01

    also for implementing full signal path symmetry in real-time oscilloscopes to provide performance and signal fidelity (i.e. lower noise and jitter). In this paper the key digital signal processing (DSP) subsystems required to achieve signal slicing are surveyed. It also presents, for the first time, a...

  2. The biological networks in studying cell signal transduction complexity: The examples of sperm capacitation and of endocannabinoid system

    Science.gov (United States)

    Bernabò, Nicola; Barboni, Barbara; Maccarrone, Mauro

    2014-01-01

    Cellular signal transduction is a complex phenomenon, which plays a central role in cell surviving and adaptation. The great amount of molecular data to date present in literature, together with the adoption of high throughput technologies, on the one hand, made available to scientists an enormous quantity of information, on the other hand, failed to provide a parallel increase in the understanding of biological events. In this context, a new discipline arose, the systems biology, aimed to manage the information with a computational modeling-based approach. In particular, the use of biological networks has allowed the making of huge progress in this field. Here we discuss two possible application of the use of biological networks to explore cell signaling: the study of the architecture of signaling systems that cooperate in determining the acquisition of a complex cellular function (as it is the case of the process of activation of spermatozoa) and the organization of a single specific signaling systems expressed by different cells in different tissues (i.e. the endocannabinoid system). In both the cases we have found that the networks follow a scale free and small world topology, likely due to the evolutionary advantage of robustness against random damages, fastness and specific of information processing, and easy navigability. PMID:25379139

  3. Interrogating a cell signalling network sensitively monitors cell fate transition during early differentiation of mouse embryonic stem cells

    Institute of Scientific and Technical Information of China (English)

    LIU; Yi-Hsin; HO; Chih-ming

    2010-01-01

    The different cell types in an animal are often considered to be specified by combinations of transcription factors,and defined by marker gene expression.This paradigm is challenged,however,in stem cell research and application.Using a mouse embryonic stem cell(mESC) culture system,here we show that the expression level of many key stem cell marker genes/transcription factors such as Oct4,Sox2 and Nanog failed to monitor cell status transition during mESC differentiation.On the other hand,the response patterns of cell signalling network to external stimuli,as monitored by the dynamics of protein phosphorylation,changed dramatically.Our results also suggest that an irreversible alternation in the cell signalling network precedes the adjustment of transcription factor levels.This is consistent with the notion that signal transduction events regulate cell fate specification.We propose that interrogating a cell signalling network can assess the cell property more precisely,and provide a sensitive measurement for the early events in cell fate transition.We wish to bring attention to the potential problem of cell identification using a few marker genes,and suggest a novel methodology to address this issue.

  4. Towards systematic discovery of signaling networks in budding yeast filamentous growth stress response using interventional phosphorylation data.

    Directory of Open Access Journals (Sweden)

    Yan Zhang

    Full Text Available Reversible phosphorylation is one of the major mechanisms of signal transduction, and signaling networks are critical regulators of cell growth and development. However, few of these networks have been delineated completely. Towards this end, quantitative phosphoproteomics is emerging as a useful tool enabling large-scale determination of relative phosphorylation levels. However, phosphoproteomics differs from classical proteomics by a more extensive sampling limitation due to the limited number of detectable sites per protein. Here, we propose a comprehensive quantitative analysis pipeline customized for phosphoproteome data from interventional experiments for identifying key proteins in specific pathways, discovering the protein-protein interactions and inferring the signaling network. We also made an effort to partially compensate for the missing value problem, a chronic issue for proteomics studies. The dataset used for this study was generated using SILAC (Stable Isotope Labeling with Amino acids in Cell culture technique with interventional experiments (kinase-dead mutations. The major components of the pipeline include phosphopeptide meta-analysis, correlation network analysis and causal relationship discovery. We have successfully applied our pipeline to interventional experiments identifying phosphorylation events underlying the transition to a filamentous growth form in Saccharomyces cerevisiae. We identified 5 high-confidence proteins from meta-analysis, and 19 hub proteins from correlation analysis (Pbi2p and Hsp42p were identified by both analyses. All these proteins are involved in stress responses. Nine of them have direct or indirect evidence of involvement in filamentous growth. In addition, we tested four of our predicted proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p, by interventional phenotypic experiments and all of them present differential invasive growth, providing prospective validation of our approach. This comprehensive

  5. Coded Single-Tone Signaling and Its Application to Resource Coordination and Interference Management in Femtocell Networks

    CERN Document Server

    Yang, C; Wang, J

    2011-01-01

    Resource coordination and interference management is the key to achieving the benefits of femtocell networks. Over-the-air signaling is one of the most effective means for distributed dynamic resource coordination and interference management. However, the design of this type of signal is challenging. In this paper, we address the challenges and propose an effective solution, referred to as coded single-tone signaling (STS). The proposed coded STS scheme possesses certain highly desirable properties, such as no dedicated resource requirement (no overhead), no near-and-far effect, no inter-signal interference (no multi-user interference), low peak-to-average power ratio (deep coverage). In addition, the proposed coded STS can fully exploit frequency diversity and provides a means for high quality wideband channel estimation. The coded STS design is demonstrated through a concrete numerical example. Performance of the proposed coded STS and its effect on cochannel traffic channels are evaluated through simulatio...

  6. Acceptance of evidence-supported hypotheses generates a stronger signal from an underlying functionally-connected network.

    Science.gov (United States)

    Whitman, J C; Takane, Y; Cheung, T P L; Moiseev, A; Ribary, U; Ward, L M; Woodward, T S

    2016-02-15

    Choosing one's preferred hypothesis requires multiple brain regions to work in concert as a functionally connected network. We predicted that a stronger network signal would underlie cognitive coherence between a hypothesis and the available evidence. In order to identify such functionally connected networks in magnetoencephalography (MEG) data, we first localized the generators of changes in oscillatory power within three frequency bands, namely alpha (7-13Hz), beta (18-24Hz), and theta (3-7Hz), with a spatial resolution of 5mm and temporal resolution of 50ms. We then used principal component analysis (PCA) to identify functionally connected networks reflecting co-varying post-stimulus changes in power. As predicted, PCA revealed a functionally connected network with a stronger signal when the evidence supported accepting the hypothesis being judged. This difference was driven by beta-band power decreases in the left dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex (PCC), and midline occipital cortex. PMID:26702776

  7. Efficient Hybrid Grid Synthesis Method Based on Genetic Algorithm for Power/Ground Network Optimization with Dynamic Signal Consideration

    Science.gov (United States)

    Yang, Yun; Kimura, Shinji

    This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.

  8. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

    International Nuclear Information System (INIS)

    In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies

  9. Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

    KAUST Repository

    Muraro, D.

    2013-12-31

    As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

  10. Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe 0νββ-decay searches

    Energy Technology Data Exchange (ETDEWEB)

    Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O. [Max-Planck-Institut fuer Physik, Munich (Germany)

    2015-07-15

    A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples. (orig.)

  11. Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe 0νββ-decay searches

    International Nuclear Information System (INIS)

    A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples. (orig.)

  12. The endocrine regulation network of growth hormone synthesis and secretion in fish: Emphasis on the signal integration in somatotropes

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In teleosts, growth hormone (GH) production is governed by multiple neuroendocrine factors from the hypothalamus and other regulators from the pituitary and peripheral organs. Exploring the principles followed by pituitary somatotropes when differentiating and integrating the signals from these regulators at the cellular and intracellular level is essential for understanding the endocrine regulation network of growth hormone synthesis and secretion in fish. This paper discusses recent advances in the action mechanisms of GH regulation factors, including the neuroendocrine regulators, pituitary level factors and peripheral factors, primarily involved in their receptor systems as well as in post-receptor signal transduction pathways.

  13. Alternative networks of the signal system and control of functionality of mobile radio systems

    Directory of Open Access Journals (Sweden)

    Pishchin Oleg Nikolaevich

    2010-08-01

    Full Text Available The necessity to increase the quantity of controllable parameters of objects in mobile networks is investigated in the paper. The control of the basic parameters of radioelectronic means of radiation functionality will allow to predict some changes in the system, and to make decisions in time on recovery of its quality. It is offered to use wireless sensor networks in order to start controlling the ex-tended quantity of parameters in networks of public usage. The distinctions of Mesh-networks technology and technologies based on the platform ZigBee, used as alarm networks in mobile radio systems, are investigated.

  14. Identification of Neuronal Network Properties from the Spectral Analysis of Calcium Imaging Signals in Neuronal Cultures

    Directory of Open Access Journals (Sweden)

    Miguel Valencia

    2013-12-01

    Full Text Available Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

  15. Northern Finland Seismological Network: a tool to analyse long-period seismological signals

    Science.gov (United States)

    Kozlovskaya, Elena; Hurskainen, Riitta

    2014-05-01

    Sodankylä Geophysical Observatory of Oulu University (SGO) is located at 67° 22' N, 26° 38' E in the middle of Finnish Lapland. It was established in 1913 and since then has gained a long experience in carrying out multidisciplinary geophysical observations in Arctic environment. Seismological observations at the University of Oulu and SGO have been carried out since 1965. During 2005-2008 the SGO modernized own sort-period permanent seismic network, enhanced the number of stations and equipped them with the VBB seismic sensors. The stations are located at latitudes from 650 N to 680 N. They form the Northern Finland Seismological Network (NFSN) that will be the part of Finnish EPOS research infrastructure in the future. The continuous seismic data of the NFSN are archived in the GFZ Seismological Data Archive of the GeoForschungsZentrum Potsdam (Germany) and in the own backup archive of the SGO. At the moment, the data of the NFSN are routinely used for monitoring of seismic activity in Northern Europe and world-wide and information about seismic events is published in several on-line bulletins. Due to the recent mineral exploration and mining boom in northern Finland, a new task for the NFSN will be recording and analysis of mining-induced seismicity and estimating of seismic hazard associated with it. During installation of instruments of the NFSN, particular measures were taken in order to improve instruments performance at long periods. In Arctic conditions the performance of broadband seismic instruments is affected by large ambient temperature variations and geomagnetic field disturbances (geomagnetic pulsations). In 2007-2009 the NFSN was a part of the POLENET/LAPNET IPY project. In addition to lithosphere structure studies, the project aimed at registration of long-period glacial seismic events originating from Greenland Ice Sheet. Analysis of data recorded by the NFSN during the IPY demonstrated that the network is capable to record not only long

  16. In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

    OpenAIRE

    Yin, Lianhong; Zheng, Lingli; Xu, Lina; Dong, Deshi; Han, Xu; Qi, Yan; Zhao, Yanyan; Xu, Youwei; Peng, Jinyong

    2015-01-01

    Background Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. Methods In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targe...

  17. Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

    OpenAIRE

    Rahmani-Badi, Azadeh; Sepehr, Shayesteh; Fallahi, Hossein; Heidari-Keshel, Saeed

    2015-01-01

    Many bacterial pathogens use quorum-sensing (QS) signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF), is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized a...

  18. Electroencephalogram Signals Processing for the Diagnosis of Petit mal and Grand mal Epilepsies Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    M. R. Arab

    2010-04-01

    Full Text Available In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for theclassification of grand mal (clonic stage and petit mal (absence epilepsies into healthy, ictal and interictal (EEGs. Preprocessingis included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement as well as an eyeballmovement artifact using the Discrete Wavelet Transformation (DWT. Denoising EEG signals from the AC power supplyfrequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accuracy of theprocessing stage (wavelet transform and neural network. The EEGs signals are categorized into normal and petit mal and clonicepilepsy by an expert neurologist. The categorization is confirmed by the Fast Fourier Transform (FFT analysis. The datasetincludes waves such as sharp, spike and spike‐slow wave. Through the Countinous Wavelet Transform (CWT of EEG records,transient features are accurately captured and separated and used as classifier input. We introduce a two‐stage classifier basedon the Learning Vector Quantization (LVQ neural network localized in both time and frequency contexts. The particularcoefficients of the Continuous Wavelet Transform (CWT are networks. The simulation results are very promising and theaccuracy of the proposed method obtained is of about 80%.

  19. A Job Market Signaling Scheme for Incentive and Trust Management in Vehicular Ad Hoc Networks

    OpenAIRE

    Haddadou, Nadia; Rachedi, Abderrezak; Ghamri-Doudane, Yacine

    2015-01-01

    In collaborative wireless networks with a low infrastructure, the presence of misbehaving nodes can have a negative impact on network performance. In particular, we are interested in dealing with this nasty presence in road safety applications, based on vehicular ad hoc networks (VANETs). In this work, we consider as harmful the presence of malicious nodes, which spread false and forged data; and selfish nodes, which cooperate only for their own benefit. To deal with this, we propose a Distri...

  20. A neural network model with dopamine-like reinforcement signal that learns a spatial delayed response task.

    Science.gov (United States)

    Suri, R E; Schultz, W

    1999-01-01

    This study investigated how the simulated response of dopamine neurons to reward-related stimuli could be used as reinforcement signal for learning a spatial delayed response task. Spatial delayed response tasks assess the functions of frontal cortex and basal ganglia in short-term memory, movement preparation and expectation of environmental events. In these tasks, a stimulus appears for a short period at a particular location, and after a delay the subject moves to the location indicated. Dopamine neurons are activated by unpredicted rewards and reward-predicting stimuli, are not influenced by fully predicted rewards, and are depressed by omitted rewards. Thus, they appear to report an error in the prediction of reward, which is the crucial reinforcement term in formal learning theories. Theoretical studies on reinforcement learning have shown that signals similar to dopamine responses can be used as effective teaching signals for learning. A neural network model implementing the temporal difference algorithm was trained to perform a simulated spatial delayed response task. The reinforcement signal was modeled according to the basic characteristics of dopamine responses to novel stimuli, primary rewards and reward-predicting stimuli. A Critic component analogous to dopamine neurons computed a temporal error in the prediction of reinforcement and emitted this signal to an Actor component which mediated the behavioral output. The spatial delayed response task was learned via two subtasks introducing spatial choices and temporal delays, in the same manner as monkeys in the laboratory. In all three tasks, the reinforcement signal of the Critic developed in a similar manner to the responses of natural dopamine neurons in comparable learning situations, and the learning curves of the Actor replicated the progress of learning observed in the animals. Several manipulations demonstrated further the efficacy of the particular characteristics of the dopamine

  1. HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks.

    Science.gov (United States)

    Dai, Xinbin; Li, Jun; Liu, Tingsong; Zhao, Patrick Xuechun

    2016-01-01

    The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many 'unknown' yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/. PMID:26657893

  2. Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

    Directory of Open Access Journals (Sweden)

    Gaelle Bettus

    Full Text Available In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD signal derived from resting state functional magnetic resonance imaging (fMRI reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG and resting-state functional MRI (fMRI in 5 patients suffering from intractable temporal lobe epilepsy (TLE. Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to non-affected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal. This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional

  3. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

    International Nuclear Information System (INIS)

    The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods

  4. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients' Consciousness Level Based on Anesthesiologists Experience

    Science.gov (United States)

    Jiang, George J. A.; Fan, Shou-Zen; Abbod, Maysam F.; Huang, Hui-Hsun; Lan, Jheng-Yan; Tsai, Feng-Fang; Chang, Hung-Chi; Yang, Yea-Wen; Chuang, Fu-Lan; Chiu, Yi-Fang; Jen, Kuo-Kuang; Wu, Jeng-Fu; Shieh, Jiann-Shing

    2015-01-01

    Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully. PMID:25738152

  5. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  6. Signal Delay in General RC Networks with Application to Timing Simulation of Digital Integrated Circuits

    OpenAIRE

    Lin, Tzu-Mu; Mead, Carver A.

    1983-01-01

    Modeling digital MOS circuits by RC networks has become a well accepted practice for estimating delays. In 1981, Penfield and Rubinstein proposed a method to bound the delays of the nodes in an RC tree network. In this paper, we address the problem of dynamic timing simulation under RC-based models. Based upon the delay of Elmore, a single value of delay is derived for any node in a general RC network. The effects of parallel connections and stored charges are properly taken into con...

  7. Global effects of kinase inhibitors on signaling networks revealed by quantitative phosphoproteomics

    DEFF Research Database (Denmark)

    Pan, Cuiping; Olsen, Jesper V; Daub, Henrik;

    2009-01-01

    Aberrant signaling causes many diseases, and manipulating signaling pathways with kinase inhibitors has emerged as a promising area of drug research. Most kinase inhibitors target the conserved ATP-binding pocket; therefore specificity is a major concern. Proteomics has previously been used to id...

  8. Growth Factor Signaling and Memory Formation: Temporal and Spatial Integration of a Molecular Network

    Science.gov (United States)

    Kopec, Ashley M.; Carew, Thomas J.

