Sample records for links signaling network

  1. SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks.

    PubMed

    Fazekas, Dávid; Koltai, Mihály; Türei, Dénes; Módos, Dezső; Pálfy, Máté; Dúl, Zoltán; Zsákai, Lilian; Szalay-Bekő, Máté; Lenti, Katalin; Farkas, Illés J; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

    2013-01-18

    Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., Bio

  2. SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks

    PubMed Central

    2013-01-01

    Background Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats

  3. Pathway perturbations in signaling networks: Linking genotype to phenotype.

    PubMed

    Li, Yongsheng; McGrail, Daniel J; Latysheva, Natasha; Yi, Song; Babu, M Madan; Sahni, Nidhi

    2018-05-10

    Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Cascaded multiplexed optical link on a telecommunication network for frequency dissemination.

    PubMed

    Lopez, Olivier; Haboucha, Adil; Kéfélian, Fabien; Jiang, Haifeng; Chanteau, Bruno; Roncin, Vincent; Chardonnet, Christian; Amy-Klein, Anne; Santarelli, Giorgio

    2010-08-02

    We demonstrate a cascaded optical link for ultrastable frequency dissemination comprised of two compensated links of 150 km and a repeater station. Each link includes 114 km of Internet fiber simultaneously carrying data traffic through a dense wavelength division multiplexing technology, and passes through two routing centers of the telecommunication network. The optical reference signal is inserted in and extracted from the communication network using bidirectional optical add-drop multiplexers. The repeater station operates autonomously ensuring noise compensation on the two links and the ultra-stable signal optical regeneration. The compensated link shows a fractional frequency instability of 3 x 10(-15) at one second measurement time and 5 x 10(-20) at 20 hours. This work paves the way to a wide dissemination of ultra-stable optical clock signals between distant laboratories via the Internet network.

  5. 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.

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

    PubMed

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

    2010-07-01

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

  7. 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.

  8. 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

  9. 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.

  10. Quantifying oncogenic phosphotyrosine signaling networks through systems biology.

    PubMed

    Del Rosario, Amanda M; White, Forest M

    2010-02-01

    Pathways linking oncogenic mutations to increased proliferative or migratory capacity are poorly characterized, yet provide potential targets for therapeutic intervention. As tyrosine phosphorylation signaling networks are known to mediate proliferation and migration, and frequently go awry in cancers, a comprehensive understanding of these networks in normal and diseased states is warranted. To this end, recent advances in mass spectrometry, protein microarrays, and computational algorithms provide insight into various aspects of the network including phosphotyrosine identification, analysis of kinase/phosphatase substrates, and phosphorylation-mediated protein-protein interactions. Here we detail technological advances underlying these system-level approaches and give examples of their applications. By combining multiple approaches, it is now possible to quantify changes in the phosphotyrosine signaling network with various oncogenic mutations, thereby unveiling novel therapeutic targets. Copyright 2009 Elsevier Ltd. All rights reserved.

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

    PubMed

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

    2015-12-28

    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.

  12. A CXCL1 paracrine network links cancer chemoresistance and metastasis

    PubMed Central

    Acharyya, Swarnali; Oskarsson, Thordur; Vanharanta, Sakari; Malladi, Srinivas; Kim, Juliet; Morris, Patrick G.; Manova-Todorova, Katia; Leversha, Margaret; Hogg, Nancy; Seshan, Venkatraman E.; Norton, Larry; Brogi, Edi; Massagué, Joan

    2012-01-01

    Metastasis and chemoresistance in cancer are linked phenomena but the molecular basis for this link is unknown. We uncovered a network of paracrine signals between carcinoma, myeloid and endothelial cells that drives both processes in breast cancer. Cancer cells that overexpress CXCL1 and 2 by transcriptional hyperactivation or 4q21 amplification are primed for survival in metastatic sites. CXCL1/2 attract CD11b+Gr1+ myeloid cells into the tumor, which produce chemokines including S100A8/9 that enhance cancer cell survival. While chemotherapeutic agents kill cancer cells, these treatments trigger a parallel stromal reaction leading to TNF-α production by endothelial and other stromal cells. TNF-α heightens the expression of CXCL1/2 in cancer cells, thus amplifying the CXCL1/2-S100A8/9 loop and causing chemoresistance. CXCR2 blockers break this cycle, augmenting the efficacy of chemotherapy against breast tumors and particularly against metastasis. This network of endothelial-carcinoma-myeloid signaling interactions provides a mechanism linking chemoresistance and metastasis, with opportunities for intervention. PMID:22770218

  13. 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.

  14. Signaling mechanisms underlying the robustness and tunability of the plant immune network

    PubMed Central

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

    2014-01-01

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

  15. 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.

  16. 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

  17. Ubiquitousness of link-density and link-pattern communities in real-world networks

    NASA Astrophysics Data System (ADS)

    Šubelj, L.; Bajec, M.

    2012-01-01

    Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness among the nodes, revealing link-pattern communities. We here propose a propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities appear ubiquitous in nature and design.

  18. Toward Continental-scale Rainfall Monitoring Using Commercial Microwave Links From Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. We have previously shown how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ˜35,500 km2), based on an unprecedented number of links (˜2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrated the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. The presentation will focus on the potential for upscaling this technique to continental-scale rainfall monitoring in Europe. In addition, several examples of recent applications of this technique on other continents (South America, Africa, Asia and Australia) will be given.

  19. 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

  20. The Hippo signal transduction network for exercise physiologists

    PubMed Central

    Hamilton, D. Lee; Tremblay, Annie M.

    2016-01-01

    The ubiquitous transcriptional coactivators Yap (gene symbol Yap1) and Taz (gene symbol Wwtr1) regulate gene expression mainly by coactivating the Tead transcription factors. Being at the center of the Hippo signaling network, Yap and Taz are regulated by the Hippo kinase cassette and additionally by a plethora of exercise-associated signals and signaling modules. These include mechanotransduction, the AKT-mTORC1 network, the SMAD transcription factors, hypoxia, glucose homeostasis, AMPK, adrenaline/epinephrine and angiotensin II through G protein-coupled receptors, and IL-6. Consequently, exercise should alter Hippo signaling in several organs to mediate at least some aspects of the organ-specific adaptations to exercise. Indeed, Tead1 overexpression in muscle fibers has been shown to promote a fast-to-slow fiber type switch, whereas Yap in muscle fibers and cardiomyocytes promotes skeletal muscle hypertrophy and cardiomyocyte adaptations, respectively. Finally, genome-wide association studies in humans have linked the Hippo pathway members LATS2, TEAD1, YAP1, VGLL2, VGLL3, and VGLL4 to body height, which is a key factor in sports. PMID:26940657

  1. Adaptive Reliable Routing Protocol Using Combined Link Stability Estimation for Mobile Ad hoc Networks

    NASA Astrophysics Data System (ADS)

    Vadivel, R.; Bhaskaran, V. Murali

    2010-10-01

    The main reason for packet loss in ad hoc networks is the link failure or node failure. In order to increase the path stability, it is essential to distinguish and moderate the failures. By knowing individual link stability along a path, path stability can be identified. In this paper, we develop an adaptive reliable routing protocol using combined link stability estimation for mobile ad hoc networks. The main objective of this protocol is to determine a Quality of Service (QoS) path along with prolonging the network life time and to reduce the packet loss. We calculate a combined metric for a path based on the parameters Link Expiration Time, Node Remaining Energy and Node Velocity and received signal strength to predict the link stability or lifetime. Then, a bypass route is established to retransmit the lost data, when a link failure occurs. By simulation results, we show that the proposed reliable routing protocol achieves high delivery ratio with reduced delay and packet drop.

  2. Link-quality measurement and reporting in wireless sensor networks.

    PubMed

    Chehri, Abdellah; Jeon, Gwanggil; Choi, Byoungjo

    2013-03-04

    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.

  3. 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

  4. 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

  5. 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.

  6. RAINLINK: Retrieval algorithm for rainfall monitoring employing microwave links from a cellular communication network

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, R.; Overeem, A.; Leijnse, H.; Rios Gaona, M. F.

    2017-12-01

    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 for one decade. They are particularly interesting for those countries where few surface rainfall observations are available. Yet to date no operational (real-time) link-based rainfall products are available. To advance the process towards operational application and upscaling of this technique, there is a need for freely available, user-friendly computer code for microwave link data processing and rainfall mapping. Such software is now available as R package "RAINLINK" on GitHub (https://github.com/overeem11/RAINLINK). It contains a working example to compute link-based 15-min rainfall maps for the entire surface area of The Netherlands for 40 hours from real microwave link data. This is a working example using actual data from an extensive network of commercial microwave links, for the first time, which will allow users to test their own algorithms and compare their results with ours. The package consists of modular functions, which facilitates running only part of the algorithm. The main processings steps are: 1) Preprocessing of link data (initial quality and consistency checks); 2) Wet-dry classification using link data; 3) Reference signal determination; 4) Removal of outliers ; 5) Correction of received signal powers; 6) Computation of mean path-averaged rainfall intensities; 7) Interpolation of rainfall intensities ; 8) Rainfall map visualisation. Some applications of RAINLINK will be shown based on microwave link data from a

  7. 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. © 2013 European Sleep Research Society.

  8. 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.

  9. 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.

  10. A comprehensive comparison of network similarities for link prediction and spurious link elimination

    NASA Astrophysics Data System (ADS)

    Zhang, Peng; Qiu, Dan; Zeng, An; Xiao, Jinghua

    2018-06-01

    Identifying missing interactions in complex networks, known as link prediction, is realized by estimating the likelihood of the existence of a link between two nodes according to the observed links and nodes' attributes. Similar approaches have also been employed to identify and remove spurious links in networks which is crucial for improving the reliability of network data. In network science, the likelihood for two nodes having a connection strongly depends on their structural similarity. The key to address these two problems thus becomes how to objectively measure the similarity between nodes in networks. In the literature, numerous network similarity metrics have been proposed and their accuracy has been discussed independently in previous works. In this paper, we systematically compare the accuracy of 18 similarity metrics in both link prediction and spurious link elimination when the observed networks are very sparse or consist of inaccurate linking information. Interestingly, some methods have high prediction accuracy, they tend to perform low accuracy in identification spurious interaction. We further find that methods can be classified into several cluster according to their behaviors. This work is useful for guiding future use of these similarity metrics for different purposes.

  11. 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%.

  12. 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%.

  13. A link prediction method for heterogeneous networks based on BP neural network

    NASA Astrophysics Data System (ADS)

    Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

    2018-04-01

    Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

  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. Indoor communications networks realized through hybrid free-space optical and Wi-Fi links

    NASA Astrophysics Data System (ADS)

    Liverman, Spencer; Wang, Qiwei; Chu, Yu-Chung; Borah, Anindita; Wang, Songtao; Natarajan, Arun; Nguyen, Thinh; Wang, Alan X.

    2018-01-01

    Recently, free-space optical (FSO) networks have been investigated as a potential replacement for traditional WiFi networks due to their large bandwidth potentials. However, FSO networks often suffer from a lack of mobility. We present a hybrid free-space optical and radio frequency (RF) system that we have named WiFO, which seamlessly integrates free-space optical links with pre-existing WiFi networks. The free-space optical link in this system utilizes infrared LEDs operating at a wavelength of 850nm and is capable of transmitting 50Mbps over a three-meter distance. In this hybrid system, optical transmitters are embedded periodically throughout the ceiling of a workspace. Each transmitter directs an optical signal downward in a diffuse light cone, establishing a line of sight optical link. Line of sight communications links have an intrinsic physical layer of security due to the fact that a user must be directly in the path of transmission to access the link; however, this feature also poses a challenge for mobility. In our system, if the free-space optical link is interrupted, a control algorithm redirects traffic over a pre-existing WiFi link ensuring uninterrupted transmissions. After data packets are received, acknowledgments are sent back to a central access point via a WiFi link. As the demand for wireless bandwidth continues to increase exponentially, utilizing the unregulated bandwidth contained within optical spectrum will become necessary. Our fully functional hybrid free-space optical and WiFi prototype system takes full advantage of the untapped bandwidth potential in the optical spectrum, while also maintaining the mobility inherent in WiFi networks.

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

    PubMed

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

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

  17. 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:…

  18. 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.

  19. 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.

  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. 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

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. Nonreciprocal signal routing in an active quantum network

    NASA Astrophysics Data System (ADS)

    Metelmann, A.; Türeci, H. E.

    2018-04-01

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

  7. 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

  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. 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

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

    PubMed

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

    2010-10-07

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

  11. Link Power Budget and Traffict QoS Performance Analysis of Gygabit Passive Optical Network

    NASA Astrophysics Data System (ADS)

    Ubaidillah, A.; Alfita, R.; Toyyibah

    2018-01-01

    Data service of telecommunication network is needed widely in the world; therefore extra wide bandwidth must be provided. For this case, PT. Telekomunikasi Tbk. applies GPON (Gigabit Passive Optical Network) as optical fibre based on telecommunication network system. GPON is a point to a multipoint technology of FTTx (Fiber to The x) that transmits information signals to the subscriber over optical fibre. In GPON trunking system, from OLT (Optical Line Terminal), the network is split to many ONT (Optical Network Terminal) of the subscribers, so it causes path loss and attenuation. In this research, the GPON performance is measured from the link power budget system and the Quality of Service (QoS) of the traffic. And the observation result shows that the link power budget system of this GPON is in good condition. The link power budget values from the mathematical calculation and direct measurement are satisfy the ITU-T G984 Class B standard, that the power level must be between -8 dBm to -27 dBm. While from the traffic performance, the observation result shows that the network resource utility of the subscribers of the observed area is not optimum. The mean of subscriber utility rate is 27.985 bps for upstream and 79.687 bps for downstream. While maximally, It should be 60.800 bps for upstream and 486.400 bps for downstream.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. Percolation of networks with directed dependency links

    NASA Astrophysics Data System (ADS)

    Niu, Dunbiao; Yuan, Xin; Du, Minhui; Stanley, H. Eugene; Hu, Yanqing

    2016-04-01

    The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdős-Rényi networks.

  19. Signal propagation in cortical networks: a digital signal processing approach.

    PubMed

    Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

    2009-01-01

    This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

  20. Nonreciprocal Signal Routing in an Active Quantum Network

    NASA Astrophysics Data System (ADS)

    Tureci, Hakan E.; Metelmann, Anja

    As superconductor quantum technologies are moving towards large-scale integrated circuits, a robust and flexible approach to routing photons at the quantum level becomes a critical problem. Active circuits, which contain driven linear or non-linear elements judiciously embedded in the circuit offer a viable solution. We present a general strategy for routing non-reciprocally quantum signals between two sites of a given lattice of resonators, implementable with existing superconducting circuit components. Our approach makes use of a dual lattice of superconducting non-linear elements on the links connecting the nodes of the main lattice. Solutions for spatially selective driving of the link-elements can be found, which optimally balance coherent and dissipative hopping of microwave photons to non-reciprocally route signals between two given nodes. In certain lattices these optimal solutions are obtained at the exceptional point of the scattering matrix of the network. The presented strategy provides a design space that is governed by a dynamically tunable non-Hermitian generator that can be used to minimize the added quantum noise as well. This work was supported by the U.S. Army Research Office (ARO) under Grant No. W911NF-15-1-0299.

  1. A conserved signaling network monitors delivery of sphingolipids to the plasma membrane in budding yeast

    PubMed Central

    Clarke, Jesse; Dephoure, Noah; Horecka, Ira; Gygi, Steven; Kellogg, Douglas

    2017-01-01

    In budding yeast, cell cycle progression and ribosome biogenesis are dependent on plasma membrane growth, which ensures that events of cell growth are coordinated with each other and with the cell cycle. However, the signals that link the cell cycle and ribosome biogenesis to membrane growth are poorly understood. Here we used proteome-wide mass spectrometry to systematically discover signals associated with membrane growth. The results suggest that membrane trafficking events required for membrane growth generate sphingolipid-dependent signals. A conserved signaling network appears to play an essential role in signaling by responding to delivery of sphingolipids to the plasma membrane. In addition, sphingolipid-dependent signals control phosphorylation of protein kinase C (Pkc1), which plays an essential role in the pathways that link the cell cycle and ribosome biogenesis to membrane growth. Together these discoveries provide new clues as to how growth-­dependent signals control cell growth and the cell cycle. PMID:28794263

  2. 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.

  3. Continuum mechanical model for cross-linked actin networks with contractile bundles

    NASA Astrophysics Data System (ADS)

    Ferreira, J. P. S.; Parente, M. P. L.; Natal Jorge, R. M.

    2018-01-01

    In the context of a mechanical approach to cell biology, there is a close relationship between cellular function and mechanical properties. In recent years, an increasing amount of attention has been given to the coupling between biochemical and mechanical signals by means of constitutive models. In particular, on the active contractility of the actin cytoskeleton. Given the importance of the actin contraction on the physiological functions, this study propose a constitutive model to describe how the filamentous network controls its mechanics actively. Embedded in a soft isotropic ground substance, the network behaves as a viscous mechanical continuum, comprised of isotropically distributed cross-linked actin filaments and actomyosin bundles. Trough virtual rheometry experiments, the present model relates the dynamics of the myosin motors with the network stiffness, which is to a large extent governed by the time-scale of the applied deformations/forces.

  4. 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.

  5. Network Modeling Reveals Prevalent Negative Regulatory Relationships between Signaling Sectors in Arabidopsis Immune Signaling

    PubMed Central

    Sato, Masanao; Tsuda, Kenichi; Wang, Lin; Coller, John; Watanabe, Yuichiro; Glazebrook, Jane; Katagiri, Fumiaki

    2010-01-01

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

  6. Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

    PubMed

    Zhang, Fan; Liu, Runsheng; Zheng, Jie

    2016-12-23

    Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

  7. Packet error rate analysis of decode-and-forward free-space optical cooperative networks in the presence of random link blockage

    NASA Astrophysics Data System (ADS)

    Zdravković, Nemanja; Cvetkovic, Aleksandra; Milic, Dejan; Djordjevic, Goran T.

    2017-09-01

    This paper analyses end-to-end packet error rate (PER) of a free-space optical decode-and-forward cooperative network over a gamma-gamma atmospheric turbulence channel in the presence of temporary random link blockage. Closed-form analytical expressions for PER are derived for the cases with and without transmission links being prone to blockage. Two cooperation protocols (denoted as 'selfish' and 'pilot-adaptive') are presented and compared, where the latter accounts for the presence of blockage and adapts transmission power. The influence of scintillation, link distance, average transmitted signal power, network topology and probability of an uplink and/or internode link being blocked are discussed when the destination applies equal gain combining. The results show that link blockage caused by obstacles can degrade system performance, causing an unavoidable PER floor. The implementation of the pilot-adaptive protocol improves performance when compared to the selfish protocol, diminishing internode link blockage and lowering the PER floor, especially for larger networks.

  8. 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.

  9. 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.

  10. 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

  11. Discrete dynamic modeling of cellular signaling networks.

    PubMed

    Albert, Réka; Wang, Rui-Sheng

    2009-01-01

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

  12. 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%.

  13. 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.).

  14. Link and Network Layers Design for Ultra-High-Speed Terahertz-Band Communications Networks

    DTIC Science & Technology

    2017-01-01

    throughput, and identify the optimal parameter values for their design (Sec. 6.2.3). Moreover, we validate and test the scheme with experimental data obtained...LINK AND NETWORK LAYERS DESIGN FOR ULTRA-HIGH- SPEED TERAHERTZ-BAND COMMUNICATIONS NETWORKS STATE UNIVERSITY OF NEW YORK (SUNY) AT BUFFALO JANUARY...TYPE FINAL TECHNICAL REPORT 3. DATES COVERED (From - To) FEB 2015 – SEP 2016 4. TITLE AND SUBTITLE LINK AND NETWORK LAYERS DESIGN FOR ULTRA-HIGH

  15. Community detection in complex networks using link prediction

    NASA Astrophysics Data System (ADS)

    Cheng, Hui-Min; Ning, Yi-Zi; Yin, Zhao; Yan, Chao; Liu, Xin; Zhang, Zhong-Yuan

    2018-01-01

    Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

  16. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  17. 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

  18. 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.

  19. 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.

  20. Full-duplex radio over fiber link with colorless source-free base station based on single sideband optical mm-wave signal with polarization rotated optical carrier

    NASA Astrophysics Data System (ADS)

    Ma, Jianxin

    2016-07-01

    A full-duplex radio-over fiber (RoF) link scheme based on single sideband (SSB) optical millimeter (mm)-wave signal with polarization-rotated optical carrier is proposed to realize the source-free colorless base station (BS), in which a polarization beam splitter (PBS) is used to abstract part of the optical carrier for conveying the uplink data. Since the optical carrier for the uplink does not bear the downlink signal, no cross-talk from the downlink contaminates the uplink signal. The simulation results demonstrate that both down- and up-links maintain good performance. The mm-wave signal distribution network based on the proposed full duplex fiber link scheme can use the uniform source-free colorless BSs, which makes the access system very simpler.

  1. 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.

  2. 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.

  3. 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

  4. Signaling networks in joint development

    PubMed Central

    Salva, Joanna E.; Merrill, Amy E.

    2016-01-01

    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 unite adjacent bones through either a hyaline cartilage or 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. PMID:27859991

  5. 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.

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

    PubMed

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

    2010-09-01

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

  7. 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.'

  8. Enhancing robustness of interdependent network by adding connectivity and dependence links

    NASA Astrophysics Data System (ADS)

    Cui, Pengshuai; Zhu, Peidong; Wang, Ke; Xun, Peng; Xia, Zhuoqun

    2018-05-01

    Enhancing robustness of interdependent networks by adding connectivity links has been researched extensively, however, few of them are focusing on adding both connectivity and dependence links to enhance robustness. In this paper, we aim to study how to allocate the limited costs reasonably to add both connectivity and dependence links. Firstly, we divide the attackers into stubborn attackers and smart attackers according to whether would they change their attack modes with the changing of network structure; Then by simulations, link addition strategies are given separately according to different attackers, with which we can allocate the limited costs to add connectivity links and dependence links reasonably and achieve more robustness than only adding connectivity links or dependence links. The results show that compared to only adding connectivity links or dependence links, allocating the limited resources reasonably and adding both connectivity links and dependence links could bring more robustness to the interdependent networks.

  9. Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

    NASA Astrophysics Data System (ADS)

    Ma, Xiaoke; Sun, Penggang; Wang, Yu

    2018-04-01

    Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

  10. A Signaling Network Induced by β2 Integrin Controls the Polarization of Lytic Granulesin Cytotoxic Cells

    PubMed Central

    Zhang, Minggang; March, Michael E.; Lane, William S.; Long, Eric O.

    2014-01-01

    Cytotoxic lymphocyte skill target cells by polarized release of the content of perforin-containing granules. In natural killer cells, the binding of β2 integrin to its ligand ICAM-1 is sufficient to promote not only adhesion but also lytic granule polarization. This provided a unique opportunity to study polarization in the absence of degranulation, and β2 integrin signaling independently of inside-out signals from other receptors. Using an unbiased proteomics approach we identified a signaling network centered on an integrin-linked kinase (ILK)–Pyk2–Paxillin core that was required for granule polarization. Downstream of ILK, the highly conserved Cdc42–Par6 signaling pathway that controls cell polarity was activated and required for granule polarization. These results delineate two connected signaling networks induced upon β2 integrin engagement alone, which are integrated to control polarization of the microtubule organizing center and associated lytic granules toward the site of contact with target cells during cellular cytotoxicity. PMID:25292215

  11. 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

  12. 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.

  13. Effect of link oriented self-healing on resilience of networks

    NASA Astrophysics Data System (ADS)

    Shang, Yilun

    2016-08-01

    Many real, complex systems, such as the human brain and skin with their biological networks or intelligent material systems consisting of composite functional liquids, exhibit a noticeable capability of self-healing. Here, we study a network model with arbitrary degree distributions possessing natural link oriented recovery mechanisms, whereby a failed link can be recovered if its two end nodes maintain a sufficient proportion of functional links. These mechanisms are pertinent for many spontaneous healing and manual repair phenomena, interpolating smoothly between complete healing and no healing scenarios. We show that the self-healing strategies have profound impact on resilience of homogeneous and heterogeneous networks employing a percolation threshold, fraction of giant cluster, and link robustness index. The self-healing effect induces distinct resilience characteristics for scale-free networks under random failures and intentional attacks, and a resilience crossover has been observed at certain level of self-healing. Our work highlights the significance of understanding the competition between healing and collapsing in the resilience of complex networks.

  14. To trade or not to trade: Link prediction in the virtual water network

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

    In the international trade network, links express the (temporary) presence of a commercial exchange of goods between any two countries. Given the dynamical behaviour of the trade network, where links are created and dismissed every year, predicting the link activation/deactivation is an open research question. Through the international trade network of agricultural goods, water resources are 'virtually' transferred from the country of production to the country of consumption. We propose a novel methodology for link prediction applied to the network of virtual water trade. Starting from the assumption of having links between any two countries, we estimate the associated virtual water flows by means of a gravity-law model using country and link characteristics as drivers. We consider the links with estimated flows higher than 1000 m3/year as active links, while the others as non-active links. Flows traded along estimated active links are then re-estimated using a similar but differently-calibrated gravity-law model. We were able to correctly model 84% of the existing links and 93% of the non-existing links in year 2011. It is worth to note that the predicted active links carry 99% of the global virtual water flow; hence, missed links are mainly those where a minimum volume of virtual water is exchanged. Results indicate that, over the period from 1986 to 2011, population, geographical distances between countries, and agricultural efficiency (through fertilizers use) are the major factors driving the link activation and deactivation. As opposed to other (network-based) models for link prediction, the proposed method is able to reconstruct the network architecture without any prior knowledge of the network topology, using only the nodes and links attributes; it thus represents a general method that can be applied to other networks such as food or value trade networks.

  15. 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

  16. Signal to noise ratio calculation for fiber optics links

    NASA Technical Reports Server (NTRS)

    Lau, K. Y.

