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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. 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

  3. 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.

  4. Bioinformatics analyses for signal transduction networks

    Institute of Scientific and Technical Information of China (English)

    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.

  5. 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...

  6. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  7. Reconstruction of periodic signals using neural networks

    Directory of Open Access Journals (Sweden)

    José Danilo Rairán Antolines

    2014-01-01

    Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

  8. 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

  9. Quantitative phosphoproteomics to characterize signaling networks

    DEFF Research Database (Denmark)

    Rigbolt, Kristoffer T G; Blagoev, Blagoy

    2012-01-01

    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 phosphoproteomics technology that have facilitated the application of phosphoproteomics to signaling networks and introduce examples of recent system-wide applications of quantitative phosphoproteomics. Despite the great advances in phosphoproteomics technology there are still several outstanding issues and we...

  10. Proteomic Study of the Brassinosteroid Signalling Network

    Institute of Scientific and Technical Information of China (English)

    Zhiyong Wang

    2012-01-01

    Plant growth is controlled by multiple environmental signals and endogenous hormones.In particular,brassinosteroid (BR) regulates a wide range of developmental processes throughout the life cycle of plants.BR acts through a receptor kinase signalling pathway,and BR signalling crosstalk with many other signalling pathways including light and gibberellin pathways as well as other receptor kinase pathways.My lab uses a combination of genetic,proteomic,and genomic approaches to elucidate not only the BR signaling pathway but also the global organization of the signaling network.We have successfully used proteomics to identify new components of the BR signalling pathway and to elucidated the mechanisms of signal transduction from the BRI1 receptor kinase to the BZR1 transcription factor.We have further uncovered mechanisms of crosstalk between different receptor kinase pathways,and we are dissecting the molecular mechanisms underlying signalling crosstalk and specificity.Our recent proteomic analysis of BR-regulated nuclear proteins has identified a potential link for BR regulation of flowering through RNA splicing and epigenetic mechanisms.I will discuss strategies and potential pitfalls in using proteomics to study signal transduction in plants.

  11. 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 m...

  12. 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.

  13. Collective Calcium Signaling of Defective Multicellular Networks

    Science.gov (United States)

    Potter, Garrett; Sun, Bo

    2015-03-01

    A communicating multicellular network processes environmental cues into collective cellular dynamics. We have previously demonstrated that, when excited by extracellular ATP, fibroblast monolayers generate correlated calcium dynamics modulated by both the stimuli and gap junction communication between the cells. However, just as a well-connected neural network may be compromised by abnormal neurons, a tissue monolayer can also be defective with cancer cells, which typically have down regulated gap junctions. To understand the collective cellular dynamics in a defective multicellular network we have studied the calcium signaling of co-cultured breast cancer cells and fibroblast cells in various concentrations of ATP delivered through microfluidic devices. Our results demonstrate that cancer cells respond faster, generate singular spikes, and are more synchronous across all stimuli concentrations. Additionally, fibroblast cells exhibit persistent calcium oscillations that increase in regularity with greater stimuli. To interpret these results we quantitatively analyzed the immunostaining of purigenic receptors and gap junction channels. The results confirm our hypothesis that collective dynamics are mainly determined by the availability of gap junction communications.

  14. 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...

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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...

  20. 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...

  1. 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

  2. Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

    CERN Document Server

    Palotai, Robin

    2009-01-01

    Complex systems are successfully reduced to interacting elements via the network concept. Transport plays a key role in the survival of networks. For example the specialized signaling cascades of cellular networks filter noise and efficiently adapt the network structure to new stimuli. However, our general understanding of transport mechanisms and signaling pathways in complex systems is yet limited. Here we summarize the key network structures involved in transport, list the solutions available to overloaded systems for relaxing their load and outline a possible method for the computational determination of signaling pathways. We highlight that in addition to hubs, bridges and the network skeleton, the overlapping modular structure is also essential in network transport. Moreover, by locating network elements in the space of overlapping network modules and evaluating their distance in this "module space", it may be possible to approximate signaling pathways computationally, which, in turn could serve the ide...

  3. Bio-inspired signal transduction with heterogeneous networks of nanoscillators

    Science.gov (United States)

    Cervera, Javier; Manzanares, José A.; Mafé, Salvador

    2012-02-01

    Networks of single-electron transistors mimic some of the essential properties of neuron populations, because weak electrical signals trigger network oscillations with a frequency proportional to the input signal. Input potentials representing the pixel gray level of a grayscale image can then be converted into rhythms and the image can be recovered from these rhythms. Networks of non-identical nanoscillators complete the noisy transduction more reliably than identical ones. These results are important for signal processing schemes and could support recent studies suggesting that neuronal variability enhances the processing of biological information.

  4. 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.

  5. 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....

  6. 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.

  7. DEVELOPMENT OF NEURAL NETWORK MODEL FOR CLASSIFICATION OF CAVITATION SIGNALS

    Directory of Open Access Journals (Sweden)

    KALYANASUNDARAM PERUMAL

    2011-10-01

    Full Text Available This paper deals with the early detection of cavitation by classification of cavitation signal into no, incipient and developed cavitation signal using artificial neural network model. This ANN model diagnoses the cavitation signal based on amplitude of rms vibration signal acquired from accelerometer, in order to find the different stages of cavitation. The classification results shows that feed forward network employing resilient back propagation algorithm was effective to distinct between the classes based on the good selection of input files for training the network. The proposed ANN model with resilient algorithm gives better performance and classification rate. The classification rate was 72.96% for the training sets and 75.57% for test data sets. It is concluded that the performance of the neural network is carried out irrespective of zones and it is optimum, and the errors are very less. The paper also discusses the future research directions.

  8. 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)

  9. 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...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, 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...

  10. 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...

  11. 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, ...

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    Primary cilia are microtubule-based, sensory organelles that emerge from the centrosomal mother centriole to project from the surface of most quiescent cells in the human body. Ciliary entry is a tightly controlled process, involving diffusion barriers and gating complexes that maintain a unique...... this 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...... on formation of the primary cilium and CDE at the pocket region. The ciliary protein Inversin functions as a molecular switch between canonical and non-canonical Wnt signaling. In paper II, we show that Inversin and the primary cilium control Wnt signaling and are required for polarization and cell migration...

  18. Timing and time signal distribution in digital communications networks

    Science.gov (United States)

    Kihara, Masami; Imaoka, Atushi

    1992-06-01

    The timing signal distribution characteristics of a digital communications network are evaluated to determine the Maximum Time Interval Error (MTIE) of the network; reference is made to the performance of network components such as transmission systems, slave clocks and timing distribution systems in intraoffices. The MTIE of each component is measured and used to determine the allowable MTIE of that component. The maximum number of slave node chains is shown to be 20. Time signal distribution performance is detailed. It is shown that time synchronization accuracy is of the order of submicroseconds between nodes separated by 2400 km over a two year period. For intra-office time signal distribution, the relative time accuracy is less than 3 nanoseconds using an 8 Mb/s round trip digital interface to connect a time signal supply in an office to dispersed equipment.

  19. 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.

  20. 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

  1. 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.

  2. 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...

  3. 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

  4. 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...

  5. 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 ...

  6. 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.

  7. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6.

    Science.gov (United States)

    Samuelraj, Ananthi Jebaseeli; Jayapal, Sundararajan

    2015-01-01

    Proxy Mobile IPV6 (PMIPV6) is a network based mobility management protocol which supports node's mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO) in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node's mobility should be modified to support group nodes' mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

  8. Optical Performance Monitoring and Signal Optimization in Optical Networks

    DEFF Research Database (Denmark)

    Petersen, Martin Nordal

    2006-01-01

    -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......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...

  9. 1st International Conference on Signal, Networks, Computing, and Systems

    CERN Document Server

    Mohapatra, Durga; Nagar, Atulya; Sahoo, Manmath

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

  10. 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.

  11. 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.

  12. An artificial network model for estimating the network structure underlying partially observed neuronal signals.

    Science.gov (United States)

    Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

    2014-01-01

    Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed.

  13. 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.

  14. The fidelity of dynamic signaling by noisy biomolecular networks.

    Directory of Open Access Journals (Sweden)

    Clive G Bowsher

    Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.

  15. 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.

  16. 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

  17. Network regulation of calcium signal in stomatal development

    Institute of Scientific and Technical Information of China (English)

    Zhu-xia SHEN; Gen-xuan WANG; Zhi-qiang LIU; Hao ZHANG; Mu-qing QIU; Xing-zheng ZHAO; Yi GAN

    2006-01-01

    Aim: Each cell is the production of multiple signal transduction programs involving the expression of thousands of genes. This study aims to gain insights into the gene regulation mechanisms of stomatal development and will investigate the relationships among some signaling transduction pathways. Methods: Nail enamel printing was conducted to observe the stomatal indices of wild type and 10 mutants (plant hormone mutants, Pi-starvation induced CaM mutants and Pi-starvation-response mutant) in Arabidopsis, and their stomatal indices were analyzed by ANOVA. We analyzed the stomatal indices of 10 Arabidopsis mutants were analyzed by a model PRGE (potential relative effect of genes) to research relations among these genes. Results: In wild type and 10 mutants, the stomatal index didn't differ with respect to location on the lower epidermis. Compared with wild type, the stomatal indices of 10 mutants all decreased significantly. Moreover, significant changes and interactions might exist between some mutant genes. Conclusion: It was the stomatal intensity in Arabidopsis might be highly sensitive to most mutations in genome. While the effect of many gene mutations on the stomatal index might be negative, we also could assume the stomatal development was regulated by a signal network in which one signal transduction change might influence the stomatal development more or less, and the architecture might be reticulate. Furthermore, we could speculate that calcium was a hub in stomatal development signal regulation network, and other signal transduction pathways regulated stomtal development by influencing or being influenced by calcium signal transduction pathways.

  18. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN) fo...

  19. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. 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

  7. 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.'

  8. 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

  9. 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

  10. 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...

  11. 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.

  12. 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.

  13. Design principles of nuclear receptor signaling: How complex networking improves signal transduction

    NARCIS (Netherlands)

    A.N. Kolodkin (Alexey); F.J. Bruggeman (Frank); N. Plant (Nick); M.J. Moné (Martijn); B.M. Bakker (Barbara); M.J. Campbell (Moray); J.P.T.M. van Leeuwen (Hans); C. Carlberg (Carsten); J.L. Snoep (Jacky); H.V. Westerhoff (Hans)

    2010-01-01

    textabstractThe topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of design aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic

  14. Design principles of nuclear receptor signaling : how complex networking improves signal transduction

    NARCIS (Netherlands)

    Kolodkin, Alexey N.; Bruggeman, Frank J.; Plant, Nick; Mone, Martijn J.; Bakker, Barbara M.; Campbell, Moray J.; van Leeuwen, Johannes P. T. M.; Carlberg, Carsten; Snoep, Jacky L.; Westerhoff, Hans V.

    2010-01-01

    The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of 'design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of

  15. 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

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  20. 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.

  1. Phosphoinositide pathway and the signal transduction network in neural development

    Institute of Scientific and Technical Information of China (English)

    Vincenza Rita Lo Vasco

    2012-01-01

    The development of the nervous system is under the strict control of a number of signal transduction pathways,often interconnected.Among them,the phosphoinositide (PI) pathway and the related phospholipase C (PI-PLC) family of enzymes have been attracting much attention.Besides their well-known role in the regulation of intracellular calcium levels,PI-PLC enzymes interact with a number of molecules belonging to further signal transduction pathways,contributing to a specific and complex network in the developing nervous system.In this review,the connections of PI signalling with further transduction pathways acting during neural development are discussed,with special regard to the role of the PI-PLC family of enzymes.

  2. Computational models of signalling networks for non-linear control.

    Science.gov (United States)

    Fuente, Luis A; Lones, Michael A; Turner, Alexander P; Stepney, Susan; Caves, Leo S; Tyrrell, Andy M

    2013-05-01

    Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. Control of cancer-related signal transduction networks

    Science.gov (United States)

    Albert, Reka

    2013-03-01

    Intra-cellular signaling networks are crucial to the maintenance of cellular homeostasis and for cell behavior (growth, survival, apoptosis, movement). Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development and/or the promotion of cell migration and metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. We recently demonstrated the effectiveness of network-based targeted manipulations on mitigating the disease T cell large granular lymphocyte (T-LGL) leukemia. The root of this disease is the abnormal survival of T cells which, after successfully fighting an infection, should undergo programmed cell death. We synthesized the relevant network of within-T-cell interactions from the literature, integrated it with qualitative knowledge of the dysregulated (abnormal) states of several network components, and formulated a Boolean dynamic model. The model indicated that the system possesses a steady state corresponding to the normal cell death state and a T-LGL steady state corresponding to the abnormal survival state. For each node, we evaluated the restorative manipulation consisting of maintaining the node in the state that is the opposite of its T-LGL state, e.g. knocking it out if it is overexpressed in the T-LGL state. We found that such control of any of 15 nodes led to the disappearance of the T-LGL steady state, leaving cell death as the only potential outcome from any initial condition. In four additional cases the probability of reaching the T-LGL state decreased dramatically, thus these nodes are also possible control

  15. 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.

  16. 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.

  17. Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling.

    Directory of Open Access Journals (Sweden)

    Song Li

    2006-10-01

    Full Text Available Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K(+ efflux through slowly activating K(+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited.

  18. 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.

  19. Signal Transduction at the Single-Cell Level: Approaches to Study the Dynamic Nature of Signaling Networks.

    Science.gov (United States)

    Handly, L Naomi; Yao, Jason; Wollman, Roy

    2016-09-25

    Signal transduction, or how cells interpret and react to external events, is a fundamental aspect of cellular function. Traditional study of signal transduction pathways involves mapping cellular signaling pathways at the population level. However, population-averaged readouts do not adequately illuminate the complex dynamics and heterogeneous responses found at the single-cell level. Recent technological advances that observe cellular response, computationally model signaling pathways, and experimentally manipulate cells now enable studying signal transduction at the single-cell level. These studies will enable deeper insights into the dynamic nature of signaling networks.

  20. Signal integration by chloroplast phosphorylation networks: An update

    Directory of Open Access Journals (Sweden)

    Anna eSchoenberg

    2012-11-01

    Full Text Available Forty years after the initial discovery of light-dependent protein phosphorylation at the thylakoid membrane system, we are now beginning to understand the roles of chloroplast phosphorylation networks in their function to decode and mediate information on the metabolic status of the organelle to long-term adaptations in plastid and nuclear gene expression. With the help of genetics and functional genomics tools, chloroplast kinases and several hundred phosphoproteins were identified that now await detailed functional characterization. The regulation and the target protein spectrum of some kinases are understood, but this information is fragmentary with respect to kinase and target protein crosstalk in a changing environment. In this review we will highlight the most recent advances in the field and discuss approaches that might lead to a comprehensive understanding of plastid signal integration by protein phosphorylation.

  1. Network coding based joint signaling and dynamic bandwidth allocation scheme for inter optical network unit communication in passive optical networks

    Science.gov (United States)

    Wei, Pei; Gu, Rentao; Ji, Yuefeng

    2014-06-01

    As an innovative and promising technology, network coding has been introduced to passive optical networks (PON) in recent years to support inter optical network unit (ONU) communication, yet the signaling process and dynamic bandwidth allocation (DBA) in PON with network coding (NC-PON) still need further study. Thus, we propose a joint signaling and DBA scheme for efficiently supporting differentiated services of inter ONU communication in NC-PON. In the proposed joint scheme, the signaling process lays the foundation to fulfill network coding in PON, and it can not only avoid the potential threat to downstream security in previous schemes but also be suitable for the proposed hybrid dynamic bandwidth allocation (HDBA) scheme. In HDBA, a DBA cycle is divided into two sub-cycles for applying different coding, scheduling and bandwidth allocation strategies to differentiated classes of services. Besides, as network traffic load varies, the entire upstream transmission window for all REPORT messages slides accordingly, leaving the transmission time of one or two sub-cycles to overlap with the bandwidth allocation calculation time at the optical line terminal (the OLT), so that the upstream idle time can be efficiently eliminated. Performance evaluation results validate that compared with the existing two DBA algorithms deployed in NC-PON, HDBA demonstrates the best quality of service (QoS) support in terms of delay for all classes of services, especially guarantees the end-to-end delay bound of high class services. Specifically, HDBA can eliminate queuing delay and scheduling delay of high class services, reduce those of lower class services by at least 20%, and reduce the average end-to-end delay of all services over 50%. Moreover, HDBA also achieves the maximum delay fairness between coded and uncoded lower class services, and medium delay fairness for high class services.

  2. 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...

  3. Integrative Signaling Networks of Membrane Guanylate Cyclases: Biochemistry and Physiology

    Science.gov (United States)

    Sharma, Rameshwar K.; Duda, Teresa; Makino, Clint L.

    2016-01-01

    This monograph presents a historical perspective of cornerstone developments on the biochemistry and physiology of mammalian membrane guanylate cyclases (MGCs), highlighting contributions made by the authors and their collaborators. Upon resolution of early contentious studies, cyclic GMP emerged alongside cyclic AMP, as an important intracellular second messenger for hormonal signaling. However, the two signaling pathways differ in significant ways. In the cyclic AMP pathway, hormone binding to a G protein coupled receptor leads to stimulation or inhibition of an adenylate cyclase, whereas the cyclic GMP pathway dispenses with intermediaries; hormone binds to an MGC to affect its activity. Although the cyclic GMP pathway is direct, it is by no means simple. The modular design of the molecule incorporates regulation by ATP binding and phosphorylation. MGCs can form complexes with Ca2+-sensing subunits that either increase or decrease cyclic GMP synthesis, depending on subunit identity. In some systems, co-expression of two Ca2+ sensors, GCAP1 and S100B with ROS-GC1 confers bimodal signaling marked by increases in cyclic GMP synthesis when intracellular Ca2+ concentration rises or falls. Some MGCs monitor or are modulated by carbon dioxide via its conversion to bicarbonate. One MGC even functions as a thermosensor as well as a chemosensor; activity reaches a maximum with a mild drop in temperature. The complexity afforded by these multiple limbs of operation enables MGC networks to perform transductions traditionally reserved for G protein coupled receptors and Transient Receptor Potential (TRP) ion channels and to serve a diverse array of functions, including control over cardiac vasculature, smooth muscle relaxation, blood pressure regulation, cellular growth, sensory transductions, neural plasticity and memory.

