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Sample records for global signaling network

The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

Science.gov (United States)

Murphy, Kevin; Birn, Rasmus M; Handwerker, Daniel A; Jones, Tyler B; Bandettini, Peter A

2009-02-01

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.
Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

Science.gov (United States)

Wong, Chi Wah; Olafsson, Valur; Tal, Omer; Liu, Thomas T

2012-10-15

Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. Copyright © 2012 Elsevier Inc. All rights reserved.
Global Optimization for Transport Network Expansion and Signal Setting

OpenAIRE

Liu, Haoxiang; Wang, David Z. W.; Yue, Hao

2015-01-01

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 pr...
Organization of signal flow in directed networks

International Nuclear Information System (INIS)

Bányai, M; Bazsó, F; Négyessy, L

2011-01-01

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
The Not-So-Global Blood Oxygen Level-Dependent Signal.

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Billings, Jacob; Keilholz, Shella

2018-04-01

Global signal regression is a controversial processing step for resting-state functional magnetic resonance imaging, partly because the source of the global blood oxygen level-dependent (BOLD) signal remains unclear. On the one hand, nuisance factors such as motion can readily introduce coherent BOLD changes across the whole brain. On the other hand, the global signal has been linked to neural activity and vigilance levels, suggesting that it contains important neurophysiological information and should not be discarded. Any widespread pattern of coordinated activity is likely to contribute appreciably to the global signal. Such patterns may include large-scale quasiperiodic spatiotemporal patterns, known also to be tied to performance on vigilance tasks. This uncertainty surrounding the separability of the global BOLD signal from concurrent neurological processes motivated an examination of the global BOLD signal's spatial distribution. The results clarify that although the global signal collects information from all tissue classes, a diverse subset of the BOLD signal's independent components contribute the most to the global signal. Further, the timing of each network's contribution to the global signal is not consistent across volunteers, confirming the independence of a constituent process that comprises the global signal.
Altered Global Signal Topography in Schizophrenia.

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Yang, Genevieve J; Murray, John D; Glasser, Matthew; Pearlson, Godfrey D; Krystal, John H; Schleifer, Charlie; Repovs, Grega; Anticevic, Alan

2017-11-01

Schizophrenia (SCZ) is a disabling neuropsychiatric disease associated with disruptions across distributed neural systems. Resting-state functional magnetic resonance imaging has identified extensive abnormalities in the blood-oxygen level-dependent signal in SCZ patients, including alterations in the average signal over the brain-i.e. the "global" signal (GS). It remains unknown, however, if these "global" alterations occur pervasively or follow a spatially preferential pattern. This study presents the first network-by-network quantification of GS topography in healthy subjects and SCZ patients. We observed a nonuniform GS contribution in healthy comparison subjects, whereby sensory areas exhibited the largest GS component. In SCZ patients, we identified preferential GS representation increases across association regions, while sensory regions showed preferential reductions. GS representation in sensory versus association cortices was strongly anti-correlated in healthy subjects. This anti-correlated relationship was markedly reduced in SCZ. Such shifts in GS topography may underlie profound alterations in neural information flow in SCZ, informing development of pharmacotherapies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks

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Choudhury, Madhurima; Datta, Abhirup

2018-05-01

Observations of HI 21cm transition line is a promising probe into the Dark Ages and Epoch-of-Reionization. Detection of this redshifted 21cm signal is one of the key science goal for several upcoming low-frequency radio telescopes like HERA, SKA and DARE. Other global signal experiments include EDGES, LEDA, BIGHORNS, SCI-HI, SARAS. One of the major challenges for the detection of this signal is the accuracy of the foreground source removal. Several novel techniques have been explored already to remove bright foregrounds from both interferometric as well as total power experiments. Here, we present preliminary results from our investigation on application of ANN to detect 21cm global signal amidst bright galactic foreground. Following the formalism of representing the global 21cm signal by 'tanh' model, this study finds that the global 21cm signal parameters can be accurately determined even in the presence of bright foregrounds represented by 3rd order log-polynomial or higher.
Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors.

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Zhao, Li; Alsop, David C; Detre, John A; Dai, Weiying

2017-01-01

Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.
Global Plant Stress Signaling: Reactive Oxygen Species at the Cross-Road

Directory of Open Access Journals (Sweden)

Nasser eSewelam

2016-02-01

Full Text Available Current technologies have changed biology into a data-intensive field and significantly increased our understanding of signal transduction pathways in plants. However, global defense signaling networks in plants have not been established yet. Considering the apparent intricate nature of signaling mechanisms in plants (due to their sessile nature, studying the points at which different signaling pathways converge, rather than the branches, represents a good start to unravel global plant signaling networks. In this regard, growing evidence shows that the generation of reactive oxygen species (ROS is one of the most common plant responses to different stresses, representing a point at which various signaling pathways come together. In this review, the complex nature of plant stress signaling networks will be discussed. An emphasis on different signaling players with a specific attention to ROS as the primary source of the signaling battery in plants will be presented. The interactions between ROS and other signaling components, e.g. calcium, redox homeostasis, membranes, G-proteins, MAPKs, plant hormones and transcription factors will be assessed. A better understanding of the vital roles ROS are playing in plant signaling would help innovate new strategies to improve plant productivity under the circumstances of the increasing severity of environmental conditions and the high demand of food and energy worldwide
A method to determine the necessity for global signal regression in resting-state fMRI studies.

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Chen, Gang; Chen, Guangyu; Xie, Chunming; Ward, B Douglas; Li, Wenjun; Antuono, Piero; Li, Shi-Jiang

2012-12-01

In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures. Copyright © 2012 Wiley Periodicals, Inc.
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...... and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase...
Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal.

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Wen, Haiguang; Liu, Zhongming

2016-06-01

Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ∼5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels. This study provides new insights into the neural origin of resting-state fMRI. Results demonstrate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized and directly coupled with the global component of spontaneous fMRI signals, in contrast to modularly synchronized fluctuations in oscillatory neural activity. These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for global and modular patterns of functional connectivity observed
Development of global cortical networks in early infancy.

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Homae, Fumitaka; Watanabe, Hama; Otobe, Takayuki; Nakano, Tamami; Go, Tohshin; Konishi, Yukuo; Taga, Gentaro

2010-04-07

Human cognition and behaviors are subserved by global networks of neural mechanisms. Although the organization of the brain is a subject of interest, the process of development of global cortical networks in early infancy has not yet been clarified. In the present study, we explored developmental changes in these networks from several days to 6 months after birth by examining spontaneous fluctuations in brain activity, using multichannel near-infrared spectroscopy. We set up 94 measurement channels over the frontal, temporal, parietal, and occipital regions of the infant brain. The obtained signals showed complex time-series properties, which were characterized as 1/f fluctuations. To reveal the functional connectivity of the cortical networks, we calculated the temporal correlations of continuous signals between all the pairs of measurement channels. We found that the cortical network organization showed regional dependency and dynamic changes in the course of development. In the temporal, parietal, and occipital regions, connectivity increased between homologous regions in the two hemispheres and within hemispheres; in the frontal regions, it decreased progressively. Frontoposterior connectivity changed to a "U-shaped" pattern within 6 months: it decreases from the neonatal period to the age of 3 months and increases from the age of 3 months to the age of 6 months. We applied cluster analyses to the correlation coefficients and showed that the bilateral organization of the networks begins to emerge during the first 3 months of life. Our findings suggest that these developing networks, which form multiple clusters, are precursors of the functional cerebral architecture.
Hierarchical feedback modules and reaction hubs in cell signaling networks.

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

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.
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.
Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

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. PMID:25951347
Construction of a global pain systems network highlights phospholipid signaling as a regulator of heat nociception.

Directory of Open Access Journals (Sweden)

G Gregory Neely

Full Text Available The ability to perceive noxious stimuli is critical for an animal's survival in the face of environmental danger, and thus pain perception is likely to be under stringent evolutionary pressure. Using a neuronal-specific RNAi knock-down strategy in adult Drosophila, we recently completed a genome-wide functional annotation of heat nociception that allowed us to identify α2δ3 as a novel pain gene. Here we report construction of an evolutionary-conserved, system-level, global molecular pain network map. Our systems map is markedly enriched for multiple genes associated with human pain and predicts a plethora of novel candidate pain pathways. One central node of this pain network is phospholipid signaling, which has been implicated before in pain processing. To further investigate the role of phospholipid signaling in mammalian heat pain perception, we analysed the phenotype of PIP5Kα and PI3Kγ mutant mice. Intriguingly, both of these mice exhibit pronounced hypersensitivity to noxious heat and capsaicin-induced pain, which directly mapped through PI3Kγ kinase-dead knock-in mice to PI3Kγ lipid kinase activity. Using single primary sensory neuron recording, PI3Kγ function was mechanistically linked to a negative regulation of TRPV1 channel transduction. Our data provide a systems map for heat nociception and reinforces the extraordinary conservation of molecular mechanisms of nociception across different species.
Construction of a Global Pain Systems Network Highlights Phospholipid Signaling as a Regulator of Heat Nociception

Science.gov (United States)

Mair, Norbert; Racz, Ildiko; Milinkeviciute, Giedre; Meixner, Arabella; Nayanala, Swetha; Griffin, Robert S.; Belfer, Inna; Dai, Feng; Smith, Shad; Diatchenko, Luda; Marengo, Stefano; Haubner, Bernhard J.; Novatchkova, Maria; Gibson, Dustin; Maixner, William; Pospisilik, J. Andrew; Hirsch, Emilio; Whishaw, Ian Q.; Zimmer, Andreas; Gupta, Vaijayanti; Sasaki, Junko; Kanaho, Yasunori; Sasaki, Takehiko; Kress, Michaela; Woolf, Clifford J.; Penninger, Josef M.

2012-01-01

The ability to perceive noxious stimuli is critical for an animal's survival in the face of environmental danger, and thus pain perception is likely to be under stringent evolutionary pressure. Using a neuronal-specific RNAi knock-down strategy in adult Drosophila, we recently completed a genome-wide functional annotation of heat nociception that allowed us to identify α2δ3 as a novel pain gene. Here we report construction of an evolutionary-conserved, system-level, global molecular pain network map. Our systems map is markedly enriched for multiple genes associated with human pain and predicts a plethora of novel candidate pain pathways. One central node of this pain network is phospholipid signaling, which has been implicated before in pain processing. To further investigate the role of phospholipid signaling in mammalian heat pain perception, we analysed the phenotype of PIP5Kα and PI3Kγ mutant mice. Intriguingly, both of these mice exhibit pronounced hypersensitivity to noxious heat and capsaicin-induced pain, which directly mapped through PI3Kγ kinase-dead knock-in mice to PI3Kγ lipid kinase activity. Using single primary sensory neuron recording, PI3Kγ function was mechanistically linked to a negative regulation of TRPV1 channel transduction. Our data provide a systems map for heat nociception and reinforces the extraordinary conservation of molecular mechanisms of nociception across different species. PMID:23236288
Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

Science.gov (United States)

Erdoğan, Sinem B; Tong, Yunjie; Hocke, Lia M; Lindsey, Kimberly P; deB Frederick, Blaise

2016-01-01

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
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.

Global asymptotical ω-periodicity of a fractional-order non-autonomous neural networks.

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Chen, Boshan; Chen, Jiejie

2015-08-01

We study the global asymptotic ω-periodicity for a fractional-order non-autonomous neural networks. Firstly, based on the Caputo fractional-order derivative it is shown that ω-periodic or autonomous fractional-order neural networks cannot generate exactly ω-periodic signals. Next, by using the contraction mapping principle we discuss the existence and uniqueness of S-asymptotically ω-periodic solution for a class of fractional-order non-autonomous neural networks. Then by using a fractional-order differential and integral inequality technique, we study global Mittag-Leffler stability and global asymptotical periodicity of the fractional-order non-autonomous neural networks, which shows that all paths of the networks, starting from arbitrary points and responding to persistent, nonconstant ω-periodic external inputs, asymptotically converge to the same nonconstant ω-periodic function that may be not a solution. Copyright © 2015 Elsevier Ltd. All rights reserved.
Early warning model based on correlated networks in global crude oil markets

Science.gov (United States)

Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

2018-01-01

Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.
Signal Processing and Neural Network Simulator

Science.gov (United States)

Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

1995-04-01

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.
Global Operations Networks

DEFF Research Database (Denmark)

In the current context of global economic liberalisation and technological advancements, industrial companies are less likely to generate value in the traditional vertically integrated chain. Instead, they are doing so by means of elaborate cross-border and cross-organisational networks. As a rule......, these networks are configured on a global basis and consist of diverse and interdependent affiliates (linked both through ownership and non-equity relationships), which are engaged in an exchange of goods, services and information. The Scandinavian context is no exception to this trend. Nevertheless......, international comparative studies providing comprehensive insights from it are still rare. With the objective of bridging this gap, Global Operations Networks (GONE) project (sponsored by the Danish Research Council) brought together numerous academic and industrial partners from Denmark, Sweden and Finland...
Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

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

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Matoza, R. S.; Landes, M.; Le Pichon, A.; Ceranna, L.; Brown, D.

2011-12-01

The International Monitoring System (IMS) includes a global network of infrasound arrays, which is designed to detect atmospheric nuclear explosions anywhere on the planet. The infrasound network also has potential application in detection of natural hazards such as large volcanic explosions and severe weather. Ambient noise recorded by the network includes incoherent wind noise and coherent infrasound. We present a statistical analysis of coherent infrasound recorded by the IMS network. We have applied broadband (0.01 to 5 Hz) array processing systematically to the multi-year IMS historical dataset (2005-present) using an implementation of the Progressive Multi-Channel Correlation (PMCC) algorithm in log-frequency space. We show that IMS arrays consistently record coherent ambient infrasound across the broad frequency range from 0.01 to 5 Hz when wind-noise levels permit. Multi-year averaging of PMCC detection bulletins emphasizes continuous signals such as oceanic microbaroms, as well as persistent transient signals such as repetitive volcanic, surf, or anthropogenic activity (e.g., mining or industrial activity). While many of these continuous or repetitive signals are of interest in their own right, they may dominate IMS array detection bulletins and obscure or complicate detection of specific signals of interest. The new PMCC detection bulletins have numerous further applications, including in volcano and microbarom studies, and in IMS data quality assessment.
Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

DEFF Research Database (Denmark)

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

2009-01-01

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

DEFF Research Database (Denmark)

Hansen, Zaza Nadja Lee; Srai, Jagjit Singh

2011-01-01

, network configuration of global R&D has tended to focus on strategic elements with limited attention given operational effectiveness, or to interfaces with downstream manufacturing operations. Within OM literature, the drivers of configuration of global networks within, engineering, production, supply...... to R&D networks emerged, e.g. product features were more prominent in R&D networks. Furthermore, the study has shown extensive interaction with other operations, including many downstream manufacturing operations. By extending the OM configuration concepts to the configuration of R&D networks......Companies are increasingly globalising their R&D activities, both within the firms and with external partners, with consequent implications for their interaction with manufacturing operations. Previous research in R&D networks has focused on coordination, governance and support elements. However...
Meteorological applications of a surface network of Global Positioning System receivers

NARCIS (Netherlands)

Haan, de S.

2008-01-01

This thesis presents meteorological applications of water vapour observations from a surface network of Global Positioning System (GPS) receivers. GPS signals are delayed by the atmo¬sphere due to atmospheric refraction and bending. Mapped to the zenith, this delay is called Zenith Total Delay
Signal-regulated systems and networks

CSIR Research Space (South Africa)

Van Zyl, TL

2010-07-01

Full Text Available The article presents the use of signal regulatory networks (SRNs), a biologically inspired model based on gene regulatory networks. SRNs are a way of understanding a class of self-organizing IT systems, signal-regulated systems (SRSs). This article...
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

2016-03-14

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.
Global tree network for computing structures enabling global processing operations

Science.gov (United States)

Blumrich; Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

2010-01-19

A system and method for enabling high-speed, low-latency global tree network communications among processing nodes interconnected according to a tree network structure. The global tree network enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the tree via links to facilitate performance of low-latency global processing operations at nodes of the virtual tree and sub-tree structures. The global operations performed include one or more of: broadcast operations downstream from a root node to leaf nodes of a virtual tree, reduction operations upstream from leaf nodes to the root node in the virtual tree, and point-to-point message passing from any node to the root node. The global tree network is configurable to provide global barrier and interrupt functionality in asynchronous or synchronized manner, and, is physically and logically partitionable.
Global Operations Networks in Motion

DEFF Research Database (Denmark)

Slepniov, Dmitrij; Jørgensen, Claus; Wæhrens, Brian Vejrum

2009-01-01

This paper addresses the phenomenon of global operations networks and how they change over time. The paper is based on the cases of three Danish companies and their global operations networks. It finds a number of common patterns highlighting some organisational effects and managerial challenges...... the companies face regarding rapid changes in their networks configurations and capabilities. The paper details the variables determining these changes and suggests how the on-going interplay between the focal organisation, its network partners, and their various contextual conditions can be approached....
State Support: A Prerequisite for Global Health Network Effectiveness Comment on "Four Challenges that Global Health Networks Face".

Science.gov (United States)

Marten, Robert; Smith, Richard D

2017-07-24

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks' success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks' effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computer Networks and Globalization

Directory of Open Access Journals (Sweden)

J. Magliaro

2007-07-01

Full Text Available Communication and information computer networks connect the world in ways that make globalization more natural and inequity more subtle. As educators, we look at these phenomena holistically analyzing them from the realist’s view, thus exploring tensions, (in equity and (injustice, and from the idealist’s view, thus embracing connectivity, convergence and development of a collective consciousness. In an increasingly market- driven world we find examples of openness and human generosity that are based on networks, specifically the Internet. After addressing open movements in publishing, software industry and education, we describe the possibility of a dialectic equilibrium between globalization and indigenousness in view of ecologically designed future smart networks
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.
A Miniaturized Laser Heterodyne Radiometer for a Global Ground-Based Column Carbon Monitoring Network

Science.gov (United States)

Wilson, Emily L.; Melroy, Hilary R.; Miller, J. Houston; McLinden, Matthew L.; Ott, Lesley E.; Holben, Brent

2012-01-01

We present progress in the development of a passive, miniaturized Laser Heterodyne Radiometer (mini-LHR) that will measure key greenhouse gases (C02, CH4, CO) in the atmospheric column as well as their respective altitude profiles, and O2 for a measure of atmospheric pressure. Laser heterodyne radiometry is a spectroscopic method that borrows from radio receiver technology. In this technique, a weak incoming signal containing information of interest is mixed with a stronger signal (local oscillator) at a nearby frequency. In this case, the weak signal is sunlight that has undergone absorption by a trace gas of interest and the local oscillator is a distributive feedback (DFB) laser that is tuned to a wavelength near the absorption feature of the trace gas. Mixing the sunlight with the laser light, in a fast photoreceiver, results in a beat signal in the RF. The amplitude of the beat signal tracks the concentration of the trace gas in the atmospheric column. The mini-LHR operates in tandem with AERONET, a global network of more than 450 aerosol sensing instruments. This partnership simplifies the instrument design and provides an established global network into which the mini-LHR can rapidly expand. This network offers coverage in key arctic regions (not covered by OCO-2) where accelerated warming due to the release of CO2 and CH4 from thawing tundra and permafrost is a concern as well as an uninterrupted data record that will both bridge gaps in data sets and offer validation for key flight missions such as OCO-2, OCO-3, and ASCENDS. Currently, the only ground global network that routinely measures multiple greenhouse gases in the atmospheric column is TCCON (Total Column Carbon Observing Network) with 18 operational sites worldwide and two in the US. Cost and size of TCCON installations will limit the potential for expansion, We offer a low-cost $30Klunit) solution to supplement these measurements with the added benefit of an established aerosol optical depth
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

Directory of Open Access Journals (Sweden)

Hongli Liu

2009-01-01

Full Text Available We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.
State Support: A Prerequisite for Global Health Network Effectiveness; Comment on “Four Challenges that Global Health Networks Face”

Directory of Open Access Journals (Sweden)

Robert Marten

2018-03-01

Full Text Available Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research.

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
Global characterization of signalling networks associated with tamoxifen resistance in breast cancer

DEFF Research Database (Denmark)

Browne, Brigid C.; Hochgräfe, Falko; Wu, Jianmin

2013-01-01

R cells. Phosphorylation of the tyrosine kinase Yes and expression of the actin‐binding protein myristoylated alanine‐rich C‐kinase substrate (MARCKS) were increased two‐ and eightfold in TamR cells respectively, and these proteins were selected for further analysis. Knockdown of either protein in Tam......Acquired resistance to the anti‐estrogen tamoxifen remains a significant challenge in breast cancer management. In this study, we used an integrative approach to characterize global protein expression and tyrosine phosphorylation events in tamoxifen‐resistant MCF7 breast cancer cells (Tam...... was perturbed in TamR cells, together with pathways enriched for proteins associated with growth factor, cell–cell and cell matrix‐initiated signalling. Consistent with known roles for Ras/MAPK and PI3‐kinase signalling in tamoxifen resistance, tyrosine‐phosphorylated MAPK1, SHC1 and PIK3R2 were elevated in Tam...
Managing Evolving Global Operations Networks

DEFF Research Database (Denmark)

Mykhaylenko, Alona; Wæhrens, Brian Vejrum; Johansen, John

2015-01-01

For many globally dispersed organisations, the home base (HB) is a historic locus of integrative and coordinating efforts that safeguard overall performance. However, the dynamism of global operations networks is increasingly pulling the centre of gravity away from the HB and dispersing it across...... the network, challenging the HB’s ability to sustain its centrality over time. To counteract this tendency, this paper addresses the gap in the literature regarding the development of the network management capability of the HB within the context of its network. Data was collected through a retrospective...... longitudinal case study of an intra-organisational operations network of one OEM and its three foreign subsidiaries. The findings suggest a row of strategic roles and corresponding managerial capabilities, which the HB needs to develop depending on the changing subsidiaries’ competencies and HB...
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
Labour in Global Production Networks

DEFF Research Database (Denmark)

Lund-Thomsen, Peter; Nadvi, Khalid; Chan, Anita

A critical challenge facing developing country producers is to meet international labour standards and codes of conduct in order to engage in global production networks. Evidence of gains for workers from compliance with such standards and codes remains limited and patchy. This paper focuses...... on the global football industry, a sector dominated by leading global brands who manage dispersed global production networks. It assesses the work conditions for football stitchers engaged in different forms of work organisation, factories, stitching centres, and home-based settings, in Pakistan, India......, and China. It draws on detailed qualitative primary field research with football stitching workers and producers in these three countries. The paper explains how, and why, work conditions of football stitchers differ across these locations through an analytical framework that interweaves both global...
Global action networks: agents for collective action

NARCIS (Netherlands)

Glasbergen, P.

2010-01-01

Global action networks (GANs) are civil society initiated multi-stakeholder arrangements that aim to fulfill a leadership role for systemic change in global governance for sustainable development. The paper develops a network approach to study some of these GANs as motivators of global collective
Global Electricity Trade Network: Structures and Implications

Science.gov (United States)

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825
EEG signal classification using PSO trained RBF neural network for epilepsy identification

Directory of Open Access Journals (Sweden)

Sandeep Kumar Satapathy

Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning
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
The UNESCO Global Network of National Geoparks

Science.gov (United States)

Mc Keever1, P.; Zouros, N.; Patzak, M.; Missotten, R.

2009-12-01

The UNESCO Global Network of National Geoparks was founded in 2004, following the model successfully established by the European Geoparks Network in 2000. It now comprises 63 members in 19 nations across the world. A Global Geopark is an area with geological heritage of international value but where that heritage is being used for the sustainable economic benefit if the local inhabitants, primarily through education and tourism. Supported by IUGS and IUCN, the aim of the Global Geoparks Network is to facilitate exchange and sharing between members to assist in the protection and conservation of the geological heritage of our planet but to do so in way where local communities can take ownership of these special places and where they can get some sustainable economic benefit from them. While allowing for the sustainable economic development of geoparks, the network explicitly forbids the destruction or sale of the geological value of a geopark. This paper outlines the ethos of the Global Geoparks Network and describes the typical activities of geoparks and how the network functions. Using two examples it also illustrates how members of the Global Geoparks Network provide good examples as tools not only for holistic nature conservation but also for economic development.
Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.
Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

Science.gov (United States)

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti
A Network of Networks Perspective on Global Trade.

Science.gov (United States)

Maluck, Julian; Donner, Reik V

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990-2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector's role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network's substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed to
Coherence resonance in globally coupled neuronal networks with different neuron numbers

International Nuclear Information System (INIS)

Ning Wei-Lian; Zhang Zheng-Zhen; Zeng Shang-You; Luo Xiao-Shu; Hu Jin-Lin; Zeng Shao-Wen; Qiu Yi; Wu Hui-Si

2012-01-01

Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance (0.1 mS/cm 2 ) is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding. (interdisciplinary physics and related areas of science and technology)
Neural networks in signal processing

International Nuclear Information System (INIS)

Govil, R.

2000-01-01

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

Science.gov (United States)

Maluck, Julian; Donner, Reik V.

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990–2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector’s role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network’s substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed
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......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....
Ensuring dynamic strategic fit of firms that compete globally in alliances and networks: proposing the Global SNA - Strategic Network Analysis - framework

Directory of Open Access Journals (Sweden)

T. Diana L. Van Aduard de Macedo-Soares

2011-02-01

Full Text Available In order to sustain their competitive advantage in the current increasingly globalized and turbulent context, more and more firms are competing globally in alliances and networks that oblige them to adopt new managerial paradigms and tools. However, their strategic analyses rarely take into account the strategic implications of these alliances and networks, considering their global relational characteristics, admittedly because of a lack of adequate tools to do so. This paper contributes to research that seeks to fill this gap by proposing the Global Strategic Network Analysis - SNA - framework. Its purpose is to help firms that compete globally in alliances and networks to carry out their strategic assessments and decision-making with a view to ensuring dynamic strategic fit from both a global and relational perspective.
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.
Logistical networking: a global storage network

International Nuclear Information System (INIS)

Beck, Micah; Moore, Terry

2005-01-01

The absence of an adequate distributed storage infrastructure for data buffering has become a significant impediment to the flow of work in the wide area, data intensive collaborations that are increasingly characteristic of leading edge research in several fields. One solution to this problem, pioneered under DOE's SciDAC program, is Logistical Networking, which provides a framework for a globally scalable, maximally interoperable storage network based on the Internet Backplane Protocol (IBP). This paper provides a brief overview of the Logistical Networking (LN) architecture, the middleware developed to exploit its value, and a few of the applications that some of research communities have made of it

Probabilistic encoding of stimulus strength in astrocyte global calcium signals.

Science.gov (United States)

Croft, Wayne; Reusch, Katharina; Tilunaite, Agne; Russell, Noah A; Thul, Rüdiger; Bellamy, Tomas C

2016-04-01

Astrocyte calcium signals can range in size from subcellular microdomains to waves that spread through the whole cell (and into connected cells). The differential roles of such local or global calcium signaling are under intense investigation, but the mechanisms by which local signals evolve into global signals in astrocytes are not well understood, nor are the computational rules by which physiological stimuli are transduced into a global signal. To investigate these questions, we transiently applied receptor agonists linked to calcium signaling to primary cultures of cerebellar astrocytes. Astrocytes repetitively tested with the same stimulus responded with global signals intermittently, indicating that each stimulus had a defined probability for triggering a response. The response probability varied between agonists, increased with agonist concentration, and could be positively and negatively modulated by crosstalk with other signaling pathways. To better understand the processes determining the evolution of a global signal, we recorded subcellular calcium "puffs" throughout the whole cell during stimulation. The key requirement for puffs to trigger a global calcium wave following receptor activation appeared to be the synchronous release of calcium from three or more sites, rather than an increasing calcium load accumulating in the cytosol due to increased puff size, amplitude, or frequency. These results suggest that the concentration of transient stimuli will be encoded into a probability of generating a global calcium response, determined by the likelihood of synchronous release from multiple subcellular sites. © 2015 Wiley Periodicals, Inc.
A fuzzy neural network for sensor signal estimation

International Nuclear Information System (INIS)

Na, Man Gyun

2000-01-01

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

DEFF Research Database (Denmark)

Wad, Peter

2014-01-01

Globalization transforms workforces of transnational corporation from predominantly home countrydominated workforces into foreign-dominated, multinational workforces. Thus, the national grounding of trade unions as the key form of labor organizing is challenged by new multinational compositions...... and cross-border relocations of corporate employment affecting working conditions of employees and trade unions in local places. We assume that economic globalization is characterized by expanding global corporate network of vertically and horizontally integrated (equity-based) and disintegrated (nonequity......-based) value chains. We also assume that globalization can both impede and enable labor empowerment. Based on these premises the key question is, how can labor leverage effective power against management in global corporate networks? This question is split into two subquestions: a) How can labor theoretically...
Automated detection and cataloging of global explosive volcanism using the International Monitoring System infrasound network

Science.gov (United States)

Matoza, Robin S.; Green, David N.; LeÂ Pichon, Alexis; Shearer, Peter M.; Fee, David; Mialle, Pierrick; Ceranna, Lars

2017-04-01

We experiment with a new method to search systematically through multiyear data from the International Monitoring System (IMS) infrasound network to identify explosive volcanic eruption signals originating anywhere on Earth. Detecting, quantifying, and cataloging the global occurrence of explosive volcanism helps toward several goals in Earth sciences and has direct applications in volcanic hazard mitigation. We combine infrasound signal association across multiple stations with source location using a brute-force, grid-search, cross-bearings approach. The algorithm corrects for a background prior rate of coherent unwanted infrasound signals (clutter) in a global grid, without needing to screen array processing detection lists from individual stations prior to association. We develop the algorithm using case studies of explosive eruptions: 2008 Kasatochi, Alaska; 2009 Sarychev Peak, Kurile Islands; and 2010 Eyjafjallajökull, Iceland. We apply the method to global IMS infrasound data from 2005-2010 to construct a preliminary acoustic catalog that emphasizes sustained explosive volcanic activity (long-duration signals or sequences of impulsive transients lasting hours to days). This work represents a step toward the goal of integrating IMS infrasound data products into global volcanic eruption early warning and notification systems. Additionally, a better understanding of volcanic signal detection and location with the IMS helps improve operational event detection, discrimination, and association capabilities.
Processing of seismic signals from a seismometer network

International Nuclear Information System (INIS)

Key, F.A.; Warburton, P.J.

1983-08-01

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)
Developmental changes of BOLD signal correlations with global human EEG power and synchronization during working memory.

Directory of Open Access Journals (Sweden)

Lars Michels

Full Text Available In humans, theta band (5-7 Hz power typically increases when performing cognitively demanding working memory (WM tasks, and simultaneous EEG-fMRI recordings have revealed an inverse relationship between theta power and the BOLD (blood oxygen level dependent signal in the default mode network during WM. However, synchronization also plays a fundamental role in cognitive processing, and the level of theta and higher frequency band synchronization is modulated during WM. Yet, little is known about the link between BOLD, EEG power, and EEG synchronization during WM, and how these measures develop with human brain maturation or relate to behavioral changes. We examined EEG-BOLD signal correlations from 18 young adults and 15 school-aged children for age-dependent effects during a load-modulated Sternberg WM task. Frontal load (in-dependent EEG theta power was significantly enhanced in children compared to adults, while adults showed stronger fMRI load effects. Children demonstrated a stronger negative correlation between global theta power and the BOLD signal in the default mode network relative to adults. Therefore, we conclude that theta power mediates the suppression of a task-irrelevant network. We further conclude that children suppress this network even more than adults, probably from an increased level of task-preparedness to compensate for not fully mature cognitive functions, reflected in lower response accuracy and increased reaction time. In contrast to power, correlations between instantaneous theta global field synchronization and the BOLD signal were exclusively positive in both age groups but only significant in adults in the frontal-parietal and posterior cingulate cortices. Furthermore, theta synchronization was weaker in children and was--in contrast to EEG power--positively correlated with response accuracy in both age groups. In summary we conclude that theta EEG-BOLD signal correlations differ between spectral power and
MANAGING GLOBAL OPERATIONS NETWORKS IN MOTION

DEFF Research Database (Denmark)

Slepniov, Dmitrij; Jørgensen, Claus; Sørensen, Brian Vejrum

2008-01-01

capabilities and intensified need for transfer, assimilation and augmentation of activities and know-how within the network. Based on these the paper highlights some organisational effects and managerial challenges the companies face regarding rapid changes in their networks configurations and capabilities.......Most industrial companies are, for reasons related to cost, market access or knowledge, working with some aspect of offshore operations. This may take form of captive operations or through outsourcing of activities overseas. With this trend, global operations networks are emerging resulting...... in corporate strategic repositioning, re-configurations of sites, and changes to the underlying capabilities. The paper is based on cases of 3 Danish companies and their global supply networks. These networks are not in a steady state, they evolve as a consequence of the ongoing co-evolution between the focal...
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.
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.
Strategic Knowledge Networks for Global Education

Science.gov (United States)

Peterson, J. Fiona

2009-01-01

The inherent opportunities for communication, collaboration and experiential learning in an online and global network create the impetus for the new network paradigm in higher education. A strategic knowledge network in education was designed and developed to build "Mode 2" knowledge capabilities; create new knowledge for innovative…
Servicing a globally broadcast interrupt signal in a multi-threaded computer

Science.gov (United States)

Attinella, John E.; Davis, Kristan D.; Musselman, Roy G.; Satterfield, David L.