    1980-01-01

    The signal to noise ratio (SNR) effect upon the maximum transmission length of a fiberoptic system is discussed. The relationships of different system parameters are discussed. A general formula to obtain the SNR of a single mode fiberoptic system is derived. The SNR attainable with single mode and multimode fiber optics links was calculated from fundamental noise considerations. It was found that for single mode fibers, laser noise dominates the noise contributions for links less than 30 km long, while thermal noise dominates for longer links. Multimode fibers degrade SNR for long links because of intermode dispersion. For frequency standard transmission, as long as the baseband modulation signals are within the bandwidth of the fibers, respectable SNR can be attained with low loss fibers (approximately 1 dB/km) for links as long as 70 km. For wideband transmission SNR is decreased by a factor equal to the ratio of the bandwidth.

  17. 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

  18. Link Prediction in Evolving Networks Based on Popularity of Nodes.

    PubMed

    Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

    2017-08-02

    Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

  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. Design of MOEMS adjustable optical delay line to reduce link set-up time in a tera-bit/s optical interconnection network.

    PubMed

    Jing, Wencai; Zhang, Yimo; Zhou, Ge

    2002-07-15

    A new structure for bit synchronization in a tera-bit/s optical interconnection network has been designed using micro-electro-mechanical system (MEMS) technique. Link multiplexing has been adopted to reduce data packet communication latency. To eliminate link set-up time, adjustable optical delay lines (AODLs) have been adopted to shift the phases of the distributed optical clock signals for bit synchronization. By changing the optical path distance of the optical clock signal, the phase of the clock signal can be shifted at a very high resolution. A phase-shift resolution of 0.1 ps can be easily achieved with 30-microm alternation of the optical path length in vacuum.

  1. A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks

    NASA Astrophysics Data System (ADS)

    Yasami, Yasser; Safaei, Farshad

    2018-02-01

    The traditional complex network theory is particularly focused on network models in which all network constituents are dealt with equivalently, while fail to consider the supplementary information related to the dynamic properties of the network interactions. This is a main constraint leading to incorrect descriptions of some real-world phenomena or incomplete capturing the details of certain real-life problems. To cope with the problem, this paper addresses the multilayer aspects of dynamic complex networks by analyzing the properties of intrinsically multilayered co-authorship networks, DBLP and Astro Physics, and presenting a novel multilayer model of dynamic complex networks. The model examines the layers evolution (layers birth/death process and lifetime) throughout the network evolution. Particularly, this paper models the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model considering feature cascade, and thereby formulates the link generation process for intra-layer and inter-layer links. Although adjacency matrixes are useful to describe the traditional single-layer networks, such a representation is not sufficient to describe and analyze the multilayer dynamic networks. This paper also extends a generalized mathematical infrastructure to address the problems issued by multilayer complex networks. The model inference is performed using some Markov Chain Monte Carlo sampling strategies, given synthetic and real complex networks data. Experimental results indicate a tremendous improvement in the performance of the proposed multilayer model in terms of sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, F1-score, Matthews correlation coefficient, and accuracy for two important applications of missing link prediction and future link forecasting. The experimental results also indicate the strong predictivepower of the proposed model for the application of

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

    PubMed

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

    2014-01-01

    Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  3. Radar signal transmission and switching over optical networks

    NASA Astrophysics Data System (ADS)

    Esmail, Maged A.; Ragheb, Amr; Seleem, Hussein; Fathallah, Habib; Alshebeili, Saleh

    2018-03-01

    In this paper, we experimentally demonstrate a radar signal distribution over optical networks. The use of fiber enables us to distribute radar signals to distant sites with a low power loss. Moreover, fiber networks can reduce the radar system cost, by sharing precise and expensive radar signal generation and processing equipment. In order to overcome the bandwidth challenges in electrical switches, a semiconductor optical amplifier (SOA) is used as an all-optical device for wavelength conversion to the desired port (or channel) of a wavelength division multiplexing (WDM) network. Moreover, the effect of chromatic dispersion in double sideband (DSB) signals is combated by generating optical single sideband (OSSB) signals. The optimal values of the SOA device parameters required to generate an OSSB with a high sideband suppression ratio (SSR) are determined. We considered various parameters such as injection current, pump power, and probe power. In addition, the effect of signal wavelength conversion and transmission over fiber are studied in terms of signal dynamic range.

  4. Information flow in a network of dispersed signalers-receivers

    NASA Astrophysics Data System (ADS)

    Halupka, Konrad

    2017-11-01

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

  5. Impulse-induced optimum signal amplification in scale-free networks.

    PubMed

    Martínez, Pedro J; Chacón, Ricardo

    2016-04-01

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

  6. Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks.

    PubMed

    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

    2008-05-16

    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 beta-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.

  7. 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.

  8. 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

  9. 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

  10. SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

    PubMed

    Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu

    2006-11-27

    Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

  11. 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.

  12. 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

  13. 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.

  14. An improved approximate network blocking probability model for all-optical WDM Networks with heterogeneous link capacities

    NASA Astrophysics Data System (ADS)

    Khan, Akhtar Nawaz

    2017-11-01

    Currently, analytical models are used to compute approximate blocking probabilities in opaque and all-optical WDM networks with the homogeneous link capacities. Existing analytical models can also be extended to opaque WDM networking with heterogeneous link capacities due to the wavelength conversion at each switch node. However, existing analytical models cannot be utilized for all-optical WDM networking with heterogeneous structure of link capacities due to the wavelength continuity constraint and unequal numbers of wavelength channels on different links. In this work, a mathematical model is extended for computing approximate network blocking probabilities in heterogeneous all-optical WDM networks in which the path blocking is dominated by the link along the path with fewer number of wavelength channels. A wavelength assignment scheme is also proposed for dynamic traffic, termed as last-fit-first wavelength assignment, in which a wavelength channel with maximum index is assigned first to a lightpath request. Due to heterogeneous structure of link capacities and the wavelength continuity constraint, the wavelength channels with maximum indexes are utilized for minimum hop routes. Similarly, the wavelength channels with minimum indexes are utilized for multi-hop routes between source and destination pairs. The proposed scheme has lower blocking probability values compared to the existing heuristic for wavelength assignments. Finally, numerical results are computed in different network scenarios which are approximately equal to values obtained from simulations. Since January 2016, he is serving as Head of Department and an Assistant Professor in the Department of Electrical Engineering at UET, Peshawar-Jalozai Campus, Pakistan. From May 2013 to June 2015, he served Department of Telecommunication Engineering as an Assistant Professor at UET, Peshawar-Mardan Campus, Pakistan. He also worked as an International Internship scholar in the Fukuda Laboratory, National

  15. Networked Airborne Communications Using Adaptive Multi Beam Directional Links

    DTIC Science & Technology

    2016-03-05

    Networked Airborne Communications Using Adaptive Multi-Beam Directional Links R. Bruce MacLeod Member, IEEE, and Adam Margetts Member, IEEE MIT...provide new techniques for increasing throughput in airborne adaptive directional net- works. By adaptive directional linking, we mean systems that can...techniques can dramatically increase the capacity in airborne networks. Advances in digital array technology are beginning to put these gains within reach

  16. Signaling in large-scale neural networks.

    PubMed

    Berg, Rune W; Hounsgaard, Jørn

    2009-02-01

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

  17. Topology control algorithm for wireless sensor networks based on Link forwarding

    NASA Astrophysics Data System (ADS)

    Pucuo, Cairen; Qi, Ai-qin

    2018-03-01

    The research of topology control could effectively save energy and increase the service life of network based on wireless sensor. In this paper, a arithmetic called LTHC (link transmit hybrid clustering) based on link transmit is proposed. It decreases expenditure of energy by changing the way of cluster-node’s communication. The idea is to establish a link between cluster and SINK node when the cluster is formed, and link-node must be non-cluster. Through the link, cluster sends information to SINK nodes. For the sake of achieving the uniform distribution of energy on the network, prolongate the network survival time, and improve the purpose of communication, the communication will cut down much more expenditure of energy for cluster which away from SINK node. In the two aspects of improving the traffic and network survival time, we find that the LTCH is far superior to the traditional LEACH by experiments.

  18. 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.

  19. Synchronization transmission of laser pattern signal within uncertain switched network

    NASA Astrophysics Data System (ADS)

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

    2017-06-01

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

  20. A conserved signaling network monitors delivery of sphingolipids to the plasma membrane in budding yeast.

    PubMed

    Clarke, Jesse; Dephoure, Noah; Horecka, Ira; Gygi, Steven; Kellogg, Douglas

    2017-10-01

    In budding yeast, cell cycle progression and ribosome biogenesis are dependent on plasma membrane growth, which ensures that events of cell growth are coordinated with each other and with the cell cycle. However, the signals that link the cell cycle and ribosome biogenesis to membrane growth are poorly understood. Here we used proteome-wide mass spectrometry to systematically discover signals associated with membrane growth. The results suggest that membrane trafficking events required for membrane growth generate sphingolipid-dependent signals. A conserved signaling network appears to play an essential role in signaling by responding to delivery of sphingolipids to the plasma membrane. In addition, sphingolipid-dependent signals control phosphorylation of protein kinase C (Pkc1), which plays an essential role in the pathways that link the cell cycle and ribosome biogenesis to membrane growth. Together these discoveries provide new clues as to how growth--dependent signals control cell growth and the cell cycle. © 2017 Clarke et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  1. Analysis of second order harmonic distortion due to transmitter non-linearity and chromatic and modal dispersion of optical OFDM SSB modulated signals in SMF-MMF fiber links

    NASA Astrophysics Data System (ADS)

    Patel, Dhananjay; Singh, Vinay Kumar; Dalal, U. D.

    2017-01-01

    Single mode fibers (SMF) are typically used in Wide Area Networks (WAN), Metropolitan Area Networks (MAN) and also find applications in Radio over Fiber (RoF) architectures supporting data transmission in Fiber to the Home (FTTH), Remote Antenna Units (RAUs), in-building networks etc. Multi-mode fibers (MMFs) with low cost, ease of installation and low maintenance are predominantly (85-90%) deployed in-building networks providing data access in local area networks (LANs). The transmission of millimeter wave signals through the SMF in WAN and MAN, along with the reuse of MMF in-building networks will not levy fiber reinstallation cost. The transmission of the millimeter waves experiences signal impairments due to the transmitter non-linearity and modal dispersion of the MMF. The MMF exhibiting large modal dispersion limits the bandwidth-length product of the fiber. The second and higher-order harmonics present in the optical signal fall within the system bandwidth. This causes degradation in the received signal and an unwanted radiation of power at the RAU. The power of these harmonics is proportional to the non-linearity of the transmitter and the modal dispersion of the MMF and should be maintained below the standard values as per the international norms. In this paper, a mathematical model is developed for Second-order Harmonic Distortion (HD2) generated due to non-linearity of the transmitter and chromatic-modal dispersion of the SMF-MMF optic link. This is also verified using a software simulation. The model consists of a Mach Zehnder Modulator (MZM) that generates two m-QAM OFDM Single Sideband (SSB) signals based on phase shift of the hybrid coupler (90° and 120°). Our results show that the SSB signal with 120° hybrid coupler has suppresses the higher-order harmonics and makes the system more robust against the HD2 in the SMF-MMF optic link.

  2. Application of Neutral Networks to Seismic Signal Discrimination

    DTIC Science & Technology

    1993-05-15

    AD-A276 626 PL-TR-93-2154 Application of Neural Networks to Seismic Signal Discrimination James A. Cercone V. Shane Foster W. Mike Clark Larry... Networks to Seismic Signal Discrimination PE 61101E PR 1DMO TA DA WU AA .AUTHOR(S) Stephen Goodman John Martin C James A. Cercone Don J. Smith G...of Technology Applications of Neural Networks to Seismic Classification project. The first year of research focused on identification and collection

  3. 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. © 2016 The Author(s).

  4. Suppression of optical beat interference-noise in orthogonal frequency division multiple access-passive optical network link using self-homodyne balanced detection

    NASA Astrophysics Data System (ADS)

    Won, Yong-Yuk; Jung, Sang-Min; Han, Sang-Kook

    2014-08-01

    A new technique, which reduces optical beat interference (OBI) noise in orthogonal frequency division multiple access-passive optical network (OFDMA-PON) links, is proposed. A self-homodyne balanced detection, which uses a single laser for the optical line terminal (OLT) as well as for the optical network unit (ONU), reduces OBI noise and also improves the signal to noise ratio (SNR) of the discrete multi-tone (DMT) signal. The proposed scheme is verified by transmitting quadrature phase shift keying (QPSK)-modulated DMT signal over a 20-km single mode fiber. The optical signal to noise ratio (OSNR), that is required for BER of 10-5, is reduced by 2 dB in the balanced detection compared with a single channel due to the cancellation of OBI noise in conjunction with the local laser.

  5. The network of photodetectors and diode lasers of the CMS Link alignment system

    NASA Astrophysics Data System (ADS)

    Arce, P.; Barcala, J. M.; Calvo, E.; Ferrando, A.; Josa, M. I.; Molinero, A.; Navarrete, J.; Oller, J. C.; Brochero, J.; Calderón, A.; Fernández, M. G.; Gómez, G.; González-Sánchez, F. J.; Martínez-Rivero, C.; Matorras, F.; Rodrigo, T.; Ruiz-Árbol, P.; Scodellaro, L.; Sobrón, M.; Vila, I.; Virto, A. L.; Fernández, J.; Raics, P.; Szabó, Zs.; Trócsnyi, Z.; Ujvári, B.; Zilizi, Gy.; Béni, N.; Christian, G.; Imrek, J.; Molnar, J.; Novak, D.; Pálinkás, J.; Székely, G.; Szillási, Z.; Bencze, G. L.; Vestergombi, G.; Benettoni, M.; Gasparini, F.; Montecassiano, F.; Rampazzo, M.; Zago, M.; Benvenuti, A.; Reithler, H.; Jiang, C.

    2018-07-01

    The central feature of the CMS Link alignment system is a network of Amorphous Silicon Position Detectors distributed throughout the muon spectrometer that are connected by multiple laser lines. The data collected during the years from 2008 to 2015 is presented confirming an outstanding performance of the photo sensors during more than seven years of operation. Details of the photo sensor readout of the laser signals are presented. The mechanical motions of the CMS detector are monitored using these photosensors and good agreement with distance sensors is obtained.

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chinthavali, Supriya

    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) andmore » the criticality index is found to be effective for one test network to identify the vulnerable nodes.« less

  7. 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.

  8. 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.

  9. Detection test of wireless network signal strength and GPS positioning signal in underground pipeline

    NASA Astrophysics Data System (ADS)

    Li, Li; Zhang, Yunwei; Chen, Ling

    2018-03-01

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

  10. The protist, Monosiga brevicollis, has a tyrosine kinase signaling network more elaborate and diverse than found in any known metazoan.

    PubMed

    Manning, Gerard; Young, Susan L; Miller, W Todd; Zhai, Yufeng

    2008-07-15

    Tyrosine kinase signaling has long been considered a hallmark of intercellular communication, unique to multicellular animals. Our genomic analysis of the unicellular choanoflagellate Monosiga brevicollis discovers a remarkable count of 128 tyrosine kinases, 38 tyrosine phosphatases, and 123 phosphotyrosine (pTyr)-binding SH2 proteins, all higher counts than seen in any metazoan. This elaborate signaling network shows little orthology to metazoan counterparts yet displays many innovations reminiscent of metazoans. These include extracellular domains structurally related to those of metazoan receptor kinases, alternative methods for membrane anchoring and phosphotyrosine interaction in cytoplasmic kinases, and domain combinations that link kinases to small GTPase signaling and transcription. These proteins also display a wealth of combinations of known signaling domains. This uniquely divergent and elaborate signaling network illuminates the early evolution of pTyr signaling, explores innovative ways to traverse the cellular signaling circuitry, and shows extensive convergent evolution, highlighting pervasive constraints on pTyr signaling.

  11. eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

    PubMed

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

    2018-05-25

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

  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. 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.

  14. Space Link Extension Protocol Emulation for High-Throughput, High-Latency Network Connections

    NASA Technical Reports Server (NTRS)

    Tchorowski, Nicole; Murawski, Robert

    2014-01-01

    New space missions require higher data rates and new protocols to meet these requirements. These high data rate space communication links push the limitations of not only the space communication links, but of the ground communication networks and protocols which forward user data to remote ground stations (GS) for transmission. The Consultative Committee for Space Data Systems, (CCSDS) Space Link Extension (SLE) standard protocol is one protocol that has been proposed for use by the NASA Space Network (SN) Ground Segment Sustainment (SGSS) program. New protocol implementations must be carefully tested to ensure that they provide the required functionality, especially because of the remote nature of spacecraft. The SLE protocol standard has been tested in the NASA Glenn Research Center's SCENIC Emulation Lab in order to observe its operation under realistic network delay conditions. More specifically, the delay between then NASA Integrated Services Network (NISN) and spacecraft has been emulated. The round trip time (RTT) delay for the continental NISN network has been shown to be up to 120ms; as such the SLE protocol was tested with network delays ranging from 0ms to 200ms. Both a base network condition and an SLE connection were tested with these RTT delays, and the reaction of both network tests to the delay conditions were recorded. Throughput for both of these links was set at 1.2Gbps. The results will show that, in the presence of realistic network delay, the SLE link throughput is significantly reduced while the base network throughput however remained at the 1.2Gbps specification. The decrease in SLE throughput has been attributed to the implementation's use of blocking calls. The decrease in throughput is not acceptable for high data rate links, as the link requires constant data a flow in order for spacecraft and ground radios to stay synchronized, unless significant data is queued a the ground station. In cases where queuing the data is not an option

  15. Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

    PubMed Central

    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

  16. 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.

  17. Modeling evolution of crosstalk in noisy signal transduction networks

    NASA Astrophysics Data System (ADS)

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

    2018-02-01

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

  18. 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.

  19. Modeling and Simulation of Bus Dispatching Policy for Timed Transfers on Signalized Networks

    NASA Astrophysics Data System (ADS)

    Cho, Hsun-Jung; Lin, Guey-Shii

    2007-12-01

    The major work of this study is to formulate the system cost functions and to integrate the bus dispatching policy with signal control. The integrated model mainly includes the flow dispersion model for links, signal control model for nodes, and dispatching control model for transfer terminals. All such models are inter-related for transfer operations in one-center transit network. The integrated model that combines dispatching policies with flexible signal control modes can be applied to assess the effectiveness of transfer operations. It is found that, if bus arrival information is reliable, an early dispatching decision made at the mean bus arrival times is preferable. The costs for coordinated operations with slack times are relatively low at the optimal common headway when applying adaptive route control. Based on such findings, a threshold function of bus headway for justifying an adaptive signal route control under various time values of auto drivers is developed.

  20. Analysis of Network Vulnerability Under Joint Node and Link Attacks

    NASA Astrophysics Data System (ADS)

    Li, Yongcheng; Liu, Shumei; Yu, Yao; Cao, Ting

    2018-03-01

    The security problem of computer network system is becoming more and more serious. The fundamental reason is that there are security vulnerabilities in the network system. Therefore, it’s very important to identify and reduce or eliminate these vulnerabilities before they are attacked. In this paper, we are interested in joint node and link attacks and propose a vulnerability evaluation method based on the overall connectivity of the network to defense this attack. Especially, we analyze the attack cost problem from the attackers’ perspective. The purpose is to find the set of least costs for joint links and nodes, and their deletion will lead to serious network connection damage. The simulation results show that the vulnerable elements obtained from the proposed method are more suitable for the attacking idea of the malicious persons in joint node and link attack. It is easy to find that the proposed method has more realistic protection significance.

  1. Motion in partially and fully cross-linked F-actin networks

    NASA Astrophysics Data System (ADS)

    Morris, Eliza; Ehrlicher, Allen; Weitz, David

    2012-02-01

    Single molecule experiments have measured stall forces and procession rates of molecular motors on isolated cytoskeletal fibers in Newtonian fluids. But in the cell, these motors are transporting cargo through a highly complex cytoskeletal network. To compare these single molecule results to the forces exerted by motors within the cell, an evaluation of the response of the cytoskeletal network is needed. Using magnetic tweezers and fluorescence confocal microscopy we observe and quantify the relationship between bead motion and filament response in F-actin networks both partially and fully cross-linked with filamin We find that when the transition from full to partial cross-linking is brought about by a decrease in cross-linker concentration there is a simultaneous decline in the elasticity of the network, but the response of the bead remains qualitatively similar. However, when the cross-linking is reduced through a shortening of the F-actin filaments the bead response is completely altered. The characteristics of the altered bead response will be discussed here.

  2. Fiber fault location utilizing traffic signal in optical network.

    PubMed

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

    2013-10-07

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

  3. A traffic analyzer for multiple SpaceWire links

    NASA Astrophysics Data System (ADS)

    Liu, Scige J.; Giusi, Giovanni; Di Giorgio, Anna M.; Vertolli, Nello; Galli, Emanuele; Biondi, David; Farina, Maria; Pezzuto, Stefano; Spinoglio, Luigi

    2014-07-01

    Modern space missions are becoming increasingly complex: the interconnection of the units in a satellite is now a network of terminals linked together through routers, where devices with different level of automation and intelligence share the same data-network. The traceability of the network transactions is performed mostly at terminal level through log analysis and hence it is difficult to verify in real time the reliability of the interconnections and the interchange protocols. To improve and ease the traffic analysis in a SpaceWire network we implemented a low-level link analyzer, with the specific goal to simplify the integration and test phases in the development of space instrumentation. The traffic analyzer collects signals coming from pod probes connected in-series on the interested links between two SpaceWire terminals. With respect to the standard traffic analyzers, the design of this new tool includes the possibility to internally reshape the LVDS signal. This improvement increases the robustness of the analyzer towards environmental noise effects and guarantees a deterministic delay on all analyzed signals. The analyzer core is implemented on a Xilinx FPGA, programmed to decode the bidirectional LVDS signals at Link and Network level. Successively, the core packetizes protocol characters in homogeneous sets of time ordered events. The analyzer provides time-tagging functionality for each characters set, with a precision down to the FPGA Clock, i.e. about 20nsec in the adopted HW environment. The use of a common time reference for each character stream allows synchronous performance measurements. The collected information is then routed to an external computer for quick analysis: this is done via high-speed USB2 connection. With this analyzer it is possible to verify the link performances in terms of induced delays in the transmitted signals. A case study focused on the analysis of the Time-Code synchronization in presence of a SpaceWire Router is

  4. 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.

  5. 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.

  6. 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

  7. Statistical similarity measures for link prediction in heterogeneous complex networks

    NASA Astrophysics Data System (ADS)

    Shakibian, Hadi; Charkari, Nasrollah Moghadam

    2018-07-01

    The majority of the link prediction measures in heterogeneous complex networks rely on the nodes connectivities while less attention has been paid to the importance of the nodes and paths. In this paper, we propose some new meta-path based statistical similarity measures to properly perform link prediction task. The main idea in the proposed measures is to drive some co-occurrence events in a number of co-occurrence matrices that are occurred between the visited nodes obeying a meta-path. The extracted co-occurrence matrices are analyzed in terms of the energy, inertia, local homogeneity, correlation, and information measure of correlation to determine various information theoretic measures. We evaluate the proposed measures, denoted as link energy, link inertia, link local homogeneity, link correlation, and link information measure of correlation, using a standard DBLP network data set. The results of the AUC score and Precision rate indicate the validity and accuracy of the proposed measures in comparison to the popular meta-path based similarity measures.

  8. 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

  9. Nonlinear channel equalization for QAM signal constellation using artificial neural networks.

    PubMed

    Patra, J C; Pal, R N; Baliarsingh, R; Panda, G

    1999-01-01

    Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

  10. G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

    PubMed Central

    Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

    2016-01-01

    The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

  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. 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

  13. 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.

  14. Effects of adaptive dynamical linking in networked games

    NASA Astrophysics Data System (ADS)

    Yang, Zhihu; Li, Zhi; Wu, Te; Wang, Long

    2013-10-01

    The role of dynamical topologies in the evolution of cooperation has received considerable attention, as some studies have demonstrated that dynamical networks are much better than static networks in terms of boosting cooperation. Here we study a dynamical model of evolution of cooperation on stochastic dynamical networks in which there are no permanent partners to each agent. Whenever a new link is created, its duration is randomly assigned without any bias or preference. We allow the agent to adaptively adjust the duration of each link during the evolution in accordance with the feedback from game interactions. By Monte Carlo simulations, we find that cooperation can be remarkably promoted by this adaptive dynamical linking mechanism both for the game of pairwise interactions, such as the Prisoner's Dilemma game (PDG), and for the game of group interactions, illustrated by the public goods game (PGG). And the faster the adjusting rate, the more successful the evolution of cooperation. We also show that in this context weak selection favors cooperation much more than strong selection does. What is particularly meaningful is that the prosperity of cooperation in this study indicates that the rationality and selfishness of a single agent in adjusting social ties can lead to the progress of altruism of the whole population.

  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. 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.

  17. Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

    PubMed

    Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

    2017-12-05

    Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.

  18. Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

    PubMed Central

    Berlusconi, Giulia; Calderoni, Francesco; Parolini, Nicola; Verani, Marco; Piccardi, Carlo

    2016-01-01

    The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities. PMID:27104948

  19. Assuring SS7 dependability: A robustness characterization of signaling network elements

    NASA Astrophysics Data System (ADS)

    Karmarkar, Vikram V.

    1994-04-01

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

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

    PubMed

    Liang, Xiaoming; Liu, Zonghua; Li, Baowen

    2009-10-01

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

  2. Space Link Extension (SLE) Emulation for High-Throughput Network Communication

    NASA Technical Reports Server (NTRS)

    Murawski, Robert W.; Tchorowski, Nicole; Golden, Bert

    2014-01-01

    As the data rate requirements for space communications increases, significant stress is placed not only on the wireless satellite communication links, but also on the ground networks which forward data from end-users to remote ground stations. These wide area network (WAN) connections add delay and jitter to the end-to-end satellite communication link, effects which can have significant impacts on the wireless communication link. It is imperative that any ground communication protocol can react to these effects such that the ground network does not become a bottleneck in the communication path to the satellite. In this paper, we present our SCENIC Emulation Lab testbed which was developed to test the CCSDS SLE protocol implementations proposed for use on future NASA communication networks. Our results show that in the presence of realistic levels of network delay, high-throughput SLE communication links can experience significant data rate throttling. Based on our observations, we present some insight into why this data throttling happens, and trace the probable issue back to non-optimal blocking communication which is sup-ported by the CCSDS SLE API recommended practices. These issues were presented as well to the SLE implementation developers which, based on our reports, developed a new release for SLE which we show fixes the SLE blocking issue and greatly improves the protocol throughput. In this paper, we also discuss future developments for our end-to-end emulation lab and how these improvements can be used to develop and test future space communication technologies.