  4. 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.

  5. 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...

  6. Signal processing using artificial neural network for BOTDA sensor system.

    Science.gov (United States)

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy. PMID:27136863

  7. 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.

  8. 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...

  9. Network dynamics for optimal compressive-sensing input-signal recovery.

    Science.gov (United States)

    Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2014-10-01

    By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.

  10. 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 ...

  11. 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.

  12. Simultaneous multichannel signal transfers via chaos in a recurrent neural network.

    Science.gov (United States)

    Soma, Ken-ichiro; Mori, Ryota; Sato, Ryuichi; Furumai, Noriyuki; Nara, Shigetoshi

    2015-05-01

    We propose neural network model that demonstrates the phenomenon of signal transfer between separated neuron groups via other chaotic neurons that show no apparent correlations with the input signal. The model is a recurrent neural network in which it is supposed that synchronous behavior between small groups of input and output neurons has been learned as fragments of high-dimensional memory patterns, and depletion of neural connections results in chaotic wandering dynamics. Computer experiments show that when a strong oscillatory signal is applied to an input group in the chaotic regime, the signal is successfully transferred to the corresponding output group, although no correlation is observed between the input signal and the intermediary neurons. Signal transfer is also observed when multiple signals are applied simultaneously to separate input groups belonging to different memory attractors. In this sense simultaneous multichannel communications are realized, and the chaotic neural dynamics acts as a signal transfer medium in which the signal appears to be hidden.

  13. An Approach to High-Order Cumulants Used to Detect Multifrequency Signals in Telephone Networks

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In accordance with the detecting process of multi-frequency signals between the offices in telephone networks, and in contrast with the autocorrelation method used to handle the multi-frequency signals, a fast,inexpensive and unbiased of cumulants estimation method is adopted in detecting signals. This detecting method is better for resisting noise performance and more practical than the autocorrelation method.

  14. 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.

  15. 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.

  16. 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.

  17. Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation.

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Ma

    Full Text Available A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.

  18. 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.

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

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-22

    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.

  20. 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...

  1. 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.

  2. 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... The interferon signaling network and transcription factor C/EBP-beta. PubmedID 18163952 Title The interfero...n signaling network and transcription factor C/EBP-beta. Authors Li H, Gade P, Xi

  3. Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.

    Science.gov (United States)

    Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2014-11-01

    Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.

  4. Iterative Learning Control Approach for Signaling Split in Urban Traffic Networks with Macroscopic Fundamental Diagrams

    Directory of Open Access Journals (Sweden)

    Fei Yan

    2015-01-01

    Full Text Available Recent analysis of field experiments in cities revealed that a macroscopic fundamental diagram (MFD relating network outflow and network vehicle accumulation exists in the urban traffic networks. It has been further confirmed that an MFD is well defined if the network has regular network topology and homogeneous spatial distribution of vehicle accumulation. However, many real urban networks have different levels of heterogeneity in the spatial distribution of vehicle accumulation. In order to improve the mobility in heterogeneously congested networks, we propose an iterative learning control approach for signaling split, which aims at distributing the accumulation in the networks as homogeneously as possible and ensuring the networks have a larger outflow. The asymptotic convergence of the proposed approach is proved by rigorous analysis and the effectiveness is further demonstrated by extensive simulations.

  5. 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...

  6. 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

  7. 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...

  8. Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

    Science.gov (United States)

    Zhang, Zipeng; Fan, Tao; Wang, Shuqing

    It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

  9. Construction of cell type-specific logic models of signaling networks using CellNOpt.

    Science.gov (United States)

    Morris, Melody K; Melas, Ioannis; Saez-Rodriguez, Julio

    2013-01-01

    Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. 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...

  15. Sensor selection for received signal strength-based source localization in wireless sensor networks

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained...

  16. Knowledge representation model for systems-level analysis of signal transduction networks.

    Science.gov (United States)

    Lee, Dong-Yup; Zimmer, Ralf; Lee, Sang-Yup; Hanisch, Daniel; Park, Sunwon

    2004-01-01

    A Petri-net based model for knowledge representation has been developed to describe as explicitly and formally as possible the molecular mechanisms of cell signaling and their pathological implications. A conceptual framework has been established for reconstructing and analyzing signal transduction networks on the basis of the formal representation. Such a conceptual framework renders it possible to qualitatively understand the cell signaling behavior at systems-level. The mechanisms of the complex signaling network are explored by applying the established framework to the signal transduction induced by potent proinflammatory cytokines, IL-1beta and TNF-alpha The corresponding expert-knowledge network is constructed to evaluate its mechanisms in detail. This strategy should be useful in drug target discovery and its validation.

  17. 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).

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof

    CERN Document Server

    Tyukin, Ivan; van Leeuwen, Cees

    2007-01-01

    We address the important theoretical question why a recurrent neural network with fixed weights can adaptively classify time-varied signals in the presence of additive noise and parametric perturbations. We provide a mathematical proof assuming that unknown parameters are allowed to enter the signal nonlinearly and the noise amplitude is sufficiently small.

  5. 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.

  6. An improved response surface methodology algorithm with an application to traffic signal optimization for urban networks

    Energy Technology Data Exchange (ETDEWEB)

    Joshi, S.S.; Rathi, A.K. [Oak Ridge National Lab., TN (United States); Tew, J.D. [Consolidated Freightways, Inc., Portland, OR (United States). ISQ Dept.

    1995-12-31

    This paper illustrates the use of the simulation-optimization technique of response surface methodology (RSM) in traffic signal optimization of urban networks. It also quantifies the gains of using the common random number (CRN) variance reduction strategy in such an optimization procedure. An enhanced RSM algorithm which employs conjugate gradient search techniques and successive second-order models is presented instead of the conventional approach. An illustrative example using an urban traffic network exhibits the superiority of using the CRN strategy ovr direct simulation in performing traffic signal optimization. Relative performance of the two strategies is quantified with computational results using the total network-wide delay as the measure of effectivness.

  7. Modeling Signal Transduction Networks: A comparison of two Stochastic Kinetic Simulation Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Pettigrew, Michel F.; Resat, Haluk

    2005-09-15

    Simulations of a scalable four compartment reaction model based on the well known epidermal growth factor receptor (EGFR) signal transduction system are used to compare two stochastic algorithms ? StochSim and the Gibson-Gillespie. It is concluded that the Gibson-Gillespie is the algorithm of choice for most realistic cases with the possible exception of signal transduction networks characterized by a moderate number (< 100) of complex types, each with a very small population, but with a high degree of connectivity amongst the complex types. Keywords: Signal transduction networks, Stochastic simulation, StochSim, Gillespie

  8. P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

    Science.gov (United States)

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

    Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

  9. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Fuente, Alberto de la; Fotia, Giorgio; Maggio, Fabio; Mancosu, Gianmaria; Pieroni, Enrico [CRS4 Bioinformatica, Parco Tecnologico POLARIS, Ed.1, Loc Piscinamanna, Pula (Italy)], E-mail: alf@crs4.it

    2008-06-06

    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.

  11. Dose-to-duration encoding and signaling beyond saturation in intracellular signaling networks.

    Directory of Open Access Journals (Sweden)

    Marcelo Behar

    2008-10-01

    Full Text Available The cellular response elicited by an environmental cue typically varies with the strength of the stimulus. For example, in the yeast Saccharomyces cerevisiae, the concentration of mating pheromone determines whether cells undergo vegetative growth, chemotropic growth, or mating. This implies that the signaling pathways responsible for detecting the stimulus and initiating a response must transmit quantitative information about the intensity of the signal. Our previous experimental results suggest that yeast encode pheromone concentration as the duration of the transmitted signal. Here we use mathematical modeling to analyze possible biochemical mechanisms for performing this "dose-to-duration" conversion. We demonstrate that modulation of signal duration increases the range of stimulus concentrations for which dose-dependent responses are possible; this increased dynamic range produces the counterintuitive result of "signaling beyond saturation" in which dose-dependent responses are still possible after apparent saturation of the receptors. We propose a mechanism for dose-to-duration encoding in the yeast pheromone pathway that is consistent with current experimental observations. Most previous investigations of information processing by signaling pathways have focused on amplitude encoding without considering temporal aspects of signal transduction. Here we demonstrate that dose-to-duration encoding provides cells with an alternative mechanism for processing and transmitting quantitative information about their surrounding environment. The ability of signaling pathways to convert stimulus strength into signal duration results directly from the nonlinear nature of these systems and emphasizes the importance of considering the dynamic properties of signaling pathways when characterizing their behavior. Understanding how signaling pathways encode and transmit quantitative information about the external environment will not only deepen our

  12. 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

  13. Orchestrating redox signaling networks through regulatory cysteine switches.

    Science.gov (United States)

    Paulsen, Candice E; Carroll, Kate S

    2010-01-15

    Hydrogen peroxide (H(2)O(2)) acts as a second messenger that can mediate intracellular signal transduction via chemoselective oxidation of cysteine residues in signaling proteins. This Review presents current mechanistic insights into signal-mediated H(2)O(2) production and highlights recent advances in methods to detect reactive oxygen species (ROS) and cysteine oxidation both in vitro and in cells. Selected examples from the recent literature are used to illustrate the diverse mechanisms by which H(2)O(2) can regulate protein function. The continued development of methods to detect and quantify discrete cysteine oxoforms should further our mechanistic understanding of redox regulation of protein function and may lead to the development of new therapeutic strategies.

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

    DEFF Research Database (Denmark)

    Fjelde, Tina

    2002-01-01

    Gbit/s demultiplexing and 2x10 to 20 Gbit/s multiplexing. Lastly, the IWC’s capabilities as an optical logic gate for enabling more complex signal processing are demonstrated and four applications hereof are discussed. Logic OR and AND are verified in full at 10 Gbit/s using PRBS sequences coupled......, involving all-optical pattern recognition by bit-wise sampling at multiple wavelengths, optical identification of bit differences in data segments through a combination of logic XOR and AND, and all optical bit sequence replacement through logic OR and AND. The schemes enable important signal processing...

  15. 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.

  16. 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

  17. Cilia and coordination of signaling networks during heart development

    DEFF Research Database (Denmark)

    Koefoed, Karen; Veland, Iben Rønn; Pedersen, Lotte Bang;

    2014-01-01

    Primary cilia are unique sensory organelles that coordinate a wide variety of different signaling pathways to control cellular processes during development and in tissue homeostasis. Defects in function or assembly of these antenna-like structures are therefore associated with a broad range...

  18. Exploring signal transduction networks using mass spectrometry-based proteomics

    NARCIS (Netherlands)

    Meijer, L.A.T.

    2012-01-01

    Mass spectrometry (MS)-based proteomics can be used to answer a diversity of biological questions. In this thesis, we describe the application of several MS-based proteomics approaches to get insight into several aspects of signal transduction. In Chapter 2, quantitative global phosphoproteomics are

  19. 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...

  20. Integrated signaling networks in plant responses to sedentary endoparasitic nematodes: a perspective.

    Science.gov (United States)

    Li, Ruijuan; Rashotte, Aaron M; Singh, Narendra K; Weaver, David B; Lawrence, Kathy S; Locy, Robert D

    2015-01-01

    Sedentary plant endoparasitic nematodes can cause detrimental yield losses in crop plants making the study of detailed cellular, molecular, and whole plant responses to them a subject of importance. In response to invading nematodes and nematode-secreted effectors, plant susceptibility/resistance is mainly determined by the coordination of different signaling pathways including specific plant resistance genes or proteins, plant hormone synthesis and signaling pathways, as well as reactive oxygen signals that are generated in response to nematode attack. Crosstalk between various nematode resistance-related elements can be seen as an integrated signaling network regulated by transcription factors and small RNAs at the transcriptional, posttranscriptional, and/or translational levels. Ultimately, the outcome of this highly controlled signaling network determines the host plant susceptibility/resistance to nematodes.

  1. 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

  2. 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.

  3. 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 ...

  4. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  5. Plant gravitropic signal transduction: A network analysis leads to gene discovery

    Science.gov (United States)

    Wyatt, Sarah

    Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.” Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes. Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections. Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected

  6. 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

  7. Tolerance to drought and salt stress in plants: Unraveling the signaling networks

    Directory of Open Access Journals (Sweden)

    Dortje eGolldack

    2014-04-01

    Full Text Available Tolerance of plants to abiotic stressors such as drought and salinity is triggered by complex multicomponent signaling pathways to restore cellular homeostasis and promote survival. Major plant transcription factor families such as bZIP, NAC, AP2/ERF and MYB orchestrate regulatory networks underlying abiotic stress tolerance. Sucrose nonfermenting 1-related protein kinase 2 (SnRK2 and MAPK pathways contribute to initiation of stress adaptive downstream responses and promote plant growth and development. As a convergent point of multiple abiotic cues, cellular effects of environmental stresses are not only imbalances of ionic and osmotic homeostasis but also impaired photosynthesis, cellular energy depletion, and redox imbalances. Recent evidence of regulatory systems that link sensing and signaling of environmental conditions and the intracellular redox status have shed light on interfaces of stress and energy signaling. ROS (reactive oxygen species cause severe cellular damage by peroxidation and de-esterification of membrane lipids, however, current models also define a pivotal signaling function of ROS in triggering tolerance against stress. Recent research advances suggest and support a regulatory role of ROS in the cross talks of stress triggered hormonal signaling such as the abscisic acid (ABA pathway and endogenously induced redox and metabolite signals. Here, we discuss and review the versatile molecular convergence in the abiotic stress responsive signaling networks in the context of ROS and lipid derived signals and the specific role of stomatal signaling.

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

    Directory of Open Access Journals (Sweden)

    Xiaojian Hu

    2015-01-01

    Full Text Available 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.

  9. 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.

  10. 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.

    2015-01-01

    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

  11. 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.

  12. 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

  13. 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.

  14. Communication efficiency and congestion of signal traffic in large-scale brain networks.

    Science.gov (United States)

    Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

    2014-01-01

    The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

  15. 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.

  16. 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 ...

  17. Drastic disorded-induced reduction of signal amplification in scale-free networks

    CERN Document Server

    Chacón, Ricardo

    2014-01-01

    Understanding information transmission across a network is a fundamental task for controlling and manipulating both biological and man-made 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 star-like 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.

  18. Cisplatin ototoxicity involves cytokines and STAT6 signaling network

    Institute of Scientific and Technical Information of China (English)

    Hyung-Jin Kim; Jeong-Dug Sul; Channy Park; Sang-Young Chung; Sung-Kyun Moon; David J Lim; Hong-Seob So; Raekil Park; Gi-Su Oh; Jeong-Han Lee; Ah-Ra Lyu; Hye-Min Ji; Sang-Heon Lee; Jeho Song; Sung-Joo Park; Yong-Ouk You

    2011-01-01

    We herein investigated the role of the STAT signaling cascade in the production of pro-inflammatory cytokines and cisplatin ototoxicity. A significant hearing impairment caused by cisplatin injection was observed in Balb/c (wild type,WT) and STAT4-/-,but not in STAT6-/- mice. Moreover,the expression levels of the protein and mRNA of proinflammatory cytokines,including TNF-α,IL-1β,and IL-6,were markedly increased in the serum and cochlea of WT and STAT4+,but not STAT6-/- mice. Organotypic culture revealed that the shape of stereocilia bundles and arrays of sensory hair cell layers in the organ of Corti from STAT6-/- mice were intact after treatment with cisplatin,whereas those from WT and STAT4-/- mice were highly distorted and disarrayed after the treatment. Cisplatin induced the phosphorylation of STAT6 in HEI-OC1 auditory cells,and the knockdown of STAT6 by STAT6-specific siRNA significantly protected HEI-OC1 auditory cells from cisplatin-induced cell death and inhibited pro-inflammatory cytokine production. We further demonstrated that IL-4 and IL-13 induced by cisplatin modulated the phosphorylation of STAT6 by binding with IL-4 receptor alpha and IL-13Rα1. These findings suggest that STAT6 signaling plays a pivotal role in cisplatin-mediated pro-inflammatory cytokine production and ototoxicity.

  19. 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

  20. Molecular signaling networks in regulation of immunity and disease

    DEFF Research Database (Denmark)

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

    the proinflammatory properties of common gut commensals. We are currently looking into the mechanisms behind the antiinflammatory effects of the microbial fermentation products with a specific interest in the complex interactions between enzymes catalyzing posttranslational modifications, transcription factors......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...

  1. 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...

  2. 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...

  3. Joint Implementation of Signal Control and Congestion Pricing in Transportation Network

    Directory of Open Access Journals (Sweden)

    Wei Mao

    2013-01-01

    Full Text Available The policy of jointly implementing signal control and congestion pricing in the transportation network is investigated. Bilevel programs are developed to model the simultaneous optimization of signal setting and congestion toll. The upper level aims to maximize the network reserve capacity or minimize the total travel time, subject to signal setting and toll constraints. The lower level is a deterministic user equilibrium problem given a plan of signal setting and congestion charge. Then the bilevel programs are transferred into the equivalent single level programs, and the solution methods are discussed. Finally, a numerical example is presented to illustrate the concepts and methods, and it is shown that the joint implementation policy can achieve promising results.