2015-12-29

Methods, apparatuses, and computer program products for servicing a globally broadcast interrupt signal in a multi-threaded computer comprising a plurality of processor threads. Embodiments include an interrupt controller indicating in a plurality of local interrupt status locations that a globally broadcast interrupt signal has been received by the interrupt controller. Embodiments also include a thread determining that a local interrupt status location corresponding to the thread indicates that the globally broadcast interrupt signal has been received by the interrupt controller. Embodiments also include the thread processing one or more entries in a global interrupt status bit queue based on whether global interrupt status bits associated with the globally broadcast interrupt signal are locked. Each entry in the global interrupt status bit queue corresponds to a queued global interrupt.
State Support: A Prerequisite for Global Health Network Effectiveness

Science.gov (United States)

Marten, Robert; Smith, Richard D.

2018-01-01

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. PMID:29524958
Anthropomorphic Networks as Representatives of Global Consciousness

Directory of Open Access Journals (Sweden)

Sergii Yahodzinskyi

2018-02-01

Full Text Available There has been analyzed a phenomenon of global consciousness, and its cultural and historical, civilizational dimensions have been substantiated. There has been demonstrated that the concept of planetary consciousness, global thinking, noosphere was described for the first time in the philosophy of cosmism. However, in modern conditions ideas of representatives of the naturalistic philosophical direction of cosmism have not lost their heuristic potential. They can be reconsidered in a new fashion within the context of emerging anthropomorphic (human dimension networks. There has been proved that global consciousness is a component of the social and cultural potential of global information networks defining vectors to prospects of humanity progress in the 21st century. Relying on methodology of the structural and functional analysis, the author arrives at a conclusion about global networks obtaining the status of representatives of global consciousness. This is the area of networks where all relevant information is concentrated – from statistical data to scientific and technical information. Access to these data is limited by human abilities and is realized in the form of discrete requests with using heuristic algorithms of information procession. A suggestion is introduced considering the fact that modern society being a self-organized system seeks to gain stable condition. Anthropomorphic networks are means of decreasing social entropy, which is growing as a result of any kind of human intervention into social processes. Thus, for the first time a human is challenged by their intellect, ability to create, discover and control.
Information flow in a network of dispersed signalers-receivers

Science.gov (United States)

Halupka, Konrad

2017-11-01

I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.
Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells.

Directory of Open Access Journals (Sweden)

Aurélien Naldi

2017-03-01

Full Text Available The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform.
The network of global corporate control.

Science.gov (United States)

Vitali, Stefania; Glattfelder, James B; Battiston, Stefano

2011-01-01

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic "super-entity" that raises new important issues both for researchers and policy makers.
The CERN Global Network opens its doors to companies

CERN Multimedia

Francesco Poppi

2010-01-01

Six months after its launch, the CERN Global Network already has almost one thousand members. Today, it is opening its doors to companies from CERN's Member States. This will open up a variety of new professional and career opportunities to all the members and will enhance the networking capabilities of all parties involved. Screenshot of the CERN Global Network website. A new item has recently appeared on the top menu of the Network's website: “Organisations”. This is the entry point for companies and, later, research institutes, wishing to join. “The CERN Global Network brings together hundreds of people who have worked at or with CERN and who have a wealth of skills and expertise. Thanks to the Network, the job opportunities made available by the companies will become visible to the wider community,” says Linda Orr-Easo, a member of the Knowledge and Technology Transfer Group and the CERN Global Network Manager. In addition to creating new career opp...
E-njoy the first CERN Global Network e-vent!

CERN Multimedia

CERN Bulletin

2010-01-01

Empowered by the considerable interest it received after it was launched, the CERN Global Network takes off and organizes the first e-vent, which will be a special talk on science communication that will be held on 29 June at 4.30 p.m. in the Council Chamber. You can experience it live on the Global Network site and, if you are a Member, provide feedback. Stay linked! On the CERN Global Network webpage, you will be able to choose the topic of the next e-vents. Seven weeks after its launch, about 600 people have already joined the CERN Global Network and six thematic groups have been created. The whole idea of joining the Network is to stay connected or reconnect with life at CERN where seminars, talks and discussions are undoubtedly a very important and much appreciated part of it. This is where the e-vents come into play. “The e-vents enable members of the Global Network to participate in selected events taking place at CERN, such as lectures or panel discussions. They will...
Development of the Global Measles Laboratory Network.

Science.gov (United States)

Featherstone, David; Brown, David; Sanders, Ray

2003-05-15

The routine reporting of suspected measles cases and laboratory testing of samples from these cases is the backbone of measles surveillance. The Global Measles Laboratory Network (GMLN) has developed standards for laboratory confirmation of measles and provides training resources for staff of network laboratories, reference materials and expertise for the development and quality control of testing procedures, and accurate information for the Measles Mortality Reduction and Regional Elimination Initiative. The GMLN was developed along the lines of the successful Global Polio Laboratory Network, and much of the polio laboratory infrastructure was utilized for measles. The GMLN has developed as countries focus on measles control activities following successful eradication of polio. Currently more than 100 laboratories are part of the global network and follow standardized testing and reporting procedures. A comprehensive laboratory accreditation process will be introduced in 2002 with six quality assurance and performance indicators.
Global dissipativity of continuous-time recurrent neural networks with time delay

International Nuclear Information System (INIS)

Liao Xiaoxin; Wang Jun

2003-01-01

This paper addresses the global dissipativity of a general class of continuous-time recurrent neural networks. First, the concepts of global dissipation and global exponential dissipation are defined and elaborated. Next, the sets of global dissipativity and global exponentially dissipativity are characterized using the parameters of recurrent neural network models. In particular, it is shown that the Hopfield network and cellular neural networks with or without time delays are dissipative systems

Synchronization transmission of laser pattern signal within uncertain switched network

Science.gov (United States)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.
Quantitative phosphoproteomics to characterize signaling networks

DEFF Research Database (Denmark)

Rigbolt, Kristoffer T G; Blagoev, Blagoy

2012-01-01

for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify......Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform...... 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...
Structure and evolution of the global seafood trade network

Science.gov (United States)

Gephart, Jessica A.; Pace, Michael L.

2015-12-01

The food production system is increasingly global and seafood is among the most highly traded commodities. Global trade can improve food security by providing access to a greater variety of foods, increasing wealth, buffering against local supply shocks, and benefit the environment by increasing overall use efficiency for some resources. However, global trade can also expose countries to external supply shocks and degrade the environment by increasing resource demand and loosening feedbacks between consumers and the impacts of food production. As a result, changes in global food trade can have important implications for both food security and the environmental impacts of production. Measurements of globalization and the environmental impacts of food production require data on both total trade and the origin and destination of traded goods (the network structure). While the global trade network of agricultural and livestock products has previously been studied, seafood products have been excluded. This study describes the structure and evolution of the global seafood trade network, including metrics quantifying the globalization of seafood, shifts in bilateral trade flows, changes in centrality and comparisons of seafood to agricultural and industrial trade networks. From 1994 to 2012 the number of countries trading in the network remained relatively constant, while the number of trade partnerships increased by over 65%. Over this same period, the total quantity of seafood traded increased by 58% and the value increased 85% in real terms. These changes signify the increasing globalization of seafood products. Additionally, the trade patterns in the network indicate: increased influence of Thailand and China, strengthened intraregional trade, and increased exports from South America and Asia. In addition to characterizing these network changes, this study identifies data needs in order to connect seafood trade with environmental impacts and food security outcomes.
Analysis and logical modeling of biological signaling transduction networks

Science.gov (United States)

Sun, Zhongyao

The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.
GLOBAL CONVERGENCE FOR THE XOR BOOLEAN NETWORKS

OpenAIRE

Ho, Juei-Ling

2009-01-01

Shih and Ho have proved a global convergent theorem for boolean network: if a map from $\\{0,1\\}^{n}$ to itself defines a boolean network has the conditions: (1) each column of the discrete Jacobian matrix of each element of $\\{0,1\\}^{n}$ is either a unit vector or a zero vector; (2) all the boolean eigenvalues of the discrete Jacobian matrix of this map evaluated at each element of $\\{0,1\\}^{n}$ are zero, then it has a unique fixed point and this boolean network is global convergent to the fi...
Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

Directory of Open Access Journals (Sweden)

Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models
Are we connected? : Ports in Global Networks

NARCIS (Netherlands)

R.A. Zuidwijk (Rob)

2015-01-01

markdownabstractAbstract Global supply chains are built on organizational, information, and logistics networks. Ports are connected via these networks and also need to connect these networks. Synchromodality is an innovative concept for container transportation, and the port plays an important
Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

Science.gov (United States)

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.
Network organization is globally atypical in autism: A graph theory study of intrinsic functional connectivity.

Science.gov (United States)

Keown, Christopher L; Datko, Michael C; Chen, Colleen P; Maximo, José Omar; Jahedi, Afrooz; Müller, Ralph-Axel

2017-01-01

Despite abundant evidence of brain network anomalies in autism spectrum disorder (ASD), findings have varied from broad functional underconnectivity to broad overconnectivity. Rather than pursuing overly simplifying general hypotheses ('under' vs. 'over'), we tested the hypothesis of atypical network distribution in ASD (i.e., participation of unusual loci in distributed functional networks). We used a selective high-quality data subset from the ABIDE datashare (including 111 ASD and 174 typically developing [TD] participants) and several graph theory metrics. Resting state functional MRI data were preprocessed and analyzed for detection of low-frequency intrinsic signal correlations. Groups were tightly matched for available demographics and head motion. As hypothesized, the Rand Index (reflecting how similar network organization was to a normative set of networks) was significantly lower in ASD than TD participants. This was accounted for by globally reduced cohesion and density, but increased dispersion of networks. While differences in hub architecture did not survive correction, rich club connectivity (among the hubs) was increased in the ASD group. Our findings support the model of reduced network integration (connectivity with networks) and differentiation (or segregation; based on connectivity outside network boundaries) in ASD. While the findings applied at the global level, they were not equally robust across all networks and in one case (greater cohesion within ventral attention network in ASD) even reversed.
Resistance Genes in Global Crop Breeding Networks.

Science.gov (United States)

Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

2017-10-01

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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.
From Local to Global Dilemmas in Social Networks

OpenAIRE

Pinheiro, Fl?vio L.; Pacheco, Jorge M.; Santos, Francisco C.

2012-01-01

Social networks affect in such a fundamental way the dynamics of the population they support that the global, population-wide behavior that one observes often bears no relation to the individual processes it stems from. Up to now, linking the global networked dynamics to such individual mechanisms has remained elusive. Here we study the evolution of cooperation in networked populations and let individuals interact via a 2-person Prisoner's Dilemma ? a characteristic defection dominant social ...
Detection test of wireless network signal strength and GPS positioning signal in underground pipeline

Science.gov (United States)

Li, Li; Zhang, Yunwei; Chen, Ling

2018-03-01

In order to solve the problem of selecting positioning technology for inspection robot in underground pipeline environment, the wireless network signal strength and GPS positioning signal testing are carried out in the actual underground pipeline environment. Firstly, the strength variation of the 3G wireless network signal and Wi-Fi wireless signal provided by China Telecom and China Unicom ground base stations are tested, and the attenuation law of these wireless signals along the pipeline is analyzed quantitatively and described. Then, the receiving data of the GPS satellite signal in the pipeline are tested, and the attenuation of GPS satellite signal under underground pipeline is analyzed. The testing results may be reference for other related research which need to consider positioning in pipeline.
Brain Network Analysis from High-Resolution EEG Signals

Science.gov (United States)

de Vico Fallani, Fabrizio; Babiloni, Fabio

Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular
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 characterization of signalling events at the levels of post-translational modifications, protein-protein interactions and changes in protein expression. This technology delivers accurate and unbiased information about the quantitative changes of thousands of proteins and their modifications in response to any...... 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....
The global structure of knowledge network

NARCIS (Netherlands)

Angelopoulos, Spyros; Lomi, Alessandro

2017-01-01

In this paper, we treat patent citations as knowledge networks connecting pieces of formalized knowledge and people, and focus on how ideas are connected, rather than how they are protected. We focus on the global structural properties of formalized knowledge network, and more specifically on the
Global network centrality of university rankings

Science.gov (United States)

Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna

2017-10-01

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.
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.
All-optical network coding for DPSK signals

DEFF Research Database (Denmark)

An, Yi; Da Ros, Francesco; Peucheret, Christophe

2013-01-01

All-optical network coding for path protection is experimentally demonstrated using four-wave mixing in SOAs for10 Gbit/s NRZ-DPSK signals with error free performance. The total power penalty after two cascaded XOR stage is only 2 dB.......All-optical network coding for path protection is experimentally demonstrated using four-wave mixing in SOAs for10 Gbit/s NRZ-DPSK signals with error free performance. The total power penalty after two cascaded XOR stage is only 2 dB....
Global synchronization of a class of delayed complex networks

International Nuclear Information System (INIS)

Li Ping; Yi Zhang; Zhang Lei

2006-01-01

Global synchronization of a class of complex networks with time-varying delays is investigated in this paper. Some sufficient conditions are derived. These conditions show that the synchronization of delayed complex networks can be determined by their topologies. In addition, these conditions are simply represented in terms of the networks coupling matrix and are easy to be checked. A typical example of complex networks with chaotic nodes is employed to illustrate the obtained global synchronization results

Modeling evolution of crosstalk in noisy signal transduction networks

Science.gov (United States)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.
Mathematical Modelling Plant Signalling Networks

KAUST Repository

Muraro, D.; Byrne, H.M.; King, J.R.; Bennett, M.J.

2013-01-01

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
Fiber fault location utilizing traffic signal in optical network.

Science.gov (United States)

Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

2013-10-07

We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.
Dopamine D1 signaling organizes network dynamics underlying working memory.

Science.gov (United States)

Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

2016-06-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.
Global Synoptic Climatology Network (GSCN)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Dataset DSI-9290 is the result of a joint effort to create a Global Synoptic Climatology Network among the Meteorological Service of Canada (Downsview, Ontario and...
Information content in reflected global navigation satellite system signals

DEFF Research Database (Denmark)

Høeg, Per; Carlstrom, Anders

2011-01-01

The direct signals from satellites in global satellite navigation satellites systems (GNSS) as, GPS, GLONASS and GALILEO, constitute the primary source for positioning, navigation and timing from space. But also the reflected GNSS signals contain an important information content of signal travel...
Application of neural networks to signal prediction in nuclear power plant

International Nuclear Information System (INIS)

Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

1993-01-01

This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well
The global transmission network of HIV-1.

Science.gov (United States)

Wertheim, Joel O; Leigh Brown, Andrew J; Hepler, N Lance; Mehta, Sanjay R; Richman, Douglas D; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-01-15

Human immunodeficiency virus type 1 (HIV-1) is pandemic, but its contemporary global transmission network has not been characterized. A better understanding of the properties and dynamics of this network is essential for surveillance, prevention, and eventual eradication of HIV. Here, we apply a simple and computationally efficient network-based approach to all publicly available HIV polymerase sequences in the global database, revealing a contemporary picture of the spread of HIV-1 within and between countries. This approach automatically recovered well-characterized transmission clusters and extended other clusters thought to be contained within a single country across international borders. In addition, previously undescribed transmission clusters were discovered. Together, these clusters represent all known modes of HIV transmission. The extent of international linkage revealed by our comprehensive approach demonstrates the need to consider the global diversity of HIV, even when describing local epidemics. Finally, the speed of this method allows for near-real-time surveillance of the pandemic's progression.
Mapping the follicle-stimulating hormone-induced signalling networks

Directory of Open Access Journals (Sweden)

Pauline eGloaguen

2011-10-01

Full Text Available Follicle-stimulating hormone (FSH is a central regulator of male and female reproductive function. Over the last decade, there has been a growing perception of the complexity associated with FSH-induced cellular signalling. It is now clear that the canonical Gs/cAMP/PKA pathway is not the sole mechanism that must be considered in FSH biological actions. In parallel, consistent with the emerging concept of biased agonism, several examples of ligand-mediated selective signalling pathway activation by gonadotropin receptors have been reported. In this context, it is important to gain an integrative view of the signalling pathways induced by FSH and how they interconnect to form a network. In this review, we propose a first attempt at building topological maps of various pathways known to be involved in the FSH-induced signalling network. We discuss the multiple facets of FSH-induced signalling and how they converge to the hormone integrated biological response. Despite of their incompleteness, these maps of the FSH-induced signalling network represent a first step towards gaining a system-level comprehension of this hormone’s actions, which may ultimately facilitate the discovery of novel regulatory processes and therapeutic strategies for infertilities and non-steroidal contraception.
Towards a global quantum network

Science.gov (United States)

Simon, Christoph

2017-11-01

The creation of a global quantum network is now a realistic proposition thanks to developments in satellite and fibre links and quantum memory. Applications will range from secure communication and fundamental physics experiments to a future quantum internet.
Evolution of SH2 domains and phosphotyrosine signalling networks

Science.gov (United States)

Liu, Bernard A.; Nash, Piers D.

2012-01-01

Src homology 2 (SH2) domains mediate selective protein–protein interactions with tyrosine phosphorylated proteins, and in doing so define specificity of phosphotyrosine (pTyr) signalling networks. SH2 domains and protein-tyrosine phosphatases expand alongside protein-tyrosine kinases (PTKs) to coordinate cellular and organismal complexity in the evolution of the unikont branch of the eukaryotes. Examination of conserved families of PTKs and SH2 domain proteins provides fiduciary marks that trace the evolutionary landscape for the development of complex cellular systems in the proto-metazoan and metazoan lineages. The evolutionary provenance of conserved SH2 and PTK families reveals the mechanisms by which diversity is achieved through adaptations in tissue-specific gene transcription, altered ligand binding, insertions of linear motifs and the gain or loss of domains following gene duplication. We discuss mechanisms by which pTyr-mediated signalling networks evolve through the development of novel and expanded families of SH2 domain proteins and the elaboration of connections between pTyr-signalling proteins. These changes underlie the variety of general and specific signalling networks that give rise to tissue-specific functions and increasingly complex developmental programmes. Examination of SH2 domains from an evolutionary perspective provides insight into the process by which evolutionary expansion and modification of molecular protein interaction domain proteins permits the development of novel protein-interaction networks and accommodates adaptation of signalling networks. PMID:22889907
Multinational Firms and the Management of Global Networks

DEFF Research Database (Denmark)

De Marchi, Valentina; Maria, Eleonora Di; Ponte, Stefano

2014-01-01

This paper aims at enriching the literature on international business (IB) studies to include insights from Global Value Chain (GVC) analysis to better explain how MNCs can orchestrate a global network organization. A first important contribution of the GVC literature is that it shifts the focus...... from single firms to their value chains, providing instruments to study how activities are split and organized among different firms at the industry level, and how MNCs can implement different governing mechanisms within a network-based setting. The GVC literature also highlights that retailers (as...... can manage their network relationships in a global scenario. Finally, through their focus on upgrading, GVC studies suggest that knowledge flows and innovation dynamics taking place within value chains are as important as those taking place within the MNC’s organizational border. We conclude...
IMPLICATIONS OF SOCIAL RESPONSIBILITY DISCLOSURE ON GLOBAL PRODUCTION NETWORK

OpenAIRE

Le Bo; Dan Shen; Jin Jun Bo

2014-01-01

This paper aims to discuss effectiveness of social responsibility disclosure in promoting global production network. Through a critical review on the theoretical development from supply chain to global production network, the global supply chain management of Apple Inc., as a case, is investigated, with focus on corporate and NGOs’ social disclosure on the environmental and labor rights' issues of its suppliers in China. The paper concludes that effectiveness of corporate social disclosure on...
A new criterion to global exponential periodicity for discrete-time BAM neural network with infinite delays

International Nuclear Information System (INIS)

Zhou Tiejun; Liu Yuehua; Li Xiaoping; Liu Yirong

2009-01-01

The discrete-time bidirectional associative memory neural network with periodic coefficients and infinite delays is studied. And not by employing the continuation theorem of coincidence degree theory as other literatures, but by constructing suitable Liapunov function, using fixed point theorem and some analysis techniques, a sufficient criterion is obtained which ensures the existence and global exponential stability of periodic solution for the type of discrete-time BAM neural network. The obtained result is less restrictive to the BAM neural networks than previously known criteria. Furthermore, it can be applied to the BAM neural network which signal transfer functions are neither bounded nor differentiable. In addition, an example and its numerical simulation are given to illustrate the effectiveness of the obtained result
Running a network on a shoestring: the Global Invasive Species Information Network

Science.gov (United States)

Jarnevich, Catherine S.; Simpson, Annie; Graham, James J; Newman, Gregory J.; Bargeron, Chuck T.

2015-01-01

The Global Invasive Species Information Network (GISIN) was conceptualized in 2004 to aggregate and disseminate invasive species data in a standardized way. A decade later the GISIN community has implemented a data portal and three of six GISIN data aggregation models in the GISIN data exchange Protocol, including invasive species status information, resource URLs, and occurrence data. The portal is based on a protocol developed by representatives from 15 countries and 27 organizations of the global invasive species information management community. The GISIN has 19 data providers sharing 34,343 species status records, 1,693,073 occurrences, and 15,601 resource URLs. While the GISIN's goal is to be global, much of its data and funding are provided by the United States. Several initiatives use the GISIN as their information backbone, such as the Great Lakes Early Detection Network (GLEDN) and the North American Invasive Species Network (NAISN). Here we share several success stories and organizational challenges that remain.
Assuring SS7 dependability: A robustness characterization of signaling network elements

Science.gov (United States)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.
Four Challenges That Global Health Networks Face

Directory of Open Access Journals (Sweden)

Jeremy Shiffman

2017-04-01

Full Text Available Global health networks, webs of individuals and organizations with a shared concern for a particular condition, have proliferated over the past quarter century. They differ in their effectiveness, a factor that may help explain why resource allocations vary across health conditions and do not correspond closely with disease burden. Drawing on findings from recently concluded studies of eight global health networks—addressing alcohol harm, early childhood development (ECD, maternal mortality, neonatal mortality, pneumonia, surgically-treatable conditions, tobacco use, and tuberculosis—I identify four challenges that networks face in generating attention and resources for the conditions that concern them. The first is problem definition: generating consensus on what the problem is and how it should be addressed. The second is positioning: portraying the issue in ways that inspire external audiences to act. The third is coalition-building: forging alliances with these external actors, particularly ones outside the health sector. The fourth is governance: establishing institutions to facilitate collective action. Research indicates that global health networks that effectively tackle these challenges are more likely to garner support to address the conditions that concern them. In addition to the effectiveness of networks, I also consider their legitimacy, identifying reasons both to affirm and to question their right to exert power.
Weak signal transmission in complex networks and its application in detecting connectivity.

Science.gov (United States)

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

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

Directory of Open Access Journals (Sweden)

Dennis Sayers

1995-05-01

Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.
Global operations networks in motion: Managing configurations and capabilities

DEFF Research Database (Denmark)

Slepniov, Dmitrij; Wæhrens, Brian Vejrum; Jørgensen, Claus

2010-01-01

In the past, the ‘Made in the World’ label, although capturing what may lie ahead, seemed awkward and futuristic. Today, it has become a reality. An ample array of global products are built up of numerous components and modules manufactured by global networks of differentiated partners rather than...... within the boundaries of one national entity. The purpose of this paper is to contribute to bridging the empirical gap in the area of global operations networks and provide insights into how they change over time. The paper is based on the cases of three Danish companies and their global operations...

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

2012-11-05

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.
Evolution of the global virtual water trade network.

Science.gov (United States)

Dalin, Carole; Konar, Megan; Hanasaki, Naota; Rinaldo, Andrea; Rodriguez-Iturbe, Ignacio

2012-04-17

Global freshwater resources are under increasing pressure from economic development, population growth, and climate change. The international trade of water-intensive products (e.g., agricultural commodities) or virtual water trade has been suggested as a way to save water globally. We focus on the virtual water trade network associated with international food trade built with annual trade data and annual modeled virtual water content. The evolution of this network from 1986 to 2007 is analyzed and linked to trade policies, socioeconomic circumstances, and agricultural efficiency. We find that the number of trade connections and the volume of water associated with global food trade more than doubled in 22 years. Despite this growth, constant organizational features were observed in the network. However, both regional and national virtual water trade patterns significantly changed. Indeed, Asia increased its virtual water imports by more than 170%, switching from North America to South America as its main partner, whereas North America oriented to a growing intraregional trade. A dramatic rise in China's virtual water imports is associated with its increased soy imports after a domestic policy shift in 2000. Significantly, this shift has led the global soy market to save water on a global scale, but it also relies on expanding soy production in Brazil, which contributes to deforestation in the Amazon. We find that the international food trade has led to enhanced savings in global water resources over time, indicating its growing efficiency in terms of global water use.
The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

Energy Technology Data Exchange (ETDEWEB)

Krishnan, J. [Department of Chemical Engineering, Centre for Process Systems Engineering, Institute for Systems and Synthetic Biology, Imperial College London, London SW7 2AZ (United Kingdom); Mois, Kristina; Suwanmajo, Thapanar [Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ (United Kingdom)

2014-11-07

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

Science.gov (United States)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological
Global asymptotic stability of delayed Cohen-Grossberg neural networks

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

In this letter, the global asymptotic stability of a class of Cohen-Grossberg neural networks with time-varying delays is discussed. A new set of sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence. Our criteria represent an extension of the existing results in literatures. An example is also presented to compare our results with the previous results
mTORC1 Is a Major Regulatory Node in the FGF21 Signaling Network in Adipocytes

Directory of Open Access Journals (Sweden)

Annabel Y. Minard

2016-09-01

Full Text Available FGF21 improves the metabolic profile of obese animals through its actions on adipocytes. To elucidate the signaling network responsible for mediating these effects, we quantified dynamic changes in the adipocyte phosphoproteome following acute exposure to FGF21. FGF21 regulated a network of 821 phosphosites on 542 proteins. A major FGF21-regulated signaling node was mTORC1/S6K. In contrast to insulin, FGF21 activated mTORC1 via MAPK rather than through the canonical PI3K/AKT pathway. Activation of mTORC1/S6K by FGF21 was surprising because this is thought to contribute to deleterious metabolic effects such as obesity and insulin resistance. Rather, mTORC1 mediated many of the beneficial actions of FGF21 in vitro, including UCP1 and FGF21 induction, increased adiponectin secretion, and enhanced glucose uptake without any adverse effects on insulin action. This study provides a global view of FGF21 signaling and suggests that mTORC1 may act to facilitate FGF21-mediated health benefits in vivo.
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.
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.
Global value chains: Building blocks and network dynamics

Science.gov (United States)

Tsekeris, Theodore

2017-12-01

The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.
Functional alignment of regulatory networks: a study of temperate phages.

Directory of Open Access Journals (Sweden)

Ala Trusina

2005-12-01

Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.
Load-induced modulation of signal transduction networks.

Science.gov (United States)

Jiang, Peng; Ventura, Alejandra C; Sontag, Eduardo D; Merajver, Sofia D; Ninfa, Alexander J; Del Vecchio, Domitilla

2011-10-11

Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.
The complex network of global cargo ship movements.

Science.gov (United States)

Kaluza, Pablo; Kölzsch, Andrea; Gastner, Michael T; Blasius, Bernd

2010-07-06

Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features that set it apart from other transportation networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.
Plasticity of the MAPK signaling network in response to mechanical stress.

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Andrea M Pereira

Full Text Available Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.
Global robust stability of delayed recurrent neural networks

International Nuclear Information System (INIS)

Cao Jinde; Huang Deshuang; Qu Yuzhong

2005-01-01

This paper is concerned with the global robust stability of a class of delayed interval recurrent neural networks which contain time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. A new sufficient condition is presented for the existence, uniqueness, and global robust stability of equilibria for interval neural networks with time delays by constructing Lyapunov functional and using matrix-norm inequality. An error is corrected in an earlier publication, and an example is given to show the effectiveness of the obtained results
Signaling mechanisms underlying the robustness and tunability of the plant immune network

Science.gov (United States)

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900
ECG Identification System Using Neural Network with Global and Local Features

Science.gov (United States)

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…
Links that speak: the global language network and its association with global fame.

Science.gov (United States)

Ronen, Shahar; Gonçalves, Bruno; Hu, Kevin Z; Vespignani, Alessandro; Pinker, Steven; Hidalgo, César A

2014-12-30

Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language's centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.
Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

Directory of Open Access Journals (Sweden)

F. Bagheri

2013-02-01

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

International Nuclear Information System (INIS)

Ji, Qiang; Zhang, Hai-Ying; Fan, Ying

2014-01-01

Highlights: • A global oil trade core network is analyzed using complex network theory. • The global oil export core network displays a scale-free behaviour. • The current global oil trade network can be divided into three trading blocs. • The global oil trade network presents a ‘robust and yet fragile’ characteristic. - Abstract: The Global oil trade pattern becomes increasingly complex, which has become one of the most important factors affecting every country’s energy strategy and economic development. In this paper, a global oil trade core network is constructed to analyze the overall features, regional characteristics and stability of the oil trade using complex network theory. The results indicate that the global oil export core network displays a scale-free behaviour, in which the trade position of nodes presents obvious heterogeneity and the ‘hub nodes’ play a ‘bridge’ role in the formation process of the trade network. The current global oil trade network can be divided into three trading blocs, including the ‘South America-West Africa-North America’ trading bloc, the ‘Middle East–Asian–Pacific region’ trading bloc, and ‘the former Soviet Union–North Africa–Europe’ trading bloc. Geopolitics and diplomatic relations are the two main reasons for this regional oil trade structure. Moreover, the global oil trade network presents a ‘robust but yet fragile’ characteristic, and the impacts of trade interruption always tend to spread throughout the whole network even if the occurrence of export disruptions is localised

Alignment of global supply networks based on strategic groups of supply chains

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Nikos G. Moraitakis

2017-09-01

Full Text Available Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic supply chain perspective in order to align regional competitive priorities and supply chain structures. The objective of this research is hence to develop an approach for the supply-chain-based alignment of complex global supply networks. Method: We review existing literature from the fields of supply chain and network management, strategic sourcing, and strategic management. Based on the literature review and theoretical and practical considerations we deduce a conceptual approach to consider upstream supply chain structures in supply network alignment initiatives. Results: On the basis of these considerations and current empirical literature we transfer strategic group theory to the supply network management context. The proposed approach introduces strategic groups of supply chains as a segmentation criterion for complex global supply networks which enables the network-wide alignment of competitive priorities. Conclusion: Supply-chain-based segmentation of global supply network structures can effectively reduce the complexity, firms face when aiming to strategically align their supply chains on a holistic level. The results of this research are applicable for certain types of global supply networks and can be used for network alignment and strategy development. The approach can furthermore generate insights useable for negotiation support with suppliers.
Association of structural global brain network properties with intelligence in normal aging.