  3. 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

  4. 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…

  5. Modeling of cell signaling pathways in macrophages by semantic networks

    PubMed Central

    Hsing, Michael; Bellenson, Joel L; Shankey, Conor; Cherkasov, Artem

    2004-01-01

    Background Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Results We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages. Conclusions We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed

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

    PubMed

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

    2012-11-05

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

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

    PubMed Central

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

    2014-01-01

    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. PMID:24468912

  8. Signalling Network Construction for Modelling Plant Defence Response

    PubMed Central

    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

  9. Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

    PubMed Central

    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. PMID:24386268

  10. Reconstruction of the temporal signaling network in Salmonella-infected human cells.

    PubMed

    Budak, Gungor; Eren Ozsoy, Oyku; Aydin Son, Yesim; Can, Tolga; Tuncbag, Nurcan

    2015-01-01

    Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

  11. 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

  12. 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

  13. The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

    PubMed

    Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

    2008-02-29

    Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using

  14. The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

    PubMed Central

    Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

    2008-01-01

    Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using

  15. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

  16. 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.

  17. 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

  18. A fresh look at functional link neural network for motor imagery-based brain-computer interface.

    PubMed

    Hettiarachchi, Imali T; Babaei, Toktam; Nguyen, Thanh; Lim, Chee P; Nahavandi, Saeid

    2018-05-04

    Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications. In this study we introduce a higher order neural network, namely the functional link neural network (FLNN) as a classifier for motor imagery (MI)-based BCI systems, to remedy the drawbacks in MLP. We compare the proposed method with competing classifiers such as linear decomposition analysis, naïve Bayes, k-nearest neighbours, support vector machine and three MLP architectures. Two multi-class benchmark datasets from the BCI competitions are used. Common spatial pattern algorithm is utilized for feature extraction to build classification models. FLNN reports the highest average Kappa value over multiple subjects for both the BCI competition datasets, under similarly preprocessed data and extracted features. Further, statistical comparison results over multiple subjects show that the proposed FLNN classification method yields the best performance among the competing classifiers. Findings from this study imply that the proposed method, which has less computational complexity compared to the MLP, can be implemented effectively in practical MI-based BCI systems. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. 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:…

  20. 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

  1. 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…

  2. 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

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

    NASA Astrophysics Data System (ADS)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

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

  4. Whole genome siRNA cell-based screen links mitochondria to Akt signaling network through uncoupling of electron transport chain

    PubMed Central

    Senapedis, William T.; Kennedy, Caleb J.; Boyle, Patrick M.; Silver, Pamela A.

    2011-01-01

    Forkhead transcription factors (FOXOs) alter a diverse array of cellular processes including the cell cycle, oxidative stress resistance, and aging. Insulin/Akt activation directs phosphorylation and cytoplasmic sequestration of FOXO away from its target genes and serves as an endpoint of a complex signaling network. Using a human genome small interfering RNA (siRNA) library in a cell-based assay, we identified an extensive network of proteins involved in nuclear export, focal adhesion, and mitochondrial respiration not previously implicated in FOXO localization. Furthermore, a detailed examination of mitochondrial factors revealed that loss of uncoupling protein 5 (UCP5) modifies the energy balance and increases free radicals through up-regulation of uncoupling protein 3 (UCP3). The increased superoxide content induces c-Jun N-terminal kinase 1 (JNK1) kinase activity, which in turn affects FOXO localization through a compensatory dephosphorylation of Akt. The resulting nuclear FOXO increases expression of target genes, including mitochondrial superoxide dismutase. By connecting free radical defense and mitochondrial uncoupling to Akt/FOXO signaling, these results have implications in obesity and type 2 diabetes development and the potential for therapeutic intervention. PMID:21460183

  5. Whole genome siRNA cell-based screen links mitochondria to Akt signaling network through uncoupling of electron transport chain.

    PubMed

    Senapedis, William T; Kennedy, Caleb J; Boyle, Patrick M; Silver, Pamela A

    2011-05-15

    Forkhead transcription factors (FOXOs) alter a diverse array of cellular processes including the cell cycle, oxidative stress resistance, and aging. Insulin/Akt activation directs phosphorylation and cytoplasmic sequestration of FOXO away from its target genes and serves as an endpoint of a complex signaling network. Using a human genome small interfering RNA (siRNA) library in a cell-based assay, we identified an extensive network of proteins involved in nuclear export, focal adhesion, and mitochondrial respiration not previously implicated in FOXO localization. Furthermore, a detailed examination of mitochondrial factors revealed that loss of uncoupling protein 5 (UCP5) modifies the energy balance and increases free radicals through up-regulation of uncoupling protein 3 (UCP3). The increased superoxide content induces c-Jun N-terminal kinase 1 (JNK1) kinase activity, which in turn affects FOXO localization through a compensatory dephosphorylation of Akt. The resulting nuclear FOXO increases expression of target genes, including mitochondrial superoxide dismutase. By connecting free radical defense and mitochondrial uncoupling to Akt/FOXO signaling, these results have implications in obesity and type 2 diabetes development and the potential for therapeutic intervention.

  6. 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).

  7. 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

  8. 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

  9. Neural Networks For Demodulation Of Phase-Modulated Signals

    NASA Technical Reports Server (NTRS)

    Altes, Richard A.

    1995-01-01

    Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

  10. Load-induced modulation of signal transduction networks.

    PubMed

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

    2011-10-11

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

  11. SDN control of optical nodes in metro networks for high capacity inter-datacentre links

    NASA Astrophysics Data System (ADS)

    Magalhães, Eduardo; Perry, Philip; Barry, Liam

    2017-11-01

    Worldwide demand for bandwidth has been growing fast for some years and continues to do so. To cover this, mega datacentres need scalable connectivity to provide rich connectivity to handle the heavy traffic across them. Therefore, hardware infrastructures must be able to play different roles according to service and traffic requirements. In this context, software defined networking (SDN) decouples the network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstracted for applications and network services. In addition, elastic optical networking (EON) technologies enable efficient spectrum utilization by allocating variable bandwidth to each user according to their actual needs. In particular, flexible transponders and reconfigurable optical add/drop multiplexers (ROADMs) are key elements since they can offer degrees of freedom to self adapt accordingly. Thus, it is crucial to design control methods in order to optimize the hardware utilization and offer high reconfigurability, flexibility and adaptability. In this paper, we propose and analyze, using a simulation framework, a method of capacity maximization through optical power profile manipulation for inter datacentre links that use existing metropolitan optical networks by exploiting the global network view afforded by SDN. Results show that manipulating the loss profiles of the ROADMs in the metro-network can yield optical signal-to-noise ratio (OSNR) improvements up to 10 dB leading to an increase in 112% in total capacity.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks.

    PubMed

    Jin, Zhigang; Wang, Ning; Su, Yishan; Yang, Qiuling

    2018-02-07

    Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider's sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider's trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15-33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20-58% for a typical network's setting.

  18. Memory Dynamics in Cross-linked Actin Networks

    NASA Astrophysics Data System (ADS)

    Scheff, Danielle; Majumdar, Sayantan; Gardel, Margaret

    Cells demonstrate the remarkable ability to adapt to mechanical stimuli through rearrangement of the actin cytoskeleton, a cross-linked network of actin filaments. In addition to its importance in cell biology, understanding this mechanical response provides strategies for creation of novel materials. A recent study has demonstrated that applied stress can encode mechanical memory in these networks through changes in network geometry, which gives rise to anisotropic shear response. Under later shear, the network is stiffer in the direction of the previously applied stress. However, the dynamics behind the encoding of this memory are unknown. To address this question, we explore the effect of varying either the rigidity of the cross-linkers or the length of actin filament on the time scales required for both memory encoding and over which it later decays. While previous experiments saw only a long-lived memory, initial results suggest another mechanism where memories relax relatively quickly. Overall, our study is crucial for understanding the process by which an external stress can impact network arrangement and thus the dynamics of memory formation.

  19. Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  20. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

    NASA Astrophysics Data System (ADS)

    Türker, Ilker; Sulak, Eyüb Ekmel

    2018-02-01

    Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

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

    PubMed Central

    Creixell, Pau; Schoof, Erwin M.; Simpson, Craig D.; Longden, James; Miller, Chad J.; Lou, Hua Jane; Perryman, Lara; Cox, Thomas R.; Zivanovic, Nevena; Palmeri, Antonio; Wesolowska-Andersen, Agata; Helmer-Citterich, Manuela; Ferkinghoff-Borg, Jesper; Itamochi, Hiroaki; Bodenmiller, Bernd; Erler, Janine T.; Turk, Benjamin E.; Linding, Rune

    2015-01-01

    Summary Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks. PMID:26388441

  6. 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.

  7. A Mobility-Aware QoS Signaling Protocol for Ambient Networks

    NASA Astrophysics Data System (ADS)

    Jeong, Seong-Ho; Lee, Sung-Hyuck; Bang, Jongho

    Mobility-aware quality of service (QoS) signaling is crucial to provide seamless multimedia services in the ambient environment where mobile nodes may move frequently between different wireless access networks. The mobility of an IP-based node in ambient networks affects routing paths, and as a result, can have a significant impact on the operation and state management of QoS signaling protocols. In this paper, we first analyze the impact of mobility on QoS signaling protocols and how the protocols operate in mobility scenarios. We then propose an efficient mobility-aware QoS signaling protocol which can operate adaptively in ambient networks. The key features of the protocol include the fast discovery of a crossover node where the old and new paths converge or diverge due to handover and the localized state management for seamless services. Our analytical and simulation/experimental results show that the proposed/implemented protocol works better than existing protocols in the IP-based mobile environment.

  8. Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

    DTIC Science & Technology

    2009-03-01

    of biological networks. Nature Biotechnology, 23(9):961–966, 2005. [18] A. Ma’ayan, S. L Jenkins, S. Neves, A. Hasseldine, E. Grace, B . Dubin-Thaler...functions of biochemical networks. Trends Biochemical Sci 31: 284–291. 56. Blinov ML, Faeder JR, Goldstein B , Hlavacek WS (2006) A network model of early...mean intensity value, red - increased intensity of signal and green - decreased intensity of signal. Lap- Lapatinib, Das- Dasatinib, C-control, A& B

  9. Effect of Cross-Linking on Free Volume Properties of PEG Based Thiol-Ene Networks

    NASA Astrophysics Data System (ADS)

    Ramakrishnan, Ramesh; Vasagar, Vivek; Nazarenko, Sergei

    According to the Fox and Loshaek theory, in elastomeric networks, free volume decreases linearly with the cross-link density increase. The aim of this study is to show whether the poly(ethylene glycol) (PEG) based multicomponent thiol-ene elastomeric networks demonstrate this model behavior? Networks with a broad cross-link density range were prepared by changing the ratio of the trithiol crosslinker to PEG dithiol and then UV cured with PEG diene while maintaining 1:1 thiol:ene stoichiometry. Pressure-volume-temperature (PVT) data of the networks was generated from the high pressure dilatometry experiments which was fit using the Simha-Somcynsky Equation-of-State analysis to obtain the fractional free volume of the networks. Using Positron Annihilation Lifetime Spectroscopy (PALS) analysis, the average free volume hole size of the networks was also quantified. The fractional free volume and the average free volume hole size showed a linear change with the cross-link density confirming that the Fox and Loshaek theory can be applied to this multicomponent system. Gas diffusivities of the networks showed a good correlation with free volume. A free volume based model was developed to describe the gas diffusivity trends as a function of cross-link density.

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

    NASA Astrophysics Data System (ADS)

    Shin, Yong-Jun

    2016-04-01

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

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

    PubMed

    Shin, Yong-Jun

    2016-04-22

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

  12. Agent-based real-time signal coordination in congested networks.

    DOT National Transportation Integrated Search

    2014-01-01

    This study is the continuation of a previous NEXTRANS study on agent-based reinforcement : learning methods for signal coordination in congested networks. In the previous study, the : formulation of a real-time agent-based traffic signal control in o...

  13. 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.

  14. The Role of Margin in Link Design and Optimization

    NASA Technical Reports Server (NTRS)

    Cheung, K.

    2015-01-01

    Link analysis is a system engineering process in the design, development, and operation of communication systems and networks. Link models that are mathematical abstractions representing the useful signal power and the undesirable noise and attenuation effects (including weather effects if the signal path transverses through the atmosphere) that are integrated into the link budget calculation that provides the estimates of signal power and noise power at the receiver. Then the link margin is applied which attempts to counteract the fluctuations of the signal and noise power to ensure reliable data delivery from transmitter to receiver. (Link margin is dictated by the link margin policy or requirements.) A simple link budgeting approach assumes link parameters to be deterministic values typically adopted a rule-of-thumb policy of 3 dB link margin. This policy works for most S- and X-band links due to their insensitivity to weather effects. But for higher frequency links like Ka-band, Ku-band, and optical communication links, it is unclear if a 3 dB link margin would guarantee link closure. Statistical link analysis that adopted the 2-sigma or 3-sigma link margin incorporates link uncertainties in the sigma calculation. (The Deep Space Network (DSN) link margin policies are 2-sigma for downlink and 3-sigma for uplink.) The link reliability can therefore be quantified statistically even for higher frequency links. However in the current statistical link analysis approach, link reliability is only expressed as the likelihood of exceeding the signal-to-noise ratio (SNR) threshold that corresponds to a given bit-error-rate (BER) or frame-error-rate (FER) requirement. The method does not provide the true BER or FER estimate of the link with margin, or the required signalto-noise ratio (SNR) that would meet the BER or FER requirement in the statistical sense. In this paper, we perform in-depth analysis on the relationship between BER/FER requirement, operating SNR, and

  15. 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

  16. 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.

  17. 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.

  18. [Early prenatal interview: implementation of a sheet link "carried" by patient. The Aurore perinatal network experience].

    PubMed

    Dupont, C; Gonnaud, F; Touzet, S; Luciani, F; Perié, M-A; Molenat, F; Evrard, A; Fernandez, M-P; Roy, J; Rudigoz, R-C

    2008-11-01

    Early prenatal interview has needed the implementation of a new communication tool between follow-up pregnancy professionals: a link sheet filled and carried by patients. To assess the utilization of link sheet by trained professionals, the contribution of the interview and the patient acceptation of the link sheet. Descriptive survey from the database of link sheets returned by professionals to Aurore perinatal network and semi-guided interviews with 100 randomized patients. One thousand one hundred and nineteen link sheets were sent to Aurore perinatal network by 55 professionals out of 78 trained. For primipare, precocious prenatal interview contribution has concerned health care security (60%) and emotional security (56%). For multipare, this contribution has concerned mainly emotional security (80%). No interviewed patient has refused link sheet principle. Link sheet principle, like implemented by Aurore perinatal network, seems pertinent to professionals and patients but it constitutes only one of the elements of network elaboration of personalized care.

  19. 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

  20. 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.

  1. Cluster synchronization transmission of different external signals in discrete uncertain network

    NASA Astrophysics Data System (ADS)

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

    2018-07-01

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

  2. A new similarity measure for link prediction based on local structures in social networks

    NASA Astrophysics Data System (ADS)

    Aghabozorgi, Farshad; Khayyambashi, Mohammad Reza

    2018-07-01

    Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.

  3. 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.

  4. Link performance model for filter bank based multicarrier systems

    NASA Astrophysics Data System (ADS)

    Petrov, Dmitry; Oborina, Alexandra; Giupponi, Lorenza; Stitz, Tobias Hidalgo

    2014-12-01

    This paper presents a complete link level abstraction model for link quality estimation on the system level of filter bank multicarrier (FBMC)-based networks. The application of mean mutual information per coded bit (MMIB) approach is validated for the FBMC systems. The considered quality measure of the resource element for the FBMC transmission is the received signal-to-noise-plus-distortion ratio (SNDR). Simulation results of the proposed link abstraction model show that the proposed approach is capable of estimating the block error rate (BLER) accurately, even when the signal is propagated through the channels with deep and frequent fades, as it is the case for the 3GPP Hilly Terrain (3GPP-HT) and Enhanced Typical Urban (ETU) models. The FBMC-related results of link level simulations are compared with cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) analogs. Simulation results are also validated through the comparison to reference publicly available results. Finally, the steps of link level abstraction algorithm for FBMC are formulated and its application for system level simulation of a professional mobile radio (PMR) network is discussed.

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

    PubMed Central

    Shin, Yong-Jun

    2016-01-01

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

  6. Link-prediction to tackle the boundary specification problem in social network surveys

    PubMed Central

    De Wilde, Philippe; Buarque de Lima-Neto, Fernando

    2017-01-01

    Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes. PMID:28426826

  7. Signal propagation and logic gating in networks of integrate-and-fire neurons.

    PubMed

    Vogels, Tim P; Abbott, L F

    2005-11-16

    Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

  8. 47 CFR 73.4157 - Network signals which adversely affect affiliate broadcast service.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 4 2011-10-01 2011-10-01 false Network signals which adversely affect affiliate broadcast service. 73.4157 Section 73.4157 Telecommunication FEDERAL COMMUNICATIONS COMMISSION....4157 Network signals which adversely affect affiliate broadcast service. See Public Notice, FCC 79-387...

  9. 47 CFR 73.4157 - Network signals which adversely affect affiliate broadcast service.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 4 2010-10-01 2010-10-01 false Network signals which adversely affect affiliate broadcast service. 73.4157 Section 73.4157 Telecommunication FEDERAL COMMUNICATIONS COMMISSION....4157 Network signals which adversely affect affiliate broadcast service. See Public Notice, FCC 79-387...

  10. An auxiliary optimization method for complex public transit route network based on link prediction

    NASA Astrophysics Data System (ADS)

    Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

    2018-02-01

    Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

  11. Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches.

    PubMed

    Enns, Eva A; Brandeau, Margaret L

    2015-04-21

    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 Erdös-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 Erdös-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 which

  12. 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

  13. Elasticity in Physically Cross-Linked Amyloid Fibril Networks.

    PubMed

    Cao, Yiping; Bolisetty, Sreenath; Adamcik, Jozef; Mezzenga, Raffaele

    2018-04-13

    We provide a constitutive model of semiflexible and rigid amyloid fibril networks by combining the affine thermal model of network elasticity with the Derjaguin-Landau-Vervey-Overbeek (DLVO) theory of electrostatically charged colloids. When compared to rheological experiments on β-lactoglobulin and lysozyme amyloid networks, this approach provides the correct scaling of elasticity versus both concentration (G∼c^{2.2} and G∼c^{2.5} for semiflexible and rigid fibrils, respectively) and ionic strength (G∼I^{4.4} and G∼I^{3.8} for β-lactoglobulin and lysozyme, independent from fibril flexibility). The pivotal role played by the screening salt is to reduce the electrostatic barrier among amyloid fibrils, converting labile physical entanglements into long-lived cross-links. This gives a power-law behavior of G with I having exponents significantly larger than in other semiflexible polymer networks (e.g., actin) and carrying DLVO traits specific to the individual amyloid fibrils.

  14. Pre-configured polyhedron based protection against multi-link failures in optical mesh networks.

    PubMed

    Huang, Shanguo; Guo, Bingli; Li, Xin; Zhang, Jie; Zhao, Yongli; Gu, Wanyi

    2014-02-10

    This paper focuses on random multi-link failures protection in optical mesh networks, instead of single, the dual or sequential failures of previous studies. Spare resource efficiency and failure robustness are major concerns in link protection strategy designing and a k-regular and k-edge connected structure is proved to be one of the optimal solutions for link protection network. Based on this, a novel pre-configured polyhedron based protection structure is proposed, and it could provide protection for both simultaneous and sequential random link failures with improved spare resource efficiency. Its performance is evaluated in terms of spare resource consumption, recovery rate and average recovery path length, as well as compared with ring based and subgraph protection under probabilistic link failure scenarios. Results show the proposed novel link protection approach has better performance than previous works.

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

    PubMed Central

    Sun, Jingchun; Zhao, Min; Jia, Peilin; Wang, Lily; Wu, Yonghui; Iverson, Carissa; Zhou, Yubo; Bowton, Erica; Roden, Dan M.; Denny, Joshua C.; Aldrich, Melinda C.; Xu, Hua; Zhao, Zhongming

    2015-01-01

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

  16. Design and modelling of a link monitoring mechanism for the Common Data Link (CDL)

    NASA Astrophysics Data System (ADS)

    Eichelberger, John W., III

    1994-09-01

    The Common Data Link (CDL) is a full duplex, point-to-point microwave communications system used in imagery and signals intelligence collection systems. It provides a link between two remote Local Area Networks (LAN's) aboard collection and surface platforms. In a hostile environment, there is an overwhelming need to dynamically monitor the link and thus, limit the impact of jamming. This work describes steps taken to design, model, and evaluate a link monitoring system suitable for the CDL. The monitoring system is based on features and monitoring constructs of the Link Control Protocol (LCP) in the Point-to-Point Protocol (PPP) suite. The CDL model is based on a system of two remote Fiber Distributed Data Interface (FDDI) LAN's. In particular, the policies and mechanisms associated with monitoring are described in detail. An implementation of the required mechanisms using the OPNET network engineering tool is described. Performance data related to monitoring parameters is reported. Finally, integration of the FDDI-CDL model with the OPNET Internet model is described.

  17. Seismic signal auto-detecing from different features by using Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

  18. 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.

  19. Modeling of cortical signals using echo state networks

    NASA Astrophysics Data System (ADS)

    Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

    2009-10-01

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

  20. 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

  1. CBL-CIPK network for calcium signaling in higher plants

    NASA Astrophysics Data System (ADS)

    Luan, Sheng

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

  2. 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

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

    PubMed

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

    2016-06-01

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

  4. Effectiveness evaluation of double-layered satellite network with laser and microwave hybrid links based on fuzzy analytic hierarchy process

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Rao, Qiaomeng

    2018-01-01

    In order to solve the problem of high speed, large capacity and limited spectrum resources of satellite communication network, a double-layered satellite network with global seamless coverage based on laser and microwave hybrid links is proposed in this paper. By analyzing the characteristics of the double-layered satellite network with laser and microwave hybrid links, an effectiveness evaluation index system for the network is established. And then, the fuzzy analytic hierarchy process, which combines the analytic hierarchy process and the fuzzy comprehensive evaluation theory, is used to evaluate the effectiveness of the double-layered satellite network with laser and microwave hybrid links. Furthermore, the evaluation result of the proposed hybrid link network is obtained by simulation. The effectiveness evaluation process of the proposed double-layered satellite network with laser and microwave hybrid links can help to optimize the design of hybrid link double-layered satellite network and improve the operating efficiency of the satellite system.

  5. Gene expression links functional networks across cortex and striatum.

    PubMed

    Anderson, Kevin M; Krienen, Fenna M; Choi, Eun Young; Reinen, Jenna M; Yeo, B T Thomas; Holmes, Avram J

    2018-04-12

    The human brain is comprised of a complex web of functional networks that link anatomically distinct regions. However, the biological mechanisms supporting network organization remain elusive, particularly across cortical and subcortical territories with vastly divergent cellular and molecular properties. Here, using human and primate brain transcriptional atlases, we demonstrate that spatial patterns of gene expression show strong correspondence with limbic and somato/motor cortico-striatal functional networks. Network-associated expression is consistent across independent human datasets and evolutionarily conserved in non-human primates. Genes preferentially expressed within the limbic network (encompassing nucleus accumbens, orbital/ventromedial prefrontal cortex, and temporal pole) relate to risk for psychiatric illness, chloride channel complexes, and markers of somatostatin neurons. Somato/motor associated genes are enriched for oligodendrocytes and markers of parvalbumin neurons. These analyses indicate that parallel cortico-striatal processing channels possess dissociable genetic signatures that recapitulate distributed functional networks, and nominate molecular mechanisms supporting cortico-striatal circuitry in health and disease.

  6. Elasticity in Physically Cross-Linked Amyloid Fibril Networks

    NASA Astrophysics Data System (ADS)

    Cao, Yiping; Bolisetty, Sreenath; Adamcik, Jozef; Mezzenga, Raffaele

    2018-04-01

    We provide a constitutive model of semiflexible and rigid amyloid fibril networks by combining the affine thermal model of network elasticity with the Derjaguin-Landau-Vervey-Overbeek (DLVO) theory of electrostatically charged colloids. When compared to rheological experiments on β -lactoglobulin and lysozyme amyloid networks, this approach provides the correct scaling of elasticity versus both concentration (G ˜c2.2 and G ˜c2.5 for semiflexible and rigid fibrils, respectively) and ionic strength (G ˜I4.4 and G ˜I3.8 for β -lactoglobulin and lysozyme, independent from fibril flexibility). The pivotal role played by the screening salt is to reduce the electrostatic barrier among amyloid fibrils, converting labile physical entanglements into long-lived cross-links. This gives a power-law behavior of G with I having exponents significantly larger than in other semiflexible polymer networks (e.g., actin) and carrying DLVO traits specific to the individual amyloid fibrils.

  7. S-nitrosylation of EGFR and Src activates an oncogenic signaling network in human basal-like breast cancer.

    PubMed

    Switzer, Christopher H; Glynn, Sharon A; Cheng, Robert Y-S; Ridnour, Lisa A; Green, Jeffrey E; Ambs, Stefan; Wink, David A

    2012-09-01

    Increased inducible nitric oxide synthase (NOS2) expression in breast tumors is associated with decreased survival of estrogen receptor negative (ER-) breast cancer patients. We recently communicated the preliminary observation that nitric oxide (NO) signaling results in epidermal growth factor receptor (EGFR) tyrosine phosphorylation. To further define the role of NO in the pathogenesis of ER- breast cancer, we examined the mechanism of NO-induced EGFR activation in human ER- breast cancer. NO was found to activate EGFR and Src by a mechanism that includes S-nitrosylation. NO, at physiologically relevant concentrations, induced an EGFR/Src-mediated activation of oncogenic signal transduction pathways (including c-Myc, Akt, and β-catenin) and the loss of PP2A tumor suppressor activity. In addition, NO signaling increased cellular EMT, expression and activity of COX-2, and chemoresistance to adriamycin and paclitaxel. When connected into a network, these concerted events link NO to the development of a stem cell-like phenotype, resulting in the upregulation of CD44 and STAT3 phosphorylation. Our observations are also consistent with the finding that NOS2 is associated with a basal-like transcription pattern in human breast tumors. These results indicate that the inhibition of NOS2 activity or NO signaling networks may have beneficial effects in treating basal-like breast cancer patients.

