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Sample records for links signaling network

  1. Ras and Nox: linked signaling networks?

    PubMed Central

    Wu, Ru Feng; Terada, Lance S.

    2009-01-01

    Both Ras and Nox represent ancient gene families which control a broad range of cellular responses. Both families mediate signals governing motility, differentiation, and proliferation, and both inhabit overlapping subcellular microdomains. Yet little is known of the precise functional relationship between these two ubiquitous families. In this review, we examine the interface where these two large fields meet. PMID:19501154

  2. Novel links in the plant TOR kinase signaling network

    PubMed Central

    Xiong, Yan; Sheen, Jen

    2015-01-01

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

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

    PubMed

    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.

  4. Choline-releasing glycerophosphodiesterase EDI3 links the tumor metabolome to signaling network activities

    PubMed Central

    Marchan, Rosemarie; Lesjak, Michaela S.; Stewart, Joanna D.; Winter, Roland; Seeliger, Janine; Hengstler, Jan G.

    2012-01-01

    Recently, EDI3 was identified as a key factor for choline metabolism that controls tumor cell migration and is associated with metastasis in endometrial carcinomas. EDI3 cleaves glycerophosphocholine (GPC) to form choline and glycerol-3-phosphate (G3P). Choline is then further metabolized to phosphatidylcholine (PtdC), the major lipid in membranes and a key player in membrane-mediated cell signaling. The second product, G3P, is a precursor molecule for several lipids with central roles in signaling, for example lysophosphatidic acid (LPA), phosphatidic acid (PA) and diacylglycerol (DAG). LPA activates intracellular signaling pathways by binding to specific LPA receptors, including membrane-bound G protein-coupled receptors and the intracellular nuclear receptor, PPARγ. Conversely, PA and DAG mediate signaling by acting as lipid anchors that bind and activate several signaling proteins. For example, binding of GTPases and PKC to PA and DAG, respectively, increases the activation of signaling networks, mediating processes such as migration, adhesion, proliferation or anti-apoptosis—all relevant for tumor development. We present a concept by which EDI3 either directly generates signaling molecules or provides “membrane anchors” for downstream signaling factors. As a result, EDI3 links choline metabolism to signaling activities resulting in a more malignant phenotype. PMID:23114620

  5. The cellular and signaling networks linking the immune system and metabolism in disease.

    PubMed

    Osborn, Olivia; Olefsky, Jerrold M

    2012-03-06

    It is now recognized that obesity is driving the type 2 diabetes epidemic in Western countries. Obesity-associated chronic tissue inflammation is a key contributing factor to type 2 diabetes and cardiovascular disease, and a number of studies have clearly demonstrated that the immune system and metabolism are highly integrated. Recent advances in deciphering the various cellular and signaling networks that participate in linking the immune and metabolic systems together have contributed to understanding of the pathogenesis of metabolic diseases and may also inform new therapeutic strategies based on immunomodulation. Here we discuss how these various networks underlie the etiology of the inflammatory component of insulin resistance, with a particular focus on the central roles of macrophages in adipose tissue and liver.

  6. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

    PubMed Central

    Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Epperlein, Jonathan; Samaga, Regina; Lauffenburger, Douglas A; Klamt, Steffen; Sorger, Peter K

    2009-01-01

    Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks. PMID:19953085

  7. Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks

    PubMed Central

    Neves, Susana R.; Tsokas, Panayiotis; Sarkar, Anamika; Grace, Elizabeth A.; Rangamani, Padmini; Taubenfeld, Stephen M.; Alberini, Cristina M.; Schaff, James C.; Blitzer, Robert D.; Moraru, Ion I.; Iyengar, Ravi

    2009-01-01

    Summary The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the β-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells. PMID:18485874

  8. Proteomic screening identifies a YAP-driven signaling network linked to tumor cell proliferation in human schwannomas

    PubMed Central

    Boin, Alizée; Couvelard, Anne; Couderc, Christophe; Brito, Isabel; Filipescu, Dan; Kalamarides, Michel; Bedossa, Pierre; De Koning, Leanne; Danelsky, Carine; Dubois, Thierry; Hupé, Philippe; Louvard,, Daniel; Lallemand, Dominique

    2014-01-01

    Background Inactivation of the NF2 gene predisposes to neurofibromatosis type II and the development of schwannomas. In vitro studies have shown that loss of NF2 leads to the induction of mitogenic signaling mediated by receptor tyrosine kinases (RTKs), MAP kinase, AKT, or Hippo pathways. The goal of our study was to evaluate the expression and activity of these signaling pathways in human schwannomas in order to identify new potential therapeutic targets. Methods Large sets of human schwannomas, totaling 68 tumors, were analyzed using complementary proteomic approaches. RTK arrays identified the most frequently activated RTKs. The correlation between the expression and activity of signaling pathways and proliferation of tumor cells using Ki67 marker was investigated by reverse-phase protein array (RRPA). Finally, immunohistochemistry was used to evaluate the expression pattern of signaling effectors in the tumors. Results We showed that Her2, Her3, PDGFRß, Axl, and Tie2 are frequently activated in the tumors. Furthermore, RRPA demonstrated that Ki67 levels are linked to YAP, p-Her3, and PDGFRß expression levels. In addition, Her2, Her3, and PDGFRß are transcriptional targets of Yes-associated protein (YAP) in schwannoma cells in culture. Finally, we observed that the expression of these signaling effectors is very variable between tumors. Conclusions Tumor cell proliferation in human schwannomas is linked to a signaling network controlled by the Hippo effector YAP. Her2, Her3, PDGFRß, Axl, and Tie2, as well as YAP, represent potentially valuable therapeutic targets. However, the variability of their expression between tumors may result in strong differences in the response to targeted therapy. PMID:24558021

  9. "Conjectural" links in complex networks

    NASA Astrophysics Data System (ADS)

    Snarskii, A. A.; Zorinets, D. I.; Lande, D. V.

    2016-11-01

    This paper introduces the concept of Conjectural Link for Complex Networks, in particular, social networks. Conjectural Link we understand as an implicit link, not available in the network, but supposed to be present, based on the characteristics of its topology. It is possible, for example, when in the formal description of the network some connections are skipped due to errors, deliberately hidden or withdrawn (e.g. in the case of partial destruction of the network). Introduced a parameter that allows ranking the Conjectural Link. The more this parameter - the more likely that this connection should be present in the network. This paper presents a method of recovery of partially destroyed Complex Networks using Conjectural Links finding. Presented two methods of finding the node pairs that are not linked directly to one another, but have a great possibility of Conjectural Link communication among themselves: a method based on the determination of the resistance between two nodes, and method based on the computation of the lengths of routes between two nodes. Several examples of real networks are reviewed and performed a comparison to know network links prediction methods, not intended to find the missing links in already formed networks.

  10. Deblurring Signal Network Dynamics.

    PubMed

    Kamps, Dominic; Dehmelt, Leif

    2017-09-15

    To orchestrate the function and development of multicellular organisms, cells integrate intra- and extracellular information. This information is processed via signal networks in space and time, steering dynamic changes in cellular structure and function. Defects in those signal networks can lead to developmental disorders or cancer. However, experimental analysis of signal networks is challenging as their state changes dynamically and differs between individual cells. Thus, causal relationships between network components are blurred if lysates from large cell populations are analyzed. To directly study causal relationships, perturbations that target specific components have to be combined with measurements of cellular responses within individual cells. However, using standard single-cell techniques, the number of signal activities that can be monitored simultaneously is limited. Furthermore, diffusion of signal network components limits the spatial precision of perturbations, which blurs the analysis of spatiotemporal processing in signal networks. Hybrid strategies based on optogenetics, surface patterning, chemical tools, and protein design can overcome those limitations and thereby sharpen our view into the dynamic spatiotemporal state of signal networks and enable unique insights into the mechanisms that control cellular function in space and time.

  11. PIPELINES AS COMMUNICATION NETWORK LINKS

    SciTech Connect

    Kelvin T. Erickson; Ann Miller; E. Keith Stanek; C.H. Wu; Shari Dunn-Norman

    2005-03-14

    This report presents the results of an investigation into two methods of using the natural gas pipeline as a communication medium. The work addressed the need to develop secure system monitoring and control techniques between the field and control centers and to robotic devices in the pipeline. In the first method, the pipeline was treated as a microwave waveguide. In the second method, the pipe was treated as a leaky feeder or a multi-ground neutral and the signal was directly injected onto the metal pipe. These methods were tested on existing pipeline loops at UMR and Batelle. The results reported in this report indicate the feasibility of both methods. In addition, a few suitable communication link protocols for this network were analyzed.

  12. DLG5 connects cell polarity and Hippo signaling protein networks by linking PAR-1 with MST1/2.

    PubMed

    Kwan, Julian; Sczaniecka, Anna; Arash, Emad Heidary; Nguyen, Liem; Chen, Chia-Chun; Ratkovic, Srdjana; Klezovitch, Olga; Attisano, Liliana; McNeill, Helen; Emili, Andrew; Vasioukhin, Valeri

    2016-12-15

    Disruption of apical-basal polarity is implicated in developmental disorders and cancer; however, the mechanisms connecting cell polarity proteins with intracellular signaling pathways are largely unknown. We determined previously that membrane-associated guanylate kinase (MAGUK) protein discs large homolog 5 (DLG5) functions in cell polarity and regulates cellular proliferation and differentiation via undefined mechanisms. We report here that DLG5 functions as an evolutionarily conserved scaffold and negative regulator of Hippo signaling, which controls organ size through the modulation of cell proliferation and differentiation. Affinity purification/mass spectrometry revealed a critical role of DLG5 in the formation of protein assemblies containing core Hippo kinases mammalian ste20 homologs 1/2 (MST1/2) and Par-1 polarity proteins microtubule affinity-regulating kinases 1/2/3 (MARK1/2/3). Consistent with this finding, Hippo signaling is markedly hyperactive in mammalian Dlg5(-/-) tissues and cells in vivo and ex vivo and in Drosophila upon dlg5 knockdown. Conditional deletion of Mst1/2 fully rescued the phenotypes of brain-specific Dlg5 knockout mice. Dlg5 also interacts genetically with Hippo effectors Yap1/Taz Mechanistically, we show that DLG5 inhibits the association between MST1/2 and large tumor suppressor homologs 1/2 (LATS1/2), uses its scaffolding function to link MST1/2 with MARK3, and inhibits MST1/2 kinase activity. These data reveal a direct connection between cell polarity proteins and Hippo, which is essential for proper development of multicellular organisms.

  13. Electronic network links physicists

    NASA Astrophysics Data System (ADS)

    Now that AGU is a member of the American Institute of Physics (AIP) (see item on page 150), AGU members may access Pi-NET, the Physics Information Network developed by AIP on behalf of its member societies. The electronic network will be available after March 31, 1986, to members of AIP member and affiliate societies for the cost of a local telephone call.This electronic database service provides listings of job opportunities in physics, calendars of meetings, advance abstracts of more than 40 journals published by AIP and member societies, brief announcements and news items, titles of journal articles from AIP's Searchable Physics Information Notices (SPIN) database, and an electronic catalog and on-line ordering of publications from AIP and member societies. AGU journals will be included in the database shortly.

  14. Covalently linked organic networks

    NASA Astrophysics Data System (ADS)

    Tsotsalas, Manuel; Addicoat, Matthew

    2015-02-01

    In this review, we intend to give an overview of the synthesis of well-defined covalently-bound organic network materials such as covalent organic frameworks (COFs), conjugated microporous frameworks (CMPs) and other “ideal polymer networks” and discuss the different approaches in their synthesis and their potential applications. In addition we will describe the common computational approaches and highlight recent achievements in the computational study of their structure and properties. For further information the interested reader is referred to several excellent and more detailed reviews dealing with the synthesis [Dawson 2012; Ding 2013; Feng 2012] and computational aspects [Han 2009; Colón 2014] of the materials presented here.

  15. Cytokinin signaling networks.

    PubMed

    Hwang, Ildoo; Sheen, Jen; Müller, Bruno

    2012-01-01

    Despite long-standing observations on diverse cytokinin actions, the discovery path to cytokinin signaling mechanisms was tortuous. Unyielding to conventional genetic screens, experimental innovations were paramount in unraveling the core cytokinin signaling circuitry, which employs a large repertoire of genes with overlapping and specific functions. The canonical two-component transcription circuitry involves His kinases that perceive cytokinin and initiate signaling, as well as His-to-Asp phosphorelay proteins that transfer phosphoryl groups to response regulators, transcriptional activators, or repressors. Recent advances have revealed the complex physiological functions of cytokinins, including interactions with auxin and other signal transduction pathways. This review begins by outlining the historical path to cytokinin discovery and then elucidates the diverse cytokinin functions and key signaling components. Highlights focus on the integration of cytokinin signaling components into regulatory networks in specific contexts, ranging from molecular, cellular, and developmental regulations in the embryo, root apical meristem, shoot apical meristem, stem and root vasculature, and nodule organogenesis to organismal responses underlying immunity, stress tolerance, and senescence.

  16. Neural Network Communications Signal Processing

    DTIC Science & Technology

    1994-08-01

    This final technical report describes the research and development- results of the Neural Network Communications Signal Processing (NNCSP) Program...The objectives of the NNCSP program are to: (1) develop and implement a neural network and communications signal processing simulation system for the...purpose of exploring the applicability of neural network technology to communications signal processing; (2) demonstrate several configurations of the

  17. Link weight evolution in a network of coupled chemical oscillators

    NASA Astrophysics Data System (ADS)

    Ke, Hua; Tinsley, Mark R.; Steele, Aaron; Wang, Fang; Showalter, Kenneth

    2014-05-01

    Link weight evolution is studied in a network of coupled chemical oscillators. Oscillators are perturbed by adjustments in imposed light intensity based on excitatory or inhibitory links to other oscillators undergoing excitation. Experimental and modeling studies demonstrate that the network is capable of producing sustained coordinated activity. The individual nodes of the network exhibit incoherent firing events; however, a dominant frequency can be discerned within the collective signal by Fourier analysis. The introduction of spike-timing-dependent plasticity yields a network that evolves to a stable unimodal link weight distribution.

  18. Link Budget Analysis for Undersea Acoustic Signaling

    DTIC Science & Technology

    2002-06-01

    wireless communications for estimating signal-to- noise ratio ( SNR ) at the receiver. Link-budget analysis considers transmitter power, transmitter...is represented as an intermediate result called the channel SNR . The channel SNR includes ambient-noise and transmission-loss components. Several...to satellite and wireless communications for estimating signal-to-noise ratio ( SNR ) at the receiver. Link-budget analysis considers transmitter

  19. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    PubMed Central

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. PMID:22164070

  20. LinkMind: link optimization in swarming mobile sensor networks.

    PubMed

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

  1. Growing Networks with Positive and Negative Links

    NASA Astrophysics Data System (ADS)

    Dech, Corynne; Antwi, Shadrack; Shaw, Leah

    Scale-free networks grown via preferential attachment have been used to model real-world networks such as the Internet, citation networks, and social networks. Here we investigate signed scale-free networks where an edge represents a positive or negative connection. We present analytic results and simulation for a growing signed network model. We compare the signed network to an unsigned scale-free network. We discuss several options for preferential attachment in a signed network that could be further adapted to model the accumulation of links over time in real-world signed networks.

  2. Regulation patterns in signaling networks of cancer

    PubMed Central

    2010-01-01

    Background Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. Results We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. Conclusion Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed. PMID:21110851

  3. The Hedgehog Signal Transduction Network

    PubMed Central

    Robbins, David J.; Fei, Dennis Liang; Riobo, Natalia A.

    2013-01-01

    Hedgehog (Hh) proteins regulate the development of a wide range of metazoan embryonic and adult structures, and disruption of Hh signaling pathways results in various human diseases. Here, we provide a comprehensive review of the signaling pathways regulated by Hh, consolidating data from a diverse array of organisms in a variety of scientific disciplines. Similar to the elucidation of many other signaling pathways, our knowledge of Hh signaling developed in a sequential manner centered on its earliest discoveries. Thus, our knowledge of Hh signaling has for the most part focused on elucidating the mechanism by which Hh regulates the Gli family of transcription factors, the so-called “canonical” Hh signaling pathway. However, in the past few years, numerous studies have shown that Hh proteins can also signal through Gli-independent mechanisms collectively referred to as “noncanonical” signaling pathways. Noncanonical Hh signaling is itself subdivided into two distinct signaling modules: (i) those not requiring Smoothened (Smo) and (ii) those downstream of Smo that do not require Gli transcription factors. Thus, Hh signaling is now proposed to occur through a variety of distinct context-dependent signaling modules that have the ability to crosstalk with one another to form an interacting, dynamic Hh signaling network. PMID:23074268

  4. Cross-linked structure of network evolution

    SciTech Connect

    Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Porter, Mason A.; Mucha, Peter J.

    2014-03-15

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  5. Link prediction in multiplex online social networks

    PubMed Central

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin

    2017-01-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%. PMID:28386441

  6. Cross-linked structure of network evolution

    NASA Astrophysics Data System (ADS)

    Bassett, Danielle S.; Wymbs, Nicholas F.; Porter, Mason A.; Mucha, Peter J.; Grafton, Scott T.

    2014-03-01

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  7. Link prediction in multiplex online social networks.

    PubMed

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  8. Link prediction in multiplex online social networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  9. Toward link predictability of complex networks

    PubMed Central

    Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H. Eugene

    2015-01-01

    The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. PMID:25659742

  10. Toward link predictability of complex networks.

    PubMed

    Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H Eugene

    2015-02-24

    The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.

  11. Influence of Reciprocal Links in Social Networks

    PubMed Central

    Zhu, Yu-Xiao; Zhang, Xiao-Guang; Sun, Gui-Quan; Tang, Ming; Zhou, Tao; Zhang, Zi-Ke

    2014-01-01

    How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven't displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks. PMID:25072242

  12. Influence of reciprocal links in social networks.

    PubMed

    Zhu, Yu-Xiao; Zhang, Xiao-Guang; Sun, Gui-Quan; Tang, Ming; Zhou, Tao; Zhang, Zi-Ke

    2014-01-01

    How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven't displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks.

  13. Bounded link prediction in very large networks

    NASA Astrophysics Data System (ADS)

    Cui, Wei; Pu, Cunlai; Xu, Zhongqi; Cai, Shimin; Yang, Jian; Michaelson, Andrew

    2016-09-01

    Evaluating link prediction methods is a hard task in very large complex networks due to the prohibitive computational cost. However, if we consider the lower bound of node pairs' similarity scores, this task can be greatly optimized. In this paper, we study CN index in the bounded link prediction framework, which is applicable to enormous heterogeneous networks. Specifically, we propose a fast algorithm based on the parallel computing scheme to obtain all node pairs with CN values larger than the lower bound. Furthermore, we propose a general measurement, called self-predictability, to quantify the performance of similarity indices in link prediction, which can also indicate the link predictability of networks with respect to given similarity indices.

  14. Porous Cross-Linked Polyimide Networks

    NASA Technical Reports Server (NTRS)

    Meador, Mary Ann B. (Inventor); Guo, Haiquan (Inventor)

    2015-01-01

    Porous cross-linked polyimide networks are provided. The networks comprise an anhydride end-capped polyamic acid oligomer. The oligomer (i) comprises a repeating unit of a dianhydride and a diamine and terminal anhydride groups, (ii) has an average degree of polymerization of 10 to 50, (iii) has been cross-linked via a cross-linking agent, comprising three or more amine groups, at a balanced stoichiometry of the amine groups to the terminal anhydride groups, and (iv) has been chemically imidized to yield the porous cross-linked polyimide network. Also provided are porous cross-linked polyimide aerogels comprising a cross-linked and imidized anhydride end-capped polyamic acid oligomer, wherein the oligomer comprises a repeating unit of a dianhydride and a diamine, and the aerogel has a density of 0.10 to 0.333 g/cm.sup.3 and a Young's modulus of 1.7 to 102 MPa. Also provided are thin films comprising aerogels, and methods of making porous cross-linked polyimide networks.

  15. Missing and forbidden links in mutualistic networks

    PubMed Central

    Olesen, Jens M.; Bascompte, Jordi; Dupont, Yoko L.; Elberling, Heidi; Rasmussen, Claus; Jordano, Pedro

    2011-01-01

    Ecological networks are complexes of interacting species, but not all potential links among species are realized. Unobserved links are either missing or forbidden. Missing links exist, but require more sampling or alternative ways of detection to be verified. Forbidden links remain unobservable, irrespective of sampling effort. They are caused by linkage constraints. We studied one Arctic pollination network and two Mediterranean seed-dispersal networks. In the first, for example, we recorded flower-visit links for one full season, arranged data in an interaction matrix and got a connectance C of 15 per cent. Interaction accumulation curves documented our sampling of interactions through observation of visits to be robust. Then, we included data on pollen from the body surface of flower visitors as an additional link ‘currency’. This resulted in 98 new links, missing from the visitation data. Thus, the combined visit–pollen matrix got an increased C of 20 per cent. For the three networks, C ranged from 20 to 52 per cent, and thus the percentage of unobserved links (100 − C) was 48 to 80 per cent; these were assumed forbidden because of linkage constraints and not missing because of under-sampling. Phenological uncoupling (i.e. non-overlapping phenophases between interacting mutualists) is one kind of constraint, and it explained 22 to 28 per cent of all possible, but unobserved links. Increasing phenophase overlap between species increased link probability, but extensive overlaps were required to achieve a high probability. Other kinds of constraint, such as size mismatch and accessibility limitations, are briefly addressed. PMID:20843845

  16. Linking brain imaging signals to visual perception.

    PubMed

    Welchman, Andrew E; Kourtzi, Zoe

    2013-11-01

    The rapid advances in brain imaging technology over the past 20 years are affording new insights into cortical processing hierarchies in the human brain. These new data provide a complementary front in seeking to understand the links between perceptual and physiological states. Here we review some of the challenges associated with incorporating brain imaging data into such "linking hypotheses," highlighting some of the considerations needed in brain imaging data acquisition and analysis. We discuss work that has sought to link human brain imaging signals to existing electrophysiological data and opened up new opportunities in studying the neural basis of complex perceptual judgments. We consider a range of approaches when using human functional magnetic resonance imaging to identify brain circuits whose activity changes in a similar manner to perceptual judgments and illustrate these approaches by discussing work that has studied the neural basis of 3D perception and perceptual learning. Finally, we describe approaches that have sought to understand the information content of brain imaging data using machine learning and work that has integrated multimodal data to overcome the limitations associated with individual brain imaging approaches. Together these approaches provide an important route in seeking to understand the links between physiological and psychological states.

  17. Link-space formalism for network analysis.

    PubMed

    Smith, David M D; Lee, Chiu Fan; Onnela, Jukka-Pekka; Johnson, Neil F

    2008-03-01

    We introduce the link-space formalism for analyzing network models with degree-degree correlations. The formalism is based on a statistical description of the fraction of links l(i,j) connecting nodes of degrees i and j. To demonstrate its use, we apply the framework to some pedagogical network models, namely, random attachment, Barabási-Albert preferential attachment, and the classical Erdos and Rényi random graph. For these three models the link-space matrix can be solved analytically. We apply the formalism to a simple one-parameter growing network model whose numerical solution exemplifies the effect of degree-degree correlations for the resulting degree distribution. We also employ the formalism to derive the degree distributions of two very simple network decay models, more specifically, that of random link deletion and random node deletion. The formalism allows detailed analysis of the correlations within networks and we also employ it to derive the form of a perfectly nonassortative network for arbitrary degree distribution.

  18. Rainfall monitoring with microwave link networks -state of the art

    NASA Astrophysics Data System (ADS)

    de Vos, Lotte; Overeem, Aart; Ríos Gaona, Manuel; van Leth, Tommy; Uijlenhoet, Remko

    2017-04-01

    For the purpose of hydrological applications, meteorology, climate monitoring and agriculture, accurate high resolution rainfall monitoring is highly desirable. Often used techniques to measure rainfall include rain gauge networks and radar. However, accurate rainfall information is lacking in large areas in the world, and the number of rain gauges is even severely declining in Europe, South-America and Africa. The investments required for the installation and maintenance of dense sensor networks can form a large obstacle. Over the past decade, various investigations have shown that microwave links from cellular communication networks may be used for rainfall monitoring. These commercial networks are installed for the purpose of cellular communication. These consist of antennas that transmit microwave link signals through the atmosphere over a path of typically several kilometers. Microwave signals are sensitive to rainfall at the frequencies that are typically used. The loss of signal (attenuation) over the link-path, which is logged in real-time by cellular communication companies for quality monitoring, can therefore be interpreted as a rainfall measurement. In recent years, various techniques have been developed to quantitatively determine rainfall from these microwave link attenuations. An overview of error sources in this process, quantitative rainfall determination techniques, as well as the results of various validation studies are provided. These studies show that there is considerable potential in using commercial microwave link networks for rainfall monitoring. This is a promising development, as these networks cover 20% of the land surface of the earth and have high density, especially in urban areas where there is generally a lack of in situ ground measurements.

  19. Signaling networks in joint development.

    PubMed

    Salva, Joanna E; Merrill, Amy E

    2017-04-01

    Here we review studies identifying regulatory networks responsible for synovial, cartilaginous, and fibrous joint development. Synovial joints, characterized by the fluid-filled synovial space between the bones, are found in high-mobility regions and are the most common type of joint. Cartilaginous joints such as the intervertebral disc unite adjacent bones through either a hyaline cartilage or a fibrocartilage intermediate. Fibrous joints, which include the cranial sutures, form a direct union between bones through fibrous connective tissue. We describe how the distinct morphologic and histogenic characteristics of these joint classes are established during embryonic development. Collectively, these studies reveal that despite the heterogeneity of joint strength and mobility, joint development throughout the skeleton utilizes common signaling networks via long-range morphogen gradients and direct cell-cell contact. This suggests that different joint types represent specialized variants of homologous developmental modules. Identifying the unifying aspects of the signaling networks between joint classes allows a more complete understanding of the signaling code for joint formation, which is critical to improving strategies for joint regeneration and repair. Developmental Dynamics 246:262-274, 2017. © 2016 Wiley Periodicals, Inc.

  20. Full-duplex RoF link with broadband mm-wave signal in W-band based on WDM-PON access network with optical mm-wave local oscillator broadcasting

    NASA Astrophysics Data System (ADS)

    Ma, Jianxin; Zhang, Ruijiao; Li, Yanjie; Zhang, Qi; Yu, Jianguo

    2015-02-01

    A novel full-duplex link with an optical mm-wave local oscillator broadcasting for broadband millimeter (mm)-wave wireless access in W-band is proposed based on the WDM-PON-RoF. In our scheme, a universal optical mm-wave local oscillator in W-band is distributed over the whole network to up-convert the downlink IF optical signal, which not only improves the spectrum efficiency by reducing the bandwidth requirement of each downlink, but also decreases the degradation caused by the fiber chromatic dispersion. Moreover, since the incoherently down-converted uplink signal is modulated on the reused blank optical carrier extracted from the downlink signal, the base stations (BSs) need no optical source, and so its structure is simplified. The numerical simulation results agree well with the theoretical analysis and show that the proposed full-duplex link for the W-band wireless access based on WDM-PON-RoF maintains good performance with cost effective implement.

  1. Neural Network Classification of Cerebral Embolic Signals

    DTIC Science & Technology

    2007-11-02

    application of new signal processing techniques to the analysis and classification of embolic signals. We applied a Wavelet Neural Network algorithm...to approximate the embolic signals, with the parameters of the wavelet nodes being used to train a Neural Network to classify these signals as resulting from normal flow, or from gaseous or solid emboli.

  2. Signalling mechanisms linking hepatic glucose and lipid metabolism.

    PubMed

    Weickert, M O; Pfeiffer, A F H

    2006-08-01

    Fatty liver and hepatic triglyceride accumulation are strongly associated with obesity, insulin resistance and type 2 diabetes, and are subject to nutritional influences. Hepatic regulation of glucose and lipid homeostasis is influenced by a complex system of hormones, hormonally regulated signalling pathways and transcription factors. Recently, considerable progress has been made in elucidating molecular pathways and potential factors that are affected in insulin-resistant states. In this review we discuss some of the key factors that are involved in both the regulation of glucose and lipid metabolism in the liver. Understanding the molecular network that links hepatic lipid accumulation and impaired glucose metabolism may provide targets for dietary or pharmacological interventions.

  3. Dopamine D1 signaling organizes network dynamics underlying working memory.

    PubMed

    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.

  4. Dopamine D1 signaling organizes network dynamics underlying working memory

    PubMed Central

    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

    2016-01-01

    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. PMID:27386561

  5. Visualizing Transmedia Networks: Links, Paths and Peripheries

    ERIC Educational Resources Information Center

    Ruppel, Marc Nathaniel

    2012-01-01

    'Visualizing Transmedia Networks: Links, Paths and Peripheries' examines the increasingly complex rhetorical intersections between narrative and media ("old" and "new") in the creation of transmedia fictions, loosely defined as multisensory and multimodal stories told extensively across a diverse media set. In order…

  6. Visualizing Transmedia Networks: Links, Paths and Peripheries

    ERIC Educational Resources Information Center

    Ruppel, Marc Nathaniel

    2012-01-01

    'Visualizing Transmedia Networks: Links, Paths and Peripheries' examines the increasingly complex rhetorical intersections between narrative and media ("old" and "new") in the creation of transmedia fictions, loosely defined as multisensory and multimodal stories told extensively across a diverse media set. In order…

  7. Singlet oxygen signaling links photosynthesis to translation and plant growth.

    PubMed

    Reinbothe, Christiane; Pollmann, Stephan; Reinbothe, Steffen

    2010-09-01

    Translation is a major target of metabolic and growth control in animals and plants. Changes in the phosphorylation status of ribosomal protein S6 are responsible for rapid adjustments in the growth pattern of higher plants in response to changes in the environment. In this review, we illuminate some common and unique aspects of translational control in animals and plants and discuss recent studies that link photosynthesis to growth via specific signal transduction cascades, one of which relies on singlet oxygen and the plant growth regulator jasmonic acid (JA). It is the aim of this review to discuss the role of the target of rapamycin (TOR) signaling network in plants and what mechanisms could contribute to growth control in response to the changing environment.

  8. Link-Quality Measurement and Reporting in Wireless Sensor Networks

    PubMed Central

    Chehri, Abdellah; Jeon, Gwanggil; Choi, Byoungjo

    2013-01-01

    Wireless Sensor networks (WSNs) are created by small hardware devices that possess the necessary functionalities to measure and exchange a variety of environmental data in their deployment setting. In this paper, we discuss the experiments in deploying a testbed as a first step towards creating a fully functional heterogeneous wireless network-based underground monitoring system. The system is mainly composed of mobile and static ZigBee nodes, which are deployed on the underground mine galleries for measuring ambient temperature. In addition, we describe the measured results of link characteristics such as received signal strength, latency and throughput for different scenarios. PMID:23459389

  9. Signal Approximation with a Wavelet Neural Network

    DTIC Science & Technology

    1992-12-01

    specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the...accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural network .

  10. Noise-processing by signaling networks.

    PubMed

    Kontogeorgaki, Styliani; Sánchez-García, Rubén J; Ewing, Rob M; Zygalakis, Konstantinos C; MacArthur, Ben D

    2017-04-03

    Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways in which signaling networks process environmental noise are not well understood. Here we outline a mathematical framework that relates a network's structure to its capacity to process noise, and use this framework to dissect the noise-processing ability of signaling networks. We find that complex networks that are dense in directed paths are poor noise processors, while those that are sparse and strongly directional process noise well. These results suggest that while cross-talk between signaling pathways may increase the ability of signaling networks to integrate multiple stimuli, too much cross-talk may compromise the ability of the network to distinguish signal from noise. To illustrate these general results we consider the structure of the signalling network that maintains pluripotency in mouse embryonic stem cells, and find an incoherent feedforward loop structure involving Stat3, Tfcp2l1, Esrrb, Klf2 and Klf4 is particularly important for noise-processing. Taken together these results suggest that noise-processing is an important function of signaling networks and they may be structured in part to optimize this task.

  11. Signaling Networks that Regulate Cell Migration

    PubMed Central

    Devreotes, Peter; Horwitz, Alan Rick

    2015-01-01

    SUMMARY Stimuli that promote cell migration, such as chemokines, cytokines, and growth factors in metazoans and cyclic AMP in Dictyostelium, activate signaling pathways that control organization of the actin cytoskeleton and adhesion complexes. The Rho-family GTPases are a key convergence point of these pathways. Their effectors include actin regulators such as formins, members of the WASP/WAVE family and the Arp2/3 complex, and the myosin II motor protein. Pathways that link to the Rho GTPases include Ras GTPases, TorC2, and PI3K. Many of the molecules involved form gradients within cells, which define the front and rear of migrating cells, and are also established in related cellular behaviors such as neuronal growth cone extension and cytokinesis. The signaling molecules that regulate migration can be integrated to provide a model of network function. The network displays biochemical excitability seen as spontaneous waves of activation that propagate along the cell cortex. These events coordinate cell movement and can be biased by external cues to bring about directed migration. PMID:26238352

  12. Enhanced photoacoustic signal from DNA assembled gold nanoparticle networks

    NASA Astrophysics Data System (ADS)

    Buchkremer, A.; Beckmann, M. F.; Linn, M.; Ruff, J.; Rosencrantz, R. R.; von Plessen, G.; Schmitz, G.; Simon, U.

    2014-12-01

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

  13. A Network of Mitogen-Activated Protein Kinases Links G Protein-Coupled Receptors to the c-jun Promoter: a Role for c-Jun NH2-Terminal Kinase, p38s, and Extracellular Signal-Regulated Kinase 5

    PubMed Central

    Marinissen, Maria Julia; Chiariello, Mario; Pallante, Michael; Gutkind, J. Silvio

    1999-01-01

    The expression of the c-jun proto-oncogene is rapidly induced in response to mitogens acting on a large variety of cell surface receptors. The resulting functional activity of c-Jun proteins appears to be critical for cell proliferation. Recently, we have shown that a large family of G protein-coupled receptors (GPCRs), represented by the m1 muscarinic receptor, can initiate intracellular signaling cascades that result in the activation of mitogen-activated protein kinases (MAPK) and c-Jun NH2-terminal kinases (JNK) and that the activation of JNK but not of MAPK correlated with a remarkable increase in the expression of c-jun mRNA. Subsequently, however, we obtained evidence that GPCRs can potently stimulate the activity of the c-jun promoter through MEF2 transcription factors, which do not act downstream from JNK. In view of these observations, we set out to investigate further the nature of the signaling pathway linking GPCRs to the c-jun promoter. Utilizing NIH 3T3 cells, we found that GPCRs can activate the c-jun promoter in a JNK-independent manner. Additionally, we demonstrated that these GPCRs can elevate the activity of novel members of the MAPK family, including ERK5, p38α, p38γ, and p38δ, and that the activation of certain kinases acting downstream from MEK5 (ERK5) and MKK6 (p38α and p38γ) is necessary to fully activate the c-jun promoter. Moreover, in addition to JNK, ERK5, p38α, and p38γ were found to stimulate the c-jun promoter by acting on distinct responsive elements. Taken together, these results suggest that the pathway linking GPCRs to the c-jun promoter involves the integration of numerous signals transduced by a highly complex network of MAPK, rather than resulting from the stimulation of a single linear protein kinase cascade. Furthermore, our findings suggest that each signaling pathway affects one or more regulatory elements on the c-jun promoter and that the transcriptional response most likely results from the temporal integration

  14. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  15. Improving interdependent networks robustness by adding connectivity links

    NASA Astrophysics Data System (ADS)

    Ji, Xingpei; Wang, Bo; Liu, Dichen; Chen, Guo; Tang, Fei; Wei, Daqian; Tu, Lian

    2016-02-01

    Compared with a single and isolated network, interdependent networks have two types of links: connectivity link and dependency link. This paper aims to improve the robustness of interdependent networks by adding connectivity links. Firstly, interdependent networks failure model and four frequently used link addition strategies are briefly reviewed. Furthermore, by defining inter degree-degree difference, two novel link addition strategies are proposed. Finally, we verify the effectiveness of our proposed link addition strategies by comparing with the current link addition strategies in three different network models. The simulation results show that, given the number of added links, link allocation strategies have great effects on the robustness of interdependent networks, i.e., the double-network link allocation strategy is superior to single-network link allocation strategy. Link addition strategies proposed in this paper excel the current strategies, especially for BA interdependent networks. Moreover, our work can provide guidance on how to allocate limited resources to an existing interdependent networks system and optimize its topology to avoid the potential cascade failures.

  16. Analysis and logical modeling of biological signaling transduction networks

    NASA Astrophysics Data System (ADS)

    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.

  17. Signal reachability facilitates characterization of probabilistic signaling networks

    PubMed Central

    2015-01-01

    Background Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times. Results In this paper, we present novel methods for characterizing probabilistic signaling networks. Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. We characterize such networks with respect to (i) centrality of individual nodes, (ii) stability of the entire network, and (iii) important functions served by the network. We use these methods to characterize major H. sapiens signaling networks including Wnt, ErbB and MAPK. PMID:26679404

  18. Identification of hybrid node and link communities in complex networks.

    PubMed

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  19. Identification of hybrid node and link communities in complex networks

    NASA Astrophysics Data System (ADS)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  20. Identification of hybrid node and link communities in complex networks

    PubMed Central

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-01-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

  1. Accelerating coordination in temporal networks by engineering the link order

    PubMed Central

    Masuda, Naoki

    2016-01-01

    Social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later. The order with which the links in a given network are sequentially used, which we call the link order, may be a strong determinant of dynamical behaviour on networks, potentially adding a new dimension to effects of temporal networks relative to static networks. Here we study the effect of the link order on linear coordination (i.e., synchronisation) dynamics. We show that the coordination speed considerably depends on specific orders of links. In addition, applying each single link for a long time to ensure strong pairwise coordination before moving to a next pair of individuals does not often enhance coordination of the entire network. We also implement a simple greedy algorithm to optimise the link order in favour of fast coordination. PMID:26916093

  2. Utility-Based Link Recommendation in Social Networks

    ERIC Educational Resources Information Center

    Li, Zhepeng

    2013-01-01

    Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networking platforms. Salient examples of link recommendation include "people you may know"' on Facebook and "who to follow" on Twitter. A social networking platform has two types of stakeholder:…

  3. Utility-Based Link Recommendation in Social Networks

    ERIC Educational Resources Information Center

    Li, Zhepeng

    2013-01-01

    Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networking platforms. Salient examples of link recommendation include "people you may know"' on Facebook and "who to follow" on Twitter. A social networking platform has two types of stakeholder:…

  4. Robust bidirectional links for photonic quantum networks.

    PubMed

    Xu, Jin-Shi; Yung, Man-Hong; Xu, Xiao-Ye; Tang, Jian-Shun; Li, Chuan-Feng; Guo, Guang-Can

    2016-01-01

    Optical fibers are widely used as one of the main tools for transmitting not only classical but also quantum information. We propose and report an experimental realization of a promising method for creating robust bidirectional quantum communication links through paired optical polarization-maintaining fibers. Many limitations of existing protocols can be avoided with the proposed method. In particular, the path and polarization degrees of freedom are combined to deterministically create a photonic decoherence-free subspace without the need for any ancillary photon. This method is input state-independent, robust against dephasing noise, postselection-free, and applicable bidirectionally. To rigorously quantify the amount of quantum information transferred, the optical fibers are analyzed with the tools developed in quantum communication theory. These results not only suggest a practical means for protecting quantum information sent through optical quantum networks but also potentially provide a new physical platform for enriching the structure of the quantum communication theory.