    2013-01-01

    Growth factor (GF) signaling is critically important for developmental plasticity. It also plays a crucial role in adult plasticity, such as that required for memory formation. Although different GFs interact with receptors containing distinct types of kinase domains, they typically signal through converging intracellular cascades (e.g.,…

  9. Stationary analysis of signals and ratio decay determination in BWR type reactors by neuronal network

    International Nuclear Information System (INIS)

    The signals registered in the nuclear plants have non stationary characteristics, in numerous times. This made difficult the application of the methods of analysis. There are determinate temporal intervals in that the signal is stationary with determinate mean, value together of zones with corrupt registers, and other zones with mean value distinct, but stationary during a temporal interval. The methodology consist in a stationary analysis to the signal received of the nuclear plant. With the Gabor Transformation are determined the temporal intervals of the stationary signals, synthesised it, as previous phase to the application of the methods of the analysis of stability parameters with methods ARMA, SVD, Neural Net,... to the reconstructed signal. 4 refs. (Author)

  10. A signaling network for patterning of neuronal connectivity in the Drosophila brain.

    Directory of Open Access Journals (Sweden)

    Mohammed Srahna

    2006-10-01

    Full Text Available The precise number and pattern of axonal connections generated during brain development regulates animal behavior. Therefore, understanding how developmental signals interact to regulate axonal extension and retraction to achieve precise neuronal connectivity is a fundamental goal of neurobiology. We investigated this question in the developing adult brain of Drosophila and find that it is regulated by crosstalk between Wnt, fibroblast growth factor (FGF receptor, and Jun N-terminal kinase (JNK signaling, but independent of neuronal activity. The Rac1 GTPase integrates a Wnt-Frizzled-Disheveled axon-stabilizing signal and a Branchless (FGF-Breathless (FGF receptor axon-retracting signal to modulate JNK activity. JNK activity is necessary and sufficient for axon extension, whereas the antagonistic Wnt and FGF signals act to balance the extension and retraction required for the generation of the precise wiring pattern.

  11. Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling

    Science.gov (United States)

    Bui, Francis Minhthang; Hatzinakos, Dimitrios

    2007-12-01

    As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN), which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSN's has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1) a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2) a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSN's, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG) signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSN's.

  12. Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling

    Directory of Open Access Journals (Sweden)

    Dimitrios Hatzinakos

    2008-03-01

    Full Text Available As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN, which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSN's has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1 a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2 a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSN's, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSN's.

  13. Assessment of signal quality in 10 Gbit/s all-optical networks with wavelength converters

    DEFF Research Database (Denmark)

    Poulsen, Henrik Nørskov; Stubkjær, Kristian

    Detailed modelling is used to assess the performance of an all-optical network. We predict that more than 10 all-optical cross connects interconnected by dispersion compensated single mode fiber can be cascaded at 10 Gbit/s......Detailed modelling is used to assess the performance of an all-optical network. We predict that more than 10 all-optical cross connects interconnected by dispersion compensated single mode fiber can be cascaded at 10 Gbit/s...

  14. Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases.

    Science.gov (United States)

    Vafaee, Fatemeh

    2016-01-01

    Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed. PMID:26906975

  15. Optimization of a neural network model for signal-to-background prediction in gamma-ray spectrometry

    International Nuclear Information System (INIS)

    The artificial neural network (ANN) model was optimized for the prediction of signal-to-background (SBR) ratio as a function of the measurement time in gamma-ray spectrometry. The network parameters: learning rate (α), momentum (μ), number of epochs (E) and number of nodes in hidden layer (N) were optimized simultaneously employing variable-size simplex method. The most accurate model with the root mean square (RMS) error of 0.073 was obtained using ANN with online backpropagation randomized (OBPR) algorithm with α = 0.27, μ 0.36, E = 14800 and N = 9. Most of the predicted and experimental SBR values for the eight radionuclides (226Ra, 214Bi, 235U, 40K, 232Th, 134Cs, 137Cs and 7Be), studied in this work, reasonably agreed to within 15 %, which was satisfactory accuracy. (author)

  16. CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2014-03-01

    Full Text Available Currently used diagnostics systems are not always efficient and do not give straightforward results which allow for the assessment of the technological condition of the engine or for the identification of the possible damages in their early stages of development. Growing requirements concerning durability, reliability, reduction of costs to minimum and decrease of negative influence on the natural environment are the reasons why there is a need to acquire information about the technological condition of each of the elements of a vehicle during its exploitation. One of the possibilities to achieve information about technological condition of a vehicle are vibroacoustic phenomena. Symptoms of defects, achieved as a result of advanced methods of vibroacoustic signals processing can serve as models which can be used during construction of intelligent diagnostic system based on artificial neural networks. The work presents conception of use artificial neural networks in the task of combustion engines diagnosis.

  17. Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks

    Directory of Open Access Journals (Sweden)

    Elke Roth-Mandutz

    2014-09-01

    Full Text Available The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated.

  18. Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

    Directory of Open Access Journals (Sweden)

    Tran Hoai Linh

    2014-09-01

    Full Text Available The paper presents a new system for ECG (ElectroCardioGraphy signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron, modified TSK (Takagi-Sugeno-Kang and the SVM (Support Vector Machine, will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution

  19. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    KAUST Repository

    Yun, Kil-Young

    2010-01-25

    Background: The transcriptional regulatory network involved in low temperature response leading to acclimation has been established in Arabidopsis. In japonica rice, which can only withstand transient exposure to milder cold stress (10C), an oxidative-mediated network has been proposed to play a key role in configuring early responses and short-term defenses. The components, hierarchical organization and physiological consequences of this network were further dissected by a systems-level approach.Results: Regulatory clusters responding directly to oxidative signals were prominent during the initial 6 to 12 hours at 10C. Early events mirrored a typical oxidative response based on striking similarities of the transcriptome to disease, elicitor and wounding induced processes. Targets of oxidative-mediated mechanisms are likely regulated by several classes of bZIP factors acting on as1/ocs/TGA-like element enriched clusters, ERF factors acting on GCC-box/JAre-like element enriched clusters and R2R3-MYB factors acting on MYB2-like element enriched clusters.Temporal induction of several H2O2-induced bZIP, ERF and MYB genes coincided with the transient H2O2spikes within the initial 6 to 12 hours. Oxidative-independent responses involve DREB/CBF, RAP2 and RAV1 factors acting on DRE/CRT/rav1-like enriched clusters and bZIP factors acting on ABRE-like enriched clusters. Oxidative-mediated clusters were activated earlier than ABA-mediated clusters.Conclusion: Genome-wide, physiological and whole-plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress and developmental responses leads to modulated growth and vigor maintenance contributing to a delay of plastic injuries. 2010 Yun et al; licensee BioMed Central Ltd.

  20. New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

    Science.gov (United States)

    Cao, Yuting; Wen, Shiping; Chen, Michael Z Q; Huang, Tingwen; Zeng, Zhigang

    2016-09-01

    This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper. PMID:27295505

  1. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

    OpenAIRE

    Shuqiang Wang; Yanyan Shen; Changhong Shi; Tao Wang; Zhiming Wei; Hanxiong Li

    2014-01-01

    Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the ...

  2. Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network

    OpenAIRE

    Ali Reza Mehri Dehnavi; Iman Farahabadi; Hossain Rabbani; Amin Farahabadi; Mohamad Parsa Mahjoob; Nasser Rajabi Dehnavi

    2011-01-01

    Background: Various techniques are used in diagnosing cardiac diseases. The electrocardiogram is one of these tools in common use. In this study vectorcardiogram (VCG) signals are used as a tool for detection of cardiac ischemia. Methods: VCG signals used in this study were obtained form 60 patients suspected to have ischemia disease and 10 normal candidates. Verification of the ischemia had done by the cardiologist during strain test by the evaluation of electrocardiogram (ECG) records ...

  3. Location Estimation Using Space-Time Signal Processing in RFID Wireless Sensor Networks

    OpenAIRE

    Chang-Heon Oh

    2013-01-01

    We consider real-time locating systems (RTLS) consisting of radio frequency identification (RFID) tags and multiantenna readers to propose a novel location estimation algorithm based on the concept of space-time signature matching for multipath environments. In contrast to previous fingerprint-based approaches that rely on received signal strength (RSS) information only, the proposed algorithm uses angle, delay, and RSS information from the received signal to form a signature, which in turn i...

  4. Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

    OpenAIRE

    Tran Hoai Linh; Pham Van Nam; Vuong Hoang Nam

    2014-01-01

    The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to...

  5. Reconstruction of Protein-Protein Interaction Network of Insulin Signaling in Homo Sapiens

    OpenAIRE

    Saliha Durmuş Tekir; Pelin Ümit; Aysun Eren Toku; Kutlu Ö. Ülgen

    2010-01-01

    Diabetes is one of the most prevalent diseases in the world. Type 1 diabetes is characterized by the failure of synthesizing and secreting of insulin because of destroyed pancreatic β-cells. Type 2 diabetes, on the other hand, is described by the decreased synthesis and secretion of insulin because of the defect in pancreatic β-cells as well as by the failure of responding to insulin because of malfunctioning of insulin signaling. In order to understand the signaling mechanisms of responding ...

  6. System Impact of Cascaded All-Optical Wavelength Conversion of D(QPSK Signals in Transparent Optical Networks

    Directory of Open Access Journals (Sweden)

    Robert Elschner

    2010-02-01

    Full Text Available We will compare techniques for all-optical wavelength conversion of differentially phase-modulated signals using four-wave mixing and super-continuum generation. For the super-continuum generation, a relation between the conversion efficieny and the nonlinear phase distortion will be derived and it will be shown that this technique is not suitable for the conversion of phase-modulated signals. For the four-wave mixing, techniques for the improvement of the conversion efficiency will be studied. Mainly the suppression of Brillouin scattering and its impact on phase-distortions will be discussed. A detailed discussion of its cascadability in transparent optical networks will conclude the contribution. The introduction of a maximum outage probability can significantly relax the OSNR requirements.

  7. Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

    Science.gov (United States)

    Azim, Shafquat; Zubair, Haseeb; Srivastava, Sanjeev K; Bhardwaj, Arun; Zubair, Asif; Ahmad, Aamir; Singh, Seema; Khushman, Moh'd; Singh, Ajay P

    2016-01-01

    We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer. PMID:27354262

  8. A maximum entropy approach to separating noise from signal in bimodal affiliation networks

    CERN Document Server

    Dianati, Navid

    2016-01-01

    In practice, many empirical networks, including co-authorship and collocation networks are unimodal projections of a bipartite data structure where one layer represents entities, the second layer consists of a number of sets representing affiliations, attributes, groups, etc., and an inter-layer link indicates membership of an entity in a set. The edge weight in the unimodal projection, which we refer to as a co-occurrence network, counts the number of sets to which both end-nodes are linked. Interpreting such dense networks requires statistical analysis that takes into account the bipartite structure of the underlying data. Here we develop a statistical significance metric for such networks based on a maximum entropy null model which preserves both the frequency sequence of the individuals/entities and the size sequence of the sets. Solving the maximum entropy problem is reduced to solving a system of nonlinear equations for which fast algorithms exist, thus eliminating the need for expensive Monte-Carlo sam...

  9. Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections

    Directory of Open Access Journals (Sweden)

    Leyre Azpilicueta

    2016-07-01

    Full Text Available With the growing demand of Intelligent Transportation Systems (ITS for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network.

  10. Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections.

    Science.gov (United States)

    Azpilicueta, Leyre; López-Iturri, Peio; Aguirre, Erik; Martínez, Carlos; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

    2016-01-01

    With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network. PMID:27455270

  11. DMPD: The involvement of the interleukin-1 receptor-associated kinases (IRAKs) incellular signaling networks controlling inflammation. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 18249132 The involvement of the interleukin-1 receptor-associated kinases (IRAKs) i...2008 Jan 30. (.png) (.svg) (.html) (.csml) Show The involvement of the interleukin-1 receptor-associated kin...ases (IRAKs) incellular signaling networks controlling inflammation. PubmedID 18249132 Title The... involvement of the interleukin-1 receptor-associated kinases (IRAKs) incellular signaling ne

  12. Transient and small signal stability of a two area HVAC power network interconnected with an HVDC link

    Energy Technology Data Exchange (ETDEWEB)

    Azimoh, L.C.; Oyedokun, D.T.; Chowdhury, S.; Chowdhury, S.P.; Folly, K.A. [Cape Town Univ., Cape Town (South Africa). Dept. of Electrical Engineering

    2009-07-01

    Despite the fact that the conditions necessary for safe and stable operation of power networks are sometimes at variance with economic considerations, power system operators are responsible for supplying safe and economical electric power to customers. Customer demand cannot be met without a stable and reliable power supply. Therefore, power system stability is of crucial importance in this market. This paper presented an analysis of transient and small signal stability of a two-area multi-machine power system. Different aspects were investigated including the responses of generator speed, the terminal voltage, the rotor angle difference and power transmitted, after a transient perturbation of a single phase to ground fault. The study also examined the effect of small signal disturbance of power systems when a direct current (DC) link was interconnected with a weak alternating (AC) link. Using the two-area power system model, the small signal stabilities of three different transmission systems were investigated, notably a high voltage alternating current (HVAC) link; a high voltage direct current (HVDC) link; and the hybrid HVAC/HVDC link. The paper discussed the fundamentals of HVAC and HVDC transmission. Rotor angle stability was also presented. An HVAC/HVDC hybrid network model was described. Simulation results were also provided for power flow; transient stability analysis; and small signal stability analysis. It was concluded that the transient stability test demonstrated that the system is relatively stable and returns to pre-fault condition about 10 seconds after perturbation. The study showed that when a fault occurs in transmission lines, the system responds according to the nature of the fault and the strength of the system.. 9 refs., 3 tabs., 8 figs., 1 appendix.

  13. Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks

    Directory of Open Access Journals (Sweden)

    Daniel J. DeDora

    2016-05-01

    Full Text Available Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR, correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS, against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain’s default mode network.

  14. Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.

    Science.gov (United States)

    DeDora, Daniel J; Nedic, Sanja; Katti, Pratha; Arnab, Shafique; Wald, Lawrence L; Takahashi, Atsushi; Van Dijk, Koene R A; Strey, Helmut H; Mujica-Parodi, Lilianne R

    2016-01-01

    Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS-and not tSNR-is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network. PMID:27199643

  15. Functional characterization of FLT3 receptor signaling deregulation in acute myeloid leukemia by single cell network profiling (SCNP.

    Directory of Open Access Journals (Sweden)

    David B Rosen

    Full Text Available BACKGROUND: Molecular characterization of the FMS-like tyrosine kinase 3 receptor (FLT3 in cytogenetically normal acute myeloid leukemia (AML has recently been incorporated into clinical guidelines based on correlations between FLT3 internal tandem duplications (FLT3-ITD and decreased disease-free and overall survival. These mutations result in constitutive activation of FLT3, and FLT3 inhibitors are currently undergoing trials in AML patients selected on FLT3 molecular status. However, the transient and partial responses observed suggest that FLT3 mutational status alone does not provide complete information on FLT3 biological activity at the individual patient level. Examination of variation in cellular responsiveness to signaling modulation may be more informative. METHODOLOGY/PRINCIPAL FINDINGS: Using single cell network profiling (SCNP, cells were treated with extracellular modulators and their functional responses were quantified by multiparametric flow cytometry. Intracellular signaling responses were compared between healthy bone marrow myeloblasts (BMMb and AML leukemic blasts characterized as FLT3 wild type (FLT3-WT or FLT3-ITD. Compared to healthy BMMb, FLT3-WT leukemic blasts demonstrated a wide range of signaling responses to FLT3 ligand (FLT3L, including elevated and sustained PI3K and Ras/Raf/Erk signaling. Distinct signaling and apoptosis profiles were observed in FLT3-WT and FLT3-ITD AML samples, with more uniform signaling observed in FLT3-ITD AML samples. Specifically, increased basal p-Stat5 levels, decreased FLT3L induced activation of the PI3K and Ras/Raf/Erk pathways, decreased IL-27 induced activation of the Jak/Stat pathway, and heightened apoptotic responses to agents inducing DNA damage were observed in FLT3-ITD AML samples. Preliminary analysis correlating these findings with clinical outcomes suggests that classification of patient samples based on signaling profiles may more accurately reflect FLT3 signaling

  16. Signal Transmission in a Human Body Medium-Based Body Sensor Network Using a Mach-Zehnder Electro-Optical Sensor

    OpenAIRE

    Yong Song; Qun Hao; Kai Zhang; Jingwen Wang; Xuefeng Jin; He Sun

    2012-01-01

    The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs) used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO) sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiv...