  8. 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.

  9. Watchdog Sensor Network with Multi-Stage RF Signal Identification and Cooperative Intrusion Detection

    DTIC Science & Technology

    2012-03-01

    detection and physical layer authentication in mobile Ad Hoc networks and wireless sensor networks (WSNs) have been investigated. Résume Le rapport...IEEE 802.16 d and e (WiMAX); (b) IEEE 802.11 (Wi-Fi) family of a, b, g, n, and s (c) Sensor networks based on IEEE 802.15.4: Wireless USB, Bluetooth... sensor network are investigated for standard compatible wireless signals. The proposed signal existence detection and identification process consists

  10. Epidemics in Adaptive Social Networks with Temporary Link Deactivation

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Disease spread in a society depends on the topology of the network of social contacts. Moreover, individuals may respond to the epidemic by adapting their contacts to reduce the risk of infection, thus changing the network structure and affecting future disease spread. We propose an adaptation mechanism where healthy individuals may choose to temporarily deactivate their contacts with sick individuals, allowing reactivation once both individuals are healthy. We develop a mean-field description of this system and find two distinct regimes: slow network dynamics, where the adaptation mechanism simply reduces the effective number of contacts per individual, and fast network dynamics, where more efficient adaptation reduces the spread of disease by targeting dangerous connections. Analysis of the bifurcation structure is supported by numerical simulations of disease spread on an adaptive network. The system displays a single parameter-dependent stable steady state and non-monotonic dependence of connectivity on link deactivation rate.

  11. A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aziz, H. M. Abdul; Ukkusuri, Satish V.

    We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

  12. A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

    DOE PAGES

    Aziz, H. M. Abdul; Ukkusuri, Satish V.

    2017-06-29

    We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

  13. Advanced development and calibration of the network robustness index to identify critical road network links.

    DOT National Transportation Integrated Search

    2010-05-31

    In this research project, transportation flexibility and reliability concepts are extended and applied : to a new method for identifying the most critical links in a road network. Current transportation : management practices typically utilize locali...

  14. Is the kinetoplast DNA a percolating network of linked rings at its critical point?

    NASA Astrophysics Data System (ADS)

    Michieletto, Davide; Marenduzzo, Davide; Orlandini, Enzo

    2015-05-01

    In this work we present a computational study of the kinetoplast genome, modelled as a large number of semiflexible unknotted loops, which are allowed to link with each other. As the DNA density increases, the systems shows a percolation transition between a gas of unlinked rings and a network of linked loops which spans the whole system. Close to the percolation transition, we find that the mean valency of the network, i.e. the average number of loops which are linked to any one loop, is around three, as found experimentally for the kinetoplast DNA (kDNA). Even more importantly, by simulating the digestion of the network by a restriction enzyme, we show that the distribution of oligomers, i.e. structures formed by a few loops which remain linked after digestion, quantitatively matches experimental data obtained from gel electrophoresis, provided that the density is, once again, close to the percolation transition. With respect to previous work, our analysis builds on a reduced number of assumptions, yet can still fully explain the experimental data. Our findings suggest that the kDNA can be viewed as a network of linked loops positioned very close to the percolation transition, and we discuss the possible biological implications of this remarkable fact.

  15. Linking the proteins--elucidation of proteome-scale networks using mass spectrometry.

    PubMed

    Pflieger, Delphine; Gonnet, Florence; de la Fuente van Bentem, Sergio; Hirt, Heribert; de la Fuente, Alberto

    2011-01-01

    Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks. Copyright © 2010 Wiley Periodicals, Inc.

  16. Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

    PubMed Central

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

  17. An intelligent anti-jamming network system of data link

    NASA Astrophysics Data System (ADS)

    Fan, Xiangrui; Lin, Jingyong; Liu, Jiarun; Zhou, Chunmei

    2017-10-01

    Data link is the key information system for the cooperation of weapons, single physical layer anti-jamming technology has been unable to meet its requirements. High dynamic precision-guided weapon nodes like missiles, anti-jamming design of data link system need to have stronger pertinence and effectiveness: the best anti-jamming communication mode can be selected intelligently in combat environment, in real time, guarantee the continuity of communication. We discuss an anti-jamming intelligent networking technology of data link based on interference awareness, put forward a model of intelligent anti-jamming system, and introduces the cognitive node protocol stack model and intelligent anti-jamming method, in order to improve the data chain of intelligent anti-jamming ability.

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

    NASA Astrophysics Data System (ADS)

    Zhang, Zipeng; Fan, Tao; Wang, Shuqing

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

  19. Full-duplex bidirectional transmission of 10-Gb/s millimeter-wave QPSK signal in E-band optical wireless link.

    PubMed

    Fang, Yuan; Yu, Jianjun; Chi, Nan; Xiao, Jiangnan

    2014-01-27

    We experimentally demonstrated full-duplex bidirectional transmission of 10-Gb/s millimeter-wave (mm-wave) quadrature phase shift keying (QPSK) signal in E-band (71-76 GHz and 81-86 GHz) optical wireless link. Single-mode fibers (SMF) are connected at both sides of the antenna for uplink and downlink which realize 40-km SMF and 2-m wireless link for bidirectional transmission simultaneously. We utilized multi-level modulation format and coherent detection in such E-band optical wireless link for the first time. Mm-wave QPSK signal is generated by photonic technique to increase spectrum efficiency and received signal is coherently detected to improve receiver sensitivity. After the coherent detection, digital signal processing is utilized to compensate impairments of devices and transmission link.

  20. A new way to improve the robustness of complex communication networks by allocating redundancy links

    NASA Astrophysics Data System (ADS)

    Shi, Chunhui; Peng, Yunfeng; Zhuo, Yue; Tang, Jieying; Long, Keping

    2012-03-01

    We investigate the robustness of complex communication networks on allocating redundancy links. The protecting key nodes (PKN) strategy is proposed to improve the robustness of complex communication networks against intentional attack. Our numerical simulations show that allocating a few redundant links among key nodes using the PKN strategy will significantly increase the robustness of scale-free complex networks. We have also theoretically proved and demonstrated the effectiveness of the PKN strategy. We expect that our work will help achieve a better understanding of communication networks.

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  3. LWT Based Sensor Node Signal Processing in Vehicle Surveillance Distributed Sensor Network

    NASA Astrophysics Data System (ADS)

    Cha, Daehyun; Hwang, Chansik

    Previous vehicle surveillance researches on distributed sensor network focused on overcoming power limitation and communication bandwidth constraints in sensor node. In spite of this constraints, vehicle surveillance sensor node must have signal compression, feature extraction, target localization, noise cancellation and collaborative signal processing with low computation and communication energy dissipation. In this paper, we introduce an algorithm for light-weight wireless sensor node signal processing based on lifting scheme wavelet analysis feature extraction in distributed sensor network.

  4. 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.

  5. Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

    PubMed Central

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

    2012-01-01

    To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response. PMID:22479638

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

    PubMed

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

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

  7. An Interaction Library for the FcεRI Signaling Network

    DOE PAGES

    Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.; ...

    2014-04-15

    Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

  8. An Interaction Library for the FcεRI Signaling Network

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.

    Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

  9. Disrupted Intrinsic Networks Link Amyloid-β Pathology and Impaired Cognition in Prodromal Alzheimer's Disease.

    PubMed

    Koch, Kathrin; Myers, Nicholas E; Göttler, Jens; Pasquini, Lorenzo; Grimmer, Timo; Förster, Stefan; Manoliu, Andrei; Neitzel, Julia; Kurz, Alexander; Förstl, Hans; Riedl, Valentin; Wohlschläger, Afra M; Drzezga, Alexander; Sorg, Christian

    2015-12-01

    Amyloid-β pathology (Aβ) and impaired cognition characterize Alzheimer's disease (AD); however, neural mechanisms that link Aβ-pathology with impaired cognition are incompletely understood. Large-scale intrinsic connectivity networks (ICNs) are potential candidates for this link: Aβ-pathology affects specific networks in early AD, these networks show disrupted connectivity, and they process specific cognitive functions impaired in AD, like memory or attention. We hypothesized that, in AD, regional changes of ICNs, which persist across rest- and cognitive task-states, might link Aβ-pathology with impaired cognition via impaired intrinsic connectivity. Pittsburgh compound B (PiB)-positron emission tomography reflecting in vivo Aβ-pathology, resting-state fMRI, task-fMRI, and cognitive testing were used in patients with prodromal AD and healthy controls. In patients, default mode network's (DMN) functional connectivity (FC) was reduced in the medial parietal cortex during rest relative to healthy controls, relatively increased in the same region during an attention-demanding task, and associated with patients' cognitive impairment. Local PiB-uptake correlated negatively with DMN connectivity. Importantly, corresponding results were found for the right lateral parietal region of an attentional network. Finally, structural equation modeling confirmed a direct influence of DMN resting-state FC on the association between Aβ-pathology and cognitive impairment. Data provide evidence that disrupted intrinsic network connectivity links Aβ-pathology with cognitive impairment in early AD. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  10. Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

    PubMed

    Grooms, Joshua K; Thompson, Garth J; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H; Epstein, Charles M; Keilholz, Shella D

    2017-06-01

    A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

  11. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks.

    PubMed

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

    2015-04-28

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

  12. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    PubMed Central

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

    2015-01-01

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

  13. Mapping edge-based traffic measurements onto the internal links in MPLS network

    NASA Astrophysics Data System (ADS)

    Zhao, Guofeng; Tang, Hong; Zhang, Yi

    2004-09-01

    Applying multi-protocol label switching techniques to IP-based backbone for traffic engineering goals has shown advantageous. Obtaining a volume of load on each internal link of the network is crucial for traffic engineering applying. Though collecting can be available for each link, such as applying traditional SNMP scheme, the approach may cause heavy processing load and sharply degrade the throughput of the core routers. Then monitoring merely at the edge of the network and mapping the measurements onto the core provides a good alternative way. In this paper, we explore a scheme for traffic mapping with edge-based measurements in MPLS network. It is supposed that the volume of traffic on each internal link over the domain would be mapped onto by measurements available only at ingress nodes. We apply path-based measurements at ingress nodes without enabling measurements in the core of the network. We propose a method that can infer a path from the ingress to the egress node using label distribution protocol without collecting routing data from core routers. Based on flow theory and queuing theory, we prove that our approach is effective and present the algorithm for traffic mapping. We also show performance simulation results that indicate potential of our approach.

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

    PubMed

    Chacón, Ricardo; Martínez, Pedro J

    2015-07-01

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

  15. 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…

  16. Traversing the Links between Heavy Metal Stress and Plant Signaling

    PubMed Central

    Jalmi, Siddhi K.; Bhagat, Prakash K.; Verma, Deepanjali; Noryang, Stanzin; Tayyeba, Sumaira; Singh, Kirti; Sharma, Deepika; Sinha, Alok K.

    2018-01-01

    Plants confront multifarious environmental stresses widely divided into abiotic and biotic stresses, of which heavy metal stress represents one of the most damaging abiotic stresses. Heavy metals cause toxicity by targeting crucial molecules and vital processes in the plant cell. One of the approaches by which heavy metals act in plants is by over production of reactive oxygen species (ROS) either directly or indirectly. Plants act against such overdose of metal in the environment by boosting the defense responses like metal chelation, sequestration into vacuole, regulation of metal intake by transporters, and intensification of antioxidative mechanisms. This response shown by plants is the result of intricate signaling networks functioning in the cell in order to transmit the extracellular stimuli into an intracellular response. The crucial signaling components involved are calcium signaling, hormone signaling, and mitogen activated protein kinase (MAPK) signaling that are discussed in this review. Apart from signaling components other regulators like microRNAs and transcription factors also have a major contribution in regulating heavy metal stress. This review demonstrates the key role of MAPKs in synchronously controlling the other signaling components and regulators in metal stress. Further, attempts have been made to focus on metal transporters and chelators that are regulated by MAPK signaling. PMID:29459874

  17. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Dasgupta, Hirak

    2016-12-01

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

  18. Minimal Network Topologies for Signal Processing during Collective Cell Chemotaxis.

    PubMed

    Yue, Haicen; Camley, Brian A; Rappel, Wouter-Jan

    2018-06-19

    Cell-cell communication plays an important role in collective cell migration. However, it remains unclear how cells in a group cooperatively process external signals to determine the group's direction of motion. Although the topology of signaling pathways is vitally important in single-cell chemotaxis, the signaling topology for collective chemotaxis has not been systematically studied. Here, we combine mathematical analysis and simulations to find minimal network topologies for multicellular signal processing in collective chemotaxis. We focus on border cell cluster chemotaxis in the Drosophila egg chamber, in which responses to several experimental perturbations of the signaling network are known. Our minimal signaling network includes only four elements: a chemoattractant, the protein Rac (indicating cell activation), cell protrusion, and a hypothesized global factor responsible for cell-cell interaction. Experimental data on cell protrusion statistics allows us to systematically narrow the number of possible topologies from more than 40,000,000 to only six minimal topologies with six interactions between the four elements. This analysis does not require a specific functional form of the interactions, and only qualitative features are needed; it is thus robust to many modeling choices. Simulations of a stochastic biochemical model of border cell chemotaxis show that the qualitative selection procedure accurately determines which topologies are consistent with the experiment. We fit our model for all six proposed topologies; each produces results that are consistent with all experimentally available data. Finally, we suggest experiments to further discriminate possible pathway topologies. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  19. 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.

  20. Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

    PubMed Central

    2012-01-01

    Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

  1. In-field Raman amplification on coherent optical fiber links for frequency metrology.

    PubMed

    Clivati, C; Bolognini, G; Calonico, D; Faralli, S; Mura, A; Levi, F

    2015-04-20

    Distributed Raman amplification (DRA) is widely exploited for the transmission of broadband, modulated signals used in data links, but not yet in coherent optical links for frequency metrology, where the requirements are rather different. After preliminary tests on fiber spools, in this paper we deeper investigate Raman amplification on deployed in-field optical metrological links. We actually test a Doppler-stabilized optical link both on a 94 km-long metro-network implementation with multiplexed ITU data channels and on a 180 km-long dedicated fiber haul connecting two cities, where DRA is employed in combination with Erbium-doped fiber amplification (EDFA). The performance of DRA is detailed in both experiments, indicating that it does not introduce noticeable penalties for the metrological signal or for the ITU data channels. We hence show that Raman amplification of metrological signals can be compatible with a wavelength division multiplexing architecture and that it can be used as an alternative or in combination with dedicated bidirectional EDFAs. No deterioration is noticed in the coherence properties of the delivered signal, which attains frequency instability at the 10(-19) level in both cases. This study can be of interest also in view of the undergoing deployment of continental fiber networks for frequency metrology.

  2. Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder.

    PubMed

    Dong, Guangheng; Lin, Xiao; Hu, Yanbo; Xie, Chunming; Du, Xiaoxia

    2015-03-17

    Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD.

  3. 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

  4. All Optical Solution for Free Space Optics Point to Point Links

    NASA Astrophysics Data System (ADS)

    Hirayama, Daigo

    Optical network systems are quickly replacing electrical network systems. Optical systems provide better bandwidth, faster data rates, better security to networks, and are less susceptible to noise. Free Space Optics (systems) still rely on numerous electrical systems such as the modulation and demodulation systems to convert optical signals to electrical signals for the transmitting laser. As the concept of the entirely optical network becomes more realizable, the electrical components of the FSO system will become a hindrance to communications. The focus of this thesis is to eliminate the electrical devices for the FSO point to point links by replacing them with optical devices. The concept is similar to an extended beam connector. However, where an extended beam connector deals with a gap of a few millimeters, my focus looks at distances from 100 meters to one kilometer. The aim is to achieve a detectable signal of 1nW at a distance of 500 meters at a wavelength of 1500-1600nm. This leads to application in building to building links and mobile networks. The research examines the design of the system in terms of generating the wave, the properties of the fiber feeding the wave, and the power necessary to achieve a usable distance. The simulation is executed in Code V by Synopsys, which is an industry standard to analyze optical systems. A usable device with a range of around 500m was achieved with an input power of 1mW. The approximations of the phase function resulted in some aberrations to the profile of the beam, but were not very detrimental to the function of the device. The removal of electrical devices from a FSO point to point link decreased the power used to establish the link and decreased the cost.

  5. Space Link Extension (SLE) Emulation for High-Throughput Network Communication

    NASA Technical Reports Server (NTRS)

    Murawski, Robert; Tchorowski, Nicole; Golden, Bert

    2014-01-01

    As the data rate requirements for space communications increases, signicant stressis placed not only on the wireless satellite communication links, but also on the groundnetworks which forward data from end-users to remote ground stations. These wide areanetwork (WAN) connections add delay and jitter to the end-to-end satellite communicationlink, eects which can have signicant impacts on the wireless communication link. It isimperative that any ground communication protocol can react to these eects such that theground network does not become a bottleneck in the communication path to the satellite.In this paper, we present our SCENIC Emulation Lab testbed which was developed to testthe CCSDS SLE protocol implementations proposed for use on future NASA communica-tion networks. Our results show that in the presence of realistic levels of network delay,high-throughput SLE communication links can experience signicant data rate throttling.Based on our observations, we present some insight into why this data throttling happens,and trace the probable issue back to non-optimal blocking communication which is sup-ported by the CCSDS SLE API recommended practices. These issues were presented aswell to the SLE implementation developers which, based on our reports, developed a newrelease for SLE which we show xes the SLE blocking issue and greatly improves the pro-tocol throughput. In this paper, we also discuss future developments for our end-to-endemulation lab and how these improvements can be used to develop and test future spacecommunication technologies.

  6. A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

    PubMed

    Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

    2018-06-21

    Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

  7. 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.

  8. 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.

  9. Star polymers as unit cells for coarse-graining cross-linked networks

    NASA Astrophysics Data System (ADS)

    Molotilin, Taras Y.; Maduar, Salim R.; Vinogradova, Olga I.

    2018-03-01

    Reducing the complexity of cross-linked polymer networks by preserving their main macroscale properties is key to understanding them, and a crucial issue is to relate individual properties of the polymer constituents to those of the reduced network. Here we study polymer networks in a good solvent, by considering star polymers as their unit elements, and first quantify the interaction between their centers of masses. We then reduce the complexity of a network by replacing sets of its bridged star polymers by equivalent effective soft particles with dense cores. Our coarse graining allows us to approximate complex polymer networks by much simpler ones, keeping their relevant mechanical properties, as illustrated in computer experiments.

  10. 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

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

    PubMed

    Mailloux, Ryan J; Treberg, Jason R

    2016-08-01

    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

  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. 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

  14. 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

  15. Linking Climate Risk, Policy Networks and Adaptation Planning in Public Lands

    NASA Astrophysics Data System (ADS)

    Lubell, M.; Schwartz, M.; Peters, C.

    2014-12-01

    Federal public land management agencies in the United States have engaged a variety of planning efforts to address climate adaptation. A major goal of these efforts is to build policy networks that enable land managers to access information and expertise needed for responding to local climate risks. This paper investigates whether the perceived and modeled climate risk faced by different land managers is leading to larger networks or more participating in climate adaptation. In theory, the benefits of climate planning networks are larger when land managers are facing more potential changes. The basic hypothesis is tested with a survey of public land managers from hundreds of local and regional public lands management units in the Southwestern United States, as well as other stakeholders involved with climate adaptation planning. All survey respondents report their perceptions of climate risk along a variety of dimensions, as well as their participation in climate adaptation planning and information sharing networks. For a subset of respondents, we have spatially explicity GIS data about their location, which will be linked with downscaled climate model data. With the focus on climate change, the analysis is a subset of the overall idea of linking social and ecological systems.

  16. Capacity upgrade in short-reach optical fibre networks: simultaneous 4-PAM 20 Gbps data and polarization-modulated PPS clock signal using a single VCSEL carrier

    NASA Astrophysics Data System (ADS)

    Isoe, G. M.; Wassin, S.; Gamatham, R. R. G.; Leitch, A. W. R.; Gibbon, T. B.

    2017-11-01

    In this work, a four-level pulse amplitude modulation (4-PAM) format with a polarization-modulated pulse per second (PPS) clock signal using a single vertical cavity surface emitting laser (VCSEL) carrier is for the first time experimentally demonstrated. We propose uncomplex alternative technique for increasing capacity and flexibility in short-reach optical communication links through multi-signal modulation onto a single VCSEL carrier. A 20 Gbps 4-PAM data signal is directly modulated onto a single mode 10 GHz bandwidth VCSEL carrier at 1310 nm, therefore, doubling the network bit rate. Carrier spectral efficiency is further maximized by exploiting the inherent orthogonal polarization switching of the VCSEL carrier with changing bias in transmission of a PPS clock signal. We, therefore, simultaneously transmit a 20 Gbps 4-PAM data signal and a polarization-based PPS clock signal using a single VCSEL carrier. It is the first time a signal VCSEL carrier is reported to simultaneously transmit a directly modulated 20 Gbps 4-PAM data signal and a polarization-based PPS clock signal. We further demonstrate on the design of a software-defined digital signal processing (DSP)-assisted receiver as an alternative to costly receiver hardware. Experimental results show that a 3.21 km fibre transmission with simultaneous 20 Gbps 4-PAM data signal and polarization-based PPS clock signal introduced a penalty of 3.76 dB. The contribution of polarization-based PPS clock signal to this penalty was found out to be 0.41 dB. Simultaneous distribution of data and timing clock signals over shared network infrastructure significantly increases the aggregated data rate at different optical network units (ONUs), without costly investment.

  17. Potential of commercial microwave link network derived rainfall for river runoff simulations

    NASA Astrophysics Data System (ADS)

    Smiatek, Gerhard; Keis, Felix; Chwala, Christian; Fersch, Benjamin; Kunstmann, Harald

    2017-03-01

    Commercial microwave link networks allow for the quantification of path integrated precipitation because the attenuation by hydrometeors correlates with rainfall between transmitter and receiver stations. The networks, operated and maintained by cellphone companies, thereby provide completely new and country wide precipitation measurements. As the density of traditional precipitation station networks worldwide is significantly decreasing, microwave link derived precipitation estimates receive increasing attention not only by hydrologists but also by meteorological and hydrological services. We investigate the potential of microwave derived precipitation estimates for streamflow prediction and water balance analyses, exemplarily shown for an orographically complex region in the German Alps (River Ammer). We investigate the additional value of link derived rainfall estimations combined with station observations compared to station and weather radar derived values. Our river runoff simulation system employs a distributed hydrological model at 100 × 100 m grid resolution. We analyze the potential of microwave link derived precipitation estimates for two episodes of 30 days with typically moderate river flow and an episode of extreme flooding. The simulation results indicate the potential of this novel precipitation monitoring method: a significant improvement in hydrograph reproduction has been achieved in the extreme flooding period that was characterized by a large number of local strong precipitation events. The present rainfall monitoring gauges alone were not able to correctly capture these events.

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

    PubMed Central

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

    2016-01-01

    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. PMID:28087714

  19. Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules.

    PubMed

    Saez-Rodriguez, Julio; Gayer, Stefan; Ginkel, Martin; Gilles, Ernst Dieter

    2008-08-15

    The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism. Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot. Supplementary data are available at Bioinformatics online.

  20. Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks

    PubMed Central

    Ollivier, Julien F.; Soyer, Orkun S.

    2016-01-01

    Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications. PMID:27163612

  1. A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks

    PubMed Central

    Wang, Ning; Su, Yishan; Yang, Qiuling

    2018-01-01

    Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting. PMID:29414898

  2. Predicting missing links and identifying spurious links via likelihood analysis

    NASA Astrophysics Data System (ADS)

    Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

    2016-03-01

    Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

  3. Predicting missing links and identifying spurious links via likelihood analysis

    PubMed Central

    Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

    2016-01-01

    Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

  4. Dexras1 links glucocorticoids to insulin-like growth factor-1 signaling in adipogenesis

    PubMed Central

    Kim, Hyo Jung; Cha, Jiyoung Y.; Seok, Jo Woon; Choi, Yoonjeong; Yoon, Bo Kyung; Choi, Hyeonjin; Yu, Jung Hwan; Song, Su Jin; Kim, Ara; Lee, Hyemin; Kim, Daeun; Han, Ji Yoon; Kim, Jae-woo

    2016-01-01

    Glucocorticoids are associated with obesity, but the underlying mechanism by which they function remains poorly understood. Previously, we showed that small G protein Dexras1 is expressed by glucocorticoids and leads to adipocyte differentiation. In this study, we explored the mechanism by which Dexras1 mediates adipogenesis and show a link to the insulin-like growth factor-1 (IGF-1) signaling pathway. Without Dexras1, the activation of MAPK and subsequent phosphorylation of CCAAT/enhancer binding protein β (C/EBPβ) is abolished, thereby inhibiting mitotic clonal expansion and further adipocyte differentiation. Dexras1 translocates to the plasma membrane upon insulin or IGF-1 treatment, for which the unique C-terminal domain (amino acids 223–276) is essential. Dexras1-dependent MAPK activation is selectively involved in the IGF-1 signaling, because another Ras protein, H-ras localized to the plasma membrane independently of insulin treatment. Moreover, neither epidermal growth factor nor other cell types shows Dexras1-dependent MAPK activation, indicating the importance of Dexras1 in IGF-1 signaling in adipogenesis. Dexras1 interacts with Shc and Raf, indicating that Dexras1-induced activation of MAPK is largely dependent on the Shc-Grb2-Raf complex. These results suggest that Dexras1 is a critical mediator of the IGF-1 signal to activate MAPK, linking glucocorticoid signaling to IGF-1 signaling in adipogenesis. PMID:27345868

  5. A logic-based method to build signaling networks and propose experimental plans.

    PubMed

    Rougny, Adrien; Gloaguen, Pauline; Langonné, Nathalie; Reiter, Eric; Crépieux, Pascale; Poupon, Anne; Froidevaux, Christine

    2018-05-18

    With the dramatic increase of the diversity and the sheer quantity of biological data generated, the construction of comprehensive signaling networks that include precise mechanisms cannot be carried out manually anymore. In this context, we propose a logic-based method that allows building large signaling networks automatically. Our method is based on a set of expert rules that make explicit the reasoning made by biologists when interpreting experimental results coming from a wide variety of experiment types. These rules allow formulating all the conclusions that can be inferred from a set of experimental results, and thus building all the possible networks that explain these results. Moreover, given an hypothesis, our system proposes experimental plans to carry out in order to validate or invalidate it. To evaluate the performance of our method, we applied our framework to the reconstruction of the FSHR-induced and the EGFR-induced signaling networks. The FSHR is known to induce the transactivation of the EGFR, but very little is known on the resulting FSH- and EGF-dependent network. We built a single network using data underlying both networks. This leads to a new hypothesis on the activation of MEK by p38MAPK, which we validate experimentally. These preliminary results represent a first step in the demonstration of a cross-talk between these two major MAP kinases pathways.