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Florian U Fischer

Full Text Available Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60-85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.
Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

Science.gov (United States)

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994
Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

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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.
Discrete dynamic modeling of T cell survival signaling networks

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Zhang, Ranran

2009-03-01

Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: (i) Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (ii) Sphingosine kinase 1 and NFκB are essential for the long-term survival of T cells in T-LGL leukemia. (iii) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).
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.
Solidarity Action in Global Labor Networks. Four Cases of Workplace Organizing at Foreign Affiliates in the Global South

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Peter Wad

2014-03-01

Full Text Available Globalization transforms workforces of transnational corporation from predominantly home countrydominated workforces into foreign-dominated, multinational workforces. Thus, the national grounding of trade unions as the key form of labor organizing is challenged by new multinational compositions and cross-border relocations of corporate employment affecting working conditions of employees and trade unions in local places. We assume that economic globalization is characterized by expanding global corporate network of vertically and horizontally integrated (equity-based and disintegrated (nonequity-based value chains. We also assume that globalization can both impede and enable labor empowerment. Based on these premises the key question is, how can labor leverage effective power against management in global corporate networks? This question is split into two subquestions: a How can labor theoretically reorganize from national unions and industrial relations institutions into global labor networks that allow prolabor improvement in global workplaces? b How and why has labor in a globalized economy secured the core International Labor Organization (ILO international labor right to organize companies and conduct collective bargaining? The Global Labor Network perspective is adopted as an analytical framework. Empirically, a comparative case methodology is applied comprising four more or less successful industrial disputes where labor achieved the right to organize and undertake collective bargaining. The disputes took place in affiliated factories of foreign transnational corporations located in Malaysia, the Philippines, Sri Lanka, and Turkey. The conclusion is that the combination of global labor capabilities and global labor strategizing must generate strategic labor power that adequately matches the weaknesses of the counterpart’s global corporate network in order to achieve prolabor outcomes. The most efficient solidarity action was leveraged
Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

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Zhi Zheng

2012-01-01

Full Text Available A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.
Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

Science.gov (United States)

Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.
Optical Performance Monitoring and Signal Optimization in Optical Networks

DEFF Research Database (Denmark)

Petersen, Martin Nordal

2006-01-01

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

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Rok Berlot

2016-12-01

Full Text Available Background: Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. Objective: To determine the contribution of both localised white matter tract damage and disruption of global network architecture to cognitive control, in older age and Mild Cognitive Impairment (MCI.Methods: 25 patients with MCI and 20 age, sex and intelligence-matched healthy volunteers were investigated with 3 Tesla structural magnetic resonance imaging (MRI. Cognitive control and episodic memory were evaluated with established tests. Structural network graphs were constructed from diffusion MRI-based whole-brain tractography. Their global measures were calculated using graph theory. Regression models utilized both global network metrics and microstructure of specific connections, known to be critical for each domain, to predict cognitive scores. Results: Global efficiency and the mean clustering coefficient of networks were reduced in MCI. Cognitive control was associated with global network topology. Episodic memory, in contrast, correlated with individual temporal tracts only. Relationships between cognitive control and network topology were attenuated by addition of single tract measures to regression models, consistent with a partial mediation effect. The mediation effect was stronger in MCI than healthy volunteers, explaining 23-36% of the effect of cingulum microstructure on cognitive control performance. Network clustering was a significant mediator in the relationship between tract microstructure and cognitive control in both groups. Conclusions: The status of critical connections and large-scale network topology are both important for maintenance of cognitive control in MCI. Mediation via large-scale networks is more important in patients with MCI than healthy volunteers. This effect is domain-specific, and true for cognitive
How to most effectively expand the global surface ozone observing network

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E. D. Sofen

2016-02-01

Full Text Available Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere–biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean. Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12–17 % show significant gaps. Antarctica is surprisingly well observed (78 %. Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics are significantly under-observed. The current network is unlikely to see the impact of the El Niño–Southern Oscillation (ENSO but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new
How to most effectively expand the global surface ozone observing network

Science.gov (United States)

Sofen, E. D.; Bowdalo, D.; Evans, M. J.

2016-02-01

Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere-biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean). Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12-17 %) show significant gaps. Antarctica is surprisingly well observed (78 %). Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics) are significantly under-observed. The current network is unlikely to see the impact of the El Niño-Southern Oscillation (ENSO) but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new sites which would help to close
OPNET simulation Signaling System No.7 (SS7) network interfaces

OpenAIRE

Ow, Kong Chung.

2000-01-01

This thesis presents an OPNET model and simulation of the Signaling System No.7 (SS7) network, which is dubbed the world's largest data communications network. The main focus of the study is to model one of its levels, the Message Transfer Part Level 3, in accordance with the ITU.T recommendation Q.704. An overview of SS7 that includes the evolution and basics of SS7 architecture is provided to familarize the reader with the topic. This includes the protocol stack, signaling points, signaling...
Reverse engineering GTPase programming languages with reconstituted signaling networks.

Science.gov (United States)

Coyle, Scott M

2016-07-02

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

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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.
Global exponential stability for nonautonomous cellular neural networks with delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

In this Letter, by utilizing Lyapunov functional method and Halanay inequalities, we analyze global exponential stability of nonautonomous cellular neural networks with delay. Several new sufficient conditions ensuring global exponential stability of the network are obtained. The results given here extend and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results
Polycentrism in Global Health Governance Scholarship; Comment on “Four Challenges That Global Health Networks Face”

Directory of Open Access Journals (Sweden)

Jale Tosun

2018-01-01

Full Text Available Drawing on an in-depth analysis of eight global health networks, a recent essay in this journal argued that global health networks face four challenges to their effectiveness: problem definition, positioning, coalition-building, and governance. While sharing the argument of the essay concerned, in this commentary, we argue that these analytical concepts can be used to explicate a concept that has implicitly been used in global health governance scholarship for quite a few years. While already prominent in the discussion of climate change governance, for instance, global health governance scholarship could make progress by looking at global health governance as being polycentric. Concisely, polycentric forms of governance mix scales, mechanisms, and actors. Drawing on the essay, we propose a polycentric approach to the study of global health governance that incorporates coalitionbuilding tactics, internal governance and global political priority as explanatory factors.
Links that speak: The global language network and its association with global fame

Science.gov (United States)

Ronen, Shahar; Gonçalves, Bruno; Hu, Kevin Z.; Vespignani, Alessandro; Pinker, Steven; Hidalgo, César A.

2014-01-01

Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce. PMID:25512502
Signal Transduction Pathways of TNAP: Molecular Network Analyses.

Science.gov (United States)

Négyessy, László; Györffy, Balázs; Hanics, János; Bányai, Mihály; Fonta, Caroline; Bazsó, Fülöp

2015-01-01

Despite the growing body of evidence pointing on the involvement of tissue non-specific alkaline phosphatase (TNAP) in brain function and diseases like epilepsy and Alzheimer's disease, our understanding about the role of TNAP in the regulation of neurotransmission is severely limited. The aim of our study was to integrate the fragmented knowledge into a comprehensive view regarding neuronal functions of TNAP using objective tools. As a model we used the signal transduction molecular network of a pyramidal neuron after complementing with TNAP related data and performed the analysis using graph theoretic tools. The analyses show that TNAP is in the crossroad of numerous pathways and therefore is one of the key players of the neuronal signal transduction network. Through many of its connections, most notably with molecules of the purinergic system, TNAP serves as a controller by funnelling signal flow towards a subset of molecules. TNAP also appears as the source of signal to be spread via interactions with molecules involved among others in neurodegeneration. Cluster analyses identified TNAP as part of the second messenger signalling cascade. However, TNAP also forms connections with other functional groups involved in neuronal signal transduction. The results indicate the distinct ways of involvement of TNAP in multiple neuronal functions and diseases.

Variability of signal-to-noise ratio and the network analysis of gravitational wave burst signals

International Nuclear Information System (INIS)

Mohanty, S D; Rakhmanov, M; Klimenko, S; Mitselmakher, G

2006-01-01

The detection and estimation of gravitational wave burst signals, with a priori unknown polarization waveforms, requires the use of data from a network of detectors. Maximizing the network likelihood functional over all waveforms and sky positions yields point estimates for them as well as a detection statistic. However, the transformation from the data to estimates can become ill-conditioned over parts of the sky, resulting in significant errors in estimation. We modify the likelihood procedure by introducing a penalty functional which suppresses candidate solutions that display large signal-to-noise ratio (SNR) variability as the source is displaced on the sky. Simulations show that the resulting network analysis method performs significantly better in estimating the sky position of a source. Further, this method can be applied to any network, irrespective of the number or mutual alignment of detectors
Measurements of the global 21-cm signal from the Cosmic Dawn

Science.gov (United States)

Bernardi, Gianni

2018-05-01

The sky-averaged (global) 21-cm signal is a very promising probe of the Cosmic Dawn, when the first luminous sources were formed and started to shine in a substantially neutral intergalactic medium. I here report on the status and early result of the Large-Aperture Experiment to Detect the Dark Age that focuses on observations of the global 21-cm signal in the 16 <~ z <~ 30 range.
Hybrid digital signal processing and neural networks applications in PWRs

International Nuclear Information System (INIS)

Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

1991-01-01

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

Directory of Open Access Journals (Sweden)

Kuo Zhang

2018-01-01

Full Text Available The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA on the neuroendocrine-immune (NEI network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.
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...... also for implementing full signal path symmetry in real-time oscilloscopes to provide performance and signal fidelity (i.e. lower noise and jitter). In this paper the key digital signal processing (DSP) subsystems required to achieve signal slicing are surveyed. It also presents, for the first time...... 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....
Signaling pathway networks mined from human pituitary adenoma proteomics data

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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
Digital Signal Processing and Control for the Study of Gene Networks

Science.gov (United States)

Shin, Yong-Jun

2016-04-01

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

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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
Overload Control in a SIP Signaling Network

OpenAIRE

Masataka Ohta

2007-01-01

The Internet telephony employs a new type of Internet communication on which a mutual communication is realized by establishing sessions. Session Initiation Protocol (SIP) is used to establish sessions between end-users. For unreliable transmission (UDP), SIP message should be retransmitted when it is lost. The retransmissions increase a load of the SIP signaling network, and sometimes lead to performance degradation when a network is overloaded. The paper proposes an overload control for a S...
Adaptive approach to global synchronization of directed networks with fast switching topologies

International Nuclear Information System (INIS)

Qin Buzhi; Lu Xinbiao

2010-01-01

Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
Local and global responses in complex gene regulation networks

Science.gov (United States)

Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

2009-04-01

An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.
Sensor signal analysis by neural networks for surveillance in nuclear reactors

International Nuclear Information System (INIS)

Keyvan, S.; Rabelo, L.C.

1992-01-01

The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs
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.
Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

Directory of Open Access Journals (Sweden)

Ananthi Jebaseeli Samuelraj

2015-01-01

Full Text Available 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.
Applications of autoassociative neural networks for signal validation in accident management

International Nuclear Information System (INIS)

Fantoni, P.; Mazzola, A.

1994-01-01

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

International Nuclear Information System (INIS)

Fantoni, P.F.; Mazzola, A.

1996-01-01

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

Science.gov (United States)

Magliocca, N. R.

2015-12-01

Rural livelihoods and the land systems on which they depend are increasingly influenced by distant markets through economic globalization. Place-based analyses of land and livelihood system sustainability must then consider both proximate and distant influences on local decision-making. Thus, advancing land change theory in the context of economic globalization calls for a systematic understanding of the general processes as well as local contingencies shaping local responses to global signals. Synthesis of insights from place-based case studies of land and livelihood change is a path forward for developing such systematic knowledge. This paper introduces a model-based synthesis approach to investigating the influence of local socio-environmental and agent-level factors in mediating land-use and livelihood responses to changing global market signals. A generalized agent-based modeling framework is applied to six case-study sites that differ in environmental conditions, market access and influence, and livelihood settings. The largest modeled land conversions and livelihood transitions to market-oriented production occurred in sties with relatively productive agricultural land and/or with limited livelihood options. Experimental shifts in the distributions of agents' risk tolerances generally acted to attenuate or amplify responses to changes in global market signals. Importantly, however, responses of agents at different points in the risk tolerance distribution varied widely, with the wealth gap growing wider between agents with higher or lower risk tolerance. These results demonstrate model-based synthesis is a promising approach to overcome many of the challenges of current synthesis methods in land change science, and to identify generalized as well as locally contingent responses to global market signals.
Cluster synchronization transmission of different external signals in discrete uncertain network

Science.gov (United States)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.
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
The Role of Functional Interdependencies in Global Operations Networks

DEFF Research Database (Denmark)

Slepniov, Dmitrij; Wæhrens, Brian Vejrum

2011-01-01

The existing studies do not adequately address the complex interplay between co-evolving production, innovation and service networks. The widening geographical and cognitive gap between these networks means that managing their interfaces in global operations context is becoming strategically...

Global Interconnectedness - Local Authorities and Transnational Networking

Directory of Open Access Journals (Sweden)

Hans Krause Hansen

2006-09-01

Full Text Available This article argues that, in their continuous and proclaimed efforts at "modernizing" themselves, public sector organizations, also at the sub-national level, increasingly envision the new media as an object of policy making and intervention. At the same time, this focus on the new media facilitates transborder networking, taking the shape of globalizing webs that connect the actors internationally through pro- cesses af mediation and with implications for relations af authority and modes of governance. As such, these webs both constitute and facilitate a form of everyday political globalization which is on the rise. Empirically, our account is based on studies of two local authorities, the cities of Vina del Mar in Chile and Bremen in Germany, as two of the transnational networks that connect them.
Polycentrism in Global Health Governance Scholarship Comment on "Four Challenges That Global Health Networks Face".

Science.gov (United States)

Tosun, Jale

2017-05-23

Drawing on an in-depth analysis of eight global health networks, a recent essay in this journal argued that global health networks face four challenges to their effectiveness: problem definition, positioning, coalition-building, and governance. While sharing the argument of the essay concerned, in this commentary, we argue that these analytical concepts can be used to explicate a concept that has implicitly been used in global health governance scholarship for quite a few years. While already prominent in the discussion of climate change governance, for instance, global health governance scholarship could make progress by looking at global health governance as being polycentric. Concisely, polycentric forms of governance mix scales, mechanisms, and actors. Drawing on the essay, we propose a polycentric approach to the study of global health governance that incorporates coalitionbuilding tactics, internal governance and global political priority as explanatory factors. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ecological network analysis on global virtual water trade.

Science.gov (United States)

Yang, Zhifeng; Mao, Xufeng; Zhao, Xu; Chen, Bin

2012-02-07

Global water interdependencies are likely to increase with growing virtual water trade. To address the issues of the indirect effects of water trade through the global economic circulation, we use ecological network analysis (ENA) to shed insight into the complicated system interactions. A global model of virtual water flow among agriculture and livestock production trade in 1995-1999 is also built as the basis for network analysis. Control analysis is used to identify the quantitative control or dependency relations. The utility analysis provides more indicators for describing the mutual relationship between two regions/countries by imitating the interactions in the ecosystem and distinguishes the beneficiary and the contributor of virtual water trade system. Results show control and utility relations can well depict the mutual relation in trade system, and direct observable relations differ from integral ones with indirect interactions considered. This paper offers a new way to depict the interrelations between trade components and can serve as a meaningful start as we continue to use ENA in providing more valuable implications for freshwater study on a global scale.
Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

Directory of Open Access Journals (Sweden)

Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.
Network features and pathway analyses of a signal transduction cascade

Directory of Open Access Journals (Sweden)

Ryoji Yanashima

2009-05-01

Full Text Available The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.
Global and local targeted immunization in networks with community structure

International Nuclear Information System (INIS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

2015-01-01

Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)
Design concepts for a Global Telemetered Seismograph Network

Science.gov (United States)

Peterson, Jon; Orsini, Nicholas A.

1982-01-01

This study represents a first step in developing an integrated, real-time global seismic data acquisition system a Global Telemetered Seismograph Network (GTSN). The principal objective of the GTSN will be to acquire reliable, high-quality, real-time seismic data for rapid location and analysis of seismic events. A secondary, but important, objective of the GTSN is to augment the existing off-line seismic data base available for research. The deployment of the GTSN will involve a variety of interrelated activities development of the data acquisition and receiving equipment, establishment of satellite and terrestrial communication links, site selection and preparation, training of station personnel, equipment installation, and establishment of support facilities. It is a complex program and the development of a sound management plan will be essential. The purpose of this study is not to fix design goals or dictate avenues of approach but to develop working concepts that may be used as a framework for program planning.The international exchange of seismic data has been an important factor in the progress that has been made during the past two decades in our understanding of earthquakes and global tectonics. The seismic data base available for analysis and research is derived principally from the Global Seismograph Network (GSN), which is funded and managed by the U.S. Geological Survey (USGS). The GSN comprises some 120 seismograph stations located in more than 60 countries of the world. Established during the 1960 s with the installation of the World-Wide Standardized Seismograph Network (WWSSN) , the GSN has been augmented in recent years by the installation of more advanced data systems, such as the Seismic Research Observatories (SRO), the modified High-Gain LongPeriod (ASRO) seismographs, and the digital WWSSN (DWWSSN). The SRO, ASRO, and DWWSSN stations have the common, distinctive feature of digital data recording, so they are known collectively as the Global
Seismic signal auto-detecing from different features by using Convolutional Neural Network

Science.gov (United States)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.
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.
Impact of the topology of global macroeconomic network on the spreading of economic crises.

Science.gov (United States)

Lee, Kyu-Min; Yang, Jae-Suk; Kim, Gunn; Lee, Jaesung; Goh, Kwang-Il; Kim, In-mook

2011-03-31

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more "globalized" random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises.
Public road infrastructure inventory in degraded global navigation satellite system signal environments

Science.gov (United States)

Sokolova, N.; Morrison, A.; Haakonsen, T. A.

2015-04-01

Recent advancement of land-based mobile mapping enables rapid and cost-effective collection of highquality road related spatial information. Mobile Mapping Systems (MMS) can provide spatial information with subdecimeter accuracy in nominal operation environments. However, performance in challenging environments such as tunnels is not well characterized. The Norwegian Public Roads Administration (NPRA) manages the country's public road network and its infrastructure, a large segment of which is represented by road tunnels (there are about 1 000 road tunnels in Norway with a combined length of 800 km). In order to adopt mobile mapping technology for streamlining road network and infrastructure management and maintenance tasks, it is important to ensure that the technology is mature enough to meet existing requirements for object positioning accuracy in all types of environments, and provide homogeneous accuracy over the mapping perimeter. This paper presents results of a testing campaign performed within a project funded by the NPRA as a part of SMarter road traffic with Intelligent Transport Systems (ITS) (SMITS) program. The testing campaign objective was performance evaluation of high end commercial MMSs for inventory of public areas, focusing on Global Navigation Satellite System (GNSS) signal degraded environments.
All-optical signal processing for optical packet switching networks

NARCIS (Netherlands)

Liu, Y.; Hill, M.T.; Calabretta, N.; Tangdiongga, E.; Geldenhuys, R.; Zhang, S.; Li, Z.; Waardt, de H.; Khoe, G.D.; Dorren, H.J.S.; Iftekharuddin, K.M.; awwal, A.A.S.

2005-01-01

We discuss how all-optical signal processing might play a role in future all-optical packet switched networks. We introduce a concept of optical packet switches that employ entirely all-optical signal processing technology. The optical packet switch is made out of three functional blocks: the
The T cell STAT signaling network is reprogrammed within hours of bacteremia via secondary signals1

Science.gov (United States)

Hotson, Andrew N.; Hardy, Jonathan W.; Hale, Matthew B.; Contag, Christopher H.; Nolan, Garry P.

2014-01-01

The delicate balance between protective immunity and inflammatory disease is challenged during sepsis, a pathologic state characterized by aspects of both a hyper-active immune response and immunosuppression. The events driven by systemic infection by bacterial pathogens on the T cell signaling network that likely control these responses have not been illustrated in great detail. We characterized how intracellular signaling within the immune compartment is reprogrammed at the single cell level when the host is challenged with a high levels of pathogen. To accomplish this, we applied flow cytometry to measure the phosphorylation potential of key signal transduction proteins during acute bacterial challenge. We modeled the onset of sepsis by intravenous administration of avirulent strains of Listeria and E. coli to mice. Within six hours of bacterial challenge, T cells were globally restricted in their ability to respond to specific cytokine stimulations as determined by assessing the extent of STAT protein phosphorylation. Mechanisms by which this negative feedback response occurred included SOCS1 and SOCS3 gene up regulation and IL-6 induced endocystosis of the IL-6 receptor. In addition, macrophages were partially tolerized in their ability to respond to TLR agonists. Thus, in contrast to the view that there is a wholesale immune activation during sepsis, one immediate host response to blood borne bacteria was induction of a refractory period during which leukocyte activation by specific stimulations was attenuated. PMID:19494279
Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine.

Science.gov (United States)

Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

2012-01-01

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 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) 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 when the network
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
CBL-CIPK network for calcium signaling in higher plants

Science.gov (United States)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.
Structure and dynamics of the global financial network

International Nuclear Information System (INIS)

Silva, Thiago Christiano; Rubens Stancato de Souza, Sergio; Tabak, Benjamin Miranda

2016-01-01

In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network’s fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions’ risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment.
EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

Directory of Open Access Journals (Sweden)

Kumari Sonal Choudhary

2016-06-01

Full Text Available Epithelial to mesenchymal transition (EMT is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR, are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E and mesenchymal (EGFR_M networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.
EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

Science.gov (United States)

Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.
Acquisition management of the Global Transportation Network

Science.gov (United States)

2001-08-02

This report discusses the acquisition management of the Global transportation Network by the U.S. Transportation Command. This report is one in a series of audit reports addressing DoD acquisition management of information technology systems. The Glo...

Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

Directory of Open Access Journals (Sweden)

Li Wang

2013-01-01

Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.
The Global Seismographic Network (GSN): Deployment of Next Generation VBB Borehole Sensors and Improving Overall Network Noise Performance

Science.gov (United States)

Hafner, K.; Davis, P.; Wilson, D.; Sumy, D.

2017-12-01

The Global Seismographic Network (GSN) recently received delivery of the next generation Very Broadband (VBB) borehole sensors purchased through funding from the DOE. Deployment of these sensors will be underway during the end of summer and fall of 2017 and they will eventually replace the aging KS54000 sensors at approximately one-third of the GSN network stations. We will present the latest methods of deploying these sensors in the existing deep boreholes. To achieve lower noise performance at some sites, emplacement in shallow boreholes might result in lower noise performance for the existing site conditions. In some cases shallow borehole installations may be adapted to vault stations (which make up two thirds of the network), as a means of reducing tilt-induced signals on the horizontal components. The GSN is creating a prioritized list of equipment upgrades at selected stations with the ultimate goal of optimizing overall network data availability and noise performance. For an overview of the performance of the current GSN relative to selected set of metrics, we are utilizing data quality metrics and Probability Density Functions (PDFs)) generated by the IRIS Data Management Centers' (DMC) MUSTANG (Modular Utility for Statistical Knowledge Gathering) and LASSO (Latest Assessment of Seismic Station Observations) tools. We will present our metric analysis of GSN performance in 2016, and show the improvements at GSN sites resulting from recent instrumentation and infrastructure upgrades.
Neural network committees for finger joint angle estimation from surface EMG signals

Directory of Open Access Journals (Sweden)

Reddy Narender P

2009-01-01

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

NARCIS (Netherlands)

Leydesdorff, L.; Wagner, C.S.; Park, H.W.; Adams, J.

2013-01-01

The network of international co-authorship relations has been dominated by certain European nations and the USA, but this network is rapidly expanding at the global level. Between 40 and 50 countries appear in the center of the international network in 2011, and almost all (201) nations are nowadays
GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms.

Science.gov (United States)

Navia, Marlon; Campelo, José Carlos; Bonastre, Alberto; Ors, Rafael

2017-12-23

Monitoring is one of the best ways to evaluate the behavior of computer systems. When the monitored system is a distributed system-such as a wireless sensor network (WSN)-the monitoring operation must also be distributed, providing a distributed trace for further analysis. The temporal sequence of occurrence of the events registered by the distributed monitoring platform (DMP) must be correctly established to provide cause-effect relationships between them, so the logs obtained in different monitor nodes must be synchronized. Many of synchronization mechanisms applied to DMPs consist in adjusting the internal clocks of the nodes to the same value as a reference time. However, these mechanisms can create an incoherent event sequence. This article presents a new method to achieve global synchronization of the traces obtained in a DMP. It is based on periodic synchronization signals that are received by the monitor nodes and logged along with the recorded events. This mechanism processes all traces and generates a global post-synchronized trace by scaling all times registered proportionally according with the synchronization signals. It is intended to be a simple but efficient offline mechanism. Its application in a WSN-DMP demonstrates that it guarantees a correct ordering of the events, avoiding the aforementioned issues.
GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms

Science.gov (United States)

Bonastre, Alberto; Ors, Rafael

2017-01-01

Monitoring is one of the best ways to evaluate the behavior of computer systems. When the monitored system is a distributed system—such as a wireless sensor network (WSN)—the monitoring operation must also be distributed, providing a distributed trace for further analysis. The temporal sequence of occurrence of the events registered by the distributed monitoring platform (DMP) must be correctly established to provide cause-effect relationships between them, so the logs obtained in different monitor nodes must be synchronized. Many of synchronization mechanisms applied to DMPs consist in adjusting the internal clocks of the nodes to the same value as a reference time. However, these mechanisms can create an incoherent event sequence. This article presents a new method to achieve global synchronization of the traces obtained in a DMP. It is based on periodic synchronization signals that are received by the monitor nodes and logged along with the recorded events. This mechanism processes all traces and generates a global post-synchronized trace by scaling all times registered proportionally according with the synchronization signals. It is intended to be a simple but efficient offline mechanism. Its application in a WSN-DMP demonstrates that it guarantees a correct ordering of the events, avoiding the aforementioned issues. PMID:29295494
Core Support to Global Development Network (GND) - Phase II ...

International Development Research Centre (IDRC) Digital Library (Canada)

The Global Development Network (GDN) was launched by the World Bank in 1999 on the premise that good policy research, properly applied, can accelerate development and improve people's lives. Working mainly through regional networks, GDN supports economic and, increasingly, social science research in and on ...
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.
Global 4-H Network: Laying the Groundwork for Global Extension Opportunities

Science.gov (United States)

Major, Jennifer; Miller, Rhonda

2012-01-01

A descriptive study examining 4-H programs in Africa, Asia, and Europe was conducted to provide understanding and direction in the establishment of a Global 4-H Network. Information regarding structure, organizational support, funding, and programming areas was gathered. Programs varied greatly by country, and many partnered with other 4-H…
Novel results for global robust stability of delayed neural networks

International Nuclear Information System (INIS)

Yucel, Eylem; Arik, Sabri

2009-01-01

This paper investigates the global robust convergence properties of continuous-time neural networks with discrete time delays. By employing suitable Lyapunov functionals, some sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point are derived. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are also given to compare our results with previous robust stability results derived in the literature.
Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

Directory of Open Access Journals (Sweden)

Nan Jiang

2017-01-01

Full Text Available A signal design problem is studied for efficiently managing autonomous vehicles (AVs and regular vehicles (RVs simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN principle and RVs follow the user equilibrium (UE principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.
Ultrasensitivity in signaling cascades revisited: Linking local and global ultrasensitivity estimations.

Directory of Open Access Journals (Sweden)

Edgar Altszyler

Full Text Available Ultrasensitive response motifs, capable of converting graded stimuli into binary responses, are well-conserved in signal transduction networks. Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can enhance the system's ultrasensitivity, how a given combination of layers affects a cascade's ultrasensitivity remains an open question for the general case. Here, we introduce a methodology that allows us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade's ultrasensitivity. The proposed analysis framework provides a natural link between global and local ultrasensitivity descriptors and it is particularly well-suited to characterize and understand mathematical models used to study real biological systems. As a case study, we have considered three mathematical models introduced by O'Shaughnessy et al. to study a tunable synthetic MAPK cascade, and we show how our methodology can help modelers better understand alternative models.
Signal Processing Device (SPD) for networked radiation monitoring system

International Nuclear Information System (INIS)

Dharmapurikar, A.; Bhattacharya, S.; Mukhopadhyay, P.K.; Sawhney, A.; Patil, R.K.

2010-01-01

A networked radiation and parameter monitoring system with three tier architecture is being developed. Signal Processing Device (SPD) is a second level sub-system node in the network. SPD is an embedded system which has multiple input channels and output communication interfaces. It acquires and processes data from first level parametric sensor devices, and sends to third level devices in response to request commands received from host. It also performs scheduled diagnostic operations and passes on the information to host. It supports inputs in the form of differential digital signals and analog voltage signals. SPD communicates with higher level devices over RS232/RS422/USB channels. The system has been designed with main requirements of minimal power consumption and harsh environment in radioactive plants. This paper discusses the hardware and software design details of SPD. (author)
[Cellular adhesion signal transduction network of tumor necrosis factor-alpha induced hepatocellular carcinoma cells].

Science.gov (United States)

Zheng, Yongchang; Du, Shunda; Xu, Haifeng; Xu, Yiyao; Zhao, Haitao; Chi, Tianyi; Lu, Xin; Sang, Xinting; Mao, Yilei

2014-11-18

To systemically explore the cellular adhesion signal transduction network of tumor necrosis factor-alpha (TNF-α)-induced hepatocellular carcinoma cells with bioinformatics tools. Published microarray dataset of TNF-α-induced HepG2, human transcription factor database HTRI and human protein-protein interaction database HPRD were used to construct and analyze the signal transduction network. In the signal transduction network, MYC and SP1 were the key nodes of signaling transduction. Several genes from the network were closely related with cellular adhesion.Epidermal growth factor receptor (EGFR) is a possible key gene of effectively regulating cellular adhesion during the induction of TNF-α. EGFR is a possible key gene for TNF-α-induced metastasis of hepatocellular carcinoma.
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.
Globalizing Social Justice Education: The Case of The Global Solidarity Network Study e-Broad Program

Science.gov (United States)

Harrison, Yvonne D.; Kostic, Kevin; Toton, Suzanne C.; Zurek, Jerome

2010-01-01

This paper documents the development, implementation, and evaluation of "The Global Solidarity Network Study e-Broad Program (GSNSeBP)", an online social justice educational program that is blended into an onsite academic course. This global electronic program, which was developed through a partnership between Catholic Relief Services (CRS) and…
In Search of a Network Organization for Innovation: A Multilevel Analysis on Transnational Corporations' Global Innovation

DEFF Research Database (Denmark)

Hu, Yimei

2013-01-01

4 explores how transnational corporations perceive and design an internal network organization to facilitate global innovation. Based on a multiple case study of three Danish transnational corporations’ global R&D organization, this paper shows three types of network organization design...... explores how an SME develops a network organization consisting of both interfirm innovation networks and an internal network organization to facilitate its global innovation strategy. Regarding the intraorganizational network organization, market mechanism is adopted to optimize internal resource...... corporations perceive/design a network organization to facilitate their global innovation? • To what extent and how can we manage a network organization? Research focus of the dissertation is on transnational corporations’ network organization for innovation. The first research question aims to clarify...
Managerial capabilities of the home base in an intra-organisational global network

DEFF Research Database (Denmark)

Mykhaylenko, Alona

of the HB change in the process of its global intra-organisational network evolution. In particular, the four papers constituting this thesis investigate how global intra-organisational networks evolve, how the types of network management capabilities of the HB change along with such network evolution....... This investigation was conducted through a retrospective longitudinal case study of one Danish original equipment manufacturer and its three subsidiaries in China, Slovakia, and the US. The findings, first of all, support, extend, and modify the revised Uppsala globalisation model with regard to the types...
Global stability of stochastic high-order neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Wang Zidong; Fang Jianan; Liu Xiaohui

2008-01-01

High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks. In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with discrete and distributed time-delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived, which guarantee the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the stochastic high-order delayed neural networks under consideration are globally asymptotically stable in the mean square if two linear matrix inequalities (LMIs) are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also shown that the main results in this paper cover some recently published works. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria
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.

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.
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.
[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

Science.gov (United States)

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.
The Global Climatology Network Precipitation data

International Nuclear Information System (INIS)

Peterson, T.C.; Easterling, D.R.; Eischeid, J.K.