  17. Self-organized neural network for the quality control of 12-lead ECG signals

    International Nuclear Information System (INIS)

    Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

  18. Simulation of burst aggregation and signalling schemes for optical burst switched networks

    OpenAIRE

    Dumych, Stepan; Maksymyuk, Taras; Guskov, Pavlo

    2013-01-01

    In this paper the model of IP-packet aggregation in the ingress node for OBS networks proposed. The main difference of this model is packet loss probability accounting due to block sizes distribution. Simulation results determined that proposed algorithm 10 times reduces the IP-packet losses, providing the same throughput.

  19. Enhanced signaling scheme with admission control in the hybrid optical wireless (HOW) networks

    DEFF Research Database (Denmark)

    Yan, Ying; Yu, Hao; Wessing, Henrik;

    2009-01-01

    The hybrid optical wireless (HOW) network has been viewed as a promising solution to meet the increasing user bandwidth and mobility demands. Due to the basic differences in the optical and wireless technologies, a challenging problem lies in the Media Access Control (MAC) protocol design so that...

  20. CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

    Science.gov (United States)

    Li, Jie; Yu, Wan-Qing; Xu, Ding; Liu, Feng; Wang, Wei

    2009-12-01

    Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feedforward network composed of Hodgkin-Huxley (HH) neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant τsyn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of τsyn, suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks.

  1. The Interferon Signaling Network and Transcription Factor C/EBP-β

    Institute of Scientific and Technical Information of China (English)

    Hui Li; Padmaja Gade; Weihua Xiao; Dhan V.Kalvakolanu

    2007-01-01

    Cytoines like interferons (IFNs) play a central role in regulating innate and specific immunities against the pathogens and neoplastic cells. A number of signaling pathways are induced in response to IFN in various cells.One classic mechanism employed by IFNs is the JAK-STAT signaling pathway for inducing cellular responses.Here we describe the non-STAT pathways that participate in IFN-induced responses. In particular, we will focus on the role played by transcription factor C/EBP-β in mediating these responses.

  2. Converged wireline and wireless signal distribution in optical fiber access networks

    DEFF Research Database (Denmark)

    Prince, Kamau

    This thesis presents results obtained during the course of my doctoral studies into the transport of fixed and wireless signaling over a converged otpical access infrastructure. In the formulation, development and assessment of a converged paradigma for multiple-services delivery via optical access...

  3. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics

    DEFF Research Database (Denmark)

    Blagoev, Blagoy; Ong, S.E.; Kratchmarova, Irina;

    2004-01-01

    To study the global dynamics of phosphotyrosine-based signaling events in early growth factor stimulation, we developed a mass spectrometric method that converts temporal changes to differences in peptide isotopic abundance. The proteomes of three cell populations were metabolically encoded with ...

  4. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

    Directory of Open Access Journals (Sweden)

    Shuqiang Wang

    2014-01-01

    Full Text Available Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC method based on sequential Monte Carlo (SMC to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

  5. Elucidating the CXCL12/CXCR4 signaling network in chronic lymphocytic leukemia through phosphoproteomics analysis.

    Directory of Open Access Journals (Sweden)

    Morgan O'Hayre

    Full Text Available BACKGROUND: Chronic Lymphocytic Leukemia (CLL pathogenesis has been linked to the prolonged survival and/or apoptotic resistance of leukemic B cells in vivo, and is thought to be due to enhanced survival signaling responses to environmental factors that protect CLL cells from spontaneous and chemotherapy-induced death. Although normally associated with cell migration, the chemokine, CXCL12, is one of the factors known to support the survival of CLL cells. Thus, the signaling pathways activated by CXCL12 and its receptor, CXCR4, were investigated as components of these pathways and may represent targets that if inhibited, could render resistant CLL cells more susceptible to chemotherapy. METHODOLOGY/PRINCIPAL FINDINGS: To determine the downstream signaling targets that contribute to the survival effects of CXCL12 in CLL, we took a phosphoproteomics approach to identify and compare phosphopeptides in unstimulated and CXCL12-stimulated primary CLL cells. While some of the survival pathways activated by CXCL12 in CLL are known, including Akt and ERK1/2, this approach enabled the identification of additional signaling targets and novel phosphoproteins that could have implications in CLL disease and therapy. In addition to the phosphoproteomics results, we provide evidence from western blot validation that the tumor suppressor, programmed cell death factor 4 (PDCD4, is a previously unidentified phosphorylation target of CXCL12 signaling in all CLL cells probed. Additionally, heat shock protein 27 (HSP27, which mediates anti-apoptotic signaling and has previously been linked to chemotherapeutic resistance, was detected in a subset (approximately 25% of CLL patients cells examined. CONCLUSIONS/SIGNIFICANCE: Since PDCD4 and HSP27 have previously been associated with cancer and regulation of cell growth and apoptosis, these proteins may have novel implications in CLL cell survival and represent potential therapeutic targets. PDCD4 also represents a

  6. Defoliation of interior Douglas-fir elicits carbon transfer and stress signalling to ponderosa pine neighbors through ectomycorrhizal networks.

    Science.gov (United States)

    Song, Yuan Yuan; Simard, Suzanne W; Carroll, Allan; Mohn, William W; Zeng, Ren Sen

    2015-01-01

    Extensive regions of interior Douglas-fir (Pseudotsuga menziesii var. glauca, IDF) forests in North America are being damaged by drought and western spruce budworm (Choristoneura occidentalis). This damage is resulting from warmer and drier summers associated with climate change. To test whether defoliated IDF can directly transfer resources to ponderosa pine (Pinus ponderosae) regenerating nearby, thus aiding in forest recovery, we examined photosynthetic carbon transfer and defense enzyme response. We grew pairs of ectomycorrhizal IDF 'donor' and ponderosa pine 'receiver' seedlings in pots and isolated transfer pathways by comparing 35 μm, 0.5 μm and no mesh treatments; we then stressed IDF donors either through manual defoliation or infestation by the budworm. We found that manual defoliation of IDF donors led to transfer of photosynthetic carbon to neighboring receivers through mycorrhizal networks, but not through soil or root pathways. Both manual and insect defoliation of donors led to increased activity of peroxidase, polyphenol oxidase and superoxide dismutase in the ponderosa pine receivers, via a mechanism primarily dependent on the mycorrhizal network. These findings indicate that IDF can transfer resources and stress signals to interspecific neighbors, suggesting ectomycorrhizal networks can serve as agents of interspecific communication facilitating recovery and succession of forests after disturbance. PMID:25683155

  7. ENHANCING NETWORK SECURITY USING 'LEARNING-FROM-SIGNALS' AND FRACTIONAL FOURIER TRANSFORM BASED RF-DNA FINGERPRINTS

    Energy Technology Data Exchange (ETDEWEB)

    Buckner, Mark A [ORNL; Bobrek, Miljko [ORNL; Farquhar, Ethan [ORNL; Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology

    2011-01-01

    Wireless Access Points (WAP) remain one of the top 10 network security threats. This research is part of an effort to develop a physical (PHY) layer aware Radio Frequency (RF) air monitoring system with multi-factor authentication to provide a first-line of defense for network security--stopping attackers before they can gain access to critical infrastructure networks through vulnerable WAPs. This paper presents early results on the identification of OFDM-based 802.11a WiFi devices using RF Distinct Native Attribute (RF-DNA) fingerprints produced by the Fractional Fourier Transform (FRFT). These fingerprints are input to a "Learning from Signals" (LFS) classifier which uses hybrid Differential Evolution/Conjugate Gradient (DECG) optimization to determine the optimal features for a low-rank model to be used for future predictions. Results are presented for devices under the most challenging conditions of intra-manufacturer classification, i.e., same-manufacturer, same-model, differing only in serial number. The results of Fractional Fourier Domain (FRFD) RF-DNA fingerprints demonstrate significant improvement over results based on Time Domain (TD), Spectral Domain (SD) and even Wavelet Domain (WD) fingerprints.

  8. Spectrum Sharing between Cooperative Relay and Ad-hoc Networks: Dynamic Transmissions under Computation and Signaling Limitations

    CERN Document Server

    Sun, Yin; Li, Yunzhou; Zhou, Shidong; Xu, Xibin

    2010-01-01

    This paper studies a spectrum sharing scenario between an uplink cognitive relay network (CRN) and some nearby low power ad-hoc networks. In particular, the dynamic resource allocation of the CRN is analyzed, which aims to minimize the average interfering time with the ad-hoc networks subject to a minimal average uplink throughput constraint. A long term average rate formula is considered, which is achieved by a half-duplex decode-and-forward (DF) relay strategy with multi-channel transmissions. Both the source and relay are allowed to queue their data, by which they can adjust the transmission rates flexibly based on sensing and predicting the channel state and ad-hoc traffic. The dynamic resource allocation of the CRN is formulated as a non-convex stochastic optimization problem. By carefully analyzing the optimal transmission time scheduling, it is reduced to a stochastic convex optimization problem and solved by the dual optimization method. The signaling and computation processes are designed carefully t...

  9. Can scale-freeness offset delayed signal detection in neuronal networks?

    CERN Document Server

    Uzun, Rukiye; Perc, Matjaz

    2014-01-01

    First spike latency following stimulus onset is of significant physiological relevance. Neurons transmit information about their inputs by transforming them into spike trains, and the timing of these spike trains is in turn crucial for effectively encoding that information. Random processes and uncertainty that underly neuronal dynamics have been shown to prolong the time towards the first response in a phenomenon dubbed noise-delayed decay. Here we study whether Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise might have shorter response times to external stimuli just above threshold if placed on a scale-free network. We show that the heterogeneity of the interaction network may indeed eradicate slow responsiveness, but only if the coupling between individual neurons is sufficiently strong. Increasing the average degree also favors a fast response, but it is less effective than increasing the coupling strength. We also show that noise-delayed decay can be offset further by adjusting the fre...

  10. The Influence of Gaussian Signaling Approximation on Error Performance in Cellular Networks

    KAUST Repository

    Afify, Laila H.

    2015-08-18

    Stochastic geometry analysis for cellular networks is mostly limited to outage probability and ergodic rate, which abstracts many important wireless communication aspects. Recently, a novel technique based on the Equivalent-in-Distribution (EiD) approach is proposed to extend the analysis to capture these metrics and analyze bit error probability (BEP) and symbol error probability (SEP). However, the EiD approach considerably increases the complexity of the analysis. In this paper, we propose an approximate yet accurate framework, that is also able to capture fine wireless communication details similar to the EiD approach, but with simpler analysis. The proposed methodology is verified against the exact EiD analysis in both downlink and uplink cellular networks scenarios.

  11. OPEN ISSUES AND CHALLENGES IN PROVIDING TESTING AND SIGNALING IN HIGH SPEED ATM NETWORKS

    Directory of Open Access Journals (Sweden)

    Dr.S.S.Riaz Ahamed

    2011-01-01

    Full Text Available Testing asynchronous transfer mode (ATM communications and network equipment is the function of performing the testing phases required for complete evaluation of ATM products. The test phases measure andanalyze all aspects of ATM—from the characteristics of lower-layer cell transmission through to the complex ATM services used to transmit multimedia over ATM networks. ATM technology has become inextricably linked to promises of a global communications infrastructure. The expectation is that seamless integration ofvoice data and video across high-speed ATM links will provide universal access to multimedia services. ATM equipment developers, service providers, and end users are all faced with these same challenges when testing ATM products.

  12. Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

    OpenAIRE

    Chai Tong Yuen; Woo San San; Tan Ching Seong; Mohamed Rizon

    2009-01-01

    A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emot...

  13. Towards using physiological signals as cryptographic keys in body area networks

    OpenAIRE

    Karaoğlan, Duygu; Karaoglan, Duygu; Levi, Albert; Tuzcu, Volkan

    2015-01-01

    Body Area Networks (BANs) are the most important building stone of pervasive healthcare, which enables remote, continuous and real-time health monitoring. Biosensors, constituting the BANs, collect highly sensitive medical information from their hosts and communicate these data. Considering the nature of the wireless medium, the privacy requirements of the individuals and the extreme energy and storage limitations of the biosensors, BANs require a light-weight and secure key management infras...

  14. Salience network-midbrain dysconnectivity and blunted reward signals in schizophrenia

    OpenAIRE

    Gradin, Victoria; Waiter, Gordon; O'Connor, Akira Robert; Romaniuk, Liana; Stickle, Catriona; Matthews, Keith; Hall, Jeremy; Steele, Douglas

    2012-01-01

    Theories of schizophrenia propose that abnormal functioning of the neural reward system is linked to negative and psychotic symptoms, by disruption of reward processing and promotion of context-independent false associations. Recently it has been argued that an insula-anterior cingulate cortex (ACC) salience network system enables switching of brain states from the default mode to a task-related activity mode. Abnormal interaction between the insula-ACC system and reward processing regions ma...

  15. Intellect Sensing of Neural Network that Trained to Classify Complex Signals

    OpenAIRE

    Reznik, A.; Galinskaya, A.

    2003-01-01

    An experimental comparison of information features used by neural network is performed. The sensing method was used. Suboptimal classifier agreeable to the gaussian model of the training data was used as a probe. Neural nets with architectures of perceptron and feedforward net with one hidden layer were used. The experiments were carried out with spatial ultrasonic data, which are used for car’s passenger safety system neural controller learning. In this paper we show that a n...

  16. Global characterization of signalling networks associated with tamoxifen resistance in breast cancer

    DEFF Research Database (Denmark)

    Browne, Brigid C.; Hochgräfe, Falko; Wu, Jianmin;

    2013-01-01

    Acquired resistance to the anti‐estrogen tamoxifen remains a significant challenge in breast cancer management. In this study, we used an integrative approach to characterize global protein expression and tyrosine phosphorylation events in tamoxifen‐resistant MCF7 breast cancer cells (Tam...... perturbed in TamR cells, together with pathways enriched for proteins associated with growth factor, cell–cell and cell matrix‐initiated signalling. Consistent with known roles for Ras/MAPK and PI3‐kinase signalling in tamoxifen resistance, tyrosine‐phosphorylated MAPK1, SHC1 and PIK3R2 were elevated in Tam...... molecular alterations associated with the tamoxifen‐resistant phenotype, and identify MARCKS as a potential biomarker of therapeutic responsiveness that may assist in stratification of patients for optimal therapy....

  17. Quantitative phosphoproteomic analysis of the PI3K-regulated signaling network.

    Science.gov (United States)

    Gnad, Florian; Wallin, Jeffrey; Edgar, Kyle; Doll, Sophia; Arnott, David; Robillard, Liliane; Kirkpatrick, Donald S; Stokes, Matthew P; Vijapurkar, Ulka; Hatzivassiliou, Georgia; Friedman, Lori S; Belvin, Marcia

    2016-07-01

    The PI3K pathway is commonly activated in cancer. Only a few studies have attempted to explore the spectrum of phosphorylation signaling downstream of the PI3K cascade. Such insight, however, is imperative to understand the mechanisms responsible for oncogenic phenotypes. By applying MS-based phosphoproteomics, we mapped 2509 phosphorylation sites on 1096 proteins, and quantified their responses to activation or inhibition of PIK3CA using isogenic knock-in derivatives and a series of targeted inhibitors. We uncovered phosphorylation changes in a wide variety of proteins involved in cell growth and proliferation, many of which have not been previously associated with PI3K signaling. A significant update of the posttranslational modification database PHOSIDA (http://www.phosida.com) allows efficient use of the data. All MS data have been deposited in the ProteomeXchange with identifier PXD003899 (http://proteomecentral.proteomexchange.org/dataset/PXD003899). PMID:27282143

  18. Desired EEG Signals For Detecting Brain Tumor Using LMS Algorithm And Feedforward Network

    OpenAIRE

    Indu Sekhar Samant#1, Guru Kalyan Kanungo#2 , Santosh Kumar Mishra*3

    2012-01-01

    In Brain tumor diagnostic EEG is the most relevant in assesing how basic functionality is affected by the lesion.EEG continues to be an attractive tool in clinical practice due to its non invasiveness and real time depication of brain function. But the EEG signalcontains the useful information along with redundant or noise information. In this Paper Least Mean Square algorithm is used to remove the artifact in the EEG signal. , generic features present in the EEG signalare extracted using spe...