  6. Coevolution of game and network structure with adjustable linking

    NASA Astrophysics Data System (ADS)

    Qin, Shao-Meng; Zhang, Guo-Yong; Chen, Yong

    2009-12-01

    Most papers about the evolutionary game on graph assume the statistic network structure. However, in the real world, social interaction could change the relationship among people. And the change of social structure will also affect people’s strategies. We build a coevolution model of prisoner’s dilemma game and network structure to study the dynamic interaction in the real world. Differing from other coevolution models, players rewire their network connections according to the density of cooperation and other players’ payoffs. We use a parameter α to control the effect of payoff in the process of rewiring. Based on the asynchronous update rule and Monte Carlo simulation, we find that, when players prefer to rewire their links to those who are richer, the temptation can increase the cooperation density.

  7. Infraslow Electroencephalographic and Dynamic Resting State Network Activity

    PubMed Central

    Grooms, Joshua K.; Thompson, Garth J.; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H.; Epstein, Charles M.

    2017-01-01

    Abstract A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies. PMID:28462586

  8. Biophysical constraints on the computational capacity of biochemical signaling networks

    NASA Astrophysics Data System (ADS)

    Wang, Ching-Hao; Mehta, Pankaj

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

  9. Discovery of Intramolecular Signal Transduction Network Based on a New Protein Dynamics Model of Energy Dissipation

    PubMed Central

    Ma, Cheng-Wei; Xiu, Zhi-Long; Zeng, An-Ping

    2012-01-01

    A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins. PMID:22363664

  10. Sparse representation of whole-brain fMRI signals for identification of functional networks.

    PubMed

    Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

    2015-02-01

    There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

    Zhang, Junwen; Yu, Jianjun; Chi, Nan

    2016-02-01

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

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

    PubMed Central

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

    2009-01-01

    Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step. PMID:18976716

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

    PubMed

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

    2009-02-01

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

  14. 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

  15. A new traffic control design method for large networks with signalized intersections

    NASA Technical Reports Server (NTRS)

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

    1979-01-01

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

  16. An experimental study on digital predistortion for radio-over-fiber links

    NASA Astrophysics Data System (ADS)

    Vieira, Luis C.; Gomes, Nathan J.; Nkansah, Anthony

    2010-12-01

    Radio-over-fiber (RoF) has been proposed as an enabling technology for broadband networks, such as WiMAX and WiFi. Besides the inherent high bandwidth and reliability of RoF systems, they also allow the reduction of installation and maintenance cost of the remote antenna units (RAUs) and improvement in the coverage area of the base station/access point. However, the nonlinear distortion of the optical link, which stems mainly from the laser diode, may impose serious limitations on the system performance, especially when high PAPR, wideband signals are used. Thus, distortion compensation is a key issue in order to facilitate the application of the RoF technology for broadband networks. In this work, digital predistortion for directly modulated RoF links is experimentally investigated. A memory-polynomial- based predistorter model is identified from measurements of the baseband OFDM input-output signals and through the use of an indirect learning architecture. Then, a predistorted signal is generated and fed to the RoF link for comparing its output with that of the non-predistorted one. As a result of this compensation technique, an improvement of the static link linearity and the output constellation diagram has been found, with an EVM reduction of 1.73 %.

  17. 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.

  18. Combined node and link partitions method for finding overlapping communities in complex networks

    PubMed Central

    Jin, Di; Gabrys, Bogdan; Dang, Jianwu

    2015-01-01

    Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures. PMID:25715829

  19. Ultra-stable long distance optical frequency distribution using the Internet fiber network.

    PubMed

    Lopez, Olivier; Haboucha, Adil; Chanteau, Bruno; Chardonnet, Christian; Amy-Klein, Anne; Santarelli, Giorgio

    2012-10-08

    We report an optical link of 540 km for ultrastable frequency distribution over the Internet fiber network. The stable frequency optical signal is processed enabling uninterrupted propagation on both directions. The robustness and the performance of the link are enhanced by a cost effective fully automated optoelectronic station. This device is able to coherently regenerate the return optical signal with a heterodyne optical phase locking of a low noise laser diode. Moreover the incoming signal polarization variation are tracked and processed in order to maintain beat note amplitudes within the operation range. Stable fibered optical interferometer enables optical detection of the link round trip phase signal. The phase-noise compensated link shows a fractional frequency instability in 10 Hz bandwidth of 5 × 10(-15) at one second measurement time and 2 × 10(-19) at 30,000 s. This work is a significant step towards a sustainable wide area ultrastable optical frequency distribution and comparison network.

  20. Hybrid Mobile Communication Networks for Planetary Exploration

    NASA Technical Reports Server (NTRS)

    Alena, Richard; Lee, Charles; Walker, Edward; Osenfort, John; Stone, Thom

    2007-01-01

    A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.

  1. The Robustness of a Signaling Complex to Domain Rearrangements Facilitates Network Evolution

    PubMed Central

    Sato, Paloma M.; Yoganathan, Kogulan; Jung, Jae H.; Peisajovich, Sergio G.

    2014-01-01

    The rearrangement of protein domains is known to have key roles in the evolution of signaling networks and, consequently, is a major tool used to synthetically rewire networks. However, natural mutational events leading to the creation of proteins with novel domain combinations, such as in frame fusions followed by domain loss, retrotranspositions, or translocations, to name a few, often simultaneously replace pre-existing genes. Thus, while proteins with new domain combinations may establish novel network connections, it is not clear how the concomitant deletions are tolerated. We investigated the mechanisms that enable signaling networks to tolerate domain rearrangement-mediated gene replacements. Using as a model system the yeast mitogen activated protein kinase (MAPK)-mediated mating pathway, we analyzed 92 domain-rearrangement events affecting 11 genes. Our results indicate that, while domain rearrangement events that result in the loss of catalytic activities within the signaling complex are not tolerated, domain rearrangements can drastically alter protein interactions without impairing function. This suggests that signaling complexes can maintain function even when some components are recruited to alternative sites within the complex. Furthermore, we also found that the ability of the complex to tolerate changes in interaction partners does not depend on long disordered linkers that often connect domains. Taken together, our results suggest that some signaling complexes are dynamic ensembles with loose spatial constraints that could be easily re-shaped by evolution and, therefore, are ideal targets for cellular engineering. PMID:25490747

  2. 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

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

    NASA Astrophysics Data System (ADS)

    Wei, Pei; Gu, Rentao; Ji, Yuefeng

    2014-06-01

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

  4. Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

    NASA Astrophysics Data System (ADS)

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

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

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

    NASA Astrophysics Data System (ADS)

    Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

    2015-07-01

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

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

    PubMed Central

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

    2010-01-01

    The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018

  7. Signaling Network Map of Endothelial TEK Tyrosine Kinase

    PubMed Central

    Sandhya, Varot K.; Singh, Priyata; Parthasarathy, Deepak; Kumar, Awinav; Gattu, Rudrappa; Mathur, Premendu Prakash; Mac Gabhann, F.; Pandey, Akhilesh

    2014-01-01

    TEK tyrosine kinase is primarily expressed on endothelial cells and is most commonly referred to as TIE2. TIE2 is a receptor tyrosine kinase modulated by its ligands, angiopoietins, to regulate the development and remodeling of vascular system. It is also one of the critical pathways associated with tumor angiogenesis and familial venous malformations. Apart from the vascular system, TIE2 signaling is also associated with postnatal hematopoiesis. Despite the involvement of TIE2-angiopoietin system in several diseases, the downstream molecular events of TIE2-angiopoietin signaling are not reported in any pathway repository. Therefore, carrying out a detailed review of published literature, we have documented molecular signaling events mediated by TIE2 in response to angiopoietins and developed a network map of TIE2 signaling. The pathway information is freely available to the scientific community through NetPath, a manually curated resource of signaling pathways. We hope that this pathway resource will provide an in-depth view of TIE2-angiopoietin signaling and will lead to identification of potential therapeutic targets for TIE2-angiopoietin associated disorders. PMID:25371820

  8. WDM-PON access network with lightwave source centralized full-duplex link based on SSB-OOFDM for wired and 60 GHz band wireless alternative accesses

    NASA Astrophysics Data System (ADS)

    Ma, Jianxin; Wang, Zhao; Zheng, Guoli

    2014-04-01

    A novel lightwave centralized full-duplex WDM-PON access network based on single sideband optical orthogonal frequency-division multiplexing (SSB-OOFDM) is proposed for providing wired and 60-GHz band wireless accesses alternately. At the OLT, the multi-channels with 10-Gb/s 4-QAM-RF-OFDM signals are SSB modulated on the optical local oscillators (OLOs). At the RN, one OOFDM signal along with two OLOs is abstracted and switched to the corresponding HONU, where the signal can be downconverted to the 10-GHz or 60-GHz band RF-OFDM signal by one OLO for wired or wireless access, while the other one is used to bear the uplink signal. Since the HONU is free from the light sources, the system complexity and cost are reduced. Full duplex transmission over 25 km fiber have been demonstrated that the error vector magnitude (EVM) of the down- and up-link signals are much below the FEC limit for both the wired and 60-GHz band wireless access services.

  9. Ultrastable optical frequency dissemination on a multi-access fibre network

    NASA Astrophysics Data System (ADS)

    Bercy, Anthony; Lopez, Olivier; Pottie, Paul-Eric; Amy-Klein, Anne

    2016-07-01

    We report a laboratory demonstration of the dissemination of an ultrastable optical frequency signal to two distant users simultaneously using a branching network. The ultrastable signal is extracted along a main fibre link; it is optically tracked by a narrow linewidth laser diode, which light is injected in a secondary link. The propagation noise of both links is actively compensated. We implement this scheme with two links of 50-km fibre spools, the extraction being set up at the mid-point of the main link. We show that the extracted signal at the end of the secondary link exhibits a fractional frequency instability of 1.4 × 10-15 at 1-s measurement time, almost equal to the 1.3 × 10-15 instability of the main link output end. The long-term instabilities are also very similar, at a level of 3-5 × 10-20 at 3 × 104-s integration time. We also show that the setting up of this extraction device, or of a simpler one, at the main link input, can test the proper functioning of the noise rejection on this main link. This work is a significant step towards a robust and flexible ultrastable network for multi-users dissemination.

  10. Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

    PubMed Central

    2015-01-01

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

  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. 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.

  13. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. 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.

  15. Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking

    NASA Astrophysics Data System (ADS)

    Yuan, Wu-Jie; Zhou, Jian-Fang; Li, Qun; Chen, De-Bao; Wang, Zhen

    2013-08-01

    Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.

  16. 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

  17. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

    PubMed

    Knapp, Bettina; Kaderali, Lars

    2013-01-01

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

  18. Networking Sensors for Information Dominance - Joint Signal Processing and Communication Design

    DTIC Science & Technology

    2012-01-01

    2012 4. TITLE AND SUBTITLE NETWORKING SENSORS FOR INFORMATION DOMINANCE - JOINT SIGNAL PROCESSING AND COMMUNICATION DESIGN, Final Report for FA9550...Rev. 2-89) Prescribed by ANSI Std. Z39-18 298-102 Public A AFRL-OSR-VA-TR-2012-0729 NETWORKING SENSORS FOR INFORMATION DOMINANCE - JOINT

  19. Characterization of Radar Signals Using Neural Networks

    DTIC Science & Technology

    1990-12-01

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

  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. 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.

  2. 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

  3. The optical antenna system design research on earth integrative network laser link in the future

    NASA Astrophysics Data System (ADS)

    Liu, Xianzhu; Fu, Qiang; He, Jingyi

    2014-11-01

    Earth integrated information network can be real-time acquisition, transmission and processing the spatial information with the carrier based on space platforms, such as geostationary satellites or in low-orbit satellites, stratospheric balloons or unmanned and manned aircraft, etc. It is an essential infrastructure for China to constructed earth integrated information network. Earth integrated information network can not only support the highly dynamic and the real-time transmission of broadband down to earth observation, but the reliable transmission of the ultra remote and the large delay up to the deep space exploration, as well as provide services for the significant application of the ocean voyage, emergency rescue, navigation and positioning, air transportation, aerospace measurement or control and other fields.Thus the earth integrated information network can expand the human science, culture and productive activities to the space, ocean and even deep space, so it is the global research focus. The network of the laser communication link is an important component and the mean of communication in the earth integrated information network. Optimize the structure and design the system of the optical antenna is considered one of the difficulty key technologies for the space laser communication link network. Therefore, this paper presents an optical antenna system that it can be used in space laser communication link network.The antenna system was consisted by the plurality mirrors stitched with the rotational paraboloid as a substrate. The optical system structure of the multi-mirror stitched was simulated and emulated by the light tools software. Cassegrain form to be used in a relay optical system. The structural parameters of the relay optical system was optimized and designed by the optical design software of zemax. The results of the optimal design and simulation or emulation indicated that the antenna system had a good optical performance and a certain

  4. 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

  5. Small molecule schweinfurthins selectively inhibit cancer cell proliferation and mTOR/AKT signaling by interfering with trans-Golgi-network trafficking

    PubMed Central

    Bao, Xingfeng; Zheng, Wanjun; Sugi, Naoko Hata; Agarwala, Kishan L; Xu, Qunli; Wang, Zichun; Tendyke, Karen; Lee, Winnie; Parent, Lana; Li, Wei; Cheng, Hongsheng; Shen, Yongchun; Taylor, Noel; Dezso, Zoltan; Du, Hong; Kotake, Yoshihiko; Zhao, Nanding; Wang, John; Postema, Maarten; Woodall-Jappe, Mary; Takase, Yasutaka; Uenaka, Toshimitsu; Kingston, David G I; Nomoto, Kenichi

    2015-01-01

    Natural compound schweinfurthins are of considerable interest for novel therapy development because of their selective anti-proliferative activity against human cancer cells. We previously reported the isolation of highly active schweinfurthins E-H, and in the present study, mechanisms of the potent and selective anti-proliferation were investigated. We found that schweinfurthins preferentially inhibited the proliferation of PTEN deficient cancer cells by indirect inhibition of AKT phosphorylation. Mechanistically, schweinfurthins and their analogs arrested trans-Golgi-network trafficking, an intracellular vesicular trafficking system, resulting in the induction of endoplasmic reticulum stress and the suppression of both lipid raft-mediated PI3K activation and mTOR/RheB complex formation, which collectively led to an effective inhibition of mTOR/AKT signaling. The trans-Golgi-network traffic arresting effect of schweinfurthins was associated with their in vitro binding activity to oxysterol-binding proteins that are known to regulate intracellular vesicular trafficking. Moreover, schweinfurthins were found to be highly toxic toward PTEN-deficient B cell lymphoma cells, and displayed 2 orders of magnitude lower activity toward normal human peripheral blood mononuclear cells and primary fibroblasts in vitro. These results revealed a previously unrecognized role of schweinfurthins in regulating trans-Golgi-network trafficking, and linked mechanistically this cellular effect with mTOR/AKT signaling and with cancer cell survival and growth. Our findings suggest the schweinfurthin class of compounds as a novel approach to modulate oncogenic mTOR/AKT signaling for cancer treatment. PMID:25729885

  6. 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.

  7. A Ka-band (32 GHz) beacon link experiment (KABLE) with Mars Observer

    NASA Technical Reports Server (NTRS)

    Riley, A. L.; Hansen, D. M.; Mileant, A.; Hartop, R. W.

    1987-01-01

    A proposal for a Ka-Band (32 GHz) Link Experiment (KABLE) with the Mars Observer mission was submitted to NASA. The experiment will rely on the fourth harmonic of the spacecraft X-band transmitter to generate a 33.6 GHz signal. The experiment will rely also on the Deep Space Network (DSN) receiving station equipped to simultaneously receive X- and Ka-band signals. The experiment will accurately measure the spacecraft-to-Earth telecommunication link performance at Ka-band and X-band (8.4 GHz).

  8. Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

    NASA Astrophysics Data System (ADS)

    Romero, David; Molina, Arturo

    Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

  9. Capacity planning of link restorable optical networks under dynamic change of traffic

    NASA Astrophysics Data System (ADS)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2005-11-01

    Future backbone networks shall require full-survivability and support dynamic changes of traffic demands. The Generalized Survivable Networks (GSN) was proposed to meet these challenges. GSN is fully-survivable under dynamic traffic demand changes, so it offers a practical and guaranteed characterization framework for ASTN / ASON survivable network planning and bandwidth-on-demand resource allocation 4. The basic idea of GSN is to incorporate the non-blocking network concept into the survivable network models. In GSN, each network node must specify its I/O capacity bound which is taken as constraints for any allowable traffic demand matrix. In this paper, we consider the following generic GSN network design problem: Given the I/O bounds of each network node, find a routing scheme (and the corresponding rerouting scheme under failure) and the link capacity assignment (both working and spare) which minimize the cost, such that any traffic matrix consistent with the given I/O bounds can be feasibly routed and it is single-fault tolerant under the link restoration scheme. We first show how the initial, infeasible formal mixed integer programming formulation can be transformed into a more feasible problem using the duality transformation of the linear program. Then we show how the problem can be simplified using the Lagrangian Relaxation approach. Previous work has outlined a two-phase approach for solving this problem where the first phase optimizes the working capacity assignment and the second phase optimizes the spare capacity assignment. In this paper, we present a jointly optimized framework for dimensioning the survivable optical network with the GSN model. Experiment results show that the jointly optimized GSN can bring about on average of 3.8% cost savings when compared with the separate, two-phase approach. Finally, we perform a cost comparison and show that GSN can be deployed with a reasonable cost.

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

    PubMed Central

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

    2016-01-01

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

  11. Improving rainfall estimation from commercial microwave links using METEOSAT SEVIRI cloud cover information

    NASA Astrophysics Data System (ADS)

    Boose, Yvonne; Doumounia, Ali; Chwala, Christian; Moumouni, Sawadogo; Zougmoré, François; Kunstmann, Harald

    2017-04-01

    The number of rain gauges is declining worldwide. A recent promising method for alternative precipitation measurements is to derive rain rates from the attenuation of the microwave signal between remote antennas of mobile phone base stations, so called commercial microwave links (CMLs). In European countries, such as Germany, the CML technique can be used as a complementary method to the existing gauge and radar networks improving their products, for example, in mountainous terrain and urban areas. In West African countries, where a dense gauge or radar network is absent, the number of mobile phone users is rapidly increasing and so are the CML networks. Hence, the CML-derived precipitation measurements have high potential for applications such as flood warning and support of agricultural planning in this region. For typical CML bandwidths (10-40 GHz), the relationship of attenuation to rain rate is quasi-linear. However, also humidity, wet antennas or electronic noise can lead to signal interference. To distinguish these fluctuations from actual attenuation due to rain, a temporal wet (rain event occurred)/ dry (no rain event) classification is usually necessary. In dense CML networks this is possible by correlating neighboring CML time series. Another option is to use the correlation between signal time series of different frequencies or bidirectional signals. The CML network in rural areas is typically not dense enough for correlation analysis and often only one polarization and one frequency are available along a CML. In this work we therefore use cloud cover information derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer onboard the geostationary satellite METEOSAT for a wet (pixels along link are cloud covered)/ dry (no cloud along link) classification. We compare results for CMLs in Burkina Faso and Germany, which differ meteorologically (rain rate and duration, droplet size distributions) and technically (CML frequencies

  12. Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Flekova, L.; Schott, M.

    2017-10-01

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

  13. Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

    PubMed

    Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

    2018-05-21

    Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate

  14. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks

    PubMed Central

    Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

    2013-01-01

    Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

  15. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

    PubMed

    Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

    2013-01-01

    Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

  16. Analysing 21cm signal with artificial neural network

    NASA Astrophysics Data System (ADS)

    Shimabukuro, Hayato; a Semelin, Benoit

    2018-05-01

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

  17. Cellular Organization and Cytoskeletal Regulation of the Hippo Signaling Network

    PubMed Central

    Sun, Shuguo; Irvine, Kenneth D.

    2016-01-01

    The Hippo signaling network integrates diverse upstream signals to control cell fate decisions and regulate organ growth. Recent studies have provided new insights into the cellular organization of Hippo signaling, its relationship to cell-cell junctions, and how the cytoskeleton modulates Hippo signaling. Cell-cell junctions serve as platforms for Hippo signaling by localizing scaffolding proteins that interact with core components of the pathway. Interactions of Hippo pathway components with cell-cell junctions and the cytoskeleton also suggest potential mechanisms for the regulation of the pathway by cell contact and cell polarity. As our understanding of the complexity of Hippo signaling increases, a future challenge will be to understand how the diverse inputs into the pathway are integrated, and to define their respective contributions in vivo. PMID:27268910

  18. Cellular Organization and Cytoskeletal Regulation of the Hippo Signaling Network.

    PubMed

    Sun, Shuguo; Irvine, Kenneth D

    2016-09-01

    The Hippo signaling network integrates diverse upstream signals to control cell fate decisions and regulate organ growth. Recent studies have provided new insights into the cellular organization of Hippo signaling, its relationship to cell-cell junctions, and how the cytoskeleton modulates Hippo signaling. Cell-cell junctions serve as platforms for Hippo signaling by localizing scaffolding proteins that interact with core components of the pathway. Interactions of Hippo pathway components with cell-cell junctions and the cytoskeleton also suggest potential mechanisms for the regulation of the pathway by cell contact and cell polarity. As our understanding of the complexity of Hippo signaling increases, a future challenge will be to understand how the diverse inputs into the pathway are integrated and to define their respective contributions in vivo. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture.

    PubMed

    Jothi, Raja; Balaji, S; Wuster, Arthur; Grochow, Joshua A; Gsponer, Jörg; Przytycka, Teresa M; Aravind, L; Babu, M Madan

    2009-01-01

    Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

  20. The Inverse Contagion Problem (ICP) vs.. Predicting site contagion in real time, when network links are not observable

    NASA Astrophysics Data System (ADS)

    Mushkin, I.; Solomon, S.

    2017-10-01

    We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it

  1. Reprocessing and Recycling of Highly Cross-Linked Ion-Conducting Networks through Transalkylation Exchanges of C-N Bonds.

    PubMed

    Obadia, Mona M; Mudraboyina, Bhanu P; Serghei, Anatoli; Montarnal, Damien; Drockenmuller, Eric

    2015-05-13

    Exploiting exchangeable covalent bonds as dynamic cross-links recently afforded a new class of polymer materials coined as vitrimers. These permanent networks are insoluble and infusible, but the network topology can be reshuffled at high temperatures, thus enabling glasslike plastic deformation and reprocessing without depolymerization. We disclose herein the development of functional and high-value ion-conducting vitrimers that take inspiration from poly(ionic liquid)s. Tunable networks with high ionic content are obtained by the solvent- and catalyst-free polyaddition of an α-azide-ω-alkyne monomer and simultaneous alkylation of the resulting poly(1,2,3-triazole)s with a series of difunctional cross-linking agents. Temperature-induced transalkylation exchanges of C-N bonds between 1,2,3-triazolium cross-links and halide-functionalized dangling chains enable recycling and reprocessing of these highly cross-linked permanent networks. They can also be recycled by depolymerization with specific solvents able to displace the transalkylation equilibrium, and they display a great potential for applications that require solid electrolytes with excellent mechanical performances and facile processing such as supercapacitors, batteries, fuel cells, and separation membranes.

  2. Country-wide rainfall maps from cellular communication networks

    PubMed Central

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-01-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

  3. 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...

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

    PubMed

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

    2016-04-08

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

  5. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

    PubMed

    Yildirim, Özal

    2018-05-01

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

  6. 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.

  7. United Kingdom Deriving Emissions linked to Climate Change Network: greenhouse gas and ozone depleting substance measurements from a UK network of tall towers

    NASA Astrophysics Data System (ADS)

    Stanley, Kieran; O'Doherty, Simon; Young, Dickon; Grant, Aoife; Manning, Alistair; Simmonds, Peter; Oram, Dave; Sturges, Bill; Derwent, Richard

    2016-04-01

    Real-time, high-frequency measurement networks are essential for investigating the emissions of gases linked with climate change and stratospheric ozone depletion. These networks can be used to verify greenhouse gas (GHG) and ozone depleting substances (ODS) emission inventories for the Kyoto and Montreal Protocols. Providing accurate and reliable country- and region-specific emissions to the atmosphere are critical for reporting to the UN agencies. The United Kingdom Deriving Emissions linked to Climate Change (UK DECC) Network, operating since 2012, is distinguished by its capability to measure at high-frequency, the influence of all of the important species in the Kyoto and Montreal Protocols from the UK, Ireland and Continental Europe. Data obtained from the UK DECC network are also fed into the European Integrated Carbon Observation System (ICOS). This presentation will give an overview of the UK DECC Network, detailing the analytical techniques used to determine the suite of GHGs and ODSs, as well as the calibration strategy used within the network. Interannual results of key GHGs from the network will also be presented.

  8. Anti-correlated Networks, Global Signal Regression, and the Effects of Caffeine in Resting-State Functional MRI

    PubMed Central

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

    2012-01-01

    Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. PMID:22743194

  9. 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

  10. A common brain network links development, aging, and vulnerability to disease.

    PubMed

    Douaud, Gwenaëlle; Groves, Adrian R; Tamnes, Christian K; Westlye, Lars Tjelta; Duff, Eugene P; Engvig, Andreas; Walhovd, Kristine B; James, Anthony; Gass, Achim; Monsch, Andreas U; Matthews, Paul M; Fjell, Anders M; Smith, Stephen M; Johansen-Berg, Heidi

    2014-12-09

    Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

  11. Ku-band signal design study. [for space shuttle orbiter communication links

    NASA Technical Reports Server (NTRS)

    Lindsey, W. L.; Woo, K. T.