  5. Robust bidirectional links for photonic quantum networks

    PubMed Central

    Xu, Jin-Shi; Yung, Man-Hong; Xu, Xiao-Ye; Tang, Jian-Shun; Li, Chuan-Feng; Guo, Guang-Can

    2016-01-01

    Optical fibers are widely used as one of the main tools for transmitting not only classical but also quantum information. We propose and report an experimental realization of a promising method for creating robust bidirectional quantum communication links through paired optical polarization-maintaining fibers. Many limitations of existing protocols can be avoided with the proposed method. In particular, the path and polarization degrees of freedom are combined to deterministically create a photonic decoherence-free subspace without the need for any ancillary photon. This method is input state–independent, robust against dephasing noise, postselection-free, and applicable bidirectionally. To rigorously quantify the amount of quantum information transferred, the optical fibers are analyzed with the tools developed in quantum communication theory. These results not only suggest a practical means for protecting quantum information sent through optical quantum networks but also potentially provide a new physical platform for enriching the structure of the quantum communication theory. PMID:26824069

  6. The community structure of human cellular signaling network.

    PubMed

    Diao, Yuanbo; Li, Menglong; Feng, Zinan; Yin, Jiajian; Pan, Yi

    2007-08-21

    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

  7. Design and Analysis of Underwater Acoustic Networks with Reflected Links

    NASA Astrophysics Data System (ADS)

    Emokpae, Lloyd

    Underwater acoustic networks (UWANs) have applications in environmental state monitoring, oceanic profile measurements, leak detection in oil fields, distributed surveillance, and navigation. For these applications, sets of nodes are employed to collaboratively monitor an area of interest and track certain events or phenomena. In addition, it is common to find autonomous underwater vehicles (AUVs) acting as mobile sensor nodes that perform search-and-rescue missions, reconnaissance in combat zones, and coastal patrol. These AUVs are to work cooperatively to achieve a desired goal and thus need to be able to, in an ad-hoc manner, establish and sustain communication links in order to ensure some desired level of quality of service. Therefore, each node is required to adapt to environmental changes and be able to overcome broken communication links caused by external noise affecting the communication channel due to node mobility. In addition, since radio waves are quickly absorbed in the water medium, it is common for most underwater applications to rely on acoustic (or sound) rather than radio channels for mid-to-long range communications. However, acoustic channels pose multiple challenging issues, most notably the high transmission delay due to slow signal propagation and the limited channel bandwidth due to high frequency attenuation. Moreover, the inhomogeneous property of the water medium affects the sound speed profile while the signal surface and bottom reflections leads to multipath effects. In this dissertation, we address these networking challenges by developing protocols that take into consideration the underwater physical layer dynamics. We begin by introducing a novel surface-based reflection scheme (SBR), which takes advantage of the multipath effects of the acoustic channel. SBR works by using reflections from the water surface, and bottom, to establish non-line-of-sight (NLOS) communication links. SBR makes it possible to incorporate both line

  8. Protein modules and signalling networks

    NASA Astrophysics Data System (ADS)

    Pawson, Tony

    1995-02-01

    Communication between cells assumes particular importance in multicellular organisms. The growth, migration and differentiation of cells in the embryo, and their organization into specific tissues, depend on signals transmitted from one cell to another. In the adult, cell signalling orchestrates normal cellular behaviour and responses to wounding and infection. The consequences of breakdowns in this signalling underlie cancer, diabetes and disorders of the immune and cardiovascular systems. Conserved protein domains that act as key regulatory participants in many of these different signalling pathways are highlighted.

  9. Lewis Information Network (LINK): Background and overview

    NASA Technical Reports Server (NTRS)

    Schulte, Roger R.

    1987-01-01

    The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

  10. Correlations between Community Structure and Link Formation in Complex Networks

    PubMed Central

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

  11. Critical Links and Nonlocal Rerouting in Complex Supply Networks.

    PubMed

    Witthaut, Dirk; Rohden, Martin; Zhang, Xiaozhu; Hallerberg, Sarah; Timme, Marc

    2016-04-01

    Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain a stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform the critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing countermeasures and support network planning and design.

  12. Critical Links and Nonlocal Rerouting in Complex Supply Networks

    NASA Astrophysics Data System (ADS)

    Witthaut, Dirk; Rohden, Martin; Zhang, Xiaozhu; Hallerberg, Sarah; Timme, Marc

    2016-04-01

    Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain a stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform the critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing countermeasures and support network planning and design.

  13. Critical links and nonlocal rerouting in complex supply networks

    NASA Astrophysics Data System (ADS)

    Witthaut, Dirk; Rohden, Martin; Zhang, Xiaozhu; Hallerberg, Sarah; Timme, Marc

    2015-10-01

    Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood, which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing counter-measures and support network planning and design.

  14. Evolutionary link community structure discovery in dynamic weighted networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiang; Liu, Caihong; Wang, Jiajia; Wang, Xiang; Zhou, Bin; Zou, Peng

    2017-01-01

    Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.

  15. Revealing how network structure affects accuracy of link prediction

    NASA Astrophysics Data System (ADS)

    Yang, Jin-Xuan; Zhang, Xiao-Dong

    2017-08-01

    Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

  16. Optimal search strategies on complex multi-linked networks

    PubMed Central

    Di Patti, Francesca; Fanelli, Duccio; Piazza, Francesco

    2015-01-01

    In this paper we consider the problem of optimal search strategies on multi-linked networks, i.e. graphs whose nodes are endowed with several independent sets of links. We focus preliminarily on agents randomly hopping along the links of a graph, with the additional possibility of performing non-local hops to randomly chosen nodes with a given probability. We show that an optimal combination of the two jump rules exists that maximises the efficiency of target search, the optimum reflecting the topology of the network. We then generalize our results to multi-linked networks with an arbitrary number of mutually interfering link sets. PMID:25950716

  17. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Uijlenhoet, Remko; Leijnse, Hidde

    2016-04-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. We present a rainfall retrieval algorithm, which is employed to obtain rainfall maps from microwave links in a cellular communication network. We compare these rainfall maps to gauge-adjusted radar rainfall maps. The microwave link data set, as well as the developed code, a package in the open source scripting language "R", are freely available at GitHub (https://github.com/overeem11/RAINLINK). The purpose of this presentation is to promote rainfall mapping utilizing microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  18. Linked in: immunologic membrane nanotube networks.

    PubMed

    Zaccard, C R; Rinaldo, C R; Mailliard, R B

    2016-07-01

    Membrane nanotubes, also termed tunneling nanotubes, are F-actin-based structures that can form direct cytoplasmic connections and support rapid communication between distant cells. These nanoscale conduits have been observed in diverse cell types, including immune, neuronal, stromal, cancer, and stem cells. Until recently, little was known about the mechanisms involved in membrane nanotube development in myeloid origin APCs or how membrane nanotube networks support their ability to bridge innate and adaptive immunity. New research has provided insight into the modes of induction and regulation of the immune process of "reticulation" or the development of multicellular membrane nanotube networks in dendritic cells. Preprogramming by acute type 1 inflammatory mediators at their immature stage licenses mature type 1-polarized dendritic cells to reticulate upon subsequent interaction with CD40 ligand-expressing CD4(+) Th cells. Dendritic cell reticulation can support direct antigen transfer for amplification of specific T cell responses and can be positively or negatively regulated by signals from distinct Th cell subsets. Membrane nanotubes not only enhance the ability of immature dendritic cells to sense pathogens and rapidly mobilize nearby antigen-presenting cells in the peripheral tissues but also likely support communication of pathogen-related information from mature migratory dendritic cells to resident dendritic cells in lymph nodes. Therefore, the reticulation process facilitates a coordinated multicellular response for the efficient initiation of cell-mediated adaptive immune responses. Herein, we discuss studies focused on the molecular mechanisms of membrane nanotube formation, structure, and function in the context of immunity and how pathogens, such as HIV-1, may use dendritic cell reticulation to circumvent host defenses. © Society for Leukocyte Biology.

  19. Mixed-method Exploration of Social Network Links to Participation

    PubMed Central

    Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

    2015-01-01

    The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

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

    PubMed Central

    Dzeja, Petras; Terzic, Andre

    2009-01-01

    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. PMID:19468337

  1. Design and implementation of a fiber optic link for a token ring local area network

    NASA Astrophysics Data System (ADS)

    Doran, Thomas J.

    1992-09-01

    This thesis describes the design and implementation of a fiber optic link for a token ring local area network (LAN). It features the use of fiber optic channels as the transmission medium between a computer system and a wiring concentrator to convert a physical ring design into a starwired configuration. The LAN was controlled by the TMS380 LAN Adapter chipset, which provided all diagnostic and network management features to include the 4 Mb/s electrical signal for operation. Since this adapter was developed for systems using twisted wire pair adapter cables, design modifications required that the fiber link be able to simulate impedance and current characteristics of copper wire link. This allows the use of adapter diagnostic checks for ring continuity and proper ring operation. Design evaluations using test signals and adapter signals from within the computer-concentrator link showed mixed results. All transmission objectives were met but the circuit loaded down the LAN adapter causing hardware error messages.

  2. Modeling of the ground-to-SSFMB link networking features using SPW

    NASA Technical Reports Server (NTRS)

    Watson, John C.

    1993-01-01

    This report describes the modeling and simulation of the networking features of the ground-to-Space Station Freedom manned base (SSFMB) link using COMDISCO signal processing work-system (SPW). The networking features modeled include the implementation of Consultative Committee for Space Data Systems (CCSDS) protocols in the multiplexing of digitized audio and core data into virtual channel data units (VCDU's) in the control center complex and the demultiplexing of VCDU's in the onboard baseband signal processor. The emphasis of this work has been placed on techniques for modeling the CCSDS networking features using SPW. The objectives for developing the SPW models are to test the suitability of SPW for modeling networking features and to develop SPW simulation models of the control center complex and space station baseband signal processor for use in end-to-end testing of the ground-to-SSFMB S-band single access forward (SSAF) link.

  3. Neural Networks Applied to Signal Processing

    DTIC Science & Technology

    1989-09-01

    identify by block number) FIELD GROUP SUB-GROUP Neural network, backpropagation, conjugato grad- ient method, Fibonacci line search, nonlinear signal...of the First Layer Gradients ............ 31 e. Calculation of the Input Layer Gradient-. ........... 33 i%" 5. Fibonacci Line Search Parameters...conjugate gradient optimization method is presented and then applied to the neu- ral network model. The Fibonacci line search method used in conjunction

  4. PLC signal attenuation in branched networks

    SciTech Connect

    Durbak, D.W.; Stewart, J.R. )

    1990-04-01

    The application of power line carrier (PLC) to utility transmission systems can provide a reliable means of communication over short and long distances. However, PLC performance is dependent upon an adequate signal-to-noise ratio at receivers. The calculation of path attenuation of signals on the transmission line can be complicated, especially in branched networks. The calculation method described here is based on the construction of an impedance matrix using multi-phase, long line pi-equivalents for transmission lines. The method can predict PLC attenuation for a variety of network topologies and is demonstrated for two cases.

  5. Predicting links based on knowledge dissemination in complex network

    NASA Astrophysics Data System (ADS)

    Zhou, Wen; Jia, Yifan

    2017-04-01

    Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method-knowledge dissemination based link prediction (KDLP)-is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

  6. Link prediction boosted psychiatry disorder classification for functional connectivity network

    NASA Astrophysics Data System (ADS)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  7. Cold-start link prediction in multi-relational networks

    NASA Astrophysics Data System (ADS)

    Wu, Shun-yao; Zhang, Qi; Wu, Mei

    2017-10-01

    During the last decade, interaction data have accumulated exponentially in many fields and provide a new opportunity for cold start link prediction. It seems necessarily to take full advantages of diversified information. However, correlation between different interactions has to be pre-tested. Therefore, this paper abstracts complex systems as multi-relational networks, and employs latent space network model to extract low-dimensional factors of sub-networks and adopts likelihood ratio test to examine correlation between factors. Then, regression between target sub-networks and correlated auxiliary sub-networks could be established for cold start link prediction. Experiments on 8 bioinformatic data sets validate the effectiveness and potential of our strategy for network correlation analysis and cold-start link prediction.

  8. Signaling networks in the control of pluripotency.

    PubMed

    Zhao, Hanzhi; Jin, Ying

    2017-10-01

    Embryonic stem cells (ESCs) are characterized by their ability of unlimited self-renewal in vitro and pluripotent developmental potential, which endows them with great values in basic research and future clinical application. However, realization of full potential of ESCs is dependent on the elucidation of molecular mechanisms governing ESCs, among which signaling pathways play critical roles. A great deal of efforts has been made in the past decades to understand what and how signaling pathways contribute to the establishment and maintenance of pluripotency. In this review, we discuss signaling networks in both mouse and human ESCs, focusing on signals involved in the control of self-renewal and differentiation. In addition, the modulation of signaling pathways by pluripotency-associated transcription factors is also briefly summarized. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Signal transduction-associated and cell activation-linked antigens expressed in human mast cells.

    PubMed

    Valent, Peter; Ghannadan, Minoo; Hauswirth, Alexander W; Schernthaner, Gerit-Holger; Sperr, Wolfgang R; Arock, Michel

    2002-05-01

    Mast cells (MCs) are multifunctional hematopoietic effector cells that produce and release an array of biologically active mediator substances. Growth and functions of MCs are regulated by cytokines, other extracellular factors, surface and cytoplasmic receptors, oncogene products, and a complex network of signal transduction cascades. Key regulators of differentiation of MCs appear to be stem cell factor (SCF) and its tyrosine kinase receptor KIT (c-kit proto-oncogene product=CD117), downstream-acting elements, and the mi transcription factor (MITF). Signaling through KIT is negatively regulated by the signal regulatory protein (SIRP)-alpha (CD172a)-SHP-1-pathway that is disrupted in neoplastic MCs in MC proliferative disorders. Both KIT and FcepsilonRI are involved in MC activation and mediator release. Activation of MCs through FcepsilonRI is associated with increased expression of activation-linked membrane antigens as well as with signaling events involving Lyn and Syk kinases, the phosphatidylinositol-3-kinase-pathway, Ras pathway, and the phospholipase C-protein kinase C pathway. A similar network of signaling is found in SCF-activated MCs. The current article gives an overview on signal transduction-associated and activation-linked antigens expressed in human MCs. Wherever possible the functional implication of signaling pathways and antigen expression are discussed.

  10. A new mutually reinforcing network node and link ranking algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-10-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.

  11. A new mutually reinforcing network node and link ranking algorithm

    PubMed Central

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-01-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity. PMID:26492958

  12. Greased hedgehogs: new links between hedgehog signaling and cholesterol metabolism.

    PubMed

    Breitling, Rainer

    2007-11-01

    The close link between signaling by the developmental regulators of the Hedgehog family and cholesterol biochemistry has been known for some time. The morphogen is covalently attached to cholesterol in a peculiar autocatalytic reaction and embryonal disruption of cholesterol synthesis leads to malformations that mimic Hh signaling defects. Recently, it was furthermore shown that secreted Hh could hitchhike on lipoprotein particles to establish its morphogenic gradient in the developing embryo. Additionally, there is new evidence that the Hh-receptor Patched transmits the Hh signal by modulating the secretion of an inhibitory sterol molecule from the receiving cells. Here we present some of the most recent discoveries on the Hh-sterol link and discuss their implications from a systems design perspective. We predict that a robust functioning of the Hh pathway will require the involvement of more sterol metabolites, and these should be the subject of future research.

  13. Linking signal fidelity and the efficiency costs of communication.

    PubMed

    Hackett, S; Schaefer, H M; Ruxton, G D

    2014-09-01

    The handicap principle has been the overarching framework to explain the evolution and maintenance of communication. Yet, it is becoming apparent that strategic costs of signalling are not the only mechanism maintaining signal honesty. Rather, the fidelity of detecting signals can itself be strongly selected. Specifically, we argue that the fidelity of many signals will be constrained by the investment in signal generation and reception by the signaller and perceiver, respectively. Here, we model how investments in signal fidelity influence the emergence and stability of communication using a simple theoretical framework. The predictions of the model indicate that high-cost communication can be stable whereas low-cost intermediates are generally selected against. This dichotomy suggests that the most parsimonious route to the evolution of communication is for initial investment in communicative traits to be driven by noncommunicative functions. Such cues can appeal to pre-existing perceptual biases and thereby stimulate signal evolution. We predict that signal evolution will vary between systems in ways that can be linked to the economics of communication to the two parties involved. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

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

    PubMed Central

    Schaub, Marc A; Henzinger, Thomas A; Fisher, Jasmin

    2007-01-01

    Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology. PMID:17408511

  15. Prediction of missing links and reconstruction of complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng-Jun; Zeng, An

    2016-04-01

    Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

  16. Computation of signal delays in RC networks

    NASA Technical Reports Server (NTRS)

    Hidalgo, Juan Carlos; Narendran, Paliath; Chaiken, Seth

    1993-01-01

    A model for signal delay computation in RC networks is presented. The strength of the paradigm is its generality and simplicity. The definition of delay is applicable to RC meshes with potential resistive attenuating paths to ground. The algorithms can also be applied to undriven circuits (static charge sharing) and circuits with initial charge. To compute the delays, each node in the network is explored locally to derive a system of sparse linear equations. The solutions of the system are delay values based on the Elmore time constant at each point in the circuit.

  17. Signal dispersion within a hippocampal neural network

    NASA Technical Reports Server (NTRS)

    Horowitz, J. M.; Mates, J. W. B.

    1975-01-01

    A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

  18. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2015-08-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm and provide the corresponding code. Moreover, the code (in the scripting language "R") is made available including a data set of commercial microwave links. The purpose of this paper is to promote rainfall monitoring utilizing microwave links from cellular communication networks as an alternative or complementary means for global, continental-scale rainfall monitoring.

  19. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2016-06-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km2) 15 min rainfall maps can be derived from the signal attenuations of approximately 2400 microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm. Moreover, the documented, modular, and user-friendly code (a package in the scripting language "R") is made available, including a 2-day data set of approximately 2600 commercial microwave links from the Netherlands. The purpose of this paper is to promote rainfall mapping utilising microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  20. Link Prediction in Weighted Networks: A Weighted Mutual Information Model

    PubMed Central

    Zhu, Boyao; Xia, Yongxiang

    2016-01-01

    The link-prediction problem is an open issue in data mining and knowledge discovery, which attracts researchers from disparate scientific communities. A wealth of methods have been proposed to deal with this problem. Among these approaches, most are applied in unweighted networks, with only a few taking the weights of links into consideration. In this paper, we present a weighted model for undirected and weighted networks based on the mutual information of local network structures, where link weights are applied to further enhance the distinguishable extent of candidate links. Empirical experiments are conducted on four weighted networks, and results show that the proposed method can provide more accurate predictions than not only traditional unweighted indices but also typical weighted indices. Furthermore, some in-depth discussions on the effects of weak ties in link prediction as well as the potential to predict link weights are also given. This work may shed light on the design of algorithms for link prediction in weighted networks. PMID:26849659

  1. PACRG, a protein linked to ciliary motility, mediates cellular signaling.

    PubMed

    Loucks, Catrina M; Bialas, Nathan J; Dekkers, Martijn P J; Walker, Denise S; Grundy, Laura J; Li, Chunmei; Inglis, P Nick; Kida, Katarzyna; Schafer, William R; Blacque, Oliver E; Jansen, Gert; Leroux, Michel R

    2016-07-01

    Cilia are microtubule-based organelles that project from nearly all mammalian cell types. Motile cilia generate fluid flow, whereas nonmotile (primary) cilia are required for sensory physiology and modulate various signal transduction pathways. Here we investigate the nonmotile ciliary signaling roles of parkin coregulated gene (PACRG), a protein linked to ciliary motility. PACRG is associated with the protofilament ribbon, a structure believed to dictate the regular arrangement of motility-associated ciliary components. Roles for protofilament ribbon-associated proteins in nonmotile cilia and cellular signaling have not been investigated. We show that PACRG localizes to a small subset of nonmotile cilia in Caenorhabditis elegans, suggesting an evolutionary adaptation for mediating specific sensory/signaling functions. We find that it influences a learning behavior known as gustatory plasticity, in which it is functionally coupled to heterotrimeric G-protein signaling. We also demonstrate that PACRG promotes longevity in C. elegans by acting upstream of the lifespan-promoting FOXO transcription factor DAF-16 and likely upstream of insulin/IGF signaling. Our findings establish previously unrecognized sensory/signaling functions for PACRG and point to a role for this protein in promoting longevity. Furthermore, our work suggests additional ciliary motility-signaling connections, since EFHC1 (EF-hand containing 1), a potential PACRG interaction partner similarly associated with the protofilament ribbon and ciliary motility, also positively regulates lifespan.

  2. PACRG, a protein linked to ciliary motility, mediates cellular signaling

    PubMed Central

    Loucks, Catrina M.; Bialas, Nathan J.; Dekkers, Martijn P. J.; Walker, Denise S.; Grundy, Laura J.; Li, Chunmei; Inglis, P. Nick; Kida, Katarzyna; Schafer, William R.; Blacque, Oliver E.; Jansen, Gert; Leroux, Michel R.

    2016-01-01

    Cilia are microtubule-based organelles that project from nearly all mammalian cell types. Motile cilia generate fluid flow, whereas nonmotile (primary) cilia are required for sensory physiology and modulate various signal transduction pathways. Here we investigate the nonmotile ciliary signaling roles of parkin coregulated gene (PACRG), a protein linked to ciliary motility. PACRG is associated with the protofilament ribbon, a structure believed to dictate the regular arrangement of motility-associated ciliary components. Roles for protofilament ribbon–associated proteins in nonmotile cilia and cellular signaling have not been investigated. We show that PACRG localizes to a small subset of nonmotile cilia in Caenorhabditis elegans, suggesting an evolutionary adaptation for mediating specific sensory/signaling functions. We find that it influences a learning behavior known as gustatory plasticity, in which it is functionally coupled to heterotrimeric G-protein signaling. We also demonstrate that PACRG promotes longevity in C. elegans by acting upstream of the lifespan-promoting FOXO transcription factor DAF-16 and likely upstream of insulin/IGF signaling. Our findings establish previously unrecognized sensory/signaling functions for PACRG and point to a role for this protein in promoting longevity. Furthermore, our work suggests additional ciliary motility-signaling connections, since EFHC1 (EF-hand containing 1), a potential PACRG interaction partner similarly associated with the protofilament ribbon and ciliary motility, also positively regulates lifespan. PMID:27193298

  3. Brain Network Analysis from High-Resolution EEG Signals

    NASA Astrophysics Data System (ADS)

    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

  4. Linking Classrooms of the Future through Interactive Telecommunications Network.

    ERIC Educational Resources Information Center

    Cisco, Ponney G.

    This document describes an interactive television (ITV) distance education network designed to service rural schools. Phase one of the network involved the installation of over 14 miles of fiber optic cable linking two high schools, a career center, and the University of Rio Grande; phase two will bring seven high schools in economically depressed…

  5. Linking Classrooms of the Future through Interactive Telecommunications Network.

    ERIC Educational Resources Information Center

    Cisco, Ponney G.

    This document describes an interactive television (ITV) distance education network designed to service rural schools. Phase one of the network involved the installation of over 14 miles of fiber optic cable linking two high schools, a career center, and the University of Rio Grande; phase two will bring seven high schools in economically depressed…

  6. Mixed-Method Exploration of Social Network Links to Participation.

    PubMed

    Kreider, Consuelo M; Bendixen, Roxanna M; Mann, William C; Young, Mary Ellen; McCarty, Christopher

    2015-07-01

    The people who regularly interact with an adolescent form that youth's social network (SN), which may impact participation. We investigated the relationship of SNs to participation using personal network analysis and individual interviews. The sample included 36 youth, aged 11 to 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism, and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least I measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within SNs. Findings contribute to understanding the ways SNs are linked to youth participation and suggest the potential of SN factors for predicting rehabilitation outcomes.

  7. Link prediction in the network of global virtual water trade

    NASA Astrophysics Data System (ADS)

    Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

    2016-04-01

    Through the international food-trade, water resources are 'virtually' transferred from the country of production to the country of consumption. The international food-trade, thus, implies a network of virtual water flows from exporting to importing countries (i.e., nodes). Given the dynamical behavior of the network, where food-trade relations (i.e., links) are created and dismissed every year, link prediction becomes a challenge. In this study, we propose a novel methodology for link prediction in the virtual water network. The model aims at identifying the main factors (among 17 different variables) driving the creation of a food-trade relation between any two countries, along the period between 1986 and 2011. Furthermore, the model can be exploited to investigate the network configuration in the future, under different possible (climatic and demographic) scenarios. The model grounds the existence of a link between any two nodes on the link weight (i.e., the virtual water flow): a link exists when the nodes exchange a minimum (fixed) volume of virtual water. Starting from a set of potential links between any two nodes, we fit the associated virtual water flows (both the real and the null ones) by means of multivariate linear regressions. Then, links with estimated flows higher than a minimum value (i.e., threshold) are considered active-links, while the others are non-active ones. The discrimination between active and non-active links through the threshold introduces an error (called link-prediction error) because some real links are lost (i.e., missed links) and some non-existing links (i.e., spurious links) are inevitably introduced in the network. The major drivers are those significantly minimizing the link-prediction error. Once the structure of the unweighted virtual water network is known, we apply, again, linear regressions to assess the major factors driving the fluxes traded along (modelled) active-links. Results indicate that, on the one hand

  8. CrossRef: A Collaborative Linking Network.

    ERIC Educational Resources Information Center

    Pentz, Ed

    2001-01-01

    CrossRef was created to make broad-based linking efficient and scalable across a wide range of primary publishers, secondary publishers, abstracting and indexing services, and libraries. CrossRef runs a system that enables publishers to assign Digital Object Identifiers (DOIs) to articles and collects standardized metadata so the identifiers can…

  9. Network worlds : from link analysis to virtual places.

    SciTech Connect

    Joslyn, C.

    2002-01-01

    Significant progress is being made in knowledge systems through recent advances in the science of very large networks. Attention is now turning in many quarters to the potential impact on counter-terrorism methods. After reviewing some of these advances, we will discuss the difference between such 'network analytic' approaches, which focus on large, homogeneous graph strucures, and what we are calling 'link analytic' approaches, which focus on somewhat smaller graphs with heterogeneous link types. We use this venue to begin the process of rigorously defining link analysis methods, especially the concept of chaining of views of multidimensional databases. We conclude with some speculation on potential connections to virtual world architectures.

  10. Structural Preferential Attachment: Network Organization beyond the Link

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Allard, Antoine; Marceau, Vincent; Noël, Pierre-André; Dubé, Louis J.

    2011-10-01

    We introduce a mechanism which models the emergence of the universal properties of complex networks, such as scale independence, modularity and self-similarity, and unifies them under a scale-free organization beyond the link. This brings a new perspective on network organization where communities, instead of links, are the fundamental building blocks of complex systems. We show how our simple model can reproduce social and information networks by predicting their community structure and more importantly, how their nodes or communities are interconnected, often in a self-similar manner.

  11. Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests

    PubMed Central

    Ding, Xingjian; Sun, Guodong; Yang, Gaoxiang; Shang, Xinna

    2016-01-01

    Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments. PMID:27355957

  12. Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests.

    PubMed

    Ding, Xingjian; Sun, Guodong; Yang, Gaoxiang; Shang, Xinna

    2016-06-27

    Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments.

  13. Influence of Inter-Community Link Strength on Community Networks

    NASA Astrophysics Data System (ADS)

    Peng, Qian-Jin; Zhao, Ming; Zhang, Hai-Feng

    2013-09-01

    This paper mainly discusses how the strength of inter-community links affects the synchronization state of the whole network and the individual community. It is found, when the inter-community link number is neither too small nor too large and the overall coupling strength takes right values, there will be an optimal inter-community link strength value, in which the community networks are in a balance region where the individual community is maximally independent, while the information transmission remains effective among different communities. Combining the result of this paper and that of the number of inter-community links, it is easy to help us find the most effective community networks.

  14. Community detection in networks with positive and negative links

    NASA Astrophysics Data System (ADS)

    Traag, V. A.; Bruggeman, Jeroen

    2009-09-01

    Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing Potts model to incorporate negative links as well, resulting in a method similar to the clustering of signed graphs, as dealt with in social balance theory, but more general. To illustrate our method, we applied it to a network of international alliances and disputes. Using data from 1993-2001, it turns out that the world can be divided into six power blocs similar to Huntington’s civilizations, with some notable exceptions.

  15. Meta-path based heterogeneous combat network link prediction

    NASA Astrophysics Data System (ADS)

    Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

    2017-09-01

    The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

  16. Crowdsourcing network inference: the DREAM predictive signaling network challenge.

    PubMed

    Prill, Robert J; Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Sorger, Peter K; Stolovitzky, Gustavo

    2011-08-30

    Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs-that is, outsourcing the task to the interested community-may be an effective strategy for network inference.

  17. Link-Prediction Enhanced Consensus Clustering for Complex Networks.

    PubMed

    Burgess, Matthew; Adar, Eytan; Cafarella, Michael

    2016-01-01

    Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that "consume" the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

  18. Model selection for athermal cross-linked fiber networks.

    PubMed

    Shahsavari, A; Picu, R C

    2012-07-01

    Athermal random fiber networks are usually modeled by representing each fiber as a truss, a Euler-Bernoulli or a Timoshenko beam, and, in the case of cross-linked networks, each cross-link as a pinned, rotating, or welded joint. In this work we study the effect of these various modeling options on the dependence of the overall network stiffness on system parameters. We conclude that Timoshenko beams can be used for the entire range of density and beam stiffness parameters, while the Euler-Bernoulli model can be used only at relatively low network densities. In the high density-high bending stiffness range, strain energy is stored predominantly in the axial and shear deformation modes, while in the other extreme range of parameters, the energy is stored in the bending mode. The effect of the model size on the network stiffness is also discussed.

  19. Detecting link failures in complex network processes using remote monitoring

    NASA Astrophysics Data System (ADS)

    Dhal, R.; Abad Torres, J.; Roy, S.

    2015-11-01

    We study whether local structural changes in a complex network can be distinguished from passive remote time-course measurements of the network's dynamics. Specifically the detection of link failures in a network synchronization process from noisy measurements at a single network component is considered. By phrasing the detection task as a Maximum A Posteriori Probability hypothesis testing problem, we are able to obtain conditions under which the detection is (1) improved over the a priori and (2) asymptotically perfect, in terms of the network spectrum and graph. We find that, in the case where the detector has knowledge of the network's state, perfect detection is possible under general connectivity conditions regardless of the measurement location. When the detector does not have state knowledge, a remote signature permits improved but not perfect detection, under the same connectivity conditions. At its essence, detectability is achieved because of the close connection between a network's topology, its eigenvalues and local response characteristics.

  20. Acoustic signal propagation characterization of conduit networks

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Safeer

    Analysis of acoustic signal propagation in conduit networks has been an important area of research in acoustics. One major aspect of analyzing conduit networks as acoustic channels is that a propagating signal suffers frequency dependent attenuation due to thermo-viscous boundary layer effects and the presence of impedance mismatches such as side branches. The signal attenuation due to side branches is strongly influenced by their numbers and dimensions such as diameter and length. Newly developed applications for condition based monitoring of underground conduit networks involve measurement of acoustic signal attenuation through tests in the field. In many cases the exact installation layout of the field measurement location may not be accessible or actual installation may differ from the documented layout. The lack of exact knowledge of numbers and lengths of side branches, therefore, introduces uncertainty in the measurements of attenuation and contributes to the random variable error between measured results and those predicted from theoretical models. There are other random processes in and around conduit networks in the field that also affect the propagation of an acoustic signal. These random processes include but are not limited to the presence of strong temperature and humidity gradients within the conduits, blockages of variable sizes and types, effects of aging such as cracks, bends, sags and holes, ambient noise variations and presence of variable layer of water. It is reasonable to consider that the random processes contributing to the error in the measured attenuation are independent and arbitrarily distributed. The error, contributed by a large number of independent sources of arbitrary probability distributions, is best described by an approximately normal probability distribution in accordance with the central limit theorem. Using an analytical approach to model the attenuating effect of each of the random variable sources can be very complex and

  1. The glucose signaling network in yeast.

    PubMed

    Kim, Jeong-Ho; Roy, Adhiraj; Jouandot, David; Cho, Kyu Hong

    2013-11-01

    Most cells possess a sophisticated mechanism for sensing glucose and responding to it appropriately. Glucose sensing and signaling in the budding yeast Saccharomyces cerevisiae represent an important paradigm for understanding how extracellular signals lead to changes in the gene expression program in eukaryotes. This review focuses on the yeast glucose sensing and signaling pathways that operate in a highly regulated and cooperative manner to bring about glucose-induction of HXT gene expression. The yeast cells possess a family of glucose transporters (HXTs), with different kinetic properties. They employ three major glucose signaling pathways-Rgt2/Snf3, AMPK, and cAMP-PKA-to express only those transporters best suited for the amounts of glucose available. We discuss the current understanding of how these pathways are integrated into a regulatory network to ensure efficient uptake and utilization of glucose. Elucidating the role of multiple glucose signals and pathways involved in glucose uptake and metabolism in yeast may reveal the molecular basis of glucose homeostasis in humans, especially under pathological conditions, such as hyperglycemia in diabetics and the elevated rate of glycolysis observed in many solid tumors. © 2013.

  2. The glucose signaling network in yeast

    PubMed Central

    Kim, Jeong-Ho; Roy, Adhiraj; Jouandot, David; Cho, Kyu Hong

    2013-01-01

    Background Most cells possess a sophisticated mechanism for sensing glucose and responsing to it appropriately. Glucose sensing and signaling in the budding yeast Saccharomyces cerevisiae represents an important paradigm for understanding how extracellular signals lead to changes in the gene expression program in eukaryotes. Scope of review This review focuses on the yeast glucose sensing and signaling pathways that operate in a highly regulated and cooperative manner to bring about glucose-induction of HXT gene expression. Major conclusions The yeast cells possess a family of glucose transporters (HXTs), with different kinetic properties. They employ three major glucose signaling pathways— Rgt2/Snf3, AMPK, and cAMP-PKA—to express only those transporters best suited for the amounts of glucose available. We discuss the current understanding of how these pathways are integrated into a regulatory network to ensure efficient uptake and utilization of glucose. General significance Elucidating the role of multiple glucose signals and pathways involved in glucose uptake and metabolism in yeast may reveal the molecular basis of glucose homeostasis in humans, especially under pathological conditions, such as hyperglycemia in diabetics and the elevated rate of glycolysis observed in many solid tumors. PMID:23911748

  3. The effect of cross-link distributions in axially-ordered, cross-linked networks

    NASA Astrophysics Data System (ADS)

    Bennett, C. Brad; Kruczek, James; Rabson, D. A.; Matthews, W. Garrett; Pandit, Sagar A.

    2013-07-01

    Cross-linking between the constituent chains of biopolymers has a marked effect on their materials’ properties. In certain of these materials, such as fibrillar collagen, increases in cross-linking lead to an increase in the melting temperature. Fibrillar collagen is an axially-ordered network of cross-linked polymer chains exhibiting a broadened denaturation transition, which has been explained in terms of the successive denaturation with temperature of multiple species. We model axially-ordered, cross-linked materials as stiff chains with distinct arrangements of cross-link-forming sites. Simulations suggest that systems composed of chains with identical arrangements of cross-link-forming sites exhibit critical behavior. In contrast, systems composed of non-identical chains undergo a crossover. This model suggests that the arrangement of cross-link-forming sites may contribute to the broadening of the denaturation transition in fibrillar collagen.

  4. Gene Network Analysis of Glucose Linked Signaling Pathways and Their Role in Human Hepatocellular Carcinoma Cell Growth and Survival in HuH7 and HepG2 Cell Lines

    PubMed Central

    Berger, Emmanuelle; Vega, Nathalie; Weiss-Gayet, Michèle; Géloën, Alain

    2015-01-01

    Cancer progression may be affected by metabolism. In this study, we aimed to analyze the effect of glucose on the proliferation and/or survival of human hepatocellular carcinoma (HCC) cells. Human gene datasets regulated by glucose were compared to gene datasets either dysregulated in HCC or regulated by other signaling pathways. Significant numbers of common genes suggested putative involvement in transcriptional regulations by glucose. Real-time proliferation assays using high (4.5 g/L) versus low (1 g/L) glucose on two human HCC cell lines and specific inhibitors of selected pathways were used for experimental validations. High glucose promoted HuH7 cell proliferation but not that of HepG2 cell line. Gene network analyses suggest that gene transcription by glucose could be mediated at 92% through ChREBP in HepG2 cells, compared to 40% in either other human cells or rodent healthy liver, with alteration of LKB1 (serine/threonine kinase 11) and NOX (NADPH oxidases) signaling pathways and loss of transcriptional regulation of PPARGC1A (peroxisome-proliferator activated receptors gamma coactivator 1) target genes by high glucose. Both PPARA and PPARGC1A regulate transcription of genes commonly regulated by glycolysis, by the antidiabetic agent metformin and by NOX, suggesting their major interplay in the control of HCC progression. PMID:26380295

  5. ATR promotes cilia signalling: links to developmental impacts

    PubMed Central

    Stiff, Tom; Casar Tena, Teresa; O'Driscoll, Mark; Jeggo, Penny A.; Philipp, Melanie

    2016-01-01

    Mutations in ATR (ataxia telangiectasia and RAD3-related) cause Seckel syndrome (ATR-SS), a microcephalic primordial dwarfism disorder. Hitherto, the clinical manifestation of ATR deficiency has been attributed to its canonical role in DNA damage response signalling following replication fork stalling/collapse. Here, we show that ATR regulates cilia-dependent signalling in a manner that can be uncoupled from its function during replication. ATR-depleted or patient-derived ATR-SS cells form cilia of slightly reduced length but are dramatically impaired in cilia-dependent signalling functions, including growth factor and Sonic hedgehog signalling. To better understand the developmental impact of ATR loss of function, we also used zebrafish as a model. Zebrafish embryos depleted of Atr resembled ATR-SS morphology, showed a modest but statistically significant reduction in cilia length and other morphological features indicative of cilia dysfunction. Additionally, they displayed defects in left-right asymmetry including ambiguous expression of southpaw, incorrectly looped hearts and randomized localization of internal organs including the pancreas, features typically conferred by cilia dysfunction. Our findings reveal a novel role for ATR in cilia signalling distinct from its canonical function during replication and strengthen emerging links between cilia function and development. PMID:26908596

  6. Cross-linked actin networks (CLANs) in glaucoma.

    PubMed

    Bermudez, Jaclyn Y; Montecchi-Palmer, Michela; Mao, Weiming; Clark, Abbot F

    2017-06-01

    One of the major causes of decreased vision, irreversible vision loss and blindness worldwide is glaucoma. Increased intraocular pressure (IOP) is a major risk factor associated with glaucoma and its molecular mechanisms are not fully understood. The trabecular meshwork (TM) is the primary site of injury in glaucoma, and its dysfunction results in elevated IOP. The glaucomatous TM has increased extracellular matrix deposition as well as cytoskeletal rearrangements referred to as cross-linked actin networks (CLANs) that consist of dome like structures consisting of hubs and spokes. CLANs are thought to play a role in increased aqueous humor outflow resistance and increased IOP by creating stiffer TM cells and tissue. CLANs are inducible by glucocorticoids (GCs) and TGFβ2 in confluent TM cells and TM tissues. The signaling pathways of these induction agents give insight into the possible mechanisms of CLAN formation, but to date, the mechanism of CLANs regulation by these pathways has yet to be determined. Understanding the role CLANs play in IOP elevation and their mechanisms of induction and regulation may lead to novel treatment options to help prevent or intervene in glaucomatous damage to the trabecular meshwork. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Fixed-time synchronization of multi-links complex network

    NASA Astrophysics Data System (ADS)

    Zhao, Hui; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zheng, Mingwen

    2017-01-01

    In the paper, the fixed-time and finite-time synchronizations of multi-links complex network are investigated. Compared with finite-time synchronization, the settling time of fixed-time synchronization is independent of initial conditions. For uncertain multi-links complex networks, this paper further analyzes synchronization mechanism and unknown parameters based on the drive-response concept and finite-time stability theory. Novel synchronization control criteria and the result of parameters identification are, respectively, obtained in a finite time by utilizing Lyapunov function and linear matrix inequality (LMI). Besides, we give other two versions of finite-time synchronization and parameters identification for uncertain multi-links complex network with impulsive control input. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results.