1993-01-01

Several years ago, in response to growing concern about global climate change, the US National Climatic Data Center and the Carbon Dioxide Information Analysis Center undertook an effort to create a baseline global land surface climate data set called the Global Historical Climatology Network (GHCN, Vose et al., 1992). GHCN was created by merging several large existing climate data sets into one data base. Fifteen separate data sets went into the creation of the GHCN version 1.0. GHCN version 1.0 was released in 1992. It has 7,533 precipitation stations, but the number of stations varies with time. A slight majority (55%) have records in excess of 50 years, and a significant proportion (13%) have records in excess of 100 years. The longest period of record for any given station is 291 years (1697--1987 for Kew, United Kingdom)
Determination of outdoor signal propagation via visibility analysis in outdoor wireless networks

Directory of Open Access Journals (Sweden)

Mustafa Coşar

2017-02-01

Full Text Available Wireless networks on university campuses has gained importance in recent years. These networks in major areas such as university campuses, are faced with many problems during the planning, design and establishment. These problems are among the first that comes to mind, the physical properties of the campus and is selected according to the characteristics of network equipment. There is no doubt at all points of a wireless network set up in order to provide uninterrupted service and quality of the signal is expected to be good. However, it should be understood literally cannot meet these expectations. Therefore, to solve many problems to campus planning and design can be made to have acceptable signal distribution will have the appropriate use of and satisfaction with increasing effect. In this study, due to the start of construction on the North Campus of Hitit University, wireless signal spread using the current spread has been determined with the help of geographic information systems visibility analysis. An area of 56 hectares, with the total of 9 AP the acceptable signal distribution was obtained.
Stochastic effects as a force to increase the complexity of signaling networks

KAUST Repository

Kuwahara, Hiroyuki; Gao, Xin

2013-01-01

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
From global agenda-setting to domestic implementation: successes and challenges of the global health network on tobacco control.

Science.gov (United States)

Gneiting, Uwe

2016-04-01

Global policy attention to tobacco control has increased significantly since the 1990 s and culminated in the first international treaty negotiated under the auspices of the World Health Organization--the Framework Convention on Tobacco Control (FCTC). Although the political process that led to the creation of the FCTC has been extensively researched, the FCTC's progression from an aspirational treaty towards a global health governance framework with tangible policy effects within FCTC member countries has not been well-understood to date. This article analyses the role of the global health network of tobacco control advocates and scientists, which formed during the FCTC negotiations during the late 1990 s, in translating countries' commitment to the FCTC into domestic policy change. By comparing the network's influence around two central tobacco control interventions (smoke-free environments and taxation), the study identifies several scope conditions, which have shaped the network's effectiveness around the FCTC's implementation: the complexity of the policy issue and the relative importance of non-health expertise, the required scope of domestic political buy-in, the role of the general public as network allies, and the strength of policy opposition. These political factors had a greater influence on the network's success than the evidence base for the effectiveness of tobacco control interventions. The network's variable success points to a trade-off faced by global health networks between their need to maintain internal cohesion and their ability to form alliances with actors in their social environment. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2015; all rights reserved.
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

Science.gov (United States)

Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

2015-01-01

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796
China's evolving role in global production networks: Implications for Trump's trade war

OpenAIRE

Athukorala, Prema-chandra

2017-01-01

This paper examines China's evolving role in global production networks and its implications for assessing the potential impact of the "trade war" declared by President Trump. The analysis, which is based on a systematic disaggregation of trade based on global production sharing into components and final assembly, suggests that the Sino-US trade gap is a structural phenomenon driven by the pivotal role played by China within East Asia cantered production networks. The global competitiveness o...
Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

International Nuclear Information System (INIS)

Xie Wei-Hao; Zhou Bin; Liu En-Xiao; Lu Wei-Dang; Zhou Ting

2015-01-01

Many real communication networks, such as oceanic monitoring network and land environment observation network, can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue (HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue (HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity. (paper)
Global spatio-temporal patterns in human migration: a complex network perspective.

Science.gov (United States)

Davis, Kyle F; D'Odorico, Paolo; Laio, Francesco; Ridolfi, Luca

2013-01-01

Migration is a powerful adaptive strategy for humans to navigate hardship and pursue a better quality of life. As a universal vehicle facilitating exchanges of ideas, culture, money and goods, international migration is a major contributor to globalization. Consisting of countries linked by multiple connections of human movements, global migration constitutes a network. Despite the important role of human migration in connecting various communities in different parts of the world, the topology and behavior of the international migration network and its changes through time remain poorly understood. Here we show that the global human migration network became more interconnected during the latter half of the twentieth century and that migrant destination choice partly reflects colonial and postcolonial histories, language, religion, and distances. From 1960 to 2000 we found a steady increase in network transitivity (i.e. connectivity between nodes connected to the same node), a decrease in average path length and an upward shift in degree distribution, all of which strengthened the 'small-world' behavior of the migration network. Furthermore, we found that distinct groups of countries preferentially interact to form migration communities based largely on historical, cultural and economic factors.
Global spatio-temporal patterns in human migration: a complex network perspective.

Directory of Open Access Journals (Sweden)

Kyle F Davis

Full Text Available Migration is a powerful adaptive strategy for humans to navigate hardship and pursue a better quality of life. As a universal vehicle facilitating exchanges of ideas, culture, money and goods, international migration is a major contributor to globalization. Consisting of countries linked by multiple connections of human movements, global migration constitutes a network. Despite the important role of human migration in connecting various communities in different parts of the world, the topology and behavior of the international migration network and its changes through time remain poorly understood. Here we show that the global human migration network became more interconnected during the latter half of the twentieth century and that migrant destination choice partly reflects colonial and postcolonial histories, language, religion, and distances. From 1960 to 2000 we found a steady increase in network transitivity (i.e. connectivity between nodes connected to the same node, a decrease in average path length and an upward shift in degree distribution, all of which strengthened the 'small-world' behavior of the migration network. Furthermore, we found that distinct groups of countries preferentially interact to form migration communities based largely on historical, cultural and economic factors.
Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

Science.gov (United States)

Dasgupta, Hirak

2016-12-01

The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.
Globally exponential stability of neural network with constant and variable delays

International Nuclear Information System (INIS)

Zhao Weirui; Zhang Huanshui

2006-01-01

This Letter presents new sufficient conditions of globally exponential stability of neural networks with delays. We show that these results generalize recently published globally exponential stability results. In particular, several different globally exponential stability conditions in the literatures which were proved using different Lyapunov functionals are generalized and unified by using the same Lyapunov functional and the technique of inequality of integral. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks. Those conditions are less restrictive than those given in the earlier references
Enhanced Global Signal of Neutral Hydrogen Due to Excess Radiation at Cosmic Dawn

Science.gov (United States)

Feng, Chang; Holder, Gilbert

2018-05-01

We revisit the global 21 cm signal calculation incorporating a possible radio background at early times, and find that the global 21 cm signal shows a much stronger absorption feature, which could enhance detection prospects for future 21 cm experiments. In light of recent reports of a possible low-frequency excess radio background, we propose that detailed 21 cm calculations should include a possible early radio background.
A central integrator of transcription networks in plant stress and energy signalling.

Science.gov (United States)

Baena-González, Elena; Rolland, Filip; Thevelein, Johan M; Sheen, Jen

2007-08-23

Photosynthetic plants are the principal solar energy converter sustaining life on Earth. Despite its fundamental importance, little is known about how plants sense and adapt to darkness in the daily light-dark cycle, or how they adapt to unpredictable environmental stresses that compromise photosynthesis and respiration and deplete energy supplies. Current models emphasize diverse stress perception and signalling mechanisms. Using a combination of cellular and systems screens, we show here that the evolutionarily conserved Arabidopsis thaliana protein kinases, KIN10 and KIN11 (also known as AKIN10/At3g01090 and AKIN11/At3g29160, respectively), control convergent reprogramming of transcription in response to seemingly unrelated darkness, sugar and stress conditions. Sensing and signalling deprivation of sugar and energy, KIN10 targets a remarkably broad array of genes that orchestrate transcription networks, promote catabolism and suppress anabolism. Specific bZIP transcription factors partially mediate primary KIN10 signalling. Transgenic KIN10 overexpression confers enhanced starvation tolerance and lifespan extension, and alters architecture and developmental transitions. Significantly, double kin10 kin11 deficiency abrogates the transcriptional switch in darkness and stress signalling, and impairs starch mobilization at night and growth. These studies uncover surprisingly pivotal roles of KIN10/11 in linking stress, sugar and developmental signals to globally regulate plant metabolism, energy balance, growth and survival. In contrast to the prevailing view that sucrose activates plant SnRK1s (Snf1-related protein kinases), our functional analyses of Arabidopsis KIN10/11 provide compelling evidence that SnRK1s are inactivated by sugars and share central roles with the orthologous yeast Snf1 and mammalian AMPK in energy signalling.
Experimental and computational tools for analysis of signaling networks in primary cells

DEFF Research Database (Denmark)

Schoof, Erwin M; Linding, Rune

2014-01-01

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differ......Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis......; 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...
Global terrestrial water storage connectivity revealed using complex climate network analyses

Science.gov (United States)

Sun, A. Y.; Chen, J.; Donges, J.

2015-07-01

Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.
Network master planning for a global manufacturing company

OpenAIRE

Heinz, Michael Pierre

2006-01-01

Production in global, intra-organisational networks is becoming more common. In this context, the allocation of production quantities to constrained manufacturing capacity is a challenging process. Due to a volatile environment it is argued to be impossible to achieve a ‘clean’ system design with dedicated resources which exactly meets future demand. Thus, recursive ‘Network Master Planning’ (NMP) becomes necessary. The aim of this research is to generate an understanding of th...
Data-driven quantification of the robustness and sensitivity of cell signaling networks

International Nuclear Information System (INIS)

Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J; Das, Jayajit

2013-01-01

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems. (paper)

Responses to olfactory signals reflect network structure of flower-visitor interactions.

Science.gov (United States)

Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

2010-07-01

1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.
Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI.

Science.gov (United States)

Dai, Weiying; Varma, Gopal; Scheidegger, Rachel; Alsop, David C

2016-03-01

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to investigate spontaneous low-frequency signal fluctuations across brain resting state networks. However, BOLD only provides relative measures of signal fluctuations. Arterial Spin Labeling (ASL) MRI holds great potential for quantitative measurements of resting state network fluctuations. This study systematically quantified signal fluctuations of the large-scale resting state networks using ASL data from 20 healthy volunteers by separating them from global signal fluctuations and fluctuations caused by residual noise. Global ASL signal fluctuation was 7.59% ± 1.47% relative to the ASL baseline perfusion. Fluctuations of seven detected resting state networks vary from 2.96% ± 0.93% to 6.71% ± 2.35%. Fluctuations of networks and residual noise were 6.05% ± 1.18% and 6.78% ± 1.16% using 4-mm resolution ASL data applied with Gaussian smoothing kernel of 6mm. However, network fluctuations were reduced by 7.77% ± 1.56% while residual noise fluctuation was markedly reduced by 39.75% ± 2.90% when smoothing kernel of 12 mm was applied to the ASL data. Therefore, global and network fluctuations are the dominant structured noise sources in ASL data. Quantitative measurements of resting state networks may enable improved noise reduction and provide insights into the function of healthy and diseased brain. © The Author(s) 2015.
Networks of global bird invasion altered by regional trade ban.

Science.gov (United States)

Reino, Luís; Figueira, Rui; Beja, Pedro; Araújo, Miguel B; Capinha, César; Strubbe, Diederik

2017-11-01

Wildlife trade is a major pathway for introduction of invasive species worldwide. However, how exactly wildlife trade influences invasion risk, beyond the transportation of individuals to novel areas, remains unknown. We analyze the global trade network of wild-caught birds from 1995 to 2011 as reported by CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora). We found that before the European Union ban on imports of wild-caught birds, declared in 2005, invasion risk was closely associated with numbers of imported birds, diversity of import sources, and degree of network centrality of importer countries. After the ban, fluxes of global bird trade declined sharply. However, new trade routes emerged, primarily toward the Nearctic, Afrotropical, and Indo-Malay regions. Although regional bans can curtail invasion risk globally, to be fully effective and prevent rerouting of trade flows, bans should be global.
Collective signaling behavior in a networked-oscillator model

Science.gov (United States)

Liu, Z.-H.; Hui, P. M.

2007-09-01

We propose and study the collective behavior of a model of networked signaling objects that incorporates several ingredients of real-life systems. These ingredients include spatial inhomogeneity with grouping of signaling objects, signal attenuation with distance, and delayed and impulsive coupling between non-identical signaling objects. Depending on the coupling strength and/or time-delay effect, the model exhibits completely, partially, and locally collective signaling behavior. In particular, a correlated signaling (CS) behavior is observed in which there exist time durations when nearly a constant fraction of oscillators in the system are in the signaling state. These time durations are much longer than the duration of a spike when a single oscillator signals, and they are separated by regular intervals in which nearly all oscillators are silent. Such CS behavior is similar to that observed in biological systems such as fireflies, cicadas, crickets, and frogs. The robustness of the CS behavior against noise is also studied. It is found that properly adjusting the coupling strength and noise level could enhance the correlated behavior.
Architectural Design for the Global Legal Information Network

Science.gov (United States)

Kalpakis, Konstantinos

1999-01-01

In this report, we provide a summary of our activities regarding the goals, requirements analysis, design, and prototype implementation for the Global Legal Information Network, a joint effort between the Law Library of Congress and NASA.
Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Science.gov (United States)

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Directory of Open Access Journals (Sweden)

Guihua Wen

2017-01-01

Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
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
International Children's Palliative Care Network: A Global Action Network for Children With Life-Limiting Conditions.

Science.gov (United States)

Marston, Joan; Boucher, Sue; Downing, Julia

2018-02-01

The International Children's Palliative Care Network (ICPCN) is a global network of individuals and organizations working together to reach the estimated 21 million children with life-limiting conditions and life-threatening illnesses. The drive to establish the ICPCN was born from the recognition of the gaps in service provision for children's palliative care and the need to collaborate, network, and share resources. Established in 2005 during a meeting in Seoul, South Korea, the ICPCN has developed over the years into an established network with a global membership. The history of the organization is described, including some of the key events since its inception. Working in collaboration with others, ICPCN has five key focus areas: Communication; Advocacy; Research; Education; and Strategic development, and is the only international charity working globally for the rights of children with palliative care needs. Activities in these areas are discussed, along with the inter-connection between the five areas. Without the ICPCN, palliative care for children would not have developed as far as it has over the years and the organization is committed to ongoing work in this area until all children requiring palliative care have access to quality services, wherever they live around the world. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.
The Global Alzheimer's Association Interactive Network.

Science.gov (United States)

Toga, Arthur W; Neu, Scott C; Bhatt, Priya; Crawford, Karen L; Ashish, Naveen

2016-01-01

The Global Alzheimer's Association Interactive Network (GAAIN) is consolidating the efforts of independent Alzheimer's disease data repositories around the world with the goals of revealing more insights into the causes of Alzheimer's disease, improving treatments, and designing preventative measures that delay the onset of physical symptoms. We developed a system for federating these repositories that is reliant on the tenets that (1) its participants require incentives to join, (2) joining the network is not disruptive to existing repository systems, and (3) the data ownership rights of its members are protected. We are currently in various phases of recruitment with over 55 data repositories in North America, Europe, Asia, and Australia and can presently query >250,000 subjects using GAAIN's search interfaces. GAAIN's data sharing philosophy, which guided our architectural choices, is conducive to motivating membership in a voluntary data sharing network. Copyright © 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
The Global Geodetic Observing System: Space Geodesy Networks for the Future

Science.gov (United States)

Pearlman, Michael; Pavlis, Erricos; Ma, Chopo; Altamini, Zuheir; Noll, Carey; Stowers, David

2011-01-01

Ground-based networks of co-located space geodetic techniques (VLBI, SLR, GNSS. and DORIS) are the basis for the development and maintenance of the International Terrestrial Reference frame (ITRF), which is our metric of reference for measurements of global change, The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at 1 mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence, but other applications are not far behind. Recent studies including one by the US National Research Council has strongly stated the need and the urgency for the fundamental space geodesy network. Simulations are underway to examining accuracies for origin, scale and orientation of the resulting ITRF based on various network designs and system performance to determine the optimal global network to achieve this goal. To date these simulations indicate that 24 - 32 co-located stations are adequate to define the reference frame and a more dense GNSS and DORIS network will be required to distribute the reference frame to users anywhere on Earth. Stations in the new global network will require geologically stable sites with good weather, established infrastructure, and local support and personnel. GGOS wil seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to contribute in the network implementation and operation. Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in
Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

Science.gov (United States)

Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

2014-07-01

Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.
Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

DEFF Research Database (Denmark)

Dheer, D.K.; Doolla, S.; Bandyopadhyay, S.

2017-01-01

, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete...... loss and stability margin is further investigated by identifying the Pareto fronts for modified IEEE 13 bus, IEEE 33 and practical 22-bus radial distribution network with application of Reference point based Non-dominated Sorting Genetic Algorithm (R-NSGA). Results were validated by time domain......For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues...
Global similarity and local divergence in human and mouse gene co-expression networks

Directory of Open Access Journals (Sweden)

Koonin Eugene V

2006-09-01

Full Text Available Abstract Background A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species. Results At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction ( Conclusion The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.
Conducting network penetration and espionage in a global environment

CERN Document Server

Middleton, Bruce

2014-01-01

When it's all said and done, penetration testing remains the most effective way to identify security vulnerabilities in computer networks. Conducting Network Penetration and Espionage in a Global Environment provides detailed guidance on how to perform effective penetration testing of computer networks-using free, open source, and commercially available tools, including Backtrack, Metasploit, Wireshark, Nmap, Netcat, and Nessus. It also considers exploits and other programs using Python, PERL, BASH, PHP, Ruby, and Windows PowerShell.The book taps into Bruce Middleton's decades of experience wi
Biophysical constraints on the computational capacity of biochemical signaling networks

Science.gov (United States)

Wang, Ching-Hao; Mehta, Pankaj

Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).
Traffic analysis and signal processing in optical packet switched networks

DEFF Research Database (Denmark)

Fjelde, Tina

2002-01-01

/s optical packet switched network exploiting the best of optics and electronics, is used as a thread throughout the thesis. An overview of the DAVID network architecture is given, focussing on the MAN and WAN architecture as well as the MPLS-based network hierarchy. Subsequently, the traffic performance...... of the DAVID core optical packet router, which exploits wavelength conversion and fibre delay-line buffers for contention resolution, is analysed using a numerical model developed for that purpose. The robustness of the shared recirculating loop buffer with respect to´bursty traffic is demonstrated...... the injection of an additional clock signal into the IWC is presented. Results show very good transmission capabilities combined with a high-speed response. It is argued that signal regeneration is an inherent attribute of the IWC employed as a wavelength converter due to the sinusoidal transfer function...
65 Years of influenza surveillance by a WHO-coordinated global network.

Science.gov (United States)

Ziegler, Thedi; Mamahit, Awandha; Cox, Nancy J

2018-05-04

The 1918 devastating influenza pandemic left a lasting impact on influenza experts and the public, and the importance of global influenza surveillance was soon recognized. The WHO Global Influenza Surveillance Network (GISN) was founded in 1952 and renamed to Global Influenza Surveillance and Response System in 2011 upon the adoption by the World Health Assembly, of the Pandemic Influenza Preparedness Framework for the Sharing of Influenza Viruses and Access to Vaccines and Other Benefits ("PIP Framework"). The importance of influenza surveillance had been recognized and promoted by experts prior to the years leading up to the establishment of WHO. In the 65 years of its existence, the Network has grown to comprise 143 National Influenza Centers recognized by WHO, 6 WHO Collaborating Centers, 4 Essential Regulatory Laboratories, and 13 H5 Reference Laboratories. The Network has proven its excellence throughout these 65 years, providing detailed information on circulating seasonal influenza viruses, as well as immediate response to the influenza pandemics in 1957, 1968, and 2009, and to threats caused by animal influenza viruses and by zoonotic transmission of coronaviruses. For its central role in global public health, the Network has been highly recognized by its many partners and by international bodies. Several generations of world renown influenza scientists have brought the Network to where it is now and they will take it forward to the future, as influenza will remain a pre-eminent threat to humans and to animals. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Correlation and network topologies in global and local stock indices

Science.gov (United States)

Nobi, Ashadun; Lee, Sungmin; Kim, Doo Hwan; Lee, Jae Woo

2014-07-01

We examined how the correlation and network structure of the global indices and local Korean indices have changed during years 2000-2012. The average correlations of the global indices increased with time, while the local indices showed a decreasing trend except for drastic changes during the crises. A significant change in the network topologies was observed due to the financial crises in both markets. The Jaccard similarities identified the change in the market state due to a crisis in both markets. The dynamic change of the Jaccard index can be used as an indicator of systemic risk or precursors of the crisis.

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.
Globally homochiral assembly of two-dimensional molecular networks triggered by co-absorbers.

Science.gov (United States)

Chen, Ting; Yang, Wen-Hong; Wang, Dong; Wan, Li-Jun

2013-01-01

Understanding the chirality induction and amplification processes, and the construction of globally homochiral surfaces, represent essential challenges in surface chirality studies. Here we report the induction of global homochirality in two-dimensional enantiomorphous networks of achiral molecules via co-assembly with chiral co-absorbers. The scanning tunnelling microscopy investigations and molecular mechanics simulations demonstrate that the point chirality of the co-absorbers transfers to organizational chirality of the assembly units via enantioselective supramolecular interactions, and is then hierarchically amplified to the global homochirality of two-dimensional networks. The global homochirality of the network assembly shows nonlinear dependence on the enantiomeric excess of chiral co-absorber in the solution phase, demonstrating, for the first time, the validation of the 'majority rules' for the homochirality control of achiral molecules at the liquid/solid interface. Such an induction and nonlinear chirality amplification effect promises a new approach towards two-dimensional homochirality control and may reveal important insights into asymmetric heterogeneous catalysis, chiral separation and chiral crystallization.
Signal classification using global dynamical models, Part I: Theory

International Nuclear Information System (INIS)

Kadtke, J.; Kremliovsky, M.

1996-01-01

Detection and classification of signals is one of the principal areas of signal processing, and the utilization of nonlinear information has long been considered as a way of improving performance beyond standard linear (e.g. spectral) techniques. Here, we develop a method for using global models of chaotic dynamical systems theory to define a signal classification processing chain, which is sensitive to nonlinear correlations in the data. We use it to demonstrate classification in high noise regimes (negative SNR), and argue that classification probabilities can be directly computed from ensemble statistics in the model coefficient space. We also develop a modification for non-stationary signals (i.e. transients) using non-autonomous ODEs. In Part II of this paper, we demonstrate the analysis on actual open ocean acoustic data from marine biologics. copyright 1996 American Institute of Physics
Alignment of global supply networks based on strategic groups of supply chains

OpenAIRE

Nikos G. Moraitakis; Jiazhen Huo; Hans-Christian Pfohl

2017-01-01

Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic su...
Competition between global and local online social networks

Science.gov (United States)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-01

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.
Competition between global and local online social networks.

Science.gov (United States)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-27

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.
A new traffic control design method for large networks with signalized intersections

Science.gov (United States)

Leininger, G. G.; Colony, D. C.; Seldner, K.

1979-01-01

The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.
A simple model of global cascades on random networks

Science.gov (United States)

Watts, Duncan J.

2002-04-01

The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascadesherein called global cascadesthat occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.
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.
Future global SLR network evolution and its impact on the terrestrial reference frame

Science.gov (United States)

Kehm, Alexander; Bloßfeld, Mathis; Pavlis, Erricos C.; Seitz, Florian

2018-06-01

Satellite laser ranging (SLR) is an important technique that contributes to the determination of terrestrial geodetic reference frames, especially to the realization of the origin and the scale of global networks. One of the major limiting factors of SLR-derived reference frame realizations is the datum accuracy which significantly suffers from the current global SLR station distribution. In this paper, the impact of a potential future development of the SLR network on the estimated datum parameters is investigated. The current status of the SLR network is compared to a simulated potential future network featuring additional stations improving the global network geometry. In addition, possible technical advancements resulting in a higher amount of observations are taken into account as well. As a result, we find that the network improvement causes a decrease in the scatter of the network translation parameters of up to 24%, and up to 20% for the scale, whereas the technological improvement causes a reduction in the scatter of up to 27% for the translations and up to 49% for the scale. The Earth orientation parameters benefit by up to 15% from both effects.
The relation between global migration and trade networks

Science.gov (United States)

Sgrignoli, Paolo; Metulini, Rodolfo; Schiavo, Stefano; Riccaboni, Massimo

2015-01-01

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong.
Global Historical Climatology Network - Daily (GHCN-Daily), Version 3

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Historical Climatology Network - Daily (GHCN-Daily) dataset integrates daily climate observations from approximately 30 different data sources. Version 3...
A Global Network Alignment Method Using Discrete Particle Swarm Optimization.

Science.gov (United States)

Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia

2016-10-19

Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.
SARAS 2: a spectral radiometer for probing cosmic dawn and the epoch of reionization through detection of the global 21-cm signal

Science.gov (United States)

Singh, Saurabh; Subrahmanyan, Ravi; Shankar, N. Udaya; Rao, Mayuri Sathyanarayana; Girish, B. S.; Raghunathan, A.; Somashekar, R.; Srivani, K. S.

2018-04-01

The global 21-cm signal from Cosmic Dawn (CD) and the Epoch of Reionization (EoR), at redshifts z ˜ 6-30, probes the nature of first sources of radiation as well as physics of the Inter-Galactic Medium (IGM). Given that the signal is predicted to be extremely weak, of wide fractional bandwidth, and lies in a frequency range that is dominated by Galactic and Extragalactic foregrounds as well as Radio Frequency Interference, detection of the signal is a daunting task. Critical to the experiment is the manner in which the sky signal is represented through the instrument. It is of utmost importance to design a system whose spectral bandpass and additive spurious signals can be well calibrated and any calibration residual does not mimic the signal. Shaped Antenna measurement of the background RAdio Spectrum (SARAS) is an ongoing experiment that aims to detect the global 21-cm signal. Here we present the design philosophy of the SARAS 2 system and discuss its performance and limitations based on laboratory and field measurements. Laboratory tests with the antenna replaced with a variety of terminations, including a network model for the antenna impedance, show that the gain calibration and modeling of internal additive signals leave no residuals with Fourier amplitudes exceeding 2 mK, or residual Gaussians of 25 MHz width with amplitudes exceeding 2 mK. Thus, even accounting for reflection and radiation efficiency losses in the antenna, the SARAS 2 system is capable of detection of complex 21-cm profiles at the level predicted by currently favoured models for thermal baryon evolution.
Collaboration tools for the global accelerator network Workshop Report

CERN Document Server

Agarwal, D; Olson, J

2002-01-01

The concept of a ''Global Accelerator Network'' (GAN) has been put forward as a means for inter-regional collaboration in the operation of internationally constructed and operated frontier accelerator facilities. A workshop was held to allow representatives of the accelerator community and of the collaboratory development community to meet and discuss collaboration tools for the GAN environment. This workshop, called the Collaboration Tools for the Global Accelerator Network (GAN) Workshop, was held on August 26, 2002 at Lawrence Berkeley National Laboratory. The goal was to provide input about collaboration tools in general and to provide a strawman for the GAN collaborative tools environment. The participants at the workshop represented accelerator physicists, high-energy physicists, operations, technology tool developers, and social scientists that study scientific collaboration.
Collaboration tools for the global accelerator network: Workshop Report

International Nuclear Information System (INIS)

Agarwal, Deborah; Olson, Gary; Olson, Judy

2002-01-01

The concept of a ''Global Accelerator Network'' (GAN) has been put forward as a means for inter-regional collaboration in the operation of internationally constructed and operated frontier accelerator facilities. A workshop was held to allow representatives of the accelerator community and of the collaboratory development community to meet and discuss collaboration tools for the GAN environment. This workshop, called the Collaboration Tools for the Global Accelerator Network (GAN) Workshop, was held on August 26, 2002 at Lawrence Berkeley National Laboratory. The goal was to provide input about collaboration tools in general and to provide a strawman for the GAN collaborative tools environment. The participants at the workshop represented accelerator physicists, high-energy physicists, operations, technology tool developers, and social scientists that study scientific collaboration
Programs for control of an analog-signal switching network

International Nuclear Information System (INIS)

D'Ottavio, T.; Enriquez, R.; Katz, R.; Skelly, J.

1989-01-01

A suite of programs has been developed to control the network of analog-signal switching multiplexers in the AGS complex. The software is driven by a relational database which describes the architecture of the multiplexer tree and the set of available analog signals. Signals are routed through a three-layer multiplexer tree, to be made available at four consoles each with three 4-trace oscilloscopes. A menu-structured operator interface program is available at each console, to accept requests to route any available analog signal to any of that console's 12 oscilloscope traces. A common routing-server program provides automatic routing-server program provides automatic routing of requested signals through the layers of multiplexers, maintaining a reservation database to denote free and in-use trunks. Expansion of the analog signal system is easily accommodated in software by adding new signals, trunks, multiplexers, or consoles to the database. Programmatic control of the triggering signals for each of the oscilloscopes is also provided. 3 refs., 4 figs., 3 tabs
Representation of global and national conservation priorities by Colombia's Protected Area Network.

Science.gov (United States)

Forero-Medina, German; Joppa, Lucas

2010-10-12

How do national-level actions overlap with global priorities for conservation? Answering this question is especially important in countries with high and unique biological diversity like Colombia. Global biodiversity schemes provide conservation guidance at a large scale, while national governments gazette land for protection based on a combination of criteria at regional or local scales. Information on how a protected area network represents global and national conservation priorities is crucial for finding gaps in coverage and for future expansion of the system. We evaluated the agreement of Colombia's protected area network with global conservation priorities, and the extent to which the network reflects the country's biomes, species richness, and common environmental and physical conditions. We used this information to identify priority biomes for conservation. We find the dominant strategy in Colombia has been a proactive one, allocating the highest proportion of protected land on intact, difficult to access and species rich areas like the Amazon. Threatened and unique areas are disproportionately absent from Colombia's protected lands. We highlight six biomes in Colombia as conservation priorities that should be considered in any future expansion of Colombia's protected area network. Two of these biomes have less than 3% of their area protected and more than 70% of their area transformed for human use. One has less than 3% protected and high numbers of threatened vertebrates. Three biomes fall in both categories. Expansion of Colombia's Protected Area Network should consider the current representativeness of the network. We indicate six priority biomes that can contribute to improving the representation of threatened species and biomes in Colombia.
Global Talent Management in Multinational Corporations and the Role of Social Networks

NARCIS (Netherlands)

Ruel, Hubertus Johannes Maria; Bondarouk, Tatiana; Dresselhaus, Lena; Olivas-Lujan, M.R.; Bondarouk, T.V.

2013-01-01

Purpose — Current global business challenges and circumstances are responsible for the need for global talent management (GTM) within multinational corporations (MNCs). Social media and networks are becoming key channels for global communication and collaboration. For GTM in MNCs, an effective usage
Competition, transmission and pattern evolution: A network analysis of global oil trade

International Nuclear Information System (INIS)

Zhang, Hai-Ying; Ji, Qiang; Fan, Ying

2014-01-01

This paper studies the competition among oil importers using complex network theory, combined with several alternative measures of competition intensity, to analyze the evolution of the pattern and transmission of oil-trading competition. The results indicate that oil trade has formed a global competition pattern and that the role played by the Asian-Pacific region in the evolution of this competition pattern is becoming increasingly prominent. In addition, global competition intensity has continued to rise, and non-OECD countries have become the main driving force for this increase in global competition intensity. The large oil importers are the most significant parts of the global oil-trading competition pattern. They are not only the major participants in the competition for oil resources but also play important roles in the transmission of oil-trading competition. China and the United States especially display the feature of globalization, whose impacts of transmission reach across the whole oil-trading competition network. Finally, a “5C” (changeability, contestability, cooperation, commitment and circumstances) policy framework is put forward to maintain the stability of oil trade and improve the energy security of oil importers in various aspects. - Highlights: • An oil-trading competition network is constructed using complex network theory. • Oil trade has formed a global competition pattern and its intensity has kept rising. • The status of the Asian-Pacific region in the competition pattern becomes prominent. • Large oil importers play important roles in transmitting the trading competition. • A “5C” policy framework is put forward to cope with the intensive competition

Sierra Stars Observatory Network: An Accessible Global Network

Science.gov (United States)

Williams, Richard; Beshore, Edward

2011-03-01

The Sierra Stars Observatory Network (SSON) is a unique partnership among professional observatories that provides its users with affordable high-quality calibrated image data. SSON comprises observatories in the Northern and Southern Hemisphere and is in the process of expanding to a truly global network capable of covering the entire sky 24 hours a day in the near future. The goal of SSON is to serve the needs of science-based projects and programs. Colleges, universities, institutions, and individuals use SSON for their education and research projects. The mission of SSON is to promote and expand the use of its facilities among the thousands of colleges and schools worldwide that do not have access to professional-quality automated observatory systems to use for astronomy education and research. With appropriate leadership and guidance educators can use SSON to help teach astronomy and do meaningful scientific projects. The relatively small cost of using SSON for this type of work makes it affordable and accessible for educators to start using immediately. Remote observatory services like SSON need to evolve to better support education and research initiatives of colleges, institutions and individual investigators. To meet these needs, SSON is developing a sophisticated interactive scheduling system to integrate among the nodes of the observatory network. This will enable more dynamic observations, including immediate priority interrupts, acquiring moving objects using ephemeris data, and more.
Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

Science.gov (United States)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.
Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

Directory of Open Access Journals (Sweden)

Yang Dan

2008-12-01

Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.
Design principles of nuclear receptor signaling: how complex networking improves signal transduction

Science.gov (United States)

Kolodkin, Alexey N; Bruggeman, Frank J; Plant, Nick; Moné, 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 increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018
The role of “network of cities” in construction of global urban culture

OpenAIRE

Baycan-Levent, Tüzin; Kundak, Seda; Gülümser, Aliye Ahu

2004-01-01

The globalization process has led to an increased interaction between cities and to a new urban system/network in which they need to be competitive and complementary at the same time. “Network of cities”, such as World Cities, Eurocities or Sister Cities are among the well known examples of interaction and cooperation of the cities at the regional and global level. The cities of different regions and countries tend to share their experiences and their cultures within these networks in order t...
Computers and networks in the age of globalization

DEFF Research Database (Denmark)

Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

In modernity, an individual identity was constituted from civil society, while in a globalized network society, human identity, if it develops at all, must grow from communal resistance. A communal resistance to an abstract conceptualized world, where there is no possibility for perception and ex...
Tracking Neuronal Connectivity from Electric Brain Signals to Predict Performance.