  4. 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.

  5. 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...

  6. 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.

  7. 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)

  8. 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.

  9. 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 ...

  10. A Neural Network Approach to Blind Estimation of PN Spreading Sequence in DS/SS Signals

    Institute of Scientific and Technical Information of China (English)

    ZHANG Tian-qi; ZHOU Zheng-zhong

    2004-01-01

    In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.

  11. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

    OpenAIRE

    Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio

    2013-01-01

    Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and gen...

  12. 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

  13. Paradigms and Paradox in the Ethylene Signaling Pathway and Interaction Network

    Institute of Scientific and Technical Information of China (English)

    Qiong Zhao; Hong-Wei Guo

    2011-01-01

    Phytohormone ethylene plays pivotal roles in plant response to developmental and environmental signals.During the past few years, the emerging evidence has led us to a new understanding of the signaling mechanisms and regulatory networks of the ethylene action. In this review, we focus on the major advances made in the past three years,particularly the findings leading to new paradigms and the observations under debate. With the recent demonstration of the regulation of the protein stability of numerous key signaling components including EIN3, ELL1, EIN2, ETR2, EBF1/EBF2,and ETP1/ETP2, we highlight proteasome-dependent protein degradation as an essential regulatory mechanism that is widely adopted in the ethylene signaling pathway. We also discuss the implication of the negative feedback mechanism in the ethylene signaling pathway in light of ethylene-induced ETR2 and EBF2 gene expression. Meanwhile, we summarize the controversy on the involvement of MKK9-MPK3/6 cascade in the ethylene signaling versus biosynthesis pathway, and discuss the possible role of this MAPK module in the ethylene action. Finally, we describe the complex interactions be-tween ethylene and other signaling pathways including auxin, light, and plant innate immunity, and propose that EIN3/ElL1 act as a convergence point in the ethylene-initiated signaling network.

  14. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    Science.gov (United States)

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-01-01

    Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

  15. 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.

  16. Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D.

    2014-10-01

    Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

  17. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.;

    2015-01-01

    Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified...

  18. 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.

  19. 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.

  20. 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.

  1. Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

    Directory of Open Access Journals (Sweden)

    Jingchun Sun

    2015-06-01

    Full Text Available A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet. The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D and cancer, one T2D genome-wide association study (GWAS dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11 and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1 develops a novel framework to construct drug-specific signal transduction networks; 2 provides insights into the molecular mode of metformin; 3 serves a model for exploring

  2. 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.

  3. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  4. 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....

  5. Best Signal Quality in Cellular Networks: Asymptotic Properties and Applications to Mobility Management in Small Cell Networks

    Directory of Open Access Journals (Sweden)

    Baccelli François

    2010-01-01

    Full Text Available The quickly increasing data traffic and the user demand for a full coverage of mobile services anywhere and anytime are leading mobile networking into a future of small cell networks. However, due to the high-density and randomness of small cell networks, there are several technical challenges. In this paper, we investigate two critical issues: best signal quality and mobility management. Under the assumptions that base stations are uniformly distributed in a ring-shaped region and that shadowings are lognormal, independent, and identically distributed, we prove that when the number of sites in the ring tends to infinity, then (i the maximum signal strength received at the center of the ring tends in distribution to a Gumbel distribution when properly renormalized, and (ii it is asymptotically independent of the interference. Using these properties, we derive the distribution of the best signal quality. Furthermore, an optimized random cell scanning scheme is proposed, based on the evaluation of the optimal number of sites to be scanned for maximizing the user data throughput.

  6. 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

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

    NARCIS (Netherlands)

    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

  8. 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...

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

    DEFF Research Database (Denmark)

    Schoof, Erwin M; Linding, Rune

    2014-01-01

    ; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network...

  10. Comparing signaling networks between normal and transformed hepatocytes using discrete logical models.

    Science.gov (United States)

    Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Zhang, Mingsheng; Morris, Melody K; Lauffenburger, Douglas A; Sorger, Peter K

    2011-08-15

    Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.

  11. 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.

  12. Yamanaka factors critically regulate the developmental signaling network in mouse embryonic stem cells

    Institute of Scientific and Technical Information of China (English)

    Xiaosong Liu; Jinyan Huang; Taotao Chen; Ying Wang; Shunmei Xin; Jian Li; Gang Pei; Jiuhong Kang

    2008-01-01

    Yamanaka factors (Oct3/4,Sox2,KIf4,c-Myc) are highly expressed in embryonic stem (ES) cells,and their overexpression can induce pluripotency in both mouse and human somatic cells,indicating that these factors regulate the developmental signaling network necessary for ES cell pluripotency.However,systemic analysis of the signaling pathways regulated by Yamanaka factors has not yet been fully described.In this study,we identified the target promoters of endogenous Yamanaka factors on a whole genome scale using ChIP (chromatin immunoprecipitation)-on-chip in E14.1 mouse ES cells,and we found that these four factors co-occupied 58 promoters.Interestingly,when Oct4 and Sox2 were analyzed as core factors,Kif4 functioned to enhance the core factors for development regulation,whereas c-Myc seemed to play a distinct role in regulating metabolism.The pathway analysis revealed that Yamanaka factors collectively regulate a developmental signaling network composed of 16 developmental signaling pathways,nine of which represent earlier unknown pathways in ES cells,including apoptosis and cellcycle pathways.We further analyzed data from a recent study examining Yamanaka factors in mouse ES cells.Interestingly,this analysis also revealed 16 developmental signaling pathways,of which 14 pathways overlap with the ones revealed by this study,despite that the target genes and the signaling pathways regulated by each individual Yamanaka factor differ significantly between these two datasets.We suggest that Yamanaka factors critically regulate a developmental signaling network composed of approximately a dozen crucial developmental signaling pathways to maintain the pluripotency of ES cells and probably also to induce pluripotent stem cells.

  13. Neuroblastoma tyrosine kinase signaling networks involve FYN and LYN in endosomes and lipid rafts.

    Directory of Open Access Journals (Sweden)

    Juan Palacios-Moreno

    2015-04-01

    Full Text Available Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment. Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development. It is known that defects in these mechanisms cause neuroblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endosomes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half of the receptor tyrosine kinases in the human genome. Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data. Clusters of proteins in these networks are indicative of functional signaling pathways. The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases, especially FYN and LYN. Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases, ALK and KIT. The results suggest a mechanism to distinguish signaling responses to activation of different receptors, or combinations of receptors, that govern the behavior of the neural crest, which gives rise to neuroblastoma.

  14. 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

  15. 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.

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

    DEFF Research Database (Denmark)

    Saldaña Cercos, Silvia; Wagner, Christoph; Vegas Olmos, Juan José;

    2015-01-01

    Methods to upgrade the network infrastructure to cope with current traffic demands has attracted increasing research efforts. A promising alternative is signal slicing. Signal slicing aims at re-using low bandwidth equipment to satisfy high bandwidth traffic demands. This technique has been used......, a comprehensive DSP power consumption analysis for both WDM and TDM systems at 1 Gbps and 10 Gbps, discussing latency penalties for each approach. For 1 Gbps WDM system 278 pJ per information bit for 4 slices is reported at 105 ns latency penalties, whereas 3898.4 pJ per information bit at 183.5 µs latency...... penalty is reported for 10 Gbps. Power savings of the order of hundreds of Watts can be obtained when using signal slicing as an alternative to 10 Gbps implemented access networks....

  17. An application of mapping neural networks and a digital signal processor for cochlear neuroprostheses.

    Science.gov (United States)

    Zadák, J; Unbehauen, R

    1993-01-01

    Cochlear neuroprostheses strive to restore the sensation of hearing to patients with a profound sensorineural deafness. They exhibit a stimulation of the surviving auditory nerve neurons by electrical currents delivered through electrodes placed on or within the cochlea. The present article describes a new method for an efficient derivation of the required information from the incoming speech signal necessary for the implant stimulation. Also some realization aspects of the new approach are addressed. In the new strategy, a multilayer neural network is employed in the formant frequency estimation having some suitable speech signal descriptors as particular input signals. The proposed method allows us a fast formant frequency estimation necessary for the implant stimulation. With the developed strategy, the prosthesis can be adjusted to the environment which the patient is supposed to live in. Moreover, the neural network concept offers us an alternative for dealing with the areas of neural loss or "holes" in the frequency map of the patient's ear.

  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. 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.

  20. Robust signal recognition algorithm based on machine learning in heterogeneous networks

    Institute of Scientific and Technical Information of China (English)

    Xiaokai Liu; Rong Li; Chenglin Zhao; Pengbiao Wang

    2016-01-01

    There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio (SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recogni-tion method based upon the original and latest updated ver-sion of the extreme learning machine (ELM) to help users to switch between networks. The ELM utilizes signal characte- ristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analyticaly, which result in lower complexity. Theoreticaly, the algorithm tends to ofer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE mod-els in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized suc-cessfuly to achieve a 95% accuracy in a low SNR (0 dB) environment in the time-varying multipath Rayleigh fading channel.

  1. 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.

  2. Nonlinear optical signal processing for high-speed, spectrally efficient fiber optic systems and networks

    Science.gov (United States)

    Zhang, Bo

    The past decade has witnessed astounding boom in telecommunication network traffic. With the emergence of multimedia over Internet, the high-capacity optical transport systems have started to shift focus from the core network towards the end users. This trend leads to diverse optical networks with transparency and reconfigurability requirement. As single channel data rate continues to increase and channel spacing continues to shrink for high capacity, high spectral efficiency, the workload on conventional electronic signal processing elements in the router nodes continues to build up. Performing signal processing functions in the optical domain can potentially alleviate the speed bottleneck if the unique optical properties are efficiently leveraged to assist electronic processing methodologies. Ultra-high bandwidth capability along with the promise for multi-channel and format-transparent operation make optical signal processing an attractive technology which is expected to have great impact on future optical networks. For optical signal processing applications in fiber-optic network and systems, a laudable goal would be to explore the unique nonlinear optical processes in novel photonic devices. This dissertation investigates novel optical signal processing techniques through simulations and experimental demonstrations, analyzes limitations of these nonlinear processing elements and proposes techniques to enhance the system performance or designs for functional photonic modules. Two key signal-processing building blocks for future optical networks, namely slow-light-based tunable optical delay lines and SOA-based high-speed wavelength converters, are presented in the first part of the dissertation. Phase preserving and spectrally efficient slow light are experimentally demonstrated using advanced modulation formats. Functional and novel photonic modules, such as multi-channel synchronizer and variable-bit-rate optical time division multiplexer are designed and

  3. Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal

    Directory of Open Access Journals (Sweden)

    V. R. Mankar

    2009-01-01

    Full Text Available The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG. These EMG signals are low-frequency and lower-magnitude signals. In this paper, it is presented that Jordan/Elman neural network can be effectively used for EMG signal noise removal, which is a typical nonlinear multivariable regression problem, as compared with other types of neural networks. Different neural network (NN models with varying parameters were considered for the design of adaptive neural-network-based filter which is a typical SISO system. The performance parameters, that is, MSE, correlation coefficient, N/P, and t, are found to be in the expected range of values.

  4. Network Organization and Evolution of Signaling Network in LTE Era%LTE时代信令网网络组织与演进

    Institute of Scientific and Technical Information of China (English)

    刘涛; 陈凤莲

    2015-01-01

    Used in LTE network all IP network architecture, compared with 2G / 3G era to TDM network organization is completely different, LTE network between each element communication, 2G / 3G and LTE networks, ifxed telephone network exchange to realize. Signaling from the developing trend, diameter protocol will replace traditional SS7 protocol, become widely used in LTE network core network communication protocol, signaling network in the future development trend of IP, diameter signaling network long-term and SS7 signaling network coexist.%LTE网络采用全IP网络架构,对比2G/3G时代以TDM为主的网络组织完全不同,LTE网络内部各网元间通信、LTE网与2G/3G网、固定电话网等网间互通如何实现。从信令演进趋势看,Diameter协议将取代传统的SS7协议,成为LTE网络广泛应用的核心网网元间通信协议,未来信令网络的发展趋势为IP化,Diameter信令网将长期与SS7信令网共存。

  5. A comparative study of Macroscopic Fundamental Diagrams of urban road networks governed by different traffic signal systems

    CERN Document Server

    Zhang, Lele; de Gier, Jan

    2011-01-01

    Using a stochastic cellular automaton model for urban traffic flow, we study and compare Macroscopic Fundamental Diagrams (MFDs) of arterial road networks governed by different types of adaptive traffic signal systems. In particular, we simulate realistic signal systems that include signal linking and adaptive cycle times, and compare their performance against a network using highly adaptive self-organizing traffic signals. We find that for networks with time- independent boundary conditions, well-defined stationary MFDs are observed, whose shape depends on the particular signal system used, and also on the level of heterogeneity in the system. We find that the spatial heterogeneity of both density and flow provide important indicators of network performance. We also study networks with time-dependent boundary conditions, containing morning and afternoon peaks. In this case, intricate hysteresis loops are observed in the MFDs which are strongly correlated with the density heterogeneity. Our results show that ...

  6. 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.

  7. Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

    Science.gov (United States)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-10-01

    Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

  8. 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.

  9. 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

    2016-04-08

    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.

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase

    Directory of Open Access Journals (Sweden)

    Nudelman Irina

    2010-10-01

    Full Text Available Abstract Background Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. Description We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to

  19. 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.

  20. Transient epileptiform signaling during neuronal network development: regulation by external stimulation and bimodal GABAergic activity.

    Science.gov (United States)

    Zemianek, Jill M; Shultz, Abraham M; Lee, Sangmook; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2013-04-01

    A predominance of excitatory activity, with protracted appearance of inhibitory activity, accompanies cortical neuronal development. It is unclear whether or not inhibitory neuronal activity is solicited exclusively by excitatory neurons or whether the transient excitatory activity displayed by developing GABAergic neurons contributes to an excitatory threshold that fosters their conversion to inhibitory activity. We addressed this possibility by culturing murine embryonic neurons on multi-electrode arrays. A wave of individual 0.2-0.4 mV signals ("spikes") appeared between approx. 20-30 days in culture, then declined. A transient wave of high amplitude (>0.5 mV) epileptiform activity coincided with the developmental decline in spikes. Bursts (clusters of ≥3 low-amplitude spikes within 0.7s prior to returning to baseline) persisted following this decline. Addition of the GABAergic antagonist bicuculline initially had no effect on signaling, consistent with delayed development of GABAergic synapses. This was followed by a period in which bicuculline inhibited overall signaling, confirming that GABAergic neurons initially display excitatory activity in ex vivo networks. Following the transient developmental wave of epileptiform signaling, bicuculline induced a resurgence of epileptiform signaling, indicating that GABAergic neurons at this point displayed inhibitory activity. The appearance of transition after the developmental and decline of epileptiform activity, rather than immediately after the developmental decline in lower-amplitude spikes, suggests that the initial excitatory activity of GABAergic neurons contributes to their transition into inhibitory neurons, and that inhibitory GABAergic activity is essential for network development. Prior studies indicate that a minority (25%) of neurons in these cultures were GABAergic, suggesting that inhibitory neurons regulate multiple excitatory neurons. A similar robust increase in signaling following cessation of

  1. 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

  2. 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.

  3. Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Elham Ghoochani

    2011-03-01

    Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

  4. Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks.

    Science.gov (United States)

    Foremski, Paweł; Gorawski, Michał; Grochla, Krzysztof; Polys, Konrad

    2015-09-02

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.

  5. 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.

  6. An empirical Bayesian approach for model-based inference of cellular signaling networks

    Directory of Open Access Journals (Sweden)

    Klinke David J

    2009-11-01

    Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

  7. 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

  8. Mapping the functional versatility and fragility of Ras GTPase signaling circuits through in vitro network reconstitution.

    Science.gov (United States)

    Coyle, Scott M; Lim, Wendell A

    2016-01-01

    The Ras-superfamily GTPases are central controllers of cell proliferation and morphology. Ras signaling is mediated by a system of interacting molecules: upstream enzymes (GEF/GAP) regulate Ras's ability to recruit multiple competing downstream effectors. We developed a multiplexed, multi-turnover assay for measuring the dynamic signaling behavior of in vitro reconstituted H-Ras signaling systems. By including both upstream regulators and downstream effectors, we can systematically map how different network configurations shape the dynamic system response. The concentration and identity of both upstream and downstream signaling components strongly impacted the timing, duration, shape, and amplitude of effector outputs. The distorted output of oncogenic alleles of Ras was highly dependent on the balance of positive (GAP) and negative (GEF) regulators in the system. We found that different effectors interpreted the same inputs with distinct output dynamics, enabling a Ras system to encode multiple unique temporal outputs in response to a single input. We also found that different Ras-to-GEF positive feedback mechanisms could reshape output dynamics in distinct ways, such as signal amplification or overshoot minimization. Mapping of the space of output behaviors accessible to Ras provides a design manual for programming Ras circuits, and reveals how these systems are readily adapted to produce an array of dynamic signaling behaviors. Nonetheless, this versatility comes with a trade-off of fragility, as there exist numerous paths to altered signaling behaviors that could cause disease. PMID:26765565

  9. 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..

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Discrete dynamics model for the speract-activated Ca2+ signaling network relevant to sperm motility.