  8. The organization of strong links in complex networks

    NASA Astrophysics Data System (ADS)

    Pajevic, Sinisa; Plenz, Dietmar

    2012-05-01

    Many complex systems reveal a small-world topology, which allows simultaneously local and global efficiency in the interaction between system constituents. Here, we report the results of a comprehensive study that investigates the relation between the clustering properties in such small-world systems and the strength of interactions between its constituents, quantified by the link weight. For brain, gene, social and language networks, we find a local integrative weight organization in which strong links preferentially occur between nodes with overlapping neighbourhoods; we relate this to global robustness of the clustering to removal of the weakest links. Furthermore, we identify local learning rules that establish integrative networks and improve network traffic in response to past traffic failures. Our findings identify a general organization for complex systems that strikes a balance between efficient local and global communication in their strong interactions, while allowing for robust, exploratory development of weak interactions.

  9. Deciphering signaling networks in osteosarcoma pathobiology

    PubMed Central

    Adamopoulos, Christos; Gargalionis, Antonios N; Basdra, Efthimia K

    2016-01-01

    Osteosarcoma is the most frequent type of primary bone tumors among children and adolescents. During the past years, little progress has been made regarding prognosis of osteosarcoma patients, especially for those with metastatic disease. Genomic instability and gene alterations are common, but current data do not reveal a consistent and repeatable pattern of osteosarcoma development, thus paralleling the tumor's high heterogeneity. Critical signal transduction pathways have been implicated in osteosarcoma pathobiology and are being evaluated as therapeutic targets, including receptor activator for nuclear factor-κB (RANK), Wnt, Notch, phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin, and mechanotransduction pathways. Herein, we recapitulate and discuss recent advances in the context of molecular mechanisms and signaling networks that contribute to osteosarcoma progression and metastasis, towards patient-tailored and novel-targeted treatments. PMID:27190271

  10. Deciphering signaling networks in osteosarcoma pathobiology.

    PubMed

    Adamopoulos, Christos; Gargalionis, Antonios N; Basdra, Efthimia K; Papavassiliou, Athanasios G

    2016-06-01

    Osteosarcoma is the most frequent type of primary bone tumors among children and adolescents. During the past years, little progress has been made regarding prognosis of osteosarcoma patients, especially for those with metastatic disease. Genomic instability and gene alterations are common, but current data do not reveal a consistent and repeatable pattern of osteosarcoma development, thus paralleling the tumor's high heterogeneity. Critical signal transduction pathways have been implicated in osteosarcoma pathobiology and are being evaluated as therapeutic targets, including receptor activator for nuclear factor-κB (RANK), Wnt, Notch, phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin, and mechanotransduction pathways. Herein, we recapitulate and discuss recent advances in the context of molecular mechanisms and signaling networks that contribute to osteosarcoma progression and metastasis, towards patient-tailored and novel-targeted treatments. © 2016 by the Society for Experimental Biology and Medicine.

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

    NASA Astrophysics Data System (ADS)

    Squartini, Tiziano; van Lelyveld, Iman; Garlaschelli, Diego

    2013-11-01

    The financial crisis clearly illustrated the importance of characterizing the level of `systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

  12. Prediction of Links and Weights in Networks by Reliable Routes

    PubMed Central

    Zhao, Jing; Miao, Lili; Yang, Jian; Fang, Haiyang; Zhang, Qian-Ming; Nie, Min; Holme, Petter; Zhou, Tao

    2015-01-01

    Link prediction aims to uncover missing links or predict the emergence of future relationships from the current network structure. Plenty of algorithms have been developed for link prediction in unweighted networks, but only a few have been extended to weighted networks. In this paper, we present what we call a “reliable-route method” to extend unweighted local similarity indices to weighted ones. Using these indices, we can predict both the existence of links and their weights. Experiments on various real-world networks suggest that our reliable-route weighted resource-allocation index performs noticeably better than others with respect to weight prediction. For existence prediction it is either the highest or very close to the highest. Further analysis shows a strong positive correlation between the clustering coefficient and prediction accuracy. Finally, we apply our method to the prediction of missing protein-protein interactions and their confidence scores from known PPI networks. Once again, our reliable-route method shows the highest accuracy. PMID:26198206

  13. Porous Cross-Linked Polyimide-Urea Networks

    NASA Technical Reports Server (NTRS)

    Meador, Mary Ann B. (Inventor); Nguyen, Baochau N. (Inventor)

    2015-01-01

    Porous cross-linked polyimide-urea networks are provided. The networks comprise a subunit comprising two anhydride end-capped polyamic acid oligomers in direct connection via a urea linkage. The oligomers (a) each comprise a repeating unit of a dianhydride and a diamine and a terminal anhydride group and (b) are formulated with 2 to 15 of the repeating units. The subunit was formed by reaction of the diamine and a diisocyanate to form a diamine-urea linkage-diamine group, followed by reaction of the diamine-urea linkage-diamine group with the dianhydride and the diamine to form the subunit. The subunit has been cross-linked via a cross-linking agent, comprising three or more amine groups, at a balanced stoichiometry of the amine groups to the terminal anhydride groups. The subunit has been chemically imidized to yield the porous cross-linked polyimide-urea network. Also provided are wet gels, aerogels, and thin films comprising the networks, and methods of making the networks.

  14. Text mining for metabolic pathways, signaling cascades, and protein networks.

    PubMed

    Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

    2005-05-10

    The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

  15. SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

    PubMed Central

    Ma'ayan, Avi; Jenkins, Sherry L; Webb, Ryan L; Berger, Seth I; Purushothaman, Sudarshan P; Abul-Husn, Noura S; Posner, Jeremy M; Flores, Tony; Iyengar, Ravi

    2009-01-01

    Background Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge. Results SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names. Conclusion SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: . The source code can be accessed from: PMID:19154595

  16. Cascaded optical fiber link using the internet network for remote clocks comparison

    NASA Astrophysics Data System (ADS)

    Chiodo, Nicola; Quintin, Nicolas; Stefani, Fabio; Wiotte, Fabrice; Camisard, Emilie; Chardonnet, Christian; Santarelli, Giorgio; Amy-Klein, Anne; Pottie, Paul-Eric; Lopez, Olivier

    2015-12-01

    We report a cascaded optical link of 1100 km for ultra-stable frequency distribution over an Internet fiber network. The link is composed of four spans for which the propagation noise is actively compensated. The robustness and the performance of the link are ensured by five fully automated optoelectronic stations, two of them at the link ends, and three deployed on the field and connecting the spans. This device coherently regenerates the optical signal with the heterodyne optical phase locking of a low-noise laser diode. Optical detection of the beat-note signals for the laser lock and the link noise compensation are obtained with stable and low-noise fibered optical interferometer. We show 3.5 days of continuous operation of the noise-compensated 4-span cascaded link leading to fractional frequency instability of 4x10-16 at 1-s measurement time and 1x10-19 at 2000 s. This cascaded link was extended to 1480-km with the same performance. This work is a significant step towards a sustainable wide area ultra-stable optical frequency distribution and comparison network at a very high level of performance.

  17. Multi-layer photonics modeling framework for the design, analysis, and optimization of devices, links, and networks

    NASA Astrophysics Data System (ADS)

    Richter, André; Louchet, Hadrien; Arellano, Cristina; Farina, Jim; Koltchanov, Igor

    2011-01-01

    Requirements on photonics modeling vary significantly when aiming to design, analyze and optimize a single device, a complete transmission link or a complex network. Depending on the task at hand, different levels of detail for emulating the underlying physical characteristics and signal interactions are necessary. We present a multi-layer photonics modeling framework that addresses the different design challenges of devices, links and networks. Our discussed methodology is based on flexible optical signal representations, equipment models ranging from very detailed to high-level parametric, sophisticated numerical algorithms and means for automated parameter and technology variation and optimization. We discuss applications such as the detailed modeling on photonics integrated circuit level, the characterization of a high-speed transmission link utilizing multilevel modulation and coherent detection, the parametric analysis of transmission links and network dynamics, and the cost-optimized placement of equipment in moderately complex networks.

  18. Physiological stress links parasites to carotenoid-based colour signals.

    PubMed

    Mougeot, F; Martínez-Padilla, J; Bortolotti, G R; Webster, L M I; Piertney, S B

    2010-03-01

    Vertebrates commonly use carotenoid-based traits as social signals. These can reliably advertise current nutritional status and health because carotenoids must be acquired through the diet and their allocation to ornaments is traded-off against other self-maintenance needs. We propose that the coloration more generally reveals an individual's ability to cope with stressful conditions. We tested this idea by manipulating the nematode parasite infection in free-living red grouse (Lagopus lagopus scoticus) and examining the effects on body mass, carotenoid-based coloration of a main social signal and the amount of corticosterone deposited in feathers grown during the experiment. We show that parasites increase stress and reduce carotenoid-based coloration, and that the impact of parasites on coloration was associated with changes in corticosterone, more than changes in body mass. Carotenoid-based coloration appears linked to physiological stress and could therefore reveal an individual's ability to cope with stressors.

  19. Cascaded optical link on a telecommunication fiber network for ultra-stable frequency dissemination

    NASA Astrophysics Data System (ADS)

    Lopez, Olivier; Chiodo, Nicola; Stefani, Fabio; Wiotte, Fabrice; Quintin, Nicolas; Bercy, Anthony; Chardonnet, Christian; Santarelli, Giorgio; Pottie, Paul-Eric; Amy-Klein, Anne

    2015-03-01

    The transfer of ultra-stable frequencies between distant laboratories is required by many applications in time and frequency metrology, fundamental physics, particle accelerators and astrophysics. Optical fiber links have been intensively studied for a decade and brought the potential to transfer frequency with a very high accuracy and stability thanks to an active compensation of the propagation noise. We are currently developing an optical metrological network using the fibers of the French National Research and Education Network. Using the so-called dark-channel approach, the ultrastable signal is copropagating with data traffic using wavelength division multiplexing. Due to significant reflections and losses along the fibers, which cannot be compensated with amplifiers, we have developed some repeater stations for the metrological signal. These remotely-operated stations amplify the ultrastable signal and compensate the propagation noise. The link is thus composed of a few cascaded spans. It gives the possibility to increase the noise correction bandwidth, which is proportional to the inverse of the fiber length for each span. These stations are a key element for the deployment of a reliable and large scale metrological network. We report here on the implementation of a two-spans cascaded link of 740 km reaching a relative stability of a few 10-20 after 103 s averaging time. Extension to longer links and alternative transfer methods will be discussed.

  20. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    cortex by the application of lateral interactions during the learning phase. The organization of the mature network is compared to that found in the macaque monkey by several analytical tests. The capacity of the network to process images is investigated. By a method of reconstructing the input images in terms of V1 activities, the simulations show that images can be faithfully represented in V1 by the proposed network. The signal-to-noise ratio of the image is improved by the representation, and compression ratios of well over two-hundred are possible. Lateral interactions between V1 neurons sharpen their orientational tuning. We further study the dynamics of the processing, showing that the rate of decrease of the error of the reconstruction is maximized for the receptive fields used. Lastly, we employ a Fokker-Planck equation for a more detailed prediction of the error value vs. time. The Fokker-Planck equation for an underdamped system with a driving force is derived, yielding an energy-dependent diffusion coefficient which is the integral of the spectral densities of the force and the velocity of the system. The theory is applied to correlated noise activation and resonant activation. Simulation results for the error of the network vs time are compared to the solution of the Fokker-Planck equation.

  1. Collective Calcium Signaling of Defective Multicellular Networks

    NASA Astrophysics Data System (ADS)

    Potter, Garrett; Sun, Bo

    2015-03-01

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

  2. MSAT signalling and network management architectures

    NASA Technical Reports Server (NTRS)

    Garland, Peter; Keelty, J. Malcolm

    1989-01-01

    Spar Aerospace has been active in the design and definition of Mobile Satellite Systems since the mid 1970's. In work sponsored by the Canadian Department of Communications, various payload configurations have evolved. In addressing the payload configuration, the requirements of the mobile user, the service provider and the satellite operator have always been the most important consideration. The current Spar 11 beam satellite design is reviewed, and its capabilities to provide flexibility and potential for network growth within the WARC87 allocations are explored. To enable the full capabilities of the payload to be realized, a large amount of ground based Switching and Network Management infrastructure will be required, when space segment becomes available. Early indications were that a single custom designed Demand Assignment Multiple Access (DAMA) switch should be implemented to provide efficient use of the space segment. As MSAT has evolved into a multiple service concept, supporting many service providers, this architecture should be reviewed. Some possible signalling and Network Management solutions are explored.

  3. Extended resource allocation index for link prediction of complex network

    NASA Astrophysics Data System (ADS)

    Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

    2017-08-01

    Recently, a number of similarity-based methods have been proposed to predict the missing links in complex network. Among these indices, the resource allocation index performs very well with lower time complexity. However, it ignores potential resources transferred by local paths between two endpoints. Motivated by the resource exchange taking places between endpoints, an extended resource allocation index is proposed. Empirical study on twelve real networks and three synthetic dynamic networks has shown that the index we proposed can achieve a good performance, compared with eight mainstream baselines.

  4. Entropy-based link prediction in weighted networks

    NASA Astrophysics Data System (ADS)

    Xu, Zhongqi; Pu, Cunlai; Ramiz Sharafat, Rajput; Li, Lunbo; Yang, Jian

    2017-01-01

    Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \\cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.

  5. Linking chordate gene networks to cellular behavior in ascidians.

    PubMed

    Davidson, Brad; Christiaen, Lionel

    2006-01-27

    Embryos of simple chordates called ascidians (sea squirts) have few cells, develop rapidly, and are transparent, enabling the in vivo fluorescent imaging of labeled cell lineages. Ascidians are also simple genetically, with limited redundancy and compact regulatory regions. This cellular and genetic simplicity is now being exploited to link comprehensive gene networks to the cellular events underlying morphogenesis.

  6. Adaptive link-layer intelligence for enhanced ad hoc networking

    NASA Astrophysics Data System (ADS)

    MacLennan, James; Walburg, Bill; Nevins, Larry; Hartup, David

    2005-05-01

    Networked radio systems that utilize self-forming, fault tolerant techniques offer needed communication functions for Homeland Security and Law Enforcement agencies. The use of an ad hoc mesh network architecture solves some of the problems inherent to the wireless physical layer such as interferers, multi-path fading, shadowing, and loss of line-of-site. These effects severely limit the performance of current 802.11 wireless network implementations. This paper describes the use of Adaptive Link-layer Intelligence for Enhanced ad hoc Networking. This technology enhances recognition and characterization of sources of wireless channel perturbations and predicts their effects on wireless link quality. Identifying and predicting channel problems at the link level improves dynamic route discovery, circumvents channel disruptions before they cause a link failure, and increases communications reliability and data rate. First Responders, Homeland Security, and Law Enforcement agencies operating in locations lacking infrastructure, such as Urban Search and Rescue (USAR) operations, can benefit from increased communications reliability in highly impaired channels that are typical in disaster response scenarios.

  7. Home-School Links: Networking the Learning Community.

    ERIC Educational Resources Information Center

    1996

    The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

  8. Link prediction based on local weighted paths for complex networks

    NASA Astrophysics Data System (ADS)

    Yao, Yabing; Zhang, Ruisheng; Yang, Fan; Yuan, Yongna; Hu, Rongjing; Zhao, Zhili

    As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

  9. Quantitative analysis of intracellular communication and signaling errors in signaling networks

    PubMed Central

    2014-01-01

    Background Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs. Results In this study we propose to model intracellular signaling network as communication channels. We define the fundamental concepts of transmission error and signaling capacity for intracellular signaling networks, and devise proper methods for computing these parameters. The developed systematic methodology quantitatively shows how the signals that ligands provide upon binding can be lost in a pathological signaling network, due to the presence of some dysfunctional molecules. We show the lost signals result in message transmission error, i.e., incorrect regulation of target proteins at the network output. Furthermore, we show how dysfunctional molecules affect the signaling capacity of signaling networks and how the contributions of signaling molecules to the signaling capacity and signaling errors can be computed. The proposed approach can quantify the role of dysfunctional signaling molecules in the development of the pathology. We present experimental data on caspese3 and T cell signaling networks to demonstrate the biological relevance of the developed method and its predictions. Conclusions This study demonstrates how signal transmission and distortion in pathological signaling networks can be modeled and studied using the proposed methodology. The new methodology determines how much the functionality of molecules in a network can affect the signal transmission and regulation of the end molecules such as transcription factors. This can lead to the identification of novel critical molecules in signal transduction networks. Dysfunction of these

  10. Locating inefficient links in a large-scale transportation network

    NASA Astrophysics Data System (ADS)

    Sun, Li; Liu, Like; Xu, Zhongzhi; Jie, Yang; Wei, Dong; Wang, Pu

    2015-02-01

    Based on data from geographical information system (GIS) and daily commuting origin destination (OD) matrices, we estimated the distribution of traffic flow in the San Francisco road network and studied Braess's paradox in a large-scale transportation network with realistic travel demand. We measured the variation of total travel time Δ T when a road segment is closed, and found that | Δ T | follows a power-law distribution if Δ T < 0 or Δ T > 0. This implies that most roads have a negligible effect on the efficiency of the road network, while the failure of a few crucial links would result in severe travel delays, and closure of a few inefficient links would counter-intuitively reduce travel costs considerably. Generating three theoretical networks, we discovered that the heterogeneously distributed travel demand may be the origin of the observed power-law distributions of | Δ T | . Finally, a genetic algorithm was used to pinpoint inefficient link clusters in the road network. We found that closing specific road clusters would further improve the transportation efficiency.

  11. Architecture and design of optical path networks utilizing waveband virtual links

    NASA Astrophysics Data System (ADS)

    Ito, Yusaku; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi

    2016-02-01

    We propose a novel optical network architecture that uses waveband virtual links, each of which can carry several optical paths, to directly bridge distant node pairs. Future photonic networks should not only transparently cover extended areas but also expand fiber capacity. However, the traversal of many ROADM nodes impairs the optical signal due to spectrum narrowing. To suppress the degradation, the bandwidth of guard bands needs to be increased, which degrades fiber frequency utilization. Waveband granular switching allows us to apply broader pass-band filtering at ROADMs and to insert sufficient guard bands between wavebands with minimum frequency utilization offset. The scheme resolves the severe spectrum narrowing effect. Moreover, the guard band between optical channels in a waveband can be minimized, which increases the number of paths that can be accommodated per fiber. In the network, wavelength path granular routing is done without utilizing waveband virtual links, and it still suffers from spectrum narrowing. A novel network design algorithm that can bound the spectrum narrowing effect by limiting the number of hops (traversed nodes that need wavelength path level routing) is proposed in this paper. This algorithm dynamically changes the waveband virtual link configuration according to the traffic distribution variation, where optical paths that need many node hops are effectively carried by virtual links. Numerical experiments demonstrate that the number of necessary fibers is reduced by 23% compared with conventional optical path networks.

  12. TCP flow control using link layer information in mobile networks

    NASA Astrophysics Data System (ADS)

    Koga, Hiroyuki; Kawahara, Kenji; Oie, Yuji

    2002-07-01

    Mobile Networks have been expanding and IMT-2000 further increases their available bandwidth over wireless links. However, TCP, which is a reliable end-to-end transport protocol, is tuned to perform well in wired networks where bit error rates are very low and packet loss occurs mostly because of congestion. Although a TCP sender can execute flow control to utilize as much available bandwidth as possible in wired networks, it cannot work well in wireless networks characterized by high bit error rates. In the next generation mobile systems, sophisticated error recovery technologies of FEC and ARQ are indeed employed over wireless links, i.e., over Layer 2, to avoid performance degradation of upper layers. However, multiple retransmissions by Layer 2 ARQ can adversely increase transmission delay of TCP segments, which will further make TCP unnecessarily increase RTO (Retransmission TimeOut). Furthermore, a link bandwidth assigned to TCP flows can change in response to changing air conditions to use wireless links efficiently. TCP thus has to adapt its transmission rate according to the changing available bandwidth. The major goal of this study is to develop a receiver-based effective TCP flow control without any modification on TCP senders, which are probably connected with wired networks. For this end, we propose a TCP flow control employing some Layer 2 information on a wireless link at the mobile station. Our performance evaluation of the proposed TCP shows that the receiver-based TCP flow control can moderate the performance degradation very well even if FER on Layer 2 is high.

  13. Rainfall retrieval in urban areas using commercial microwave links from mobile networks: A modelling feasibility study

    NASA Astrophysics Data System (ADS)

    Zohidov, Bahtiyor; Andrieu, Hervé; Servières, Myriam; Normand, Nicolas

    2014-05-01

    Rainfall is usually measured by networks of rain gauges and weather radars. Many cities worldwide are not supplied with these devices; however, they are generally equipped with mobile telecommunication networks. Mobile networks use atmospheric Hyper-Frequency (HF) links whose transmitted signal power is attenuated by rainfall. Measuring that signal attenuation along each link could allow the measurement of path-averaged rainfall [Leijnse et al 2007, Overeem et al 2013, Messer et al 2006, Guili et al 1991, Zinevich et al 2008, Cuccoli et al 2011]. As HF links are concentrated in cities, these networks could constitute a self-sufficient approach to monitoring rainfall in urban areas. We adopt a simulation approach in order to study the feasibility of mapping rainfall fields at the city scale by means of existing HF links. Our domain of study is the central part of the city of Nantes, France, where the density of cellular networks is greatest. As a basis, we use a data set consisting of hundreds of weather radar images recorded by the Météo-France C band weather radar at high spatial (250m x 250m) and temporal (5 minute) resolutions located about 10 km north of the center of Nantes. We convert these images into rainfall maps using the Z-R relation and consider them as reference rainfall fields. The simulation is performed as follows. First, we simulate the measurement of total attenuation along each HF link using a rain-attenuation model based on Mie theory and a known drop size distribution in a continental temperate climate. This procedure is applied for 256 real radio links operating at different frequencies (18, 23, 38 GHz) with lengths ranging from 0.4 to 16 km. This helps us to substitute the attenuation data for the signal power received from microwave links. Error sources affecting measurement accuracy are introduced as a zero-mean Gaussian distributed random variable with variance of 10% of total attenuation. The retrieval of the rainfield is performed by a

  14. Studies on the signal amplification in weighted and unweighted small-world networks

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Wang, Jianjun; Ma, Fuqiu

    2017-02-01

    Weighted and unweighted networks composed of coupled bistable oscillators with small-world topology are investigated under the co-presence of a weak signal and multiplicative Gaussian white noise. As the noise intensity is adjusted to one or two optimal values, the temporal periodicity of the output of the system reaches the maximum, indicating the occurrence of stochastic resonance (SR) or stochastic bi-resonance (SBR). The resonance behavior is strongly-dependent on the coupling strength in both networks. At a weak coupling, SR more likely takes place; whereas at a strong coupling, SBR is prone to occur. Compared with unweighted networks, the span of coupling strength for SBR is narrower in weighted networks. In addition, the weak signal cannot be amplified so effectively in the weighted networks as in the unweighted networks, attributing to the weakening effect of the link weight on the coupling between oscillators and the heterogeneity of the whole network connectivity caused by the weight distribution.

  15. Linking Individual and Collective Behavior in Adaptive Social Networks

    NASA Astrophysics Data System (ADS)

    Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.

    2016-03-01

    Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

  16. Neural network-based sensor signal accelerator.

    SciTech Connect

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  17. Logic integer programming models for signaling networks.

    PubMed

    Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

    2009-05-01

    We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

  18. Similarity indices based on link weight assignment for link prediction of unweighted complex networks

    NASA Astrophysics Data System (ADS)

    Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

    2017-01-01

    Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

  19. Towards operational rainfall monitoring with microwave links from cellular telecommunication networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2017-04-01

    The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. Microwave links from cellular communication networks have been proposed as a promising new rainfall measurement technique since one decade. They are particularly interesting for those countries where few surface rainfall observations are available. Yet too date no operational (real-time) link-based rainfall products are available. To advance the process towards operational application and upscaling of this technique, long time series should be analyzed for different networks and climates. Here the potential for long-term large-scale operational rainfall monitoring is demonstrated by utilizing a 2.5-year data set from a cellular communication network. The data set consists of roughly 2,000 links covering the land surface of the Netherlands (35,500 square kilometers). The quality of link rainfall maps is thoroughly quantified by an extensive validation against independent gauge-adjusted radar rainfall maps for, among others, different seasons and extremes. One of the goals is to quantify whether the cellular telecommunication network can yield rainfall maps of comparable quality as those based on automatic rain gauge data (with a density of 1 gauge per 1000 square kilometers). Developing countries will usually have rain gauge networks with a lower density (and little or no weather radars). This helps to assess the possibly added value of link-based rainfall estimates with respect to those from existing rain gauge networks. Moreover, this shows the possibly added value of link rainfall estimates for adjustment of radar rainfall images. The results further confirm

  20. Capacity and performance analysis of signaling networks in multivendor environments

    NASA Astrophysics Data System (ADS)

    Bafutto, Marcos; Kuehn, Paul J.; Willmann, Gert

    1994-04-01

    The load of common channel signaling networks is being increased through the introduction of new services such as supplementary services or mobile communication services. This may lead to a performance degradation of the signaling network, which affects both the quality of the new services and of the services already offered by the network. In this paper, a generic modeling methodology for the signaling load and the signaling network performance as a result of the various communication services is extended in order to include certain implementation-dependent particularities. The models are obtained by considering the protocol functions of Signaling System No. 7 as specified by the CCITT, as well as the information flows through these functions. With this approach, virtual processor models are derived which can be mapped onto particular implementations. This allows the analysis of signaling networks in a multivendor environment. Using these principles, a signaling network planning tool concept has been developed which provides the distinct loading of hardware and software signaling network resources, and on which hierarchical performance analysis and planning procedures are based. This allows to support the planning of signaling networks according to given service, load, and grade-of-service figures. A simple case study outlines the application of the tool concept to a network supporting Freephone, Credit Card, and ISDN voice services.

  1. Asynchronous transfer mode link performance over ground networks

    NASA Technical Reports Server (NTRS)

    Chow, E. T.; Markley, R. W.

    1993-01-01

    The results of an experiment to determine the feasibility of using asynchronous transfer mode (ATM) technology to support advanced spacecraft missions that require high-rate ground communications and, in particular, full-motion video are reported. Potential nodes in such a ground network include Deep Space Network (DSN) antenna stations, the Jet Propulsion Laboratory, and a set of national and international end users. The experiment simulated a lunar microrover, lunar lander, the DSN ground communications system, and distributed science users. The users were equipped with video-capable workstations. A key feature was an optical fiber link between two high-performance workstations equipped with ATM interfaces. Video was also transmitted through JPL's institutional network to a user 8 km from the experiment. Variations in video depending on the networks and computers were observed, the results are reported.

  2. Predicting top-L missing links with node and link clustering information in large-scale networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhihao; Lin, Youfang; Wan, Huaiyu; Jamil, Waleed

    2016-08-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems, such as social, biological, and technological systems. The prediction of missing links in incomplete complex networks aims to estimate the likelihood of the existence of a link between a pair of nodes. Various topological features of networks have been applied to develop link prediction methods. However, the exploration of features of links is still limited. In this paper, we demonstrate the power of node and link clustering information in predicting top -L missing links. In the existing literature, link prediction algorithms have only been tested on small-scale and middle-scale networks. The network scale factor has not attracted the same level of attention. In our experiments, we test the proposed method on three groups of networks. For small-scale networks, since the structures are not very complex, advanced methods cannot perform significantly better than classical methods. For middle-scale networks, the proposed index, combining both node and link clustering information, starts to demonstrate its advantages. In many networks, combining both node and link clustering information can improve the link prediction accuracy a great deal. Large-scale networks with more than 100 000 links have rarely been tested previously. Our experiments on three large-scale networks show that local clustering information based methods outperform other methods, and link clustering information can further improve the accuracy of node clustering information based methods, in particular for networks with a broad distribution of the link clustering coefficient.

  3. Spatial Modeling of Cell Signaling Networks

    PubMed Central

    Cowan, Ann E.; Moraru, Ion I.; Schaff, James C.; Slepchenko, Boris M.; Loew, Leslie M.

    2012-01-01

    The shape of a cell, the sizes of subcellular compartments and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic vs, stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling. PMID:22482950

  4. Speciation network in Laurasiatheria: retrophylogenomic signals.

    PubMed

    Doronina, Liliya; Churakov, Gennady; Kuritzin, Andrej; Shi, Jingjing; Baertsch, Robert; Clawson, Hiram; Schmitz, Juergen

    2017-03-15

    Rapid species radiation due to adaptive changes or occupation of new ecospaces challenges our understanding of ancestral speciation and the relationships of modern species. At the molecular level, rapid radiation with successive speciations over short time periods - too short to fix polymorphic alleles - is described as incomplete lineage sorting. Incomplete lineage sorting leads to random fixation of genetic markers and hence random signals of relationships in phylogenetic reconstructions. The situation is further complicated when you consider that the genome is a mosaic of ancestral and modern incompletely sorted sequence blocks that leads to reconstructed affiliations to one or the other relatives depending on the fixation of their shared ancestral polymorphic alleles. The laurasiatherian relationships among Chiroptera, Perissodactyla, Cetartiodactyla, and Carnivora present a prime example for such enigmatic affiliations. We performed whole-genome screenings for phylogenetically diagnostic retrotransposon insertions involving the representatives bat (Chiroptera), horse (Perissodactyla), cow (Cetartiodactyla), and dog (Carnivora), and extracted among 162 thousand preselected cases 102 virtually noise-free, phylogenetically informative retroelements to draw a complete picture of the highly complex evolutionary relations within Laurasiatheria. All possible evolutionary scenarios received considerable retrotransposon support, leaving us with a network of affiliations. However, the Cetartiodactyla-Carnivora relationship as well as the basal position of Chiroptera and an ancestral laurasiatherian hybridization process did exhibit some very clear, distinct signals. The significant accordance of retrotransposon presence/absence patterns and flanking nucleotide changes suggest an important influence of mosaic genome structures in the reconstruction of species histories.

  5. Mutual information model for link prediction in heterogeneous complex networks

    PubMed Central

    Shakibian, Hadi; Moghadam Charkari, Nasrollah

    2017-01-01

    Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices. PMID:28344326

  6. Uncovering the essential links in online commercial networks

    PubMed Central

    Zeng, Wei; Fang, Meiling; Shao, Junming; Shang, Mingsheng

    2016-01-01

    Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most of the relevant information. With the backbone, a system can generate satisfactory recommenda- tions while saving much computing resource. In this paper, we propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, however, consuming only 20% links. The experimental results show that our method outperforms the alternative backbone extraction methods. Moreover, the structure of the information backbone is studied in detail. Finally, we highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system. PMID:27682464

  7. Uncovering the essential links in online commercial networks

    NASA Astrophysics Data System (ADS)

    Zeng, Wei; Fang, Meiling; Shao, Junming; Shang, Mingsheng

    2016-09-01

    Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most of the relevant information. With the backbone, a system can generate satisfactory recommenda- tions while saving much computing resource. In this paper, we propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, however, consuming only 20% links. The experimental results show that our method outperforms the alternative backbone extraction methods. Moreover, the structure of the information backbone is studied in detail. Finally, we highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system.

  8. Spatial signals link exit from mitosis to spindle position.

    PubMed

    Falk, Jill Elaine; Tsuchiya, Dai; Verdaasdonk, Jolien; Lacefield, Soni; Bloom, Kerry; Amon, Angelika

    2016-05-11

    In budding yeast, if the spindle becomes mispositioned, cells prevent exit from mitosis by inhibiting the mitotic exit network (MEN). The MEN is a signaling cascade that localizes to spindle pole bodies (SPBs) and activates the phosphatase Cdc14. There are two competing models that explain MEN regulation by spindle position. In the 'zone model', exit from mitosis occurs when a MEN-bearing SPB enters the bud. The 'cMT-bud neck model' posits that cytoplasmic microtubule (cMT)-bud neck interactions prevent MEN activity. Here we find that 1) eliminating cMT- bud neck interactions does not trigger exit from mitosis and 2) loss of these interactions does not precede Cdc14 activation. Furthermore, using binucleate cells, we show that exit from mitosis occurs when one SPB enters the bud despite the presence of a mispositioned spindle. We conclude that exit from mitosis is triggered by a correctly positioned spindle rather than inhibited by improper spindle position.

  9. An investigation of spatial signal transduction in cellular networks.

    PubMed

    Alam-Nazki, Aiman; Krishnan, J

    2012-07-05

    Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

  10. An investigation of spatial signal transduction in cellular networks

    PubMed Central

    2012-01-01

    Background Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. Results In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. Conclusions Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual

  11. Statistical signal processing in sensor networks

    NASA Astrophysics Data System (ADS)

    Guerriero, Marco

    In this dissertation we focus on decentralized signal processing in Sensor Networks (SN). Four topics are studied: (i) Direction of Arrival (DOA) estimation using a Wireless Sensor network (WSN), (ii) multiple target tracking in large SN, (iii) decentralized target detection in SN and (iv) decentralized sequential detection in SN with communication constraints. The first topic of this thesis addresses the problem of estimating the DOA of an acoustic wavefront using a a WSN made of isotropic (hence individually useless) sensors. The WSN was designed according to the SENMA (SEnsor Network with Mobile Agents) architecture with a mobile agent (MA) that successively queries the sensors lying inside its field of view. We propose both fast/simple and optimal DOA-estimation schemes, and an optimization of the MAs observation management is also carried out, with the surprising finding that the MA ought to orient itself at an oblique angle to the expected DOA, rather than directly toward it. We also consider the extension to multiple sources; intriguingly, per-source DOA accuracy is higher when there is more than one source. In all cases, performance is investigated by simulation and compared, when appropriate, with asymptotic bounds; these latter are usually met after a moderate number of MA dwells. In the second topic, we study the problem of tracking multiple targets in large SN. While these networks hold significant potential for surveillance, it is of interest to address fundamental limitations in large-scale implementations. We first introduce a simple analytical tracker performance model. Analysis of this model suggests that scan-based tracking performance improves with increasing numbers of sensors, but only to a certain point beyond which degradation is observed. Correspondingly, we address model-based optimization of the local sensor detection threshold and the number of sensors. Next, we propose a two-stage tracking approach (fuse-before-track) as a possible

  12. Viscoelastic Nanomechanics of Ionically Cross-linked Polyelectrolyte Networks

    NASA Astrophysics Data System (ADS)

    Han, Biao; Lee, Daeyeon; Han, Lin

    2015-03-01

    Understanding the mechanics of ionic polyelectrolyte networks is critical for applications where nm-to-um mechanics is the key to success. This study aims to reveal the roles of ionic cross-links and fixed charges in the viscoelasticity of layer-by-layer poly(allylamine hydrochloride)/poly(acrylic acid) microfilms, PAH/PAA, a complex held by pH-sensitive amine-carboxyl links. AFM-nanoindentation and force relaxation (tip R =12.5um) was performed at ionic strength(IS) =0.01-1.0M, pH =5.5-2.0 (pKa of PAA =2.3). When pH changes from 5.5 to 2.0, the films swell for 4x from densely linked, net neutral state to loosely linked, positively charged one. A >100x reduction in indentation modulus was observed at all IS, suggesting the dominance of decrease in cross-link density. In most states, more than 90% force relaxation was observed, where cross-link breaking/reformation likely dominates viscoelasticity. However, at pH =2.5 and IS =0.01M, when electrical double layer repulsion is important (Debye length =3nm), relaxation was about 60%, highlighting the contribution of fixed charges. In summary, this study revealed unique viscoelastic behaviors of PAH/PAA due to the pH- and IS-dependent cross-link and charge densities.

  13. Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links.

    PubMed

    Shen, Hao; Zhu, Yanzheng; Zhang, Lixian; Park, Ju H

    2017-02-01

    This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.

  14. mTOR links oncogenic signaling to tumor cell metabolism.

    PubMed

    Yecies, Jessica L; Manning, Brendan D

    2011-03-01

    As a key regulator of cell growth and proliferation, the mammalian target of rapamycin (mTOR) complex 1 (mTORC1) has been the subject of intense investigation for its role in tumor development and progression. This research has revealed a signaling network of oncogenes and tumor suppressors lying upstream of mTORC1, and oncogenic perturbations to this network result in the aberrant activation of this kinase complex in the majority of human cancers. However, the molecular events downstream of mTORC1 contributing to tumor cell growth and proliferation are just coming to light. In addition to its better-known functions in promoting protein synthesis and suppressing autophagy, mTORC1 has emerged as a key regulator of cellular metabolism. Recent studies have found that mTORC1 activation is sufficient to stimulate an increase in glucose uptake, glycolysis, and de novo lipid biosynthesis, which are considered metabolic hallmarks of cancer, as well as the pentose phosphate pathway. Here, we focus on the molecular mechanisms of metabolic regulation by mTORC1 and the potential consequences for anabolic tumor growth and therapeutic strategies.

  15. Epidemics with temporary link deactivation in scale-free networks

    PubMed Central

    Shkarayev, Maxim S.; Tunc, Ilker; Shaw, Leah B.

    2014-01-01

    During an epidemic, people may adapt or alter their social contacts to avoid infection. Various adaptation mechanisms have been studied previously. Recently, a new adaptation mechanism was presented in [1], where susceptible nodes temporarily deactivate their links to infected neighbors and reactivate when their neighbors recover. Considering the same adaptation mechanism on a scale-free network, we find that the topology of the subnetwork consisting of active links is fundamentally different from the original network topology. We predict the scaling exponent of the active degree distribution and derive mean-field equations by using improved moment closure approximations based on the conditional distribution of active degree given the total degree. These mean field equations show better agreement with numerical simulation results than the standard mean field equations based on a homogeneity assumption. PMID:25419231

  16. Photoreceptor signaling networks in plant responses to shade.

    PubMed

    Casal, Jorge J

    2013-01-01

    The dynamic light environment of vegetation canopies is perceived by phytochromes, cryptochromes, phototropins, and UV RESISTANCE LOCUS 8 (UVR8). These receptors control avoidance responses to preclude exposure to limiting or excessive light and acclimation responses to cope with conditions that cannot be avoided. The low red/far-red ratios of shade light reduce phytochrome B activity, which allows PHYTOCHROME INTERACTING FACTORS (PIFs) to directly activate the transcription of auxin-synthesis genes, leading to shade-avoidance responses. Direct PIF interaction with DELLA proteins links gibberellin and brassinosteroid signaling to shade avoidance. Shade avoidance also requires CONSTITUTIVE PHOTOMORPHOGENESIS 1 (COP1), a target of cryptochromes, phytochromes, and UVR8. Multiple regulatory loops and the input of the circadian clock create a complex network able to respond even to subtle threats of competition with neighbors while still compensating for major environmental fluctuations such as the day-night cycles.