    1977-01-01

    The acquisition/tracking performance of a practical squaring loop in which the times two multiplier is mechanized as a limiter/multiplier combination is evaluated. This squaring approach serves to produce the absolute value of the arriving signal as opposed to the perfect square law action which is required in order to render acquisition and tracking performance equivalent to that of a Costas loop. The Ku-Band orbiter signal design for the forward link is assessed. Acquisition time results and acquisition and tracking thresholds are summarized. A tradeoff study which pertains to bit synchronization techniques for the high rate Ku-Band channel is included and an optimum selection is made based upon the appropriate design constraints.

  12. Network analysis of physics discussion forums and links to course success

    NASA Astrophysics Data System (ADS)

    Traxler, Adrienne; Gavrin, Andrew; Lindell, Rebecca

    2017-01-01

    Large introductory science courses tend to isolate students, with negative consequences for long-term retention in college. Many active learning courses build collaboration and community among students as an explicit goal, and social network analysis has been used to track the development and beneficial effects of these collaborations. Here we supplement such work by conducting network analysis of online course discussion forums in two semesters of an introductory physics class. Online forums provide a tool for engaging students with each other outside of class, and offer new opportunities to commuter or non-traditional students with limited on-campus time. We look for correlations between position in the forum network (centrality) and final course grades. Preliminary investigation has shown weak correlations in the very dense full-semester network, so we will consider reduced ''backbone'' networks that highlight the most consistent links between students. Future work and implications for instruction will also be discussed.

  13. 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.

  14. Altering the threshold of an excitable signal transduction network changes cell migratory modes.

    PubMed

    Miao, Yuchuan; Bhattacharya, Sayak; Edwards, Marc; Cai, Huaqing; Inoue, Takanari; Iglesias, Pablo A; Devreotes, Peter N

    2017-04-01

    The diverse migratory modes displayed by different cell types are generally believed to be idiosyncratic. Here we show that the migratory behaviour of Dictyostelium was switched from amoeboid to keratocyte-like and oscillatory modes by synthetically decreasing phosphatidylinositol-4,5-bisphosphate levels or increasing Ras/Rap-related activities. The perturbations at these key nodes of an excitable signal transduction network initiated a causal chain of events: the threshold for network activation was lowered, the speed and range of propagating waves of signal transduction activity increased, actin-driven cellular protrusions expanded and, consequently, the cell migratory mode transitions ensued. Conversely, innately keratocyte-like and oscillatory cells were promptly converted to amoeboid by inhibition of Ras effectors with restoration of directed migration. We use computational analysis to explain how thresholds control cell migration and discuss the architecture of the signal transduction network that gives rise to excitability.

  15. Transport link scanner: simulating geographic transport network expansion through individual investments

    NASA Astrophysics Data System (ADS)

    Jacobs-Crisioni, C.; Koopmans, C. C.

    2016-07-01

    This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness is defined as a function of variables in which revenue and broader societal benefits may play a role and can be based on empirically underpinned parameters that may differ according to private or public interests. The choice set is selected from an exhaustive set of links and presumably contains those investment options that best meet private operator's objectives by balancing the revenues of additional fare against construction costs. The investment options consist of geographically plausible routes with potential detours. These routes are generated using a fine-meshed regularly latticed network and shortest path finding methods. Additionally, two indicators of the geographic accuracy of the simulated networks are introduced. A historical case study is presented to demonstrate the model's first results. These results show that the modelled networks reproduce relevant results of the historically built network with reasonable accuracy.

  16. Information transmission and signal permutation in active flow networks

    NASA Astrophysics Data System (ADS)

    Woodhouse, Francis G.; Fawcett, Joanna B.; Dunkel, Jörn

    2018-03-01

    Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

  17. A switchable spin-wave signal splitter for magnonic networks

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

  18. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    PubMed

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  19. Rate-based congestion control in networks with smart links, revision. B.S. Thesis - May 1988

    NASA Technical Reports Server (NTRS)

    Heybey, Andrew Tyrrell

    1990-01-01

    The author uses a network simulator to explore rate-based congestion control in networks with smart links that can feed back information to tell senders to adjust their transmission rates. This method differs in a very important way from congestion control in which a congested network component just drops packets - the most commonly used method. It is clearly advantageous for the links in the network to communicate with the end users about the network capacity, rather than the users unilaterally picking a transmission rate. The components in the middle of the network, not the end users, have information about the capacity and traffic in the network. The author experiments with three different algorithms for calculating the control rate to feed back to the users. All of the algorithms exhibit problems in the form of large queues when simulated with a configuration modeling the dynamics of a packet-voice system. However, the problems are not with the algorithms themselves, but with the fact that feedback takes time. If the network steady-state utilization is low enough that it can absorb transients in the traffic through it, then the large queues disappear. If the users are modified to start sending slowly, to allow the network to adapt to a new flow without causing congestion, a greater portion of the network's bandwidth can be used.

  20. Linking Transnational Logics: A Feminist Rhetorical Analysis of Public Policy Networks

    ERIC Educational Resources Information Center

    Dingo, Rebecca

    2008-01-01

    In this article, the author investigates the circulation and appropriation of representations of women in public policy. The author effectively mobilizes the metaphor of the network to examine the discursive intersections and transnational links between U.S. welfare programs and the World Bank gender mainstreaming policies. Her analysis reveals…

  1. Reliable Broadcast under Cascading Failures in Interdependent Networks

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Duan, Sisi; Lee, Sangkeun; Chinthavali, Supriya

    Reliable broadcast is an essential tool to disseminate information among a set of nodes in the presence of failures. We present a novel study of reliable broadcast in interdependent networks, in which the failures in one network may cascade to another network. In particular, we focus on the interdependency between the communication network and power grid network, where the power grid depends on the signals from the communication network for control and the communication network depends on the grid for power. In this paper, we build a resilient solution to handle crash failures in the communication network that may causemore » cascading failures and may even partition the network. In order to guarantee that all the correct nodes deliver the messages, we use soft links, which are inactive backup links to non-neighboring nodes that are only active when failures occur. At the core of our work is a fully distributed algorithm for the nodes to predict and collect the information of cascading failures so that soft links can be maintained to correct nodes prior to the failures. In the presence of failures, soft links are activated to guarantee message delivery and new soft links are built accordingly for long term robustness. Our evaluation results show that the algorithm achieves low packet drop rate and handles cascading failures with little overhead.« less

  2. Noninvasive characterization of the Trecate (Italy) crude-oil contaminated site: links between contamination and geophysical signals.

    PubMed

    Cassiani, Giorgio; Binley, Andrew; Kemna, Andreas; Wehrer, Markus; Orozco, Adrian Flores; Deiana, Rita; Boaga, Jacopo; Rossi, Matteo; Dietrich, Peter; Werban, Ulrike; Zschornack, Ludwig; Godio, Alberto; JafarGandomi, Arash; Deidda, Gian Piero

    2014-01-01

    The characterization of contaminated sites can benefit from the supplementation of direct investigations with a set of less invasive and more extensive measurements. A combination of geophysical methods and direct push techniques for contaminated land characterization has been proposed within the EU FP7 project ModelPROBE and the affiliated project SoilCAM. In this paper, we present results of the investigations conducted at the Trecate field site (NW Italy), which was affected in 1994 by crude oil contamination. The less invasive investigations include ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and electromagnetic induction (EMI) surveys, together with direct push sampling and soil electrical conductivity (EC) logs. Many of the geophysical measurements were conducted in time-lapse mode in order to separate static and dynamic signals, the latter being linked to strong seasonal changes in water table elevations. The main challenge was to extract significant geophysical signals linked to contamination from the mix of geological and hydrological signals present at the site. The most significant aspects of this characterization are: (a) the geometrical link between the distribution of contamination and the site's heterogeneity, with particular regard to the presence of less permeable layers, as evidenced by the extensive surface geophysical measurements; and (b) the link between contamination and specific geophysical signals, particularly evident from cross-hole measurements. The extensive work conducted at the Trecate site shows how a combination of direct (e.g., chemical) and indirect (e.g., geophysical) investigations can lead to a comprehensive and solid understanding of a contaminated site's mechanisms.

  3. The signal extraction of fetal heart rate based on wavelet transform and BP neural network

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

  4. 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…

  5. Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

    PubMed Central

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

    2011-01-01

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

  6. Physiological β-catenin signaling controls self-renewal networks and generation of stem-like cells from nasopharyngeal carcinoma.

    PubMed

    Cheng, Yue; Cheung, Arthur Kwok Leung; Ko, Josephine Mun Yee; Phoon, Yee Peng; Chiu, Pui Man; Lo, Paulisally Hau Yi; Waterman, Marian L; Lung, Maria Li

    2013-09-27

    A few reports suggested that low levels of Wnt signaling might drive cell reprogramming, but these studies could not establish a clear relationship between Wnt signaling and self-renewal networks. There are ongoing debates as to whether and how the Wnt/β-catenin signaling is involved in the control of pluripotency gene networks. Additionally, whether physiological β-catenin signaling generates stem-like cells through interactions with other pathways is as yet unclear. The nasopharyngeal carcinoma HONE1 cells have low expression of β-catenin and wild-type expression of p53, which provided a possibility to study regulatory mechanism of stemness networks induced by physiological levels of Wnt signaling in these cells. Introduction of increased β-catenin signaling, haploid expression of β-catenin under control by its natural regulators in transferred chromosome 3, resulted in activation of Wnt/β-catenin networks and dedifferentiation in HONE1 hybrid cell lines, but not in esophageal carcinoma SLMT1 hybrid cells that had high levels of endogenous β-catenin expression. HONE1 hybrid cells displayed stem cell-like properties, including enhancement of CD24(+) and CD44(+) populations and generation of spheres that were not observed in parental HONE1 cells. Signaling cascades were detected in HONE1 hybrid cells, including activation of p53- and RB1-mediated tumor suppressor pathways, up-regulation of Nanog-, Oct4-, Sox2-, and Klf4-mediated pluripotency networks, and altered E-cadherin expression in both in vitro and in vivo assays. qPCR array analyses further revealed interactions of physiological Wnt/β-catenin signaling with other pathways such as epithelial-mesenchymal transition, TGF-β, Activin, BMPR, FGFR2, and LIFR- and IL6ST-mediated cell self-renewal networks. Using β-catenin shRNA inhibitory assays, a dominant role for β-catenin in these cellular network activities was observed. The expression of cell surface markers such as CD9, CD24, CD44, CD90, and CD133

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

    PubMed

    Anderson, Brian A

    2017-03-01

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

  8. A KST framework for correlation network construction from time series signals

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

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

    PubMed Central

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

    2016-01-01

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

  10. Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

    PubMed

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

    2012-10-15

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

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

    PubMed Central

    Newaz, Khalique; Sriram, K.; Bera, Debajyoti

    2015-01-01

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

  12. Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.

    PubMed

    Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E

    2017-07-01

    Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

  13. A novel DLX3-PKC integrated signaling network drives keratinocyte differentiation.

    PubMed

    Palazzo, Elisabetta; Kellett, Meghan D; Cataisson, Christophe; Bible, Paul W; Bhattacharya, Shreya; Sun, Hong-Wei; Gormley, Anna C; Yuspa, Stuart H; Morasso, Maria I

    2017-04-01

    Epidermal homeostasis relies on a well-defined transcriptional control of keratinocyte proliferation and differentiation, which is critical to prevent skin diseases such as atopic dermatitis, psoriasis or cancer. We have recently shown that the homeobox transcription factor DLX3 and the tumor suppressor p53 co-regulate cell cycle-related signaling and that this mechanism is functionally involved in cutaneous squamous cell carcinoma development. Here we show that DLX3 expression and its downstream signaling depend on protein kinase C α (PKCα) activity in skin. We found that following 12-O-tetradecanoyl-phorbol-13-acetate (TPA) topical treatment, DLX3 expression is significantly upregulated in the epidermis and keratinocytes from mice overexpressing PKCα by transgenic targeting (K5-PKCα), resulting in cell cycle block and terminal differentiation. Epidermis lacking DLX3 (DLX3cKO), which is linked to the development of a DLX3-dependent epidermal hyperplasia with hyperkeratosis and dermal leukocyte recruitment, displays enhanced PKCα activation, suggesting a feedback regulation of DLX3 and PKCα. Of particular significance, transcriptional activation of epidermal barrier, antimicrobial peptide and cytokine genes is significantly increased in DLX3cKO skin and further increased by TPA-dependent PKC activation. Furthermore, when inhibiting PKC activity, we show that epidermal thickness, keratinocyte proliferation and inflammatory cell infiltration are reduced and the PKC-DLX3-dependent gene expression signature is normalized. Independently of PKC, DLX3 expression specifically modulates regulatory networks such as Wnt signaling, phosphatase activity and cell adhesion. Chromatin immunoprecipitation sequencing analysis of primary suprabasal keratinocytes showed binding of DLX3 to the proximal promoter regions of genes associated with cell cycle regulation, and of structural proteins and transcription factors involved in epidermal differentiation. These results indicate

  14. Real-time in Situ Signal-to-noise Ratio Estimation for the Assessment of Operational Communications Links

    NASA Technical Reports Server (NTRS)

    Manning, Robert M.

    2002-01-01

    The work presented here formulates the rigorous statistical basis for the correct estimation of communication link SNR of a BPSK, QPSK, and for that matter, any M-ary phase-modulated digital signal from what is known about its statistical behavior at the output of the receiver demodulator. Many methods to accomplish this have been proposed and implemented in the past but all of them are based on tacit and unwarranted assumptions and are thus defective. However, the basic idea is well founded, i.e., the signal at the output of a communications demodulator has convolved within it the prevailing SNR characteristic of the link. The acquisition of the SNR characteristic is of the utmost importance to a communications system that must remain reliable in adverse propagation conditions. This work provides a correct and consistent mathematical basis for the proper statistical 'deconvolution' of the output of a demodulator to yield a measure of the SNR. The use of such techniques will alleviate the need and expense for a separate propagation link to assess the propagation conditions prevailing on the communications link. Furthermore, they are applicable for every situation involving the digital transmission of data over planetary and space communications links.

  15. 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.

  16. 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.

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

    PubMed Central

    Nolin, David A.

    2012-01-01

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

  18. Signaling Networks among Stem Cell Precursors, Transit-Amplifying Progenitors, and their Niche in Developing Hair Follicles.

    PubMed

    Rezza, Amélie; Wang, Zichen; Sennett, Rachel; Qiao, Wenlian; Wang, Dongmei; Heitman, Nicholas; Mok, Ka Wai; Clavel, Carlos; Yi, Rui; Zandstra, Peter; Ma'ayan, Avi; Rendl, Michael

    2016-03-29

    The hair follicle (HF) is a complex miniorgan that serves as an ideal model system to study stem cell (SC) interactions with the niche during growth and regeneration. Dermal papilla (DP) cells are required for SC activation during the adult hair cycle, but signal exchange between niche and SC precursors/transit-amplifying cell (TAC) progenitors that regulates HF morphogenetic growth is largely unknown. Here we use six transgenic reporters to isolate 14 major skin and HF cell populations. With next-generation RNA sequencing, we characterize their transcriptomes and define unique molecular signatures. SC precursors, TACs, and the DP niche express a plethora of ligands and receptors. Signaling interaction network analysis reveals a bird's-eye view of pathways implicated in epithelial-mesenchymal interactions. Using a systematic tissue-wide approach, this work provides a comprehensive platform, linked to an interactive online database, to identify and further explore the SC/TAC/niche crosstalk regulating HF growth. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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

    PubMed

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

    2014-01-01

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

  20. Optical-wireless-optical full link for polarization multiplexing quadrature amplitude/phase modulation signal transmission.

    PubMed

    Li, Xinying; Yu, Jianjun; Chi, Nan; Zhang, Junwen

    2013-11-15

    We propose and experimentally demonstrate an optical wireless integration system at the Q-band, in which up to 40 Gb/s polarization multiplexing multilevel quadrature amplitude/phase modulation (PM-QAM) signal can be first transmitted over 20 km single-mode fiber-28 (SMF-28), then delivered over a 2 m 2 × 2 multiple-input multiple-output wireless link, and finally transmitted over another 20 km SMF-28. The PM-QAM modulated wireless millimeter-wave (mm-wave) signal at 40 GHz is generated based on the remote heterodyning technique, and demodulated by the radio-frequency transparent photonic technique based on homodyne coherent detection and baseband digital signal processing. The classic constant modulus algorithm equalization is used at the receiver to realize polarization demultiplexing of the PM-QAM signal. For the first time, to the best of our knowledge, we realize the conversion of the PM-QAM modulated wireless mm-wave signal to the optical signal as well as 20 km fiber transmission of the converted optical signal.

  1. The Calcineurin Signaling Network Evolves Via Conserved Kinase–Phosphatase Modules That Transcend Substrate Identity

    PubMed Central

    Bodenmiller, Bernd; Wanka, Stefanie; Landry, Christian R.; Aebersold, Ruedi; Cyert, Martha S.

    2014-01-01

    Summary To define the first functional network for calcineurin, the conserved Ca2+/calmodulin-regulated phosphatase, we systematically identified its substrates in S. cerevisiae using phosphoproteomics and bioinformatics, followed by co-purification and dephosphorylation assays. This study establishes new calcineurin functions and reveals mechanisms that shape calcineurin network evolution. Analyses of closely related yeasts show that many proteins were recently recruited to the network by acquiring a calcineurin-recognition motif. Calcineurin substrates in yeast and mammals are distinct due to network rewiring but surprisingly are phosphorylated by similar kinases. We postulate that co-recognition of conserved substrate features, including phosphorylation and docking motifs, preserves calcineurin-kinase opposition during evolution. One example we document is a composite docking site that confers substrate recognition by both calcineurin and MAPK. We propose that conserved kinase-phosphatase pairs define the architecture of signaling networks and allow other connections between kinases and phosphatases to develop and establish common regulatory motifs in signaling networks. PMID:24930733

  2. Using LinkedIn in the Marketing Classroom: Exploratory Insights and Recommendations for Teaching Social Media/Networking

    ERIC Educational Resources Information Center

    McCorkle, Denny E.; McCorkle, Yuhua Li

    2012-01-01

    With the rapid growth of social networking and media comes their consideration for use in the marketing classroom. Social networking skills are becoming essential for personal branding (e.g., networking, self-marketing) and corporate/product branding (e.g., marketing communication). This paper addresses the use of LinkedIn (i.e., an online…

  3. An Alzheimer Disease-linked Rare Mutation Potentiates Netrin Receptor Uncoordinated-5C-induced Signaling That Merges with Amyloid β Precursor Protein Signaling.

    PubMed

    Hashimoto, Yuichi; Toyama, Yuka; Kusakari, Shinya; Nawa, Mikiro; Matsuoka, Masaaki

    2016-06-03

    A missense mutation (T835M) in the uncoordinated-5C (UNC5C) netrin receptor gene increases the risk of late-onset Alzheimer disease (AD) and also the vulnerability of neurons harboring the mutation to various insults. The molecular mechanisms underlying T835M-UNC5C-induced death remain to be elucidated. In this study, we show that overexpression of wild-type UNC5C causes low-grade death, which is intensified by an AD-linked mutation T835M. An AD-linked survival factor, calmodulin-like skin protein (CLSP), and a natural ligand of UNC5C, netrin1, inhibit this death. T835M-UNC5C-induced neuronal cell death is mediated by an intracellular death-signaling cascade, consisting of death-associated protein kinase 1/protein kinase D/apoptosis signal-regulating kinase 1 (ASK1)/JNK/NADPH oxidase/caspases, which merges at ASK1 with a death-signaling cascade, mediated by amyloid β precursor protein (APP). Notably, netrin1 also binds to APP and partially inhibits the death-signaling cascade, induced by APP. These results may provide new insight into the amyloid β-independent pathomechanism of AD. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  4. Duplicate retention in signalling proteins and constraints from network dynamics.

    PubMed

    Soyer, O S; Creevey, C J

    2010-11-01

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

  5. An integrated analog O/E/O link for multi-channel laser neurons

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nahmias, Mitchell A., E-mail: mnahmias@princeton.edu; Tait, Alexander N.; Tolias, Leonidas

    2016-04-11

    We demonstrate an analog O/E/O electronic link to allow integrated laser neurons to accept many distinguishable, high bandwidth input signals simultaneously. This device utilizes wavelength division multiplexing to achieve multi-channel fan-in, a photodetector to sum signals together, and a laser cavity to perform a nonlinear operation. Its speed outpaces accelerated-time neuromorphic electronics, and it represents a viable direction towards scalable networking approaches.

  6. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

    NASA Technical Reports Server (NTRS)

    Smith, Aaron; Evans, Michael; Downey, Joseph

    2017-01-01

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

  7. 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. Copyright © 1991. Published by Elsevier Ltd.

  8. Effects of topologies on signal propagation in feedforward networks

    NASA Astrophysics Data System (ADS)

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

    2018-01-01

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

  9. Effects of topologies on signal propagation in feedforward networks.

    PubMed

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

    2018-01-01

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

  10. Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

    PubMed

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

    2011-11-01

    Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

  11. A New Signaling Architecture THREP with Autonomous Radio-Link Control for Wireless Communications Systems

    NASA Astrophysics Data System (ADS)

    Hirono, Masahiko; Nojima, Toshio

    This paper presents a new signaling architecture for radio-access control in wireless communications systems. Called THREP (for THREe-phase link set-up Process), it enables systems with low-cost configurations to provide tetherless access and wide-ranging mobility by using autonomous radio-link controls for fast cell searching and distributed call management. A signaling architecture generally consists of a radio-access part and a service-entity-access part. In THREP, the latter part is divided into two steps: preparing a communication channel, and sustaining it. Access control in THREP is thus composed of three separated parts, or protocol phases. The specifications of each phase are determined independently according to system requirements. In the proposed architecture, the first phase uses autonomous radio-link control because we want to construct low-power indoor wireless communications systems. Evaluation of channel usage efficiency and hand-over loss probability in the personal handy-phone system (PHS) shows that THREP makes the radio-access sub-system operations in a practical application model highly efficient, and the results of a field experiment show that THREP provides sufficient protection against severe fast CNR degradation in practical indoor propagation environments.

  12. Rainfall estimation from microwave links in São Paulo, Brazil.

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2017-04-01

    Rainfall estimation from microwave link networks has been successfully demonstrated in countries such as the Netherlands, Israel and Germany. The path-averaged rainfall intensity can be computed from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities to retrieve rainfall rates at high spatiotemporal resolutions very close to the ground surface. High spatiotemporal resolutions and closer-to-ground measurements are highly appreciated, especially in urban catchments where high-impact events such as flash-floods develop in short time scales. We evaluate here this rainfall measurement technique for a tropical climate, something that has hardly been done previously. This is highly relevant since many countries with few surface rainfall observations are located in the tropics. The test-bed is the Brazilian city of São Paulo. The performance of 16 microwave links was evaluated, from a network of 200 links, for the last 3 months of 2014. The open software package RAINLINK was employed to obtain link rainfall estimates. The evaluation was done through a dense automatic gauge network. Results are promising and encouraging, especially for short links for which a high correlation (> 0.9) and a low bias (< 5%) were obtained.

  13. Mutations in the inositol polyphosphate-5-phosphatase E gene link phosphatidyl inositol signaling to the ciliopathies

    PubMed Central

    Bielas, Stephanie L.; Silhavy, Jennifer L.; Brancati, Francesco; Kisseleva, Marina V.; Al-Gazali, Lihadh; Sztriha, Laszlo; Bayoumi, Riad A.; Zaki, Maha S.; Abdel-Aleem, Alice; Rosti, Ozgur; Kayserili, Hulya; Swistun, Dominika; Scott, Lesley C.; Bertini, Enrico; Boltshauser, Eugen; Fazzi, Elisa; Travaglini, Lorena; Field, Seth J.; Gayral, Stephanie; Jacoby, Monique; Schurmans, Stephane; Dallapiccola, Bruno; Majerus, Philip W.; Valente, Enza Maria; Gleeson, Joseph G.

    2009-01-01

    Phosphotidylinositol (PtdIns) signaling is tightly regulated, both spatially and temporally, by subcellularly localized PtdIns kinases and phosphatases that dynamically alter downstream signaling events 1. Joubert Syndrome (JS) characterized by a specific midbrain-hindbrain malformation (“molar tooth sign”) and variably associated retinal dystrophy, nephronophthisis, liver fibrosis and polydactyly 2, and is included in the newly emerging group of “ciliopathies”. In patients linking to JBTS1, we identified mutations in the INPP5E gene, encoding inositol polyphosphate-5-phosphatase E, which hydrolyzes the 5-phosphate of PtdIns(3,4,5)P3 and PtdIns(4,5)P2. Mutations clustered in the phosphatase domain and impaired 5-phosphatase activity, resulting in altered cellular PtdIns ratios. INPP5E localized to cilia in major organs affected in JS, and mutations promoted premature destabilization of cilia in response to stimulation. Thus, these data links PtdIns signaling to the primary cilium, a cellular structure that is becoming increasingly appreciated for its role in mediating cell signals and neuronal function. PMID:19668216

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

    PubMed Central

    Gruszka, Damian

    2013-01-01

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

  15. Reverse Engineering a Signaling Network Using Alternative Inputs

    PubMed Central

    Tanaka, Hiromasa; Yi, Tau-Mu

    2009-01-01

    One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an “AIs-Deletions matrix” that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams. PMID:19898612

  16. Domain-general signals in the cingulo-opercular network for visuospatial attention and episodic memory

    PubMed Central

    Sestieri, Carlo; Corbetta, Maurizio; Spadone, Sara; Romani, Gian Luca; Shulman, Gordon L.