  19. Therapeutic resistance in cancer: microRNA regulation of EGFR signaling networks

    International Nuclear Information System (INIS)

    Receptor tyrosine kinases (RTKs) such as the epidermal growth factor receptor (EGFR) regulate cellular homeostatic processes. EGFR activates downstream signaling cascades that promote tumor cell survival, proliferation and migration. Dysregulation of EGFR signaling as a consequence of overexpression, amplification and mutation of the EGFR gene occurs frequently in several types of cancers and many become dependent on EGFR signaling to maintain their malignant phenotypes. Consequently, concerted efforts have been mounted to develop therapeutic agents and strategies to effectively inhibit EGFR. However, limited therapeutic benefits to cancer patients have been derived from EGFR-targeted therapies. A well-documented obstacle to improved patient survival is the presence of EGFR-inhibitor resistant tumor cell variants within heterogeneous tumor cell masses. Here, we summarize the mechanisms by which tumors resist EGFR-targeted therapies and highlight the emerging role of microRNAs (miRs) as downstream effector molecules utilized by EGFR to promote tumor initiation, progression and that play a role in resistance to EGFR inhibitors. We also examine evidence supporting the utility of miRs as predictors of response to targeted therapies and novel therapeutic agents to circumvent EGFR-inhibitor resistance mechanisms

  20. A sophisticated network of signaling pathways regulates stomatal defenses to bacterial pathogens.

    Science.gov (United States)

    Arnaud, Dominique; Hwang, Ildoo

    2015-04-01

    Guard cells are specialized cells forming stomatal pores at the leaf surface for gas exchanges between the plant and the atmosphere. Stomata have been shown to play an important role in plant defense as a part of the innate immune response. Plants actively close their stomata upon contact with microbes, thereby preventing pathogen entry into the leaves and the subsequent colonization of host tissues. In this review, we present current knowledge of molecular mechanisms and signaling pathways implicated in stomatal defenses, with particular emphasis on plant-bacteria interactions. Stomatal defense responses begin from the perception of pathogen-associated molecular patterns (PAMPs) and activate a signaling cascade involving the production of secondary messengers such as reactive oxygen species, nitric oxide, and calcium for the regulation of plasma membrane ion channels. The analyses on downstream molecular mechanisms implicated in PAMP-triggered stomatal closure have revealed extensive interplays among the components regulating hormonal signaling pathways. We also discuss the strategies deployed by pathogenic bacteria to counteract stomatal immunity through the example of the phytotoxin coronatine. PMID:25661059

  1. Time signal distribution in communication networks based on synchronous digital hierarchy

    Science.gov (United States)

    Imaoka, Atsushi; Kihara, Masami

    1993-01-01

    A new method that uses round-trip paths to accurately measure transmission delay for time synchronization is proposed. The performance of the method in Synchronous Digital Hierarchy networks is discussed. The feature of this method is that it separately measures the initial round trip path delay and the variations in round-trip path delay. The delay generated in SDH equipment is determined by measuring the initial round-trip path delay. In an experiment with actual SDH equipment, the error of initial delay measurement was suppressed to 30ns.

  2. Intelligent Services in Converged Networks - Evolution steps in the signalling arena

    DEFF Research Database (Denmark)

    Soler-Lucas, José; Fosgerau, Anders; Grabner, Boris

    The paper aims to present the authors' view of the future of telephony. While voice transport over IP is no longer a dream but a reality, the capacity to offer IN-like services, as value added services within VoIP environments, has still been rarely treated and implemented. We present an overview...... on the subject and the work currently in development, within the IST project GEMINI, towards the implementation of IN IP-based services and its interoperability with traditional PSTN-SS7-IN networks....

  3. Structural and functional robustness of the adaptive-sorting signaling network

    Science.gov (United States)

    Pang, Ning-Ning

    2016-06-01

    A major task of study on ligand discrimination by T cells is the construction of a mechanistic model to account for threshold setting in response to variant ligands interacting with the same T-cell receptors. Recently, Lalanne and Francois in a seminal paper (2013 Phys. Rev. Lett. 110 218102) have addressed this question by constructing minimal core circuits such that the biological outputs can satisfy the essential properties of early T-cell activation. To make this core set of network topology a valuable tool for synthetic biologists to robustly engineer biological circuits, we are motivated to ask a general question: is adaptive response encoded by the proposed circuit topology structurally stable, regardless of the values of the kinetic parameters? This has particularly relevant effects for the network reliability, since failures in ligand discrimination result in either infection or autoimmune diseases. To the best of our knowledge, a rigorous and complete mathematical proof of this issue is still lacking in the literature. In this paper, by giving a rigorous mathematical proof, we have shown that this regulatory circuitry is appropriately designed and the existence, uniqueness, and globally asymptotic attractiveness of the steady state are preserved. Moreover, we further generalize the adaptive sorting module and undertake an extensive analysis on the trade-off between antagonism and sensitivity of T-cell ligand discrimination in various cellular conditions. Notably, the optimal phosphorylation step in which to place the regulatory motif is analytically obtained and numerically confirmed. Finally, relevant experimental facts and biological implications are discussed.

  4. Searches for Exotic Transient Signals with a Global Network of Optical Magnetometers for Exotic Physics

    CERN Document Server

    Pustelny, S

    2016-01-01

    In this letter, we describe a novel scheme for searching for physics beyond the Standard Model. The idea is based on correlation of time-synchronized readouts of distant ($\\gtrsim$100~km) optical magnetometers. Such an approach limits hard-to-identify local transient noise, providing the system with unique capabilities of identification of global transient events. Careful analysis of the signal can reveal the nature of the events (e.g., its nonmagnetic origin), which opens avenues for new class of exotic-physics searches (searches for global transient exotic spin couplings) and tests of yet unverified theoretical models.

  5. Quasi steady-state approximations in complex intracellular signal transduction networks - a word of caution

    DEFF Research Database (Denmark)

    Pedersen, Morten Gram; Bersani, A.M.; Bersani, E.

    2008-01-01

    Enzyme reactions play a pivotal role in intracellular signal transduction. Many enzymes are known to possess Michaelis-Menten (MM) kinetics and the MM approximation is often used when modeling enzyme reactions. However, it is known that the MM approximation is only valid at low enzyme....... Starting from a single reaction and arriving at the mitogen activated protein kinase (MAPK) cascade, we give several examples of biologically realistic scenarios where the MM approximation leads to quantitatively as well as qualitatively wrong conclusions, and show that the tQSSA improves the accuracy of...

  6. Transforming growth factor-beta signaling network regulates plasticity and lineage commitment of lung cancer cells

    OpenAIRE

    Ischenko, I; Liu, J.; Petrenko, O; Hayman, M J

    2014-01-01

    Identification of target cells in lung tumorigenesis and characterization of the signals that control their behavior is an important step toward improving early cancer diagnosis and predicting tumor behavior. We identified a population of cells in the adult lung that bear the EpCAM+CD104+CD49f+CD44+CD24loSCA1+ phenotype and can be clonally expanded in culture, consistent with the properties of early progenitor cells. We show that these cells, rather than being restricted to one tumor type, ca...

  7. MicroRNA-455 regulates brown adipogenesis via a novel HIF1an-AMPK-PGC1α signaling network

    DEFF Research Database (Denmark)

    Zhang, Hongbin; Guan, Meiping; Townsend, Kristy L;

    2015-01-01

    Brown adipose tissue (BAT) dissipates chemical energy as heat and can counteract obesity. MicroRNAs are emerging as key regulators in development and disease. Combining microRNA and mRNA microarray profiling followed by bioinformatic analyses, we identified miR-455 as a new regulator of brown...... adipogenesis. miR-455 exhibits a BAT-specific expression pattern and is induced by cold and the browning inducer BMP7. In vitro gain- and loss-of-function studies show that miR-455 regulates brown adipocyte differentiation and thermogenesis. Adipose-specific miR-455 transgenic mice display marked browning of...... data reveal a novel microRNA-regulated signaling network that controls brown adipogenesis and may be a potential therapeutic target for human metabolic disorders....

  8. Application of network properties and signal strength to identify face-to-face links in an electronic dataset

    CERN Document Server

    Sekara, Vedran

    2014-01-01

    Understanding how people interact and socialize is important in many contexts, from disease control to urban planning. Datasets that capture this specific aspect of human life have increased in size and availability over the last few years. We have yet to understand, however, to what extent such electronic datasets may serve as a valid proxy for real life face-to-face interactions. For an observational dataset, gathered by mobile phones, we attack the problem of identifying transient and non-important links, as well as how to highlight important interactions. Using the Bluetooth signal strength parameter to distinguish between observations, we demonstrate that weak links, compared to strong links, have a lower probability of being observed at later times, while such links--on average--also have lower link-weights and a lower probability of sharing an online friendship. Further, the role of link-strength is investigated in relation to social network properties.

  9. Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

    Directory of Open Access Journals (Sweden)

    Che-Ting Kuo

    2015-02-01

    Full Text Available This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS with a dynamic solution agent algorithm water flow like algorithm (WFA, for nonlinear system identification and blind source separation (BSS problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

  10. A human body model for efficient numerical characterization of UWB signal propagation in wireless body area networks.

    Science.gov (United States)

    Lim, Hooi Been; Baumann, Dirk; Li, Er-Ping

    2011-03-01

    Wireless body area network (WBAN) is a new enabling system with promising applications in areas such as remote health monitoring and interpersonal communication. Reliable and optimum design of a WBAN system relies on a good understanding and in-depth studies of the wave propagation around a human body. However, the human body is a very complex structure and is computationally demanding to model. This paper aims to investigate the effects of the numerical model's structure complexity and feature details on the simulation results. Depending on the application, a simplified numerical model that meets desired simulation accuracy can be employed for efficient simulations. Measurements of ultra wideband (UWB) signal propagation along a human arm are performed and compared to the simulation results obtained with numerical arm models of different complexity levels. The influence of the arm shape and size, as well as tissue composition and complexity is investigated. PMID:21062677

  11. Optical Switching for Dynamic Distribution of Wireless-Over-Fiber Signals in Active Optical Networks

    DEFF Research Database (Denmark)

    Vegas Olmos, Juan José; Rodes, Guillermo; Tafur Monroy, Idelfonso

    2012-01-01

    In this paper, we report on an experimental validation of dynamic distribution of wireless-over-fiber by employing optical switching using semiconductor optical amplifiers; we also provide a channel distribution scheme and a generic topology for such an optical switch. The experiment consists of a...... results show a negligible power penalty on each channel for both the best and the worst case in terms of inter-channel crosstalk. The presented system is highly scalable both in terms of port count and throughput, a desirable feature in highly branched access networks, and is modulation- and frequency...... four wavelength-division-multiplexed channel system operating on a WiMax frequency band and employing an orthogonal-frequency-division-multiplexing modulation at 625 Mbits/s per channel, transmission of the data over 20 km of optical fiber, and active switching in a 1 × 16 active optical switch. The...

  12. Neural Network Approach to Automated Condition Classification of a Check Valve by Acoustic Emission Signals

    International Nuclear Information System (INIS)

    This paper presents new techniques under development for monitoring the health and vibration of the active components in nuclear power plants, The purpose of this study is to develop an automated system for condition classification of a check valve one of the components being used extensively in a safety system of a nuclear power plant. Acoustic emission testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disc movement for valve failure such as wear and leakage due to foreign object interference in a check valve, It is clearly demonstrated that the evaluation of different types of failure types such as disc wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters, It is also shown that the leak size can be determined with an artificial neural network

  13. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

    Science.gov (United States)

    Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

    2014-01-01

    This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

  14. Forecast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

    International Nuclear Information System (INIS)

    A feed-forward neural network is used to forecast major and minor disruptions in TEXT tokamak discharges. Using the experimental data of soft X-ray signals as input data, the neural net is trained with one disruptive plasma discharge, while a different disruptive discharge is used for validation. After proper training, the net works with the same set of weights, it is then used to forecast disruptions in two other different plasma discharges. It is observed that the neural net is capable of predicting the onset of a disruption up to 3.12 ms in advance. From what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism is associated with an increase in the m=2 magnetic island, that disturbs the central part of the plasma column afterwards, or the initial perturbation has first occurred in the central part of the plasma column and then the m=2 MHD mode is destabilized. (author)

  15. Physically secured orthogonal frequency division multiplexing-passive optical network employing noise-based encryption and signal recovery process

    Science.gov (United States)

    Jin, Wei; Zhang, Chongfu; Yuan, Weicheng

    2016-02-01

    We propose a physically enhanced secure scheme for direct detection-orthogonal frequency division multiplexing-passive optical network (DD-OFDM-PON) and long reach coherent detection-orthogonal frequency division multiplexing-passive optical network (LRCO-OFDM-PON), by employing noise-based encryption and channel/phase estimation. The noise data generated by chaos mapping are used to substitute training sequences in preamble to realize channel estimation and frame synchronization, and also to be embedded on variable number of key-selected randomly spaced pilot subcarriers to implement phase estimation. Consequently, the information used for signal recovery is totally hidden as unpredictable noise information in OFDM frames to mask useful information and to prevent illegal users from correctly realizing OFDM demodulation, and thereby enhancing resistance to attackers. The levels of illegal-decryption complexity and implementation complexity are theoretically discussed. Through extensive simulations, the performances of the proposed channel/phase estimation and the security introduced by encrypted pilot carriers have been investigated in both DD-OFDM and LRCO-OFDM systems. In addition, in the proposed secure DD-OFDM/LRCO-OFDM PON models, both legal and illegal receiving scenarios have been considered. These results show that, by utilizing the proposed scheme, the resistance to attackers can be significantly enhanced in DD-OFDM-PON and LRCO-OFDM-PON systems without performance degradations.

  16. Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

    Directory of Open Access Journals (Sweden)

    St Laurent Georges

    2010-03-01

    Full Text Available Abstract Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4

  17. Razvoj omrežja SIGNAL in tržna vrednost določanja položaja : The development of the SIGNAL network and market value of positioning

    Directory of Open Access Journals (Sweden)

    Dalibor Radovan

    2007-01-01

    Full Text Available The Slovenian permanent GPS stations network named SIGNAL is presented. The discussion about the users, user-access statistics and operation cost items is included. Positive and negative sides of various future financing and tariff systems are analysed. The articleconcludes with the discussion on the factual usability of such positioning system in the light of Galileo and GLONASS development.

  18. Moonlighting kinases with guanylate cyclase activity can tune regulatory signal networks

    KAUST Repository

    Irving, Helen R.

    2012-02-01

    Guanylate cyclase (GC) catalyzes the formation of cGMP and it is only recently that such enzymes have been characterized in plants. One family of plant GCs contains the GC catalytic center encapsulated within the intracellular kinase domain of leucine rich repeat receptor like kinases such as the phytosulfokine and brassinosteroid receptors. In vitro studies show that both the kinase and GC domain have catalytic activity indicating that these kinase-GCs are examples of moonlighting proteins with dual catalytic function. The natural ligands for both receptors increase intracellular cGMP levels in isolated mesophyll protoplast assays suggesting that the GC activity is functionally relevant. cGMP production may have an autoregulatory role on receptor kinase activity and/or contribute to downstream cell expansion responses. We postulate that the receptors are members of a novel class of receptor kinases that contain functional moonlighting GC domains essential for complex signaling roles.

  19. Symmetry breaking in a bulk-surface reaction-diffusion model for signalling networks

    Science.gov (United States)

    Rätz, Andreas; Röger, Matthias

    2014-08-01

    Signalling molecules play an important role for many cellular functions. We investigate here a general system of two membrane reaction-diffusion equations coupled to a diffusion equation inside the cell by a Robin-type boundary condition and a flux term in the membrane equations. A specific model of this form was recently proposed by the authors for the GTPase cycle in cells. We investigate here a putative role of diffusive instabilities in cell polarization. By a linearized stability analysis, we identify two different mechanisms. The first resembles a classical Turing instability for the membrane subsystem and requires (unrealistically) large differences in the lateral diffusion of activator and substrate. On the other hand, the second possibility is induced by the difference in cytosolic and lateral diffusion and appears much more realistic. We complement our theoretical analysis by numerical simulations that confirm the new stability mechanism and allow us to investigate the evolution beyond the regime where the linearization applies.