  17. CD-Based Indices for Link Prediction in Complex Network

    PubMed Central

    Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

    2016-01-01

    Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks. PMID:26752405

  18. CD-Based Indices for Link Prediction in Complex Network.

    PubMed

    Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

    2016-01-01

    Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

  19. Curing critical links in oscillator networks as power flow models

    NASA Astrophysics Data System (ADS)

    Rohden, Martin; Witthaut, Dirk; Timme, Marc; Meyer-Ortmanns, Hildegard

    2017-01-01

    Modern societies crucially depend on the robust supply with electric energy so that blackouts of power grids can have far reaching consequences. Typically, large scale blackouts take place after a cascade of failures: the failure of a single infrastructure component, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Improving the robustness of a network to prevent such secondary failures is thus key for assuring a reliable power supply. In this article we analyze the nonlocal rerouting of power flows after transmission line failures for a simplified AC power grid model and compare different strategies to improve network robustness. We identify critical links in the grid and compute alternative pathways to quantify the grid’s redundant capacity and to find bottlenecks along the pathways. Different strategies are developed and tested to increase transmission capacities to restore stability with respect to transmission line failures. We show that local and nonlocal strategies typically perform alike: one can equally well cure critical links by providing backup capacities locally or by extending the capacities of bottleneck links at remote locations.

  20. Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

    NASA Astrophysics Data System (ADS)

    Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

    2017-10-01

    So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

  1. Defining a Modular Signalling Network from the Fly Interactome

    PubMed Central

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

    2008-01-01

    Background Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Results Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Conclusion Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome. PMID:18489752

  2. Autoclavable highly cross-linked polyurethane networks in ophthalmology.

    PubMed

    Bruin, P; Meeuwsen, E A; van Andel, M V; Worst, J G; Pennings, A J

    1993-11-01

    Highly cross-linked aliphatic polyurethane networks have been prepared by the bulk step reaction of low molecular weight polyols and hexamethylenediisocyanate (HDI). These polyurethane networks are optically transparent, colourless and autoclavable amorphous glassy thermosets, which are suited for use in ophthalmic applications such as intraocular lenses and keratoprostheses. The properties of these glassy polyurethanes, obtained from the reaction of the low molecular weight polyols triisopropanolamine (TIPA) or tetrakis (2-hydroxypropyl)ethylenediamine (Quadrol) and HDI in stoichiometric proportions, have been investigated in more detail. The glassy Quadrol/HDI-based polyurethane exhibits a reduction in ultimate glass transition temperature from 85 to 48 degrees C by uptake of 1% of water, and good ultimate mechanical properties (tensile strength 80-85 MPa, elongation at break ca 15%, modulus ca 1.5 GPa). IR spectra of these hydrophobic polyurethane networks revealed the absence of an isocyanate absorption, indicating that all isocyanates, apparently, had reacted during the cross-linking reaction. The biocompatibility could be increased by grafting tethered polyacrylamide chains onto the surface during network formation. These transparent cross-linked polyurethanes did not transmit UV light up to 400 nm, by incorporation of a small amount of the UV absorbing chromophore Coumarin 102, and could be sterilized simply by autoclaving. They were implanted in rabbit eyes, either in the form of small circular disks or in the form of a keratoprosthesis (artificial cornea). It was shown that the material was well tolerated by the rabbit eyes. Serious opacification of the cornea, a direct result of an adverse reaction to the implant, was never seen. Even 1 yr after implantation of a polyurethane keratoprosthesis the eye was still 'quiet'.

  3. Pipelined chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2010-02-01

    A novel nonlinear adaptive filter with pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN) is presented in this paper, which uses a modification real-time recurrent learning algorithm. The PCFLARNN consists of a number of simple small-scale Chebyshev functional link artificial recurrent neural network (CFLARNN) modules. Compared to the standard recurrent neural network (RNN), those modules of PCFLARNN can simultaneously be performed in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Furthermore, contrasted with the architecture of a pipelined RNN (PRNN), each module of PCFLARNN is a CFLARNN whose nonlinearity is introduced by enhancing the input pattern with Chebyshev functional expansion, whereas the RNN of each module in PRNN utilizing linear input and first-order recurrent term only fails to utilize the high-order terms of inputs. Therefore, the performance of PCFLARNN can further be improved at the cost of a slightly increased computational complexity. In addition, due to the introduced nonlinear functional expansion of each module in PRNN, the number of input signals can be reduced. Computer simulations have demonstrated that the proposed filter performs better than PRNN and RNN for nonlinear colored signal prediction, nonstationary speech signal prediction, and chaotic time series prediction.

  4. Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links.

    PubMed

    Teramae, Jun-nosuke; Tsubo, Yasuhiro; Fukai, Tomoki

    2012-01-01

    The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.

  5. Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links

    NASA Astrophysics Data System (ADS)

    Teramae, Jun-Nosuke; Tsubo, Yasuhiro; Fukai, Tomoki

    2012-07-01

    The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.

  6. Preserving Energy Using Link Protocol in Wireless Networks

    NASA Astrophysics Data System (ADS)

    Kanavalli, Anita; Geetha, T. L.; Shenoy, P. Deepa; Venugopal, K. R.; Patnaik, L. M.

    A sensor node is equipped with a limited energy source and hence has a lifetime dependent on that source. In a wireless sensor network, each node can originate data and also has to route data. When a few nodes deplete their energy resources, topology changes occur which may require rerouting of data packets. An Automatic Repeat reQuest (ARQ) protocol uses a mechanism to improve transmission reliability of a communication link by means of packet retransmission. The purpose of this work is to design a link quality aware ARQ protocol which only retransmits packets only when it gets acknowledgement from the receiver i.e. only when link is good. The protocol makes necessary comparisons through simulations and draw conclusions based on the results obtained. The main aim of this study is to determine a protocol that is efficient in terms of preserving the limited energy supply for sensor nodes and use a SimpleLink module for this purpose. The SimpleLink module consists of a transmitter and sender both will Adopt ARQ transmission protocol.

  7. Spatial signals link exit from mitosis to spindle position

    PubMed Central

    Falk, Jill Elaine; Tsuchiya, Dai; Verdaasdonk, Jolien; Lacefield, Soni; Bloom, Kerry; Amon, Angelika

    2016-01-01

    In budding yeast, if the spindle becomes mispositioned, cells prevent exit from mitosis by inhibiting the mitotic exit network (MEN). The MEN is a signaling cascade that localizes to spindle pole bodies (SPBs) and activates the phosphatase Cdc14. There are two competing models that explain MEN regulation by spindle position. In the 'zone model', exit from mitosis occurs when a MEN-bearing SPB enters the bud. The 'cMT-bud neck model' posits that cytoplasmic microtubule (cMT)-bud neck interactions prevent MEN activity. Here we find that 1) eliminating cMT– bud neck interactions does not trigger exit from mitosis and 2) loss of these interactions does not precede Cdc14 activation. Furthermore, using binucleate cells, we show that exit from mitosis occurs when one SPB enters the bud despite the presence of a mispositioned spindle. We conclude that exit from mitosis is triggered by a correctly positioned spindle rather than inhibited by improper spindle position. DOI: http://dx.doi.org/10.7554/eLife.14036.001 PMID:27166637

  8. Signal Destruction Tunes the Zone of Activation in Spatially Distributed Signaling Networks.

    PubMed

    Silva, Kalinga Pavan; Chellamuthu, Prithiviraj; Boedicker, James Q

    2017-03-14

    Diverse microbial communities coordinate group behaviors through signal exchange, such as the exchange of acyl-homoserine lactones (AHLs) by Gram-negative bacteria. Cellular communication is prone to interference by neighboring microbes. One mechanism of interference is signal destruction through the production of an enzyme that cleaves the signaling molecule. Here we examine the ability of one such interference enzyme, AiiA, to modulate signal propagation in a spatially distributed system of bacteria. We have developed an experimental assay to measure signal transduction and implement a theoretical model of signaling dynamics to predict how the system responds to interference. We show that titration of an interfering strain into a signaling network tunes the spatial range of activation over the centimeter length scale, quantifying the robustness of the signaling network to signal destruction and demonstrating the ability to program systems-level responses of spatially heterogeneous cellular networks.

  9. Directed networks' different link formation mechanisms causing degree distribution distinction

    NASA Astrophysics Data System (ADS)

    Behfar, Stefan Kambiz; Turkina, Ekaterina; Cohendet, Patrick; Burger-Helmchen, Thierry

    2016-11-01

    Within undirected networks, scientists have shown much interest in presenting power-law features. For instance, Barabási and Albert (1999) claimed that a common property of many large networks is that vertex connectivity follows scale-free power-law distribution, and in another study Barabási et al. (2002) showed power law evolution in the social network of scientific collaboration. At the same time, Jiang et al. (2011) discussed deviation from power-law distribution; others indicated that size effect (Bagrow et al., 2008), information filtering mechanism (Mossa et al., 2002), and birth and death process (Shi et al., 2005) could account for this deviation. Within directed networks, many authors have considered that outlinks follow a similar mechanism of creation as inlinks' (Faloutsos et al., 1999; Krapivsky et al., 2001; Tanimoto, 2009) with link creation rate being the linear function of node degree, resulting in a power-law shape for both indegree and outdegree distribution. Some other authors have made an assumption that directed networks, such as scientific collaboration or citation, behave as undirected, resulting in a power-law degree distribution accordingly (Barabási et al., 2002). At the same time, we claim (1) Outlinks feature different degree distributions than inlinks; where different link formation mechanisms cause the distribution distinctions, (2) in/outdegree distribution distinction holds for different levels of system decomposition; therefore this distribution distinction is a property of directed networks. First, we emphasize in/outlink formation mechanisms as causal factors for distinction between indegree and outdegree distributions (where this distinction has already been noticed in Barker et al. (2010) and Baxter et al. (2006)) within a sample network of OSS projects as well as Java software corpus as a network. Second, we analyze whether this distribution distinction holds for different levels of system decomposition: open

  10. Epileptic seizures as condensed sleep: an analysis of network dynamics from electroencephalogram signals.

    PubMed

    Gast, Heidemarie; Müller, Markus; Rummel, Christian; Roth, Corinne; Mathis, Johannes; Schindler, Kaspar; Bassetti, Claudio L

    2014-06-01

    Both deepening sleep and evolving epileptic seizures are associated with increasing slow-wave activity. Larger-scale functional networks derived from electroencephalogram indicate that in both transitions dramatic changes of communication between brain areas occur. During seizures these changes seem to be 'condensed', because they evolve more rapidly than during deepening sleep. Here we set out to assess quantitatively functional network dynamics derived from electroencephalogram signals during seizures and normal sleep. Functional networks were derived from electroencephalogram signals from wakefulness, light and deep sleep of 12 volunteers, and from pre-seizure, seizure and post-seizure time periods of 10 patients suffering from focal onset pharmaco-resistant epilepsy. Nodes of the functional network represented electrical signals recorded by single electrodes and were linked if there was non-random cross-correlation between the two corresponding electroencephalogram signals. Network dynamics were then characterized by the evolution of global efficiency, which measures ease of information transmission. Global efficiency was compared with relative delta power. Global efficiency significantly decreased both between light and deep sleep, and between pre-seizure, seizure and post-seizure time periods. The decrease of global efficiency was due to a loss of functional links. While global efficiency decreased significantly, relative delta power increased except between the time periods wakefulness and light sleep, and pre-seizure and seizure. Our results demonstrate that both epileptic seizures and deepening sleep are characterized by dramatic fragmentation of larger-scale functional networks, and further support the similarities between sleep and seizures.

  11. Community detection, link prediction, and layer interdependence in multilayer networks.

    PubMed

    De Bacco, Caterina; Power, Eleanor A; Larremore, Daniel B; Moore, Cristopher

    2017-04-01

    Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

  12. Underwater Electromagnetic Sensor Networks-Part I: Link Characterization.

    PubMed

    Quintana-Díaz, Gara; Mena-Rodríguez, Pablo; Pérez-Álvarez, Iván; Jiménez, Eugenio; Dorta-Naranjo, Blas-Pablo; Zazo, Santiago; Pérez, Marina; Quevedo, Eduardo; Cardona, Laura; Hernández, J Joaquín

    2017-01-19

    Underwater Wireless Sensor Networks (UWSNs) using electromagnetic (EM) technology in marine shallow waters are examined, not just for environmental monitoring but for further interesting applications. Particularly, the use of EM waves is reconsidered in shallow waters due to the benefits offered in this context, where acoustic and optical technologies have serious disadvantages. Sea water scenario is a harsh environment for radiocommunications, and there is no standard model for the underwater EM channel. The high conductivity of sea water, the effect of seabed and the surface make the behaviour of the channel hard to predict. This justifies the need of link characterization as the first step to approach the development of EM underwater sensor networks. To obtain a reliable link model, measurements and simulations are required. The measuring setup for this purpose is explained and described, as well as the procedures used. Several antennas have been designed and tested in low frequency bands. Agreement between attenuation measurements and simulations at different distances was analysed and made possible the validation of simulation setups and the design of different communications layers of the system. This leads to the second step of this work, where data and routing protocols for the sensor network are examined.

  13. Community detection, link prediction, and layer interdependence in multilayer networks

    NASA Astrophysics Data System (ADS)

    De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher

    2017-04-01

    Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

  14. Hybrid wireless underground sensor networks - Quantification of signal attenuation due to soil adsorption

    NASA Astrophysics Data System (ADS)

    Huisman, J. A.; Bogena, H. R.; Meier, H.; Weuthen, A.; Rosenbaum, U.; Vereecken, H.

    2008-12-01

    A promising new technology for environmental monitoring is the wireless sensor network. Recently, we developed a sensor network for the near real-time monitoring of soil moisture. This network is based on the new low-cost ZigBee radio network that operates on top of the IEEE 802.15.4 standard. The sensor network consists of soil moisture sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the soil and linked wirelessly with nearby aboveground router devices (i.e. it is a hybrid wireless sensor network). A key concern with these hybrid networks is the quality of the wireless link through the soil. In this study, we measured and modelled the signal attenuation of a 5 cm soil layer. We developed a laboratory experimental setup to measure signal attenuation for a range of soil water contents and bulk electrical conductivities. These data were then used to validate a semi-empirical electromagnetic model simulating signal attenuation. The validated model was then used to show how wireless sensor transmitter range is affected by soil adsorption for different soil conditions.

  15. Consideration on common channel signaling evolution for global intelligent networking

    NASA Astrophysics Data System (ADS)

    Fujioka, Masanobu; Wakahara, Yasushi

    1994-04-01

    With the evolution of digital networks and intelligent network (IN) capabilities, the role of common channel signaling has become more and more important. In respect to IN, common channel signaling would play a significant role not only inside one network but also over multiple networks. International credit card validation and internetworking for digital mobile services represented by GSM (Global System for Mobile Communications) are examples which utilize internetwork signaling capabilities in the framework of the initial-phase IN. Enhanced service providers (ESP's) may access the public network through the common channel signaling interface to make use of the IN capabilities, as is being discussed in terms of ONA (Open Network Architecture) or ONP (Open Network Provision). This paper first identifies various scenarios where internetwork signaling interactions would take place in the framework of IN in the forthcoming era. It then identifies various requirements to cope with these scenarios. It finally discusses the directions for evolution of common channel signaling toward global intelligent networking.

  16. Network component analysis: reconstruction of regulatory signals in biological systems.

    PubMed

    Liao, James C; Boscolo, Riccardo; Yang, Young-Lyeol; Tran, Linh My; Sabatti, Chiara; Roychowdhury, Vwani P

    2003-12-23

    High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

  17. Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling*

    PubMed Central

    Ryall, Karen A.; Holland, David O.; Delaney, Kyle A.; Kraeutler, Matthew J.; Parker, Audrey J.; Saucerman, Jeffrey J.

    2012-01-01

    Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression. PMID:23091058

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

    PubMed Central

    Squartini, Tiziano; van Lelyveld, Iman; Garlaschelli, Diego

    2013-01-01

    The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies. PMID:24285089

  19. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

    PubMed

    Kato, Ayaka; Morita, Kenji

    2016-10-01

    It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value

  20. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation

    PubMed Central

    Morita, Kenji

    2016-01-01

    It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of ‘Go’ or ‘No-Go’ selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of ‘Go’ values towards a goal, and (2) value-contrasts between ‘Go’ and ‘No-Go’ are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological

  1. Simulation of Fracture Nucleation in Cross-Linked Polymer Networks

    NASA Astrophysics Data System (ADS)

    Moller, J. C.; Barr, S. A.; Schultz, E. J.; Breitzman, T. D.; Berry, R. J.

    2013-02-01

    A novel atomistic simulation method is developed whereby polymer systems can undergo strain-rate-controlled deformation while bond scission is enabled. The aim is to provide insight into the nanoscale origins of fracture. Various highly cross-linked epoxy systems including various resin chain lengths and levels of nonreactive dilution were examined. Consistent with the results of physical experiments, cured resin strength increased and ductility decreased with increasing cross-link density. An analysis of dihedral angle activity shows the locations in the molecular network that are most absorptive of mechanical energy. Bond scission occurred principally at cross-link sites as well as between phenyl rings in the bisphenol moiety. Scissions typically occurred well after yield and were accompanied by steady increases in void size and dihedral angle motion between bisphenol moieties and at cross-link sites. The methods developed here could be more broadly applied to explore and compare the atomistic nature of deformation for various polymers such that mechanical and fracture properties could be tuned in a rational way. This method and its results could become part of a solution system that spans multiple length and time scales and that could more completely represent such mechanical events as fracture.

  2. Radar signal categorization using a neural network

    NASA Technical Reports Server (NTRS)

    Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

    1991-01-01

    Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  4. Hub-activated signal transmission in complex networks

    NASA Astrophysics Data System (ADS)

    Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

    2014-03-01

    A wide range of networked systems exhibit highly connected nodes (hubs) as prominent structural elements. The functional roles of hubs in the collective nonlinear dynamics of many such networks, however, are not well understood. Here, we propose that hubs in neural circuits may activate local signal transmission along sequences of specific subnetworks. Intriguingly, in contrast to previous suggestions of the functional roles of hubs, here, not the hubs themselves, but nonhub subnetworks transfer the signals. The core mechanism relies on hubs and nonhubs providing activating feedback to each other. It may, thus, induce the propagation of specific pulse and rate signals in neuronal and other communication networks.

  5. Establishing a Statistical Link between Network Oscillations and Neural Synchrony

    PubMed Central

    Zhou, Pengcheng; Burton, Shawn D.; Snyder, Adam C.; Smith, Matthew A.; Urban, Nathaniel N.; Kass, Robert E.

    2015-01-01

    Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. PMID:26465621

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

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-03-01

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

  7. Assessing the weather monitoring capabilities of cellular microwave link networks

    NASA Astrophysics Data System (ADS)

    Fencl, Martin; Vrzba, Miroslav; Rieckermann, Jörg; Bareš, Vojtěch

    2016-04-01

    Using of microwave links for rainfall monitoring was suggested already by (Atlas and Ulbrich, 1977). However, this technique attracted broader attention of scientific community only in the recent decade, with the extensive growth of cellular microwave link (CML) networks, which form the backbone of today's cellular telecommunication infrastructure. Several studies have already shown that CMLs can be conveniently used as weather sensors and have potential to provide near-ground path-integrated observations of rainfall but also humidity or fog. However, although research is still focusing on algorithms to improve the weather sensing capabilities (Fencl et al., 2015), it is not clear how to convince cellular operators to provide the power levels of their network. One step in this direction is to show in which regions or municipalities the networks are sufficiently dense to provide/develop good services. In this contribution we suggest a standardized approach to evaluate CML networks in terms of rainfall observation and to identify suitable regions for CML rainfall monitoring. We estimate precision of single CML based on its sensitivity to rainfall, i.e. as a function of frequency, polarization and path length. Capability of a network to capture rainfall spatial patterns is estimated from the CML coverage and path lengths considering that single CML provides path-integrated rain rates. We also search for suitable predictors for regions where no network topologies are available. We test our approach on several European networks and discuss the results. Our results show that CMLs are very dense in urban areas (> 1 CML/km2), but less in rural areas (< 0.02 CML/km2). We found a strong correlation between a population and CML network density (e.g. R2 = 0.97 in Czech Republic), thus population could be a simple proxy to identify suitable regions for CML weather monitoring. To enable a simple and efficient assessment of the CML monitoring potential for any region worldwide

  8. Specification of spatial relationships in directed graphs of cell signaling networks.

    PubMed

    Lipshtat, Azi; Neves, Susana R; Iyengar, Ravi

    2009-03-01

    Graph theory provides a useful and powerful tool for the analysis of cellular signaling networks. Intracellular components such as cytoplasmic signaling proteins, transcription factors, and genes are connected by links, representing various types of chemical interactions that result in functional consequences. However, these graphs lack important information regarding the spatial distribution of cellular components. The ability of two cellular components to interact depends not only on their mutual chemical affinity but also on colocalization to the same subcellular region. Localization of components is often used as a regulatory mechanism to achieve specific effects in response to different receptor signals. Here we describe an approach for incorporating spatial distribution into graphs and for the development of mixed graphs where links are specified by mutual chemical affinity as well as colocalization. We suggest that such mixed graphs will provide more accurate descriptions of functional cellular networks and their regulatory capabilities and aid in the development of large-scale predictive models of cellular behavior.

  9. Information routing driven by background chatter in a signaling network.

    PubMed

    Domedel-Puig, Núria; Rué, Pau; Pons, Antonio J; García-Ojalvo, Jordi

    2011-12-01

    Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity -or chatter- that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios.

  10. Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways.

    PubMed

    Khurana, Vikram; Peng, Jian; Chung, Chee Yeun; Auluck, Pavan K; Fanning, Saranna; Tardiff, Daniel F; Bartels, Theresa; Koeva, Martina; Eichhorn, Stephen W; Benyamini, Hadar; Lou, Yali; Nutter-Upham, Andy; Baru, Valeriya; Freyzon, Yelena; Tuncbag, Nurcan; Costanzo, Michael; San Luis, Bryan-Joseph; Schöndorf, David C; Barrasa, M Inmaculada; Ehsani, Sepehr; Sanjana, Neville; Zhong, Quan; Gasser, Thomas; Bartel, David P; Vidal, Marc; Deleidi, Michela; Boone, Charles; Fraenkel, Ernest; Berger, Bonnie; Lindquist, Susan

    2017-02-22

    Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To "humanize" this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy.

  11. Non-linear dimensionality reduction of signaling networks

    PubMed Central

    Ivakhno, Sergii; Armstrong, J Douglas

    2007-01-01

    Background Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling. Results Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares – Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF

  12. Non-linear dimensionality reduction of signaling networks.

    PubMed

    Ivakhno, Sergii; Armstrong, J Douglas

    2007-06-08

    Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling. Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares - Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction

  13. Neural network analysis of oxygenation signals in infants during sleep.

    PubMed

    Taktak, A F; Simpson, S; Patel, S; Meyer, G

    2000-08-01

    The use of artificial neural networks (ANNs) to interpret sleep monitoring signals is described. Recordings from ten infants with apparent life threatening episodes were assigned into training feedforward R-PROP networks. In order to separate good signal from artefact, 60 second time frames of SaO2 and TcPO2 signals were processed and the mean and standard deviation values were used as inputs to the networks. Intra-human errors were minimized using this method whilst inter-human errors remained significant. To decrease the latter, the number of hidden units was increased to eight. Sensitivity figures of the SaO2 network were 0.93 and 0.9 for the training and test sets respectively whilst the specificity figures were 0.7 and 0.65 respectively. For the TcPO2 signals the above figures were 0.92, 0.85, 0.77 and 0.61 respectively.

  14. Elementary signaling modes predict the essentiality of signal transduction network components

    PubMed Central

    2011-01-01

    Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The

  15. Linking Social Media Reports to Network Indicators of DoS Attacks

    DTIC Science & Technology

    2015-02-15

    reporting in fast-paced social media such as Twitter, but these reports are rarely linked to quantiable network behavior. A data set of network-based...FEB 2015 2. REPORT TYPE N/A 3. DATES COVERED 4. TITLE AND SUBTITLE Linking Social Media Reports to Network Indicators of DoS Attacks 5a...Random sample of 30 (D, E) pairs yielding 21 unique entities (E) Linking Social Media Reports to Network Indicators of DoS Attacks Evan Wright

  16. Link-state-estimation-based transmission power control in wireless body area networks.

    PubMed

    Kim, Seungku; Eom, Doo-Seop

    2014-07-01

    This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.

  17. Information transduction capacity of noisy biochemical signaling networks

    PubMed Central

    Cheong, Raymond; Rhee, Alex; Wang, Chiaochun Joanne; Nemenman, Ilya; Levchenko, Andre

    2014-01-01

    Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple pathways together. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells. PMID:21921160

  18. Ultrasensitivity in signaling cascades revisited: Linking local and global ultrasensitivity estimations.

    PubMed

    Altszyler, Edgar; Ventura, Alejandra C; Colman-Lerner, Alejandro; Chernomoretz, Ariel

    2017-01-01

    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.

  19. Ultrasensitivity in signaling cascades revisited: Linking local and global ultrasensitivity estimations

    PubMed Central

    Altszyler, Edgar; Ventura, Alejandra C.; Colman-Lerner, Alejandro; Chernomoretz, Ariel

    2017-01-01

    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. PMID:28662096

  20. Linking structure and activity in nonlinear spiking networks

    PubMed Central

    Josić, Krešimir; Shea-Brown, Eric

    2017-01-01

    Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function. PMID:28644840

  1. Predictors of the peak width for networks with exponential links

    USGS Publications Warehouse

    Troutman, B.M.; Karlinger, M.R.

    1989-01-01

    We investigate optimal predictors of the peak (S) and distance to peak (T) of the width function of drainage networks under the assumption that the networks are topologically random with independent and exponentially distributed link lengths. Analytical results are derived using the fact that, under these assumptions, the width function is a homogeneous Markov birth-death process. In particular, exact expressions are derived for the asymptotic conditional expectations of S and T given network magnitude N and given mainstream length H. In addition, a simulation study is performed to examine various predictors of S and T, including N, H, and basin morphometric properties; non-asymptotic conditional expectations and variances are estimated. The best single predictor of S is N, of T is H, and of the scaled peak (S divided by the area under the width function) is H. Finally, expressions tested on a set of drainage basins from the state of Wyoming perform reasonably well in predicting S and T despite probable violations of the original assumptions. ?? 1989 Springer-Verlag.

  2. Signal Classification Using The Mean Separator Neural Network

    DTIC Science & Technology

    2000-03-01

    and investigated. One modification involved input data preconditioning prior to neural network processing. A second modification incorporated...decision-making capacity. More data is not needed; enhanced information and knowledge are essential. This study built upon the Mean Separator Neural ... Network (MSNN) signal classification tool originally proposed by Duzenli (1998) and modified it for increased robustness. MSNN variants were developed

  3. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    SciTech Connect

    Chinthavali, Supriya

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  4. Time Score: A New Feature for Link Prediction in Social Networks

    NASA Astrophysics Data System (ADS)

    Munasinghe, Lankeshwara; Ichise, Ryutaro

    Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

  5. Parameter space exploration within dynamic simulations of signaling networks.

    PubMed

    De Ambrosi, Cristina; Barla, Annalisa; Tortolina, Lorenzo; Castagnino, Nicoletta; Pesenti, Raffaele; Verri, Alessandro; Ballestrero, Alberto; Patrone, Franco; Parodi, Silvio

    2013-02-01

    We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  7. Robustness of interdependent networks with different link patterns against cascading failures

    NASA Astrophysics Data System (ADS)

    Wang, Jianwei; Jiang, Chen; Qian, Jianfei

    2014-01-01

    Taking into account the load, the load redistribution, and the node capacity, we study the robustness of two interdependent networks A and B against cascading failures, where each node in network A depends on one node in network B, and vice versa. We adopt three kinds of link patterns between two interdependent networks, i.e., the assortative link (AL), the disassortative link (DL), the random link (RL), where the RA refers to connect randomly two nodes in networks A and B, the AL refers to that high-degree (low-degree) nodes in A network link high-degree (low-degree) nodes in B network, while the DL refers to the fact that high-degree nodes in A network link low-degree nodes in B network. We investigate the robustness of the interdependent networks constructed by two artificial networks and the power grid, taking into account two stages of the cascading propagation. We numerically find that both the different link patterns and the parameters in the cascading model have important effects on dramatically improving the robustness of the interdependent networks against cascading failures. In addition, we obtain the better link pattern and the matching network structure to effectively avoid the cascading propagation.

  8. Mechanical behavior of highly cross-linked polymer networks and its links to microscopic structure.

    PubMed

    Mukherji, Debashish; Abrams, Cameron F

    2009-06-01

    Highly cross-linked polymer (HCP) networks are becoming increasingly important as high-performance adhesives and multifunctional composite materials. Because of their cross-linked molecular architectures, HCPs can be strong but brittle. One key goal in improving the performance of an HCP is to increase toughness without sacrificing strength. Using large scale molecular-dynamics simulation, we compare and characterize the mechanical behavior of two model HCPs under tensile deformation. In the first case, bond angles among any three connected monomers are unconstrained and in the second case we impose harmonic tetrahedral bond angle constraints. We perform a detailed microstructural analysis that establishes a unique correlation between macroscopic mechanical behavior and the microscopic structure of an HCP. While, in the unconstrained system, strain-hardening behavior is observed that is attributed to the formation of microvoids, the void growth is completely arrested in the constrained system and no strain hardening is observed. Moreover, after the initial strain-hardening phase, the unconstrained system displays the same stress-strain behavior as that of a constrained network. Strain hardening makes the unconstrained system ductile while it retains the same tensile strength as the constrained system. We suggest that bond angle flexibility of cross-linkers might be a possible means to control ductility in an HCP network at a constant cross-linker density. We have also studied the effect of temperature, strain rate, and intermonomer nonbonded interaction strength on the stress-strain behavior. Interestingly at a strong intermonomer nonbonded interaction strength, no strain hardening is observed even in the unconstrained system and fracture sets in at around 1% strain, similar to what is observed in an experimental system such as epoxy and vinyl-ester based thermosets. This indicates that strong nonbonded interactions play a key role in making an HCP strong but

  9. Capacity Limit, Link Scheduling and Power Control in Wireless Networks

    ERIC Educational Resources Information Center

    Zhou, Shan

    2013-01-01

    The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different…

  10. Capacity Limit, Link Scheduling and Power Control in Wireless Networks

    ERIC Educational Resources Information Center

    Zhou, Shan

    2013-01-01

    The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different…

  11. Enhanced correlation of received power-signal fluctuations in bidirectional optical links

    NASA Astrophysics Data System (ADS)

    Minet, Jean; Vorontsov, Mikhail A.; Polnau, Ernst; Dolfi, Daniel

    2013-02-01

    A study of the correlation between the power signals received at both ends of bidirectional free-space optical links is presented. By use of the quasi-optical approximation, we show that an ideal (theoretically 100%) power-signal correlation can be achieved in optical links with specially designed monostatic transceivers based on single-mode fiber collimators. The theoretical prediction of enhanced correlation is supported both by experiments conducted over a 7 km atmospheric path and wave optics numerical analysis of the corresponding bidirectional optical link. In the numerical simulations, we also compare correlation properties of received power signals for different atmospheric conditions and for optical links with monostatic and bistatic geometries based on single-mode fiber collimator and on power-in-the-bucket transceiver types. Applications of the observed phenomena for signal fading mitigation and turbulence-enhanced communication link security in free-space laser communication links are discussed.

  12. Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

    PubMed

    Mall, Susmita; Chakraverty, S

    2016-08-01

    Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

  13. Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

    PubMed

    Koehler, Jacob; Rawlings, Chris; Verrier, Paul; Mitchell, Rowan; Skusa, Andre; Ruegg, Alexander; Philippi, Stephan

    2005-01-01

    The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

  14. Signaled and Unsignaled Terminal Links in Concurrent Chains I: Effects of Reinforcer Probability and Immediacy

    ERIC Educational Resources Information Center

    Mattson, Karla M.; Hucks, Andrew; Grace, Randolph C.; McLean, Anthony P.

    2010-01-01

    Eight pigeons responded in a three-component concurrent-chains procedure, with either independent or dependent initial links. Relative probability and immediacy of reinforcement in the terminal links were both varied, and outcomes on individual trials (reinforcement or nonreinforcement) were either signaled or unsignaled. Terminal-link fixed-time…

  15. Random neural networks with multiple classes of signals.

    PubMed

    Gelenbe, E; Fourneau, J M

    1999-05-15

    By extending the pulsed recurrent random neural network (RNN) discussed in Gelenbe (1989, 1990, 1991), we propose a recurrent random neural network model in which each neuron processes several distinctly characterized streams of "signals" or data. The idea that neurons may be able to distinguish between the pulses they receive and use them in a distinct manner is biologically plausible. In engineering applications, the need to process different streams of information simultaneously is commonplace (e.g., in image processing, sensor fusion, or parallel processing systems). In the model we propose, each distinct stream is a class of signals in the form of spikes. Signals may arrive to a neuron from either the outside world (exogenous signals) or other neurons (endogenous signals). As a function of the signals it has received, a neuron can fire and then send signals of some class to another neuron or to the outside world. We show that the multiple signal class random model with exponential interfiring times, Poisson external signal arrivals, and Markovian signal movements between neurons has product form; this implies that the distribution of its state (i.e., the probability that each neuron of the network is excited) can be computed simply from the solution of a system of 2Cn simultaneous nonlinear equations where C is the number of signal classes and n is the number of neurons. Here we derive the stationary solution for the multiple class model and establish necessary and sufficient conditions for the existence of the stationary solution. The recurrent random neural network model with multiple classes has already been successfully applied to image texture generation (Atalay & Gelenbe, 1992), where multiple signal classes are used to model different colors in the image.

  16. Teleconnection Paths via Climate Network Direct Link Detection.

    PubMed

    Zhou, Dong; Gozolchiani, Avi; Ashkenazy, Yosef; Havlin, Shlomo

    2015-12-31

    Teleconnections describe remote connections (typically thousands of kilometers) of the climate system. These are of great importance in climate dynamics as they reflect the transportation of energy and climate change on global scales (like the El Niño phenomenon). Yet, the path of influence propagation between such remote regions, and weighting associated with different paths, are only partially known. Here we propose a systematic climate network approach to find and quantify the optimal paths between remotely distant interacting locations. Specifically, we separate the correlations between two grid points into direct and indirect components, where the optimal path is found based on a minimal total cost function of the direct links. We demonstrate our method using near surface air temperature reanalysis data, on identifying cross-latitude teleconnections and their corresponding optimal paths. The proposed method may be used to quantify and improve our understanding regarding the emergence of climate patterns on global scales.

  17. Photoinduced Plasticity in Cross-Linked Liquid Crystalline Networks.

    PubMed

    McBride, Matthew K; Hendrikx, Matthew; Liu, Danqing; Worrell, Brady T; Broer, Dirk J; Bowman, Christopher N

    2017-02-24

    Photoactivated reversible addition fragmentation chain transfer (RAFT)-based dynamic covalent chemistry is incorporated into liquid crystalline networks (LCNs) to facilitate spatiotemporal control of alignment, domain structure, and birefringence. The RAFT-based bond exchange process, which leads to stress relaxation, is used in a variety of conditions, to enable the LCN to achieve a near-equilibrium structure and orientation upon irradiation. Once formed, and in the absence of subsequent triggering of the RAFT process, the (dis)order in the LCN and its associated birefringence are evidenced at all temperatures. Using this approach, the birefringence, including the formation of spatially patterned birefringent elements and surface-active topographical features, is selectively tuned by adjusting the light dose, temperature, and cross-linking density.

  18. Reduction of complex signaling networks to a representative kernel.

    PubMed

    Kim, Jeong-Rae; Kim, Junil; Kwon, Yung-Keun; Lee, Hwang-Yeol; Heslop-Harrison, Pat; Cho, Kwang-Hyun

    2011-05-31

    The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network's kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

  19. Structural permeability of complex networks to control signals

    PubMed Central

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

    2015-01-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met. PMID:26391186

  20. Telesonar Signaling and Seaweb Underwater Wireless Networks

    DTIC Science & Technology

    2001-04-01

    personnel and mobile undersea systems as network nodes, contributing to this effort are Bob Creber, Chris Fletcher, The annual Seaweb and Sublink...Oceanographic deployable devices that will augment high-value space Partnership Program (NOPP), Jim Eckman (ONR and naval platforms. Distributed system

  1. Dissection of salicylic acid-mediated defense signaling networks

    PubMed Central

    2009-01-01

    The small phenolic molecule salicylic acid (SA) plays a key role in plant defense. Significant progress has been made recently in understanding SA-mediated defense signaling networks. Functional analysis of a large number of genes involved in SA biosynthesis and regulation of SA accumulation and signal transduction has revealed distinct but interconnecting pathways that orchestrate the control of plant defense. Further studies utilizing combinatorial approaches in genetics, molecular biology, biochemistry and genomics will uncover finer details of SA-mediated defense networks as well as further insights into the crosstalk of SA with other defense signaling pathways. The complexity of defense networks illustrates the capacity of plants to integrate multiple developmental and environmental signals into a tight control of the costly defense responses. PMID:19820324

  2. Plasma membrane regulates Ras signaling networks

    PubMed Central

    Chavan, Tanmay Sanjeev; Muratcioglu, Serena; Marszalek, Richard; Jang, Hyunbum; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth; Gaponenko, Vadim

    2015-01-01

    Ras GTPases activate more than 20 signaling pathways, regulating such essential cellular functions as proliferation, survival, and migration. How Ras proteins control their signaling diversity is still a mystery. Several pieces of evidence suggest that the plasma membrane plays a critical role. Among these are: (1) selective recruitment of Ras and its effectors to particular localities allowing access to Ras regulators and effectors; (2) specific membrane-induced conformational changes promoting Ras functional diversity; and (3) oligomerization of membrane-anchored Ras to recruit and activate Raf. Taken together, the membrane does not only attract and retain Ras but also is a key regulator of Ras signaling. This can already be gleaned from the large variability in the sequences of Ras membrane targeting domains, suggesting that localization, environment and orientation are important factors in optimizing the function of Ras isoforms. PMID:27054048

  3. Plasma membrane regulates Ras signaling networks.

    PubMed

    Chavan, Tanmay Sanjeev; Muratcioglu, Serena; Marszalek, Richard; Jang, Hyunbum; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth; Gaponenko, Vadim

    2015-01-01

    Ras GTPases activate more than 20 signaling pathways, regulating such essential cellular functions as proliferation, survival, and migration. How Ras proteins control their signaling diversity is still a mystery. Several pieces of evidence suggest that the plasma membrane plays a critical role. Among these are: (1) selective recruitment of Ras and its effectors to particular localities allowing access to Ras regulators and effectors; (2) specific membrane-induced conformational changes promoting Ras functional diversity; and (3) oligomerization of membrane-anchored Ras to recruit and activate Raf. Taken together, the membrane does not only attract and retain Ras but also is a key regulator of Ras signaling. This can already be gleaned from the large variability in the sequences of Ras membrane targeting domains, suggesting that localization, environment and orientation are important factors in optimizing the function of Ras isoforms.