Science.gov (United States)

Vecchio, Fabrizio; Miraglia, Francesca; Rossini, Paolo Maria

2018-05-01

The human brain is a complex container of interconnected networks. Network neuroscience is a recent venture aiming to explore the connection matrix built from the human brain or human "Connectome." Network-based algorithms provide parameters that define global organization of the brain; when they are applied to electroencephalographic (EEG) signals network, configuration and excitability can be monitored in millisecond time frames, providing remarkable information on their instantaneous efficacy also for a given task's performance via online evaluation of the underlying instantaneous networks before, during, and after the task. Here we provide an updated summary on the connectome analysis for the prediction of performance via the study of task-related dynamics of brain network organization from EEG signals.
Global Nuclear Safety and Security Network

International Nuclear Information System (INIS)

Guo Lingquan

2013-01-01

The objectives of the Regulatory Network are: - to contribute to the effectiveness of nuclear regulatory systems; - to contribute to continuous enhancements, and - to achieve and promote radiation and nuclear safety and security by: • Enhancing the effectiveness and efficiency of international cooperation in the regulation of nuclear and radiation safety of facilities and activities; • Enabling adequate access by regulators to relevant safety and security information; • Promoting dissemination of information on safety and security issues as well as information of good practices for addressing and resolving these issues; • Enabling synergies among different web based networks with a view to strengthening and enhancing the global nuclear safety framework and serving the specific needs of regulators and international organizations; • Providing additional information to the public on international regulatory cooperation in safety and security matters
Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

Science.gov (United States)

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040
Correlation and network topologies in global and local stock indices

DEFF Research Database (Denmark)

Nobi, A.; Lee, S.; Kim, D. H.

2014-01-01

the crises. A significant change in the network topologies was observed due to the financial crises in both markets. The Jaccard similarities identified the change in the market state due to a crisis in both markets. The dynamic change of the Jaccard index can be used as an indicator of systemic risk......We examined how the correlation and network structure of the global indices and local Korean indices have changed during years 2000-2012. The average correlations of the global indices increased with time, while the local indices showed a decreasing trend except for drastic changes during...... or precursors of the crisis. (C) 2014 Elsevier B.V. All rights reserved....
Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits
SONG-China Project: A Global Automated Observation Network

Science.gov (United States)

Yang, Z. Z.; Lu, X. M.; Tian, J. F.; Zhuang, C. G.; Wang, K.; Deng, L. C.

2017-09-01

Driven by advancements in technology and scientific objectives, data acquisition in observational astronomy has been changed greatly in recent years. Fully automated or even autonomous ground-based network of telescopes has now become a tendency for time-domain observational projects. The Stellar Observations Network Group (SONG) is an international collaboration with the participation and contribution of the Chinese astronomy community. The scientific goal of SONG is time-domain astrophysics such as asteroseismology and open cluster research. The SONG project aims to build a global network of 1 m telescopes equipped with high-precision and high-resolution spectrographs, and two-channel lucky-imaging cameras. It is the Chinese initiative to install a 50 cm binocular photometry telescope at each SONG node sharing the network platform and infrastructure. This work is focused on design and implementation in technology and methodology of SONG/50BiN, a typical ground-based network composed of multiple sites and a variety of instruments.
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.
Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

Science.gov (United States)

Knapp, Bettina; Kaderali, Lars

2013-01-01

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
Characterization of Radar Signals Using Neural Networks

Science.gov (United States)

1990-12-01

e***e*e*eeeeeeeeeeeesseeeeeese*eee*e*e************s /* Function Name: load.input.ptterns Number: 4.1 /* Description: This function determines wether ...XSE.last.layer Number: 8.5 */ /* Description: The function determines wether to backpropate the *f /* parameter by the sigmoidal or linear update...Sigmoidal Function," Mathematics of Control, Signals and Systems, 2:303-314 (March 1989). 6. Dayhoff, Judith E. Neural Network Architectures. New York: Van
A global interaction network maps a wiring diagram of cellular function

Science.gov (United States)

Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N.; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D.; Pelechano, Vicent; Styles, Erin B.; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S.; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F.; Li, Sheena C.; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; Luis, Bryan-Joseph San; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W.; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G.; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M.; Moore, Claire L.; Rosebrock, Adam P.; Caudy, Amy A.; Myers, Chad L.; Andrews, Brenda; Boone, Charles

2017-01-01

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing over 23 million double mutants, identifying ~550,000 negative and ~350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. PMID:27708008
A scalable global positioning system-free localization scheme for underwater wireless sensor networks

KAUST Repository

Mohammed, A.M.

2013-05-07

Seaweb is an acoustic communication technology that enables communication between sensor nodes. Seaweb technology utilizes the commercially available telesonar modems that has developed link and network layer firmware to provide a robust undersea communication capability. Seaweb interconnects the underwater nodes through digital signal processing-based modem by using acoustic links between the neighboring sensors. In this paper, we design and investigate a global positioning system-free passive localization protocol by integrating the innovations of levelling and localization with the Seaweb technology. This protocol uses the range data and planar trigonometry principles to estimate the positions of the underwater sensor nodes. Moreover, for precise localization, we consider more realistic conditions namely, (a) small displacement of sensor nodes due to watch circles and (b) deployment of sensor nodes over non-uniform water surface. Once the nodes are localized, we divide the whole network field into circular levels and sectors to minimize the traffic complexity and thereby increases the lifetime of the sensor nodes in the network field. We then form the mesh network inside each of the sectors that increases the reliability. The algorithm is designed in such a way that it overcomes the ambiguous nodes errata and reflected paths and therefore makes the algorithm more robust. The synthetic network geometries are so designed which can evaluate the algorithm in the presence of perfect or imperfect ranges or in case of incomplete data. A comparative study is made with the existing algorithms which proves the efficiency of our newly proposed algorithm. 2013 Mohammed et al.
Indications of marine bioinvasion from network theory. An analysis of the global cargo ship network

Science.gov (United States)

Kölzsch, A.; Blasius, B.

2011-12-01

The transport of huge amounts of small aquatic organisms in the ballast tanks and at the hull of large cargo ships leads to ever increasing rates of marine bioinvasion. In this study, we apply a network theoretic approach to examine the introduction of invasive species into new ports by global shipping. This is the first stage of the invasion process where it is still possible to intervene with regulating measures. We compile a selection of widely used and newly developed network properties and apply these to analyse the structure and spread characteristics of the directed and weighted global cargo ship network (GCSN). Our results reveal that the GCSN is highly efficient, shows small world characteristics and is positive assortative, indicating that quick spread of invasive organisms between ports is likely. The GCSN shows strong community structure and contains two large communities, the Atlantic and Pacific trading groups. Ports that appear as connector hubs and are of high centralities are the Suez and Panama Canal, Singapore and Shanghai. Furthermore, from robustness analyses and the network's percolation behaviour, we evaluate differences of onboard and in-port ballast water treatment, set them into context with previous studies and advise bioinvasion management strategies.
Global synchronization of general delayed complex networks with stochastic disturbances

International Nuclear Information System (INIS)

Tu Li-Lan

2011-01-01

In this paper, global synchronization of general delayed complex networks with stochastic disturbances, which is a zero-mean real scalar Wiener process, is investigated. The networks under consideration are continuous-time networks with time-varying delay. Based on the stochastic Lyapunov stability theory, Ito's differential rule and the linear matrix inequality (LMI) optimization technique, several delay-dependent synchronous criteria are established, which guarantee the asymptotical mean-square synchronization of drive networks and response networks with stochastic disturbances. The criteria are expressed in terms of LMI, which can be easily solved using the Matlab LMI Control Toolbox. Finally, two examples show the effectiveness and feasibility of the proposed synchronous conditions. (general)
Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays is studied. Some sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence by using different Lyapunov functionals. Our criteria represent an extension of the existing results in literatures. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks. Those conditions are less restrictive than those given in the earlier reference

Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

Directory of Open Access Journals (Sweden)

S. N. Kale

2009-01-01

Full Text Available Electromyography (EMG signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP and Radial Basis Function NN (RBF models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.
Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

International Nuclear Information System (INIS)

Jie, Li; Wan-Qing, Yu; Ding, Xu; Feng, Liu; Wei, Wang

2009-01-01

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 τ syn 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 τ syn , 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. (cross-disciplinary physics and related areas of science and technology)
Approach on a global HTGR R and D network

International Nuclear Information System (INIS)

Lensa, W. von

1997-01-01

The present situation of nuclear power in general and of the innovative nuclear reactor systems in particular requires more comprehensive, coordinated R and D efforts on a broad international level to respond to today's requirements with respect to public and economic acceptance as well as to globalization trends and global environmental problems. HTGR technology development has already reached a high degree of maturity that will be complemented by the operation of the two new test reactors in Japan and China, representing technological milestones for the demonstration of HTGR safety characteristics and Nuclear Process Heat generation capabilities. It is proposed by the IAEA 'International Working Group on Gas-Cooled Reactors' to establish a 'Global HTGR R and D Network' on basic HTGR technology for the stable, long-term advancement of the specific HTGR features and as a basis for the future market introduction of this innovative reactor system. The background and the motivation for this approach are illustrated, as well as first proposals on the main objectives, the structure and the further procedures for the implementation of such a multinational working sharing R and D network. Modern telecooperation methods are foreseen as an interactive tool for effective communication and collaboration on a global scale. (author)
Music Signal Processing Using Vector Product Neural Networks

Science.gov (United States)

Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

2017-05-01

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.
Global exponential stability of mixed discrete and distributively delayed cellular neural network

International Nuclear Information System (INIS)

Yao Hong-Xing; Zhou Jia-Yan

2011-01-01

This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov—Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result. (general)
Transfer of optical signals around bends in two-dimensional linear photonic networks

International Nuclear Information System (INIS)

Nikolopoulos, G M

2015-01-01

The ability to navigate light signals in two-dimensional networks of waveguide arrays is a prerequisite for the development of all-optical integrated circuits for information processing and networking. In this article, we present a theoretical analysis of bending losses in linear photonic lattices with engineered couplings, and discuss possible ways for their minimization. In contrast to previous work in the field, the lattices under consideration operate in the linear regime, in the sense that discrete solitons cannot exist. The present results suggest that the functionality of linear waveguide networks can be extended to operations that go beyond the recently demonstrated point-to-point transfer of signals, such as blocking, routing, logic functions, etc. (paper)
The global health network on alcohol control: successes and limits of evidence-based advocacy.

Science.gov (United States)

Schmitz, Hans Peter

2016-04-01

Global efforts to address alcohol harm have significantly increased since the mid-1990 s. By 2010, the World Health Organization (WHO) had adopted the non-binding Global Strategy to Reduce the Harmful Use of Alcohol. This study investigates the role of a global health network, anchored by the Global Alcohol Policy Alliance (GAPA), which has used scientific evidence on harm and effective interventions to advocate for greater global public health efforts to reduce alcohol harm. The study uses process-tracing methodology and expert interviews to evaluate the accomplishments and limitations of this network. The study documents how network members have not only contributed to greater global awareness about alcohol harm, but also advanced a public health approach to addressing this issue at the global level. Although the current network represents an expanding global coalition of like-minded individuals, it faces considerable challenges in advancing its cause towards successful implementation of effective alcohol control policies across many low- and middle-income countries (LMICs). The analysis reveals a need to transform the network into a formal coalition of regional and national organizations that represent a broader variety of constituents, including the medical community, consumer groups and development-focused non-governmental organizations. Considering the growing harm of alcohol abuse in LMICs and the availability of proven and cost-effective public health interventions, alcohol control represents an excellent 'buy' for donors interested in addressing non-communicable diseases. Alcohol control has broad beneficial effects for human development, including promoting road safety and reducing domestic violence and health care costs across a wide variety of illnesses caused by alcohol consumption. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2015; all rights reserved.
Global efficiency of local immunization on complex networks.

Science.gov (United States)

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J

2013-01-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.
Global efficiency of local immunization on complex networks

Science.gov (United States)

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

2013-07-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.
The Theoretical Aspects of the Development of Global Production Networks and Value Chains: the New Paradigm of Globalization

Directory of Open Access Journals (Sweden)

Cherkas Nataliia I.

2018-03-01

Full Text Available The article is aimed at systematizing the contemporary perceptions of the changing paradigms of globalization and international competition as a result of the spread of global networks and value chains. The development of global value chains (GVC occurred as a result of two distributions of globalization: (1 global competition is manifested at the level of sectors and companies (from the mid-nineteenth century (2 the concept of trade in tasks arises (at the end of XX century. The publication analyzes the impact of globalization on the international competitiveness of both the EU and the developing countries in the trade of final products and tasks. The model takes into consideration differences in wages, technology gap and trade costs, and provides for assessing the comparative advantages of individual sectors or segments of GVC. Features of the conception of global production networks have been identified as: «imports for production» and «imports for exports», which define international competitiveness on the basis of creation of the intrinsic value added. It is determined that the competitiveness of the economy is determined by the country’s positions in the GVC, and the increase in productivity of companies depends on their involvement in the segments (tasks with a high level of value added.
Analysis of market signals in a competitive electricity market using components of network rental

International Nuclear Information System (INIS)

Amarasinghe, L.Y.C.; Annakkage, U.D.

2009-01-01

In the competitive electricity market, Locational Marginal Prices (LMPs) are important pricing signals for the participants as the effects of transmission losses and binding constraints are embedded in LMPs. While these LMPs provide valuable information at each location, they do not provide a detailed description in terms of contributing terms. The LMP components, on the other hand, show the explicit decomposition of LMP into contributing components, and thus, can be considered as better market signals. However, the effects of transmission losses cannot be explicitly seen from the LMP components. In this paper, the components of network rental is proposed to be used as a method in analyzing market signals, by decomposing the network rental into contributing components among the consumers. Since, the network rental is the surplus paid by all the consumers, components of network rental show how each consumer has actually overpaid due to losses and each binding constraint separately. A case study is also presented to demonstrate the potential of this proposed method in market signal analysis. (author)
Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

Science.gov (United States)

Flekova, L.; Schott, M.

2017-10-01

Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.
Analysing 21cm signal with artificial neural network

Science.gov (United States)

Shimabukuro, Hayato; a Semelin, Benoit

2018-05-01

The 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.
Multi-Scale Factor Analysis of High-Dimensional Brain Signals

KAUST Repository

Ting, Chee-Ming

2017-05-18

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.
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.
Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

Science.gov (United States)

Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

2018-04-01

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.
Global stability of a susceptible-infected-susceptible epidemic model on networks with individual awareness

International Nuclear Information System (INIS)

Li Ke-Zan; Xu Zhong-Pu; Zhu Guang-Hu; Ding Yong

2014-01-01

Recent research results indicate that individual awareness can play an important influence on epidemic spreading in networks. By local stability analysis, a significant conclusion is that the embedded awareness in an epidemic network can increase its epidemic threshold. In this paper, by using limit theory and dynamical system theory, we further give global stability analysis of a susceptible-infected-susceptible (SIS) epidemic model on networks with awareness. Results show that the obtained epidemic threshold is also a global stability condition for its endemic equilibrium, which implies the embedded awareness can enhance the epidemic threshold globally. Some numerical examples are presented to verify the theoretical results. (interdisciplinary physics and related areas of science and technology)
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

Science.gov (United States)

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.
Using neural networks to enhance the Higgs boson signal at hadron colliders

International Nuclear Information System (INIS)

Field, R.D.; Kanev, Y.; Tayebnejad, M.; Griffin, P.A.

1995-01-01

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

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

International Nuclear Information System (INIS)

Keshri, Neha; Mishra, Bimal Kumar

2014-01-01

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
Sustaining a Global Social Network: a quasi-experimental study.

Science.gov (United States)

Benton, D C; Ferguson, S L

2017-03-01

To examine the longer term impact on the social network of participating nurses in the Global Nursing Leadership Institute (GNLI2013) through using differing frequencies of follow-up to assess impact on maintenance of network cohesion. Social network analysis is increasingly been used by nurse researchers, however, studies tend to use single point-in-time descriptive methods. This study utilizes a repeated measures, block group, control-intervention, quasi-experimental design. Twenty-eight nurse leaders, competitively selected through a double-blind peer review process, were allocated to five action learning-based learning groups. Network architecture, measures of cohesion and node degree frequency were all used to assess programme impact. The programme initiated and sustained connections between nurse leaders drawn from a geographically dispersed heterogeneous group. Modest inputs of two to three e-mails over a 6-month period seem sufficient to maintain connectivity as indicated by measures of network density, diameter and path length. Due to the teaching methodology used, the study sample was relatively small and the follow-up data collection took place after a relatively short time. Replication and further cohort data collection would be advantageous. In an era where many policy solutions are being debated and initiated at the global level, action learning leadership development that utilizes new technology follow-up appears to show significant impact and is worthy of wider application. The approach warrants further inquiry and testing as to its longer term effects on nursing's influence on policy formulation and implementation. © 2016 International Council of Nurses.
Global robust exponential stability for interval neural networks with delay

International Nuclear Information System (INIS)

Cui Shihua; Zhao Tao; Guo Jie

2009-01-01

In this paper, new sufficient conditions for globally robust exponential stability of neural networks with either constant delays or time-varying delays are given. We show the sufficient conditions for the existence, uniqueness and global robust exponential stability of the equilibrium point by employing Lyapunov stability theory and linear matrix inequality (LMI) technique. Numerical examples are given to show the approval of our results.
Signalling design and architecture for a proposed mobile satellite network

Science.gov (United States)

Yan, T.-Y.; Cheng, U.; Wang, C.

1990-01-01

In a frequency-division/demand-assigned multiple-access (FD/DAMA) architecture, each mobile subscriber must make a connection request to the Network Management Center before transmission for either open-end or closed-end services. Open-end services are for voice calls and long file transfer and are processed on a blocked-call-cleared basis. Closed-end services are for transmitting burst data and are processed on a first-come first-served basis. This paper presents the signalling design and architecture for non-voice services of an FD/DAMA mobile satellite network. The connection requests are made through the recently proposed multiple channel collision resolution scheme which provides a significantly higher throughput than the traditional slotted ALOHA scheme. For non-voice services, it is well known that retransmissions are necessary to ensure the delivery of a message in its entirety from the source to destination. Retransmission protocols for open-end and closed-end data transfer are investigated. The signal structure for the proposed network is derived from X-25 standards with appropriate modifications. The packet types and their usages are described in this paper.
Beyond the network effect: towards an alternative understanding of global urban organizations

NARCIS (Netherlands)

James, P.; Verrest, H.; Gupta, J.; Pfeffer, K.; Verrest, H.; Ros-Tonen, M.

2015-01-01

Global organizations providing network relations for cities are bourgeoning. Organizations such as Metropolis, UN-Habitat, ICLEI - Local Governments for Sustainability, the Global Compact Cities Programme, and the C40, as well as City-to-City arrangements, have become increasingly important to
The Pan-University Network for Global Health: framework for collaboration and review of global health needs.

Science.gov (United States)

Winchester, M S; BeLue, R; Oni, T; Wittwer-Backofen, U; Deobagkar, D; Onya, H; Samuels, T A; Matthews, S A; Stone, C; Airhihenbuwa, C

2016-04-21

In the current United Nations efforts to plan for post 2015-Millennium Development Goals, global partnership to address non-communicable diseases (NCDs) has become a critical goal to effectively respond to the complex global challenges of which inequity in health remains a persistent challenge. Building capacity in terms of well-equipped local researchers and service providers is a key to bridging the inequity in global health. Launched by Penn State University in 2014, the Pan University Network for Global Health responds to this need by bridging researchers at more than 10 universities across the globe. In this paper we outline our framework for international and interdisciplinary collaboration, as well the rationale for our research areas, including a review of these two themes. After its initial meeting, the network has established two central thematic priorities: 1) urbanization and health and 2) the intersection of infectious diseases and NCDs. The urban population in the global south will nearly double in 25 years (approx. 2 billion today to over 3.5 billion by 2040). Urban population growth will have a direct impact on global health, and this growth will be burdened with uneven development and the persistence of urban spatial inequality, including health disparities. The NCD burden, which includes conditions such as hypertension, stroke, and diabetes, is outstripping infectious disease in countries in the global south that are considered to be disproportionately burdened by infectious diseases. Addressing these two priorities demands an interdisciplinary and multi-institutional model to stimulate innovation and synergy that will influence the overall framing of research questions as well as the integration and coordination of research.
Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

International Nuclear Information System (INIS)

Wei Duqu; Luo Xiaoshu; Zou Yanli

2008-01-01

We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network
Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

Science.gov (United States)

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific
Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

Directory of Open Access Journals (Sweden)

Xia Liang

Full Text Available Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation, global signal presence (regressed or not and frequency band selection [slow-5 (0.01-0.027 Hz versus slow-4 (0.027-0.073 Hz] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR. The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics
Filtering and spectral processing of 1-D signals using cellular neural networks

NARCIS (Netherlands)

Moreira-Tamayo, O.; Pineda de Gyvez, J.

1996-01-01

This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This
Analysing the Outbound logistics process enhancements in Nokia-Siemens Networks Global Distribution Center

OpenAIRE

Marjeta, Katri

2011-01-01

Marjeta, Katri. 2011. Analysing the outbound logistics process enhancements in Nokia-Siemens Networks Global Distribution Center. Master´s thesis. Kemi-Tornio University of Applied Sciences. Business and Culture. Pages 57. Due to confidentiality issues, this work has been modified from its original form. The aim of this Master Thesis work is to describe and analyze the outbound logistics process enhancement projects executed in Nokia-Siemens Networks Global Distribution Center after the N...
The Transition from Alliance Networks to Multilateral Alliances in the Global Airline Industry

Directory of Open Access Journals (Sweden)

Sergio G. Lazzarini

2008-01-01

Full Text Available This study examines conditions in which alliance networks (informal webs of bilateral entanglements between firms may or may not evolve into multilateral alliances (broad, formal multiple-firm arrangements. I offer a theory to explain the formation of multilateral alliances based on both the resource profile and the structure of existing interfirm networks, and provide an initial test of that theory in the context of the global airline industry. Using data from 75 global airlines and their alliances, I propose a methodology to retrieve samples of alliance networks and then use regression analysis to assess how the resource profile and the structure of these networks influence their formalization into multilateral alliances. I find that multilateral alliances are more likely to emerge when alliance networks exhibit high resource diversity and network structure characterized by moderate density and high centralization. Apparently, while highly sparse networks reduce actors’ awareness of their potential joint collaboration, highly dense or embedded networks substitute for the need for formal controls accompanying multilateral agreements. The effect of centralization suggests that the formation of multilateral alliances tends to be triggered by leading actors directly connected to other network members.
A switchable spin-wave signal splitter for magnonic networks

Science.gov (United States)

Heussner, F.; Serga, A. A.; Brächer, T.; Hillebrands, B.; Pirro, P.

2017-09-01

The influence of an inhomogeneous magnetization distribution on the propagation of caustic-like spin-wave beams in unpatterned magnetic films has been investigated by utilizing micromagnetic simulations. Our study reveals a locally controllable and reconfigurable tractability of the beam directions. This feature is used to design a device combining split and switch functionalities for spin-wave signals on the micrometer scale. A coherent transmission of spin-wave signals through the device is verified. This attests the applicability in magnonic networks where the information is encoded in the phase of the spin waves.
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.
The signal extraction of fetal heart rate based on wavelet transform and BP neural network

Science.gov (United States)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical 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 correctness rate of BP network recognition is about 83.3% by using the above measure.
A System to Produce Precise Global GPS Network Solutions for all Geodetic GPS Stations in the World

Science.gov (United States)

Blewitt, G.; Kreemer, C. W.

2010-12-01

We have developed an end-to-end system that automatically seeks and routinely retrieves geodetic GPS data from ~5000 stations (currently) around the globe, reduces the data into unique, daily global network solutions, and produces high precision time series for station coordinates ready for time-series analysis, geophysical modeling and interpretation. Moreover, “carrier range” data are produced for all stations, enabling epoch-by-epoch tracking of individual station motions by precise point positioning for investigation of sub-daily processes, such as post-seismic after-slip and ocean tidal loading. Solutions are computed in a global reference frame aligned to ITRF, and optionally in user-specified continental-scale reference frames that can filter out common-mode signals to enhance regional strain anomalies. We describe the elements of this system, the underlying signal processing theory, the products, operational statistics, and scientific applications of our system. The system is fundamentally based on precise point positioning using JPL's GIPSY OASIS II software, coupled with ambiguity resolution and a global network adjustment of ~300,000 parameters per day using our newly developed Ambizap3 software. The system is designed to easily and efficiently absorb stations that deliver data very late, by recycling prior computations in the network adjustment, such that the resulting network solution is identical to starting from scratch. Thus, it becomes possible to trawl continuously the Internet for late arriving data, or for newly discovered data, and seamlessly update all GPS station time series using the new information content. As new stations are added to the processing archive, automated e-mail requests are made to H.-G. Scherneck's server at Chalmers University to compute ocean loading coefficients used by the station motion model. Rinex file headers are parsed and compared with alias tables in order to infer the correct receiver type and antenna
eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

Science.gov (United States)

Clarke, Daniel J B; Kuleshov, Maxim V; Schilder, Brian M; Torre, Denis; Duffy, Mary E; Keenan, Alexandra B; Lachmann, Alexander; Feldmann, Axel S; Gundersen, Gregory W; Silverstein, Moshe C; Wang, Zichen; Ma'ayan, Avi

2018-05-25

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.
Novel global robust stability criterion for neural networks with delay

International Nuclear Information System (INIS)

Singh, Vimal

2009-01-01

A novel criterion for the global robust stability of Hopfield-type interval neural networks with delay is presented. An example illustrating the improvement of the present criterion over several recently reported criteria is given.
Local communities obstruct global consensus: Naming game on multi-local-world networks

Science.gov (United States)

Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

2018-02-01

Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.
Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

Science.gov (United States)

Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

2011-01-01

Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

Science.gov (United States)

Anderson, Brian A

2017-03-01

Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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,
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.
A KST framework for correlation network construction from time series signals

Science.gov (United States)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.
Nonreciprocal signal routing in an active quantum network

Science.gov (United States)

Metelmann, A.; Türeci, H. E.

2018-04-01

As superconductor quantum technologies are moving towards large-scale integrated circuits, a robust and flexible approach to routing photons at the quantum level becomes a critical problem. Active circuits, which contain parametrically driven elements selectively embedded in the circuit, offer a viable solution. Here, we present a general strategy for routing nonreciprocally quantum signals between two sites of a given lattice of oscillators, implementable with existing superconducting circuit components. Our approach makes use of a dual lattice of overdamped oscillators linking the nodes of the main lattice. Solutions for spatially selective driving of the lattice elements can be found, which optimally balance coherent and dissipative hopping of microwave photons to nonreciprocally route signals between two given nodes. In certain lattices these optimal solutions are obtained at the exceptional point of the dynamical matrix of the network. We also demonstrate that signal and noise transmission characteristics can be separately optimized.
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.
Network Fictions and the Global Unhomely

Directory of Open Access Journals (Sweden)

Aris Mousoutzanis

2016-04-01

Full Text Available The paper suggests that the increasing proliferation of network fictions in literature, film, television and the internet may be interpreted through a theoretical framework that reconceptuallises the originally strictly psychoanalytic concept of the 'Unheimlich' (Freud’s idea of the ‘unhomely’ or ‘uncanny’ within the context of political, economic and cultural disources fo globalisation. ‘Network fictions’ are those texts consisting of multiple interlocking narratives set in various times and places that explore the interconnections of characters and events across different storylines: novels such as William Gibson’s 'Pattern Recognition' (2003, Hari Kunzro’s 'Transmission' (2005 and 'Gods Without Men' (2011, David Mitchell’s 'Cloud Atlas' (2004, or Rana Dasgupta’s 'Tokyo Cancelled' (2005 are some examples. My argument is that central to these fictions is a sense of a ‘global unhomely’. The sense of displacement, unhomeliness and global mobility that is conveyed in these fictions is fundamental to the experience of the 'Unheimlich'. In addition, the ability of the concept to convey a combined sense of the familiar and the strange is useful in exploring the ways in which these fictions engage with theoretical debates on globalisation that perceive the interaction between global flows and local cultures either in terms of homogenisation and uniformity or of heterogenisation and hybridity. Moreover, the repetitive temporality of the 'Unheimlich' is another distinctive aspect that allows a reading of the disjunctive, non-linear temporal structure of these fictions from this perspective. The ‘repetition compulsion’, however, that Freud considered to be an example of uncanniness was also theorised by him as a post-traumatic symptom, and this implicit association of uncanniness with post-traumatic experience also allows to interpret the persistent preoccupation of these fictions with suffering and disaster, as well as
Only (Dis-)Connect: Pentecostal Global Networking as Revelation and Concealment

OpenAIRE

Coleman, Simon

2013-01-01

Contemporary forms of Pentecostalism, such as that of the Faith Movement, are often represented as inherently global, constituting a religion ‘made to travel’ and to missionize across the world. I argue that while much attention has been paid to proselytization as a catalyst in encouraging transnational activities among such Christians, more analysis is needed of how Pentecostalists represent each other in the construction of global imaginaries. The imagined and enacted networks that result a...
Power and Networks in Worldwide Knowledge Coordination: The Case of Global Science

Science.gov (United States)

King, Roger

2011-01-01

The article considers the global governance of knowledge systems, exploring concepts of power, networks, standards (defined as normative practices), and structuration. The focus is on science as a form of predominantly private global governance, particularly the self-regulatory and collaborative processes stretching across time and space. These…
Global Asymptotic Stability of Switched Neural Networks with Delays

Directory of Open Access Journals (Sweden)

Zhenyu Lu

2015-01-01

Full Text Available This paper investigates the global asymptotic stability of a class of switched neural networks with delays. Several new criteria ensuring global asymptotic stability in terms of linear matrix inequalities (LMIs are obtained via Lyapunov-Krasovskii functional. And here, we adopt the quadratic convex approach, which is different from the linear and reciprocal convex combinations that are extensively used in recent literature. In addition, the proposed results here are very easy to be verified and complemented. Finally, a numerical example is provided to illustrate the effectiveness of the results.
Nonlinear Silicon Photonic Signal Processing Devices for Future Optical Networks

Directory of Open Access Journals (Sweden)

Cosimo Lacava

2017-01-01

Full Text Available In this paper, we present a review on silicon-based nonlinear devices for all optical nonlinear processing of complex telecommunication signals. We discuss some recent developments achieved by our research group, through extensive collaborations with academic partners across Europe, on optical signal processing using silicon-germanium and amorphous silicon based waveguides as well as novel materials such as silicon rich silicon nitride and tantalum pentoxide. We review the performance of four wave mixing wavelength conversion applied on complex signals such as Differential Phase Shift Keying (DPSK, Quadrature Phase Shift Keying (QPSK, 16-Quadrature Amplitude Modulation (QAM and 64-QAM that dramatically enhance the telecom signal spectral efficiency, paving the way to next generation terabit all-optical networks.
Building dynamic capabilities in large global advertising agency networks: managing the shift from mass communication to digital interactivity

DEFF Research Database (Denmark)

Suheimat, Wisam; Prætorius, Thim; Brambini-Pedersen, Jan Vang

2018-01-01

Interactive digital technologies result in significant managerial challenges for the largest global advertising agency networks. This paper, based on original data from in-depth case research in three of the largest global advertising networks, investigates how advertising agency networks manage...
A global genetic interaction network maps a wiring diagram of cellular function.