  20. Parameter estimation for compact binary coalescence signals with the first generation gravitational-wave detector network

    CERN Document Server

    Aasi, J; Abbott, B P; Abbott, R; Abbott, T D; Abernathy, M; Accadia, T; Acernese, F; Adams, C; Adams, T; Addesso, P; Adhikari, R; Affeldt, C; Agathos, M; Agatsuma, K; Ajith, P; Allen, B; Allocca, A; Ceron, E Amador; Amariutei, D; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Ast, S; Aston, S M; Astone, P; Atkinson, D; Aufmuth, P; Aulbert, C; Aylott, B E; Babak, S; Baker, P; Ballardin, G; Ballmer, S; Bao, Y; Barayoga, J C B; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Bastarrika, M; Basti, A; Batch, J; Bauchrowitz, J; Bauer, Th S; Bebronne, M; Beck, D; Behnke, B; Bejger, M; Beker, M G; Bell, A S; Bell, C; Belopolski, I; Benacquista, M; Berliner, J M; Bertolini, A; Betzwieser, J; Beveridge, N; Beyersdorf, P T; Bhadbade, T; Bilenko, I A; Billingsley, G; Birch, J; Biswas, R; Bitossi, M; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bland, B; Blom, M; Bock, O; Bodiya, T P; Bogan, C; Bond, C; Bondarescu, R; Bondu, F; Bonelli, L; Bonnand, R; Bork, R; Born, M; Boschi, V; Bose, S; Bosi, L; Bouhou, B; Braccini, S; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Breyer, J; Briant, T; Bridges, D O; Brillet, A; Brinkmann, M; Brisson, V; Britzger, M; Brooks, A F; Brown, D A; Bulik, T; Bulten, H J; Buonanno, A; Burguet--Castell, J; Buskulic, D; Buy, C; Byer, R L; Cadonati, L; Cagnoli, G; Calloni, E; Camp, J B; Campsie, P; Cannon, K; Canuel, B; Cao, J; Capano, C D; Carbognani, F; Carbone, L; Caride, S; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C; Cesarini, E; Chalermsongsak, T; Charlton, P; Chassande-Mottin, E; Chen, W; Chen, X; Chen, Y; Chincarini, A; Chiummo, A; Cho, H S; Chow, J; Christensen, N; Chua, S S Y; Chung, C T Y; Chung, S; Ciani, G; Clara, F; Clark, D E; Clark, J A; Clayton, J H; Cleva, F; Coccia, E; Cohadon, P -F; Colacino, C N; Colla, A; Colombini, M; Conte, A; Conte, R; Cook, D; Corbitt, T R; Cordier, M; Cornish, N; Corsi, A; Costa, C A; Coughlin, M; Coulon, J -P; Couvares, P; Coward, D M; Cowart, M; Coyne, D C; Creighton, J D E; Creighton, T D; Cruise, A M; Cumming, A; Cunningham, L; Cuoco, E; Cutler, R M; Dahl, K; Damjanic, M; Danilishin, S L; D'Antonio, S; Danzmann, K; Dattilo, V; Daudert, B; Daveloza, H; Davier, M; Daw, E J; Dayanga, T; De Rosa, R; DeBra, D; Debreczeni, G; Degallaix, J; Del Pozzo, W; Dent, T; Dergachev, V; DeRosa, R; Dhurandhar, S; Di Fiore, L; Di Lieto, A; Di Palma, I; Emilio, M Di Paolo; Di Virgilio, A; Díaz, M; Dietz, A; Donovan, F; Dooley, K L; Doravari, S; Dorsher, S; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Dumas, J -C; Dwyer, S; Eberle, T; Edgar, M; Edwards, M; Effler, A; Ehrens, P; Endröczi, G; Engel, R; Etzel, T; Evans, K; Evans, M; Evans, T; Factourovich, M; Fafone, V; Fairhurst, S; Farr, B F; Farr, W M; Favata, M; Fazi, D; Fehrmann, H; Feldbaum, D; Ferrante, I; Ferrini, F; Fidecaro, F; Finn, L S; Fiori, I; Fisher, R P; Flaminio, R; Foley, S; Forsi, E; Forte, L A; Fotopoulos, N; Fournier, J -D; Franc, J; Franco, S; Frasca, S; Frasconi, F; Frede, M; Frei, M A; Frei, Z; Freise, A; Frey, R; Fricke, T T; Friedrich, D; Fritschel, P; Frolov, V V; Fujimoto, M -K; Fulda, P J; Fyffe, M; Gair, J; Galimberti, M; Gammaitoni, L; Garcia, J; Garufi, F; Gáspár, M E; Gelencser, G; Gemme, G; Genin, E; Gennai, A; Gergely, L Á; Ghosh, S; Giaime, J A; Giampanis, S; Giardina, K D; Giazotto, A; Gil-Casanova, S; Gill, C; Gleason, J; Goetz, E; González, G; Gorodetsky, M L; Goßler, S; Gouaty, R; Graef, C; Graff, P B; Granata, M; Grant, A; Gray, C; Greenhalgh, R J S; Gretarsson, A M; Griffo, C; Grote, H; Grover, K; Grunewald, S; Guidi, G M; Guido, C; Gupta, R; Gustafson, E K; Gustafson, R; Hallam, J M; Hammer, D; Hammond, G; Hanks, J; Hanna, C; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Hartman, M T; Haster, C -J; Haughian, K; Hayama, K; Hayau, J -F; Heefner, J; Heidmann, A; Heintze, M C; Heitmann, H; Hello, P; Hemming, G; Hendry, M A; Heng, I S; Heptonstall, A W; Herrera, V; Heurs, M; Hewitson, M; Hild, S; Hoak, D; Hodge, K A; Holt, K; Holtrop, M; Hong, T; Hooper, S; Hough, J; Howell, E J; Hughey, B; Husa, S; Huttner, S H; Huynh-Dinh, T; Ingram, D R; Inta, R; Isogai, T; Ivanov, A; Izumi, K; Jacobson, M; James, E; Jang, Y J; Jaranowski, P; Jesse, E; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; Kalmus, P; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Kasprzack, M; Kasturi, R; Katsavounidis, E; Katzman, W; Kaufer, H; Kaufman, K; Kawabe, K; Kawamura, S; Kawazoe, F; Keitel, D; Kelley, D; Kells, W; Keppel, D G; Keresztes, Z; Khalaidovski, A; Khalili, F Y; Khazanov, E A; Kim, B K; Kim, C; Kim, H; Kim, K; Kim, N; Kim, Y M; King, P J; Kinzel, D L; Kissel, J S; Klimenko, S; Kline, J; Kokeyama, K; Kondrashov, V; Koranda, S; Korth, W Z; Kowalska, I; Kozak, D; Kringel, V; Krishnan, B; Królak, A; Kuehn, G; Kumar, P; Kumar, R; Kurdyumov, R; Kwee, P; Lam, P K; Landry, M; Langley, A; Lantz, B; Lastzka, N; Lawrie, C; Lazzarini, A; Roux, A Le; Leaci, P; Lee, C H; Lee, H K; Lee, H M; Leong, J R; Leonor, I; Leroy, N; Letendre, N; Lhuillier, V; Li, J; Li, T G F; Lindquist, P E; Litvine, V; Liu, Y; Liu, Z; Lockerbie, N A; Lodhia, D; Logue, J; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J; Lubinski, M; Lück, H; Lundgren, A P; Macarthur, J; Macdonald, E; Machenschalk, B; MacInnis, M; Macleod, D M; Mageswaran, M; Mailand, K; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Mandel, I; Mandic, V; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A; Maros, E; Marque, J; Martelli, F; Martin, I W; Martin, R M; Marx, J N; Mason, K; Masserot, A; Matichard, F; Matone, L; Matzner, R A; Mavalvala, N; Mazzolo, G; McCarthy, R; McClelland, D E; McGuire, S C; McIntyre, G; McIver, J; Meadors, G D; Mehmet, M; Meier, T; Melatos, A; Melissinos, A C; Mendell, G; Menéndez, D F; Mercer, R A; Meshkov, S; Messenger, C; Meyer, M S; Miao, H; Michel, C; Milano, L; Miller, J; Minenkov, Y; Mingarelli, C M F; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moe, B; Mohan, M; Mohapatra, S R P; Moraru, D; Moreno, G; Morgado, N; Morgia, A; Mori, T; Morriss, S R; Mosca, S; Mossavi, K; Mours, B; Mow--Lowry, C M; Mueller, C L; Mueller, G; Mukherjee, S; Mullavey, A; Müller-Ebhardt, H; Munch, J; Murphy, D; Murray, P G; Mytidis, A; Nash, T; Naticchioni, L; Necula, V; Nelson, J; Neri, I; Newton, G; Nguyen, T; Nishizawa, A; Nitz, A; Nocera, F; Nolting, D; Normandin, M E; Nuttall, L; Ochsner, E; O'Dell, J; Oelker, E; Ogin, G H; Oh, J J; Oh, S H; Oldenberg, R G; O'Reilly, B; O'Shaughnessy, R; Osthelder, C; Ott, C D; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Page, A; Palladino, L; Palomba, C; Pan, Y; Pankow, C; Paoletti, F; Paoletti, R; Papa, M A; Parisi, M; Pasqualetti, A; Passaquieti, R; Passuello, D; Pedraza, M; Penn, S; Perreca, A; Persichetti, G; Phelps, M; Pichot, M; Pickenpack, M; Piergiovanni, F; Pierro, V; Pihlaja, M; Pinard, L; Pinto, I M; Pitkin, M; Pletsch, H J; Plissi, M V; Poggiani, R; Pöld, J; Postiglione, F; Poux, C; Prato, M; Predoi, V; Prestegard, T; Price, L R; Prijatelj, M; Principe, M; Privitera, S; Prodi, G A; Prokhorov, L G; Puncken, O; Punturo, M; Puppo, P; Quetschke, V; Quitzow-James, R; Raab, F J; Rabeling, D S; Rácz, I; Radkins, H; Raffai, P; Rakhmanov, M; Ramet, C; Rankins, B; Rapagnani, P; Raymond, V; Re, V; Reed, C M; Reed, T; Regimbau, T; Reid, S; Reitze, D H; Ricci, F; Riesen, R; Riles, K; Roberts, M; Robertson, N A; Robinet, F; Robinson, C; Robinson, E L; Rocchi, A; Roddy, S; Rodriguez, C; Rodruck, M; Rolland, L; Rollins, J G; Romano, R; Romie, J H; Rosińska, D; Röver, C; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Salemi, F; Sammut, L; Sandberg, V; Sankar, S; Sannibale, V; Santamaría, L; Santiago-Prieto, I; Santostasi, G; Saracco, E; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Savage, R L; Schilling, R; Schnabel, R; Schofield, R M S; Schulz, B; Schutz, B F; Schwinberg, P; Scott, J; Scott, S M; Seifert, F; Sellers, D; Sentenac, D; Sergeev, A; Shaddock, D A; Shaltev, M; Shapiro, B; Shawhan, P; Shoemaker, D H; Sidery, T L; Siemens, X; Sigg, D; Simakov, D; Singer, A; Singer, L; Sintes, A M; Skelton, G R; Slagmolen, B J J; Slutsky, J; Smith, J R; Smith, M R; Smith, R J E; Smith-Lefebvre, N D; Somiya, K; Sorazu, B; Speirits, F C; Sperandio, L; Stefszky, M; Steinert, E; Steinlechner, J; Steinlechner, S; Steplewski, S; Stochino, A; Stone, R; Strain, K A; Strigin, S E; Stroeer, A S; Sturani, R; Stuver, A L; Summerscales, T Z; Sung, M; Susmithan, S; Sutton, P J; Swinkels, B; Szeifert, G; Tacca, M; Taffarello, L; Talukder, D; Tanner, D B; Tarabrin, S P; Taylor, R; ter Braack, A P M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Thüring, A; Titsler, C; Tokmakov, K V; Tomlinson, C; Toncelli, A; Tonelli, M; Torre, O; Torres, C V; Torrie, C I; Tournefier, E; Travasso, F; Traylor, G; Tse, M; Ugolini, D; Vahlbruch, H; Vajente, G; Brand, J F J van den; Broeck, C Van Den; van der Putten, S; van Veggel, A A; Vass, S; Vasuth, M; Vaulin, R; Vavoulidis, M; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Vetrano, F; Viceré, A; Villar, A E; Vinet, J -Y; Vitale, S; Vocca, H; Vorvick, C; Vyatchanin, S P; Wade, A; Wade, L; Wade, M; Waldman, S J; Wallace, L; Wan, Y; Wang, M; Wang, X; Wanner, A; Ward, R L; Was, M; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Wessels, P; West, M; Westphal, T; Wette, K; Whelan, J T; Whitcomb, S E; White, D J; Whiting, B F; Wiesner, K; Wilkinson, C; Willems, P A; Williams, L; Williams, R; Willke, B; Wimmer, M; Winkelmann, L; Winkler, W; Wipf, C C; Wiseman, A G; Wittel, H; Woan, G; Wooley, R; Worden, J; Yablon, J; Yakushin, I; Yamamoto, H; Yamamoto, K; Yancey, C C; Yang, H; Yeaton-Massey, D; Yoshida, S; Yvert, M; Zadrożny, A; Zanolin, M; Zendri, J -P; Zhang, F; Zhang, L; Zhao, C; Zotov, N; Zucker, M E; Zweizig, J

    2013-01-01

    Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a "blind injection" wher...

  1. EMG signals characterization in three states of contraction by fuzzy network and feature extraction

    CERN Document Server

    Mokhlesabadifarahani, Bita

    2015-01-01

    Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.

  2. Signaling Networks among Stem Cell Precursors, Transit-Amplifying Progenitors, and their Niche in Developing Hair Follicles.

    Science.gov (United States)

    Rezza, Amélie; Wang, Zichen; Sennett, Rachel; Qiao, Wenlian; Wang, Dongmei; Heitman, Nicholas; Mok, Ka Wai; Clavel, Carlos; Yi, Rui; Zandstra, Peter; Ma'ayan, Avi; Rendl, Michael

    2016-03-29

    The hair follicle (HF) is a complex miniorgan that serves as an ideal model system to study stem cell (SC) interactions with the niche during growth and regeneration. Dermal papilla (DP) cells are required for SC activation during the adult hair cycle, but signal exchange between niche and SC precursors/transit-amplifying cell (TAC) progenitors that regulates HF morphogenetic growth is largely unknown. Here we use six transgenic reporters to isolate 14 major skin and HF cell populations. With next-generation RNA sequencing, we characterize their transcriptomes and define unique molecular signatures. SC precursors, TACs, and the DP niche express a plethora of ligands and receptors. Signaling interaction network analysis reveals a bird's-eye view of pathways implicated in epithelial-mesenchymal interactions. Using a systematic tissue-wide approach, this work provides a comprehensive platform, linked to an interactive online database, to identify and further explore the SC/TAC/niche crosstalk regulating HF growth. PMID:27009580

  3. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Quan Liu

    2015-01-01

    Full Text Available In order to build a reliable index to monitor the depth of anesthesia (DOA, many algorithms have been proposed in recent years, one of which is sample entropy (SampEn, a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN model using bispectral index (BIS or expert assessment of conscious level (EACL as the target. To test the performance of the new index’s sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD. The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

  4. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

    Science.gov (United States)

    Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

    2015-01-01

    In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately. PMID:26491464

  5. Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal.

    Science.gov (United States)

    Whitman, Jennifer C; Ward, Lawrence M; Woodward, Todd S

    2013-01-01

    Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks. PMID:23504590

  6. Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

    Directory of Open Access Journals (Sweden)

    Jennifer C Whitman

    2013-03-01

    Full Text Available Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG / MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.

  7. Integrated transcriptomic and proteomic analysis identifies protein kinase CK2 as a key signaling node in an inflammatory cytokine network in ovarian cancer cells

    Science.gov (United States)

    Kulbe, Hagen; Iorio, Francesco; Chakravarty, Probir; Milagre, Carla S.; Moore, Robert; Thompson, Richard G.; Everitt, Gemma; Canosa, Monica; Montoya, Alexander; Drygin, Denis; Braicu, Ioana; Sehouli, Jalid; Saez-Rodriguez, Julio; Cutillas, Pedro R.; Balkwill, Frances R.

    2016-01-01

    We previously showed how key pathways in cancer-related inflammation and Notch signaling are part of an autocrine malignant cell network in ovarian cancer. This network, which we named the “TNF network”, has paracrine actions within the tumor microenvironment, influencing angiogenesis and the immune cell infiltrate. The aim of this study was to identify critical regulators in the signaling pathways of the TNF network in ovarian cancer cells that might be therapeutic targets. To achieve our aim, we used a systems biology approach, combining data from phospho-proteomic mass spectrometry and gene expression array analysis. Among the potential therapeutic kinase targets identified was the protein kinase Casein kinase II (CK2). Knockdown of CK2 expression in malignant cells by siRNA or treatment with the specific CK2 inhibitor CX-4945 significantly decreased Notch signaling and reduced constitutive cytokine release in ovarian cancer cell lines that expressed the TNF network as well as malignant cells isolated from high grade serous ovarian cancer ascites. The expression of the same cytokines was also inhibited after treatment with CX-4945 in a 3D organotypic model. CK2 inhibition was associated with concomitant inhibition of proliferative activity, reduced angiogenesis and experimental peritoneal ovarian tumor growth. In conclusion, we have identified kinases, particularly CK2, associated with the TNF network that may play a central role in sustaining the cytokine network and/or mediating its effects in ovarian cancer. PMID:26871292

  8. [Simulation Analysis of the Pulse Signal on the Electricity Network of Cardiovascular System].