  4. Luminal signalling links cell communication to tissue architecture during organogenesis.

    PubMed

    Durdu, Sevi; Iskar, Murat; Revenu, Celine; Schieber, Nicole; Kunze, Andreas; Bork, Peer; Schwab, Yannick; Gilmour, Darren

    2014-11-06

    Morphogenesis is the process whereby cell collectives are shaped into differentiated tissues and organs. The self-organizing nature of morphogenesis has been recently demonstrated by studies showing that stem cells in three-dimensional culture can generate complex organoids, such as mini-guts, optic-cups and even mini-brains. To achieve this, cell collectives must regulate the activity of secreted signalling molecules that control cell differentiation, presumably through the self-assembly of microenvironments or niches. However, mechanisms that allow changes in tissue architecture to feedback directly on the activity of extracellular signals have not been described. Here we investigate how the process of tissue assembly controls signalling activity during organogenesis in vivo, using the migrating zebrafish lateral line primordium. We show that fibroblast growth factor (FGF) activity within the tissue controls the frequency at which it deposits rosette-like mechanosensory organs. Live imaging reveals that FGF becomes specifically concentrated in microluminal structures that assemble at the centre of these organs and spatially constrain its signalling activity. Genetic inhibition of microlumen assembly and laser micropuncture experiments demonstrate that microlumina increase signalling responses in participating cells, thus allowing FGF to coordinate the migratory behaviour of cell groups at the tissue rear. As the formation of a central lumen is a self-organizing property of many cell types, such as epithelia and embryonic stem cells, luminal signalling provides a potentially general mechanism to locally restrict, coordinate and enhance cell communication within tissues.

  5. Towards blueprints for network architecture, biophysical dynamics and signal transduction.

    PubMed

    Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric

    2006-12-15

    We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

  6. Encoding Hydrogel Mechanics via Network Cross-Linking Structure

    PubMed Central

    2015-01-01

    The effects of mechanical cues on cell behaviors in 3D remain difficult to characterize as the ability to tune hydrogel mechanics often requires changes in the polymer density, potentially altering the material’s biochemical and physical characteristics. Additionally, with most PEG diacrylate (PEGDA) hydrogels, forming materials with compressive moduli less than ∼10 kPa has been virtually impossible. Here, we present a new method of controlling the mechanical properties of PEGDA hydrogels independent of polymer chain density through the incorporation of additional vinyl group moieties that interfere with the cross-linking of the network. This modification can tune hydrogel mechanics in a concentration dependent manner from <1 to 17 kPa, a more physiologically relevant range than previously possible with PEG-based hydrogels, without altering the hydrogel’s degradation and permeability. Across this range of mechanical properties, endothelial cells (ECs) encapsulated within MMP-2/MMP-9 degradable hydrogels with RGDS adhesive peptides revealed increased cell spreading as hydrogel stiffness decreased in contrast to behavior typically observed for cells on 2D surfaces. EC-pericyte cocultures exhibited vessel-like networks within 3 days in highly compliant hydrogels as compared to a week in stiffer hydrogels. These vessel networks persisted for at least 4 weeks and deposited laminin and collagen IV perivascularly. These results indicate that EC morphogenesis can be regulated using mechanical cues in 3D. Furthermore, controlling hydrogel compliance independent of density allows for the attainment of highly compliant mechanical regimes in materials that can act as customizable cell microenvironments. PMID:26082943

  7. Effectiveness of Link Prediction for Face-to-Face Behavioral Networks

    PubMed Central

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30–0.45 and a recall of 0.10–0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks. PMID:24339956

  8. Effectiveness of link prediction for face-to-face behavioral networks.

    PubMed

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30-0.45 and a recall of 0.10-0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

  9. Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters

    PubMed Central

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2014-01-01

    Experimental measurements of biochemical noise have primarily focused on sources of noise at the gene expression level due to limitations of existing noise decomposition techniques. Here, we introduce a mathematical framework that extends classical extrinsic–intrinsic noise analysis and enables mapping of noise within upstream signaling networks free of such restrictions. The framework applies to systems for which the responses of interest are linearly correlated on average, although the framework can be easily generalized to the nonlinear case. Interestingly, despite the high degree of complexity and nonlinearity of most mammalian signaling networks, three distinct tumor necrosis factor (TNF) signaling network branches displayed linearly correlated responses, in both wild-type and perturbed versions of the network, across multiple orders of magnitude of ligand concentration. Using the noise mapping analysis, we find that the c-Jun N-terminal kinase (JNK) pathway generates higher noise than the NF-κB pathway, whereas the activation of c-Jun adds a greater amount of noise than the activation of ATF-2. In addition, we find that the A20 protein can suppress noise in the activation of ATF-2 by separately inhibiting the TNF receptor complex and JNK pathway through a negative feedback mechanism. These results, easily scalable to larger and more complex networks, pave the way toward assessing how noise propagates through cellular signaling pathways and create a foundation on which we can further investigate the relationship between signaling system architecture and biological noise. PMID:25404303

  10. Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters.

    PubMed

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2014-12-02

    Experimental measurements of biochemical noise have primarily focused on sources of noise at the gene expression level due to limitations of existing noise decomposition techniques. Here, we introduce a mathematical framework that extends classical extrinsic-intrinsic noise analysis and enables mapping of noise within upstream signaling networks free of such restrictions. The framework applies to systems for which the responses of interest are linearly correlated on average, although the framework can be easily generalized to the nonlinear case. Interestingly, despite the high degree of complexity and nonlinearity of most mammalian signaling networks, three distinct tumor necrosis factor (TNF) signaling network branches displayed linearly correlated responses, in both wild-type and perturbed versions of the network, across multiple orders of magnitude of ligand concentration. Using the noise mapping analysis, we find that the c-Jun N-terminal kinase (JNK) pathway generates higher noise than the NF-κB pathway, whereas the activation of c-Jun adds a greater amount of noise than the activation of ATF-2. In addition, we find that the A20 protein can suppress noise in the activation of ATF-2 by separately inhibiting the TNF receptor complex and JNK pathway through a negative feedback mechanism. These results, easily scalable to larger and more complex networks, pave the way toward assessing how noise propagates through cellular signaling pathways and create a foundation on which we can further investigate the relationship between signaling system architecture and biological noise.

  11. Evolution of SH2 domains and phosphotyrosine signalling networks

    PubMed Central

    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

  12. Edge-based sensitivity analysis of signaling networks by using Boolean dynamics.

    PubMed

    Trinh, Hung-Cuong; Kwon, Yung-Keun

    2016-09-01

    Biological networks are composed of molecular components and their interactions represented by nodes and edges, respectively, in a graph model. Based on this model, there were many studies with respect to effects of node-based mutations on the network dynamics, whereas little attention was paid to edgetic mutations so far. In this paper, we defined an edgetic sensitivity measure that quantifies how likely a converging attractor is changed by edge-removal mutations in a Boolean network model. Through extensive simulations based on that measure, we found interesting properties of highly sensitive edges in both random and real signaling networks. First, the sensitive edges in random networks tend to link two end nodes both of which are susceptible to node-knockout mutations. Interestingly, it was analogous to an observation that the sensitive edges in human signaling networks are likely to connect drug-target genes. We further observed that the edgetic sensitivity predicted drug-targets better than the node-based sensitivity. In addition, the sensitive edges showed distinguished structural characteristics such as a lower connectivity, more involving feedback loops and a higher betweenness. Moreover, their gene-ontology enrichments were clearly different from the other edges. We also observed that genes incident to the highly sensitive interactions are more central by forming a considerably large connected component in human signaling networks. Finally, we validated our approach by showing that most sensitive interactions are promising edgetic drug-targets in p53 cancer and T-cell apoptosis networks. Taken together, the edgetic sensitivity is valuable to understand the complex dynamics of signaling networks. kwonyk@ulsan.ac.kr Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Slm35 links mitochondrial stress response and longevity through TOR signaling pathway

    PubMed Central

    Jose, L. Aguilar-Lopez; Laboy, Raymond; Fabiola, Jaimes-Miranda; Garay, Erika; Alexander, DeLuna; Funes, Soledad

    2016-01-01

    In most eukaryotic cells mitochondria are essential organelles involved in a great variety of cellular functions. One of the physiological processes linked to mitochondria is aging, a gradual process of damage accumulation that eventually promotes cell death. Aging depends on a balance between mitochondrial biogenesis, function and degradation. It has been previously shown that Tor1, Sch9 and Ras2 are activated in response to nutrient availability and regulate cell growth and division. A deficiency in any of these genes promotes lifespan extension and cell protection during oxidative and heat shock stress. In this work we report that in Saccharomyces cerevisiae, the uncharacterized mitochondrial protein Slm35 is functionally linked with the TOR signaling pathway. A Δtor1Δslm35 strain shows a severe decrease in lifespan and is unable to contend with oxidative and heat shock stresses. Specifically, this mutant shows decreased catalase activity indicating a misregulation of ROS scavenging mechanisms. In this study we show that Slm35 is also relevant for mitochondrial network dynamics and mitophagy. The results presented here suggest that Slm35 plays an important role connecting mitochondrial function with cytosolic responses and cell adaptation to stress and aging. PMID:27922823

  14. Slm35 links mitochondrial stress response and longevity through TOR signaling pathway.

    PubMed

    Aguilar-Lopez, Jose L; Laboy, Raymond; Jaimes-Miranda, Fabiola; Garay, Erika; DeLuna, Alexander; Funes, Soledad

    2016-12-02

    In most eukaryotic cells mitochondria are essential organelles involved in a great variety of cellular functions. One of the physiological processes linked to mitochondria is aging, a gradual process of damage accumulation that eventually promotes cell death. Aging depends on a balance between mitochondrial biogenesis, function and degradation. It has been previously shown that Tor1, Sch9 and Ras2 are activated in response to nutrient availability and regulate cell growth and division. A deficiency in any of these genes promotes lifespan extension and cell protection during oxidative and heat shock stress. In this work we report that in Saccharomyces cerevisiae, the uncharacterized mitochondrial protein Slm35 is functionally linked with the TOR signaling pathway. A Δtor1Δslm35 strain shows a severe decrease in lifespan and is unable to contend with oxidative and heat shock stresses. Specifically, this mutant shows decreased catalase activity indicating a misregulation of ROS scavenging mechanisms. In this study we show that Slm35 is also relevant for mitochondrial network dynamics and mitophagy. The results presented here suggest that Slm35 plays an important role connecting mitochondrial function with cytosolic responses and cell adaptation to stress and aging.

  15. MiRNA-34 intrinsically links p53 tumor suppressor and Wnt signaling.

    PubMed

    Cha, Yong Hoon; Kim, Nam Hee; Park, Changbum; Lee, Inhan; Kim, Hyun Sil; Yook, Jong In

    2012-04-01

    Though tumor suppressor p53 and the canonical Wnt cascade have been extensively studied for the last 30 years, due to their important physiological roles, the two signaling pathways have been largely considered independent. Recently, the miR-34 family was found to directly link p53 and Wnt, revealing the tight connection between loss of tumor suppressor function and activation of oncogenic signaling. These observations demonstrate that miR-34, known to be directly downstream of p53, targets a set of highly conserved sites in the UTR of Wnt and EMT genes, specifically WNT1, WNT3, LRP6, AXIN2, β-catenin, LEF1 and Snail, resulting in suppression of TCF/LEF transcriptional activity and the EMT program. The loss of p53 function increases Wnt activities and promotes the Snail-dependent EMT program at multiple levels in a miR-34/UTR-specific manner. The TCF/LEF transcriptional signature was closely associated with functionality of p53 and miR-34 in clinical samples, suggesting the pervasive impact of miR-34 loss on the oncogenic pathway in human cancer. Here, we review recent findings on ceRNA in light of novel data to elucidate the physiological relevance of the p53-miR-34-Wnt network, which encompasses sets of genes and directions of signaling. As loss of wt-p53 or hyperactivation of Wnt is critical in maintaining cancer stem cell properties and in establishing the metastatic program, these observations indicate a mechanism of miR-mediated quasi-sufficiency which connects tumor suppressor and oncogenic signaling pathways, supporting a continuum model of human cancer.

  16. Ultrahigh-capacity access network architecture for mobile data backhaul using integrated W-band wireless and free-space optical links with OAM multiplexing.

    PubMed

    Fang, Yuan; Yu, Jianjun; Zhang, Junwen; Chi, Nan; Xiao, Jiangnan; Chang, Gee-Kung

    2014-07-15

    In this Letter, we propose and experimentally demonstrate a novel access network architecture using hybrid integrated W-band wireless and free-space optical (FSO) links with orbital angular momentum (OAM) multiplexing. The transmission of a 20 GBd quadrature phase-shift keying signal modulated over 10 OAM modes has been demonstrated over a 0.6 m FSO link and a 0.4 m W-band wireless link at 100 GHz. The experimental results show that the architecture can support future ultrahigh-capacity, converged optical-wireless access networks that require extra bandwidth and system flexibility in mobile data networks.

  17. Comorbidities of Psoriasis - Exploring the Links by Network Approach.

    PubMed

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-01-01

    Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities.

  18. Comorbidities of Psoriasis - Exploring the Links by Network Approach

    PubMed Central

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-01-01

    Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities. PMID:26966903

  19. Link prediction in social network based on local information and attributes of nodes

    NASA Astrophysics Data System (ADS)

    Liang, Yingying; Huang, Lan; Wang, Zhe

    2017-08-01

    Link prediction is essential to both research areas and practical applications. In order to make full use of information of the network, we proposed a new method to predict links in the social network. Firstly, we extracted topological information and attributes of nodes in the social network. Secondly, we integrated them into feature vectors. Finally, we used XGB classifier to predict links using feature vectors. Through expanding information source, experiments on a co-authorship network suggest that our method can improve the accuracy of link prediction significantly.

  20. Characterization of Radar Signals Using Neural Networks

    DTIC Science & Technology

    1990-12-01

    can take hours (23:466) (14: 4 ). Once trained, these networks usually provide high accuracy for pattern classification while -requiring relatively short...parameters. Even with the use of high speed computers, this task is computationally intense- and can take several seconds to properly-classify the radarbignal...N aWE= -- . dn)lo0(1-y) (B.108) W"L N,"i - _n= 4 Thus aCE 1 N a OwK, -N _[dn_ -log(y) (1 - d,)w log(1 - Y)] (B.109) Taking the derivative provides

  1. Survivable Lightpath Provisioning in WDM Mesh Networks Under Shared Path Protection and Signal Quality Constraints

    NASA Astrophysics Data System (ADS)

    Yang, Xi; Shen, Lu; Ramamurthy, Byrav

    2005-04-01

    This paper addresses the problem of survivable lightpath provisioning in wavelength-division-multiplexing (WDM) mesh networks, taking into consideration optical-layer protection and some realistic optical signal quality constraints. The investigated networks use sparsely placed optical-electrical-optical (O/E/O) modules for regeneration and wavelength conversion. Given a fixed network topology with a number of sparsely placed O/E/O modules and a set of connection requests, a pair of link-disjoint lightpaths is established for each connection. Due to physical impairments and wavelength continuity,both the working and protection lightpaths need to be regenerated at some intermediate nodes to overcome signal quality degradation and wavelength contention. In the present paper, resource-efficient provisioning solutions are achieved with the objective of maximizing resource sharing. The authors propose a resource-sharing scheme that supports three kinds of resource-sharing scenarios, including a conventional wavelength-link sharing scenario, which shares wavelength links between protection lightpaths, and two new scenarios, which share O/E/O modules between protection lightpaths and between working and protection lightpaths. An integer linear programming (ILP)-based solution approach is used to find optimal solutions. The authors also propose a local optimization heuristic approach and a tabu search heuristic approach to solve this problem for real-world,large mesh networks. Numerical results show that our solution approaches work well under a variety of network settings and achieves a high level of resource-sharing rates (over 60% for O/E/O modules and over 30% for wavelength links), which translate into great savings in network costs.

  2. O-linked-N-acetylglucosamine cycling and insulin signaling are required for the glucose stress response in Caenorhabditis elegans.

    PubMed

    Mondoux, Michelle A; Love, Dona C; Ghosh, Salil K; Fukushige, Tetsunari; Bond, Michelle; Weerasinghe, Gayani R; Hanover, John A; Krause, Michael W

    2011-06-01

    In a variety of organisms, including worms, flies, and mammals, glucose homeostasis is maintained by insulin-like signaling in a robust network of opposing and complementary signaling pathways. The hexosamine signaling pathway, terminating in O-linked-N-acetylglucosamine (O-GlcNAc) cycling, is a key sensor of nutrient status and has been genetically linked to the regulation of insulin signaling in Caenorhabditis elegans. Here we demonstrate that O-GlcNAc cycling and insulin signaling are both essential components of the C. elegans response to glucose stress. A number of insulin-dependent processes were found to be sensitive to glucose stress, including fertility, reproductive timing, and dauer formation, yet each of these differed in their threshold of sensitivity to glucose excess. Our findings suggest that O-GlcNAc cycling and insulin signaling are both required for a robust and adaptable response to glucose stress, but these two pathways show complex and interdependent roles in the maintenance of glucose-insulin homeostasis.

  3. O-Linked-N-Acetylglucosamine Cycling and Insulin Signaling Are Required for the Glucose Stress Response in Caenorhabditis elegans

    PubMed Central

    Mondoux, Michelle A.; Love, Dona C.; Ghosh, Salil K.; Fukushige, Tetsunari; Bond, Michelle; Weerasinghe, Gayani R.; Hanover, John A.; Krause, Michael W.

    2011-01-01

    In a variety of organisms, including worms, flies, and mammals, glucose homeostasis is maintained by insulin-like signaling in a robust network of opposing and complementary signaling pathways. The hexosamine signaling pathway, terminating in O-linked-N-acetylglucosamine (O-GlcNAc) cycling, is a key sensor of nutrient status and has been genetically linked to the regulation of insulin signaling in Caenorhabditis elegans. Here we demonstrate that O-GlcNAc cycling and insulin signaling are both essential components of the C. elegans response to glucose stress. A number of insulin-dependent processes were found to be sensitive to glucose stress, including fertility, reproductive timing, and dauer formation, yet each of these differed in their threshold of sensitivity to glucose excess. Our findings suggest that O-GlcNAc cycling and insulin signaling are both required for a robust and adaptable response to glucose stress, but these two pathways show complex and interdependent roles in the maintenance of glucose–insulin homeostasis. PMID:21441213

  4. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

    PubMed Central

    Jebaseeli Samuelraj, Ananthi; 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. PMID:26366431

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

    PubMed

    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.

  6. Identifying network motifs that buffer front-to-back signaling in polarized neutrophils.

    PubMed

    Wang, Yanqin; Ku, Chin-Jen; Zhang, Elizabeth R; Artyukhin, Alexander B; Weiner, Orion D; Wu, Lani F; Altschuler, Steven J

    2013-05-30

    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.

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

    NASA Astrophysics Data System (ADS)

    Fonley, Morgan; Mantilla, Ricardo; Small, Scott J.; Curtu, Rodica

    2016-07-01

    Several authors have reported diel oscillations in streamflow records and have hypothesized that these oscillations are linked to evapotranspiration cycles in the watershed. The timing of oscillations in rivers, however, lags behind those of temperature and evapotranspiration in hillslopes. Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low-flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical model to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for the case of uniform velocities in space over all river links. We then use our analytic solution to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, we have tested our theoretical formulation in the Dry Creek Experimental Watershed, where oscillations are clearly observed in streamflow records. We find that our solution produces streamflow values and fluctuations that are similar to those observed in the summer of 2011.

  8. Mammalian circadian signaling networks and therapeutic targets.

    PubMed

    Liu, Andrew C; Lewis, Warren G; Kay, Steve A

    2007-10-01

    Virtually all cells in the body have an intracellular clockwork based on a negative feedback mechanism. The circadian timekeeping system in mammals is a hierarchical multi-oscillator network, with the suprachiasmatic nuclei (SCN) acting as the central pacemaker. The SCN synchronizes to daily light-dark cycles and coordinates rhythmic physiology and behavior. Synchronization in the SCN and at the organismal level is a key feature of the circadian clock system. In particular, intercellular coupling in the SCN synchronizes neuron oscillators and confers robustness against perturbations. Recent advances in our knowledge of and ability to manipulate circadian rhythms make available cell-based clock models, which lack strong coupling and are ideal for target discovery and chemical biology.

  9. Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wu, Chien-Hsien; Lin, Ching-An

    We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.

  10. Signal processing techniques for synchronization of wireless sensor networks

    NASA Astrophysics Data System (ADS)

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

    2010-11-01

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

  11. Evolution of Hormone Signaling Networks in Plant Defense.

    PubMed

    Berens, Matthias L; Berry, Hannah M; Mine, Akira; Argueso, Cristiana T; Tsuda, Kenichi

    2017-08-04

    Studies with model plants such as Arabidopsis thaliana have revealed that phytohormones are central regulators of plant defense. The intricate network of phytohormone signaling pathways enables plants to activate appropriate and effective defense responses against pathogens as well as to balance defense and growth. The timing of the evolution of most phytohormone signaling pathways seems to coincide with the colonization of land, a likely requirement for plant adaptations to the more variable terrestrial environments, which included the presence of pathogens. In this review, we explore the evolution of defense hormone signaling networks by combining the model plant-based knowledge about molecular components mediating phytohormone signaling and cross talk with available genome information of other plant species. We highlight conserved hubs in hormone cross talk and discuss evolutionary advantages of defense hormone cross talk. Finally, we examine possibilities of engineering hormone cross talk for improvement of plant fitness and crop production.

  12. Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

    PubMed

    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.

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

    PubMed Central

    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

  14. Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling.

    PubMed

    Hill, Steven M; Nesser, Nicole K; Johnson-Camacho, Katie; Jeffress, Mara; Johnson, Aimee; Boniface, Chris; Spencer, Simon E F; Lu, Yiling; Heiser, Laura M; Lawrence, Yancey; Pande, Nupur T; Korkola, James E; Gray, Joe W; Mills, Gordon B; Mukherjee, Sach; Spellman, Paul T

    2017-01-25

    Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

    Hinczewski, Michael; Thirumalai, D.

    2014-10-01

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

  16. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

    PubMed Central

    Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

    2017-01-01

    Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201

  17. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

    PubMed

    Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

    2017-08-03

    Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

  18. Protein S-glutathionlyation links energy metabolism to redox signaling in mitochondria

    PubMed Central

    Mailloux, Ryan J.; Treberg, Jason R.

    2015-01-01

    At its core mitochondrial function relies on redox reactions. Electrons stripped from nutrients are used to form NADH and NADPH, electron carriers that are similar in structure but support different functions. NADH supports ATP production but also generates reactive oxygen species (ROS), superoxide (O2·-) and hydrogen peroxide (H2O2). NADH-driven ROS production is counterbalanced by NADPH which maintains antioxidants in an active state. Mitochondria rely on a redox buffering network composed of reduced glutathione (GSH) and peroxiredoxins (Prx) to quench ROS generated by nutrient metabolism. As H2O2 is quenched, NADPH is expended to reactivate antioxidant networks and reset the redox environment. Thus, the mitochondrial redox environment is in a constant state of flux reflecting changes in nutrient and ROS metabolism. Changes in redox environment can modulate protein function through oxidation of protein cysteine thiols. Typically cysteine oxidation is considered to be mediated by H2O2 which oxidizes protein thiols (SH) forming sulfenic acid (SOH). However, problems begin to emerge when one critically evaluates the regulatory function of SOH. Indeed SOH formation is slow, non-specific, and once formed SOH reacts rapidly with a variety of molecules. By contrast, protein S-glutathionylation (PGlu) reactions involve the conjugation and removal of glutathione moieties from modifiable cysteine residues. PGlu reactions are driven by fluctuations in the availability of GSH and oxidized glutathione (GSSG) and thus should be exquisitely sensitive to changes ROS flux due to shifts in the glutathione pool in response to varying H2O2 availability. Here, we propose that energy metabolism-linked redox signals originating from mitochondria are mediated indirectly by H2O2 through the GSH redox buffering network in and outside mitochondria. This proposal is based on several observations that have shown that unlike other redox modifications PGlu reactions fulfill the requisite

  19. Links among resting-state default-mode network, salience network, and symptomatology in schizophrenia.

    PubMed

    Orliac, François; Naveau, Mickael; Joliot, Marc; Delcroix, Nicolas; Razafimandimby, Annick; Brazo, Perrine; Dollfus, Sonia; Delamillieure, Pascal

    2013-08-01

    Neuroimaging data support the idea that schizophrenia is a brain disorder with altered brain structure and function. New resting-state functional connectivity techniques allow us to highlight synchronization of large-scale networks, such as the default-mode network (DMN) and salience network (SN). A large body of work suggests that disruption of these networks could give rise to specific schizophrenia symptoms. We examined the intra-network connectivity strength and gray matter content (GMC) of DMN and SN in 26 schizophrenia patients using resting-state functional magnetic resonance imaging and voxel-based morphometry. Resting-state data were analyzed with independent component analysis and dual-regression techniques. We reported reduced functional connectivity within both DMN and SN in patients with schizophrenia. Concerning the DMN, patients showed weaker connectivity in a cluster located in the right paracingulate cortex. Moreover, patients showed decreased GMC in this cluster. With regard to the SN, patients showed reduced connectivity in the left and right striatum. Decreased connectivity in the paracingulate cortex was correlated with difficulties in abstract thinking. The connectivity decrease in the left striatum was correlated with delusion and depression scores. Correlation between the connectivity of DMN frontal regions and difficulties in abstract thinking emphasizes the link between negative symptoms and the likely alteration of the frontal medial cortex in schizophrenia. Correlation between the connectivity of SN striatal regions and delusions supports the aberrant salience hypothesis. This work provides new insights into dysfunctional brain organization in schizophrenia and its contribution to specific schizophrenia symptoms.

  20. Dynamics of a sensory signaling network in a unicellular eukaryote.

    PubMed

    Foster, Kenneth W; Josef, Keith; Saranak, Jureepan; Tuck, Ned

    2006-01-01

    The processing components and the dynamic signaling network that an individual cell uses to do signal integration and make decisions based on multiple sensory inputs are being identified in a well studied free-swimming unicellular green algal model organism, Chlamydomonas. It has many sensory photoreceptors and measurable behavior associated with its orienting and swimming with respect to light sources in its environment. Study of the dynamics of the beating of its two steering cilia reveals their complex specialization.

  1. VLSI Neural Networks Help To Compress Video Signals

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Sheu, Bing J.

    1996-01-01

    Advanced analog/digital electronic system for compression of video signals incorporates artificial neural networks. Performs motion-estimation and image-data-compression processing. Effectively eliminates temporal and spatial redundancies of sequences of video images; processes video image data, retaining only nonredundant parts to be transmitted, then transmits resulting data stream in form of efficient code. Reduces bandwidth and storage requirements for transmission and recording of video signal.

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

    NASA Astrophysics Data System (ADS)

    Wyatt, Sarah

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

  3. Hedgehog Signaling Links Chronic Inflammation to Gastric Cancer Precursor Lesions.

    PubMed

    Merchant, Juanita L; Ding, Lin

    2017-03-01

    Since its initial discovery in Drosophila, Hedgehog (HH) signaling has long been associated with foregut development. The mammalian genome expresses 3 HH ligands, with sonic hedgehog (SHH) levels highest in the mucosa of the embryonic foregut. More recently, interest in the pathway has shifted to improving our understanding of its role in gastrointestinal cancers. The use of reporter mice proved instrumental in our ability to probe the expression pattern of SHH ligand and the cell types responding to canonical HH signaling during homeostasis, inflammation, and neoplastic transformation. SHH is highly expressed in parietal cells and is required for these cells to produce gastric acid. Furthermore, myofibroblasts are the predominant cell type responding to HH ligand in the uninfected stomach. Chronic infection caused by Helicobacter pylori and associated inflammation induces parietal cell atrophy and the expansion of metaplastic cell types, a precursor to gastric cancer in human subjects. During Helicobacter infection in mice, canonical HH signaling is required for inflammatory cells to be recruited from the bone marrow to the stomach and for metaplastic development. Specifically, polarization of the invading myeloid cells to myeloid-derived suppressor cells requires the HH-regulated transcription factor GLI1, thereby creating a microenvironment favoring wound healing and neoplastic transformation. In mice, GLI1 mediates the phenotypic shift to gastric myeloid-derived suppressor cells by directly inducing Schlafen 4 (slfn4). However, the human homologs of SLFN4, designated SLFN5 and SLFN12L, also correlate with intestinal metaplasia and could be used as biomarkers to predict the subset of individuals who might progress to gastric cancer and benefit from treatment with HH antagonists.

  4. PD-1 Coinhibitory Signals: The Link Between Pathogenesis and Protection

    PubMed Central

    Kulpa, Deanna A.; Lawani, Mariam; Cooper, Anthony; Peretz, Yoav; Ahlers, Jeff; Sékaly, Rafick-Pierre

    2013-01-01

    In the majority of HIV-1 infected individuals, the adaptive immune response drives virus escape resulting in persistent viremia and a lack of immune-mediated control. The expression of negative regulatory molecules such as PD-1 during chronic HIV infection provides a useful marker to differentiate functional memory T cell subsets and the frequency of T cells with an exhausted phenotype. In addition, cell-based measurements of virus persistence equate with activation markers and the frequency of CD4 T cells expressing PD-1. High-level expression of PD-1 and its ligands PD-L1 and - L2 are found on hematopoietic and non-hematopoietic cells, which are regulated by chronic antigen stimulation, Type 1 and Type II interferons (IFNs), and homeostatic cytokines. In HIV infected subjects, PD-1 levels on CD4 and CD8 T cells continue to remain high following combination anti-retroviral therapy (cART). System biology approaches have begun to elucidate signal transduction pathways regulated by PD-1 expression in CD4 and CD8 T cell subsets that become dysfunctional through chronic TCR activation and PD-1 signaling. In this review, we summarize our current understanding of transcriptional signatures and signal transduction pathways associated with immune exhaustion with a focus on recent work in our laboratory characterizing the role of PD-1 in T cell dysfunction and HIV pathogenesis. We also highlight the therapeutic potential of blocking PD-1-PD-L1 and other immune checkpoints for activating potent cellular immune responses against chronic viral infections and cancer. PMID:23548749

  5. Biodiversity conservation in metacommunity networks: linking pattern and persistence.

    PubMed

    Economo, Evan P

    2011-06-01

    A central goal of conservation science is to identify the most important habitat patches for maintaining biodiversity on a landscape. Spatial biodiversity patterns are often used for such assessments, and patches that harbor unique diversity are generally prioritized over those with high community similarity to other areas. This places an emphasis on biodiversity representation, but removing a patch can have cascading effects on biodiversity persistence in the remaining ecological communities. Metacommunity theory provides a mechanistic route to the linking of biodiversity patterns on a landscape with the subsequent dynamics of diversity loss after habitat is degraded. Using spatially explicit neutral theory, I focus on the situation where spatial patterns of diversity and similarity are generated by the structure of dispersal networks and not environmental gradients. I find that gains in biodiversity representation are nullified by losses in persistence, and as a result the effects of removing a patch on metacommunity diversity are essentially independent of complementarity or other biodiversity patterns. In this scenario, maximizing protected area and not biodiversity representation is the key to maintaining diversity in the long term. These results highlight the need for a broader understanding of how conservation paradigms perform under different models of metacommunity dynamics.

  6. Recurrent Network Dynamics; a Link between Form and Motion

    PubMed Central

    Joukes, Jeroen; Yu, Yunguo; Victor, Jonathan D.; Krekelberg, Bart

    2017-01-01

    To discriminate visual features such as corners and contours, the brain must be sensitive to spatial correlations between multiple points in an image. Consistent with this, macaque V2 neurons respond selectively to patterns with well-defined multipoint correlations. Here, we show that a standard feedforward model (a cascade of linear–non-linear filters) does not capture this multipoint selectivity. As an alternative, we developed an artificial neural network model with two hierarchical stages of processing and locally recurrent connectivity. This model faithfully reproduced neurons’ selectivity for multipoint correlations. By probing the model, we gained novel insights into early form processing. First, the diverse selectivity for multipoint correlations and complex response dynamics of the hidden units in the model were surprisingly similar to those observed in V1 and V2. This suggests that both transient and sustained response dynamics may be a vital part of form computations. Second, the model self-organized units with speed and direction selectivity that was correlated with selectivity for multipoint correlations. In other words, the model units that detected multipoint spatial correlations also detected space-time correlations. This leads to the novel hypothesis that higher-order spatial correlations could be computed by the rapid, sequential assessment and comparison of multiple low-order correlations within the receptive field. This computation links spatial and temporal processing and leads to the testable prediction that the analysis of complex form and motion are closely intertwined in early visual cortex. PMID:28360844

  7. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks

    PubMed Central

    Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E.

    2013-01-01

    Multi-component signal transduction pathways and gene regulatory circuits underpin integrated cellular responses to perturbations. A recurring set of network motifs serve as the basic building blocks of these molecular signalling networks. This review focuses on ultrasensitive response motifs (URMs) that amplify small percentage changes in the input signal into larger percentage changes in the output response. URMs generally possess a sigmoid input–output relationship that is steeper than the Michaelis–Menten type of response and is often approximated by the Hill function. Six types of URMs can be commonly found in intracellular molecular networks and each has a distinct kinetic mechanism for signal amplification. These URMs are: (i) positive cooperative binding, (ii) homo-multimerization, (iii) multistep signalling, (iv) molecular titration, (v) zero-order covalent modification cycle and (vi) positive feedback. Multiple URMs can be combined to generate highly switch-like responses. Serving as basic signal amplifiers, these URMs are essential for molecular circuits to produce complex nonlinear dynamics, including multistability, robust adaptation and oscillation. These dynamic properties are in turn responsible for higher-level cellular behaviours, such as cell fate determination, homeostasis and biological rhythm. PMID:23615029

  8. Data link level interconnection of remote Fiber Distributed Data Interface Local Area Networks (FDDI LANs) through the Critical Data Link (CDL)

    NASA Astrophysics Data System (ADS)

    Karayakaylar, Selcuk

    1994-06-01

    This thesis deals with the features and performance of a network interface device to interconnect two remote Fiber Distributed Data Interface (FDDI) Local Area Networks (LAN's) through the Critical Data Link (CDL) which is a full-duplex, jam-resistant, point-to-point microwave communications system for use in imagery and signals intelligence collection systems. In particular, OPNET, a commercially available network engineering tool is used to model a medium access level remote bridge interface connecting two LAN's. The effectiveness of two different load balancing techniques used to distribute traffic over the multiple channels of the CDL has been studied. Also, the effect of different jamming patterns on the bit error rate seen by the users has been studied.

  9. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    PubMed

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  10. Linking Behavior in the Physics Education Research Coauthorship Network

    ERIC Educational Resources Information Center

    Anderson, Katharine A.; Crespi, Matthew; Sayre, Eleanor C.

    2017-01-01

    There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real…

  11. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells.

    PubMed

    Naldi, Aurélien; Larive, Romain M; Czerwinska, Urszula; Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Coopman, Peter J; Radulescu, Ovidiu

    2017-03-01

    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.

  12. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells

    PubMed Central

    Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Radulescu, Ovidiu

    2017-01-01

    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. PMID:28306714

  13. Signal-Response Modeling of Partial Hormone Feedback Networks

    PubMed Central

    Johnson, Michael L.; Veldhuis, Paula P.; Evans, William S.

    2009-01-01

    Background Endocrine feedback control networks are typically complex and contain multiple hormones, pools, and compartments. The hormones themselves commonly interact via multiple pathways and targets within the networks, and a complete description of such relationships may involve hundreds of parameters. In addition, it is often difficult, if not impossible, to collect experimental data pertaining to every component within the network. Therefore, the complete simultaneous analysis of such networks is challenging. Nevertheless, an understanding of these networks is critical for furthering our knowledge of hormonal regulation in both physiologic and pathophysiologic conditions. Methods We propose a novel approach for the analysis of dose-response relationships of subsets of hormonal feedback networks. The algorithm and signal-response quantification (SRQuant) software is based on convolution integrals, and tests whether several discretely measured input signals can be individually delayed, spread in time, transformed, combined, and discretely convolved with an elimination function to predict the time course of the concentration of an output hormone. Signal-response quantification is applied to examples from the endocrine literature to demonstrate its applicability to the analysis of the different endocrine networks. Results In one example, SRQuant determines the dose-response relationship by which one hormone regulates another, highlighting its advantages over other traditional methods. In a second example, for the first time (to the best of our knowledge), we show that the secretion of glucagon may be jointly controlled by the β and the δ cells. Conclusion We have developed a novel convolution integral-based approach, algorithm, and software (SRQuant) for the analysis of dose-response relationships within subsets of complex endocrine feedback control networks. PMID:20046649

  14. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

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

    2012-01-01

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

  15. Discrete dynamic modeling of T cell survival signaling networks

    NASA Astrophysics Data System (ADS)

    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).

  16. Efficient network disintegration under incomplete information: the comic effect of link prediction.

    PubMed

    Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

    2016-03-10

    The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the "comic effect" of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

  17. Efficient network disintegration under incomplete information: the comic effect of link prediction

    NASA Astrophysics Data System (ADS)

    Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

    2016-03-01

    The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

  18. Efficient network disintegration under incomplete information: the comic effect of link prediction

    PubMed Central

    Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

    2016-01-01

    The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized. PMID:26960247

  19. A neural network that links brain function, white-matter structure and risky behavior.

    PubMed

    Kohno, Milky; Morales, Angelica M; Guttman, Zoe; London, Edythe D

    2017-04-01

    The ability to evaluate the balance between risk and reward and to adjust behavior accordingly is fundamental to adaptive decision-making. Although brain-imaging studies consistently have shown involvement of the dorsolateral prefrontal cortex, anterior insula and striatum during risky decision-making, activation in a neural network formed by these regions has not been linked to structural connectivity. Therefore, in this study, white-matter connectivity was measured with diffusion-weighted imaging in 40 healthy research participants who performed the Balloon Analogue Risk Task, a test of risky decision-making, during fMRI. Fractional anisotropy within a network that includes white-matter pathways connecting four regions (the prefrontal cortex, insula and midbrain to the striatum) was positively correlated with the number of risky choices and total amount earned on the task, and with the parametric modulation of activation in regions within the network to the level of risk during choice selection. Furthermore, analysis using a mixed model demonstrated how relationships of the parametric modulation of activation in each of the four aforementioned regions are related to risk probabilities, and how previous trial outcomes and task progression influence the choice to take risk. The present findings provide the first direct evidence that white-matter integrity is linked to function within previously identified components of a network that is activated during risky decision-making, and demonstrate that the integrity of white-matter tracts is critical in consolidating and processing signals between cortical and striatal circuits during the decision-making process.

  20. Neural network approach to classification of infrasound signals

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang

    As part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear Test-Ban Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the man-made and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the in-house developed weighted least-squares algorithm. Classifying commonly observed low-frequency (0.015--0.1 Hz) signals at out stations, namely mountain associated waves and high trace-velocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the trace-velocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (2004--2007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two well-known detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with

  1. A comprehensive map of the mTOR signaling network

    PubMed Central

    Caron, Etienne; Ghosh, Samik; Matsuoka, Yukiko; Ashton-Beaucage, Dariel; Therrien, Marc; Lemieux, Sébastien; Perreault, Claude; Roux, Philippe P; Kitano, Hiroaki

    2010-01-01

    The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. PMID:21179025

  2. SSL: Signal Similarity-Based Localization for Ocean Sensor Networks.

    PubMed

    Chen, Pengpeng; Ma, Honglu; Gao, Shouwan; Huang, Yan

    2015-11-24

    Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes' positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes' positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes' relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

  3. SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

    PubMed Central

    Chen, Pengpeng; Ma, Honglu; Gao, Shouwan; Huang, Yan

    2015-01-01

    Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes’ positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes’ positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes’ relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks. PMID:26610520

  4. Dictyostelium uses ether-linked inositol phospholipids for intracellular signalling.

    PubMed

    Clark, Jonathan; Kay, Robert R; Kielkowska, Anna; Niewczas, Izabella; Fets, Louise; Oxley, David; Stephens, Len R; Hawkins, Phillip T

    2014-10-01

    Inositol phospholipids are critical regulators of membrane biology throughout eukaryotes. The general principle by which they perform these roles is conserved across species and involves binding of differentially phosphorylated inositol head groups to specific protein domains. This interaction serves to both recruit and regulate the activity of several different classes of protein which act on membrane surfaces. In mammalian cells, these phosphorylated inositol head groups are predominantly borne by a C38:4 diacylglycerol backbone. We show here that the inositol phospholipids of Dictyostelium are different, being highly enriched in an unusual C34:1e lipid backbone, 1-hexadecyl-2-(11Z-octadecenoyl)-sn-glycero-3-phospho-(1'-myo-inositol), in which the sn-1 position contains an ether-linked C16:0 chain; they are thus plasmanylinositols. These plasmanylinositols respond acutely to stimulation of cells with chemoattractants, and their levels are regulated by PIPKs, PI3Ks and PTEN. In mammals and now in Dictyostelium, the hydrocarbon chains of inositol phospholipids are a highly selected subset of those available to other phospholipids, suggesting that different molecular selectors are at play in these organisms but serve a common, evolutionarily conserved purpose.