    2014-01-01

    We investigated the functional properties of a previously described cingulo-opercular network (CON) putatively involved in cognitive control. Analyses of common fMRI task-evoked activity during perceptual and episodic memory search tasks that differently recruited the dorsal attention (DAN) and default mode network (DMN) established the generality of this network. Regions within the CON (anterior insula/frontal operculum and anterior cingulate/presupplementary cortex) displayed sustained signals during extended periods in which participants searched for behaviourally relevant information in a dynamically changing environment or from episodic memory in the absence of sensory stimulation. The CON was activated during all phases of both tasks, which involved trial initiation, target detection, decision and response, indicating its consistent involvement in a broad range of cognitive processes. Functional connectivity analyses showed that the CON flexibly linked with the DAN or DMN regions during perceptual or memory search, respectively. Aside from the CON, only a limited number of regions, including the lateral prefrontal cortex, showed evidence of domain-general, sustained activity, although in some cases the common activations may have reflected the functional-anatomical variability of domain-specific regions rather than a true domain-generality. These additional regions also showed task-dependent functional connectivity with the DMN and DAN, suggesting that this feature is not a specific marker of cognitive control. Finally, multivariate clustering analyses separated the CON from other fronto-parietal regions previously associated with cognitive control, indicating a unique fingerprint. We conclude that the CON’s functional properties and interactions with other brain regions support a broad role in cognition, consistent with its characterization as a task-control network. PMID:24144246

  17. Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

    PubMed Central

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

    2014-01-01

    The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication. PMID:24415931

  18. Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

    PubMed

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

    2014-06-01

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

  19. A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

    PubMed Central

    Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

    2014-01-01

    We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

  20. A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

    PubMed

    Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

    2014-01-01

    We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

  1. Signaling network of the Btk family kinases.

    PubMed

    Qiu, Y; Kung, H J

    2000-11-20

    The Btk family kinases represent new members of non-receptor tyrosine kinases, which include Btk/Atk, Itk/Emt/Tsk, Bmx/Etk, and Tec. They are characterized by having four structural modules: PH (pleckstrin homology) domain, SH3 (Src homology 3) domain, SH2 (Src homology 2) domain and kinase (Src homology 1) domain. Increasing evidence suggests that, like Src-family kinases, Btk family kinases play central but diverse modulatory roles in various cellular processes. They participate in signal transduction in response to virtually all types of extracellular stimuli which are transmitted by growth factor receptors, cytokine receptors, G-protein coupled receptors, antigen-receptors and integrins. They are regulated by many non-receptor tyrosine kinases such as Src, Jak, Syk and FAK family kinases. In turn, they regulate many of major signaling pathways including those of PI3K, PLCgamma and PKC. Both genetic and biochemical approaches have been used to dissect the signaling pathways and elucidate their roles in growth, differentiation and apoptosis. An emerging new role of this family of kinases is cytoskeletal reorganization and cell motility. The physiological importance of these kinases was amply demonstrated by their link to the development of immunodeficiency diseases, due to germ-line mutations. The present article attempts to review the structure and functions of Btk family kinases by summarizing our current knowledge on the interacting partners associated with the different modules of the kinases and the diverse signaling pathways in which they are involved.

  2. A Study of an Optical Lunar Surface Communications Network with High Bandwidth Direct to Earth Link

    NASA Technical Reports Server (NTRS)

    Wilson, K.; Biswas, A.; Schoolcraft, J.

    2011-01-01

    Analyzed optical DTE (direct to earth) and lunar relay satellite link analyses, greater than 200 Mbps downlink to 1-m Earth receiver and greater than 1 Mbps uplink achieved with mobile 5-cm lunar transceiver, greater than 1Gbps downlink and greater than 10 Mpbs uplink achieved with 10-cm stationary lunar transceiver, MITLL (MIT Lincoln Laboratory) 2013 LLCD (Lunar Laser Communications Demonstration) plans to demonstrate 622 Mbps downlink with 20 Mbps uplink between lunar orbiter and ground station; Identified top five technology challenges to deploying lunar optical network, Performed preliminary experiments on two of challenges: (i) lunar dust removal and (ii)DTN over optical carrier, Exploring opportunities to evaluate DTN (delay-tolerant networking) over optical link in a multi-node network e.g. Desert RATS.

  3. Dependency links can hinder the evolution of cooperation in the prisoner's dilemma game on lattices and networks.

    PubMed

    Wang, Xuwen; Nie, Sen; Wang, Binghong

    2015-01-01

    Networks with dependency links are more vulnerable when facing the attacks. Recent research also has demonstrated that the interdependent groups support the spreading of cooperation. We study the prisoner's dilemma games on spatial networks with dependency links, in which a fraction of individual pairs is selected to depend on each other. The dependency individuals can gain an extra payoff whose value is between the payoff of mutual cooperation and the value of temptation to defect. Thus, this mechanism reflects that the dependency relation is stronger than the relation of ordinary mutual cooperation, but it is not large enough to cause the defection of the dependency pair. We show that the dependence of individuals hinders, promotes and never affects the cooperation on regular ring networks, square lattice, random and scale-free networks, respectively. The results for the square lattice and regular ring networks are demonstrated by the pair approximation.

  4. Processes entangling interactions in communities: forbidden links are more important than abundance in a hummingbird-plant network.

    PubMed

    Vizentin-Bugoni, Jeferson; Maruyama, Pietro Kiyoshi; Sazima, Marlies

    2014-04-07

    Understanding the relative importance of multiple processes on structuring species interactions within communities is one of the major challenges in ecology. Here, we evaluated the relative importance of species abundance and forbidden links in structuring a hummingbird-plant interaction network from the Atlantic rainforest in Brazil. Our results show that models incorporating phenological overlapping and morphological matches were more accurate in predicting the observed interactions than models considering species abundance. This means that forbidden links, by imposing constraints on species interactions, play a greater role than species abundance in structuring the ecological network. We also show that using the frequency of interaction as a proxy for species abundance and network metrics to describe the detailed network structure might lead to biased conclusions regarding mechanisms generating network structure. Together, our findings suggest that species abundance can be a less important driver of species interactions in communities than previously thought.

  5. Processes entangling interactions in communities: forbidden links are more important than abundance in a hummingbird–plant network

    PubMed Central

    Vizentin-Bugoni, Jeferson; Maruyama, Pietro Kiyoshi; Sazima, Marlies

    2014-01-01

    Understanding the relative importance of multiple processes on structuring species interactions within communities is one of the major challenges in ecology. Here, we evaluated the relative importance of species abundance and forbidden links in structuring a hummingbird–plant interaction network from the Atlantic rainforest in Brazil. Our results show that models incorporating phenological overlapping and morphological matches were more accurate in predicting the observed interactions than models considering species abundance. This means that forbidden links, by imposing constraints on species interactions, play a greater role than species abundance in structuring the ecological network. We also show that using the frequency of interaction as a proxy for species abundance and network metrics to describe the detailed network structure might lead to biased conclusions regarding mechanisms generating network structure. Together, our findings suggest that species abundance can be a less important driver of species interactions in communities than previously thought. PMID:24552835

  6. Crosstalk cancellation on linearly and circularly polarized communications satellite links

    NASA Technical Reports Server (NTRS)

    Overstreet, W. P.; Bostian, C. W.

    1979-01-01

    The paper discusses the cancellation network approach for reducing crosstalk caused by depolarization on a dual-polarized communications satellite link. If the characteristics of rain depolarization are sufficiently well known, the cancellation network can be designed in a way that reduces system complexity, the most important parameter being the phase of the cross-polarized signal. Relevant theoretical calculations and experimental data are presented. The simplicity of the cancellation system proposed makes it ideal for use with small domestic or private earth terminals.

  7. Neuropeptide Signaling Networks and Brain Circuit Plasticity.

    PubMed

    McClard, Cynthia K; Arenkiel, Benjamin R

    2018-01-01

    The brain is a remarkable network of circuits dedicated to sensory integration, perception, and response. The computational power of the brain is estimated to dwarf that of most modern supercomputers, but perhaps its most fascinating capability is to structurally refine itself in response to experience. In the language of computers, the brain is loaded with programs that encode when and how to alter its own hardware. This programmed "plasticity" is a critical mechanism by which the brain shapes behavior to adapt to changing environments. The expansive array of molecular commands that help execute this programming is beginning to emerge. Notably, several neuropeptide transmitters, previously best characterized for their roles in hypothalamic endocrine regulation, have increasingly been recognized for mediating activity-dependent refinement of local brain circuits. Here, we discuss recent discoveries that reveal how local signaling by corticotropin-releasing hormone reshapes mouse olfactory bulb circuits in response to activity and further explore how other local neuropeptide networks may function toward similar ends.

  8. The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

    PubMed Central

    Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

    2017-01-01

    Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

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

    PubMed Central

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

    2015-01-01

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown. PMID:26340633

  10. Signal identification in addictovigilance: the functioning of the French system.

    PubMed

    Jouanjus, Emilie; Gibaja, Valérie; Kahn, Jean-Pierre; Haramburu, Françoise; Daveluy, Amélie

    2015-01-01

    The French addictovigilance network (addictovigilance: surveillance of addiction), composed of 13 Addictovigilance Centres, was set up in 1990 in order to achieve reliable surveillance and evaluation of abuse and dependence cases due to psychoactive substances (alcohol and tobacco excepted). The detection of safety signals is one of the roles of the addictovigilance centres. Signals from spontaneous reports need to be analyzed before further communication. In addictovigilance, signals may be linked to adverse effects (deaths, pathological signs), to products (new psychoactive substances with potentially dangerous effects) or to practices (new administration routes, new contexts of use). These signals are provided by numerous partners among whom the addictovigilance network has to raise awareness about information that may possibly be an alert signal. The watchful attitude of all partners will make it possible that signals will be, after analyze, considered as true alerts. The addictovigilance network collects data, assess the potential for addiction of psychoactive drugs to provide information on the risk of addiction and give opinions for public health decisions (harm reduction or prevention programs, psychoactive substances control, health alerts). © 2015 Société Française de Pharmacologie et de Thérapeutique.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-04-09

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

  13. Earth-Space Link Attenuation Estimation via Ground Radar Kdp

    NASA Technical Reports Server (NTRS)

    Bolen, Steven M.; Benjamin, Andrew L.; Chandrasekar, V.

    2003-01-01

    A method of predicting attenuation on microwave Earth/spacecraft communication links, over wide areas and under various atmospheric conditions, has been developed. In the area around the ground station locations, a nearly horizontally aimed polarimetric S-band ground radar measures the specific differential phase (Kdp) along the Earth-space path. The specific attenuation along a path of interest is then computed by use of a theoretical model of the relationship between the measured S-band specific differential phase and the specific attenuation at the frequency to be used on the communication link. The model includes effects of rain, wet ice, and other forms of precipitation. The attenuation on the path of interest is then computed by integrating the specific attenuation over the length of the path. This method can be used to determine statistics of signal degradation on Earth/spacecraft communication links. It can also be used to obtain real-time estimates of attenuation along multiple Earth/spacecraft links that are parts of a communication network operating within the radar coverage area, thereby enabling better management of the network through appropriate dynamic routing along the best combination of links.

  14. 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

  15. Safe and effective error rate monitors for SS7 signaling links

    NASA Astrophysics Data System (ADS)

    Schmidt, Douglas C.

    1994-04-01

    This paper describes SS7 error monitor characteristics, discusses the existing SUERM (Signal Unit Error Rate Monitor), and develops the recently proposed EIM (Error Interval Monitor) for higher speed SS7 links. A SS7 error monitor is considered safe if it ensures acceptable link quality and is considered effective if it is tolerant to short-term phenomena. Formal criteria for safe and effective error monitors are formulated in this paper. This paper develops models of changeover transients, the unstable component of queue length resulting from errors. These models are in the form of recursive digital filters. Time is divided into sequential intervals. The filter's input is the number of errors which have occurred in each interval. The output is the corresponding change in transmit queue length. Engineered EIM's are constructed by comparing an estimated changeover transient with a threshold T using a transient model modified to enforce SS7 standards. When this estimate exceeds T, a changeover will be initiated and the link will be removed from service. EIM's can be differentiated from SUERM by the fact that EIM's monitor errors over an interval while SUERM's count errored messages. EIM's offer several advantages over SUERM's, including the fact that they are safe and effective, impose uniform standards in link quality, are easily implemented, and make minimal use of real-time resources.

  16. An evidential link prediction method and link predictability based on Shannon entropy

    NASA Astrophysics Data System (ADS)

    Yin, Likang; Zheng, Haoyang; Bian, Tian; Deng, Yong

    2017-09-01

    Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster-Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.

  17. The application of neural networks to myoelectric signal analysis: a preliminary study.

    PubMed

    Kelly, M F; Parker, P A; Scott, R N

    1990-03-01

    Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

  18. Self-organizing path integration using a linked continuous attractor and competitive network: path integration of head direction.

    PubMed

    Stringer, Simon M; Rolls, Edmund T

    2006-12-01

    A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attractor network to the next head direction based on the incoming rotation signal. An associative synaptic modification rule with a short term memory trace enables preceding combination cell activity during training to be associated with the next position in the continuous attractor network. The network accounts for the presence of neurons found in the brain that respond to combinations of head direction and angular head rotation velocity. Analogous networks in the hippocampal system could self-organize to perform path integration of place and spatial view representations.

  19. Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning.

    PubMed

    Korcsmaros, Tamas; Dunai, Zsuzsanna A; Vellai, Tibor; Csermely, Peter

    2013-09-01

    The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

  20. High-speed digital fiber optic links for satellite traffic

    NASA Technical Reports Server (NTRS)

    Daryoush, A. S.; Ackerman, E.; Saedi, R.; Kunath, R. R.; Shalkhauser, K.

    1989-01-01

    Large aperture phased array antennas operating at millimeter wave frequencies are designed for space-based communications and imaging platforms. Array elements are comprised of active T/R modules which are linked to the central processing unit through high-speed fiber-optic networks. The system architecture satisfying system requirements at millimeter wave frequency is T/R level data mixing where data and frequency reference signals are distributed independently before mixing at the T/R modules. This paper demonstrates design procedures of a low loss high-speed fiber-optic link used for transmission of data signals over 600-900 MHz bandwidth inside satellite. The fiber-optic link is characterized for transmission of analog and digital data. A dynamic range of 79 dB/MHz was measured for analog data over the bandwidth. On the other hand, for bursted SMSK satellite traffic at 220 Mbps rates, BER of 2 x 10 to the -7th was measured for E(b)/N(o) of 14.3 dB.

  1. Fiber-Optic Communication Links Suitable for On-Board Use in Modern Aircraft

    NASA Technical Reports Server (NTRS)

    Nguyen, Hung; Ngo, Duc; Alam, Mohammad F.; Atiquzzaman, Mohammed; Sluse, James; Slaveski, Filip

    2004-01-01

    The role of the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Centers has been focused mainly on the improvement of air transportation safety, with particular emphasis on air transportation communication systems in on-board aircraft. The conventional solutions for digital optical communications systems specifically designed for local/metro area networks are, unfortunately, not capable of transporting the microwave and millimeter RF signals used in avionics systems. Optical networks capable of transporting RF signals are substantially different from the standard digital optical communications systems. The objective of this paper is to identify a number of different communication link architectures for RF/fiber optic transmission using a single backbone fiber for carrying VHF and UHF RF signals in the aircraft. To support these architectures, two approaches derived from both hybrid RF-optical and all-optical processing methodologies are discussed with single and multiple antennas for explicitly transporting VHF and UHF signals, while the relative merits and demerits of each architecture are also addressed. Furthermore, the experimental results of wavelength division multiplexing (WDM) link architecture from our test-bed platform, configured for aircraft environment to support simultaneous transmission of multiple RF signals over a single optical fiber, exhibit no appreciable signal degradation at wavelengths of both 1330 and 1550 nm, respectively. Our measurements of signal to noise ratio carried out for the transmission of FM and AM analog modulated signals at these wavelengths indicate that WDM is a fiber optic technology which is potentially suitable for avionics applications.

  2. A novel DLX3–PKC integrated signaling network drives keratinocyte differentiation

    PubMed Central

    Palazzo, Elisabetta; Kellett, Meghan D; Cataisson, Christophe; Bible, Paul W; Bhattacharya, Shreya; Sun, Hong-wei; Gormley, Anna C; Yuspa, Stuart H; Morasso, Maria I

    2017-01-01

    Epidermal homeostasis relies on a well-defined transcriptional control of keratinocyte proliferation and differentiation, which is critical to prevent skin diseases such as atopic dermatitis, psoriasis or cancer. We have recently shown that the homeobox transcription factor DLX3 and the tumor suppressor p53 co-regulate cell cycle-related signaling and that this mechanism is functionally involved in cutaneous squamous cell carcinoma development. Here we show that DLX3 expression and its downstream signaling depend on protein kinase C α (PKCα) activity in skin. We found that following 12-O-tetradecanoyl-phorbol-13-acetate (TPA) topical treatment, DLX3 expression is significantly upregulated in the epidermis and keratinocytes from mice overexpressing PKCα by transgenic targeting (K5-PKCα), resulting in cell cycle block and terminal differentiation. Epidermis lacking DLX3 (DLX3cKO), which is linked to the development of a DLX3-dependent epidermal hyperplasia with hyperkeratosis and dermal leukocyte recruitment, displays enhanced PKCα activation, suggesting a feedback regulation of DLX3 and PKCα. Of particular significance, transcriptional activation of epidermal barrier, antimicrobial peptide and cytokine genes is significantly increased in DLX3cKO skin and further increased by TPA-dependent PKC activation. Furthermore, when inhibiting PKC activity, we show that epidermal thickness, keratinocyte proliferation and inflammatory cell infiltration are reduced and the PKC-DLX3-dependent gene expression signature is normalized. Independently of PKC, DLX3 expression specifically modulates regulatory networks such as Wnt signaling, phosphatase activity and cell adhesion. Chromatin immunoprecipitation sequencing analysis of primary suprabasal keratinocytes showed binding of DLX3 to the proximal promoter regions of genes associated with cell cycle regulation, and of structural proteins and transcription factors involved in epidermal differentiation. These results indicate

  3. Adding signals to coordinated traffic signal systems.

    DOT National Transportation Integrated Search

    1983-08-01

    The purpose of this research was to investigate the effect of adding or : removing traffic signals within a coordinated, signal-controlled street network. : The report includes a discussion of coordinated signal systems; arterial street : network con...

  4. Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise

    NASA Astrophysics Data System (ADS)

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

    1989-03-01

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

  5. Identification of critical regulatory genes in cancer signaling network using controllability analysis

    NASA Astrophysics Data System (ADS)

    Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

    2017-05-01

    Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

  6. Cell Size and Growth Rate Are Modulated by TORC2-Dependent Signals.

    PubMed

    Lucena, Rafael; Alcaide-Gavilán, Maria; Schubert, Katherine; He, Maybo; Domnauer, Matthew G; Marquer, Catherine; Klose, Christian; Surma, Michal A; Kellogg, Douglas R

    2018-01-22

    The size of all cells, from bacteria to vertebrates, is proportional to the growth rate set by nutrient availability, but the underlying mechanisms are unknown. Here, we show that nutrients modulate cell size and growth rate via the TORC2 signaling network in budding yeast. An important function of the TORC2 network is to modulate synthesis of ceramide lipids, which play roles in signaling. TORC2-dependent control of ceramide signaling strongly influences both cell size and growth rate. Thus, cells that cannot make ceramides fail to modulate their growth rate or size in response to changes in nutrients. PP2A associated with the Rts1 regulatory subunit (PP2A Rts1 ) is embedded in a feedback loop that controls TORC2 signaling and helps set the level of TORC2 signaling to match nutrient availability. Together, the data suggest a model in which growth rate and cell size are mechanistically linked by ceramide-dependent signals arising from the TORC2 network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Achieving fast and stable failure detection in WDM Networks

    NASA Astrophysics Data System (ADS)

    Gao, Donghui; Zhou, Zhiyu; Zhang, Hanyi

    2005-02-01

    In dynamic networks, the failure detection time takes a major part of the convergence time, which is an important network performance index. To detect a node or link failure in the network, traditional protocols, like Hello protocol in OSPF or RSVP, exchanges keep-alive messages between neighboring nodes to keep track of the link/node state. But by default settings, it can get a minimum detection time in the measure of dozens of seconds, which can not meet the demands of fast network convergence and failure recovery. When configuring the related parameters to reduce the detection time, there will be notable instability problems. In this paper, we analyzed the problem and designed a new failure detection algorithm to reduce the network overhead of detection signaling. Through our experiment we found it is effective to enhance the stability by implicitly acknowledge other signaling messages as keep-alive messages. We conducted our proposal and the previous approaches on the ASON test-bed. The experimental results show that our algorithm gives better performances than previous schemes in about an order magnitude reduction of both false failure alarms and queuing delay to other messages, especially under light traffic load.

  8. Linearized Optically Phase-Modulated Fiber Optic Links for Microwave Signal Transport

    DTIC Science & Technology

    2009-03-03

    detectors (with internal 50- Ohm resistors) capable of 40-mA dc current per detector. With this link, the linearized SFDR would improve to 133 dB/Hz4/5...the IF) limitation on the signal. All calculations consider the 3dB power loss from the hybrid combiner and 6dB loss from parallel 50- Ohm resistors...283. [25] M. Nazarathy, J. Berger, A. Ley , I. Levi, and Y. Kagan, “Externally Modulated 80 Channel Am Catv Fiber-to-feeder Distribution System Over

  9. Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

    2001-07-01

    Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  11. Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics

    PubMed Central

    2009-01-01

    Background The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. Results Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. Conclusions The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks. PMID:20028552

  12. Harmonic stochastic resonance-enhanced signal detecting in NW small-world neural network

    NASA Astrophysics Data System (ADS)

    Wang, Dao-Guang; Liang, Xiao-Ming; Wang, Jing; Yang, Cheng-Fang; Liu, Kai; Lü, Hua-Ping

    2010-11-01

    The harmonic stochastic resonance-enhanced signal detecting in Newman-Watts small-world neural network is studied using the Hodgkin-Huxley dynamical equation with noise. If the connection probability p, coupling strength gsyn and noise intensity D matches well, higher order resonance will be found and an optimal signal-to-noise ratio will be obtained. Then, the reasons are given to explain the mechanism of this appearance.

  13. Coordinated photomorphogenic UV-B signaling network captured by mathematical modeling.

    PubMed

    Ouyang, Xinhao; Huang, Xi; Jin, Xiao; Chen, Zheng; Yang, Panyu; Ge, Hao; Li, Shigui; Deng, Xing Wang

    2014-08-05

    Long-wavelength and low-fluence UV-B light is an informational signal known to induce photomorphogenic development in plants. Using the model plant Arabidopsis thaliana, a variety of factors involved in UV-B-specific signaling have been experimentally characterized over the past decade, including the UV-B light receptor UV resistance locus 8; the positive regulators constitutive photomorphogenesis 1 and elongated hypocotyl 5; and the negative regulators cullin4, repressor of UV-B photomorphogenesis 1 (RUP1), and RUP2. Individual genetic and molecular studies have revealed that these proteins function in either positive or negative regulatory capacities for the sufficient and balanced transduction of photomorphogenic UV-B signal. Less is known, however, regarding how these signaling events are systematically linked. In our study, we use a systems biology approach to investigate the dynamic behaviors and correlations of multiple signaling components involved in Arabidopsis UV-B-induced photomorphogenesis. We define a mathematical representation of photomorphogenic UV-B signaling at a temporal scale. Supplemented with experimental validation, our computational modeling demonstrates the functional interaction that occurs among different protein complexes in early and prolonged response to photomorphogenic UV-B.

  14. Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  15. Broadband nanophotonic wireless links and networks using on-chip integrated plasmonic antennas.

    PubMed

    Yang, Yuanqing; Li, Qiang; Qiu, Min

    2016-01-19

    Owing to their high capacity and flexibility, broadband wireless communications have been widely employed in radio and microwave regimes, playing indispensable roles in our daily life. Their optical analogs, however, have not been demonstrated at the nanoscale. In this paper, by exploiting plasmonic nanoantennas, we demonstrate the complete design of broadband wireless links and networks in the realm of nanophotonics. With a 100-fold enhancement in power transfer superior to previous designs as well as an ultrawide bandwidth that covers the entire telecommunication wavelength range, such broadband nanolinks and networks are expected to pave the way for future optical integrated nanocircuits.

  16. A comparative signaling cost analysis of Macro Mobility scheme in NEMO (MM-NEMO) with mobility management protocol

    NASA Astrophysics Data System (ADS)

    Islam, Shayla; Abdalla, Aisha H.; Habaebi, Mohamed H.; Latif, Suhaimi A.; Hassan, Wan H.; Hasan, Mohammad K.; Ramli, H. A. M.; Khalifa, Othman O.

    2013-12-01

    NEMO BSP is an upgraded addition to Mobile IPv6 (MIPv6). As MIPv6 and its enhancements (i.e. HMIPv6) possess some limitations like higher handoff latency, packet loss, NEMO BSP also faces all these shortcomings by inheritance. Network Mobility (NEMO) is involved to handle the movement of Mobile Router (MR) and it's Mobile Network Nodes (MNNs) during handoff. Hence it is essential to upgrade the performance of mobility management protocol to obtain continuous session connectivity with lower delay and packet loss in NEMO environment. The completion of handoff process in NEMO BSP usually takes longer period since MR needs to register its single primary care of address (CoA) with home network that may cause performance degradation of the applications running on Mobile Network Nodes. Moreover, when a change in point of attachment of the mobile network is accompanied by a sudden burst of signaling messages, "Signaling Storm" occurs which eventually results in temporary congestion, packet delays or even packet loss. This effect is particularly significant for wireless environment where a wireless link is not as steady as a wired link since bandwidth is relatively limited in wireless link. Hence, providing continuous Internet connection without any interruption through applying multihoming technique and route optimization mechanism in NEMO are becoming the center of attention to the current researchers. In this paper, we propose a handoff cost model to compare the signaling cost of MM-NEMO with NEMO Basic Support Protocol (NEMO BSP) and HMIPv6.The numerical results shows that the signaling cost for the MM-NEMO scheme is about 69.6 % less than the NEMO-BSP and HMIPv6.

  17. Device design and signal processing for multiple-input multiple-output multimode fiber links

    NASA Astrophysics Data System (ADS)

    Appaiah, Kumar; Vishwanath, Sriram; Bank, Seth R.

    2012-01-01

    Multimode fibers (MMFs) are limited in data rate capabilities owing to modal dispersion. However, their large core diameter simplifies alignment and packaging, and makes them attractive for short and medium length links. Recent research has shown that the use of signal processing and techniques such as multiple-input multiple-output (MIMO) can greatly improve the data rate capabilities of multimode fibers. In this paper, we review recent experimental work using MIMO and signal processing for multimode fibers, and the improvements in data rates achievable with these techniques. We then present models to design as well as simulate the performance benefits obtainable with arrays of lasers and detectors in conjunction with MIMO, using channel capacity as the metric to optimize. We also discuss some aspects related to complexity of the algorithms needed for signal processing and discuss techniques for low complexity implementation.