    Science.gov (United States)

    Liu, Ying; Yin, Yanfei; Zhang, Defa; Wang, Menghong; Bi, Yongqiang

    2015-12-01

    Pulse waves contain abundant physiological and pathological information of human body. Research of the relationship between pulse wave and human cardiovascular physiological parameters can not only help clinical diagnosis and treatment of cardiovascular diseases, but also contribute to develop many new medical instruments. Based on the traditional double elastic cavity model, the human cardiovascular system was established by using the electric network model in this paper. The change of wall pressure and blood flow in artery was simulated. And the influence of the peripheral resistance and vessel compliance to the distribution of blood flow in artery was analyzed. The simulation results were compared with the clinical monitoring results to predict the physiological and pathological state of human body. The result showed that the simulation waveform of arterial wall pressure and blood flow was stabile after the second cardiac cycle. With the increasing of peripheral resistance, the systolic blood pressure of artery increased, the diastolic blood pressure had no significant change, and the pulse pressure of artery increased gradually. With the decreasing of vessel compliance, the vasoactivity became worse and the pulse pressure increased correspondingly. The simulation results were consistent with the clinical monitoring results. The increasing of peripheral resistance and decreasing of vascular compliance indicated that the incidence of hypertension and atherosclerosis was increased. PMID:27079088

  9. Signaling networks and cell motility: a computational approach using a phase field description.

    Science.gov (United States)

    Marth, Wieland; Voigt, Axel

    2014-07-01

    The processes of protrusion and retraction during cell movement are driven by the turnover and reorganization of the actin cytoskeleton. Within a reaction-diffusion model which combines processes along the cell membrane with processes within the cytoplasm a Turing type instability is used to form the necessary polarity to distinguish between cell front and rear and to initiate the formation of different organizational arrays within the cytoplasm leading to protrusion and retraction. A simplified biochemical network model for the activation of GTPase which accounts for the different dimensionality of the cell membrane and the cytoplasm is used for this purpose and combined with a classical Helfrich type model to account for bending and stiffness effects of the cell membrane. In addition streaming within the cytoplasm and the extracellular matrix is taken into account. Combining these phenomena allows to simulate the dynamics of cells and to reproduce the primary phenomenology of cell motility. The coupled model is formulated within a phase field approach and solved using adaptive finite elements. PMID:23835784

  10. Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

    Science.gov (United States)

    Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

    2013-10-01

    The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance. PMID:23924412

  11. Bidirectional regulation of angiogenesis by phytoestrogens through estrogen receptor-mediated signaling networks.

    Science.gov (United States)

    Liu, Hai-Xin; Wang, Yu; Lu, Qing; Yang, Ming-Zhu; Fan, Guan-Wei; Karas, Richard H; Gao, Xiu-Mei; Zhu, Yan

    2016-04-01

    Sex hormone estrogen is one of the most active intrinsic angiogenesis regulators; its therapeutic use has been limited due to its carcinogenic potential. Plant-derived phytoestrogens are attractive alternatives, but reports on their angiogenic activities often lack in-depth analysis and sometimes are controversial. Herein, we report a data-mining study with the existing literature, using IPA system to classify and characterize phytoestrogens based on their angiogenic properties and pharmacological consequences. We found that pro-angiogenic phytoestrogens functioned predominantly as cardiovascular protectors whereas anti-angiogenic phytoestrogens played a role in cancer prevention and therapy. This bidirectional regulation were shown to be target-selective and, for the most part, estrogen-receptor-dependent. The transactivation properties of ERα and ERβ by phytoestrogens were examined in the context of angiogenesis-related gene transcription. ERα and ERβ were shown to signal in opposite ways when complexed with the phytoestrogen for bidirectional regulation of angiogenesis. With ERα, phytoestrogen activated or inhibited transcription of some angiogenesis-related genes, resulting in the promotion of angiogenesis, whereas, with ERβ, phytoestrogen regulated transcription of angiogenesis-related genes, resulting in inhibition of angiogenesis. Therefore, the selectivity of phytoestrogen to ERα and ERβ may be critical in the balance of pro- or anti-angiogenesis process. PMID:27114311

  12. Final Report [The c-Abl signaling network in the radioadaptive response

    Energy Technology Data Exchange (ETDEWEB)

    Chi-Min, Yuan

    2014-01-28

    The radioadaptive response, or radiation hormesis, i.e. a low dose of radiation can protect cells and organisms from the effects of a subsequent higher dose, is a widely recognized phenomenon. Mechanisms underlying such radiation hormesis, however, remain largely unclear. Preliminary studies indicate an important role of c-Abl signaling in mediating the radioadaptive response. We propose to investigate how c-Abl regulates the crosstalk between p53 and NFκB in response to low doses irradiation. We found in our recent study that low dose IR induces a reciprocal p53 suppression and NFκB activation, which induces HIF-a and subsequently a metabolic reprogramming resulting in a transition from oxidative phosphorylation to glycolysis. Of importance is that this glycolytic switch is essential for the radioadaptive response. This low-dose radiationinduced HIF1α activation was in sharp contrast with the high-dose IR-induced p53 activation and HIF1α inhibition. HIF1α and p53 seem to play distinct roles in mediating the radiation dose-dependent metabolic response. The induction of HIF1α-mediated glycolysis is restricted to a low dose range of radiation, which may have important implications in assessing the level of radiation exposure and its potential health risk. Our results support a dose-dependent metabolic response to IR. When IR doses are below the threshold of causing detectable DNA damage (<0.2Gy) and thus little p53 activation, HIF1α is induced resulting in induction of glycolysis and increased radiation resistance. When the radiation dose reaches levels eliciting DNA damage, p53 is activated and diminishes the activity of HIF1α and glycolysis, leading to the induction of cell death. Our work challenges the LNT model of radiation exposure risk and provides a metabolic mechanism of radioadaptive response. The study supports a need for determining the p53 and HIF1α activity as a potential reliable biological readout of radiation exposure in humans. The

  13. Signal Transmission in a Human Body Medium-Based Body Sensor Network Using a Mach-Zehnder Electro-Optical Sensor

    Directory of Open Access Journals (Sweden)

    Yong Song

    2012-11-01

    Full Text Available The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiving circuit. Meanwhile, in order to verify the frequency response properties and determine the suitable parameters of the developed system, in-vivo measurements have been implemented under conditions of different carrier frequencies, baseband frequencies and signal transmission paths. Results indicate that the proposed system will help to achieve reliable and high speed signal transmission of BSN based on the human body medium.

  14. Signal integrity design and analysis of Cat 6 network signals%Cat 6网络接口信号完整性设计及分析

    Institute of Scientific and Technical Information of China (English)

    胡玉琛; 刘一清

    2015-01-01

    This paper introduces the method of network signal routing in printed circuit board (PCB) for Cat 6 .The bandwidth in Cat 6 reaches 250 MHz ,which is 1 .5 times higher than Cat 5 ,so the problem of crosstalk is more serious .This design utilize interdigital capacitors which are widely used in microwave and radiofrequency field to successfully solve the problem of crosstalk ,and this paper proposes a new method of designing interdigital capacitors in PCBs .Meanwhile ,solid capacitors are not used in the design that it can reduce the cost of PCB effectively .Finally ,the design is simulated by 3‐D high‐frequency electromagnetic simulation software CST ,which improves the efficiency and precision of the design .%介绍了六类线标准(Cat 6)中网络信号在印制电路板(PCB)中的走线设计方法。在六类线标准中带宽达到了250 M Hz ,比通常使用的五类线标准整整高出了1.5倍,因此随之而来的信号串扰问题也更为严重。本设计采用了微波射频领域中使用的交指电容滤波器,成功地解决了信号串扰问题,并且本文提出了一种在PCB中设计交指电容的全新方法。同时由于没有采用实体电容,有效地降低了PCB的设计成本。最终结合三维高频电磁仿真软件CST对本设计进行了仿真,提高了设计效率和精度[7]。

  15. Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules

    Science.gov (United States)

    Menon, Govind; Krishnan, J.

    2016-07-01

    While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

  16. VSNL1 Co-expression networks in aging include calcium signaling, synaptic plasticity, and Alzheimer’s disease pathways

    Directory of Open Access Journals (Sweden)

    C W Lin

    2015-03-01

    Full Text Available The Visinin-like 1 (VSNL1 gene encodes Visinin-like protein 1, a peripheral biomarker for Alzheimer disease (AD. Little is known, however, about normal VSNL1 expression in brain and the biologic networks in which it participates. Frontal cortex gray matter from 209 subjects without neurodegenerative or psychiatric illness, ranging in age from 16–91, were processed on Affymetrix GeneChip 1.1 ST and Human SNP Array 6.0. VSNL1 expression was unaffected by age and sex, and not significantly associated with SNPs in cis or trans. VSNL1 was significantly co-expressed with genes in pathways for Calcium Signaling, AD, Long Term Potentiation, Long Term Depression, and Trafficking of AMPA Receptors. The association with AD was driven, in part, by correlation with amyloid precursor protein (APP expression. These findings provide an unbiased link between VSNL1 and molecular mechanisms of AD, including pathways implicated in synaptic pathology in AD. Whether APP may drive increased VSNL1 expression, VSNL1 drives increased APP expression, or both are downstream of common pathogenic regulators will need to be evaluated in model systems.

  17. Differential roles of AVP and VIP signaling in the postnatal changes of neural networks for coherent circadian rhythms in the SCN

    Science.gov (United States)

    Ono, Daisuke; Honma, Sato; Honma, Ken-ichi

    2016-01-01

    The suprachiasmatic nucleus (SCN) is the site of the master circadian clock in mammals. The SCN neural network plays a critical role in expressing the tissue-level circadian rhythm. Previously, we demonstrated postnatal changes in the SCN network in mice, in which the clock gene products CRYPTOCHROMES (CRYs) are involved. Here, we show that vasoactive intestinal polypeptide (VIP) signaling is essential for the tissue-level circadian PER2::LUC rhythm in the neonatal SCN of CRY double-deficient mice (Cry1,2−/−). VIP and arginine vasopressin (AVP) signaling showed redundancy in expressing the tissue-level circadian rhythm in the SCN. AVP synthesis was significantly attenuated in the Cry1,2−/− SCN, which contributes to aperiodicity in the adult mice together with an attenuation of VIP signaling as a natural process of ontogeny. The SCN network consists of multiple clusters of cellular circadian rhythms that are differentially integrated by AVP and VIP signaling, depending on the postnatal period.

  18. Differential roles of AVP and VIP signaling in the postnatal changes of neural networks for coherent circadian rhythms in the SCN.

    Science.gov (United States)

    Ono, Daisuke; Honma, Sato; Honma, Ken-Ichi

    2016-09-01

    The suprachiasmatic nucleus (SCN) is the site of the master circadian clock in mammals. The SCN neural network plays a critical role in expressing the tissue-level circadian rhythm. Previously, we demonstrated postnatal changes in the SCN network in mice, in which the clock gene products CRYPTOCHROMES (CRYs) are involved. Here, we show that vasoactive intestinal polypeptide (VIP) signaling is essential for the tissue-level circadian PER2::LUC rhythm in the neonatal SCN of CRY double-deficient mice (Cry1,2 (-/-) ). VIP and arginine vasopressin (AVP) signaling showed redundancy in expressing the tissue-level circadian rhythm in the SCN. AVP synthesis was significantly attenuated in the Cry1,2 (-/-) SCN, which contributes to aperiodicity in the adult mice together with an attenuation of VIP signaling as a natural process of ontogeny. The SCN network consists of multiple clusters of cellular circadian rhythms that are differentially integrated by AVP and VIP signaling, depending on the postnatal period. PMID:27626074

  19. Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

    Directory of Open Access Journals (Sweden)

    Irina Popova

    2013-08-01

    Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.

  20. Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals

    Science.gov (United States)

    Mekanik, F.; Imteaz, M. A.; Talei, A.

    2016-05-01

    Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill

  1. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; de Fries, Louise Skovlund

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social...

  2. The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component

    International Nuclear Information System (INIS)

    In this paper, Robust AutoAssociative Neural Networks (RAANN) are applied to a series of signals produced by the Halden simulator of the 1200MWe BWR Forsmark-3 plant in Sweden. The applications concern: - correction of drifts and gross errors in sensors, for diagnostic and control purposes, - cluster analysis, to individuate a failed component and the intensity of the failure, - forecasting system signals, for safety or economic purposes, - reconstruction of unmeasured signals (virtual sensors). In the attainment of the above results, the geometric interpretation of the mapping performed by the network, propounded in Part I of this work, has provided a reasoned choice of the most critical free parameter, i.e., the number f of nodes of the bottleneck layer, thus allowing a deep understanding of the network functioning and also avoiding the traditional and troubling procedure of selection by trial-and-error. The theoretical basis of this analysis, discussed in details in the companion paper, is founded on the idea of dimension and in particular of fractal dimension, which has been used as a numerical estimator of f

  3. A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

    OpenAIRE

    Ali Yener Mutlu; Edward Bernat; Selin Aviyente

    2012-01-01

    In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization...

  4. Data-analysis strategy for detecting gravitational-wave signals from inspiraling compact binaries with a network of laser-interferometric detectors

    International Nuclear Information System (INIS)

    A data-analysis strategy based on the maximum-likelihood method (MLM) is presented for the detection of gravitational waves from inspiraling compact binaries with a network of laser-interferometric detectors having arbitrary orientations and arbitrary locations around the globe. For simplicity, we restrict ourselves to the Newtonian inspiral wave form. However, the formalism we develop here is also applicable to a wave form with post-Newtonian (PN) corrections. The Newtonian wave form depends on eight parameters: the distance r to the binary, the phase δc of the wave form at the time of final coalescence, the polarization-ellipse angle ψ, the angle of inclination ε of the binary orbit to the line of sight, the source-direction angles (θ,φ), the time of final coalescence tc at the fiducial detector, and the chirp time ξ. All these parameters are relevant for a chirp search with multiple detectors, unlike the case of a single detector. The primary construct on which the MLM is based is the network likelihood ratio (LR). We obtain this ratio here. For the Newtonian inspiral wave form, the LR is a function of the eight signal parameters. In the MLM-based detection strategy, the LR must be maximized over all of these parameters. Here, we show that it is possible to maximize it analytically with respect to four of the eight parameters, namely, (r,deltac,ψ,ε). Maximization over the time of arrival is handled most efficiently by using the fast-Fourier-transform algorithm, as in the case of a single detector. This not only allows us to scan the parameter space continuously over these five parameters but also cuts down substantially on the computational costs. The analytical maximization over the four parameters yields the optimal statistic on which the decision must be based. The value of the statistic also depends on the nature of the noises in the detectors. Here, we model these noises to be mainly Gaussian, stationary, and uncorrelated for every pair of detectors

  5. Twist induces epithelial-mesenchymal transition and cell motility in breast cancer via ITGB1-FAK/ILK signaling axis and its associated downstream network.

    Science.gov (United States)

    Yang, Jiajia; Hou, Yixuan; Zhou, Mingli; Wen, Siyang; Zhou, Jian; Xu, Liyun; Tang, Xi; Du, Yan-e; Hu, Ping; Liu, Manran

    2016-02-01

    Twist, a highly conserved basic Helix-Loop-Helix transcription factor, functions as a major regulator of epithelial-mesenchymal transition (EMT) and tumor metastasis. In different cell models, signaling pathways such as TGF-β, MAPK/ERK, WNT, AKT, JAK/STAT, Notch, and P53 have also been shown to play key roles in the EMT process, yet little is known about the signaling pathways regulated by Twist in tumor cells. Using iTRAQ-labeling combined with 2D LC-MS/MS analysis, we identified 194 proteins with significant changes of expression in MCF10A-Twist cells. These proteins reportedly play roles in EMT, cell junction organization, cell adhesion, and cell migration and invasion. ECM-receptor interaction, MAPK, PI3K/AKT, P53 and WNT signaling were found to be aberrantly activated in MCF10A-Twist cells. Ingenuity Pathways Analysis showed that integrin β1 (ITGB1) acts as a core regulator in linking integrin-linked kinase (ILK), Focal-adhesion kinase (FAK), MAPK/ERK, PI3K/AKT, and WNT signaling. Increased Twist and ITGB1 are associated with breast tumor progression. Twist transcriptionally regulates ITGB1 expression. Over-expression of ITGB1 or Twist in MCF10A led to EMT, activation of FAK/ILK, MAPK/ERK, PI3K/AKT, and WNT signaling. Knockdown of Twist or ITGB1 in BT549 and Hs578T cells decreased activity of FAK, ILK, and their downstream signaling, thus specifically impeding EMT and cell invasion. Knocking down ILK or inhibiting FAK, MAPK/ERK, or PI3K/AKT signaling also suppressed Twist-driven EMT and cell invasion. Thus, the Twist-ITGB1-FAK/ILK pathway and their downstream signaling network dictate the Twist-induced EMT process in human mammary epithelial cells and breast cancer cells. PMID:26693891

  6. Genetic reductionist approach for dissecting individual roles of GGDEF proteins within the c-di-GMP signaling network in Salmonella

    OpenAIRE

    Solano, Cristina; García, Begoña; Latasa, Cristina; Toledo-Arana, Alejandro; Zorraquino, Violeta; Valle, Jaione; Casals, Joan; Pedroso, Enrique; Lasa, Iñigo

    2009-01-01

    Bacteria have developed an exclusive signal transduction system involving multiple diguanylate cyclase and phosphodiesterase domain-containing proteins (GGDEF and EAL/HD-GYP, respectively) that modulate the levels of the same diffusible molecule, 3′-5′-cyclic diguanylic acid (c-di-GMP), to transmit signals and obtain specific cellular responses. Current knowledge about c-di-GMP signaling has been inferred mainly from the analysis of recombinant bacteria that either lack or overproduce individ...