Science.gov (United States)

Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

2016-09-23

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. Copyright © 2016, American Association for the Advancement of Science.
Global existence of periodic solutions of BAM neural networks with variable coefficients

International Nuclear Information System (INIS)

Guo Shangjiang; Huang Lihong; Dai Binxiang; Zhang Zhongzhi

2003-01-01

In this Letter, we study BAM (bidirectional associative memory) networks with variable coefficients. By some spectral theorems and a continuation theorem based on coincidence degree, we not only obtain some new sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the periodic solution but also estimate the exponentially convergent rate. Our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity. Moreover, these conclusions are presented in terms of system parameters and can be easily verified for the globally Lipschitz and the spectral radius being less than 1. Therefore, our results should be useful in the design and applications of periodic oscillatory neural circuits for neural networks with delays
REMOTE OPERATIONS IN A GLOBAL ACCELERATOR NETWORK

International Nuclear Information System (INIS)

PEGGS, S.; SATOGATA, T.; AGARWAL, D.; RICE, D.

2003-01-01

The INTRODUCTION to this paper summarizes the history of the Global Accelerator Network (GAN) concept and the recent workshops that discussed the relationship between GAN and Remote Operations. The REMOTE OPERATIONS SCENARIOS section brings out the organizational philosophy embodied in GAN-like and to non-GAN-like scenarios. The set of major TOPICS RAISED AT THE WORKSHOPS are only partially resolved. COLLABORATION TOOLS are described and discussed, followed by examples of REMOTE ACCELERATOR CONTROL PROJECTS around the world
Remote operations in a global accelerator network

International Nuclear Information System (INIS)

Peggs, Steve; Satogata, Todd; Agarwal, Deborah; Rice, David

2003-01-01

The INTRODUCTION to this paper summarizes the history of the Global Accelerator Network (GAN) concept and the recent workshops that discussed the relationship between GAN and Remote Operations. The REMOTE OPERATIONS SCENARIOS section brings out the organizational philosophy embodied in GAN-like and to non-GAN-like scenarios. The set of major TOPICS RAISED AT THE WORKSHOPS are only partially resolved. COLLABORATION TOOLS are described and discussed, followed by examples of REMOTE ACCELERATOR CONTROL PROJECTS around the world
REMOTE OPERATIONS IN A GLOBAL ACCELERATOR NETWORK.

Energy Technology Data Exchange (ETDEWEB)

PEGGS,S.; SATOGATA,T.; AGARWAL,D.; RICE,D.

2003-05-12

The INTRODUCTION to this paper summarizes the history of the Global Accelerator Network (GAN) concept and the recent workshops that discussed the relationship between GAN and Remote Operations. The REMOTE OPERATIONS SCENARIOS section brings out the organizational philosophy embodied in GAN-like and to non-GAN-like scenarios. The set of major TOPICS RAISED AT THE WORKSHOPS are only partially resolved. COLLABORATION TOOLS are described and discussed, followed by examples of REMOTE ACCELERATOR CONTROL PROJECTS around the world.
CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change

Energy Technology Data Exchange (ETDEWEB)

Anderson-Teixeira, Kristina J. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Davies, Stuart J. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; National Museum of Natural History, Washington, DC (United States). Dept. of Botany; Bennett, Amy C. [Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Gonzalez-Akre, Erika B. [Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Muller-Landau, Helene C. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Joseph Wright, S. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Abu Salim, Kamariah [Univ. of Brunei Darussalam, Bandar Seri Begawan (Brunei). Faculty of Science. Environmental and Life Sciences; Almeyda Zambrano, Angélica M. [Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Stanford Univ., CA (United States). Stanford Woods Inst. for the Environment; Univ. of Alabama, Tuscaloosa, AL (United States). Dept. of Geography; Alonso, Alfonso [Smithsonian Conservation Biology Inst., Washington, DC (United States). National Zoological Park. Center for Conservation Education and Sustainability; Baltzer, Jennifer L. [Wilfrid Laurier Univ., Waterloo, ON (Canada). Dept. of Biology; Basset, Yves [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Bourg, Norman A. [Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Broadbent, Eben N. [Smithsonian Conservation Biology Inst. (SCBI), Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Stanford Univ., CA (United States). Stanford Woods Inst. for the Environment; Univ. of Alabama, Tuscaloosa, AL (United States). Dept. of Geography; Brockelman, Warren Y. [Mahidol Univ., Bangkok (Thailand). Dept. of Biology; Bunyavejchewin, Sarayudh [Dept. of National Parks, Wildlife and Plant Conservation, Bangkok (Thailand). Research Office; Burslem, David F. R. P. [Univ. of Aberdeen (United Kingdom). School of Biological Sciences; Butt, Nathalie [Univ. of Queensland, St. Lucia (Australia). School of Biological Sciences; Univ. of Oxford (United Kingdom). School of Geography and the Environment. Environmental Change Inst.; Cao, Min [Chinese Academy of Sciences (CAS), Kunming (China). Xishuangbanna Tropical Botanical Garden. Key Lab. of Tropical Forest Ecology; Cardenas, Dairon [Sinchi Amazonic Inst. of Scientific Research, Bogota (Colombia); Chuyong, George B. [Univ. of Buea (Cameroon). Dept. of Botany and Plant Physiology; Clay, Keith [Indiana Univ., Bloomington, IN (United States). Dept. of Biology; Cordell, Susan [USDA Forest Service, Hilo, HI (United States). Inst. of Pacific Islands Forestry; Dattaraja, Handanakere S. [Indian Inst. of Science, Bangalore (India). Centre for Ecological Sciences; Deng, Xiaobao [Chinese Academy of Sciences (CAS), Kunming (China). Xishuangbanna Tropical Botanical Garden. Key Lab. of Tropical Forest Ecology; Detto, Matteo [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Du, Xiaojun [Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Botany; Duque, Alvaro [Univ. Nacional de Colombia, Medellin (Colombia). Dept. de Ciencias Forestales; Erikson, David L. [National Museum of Natural History, Washington, DC (United States). Dept. of Botany; Ewango, Corneille E. N. [Okapi Wildlife Reserve, Epulu (Democratic Republic of the Congo). Centre de Formation et de Recherche en Conservation Forestiere (CEFRECOF); Fischer, Gunter A. [Kadoorie Farm and Botanic Garden, Tai Po, Hong Kong (China); Fletcher, Christine [Forest Research Inst. Malaysia (FRIM), Selangor (Malaysia); Foster, Robin B. [The Field Museum, Chicago, IL (United States). Botany Dept.; Giardina, Christian P. [USDA Forest Service, Hilo, HI (United States). Inst. of Pacific Islands Forestry; Gilbert, Gregory S. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Univ. of California, Santa Cruz, CA (United States). Environmental Studies Dept.; Gunatilleke, Nimal [Univ. of Peradeniya (Sri Lanka). Faculty of Science. Dept. of Botany; Gunatilleke, Savitri [Univ. of Peradeniya (Sri Lanka). Faculty of Science. Dept. of Botany; Hao, Zhanqing [Chinese Academy of Sciences (CAS), Shenyang (China). State Key Lab. of Forest and Soil Ecology. Inst. of Applied Ecology; Hargrove, William W. [USDA-Forest Service Station Headquarters, Asheville, NC (United States). Eastern Forest Environmental Threat Assessment Center; Hart, Terese B. [Lukuru Wildlife Research Foundation, Kinshasa (Democratic Republic of the Congo). Tshuapa-Lomami-Lualaba Project; Hau, Billy C. H. [Univ. of Hong Kong (China). School of Biological Sciences. Kadoorie Inst.; He, Fangliang [Univ. of Alberta, Edmonton, AB (Canada). Dept. of Renewable Resources; Hoffman, Forrest M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences Group; Howe, Robert W. [Univ. of Wisconsin, Green Bay, WI (United States). Dept. of Natural and Applied Sciences; Hubbell, Stephen P. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Univ. of California, Los Angeles, CA (United States). Dept. of Ecology and Evolutionary Biology; Inman-Narahari, Faith M. [Univ. of Hawaii, Honolulu, HI (United States). College of Tropical Agriculture and Human Resources; Jansen, Patrick A. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Wageningen Univ. (Netherlands). Resource Ecology Group; Jiang, Mingxi [Chinese Academy of Sciences (CAS), Wuhan (China). Wuhan Botanical Garden; Johnson, Daniel J. [Indiana Univ., Bloomington, IN (United States). Dept. of Biology; Kanzaki, Mamoru [Kyoto Univ. (Japan). Graduate School of Agriculture; Kassim, Abdul Rahman [Forest Research Inst. Malaysia (FRIM), Selangor (Malaysia); Kenfack, David [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; National Museum of Natural History, Washington, DC (United States). Dept. of Botany; Kibet, Staline [National Museums of Kenya, Nairobi (Kenya); Univ. of Nairobi (Kenya). Land Resource Management and Agricultural Technology Dept.; Kinnaird, Margaret F. [Mpala Research Centre, Nanyuki (Kenya); Wildlife Conservation Society, New York, NY (United States). Global Conservation Programs; Korte, Lisa [Smithsonian Conservation Biology Inst., Washington, DC (United States). National Zoological Park. Center for Conservation Education and Sustainability; Kral, Kamil [Silva Tarouca Research Inst., Brno (Czech Republic). Dept. of Forest Ecology; Kumar, Jitendra [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences Group; Larson, Andrew J. [Univ. of Montana, Missoula, MT (United States). College of Forestry and Conservation. Dept. of Forest Management; Li, Yide [Chinese Academy of Forestry, Guangzhou (China). Research Inst. of Tropical Forestry; Li, Xiankun [Chinese Academy of Sciences (CAS), Guilin (China). Guangxi Inst. of Botany; Liu, Shirong [Chinese Academy of Forestry, Beijing (China). Research Inst. of Forest Ecology, Environment and Protection; Lum, Shawn K. Y. [Nanyang Technological Univ. (Singapore). National Inst. of Education. Natural Sciences and Science Education Academic Group; Lutz, James A. [Utah State Univ., Logan, UT (United States). Wildland Resources Dept.; Ma, Keping [Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Botany; Maddalena, Damian M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences Group; Makana, Jean-Remy [Wildlife Conservation Society, Brazzaville (Democratic Republic of the Congo); Malhi, Yadvinder [Univ. of Oxford (United Kingdom). School of Geography and the Environment. Environmental Change Inst.; Marthews, Toby [Univ. of Oxford (United Kingdom). School of Geography and the Environment. Environmental Change Inst.; Mat Serudin, Rafizah [Univ. of Brunei Darussalam, Bandar Seri Begawan (Brunei). Faculty of Science. Environmental and Life Sciences; McMahon, Sean M. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Smithsonian Environmental Research Center, Edgewater, MD (United States). Forest Ecology Group; McShea, William J. [Smithsonian Conservation Biology Inst., Front Royal, VA (United States). National Zoological Park. Conservation Ecology Center; Memiaghe, Hervé R. [Inst. de Recherche en Ecologie Tropicale, Libreville (Gabon). Centre National de la Recherche Scientifique et Technologique; Mi, Xiangcheng [Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Botany; Mizuno, Takashi [Kyoto Univ. (Japan). Graduate School of Agriculture; Morecroft, Michael [Natural England, Sheffield (United Kingdom); Myers, Jonathan A. [Washington Univ., St. Louis, MO (United States). Dept. of Biology; Novotny, Vojtech [New Guinea Binatang Research Centre, Madang (Papua New Guinea); Univ. of South Bohemia, Ceske Budejovice (Czech Republic). Academy of Sciences of the Czech Republic. Faculty of Science. Biology Centre; de Oliveira, Alexandre A. [Univ. of Sao Paulo (Brazil). Inst. of Biosciences. Ecology Dept.; Ong, Perry S. [Univ. of the Philippines Diliman, Quezon City (Philippines). Inst. of Biology; Orwig, David A. [Harvard Univ., Petersham, MA (United States). Harvard Forest; Ostertag, Rebecca [Univ. of Hawaii, Hilo, HI (United States). Dept. of Biology; den Ouden, Jan [Wageningen Univ. (Netherlands). Forest Ecology and Forest Management Group; Parker, Geoffrey G. [Smithsonian Environmental Research Center, Edgewater, MD (United States). Forest Ecology Group; Phillips, Richard P. [Indiana Univ., Bloomington, IN (United States). Dept. of Biology; Sack, Lawren [Univ. of California, Los Angeles, CA (United States). Dept. of Ecology and Evolutionary Biology; Sainge, Moses N. [Tropical Plant Exploration Group (TroPEG), Mundemba (Cameroon); Sang, Weiguo [Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Botany; Sri-ngernyuang, Kriangsak [Maejo Univ., Chiang Mai (Thailand). Faculty of Architecture and Environmental Design; Sukumar, Raman [Indian Inst. of Science, Bangalore (India). Centre for Ecological Sciences; Sun, I-Fang [National Dong Hwa Univ., Hualian (Taiwan). Dept. of Natural Resources and Environmental Studies; Sungpalee, Witchaphart [Maejo Univ., Chiang Mai (Thailand). Faculty of Architecture and Environmental Design; Suresh, Hebbalalu Sathyanarayana [Indian Inst. of Science, Bangalore (India). Centre for Ecological Sciences; Tan, Sylvester [Sarawak Forest Dept., Kuching (Malaysia); Thomas, Sean C. [Univ. of Toronto, ON (Canada). Faculty of Forestry; Thomas, Duncan W. [Washington State Univ., Vancouver, WA (United States). School of Biological Sciences; Thompson, Jill [Centre for Ecology and Hydrology, Penicuik, Scotland (United Kingdom); Univ. of Puerto Rico Rio Pedras, San Juan (Puerto Rico). Dept. of Environmental Science. Inst. for Tropical Ecosystem Studies; Turner, Benjamin L. [Smithsonian Tropical Research Inst. (STRI), Panama (Panama). Center for Tropical Forest Science. Forest Global Earth Observatory; Uriarte, Maria [Columbia Univ., New York, NY (United States). Dept. of Ecology, Evolution and Environmental Biology; Valencia, Renato [Pontifical Catholic Univ. of Ecuador, Quito (Ecuador). Dept. of Biological Sciences; Vallejo, Marta I. [Inst. Alexander von Humboldt, Bogota (Colombia); Vicentini, Alberto [National Inst. of Amazonian Research (INPA), Manaus (Brazil); Vrška, Tomáš [Silva Tarouca Research Inst., Brno (Czech Republic). Dept. of Forest Ecology; Wang, Xihua [East China Normal Univ. (ECNU), Shanghai (China). School of Ecological and Environmental Sciences; Wang, Xugao [Lukuru Wildlife Research Foundation, Kinshasa (Democratic Republic of the Congo). Tshuapa-Lomami-Lualaba Project; Weiblen, George [Univ. of Minnesota, St. Paul, MN (United States). Dept. of Plant Biology; Wolf, Amy [Univ. of Wisconsin, Green Bay, WI (United States). Dept. of Biology. Dept. of Natural and Applied Sciences; Xu, Han [Chinese Academy of Forestry, Guangzhou (China). Research Inst. of Tropical Forestry; Yap, Sandra [Univ. of the Philippines Diliman, Quezon City (Philippines). Inst. of Biology; Zimmerman, Jess [Univ. of Puerto Rico Rio Piedras, San Juan (Puerto Rico). Dept. of Environmental Science. Inst. for Tropical Ecosystem Studies

2014-09-25

Global change is impacting forests worldwide, threatening biodiversity and ecosystem services, including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamic research sites useful for characterizing forest responses to global change. The broad suite of measurements made at the CTFS-ForestGEO sites make it possible to investigate the complex ways in which global change is impacting forest dynamics. ongoing research across the network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in a era of global change
Quantitative Models of Imperfect Deception in Network Security using Signaling Games with Evidence

OpenAIRE

Pawlick, Jeffrey; Zhu, Quanyan

2017-01-01

Deception plays a critical role in many interactions in communication and network security. Game-theoretic models called "cheap talk signaling games" capture the dynamic and information asymmetric nature of deceptive interactions. But signaling games inherently model undetectable deception. In this paper, we investigate a model of signaling games in which the receiver can detect deception with some probability. This model nests traditional signaling games and complete information Stackelberg ...
Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

Science.gov (United States)

Brown, Patrick T.; Li, Wenhong; Cordero, Eugene C.; Mauget, Steven A.

2015-01-01

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal. PMID:25898351
Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

Science.gov (United States)

Brown, Patrick T; Li, Wenhong; Cordero, Eugene C; Mauget, Steven A

2015-04-21

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.
Duplicate retention in signalling proteins and constraints from network dynamics.

Science.gov (United States)

Soyer, O S; Creevey, C J

2010-11-01

Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.
The Global Environment Radiation Monitoring Network (GERMON)

International Nuclear Information System (INIS)

Zakheim, B.J.; Goellner, D.A.

1994-01-01

Following the Chernobyl accident in 1986, a group of experts from the World Health Organization (WHO) and the United Nations Environment Program (UNEP) met in France to discuss and develop the basic principles of a global environmental radiation monitoring network (GERMON). The basic functions of this network were to provide regular reports on environmental radiation levels and to be in a position to provide reliable and accurate radiation measurements on a quick and accurate radiation measurements on a quick turnaround basis in the event of a major radiation release. By 1992, although 58 countries had indicated an interest in becoming a part of the GERMON system, only 16 were providing data on a regular basis. This paper traces the history of GERMON from its inception in 1987 through its activities during 1993-4. It details the objectives of the network, describes functions, lists its participants, and presents obstacles in the current network. The paper examines the data requirements for radiological emergency preparedness and offers suggestions for the current system. The paper also describes the growing need for such a network. To add a domestic perspective, the authors present a summary of the environmental monitoring information system that was used by the NRC in 1986 in its analyses of the Chernobyl incident. Then we will use this 1986 experience to propose a method for the use of GERMON should a similar occasion arise in the future
Experimental observation of chimera and cluster states in a minimal globally coupled network

Energy Technology Data Exchange (ETDEWEB)

Hart, Joseph D. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Bansal, Kanika [Department of Mathematics, University at Buffalo, SUNY Buffalo, New York 14260 (United States); US Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005 (United States); Murphy, Thomas E. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742 (United States); Roy, Rajarshi [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742 (United States)

2016-09-15

A “chimera state” is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.
Forced disappearance in an era of globalization: biopolitics, shadow networks, and imagined worlds.

Science.gov (United States)

Rozema, Ralph

2011-01-01

In this article, I argue that the practice of forced disappearance of persons on the part of paramilitary groups has become linked to specific processes of globalization. Global flows related to biopolitics, global crime networks, and dehumanizing imaginations reproduced by mass media together constitute a driving force behind forced disappearances. Drawing on ethnographic fieldwork in the Colombian city of Medellín, I analyze how these global flows interact with local armed actors, helping create a climate conducive to forced disappearance. These mechanisms in Colombia show similarities to those in some African and Asian countries. Gaining insight into the mechanisms behind forced disappearance may help prevent it from occurring in the future. Enhancing social inclusion of residents, unraveling the transnational crime networks in which perpetrators are involved, and disseminating rehumanizing images of victims all contribute to curbing the practice of forced disappearance.
EEG signal classification based on artificial neural networks and amplitude spectra features

Science.gov (United States)

Chojnowski, K.; FrÄ czek, J.

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.
Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

Science.gov (United States)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.
Effects of topologies on signal propagation in feedforward networks

Science.gov (United States)

Zhao, Jia; Qin, Ying-Mei; Che, Yan-Qiu

2018-01-01

We systematically investigate the effects of topologies on signal propagation in feedforward networks (FFNs) based on the FitzHugh-Nagumo neuron model. FFNs with different topological structures are constructed with same number of both in-degrees and out-degrees in each layer and given the same input signal. The propagation of firing patterns and firing rates are found to be affected by the distribution of neuron connections in the FFNs. Synchronous firing patterns emerge in the later layers of FFNs with identical, uniform, and exponential degree distributions, but the number of synchronous spike trains in the output layers of the three topologies obviously differs from one another. The firing rates in the output layers of the three FFNs can be ordered from high to low according to their topological structures as exponential, uniform, and identical distributions, respectively. Interestingly, the sequence of spiking regularity in the output layers of the three FFNs is consistent with the firing rates, but their firing synchronization is in the opposite order. In summary, the node degree is an important factor that can dramatically influence the neuronal network activity.
Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

Science.gov (United States)

Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M

2011-11-01

Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.
The global historical climatology network: Long-term monthly temperature, precipitation, and pressure data

International Nuclear Information System (INIS)

Vose, R.S.; Schmoyer, R.L.; Peterson, T.C.; Steurer, P.M.; Heim, R.R. Jr.; Karl, T.R.; Eischeid, J.K.

1992-01-01

Interest in global climate change has risen dramatically during the past several decades. In a similar fashion, the number of data sets available to study global change has also increased. Unfortunately, many different organizations and researchers have compiled these data sets, making it confusing and time consuming for individuals to acquire the most comprehensive data. In response to this rapid growth in the number of global data sets, DOE's Carbon Dioxide Information Analysis Center (CDIAC) and NOAA's National Climatic Data Center (NCDC) established the Global Historical Climatology Network (GHCN) project. The purpose of this project is to compile an improved data set of long-term monthly mean temperature, precipitation, sea level pressure, and station pressure for as dense a network of global stations as possible. Specifically, the GHCN project seeks to consolidate the numerous preexisting national-, regional-, and global-scale data sets into a single global data base; to subject the data to rigorous quality control; and to update, enhance, and distribute the data set at regular intervals. The purpose of this paper is to describe the compilation and contents of the GHCN data base (i.e., GHCN Version 1.0)
Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase.

Science.gov (United States)

Patil, Sonali; Pincas, Hanna; Seto, Jeremy; Nudelman, German; Nudelman, Irina; Sealfon, Stuart C

2010-10-07

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. 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 pathogen detection. This map represents a navigable
Global exponential stability of BAM neural networks with delays and impulses

International Nuclear Information System (INIS)

Li Yongkun

2005-01-01

Sufficient conditions are obtained for the existence and global exponential stability of a unique equilibrium of a class of two-layer heteroassociative networks called bidirectional associative memory (BAM) networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. An illustrative example is given to demonstrate the effectiveness of the obtained results
Recurrence network analysis of experimental signals from bubbly oil-in-water flows

Energy Technology Data Exchange (ETDEWEB)

Gao, Zhong-Ke; Zhang, Xin-Wang; Du, Meng [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Jin, Ning-De, E-mail: ndjin@tju.edu.cn [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2013-02-04

Based on the signals from oil–water two-phase flow experiment, we construct and analyze recurrence networks to characterize the dynamic behavior of different flow patterns. We first take a chaotic time series as an example to demonstrate that the local property of recurrence network allows characterizing chaotic dynamics. Then we construct recurrence networks for different oil-in-water flow patterns and investigate the local property of each constructed network, respectively. The results indicate that the local topological statistic of recurrence network is very sensitive to the transitions of flow patterns and allows uncovering the dynamic flow behavior associated with chaotic unstable periodic orbits.
Recurrence network analysis of experimental signals from bubbly oil-in-water flows

International Nuclear Information System (INIS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Du, Meng; Jin, Ning-De

2013-01-01

Based on the signals from oil–water two-phase flow experiment, we construct and analyze recurrence networks to characterize the dynamic behavior of different flow patterns. We first take a chaotic time series as an example to demonstrate that the local property of recurrence network allows characterizing chaotic dynamics. Then we construct recurrence networks for different oil-in-water flow patterns and investigate the local property of each constructed network, respectively. The results indicate that the local topological statistic of recurrence network is very sensitive to the transitions of flow patterns and allows uncovering the dynamic flow behavior associated with chaotic unstable periodic orbits.
Virtual Global Accelerator Network (VGAN)(LCC-0083)

International Nuclear Information System (INIS)

Larsen, R

2003-01-01

The concept of a Global Accelerator Network (GAN) has been proposed by key members of ICFA as a cornerstone of a future International Linear Collider (LC). GAN would provide a tool for the participants of an international collaboration to participate in the actual running of the machine from different parts of the world. Some technical experts view the concept as technologically trivial, and instead point out the significant sociological, organizational and administrative problems that must be surmounted in creating a truly workable system. This note proposes that many real issues can be explored by building a simulator (VGAN) consisting of a virtual accelerator model, a global controls model, and a functioning human organizational model, a tool that would explore and resolve many real problems of GAN and the LC enterprise during the LC preliminary design and testing phase
Connection Setup Signaling Scheme with Flooding-Based Path Searching for Diverse-Metric Network

Science.gov (United States)

Kikuta, Ko; Ishii, Daisuke; Okamoto, Satoru; Oki, Eiji; Yamanaka, Naoaki

Connection setup on various computer networks is now achieved by GMPLS. This technology is based on the source-routing approach, which requires the source node to store metric information of the entire network prior to computing a route. Thus all metric information must be distributed to all network nodes and kept up-to-date. However, as metric information become more diverse and generalized, it is hard to update all information due to the huge update overhead. Emerging network services and applications require the network to support diverse metrics for achieving various communication qualities. Increasing the number of metrics supported by the network causes excessive processing of metric update messages. To reduce the number of metric update messages, another scheme is required. This paper proposes a connection setup scheme that uses flooding-based signaling rather than the distribution of metric information. The proposed scheme requires only flooding of signaling messages with requested metric information, no routing protocol is required. Evaluations confirm that the proposed scheme achieves connection establishment without excessive overhead. Our analysis shows that the proposed scheme greatly reduces the number of control messages compared to the conventional scheme, while their blocking probabilities are comparable.
Remote consultation and diagnosis in medical imaging using a global PACS backbone network

Science.gov (United States)

Martinez, Ralph; Sutaria, Bijal N.; Kim, Jinman; Nam, Jiseung

1993-10-01

A Global PACS is a national network which interconnects several PACS networks at medical and hospital complexes using a national backbone network. A Global PACS environment enables new and beneficial operations between radiologists and physicians, when they are located in different geographical locations. One operation allows the radiologist to view the same image folder at both Local and Remote sites so that a diagnosis can be performed. The paper describes the user interface, database management, and network communication software which has been developed in the Computer Engineering Research Laboratory and Radiology Research Laboratory. Specifically, a design for a file management system in a distributed environment is presented. In the remote consultation and diagnosis operation, a set of images is requested from the database archive system and sent to the Local and Remote workstation sites on the Global PACS network. Viewing the same images, the radiologists use pointing overlay commands, or frames to point out features on the images. Each workstation transfers these frames, to the other workstation, so that an interactive session for diagnosis takes place. In this phase, we use fixed frames and variable size frames, used to outline an object. The data pockets for these frames traverses the national backbone in real-time. We accomplish this feature by using TCP/IP protocol sockets for communications. The remote consultation and diagnosis operation has been tested in real-time between the University Medical Center and the Bowman Gray School of Medicine at Wake Forest University, over the Internet. In this paper, we show the feasibility of the operation in a Global PACS environment. Future improvements to the system will include real-time voice and interactive compressed video scenarios.
Does Global Astrocytic Calcium Signaling Participate in Awake Brain State Transitions and Neuronal Circuit Function?

DEFF Research Database (Denmark)

Kjaerby, Celia; Rasmussen, Rune; Andersen, Mie

2017-01-01

of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca(2+) signaling in response to activation of the noradrenergic system, but whether...... astrocytic Ca(2+) signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca(2+) signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore......We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release...
Network advocacy and the emergence of global attention to newborn survival.

Science.gov (United States)

Shiffman, Jeremy

2016-04-01

Globally 2.9 million babies die each year before reaching 28 days of life. Over the past quarter century, neonatal mortality has declined at a slower pace than post-neonatal under-five mortality: in consequence newborns now comprise 44% of all deaths to children under five years. Despite high numbers of newborn deaths, global organizations and national governments paid little attention to the issue until 2000, and resources, while growing since then, remain inadequate. This study examines the factors behind these patterns of policy attention: the delayed emergence of attention, its sudden appearance in 2000, its growth thereafter, but the dearth of resources to date. Drawing on a framework on global health networks grounded in collective action theory, the study finds that a newborn survival network helped to shift perceptions about the problem's severity and tractability, contributing to the rise of global attention. Its efforts were facilitated by pressure on governments to achieve the child survival Millennium Development Goal and by growing awareness that the neonatal period constituted a growing percentage of under-five mortality, a fact the network publicized. The network's relatively recent emergence, its predominantly technical rather than political composition and strategies, and its inability to date to find a framing of the issue that has convinced national political leaders of the issue's urgency, in part explain the insufficiency of resources. However, since 2010 a number of non-health oriented inter-governmental organizations have begun to pay attention to the issue, and several countries with high neonatal mortality have created national plans, developments which augur well for the future. The study points to two broader implications concerning how neglected global health issues come to attract attention: priority emerges from a confluence of factors, rather than any single cause; and growth in priority may depend on the creation of a broader

Shifting Tides in Global Higher Education: Agency, Autonomy, and Governance in the Global Network. Global Studies in Education, Volume 9

Science.gov (United States)

Witt, Mary Allison

2011-01-01

The increasing connection among higher education institutions worldwide is well documented. What is less understood is how this connectivity is enacted and manifested on specific levels of the global education network. This book details the planning process of a multi-institutional program in engineering between institutions in the US and…
Global stability of an SIR model with differential infectivity on complex networks

Science.gov (United States)

Yuan, Xinpeng; Wang, Fang; Xue, Yakui; Liu, Maoxing

2018-06-01

In this paper, an SIR model with birth and death on complex networks is analyzed, where infected individuals are divided into m groups according to their infection and contact between human is treated as a scale-free social network. We obtain the basic reproduction number R0 as well as the effects of various immunization schemes. The results indicate that the disease-free equilibrium is locally and globally asymptotically stable in some conditions, otherwise disease-free equilibrium is unstable and exists an unique endemic equilibrium that is globally asymptotically stable. Our theoretical results are confirmed by numerical simulations and a promising way for infectious diseases control is suggested.
Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

Science.gov (United States)

Szalay, Kristóf Z; Nussinov, Ruth; Csermely, Peter

2014-06-01

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Tracing the Slow Food Movement: local foodscapes and global networks

NARCIS (Netherlands)

Hendrikx, B.; Dormans, S.E.M.; Lagendijk, A.

2012-01-01

Over the last two decades alternative food practices have mushroomed across the globe. This proliferation has changed local food scapes, infusing localities with new ideas and ways of food production, circulation and consumption. It has also created global networks of innovation and
Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

Science.gov (United States)

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

2013-09-15

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 generate reliable predictions. We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary materials are available at Bioinformatics online. santiago.videla@irisa.fr.
The Continuing Growth of Global Cooperation Networks in Research: A Conundrum for National Governments.