  18. Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

    PubMed

    Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

    2016-03-22

    Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

  19. Optimization and throughput estimation of optical ground networks for LEO-downlinks, GEO-feeder links and GEO-relays

    NASA Astrophysics Data System (ADS)

    Fuchs, Christian; Poulenard, Sylvain; Perlot, Nicolas; Riedi, Jerome; Perdigues, Josep

    2017-02-01

    Optical satellite communications play an increasingly important role in a number of space applications. However, if the system concept includes optical links to the surface of the Earth, the limited availability due to clouds and other atmospheric impacts need to be considered to give a reliable estimate of the system performance. An OGS network is required for increasing the availability to acceptable figures. In order to realistically estimate the performance and achievable throughput in various scenarios, a simulation tool has been developed under ESA contract. The tool is based on a database of 5 years of cloud data with global coverage and can thus easily simulate different optical ground station network topologies for LEO- and GEO-to-ground links. Further parameters, like e.g. limited availability due to sun blinding and atmospheric turbulence, are considered as well. This paper gives an overview about the simulation tool, the cloud database, as well as the modelling behind the simulation scheme. Several scenarios have been investigated: LEO-to-ground links, GEO feeder links, and GEO relay links. The key results of the optical ground station network optimization and throughput estimations will be presented. The implications of key technical parameters, as e.g. memory size aboard the satellite, will be discussed. Finally, potential system designs for LEO- and GEO-systems will be presented.

  20. Vector neural network signal integration for radar application

    NASA Astrophysics Data System (ADS)

    Bierman, Gregory S.

    1994-07-01

    The Litton Data Systems Vector Neural Network (VNN) is a unique multi-scan integration algorithm currently in development. The target of interest is a low-flying cruise missile. Current tactical radar cannot detect and track the missile in ground clutter at tactically useful ranges. The VNN solves this problem by integrating the energy from multiple frames to effectively increase the target's signal-to-noise ratio. The implementation plan is addressing the APG-63 radar. Real-time results will be available by March 1994.

  1. Microwave analog fiber-optic link for use in the deep space network

    NASA Technical Reports Server (NTRS)

    Logan, R. T., Jr.; Lutes, G. F.; Maleki, L.

    1990-01-01

    A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.

  2. LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

    NASA Astrophysics Data System (ADS)

    Berahmand, Kamal; Bouyer, Asgarali

    2018-03-01

    Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

  3. Relating microstructure to rheology of a bundled and cross-linked F-actin network in vitro

    NASA Astrophysics Data System (ADS)

    Shin, J. H.; Gardel, M. L.; Mahadevan, L.; Matsudaira, P.; Weitz, D. A.

    2004-06-01

    The organization of individual actin filaments into higher-order structures is controlled by actin-binding proteins (ABPs). Although the biological significance of the ABPs is well documented, little is known about how bundling and cross-linking quantitatively affect the microstructure and mechanical properties of actin networks. Here we quantify the effect of the ABP scruin on actin networks by using imaging techniques, cosedimentation assays, multiparticle tracking, and bulk rheology. We show how the structure of the actin network is modified as the scruin concentration is varied, and we correlate these structural changes to variations in the resultant network elasticity.

  4. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

    PubMed

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

    2012-10-18

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

  5. Proposal and performance analysis on the PDM microwave photonic link for the mm-wave signal with hybrid QAM-MPPM-RZ modulation

    NASA Astrophysics Data System (ADS)

    Tian, Bo; Zhang, Qi; Ma, Jianxin; Tao, Ying; Shen, Yufei; Wang, Yang; Zhang, Geng; Zhou, Wenmao; Zhao, Yi; Pan, Xiaolong

    2018-07-01

    A polarization division multiplexed (PDM) microwave photonic link for the millimeter (MM)-wave signal with hybrid modulation scheme is proposed in this paper, which is based on the combination of quadrature amplitude modulation, multi-pulse pulse-position modulation and return to zero modulation (QAM-MPPM-RZ). In this scheme, the two orthogonal polarization states enable simultaneous transmission of four data flows, which can provide different services for users according to the data rate requirement. To generate hybrid QAM-MPPM-RZ mm-wave signal, the QAM mm-wave signal is directly modulated by MPPM-RZ signal without using digital signal processing (DSP) devices, which reduces the overhead of the encoding process. Then, the generated QAM-MPPM-RZ mm-wave signal is transmitted in PDM microwave photonic link based on SSB modulation. The sparsity characteristic of QAM-MPPM-RZ not only improves the power efficiency, but also decreases the degradation caused by the fiber chromatic dispersion. The simulation results show that, under the constraint of the same transmitted data rate, the PDM microwave photonic link with 50 GHz QAM-MPPM-RZ mm-wave signal achieves much lower levels of bit-error rate than ordinary 32-QAM. In addition, the increase of laser linewidth brings no additional impact to the proposed scheme.

  6. Growing knowledge of the mTOR signaling network.

    PubMed

    Huang, Kezhen; Fingar, Diane C

    2014-12-01

    The kinase mTOR (mechanistic target of rapamycin) integrates diverse environmental signals and translates these cues into appropriate cellular responses. mTOR forms the catalytic core of at least two functionally distinct signaling complexes, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). mTORC1 promotes anabolic cellular metabolism in response to growth factors, nutrients, and energy and functions as a master controller of cell growth. While significantly less well understood than mTORC1, mTORC2 responds to growth factors and controls cell metabolism, cell survival, and the organization of the actin cytoskeleton. mTOR plays critical roles in cellular processes related to tumorigenesis, metabolism, immune function, and aging. Consequently, aberrant mTOR signaling contributes to myriad disease states, and physicians employ mTORC1 inhibitors (rapamycin and analogs) for several pathological conditions. The clinical utility of mTOR inhibition underscores the important role of mTOR in organismal physiology. Here we review our growing knowledge of cellular mTOR regulation by diverse upstream signals (e.g. growth factors; amino acids; energy) and how mTORC1 integrates these signals to effect appropriate downstream signaling, with a greater emphasis on mTORC1 over mTORC2. We highlight dynamic subcellular localization of mTORC1 and associated factors as an important mechanism for control of mTORC1 activity and function. We will cover major cellular functions controlled by mTORC1 broadly. While significant advances have been made in the last decade regarding the regulation and function of mTOR within complex cell signaling networks, many important findings remain to be discovered. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants.

    PubMed

    Varala, Kranthi; Marshall-Colón, Amy; Cirrone, Jacopo; Brooks, Matthew D; Pasquino, Angelo V; Léran, Sophie; Mittal, Shipra; Rock, Tara M; Edwards, Molly B; Kim, Grace J; Ruffel, Sandrine; McCombie, W Richard; Shasha, Dennis; Coruzzi, Gloria M

    2018-06-19

    This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our "just-in-time" analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to "prune" the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF "N-specificity" index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs-CRF4, SNZ, CDF1, HHO5/6, and PHL1-validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15 NO 3 - uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal "transcriptional logic" for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine. Copyright © 2018 the Author(s). Published by PNAS.

  8. Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  9. Sequential optimization of methotrexate encapsulation in micellar nano-networks of polyethyleneimine ionomer containing redox-sensitive cross-links

    PubMed Central

    Abolmaali, Samira Sadat; Tamaddon, Ali; Yousefi, Gholamhossein; Javidnia, Katayoun; Dinarvand, Rasoul

    2014-01-01

    A functional polycation nanonetwork was developed for delivery of water soluble chemotherapeutic agents. The complexes of polyethyleneimine grafted methoxy polyethylene glycol (PEI-g-mPEG) and Zn2+ were utilized as the micellar template for cross-linking with dithiodipropionic acid, followed by an acidic pH dialysis to remove the metal ion from the micellar template. The synthesis method was optimized according to pH, the molar ratio of Zn2+, and the cross-link ratio. The atomic force microscopy showed soft, discrete, and uniform nano-networks. They were sensitive to the simulated reductive environment as determined by Ellman’s assay. They showed few positive ζ potential and an average hydrodynamic diameter of 162±10 nm, which decreased to 49±11 nm upon dehydration. The ionic character of the nano-networks allowed the achievement of a higher-loading capacity of methotrexate (MTX), approximately 57% weight per weight, depending on the cross-link and the drug feed ratios. The nano-networks actively loaded with MTX presented some suitable properties, such as the hydrodynamic size of 117±16 nm, polydispersity index of 0.22, and a prolonged swelling-controlled release profile over 24 hours that boosted following reductive activation of the nanonetwork biodegradation. Unlike the PEI ionomer, the nano-networks provided an acceptable cytotoxicity profile. The drug-loaded nano-networks exhibited more specific cytotoxicity against human hepatocellular carcinoma cells if compared to free MTX at concentrations above 1 μM. The enhanced antitumor activity in vitro might be attributed to endocytic entry of MTX-loaded nano-networks that was found in the epifluorescence microscopy experiment for the fluorophore-labeled nano-networks. PMID:24944513

  10. Sequential optimization of methotrexate encapsulation in micellar nano-networks of polyethyleneimine ionomer containing redox-sensitive cross-links.

    PubMed

    Abolmaali, Samira Sadat; Tamaddon, Ali; Yousefi, Gholamhossein; Javidnia, Katayoun; Dinarvand, Rasoul

    2014-01-01

    A functional polycation nanonetwork was developed for delivery of water soluble chemotherapeutic agents. The complexes of polyethyleneimine grafted methoxy polyethylene glycol (PEI-g-mPEG) and Zn(2+) were utilized as the micellar template for cross-linking with dithiodipropionic acid, followed by an acidic pH dialysis to remove the metal ion from the micellar template. The synthesis method was optimized according to pH, the molar ratio of Zn(2+), and the cross-link ratio. The atomic force microscopy showed soft, discrete, and uniform nano-networks. They were sensitive to the simulated reductive environment as determined by Ellman's assay. They showed few positive ζ potential and an average hydrodynamic diameter of 162±10 nm, which decreased to 49±11 nm upon dehydration. The ionic character of the nano-networks allowed the achievement of a higher-loading capacity of methotrexate (MTX), approximately 57% weight per weight, depending on the cross-link and the drug feed ratios. The nano-networks actively loaded with MTX presented some suitable properties, such as the hydrodynamic size of 117±16 nm, polydispersity index of 0.22, and a prolonged swelling-controlled release profile over 24 hours that boosted following reductive activation of the nanonetwork biodegradation. Unlike the PEI ionomer, the nano-networks provided an acceptable cytotoxicity profile. The drug-loaded nano-networks exhibited more specific cytotoxicity against human hepatocellular carcinoma cells if compared to free MTX at concentrations above 1 μM. The enhanced antitumor activity in vitro might be attributed to endocytic entry of MTX-loaded nano-networks that was found in the epifluorescence microscopy experiment for the fluorophore-labeled nano-networks.

  11. Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer.

    PubMed

    Yeung, Tsz-Lun; Sheng, Jianting; Leung, Cecilia S; Li, Fuhai; Kim, Jaeyeon; Ho, Samuel Y; Matzuk, Martin M; Lu, Karen H; Wong, Stephen T C; Mok, Samuel C

    2018-05-31

    Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided. Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04

  12. Dependency Links Can Hinder the Evolution of Cooperation in the Prisoner’s Dilemma Game on Lattices and Networks

    PubMed Central

    Wang, Xuwen; Nie, Sen; Wang, Binghong

    2015-01-01

    Networks with dependency links are more vulnerable when facing the attacks. Recent research also has demonstrated that the interdependent groups support the spreading of cooperation. We study the prisoner’s dilemma games on spatial networks with dependency links, in which a fraction of individual pairs is selected to depend on each other. The dependency individuals can gain an extra payoff whose value is between the payoff of mutual cooperation and the value of temptation to defect. Thus, this mechanism reflects that the dependency relation is stronger than the relation of ordinary mutual cooperation, but it is not large enough to cause the defection of the dependency pair. We show that the dependence of individuals hinders, promotes and never affects the cooperation on regular ring networks, square lattice, random and scale-free networks, respectively. The results for the square lattice and regular ring networks are demonstrated by the pair approximation. PMID:25798579

  13. Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

    PubMed

    Truong, Cong-Doan; Kwon, Yung-Keun

    2017-12-21

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

  14. Where is the comfort in comfort foods? Mechanisms linking fat signaling, reward, and emotion.

    PubMed

    Weltens, N; Zhao, D; Van Oudenhove, L

    2014-03-01

    Food in general, and fatty foods in particular, have obtained intrinsic reward value throughout evolution. This reward value results from an interaction between exteroceptive signals from different sensory modalities, interoceptive hunger/satiety signals from the gastrointestinal tract to the brain, as well as ongoing affective and cognitive processes. Further evidence linking food to emotions stems from folk psychology ('comfort foods') and epidemiological studies demonstrating high comorbidity rates between disorders of food intake, including obesity, and mood disorders such as depression. This review paper aims to give an overview of current knowledge on the neurophysiological mechanisms underlying the link between (fatty) foods, their reward value, and emotional responses to (anticipation of) their intake in humans. Firstly, the influence of exteroceptive sensory signals, including visual, olfactory ('anticipatory food reward'), and gustatory ('consummatory food reward'), on the encoding of reward value in the (ventral) striatum and of subjective pleasantness in the cingulate and orbitofrontal cortex will be discussed. Differences in these pathways and mechanisms between lean and obese subjects will be highlighted. Secondly, recent studies elucidating the mechanisms of purely interoceptive fatty acid-induced signaling from the gastrointestinal tract to the brain, including the role of gut peptides, will be presented. These studies have demonstrated that such subliminal interoceptive stimuli may impact on hedonic circuits in the brain, and thereby influence the subjective and neural responses to negative emotion induction. This suggests that the effect of foods on mood may even occur independently from their exteroceptive sensory properties. © 2014 John Wiley & Sons Ltd.

  15. The Security Aspects of Wireless Local Area Network (WLAN)

    DTIC Science & Technology

    2003-09-01

    by wireless links to enable devices to communicate. In a Bluetooth network, mobile routers control the changing network topologies of these... Bluetooth Bluetooth is a simple peer-to-peer protocol created to connect multiple consumer mobile information devices (cellular phones, laptops...technology [Ref 2]. Bluetooth enables mobile devices to avoid interference from other signals by hopping to a new frequency after transmitting or

  16. Network and Atomistic Simulations Unveil the Structural Determinants of Mutations Linked to Retinal Diseases

    PubMed Central

    Mariani, Simona; Dell'Orco, Daniele; Felline, Angelo; Raimondi, Francesco; Fanelli, Francesca

    2013-01-01

    A number of incurable retinal diseases causing vision impairments derive from alterations in visual phototransduction. Unraveling the structural determinants of even monogenic retinal diseases would require network-centered approaches combined with atomistic simulations. The transducin G38D mutant associated with the Nougaret Congenital Night Blindness (NCNB) was thoroughly investigated by both mathematical modeling of visual phototransduction and atomistic simulations on the major targets of the mutational effect. Mathematical modeling, in line with electrophysiological recordings, indicates reduction of phosphodiesterase 6 (PDE) recognition and activation as the main determinants of the pathological phenotype. Sub-microsecond molecular dynamics (MD) simulations coupled with Functional Mode Analysis improve the resolution of information, showing that such impairment is likely due to disruption of the PDEγ binding cavity in transducin. Protein Structure Network analyses additionally suggest that the observed slight reduction of theRGS9-catalyzed GTPase activity of transducin depends on perturbed communication between RGS9 and GTP binding site. These findings provide insights into the structural fundamentals of abnormal functioning of visual phototransduction caused by a missense mutation in one component of the signaling network. This combination of network-centered modeling with atomistic simulations represents a paradigm for future studies aimed at thoroughly deciphering the structural determinants of genetic retinal diseases. Analogous approaches are suitable to unveil the mechanism of information transfer in any signaling network either in physiological or pathological conditions. PMID:24009494

  17. 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.

  18. Optimal control of epidemic information dissemination over networks.

    PubMed

    Chen, Pin-Yu; Cheng, Shin-Ming; Chen, Kwang-Cheng

    2014-12-01

    Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.

  19. A molecular signaling approach to linking intraspecific variation and macro-evolutionary patterns.

    PubMed

    Swanson, Eli M; Snell-Rood, Emilie C

    2014-11-01

    Macro-evolutionary comparisons are a valued tool in evolutionary biology. Nevertheless, our understanding of how systems involved in molecular signaling change in concert with phenotypic diversification has lagged. We argue that integrating our understanding of the evolution of molecular signaling systems with phylogenetic comparative methods is an important step toward understanding the processes linking variation among individuals with variation among species. Focusing mostly on the endocrine system, we discuss how the complexity and mechanistic nature of molecular signaling systems may influence the application and interpretation of macro-evolutionary comparisons. We also detail five hypotheses concerning the role that physiological mechanisms can play in shaping macro-evolutionary patterns, and discuss ways in which these hypotheses could influence phenotypic diversification. Finally, we review a series of tools able to analyze the complexity of physiological systems and the way they change in concert with the phenotypes for which they coordinate development. © The Author 2014. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

  20. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms

    PubMed Central

    2012-01-01

    Background Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Results Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Conclusions Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context. PMID:23079107

  1. Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling

    PubMed Central

    Li, Song; Assmann, Sarah M; Albert, Réka

    2006-01-01

    Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited. PMID:16968132

  2. Encoding Hydrogel Mechanics via Network Cross-Linking Structure.

    PubMed

    Schweller, Ryan M; West, Jennifer L

    2015-05-11

    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.

  3. Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

    NASA Astrophysics Data System (ADS)

    Gao, Zhong-Ke; Wang, Zi-Bo; Yang, Yu-Xuan; Li, Shan; Dang, Wei-Dong; Mao, Xiao-Qian

    2018-09-01

    Brain-computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human-machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical 'Oddball' paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

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

    PubMed

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

    2014-01-01

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

  5. Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks

    PubMed Central

    Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

    2014-01-01

    A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic–phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits. PMID:24723897

  6. Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks.

    PubMed

    Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

    2014-01-01

    A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.

  7. Simulator of Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

    2005-01-01

    Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

  8. Dynamic control of type I IFN signalling by an integrated network of negative regulators.

    PubMed

    Porritt, Rebecca A; Hertzog, Paul J

    2015-03-01

    Whereas type I interferons (IFNs) have critical roles in protection from pathogens, excessive IFN responses contribute to pathology in both acute and chronic settings, pointing to the importance of balancing activating signals with regulatory mechanisms that appropriately tune the response. Here we review evidence for an integrated network of negative regulators of IFN production and action, which function at all levels of the activating and effector signalling pathways. We propose that the aim of this extensive network is to limit tissue damage while enabling an IFN response that is temporally appropriate and of sufficient magnitude. Understanding the architecture and dynamics of this network, and how it differs in distinct tissues, will provide new insights into IFN biology and aid the design of more effective therapeutics. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  9. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    PubMed

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

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

  10. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals

    PubMed Central

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-01-01

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process. PMID:28590456

  11. High-precision multi-node clock network distribution.

    PubMed

    Chen, Xing; Cui, Yifan; Lu, Xing; Ci, Cheng; Zhang, Xuesong; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

    2017-10-01

    A high precision multi-node clock network for multiple users was built following the precise frequency transmission and time synchronization of 120 km fiber. The network topology adopts a simple star-shaped network structure. The clock signal of a hydrogen maser (synchronized with UTC) was recovered from a 120 km telecommunication fiber link and then was distributed to 4 sub-stations. The fractional frequency instability of all substations is in the level of 10 -15 in a second and the clock offset instability is in sub-ps in root-mean-square average.

  12. Enhancement of electrical signaling in neural networks on graphene films.

    PubMed

    Tang, Mingliang; Song, Qin; Li, Ning; Jiang, Ziyun; Huang, Rong; Cheng, Guosheng

    2013-09-01

    One of the key challenges for neural tissue engineering is to exploit supporting materials with robust functionalities not only to govern cell-specific behaviors, but also to form functional neural network. The unique electrical and mechanical properties of graphene imply it as a promising candidate for neural interfaces, but little is known about the details of neural network formation on graphene as a scaffold material for tissue engineering. Therapeutic regenerative strategies aim to guide and enhance the intrinsic capacity of the neurons to reorganize by promoting plasticity mechanisms in a controllable manner. Here, we investigated the impact of graphene on the formation and performance in the assembly of neural networks in neural stem cell (NSC) culture. Using calcium imaging and electrophysiological recordings, we demonstrate the capabilities of graphene to support the growth of functional neural circuits, and improve neural performance and electrical signaling in the network. These results offer a better understanding of interactions between graphene and NSCs, also they clearly present the great potentials of graphene as neural interface in tissue engineering. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Promoting Wired Links in Wireless Mesh Networks: An Efficient Engineering Solution

    PubMed Central

    Barekatain, Behrang; Raahemifar, Kaamran; Ariza Quintana, Alfonso; Triviño Cabrera, Alicia

    2015-01-01

    Wireless Mesh Networks (WMNs) cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP) that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic). Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP), WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes. PMID:25793516

  14. Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint

    PubMed Central

    Wang, Qiu; Cheang, Chak Fong

    2017-01-01

    Extensive attention has been given to the use of cognitive radio technology in underwater acoustic networks since the acoustic spectrum became scarce due to the proliferation of human aquatic activities. Most of the recent studies on underwater cognitive acoustic networks (UCANs) mainly focus on spectrum management or protocol design. Few efforts have addressed the quality-of-service (QoS) of UCANs. In UCANs, secondary users (SUs) have lower priority to use acoustic spectrum than primary users (PUs) with higher priority to access spectrum. As a result, the QoS of SUs is difficult to ensure in UCANs. This paper proposes an analytical model to investigate the link connectivity and the probability of coverage of SUs in UCANs. In particular, this model takes both topological connectivity and spectrum availability into account, though spectrum availability has been ignored in most recent studies. We conduct extensive simulations to evaluate the effectiveness and the accuracy of our proposed model. Simulation results show that our proposed model is quite accurate. Besides, our results also imply that the link connectivity and the probability of coverage of SUs heavily depend on both the underwater acoustic channel conditions and the activities of PUs. PMID:29215561

  15. Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

    PubMed

    Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

    2016-03-01

    Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. © 2016 American Institute of Chemical Engineers.

  16. HRD Motif as the Central Hub of the Signaling Network for Activation Loop Autophosphorylation in Abl Kinase.

    PubMed

    La Sala, Giuseppina; Riccardi, Laura; Gaspari, Roberto; Cavalli, Andrea; Hantschel, Oliver; De Vivo, Marco

    2016-11-08

    A number of structural factors modulate the activity of Abelson (Abl) tyrosine kinase, whose deregulation is often related to oncogenic processes. First, only the open conformation of the Abl kinase domain's activation loop (A-loop) favors ATP binding to the catalytic cleft. In this regard, the trans-autophosphorylation of the Y412 residue, which is located along the A-loop, favors the stability of the open conformation, in turn enhancing Abl activity. Another key factor for full Abl activity is the formation of active conformations of the catalytic DFG motif in the Abl kinase domain. Furthermore, binding of the SH2 domain to the N-lobe of the Abl kinase was recently demonstrated to have a long-range allosteric effect on the stabilization of the A-loop open state. Intriguingly, these distinct structural factors imply a complex signal transmission network for controlling the A-loop's flexibility and conformational preference for optimal Abl function. However, the exact dynamical features of this signal transmission network structure remain unclear. Here, we report on microsecond-long molecular dynamics coupled with enhanced sampling simulations of multiple Abl model systems, in the presence or absence of the SH2 domain and with the DFG motif flipped in two ways (in or out conformation). Through comparative analysis, our simulations augment the interpretation of the existing Abl experimental data, revealing a dynamical network of interactions that interconnect SH2 domain binding with A-loop plasticity and Y412 autophosphorylation in Abl. This signaling network engages the DFG motif and, importantly, other conserved structural elements of the kinase domain, namely, the EPK-ELK H-bond network and the HRD motif. Our results show that the signal propagation for modulating the A-loop spatial localization is highly dependent on the HRD motif conformation, which thus acts as the central hub of this (allosteric) signaling network controlling Abl activation and function.

  17. A coherent fiber link for very long baseline interferometry.

    PubMed

    Clivati, Cecilia; Costanzo, Giovanni A; Frittelli, Matteo; Levi, Filippo; Mura, Alberto; Zucco, Massimo; Ambrosini, Roberto; Bortolotti, Claudio; Perini, Federico; Roma, Mauro; Calonico, Davide

    2015-11-01

    We realize a coherent fiber link for application in very long baseline interferometry (VLBI) for radio astronomy and geodesy. A 550-km optical fiber connects the Italian National Metrological Institute (INRIM) to a radio telescope in Italy and is used for the primary Cs fountain clock stability and accuracy dissemination. We use an ultrastable laser frequency- referenced to the primary standard as a transfer oscillator; at the radio telescope, an RF signal is generated from the laser by using an optical frequency comb. This scheme now provides the traceability of the local maser to the SI second, realized by the Cs fountain at the 1.7 × 10(-16) accuracy. The fiber link never limits the experiment and is robust enough to sustain radio astronomical campaigns. This experiment opens the possibility of replacing the local hydrogen masers at the VLBI sites with optically-synthesized RF signals. This could improve VLBI resolution by providing more accurate and stable frequency references and, in perspective, by enabling common- clock VLBI based on a network of telescopes connected by fiber links.

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

    NASA Technical Reports Server (NTRS)

    Navarro, Robert

    2006-01-01

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

  19. 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.

  20. Software-defined Quantum Networking Ecosystem

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Humble, Travis S.; Sadlier, Ronald

    The software enables a user to perform modeling and simulation of software-defined quantum networks. The software addresses the problem of how to synchronize transmission of quantum and classical signals through multi-node networks and to demonstrate quantum information protocols such as quantum teleportation. The software approaches this problem by generating a graphical model of the underlying network and attributing properties to each node and link in the graph. The graphical model is then simulated using a combination of discrete-event simulators to calculate the expected state of each node and link in the graph at a future time. A user interacts withmore » the software by providing an initial network model and instantiating methods for the nodes to transmit information with each other. This includes writing application scripts in python that make use of the software library interfaces. A user then initiates the application scripts, which invokes the software simulation. The user then uses the built-in diagnostic tools to query the state of the simulation and to collect statistics on synchronization.« less