    Science.gov (United States)

    Espinal, Jesús; Aldana, Maximino; Guerrero, Adán; Wood, Christopher; Darszon, Alberto; Martínez-Mekler, Gustavo

    2011-01-01

    Understanding how spermatozoa approach the egg is a central biological issue. Recently a considerable amount of experimental evidence has accumulated on the relation between oscillations in intracellular calcium ion concentration ([Ca2+]i) in the sea urchin sperm flagellum, triggered by peptides secreted from the egg, and sperm motility. Determination of the structure and dynamics of the signaling pathway leading to these oscillations is a fundamental problem. However, a biochemically based formulation for the comprehension of the molecular mechanisms operating in the axoneme as a response to external stimulus is still lacking. Based on experiments on the S. purpuratus sea urchin spermatozoa, we propose a signaling network model where nodes are discrete variables corresponding to the pathway elements and the signal transmission takes place at discrete time intervals according to logical rules. The validity of this model is corroborated by reproducing previous empirically determined signaling features. Prompted by the model predictions we performed experiments which identified novel characteristics of the signaling pathway. We uncovered the role of a high voltage-activated Ca2+ channel as a regulator of the delay in the onset of fluctuations after activation of the signaling cascade. This delay time has recently been shown to be an important regulatory factor for sea urchin sperm reorientation. Another finding is the participation of a voltage-dependent calcium-activated K+ channel in the determination of the period of the [Ca2+]i fluctuations. Furthermore, by analyzing the spread of network perturbations we find that it operates in a dynamically critical regime. Our work demonstrates that a coarse-grained approach to the dynamics of the signaling pathway is capable of revealing regulatory sperm navigation elements and provides insight, in terms of criticality, on the concurrence of the high robustness and adaptability that the reproduction processes are predicted

  15. Discrete dynamics model for the speract-activated Ca2+ signaling network relevant to sperm motility.

    Directory of Open Access Journals (Sweden)

    Jesús Espinal

    Full Text Available Understanding how spermatozoa approach the egg is a central biological issue. Recently a considerable amount of experimental evidence has accumulated on the relation between oscillations in intracellular calcium ion concentration ([Ca2+]i in the sea urchin sperm flagellum, triggered by peptides secreted from the egg, and sperm motility. Determination of the structure and dynamics of the signaling pathway leading to these oscillations is a fundamental problem. However, a biochemically based formulation for the comprehension of the molecular mechanisms operating in the axoneme as a response to external stimulus is still lacking. Based on experiments on the S. purpuratus sea urchin spermatozoa, we propose a signaling network model where nodes are discrete variables corresponding to the pathway elements and the signal transmission takes place at discrete time intervals according to logical rules. The validity of this model is corroborated by reproducing previous empirically determined signaling features. Prompted by the model predictions we performed experiments which identified novel characteristics of the signaling pathway. We uncovered the role of a high voltage-activated Ca2+ channel as a regulator of the delay in the onset of fluctuations after activation of the signaling cascade. This delay time has recently been shown to be an important regulatory factor for sea urchin sperm reorientation. Another finding is the participation of a voltage-dependent calcium-activated K+ channel in the determination of the period of the [Ca2+]i fluctuations. Furthermore, by analyzing the spread of network perturbations we find that it operates in a dynamically critical regime. Our work demonstrates that a coarse-grained approach to the dynamics of the signaling pathway is capable of revealing regulatory sperm navigation elements and provides insight, in terms of criticality, on the concurrence of the high robustness and adaptability that the reproduction

  16. Advanced digital signal processing for short-haul and access network

    Science.gov (United States)

    Zhang, Junwen; Yu, Jianjun; Chi, Nan

    2016-02-01

    Digital signal processing (DSP) has been proved to be a successful technology recently in high speed and high spectrum-efficiency optical short-haul and access network, which enables high performances based on digital equalizations and compensations. In this paper, we investigate advanced DSP at the transmitter and receiver side for signal pre-equalization and post-equalization in an optical access network. A novel DSP-based digital and optical pre-equalization scheme has been proposed for bandwidth-limited high speed short-distance communication system, which is based on the feedback of receiver-side adaptive equalizers, such as least-mean-squares (LMS) algorithm and constant or multi-modulus algorithms (CMA, MMA). Based on this scheme, we experimentally demonstrate 400GE on a single optical carrier based on the highest ETDM 120-GBaud PDM-PAM-4 signal, using one external modulator and coherent detection. A line rate of 480-Gb/s is achieved, which enables 20% forward-error correction (FEC) overhead to keep the 400-Gb/s net information rate. The performance after fiber transmission shows large margin for both short range and metro/regional networks. We also extend the advanced DSP for short haul optical access networks by using high order QAMs. We propose and demonstrate a high speed multi-band CAP-WDM-PON system on intensity modulation, direct detection and digital equalizations. A hybrid modified cascaded MMA post-equalization schemes are used to equalize the multi-band CAP-mQAM signals. Using this scheme, we successfully demonstrates 550Gb/s high capacity WDMPON system with 11 WDM channels, 55 sub-bands, and 10-Gb/s per user in the downstream over 40-km SMF.

  17. Control mechanism to prevent correlated message arrivals from degrading signaling no. 7 network performance

    Science.gov (United States)

    Kosal, Haluk; Skoog, Ronald A.

    1994-04-01

    Signaling System No. 7 (SS7) is designed to provide a connection-less transfer of signaling messages of reasonable length. Customers having access to user signaling bearer capabilities as specified in the ANSI T1.623 and CCITT Q.931 standards can send bursts of correlated messages (e.g., by doing a file transfer that results in the segmentation of a block of data into a number of consecutive signaling messages) through SS7 networks. These message bursts with short interarrival times could have an adverse impact on the delay performance of the SS7 networks. A control mechanism, Credit Manager, is investigated in this paper to regulate incoming traffic to the SS7 network by imposing appropriate time separation between messages when the incoming stream is too bursty. The credit manager has a credit bank where credits accrue at a fixed rate up to a prespecified credit bank capacity. When a message arrives, the number of octets in that message is compared to the number of credits in the bank. If the number of credits is greater than or equal to the number of octets, then the message is accepted for transmission and the number of credits in the bank is decremented by the number of octets. If the number of credits is less than the number of octets, then the message is delayed until enough credits are accumulated. This paper presents simulation results showing delay performance of the SS7 ISUP and TCAP message traffic with a range of correlated message traffic, and control parameters of the credit manager (i.e., credit generation rate and bank capacity) are determined that ensure the traffic entering the SS7 network is acceptable. The results show that control parameters can be set so that for any incoming traffic stream there is no detrimental impact on the SS7 ISUP and TCAP message delay, and the credit manager accepts a wide range of traffic patterns without causing significant delay.

  18. 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.

  19. 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.

  20. 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.

  1. Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks.

    Science.gov (United States)

    Trinh, Hung-Cuong; Kwon, Yung-Keun

    2015-11-01

    Efficiently identifying functionally important genes in order to understand the minimal requirements of normal cellular development is challenging. To this end, a variety of structural measures have been proposed and their effectiveness has been investigated in recent literature; however, few studies have shown the effectiveness of dynamics-based measures. This led us to investigate a dynamic measure to identify functionally important genes, and the effectiveness of which was verified through application on two large-scale human signaling networks. We specifically consider Boolean sensitivity-based dynamics against an update-rule perturbation (BSU) as a dynamic measure. Through investigations on two large-scale human signaling networks, we found that genes with relatively high BSU values show slower evolutionary rate and higher proportions of essential genes and drug targets than other genes. Gene-ontology analysis showed clear differences between the former and latter groups of genes. Furthermore, we compare the identification accuracies of essential genes and drug targets via BSU and five well-known structural measures. Although BSU did not always show the best performance, it effectively identified the putative set of genes, which is significantly different from the results obtained via the structural measures. Most interestingly, BSU showed the highest synergy effect in identifying the functionally important genes in conjunction with other measures. Our results imply that Boolean-sensitive dynamics can be used as a measure to effectively identify functionally important genes in signaling networks.

  2. 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.

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    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

  6. 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.

  7. 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.

  8. 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.

  9. In-vitro exposure of neuronal networks to the GSM-1800 signal.

    Science.gov (United States)

    Moretti, Daniela; Garenne, André; Haro, Emmanuelle; Poulletier de Gannes, Florence; Lagroye, Isabelle; Lévêque, Philippe; Veyret, Bernard; Lewis, Noëlle

    2013-12-01

    The central nervous system is the most likely target of mobile telephony radiofrequency (RF) field exposure in terms of biological effects. Several electroencephalography (EEG) studies have reported variations in the alpha-band power spectrum during and/or after RF exposure, in resting EEG and during sleep. In this context, the observation of the spontaneous electrical activity of neuronal networks under RF exposure can be an efficient tool to detect the occurrence of low-level RF effects on the nervous system. Our research group has developed a dedicated experimental setup in the GHz range for the simultaneous exposure of neuronal networks and monitoring of electrical activity. A transverse electromagnetic (TEM) cell was used to expose the neuronal networks to GSM-1800 signals at a SAR level of 3.2 W/kg. Recording of the neuronal electrical activity and detection of the extracellular spikes and bursts under exposure were performed using microelectrode arrays (MEAs). This work provides the proof of feasibility and preliminary results of the integrated investigation regarding exposure setup, culture of the neuronal network, recording of the electrical activity, and analysis of the signals obtained under RF exposure. In this pilot study on 16 cultures, there was a 30% reversible decrease in firing rate (FR) and bursting rate (BR) during a 3 min exposure to RF. Additional experiments are needed to further characterize this effect.

  10. 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.

  11. 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...

  12. 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 ...

  13. Higher-order-statistics-based radial basis function networks for signal enhancement.

    Science.gov (United States)

    Lin, Bor-Shyh; Lin, Bor-Shing; Chong, Fok-Ching; Lai, Feipei

    2007-05-01

    In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.

  14. 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

  15. β-Spectrin regulates the hippo signaling pathway and modulates the basal actin network.

    Science.gov (United States)

    Wong, Kenneth Kin Lam; Li, Wenyang; An, Yanru; Duan, Yangyang; Li, Zhuoheng; Kang, Yibin; Yan, Yan

    2015-03-01

    Emerging evidence suggests functional regulation of the Hippo pathway by the actin cytoskeleton, although the detailed molecular mechanism remains incomplete. In a genetic screen, we identified a requirement for β-Spectrin in the posterior follicle cells for the oocyte repolarization process during Drosophila mid-oogenesis. β-spectrin mutations lead to loss of Hippo signaling activity in the follicle cells. A similar reduction of Hippo signaling activity was observed after β-Spectrin knockdown in mammalian cells. We further demonstrated that β-spectrin mutations disrupt the basal actin network in follicle cells. The abnormal stress fiber-like actin structure on the basal side of follicle cells provides a likely link between the β-spectrin mutations and the loss of the Hippo signaling activity phenotype.

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST.

    Directory of Open Access Journals (Sweden)

    Panuwat Trairatphisan

    Full Text Available Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation.In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF signalling pathway in Gastrointestinal Stromal Tumour (GIST. We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion.

  1. 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 ncellular signaling networks controlling inflammation. Ringwood L, Li L. Cytokine. 2008 Apr;42(1):1-7. Epub ...ases (IRAKs) incellular signaling networks controlling inflammation. PubmedID 182...49132 Title The involvement of the interleukin-1 receptor-associated kinases (IRAKs) incellular signaling networks controlling

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

    Directory of Open Access Journals (Sweden)

    Harish Babu

    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.

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. 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.

  7. A Novel Approach for Transmission of 56 Gbit/s NRZ Signal in Access Network Using Spectrum Slicing Technique

    DEFF Research Database (Denmark)

    Spolitis, S.; Vegas Olmos, Juan José; Bobrovs, V.;

    2013-01-01

    We present the spectrum slicing and stitching concept for high-capacity low optics complexity optical access networks. Spectrum slicing and stitching of a 56 Gbit/s NRZ electrical signal is experimentally demonstrated for the first time....

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Selective control of the apoptosis signaling network in heterogeneous cell populations.

    Directory of Open Access Journals (Sweden)

    Diego Calzolari

    Full Text Available BACKGROUND: Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective. METHODOLOGY AND PRINCIPAL FINDINGS: Apoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions, which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies. CONCLUSIONS AND SIGNIFICANCE: We have explored two methods for the study of selectivity in cell populations. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on

  19. A Cytokine Signalling Network for the Regulation of Inducible Nitric Oxide Synthase Expression in Rheumatoid Arthritis

    Science.gov (United States)

    Dey, Poulami; Panga, Venugopal; Raghunathan, Srivatsan

    2016-01-01

    In rheumatoid arthritis (RA), nitric oxide (NO) is implicated in inflammation, angiogenesis and tissue destruction. The enzyme inducible nitric oxide synthase (iNOS) is responsible for the localised over-production of NO in the synovial joints affected by RA. The pro- and anti-inflammatory cytokines stimulate the synovial macrophages and the fibroblast-like synoviocytes to express iNOS. Therefore, the cytokine signalling network underlying the regulation of iNOS is essential to understand the pathophysiology of the disease. By using information from the literature, we have constructed, for the first time, the cytokine signalling network involved in the regulation of iNOS expression. Using the differential expression patterns obtained by re-analysing the microarray data on the RA synovium and the synovial macrophages available in the Gene Expression Omnibus (GEO) database, we aimed to establish the role played by the network genes towards iNOS regulation in the RA synovium. Our analysis reveals that the network genes belonging to interferon (IFN) and interleukin-10 (IL-10) pathways are always up-regulated in the RA synovium whereas the genes which are part of the anti-inflammatory transforming growth factor-beta (TGF-β) signalling pathway are mostly down-regulated. We observed a consistent up-regulation of the transcription factor signal transducers and activators of transcription 1 (STAT1) in the RA synovium and the macrophages. Interestingly, we found a consistent up-regulation of the iNOS interacting protein ras-related C3 botulinum toxin substrate 2 (RAC2) in the RA synovium as well as the macrophages. Importantly, we have constructed a model to explain the impact of IFN and IL-10 pathways on Rac2-iNOS interaction leading to over-production of NO and thereby causing chronic inflammation in the RA synovium. The interplay between STAT1 and RAC2 in the regulation of NO could have implications for the identification of therapeutic targets for RA. PMID:27626941

  20. 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

    to identify the direct targets of kinase inhibitors upon affinity purification from cellular extracts. Here we introduce a complementary approach to evaluate the effects of kinase inhibitors on the entire cell signaling network. We used triple labeling SILAC (stable isotope labeling by amino acids in cell......-ABL, which is the cause of chronic myelogenous leukemia, affected nearly 1,000 phosphopeptides. In addition to the proximal effects on ABL and its immediate targets, dasatinib broadly affected the downstream MAPK pathways. Pathway mapping of regulated sites implicated a variety of cellular functions...

  1. 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.

  2. 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

    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...

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

    Directory of Open Access Journals (Sweden)

    Ainhoa eMartinez-Medina

    2013-06-01

    Full Text Available Root colonization by selected Trichoderma isolates can activate in the plant a systemic defence response that is effective against a broad spectrum of plant pathogens. Diverse plant hormones play pivotal roles in the regulation of the defence signalling network that leads to the induction of systemic resistance triggered by beneficial organisms (ISR. Among them, jasmonic acid (JA and ethylene (ET signalling pathways are generally essential for ISR. However, Trichoderma ISR (TISR is believed to involve a wider variety of signalling 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 defence 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 signalling pathways for a functional TISR. Expression analysis of several hormone related marker genes point to the role of priming for enhanced JA-dependent defence responses upon pathogen infection. Together, our results indicate that although TISR induced in tomato against the 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. Stimulation with a low-amplitude, digitized synaptic signal to invoke robust activity within neuronal networks on multielectrode arrays.

    Science.gov (United States)

    Zemianek, Jill M; Serra, Michael; Guaraldi, Mary; Shea, Thomas B

    2012-03-01

    Multielectrode arrays (MEAs) are used for analysis of neuronal activity. Here we report two variations on commonly accepted techniques that increase the precision of extracellular electrical stimulation: (i) the use of a low-amplitude recorded spontaneous synaptic signal as a stimulus waveform and (ii) the use of a specific electrode within the array adjacent to the stimulus electrode as a hard-grounded stimulus signal return path. Both modifications remained compatible with manipulation of neuronal networks. In addition, localized stimulation with the low-amplitude synaptic signal allowed selective stimulation or inhibition of otherwise spontaneous signals. These findings indicate that minimizing the area of the culture impacted by external stimulation allows modulation of signaling patterns within subpopulations of neurons in culture. The simple modifications described herein may be useful for precise monitoring and manipulation of neuronal networks.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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...

  19. 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.

  20. Noise-enhanced nonlinear response and the role of modular structure for signal detection in neuronal networks.

    Science.gov (United States)

    Lopes, M A; Lee, K-E; Goltsev, A V; Mendes, J F F

    2014-11-01

    We show that sensory noise can enhance the nonlinear response of neuronal networks, and when delivered together with a weak signal, it improves the signal detection by the network. We reveal this phenomenon in neuronal networks that are in a dynamical state preceding a saddle-node bifurcation corresponding to the appearance of sustained network oscillations. In this state, even a weak subthreshold pulse can evoke a large-amplitude oscillation of neuronal activity. The signal-to-noise ratio reaches a maximum at an optimum level of sensory noise, manifesting stochastic resonance (SR) at the population level. We demonstrate SR by use of simulations and numerical integration of rate equations in a cortical model. Using this model, we mimic the experiments of Gluckman et al. [Phys. Rev. Lett. 77, 4098 (1996)PRLTAO0031-900710.1103/PhysRevLett.77.4098] that have given evidence of SR in mammalian brain. We also study neuronal networks in which neurons are grouped in modules and every module works in the regime of SR. We find that even a few modules can strongly enhance the reliability of signal detection in comparison with the case when a modular organization is absent.