Directory of Open Access Journals (Sweden)

Caroline S Wagner

Full Text Available Global collaboration continues to grow as a share of all scientific cooperation, measured as coauthorships of peer-reviewed, published papers. The percent of all scientific papers that are internationally coauthored has more than doubled in 20 years, and they account for all the growth in output among the scientifically advanced countries. Emerging countries, particularly China, have increased their participation in global science, in part by doubling their spending on R&D; they are increasingly likely to appear as partners on internationally coauthored scientific papers. Given the growth of connections at the international level, it is helpful to examine the phenomenon as a communications network and to consider the network as a new organization on the world stage that adds to and complements national systems. When examined as interconnections across the globe over two decades, a global network has grown denser but not more clustered, meaning there are many more connections but they are not grouping into exclusive 'cliques'. This suggests that power relationships are not reproducing those of the political system. The network has features an open system, attracting productive scientists to participate in international projects. National governments could gain efficiencies and influence by developing policies and strategies designed to maximize network benefits-a model different from those designed for national systems.
Global Stability of Complex-Valued Genetic Regulatory Networks with Delays on Time Scales

Directory of Open Access Journals (Sweden)

Wang Yajing

2016-01-01

Full Text Available In this paper, the global exponential stability of complex-valued genetic regulatory networks with delays is investigated. Besides presenting conditions guaranteeing the existence of a unique equilibrium pattern, its global exponential stability is discussed. Some numerical examples for different time scales.
Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

Science.gov (United States)

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

2015-01-01

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. PMID:26340633
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
Engineering satellite-based navigation and timing global navigation satellite systems, signals, and receivers

CERN Document Server

Betz, J

2016-01-01

This book describes the design and performance analysis of satnav systems, signals, and receivers. It also provides succinct descriptions and comparisons of all the world’s satnav systems. Its comprehensive and logical structure addresses all satnav signals and systems in operation and being developed. Engineering Satellite-Based Navigation and Timing: Global Navigation Satellite Systems, Signals, and Receivers provides the technical foundation for designing and analyzing satnav signals, systems, and receivers. Its contents and structure address all satnav systems and signals: legacy, modernized, and new. It combines qualitative information with detailed techniques and analyses, providing a comprehensive set of insights and engineering tools for this complex multidisciplinary field. Part I describes system and signal engineering including orbital mechanics and constellation design, signal design principles and underlying considerations, link budgets, qua tifying receiver performance in interference, and e...
Monthly Summaries of the Global Historical Climatology Network - Daily (GHCN-D)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly Summaries of Global Historical Climatology Network (GHCN)-Daily is a dataset derived from GHCN-Daily. The data are produced by computing simple averages or...
Indications of marine bioinvasion from network theory. An analysis of the global cargo ship network

NARCIS (Netherlands)

Kölzsch, A.; Blasius, B.

2011-01-01

The transport of huge amounts of small aquatic organisms in the ballast tanks and at the hull of large cargo ships leads to ever increasing rates of marine bioinvasion. In this study, we apply a network theoretic approach to examine the introduction of invasive species into new ports by global
European network for research in global change (ENRICH)

Energy Technology Data Exchange (ETDEWEB)

Ghazi, A [European Commission, Bruxelles (Belgium). DG XII/JRC

1996-12-31

While approaching the beginning of the twenty first century, the scientific community is faced with the formidable tasks of monitoring and detecting, understanding and predicting changes in the Earth System and its interactions with human beings. A crucial challenge is to make scientific research results accessible and usable for those involved in the decision making process related to the concept of Sustainable Development. Major international scientific programmes under the umbrella of ICSU, such as the IGBP and WCRP, are dealing with these issues. Although there exist many well developed global change research programmes in several European countries and effective collaboration networks between research institutes, there is an urgent need for overall communication with a view to promoting wider international links ensuring complementarity, synergy and coherence. Recognizing the importance of promoting coherence in research and utilising research results for various European Union (EU) policies, the European Commissioner responsible for Science, Research and Development wrote in March 1992 to all the EU Research Ministers to propose an initiative in this domain. In a rapid response, a group of Senior Experts from the EU Member States was set up in April 1992. This Group established a Task Force to develop the concept of the European Network for Research In Global CHange (ENRICH) which was approved in July 1993
European network for research in global change (ENRICH)

Energy Technology Data Exchange (ETDEWEB)

Ghazi, A. [European Commission, Bruxelles (Belgium). DG XII/JRC

1995-12-31

While approaching the beginning of the twenty first century, the scientific community is faced with the formidable tasks of monitoring and detecting, understanding and predicting changes in the Earth System and its interactions with human beings. A crucial challenge is to make scientific research results accessible and usable for those involved in the decision making process related to the concept of Sustainable Development. Major international scientific programmes under the umbrella of ICSU, such as the IGBP and WCRP, are dealing with these issues. Although there exist many well developed global change research programmes in several European countries and effective collaboration networks between research institutes, there is an urgent need for overall communication with a view to promoting wider international links ensuring complementarity, synergy and coherence. Recognizing the importance of promoting coherence in research and utilising research results for various European Union (EU) policies, the European Commissioner responsible for Science, Research and Development wrote in March 1992 to all the EU Research Ministers to propose an initiative in this domain. In a rapid response, a group of Senior Experts from the EU Member States was set up in April 1992. This Group established a Task Force to develop the concept of the European Network for Research In Global CHange (ENRICH) which was approved in July 1993
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.

2013-01-01

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
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

2013-04-09

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.
Perturbation Biology: Inferring Signaling Networks in Cellular Systems

Science.gov (United States)

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245
Identifying Network Motifs that Buffer Front-to-Back Signaling in Polarized Neutrophils

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Yanqin Wang

2013-05-01

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

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Zhang, Guodong; Zeng, Zhigang; Hu, Junhao

2018-01-01

This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Global robust exponential stability analysis for interval recurrent neural networks

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.; Zou Yun

2004-01-01

This Letter investigates the problem of robust global exponential stability analysis for interval recurrent neural networks (RNNs) via the linear matrix inequality (LMI) approach. The values of the time-invariant uncertain parameters are assumed to be bounded within given compact sets. An improved condition for the existence of a unique equilibrium point and its global exponential stability of RNNs with known parameters is proposed. Based on this, a sufficient condition for the global robust exponential stability for interval RNNs is obtained. Both of the conditions are expressed in terms of LMIs, which can be checked easily by various recently developed convex optimization algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition

Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

International Nuclear Information System (INIS)

Vannucci, A.; Oliveira, K.A.; Tajima, T.

1999-01-01

A feedforward neural network with two hidden layers is used to forecast major and minor disruptive instabilities in TEXT tokamak discharges. Using the experimental data of soft X ray signals as input data, the neural network is trained with one disruptive plasma discharge, and a different disruptive discharge is used for validation. After being properly trained, the networks, with the same set of weights, are used to forecast disruptions in two other plasma discharges. It is observed that the neural network is able to predict the occurrence of a disruption more than 3 ms in advance. This time interval is almost 3 times longer than the one already obtained previously when a magnetic signal from a Mirnov coil was used to feed the neural networks. Visually no indication of an upcoming disruption is seen from the experimental data this far back from the time of disruption. Finally, by observing the predictive behaviour of the network for the disruptive discharges analysed and comparing the soft X ray data with the corresponding magnetic experimental signal, it is conjectured about where inside the plasma column the disruption first started. (author)
Earthquake Monitoring with the MyShake Global Smartphone Seismic Network

Science.gov (United States)

Inbal, A.; Kong, Q.; Allen, R. M.; Savran, W. H.

2017-12-01

Smartphone arrays have the potential for significantly improving seismic monitoring in sparsely instrumented urban areas. This approach benefits from the dense spatial coverage of users, as well as from communication and computational capabilities built into smartphones, which facilitate big seismic data transfer and analysis. Advantages in data acquisition with smartphones trade-off with factors such as the low-quality sensors installed in phones, high noise levels, and strong network heterogeneity, all of which limit effective seismic monitoring. Here we utilize network and array-processing schemes to asses event detectability with the MyShake global smartphone network. We examine the benefits of using this network in either triggered or continuous modes of operation. A global database of ground motions measured on stationary phones triggered by M2-6 events is used to establish detection probabilities. We find that the probability of detecting an M=3 event with a single phone located 20 nearby phones closely match the regional catalog locations. We use simulated broadband seismic data to examine how location uncertainties vary with user distribution and noise levels. To this end, we have developed an empirical noise model for the metropolitan Los-Angeles (LA) area. We find that densities larger than 100 stationary phones/km2 are required to accurately locate M 2 events in the LA basin. Given the projected MyShake user distribution, that condition may be met within the next few years.
Tensor network decompositions in the presence of a global symmetry

International Nuclear Information System (INIS)

Singh, Sukhwinder; Pfeifer, Robert N. C.; Vidal, Guifre

2010-01-01

Tensor network decompositions offer an efficient description of certain many-body states of a lattice system and are the basis of a wealth of numerical simulation algorithms. We discuss how to incorporate a global symmetry, given by a compact, completely reducible group G, in tensor network decompositions and algorithms. This is achieved by considering tensors that are invariant under the action of the group G. Each symmetric tensor decomposes into two types of tensors: degeneracy tensors, containing all the degrees of freedom, and structural tensors, which only depend on the symmetry group. In numerical calculations, the use of symmetric tensors ensures the preservation of the symmetry, allows selection of a specific symmetry sector, and significantly reduces computational costs. On the other hand, the resulting tensor network can be interpreted as a superposition of exponentially many spin networks. Spin networks are used extensively in loop quantum gravity, where they represent states of quantum geometry. Our work highlights their importance in the context of tensor network algorithms as well, thus setting the stage for cross-fertilization between these two areas of research.
Global Production Networks and International Inequality: Making a Case for a Meso-Level Turn in Macro-Comparative Sociology

Directory of Open Access Journals (Sweden)

Mathew Mahutga

2015-08-01

Full Text Available In this article, I extend recent macro-comparative empirical research on the developmental implications of global production networks. I draw from theories of commodity/value chains, global production networks and economic sociology to identify three contending theoretical perspectives for exactly how the developmental returns to network participants should be distributed-cooperation, exploitation and differential gains-and derive testable hypotheses for each. Adding to recent empirical advances for measuring the average network position of firms at the country level, I evaluate these hypotheses by way of dynamic panel regression models of hourly wage rates in the garment and transportation equipment industries. The results suggest that macro-sociological theories linking underdevelopment to the structure of the world-economy, as well as theories of the distribution of the gains from network participation, miss important variation at the industry level. Cooperation provides a poor account of the distribution of the gains from network participation. Instead, both industries appear to distribute the gains from network participation differentially across network participants. However, the extent of this inequality increases, and the garment industry transitions to exploitation, when global production networks become entrenched organizational logics. Variation in the distribution of the returns to network participation is explicable only by accounting for production-network governance as it varies across industries and over time. I conclude by highlighting the analytical utility to macro-comparative sociology of a turn toward the mesa-level of global industries.
AN INITIATIVE FOR CONSTRUCTION OF NEW-GENERATION LUNAR GLOBAL CONTROL NETWORK USING MULTI-MISSION DATA

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K. Di

2017-07-01

Full Text Available A lunar global control network provides geodetic datum and control points for mapping of the lunar surface. The widely used Unified Lunar Control Network 2005 (ULCN2005 was built based on a combined photogrammetric solution of Clementine images acquired in 1994 and earlier photographic data. In this research, we propose an initiative for construction of a new-generation lunar global control network using multi-mission data newly acquired in the 21st century, which have much better resolution and precision than the old data acquired in the last century. The new control network will be based on a combined photogrammetric solution of an extended global image and laser altimetry network. The five lunar laser ranging retro-reflectors, which can be identified in LROC NAC images and have cm level 3D position accuracy, will be used as absolute control points in the least squares photogrammetric adjustment. Recently, a new radio total phase ranging method has been developed and used for high-precision positioning of Chang’e-3 lander; this shall offer a new absolute control point. Systematic methods and key techniques will be developed or enhanced, including rigorous and generic geometric modeling of orbital images, multi-scale feature extraction and matching among heterogeneous multi-mission remote sensing data, optimal selection of images at areas of multiple image coverages, and large-scale adjustment computation, etc. Based on the high-resolution new datasets and developed new techniques, the new generation of global control network is expected to have much higher accuracy and point density than the ULCN2005.
Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

Science.gov (United States)

Wilczynski, Bartek; Furlong, Eileen E M

2010-04-15

Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.
Global Horizontal Control Network of Shanghai Synchrotron Radiation Facility

International Nuclear Information System (INIS)

Yu Chenghao; Ke Ming; Du Hanwen; Yin Lixin; Zhao Zhentang; Dong Lan; Huang Kaixi

2009-01-01

As a national big scientific engineering, Shanghai Synchrotron Radiation Facility (SSRF) has rigid requirement to the components with sub-millimeter accuracy. In the process of survey and positioning global control network is a connecting link, which determines the position relationship between building and accelerator devices, and provides high accuracy datum to local control network. Within the designing process, building and devices are very restrict. While among observation, it's hard to be observed and abound with disadvantages. With continuous optimization and careful operation, super-high accuracy of 0.3 mm within 400 m circumference was achieved and slab's periodic movement could be seen through 3 times measurement. (authors)
Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Directory of Open Access Journals (Sweden)

Helmut Schmidt

2014-11-01

Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic
Global network structure of dominance hierarchy of ant workers.

Science.gov (United States)

Shimoji, Hiroyuki; Abe, Masato S; Tsuji, Kazuki; Masuda, Naoki

2014-10-06

Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise

Science.gov (United States)

Keeler, James D.; Pichler, Elgar E.; Ross, John

1989-03-01

We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.
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
Spike propagation in driven chain networks with dominant global inhibition

International Nuclear Information System (INIS)

Chang Wonil; Jin, Dezhe Z.

2009-01-01

Spike propagation in chain networks is usually studied in the synfire regime, in which successive groups of neurons are synaptically activated sequentially through the unidirectional excitatory connections. Here we study the dynamics of chain networks with dominant global feedback inhibition that prevents the synfire activity. Neural activity is driven by suprathreshold external inputs. We analytically and numerically demonstrate that spike propagation along the chain is a unique dynamical attractor in a wide parameter regime. The strong inhibition permits a robust winner-take-all propagation in the case of multiple chains competing via the inhibition.
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.
The scaling structure of the global road network.

Science.gov (United States)

Strano, Emanuele; Giometto, Andrea; Shai, Saray; Bertuzzo, Enrico; Mucha, Peter J; Rinaldo, Andrea

2017-10-01

Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.
Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

Directory of Open Access Journals (Sweden)

Yundi Chu

2015-01-01

Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

Science.gov (United States)

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

International Nuclear Information System (INIS)

Kim, Kyungmin; Lee, Hyun Kyu; Harry, Ian W; Hodge, Kari A; Kim, Young-Min; Lee, Chang-Hwan; Oh, John J; Oh, Sang Hoon; Son, Edwin J

2015-01-01

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%–14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs. (paper)
Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays.

Science.gov (United States)

Zhang, Zhengqiu; Liu, Wenbin; Zhou, Dongming

2012-01-01

In this paper, we first discuss the existence of a unique equilibrium point of a generalized Cohen-Grossberg BAM neural networks of neutral type delays by means of the Homeomorphism theory and inequality technique. Then, by applying the existence result of an equilibrium point and constructing a Lyapunov functional, we study the global asymptotic stability of the equilibrium solution to the above Cohen-Grossberg BAM neural networks of neutral type. In our results, the hypothesis for boundedness in the existing paper, which discussed Cohen-Grossberg neural networks of neutral type on the activation functions, are removed. Finally, we give an example to demonstrate the validity of our global asymptotic stability result for the above neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.
Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

Institute of Scientific and Technical Information of China (English)

Zhang Junbin; Cai Jinyan; Meng Yafeng

2016-01-01

Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.
Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo

2008-01-01

In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results

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....
Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring

NARCIS (Netherlands)

Tuma, M.; Rorbech, V.; Prior, M.; Igel, C.

2016-01-01

We designed and jointly optimized an integrated signal processing chain for detection and classification of long-range passive-acoustic underwater signals recorded by the global geophysical monitoring network of the Comprehensive Nuclear-Test-Ban Treaty Organization. Starting at the level of raw
Structure and relationships within global manufacturing virtual networks

Directory of Open Access Journals (Sweden)

José Ramón Vilana

2009-04-01

Full Text Available Global Manufacturing Virtual Networks (GMVNs are dynamically changing organizations formed by Original Equipment Manufacturers (OEMs, Contract Manufacturers (CMs, turn-key and component suppliers, R+D centres and distributors. These networks establish a new type of vertical and horizontal relations between independent companies or even competitors where it is not needed to maintain internal manufacturing resources but to manage and share the network resources. The fluid relations that exist within the GMVNs allow them a very permeable organization easy to connect and disconnect from one to each other as well as to choose a set of partners with specific attributes. The result is a highly flexible system characterized by low barriers to entry and exit, geographic flexibility, low costs, rapid technological diffusion, high diversification through contract manufacturers and exceptional economies of scale. Anyhow, there are three major drawbacks in the GMVNs that should be considered at the beginning of this type of collaborations: 1 the risk of contract manufacturers to develop their own end-products in competition with their customers; 2 the technology transfer between competitors OEMs through other members of the GMVN and 3 the lose of process expertise by the OEMs the more they outsource manufacturing processes to the network.
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.
Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

Science.gov (United States)

Lebedeva, Galina; Sorokin, Anatoly; Faratian, Dana; Mullen, Peter; Goltsov, Alexey; Langdon, Simon P.; Harrison, David J.; Goryanin, Igor

2012-01-01

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a
A global, multi-disciplinary, multi-sectorial initiative to combat leptospirosis: Global Leptospirosis Environmental Action Network (GLEAN).

Science.gov (United States)

Durski, Kara N; Jancloes, Michel; Chowdhary, Tej; Bertherat, Eric

2014-06-05

Leptospirosis has emerged as a major public health problem in both animals and humans. The true burden of this epidemic and endemic disease is likely to be grossly under-estimated due to the non-specific clinical presentations of the disease and the difficulty of laboratory confirmation. The complexity that surrounds the transmission dynamics, particularly in epidemic situations, requires a coordinated, multi-disciplinary effort. Therefore, the Global Leptospirosis Environmental Action Network (GLEAN) was developed to improve global and local strategies of how to predict, prevent, detect, and intervene in leptospirosis outbreaks in order to prevent and control leptospirosis in high-risk populations.
CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

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Terfve, Camille; Cokelaer, Thomas; Henriques, David; MacNamara, Aidan; Goncalves, Emanuel; Morris, Melody K; van Iersel, Martijn; Lauffenburger, Douglas A; Saez-Rodriguez, Julio

2012-10-18

Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
Application of the minimum fuel neural network to music signals

DEFF Research Database (Denmark)

Harbo, Anders La-Cour

2004-01-01

) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up......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...
Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

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The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible un...
Global gene expression profiling displays a network of dysregulated genes in non-atherosclerotic arterial tissue from patients with type 2 diabetes

Directory of Open Access Journals (Sweden)

Skov Vibe

2012-02-01

Full Text Available Abstract Background Generalized arterial alterations, such as endothelial dysfunction, medial matrix accumulations, and calcifications are associated with type 2 diabetes (T2D. These changes may render the vessel wall more susceptible to injury; however, the molecular characteristics of such diffuse pre-atherosclerotic changes in diabetes are only superficially known. Methods To identify the molecular alterations of the generalized arterial disease in T2D, DNA microarrays were applied to examine gene expression changes in normal-appearing, non-atherosclerotic arterial tissue from 10 diabetic and 11 age-matched non-diabetic men scheduled for a coronary by-pass operation. Gene expression changes were integrated with GO-Elite, GSEA, and Cytoscape to identify significant biological pathways and networks. Results Global pathway analysis revealed differential expression of gene-sets representing matrix metabolism, triglyceride synthesis, inflammation, insulin signaling, and apoptosis. The network analysis showed a significant cluster of dysregulated genes coding for both intra- and extra-cellular proteins associated with vascular cell functions together with genes related to insulin signaling and matrix remodeling. Conclusions Our results identify pathways and networks involved in the diffuse vasculopathy present in non-atherosclerotic arterial tissue in patients with T2D and confirmed previously observed mRNA-alterations. These abnormalities may play a role for the arterial response to injury and putatively for the accelerated atherogenesis among patients with diabetes.
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.
Decentralized Hierarchical Controller Design for Selective Damping of Inter Area Oscillations Using PMU Signals

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Ashfaque Ahmed Hashmani

2011-07-01

Full Text Available This paper deals with the decentralized hierarchical PSS (Power System Stabilizer controller design to achieve a better damping of specific inter-area oscillations. The two-level decentralized hierarchical structure consists of two PSS controllers. The first level controller is a local PSS controller for each generator to damp local mode in the area where controller is located. This controller uses only local signals as input signals. The local signal comes from the generator at which the controller is located. The secondary level controller is a multivariable decentralized global PSS controller to damp inter-area modes. This controller uses selected suitable wide area PMU (Phasor Measurement Units signals as inputs. The PMU or global signals are taken from network locations where the oscillations are well observable. The global controller uses only those global input signals in which the assigned single inter-area mode is most observable and is located at a generator that is most effective in controlling the assigned mode. The global controller works mainly in a frequency band given by the natural frequency of the assigned mode. The effectiveness of the resulting hierarchical controller is demonstrated through simulation studies conducted on a test power system.
Comparing global alcohol and tobacco control efforts: network formation and evolution in international health governance.

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Gneiting, Uwe; Schmitz, Hans Peter

2016-04-01

Smoking and drinking constitute two risk factors contributing to the rising burden of non-communicable diseases in low- and middle-income countries. Both issues have gained increased international attention, but tobacco control has made more sustained progress in terms of international and domestic policy commitments, resources dedicated to reducing harm, and reduction of tobacco use in many high-income countries. The research presented here offers insights into why risk factors with comparable levels of harm experience different trajectories of global attention. The analysis focuses particular attention on the role of dedicated global health networks composed of individuals and organizations producing research and engaging in advocacy on a given health problem. Variation in issue characteristics and the policy environment shape the opportunities and challenges of global health networks focused on reducing the burden of disease. What sets the tobacco case apart was the ability of tobacco control advocates to create and maintain a consensus on policy solutions, expand their reach in low- and middle-income countries and combine evidence-based research with advocacy reaching beyond the public health-centered focus of the core network. In contrast, a similar network in the alcohol case struggled with expanding its reach and has yet to overcome divisions based on competing problem definitions and solutions to alcohol harm. The tobacco control network evolved from a group of dedicated individuals to a global coalition of membership-based organizations, whereas the alcohol control network remains at the stage of a collection of dedicated and like-minded individuals. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2016; all rights reserved.
Biasing vector network analyzers using variable frequency and amplitude signals

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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.
AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling.

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Dietz, Karl-Josef; Vogel, Marc Oliver; Viehhauser, Andrea

2010-09-01

To optimize acclimation responses to environmental growth conditions, plants integrate and weigh a diversity of input signals. Signal integration within the signalling networks occurs at different sites including the level of transcription factor activation. Accumulating evidence assigns a major and diversified role in environmental signal integration to the family of APETALA 2/ethylene response element binding protein (AP2/EREBP) transcription factors. Presently, the Plant Transcription Factor Database 3.0 assigns 147 gene loci to this family in Arabidopsis thaliana, 200 in Populus trichocarpa and 163 in Oryza sativa subsp. japonica as compared to 13 to 14 in unicellular algae ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). AP2/EREBP transcription factors have been implicated in hormone, sugar and redox signalling in context of abiotic stresses such as cold and drought. This review exemplarily addresses present-day knowledge of selected AP2/EREBP with focus on a function in stress signal integration and retrograde signalling and defines AP2/EREBP-linked gene networks from transcriptional profiling-based graphical Gaussian models. The latter approach suggests highly interlinked functions of AP2/EREBPs in retrograde and stress signalling.
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.
Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

Science.gov (United States)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.
Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

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Deco, Gustavo; Hagmann, Patric; Hudetz, Anthony G; Tononi, Giulio

2014-12-01

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Networks in Cell Biology

Science.gov (United States)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.
Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

Science.gov (United States)

Truong, Cong-Doan; Kwon, Yung-Keun

2017-12-21

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis. Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior.

Science.gov (United States)

Gao, Zhong-Ke; Dang, Wei-Dong; Yang, Yu-Xuan; Cai, Qing

2017-03-01

The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.
Global and local missions of cAMP signaling in neural plasticity, learning and memory

Directory of Open Access Journals (Sweden)

Daewoo eLee

2015-08-01

Full Text Available The fruit fly Drosophila melanogaster has been a popular model to study cAMP signaling and resultant behaviors due to its powerful genetic approaches. All molecular components (AC, PDE, PKA, CREB, etc essential for cAMP signaling have been identified in the fly. Among them, adenylyl cyclase (AC gene rutabaga and phosphodiesterase (PDE gene dunce have been intensively studied to understand the role of cAMP signaling. Interestingly, these two mutant genes were originally identified on the basis of associative learning deficits. This commentary summarizes findings on the role of cAMP in Drosophila neuronal excitability, synaptic plasticity and memory. It mainly focuses on two distinct mechanisms (global versus local regulating excitatory and inhibitory synaptic plasticity related to cAMP homeostasis. This dual regulatory role of cAMP is to increase the strength of excitatory neural circuits on one hand, but to act locally on postsynaptic GABA receptors to decrease inhibitory synaptic plasticity on the other. Thus the action of cAMP could result in a global increase in the neural circuit excitability and memory. Implications of this cAMP signaling related to drug discovery for neural diseases are also described.
Constraining Cosmic Dawn and Cosmological Reionization via the global redshifted 21-cm signal

Science.gov (United States)

Singh, Saurabh

2018-01-01

The formation of first stars and consequent thermal evolution in baryons during Cosmic Dawn and the Epoch of Reionization (EoR) is poorly constrained. The 21-cm line transition of neutral hydrogen is one of the richest probes of the astrophysics during this era. The signal has the potential to reveal the nature and timing of the emergence of first stars, first light, and the consequent evolution in thermal and ionization state of the baryons.The detection of the global redshifted 21-cm signal, which represents the mean thermal history of the gas, is challenging since it is extremely faint and seen through orders of magnitude stronger contributions from Galactic and extragalactic foregrounds. Man-made terrestrial Radio Frequency Interference (RFI) and the exacting tolerances required on instrument systematics make the detection even more daunting.The design considerations for a precision spectral radiometer are first listed, and a comparison is made of different radiometer configurations, including short and zero baseline interferometers along with methods to enhance the response. We discuss the relative merits of different methods.We then describe SARAS 2, a spectral radiometer custom-designed for precision measurement of the global 21-cm signal. SARAS 2 has been designed to have a system transfer function and internal systematics – both multiplicative and additive – to be spectrally smooth so as to allow a separation of foregrounds and systematics from plausible and predicted global cosmological 21-cm signals. The algorithms for calibration and RFI mitigation are carefully developed so that they do not introduce spectral features that may confuse the detection of the 21-cm signal.We present the outcomes for cosmology from analysis of 60 hr observing with the radiometer deployed at the Timbaktu Collective in Southern India. The detailed analysis of the data reveals an RMS noise level of 11 mK, without being limited by systematic structures. The likelihood
Promoting Simulation Globally: Networking with Nursing Colleagues Across Five Continents.

Science.gov (United States)

Alfes, Celeste M; Madigan, Elizabeth A

Simulation education is gaining momentum internationally and may provide the opportunity to enhance clinical education while disseminating evidence-based practice standards for clinical simulation and learning. There is a need to develop a cohesive leadership group that fosters support, networking, and sharing of simulation resources globally. The Frances Payne Bolton School of Nursing at Case Western Reserve University has had the unique opportunity to establish academic exchange programs with schools of nursing across five continents. Although the joint and mutual simulation activities have been extensive, each international collaboration has also provided insight into the innovations developed by global partners.
Climate change signal and uncertainty in CMIP5-based projections of global ocean surface wave heights

Science.gov (United States)

Wang, Xiaolan L.; Feng, Yang; Swail, Val R.

2015-05-01

This study uses the analysis of variance approaches to quantify the climate change signal and uncertainty in multimodel ensembles of statistical simulations of significant wave height (Hs), which are based on the CMIP5 historical, RCP4.5 and RCP8.5 forcing scenario simulations of sea level pressure. Here the signal of climate change refers to the temporal variations caused by the prescribed forcing. "Significant" means "significantly different from zero at 5% level." In a four-model ensemble of Hs simulations, the common signal—the signal that is simulated in all the four models—is found to strengthen over time. For the historical followed by RCP8.5 scenario, the common signal in annual mean Hs is found to be significant in 16.6% and 82.2% of the area by year 2005 and 2099, respectively. The global average of the variance proportion of the common signal increases from 0.75% in year 2005 to 12.0% by year 2099. The signal is strongest in the eastern tropical Pacific (ETP), featuring significant increases in both the annual mean and maximum of Hs in this region. The climate model uncertainty (i.e., intermodel variability) is significant nearly globally; its magnitude is comparable to or greater than that of the common signal in most areas, except in the ETP where the signal is much larger. In a 20-model ensemble of Hs simulations for the period 2006-2099, the model uncertainty is found to be significant globally; it is about 10 times as large as the variability between the RCP4.5 and RCP8.5 scenarios. The copyright line for this article was changed on 10 JUNE 2015 after original online publication.
Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

Science.gov (United States)

Moon, Jaegeun; Jung, Im Y; Yoo, Jaesoo

2017-04-02

Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.
Identifying novel targets of oncogenic EGF receptor signaling in lung cancer through global phosphoproteomics.

Science.gov (United States)

Zhang, Xu; Belkina, Natalya; Jacob, Harrys Kishore Charles; Maity, Tapan; Biswas, Romi; Venugopalan, Abhilash; Shaw, Patrick G; Kim, Min-Sik; Chaerkady, Raghothama; Pandey, Akhilesh; Guha, Udayan

2015-01-01

Mutations in the epidermal growth factor receptor (EGFR) kinase domain occur in 10-30% of lung adenocarcinoma and are associated with tyrosine kinase inhibitor (TKI) sensitivity. We sought to identify the immediate direct and indirect phosphorylation targets of mutant EGFRs in lung adenocarcinoma. We undertook SILAC strategy, phosphopeptide enrichment, and quantitative MS to identify dynamic changes of phosphorylation downstream of mutant EGFRs in lung adenocarcinoma cells harboring EGFR(L858R) and EGFR(L858R/T790M) , the TKI-sensitive, and TKI-resistant mutations, respectively. Top canonical pathways that were inhibited upon erlotinib treatment in sensitive cells, but not in the resistant cells include EGFR, insulin receptor, hepatocyte growth factor, mitogen-activated protein kinase, mechanistic target of rapamycin, ribosomal protein S6 kinase beta 1, and Janus kinase/signal transducer and activator of transcription signaling. We identified phosphosites in proteins of the autophagy network, such as ULK1 (S623) that is constitutively phosphorylated in these lung adenocarcinoma cells; phosphorylation is inhibited upon erlotinib treatment in sensitive cells, but not in resistant cells. Finally, kinase-substrate prediction analysis from our data indicated that substrates of basophilic kinases from, AGC and Calcium and calmodulin-dependent kinase groups, as well as STE group kinases were significantly enriched and those of proline-directed kinases from, CMGC and Casein kinase groups were significantly depleted among substrates that exhibited increased phosphorylation upon EGF stimulation and reduced phosphorylation upon TKI inhibition. This is the first study to date to examine global phosphorylation changes upon erlotinib treatment of lung adenocarcinoma cells and results from this study provide new insights into signaling downstream of mutant EGFRs in lung adenocarcinoma. All MS data have been deposited in the ProteomeXchange with identifier PXD001101 (http
Contributions of a global network of tree diversity experiments to sustainable forest plantations.

Science.gov (United States)

Verheyen, Kris; Vanhellemont, Margot; Auge, Harald; Baeten, Lander; Baraloto, Christopher; Barsoum, Nadia; Bilodeau-Gauthier, Simon; Bruelheide, Helge; Castagneyrol, Bastien; Godbold, Douglas; Haase, Josephine; Hector, Andy; Jactel, Hervé; Koricheva, Julia; Loreau, Michel; Mereu, Simone; Messier, Christian; Muys, Bart; Nolet, Philippe; Paquette, Alain; Parker, John; Perring, Mike; Ponette, Quentin; Potvin, Catherine; Reich, Peter; Smith, Andy; Weih, Martin; Scherer-Lorenzen, Michael

2016-02-01

The area of forest plantations is increasing worldwide helping to meet timber demand and protect natural forests. However, with global change, monospecific plantations are increasingly vulnerable to abiotic and biotic disturbances. As an adaption measure we need to move to plantations that are more diverse in genotypes, species, and structure, with a design underpinned by science. TreeDivNet, a global network of tree diversity experiments, responds to this need by assessing the advantages and disadvantages of mixed species plantations. The network currently consists of 18 experiments, distributed over 36 sites and five ecoregions. With plantations 1-15 years old, TreeDivNet can already provide relevant data for forest policy and management. In this paper, we highlight some early results on the carbon sequestration and pest resistance potential of more diverse plantations. Finally, suggestions are made for new, innovative experiments in understudied regions to complement the existing network.
Immediate causality network of stock markets

Science.gov (United States)

Zhou, Li; Qiu, Lu; Gu, Changgui; Yang, Huijie

2018-02-01

Extensive works show that a network of stocks within a single stock market stores rich information on evolutionary behaviors of the system, such as collapses and/or crises. But a financial event covers usually several markets or even the global financial system. This mismatch of scale leads to lack of concise information to coordinate the event. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maximum, which can act as an early warning signal of financial crises. The markets in America are monodirectionally and strongly influenced by that in Europe and act as the center. Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system. This method can be extended straightly to find early warning signals for physiological and ecological systems, etc.
An input feature selection method applied to fuzzy neural networks for signal esitmation

International Nuclear Information System (INIS)

Na, Man Gyun; Sim, Young Rok

2001-01-01

It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods
Investigation of global and local network properties of music perception with culturally different styles of music.