  7. Receptors rather than signals change in expression in four physiological regulatory networks during evolutionary divergence in threespine stickleback.

    Science.gov (United States)

    Di Poi, Carole; Bélanger, Dominic; Amyot, Marc; Rogers, Sean; Aubin-Horth, Nadia

    2016-07-01

    The molecular mechanisms underlying behavioural evolution following colonization of novel environments are largely unknown. Molecules that interact to control equilibrium within an organism form physiological regulatory networks. It is essential to determine whether particular components of physiological regulatory networks evolve or if the network as a whole is affected in populations diverging in behavioural responses, as this may affect the nature, amplitude and number of impacted traits. We studied the regulation of four physiological regulatory networks in freshwater and marine populations of threespine stickleback raised in a common environment, which were previously characterized as showing evolutionary divergence in behaviour and stress reactivity. We measured nineteen components of these networks (ligands and receptors) using mRNA and monoamine levels in the brain, pituitary and interrenal gland, as well as hormone levels. Freshwater fish showed higher expression in the brain of adrenergic (adrb2a), serotonergic (htr2a) and dopaminergic (DRD2) receptors, but lower expression of the htr2b receptor. Freshwater fish also showed higher expression of the mc2r receptor of the glucocorticoid axis in the interrenals. Collectively, our results suggest that the inheritance of the regulation of these networks may be implicated in the evolution of behaviour and stress reactivity in association with population divergence. Our results also suggest that evolutionary change in freshwater threespine stickleback may be more associated with the expression of specific receptors rather than with global changes of all the measured constituents of the physiological regulatory networks. PMID:27146328

  8. Biased Gs versus Gq proteins and β-arrestin signaling in the NK1 receptor determined by interactions in the water hydrogen bond network.

    Science.gov (United States)

    Valentin-Hansen, Louise; Frimurer, Thomas M; Mokrosinski, Jacek; Holliday, Nicholas D; Schwartz, Thue W

    2015-10-01

    X-ray structures, molecular dynamics simulations, and mutational analysis have previously indicated that an extended water hydrogen bond network between trans-membranes I-III, VI, and VII constitutes an allosteric interface essential for stabilizing different active and inactive helical constellations during the seven-trans-membrane receptor activation. The neurokinin-1 receptor signals efficiently through Gq, Gs, and β-arrestin when stimulated by substance P, but it lacks any sign of constitutive activity. In the water hydrogen bond network the neurokinin-1 has a unique Glu residue instead of the highly conserved AspII:10 (2.50). Here, we find that this GluII:10 occupies the space of a putative allosteric modulating Na(+) ion and makes direct inter-helical interactions in particular with SerIII:15 (3.39) and AsnVII:16 (7.49) of the NPXXY motif. Mutational changes in the interface between GluII:10 and AsnVII:16 created receptors that selectively signaled through the following: 1) Gq only; 2) β-arrestin only; and 3) Gq and β-arrestin but not through Gs. Interestingly, increased constitutive Gs but not Gq signaling was observed by Ala substitution of four out of the six core polar residues of the network, in particular SerIII:15. Three residues were essential for all three signaling pathways, i.e. the water-gating micro-switch residues TrpVI:13 (6.48) of the CWXP motif and TyrVII:20 (7.53) of the NPXXY motif plus the totally conserved AsnI:18 (1.50) stabilizing the kink in trans-membrane VII. It is concluded that the interface between position II:10 (2.50), III:15 (3.39), and VII:16 (7.49) in the center of the water hydrogen bond network constitutes a focal point for fine-tuning seven trans-membrane receptor conformations activating different signal transduction pathways. PMID:26269596

  9. Investigations of Escherichia coli promoter sequences with artificial neural networks: new signals discovered upstream of the transcriptional startpoint

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Engelbrecht, Jacob

    1995-01-01

    along the promoter region. The spacing of all signals correspond to the helical periodicity of DNA, meaning that the signals are all present on the same face of the DNA helix in the promoter region. This is consistent with a model where the RNA polymerase contacts the promoter on one side of the DNA...

  10. Coherent spectral amplitude coded label detection for DQPSK payload signals in packet-switched metropolitan area networks

    DEFF Research Database (Denmark)

    Osadchiy, Alexey Vladimirovich; Guerrero Gonzalez, Neil; Jensen, Jesper Bevensee; Tafur Monroy, Idelfonso

    2011-01-01

    We report on an experimental demonstration of a frequency swept local oscillator-based spectral amplitude coding (SAC) label detection for DQPSK signals after 40km of fiber transmission. Label detection was performed for a 10.7Gbaud DQPSK signal labeled with a SAC label composed of four-frequency...

  11. Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures.

    Science.gov (United States)

    Ito, Kengo; Tsutsumi, Yu; Date, Yasuhiro; Kikuchi, Jun

    2016-04-15

    The abundant observation of chemical fragment information for molecular complexities is a major advantage of biological NMR analysis. Thus, the development of a novel technique for NMR signal assignment and metabolite identification may offer new possibilities for exploring molecular complexities. We propose a new signal assignment approach for metabolite mixtures by assembling H-H, H-C, C-C, and Q-C fragmental information obtained by multidimensional NMR, followed by the application of graph and network theory. High-speed experiments and complete automatic signal assignments were achieved for 12 combined mixtures of (13)C-labeled standards. Application to a (13)C-labeled seaweed extract showed 66 H-C, 60 H-H, 326 C-C, and 28 Q-C correlations, which were successfully assembled to 18 metabolites by the automatic assignment. The validity of automatic assignment was supported by quantum chemical calculations. This new approach can predict entire metabolite structures from peak networks of biological extracts. PMID:26789380

  12. Quantitative phosphoproteomic analysis of signaling downstream of the prostaglandin e2/g-protein coupled receptor in human synovial fibroblasts: potential antifibrotic networks.

    Science.gov (United States)

    Gerarduzzi, Casimiro; He, QingWen; Antoniou, John; Di Battista, John A

    2014-11-01

    The Prostaglandin E2 (PGE2) signaling mechanism within fibroblasts is of growing interest as it has been shown to prevent numerous fibrotic features of fibroblast activation with limited evidence of downstream pathways. To understand the mechanisms of fibroblasts producing tremendous amounts of PGE2 with autocrine effects, we apply a strategy of combining a wide-screening of PGE2-induced kinases with quantitative phosphoproteomics. Our large-scale proteomic approach identified a PKA signal transmitted through phosphorylation of its substrates harboring the R(R/X)X(S*/T*) motif. We documented 115 substrates, of which 72 had 89 sites with a 2.5-fold phosphorylation difference in PGE2-treated cells than in untreated cells, where approximately half of such sites were defined as being novel. They were compiled by networking software to focus on highlighted activities and to associate them with a functional readout of fibroblasts. The substrates were associated with a variety of cellular functions including cytoskeletal structures (migration/motility), regulators of G-protein coupled receptor function, protein kinases, and transcriptional/translational regulators. For the first time, we extended the PGE2 pathway into an elaborate network of interconnecting phosphoproteins, providing vital information to a once restricted signalosome. These data provide new insights into eicosanoid-initiated cell signaling with regards to the regulation of fibroblast activation and the identification of new targets for evidenced-based pharmacotherapy against fibrosis. PMID:25223752

  13. Chronic morphine treatment attenuates cell growth of human BT474 breast cancer cells by rearrangement of the ErbB signalling network.

    Directory of Open Access Journals (Sweden)

    Inka Regine Weingaertner

    Full Text Available There is increasing evidence that opioid analgesics may interfere with tumour growth. It is currently thought that these effects are mediated by transactivation of receptor tyrosine kinase (RTK-controlled ERK1/2 and Akt signalling. The growth of many breast cancer cells is dependent on hyperactive ErbB receptor networks and one of the most successful approaches in antineoplastic therapy during the last decade was the development of ErbB-targeted therapies. However, the response rates of single therapies are often poor and resistance mechanisms evolve rapidly. To date there is no information about the ability of opioid analgesics to interfere with the growth of ErbB-driven cancers.Here we demonstrate that ErbB2 overexpressing BT474 human breast cancer cells carry fully functional endogenous µ-opioid receptors. Most interestingly, the acute opioid effects on basal and Heregulin-stimulated ERK1/2 and Akt phosphorylation changed considerably during chronic Morphine treatment. Investigation of the underlying mechanism by the use of protein kinase inhibitors and co-immunoprecipitation studies revealed that chronic Morphine treatment results in rearrangement of the ErbB signalling network leading to dissociation of ERK1/2 from Akt signalling and a switch from ErbB1/ErbB3 to ErbB1/ErbB2-dependent cell growth. In chronically Morphine-treated cells Heregulin-stimulated ERK1/2 signalling is redirected via a newly established PI3K- and metalloproteinase-dependent feedback loop. Together, these alterations result in apoptosis of BT474 cells. A similar switch in Heregulin-stimulated ERK1/2 signalling from an ErbB2-independent to an ErbB2-, PI3K- and metalloproteinase-dependent mechanism was also observed in κ-opioid receptor expressing SKBR3 human mammary adenocarcinoma cells.The present data demonstrate that the ErbB receptor network of human breast cancer cells represents a target for chronic Morphine treatment. Rearrangement of ErbB signalling by chronic

  14. Real-time quality control of pipes using neural network prediction error signals for defect detection in time area

    Science.gov (United States)

    Akhmetshin, Alexander M.; Gvozdak, Andrey P.

    1999-08-01

    The magnetic-induction method of quality control of seamless pipes in real-time characterized by a high level of structural noises having the composite law of an elementary probability law varying from batch to a batch, of a varying form. The traditional method of a detection of defects of pipes is depend to usage of ethanol defects. However shape of actual defects is casual, that does not allow to use methods of an optimum filtration for their detection. Usage of adaptive variants of a Kalman filter not ensures the solutions of a problem of a detection because of poor velocity of adaptation and small relation a signal/the correlated noise. For the solution of a problem was used structural Adaptive Neuro-Fuzzy Inference System (ANFIS) which was trained by delivery of every possible variants of signals without defects of sites of pipes filed by transducer system. As an analyzable signal the error signal of the prognosis ANFIS was considered. The carried out experiments have shown, that the method allows to ooze a signal of casual extended defects even in situations when a signal-noise ratio was less unity and the traditional amplitudes methods of selection of signals of defects did not determine.

  15. Propofol-Induced Frontal Cortex Disconnection: A Study of Resting-State Networks, Total Brain Connectivity, and Mean BOLD Signal Oscillation Frequencies.

    Science.gov (United States)

    Guldenmund, Pieter; Gantner, Ithabi S; Baquero, Katherine; Das, Tushar; Demertzi, Athena; Boveroux, Pierre; Bonhomme, Vincent; Vanhaudenhuyse, Audrey; Bruno, Marie-Aurélie; Gosseries, Olivia; Noirhomme, Quentin; Kirsch, Muriëlle; Boly, Mélanie; Owen, Adrian M; Laureys, Steven; Gómez, Francisco; Soddu, Andrea

    2016-04-01

    Propofol is one of the most commonly used anesthetics in the world, but much remains unknown about the mechanisms by which it induces loss of consciousness. In this resting-state functional magnetic resonance imaging study, we examined qualitative and quantitative changes of resting-state networks (RSNs), total brain connectivity, and mean oscillation frequencies of the regional blood oxygenation level-dependent (BOLD) signal, associated with propofol-induced mild sedation and loss of responsiveness in healthy subjects. We found that detectability of RSNs diminished significantly with loss of responsiveness, and total brain connectivity decreased strongly in the frontal cortex, which was associated with increased mean oscillation frequencies of the BOLD signal. Our results suggest a pivotal role of the frontal cortex in propofol-induced loss of responsiveness. PMID:26650183

  16. A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

    Science.gov (United States)

    Mutlu, Ali Yener; Bernat, Edward; Aviyente, Selin

    2012-01-01

    In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity. PMID:22934122

  17. 一种新型嵌入式网络信号机研究与设计%Study and Design of Embedded Network Traffic Signal Controller

    Institute of Scientific and Technical Information of China (English)

    肖圣兵; 陈锋

    2012-01-01

    Traffic signal controller is one of the main foundations for Intelligent Traffic System. In order to meet intellectualization and network requirements, a novel embedded network signal controller is investigated. ARM and CPLD are used to design hardware structure of signal controller, and humanized user interface is developed using QT/Embedded based on touch screen. Multiple communication interfaces are supported and the communication protocol is defined by XML. The mode of two level conflict check is introduced to avoid green conflict. Single point fuzzy control module is developed to deal with dynamic traffic flow, so as to increase traffic capacity in intersection. Connecting to traffic simulation software that simulates the actual intersectiont experiments show that traffic signal controller works stably, and has excellent performance.%交通信号机是智能交通系统的基础之一,为了满足信号机的网络化和智能化的要求,研究并提出了一种新型的嵌入式网络信号机,该信号机的硬件结构采用ARM和CPLD,以触摸屏为基础采用QT/Embedded设计了人性化的人机接口,信号机支持多种通信接口,其协议采用XML予以定义;为了避免绿冲突,以两级模式进行冲突判断;单点模糊控制机制的设计能够适应动态的交通流状况,有效提高交叉口的通行能力;连接交通仿真软件,模拟实际路口,实验显示该信号机工作稳定,性能优越.

  18. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

    International Nuclear Information System (INIS)

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca2+-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit β (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: ► We performed brain proteomic analysis of rats exposed to the neurotoxicant, Aroclor 1254. ► Cerebellum and

  19. The sigmaS subunit of RNA polymerase as a signal integrator and network master regulator in the general stress response in Escherichia coli.

    Science.gov (United States)

    Klauck, Eberhard; Typas, Athanasios; Hengge, Regine

    2007-01-01

    The sigmaS (RpoS) subunit of RNA polymerase in Escherichia coli is a key master regulator which allows this bacterial model organism and important pathogen to adapt to and survive environmentally rough times. While hardly present in rapidly growing cells, sigmaS strongly accumulates in response to many different stress conditions, partly replaces the vegetative sigma subunit in RNA polymerase and thereby reprograms this enzyme to transcribe sigmaS-dependent genes (up to 10% of the E. coli genes). In this review, we summarize the extremely complex regulation of sigmaS itself and multiple signal input at the level of this master regulator, we describe the way in which sigmaS specifically recognizes "stress" promoters despite their similarity to vegetative promoters, and, while being far from comprehensive, we give a short overview of the far-reaching physiological impact of sigmaS. With sigmaS being a central and multiple signal integrator and master regulator of hundreds of genes organized in regulatory cascades and sub-networks or regulatory modules, this system also represents a key model system for analyzing complex cellular information processing and a starting point for understanding the complete regulatory network of an entire cell. PMID:17725229

  20. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

    Directory of Open Access Journals (Sweden)

    Jiann-Shing Shieh

    2013-08-01

    Full Text Available EEG (Electroencephalography signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module. Multivariate empirical mode decomposition (MEMD algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3 is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC curve (AUC of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

  1. CCN2 is required for the TGF-β induced activation of Smad1-Erk1/2 signaling network.

    Directory of Open Access Journals (Sweden)

    Sashidhar S Nakerakanti

    Full Text Available Connective tissue growth factor (CCN2 is a multifunctional matricellular protein, which is frequently overexpressed during organ fibrosis. CCN2 is a mediator of the pro-fibrotic effects of TGF-β in cultured cells, but the specific function of CCN2 in the fibrotic process has not been elucidated. In this study we characterized the CCN2-dependent signaling pathways that are required for the TGF-β induced fibrogenic response. By depleting endogenous CCN2 we show that CCN2 is indispensable for the TGF-β-induced phosphorylation of Smad1 and Erk1/2, but it is unnecessary for the activation of Smad3. TGF-β stimulation triggered formation of the CCN2/β(3 integrin protein complexes and activation of Src signaling. Furthermore, we demonstrated that signaling through the α(vβ(3 integrin receptor and Src was required for the TGF-β induced Smad1 phosphorylation. Recombinant CCN2 activated Src and Erk1/2 signaling, and induced phosphorylation of Fli1, but was unable to stimulate Smad1 or Smad3 phosphorylation. Additional experiments were performed to investigate the role of CCN2 in collagen production. Consistent with the previous studies, blockade of CCN2 abrogated TGF-β-induced collagen mRNA and protein levels. Recombinant CCN2 potently stimulated collagen mRNA levels and upregulated activity of the COL1A2 promoter, however CCN2 was a weak inducer of collagen protein levels. CCN2 stimulation of collagen was dose-dependent with the lower doses (<50 ng/ml having a stimulatory effect and higher doses having an inhibitory effect on collagen gene expression. In conclusion, our study defines a novel CCN2/α(vβ(3 integrin/Src/Smad1 axis that contributes to the pro-fibrotic TGF-β signaling and suggests that blockade of this pathway may be beneficial for the treatment of fibrosis.