  1. 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.

  2. UHF front-end feeding RFID-based body sensor networks by exploiting the reader signal

    Science.gov (United States)

    Pasca, M.; Colella, R.; Catarinucci, L.; Tarricone, L.; D'Amico, S.; Baschirotto, A.

    2016-05-01

    This paper presents an integrated, high-sensitivity UHF radio frequency identification (RFID) power management circuit for body sensor network applications. The circuit consists of a two-stage RF-DC Dickson's rectifier followed by an integrated five-stage DC-DC Pelliconi's charge pump driven by an ultralow start-up voltage LC oscillator. The DC-DC charge pump interposed between the RF-DC rectifier and the output load provides the RF to load isolation avoiding losses due to the diodes reverse saturation current. The RF-DC rectifier has been realized on FR4 substrate, while the charge pump and the oscillator have been realized in 180 nm complementary metal oxide semiconductor (CMOS) technology. Outdoor measurements demonstrate the ability of the power management circuit to provide 400 mV output voltage at 14 m distance from the UHF reader, in correspondence of -25 dBm input signal power. As demonstrated in the literature, such output voltage level is suitable to supply body sensor network nodes.

  3. 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.

  4. Inhibition of GSK3 by lithium, from single molecules to signaling networks

    Directory of Open Access Journals (Sweden)

    Laure eFreland

    2012-02-01

    Full Text Available For more than 60 years, the mood stabilizer lithium has been used alone or in combination for the treatment of bipolar disorder, schizophrenia, depression and other mental illnesses. Despite this long history, the molecular mechanisms trough which lithium regulates behavior are still poorly understood. Among several targets, lithium has been shown to directly inhibit glycogen synthase kinase 3 alpha and beta (GSK3α and GSK3β. However in vivo, lithium also inhibits GSK3 by regulating the activity of other mechanisms like the formation of a signaling complex comprised of beta-arrestin 2 and Akt. Here, we provide an overview of in vivo evidence supporting a role for inhibition of GSK3 in some behavioral effects of lithium. We also explore how regulation of GSK3 by lithium within a signaling network involving several molecular targets and cell surface receptors (e.g. G protein coupled receptors and receptor tyrosine kinases may provide cues to its relative pharmacological selectivity and its effects on disease mechanisms. A better understanding of these intricate actions of lithium at a systems level may allow the rational development of better mood stabilizer drugs with enhanced selectivity, efficacy and lesser side effects.

  5. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Kyunghyun Park

    Full Text Available As pharmacodynamic drug-drug interactions (PD DDIs could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

  6. 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.

  7. 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.

  8. 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

  9. 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 ...

  10. 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...

  11. 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 ...

  12. Information technology -- Telecommunications and information exchange between systems -- Corporate telecommunication networks -- Signalling interworking between QSIG and H.323 -- Generic functional protocol for the support of supplementary services

    CERN Document Server

    International Organization for Standardization. Geneva

    2001-01-01

    Information technology -- Telecommunications and information exchange between systems -- Corporate telecommunication networks -- Signalling interworking between QSIG and H.323 -- Generic functional protocol for the support of supplementary services

  13. [Disease resistance signal transfer between roots of different tomato plants through common arbuscular mycorrhiza networks].

    Science.gov (United States)

    Xie, Li-Jun; Song, Yuan-Yuan; Zeng, Ren-Sen; Wang, Rui-Long; Wei, Xiao-Chen; Ye, Mao; Hu, Lin; Zhang, Hui

    2012-05-01

    Common mycorrhizal networks (CMNs) are the underground conduits of nutrient exchange between plants. However, whether the CMNs can serve as the underground conduits of chemical communication to transfer the disease resistance signals between plants are unknown. By inoculating arbuscular mycorrhizal fungus (AMF) Glomus mosseae to establish CMNs between 'donor' and 'receiver' tomato plants, and by inoculating Alternaria solani, the causal agent of tomato early blight disease, to the 'donor' plants, this paper studied whether the potential disease resistance signals can be transferred between the 'donor' and 'receiver' plants roots. The real time RT-PCR analysis showed that after inoculation with A. solani, the AMF-inoculated 'donor' plants had strong expression of three test defense-related genes in roots, with the transcript levels of the phenylalanine ammonia-lyase (PAL), lipoxygenase (LOX) and chitinase (PR3) being significantly higher than those in the roots of the 'donor' plants only inoculated with A. solani, not inoculated with both A. solani and AMF, and only inoculated with AMF. More importantly, in the presence of CMNs, the expression levels of the three genes in the roots of the 'receiver' plants were significantly higher than those of the 'receiver' plants without CMNs connection, with the connection blocking, and with the connection but the 'donor' plants not A. solani-inoculated. Compared with the control (without CMNs connection), the transcript level of the PAL, LOX and PR3 in the roots of the 'receiver' plants having CMNs connection with the 'donor' plants was 4.2-, 4.5- and 3.5-fold higher, respectively. In addition, the 'donor' plants activated their defensive responses more quickly than the 'receiver' plants (18 and 65 h vs. 100 and 140 h). These findings suggested that the disease resistance signals produced by the pathogen-induced 'donor' tomato plant roots could be transferred to the 'receiver' plant roots through CMNs.

  14. Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

    Science.gov (United States)

    Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

    2016-10-01

    Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

  15. 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.

  16. 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...nd the innate immune system: crosstalk with the toll-likereceptor signaling network. Authors Chinenov Y, Rog

  17. 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...

  18. 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...

  19. 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.

  20. 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.

  1. 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

    2016-07-05

    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.

  2. 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

  3. 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.

  4. NOISE CHARACTERISTIC AND SEASONAL SIGNALS IN THE RE-PROCESSED EUREF PERMANENT NETWORK COORDINATE TIME SERIES

    Science.gov (United States)

    Kenyeres, A.; Williams, S. D.; Figurski, M.; van Dam, T. M.; Szafranek, K.

    2009-12-01

    Previous analyses of periodic signals present in continuous GPS time series showed that the amplitude and phase of the derived seasonal term mostly disagree with surface mass loading models. The CGPS results appeared to over-estimate the amplitude of the seasonal term and the estimated amplitudes and/or phases were poorly coherent with the loading models, especially at sites close to coastal areas. The studies concluded that the GPS results are distorted by analysis artifacts (such as ocean tide loading, aliasing, and antenna phase centre variation models), monument thermal effects, and multipath. In addition, the actual CGPS time series were inhomogeneous in terms of processing strategy, applied models and reference frame alignment. With the introduction of absolute antenna phase centre variation models an effort, within the EUREF Permanent Network, was initiated to produce a complete GPS re-analysis from global to local levels. A test re-processing of all EPN observations from 1996 to 2007 has already been completed by the Military University of Technology (MUT), Warsaw, Poland and cumulative EPN solutions, from the daily SINEX files, have been created using the CATREF software. We used a combination of Weighted Least Squares, Maximum Likelihood Estimation (MLE), Empirical Orthogonal Functions (EOF’s) and Wavelets to analyze the data for their spatial and temporal noise characteristics and investigate the periodic signals. We find that the noise levels in the re-processed daily solutions is reduced compared to past solutions, but the noise spectra is still represented by a combination of flicker noise and white noise. The amplitudes of the seasonal term have generally decreased and the spatial distribution of the phase lag appears to be more uniform. Comparisons of the estimated annual variations with combined loading models (NCEP + LaD - World - Fraser + ECCO) and the vertical displacement model of the GRACE R4 gravity fields show an improved agreement

  5. Joint transfer of time and frequency signals and multi-point synchronization via fiber network

    Science.gov (United States)

    Nan, Cheng; Wei, Chen; Qin, Liu; Dan, Xu; Fei, Yang; You-Zhen, Gui; Hai-Wen, Cai

    2016-01-01

    A system of jointly transferring time signals with a rate of 1 pulse per second (PPS) and frequency signals of 10 MHz via a dense wavelength division multiplex-based (DWDM) fiber is demonstrated in this paper. The noises of the fiber links are suppressed and compensated for by a controlled fiber delay line. A method of calibrating and characterizing time is described. The 1PPS is synchronized by feed-forward calibrating the fiber delays precisely. The system is experimentally examined via a 110 km spooled fiber in laboratory. The frequency stabilities of the user end with compensation are 1.8×10-14 at 1 s and 2.0×10-17 at 104 s average time. The calculated uncertainty of time synchronization is 13.1 ps, whereas the direct measurement of the uncertainty is 12 ps. Next, the frequency and 1PPS are transferred via a metropolitan area optical fiber network from one central site to two remote sites with distances of 14 km and 110 km. The frequency stabilities of 14 km link reach 3.0×10-14 averaged in 1 s and 1.4×10-17 in 104 s respectively; and the stabilities of 110 km link are 8.3×10-14 and 1.7×10-17, respectively. The accuracies of synchronization are estimated to be 12.3 ps for the 14 km link and 13.1 ps for the 110 km link, respectively. Project supported by the National Natural Science Foundation of China (Grant No. 61405227).

  6. Phytochrome and retrograde signalling pathways coverage to antogonistically regulate a light-induced transcription network

    Science.gov (United States)

    Plastid-to-nucleus retrograde signals emitted by dysfunctional chloroplasts impact photomorphogenic development, but the molecular link between retrograde and photosensory-receptor signaling has remained undefined. Here, we show that the phytochrome (phy) and retrograde signaling pathways converge a...

  7. Transcriptional Regulatory Networks Activated by PI3K and ERK Transduced Growth Signals in Human Glioblastoma Cells

    Institute of Scientific and Technical Information of China (English)

    Peter M. Haverty; Zhi-Ping Weng; Ulla Hansen

    2005-01-01

    Determining how cells regulate their transcriptional response to extracellular signals is key to the understanding of complex eukaryotic systems. This study was initiated with the goals of furthering the study of mammalian transcriptional regulation and analyzing the relative benefits of related computational methodologies. One dataset available for such an analysis involved gene expression profiling of the early growth factor response to platelet derived growth factor (PDGF)in a human glioblastoma cell line; this study differentiated genes whose expression was regulated by signaling through the phosphoinositide-3-kinase (PI3K) versus the extracellular-signal regulated kinase (ERK) pathways. We have compared the inferred transcription factors from this previous study with additional predictions of regulatory transcription factors using two alternative promoter sequence analysis techniques. This comparative analysis, in which the algorithms predict overlapping,although not identical, sets of factors, argues for meticulous benchmarking of promoter sequence analysis methods to determine the positive and negative attributes that contribute to their varying results. Finally, we inferred transcriptional regulatory networks deriving from various signaling pathways using the CARRIE program suite. These networks not only included previously described transcriptional features of the response to growth signals, but also predicted new regulatory features for the propagation and modulation of the growth signal.

  8. 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

  9. Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems.

    Science.gov (United States)

    Parodi, Silvio; Riccardi, Giuseppe; Castagnino, Nicoletta; Tortolina, Lorenzo; Maffei, Massimo; Zoppoli, Gabriele; Nencioni, Alessio; Ballestrero, Alberto; Patrone, Franco

    2016-01-01

    Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.

  10. Conventional and novel Gγ protein families constitute the heterotrimeric G-protein signaling network in soybean.

    Directory of Open Access Journals (Sweden)

    Swarup Roy Choudhury

    Full Text Available Heterotrimeric G-proteins comprised of Gα, Gβ and Gγ proteins are important signal transducers in all eukaryotes. The Gγ protein of the G-protein heterotrimer is crucial for its proper targeting at the plasma membrane and correct functioning. Gγ proteins are significantly smaller and more diverse than the Gα and Gβ proteins. In model plants Arabidopsis and rice that have a single Gα and Gβ protein, the presence of two canonical Gγ proteins provide some diversity to the possible heterotrimeric combinations. Our recent analysis of the latest version of the soybean genome has identified ten Gγ proteins which belong to three distinct families based on their C-termini. We amplified the full length cDNAs, analyzed their detailed expression profile by quantitative PCR, assessed their localization and performed yeast-based interaction analysis to evaluate interaction specificity with different Gβ proteins. Our results show that ten Gγ genes are retained in the soybean genome and have interesting expression profiles across different developmental stages. Six of the newly identified proteins belong to two plant-specific Gγ protein families. Yeast-based interaction analyses predict some degree of interaction specificity between different Gβ and Gγ proteins. This research thus identifies a highly diverse G-protein network from a plant species. Homologs of these novel proteins have been previously identified as QTLs for grain size and yield in rice.

  11. A system for improving fall detection performance using critical phase fall signal and a neural network

    Directory of Open Access Journals (Sweden)

    Patimakorn Jantaraprim

    2012-12-01

    Full Text Available We present a system for improving fall detection performance using a short time min-max feature based on the specificsignatures of critical phase fall signal and a neural network as a classifier. Two subject groups were tested: Group A involvingfalls and activities by young subjects; Group B testing falls by young and activities by elderly subjects. The performance wasevaluated by comparing the short time min-max with a maximum peak feature using a feed-forward backpropagation networkwith two-fold cross validation. The results, obtained from 672 sequences, show that the proposed method offers a betterperformance for both subject groups. Group B’s performance is higher than Group A’s. The best performances are 98.2%sensitivity and 99.3% specificity for Group A, and 99.4% sensitivity and 100% specificity for Group B. The proposed systemuses one sensor for a body’s position, without a fixed threshold for 100% sensitivity or specificity and without additionalprocessing of posture after a fall.

  12. 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..

  13. Perturbation waves in proteins and protein networks: Applications of percolation and game theories in signaling and drug design

    CERN Document Server

    Antal, Miklos A; Csermely, Peter

    2008-01-01

    The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein molecules and in protein-protein interaction networks (interactomes). We assess the possibilities and critically review the initial attempts for the application of game theory to the often rather complicated process, when two protein molecules approach each other, mutually adjust their conformations via multiple communication steps and finally, bind to each other. We also summarize available data on the application of percolation theory for the prediction of amino acid network- and interactome-dynamics. Furthermore, we give an overview of the dissection of signals and noise in the cellular context of various perturbations. Finally, we propose possible applications of the reviewed methodologies in drug design.

  14. 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.

  15. 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

  16. 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

  17. Activated PTHLH Coupling Feedback Phosphoinositide to G-Protein Receptor Signal-Induced Cell Adhesion Network in Human Hepatocellular Carcinoma by Systems-Theoretic Analysis

    Directory of Open Access Journals (Sweden)

    Lin Wang

    2012-01-01

    Full Text Available Studies were done on analysis of biological processes in the same high expression (fold change ≥2 activated PTHLH feedback-mediated cell adhesion gene ontology (GO network of human hepatocellular carcinoma (HCC compared with the corresponding low expression activated GO network of no-tumor hepatitis/cirrhotic tissues (HBV or HCV infection. Activated PTHLH feedback-mediated cell adhesion network consisted of anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolism, cell adhesion, cell differentiation, cell-cell signaling, G-protein-coupled receptor protein signaling pathway, intracellular transport, metabolism, phosphoinositide-mediated signaling, positive regulation of transcription, regulation of cyclin-dependent protein kinase activity, regulation of transcription, signal transduction, transcription, and transport in HCC. We proposed activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network. Our hypothesis was verified by the different activated PTHLH feedback-mediated cell adhesion GO network of HCC compared with the corresponding inhibited GO network of no-tumor hepatitis/cirrhotic tissues, or the same compared with the corresponding inhibited GO network of HCC. Activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network included BUB1B, GNG10, PTHR2, GNAZ, RFC4, UBE2C, NRXN3, BAP1, PVRL2, TROAP, and VCAN in HCC from GEO dataset using gene regulatory network inference method and our programming.

  18. 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.

  19. 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.)

  20. 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.

  1. The early ELF signals of the gigantic jets captured by the Taiwan ground observation network

    Science.gov (United States)

    Chen, A. B. C.; Huang, P. H.; Su, H. T.; Hsu, R. R.

    2015-12-01

    The in-cloud ignition process of gigantic jets and blue jets receives attentions and discussions in the past years. The polarity and the position of their breakdown were proposed by Krehbiel et al. [2008] but no concrete observational evidence to support it directly. ELF spectrogram is a good tool to explore the electric activities, but traditional spectrograms are generated by a Fourier transform which obtain the frequency information through an integration operation. However the integration greatly limits the lowest frequency revealed by spectrogram and buries the important transient features. In this study, we applied a new but widely-used method, the Hilbert-Huang transform (HHT), to explore the spectrogram. Instead of the integration, HHT obtains the frequency information by differentiating on the phase angle, and become a powerful tool to reveal the fast frequency variation associated with transient luminous events. More than 100 transient luminous events including 25 gigantic jets observed by Taiwan ground optical observation network were analyzed. The results indicate that approximately 70% of gigantic jets can identify a rapid frequency variation in the interval of 300-600 milliseconds before main surge discharge, and this early feature can not find a clear corresponding amplitude variation in its sferic. Since this early signal can not be identified from the traditional Fourier spectrogram, but clear in-cloud lightning was registered correspondingly by the ground optical observation. In contrast to gigantic jets, this feature of early frequency change can be seen only in less than 30% of sprites and elves. These observational evidences are able to provide new constraints on the early discharge process of gigantic jets in clouds.

  2. 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

  3. 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...