Science.gov (United States)

Li, Yan; Rui, Xue; Li, Shuyu; Pu, Fang

2014-11-01

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved. Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties. Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception. Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception. Copyright © 2014 Elsevier Ltd. All rights reserved.
Building a Global Ocean Science Education Network

Science.gov (United States)

Scowcroft, G. A.; Tuddenham, P. T.; Pizziconi, R.

2016-02-01

It is imperative for ocean science education to be closely linked to ocean science research. This is especially important for research that addresses global concerns that cross national boundaries, including climate related issues. The results of research on these critical topics must find its way to the public, educators, and students of all ages around the globe. To facilitate this, opportunities are needed for ocean scientists and educators to convene and identify priorities and strategies for ocean science education. On June 26 and 27, 2015 the first Global Ocean Science Education (GOSE) Workshop was convened in the United States at the University of Rhode Island Graduate School of Oceanography. The workshop, sponsored by the Consortium for Ocean Science Exploration and Engagement (COSEE) and the College of Exploration, had over 75 participants representing 15 nations. The workshop addressed critical global ocean science topics, current ocean science research and education priorities, advanced communication technologies, and leveraging international ocean research technologies. In addition, panels discussed elementary, secondary, undergraduate, graduate, and public education across the ocean basins with emphasis on opportunities for international collaboration. Special presentation topics included advancements in tropical cyclone forecasting, collaborations among Pacific Islands, ocean science for coastal resiliency, and trans-Atlantic collaboration. This presentation will focus on workshop outcomes as well as activities for growing a global ocean science education network. A summary of the workshop report will also be provided. The dates and location for the 2016 GOES Workshop will be announced. See http://www.coexploration.net/gose/index.html
Structuring energy supply and demand networks in a general equilibrium model to simulate global warming control strategies

International Nuclear Information System (INIS)

Hamilton, S.; Veselka, T.D.; Cirillo, R.R.

1991-01-01

Global warming control strategies which mandate stringent caps on emissions of greenhouse forcing gases can substantially alter a country's demand, production, and imports of energy products. Although there is a large degree of uncertainty when attempting to estimate the potential impact of these strategies, insights into the problem can be acquired through computer model simulations. This paper presents one method of structuring a general equilibrium model, the ENergy and Power Evaluation Program/Global Climate Change (ENPEP/GCC), to simulate changes in a country's energy supply and demand balance in response to global warming control strategies. The equilibrium model presented in this study is based on the principle of decomposition, whereby a large complex problem is divided into a number of smaller submodules. Submodules simulate energy activities and conversion processes such as electricity production. These submodules are linked together to form an energy supply and demand network. Linkages identify energy and fuel flows among various activities. Since global warming control strategies can have wide reaching effects, a complex network was constructed. The network represents all energy production, conversion, transportation, distribution, and utilization activities. The structure of the network depicts interdependencies within and across economic sectors and was constructed such that energy prices and demand responses can be simulated. Global warming control alternatives represented in the network include: (1) conservation measures through increased efficiency; and (2) substitution of fuels that have high greenhouse gas emission rates with fuels that have lower emission rates. 6 refs., 4 figs., 4 tabs
Embedding reward signals into perception and cognition

Directory of Open Access Journals (Sweden)

Luiz Pessoa

2010-09-01

Full Text Available Despite considerable interest in the neural basis of valuation, how valuation affects cognitive processing has received relatively less attention. Here, we review evidence from recent behavioral and neuroimaging studies supporting the notion that motivation can enhance perceptual and executive control processes to achieve more efficient goal-directed behavior. Specifically, in the context of cognitive tasks offering monetary gains, improved behavioral performance has been repeatedly observed in conjunction with elevated neural activations in task-relevant perceptual, cognitive, and reward-related regions. We address the neural basis of motivation-cognition interactions by suggesting various modes of communication between relevant neural networks: (1 global hub regions may integrate information from multiple inputs providing a communicative link between specialized networks, (2 point-to-point interactions allow for more specific cross-network communication, and (3 diffuse neuromodulatory systems can relay motivational signals to cortex and enhance signal processing. Together, these modes of communication allow information regarding motivational significance to reach relevant brain regions and shape behavior.
Medical reliable network using concatenated channel codes through GSM network.

Science.gov (United States)

Ahmed, Emtithal; Kohno, Ryuji

2013-01-01

Although the 4(th) generation (4G) of global mobile communication network, i.e. Long Term Evolution (LTE) coexisting with the 3(rd) generation (3G) has successfully started; the 2(nd) generation (2G), i.e. Global System for Mobile communication (GSM) still playing an important role in many developing countries. Without any other reliable network infrastructure, GSM can be applied for tele-monitoring applications, where high mobility and low cost are necessary. A core objective of this paper is to introduce the design of a more reliable and dependable Medical Network Channel Code system (MNCC) through GSM Network. MNCC design based on simple concatenated channel code, which is cascade of an inner code (GSM) and an extra outer code (Convolution Code) in order to protect medical data more robust against channel errors than other data using the existing GSM network. In this paper, the MNCC system will provide Bit Error Rate (BER) equivalent to the BER for medical tele monitoring of physiological signals, which is 10(-5) or less. The performance of the MNCC has been proven and investigated using computer simulations under different channels condition such as, Additive White Gaussian Noise (AWGN), Rayleigh noise and burst noise. Generally the MNCC system has been providing better performance as compared to GSM.
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.
Global product development interaction between local networks: A study of the Danish food industry

DEFF Research Database (Denmark)

Kristensen, Preben Sander

A study of the Danish foods industry shows that producers of food products largely ignore home marekt demand in their product development activities. They have built up and maintain development of end-user products in interaction with customers in distant sophisticated markets. Concurrently...... view of actors in the global end-user customer market and companies' euclidean view of actors in thelocal business-to-business market. In pr companies combine these two market views by interacting in networks: The global industrial network links various functions which again are each part of a local...... their development of end-user pr through global interaction. It is precisely by not interacting with home market end-user demand, but rather by deriving an industrial home market demand from changing end-user markets that the complex has avoided being insulated....
Global asymptotic stabilization of large-scale hydraulic networks using positive proportional controls

DEFF Research Database (Denmark)

Jensen, Tom Nørgaard; Wisniewski, Rafal

2014-01-01

An industrial case study involving a large-scale hydraulic network underlying a district heating system subject to structural changes is considered. The problem of controlling the pressure drop across the so-called end-user valves in the network to a designated vector of reference values under...... directional actuator constraints is addressed. The proposed solution consists of a set of decentralized positively constrained proportional control actions. The results show that the closed-loop system always has a globally asymptotically stable equilibrium point independently on the number of end......-users. Furthermore, by a proper design of controller gains the closed-loop equilibrium point can be designed to belong to an arbitrarily small neighborhood of the desired equilibrium point. Since there exists a globally asymptotically stable equilibrium point independently on the number of end-users in the system...
Experimental video signals distribution MMF network based on IEEE 802.11 standard

Science.gov (United States)

Kowalczyk, Marcin; Maksymiuk, Lukasz; Siuzdak, Jerzy

2014-11-01

The article was focused on presentation the achievements in a scope of experimental research on transmission of digital video streams in the frame of specially realized for this purpose ROF (Radio over Fiber) network. Its construction was based on the merge of wireless IEEE 802.11 network, popularly referred as Wi-Fi, with a passive optical network PON based on multimode fibers MMF. The proposed approach can constitute interesting proposal in area of solutions in the scope of the systems monitoring extensive, within which is required covering of a large area with ensuring of a relatively high degree of immunity on the interferences transmitted signals from video IP cameras to the monitoring center and a high configuration flexibility (easily change the deployment of cameras) of such network.
Global existence of periodic solutions on a simplified BAM neural network model with delays

International Nuclear Information System (INIS)

Zheng Baodong; Zhang Yazhuo; Zhang Chunrui

2008-01-01

A simplified n-dimensional BAM neural network model with delays is considered. Some results of Hopf bifurcations occurring at the zero equilibrium as the delay increases are exhibited. Global existence of periodic solutions are established using a global Hopf bifurcation result of Wu [Wu J. Symmetric functional-differential equations and neural networks with memory. Trans Am Math Soc 1998;350:4799-838], and a Bendixson criterion for higher dimensional ordinary differential equations due to Li and Muldowney [Li MY, Muldowney J. On Bendixson's criterion. J Differ Equations 1994;106:27-39]. Finally, computer simulations are performed to illustrate the analytical results found

Bayesian inversion of the global present-day GIA signal uncertainty from RSL data

Science.gov (United States)

Caron, Lambert; Ivins, Erik R.; Adhikari, Surendra; Larour, Eric

2017-04-01

Various geophysical signals measured in the process of studying the present-day climate change (such as changes in the Earth gravitational potential, ocean altimery or GPS data) include a secular Glacial Isostatic Adjustment contribution that has to be corrected for. Yet, one of the current major challenges that Glacial Isostatic Adjustment modelling is currently struggling with is to accurately determine the uncertainty of the predicted present-day GIA signal. This is especially true at the global scale, where coupling between ice history and mantle rheology greatly contributes to the non-uniqueness of the solutions. Here we propose to use more than 11000 paleo sea level records to constrain a set of GIA Bayesian inversions and thoroughly explore its parameters space. We include two linearly relaxing models to represent the mantle rheology and couple them with a scalable ice history model in order to better assess the non-uniqueness of the solutions. From the resulting estimates of the Probability Density Function, we then extract maps of uncertainty affecting the present-day vertical land motion and geoid due to GIA at the global scale, and their associated expectation of the signal.
Analyzing the impact of global financial crisis on the interconnectedness of Asian stock markets using network science

OpenAIRE

Jitendra Aswani

2015-01-01

As importance of Asian Stock Markets (ASM) has increased after the globalization, it is become significant to know how this network of ASM behaves on the onset of financial crises. For this study, the Global Financial Crisis is considered whose origin was in the developed country, US, unlike the Asian crisis of 1997. To evaluate the impact of financial crisis on the ASM, network theory is used as a tool here. Network modeling of stock markets is useful as it can help to avert the spillover of...
How Sustainable are Benefits from Global Production Networks? Malaysia's Upgrading Prospects in the Electronics Industry

OpenAIRE

Dieter Ernst

2003-01-01

The paper introduces an operational definition of industrial upgrading (IU and documents the emergence of complex, multi-tier "networks of networks" which provide new opportunities for IU, but which also raise threshold requirements for participating in these networks. I highlight structural weaknesses of the Malaysian electronics industry that constrain its upgrading prospects; assess current policies that try to link cluster development and global network integration; discuss adjustments in...
Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

Science.gov (United States)

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-06-07

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.
The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.

Science.gov (United States)

Mykhalovskiy, Eric; Weir, Lorna

2006-01-01

The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area--the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. This commentary outlines the operation and development of GPHIN and compares it to ProMED-mail, another Internet-based approach to global health surveillance. We argue that GPHIN has created an important shift in the relationship of public health and news information. By exiting the pyramid of official reporting, GPHIN has created a new monitoring technique that has disrupted national boundaries of outbreak notification, while creating new possibilities for global outbreak response. By incorporating news within the emerging apparatus of global infectious disease surveillance, GPHIN has effectively responded to the global media's challenge to official country reporting of outbreak and enhanced the effectiveness and credibility of international public health.
Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

International Nuclear Information System (INIS)

Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

2010-01-01

Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)
Towards the understanding of network information processing in biology

Science.gov (United States)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.
Status report on the USGS component of the Global Seismographic Network

Science.gov (United States)

Gee, L. S.; Bolton, H. F.; Derr, J.; Ford, D.; Gyure, G.; Hutt, C. R.; Ringler, A.; Storm, T.; Wilson, D.

2010-12-01

As recently as four years ago, the average age of a datalogger in the portion of the Global Seismographic Network (GSN) operated by the United States Geological Survey (USGS) was 16 years - an eternity in the lifetime of computers. The selection of the Q330HR in 2006 as the “next generation” datalogger by an Incorporated Research Institutions for Seismology (IRIS) selection committee opened the door for upgrading the GSN. As part of the “next generation” upgrades, the USGS is replacing a single Q680 system with two Q330HRs and a field processor to provide the same capability. The functionality includes digitizing, timing, event detection, conversion into miniSEED records, archival of miniSEED data on the ASP and telemetry of the miniSEED data using International Deployment of Accelerometers (IDA) Authenticated Disk Protocol (IACP). At many sites, Quanterra Balers are also being deployed. The Q330HRs feature very low power consumption (which will increase reliability) and higher resolution than the Q680 systems. Furthermore, this network-wide upgrade provides the opportunity to correct known station problems, standardize the installation of secondary sensors and accelerometers, replace the feedback electronics of STS-1 sensors, and perform checks of absolute system sensitivity and sensor orientation. The USGS upgrades began with ANMO in May, 2008. Although we deployed Q330s at KNTN and WAKE in the fall of 2007 (and in the installation of the Caribbean network), these deployments did not include the final software configuration for the GSN upgrades. Following this start, the USGS installed six additional sites in FY08. With funding from the American Recovery and Reinvestment Act and the USGS GSN program, 14 stations were upgraded in FY09. Twenty-one stations are expected to be upgraded in FY10. These systematic network-wide upgrades will improve the reliability and data quality of the GSN, with the end goal of providing the Earth science community high
Strengthening and sustainability of national immunization technical advisory groups (NITAGs) globally: Lessons and recommendations from the founding meeting of the global NITAG network.

Science.gov (United States)

Adjagba, Alex; MacDonald, Noni E; Ortega-Pérez, Inmaculada; Duclos, Philippe

2017-05-25

National Immunization Technical Advisory Groups (NITAGs) provide independent, evidence-informed advice to assist their governments in immunization policy formation. However, many NITAGs face challenges in fulfilling their roles. Hence the many requests for formation of a network linking NITAGs together so they can learn from each other. To address this request, the Health Policy and Institutional Development (HPID) Center (a WHO Collaborating Center at the Agence de Médecine Préventive - AMP), in collaboration with WHO, organized a meeting in Veyrier-du-Lac, France, on 11 and 12 May 2016, to establish a Global NITAG Network (GNN). The meeting focused on two areas: the requirements for (a) the establishment of a global NITAG collaborative network; and (b) the global assessment/evaluation of the performance of NITAGs. 35 participants from 26 countries reviewed the proposed GNN framework documents and NITAG performance evaluation. Participants recommended that a GNN should be established, agreed on its governance, function, scope and a proposed work plan as well as setting a framework for NITAG evaluation. Copyright © 2017.
Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

International Nuclear Information System (INIS)

Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

2015-01-01

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

International Nuclear Information System (INIS)

Wan Li; Zhou Qinghua

2007-01-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

Science.gov (United States)

Wan, Li; Zhou, Qinghua

2007-11-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established.
Whole-brain structural topology in adult attention-deficit/hyperactivity disorder: Preserved global - disturbed local network organization.

Science.gov (United States)

Sidlauskaite, Justina; Caeyenberghs, Karen; Sonuga-Barke, Edmund; Roeyers, Herbert; Wiersema, Jan R

2015-01-01

Prior studies demonstrate altered organization of functional brain networks in attention-deficit/hyperactivity disorder (ADHD). However, the structural underpinnings of these functional disturbances are poorly understood. In the current study, we applied a graph-theoretic approach to whole-brain diffusion magnetic resonance imaging data to investigate the organization of structural brain networks in adults with ADHD and unaffected controls using deterministic fiber tractography. Groups did not differ in terms of global network metrics - small-worldness, global efficiency and clustering coefficient. However, there were widespread ADHD-related effects at the nodal level in relation to local efficiency and clustering. The affected nodes included superior occipital, supramarginal, superior temporal, inferior parietal, angular and inferior frontal gyri, as well as putamen, thalamus and posterior cerebellum. Lower local efficiency of left superior temporal and supramarginal gyri was associated with higher ADHD symptom scores. Also greater local clustering of right putamen and lower local clustering of left supramarginal gyrus correlated with ADHD symptom severity. Overall, the findings indicate preserved global but altered local network organization in adult ADHD implicating regions underpinning putative ADHD-related neuropsychological deficits.
Basic principles of the WHO/UNEP global environmental radiation network

International Nuclear Information System (INIS)

1988-01-01

After the accident at Chernobyl, attempts were made to improve radiation monitoring capabilities and the exchange of information at both national and international levels. As part of these efforts it is proposed to establish a Global Environmental Radiation Monitoring Network (GERMON). This report contains an overview of existing national and international programmes, and makes suggestions about the structure and operational requirements of GERMON. Annexes present the existing WHO environmental radioactivity monitoring network; give the measured CS-137 activities in milk samples in France, Sweden, Canada and the USA from 1974 to 1985; and reproduce the text of the Convention on Early Notification of a Nuclear Accident
Prediction of Global Solar Radiation in India Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Rajiv Gupta

2016-06-01

Full Text Available Increasing global warming and decreasing fossil fuel reserves has necessitated the use of renewable energy resources like solar energy in India. To maximize return on a solar farm, it had to be set up at a place with high solar radiation. The solar radiation values are available only for a small number of places and must be interpolated for the rest. This paper utilizes Artificial Neural Network in interpolation, by obtaining a function with input as combinations of 7 geographical and meteorological parameters affecting radiation, and output as global solar radiation. Data considered was of past 9 years for 13 Indian cities. Low error values and high coefficient of determination values thus obtained, verified that the results were accurate in terms of the original solar radiation data known. Thus, artificial neural network can be used to interpolate the solar radiation for the places of interest depending on the availability of the data.
Global innovation networks and university-firm interactions: an exploratory survey analysis

Directory of Open Access Journals (Sweden)

Gustavo Britto

2015-02-01

Full Text Available The literature on Global Innovation Networks has contributed to identify changes in the innovation activities of multinational corporations. Although university-firm interactions are seen as an important factor for the emergence of GINs, their role has received limited attention. This paper aims to fill this gap in two ways. First, it carries out an exploratory analysis of an original survey dataset, of firms in three industrial sectors from nine developed and developing countries. Second, the paper analyses whether the role of universities in global innovation networks is related to national systems of innovation with varying degrees of maturity. Multiple correspondence analysis and a Probit model are used to establish the relevance of key factors in driving GINs. The results identify distinctive profiles constructed mainly according to firm characteristics, but reflecting country specific patterns of association. The Probit model confirms that internationalization processes and the existence of local interactions substantially increase the probability of interactions with international institutions.
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..
Globally exponential synchronization in an array of asymmetric coupled neural networks

International Nuclear Information System (INIS)

Lu Jianquan; Ho, Daniel W.C.; Liu Ming

2007-01-01

In this Letter, we study the globally exponential synchronization in an array of linearly coupled neural networks with delayed coupling. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the real-world network. The difficulty arising from the asymmetry of the coupling matrix has been overcame in this work. Some synchronization criteria are given in terms of strict linear matrix inequalities (LMIs), which can be efficiently solved by using interior point algorithm. Some previous synchronization results are generalized. Numerical simulation is also given to verify our theoretical analysis
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.
Effects of local and global network connectivity on synergistic epidemics

Science.gov (United States)

Broder-Rodgers, David; Pérez-Reche, Francisco J.; Taraskin, Sergei N.

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Complexity in human transportation networks: a comparative analysis of worldwide air transportation and global cargo-ship movements

Science.gov (United States)

Woolley-Meza, O.; Thiemann, C.; Grady, D.; Lee, J. J.; Seebens, H.; Blasius, B.; Brockmann, D.

2011-12-01

We present a comparative network-theoretic analysis of the two largest global transportation networks: the worldwide air-transportation network (WAN) and the global cargo-ship network (GCSN). We show that both networks exhibit surprising statistical similarities despite significant differences in topology and connectivity. Both networks exhibit a discontinuity in node and link betweenness distributions which implies that these networks naturally segregate into two different classes of nodes and links. We introduce a technique based on effective distances, shortest paths and shortest path trees for strongly weighted symmetric networks and show that in a shortest path tree representation the most significant features of both networks can be readily seen. We show that effective shortest path distance, unlike conventional geographic distance measures, strongly correlates with node centrality measures. Using the new technique we show that network resilience can be investigated more precisely than with contemporary techniques that are based on percolation theory. We extract a functional relationship between node characteristics and resilience to network disruption. Finally we discuss the results, their implications and conclude that dynamic processes that evolve on both networks are expected to share universal dynamic characteristics.
Global investigation of interleukin-1β signaling in primary β-cells using quantitative phosphoproteomics

DEFF Research Database (Denmark)

Engholm-Keller, Kasper; Størling, Joachim; Pociot, Flemming

in β-cells by which this cytokine can modulate cell-matrix interactions during inflammation, a regulation shown in other cell types. Further data analysis is currently ongoing, and the collective results of the experiments will hopefully facilitate additional insights into the effect of IL-1β......Novel Aspect: Global phosphoproteomic analysis of cytokine signaling in primary β-cells Introduction The insulin-producing β-cells of the pancreatic islets of Langerhans are targeted by aberrant immune system responses in diabetes mellitus involving cytokines, especially interleukin-1β (IL-1 β......), which initiate apoptosis of the β-cells. As only limited amounts of primary β-cells can be isolated from model organisms like mouse and rat, global phosphoproteomic analysis of these signaling events by mass spectrometry has generally been unfeasible. We have therefore developed a strategy...
Best response game of traffic on road network of non-signalized intersections

Science.gov (United States)

Yao, Wang; Jia, Ning; Zhong, Shiquan; Li, Liying

2018-01-01

This paper studies the traffic flow in a grid road network with non-signalized intersections. The nature of the drivers in the network is simulated such that they play an iterative snowdrift game with other drivers. A cellular automata model is applied to study the characteristics of the traffic flow and the evolution of the behaviour of the drivers during the game. The drivers use best-response as their strategy to update rules. Three major findings are revealed. First, the cooperation rate in simulation experiences staircase-shaped drop as cost to benefit ratio r increases, and cooperation rate can be derived analytically as a function of cost to benefit ratio r. Second, we find that higher cooperation rate corresponds to higher average speed, lower density and higher flow. This reveals that defectors deteriorate the efficiency of traffic on non-signalized intersections. Third, the system experiences more randomness when the density is low because the drivers will not have much opportunity to update strategy when the density is low. These findings help to show how the strategy of drivers in a traffic network evolves and how their interactions influence the overall performance of the traffic system.
Dynamic optical routing and simultaneous generation of millimeter-wave signals for in-building access network

NARCIS (Netherlands)

Zou, S.; Okonkwo, C.M.; Cao, Z.; Tran, N.C.; Tangdiongga, E.; Koonen, A.M.J.

2012-01-01

Two-stage optical routing using SOA and integrated micro-ring resonator, and remote generation of millimeter-wave signals by optical frequency multiplication is demonstrated for inbuilding network. Both 150Mb/s 64-QAM and 802.11a WLAN signal at 38GHz are transmitted.
European network infrastructures of observatories for terrestrial Global Change research

Science.gov (United States)

Vereecken, H.; Bogena, H.; Lehning, M.

2009-04-01

The earth's climate is significantly changing (e.g. IPCC, 2007) and thus directly affecting the terrestrial systems. The number and intensity hydrological extremes, such as floods and droughts, are continually increasing, resulting in major economical and social impacts. Furthermore, the land cover in Europe has been modified fundamentally by conversions for agriculture, forest and for other purposes such as industrialisation and urbanisation. Additionally, water resources are more than ever used for human development, especially as a key resource for agricultural and industrial activities. As a special case, the mountains of the world are of significant importance in terms of water resources supply, biodiversity, economy, agriculture, traffic and recreation but particularly vulnerable to environmental change. The Alps are unique because of the pronounced small scale variability they contain, the high population density they support and their central position in Europe. The Alps build a single coherent physical and natural environment, artificially cut by national borders. The scientific community and governmental bodies have responded to these environmental changes by performing dedicated experiments and by establishing environmental research networks to monitor, analyse and predict the impact of Global Change on different terrestrial systems of the Earths' environment. Several European network infrastructures for terrestrial Global Change research are presently immerging or upgrading, such as ICOS, ANAEE, LifeWatch or LTER-Europe. However, the strongest existing networks are still operating on a regional or national level and the historical growth of such networks resulted in a very heterogeneous landscape of observation networks. We propose therefore the establishment of two complementary networks: The NetwOrk of Hydrological observAtories, NOHA. NOHA aims to promote the sustainable management of water resources in Europe, to support the prediction of
The feasibility of implementing an ecological network in The Netherlands under conditions of global change

NARCIS (Netherlands)

Bakker, M.M.; Alam, S.J.; Dijk, van J.; Rounsevell, T.; Spek, T.; Brink, van den A.

2015-01-01

Context Both global change and policy reform will affect the implementation of the National Ecological Network (NEN) in the Netherlands. Global change refers to a combination of changing groundwater tables arising from climate change and improved economic prospects for farming. Policy reform refers
The feasibility of implementing an ecological network in The Netherlands under conditions of global change

NARCIS (Netherlands)

Bakker, Martha; Alam, Shah Jamal; van Dijk, Jerry; Rounsevell, Mark; Spek, Teun; van den Brink, Adri

2015-01-01

Context: Both global change and policy reform will affect the implementation of the National Ecological Network (NEN) in the Netherlands. Global change refers to a combination of changing groundwater tables arising from climate change and improved economic prospects for farming. Policy reform refers
Security Enhancement of Wireless Sensor Networks Using Signal Intervals

Directory of Open Access Journals (Sweden)

Jaegeun Moon

2017-04-01

Full Text Available Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP, the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.
Automation of seismic network signal interpolation: an artificial intelligence approach

International Nuclear Information System (INIS)

Chiaruttini, C.; Roberto, V.

1988-01-01

After discussing the current status of the automation in signal interpretation from seismic networks, a new approach, based on artificial-intelligence tecniques, is proposed. The knowledge of the human expert analyst is examined, with emphasis on its objects, strategies and reasoning techniques. It is argued that knowledge-based systems (or expert systems) provide the most appropriate tools for designing an automatic system, modelled on the expert behaviour
Signaling Network Assessment of Mutations and Copy Number Variations Predict Breast Cancer Subtype-Specific Drug Targets

Directory of Open Access Journals (Sweden)

Naif Zaman

2013-10-01

Full Text Available Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.
Cytotoxic Vibrio T3SS1 Rewires Host Gene Expression to Subvert Cell Death Signaling and Activate Cell Survival Networks

Science.gov (United States)

De Nisco, Nicole J.; Kanchwala, Mohammed; Li, Peng; Fernandez, Jessie; Xing, Chao; Orth, Kim

2017-01-01

Bacterial effectors are potent manipulators of host signaling pathways. The marine bacterium Vibrio parahaemolyticus (V. para), delivers effectors into host cells through two type three secretion systems (T3SS). The ubiquitous T3SS1 is vital for V. para survival in the environment, whereas T3SS2 causes acute gastroenteritis in human hosts. Although the natural host is undefined, T3SS1 effectors attack highly conserved cellular processes and pathways to orchestrate non-apoptotic cell death. Much is known about how T3SS1 effectors function in isolation, but we wanted to understand how their concerted action globally affects host cell signaling. To assess the host response to T3SS1, we compared gene expression changes over time in primary fibroblasts infected with V. para that have a functional T3SS1 (T3SS1+) to those in cells infected with V. para lacking T3SS1 (T3SS1−). Overall, the host transcriptional response to both T3SS1+ and T3SS1− V. para was rapid, robust, and temporally dynamic. T3SS1 re-wired host gene expression by specifically altering the expression of 398 genes. Although T3SS1 effectors target host cells at the posttranslational level to cause cytotoxicity, network analysis indicated that V. para T3SS1 also precipitates a host transcriptional response that initially activates cell survival and represses cell death networks. The increased expression of several key pro-survival transcripts mediated by T3SS1 was dependent on a host signaling pathway that is silenced later in infection by the posttranslational action of T3SS1. Taken together, our analysis reveals a complex interplay between roles of T3SS1 as both a transcriptional and posttranslational manipulator of host cell signaling. PMID:28512145
High-speed quantum networking by ship

Science.gov (United States)

Devitt, Simon J.; Greentree, Andrew D.; Stephens, Ashley M.; van Meter, Rodney

2016-11-01

Networked entanglement is an essential component for a plethora of quantum computation and communication protocols. Direct transmission of quantum signals over long distances is prevented by fibre attenuation and the no-cloning theorem, motivating the development of quantum repeaters, designed to purify entanglement, extending its range. Quantum repeaters have been demonstrated over short distances, but error-corrected, global repeater networks with high bandwidth require new technology. Here we show that error corrected quantum memories installed in cargo containers and carried by ship can provide a exible connection between local networks, enabling low-latency, high-fidelity quantum communication across global distances at higher bandwidths than previously proposed. With demonstrations of technology with sufficient fidelity to enable topological error-correction, implementation of the quantum memories is within reach, and bandwidth increases with improvements in fabrication. Our approach to quantum networking avoids technological restrictions of repeater deployment, providing an alternate path to a worldwide Quantum Internet.
Automated embolic signal detection using Deep Convolutional Neural Network.

Science.gov (United States)

Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

2017-07-01

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.
A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

Directory of Open Access Journals (Sweden)

Adacher Ludovica

2017-12-01

Full Text Available In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM, particle swarm optimization (PSO and the genetic algorithm (GA are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA. Numerical experiments on a real test network are reported.
TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks.

Science.gov (United States)

Chua, Huey Eng; Bhowmick, Sourav S; Zheng, Jie

2017-09-22

Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, a user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove. hechua@ntu.edu.sg or assourav@ntu.edu.sg. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
U.S. Geological Survey Global Seismographic Network - Five-Year Plan 2006-2010

Science.gov (United States)

Leith, William S.; Gee, Lind S.; Hutt, Charles R.

2009-01-01

The Global Seismographic Network provides data for earthquake alerting, tsunami warning, nuclear treaty verification, and Earth science research. The system consists of nearly 150 permanent digital stations, distributed across the globe, connected by a modern telecommunications network. It serves as a multi-use scientific facility and societal resource for monitoring, research, and education, by providing nearly uniform, worldwide monitoring of the Earth. The network was developed and is operated through a partnership among the National Science Foundation (http://www.nsf.gov), the Incorporated Research Institutions for Seismology (http://www.iris.edu/hq/programs/gsn), and the U.S. Geological Survey (http://earthquake.usgs.gov/gsn).
Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

KAUST Repository

Douik, Ahmed

2016-03-28

In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs\\' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.
Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

International Nuclear Information System (INIS)

Stueckle, Todd A.; Lu, Yongju; Davis, Mary E.; Wang, Liying; Jiang, Bing-Hua; Holaskova, Ida; Schafer, Rosana; Barnett, John B.; Rojanasakul, Yon

2012-01-01

Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As 2 O 3
Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

Energy Technology Data Exchange (ETDEWEB)

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

2012-06-01

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

Science.gov (United States)

Radeck, Jara; Fritz, Georg; Mascher, Thorsten

2017-02-01

The cell envelope stress response (CESR) encompasses all regulatory events that enable a cell to protect the integrity of its envelope, an essential structure of any bacterial cell. The underlying signaling network is particularly well understood in the Gram-positive model organism Bacillus subtilis. It consists of a number of two-component systems (2CS) and extracytoplasmic function σ factors that together regulate the production of both specific resistance determinants and general mechanisms to protect the envelope against antimicrobial peptides targeting the biogenesis of the cell wall. Here, we summarize the current picture of the B. subtilis CESR network, from the initial identification of the corresponding signaling devices to unraveling their interdependence and the underlying regulatory hierarchy within the network. In the course of detailed mechanistic studies, a number of novel signaling features could be described for the 2CSs involved in mediating CESR. This includes a novel class of so-called intramembrane-sensing histidine kinases (IM-HKs), which-instead of acting as stress sensors themselves-are activated via interprotein signal transfer. Some of these IM-HKs are involved in sensing the flux of antibiotic resistance transporters, a unique mechanism of responding to extracellular antibiotic challenge.

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