  2. A framework for the establishment of a cnidarian gene regulatory network for "endomesoderm" specification: the inputs of ß-catenin/TCF signaling.

    Directory of Open Access Journals (Sweden)

    Eric Röttinger

    Full Text Available Understanding the functional relationship between intracellular factors and extracellular signals is required for reconstructing gene regulatory networks (GRN involved in complex biological processes. One of the best-studied bilaterian GRNs describes endomesoderm specification and predicts that both mesoderm and endoderm arose from a common GRN early in animal evolution. Compelling molecular, genomic, developmental, and evolutionary evidence supports the hypothesis that the bifunctional gastrodermis of the cnidarian-bilaterian ancestor is derived from the same evolutionary precursor of both endodermal and mesodermal germ layers in all other triploblastic bilaterian animals. We have begun to establish the framework of a provisional cnidarian "endomesodermal" gene regulatory network in the sea anemone, Nematostella vectensis, by using a genome-wide microarray analysis on embryos in which the canonical Wnt/ß-catenin pathway was ectopically targeted for activation by two distinct pharmaceutical agents (lithium chloride and 1-azakenpaullone to identify potential targets of endomesoderm specification. We characterized 51 endomesodermally expressed transcription factors and signaling molecule genes (including 18 newly identified with fine-scale temporal (qPCR and spatial (in situ analysis to define distinct co-expression domains within the animal plate of the embryo and clustered genes based on their earliest zygotic expression. Finally, we determined the input of the canonical Wnt/ß-catenin pathway into the cnidarian endomesodermal GRN using morpholino and mRNA overexpression experiments to show that NvTcf/canonical Wnt signaling is required to pattern both the future endomesodermal and ectodermal domains prior to gastrulation, and that both BMP and FGF (but not Notch pathways play important roles in germ layer specification in this animal. We show both evolutionary conserved as well as profound differences in endomesodermal GRN structure compared

  3. Searching joint association signals in CATIE schizophrenia genome-wide association studies through a refined integrative network approach

    OpenAIRE

    2012-01-01

    Background Genome-wide association studies (GWAS) have generated a wealth of valuable genotyping data for complex diseases/traits. A large proportion of these data are embedded with many weakly associated markers that have been missed in traditional single marker analyses, but they may provide valuable insights in dissecting the genetic components of diseases. Gene set analysis (GSA) augmented by protein-protein interaction network data provides a promising way to examine GWAS data by analyzi...

  4. Al-optical signal processing subsystem based on highly-nonlinear fibres and their limitations for networking applications

    Czech Academy of Sciences Publication Activity Database

    Karásek, Miroslav; Teixeira, A.; Beleffi, G. M. T.; Luis, R.

    Vol. 4534, - (2007), s. 78-85. ISSN 0302-9743. [International IFIP TC6 Conference on Optical Network Design & Modelling ONDM 2007 /11./. Athens, 29.05.2007-31.05.2007] R&D Projects: GA MŠk 1P05OC001 Institutional research plan: CEZ:AV0Z20670512 Keywords : optical communication * optical fibres * wavelenght division multiplexing Subject RIV: BH - Optics, Masers, Lasers Impact factor: 0.402, year: 2005

  5. Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

    OpenAIRE

    Che-Ting Kuo; Ching-Hung Lee

    2015-01-01

    This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS) with a dynamic solution agent algorithm water flow like algorithm (WFA), for nonlinear system identification and blind source separation (BSS) problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA ...

  6. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

    Energy Technology Data Exchange (ETDEWEB)

    Kodavanti, Prasada Rao S., E-mail: kodavanti.prasada@epa.gov [Neurotoxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Osorio, Cristina [Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States); Royland, Joyce E.; Ramabhadran, Ram [Genetic and Cellular Toxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Alzate, Oscar [Department of Cellular and Developmental Biology, University of North Carolina at Chapel Hill, North Carolina (United States); Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States)

    2011-11-15

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca{sup 2+}-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit {beta} (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: Black-Right-Pointing-Pointer We performed brain proteomic analysis of rats exposed to the neurotoxicant

  7. Evaluation of phosphopeptide enrichment strategies for quantitative TMT analysis of complex network dynamics in cancer-associated cell signalling

    OpenAIRE

    Benedetta Lombardi; Nigel Rendell; Mina Edwards; Matilda Katan; Jasminka Godovac Zimmermann

    2015-01-01

    Defining alterations in signalling pathways in normal and malignant cells is becoming a major field in proteomics. A number of different approaches have been established to isolate, identify and quantify phosphorylated proteins and peptides. In the current report, a comparison between SCX prefractionation versus an antibody based approach, both coupled to TiO2 enrichment and applied to TMT labelled cellular lysates, is described. The antibody strategy was more complete for enriching phosphope...

  8. Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network

    Directory of Open Access Journals (Sweden)

    Shane Lee

    2013-12-01

    Full Text Available Gamma frequency rhythms have been implicated in numerous studies for their role in healthy and abnormal brain function. The frequency band has been described to encompass as broad a range as 30–150 Hz. Crucial to understanding the role of gamma in brain function is an identification of the underlying neural mechanisms, which is particularly difficult in the absence of invasive recordings in macroscopic human signals such as those from magnetoencephalography (MEG and electroencephalography (EEG. Here, we studied features of current dipole (CD signals from two distinct mechanisms of gamma generation, using a computational model of a laminar cortical circuit designed specifically to simulate CDs in a biophysically principled manner (Jones et al., 2007; Jones et al., 2009. We simulated spiking pyramidal interneuronal gamma (PING whose period is regulated by the decay time constant of GABAA-mediated synaptic inhibition and also subthreshold gamma driven by gamma-periodic exogenous excitatory synaptic drive. Our model predicts distinguishable CD features created by spiking PING compared to subthreshold driven gamma that can help to disambiguate mechanisms of gamma oscillations in human signals. We found that gamma rhythms in neocortical layer 5 can obscure a simultaneous, independent gamma in layer 2/3. Further, we arrived at a novel interpretation of the origin of high gamma frequency rhythms (100–150 Hz, showing that they emerged from a specific temporal feature of CDs associated with single cycles of PING activity and did not reflect a separate rhythmic process. Last we show that the emergence of observable subthreshold gamma required highly coherent exogenous drive. Our results are the first to demonstrate features of gamma oscillations in human current source signals that distinguish cellular and circuit level mechanisms of these rhythms and may help guide understanding of their functional role.

  9. Nitro-Fatty Acids in Plant Signaling: Nitro-Linolenic Acid Induces the Molecular Chaperone Network in Arabidopsis.

    Science.gov (United States)

    Mata-Pérez, Capilla; Sánchez-Calvo, Beatriz; Padilla, María N; Begara-Morales, Juan C; Luque, Francisco; Melguizo, Manuel; Jiménez-Ruiz, Jaime; Fierro-Risco, Jesús; Peñas-Sanjuán, Antonio; Valderrama, Raquel; Corpas, Francisco J; Barroso, Juan B

    2016-02-01

    Nitro-fatty acids (NO2-FAs) are the product of the reaction between reactive nitrogen species derived of nitric oxide (NO) and unsaturated fatty acids. In animal systems, NO2-FAs are considered novel signaling mediators of cell function based on a proven antiinflammatory response. Nevertheless, the interaction of NO with fatty acids in plant systems has scarcely been studied. Here, we examine the endogenous occurrence of nitro-linolenic acid (NO2-Ln) in Arabidopsis and the modulation of NO2-Ln levels throughout this plant's development by mass spectrometry. The observed levels of this NO2-FA at picomolar concentrations suggested its role as a signaling effector of cell function. In fact, a transcriptomic analysis by RNA-seq technology established a clear signaling role for this molecule, demonstrating that NO2-Ln was involved in plant defense response against different abiotic-stress conditions, mainly by inducing heat shock proteins and supporting a conserved mechanism of action in both animal and plant defense processes. Bioinformatics analysis revealed that NO2-Ln was also involved in the response to oxidative stress conditions, mainly depicted by H2O2, reactive oxygen species, and oxygen-containing compound responses, with a high induction of ascorbate peroxidase expression. Closely related to these results, NO2-Ln levels significantly rose under several abiotic-stress conditions such as wounding or exposure to salinity, cadmium, and low temperature, thus validating the outcomes found by RNA-seq technology. Jointly, to our knowledge, these are the first results showing the endogenous presence of NO2-Ln in Arabidopsis (Arabidopsis thaliana) and supporting the strong signaling role of these molecules in the defense mechanism against different abiotic-stress situations. PMID:26628746

  10. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

    OpenAIRE

    Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh

    2015-01-01

    In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different...

  11. The biological networks in studying cell signal transduction complexity: The examples of sperm capacitation and of endocannabinoid system

    OpenAIRE

    Nicola Bernabò; Barbara Barboni; Mauro Maccarrone

    2014-01-01

    Cellular signal transduction is a complex phenomenon, which plays a central role in cell surviving and adaptation. The great amount of molecular data to date present in literature, together with the adoption of high throughput technologies, on the one hand, made available to scientists an enormous quantity of information, on the other hand, failed to provide a parallel increase in the understanding of biological events. In this context, a new discipline arose, the systems biology, aimed to ma...

  12. Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

    Directory of Open Access Journals (Sweden)

    Craigon Marie

    2009-08-01

    Full Text Available Abstract Background Interferons (IFNs are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs. Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ. Results Transfection of murine bone-marrow derived macrophages (BMDMs with a non-targeting (control siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000 prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response. Conclusion Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated

  13. Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks

    OpenAIRE

    DeDora, Daniel J.; Nedic, Sanja; Katti, Pratha; Arnab, Shafique; Wald, Lawrence L.; Takahashi, Atsushi; Dijk, Koene R.A.Van; Strey, Helmut H.; Mujica-Parodi, Lilianne R.

    2015-01-01

    Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic f...

  14. Evaluation of phosphopeptide enrichment strategies for quantitative TMT analysis of complex network dynamics in cancer-associated cell signalling

    Directory of Open Access Journals (Sweden)

    Benedetta Lombardi

    2015-03-01

    Full Text Available Defining alterations in signalling pathways in normal and malignant cells is becoming a major field in proteomics. A number of different approaches have been established to isolate, identify and quantify phosphorylated proteins and peptides. In the current report, a comparison between SCX prefractionation versus an antibody based approach, both coupled to TiO2 enrichment and applied to TMT labelled cellular lysates, is described. The antibody strategy was more complete for enriching phosphopeptides and allowed the identification of a large set of proteins known to be phosphorylated (715 protein groups with a minimum number of not previously known phosphorylated proteins (2.

  15. Simultaneous Generations of Independent Millimeter Wave and 10 Gbit/s Wired Signal by Single Electrode Modulator in TDM-PON Network

    Science.gov (United States)

    Niazi, Shahab Ahmad; Zhang, Xiaoguang; Xi, Lixia; Idress, Muhammad

    2013-03-01

    We propose and present a cost effective and simple technique of simultaneous generation and propagation of millimeter wave with recently standardized 10 giga bit passive optical network (GPON) by mixing of 2.5 Gbit/s, 30 GHz radio wave with 10 Gbit/s based band signal and then modulated by single Mach Zehnder modulator (MZM). In this scheme, we have applied 1490 nm for downstream, 1310 nm for upstream transmission and ON OFF keying (OOK) modulation format to make it fully align with existing standards and infrastructure. Simulation results show error free transmission performance with negligible power penalty over 25 km bidirectional fiber. We also highlight the principles and discuss the main technical challenges for commercial realization of 60 GHz spectrum.

  16. Damage localization using a power-efficient distributed on-board signal processing algorithm in a wireless sensor network

    International Nuclear Information System (INIS)

    A distributed on-board algorithm that is embedded and executed within a group of wireless sensors to locate structural damages in isotropic plates is presented. The algorithm is based on an energy-decay model of Lamb waves and singular value decomposition (SVD) to determine damage locations. A sensor group consists of a small number of sensors, each of which independently collects wave signals and evaluates wave energy upon an external triggering signal sent from a base station. The energy values, usually a few bytes in length, are then sent to the base station to determine the presence and location of damages. In comparison with traditional centralized approaches in which whole datasets are required to be transmitted, the proposed algorithm yields much less wireless communication traffic, yet with a modest amount of computation required within sensors. Experiments have shown that the algorithm is robust to locate damage for isotropic plate structures and is very power efficient, with more than an order-of-magnitude power saving

  17. Logical network of genotoxic stress-induced NF-kappaB signal transduction predicts putative target structures for therapeutic intervention strategies

    Directory of Open Access Journals (Sweden)

    Rainer Poltz

    2009-12-01

    Full Text Available Rainer Poltz1, Raimo Franke1,#, Katrin Schweitzer1, Steffen Klamt2, Ernst-Dieter Gilles2, Michael Naumann11Institute of Experimental Internal Medicine, Otto von Guericke University, Magdeburg, Germany; 2Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; #Present address: Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, GermanyAbstract: Genotoxic stress is induced by a broad range of DNA-damaging agents and could lead to a variety of human diseases including cancer. DNA damage is also therapeutically induced for cancer treatment with the aim to eliminate tumor cells. However, the effectiveness of radio- and chemotherapy is strongly hampered by tumor cell resistance. A major reason for radio- and chemotherapeutic resistances is the simultaneous activation of cell survival pathways resulting in the activation of the transcription factor nuclear factor-kappa B (NF-κB. Here, we present a Boolean network model of the NF-κB signal transduction induced by genotoxic stress in epithelial cells. For the representation and analysis of the model, we used the formalism of logical interaction hypergraphs. Model reconstruction was based on a careful meta-analysis of published data. By calculating minimal intervention sets, we identified p53-induced protein with a death domain (PIDD, receptor-interacting protein 1 (RIP1, and protein inhibitor of activated STAT y (PIASy as putative therapeutic targets to abrogate NF-κB activation resulting in apoptosis. Targeting these structures therapeutically may potentiate the effectiveness of radio- and chemotherapy. Thus, the presented model allows a better understanding of the signal transduction in tumor cells and provides candidates as new therapeutic target structures.Keywords: apoptosis, Boolean network, cancer therapy, DNA-damage response, NF-κB

  18. A data-analysis strategy for detecting gravitational-wave signals from inspiraling compact binaries with a network of laser-interferometric detectors

    CERN Document Server

    Pai, A; Bose, S; Pai, Archana; Dhurandhar, Sanjeev; Bose, Sukanta

    2001-01-01

    A data-analysis strategy based on the maximum-likelihood method (MLM) is presented for the detection of gravitational waves from inspiraling compact binaries with a network of laser-interferometric detectors having arbitrary orientations and arbitrary locations around the globe. The MLM is based on the network likelihood ratio (LR), which is a function of eight signal-parameters that determine the Newtonian inspiral waveform. In the MLM-based strategy, the LR must be maximized over all of these parameters. Here, we show that it is possible to maximize it analytically over four of the eight parameters. Maximization over a fifth parameter, the time of arrival, is handled most efficiently by using the Fast-Fourier-Transform algorithm. This allows us to scan the parameter space continuously over these five parameters and also cuts down substantially on the computational costs. Maximization of the LR over the remaining three parameters is handled numerically. This includes the construction of a bank of templates o...

  19. Neural Networks for the Extraction of the ΛC Signal in p-Pb collisions at 
 √sNN = 5.02 TeV

    CERN Document Server

    Giampaolo, Alberto

    2016-01-01

    The charmed baryon ΛC is of interest for the characterization of the quark-gluon plasma (QGP) created in Pb-Pb collisions, due to its sensitivity to c-quark thermalization and to the hadronization mechanisms. The measurement in pp an p-Pb collisions is of interest both as a reference for the Pb- Pb result and in the context of recent observations suggesting the possible creation of a QGP in small colliding systems. This project is focused on the study of the extraction of the ΛC signal in p-Pb collisions with the ALICE detector, through the usage of deep learning, a machine learning technique. In a few weeks we were able to reproduce the results of the existing BDT analysis with a simple shallow networks. In the 6 to 8 pT bin, deep networks using low-level variables get close to the performance of the topological variable analysis, but with the architectures tested in this project they do not seem to be able to outperform it.

  20. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

    Directory of Open Access Journals (Sweden)

    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.