  4. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

    Science.gov (United States)

    Kuperstein, I; Bonnet, E; Nguyen, H-A; Cohen, D; Viara, E; Grieco, L; Fourquet, S; Calzone, L; Russo, C; Kondratova, M; Dutreix, M; Barillot, E; Zinovyev, A

    2015-01-01

    Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses

  5. Functional and gene network analyses of transcriptional signatures characterizing pre-weaned bovine mammary parenchyma or fat pad uncovered novel inter-tissue signaling networks during development

    Directory of Open Access Journals (Sweden)

    Lewin Harris A

    2010-05-01

    Full Text Available Abstract Background The neonatal bovine mammary fat pad (MFP surrounding the mammary parenchyma (PAR is thought to exert proliferative effects on the PAR through secretion of local modulators of growth induced by systemic hormones. We used bioinformatics to characterize transcriptomics differences between PAR and MFP from ~65 d old Holstein heifers. Data were mined to uncover potential crosstalk through the analyses of signaling molecules preferentially expressed in one tissue relative to the other. Results Over 9,000 differentially expressed genes (DEG; False discovery rate ≤ 0.05 were found of which 1,478 had a ≥1.5-fold difference between PAR and MFP. Within the DEG highly-expressed in PAR vs. MFP (n = 736 we noted significant enrichment of functions related to cell cycle, structural organization, signaling, and DNA/RNA metabolism. Only actin cytoskeletal signaling was significant among canonical pathways. DEG more highly-expressed in MFP vs. PAR (n = 742 belong to lipid metabolism, signaling, cell movement, and immune-related functions. Canonical pathways associated with metabolism and signaling, particularly immune- and metabolism-related were significantly-enriched. Network analysis uncovered a central role of MYC, TP53, and CTNNB1 in controlling expression of DEG highly-expressed in PAR vs. MFP. Similar analysis suggested a central role for PPARG, KLF2, EGR2, and EPAS1 in regulating expression of more highly-expressed DEG in MFP vs. PAR. Gene network analyses revealed putative inter-tissue crosstalk between cytokines and growth factors preferentially expressed in one tissue (e.g., ANGPTL1, SPP1, IL1B in PAR vs. MFP; ADIPOQ, IL13, FGF2, LEP in MFP vs. PAR with DEG preferentially expressed in the other tissue, particularly transcription factors or pathways (e.g., MYC, TP53, and actin cytoskeletal signaling in PAR vs. MFP; PPARG and LXR/RXR Signaling in MFP vs. PAR. Conclusions Functional analyses underscored a reciprocal influence in

  6. 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...

  7. Calcium signal network of GPCR and CaCC signal transduction pathways%GPCR和CaCC信号传导中的钙信号网络

    Institute of Scientific and Technical Information of China (English)

    冯莹柱; 王伯初

    2013-01-01

    G蛋白偶联受体(G protein-coupled receptor,GPCR)作为最大的一类人膜蛋白受体家族和最重要的药物靶标而倍受关注,其中钙离子在细胞内信号传导级联放大中起了关键的作用.阐述了GPCR和钙激活的氯离子通道蛋白(calcium-activated chloride channel,CaCC)中的钙信号网络与生理功能以及如何干扰阻断该网络,为药物设计和很多疾病的治疗提供了依据.%G protein-coupled receptors (GPCR) are the largest class of human membrane protein receptors and drug targets which attract much attention, and calciumion plays a key role in intracellular signaling cascades amplification. This review focuses on calcium signal network of GPCR and calcium-activated chloride channel (CaCC) and how to interfere and block the network. It will provide a reference for the research of drug design and therapy of many diseases.

  8. 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

    2016-06-29

    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 networks by in silico analysis. Further analyses placed genes in our RNA sequencing-generated dataset to several canonical signalling pathways, such as cell-cycle control, DNA-damage and -repair responses, p53 and HIF1α. Importantly, we observed downregulation of the pancreatic adenocarcinoma signaling pathway in MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and 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.

  9. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    C. R. Hema

    2008-01-01

    Full Text Available Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.

  10. Space-time signal processing for distributed pattern detection in sensor networks

    Science.gov (United States)

    Paffenroth, Randy C.; Du Toit, Philip C.; Scharf, Louis L.; Jayasumana, Anura P.; Banadara, Vidarshana; Nong, Ryan

    2012-05-01

    We present a theory and algorithm for detecting and classifying weak, distributed patterns in network data that provide actionable information with quantiable measures of uncertainty. Our work demonstrates the eectiveness of space-time inference on graphs, robust matrix completion, and second order analysis for the detection of distributed patterns that are not discernible at the level of individual nodes. Motivated by the importance of the problem, we are specically interested in detecting weak patterns in computer networks related to Cyber Situational Awareness. Our focus is on scenarios where the nodes (terminals, routers, servers, etc.) are sensors that provide measurements (of packet rates, user activity, central processing unit usage, etc.) that, when viewed independently, cannot provide a denitive determination of the underlying pattern, but when fused with data from across the network both spatially and temporally, the relevant patterns emerge. The approach is applicable to many types of sensor networks including computer networks, wireless networks, mobile sensor networks, and social networks, as well as in contexts such as databases and disease outbreaks.

  11. PBO H2O: Monitoring the Terrestrial Water Cycle with reflected GPS signals recorded by the Plate Boundary Observatory Network

    Science.gov (United States)

    Small, E. E.; Fairfax, E. J.; Chew, C. C.; Larson, K. M.

    2015-12-01

    Data from NSF's EarthScope Plate Boundary Observatory (PBO), and similar GPS networks worldwide, can be used to monitor the terrestrial water cycle. GPS satellites transmit L-band microwave signals, which are strongly influenced by water at the surface of the Earth. GPS signals take two different paths: (1) the "direct" signal travels from the satellite to the antenna; (2) the "reflected" signal interacts with the Earth's surface before travelling to the antenna. The direct signal is used by geophysicists to measure the position of the antenna. By analyzing these GPS data over multiple years, the motion of the site can be estimated. The effects of reflected signals are generally ignored by geophysicists because they are small. This is not happenstance, as significant effort has been made to design and deploy a GPS antenna that suppresses ground reflections. Our group has developed a remote sensing technique to retrieve terrestrial water cycle variables from GPS data. We extract the water cycle products from signal strength data that measures the interference between the direct and reflected GPS signals. The sensing footprint is intermediate in scale between in situ observations and most remote sensing measurements. Snow depth, snow water equivalent (SWE), near surface soil moisture, and an index of vegetation water content are currently estimated from nearly 500 PBO sites. These PBO H2O products are updated daily and are available online (http://xenon.colorado.edu/portal/index.php). Validation studies show that retrieved products are of sufficient quality to be used in a variety of applications. The root mean square error (RMSE) of GPS-based SWE is 2 cm, based on a comparison to snow survey data at nearly 20 GPS sites. The RMSE of near surface volumetric soil moisture is < 0.04 cm3 cm-3, sufficient for validation of SMAP soil moisture and similar products.

  12. Genome-wide network analysis of Wnt signaling in three pediatric cancers

    Science.gov (United States)

    Bao, Ju; Lee, Ho-Jin; Zheng, Jie J.

    2013-10-01

    Genomic structural alteration is common in pediatric cancers, and analysis of data generated by the Pediatric Cancer Genome Project reveals such tumor-related alterations in many Wnt signaling-associated genes. Most pediatric cancers are thought to arise within developing tissues that undergo substantial expansion during early organ formation, growth and maturation, and Wnt signaling plays an important role in this development. We examined three pediatric tumors--medullobastoma, early T-cell precursor acute lymphoblastic leukemia, and retinoblastoma--that show multiple genomic structural variations within Wnt signaling pathways. We mathematically modeled this pathway to investigate the effects of cancer-related structural variations on Wnt signaling. Surprisingly, we found that an outcome measure of canonical Wnt signaling was consistently similar in matched cancer cells and normal cells, even in the context of different cancers, different mutations, and different Wnt-related genes. Our results suggest that the cancer cells maintain a normal level of Wnt signaling by developing multiple mutations.

  13. 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

  14. 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

  15. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.; Yan, W. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

    1993-11-01

    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.

  17. Mapping network motif tunability and robustness in the design of synthetic signaling circuits.

    Directory of Open Access Journals (Sweden)

    Sergio Iadevaia

    Full Text Available Cellular networks are highly dynamic in their function, yet evolutionarily conserved in their core network motifs or topologies. Understanding functional tunability and robustness of network motifs to small perturbations in function and structure is vital to our ability to synthesize controllable circuits. In establishing core sets of network motifs, we selected topologies that are overrepresented in mammalian networks, including the linear, feedback, feed-forward, and bifan circuits. Static and dynamic tunability of network motifs were defined as the motif ability to respectively attain steady-state or transient outputs in response to pre-defined input stimuli. Detailed computational analysis suggested that static tunability is insensitive to the circuit topology, since all of the motifs displayed similar ability to attain predefined steady-state outputs in response to constant inputs. Dynamic tunability, in contrast, was tightly dependent on circuit topology, with some motifs performing superiorly in achieving observed time-course outputs. Finally, we mapped dynamic tunability onto motif topologies to determine robustness of motif structures to changes in topology and identify design principles for the rational assembly of robust synthetic networks.

  18. 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

  19. 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.

  20. 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

  1. Antithetical NFATc1-Sox2 and p53-miR200 signaling networks govern pancreatic cancer cell plasticity.

    Science.gov (United States)

    Singh, Shiv K; Chen, Nai-Ming; Hessmann, Elisabeth; Siveke, Jens; Lahmann, Marlen; Singh, Garima; Voelker, Nadine; Vogt, Sophia; Esposito, Irene; Schmidt, Ansgar; Brendel, Cornelia; Stiewe, Thorsten; Gaedcke, Jochen; Mernberger, Marco; Crawford, Howard C; Bamlet, William R; Zhang, Jin-San; Li, Xiao-Kun; Smyrk, Thomas C; Billadeau, Daniel D; Hebrok, Matthias; Neesse, Albrecht; Koenig, Alexander; Ellenrieder, Volker

    2015-02-12

    In adaptation to oncogenic signals, pancreatic ductal adenocarcinoma (PDAC) cells undergo epithelial-mesenchymal transition (EMT), a process combining tumor cell dedifferentiation with acquisition of stemness features. However, the mechanisms linking oncogene-induced signaling pathways with EMT and stemness remain largely elusive. Here, we uncover the inflammation-induced transcription factor NFATc1 as a central regulator of pancreatic cancer cell plasticity. In particular, we show that NFATc1 drives EMT reprogramming and maintains pancreatic cancer cells in a stem cell-like state through Sox2-dependent transcription of EMT and stemness factors. Intriguingly, NFATc1-Sox2 complex-mediated PDAC dedifferentiation and progression is opposed by antithetical p53-miR200c signaling, and inactivation of the tumor suppressor pathway is essential for tumor dedifferentiation and dissemination both in genetically engineered mouse models (GEMM) and human PDAC. Based on these findings, we propose the existence of a hierarchical signaling network regulating PDAC cell plasticity and suggest that the molecular decision between epithelial cell preservation and conversion into a dedifferentiated cancer stem cell-like phenotype depends on opposing levels of p53 and NFATc1 signaling activities.

  2. 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.,…

  3. Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals.

    Science.gov (United States)

    Meza-Kubo, Victoria; Morán, Alberto L; Carrillo, Ivan; Galindo, Gilberto; García-Canseco, Eloisa

    2016-08-01

    The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results.

  4. 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)

  5. 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.

  6. 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.

  7. IMMUNE RBF NETWORK AND ITS APPLICATION IN THE MODULATION-STYLE RECOGNITION OF RADAR SIGNALS

    Institute of Scientific and Technical Information of China (English)

    Gong Xinbao; Zang Xiaogang; Zhou Xilang; Hu Guangrui

    2003-01-01

    Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.

  8. 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)

  9. Transmission and pass-drop operations of mixed baudrate Nyquist OTDM-WDM signals for all-optical elastic network.

    Science.gov (United States)

    Tan, Hung Nguyen; Inoue, Takashi; Kurosu, Takayuki; Namiki, Shu

    2013-08-26

    We propose the use of Nyquist OTDM-WDM signal for highly efficient, fully elastic all-optical networks. With the possibility of generation of ultra-coarse yet flexible granular channels, Nyquist OTDM-WDM can eliminate guard-bands in conventional WDM systems, and hence improves the spectral efficiency in network perspective. In this paper, transmission and pass-drop operations of mixed baudrate Nyquist OTDM-WDM channels from 43 Gbaud to dual-polarization 344 Gbaud are successfully demonstrated over 320 km fiber link with four FlexGrid-compatible WSS nodes. A stable clock recovery is also carried out for different baudrate Nyquist OTDMs by optical null-header insertion technique.

  10. 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.

  11. Electrochemical Detection of Amyloid-β Oligomers Based on the Signal Amplification of a Network of Silver Nanoparticles.

    Science.gov (United States)

    Xia, Ning; Wang, Xin; Zhou, Binbin; Wu, Yangyang; Mao, Wenhui; Liu, Lin

    2016-08-01

    Amyloid-β oligomers (AβOs) are the most important toxic species in the brain of Alzheimer's disease (AD) patient. AβOs, therefore, are considered reliable molecular biomarkers for the diagnosis of AD. Herein, we reported a simple and sensitive electrochemical method for the selective detection of AβOs using silver nanoparticles (AgNPs) as the redox reporters and PrP(95-110), an AβOs-specific binding peptide, as the receptor. Specifically, adamantine (Ad)-labeled PrP(95-110), denoted as Ad-PrP(95-110), induced the aggregation and color change of AgNPs and the follow-up formation of a network of Ad-PrP(95-110)-AgNPs. Then, Ad-PrP(95-110)-AgNPs were anchored onto a β-cyclodextrin (β-CD)-covered electrode surface through the host-guest interaction between Ad and β-CD, thus producing an amplified electrochemical signal through the solid-state Ag/AgCl reaction by the AgNPs. In the presence of AβOs, Ad-PrP(95-110) interacted specifically with the AβOs, thus losing the capability to bind AgNPs and to induce the formation of an AgNPs-based network on the electrode surface. Consequently, the electrochemical signal decreased with an increase in the concentration of AβOs in the range of 20 pM to 100 nM. The biosensor had a detection limit of 8 pM and showed no response to amyloid-β monomers (AβMs) and fibrils (AβFs). On the basis of the well-defined and amplified electrochemical signal of the AgNPs-based network architecture, these results should be valuable for the design of novel electrochemical biosensors by marrying specific receptors.

  12. 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.

  13. Signal processing in the TGF-beta superfamily ligand-receptor network.

    Directory of Open Access Journals (Sweden)

    Jose M G Vilar

    2006-01-01

    Full Text Available The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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 ...

  19. 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 ...

  20. The protein interaction network of a taxis signal transduction system in a Halophilic Archaeon

    Directory of Open Access Journals (Sweden)

    Schlesner Matthias

    2012-11-01

    Full Text Available Abstract Background The taxis signaling system of the extreme halophilic archaeon Halobacterium (Hbt. salinarum differs in several aspects from its model bacterial counterparts Escherichia coli and Bacillus subtilis. We studied the protein interactions in the Hbt. salinarum taxis signaling system to gain an understanding of its structure, to gain knowledge about its known components and to search for new members. Results The interaction analysis revealed that the core signaling proteins are involved in different protein complexes and our data provide evidence for dynamic interchanges between them. Fifteen of the eighteen taxis receptors (halobacterial transducers, Htrs can be assigned to four different groups depending on their interactions with the core signaling proteins. Only one of these groups, which contains six of the eight Htrs with known signals, shows the composition expected for signaling complexes (receptor, kinase CheA, adaptor CheW, response regulator CheY. From the two Hbt. salinarum CheW proteins, only CheW1 is engaged in signaling complexes with Htrs and CheA, whereas CheW2 interacts with Htrs but not with CheA. CheY connects the core signaling structure to a subnetwork consisting of the two CheF proteins (which build a link to the flagellar apparatus, CheD (the hub of the subnetwork, two CheC complexes and the receptor methylesterase CheB. Conclusions Based on our findings, we propose two hypotheses. First, Hbt. salinarum might have the capability to dynamically adjust the impact of certain Htrs or Htr clusters depending on its current needs or environmental conditions. Secondly, we propose a hypothetical feedback loop from the response regulator to Htr methylation made from the CheC proteins, CheD and CheB, which might contribute to adaptation analogous to the CheC/CheD system of B. subtilis.

  1. 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 ...

  2. 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...

  3. 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

  4. 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.

  5. 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...

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. Self-organized neural network for the quality control of 12-lead ECG signals.

    Science.gov (United States)

    Chen, Yun; Yang, Hui

    2012-09-01

    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.

  12. The Wnt pathway: a key network in cell signalling dysregulated by viruses.

    Science.gov (United States)

    van Zuylen, Wendy J; Rawlinson, William D; Ford, Caroline E

    2016-09-01

    Viruses are obligate parasites dependent on host cells for survival. Viral infection of a cell activates a panel of pattern recognition receptors that mediate antiviral host responses to inhibit viral replication and dissemination. Viruses have evolved mechanisms to evade and subvert this antiviral host response, including encoding proteins that hijack, mimic and/or manipulate cellular processes such as the cell cycle, DNA damage repair, cellular metabolism and the host immune response. Currently, there is an increasing interest whether viral modulation of these cellular processes, including the cell cycle, contributes to cancer development. One cellular pathway related to cell cycle signalling is the Wnt pathway. This review focuses on the modulation of this pathway by human viruses, known to cause (or associated with) cancer development. The main mechanisms where viruses interact with the Wnt pathway appear to be through (i) epigenetic modification of Wnt genes; (ii) cellular or viral miRNAs targeting Wnt genes; (iii) altering specific Wnt pathway members, often leading to (iv) nuclear translocation of β-catenin and activation of Wnt signalling. Given that diverse viruses affect this signalling pathway, modulating Wnt signalling could be a generalised critical process for the initiation or maintenance of viral pathogenesis, with resultant dysregulation contributing to virus-induced cancers. Further study of this virus-host interaction may identify options for targeted therapy against Wnt signalling molecules as a means to reduce virus-induced pathogenesis and the downstream consequences of infection. Copyright © 2016 John Wiley & Sons, Ltd.