  5. Robust allocation of weighted dependency links in cyber-physical networks

    NASA Astrophysics Data System (ADS)

    Li, Xin; Wu, Haotian; Scoglio, Caterina; Gruenbacher, Don

    2015-09-01

    Interdependent network models are often used to show how one network has an effect on another network through dependencies. In this paper, we propose a novel interdependent network model which consists of two individual networks with unequal numbers of nodes and one-to-multiple weighted dependency links between the two networks. Based on realistic assumptions, this model differs from previous works that considered equal numbers of nodes in the two networks and identical dependency links. We formulate an optimization problem to allocate dependency links using least resources. This novel model enhances the practicability of traditional cyber-physical system structures, but it makes the dependency link deployment problem more complex and the optimization problem cannot be solved in large networks. To overcome this problem, we propose a new algorithm based on a revised network flow method. Extensive simulations on random networks and real networks show that our deployment method produces topologies that are more robust than the ones obtained by other deployment techniques. Results indicate that our algorithm is efficient and cost-effective in designing robust interdependent networks, and our deployment method is suitable for networks of any size.

  6. Signal bi-amplification in networks of unidirectionally coupled MEMS

    NASA Astrophysics Data System (ADS)

    Tchakui, Murielle Vanessa; Woafo, Paul; Colet, Pere

    2016-01-01

    The purpose of this paper is to analyze the propagation and the amplification of an input signal in networks of unidirectionally coupled micro-electro-mechanical systems (MEMS). Two types of external excitations are considered: sinusoidal and stochastic signals. We show that sinusoidal signals are amplified up to a saturation level which depends on the transmission rate and despite MEMS being nonlinear the sinusoidal shape is well preserved if the number of MEMS is not too large. However, increasing the number of MEMS, there is an instability that leads to chaotic behavior and which is triggered by the amplification of the harmonics generated by the nonlinearities. We also show that for stochastic input signals, the MEMS array acts as a band-pass filter and after just a few elements the signal has a narrow power spectra.

  7. Approaches to identify kinase dependencies in cancer signalling networks.

    PubMed

    Dermit, Maria; Dokal, Arran; Cutillas, Pedro R

    2017-09-01

    Cells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine. © 2017 Federation of European Biochemical Societies.

  8. Reverse engineering GTPase programming languages with reconstituted signaling networks

    PubMed Central

    Coyle, Scott M.

    2016-01-01

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

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

    PubMed

    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.

  10. The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer

    PubMed Central

    Li, Lei; Tibiche, Chabane; Fu, Cong; Kaneko, Tomonori; Moran, Michael F.; Schiller, Martin R.; Li, Shawn Shun-Cheng; Wang, Edwin

    2012-01-01

    Phosphotyrosine (pTyr) signaling, which plays a central role in cell–cell and cell–environment interactions, has been considered to be an evolutionary innovation in multicellular metazoans. However, neither the emergence nor the evolution of the human pTyr signaling system is currently understood. Tyrosine kinase (TK) circuits, each of which consists of a TK writer, a kinase substrate, and a related reader, such as Src homology (SH) 2 domains and pTyr-binding (PTB) domains, comprise the core machinery of the pTyr signaling network. In this study, we analyzed the evolutionary trajectories of 583 literature-derived and 50,000 computationally predicted human TK circuits in 19 representative eukaryotic species and assigned their evolutionary origins. We found that human TK circuits for intracellular pTyr signaling originated largely from primitive organisms, whereas the inter- or extracellular signaling circuits experienced significant expansion in the bilaterian lineage through the “back-wiring” of newly evolved kinases to primitive substrates and SH2/PTB domains. Conversely, the TK circuits that are involved in tissue-specific signaling evolved mainly in vertebrates by the back-wiring of vertebrate substrates to primitive kinases and SH2/PTB domains. Importantly, we found that cancer signaling preferentially employs the pTyr sites, which are linked to more TK circuits. Our work provides insights into the evolutionary paths of the human pTyr signaling circuits and suggests the use of a network approach for cancer intervention through the targeting of key pTyr sites and their associated signaling hubs in the network. PMID:22194470

  11. Optimal Signal Processing in Small Stochastic Biochemical Networks

    PubMed Central

    Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

    2007-01-01

    We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

  12. Effects of link-orientation methods on robustness against cascading failures in complex networks

    NASA Astrophysics Data System (ADS)

    Jiang, Zhong-Yuan; Ma, Jian-Feng; Shen, Yu-Long; Zeng, Yong

    2016-09-01

    Unidirectional and bidirectional links may coexist in many realistic networked complex systems such as the city transportation networks. Even more, for some considerations, several bidirectional links are shifted to unidirectional ones. Many link-orientation strategies might be employed, including High-to-Low, Low-to-High and Random direction-determining methods, abbreviated as HTLDD, LTHDD and RDD respectively. Traffic passing through a unidirectional link is restricted to one-side direction. In real complex systems, nodes are correlated with each other. The failure from an initial node may be propagated iteratively, resulting in a large scale of failures of other nodes, called cascade phenomenon which may damage the safety or security of the networked system. Assuming that traffic load on any failed node can be redistributed to its non-failed neighbors, in this work, we try to reveal the effects of unidirectional links on network robustness against cascades. Extensive simulations have been implemented on kinds of networks including Scale-Free networks, Small-World networks, and Erdös-Rényi random networks. The results showed that all of the above three direction-determining methods decrease the robustness of the original networks against cascading failure. This work can help network designers and managers understand the robustness of network well and efficiently prevent the safety events.

  13. Signalling network construction for modelling plant defence response.

    PubMed

    Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

    2012-01-01

    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 utilised for

  14. p62 links autophagy and Nrf2 signaling

    PubMed Central

    Jiang, Tao; Harder, Bryan; Rojo de la Vega, Montserrat; Wong, Pak K.; Chapman, Eli; Zhang, Donna D.

    2015-01-01

    The Nrf2-Keap1-ARE pathway is a redox and xenobiotic sensitive signaling axis that functions to protect cells against oxidative stress, environmental toxicants, and harmful chemicals through the induction of cytoprotective genes. To enforce strict regulation, cells invest a great deal of energy into the maintenance of the Nrf2 pathway to ensure rapid induction upon cellular insult and rapid return to basal levels once the insult is mitigated. Because of the protective role of Nrf2 transcriptional programs, controlled activation of the pathway has been recognized as a means for chemoprevention. On the other hand, constitutive activation of Nrf2, due to somatic mutations of genes that control Nrf2 degradation, promotes carcinogenesis and imparts chemoresistance to cancer cells. Autophagy, a bulk protein degradation process, is another tightly regulated complex cellular process that functions as a cellular quality control system to remove damaged proteins or organelles. Low cellular nutrient levels can also activate autophagy, which acts to restore metabolic homeostasis through the degradation of macromolecules to provide nutrients. Recently, these two cellular pathways were shown to intersect through the direct interaction between p62 (an autophagy adaptor protein) and Keap1 (the Nrf2 substrate adaptor for the Cul3 E3 ubiquitin ligase). Dysregulation of autophagy was shown to result in prolonged Nrf2 activation in a p62-dependent manner. In this review, we will discuss the progress that has been made in dissecting the intersection of these two pathways and the potential tumor-promoting role of prolonged Nrf2 activation. PMID:26117325

  15. Teaming Up: Linking Collaboration Networks, Collective Efficacy, and Student Achievement

    ERIC Educational Resources Information Center

    Moolenaar, Nienke M.; Sleegers, Peter J. C.; Daly, Alan J.

    2012-01-01

    Improving student achievement through teacher collaboration networks is a current focus of schools in many countries. Yet, empirical evidence on the relationship between teacher networks and student achievement and mechanisms that may explain this relationship is limited. This study examined the relationship between teacher networks and student…

  16. Topic Models for Link Prediction in Document Networks

    ERIC Educational Resources Information Center

    Kataria, Saurabh

    2012-01-01

    Recent explosive growth of interconnected document collections such as citation networks, network of web pages, content generated by crowd-sourcing in collaborative environments, etc., has posed several challenging problems for data mining and machine learning community. One central problem in the domain of document networks is that of "link…

  17. Topic Models for Link Prediction in Document Networks

    ERIC Educational Resources Information Center

    Kataria, Saurabh

    2012-01-01

    Recent explosive growth of interconnected document collections such as citation networks, network of web pages, content generated by crowd-sourcing in collaborative environments, etc., has posed several challenging problems for data mining and machine learning community. One central problem in the domain of document networks is that of "link…

  18. Teaming Up: Linking Collaboration Networks, Collective Efficacy, and Student Achievement

    ERIC Educational Resources Information Center

    Moolenaar, Nienke M.; Sleegers, Peter J. C.; Daly, Alan J.

    2012-01-01

    Improving student achievement through teacher collaboration networks is a current focus of schools in many countries. Yet, empirical evidence on the relationship between teacher networks and student achievement and mechanisms that may explain this relationship is limited. This study examined the relationship between teacher networks and student…

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

    NASA Astrophysics Data System (ADS)

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

    2004-10-01

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

  20. Molecular basis for specificity of the Met1-linked polyubiquitin signal.

    PubMed

    Elliott, Paul R

    2016-12-15

    The post-translational modification of proteins provides a rapid and versatile system for regulating all signalling pathways. Protein ubiquitination is one such type of post-translational modification involved in controlling numerous cellular processes. The unique ability of ubiquitin to form polyubiquitin chains creates a highly complex code responsible for different subsequent signalling outcomes. Specialised enzymes ('writers') generate the ubiquitin code, whereas other enzymes ('erasers') disassemble it. Importantly, the ubiquitin code is deciphered by different ubiquitin-binding proteins ('readers') functioning to elicit particular cellular responses. Ten years ago, the methionine1 (Met1)-linked (linear) polyubiquitin code was first identified and the intervening years have witnessed a seismic shift in our understanding of Met1-linked polyubiquitin in cellular processes, particularly inflammatory signalling. This review will discuss the molecular mechanisms of specificity determination within Met1-linked polyubiquitin signalling.

  1. Molecular basis for specificity of the Met1-linked polyubiquitin signal

    PubMed Central

    Elliott, Paul R.

    2016-01-01

    The post-translational modification of proteins provides a rapid and versatile system for regulating all signalling pathways. Protein ubiquitination is one such type of post-translational modification involved in controlling numerous cellular processes. The unique ability of ubiquitin to form polyubiquitin chains creates a highly complex code responsible for different subsequent signalling outcomes. Specialised enzymes (‘writers’) generate the ubiquitin code, whereas other enzymes (‘erasers’) disassemble it. Importantly, the ubiquitin code is deciphered by different ubiquitin-binding proteins (‘readers’) functioning to elicit particular cellular responses. Ten years ago, the methionine1 (Met1)-linked (linear) polyubiquitin code was first identified and the intervening years have witnessed a seismic shift in our understanding of Met1-linked polyubiquitin in cellular processes, particularly inflammatory signalling. This review will discuss the molecular mechanisms of specificity determination within Met1-linked polyubiquitin signalling. PMID:27913667

  2. Fragmentation transition in a coevolving network with link-state dynamics.

    PubMed

    Carro, A; Vazquez, F; Toral, R; San Miguel, M

    2014-06-01

    We study a network model that couples the dynamics of link states with the evolution of the network topology. The state of each link, either A or B, is updated according to the majority rule or zero-temperature Glauber dynamics, in which links adopt the state of the majority of their neighboring links in the network. Additionally, a link that is in a local minority is rewired to a randomly chosen node. While large systems evolving under the majority rule alone always fall into disordered topological traps composed by frustrated links, any amount of rewiring is able to drive the network to complete order, by relinking frustrated links and so releasing the system from traps. However, depending on the relative rate of the majority rule and the rewiring processes, the system evolves towards different ordered absorbing configurations: either a one-component network with all links in the same state or a network fragmented in two components with opposite states. For low rewiring rates and finite-size networks there is a domain of bistability between fragmented and nonfragmented final states. Finite-size scaling indicates that fragmentation is the only possible scenario for large systems and any nonzero rate of rewiring.

  3. Fragmentation transition in a coevolving network with link-state dynamics

    NASA Astrophysics Data System (ADS)

    Carro, A.; Vazquez, F.; Toral, R.; San Miguel, M.

    2014-06-01

    We study a network model that couples the dynamics of link states with the evolution of the network topology. The state of each link, either A or B, is updated according to the majority rule or zero-temperature Glauber dynamics, in which links adopt the state of the majority of their neighboring links in the network. Additionally, a link that is in a local minority is rewired to a randomly chosen node. While large systems evolving under the majority rule alone always fall into disordered topological traps composed by frustrated links, any amount of rewiring is able to drive the network to complete order, by relinking frustrated links and so releasing the system from traps. However, depending on the relative rate of the majority rule and the rewiring processes, the system evolves towards different ordered absorbing configurations: either a one-component network with all links in the same state or a network fragmented in two components with opposite states. For low rewiring rates and finite-size networks there is a domain of bistability between fragmented and nonfragmented final states. Finite-size scaling indicates that fragmentation is the only possible scenario for large systems and any nonzero rate of rewiring.

  4. Cascading failure in interconnected weighted networks based on the state of link

    NASA Astrophysics Data System (ADS)

    Ding, Chao; Yao, Hong; Du, Jun; Peng, Xingzhao; Wang, Zhe; Zhao, Jingbo

    Recently the robustness of coupled network under cascading failure has attracted a lot of attention. In this paper, we investigate the cascading failure of the interconnected weighted networks based on the state of link. The load on one link is defined by a function of the strength of the two nodes at the ends of that link, using four intentional attack strategies, we study the invulnerability of the interconnected weighted networks when cascading failure occurs. Our studies show that when the link with highest load is attacked, the damage to the network will be more serious by attacking the inner-link with highest load than that caused by attacking the coupling link with highest load, and no matter how the coupling links distribute, there are two thresholds. In addition, we find that the larger the weight increment in the model or the smaller the network’s mean clustering coefficient, the stronger the ability of the network to resist cascading failure when the inner-link with highest load is attacked, while the weaker the ability of the network to suppress the cascading failure when the inner-link with lowest load is attacked.

  5. Signal Transduction Pathways of TNAP: Molecular Network Analyses.

    PubMed

    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.

  6. Reducing the Spectral Radius of a Torus Network by Link Removal

    PubMed Central

    Yang, Xiaofan; Li, Pengdeng; Yang, Lu-Xing; Wu, Yingbo

    2016-01-01

    The optimal link removal (OLR) problem aims at removing a given number of links of a network so that the spectral radius of the residue network obtained by removing the links from the network attains the minimum. Torus networks are a class of regular networks that have witnessed widespread applications. This paper addresses three subproblems of the OLR problem for torus networks, where two or three or four edges are removed. For either of the three subproblems, a link-removing scheme is described. Exhaustive searches show that, for small-sized tori, each of the proposed schemes produces an optimal solution to the corresponding subproblem. Monte-Carlo simulations demonstrate that, for medium-sized tori, each of the three schemes produces a solution to the corresponding subproblem, which is optimal when compared to a large set of randomly produced link-removing schemes. Consequently, it is speculated that each of the three schemes produces an optimal solution to the corresponding subproblem for all torus networks. The set of links produced by each of our schemes is evenly distributed over a network, which may be a common feature of an optimal solution to the OLR problem for regular networks. PMID:27171372

  7. Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

    PubMed Central

    Huang, Shao-shan Carol; Clarke, David C.; Gosline, Sara J. C.; Labadorf, Adam; Chouinard, Candace R.; Gordon, William; Lauffenburger, Douglas A.; Fraenkel, Ernest

    2013-01-01

    Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets. PMID:23408876

  8. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

    PubMed Central

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

    2016-01-01

    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. PMID:27253373

  9. Next generation of network medicine: interdisciplinary signaling approaches.

    PubMed

    Korcsmaros, Tamas; Schneider, Maria Victoria; Superti-Furga, Giulio

    2017-02-20

    In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

  10. Signal Processing in Periodically Forced Gradient Frequency Neural Networks

    PubMed Central

    Kim, Ji Chul; Large, Edward W.

    2015-01-01

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

  11. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    PubMed

    Kim, Ji Chul; Large, Edward W

    2015-01-01

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

  12. Programs for control of an analog-signal switching network

    SciTech Connect

    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.

  13. Collective signaling behavior in a networked-oscillator model

    NASA Astrophysics Data System (ADS)

    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.

  14. Therapeutics Targeting FGF Signaling Network in Human Diseases.

    PubMed

    Katoh, Masaru

    2016-12-01

    Fibroblast growth factor (FGF) signaling through its receptors, FGFR1, FGFR2, FGFR3, or FGFR4, regulates cell fate, angiogenesis, immunity, and metabolism. Dysregulated FGF signaling causes human diseases, such as breast cancer, chondrodysplasia, gastric cancer, lung cancer, and X-linked hypophosphatemic rickets. Recombinant FGFs are pro-FGF signaling therapeutics for tissue and/or wound repair, whereas FGF analogs and gene therapy are under development for the treatment of cardiovascular disease, diabetes, and osteoarthritis. FGF traps, anti-FGF/FGFR monoclonal antibodies (mAbs), and small-molecule FGFR inhibitors are anti-FGF signaling therapeutics under development for the treatment of cancer, chondrodysplasia, and rickets. Here, I discuss the benefit-risk and cost-effectiveness issues of precision medicine targeting FGFRs, ALK, EGFR, and FLT3. FGFR-targeted therapy should be optimized for cancer treatment, focusing on genomic tests and recurrence.

  15. Signaling and Gene Regulatory Networks in Mammalian Lens Development.

    PubMed

    Cvekl, Ales; Zhang, Xin

    2017-10-01

    Ocular lens development represents an advantageous system in which to study regulatory mechanisms governing cell fate decisions, extracellular signaling, cell and tissue organization, and the underlying gene regulatory networks. Spatiotemporally regulated domains of BMP, FGF, and other signaling molecules in late gastrula-early neurula stage embryos generate the border region between the neural plate and non-neural ectoderm from which multiple cell types, including lens progenitor cells, emerge and undergo initial tissue formation. Extracellular signaling and DNA-binding transcription factors govern lens and optic cup morphogenesis. Pax6, c-Maf, Hsf4, Prox1, Sox1, and a few additional factors regulate the expression of the lens structural proteins, the crystallins. Extensive crosstalk between a diverse array of signaling pathways controls the complexity and order of lens morphogenetic processes and lens transparency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. On-Board Fiber-Optic Network Architectures for Radar and Avionics Signal Distribution

    NASA Technical Reports Server (NTRS)

    Alam, Mohammad F.; Atiquzzaman, Mohammed; Duncan, Bradley B.; Nguyen, Hung; Kunath, Richard

    2000-01-01

    Continued progress in both civil and military avionics applications is overstressing the capabilities of existing radio-frequency (RF) communication networks based on coaxial cables on board modem aircrafts. Future avionics systems will require high-bandwidth on- board communication links that are lightweight, immune to electromagnetic interference, and highly reliable. Fiber optic communication technology can meet all these challenges in a cost-effective manner. Recently, digital fiber-optic communication systems, where a fiber-optic network acts like a local area network (LAN) for digital data communications, have become a topic of extensive research and development. Although a fiber-optic system can be designed to transport radio-frequency (RF) signals, the digital fiber-optic systems under development today are not capable of transporting microwave and millimeter-wave RF signals used in radar and avionics systems on board an aircraft. Recent advances in fiber optic technology, especially wavelength division multiplexing (WDM), has opened a number of possibilities for designing on-board fiber optic networks, including all-optical networks for radar and avionics RF signal distribution. In this paper, we investigate a number of different novel approaches for fiber-optic transmission of on-board VHF and UHF RF signals using commercial off-the-shelf (COTS) components. The relative merits and demerits of each architecture are discussed, and the suitability of each architecture for particular applications is pointed out. All-optical approaches show better performance than other traditional approaches in terms of signal-to-noise ratio, power consumption, and weight requirements.

  17. Novel Lys63-linked ubiquitination of IKKβ induces STAT3 signaling

    PubMed Central

    Gallo, Leandro H; Meyer, April N; Motamedchaboki, Khatereh; Nelson, Katelyn N; Haas, Martin; Donoghue, Daniel J

    2014-01-01

    NFκB signaling plays a significant role in human disease, including breast and ovarian carcinoma, insulin resistance, embryonic lethality and liver degeneration, rheumatoid arthritis, aging and Multiple Myeloma (MM). Inhibitor of κB (IκB) kinase β (IKKβ) regulates canonical Nuclear Factor κB (NFκB) signaling in response to inflammation and cellular stresses. NFκB activation requires Lys63-linked (K63-linked) ubiquitination of upstream proteins such as NEMO or TAK1, forming molecular complexes with membrane-bound receptors. We demonstrate that IKKβ itself undergoes K63-linked ubiquitination. Mutations in IKKβ at Lys171, identified in Multiple Myeloma and other cancers, lead to a dramatic increase in kinase activation and K63-linked ubiquitination. These mutations also result in persistent activation of STAT3 signaling. Liquid chromatography (LC)-high mass accuracy tandem mass spectrometry (MS/MS) analysis identified Lys147, Lys418, Lys555 and Lys703 as predominant ubiquitination sites in IKKβ. Specific inhibition of the UBC13-UEV1A complex responsible for K63-linked ubiquitination establishes Lys147 as the predominant site of K63-ubiquitin conjugation and responsible for STAT3 activation. Thus, IKKβ activation leads to ubiquitination within the kinase domain and assemblage of a K63-ubiquitin conjugated signaling platform. These results are discussed with respect to the importance of upregulated NFκB signaling known to occur frequently in MM and other cancers. PMID:25486864

  18. Music Signal Processing Using Vector Product Neural Networks

    NASA Astrophysics Data System (ADS)

    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.

  19. Characterization of RF signal transmission using FSO links considering atmospheric effects

    NASA Astrophysics Data System (ADS)

    Mohammad Shah, Alam; Dat, Pham Tien; Kazaura, Kamugisha; Wakamori, Kazuhiko; Suzuki, Toshiji; Omae, Kazunori; Matsumoto, Mitsuji; Aburakawa, Yuji; Takahashi, Koichi; Nakamura, Takuya; Higashino, Takeshi; Tsukamoto, Katsutoshi; Komaki, Shozo

    2008-02-01

    Radio on Free-Space Optics (RoFSO) communication systems have attracted a considerable attention for a variety of applications where optical fibers are not feasible, especially in rural areas, to provide ubiquitous wireless services quickly and more effectively. RoFSO links can be used to transmit signals like cellular W-CDMA, terrestrial digital TV or WLAN signals. In spite of its potential, such links are highly dependent on the deployment environment characteristics in particular the weather conditions. Severity and duration of the atmospheric effects have direct impact on the availability of the links as well as on the quality of RF signal transmitted over it. Thus, the necessity of investigating the effects of various weather conditions on RF signal transmission using FSO links. In collaboration with several institutions, we are currently developing an advanced Dense Wavelength Division Multiplexing (DWDM) RoFSO antenna capable of transporting multiple RF signals. As preliminary work, we are conducting experiments on a 1 km link using an off-the-shelf Radio Frequency - FSO (RF-FSO) antenna, with the objective of obtaining and characterizing performance related parameters of RF-FSO transmission in operational environment. As an example, we examine the influence of atmospheric turbulence on the transmission quality of W-CDMA signal. Among the performance metric of interest is the Adjacent Channel Leakage Power Ratio (ACLR) which will be measured, analyzed and correlated with the weather conditions. An atmospheric fluctuation model for estimating the communication quality of RF signal transmission on FSO links is being developed. Also the obtained results will be used for the deployment environment characterization as well as baseline for the design and performance evaluation of new advanced DWDM RoFSO communication systems we are currently developing.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    PubMed Central

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

    2016-01-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

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

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-10-01

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

  4. Planarian Hh signaling regulates regeneration polarity and links Hh pathway evolution to cilia.

    PubMed

    Rink, Jochen C; Gurley, Kyle A; Elliott, Sarah A; Sánchez Alvarado, Alejandro

    2009-12-04

    The Hedgehog (Hh) signaling pathway plays multiple essential roles during metazoan development, homeostasis, and disease. Although core protein components are highly conserved, the variations in Hh signal transduction mechanisms exhibited by existing model systems (Drosophila, fish, and mammals) are difficult to understand. We characterized the Hh pathway in planarians. Hh signaling is essential for establishing the anterior/posterior axis during regeneration by modulating wnt expression. Moreover, RNA interference methods to reduce signal transduction proteins Cos2/Kif27/Kif7, Fused, or Iguana do not result in detectable Hh signaling defects; however, these proteins are essential for planarian ciliogenesis. Our study expands the understanding of Hh signaling in the animal kingdom and suggests an ancestral mechanistic link between Hh signaling and the function of cilia.

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

    PubMed Central

    2010-01-01

    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 pathogen detection

  6. An Improved Network Strain Filter for Detecting Transient Deformation Signals

    NASA Astrophysics Data System (ADS)

    Ohtani, R.; McGuire, J.; Segall, P.

    2008-12-01

    We have developed a tool to detect transient signals such as aseismic fault slip and magmatic intrusion automatically from large-scale (principally GPS) geodetic arrays, referred to as a Network Strain Filter (NSF). The NSF is capable of detecting transient signals in large data sets which may be difficult to identify by visual inspection of individual time series. The underlying principle is to exploit the spatially coherent nature of tectonic signals. The NSF models GPS displacement time series as a sum of contributions from tectonic transients, steady motion due to secular deformation, site-specific local benchmark motion, reference frame errors, and white noise. Transient deformation is represented by a spatial wavelet basis with time varying coefficients estimated using Kalman filtering techniques. A "hyperparameter" is also estimated to constrain the amount of temporal smoothness of the tectonic deformation. As station distribution is irregular and wavelets have local support (non-zero only over a localized domain), the design matrix is generally ill-conditioned. We investigate two strategies for regularizing the problem. The first is explicit spatial smoothing of the transient deformation. The second is to simply exclude wavelet bases that don't span some minimum number of stations. In this case, the smallest wavelet scale is determined such that the residual variance is consistent with the a priori errors of the data. Similarly the degree of spatial smoothing is determined by a priori knowledge of the data errors. To test the performance of the NSF, we carried out numerical tests using the southern California Integrated GPS Network (SCIGN) station distribution with synthetic transients of variable signal to noise ratio. We tested a six-year-long time series with a slow slip event with a duration of three years. Due to the long duration of the transient event, the contributions from secular motion and benchmark wobble make it difficult to identify the

  7. Classification of radar jammer FM signals using a neural network

    NASA Astrophysics Data System (ADS)

    Mendoza, Ariadna; Soto, Alberto; Flores, Benjamin C.

    2017-05-01

    We propose an approach based on artificial Neural Networks (NN) to classify wideband radar jammer signals for more efficient use of countermeasures. A robust NN is be used to correctly differentiate Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). We compare the performance of the NN using the samples of the power spectrum versus the autocorrelation. Prior experiments showed that frequency-domain moments of the FM signal itself are better descriptors than time-domain moments. Using simulated wideband FM radar signals, we compute a set of N autocorrelation and spectra and feed them to the NN which has ten hidden layers. For training purposes, the autocorrelations or spectra sets are divided into three groups, 75% for training, 15% for validating and 15% for testing. For the power spectra set, we observe that a Signal to Noise Ratio (SNR) of 5dB allows the network to approach an average of 5% percent Probability of Error (PE). Training with the autocorrelation set yields comparable results. For an SNR of 5dB, the average PE reached an average of 0.3%. In both instances, the NN reaches zero percent PE at an SNR of 10dB.

  8. Distributed Signal Processing for Wireless EEG Sensor Networks.

    PubMed

    Bertrand, Alexander

    2015-11-01

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

  9. Analysis of the establishment of the global synchronization in complex networks with different topologies of links

    NASA Astrophysics Data System (ADS)

    Hramov, Alexander E.; Kharchenko, Alexander A.; Khramova, Marina V.; Koronovskii, Alexey A.; Kurovskaya, Maria K.; Makarov, Vladimir V.; Moskalenko, Olga I.; Pavlov, Alexey N.; Dana, Syamal K.

    2016-03-01

    In the present paper the mechanism of the global synchronization onset through the formation of the synchronous clusters in complex networks with different topologies of links (scale-free networks, small-world networks, random networks) is studied. We consider the dependencies of integral characteristics of synchronous dynamics (synchronization measure, number of synchronous clusters, etc) on coupling strength between nodes. As a basic element of the node oscillator we consider Kuramoto phase oscillator.

  10. Strain hardening, avalanches, and strain softening in dense cross-linked actin networks

    NASA Astrophysics Data System (ADS)

    Åström, Jan A.; Kumar, P. B. Sunil; Vattulainen, Ilpo; Karttunen, Mikko

    2008-05-01

    Actin filament networks enable the cytoskeleton to adjust to internal and external forcing. These dynamic networks can adapt to changes by dynamically adjusting their cross-links. Here, we model actin filaments as cross-linked elastic fibers of finite dimensions, with the cross-links being approximately 1μm apart, and employ a full three-dimensional model to study their elastic properties by computer simulations. The results show compelling evidence that dense actin networks are characterized by (a) strain hardening without entropic elasticity, (b) avalanches of cross-link slippage leading to strain softening in the case of breakable cross-links, and (c) spontaneous formation of stress fibers in the case of dynamic cross-link formation and destruction.

  11. The vulnerability of the global container shipping network to targeted link disruption

    NASA Astrophysics Data System (ADS)

    Viljoen, Nadia M.; Joubert, Johan W.

    2016-11-01

    Using complex network theory to describe the relational geography of maritime networks has provided great insights regarding their hierarchy and evolution over the past two decades. Unlike applications in other transport fields, notably air transport, complex network theory has had limited application in studying the vulnerability of maritime networks. This study uses targeted link disruption to investigate the strategy specific vulnerability of the network. Although nodal infrastructure such as ports can render a network vulnerable as a result of labour strikes, trade embargoes or natural disasters, it is the shipping lines connecting the ports that are more probably disrupted, either from within the industry, or outside. In this paper, we apply and evaluate two link-based disruption strategies on the global container shipping network, one based on link betweenness, and the other on link salience, to emulate the impact of large-scale service reconfiguration affecting priority links. The results show that the network is by and large robust to such reconfiguration. Meanwhile the flexibility of the network is reduced by both strategies, but to a greater degree by betweenness, resulting in a reduction of transshipment and dynamic rerouting potential amongst the busiest port regions. The results further show that the salience strategy is highly effective in reducing the commonality of shortest path sets, thereby diminishing opportunities for freight consolidation and scale economies.

  12. NASA 60 GHz intersatellite communication link definition study. Addendum A: Mixed baseband and IF signals

    NASA Technical Reports Server (NTRS)

    1986-01-01

    As part of a definition study for a 60 GHz intersatellite communications link system (ICLS), baseline design concepts for a channelized crosslink were identified. The crosslink would allow communications between geostationary satellites of the planned Tracking and Data Acquisition System (TDAS) and would accommodate a mixture of frequency translation coherent links (bent pipe links) and baseband-in/baseband-out links (mod/demod links). A 60 GHz communication system was developed for sizing and analyzing the crosslink. For the coherent links this system translates incoming signals directly up to the 60 GHz band; trunks the signals across from one satellite to a second satellite at 60 GHz then down converts to the proper frequency for re-transmission from the second satellite without converting to any intermediate frequencies. For the baseband-in/baseband-out links the baseband data is modulated on to the 60 GHz carrier at the transmitting satellite and demodulated at the receiving satellite. The frequency plan, equipment diagrams, and link calculations are presented along with results from sizing and reliability analyses.

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

    PubMed

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

    2011-08-01

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

  14. Integration and analysis of neighbor discovery and link quality estimation in wireless sensor networks.

    PubMed

    Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

    2014-01-01

    Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

  15. Visualizing weighted networks: a performance comparison of adjacency matrices versus node-link diagrams

    NASA Astrophysics Data System (ADS)

    McIntire, John P.; Osesina, O. Isaac; Bartley, Cecilia; Tudoreanu, M. Eduard; Havig, Paul R.; Geiselman, Eric E.

    2012-06-01

    Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence, cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey information to a viewer. This is particularly true for weighted networks which include the strength of connections between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The results should help shed light on effective methods of visualizing network data for some representative analysis tasks, with the ultimate goal of improving usability and performance for viewers of network data displays.

  16. Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

    PubMed Central

    Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

    2014-01-01

    Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications. PMID:24678277

  17. Traffic Management in ATM Networks Over Satellite Links

    NASA Technical Reports Server (NTRS)

    Goyal, Rohit; Jain, Raj; Goyal, Mukul; Fahmy, Sonia; Vandalore, Bobby; vonDeak, Thomas

    1999-01-01

    This report presents a survey of the traffic management Issues in the design and implementation of satellite Asynchronous Transfer Mode (ATM) networks. The report focuses on the efficient transport of Transmission Control Protocol (TCP) traffic over satellite ATM. First, a reference satellite ATM network architecture is presented along with an overview of the service categories available in ATM networks. A delay model for satellite networks and the major components of delay and delay variation are described. A survey of design options for TCP over Unspecified Bit Rate (UBR), Guaranteed Frame Rate (GFR) and Available Bit Rate (ABR) services in ATM is presented. The main focus is on traffic management issues. Several recommendations on the design options for efficiently carrying data services over satellite ATM networks are presented. Most of the results are based on experiments performed on Geosynchronous (GEO) latencies. Some results for Low Earth Orbits (LEO) and Medium Earth Orbit (MEO) latencies are also provided.

  18. Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

    PubMed Central

    Lyamzin, Dmitry R.; Barnes, Samuel J.; Donato, Roberta; Garcia-Lazaro, Jose A.; Keck, Tara

    2015-01-01

    Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks. PMID:26019325

  19. Modeling Signaling Networks to Advance New Cancer Therapies.

    PubMed

    Saez-Rodriguez, Julio; MacNamara, Aidan; Cook, Simon

    2015-01-01

    Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

  20. A signaling network in phenylephrine-induced benign prostatic hyperplasia.

    PubMed

    Kim, Jayoung; Yanagihara, Yutaka; Kikugawa, Tadahiko; Ji, Mihee; Tanji, Nozomu; Masayoshi, Yokoyama; Freeman, Michael R

    2009-08-01

    Benign prostatic hyperplasia (BPH) is an age-related disease of unknown etiology characterized by prostatic enlargement and coinciding with distinctive alterations in tissue histomorphology. To identify the molecular mechanisms underlying the development of BPH, we conducted a DNA microarray study using a previously described animal model in which chronic alpha(1)-adrenergic stimulation by repeated administration of phenylephrine evokes histomorphological changes in the rat prostate that resemble human BPH. Bioinformatic tools were applied to microarray data obtained from prostate tissue to construct a network model of potentially relevant signal transduction pathways. Significant involvement of inflammatory pathways was demonstrable, including evidence for activation of a TGF-beta signaling cascade. The heterodimeric protein clusterin (apolipoprotein J) was also identified as a prominent node in the network. Responsiveness of TGF-beta signaling and clusterin gene and protein expression were confirmed independently of the microarray data, verifying some components of the model. This is the first attempt to develop a comprehensive molecular network for histological BPH induced by adrenergic activation. The study also implicated clusterin as a novel biochemical target for therapy.

  1. Hijacking common mycorrhizal networks for herbivore-induced defence signal transfer between tomato plants.

    PubMed

    Song, Yuan Yuan; Ye, Mao; Li, Chuanyou; He, Xinhua; Zhu-Salzman, Keyan; Wang, Rui Long; Su, Yi Juan; Luo, Shi Ming; Zeng, Ren Sen

    2014-01-28

    Common mycorrhizal networks (CMNs) link multiple plants together. We hypothesized that CMNs can serve as an underground conduit for transferring herbivore-induced defence signals. We established CMN between two tomato plants in pots with mycorrhizal fungus Funneliformis mosseae, challenged a 'donor' plant with caterpillar Spodoptera litura, and investigated defence responses and insect resistance in neighbouring CMN-connected 'receiver' plants. After CMN establishment caterpillar infestation on 'donor' plant led to increased insect resistance and activities of putative defensive enzymes, induction of defence-related genes and activation of jasmonate (JA) pathway in the 'receiver' plant. However, use of a JA biosynthesis defective mutant spr2 as 'donor' plants resulted in no induction of defence responses and no change in insect resistance in 'receiver' plants, suggesting that JA signalling is required for CMN-mediated interplant communication. These results indicate that plants are able to hijack CMNs for herbivore-induced defence signal transfer and interplant defence communication.

  2. Living ordered neural networks as model systems for signal processing

    NASA Astrophysics Data System (ADS)

    Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.

    2007-06-01

    Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.

  3. Critical Performance Enhancement of Ultrahigh-Bandwidth Microwave Photonic Links through Nonlinear Photonic Signal Processing

    DTIC Science & Technology

    2013-04-11

    four-wave mixing (FWM) interaction such that in an amplifier-less link we are thermally noise limited after photodetection due to a low received...link with self-phase modulation based enhancement and balanced detection. optical signal. During this quarter we have constructed the sampling-based...photodetector. Furthermore, 8-dB of signal gain, a 3.6-dB improvement in OIP3, and a 3.1 -dB improvement in OIP2. the use of the balanced detector allows for

  4. Critical effects of overlapping of connectivity and dependence links on percolation of networks

    NASA Astrophysics Data System (ADS)

    Li, Ming; Liu, Run-Ran; Jia, Chun-Xiao; Wang, Bing-Hong

    2013-09-01

    In a recent work Parshani et al (2011 Proc. Natl Acad. Sci. USA 108 1007), dependence links have been introduced to the percolation model and used to study the robustness of the networks with such links, which shows that the networks are more vulnerable than the classical networks with only connectivity links. This model usually demonstrates a first order transition, rather than the second order transition found in classical network percolation. In this paper, considering the real situation that the interdependent nodes are usually connected, we study the cascading dynamics of networks when dependence links partially overlap with connectivity links. We find that the percolation transitions are not always sharpened by making nodes interdependent. For a high fraction of overlapping, the network is robust for random failures, and the percolation transition is second order, while for a low fraction of overlapping, the percolation process shows a first order transition. This work demonstrates that the crossover between two types of transitions does not only depend on the density of dependence links but also on the overlapping fraction of connectivity and dependence links. Using generating function techniques, we present exact solutions for the size of the giant component and the critical point, which are in good agreement with the simulations.

  5. Multi-Hop Link Capacity of Multi-Route Multi-Hop MRC Diversity for a Virtual Cellular Network

    NASA Astrophysics Data System (ADS)

    Daou, Imane; Kudoh, Eisuke; Adachi, Fumiyuki

    In virtual cellular network (VCN), proposed for high-speed mobile communications, the signal transmitted from a mobile terminal is received by some wireless ports distributed in each virtual cell and relayed to the central port that acts as a gateway to the core network. In this paper, we apply the multi-route MHMRC diversity in order to decrease the transmit power and increase the multi-hop link capacity. The transmit power, the interference power and the link capacity are evaluated for DS-CDMA multi-hop VCN by computer simulation. The multi-route MHMRC diversity can be applied to not only DS-CDMA but also other access schemes (i. e. MC-CDMA, OFDM, etc.).

  6. Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

    PubMed Central

    Wu, Liantao; Yu, Kai; Cao, Dongyu; Hu, Yuhen; Wang, Zhi

    2015-01-01

    Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links. PMID:26287195

  7. Linking behavior in the physics education research coauthorship network

    NASA Astrophysics Data System (ADS)

    Anderson, Katharine A.; Crespi, Matthew; Sayre, Eleanor C.

    2017-06-01

    There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real time. In this paper, we use thirty years of data from an emerging scientific community to look at that crucial early stage in the development of a social network. We show that when the field was very young, islands of individual researchers labored in relative isolation, and the coauthorship network was disconnected. Thirty years later, rather than a cluster of individuals, we find a true collaborative community, bound together by a robust collaboration network. However, this change did not take place gradually—the network remained a loose assortment of isolated individuals until the mid 2000s, when those smaller parts suddenly knit themselves together into a single whole. In the rest of this paper, we consider the role of three factors in these observed structural changes: growth, changes in social norms, and the introduction of institutions such as field-specific conferences and journals. We have data from the very earliest years of the field, a period which includes the introduction of two different institutions: the first field-specific conference, and the first field-specific journals. We also identify two relevant behavioral shifts: a discrete increase in coauthorship coincident with the first conference, and a shift among established authors away from collaborating with outsiders, towards collaborating with each other. The interaction of these factors gives us insight into the formation of collaboration networks more broadly.

  8. A preferential attachment strategy for connectivity link addition strategy in improving the robustness of interdependent networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingyuan; Cao, Jianye; Li, Rui; Zhao, Tianfang

    2017-10-01

    Given the same two networks and only one-to-one interlinks are allowed, apparently interdependent networks coupled by these two networks has the optimal robustness when we connect every pair of the same nodes in these two networks. According to the structure of this interdependent network with the optimal robustness, we propose a preferential attachment strategy. And by applying this preferential attachment strategy to three existing connectivity link addition strategies RA (random addition strategy), LD (low degree addition strategy) and LIDD (low inter degree-degree difference addition strategy), we find that each improved strategy is obviously better than before in improving the robustness of interdependent networks. Our findings can provide guidance on connectivity link addition strategy to improve robustness of interdependent networks against cascading failures.

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

    PubMed

    Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

    2013-01-01

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

  10. Random Deep Belief Networks for Recognizing Emotions from Speech Signals

    PubMed Central

    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. PMID:28356908

  11. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    NASA Astrophysics Data System (ADS)

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-11-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  12. Oma1 Links Mitochondrial Protein Quality Control and TOR Signaling To Modulate Physiological Plasticity and Cellular Stress Responses.

    PubMed

    Bohovych, Iryna; Kastora, Stavroula; Christianson, Sara; Topil, Danelle; Kim, Heejeong; Fangman, Teresa; Zhou, You J; Barrientos, Antoni; Lee, Jaekwon; Brown, Alistair J P; Khalimonchuk, Oleh

    2016-09-01

    A network of conserved proteases known as the intramitochondrial quality control (IMQC) system is central to mitochondrial protein homeostasis and cellular health. IMQC proteases also appear to participate in establishment of signaling cues for mitochondrion-to-nucleus communication. However, little is known about this process. Here, we show that in Saccharomyces cerevisiae, inactivation of the membrane-bound IMQC protease Oma1 interferes with oxidative-stress responses through enhanced production of reactive oxygen species (ROS) during logarithmic growth and reduced stress signaling via the TORC1-Rim15-Msn2/Msn4 axis. Pharmacological or genetic prevention of ROS accumulation in Oma1-deficient cells restores this defective TOR signaling. Additionally, inactivation of the Oma1 ortholog in the human fungal pathogen Candida albicans also alters TOR signaling and, unexpectedly, leads to increased resistance to neutrophil killing and virulence in the invertebrate animal model Galleria mellonella Our findings reveal a novel and evolutionarily conserved link between IMQC and TOR-mediated signaling that regulates physiological plasticity and pancellular oxidative-stress responses.

  13. Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

    PubMed Central

    Chasman, Deborah; Ho, Yi-Hsuan; Berry, David B; Nemec, Corey M; MacGilvray, Matthew E; Hose, James; Merrill, Anna E; Lee, M Violet; Will, Jessica L; Coon, Joshua J; Ansari, Aseem Z; Craven, Mark; Gasch, Audrey P

    2014-01-01

    Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology. PMID:25411400

  14. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

    PubMed

    VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

    2014-03-01

    Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

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

    PubMed Central

    Welcome, Menizibeya O.; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2015-01-01

    Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose) regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning. PMID:25653876

  16. Sweet taste receptor signaling network: possible implication for cognitive functioning.

    PubMed

    Welcome, Menizibeya O; Mastorakis, Nikos E; Pereverzev, Vladimir A

    2015-01-01

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

  17. Rapid Exact Signal Scanning With Deep Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Thom, Markus; Gritschneder, Franz

    2017-03-01

    A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.

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

    PubMed

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

    2013-05-01

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

  19. Calcium/calmodulin-mediated signal network in plants

    NASA Technical Reports Server (NTRS)

    Yang, Tianbao; Poovaiah, B. W.

    2003-01-01

    Various extracellular stimuli elicit specific calcium signatures that can be recognized by different calcium sensors. Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes. In recent years, completion of the Arabidopsis genome project and advances in functional genomics have helped to identify and characterize numerous calmodulin-binding proteins in plants. There are some similarities in Ca(2+)/calmodulin-mediated signaling in plants and animals. However, plants possess multiple calmodulin genes and many calmodulin target proteins, including unique protein kinases and transcription factors. Some of these proteins are likely to act as "hubs" during calcium signal transduction. Hence, a better understanding of the function of these calmodulin target proteins should help in deciphering the Ca(2+)/calmodulin-mediated signal network and its role in plant growth, development and response to environmental stimuli.

  20. Calcium/calmodulin-mediated signal network in plants

    NASA Technical Reports Server (NTRS)

    Yang, Tianbao; Poovaiah, B. W.

    2003-01-01

    Various extracellular stimuli elicit specific calcium signatures that can be recognized by different calcium sensors. Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes. In recent years, completion of the Arabidopsis genome project and advances in functional genomics have helped to identify and characterize numerous calmodulin-binding proteins in plants. There are some similarities in Ca(2+)/calmodulin-mediated signaling in plants and animals. However, plants possess multiple calmodulin genes and many calmodulin target proteins, including unique protein kinases and transcription factors. Some of these proteins are likely to act as "hubs" during calcium signal transduction. Hence, a better understanding of the function of these calmodulin target proteins should help in deciphering the Ca(2+)/calmodulin-mediated signal network and its role in plant growth, development and response to environmental stimuli.

  1. Signal-BNF: a Bayesian network fusing approach to predict signal peptides.

    PubMed

    Zheng, Zhi; Chen, Youying; Chen, Liping; Guo, Gongde; Fan, Yongxian; Kong, Xiangzeng

    2012-01-01

    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.

  2. Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

    PubMed Central

    Zheng, Zhi; Chen, Youying; Chen, Liping; Guo, Gongde; Fan, Yongxian; Kong, Xiangzeng

    2012-01-01

    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. PMID:23118510

  3. FORUM: Ecological networks: the missing links in biomonitoring science

    PubMed Central

    Gray, Clare; Baird, Donald J; Baumgartner, Simone; Jacob, Ute; Jenkins, Gareth B; O'Gorman, Eoin J; Lu, Xueke; Ma, Athen; Pocock, Michael J O; Schuwirth, Nele; Thompson, Murray; Woodward, Guy

    2014-01-01

    Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems. Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses. Synthesis and applications. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground-truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network-based perspective. PMID:25558087

  4. What the [beep]? Six-month-olds Link Novel Communicative Signals to Meaning

    PubMed Central

    Ferguson, Brock; Waxman, Sandra R.

    2017-01-01

    Over the first year, infants tune in to the signals of their native language and begin to link them to meaning. Here, we ask whether infants, like adults, can also infer the communicative function of otherwise arbitrary signals (here, tone sequences) and link these to meaning as well. We examined 6-month-olds’ object categorization in the context of sine-wave tones, a signal that fails to support categorization at any point during their first year. However, before the categorization task, we exposed infants to tones in one of two vignettes. In one, the tones were produced by an actor in a rich communicative exchange; in the other, infants heard the very same tones, but these were uncoupled from the actors’ activity. Infants exposed to the communicative vignette successfully formed object categories in the subsequent test; those exposed to the non-communicative vignette failed, performing identically to infants with no prior exposure to this novel signal. This reveals in 6-month-old infants a remarkable flexibility in identifying which signals in the ambient environment are communicative and in linking these signals to core cognitive capacities including categorization. PMID:26433024

  5. What the [beep]? Six-month-olds link novel communicative signals to meaning.

    PubMed

    Ferguson, Brock; Waxman, Sandra R

    2016-01-01

    Over the first year, infants tune into the signals of their native language and begin to link them to meaning. Here, we ask whether infants, like adults, can also infer the communicative function of otherwise arbitrary signals (here, tone sequences) and link these to meaning as well. We examined 6-month-olds' object categorization in the context of sine-wave tones, a signal that fails to support categorization at any point during their first year. However, before the categorization task, we exposed infants to tones in one of two vignettes. In one, the tones were produced by an actor in a rich communicative exchange; in the other, infants heard the very same tones, but these were uncoupled from the actors' activity. Infants exposed to the communicative vignette successfully formed object categories in the subsequent test; those exposed to the non-communicative vignette failed, performing identically to infants with no prior exposure to this novel signal. This reveals in 6-month-old infants a remarkable flexibility in identifying which signals in the ambient environment are communicative and in linking these signals to core cognitive capacities including categorization. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks.

    PubMed

    Katiravan, Jeevaa; Sylvia, D; Rao, D Srinivasa

    2015-01-01

    In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

  7. Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks

    PubMed Central

    Katiravan, Jeevaa; Sylvia, D.; Rao, D. Srinivasa

    2015-01-01

    In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network. PMID:26167529

  8. Full duplex fiber link for alternative wired and wireless access based on SSB optical millimeter-wave with 4-PAM signal

    NASA Astrophysics Data System (ADS)

    Ma, Jianxin; Zhang, Junjie

    2015-03-01

    A novel full-duplex fiber-wireless link based on single sideband (SSB) optical millimeter (mm)-wave with 10 Gbit/s 4-pulse amplitude modulation (PAM) signal is proposed to provide alternative wired and 40 GHz wireless accesses for the user terminals. The SSB optical mm-wave with 4-PAM signal consists of two tones: one bears the 4-PAM signal and the other is unmodulated with high power. After transmission over the fiber to the hybrid optical network unit (HONU), the SSB optical mm-wave signal can be decomposed by fiber Bragg gratings (FBGs) as the SSB optical mm-wave signal with reduced carrier-to-sideband ratio (the baseband 4-PAM optical signal) and the uplink optical carrier for the wireless (wired) access. This makes the HONU free from the laser source. For the uplink, since the wireless access signal is converted to the baseband by power detection, both the transmitter in the HONU and the receiver in optical line terminal (OLT) are co-shared for both wireless and wired accesses, which makes the full duplex link much simpler. In our scheme, the optical electrical field of the square-root increment level 4-PAM signal assures an equal level spacing receiving for both the downlink wired and wireless accesses. Since the downlink wireless signal is down-converted to the baseband by power detection, RF local oscillator is unnecessary. To confirm the feasibility of our proposed scheme, a simulation full duplex link with 40 GHz SSB optical mm-wave with 10 Gbit/s 4-PAM signal is built. The simulation results show that both down- and up-links for either wired or wireless access can keep good performance even if the link length of the SSMF is extended to 40 km.

  9. Lysine 63-linked polyubiquitin chain may serve as a targeting signal for the 26S proteasome

    PubMed Central

    Saeki, Yasushi; Kudo, Tai; Sone, Takayuki; Kikuchi, Yoshiko; Yokosawa, Hideyoshi; Toh-e, Akio; Tanaka, Keiji

    2009-01-01

    Recruitment of substrates to the 26S proteasome usually requires covalent attachment of the Lys48-linked polyubiquitin chain. In contrast, modifications with the Lys63-linked polyubiquitin chain and/or monomeric ubiquitin are generally thought to function in proteasome-independent cellular processes. Nevertheless, the ubiquitin chain-type specificity for the proteasomal targeting is still poorly understood, especially in vivo. Using mass spectrometry, we found that Rsp5, a ubiquitin-ligase in budding yeast, catalyzes the formation of Lys63-linked ubiquitin chains in vitro. Interestingly, the 26S proteasome degraded well the Lys63-linked ubiquitinated substrate in vitro. To examine whether Lys63-linked ubiquitination serves in degradation in vivo, we investigated the ubiquitination of Mga2-p120, a substrate of Rsp5. The polyubiquitinated p120 contained relatively high levels of Lys63-linkages, and the Lys63-linked chains were sufficient for the proteasome-binding and subsequent p120-processing. In addition, Lys63-linked chains as well as Lys48-linked chains were detected in the 26S proteasome-bound polyubiquitinated proteins. These results raise the possibility that Lys63-linked ubiquitin chain also serves as a targeting signal for the 26S proteaseome in vivo. PMID:19153599

  10. Parallel Decentralized Network for the Detection of Unknown Signals Through Wireless Nakagami Fading Channels

    NASA Astrophysics Data System (ADS)

    El-Bahaie, Ebtehal H.; Al-Hussaini, Emad K.

    2010-12-01

    In this paper analytical and simulation results for the decentralized detection of unknown signals are introduced. Parallel sensor network scheme is assumed. Additive White Gaussian Noise (AWGN) and Nakagami fading are assumed in both links, that is, from the source to the decentralized local sensors and from the sensors to the fusion center. Furthermore, diversity employing Square Law Combining (SLC) or Square Law Selection (SLS) is considered at each sensor, for both independent and correlated branches. Appreciable improvements are obtained with the increase of the number of sensors and the diversity employment.

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

    PubMed Central

    Nguyen, Thanh-Phuong; Morine, Melissa J.

    2014-01-01

    Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI) to model the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration. PMID:24551850

  12. K-Lysine acetyltransferase 2a regulates a hippocampal gene expression network linked to memory formation

    PubMed Central

    Stilling, Roman M; Rönicke, Raik; Benito, Eva; Urbanke, Hendrik; Capece, Vincenzo; Burkhardt, Susanne; Bahari-Javan, Sanaz; Barth, Jonas; Sananbenesi, Farahnaz; Schütz, Anna L; Dyczkowski, Jerzy; Martinez-Hernandez, Ana; Kerimoglu, Cemil; Dent, Sharon YR; Bonn, Stefan; Reymann, Klaus G; Fischer, Andre

    2014-01-01

    Neuronal histone acetylation has been linked to memory consolidation, and targeting histone acetylation has emerged as a promising therapeutic strategy for neuropsychiatric diseases. However, the role of histone-modifying enzymes in the adult brain is still far from being understood. Here we use RNA sequencing to screen the levels of all known histone acetyltransferases (HATs) in the hippocampal CA1 region and find that K-acetyltransferase 2a (Kat2a)—a HAT that has not been studied for its role in memory function so far—shows highest expression. Mice that lack Kat2a show impaired hippocampal synaptic plasticity and long-term memory consolidation. We furthermore show that Kat2a regulates a highly interconnected hippocampal gene expression network linked to neuroactive receptor signaling via a mechanism that involves nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). In conclusion, our data establish Kat2a as a novel and essential regulator of hippocampal memory consolidation. PMID:25024434

  13. Phosphatase Specificity and Pathway Insulation in Signaling Networks

    PubMed Central

    Rowland, Michael A.; Harrison, Brian; Deeds, Eric J.

    2015-01-01

    Phosphatases play an important role in cellular signaling networks by regulating the phosphorylation state of proteins. Phosphatases are classically considered to be promiscuous, acting on tens to hundreds of different substrates. We recently demonstrated that a shared phosphatase can couple the responses of two proteins to incoming signals, even if those two substrates are from otherwise isolated areas of the network. This finding raises a potential paradox: if phosphatases are indeed highly promiscuous, how do cells insulate themselves against unwanted crosstalk? Here, we use mathematical models to explore three possible insulation mechanisms. One approach involves evolving phosphatase KM values that are large enough to prevent saturation by the phosphatase’s substrates. Although this is an effective method for generating isolation, the phosphatase becomes a highly inefficient enzyme, which prevents the system from achieving switch-like responses and can result in slow response kinetics. We also explore the idea that substrate degradation can serve as an effective phosphatase. Assuming that degradation is unsaturatable, this mechanism could insulate substrates from crosstalk, but it would also preclude ultrasensitive responses and would require very high substrate turnover to achieve rapid dephosphorylation kinetics. Finally, we show that adaptor subunits, such as those found on phosphatases like PP2A, can provide effective insulation against phosphatase crosstalk, but only if their binding to substrates is uncoupled from their binding to the catalytic core. Analysis of the interaction network of PP2A’s adaptor domains reveals that although its adaptors may isolate subsets of targets from one another, there is still a strong potential for phosphatase crosstalk within those subsets. Understanding how phosphatase crosstalk and the insulation mechanisms described here impact the function and evolution of signaling networks represents a major challenge for

  14. Phosphatase specificity and pathway insulation in signaling networks.

    PubMed

    Rowland, Michael A; Harrison, Brian; Deeds, Eric J

    2015-02-17

    Phosphatases play an important role in cellular signaling networks by regulating the phosphorylation state of proteins. Phosphatases are classically considered to be promiscuous, acting on tens to hundreds of different substrates. We recently demonstrated that a shared phosphatase can couple the responses of two proteins to incoming signals, even if those two substrates are from otherwise isolated areas of the network. This finding raises a potential paradox: if phosphatases are indeed highly promiscuous, how do cells insulate themselves against unwanted crosstalk? Here, we use mathematical models to explore three possible insulation mechanisms. One approach involves evolving phosphatase KM values that are large enough to prevent saturation by the phosphatase's substrates. Although this is an effective method for generating isolation, the phosphatase becomes a highly inefficient enzyme, which prevents the system from achieving switch-like responses and can result in slow response kinetics. We also explore the idea that substrate degradation can serve as an effective phosphatase. Assuming that degradation is unsaturatable, this mechanism could insulate substrates from crosstalk, but it would also preclude ultrasensitive responses and would require very high substrate turnover to achieve rapid dephosphorylation kinetics. Finally, we show that adaptor subunits, such as those found on phosphatases like PP2A, can provide effective insulation against phosphatase crosstalk, but only if their binding to substrates is uncoupled from their binding to the catalytic core. Analysis of the interaction network of PP2A's adaptor domains reveals that although its adaptors may isolate subsets of targets from one another, there is still a strong potential for phosphatase crosstalk within those subsets. Understanding how phosphatase crosstalk and the insulation mechanisms described here impact the function and evolution of signaling networks represents a major challenge for

  15. Real time data acquisition of commercial microwave link networks for hydrometeorological applications

    NASA Astrophysics Data System (ADS)

    Chwala, C.; Keis, F.; Kunstmann, H.

    2015-11-01

    The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted- and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to one second. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real time transfer to our data base. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine skiing resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of one second. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application.

  16. Real-time data acquisition of commercial microwave link networks for hydrometeorological applications

    NASA Astrophysics Data System (ADS)

    Chwala, Christian; Keis, Felix; Kunstmann, Harald

    2016-03-01

    The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open-source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to 1 s. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real-time transfer to our database. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine ski resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of 1 s. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application.

  17. TSLP Signaling Network Revealed by SILAC-Based Phosphoproteomics*

    PubMed Central

    Zhong, Jun; Kim, Min-Sik; Chaerkady, Raghothama; Wu, Xinyan; Huang, Tai-Chung; Getnet, Derese; Mitchell, Christopher J.; Palapetta, Shyam M.; Sharma, Jyoti; O'Meally, Robert N.; Cole, Robert N.; Yoda, Akinori; Moritz, Albrecht; Loriaux, Marc M.; Rush, John; Weinstock, David M.; Tyner, Jeffrey W.; Pandey, Akhilesh

    2012-01-01

    Thymic stromal lymphopoietin (TSLP) is a cytokine that plays diverse roles in the regulation of immune responses. TSLP requires a heterodimeric receptor complex consisting of IL-7 receptor α subunit and its unique TSLP receptor (gene symbol CRLF2) to transmit signals in cells. Abnormal TSLP signaling (e.g. overexpression of TSLP or its unique receptor TSLPR) contributes to the development of a number of diseases including asthma and leukemia. However, a detailed understanding of the signaling pathways activated by TSLP remains elusive. In this study, we performed a global quantitative phosphoproteomic analysis of the TSLP signaling network using stable isotope labeling by amino acids in cell culture. By employing titanium dioxide in addition to antiphosphotyrosine antibodies as enrichment methods, we identified 4164 phosphopeptides on 1670 phosphoproteins. Using stable isotope labeling by amino acids in cell culture-based quantitation, we determined that the phosphorylation status of 226 proteins was modulated by TSLP stimulation. Our analysis identified activation of several members of the Src and Tec families of kinases including Btk, Lyn, and Tec by TSLP for the first time. In addition, we report TSLP-induced phosphorylation of protein phosphatases such as Ptpn6 (SHP-1) and Ptpn11 (Shp2), which has also not been reported previously. Co-immunoprecipitation assays showed that Shp2 binds to the adapter protein Gab2 in a TSLP-dependent manner. This is the first demonstration of an inducible protein complex in TSLP signaling. A kinase inhibitor screen revealed that pharmacological inhibition of PI-3 kinase, Jak family kinases, Src family kinases or Btk suppressed TSLP-dependent cellular proliferation making them candidate therapeutic targets in diseases resulting from aberrant TSLP signaling. Our study is the first phosphoproteomic analysis of the TSLP signaling pathway that greatly expands our understanding of TSLP signaling and provides novel therapeutic targets

  18. Atmospheric turbulence-induced signal fades on optical heterodyne communication links

    NASA Astrophysics Data System (ADS)

    Winick, K. A.

    1986-06-01

    The three basic atmospheric propagation effects, absorption, scattering, and turbulence, are reviewed. A simulation approach is then developed to determine signal fade probability distributions on heterodyne-detected satellite links which operate through naturally occurring atmospheric turbulence. The calculations are performed on both angle-tracked and nonangle-tracked downlinks, and on uplinks, with and without adaptive optics. Turbulence-induced degradations in communication performance are determined using signal fade probability distributions, and it is shown that the average signal fade can be a poor measure of the performance degradation.

  19. Discovering link communities in complex networks by an integer programming model and a genetic algorithm.

    PubMed

    Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

    2013-01-01

    Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.

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

    USDA-ARS?s Scientific Manuscript database

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

  1. Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.

    PubMed

    Hsieh, Wen-Tsong; Tzeng, Ke-Rung; Ciou, Jin-Shuei; Tsai, Jeffrey Jp; Kurubanjerdjit, Nilubon; Huang, Chien-Hung; Ng, Ka-Lok

    2015-01-01

    Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs). Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called coupled motif structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions. In this study, we integrate transcription factors (TFs), microRNAs (miRNAs), miRNA targets and network motifs information to build the cancer-related TF-miRNA-motif networks (TMMN). This allows us to examine the role of network motifs in cancer formation at different levels of regulation, i.e. transcription initiation (TF → miRNA), gene-gene interaction (CMS), and post-transcriptional regulation (miRNA → target genes). Among the cancer networks and STNs we considered, it is found that there is a substantial amount of crosstalking through motif interconnections, in particular, the crosstalk between prostate cancer network and PI3K-Akt STN.To validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research.

  2. Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

    PubMed Central

    2015-01-01

    Abstract Background Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs). Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called coupled motif structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions. Results In this study, we integrate transcription factors (TFs), microRNAs (miRNAs), miRNA targets and network motifs information to build the cancer-related TF-miRNA-motif networks (TMMN). This allows us to examine the role of network motifs in cancer formation at different levels of regulation, i.e. transcription initiation (TF → miRNA), gene-gene interaction (CMS), and post-transcriptional regulation (miRNA → target genes). Among the cancer networks and STNs we considered, it is found that there is a substantial amount of crosstalking through motif interconnections, in particular, the crosstalk between prostate cancer network and PI3K-Akt STN. Conclusions To validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research. PMID:25707690

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Neuronal network disintegration: common pathways linking neurodegenerative diseases

    PubMed Central

    Ahmed, Rebekah M; Devenney, Emma M; Irish, Muireann; Ittner, Arne; Naismith, Sharon; Ittner, Lars M; Rohrer, Jonathan D; Halliday, Glenda M; Eisen, Andrew; Hodges, John R; Kiernan, Matthew C

    2016-01-01

    Neurodegeneration refers to a heterogeneous group of brain disorders that progressively evolve. It has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels and therefore traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Neuronal network disintegration is fundamental to neurodegeneration, and concepts based around such a concept may better explain the overlap between their clinical and pathological phenotypes. In this Review, promoters of overlap in neurodegeneration incorporating behavioural, cognitive, metabolic, motor, and extrapyramidal presentations will be critically appraised. In addition, evidence that may support the existence of large-scale networks that might be contributing to phenotypic differentiation will be considered across a neurodegenerative spectrum. Disintegration of neuronal networks through different pathological processes, such as prion-like spread, may provide a better paradigm of disease and thereby facilitate the identification of novel therapies for neurodegeneration. PMID:27172939

  5. Leveraging Social Links for Trust and Privacy in Networks

    NASA Astrophysics Data System (ADS)

    Cutillo, Leucio Antonio; Molva, Refik; Strufe, Thorsten

    Existing on-line social networks (OSN) such as Facebook suffer from several weaknesses regarding privacy and security due to their inherent handling of personal data. As pointed out in [4], a preliminary analysis of existing OSNs shows that they are subject to a number of vulnerabilities, ranging from cloning legitimate users to sybil attacks through privacy violations. Starting from these OSN vulnerabilities as the first step of a broader research activity, we came up with a new approach that is very promising in re-visiting security and privacy problems in distributed systems and networks. We suggest a solution that both aims at avoiding any centralized control and leverages on the real life trust between users, that is part of the social network application itself. An anonymization technique based on multi-hop routing among trusted nodes guarantees privacy in data access and, generally speaking, in all the OSN operations.

  6. Effects of active links on epidemic transmission over social networks

    NASA Astrophysics Data System (ADS)

    Zhu, Guanghu; Chen, Guanrong; Fu, Xinchu

    2017-02-01

    A new epidemic model with two infection periods is developed to account for the human behavior in social network, where newly infected individuals gradually restrict most of future contacts or are quarantined, causing infectivity change from a degree-dependent form to a constant. The corresponding dynamics are formulated by a set of ordinary differential equations (ODEs) via mean-field approximation. The effects of diverse infectivity on the epidemic dynamics ​are examined, with a behavioral interpretation of the basic reproduction number. Results show that such simple adaptive reactions largely determine the impact of network structure on epidemics. Particularly, a theorem proposed by Lajmanovich and Yorke in 1976 is generalized, so that it can be applied for the analysis of the epidemic models with multi-compartments especially network-coupled ODE systems.

  7. High-Dynamic-Range Fiber-Optic Link For Microwave Signals

    NASA Technical Reports Server (NTRS)

    Logan, Ronald T., Jr.; Lutes, George F.

    1992-01-01

    Ultrastable fiber-optic communications system transmits microwave signals between antenna sites of Deep Space Network (DSN) and central processing station several kilometers away. Permits relocation of critical components from front-end areas of DSN antennas to central location, permitting radio-frequency (RF) antenna arraying, improving DSN flexibility, maintainability, and system performance. Also useful in commercial analog and digital communications.

  8. Query and Visualization of extremely large network datasets over the web using Quadtree based KML Regional Network Links

    SciTech Connect

    Dadi, Upendra; Liu, Cheng; Vatsavai, Raju

    2009-01-01

    Geographic data sets are often very large in size. Interactive visualization of such data at all scales is not easy because of the limited resolution of the monitors and inability of visualization applications to handle the volume of data. This is especially true for large vector datasets. The end user s experience is frequently unsatisfactory when exploring such data over the web using a naive application. Network bandwidth is another contributing factor to the low performance. In this paper, a Quadtree based technique to visualize extremely large spatial network datasets over the web is described. It involves using custom developed algorithms leveraging a PostGIS database as the data source and Google Earth as the visualization client. This methodology supports both point and range queries along with non-spatial queries. This methodology is demonstrated using a network dataset consisting of several million links. The methodology is based on using some of the powerful features of KML (Keyhole Markup Language). Keyhole Markup Language (KML) is an Open Geospatial Consortium (OGC) standard for displaying geospatial data on Earth browsers. One of the features of KML is the notion of Network Links. Using network links, a wide range of geospatial data sources such as geodatabases, static files and geospatial data services can be simultaneously accessed and visualized seamlessly. Using the network links combined with Level of Detail principle, view based rendering and intelligent server and client-side caching, scalability in visualizing extremely large spatial datasets can be achieved.

  9. Building Student Networks with LinkedIn: The Potential for Connections, Internships, and Jobs

    ERIC Educational Resources Information Center

    Peterson, Robert M.; Dover, Howard F.

    2014-01-01

    Networking is a chance to interact with people, build friendships or business partners, identify opportunities, and create value. Technology has made this process easier, since individuals can readily contact others who were previously unknown. In the professional world, LinkedIn has become the standard way to build virtual and personal networks.…

  10. Building Student Networks with LinkedIn: The Potential for Connections, Internships, and Jobs

    ERIC Educational Resources Information Center

    Peterson, Robert M.; Dover, Howard F.

    2014-01-01

    Networking is a chance to interact with people, build friendships or business partners, identify opportunities, and create value. Technology has made this process easier, since individuals can readily contact others who were previously unknown. In the professional world, LinkedIn has become the standard way to build virtual and personal networks.…

  11. Networking for Innovation in South Wales. Experiences in Developing Productive Links between University and Industry.

    ERIC Educational Resources Information Center

    Jones, Haydn; Jenkins, Sarah

    1999-01-01

    To develop productive links between industry and university, the style and ethos of networks focused on innovation are crucial. Cardiff University (Wales) has an innovation network that stimulates collaborative work and technology transfer through its informal open nature that encourages partnership. (SK)

  12. Social Networks: A Link between Psychiatric Epidemiology and Community Mental Health.

    ERIC Educational Resources Information Center

    Llamas, Robert; And Others

    1981-01-01

    Examines the concept of social network as a mediating construct linking psychiatric epidemiology and community mental health. Presents a selective review of studies investigating the structural and interactional features of the social networks of psychiatrically impaired persons. Implications of results are discussed. (Author)

  13. Analysis and research on technology system of space broadband network based on laser link

    NASA Astrophysics Data System (ADS)

    Chang, Chengwu; Fei, Ligang; Chen, Erhu; Kou, Baohua; Cheng, Liyu

    2015-10-01

    Space broadband network based on laser link represents the future development direction, Europe, the United States, Japan and other space powers have been researching the theory of space laser communication and the key technology constantly, and have carried out the key technology test of inter-satellite laser communication and satellite-ground laser communication on orbit. However, what is the technology system of space broadband network based on laser link? up to now, it is still controversial, such as wavelength, coding, and modulation mode, exchange mode and so on. Here, by analyzing all kinds of space laser communication test and its technology parameters, combined with the application requirement of space broadband network, a set of technology system for space broadband network based on laser link is put forward, although just a preliminary research result. At first, this paper introduces the basic conception of space broadband network based on laser link, defines the space laser broadband network technology system and its research scope. Then analyze the main contents of space laser broadband network technology system, especially the technical route choice involved, and by studying, the related suggestions are given. Finally, with the development of space broadband network, the issue of standardization in space laser communication technology system is put forward, in order to cause attaches great importance to scientific research institutes and relevant experts.

  14. Imbalanced Functional Link between Valuation Networks in Abstinent Heroin-Dependent Subjects

    PubMed Central

    Xie, Chunming; Shao, Yongcong; Ma, Lin; Zhai, Tianye; Ye, Enmao; Fu, Liping; Bi, Guohua; Chen, Gang; Cohen, Alex; Li, Wenjun; Chen, Guangyu; Yang, Zheng; Li, Shi-Jiang

    2015-01-01

    Using neuroeconomic approaches, our findings demonstrate that the underlying duality of the β-δ discounting networks that jointly influence valuation is impaired to a pathogenic state in abstinent heroin dependents. The imbalanced functional link between the β-δ networks for valuation may orchestrate the irrational choice in drug addiction. PMID:23207652

  15. The Nature and Implications of a Resource Network as a Support System for Linking Agents.

    ERIC Educational Resources Information Center

    Reisinger, Carol

    A resource network can serve as a strategy for personnel-multiplying by performing many activities and functions that the linking agent would otherwise have to perform; e.g., such a network can provide an organized method for identifying and collecting resources, as well surveying the kinds of resources available. The Illinois Resource and…

  16. Use of social network analysis to describe service links for farmers' mental health.

    PubMed

    Fuller, Jeffrey; Kelly, Brian; Sartore, Gina; Fragar, Lynne; Tonna, Anne; Pollard, Georgia; Hazell, Trevor

    2007-04-01

    The primary mental health care needs of farmers require that service innovations incorporate rural support workers into a local service network. This component of the FarmLink pilot sought to develop a social network analysis method that would describe local mental health-related human service networks. The purpose is to inform improvements in this network and to serve as a baseline against which such improvements can be evaluated. A pilot survey of rural human service providers who deal with mental health-related issues among farmers about their self-reported links between each other. Service delivery agencies associated with a small rural town in New South Wales. Twenty-five agents from a range of human services involved in rural human support services to farmers, such as from agricultural and drought support, welfare, primary health care and education. Telephone interview prior to the conduct of a Mental Health First Aid seminar and a Farmers Mental Health and Wellbeing workshop. Agent self-reported service links over the past three months for information exchange, client referrals and working together in relation to helping farmers for mental health, emotional health or stress-related problems. Analysis trialled on the 'made referrals' link shows the network influence, prominence and intermediary status of the rural financial counsellor. Within the limitations of recalled self-report data, social network analysis provides a useful network description for informing and evaluating service network improvements.

  17. Enhancing traffic capacity of scale-free networks by link-directed strategy

    NASA Astrophysics Data System (ADS)

    Ma, Jinlong; Han, Weizhan; Guo, Qing; Zhang, Shuai

    2016-08-01

    The transport efficiency of a network is strongly related to the underlying structure. In this paper, we propose an efficient strategy named high-betweenness-first (HBF) for the purpose of improving the traffic handling capacity of scale-free networks by limiting a fraction of undirected links to be unidirectional ones based on the links’ betweenness. Compared with the high-degree-first (HDF) strategy, the traffic capacity can be more significantly enhanced under the proposed link-directed strategy with the shortest path (SP) routing protocol. Simulation results in the Barabási-Albert (BA) model for scale-free networks show that the critical generating rate Rc which can evaluate the overall traffic capacity of a network system is larger after applying the HBF strategy, especially with nonrandom direction-determining rules. Because of the strongly improved traffic capacity, this work is helpful to design and optimize modern communication networks such as the software defined network.

  18. The Hippo signal transduction network in skeletal and cardiac muscle.

    PubMed

    Wackerhage, Henning; Del Re, Dominic P; Judson, Robert N; Sudol, Marius; Sadoshima, Junichi

    2014-08-05

    The discovery of the Hippo pathway can be traced back to two areas of research. Genetic screens in fruit flies led to the identification of the Hippo pathway kinases and scaffolding proteins that function together to suppress cell proliferation and tumor growth. Independent research, often in the context of muscle biology, described Tead (TEA domain) transcription factors, which bind CATTCC DNA motifs to regulate gene expression. These two research areas were joined by the finding that the Hippo pathway regulates the activity of Tead transcription factors mainly through phosphorylation of the transcriptional coactivators Yap and Taz, which bind to and activate Teads. Additionally, many other signal transduction proteins crosstalk to members of the Hippo pathway forming a Hippo signal transduction network. We discuss evidence that the Hippo signal transduction network plays important roles in myogenesis, regeneration, muscular dystrophy, and rhabdomyosarcoma in skeletal muscle, as well as in myogenesis, organ size control, and regeneration of the heart. Understanding the role of Hippo kinases in skeletal and heart muscle physiology could have important implications for translational research. Copyright © 2014, American Association for the Advancement of Science.

  19. Abnormalities of signal transduction networks in chronic schizophrenia.

    PubMed

    McGuire, Jennifer L; Depasquale, Erica A; Funk, Adam J; O'Donnovan, Sinead M; Hasselfeld, Kathryn; Marwaha, Shruti; Hammond, John H; Hartounian, Vahram; Meador-Woodruff, James H; Meller, Jarek; McCullumsmith, Robert E

    2017-09-12

    Schizophrenia is a serious neuropsychiatric disorder characterized by disruptions of brain cell metabolism, microstructure, and neurotransmission. All of these processes require coordination of multiple kinase-mediated signaling events. We hypothesize that imbalances in kinase activity propagate through an interconnected network of intracellular signaling with potential to simultaneously contribute to many or all of the observed deficits in schizophrenia. We established a workflow distinguishing schizophrenia-altered kinases in anterior cingulate cortex using a previously published kinome array data set. We compared schizophrenia-altered kinases to haloperidol-altered kinases, and identified systems, functions, and regulators predicted using pathway analyses. We used kinase inhibitors with the kinome array to test hypotheses about imbalance in signaling and conducted preliminary studies of kinase proteins, phosphoproteins, and activity for kinases of interest. We investigated schizophrenia-associated single nucleotide polymorphisms in one of these kinases, AKT, for genotype-dependent changes in AKT protein or activity. Kinome analyses identified new kinases as well as some previously implicated in schizophrenia. These results were not explained by chronic antipsychotic treatment. Kinases identified in our analyses aligned with cytoskeletal arrangement and molecular trafficking. Of the kinases we investigated further, AKT and (unexpectedly) JNK, showed the most dysregulation in the anterior cingulate cortex of schizophrenia subjects. Changes in kinase activity did not correspond to protein or phosphoprotein levels. We also show that AKT single nucleotide polymorphism rs1130214, previously associated with schizophrenia, influenced enzyme activity but not protein or phosphoprotein levels. Our data indicate subtle changes in kinase activity and regulation across an interlinked kinase network, suggesting signaling imbalances underlie the core symptoms of schizophrenia

  20. LINKEDIN TRILOGY: Part 1. Top 10 Reasons You Should NOT Join LinkedIn Professional Network!

    ERIC Educational Resources Information Center

    Berk, Ronald A.

    2013-01-01

    Disclaimer: I have been an active "free" user of LinkedIn for 5.463 years with more than 3000 (1st degree) connections from all over the world. I have no vested interest in LinkedIn other than as a user of the services it provides. Despite the fact that LinkedIn was originally designed as a network for business professionals, not…

  1. LINKEDIN TRILOGY: Part 1. Top 10 Reasons You Should NOT Join LinkedIn Professional Network!

    ERIC Educational Resources Information Center

    Berk, Ronald A.

    2013-01-01

    Disclaimer: I have been an active "free" user of LinkedIn for 5.463 years with more than 3000 (1st degree) connections from all over the world. I have no vested interest in LinkedIn other than as a user of the services it provides. Despite the fact that LinkedIn was originally designed as a network for business professionals, not…

  2. ASSESSING HEADWATER STREAMS: LINKING LANDSCAPES TO STREAM NETWORKS

    EPA Science Inventory

    Headwater streams represent a significant land-water boundary and drain 70-80% of the landscape. Headwater streams are vital components to drainage systems and are directly linked to our downstream rivers and lakes. However, alteration and loss of headwater streams have occurre...