  13. 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.

  14. 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...

  15. Stochastic responses may allow genetically diverse cell populations to optimize performance with simpler signaling networks.

    Directory of Open Access Journals (Sweden)

    Christopher C Govern

    Full Text Available Two theories have emerged for the role that stochasticity plays in biological responses: first, that it degrades biological responses, so the performance of biological signaling machinery could be improved by increasing molecular copy numbers of key proteins; second, that it enhances biological performance, by enabling diversification of population-level responses. Using T cell biology as an example, we demonstrate that these roles for stochastic responses are not sufficient to understand experimental observations of stochastic response in complex biological systems that utilize environmental and genetic diversity to make cooperative responses. We propose a new role for stochastic responses in biology: they enable populations to make complex responses with simpler biochemical signaling machinery than would be required in the absence of stochasticity. Thus, the evolution of stochastic responses may be linked to the evolvability of different signaling machineries.

  16. 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.

  17. 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.

  18. 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...

  19. Profiling DNA damage-induced phosphorylation in budding yeast reveals diverse signaling networks.

    Science.gov (United States)

    Zhou, Chunshui; Elia, Andrew E H; Naylor, Maria L; Dephoure, Noah; Ballif, Bryan A; Goel, Gautam; Xu, Qikai; Ng, Aylwin; Chou, Danny M; Xavier, Ramnik J; Gygi, Steven P; Elledge, Stephen J

    2016-06-28

    The DNA damage response (DDR) is regulated by a protein kinase signaling cascade that orchestrates DNA repair and other processes. Identifying the substrate effectors of these kinases is critical for understanding the underlying physiology and mechanism of the response. We have used quantitative mass spectrometry to profile DDR-dependent phosphorylation in budding yeast and genetically explored the dependency of these phosphorylation events on the DDR kinases MEC1, RAD53, CHK1, and DUN1. Based on these screens, a database containing many novel DDR-regulated phosphorylation events has been established. Phosphorylation of many of these proteins has been validated by quantitative peptide phospho-immunoprecipitation and examined for functional relevance to the DDR through large-scale analysis of sensitivity to DNA damage in yeast deletion strains. We reveal a link between DDR signaling and the metabolic pathways of inositol phosphate and phosphatidyl inositol synthesis, which are required for resistance to DNA damage. We also uncover links between the DDR and TOR signaling as well as translation regulation. Taken together, these data shed new light on the organization of DDR signaling in budding yeast. PMID:27298372

  20. 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 ...

  1. 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...

  2. 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

  3. 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...

  4. 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

  5. Pathway Network Analyses for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signaling.

    Science.gov (United States)

    Wen, Ya; Alshikho, Mohamad J; Herbert, Martha R

    2016-01-01

    We used established databases in standard ways to systematically characterize gene ontologies, pathways and functional linkages in the large set of genes now associated with autism spectrum disorders (ASDs). These conditions are particularly challenging--they lack clear pathognomonic biological markers, they involve great heterogeneity across multiple levels (genes, systemic biological and brain characteristics, and nuances of behavioral manifestations)-and yet everyone with this diagnosis meets the same defining behavioral criteria. Using the human gene list from Simons Foundation Autism Research Initiative (SFARI) we performed gene set enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database, and then derived a pathway network from pathway-pathway functional interactions again in reference to KEGG. Through identifying the GO (Gene Ontology) groups in which SFARI genes were enriched, mapping the coherence between pathways and GO groups, and ranking the relative strengths of representation of pathway network components, we 1) identified 10 disease-associated and 30 function-associated pathways 2) revealed calcium signaling pathway and neuroactive ligand-receptor interaction as the most enriched, statistically significant pathways from the enrichment analysis, 3) showed calcium signaling pathways and MAPK signaling pathway to be interactive hubs with other pathways and also to be involved with pervasively present biological processes, 4) found convergent indications that the process "calcium-PRC (protein kinase C)-Ras-Raf-MAPK/ERK" is likely a major contributor to ASD pathophysiology, and 5) noted that perturbations associated with KEGG's category of environmental information processing were common. These findings support the idea that ASD-associated genes may contribute not only to core features of ASD themselves but also to vulnerability to other chronic and systemic problems potentially including cancer, metabolic conditions

  6. Microscopy and Bioinformatic Analyses of Lipid Metabolism Implicate a Sporophytic Signaling Network Supporting Pollen Development in Arabidopsis

    Institute of Scientific and Technical Information of China (English)

    Yixing Wang; Hong Wu; Ming Yang

    2008-01-01

    The Arabidopsis sporophytic tapetum undergoes a programmed degeneration process to secrete lipid and other materials to support pollen development.However,the molecular mechanism regulating the degeneration process is unknown.To gain insight into this molecular mechanism,we first determined that the most critical period for tapetal secretion to support pollen development iS from the vacuolate microspore stage to the early binucleate pollen stage.We then analyzed the expression of enzymes responsible for lipid biosynthesis and degradation with available in-silico data.The genes for these enzymes that are expressed in the stamen but not in the concurrent uninucleate microspore and binucleate pollen are of particular interest,as they presumably hold the clues to unique molecular processes in the sporophytic tissues compared to the gametophytic tissue.No gene for lipid biosynthesis but a single gene encoding a patatin-like protein likely for lipid mobilization was identified based on the selection criterion.A search for genes co-expressed with this gene identified additional genes encoding typical signal transduction components such as a leucine-rich repeat receptor kinase,an extra-large G-protein,other protein kinases,and transcription factors.In addition,proteases,cell wall degradation enzymes,and other proteins were also identified.These proteins thus may be components of a signaling network leading to degradation of a broad range of cellular components.Since a broad range of degradation activities is expected to occur only in the tapetal degeneration process at this stage in the stamen,it iS further hypothesized that the signaling network acts in the tapetal degeneration process.

  7. 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.

  8. 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...

  9. 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.

  10. 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...

  11. 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...

  12. 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

  13. Signaling Networks Involving Reactive Oxygen Species and Ca2+ in Plants

    Science.gov (United States)

    Kuchitsu, Kazuyuki

    2013-01-01

    Although plants never evolved central information processing organs such as brains, plants have evolved distributed information processing systems and are able to sense various environmental changes and reorganize their body plan coordinately without moving. Recent molecular biological studies revealed molecular bases for elementary processes of signal transduction in plants. Though reactive oxygen species (ROS) are highly toxic substances produced through aerobic respiration and photosynthesis, plants possess ROS-producing enzymes whose activity is highly regulated by binding of Ca2+. In turn, Ca2+- permeable channel proteins activated by ROS are shown to be localized to the cell membrane. These two components are proposed to constitute a positive feedback loop to amplify cellular signals. Such molecular physiological studies should be important steps to understand information processing systems in plants and future application for technology related to environmental, energy and food sciences.

  14. Unravelling druggable signalling networks that control F508del-CFTR proteostasis.

    Science.gov (United States)

    Hegde, Ramanath Narayana; Parashuraman, Seetharaman; Iorio, Francesco; Ciciriello, Fabiana; Capuani, Fabrizio; Carissimo, Annamaria; Carrella, Diego; Belcastro, Vincenzo; Subramanian, Advait; Bounti, Laura; Persico, Maria; Carlile, Graeme; Galietta, Luis; Thomas, David Y; Di Bernardo, Diego; Luini, Alberto

    2015-12-23

    Cystic fibrosis (CF) is caused by mutations in CF transmembrane conductance regulator (CFTR). The most frequent mutation (F508del-CFTR) results in altered proteostasis, that is, in the misfolding and intracellular degradation of the protein. The F508del-CFTR proteostasis machinery and its homeostatic regulation are well studied, while the question whether 'classical' signalling pathways and phosphorylation cascades might control proteostasis remains barely explored. Here, we have unravelled signalling cascades acting selectively on the F508del-CFTR folding-trafficking defects by analysing the mechanisms of action of F508del-CFTR proteostasis regulator drugs through an approach based on transcriptional profiling followed by deconvolution of their gene signatures. Targeting multiple components of these signalling pathways resulted in potent and specific correction of F508del-CFTR proteostasis and in synergy with pharmacochaperones. These results provide new insights into the physiology of cellular proteostasis and a rational basis for developing effective pharmacological correctors of the F508del-CFTR defect.

  15. Therapeutic resistance in cancer:microRNA regulation of EGFR signaling networks

    Institute of Scientific and Technical Information of China (English)

    German G. Gomez; Jill Wykosky; Ciro Zanca; Frank B. Furnari; Webster K. Cavenee

    2013-01-01

    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 beneifts 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.

  16. Intelligent Services in Converged Networks - Evolution steps in the signalling arena

    DEFF Research Database (Denmark)

    Soler-Lucas, José; Fosgerau, Anders; Grabner, Boris

    2003-01-01

    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....

  17. 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.

  18. 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.

  19. Time-dependent, glucose-regulated Arabidopsis Regulator of G-protein Signaling 1 network

    Directory of Open Access Journals (Sweden)

    Dinesh Kumar Jaiswal

    2016-04-01

    Full Text Available Plants lack 7-transmembrane, G-protein coupled receptors (GPCRs because the G alpha subunit of the heterotrimeric G protein complex is “self-activating”—meaning that it spontaneously exchanges bound GDP for GTP without the need of a GPCR. In lieu of GPCRs, most plants have a seven transmembrane receptor-like regulator of G-protein signaling (RGS protein, a component of the complex that keeps G-protein signaling in its non-activated state. The addition of glucose physically uncouples AtRGS1 from the complex through specific endocytosis leaving the activated G protein at the plasma membrane. The complement of proteins in the AtRGS1/G-protein complex over time from glucose-induced endocytosis was profiled by immunoprecipitation coupled to mass spectrometry (IP-MS. A total of 119 proteins in the AtRGS1 complex were identified. Several known interactors of the complex were identified, thus validating the approach, but the vast majority (93/119 were not known previously. AtRGS1 protein interactions were dynamically modulated by d-glucose. At low glucose levels, the AtRGS1 complex is comprised of proteins involved in transport, stress and metabolism. After glucose application, the AtRGS1 complex rapidly sheds many of these proteins and recruits other proteins involved in vesicular trafficking and signal transduction. The profile of the AtRGS1 components answers several questions about the type of coat protein and vesicular trafficking GTPases used in AtRGS1 endocytosis and the function of endocytic AtRGS1.

  20. 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.

  1. 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...

  2. 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.

  3. 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.

  4. The role of docking interactions in mediating signaling input, output, and discrimination in the yeast MAPK network.

    Science.gov (United States)

    Reményi, Attila; Good, Matthew C; Bhattacharyya, Roby P; Lim, Wendell A

    2005-12-22

    Cells use a network of mitogen-activated protein kinases (MAPKs) to coordinate responses to diverse extracellular signals. Here, we examine the role of docking interactions in determining connectivity of the yeast MAPKs Fus3 and Kss1. These closely related kinases are activated by the common upstream MAPK kinase Ste7 yet generate distinct output responses, mating and filamentous growth, respectively. We find that docking interactions are necessary for communication with the kinases and that they can encode subtle differences in pathway-specific input and output. The cell cycle arrest mediator Far1, a mating-specific substrate, has a docking motif that selectively binds Fus3. In contrast, the shared partner Ste7 has a promiscuous motif that binds both Fus3 and Kss1. Structural analysis reveals that Fus3 interacts with specific and promiscuous peptides in conformationally distinct modes. Induced fit recognition may allow docking peptides to achieve discrimination by exploiting subtle differences in kinase flexibility. PMID:16364919

  5. 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

  6. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

    Science.gov (United States)

    Mani-Varnosfaderani, Ahmad; Kanginejad, Atefeh; Gilany, Kambiz; Valadkhani, Abolfazl

    2016-10-12

    The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets. PMID:27662759

  7. 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

  8. 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.

  9. 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.

  10. Bistability in the Rac1, PAK, and RhoA Signaling Network Drives Actin Cytoskeleton Dynamics and Cell Motility Switches

    Science.gov (United States)

    Byrne, Kate M.; Monsefi, Naser; Dawson, John C.; Degasperi, Andrea; Bukowski-Wills, Jimi-Carlo; Volinsky, Natalia; Dobrzyński, Maciej; Birtwistle, Marc R.; Tsyganov, Mikhail A.; Kiyatkin, Anatoly; Kida, Katarzyna; Finch, Andrew J.; Carragher, Neil O.; Kolch, Walter; Nguyen, Lan K.; von Kriegsheim, Alex; Kholodenko, Boris N.

    2016-01-01

    Summary Dynamic interactions between RhoA and Rac1, members of the Rho small GTPase family, play a vital role in the control of cell migration. Using predictive mathematical modeling, mass spectrometry-based quantitation of network components, and experimental validation in MDA-MB-231 mesenchymal breast cancer cells, we show that a network containing Rac1, RhoA, and PAK family kinases can produce bistable, switch-like responses to a graded PAK inhibition. Using a small chemical inhibitor of PAK, we demonstrate that cellular RhoA and Rac1 activation levels respond in a history-dependent, bistable manner to PAK inhibition. Consequently, we show that downstream signaling, actin dynamics, and cell migration also behave in a bistable fashion, displaying switches and hysteresis in response to PAK inhibition. Our results demonstrate that PAK is a critical component in the Rac1-RhoA inhibitory crosstalk that governs bistable GTPase activity, cell morphology, and cell migration switches. PMID:27136688

  11. A protein–protein interaction network linking the energy-sensor kinase SnRK1 to multiple signaling pathways in Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Madlen Nietzsche

    2016-04-01

    Full Text Available In plants, the sucrose non-fermenting (SNF1-related protein kinase 1 (SnRK1 represents a central integrator of low energy signaling and acclimation towards many environmental stress responses. Although SnRK1 acts as a convergent point for many different environmental and metabolic signals to control growth and development, it is currently unknown how these many different signals could be translated into a cell-type or stimulus specific response since many components of SnRK1-regulated signaling pathways remain unidentified. Recently, we have demonstrated that proteins containing a domain of unknown function (DUF 581 interact with the catalytic α subunits of SnRK1 (AKIN10/11 from Arabidopsis thaliana and could potentially act as mediators conferring tissue- and stimulus-type specific differences in SnRK1 regulation. To further extend the SnRK1 signaling network in plants, we systematically screened for novel DUF581 interaction partners using the yeast two-hybrid system. A deep and exhaustive screening identified 17 interacting partners for 10 of the DUF581 proteins tested. Many of these novel interaction partners are implicated in cellular processes previously associated with SnRK1 signaling. Furthermore, we mined publicly available interaction data to identify additional DUF581 interacting proteins. A protein–protein interaction network resulting from our studies suggests connections between SnRK1 signaling and other central signaling pathways involved in growth regulation and environmental responses. These include TOR and MAP-kinase signaling as well as hormonal pathways. The resulting protein–protein interaction network promises to be effective in generating hypotheses to study the precise mechanisms SnRK1 signaling on a functional level.

  12. 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.

  13. 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.

  14. PPARγ Networks in Cell Signaling: Update and Impact of Cyclic Phosphatidic Acid

    Directory of Open Access Journals (Sweden)

    Tamotsu Tsukahara

    2013-01-01

    Full Text Available Lysophospholipid (LPL has long been recognized as a membrane phospholipid metabolite. Recently, however, the LPL has emerged as a candidate for diagnostic and pharmacological interest. LPLs include lysophosphatidic acid (LPA, alkyl glycerol phosphate (AGP, cyclic phosphatidic acid (cPA, and sphingosine-1-phosphate (S1P. These biologically active lipid mediators serve to promote a variety of responses that include cell proliferation, migration, and survival. These LPL-related responses are mediated by cell surface G-protein-coupled receptors and also intracellular receptor peroxisome proliferator-activated receptor gamma (PPARγ. In this paper, we focus mainly on the most recent findings regarding the biological function of nuclear receptor-mediated lysophospholipid signaling in mammalian systems, specifically as they relate to health and diseases. Also, we will briefly review the biology of PPARγ and then provide an update of lysophospholipids PPARγ ligands that are under investigation as a therapeutic compound and which are targets of PPARγ relevant to diseases.

  15. 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.

  16. Parameter estimation for compact binary coalescence signals with the first generation gravitational-wave detector network

    CERN Document Server

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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; 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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; 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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...

  17. 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

  18. 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.

  19. 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

  20. Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model.

    Science.gov (United States)

    BagheriMofidi, Seyed Mehdi; Pouladian, Majid; Jameie, Seyed Behnamedin; Abbaspour Tehrani-Fard, Ali

    2016-09-01

    Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot-savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm(3). With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.

  1. 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.

  2. 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

  3. Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

    Directory of Open Access Journals (Sweden)

    Jesse ePalma

    2012-06-01

    Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

  4. 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

  5. A Method of Signal Timing Optimization for Spillover Dissipation in Urban Street Networks

    Directory of Open Access Journals (Sweden)

    Dongfang Ma

    2013-01-01

    Full Text Available The precise identification and quick dissipation of spillovers are critically important in a traffic control system, especially when heavy congestion occurs. This paper first presents a calculation method for the occupancy per cycle under different traffic conditions and identifies the threshold of occupancy that characterizes the formation of spillovers. Then, capacity adjustments are determined for the incoming and outgoing streams of bottleneck links, with the aim of dissipating the queue to a permissible length within a given period of time, and optimization schemes are defined to calculate splits for the upstream and downstream intersections. Finally, taking average vehicular delay, outputs per cycle, and maximum queue length on the bottleneck link as the evaluation indices, the method of dissipating spillovers proposed in this paper is evaluated using a VISSIM simulation. The results show that the maximum queue length on the bottleneck link and the average vehicular delay at the upstream and downstream intersections decrease significantly under the new signal control plan; meanwhile, the new control schemes have little influence on the outputs of the two intersections per cycle.