  3. ASSESSING HEADWATER STREAMS: LINKING LANDSCAPES TO STREAM NETWORKS

    EPA Science Inventory

    Headwater streams represent a significant land-water boundary and drain 70-80% of the landscape. Headwater streams are vital components to drainage systems and are directly linked to our downstream rivers and lakes. However, alteration and loss of headwater streams have occurre...

  4. Speech perception: linking comprehension across a cortical network.

    PubMed

    Ghazanfar, Asif A; Pinsk, Mark A

    2007-06-05

    Listening to speech amidst noise is facilitated by a variety of cues, including the predictable use of certain words in certain contexts. A recent fMRI study of the interaction between noise and semantic predictability has identified a cortical network involved in speech comprehension.

  5. Integration of Brassinosteroid Signal Transduction with the Transcription Network for Plant Growth Regulation in Arabidopsis

    PubMed Central

    Sun, Yu; Fan, Xi-Ying; Cao, Dong-Mei; He, Kun; Tang, Wenqiang; Zhu, Jia-Ying; He, Jun-Xian; Bai, Ming-Yi; Zhu, Shengwei; Oh, Eunkyoo; Patil, Sunita; Kim, Tae-Wuk; Ji, Hongkai; Wong, Wing Hong; Rhee, Seung Y.; Wang, Zhi-Yong

    2010-01-01

    SUMMARY Brassinosteroids (BRs) regulate a wide range of developmental and physiological processes in plants through a receptor-kinase signaling pathway that controls the BZR transcription factors. Here we use transcript profiling and chromatin-immunoprecipitation microarray (ChIP-chip) experiments to identify 953 BR-regulated BZR1 target (BRBT) genes. Functional studies of selected BRBTs further demonstrate roles in BR-promotion of cell elongation. The BRBT genes reveal numerous molecular links between the BR signaling pathway and downstream components involved in developmental and physiological processes. Furthermore, the results reveal extensive crosstalk between BR and other hormonal and light signaling pathways at multiple levels. For example, BZR1 not only controls the expression of many signaling components of other hormonal and light pathways, but also co-regulates common target genes with light-signaling transcription factors. Our results provide a genomic map of steroid hormone actions in plants, which reveals a regulatory network that integrates hormonal and light signaling pathways for plant growth regulation. PMID:21074725

  6. Predicting brain network changes in Alzheimer's disease with link prediction algorithms.

    PubMed

    Sulaimany, Sadegh; Khansari, Mohammad; Zarrineh, Peyman; Daianu, Madelaine; Jahanshad, Neda; Thompson, Paul M; Masoudi-Nejad, Ali

    2017-03-28

    Link prediction is a promising research area for modeling various types of networks and has mainly focused on predicting missing links. Link prediction methods may be valuable for describing brain connectivity, as it changes in Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Here, we analyzed 3-tesla whole-brain diffusion-weighted images from 202 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) - 50 healthy controls, 72 with earlyMCI (eMCI) and 38 with lateMCI (lMCI) and 42 AD patients. We introduce a novel approach for Mixed Link Prediction (MLP) to test and define the percent of predictability of each heightened stage of dementia from its previous, less impaired stage, in the simplest case. Using well-known link prediction algorithms as the core of MLP, we propose a new approach that predicts stages of cognitive impairment by simultaneously adding and removing links in the brain networks of elderly individuals. We found that the optimal algorithm, called "Adamic and Adar", had the best fit and most accurately predicted the stages of AD from their previous stage. When compared to the other link prediction algorithms, that mainly only predict the added links, our proposed approach can more inclusively simulate the brain changes during disease by both adding and removing links of the network. Our results are also in line with computational neuroimaging and clinical findings and can be improved for better results.

  7. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

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

  8. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    PubMed Central

    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

  9. Arachidonic Acid: An Evolutionarily Conserved Signaling Molecule Modulates Plant Stress Signaling Networks[C][W

    PubMed Central

    Savchenko, Tatyana; Walley, Justin W.; Chehab, E. Wassim; Xiao, Yanmei; Kaspi, Roy; Pye, Matthew F.; Mohamed, Maged E.; Lazarus, Colin M.; Bostock, Richard M.; Dehesh, Katayoon

    2010-01-01

    Fatty acid structure affects cellular activities through changes in membrane lipid composition and the generation of a diversity of bioactive derivatives. Eicosapolyenoic acids are released into plants upon infection by oomycete pathogens, suggesting they may elicit plant defenses. We exploited transgenic Arabidopsis thaliana plants (designated EP) producing eicosadienoic, eicosatrienoic, and arachidonic acid (AA), aimed at mimicking pathogen release of these compounds. We also examined their effect on biotic stress resistance by challenging EP plants with fungal, oomycete, and bacterial pathogens and an insect pest. EP plants exhibited enhanced resistance to all biotic challenges, except they were more susceptible to bacteria than the wild type. Levels of jasmonic acid (JA) were elevated and levels of salicylic acid (SA) were reduced in EP plants. Altered expression of JA and SA pathway genes in EP plants shows that eicosapolyenoic acids effectively modulate stress-responsive transcriptional networks. Exogenous application of various fatty acids to wild-type and JA-deficient mutants confirmed AA as the signaling molecule. Moreover, AA treatment elicited heightened expression of general stress-responsive genes. Importantly, tomato (Solanum lycopersicum) leaves treated with AA exhibited reduced susceptibility to Botrytis cinerea infection, confirming AA signaling in other plants. These studies support the role of AA, an ancient metazoan signaling molecule, in eliciting plant stress and defense signaling networks. PMID:20935246

  10. Cascading failures in coupled networks with both inner-dependency and inter-dependency links

    NASA Astrophysics Data System (ADS)

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-05-01

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

  11. Cascading failures in coupled networks with both inner-dependency and inter-dependency links

    PubMed Central

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-01-01

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well. PMID:27142883

  12. Real time data acquisition of a countrywide commercial microwave link network

    NASA Astrophysics Data System (ADS)

    Chwala, Christian; Keis, Felix; Kunstmann, Harald

    2015-04-01

    Research in recent years has shown that data from commercial microwave link networks can provide very valuable precipitation information. Since these networks comprise the backbone of the cell phone network, they provide countrywide coverage. However acquiring the necessary data from the network operators is still difficult. Data is usually made available for researchers with a large time delay and often at irregular basis. This of course hinders the exploitation of commercial microwave link data in operational applications like QPE forecasts running at national meteorological services. To overcome this, we have developed a custom software in joint cooperation with our industry partner Ericsson. The software is installed on a dedicated server at Ericsson and is capable of acquiring data from the countrywide microwave link network in Germany. In its current first operational testing phase, data from several hundred microwave links in southern Germany is recorded. All data is instantaneously sent to our server where it is stored and organized in an emerging database. Time resolution for the Ericsson data is one minute. The custom acquisition software, however, is capable of processing higher sampling rates. Additionally we acquire and manage 1 Hz data from four microwave links operated by the skiing resort in Garmisch-Partenkirchen. We will present the concept of the data acquisition and show details of the custom-built software. Additionally we will showcase the accessibility and basic processing of real time microwave link data via our database web frontend.

  13. Cascading failures in coupled networks with both inner-dependency and inter-dependency links.

    PubMed

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-05-04

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

  14. Sugar signalling and antioxidant network connections in plant cells.

    PubMed

    Bolouri-Moghaddam, Mohammad Reza; Le Roy, Katrien; Xiang, Li; Rolland, Filip; Van den Ende, Wim

    2010-05-01

    Sugars play important roles as both nutrients and regulatory molecules throughout plant life. Sugar metabolism and signalling function in an intricate network with numerous hormones and reactive oxygen species (ROS) production, signalling and scavenging systems. Although hexokinase is well known to fulfil a crucial role in glucose sensing processes, a scenario is emerging in which the catalytic activity of mitochondria-associated hexokinase regulates glucose-6-phosphate and ROS levels, stimulating antioxidant defence mechanisms and the synthesis of phenolic compounds. As a new concept, it can be hypothesized that the synergistic interaction of sugars (or sugar-like compounds) and phenolic compounds forms part of an integrated redox system, quenching ROS and contributing to stress tolerance, especially in tissues or organelles with high soluble sugar concentrations.

  15. Deep Space Network Capabilities for Receiving Weak Probe Signals

    NASA Technical Reports Server (NTRS)

    Asmar, Sami; Johnston, Doug; Preston, Robert

    2005-01-01

    Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

  16. EU-ADR healthcare database network vs. spontaneous reporting system database: preliminary comparison of signal detection.

    PubMed

    Trifirò, Gianluca; Patadia, Vaishali; Schuemie, Martijn J; Coloma, Preciosa M; Gini, Rosa; Herings, Ron; Hippisley-Cox, Julia; Mazzaglia, Giampiero; Giaquinto, Carlo; Scotti, Lorenza; Pedersen, Lars; Avillach, Paul; Sturkenboom, Miriam C J M; van der Lei, Johan; Eu-Adr Group

    2011-01-01

    The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug

  17. Predicting missing links in complex networks based on common neighbors and distance

    PubMed Central

    Yang, Jinxuan; Zhang, Xiao-Dong

    2016-01-01

    The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity. PMID:27905526

  18. Controlling self-sustained spiking activity by adding or removing one network link

    NASA Astrophysics Data System (ADS)

    Xu, Kesheng; Huang, Wenwen; Li, Baowen; Dhamala, Mukesh; Liu, Zonghua

    2013-06-01

    Being able to control the neuronal spiking activity in specific brain regions is central to a treatment scheme in several brain disorders such as epileptic seizures, mental depression, and Parkinson's diseases. Here, we present an approach for controlling self-sustained oscillations by adding or removing one directed network link in coupled neuronal oscillators, in contrast to previous approaches of adding stimuli or noise. We find that such networks can exhibit a variety of activity patterns such as on-off switch, sustained spikes, and short-term spikes. We derive the condition for a specific link to be the controller of the on-off effect. A qualitative analysis is provided to facilitate the understanding of the mechanism for spiking activity by adding one link. Our findings represent the first report on generating spike activity with the addition of only one directed link to a network and provide a deeper understanding of the microscopic roots of self-sustained spiking.

  19. The structural role of weak and strong links in a financial market network

    NASA Astrophysics Data System (ADS)

    Garas, A.; Argyrakis, P.; Havlin, S.

    2008-05-01

    We investigate the properties of correlation based networks originating from economic complex systems, such as the network of stocks traded at the New York Stock Exchange (NYSE). The weaker links (low correlation) of the system are found to contribute to the overall connectivity of the network significantly more than the strong links (high correlation). We find that nodes connected through strong links form well defined communities. These communities are clustered together in more complex ways compared to the widely used classification according to the economic activity. We find that some companies, such as General Electric (GE), Coca Cola (KO), and others, can be involved in different communities. The communities are found to be quite stable over time. Similar results were obtained by investigating markets completely different in size and properties, such as the Athens Stock Exchange (ASE). The present method may be also useful for other networks generated through correlations.

  20. Space Network Time Distribution and Synchronization Protocol Development for Mars Proximity Link

    NASA Technical Reports Server (NTRS)

    Woo, Simon S.; Gao, Jay L.; Mills, David

    2010-01-01

    Time distribution and synchronization in deep space network are challenging due to long propagation delays, spacecraft movements, and relativistic effects. Further, the Network Time Protocol (NTP) designed for terrestrial networks may not work properly in space. In this work, we consider the time distribution protocol based on time message exchanges similar to Network Time Protocol (NTP). We present the Proximity-1 Space Link Interleaved Time Synchronization (PITS) algorithm that can work with the CCSDS Proximity-1 Space Data Link Protocol. The PITS algorithm provides faster time synchronization via two-way time transfer over proximity links, improves scalability as the number of spacecraft increase, lowers storage space requirement for collecting time samples, and is robust against packet loss and duplication which underlying protocol mechanisms provide.

  1. Space Network Time Distribution and Synchronization Protocol Development for Mars Proximity Link

    NASA Technical Reports Server (NTRS)

    Woo, Simon S.; Gao, Jay L.; Mills, David

    2010-01-01

    Time distribution and synchronization in deep space network are challenging due to long propagation delays, spacecraft movements, and relativistic effects. Further, the Network Time Protocol (NTP) designed for terrestrial networks may not work properly in space. In this work, we consider the time distribution protocol based on time message exchanges similar to Network Time Protocol (NTP). We present the Proximity-1 Space Link Interleaved Time Synchronization (PITS) algorithm that can work with the CCSDS Proximity-1 Space Data Link Protocol. The PITS algorithm provides faster time synchronization via two-way time transfer over proximity links, improves scalability as the number of spacecraft increase, lowers storage space requirement for collecting time samples, and is robust against packet loss and duplication which underlying protocol mechanisms provide.

  2. Abnormalities of signal transduction networks in chronic schizophrenia.

    PubMed

    McGuire, Jennifer L; Depasquale, Erica A; Funk, Adam J; O'Donnovan, Sinead M; Hasselfeld, Kathryn; Marwaha, Shruti; Hammond, John H; Hartounian, Vahram; Meador-Woodruff, James H; Meller, Jarek; McCullumsmith, Robert E

    2017-01-01

    Schizophrenia is a serious neuropsychiatric disorder characterized by disruptions of brain cell metabolism, microstructure, and neurotransmission. All of these processes require coordination of multiple kinase-mediated signaling events. We hypothesize that imbalances in kinase activity propagate through an interconnected network of intracellular signaling with potential to simultaneously contribute to many or all of the observed deficits in schizophrenia. We established a workflow distinguishing schizophrenia-altered kinases in anterior cingulate cortex using a previously published kinome array data set. We compared schizophrenia-altered kinases to haloperidol-altered kinases, and identified systems, functions, and regulators predicted using pathway analyses. We used kinase inhibitors with the kinome array to test hypotheses about imbalance in signaling and conducted preliminary studies of kinase proteins, phosphoproteins, and activity for kinases of interest. We investigated schizophrenia-associated single nucleotide polymorphisms in one of these kinases, AKT, for genotype-dependent changes in AKT protein or activity. Kinome analyses identified new kinases as well as some previously implicated in schizophrenia. These results were not explained by chronic antipsychotic treatment. Kinases identified in our analyses aligned with cytoskeletal arrangement and molecular trafficking. Of the kinases we investigated further, AKT and (unexpectedly) JNK, showed the most dysregulation in the anterior cingulate cortex of schizophrenia subjects. Changes in kinase activity did not correspond to protein or phosphoprotein levels. We also show that AKT single nucleotide polymorphism rs1130214, previously associated with schizophrenia, influenced enzyme activity but not protein or phosphoprotein levels. Our data indicate subtle changes in kinase activity and regulation across an interlinked kinase network, suggesting signaling imbalances underlie the core symptoms of schizophrenia.

  3. Private link between signal and response in Bacillus subtilis quorum sensing

    PubMed Central

    Oslizlo, Anna; Stefanic, Polonca; Dogsa, Iztok; Mandic-Mulec, Ines

    2014-01-01

    Bacteria coordinate their behavior using quorum sensing (QS), whereby cells secrete diffusible signals that generate phenotypic responses associated with group living. The canonical model of QS is one of extracellular signaling, where signal molecules bind to cognate receptors and cause a coordinated response across many cells. Here we study the link between QS input (signaling) and QS output (response) in the ComQXPA QS system of Bacillus subtilis by characterizing the phenotype and fitness of comQ null mutants. These lack the enzyme to produce the ComX signal and do not activate the ComQXPA QS system in other cells. In addition to the activation effect of the signal, however, we find evidence of a second, repressive effect of signal production on the QS system. Unlike activation, which can affect other cells, repression acts privately: the de-repression of QS in comQ cells is intracellular and only affects mutant cells lacking ComQ. As a result, the QS signal mutants have an overly responsive QS system and overproduce the secondary metabolite surfactin in the presence of the signal. This surfactin overproduction is associated with a strong fitness cost, as resources are diverted away from primary metabolism. Therefore, by acting as a private QS repressor, ComQ may be protected against evolutionary competition from loss-of-function mutations. Additionally, we find that surfactin participates in a social selection mechanism that targets signal null mutants in coculture with signal producers. Our study shows that by pleiotropically combining intracellular and extracellular signaling, bacteria may generate evolutionarily stable QS systems. PMID:24425772

  4. Matching–centrality decomposition and the forecasting of new links in networks

    PubMed Central

    Rohr, Rudolf P.; Naisbit, Russell E.; Mazza, Christian; Bersier, Louis-Félix

    2016-01-01

    Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching–centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network. PMID:26842568

  5. Matching-centrality decomposition and the forecasting of new links in networks.

    PubMed

    Rohr, Rudolf P; Naisbit, Russell E; Mazza, Christian; Bersier, Louis-Félix

    2016-02-10

    Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching-centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network.

  6. Random Regular Networks with Distance-limited Interdependent Links

    NASA Astrophysics Data System (ADS)

    Lowinger, Steven; Kornbluth, Yosef; Cwilich, Gabriel; Buldyrev, Sergey

    2014-03-01

    We study the mutual percolation of a system composed of two interdependent random regular networks. We introduce a notion of distance, d, to explore the effects of the proximity of interdependent nodes on the cascade of failures after an initial attack. The nature of the transition through which the networks disintegrate depends on the parameters of the system, which are the degree of the nodes and the maximum distance between interdependent nodes. As the distance and degree increase, the collapse at the critical threshold changes from a second-order transition to a first-order one. The critical threshold monotonically increases with distance. We find a transitional case, in which a novel type of phase transition appears. The case d = 1 can be completely solved analytically and it maps into a discrete version of the Rényi parking problem.

  7. Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

    PubMed

    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.

  8. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    PubMed Central

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

    2017-01-01

    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. PMID:28368341

  9. ATR inhibition rewires cellular signaling networks induced by replication stress.

    PubMed

    Wagner, Sebastian A; Oehler, Hannah; Voigt, Andrea; Dalic, Denis; Freiwald, Anja; Serve, Hubert; Beli, Petra

    2016-02-01

    The slowing down or stalling of replication forks is commonly known as replication stress and arises from multiple causes such as DNA lesions, nucleotide depletion, RNA-DNA hybrids, and oncogene activation. The ataxia telangiectasia and Rad3-related kinase (ATR) plays an essential role in the cellular response to replication stress and inhibition of ATR has emerged as therapeutic strategy for the treatment of cancers that exhibit high levels of replication stress. However, the cellular signaling induced by replication stress and the substrate spectrum of ATR has not been systematically investigated. In this study, we employed quantitative MS-based proteomics to define the cellular signaling after nucleotide depletion-induced replication stress and replication fork collapse following ATR inhibition. We demonstrate that replication stress results in increased phosphorylation of a subset of proteins, many of which are involved in RNA splicing and transcription and have previously not been associated with the cellular replication stress response. Furthermore, our data reveal the ATR-dependent phosphorylation following replication stress and discover novel putative ATR target sites on MCM6, TOPBP1, RAD51AP1, and PSMD4. We establish that ATR inhibition rewires cellular signaling networks induced by replication stress and leads to the activation of the ATM-driven double-strand break repair signaling.

  10. Social networks and links to isolation and loneliness among elderly HCBS clients.

    PubMed

    Medvene, Louis J; Nilsen, Kari M; Smith, Rachel; Ofei-Dodoo, Samuel; DiLollo, Anthony; Webster, Noah; Graham, Annette; Nance, Anita

    2016-01-01

    The purpose of this study was to explore the network types of HCBS clients based on the structural characteristics of their social networks. We also examined how the network types were associated with social isolation, relationship quality and loneliness. Forty personal interviews were carried out with HCBS clients to assess the structure of their social networks as indicated by frequency of contact with children, friends, family and participation in religious and community organizations. Hierarchical cluster analysis was conducted to identify network types. Four network types were found including: family (n = 16), diverse (n = 8), restricted (n = 8) and religious (n = 7). Family members comprised almost half of participants' social networks, and friends comprised less than one-third. Clients embedded in family, diverse and religious networks had significantly more positive relationships than clients embedded in restricted networks. Clients embedded in restricted networks had significantly higher social isolation scores and were lonelier than clients in diverse and family networks. The findings suggest that HCBS clients' isolation and loneliness are linked to the types of social networks in which they are embedded. The findings also suggest that clients embedded in restricted networks are at high risk for negative outcomes.

  11. Neurodegenerative and Inflammatory Pathway Components Linked to TNF-α/TNFR1 Signaling in the Glaucomatous Human Retina

    PubMed Central

    Yang, Xiangjun; Luo, Cheng; Cai, Jian; Powell, David W.; Yu, Dahai; Kuehn, Markus H.

    2011-01-01

    Purpose. This study aimed to determine retinal proteomic alterations in human glaucoma, with particular focus on links to TNF-α/TNFR1 signaling. Methods. Human retinal protein samples were obtained from 20 donors with (n = 10) or without (n = 10) glaucoma. Alterations in protein expression were individually analyzed by quantitative LC-MS/MS. Quantitative Western blot analysis with cleavage or phosphorylation site-specific antibodies was used for data validation, and cellular localization of selected proteins was determined by immunohistochemical analysis of the retina in an additional group of glaucomatous human donor eyes (n = 38) and nonglaucomatous controls (n = 30). Results. Upregulated retinal proteins in human glaucoma included a number of downstream adaptor/interacting proteins and protein kinases involved in TNF-α/TNFR1 signaling. Bioinformatic analysis of the high-throughput data established extended networks of diverse functional interactions with death-promoting and survival-promoting pathways and mediation of immune response. Upregulated pathways included death receptor-mediated caspase cascade, mitochondrial dysfunction, endoplasmic reticulum stress, calpains leading to apoptotic cell death, NF-κB and JAK/STAT pathways, and inflammasome-assembly mediating inflammation. Interestingly, retinal expression pattern of a regulator molecule, TNFAIP3, exhibited prominent variability between individual samples, and methylation of cytosine nucleotides in the TNFAIP3 promoter was found to be correlated with this variability among glaucomatous donors. Conclusions. Findings of this study reveal a number of proteins upregulated in the glaucomatous human retina that exhibit many links to TNF-α/TNFR1 signaling. By highlighting various signaling molecules and regulators involved in cell death and immune response pathways and by correlating proteomic findings with epigenetic alterations, these findings provide a framework motivating further research. PMID:21917936

  12. Criteria for Evaluating Alternative Network and Link Layer Protocols for the NASA Constellation Program Communication Architecture

    NASA Technical Reports Server (NTRS)

    Benbenek, Daniel; Soloff, Jason; Lieb, Erica

    2010-01-01

    Selecting a communications and network architecture for future manned space flight requires an evaluation of the varying goals and objectives of the program, development of communications and network architecture evaluation criteria, and assessment of critical architecture trades. This paper uses Cx Program proposed exploration activities as a guideline; lunar sortie, outpost, Mars, and flexible path options are described. A set of proposed communications network architecture criteria are proposed and described. They include: interoperability, security, reliability, and ease of automating topology changes. Finally a key set of architecture options are traded including (1) multiplexing data at a common network layer vs. at the data link layer, (2) implementing multiple network layers vs. a single network layer, and (3) the use of a particular network layer protocol, primarily IPv6 vs. Delay Tolerant Networking (DTN). In summary, the protocol options are evaluated against the proposed exploration activities and their relative performance with respect to the criteria are assessed. An architectural approach which includes (a) the capability of multiplexing at both the network layer and the data link layer and (b) a single network layer for operations at each program phase, as these solutions are best suited to respond to the widest array of program needs and meet each of the evaluation criteria.

  13. Neural network based control schemes for flexible-link manipulators: simulations and experiments.

    PubMed

    Talebi, H A.; Khorasani, K; Patel, R V.

    1998-10-01

    This paper presents simulation and experimental results on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the modified output re-definition approach. The modified output re-definition approach requires only a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the 'feedback-error-learning' approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the 'output neural network') is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. The performance of the four proposed neural network controllers is illustrated by simulation results for a two-link planar flexible manipulator and by experimental results for a single flexible-link test-bed. The networks are all trained and employed as online controllers and no off-line training is required.

  14. Link predication based on matrix factorization by fusion of multi class organizations of the network.

    PubMed

    Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

    2017-08-21

    Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF (3) here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

  15. A link-adding strategy for transport efficiency of complex networks

    NASA Astrophysics Data System (ADS)

    Ma, Jinlong; Han, Weizhan; Guo, Qing; Wang, Zhenyong; Zhang, Shuai

    2016-12-01

    The transport efficiency is one of the critical parameters to evaluate the performance of a network. In this paper, we propose an improved efficient (IE) strategy to enhance the network transport efficiency of complex networks by adding a fraction of links to an existing network based on the node’s local degree centrality and the shortest path length. Simulation results show that the proposed strategy can bring better traffic capacity and shorter average shortest path length than the low-degree-first (LDF) strategy under the shortest path routing protocol. It is found that the proposed strategy is beneficial to the improvement of overall traffic handling and delivering ability of the network. This study can alleviate the congestion in networks, and is helpful to design and optimize realistic networks.

  16. The ABA-INSENSITIVE-4 (ABI4) transcription factor links redox, hormone and sugar signaling pathways.

    PubMed

    Foyer, Christine H; Kerchev, Pavel I; Hancock, Robert D

    2012-02-01

    The cellular reduction-oxidation (redox) hub processes information from metabolism and the environment and so regulates plant growth and defense through integration with the hormone signaling network. One key pathway of redox control involves interactions with ABSCISIC ACID (ABA). Accumulating evidence suggests that the ABA-INSENSITIVE-4 (ABI4) transcription factor plays a key role in transmitting information concerning the abundance of ascorbate and hence the ability of cells to buffer oxidative challenges. ABI4 is required for the ascorbate-dependent control of growth, a process that involves enhancement of salicylic acid (SA) signaling and inhibition of jasmonic acid (JA) signaling pathways. Low redox buffering capacity reinforces SA- JA- interactions through the mediation of ABA and ABI4 to fine-tune plant growth and defense in relation to metabolic cues and environmental challenges. Moreover, ABI4-mediated pathways of sugar sensitivity are also responsive to the abundance of ascorbate, providing evidence of overlap between redox and sugar signaling pathways.

  17. Link removal for the control of stochastically evolving epidemics over networks: A comparison of approaches

    PubMed Central

    Brandeau, Margaret L.

    2015-01-01

    For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two “preventive” approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two “reactive” approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdős-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdős-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing

  18. RANK-mediated signaling network and cancer metastasis.

    PubMed

    Chu, Gina Chia-Yi; Chung, Leland W K

    2014-09-01

    Cancer metastasis is highly inefficient and complex. Common features of metastatic cancer cells have been observed using cancer cell lines and genetically reconstituted mouse and human tumor xenograft models. These include cancer cell interaction with the tumor microenvironment and the ability of cancer cells to sense extracellular stimuli and adapt to adverse growth conditions. This review summarizes the coordinated response of cancer cells to soluble growth factors, such as RANKL, by a unique feed forward mechanism employing coordinated upregulation of RANKL and c-Met with downregulation of androgen receptor. The RANK-mediated signal network was found to drive epithelial to mesenchymal transition in prostate cancer cells, promote osteomimicry and the ability of prostate cancer cells to assume stem cell and neuroendocrine phenotypes, and confer the ability of prostate cancer cells to home to bone. Prostate cancer cells with activated RANK-mediated signal network were observed to recruit and even transform the non-tumorigenic prostate cancer cells to participate in bone and soft tissue colonization. The coordinated regulation of cancer cell invasion and metastasis by the feed forward mechanism involving RANKL, c-Met, transcription factors, and VEGF-neuropilin could offer new therapeutic opportunities to target prostate cancer bone and soft tissue metastases.

  19. Neural network classification of myoelectric signal for prosthesis control.

    PubMed

    Kelly, M F; Parker, P A; Scott, R N

    1991-12-01

    An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions.

  20. RANK-mediated signaling network and cancer metastasis

    PubMed Central

    Chu, Chia-Yi Gina; Chung, Leland W. K.

    2014-01-01

    Cancer metastasis is highly inefficient and complex. Common features of metastatic cancer cells have been observed using cancer cell lines and genetically reconstituted mouse and human tumor xenograft models. These include cancer cell interaction with the tumor microenvironment, and the ability of cancer cells to sense extracellular stimuli and adapt to adverse growth conditions. This review summarizes the coordinated response of cancer cells to soluble growth factors, such as RANKL, by a unique forward feedback mechanism employing coordinated upregulation of RANKL and c-Met with downregulation of androgen receptor. The RANK-mediated signal network was found to drive epithelial to mesenchymal transition in prostate cancer cells, promote osteomimicry and the ability of prostate cancer cells to assume stem cell and neuroendocrine phenotypes, and confer the ability of prostate cancer cells to home to bone. Prostate cancer cells with activated RANK-mediated signal network were observed to recruit and even transform the non-tumorigenic prostate cancer cells to participate in bone and soft tissue colonization. The coordinated regulation of cancer cell invasion and metastasis by the forward feedback mechanism involving RANKL, c-Met, transcription factors and VEGF-neuropilin could offer new therapeutic opportunities to target prostate cancer bone and soft tissue metastases. PMID:24398859

  1. Circadian clock-dependent gating in ABA signalling networks.

    PubMed

    Seung, David; Risopatron, Juan Pablo Matte; Jones, Brian Joseph; Marc, Jan

    2012-07-01

    Plant growth and development are intimately attuned to fluctuations in environmental variables such as light, temperature and water availability. A broad range of signalling and dynamic response mechanisms allows them to adjust their physiology so that growth and reproductive capacity are optimised for the prevailing conditions. Many of the response mechanisms are mediated by the plant hormones. The hormone abscisic acid (ABA) plays a dominant role in fundamental processes such as seed dormancy and germination, regulation of stomatal movements and enhancing drought tolerance in response to the osmotic stresses that result from water deficit, salinity and freezing. Whereas plants maintain a constant vigilance, there is emerging evidence that the capacity to respond is gated by the circadian clock so that it varies with diurnal fluctuations in light, temperature and water status. Clock regulation enables plants to anticipate regular diurnal fluctuations and thereby presumably to maximise metabolic efficiency. Circadian clock-dependent gating appears to regulate the ABA signalling network at numerous points, including metabolism, transport, perception and activity of the hormone. In this review, we summarise the basic principles and recent progress in elucidating the molecular mechanisms of circadian gating of the ABA response network and how it can affect fundamental processes in plant growth and development.

  2. Time development in the early history of social networks: link stabilization, group dynamics, and segregation.

    PubMed

    Bruun, Jesper; Bearden, Ian G

    2014-01-01

    Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks' communities show that while student movement between groups are erratic in the beginning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately

  3. The stochastic link equilibrium strategy and algorithm for flow assignment in communication networks

    NASA Astrophysics Data System (ADS)

    Tao, Yang; Zhou, Xia

    2005-11-01

    Based on the mature user equilibrium (UE) theory in transportation field as well as the similarity of network flow between transportation and communication, in this paper, the user equilibrium theory was applied to communication networks, and how to apply the stochastic user equilibrium (SUE) to flow assigning in generalized communication networks was further studied. The stochastic link equilibrium (SLE) flow assignment strategy was proposed in this paper, the algorithm of SLE flow assignment was also provided. Both analyses and simulation based on the given algorithm proved that the optimal flow assignment in networks can be achieved by using this algorithm.

  4. An adaptive signaling network in melanoma inflammatory niches confers tolerance to MAPK signaling inhibition

    PubMed Central

    Luheshi, Nadia; Arozarena, Imanol; Acosta, Juan-Carlos; Kamarashev, Jivko; Frederick, Dennie T.; Cooper, Zachary A.; Flaherty, Keith T.; Smith, Michael P.

    2017-01-01

    Mitogen-activated protein kinase (MAPK) pathway antagonists induce profound clinical responses in advanced cutaneous melanoma, but complete remissions are frustrated by the development of acquired resistance. Before resistance emerges, adaptive responses establish a mutation-independent drug tolerance. Antagonizing these adaptive responses could improve drug effects, thereby thwarting the emergence of acquired resistance. In this study, we reveal that inflammatory niches consisting of tumor-associated macrophages and fibroblasts contribute to treatment tolerance through a cytokine-signaling network that involves macrophage-derived IL-1β and fibroblast-derived CXCR2 ligands. Fibroblasts require IL-1β to produce CXCR2 ligands, and loss of host IL-1R signaling in vivo reduces melanoma growth. In tumors from patients on treatment, signaling from inflammatory niches is amplified in the presence of MAPK inhibitors. Signaling from inflammatory niches counteracts combined BRAF/MEK (MAPK/extracellular signal–regulated kinase kinase) inhibitor treatment, and consequently, inhibiting IL-1R or CXCR2 signaling in vivo enhanced the efficacy of MAPK inhibitors. We conclude that melanoma inflammatory niches adapt to and confer drug tolerance toward BRAF and MEK inhibitors early during treatment. PMID:28450382

  5. Simulation study of communication link for Pioneer Saturn/Uranus atmospheric entry probe. [signal acquisition by candidate modem for radio link

    NASA Technical Reports Server (NTRS)

    Hinrichs, C. A.

    1974-01-01

    A digital simulation is presented for a candidate modem in a modeled atmospheric scintillation environment with Doppler, Doppler rate, and signal attenuation typical of the radio link conditions for an outer planets atmospheric entry probe. The results indicate that the signal acquisition characteristics and the channel error rate are acceptable for the system requirements of the radio link. The simulation also outputs data for calculating other error statistics and a quantized symbol stream from which error correction decoding can be analyzed.

  6. Automated Measurement and Signaling Systems for the Transactional Network

    SciTech Connect

    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.

  7. Control of cancer-related signal transduction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2013-03-01

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

  8. Link prediction measures considering different neighbors’ effects and application in social networks

    NASA Astrophysics Data System (ADS)

    Luo, Peng; Wu, Chong; Li, Yongli

    Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

  9. Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

    PubMed Central

    Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali

    2012-01-01

    Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250

  10. Oleuropein Mediated Targeting of Signaling Network in Cancer.

    PubMed

    Fayyaz, Sundas; Aydin, Tuba; Cakir, Ahmet; Gasparri, Maria Luisa; Panici, Pierluigi Benedetti; Farooqi, Ammad Ahmad

    2016-01-01

    Cancer is a multifaceted and genomically complex disease. Rapidly accumulating preclinical and clinical studies are emphasizing on wide ranging molecular mechanisms that underpin cancer development, progression and metastasis. Intratumor heterogeneity, loss of apoptosis, rapidly developing resistance against molecular therapeutics and off-target effects are some of the deeply studied resistance mechanisms. Data obtained through high-throughput technologies has considerably enhanced our understanding of the intracellular signaling cascades frequently dysregulated spatio-temporally. There is an ever-expanding list of synthetic and natural agents reported to activate tumor suppressor genes and inhibit oncogenes in cancer cells. Markedly reduced tumor growth has also been documented in xenografted mice administered with phytochemicals. Oleuropein is a bioactive ingredient isolated from various sources and there is evidence of complete regression of tumors in 9- 12 days in mice orally administered with Oleuropein. In this review we summarize recent developments in use of Oleuropein as an anticancer agent. Extraction and isolation of Oleuropein and how it modulates intracellular signaling network to induce apoptosis in cancer cells. Human epidermal growth factor receptor 2 (HER2) frequently overexpressed in breast cancer cells is inhibited by Oleuropein. Interestingly, trastuzumab efficacy was notably enhanced in Oleuropein treated breast cancer cells. There is still insufficient information related to Oleuropein mediated microRNA regulation in cancer cells. We still do not have information about regulation of different signaling cascades by Oleuropein which are deregulated in cancer. Future studies must converge on a deeper analysis of target molecular network of Oleuropein and its efficacy as a tumor growth inhibitor in xenografted mice.

  11. Barcoding of GPCR trafficking and signaling through the various trafficking roadmaps by compartmentalized signaling networks.

    PubMed

    Bahouth, Suleiman W; Nooh, Mohammed M

    2017-08-01

    Proper signaling by G protein coupled receptors (GPCR) is dependent on the specific repertoire of transducing, enzymatic and regulatory kinases and phosphatases that shape its signaling output. Activation and signaling of the GPCR through its cognate G protein is impacted by G protein-coupled receptor kinase (GRK)-imprinted "barcodes" that recruit β-arrestins to regulate subsequent desensitization, biased signaling and endocytosis of the GPCR. The outcome of agonist-internalized GPCR in endosomes is also regulated by sequence motifs or "barcodes" within the GPCR that mediate its recycling to the plasma membrane or retention and eventual degradation as well as its subsequent signaling in endosomes. Given the vast number of diverse sequences in GPCR, several trafficking mechanisms for endosomal GPCR have been described. The majority of recycling GPCR, are sorted out of endosomes in a "sequence-dependent pathway" anchored around a type-1 PDZ-binding module found in their C-tails. For a subset of these GPCR, a second "barcode" imprinted onto specific GPCR serine/threonine residues by compartmentalized kinase networks was required for their efficient recycling through the "sequence-dependent pathway". Mutating the serine/threonine residues involved, produced dramatic effects on GPCR trafficking, indicating that they played a major role in setting the trafficking itinerary of these GPCR. While endosomal SNX27, retromer/WASH complexes and actin were required for efficient sorting and budding of all these GPCR, additional proteins were required for GPCR sorting via the second "barcode". Here we will review recent developments in GPCR trafficking in general and the human β1-adrenergic receptor in particular across the various trafficking roadmaps. In addition, we will discuss the role of GPCR trafficking in regulating endosomal GPCR signaling, which promote biochemical and physiological effects that are distinct from those generated by the GPCR signal transduction pathway

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

    PubMed

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

    2016-07-01

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

  13. Bio-nanocapsules for signal enhancement of alkaline phosphatase-linked immunosorbent assays.

    PubMed

    Iijima, Masumi; Yamamoto, Mikako; Yoshimoto, Nobuo; Niimi, Tomoaki; Kuroda, Shun'ichi

    2013-01-01

    The bio-nanocapsules displaying about 240 molecules of immunoglobulin G Fc-binding Z domains (ZZ-BNCs) enhanced the signals of enzyme-linked immunosorbent assay by tethering the Fc regions of secondary antibodies (Abs), which were eliminated using high-molecular mass enzymes (e.g., alkaline phosphatase). By way of optimizing the distance between enzymes and Abs, ZZ-BNCs improved sensitivity independently of enzymes.

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  15. Noise figure of microwave photonic links operating under large-signal modulation and its application to optoelectronic oscillators.

    PubMed

    Hosseini, Seyyed Esmail; Banai, Ali

    2014-10-01

    The noise performance of intensity-modulation direct-detection microwave photonic links (MWPL) operating under large-signal conditions has been studied in this paper. A sinusoidal signal plus narrowband white Gaussian noise is applied at the radio frequency input of the link, and the output spectrum is derived using a nonlinear analytical approach. We show that the output SNR can be severely affected by the interaction of signal and noise due to the nonlinearity of the MWPL combined with the large input modulating signal. It is shown that the large-signal noise figure (NF) of an MWPL depends on the input power, a dependence that is not readily apparent under small-signal conditions, due to two unavoidable issues appearing in the large-signal conditions: (1) the link power gain is a function of its input power, and (2) the link power gain is not the same for the signal and noise due to the capture effect. We also have observed that if shot noise or laser relative intensity noise (RIN) is the dominant source of noise, link large-signal NF increases as the input signal power increases. We have shown that, when the MWPL is operating in the linear regime, our theoretical predictions approach the already published results on small-signal NF, which are verified by experimental data. We have shown that large-signal NF affects the noise performance of optoelectronic oscillators because they contain MWPLs at saturation.

  16. Multi-link faults