  6. PPAR γ Networks in Cell Signaling: Update and Impact of Cyclic Phosphatidic Acid.

    Science.gov (United States)

    Tsukahara, Tamotsu

    2013-01-01

    Lysophospholipid (LPL) has long been recognized as a membrane phospholipid metabolite. Recently, however, the LPL has emerged as a candidate for diagnostic and pharmacological interest. LPLs include lysophosphatidic acid (LPA), alkyl glycerol phosphate (AGP), cyclic phosphatidic acid (cPA), and sphingosine-1-phosphate (S1P). These biologically active lipid mediators serve to promote a variety of responses that include cell proliferation, migration, and survival. These LPL-related responses are mediated by cell surface G-protein-coupled receptors and also intracellular receptor peroxisome proliferator-activated receptor gamma (PPAR γ ). In this paper, we focus mainly on the most recent findings regarding the biological function of nuclear receptor-mediated lysophospholipid signaling in mammalian systems, specifically as they relate to health and diseases. Also, we will briefly review the biology of PPAR γ and then provide an update of lysophospholipids PPAR γ ligands that are under investigation as a therapeutic compound and which are targets of PPAR γ relevant to diseases. PMID:23476786

  7. 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

  8. 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.

  9. 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]。

  10. Landscape mapping of functional proteins in insulin signal transduction and insulin resistance: a network-based protein-protein interaction analysis.

    Directory of Open Access Journals (Sweden)

    Chiranjib Chakraborty

    Full Text Available The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins.

  11. 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.

  12. 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

    2016-07-21

    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.

  13. Signalling networks associated with urokinase-type plasminogen activator (uPA) and its inhibitor PAI-1 in breast cancer tissues: new insights from protein microarray analysis.

    Science.gov (United States)

    Wolff, Claudia; Malinowsky, Katharina; Berg, Daniela; Schragner, Kerstin; Schuster, Tibor; Walch, Axel; Bronger, Holger; Höfler, Heinz; Becker, Karl-Friedrich

    2011-01-01

    The urokinase-type plasminogen activator (uPA) and the main uPA inhibitor PAI-1 play important roles in cell migration and invasion in both physiological and pathological contexts. Both factors are clinically applicable predictive markers in node-negative breast cancer patients that are used to stratify patients for adjuvant chemotherapy. In addition to their classical functions in plasmin regulation, both factors are key components in cancer-related cell signalling. Such signalling cascades are well described in cell culture systems, but a better understanding of uPA- and PAI-1-associated signalling networks in clinical tissues is needed. We examined the expression of uPA, PAI-1, and 21 signalling molecules in 201 primary breast cancer tissues using protein microarrays. Expression of uPA was significantly correlated with the expression of ERK and Stat3, while expression of PAI-1 was correlated with the uPA receptor and Akt activation, presumably via integrin and HER-receptor signalling. Analysis of uPA expression did not reveal any significant correlation with staging, grading or age of the patients. The PAI-1 expression was correlated with nodal stage. Network monitoring for uPA and PAI-1 in breast cancer reveals interactions with main signalling cascades and extends the findings from cell culture experiments. Our results reveal possible mechanisms underlying cancer development.

  14. Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network.

    Science.gov (United States)

    Murakami, Yohei; Takada, Shoji

    2013-01-01

    When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC) is a useful method. Conventional MCMC needs likelihood to evaluate a posterior distribution of acceptable parameters, while the approximate Bayesian computation (ABC) MCMC evaluates posterior distribution with use of qualitative fitness measure. However, none of these algorithms can deal with mixture of quantitative, i.e., likelihood, and qualitative fitness measures simultaneously. Here, to deal with this mixture, we formulated Bayesian formula for hybrid fitness measures (HFM). Then we implemented it to MCMC (MCMC-HFM). We tested MCMC-HFM first for a kinetic toy model with a positive feedback. Inferring kinetic parameters mainly related to the positive feedback, we found that MCMC-HFM reliably infer them using both qualitative and quantitative fitness measures. Then, we applied the MCMC-HFM to an apoptosis signal transduction network previously proposed. For kinetic parameters related to implicit positive feedbacks, which are important for bistability and irreversibility of the output, the MCMC-HFM reliably inferred these kinetic parameters. In particular, some kinetic parameters that have experimental estimates were inferred without using these data and the results were consistent with experiments. Moreover, for some parameters, the mixed use of quantitative and qualitative fitness measures narrowed down the acceptable range of parameters.

  15. 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.

  16. 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.

  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

    2016-09-01

    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

  18. 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.

  19. 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

  20. Prolonged abstinence from developmental cocaine exposure dysregulates BDNF and its signaling network in the medial prefrontal cortex of adult rats.

    Science.gov (United States)

    Giannotti, Giuseppe; Caffino, Lucia; Calabrese, Francesca; Racagni, Giorgio; Riva, Marco A; Fumagalli, Fabio

    2014-04-01

    Although evidence exists that chronic cocaine exposure during adulthood is associated with changes in BDNF expression, whether and how cocaine exposure during adolescence modulates BDNF is still unknown. To address this issue, we exposed rats to repeated cocaine injections from post-natal day (PD) 28 to PD 42, a period that roughly approximates adolescence in humans, and we carried out a detailed analysis of the BDNF system in the medial prefrontal cortex (mPFC) of rats sacrificed 3 d (PD 45) and 48 d (PD 90) after the last cocaine treatment. We found that developmental exposure to cocaine altered transcriptional and translational mechanisms governing neurotrophin expression. Total BDNF mRNA levels, in fact, were enhanced in the mPFC of PD 90 rats exposed to cocaine in adolescence, an effect sustained by changes in BDNF exon IV through the transcription factors CaRF and NF-kB. While a profound reduction of specific BDNF-related miRNAs (let7d, miR124 and miR132) may contribute to explaining the increased proBDNF levels, the up-regulation of the extracellular proteases tPA is indicative of increased processing leading to higher levels of released mBDNF. These changes were associated with increased activation of the trkB-Akt pathway resulting in enhanced pmTOR and pS6 kinase, which ultimately produced an up-regulation of Arc and a consequent reduction of GluA1 expression in the mPFC of PD 90 cocaine-treated rats. These findings demonstrate that developmental exposure to cocaine dynamically dysregulates BDNF and its signaling network in the mPFC of adult rats, providing novel mechanisms that may contribute to cocaine-induced changes in synaptic plasticity. PMID:24345425

  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. Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Eduardo Pérez-Palma

    Full Text Available Genome-wide association studies (GWAS have successfully identified several risk loci for Alzheimer's disease (AD. Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW, defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES procedure. Comparison of these strategies revealed that ontological sub-networks (SNs involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10(-11, p<1.9×10(-11; GW and GATES, respectively. Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10(-8 in the Alzheimer's disease Neuroimaging Initiative (ADNI study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.

  3. Wireless Body Area Sensor Networks Signal Processing and Communication Framework: Survey on Sensing, Communication Technologies, Delivery and Feedback

    OpenAIRE

    Massudi Mahmuddin; Khalid A. Al-Saud; Amr Mohamed

    2012-01-01

    Problem statement: The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors. In additio...

  4. The Emerging Role of the Phosphatidylinositol 3-Kinase/ Akt/Mammalian Target of Rapamycin Signaling Network in Cancer Stem Cell Biology

    Directory of Open Access Journals (Sweden)

    James A. McCubrey

    2010-08-01

    Full Text Available The cancer stem cell theory entails the existence of a hierarchically organized, rare population of cells which are responsible for tumor initiation, self-renewal/maintenance, and mutation accumulation. The cancer stem cell proposition could explain the high frequency of cancer relapse and resistance to currently available therapies. The phosphatidylinositol 3-kinase (PI3K/Akt/mammalian target of rapamycin (mTOR signaling pathway regulates a wide array of physiological cell functions which include differentiation, proliferation, survival, metabolism, autophagy, and motility. Dysregulated PI3K/Akt/mTOR signaling has been documented in many types of neoplasias. It is now emerging that this signaling network plays a key role in cancer stem cell biology. Interestingly, cancer stem cells displayed preferential sensitivity to pathway inhibition when compared to healthy stem cells. This observation provides the proof-of-principle that functional differences in signaling pathways between neoplastic stem cells and healthy stem cells could be identified. In this review, we present the evidence which links the signals emanating from the PI3K/Akt/mTOR cascade with the functions of cancer stem cells, both in solid and hematological tumors. We then highlight how targeting PI3K/Akt/mTOR signaling with small molecules could improve cancer patient outcome.

  5. Full-Duplex Link Providing Alternative Wired and Wireless Broadband Access for the Wavelength-Division Multiplexing Passive Optical Network with a Uniform Converged Signal Format

    Science.gov (United States)

    Ma, Jianxin

    2014-05-01

    A full-duplex link implementing alternative wired and wireless access for wavelength-division multiplexing passive optical network is proposed with the uniformed three-tone converged optical signal, which provides a wired or wireless downlink access signal alternatively and an uplink optical carrier. The uplink optical carrier reversed by the converged optical signal makes the hybrid optical node unit free from the optical source. The simulation results show that the full-duplex link with a 10-Gb/s 16-quadrature amplitude modulation (16-QAM) downstream and 5 Gb/s binary upstream can provide both wired access with a bit-error rate below 10-9 and radio-over-fiber-based wireless access with a bit-error rate below 10-7 over 40 km of fiber without an optical source and optical amplifier in the hybrid optical node unit.

  6. Relationship between brain network pattern and cognitive performance of children revealed by MEG signals during free viewing of video.

    Science.gov (United States)

    Duan, Fang; Watanabe, Katsumi; Yoshimura, Yuko; Kikuchi, Mitsuru; Minabe, Yoshio; Aihara, Kazuyuki

    2014-04-01

    Application of graph theory to analysis of functional networks in the brain is an important research trend. Extensive research on the resting state has shown a "small-world" organization of the brain network as a whole. However, the small-worldness of children's brain networks in a working state has not yet been well characterized. In this paper, we used a custom-made, child-sized magnetoencephalography (MEG) device to collect data from children while they were watching cartoon videos. Network structures were analyzed and compared with scores on the Kaufman Assessment Battery for Children (K-ABC). The results of network analysis showed that (1) the small-world scalar showed a negative correlation with the simultaneous processing raw score, a measure of visual processing (Gv) ability, and (2) the children with higher simultaneous processing raw scores possessed network structures that can be more efficient for local information processing than children with lower scores. These results were compatible with previous studies on the adult working state. Additional results obtained from further analysis of the frontal and occipital lobes indicated that high cognitive performance could represent better local efficiency in task-related sub-networks. Under free viewing of cartoon videos, brain networks were no longer confined to their strongest small-world states; connections became clustered in local areas such as the frontal and occipital lobes, which might be a more useful configuration for handling visual processing tasks.

  7. 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

  8. 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;

    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......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...

  9. ZigBee Network Signal Anti-collision Algorithm Based on More Virtual Signal Calibration%基于多虚拟信号校验的ZigBee网络信号防冲突算法

    Institute of Scientific and Technical Information of China (English)

    段谟意; 王力群

    2012-01-01

    针对ZigBee网络节点在大规模的数据通信过程中,会因为通信时域不一致引起冲突的问题,提出了一种基于三角校验碰撞预测的放信道信号冲突机制.运用对同一节点进行通信的信号组成一个多信号虚拟校验区域.通过建立的无线传感信号碰撞冲突检测模型进行通信时域冲突的预测判断.通过时域内的信号二维区域碰撞预测,计算碰撞的可能概率.实验结果表明,运用该方法能够对大数据、对节点、长时间的ZigBee通信网络通信进行优化,提高了节点信道的通信效率.同时,这种方法还大幅降低了无线通信网络的数据碰撞的可能性.%ZigBee network nodes in the great scale of data communication, communication does not agree for time domain by the conflict is put forward based on triangle calibration collision prediction of the channel conflict put signal mechanism. The same node of communication of the signal more than a signal virtual calibration area is used. Through the establishment of the wireless sensor signal collision detection model conflict time domain is prediction of communication conflict judgment. Through the time domain signals of 2 d regional collision forecast, the collision of the may probability is calculatied. The experimental results show that the method can be used to big data, to node, long ZigBee communication network communication optimization. The efficiency of the node channel communications is improved. At the same time,this method greatly reduces the wireless communication network data of the collision of the possibilities.

  10. 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

  11. 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

  12. 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.

  13. An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

    Directory of Open Access Journals (Sweden)

    Arunachalam Vinayagam

    2016-09-01

    Full Text Available Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

  14. ErbB2-Driven Breast Cancer Cell Invasion Depends on a Complex Signaling Network Activating Myeloid Zinc Finger-1-Dependent Cathepsin B Expression

    DEFF Research Database (Denmark)

    Rafn, Bo; Nielsen, Christian Thomas Friberg; Andersen, Sofie Hagel;

    2012-01-01

    signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling......Aberrant ErbB2 receptor tyrosine kinase activation in breast cancer is strongly linked to an invasive disease. The molecular basis of ErbB2-driven invasion is largely unknown. We show that cysteine cathepsins B and L are elevated in ErbB2 positive primary human breast cancer and function...... as effectors of ErbB2-induced invasion in vitro. We identify Cdc42-binding protein kinase beta, extracellular regulated kinase 2, p21-activated protein kinase 4, and protein kinase C alpha as essential mediators of ErbB2-induced cysteine cathepsin expression and breast cancer cell invasiveness. The identified...

  15. 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.

  16. A Signaling Abnormal Handling Detection Method in IP Multimedia Subsystem Network%一种IMS网络信令异常处理检测方法

    Institute of Scientific and Technical Information of China (English)

    谢晓龙; 季新生; 刘彩霞; 刘树新

    2012-01-01

    针对IMS网络中被入侵或劫持的网络实体可能对信令消息做出恶意篡改等异常处理的问题,提出一种基于信令处理规则的IMS 网络信令异常处理检测方法.该方法基于建立的信令处理规则库,模拟实体对信令消息的正常处理并生成一条预处理消息,通过判断预处理消息与经过实体处理后的信令消息是否匹配,检测IMS网络中是否存在信令异常处理.实验结果表明,该方法对信令异常处理的检测率达到了100%.%In order to solve the problem that entities in IP Multimedia Subsystem(IMS) network hijacked by attackers may make exception handling to signaling messages, this paper proposes a detection method for signaling exception handling in IMS based on signaling handling rules. It simulates the normal handling of the entity to signaling messages and generates a pre-processing message based on the database of signaling handling rules, and then matching detection is made between the pre-processing message and the message handled by the entity to detect signaling exception handling in IMS. Experimental results prove that the detection rate of exception handling in IMS achieves 100%.

  17. WWP-1 is a novel modulator of the DAF-2 insulin-like signaling network involved in pore-forming toxin cellular defenses in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Chang-Shi Chen

    Full Text Available Pore-forming toxins (PFTs are the single largest class of bacterial virulence factors. The DAF-2 insulin/insulin-like growth factor-1 signaling pathway, which regulates lifespan and stress resistance in Caenorhabditis elegans, is known to mutate to resistance to pathogenic bacteria. However, its role in responses against bacterial toxins and PFTs is as yet unexplored. Here we reveal that reduction of the DAF-2 insulin-like pathway confers the resistance of Caenorhabditis elegans to cytolitic crystal (Cry PFTs produced by Bacillus thuringiensis. In contrast to the canonical DAF-2 insulin-like signaling pathway previously defined for aging and pathogenesis, the PFT response pathway diverges at 3-phosphoinositide-dependent kinase 1 (PDK-1 and appears to feed into a novel insulin-like pathway signal arm defined by the WW domain Protein 1 (WWP-1. In addition, we also find that WWP-1 not only plays an important role in the intrinsic cellular defense (INCED against PFTs but also is involved in innate immunity against pathogenic bacteria Pseudomonas aeruginosa and in lifespan regulation. Taken together, our data suggest that WWP-1 and DAF-16 function in parallel within the fundamental DAF-2 insulin/IGF-1 signaling network to regulate fundamental cellular responses in C. elegans.

  18. Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing

    Science.gov (United States)

    Vandoorne, Kristof; Fiers, Martin; Verstraeten, David; Schrauwen, Benjamin; Dambre, Joni; Bienstman, Peter

    2011-01-01

    Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness.

  19. Network medicine

    DEFF Research Database (Denmark)

    Pawson, Tony; Linding, Rune

    2008-01-01

    for new therapeutic intervention. We argue that by targeting the architecture of aberrant signaling networks associated with cancer and other diseases new therapeutic strategies can be implemented. Transforming medicine into a network driven endeavour will require quantitative measurements of cell...... signaling processes; we will describe how this may be performed and combined with new algorithms to predict the trajectories taken by a cellular system either in time or through disease states. We term this approach, network medicine....

  20. Wireless Body Area Sensor Networks Signal Processing and Communication Framework: Survey on Sensing, Communication Technologies, Delivery and Feedback

    Directory of Open Access Journals (Sweden)

    Massudi Mahmuddin

    2012-01-01

    Full Text Available Problem statement: The Wireless Body Area Sensor Networks (WBASNs is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors. In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena. Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques. Results and Conclusion: Wireless Sensor Networks (WSNs technologies are considered as one of the key of the research areas in computer science and healthcare application industries. Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application. Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention.